All ultra-wideband (UWB) sensor applications require hardware designed directly for their specific application. The switching of broadband radio frequency and microwave signals is an integral part of almost every piece of high-frequency equipment, whether in commercial operation or laboratory conditions. The trend of integrating various circuit structures and systems on a chip (SoC) or in a single package (SiP) is also related to the need to design these integrated switches for various measuring devices and instruments in laboratories, paradoxically for their further development. Another possible use is switching high-frequency signals in telecommunications devices, whether mobile or fixed networks, for example, for switching signals from several antennas. Based on these requirements, a high-frequency semiconductor integrated switch with NMOS transistors was designed. With these transistors, it is possible to achieve higher integration than with bipolar ones. Even though MOSFET transistors have worse frequency characteristics, we can compensate them to some extent with the precise design of the circuit and layout of the chip. This article describes the analysis and design of a high-frequency semiconductor integrated switch for UWB applications consisting of three series-parallel switches controlled by CMOS logic signals. They are primarily intended for UWB sensor systems, e.g., when switching and configuring the antenna MIMO system or when switching calibration tools. The design of the switch was implemented in low-cost 0.35 µm SiGe BiCMOS technology with an emphasis on the smallest possible attenuation and the largest possible bandwidth and isolation. The reason for choosing this technology was also that other circuit structures of UWB systems were realized in this technology. Through the simulations, individual parameters of the circuit were simulated, the layout of the chip was also created, and the parameters of the circuit were simulated with the parasitic extraction and the inclusion of parasitic elements (post-layout simulations). Subsequently, the chip was manufactured and its parameters were measured and evaluated. Based on these measurements, the designed and fabricated UWB switch was found to have the following parameters: a supply current of 2 mA at 3.3 V, a bandwidth of 6 GHz, an insertion loss (at 1 GHz) of −2.2 dB, and isolation (at 1 GHz) of  In telecommunications: The RF switches in cellular networks are essential components in cell towers and base stations, enabling efficient switching between different frequency bands and antennas ,2,3. TheIn wireless Wi-Fi and Bluetooth communication systems: The RF switches are used to manage signal paths between antennas and transceivers, improving connectivity and signal quality.In fiber optic communication: The RF switches can be used to modulate optical signals for efficient data transmission.In defence: High-frequency RF switches are crucial in radar systems for rapid switching between transmission and reception modes, enhancing target detection capabilities, and the RF switches also play a role in electronic countermeasure systems by managing signal routing to disrupt or jam enemy communication systems in scenarios of electronic warfare.In medical imaging : The RF switches can help manage signal paths, enhancing image quality and reducing interference, or are used to switch between different transducers and frequencies for diagnostic imaging.In the automotive industry: The RF switches are used in automotive radar systems (radar-based driver assistance systems) for collision avoidance, adaptive cruise control, and other safety features.In industrial and manufacturing: The RF switches can be used in non-destructive testing and quality control processes, such as material analysis and defect detection ,6,7,8.In process control: The RF switches can help with managing communication and control signals in industrial automation systems .In research and development: The RF switches are used in scientific instruments for signal routing and testing, such as in spectrum analyzers, signal generators, and calibration systems.Switching of ultra-wideband (UWB) radio frequency and microwave signals is essential to almost every high-frequency (HF) device, whether in commercial operation or laboratory conditions. Related to the trend of integrating various circuit structures on system on chip (SoC) is the need to design integrated switches for various commercial and measurement HF devices as well as instruments in laboratories, paradoxically for their further development. Hence, selecting the appropriate switch is extremely important for successful RF applications. Switch requirements can vary greatly even within a single system, such as, for example, switching and configuring the antenna MIMO system or when switching calibration tools. When equipped with the knowledge of available RF switch solutions, application requirements, and performance tradeoffs, engineers can effectively implement their designs and meet today’s advanced systems demands. Covering all possible system considerations and applications is beyond the scope of this article, therefore as an example some important and interesting applications will be introduced:The presented switch has been analyzed and designed to test its capabilities, future applications, and its integration in SoC with other circuits developed for UWB applications, especially for M-sequence based radars . These pIn the case of designing electronic switches, the choice between MOSFETs  and bipolar junction transistors (BJTs) involves careful consideration of various trade-offs. Each type of transistor has its advantages and disadvantages, and the selection depends on factors such as power consumption, speed, voltage handling capabilities, and the intended application. Therefore, in the next section, we introduce some of the trade-offs between these two transistor types in switch design.The NMOS transistors (MOSFET transistors with channel type-n) can switch the low voltages and they may have problems handling high voltage levels efficiently. The BJTs often have better voltage-handling capabilities compared to NMOS transistors. However, the NMOS transistors are generally more power-efficient in terms of static power consumption, as they have negligible current flow when turned off. On the other hand, the bipolar transistor’s dynamic power consumption can be lower due to faster switching. The NMOS transistors can switch relatively quickly due to their relatively small channel length and low on-resistance. However, they might not be as fast as some bipolar transistors, especially in high-frequency applications. In the case of the threshold voltage, the NMOS transistors require a positive gate-source voltage to turn on, typically around 0.5 V to 1.5 V, while BJTs can operate with very low base-emitter voltage levels. The higher threshold voltage might limit the use of the switch in low-voltage applications. The BJTs are less susceptible to noise due to their current-controlled operation, but the NMOS transistors can be cost-effective due to their compatibility with CMOS processes. The bipolar transistors are often more stable in performance across a wider temperature range. In conclusion, the choice between NMOS and bipolar transistors in switch design depends on the specific requirements of the application. The NMOS transistors are generally more suitable for low-power and digital applications, while bipolar transistors excel in high-speed and high-power scenarios. Because NMOS transistors are symmetrical and thus the The last, and perhaps most important, difference between the current–voltage characteristics of the two devices concerns the input current into the control terminal. While at low frequencies the gate current of the NMOS is practically zero and the input resistance looking into the gate is practically infinite, the BJT draws a base current IB that is proportional to the collector current. The finite base current and the corresponding finite input resistance looking into the base comprise a definite disadvantage of the BJT in comparison to the NMOS. Thus, as was mentioned earlier, the NMOS provides an excellent implementation of a switch, a fact that has made CMOS technology capable of realizing a host of analog circuit functions that are not possible with bipolar transistors.It can thus be seen that each of the two transistor types has its own distinct and unique advantages. Bipolar technology has been extremely useful in the design of very high-quality general-purpose circuit building blocks, such as op-amps. On the other hand, CMOS, with its very high packing density and its suitability for both digital and analog circuits, has become the technology of choice for the implementation of very large-scale integrated circuits. Nevertheless, the performance of CMOS circuits can be improved if the designer has available (on the same chip-SoC) bipolar transistors that can be employed in functions that require their high transconductance and excellent current-driving capability. A technology that allows the fabrication of high-quality bipolar transistors on the same chip as CMOS circuits is called BiCMOS.The decision should be made based on a careful evaluation of the trade-offs discussed above and an understanding of how they align with the desired functionality and performance of the switch circuit.Based on these requirements and the analysis, an interesting and useful controlled semiconductor switch for ultra-wideband radar sensor applications with NMOS transistors, implemented in low-cost 0.35 µm SiGe BiCMOS technology, was designed. Although metal-oxide-semiconductor field-effect transistors (MOSFETs) have worse frequency characteristics, we can compensate them to some extent by proper circuit and chip design. The priority was to achieve the lowest possible signal attenuation and the largest possible frequency bandwidth that the switch could handle. We will use several options to switch high-frequency signals and achieve the aforementioned desired characteristics. For example, improving an already used circuit structure for a different manufacturing technology. The design of switches can be realized by different technologies, ranging from a mechanical switch to micro-electromechanical elements (MEMS) , to a diThe main advantages of CMOS transistors used as switches are:Separation of control and switching signals due to isolated gate.Elimination of the mechanical elements of the switch, resulting in a small size, high degree of integration, high reliability, and good switching speed.Relatively inexpensive mass production with well-mastered technology.Easy integration on an SoC with control logic and other circuitry.Low power consumption resulting from voltage control of switching elements.Ability to switch both positive and negative voltages.However, we must also mention the disadvantages, which mainly include:Higher resistance and parasitic capacitance as well as associated insertion loss, and lower frequency bandwidth.Worse isolation and crosstalk at higher frequencies.Greater power limitation and associated signal distortion at higher powers.Absorptive—in the open state, a matched impedance is connected to the input instead of the output channel, which absorbs the signal;Reflective—in the open state, the switch behaves as a short or open circuit from the source’s point of view, i.e., the signal is reflected to the source.The simplest circuit enabling analog signal switching in CMOS technology is the serial single pole double through the Transmit/Receive (SPDT T/R) switch . Each swA differential SPDT T/R switch  is used By modifying the series-parallel T/R switch topology can also realize an asymmetrical SPDT T/R switch . For exaAnother solution offered is a switch with a travelling wave . Such a The next section of this paper describes the topology of the proposed semiconductor integrated switch with NMOS transistors. The third section focuses on the description of the circuit design, along with its simulations and the design of the chip layout. The design is implemented in the available low-cost 0.35 µm BiCMOS technology. The reason for choosing this technology was also that other circuit structures of UWB Systems were realized in this technology  as well.Today’s MOSFET switches are a challenge in many applications because their cutoff frequencies and breakdown voltages are constantly improving. Each technology has advantages and disadvantages that can be exploited for different applications for an optimal solution.The metal-oxide-semiconductor field-effect transistors (MOSFETs) have become an increasingly essential part of integrated circuits because they enable high density and low power dissipation. Bipolar transistors also provide many advantages in integrated circuits. For example, the gain factor per unit current is typically higher in bipolar transistors than in MOSFET ones . Thus, iThe basic element of the proposed high-frequency semiconductor switch described in this paper is an NMOS transistor. Its design has been implemented in a low-cost 0.35 µm SiGe BiCMOS technology. For basic design and calculations, it is convenient to use a model of the transistor that accurately represents its behavior in the circuit as well as its properties in the implemented fabrication technology. It should be noted that these models should not be very complex to facilitate computations. More on the description of the operation and the corresponding working conditions of MOSFET transistors, with channel type-n (NMOS) and with channel type-p (PMOS) in the enhancement mode, is given in ,24.L, may extend beyond the source and drain region Lef=LW, a length represented by Y, and a depth indicated by The surfaces of the source and the drain are the same, which shows the symmetry of the MOSFET transistor structure. Typical citances . The difBased on the physical structure, pinout arrangement, and other influences, it can be argued that in terms of high frequencies, the MOSFET transistor has parasitic capacitances that make worse the characteristics of high-frequency circuits created from these transistors. These parasitic capacitances must have the smallest possible value allows to increase these circuits’ switching speed and bandwidth. The capacitances in the MOSFET’s structure are distributed, and their exact calculation is quite difficult to determine. Using a simple approximation, we can obtain a model and values of the parasitic capacitances that can be used to analyze the transistor behavior in the high-frequency signal domain.The gate capacitance effect, characterized by the The junction capacitances between drain (D)-substrate also called the “bulk” or the body (B) and source (S)-substrate (B) semiconductor junctions.The fundamental capacitances that most affect the transistor behavior for RF signals can be divided into two main groups:The transistor behavior and the two main capacitance effects can be modelled by using a direct current (DC) NMOS transistor model and connecting the capacitances between the four terminals of the transistor as shown in ansistor ,24,25.The gate capacitance effect is characterized by citances ,26. Paralogy see  27]. As. AsCGSovThe capacitances that result from the interaction between the electrodes are denoted as W, length Y, and thickness A transistor operating in triode region has a conducting channel formed along its entire length from the source to the drain. The thickness of the channel at the drain only changes significantly as the saturation mode is approached. The capacitance tionship :(7)CGS≈2unctions ,28. The In practice, the actual shape of the area is quite complex and the concentration of impurities is not uniform. Since the voltage at these PN junctions also changes dynamically during the operation of the transistor, it is quite complicated to determine the exact number and value of these capacitances. The junction capacitances at zero bias voltage of the PN junction are determined by the capacitance  1 and 5 . The sidtion see . The CDBrelation :(8)CDB=CThis section describes the principle of operation of the high-frequency switch. The switch type was selected based on the requirements defined for the circuit design presented in As shown in e switch  was conse switch . In thisWe assume that the DC voltage source behaves as a short to ground for the high-frequency signal, therefore resistors We will use the nodal voltage analysis to calculate the signal transfer, on the basis of which we can construct the admittance matrix Equation ). In theThen, we create the resulting admittance matrix Equation  shows thThe previous analysis of the switch equivalent circuit diagrams in different modes of operation evaluated the influence of electronic components and parasitic elements on its operation and functionality. Since this analysis is the basis for the design of our precisely designed and optimized switch, the correctness of the conclusions introduced was also verified analytically based on the admittance matrix. The admittance matrix was derived from the equivalent circuit diagrams presented in the sections dealing with analysis and, as already mentioned above, also takes into account parasitic elements affecting the required properties of the proposed switch. From the size of the matrix and the number of elements, it is clear that the manual computation of the algebraic complements, the transfer function, and the subsequent depiction of the transfer function are very complex and difficult. Because of this difficulty and the potential mistakes of manual calculation, a program was created in the Matlab environment to show the insertion loss and isolation. Thus, the insertion loss and isolation characteristics were computed from the admittance matrix in Matlab and these results were compared with those found from the simulations in the Cadence CAD design tool. This comparison of the results see  clearly The behavior of the high-frequency switch in terms of high-frequency signals in the closed state is evident from Equation , by addiCGSs see . The capThe appropriate value of resistor Transistor Equation . AnotherThe operation and behavior of the switch in the open state are evident from the diagram shown in Equation . The parTransistor Equation  with theCGSs see . With hition see , and a sMinimum, i.e., as small signal attenuation as possible in the closed state ;Maximum on-chip isolation in the open state, preferably below −40 dB;A switch frequency bandwidth as wide as possible, from the lowest radio frequencies to a minimum of 10 GHz;The highest possible power that the switch can transmit without compression/distortion;A control voltage of 3.3 V for logic and switching needs;Low power consumption;A define the number of outputs, in our case three outputs for the UWB sensor systems  or for switching calibration tools .The design of the switch is implemented in BiCMOS technology and was based on the theoretical knowledge described in the previous chapters and on simulations carried out in a Cadence CAD design tool with the basic electronics components models of the 0.35 µm SiGe BiCMOS technology used. Before starting the circuit design of a broadband integrated semiconductor switch, it is necessary to establish the characteristics the designed circuit should satisfy:Based on the above requirements, the high-frequency part of the switch consists of three identical series-parallel switches. Since the switches are designed, e.g., for the calibration of RF devices, the individual outputs can be connected to the components used in the device calibration. The control logic specifies the terminal, e.g., with the corresponding calibration element. A 50 Ω load, a short circuit, and an open output are used for calibration. A detailed schematic will be given and its operation will be explained in the next section.L and width W. We keep the length L as low as possible because increasing the length increases the conduction channel resistance and parasitic capacitance. Increasing the transistor’s width decreases the open transistor’s resistivity and reduces the signal attenuation. The simulations of the characteristics in It should be noted at this point that the design and simulations do not consider the parasitic properties of the bonding pads and conductive paths on the PCB upon which the switch could be designed and implemented for a particular UWB application. However, their influence will be considered and shown through the measurement results presented in L = 0.35 µm, but the channel width differs. One has a W = 150 µm width, and the other has a W = 10 µm width. It can be seen that with the smaller transistor, the frequency bandwidth is much larger, but the attenuation is at about −8.6 dB. Conversely, with the wider transistor, the frequency bandwidth is smaller but the attenuation drops only to −1 dB. As a result, there is a trade-off to be made between attenuation and frequency bandwidth. The resulting dimension of transistor However, increasing the channel width has an undesirable effect on the frequency bandwidth of the switch. As a result of increasing the channel width, the capacitances open see . The eff2) and, since no current flows into the gate even after adding a resistor, the control voltage for the transistor is not reduced by the voltage drop across this resistor. The effect of the resistor has been simulated according to the schematic in The parasitic gate capacitances Equation . To remoThe characteristic shows the difference in bandwidth without resistor By determining the dimensions of transistor To increase the isolation in the closed state of the switch, transistor Y dimension, independent of the transistor length. The capacitance The width of the transistor directly changes the size of the junction areas of the source and drain regions see . The sizEquation . In the The design requirements for the resulting high-frequency switch indicated that more than one output was needed. Since three outputs, 50 Ω, short, and open-circuit, are primarily required for the applications under consideration, e.g., for calibration, the resulting circuitry of the proposed switch consists of three series-parallel switches see . One porIt can be seen in the characteristics that there is a rapid decrease frequency bandwidth a. ConverHowever, the resulting parameters will still change after the layout design. The change in characteristics is justified in the capacity From the previous simulations and setups, we determined the resulting transistor dimensions. Transistors Today’s ASIC design implementation flow must address different design modes, such as functional modes, test modes, and several input/output functions at different process, voltage, and temperature (PVT) corners, as its main function. Based on the analysis of the RF switch and NMOS transistor , a designsceiver , it was The problem occurs when the AC signal is connected, which has a DC value of 0 V, i.e., the potential of GND. In this case, the switch will not be closed by applying Schematic Design—A schematic based design flow and simulation were used. A typical ASIC design implementation includes schematics  for functional mode as well as the specified technology library. Upon completion of the design schematic, the final netlist (pre-layout netlist) to the physical design for place and route was provided.Physical Design—Manual placement and drawing of all devices and structures were used. Once the pre-layout netlist is available, the physical design activities will begin. During the physical design activities, the goal will be to first make sure the routed design will meet the physical design rules (Design Rule Check) and then, if both the schematic circuit and layout circuit match. Upon completion of the routed design, the physical design will provide the post-layout netlist for the final optimization, verification, and evaluation. This uses the simulation same as the simulation of the schematic except for the fact that the results will now include parasitic effects from the actual layout (Extraction). Parasitic capacitances, resistances, and inductances in the layout can strongly affect the performance of a design. Thus to evaluate the effects of parasitics and to gain a higher degree of confidence that a layout will result in a chip that meets the specifications, it is important to run post-layout simulations, as well. In our design flow, we only extracted a layout with parasitic capacitors and resistors  and only with their values above a certain chosen limit level so that their influence does not significantly affect the operation or accuracy of our design. The performance of a circuit simulated with parasitics accounted for is always worse, although closer to reality, than a schematic that does not include estimated parasitics. Thus, this analysis examines the operation of the design across a range of processes and also different conditions .Before the layout of the proposed and implemented UWB switch, it is necessary to be sure that the proposed design is working properly. Thus, in the next steps, we will be going from a schematic to the final layout (tape out). The functionality of the design at different levels was verified and the performance, timing, and power dissipation parameters important for our design were simulated. The most advanced IC technologies available to us and Cadence’s professional CAD tools have been used. Specifically, we followed the next steps in our practical ASIC design implementation:Once we are done, we will have a prototype layout for our ASIC UWB switch that can be sent for fabrication . The chip can only be sent for fabrication only if proof of functionality from post-layout simulations is provided.r = 6. The finished PCB prototype is shown in This section presents the measurement and test results of the fabricated integrated UWB switch. The realized UWB switch was wire-bonded in the package (die mounted into a QFN32 5 × 5 mm) and soldered on the custom testing a precision-designed printed circuit board (PCB), made of RO4360G2 substrate with dielectric constant εThe insertion loss and isolation of each port of the switch obtained from measurements and post-layout simulations are shown in The 50 Ω port matching measurements are shown in p was applied to port RF1. From this measurement, the attenuation and compression of the sinusoidal signal can be seen. The compression is caused by reducing the voltage difference The last measurement is shown in The aim of this paper was the design and implementation of a particularly low-cost SiGe BiCMOS technology, and the simulation as well as the realization and evaluation of the characteristics of the high-frequency semiconductor integrated switch. The analysis of the chosen solution using the theoretical knowledge of MOSFET transistors and 0.35 µm SiGe BiCMOS technology helped design the presented switch. The switch design was realized after an initial presentation of the switch issues, the fabrication technology used, and the theory associated with the MOSFET transistor models. The design was implemented and the predicted theoretical behavior of the switch was verified using simulations in a Cadence CAD design tool. Based on these simulations, the parameters of the final design of the UWB switch were improved by successively modifying the circuit parameters. Simulations were also performed on the layout of the designed chip, including parasitic elements due to the influence of the transistor placements and conductive paths on the chip, as well. The resulting post-layout simulations confirmed the predicted estimates in terms of bandwidth. The resulting design achieves bandwidths ranging from a “DC” signal up to nearly 6 GHz, with a considered −3 dB decrease. The insertion loss at low frequencies is 1.2 dB and the isolation has a minimum value of −18 dB. Power simulations were also performed on the layout. The switch achieves a 1 dB compression point at the power of around 5 dBm, which represents a power of about 2.5 mW. The designed switch performance parameters achieved are sufficient for our required switch application. The power consumption of the switch reaches the order of units of mW, thanks to the MOSFET transistors used, which are characterized by very low power consumption. There is still room for improvement in this design; the goal is to obtain the best possible parameters out of the available fabrication technology and the MOSFET transistors themselves. Although for our UWB applications, the available power and bandwidth are sufficient, we need to continue to look for ways to increase the power transmitted by the switch. Further improvements to the design consist of refining the layout created, increasing isolation, and reducing inductances and parasitic capacitances. The layout of the switch is designed so far as a block without the die pads for the bonding wires, so it would be useful to further analyze the influence of these parasitic properties, comparing them with the results that were obtained from measurements on an implemented UWB switch mounted on a development board. Its precise design took into account our applications for UWB sensor systems, namely for the switching and configuration of the UWB radar antenna MIMO system or the switching of the calibration tools, primarily intended for the calibration of the UWB sensor system (three outputs). The main achieved parameters of the designed switch compared to a commercial RF switch  and othe


The absence of artificial articular cartilage could cause the failure of artificial joints due to excessive material wear. There has been limited research on alternative materials for articular cartilage in joint prostheses, with few reducing the friction coefficient of artificial cartilage prostheses to the range of the natural cartilage friction coefficient (0.001–0.03). This work aimed to obtain and characterize mechanically and tribologically a new gel for potential application in articular replacement. Therefore, poly(hydroxyethyl methacrylate) (PHEMA)/glycerol synthetic gel was developed as a new type of artificial joint cartilage with a low friction coefficient, especially in calf serum. This glycerol material was developed via mixing HEMA and glycerin at a mass ratio of 1:1. The mechanical properties were studied, and it was found that the hardness of the synthetic gel was close to that of natural cartilage. The tribological performance of the synthetic gel was investigated using a reciprocating ball-on-plate rig. The ball samples were made of a cobalt-chromium-molybdenum (Co-Cr-Mo) alloy, and the plates were synthetic glycerol gel and two additional materials for comparison, which were ultra-high molecular polyethylene (UHMWPE) and 316L stainless steel. It was found that synthetic gel exhibited the lowest friction coefficient in both calf serum (0.018) and deionized water (0.039) compared to the other two conventional materials for knee prostheses. The surface roughness of the gel was found to be 4–5 μm through morphological analysis of wear. This newly proposed material provided a possible solution as a type of cartilage composite coating with hardness and tribological performance close to the nature of use in wear couples with artificial joints. Osteoarthritis has a high incidence in the elderly population , and it PHEMA has long been used to make contact lenses , which pIn this work, we used HEMA as the matrix and glycerol as the hardness regulator and then polymerized it in a water bath. Glycerol was selected as a gel polymerization regulator because it is a natural metabolite of the human body with good biocompatibility and is also a good lubricating candidate for joint frictional pairs. In our previous research, the optimal mass ratio of the HEMA monomer to glycerol was found to be 1:1 ,23. The The friction coefficients, wear surface profiles and surface roughness were obtained in the results. It was found that the gel exhibited the lowest friction coefficient among the other two materials, and the wear resistance performance was improved since no metal ions were observed from the metal-on-gel pair.The raw materials and reagents for the preparation of artificial cartilage prostheses are shown in HEMA and glycerin were injected into an Aluminum foil crucible, then mixed at a mass ratio of 1:1. After that, 0.2 wt% initiator ammonium persulfate was added to the foil crucible. As a preparation process for stirring, a magnetic rotor was put into the mixed solution. This crucible was placed in a magnetic stirrer with a constant temperature water bath of 70 °C, and the crucible was uniformly stirred to prepolymerise for about 15 min. When the solution was slightly viscous, the magnetic rotor was taken out, and the temperature in a constant temperature water bath was kept at 70 °C for 24 h until the reaction was complete. The flow chart for the preparation is shown in −3 m2 was subject to a compressive force. The axial pressure within the equivalent force range of 600 N to 1000 N was applied to the sample with an increment of 100 N and was controlled by a displacement speed of 2 mm/min. When each loading step was reached, the pressure was kept for 3 min and unloaded/rested for 3 min, as shown in Articular cartilage plays a dominant role in cushioning motion pressure in the human body; therefore, it is important to determine the compressive properties and elasticity of this new material . The evaThe tribological properties of the gel in sliding were investigated using a reciprocating ball-on-plate friction tester . The balls were made of a Co-Cr-Mo alloy, and the plates were made of synthetic PHEMA/glycerol gel, UHMWPE, and 316L stainless steel, respectively. Although the CoCr-stainless steel pairs are not currently used in clinics, we added this metal–metal friction pair as the control group mainly because we were interested in the changing trends of such contact pairs. The most used metal–metal materials in clinics are CoCr-on-CoCr, and one can find their frictional performance in the literature . Calf seThe reciprocating friction and wear tester is shown in In earlier studies, the range of natural normal joint contact stresses has been proposed as 0.1–2.0 MPa ,29. In tSince the synthetic gel was soft and easily deformed after compression, there was no pre-compression at the start of testing to ensure that the friction occurred at its original shape.The morphology of the samples was obtained by the 3D Optical Surface Metrology System  to observe the worn surface modification during the test. By the end of the experiment, the lubricating medium was collected, and the Cr and Mo elements in the deionized water were analyzed by an M-type oil spectrometer to evaluate the wear results during the whole experiment.After each loading step represented in The variations in the friction coefficients during the testing time for the three materials are shown in The friction coefficients of the UHMWPE plate and 316L stainless steel plate decreased with an increase in the friction time, contrary to the gel plate, and this change did not depend on the type of lubricating medium either. Among them, the frictional pair Co-Cr-Mo ball to the 316L stainless steel plate in the deionized water showed the largest decrease in friction coefficients by 37.9% from 0.178 to 0.110. For all these cases, the variation in the friction coefficients during friction time was sharper in the calf serum than in the deionized water.The overall-averaged friction coefficients for the 6 h testing were compared in The surface roughness of the plate samples before and after friction testing was extracted from the 3D micrographs, as shown in The morphology images of the initial plate surfaces (before the test) are shown in After friction testing, the gel plate maintained a relatively complete surface structure in the calf serum, with only a few scratches along the sliding direction, as shown in The ion levels of Cr and Mo collected in lubricants after wear testing are shown in It is known that in human daily activities, the articular cartilage always bears compressive stress, and such a mechanical characteristic is important for the gel’s application as an articular cartilage biomaterial . The mecThe frictional properties of gels were also partly determined by their mechanical properties . The excAn obviously different trend in the friction coefficient variations was found that the coefficient was decreased during testing in the metal-on-gel pair while the other two material pairs showed an increasing trend during this time . These vFirstly, the unpolymerized glycerol components were observed on the gel surface, and they played a role in lubrication, leading to lower initial friction. However, after some time, the glycerin was dissolved in the lubricating medium, and the benefit no longer existed. Secondly, the mass ratio of HEMA and glycerin did not reach the optimal rate of 1:1 anymore, and the change in the glycerin and water content could have reduced the mechanical strength of the gel material. As discussed in the compression test, the elasticity and hardness of the synthetic gel could be affected. Thirdly, the change in the gel components could affect the viscoelastic property of the gel. After a long-time, the continuous reciprocating of sliding the material became more viscous, resulting in larger deformation in the shear direction. This deformation increased the shear stress and contributed to an extra frictional force. The upward trend in the friction coefficient in the current gel materials was in line with the friction test of natural cartilage . It suggFor all the tested cases, a common trend was that the friction coefficients gradually reached a nearly stable value in about 4 h. It suggested that with continuing friction, the structure of the gel composition gradually reached a steady-state stage, as did the other two material pairs. The synthetic PHEMA gel plate showed the lowest friction coefficient -on-hard material pair. Avoiding the release of metal ions from the metal ball component was a great benefit of reducing adverse wear particle reactions with human joint tissues.The preliminary tribological study also had some limitations. Ammonium persulfate, as a toxic material, can be carefully considered as an initiator for the preparation of gel. Due to the short duration of testing, no gravimetric measurements of wear were taken during this work. On the other hand, the swing and torsion friction study of the hydrogel in terms of the flexion-extension and adduction-abduction of the hip and knee joints were found, and the sliding and rotation speed had a great influence on the friction behavior of the hydrogel study . The ranThe gel can only function as a cartilage replacement once sufficiently integrated with the implant or bone surface . In prevIn this work, a new artificial joint cartilage material, PHEMA/glycerol gel, was proposed and tested with mechanical compression and friction simulations, introducing the reciprocating friction and wear tester. The synthetic gel material was coated on the UHMWPE plate, sliding against a Co-Cr-Mo ball. Another two materials for the plates, UHMWPE and 316L stainless steel were tested for comparison. The conclusions are drawn below.The synthetic gel with HEMA and glycerin at a mass ratio of 1:1 exhibited excellent compressive properties in terms of elastic recovery and compression resistance.The PHEMA/glycerol gel showed a low friction coefficient which was close to the natural cartilage, and no metal ion was released in the lubricant.The friction coefficient increased during testing, probably due to the gel’s poro- and visco-elastic properties, and reached a stable structure in about 4 h.The proposed PHEMA gel material was tested and was proved to effectively reduce the friction of the artificial joint prosthesis, indicating its excellent lubrication performance. Moreover, the biological safety of this synthetic gel was confirmed to ensure that patients had little chance of suffering from osteoarthritis and allergic reactions due to wear products since no metal ions were observed through surface spectroscopic analysis after the wearing test. This new material could, therefore, be used as a cartilage composite coating for artificial joints, which could be a possible solution to reduce the rising temperature and noise, and prolong the service life of the joint prosthesis, particularly for hip and knee joints. Our results provide a new option as a potential articular cartilage substitute in clinical applications for cartilage tissue engineering due to the good porous elastic property, compression resistance, frictional property and biological safety of this synthetic PHEMA gel material.


The target of the study is to predict the inhibitory effect of amide derivatives on xanthine oxidase (XO) by building several models, which are based on the theory of the quantitative structure–activity relationship (QSAR). The heuristic method (HM) was used to linearly select descriptors and build a linear model. XGBoost was used to non-linearly select descriptors, and radial basis kernel function support vector regression (RBF SVR), polynomial kernel function SVR (poly SVR), linear kernel function SVR (linear SVR), mix-kernel function SVR (MIX SVR), and random forest (RF) were adopted to establish non-linear models, in which the MIX-SVR method gives the best result. The kernel function of MIX SVR has strong abilities of learning and generalization of established models simultaneously, which is because it is a combination of the linear kernel function, the radial basis kernel function, and the polynomial kernel function. In order to test the robustness of the models, leave-one-out cross validation (LOOCV) was adopted. In a training set,  Hyperuricemia is a chronic metabolic disorder caused by impaired purine metabolism . Excess Xanthine oxidase (XO) is a key rate-limiting enzyme in the purine metabolism pathway. It catalyzes the oxidation of hypoxanthine and xanthine to uric acid with reactive oxidants being released in the process . ExcessiCurrent therapeutic approaches for gout are mainly based on lowering serum uric acid levels; these approaches include the inhibition of XO, promotion of uric acid excretion, or alkalinization of urine . One effAmide derivatives are a newly discovered type of XO inhibitors that have significant research value. Although assessing the inhibitory effect of XO  and XGBoost methods. Comparing the performance of models developed by linear regression, support vector regression (SVR), and random forest (RF) regression, it was observed that the model built by mix kernel SVR exhibits the best predictive ability and robustness.The compounds listed in The process of descriptor calculation is the basis of constructing QSAR models. First, the structures of these compounds were drawn using ChemDraw software. The result file was then imported into HyperChem and optimized using MM+ in the molecular mechanics force field. Second, the semi-empirical method was used to carry out further optimization so that the compound could have the lowest energy. According to the principle of minimum potential energy, molecular stability is enhanced by minimizing the energy state of a system, with lower energy states resulting in greater stability. After this process, the corresponding result file was sent to MOPAC software to change the format. Finally, five classes of descriptors were calculated using CODESSA software. These classes include topological, constitutional, geometrical, quantum chemical, and electrostatic descriptors .The heuristic method, an efficient approach for descriptor selection and linear model construction, has no limitation to the size of the dataset . Before The descriptor selection process by the HM involves calculating intercorrelations between all descriptors and identifying pairs with high regression coefficients but low correlation coefficients. Non-collinear descriptors are added to these pairs to perform higher-order regression treatments, and the final equation is selected based on the maximum Fisher criterion and the highest cross-validated correlation coefficient used in the linear model .In order to improve the performance and robustness of the non-linear model, a non-linear method is used to select descriptors. XGBoost can be adopted to perform non-linear dimensionality reduction, which is mainly based on feature importance evaluation . There aThe coverage method determines feature importance by calculating the sum of the number of times that each feature occurs in all tree nodes. The higher the coverage is, the more important the feature is considered to be. This method is useful when working with datasets that have many features as it provides a quick way of identifying the most relevant features.The split gain method determines feature importance by calculating the information gain of each feature when the decision tree is split. The higher the split gain, the more important the feature is considered to be. This method is especially useful when working with datasets that have complex relationships between the features as it can capture subtler interactions than the coverage method.The split gain method is the default method used by XGBoost if no extra settings are added because the split gain method is generally more effective at capturing the underlying relationships between the features in the dataset. The coverage method provides a quick and simple way to evaluate feature importance, but it tends to favor features with a high cardinality. However, both methods have their own strengths and weaknesses, and the choice of which method to use will depend on the specific dataset and the goals of the analysis.Random forest is a popular bagging algorithm, which combines multiple decision trees to make accurate predictions in a regression. This algorithm creates multiple decision trees by randomly selecting subsets of features and data samples, and then averaging their outputs to obtain the final prediction. In this process, as the depth of the tree increases, the entropy of sample types under decision tree branches will also increase, which will seriously affect the efficiency of the decision tree and increase the risk of overfitting. Therefore, the random forest reduces the overfitting probability and improves the generalization ability of the model by pruning and other methods. These methods remove unnecessary nodes or branches from the decision tree.Random forest has the advantages of fast training and good performance in a classification, and it can handle non-linear data very well. However, compared to other regression methods like SVR, the performance of random forests may be affected by outliers in the data, which can result in reduced robustness. Therefore, its performance somewhat depends on the dataset.The support vector machine (SVM)  is a claBecause SVR is very sensitive to the scale of the input data, normalization must be executed before the SVR process to ensure the accuracy and robustness of the model to be established.After performing normalization, the next step is to establish models by SVR. First, the original problem is transformed into a dyadic problem while satisfying the Karush–Kuhn–Tucker (KTT) principle, and the Lagrange multiplier method is used on the basis of the dyadic problem. Ultimately, the problem can be simplified to solve the following quadratic convex programming problems:The initial SVR excelled in solving linear problems rather than non-linear problems, so the kernel method was then introduced to solve non-linear problems. It is an easy way that uses the kernel function to calculate the inner product in Eq. Moreover, a soft-interval SVM was introduced to enhance the robustness of the SVM, which controls the tolerance of the SVM to noise by introducing relaxation variables and penalty factors.Since the RF and MIX-SVR algorithm has many parameters that are not independent, particle swarm optimization (PSO) was used for parameter optimization in RFs and MIX SVR.PSO, an optimization algorithm based on population intelligence, was first proposed by Eberhart and Kennedy in 1995. PSO is a vector-based method, which achieves optimal search by sharing information among populations, and all iterations update the position vector and velocity vector, according to the following two equations:Based on the traditional PSO algorithm, some improvements have been made. Because some parameters can only be integer values, while some parameters take values in the whole real number domain, mixed optimization is introduced, which means that the improved algorithm will automatically match, whether it is for integer optimization or real number optimization. The principle of solving the integer search problem is based on the original real number search problem, which involves rounding the real number x generated by each operation to the nearest integer to participate in the next operation.In addition, the improvement of PSO also involved the method of linear decreasing weights, which means the pace is very large at first but decreases with each epoch. The weight varies according to Eq. A total of 646 descriptors were calculated according to the descriptor calculation step described in the descriptor calculation subsection. The number of descriptors in linear models was increased from one to seven, and the corresponding Based on the seven descriptors selected by the HM, the linear model was built and shown in Eq. The t linear , so non-Non-linear descriptor selection can better capture complex non-linear relationships in the data . As seleAll calculated descriptors exported from CODESSA were pre-processed, and non-generic descriptors were removed. As mentioned previously, XGBoost selects descriptors by calculating the importance of each descriptor based on the split gain method. The correlation matrix is shown in (1) Number of trees (NT): Increasing the number can improve the model performance at the cost of computational complexity.(2) Maximum depth of the tree (MD): The purpose of this parameter is to prevent overfitting by controlling the depth of the tree. Increasing the maximum depth can improve the model performance at the cost of the risk of overfitting.(3) Minimum number of samples required to split internal nodes (MS): The purpose of this parameter is to control the minimum size of leaf nodes to avoid overfitting. Increasing this parameter may lead to underfitting.(4) Minimum number of samples required for leaf nodes (ML): It is used in the pre-pruning of the decision tree.The RF method is a popular regression method, which performs well in many research fields . Four imThe PSO method is adopted to tune the hyperparameters; one relatively good set of parameters are NT = 522, MD = 13, MS = 7, and ML = 4. The tuning process of the model based on RFs by PSO is shown in To ensure comparability between models, the same four descriptors selected by XGBoost were used in the models built by linear-kernel SVR, polynomial kernel function (poly)-kernel SVR, and radial basis kernel function (RBF)-kernel SVR. The As shown in the results, the three models by single-kernel functions did not perform well, which is related to their characteristics. So attempts were made to get better models by combining different kernels. The radial basis kernel function is strong in fitting, which increases the possibility of overfitting to some extent. The polynomial kernel function and the linear kernel function show good generalization performances, which increases the possibility of underfitting to some extent. MIX SVR is an attempt at combining the superiority of different kernels in order to build a model with both strong generalization and fitness abilities.For SVR, the choice of the kernel function has an obverse impact on the regression results. As mentioned previously in the single-kernel SVR part, the RBF kernel is good at fitting, while the linear and poly kernel are good at generalization. The kernel function designed in this paper is a proportional combination of these three kernel functions, which improves both the generalization performance and robustness of the model. The mix kernel function is expressed as follows in Eq. After adjusting the parameters using PSO, optimum A visual comparison is shown in The MIX-SVR method can effectively fit the data without overfitting because it takes advantage of the complementary features of polynomial and radial basis kernel functions. Overall, the mix-kernel SVR method is a promising approach for use in various applications requiring accurate and reliable regression analysis results.The descriptor-labeled HD is the surface area of the hydrogen donor divided by the total area of the molecule. It has been determined to hold the greatest significance among the four descriptors being evaluated. This suggests that it is likely a variable that is strongly associated with the dependent variable or that it possesses a high degree of predictive power. In addition, this descriptor may also enhance model performance by collaborating with other descriptors. The second descriptor, ME, has the maximum electron–electron repulsive force between electron clouds in a hydrogen atom. The inter-electron repulsion affects the reaction rate to some extent. HS is the surface area of the hydrogen-bonded receptors in the molecule. Molecules with more hydrogen-bonded receptors can form tighter bindings to target proteins. HC is the surface charge of the hydrogen bonding donor atom. They are both quantum chemical descriptors, which can be used to predict their chemical properties.The performance and robustness of the models constructed by mix-kernel SVR have been verified in predicting the elopment .


The magnetic spectrometer AMS-100, which includes a superconducting coil, is designed to measure cosmic rays and detect cosmic antimatter in space. This extreme environment requires a suitable sensing solution to monitor critical changes in the structure such as the beginning of a quench in the superconducting coil. Rayleigh-scattering-based distributed optical fibre sensors (DOFS) fulfil the high requirements for these extreme conditions but require precise calibration of the temperature and strain coefficients of the optical fibre. Therefore, the fibre-dependent strain and temperature coefficients  The demand for optical fibre sensors has increased in recent years due to their unique capabilities and advantages over conventional sensors . One of In recent years, this wide range of applications has been increasingly extended to space applications, where the reliable monitoring of critical structural elements is essential . EspeciaOverall, fibre optic sensors offer a combination of high accuracy, immunity to electromagnetic interferences, lightweight and compact design, durability, multi-parameter sensing, and a long range that make them ideal to use for the harsh environment in space applications, such as for use with the magnetic spectrometer AMS-100 [Fibre optic sensors use the properties of light to measure various physical parameters. Two common types of fibre optic sensors are FBGs (fibre Bragg gratings)  and distThis work will cover the determination of the fibre-dependent strain and temperature coefficient for distributed Rayleigh-based OFDR measurements. The advantage of the proposed method in comparison to studies of the past ,18 is thThe proposed method for measuring distributed temperatures for cryogenic temperatures in the literature  and in tEquation (1) describes the basic model for spectral shift decoupling in temperature change and strain. s of 10% . The meastrument . ΔT is tThe increasing range of application areas for optical fibre sensors results in the need for more precise modelling and the separation of the actual temperature and strain applied to the fibre. Therefore, the fibre constants need to be determined precisely and calculated with a more complex model, which will be discussed in more detail in the next sections.This paper was written in parallel to a master’s thesis by Clemens Dittmar . The theThe setup includes an OBR-4613 from Luna Technology, which is a Keithly 2701 multimeter for read-out of strain gauges, thermal resistors (Pt1000), and thermally insulated chambers for slow warm-up and cryogenic tensile testing.As mentioned in the introduction, the basic model for calculating strain and temperature in Rayleigh-based OFDR measurements does not cover more complex use cases. Therefore, a more complex, non-linear model is needed. The standard model is a linear approximation of Equation (2) for weak FBGs.ve index , of the As Wang et al., explained by applying Gosh’s theory for the temperature dependence of the refractive index for glasses, the TOC of silica has a temperature dependency that is related to the band gap energy structure . They shFor a free fibre, the coefficient of thermal expansion , a spatial resolution of 0.6 mm, and a sensitivity of 1 microstrain (μm/m) and 1 °C. The Luna OBR system was chosen as the interrogation system for the determination of the OFDR-relevant fibre parameters due to its high resolution and flexibility to perform measurements in various locations, as it has a portable design. In continuous measurement mode, the software automatically performs OFDR measurements at predefined intervals. The shortest interval used here was one second. A Python script was written for which a measurement period and a fixed number of measurements could be defined. A tunable laser source, most simply with a ramp waveform, emitted light that was coupled into an optical fibre. The laser source in the LUNA OBR 4613 emits 10 mW in a possible tuning range of 1260 nm to 1340 nm. The intensity was split into two paths by a coupler that allows a light beam to be split into two paths. One path led to the interferometric measurement setup shown in The polarisation controller provided equal intensities for the two polarisation axes coming from the reference arm. The light returning from the FUT had arbitrary polarisation states and was coupled with the light from the reference arm. The light was split into two perpendicular polarisation states by a polarising beam splitter (PBS), and the intensity of the interfering signals was measured by photodiodes connected to a data acquisition board. This ensured that the interference between the light from the measurement and the reference arm was measured as a vector sum of the obtained signal in both detectors. When the laser wavelength is tuned, a periodic signal is generated at the detector, and its frequency depends on the position of the respective backscattering fibre segment. Backscattered light from positions further away along the fibre corresponds to higher frequencies. The signal sum of all segments along the fibre was decomposed into its frequency components by Fourier transformation. The frequencies then correspond to the signal locations in the fibre. The amplitude of each frequency component indicates the strength of the respective reflection ,28.The measurement of thermal and mechanical strain was carried out using strain gauges from Hottinger Brüel and Kjaer GmbH (HBM), which consist of a 350 Ω resistance grid that is sensitive to mechanical and thermal influences. In order to compensate for temperature-dependent resistance changes when the strain gauge is applied to steel, the manufacturer provides a compensation curve for each strain gauge size. Therefore, a strain gauge must always be read with a temperature sensor. The grid has a CTE equal to that of steel, and the sensor signal must be compensated if a substrate other than steel is used, as the temperature correction assumes steel as the sensor target. To determine a correction function, the strain gauges were calibrated on two materials with well-known thermal strain characterisations (NIST data). Titanium (Ti-6Al-4V)  was usedClass A Pt1000 platinum resistors were used for temperature measurement. These were read out using the four-wire technique with a Keithly 2701 multimeter. Resistance was converted to temperature with the Callendar van Dusen Equation . The staTo test the strain gauge calibration, the thermal strains of Aluminium-6061-T6511 and oxygen-free copper were measured, as their thermomechanical behaviour is known from the NIST database ,33. The The results of the measurements are shown in For the measurements, a test sample was manufactured from Aluminium 6060 with the geometry shown in The fibre used here is a SM1500(9/125)P germanium-doped single-mode fibre with core, cladding, and coating dimensions of (9/125/157 16 grey) . The adh16 grey) ,36. Acco16 grey)  and our A liquid nitrogen container was placed around the central area of the strain gauge, enclosing the central 30.0 cm of the specimen. This allowed the strain specimen to be clamped into the tensile test machine and the central area to be cooled with liquid nitrogen. One series of measurements was carried out at room temperature and two at 77 K under load control. The tension was increased to 400 N steps over 30 s and held for 10 s. The maximum force was 3600 N.For the warm-up experiment from 77 to 300 K, the aluminium tensile specimen was equipped with two Pt1000 temperature sensors at each of three positions for this test. These were thermally bonded to the aluminium at the top and bottom using copper tape and thermal paste. The OBR measurement system was controlled externally from a laboratory PC using a Python program to record one measurement per minute. The sample was placed inside a thermal box, which contained a steel-grid base with a large thermal capacity at the bottom and was supported by polycarbonate blocks to provide thermal insulation.For the higher temperature range, the specimen was installed in a climate chamber, and ten measurements were taken at each temperature level.The climatic chamber was set to 6 different temperatures from 233 K to 353 K and held constant for 30 min until the Pt1000 data had settled.To verify that the strain of the aluminium was well transferred to the fibre, a detailed 3D FEM simulation was performed in Abaqus 2020. In the simulation, the fibre (fused silica), the polyimide coating, the epoxy adhesive EP-50-5150, and the Aluminium 6060 were resolved in detail see  by C3D8RA 3D model was chosen because, with the strain in fibre strain, the measured value is orthogonal to the cross-section. To reduce the computational effort, the axisymmetric specimen was simulated in a quarter model, which had a fixed point constraint at the origin of the coordinate system and symmetry boundary conditions in the x–y plane and y–z plane. The mirroring in the x–y plane by the chosen boundary conditions guarantees that the specimen can contract and expand unhindered under the influence of temperature, and, thus, no thermally induced stresses are applied by the constraints.The mechanical material properties of aluminium , epoxy , and polFor the simulation of the fibre, the literature values of fused silica were used. The thermal expansion coefficient of 273.15 K . LiteratThe following section covers the results of the simulation, strain, and temperature sensitivity of the optical fibre.With the simulation of the linear sample described in To determine the strain sensitivity, the mean value over 2 cm of the spectral shift at the position of the strain gauge was used and plotted against the measured strain from the strain gauge. Only data from 400 N upwards (after the first force step) were considered to avoid possible systematic effects caused by the self fixing of the clamps from the tensile measurement stand. The maximum temperature variation was 0.1 K at room temperature and 0.04 K and 0.05 K at 77 K.A linear fit to the strain sensitivities was performed, and the parameters are listed in The OBR data from the warm-up measurement were processed with a reference at liquid nitrogen just before the liquid nitrogen level in the thermal box fell below the sample. This was 170 min after first LN2 contact, and only data from this data set onwards were used. The OBR data from the climate chamber measurement were processed with a reference at 293 K. The reference and measurement data sets were compared stepwise for the evaluation of the spectral shift. The step size was defined as the gauge length and gave the width of the data block used for the cross-correlation of spectral shift and time shift. The gauge length used was 2 cm with a resolution of 0.6 mm. An interquartile range filter (IQR) was applied to the fibre data to filter out the discontinuous peaks at higher temperature differences.Equation (14) was used to calculate the thermal fibre signal, which was corrected for the thermo-mechanical component. The temperature data were used to calculate the thermo-mechanical strain, The diagram in The parameters from the two adjustments are given in The temperature sensitivity determined here was dependent on the strain sensitivity, which was also true for all other temperature sensitivities reported in the literature. A comparison was, therefore, difficult, since, in the literature, a constant strain sensitivity has been assumed for the determination of the temperature sensitivity. The final calibrated fibre parameters for the strain and temperature coefficients with the overall uncertainty are summarised in An independent test was carried out with a cylinder-shaped aluminium sample. It had the dimensions of 50 mm in height, The optical fibre was glued into a groove with dimensions of It can be seen in As shown in this study, the investigation of the temperature and strain behaviour of fibre optic sensors in the range of 77 K to 353 K is crucial for the accurate monitoring of critical changes in structures. The results show that the fibre-specific strain coefficient was temperature-dependent and decreased at lower temperatures. In addition, the temperature coefficient exhibited a linear dependence on temperature, which must be taken into account for accurate temperature measurements. With the additional simulations carried out, it was shown that the strain applied to the aluminium sample corresponded to the fibre strain, and, thus, a calibration by the cryogenic tensile test setup was possible. The use of the custom-built cryogenic tensile test setup enabled accurate measurements of the OFDR spectral shift at different temperatures for applied external strain that were verified by strain gauge measurements. In addition, a test over the entire temperature range showed that the parameters determined here described the fibre optic data as well as the actual fibre parameters from Taolue et al., without requiring additional fibre parameters such as the Young’s modulus for their determination. Of particular importance was a detailed calibration of the strain gauges used for the relevant temperature range, as the manufacturer only specified this calibration curve for a single material with a constant alpha, so it was repeated here for different materials with a precisely known CTE temperature dependency. With these calibrated measurements, a non-linear model was parameterised to describe the temperature dependency of the OFDR spectral shift for fibres that were mechanically bonded into a carrier substrate. The need for this additional calibration resulted in an overall time-consuming characterisation, which limits the proposed method and can be improved in further studies. Moreover, the proposed method was tested for standard telecommunication single-mode fibres and provided a high resolution over a long range. It is, therefore, unclear whether this method works for other fibre types. A change in the fibre type could result in limitations such as a decrease in the measurement range due to high internal losses or lower resolution. There is also uncertainty as to whether the presented method can also work with polarisation maintaining, hollow core fibres, or multi-mode fibres, which should be investigated further in upcoming studies. The presented model with the SM9/125 fibre could be used to measure the strain of a support structure with integrated fibres and enabled the decoupling of the strain signal from the signal caused by the CTE of the material, which could thus be measured independently of the fibre CTE. This study confirms that fibre optic sensors could accurately determine the strain or temperature of an aluminium structure over the entire temperature range considered. This shows that optical fibres are a suitable solution for monitoring critical structural changes in space, such as on an AMS-100 magnetic spectrometer.Furthermore, the measurements underline the advantages of using fibre optic sensors in extreme environmental conditions such as space. The proposed method can be used, for example, for detecting a quench in a high-temperature super-conducting coil such as in the AMS-100. It can be triggered by external mechanical or thermal influences, among other things. More likely, however, is a temperature rise caused by current not flowing through the superconducting layer for unknown reasons. If the structure can expand locally due to the thermomechanical stress, the mechanical signal becomes dominant due to the high CTE of aluminium. This signal correlation can be investigated with the proposed sensor fibres. These fibre optic sensors offer high accuracy and immunity to electromagnetic interference. Due to their low weight and compact design, they can be easily integrated into structures with little installation space. With the precise calibration of the fibre parameters, parallel measurement of the temperature and strain is also possible for known materials, even over long distances.This paper shows a successful method to determine the fibre-specific coefficients for OFDR measurements to calculate strain and temperature from 77 to 353 K. A detailed calibration was performed using additional strain gauges and thermal resistors. Moreover, a nonlinear model was described to decouple strain and temperature with these calculated parameters for integrated sensor fibres in known materials. The calibration parameters could be tested independently with the linear model for a ring-shaped sample. The standard model showed a deviation from the measured parameters for colder temperatures, while the prediction with the non-linear model agreed with the measured spectral shift. This proves the calibration parameters. Overall, the results of this study provide important foundations for the development of distributed fibre optic sensor systems for space applications and demonstrate the potential of fibre optic sensor technology in extreme environments. Further research in this area should focus on improving the accuracy and precision of fibre optic sensors and exploring new applications and uses for fibre optic sensors in space exploration and other extreme environments.


This paper introduces a deep learning approach to photorealistic universal style transfer that extends the PhotoNet network architecture by adding extra feature-aggregation modules. Given a pair of images representing the content and the reference of style, we augment the state-of-the-art solution mentioned above with deeper aggregation, to better fuse content and style information across the decoding layers. As opposed to the more flexible implementation of PhotoNet , which targets the minimization of inference time, our method aims to achieve better image reconstruction and a more pleasant stylization. We propose several deep layer aggregation architectures to be used as wrappers over PhotoNet, to enhance the stylization and quality of the output image. Among a wide range of rendering techniques, style transfer occupies its place through artistic and photorealistic stylization. Style transfer refers to a computer vision technique that generates an output image based on two input images (reference and style images). In this process, the content of the former is transferred to the output image using the style of the latter.Two of the most common subfields of style transfer are artistic style transfer and photorealistic stylization . ArtistiPhoto editing and art: neural style transfer is already used in photo editing software for generating license-free artwork, art designing, fashion designing, etc. .Virtual reality: neural style transfer can generate virtual reality scenes and environments of great visual impact, much faster than conventional methods.Style transfer has many applications in the arts and entertainment industries, being able to produce good aesthetic and artistic results . We findTexture synthesis: extracts the main characteristics from the input images.Image reconstruction: expands the synthesized content in the desired style, preserving high-level features.Given two images—content, and reference of style—neural style transfer (NST) aims to translate the former into the texture domain of the latter. To accomplish this, two main rendering steps must be performed:From a network architecture point of view, this is accomplished using hybrids of autoencoder networks. An autoencoder is a neural network that is trained to attempt to copy its input to its output : the encThe neural style transfer is achieved by combining the encoder trained on the input image and the decoder trained on the style, so that the compressed input image is expanded in the style learned by the decoder .An encoder is a feed-forward neural network used to transform its input into a compressed, latent representation, using convolutional and MaxPool layers. The output of an encoder, typically called code or bottleneck, is passed as input to the decoder in an autoencoder architecture.J. An et al. use in  the pre-The decoder part plays the role of generating the final stylized image. The decoder is responsible for reconstructing the image from the encoded representation generated by the encoder, while also incorporating the style information from the style image.It typically consists of a series of up-sampling layers that increase the spatial resolution of the encoded feature maps, followed by a series of convolutional layers that learn to generate the final image. These convolutional layers are usually similar to those used in the encoder, but with the filter sizes and number of channels reversed.To incorporate the style information, the decoder is often augmented with additional layers that are designed to match the statistics of the style image.Our work explores different types of deep layer aggregation techniques presented by F. Yu et al. in  to augmeSkip connections are aggregation techniques used in deep neural network algorithms to improve model convergence. As the name suggests, skipping a layer in the neural network means feeding the output of one layer as the input to the next layers (instead of only to the next one) . From a F. Yu et al. investigate in  diverse Our work aims to use these techniques presented by F. Yu et al. in , expandiCurrent style transfer methods are prone to overfitting since the decoder is trained to reconstruct the style image, which can lead to extracting spatial correlations in the style image instead of only colors and color patterns. The spatial correlations should only come from the input image. Deep layer aggregation architectures are known to reduce overfitting and might obtain better results in photorealistic style-transfer; therefore, we tested multiple aggregation techniques.Each proposed aggregation strategy uses the pre-trained VGG-19 for the image classification network as the encoder and features a structurally asymmetric decoder, unlike PhotoNet, which maintains the symmetry between these two main components. Depending on the architecture’s type, deep feature aggregation is applied along the decoder to merge (concatenate) and fuse  multi-level features in a different manner. As in  depicts For each architecture, the fuse pyramid modules reduce the number of feature maps to the lowest value of the concatenation. Two exceptions to this rule are the bottleneck and output pyramid modules, which reduce the number of feature maps to 3 and 512, respectively. We performed up-sampling on smaller-scale feature maps before concatenation. Depending on the model used to perform style transfer, the number of pyramid modules and the values for the up-sampling factor vary accordingly.The iterative deep aggregation (IDA) decoder progressively aggregates and deepens the representation in the image reconstruction stage of the network. Aggregation begins at the smallest scale (right after the bottleneck feature aggregation module) and then iteratively merges larger scales throughout the decoder. The semantic refinement of the shallow features increases with the number of aggregation nodes. The tree-structured aggregation (TSA) decoder aggregates hierarchically through a tree structure of blocks to better span the feature hierarchy of the network across various depths.The reentrant aggregation (RA) decoder is a refinement of TSA that deepens aggregation by routing intermediate aggregations back into the network and improves efficiency by merging successive aggregations at the same depth.The hierarchical deep aggregation (HDA) decoder is a much deeper, generalized feature aggregation architecture that also includes IDA, which focuses on fusing resolution and scales, whereas HDA focuses on preserving and merging features from all modules and channels . We can To keep the inference time of our network close to PhotoNet , we adaporigI represents the original image and reconI represents the image reconstructed by the decoder. In addition, a perceptual loss term is introduced to enhance the reconstruction stage of the decoder,iΦ represents the output of the ith stage of the pre-trained VGG-19 . This function measures high-level semantic differences between features across the network. Thus, the overall loss function,All aggregation decoders were trained in the same manner as PhotoNet, using the MS COCO 2014 dataset. The objective was to invert deep features received from the extraction encoder back to high-quality images. The reconstruction loss is defined as the Frobenius norm between the input and inverted output images:Improved generalization: some aggregations might be better adapted to some styles, and this would be best captured by the metric of FID score.Better output quality: some aggregations might be better suited to capture different aspects of the style, and this would be best captured by the metric of reconstruction error More flexibility: some aggregations might be better suited to capturing color and spatial patterns, and this would be best captured by the metric of total variation; since the spatial coherence should come only from the input image, and the color from the style image, a very low value of total variation is a sign of content loss when doing transfer on complex images such as the ones in our dataset.By using different aggregations, it might be possible to achieve a better aesthetic effect. A better result would be comprised of improvements in one or several aspects   was usedThe discrete total variation (TV)  was compN represents the number of images from our content dataset.We also measured the feature inversion capabilities of each decoder by calculating the reconstruction error between the reconstructed image and the corresponding original image as follows,The FID and TV were computed on 768 × 512 transferred images, while the reconstruction error was calculated using pairs of 512 × 512 images.However, by analyzing the way in which our strategies transfer texture from the style reference to the content input using Out of a total of 170 votes performed on five different samples, RA was the most preferred photorealistic style transfer approach according to We compared the expected results with the measured results see , and we Regarding the TSA and HDA, the results were similar to PhotoNet .The RA aggregation technique showed improved (low) reconstruction error and was the most pleasing version in the subjective evaluation, when compared to PhotoNet or all other aggregation techniques.We computed both the inference and training time for each architecture and show the results in Aggregation is an important aspect, not only for classification or high-resolution network architectures, but also for photorealistic style transfer purposes. By addressing deep aggregation in the context of photorealistic style transfer, we demonstrated that different aggregation approaches led to different stylization capabilities.Compared to plain PhotoNet, our experiments with extra aggregation techniques led to longer training and inference times  and for RA (reentrant aggregation decoder), it led to better subjective results, proving, thus, to be a more pleasing stylization result for humans. This result was also indicated by the reconstruction error, which was lower than when using the PhotoNet alone. We checked results of total variation (TV) and the Fréchet inception (FI) scores and they were similar to PhotoNet scores. As future work, we would like to repeat the experiment on grayscale/infrared-spectrum images, and on higher-definition images when an NVIDIA GPU such as the RTX H100 with 80 GB of RAM memory becomes available.Other areas where these NST decoders might be tested are data augmentation of medical data see ,14) and  and 14])