esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain
## 6 Performance Analysis
### Methodology/Experiment Setup  
The testing environment is configured in an instance of Linux Ubuntu 64-bit v20.4.3 LTS within Oracle VM Virtual Box, hosted on a Lenovo IdeaPad Flex 5 running Windows 11 Home with a 2.40 GHz 11 th Generation Intel Core i5 processor.  
**Ethereum Setup.** The system's smart contract is written in Solidity and the migration file is written in JavaScript. The system is deployed to a local instance of a Blockchain using Truffle. Truffle enables interactions with the smart contract and Blockchain by setting up a Blockchain instance and Ethereum network using Ganache, and is used for compiling and deploying the contract to the local Ganache Blockchain.  
**Hyperledger Caliper Setup.** Hyperledger Caliper v0.4.2 is used to test the performance of our protocol on the Ethereum Blockchain network [37]. A Caliper, a Blockchain performance framework, is used to run tests on our protocol by using custom use cases that are defined by benchmark configurations and workload modules. When executed, Caliper produces standardized results that measure various metrics of the protocol's performance. These metrics are discussed in detail later. The benchmark configurations are used to define and structure the execution of the workload modules, as well as the collection of the test results. Each workload module defines the construction of a transaction and its submission to the Blockchain. Finally, a network configuration file binds the Caliper tool to our Ethereum network hosting our smart contract. The network is referred to as the SUT (System Under Test). In our implementation, workloads were written for Register, Vote, and Verify. Each benchmark is broken into different rounds, with each round allowing for some type of variation from the previous round. In our case, each round executes a different workload module (i.e., a different transaction); as such, each round specifies the transaction to be executed, the total number of transactions to be submitted (txCount), and the rate at which the transactions are to be executed (txRate). By manipulating these two variables, we explore their impact on the overall performance of the Blockchain, while keeping other factors such as the environment stable. The caliper records numerous metrics while monitoring the transaction executions, including the number of successful and failedtransactions, the send rate (tps), minimum, maximum, and average latency (seconds), and throughput (tps). These metrics are written to a report that can be viewed both via console and web browser.
## 3 Finite Element Model of Near-Ground Explosion  
### Governing Equations and Computation Code  
In the continuum mechanics, the momentum equation is  
\[\sigma_{ij,j}+\rho f_{i}=\rho\ddot{x}_{i} \tag{4}\]  
where, \(\sigma_{ij,j}\) is one order partial derivative of Cauchy stress component \(\sigma_{ij}\) with respect to the coordinate variable in the \(j\)th direction, \(f_{i}\) is the volume force vector of the unit mass, \(\ddot{x}_{i}\) is the acceleration vector, and \(\rho\) is the density of air.  
The conversation equation of mass is  
\[\rho\,=\,J\rho_{0} \tag{5}\]  
where, \(\rho_{0}\) is the initial density of air, \(J=\left|\frac{\partial x_{i}}{\partial x_{j}}\right|\), \(x_{i}\) is the space coordinate in the \(i\)th direction, \(x_{j}\) is the matter coordinate in the \(j\)th direction, and \(\partial x_{i}/\partial x_{j}\) is the strain gradient with respect to \(x_{j}\).  
The energy equation is  
\[\dot{E}=V\dot{S}\dot{i}\dot{e}ij-(p+q)\dot{V} \tag{6}\]  
where, \(V\) is volume, \(\dot{e}\dot{i}j\) is strain rate tensor, and \(q\) is artificial viscidity. Deviatoric stress tensor is given by  
\[S\dot{i}j=\sigma\dot{i}j+(p+q)\dot{e}\dot{i}j \tag{7}\]  
where, \(\dot{e}_{ij}\) is Kronecker symbol and \(p\) is hydrodynamic pressure, given by  
\[p=-\frac{1}{3}\alpha kk-q. \tag{8}\]  
ANSYS/LS-DYNA 970 is a finite element computation code for dynamics suitable for impact, explosion, and other instantaneous problems, and is used to carry out the numerical simulation process. In LS-DYNA, there are three classes of methods available for generating the grids and mesh, including Eulerian, Lagrangian, and arbitrary Lagrangian-Eulerian (ALE) method. In this study, the ALE method is employed to simulate the process of the TNT explosion, the propagation of air shock waves, and the interaction of explosion shock waves with the grounds. The ALE algorithm consists of a classical Lagrangian step in which the mesh moves along with the modeled material, a rezone step in which the mesh is modified to preserve good quality through the computation of the Eulerian time step, and a remapping step in which the solution is conservatively transferred from the old mesh to the new rezoned one, and thereby performs the fluid-solid coupling transient analysis [33].
## 4 Analysis
### Simulation Experiments for the Optimal Satellite Orbit in the Common-View Mode between Two Stations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Different** \\ **SLR Stations** \\ \end{tabular} } & \multicolumn{2}{c}{**Improved Simulated Annealing**} & \multicolumn{2}{c}{**Particle Swarm Optimization**} & \multicolumn{2}{c}{\begin{tabular}{c} **Conventional Iterative** \\ **Traverse Method** \\ \end{tabular} } \\ \cline{2-7}  & \(Time_{com}\)**(S)** & \begin{tabular}{c} **Computation Time** \\ **(min)** \\ \end{tabular} & \(Time_{com}\)**(S)** & \
\begin{tabular}{c} **Computation Time** \\ **(min)** \\ \end{tabular} & \(Time_{com}\)**(S)** &
\begin{tabular}{c} **Computation Time** \\ **(min)** \\ \end{tabular} \\ \hline CC WH & & & & & \\ (R = 7000 km) & 354 & 1.503 & 354 & 10.009 & 353 & 20.213 \\ CC WH & 930 & 1.761 & 931 & 11.422 & 930 & 22.057 \\ CC WH & & & & & \\ (R = 9000 km) & 1463 & 2.165 & 1463 & 13.053 & 1463 & 26.895 \\ CC WH & & & & & \\ (R = 10,000 km) & 2009 & 2.941 & 2009 & 15.728 & 2009 & 31.804 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The computation time of the improved simulated annealing, particle swarm optimization algorithms, and the conventional iterative traverse method. Figure 9: Improved simulated annealing (SA) algorithm optimizing the optimal satellite orbit trajectory for the two stations. part of the U.S.-European satellite mission series, primarily traverses the airspace of the United States and Europe, while Ajisai, a Japanese satellite, predominantly orbits over Japan. Table 3 shows that for the common-view model between the Changchun and Wuhan stations, Ajisai has a longer common-view duration compared to Jason-3. Furthermore, through optimized satellite orbit designs, the maximum continuous common-view duration over a period of 10 days was extended approximately 8.955 times for Jason-3 and 3.337 times for Ajisai. The Improved Simulated Annealing Algorithm Optimizes Satellite Orbits in a Multi-Station Common-View Model  
The simulated annealing algorithm was used to solve the optimal satellite orbit problem in the common-view mode, taking into account parameters such as minimum orbit altitude, in-orbit flight life, and orbital period. The initial UTC time was set to 1 August 2023 at 00:00:00. The orbit radius was fixed at 12,792.55 km, with a 4-h orbital period that aligns with a divisor of Earth's rotational cycle. Based on the quarterly global performance report provided by the International Laser Ranging Service (ILRS) website, SLR station selections were selected. Initially, ten ground stations for SLR were chosen to simulate satellite orbit optimization within the Asia-Pacific region's common-view mode. Subsequently, ten SLR ground stations were selected to optimize satellite orbits within the shared observation model spanning Asia and Europe. Table 4 displays the coordinates, latitude, longitude, and elevation information for the selected stations within the Asia-Pacific region's SLR station network, while Table 5 presents the corresponding station details for the European-to-Asian region SLR station network.
## 3 Results and Discussion
### Inner Walls
#### 3.1.3 Surroundings
Each tunnel is monitored via eight measurement circles (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\), \(180^{\circ}\), \(215^{\circ}\), \(270^{\circ}\), and \(315^{\circ}\)) to assess coordination at different angles. Changes in the coordination of Tunnel 2 surroundings indicate the disturbance level induced by train-induced vibrations from the adjacent tunnel. Under the NW condition, coordination variations at all monitoring points are greater than those under T2. The most significant coordination changes occur at the vault (\(90^{\circ}\)), followed by \(180^{\circ}\), indicating that coordination changes in the Tunnel 2 surroundings correlate with monitoring distance and position. At the vault, train-induced vibrations cause particles to slide downward along the liners, leading to a decrease in coordination and stability, which also shows that the vault is the focus of dynamic characteristics. Overall, the Tunnel 2 surroundings exhibit greater fluctuations in coordination under NW condition due to the absence of internal walls when compared to the T2 condition. Under T2 condition, the surroundings with greater burial depth demonstrate higher coordination, which remains nearly constant throughout the testing period. This indicates that the lower-right region of the Tunnel 2 surroundings is less affected by vibrations and maintains greater stability. Combined with Figures 9 and 10, the static-dynamic responses of Tunnel 2 surroundings are influenced by the position of liners and surroundings, proximity to the vibration source, and burial depth. Internal walls enhance surroundings stability, while coordination is governed by distance from the vibration source and burial depth. The T2 condition demonstrates higher and more stable coordination, highlighting the structural benefits of internal walls.
## 4 Numerical Results and Discussions
### Spectral Efficiency  
#### 4.2.1 Spectral Efficiency in S\({}_{1}\)  
Figure 11 presents SE of both NU and D\({}_{2}\) in S\({}_{1}\) with respect to SNR that holds values between 0 and 30 dB. As we mentioned in the system model, SE of NU and D\({}_{2}\) solely depends on the antenna indices. Hence, their SE performance is identical in S\({}_{1}\) for the same antenna configuration. On the contrary, the SE performance of FU depends on the modulation order employed by BS and D\({}_{1}\), during \(P_{1}\) and \(P_{2}\), respectively in S\({}_{1}\). It is observed that SE curves approach to the theoretical number of data bits at high SNR. This is because of the fact that at high SNR, BER tends to zero causing SE to reach its theoretical upper limit. It is also identified that an increase in the number of receiving antennas improves SE performance with the same number of transmitting antennas because of the improvement in diversity order. In Figures 12 and 13, we illustrate SE performance of FU in S\({}_{1}\) for different PSK and QAM modulation order against SNR, considering two distinct antenna configurations. Here, SNR holds values from 0 to 30 dB. The term in legends of the figures represents the parameters as mentioned earlier in Figures 5 and 6. The SE curves in both figures approach to the theoretical number of bits with increasing SNR, as expected. It is noticed that for the same antenna configuration SE performance is better with lower order of modulation compared to the ones with higher order  
Figure 12: SE of FU in S\({}_{1}\) for \(M\)-PSK.  
Figure 13: SE of FU in S\({}_{1}\) for \(M\)-QAM.
# Industrial Dust Explosions. A Brief Review
## 2 Dust Explosion Prevention and Mitigation  
### Preventing Explosible Dust Clouds  
#### 2.1.1 Adding Clean Inert Gas  
For a given type of dust suspended in air, and a given type of added inert gas, there is a \"limiting oxygen concentration\" (LOC) in the air/gas mixture, below which the dust cloud is unable to propagate a self-sustained flame. By keeping the oxygen content below this limit throughout the process system, the possibility of dust explosions is excluded. Four types of inert gases are currently in use for this purpose, viz. carbon dioxide, water vapour, nitrogen, and rare gases. The design of gas inerting systems depends on whether the process is continuous or of the batch type. It also depends on the strength of the process equipment, and on type and source of inert gas. Two main system principles are used, viz. pressure-variation and flushing.
# Effect of Plate Curvature on Heat Source Distribution in Induction Line Heating for Plate Forming
## 1 Introduction
During the whole process, the deformation of the plate affects the heat generation distribution of the inductor. In previous studies, the electromagnetic calculation and simplified calculation of line heating were verified, but the influence of plate curvature on heat source distribution was rarely mentioned.
## 4 Discussion  
In the previous section, the different types of faults were presented and the behavior of nRMSE values seen as indicators of already known faults was discussed. It is also important to evaluate the behavior of ANN when they are fed with inputs from other PV systems. To this end, we present the behavior of all three networks in 2014, when they are fed with data from PVS1 (Figure 10).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **N** & **RMSE** & **MBE** & **MAPE** & **MAE** & **nRMSE** \\ \hline
21/12/15 & 32 & 80.28 & 368.84 & 5.98 & 370.46 & 6.8 \\
22/12/15 & 32 & 84.85 & 384.53 & 6.52 & 384.53 & 7.4 \\
23/12/15 & 33 & 91  
The specific behavior of all three networks during 2014 is statistically evaluated by the variation of nRMSE in this period.  
Figure 11 shows a similar behavior for all three models. However, certain periods are observed with an increasing trend, except those correlated with the existence of a fault. This trend is significant at the end of March, when the nRMSE is seen to significantly increase. During this period, the n3 network, which has a different type of temperature sensor's input, demonstrates smaller deviations. This behavior is also observable in Figure 12. All three networks underestimate the simulated power, as seen in Figure 12. This may indicate an error in measurement equipment in network n3.  
Figure 11: Comparison of n RMSE values for three ANNs during 2014.  
Figure 10: Comparative behavior of three ANNs fed with data from PVS1.  
Figure 13 shows the trend of percent bias error during 2014. The general trend shows that simulated values are higher than the actual values; however, there are periods at the end of March where the result is the opposite. This fact may be related to a systematic error of measurement equipment in this period. The fact that n3 has a more consistent behavior in this aspect may indicate that this is an error in the backsheet temperature sensor.  
An observation of the nRMSE and PBIAS error values points to the fact that these metrics could act as indicators for possible faults. The behavior of these values (daily) in normal operation sets a clearly observable threshold. A fault in a PV string has the larger statistical deviation from ANN simulation to actual behavior, over 30%, fault panel has  
Figure 12: A period at the end of March in which all three networks are seen to underestimate the AC power output.  
Figure 13: Evolution of percent bias of prediction during the whole year, 2014.  
a deviation of 10-20% and faults of near shading 6-10%, compared to 5% during normal operation. These deviations are dependent on the specific system type, layout, location and the type of measurement equipment. However, the proposed procedure is valid--mutatis mutandis--in all these cases.
# High Repetition Rate, TEM\({}_{00}\) Mode, Compact Sub-Nanosecond 532 nm Laser
###### Abstract  
As a critical transmitter, compact 532 nm lasers operating on high repetition and short pulse widths have been used widely for airborne or space-borne laser active remote sensing. We developed a free space pumped TEM\({}_{00}\) mode sub-nanosecond 532 nm laser that occupied a volume of less than 125 mm \(\times\) 50 mm \(\times\) 40 mm (0.25 L). The fundamental 1064 nm laser consists of a passively Q-switched composite crystal microchip laser and an off-axis, two-pass power amplifier. The pump sources were two single-emitter semiconductor laser diodes (LD) with 808 nm wavelengths and a maximum continuous wave (CW) power of 10 W each. The average power of the fundamental 1064 nm laser was 1.26 W, with the laser operating at 16 kHz repetition rates and 857 ps pulse widths. Since the beam distortion would be severe in microchip lasers due to the increase in heat load, in order to obtain a high beam quality of 532 nm, the beam distortion in our experiment amplifying the fundamental laser was compensated by adjusting the distribution of the pumping beam. Furthermore, in the critical phase matching (CPM) regime for the second harmonic generation (SHG), a Type I LiB\({}_{3}\)O\({}_{5}\) (LBO) crystal obtained 770 ps, a beam quality of M\({}^{2}\) \(<\) 1.2, and a 16 kHz pulse output at 532 nm, which was better than 0.6 W average power.  
laser remote sensing; photon-counting lidar; microchip laser; passively Q-switching; compact solid-state lasers +
Footnote †: journal:
## 3 System Model
### Decision Model  
When the terminal device \(m\) has a new task \(\lambda_{m}(t)\) in time slot \(t\), the decision model has to give the offloading scheme. The binary variable \(x_{m}^{t}\in\{0,1\}\) is used to indicate whether the current task is offloaded, \(x_{m}^{t}=0\) indicates that the task is executed on the MD, and \(x_{m}^{t}=1\) indicates that the task will be offloaded to an edge server for execution. \(y_{m,n}^{t}\in\{0,1\}\) represents the edge server to which the task is offloaded for execution, when \(y_{m,n}^{t}=1\), the task is offloaded to the edge server \(n\in\mathcal{N}\) for execution, otherwise \(y_{m,n}^{t}=0\). The tasks considered in this model are atomic level tasks, i.e., the tasks are not subdidivable, each offloaded task can be executed in only one edge server, and the tasks offloaded to the edge server for execution are constrained by \(\sum_{n\in\mathcal{N}}y_{m,n}^{t}=1,m\in\mathcal{M},t\in\mathcal{T},x_{m}^{t}=1\).
## Appendix A Speed Up Technique Results  
Table 11 displays the statistics associated with the pre-processing and testing of both of the goal-directed [PERSON]'s algorithm (A*) speed-up techniques presented in Section 5.1.1, applied to the pedestrian transport network of the Solent region. It shows that pre-processing times increase with the number of regions, which is because more regions mean more boundary nodes between adjacent regions (see \"boundary nodes\" column), and hence more corresponding calls of [PERSON]'s algorithm. For 1000 regions, pre-processing takes no longer than an hour. For each number of regions, we tested 3 versions of A* in the same set of 1000 random origin-destination queries: (i) A* (no speed ups); (ii) A* with arc flags; (iii) A* with cross-region edge labels. In each case, the same shortest-path solutions are found. The final three columns of Table 11 provide average query times, given in milliseconds. All speed-up experiments do lead to reduced query times compared to A* alone. For the case of the arc flags speed up, query times are lower the higher the number of regions, because more boundary nodes are encountered where edges can be pruned. Conversely, for the case of the cross-region edge label speed up, query times generally increase gradually the higher the number of regions, after a minimised query time for the case of 50 regions. That is, fairly large regions lead to greater portions of edges not being included in cross-region shortest paths.  
The previous experiment results are based on applying both speed-up techniques separately. However, they can be applied simultaneously, along with their optimal node region definitions. When both speed ups are applied simultaneously with their respective optimal node region definitions, query times are reduced to 3 milliseconds, which is approximately a 93% query time reduction compared to A* alone.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 3 Experimental Results and Analysis  
To verify the effectiveness of the multi-model decomposition deep learning hybrid model, experiments were performed using ocean hydrological data, and its effectiveness and prediction accuracy were evaluated using different indicators.
## 5 Experiments and Analyses
### Video Delivery Experiment  
The first experiment consisted of four parts. The first part evaluated the numbers of jobs that the Resource Broker allocated to different resource nodes, the turnaround time of a job and CPU loads of a job execution at different time points in the situation where all requests are of the same type. The second part redid the first part, but user requests were evenly divided into type-1, type-2 and type-3, and only the SPA was employed. The third part redid the second part, but only the Random algorithm was invoked where the Random algorithm is the one randomly selecting a resource node from the performance table for a user request without considering the level to which the chosen resource node belongs. The fourth part redid the second and the third parts, but users' requests were unevenly divided into types 1, 2 and 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Specification** \\ \hline Packet size of a user request (_i.e._, a data packet) & 1.5 Kbits \\ Packet delivery protocol & TCP \\ MAC & 802.11 \\ Bandwidth of a wireless link between a user node and the MCI server & 11 Mbps \\ Experimental duration & 1500 s (the 1 st part), 200 s (others) \\ Packet rate & A packet per 15 s \\ Total requests & 100 (the 1 st part), 60 (the 2 nd, 3 rd and 4 th parts) \\ Number of resource nodes & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The parameters of the experiment.
## 3 Numerical Validation and Results
### Prestress Loss
From the perspective of the influence range of local stress, the stress redistribution caused by box girder cracking was more extensive than that caused by joint damage. Therefore, for bridges with segmental joint damage, the bridge performance can be maintained by only reinforcing the corresponding segmental joint. It is insufficient to only reinforce the location of the crack in the event of structural cracks occurring within the bridge beam, however. It is imperative to also consider the redistribution of stress caused by said cracks, whose influence range is contingent upon factors such as cracking location, load amplitude and section stiffness. Targeted repair measures must be implemented based on their respective stress impact ranges so as to prevent the further propagation of existing fissures. It should be noted that the objective of our paper was to analyze the relative influence of different factors on the long-term deflection of the structure under its completed state, rather than determining its real stress during construction. In other words, the model established in this paper is limited to analyzing the relative change law of stress and displacement. However, it lacks practical significance in terms of determining the absolute value of local stress due to its failure to consider factors such as initial internal force accumulation caused by the construction process, material nonlinearity, and the redistribution of bending stress due to creep. Therefore, if an accurate investigation of the real stress distribution of the box girder is required, these aforementioned factors must be taken into consideration within the refined model. Figure 21: Stress distribution comparison between prestress loss Case VI and no-loss condition. (**a**) Main span range. (**b**) Mid-span segment.