esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Coal and Gangue Recognition Model Based on CAM-Hardswish with EfficientNetV2
## 5 Conclusions  
The aim of this paper is to efficiently recognize coal and gangue using a combination of image data preprocessing and deep learning technology. Pipeline-based coal and gangue image data preprocessing and image processing technologies were performed on the small sample of coal and gangue datasets to simulate the harsh production conditions under actual working conditions and obtain coal and gangue samples with multiscale, multimorphology, and color images. Then the network was trained after EfficientNetV2 was improved, the performance of the model was tested, and the following conclusions were drawn:  
1. Compared with the SE module in the original model, the CAM channel attention mechanism module that was introduced in this study adopted a parallel connection method that combined average pooling and max pooling. This module could efficiently extract essential image information. This solved the problem that the single pooling operation of the SE module caused the image to lose information. In order to further improve the performance of the coal and gangue recognition network, the ReLu activation function was replaced by the Hardswish activation function, which improved the recognition accuracy.
2. The original EfficientNetV2 network suffered from excessive video memory usage, which led to a decrease in training speed and efficiency. The proposed coal and gangue recognition network in this study significantly enhanced its training speed, reduced video memory usage, and shortened the training time without compromising model accuracy.
3. This study resulted in an improved EfficientNetV2 network that showcases excellent performance in recognizing coal and gangue. It can achieve a recognition rate of 98.24%, which is a significant improvement of 3.98% over the previously tested network. The improved model reduced the inference time by 6.6 ms compared with its predecessor. The effectiveness of the improved model was validated.  
Conceptualization, N.L. ([PERSON]); methodology, N.L. ([PERSON]) and J.X.; supervision, S.W. and K.Q.; investigation, J.X.; writing--original draft preparation, J.X.; writing--review and editing, S.W., K.Q. and N.L. ([PERSON]); funding acquisition, N.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grant no.: 62002285).  
Not applicable.  
Data Availability StatementData available on request due to restrictions eg privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the dataset in this paper is a self-constructed dataset and will continue to be used in subsequent studies, so we do not intend to public the dataset.  
The authors declare no conflict of interest.
# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 3 Materials and Methods
### Feature Extraction
#### 3.4.2 Full Body Feature: LIOP  
The local intensity order pattern (LIOP) feature [46] performs better in the case of low contrast and illumination changes within an image but not very well in the case of rotation and scale variation. It is also robust to many other geometric and photometric transformations, such as view-point change, image blur, and JPEG compression. It is a novel method for feature description based on intensity order. Firstly, the overall intensity order is used to divide the local patch into sub regions called ordinal bins. Next, the LIOP descriptor of each point is defined based on the relationships among the intensities of its neighboring sample points. If \(P(x)\) is a vector consisting of the intensities of the neighboring sample points of a point \(x\) in the local patch, the LIOP of the point x can be defined using Equation (7), where \(\omega(x)\) is a weighting function described in Equation (8). The LIOP descriptor is constructed by concatenating the LIOPs of points in each ordinal bin, respectively. As in [46], each image is divided into six ordinal bins, and the number of neighboring sample points is set to four, resulting in a feature vector of size 4! \(\times\) 6 = 144. Each step of this process is shown in Figure 6, and Equation (9) represents the LIOP descriptor of each bin.  
\[\omega(x)=\sum_{i,j}sgn\Bigl{(}\bigl{|}I(x_{i})-I(x_{j})\bigr{|}-T_{lp}\Bigr{)}+1 \tag{7}\]  
\[LIOP(x)=\varnothing(\gamma(P(x))) \tag{8}\]  
\[des_{i}=\sum_{x\in bin_{i}}\omega(x)LIOP(x) \tag{9}\]
## 2 FE Model Validation
### Comparison of the Simulation and Test Results under Static Load  
Figure 8 compares the FE results (the equivalent plastic strain contour map of concrete) and the final experimental failure modes of the RC columns with three different cross-sections [16]. At the peak bearing capacity, each specimen finally undergoes flexure failure, which is mainly characterized by yielding of the longitudinal tensile rebars and concrete damage concentrated at the bottom of the column, forming a plastic hinge, which shows agreement with the test results.  
Figure 9 compares the load-column top displacement curves obtained from the test [16] and numerical simulations. The experimental and simulated initial stiffnesses are essentially the same, and the errors of the peak bearing capacities of the three sets of specimens are between \(-11.43\%\) and \(6.35\%\), indicating the reliability of the bearing capacity by numerical simulation. Additionally, during the test, with the full development of flexural cracks in the concrete in the tension zone, the longitudinal rebars yielded, the concrete cover in the compression zone spalled, and all specimens eventually exhibited yielding of the tensile rebars and flexural crushing of concrete in the compression zone. However, according to the FE simulation results, there is no crushing or spallation of concrete, and the simulated damage state of the concrete differs from the failure of the actual specimens. In addition, the number of tested specimens was limited, resulting in the dispersion of the experimental data. Therefore, the experimental and simulated load-displacement curves differ to some extent. Nevertheless, the load-displacement curves and the maximum bearing capacities of the specimens obtained from the numerical simulation are in overall good agreement with the test results.
# Solving Optimal Electric Vehicle Charger Deployment Problem
## 2 Related Work
### Problem Formulation Approaches
#### 2.1.2 Distance Optimization  
This method aims to select locations that enable all potential users to receive services with the shortest distance or minimum cost [15]. Representative examples include the set cover, \(p\)-median, and \(p\)-center techniques. The set cover ensures that users receive similar levels of service, \(p\)-median aims to efficiently locate facilities with minimal movement required for service, and \(p\)-center minimizes the maximum distance between facilities and users. Distance optimization techniques allow for efficient location selection, but they require careful consideration of local information and feasibility of installation, distances between users and candidate locations, and the exact number of chargers to be installed. Consequentially, it is appropriate to use these techniques for the final location selection after considering all location analyses.
## 3 Results and Discussion
### Optimization of the 1 st Step  
In order to optimize the entire protocol, we first investigated the Michael addition/elimination one-pot process using phenylhydrazine **2a** and the \(\beta\)-nitroacrylate **1a** as reagents. Preliminary studies highlighted the almost quantitative addition of **2a** to **1a** under solvent-free and promoter-free conditions at room temperature (2 h). Further investigations (in terms of solvents, bases, and reaction temperatures) were dedicated to nitrous acid elimination, and the best result was obtained using two equivalents of TMG in MeCN at room temperature (4 h, entry f). Instead of obtaining the pure adduct **4a**, a mixture of tautomers **4a**, **4a\({}^{\prime}\)**, and **4a\({}^{\prime\prime}\)** were isolated in a ratio of approximately 54:13:33. The ratio was calculated by \({}^{1}\)H-NMR on the basis of the characteristic signal of each tautomer, as depicted in Figure 1. Nevertheless, all tautomers were suitable for the Fischer indole synthesis (Table 1).  
Figure 1: Characteristic \({}^{1}\)H-NMR signal of tautomers **4a**, **4a\({}^{\prime}\)**, and **4a\({}^{\prime\prime}\)** (tautomeric ratio 54:13:33).
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 2 Materials and Methods
### Cross-Section Segmentation and Segment Extraction
#### 2.2.1 Preliminary Classification of Tunnel Segments  
After the GuSAC algorithm removes tunnel lining joint features, the distinctions between tunnel segments become more apparent, as shown in Figure 5. Consequently, the TMatch algorithm is designed to capture these features.  
The first step of this algorithm is the preliminary classification of the tunnel point cloud, with two key parameters: the tunnel joint distance R and the minimum number of point clouds forming a segment N. These parameters jointly determine the algorithm's ability to accurately identify dense regions. In this study, the optimal parameter combination was determined through experiments to ensure the algorithm effectively segments the point cloud data into meaningful clusters while suppressing the impact of noise and outliers.  
Figure 4: GuSAC algorithm effect. (**a**) Non-tunnel structural point cloud. (**b**) Original point cloud section. (**c**) Denoised section.  
Figure 5: Joint seam boundary feature.  
The processed point cloud data output the coordinates of each point along with its corresponding classification label. Points not assigned to any cluster are labeled as 0, while other points are labeled with consecutive numbers starting from 1, ensuring the uniqueness and recognizability of each tunnel segment label. To visually demonstrate the clustering results, the processed data were visualized using CloudCompare v2.13.1 software. The visualization is shown in Figure 6 below.  
The visualization effect of the tunnel point cloud after processing with the TMatch algorithm is shown in Figure 6, where regions in different colors represent different tunnel segments. The inter-ring lining and intra-ring lining are strictly differentiated. However, there are still some detail errors in the current classification, and a further improvement in classification accuracy is needed.
## 5 The PMOD Scheduler
### Pipelined Scheduling
This adds another \(w\) time to the total cost, \(TC\), which gives us  
\[TC=2u+2\theta+\chi+\psi+v+w\leq 2u+[(N-2)\theta]+v+w \tag{14}\]  
where \(N\) is the number of communication steps. We can see that the maximum \(TC\) with pipelining is achieved if there are two communication steps of the maximum cost (this is always the case) and all the remaining \(N-2\) steps have a cost of \(\theta\) (the second largest cost). Without pipelining, the total cost \(TCW\) would be  
\[TCW=2(u+v+w)+2(\theta+v+w)+(\chi+v+w)+(\psi+v+w)>TC\]  
even for the worst-case scenario for \(TC\). In the worst-case scenario just described, the TCW is bounded by \(2(u+v+w)+[(N-2)(\theta+v+w)]\). Before proceeding to the next case, let us discuss a straightforward case, in which all the communication steps are more expensive compared to processing. This means that \(u>\theta>\chi>\psi>v\). In such a scenario, the total cost, \(TC\), depends entirely on the first stage (there is a complete overlap with the times required for processing and packing). This makes the total cost somehow smaller compared to the one computed in Equation (14):  
\[TC=2u+2\theta+\chi+\psi\leq 2u+(N-2)\theta\]  
Figure 2: Pipelined scheduling, \(u>v>w\). and, of course, this is again an improvement compared to the \(TCW\). Further improvement could be achieved if we could find a mechanism that can further reduce the transferring stage times; that is, reducing the communication times of the steps defined by the \(SM\). This will be discussed in Section 7. **(B) The processing stage is always more expensive compared to the transferring stage; \(\mathbf{v}>\mathbf{u}>\mathbf{w}\)**  
In this scenario (see Figure 3), we assume that the data streams require such an exhausting processing procedure that the processing time overcomes the transferring time; that is, \(v>u>w\). From Figure 3, it is obvious that all of the transferring time and almost all of the packing time overlaps with the processing time. Clearly, the total cost, \(TC\), is  
\[TC=u+6v+w\quad\text{or generally}\quad TC=u+Nv+w,\]  
where \(N\) is the number of communicating steps. In this case, further improvements can be made only on an application basis, as the total cost is determined by stream processing. For example, if pipelining can be used to process an application's data, we can achieve improvements at the processing stage, thus improving the total cost. However, the implementation of a scheduling scheme that is optimal for all the applications is an NP-hard problem.
###### Abstract  
With the unconventional energy sector (e.g., shale gas) increasingly focused on precision drilling and cost-effective extraction, slim-hole horizontal well technology is gaining prominence. However, drill string dynamics in narrow, complex fluid environments are not fully understood. This study presents a novel bidirectional fluid-structure interaction (FSI) model, uniquely integrating recursive algorithms with the Moving Particle Semi-implicit (MPS) method to couple drill string-wellbore contact with drilling fluid interactions. Key findings show that drilling fluid significantly impacts drill string behavior; for instance, it can reduce natural frequencies by 20-25%, while stiff formations amplify lateral resonance risks. Optimizing fluid properties can substantially cut energy losses, though TREE is marginally elevated when viscosity exceeds the threshold (\(2.5\times 10^{-5}\) m\({}^{2}\)/s). The drill string typically displaces rightward, but higher viscosity can shift it left; a moderate friction coefficient aids centering. Excessive lateral displacement impairs cuttings removal, affecting fracturing. These insights enable actionable strategies: adjusting fluid viscosity and drag reducers can optimize drill string position and enhance cleaning. This research provides a framework for energy-efficient drilling in complex reservoirs, balancing efficiency with wellbore integrity and improving outcomes in the unconventional energy sector.  
unconventional energy development; drill string; slim-hole horizontal well; fluid-structure interaction; energy dissipation Article
## 2 RFM Optimization
### Application to Push-Frame Images
#### 2.2.2 PlanetScope (Doves)  
The PlanetScope CubSats acquire data in a push-frame nadir-pointing mode (Figure 3). As with SkySat, the overlap of consecutive images from the same DOVE is very small, \(\sim\)8%, as well as the baseline. Thus, rays between tie points are almost collinear. Therefore, to perform the RFMs refinement, we select image pairs with maximum overlap, hence the maximum convergence angle. Then, we extract tie points between the selected image pairs and estimate the bias RFM correction.  
Figure 3: (**a**) A schematic representation of Doves positions on ascending and descending orbits, modified from [25]. (**b**) Projection view of Doves footprints (Dove-C- of44 and Dove-C-of36) featuring along-track overlap between subsequent acquisitions of the same dove, and across-track overlap between successive doves in the same orbit. (**c**) Projection view of Doves footprints featuring overlap between ascending (Dove-C-of24) and descending (Dove-C-1033) acquisitions. Rectangles denote individual dove footprint and each color represent the time acquisition. Dash arrows depict the direction of the dove acquisition as seen on ground.  
Figure 2: (**a**) A scheme of a single set of SkySat raw sensor footprint, three raw frames corresponding to the three 2D-CMOS image sensors; (**b**) Morecni Mine area as seen by SkySat, the black bounding boxes show the individual scenes of each detector. (**c**) Number of images overlap of SkySat tri-stereo configuration.  
In both cases, the refinement of RFMs can be additionally constrained with GCPs extracted from an external ortho-image and a DEM.  
The angle between incidence view vectors from overlapping scenes \(\theta\), also called the \"convergence angle\" [13; 26], is determined by computing the angle of intersection of two ray vectors \(u_{1}\) and \(u_{2}\),  
\[\theta=\cos^{-1}\left(\frac{u_{1}.u_{2}}{u_{1}.|u_{2}|}\right). \tag{9}\]  
The ray vector \(u\) is computed as follows: for a pixel p with coordinates \((x,y)\), we define two ground points \(M\) and \(N\), where \(M=RFM_{Z}^{-1}(x,y)\) and \(N=RFM_{Z\times 1}^{-1}(x,y)\), \(\lambda\) is a scale factor. Then, the vector \(u\) is defined with respect to these two points.  
The base-to-height (B/H) ratio is estimated, ignoring the Earth curvature, from  
\[\frac{B}{H}=2.\tan\frac{\theta}{2}. \tag{10}\]
## 4 Results
### Verification Experiment  
Using the results shown in Table 12 where the levels that optimized both responses were a pulse on-time of 10, a pulse off-time of 11, a servo voltage of 44 V, a wire tension of 50 g, a wire-speed of 7 m/min, and a voltage of 9 V, experiments were run to verify this finding.  
The results for the verification experiments are reported in Table 13 for the surface roughness response and in Table 14 for the energy consumption. In both cases, the responses found using the optimal levels previously mentioned were compared with the baseline values defined in Table 2. To ensure comparable results, the verification experiment was run five times for the 19 mm plate and five times for the 25 mm plate.  
Table 15 summarizes the comparison between the responses obtained with the optimal levels defined in this work and the baseline values. From the values, it is easy to see that the surface roughness improvement was marginal, with a 1.3% decrease in variability and a 1.2% decrease in the mean surface roughness. Considering the energy consumption, the improvement was more important, with a decrease in variability of 8% and a decrease in energy consumption of 8.4%. It is important to note that both responses improved  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{3}{c}{**Surface Roughness**} & \multicolumn{5}{c}{**Energy Consumption**} & \multirow{2}{*}{**Optimal Level**} & \multirow{2}{*}{**Optimal Value**} \\  & & \multicolumn{2}{c}{**S/N**} & \multicolumn{2}{c}{**Mean**} & & \multicolumn{2}{c}{**S/N**} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{**Optimal Level**} & \multirow{2}{*}{**Optimal Value**} \\  & **Level** & \% & **Level** & \% & **Level** & \% & & & **Level** & \% \\ \hline Ton & 1 & 68.47\% & 1 & 68.38\% & 3 & 42.88\% & 3 & 42.05\% & 1 & 10 \\ Toff & 1 & 0.19\% & 1 & 0.17\% & 1 & 0.30\% & 1 & 0.05\% & 1 & 11 \\ SV & 2 & 0.01\% & 2 & 0.00\% & 2 & 0.05\% & 2 & 0.10\% & 2 & 44 volts \\ WT & 1 & 1.18\% & 1 & 1.23\% & 3 & 4.85\% & 3 & 4.70\% & 3 & 50 gf \\ WS & 3 & 0.45\% & 3 & 0.49\% & 2 & 0.02\% & 2 & 0.20\% & 3 & 7 m/min \\ V & 1 & 22.88\% & 1 & 22.95\% & 3 & 49.65\% & 3 & 47.74\% & 3 & 9 volts \\ \hline \hline \end{tabular}
\end{table}
Table 12: Combination of factors and levels that improve both responses.  
with the selected levels for each of the variables, even when considering plates with different thicknesses.
## 2 Related Works
### Distributed Underwater Acoustic Communication Network Simulator  
The authors of this proposal [20] studied and emphasized the importance of underwater communication simulations as a way to guarantee the feasibility of a proposed solution. As mentioned in this work, real-world experiments, even when they are controlled environments, are highly costly. Thus, in order to address this issue a novel Distributed Underwater Acoustic Communication Network Simulator (DUACNS) is proposed. This simulator is based on a CORBA (Common ObjectRequest Broker) middleware. As main features, this simulation environment allows modulation and demodulation operations, as well as synthesizing received signals (based on the Bellhop ray tracing channel model). This simulation environment has three main features: firstly, a CORBA middleware, which provides a global time reference for all computers in a simulation and a cross-layer message exchange mechanism for information publishing and subscribing. Secondly, a software-defined modem based on Multi-carrier MFSK, which provides physical layer support, has also been included in the proposal. Lastly, synchronization based on the BELLHOP ray tracing model and 3D network topology is also present.  
All in all, while the proposal addresses issues that are common for distributed systems and offers a reliable framework (based on CORBA) for data interchange, its adaptability to an environment where autonomous maritime vehicles are involved in seems questionable, as the proposal seems to focus on the characteristics of the acoustic communications, rather than on the software components or the facilities that are provided for the system where it is included.
## 3 Results
### Cluster Analysis
#### 3.2.3 Lab Experiment Result: Saliency Based on Audibility Testing  
The saliency for each attribute was determined from the audibility test, where subjects were required to note how audible the differences among all stimuli were, for each of their own elicited constructs. Figure 6 presents the individual results generated by the audibility testing.  
A number of steps were employed in calculating an \"audibility index\", based on which a final classification could be outlined. The first step was grouping of similar attributes for each subject individually. The grouping was carried out based on both individual clusters and verbal feedback received from each participant regarding the similarity of their attributes. Once all the similar attributes were grouped, the most representative name was assigned and their audibility scores were averaged. For the second step, the number of occurrences for each attribute among all the subjects was counted, resulting in a further reduction in the number of attributes. The cluster analysis was also used as a basis for grouping the most similar perceptual attributes. The audibility scores were averaged for each attribute group and an audibility index was calculated as follows: a percentage of the number of occurrences as well as the percentage of the audibility scores were calculated. An equally weighted average of the two was obtained, which was defined as an audibility index. The inclusion of the number of occurrences as well as this newly calculated value allowed for an easier observation and classification of the attributes based on the audibility index. Ten attributes which are presented in Table 4 were observed to have an audibility index higher than 50.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Cluster** & **Attribute** \\ \hline
1 & Reverb spread, Envelopment, Reverb direction \\
2 & Reverb/Echo direction \\
3.1 & Direct sound (level, clarity, timbre) \\
3.2 & Source width, Early reflections, Reverb spread \\
4.1 & Perceived distance to source/source clarity, source spread \\
4.2 & Reverb level \\
5.1 & Envelopment, Reverb timbre, Room size perception \\
5.2 & Source level, General timbre, Envelopment \\
5.3 & Early reflections direction, perceived source distance/level, room timbre \\
6.1 & Perceived reverb loudness, reverb direction \\
6.2 & Envelopment, source width, source clarity \\
7 & Echo direction, source spread, source loudness, reverb spread \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lab experiment main construct categories based on clustering.  
Figure 6: Audibility rating for each subject.