esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 3 Theoretical Background
### Review of ML-Based Classification Algorithms
As a result, KNN performs better in cases where the datasets are evenly distributed; however, in cases where the datasets differ slightly, the accuracy of KNN may be affected [30]. On the plus side, normalization is critical in ensuring even representation of all feature values when feeding datasets to KNN for improved performance. Naive Bayes classifier (NBC) is a popular type of theorem-based learner; it is based on [PERSON] theorem, which defines the relationship between two conditional probabilities of a specific event based on available prior information about the event under consideration [39]. NBC is a better classifier than other models whose principles are also based on [PERSON] theorem because it presents a simpler model with a simpler computational procedure [30]. Overall, the accuracy of ML algorithms has improved over the years, as many algorithms employ techniques that would readily predict complex datasets to give an outstanding result--SVM is a unique ML algorithm that employs a hyper-plane to create its decision boundary using support vectors. It provides space for the user to define gamma parameters for decision boundaries, and its performance is based on: the distance of the sample on either side can change influence; its regularization parameter determines the distance between the decision boundary and separation; its various kernels (for nonlinear boundaries), radial-based function (RBF), and so on [40]. SVMs are known to be computationally efficient; however, as the parameter values increase, the computational speed significantly drops [30], which is a major drawback for its use on large datasets. Amongst ML-based learners, multi-layer perception (MLP) has a relatively high predictive accuracy compared to other methods. MLP is a feed-forward neural network (FFNN) with three structures by default: input, hidden, and output layers [41]. It is very efficient for both supervised and unsupervised situations due to its architecture, learning sequence, and flexibility, making it ideal for classification [30; 41]. MLP's difficulty in implementation and interpretation are some of its significant drawbacks [41].
# Compact Multilayer Yagi-Uda Based Antenna for IoT/5G Sensors
## 2 Materials and Methods  
A Yagi-Uda antenna is a directive radiating structure composed of an active element and a group of passive elements, commonly referred to as directors and reflector, with the objective to steer the radiation to a specific direction [5]. Traditionally, Yagi antennas were based on metallic wire structures, in which the directors, reflectors, and the half-wavelength dipole were created [4]. However, and because printed antennas and circuits are currently widely used in microwave applications, several researchers have implemented printed Yagi-like structures in a planar dielectric substrate. Printed antennas allow one to develop low-cost and low-profile antennas, highly versatile and with good efficiency, facilitating its integration in microwave circuits [20].  
Design concerns of such antenna lie on the estimation of the lengths of the various constituent elements of the antenna, the distance between them, and, possibly, the number of directors needed to accomplish the goals. In this work, the optimal length for the dipole is not equivalent to half-wavelength in vacuum because these antennas are to be printed on a dielectric substrate, and its dielectric constant has a direct impact, causing a reduction on the dipole's length [21]. In fact, in the literature [21], it was found that the optimal length for a printed dipole would be 0.38 \(\lambda\). Consequently,an overall reduction on the antenna's size occurs, which is a clear advantage when considering the target applications for which these antennas can be applied.  
When designing antennas for this frequency band, one of the main challenges is the choice of the dielectric substrate to use, as it has a strong impact in the design and in the performance of the antenna. Moreover, at these frequencies, because of the small size of the elements, a more cautious design is required to guarantee an appropriate performance at 24 GHz. In this work, the substrate used is the Rogers RO4350B, because of its good performance in high frequencies (as it presents low dielectric tolerance and loss), as well as its electrical stability properties over frequency. The main characteristics include a relative dielectric permittivity of \(\varepsilon_{\mathrm{r}}=3.48\), a dissipation factor of \(\tan(\delta)=0.0037\) with a thickness of h = 0.762 mm.  
Usually, in Yagi antennas, the spacing between directors varies between 0.1 \(\lambda\) and 0.3 \(\lambda\) and their length is 5% to 30% shorter than the active element [6]. On the other hand, the reflector typically is up to 5% longer than the dipole and it backs the driver [6]. This placement of the active and parasitic elements ensures the end-fire beam formation [4]. In this work, the design of two Yagi antennas is presented, where the first one follows a planar structure and the second is a multilayer assembly of dielectric substrates with the elements printed on them. Both antennas are composed by a dipole, a reflector and three director elements, which is a trade-off between the gain achieved and the overall size of the antennas.
## 4 Evaluation via Use Cases
### Refactorisation of Large-Scale Matrices
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**No.** & **Power Grid** & **K** & **N** & **NNZ** & **_d [\%]_ \\ \hline (1) & Regional EHV/HV with SL & 1000 & 11,003 & 44,623 & 0.036 \\ (2) & French EHV with SL & 2000 & 26,432 & 92,718 & 0.013 \\ (3) & French EHV/Regional HV with SL & 4000 & 56,564 & 183,278 & 0.0057 \\ (4) & French EHV with VDL & 2000 & 60,236 & 188,666 & 0.0051 \\ (5) & F. + one neighbour EHV, SL & 3000 & 47,900 & 20,5663 & 0.0089 \\ (6) & F. + one neighbour EHV, VDL & 3000 & 75,300 & 269,958 & 0.0047 \\ (7) & F. + neighg. countries EHV, SL & 7500 & 70,434 & 267,116 & 0.0054 \\ (8) & F. EHV + regional HV, SL & 4000 & 90,940 & 316,280 & 0.0038 \\ (9) & F. EHV + regional HV, VDL & 4000 & 197,288 & 586,745 & 0.0015 \\ (10) & F. + neighg. countries EHV, VDL & 7500 & 220,828 & 693,442 & 0.0014 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of matrices from the French transmission system (\(K=\) number of nodes, \(N=\) matrix size, \(\text{NNZ}=\) number of nonzeros, \(d=\) density factor according to \(\frac{\text{NNZ}}{N-N}\)). PartialNICSLU performs a partial refactorisation based on the factorisation path, which results in speedups from 7.34 to 49.01 compared to KLU. Next, we investigated the impact of an increasing ratio of varying matrix entries. Figure 9 demonstrates that the computation time increases for all matrices with an increasing ratio of varying matrix entries and that, for the largest ratio of 10%, the computation time of PartialNICSLU becomes similar to that of NICSLU. This is due to the fact that a variation of 10% of the rows means that the factorisation path already includes 48% of the entries in the **LU** matrix. Figure 8: Refactorisation times for large-scale matrices assuming a ratio of varying matrix entries of 0.1%. Figure 9: Impact of ratio of varying matrix entries on partial refactorisation times.
## 2 Study Area and Data
### Data Description and Processing
#### 2.2.4 Phenological and Meteorological Data  
Summer maize phenological and meteorological data of Huanghuaihai Plain in 2012 were derived from the National Meteorological Information Center ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn))). In this study, the observation phenology data from 32 agricultural meteorological stations are gathered, including latitude, longitude, maize fertility name, and fertility date that was converted into the day of the year (DOY) at each site. The location of agro-meteorological stations is shown in Figure 2, and the phenological information is listed in Table 1.  
The grid dataset of the daily value of surface temperature and precipitation in China with the spatial resolution of 0.5\({}^{\circ}\) was employed to obtain the meteorological elements in the study area. The grid dataset was generated by the thin plate spline (TPS) method based on the data of precipitation and air temperature of the high-density site on the ground in China in the special national information materials, as well as the GTOPO30 data. The grid dataset was defined as the CGS_WGS_1984 geographic coordinate system and was projected into the UTM_Zone_51N coordinate system. We resampled the grid data with 250 m spatial resolution by the bilinear algorithm. Based on the processed grid dataset, the annual average temperature, annual precipitation, and \(\geq\)10 \({}^{\circ}\)C accumulated temperature in Huanghuaihai Plain were calculated from the grid data with 250 m spatial resolution. In addition, the annual average temperature, annual precipitation, and \(\geq\)10 \({}^{\circ}\)C accumulated temperature at 32 agro-meteorological stations were obtained (Table 1).  
Figure 2: The spatial distribution of agro-meteorological sites in Huanghuaihai Plain.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Sites** & \multicolumn{6}{c}{Environmental Factors} & \multicolumn{6}{c}{**Phenological Information (DOY)**} \\ \hline  & & \multicolumn{3}{c}{**Average**} & \multicolumn{3}{c}{\(\geq\)**10 °C**} & \multicolumn{3}{c}{**Foretional Information (DOY)**} \\ \hline
**Name** & **Longi-ude Lattice** & **Latinque** & **Annual** & **Accunulated** & **Precipitation** & **Sowing** & **Seeding** & **Teroil** & **Sever-Leaf** & **Joint-ing** & **Tassel-ing** & **Milk-ripe** & **Matura-tion** \\  & (°E) & (°N) & **Temperature** & **(mm)** & **Sowing** & **Seeding** & **Teroil**
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 2 Related Work
### Comparison with the Proposed Framework  
The proposed framework advances the state of the art by addressing key limitations of existing approaches:  
* Integration of Stock Selection and Market Prediction: Unlike existing works that treat stock selection and market state prediction as independent tasks, our framework combines an ontology-based stock classification system with a dynamic market state prediction model.
* Multimodal Data Integration: The approach integrates three critical dimensions--financial fundamentals (via ontology), market sentiment (via BERT), and dynamic market regimes (via NMC and LSTM). This multimodal integration enhances predictive accuracy and decision-making capabilities.
* Dynamic Framework Update: By incorporating real-time financial indicators and exogenous variables such as VIX, GARCH volatility, and the Illiquidity Index (ILLIQ), the framework adapts to changing market conditions.
* Dynamic Adaptability: By incorporating real-time financial indicators and exogenous variables such as VIX, GARCH volatility, and the Illiquidity Index (ILLIQ), the framework dynamically integrates updated market information to enhance its predictive capabilities. While the framework does not involve automatic parameter adjustments typically associated with adaptive machine learning algorithms, it enables decision-making that is responsive to changing market conditions through the integration of continuously updated data sources.
* Superior Performance: Empirical results show that the proposed framework achieves a market state prediction accuracy of 97.20% and outperforms baseline models like RNN, Random Forest, and XGBoost. The stock selection and prediction-based trading strategy generated cumulative returns of 13.67%, significantly higher than the 8.62% from the Buy and Hold strategy.  
In summary, the proposed framework sets itself apart from existing methods by providing a comprehensive and unified approach to stock selection and market state prediction. It integrates ontology-based financial analysis with advanced machine learning techniques, effectively bridging technical, fundamental, and sentiment analyses. This creates a strong solution for quantitative trading. Comparative evaluations show that our method not only overcomes current limitations but also outperforms others in terms of performance, making it an important contribution to the field of financial market prediction.
# Design and Optimization of a Toothed-Plate Single-Roller Crushing Device for Waste Plastic Film
## 2 The Structure and Principles of the Crushing Device  
### Structure of the Crushing Device  
As shown in Figure 1, the primary components of the toothed-plate single-roller crushing device include a crushing component, an electrical control component, a conveyor component, a transmission component, a frame, a feeding hopper, and a discharge hopper. Within the crushing component, there are 8 toothed-plate holders uniformly welded on the outer circumference of the crushing roller. Each holder can accommodate 3 moving toothed plates, and their arrangement and quantity can be adjusted according to specific crushing requirements. The conveying component has an adjustable speed and a width of 0.8 m and is capable of meeting the needs of waste plastic film transport. The electrical control component consists of a touchscreen, a programmable controller, and a frequency converter, enabling functions such as whole-machine control and overload protection.
# Adaptive Clustering of Users in Power Domain NOMA
## 6 Results and Discussions
### Comparison with OMA Systems  
Figure 7 shows the average sum data rate of NOMA and OMA systems for two user clusters. It is possible to note that there are advantages of a NOMA system with the proposed model as there is a consistency in the increase in the sum data rate over time when compared to OMA systems.  
The slight variations over time that occur in Figure 7 are due to changes in the channel gain of each user. When users have more distant values of channel gains, the more significant the difference in the sum data rate is between a NOMA and OMA system and vice versa. The performance gains obtained in relation to the OMA in the analyzed time instants were equal to 6.3%, 8.5%, 4.6%, 5.9% and 5.0%. These values are characteristic of the channel model used, as this model does not favor a large difference in channel gain between users.
## 5 Challenges and Future Research Trends
### Channel Estimation for Other 6G Communication Scenarios  
Beyond the RIS-aided communication systems we have mentioned, there are numerous emerging scenarios for next-generation wireless communications. These scenarios include those that employ terahertz (THz) communication technology [101] and visible light communication (VLC) technology [102]. Among them, THz communications can bring massive spectrum resources that meet the requirements of 6G. Additionally, VLC adopts an ultra-high bandwidth for high-speed data transmission, offering a wide range of applications in 6G. Given these, extending deep-learning-based channel estimation research to these two communication scenarios is necessary and an important future research direction.
# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
## 4 Experimental Results and Analysis
### Performance of Different Models in Training Phase  
In order to examine the change in accuracy of the different methods during the training process, the proposed model was tested on the validation set at every 1000 iterations during training. Figure 6 illustrates the IoU metrics for the two target categories of land and water regions, with the number of iterations (Iteration/1000) in the horizontal coordinate and the IoU values in the vertical coordinate. Figure 6a,b demonstrate the IoU values for water and land areas in each validation, respectively. It can be seen from Figure 6 that the performance of U-net, Uper-net, and the FCN varies significantly at each stage of iteration, and gradually stabilizes in the later stages.  
In the test results of Fuse-ResNet, the IoU value remains at a high level, with only slight variations, and stabilizes after the 16,000 th iteration. This indicates that the proposed Fuse-ResNet achieves high-precision recognition, while maintaining good robustness. At the end of the training process, the IoU value of Fuse-ResNet for the water region category reaches 99.45%, while the values for the other models range from 97.5% to 98.6%. The IoU value of Fuse-ResNet for the land category reaches 98.06%, while the values for the other models range from 95.87% to 97.5%.  
Figure 6: Performance of different models on validation set. (**a**) IoU of water areas; (**b**) IoU of land areas.
# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
## 2 Materials and Methodology Applied to the Forecast and Estimation Model
### Econometric Data: Weather Variables, Outliers and Satellite Measures  
Influence causes or factors in a process require adequate research to avoid non-desirable effects and control their dissemination. The cause search allows us to observe changes and study their mechanisms to generate new hypotheses and design plans to modify or mitigate their effects. When constructing an explanatory model, relationships between causes and a given effect can be represented in such a way that the proportion of each cause to the effect can be computed. The aim of this paper was to find a mathematical model that relates vegetation indices for different types of crops with possible explanatory variables. Additionally, it is important to detect the presence of outliers in the given effect. Their presence can be derived from multiple causes: a single time instant in which an outstanding value is obtained; a period in which the observed behavior of the time series changes drastically or returns gradually to the previous level. To consider all the possible factors that can provoke effects in the vegetation indices obtained by satellite, we considered as part of the explanatory model the following components:  
* Weather variables, namely, temperature (T), precipitation (P), humidity (H) and radiation (R), were measured in the same time period as the satellite measures;
* Autoregressive measures obtained through the lags of the time series in the studio;
* Intervention measures to deal with outliers presented in the time series.  
On the other hand, satellite measures obtained by MODIS are collected fortnightly. To apply time series modelling techniques, data must be aggregated in monthly frequencies.  
We considered the following aggregation models for the five types of crops (Figure 2): time series obtained by the average values of available data in each month (Figure 2a); time series obtained by the maximum values of available data in each month (Figure 2b); and time series obtained by the minimum values of available data in each month (Figure 2c).  
Figure 2: _Cont_.  
In this paper, we consider the case obtained by the average values of the data available in each month, as there are no observable significant differences between the three proposed aggregation methods. In the case of unbalanced data, other aggregation measures can be used as an alternative, such as the median value. In our case, data were balanced, and the variation between the minimum and maximum values was small; therefore, the average was able to appropriately represent the sample population.  
Figure 2: Time series obtained by the average values (**a**), maximum values (**b**) and minimum values (**c**) of available data in each month.
# The Ocean-Going Autonomous Ship--Challenges and Threats
## 2 Navigational Equipment and Related Challenges
### Navigation Threats
One of the examples of counteracting the threat of GNSS disrupting, but also to identify the scale of the threat, was the DETECTOR project, co-financed by the European GNSS Agency under the 7 th Framework Program. It included the development and verification of a low-cost device for the detection of the interference in the GNSS band and the occurrence of jamming. This device was supposed to be useful for interference detection for GNSS in land transportation. Tests have proved the system's usefulness not only for reliable detection of interference, but also for determining the characteristics of interfering devices in laboratory conditions, but also during field experiments and in real conditions [26]. Figure 3: Typical GNSS vulnerabilities [22]. Until recently, GNSS receivers were vulnerable to jamming or spoofing. Only an experienced user, carefully observing certain parameters of the signal, can suspect that such a threat appears. The final effects were observed in the form of a radical decrease in the accuracy or the interrupted work of the receiver. Currently, there are GPS receiver modules with a built-in jamming threat signaling mechanism, for example Gmm-u5j [27]. There are also receivers with a multi-segment, controlled antenna (GAJT), which eliminates the signals received from a particular angle sector, thus eliminating the reception of the jammer signal.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 3 Experimental Procedures and Methods  
Polarization calibration based on the polarization calibration model proposed by [PERSON] [30] was performed following the procedure depicted in Figure 5. The process mainly consisted of two parts: ground experiments and image processing. Firstly, a flat area with no significant electromagnetic interference was selected within the experimental area to deploy ARCs and CRs. Prior to the operation of the active calibrator, external calibration and antenna rotation were conducted to ensure the accuracy of the transmitted signal RCS and polarization scattering matrix. Before the satellite transit, parameters such as working time, working frequency band, azimuth, elevation angle, delay, and RCS value were set according to the predicted transit time to ensure that the signals transmitted by the equipment were accurately received by the satellite.  
Figure 4: Overall conditions of the experimental data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Imaging Satellite** & **Imaging Time** & **Frequency** & **Center Incidence** & **Sight Direction** & **Swath** & **Imaging Mode** & **Polarization** \\ \hline C-SAR/02 &
\begin{tabular}{c} 19 June 2023 \\ 18:10:17 \\ \end{tabular} & 5.4 GHz & 31.70\({}^{\circ}\) & Right & 30 km & QPSI & Quad \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental data parameters.  
After obtaining the experimental data, the corresponding position on the image was localized according to the actual deployment latitude and longitude coordinates of the equipment. Then, the amplitude and phase of each ARC and CR were extracted and normalized. Next, based on the observed scattering matrix M of the ARC, the receiving distortion matrix R and the transmission distortion matrix T were separately calculated. Finally, using the distortion matrices calculated in the previous step, the observed matrices of each CR were corrected to obtain polarization isolation, amplitude imbalance, and phase imbalance indicators.