esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Improved Self-Collision Detection Technology
### Comparison of the Performance of Self-Collision Detection
#### 5.3.1 Comparison of the Approximation Results  
This simulation compares the efficiency of the approximation used for simplifying the robot before self-collision detection processes. If a single spherical object is used for the approximation of each component of the mobile manipulator robot and we use the methods of article [36] and this article to approximate each component of the mobile manipulator system, the results are as shown in Figure 7.  
To compare the accuracy between the approximation and the actual structure of the mobile manipulator during the self-collision detection process, we performed a quantitative calculation by comparing the volume of the approximation and the volume enveloped by the actual part surface. Assuming: the volume enveloped by an actual component surface of the mobile manipulator robot is \(V_{1}\), the volume of the corresponding approximation object is \(V_{2}\), and the approximation degree of the corresponding component using the corresponding approximation object can be expressed as \(n=\frac{V_{1}}{V_{2}}*100\%\). When the value of the approximation degree is larger, the approximation object of the corresponding component is better, and the value range of the approximation degree is between \([0,1]\). Therefore, we use the approximation degree to quantitatively compare the performance of approximating each component of the mobile manipulator robot based on different methods, which can be summarized in Table 2.  
By comparing different approximation results of the mobile manipulator robot, we can obtain the following:  
(1) Comparing Figure 7a,b, the second link, the third link, and the mobile platform are described by a large or small spherical object, and the two components will inevitably collide with each other at the initial time. This is caused by the significant difference in length and width of the manipulator and the mobile platform. (2) Comparing Figure 7c-f, the basic structure of the manipulator subsystem can be accurately described by adding key point technology, which proves the correctness of the optimized kinematic modeling method and the effectiveness of the approximating process based on the physical structure model. (3) Comparing Figure 7c-f, the connecting links and mobile platform of the robot can be described by multiple spherical objects. Moreover, the misjudgment of self-collision detection can be effectively eliminated at the initial moment. Therefore, the multiple spherical objects can improve the performance of self-collision detection. (4) Comparing Table 2 and Figure 7e,f, the approximation degrees of these links and the end-effector obtained by the optimized method are more excellent than these degrees obtained by the typical method. Furthermore, the appearance of connecting links in Figure 7f was more accurately described by the multiple spherical objects than by the same spherical objects in Figure 7e. Therefore, multiple spherical objects can be effectively used to approximate the complex components in the mobile manipulator.
## 4 Case Study and Analysis  
To validate the effectiveness of the proposed methodology, a propeller with a Gurney flap was designed for an ultra-high-altitude UAV (see Figure 15) working at an altitude of 20 km with a cruising velocity of 32 m/s. The fixed parameters and design state for the propeller are displayed in Table 4. The average working Reynolds number of the cross-sectional airfoils (\(R_{\text{average}}\)) for the propeller was estimated lower than \(6\times 10^{4}\). The relative height of Gurney flap for the cross-sectional airfoil in the optimization of the Gurney flap propeller was taken as 1%. In order to quantify and analyze the effect of the Gurney flap on the aerodynamic performance of the propeller, a propeller without a Gurney flap was completed as a comparison, which was also designed by the proposed method by making the relative height of the Gurney flap on the cross-sectional airfoil be 0. In addition, the turbulence intensity used in the CFD calculations was set as 0.1%.
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 4 Experiment
### Experimental Data  
#### 4.2.1 Public Dataset  
In order to validate the effectiveness of AMMNet, the public dataset LEVIR_CD used in the literature [4; 5; 6; 7; 8] is selected for validation and comparison.  
The LEVIR-CD dataset is recognized as a key benchmark in remote sensing, specifically designed for building change detection. It consists of 637 pairs of high-resolution remote sensing images, each with a 0.5 m/pixel resolution, covering building changes in urban China from 2002 to 2017. To meet the model and GPU memory constraints, each sample is cropped into 16 non-overlapping image patches and split into training, validation, and test sets with a 7:1:2 ratio, containing 7120, 1024, and 2048 image pairs, respectively. The dataset covers a variety of urban development scenarios and complex environments, providing rich and diverse data to support model performance validation.
## 2 Designed Methodology
### Degradation Model
#### 2.8.2 Design of a Non-Optimal EMS  
The objective of the EMS is to charge the fleet bus batteries at the lowest possible cost. This cost depends on the grid tariff, which varies during the day, and on the BESS degradation/operating cost. The EVCS power schedule is that optimized in Section 2.1.2.  
The input variables of the EMSs are the grid tariff, \(\mathbf{\xi}_{G}(t)\), the fleet load, \(P_{EVCS}(t)\), and the SOC of the BESS, \(SOC_{BESS}(t)\), while the output is the BESS power, \(P_{BESS}(t)\). Two types of EMSs are designed, an RB (EMS2) and an FL (EMS3), the latter of which is better at considering uncertainties.  
Figure 4: Operation/degradation cost calculation based on the rainflow-counting method [21].  
As shown in the flowchart in Figure 5, the RB EMS compares the grid power price with a threshold, \(\epsilon_{Gthr}\), in order to decide whether to charge or discharge (A branch) the BESS. If the price is low, the BESS is charged. Depending on the SOC, charging is either fast (for SOC < 0.5) or slow (\(P_{BESS}\) and \(P_{BESSstep\_max}\) at half their value, to avoid high DODs). This is rule is applied in order to reduce the degradation of the battery [21], and, thus, the cost of operation. Then, if \(P_{diff}(t)\), the difference between the contracted maximum power, \(P_{Gmax}\), and the fleet load at \(t\) sample interval, \(P_{EVCS}(t)\), is higher than \(P_{BESSmax}\), the maximum possible value is given to \(P_{BESS}\).  
The A branch follows the same logical structure but discharging the BESS, and, instead of using \(P_{diff}\) as the power limit, the available power to load the fleet is considered.
# LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
## 5 Conclusions  
In the process of high precision positioning based on GNSS, the carrier phase ambiguity resolution is a necessary step. However, due to the influence of complex indoor  
Figure 14: Positioning accuracy analysis when ranging accuracy is within 10 cm.  
Figure 13: Positioning accuracy analysis when ranging accuracy is within 15 cm.  
environments, the traditional carrier phase ambiguity estimation algorithm is difficult to achieve the effective ambiguity estimation under the influence of frequent cycle slips. The existing indoor location algorithms do not consider how to suppress the cycle slip effect on the ambiguity. This study presents a reliable indoor positioning method based on group phase ranging theory. Based on the multi-frequency phase characteristic of the BeiDou pseudo-satellite, the phase integer part of carrier phase is estimated by using the group phase theory. The high-precision indoor location is achieved by the factor map location technology assisted by SFLA. The method proposed in this paper is fully verified by the measured and simulated data. The advantages of the proposed algorithm are fully demonstrated under the condition of equal precision. In future work, we will carry out more research on the integrity of the algorithm and the reliability of location. In the existing algorithms, by fusing INS/MEMS and other multi-source sensors, a more lupin fusion localization method based on BeiDou pseudo-satellite is constructed by integrating sensors such as inertial sensors and visual radar.  
All authors together developed the idea that led to this paper. [PERSON] conceived the positioning method, the experiment and the data processing of the article. [PERSON] provided critical comments and contributed to the final revision of the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Plan of China (project: High Precision Positioning, Navigation and Control Technology for Large Underground Space (No. s2021 YFB3900800, 2021 YFB3900801, 2021 YFB3900802, 2021 YFB3900803, 2021 YFB3900804)).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing projects.  
The authors declare no conflict of interest.
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 8 Algorithm for Arbitrary Planar Domains  
After the statements, demonstrations, and examples described in the previous sections, it is now straightforward to establish the basis of a general algorithm for arbitrary planar domains that can be described by parametric equations, which have associated a sense of circulation along the boundary. The procedure can be stated as follows:  
1. Set up the closed region by segments defined parametrically in such a way that each segment is defined by two extremes \(z_{1}\) and \(z_{2}\), a parameter \(t\), being \(0\leqslant t\leqslant 1\), and optional points or parameters depending on the type of the segments (linear, circular arcs, Bezier curves, etc.) * Linear segments do not require any further parameters or points; * circular arcs require a third point (center), or else a radius along with the definition of its orientation; * other curves (of second or higher order) require additional elements to fully define them (3 rd order Bezier curves require two more points). 2. Normalize the domain delimiting points (vertices) relative positions by subtracting the test point \(a\) to all vertices. Figure 19: Results of algorithms applied to the regions of Figure 1. Due to limited resolution in the display above, some points may seem to be on the border, but actually, none of them are. Check the intersections of this newly relocated region with the negative half part of the horizontal axis (\(x\)). Intersections occur from \(Q2\) to \(Q3\) or vice-versa. Full intersections are accounted for an integer value (\(+1\) or \(-1\), depending on the sense, \(Q2\) to \(Q3\) (\(+\)\(\diamond\)) or \(Q3\) to \(Q2\) (\(+\)\(\diamond\)), respectively), or a partial value (\(+0.5\) or \(-0.5\)) if the segment starts or ends on the very horizontal axis (\(\mathbf{\gamma}\), \(\mathbf{\perp}\)\(\mathbf{\omega}\)), and the positive or negative contribution is determined once again if the segment goes or comes to/from quadrants \(Q2\) or \(Q3\). 4. The verification of the sense of the evolution of the path must be asserted in these intersection/touching points: it is straightforward for linear segments, but for other types of segments, the descendant gradient of \(y\) with respect to \(t\) must be used at that point to determine the sense of cross/approach to the horizontal axis. Algorithm 5 formalizes the required steps. ```
0:\(a=(a_{x},a_{y})\), \(P_{n}=(x_{n},y_{n})\) for \(n=1,2,\ldots,M\)
0:\(L_{n}\in\{0,1,2,3,\ldots\}\)//type of segment/number of control points
0:\(C_{n_{k}}=\{(xx_{k},yy_{k})\}_{n}\) for \(k=0,1,\ldots,L_{n}\)//Control points //It is admitted that \(P_{M+1}=P_{1}\). 1:if\(a\in\{P_{1},P_{2},\ldots,P_{M}\}\)then
2:return true//point\(a\)is one of the vertices
3:endif
4:\(P_{n}\gets P_{n}-a\) for \(n=1,2,\ldots,M\)
5:\(C_{n}\leftarrow C_{n}-a\) for \(n=1,2,\ldots,M\)
6:\(I_{C}\gets 0\)
7:for\(n=1\)to\(M\)do
8:\([X(t)\,,Y(t)]=f(P_{n},P_{n+1},I_{C_{n}},L_{n})\)
9:\(T\leftarrow\)solutions of \(Y(t)=0\)
10:for all\(t_{c}\)in\(T\)do
11:if\(X(t_{c})==0\)then
12:return true//point\(a\)is on border (included).
# Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
## 4 Experiment Setup
### Dataset  
The DREBIN dataset contains 5560 malware samples from 179 different families. We dropped 61 samples that had no manifest file or structural errors; then, we used the remaining 5499 samples for the experiment. We ignored permissions that were not used in any malware samples. Then, we used the remaining 96 permissions (87 built-in permissions + 9 custom ones) as the features. The nine custom permissions are as follows:com.android.alarm.permission.SET_ALARM, com.android.browser.permission.READ_HISTORY_BOOKMARKS, com.android.browser.permission.WRITE_HISTORY_BOOKMARKS, com.android.launcher.permission.INSTALL_SHORTCUT, com.android.launcher.permission.UNINSTALL_SHORTCUT, com.android.vending.BILLING, com.android.vending.CHECK_LICENSE, com.google.android.c2 dm.permission.RECEIVE, com.google.android.googleps.permission.GOGOGE_AUTH.  
Since the malware of each family share codes, they also request similar permissions. Malware tends to belong to only one family, and some families have few samples to learn through machine learning. We sorted the families in descending order of the number of samples. We experimented with 4615 malware of the top 20 families. The family name, the number of samples, and permissions are shown in Table 1.  
We evaluated the algorithms using 5-fold cross-validation. We split the dataset into 5 folds. To proportionally distribute the samples of each family on 5 folds, we divided each family into 5 subsets and associated each subset with a fold.
# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
## 2 Methods and Principles  
Multi-task learning (MTL) is an important branch in the field of machine learning that aims to improve the generalization ability and efficiency of models by learning multiple related tasks simultaneously. Based on the assumption that all tasks, or at least some, are relevant, learning multiple tasks jointly has been found empirically and theoretically to lead to better performance than learning individual tasks independently [22]. The hard-sharing mechanism of implicit layer parameters is one of the most common approaches in multi-task learning. Multiple tasks share part of the hidden layer and learn some common low-level features while retaining the output layer for different tasks. Throughout the learning process, the tasks exchange beneficial information, thereby amplifying the efficacy of each task's learning process, enhancing the model's overall generalization capabilities, and diminishing the likelihood of overfitting.  
U-Net is a model obtained by improving on the full convolutional neural network [23]. U-Net has low requirements on the size of training data, high segmentation accuracy, and is widely used in image segmentation tasks. U-Net and its variants have achieved remarkable results in the geophysical field due to good feature extraction capabilities [24; 25; 26]. The architecture of U-Net is inherently symmetrical, featuring a contraction path (encoder), dedicated to feature extraction, and an expansion path (decoder), which employs up-sampling through transposed convolution to restore the feature map to the original image's dimensions. U-Net facilitates the transfer of features from the contraction to the expansion path via skip connections, effectively integrating surface-level high-resolution details with profound abstract features.  
The input data to the network are low-SNR grayscale microseismic recording maps. The encoder first resizes the input data using a convolutional layer, followed by a Residual (RES) module and a maximum pooling operation. Decoder 1 outputs a Gaussian mixture distribution containing information about the location of the microseismic source after an up-sampling, convolution, and convolution and coordinate attention (CCA) module. Decoder 2 outputs a microseismic record after noise suppression after an up-sampling, convolution, and convolution and squeeze-and-excitation attention (CSEA) module.  
The following section describes RES module, CCA module, CSEA module, and the overall architecture of the MA_W-Net network:
## 6 Optimization of Energy Distribution
### Optimal Travel Path  
The UAV must move along the shortest Hamiltonian cycle, and the shortest travel path can be found by solving the problem called the Traveling Salesman Problem (TSP) [28]. The symbol \(D_{TSP}\) represents the distance traveled through the shortest Hamiltonian cycle, and let \(\tau_{TSP}=dfracD_{TSP}V\). The optimal travel path is  
\[\tau_{TSP}+\tau_{space}+\tau_{ivac}+\sum_{i}^{N}\tau_{i}=\tau \tag{12}\]  
\[\tau_{vac}=\tau_{space}+\tau_{vac} \tag{13}\]  
By our proposed system, the min \(\dfrac{\tau_{vac}}{\tau}\) was obtained, \(\tau_{suc}\) is the time spent on the service station to be charged by a UAV, \(\tau_{ivac}\) is the charging period to maximize the energy from a UAV at some points along the physical path, and \(\tau_{vac}\) is period of time as mentioned in Section 6 case(1) such that max \(\dfrac{\tau_{vac}}{\tau}\) has been satisfied in OPT by the near optimal solution procedure in our proposed solution.  
Here, in an optimized research solution with the maximum \(\dfrac{\tau_{vac}}{\tau}\), the UAV must have traveled through the shortest Hamiltonian cycle that links all of the sensor nodes and the initial point (service station).  
OPT max \(\dfrac{\tau_{vac}}{\tau}\) s.t  
\[\sum_{j\in N}^{j\
eq i}f_{ij}+f_{IB}(ieN)-\sum_{k\in N}^{k\
eq i}f_{ki}=R_{i}( ieN) \tag{14}\]  
\[\rho.\sum_{k\in N}^{k\
eq i}f_{ki}+\sum_{j\in N}^{j\
eq i}C_{ij}f_{ij}+C_{iB}f_ {IB}-P_{i}=0(ieN) \tag{15}\]  
\[\tau-\sum_{i\in N}\tau_{i}-\tau_{vac}=\tau_{TSP} \tag{16}\]  
\[\tau p_{i}-U\tau_{i}=0(ieN) \tag{17}\]  
\[\tau-\tau_{i}p_{i}\leqslant E_{max}-E_{min},ieN \tag{18}\]  
\(f_{ij},f_{IB},\tau_{i},\tau,\tau_{vac},p_{i}\geq 0(i,j\in N,i\
eq j)\).  
In this research problem, the rate of flow \(f_{ij}\) and \(f_{IB}\); time periods \(\tau\), \(\tau_{i}\), and \(\tau_{vac}\); and energy utilization \(p_{i}\) are the variables used to denote the optimization, and \(R_{i},\rho,C_{ij},C_{iB},U,E_{max},\)\(E_{min},and\tau_{TSP}\) are constants. This research issue has both a non-linear term \(\dfrac{\tau_{vac}}{\tau}\) and nonlinear terms \((p_{i}\) and \(\tau_{i}p_{i})\). Of note, there are two possible outcomes for the optimization problem OPT: either the solution is optimal or infeasible, denoted by OPTS. There are various scenarios where the outcomes may occur later, e.g., (i) the UAV energy charged rate is varied, and it may be very low or small or may be large; (ii) the interval between UAVs is high. Due to the problem constraints, OPT could not be held. These are some of restrictions for a UAV to regain some network energy with a maximized lifetime in a sensor network [28].
## 2 Materials and Methods
### Photoelectrochemical Measurements  
Photoelectrochemical tests were conducted using a three-electrode cell. Working electrodes comprising a planar silicon photoelectrode, nanowire structured silicon photoelectrode and FTO glass were used. Graphite rods were used as counter electrodes and Ag/AgCl (3M NaCl) as reference electrodes. Each reference electrode was calibrated to \(-\)0.201 V (vs. RHE) in a proton-rich aqueous solution of 1 M perchloric acid and purged with a high-purity saturated H\({}_{2}\) at 25 \({}^{\circ}\)C. For solar irradiation, a 300 W Xenon lamp was used as the light source at a light intensity of 100 mW/cm\({}^{2}\) (Air Mass 1.5 Global condition glass filter).
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 1 Introduction  
With the proliferation of various underwater applications such as underwater environment monitoring, offshore structural health monitoring and target tracking, underwater acoustic sensor networks (UASNs) have received much attention. Due to the high attenuation of electromagnetic waves in underwater environments, acoustic communications are typically used in underwater sensor networks. Recently, security has become one of the important concerns in UASNs [1]. We summarize related works on security of UASNs as follows.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 4 Machine Learning Algorithms for SON
### ML Algorithms Taxonomy
#### 4.1.3 Reinforcement Learning (RL)
The Bellman equation describing the values received by an agent during the process use for trying to find the most optimal action to take at each step can be written as:  
\[\text{V(s)}=\text{max}\left[\text{R(s,a)}+\gamma\text{V(s^{\prime})}\right] \tag{1}\]  
The key terms of the equation are:  
* Action performed by the agent is \"a\". * State instance by performing an action by the agent is \"s\". * The reward received for each beneficial action or not is \"R\". * The discount factor is Gamma \"\(\gamma\)\". * V(s): Corresponds to the value calculated at a specific point. * R(s,a): The reward at a particular state after an action is performed. * \(\gamma\) = Corresponds to the discount factor that determines if the agent as regards the beneficial the rewards received in the near future compared to those in the immediate future. * V(s\({}^{\prime}\)) = The value obtained at the previous state. As far as 5G networks are concerned, recent approaches and proposals involving RL algorithms regard power and interference control and they can be a perfect match for the development of intelligent B5G SON platforms [84; 85; 12; 86; 87]. The UEs and RAN nodes can run RL and DRL algorithms so that real-time network optimization activities can be fully automated and able to be effective in real-time and continuously changing radio propagation environments.
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
## 3 Aquatic Platform
### IoT Wireless Sensor Kit  
#### 3.2.1 Prototype Setup  
The design of the system USV is shown in Figure 2. The designed model is divided into four main parts, as described in Figure 3, which are the data acquisition, the central processing unit, the communication module and the power unit. The hull of the USV is built using ABS and the base of the buoy is made of styrofoam and plastic. The electronic devices for aquatic monitoring are installed inside the hull or in a water-proof box in the buoy.  
Figure 2: Overall prototype design.