esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 3 The Proposed System Methodology
### Head Counting Using Cross-Line Judgement  
First, the counting line and region are specified in each frame of a video. Secondly, the movements of the heads are detected, i.e., either the heads are moving from left to right, right to left, upward to downward or downward to upward in direction. Hence, the relative location between heads and crossing lines can be determined and this information is further used to detect the position of the initial movement of the heads using the horizontal and vertical crossing lines. So if the crossing line is aligned horizontally, then the directions of the moving heads are calculated as in Equation (18), and, if the crossing line is aligned vertically, then the directions of the moving heads are calculated using Equation (19);  
\[\begin{split}{\mathit{Direc}}_{head}=\{&\begin{array}[ ]{l}up\_to\_down,\,y_{\mathit{head}_{init}}<\!\!y_{u}\\ down\_to\_up,\,y_{\mathit{head}_{init}}>\!\!y_{d}\end{array}\\ \\ {\mathit{Direc}}_{head}=\{&\begin{array}{l}left\_to\_right,\,x_{\mathit{head} _{init}}<\!\!x_{l}\\ right\_to\_left,\,x_{\mathit{head}_{init}}>\!\!x_{r}\end{array}\\ \\ \end{array}\\ \end{split} \tag{18}\]  
where \(\mathit{Direc}_{head}\) denotes the initial movement of the head. \(x_{\mathit{head}_{init}}\) and \(y_{\mathit{head}_{init}}\) denotes the initial \(x\) and \(y\) coordinates of the center of the heads.  
After detecting the direction of the initial head movements, the heads are counted based on the cross-line judgment as in [35];  
\[\begin{split} Head_{count}=\left\{\begin{array}{l}left\_to\_right, \,\mathit{Direc}_{head}=left\_to\_right and\,x_{\mathit{head}}>x_{r}\\ right\_to\_left,\,\mathit{Direc}_{head}=right\_to\_left and\,x_{ \mathit{head}}<x_{l}\\ up\_to\_down,\,\mathit{Direc}_{head}=up\_to\_down and\,y_{ \mathit{head}}>y_{d}\\ down\_to\_up,\,\mathit{Direc}_{head}=down\_to\_up and\,y_{ \mathit{head}}<y_{u}\end{array}\\ \\ \end{array}\\ \end{split}\right. \tag{20}\]  
where \(\mathit{Head}_{count}\) denotes the direction of the heads being counted, \(x_{\mathit{head}}\) and \(y_{\mathit{head}}\) are the \(x\) and \(y\) coordinates of the center of the heads. If one of the above conditions is true, a counter of \(1\) is added in \(\mathit{Head}_{count}\), otherwise the head will be discarded. Figure 11 shows the results obtained for head counting over the PCDS dataset.  
Figure 10: Convergence plot of the CNN using 250 epochs of all three benchmark datasets.
# Sequential Characteristics Based Operators Disassembly Quantization Method for LSTM Layers
## 4 Experiments
### Comparison with the State of the Art
#### 4.2.1 Character-Level Language Prediction  
The LSTM takes as input a character sequence and predicts the next character at each time step. The training objective is the cross-entropy loss over target sequences, and performance is evaluated by bits per character (BPC). Experiments were performed on two benchmark datasets: (i) [PERSON]'s War and Peace and (ii) Penn Treebank Corpus. On War and Peace and Penn Treebank, we used a one-layer LSTM with 512 hidden units as in [34]. Adam is used as the optimizer.  
Table 3 shows the testing BPC values and size of LSTM parameters. The method with the lowest BPC in each group is highlighted.  
**Comparison with Full-Precision LSTM:** On both the Penn Treebank and the War and Peace datasets, the 2-bit, 4-bit, and 8-bit quantized LSTM performs similarly to the full-precision baseline. Moreover, compared with the full-precision LSTM, the 1-bit quantized LSTM has competitive performance but requires much less storage.  
**Comparison of Different Quantization Methods:** For the quantized 1-bit LSTM, BWN and LAB perform significantly better than BCN. They have an additional scaling parameter, which is empirically smaller than 1, and can thus alleviate the exploding-gradient problem. Regardless of the dataset, compared to other comparison methods, our quantization method achieves the lowest BPC values by 1.446 and 1.723 in the 2-bit  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Precision**} & \multirow{2}{*}{
\begin{tabular}{c} **Quantization** \\ **Method** \\ \end{tabular} } & \multicolumn{4}{c}{**Penn Treebank**} & \multicolumn{4}{c}{**War and Peace**} \\ \cline{3-8}  & & & \multicolumn{2}{c}{**Units = 512**} & \multicolumn{4}{c}{**Units = 512**} \\ \cline{3-8}  & & **BPC** & **Variation** & **Size** & **BPC** & **Variation** & **Size** \\ \hline
32-bit & [35] & - & 1.45 & / & 4504 & 1.72 & / & 4800 \\ \hline \multirow{3}{*}{\(1\)-bit} & [26] & BWN & **1.600** & **0.15** & 149 & **1.822** & **0.102** & 158 \\  & [27] & LAB & 1.601 & 0.151 & 149 & 1.887 & 0.167 & 158 \\  & [25] & BCN & 5.840 & 4.39 & 149 & 6.633 & 4.913 & 158 \\  & / & Ours & 2.133 & 0.683 & 149 & 2.357 & 0.637 & 158 \\ \hline \multirow{3}{*}{\(2\)-bit} & [28] & TWN & 1.517 & 0.067 & 289 & 1.754 & 0.034 & 308 \\  & [29] & LAT & 1.536 & 0.086 & 289 & 1.828 & 0.108 & 308 \\ \cline{1-1}  & / & TCN & 5.840 & 4.39 & 289 & 3.493 & 1.773 & 308 \\ \cline{1-1}  & / & Ours & **1.446** & **0.004** & 289 & **1.723** & **0.063** & 308 \\ \hline
4-bit & / & Ours & **1.572** & **0.122**
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 3 Background
### Automatic Speaker Recognition Challenges  
**Speech Pathology.** Medical issues, such as dysphonia, dysarthria, and cleft lip and palate, can lead to breaks or interruptions in speech. As stated by [34], pathological speech is more vulnerable to privacy breaches compared to healthy speech when applied to ASV systems.  
**Background Noise.** The accuracy of speaker recognition systems is notably affected by background noise [35]. This is because clean environmental recordings are utilised during training, while speakers often speak in noisy conditions during testing.  
**Children's Voices.** Recognising and accurately identifying children's speech can be more difficult than identifying adults' speech. This is because children's vocal tracts and language skills are still developing, which causes their speech to be less clear, less stable, and more variable compared to adults. Additionally, children may not have the same level of control over their speech as adults do. This makes it challenging for recognition systems to identify both the speech and the speaker accurately [36].
## 2 Fuzzy Logic Overview  
The general structure of a fuzzy logic-based decision system is depicted in Figure 1, which consists of three main building blocks: the fuzzifier, the inference engine, and the de-fuzzifier [21]. The fuzzy logic system (FLS) knowledge is represented by fuzzy variables that can take any values between 0 and 1, as opposed to the binary logic system; further, it compromises a set of IF-THEN rules. To illustrate this, consider an input variable that has an exact value that is measured, for example, from a weather temperature sensor--in fuzzy logic, these exact values are called crisp values. Next, the fuzzifier will group the crisp input values and map them to a fuzzy set using the membership function. The fuzzifier output named the fuzzy linguistic variables are used to describe the system qualitatively. For example, they can describe the weather on the basis of the measured temperature as hot, cold, warm, freezing, and so on.  
Figure 1: Fuzzy logic system structure [29].  
After that, the inference engine shown in Figure 2 maps the input values to a certain fuzzy output on thebasis of a set of IF-THEN rules, and the fuzzy outputs of these rules are then aggregated using the aggregator function. Aggregation is the process by which several fuzzy values are mapped to a single fuzzy set that represents the output rules of the fuzzy rules. For example, the weighted means function can be used as an aggregator to map several fuzzy values to a single value that will be used in the de-fuzzification process. Finally, to get a deterministic decision from the fuzzy logic system, the aggregator output is fed to the de-fuzzification process, which is responsible for getting a crisp output (non-fuzzy output) using membership functions analogous to the one used in the Fuzzifier stage. The choice of the appropriate de-fuzzifier function is application-specific. However, centroid of area (COA); bisector of area (BOA); and the mean, smallest, and largest of maximum (MOM, SOM, LOM, respectively) are the most commonly used functions [30].
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 2 Cut-In Scenario Classification and Cut-In Possibility Quantification
### Finite State Machine for Cut-In Scenarios  
The finite state machine is shown in Figure 2. The host vehicle can adopt different control strategies when facing a cut-in vehicle through the conversion of the FSM. The FSM includes two states in total, in which the cut-in vehicle state contains two sub-states; corresponding to three scenarios.  
Driving scenario 1: When the cut-in vehicle does not exist, or completes a lane change, the FSM remains in the following the preceding vehicle state, which means that the host vehicle can use the preceding vehicle in front of it in the lane as a reference to stably follow the object.  
Driving scenario 2: When the host vehicle recognizes a potential cut-in vehicle using the target screening method mentioned below, if the longitudinal relative distance (which can be measured using a corner radar equipped on the front of the host vehicle) between the host vehicle and the preceding vehicle in front of it in the lane meets the cut-in vehicle safety distance model, the FSM is switched to follow the cut-in state. At this time, the host vehicle does not need to brake in advance to maintain a safe relative distance with the cut-in vehicle. When the cut-in vehicle reaches the center line of the lane, the host vehicle stably changes the follow object, from the preceding vehicle, to the cut-in vehicle.  
Driving scenario 3: When a nearby vehicle performs a cut-in maneuver, and the relative distance between the host vehicle and the cut-in vehicle does not meet the safety distance model, the FSM is switched to yield to the cut-in vehicle state. Before the nearby vehicle cuts in, the host vehicle changes the following target to the cut-in vehicle. At the same time, the cut-in possibility is used as a reference trajectory for the MPC. At this time, the host vehicle under the control of the MPC-ACC start to perform a braking action, which increases the relative distance from the preceding vehicle in front of it in the lane to provide a safe space for the nearby vehicle to cut into. With the successful cut-in maneuver completed, the host vehicle can smoothly follow the cut-in vehicle.
## 5 Conclusions  
This paper proposes a new label-clip-splice strategy to improve GA with a multipoint approximation method to realize simultaneous size and topology optimization for large-scale trusses efficiently. A mathematical model with continuous size design variables and discrete topology design variables is established based on the ground structure. A branched multipoint approximation is used for implicit functions in the model to conduct the first-level approximation problem. GA is selected to address the mixed variables optimization. To solve the problem of a large number of topology variables for GA resulting in a decrease in the approximation effect for BMA and convergence difficulties for large-scale truss optimization, a label-clip-splice strategy is proposed to shrink the search domain gradually and reasonably with iterations.  
In addition to verifying the improvement of the label-clip-splice strategy on the hybrid method of GA with BMA for large-scale truss optimization, the numerical simulations in this paper also suggest the efficacy of the presented approach in finding the optimal design of large-scale truss optimization with mixed variables. The performance of the proposed method in reducing the number of structural analyses is significant and is far superior to other methods. Furthermore, the proposed method shows its potential applicability for the optimum design of practical structures with the extensive ground structure method and structural analyses from the finite element method.  
Conceptualization, T.D.; methodology, T.D. and H.H.; validation, Z.D.; formal analysis, T.D.; writing--original draft preparation, T.D.; writing--review and editing, T.D. and Z.Y.; supervision, S.C. and C.H.; project administration, S.C. and C.H.; funding acquisition, S.C. and H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [the National Natural Science Foundation of China] grant number [11672016].  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline No. & Area & No. & Area & No. & Area & No. & Area \\ \hline
**1–12** & 0 & 56 & 0.9887 & 96 & 0.6406 & 171 & 0.1 \\
**13** & 0.2199 & 57–60 & 0 & 97–128 & 0 & 172–238 & 0 \\
**14–19** & 0 & 61 & 0.3213 & 129 & 1.4555 & 239 & 2.847 \\
**20** & 0.9186 & 62–63 & 0 & 130 & 0 & 240–317 & 0 \\
**21–35** & 0 & 64 & 0.3215 & 131 & 0.5037 & 318 & 0.1578 \\
**36** & 0.4054 & 65 & 0.7449 & 132 & 0 & 319–331 & 0 \\
**37–43** & 0 & 66–88 & 0 & 133 & 0.957 & & \\
**44** & 0.1221 & 89 & 0.6388 & 134–169 & 0 & & \\
**45–55** & 0 & 90–95 & 0 & 170 & 0.1438 & & \\ \hline \end{tabular}
\end{table}
Table 6: Optimal design of the 622-bar truss.  
\begin{table}
\begin{tabular}{c c c} \hline  & Literature & Present \\ \hline Iteration number & & 124 \\ Labeled variables & / & 314 \\ Critical constraint & 0.000 & 0.000 \\ Weight & / & 50.522 \\ Structural analysis & 10,783 & 139 \\ Percentage difference (\%) & 1.29 & - \\ \hline \end{tabular}
\end{table}
Table 5: Optimization results of the 662-bar truss.
# Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement Learning
## 3 Modelling of the CDCN
### The Utility Function Model for Cloud Users
If we also assume that the round-trip time processes for each communication path are statistically independent and have _truncated normal_ distributions with parameter vector \((\mu,\sigma,x,y)\), we can write, according to Equation (6) [62]:  
\[\mathbb{E}[\mathcal{H}^{(i)}(t)]=\mu^{(i)}_{\text{sup}}+\sigma^{(i)}_{\text{ sup}}\sqrt{\frac{2}{\pi}}\times\frac{\mathbf{exp}\{-\frac{1}{2}(x^{(i)}_{\text{ sup}})^{2}\}-\mathbf{exp}\{-\frac{1}{2}(y^{(i)}_{\text{sup}})^{2}\}}{ \mathbf{erf}(\frac{y^{(i)}_{\text{sup}})}{\sqrt{2}})-\mathbf{erf}(\frac{y^{(i )}_{\text{sup}}}{\sqrt{2}})} \tag{6}\]  
where \(\mathbf{erf}(\cdot)\) is the _error function_ and \(\mu^{(i)}_{\text{sup}}\) and \(\sigma^{(i)}_{\text{sup}}\) are the mean and standard deviation of the normal distribution associated with the largest round-trip time path and \(x^{(i)}_{\text{sup}}\) and \(y^{(i)}_{\text{sup}}\) are their corresponding positive truncation parameters, and we have \(x^{(i)}_{\text{sup}}<y^{(i)}_{\text{sup}}\). For a clearer illustration of user utility modelling, in Figure 2, the user utility function calculation is summarised. As can be verified in Figure 2, for calculating each user utility function, we need the cloud data centres' resource pool info (CPU, RAM, storage, BW) and expected packet loss and round-trip times associated with each user session.
## 2 Materials and Methods
### Experimental Methods  
Different jet stream pressures, impact angles, and water jet bit rotation speeds were used to study the effects of water jets on the specimens. The common experimental parameters were an erosion time of 30 s (starting when the jet pressure was stable) and a jet impact distance of 20 mm. The depth, width, and volume of the crushed pit formed in the specimen were used as indexes for evaluating rock-breaking performance. The depths and widths of the crushed pits were measured by digital vernier calipers and the pit volumes were determined by measuring the volume of sand needed to fill each pit. Each specimen was tested three times and the results were averaged. The specimens  
Figure 3: Illustration showing the water jet bit used in the experiments: (**a**) top view of the bits; (**b**) front view of the bits.  
Figure 2: A synthetic coal specimen, i.e., a 100 mm cube.  
were named by numbers representing the pump pressure, the rotation speed, and the impact angle. For example, the specimen name 6-100-0 indicates that the specimen was tested with a 6 MPa jet rotating at 100 rpm impacting at an angle of 0\({}^{\circ}\).  
The schemes for the three sets of water jet rotary impact rock-breaking tests performed are described below.  
Experimental suite 1: water jet impact angle 0\({}^{\circ}\); bit rotation none (0 rpm) and 100 rpm; and water jet pressures 3, 6, 9, 12, 15, and 18 MPa. Experimental suite 2: water jet impact angle 0\({}^{\circ}\); bit rotation speeds 0, 50, 100, 150, 200, and 250 rpm; and water jet pressures 6 and 12 MPa. Experimental suite 3: tests were conducted with water jet bits providing different impact angles (0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\), and 30\({}^{\circ}\)); the erosion test was carried out at rotation speeds of 0 and 100 rpm and water jet pressures of 6 and 12 MPa.
## 1 Introduction
The above analysis shows that the existing hyperspectral target detection algorithms are mainly oriented to land domain scenarios, and there are few research cases for underwater target detection. Underwater target detection is a very interesting problem: many authors have proved that hyperspectral imagery could enable to retrieve efficiently bathymetry, coral reefs and sea grass [12; 13; 14; 15]. However, only a few ones have dealt with the detection problem in such a context. For example, in [16], the purpose of the authors is more to classify pixels rather than to detect spared target pixels among the background. Most similarity criteria of hyperspectral pixels consider a single spectral dimension, and multi-source information including global, local, spectral, shape and other information is less comprehensive consideration. Afterward, it is difficult for the existing hyperspectral target detection algorithms to strike a balance between efficiency and accuracy, and further research is still required to improve the detection efficiency while meeting the accuracy requirements. In this paper, we try to present a new method of detecting underwater targets in hyperspectral imaging, which not only guarantees the detection probability but also considers the efficiency. The development of the new algorithm is based on the hyperspectral images of underwater targets acquired by an unmanned airborne hyperspectral camera.
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 3 Proposed Method
### Optimization Stage  
The core idea of the PSF algorithm in the optimization stage is to simulate the stamping forming technique. Initially, the pressure is applied to the preliminary path along the direction of the global path, gradually making it closer to the global path. In this process, the path encounters resistance generated by obstacles, causing deformation. Additionally, due to the limitation of the robot's velocity, the preliminary path will not undergo extensive deformations. Therefore, the preliminary path can be adjusted in real time and multiobjective-optimized from three aspects: the distance between the preliminary path and the global path, the distance between the preliminary path and obstacles, and the kinematic constraints of the robot's motion. This enables the generation of an optimal local path, denoted as \(B^{*}\). \(B^{*}\) can be obtained by minimizing a weighted nonlinear least squares cost function. The cost function consists of the objective \(T\) and penalty \(P\), expressed as  
\[B^{*}=\arg\min_{B}\sum_{\epsilon}\gamma_{\epsilon}f_{\epsilon}(B),\ \ \ e\in\{T,P\} \tag{11}\]  
where \(f_{T}\) represents the objective function concerning constraints on the deviation of the local path, and \(f_{P}\) represents the penalty function regarding constraints on obstacles and velocity. \(\gamma_{\epsilon}\) denotes the weights among these terms. The G2O framework can be utilized to solve the above functions [23].
## 2 Related Work
We test the system's effectiveness on two distinct databases, ISIC 2020 and DermIS, highlighting its adaptability to varying datasets. Our approach involves training, validation, and testing on 700, 300, and 200 images, respectively, showcasing rigorous empirical evaluation. Moreover, we conduct a comprehensive comparison of our system's performance against four other existing applications that utilize CNN combinations, such as Google Teachable Machine, Microsoft Azure Machine Learning (ML), Google Vertex AI, and Salesforce Einstein Vision. This comparison underscores our proposed system's superior performance. The structure of this study is as follows: Section 3 provides insights into the utilized databases and outlines the various CNN architectures applied for skin lesion detection. The methodology employed for validating system performance is also detailed. Section 4 delves into the experimental results. Section 5 engages in a discussion of the proposed methodologies and includes a comparison with existing systems. Finally, Section 6 presents the conclusion of this research.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges
### UOC--Today's Research Challenges
#### 7.2.7 High-Speed UOC Transmission Link to Implement 5G/6G and the IoT  
UOC technology capable of delivering high-speed transmission is essential ro truly implement the IoT in 5G/6G architecture. IoT coverage and performance will undoubtedly improve with the use of high-speed UOC links offering 10 s or more Gbit/s speeds. It may be possible to develop high-speed long-reach UOC links to integrate 5G/6G, and the IoT using advanced modulation schemes such as a polarization division multiplexed 16-level quadrature amplitude modulation (PDM-16-QAM)-based UOC link at 160 Gbit/s, using coherent detection and advanced digital signal processing at the receiver end terminals. Recently, researchers reported [499] that integrating the IoT with 5G/6G will focus on machine-type communications (MTC), and analyzed the sources of delay in end-to-end path MTC traffic in both uplink and downlink directions, specifically addressing performance optimization for the uplink direction of MTC and the mobile core of 5G/6G systems. Additionally, the authors of [499] highlighted how the IoT is evolving from a sensor-driven paradigm to one that integrates drones, robotics, actuators, and architectural components to reduce energy intake, control overhead, and delay.