esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 3 Background
### Channel Model  
We give an algorithm of calculating packet error rate (PER) for two neighborhood nodes in underwater channel here. The main factors that affect the transmission performance of underwater channel are propagation loss, ambient noise and fading. For propagation loss, [PERSON] gave an empirical formula in 1967 [22] as shown in Equation (1).  
\[TL\left(d,f\right)=A_{0}d^{k}a(f)^{d} \tag{1}\]  
Here \(A_{0}\) is a normalizing parameter. \(d\) is the transmission range. \(k\) is the spread factor and \(k=1.5\) is usually taken as representative of practical spreading based on a partially bounded sphere for shallow sea channel. The sound wave absorption loss \(a\left(f\right)\) is modeled by the famous [PERSON] formula [23] in Equation (2)  
\[10\log a(f)=\left\{\begin{array}{ll}0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2} }{4100+f^{2}}+2.75\cdot 10^{-4}f^{2}+0.003,&f>200\text{Hz}\\ 0.002+0.11\frac{f^{2}}{1+f^{2}}+0.011f^{2},&f<200\text{Hz}\end{array}\right. \tag{2}\]  
The ambient noise in underwater channel is mainly affected by four factors, including turbulence \(N_{t}\), shipping \(N_{s}\), waves \(N_{w}\) and thermal noise \(N_{th}\). According to the famous Wenz noise power spectral density [24], the effect of ambient noise can be described as in Equations (3) and (4):  
\[NL\left(f\right)=N_{t}\left(f\right)+N_{s}\left(f\right)+N_{w}\left(f\right)+N _{th}\left(f\right) \tag{3}\]  
where  
\[\begin{array}{l}10\log N_{t}\left(f\right)=17-30\log f\\ 10\log N_{s}\left(f\right)=40+20\left(s-0.5\right)+26\log f-60\log\left(f+0.0 3\right)\\ 10\log N_{w}\left(f\right)=50+7.5w^{1/2}+20\log f-40\log\left(f+0.4\right)\\ 10\log N_{th}\left(f\right)=-15+20\log f\end{array} \tag{4}\]  
Here \(s\) is the shipping activity factor and \(k=0.5\) for average shipping activity. \(w\) is the wind velocity in \(m/s\). According to the passive sonar equation [22], the SNR per bit for underwater channel can be described as in Equation (5):  
\[\gamma_{b}=SL-TL-NL+DI \tag{5}\]  
where \(SL\) is the source power level and \(DI\) denotes the directivity coefficient. For omnidirectional hydrophones, \(DI=0\). \(TL\) is the propagation loss and \(NL\) is the noise power spectral density. As we know, the underwater channel suffers severe multipath effects. Here we suppose that the signal mode type is BPSK and the average bit error rate (BER) \(P_{b}\) can be obtained according to Rayleigh channel model [25] in Equations (6) and (7):  
\[P_{b}=\frac{1}{2}\left(1-\sqrt{\frac{\gamma_{s}}{1+\gamma_{s}}}\right) \tag{6}\]  
where  
\[\gamma_{s}=10^{\gamma_{b}/10} \tag{7}\]  
Thus, for a packet with \(L\) bits, the PER \(P_{p}\) can be obtained as in Equation (8):  
\[P_{p}=1-(1-P_{b})^{L} \tag{8}\]As the nodes in underwater applications always move passively with water currents, the PER between two nodes varies with distance. So we should monitor channel changes and make route decisions based on the real-time \(P_{p}\).
## 5 Experimental Part  
### Experimental Test Bench  
A test bench was built for conducting experiments. The view of the test bench is presented in Figure 11.  
The test bench consists of the Hirata Cartesian robot, the data collection system based on Dewetron, and three vibration sensors installed on the grab system of the robot and the top and bottom of the test bench.  
The main sensor for measuring robot vibrations is installed on the Z-axis, directly on the gripping system. This placement allows the tracking of vibrations along both axes of the robot. The sensors placed above and below the laboratory stand structure are intended for measuring the stand's vibrations to eliminate additional noise from the main signal. This approach helps mitigate the influence of the stand's vibrations on the magnitude of the main sensor's signal. Accelerometers of the DIS QG40N-series (DIS Sensors, Soest, Netherlands) were used for the experiment. The technical specifications of the sensor are presented in Table 3.  
Gearbox faults were sequentially introduced into the robot's structure. To obtain the necessary data, artificial faults were separately created in the gearbox, namely, overtension of the tooth belt transmission and heating of the screw transmission. For overtension of the belt, a displacement of the transmission pulleys was performed, as this type of damage is the most common. For heating the worm, lubrication was removed, simulating the case of contamination/drying of lubrication in the transmission.  
Figure 11: View of the experimental test bench.  
During the experiment, several conditions were established: a minimum rotation speed of the robot's servo motor was set (200 rpm/s) to avoid unwanted damage. Measurements were conducted along two axes of the robot, X and Y, the robot movements were linear to track changes in the reference and faulty signals.  
The experiment consisted of the following steps:  
1. Data acquisition, collection of all data from vibration sensors by unfaulty (reference) and faulty signals. The vibrational signal, captured by accelerometers, was converted into an electrical signal, and proceeded into Dewetron inputs for processing, visualization, and storage.
2. Data processing, transformation of output vibration signals into spectra to obtain vibration amplitude analysis. Following data collection, Fourier transform analysis was performed to analyze the acquired data. Also, to eliminate unwanted noise by comparing signals from the accelerometers, Matlab (9.10.0.1602886, R2021a) filters were used.
3. Compilation of fuzzy logic rules library based on the spectra. Based on spectral analysis, data collection was conducted, and a rule base for the fuzzy logic algorithm was developed. This formed the foundation for the diagnostic and control system.
4. Development of a fuzzy logic control system.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 5 Solving a Pseudo-Static Scenario  
For small flying spaces, solutions could be found through exhaustive search by assessing the objective function (i.e., see (9)) for every grid edge for an increasing number of UAVs, starting with one (\(D=1\)). If no valid solutions were found, the number of drones should be increased until a solution is found, or a maximum number of UAVs is reached (\(D=D_{\text{max}}\)). Once a solution is found, the search method has to consider launching an extra drone to ensure that the objective function value cannot be further reduced (i.e., increasing the number of UAVs could reduce the UAV turnover rate). However, although exhaustive search is feasible in very small areas, it is not generally feasible in most real-life scenarios, for assessing a solution with \(D\) drones would require to evaluate \(\sum_{d=1}^{D+1}\left(|\mathcal{P}|/d\right)\) possibilities. Thus, we suggest using heuristic search algorithms as a general method to find solutions to our optimization problem. Although several other metaheuristic methods are applicable, we opt for using Genetic Algorithms to find a sub-optimal solution due to its simplicity and proven effectiveness in similar problems [12; 40; 49], since the contribution of this work is not focused on the performance of the metaheuristics method.
# Crushing Analysis and Optimization of Adjacent Variable Thickness Hexagonal Tubes
## 4 Conclusions  
In this study, we proposed an AVTHT and investigated its crushing performance under axial loading and multiple oblique loadings. By performing simulations and experiments on four configurations of AVTHTs under axial loading, we validated the FE models. Then, we investigated the crushing performance of AVTHTs with various thickness configurations, loading angles, and loading patterns by the FE models. Finally, the crashworthiness MOD was adopted to minimize \(\mathrm{F_{maxw}}\) and maximize \(\mathrm{SEA_{w}}\) using the MOGA. Conclusions can be drawn as follows:  
* The manufactured AVTHTs could exhibit a progressive deformation mode with generating folds under axial loading, whether in experiments or simulations;
* AVTHTs under several types of loading angles (\(0^{\circ}\), \(10^{\circ}\), \(20^{\circ}\), and \(30^{\circ}\)) and patterns exhibited different deformation modes, as well as force-displacement characteristics and crashworthiness indices. The changing of plate thickness configuration could change the crushing performance of AVTHTs under axial and oblique loadings;
* The results of optimization determined the thickness of the design domains and indicated that a certain thickness range should be avoided, such as the ranges of \(1.55\leq t1\leq 1.6\) and \(1.85\leq t1\leq 1.95\). In the pareto results, increasing \(t1\) would not always increase the \(\mathrm{F_{maxw}}\) and \(\mathrm{SEA_{w}}\). For example, when \(1.75\leq t1\leq 1.8\), increasing \(t1\) would lead to decline of \(\mathrm{F_{maxw}}\) and \(\mathrm{SEA_{w}}\).  
Together, the research studied the crushing performance of AVTHTs under multiple loadings. The optimization results could provide a good design domain for AVTHTs, which is worthy of the remarkable crashworthiness performance of railway vehicles.  
Conceptualization, K.X. and P.X.; methodology, K.X.; validation, J.X.; formal analysis, K.X.; investigation, K.X.; resources, K.X. and S.Y.; data curation, K.X.; writing--original draft preparation, K.X.; writing--review and editing, S.Y.; supervision, Q.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China, funding number 2021 YFB3703801.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Implementation of Constellation Dynamic Selection in ARAIM
### Detailed Implementation of Constellation Dynamic Selection  
The detailed implementation is given in Figure 4. The implementation can be divided into three parts: Baseband processing, constellation dynamic selection and ARAIM calculation. For the baseband processing, it is a classical multi-constellation receiver including signal acquisition, channel tracking and navigation message demodulation. All the signals from four constellations are processed to get the all-in-view satellite geometry, pseudoranges, and initial positioning results. Then the constellations will be evaluated the geometry performance to get the best two constellations. Only these two constellations will be selected to assess the present integrity.  
The next step is to find the criterion for constellation dynamic selection. As the basis of ARAIM user algorithm is the improved geometry from multi-constellation, the criterion should evaluate the geometry scientifically and reasonably. Furthermore, the criterion should focus on the geometry consistency at vertical component as the ARAIM focuses on providing vertical guidance. The vertical protect performance is also the biggest challenge for ARAIM application, especially the EMT requirement in LPV-200, which is also for evaluating the vertical positioning performance. Based on the research work in [24], we also noticed that in ARAIM with two constellations, the availability is related to the VDOP of spatial geometry closely. Finally, DOP values are easy to access. Considering both the performance requirements and engineering implementation, the vertical dilution of precision (VDOP) is the best choice for selecting the best two constellations. After determination of the selection criterion, the implementation can be further divided into two steps: Jacobian matrix separation and the best constellation combination selection.
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 2 Preliminaries
### Band Fusion-Based Edge Feature Extraction  
As mentioned above, edge characteristics-based perceptual hash can achieve higher precision for MS image integrity authentication, so we adopt the band feature fusion technique in order to obtain the robust edge feature of the band sets separated by band-clustering.  
So far, many fusion algorithms have been developed for multispectral band fusion. One of the most important fusion techniques is the wavelet-based method, which usually uses the discrete wavelet transform (DWT) in the fusion [22]. Since the DWT of image signals produces a non-redundant image representation, it can provide better spatial and spectral localization of image information as compared to other multiresolution representations. Therefore, we adopt DWT-based fusion techniques to obtain the robust fusion features of the band subset.  
The key step of DWT-based fusion techniques is to define a fusion rule to create a new composite multiresolution representation, which uses different fusion rules to deal with various frequency bands. The process of applying the DWT can be represented as a set of filters. After first-level decomposition, the image is decomposed into a low frequency sub-image (\(LL_{1}\)) which is the approximation of the original image and a group of high-frequency sub-images (\(LH_{1}\), \(HH_{1}\), and \(HL_{1}\)) which contain abundant edge information, as shown in Figure 3a. The low frequency sub-image is the approximation of the original signals for this level and can be further decomposed until the desired resolution is reached. Figure 3b illustrates wavelet decomposition of an image at level 2. In Figure 3b, \(LL_{2}\), \(HL_{2}\), \(LH_{2}\), and \(HH_{2}\) are the sub-images produced after the \(LL_{1}\) sub-image that are being further decomposed. Since the second-level high-pass sub-images (also called middle frequency sub-images) \(LH_{2}\), \(HH_{2}\), and \(HL_{2}\) also contain abundant edge information, and are more robust than high frequency ones, but more fragile than low frequency ones, we use middle-frequency sub-images to extract the robust bits.  
Figure 3: Two-dimensional discrete wavelet transform of 2D signals: (**a**) One-level discrete wavelet transform (DWT)\({}_{2}\) (**b**) Two-level DWT.
## 2 The Establishment of Time-Space Network  
### Establish the Time-Space Network  
The time-space network is composed of a large number of nodes and various arcs. While establishing the network, the simplification of empty-driving arcs should be considered at the generation stage. We do not distinguish the vehicles belonging to different parking yards, and there is no fixed allotment relationship between vehicles and parking yards. As long as the number of vehicles in the yard is unchanged, there is no necessity that the vehicles must return to the original departing parking yard. Therefore, the yards can be regarded as general stations, and used to express the connection between yards and other stations in the same network. The processes of establishing multi-type pure electric buses system time-space network with the characteristic of recharging requirement are as follows:  
1. Generate task nodes and task arcs. Generate the task start node and task end node according to the route timetable, and each node has its own time and space attributes. Assuming that there are \(N\) task vehicles, \(S_{i}\) is the departure site of number \(i\); \(e_{i}\) is the arrival site of number \(i\); \(d_{i}\) is the departure time of number \(i\); \(\mathsf{a}_{i}\) is the arrival time of number \(i\). According to the above process, the start node \(\mathsf{t}(S_{i},d_{i})\) can be formed, and the start node of the task can be arranged in chronological order to form a set of task start nodes T; the end node \(\mathsf{e}(e_{i},\mathsf{a}_{i})\) can be arranged in chronological order to form a set of end-of-task nodes E. Task arc: (t, e), \(\mathsf{t}\in\mathsf{T},\mathsf{e}\in\mathsf{E}\). The set of task arcs is \(\mathsf{A}_{\text{task}}\). Connect the task start node with the corresponding task end node to generate the task arc.
2. Generate the parking lot node, exit arc, and entry arc. According to the task start node set, the task end node set and the empty-driving time between each station and parking yard, set up the corresponding exit node and entry node (collectively regarded as the parking yard node). Then set up the exit arc connecting the exit node and task start node, and the entry arc connecting the task end node and entry node.
3. Generate the waiting arc. Sort by stations first, then arrange them in time order and generate a collection of all the nodes.
4. Generate the empty-driving arc. Take out the task end node set and generate a DHE set according to the sequence of the task end time from the smallest to the largest. Sort the task start nodes according to the stations firstly, and then generate a T set according to the sequence of time from the smallest to the largest. Initialize the empty-driving arc.
# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 4 Proposed Resource Management Solution
### Data Offloading Process  
At this stage, priority based task offloading criteria are given to balance the energy consumption among all relays. The proposed algorithm considers three key parameters: residual energy, computation ability \(F_{i}\), and task \(C_{i}\). Since a relay with a higher residual energy and computationcapacity is more likely to handle \(C_{i}\) under QoS constraints, the priority task offloading receiver selection function can be formulated as:  
\[\mathcal{Z}(j)=\frac{C_{i}}{E_{j}F_{j}}, \tag{25}\]  
where \(E_{j}\) and \(F_{j}\) represent the residual energy and computation resource of relay \(j\). Relay \(j\), which has a higher \(\mathcal{Z}(j)\) value, does not have enough resources in terms of computation resource or residual energy. The offloading requests that relay \(i\) delivers task \(C_{i}\) to relay \(j\) with the minimal value \(\mathcal{Z}(j)\). One should note that the nearest relay may not be able to accept the task due to a lack of computation resources. By doing so, the relay with the appropriate resources takes the computing task described by Equations (9) and (10). If relay \(j\) can execute the task locally, as mentioned in Equations (5)-(8), in a similar way to Section 4.2, numerous key QoS metrics are obtained and one round finishes. However, if relay \(j\) cannot execute the task, this will be offloaded to the C-MEC. The detailed information is given in Algorithm 2.  
```
Initialize\(F_{i}\), \(D_{i}\), \(T_{i}^{\prime}\), \(C_{i}\), \(E_{res}\)  The task with a size of \(C_{i}\) received according to Algorithm 1;  Update \(U_{i}\) according to Equation (3); if\(E_{i}^{res}<E_{i}^{C_{i}}+E_{i}^{D_{i}}\) or \(T_{i}^{C}\leq T_{i}^{\prime}\)Then  execute the task locally according to Equations (5)-(8) Else \(\mathcal{Z}(j)\) with a minimal value is selected according to Equation (25);  offload task \(C_{i}\) to relay \(j\) according to Equations (9)-(12); Endif While\(E_{j}\geq E_{j}^{C_{i}}+E_{j}^{D_{i}}\)do  execute the task on \(j\)-th relay according to Equations (5)-(8); Else  offload task \(C_{i}\) to the C-MEC according to Equations (9)-(12); Endwhile
```  
**Algorithm 2** Proposed Iterative Task Offloading Strategy
# Impacts of Lightning-Induced Overvoltage on a Hybrid Solar PV-Battery Energy Storage System
## 4 EMTP-RV Model
### Return Stroke Current Model  
There are a several types of mathematical expressions that can be used to model lightning current waveshapes. The most commonly used are the double exponential and Heidler functions. The double exponential function is usually known as the simplest function, while the Heidler function produces more realistic results and is also recommended by standard IEC 62305-1 [56]. This is because the Heidler function starts with steepness 0 at equals 0, while the double exponential function starts with t equals 0 with the highest steepness. Hence, the Heidler function is used in this paper to represent the lightning waveshape.  
The effects of lightning-induced voltage were simulated using the lightning waveshape 10/350, with varied lightning current amplitudes within the range of 2 to 200 kA, as referred to in the International Council on Large Electric Systems (CIGRE) distribution. The lightning waveshape of 10/350 was chosen as part of the international standard waveform for testing and also to represent the lightning severity (major stress of electrical and mechanical) within the surge protection device [11; 57; 58].  
In this study, three lightning current amplitudes were selected, namely 3, 19, and 169 kA. These values were selected based on statistical lightning activity data in 2017 at Solar PV Farm Puchong Gateway, Selangor [59]. According to the abovementioned statistical data, the probability of lightning occurrence is 95% at 3 kA, 50% at 19 kA, and 5% at 169 kA [59] as Figure 9. Hence, the lightning waveshapes were modeled in the  
Figure 8: AC voltage outputs before the transformer.  
Figure 7: DC voltage outputs: (**a**) solar PV; (**b**) battery energy storage.  
Electro-Magnetic Transient Program--Restructured Version (EMTP-RV) software using the Heidler equation, as expressed in Equation (1):  
\[\mathrm{i}(t)=\frac{\mathrm{I}_{\mathrm{m}}}{\eta}\frac{\left(\frac{\mathrm{t}}{ \tau_{1}}\right)^{n}}{1+\left(\frac{\mathrm{t}}{\tau_{1}}\right)^{n}}\exp^{(- \frac{\mathrm{t}}{\tau_{2}})} \tag{1}\]  
where \(\mathrm{I}_{\mathrm{m}}\) is the lightning peak current, \(\eta\) is the correction factor of the peak current, \(\tau_{1}\) is the rise time constant, \(\tau_{2}\) is the fall time constant, and n is the steepness factor. Table 7 tabulates the parameters of the Heidler equation used to model the lightning waveshape.
# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
###### Abstract
Besides, localization measures are designed to provide quantitative values of the central location of the sample. These values will be helpful to identify differences between several of the vessel's routes. This paper proposes an approach to data selection to reduce computational cost. This work does not focus on precision, but it shows a precision of 76.15% and an accuracy of 81.043% for vessel position predictions through time. The paper is organized as follows. Section 2 shows a brief description of the related works. Section 3 describes the PreMovEst method for selecting the best AIS data in predicting vessel movements. Section 4 presents preliminary experiments to show the data behavior and adjust the data before selection. Section 5 describes the operations of selecting routes by clustering using the best statistical values. Section 6 presents the results of the experiments. Section 7 summarizes the conclusions drawn from this work.