esa-sceva/satcom-chunk-collection huggingface dataset:
# The Multi-Branch Deep-Learning-Based Approach to Heart Dysfunction Classification
## 3 Experiments
### Scenarios  
To thoroughly verify the capabilities of the proposed solution, we introduced seven scenarios described below.  
* Scenario A--the first set of experiments was performed to establish the benchmark results and obtain a starting baseline for further comparison. We used a single-branch architecture and tested all branches separately, using only the LSTM (as the most often used) architecture as the core network. The basis of this scenario is an extension of the work published previously [18]. In our experiments, we employed a pruned ECG signal sample of length 0.7 s (0.2 s before the peak and 0.5 s after the R peak).
* Scenario B--the second experiment tests possible differences between core network architectures working on different branches. As such, for each of the core networks, tests of single branches were performed for four branches: \(\alpha,\beta,\delta\), and \(\epsilon\). Omitted in the experiment were the branches \(\gamma\), as domain knowledge alone proved to be inefficient, and \(\zeta\), because the \(\epsilon\) branch baseline drift removal worked better. An enhanced input signal window (3 s of signal) was used, and dropout layers were added.
* Scenario C--core network multi-branch architectures were tested with dropout layers embedded in the model. This comparison aimed to check if the combination improves model performance despite some branches offering no performance increase alone when all inputs are presented independently. This way, it can be checked if different perspectives (in the form of differently prepared independent inputs) can provide some improvement, even when such input alone works worse than the raw ECG signal.
* Scenario D--during this set of experiments, we measured and compared the performance of deeper architectures with an increased number of neurons in hidden layers. Multi-branch architecture was used for each of the core networks.
* Scenario E--in the third scenario, we test a multi-branch architecture with LSTM with and without dropout layers and different pruning approaches. The goal was to establish a strategy to increase the network's generalizing capability and improve overall performance while reducing the risk of overfitting.
* Scenario F--this experiment is an ablation study. The aim is to verify whether the exclusion of a given single branch from the initial complete set of six branches would change the overall performance of the architecture. Thus, it can be checked if any information from one of the independent branches could be considered excessive and removed to increase the architecture's performance.
* Scenario G--based on the results from scenarios D and E, additional tests on all three larger core networks were conducted, using different pruning strategies to check if the results from scenario D can be improved by applying different pruning techniques, especially for the underperforming networks.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 1 Introduction
Based on the simulation results, the influences of material properties, laser power and laser scanning speed, thermal image acquisition frequency on the temperature gradient at the crack were analyzed, and the windows for selecting laser scanning parameters were proposed for iron-based and nickel-based alloy materials. The optimized thermographic process parameters were applied to the surface crack detection of real repaired components, and the thermal image sequence based on laser scanning was used for post-processing. Then, the processing results of the one-order gradient algorithm, the second-order gradient algorithm and the edge detection algorithms were compared. The results show that the edge detection algorithm based on Prewitt operator has high precision in locating crack edges, and can achieve fast crack extraction with high signal-to-noise ratio (SNR). The detection sensitivity of crack width can reach 10 \(\upmu\)m. To further study the influence of surface roughness on thermographic detection, repair layer with and without polishing process are detected. The results show that the periodic temperature distribution is caused by the periodic cladding tracks. When the amplitude of surface fluctuation is small, the global threshold segmentation algorithm can effectively eliminate the cladding track noise. When the amplitude of surface fluctuation is large, cladding noise has a great influence on crack identification results. The framework and technical route are shown in Figure 1.
# An Improved Fuzzy Vector Signature with Reusability
## 3 Definitions  
### Syntax of Fuzzy Vector Signature  
Let \(\mathcal{W}\) be a fuzzy (biometric) space with the Hamming distance metric \(\mathcal{M}\) and \(w\) is a sample of random variable \(W\in\mathcal{W}\). A fuzzy vector signature (FVS) scheme [20] is defined by three algorithms (**Setup**, **Sign**, **Verify**) as follows:  
* [noitemsep]
* \(\mathbf{Setup}(1^{\lambda},w,n,d,\ell,t)\): The setup algorithm takes as input the security parameter \(1^{\lambda}\), a sample of fuzzy (biometric) data \(w\gets W\), the length \(n\) of \(w\), the number \(d\) of subsets, the number \(\ell\) of elements included into each subset, and the maximum number \(t\) of errors that can be tolerated. It generates a signing parameter \(\mathsf{SP}\) and verification key \(\mathsf{VK}_{w}\) corresponding to \(w\). Here, \(t/n\) is said to be the error tolerance rate.
* \(\mathbf{Sign}(\mathsf{SP},w^{\prime},m)\): The signature generation algorithm takes as input a signing parameter \(\mathsf{SP}\), a sample of fuzzy (biometric) data \(w^{\prime}\gets W\) of length \(n\), and a message \(m\). It generates a signature \(\sigma_{w^{\prime}}\) corresponding to \(w^{\prime}\).  
\(\bullet\)**Verify\((\mathsf{VK}_{w},\sigma_{w^{\prime}},m)\)**: The verification algorithm takes as input a verification key \(\mathsf{VK}_{w}\), a signature \(\sigma_{w^{\prime}}\), and a message \(m\). If a signature \(\sigma_{w^{\prime}}\) is valid under the condition that \(\mathbf{dis}(w,w^{\prime})\leq t\), it outputs 1; otherwise 0.  
**Correctness**. Let \(\delta\) be the probability that the **Verify** algorithm outputs 0, and let \(\mathbf{dis}(w,w^{\prime})\leq t\) for two sample of fuzzy (biometric) data \(w,w^{\prime}\in\mathcal{M}\). For the signing parameter \(\mathsf{SP}\) and the verification key \(\mathsf{VK}_{w}\), if \(\sigma_{w^{\prime}}\leftarrow\mathbf{Sign}(\mathsf{SP},w^{\prime},m)\), then \(\Pr[\mathbf{Verify}(\mathsf{VK}_{w},\sigma_{w^{\prime}},m)\!=\!1]\geq 1-\delta\).
## 3 Preliminaries
### Local Maxima Problem  
To accommodate temperature transfer function in the network scenario, we must solve the problem of local maxima to ensure that forwarding by the warmest path will always end up in the data sink. When \(\xi>1\), the probability of local maxima is \(\geq\)0, therefore we modify the original function and use \(\xi_{NORM}\in\langle 0,1\rangle\). Maximum temperature is in the data sink, \(\phi_{MAX}\), and the temperature of every non-sink node is calculated as follows:  
\[\phi_{i}=\phi_{i}+(\phi_{j}-\phi_{i})\cdot\xi_{i_{NORM}} \tag{1}\]  
The data sinks are thermal sources and all other sensor nodes are conducting temperature depending on their own properties. The only condition which must be kept when forwarding the packets in the network, is that there exists path from any node to any sink in the network and the temperature values of all nodes along the path are strictly increasing towards the data sink. This is accomplished by normalizing \(\xi\), \(\xi_{NORM}\in\langle 0,1\rangle\) (see Theorem 1).  
**Theorem 1**.: _Node \(N_{i}\) is non-sink node connected to the network and there exists at least one path from node \(N_{i}\) to the sink. If \(\xi_{NORM}\in\langle 0,1\rangle\), then there exists a neighbor of \(N_{i}\), \(N_{j}\), and \(\phi_{i}<\phi_{j}\)._  
Proof.: By contradiction. Assume that there is no such neighbor having \(\phi_{i}<\phi_{j}\), so for every neighbor \(N_{j}\), \(\phi_{i}\geq\phi_{j}\). Then, from the iterative calculation of the temperature, \(\phi_{i}\), we can write:  
\[\phi_{i}=\phi_{i_{PREV}}+(\phi_{j}-\phi_{i_{PREV}})\cdot\xi_{i_{NORM}}\quad \land\quad\phi_{i}\geq\phi_{j}\]  
Thus  
\[\phi_{i_{PREV}}+(\phi_{j}-\phi_{i_{PREV}})\cdot\xi_{i_{NORM}}\geq\phi_{j}\]  
\[\xi_{NORM}\geq 1\]  
which is in contradiction with the previous assumption that \(\xi_{NORM}\in\langle 0,1\rangle\).
## 5 Analyses and Results
### Results of Measurement Model Assessment  
Two main procedures were utilized as a part of the CFA to test the measurement model. The first approach is testing model fit criteria and second, assessing the reliability and validity of the model.
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 4 Preliminary Design of an IoT-Aided Building Fire Evacuation Control System
### Potential Services and Applications
#### 4.3.3 Evacuation Decision Support  
The evacuation decision support module works for the major purpose of evacuation control, including the planning of evacuation strategies and subsequently the evacuation guidance. For the planning of evacuation strategies, evacuation simulation provides a possible approach. Evacuation simulation is a computer-based approach to determine the total egress time based on the simulation of crowd dynamics and pedestrian motion. The existing software or modeling approach include FDS + Evac [75], SGEM [76], agent-based modeling (ABM) [77], and cellular automata (CA) [78]. By determining the input parameters, such as building geometries, initial occupants' locations, evacuation strategies, and individual behaviors and interactions, evacuation simulation can predict the evacuation process and evaluate the evacuation with some indicators, including total egress time, travel distance, door flow rate, etc. From these indicators, whether or not the evacuation strategy is acceptable can be determined. In the currently proposed system, the data collection of people counting for each floor is dynamically linked with the simulation model. In addition, the hazardous areas and characteristics of the ongoing fire analyzed in the fire risk assessment module are also connected to the evacuation model. The evacuation simulation may become more adaptable to the real fire situation on-site by considering these dynamic parameter inputs. With such dynamic evacuation modeling to assess the effectiveness of different evacuation strategies, such as evacuating occupants with different sequences, whether evacuating occupants with the help of elevators, etc., safety and effectiveness of implementing the building evacuation can be significantly improved.  
As for the evacuation guidance part, it is meant to make the planned evacuation strategies come into action. With the control of the exit and directional signs, the evacuation decision support module can deliver the planned evacuation route to evacuees. Moreover, the state and condition of each exit and directional sign can be monitored and viewed in the BIM-based platform. Furthermore, considering that it may need to control the sequence of evacuation for each floor, the evacuation decision support module also enables sending voice alarms and instructions to the designated areas or floors via PA systems.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 2 Development of BMS
### Device Control  
An Arduino Uno Rev 2 Wi-Fi circuit board, equipped with the ATmega4809 microcontroller, was chosen due to its data logging, pre-processing, and transmission capabilities [37]. It had a clock speed of 16 MHz with 48 kB of flash memory and Electrically Erasable Programmable Read-Only Memory (EEPROM) of 256 Bytes providing 16 digital input and output pins. This device managed a network of sensors, including 'temperature sensors' and 'current and voltage monitor sensors' (obtained from Adafruit Industries). Its internal 5 V regulator, with a maximum current output of 0.8 A, was used to satisfy the input current and voltage requirements of the sensor network. Table 1 provides a detailed overview of the various connections made using an Arduino board with various sensors, as also depicted in Figure 1. This setup has five different temperature sensors for collecting four cell surface temperatures and one ambient temperature. Serial clock (SCLK), serial data out (SDO), serial data in (SDI), and chip select (CS) were all signals utilized in the serial peripheral interface (SPI) protocol to connect the sensors to the microcontroller. Table 2 gives information on the required power for these sensors and their usage in collecting parameters like voltage, current, and temperature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Connection** & **Use** \\ \hline Power and Serial & USB & 5 V power supply as well as serial communication to the main CPU \\ \hline Ground & GND & Common star ground for every component in the sensor network \\ \hline Shunt V+ & Current Sensor V+ & Positive Kelvin connection from current shunt to battery positive \\ \hline Shunt V\(-\) & Current Sensor V\(-\) & Negative Kelvin connection from current shunt to load positive \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the electrical terminals on the sensor network wiring harness.  
Figure 1: A detailed schematic of the BMS sensor network, illustrating the integration of current, voltage, and temperature sensors with a microcontroller and communication module for real-time monitoring and data transmission.
# New RED-Type TCP-AQM Algorithms Based on Beta Distribution Drop Functions
## 4 The Beta RED Scheme
### Beta RED Algorithm
Summarizing, in BetaRED, once the target queue length \(q_{\text{target}}\) is set, we have to adjust the parameters \(q_{\text{min}}\), \(q_{\text{max}}\), \(p_{\text{max}}\), \(w\) and \(\theta\). The rest of the algorithm works exactly the same as RED. For the sake of completeness, we present an outline of the pseudo-code for BetaRED in Algorithm 1. ```
1: set tunable parameters: \(q_{\text{target}}\), \(q_{\text{min}}\), \(q_{\text{max}}\), \(w\), \(p_{\text{max}}\), \(\theta\)
2:\(\mu=\frac{q_{\text{target}}-q_{\text{min}}}{q_{\text{max}}-q_{\text{min}}}\); \(\sigma=\theta\cdot\sqrt{\mu(1-\mu)}\);
3:for each arriving packetdo
4: calculate new \(q_{\text{avg}}=(1-w)\cdot q_{\text{avg}}+w\cdot q_{\text{cur}}\)
5:if\(q_{\text{avg}}\leq q_{\text{min}}\)then
6:\(p=0\)
7:elseif\(q_{\text{avg}}\geq q_{\text{max}}\)then
8:\(p=1\)
9:else
10:\(p=p_{\text{max}}\cdot\tilde{I}_{z(q_{\text{avg}})}(\mu,\sigma)\)
11:endif
12: with probability \(p\), drop arriving packet
13:endfor
```  
**Algorithm 1** Pseudo-code outline for the Beta RED algorithm.
## 3 Results and Discussion  
As shown in Figures 3 and 5, which represent Google Maps captures of the airports, the houses are located close to the runway. Thus, it is necessary to assess the impact of aircraft engine noise on these buildings during the maximum regime and to estimate the noise reduction provided by the proposed solution.  
The results are displayed in Figure 17.  
To simulate the noise produced by an aircraft during ground operation (taxing), the source depicted in Figure 17 was duplicated, assuming a mirroring of the directivity vectors. Additionally, the calculation area was expanded to 1000 m \(\times\) 700 m and the grid resolution was increased to 10 m, then the houses in the neighboring residential area were positioned. The resulting output is shown in Figure 18.  
Figure 17: The noise contour obtained for a single CFM56 engine.  
For this study, the area of buildings closest to the runway was selected, as shown in Figure 19.  
By utilizing the input data described above, a noise contour incorporating the resonance-absorbent screen was obtained, as shown in Figure 20.  
Figure 19: Residential area near the Baneasa airport runway.  
Figure 18: Noise contour mapped for two CFM56 engines.  
By comparing the two noise contours, it is clear that the reduction achieved by incorporating the redesigned resonance-absorbent screen can reach up to 15 dB, depending on the location of the houses. There are no houses that are exposed to more than 65 dB in the model, according to Figure 20.  
This reduction can be further improved by increasing the height of the screen. Moreover, in areas where houses are closer to the runway, a higher screen height can be used than in those areas where the houses are further away from the runway. It should be noted that the reduction obtained via the prediction software program is consistent with that obtained through calculation. The difference between the two results is due to the positioning of the noise source; in the prediction, the center of the runway is treated as the noise source, while in the calculation, the location closest to the houses is designated as the noise source.
# Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
## 4 Spatial Baseline Optimization Method for SMS-TomoSAR systems  
### Baseline Model Construction  
Under the given experimental scenario parameters, especially the number of baseline and baseline spans, the effect of the relative intensity of the vertical baseline on the imaging performance of SMS-TomoSAR systems is explored. The vertical baseline \(\left\{b_{\perp p}|p=1,\ \ldots,\ MN-1\right\}\) was planned according to Figure 3. The image centered by the relative position (considered for image correlation and other factors) was selected as the main image, and the baselines on both sides of the main image were assumed to be symmetrically distributed. Taking the \(\left(MN-1\right)/2,\ \ldots,\ MN-1,\ MN\) images as an example (herein referred to as the Positive Baseline Image Group), the baseline interval of the image group satisfied the equivalent relationship:  
\[a_{n}=a_{1}q^{n-1}\quad\ n=1,\ \ldots,\ (MN-1)/2, \tag{5}\]  
\[\frac{B_{span}}{2}=\frac{a_{1}[1-q^{(MN-1)/2}]}{1-q}, \tag{6}\]  
where \(q\) is the baseline interval geometric coefficient, and \(B_{span}\) is the baseline span. Since only the positive baseline image group was analyzed here, the positive baseline span was \(B_{span}/2\). It can be easily obtained from the above baseline analysis that when \(q>1\), \(a_{1}<a_{2}<\cdots<a_{(MN-1)/2}\), that is, the farther from the main image, the more dispersed the baseline distribution. When \(q<1\), \(a_{1}>a_{2}>\cdots>a_{(MN-1)/2}\), that is, the farther from the main image, the denser the baseline distribution. Especially, when \(q=1\), \(a_{1}=a_{2}=\cdots=a_{(MN-1)/2}\), that is, the baseline of the image satisfies the uniform distribution. Similarly, the above analysis was satisfied for the \(1,\ 2,\ \ldots,\ (MN-1)/2\) image (i.e., the Negative Baseline Image Group), and will not be discussed again. Figure 3 shows the baseline distribution with varying \(q\).  
By adjusting \(q\) to change the baseline distribution within the fixed baseline span, image data with different non-uniform characteristics was simulated, and then the imaging and result analysis were performed to explore the influence of different non-uniform characteristics of the baseline on the imaging performance of an SMS-TomoSAR system. Finally, the experimental simulation results showed that an approximate uniform baseline distribution had better SMS-TomoSAR height-dimensional imaging performance.
# Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT
## 2 Literature Review
### Bottleneck Detection in Manufacturing Systems
```
0: time series of active periods
0: list of bottlenecks
1: sort list by starting times
2:for each starting timedo
3:if first runthen
4: pick entry with longest duration, add to \(bn\_list\)
5: update last_bn_duration, last_bn_end
6:else
7: pick entry with longest duration
8:if end of entry is after last_bn_endthen
9: add to \(bn\_list\)
10: update last_bn_duration, last_bn_end
11:else if start of entry is after last_bn_end
12: add to \(bn\_list\)
13: update last_bn_duration, last_bn_end
14:endif
15:endif
16:endfor
17:return\(bn\_list\)
```  
**Algorithm 1** Algorithm for the Active Period Method  
In contrast to the Active Period Method, the _Turning Point Method_, presented by [14], observes the shift from blockage to starvation. The duration of blockage and starvation are accumulated separately for each machine and set into relation to the observation period. If a machine is a bottleneck, the upstream machines will have a high blockage percentage and the downstream machines will have a high starvation percentage. The bottleneck machine, however, will have low values for both blockage and starvation. Thus, by analyzing the values with respect to the neighbouring machines, one can determine a bottleneck machine. Figure 6 shows an example. Here, machine M3 is the bottleneck, as it is the turning point for the ratio of blockage and starvation percentages. Figure 5: Example of Active Period Method. Another method of determining bottleneck machines is suggested in [15], proposing a walk through the manufacturing layout. This method also uses blockage and starvation periods of machines. When there are machines which are starved, the bottleneck has to be upstream, which means it is somewhere in a process before the starved machines. If there are blocked machines, the bottleneck has to be downstream; thus, it occurs in some later processes. During a walk through the production site, all streams are marked whether the bottleneck is up- or downstream, leading to a set of one or more bottleneck machines. Ref. [16] presented Minerva is a reinforcement learning-based technique, footing on the arrival of jobs coming from the ERP program, which are assigned to machine pools. This method determines a pool of machines as bottlenecks indirectly by maximizing the throughput of the system by an optimal job scheduling. This leads to a machine pool whose capacity needs to be expanded and therefore is a bottleneck pool.
# Multiple SSO Space Debris Flyby Trajectory Design Based on Cislunar Orbit
## 7 Conclusions  
The paper investigates a one-to-many Chinese SSO space debris flyby mission. The debris in this multiple-SSO-object flyby mission are chosen by analyzing the distribution characteristics of the orbital location, type, source, and size of the SSO space cataloged objects. Based on the ergodic searching method, pruning function, and local optimization, the optimal orbit of the optimal two-impulse transfer and flyby when the spacecraft departs from three types of resonant DRO are obtained. The results show that transferring from the 3:2 resonant DRO has the smallest velocity increment. A semi-analytical optimization method is proposed to design the optimal large elliptical transfer orbits when the NSGA-II algorithm cannot obtain the Pareto-optimal orbit. The simulation results show that all target SSO space debris in the database are near-coplanar flown by at least once. The total velocity increment of the mission is 1.39 km/s, and the total time of flight is 2.23 years. The results of this paper can provide the basis for subsequent multiple space debris removal missions.  
Conceptualization, S.Z. and S.W.; methodology, S.Z.; validation, S.Z. and S.W.; formal analysis, S.Z.; investigation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, S.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA30010500, and the Lab funding of the Key Laboratory of Space Utilization, Chinese Academy of Sciences, Grant No. CXJJ-22S021.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.