esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Borehole Resistivity Monitoring of Overburden  
### Basis of Physical Property Exploration  
The overburden of the working face will form a caving zone, a water-flowing fractured zone and a bending subsidence zone after mining (hereinafter referred to as \"three zones\"). Under the condition in which there is no water-filled fracture, different zones have different resistivity characteristics. Based on the previous research results, the caving zone resistivity is the highest, and is usually more than three times greater than the original rock strata resistivity. The fractured zone is above the caving zone, and the resistivity is usually more than 1.5 times greater than the resistivity of the original rock strata. The bending subsidence zone is above the fracture zone, the fractures are separated locally, and the resistivity value increases slightly [22].  
Single-hole resistivity detection is carried out by constructing electrode device holes in the roof of the coal seam [23; 24]. Electrodes with different spacing are buried for single-hole measurement. Based on the electrode points in a single hole, two-dimensional geo-electric cross-section measurement is conducted, providing a large amount of data and extensive information. The single-hole electrical method uses the high-density resistivity method for corresponding treatment. Its physical premise is the electrical conductivity difference between underground media. Similarly to the conventional resistivity method, it supplies current I to the ground through the \(A\) and \(B\) power supply electrodes and then measures the potential difference \(\Delta V\) between the \(M\) and \(N\) measurement electrodes to obtain the apparent resistivity value of each point.  
In practical applications, the single-hole resistivity test has been improved to form a three-dimensional test space for boreholes. Usually, two boreholes are constructed in the roof, and a hole-hole CT test system is designed to obtain corresponding potential data. Since the data inversion of the hole-to-hole resistivity method increases the constraint effect of the electrode in the borehole, the result is more accurate than that of the single-hole resistivity test system. This is convenient for directly comparing the resistivity difference in rock deformation and failure, thereby improving the judgment accuracy [25; 26].  
Generally, when the coal strata are not affected by mining, a resistivity sensing unit is installed in the roof borehole, grouting is carried out to seal the borehole, and the background resistivity value is collected. In the later stage, with the mining of the working face, parameters such as apparent resistivity in the borehole are dynamically tested regularly, which can reflect the dynamic change in rock strata failure near each electrode.  
Figure 5: Profile of working face plastic zone. (**a**) Excavation 20 m, (**b**) Excavation 100 m, (**c**) Excavation 200 m.
## 3 Results  
### Polymer Identification by Raman Spectroscopy  
The preliminary recognition of polymers constituting the calibration dataset by Raman spectroscopy is shown in Figure 3. More than 50% of the analyzed particles are PE, followed by PP (24%), EPS (19%) and PS (4%).
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 3 Materials and Methods
### Uniform Manifold Approximation and Projection (UMAP)  
UMAP (Uniform Manifold Approximation and Projection) is a nonlinear dimensionality reduction technique that leverages manifold learning and algebraic topology to preserve both local and global data structures in low-dimensional embeddings. Unlike linear methods such as PCA, UMAP constructs a high-dimensional graph representation of the data and optimizes a low-dimensional counterpart by minimizing the cross-entropy between topological representations. The algorithm operates through two key phases: (1) computing a fuzzy simplicial set representation of the input data using nearest-neighbor graphs with adaptive kernel bandwidths, and (2) optimizing the low-dimensional embedding through stochastic gradient descent to maintain the topological relationships. Key parameters include n_neighbors, which balances local versus global structure preservation (typically 15-200), and min_dist, controlling cluster compactness (default 0.1). Comparative studies demonstrate UMAP's superior scalability and preservation of inter-cluster distances relative to t-SNE, particularly for high-dimensional feature spaces like image embeddings or genomic data. In malware analysis, UMAP has proven effective for visualizing hierarchical feature relationships and detecting anomalous patterns [36].
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 6 Conclusions  
Along this article, we have defined eTorii, a protocol that allows routing traffic in DCNs in an efficient, scalable and flexible way. Despite being a distributed protocol, its configuration is automatic and it reacts fast to network failures and changes, as many alternative routes are usually deployed when the HLMAC address assignment is realized.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Network topology & Spine-Leaf (4 \(\times\) 4) \\ Hosts per Leaf & 20 \\ Flow distribution & Random Extra-Leaf \\ Flow size distribution & Web Search [2] \& Data Mining [36] \\ Network traffic load (\%) & 10, 20, 40 y 60\% \\ Link speed & 100 Mbps \\ Execution time & 1800 s \\ Transitory time & 800 s \\ Number of executions & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Test 2 testbed parameters.  
Figure 11: Average throughput classified by traffic type.  
Figure 10: Data center flow size CDFs (Reprinted with permission from ref. [37]. Copyright 2017 IEEE).  
We have implemented and evaluated a prototype of eTorii in the Ryu SDN controller and with the Mininet emulator. The evaluation shows routing is correctly balanced and the obtained throughput directly competes with the standardized protocol ECMP, being even better for elephant and mouse flows, which are the most common in DCNs.  
As future work, we envision an in-depth analysis of eTorii in diverse DCN topologies to measure the average amount of table entries required for routing to evaluate its scalability in detail, as well as the convergence time to prove its resilience and flexibility. In particular, we would like to focus on the effect of topological changes. Although eTorii is able to re-route at zero cost most of times, we should analyze in detail how the proposed recovery mechanism affects the overall throughput performance, packet loss and scalability. Additionally, a high-performance implementation, for example using P4 hardware targets or similar, could be advisable to show the application of eTorii to cloud computing networks.  
Conceptualization, E.R.; methodology, I.M.-Y.; software, M.B.-M.; validation, I.M.-Y. and J.A.-H.; formal analysis, E.R.; investigation, J.M.A.; resources, J.M.A.; data curation, J.A.-H.; writing--original draft preparation, E.R.; writing--review and editing, I.M.-Y.; visualization, J.A.-H. and J.M.A.; supervision, E.R. and I.M; project administration, E.R. and I.M.-Y.; funding acquisition, E.R. and I.M.-Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grants from Comunidad de Madrid through Project TAPIR-CM (S2018/TCS-4496) and Project IRIS-CM (CM/JIN/2019-039), and from Junta de Comunidades de Castilla la Mancha through Project IRIS-JCCM (SBPLY/19/180501/000324).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Method for Evaluating the Spatial Layout of Fire Stations in Chemical Industrial Parks
## 2 Methods
### General Framework
* Calculate the difference between the estimated fire rescue time and the required fire rescue time, \(t_{ij}-X_{i},j=1,2,\ldots\) k, and make the following judgments: (**a**) If \(t_{ij}-X_{i}<0\), then C\({}_{i}\) = 1, where C\({}_{i}\) is the first state register of the point \(R_{i}\), and C\({}_{i}\) = 1 indicates that the point \(R_{i}\) is covered by fire station \(F_{j}\); (**b**) If the situation \(t_{ij}-X_{i}<0\) occurs more than once, then \(H_{i}=1\), where \(H_{i}\) is the second status register of the point \(R_{i}\), and \(H_{i}\) = 1 indicates that the point \(R_{i}\) is covered by two or more fire stations. * Traverse \(R_{i}\), \(i=1\), 2, \(\ldots\) n, calculate C = \(\Sigma C_{i}\), where C represents the number of PTBRs covered by fire stations. Then, calculate \(H\) = \(\Sigma H_{i}\), where \(H\) represents the number of PTBRs covered by two or more fire stations. * Calculate the coverage rate of fire stations as \[\alpha=\frac{\text{Number of covered PTBR}}{\text{Total number of PTBR}}=\frac{\text{C}}{|\text{R}|}\] (3)
* Calculate the overlap-coverage rate of fire stations as \[\gamma=\frac{\text{Number of crossly covered PTBR}}{\text{Number of covered PTBR}}=\frac{H}{\text{C}}\] (4)
* Determine the standard \(\alpha_{s}\) for the coverage rate of fire stations. If \(\alpha\geq\alpha_{s}\), the coverage rate of fire stations meets the standard. * Determine the standard \(\gamma_{s}\) for the overlap-coverage rate of fire stations. If \(\gamma\leq\gamma_{s}\), the overlap-coverage rate of fire stations meets the standard. This method can also analyze the average fire rescue time, i.e., \(t_{average}\) as  
\[t_{average}=\sum_{i=1}^{\rm{n}}\sum_{j=1}^{\rm{k}}t_{ij}/\rm{n}\times\rm{k} \tag{5}\]
## 3 Proposed Methodology
### Feature Selection MFB-FA (Modified Flashing Behaviour-Based Firefly Algorithm)
Random Initialization: Though random initialization could assure initial FFs are homogeneously distributed in solution space, the solution quality seems to be uncertain as the FFs seem to go far from the global optimum. When initial FFs seem to be not only homogeneously distributed but also with high quality, it would assist in the betterment of the mass of FFs, thereby preventing the approach of getting prematurely trapped in local optima to a certain extent. 2. The light-absorption coefficient corresponding to \(\beta\) exists as the constant value. In this case, the \(\beta\) value finds the attraction of all FFs. Generally, \(\beta\in[0,10]\) might be recommended, and it is convenient. Nevertheless, using fixed values for all issues is irrational. The absorption coefficient has to differ with iterations in the searching space. Thus, in this research, \(\beta\) is tuned with the chaotic maps without using a constant value. In stochastic-searching optimization approaches, the methodologies using chaotic variables rather than random variables are termed chaotic optimization algorithms. In such algorithms, due to ergodicity and non-repeatability of chaos, it could accomplish complete searches at a higher speed than the stochastic searches that rely on probabilities. Thus, in this study, a chaotic map is used for initializing the FF population and tuning the absorption coefficient. The MFB-FA Algorithm 1 is presented below. ```
Input : Size of the population Output : Best optimal solution \((\text{best}_{\text{subset}})\) Begin set params for Modified_FA(); \(\text{init}_{\text{population}}\); \(\text{Det of light intensity\_I}()\); \(\text{Object(fun)}=\text{f(q)},\text{q}=(\text{q}_{1},\dots,\text{q}_{\text{d}}) \text{T}\) Generate an initial chaotic population of fireflies \(\text{q}_{i},i=1,2,\dots\text{n}\) compute the \({}^{\prime}\)I\({}^{\prime}\) so that it is associated with f(q) while(T \(\leq\) Max Iteration) Define absorption coefficient \(\beta\) with chaos Gaussian Map for m = 1 : i (i ff) for n = 1 : i (i ff) if \(\text{I}_{\text{m}}>\text{I}_{\text{n}}\) move ff towards n; end if Vary attractiveness withdistance Dist via \(\exp^{-\beta\text{Dist}^{2}}\) Evaluation of new solutions and updating of light intensity (LI) end for end for Firefly ranking and determination of best solution; T = T + 1 end while end
```  
**Algorithm 1** MFB-FA: Modified Flashing Behaviour-based Firefly Algorithm
# The Effect of Meat and Bone Meal on Yield and Herbage Quality in Silage Maize
## 3 Results and Discussion
### Macronutrient Content of Maize Herbage  
The macronutrient composition of whole-crop maize herbage grown for silage remained stable: 1.93-2.08 g P, 11.21-4.6 g K, 1.07-1.34 g Ca, and 1.02-1.08 g Mg\(\cdot\)kg\({}^{-1}\) DM, and it was not significantly modified by fertilization (Table 4). Significant differences in the content of the above nutrients in herbage were noted between years of the study (\(p\leq 0.05\)). Herbage was most abundant in nutrients in 2020, and significant differences in the concentrations of P, Ca, and Mg were observed relative to 2019, except for the K content, which was identical in both years. No significant changes were noted in the concentrations of P and Ca in maize herbage between 2014 and 2019. In turn, the Mg content varied significantly across the years. According to INRA guidelines [49], maize green forage for ruminants should contain 3.0 g P and 4.0 g Ca\(\cdot\)kg\({}^{-1}\) DM. The results of previous studies indicate that MBM positively affects the P content of maize. The grain of maize fertilized with a high dose of MBM (5.0 Mg\(\cdot\)ha\({}^{-1}\)) applied every two years was characterized by a significantly higher P concentration relative to the remaining treatments, including the NPK treatment [13]. [PERSON] et al. [15] observed a significant increase in the P content of maize grain after three years of MBM application at 1.0, 2.0, and 3.0 Mg\(\cdot\)ha\({}^{-1}\), compared with its single application. A high P concentration in maize grain depends on P uptake from soil fertilized with MBM [23]. According to some authors, as much as 50% of P from MBM is available to plants already in the first year after application [12]. Other authors claim that 5-28% of P is available in the first year after MBM application, 63% in the second year, and 69-87% in the third year [21]. Therefore, some researchers do not recommend P fertilization in the year following MBM application [50].  
Figure 3: Interaction between fertilization and year of study for the crude fiber (CF) content of maize herbage; a, b, c within a year (\(p\leq 0.05\)).  
It should be noted that the low abundance of plant-available P in soil increases the fertilizing effect of MBM [25; 51]. In the current experiment, soil was moderately abundant in plant-available P and slightly acidic; thus, maize plants could absorb P from soil reserves that partially met their requirements, and no significant differences were noted in the P content of herbage.
# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 2 Basic of Cache Based on Popularity and Class
### Background and Related Research  
Caching strategies can be grouped into cache placement, content selection, and policy [4]. Cache placement is a mechanism used to decide the cache location of the data. Cache content selection determines the content to be cached or deleted from the content store. Cache policy regulates the cooperation mechanism between router nodes, coordination between nodes, etc.  
One of the cache placement techniques is Leave Copy Down (LCD). This technique give an additional bit, called 'flag', on the data. When an interest packet enters the router, it changes the flag bit in the packet to '1'. The downstream router that receives the data stores a copy in the content store [21]. LCD technique places data closer to consumers for any popular content. Every time there is a consumer request, the data are being copied to the one-hop router closer to the consumer.  
A caching scheme similar to LCD is used on WAVE algorithm [16], which is a data file that is divided into several smaller packages called chunks. Interest packages come from users and contain requests for content. When a consumer sends an interest packet, the content chunk 'A' is sent from the producer to the one-hop downstream router to be stored, before it is sent to the consumer. Furthermore, when the next consumer sends an interest packet to request the same content, the next downstream router saves the chunk 'A' before sending it to the consumer. Every new request for the same content shifts the chunk 'A' that needs to be kept at the router one hop closer to the consumer.  
The idea of the LCD and WAVE mechanism is modified and applied to our new cache algorithm, which is Cache Based on the Popularity and Class (CAPIC) algorithm as the cache placement method. In the CAPIC algorithm, data are cached one hop closer to the consumer for each interest packet sent by the consumer. On the router, the cache policy is enforced to run on differentiated cache proportion of each content class. When the content store is full, the cache replacement technique is used to select the content to be removed. This mechanism draws the content closer to the consumer every time it is requested.
## 5 Discussion
The 12.8% parameter reduction while maintaining enhanced performance suggests that TSA provides an effective approach to the model complexity-accuracy balance, which is relevant for edge computing applications in industrial Internet of Things deployments. The current implementation presents several limitations requiring acknowledgment. The dataset scale, while representative of typical small-sample industrial scenarios, limits comprehensive evaluation across diverse shipbuilding environments and component types. The current evaluation focuses primarily on geometric component classification, and performance in surface defect detection remains unexplored. Practical deployment of TSA involves several implementation considerations that warrant discussion. The triplet parallel processing architecture, although reduces parameter count and theoretical computational complexity, may exhibit varying inference performance across different hardware platforms due to memory bandwidth constraints and parallel processing efficiency differences. The mechanism incorporates architectural parameters such as group normalization settings that are optimized for the current dataset characteristics. However, while these demonstrate robust performance in the current evaluation, their optimal configuration may vary when applied to different industrial contexts with varying data distributions or challenging visual conditions such as extreme metallic reflections under high-intensity lighting, where the separation mechanism's feature discrimination capabilities could be compromised. Future research directions should prioritize addressing current limitations and expanding the method's robustness and applicability. Adaptive threshold learning mechanisms warrant investigation to enable automatic parameter optimization across different manufacturing environments. Robust preprocessing strategies for challenging illumination conditions require development to address metallic surface reflections and varying lighting scenarios common in industrial settings. Validation across additional small-sample industrial detection tasks would help establish the broader applicability of TSA beyond ship component recognition. Integration with emerging vision-language models presents opportunities for enhanced semantic understanding in manufacturing quality control applications, potentially enabling more sophisticated defect classification and process optimization capabilities.
# A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes
## 6 Conclusions  
This paper presents a 3D rope-driven robot and a full-coverage path-planning algorithm to realize the global cleaning of complex glass curtain walls. By designing a five-rope parallel configuration, the robot was made able to overcome obstacles to complete three-DOF motion. The wall model was built based on the grid method to mark obstacles, which was then decomposed according to the wall curvature to more thoroughly clean the subareas. To overcome the smooth obstacle avoidance problem for robots on complex walls, a full-coverage path-planning algorithm based on heuristic principles was proposed. By specifying the direction priority, the path of the robot body can be autonomously planned according to the distribution of wall obstacles. Using the proposed dead zone escape strategy can ensure the shortest escape path and maximize cleaning efficiency.  
A series of experiments were tailored to verify the performance of the robot and the effectiveness of the algorithm. The results show that the robot can avoid obstacles and achieve full-coverage movement on the complex facades of the experimental platform. The proposed algorithm planned an efficient global cleaning path for a rope-driven robot with a maximum position error of 7.3 mm. According to these results, the proposed priority heuristic algorithm increases the robot operation coverage rate on complex walls by independently planning the work path according to the distribution of obstacles.  
This study provides a new idea for robot structure design and path planning for complex facade cleaning, which can be widely used in other scenes operated by rope-driven or crawling robots. Different from the existing methods that deal with planar CPPS,  
Figure 15: Body position errors.  
we expanded the surface to a plane and then solved the global cleaning path planning problem considering obstacles, so no comparison with traditional CPPS was possible. In future work, we will compare our method with existing CPP techniques for flat surfaces and further improve the obstacle avoidance performance of the robot.  
Conceptualization, D.Z.; formal analysis, D.Z. and Y.L.; resources, M.L. and M.Z.; funding acquisition, M.L. and M.Z.; methodology, Y.Z. and Z.L.; investigation, P.J. and J.W.; validation, X.J. and G.W.; writing--original draft, D.Z. and Y.L.; writing--review and editing, P.J. and J.W. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data from in this study are available in the article.  
The authors declare no conflict of interest.
## 2 Literature Overview
### Challenges and Future Directions  
Despite these advancements, challenges such as data privacy, interoperability, and the need for explainable AI persist. Addressing these challenges will require continued innovation, particularly in developing multimodal systems, personalized query interpretation, and ensuring fairness in healthcare AI systems [27, 28].
## 3 Methodology
### Feature Extraction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Techniques for Extracted Feature** & **Feature Number (k)** & **Equations of Detailed \& Approximation Coefficients** \\ \hline
1. Mean & k = 1,..., 12 & \(\mu=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}\) \\
2. Standard deviation & k = 13,..., 24 & \(\sigma=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(x_{i}-\mu\right)^{2}}{N-1}}\) \\
3. Skewness & k = 25,..., 36 & \(x_{skewness}=\frac{1}{N}\sum\limits_{i=1}^{N}\frac{\left(x_{i}-\mu\right)^{3}}{ \sigma^{3}}\) \\
4. Kurtosis & k = 37,..., 48 & \(x_{kurtosis}=\frac{1}{N}\sum\limits_{i=1}^{\frac{1}{N}\left(x_{i}-\mu\right)^{4}}\) \\
5. RMS & k = 49,..., 60 & \(X_{rms}=\frac{\sqrt{\frac{N}{\sum\limits_{i=1}^{N}x_{i}^{2}}}}{\frac{\mu}{N}}\) \\
6. Form factor & k = 61,..., 72 & \(F_{f}=\frac{\sqrt{\frac{N}{\sum\limits_{i=1}^{N}}}}{\frac{\mu}{N}}\) \\
7. Crest factor & k = 73,..., 84 & \(C_{f}=\frac{\sqrt{\frac{N}{\sum\limits_{i=1}^{N}}}}{\frac{\mu}{N}}\) \\
8. Energy & k = 85,..., 96 & \(E=\sum\limits_{i=1}^{N}{x_{i}}^{2}\) \\
9. Shannon- entropy & k = 97,..., 108 & \(H_{s}=-\sum\limits_{i=1}^{N}{x_{i}}^{2}\log({x_{i}}^{2})\) \\
10. Log-energy entropy & k = 109,..., 120 & \(H_{e}=-\sum\limits_{i=1}^{N}\log({x_{i}}^{2})\) \\
11. Interquartile range & k = 121,..., 132 & \(IQR=x_{i}(\%25 th)-x_{i}(\%25 th)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The formulae used for feature extraction from the current signal (x represents the signal vector). Figure 10: Wavelet decomposition of the ‘sym4’ mother wavelet of the inner fault bearing.