esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Impact of Low-Frequency Random Loading and Unloading on Silo Structures
## 2 Reinforced Concrete Silo Force Analysis
### Analysis of Silo Wall Pressure Monitoring
#### 2.2.4 Analysis of Test Results  
Figure 6 presents the pressure diagram of the continuous monitoring at Point 7 over three days. Taking the intervals between the appearance of the lowest points D1, D2, D3, D4, D5, etc., at Point 7 on June 3 and 4, 2021, as examples, we find that TD2-TD1 = 60 min, 60 min; TD3-TD2 = 47 min, 43 min; TD4-TD3 = 60 min, 65 min; and TD5-TD4 = 48 min, 42 min. It is observed that the time intervals between the appearance of the lowest pressure points follow a 4:3 ratio, which is consistent with the unloading cycle time. The minimum pressure points are monitored at the beginning of unloading.  
According to the unloading rules, unloading occurs at regular intervals, with the unloading port being changed every 15 min. Therefore, during the material loading process, the maximum pressure point appears 15 min after the start of unloading. Consequently, there are three peak points, G1, G2, and G3, between D1 and D2 and two peak points, G4 and G5, between D2 and D3.  
From the figure, it is found that TG1-TD1 = 17 min, 13 min; TG2-TG1 = 12 min, 17 min; TG3-TG2 = 18 min, 18 min; and TD2-TG3 = 13 min, 12 min, which is consistent with the unloading cycle time, with intervals of approximately 15 min.  
Figure 5: Layout drawing of pressure sensors on inner surface of bin wall. (The numbers 1–40 in the figure represent the serial numbers of sensors installed at different heights and regions on the silo wall. The purple dots indicate four groups of sensors located at the same elevation but in different areas of the most vulnerable region on the silo wall.)  
Figure 6: Three consecutive days of measured pressure charts at monitoring Point 7.  
After discovering this pattern from the big data based on the monitoring process, we selected pressure data from four groups (groups 7, 20, 30, and 37) at an elevation of 31.5 m for certain time periods, as shown in Figure 7.  
An analysis of the measured results reveals that the horizontal pressure values at the same elevation and time are different, indicating a noticeable time lag, which is referred to as the wheel effect. From this, we can infer that during the silo discharge process, the circumferential tension on the silo wall is uneven, resulting in bending and torsion moments on the wall. This causes excessive local tension in some areas, leading to skewed stress and cracking of the silo wall. Additionally, there is a pattern of pressure fluctuation with one minimum point, followed by three maximum points, then another minimum point, and finally, two maximum points. This is consistent with the aforementioned analysis. Through the analysis of multiple sets of load data, extracted from both discharge and non-discharge areas, it is found that the load in the non-discharge area is approximately 1.43 times that of the discharge area.
# Numerical Simulation Analysis Method for Rockburst Control in Deeply Buried Caverns
## 4 Numerical Analysis Method for Relieving Rockburst through Borehole Stress Release  
According to the energy theory of rockburst, a rockburst may occur when the energy accumulated in the rock mass reaches a certain extent during the underground cavern excavation. For relatively light rockburst, drilling holes to release part of the energy from the tunnel face or injecting water to reduce the surface tension of the surrounding rock to reduce stress concentration is an effective measure to alleviate the occurrence of rockburst. Drilling holes on the tunnel face creates a cavity effect in the rock mass, releasing some of the rock mass energy and regulating the stress. To reasonably simulate the energy release and stress adjustment effects of drilling holes, we propose to use implicit hole elements to simulate the energy release effect of drilling.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 6 Keetchi--Case Studies
### Use Case 2: Two People Passing by on the Street  
Use Case 2 shows the situation when two people pass by each other on the street. We consider the simplest case, where people approach each other from two opposite directions, pass by each other and part again.  
In general, Epidemic will again first send a summary vector and only later data. Depending on the contact duration, it will either manage to exchange successfully all data from both devices, exchange successfully only in one direction or manage only few packets in one direction. Keetchi will start exchanging data straight away and even in high speed scenarios will successfully exchange at least the most popular messages in both directions. The exact numbers of how many packets can be exchanged with both protocols are summarised in Figure 6b, where the protocols are explored with different contact durations. One can see that even walking speeds (1 m/s) cause severe problems to both protocols, because of the relatively large data size (10,000 B) and relatively low communication speeds. This scenario demonstrates well the general problems of OppNets, when realistically evaluated.  
Keetchi has two advantages over Epidemic: (1) it exchanges messages in both directions, instead of one-way; and (2) it exchanges more messages in total than Epidemic. In terms of overhead, Keetchi is always slightly better than Epidemic, since it does not need summary vectors and data requests.  
**Disseminating popular messages.** In this scenario, there is a big difference between Epidemic and Keetchi in terms of how many popular messages will be successfully exchanged. With Epidemic, this will happen by chance--if we assume that Y% of all available messages were successfully exchanged, then also Y% of popular messages will be exchanged too. With Keetchi and short contacts, which do not allow the exchange of all messages, the exchange will be focused first on popular messages to deliver as many popular messages as possible. In summary, we can say that, in a mobile scenario, Epidemic will waste a lot of resources to exchange unpopular messages, while Keetchi will focus on popular ones.
###### Abstract  
The reliable generation of dissipative Kerr solitons (DKSs) enables applications in communications, metrology, optical clocks, and, more recently, artificial intelligence. We show how single DKS can be generated by Si\({}_{3}\)N\({}_{4}\) dual-coupled microring resonators (DCMs). We modeled this coupled structure using the Lugiato-Lefever equation (LLE), including mode interactions in the dispersion profile. We also characterized the pump power and detuning parameter space for several mode interaction strengths and frequencies, and we found parameters for which a DKS could be deterministically obtained using a single, adiabatic frequency sweep with a constant pump power. We demonstrated deterministic single DKS generation for this path by simulating 200 times with different random noise inputs. This result paves the way for reliable, inexpensive, and deterministic single DKS generation in a simple setup.  
photonic; microring resonator; solitons +
Footnote †: journal: Applied sciences
## 4 Implementation of RUL Prediction Based on Proposed Method
### Experimental Verification and Analysis  
#### 4.4.1 Experimental Result Analysis  
The prediction results of all tested bearings in the experiment are shown in Figure 15. It can be observed that the predicted values fluctuated around the actual values, and the overall trends were closely aligned, which validates the effectiveness of the proposed method. Further analysis revealed that in all three experimental groups, the predicted RUL remained nearly flat during the initial stage, indicating that the model struggled to capture the degradation trend at this phase. As noted in previous research on early-stage RUL prediction of rolling bearings [41], the degree of degradation is minimal in the early operational phase, leading to negligible variations in the acquired signals. Consequently, the extracted features show little change, making it difficult for the prediction model to learn meaningful degradation patterns, and resulting in flat RUL predictions. A comparative analysis revealed that the prediction results for bearing B1-3 exhibited relatively small fluctuations, whereas bearings B2-1 and B2-6 showed greater volatility. This may be attributed to the fact that B1-3 underwent a gradual degradation process, while B2-1 and B2-6 exhibited abrupt degradation behaviors. At the same time, this is consistent with the difficulties in predicting the remaining useful life of bearings with sudden failure patterns, as highlighted in the literature [28].
# Pockels Effect at the Interface between Water and Ti Electrode
## 3 Experimental Results
### Electroreflectance Spectra
#### 3.2.3 Electroreflectance Spectra under Steady-State UV Irradiation  
Titanium dioxide is known as a photocatalyst and exhibits strong oxidizing power when irradiated with UV light. The Honda-Fujishima effect [17] is one of the photocatalytic effects using titanium dioxide: UV light irradiation on a titanium dioxide electrode decreases the bias voltage required for the electrolysis of water. We measured the effect of UV light irradiation on the Pockels effect due to the change in optical constants at the electrode interface. Figure 6 shows the electroreflectance spectra upon UV light irradiation. The sign  
Figure 4: AC voltage dependence of electroreflectance spectrum.  
Figure 3: Electroreflectance spectra of the Ti electrode, which has been used for more than 30 h, in 0.1 M NaCl aqueous solution at 2 V and 20 Hz. \(X\) and \(Y\) represent the cos and sin components of the lock-in signal.  
of the \(X\) signal is reversed by UV light irradiation, where there was a delay in response with irradiation (significant \(Y\) component), while there was little delay in response (little \(Y\) component) without UV light irradiation. The magnitude of the signal (\(\sqrt{X^{2}+Y^{2}}\) did not change substantially. Note that no bubbles due to the photocatalytic effect were visually observed during this experiment.
# A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System
## 3 Materials and Methods
### Multiple Object Tracking
#### 3.3.3 Metrics and Optimization of Matching  
In the tracking task, _IoU_[42] is used to measure the matching degree between the detection box and the prediction box. In the matching stage of DeepSORT, _IoU_ is applied to process the unmatched detection results and tracking results, and the cost matrix is computed. The Hungarian algorithm uses this matrix to further solve the assignment problem. Finally, the object trace is formed.  
_IoU_ is the ratio of intersection to union, and the equation is as follows:  
\[IoU(A,B)=\frac{\left|A\cap B\right|}{\left|A\cup B\right|}, \tag{11}\]  
As shown in Figure 6, the red box \(A\) and the blue box \(B\) represent the detection box and the prediction box, respectively. The purple box is the overlap area of \(A\) and \(B\).  
However, there are the following problems when using _IoU_ to measure the degree of overlap between the detection box and the prediction box:  
1. When there is no intersection between the detection box and the prediction box, the relative distance between the two cannot be reflected at this time. This case is shown in Figure 7.
2. When the intersection of the detection box and the prediction box exists and _IoU_ is equal, it is impossible to distinguish the matching effect in different overlapping states using _IoU_. This case is shown in Figure 8.  
Figure 6: The schematic diagram of _IoU_.  
Figure 7: The case of \(IoU=0\).  
In order to solve the above problems, this paper adopts \(DIoU\) (Distance Intersection over Union) [43] instead of \(IoU\) in DeepSORT, and its equation is as follows:  
\[DIoU(A,B)=IoU-\frac{\rho^{2}(a,b)}{c^{2}}, \tag{12}\]  
where \(a\) and \(b\) denote the center points of \(A\) and \(B\), respectively. The purple dotted box represents the minimum bounding rectangle covering the two boxes, and the diagonal length labeled \(c\). \(\rho^{2}(a,b)\) is the Euclidean distance labeled \(d\), as shown in Figure 9.  
For the detection box and prediction box, \(DIoU\) considers three important factors at the same time: the overlap area, the non-overlap area and the central point distance. It can describe the matching relationship more comprehensively.
## 2 Materials and Methods
### Model Optimization  
After conducting feature selection, eight influential factors (including the temperature, humidity, illuminance, O\({}_{2}\) concentration, PM\({}_{2.5}\) concentration, PM\({}_{10}\) concentration, indoor population, and past CO\({}_{2}\) concentration) were identified and utilized as input variables for the RF-TPE-LSTM model. The study involved training, validating, and testing the models on a computer with the following configuration: Ubuntu 20.04 LTS operating system, an NVIDIA RTX A4000 GPU (NVIDIA Corporation, Santa Clara, CA, USA), an Intel Xeon 4210R CPU @ 3.20 GHz processor (Intel Corporation, Santa Clara, CA, USA), and 64 GB of RAM (Kingston Technology, Fountain Valley, CA, USA). Hyperopt was employed to iteratively adjust and optimize the hyperparameters, and the model's hyperparameter optimization ranges are shown in Table 3, ultimately achieving the optimal combination.  
To assess the prediction performance of the RF-TPE-LSTM model, two aspects were considered. Firstly, in terms of the prediction time, historical 60 min data were utilized to forecast the classroom at various future time points (10, 20, 30, 40, and 50 min), with the optimal prediction time determined to be 10 min. Secondly, the RF-TPE-LSTM model was compared with state-of-the-art methods, namely, the RNN [39], BPNN [40], LSTM [23], and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Parameter Value** \\ \hline Number of input layer nodes & 7 \\ Number of hidden layers & 1 \\ Number of output layer nodes & 1 \\ Loss function & MSE \\ Activation function & ReLU \\ Optimization function & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 2: LSTM model parameters.  
Figure 7: Hyperopt workflow based on the TPE algorithm.  
Optuna-LSTM [41]. Additionally, the prediction performance was evaluated both with and without the use of TPE.
# A Fluid-Structure Coupling Analysis of a Far-Field Flat Mirror for AliCPT-1 Telescope Calibration
## 4 Structural Analysis
### Results Analysis  
The analytical results show that the maximum FFF deformation of 0.059 mm occurs in the mirror surface under 17.5 m/s wind speed applied in the front wind direction. The maximum stress of 4.5 MPa is concentrated at the interface between the support rod and the base plate, as illustrated in Figures 14 and 15.  
Figure 14: Results after applying surface pressure calculation: (**a**) Deformation contour plot of the reflective mirror under front wind. (**b**) Stress contour plot of the reflective mirror under front wind.  
Figure 13: Distribution of pressure loads.  
The surface of the FFF mirror, measured with a three-coordinate measuring machine, indicates deformations of 0.05 mm resulting from processing and self-weight. The final Root Mean Square (RMS) flatness is obtained by combining the 0.05 mm with 0.0587 mm, which is 0.077 mm. This value is less than the required flatness of 0.1 mm. The material at this location is AL6061, and its yield strength is 280 MPa, which shows that the FFF can be used normally without damage in this extreme working condition. Maximum deformation and stress in backward wind pressure have an even lesser influence on the FFF. The result showed that the telescope can be calibrated in this extreme operating condition.  
This study employed a boundary condition of an instantaneous wind speed of 17.5 m/s, which occurred only at specific times in the last three years: 5:53 a.m. on 13 January 2020 and 2:19 a.m. on 14 January 2020; normally, the chosen weather had relatively low winds for calibration work for equipment safety. Furthermore, the maximum wind speeds are from wind directions of 40 degrees southeast, not in the vertical direction of the FFF mirror, which will have a lesser influence on the mirror. So, actually, the deformation of the FFF mirror is smaller compared to the analyzed conditions.
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 4 Experimental Results  
Experimental results validate the proposed SoC architecture for EMG acquisition with a sampling frequency of 8 kSPS. The effectiveness of the developed prototype is compared with commercial systems, showing significant improvements in signal-to-noise ratio and overall performance. A discussion on latency and effectiveness in the context of active rehabilitation therapies is included. The final setup for the system validation is shown in Figure 2.  
Four key experimental tests were conducted to evaluate the system's performance. These were the following: functional validation, latency assessment, time and frequency analysis of captured EMG signals, and resource assessment on SoC-FPGA. Prior to the description of the experimental procedure, the EMG signal acquisition protocol performed in this study is described below.
## 2 Materials and Methods
### Experimental Design
#### 2.7.4 Experimental Parameters and Configurations  
In addition to varying the agent algorithms (DQN, DDPG, PPO, and SAC) (Section 2.3) and the two types of feature extractors (MLP and CNN) (Section 2.5), several key parameters were adjusted across experiments to evaluate agent performance under diverse conditions. One critical parameter is the rebalancing frequency, which was set to either continuous rebalancing (\(f=1\)) or periodic rebalancing every 10 periods (approximately bi-weekly). The lookback period--the number of historical data points used for decision-making--was also varied, with configurations of 16 and 28 periods to examine the influence of shorter versus longer historical contexts (Section 2.1).  
Additionally, the experiments varied the type of data input into the RL agents, either using raw OHLC prices or incorporating technical indicators derived from these prices (Section 2.4). This setup allows for an analysis of how the inclusion of more complex market signals influences the agents' portfolio optimization strategies.  
The set of financial instruments remained constant across all experiments, consisting of six indices plus a risk-free asset (Section 2.7.1). Each experiment consisted of 252 periods, approximating one full investment year.  
To address the convergence challenges observed in certain RL agents, the best-performing model--defined as the one with the highest average reward over the preceding 100 episodes--was retained for testing with the evaluation set. To maintain consistency across agents, a batch size of 256 and a total of 10,000,000 timesteps were applied to all training runs. These standardized parameters ensured a rigorous and fair comparison between different agent configurations and experimental setups. Table 4 summarizes the different options for each of the experiment parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Option** & **Total Options** \\ \hline \multirow{3}{*}{RL agent} & DQN & \multirow{3}{*}{4} \\  & DDPG & \\  & PPO & \\  & SAC & \\ \hline \multirow{3}{*}{Feature extractor} & MLP & \multirow{3}{*}{2} \\  & CNN & \\ \hline \multirow{3}{*}{Rebalancing frequency} & continuous (1) & \multirow{3}{*}{2} \\  & bi-weekly (10) & \\ \hline \multirow{3}{*}{Lookback periods} & 16 & \multirow{3}{*}{2} \\  & 28 & \\ \hline \multirow{3}{*}{Environmental signals} & OHLC (dim = 24) & \multirow{3}{*}{2} \\  & OHLC + technical indicators & \\ \cline{1-1}  & (dim = 72) & \\ \hline \hline \end{tabular} Experimental configurations for a total of 64 experiments.  
\end{table}
Table 4: Experiment parameters and configuration options.
## 2 Information Granulation and Classification Systems
### An Introduction to Simplicial Complexes
By considering an \(\epsilon\)-ball around each point \(x\in\mathcal{P}\), it is possible to intersect said ball with \(V(x)\), leading to a restricted Voronoi region and the nerve of the set of restricted Voronoi regions for all points in \(\mathcal{P}\) is the Alpha complex. **Vietoris-Rips complex:**: for each subset \(S\subset\mathcal{P}\) of points, check whether all of their pairwise distances are below \(\epsilon\). If so, \(S\) is a valid simplex to be included in the Vietoris-Rips complex. Cech complex, Alpha complex and Vietoris-Rips complex strictly depend on \(\epsilon\), which somehow determines the'resolution' of the simplicial complex. Amongst the three, the Vietoris-Rips is the most used due to lower computational complexity and intuitiveness [59]. Indeed, the latter can be easily evaluated as follows [62]:  
1. build the Vietoris-Rips neighbourhood graph \(\mathcal{G}_{\text{VR}}(\mathcal{V},\mathcal{E})\) where \(\mathcal{V}\) is the set of vertices and \(\mathcal{E}\) is the set of edges, hence \(\mathcal{V}\equiv\mathcal{P}\) and \(\epsilon(v_{i},v_{j})\in\mathcal{E}\) if \(d(v_{i},v_{j})\leq\epsilon\) for any two nodes \(v_{i},v_{j}\in\mathcal{V}\) with \(i\
eq j\)
2. evaluate all maximal cliques in \(\mathcal{G}_{\text{VR}}\). The second step is due to the fact that the Vietoris-Rips complex is dually definable as the Clique complex of the Vietoris-Rips neighbourhood graph. The latter complex [48; 63; 64] is defined as follows:  
**Clique complex:**: for a given underlying graph \(\mathcal{G}\), the Clique complex is the simplicial complex formed by the set of vertices of its (maximal) cliques. In other words, a clique of \(k\) vertices is represented by a simplex of order \((k-1)\). Despite its'minimalistic' definition, proving that the Clique complex is a valid simplicial complex is straightforward: any subset of a clique is also a clique, meeting the requirement of being closed with respect to inclusions of the faces. A useful facet of the Clique complex relies on its parameter-free peculiarity: if the underlying 1-skeleton is available beforehand, one can directly use the Clique complex which not only does not need any scale parameter (e.g., \(\epsilon\) for the Vietoris-Rips complex and the Alpha complex) but also encodes the same information as the underlying graph and additionally completes a topological object with its fullest possible simplicial structure, being it a canonical polyadic extension of existing networks (1-skeletons) [65]. Further, it is noteworthy that from the set of cliques it is possible to recover the \(k\)-faces of the simplices by extracting all \((k+1)\)-combinations of these cliques. This is crucial when one wants to study the homology of the simplicial complex which is, however, out of the scope of this paper [66; 67]. Despite enumerating the maximal cliques being well-known as an NP-complete problem, several heuristics can be found in the literature [68; 69; 70].
## 4 Simulation and Evaluation
### Robustness  
To assess the resilience of Multi-MAF, we fixed the number of sources in 50 nodes with a Uniform distribution in the simulation time, and varied the probability of node failures from 0% to 50% randomly during the simulation. When a node fails, it stays inactive until the end of simulation. Figure 10(a) shows that all algorithms presented a similar performance regarding the delivery ratio (relation between received and data packet sent), because in all cases the time to recover from a failure was the same (10 times the data rate). Regarding the energy consumption [Figure 10(b)], EF-Tree outperforms SID. The reason is that in the SID algorithm, sources have to flood messages for path discovery, whereas the EF-Tree algorithm rebuilds the tree for the entire network at once. Multi-MAF's average performance stayed between EF-Tree and SID performances, because it can operate in one of these two modes during the network lifetime. As we can see, the results present large confidence intervals. This is due to the random node failures occurring at important locations of the network with a high effect on the overall data delivery ratio and energy consumption.