esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 4 Dynamic Edge-Server Placement  
Dynamic edge-server placement involves several steps. In the approach proposed herein, the optimal edge-server placement for the current moment is calculated using the improved SO (ISO) algorithm. Next, the dynamic edge-server placement algorithm is used to adjust the number of edge servers and their locations in accordance with the environment changes. Then, the minimum movement cost between the edge-server placement at the current moment and that at the next moment is derived based on the minimum placement-cost algorithm. Finally, the total cost at the next moment is computed. To optimize the placement of edge servers for the ISO algorithm, we employed a mapping strategy that transforms multiple unrelated variables into coordinates representing the positions. Specifically, we used the first and second dimensions of the algorithm variable 'dim' to determine the coordinates of the first position, and the third and fourth dimensions to determine the coordinates of the second position, and so on. By employing this mapping approach, we were able to convert the original multidimensional optimization problem into a two-dimensional problem within the spatial domain. This allowed us to utilize the ISO algorithm to search for optimal solutions in this two-dimensional space.
# Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis
## 3 Limitations and Improvements of MOMEDA
### Limitations of the MOMEDA Method  
In the single fault diagnosis, MOMEDA has certain limitations in the extraction of weak faults. The periodic impact it finds may be false components and cannot be accurately identified for fault features [33; 34; 35].  
To illustrate the impact of different noise on MOMEDA performance, an analog signal is constructed, as shown in Equation (37), for comparison.  
\[\begin{array}{l}x_{1}(t)=A_{m}\times\exp(-\frac{g}{T_{m}})\sin(2\pi f_{a}t) \\ x_{2}(t)=x_{1}(t)+noise\end{array} \tag{37}\]  
where \(x(t)\) a is the periodic impact signal, \(A_{m}\) is the amplitude of the impact, \(g\) is the damping coefficient, \(T_{m}\) is the period of the impact, and \(f_{a}\) is the natural frequency of the shaft. The parameter is set to the following: \(g=0.1\), \(T_{m}=0.02\) s, \(f_{a}=320\) Hz.  
In order to further verify the effect of noise on MOMEDA extraction faults, different noise sizes are selected for simulation analysis. The extraction effect of MOMEDA is shown in Figures 1-3. It can be seen from the Figures 1-3 that, as the signal-to-noise ratio decreases, the 50 Hz shock period is gradually submerged by noise. In Figure 1d or Figure 2d, the fault period (T = 50) can also be extracted,but as the signal-to-noise ratio decreases again, the fault period in the envelope spectrum in Figure 3d cannot be extracted accurately. Figure 1a is the simulated bearing fault impact signal. Figure 1b is the noise of 7.76 dB, Figure 1c shows the signal diagram with 7.76 dB noise added, Figure 1d shows the MOMEDA extracts the result when 7.76 dB noise is added, and Figure 1e shows the results of the envelope spectrum. Figure 2a is the simulated bearing fault impact signal, Figure 2b is the noise of 1.74 dB, Figure 2c shows the signal diagram with 1.74 dB noise added, Figure 2d shows the MOMEDA extracts the result when 1.74 dB noise is added, and Figure 2e shows the results of the envelope spectrum. Figure 3a is the simulated bearing fault impact signal, Figure 3b is the noise of \(-\)3.1
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 4 The Proposed Separable Data Hiding System
### Data Extraction  
In this phase, we consider that the receiver only has the data-hiding key. With the aid of data-hiding key, the receiver extracts the embedded information bits by adopting following steps.  
Step 1: The receiver pseudo-randomly selects \(Q\) pixels from the embedded encrypted image according to the data-hiding key as did in the data hiding phase. Let \(I^{\prime\prime}(1)\), \(I^{\prime\prime}(2)\),..., \(I^{\prime\prime}(Q)\), be the \(Q\) selected pixels in the embedded encrypted image.  
Step 2: The \(t\) th bits of \(I^{\prime\prime}(1)\), \(I^{\prime\prime}(2)\),..., \(I^{\prime\prime}(Q)\) pixels are collected and these bits are the permuted extracted information bits. Let \(E(1)\), \(E(2)\),..., \(E(Q)\) be the extracted bits. The \(t\) th bits can be extracted by using following equation  
\[E(d)=\bigg{\lfloor}\frac{I^{\prime\prime}(d)}{2^{t-1}}\bigg{\rfloor}\text{mod }2,\ 1\leq d\leq Q \tag{14}\]  
Step 3: By exploiting the data-hiding key, the permuted extracted bits \(E(1)\), \(E(2)\),..., \(E(Q)\) are inverse permuted to get the actual information bits.  
Note that, because of the pseudo-random pixel selection and permutation of information bits, any attacker without the data-hiding key cannot obtain the pixel locations, and therefore cannot extract the embedded data. Furthermore, although the receiver having the data-hiding key can successfully extract the embedded data, he cannot get any information about the original image content.
## 10 Discussion  
This paper focuses on the autonomous displacement of the MAR system and has achieved innovative results in system control algorithms and structural optimization. The proposed new differential tracker, SYSTD, effectively solves the problem of tracking the position feedback signal of the MRC. SYSTD is constructed based on specific elementary functions, and its stability has been verified through theoretical derivation. Through phase-plane analysis, a theoretical basis for its parameter selection is provided, and the detailed parameter tuning method enhances its operability in practical applications. Simulation results show that SYSTD achieves a good balance between tracking speed and stability. Compared with traditional TD and MTD, it has a faster tracking speed and better filtering effect. This not only improves the tracking accuracy of the MAR system for the position feedback signal of the MRC but also provides new ideas for the control algorithm design of other similar systems. In terms of system structure optimization, the method of estimating the position of the MRC by winding survey coils along the horizontal control coils realizes sensorless control. This innovation simplifies the system structure, reduces costs, and improves system reliability. Combined with the precise observation of relevant signals by SYSTD, the accuracy of position estimation is further improved, providing a more advantageous solution for the practical application of the MAR system. However, this paper also has certain limitations. During the experiment, it was found that the overshoot of the actual system is larger than that of the simulation results, and the time to enter a stable state is also longer. This may be due to the more complex interference factors in the actual environment, which are difficult to fully simulate in the simulation. Future research can consider further optimizing the control algorithm to enhance its adaptability to complex actual environments and improve the stability of system performance.  
This study has limitations: (1) environmental disturbances (e.g., electromagnetic noise) may degrade performance in industrial settings; (2) long-term thermal effects on coil resistance were not modeled. Future work will integrate real-time disturbance observers and temperature compensation modules. Additionally, we plan to extend the control framework to 3D multi-axis coordination for complex trajectory tracking.
# Multi-Lidar System Localization and Mapping with Online Calibration
## 3 Back-end Optimization and Map Construction  
The back-end optimization in this study is based on the graph optimization library GTSAM. The global pose is optimized by constructing a pose graph supplemented with loop-back and ground constraint factors. Additionally, key frames are extracted, and their feature point clouds are matched to the global map for comprehensive map generation and updating.
## 3 Results and Analysis
### Comparison and Accuracy Analysis  
In a comprehensive comparison, the estimation models developed under different kinds of methods were predictive and similar for kiwifruit sweetness in all four periods. The accuracy of validation models under both SPA and CARS features showed that the estimation accuracy for each harvest period (T1, T2, and T3) were lower than that in the full-harvest period (T4). From the modeling methods, the prediction accuracy of the LSSVM and the BP methods were lower than that of the PLS and the MLR under each harvesting period (T1, T2, and T3) due to the fact that the nonlinear model required a greater number of samples, whereas the models for the full-harvest period (T4), the LSSVM and the BP, were significantly superior to the rest of the two algorithms. Comparing the scatter distribution of T4 period in Figures 6 and 8, the dispersion of scatters between the measured and predicted values in verification dataset was smaller with SPA feature selection method than CARS, and the fitting line was closer to the 1:1 line.  
Under the SPA feature selection method, the best models for estimating the sweetness of kiwifruit at T1, T2, and T3 periods were MSC-SPA-MLR, SNV-SPA-MLR, and MSC-SPA-MLR, which could explain 52%, 33%, and 59% of the variation in kiwifruit sweetness levels, respectively. The best validation model for the whole harvest period was MSC-SPA-LSVM, which could explain up to 79% of the variability in kiwifruit sweetness. The best models for kiwifruit sweetness from T1 to T3 period under the CARS feature selection method were MSC-CARS-PLS, MSC-CARS-PLS, and SNV-CARS-PLS, which could explain 47%, 33%, and 55% of the variation in kiwifruit sweetness levels, respectively. The best validation model for the whole harvest period was MSC-CARS-BP, which could explain up to 77% of the variability in kiwifruit sweetness levels. Comparing the scatter distributions in Figures 6 and 8, it could be seen that during the T4 period, the scatter distribution under the SPA algorithm was more concentrated and closer to the 1:1 line. In contrast, under the CARS algorithm, the sweetness predicted for measured values of 6-10% was mostly around 8%, while those with 14-18% are predicted to be about 16%. Overall, the accuracy of estimation models with features from SPA were higher than those from the CARS algorithm.  
Figure 8: Scatter distribution between the predicted and measured sweetness in each harvest stage with features by CARS algorithm.
## 3 Results and Discussion
### BOD and OUR Measurements
As we mentioned in the Introduction, at a 1 kGy dose, practically all ofloxacin molecules underwent some transformation [12]. In the transformations, the pharmacophore parts of ofloxacin molecules were modified, and, as the results show, the transformation products did not have antibacterial potency. Our results are similar to those of [PERSON] et al. [12]. They used an agar diffusion test also at 0.1 mmol dm\({}^{-3}\) antibiotic concentration in 2 and 3 kGy irradiated solutions, and after applying _E. coli_ test bacteria found complete disappearance of antibacterial effect. In purified wastewater, we observed inhibition zones even at 2 kGy dose. This matrix has a relatively high organic molecule content (high initial COD and TOC values), and as we suppose a large part of reactive radicals formed during water radiolysis disappear in reaction with them. Therefore, a lower number of radicals are available for reacting with ofloxacin.
## 1 Introduction  
Energy scarcity and environmental deterioration pose significant challenges to humanity, necessitating a focus on energy conservation and emission reduction in architecture. This study explores thermal reflective insulation coatings for buildings, which involve placing low thermal conductivity air between layers of highly reflective aluminum films to form a composite material with superior insulation properties. These materials, particularly focused on barrier, reflective, and radiation insulation, are widely used in building walls and other applications [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Organic thermal insulation materials, such as polyurethane foam, expanded polystyrene, and phenolic foam, are recognized for their light weight and ease of processing [16; 17; 18; 19; 20; 21; 22; 23]. Conversely, inorganic thermal insulation materials are increasingly valued for their excellent thermal stability and eco-friendly characteristics [24; 25; 26]. Previous research has shown that different substrates and novel nano-composite inorganic materials vary in terms of their effects on sunlight reflection. [PERSON] et al. performed extensive tests on new heat-reflecting coatings, assessing their suitability as substrates with anti-corrosion, alkaline resistance, and self-quenching properties at temperatures below 200 \({}^{\circ}\)C [27]. [PERSON] developed a novel composite material, CLC brick, for use in building wall design, examining its potential to reduce energy costs [28]. [PERSON] et al. created terbium-doped cerium oxide nanocomposites with a near-infrared solar reflectivity of 85.54% using a green synthesis method, ball milling, and high-temperature calcination [29]. [PERSON] et al. devised a method to prepare radiation-cooled coatings that enhance the sunlight scattering efficiency by randomly distributing Y203 nanoparticles in PDMS [30]. [PERSON] et al. explored the effects of applying DHR and RR materials to building facades on the outdoor thermal environment [31]. Currently, other research on new inorganic coatings primarily focuses on aspects such as corrosion resistance, alkaline resistance, and preparation technology.  
This study investigates the reflection characteristics of thermal reflective coatings on different walls, systematically assessing the impact of the wall composition, light sourceirradiation angle, light source irradiation duration, and other factors on the efficacy of the thermal reflection. The aim is to thoroughly explore the mechanisms of thermal reflection.
# Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition
## 3 Methodology  
The proposed method as shown in Figure 1 starts with reading frames, extracting skeleton features, preprocessing and finally training CNN-LSTM network. In start the proposed system reads each frame from the input video and extract skeleton features using OpenPose [9]. The OpenPose outputs 25 joints location in the form of X, Y coordinates and confidence map but for the proposed system only X, Y coordinates are used. When we trained our proposed model including confidence maps results were similar, so instead of using X,Y coordinates and confidence map, the proposed research selects only X,Y coordinates for HAR system. Input layer parameters of proposed model are set as 1 \(\times\) 25 \(\times\) 2, where 1 shows that data for each frame will come and 25 \(\times\) 2 represents 25 joint-location (X,Y) coordinates. Finally, the X, Y coordinates of extracted skeleton features are fed into the CNN-LSTM system for the classification process. CNN-LSTM architecture as shown in Figure 2 is designed in a way that it can work on skeleton features directly instead of generating heatmaps from skeleton features for the training of deep learning architecture. The proposed CNN-LSTM architecture consists of 12 layers starting with input layer for taking skeleton features as an input. The dimensions of an input layer are 1 \(\times\) 25 \(\times\) 2, where 1 shows that data are of a single frame and 25 \(\times\) 2 are the X, Y coordinates of 25 joints locations. A time-distributed CNN layer with 16 filters of size 3 \(\times\) 3 is used, and for feature extraction, ReLU activation is used on key points of each frame. CNNs are very good at pulling out spatial features that are not affected by scale or rotation. The CNN layer can extract spatial features and angles between the key points in a frame [39]. Batch normalization is used to speed up convergence on the CNN output. The next layer is a dropout layer, which randomly drops some of the weights to avoid overfitting. The CNN output is then flattened and sent to the LSTM layer, which has 20 units and a unit forget bias of 0.5. LSTM is used to see how the features extracted by the CNN layer change over time. This takes advantage of the fact that video input comes in a certain order.  
Each frame's output from the LSTM layer is sent to a time-distributed fully connected layer with number of outputs depending on the number of classes and Softmax activation. Each of these eighteen outputs tells you, in terms of cross-entropy, how likely it is that the corresponding action is being performed.  
Figure 1: Methodology of the proposed skeleton based CNN-LSTM HAR system.  
Figure 2: CNN-LSTM Architecture.
## 4 Experimental Considerations
### Experimental Conditions and Limitations  
Nonlinear Thomson scattering modulated radiation characteristics research in the experimental aspects, there are still some technical challenges, including CEP stability control, angular resolution accuracy, and electron beam-laser synchronization and other issues. At the same time, there are some limitations in the experimental conditions; first, the experimental environment should simulate a vacuum to avoid the influence of air scattering; second, the laser intensity should be ensured in \(10^{18}\sim 10^{19}\)W/cm\({}^{2}\). If it is lower than this range, the nonlinear effect is weak, and higher than this range may trigger the interference of QED effect; third, there are some requirements in the quality of the electron beam, the energy dispersion needs to be less than 5%, the transverse size should be smaller than the laser wavelength.
## 3 Results  
In this paper, simulation experiments were carried out on the proposed optimization mechanism, and the effectiveness of the proposed mechanism is demonstrated by the analysis of simulation results. The optimization mechanism proposed in this paper optimizes the queue lengths and numbers of the output ports of the onboard switches, mainly acting on the internals of the satellites to reduce the queuing delay. The inter-satellite distances mainly affect the propagation delay and have no impact on the queuing delay. Therefore, the proposed optimization mechanism can be flexibly applied to various configurations of large-scale satellite networks. To simplify the complexity of simulation experiments, the simulation experiments in this study employed the Iridium constellation [22], which is composed of 66 LEO satellites divided into six orbital planes.  
The topology of the LEO satellite communication network adopted in this simulation is shown in Figure 6. This paper considers two types of satellite formations:  
1. Cluster formation: one satellite in each of three adjacent orbital planes, numbered 17, 28, and 39. They can correspond to S3, S4, and S5 in Figure 4.  
2. Trailing formation: three adjacent satellites in the same orbital plane; their numbers are 27, 28, 29. Similarly, they can correspond to S3', S4', and S5' in Figure 4.  
For the simulation, we describe a concise scenario to illustrate the behavior of traffic. Satellite communication technology is constantly advancing, especially in the field of inter-satellite links. Thanks to technological innovations such as laser communication systems, higher data rates are becoming increasingly feasible. Therefore, the data transmission rate of the satellite node's output port was set to \(R=1\) Gb/s, and the transmission slot \(T_{c}\) was set to the time it takes for a queue to be completely emptied, that is, \(T_{c}=\frac{I}{R}\).  
There are various types of services in satellite communication networks, and the packet size may vary greatly. To simulate diverse and realistic traffic, we sample packet sizes from a uniform distribution ranging from 64 to 1500 bytes. This range covers the typical packet sizes of small control signals and large data payloads.  
In a dynamic network environment, data traffic is unpredictable. Satellite communication networks often deal with bursty and sporadic traffic patterns, where packets can  
Figure 6: The topology of LEO satellite communication network adopted in this study, where the arrows indicate the directions of the satellite orbits.  
arrive at any time within a transmission slot. To simulate this randomness, we sampled the arrival time from a uniform distribution ranging from 0 to \(T_{c}\).  
The main parameters in the simulation are shown in Table 1.
## 1 Introduction
[PERSON] et al. [20] conducted finite element simulation and structural dynamic response analysis on the load spectra of hydraulic damper for the axle box of a certain electric locomotive. The results show that if the damper contains welding defects, it is prone to premature failure. [PERSON] et al. [21] conducted a series of physical and chemical tests and finite element simulation on the failure of girth joints at a natural gas station inspected via X-rays, and the results show that welding defects are original failure factors that induce crack initiation and propagation through walls. For fatigue cracks in fillet welds of traction rod brackets on multiple electric locomotives of a certain model in service before reaching the design life, [PERSON] et al. [22] experimentally observed the failure thereof and found that fatigue cracks are related to pores in welds, incomplete fusion, and assembly clearance at the root of the fillet welds. [PERSON] et al. [23] investigated the influences of repair welding on fatigue strength of bogie frames of railway locomotives and compared the fatigue performance of fillet joints not subjected to repair welding and repair-welded fillet joints of SM490A steel. Fatigue test results imply that the S-N curves of repair-welded fillet joints are lower than those of the joints without repair welding. Moreover, they also compared changes in residual stress and the results indicate that the residual stress of repair-welded samples is much greater than that of samples not subjected to repair by welding. For medium-strength welded structural steel widely used in practice, [PERSON] et al. [24] studied influences of post-weld thermal treatments on materials, and the results indicate that post-weld thermal treatments remove the residual stress and slightly improve the fatigue strength of samples. This all shows that welding defects significantly affect the fatigue strength of bogie frames; therefore, research into the influences of welding defects on welding residual stress is justified. The aforementioned research reveals that welding defects are key factors that influence the strength of bogie frames. At present, influences of welding defects on the fatigue performance of bogie frames have been extensively studied, while influences of defects on fatigue performance of welded bogie frames remain a focus of much research due to the complexity of such defects and their influences. Therefore, the finite element method (FEM) was adopted to explore influences of defects (assembly clearance, incomplete fusion, incomplete penetration, and pores) according to the International Union of Railways (UIC) standard UIC 615-4. It also developed and evaluated the effect of increasing weld thickness on the fatigue reliability of the bogie frame, formulated a repair plan for in-service equipment based on the new manufacturing plan and evaluated the influence of repair welding stress. Research results are of guiding significance for maintaining locomotives in service and improving new locomotives and provide reference for the assessment of fatigue performance of other welded structures.