esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed System
### Pre-Processing  
Usually, some lines or small size regions are formed around the silhouette due to segmentation errors, loose clothing of the subject under consideration, and other noise. These unnecessary regions do not offer any important information for action recognition, but rather create problems for the feature-extraction algorithm and increase the complexity. By removing these small regions, complexity can be reduced and the feature-extraction process can be made more accurate. In our case, a region was considered as small and unnecessary if its area is less than 1/10 of the silhouette. This threshold was set based on an observation on 1000 silhouette images of different actors and actions. An example of the silhouette before and after noise removal is shown in first row of Figure 1.
# A Comparison of an Adaptive Self-Guarded Honeypot with Conventional Honeypots
## 4 Description of Honeypot Systems
### Asgard: Adaptive Self-Guarded Honeypot  
Asgard is our adaptive honeypot system that we developed in [6], which distinguishes itself from the other adaptive honeypots because rather than allowing the attackers to freely attack and compromise the system, it learns to limit the risk of being compromised by blocking or substituting the execution of programs deemed malicious. The system is disguised as a vulnerable and fully functional Linux system for this reason, it is also considered as a high-interaction honeypot. The vulnerable aspect of this system is to allow easy remote access through the SSH protocol by using a brute force or a dictionary attack. After they gain access to the system, they could open an interactive shell session or simply request to execute Linux commands, such as in any normal Linux system. Whenever an attacker enters a command, the system intercepts and analyses it, and decides to either _allow_, _block_ or _substitute_ the execution of that command. To learn to decide on the action, Asgard is an RL agent that observes its environment state, which consists of the entered command only, and learns to take actions.
## 3 System Model  
In this model, we have \(n\) mobile devices \(M=\{m_{1},m_{2},...,m_{n}\}\) and \(m\) edge nodes \(E=\{e_{1},e_{2},...,e_{m}\}\). Mobile devices and edge nodes can have wireless connections to each other. Additionally, tasks can arrive at any time in mobile devices and should be computed either in the mobile device or offloaded to an edge node. In the following subsections, the task model, mobile devices, and edge nodes are described in more detail.
## 4 Experimental Design and Results
### Efficiency of the Algorithms  
Improvement percentage (IP) values, defined in Equation (5), were calculated to enable a comparative analysis of the algorithms' performance.  
\[\textit{Improvement Percentage}=|\frac{\textit{new value}-\textit{original value}}{\textit{original value}}\times 100\%\mid \tag{5}\]  
IP values were calculated for each image individually to facilitate a comparison of the algorithms.  
Figure 11: BPP values of the algorithms (lower is better).  
Figure 12: SS values of the algorithms (higher is better).  
Based on the comparative information presented in Table 7, the following analyses can be derived:  
The DEA demonstrated significant success across all image types within the datasets, proving its effectiveness. To evaluate overall performance, average IP values were calculated for all images.  
Compared to the original images, the results indicated that:  
* The HCA achieved an IP value of 13.81;
* The LPHEA achieved an IP value of 14.03;
* The HBILES algorithm achieved an IP value of 41.16;
* The DEA achieved an IP value of 45.12.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Image Name**} & \multicolumn{5}{c}{**Encoding Algorithms**} & \multicolumn{5}{c}{**Compression Algorithms**} \\ \cline{2-10}  & \multirow{2}{*}{**HCA [23]**} & **LPHEA** & **HBILES** & \multirow{2}{*}{**DEA**} & **BPG** & **LS** & **2000** & **S+DEA** \\  & & & [30] & **[29]** & & [55] & [56] & [57] & \\ \hline \(1  
Meanwhile, the DEA outperformed all benchmark encoding algorithms, demonstrating its superior performance.  
When analyzing the average values across all images, it is evident that the S+DEA consistently outperformed BPG and JPEG2000. However, it lagged slightly behind JPEG-LS by a very small margin.  
To further analyze this situation, a dataset-specific evaluation was required. The assessments conducted for each dataset are presented below.  
* In Dataset 1, the S+DEA outperformed the other compression algorithms, achieving an average IP value of 45.12;
* In Dataset 2, which consists of large satellite images, the S+DEA achieved an average IP value of 59.64, significantly outperforming BPG, JPEG2000 and JPEG-LS;
* In Dataset 3, which consists of medical images, the BPG, JPEG-LS, JPEG2000 and S+DEA algorithms achieved an average IP value of 50.26, 58.76, 58.58, and 51.66, respectively. The S+DEA performed better than BPG but fell slightly short of the performance levels achieved by JPEG-LS and JPEG2000. JPEG-LS and JPEG2000 achieved a certain level of success in medical imaging due to their encoding methods, which predict pixel values based on their neighboring pixels,
* In Dataset 4, the S+DEA compression algorithm outperformed the benchmark algorithms BPG, JPEG2000, and JPEG-LS, achieving an average IP value of 37.05.
## 2 Materials and Methods
### Machine Learning Algorithm (LSTM)  
LSTM networks constitute a type of neural network architecture being built upon a recurrent manner, via introducing memory cells, as well as the in-between connections, with the intention of constructing a graph directed in a sequence. In a general sense, recurrent neural networks process the sequences by employing these memory cells in a dissimilar way as compared to simple artificial neural networks. Although they are designed for handling problems with a sequential nature, recurrent neural networks frequently comfort the problem pertaining to vanishing gradients or being unable to \"memorize\" many sequential data. However, the characteristic cell structures found in LSTMs, also called gates, render the network capable of variating the retained information [63], while they can regulate which part of information is going to be either discarded or stored in the long memory. This leads to the much desired optimization of the memorizing process. Problems with dynamic sequential behavior have been proven to be suitable for such kinds of problems. HAR can fall under this premise, because all activities are time-dependent sequences, which makes LSTM a suitable algorithm for the problem the present study tackles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-Activity** & **Assigned Value** & **One-Hot Vector** \\ \hline Standing & 0 & (1,0,0,0,0,0) \\ Walking (without crate) & 1 & (0,1,0,0,0,0) \\ Bending & 2 & (0,0,1,0,0,0) \\ Lifting crate & 3 & (0,0,0,1,0,0) \\ Walking (with crate) & 4 & (0,0,0,0,1,0) \\ Placing crate & 5 & (0,0,0,0,0,1) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sub-activity description, assigned value, and vectorial transformation.
# Aerospace
## 5 Single Small Satellite to Venus Orbit as Secondary Payload
### Mission Requirements  
The study requirements are as follows:  
1. The mission should deliver a 25 kg small satellite as a secondary payload on a Venus orbiter mission to a 200 \(\times\) 2000 km near-polar Venus orbit.
2. The concept must allow for at least 0.4 deg. of Theoretical Corridor Width and accommodate a \(+/-0.1\) deg. 3\(\sigma\) EFPA error at entry interface.
3. The maximum peak deceleration during aerocapture is not to exceed 10 g.
4. The maximum peak stagnation-point heat rate is not to exceed 240 W/cm\({}^{2}\)
5. The stagnation-point total heat load is not to exceed 25 kJ/cm\({}^{2}\).
6. The orbiter deflection maneuver must not exceed 10 m/s.
7. The deflection maneuver must be performed no earlier than 2 days before entry.
## 5 Numerical Experiments
### Sensitivity Analysis  
In Cases 1 to 4, where price sensitivity was varied, the breakdown of the four types of costs for the optimal solution obtained using DP is shown in Figure 4(a)-d.  
A sensitivity analysis of the model was conducted based on the results of Case 1. In Case 2, where price sensitivity is low, customer demand is less influenced by price, making it easier to sell at the standard price across all periods. As a result, inventory  
Figure 4: Computation time of PPO for each dimension.  
Figure 5: Breakdown of costs in Cases 1 to 4.  
levels increase, and the proportion of holding costs rises by an average of 6.4%. In Case 3, where price sensitivity is high and the differences between periods are large, the results were almost the same as those in Case 1. In Case 4, where price sensitivity is high and the differences between periods are small, customer demand for all inventory is highly sensitive to price. Consequently, adjusting sales volume through pricing becomes easier, leading to a reduction in the proportion of waste, with an average decrease of 11.9%.  
In the numerical experiments with varying price sensitivity, when price sensitivity is low, customer demand is less affected by price and time periods, suggesting that sales can remain stable even without discounts. The breakdown of costs in this case indicates that, in a stable selling environment conducive to generating profits, order quantities increase, and the proportion of ordering and holding costs becomes relatively large. On the other hand, when price sensitivity is high and there are significant differences between time periods, changes in customer demand vary greatly from period to period, making pricing more difficult and leading to an increase in costs. Additionally, when price sensitivity is high but the differences between time periods are small, inventory sales for specific periods become easier through price setting, which can lead to cost reductions. From the cost breakdown in this case, it is evident that a reduction in unsold inventory is possible, which decreases the amount of stock that is spoiled.  
The insights from the sensitivity analysis conducted by varying price sensitivity are summarized below. When customers' price sensitivity is low, stable sales can be achieved even at the standard price. In such cases, it becomes easier to increase profits, leading to a tendency for order quantities to rise. However, this also increases ordering and holding costs. On the other hand, when price sensitivity is high and there are significant differences between periods, pricing becomes more challenging, which contributes to an overall increase in costs. Conversely, if price sensitivity is high but the differences between periods are small, it becomes possible to sell inventory for specific periods through strategic price setting. This can lead to cost reduction by minimizing waste.
# Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
## 4 Results and Discussion
### LSTM Prediction Effect Analysis  
In this paper, data from 10 consecutive working days are collected to validate the proposed method. The operation data are divided into two parts, the former 5 days is used for model training and pattern recognition and the latter 5 days for verifying the model prediction effect. The prediction results are shown in Figure 5, whose horizontal axis represents different working days (separated by short green lines) and the vertical axis represents the cooling capacity. It can obviously be seen that the cooling load in consecutive working days shows a strong periodic trend. A brief peak in the cooling load exists each day at 8-10 a.m., and this is mainly because it is the start of work. In addition, the cooling load is relatively stable throughout the day, and normally only one compressor is on. Analyzing the prediction results, it can be found that the predicted values of the cooling load match well with the test values. When dealing with longer periods, the model allows for continuous updating and iterating with the latest historical data, thereby guaranteeing prediction accuracy.  
The errors between the test and predicted values of the cooling load are shown in Figure 6, where 65% of the predicted cold load results can converge within the 20% error line with the R\({}^{2}\) of 0.8791. It is important to note that when adopting a peak shifting strategy, accurate time-to-time prediction is not necessary, and total load forecast is more important. From this point of view, the prediction accuracy is sufficient for peak shifting, and the LSTM algorithm is suitable for cooling load forecasting of cold stores.  
Figure 5: Comparison of the test and prediction cooling load over 5 working days (separated by short green lines).
# An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals
## 5 Conclusions  
Aiming at the problem of inaccurate ranging by current indoor positioning methods, it is difficult to meet the problem of high-precision positioning. Based on the homologous base station, this paper proposes a DFEC ranging method. In this way, by using the phase constraint relationship between the dual-frequency signals, an equation for measuring the propagation distance using the phase within the cycle is constructed. This method has the following advantages: (1) It is not necessary to know the transmission time and reception time of the signal, and it avoids the dependence of ranging on time; (2) When a cycle slip occurs, carrier phase cycle slip is usually introduced, this method does not have the problem of the ambiguity of whole cycles; (3) Through testing in wired and wireless environments, it is found that in the semi-shielded and fully-shielded areas of the signal, although affected by the multipath signal, the ranging method can still meet the positioning accuracy of 93% probability. In future work, we will further use this ranging method to conduct comprehensive test verification on WIFI RTT, 5G and dual-frequency multi-system pseudo satellites. By analyzing the feasibility of various positioning methods, we will lay the foundation for further multi-source fusion basis. Thus, a ranging method using multiple means and multiple frequency phases to constrain is used to improve the accuracy and stability of indoor environment ranging and ensure the service demand of continuous reading high-precision positioning.  
All authors contributed to the manuscript and discussed the results. All authors together developed the idea that led to this paper. [PERSON] and [PERSON] conceived the experiments and analyzed the data. [PERSON] and [PERSON] performed data processing and contributed to the manuscript. [PERSON] and [PERSON] provided critical comments and contributed to the final revision of the paper. [PERSON] contributed to the expression and the design of programs. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 41774027). This work was supported in part by the National Key Research and Development Plan of China (project: Indoor Hybrid Intelligent Positioning and Indoor GIS Technology (No. 2016 YFB0502100, 2016 YFB0502101)).  
The authors declare no conflict of interest.
###### Abstract  
As systems in industry become increasingly interconnected and sophisticated, the task of fault detection and diagnosis becomes significantly more difficult. Predictive maintenance, in conjunction with sophisticated multimodal learning methods, has been found to be an effective solution for tackling such challenges. Presently, data are collected across numerous sources, ranging from sensors and operational variables to environmental variables, making it vital to combine these heterogeneous data for effective diagnostics. Advanced learning methods like deep learning, transfer learning, and hybrid models are tailored to processing and aggregating such disparate streams of data, thereby leading to higher diagnostic accuracy. This leads to more efficient and reliable predictive maintenance methods. This paper provides a comprehensive review of how various learning methods are applied to fault diagnosis in interconnected systems, particularly in predictive maintenance. It examines different approaches that integrate data across domains, evaluating how each contributes to improved fault detection and enhanced system reliability. Additionally, it addresses emerging research areas, such as real-time fault detection, innovative data fusion processes, and the increasing application in power grids, manufacturing, and the automation sector. This paper serves as a valuable resource for both researchers and practitioners, emphasizing the significant potential of multimodal learning in advancing fault diagnosis and predictive maintenance within increasingly interconnected and complex systems.  
deep learning; fault diagnosis; interconnected systems +
Footnote †: journal: Applied sciences  
1][PERSON] [1], [PERSON] [2,*]\({}^{\copyright}\), [PERSON] [2] and [PERSON] [1]\({}^{\copyright}\)
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 3 Results  
A systematic review was conducted to identify studies that assessed AV deployment in rural areas from 2015 to the present. Over the years, the volume of studies on AV deployment in rural settings has risen (Figure 2), reflecting technological and societal  
Figure 1: Methodology chart.  
factors. Initial research between 2015 and 2017 was limited [7; 8; 9; 10; 11], largely due to AV technology still being in its early stages and fewer real-world applications in non-urban contexts. Interest expanded as the technology advanced and garnered more policy and funding support, particularly around 2018-2020 [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35] when government agencies and private industry began actively investing in rural-focused AV research. Concurrently, real-world pilot programs and heightened public awareness fueled an increase in publications as researchers explored issues such as infrastructure readiness, safety, and community acceptance. A slight plateau around 2021 [27; 29; 30; 31; 33; 34] may be attributed to pandemic-related slowdowns; however, publications rebounded as conditions improved and new avenues of study emerged. The continued rise in the number of studies from 2022 onward illustrates a growing recognition of rural areas as critical environments for testing and implementing AV technology [6; 7; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66].  
The review identified eight key themes essential to AV deployment: benefits, challenges, user perception, long-term adoption, cost feasibility, physical and digital infrastructure needs, and technological advancements (Figure 3).  
These themes stem from four assessment approaches: field tests, simulations, interviews/surveys, and conceptual evaluations (Figure 4). Field tests provide crucial insights into AV performance under real-world conditions, simulations complement these tests by modeling AV behavior in controlled environments. Combined, these methods highlight deployment challenges, infrastructure needs, and necessary technological upgrades. User perception and acceptance, gathered through interviews and surveys, play an important  
Figure 3: Key themes in AV deployment feasibility assessment in rural areas.  
role in shaping long-term planning and adoption strategies. These insights are tied to studies on economic feasibility, which assess the financial sustainability of AV implementation by evaluating the necessary investments. Together, they provide a holistic understanding of how to optimize AV deployment in rural areas, balancing public acceptance with economic viability.
## 3 DASH Interferometer Mount
### Finite Element Analysis  
A finite element model of the DASH interferometer assembly can then be established to analyze the modal and random vibration responses, as well as to verify the correctness of the theoretical analysis. Table 4 lists the properties of the primary materials used in the DASH interferometer assembly.  
Figure 11 illustrates the deformation of the interferometer assembly. The analysis results show that the DASH interferometer moves together with the flexible structure, and the maximum deformation is located at the wing position of the flexible structure.  
Modal analysis was then performed to evaluate the rigid-body performance of the structural frame in comparison to that of the DASH interferometer. Table 5 summarizes the comparative analysis values of the natural frequencies of the structural frame and DASH interferometer. Note that the natural frequencies of the structural frame are much higher than those of the DASH interferometer in all three directions. The structural frame can be defined as a rigid structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Density/kg\(\cdot\)m\({}^{-3}\)** & **Young’s Modulus/GPa** & **Poisson’s Ratio** \\ \hline TC4 & 4400 & 109 & 0.28 \\ Invar & 8100 & 145 & 0.25 \\ Fused silica & 2200 & 73.1 & 0.2 \\ CaF2 & 3180 & 75.8 & 0.28 \\ N-SF57 & 3500 & 96 & 0.26 \\ Glue & 1 & 14 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Properties of primary materials used in the DASH interferometer assembly.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(K_{TX}\) & 17,527 N/mm \\ \(K_{TV}\) & 18,322 N/mm \\ \(K_{TZ}\) & 40,050 N/mm \\ \(f_{y}\) & 666.65 Hz \\ \(f_{y}\) & 681.61 Hz \\ \(f_{z}\) & 1007.72 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Spring constants and natural frequencies in three directions.  
The maximum stress on the mechanical element was 65.56 MPa, which occurred at the wing position of the flexible structure, and the maximum stress on the optical element was 0.56 MPa. All stresses were less than the tensile strength of the material.  
The maximum shear stress \(\sigma_{max}\) at the bonding surface between the structure and optical elements was 3.4 MPa. According to the safety margin equation (\(MS=\sigma_{b}/(\sigma_{max}\times 1.2)\)), which defined the safe coefficient as 1.2, the safety margin (MS) must be higher than one. Considering the shear strength (\(\sigma_{b}=14\) MPa) of the epoxy adhesive (8217) as the structural adhesive, the safety margin was 3.4, which is higher than one. The maximum shear stress of the bonding surfaces between the optical elements was 0.33 MPa. Considering the shear strength (20 MPa) of the ultraviolet photosensitive adhesive (NOA61), the safety margin was 49.5, which is also higher than one. Therefore, all values had a high safety margin.