esa-sceva/satcom-chunk-collection huggingface dataset:
# Region-Based Approach for Machining Time Improvement in Robot Surface Finishing
## 3 Materials and Methods
### Surface Subdivision and Machining Time Estimation  
#### 3.3.1 Clustering  
As observed in Figure 1, the ellipse data already form distinct areas of similarity, but relying solely on intuition and visual inspection is insufficient. Hence, clustering algorithms such as K-means can be used to subdivide the workpiece into clusters. K-means clustering is a popular unsupervised machine learning algorithm used to partition a dataset into distinct clusters with similar data subsets, where each data point belongs to the cluster with the nearest mean (centroid). The algorithm assigns data points to clusters iteratively, and updates the centroids by calculating the mean of all points within each cluster. This process repeats until the centroids stabilize or a predefined number of iterations is reached [40].  
The data are partitioned into clusters using the squared Euclidean distance metric, where \(K\) is the number of clusters. The algorithm allows up to an arbitrary number of iterations to converge. Empty clusters are handled by creating a new cluster with a single point, and the algorithm runs several times with different initial centroids to avoid the local minima. The centroids can be initialized using the K-means++ algorithm [41], which ensures better initial placement.  
For each surface point \(P_{i}\) on the workpiece, a multidimensional feature vector \(F_{i}\) (19) is constructed by combining the position vector \(P_{i}\), surface unit normal vector \(\hat{n}_{i}\), minimal possible speed \(\sigma_{\min,i}\) (corresponding to the minor ellipse semi-axis length), and maximal possible speed \(\sigma_{\max,i}\) (corresponding to the major ellipse semi-axis length).  
\[F_{i}=\left[P_{ix}\ P_{iy}\ P_{iz}\ \hat{n}_{ix}\ \hat{n}_{iy}\ \hat{n}_{iz}\ \sigma_{\min,i}\ \sigma_{\max,i}\right] \tag{19}\]  
The Z-Score normalization [42] is used as  
\[\widetilde{F}_{i}=\frac{F_{i}-\mu}{s} \tag{20}\]  
\(\widetilde{F}_{i}\) represents the normalized clusterization vector \(F_{i}\), while \(\mu\) and \(s\) denote the feature vectors' mean value and Standard Deviation.
## 5 Experimental Results and Analysis
### Performance
#### 5.5.4 Discussion  
In our research, to enhance the model's performance in detecting small targets, such as the heads of students seated at the back, we incorporated a combination of the Focal Modulation module and the Scale-aware Attention from the Dyhead module. This integration significantly boosts the model's capability in feature extraction and fusion under complex classroom environments, thereby enhancing the detection accuracy for occlusion and small  
Figure 11: Comparison of loss functions.  
Figure 10: The confusion matrix of SBD-Net in the analysis of different behavioral detection effects.  
objects. Specifically, Focal Modulation employs a multi-level feature fusion mechanism that aggregates context information from short to long distances across different positions in the feature map. Through deep convolutional encoding, the FM module focuses and merges features from various scales at each query location, capturing fine local details. Subsequently, when the model makes inputs into the Dyhead module, its Scale-aware Attention dynamically adjusts the importance of different scale features. Using 1 \(\times\) 1 convolution operations, Dyhead's Scale-aware Attention allocates attention weights based on the prominence of different scales in the feature maps, enabling the model to focus more on extracting features of occlusion and small objects.  
To validate the scalability of our proposed method in student behavior detection, we conducted experiments using two public datasets. The SCBehavior dataset includes younger students in K-12 education, while SCB-U involves older students. The results demonstrate that our proposed method exhibits strong scalability in recognizing student behaviors across different grade levels.
###### Abstract  
The performance of extracted coagulant from the sugarcane bagsase was tested using synthetic wastewater for turbidity removal. Sugarcane bagsase was selected because it is available in abundance as a waste. This study was carried out to analyze the effect of the extraction process in optimizing the active coagulant agent of bagsase as a natural coagulant for optimum turbidity removal. Bagsase was characterized in terms of physical, chemical and morphological properties. The results showed bagsase has very high polysaccharide content which can act as an active coagulant agent together with hemicellulose and lignin. The extraction process for degradation of lignin and hemicellulose was run based on two different solvents (NaOH and H\({}_{2}\)SO\({}_{4}\)) with varying concentrations from 2% to 10% at different extraction temperatures varied from 60 \({}^{\circ}\)C to 180 \({}^{\circ}\)C for various extraction times (0.5 h to 3 h). The optimum polysaccharide content extracted from bagsase was 697.5 mg/mL by using 2% NaOH at 120 \({}^{\circ}\)C for 2 h extraction. The coagulation process using extracted bagsase showed the removal of suspended solids up to 95.9% under optimum conditions. The concentration of polysaccharides as the active coagulant agent plays a vital role where high polysaccharides content removes most turbidity at a lower dosage. Bagsase has the potential to be an alternative coagulating agent due to its efficiency, and eco-friendly properties for the treatment of wastewater.  
bagsase; active coagulant agent; polysaccharides
## 3 Results
### Analysis of Spatial Correlation between Snow Cover and NDVI
#### 3.3.2 Spatial Differentiation of Correlation for Different Vegetation Types  
The QTP has a rich diversity of vegetation types, which show different patterns of NDVI changes in recent years [32]. Hence, the correlation between vegetation and snow changes based on NDVI also varies depending on the vegetation types. In this paper, we used the latest distribution map of basic vegetation types on the plateau [33] to examine the spatial distribution and NDVI-snow depth correlation of the main vegetation types, namely grassland, forest (subdivided into evergreen coniferous forest, coniferous and broad-leaved mixed forest and evergreen broad-leaved forest), alpine vegetation and desert. The results are presented in the figure. Firstly, Figure 9 illustrates the spatial distribution of principal grassland vegetation types on the QTP. Compared with the NDVI-snow depth correlation depicted in Figure 9b, a substantial alignment is observed with areas exhibiting a significant negative correlation. Notably, the grassland at the southern slopes of the Qilian Mountains, Qinghai Plateau, Kunlun Mountain, Himalayas, and the Hengduan Mountain ranges highly overlap with the distribution of significantly negative correlation. In essence, most of the high-altitude grassland areas on the plateau demonstrate a marked negative correlation between NDVI and snow depth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Altitude/m**} & **Significant** & **Non-Significant** & **Non-Significant** & **Significant** \\  & **Negative** & **Negative** & **Positive** & **Positive** \\  & **Correlation (\%)** & **Correlation (\%)** & **Correlation (\%)** & **Correlation (\%)** \\ \hline \(<\)3000 & 14.25 & 31.30 & 36.01 & 18.44 \\
3000-4000 & 31.33 & 37.91 & 23.43 & 7.32 \\
4000-5000 & 41.80 & 30.91 & 18.13 & 9.16 \\ \(>\)5000 & 48.86 & 28.03 & 18.36 & 4.74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Significant area shares in relation to elevation (statistics for different elevation subdivisions).  
Figure 9: Spatial relationship between grassland and snow cover vegetation on the QTP: (**a**) spatial distribution of grassland; (**b**) spatial distribution of significant negative correlation.  
Figure 10 compares the spatial distribution of forest and desert grassland vegetation types with the positive NDVI-snow depth correlation distribution. A considerable congruence is observed between the two distributions across the plateau, particularly in the desert grassland areas in the Qaidam Basin, the southern slopes of the Kunlun Mountains and the northern plateau, where the snow-NDVI relationship exhibits a marked positive correlation, and statistically significant. The \"Yijiang Lianghe\" basin and the Hengduan Mountain region in Tibet are characteristic forested areas on the plateau (Figure 10a); the positive correlation distribution in Figure 10b resembles them. In other words, the areas with desert grassland and forest vegetation types on the QTP tend to show a positive and significant correlation between NDVI and snow depth variations.
# Degradation Assessment of In-Service Transformer Oil Based on Electrical and Chemical Properties
###### Abstract  
In order to ensure the long-term reliability and safety of power transformers, it is important to continuously monitor the characteristics of insulating oil, which not only helps in understanding its behavior over time but also ensures the safety of the equipment. The current study analyzes in-service insulating oil with the aim of relating deterioration and changes in the oil with service aging. Insulating oil samples were collected from three power transformers, with a voltage level of 220 kV and 132 kV, installed at a 220 kV substation. Electrical and chemical characteristics were obtained, and the impact of service aging and the relationships among load variation, oil, and winding temperatures with the characteristics were evaluated. Variations in the dielectric dissipation factor and breakdown voltage with service aging were recorded for all transformers, while the moisture content increased with each service year. Among the concentrations of gases present in the insulating oil, carbon monoxide, oxygen, and nitrogen concentrations increased after each service year. The impact of load variation on the breakdown voltage of the 132 kV transformer oil was more prominent than for the 220 kV transformers. The analysis of gas ratios and moisture content identified the degradation of cellulose insulation in all transformers, which was due to the presence of electrical faults.  
in-service transformer; insulating oil; dielectric dissipation factor; breakdown voltage; dissolved gas analysis; load variation; cellulose insulation; condition monitoring +
Footnote †: journal: Applied sciences
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 5 Evaluation Methodology
### Network Topology  
The topology of the network used to evaluate the system is shown in Figure 7. The network is composed of four elements, a surveillance camera, intermediate nodes, the Integrated Command and Control Center (ICCC), and wireless links.  
The surveillance camera generates information. The intermediate nodes deliver information to the network sink, i.e., the ICCC. The ICCC is responsible for collecting sensed information. The wireless links transmit information between nodes. All nodes in the network topology are connected to OpenFlow switches. Each OpenFlow switch is connected to a remote controller that is running the MPResiSDN algorithm. Figure 8 illustrates the three diverse paths that are used in the evaluation scenario.
# Environmental Remediation of Metribuzin Herbicide by Mesoporous Carbon--Rich from Wheat Straw
## 3 Results and Discussion
### Adsorption Kinetic Modeling  
Any adsorbent unit's design and optimization are controlled by accurate and relevant kinetic data. The kinetic data were examined to predict the extent of adsorption and to distinguish whether the adsorption process was chemical or physical sorption [63].  
The amount of adsorbed metribuzin per unit mass of the biochar and wheat straw (q\({}_{\text{f}}\)) is plotted versus time, which shows the time dependence of metribuzin removal by WS and WSB (Figure 3). Through both cases (pseudo second order and first order model), a fast removal rate is noticed in the early stage, followed by a slower pattern before equilibrium is reached [73].  
In terms of determination coefficients--R\({}^{2}\), the pseudo second order model was more accurate for fitting the experimental data than the pseudo first order model, according to the parameters obtained (Table 2).  
Figure 2: FTIR spectra of (**a**) raw WS (**b**) WS biochar.  
The pseudo second order model exhibited higher R\({}^{2}\) values within the range of 0.996-0.998 compared to the R\({}^{2}\) of 0.531-0.726 for the first order model. These observations are also consistent with previous research on biochar mediated metribuzin adsorption [74; 75]. According to the parameters of the pseudo second order model, the values indicate adsorption of the chemisorption type. The increase in metribuzin concentration from 5 mg L\({}^{-1}\) to 100 mg L\({}^{-1}\) enhanced the predicted metribuzin uptake from 1.453 to 1.565 mg g\({}^{-1}\). The same increase in metribuzin concentration decreased the K\({}_{2}\) values from 0.956 to 0.735. Due to the greater number of metribuzin molecules and fewer actives sites, a decrease in the adsorption rate was noticed. The pseudo second order model is frequently superior to the pseudo first order model in explaining the kinetics of adsorption phenomena and fits better with the experimental data, according to the majority of adsorption studies in the literature [76].  
Based on FTIR data, the abundance of biochar functional groups (OH, COO\({}^{-}\), R-O\({}^{-}\)) and the possible adsorption rate of metribuzin were mostly linked to ion exchange or surface complexation of biochar functional groups and metribuzin molecules. Furthermore, the mesoporous structure of wheat straw biochar stimulates metribuzin retention in the pore channel by physicochemical adsorption. According to [77], within advantageous circumstances, physisorption and chemisorption processes may concomitantly or alternatively take place. These results indicate that the adsorption rate depends more on the available active sites in the biochar than on the concentration of metribuzin.
# Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation
## 3 Detail Guided Multilateral Segmentation Network  
### Network Structure  
The semantic segmentation network is composed of three paths: context fusion path, detail guidance path and semantic information supplement path. The context fusion path is used to extract the main features for semantic segmentation, the spatial detail guidance path is used to guide the context path to learn and retain the spatial detail information in the deeper network, and the semantic information supplement path provides the connection between the local information and the global information to supplement the semantic information. The overall framework of DGMNet is shown in Figure 2.  
First, the input is passed to the context path and the detail guidance path. In the detail guidance path, the Gaussian Laplacian operator is used to extract the edge information of the image, and the feature maps of different sizes containing rich detail information are  
Figure 2: DGMNet network structure.  
obtained after a simple convolution operation. The features obtained by the lightweight backbone network and the detail guidance path are input into the feature interaction module, and the output features are fused with the features of the lightweight backbone network, so that the network can learn more detailed information. In the context path, the outputs of different layers of the lightweight backbone network will pass through the context feature fusion network, so as to extract features with rich semantic information and a large receptive field. At the same time, the convolutional features of the last layer of the backbone network are used to construct the graph structure in the semantic information supplement path. Then, the three features are effectively fused through the feature fusion module, and the transposed convolution is used for decoding. Finally, segmentation is carried out according to the features with rich semantic information and detailed information extracted from the network.  
### Network Structure  
#### 3.2.1 Context Fusion Path  
In order to obtain various features with rich feature information and reduce the consumption of hardware resources by the model, a lightweight backbone network is used to encode visual features in the context path to obtain feature maps with rich semantic information and large receptive fields. The last three output features of different scales and levels extracted by the lightweight backbone network are fused, so that the model can obtain more context information when performing semantic segmentation on the original input image. In addition, in order to improve the segmentation performance, the proposed full attention module (FAM) is used in the multi-scale fusion to refine the output features.
# Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
## 3 The Proposed Approach MIMLELM
### Transformation from SIML to SISL  
After transforming the MIML examples \((X_{i},Y_{i})\) to the SIML examples \((z_{i},Y_{i})\), \(i=1\), \(2\),\(\ldots\)\(m\), the SIML learning task can be further transformed into a traditional supervised learning task SISL, _i.e._, learning a function \(f_{SISL}\): Z \(\times\)\(Y\)\(\rightarrow\)\(\{-1,+1\}\). For this goal, we can implement the transformation from SIML to SISL in such a way that for any \(y\in Y\), \(f_{SISL}(z_{i},y)=+1\) if \(y\in Y_{i}\), and \(-1\) otherwise. That is, \(f_{SISL}=\{y|f_{SISL}(z_{i},y)=+1\}\).  
Figure 3 gives a simple illustration of this transformation. For a multi-label dataset, there are some instances that have more than one class label. It is hard for us to train the classifiers directly  
Figure 2: The process of transforming multi-instance examples into single-instance examples.  
over the multi-label datasets. An intuitive solution to this problem is to use every multi-label data more than once when training. This is rational because every SIML example could be considered as a set of SISLs, where each SISL is of the same instance, but with a different label. Concretely, each SIML example is taken as a positive SISL example of all the classes to which it belongs. As shown in Figure 3, every circle represents an SIML example. In particular, each example in area Ais of two class labels \"\(\bigcirc\)\" and \"\(\times\)\", while the other examples are of either the \"\(\bigcirc\)\" label or the \"\(\times\)\" label. According to the transformation from SIML to SISL mentioned above, an SIML example, say \((X_{u},\{\bigcirc,\times\})\) in area \(A\) should be transformed into two SISL examples, \((X_{u_{1}},\bigcirc)\) and \((X_{u_{1}},\times)\). Consequently, when training the \"\(\bigcirc\)\" model, \((X_{u},\{\bigcirc,\times\})\) is considered as \((X_{u_{1}},\bigcirc)\); otherwise, it is considered as \((X_{u_{1}},\times)\). In this way, the SIML examples in area A is ensured to be used as a positive example both in classes \"\(\bigcirc\)\" and \"\(\times\)\". This method can more effectively make full use of the data and make the experiment result closer to the true one.
## 4 Discussion
### Effect of Vibration Frequency on Mu Rhythm  
To set vibratory conditions for the Achilles tendon during stair climbing, mu rhythm reduction rate according to vibration frequencies was analyzed. As a result, it was confirmed that the mu rhythm reduction rate differs according to the applied vibration frequencies (Figure 1). This means that the vibratory stimulus applied to the Achilles tendon affected the sensorimotor cortex, and that acceptance of the vibratory sense in the Achilles tendon varies according to vibration frequencies. Depending on  
Figure 5: Mean value (\(\pm\) standard deviation) of co-activation of each gait phase according to the vibratory stimulation conditions at the sub-threshold intensity of each muscle group: (**a**) TA-GL group, (**b**) TA-GM group, (**c**) TA-SOL group, * indicates statistical significance through post-hoc tests between vibratory stimulus conditions (\(p<0.05\)).  
the distribution and density of the receptor [26] and the frequency of firing [12], the vibration frequency and sensing capacity to be detected vary, and the amount of information transmitted through the Ia afferent differs. Therefore, a difference occurs in the signal transmitted to the sensorimotor cortex according to vibration frequency, while a difference in the mu rhythm reduction rate occurs in response to changes in the vibration frequency. This is in line with the previous study, which reported that the somatosensory response to vibrotactile stimuli is frequency-dependent [27]. In addition, [PERSON] et al. [28] have confirmed that vibrations that were repeatedly applied to the flexor carpi radialis muscles during voluntary contractions can induce excitatory changes in the primary motor cortex. [PERSON] et al. [29] have also shown an increase in MEP due to vibrations of 80 and 120 Hz applied to the extensor carpi radialis muscle. In addition, [PERSON] et al. [30] have reported that the MEP of the transcranial magnetic stimulation was enhanced by vibrations applied to the extensor carpi radialis muscles. An increase of MEP occurs with increased excitability of the motoneuron pool, and there are also changes in the cortical responsiveness and in the operation of the intracortical circuits [31]. MEP varies with vibration frequency, and a change in MEP implies a decrease or an increase in the number of motor neurons that can respond to the excitation of a given motor neuron pool [32]. Therefore, in this study, the change in mu rhythm indicates that vibration applied to the Achilles tendon induced an excitatory change in the sensorimotor cortex, which affects both the motor neuron pool excitability and the target muscle activity.
## 3 Results
### Taxonomic Affiliation  
At the genus level, we observed significant differences in microbiota composition between control and Groups A, C, and D (Figure 4). In Group A, _Coprococus_ (\(p\) = 0.034) and _Prevotella_ (\(p\) = 0.035) were more abundant, while _Akkermansia_ (\(p\) < 0.01) showed a significantly lower relative abundance than that in the control group (Figure 4a). In comparison with the control group, Group C showed more abundant _Oscillospira_ (\(p\) < 0.01), and less abundant _Akkermansia_ (\(p\) < 0.01), _Lactococcus_ (\(p\) < 0.01), _Oxalobacter_ (\(p\) < 0.01), _Sphaerochaeta_ (\(p\) < 0.01), _Paludibacter_ (\(p\) < 0.01), _Fibrobacter_ (\(p\) < 0.01), _Annarovibrio_ (\(p\) < 0.01), _Pseudomonas_ (\(p\) = 0.012), _Mogibacterium_ (\(p\) = 0.013), _Pseudoramibacter_Eubacterium_ (\(p\) = 0.018), _YRC22_ (\(p\) = 0.018), _Flexispira_ (\(p\) = 0.021), _SMB53_ (\(p\) = 0.025), _AF12_ (\(p\) = 0.031), and _Roseburia_ (\(p\) = 0.038) (Figure 4b). For Group D, lower relative abundances were observed in _Allobactulum_ (\(p\) < 0.01), _Rikenchia_ (\(p\) = 0.023), _Shuttleworthia_ (\(p\) = 0.028), _Epulopiscium_ (\(p\) = 0.029), _Bilophila_ (\(p\) = 0.031), _Dorea_ (\(p\) = 0.033), _Fibrobacter_ (\(p\) = 0.047), and _TG5_ (\(p\) = 0.049) compared to the control group. Samples of Group B and E were not analyzed for the relative abundance due to the limited sample size.  
Figure 4: Relative abundance between control and groups A, C, and D with significant difference in bacterial genera. Extended error bar plots (**a–c**), display the differences in mean abundance of taxa in Groups A, C, and D compared to control (\(p\) < 0.05, confidence intervals = 95%), respectively. Analysis is performed with STAMP2.1.3 and the \(p\) values are based on t-test. _Prevotella_ with square brackets is proposed by the Greengenes curators and indicating the recommended taxonomy.