esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Predictive Equations and Machine Learning Models
### Machine Learning Methodologies
#### 2.3.2 Tabular Generative Adversarial Networks (TGAN)  
The availability of large and high-quality datasets is necessary for the use of machine learning techniques in the area of engineering systems modeling. However, the generation of statistically significant amounts of data points using traditional experimental methods can be a costly process. Generative adversarial networks (GANs) have been proposed recently as an alternative approach for generating large synthetic datasets on which state-of-the-art ML models can be trained [50; 51]. GANs consist of two components called the generative model and the discriminator model. These two components are trained at the same time, whereby the generative model constantly generates data samples which increasingly resemble the data points from the real dataset, and the discriminator model classifies the newly generated points as real or synthetic. The aim of this training process is to bring the generative model to the point where the newly generated data points are indistinguishable by the discriminator model from real data [52]. In this study, the synthetic dataset was generated using the CTGAN software library for the Python programming language [53]. The synthetic data points were generated in such a way that the features had a similar probability distribution to the original data and the same upper and lower bounds. The process of generative model training is described in Equation (4).  
\[\underset{G}{\mathrm{min}}\underset{D}{\mathrm{max}}V(D,G)=E_{x\sim p_{data}( x)}[\log D(\mathbf{x})]+E_{z\sim p_{z}(z)}[\log(1-D(G(z)))] \tag{4}\]  
In Equation (4), \(\mathrm{x},\mathrm{p_{data}(x)},\mathrm{z},\mathrm{p_{z}(z)},\mathrm{D(x)}\), and \(\mathrm{G(x)}\) denote a data point, the probability distribution of the real dataset, a random noise vector, the prior probability distribution of \(z\), the discriminator model and the generator model, respectively. Equation (4) describes a mini-max game between the generator (\(\mathbf{G}\)) and the discriminator (\(\mathbf{D}\)) with the value function \(\boldsymbol{V(D,G)}\)[52].
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 3 Preliminaries
### Homomorphic Encryption
#### 3.1.1 Partially Homomorphic Encryption  
PHE allows performing an unlimited number of a single type of allowed operations on encrypted data. The algorithms used for applications in which PHE schemes are applied support only additions or multiplications, not both. Among the major PHE schemes are RSA, GM, El-Gamal, Benaloh, NS, OU, Pailier, DJ, KTX, and Galbraith [32].
###### Abstract  
This study presents the development of a number of finite element (FE) models of the pelvis using different continuum and structural modelling approaches. Four FE models were developed using different modelling approaches: continuum isotropic, continuum orthotropic, hybrid isotropic and hybrid orthotropic. The models were subjected to an iterative adaptation process based on the Mechanostat principle. Each model was adapted to a number of common daily living activities (walking, stair ascent, stair descent, sit-to-stand and stand-to-sit) by applying onto it joint and muscle loads derived using a musculoskeletal modelling framework. The resulting models, along with a structural model previously developed by the authors, were compared visually in terms of bone architecture, and their response to a single load case was compared to a continuum FE model derived from computed tomography (CT) imaging data. The main findings of this study were that the continuum orthotropic model was the closest to the CT derived model in terms of load response albeit having less total bone volume, suggesting that the role of material directionality in influencing the maximum orthotropic Young's modulus should be included in continuum bone adaptation models. In addition, the hybrid models, where trabecular and cortical bone were distinguished, had similar outcomes, suggesting that the approach to modelling trabecular bone is less influential when the cortex is modelled separately.  
biomechanics; finite element modelling; pelvis; bone adaptation; musculoskeletal modelling 1
## 2 Generation of Frequency Distribution Schemes
In this way, we can generate the corresponding distribution scheme under different frequency domains. Subsequently, we evaluate which distribution scheme is optimal based on factors such as the offset frequency duration, the number of switching, and the possibility of low-frequency optimization.
# Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments
## 4 Experiments and Discussion  
### Experimental Setup  
The proposed method was evaluated using simulations based on ROS and Gazebo, executed on a server equipped with an Intel i9-13900k CPU and an NVIDIA RTX 4080 GPU, utilizing Docker containers running ROS Melodic. Figure 4 illustrates our simulation environment: Figure 4a represents the Turtlebot3 robot; Figure 4b shows the Gazebo simulator with two Turtlebot3 robots in the custom map, with the blue fan-shaped lines indicating the scanning range of the LiDAR; and Figure 4c shows the visualization tool Rviz, with the white areas representing the regions already explored by the LiDAR.  
Figure 3: An action space transformation diagram: (**a**) the alternative direction, (**b**) the target coordinates.  
Our paper focuses on strategies for multiple robots to collaboratively explore unknown environments, with the experimental background being set in disaster rescue scenarios, such as post-fire buildings or chemical plant leaks. We use three simulation maps: World1 (20 m \(\times\) 20 m corridor) corresponds to a real-world scenario of building hallways, which require the exploration of narrow spaces; World2 (40 m \(\times\) 40 m hall) corresponds to real-world scenarios like warehouses or conference halls, requiring large-scale coverage; and World3 (12 m \(\times\) 17 m office interior) corresponds to individual rooms or offices with complex layouts. These maps are shown in Figure 5.  
Each TurtleBot3 robot is equipped with a 360\({}^{\circ}\) single-line LiDAR sensor (with an effective range of 5 m and a sampling frequency of 720 Hz). The robot parameters are configured with a maximum speed of 1.0 m/s, an acceleration of 0.3 m/s\({}^{2}\), a friction coefficient of 0.5 N \(\cdot\) s/m, and an inertia coefficient of 50 kg. These settings ensure accurate environmental perception, smooth motion, and high-quality simulation data while minimizing potential inconsistencies from the Gazebo physics engine.  
Training episodes terminate upon goal achievement or reaching the maximum time steps. The key training parameters are summarized in Table 1. Data from each episode--including observations, shared observations, rewards, actions, and termination flags--are stored in an experience replay buffer. Neural networks are trained by sampling batches from this buffer.  
Figure 4: Simulator environment: (**a**) Turtlebot3 robot, (**b**) Gazebo simulator, (**c**) visualization tool named Rviz.  
Figure 5: Three training maps: World I is a corridor, World II is a shopping mall, and World III is an art museum with a unique shape.
# Multifunction RF Systems for Naval Platforms
## 5 MFRF System Resource Management
### Development of Modelling and Simulation Capability  
Evaluation of resource management techniques for multifunction systems in complex scenarios relies heavily on modelling and simulation. A modelling tool for resource management would need to include the following capabilities:  
* model for a real-time scheduler that accounts for radar, ES, EA and communications tasks.
* modelling of radar search and track modes.
* dynamic arbitrary partitioning of arrays to accommodate operation of multiple RF functions that in general are activated at different points in time.
* separation of Rx and Tx modes to provide the flexibility of utilizing different transmit/receive array gains and beamwidths, as would be the case if a MFRF system was configured with separate Rx and Tx arrays for example.
* inclusion of threat emitters in the scenario to stimulate the ES function.
* modelling of ES, EA and communication functions, including hand-off of threat emitter information from the ES to the EA function.  
With regard to the last item, it would likely suffice to implement relatively simple models for ES, EA and communications functions, since the purpose of the study would be to evaluate resource management techniques rather than the effectiveness of specific waveforms or algorithms used in these functions. For example, when EA is activated in the model, it can simply be assumed that suitable jamming waveforms are being used without the need to explicitly model them. The focus instead would be on modelling the action taken by the RAM at that instant to provide the EA function with sufficient system resources to transmit at the required EIRP for the length of time deemed necessary to likely defeat the threat. In the case of ES, modelling of the emission detection process would be implemented in a similar fashion to the radar range equation-based scheme utilized for modelling radar detections, except that a one-way version of the radar range equation would be used in the case of ES. Beyond that, modelling of the ES function would include assignment of system resources by the RAM to allow continuous monitoring for threat emissions across the frequency spectrum, and timely coverage of the required surveillance volume with a receive beamwidth narrow enough to achieve required AoA measurement accuracies.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 6 Open Issue and Future Direction
### D2d  
One of the most important characteristics of B5G cellular networks is the use of millimeter-wave and terahertz transmission. Due to the operation of mm-wave and terahertz communication on a wider frequency spectrum (30-300 GHz) and (300 GHz-10 THz), respectively, these frequencies have the potential to deliver extraordinarily high data rates for mobile devices, potentially providing massive network capacity [7; 175]. However, they have many significant propagation features that are distinct from those of the microwave band, leading to several challenges regarding interference management (IM). In B5G cellular networks enabling D2D communication, several scenarios are introduced within each cell to mitigate interference. However, novel IM strategies must be suggested that consider directional interference in B5G cellular networks to support a wide range of D2D communication [176]. On the other hand, IM is one of the main issues when D2D technology is integrated with SCs in underlay in-band D2D communication. Due to the transmission power of each BS being different, IM and RA problems for underlay spectrum sharing are more difficult in multi-tier HetNet compared with conventional single-tier systems. To increase spectrum efficiency, interference between D2D links must be taken into consideration and managed efficiently. Additionally, the mode selection strategies must be adopted in this heterogeneous environment to make dynamic decisions depending on the network's condition. As a result, it is important to investigate how to achieve efficient IM [177].
# Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning
## 2 Clutch Temperature Prediction Model Construction Based on Bi-LSTM and Migration Learning
### Data Acquisition and Processing  
In this paper, experimental data are obtained by using the comprehensive clutch test rig model LZDZX-430A, which is mainly used to simulate and test the working performance of clutches under different operating conditions. It can evaluate the durability, friction characteristics, wear, and ability to transmit the torque of the clutch by simulating the actual operating condition. Temperature measurement is carried out by taking points at different locations on the surface of the clutch pressure plate, as depicted in Figure 3.  
In this paper, a cycle of 30 s, i.e., a complete slip-grinding, is used, and the temperature data of three different positions of a platen are obtained. The platen temperature profile is shown in Figure 4.  
The clutch pressure plate selected in this paper is Fuda (FD) and Valeo clutch pressure plate. The weight of the pressure plate of FD is 28.5 kg, the inner hole of the pressure plate is \(\varphi\)240, the pressure is 32,000 N, the inner hole of the friction plate is \(\varphi\)240, and the amount of waveform is 0.7. The weight of Valeo's platen is 28.5 kg, the inner hole of the platen is \(\varphi\)260, the pressure is 41,000 N, the inner hole of the friction plate is \(\varphi\)260, and the amount of waveform is 0.7. The platen is shown in Figure 5.  
Figure 3: Experimental equipment: (**A**) Physical drawing of LZDZX-430A table; (**B**) platen installation diagram; (**C**) platen assembly diagram; and (**D**) thermocouple punching location diagram.  
Figure 2: Schematic representation of Bi-LSTM.  
Figure 4: Platen temperature profile (**a**) is the complete temperature profile, and (**b**) is one cycle temperature profile.  
Figure 5: Clutch pressure plate: (**A**) Fuda platen and follower plate before experimentation; (**B**) Fuda platen and follower plate after experimentation; (**C**) Valeo platen and follower before experimentation; and (**D**) Valeo platen and follower after experimentation.  
Since this paper is designed to predict the temperature profile at a future time, a previous 30 s is used as a basis for predicting the temperature for the next 30 s. Therefore, parameters such as time, termination time, pressure (N), waveform sheet, and platen temperature of the previous cycle are taken as inputs, and the temperature of the platen in the next 30 s is taken as output. The first 6 s is taken as the slip-grinding time and its time interval is taken as 0.05 s; to reduce the computation time, the remaining 24 s is taken as the cooling time, and its time interval is taken as 1 s.
## 5 Implementation Details
### Inference Optimization  
When performing model inference on ciphertexts, some ciphertext packing techniques and special convolution methods are required to reduce the complexity of the inference process. [PERSON] et al. [34] proposed a data structure called the graph block tensor and an interleaved packing method that can effectively reduce the latency and memory consumption of ciphertext inference, and can easily adapt the output of one convolutional layer as the input for the next convolutional layer. The graph block tensor is a data structure that packs tensors into fixed-size blocks according to the requirements of homomorphic encryption and allows them to be manipulated similarly to regular tensors [34]. We use this approach, and the shape and packing of the tensor blocks for the three models are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \(\mathbf{\lambda}\) & \begin{tabular}{c} **Integer** \\ **Precision** \\ \end{tabular} & \begin{tabular}{c} **Fractional** \\ **Precision** \\ \end{tabular} & \
\begin{tabular}{c} **Evaluation** \\ **Level** \\ \end{tabular} &
\begin{tabular}{c} **Degree** \\ **L** \\ \end{tabular} \\ \hline CNN-6 & 128 & 10 & 50 & 14 & \(2^{15}\) & - \\ AlexNet & 128 & 10 & 39 & 20 & \(2^{15}\) & - \\ ResNet-20 & 128 & 12 & 48 & 9 & \(2^{16}\) & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Safety parameters configuration.  
Given an image input \(I[w_{I},h_{I},c,b]\), each dimension represents the height and width, number of channels and batch of the image, and the convolution kernel size \(F[w_{F},h_{F},c,f]\); each dimension represents the height and width, number of input channels, and number of output channels of the convolution kernel (i.e. the number of convolution kernels) respectively. The two are multiplied to obtain \(O[w,h,c,b,f]\). For a particular block shape \([t_{1},t_{2},t_{3},t_{4},t_{5}]\) and packing method, C-W-H-F-B can be decomposed and rearranged to \(O\left[\frac{c}{t_{1}}\frac{w}{t_{2}}\frac{h}{t_{3}}\frac{f}{t_{4}}\frac{b}{t_{5 }}\right]\) for \(O\).
# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 3 Methodology
### Estimation of Photovoltaic Technical Potential  
For the estimation of the photovoltaic technical potential, historical time series of temperature and wind speed provided by PVGIS were used in addition to the irradiation on the POA. The power output of the photovoltaic installations, according to the model of [PERSON] et al. [35], depends on irradiation, weather conditions, system characteristics, and system losses:  
\[P_{\rm pv}=P_{\rm POA}\cdot P_{\rm STC}\cdot\eta_{r}\cdot(1-L) \tag{11}\]  
where \(P_{\rm POA}\) represents the irradiance in the plane of the arrays, and \(P_{\rm STC}\) represents the power output from the photovoltaic modules determined under standard test conditions; in our case, it defined as \(P_{\rm STC}=A\cdot\eta_{n}=182\,\mathrm{W}\), where \(\eta_{n}=0.182\) represents the nominal (18.2%) efficiency [33] and \(A=1\) m\({}^{2}\) is the area covered by a single pixel of the raster. Relative efficiency \(\eta_{r}\) is calculated as follows:  
\[\eta_{r}=1+k_{1}\log(P^{\prime})+k_{2}\log(P^{\prime})^{2}+k_{3}T_{m}+k_{4}T_{ m}\log(P^{\prime})+k_{5}T_{m}\log(P^{\prime})^{2}+k_{6}T_{m}^{2} \tag{12}\]  
where \(P^{\prime}=P_{\rm POA}/1000\) and parameters \(k_{i}\), which depend on the solar panel technology used, were calibrated on the basis of measurements by ESTI, the Joint Research Centre's European Solar Test Installation laboratory [35]. Temperature module \(T_{m}\) is defined as follows:  
\[T_{m}=T_{a}-25^{\circ}+\frac{P_{\rm POA}}{U_{0}+U_{1}\cdot W_{\rm s}} \tag{13}\]  
where \(T_{a}\) is the ambient temperature [46], \(W_{\rm s}\) is the wind speed, and coefficients \(U_{0}=26.9\) and \(U_{1}=6.2\) are determined in [47]. Lastly, generic system loss \(L=14.0\%\), as recommended by PVGIS, was considered. Such system losses include the reduction of the panel efficiency over time and the losses due to the transfer of power from the photovoltaic panels to the electrical grid via cables and inverter [7].  
By integrating power output \(P_{\rm pv}\) from Equation (11) over a one-year period, we obtained the average energy \(E_{\rm pv}\) generated annually by a hypothetical 1 m\({}^{2}\) sized panel installed on a portion of a roof characterized by specific irradiation values, weather conditions, tilt, and orientation. Thus, we obtained the technical potential map, an extract of which is shown as an example in Figure 8c, where each pixel represents the amount of energy \(E_{\rm pv}\) that could be produced annually by photovoltaic modules characterized by a 1 m\({}^{2}\) surface, efficiency of 18.2% and system losses of 14.0%.  
Figure 8: Maps showing, for the rooftop surfaces within the Municipality of Cagliari, (**a**) the yearly average values per m\({}^{2}\) of GHI, (**b**) irradiation on the POA, (**c**) and photovoltaic potential; (**d**) average LCOE.
# A Multichannel, High-Bandwidth Wirelane Receiver for D2D Interconnects
###### Abstract  
This paper proposes a multichannel and high-bandwidth (BW) receiver for standard packaging die-to-die (D2D) interconnects. The receiver adopts forward clock (FCK) architecture of the high-density transmission standard, which consists of 16 high-speed data paths and a pair of low-speed differential clocks for 512 Gbps BW. To reduce the chip area and power consumption, a common minimal phase-locked loop (MINI-PLL) and data adjustment (CDA) circuit to replaces the clock data recovery circuit (CDR) in the traditional receiver. A delay-matching circuit is adopted to combat PVT variation and lane skew. In addition, a high linearity phase interpolator (PI) circuit design is used in the minimum phase-locked loop (MINI-PLL) to adjust the clock phase and improve the clock jitter performance. Using 28 nm CMOS technology, the overall link power consumption is 1.56 pJ/b. Bit error rate (BER) is less than \(10^{-15}\) under the real S-parameters with a channel loss of 10 db@16 GHz.  
die-to-die; phase interpolator; forward clock; jitter performance; bit error rate +
Footnote †: journal: Electronics Letters  
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Footnote 0: Copyright** © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))).
## 3 Theoretical Foundation and Numerical Model Construction
### Numerical Model
#### 3.2.2 Pore Water Pressure  
Under water impoundment conditions, hydrostatic pressure varying with elevation is applied on the slope surface below the water table. The groundwater level within the rock mass is determined from seepage field simulation results under different water level scenarios. [PERSON] et al. [25] investigated the distribution of water pressure on  
Figure 4: The simulation model of Fushun Open-Pit Mine.  
Figure 5: High precision tetrahedral mesh of the open-pit mine. (**a**) Lithology 3D modeling diagram, (**b**) Mesh 3D model diagram.  
open-pit mine slopes and summarized the variation in pore pressure with depth measured by borehole piezometers. They found that the ratio of deep slope pore water pressure to hydrostatic pressure is approximately 0.7. Figure 6 illustrates the variation in pore water pressure in the slope rock mass with piezometer burial depth. The slope of the linearly fitted curve, representing the water pressure coefficient, is approximately 0.61. Therefore, in the simulation, neglecting capillary effects in the unsaturated zone, the pore water pressure in the slope rock mass is assigned according to the following equation:  
\[P(x,y)=k_{w}\cdot\text{Max}(f(x)-y,0)\cdot\gamma_{w}g \tag{5}\]  
In the equation, \((x,y)\) denotes the coordinates of an arbitrary point in the numerical model; \(k_{w}\) is the water pressure coefficient; \(P(x,y)\) represents the pore water pressure at that point, with units of Pascal (Pa); \(f(x)\) is the interpolation function of the phreatic surface; the Max () function returns the maximum value of its two arguments; \(\gamma_{w}\) and \(g\) are the unit weight of water and gravitational acceleration, respectively, with units of kg/m\({}^{3}\) and m/s\({}^{2}\).