esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Validating and Testing the Model  
From the calibration process, a set of sensor-specific model parameters was obtained, that is, the spectral bands above the F statistic threshold, the PLS linear transformation coefficients, the number of LV, the LDA coefficients, and the probability threshold. In other words, a model was created for each sensor based on the machine learning process, which found the best parameters for each sensor. Each model was applied to the corresponding sensor resampled validation database, to calculate the accuracy metrics (i.e., quantitative validation).  
Then, to test the models, and to demonstrate how practical they are, the models were applied to the entire image, thus creating a series of thematic maps (a map for each sensor) that illustrate the predicted location of clean and oil pixels. Each map gave a broader view of the performances per sensor. Two scenes (one containing both oil and clean pixels, and the other containing only clean pixels) were selected in order to closely examine the classification results. These maps were later also spatially resampled (see spatial resampling under Results).  
The testing process of the models was done qualitatively and not quantitatively (the reason for that is discussed further in the discussion part of this paper). This was done by visually evaluating and interpreting the agreement between the classified image to the Fenix true-color image and the EROS image.
## 4 Results and Discussion
### Case Study 2
#### 4.3.2 Application of RTO  
The objective function was solved by the proposed RTO with vague parameters. The ideal estimations of TMS and aggregate working time found are shown in Table 12. They similarly show the comparative delayed consequence of RTO with other metaheuristic and numerical procedures in the literature. In this depiction, no encroachment and miscoordination of the relays have been found. The time taken by relay R1 to work is the least for a fault at point C and is most extreme for a fault at point A. This is attractive on the grounds that, for a fault at point C, relay 1 is the first to work. Meanwhile, for a fault at point A, relay 7 ought to get the principal opportunity to work. On the off chance that it neglects to work, relay 4 should assume a control tripping activity. If relay 4 likewise neglects to work, relay 1 should assume control over the tripping activity at that point. The values are given in Table 12 ensure that the relays will work at any rate possible time for a fault whenever in the system. The objective value acquired over the span of the simulation for the best candidate course of action in each iteration is shown in Figure 12, which exhibits that the convergence is faster and get the best qualities in a lesser number of iterations. Additionally, the RTO outmatched the simplex method for this case and obtained an agreeable and better outcome compared to the simplex technique. The graphical image of the improved aggregate working time appears in Figure 13 and shows that the total operating time is streamlined up to the ideal and optimum value when compared to the simplex method, as mentioned in the literature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**TMS** & **SM [(39) (\(\geq\)0.2)** & **RTO (\(\geq\)0.2)** \\ \hline TMS 1 & 0.23829 & 0.0852 \\ TMS 2 & 0.12 & 0.0250 \\ TMS 3 & 0.36036 & 0.0953 \\ TMS 4 & 0.0319 & 0.0250 \\ TMS 5 & 0.02773 & 0.0250 \\ TMS 6 & 0.025 & 0.0250 \\ TMS 7 & 0.08 & 0.0250 \\ _Top (z)_ & **15.7068** & **5.1756** \\ \hline \hline \end{tabular}
\end{table}
Table 12: Optimal TMS for Case 3.
# Development and Verification of a Simulation Model of an Automated Assembly Line
## 5 Verification of the Simulation Model
The total divergence time for four pallets is 6 s, while for five pallets, it is 14 s, and for six pallets, it is 22 s.  
Figure 19: Comparison of the arrival time of pallets to the stands—scenario 4: (a) reality; (b) simulation. Figure 20: Comparison of the arrival time of pallets to the stands—scenario 5: (a) reality; (b) simulation. In order to correct the resulting discrepancy and not exceed the difference by more than 10% of real time, it was decided to develop a coefficient. It was called the coefficient of time variation \(W(t)\). It adds seconds to the simulation duration result. Its dependence on the number of discuses on the line is shown in Table 2.  
The coefficient was also divided into individual pallets depending on the scenario. The coefficient relationships for each ray are shown in Table 3.  
To facilitate the calculation and correction of the simulation results, an Excel file has been developed that will calculate the correct result. The user's task is only to enter the simulation time of individual pallets (products) into the table. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Time Discrepancy (s)**} \\ \hline  & **Scenario 4** & **Scenario 5** & **Scenario 6** \\ \hline
**Total Time** & **6** & **14** & **22** \\ \hline Pallet 1 & 10.2 & 19.0 & 28.2 \\ Pallet 2 & 8.4 & 18.2 & 27.3 \\ Pallet 3 & 7.2 & 16.8 & 26.4 \\ Pallet 4 & 6.0 & 16.0 & 25.1 \\ Pallet 5 & - & 14.0 & 24.2 \\ Pallet 6 & - & - & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of time discrepancies. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Number of Pallets (Products) in the Simulation**} & \multicolumn{1}{c}{\(W(t)\) Coefficient Value (s)**} \\ \hline
1 & 0 \\
2 & 0 \\
3 & 0 \\
4 & 6 \\
5 & 14 \\
6 & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dependence of the \(W(t)\) coefficient on the number of pallets. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Scenario for 4 Pallets**} & \multicolumn{1}{c}{\(W(t)\) = 6**} \\ \hline
1 pallet & 1.7 \(W(t)\) \\
2 pallets & 1.4 \(W(t)\) \\
3 pallets & 1.2 \(W(t)\) \\
4 pallets & 1.W(t) \\ \hline
**Scenario for 5 Pallets** & \(W(t)\) = 14 \\ \hline
1 pallet & 1.36 \(W(t)\) \\
2 pallets & 1.3 \(W(t)\) \\
3 pallets & 1.2 \(W(t)\) \\
4 pallets & 1.14 \(W(t)\) \\
5 pallets & 1 \(W(t)\) \\ \hline
**Scenario for 6 Pallets** & \(W(t)\) = 22 \\ \hline
1 pallet & 1.28 \(W(t)\) \\
2 pallets & 1.24 \(W(t)\) \\
3 pallets & 1.2 \(W(t)\) \\
4 pallets & 1.14 \(W(t)\) \\
5 pallets & 1.1 \(W(t)\) \\
6 pallets & 1 \(W(t)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The dependence of the \(W(t)\) coefficient for each pallet depending on the scenario. Table 4 summarizes the calculated and corrected simulation times with the measured real times. The obtained simulation results slightly differ from real times. Deviations in time do not exceed more than 10% of the real time.
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 4 Proposed Defense System
### A Defense System  
To achieve these goals, we design a defense system based on the observations from an adversarial audio. As shown in Figure 3 in Section 3, the adversarial audio is simply the clean illegal audio with well-designed perturbations that are similar to white noise. If such perturbations can be removed or modified, adversarial attacks would lose the efficiency against the SV system. Based on this intuition, we propose a defense system as shown in Figure 4. Compared with Figure 1, in Figure 4 we add an additional module, i.e., a plugin function, before the recognition module to an SV system. Such a plugin function is used to preprocess an input audio to either forcefully remove the perturbations or intentionally modify the perturbations. In the following sections we will discuss two options for the plugin function: denoising and noise-adding.  
It can be observed that the proposed defense system is compatible with an existing SV system and can be applied to any SV system. It does not require to change the internal structure of an SV system. Moreover, the overhead of the defense method is only on the plugin function. If such a plugin is light weight, it will not introduce much additional computation to the SV system. Furthermore, the main goal of the plugin function is to modify the input audio, so that the SV system can reject adversarial audios, but can have the similar decisions on normal audios as in the SV system without the plugin. As our first attempt, we study denoising and noise-adding functions in this work. But other functions can be applied here as well.
## 3 Experiments and Discussions
### Limitations of the Proposed Method  
The proposed method has an advantage in computation complexity and accuracy. However, it has limitations too. (1) The proposed method runs in block by block. So, it is impossible to run the method in real time. The delay from inputting heart sound signal to outputting the split for a normal subject is three minutes or more. (2) A preprocessing is needed for the collection of S2s in which the S2s are aligned in time domain to facilitate average operation. If the alignment is missed or not well done, the estimated aortic and pulmonary components will be inaccurate, and consequently the performance of split estimation will degrade.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Subject** \\ **Index** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Number** \\ **of S2s** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **HVD Method** [7] \\ \end{tabular} } & \multicolumn{2}{c}{**STFT Method [4]**} & \multicolumn{2}{c}{**Proposed Method**} \\ \cline{3-8}  & & & **CPUtime** & **RMSerror** & **CPUtime** & **RMSerror** & **CPUtime** & **RMSerror** \\  & & **(s)** & **(ms)** & **(s)** & **(ms)** & **(s)** & **(ms)** \\ \hline
1 & 344 & 942.1 & 13.2 & 11.5 & 6.0 & 1.3 & 5.8 \\
2 & 331 & 948.1 & 16.7 & 11.4 & 22.3 & 1.2 & 4.9 \\
3 & 360 & 971.4 & _27.7_ & 11.6 & 14.3 & 1.2 & 10.8 \\
4 & 367 & 981.1 & 7.3 & 13.1 & 14.8 & 1.1 & 4.9 \\
5 & 290 & 830.1 & 34.5 & 10.1 & 21.5 & 0.9 & 6.8 \\
6 & 311 & 843.3 & 21.4 & 10.6 & 7.9 & 1.0 & 6.3 \\
7 & 299 & 799.3 & 8.9 & 10.2 & 8.6 & 0.9 & 5.4 \\
8 & 281 & 773.5 & 10.1 & 9.5 & 10.0 & 0.9 & 5.3 \\
9 & 312 & 847.3 & 23.2 & 10.4 & 16.7 & 0.9 & 7.9 \\
10 & 285 & 776.7 & 21.7 & 9.7 & 35.7 & 0.9 & 7.1 \\
11 & 286 & 765.3 & 22.2 & 10.4 & 12.9 & 0.9 & 7.0 \\
12 & 301 & 804.2 & 32.4 & 10.2 & 11.4 & 1.0 & 4.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of CPU time and RMS error among the three methods.
## 5 Simulation Experimental Results and Discussions
### Comprehensive Balance Method Analysis  
Table 11 presents that for various performance indicators, the influence of different factors is significantly different, making it difficult to uniformly determine the priority order of three factors among the four indicators. In addition, the importance of various indicators is contradictory. Therefore, it is necessary to comprehensively investigate all factors and indicators to extract the optimal solution. For multi-indicator orthogonal experiment analysis methods, the comprehensive balance method and the comprehensive scoring method are commonly utilized. However, due to the difficulty in reasonably selecting scoring criteria and weights for each indicator, this study employs the comprehensive balance method to examine the experimental results [53]. The core of the comprehensive balance approach is to identify the primary and secondary influencing factors for each indicator and their optimal solutions through an intuitive analysis of individual indicators. Then, domain knowledge and practical experience are combined to perform a comprehensive analysis and comparison of the indicators to ultimately derive the best overall solution.  
In the comprehensive balance method analysis, the following principles must be followed: (1) Priority principle: When the influence of a factor on different indicators is significantly different, this factor should be prioritized as the main influencing factor at its optimal level. (2) Majority principle: When the influence of a factor on different indicators is similar, the principle of \"minority follows majority\" should be followed, and the level that often appears as the optimal level should be selected. (3) Weight principle: When different indicators have different levels of importance, such as when comprehensively judging the optimal levels of factors, the more important indicators should be prioritized. (4) Efficiency principle: When the discrepancy in the levels of a factor is small, the principle of improving efficiency and reducing consumption should be followed to select the appropriate level.  
Based on Figure 9, Table 11, and the principles of the comprehensive balance method, the experimental results have been comprehensively analyzed with the following specific analysis steps:
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 4 Discussion
### Socioeconomic Influences on Water Area Dynamics in Kashgar  
Over the past few decades, particularly since 2014 [45], the Kashgar region has undergone rapid population growth and economic development. This has fueled industrialization and urbanization, leading to the conversion of surface water bodies into farmland or construction land. Consequently, the area of water bodies in Kashgar has exhibited a continuous decline between 2003 and 2023.  
Literature findings [45] indicate that since 1972, cultivated land in Kashgar has expanded continuously, urbanization has intensified, and the area of forests and water bodies has shown a downward trend. Snow cover has also been subject to fluctuations driven by both climate change and anthropogenic activities. These changes, in turn, influence snow and ice melt and runoff patterns, ultimately impacting water body dynamics across the entire Kashgar region.  
According to [46], the ecological security of the Kashgar region is transitioning from a state of \"overall security\" to \"relative security\" and ultimately towards \"security\". This trajectory suggests a positive outlook for the protection and enhancement of local water security. The future ecological prospects for the Kashgar region appear relatively optimistic.
## 2 Materials and Methods
### Feature Extraction
#### 2.2.1 Local Binary Patterns  
Local Binary Patterns (LBP) are a simple but effective texture descriptor which label every pixel of the image analyzing its neighborhood. It identifies if the grey level of every neighbor pixel is above a certain threshold and codifies this comparison with a binary number. This descriptor has become very popular due to its good classification accuracy and its low computational cost, which allows real-time image processing in many applications. In addition, this descriptor has a great robustness when there are varying lighting conditions [48; 49].  
On its basic version, LBP operator works with a \(3\times 3\) matrix that goes across the image pixel by pixel, identifying the grey values of its eight neighbors and taking as a threshold the grey value of the central pixel. Thus, the binary code is obtained as follows: if the neighbor pixels has a lower value than the central one, they will coded as 0; otherwise, their code will be 1. Finally, each binary value is weighted by its corresponding power of two and added to obtain the LBP code of the pixel. In Figure 2, a graphic example is shown.  
Figure 1: Facial expression of physical distress is the most consistent behavioral indicator of pain in infants.  
Figure 2: Graphic example of the LBP descriptor.  
This descriptor has been extended over the years, so that it can be used in circle neighborhoods of different sizes. In this circular version, neighbors are equally spaced, allowing the use of any radio and any number of neighboring pixels. Once the codes of all pixels are obtained, a histogram is created. It is also common to divide the image into cells, so that a histogram per cell would be obtained, being finally concatenated. In addition, the LBP descriptor has uniformity, which reduces negligible information significantly, and therefore it provides low computational cost and invariance to rotations, which become two important properties when applied to facial expression recognition in mobile and wearable devices [50].
## 4 Results and Discussion
### Discussion
#### 4.2.1 Landslides
The field investigation showed substantial small-scale damage, although the slope was relatively stable, and the observations were consistent with the results of the SBAS-InSAR analysis. The slope was affected by natural factors and engineering activities. When the local damage is severe, widespread damage may occur. At this time, a landslide is highly likely, posing a significant threat to the lives and property of local residents. Figure 8: Field photos of potential landslide 8. (**a**) Velocity map of the landslide. (**b**) Crack at the trailing edge. (**c**) Accumulation area of the rock and soil. (**d**,**e**) A small-scale collapse. (**f**) A small-scale landslide. (**g**) Cracks in the road. (**h**) Cracked retaining wall. Note: the red curve in (**a**) represents the boundary of the landslide, and the blue arrow in (**b**) represents the direction of slipping.
## 3 Results
### Cavity of Length \(L=16\lambda\)  
#### 3.2.1 Amplitudes in the Cavity  
In this section, we consider the open-field cavity of length \(L=16\lambda\) and we use the same parameters as in Section 3.1.1. The shape of the source and harmonic amplitudes inside the cavity is shown in Figure 5. As we can see, the maximum amplitude is given for the source frequency, \(f\), and its position is at the source. The maximum of the 2 nd harmonic amplitude is 32.3% (of \(p_{s}\)) and its position is at \(x=3.45\lambda\), the maximum of the 3 rd harmonic amplitude is 19.4% (of \(p_{s}\)) and its position is at \(x=4.12\lambda\), and the 4 th harmonic amplitude is 13.7% (of \(p_{s}\)) and its position is at \(x=4.42\lambda\).
# Research on the Roof Failure Law of Downward Mining of Gently Inclined Coal Seams at Close Range
## 2 Overview of the Mining Area  
### Mining Conditions  
The Yindonggou Coal Mine is located in the northern part of the Wangwa Mining Area, Pengyang County, Ningxia Hui Autonomous Region. The mine has a north-south length of approximately 5.19 km, an east-west width of about 3.75 km, and a total area of 12.461 km\({}^{2}\). The designed production capacity is 3.0 Mt/a, with a service life of 56.0 years. The mine uses a slanted shaft with single-level development, employing a fully mechanized one-pass full-height coal mining process. Currently, the main mining face is located in the 11 th mining district, and the excavation layout of the mining works is shown in Figure 1. The mine is currently recovering coal from the 11040 mining face.  
The 11040 mining face is located in the southern wing of the 11 th mining district of the Yindonggou Coal Mine and is the first mining face of the 4 th coal seam. The coal seam is 2.7 m thick and is classified as a medium-thick coal seam. The mineable strike length is 773 m, with an inclined length of 266 m and an average dip angle of 15\({}^{\circ}\), making it a gently inclined coal seam. The burial depth is approximately 465 m. On both the east and west sides, the coal seam is surrounded by entities from the 4 th coal seam. The roof rock layer primarily consists of fine sandstone, with small amounts of mudstone and carbonaceous mudstone, ranging in thickness from 1.2 to 11.7 m, with an average thickness of 4.3 m. The pseudo-roof consists mainly of carbonaceous mudstone, with a thickness of 0.5 m. The floor rock layer is mainly fine sandstone, with small amounts of fine-grained sandstone in some areas, ranging in thickness from 1.44 to 7.53 m, with an average thickness of 4.38 m. The pseudo-floor is not developed.  
The main tunnels of the northern adjacent mine are protected by coal pillars, while the southern side is adjacent to the Yindongzi old kiln (with a 55 m protective coal pillar). Above, there are the 110204 and 110104 goat areas. The coal seam thickness of the 110104 working face is 2.9 m, with an interlayer distance of about 8 m from the 110204 working face. The coal seam thickness of the 110204 working face is 3.8 m, with an interlayer distance of about 23 m from the 110404 working face. As shown in Figure 2.  
Figure 1: The layout plan of the mining and excavation project in the 11 th mining area of Yindonggou Coal Mine.
# Effects of the Ventilation System by Using Jet Fans during a Fire in Road Tunnels
###### Abstract  
People inside a road tunnel may be in grave danger if there is a fire. When a fire is developing, mechanical ventilation can be crucial in protecting occupants and facilitating the arrival of emergency crews. In this paper, a new calculation method that can analyze the evolution of the fire and of the airflow is illustrated. An assumed fire inside an 800 m long one-way road tunnel in Northern Italy was dealt with using the developed method. Subsequently, the acquired results were successfully compared to those of numerical modeling. By varying the position of the fire inside the studied tunnel, it was then possible to detect situations of high risk for the people when the fire is located at a limited distance from the upstream entrance. The newly developed calculation procedure was then able to verify how a simple different positioning of the pairs of jet fans inside the tunnel can eliminate the risks that people intent on escaping towards the upstream entrance can be reached by the fire fumes (back-layering phenomenon). It can therefore be a useful tool to improve the jet fan design in one-way road tunnels in accordance with safety principles.  
ventilation system; jet fan; tunnel fire simulation; fire toxic fumes; back-layering length Article