esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions and Future Works  
Energy consumption is a challenging design in the conception of wireless networks. Long distance communication channels used by cluster heads to reach the base station consume a significant amount of energy and accelerate the appearance of dead nodes and congestion problems. In this context, we present a new distributed approach for k-hop intra-clustering called Distributed Clustering based 2-Hop Connectivity (DC2 HC) for large  
Figure 16: Last node death according to different nodes density with (**a**) \(k=1\), (**b**) \(k=2\), (**c**) \(k=3\).  
Figure 17: Average number of alive nodes over execution time with \(\delta=1000\) deployed nodes and (**a**) \(k=1\), (**b**) \(k=2\), (**c**) \(k=3\).  
networks. The approach optimizes the set of representative cluster heads and extends the network lifetime. The cluster heads election is performed based on the two-hop neighbors connectivity of nodes to strength the cluster connectivity. It also considers the residual energy of nodes to balance the energy consumption among larger clusters. We proved that the proposed approach has a linear time complexity of \(O(|V|+k)\) rounds, where \(|V|\) is the number of nodes in the network. Various simulation experiments have been performed to evaluate the performance of the proposed scheme with different parameters. Simulation results show that the proposed algorithm provides better performance compared to similar algorithms. The cluster heads cardinality is reduced by 40.1%, the energy consumption is enhanced by 21.7% and the network lifetime is extended by 20.8%. As future work, we plan to study the effect of introducing the State of Health (\(SoH\)) of rechargeable batteries [60] during the clustering and integrating an optimized attack prevention schemes [61] to avoid the sudden death of network device and further increase the network durability.  
[PERSON] conceived the idea, performed the analysis and wrote the first draft of the paper; [PERSON] and [PERSON] reviewed, edited and finalized the writeup of the paper. [PERSON] assisted with the experiment design, the data processing and revision. All authors have read and agreed to the submitted version of the manuscript.  
This research was funded by UAEU-UPAR, grant number 31T102-UPAR-1-2017.  
Not applicable.  
The authors declare no conflict of interest.
# Habitat Loss and Other Threats to the Survival of _Parnasius apollo_ ([PERSON], 1758) in Serbia
## 2 Materials and Methods
### Vegetation Indices and Remote Sensing  
Accurate classification of vegetation in forested landscapes requires robust indicators of canopy condition, including parameters such as vegetative vigor, chlorophyll concentration, growth dynamics, canopy structure, and leaf traits. High-resolution remote sensing platforms, particularly Planet Explorer and Sentinel-2, offer the spatial and spectral resolution necessary for monitoring these dynamics across heterogeneous terrain.  
In this study, vegetation indices (VIs) derived from Planet Explorer imagery were utilized to classify vegetation types within mixed forest ecosystems [70]. A central methodological challenge in using VIs for dense vegetation lies in the saturation effect, where index values plateau beyond a certain threshold of biomass or chlorophyll content. This limitation is especially evident with conventional indices such as the Normalized Difference Vegetation Index (NDVI), which often fails to differentiate structural or biochemical variations in high-biomass forests.  
To overcome this, we incorporated several advanced vegetation indices that have demonstrated improved sensitivity and resistance to saturation under dense canopy conditions [71]. Among the tested indices, the Modified Triangular Vegetation Index 1 (MTVI1), Modified Non-Linear Index (MNLI), Triangular Greenness Index (TGI), Sentinel-2 Triangular Vegetation Index (STVI), and Triangular Vegetation Index (TRV) showed superior performance. These indices effectively captured structural and physiological variation in complex forest environments, maintaining responsiveness under high biomass and LAI conditions [71; 72].  
Specifically, MTVI1, MNLI, and STVI demonstrated strong sensitivity in conditions of elevated Leaf Area Index (LAI), outperforming traditional indices by retaining discriminative capacity in dense canopies [73]. Their application enabled accurate quantification of vegetation heterogeneity, which is essential for ecological assessment and landscape monitoring in mixed forest-grassland systems.
# Victim Detection and Localization in Emergencies
## 2 Challenges of Detection and Localization of Victims under Rubble
In contrast, underground victims are extremely challenging to find due to visibility and sound being blocked by rubble. In [14], they implemented an audio-processing-based human detector with a UAV even under rubble. The victims needed to be located in three dimensions, in order to better assess their situation, communicate with them, and estimate their chances of survival. Moreover, the precision requirements were higher, since rescuing underground victims often involves complex and risky procedures to liberate them. Finding Individuals for Disaster and Emergency Response (FINDER) uses radar for detecting heartbeats or breathing variations of the victims [22]. However, underground techniques are extremely complex and cover a limited area. To overcome this limitation, the DronAid system proposed in [15] uses Passive Infrared (PIR) sensors mounted on a UAV to scan the rubble, looking for victims trapped near the surface. Table 1 compares the different victim detection and localization systems described above compared with the proposed method. The crucial characteristics that we compared were: the capability of finding individuals under rubble, a fast deployment to find victims, and the precision of the victim's location.
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 3 Material and Methods
### Data Acquisition Part  
This is the part in which we collected data about the current consumption for a given location targeting to know the impact of RSSI on the current consumption in that particular area. For each  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Applications** & **AT Command** & **Explanations** \\ \hline \multirow{5}{*}{AT Command for GPRS} & AT+CREG? & Network registration \\  & AT+SAPBR=3,1, & Connecting to GPRS \\  & AT+SAPBR=1,1 & Activation for bear profile \\  & AT+HITTPNIT & Initialization for HTTP Services \\  & AT+HITTPNARA & Set HTTP Parameter values \\  & AT+HITPACTION & HTTP Method action \\  & AT+HITTPREAD & HTTP Read server response \\  & AT+HITTPERM & Terminate HTTP Service \\ AT Command for RSSI & AT+CSQ=? & Signal quality report \\ \hline \hline \end{tabular}
\end{table}
Table 2: Some AT Commands used in this paper.  
Figure 5: System configuration.  
record, we spent 24 h. To achieve this, we used an Arduino Uno based microcontroller, an ACS712 (Shenzhen Hongxuan Electronic Co., Ltd., Shenzhen, China) current sensor, and Ni LabVIEW [21]. The picture for our experiment is shown in Figure 6.  
Current data from six locations (with their RSSI values) have been recorded considering four different scenarios.
# Perspective of Using Vertically Oriented Graphene as an Electrochemical Biosensing Platform
## 3 Results and Discussions
### AFM Analysis  
The surface roughness of VG can be interpreted by analyzing data obtained from scanning the surface with an atomic force microscope (AFM) presented in Figure 3. Roughness refers to surface variations in height, and AFM analysis can provide detailed information about these variations. With the help of AFM analysis, the topography of the surfaces of the vertical graphene samples was carried out, and the roughness was determined for the sample.  
The roughness of a sample refers to the asperities and variability in the surface relief of that sample. It measures the level of unevenness or irregularity of the surface. The roughness is characterized by the difference in the height of the points on the surface in relation to a reference plane. R\({}_{\text{MS}}\) and R\({}_{\text{q}}\) are two standard parameters used to quantify surface roughness in topographic analysis by AFM. These parameters are used to measure the average roughness and the variation in roughness on the analyzed surface. The value of R\({}_{\text{MS}}\) is 48.32 \(\pm\) 1.31 nm, and the value of R\({}_{\text{q}}\) is 39.33 \(\pm\) 1.10 nm. The result of the AFM analysis of the vertical graphene is correlated with the images obtained from the SEM analysis. The fact that vertical graphene has a high roughness indicates that its structure consists of vertical graphene walls with different heights. Some intertwine, giving it open edges with sharp tips and presenting irregularities on top. This characteristic offers an advantage regarding cell adhesion on the VG surface because the cells can adhere to the VG walls.
###### Abstract
However, due to the limitations of the observation conditions, studies applying the EC method to observe CH\({}_{4}\) fluxes in paddy fields are rare, with only a few studies concentrated on the US [43; 44; 45], Italy [46; 47], Philippines [48], Indonesia, Japan [41], Korea [1; 31] and China [2; 34; 49; 50]. These studies reported that the pattern and magnitude of CH\({}_{4}\) fluxes were site dependent and varied with time. Additionally, [PERSON] et al. [44] conducted a 6.5-year observation in California, which represents the only study at an inter-seasonal timescale. Hence, more studies are urgently needed. The paddy fields in the middle and lower reaches of the Yangtze River are cultivated in a double-cropping pattern that benefits from suitable water and thermal conditions. Currently, with the development of the economy and urbanization, job opportunities in the industry and service sectors are increasing rapidly [51], and wages are much greater in cities than in rural areas. These conditions have resulted in a substantial labour shortage in traditional paddy fields. Hence, rice cultivation is shifting from transplanting to direct seeding. Alternate wetting and drying (AWD) irrigation is also currently practiced in southern China [50]. Compared with traditional irrigation patterns, AWD has advantages for reducing water use [52; 53] because paddy fields are dried out for approximately 3-5 days after the standing water depth falls to zero. Studies have reported that CH\({}_{4}\) emissions in paddy fields are regulated by SWC [27; 39]; therefore, changes in irrigation patterns will consequently influence CH\({}_{4}\) exchange in this region. The changes in planting and irrigation patterns provide a challenge for estimating regional CH\({}_{4}\) emissions, and studies on the variations in CH\({}_{4}\) emissions and their biophysical controls are urgently needed. Therefore, a four-season observational study of CH\({}_{4}\) fluxes was conducted in two double-cropping paddy fields in southern China. This paper reports the variations and drivers of CH\({}_{4}\) fluxes at diurnal, seasonal and inter-seasonal timescales. The main objectives of this study are to (1) explore the variations in CH\({}_{4}\) fluxes at diurnal and seasonal timescales; (2) investigate the dominant drivers at diurnal and seasonal timescales; and (3) measure and compare CH\({}_{4}\) fluxes at inter-seasonal timescales. This study contributes to the understanding of CH\({}_{4}\) emissions and provides a strategy for reducing regional GHG emissions.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Granulation,** & **Concentration** & **\(\Delta\)S, J/mol** & \(\Delta\)H, J/(mol \(\cdot\) K)** & \(\Delta\)**G, J/(mol \(\cdot\) K)** \\ \hline
1.0–1.6 & 0.5 & 77.321 & \(-\)29,099.00 & \(-\)7254.18 \(\div-\)6073.08 \\ \hline
1.0–1.6 & 1.0 & 108.980 & \(-\)38,475.53 & \(-\)7953.77 \(\div-\)5947.07 \\ \hline
1.0–1.6 & 2.0 & 1.238 & \(-\)7015.85 & \(-\)6720.82 \(\div-\)6419.69 \\ \hline
1.6–2.5 & 0.5 & 52.998 & \(-\)21,633.86 & \(-\)6346.95 \(\div-\)5601.56 \\ \hline
1.6–2.5 & 1.0 & 132.284 & \(-\)32,214.26 & \(-\)7603.71 \(\div-\)5256.99 \\ \hline
1.6–2.5 & 2.0 & 87.970 & \(-\)32,214.26 & \(-\)8151.18 \(\div-\)6229.76 \\ \hline
2.0–5.0 & 0.5 & 0.853 & \(-\)6992.24 & \(-\)6146.72 \(\div-\)6673.73 \\ \hline
2.0–5.0 & 1.0 & 92.028 & \(-\)32,647.42 & \(-\)6835.24 \(\div-\)5440.45 \\ \hline
2.0–5.0 & 2.0 & 28.528 & \(-\)15,138.13 & \(-\)7258.97 \(\div-\)6816.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thermodynamic function values. Figure 11: Examples of the thermodynamic relationships in the sorption process. Negative free energy values suggest that the phosphate adsorption process is spontaneous over the studied temperature range. It can also be noted that \(\Delta\)G increases with increasing solution temperatures, which can be explained by adsorption becoming slightly more challenging with increasing temperatures [53].
## 4 Experiments
### Two-Level Attention Model  
The Res-CNN structure is shown in Table 1, where \(bs\) in the blue box of \(Conv6\) takes 192 in the first two residual blocks and 320 in the last residual block. OARes-CNN embeds an object-level attention module in the Res-CNN structure. PARes-CNN adds a pixel-level attention module to the Res-CNN structure. Their detailed structures are shown in the upper and lower halves of Figure 1. TA-CNN introduces a two-level attention module simultaneously in the Res-CNN structure. For example, in Table 1, convolutional blocks, such as \(Conv1\) and \(Conv2\), are followed by a TA-block. At the same time, we conducted feature fusion for two levels of attention. TA-CNN_ADD indicates that a summation operation is performed on two-level attention features, and TA-CNN_SORT shows that the SORT method is performed on two-level attention features.  
The experimental data shown below was obtained without using pre-training networks and fine-tuning techniques. As shown in Table 2, Res-CNN was better, in terms of classification accuracy and time complexity, than B-CNN; due to the use of residual structure, and as Res-CNN does not adopt the local pairwise feature interaction methods, so its number of parameters is also greatly reduced. Compared to Res-CNN, OARes-CNN increased the classification accuracy by 0.58%, as we introduced an object-level attention mechanism. PARes-CNN introduced a pixel-level attention mechanism, and achieved similar results. However, single-level attention is not obvious enough to improve network performance. TA-CNN focuses on the classified images by using a two-level attention. TA-CNN_SORT achieved better results than TA-CNN_ADD, as the former learned higher-level feature representations. Here, we define TA-CNN_SORT as TA-CNN. Compared with Res-CNN, the classification accuracies of TA-CNN on the datasets Stanford Cars and FGVC-Aircraft increased by 0.93% and 0.86%, respectively. Correspondingly, the network parameters have increased by about 1.69\(M\), and the time complexity has increased slightly. It is worth mentioning that, without using pre-training conditions, compared with B-CNN, the classification accuracy of TA-CNN increased by 4.61% for the FGVC-Aircraft dataset.
# Removal of Chromium from Synthetic Wastewater Using Modified Maghemite Nanoparticles
## 4 Conclusions  
The influence of process parameters, i.e., pH, adsorbent dose, and initial concentration, on chromium removal were investigated by performing statistically designed experiments. The data obtained from ANOVA establishes that the model was highly significant. The results showed that as pH and initial concentration increases chromium removal decreases, but as adsorbent dose increases chromium removal increases. Concerning the interaction effects, adsorbent dose and initial concentration showed positive effects, while the interaction between pH and initial concentration showed negative effects. Adsorbent dose also showed a significant squared effect in the experiment. The maximum chromium removal (95.5%) was observed at pH = 2.6, adsorbent dose = 5 g/L, and initial chromium concentration = 20 mg/L. The proposed model equation can certainly provide a good estimation of chromium removal in large-scale systems, such as tannery industries. A systematic study on the removal of chromium using MNPs can provide a practical solution for tannery industries located around Vellore (India) to reduce waste discharges, recover useful materials, and reduce or prevent pollution. We would suggest further research to examine the regeneration aspect of MNPs and their subsequent use in order to make them more economical for industrial use.  
Conceptualization, S.K.B.; formal analysis, B.M. and A.V.; investigation, S.S., G.T., and A.R.; methodology, S.K.B.; resources, S.K.B.; supervision, S.K.B.; writing--original draft, S.S., G.T., and A.R.; writing--review and editing, B.M., A.V., E.R.R., and A.P. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the management of Vellore Institute of Technology, Vellore, for supporting this work through the university research seed grant.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Kinematic Data Analysis  
#### 2.3.1 Cycle-Averaged Kinematic Parameters Definition  
Two trajectory-related parameters were calculated for each movement cycle: Smoothness Metric (_SM_), which is the number of peaks detected in the velocity profile obtained per trajectory, and Efficiency Metric (_EM_), as the length of the trajectory, both computed in accordance with [16], as defined in Equations (1) and (2), respectively, where \(x_{i}\), \(y_{i}\), \(z_{i}\) represent the Cartesian coordinates at frame \(i\), \(\Delta t\) is the time difference between two frames, and \(L\) is the total trajectory length.  
\[\begin{array}{l}SM=\sum\limits_{i=2}^{L-1}\delta(v_{i})\hskip 28.452756 pt \delta(v_{i})=\left\{\begin{array}{ll}1,&\text{if }v_{i-1}<\ v_{i}>\ v_{i+1}\\ 0,&\text{otherwise}\end{array}\right.\\ \\ v_{i}=\sqrt{\dot{x}_{i}^{2}+\dot{y}_{i}^{2}+\dot{z}_{i}^{2}}\hskip 11.381102 pt \dot{x}_{i}=\frac{x_{i}-x_{(i-1)}}{\Delta t},\ \dot{y}_{i}=\frac{y_{i}-y_{(i-1)}}{\Delta t},\ \dot{z}_{i}=\frac{z_{i}-z_{(i-1)}}{ \Delta t}\\ \\ EM=\sum\limits_{i=2}^{L}\sqrt{\left(x_{i}-x_{\{i-1\}}\right)^{2}+\left(y_{i}-y _{\{i-1\}}\right)^{2}+\left(z_{i}-z_{\{i-1\}}\right)^{2}}\end{array} \tag{1}\]  
Ten additional parameters were obtained from the five hand kinematic synergies (_Thumb_F_, _MCPs_F_, _PIP_S_, _Thumb_A_, _Fingers_A_) for each movement cycle. These parameters captured central tendency and dispersion using the median and range, as the Shapiro-Wilk test indicated non-normal distribution. The range, defined as the difference between maximum and minimum values, was used instead of the interquartile rangebecause the parameters were normalised to reference maximal and minimal values [23]. These parameters are labelled to reflect the metric: _KinematicSurgery_M_ for the median and _KinematicSurgery_R_ for the range (e.g., _Thumb_F_M_ and _Thumb_F_R_ for median and range of _Thumb_F_, respectively).
# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 3 Results  
Our sample data consist of radar images of Antarctica acquired between 2009 and 2011 (i.e., 2009_Antarctica DC8, 2010 Antarctica DC8 and 2011 Antarctica TO). The first and second data sets (i.e., 2009 and 2010) were obtained by MCoRDS with bandwidth of 9.5 MHZ and transmission power of 550 W. The third data set (i.e., 2011) was acquired by MCoRDS2 with bandwidth of 30 MHZ and transmission power of 1050 W. For all data sets, pulse compression and windowing algorithms are used to improve the range resolution of the image, and synthetic aperture radar (SAR) processing is used to improve the azimuth resolution. The range resolution of radar image obtained by MCoRDS and MCoRDS2 is 13.6 m and 4.3 m respectively. The azimuth resolution of all images is 25 m. Moreover, a minimum variance distortionless response technique is employed to suppress the clutter from the cross-track direction [26]. In this work, 360 images are used for training and 100 images are used for testing. All images of training and testing contain all target classes, i.e., free space, layers, bedrock, and noise (including EFZ). Although the radar image dataset is small, the network can accelerate the convergence speed of gradient descent and obtain better classification performance, due to the ResNet model which is pretrained by the ILSVRC-2012-CLS image classification dataset in the training phase. Our implementation is built on TensorFlow. The batch normalization parameters are trained with decay = 0.9997. Momentum and weight decay coefficients are set to 0.9 and 0.0002 respectively. Batchsize is set to 4 with 10,240 iterations. We employ a 'poly' learning rate policy where the initial  
Figure 5: The decoder concatenates the high-level and the low-level features.  
learning rate is 0.007. During training, we set the image crop size to 513, randomly flip the images from right to left, and scale the input images from 0.5 to 2.0.  
Like other classification methods of the ice subsurface targets [10; 11], this paper employs overall accuracy (OA) to evaluate classification performance of our network. Instead of taking OA as the evaluation of window sample classification (i.e., each window contains multiple pixels) as in [10] and [11], we take OA to measure the image pixel classification results. OA represents the percentage of predicted correct classification to total samples. At the same time, we also use kappa coefficient as the evaluation metric, which integrates the diagonal and non-diagonal terms of the confusion matrix.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 4 Path Planning
### Significant Points Extraction  
\[PathSet=\{\mathbf{P}(0),\mathbf{P}(1),\ldots,\mathbf{P}(n-1)\} \tag{7}\]  
The above path point set is produced by A-heuristic algorithm (illustrated in Figure 5a) still needs to be reprocessed before the smoothing path algorithm plays its role. Since A-heuristic has a limit in searching direction and distance between consecutive checking points, it produces the oscillated path that makes robot difficultly continuously follows [16], i.e., directly smoothing this path point set for robot trajectory produce large curvature trajectory and make robot decelerates to follow trajectory or slipped out trajectory many times. One more significant reason is reducing memory costs when smoothing path because the greater number of path point set causes increasing memory using to produce a smooth trajectory. First, by removing points in the same line, we can greatly reduce the number of points in the path point set. The significant point set can be present as (8) and illustrated in Figure 5b corresponding to the current example.  
Figure 4: (**a**) Number of steps that the window which has the lowest h score of open set converges the target spot; (**b**) total spots of open set and closed set during the search.  
Figure 5: (**a**) Path point set is produced by A-Heuristic algorithm; (**b**) significant point set; (**c**) reduced significant point set by using regression search method.  
\[significantSet=\{\mathbf{P}(i)\big{|}\mathbf{P}(i+1)-2\mathbf{P}(i)+\mathbf{P}(i-1)\
eq 0,\mathbf{P}(i)\in pathSet\}\cup\{\mathbf{P}(0),\mathbf{P}(n-1)\} \tag{8}\]  
\[k=\{1,2,\ldots,n-2\}\]  
Geometrically, the A-heuristic algorithm produces redundant triangular paths caused by its limit in the searching direction. The shortening triangular path by the regression search method is employed. The robot sequentially checks the segmented line between the pairs of point \(\{\mathbf{P}_{i}\mathbf{P}_{i+2}\}\) with the obstacle-scaled map \(\mathbf{M}_{i,j}\). If the line segment does not contain any obstacle the point \(\mathbf{P}_{i+1}\) will be removed, otherwise, the algorithm will continuously be checking \(\{\mathbf{P}_{i+1},\mathbf{P}_{i+3}\}\). This process will be repeated until no points are disqualified. The Figure 5c illustrates the significant point set in Figure 5b that is applied the regression search method.