esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antenna Single Element
### Aperture Coupling Feeding  
The antenna feeding is performed by means of a coupling aperture or slot that is etched on the ground plane. The bottom patch is electromagnetically coupled by this aperture slot, which at the same time is fed by a microstrip line that is placed below the ground plane. A side view of this feeding topology can be seen in Figure 2. This excitation technique allows for an independent design of the radiation and the feeding parts of the antenna. The electrical properties of each feeding layer are shown in Table 1.  
The desired dual-polarization operation of the antenna requires the use of two orthogonal slots in order to excite the corresponding polarization. The H-shape smooths the field distribution at the slot edges, thus improving the coupling between the slot and the bottom patch [36]. Each slot is shifted with a small offset in order to maximize the port isolation (\(S_{21}\)), and it is fed by its respective microstrip line. Each line is placed on different FR4 substrate heights to reduce the coupling between both polarizations. The width of both lines is set to yield an impedance of 50 \(\Omega\). Figure 3 shows the spatial disposition of the slots and the feeding lines. The corresponding geometrical values are displayed in Table 2.  
From now and on, the horizontal polarization (H) is excited by the slot 1 and its corresponding line 1. The same assumption is made with the vertical polarization (V) fed by the slot 2 and line 2. According to this reference, the electrical field of the horizontal polarization is along the \(x\)-axis and along \(y\) for the vertical polarization. The bending of the line 2 is done in order to place the two antenna connectors on opposite sides for further mechanical purposes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Substrate** & \(\varepsilon_{r}\) & **Thickness** \\ \hline Substrate Patch 1 & Rogers RT/Duroid 6006 & 6.15 & \(h_{sp1}=1.27\) mm \\ Foam 1 & Rohacell HF 51 & 1.06 & \(h_{foam1}=18\) mm \\ Substrate Patch 1 & Rogers RT/Duroid 6006 & 6.15 & \(h_{sp2}=1.27\) mm \\ Foam 2 & Rohacell HF 51 & 1.06 & \(h_{foam2}=4\) mm \\ Feed Substrate 1 & FR4 Epoxy & 4.4 & \(h_{foam1}=1.27\) mm \\ Feed Substrate 2 & FR4 Epoxy & 4.4 & \(h_{foam2}=1.27\) mm \\ Foam 3 & Rohacell HF 51 & 1.06 & \(h_{foam3}=5\) mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Antenna layer properties.  
Figure 3: Antenna feeding layout and constructed prototype.
## 2 Methods
### Support Vector Regression  
SVMs can be used for classification and regression [44; 45]. In the case of regression, support vector machines are termed as support vector regression and is closely related to statistical learning. To build a regression model, a training data set \(\mathcal{T}\) is required:  
\[\mathcal{T}=\{\vec{X},\vec{Y}\}, \tag{3}\]  
where \(\vec{X}=\{x_{1},\ldots,x_{K}\}\) are the input and \(\vec{Y}=\{y_{1},\ldots,y_{K}\}\) the output sets. The goal of the SVR is to approximate the data set via a regression model \(F\) similar to:  
\[F(x)=\sum_{l=1}^{M}a_{l}x_{l}+b, \tag{4}\]  
where the coefficients \(b\) and \(a_{l},\forall l=1,\ldots,M\) are unknown and have to be identified using the training data set \(\mathcal{T}\). Please note that the SVR was originally developed for linear regression problems but can be easily extended to nonlinear regression problems due to the so-called Kernel trick [46]. Moreover, empirical models based on SVR are robust to outliers, that is, data that are corrupted by large random errors or offsets. In addition, the SVR only considers those coefficients \(a_{i}\) that are relevant and sets non-relevant coefficients to zero, which simplifies the model building and ensures well-posed identification problems in the case of limited data; that is, \(K<M\). Thus, outliers and limited data have less impact on SVR models compared to ordinary regression techniques. For the technical details of SVR, the interested reader is referred to [46; 47; 48] and references therein. In the present study, frequency dependent NFR data sets during cycle ageing are included in the training data sets. Technically, we used the following SVR setting within the R statistical computing environment including the e1071 library: radial basis kernel, an epsilon value in the insensitive-loss function of 0.1, and a C-constant of the regularisation term in the Lagrange formulation of 8.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 7 Security Analysis
### Security Attributes
#### 7.1.4 Fairness  
The PVPBC voting system guarantees that the election result will not be known during the election period. Therefore, voters cannot be influenced. This property is achieved by separating the election phase from the post-election phase. During the election phase, cast votes are published to the DLT in an encrypted form alongside commitments and encrypted tracking numbers. The ballot is only mixed, shuffled, and decrypted during the post-election phase by a set of tellers.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology  
### Ground-Based Vicarious Radiometric Calibration  
In general terms, ground-based vicarious radiometric calibration uses scenes of known spectral radiance and compares them to a sensor under a test to evaluate its absolute radiometric calibration. In the solar-reflective regime, the creation of a scene with known spectral radiance requires knowledge of the surface reflectance [25] and the atmospheric transmittance [18], as well as knowledge of the atmospheric scattering properties. The surface reflectance is typically measured by ground instrumentation that may or may not require on-site personnel. Atmospheric measurements are made using suitable spectroradiometers, with two examples being the Automated Solar Radiometer (ASR) [26; 27; 28] and the Cimel CE-318 solar-lunar photometer [29; 30]. In general terms, the results of these two measurements are used in a radiative transfer code to determine the at-sensor spectral radiance for a given airborne or spaceborne instrument [31]. The work described in this paper uses two independent ground-based techniques, the first of which uses a system of auto  
Figure 1: The relative spectral response (RSR) for the Landsat 9 OLI multispectral bands 1–5 (**left**) as well as 6 and 7 (**right**). The panchromatic band 8 is omitted for clarity, and the cirrus band 9 is also excluded since it is not used in this work.  
mated instruments to make surface reflectance and atmospheric measurements (RadCaTS), while the other uses ground-based personnel for similar measurements (reflectance-based approach). Each of these systems is described in the next two sections, with the RadCaTS being introduced first since it is now the primary data collection technique used by the University of Arizona (U Arizona).
## 3 Materials and Methods
### RGB-D and IRT Images Fusion Module
#### 3.4.3 Thermal and RGB-D Image Fusion  
A one-to-one pixel correspondence between sensors is needed, to be able to overlap the thermal view with the RGB one. The process to obtain this correspondence is known as registration between sensors. The color and depth sensors of the RGB-D camera are already registered, such that only registering the IRT camera with the RGB-D one is required. The registration process can be performed as follows: First, the 3D point cloud is calculated from the depth image (\(I_{depth}\)) using the RGB camera intrinsic parameter; then, this 3D point cloud is transformed using the rigid transformation defined by the extrinsic parameters obtained in the calibration \([R_{C,T},t_{C,T}]\) (see Figure 7); finally, this 3D point cloud is projected, using Equation (4), onto the IRT image plane using the IRT camera intrinsic parameters. The 3D projected points give the correspondence between IRT pixels and RGB pixels, obtaining a registered IRT image. Then, a more explainable image can be obtained by performing a blending procedure to mix the color image \(I_{\text{rgb}}\) and the registered IRT image \(I_{\text{thermal}}\) using alpha channel mixing.  
In addition, knowing the object pose allows us to show only the temperature of the object and not that of the whole scene. Moreover, it is accurate enough to even extract the temperature of each object component. This segmentation is performed by calculating a mask \(I_{mask}=I_{model\text{-}depth}>0\) and applying it onto the registered IRT image of \(I_{\text{thermal-rgb}}\), such that we have:  
\[I_{compose}=\alpha\cdot I_{\text{thermal-rgb}}\cdot I_{mask}+(1-\alpha) \cdot I_{\text{rgb}}. \tag{16}\]  
In Figures 1 and 8, two examples of the fusion of RGB and IRT information with different \(\alpha\) values can be seen.
# 2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar
## 2 Signal Model and Principle of the Range and Velocity Estimation  
### Signal Model  
The transmitted signal of the FMCW radar [31] can be expressed as:  
\[\begin{split} s^{\text{TX}}(t)&=a_{t}\exp\biggl{\{} j2\pi(f_{0}t+\int_{0}^{t}\mu tdt)+\varphi_{0}\biggr{\}}\\ &=a_{t}\exp\biggl{\{}j2\pi(f_{0}t+\frac{1}{2}\mu^{2})+\varphi_{0} \biggr{\}},\text{for }0\leq t\leq T_{c}\end{split} \tag{1}\]  
where \(a_{t}\), \(f_{0}\), \(\mu\), \(\varphi_{0}\) and \(T_{c}\) denote the complex amplitude, carrier frequency, frequency slope, initial phase and ramp duration of the transmitted signal, respectively.  
The received signal is reflected by \(D\) targets and can be expressed as follows:  
\[\begin{split} s^{\text{RX}}(t)&=\sum_{i=1}^{D}a_{r( i)}s^{\text{TX}}(t-\tau_{i})+n_{z}(t)\\ &=\sum_{i=1}^{D}a_{r(i)}\exp\biggl{\{}j2\pi(f_{0}(t-\tau_{i})+ \frac{1}{2}\mu(t-\tau_{i})^{2})+\varphi_{1i}\biggr{\}}+n_{z}(t)\end{split} \tag{2}\]  
where \(a_{r(i)}\), \(\tau_{i}\) and \(\varphi_{1i}\) denote the strength, time delay and phase of the \(i\)th reflected signal, respectively, and \(n_{z}(t)\) is the noise component.  
By mixing \(s^{\text{TX}}(t)\) with \(s^{\text{RX}}(t)\) and passing it through a low-pass filter (LPF), the analog IF signal of \(D\) targets is expressed as:  
\[x(t)=\sum_{i=1}^{D}a_{rt(i)}\exp\biggl{\{}j2\pi(f_{0}\tau_{i}+\mu\tau_{i}t- \frac{1}{2}\mu\tau_{i}^{2})+\varphi_{rt}\biggr{\}}+n_{z}(t) \tag{3}\]
## 5 Results and Discussions
### Effect of Assembly Errors
When the center distance deviation is considered, the number of contact pins will increase or decrease with the rotation of the cycloid gear, while the number of contact pins will increase under the influence of the misalignment deviations. Although the increase in the number of contact pins helps to improve the force condition of each contact pin in theory, the misalignment deviations will cause serious eccentric load on the tooth surface of the contact pins. This will increase the MCS and shorten the contact fatigue lifetime of the tooth surface, and the uneven load distribution on the tooth surface will cause the transmission accuracy to decrease and at the same time accelerate the tooth surface wear and reduce the transmission accuracy lifetime of the cycloid-pin gear pair.
# Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions
###### Abstract  
The purpose of this research was to improve the performance of a wireless body area sensor network, operating on a person in the seated and standing positions. Optimization-focused on both the on-body transmission channel and off-body link performance. The system consists of three nodes. One node (on the user's head) is fixed, while the positions of the other two (one on the user's trunk and the other on one leg) with respect to the body (local coordinates) are design variables. The objective function used in the design process is characterized by two components: the first controls the wireless channel for on-body data transmission between the three sensor nodes, while the second controls the off-body transmission between the nodes and a remote transceiver. The optimal design procedure exploits a low-cost Estra, which is an evolutionary strategy optimization algorithm linked with Remcom XFdtd, a full-wave Finite-Difference Time-Domain (FDTD) electromagnetic field analysis package. The Pareto-like approach applied in this study searches for a non-dominated solution that gives the best compromise between on-body and off-body performance.  
WABAN; wearable antenna; human body model; FDTD analysis; evolutionary optimization algorithms; computational electromagnetics +
Footnote †: journal: Sensors
## 6 Conclusions  
This paper proposed D-ST, a solution that combines the flexibility of online EDF scheduling with the temporal isolation offered by the transmission gate mechanism. D-ST is able to satisfy the requirements of all the RT traffic classes typically found in automotive communications while maintaining the full compliance with the IEEE 802.1Q-2018 standard. This property makes D-ST able to support a broad range of automotive applications over TSN networks, without hardware modifications in the Ethernet switches.  
Future work will address how to exploit stochastic [62; 63; 64] and worst-case [39] analysis for admission control to automatically admit new flows in the network or reject them. Furthermore, extensive experiments with commercial off-the-shelf (COTS) devices will be carried on.  
Conceptualization, L.L., L.L.B. and G.P.; methodology, L.L., L.L.B. and G.P.; investigation, L.L., L.L.B. and G.P.; writing--original draft preparation, L.L., L.L.B. and G.P.; writing--review and editing, L.L., L.L.B. and G.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Italian Ministry of Research through the PRIN 2017 Program, Project SPHERE--Software architecture for Predictable HEterogeneous REal-time systems, project number 20172 NNB4T.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Time Unit \(\boldsymbol{u}\)(\(\upmu\)s)** & **Number of Stream** & **Number of Queues** & **Used for EDF** \\  & & **Gates (N)** & **Transmissions per** \\  & & & **Ethernet Port (Q)** \\ \hline D-TSN & 220 & 8 & 8 \\ D-ST & 220 & 7 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network configuration parameters for D-TSN and D-ST.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flow** & **Deadline (ms)** & \
\begin{tabular}{c} **D-ST** \\ **Max _e2 eDelay_ (ms)** \\ \end{tabular} &
\begin{tabular}{c} **D-TSN** \\ **Max _e2 eDelay_ (ms)** \\ \end{tabular} \\ \hline LiDAR & 10 & 0.01 & 8.29 \\ Ultrasonic & 20 & 0.01 & 18.26 \\ ADAS sensors & 1 & 0.45 & 0.45 \\ Video & 10 & 8.50 & 8.28 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation results: maximum end-to-end delay.  
Data Availability Statement: The data underlying this article will be shared on reasonable request from the corresponding author.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### Analyses--Instrumental Methods and Measurements  
Water contact angle analysis (WCA) was performed by the sessile drop technique (5 \(\mu\)L) at room temperature and atmospheric pressure with a Kruss DSA100 goniometer. Three independent measurements were performed for each sample, each with a 5 \(\mu\)L water drop, and the obtained results were averaged.  
Differential scanning calorimetry (DSC) was performed using a NETZSCH204 F1 Phoenix calorimeter. Samples of 6 \(\pm\) 0.2 mg were placed in an aluminum crucible with a punctured lid. The measurements were performed under nitrogen in the temperature range of 20-220 \({}^{\circ}\)C and at a 10 \({}^{\circ}\)C/min heating rate.  
For tensile strength tests using the L-FDM technique, paddle-type 1 BA specimens were printed according to the PN-EN ISO 527-1:2020-0133 standards.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Slicer** & **Creality 1.2.3.** \\ \hline layer height [mm] & 0.2 \\ \hline first layer speed/printing speed [mm/s] & 20/50—horizontal, 20/30—vertical \\ \hline print/bed temp. [\({}^{\circ}\)C] & 220/60 for PLA and 230/70 for PETG \\ \hline number of shells & 2 \\ \hline infill [\%] & 100 \\ \hline infill angle [\({}^{\circ}\)] & 45 \\ \hline fan speed [\%] & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Printing parameters.  
The tensile tests of the printed samples were carried out using the INSTRON 5969 universal testing machine with a maximum load force of 50 kN. The testing machine was set to a travel speed of 2 mm/min for the tensile strength measurements.  
Charpy impact test (with no notch) was performed on an Instron Coast 9050 impact machine according to ISO 179-135.  
The morphology and microstructure of the prepared composites were observed by scanning electron microscopy (SEM). The imaging was performed in three SE modes. The surface observations were analyzed using a Hitachi SU70 scanning electron microscope equipped with an energy dispersive spectrometer (EDS) for chemical analysis.  
Light microscopy Images of the surface and fractures of the composites were taken using KEYENCE VHX-7000 digital microscope (Keyence International, Mechelen, Belgium, NV/SA) with VH-Z100R wide-angle zoom lens at \(\times\)100 magnification. Images were taken with depth composition and the aid of 3D imaging-built software.
## 4 Results
### Sensitivity Analysis  
The sensitivity analysis plays a significant role in understanding the contribution of individual variables to the model and can give suggestions on applying the model better. In the study, we followed the procedures used in a prior study by [PERSON]. et al. (2012) to conduct the sensitivity analysis. Table 6 gives the initial and conventional values of variables as references to examine the sensitivity of the SEB-A method. The direct inputs of Equation (5), \(Rn-G\) and LST - \(T_{a-loc}\), were analyzed (the \(LE_{exo}\) was ignored here because it is added to Equation (2) directly, and the sensitivity of it is similar to \(R_{n}-G\)). Perturbations of \(Rn-G\) were specified as [\(-20\%\), \(20\%\)], with a variation step of \(10\%\). Perturbations of LST - \(T_{a-loc}\) were also specified as [\(-20\%\), \(20\%\)], with a variation step of \(10\%\).  
Tables 7 and 8 show the deviations in ET computed after perturbing \(Rn-G\) and LST - \(T_{a-loc}\) from their initial values individually. \(Rn-G\) is positively correlated with ET, but LST - \(T_{a-loc}\) is negatively correlated with ET. When comparing the sensitivity of \(Rn-G\) with that of LST - \(T_{a-loc}\), \(Rn-G\) is found to be more sensitive than LST - \(T_{a-oc}\) to ET. A \(10\%\) increase in \(Rn-G\) could result in a \(30\) W/m\({}^{2}\) increase in ET estimates, whereas a \(20\%\) increase in LST - \(T_{a-loc}\) only results in a \(17.3\) W/m\({}^{2}\) decrease in ET estimates. The sensitivity results indicate that the SEB-A method requests accurate estimates of \(Rn-G\), but it is less constrained with LST - \(T_{a-loc}\).
## 3 Performance Evaluations
### Evaluating the Performance of Multi-Model and Single-Model Gait-Based KAM Prediction Systems  
The Multi-model and Single-model Gait-based KAM Prediction Systems were evaluated to compare their classification performance and predictive accuracy across multiple gait patterns. Figure 6 shows the Confusion Matrix for the Multi-model Gait-based KAM Prediction System. This matrix visualizes how the model predicted each actual gait pattern and highlights the classification accuracy for each specific pattern. As shown in the graph, the normal (94.03%), toe in (92.59%), and toe out (89.77%) patterns, which have clear and consistent characteristics, are predicted accurately. However, the trunk lean pattern, which exhibits irregular and complex characteristics, is predicted with a lower accuracy (82.23%) compared to the other patterns. This suggests that while the model effectively captures the stable features of certain gait types, it has more difficulty with variable patterns like trunk lean. Based on these results, we calculated the overall Accuracy and F1 Score, as shown in Table 5, both of which are around 0.9 or higher.  
Figure 7 shows a comparison of the actual versus predicted KAM values for both systems, demonstrating that the Multi-model system's predictions more closely align with the actual measurements than the Single-model system. This alignment signifies the Multi  
Figure 6: Confusion matrix of Multi-model Gait-based KAM Prediction System (Knee Thrust 97.15%, Normal 94.03%, Toe in 92.59%, Toe out 89.77%, Trunk lean 82.23%).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Metrics** & **Value** \\ \hline Accuracy & 0.9288 \\ F1 Score & 0.9077 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy of the Multi-model Gait-based KAM Prediction System.  
model system's robustness in predicting KAM values across diverse gait patterns, as it leverages gait classification to adjust predictions according to specific gait patterns. In contrast, the Single-model system, which does not incorporate prior gait pattern classification, exhibits a larger prediction error and less alignment with the actual values.  
Further analysis of the predictive accuracy is shown in Figure 8, which plots the actual KAM values against the predicted values for both systems. In this comparison, the closer clustering of data points along the \(y=x\) line in the Multi-model system demonstrates its ability to handle diverse gait patterns more accurately, whereas the Single-model system shows greater deviation from the ideal line. This comparison underscores the advantage of the Multi-model approach, which incorporates pattern-specific classification steps before KAM prediction, allowing for more precise KAM estimation across the varied gait types.
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 7 Conclusions  
Integrated circuit testing is a critical process for ensuring the quality and reliability of integrated circuit products. In the post-Moore's Law era, with the continuous evolution of integrated circuit fabrication technologies and the development of SoC and chiplet technologies, the number of IP cores and chiplets integrated on a single chip is increasing, leading to more complex functionalities. This has resulted in a significant surge in the volume of test data, while the performance improvement in test equipment lags behind the advancements in design technology, creating a bottleneck in the testing of very large-scale integrated circuits. To address this bottleneck, this paper summarizes and analyzes the existing integrated circuit test compression techniques from three aspects: test compression-oriented coding methods, scan chain structure optimization, and enhancement of coding compression efficiency. In view of the limitations of current research, the paper also prospects the future development trends of integrated circuit testing, with the aim of providing guidance and recommendations for the advancement of test compression technology.  
Conceptualization, D.Y.; methodology, S.Z.; investigation, L.C.; writing--original draft preparation, L.Z.; writing--review and editing, W.Z.; visualization, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.