esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling of Coupled Hysteresis  
The experimental results in Section 2 show that not only do PAMs have inherent hysteresis properties, outputs of different PAMs distributed parallelly in the soft manipulator, but they also depend on each other and show strongly stiffness-dependent characteristics. To describe this complex behavior, a stiffness-dependent and coupled hysteresis model (SDCHM) consisting of three parts is established, as shown in Figure 6. Generalized Prandtl-Ishlinskii (GPI) models GPI\({}_{1}\), GPI\({}_{2}\), and GPI\({}_{3}\) are used to model the inherent hysteresis behavior of each contractile PAM, respectively. The GPI model features a simple structure and high flexibility, suitable for addressing PAM hysteresis with dead zones and asymmetry. Furthermore, the GPI model has an analytical inverse solution, providing convenience for the inverse compensator derivation. A linear coupling structure is cascaded in series in the actuator coupling submodule. \(G_{ij}(i=1,2,3;j=1,2,3)\) are gains that describe the input-output coupling in the manipulator system. For circumventing simplifying assumptions and facilitating the completion of compensators, inspired by data-driven modeling approaches, the stiffness dependence of the output behavior of the soft manipulator and other unmodeled errors in the first two subparts are fitted in the stiffness-dependent submodule \(g(\cdot)\) using a T-S fuzzy neural network (TSFNN) [35]. The inflation pressure of \(p_{1}\), \(p_{2}\), and \(p_{3}\) in contractile PAMs firstly acts upon the inherent hysteresis submodule, and outputs \(v_{1}\), \(v_{2}\), and \(v_{3}\) from the inherent hysteresis submodule are subsequently input into the actuator coupling submodule. Before the final calculated displacement of \(\hat{y}_{k}(k=1,\ 2,\ 3)\) is  
Figure 5: Stiffness effects on coupled hysteresis output of the manipulator system. (**a**) \(p_{1}-y_{1}\); (**b**) \(p_{1}-y_{2}\); (**c**) \(p_{1}-y_{3}\).  
generated, the actuator coupling submodule outputs \(h_{k}(k=1,\ 2,\ 3)\) and inflation pressure \(p_{0}\) in the extensor PAM flow into the stiffness-dependent submodule for further calculation.
## 2 Basis of the QuEChERS Method
### Miniaturization of the QuECHERS Procedure  
Currently, one important trend in the analytical chemistry field is the miniaturization of conventional extraction methods to develop environmentally friendly methodologies, which mainly involve minimum consumption of solvents and samples in order to comply with the GAC principles [52; 53; 54; 55; 56]. Based on this, miniaturization is one of the modifications that the QuECHERS procedure has experienced in the last years [15]. For instance, several authors have proposed its miniaturization for the determination of a wide variety of analytes in a broad range of matrices, such as for the simultaneous analysis of perchlorate and bromate in fruits and vegetables [57], the determination of antibiotics in human urine and serum [58], the determination of zearalenone in cereals [59], the multicomponent extraction of phenolic compounds in baby foods [60], the analysis of psychotropic drugs in blood and serum [61], the simultaneous determination of pesticides in human serum [62] and in odonata nymphs [63], and the extraction of bisphenol A from human urine samples [64]. Regarding PAs, two works have successfully proposed the miniaturization of the QuECHERS strategy in different aromatic herb samples, including oregano, thyme, rosemary, basil and herbs de Provence [27; 28]. In both works, satisfactory recovery results were achieved, and the methods were properly validated fulfilling the criteria set in the validation guidelines. Miniaturization was achieved by reducing, by ten times, the amount of sample, the volume of solvents and the amount of partitioning salts employed according to the original QuECHERS procedure (Table 1). Accordingly, for the determination of 21 PAs and PANOs in oregano samples, the miniaturized QuECHERS procedure proposed using 0.2 g of dry oregano, 1 mL of ACN as extraction solvent, 0.65 g of partitioning salts (keeping the proportion 4:1:1:0.5 of MgSO\({}_{4}\):NaCl: trisodium citrate dihydrate:disodium hydrogen citrate sesquihydrate) and 175 mg of clean-up sorbents [27]. For thyme, basil, rosemary and herbs de Provence samples, the miniaturized QuECHERS procedure was reduced in the same way; however, a second extraction cycle with 0.5 mL of ACN before the clean-up step and an elution step with 250 \(\mu\)L of ACN after the clean-up step were required to improve the extraction efficiency of some analytes, leading to 0.2 g of dry herb sample, 1.75 mL of ACN as extraction solvent, 0.65 g of partitioning salts (keeping the proportion 4:1:1:0.5 of MgSO\({}_{4}\):NaCl: trisodium citrate dihydrate:disodium hydrogen citrate sesquihydrate) and 175 mg of clean-up sorbents [28]. In comparison to the original QuECHERS method, these miniaturized procedures produce minimal waste amounts and involve considerably less sample, partitioning salts and solvent amounts. Moreover, the good analytical performance of the two procedures highlights the possibility to successfully miniaturize the QuECHERS strategy, leading to improved cost-effective and environmentally friendly microextraction methods, which meet the GAC principles.
## 2 Materials and Methods
### Carrageenan-Induced Rat Paw edema Measurement  
A total of 30 male Wistar rats (200-250 g) were procured from BioLASCO, Taipei, Taiwan. The Institutional Animal Care and Use Committee, Taipei Medical University (LAC-2015-0267) approved animal experiments and care procedures. The animals were maintained at a condition with 22 \(\pm\) 4 \({}^{\circ}\)C temperature, 50 \(\pm\) 20% humidity and 12 h light/dark cycle in animal center. They had access to standard rodent pellet food and water ad libitum. The rats were anesthetized by 5% isoflurane. Animals were grouped as: (i) vehicle control (20 \(\upmu\)L, 0.1% DMSO); (ii) carrageenan control; (iii) standard anti-inflammatory drug indomethacin (5 mg/kg b.w.); and (iv) and (v) 1 and 2 mg/kg of TQ-6, respectively. A carrageenan-induced paw edema was measured as described previously [15], with some minor modifications. Paw thickness was measured before (0 h) or after 1, 2, 4, 6, and 24 h carrageenan injection.
## 4 Conclusions and Recommendations
### Conclusions
Furthermore, the integration of satellite products such as SMAP/Sentinel, CGLS Sentinel-1, and ERA5-Land contributes to robust, detailed, and precise SM monitoring. This comprehensive approach, combining diverse data sources and modeling techniques, establishes an effective framework for addressing the complex dynamics of SM. * UAVs offer promising potential for detailed SM monitoring due to their high-resolution capabilities. However, their high costs, expertise requirements, and dependence on weather conditions, along with limited resources, necessitate the development of methods to integrate UAV data with satellite observations. This approach can provide reliable estimates over vast tracts of land typical of the western US, optimizing both feasibility and cost-effectiveness in large-scale SM monitoring. Additionally, a gap was identified to demonstrate how RGB/multispectral imaging can be used to \"substitute\" the use of expensive shortwave infrared (SWIR) sensors. Demonstrating how RGB or multispectral imaging can effectively replace SM estimation with SWIR in UAVs presents an important area for future research. * Despite advancements, challenges remain, particularly in the application of interpolation methods and the need for high-resolution, precise data for disturbed vegetation in semi-arid regions.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### Number of SUT Samples to Test
#### 4.2.1 Considerations on Number of SUT Samples in TID Tests  
In our TID tests, such part-to-part variability was also observed. Let us take an example of the MOSFET switching time degradation during X-rays tests (see Figure 3). In all PoL modules that were tested, the MOSFET transistors were from the same production lot. Let us focus on test results for PoL 2D boards: even if the test configuration was the same, there might be observed faster degradation of the SUT010 MOSFET (blue colour) when compared to the SUT011 MOSFET (red colour), This difference is supposed to be the main factor explaining why the system-level degradation, and--finally--functional failure of the SUT010 device is observed at lower TID level than for SUT011. This result shows that the part-to-part variability might be transformed into the SUT-to-SUT variability: TID testing of only one SUT doesn't give full information about the TID performance of givendevice series, even if the components in the series are from the corresponding production lots. Testing of a higher number of SUTs is required to give reliable information on the TID radiation performance of the system.  
On the other hand, if some risk might be accepted, one of the ways to reduce the required number of SUT samples would be to irradiate this reduced SUT sample set to a much higher TID level than expected in the final application of the system. In [9] it was chosen to irradiate the system (equipment) to the level 300 times higher than expected in the target application, to accept it for use. Although such margin is typically difficult to achieve for space applications, this approach might be sometimes feasible. If it is possible to irradiate the system up to the catastrophic failure and if it is possible to track the source of failure (such as in an example of X-ray tests [22]), this information might be also useful for further analysis of the TID level that system may withstand.
# Malware Classification Based on Shallow Neural Network
## 3 Classification Methodology
### Overview of Classification Model  
The SNNMAC, the malware classification model we proposed in this paper, is for portable executable (PE) files, the binary executable file format on Windows. First, the SNNMAC disassembles malware sample files and extracts opcode sequences from the.asm file. Then, it applies the control transfer instruction-based n-gram on the sequences to obtain an n-gram dataset. Low-frequency word deletion and the hash trick are also performed in this step. Later, the embedding layer transfers every n-gram into a fixed-length vector, and the hidden layer produces a file feature for the output layer to decide the final label.  
Concretely, the classification process of the SNNMAC can be divided into two stages. As shown in Figure 1, the first stage is to process the malware sample file. It generates n-gram data as the input of the next stage. The second stage takes a shallow neural network to learn from the n-grams and outputs the final classification result.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 6 Conclusions  
In this study, an ML-based technique for the identification of V2X communication signals without any prior information is proposed. It combines the use of robust features based on time-frequency analysis along with the random forest classifier.  
First, we present the model of the three considered signals as well as their physical layer parameters. A comparison of these parameters shows that LTE-V2X and NR-V2X have similar properties, in particular those related to the periodicity of signals. Their instantaneous frequency is thus extracted to distinguish between them, then passed through the SVD algorithm to reduce their dimensionality.  
By implementing the random forest classifier, the results show the effectiveness of our approach and the superiority of IF as a distinctive feature when compared to the cyclostationarity-based feature utilized in many existing studies. Moreover, comparative analysis with the statistical approach indicates that the latter is not suitable for identifying signals that have similar CFs, and that it is highly dependent on the SNR level.  
In subsequent studies, the performance of the proposed identification technique can be explored on vehicular signals affected by multi-path fading channels. Furthermore, the proposed technique can also be used for real-world applications such as dynamic spectrum access or jamming signals detection.  
Conceptualization, C.S. and F.E.; methodology, C.S.; software, C.S.; validation, F.E.; formal analysis, C.S.; investigation, C.S.; resources, F.E.; data curation, C.S.; writing--original draft preparation, C.S.; writing--review and editing, C.S.; visualization, C.S.; supervision, F.E.; project administration, F.E.; funding acquisition, F.E. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the project ELSAT 2020-OS4 ORIO. It is co-financed by the European Union with the European Regional Development Fund, the French state and the Region Hauts de France.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Experiment
### Discussion and Analysis
#### 4.3.1 Mutual Coupling Analysis  
Due to the limitations of the natural frequencies, the mode shapes of frequencies, and inertial effects, the low-frequency acoustic sensors, such as transducers are usually large [43]. For engineering implementation, the minimum spacing of the array elements is constrained. The distance between adjacent element of the off-grid array designed using GEWOA can be less than half the wavelength, which results in the array element falling in the radiated sound field of that adjacent element.  
Figure 18: Beampatterns of sparse arrays designed using different methods.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Arrays** & **DC Array** & **GWO Array** & **GEWOA** \\ \hline PSLL(dB) & \(-15.34\) dB & \(-15.82\) dB & \(-17.58\) dB \\ Threshold Time(s) & 122.41 s & 20.54 s & 14.39 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The PSLLs of arrays designed using different algorithms.  
The mutual coupling phenomenon occurs between adjacent array elements. This increases with the decrease of array element spacing, and directly affects the setting of the minimum spacing of the array element. According to the mutual radiation impedance model [44], the mutual radiation impedance \(X_{AB}\) of an array element in the radiation field of another array element is  
\[X_{AB}=\rho cSka^{2}/d_{AB}. \tag{21}\]  
where \(\rho\) is the medium density. \(c\) is the speed of sound. \(S\) is the surface area of the array element. \(a\) is the radius of the sensor element. \(d_{AB}\) is the distance of two elements. \(k\) is the wave number assuming the resonant frequencies of the array element is 3.5 kHz. The radius of the array element is 0.4 m. The curve of the radiation impedance changing with the array element spacing is shown in Figure 19.  
It can be seen that \(d/\lambda\geq 0.4\), mutual-impedance is less than its self-impedance. In the off grid array design, the operating frequency is 300 Hz, and the minimum spacing of array elements is set to 2 m due to engineering requirements. Since the minimum spacing is greater than 0.4 times the wavelength of the resonant frequency, the mutual radiation effect is less than the self-radiation, and it can be disregarded.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 6 Field Data Analysis
### Land Vehicle Test
The use of only a C/N\({}_{0}\)-based weighting shows generally low performance in mitigating multipath errors. The improvement obtained with the Code-Minus-Carrier (CMC)-based corrections decreases from static to vehicular scenarios with performance mostly limited by cycle slips caused by obstructions and antenna motion. For the detection/exclusion and detection/de-weighting techniques, the performance is limited by measurement geometry and the percentage of affected measurements. For all but the downtown test scenario, the combined methods show higher performance compared to each single technique. As discussed previously, in the downtown test, since the multipath behaves more like noise than bias, the C/N\({}_{0}\)-based weighting shows slightly higher performance before applying any exclusion or correction. Figure 17: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Signal**} & **Positioning** & **East Mean** & **North** & **Height** & **East RMS** & **North** & **Height** \\  & **Approach** & **(m)** & **Mean (m)** & **Mean (m)** & **Mean (m)** & **RMS (m)** & **RMS (m)** \\ \hline \multirow{3}{*}{Low Multipath Area} & A1 & \(-\)1.10 & \(-\)0.05 & 1.38 & 1.26 & 0.49 & 1.49 \\  & A2 & \(-\)1.09 & \(-\)0.13 & 1.31 & 1.27 & 0.52 & 1.48 \\  & A3 & \(-\)1.08 & \(-\)0.03 & 1.32 & 1.25 & 0.46 & 1.44 \\  & A4 & \(-\)1.07 & \(-\)0.11 & 1.29 & 1.25 & 0.49 & 1.45 \\ \hline \multirow{3}{*}{Campus MP} & A1 & \(-\)0.07 & 1.82 & 4.49 & 1.05 & 5.44 & 8.35 \\  & A2 & 0.05 & 0.42 & 2.67 & 1.03 & 1.95 & 3.62 \\ \cline{1-1}  & A3 & 0.62 & 2.13 & 3.68 & 1.14 & 5.09 & 7.48 \\ \cline{1-1}  & A4 & 0.36 & 0.68 & 1.73 & 1.63 & 1.44 & 2.80 \\ \hline \multirow{3}{*}{Downtown Area} & A1 & \(-\)0.11 & 2.08 & 3.29 & 12.50 & 15.38 & 17.19 \\  & A2 & 0.35 & 2.38 & 3.58 & 12.95 & 15.27 & 17.29 \\ \cline{1-1}  & A3 & \(-\)0.15 & 2.06 & 3.35 & 13.12 & 15.41 & 18.09 \\ \cline{1-1}  & A4 & 0.38 & 2.37 & 3.57 & 12.55 & 14.95 & 18.19 \\ \hline \hline \end{tabular}  
* A1: C/N\({}_{0}\)-based LS solution with no correction, exclusion (or de-weighting): A2: C/N\({}_{0}\)-based LS solution with detection and exclusion (or de-weighting): A3: C/N\({}_{0}\)-based LS solution with CMC-based error correction; A4: Combination of a preceding multipath detection and exclusion (or de-weighting) followed by a CMC-based error correction and a C/N\({}_{0}\)-based weighting.  
\end{table} 
Table 3: Position errors for GPS L1—Land vehicle test.
# Compatibility Analysis Between RedCap Non-Public Networks and 5G NR in TDD FR1 and FR2 Bands
## 3 Key Limitations of RedCap's Technological Capabilities
Here, data rate requirements shift from the downlink to the uplink (or at least become symmetrical). While conventional smartphones with human-driven applications emphasize data in the downlink direction, emerging UE types like industrial sensors or closed-circuit television (CCTV) cameras prioritize data transmission in the uplink direction. Consequently, operators may need to reconsider their prevailing TDD UL/DL configuration, historically inclined towards downlink. The dynamic nature of the 5G NR TDD UL/DL configuration permits operators to configure a non-public network (e.g., within a factory) with symmetric UL/DL resources, while outside the factory, downlink resources dominate. This can potentially lead to harmful interference from the private networks that used RedCap to the public networks in the FR1 and FR2 frequency ranges. 5G NR public networks rely highly on the frequency bands n77 and n78 (mostly 3400-3800 MHz) and on band n258 (24.25-27.5 GHz). In this study, we try to estimate the degree to which RedCap private networks' deployment in the urban environment may affect public TDD 5G networks in the FR1 and FR2 frequency bands.
## 2 Materials and Methods
### Laser Irradiation Setup  
Figure 2 shows the schematic illustration of laser irradiation setup in bead-on-plate welding experiment. A picosecond pulsed laser of 1064 nm wavelength and 12.5 ps pulse duration was used as a laser source, which was tightly focused inside the glass specimen by an objective lens with spherical aberration correction. For fusion micro-welding by using ultrashort pulsed laser, 10 ps in pulse duration is typically preferable [25]. As this extreme short pulse duration makes it easy to achieve very high peak laser intensity with low pulse energies, and it shows better results such as nonlinear absorption inside transparent materials and melting phenomena by combining a high pulse repetition rate [26; 27]. In order to focus on discussing the influence of numerical aperture, pulse duration was fixed at 12.5 ps in this study. N.A. 0.45, 0.65, and 0.85 of objective lenses with spherical aberration correction were used. The specifications of laser oscillator are shown in Table 2. The borosilicate glass (Schott, D263) specimen was fixed on a jig and scanned linearly by using several combinations of processing parameters. The laser beam was focused at 300 \(\upmu\)m below the top surface of glass specimen, and molten area was formed inside the glass specimen by the laser irradiation [18]. The geometrical contour of one molten area was similar to tear drop, thus this kind of molten area is referred to as \"teardrop-shaped\" in this paper. Then, the molten area of glass specimen was observed by using an optical microscope. The observation directions such as top view, side view, and cross sectional view are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} Density \(\rho\) (g/cm\({}^{3}\)) & 2.51 \\ Specific heat \(c\) [ J/(kg\(\cdot\)K) ] & 2.2 \\ Melting temperature \(\theta\)\({}_{\mathrm{m}}\) (K) & 1100 \\ Coefficient of thermal expansion \(\alpha\) (1/K) & 7.2 \(\times\) 10\({}^{-6}\) \\ Young’s modulus \(E_{\mathrm{Y}}\) (kN/mm\({}^{2}\)) & 72.9 \\ Refractive index \(N_{\mathrm{R}}\) & 1.5 \\ Poisson’s ratio \(\mu\) & 0.208 \\ Knoop hardness & 470 \\ \end{tabular}
\end{table}
Table 1: Specifications of borosilicate glass D263.  
Figure 3: Schematic illustration of observation direction by optical microscope.  
Figure 2: Schematic illustration of laser irradiation setup in bead-on-plate welding experiment.
# Wave Diffraction by Metamaterial-Coated Wedges: The UAPO Solution for Skew Incidence
## 4 Numerical Examples
In order to obtain reliable data, automatic adaptive meshes from \(0.000075\lambda_{0}\) to \(0.075\lambda_{0}\) have been used. Save for Figure 8, the Figures from 6 to 10 contain: (a) the amplitude of the UAPO-based field component and the corresponding Comsol Multiphysics(r) data; (b) a comparison between the related phases. One should note that the reported plots show large fluctuations, so providing hard test-beds. Figures 6 and 7 are relevant to \(E^{i}_{\beta^{\prime}}=1,E^{i}_{\phi^{\prime}}=0\) and \(E^{i}_{\beta^{\prime}}=0,E^{i}_{\phi^{\prime}}=1\), respectively, when \(\phi^{\prime}=80^{\circ}\), and despite the fast variability of amplitude and phase of the field, the UAPO-based data fit very well those attained by Comsol Multiphysics(r). Moreover, the interested reader can surely appreciate Figure 8a,b, where the UAPO diffracted field components are compared with the corresponding Comsol Multiphysics(r) data that were obtained by extracting the GO field contributions. The agreements are very encouraging. The last figures refer to \(\phi^{\prime}=40^{\circ}\). Accordingly, one surface of the wedge is involved in the evaluation of the UAPO diffracted field as in Figures 4 and 5. Good results are again achieved, but some of the lobes of the amplitude patterns are not correctly recovered and the phase values show misalignments in correspondence of the angular region centered on the incident field boundary. It is authors' opinion that such a case suffers from the absence of the diffraction contribution from the internal PEC wedge.