esa-sceva/satcom-chunk-collection huggingface dataset:
# Regression Equations for Estimating Landslide-Triggering Factors Using Soil Characteristics
## 3 Results
### Derivation of Regression Equations for Evaluating Landslide-Triggering Factors
#### 3.5.1 Regression Equations of Permeability Coefficient
According to the results of regression analysis for the gneiss and granite regions, variance analysis, which is a measure of the accuracy of an equation, had a significant probability of P = 0.000, and the R-square representing explanatory power was about 83% in gneiss soil and about 93% in granite soil. This means that the regression equation was suitable. With this, the regression equations for estimating permeability coefficient in different geological conditions can be summarized as follows, using the unstandardized coefficient of regression equation results:  
\[\mathrm{k}_{\mathrm{gn}}=(1.488\times 10^{-02}\times e)+(1.076\times D_{10})+(-1. 629\times 10^{-04}\times c_{u})-(1.893\times 10^{-02}) \tag{2}\]  
\[\mathrm{k}_{\mathrm{gr}}=(8.281\times 10^{-03}\times e)+(0.639\times D_{10})+(-2. 766\times 10^{-05}\times c_{u})-(9.907\times 10^{-03}) \tag{3}\]  
where \(\mathrm{k}_{\mathrm{gn}}\) is the permeability coefficient of gneiss soil, \(\mathrm{k}_{\mathrm{gr}}\) is the permeability coefficient of granite soil, \(D_{10}\) is the effective diameter, \(e\) is the void ratio, and \(c_{u}\) is uniformity coefficient, respectively.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Future Directions
#### Optimal Sensor Position  
Water distribution network monitoring is crucial. owing to the consequential effects of pipe bursts and contaminated water on public health. Intensive monitoring of the WDN pipes where sensors are placed at a strategic position under a limited budget will improve water quality and leakage awareness. In the last few years, research studies have been conducted on sensor placement under this budget constraint [158; 159; 160; 161]. [PERSON] et al. [158] presented a critical survey of the optimal sensor placement for a contaminant warning system in a water distribution network. Unfortunately, optimal placement of sensors has not been achieved under cost constraints and has received notable attention. Thus, further research studies are needed to develop efficient and rapid algorithms to solve problems related to optimal sensor position along the pipe.
## 3 Case Study Used in Assumption Analysis
### Activity 2: Analysis of UX with the \"DELIVERED\" Solution by the Development Team  
A video containing a recording of a user test was shown individually to each participant. The video was recorded remotely through a videoconference, where the user, in addition to having a camera filming his interaction with the device, shared the screen of the smart glasses. The objective was to see if the developers were empathetic with the user's reaction when using the solution. This video was chosen because it clearly displayed users using the solution and reporting their opinions about usage, and being possible to observe where the user presented their difficulties with the solution. For example, the video showed that the app faced a lot of resistance at first due to the new interaction paradigm. Five electricians had difficulty looking at a tiny screen for a long time. They reported problems such as blurred vision, headaches, and fatigue. This was compounded by the fact that many electricians had vision problems and wore corrective lenses. Another complaint was about the need to memorize commands (this was later resolved with the addition of a glossary of commands). Due to ergonomic issues, features that required the camera, such as augmented reality and object detection, were initially not well received. Navigation was confusing for users as more steps were taken than necessary to reach a goal (the team later simplified navigation by removing branch paths).
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 6 Experimental Results and Discussion  
A two-day F-SAR flight campaign was conducted in June 2016 in the North Sea [9]. All radar data, in total more than 1 TB, were acquired fully polarimetric and simultaneously in X- and L-band. Additionally, the AIS data transmitted by the ships were collected for ground truth purposes. At the first day of the campaign the island Helgoland and the town Cuxhaven including the coastal areas and ships of opportunity were observed, mainly during linear flight tracks but also during a circular track with the radar antenna pointing not to the circle center but to the opposite direction (cf. red circle in Figure 21, top). At the second day, a dedicated experiment with a controlled ship operated by the German federal police was carried out. The ship moved with velocities of 0 to 20 kn (knot) between three different waypoints. The circular flight tracks, this time with the antenna pointing to the circle center, were flown with a radius of 5600 m resulting in a total ship observation time of approx. 400 s (=6.7 min) per circle (cf. red circle in Figure 21, bottom) [9].  
A small part of the acquired fully polarimetric radar data as well as the corresponding AIS data is shown in Figure 22.  
Figure 23 shows the Google Earth image of the scene where the linear and circular flight experiments were conducted.  
Figure 21: Flight tracks flown during the two-day North Sea campaign (top: day 1; bottom: day 2).  
In Table 2 the X- and L-band system and acquisition geometry parameters used during the experiments are listed.  
Figure 23: **(a)** Google Earth image showing a part of the test site. The region within the white box marks the area where the data were acquired during (**b**) a linear and (**c**) a circular F-SAR flight track. The red and yellow ellipses in (**b,c**) are the 3 dB antenna footprints of the X- and L-band antenna, respectively.  
Figure 22: Simultaneously acquired fully polarimetric X- and L-band data of the region around Cuxhaven and the corresponding AIS data.
## 4 Simulation Results
### Simulation Results for the Second Control Approach Based on the Bounded-Differentiable-Disturbance  
As in the previous part, the disturbance signals are composed according to the assumptions of this approach. Therefore, we compose them as  
\[\begin{bmatrix}\tau_{v1}+\tau_{1}\\ \tau_{v2}+\tau_{2}\end{bmatrix}=\begin{bmatrix}0.25\ sin(2t)+0.10\ \text{Nm}\\ 0.40\ sin(4t)+0.15\ \text{Nm}\end{bmatrix}\]  
where they are clearly bounded and differentiable. The bounded values for the overall disturbance can be directly calculated from the above equation and are  
\[\begin{bmatrix}-0.15\ \text{Nm}\\ -0.25\ \text{Nm}\end{bmatrix}\leq\begin{bmatrix}\tau_{v1}+\tau_{1}\\ \tau_{v2}+\tau_{1}\end{bmatrix}\leq\begin{bmatrix}0.35\ \text{Nm}\\ 0.55\ \text{Nm}\end{bmatrix}.\]  
For this approach, we select the gains in (30) as \(K_{2}=75\), \(K_{3}=100\), \(\sigma_{1}=\text{diag}[40\ \ 50]\), and \(\sigma_{2}=\text{diag}[30\ \ 40]\). The performance of the control inputs for this approach is compared with the PD control as shown in Figure 5, where the gains and parameters of the PD control are the same as in Section 4.1.  
Figure 4: Performance in terms of drifts and systematic errors for the first proposed approach compared with the PD approach: (**a**,**b**) desired and actual displacements of joint 1 and joint 2, respectively; (**c**,**d**) angular displacement errors of joint 1 and joint 2, respectively.  
Figure 5: Tracking errors and control inputs for the second proposed approach compared with the PD approach: (**a**,**b**) angular displacement errors of joint 1 and joint 2, respectively; (**c**,**d**) input torques of joint 1 and joint 2, respectively.  
This controller performs better in terms of accuracy and control effort because it drives the error to zero, but it is restricted by Assumption 6. By contrast, the preceding controller drives the error to a value close to zero by only considering Assumption 1. Same as the preceding approach, the performance of the proposed controller in terms of drifts and systematic errors is presented in Figure 6.  
In both proposed approaches, the torques have higher oscillations w.r.t. the PD controller; however, the oscillations are not beyond the classical bandwidth limits of the actuators.
## 3 Metadata Design
### Metadata Generation Process and Validation  
Creating high-quality metadata are streamlined through carefully designed workflows that minimize errors and ensure that critical information is captured from the outset. * **Template-Based Generation:** Predefined templates guide researchers to fill out the required fields for each measurement technique. These standardized forms reduce omissions, prevent inconsistencies, and simplify the metadata creation process. * **Automated Metadata Extraction:** Key metadata such as sample information and experimental proposal details are automatically retrieved from dedicated databases specifically built for these purposes. When metadata are embedded within the data files, the system extracts the relevant information directly from these embedded files. This approach significantly reduces the need for manual input, thereby lowering the risk of human error and ensuring that the recorded metadata remain accurate and up to date. * **Standardized, Machine-Readable Formats:** By segregating metadata from raw measurement data and maintaining them in a unified, structured format, FC-BENTEN preserves compatibility with existing analytical tools. Researchers can continue to use familiar data analysis workflows while benefiting from consistently formatted metadata that enhance searchability, interpretation, and interoperability. The metadata generation workflow illustrated in Figure 16 begins with the researchers selecting the appropriate template. They then input the parameters for measurements, processing, and analysis. YAML was used as the input format owing to its hierarchical structure, which is readable and easy to edit. Additionally, YAML supports comments, allowing researchers to clearly document instructions for metadata input. For experimental proposals and samples, the associated metadata were extracted using identifiers, such as proposals or sample IDs. These data points were integrated into a metadata workflow to form a unified, comprehensive record. Metadata for analysis-requested samples, summarized in Table 2, is initially submitted through Excel-based forms on the FC-Platform, serving as the foundation for constructing a dedicated database. For standard reference materials, relevant metadata, including supplier information and chemical composition, were systematically linked to the sample IDs. Selected examples of the metadata and data utilized for key measurement techniques (e.g., XAFS, HAXPES, XRD, PDF, and SAXS) are listed in Table 3. These examples illustrate how metadata ensure consistency across varying experimental contexts and highlight their role in effective data utilization and integration.
## 1 Introduction  
The simultaneous rise in land surface data availability, digital imaging technology, and inexpensive computational power since the turn of the millennium have the potential to greatly advance the developing fields of land-change [1,2] and urbanization science [3]. While encompassing remote sensing, scientists from both disciplines are particularly interested in examining coupled human-ecosystem interactions and applying land-cover/land-use (LCLU) mapping and change data to managing and perpetuating ecosystem services [4, 5, 6, 7]. New space-based platforms, such as IKONOS (1 m), SPOT5 (2.5-5 m), QuickBird (0.6-2.4 m), and WorldView (1-2 m), as well as plane-based aerial imaging (0.1-2 m), have extensively increased the amount of high resolution digital imagery available to researchers and policy makers worldwide [8, 9, 10, 11, 12].  
Analytical techniques for this data were largely developed with lower resolution imagery, such as Landsat (30 m) and MODIS (250 m) data, and a great deal of debate remains as to whether those techniques apply well to data with smaller pixel sizes [13,14]. In the past decade, geographic object based image analysis (GEOBIA) and object based change detection (OBCD) have increasingly been used to map land cover and land cover change with these higher resolution data types by grouping the pixels back into whole landscape features [15, 16, 17]. With OBCD, pixels are grouped into polygon objects for analysis rather than being treated individually. Object-based image analysis has been an important step in bridging the gap between the extensive analytical power of pixel-based image analysis and the decades-old practice of air photo interpretation, which has been vital to regional land managers and policy makers since the 1940s [13,18, 19, 20].  
However, while object-based techniques have helped with image interpretation, applying this data to the assessment of coupled human-ecosystem interactions requires integrating multiple data types [21]. Data mismatches may occur among raster images from potentially different sources and/or times [22, 23, 24], and vector GIS data, such as parcel boundaries, stream lines, zoning data, building footprints [25, 26, 27], and point data, such as locations of toxic sources, building permits, or species observations [28], may all contain positional and attribute error. In addition to the scaling issues across different data sets [29,30], error in individual data sources compounds overall model errors when multiple data sets are integrated [31,32].
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 3 System Model  
A novel routing protocol, called TBRL, for dynamic topology in critical underwater monitoring missions is proposed. In TBRL, reliable links are established between the nodes at different depth levels. It is a reliable, delay sensitive, energy efficient, and efficient topology discovery routing protocol that uses multi-sink architecture. Several surface sinks are used to collect data that are being sent from sensor nodes on the water surface and some nodes are anchored at the bottom. The remaining nodes are deployed at different levels between the water surface and the ground. The nodes will forward data packets that are based on a link quality. By considering reliability, any node used for the data collection delivers the data towards the next nodes. Data packets that reach any of the surface sinks are considered to be successfully delivered to the final destination. Figure 1 shows the flow chart of TBRL. These sinks have the facility of radio communication, which enables them to communicate with each other at higher bandwidths and lower propagation delays.  
TBRL completes its communication process in three phases:  
* dynamic topology discovery algorithm,
* two nodes reliable model, and
* topology changes of existing paths.  
Phase one is to assign nodes' location by considering their depth and water speed at different depth levels. In the second phase, nodes forward the data packets towards the next nodes or sinks by ensuring the reliability of a link. In the third phase, all of the possible reliable paths are taken as an input parameter and return the less affected path.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Protocol Name** & **Weakness** & **Proposed TBRL** \\ \hline DBR [30] & Used greedy approach in term of depth and not considered links quality between nodes & Next node selection based on link quality and depth threshold is defined for routing process \\ \hline R-ERP2R [31] & Maximum range of physical distance is not set, it takes greedy approach to choose the next one and only makes one path for sending the data & Physical distance is set and it takes account multiple possible paths from source to sink, after that it sorts the path and chooses the optimal one \\ \hline \multirow{4}{*}{3H-RM [32]} & There is no mechanism introduced for the acknowledgment process when step size is less than 3 thus it makes higher probability of packets duplication at sink node & 2N-RM consists of two nodes reliability model and no step size is taken. When data reached at sink node, there is a zero probability of duplication data packet at sink node due to one to one relationship. \\ \hline \multirow{3}{*}{2H-ACK [33]} & When second node becomes sink, it has no choice for taking acknowledgment from other receiver and send it to source node & Two nodes ensure the reliability by taking the consideration of ETX value and have proper mechanism to acknowledge the source node. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
## 2 Materials and Methods
### Machine Learning Classification Models
#### 2.4.3 Random Forest (RF)  
Random forest (RF) is an ensemble learning algorithm with multiple decision trees (DT). It utilizes bootstrapping from the original training data to choose random samples, with the samples not selected being termed as out-of-bag samples (OOB). Then, each DT is trained using a randomly selected subset of features from the OOB samples. This design ensures the independence and diversity of DTs. Finally, each DT can make predictions and generate the results through voting. RF can handle high-dimensional and large volumes of training samples, demonstrating good scalability. In this work, we obtained the best performance when the number of decision trees was 50.
## 4 Experimental Results
### Automatic Solar Tracking Results
#### 4.1.1 Instantaneous Solar Tracking Error  
ISTE represents the actual angular difference between, where the tracker is pointing and where the Sun is at that moment [1]. The azimuth (\(\alpha\)) and zenith (\(\beta\)) ISTEs can be directly observed by the digital sensor ISSX software, or they can be calculated with the PPS by Equations (11) and (12), respectively; where \(h=300\) mm is the separation between the two parallel plates, \(x\) and \(y\) are the projections on the Cartesian plane of the distance between the centroid of the projection of the Sun's rays and the center of the concentric circles of the PPS, as seen in Figure 8. It is important to mention that due to its construction, the PPS can only measure small angles since its FOV is about \(\pm 15^{\circ}\). As the standard suggests, the total ISTE (\(\theta\)) is obtained by Equation (13). Note that the total ISTE is always positive:  
\[\alpha=\arctan(x/h), \tag{11}\]  
\[\beta=\arctan(y/h), \tag{12}\]  
\[\theta=\arctan(v/h)=\arctan\sqrt{x^{2}+y^{2}}/h. \tag{13}\]  
Solar tracking under clear sky conditions (8 October 2021) was performed, and the ISTEs were measured by the DSS-MEMS and the PPS, which only monitor the tracking accuracy and do not influence the system control. These sensors were installed in the center of the tracking plane at the point of minimum deflection; see Figure 9. Direct normal irradiance (DNI) values were measured by the DSS-MEMS. Table 2 shows the experimental results obtained in 2-h intervals. The total mean absolute ISTEs obtained with the DSS-MEMS and the PPS were 0.37\({}^{\circ}\) and 0.52\({}^{\circ}\), respectively. It is worth mentioning that because of the high-precision solar tracking, the measurements of the ISTEs obtained by means of the PPS are complex since the projection of the Sun's rays is wide enough and falls between several measuring circles on the receiving plate; see Figure 8b. ISTEs were not measured under cloudy conditions because the diffuse environmental radiation would considerably alter the measurements.  
Figure 9: Solar sensors and devices for the solar tracking characterization of the designed STS.
## 4 Discussion of Results
### Elapsed Runtimes  
LSTM networks are computationally expensive and require at least four times the resources required by 1D-CNNs or random forests, according to our experiments. When runtime is not a priority, LSTM networks are recommended. On the other hand, when runtime is a priority, random forests are the learning architecture of choice. One-dimensional CNNs still have a place when the runtime is of absolute significance and the accuracy of the summary is not highly prioritized or is not intended to be relied upon in a sensitive application. Recall that in this work, we used cross-validation with K = 5 to generate the results; the results reported in the experiment are for both K = 5 and K = 1.  
In conclusion, as the proposed feature set contains only 64 variables, the model generation and testing time is very fast in comparison to typical works in which hundreds or thousands of CNN features are used.
# Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence
## 2 Methodology
### EEG Analysis  
The bandwidth of EEG signals falls in the following rhythms as shown in Table 2, i.e., alpha, beta, gamma, delta, and theta, respectively. These rhythms identify the human brain activity levels.  
The delta and theta frequencies are dominant while sleeping and unconscious, whereas the gamma frequencies are dominant during hyper-activities, also known as exciting activities. Therefore, the desired dominant frequencies for this study are alpha (8 Hz-13 Hz) and beta (13 Hz-32 Hz), respectively. With the continuous thinking of moving the arm left or right, the frequencies are generated in the 8 Hz-32 Hz bandwidth. Moreover, the EEG headsets are responsible for acquiring data from the brain and vectorizing it into numerical form.
## 3 Proposed Scheme
### Computational Complexity  
In this sub-section, we analyze the computational complexity of our proposed algorithm. The complexity of the sub-channel resource allocation to cellular users and M-D2D groups is \(O(N_{\text{C}}\times N_{\text{G}})\). Then, \([N_{D}\times O(N_{\text{G}})]\) is the complexity of the matching-based sub-channel resource allocation within the M-D2D group. These resource allocations are conducted in parallel. Therefore, the overall complexity of the proposed scheme is \([O(N_{\text{C}}\times N_{\text{G}})+N_{D}\times O(N_{\text{G}})]\). This is due to the exhaustive search for the resource reuse partner and hence makes the proposed scheme impractical for the dense networks. Therefore, in order to minimize the complexity of the proposed scheme, we proposed a metaheuristic-tabu search algorithm.