esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Geographic Area to Be Studied  
Yunnan Province is located between 21\({}^{\circ}\)8\({}^{\prime}\)-29\({}^{\circ}\)15\({}^{\prime}\) N and 97\({}^{\circ}\)31\({}^{\prime}\)-106\({}^{\circ}\)11\({}^{\prime}\) E, which crosses the Yangtze River, Pearl River, Yuanjiang River, Lancang River, Nujiang River and Daying River. It is an important ecological area in the upper reaches of the Yangtze River. As shown in Figure 1, it has obvious traffic location advantages, unique resources and prominent ecological status. The total population of Yunnan Province is 47.209 million, and the urbanization level is 50.05%, of which the minority population accounts for 33.12%. The terrain is mainly mountainous, with a mountainous area of 331,100 km\({}^{2}\), accounting for 84% of the total area of the province. The terrain is high in the northwest and low in the southeast, and it drops step by step from north to south. The climate is mainly subtropical and tropical monsoon climate, and Northwest Yunnan belongs to plateau mountain climate. The lake water area is about 110 \(\times\) 10\({}^{3}\) ha, accounting for 0.28% of the total area of the province. The forest area is 2392.65 \(\times\) 10\({}^{3}\) ha, with a forest coverage rate of 65.0%. Moreover, 85% of the annual precipitation is concentrated in May to October. In terms of ecological and economic status, the region plays an important strategic role in maintaining the balance between economic development and ecological protection of the Yangtze River economic belt. However, although Yunnan Province is a region with relatively rich water resources in China, the overall situation is not optimistic due to the uneven spatial and temporal distribution of water resources in the region [29].
## 4 Methodology
### Feature Engineering and Selection  
After simulation, we merged diffusion results with network metrics to create a comprehensive dataset for analysis. The final dataset for the synthetic networks contained 3600 rows and 40 columns after cleaning.  
We implemented a robust feature selection process to address several challenges:  
1. High dimensionality relative to observations;
2. Multicollinearity among structural features;
3. Potential for overfitting due to feature redundancy.  
Our feature selection approach combined correlation analysis with mutual information to capture both linear and non-linear relationships. We identified features with a high correlation (r > 0.8) to address multicollinearity.  
Figure 16: Coverage by delay type. Box plots show standard statistical summaries with outliers marked as circles.  
Figure 17: Coverage by spread probability in Synthetic Networks. Box plots show standard statistical summaries with outliers marked as circles.  
The top features by correlation with coverage included the following (Figure 18):  
* avg_degree (r = 0.628);
* peak_speed_time (r = 0.582);
* edges (r = 0.549);
* max_betweenness_centrality (r = \(-\)0.557);
* min_degree (r = 0.547).  
After mutual information analysis, we normalized and combined both the correlation and mutual information scores to rank the features. This resulted in selecting 20 features for the final model, including the following (Figure 19):  
* avg_degree (score = 0.946);
* edges (score = 0.928);
* spectral_radius (score = 0.932);
* max_betweenness_centrality (score = 0.915);
* avg_degree_centrality (score = 0.834).  
We also created cross-validation groups based on network_id to prevent data leakage. This ensured that observations from the same network would not appear in both the training and test sets during cross-validation.  
Figure 18: Top 10 features by correlation with coverage. Blue bars indicate positive correlation with coverage, red bars indicate negative correlation with coverage.
# An Internet of Things (IoT) Application on Volcano Monitoring
## 3 IoT Thermometers Network  
An IoT-based, reliable, robust, low-cost, low-power, and scalable wireless network of thermometers for monitoring the soil temperature in volcanic areas is proposed. This wireless network is composed by three main elements: the gateway, the repeater, and the end devices or thermometers. The network operation can be summarized as follows: The gateway, located in a place with internet access, manages the network and acts as a transparent bridge that relays messages between end devices and a central network server. Every sampling period, it communicates with the repeater to start the temperature measuring process. The repeater will retransmit the order to each end device (its number will depend on the points to be monitored) and wait for the returned answers. Every end device will identify the command and execute the associated task (measure, return RSSI value...), returning to the repeater the required information. The latter will collect packets from each end device, and it will retransmit altogether to the gateway. Data received by the gateway will be processed and stored locally on a SD card, and then, these data will be synchronized to the data analysis center (Figure 2).  
Figure 1: Tested links in Las Car\(\acute{\text{a}}\)adas del Teide caldera.
## 2 The Constrained Multi-Objective Component Deployment Problem
### Multi-Objective Formulation  
The communication overhead conflicts with the reliability objective because a system is more reliable when the software components are deployed to different hosts. The failure of a host in such a configuration only compromises the functionality of a single component. Co-locating several components on the same hardware unit minimises the communication overhead. In this study, we consider a bi-objective problem with reliability (Equation (7)) as the first and communication (Equation (8)) overhead as the second objective.
## 3 Collision-Free Hybrid MAC Protocol Based on Pipeline Parallel Transmission
### Network Model  
To simplify the discussion, we first consider a one-hop cluster in a distributed underwater acoustic network, which has the following characteristics:  
* All nodes in the cluster are static, randomly distributed and can reach each other directly using an omnidirectional and half-duplex underwater acoustic modem.
* Nodes share a common clock since time synchronization among distributed nodes is achievable in underwater wireless networks [16].
* A delay table that contains all the inter-nodal propagation delays in the cluster is stored by each node, which is obtained in the network initialization stage by measuring the round-trip time and exchanging information with its neighbors [15; 18].
* All nodes have one control channel and at least one data channel.
* Each node is equipped with a single transceiver, which means it can only listen, send or receive on one specific channel at the same time.
# Profiling Croatian Whisky Using GC/MS-MS and UHPLC-QTOF
## 3 Results and Discussion
### Aromatic Profile of Croatian Whisky Determined by SPME-GC-MS
#### 3.3.1. Sugars in Croatian Whisky  
In Croatian whiskies, several sugars were detected, including D-glucose, isomaltulose, raffinose, sorbitol, and arabinose. These sugars originate from multiple sources, such as raw materials, produced during enzymatic degradation of starches and cell walls as well as from the oak cask used in maturation and its possible previous contents, especially sweet wine. D-glucose is an essential component of the grains in the mash. During fermentation by yeast in the whisky-making process, it produces ethanol and other by-products. Any presence of residual glucose in the final product can impact the sweetness and body of the whisky [31; 37]. Isomaltulose, a disaccharide similar to sucrose, may be derived from a breakdown of sugars in the grains. Its presence in whisky could contribute to a different type of sweetness because it is metabolized more slowly by humans compared to sucrose [1; 37]. Raffinose, a trisaccharide of galactose, glucose, and fructose, is often found in small amounts after the breakdown of complex plant materials during mashing. It has the potential to influence viscosity and mouthfeel in the body due to its sugar component [20; 31]. Sorbitol, a sugar alcohol commonly found in fruits, can contribute mild sweetness and influence the mouthfeel of the whisky. Arabinose, a pentose sugar, is found in the raw materials or through the breakdown of hemicellulose from grains. It contributes to the sweetness and complexity of whisky by adding a sweet note, and it is likely derived from the breakdown of polysaccharides from grains or wooden casts used to age the spirits [37]. The presence of pentose sugars is also important for the production of key congeners through the Maillard reactions [41].
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
## 4 Proposed Imaging Method  
In this section, based on the proposed range model and the derived 2-D spectrum of the target, a novel ground moving target imaging method is presented. First, the imaging process is modeled as an optimization problem. Then, the details on utilizing the GA to solve the optimization problem are described. Finally, the flowchart of the proposed imaging method is presented.
# Communication Technologies in Emergency Situations
## 4 Emergency Communications Technologies--A Systematic Literature Review
### Satellite Communications Technologies
#### 4.2.3 Warning Systems Communications  
Warning Systems (WS) Communications have only been identified tangentially in a couple of works. For example, the public-warning system (PWS) is one of the use cases for the PS-LTE system presented in [48]. Earthquake sensor nodes could be installed to gather the shock information and transmit it to the CIRS to enable coordination operations. The CIRSs request mobile operators to broadcast public warning alerts to the users in their vicinity. The public warning system (PWS) is an alert-based system that is used for the delivery of short messages in case of emergency or disaster situations. The paper claims that many Public Warning Systems have been deployed all over the world, such as commercial mobile alert system (CMAS), earthquake and tsunami warning system (ETWS), Korean public alert system (KPAS), European (EU)-ALERT, and Austria public alert system. The notification latency varies with these systems, but usually it should be delivered within four seconds to users in the notification area.  
Additionally, in [45], the authors propose to link their proposed app to official emergency alert systems, such as the Wireless Emergency Alert (WEA) from the US, because most cellular service providers in the US implement WEA supported in all Android/IOS phones. Indeed, WEA messages ride on the control channel and thus are not affected by the network congestion.
# Reducing System Load of Effective Video Using a Network Model
## 4 Simulations
### System Configuration  
In this section, we show the proposed model. The experimental method in Figure 18 shows an example of image segmentation. When dividing the image, the image is adjusted to the absolute size, so the area can be divided according to the location of the FoV. This method is a flexible and objective method that can change the viewpoint by extracting a box-shaped image in units of regions. Algorithms and computational methods of networks are configured according to each domain.  
Figures 19 and 20 show the results of applying the image segmentation method to 360\({}^{\circ}\) video and general video. Since the image is scaled and segmented based on its absolute size, the area can be segmented according to the position of the FoV. As shown in Figure 18, this method can be easily and quickly applied to visual viewpoint transformation because it is extracted in the shape of a box. Therefore, flexible calculations are possible when applying networks and algorithms.  
Figure 17: Simulation Process.  
Figure 18: Location calculation by area.  
Figure 21 shows how the client and server behave according to user requests.  
The server prepares video for output through preprocessing.  
When the user's request is confirmed, the requested video is sent. Whenever the user's field of view is switched, the client sends an event to the server and requests the video it needs. When the server receives the video, each network works to output the video.  
Figure 21: Client system configuration.  
Figure 19: (a) Game user viewpoint (360 degrees). (b) User non-viewpoint screen.  
Figure 20: (a) 2D game split into (b) FoV and (c) non-FoV.  
Figure 22 shows how video is handled by the network depending on the user's point of view. In this simulation, the image was segmented into nine parts and divided into FoV and non-FoV areas according to the user's point of view. It shows how the image is outputted after SRGAN is applied to the FoV area and DAIN and SST algorithms are applied to the non-FoV area.
# Small-Size Square Ring 1-Bit Reconfigurable Transmitarray Unit Cell for C-Band Applications
## 2 Unit-Cell Design
Choosing the antenna topology, as well as the substrate parameters, allows maintaining the low ripple level of the unit-cell response with extended bandwidth, and, as a result, to reduce the insertion loss. The proposed unit-cell antennas have reduced size with respect to the standard patch due to the additional cutout in the center of the patches, thus providing square ring design. To study the influence the antenna layout, as well as the substrate parameters on insertion loss the full-wave EM simulation of the proposed unit-cell design is provided, Figure 2. The insertion loss is presented as a function of the cutout lateral dimensions for different substrate thickness while maintaining the reflection coefficient modules lower than \(-20\) dB. The lateral dimensions of the cutout area were chosen \(5\times 5\) mm\({}^{2}\). As a result, the square ring antenna area was decreased up to 28% with respect to standard patch antenna, which is equal to \(11.5\times 11.5\) mm\({}^{2}\) (\(0.44\lambda_{\text{g}}\times 0.44\lambda_{\text{g}}\)) and \(13\times 13\) mm\({}^{2}\) (\(0.5\lambda_{\text{g}}\times 0.5\lambda_{\text{g}}\)), correspondingly. On the other hand, the reduced antenna area brings additional losses that estimated as 0.2 dB higher than for traditional design. Nevertheless, we expect reduced mutual coupling between unit-cells arranged to a transmitarray. In addition, a denser allocation of unit-cells in a transmitarray will allow us to reduce a gain loss for beam steering [19].
# Structural Optimization of Slotting Nozzle Used to Improve Coal-Seam Permeability
## 2 Structural Optimization of Slotting Nozzle Design
### Numerical Experiment Results and Discussion  
The performance of a slotting nozzle is mainly related to stable segment length \(L\), convergent angle \(\theta\), and straight segment length \(l\). To evaluate the influence of each parameter on its performance and to provide a basis for the processing accuracy of each parameter, the significance of each parameter was analyzed. The results are shown in Table 3.  
As seen from Table 3, the convergent angle \(\theta\) had the largest range; the ranges of the stable segment length \(L\) and straight segment length \(l\) were similar. The data show that the convergent angle \(\theta\) had the greatest influence on slotting nozzle performance, while the influences of stable segment length \(L\) and straight segment length \(l\) were small. According to the magnitude of range, the primary and secondary orders of parameters affecting nozzle performance are: nozzle convergent angle \(\theta>\) stable segment length \(L>\) straight segment length \(l\).  
Taking the influencing factors as the abscissa and the water jet pressure at a distance of 150 mm from the outlet as the ordinate, the trends of slotting nozzle structure parameters and performance were plotted, as shown in Figure 5.  
When the total length of the slotting nozzle was fixed, the dynamic pressure gradually decreased and the rate of decrease slowed down with an increase of the length of the stable section \(L\). With an increase of convergent angle \(\theta\), the dynamic pressure increased quadratically and the rate of change gradually decreased. With an increase of straight segment length \(l\), the dynamic pressure decreased quadratically and the rate of change gradually increased.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Stable Segment** & **Convergent Angle** & **Straight Segment** & **Dynamic Pressure** \\
**Number** & **Length \(L\) (mm)** & \(\theta\) (°) & **Length \(l\) (mm)** & \(p_{k2}\) (MPa)** \\ \hline
1 & 0 & 10 & 3 & 5.04 \\
2 & 0 & 20 & 6 & 5.29 \\
3 & 0 & 30 & 9 & 5.39 \\
4 & 0 & 40 & 12 & 5.33 \\
6 &
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 6 Discussion of Findings and Consideration for Processing Approaches
### Constant Effective Velocity Detection in a Time-Scale Analysis Approach  
A first step to address the linear movement for high time-bandwidth products is to treat the Doppler effect in its original scaling relations even in the case of a constant effective velocity (Section 3). This can be broken down to the question how to properly treat the fine resolutions compared to the Doppler scaling relations for moving target detection. The task becomes 'how to find the correct scale to treat and to determine the velocity correctly'. The optimal processing technique would thereby address the whole movement effects with a proper method, in short processing time to reduce the added latency and embody a sufficiently low computational complexity and implementation effort. One straight forward approach is a time (range)-scale analysis [1] that results in a modified version of the cross-ambiguity function \(\chi(\tau,f_{\mathrm{d}})\)[13]. The modified function with scaling relations then yields  
\[\chi_{\mathrm{mod}}(\tau_{0},s)=\int_{t_{0}}^{t_{0}+T_{\mathrm{cov}}}y_{ \mathrm{rx}}(t)\,\frac{1}{|x_{\mathrm{ref}}|}\,x_{\mathrm{ref}}^{*}\bigg{(} \frac{t-\tau_{0}}{s}\bigg{)}\mathrm{d}\,t\,\,\,\mathrm{for}\,\,\{s=const\mid t _{0}\leq t<t_{0}+T_{\mathrm{cov}}\}. \tag{28}\]  
The time-scale approach as described in Equation (28) addresses primarily the decreased accuracy due to the involved spread (Section 5.1). Hereby, \(t_{0}\) marks the beginning of the CPI with duration \(T_{\mathrm{cov}}\) and the time offset \(\tau_{0}=R_{\mathrm{bi}}(t_{0})/c\) relates to the bistatic target range at a fixed reference point \(t_{0}\) in time. The analysed scale factor \(s\) gives then according to (13) the estimated effective target velocity by \(v_{\mathrm{est}}\approx c\,\left(1-s\right)/\left(1+s\right)\). In addition to a regained resolution for linearly moving targets, this also improves the capability for an accurate multistatic localisation because the bistatic range \(R_{\mathrm{bi}}(t_{0})\) at a common reference time \(t_{0}\) will no longer be worsen by the linear movement extension. This approach is in line with the case described in Section 3.2 and the inherent processing model is therefore a continuation of the classical assumption (Section 2.2) of a constant effective velocityand confined scatterers. It can thereby achieve an increase in the realised coherent integration gain as long as the otherwise extended target return stays similar to a uniform and rectangular spread without deviation (Section 6.4).
# A Novel Adaptive FCM with Cooperative Multi-Population Differential Evolution Optimization
## 2 Background  
Fuzzy c-means or FCM is a partitional clustering algorithm based on the notion of fuzzy membership of objects in a dataset to a cluster. FCM is useful when the natural partitions in a dataset are not evident or very well defined. A data object \(x_{k}\) has a certain membership \(u_{ik}\) (which takes values between zero and one, not including 0 or 1) in a cluster \(C_{i}\), which is seen as the partial (fuzzy) belongingness of the data point to that cluster, subject to the constraint that the sum of memberships across all clusters is unity and the contribution of memberships of all data points to any particular cluster is always less than the cardinality of the dataset \(n\) (\(k\) = 1,2,..., \(n\)).  
\[\sum\limits_{i=1}^{c}u_{ik}=1;\ 0<\sum\limits_{k=1}^{n}u_{ik}<n \tag{1}\]  
The fuzzy sum-of-squared-error objective function is the least squares estimator function,  
\[J_{FCM}=\sum\limits_{i=1}^{c}\sum\limits_{k=1}^{n}u_{ik}^{m}||x_{k}-v_{i}||^{2} \tag{2}\]  
The exponent \(m\) is called the fuzzifier which determines the fuzziness of the partition, \(m\in[1,\infty)\) and \(||x_{k}-v_{i}||_{2}=(x_{k}-v_{i})^{\prime}A(x_{k}-v_{i})\) is the distance between cluster prototype \(v_{i}\) and data object \(x_{k}\) where \(A\) is the norm matrix. The identity norm matrix \(A=I\) yields Euclidean distance and results in spherical clusters while other norms are used for elliptical clusters, etc. In this paper, the Euclidean distance has been used. The FCM-AO minimizes the functional in Equation (2) by iteratively calculating cluster prototypes and updating memberships until there is no change in the termination criterion, usually as measured by a change in memberships between two successive iterations.  
\[v_{i}=\frac{\sum\limits_{k=1}^{n}u_{ik}^{m}x_{k}}{\sum\limits_{k=1}^{n}u_{ik} ^{m}}\ \ \ \ \forall i=1,\ 2,\ldots,c \tag{3}\]  
\[u_{ik}=\frac{1}{\sum\limits_{j=1}^{c}\left[\frac{||x_{k}-v_{j}||^{2}}{||x_{k}- v_{j}||^{2}}\right]^{\frac{1}{m-1}}} \tag{4}\]  
The equations presented for FCM are from [4]. The algorithm often starts by initializing cluster prototypes randomly, then calculating memberships using Equation (4), followed by recalculating prototypes using Equation (3) until convergence. The final membership matrix \(\mathbf{U}=\left[u_{ik}\right]_{c\times n}\) is sensitive to the initialization of cluster prototypes. In addition, the iterative optimization scheme assumes that the number of clusters \(c\) is known. The only parameter to tune is the fuzzifier \(m\) which depending on the fuzzification required is often set to \(m=2\) for most clustering problems (especially one involving spherical clusters).
# Solar Filament Detection Based on an Improved Deep Learning Model
## 3 Hybrid CNN-Transformer Model
### Hybrid CNN-Transformer Model  
Figure 6 shows a full-disk H\(\alpha\) image observed on 21 October 2013 at 19:50:25 UT. The filaments in the four red boxes were selected for analysis: a broken, long filament in box 1, a large flaky filament in rectangle 2, a broken circular filament in rectangle 3, and a small filament in rectangle 4. The structures within rectangles 5 and 6 are sunspots. They are similar to filaments. When manually detecting these filaments, it is necessary to use detailed features such as the brightness and edges of the filaments. It is also important to consider global features such as shapes and overall structure.  
Existing methods are less capable of utilizing global features. They are prone to be overly sensitive to local features, resulting in misdetections of background noise and sunspots such as the structures in rectangles 5 and 6. Therefore, fully utilizing global features will improve the method's resistance to interference.  
Figure 5: Structure of the Transformer layer.  
Figure 6: A full-disk H\(\alpha\) image observed on 21 October 2013 at 19:50:25 UT.  
To effectively combine local detail information with global information, researchers have experimented with introducing Transformer into CNN, leading to the development of various Hybrid CNN-Transformer models. Among these, SETR [29] and TransUnet [30] are notable examples. SETR employs Transformer for feature encoding and employs CNN to decode the global information obtained from Transformer. TransUnet inserts Transformer between the encoder and decoder of U-Net, leveraging the local detail information extracted by the U-Net encoder as the input features for Transformer.  
Introducing Transformer into CNN can enable the network to combine global information with local detail information. We applied this method to the field of solar filament detection. It can better deal with features of the textures, edges, locations, and structures of solar filaments, thus achieving better solar filament detection performance.