esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results
### Prediction Based on Dynamic Data  
The prediction of BOS variables (i.e., melt temperature and carbon concentration in the melt) is based on machine models trained on a large dataset of combined static and dynamic data. The idea of merged or paired static and dynamic data was drawn in Figure 3. The database from the BOS operation contained 872 meltings (i.e., rows in the table) with static data (i.e., columns in the table). Essential static data for each melting were the initial and final measured melt temperature and carbon concentration in the melt. Other essential data was the total amount of oxygen injected in a given melt. The database of dynamic data contained process variables measured continuously during melting. The concentration of CO and CO\({}_{2}\) in the waste gas, temperature of waste gas, and flow rate of the blowing oxygen were provided from each melt. These process data were recorded for every melting with a sampling period of 1 s. An example of dynamic data behavior from the selected melting is shown in Figure 19. The training datasets for temperature and carbon content modeling were created within data mining. These big datasets were created by merging static and dynamic data (see Figure 2). For each initial and final measured melt temperature from the static dataset, initial and final observations (i.e., CO, CO\({}_{2}\), waste gas temperature, and the accumulated amount of blown oxygen) from the dataset of dynamic data were assigned for every melting. The same procedure was applied to the carbon concentration in the melt. Thus, pairs of inputs (i.e., input observations) and outputs (i.e., targets) were created as huge data matrixes (i.e., big dataset #1 and big dataset #2). As part of the data processing, meltings whose dynamic data were not satisfactory (e.g., there was missing data) were excluded from these datasets. Furthermore, meltings whose dynamic data were used to test the performance of machine models were excluded. This way, big filtered datasets were obtained and used to train machine models.
## 2 Materials and Methods
### Road Mapping  
Road map extraction is an important and fundamental task in the land cover mapping field [29; 30; 31], and is the core of the proposed method. We build a network to extract roads automatically. The network adopts the framework of the U-Net [32], which has an encoder-decoder structure. It can be split into three parts: encoder, middle bridge, and decoder. The encoder part is a ResNet34. The middle bridge part uses several dilated convolution layers with skip connection to connect the encoder part to the decoder part, with the dilated convolution stacked in cascade mode. This can enlarge the receptive field and preserve detailed spatial information. The decoder part uses transposed convolution layers to conduct up-sampling such that they can be connected to the corresponding feature map obtained from the encoder part. It uses the ImageNet pretrained model for initialization to accelerate network convergence and improve performance.  
Binary Cross Entropy (BCE) is the most widely used loss function in road extraction tasks [30]. The pixel-wise difference is calculated between the predicted and the true value for backward propagation. \(BCE\) is defined as  
\[l_{BCE}=-\frac{1}{N}\sum\limits_{i=1}^{N}(g_{i}\text{log}(y_{i})+(1-g_{i})(1- log(y_{i}))) \tag{18}\]where \(y_{i}\) is the ground truth label. \(\hat{y}_{i}\) is the predicted probability of pixel \(i\) being road. To evaluate the model performance globally, Intersection over Union (IoU) is used as another loss function. _IoU_ is defined as Equation (15),  
\[l_{IOU}=1-\frac{\sum_{i=1}^{N}y_{i}\hat{y}_{i}}{\sum_{i=1}^{N}(y_{i}+\hat{y}_{i} -y_{i}\hat{y}_{i})} \tag{19}\]  
The \(BCE\) loss measures the difference between the predicted map and the ground truth in the pixel level. The _IoU_ loss measures the performance of the model globally. The overall loss is calculated as  
\[loss=l_{BCE}+l_{IOU} \tag{20}\]  
The convolutional neural network is highly dependent on datasets. Based on the public Massachusetts Roads Dataset, we created datasets to train the network, and then used it to draw the road map of Jilin-1 and Skybox. We split the whole Jilin-1 and Skybox images into several patches, each 256 \(\times\) 256, and used them to train the network for Jilin-1 and Skybox imagery, respectively. Study area images are reserved as test images to obtain the final road results. By using identified roads, we can eliminate many pseudo and false moving objects that are far away from any road.
# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
## 3 Materials and Methods
### The Gearbox Subsystem
It involves calculating the means to find the reference signal, subtractions to find the residuals, and moving averages, both to smooth the residuals and to detect outliers. Let us now summarise them. The median of dataset \(\mathbf{x}\) of \(n\) values, \(median(\mathbf{x})\), is computed by first ordering the values of this data set from smallest to largest such that the smallest is \(x_{(1)}\) and the largest is \(x_{(n)}\); then, depending on whether \(n\) is even or odd, one has:  
\[median(\mathbf{x})= \begin{cases}\begin{array}{c}x_{\left(\frac{n+1}{2} \right)}&if\ n\ is\ odd\\ \\ \frac{1}{2}\left(x_{\left(\frac{n}{2}\right)}+x_{\left(\frac{n}{2}+1\right)} \right)&if\ n\ is\ even\end{array}\end{cases}  \tag{1}\]  
To perform the smoothing of the signal \(x(i)\), a simple moving average (SMA) filter with a temporal window of N samples is used, according to the following expression:  
\[y(n)=\frac{1}{N}\sum_{i=n-N+1}^{n}x(i), \tag{2}\]  
which can be effectively computed by the following recursive form:  
Figure 10: Simplified diagram of the proposed method. Figure 9: At the top, the residue of the signals \(wtrm\_avg\_Gbx\_OilPres\) is depicted. The same signals smoothed by a 50-sample moving average filter are depicted at the bottom. \[y(n)=y(n-1)+\frac{x(n)-x(n-N)}{N} \tag{3}\]  
One way to interpret the smoothing is to determine the Discrete Fourier Transform (DFT) of the filter through the study of the Z transform of any of the expressions (2) or (3):  
\[H(z)=\frac{Y(z)}{X(z)}=\frac{1}{N}\frac{1-z^{-N}}{1-z^{-1}} \tag{4}\]  
When replacing \(z\) by \(e^{-j\omega}\) in (4), and performing some operations, the DFT is obtained, in terms of the discrete frequency \(\omega\), which is:  
\[H(e^{j\omega})=e^{-j\omega\frac{N-1}{2}}\frac{1}{N}\frac{sin\left(\omega\frac {N}{Z}\right)}{sin\left(\frac{\omega}{Z}\right)} \tag{5}\]  
Note that \(N\), the length of the moving window, controls the pass-band of this simple low-pass filter, so that the larger the \(N\), the narrower the pass-band. Finally, note that the same moving average filter has been used to filter outliers. The samples within the temporal moving window are used to calculate the standard deviation \(\sigma(n)\) when the filter is used for this purpose. Then, if \(|x(n)-y(n)|>3.2\sigma(n)\), \(x(n)\) will be labelled as an outlier.
# Two-Stage Beamforming Technique for GNSS Applications +
###### Abstract  
In this paper, we introduce a robust beamforming technique using array antennas. The proposed solution constitutes two stages; the first stage exploits the space-alternating generalized expectation-maximization (SAGE) algorithm to decompose the received GNSS signal into its constituent signals, i.e., direct and reflected signals. The SAGE algorithm estimates the angle of arrival (AoA) and the received covariance matrix for both the direct and reflected signals. The second stage, on the other hand, utilizes the Minimum Variance Distortionless Response (MVDR) algorithm to produce the weight vector that steers the main beam towards the satellite's direction and the nulls towards the multipath effect. The MVDR uses the AoA of the direct path and the covariance matrix of the reflected path to minimize the multipath effect. The experimental results reveal that the proposed technique improves the received signal strength and the location estimation accuracy, as compared to a single-antenna system. Furthermore, the proposed technique outperforms the traditional MVDR technique in the tested environment. Finally, the 95% 3D position error of the proposed solution is 5.2 m, and the position dilution of precision (pdop) is 0.84.  
P 2025 88, 45
# Collision Detection Method Using Self Interference Cancelation for Random Access Multiuser MIMO
###### Abstract  
This paper proposes an interference detection method for multiuser-multiple input multiple output (MU-MIMO) transmission, which utilizes periodical preamble signals in the frequency domain and the concept of full-duplex transmission when assuming _idle_ antennas at the access point (AP) in MU-MIMO. In the propose method, collision detection (CD) of MU-MIMO is achieved by utilizing _asynchronous_ MU-MIMO called random access MU-MIMO. In random access MU-MIMO, several antennas that are not used for the transmission exist, due to _asynchronous_ MU-MIMO. Hence, _idle_ antennas at the AP can receive preamble signals while the transmit antennas at the AP transmit the preamble signals: this procedure is regarded as full-duplex transmission, which cancels the self-interference between AP antennas. The interference can be detected by subtracting the short preamble signal, which is multiplied by the estimated channel response using the received signal after the FFT processing. Moreover, we utilize dual polarization to reduce the mutual coupling between transmit and receive antennas at the AP. Through a computer simulation, it is shown that the proposed method can successfully detect collision from other user terminals (UTs) with OFDM signals when the interfering power from the interfering user terminal (IT) is greater than the noise power. In addition, the interfering power from IT at the AP and the desired user terminal (DT) is measured in an actual indoor environment, and the possibility of using the proposed method at the AP is discussed by using the measurement results.  
random access multiuser MIMO; collision detection; short preamble signal; self-interference +
Footnote †: journal: Electronics Letters
# Acoustic, Myoelectric, and Aerodynamic Parameters of
## 5 Conclusions  
In accordance with the objective of the present study, which aimed to evaluate the conditions of the normal and dysphonic voices in adults from acoustic, aerodynamic, and surface electromyographic parameters, it can be highlighted that the results from the included studies show differences between both voices.  
The most studied acoustic parameters were frequency and intensity, which tend to decrease with the presence of dysphonia and disturbance parameters (jitter, shimmer, and HNR) that tend to increase in vocal pathology. Regarding the aerodynamic measurements, maximum phonation time (MPT) and subglottic pressure (SGP) were the most analyzed, with MPT being generally higher in people without vocal pathology and SGP higher in people with dysphonia. The existence of differences in surface electromyographic parameters cannot be determined due to the small number of articles that discuss this topic.  
The small number of articles, the variability of the populations analyzed, the variety of instruments used, and aspects examined related to the voice, hinder the establishment of normative criteria to objectively define what is a normal voice and a dysphonic voice.  
Studying the acoustic, aerodynamic, and electromyographic parameters with more precision, both of the normal and dysphonic voice, will allow health professionals working in the field of voice (speech therapy, otorhinolaryngology, phoniatrics, etc.) to establish a diagnostic and detailed consensus of the vocal pathology in question, favoring the communication and generalization of the results worldwide.  
Therefore, more studies are needed to deepen the development and implementation of study protocols. This would favor the homogeneity of the records in order to be able to generate the necessary data to establish thresholds of normality and pathology, as well as allow the comparison of results between different study groups.  
Conceptualization, C.P.-H. and J.L.S.; methodology, C.P.-H. and J.L.S.; investigation, C.P.-H. and J.L.S.; resources, C.P.-H., V.R.-C. and L.B.; data curation, J.L.S.; writing--original draft preparation, C.P.-H.; writing--review and editing, J.L.S.; supervision, V.R.-C. and L.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Metrics for Jamming Attack Detection
### Energy Consumption Amount (ECA)  
[PERSON] _et al._[3] define ECA as the _approximated energy amount consumed in a specified time for a sensor network_. It can be calculated by measuring the drop in the battery (power-supply) voltage (\(v\)) of the node and multiplying its squared value with the time duration and then by dividing the result with the average electrical load (resistance) of the node. The authors argue that certain jammers force sensor nodes to remain in BACKGROUND period even if they should have switched to IDLE mode, causing them to consume more energy than the normal. They suggest that this consequence can be used to distinguish the normal and jamming scenarios from each other. This metric has two pit falls: firstly, the sampling of the threshold energy consumptions of the node by itself under different traffic-load conditions is a tall order, and secondly, there may not be any perceptible energy consumption differential when the jammer is attacking in a way which does not involve the carrier capture, or when the jammer is resorting to simple power attack.
# First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS
## 4 Discussion
### Correlation to Local Time  
Another aspect to highlight is the correlation between local time and the occurrence of high \(S_{4}\) events, as shown in the set of histograms in Figure 18. Some of them show a higher probability of finding high \(S_{4}\) values during hours around the sunrise and sunset. In particular, plots on 1 and 24 April, which are also very similar to each other, reveal peaks around 9 h in the morning and 8-9 h in the evening. It is also important to remark that the sunrise and sunset do not occur at the same time in all the regions covered in this graphs, which are all the oceans and seas with latitudes from 40\({}^{\circ}\)N to 40\({}^{\circ}\)S. Considering April is close to the equinox, the span between local sunrise and sunset is not very large from 40\({}^{\circ}\)N to 40\({}^{\circ}\)S: around 30 min. It would be exactly zero on the equinoxes. This difference is much higher for 25 July, around 2 h 20 min.  
In this way, being the sunrise and sunset the most active periods in terms of ionospheric activity, in the equinoxes the peak in these histograms should be even higher than in the solstices because, during the equinoxes, sunrise and sunset happen at the same time for all latitudes. This can be observed in the July histogram (Figure 18c), but it is not evident in the November's one (Figure 18f). However, it is important to consider that the measurements used for these histograms have no sea roughness filter, and they use all the data over oceans within CYGNSS available latitudes.
## 2 Materials and Methods
### Methods
#### 2.3.6 Results Validation  
Validation tools are used to evaluate the predictive performance of a model applied to analyze landslide susceptibility. In this research, as in the previous stages, the predictive ability of the models obtained with the applied algorithm was validated using the area under the receiver operating characteristic (ROC) curve (AUC) method, which, besides being a widely used and intuitive metric, allows an easy comparison of different modeling approaches [64]. As mentioned in previous steps, the \"area under the curve\" method quantitatively summarizes the ROC curve, describing the predictive ability of a model to predict landslide susceptibility [7,65]. The AUC value quantifies this capacity with values fluctuating between 0 and 1. The closer the AUC value is to 1, the greater the accuracy and reliability of the model; whereas, if the value is 0.5 or less, the prediction is a random guess. Generally, an AUC value of 0.75 or higher indicates that the model would have good predictive performance [64]. The ROC AUC was applied on test data (30%) and was performed with ROCR 1.0-11 [66], pROC 1.18.5 [67] and caret 6.0-94 [63] R software packages.  
This study also used the F-Score as a validation metric. F-Score presents a balanced view of the classified data as it is the harmonic mean of the data. In the previous stages of this research, details of this metric have been explained [7,12], however, it is important  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Version/Amount** \\ \hline Operating System & Ubuntu 20.04.5 LTS \\ Cores & 16 \\ RAM & 32 GB \\ GPU & 1 \\ R version & 4.2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main HPC features for algorithms execution.  
to note that this metric works adequately with unbalanced data, considering not only the number of prediction errors but also the type of errors that are committed [68]. It should be noted that the higher the F-Score value, the fewer false positives and false negatives the prediction will have, which makes this metric very efficient for implementation in various ML applications [69].  
Additionally, in this study, the percentage of landslides for each susceptibility level was analyzed as a validation metric. This method consists of applying spatial analysis techniques to count the number of points corresponding to landslides that fall within each susceptibility level. It is inferred that most of the points should coincide with the high and very high susceptibility levels.
## 2 Eruption Forecasting Algorithm
The final color designation is set according to the point in the algorithm where processing is terminated. For example, if the algorithm is activated by a new unrest episode and is not allowed to progress past the eruption node, because the probability of occurrence does not exceed a predefined decision threshold, then the final hazard level is set to orange. However, if the algorithm were to progress to the intensity node the hazard level is set to red. \begin{table}
\begin{tabular}{l l|c c c c|c c c c} \hline \hline  & \multicolumn{2}{c}{**Episode**} & \multicolumn{2}{c}{**Response Variable**} & \multicolumn{4}{c}{**Independent Variable**} \\ \hline
2000 & Hekla\({}^{(2,3,4)}\) & 3 & 1 & 1 & 1 & 196 & 3.4e + 20 & 15 & 9 & [30, 31] \\
2000 & Eyjafjallajökull\({}^{(2,3,4)}\) & 0 & 0 & 1 & 1 & 170 & 4.1e + 20 & 365 & 2 & [32] \\
2001 & Etna\({}^{(2,3,4)}\) & 2 & 1 & 1 & 1 & 414 & 1.0e + 23 & 28 & 115 & [33] \\
2001 & Okmok\({}^{(4)}\) & 0 & 0 & 0 & 1 & 19 & 8.1e + 21 & 2 & 16 & [34] \\
2001 & Aniakchak\({}^{(2,3,4)}\) & 0 & 0 & 0 & 1 & 13 & 5.8e + 20 & 64 & 1 & [35] \\
2002 & Hood\({}^{(2,3,4)}\) & 0 & 0 & 0 & 0 & 86 & 7.8e + 22 & 60 & 2 & [36] \\
2002 & Etna\({}^{(2,3,4)}\) & 3 & 1 & 1 & 1 & 353 & 2.1e + 23 & 94 & 115 & [37] \\
2003 & Veniaminof\({}^{(2)}\) & 2 & 1 & 1 & 1 & 103 & 6.2e + 20 & 1050 & 22 & [38] \\
2004 & Grimsvötn\({}^{(2,3,4)}\) & 3 & 1 & 0 & 0 & 920 & 6.3e + 21.9 & 490 & 29 & [39] \\
2004 & Spurr\({}^{(3,4)}\) & 0 & 0 & 0 & 0 & 2743 & 5.1e + 20 & 239 & 2 & [40, 41] \\
2004 & Etna\({}^{(2,3,4)}\) & 1 & 1 & 1 & 156 & 4.8e + 21 & 186 & 115 & [42] \\
2004 & Saint Helens\({}^{(2,3,4)}\) & 2 & 1 & 1 & 1 & 1094 & 1.5e + 23 & 21 & 14 & [43] \\
2005 & Augustine\({}^{(2,3,4)}\) & 3 & 1 & 1 & 1 & 2007 & 3.1e + 20 & 80 & 9 & [44] \\
2006 & Korovin\({}^{(2,3,4)}\) & 1 & 1 & 0 & 1 & 377 & 1.4e + 21 & 329 & 7 & [45, 46] \\
2007 & Pavlof\({}^{(2)}\) & 2 & 1 & 1 & 0 & 2 & 8.8e + 18 & 30 & 39 & [47] \\
2007 & Uptyppingar\({}^{(2,3,4)}\) & 0 & 0 & 1 & 0 & 3124 & 6.5e + 20 & 133 & 0 & [48] \\
2008 & Yellowstone\({}^{(2,3)}\) & 0 & 0 & 1 & 1 & 2594 & 6.9e + 22 & 49 & 0 & [49] \\
2008 & Paricutin\({}^{(2,3,4)}\) & 0 & 0 & 0 & 0 & 0 & 0 & 21900 & 1 & [50] \\
2008 & Hengill & 0 & 0 & 0 & 0 & 3309 & 1.1e + 24 & 10 & 0 & [51] \\
2008 & Okmok\({}^{(2,3,4)}\) & 4 & 1 & 1 & 1 & 464 & 4.9e + 21 & 100 & 16 & [34, 52] \\
2008 & Kasatochi\({}^{(2,3,4)}\) & 4 & 1 & 1 & 0 & 1489 & 7.4e + 24 & 22 & 1 & [53] \\
2009 & Redoubt\({}^{(2,3,4)}\) & 3 & 1 & 1 & 1 & 4219 & 3.9e + 21 & 365 & 6 & [31] \\
2010 & Eyjafjallajökull\({}^{(2,3,4)}\) & 4 & 1 & 1 & 1 & 4019 & 1.1e + 22 & 100 & 2 & [54] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 3 The Proposed Mechanism
How can the network players, strategies, and utility functions be defined? All similar questions are addressed and formulated. The key factors used in the work are formalized before being processed in the evolutionary game and evaluation process. These factors are discussed in the Section 3.4, while Section 3.5 covers the division of nodes, and Section 3.6 comprises the assessment by nodes. The list of parameters is mentioned. Those are used for the assessment of nodes. Also, an algorithm is applied in order to filter out the eligible nodes by BS. Section 3.7 explains the CH selection process, which is initiated by taking the sets of suitable, not suitable, and prohibited nodes. A CH selection game is discussed in detail and the last Section 3.8, covers the continual assessment by BS. The list of symbols is given in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S\#** & **Symbol** & **Description** \\ \hline
1. & CH & Cluster head \\
2. & \(Cluster_{ID}\) & Cluster with an identity number \\
3. & K\({}^{\text{T}}\) & Total number of clusters at time T \\
4. & G & Game model \\
5. & N & Nodes/number of nodes \\
6. & S & Strategies \\
7. & U & Utility function \\
8. & CM & Cluster member \\
9. & \(CostE_{i}\) & Energy cost of node \(i\) \\
10. & \(Cost_{i}(Sense)\) & Cost of sensing \\
11. & \(Cost_{i}(Process)\) & Cost of processing data \\
12. & \(Cost_{i}(agg)\) & Cost of aggregation of data \\
13. & \(Cost_{i}(Packet-Trans)\) & Cost of a single packet transmission \\
14. & \(Cost_{CH}\) & Energy cost of cluster head \\
15. & \(Cost_{CM}\) & Energy cost of cluster member \\
16. & \(HpL_{i}\) & Hop level of node \(i\) \\
17. & \(HpL^{max}\) & Maximum hop levels in the network \\
18. & \(fb\) & The ratio of forward nodes to backward nodes \\
19. & \(d_{i,j}\) & Distance between nodes \(i\) and \(j\) \\
20. & \(CN\) & Closed neighbors \\
21. & \(CNDist^{Thres}\) & Threshold distance to determine CNs set \\
22. & \(P\big{(}d_{i,j}\big{)}\) & Propagation model for the distance between nodes \(i\) and \(j\) \\
23. & \(C_{t}\) & Transceiver characteristics \\
24. & \(P_{t}\) & Transmission power \\
25. & \(A_{i}\) & Importance of node \(i\) \\
26. & \(cn\) & Number of CNs \\
27. & \(Part^{Thres}\) & The threshold value for participation \\
28. & \(p_{CM}\) & The participation level of cluster member \\
29. & \(TSS_{CM}\) & Total Session Sent by the cluster member \\
30. & A & Set of nodes suitable for CH \\
31. & B & Set of nodes not suitable for CH \\
32. & PCH & Set of nodes prohibited for CH \\
33. & TE & Threshold value for eligibility of being CH \\
34. & \(\Pi_{i}\) & Profit value assigned to the node by BS \\
35. & \(\pi_{i}\) & Penalty value assigned to the node by BS \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of Symbols.
## 2 Dynamic Health Intelligent Evaluation Model  
In order to analyze the health deterioration of satellites under time-varying and extreme thermal loads, the dynamic health intelligent evaluation model (DHIEM) is presented in this paper. Meanwhile, new definitions such as health degree and failure factor and new topological system (satellite-subsystems-components) considering the reliability relationship are proposed to characterize the dynamic performance of health deterioration. This section introduces the principle and algorithm of the model in detail.