esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 4 Methodology  
The proposed algorithm for automated traffic zone determination uses data from the WAFE platform [17], a popular crowd-sourced navigation application. WAFE provides real-time information on traffic congestion, accidents, road closures, and other incidents. The algorithm is based on automatically generated congestion values only. The research is based on the WAFE platform, as this was the data source used by the project that initiated the research. It is possible to use any other data source that can provide data of a similar quality and quantity. From this perspective, the research is intended to demonstrate the feasibility of the theory.  
The WAFE-generated data set contains the data generated and collected for a one-year period. The data is in a json file and covers the whole year 2019. Territorially, it covers the 11 th district of Budapest, Hungary. During the data filtering process, entries from regions and time periods outside the target area were also included and had to be excluded for subsequent analysis.  
The data set consists of two parts. On the one hand, it contains information reported by the users, on the other hand, it contains data automatically generated by WAFE. The analysis utilized the latter, which means that only the data automatically generated by WAFE were taken into account. The data set is organized as follows (Table 1).  
Using these data, the algorithm aims to identify groups of areas of similar behavior and to delineate traffic zones. The program consists of three distinct parts, the first sub-program is responsible for data processing, the second sub-program contains the clustering procedures, while the third sub-program helps in visualizing the result. The subprograms are divided into different modules which perform logically separable tasks within the subprograms. The key steps of the methodology are the following.  
Pre-processing: The raw data obtained from WAFE are preprocessed to remove outliers, filter irrelevant information, and aggregate incidents into meaningful clusters of congestion. This preprocessing step helps clean the data and ensures that only relevant information is used for zone determination.  
Temporal and spatial analysis: From the preprocessed data, the algorithm creates time series of traffic volumes, based on the congestion data. The data are then subjected to spatial analysis techniques to identify clusters of time series based on their coordinates.  
Zone delineation: Based on the results of the spatial analysis, the algorithm delineates the traffic zones by grouping spatially contiguous clusters. The size and shape of these zones are determined based on the area of similar time series next to each other, with the goal of creating homogeneous zones that capture similar traffic patterns.  
The program is written in Python 3.9 [18].
## 3 Research Method
### Model Metrics
#### 3.5.3 Mean Absolute Error, MAE  
The mean absolute error is the average of the absolute errors between the predicted value and the true value, and is calculated as:  
\[\text{MAE}=\frac{1}{n}{\sum\limits_{i=1}^{n}}|y_{i}-\hat{y_{i}}|\]  
MAE directly calculates the absolute error between the predicted value and the true value, avoiding the amplification effect caused by the square of the error, so it is more robust to outliers. In the PM\({}_{2.5}\) concentration prediction study, due to the complexity of environmental factors, some abnormal PM\({}_{2.5}\) concentration values may appear (such as high concentration values caused by sudden pollution events). MAE can more robustly reflect the average prediction error of the model and will not produce large fluctuations due to individual outliers. Compared with MSE and RMSE, MAE focuses more on measuring the average prediction error of the model on most samples.
###### Abstract
A number of amplifiers and oscillators operating in the Ka-Band and other higher-frequency bands employing multiple beams and extended interaction structures are being explored globally [19; 29; 30; 31; 32; 33]. The arrangement of this paper is as follows: In Section 1, a short introduction of multiple beam klystrons and their application scopes are stated; the design approach of the RF section of the proposed ladder-type multibeam EIK is provided in Section 2; in Section 3, the simulation results of the intermediate cavities, input/output cavities, and beam-wave interaction are presented; and the conclusion of this work is given in Section 4, followed by the references.
## 2 Materials and Methods
### Sentinel-1 Level-2 OCN Data  
The Sentinel-1 satellite constellation consists of three SAR satellites: Sentinel-1A, -1B, and -1C. Sentinel-1A and -1B were launched in April 2014 and April 2016, respectively, with Sentinel-1C joining the mission in December 2024. Currently, Sentinel-1A and -1C remain operational, while Sentinel-1B ceased functioning in January 2021 due to a power failure. The Sentinel-1 Level-2 Ocean (OCN) product provides Doppler shift measurements, wind speed estimates derived from VV-polarized radar backscatter, and wind direction obtained from the ECMWF reanalysis data. Operating in the interferometric wide (IW) mode, the OCN product offers a 250 km swath at a spatial resolution of 1 km. The incidence angle across the three IW sub-swaths ranges from 30\({}^{\circ}\) (near range) to 46\({}^{\circ}\) (far range), segmented into intervals of 30-36\({}^{\circ}\), 36-42\({}^{\circ}\), and 42-46\({}^{\circ}\). These data are freely available through the Copernicus Open Access Hub. For this study, a total of 6431 Sentinel-1 SAR acquisitions in IW mode covering the SCS were collected between 5 November 2017 and 31 October 2023. Their geographical distribution is illustrated in Figure 1.  
Figure 1: Geographic distribution of the 6431 Sentinel-1 SAR scenes (red rectangles) and the trajectories of the drifting buoys (colored-dot lines) collected in the SCS between November 2017 and October 2023. The colorbar represents the drifters’ measured surface ocean current velocities, in units of m/s.
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
###### Abstract  
In wireless sensor networks, it is important to use the right number of sensors to optimize the network and consider the key design and cost. Due to the limited power of sensors, important issues include how to control the state of the sensor through an automatic control algorithm and how to power-save and efficiently distribute work. However, sensor nodes are usually deployed in dangerous or inaccessible locations. Therefore, it is difficult and impractical to supply power to sensors through humans. In this study, we propose a high reliability control algorithm with fast convergence and strong self-organization ability called the sensor activity control algorithm (SACA), which can efficiently control the number of sensors in the active state and extend their use time. In the next round, SACA considers the relationship between the total number of active sensors and the target value and determines the state of the sensor. The data transmission technology of random access is used between the sensor and the base station. Therefore, the sensor in the sleep state does not need to receive the feedback packet from the base station. The sensor can achieve true dormancy and power-saving effects. The experimental results show that SACA has fast convergence, strong self-organization capabilities, and power-saving advantages.  
quality of service; Gur Game; wireless sensor networks; internet of things; power saving 202222220103390/s2202022222220  
To address these issues, in [5], the authors first applied the Gur Game algorithm to control the number of active sensors and then defined the quality of service as the number of active sensors. In the Gur Game, each active sensor sends a packet to the base station. When the base station receives packets from the active sensors, it counts the total number of packets and calculates the probability of the sensor changing its state in the next round through the reward function. After several operations, the total number of active sensors gradually reaches the target value. In [6], the authors proposed the ACK algorithm, in which the base station individually sends feedback packets to each active sensor. Sleep sensors do not need to receive feedback packets, and the ACK algorithm uses a random-access mechanism to increase the lifetime of the entire network [6; 7; 8; 9; 10].  
In this study, we propose a high reliability control algorithm with fast convergence and strong self-organization ability, called the sensor activity control algorithm (SACA). In SACA, there are two sub-processes: the adjustment procedure for the weight value of sensors and the transformation procedure for the state of sensors. SACA considers the relationship between the total number of active sensors and the target value and determines the state (ON or OFF state) of the sensors in the next round. Random access transmission technology is used between the sensor and the base station such that the sleep sensors do not need to receive the feedback packet from the base station, resulting in power savings.
## 3 2D-DOA Total Variation Regularization
### Total Variation Regularization
#### 3.1.1 Total Variation Definition  
Suppose that the source \(S\) matrix with dimension \(M\times N\), according to the column sorting matrix, \(S_{i,j}\) represents any element in the matrix, \(i\) is the row vector index, \(j\) is the column vector index, \(i,j=1,\ldots,M\). The variation of any row and adjacent elements in the matrix is defined as:  
\[\left(\Delta S_{h}\right)_{ij}=\left\{\begin{matrix}S_{i,j}&j=1\\ S_{i,j}-S_{i,j-1}&1<j\leq M\end{matrix}\right. \tag{15}\]  
\[\left(\Delta S_{v}\right)_{ij}=\left\{\begin{matrix}S_{i,j}&i=1\\ S_{i,j}-S_{i-1,j}&1<i\leq N\end{matrix}\right. \tag{16}\]  
where \(\Delta S_{h}\) and \(\Delta S_{v}\) represent the first-order \(X\) axis direction variation and \(Y\) axis direction variation of the adjacent elements of matrix \(S\), respectively. The 2D total variation is defined as:  
\[TV(s)=\sum_{i,j}\Bigl{[}\left(\Delta S_{h}\right)_{i,j}+\left(\Delta S_{v} \right)_{i,j}\Bigr{]} \tag{17}\]
# Assessment of Hongtu-1 Multi-Static X-Band SAR Constellation Interferometry
## 2 Materials and Methods
### Geocoding Considering Height Correction  
The terrain height was used as one of the inputs of the geocoding process. Using a terrain height with an error \(h_{err}\) resulted in a positional error in the ground-range direction of \(h_{err}/sin\theta\). For the 42.5 deg. Incidence angle of the scene considered, this means that the positional error was about 1.5 times the height error. Consequently, it is very relevant thatthe corrected heights were used for geocoding, as height corrections \(>\) 10 m were observed for some forests and in mining and landfill areas.
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 1 Introduction
Our approach is unique in the sense that change detection based on feature points is being newly proposed, while there have been previous studies combining change detection with matching at the patch [40], image [41] and multi-scale level [42]. It is presumed that small objects have distinctive brightness patterns, and hence, produce feature points. Feature point analysis should represent the change in status of small objects better than pixel analysis. We expect our feature-based approach to be less noisy than pixel-based approaches, since feature points are less sensitive to geometric errors between previous and recent images. We tried this approach as an attempt to compensate for the disadvantages of highly time-consuming approaches. It is very difficult to apply computationally expensive supervised, segmentation, and classification approaches [43; 44] to every changed pixel [25]. Applying such an approach to changed features should be more feasible. We expect that our feature-based approach is able to represent object changes without supervision to some extent. The organization of this paper is as follows. Section 2 describes the workflow of the proposed feature-based change detection. Section 3 describes the dataset, study area and preparation of ground truth data used for the experiments. Section 4 reports the results of feature-based change detection and discusses their performance in comparison to a pixel-based approach. Section 5 concludes the findings of this paper, along with limitations and future research directions.
## 3 Materials and Methods
### Models Overview
#### 3.7.2 K-Nearest Neighbors (KNN)  
K-Nearest Neighbors (KNN) [34] is a simple, yet effective, instance-based learning algorithm. It classifies data points based on the majority vote of their \(k\)-nearest neighbors, utilizing distance metrics like Minkowski distance. In this study, KNN was applied to human activity recognition using both non-overlapping and 50% overlapping data configurations.  
The Minkowski distance, which generalizes both Euclidean and Manhattan distance, is defined as:  
\[d(x_{i},x_{j})=\left(\sum_{k=1}^{n}|x_{ik}-x_{jk}|^{p}\right)^{1/p}, \tag{13}\]  
where \(p=1\) for Manhattan distance and \(p=2\) for Euclidean distance.  
To ensure optimal performance, we used nested cross-validation, which not only tunes hyperparameters but also validates the model, thus providing a more robust evaluation. The optimal hyperparameters across both two-sensor and four-sensor configurations (non-overlapping and overlapping) remained consistent, as shown in Table 8. Specifically, a Minkowski distance with \(p=1\) (Manhattan distance), \(k=3\) neighbors, and uniform weights yielded the best results.  
KNN's simplicity in design--using local proximity to make decisions--makes it highly interpretable. Despite this, the nested cross-validation process confirmed that the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Configuration** & **Criterion** & **Max Depth** & **Min Samples Leaf** & **Min Samples Split** \\ \hline
2 Sensors—Non-overlapping & Entropy & 5 & 2 & 2 \\
2 Sensors—50\% overlapping & Entropy & 5 & 2 & 2 \\
4 Sensors—Non-overlapping & Entropy & 5 & 1 & 2 \\
4 Sensors—50\% overlapping & Gini & 5 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimal Hyperparameters for Decision Tree in Different Sensor Configurations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Configuration** & **Metric** & **n\_neighbors** & **p (Minkowski)** & **Weights** \\ \hline
2 Sensors—Non-overlapping & [PERSON] & 3 & 1 & Uniform \\
2 Sensors—50\% overlapping & [PERSON] & 3 & 1 & Uniform \\
4 Sensors—Non-overlapping & [PERSON] & 3 & 1 & Uniform \\
4 Sensors—50\% overlapping & [PERSON] & 3 & 1 & Uniform \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal Hyperparameters for KNN in Different Sensor Configurations.  
\(k=3\) setup with Manhattan distance strikes a balance between simplicity and accuracy, performing consistently well across different sensor configurations. While KNN is not as complex as deep learning models, it provides competitive results when applied in well-structured sensor setups. Its effectiveness highlights that even traditional methods can serve as reliable benchmarks in human activity recognition tasks [35].
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 3 The IACO-Based Spectrum Allocation Method
### Pseudocode of IACO  
```
Input:\(T\): network topology; \(N\): the number of node in undirected graph; \(\Delta t\): the evolution times in change rate monitoring; \(E_{max}\): the maximum of iteration; \(E_{convergence}\): the maximum evolution time with low convergence speed. Output: the optimum spectrum allocation solution \(X\)  initialization population \(N(N_{1},N_{2},N_{3},\ldots,N_{Num})\) while\(E<E_{max}\)do \(E\gets E+1\)  Interference removal Fitness Evaluation if Equation (8) and \(E_{convergence}\)then  Initialize DE with subpopulation of ACO \(E_{convergence}\gets E_{convergence}+1\)  Mutation Crossover Selection  all ants start traversal in the solution space  The ants select path by Equation (9)  Update pheromone according to Equation (11) if\({}^{1}\!E_{convergence}\)then  Initialize VNS with local optimal solution \(s\)  Construct reverse neighborhood \(N^{s}\)  Local search
```  
**Algorithm 1**An Improved Ant Colony Optimization Algorithm
## 5 Implementation and Evaluation
### Feature Selection
#### 5.3.1 Mifs  
MIFS [20], a greedy feature selection approach, measures the mutual information between the candidate features and the output class, which reduces the uncertainty of preserving the useful information. This reduced feature subset serves as an optimal feature subset for the multi-class classification problem. MIFS is advantageous over linear transform dependent methods as it considers the non-linear relationship within the features and that between the features and the output gesture class. Therefore, MIFS provides a good generalization for the training and reduces the computational time. MIFS ensures that every selected feature is an informative one as follows:  
1. Set CF \(\leftarrow\) Initial 'n' cumulant features (_Fn_ CF), S \(\leftarrow\) Empty set
2. For each cf \(\in\) CF, compute MI (GC; cf) (Compute mutual information, MI for every individual feature 'cf' that belong to the output gesture class 'GC')
3. Find the cf that maximizes MI (GC; cf) 1. Set CF \(\leftarrow\) CF\(\backslash\){cf}; Set S \(\leftarrow\) {cf}
4. Greedy selection repeats until \(|\)S\(|\) = \(k\) (where, \(k\) = 30/50/80) 1. Computation of MI between CF: for all pair of variables (cf, s) with \(\text{cf}\in\) CF, s \(\in\) S. Compute I(cf, s), if it is not already available 2. Selection of next CF: Choose feature cf as the one that maximizes I(GC; cf) \(-\)\(\beta\)\(\sum_{s\in\) S I (cf; s) Set CF \(\leftarrow\) CF\(\backslash\){cf}; set S\(\in\) S \(\cup\) {cf}
5. Output the set S containing the selected features (Selected feature subset with 30/50/80) where CF is the cumulant features, S is the set of currently selected indices of CF (30, 50 or 80) and \(\beta\) is the regularization parameter. MIFS ensures the term I(GC; cf)consist of only relevant CF and adds the CF iteratively in the feature subset S. Experiments were conducted by varying the \(\beta\) values from 0.1 to 1 as it contributes in reducing the redundancy among the CF.
## 2 Materials and Methods
### Total Polyphenol Content and Polyphenolic Profile  
The total polyphenol concentration in infusions, methanol and acidified methanolic extract of examined herbs was measured with Folin-Ciocalteu reagent [28] as was reported by [PERSON] et al. [25]. The results were calculated as the chlorogenic acid equivalent (CGA) in mg per 100 g of DM of sample as was previously reported [25].  
For the HPLC polyphenic compounds analysis, all types of extracts of herbs were used to determine polyphenolic compounds via the HPLC method using the Prominence-i LC 2030C D3 Plus system (Shimadzu, Kyoto, Japan) along with a DAD detector and a Luna Omega 5 \(\upmu\)m Polar C18, 100A, 250 \(\times\) 10 mm Phenomenex column (Torrance, CA, USA). The separation of phenolic compounds was carried out at 40 \({}^{\circ}\)C. The mobile phase was a mixture of two elements: A, 0.1% formic acid in water (\(v/v\)), and B,0.1% formic acid in methanol (\(v/v\)). The mobile phase flow rate was 1.2 mL/min. The analysis was carried out with the following gradient conditions: from 20% to 40% B for 10 min, 40% B for 10 min, from 40% to 50% B for 10 min, from 50% to 60% B for 5 min, 60% B for 5 min, from 60 to 70% B for 5 min, from 70% to 90% B for 5 min, 90% B for 5 min, from 90% to 20% B (the initial condition) for 1 min, and 20% B for 4 min. This resulted in a total run time of 60 min. The injection volume of tested samples was 20 uL. A stock standard solution (100 mg/L) of each polyphenolic compound was prepared in 70% acidified methanol (0.1% formic acid (POCH, Gliwice, Poland). The calibration curves of the polyphenol standards were made by the dilution of stock standard solutions in 0.1% formic acid in 70% methanol (\(v/v\)). All the solutions were filtered through a 0.22 um filter, as was previously reported [22; 23].  
The following polyphenolic compounds were determined based on the following standards: gallic acid, catechin, chlorogenic acid, 4-hydroxybenzoic acid, epicatechin, caffe acid, vanillic acid, syringic acid, p-coumaric acid, ferulic acid, sinapinic acid, naringin, rutin, hesperidin, rosmarinic acid, myricetin, quercetin, luteolin, kaempfer, apigenin, isorhamentin, hispidulin, acacetin, carnosol and carnosic acid (Sigma, Saint Louis, MO, USA) (based on LabSolution ver. 5.93 Shimadzu Corporation (Kyoto, Japan)). The levels of quercetin, luteolin, apigenin, and myricetin were determined only in their free aglycones form.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 3 Materials and Methods
### Modelling Using the LSTM Network
The number of LSTM layers and units is tuned through trial and error to optimise the model's generalisation capability. The dropout rate is adjusted to prevent overfitting. The fully connected layer ensures that the features extracted from the LSTM layers are appropriately structured for classification or regression tasks. Activation function of the output layer is chosen based on the problem type (e.g., _softmax_ for classification, _linear_ for regression). Solvers such as ADAM, RMSProp, or SGD are used as the training algorithm, and the optimal solver is selected by monitoring the validation error. Training parameters such as the learning rate and training duration are optimised by tracking loss function and accuracy. Initial weight values are set using Xavier or He initialisation methods to prevent vanishing or exploding gradients, thereby facilitating faster model convergence. The careful tuning of these hyper-parameters significantly enhances model performance and helps to prevent overfitting. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Configuration** & **Parameters** \\ \hline Sequential input layer & Time-series data & _Configuration_ \\ Input variable(s) & 3 lag(s) of output & _Configuration_\({}_{t-1}\), _Configuration_\({}_{t-2}\), _Configuration_\({}_{t-3}\) \\ LSTM layer(s) & 1 layer & Stack size: 80\% (modelling), 20\% (forecasting); all data normalised \\ Hidden unit (count) & 64 & Gates: _sigmoid_, State: _tanh_ \\ Dropout layer & After LSTM layer & Dropout probability: 0.2 \\ Fully connected layer & 1 unit & Number of responses = 1 (the configuration data) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyper-parameters of LSTM and GRU networks. Figure 6: Signal flow in an LSTM hidden unit.