esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Impact Absorption Tests (Drop Tests)  
The test matrix consisted of 60 impacts onto a flat anvil. After the selection process based on the penetration test results, a new sample of each selected helmet model was drop tested at 5 m/s and another sample at 8.2 m/s. Each helmet was tested on the front, lateral, and top areas (three impacts per helmet sample at each impact speed). The selected impact areas corresponded with the points B, X (right), and P, as described in the European regulation [5] and shown in Figure 2.  
A free fall guided impact machine (Model: Quebrantahuesos 6.0, +D, Pozuelo de Alarcon, Spain) was used for the impact absorption tests (see Figure 3). As the helmets tested were not of the same size, three metallic headform sizes were used (Model: 100_04_FMH, Cadex Inc., Saint-Jean-sur-Richelieu, QC, Canada) to ensure an appropriate fitting of the headform for each helmet size, as prescribed in the regulation [4; 5]. Four helmet types were tested with the 535 mm headform circumference, three with the 575 mm headform, and three with the 605 mm headform [20]. The corresponding headform masses were 4.1 kg, 4.7 kg, and 5.6 kg respectively. The headforms were positioned inside the  
Figure 1: Penetration test set-up.  
Figure 2: Impact points for the impact absorption tests.  
helmets according to the requirements of Annex 5 of ECE-22.06 [5], and the retention system was adjusted under the chin of the headforms and tightened to a tension of 75 N [15]. Before each impact, the headform was re-positioned, and the retention system re-tensioned.  
A wireless system (Model: iCONO, +D, Pozuelo de Alarcon, Spain) was used to measure the linear acceleration at the center of gravity of the headforms. The wireless system incorporates three orthogonal uniaxial accelerometers (Model: 64C-2000, MEAS, Nanshan District Shenzhen, China) and an acquisition system (Model: SLICE NANO, DTS, Seal Beach, CA, USA). Data were recorded at 10 kHz, filtered using a low-pass filter CFC-1000, and post-processed using a validated and developed in-house script of Matlab (Matlab R2013b, MathWorks, Natick, MA, USA).
# [PERSON] and Mendell-Elston Estimation of the High-Dimensional Multivariate Normal Distribution
## 2 Background  
Numerical methods for estimation of the mvn distribution have a long and fascinating history of development and many interesting accounts from varied perspectives have been presented ([15; 16; 17; 18], and references therein). Classical approaches to the problem have generally been variations on a few standard methods [19; 20]. Often, some form of numerical quadrature is involved, in which an estimate of the integral is formed by accumulating a weighted sum of integrand values at a sequence of abcissae covering the region of integration [21; 22; 23; 24]. Tetrachoric series expansions [25; 26] offer another approach to the problem, although these series may converge slowly, and in fact do not converge at all at some points in the correlation space for a given number of dimensions [27]. Other approaches have involved quadrature applied to an integral transformation of the tetrachoric series [19; 28; 29], and decomposition of the multidimensional probability into a product of conditional univariate probabilities [1; 8; 11; 12; 30; 31; 32; 33].  
In practice, the utility and applicability of any algorithm for estimating the mvn distribution is overwhelmingly constrained by the dimensionality of the problem. Fast and accurate algorithms have been described for evaluation of the univariate and multivariate normal distributions for'small' numbers of dimensions; for the frequently encountered cases of \(n=1\)[34] and \(n=2\)[35; 36; 37; 38], several algorithms are available that can in principle provide any desired accuracy. For the case \(n>2\), several algorithms (error-bounded and not) based on quadrature have been developed and their relative performance compared [17; 21; 22; 23; 24]. Monte Carlo approaches to the problem have also been developed that have desirable statistical properties and exhibit good scale properties with the number of dimensions [13; 14; 39; 40].  
As the number of dimensions reaches \(n\approx 10\), many approaches to estimating the mvn distribution become impractical. Conventional series approximations and quadrature methods grow unwieldy, and the computational burden for algorithms using these methods rapidly becomes prohibitive [13; 14; 19; 20; 41]. However, methods of estimation based on reduction or transformation of the joint \(n\)-variate distribution to a series of (typically univariate) integrals continue to scale favorably with the number of dimensions.
# Hardware Design of a High Dynamic Range Radio Frequency (RF) Harmonic Measurement System
## 3 Issues in Creating a High Dynamic Range Harmonic Measurement System
### Creating a Harmonic Free Probe Signal
#### 3.2.2 Diplexer Considerations  
This leads us to the choice of microwave diplexers for accomplishing the filtering operation. Diplexers are three-port devices which separate power entering a common input into two frequency bands; or conversely, they combine two frequency bands arriving separately into a common output [49]. Diplexers in which there are adequate guard bands between channels may be designed by suitably connecting two separate, doubly-terminated filters onto a T-junction. These devices are typically employed to connect the transmit and receive filters of a transceiver to a single antenna through a suitable three-port junction [50; 51]. Diplexers are designed to have low PIM and, therefore, will have low harmonic distortion. With that said, they will generate their own harmonics, but these will be very low, on the order of high-quality cables and connectors, i.e., \(<\!-170\) dBc. The harmonics generated by the diplexers, cables, and connectors will set a fundamental limit on the dynamic range of the system.  
As such, they have the ability to be configured differently for different applications. Diplexers can be used as a low-pass filter by terminating the high frequency port in 50 \(\Omega\) and using the common port and low frequency port. Unlike reflective filters, diplexers can be cascaded to increase their stop band attenuation. To cascade diplexers in a low-pass configuration, we terminate the high frequency port and connect the common port to the low-pass port of the next diplexer. By connecting the low-frequency port of one diplexer to the common port of another diplexer, the reflections between cascaded diplexers is reduced, when compared to the reflections when reflective filters are cascaded. By terminating the high-frequency port, the stop band frequencies have a path to 50 \(\Omega\) and the cascaded network is well-behaved. Figure 7 shows the \(|S_{21}|\) response when two diplexers are cascaded in a low-pass configuration.  
Figure 7 shows that when two diplexers are cascaded, the measured \(|S_{21}|\) closely matches the theoretical response. Comparing Figures 6 and 7, we note that cascading diplexers is more desirable than cascading reflective filters for increasing stop band attenuation.  
Figure 7: Measured and theoretical \(|S_{21}|\) response when two RBF-272 diplexers in low-pass filter configuration are cascaded.
## 2 Materials and Methods
### Input Data
#### 2.3.3 Wind Speed  
Accurate wind resource evaluation is crucial for determining the feasibility and performance of wind power systems in Aras de los Olmos. This study obtained wind data at a reference height of 50 m above ground level.  
The baseline data indicate an annual average wind speed of around 5.56 m/s, with higher velocities recorded in winter months--6.67 m/s in January and 6.57 m/s in December--and lower speeds in summer, reaching 4.38 m/s in July and 4.29 m/s in August. This seasonal fluctuation underscores the potential need for complementary energy sources during low-wind periods.  
However, it was subsequently adjusted within the HOMER to reflect local conditions, including the site altitude of 936 m above sea level and an anemometer height of 10 m.  
The site sits at a relatively high altitude and experiences notable topographical influences. Consequently, this study applies a logarithmic wind speed profile, as shown in Equation (1). This approach more accurately captures the terrain's characteristics and ensures reliable velocity estimates at the chosen anemometer height \(h\), where wind speed \(v\) is extrapolated from a reference height \(h_{ref}\)[46].  
\[v(h)=v\Big{(}h_{ref}\Big{)}\times\frac{ln(h/z_{0})}{ln\Big{(}h_{ref}/z_{0} \Big{)}} \tag{1}\]  
where \(z_{0}\) is the surface roughness length (set to 0.01 m in this case, indicating rough pasture-like terrain in the study site).  
The corrected dataset more accurately represents on-site wind conditions. This accuracy is crucial for reliably estimating turbine performance, calculating capacity factors, and ensuring robust wind power integration into the region's energy mix.
## 3 Dataset and Variables
### Selection and Definition of Explanatory Variables
However, employing many conditions or variables in fsQCA can result in overly complex solutions that may make their interpretation difficult [46]. Researchers must carefully balance model complexity and explanatory power when determining the appropriate number of conditions. Figure 1: The ten most relevant variables identified by the XGBoost algorithm. **Notes**: The figure shows the relative importance of the predictors or features. The solvency ratio (SOLL) is identified as the most important feature, with all other features ranked relative to its importance. The relative values of the other features are scaled in comparison to SOLL, highlighting their proportional significance in predicting business failure. From the ten most relevant predictors identified by the XGBoost algorithm, we have selected the top six (indicated by the red rectangle). \begin{table}
\begin{tabular}{c c} \hline \hline
**Key** & **Variables** \\ \hline \multicolumn{3}{c}{**PROFITABILITY RATIOS**} \\ \hline RTAS & ROA (Pre-tax profit) (\%) = (Pre-tax profit/Total Assets) \(\times\) 100 \\ \hline ROA & ROA (Net income) (\%) = (Net income/Total Assets) \(\times\) 100 \\ \hline CFOP & Operating cash Flow (\%) = (Cash Flow/Operating revenue) \(\times\) 100 \\ \hline \multicolumn{3}{c}{**STRUCTURAL RATIOS**} \\ \hline SOLL & Solvency ratio (\%) = (Equity/Total Assets) \(\times\) 100 \\ \hline GEAR & Indebtedness Ratio (\%) = [(Non-current liabilities + Loans)/Equity] \(\times\) 100 \\ \hline LIQR & Liquidity Ratio = (Current assets -Stocks)/Current liabilities \\ \hline \multicolumn{3}{c}{**OPERATIONAL RATIOS**} \\ \hline COLL & Collection Period (days) = (Debtors/Operating revenue) \(\times\) 360 \\ \hline \multicolumn{3}{c}{**EMPLOYEE-BASED RATIOS**} \\ \hline PPE & Profit per Employee = EBT/Number of employees \\ \hline TPR & Operating revenue per employee = Profit before taxes/Number of employees \\ \hline WCPE & Working capital per employee = Working capital/Number of employees \\ \hline \hline \end{tabular} Note: Key based on the reference in the ORBIS database [38].  
\end{table} 
Table 3: Description of the most important financial variables. tions to include in an fsQCA study. In this study, as explained, we have selected seven variables, achieving a balance between complexity and explanatory power.
# Evaluation of 8-Channel Radiative Antenna Arrays for Human Head Imaging at 10.5 Tesla
## 3 Results  
Evaluation of Interaction among Coaxial Cables and Antenna Elements with the Inductor-Shortened Dipole Antenna Array  
As shown in Figures 1 and 2, we evaluated the interaction among coaxial cables and antenna elements with an 8-channel inductor-shortened dipole antenna array in simulation and experiment. A good agreement between simulation (Figure 1b,f) and experiment (Figure 1d,h) of \(\sim\)10% was achieved for the highest B1\({}^{+}\) efficiency values. In simulation, the 8-channel dipole antenna array without coaxial cables (Figure 1b) produced 23.1% higher B1\({}^{+}\) efficiency compared to the dipole antenna array with coaxial cables (Figure 1d). The experimental B1\({}^{+}\) efficiency of the dipole antenna array with further (=7 cm gap) coaxial setup arrays was shown to be 20.7% higher compared to the tight (=2 cm gap) coaxial cable setup, as shown in Figure 1f,h. Degradation of the B1\({}^{+}\) efficiency is the disadvantage from the interference between coaxial cables and antenna elements. However, SAR efficiency values of the dipole antenna array were similar between without (Figure 2c) and with (Figure 2d) coaxial cables, because SAR values (Figure 2a,b) were also decreased as B1\({}^{+}\) efficiency values were decreased.
# Enhancing Railway Network Safety by Reproducing Wheel-Rail Electrical Contact on a Laboratory Scale
## 3 Results
### Dependence of the Resistance on the Applied Current
#### 3.2.1 Clean Contact: \(R_{c}(I)\)  
For a clean contact, Figure 10 shows the variation in current and voltage over time. The corresponding \(U\)-\(I\) characteristic is shown in Figure 11. It presents a linear-ohmic and reversible behavior. In this case, the increasing and decreasing \(U\)-\(I\) characteristics are perfectly superposable on each other. \(R_{c}(I)\) was evaluated at 10 m\(\Omega\), which is consistent with a contact on bare metal.
## 4 Discussion  
### Reliability of Localization Results for Unknown Samples  
This paper extensively demonstrated, as shown in Figure 2, that the reliability of results for unknown samples could be determined based on the SMR value and comparing the region of maximum response intensity (\(D_{\text{max}}\)) with that of maximum connected domain volume (\(D_{\text{vol}}\)). Figure 9 presents three different types of localization results. Consequently, their SMR values were plotted for analysis to assess the reliability of each result.  
As illustrated by position #3 in Figure 10, when the response from the anomalous location was significantly higher than other areas, accurate localization of the true region was achieved. However, as demonstrated by positions #1 and #2 in Figure 10, when the difference in response between the anomalous location and normal areas decreased, the prediction accuracy diminished, resulting in either low accuracy or completely erroneous predictions.  
Figure 9: Results using 19.0% moisture content grain. (**a**) A top view of the layout from Figure 6e was shown, with localization performed for hotspot areas in three different positions. (**b–d**) displayed the localization results for positions #1, #2, and #3, respectively. Only position #3 was predicted accurately. Position #2 showed relatively low prediction accuracy, while the prediction for position #1 was entirely incorrect. Consequently, the area capable of accurate prediction was limited to the region within the red box.
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 5 Results
### Evaluation of Model Performance  
#### 5.2.1 Evaluation Metrics  
The effectiveness of the near-crash event identification model is evaluated through five key metrics, ensuring a comprehensive assessment of its performance:  
(1) Accuracy (\(A\)). This metric measures the proportion of correctly predicted observations by the model out of the total number of samples:  
\[A=\frac{N_{c}}{N} \tag{15}\]  
where \(N_{c}\) is the total number of correct observations, and \(N\) is the total number of observations. (2) Precision (\(P\)). This metric measures the proportion of actual positive samples among all samples predicted as positive:  
\[P=\frac{TP}{TP+FP} \tag{16}\]  
where \(TP\) and \(FP\) are the numbers of true positive and false positive predictions, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Variable** & **Symbol** & **Definition** & **Importance** \\ \hline Vehicle speed & \(v\) & Rate of change in distance over time, reflecting changes in the state of the vehicle & 20\% \\  & & & the vehicle \\  & & & The angle between the direction the vehicle’s front is pointing and a & \\ Heading angle & \(\psi\) & reference direction (usually geographic north) & 8\% \\ Pitch angle & \(\theta\) & The angle of the vehicle’s front tilting up or down & 1\% \\ Roll angle & \(\alpha\) & The angle of the vehicle’s front tilting to one side & 2\% \\ X-axis acceleration & \(a_{x}\) & Rate of change in speed in the x-axis direction over time & 28\% \\ Y-axis acceleration & \(a_{y}\) & Rate of change in speed in the y-axis direction over time & 12\% \\ X-axis angular velocity & \(w_{x}\) & Angular velocity of the vehicle rotating around its x-axis & 3\% \\ Y-axis angular velocity & \(w_{y}\) & Angular velocity of the vehicle rotating around its y-axis & 26\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Importance of vehicle motion features.  
Figure 9: Determination of decision trees in the random forest model.  
(3) Recall (\(R\)). This metric measures the ratio of the number of correctly predicted positive samples to the total number of positive samples:  
\[R=\frac{TP}{TP+FN} \tag{17}\]  
where \(FN\) is the number of false negatives.  
(4) F1 Score (\(F_{1}\)). This metric is the harmonic mean of precision and recall metrics, which is calculated by:  
\[F_{1}=\frac{2*P*R}{P+R} \tag{18}\]  
(5) Area Under the Curve (_AUC_). The performance of the classifier is assessed by plotting the true positive rate against the false positive rate (ROC curve).
# Advanced Metamaterial-Integrated Dipole Array Antenna for Enhanced Gain in 5G Millimeter-Wave Bands
## 2 The Design of the Metamaterial-Integrated Dipole Antenna
### Metamaterial Unit Cell Design
\[n_{i}=\sqrt{\varepsilon_{i}\cdot\mu_{i}} \tag{6}\]  
Hence, the refractive index remains consistently above 1 within this specified region, and the directivity of the antenna is enhanced as per [PERSON]'s law, as previously explained. Figure 6: The properties of the metamaterial unit cell: (**a**) a schematic diagram with the design parameters, (**b**) the gain enhancement mechanism, (**c**) the characteristic response, and (**d**) the material properties.
###### Abstract  
The aim of this study was to assess the impact of the on-farm solar photovoltaic (PV) generation to offset grid electricity consumption on a commercial poultry farm in the US. Hourly electricity production by PV systems was estimated using the System Advisor Model (SAM) of the National Renewable Energy Lab (NREL) and compared with the estimated electric load demand of a broiler house. We analyzed the economic benefits of installing solar systems of three capacities under net energy metering and net energy billing pricing scenarios. Results suggested that a smaller PV installation of 35 kW\({}_{\rm p}\), while substantially short of meeting energy needs, resulted in an 85% self-consumption ratio, whereas the larger 70 kW\({}_{\rm p}\) and 105 kW\({}_{\rm p}\) PV systems, led to 59% and 42% self-consumption ratios, respectively. This is important when analyzing pricing schemes as lesser amounts of PV energy created are sold to the grid with the smallest system, and, thereby, the least pricing effects on profitability occur across pricing scenarios. Although all scenarios lead to positive net present values (NPVs), under either type of the pricing scenarios, farm business owners realize more favorable energy sales with NEM, which would spur PV system adoption. Results of this study thus provide information to both poultry farmers, considering the size of system installations and policy makers interested in affordably increasing renewable energy supplies and/or rural development.  
p 2025 18 979
## 2 Materials and Methods
### Methods
#### 2.3.4 Geodetector  
Spatial heterogeneity is one of the fundamental characteristics of geographical phenomena. Geodetector refers to statistical techniques employed to detect spatial variability and disclose the driving factors [73]. Geodetector comprises the following four components: risk, factor, ecological, and interaction detectors. The factor detector detects the spatially stratified heterogeneity (SSH) of dependent variable y (i.e., LUEmax value). It also measures the degree to which the independent variable x (which includes physiological and environmental mechanism factors) can explain the SSH of the y value. It is expressed as follows:  
\[q=1-\frac{\sum_{h=1}^{L}N_{h}\sigma_{h}^{2}}{N\sigma^{2}} \tag{8}\]  
where L represents the stratification of the dependent variable, LUEmax, or the influence factor X; N and N\({}_{\text{h}}\) are the number of sample units in strata h and the total region, respectively; and \(\sigma_{h}^{2}\) and \(\sigma^{2}\) are the variance in the h strata and the variance in the region. Generally, a larger value implies a stronger explanatory power of each factor. This study used the q value to quantify the explanatory degree of different drivers.  
Climatic and ecological factors closely associated with variations in the LUEmax were chosen as the factors influencing LUEmax. Ten environmental factors, namely, remote sensing indices (EVI, LSWI, and LAI), air temperature, soil temperature, VPD, precipitation, CO\({}_{2}\) molar fraction, and net radiation, were selected to analyze their relationship with LUEmax's change rate. Several discretization methods are available in the R language, including natural discontinuity and equal-interval discretization [74]. The parameter values for discretization grouping were set accordingly. The optimal combination of discretization methods and parameter values was determined through comparative analysis. Because the Geodetector algorithm works on discrete data, the ten continuous independent variables were transformed into discrete categories using an optimal method. Finally, the discretized data were used to perform calculations in Geodetector for factor and interaction detection.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 4 System Design and Implementation
### Machine Learning Models
This technique is used to perform classification when the negative class is either absent, poorly sampled, or poorly characterized. The method solves this problem by defining a class boundary just with the knowledge of the provided positive class. This technique has found application in many fields, with concept learning and outlier detection being some of them [59]. In our case, the scarcity of abandoned buildings for our study area prevents us from using solely the aforementioned Random Forest classifier. In this context, and since data for nontextual buildings are available, we chose to use OSVM. We set the not-abandoned buildings as the positive class and assumed that buildings that did not fulfill all the criteria set by the OSVM would be classified as negative, in other words, abandoned. The model was trained on various combinations of spectral indices and NTLs, with its accuracy varying. To evaluate the performance of OSVM, we constructed a hand-labeled geo-referenced dataset for abandoned and not-abandoned buildings across the municipality of Volos. The dataset consists of the buildings' geometries, which were extracted from OpenStreetMaps using the Overpass API and/or by hand where exact coordinates were not available. Similarly, with the Random Forest approach, we performed some statistical tests on our selected parameters. Specifically, we used the Spearman and Pearson coefficients to check if their correlation was significant at a 0.05 significance level (\(a=0.05\)). The Null \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{2}{c}{**Parameters**} & \multirow{2}{*}{**RF Accuracy**} \\ \cline{2-3} \cline{2-3}  & **Max Depth** & & **\# of Estimators** \\ \hline NDVI, NDBI & 20 & 400 & 0.663\% \\ \hline NDVI, NDBI, Average Radiance & 20 & 350 & 0.681\% \\ \hline VgNIRBI, Average Radiance & 20 & 600 & 0.666\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected Feature Combinations for Random Forest Experiments. Hypothesis that the correlation coefficient was significantly different from 0 (no correlation) was rejected in all index combinations; so, their correlation matrix was also computed (Figure 2) to choose the ones that correlated less than 0.5.