esa-sceva/satcom-chunk-collection huggingface dataset:
# Geochemical Analysis of a Multi-Layer Hydrocarbon Reservoir in the Wuerhe Area, Junggar Basin
## 2 Geological Setting and Petroleum Background
### Structural Characteristics  
In the Permian-Triassic era, the research area developed a large nose-shaped structural belt with a 10 km width and a 20 km length horizontally and two sets of deep and shallow fault systems longitudinally. The deep large-scale reverse fault was formed in the Hercynian Indonesian movement. Five reverse faults were developed from the Halalarte mountain to the Mahu depression. Among them, F3 and F5 faults control the boundary of the Wuerhe nose uplift; F4 fault is located in the middle of the nose uplift, and the deep fault section breaks upward from the Carboniferous system to the top of the Jurassic system (Figure 3c).  
Figure 3: Structural characteristics of Wuerhe nose uplift, showing the two sets of deep and shallow faults are connected with each other on the profile, forming a “Y” type combination, which provides a channel for oil migration (**a**–**c**).  
Small scale positive faults and strike-slip fault systems formed by the Yanshanian movement are developed in the shallow layer, with the characteristics of \"flower structure\" in their profile. The faults are broken upward to the ground surface and downward to the Jurassic and Triassic strata. The two sets of fault systems show a \"Y\" type combination on the section and nearly parallel extension in the plane, both striking near northeast. The interpretation scheme of shallow faults can be accurately determined through satellite photos, field observations, and wave group variation characteristics of the seismic profile in the Wuerhe bitumen vein area (Figure 3a,b). The inherited nose uplift structure and the relay communication of deep and shallow fault systems provide conditions for oil accumulation and vertical migration channel for multi-layer reservoir formation.
## 4 Discussion  
### Overall Failure Mode of the Model  
The failure patterns of the model corresponding to MSDs of 0.5 m, 1.0 m, and 1.5 m in the horsehead roadway's roof are depicted in Figure 14. The model reveals that element failure predominately occurs within the rock mass located both above and below the horsehead roadway and at the shaft's vicinity [43; 44; 45]. For instance, at a subsidence displacement of 0.5 m, the extent of rock damage above the horsehead roadway extends approximately 12 m, with the damage below reaching about 15 m. Damage diminishes with increasing distance from the horsehead roadway; notably, shaft elements situated over 75 m away exhibit no damage, as indicated by the yellow rectangular box in Figure 14a. When subsidence displacement increases to 1.0 m, the damaged rock area above the horsehead roadway expands to roughly 17 m, and the lower portion to about 16 m. In this scenario, the shaft elements beyond 75 m from the roadway show isolated instances of damage, as highlighted by the yellow elliptical box in Figure 14b. Additionally, a significant escalation in the damage range of the surrounding rock is observed at mid-shaft levels, as demarcated by the yellow rectangular box in Figure 14b.  
With the horsehead roadway's roof subsidence displacement reaching 1.5 m, the damage extent in the rock above the roadway further extends to approximately 21 m, while below it increases to about 20 m. There is a discernible pattern where increased distance from the horsehead roadway correlates with a reduced damage range in the shaft and adjacent rock. At this 1.5 m subsidence displacement, a notable aggregation of damage  
Figure 14: Overall failure mode of the model under the roof subsidence influence of the horsehead roadway.  
occurs in shaft elements exceeding 75 m from the horsehead roadway--a contrast to the conditions observed at displacements of 0.5 m and 1.0 m. Moreover, the rock in direct contact with the entire length of the shaft is affected, manifesting as a continuous band--with a width ranging from 2 to 5 m under two-dimensional visualization (indicated by the yellow rectangular box). This pattern underscores the simultaneous presence of shear and tensile failures in the rock mass surrounding the horsehead roadway. At a displacement of 0.5 m, tensile failures are primarily near the free surface, while shear failures are more distant. In the case of the shaft, shear failures predominate, with tensile failures sparsely scattered in the upper sections. When the subsidence displacement increases to 1.0 m, shear failures continue to dominate the shaft wall failure, but tensile failures become more pronounced in the upper regions. As the subsidence displacement escalates further to 1.5 m, the character of the shaft wall failure alters significantly; shear and tensile failures exist in roughly equal measure, unlike the primarily shear failure observed at the lower displacement levels of 0.5 m and 1.0 m.
# Numerical Modeling of Engineering-Scale Jointed Coal Mass and Confining Pressure Effect
## 5 Potential Application in Estimation of Abutment Pressure Distribution Ahead of Working Face
### Load-Bearing State and Estimation of Abutment Pressure Distribution of Coal Seam  
After determining the distribution of the principal stresses and the type of loading of the coal seam, a quantitative failure index of the coal seam can be obtained. The maximum and minimum principal stresses of Equation (6) could be regarded as equal to \(\sigma_{xx}\) and \(\sigma_{lx}\), respectively, and whether the coal mass destroyed or not under the same principal stress was only affected by the cohesion and internal friction angle of the coal mass. The failure tendency coefficient of the coal mass was defined as \(K_{c}\), which was only related to the compressive mechanical parameters of the coal mass for a given stress condition [49]:  
\[K_{c}=\sigma_{vx}-\frac{1+\sin\varphi}{1-\sin\varphi}\sigma_{lx}-\frac{2C\cos \varphi}{1-\sin\varphi} \tag{10}\]  
For coal seams in different zones, if \(K_{c}\geq 0\), the coal mass underwent failure under compressive conditions. If \(K_{c}<0\), the coal mass had no tendency to undergo failure. Equation (10) was essentially a modified form of the Mohr-Coulomb strength criterion, in which the tendency of the coal mass to fail increased with the increase in \(K_{c}\), and the two were positively proportional.  
It was observed that the higher the fissure degree, the faster the failure tendency coefficient of the coal seam increased, and the easier it was to reach the critical failure point. When the fissure degree increased, the vertical stress in the zone ahead of the working face was obviously reduced, which indicated that increasing the fissure degree could reduce the loads of the supports at the working face. The fitted relationship between the failure zone ahead of the working face and the fissure degree is displayed in Figure 15, with a fitted equation of \(L_{m}=1.08K_{0}^{0.86}\). The fitted results confirmed that increasing the fissure degree  
Figure 14: The distribution of failure tendency coefficient and stress in coal seam under different fissure degrees.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fissure Degree (m\({}^{-1}\))** & \(k\) & \(b\) & **C (MPa)** & \(\varphi\) (\({}^{\circ}\)) \\ \hline
33.04 & 2.87 & 11.64 & 4.26 & 17.65 \\
14.68 & 2.95 & 14.68 & 5.26 & 18.79 \\
8.26 & 3.19 & 17.01 & 5.75 & 21.90 \\
5.28 & 3.50 & 18.22 & 5.76 & 25.38 \\
3.67 & 3.55 & 20.05 & 6.28 & 25.89 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The magnitude of relevant parameters under different fissure degrees.  
of the coal mass could expand the failure zone ahead of the working face. At this time, the coal seam entered the post-peak stage for a longer period of time in advance, and the failure was more sufficient, which contributed to the top-coal caving.
# Distribution System State Estimation Using Model-Optimized Neural Networks
###### Abstract  
Maintaining reliability during power system operation relies heavily on the operator's knowledge of the system and its current state. With the increasing complexity of power systems, full system monitoring is needed. Due to the costs to install and maintain measurement devices, a cost-effective optimal placement is normally employed, and as such, state estimation is used to complete the picture. However, in order to provide accurate state estimates in the current power system climate, the models must be fully expanded to include probabilistic uncertainties and non-linear assets. Recognizing its analogous relationship with state estimation, machine learning and its ability to summarily model unseen and complex relationships between input data is used. Thus, a power system state estimator was developed using modified long short-term (LSTM) neural networks to provide quicker and more accurate state estimates over the conventional weighted least squares-based state estimator (WLS-SE). The networks are then subject to standard polynomial scheduled weight pruning to further optimize the size and memory consumption of the neural networks. The state estimators were tested on a hybrid AC/DC distribution system composed of the IEEE 34-bus AC test system and a 9-bus DC microgrid. The conventional WLS-SE has achieved a root mean square error (RMSE) of 0.0151 p.u. for voltage magnitude estimates, while the LSTM's were able to achieve RMSE's between 0.0019 p.u. and 0.0087 p.u., with the latter having 75% weight sparsity, estimates about ten times faster, and half of its full memory requirement occupied.  
p 2022 12 2022
# An Automatic Analysis System for High-Throughput _Clostridium Diffic_ Toxin Activity Screening
## 1 Introduction
### Related Works  
The cell-rounding assay was adapted from an approved clinical test used to diagnose _C. difficile_ in human patients. In this clinical test, evaluation and differentiation between adherent and rounded cells is performed by visual examination. Therefore, adaptation of this assay into a screen to identify potential anti-toxin compounds in a library composed of thousands of molecules is limited in its throughput due to the necessity of manually analyzing each image. Additionally, when including the replicates needed to achieve statistically reliable data, the resulting set of images cannot be easily analyzed manually within a reasonable time frame. To improve the high-throughput potential of this assay, we developed an algorithm to automatically count and classify cells according to the morphological changes seen upon toxin challenge. To the best of our knowledge, there is no commercial  
Figure 3: Characteristic morphology changes with TcdB challenge. (**a**) Adherent HFFs without toxin treatment exhibit diffuse cytosolic staining with a cell tracker dye. Upon TcdB treatment, actin cytoskeletal disruption leads to a progression from (**b**) partial to (**c**) complete cell rounding.  
software to analyze and classify such images in an unsupervised way, but some existing software can be adapted for counting or classification functions. For cell counting, ImageJ--an open source image processing program designed for scientific multidimensional images--contains a derivate (_Fiji_) released in 2003 to manually count cells [22; 23; 24; 25]. _Icy_, a JAVA plugin, is another interactive counter [26]. For cell classification, the shape parameter circularity in ImageJ classifies detected cells based on a function of the perimeter \(P\) and the area \(A\)[27]. However, classification of adherent cells with irregular borders remains a challenge. If circularity is not a sufficient exclusion criterion, the _BioVoxel Toolbox_[28] is an advanced particle analyzer. However, solutions are based on a permanent interaction with the user, and the detection and classification parameters are chosen manually [28]. Therefore, use of this technology does not significantly decrease the time required to analyze images.  
Additionally, without image processing and machine learning knowledge, cell counting, analysis, and classification steps can contain high error rates for large datasets. To overcome the current limitations in the automatic image analysis of phenotypic cell-based screens, we created an automated approach for the differentiation of rounded versus adherent cells exposed to _C. difficile_ toxins and potential inhibitors. Moreover, this method can be easily adapted and applied to many diagnostics and antitoxin screens for a variety of cytotoxic agents. An overall summary and comparison of the proposed and described methods are presented in Table 1.
# Automatic Selective Encryption of DICOM Images
## Appendix A
To perform the selective encryption adaptively, a security index that defined the security threats have been introduced. This security index \(I=[ \begin{array}{cc}I_{1},&I_{2},&I_{3}\end{array} ]\) is a vector that contains three elements (each element corresponds to a region (R1, R2 and R3)). Based on the description as mentioned above, Equation (A13) can be rewritten in vector form, as shown in Equation (A14):  
\[ \begin{bmatrix}T_{1}\\ T_{2}\\ T_{3}\end{bmatrix} = \begin{bmatrix}I_{1}\\ I_{2}\\ I_{3}\end{bmatrix} (T_{ROI\_AES256}-segmentation_{time}-\in)\] (A14)  
The regions' sizes can be determined by simply solving a linear system, including the constraint that (\(\sum K=1\)), by rewriting Equation (A14), as shown in Equation (A15):  
\[ \begin{cases}T_{1}=I_{1}T_{d}\\ T_{2}=I_{2}T_{d}\\ T_{3}=I_{3}T_{d}\end{cases} \] (A15)  
From Equation (A15), \(T_{d_{2}}\) and \(T_{d_{3}}\) can be rewritten as shown in Equation (A16):  
\[ \begin{cases}T_{2}=\frac{I_{2}}{I_{1}}T_{1}\\ T_{3}=\frac{I_{3}}{I_{1}}T_{1}\end{cases} \] (A16)  
Let us assume \(\frac{I_{2}}{I_{1}}\) and \(\frac{I_{3}}{I_{1}}\) are equal to \(I_{1}\) and \(I_{2}\), respectively. Now by solving for that _Ks_, considering Equation (A11), as shown in Equations (A17) and (A18):  
\[K_{2}=\frac{h_{1}(K_{1}B_{1}S_{roi}+C_{1})-C_{2}}{B_{2}S_{roi}}\] (A17)  
\[K_{3}=\frac{h_{2}(K_{1}B_{1}S_{roi}+C_{1})-C_{3}}{B_{3}S_{roi}}\] (A18)  
By substituting Equations (A17) and (A18) in (\(\sum K=1\)), then \(K_{1}\) can be determined as shown in Equation (A19). Then, \(K_{2}\) and \(K_{3}\) can be calculated using Equations (A17) and (A18):  
\[K_{1}=\frac{\left(B_{2}B_{3}+h_{1}\frac{C_{2}B_{3}}{S_{roi}}+h_{2}\frac{C_{3} B_{2}}{S_{roi}}\right)}{B_{2}B_{3}+h_{1}B_{1}B_{3}+h_{2}B_{1}B_{2}}\] (A19)  
From these equations, it can be noticed that \(K_{2}\) is directly proportional with \(h_{1}\); hence, it is also directly proportional to \(I_{2}\) and inversely proportional to \(I_{1}\). On the other hand, \(K_{3}\) is directly proportional to \(h_{2}\); hence, it is also directly proportional to \(I_{3}\) and inversely proportional to \(I_{1}\). Accordingly, for high-security risk, it is desirable to have large \(I_{1}\) and small \(I_{2}\) and \(I_{3}\); i.e., \(I=[5.0,\;1.0,\;0.005]\). At medium security threats, it is desirable to give all regions the same importance; hence, \(I_{1}\)\(I_{2}\), and \(I_{3}\) can have the same values. Finally, at a low-security level, \(I_{1}\) can have a very small value and \(I_{2}\) and \(I_{3}\) can have larger values, i.e., \(I=[1,\;2,\;3]\). It is worth here mentioning that this security index can be defined based on the need and there is no thumb rule as to how to adjust these values as long as it follows the logic explained above.
# Efficient Regression Testing of Software Product Lines by Reducing Redundant Test Executions
## 4 Test Execution Reduction Method for Efficient SPL Regression Testing
### Step 1B. Family Checksum Calculation  
This step constructs the key artifact for reducing test execution. According to Lemma 1, two test executions are equivalent if every pair of code units they traverse are identical. To check whether two code units are identical, our method compares their MD5 checksums [25]. The MD5 checksum of a code unit is computed by applying a hashing function to statements of the code unit. So, if one statement of two code units is different, their checksum values are different. Furthermore, if the checksums of two code units are the same, then they are identical. This step builds a checksum matrix that contains the checksum values of every code unit of the modified family. Using the checksum matrix, our method determines whether two code units are identical and furthermore, whether two test executions are equivalent (cf. Section 4.6). As shown in the Family Checksum function of Algorithm 1, for each product of the modified product family, it extracts all the code units from the product (lines 3, 4). Then, it builds a checksum matrix by assigning the checksum value of each code unit to the corresponding matrix index (lines 5-7). The checksum matrix is used as an input of Step 2-2.  
```
0:MPF: modified product family
0:chksumMatrix: checksum matrix
0:extractCUs(\(p\)): input a product \(p\) and return a set of code units in \(p\)
0:checksum(\(c\)): input a code unit \(c\) and return the checksum value of \(c\)
1:function FamilyChecksum(MPF)
2:CUs\(\leftarrow\emptyset\)
3:foreach\(p\in\)MPFdo:
4:CUs\(\leftarrow\)extractCUs(\(p\))
5:foreach\(c\in\)CUsdo:
6:chksumMatrix[\(p\)][\(c\)]\(\leftarrow\)checksum(\(c\))
7:endfor
8:endfor
9:returnchksumMatrix
10:endfunction
```  
**Algorithm 1** Family checksum  
Table 1 shows an example of a checksum matrix for a modified product family consisting of P1', P2' and P3'. In this example, we use a class as a code unit. P1 has Classes A and B; P2' has Classes A and C; P3' has Classes A, B and C. To simplify the example, we presented a checksum value as a single character (e.g., a, b, c and d). Two classes with the same character indicate that their checksum values are the same and so they have the identical source code. In Table 1, Class A is common to all the products of the product family and its source code is identical over all the products. Class B is used for P1' and P3' but not for P2' and its source code for P1' is identical to that for P3', too. Class C is used for P2' and P3' but not for P1' and its source code for P2' is not identical to that for P3'.
# Update class center using momentum-based smoothing
## 3 Experiments and Results
### Inference Evaluation of LightExNet on Mobile Device  
To validate the real-world deployment capability of the proposed LightExNet model, we conducted a direct on-device comparison with the standard MobileNet V2 model using the PyTorch Mobile framework on a representative mid-range Android smartphone--Redmi Note 13 5G. LightExNet is specifically designed for lightweight expression recognition, incorporating deep-shallow feature fusion, a stacked attention mechanism, and a center loss-based optimization strategy, all of which aim to improve discriminative capacity with minimal computational overhead. Device and deployment details are shown in Table 8.  
To better assess the practical inference efficiency and deployment readiness of the proposed LightExNet model, we conducted a direct comparative evaluation against the standard MobileNet V2 architecture. Both models were trained using PyTorch under consistent settings and exported to TorchScript format (.pt) for mobile deployment.  
The two models were then deployed on the Redmi Note 13 5G smartphone using the PyTorch runtime, and inference was performed directly on-device using single-frame inputs of the same resolution (224 \(\times\) 224). The testing environment was kept consistent for both models, including preprocessing operations, runtime conditions, and input sources, ensuring a fair and controlled comparison.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Scheme** & **Base** & **1** & **3** & **Params** & **FLOPs** & **Fer2013 Acc** & **CK+ Acc** & **RAF-DB Acc** \\ \hline
**MobileNet** & & & & & **3.5 M** & **312.85 M** & **67.02\%** & **93.26\%** & **81.41\%** \\
**V2** & & & & & & & & & \\ \hline
1 & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 10.86\% & 8.35\% & \(-\)2.63\% & 1.58\% & \(-\)0.05\% \\
2 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(-\)7.71\% & 5.73\% & 1.40\% & 3.07\% & 3.60\% \\
3 & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(-\)9.71\% & 7.38\% & \(-\)1.57\% & 2.62\% & 2.09\% \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(-\)6.57\% & 4.66\% & 3.21\% & 4.41\% & 5.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effectiveness of Individual Components Compared to Baseline MobileNet V2.  
Performance metrics such as inference latency, frame rate, memory usage, power consumption, and classification accuracy were measured and averaged across multiple runs. The following table presents the comparative results of LightExNet and MobileNet V2 under identical mobile deployment settings.  
Performance comparison of two algorithms is shown in Table 9.  
This mobile inference evaluation demonstrates that LightExNet achieves a better trade-off between recognition accuracy, runtime performance, and model size than MobileNet V2 on a representative mid-tier Android device. The results confirm the effectiveness of the proposed architecture for practical mobile deployment, satisfying the real-time requirements of edge-side applications such as human-computer interaction, smart terminals, and mobile health monitoring.
# A Petri Net-Based Algorithm for Solving the One-Dimensional Cutting Stock Problem
## 3 Basic Concepts
### Filtered Beam Search Algorithm  
The beam search algorithm was first used as an artificial intelligence technique for speech recognition. It has since been applied to combinatorial optimization problems, such as scheduling problems [55], the traveling salesman problem [56; 57; 58], and cutting and packing problems [59; 60; 61; 62; 63]. This method is efficient because solutions to these problems can be constructed sequentially by appending the next operation, city, or item to the partial solution based on the cost it adds. Thus, the construction of the solution can be viewed as a decision tree. At each level, the corresponding nodes represent partial solutions with given costs, while at the last level, the nodes represent complete solutions. One of these complete solutions is then selected as the best or optimal solution. The beam search algorithm performs a breadth-first search followed by a depth-first search. At each level of the tree, the nodes are branched, and those that are not promising for further branching based on a cost function evaluation are pruned. The number of nodes \(\beta\) that are branched at each level are the beams. Once these nodes have been selected, the others are discarded permanently. Since the beam search has no backtracking, it is possible to discard good solutions, but it saves much computational effort. In [64], a modification to the beam search was introduced known as the filtered beam search algorithm. As the name suggests, at each level a local evaluation is carried out over the nodes that share the same parent node. Only those nodes that satisfy a local evaluation condition are considered beam nodes, thereby filtering the  
Figure 2: Reachability tree for the PN from Figure 1.  
nodes. Once all the filtered nodes have been obtained from the parent nodes, only those that satisfy a global evaluation condition become the new beam nodes or parent nodes for the next level.  
The number of filtered nodes \(\alpha\) (filter width) and the number of beam nodes \(\beta\) (beam width) are user-defined. However, it is known that larger values require more computational effort to search the tree, while smaller values reduce the likelihood of finding the best or optimal solution. A representation of the filtered beam search algorithm is shown in Figure 3 for a solution with a sequence of \(n\) nodes. Sometimes, it is desired to maintain diversity in the search by setting \(\alpha<\beta\), which guarantees that at least one beam node is selected from a different parent node [60].