esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Metrics  
Motivated by the growing importance and need for an energy forecasting system, the ASHRAE Great Energy Predictor Shootout leveraged and unified a metric, CV, to accurately compare the methods predicting hourly energy use based on building data [33]. Root Mean Squared Error (RMSE) [34], Mean Absolute Error (MAE) [35], and coefficient of determination (R\({}^{2}\)) [36; 37] are also provided to allow in-depth insights into the results and benchmarking with other research as these metrics have also been widely used. The details of the three metrics are given as follows:  
\[\text{CV}=\frac{\sqrt{\frac{1}{\text{N}-1}\sum_{i=1}^{\text{N}}\left(\text{y} _{i}-\text{y}_{i}\right)^{2}}}{\overline{y}}\times 100 \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}(\text{y}_{i}- \text{y}_{i})^{2}} \tag{5}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{\text{N}}(\text{\hat{y}}_{i}-\text{y}_{i})^{ 2}}{\sum_{i=1}^{\text{N}}(\text{\hat{y}}_{i}-\overline{y})^{2}} \tag{6}\]  
\[\text{MAE}=\frac{\sum_{i=1}^{\text{N}}|\text{\hat{y}}_{i}-\text{y}_{i}|}{N} \tag{7}\]  
where y\({}_{i}\) is actual values; \(\text{\hat{y}}_{i}\) indicates predicted values; \(\overline{y}\) is the average value of the total energy consumption; and N is the number of samples. As seen in (4), a large numerator means an extensive error range leading to a high CV. It is noticeable that RMSE pays more attention to the error itself, while R\({}^{2}\) focuses on a ratio of the error to actual and average values.
# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 5 Simulation and Analysis  
### Experimental Data  
The experiment used six typical communication jamming signals to evaluate the recognition performance of the proposed signal identification method. These include Single-Tone Jamming, Multi-Tone Jamming, Narrow-Band Jamming, Broad-Band Jamming, Comb-Frequency Jamming, and Linear Sweeping Frequency Jamming. The specific parameters of each interference signal are detailed in Table 1. For each type of jamming signal, 140 samples were generated at each Jamming-to-Noise Ratio (JNR) level ranging from \(-10\) dB to \(10\) dB with a step size of 2 dB. The samples were divided into a 7:3 ratio for training and testing, resulting in 98 training samples per interference signal type, totaling 588 samples, and 42 testing samples per interference signal type, totaling 252 samples.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Jamming Types** & **Label** & **Parameters Setting** \\ \hline Single-Tone & Jam 0 & The frequency points \(f\) are randomly selected between 1 and 5 MHz. \\ Multi-Tone & Jam 1 & The number N of audio is a random integer between 2 and 8, and the frequency points \(f\) are same as jam1. \\ Narrow Band & Jam 2 & The center frequency point \(f_{c}\) is randomly selected between 1 and 5 MHz, and the bandwidth is set to 0.25 MHz. \\ Broad Band & Jam 3 & The bandwidth is randomly selected between 2 and 3 MHz. \\ Comb Frequency & Jam 4 & The start frequency point \(f_{s}\) is positioned at 2.5 MHz, the number N of \\  & & frequency point randomly selected between 3 and 5, and the interval between frequency points is set to 0.25 MHz. \\ Linear Sweeping Frequency & Jam 5 & The frequency linearly varies with a starting frequency between 10 and 100 MHz and a sweep rate uniformly distributed within [0.1, 1] THz/s. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameter settings for the six types of communication jamming signals.  
Figure 5: C-ResNet Network Structure.
## 2 Materials and Methods
### Measurement and Calculation of Losses Due to the Curvature  
The losses due to the curvature are measured as a function of the sun's elevation at the zenith for two solar panels with different curvatures in Chambery, France. The experience consists of outdoor measurements of the maximum power point on one curved solar roof (radius of curvature approximately 3 m) and one standard flat reference module. The modules are placed facing south with 0\({}^{\circ}\) slope (horizontally). From August 2019 to February 2020, the measurements were recorded every 5 min during daytime. From this, an approximation of a linear regression of the producible loss versus the flat reference module as a function of the sun's elevation at the zenith was computed. This regression was extrapolated to compute the curvature losses for each location and according to the sun's elevation at the zenith through a typical year.  
Figure 1: Energy and distance modeling.  
Equation (1) ([PERSON] et al. [16]) calculates the sun's elevation at the zenith as a function of the co-latitude and the declination of the sun on the 15 th of each month (_dayNum_).  
\[\text{sun elevation at zenith}=23.45\times\sin\biggl{(}\frac{2\times\pi\times( dayNum+284)}{365}\biggr{)}. \tag{1}\]  
Therefore, the model calculates the losses due to the curvature on the 15 th of every month for each city with the solar roofs considered.  
As indicated previously [6] p. 3, the monthly photovoltaic producible values are obtained from the Photovoltaic Geographical Information System (PVGIS) [17] for a flat horizontal panel for 19 European cities. Considering the average and the highest annual PV productions among these cities, the chosen localities for VIPV studies are Paris and Malaga. Figure 2 shows the monthly PV produced energy in Malaga and Paris in kWh per kWp with and without curvature correction. The values for a flat panel are directly obtained from PVGIS. The subtraction of monthly curvature losses from the values for a flat panel yields the values for a curved panel. Total production, respectively, for a flat panel and a curved panel is 1635 kWh/kWp and 1535 kWh/kWp in Malaga and 1058 kWh/kWp and 965 kWh/kWp in Paris. The losses due to curvature are 8.8% in Paris and 6.1% in Malaga for a year.
## 3 Results and Discussion
### Thermal Stability of SOM
We observed positive correlation between the amount of SOM degraded around 250 \({}^{\circ}\)C and WDPT or MED values, which confirms low thermal stability of substances responsible for SWR. FTIR data and the results of TG analysis helped to increase the explained portion of variances in WR data in the multiple regression analysis. The R\({}^{2}\) values ranged from 0.52 up to 0.89, depending on the used predictors. Whereas lower R\({}^{2}\) values were characteristic of the equations that used basic soil properties as explanatory variables, the models that used FTIR peak areas, TG data, and soil moisture showed R\({}^{2}\) > 0.7. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13010216/s1](https://www.mdpi.com/article/10.3390/app13010216/s1)]([https://www.mdpi.com/article/10.3390/app13010216/s1](https://www.mdpi.com/article/10.3390/app13010216/s1)), Tables S1 and S2: The equations obtained via multiple regression analysis and the parameters characterizing the regression models (R\({}^{2}\): coefficient of multiple determination, F: observed value of the F statistic, \(p\): error probability value of F statistics, d.f. : degrees of freedom). Figures S1 and S2: Scatter plots displaying collinearity between observed and predicted SWR data. Conceptualization, I.S. and P.D. ; Formal analysis, I.S., P.D., and Z.F. ; Methodology, I.S. and P.D. ; Visualization, I.S. and Z.F. ; Writing--original draft, review and editing, I.S., P.D. and Z.F. ; Funding acquisition, I.S. and P.D. All authors have read and agreed to the published version of the manuscript. This work was supported by the Slovak Scientific Grant Agency (the Ministry of Education, Science, Research and Sport of the Slovak Republic) for VEGA projects No. 1/0712/20 and 2/0147/21. The data that support the findings of this study are available from the corresponding author on request. The authors declare that they have no competing financial interest or personal relationships that are relevant to the content of this article.
# Development of Patient Specific Conformal 3D-Printed Devices for Dose Verification in Radiotherapy
## 2 Materials and Methods  
### Development of Personalized 3D Devices  
Fabrication of personalized 3D devices for clinical applications in radiotherapy was based on the reconstruction of a real cancer patient's model according to CT images. The conversion of the data to a printer-friendly model was achieved using a specially developed data conversion algorithm and final realization of the 3D objects by applying 3D-printing technology. For this purpose, an original set of CT scans (CT scanner Siemens Light Speed RT16, Erlangen, Germany). in DICOM format displaying an anonymous patient's anatomic area of interest (facial part with nose, cheeks and eyes covering tumor location) was selected. Surface rendering was performed using the open platform software 3D Slicer (slicer.org; US) to reconstruct a 3D model of the patient converting DICOM files to STL (stereolithography) format. The open software Blender (blender.org; Netherlands) was used to create a 3D-printed model of the desired object (3D phantom or 3D bolus) from the extracted STL files. The Zortrax M300 3D printer, which has a large printing volume (300 \(\times\) 300 \(\times\) 300 mm\({}^{3}\)) was used to print the patient-specific devices (3D phantom and 3D bolus), by using fused deposition modeling (FDM) printing technology. The extracted STL file was converted into a special code able to run the commands that regulate the printing parameters of the desired 3D objects. The whole workflow chart of the creation and printing of 3D objects is shown in Figure 1.  
Two types of 3D-printed devices were created using the technological procedure described above: patient-specific 3D phantom of the facial region of interest (Figure 2) and patient-specific 3D bolus of the same anatomic region (Figure 3). The 3D phantom was designed to be attached to the surface of a standard CT polymethyl methacrylate (PMMA) head phantom. Moreover, boluses of two different thicknesses (0.5 and 1.0 cm) were printed in order to evaluate the effect of bolus thickness on surface dose distribution. PLA material was used for the creation of desired 3D objects keeping a 90% infill ratio
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 2 Materials and Methods  
Similarity-based methods can be categorised into three main types relevant to agricultural recommendation systems: user-based, item-based, and hybrid approaches [12; 13; 14; 15; 16].  
User-based filtering generates recommendations by analysing the practices of farmers operating under similar conditions, such as climate, crop type, and farming techniques. For example, if one farmer achieves success with a particular irrigation method, that method can be recommended to others in comparable environments. This approach relies on accurate data sharing and community engagement [17].  
The item-based approach suggests treatments, seeds, and fertilisers based on the observed success of similar products. If an agricultural item is highly rated or frequently used, it can be inferred to be of high quality and, as a result, a safe recommendation.  
Hybrid methods combine both user-based and item-based approaches to produce more complete recommendations. These systems adapt to varied farmer needs and improve decision-making across agricultural tasks. While traditional systems often emphasise user-based filtering, item-based filtering has been shown to improve scalability in large datasets by focusing on item-item similarities [18].  
Beyond algorithmic function, similarity-based systems support community building by fostering farmer networks where users share experiences, solutions, and concerns. This collective knowledge helps individuals facing unfamiliar challenges learn from others with similar profiles, improving resilience and adaptability.  
This study focuses on applying similarity-based methods to improve crop lifecycles and farm efficiency, with an emphasis on the user-user approach. Each farmer is represented as a user, and the algorithm identifies the most similar users based on attributes such as soil type, climate, and pest management.  
The implementation was carried out in Java 11.0.17 (Oracle OpenJDK, Austin, TX, USA), chosen for its efficiency in handling both small and large datasets and for its support of the KNN and ANN-LSH algorithms. Farmer profiles were stored in CSV format, and Java collections were used to ensure efficient storage, retrieval, and neighbour computation.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 3 Data Preprocessing
### Adding Gaussian Noise to Curves  
The current-voltage curves measured in actual field conditions may exhibit slight noise due to various external factors. Therefore, to ensure the training data obtained through simulation more closely resemble the actual measured data, Gaussian noise is added to the simulated curves, as shown in Figure 21, making the simulated curves more similar to the curves obtained in actual field measurements. The curves with added Gaussian noise are then converted into images to serve as input data for the CNN part of the multi-input model, enhancing the model's accuracy on actual field data. After multiple tests, the study initially generates random noise with a Gaussian distribution having a mean of 0 and a standard deviation of 0.03; the noise generated under this parameter not only preserves the original features of the curve but also simulates a certain degree of noise, adds this noise to the original current data to simulate real-world environmental noise. As for the actual field data, Gaussian noise is not necessary. It can be directly input into the trained model for fault diagnosis.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods  
The EDFA model proposed in this work is specifically designed to deliver a precise estimation of the noise figure. However, it is not intended to function as an independent, stand-alone model. Instead, its purpose is to integrate within a broader digital-twin framework for network modeling while remaining entirely brand-agnostic. To fulfill these objectives, the model must adhere to the following key requirements:  
1. **Accuracy:** The model should maintain a high level of precision, ensuring that errors remain comparable to the inherent measurement uncertainties present in the training datasets.
2. **Computational efficiency:** It must be capable of processing data swiftly to prevent performance bottlenecks, particularly when handling extensive training and test datasets within the complete network model.
3. **Brand neutrality:** The model should be easily adaptable across different manufacturers and device types with minimal modifications.
4. **Usability and portability:** While not a strict requirement, the model should ideally be intuitive to use and easily portable to different environments.  
With respect to the model's input parameters, Equation (9) establishes that the noise figure is dependent on \(P_{ASE_{AMP}}\), \(G\), and \(f\). The EDFAs considered in this study operate in AGC mode, allowing adjustments to both the target gain and tilt. Consequently, the channel gain, \(G\), is primarily determined by these two parameters. Moreover, \(P_{ASE_{AMP}}\) is influenced not only by gain but also by the input signal power. Based on these considerations, the model employs the following four input parameters:  
1. **Input signal power**, expressed in dBm;
2. **Gain**, measured in dB;
3. **Tilt**, represented in dB;
4. **Channel frequency**, given in THz.  
The central objective of this study is to attain an accuracy level where the maximum observed errors in the test datasets remain within or below the theoretical uncertainty of the noise figure, as analytically derived from the measurement data.
## 3 Antenna Performance Enhancement Techniques
### Impact of Dielectric Lens on Directivity and Gain  
Electrostatic radiation transmitted in one direction by the dielectric lens has the capability to divert the direction of the electrostatic radiation in one direction, which leads to an increase in the directivity as well as gain of an antenna, as in [39]. Different geometrical shapes of dielectric lens are available, and they are designed by alignment with the same or different substrate materials.
## 2 Materials and Methods
### Near Real-Time Combined Processing
#### 2.3.3 Performance Measures  
The performance measures used to assess the flood detection accuracy were the flood detection rate (i.e., recall (or hit rate) = \(t_{p}/(t_{p}+f_{n})\)), the precision (= \(t_{p}/(t_{p}+f_{p})\)), and the critical success index (CSI = \(t_{p}/(t_{p}+f_{p}+f_{n})\)), where \(t_{p}\) = true positives, \(f_{n}\) = false negatives, and \(f_{p}\) = false positives.
## 2 Materials and Methods
### In Vitro Maturation (IVM), Oocyte Isolation, and Experimental Protocol  
Cumulus-oocyte complexes (COCs, 15/group) were collected by puncturing antral follicles from mouse ovaries using an insulin syringe. They were then subjected to in vitro maturation (IVM) at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\) in air, using alpha MEM medium supplemented with 0.23 mM pyruvate and 2 mM L-glutamine [43]. Lindane (PESTANAL, 45548) was dissolved in dimethyl sulfoxide (DMSO), and serial dilutions were prepared in culture medium to achieve the desired concentrations. In our study, DMSO was used at concentrations \(\leq\)0.1% as a vehicle control in accordance with previous studies on Lindane and other organochlorines [44]. These studies have reported that DMSO at such concentrations does not induce morphological, maturation, or ultrastructural changes in oocytes or granulosa cells [45; 46].  
To investigate Lindane toxicity, the concentrations used (1 \(\upmu\)M, 10 \(\upmu\)M, and 100 \(\upmu\)M) were based on previous reproductive toxicity studies [38; 39; 47]. Despite exceeding typical human and occupational exposure [21; 22; 23; 24; 25; 26], these concentrations reflect Lindane bioaccumulation in lipid-rich tissues such as the reproductive system and mimic chronic exposure.  
COCs were casually assigned to four experimental groups--Control, Lindane 1 \(\upmu\)M (L1), Lindane 10 \(\upmu\)M (L10), Lindane 100 \(\upmu\)M (L100)--and subjected to IVM [39]. After 16 h, metaphase II (MII) oocytes were isolated by removing cumulus cells. All experiments were performed in triplicate.
# A Novel Cross-Latch Shift Register Scheme for Low Power Applications
## 3 Analysis of Layout Area and Power Consumption of the Proposed CLSR Design
According to the above results, the proposed CLSR scheme applied to the application of shift registers achieved better performance such as area, power, and PDP, compared with that of the master-slave architecture. Moreover, to achieve low power design, recently reported works try to extremely reduce the number of transistors [28; 29; 30]. However, these flip-flops have floating problems of internal nodes. Thus, the reported flip-flops must be selected carefully for different applications. Finally, Table 2 shows a detailed performance comparison of different 256-bit shift registers. The area of the proposed CLSR scheme design is 1932.47 \(\upmu\)m\({}^{2}\). Compared with the conventional MS latch shift register (2961.88 \(\upmu\)m\({}^{2}\)), the proposed CLSR scheme design achieves a 34.76% reduction in the integrated circuitry layout area. Table 2 also records the power consumption, leakage power, and PDP at 0.9 V and 0.4 V. When operating at 0.9 V/250 MHz, the proposed design can save up to 36% power consumption and 16.90% PDP. At the same time, due to the large saving of the number of transistors, the proposed CLSR scheme can effectively suppress leakage power consumption [34]. Figure 10: Power delay product performance at different supply voltages.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 5 Learning-Based Asynchronous Stream Sensing
### Asynchronous Stream-Sensing Network  
Figure 6 presents the overall structure of our asynchronous stream-sensing network (ASensNet). The ASensNet comprises five extractors: (1) an AutoEncoder Extractor, (2) an Inception-layer-based extractor, (3) linear extractor 1, (4) linear extractor 2, and (5) a Classifier. The AutoEncoder Extractor and the Inception-layer-based extractor have a real subnetwork and an imaginary subnetwork. Real subnetworks process \(\mathbf{I}_{\text{R}}\), while imaginary subnetworks handle \(\mathbf{I}_{\text{I}}\). For each extractor, the real and imaginary subnetworks have the same structure. With Linear Extractor 1, the extracted features from subnetworks merge into a feature. Finally, to detect the number of rescue signals in a received signal, the classifier deals with a combined feature of two features extracted from both linear extractor 1 and 2. Linear extractor 2 extracts features from \(\mathbf{I}_{\text{eig}}\).  
Figure 5: Example of input data.
# Electromagnetic Characteristics Analysis of a Tubular Moving Magnet Linear Generator System
## 4 Prototype Research
### Prototype Performance  
After being validated by the prototype testing results, the simulation model is used to evaluate the performance of the linear generator prototype at the rated motion frequency of 50 Hz, the rated stroke of 36 mm, and the rated current amplitude of 15 A. The main performance parameters of the linear generator prototype are shown in the Table 4 and are compared with the latest reported tubular moving coil linear generator prototype in reference [17].  
The generating efficiency is calculated by dividing the electrical energy output by the mechanical energy input during a stroke. The electrical energy output during a stroke is obtained by integrating the instantaneous electrical power output, which is calculated by multiplying the instantaneous current by the terminal voltage of the coil. The mechanical energy input during a stroke is obtained by integrating the instantaneous mechanical power input, which is calculated by multiplying the instantaneous electromagnetic force by the motion speed of the mover.  
Compared with the reported moving coil linear generator prototype (MCLG), the new moving magnet linear generator prototype (MMLG) has a more stable structure and better power density. Meanwhile, the mover mass of the moving coil prototype is smaller than that of the moving magnet prototype. Except for the power density and the mover mass, the other parameters of the two prototypes are similar, and both can meet the requirements of the FPEs.  
Table 5 shows the losses of the designed tubular moving magnet linear generator prototype when it is working at the rated motion frequency of 50 Hz, the rated stroke of 36 mm, and the rated current amplitude of 15 A. The losses in the copper, the magnet, the core, and the shell are considered. It is obvious that the loss of the linear generator mainly comes from the copper loss, which can be cooled together with the stator. The loss in the moving magnet is relatively small.