esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
CoP movement was acquired and analyzed using LabView based software (ARS Force Plate, S2P, Ljubljana. Slovenia). Signals were sampled at 1000 Hz and filtered (0.04-10 Hz band-pass, Butterworth zero-lag fourth-order). The following parameters describing CoP movement were calculated; (i) average velocity of CoP movement (Vtot), (ii, iii) velocity of CoP movement in medial-lateral (ML) (Vml) and anterior-posterior (AP) (Vap) direction, (iv, v) average amplitude of CoP movement in ML (Aml) and AP (Aap) direction, (vi, vii)mean frequency of changes in CoP movement direction during ML (Fml) and in AP (Fap) postural sway.  
Head and neck movements during the Butterfly test were analyzed using the NeckCare software. Parameters of (i) mean and standard deviation of the time spent on target during each trial expressed in seconds (time-on-target), (ii) time head and neck spent behind the target expressed as percentage of total time (undershoot), (iii) time head and neck spent in front of the target expressed as percentage of total time (overshoot) and (iv) average mismatch between the target and the head cursor in millimeters (amplitude accuracy) were used for further analysis.  
NeckCare software was used to analyze relocation accuracy in the Head-to-Neutral position sense. Parameters such as (i) mean of the absolute total deviation from the reference position over the six trials for each measured direction (absolute error) and (ii) average magnitude of under- and over-estimation of target position (constant error) were further analyzed.
# An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks
###### Abstract  
Energy limitation is an adverse problem in designing routing protocols for underwater sensor networks (UWSNs). To prolong the network lifetime with limited battery power, an energy balanced and efficient routing protocol, called energy balanced and lifetime extended routing protocol (EBLE), is proposed in this paper. The proposed EBLE not only balances traffic loads according to the residual energy, but also optimizes data transmissions by selecting low-cost paths. Two phases are operated in the EBLE data transmission process: (1) candidate forwarding set selection phase and (2) data transmission phase. In candidate forwarding set selection phase, nodes update candidate forwarding nodes by broadcasting the position and residual energy level information. The cost value of available nodes is calculated and stored in each sensor node. Then in data transmission phase, high residual energy and relatively low-cost paths are selected based on the cost function and residual energy level information. We also introduce detailed analysis of optimal energy consumption in UWSNs. Numerical simulation results on a variety of node distributions and data load distributions prove that EBLE outperforms other routing protocols (BTM, BEAR and direct transmission) in terms of network lifetime and energy efficiency.  
underwater communication; energy consumption; energy efficiency; routing protocols; network lifetime +
Footnote †: journal: Sensors
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
## 3 Implementation
### Dee-Km
#### 3.3.1 Proposal Approach  
The following modification is proposed for the Distributed Energy-Efficient Clustering (DEEC) protocol:  
- Stage 1: Initial Energy Distribution and Clustering  
Calculate the initial cluster areas and cluster heads using the DEEC protocol. The probability of node selection as cluster head is shown in the Equation (9):  
\[p_{i}=\left\{\begin{array}{l}\frac{p_{opt}\ E(r)}{(1+m)E(r)}\ for\ normal\ nodes\\ \frac{P_{opt}\ E(r)(1+a)}{(1+m)E(r)}\ for\ advanced\ nodes\end{array}\right. \tag{9}\]  
where \(P_{opt}\) is the optimal probability of cluster head selection, \(m\) is a fraction of advanced nodes, \(a\) is the energy factor for advanced nodes, \(Ei\) represents the residual energy of node \(i\), \(E^{\prime}\) is the average energy and \(r\) represents the round.  
- Stage 2: K-Means Optimisation  
The K-Means optimisation process iteratively refines cluster formation through centroid calculation, as shown in Equation (10):  
\[\mu_{i}(w_{i})=\frac{1}{|w_{i}|}\sum_{x\in w_{i}}x \tag{10}\]  
where \(\mu_{i}\) represents the centroid of cluster \(i\), \(w_{i}\) is the set of nodes in cluster \(i\) and \(x\) represents node coordinates.  
Equation (11) calculates the distance of the node to the centroids:  
\[D=\sum_{x\in w_{i}}|x-\mu(w_{n})\,|^{\,2} \tag{11}\]Clustering is complete when there are no changes in clusters or the maximum number of iterations is reached. If either condition is not met, the process is repeated until the stopping criteria are satisfied.  
* Stage 3: Cluster Head (CH) Selection and Validation  
Spatial Validation: For each candidate CH, verify if it remains within its cluster and is closer to the base station than the cluster centroid. If so, confirm its role as CH.  
Centroid Proximity Selection: If the candidate fails validation, select the node nearest to the cluster centroid as a tentative CH.  
Energy Assessment: Evaluate the tentative CH's residual energy. If sufficient, confirm as CH; otherwise, iteratively assess the next closest node.  
Cluster Reassignment: If no node meets the energy requirement, dissolve the cluster and reassign its nodes to neighbouring clusters.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 4 Performance Analysis
### Bit Rate Analysis  
The analysis of the bit rate for the two considered modulations is shown in the left plot of Figure 3. For both modulations, as expected, we obtain an improvement of the achievable bit rate with the increment of CN0. We see that the bit rate moves to a saturation level at a given CN0. For instance, in the left plot, we see a saturation level of 1 kbps at 42 dB-Hz for the \(B=0.1\) MHz curve (i.e., blue curve). Both the saturation level and the CN0 value at which it is reached are increased with the bandwidth value. The second observation we can extract from the left plot of Figure 3 is that we obtain less and less gain in bit rate for larger bandwidths. For instance, from \(B=0.1\) MHz to \(B=0.25\) MHz, we obtain an improvement in bit rate that is proportional to the bandwidth increment (i.e., \(\times 2.5\)). However, the improvement with \(B=10\) MHz is just \(\times 20\) bit rate (instead of \(\times 100\)) with respect to \(B=0.1\) MHz. Finally, another important point extracted from the results of bit rate is the difference between achievable values for FSK- and BOK-CSS. The former provides an improvement of 1 order of magnitude for all bandwidth and CN0 values.
## 4 Conclusions  
Many examples in the existing literature indicate that the magnetic filter field system of RF negative hydrogen ion sources reduces the electron temperature and \"filters\" or \"reflects\" the \"hot\" electrons to avoid the stripping loss of \(H^{-}\) ions [17, 18, 19, 20]. However, the numerical results show that the magnetic filter field filters both \"hot\" and \"cold\" electrons, which is different from the descriptions in the literature. The numerical results show that the reflection of the electrons is caused by the magnetic mirror effect in the source. The filtering of electrons is reflected in two aspects. For one thing, most of the electrons in the expansion region are still constrained by the magnetic filter field and move along the magnetic field lines to the chamber wall. It is difficult for them to reach the extraction surface. Therefore, the magnetic filter field can reduce the temperature of the extracted electrons to a certain extent. For another thing, collisions lead to the decay of electron energy in the expansion region, where excitation collision is the dominant decay mechanism for electron energy. These two aspects together contribute to lowering the temperature of the electrons at the extraction surface.  
In this paper, the mechanism of the magnetic filter field on the \"hot\" electrons in the expansion region is studied based on the CHIPIC code. The filtering, reflection, and drift phenomena of electrons in the numerical results are presented and discussed to analyze the complex behavior of electrons in a magnetic filter field. The research in this paper helps elucidate the particle transport process under the magnetic filter field system and the objective role of the magnetic filter field system in negative hydrogen ion source devices. The numerical results help to explain the formation process of cold electrons on the extraction surface, thus providing a reference for reducing the loss probability of \(H^{-}\) ions.  
Conceptualization, M.X. and H.W.; methodology, D.L.; software, M.X., H.W., D.L. and L.L.; validation, M.X.; formal analysis, M.X., H.W., D.L. and L.L.; investigation, M.X., H.W., D.L. and L.L.; resources, M.X., H.W., D.L. and L.L.; writing--original draft preparation, M.X.; writing--review and editing, M.X. and D.L.; visualization, M.X., H.W., D.L. and L.L.; supervision, M.X., H.W., D.L. and L.L.; project administration, M.X., H.W., D.L. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA, grant number 11905026 and 12075051. This research was funded by NATIONAL KEY RESEARCH AND DEVELOPMENT PROGRAM OF CHINA, grant number 2018 YFF01013001.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Remote Sensing Analysis of Geologic Hazards
## 3 Landslides
### Warning Procedures  
[PERSON] et al. [34] developed a threshold-based early warning procedure for the Gapa Landslide (Southwest China), which was reactivated by the impoundment of a large water reservoir. They observed that the landslide deformation was strongly related to the fluctuations in reservoir water levels; thus, a crucial water level was also defined to reduce false warnings from the velocity threshold alone. The current monitoring system is composed of six permanent GNSS receivers and one water level station. A warning procedure can be activated in case of a velocity exceeding 4 mm day\({}^{-1}\) and a water level higher than 1820 a.s.l. (i.e., reservoir depth \(>\) 70 m).
## 2 Proposed Cone Cell Identification Method  
Figure 1 shows the flowchart of the proposed cone cell identification method, which comprises six steps: (1) image denoising, (2) bias field correction, (3) cone cell identification based on \(K\)-means clustering [38], (4) duplicate identification removal, (5) identification based on threshold segmentation, and (6) merging of closed identified cone cells. First, AO-SLO images are denoised by averaging multiple images registered by TV-L1 optical flow estimation [37]. Second, bias field correction [39] is applied to the denoised image, and the corrected image is isotropically magnified four times via bicubic interpolation. Third, cone cells are roughly identified by \(K\)-means clustering [38]. Fourth, duplicate identification results are removed. Fifth, a threshold is calculated by using the identification results to segment the corrected image. The segmentation results are used to generate the cone cell identification results. Finally, the identification outcome is obtained by merging closed identified cone cells [16].  
Figure 1: Flowchart of proposed method for automated cone cell identification.
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 2 Related Work  
### Data Mining and Two-Step Classification  
The field of data mining came into existence relatively recently with the stated objective of systemizing the methodologies for extracting hidden patterns or other knowledge of interest from massive datasets. For instance, data mining provides the tools for discovering latent correlations between features, thus allowing feature transformation and dimensionality reduction [2]. Both are crucial in the extract-transform-load (\(ETL\)) cycle in databases. As stated in [3], the knowledge discovery has a wide range of applications, with marketing, finance and fraud detection defining a portion of them. The process of knowledge discovery is structured in several stages, the first of which is feature selection, as presented in Figure 1. The preprocessing and transformation steps follow and lead to the main stage of data mining, where a suitable algorithm or an ad hoc version of it, extracts latent information in a form appropriate for future use [4].  
Data mining consists of various tasks, most notably visualization, cleansing, missing value completion, outlier discovery, clustering, dimensionality reduction, and classification. In its most basic form, the latter consists of assigning labels taken from a finite set to algorithmic objects, for instance graphs, vectors or countable sequences, based on a small number of known label-object pairings. A methodological approach to classification stems from the common engineering intuition to dividing hard optimization problems to simpler subtasks in order to obtain a solution. The classification techniques based on this approach are many and diverse. In the Gauss-Dantzig selector, linear programming performs very sparse model selection under certain inequality constraints and then ordinary least squares estimate the values of the model coefficients [5; 6]. Another prime example constitutes the generic scheme proposed in [7] for clustering objects stored in GIS databases, where in each clustering iteration, there is a step dependent only on non-spatial attributes followed by a step where the location-based operations are executed. In addition, in [8], a methodology for deduplicating records in databases in an unsupervised way is described, where synthetic high quality records are created based on the underlying domain and subsequently they are driven to a high performing classifier such as a support vector machine (SVM). In [9], an expectation-maximization (\(EM\))-like clustering algorithm for fuzzy data based on belief functions is implemented and compared to one-step fuzzy classifiers. Finally, an implicit two-step spatiosocial clustering of trilingual tweets is proposed in [10], where a genetic algorithm takes into account both linguistic similarity and spatial proximity models. Other applications include limiting vulnerability from cyber attacks [11] and improving healthcare services [12].
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 3 Experimental Measurements Conducted in a Cellular Network
### Data Processing  
After data collection, the measurement files were exported from NetMonitor version 1.81 in.CSV format for further processing. A preliminary inspection was conducted to identify and eliminate inconsistent records and outliers. Specifically, the following filtering criteria were applied: (i) exclusion of samples not corresponding to LTE technology; (ii) removal of signal measurements with placeholder values such as 2,147,483,647, which represent invalid or unsupported readings; and (iii) elimination of records missing essential fields such as GPS coordinates, timestamps, or key performance indicators (KPIs).  
As a result of this data cleaning process, approximately 4% of the samples were discarded in the urban dataset and 6% in the suburban dataset. These percentages were calculated by comparing the number of valid samples retained after filtering to the total number initially collected. Specifically, the final urban dataset consisted of 31,790 valid samples, while the suburban dataset retained 45,674 valid measurements. These volumes ensured sufficient data density and statistical robustness for the subsequent analysis.  
The processing and calculation of derivative parameters, as well as statistical analyses, were carried out using software tools such as Microsoft Excel (version 2306) and RStudio (version 2023.06.2+561), facilitating the efficient handling and visualization of large datasets.  
It is important to note that while data filtering improves dataset reliability by removing incomplete or invalid entries, it may also introduce slight bias. Discarded samples may have originated from low-coverage or transition zones, potentially underrepresenting critical edge conditions. However, the remaining data maintained broad spatial and sectoral coverage, ensuring sufficient analytical integrity. Future work could explore imputation or correction methods to address these exclusions more comprehensively.
## 5 Results
### Statically Configured Two Channels  
In this case, only one channel is assigned to a service, and these pairs remain unchanged during the simulation. The multi-channel environment still exists; however, if a channel is oversaturated, only the built-in DCC algorithms will be triggered in order to  
Figure 5: Simulation map: 6 \(\times\) 6 grid track for cars.  
Figure 6: Number of vehicles in the simulator for the used 500 ms generation interval.  
Figure 7: CCH CBR values—one channel (SAM, CPM, CAM, and PAM).  
control the saturation. The middle layer will not transfer the traffic to another channel: applications will transmit based on the initial channel assignment. SAM, PAM, and CAM messages are sent via CCH, while CPM is on the SCH1.  
In this situation, the CBR of the CCH channel increases until the 16 th second; after that, it decreases slightly due to DCC (Figure 8). The highest value was 0.23 and a little above 0.15 towards the end of the simulation. It is clearly visible that the CBR value is much lower in this case compared to the situation where all application data were transmitted on one channel.  
For SCH1, it is a bit different (Figure 9). Here, the CBR increases almost to the end of the simulation. The highest value was around 0.28. However, the CBR is still lower on average than if it were sent out on a single channel.
## 3 Kriging (Gaussian Process) Model
### Response Prediction  
In the metamodeling, it is of interest to predict a response, \(\mathbf{\hat{Y}}=\Gamma^{\text{Krig}}(\mathbf{x}_{*})\), for a new data point \(\mathbf{x}_{*}\), with the knowledge of the observed experimental design \(\chi_{D}=\left\{\mathbf{x}^{(1)},\mathbf{x}^{(2)},...,\mathbf{x}^{(n)} \right\}^{T}\) and its associated model responses \(\mathbf{\mathcal{Y}}_{D}=\left\{y(\mathbf{x}^{(1)}),...,y(\mathbf{x}^{(n)}) \right\}^{T}=\left\{\Gamma^{\text{Krig}}(\mathbf{x}^{(1)}),...,\Gamma^{\text{ Krig}}(\mathbf{x}^{(n)})\right\}^{T}\). The Kriging metamodel offers predictions that rely on the Gaussian characteristics of the underlying process. The Gaussian assumption postulates that the vector created by the prediction at point \(\mathbf{x}_{*}\), represented as \(\mathbf{\hat{Y}}\), and the actual model responses \(\mathbf{\mathcal{Y}}_{D}\) follow a joint Gaussian distribution defined in a vector form [16; 31; 32] by  
\[\left\{\begin{array}{c}\mathbf{\hat{Y}}(\mathbf{x}_{*})\\ \mathbf{\mathcal{Y}}_{D}\end{array}\right\}\sim N_{n+1}\bigg{(}\left\{ \begin{array}{c}\mathbf{f}^{T}(\mathbf{x}_{*})\mathbf{m}\\ \mathbf{\mathcal{F}}\mathbf{m}\end{array}\right\},\sigma^{2}\begin{cases}1& \mathbf{r}^{T}(\mathbf{x}_{*})\\ \mathbf{r}(\mathbf{x}_{*})&\mathbf{\kappa}\end{cases}\bigg{)} \tag{7}\]  
where \(\mathbf{\mathcal{F}}\) is the Kriging design matrix and each of its elements is computed as follows:  
\[\mathbf{\mathcal{F}}_{ij}=f_{j}(\mathbf{x}^{(i)}),\ \ i=1,...,n;\ \ j=1,...,q \tag{8}\]  
where \(\mathbf{r}(\mathbf{x}_{*})\) represents the vector of cross-correlations between the prediction point \(\mathbf{x}_{*}\) and each of the observations \(\mathbf{x}\), with its individual elements being expressed as \(r_{i}=\kappa\Big{(}\mathbf{x}_{*},\mathbf{x}^{(i)};\mathbf{\Theta}\Big{)},i=1,...,n\). The element of covariance matrix \(\mathbf{\kappa}\) is calculated as  
\[\kappa_{ij}=\kappa\Big{(}\mathbf{x}^{(i)},\mathbf{x}^{(j)};\mathbf{\Theta} \Big{)},\ \ i,j=1,...,n \tag{9}\]  
The Gaussian-process-based estimation of responses, \(\mathbf{\hat{Y}}\), at a new (prediction) point \(\mathbf{x}_{*}\), and the corresponding prediction variance \(\sigma_{\hat{Y}}^{2}(\mathbf{x}_{*})\) and mean \(\mu_{\hat{Y}}(\mathbf{x}_{*})\) are determined as the conditional on the observed points \((\mathbf{x})\).  
\[\mu_{\hat{Y}}(\mathbf{x}_{*})=\mathbf{f}(\mathbf{x}_{*})^{T}\mathbf{\hat{m}}+ \mathbf{r}(\mathbf{x}_{*})^{T}\mathbf{\kappa}^{-1}(\mathbf{\mathcal{Y}}_{D}-  
Figure 2 illustrates the predictive mean, variance, and confidence interval of the Gaussian process given the observed data. In Kriging, a confidence interval establishes a range of values within which the actual response value is expected to lie. This interval serves as a measure of the uncertainty inherent in the predictions generated by the Kriging model. The significance of the confidence interval lies in its ability to enhance comprehension regarding the reliability of the model predictions and to evaluate the level of confidence one can place in the estimated values.
## 2 GFPS Scheme between Two Different Uncertain Chaotic Systems  
Consider two chaotic systems (drive-response systems) with uncertain parameters given in the following form:  
\[\begin{split}\dot{x}&=f_{1}(x,\phi),\\ \dot{y}&=f_{2}(y,\psi)+u,\end{split} \tag{1}\]  
where \(x,y\in R^{n};\phi\in R^{m},\psi\in R^{l}\) are uncertain parameter vectors, and \(u\) is the controller to be designed. For simplicity, assume that \(f_{1}(0,\phi)=f_{2}(0,\psi)=0\) hold for any values of \(\phi\) and \(\psi\). If there exists a scaling function matrix \(h=diag\{h_{1}(x),\,h_{2}(x),\,\cdots,h_{n}(x)\}\), such that  
\[\lim_{t\to\infty}||y-h(x)x||=0, \tag{2}\]  
then the two chaotic systems in system (1) are said to be GFPS with respect to the scaling function matrix \(h\)[37; 40]. The two chaotic systems are said to be FPS if  
\[h_{1}(x)=h_{2}(x)=\cdots=h_{n}(x)\]  
in Equation (2). Moreover, we find MPS between the two chaotic systems if \(h=diag\{h_{1},\,h_{2},\cdots\,h_{n}\}\), \(h_{i}(i=1,2,\cdots,n)\) are real constants in Equation (2). Obviously, both FPS and MPS are specific cases of GFPS.  
The controller \(u\) and the parameter update laws in system (1) are designed as below:  
\[\begin{split} u&=J(x)f_{1}(x,\dot{\phi})-f_{2}(y, \dot{\psi})+k\delta_{1},\\ \dot{\phi}&=g_{1}(\dot{\phi}-\phi,\delta_{1}),\\ \dot{\psi}&=g_{2}(\dot{\psi}-\psi,\delta_{1}),\end{split} \tag{3}\]  
where \(J(x)=d(h(x)x)/dx\), \(k\) is the control parameter, and \(\delta_{1}=y-h(x)x\) is the synchronization error; \(\dot{\phi}\) and \(\dot{\psi}\) are the estimated values of uncertain parameter vectors \(\phi\) and \(\psi\), respectively. In addition, \(g_{1}(\dot{\phi}-\phi,0)=g_{2}(\dot{\psi}-\psi,0)=0\) hold if  
\[\dot{\phi}=\phi\quad and\quad\dot{\psi}=\psi. \tag{4}\]  
The GFPS occurring in system (1) can be regarded as a specific case of GS between the two chaotic systems, which can be detected using the auxiliary system approach [46]. Consider the following identical copy of the response system driven by the same driving signal \(x\),  
\[\begin{split}\dot{z}&=f_{2}(z,\psi)+\bar{u},\\ \bar{u}&=J(x)f_{1}(x,\bar{\phi})-f_{2}(z,\bar{\psi} )+k\delta_{2},\\ \dot{\bar{\phi}}&=g_{1}(\dot{\phi}-\phi,\delta_{2}), \\ \dot{\bar{\psi}}&=g_{2}(\ddot{\psi}-\psi,\delta_{2}), \end{split} \tag{5}\]  
in which \(z\in R^{n}\) and \(\delta_{2}=z-h(x)x\). \(\bar{\phi}\) and \(\bar{\psi}\) are the estimated values of uncertain parameter vectors \(\phi\) and \(\psi\), respectively. Additionally, \(g_{1}(\dot{\phi}-\phi,0)=g_{2}(\ddot{\psi}-\psi,0)=0\) hold for  
\[\bar{\phi}=\phi\quad and\quad\bar{\psi}=\psi. \tag{6}\]  
Then, GFPS conditions (2) and (4) lead to  
\[\lim_{t\to\infty}||y-z||=0,\quad\lim_{t\to\infty}||\dot{\phi}-\bar{\phi}||=0, \quad\lim_{t\to\infty}||\dot{\psi}-\bar{\psi}||=0. \tag{7}\]