esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation of the Proposed Method  
This section shows the prediction results of the single pile responses using the proposed \"hybrid-BEM-py curve\" method. The solutions obtained with the analyses are compared with theexperimental results observed in lateral load tests on single piles both in coarse soils (sand and gravel) and cohesive soils (clays and silts) and both in steel pipes and reinforced concrete piles. The experimental results refer to lateral load tests reported in the literature with a total of 22 case histories for single pile analyses (Table 1). The aim of the analyses is to validate the proposed method. They are conducted not as a back analysis but as a class A prediction, directly using the actual pile mechanical and geometrical properties and the soil strength and stiffness parameters according to the interpretation of the in situ and laboratory tests data. The elastic modulus used as the input is a Young's Modulus at small strain level \(E_{max}\), which can be obtained starting with the \(G_{max}\) (Shear Modulus at small strain levels) value, using the elastic relationship: \(E=2G(1+\
u)\). The results obtained highlight the possibility of providing a good forecast of the most representative aspects (pile-head displacement and rotation and maximum bending moment) of the single pile response. Figure 8 shows a comparison between the measured and computed results. In these plots, the ratio between the measured horizontal load for a given displacement level (\(y\)/D) and the measured maximum lateral load during the test is on the \(x\)-axis, while the ratio between the computed and the measured load at the same displacement level is on the \(y\)-axis. For 56 of the 65 reported points in Figure 8, the error in the load prediction is lower than 20%. The errors are higher for lower values of the relative displacements (\(y\)/D) and lower load level (\(H_{measured}\)/\(H_{max\ test}\)). This statement is referred to the cases reported in [31; 42; 45; 54]. In [45; 54] the piles were filled with grout, so the initial pile flexural rigidity could be affected by the steel/grout interaction. In [31; 42] the measured load vs. horizontal displacement plots present for lower load levels an unusual trend compared to the other cases. For all the other cases, it seems that the comparison is satisfactory. The proposed model reproduces in a very accurate way the measured data for large diameter reinforced concrete piles in sand (diamond markers in Figure 8). A comparison between the measured and computed data on a large-diameter reinforced concrete bored single pile tested in Taiwan by [PERSON] et al. [43] is presented in the following section.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 2 Related Works
### Depthwise Separable Convolution  
Depthwise separable convolution (DSC), first proposed by [PERSON] et al. [26], gained widespread recognition when it was introduced in the MobileNet model by the Google team in 2017 [27]. The fundamental concept of MobileNet involves significantly reducing computational complexity and model size by employing DSC.  
Depthwise separable convolution comprises two processes: Depthwise Convolution (DW) and Pointwise Convolution (PW). In DW, the number of convolutional kernels matches that of the input channels, thereby establishing a one-to-one correlation between channels and kernels. Consequently, in DW, the number of output feature maps matches that of the input channels. PW then convolves the output feature maps from DW with convolutional kernels, ensuring that each output feature map integrates information from all input feature maps. The schematic diagram of depthwise separable convolution is illustrated in Figure 2.  
Figure 1: Architecture of Res2 net.  
In the figure, the input image size is denoted by \(D_{\text{F}}\times D_{\text{F}}\times M\) and is convolved with convolution kernels of size \(D_{\text{K}}\times D_{\text{K}}\times M\) to obtain M-channel feature maps. Then, the M-channel feature maps are input to convolution kernels of size \(1\times 1\times N\), resulting in N-channel feature maps. The computational complexity of the entire process is as follows:  
\[D_{\text{F}}\cdot D_{\text{F}}\cdot M\cdot D_{\text{K}}\cdot D_{\text{K}}+D_{ \text{F}}\cdot D_{\text{F}}\cdot M\cdot N \tag{1}\]  
If the input image with the size \(D_{\text{F}}\times D_{\text{F}}\times M\) is convolved using regular convolution with kernels of size \(D_{\text{K}}\times D_{\text{K}}\times M\) to obtain the same feature maps as in the above process, the computational complexity is as follows:  
\[D_{\text{K}}\cdot D_{\text{K}}\cdot D_{\text{F}}\cdot D_{\text{F}}\cdot M\cdot N \tag{2}\]  
The ratio between (1) and (2) is  
\[\frac{1}{D_{\text{K}}{}^{2}}+\frac{1}{N} \tag{3}\]  
In feature extraction, the commonly chosen kernel size is \(3\times 3\). Therefore, theoretically, depthwise separable convolution reduces computation by a factor of 8-9 compared to regular convolution.  
MobileNetv3, introduced by [28], incorporates depthwise separable convolution, inverted residual blocks, and Squeeze-and-Excitation (SE) modules [29]. The input feature map is initially expanded through convolutional layers to extract additional features. Subsequently, Depthwise Convolution (DW) is applied, followed by the SE module to adjust the weights of each channel, thereby enhancing the model's accuracy. Finally, downsampling is performed through convolutional layers. When the number of input and output features matches, shortcut connections are utilized by the Bottleneck (Bneck). The structure of the Bneck network is depicted in Figure 3.  
Figure 3: Bneck of MobileNetv3 structure.  
Figure 2: Depth-separable convolution schematic.
## 2 Literature Review
Moreover, they apply FAIR-FIRST and TP&FAIR, two fairness-based packing techniques that increase system capacity and user fairness to ensure privacy-preserving. [PERSON] et al. [23] discussed electrical forecasting, which is becoming increasingly popular due to the deregulation and integration of renewable resources. Because of the limited power source, it is crucial to predict the power prediction accurately. Therefore, this paper proposes a short-term load predictor that can forecast for the next 24 h of loadingfor predicting power consumption. They used ANN (artificial neural networks) and SVM (support vector machines) technology in their project. Tunisian Power Company tests its proposed system to produce accurate and acceptable results one day in advance, with an average error rarely exceeding 2.3%. However, their computational time is very high.
## 2 Materials and Methods
### Analysis of Volatile Organic Compounds (VOCs) in GC-MS
#### 2.8.2 GC-MS Analysis Procedure  
The compounds were separated using an Agilent 7890A gas chromatography system (Agilent Technologies, Santa Clara, CA, USA) equipped with a GC DB-WAX column (30 m \(\times\) 0.25 mm \(\times\) 0.15 \(\mathrm{\SIUnitSymbolMicro m}\)) and Agilent 5975C inert MSD with Triple-Axis Detector, using He as a carrier gas. The VOCs were desorbed for 5 min by inserting the SPME fiber into a GC injector (270 \({}^{\circ}\)C). The GC oven was programmed to maintain a temperature of 70 \({}^{\circ}\)C for 1 min, then to increase to 140 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), and finally to 210 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\). The column flow rate was 1.0 mL\(\cdot\)min\({}^{-1}\). The MS was scanned in the range of 45-650 amu at 70 eV. The total analysis time was 39.33 min. Compounds were identified using NIST 14.0 database, and Linear Retention Index (LRI) was calculated with a series of n-alkanes (C7-C40).
## 3 Experimental Set-Up and Instrumentation
### Electrical Supply and Data Acquisition System  
The electrical circuit consists of a DC power supply, an active load, a metric ampere clamp, a voltmeter, a voltage divider and three mechanical relays (Figure 2).  
\begin{table}
\begin{tabular}{l} \hline Electrical Properties \\ \hline Rated capacity: 1500 F \\ Maximum series equivalent electric resistance (\(ESR\)): 0.47 m\(\Omega\) \\ Rated voltage: 2.70 V \\ Maximum continuous current: 84 A \\ \hline Thermal Properties \\ \hline Maximum operating temperature: \(65^{\circ}\) C \\ Thermal capacity: 320 J/\({}^{\circ}\)C \\ \hline Physical Properties \\ \hline Diameter (\(D\)): 60.4 mm \\ Length (\(L\)): 85 mm \\ Maximum torque supported (by threading): 14 N m \\ \hline \end{tabular}
\end{table}
Table 1: Used supercapacitor specifications.  
Figure 2: Power supply and data acquisition system.  
The voltmeter and the metric ampere clamp, which are connected to the module terminals, allow the measurement of the voltage and of the current. A NI acquisition unit (National Instruments) is used to process the current and the voltage across the SCs and the voltage generated by the thermocouples. The data acquisition and the electrical system are controlled using LabVIEW platform.  
Under operation, SCs undergo charge/discharge cycles. In general, SCs are operated between the nominal voltage (2.7 V) and half of this voltage (1.35 V) since in this case they can store and supply about 75% of their maximum energy without generating major electronics design problems [27; 28]. The SCs module used in this study was composed from 14 SCs connected in series. Therefore, the voltage of the module has been charged and discharged in the range 18.9-37.8 V.  
While charging, the flow of the electrical energy passes from the power supply to the SCs (Figure 3a) and when the voltage at the module terminals reaches 37.8 V, the discharge begins. The SCs discharge into the active charge (Figure 3b), which is composed of resistors, until the voltage attains 18.9 V. The relays R1, R2, and R3 are controlled from the LabVIEW program using the output of the NI Card. Depending on the experiment, the charging/discharging current is varied.
## 2 Materials and Methods
### Measures and Data Processing
#### 2.3.3 Running Initiation Key Events of Interest/Initiation Timing Error  
In both VE and RWE conditions, and following [PERSON] et al.'s method [13], three key events were identified and measured regarding the running initiation task: wave initiation, gaze release, and foot lift (Figure 1B). Wave initiation corresponds to the moment when the participant begins to lower their head as their partner approaches. It corresponds to the beginning of the preparatory movement that precedes the initiation of the run. Gaze release is much closer to the race initiation and corresponds to the moment when the athlete's head turns away to face the athletics track. Gaze release is also associated with the last moment athletes can receive visual information about their partner. Finally, the foot lift action is the main marker of performance. It corresponds to the moment when the participant lifts their rear foot (right foot during the experiment). This event is considered as the initiation of the run. The timing of each event was computed for each participant as the difference in milliseconds between the time of occurrence of each event in reference to the time of the partner's passage on the mark. The passage of the partner corresponds to the value of 0. Events preceding the partner's passage have a negative sign, and those following it have a positive sign.  
Detections of the three events were then expressed in ms relative to the avatar's passage over the mark. The detection method is detailed in [PERSON] et al.'s article [13]. The 8 trials were averaged per participant for each event and for each condition. Between-participant and within-participant variability were also measured. Between-participant variability corresponds to the standard deviation between the means of each participant for each event. Within-participant variability corresponds to the mean of intra-participant standard deviations for each event and participant. The data analysis was first carried out at the group scale (paired comparison of the 12 participants) in order to identify the general behavioral tendency. Secondly, the data analysis was performed at the participant scale (paired comparison for each participant) to precisely assess the impact of the VE on the behavior of each athlete.
## 1 Introduction
Therefore, this paper takes the Starlink constellation as a case study and proposes an innovative \"\(3+1\)\" topology control strategy, integrating static fixed links with dynamic temporary links to enhance the network flexibility and adaptability. The study explores the construction mechanism of dynamic links, treating it as a general graph maximum weight-matching problem and assessing traditional greedy algorithms' limitations in addressing such challenges. Subsequently, an elite strategy-based maximum weight-matching algorithm is introduced to optimize the dynamic link allocation, thereby enhancing the network sustainability and stability. The paper is structured into four sections. Section 1 introduces the current development background of satellite communication technology, emphasizing its significance in the global communication domain and the challenges faced by mega-constellation satellite networks, including signal transmission delays and unstable communication links. Section 2 provides a detailed topological analysis of mega-constellations, reviewing fundamental concepts and existing technologies in satellite communication networks, with a focus on inter-satellite communication issues. It elaborates on the design and implementation of the proposed \"\(3+1\)\" topology control strategy, highlighting its integration of static and dynamic links to improve network flexibility. Section 3 discusses the mechanism of dynamic link construction and optimization algorithms, presenting a maximum weight-matching algorithm with an elite retention strategy for efficient link allocation. It then evaluates the performance of these algorithms through simulation experiments and comparisons. Finally, Section 4 summarizes the paper, reviews the research findings, and outlines future research directions in satellite communication topology control.
# Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability
## 3 Materials and Methods
### Generation of the Arm's Maximum Solution Set
#### 3.1.2 Position Expression of Each Joint in Global Coordinate
The variable is \(\theta_{i}\), for convenience, this study uses \(si\) for \(\sin\theta_{i}\) and \(ci\) for \(\cos\theta_{i}\). According to Formula (1), the D-H transformation matrices between each two coordinate systems can be obtained as: \begin{table}
\begin{tabular}{c c c c c c} \hline \(i\) & **Joint Angles** & **Zero Position** & \(\mathbf{\alpha_{i}}\) & \(\mathbf{a_{i}}\) & \(\mathbf{d_{i}}\) \\ \hline
1 & \(\theta_{1}\) & \(-90^{\circ}\) & \(-90^{\circ}\) & 0 & 0 \\ \hline
2 & \(\theta_{2}\) & \(-90^{\circ}\) & \(-90^{\circ}\) & 0 & 0 \\ \hline
3 & \(\theta_{3}\) & \(-90^{\circ}\) & \(0^{\circ}\) & \(l\) & 0 \\ \hline
4 & \(\theta_{4}\) & \(0^{\circ}\) & \(90^{\circ}\) & \(h\) & 0 \\ \hline
5 & \(\theta_{5}\) & \(0^{\circ}\) & \(-90^{\circ}\) & 0 & 0 \\ \hline
6 & \(\theta_{6}\) & \(90^{\circ}\) & \(-90^{\circ}\) & 0 & 0 \\ \hline
7 & \(\theta_{7}\) & \(0^{\circ}\) & \(0^{\circ}\) & 0 & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: Arm’s Parameters in D-H Transformation Matrices. Figure 3: Arm Coordinate System. \[{}^{0}T_{1}= \begin{bmatrix}c1&0&-s1&0\\ s1&0&c1&0\\ 0&-1&0&0\\ 0&0&0&1\end{bmatrix} ,\,^{1}T_{2}= \begin{bmatrix}c2&0&-s2&0\\ s2&0&c2&0\\ 0&-1&0&0\\ 0&0&0&1\end{bmatrix} ,\,^{2}T_{3}= \begin{bmatrix}c3&-s3&0&lc3\\ s3&c3&0&ls3\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \]  
The transformation matrix from the shoulder joint coordinate system to the base coordinate system \({}^{0}T_{7}\) and the transformation matrix from the elbow joint coordinate system to base coordinate system \({}^{0}T_{2}\) can be obtained as follows:  
\[{}^{0}T_{7}={}^{0}T_{1}{}^{1}T_{2}{}^{2}T_{3}{}^{3}T_{4}{}^{4}T_{5}{}^{5}T_{6} {}^{6}T_{7}= \begin{bmatrix}R_{3*3}^{07}&P_{3*1}^{07}\\ {\sf O}_{1*3}&I_{1*1}\end{bmatrix}  \tag{3}\]  
\[{}^{0}T_{3}={}^{0}T_{1}{}^{1}T_{2}{}^{2}T_{3}= \begin{bmatrix}R_{3*3}^{03}&P_{3 *1}^{03}\\ {\sf O}_{1*3}&I_{1*1}\end{bmatrix}  \tag{4}\]  
The formula \(P_{3*1}^{07}= \begin{bmatrix}I*(s1*s3+c1*c2*c3)+h*[s1*s(3+4)+c1*c2*c3*(c4-s4)] \\ I*(s1*c2*c3-c1*s3)-h*[c1*s(3+4)-s1*c2*c(3+4)]\\ I*s2*c3-hs*2*c(3+4)\end{bmatrix} \) is the expression for shoulder joint coordinates;  
The formula \(P_{3*1}^{03}= \begin{bmatrix}I*s1*s3+l*c1*c2*c3\\ l*s1*c2*c3-l*c1*s3\\ -ls*2*c3\end{bmatrix} \) is the expression for elbow joint coordinates. Note: The expressions for shoulder joint coordinates and elbow joint coordinates are obtained in the case that the wrist coordinate is (0, 0, 0). According to the expressions for shoulder joint coordinates and elbow joint coordinates, different shoulder joint and elbow joint positions can be obtained by setting different values of \(\theta_{1}\) to \(\theta_{7}\). The forearm and upper arm can be represented by connecting the elbow joint to the corresponding wrist joint and the corresponding shoulder joint. The shoulder joint and elbow joint positions can represent different arm postures.
# 5G-V2X Communications and Networking for Connected and Autonomous Vehicles
###### Abstract
Departing from the architectural design of V2X communications-related aspects, the fourth paper [4] provides an overview of the status quo and of the ongoing evolution of the two most prominent radio access technologies for V2X, and vehicle-to-vehicle communications in particular, i.e., IEEE 802.11p and Cellular-V2X. A comprehensive evaluation study is conducted through large-scale simulations to shed light on the advantages and drawbacks of the two technologies and to pinpoint the main conditions and parameters that affect their performance. The fifth paper [5] targets the improvements of the reliability of vehicle-to-roadside connectivity when the User Datagram Protocol (UDP) is used to satisfy real-time requirements, by introducing redundancy into the transmitted data. To this aim, the authors propose an inter-packet error control coding scheme to improve the recovery of data with a focus on convolutional coding techniques. Simulations for the memoryless binary erasure channel, channels with memory, and IEEE 802.11p radio are provided. Moving on to more practical implementations, the authors of the sixth paper [6] investigate the usage of Long Range Wide Area Network (LoRaWAN) technology to geolocalise vehicles indoors and counteract the well-known limitations of the Global Navigation Satellite System (GNSS). In particular, the proposal achieves the localisation of a vehicle within a 20-30 m range with a low deployment cost, by leveraging the estimation of the behaviour of a LoRaWAN channel established between simple client devices and LoRaWAN gateways, and using trilateration. EURECOM acknowledges the support of its industrial members, namely, BMW Group, IABG, Monaco Telecom, Orange, SAP and Symantec. It has been a great pleasure to put together this Special Issue and gather interesting research papers, which provide and assess novel ideas and solutions in the field of 5G V2X communications and networking. We are extremely grateful to [PERSON], the Editor-in-Chief of the Future Internet journal, for the opportunity to organize this Special Issue and to all the managing Editors supporting us. We thank all the authors for submitting their papers to this Special Issue and all the volunteer reviewers for their hard work in evaluating these submissions within the tight deadlines and for their valuable remarks that have contributed to the quality of the published articles. The authors declare no conflict of interest.
## 4 Results and Discussion
### Deformation Behaviour of Single Tunnels
After the GM-1 tunnel samples, the rock tunnel prepared from GM-2 (50% POP, 30% Sand, and 20% Clay) material is tested, and the graph is obtained, as shown in Figure 10. When the single tunnels samples of GM-2 material are subjected to static loading conditions, it is observed that in the case of unlined tunnels, maximum deformation occurs at a smaller cover depth. Maximum deformation is experienced in the case of a 3 cm unlined tunnel. In the 3 cm unlined tunnel model, the maximum crown deformation of 0.29 mm is experienced at L/2 distance, whereas in the case of 5 cm unlined tunnel models, the maximum crown deformation value is 0.21 mm at L/2 distance. In the case of liner material tunnel models, minimum deformation is experienced in the case of 5 cm lined tunnel samples, and its value is 0.11 mm at L/2 distance, whereas in 3 cm lined material samples, the maximum deformation value is 0.14 mm at L/2 distance. Figure 9: Deformation Profile of lined and Unlined Tunnel for GM1 material. After conducting the experimental tests on single tunnels samples of all three model materials, a combined deformation graph is obtained. A comparison of deformation profiles obtained from experimental testing results for single tunnels of different materials is shown in Figure 11. The results show that the maximum deformation occurs in the case of unlined samples of GM-2 model material having a cover depth of 3 cm and its value is 0.29 mm at L/2 distance, i.e., at the centre of the tunnel, whereas the minimum deformation occurs in case of a lined sample of POP model material having a cover depth of 5 cm. From the above results, it has been concluded that the strength properties of rock samples cover depth, and lining material plays an essential role in the deformation of single tunnels. It can also be concluded from the results that maximum deformation is experienced in GM2 material, which is the weakest of all the model materials. In the case of the model materials, the maximum deformation occurs in the case of the model material having a cover depth of 3 cm and its value is 0.29 mm at L/2 distance, i.e., at the centre of the tunnel, whereas the minimum deformation occurs in case of a lined sample of POP model material having a cover depth of 5 cm. Figure 11: Comparison of deformation profiles for single tunnels of different materials. Figure 10: Deformation Profile of lined and Unlined Tunnel for GM2 material. of 3 cm, cover depth deformation is more as compared to 5 cm. The lowest deformation is experienced in the case of Plaster of Paris model material. In the case of Liner material, less deformation is experienced, which is due to the resistance offered by the liner material.
# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 4 Experimental Result and Analysis
### Emotion Datasets
#### 4.1.2 Emo-Db  
Seven acted emotional states are represented in the EMO-DB speech dataset: anger, disgust, boredom, joy, sadness, neutral, and fear. Ten actors who are natural German speakers contributed 535 emotional utterances in the German language. It was initiated by the ICS, Technical University, Berlin, German [40]. Five of them are actresses who are women, while the other five are actors who are men. The speech files are 3 s long on average, and they were initially collected at a sampling rate of 48 kHz before being resampled to 16 kHz.