esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
As an important alternative to the human eyes to detect, identify, and track moving targets steadily or continuously, electro-optical tracking systems (EOTSs) have drawn interest around the world [1; 2; 3]. In an EOTS, in order to provide extremely accurate results within a short response time, one of the priorities is to have timely and accurate auto-focus technology (AFT) to capture the image information of the moving objects [4; 5; 6].  
At present, there are mainly two types of AFTs [7; 8]: conventional AFTs based on measuring distance, and AFTs based on digital image processing. Conventional AFTs include the image deflection method and the position sensitive detector (PSD) measurement distance method [9], which mainly depend on infrared or ultrasonic distance measurements [10; 11] with transmitters and receivers,leading to increased cost of the systems. In addition, ultrasonic methods cannot auto-focus accurately on objects behind the glass [12], thus limiting its usage scenarios. Compared to the conventional AFTs, auto-focusing methods based on digital image processing (DIP-AF) have been found to have the advantages of integrated, miniaturized, and low-cost applications [13], such as digital camera, video camera, and microscope imaging [14; 15]. However, little attention has been paid to AFTs with telescope systems, which are critical for capturing large amounts of image information through high-resolution CCDs in applications of astronomy observations [16; 17]. To investigate the focusing performance of the telescope system, in this paper, we analyzed the effect of the image estimation function and focusing window selection, and we propose a novel adaptive dynamic focusing window selection method with high sensitivity. This paper is organized as follows: The principle of DIP-AF is firstly given in Section 2, then the image estimation functions of different methods are compared in Section 3. In Section 4, we propose a novel method to improve the detecting accuracy and speed of the DIP-AF of a telescope system. Finally, conclusions are drawn in Section 5.
###### Abstract  
Due to the strong disturbance of a mining face, the surrounding rock of the withdrawal roadway is susceptible to deformation and failure, which restricts the safe and efficient evacuation of mining equipment. To resolve this longstanding technical problem in mine production, an engineering investigation, numerical simulation, theoretical analysis, and other research methodologies were conducted in this study. Furthermore, the influence mechanism of mining-induced stress on the withdrawal roadway was revealed, the anti-disturbance principles of thick-layer anchorage of roadway roofs were elucidated, and a novel double-layer flexible support technique was proposed. The front abutment pressure, stress superposition, damage accumulation of the surrounding rock, and the fluctuation of mining-induced stress are the primary factors contributing to the significant deformation of the surrounding rock in a withdrawal roadway. However, the fluctuation of mining-induced stress has usually been ignored in previous studies, and it may be the most crucial cause of the significant deformation and instability of the surrounding rock. The thickness of the anchored rock beam is the most vital factor affecting the maximum subsidence and maximum tensile stress of the roof, and increasing the thickness of the anchored rock beam can significantly improve the stability and anti-disturbance performance of the roof. In the proposed double-layer flexible supporting technique, flexible steel strands serve as the carrier, which overcomes the constraint of the roadway height on the length of roof support components. The first layer of flexible support is used to construct a thick fundamental anchorage layer, while the second layer is employed to construct a thicker reinforced anchorage layer, facilitating the effective resistance of the roof against strong mining disturbances. The effectiveness of this technique was further validated through the application of an engineering practice. The research results have reference value for solving the difficult problem of mining roadway support.  
Keywords:withdrawn roadway; strong mining disturbance; stress fluctuation; double-layer flexible support; thick anchor +
Footnote †: journal: Applied sciences
## 5 Discussion  
### Ablation Experiments  
To validate the effectiveness of the proposed method, we conducted ablation experiments by removing individual modules used in this study. The experimental results are shown in Table 2 below.  
From the experimental results, it can be observed that when using knowledge distillation alone, compared to the baseline, the OA increased by 0.49%, IoU increased by 2.26%, \(\mathrm{P_{50}^{IoU}}\) increased by 1.61%, and \(\mathrm{P_{50}^{Boundary}}\) increased by 6.27%. All metrics showed improvement, with \(\mathrm{P_{50}^{Boundary}}\) showing a significant increase. This indicates that by using knowledge distillation, the accuracy of building boundary extraction can be greatly improved. The model effectively learns generalization knowledge from the large dataset, enabling more precise identification of diverse building boundaries. When both knowledge distillation and Dice Loss were used together, the model achieved the best performance. Compared to using knowledge distillation alone, OA increases by 0.91%, IoU increased by 1.91%, \(\mathrm{P_{50}^{IoU}}\) increased by 1.90%, and \(\mathrm{P_{50}^{Boundary}}\) increased by 2.21%. All metrics showed stable growth. This indicates that the inclusion of Dice Loss effectively mitigates the problem of building boundary adhesion, resulting in more complete extraction of building polygon instances and more accurate boundaries.  
Additionally, we calculated the GPU processing time per batch for each method during training and the number of parameters of each model. From Table 2, the Baseline achieved the least GPU processing time per batch. With the addition of distillation loss and Dice Loss, the GPU processing time for each batch increased. Compared to the baseline, the processing time per batch for the model with distillation loss and Dice Loss increased from 0.84 s to 0.90 s. We believe that this small increase in GPU processing time is acceptable due to the significant improvement in modeling results. Meanwhile, Table 2 shows that the number of parameters in each model is equal and the use of distillation loss and Dice Loss does not increase the complexity of the model.
## 1 Introduction
In this study, an automatic irrigation system was developed which can be monitored and controlled via mobile devices. In this system, in addition to the seasonal water needs of plants, irrigation is carried out by taking into account the continuous values taken from the humidity sensors.
## 5 Results and Discussion
### DBN-Based Alpha-Beta Filter Results  
The DBN learning model was constructed by using training and testing sets of 70/30. The input to the DBN was composed of temperature values and humidity values, and two output data were used. The DBN was composed of two steps. In the first step, we used supervised learning to construct the RBM network. The RBM settings were a batch size of 12, momentum of 0, and 300 epochs. In the second step, the RBM network learned the backpropagation algorithm of supervised learning. The backpropagation used a batch size of 12 and 300 epochs. The DBN was used for the \(\alpha\)-\(\beta\) filter algorithm to learn from, and the learned values of the DBN were passed through to the \(\alpha\)-\(\beta\) filter to produce the final output values. The mean-absolute-error and root-mean-square-error values were estimated for the typical alpha-beta filter and the proposed DBN-based \(\alpha\)-\(\beta\) filter to evaluate their performance. Regarding the typical method, we obtained an RMSE of 5.216 and an MAE of 3.951 when we applied the DBN with two inputs, two outputs, and the RBM. The three-fold cross-validation method was used to comprehensively calculate the performance. The best results were an RMSE of 3.605 and an MAE of 2.610. The results are shown in Table 3. The performance of our model varied depending on the number of nodes constituting the DBN.
## 5 Experiments
### Evaluation Methodology  
A time series prediction problem can be solved as a classification problem or a regression problem. The change pattern of time series is often unstable and irregular, so it is difficult to accurately predict the value at a specific time step. In previous studies [26; 48; 49], researchers have mainly focused on predicting the future trend of time series, transforming time series prediction into a classification problem to solve. In our study, the trend of the next time step is defined as one of the directions of rising (+1), falling (\(-1\)), and steady (0). The ground-truth label is determined based on the change ratio of the closing price and AQI for the stock and air quality datasets, respectively, meaning that:  
\[y=\left\{\begin{aligned} &+1,&\text{if}\ \ \frac{v_{t+1}-v_{t}}{v_{t}}\geq\alpha;\\ &-1,&\text{if}\ \ \frac{v_{t+1}-v_{t}}{v_{t}}\leq- \alpha;\\ & 0,&\text{otherwise},\end{aligned}\right. \tag{11}\]  
where \(v_{t}\) and \(v_{t+1}\) are the values of closing prices or AQI at the \(t\)-th and \((t+1)\)-th time step, respectively. \(\alpha\) is a threshold and set to be 0.50% in line with the previous work [48]. We use the accuracy, precision, recall, and \(F_{1}\) score as metrics to evaluate the performance of different algorithms. To further analyze the results, we performed paired a t-test to compare the difference between our method and the other existing methods, and found that the improvement of our method is statistically significant at the significance level of 0.05 [50]. For each method compared in our experiments, we repeat the training and testing procedures five times, and report the average performance to alleviate the fluctuations caused by random initializations.
# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 4 Results and Discussions
### The Algorithm Evaluation
(**a**) Anomalies 3 L1, L2; (**b**) anomalies 5 L1, L2; (**c**) anomalies 6 L1, L2; (**d**) anomalies 7 L1, L2; (**e**) anomalies 8 L1, L2; (**f**) anomalies 9 L1, L2; (**g**) anomalies 10 L1, L2. The color legend can be consulted in Table 4.  
A closer look at the data on the confusion matrix reveals that many samples of level 1 and level 2 anomalies are misclassified. Most readings are projected in the steady-state area and some in the transient area. Thus, the occlusion intensity is too low. The VM3 shut-off valve prevents water from entering the system. Therefore, this anomaly will only affect the system when the valve is entirely closed. The reason is that if the valve is not entirely closed, it can allow water through, regardless of the amount of water the system needs. For precisely this reason, the algorithm recognizes level 1 and level 2 anomaly 3 values as steady-state measurements when supplied. However, the new tests for anomaly 5 show the highest accuracy level at 95 per cent. Even by slightly closing the inlet water flow shut-off valve at the tank, the system enters a new state that differs significantly from the steady state, and it is like level 3. The results obtained from anomaly 6 level 1 and 2 data projection are also good. The accuracy reaches 80% because even if the VM6 valve is closed a little, the flow rate of water leaving the tank is like that of level 3. Anomaly tests of inlet and outlet air produce the lowest accuracy values. In these two scenarios, even with valves VM7 and VM9 partially closed, air still enters and exits the system without experiencing a real occlusion. Figure 17 depicts the inlet air flow rate relative to the steady state for testing anomaly 7 levels 1 and 2. Figure 18 shows the air output for the tests of anomaly 9 level 1 and level 2 compared to the steady state. The two figures attest that the trend of the anomalous air system is very similar to the trend of the steady state under both circumstances. In conclusion, despite the low-precision results, the algorithm can still evaluate system conditions. The created model can classify the presence or absence of an anomaly. For example, suppose there is a significant occlusion in the system pipelines. In that case, the algorithm can identify and classify it, but if there is only a minor occlusion, the model can only notify the problem.
## 3 Experiments
### Test Set Selection  
In this study, the authoritative GEBCO_2024 dataset (with a resolution of 15 arcseconds) was used, and two regions were carefully selected for algorithm validation: the transition zone between the forearc slope and ocean basin off the coast of Luzon (TR2: 23.5-25.0\({}^{\circ}\) N, 122.0-123.5\({}^{\circ}\) E) and the area near the Mariana Trench in the Western Pacific (TR1: 26.0-28.0\({}^{\circ}\) N, 134.0-136.0\({}^{\circ}\) E). These regions were chosen to thoroughly examine the algorithm's ability to recover both fine details and large-scale structural features in complex topographies (see Figure 2).  
The TR2 region, located at the boundary between the continental slope and the ocean basin, features smooth, continuous sedimentary layers alongside steep gradients. The average elevation in this region ranges from \(-\)4200 m to \(-\)3600 m, with significant local elevation fluctuations. This area is prone to the formation of submarine canyons, trenches, ridges, or cliffs, making it ideal for evaluating the algorithm's performance in preserving low-frequency structural continuity. In contrast, the TR1 region, located near the Mariana Trench, exhibits significant terrain variation, with elevations ranging from \(-\)3500 m to  
Figure 2: Detailed DEM parameters of test regions.  
\(-\)500 m. The topography follows a trend of first rising and then rapidly descending, indicating the coexistence of towering mountains and deep valleys or basins. The region's extreme vertical gradients (with prominent positive and negative fluctuations, near-vertical cliffs, and an average slope close to 89\({}^{\circ}\)) and pronounced horizontal gradient changes reflect a complex landscape of ravines, slopes, and terraces. These characteristics provide a rigorous test environment for evaluating the algorithm's ability to recover high-frequency micro-terrain details, such as fault zones, volcanic ridges, and localized undulations.  
All data were obtained from the GEBCO ([[https://download.gebco.net](https://download.gebco.net)]([https://download.gebco.net](https://download.gebco.net)), accessed on 15 January 2025), ensuring consistency in data format and resolution. This dual-modal validation framework, which balances both low-frequency and high-frequency terrain features, establishes a solid scientific foundation for geological interpretation, structural analysis, and detailed seafloor terrain modeling.
## 2 Implementation  
### Factorial Explosion of P  
Our new approach is based on the fact that the \(\vec{C_{P}}(t)\) curves are in fact linear combinations of the state curves \(\vec{x}(t)\) as given by Equation 6. Since the rows in **P** are linearly independent, we can solve for all but \(U(t)\) in \(\vec{x}(t)\) by inverting **P**,  
\[\sum_{i=2}^{n}x_{i}(t)=\textbf{P}^{-1}\,\vec{C}_{P}(t). \tag{9}\]  
In other words, given the experimental data \(\vec{C_{E}}(t)\) we are able to generate time-progress curves for all the species in solution except \(U(t)\). The mass-balance equation allows us to generate \(U(t)\) by subtracting the other state curves from unity,  
\[U(t)=1-I1(t)-I2(t)\ldots-In(t)-F(t) \tag{10}\]  
and allows the complete recreation of \(\vec{x}(t)\).  
We show in the supplementary material that for \(I\) intermediates, there are \(n\) different **P** matrices, as given by  
\[n=\frac{2^{I}!}{(2^{I}-I-1)!}. \tag{11}\]  
In Figure 2, we illustrate the combinatorial explosion of possible **P** matrices as a function of the number of intermediates with the blue curve. In our original implementation of the Kinfold algorithm [9] we made some simplifications to the combinatorics of **P** with several assumptions that somewhat  
Figure 2: Illustration of the combinatorial explosion in the enumeration of all possible **P** matrices as a function of the number of intermediates \(I\). In blue we plot \(n\) as given by equation Equation 11 which we derive in the supplementary material. The red curve represents the number of models (**P** matrices) that are tested when using the previous implementation of Kinfold by non-linear least squares optimization [9]. The black curve is the average number of models that now need to test based on a sampling of 100 random data sets using our new approach. Error bars represent three standard deviations and the light gray shadow the maximum and minimum values of \(n\) for each \(I\).  
offset the explosion (Figure 2, red curve). Nonetheless, we still needed to test a prohibitively large numbers of models for any system with more than two intermediates. We therefore focus our current approach on more efficiently testing all combinations of **P**.
## 3 Results and Discussion
### Effect of Fermentation on pH  
The ability of LAB to produce acid during fermentation is related to whether the growth of miscellaneous bacteria can be inhibited during product storage, and also affects the sensory characteristics of the final product. The results revealed that the five strains could reduce the pH of the substrate to varying degrees during fermentation (Table 1) and finally reach a pH of 3.15-4.25. After 8 h of fermentation, the pH values of _L. plantarum_ and _L. acidophilus_ decreased rapidly by 27.39% and 27.67%, respectively, which were distinctly different from those of other strains (\(p<0.05\)). The maximum decrease in the pH value of the two strains occurred at the same time with the index growth period of the strain. By contrast, _S. thermophilus_ has the weakest acid-producing capacity, reaching a pH of 4.25 after 48 h of fermentation, which may be attributed to _L. acidophilus_ preferring lactose to glucose as its main energy source [25]. LAB can produce lactic acid through the carbohydrate metabolism pathway. The production of various organic acids may be the main reason for the decrease in pH [26]. The above results are supported by the study of [PERSON] et al. [27]. They concluded that the pH values of different substrates decrease rapidly within 12 h after fermentation by LAB. The strains used in the production of fermented cereal beverages and fermentation time affect the pH of the product.
# BNS: A Framework for Wireless Body Area Network Realistic Simulations
## 4 Use Cases
### Analysis of Channel Access Mechanisms
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Node** & **Position** & **Node Type** & **Transmission Rate** \\ \hline
0 & Right waist & Hub & - \\
1 & Head & EEG & 86.5 Kb/s \\
2 & Left arm & Blood pressure & 1.2 Kb/s \\
3 & Left hand & Glucose Monitor & 1 Kb/s \\
4 & Chest & ECG & 192 Kb/s \\
5 & Right arm & Blood flow & 480 b/s \\
6 & Right hand & Heart rate meter & 48 b/s \\
7 & Center of waist & Medication injector & 16 Kb/s \\
8 & Left leg & blood pH & 48 b/s \\
9 & Left foot & Motion sensor & 35 Kb/s \\
10 & Right leg & Temperature sensor & 2.4 b/s \\
11 & Right foot & GPS & 96 b/s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: WBAN sensors nodes, their positions and transmission rates. The results for energy spent per transmitted application bit are represented in Figure 14. As in the results of the application PDR, the nodes were divided between nodes positioned on the head, chest and upper limbs in Figure 14, and nodes in the waist and lower limbs in Figure 14. The low PDR of lower limbs incurs in retransmissions and increases the energy spent to transmit a bit. In addition, smaller packets also increase the energy spent to transmit an application bit. Those influences can be observed in Node 10, with a low PDR and low transmission rate, but a high energy spent to transmit an application bit. The results are consistent with the PDR analysis, and the results of both MAC methods are similar. It is important to highlight the application influence on energy consumption. The result of energy spent per transmitted application bit for the network was \(180.17\pm 19.14\)\(\upmu\)J/bit for TDMA and \(170.48\pm 21.75\)\(\upmu\)J/bit for CSMA/CA. Figure 13: Application PDR for the 12 nodes topology. This simulation scenario shows that energy consumption and network PDR heavily rely on node deployment, mobility, wireless channel and the application itself. All those features are covered by the MoBAN, Goswami channel, and IEEE 11073 modules, respectively. Therefore, to design a communication protocol for WBANs, those characteristics particular to the WBAN scenario should be considered.
# Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions
## 4 Case Study
#### Prognosis Models
For example, an optimal threshold for interval C could be 300, while the same threshold would be too large for interval A, where a 150 threshold would be sufficient. The definition of such thresholds within the forecasting models allowed a significant increase in the achievable results, which were already excellent. Furthermore, another consideration that added further value to the achieved results was the fact that most errors were caused by an underestimation of the residual useful life of the machinery. This approach could be defined as \"precautionary\", as it tends to direct towards early rather than late maintenance. The cases of underestimation of the forecast are extremely small, almost negligible. Finally, considering all the above analysis, it is possible to state that CVA successfully contributes to improving the performance of the residual useful life prediction model. Without the prior analysis of the dataset with CVA and the consequent application of the contribution plots, the achievable results would have been different. An example of the achievable results for the prognosis without CVA in interval C is shown in Figure 7.  
After the optimization process, and without the prior application of CVA, the results achieved for interval C were the following:  
* MSE: 470.19;
* \(R^{2}\): 0.93. As can be seen, there was an increase in the MSE, but the most striking result was the worsening of the \(R^{2}\) index from 0.98 to 0.93. This last consideration was also evident in a graphic analysis, comparing Figures 5 and 7, where it is clear that the model was less able to predict and reproduce the trend of the residual useful life of the machinery. Even though the results achievable without the application of CVA were excellent, with the application of CVA it was possible to significantly improve the performance of the residual useful life prediction model of the machinery.