esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical
### Multi-Parameter Empirical Model for CO\({}_{2}\)-CH\({}_{4}\) Hydrate Formation  
Although the theoretical models (Chen-Guo model, vdW-P model) can accurately predict the formation conditions of CO\({}_{2}\)-CH\({}_{4}\) mixed gas hydrates, it requires a series of complex thermodynamic equilibrium calculations. In this study, reliable phase equilibrium data obtained from these theoretical models were processed through regression analysis. Using temperature and CO\({}_{2}\) mole fraction as variables and hydrate equilibrium pressure as the objective function, a simplified empirical model for predicting the phase equilibrium conditions of CO\({}_{2}\)-CH\({}_{4}\) hydrates was established.  
Firstly, temperature \(T\) (in \({}^{\circ}\)C) was taken as the variable and ln \(P\) (in MPa) as the objective function, and linear equations were fitted for different CO\({}_{2}\) mole fraction (\(y\)) (Equation (6)). Next, the two coefficients B and C in Equation (6) were separately fitted as functions of the CO\({}_{2}\) molar fraction (\(y\)), as shown in Equation (7). To ensure calculation accuracy, a fourth polynomial was used for fitting, and the values of the five parameters a, b, c, d, and e could be obtained. In some cases, a linear or quadratic polynomial  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Gas**} & \multicolumn{2}{c}{\(X\times\)} & \multirow{2}{*}{**Y/K**} & \multirow{2}{*}{**Z/K**} & \multicolumn{2}{c}{**a \(\times\)**} & \multirow{2}{*}{**b/K**} & \multirow{2}{*}{**c/K**} & \multirow{2}{*}{**g/(K/MPa)**} & \multirow{2}{*}{**D**} \\  & **10\%/MPa-1** & & & & & & & \\ \hline CO\({}_{2}\) & 1.6464 & 2799.66 & 15.9 & 963.72 & \(-6444.50\) & 36.67 & & \\ CH\({}_{4}\) & 2.3048 & 2752.29 & 23.01 & 1584.4 & \(-6591.43\) & 27.04 & 4.242 & \(-22.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constants in Chen–[PERSON] theoretical model.  
regression was also used to fit the parameters under certain conditions. Thus, a multi-parameter empirical model for predicting the phase equilibrium conditions of CO\({}_{2}\)-CH\({}_{4}\) gas hydrates can be established. The input parameters of this model are only CO\({}_{2}\) mole fraction and temperature, and the corresponding hydrate formation pressure values can be obtained through simple mathematical calculations, avoiding the complex thermodynamic equilibrium trial-and-error iterative calculations in the phase equilibrium models.  
\[\ln P=\text{B}T+\text{C} \tag{6}\]  
\[f=\text{a}y^{4}+\text{b}y^{3}+\text{c}y^{2}+\text{d}y+\text{e} \tag{7}\]
## 4 Discussion  
The data presented in Figure 2 underscore the dynamic nature of NO\({}_{2}\) emissions during TBM tunnel construction and highlight the factors that influence their levels. They emphasize the necessity for the meticulous management of TBM production, locomotive usage, and ventilation to mitigate the impact on air quality and safeguard worker health. These findings shed light on the complexity of NO\({}_{2}\) emissions in tunnel construction projects and underscore the importance of adopting effective strategies to minimize their adverse effects on the environment and the well-being of the workforce.  
To assess the compliance with the current and proposed occupational exposure limit values (OELVs) for NO\({}_{2}\), all available measurements were classified and summarized in Tables 2 and 3, respectively. Table 2 indicates that, under the current OELV, 36 measurements exceed the limits. However, under the proposed OELVs in Directive 2017/164/EU, 3457 measurements exceed the proposed limits (74% of measurements exceed the limit for NO\({}_{2}\)), indicating a substantial potential for exceeding the legal thresholds if mitigation measures are not implemented effectively.  
Table 3 presents the daily average concentration of NO\({}_{2}\), and the corresponding number of days that exceed different concentration ranges in relation to the current and proposed OELVs. It reveals that, under the current OELV, all measurements are below the OELV. However, with the proposed OELV, 209 days (60% of the days) surpass the OELV.  
Figure 4 demonstrates the percentage of days with different NO\({}_{2}\) average concentrations under the current and proposed OELVs. While there are no significant concerns for NO\({}_{2}\) concentrations within the current limits, approximately 60% of the days will surpass the limit under the proposed OELV, and 13% of the days will be close to the limits. These findings emphasize the urgent need for effective mitigation strategies to control NO\({}_{2}\) emissions during TBM tunnel construction and ensure compliance with the proposed OELV.  
Figure 3: Relation between number of rings installed and NO\({}_{2}\) (**A**). NO\({}_{2}\) emitted inside the tunnel vs. total rings installed (**B**).  
Overall, the results reveal the potential challenges in meeting the proposed OELVs for NO2 during TBM tunnel construction.
###### Abstract  
The high operating temperatures of photovoltaic (PV) panels negatively affect both electrical efficiency and material degradation rate. Combining both a water-cooling-based photovoltaic/thermal (PV/T) system and a phase-change material (PCM) with/without low concentration (LC) represents a promising solution for boosting the overall energy conversion efficiency of the PV system. This approach needs to be evaluated in harsh weather where the PCM should have a high melting temperature. Therefore, this study experimentally investigates the performance of three PV cooling systems, namely PV-PCM, PV/T-PCM, and LCPV/T-PCM, compared to a reference PV without cooling, under the weather conditions of Riyadh. The results show that the PV/T-PCM attained the highest daily average electrical and overall efficiencies of 14.2% (5% increase) and 42.7%, respectively, compared to 13.56% electrical efficiency of the reference panel. The electrical efficiency of the PV-PCM was 13.64% due to inefficient natural cooling in the afternoon. The LCPV/T-PCM recorded the best performance during the two hours around noon, with an average increase in electrical power and efficiency of 11.06% and a maximum overall efficiency of 70%. Finally, the LCPV/T-PCM system can be only effectively used to support the higher demand for electricity and thermal energy around noon; otherwise, a new design configuration with low concentration is needed to establish a higher electrical efficiency in most hours of sunlight.  
photovoltaic cooling; photovoltaic efficiency; solar low concentration; phase change material; high ambient temperature Article  
Experimental Evaluation of a Solar Low-Concentration Photovoltaic/Thermal System Combined with a Phase-Change Material Cooling Technique  
10.390/app13010025  
13.25[[https://doi.org/10.3390/app13010025](https://doi.org/10.3390/app13010025)]([https://doi.org/10.3390/app13010025](https://doi.org/10.3390/app13010025))
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Fine-Tuning Deep CNN
#### 3.4.3 Fine-VGG-16  
For an input image size of 64 \(\times\) 64 layers, the output size of Max Pooling5 equaled \(2\times 2\times 512\). The output of the first and second fully connected layers (i.e., FC1 and FC2) was fine-tuned to obtain a 512-dimensional vector, and the output of FC3 layer equaled 4 or 6 depending on the dataset considered.
## 3 Methodology
### Prediction Model Building and Testing  
The aim of the study was to build a model that can be used to predict Li-ion battery capacity using fPCA. The following is the process of capacity prediction model building and testing.  
1. The monitoring data of the Li-ion battery comprise 168 cycles of voltage, current, and temperature. To begin, we considered the initial \(N=100\) cycles as training data to build a prediction model.
2. The original discretized measurements are transformed to smooth curves by applying the B-spline basis expansion. The number of knots is chosen, for each of voltage, current, and temperature, such that the fitted model has the lowest BIC value.
3. The fPCA technique is performed on each monitoring variable, and the mean function \(\mu(t)\), fPC scores \(\xi_{ik},\,\,k=1,\,\,\dots,\,\,K\), and corresponding eigenfunctions are obtained for each predictor. These fPC scores characterize the status of the battery at the corresponding cycle.
4. The LASSO regression model is trained based on fPC scores. The LASSO complexity parameter is chosen by the K-fold cross-validation with 5 folds.
5. To test the model, the monitoring variables' measurements for the forthcoming \(P=20\) cycles are predicted. Simple linear regression was used for this task for each time point. For the voltage measurements for a time point of \(j=1\), for example, we fit the following model to the initial 100 cycles of data: \[v_{i1}=\beta_{0}+\beta_{1}i+\varepsilon_{i1},\,\,\,i=1,\,\dots,\,100\] (4)  
Then, the fitted model is extrapolated to produce \(\hat{v}_{i1},\,\,i=101,\,\,\dots,\,120\). This process is repeated for each time point of \(j=1,\,\,\dots,\,\,m_{i}\). Merging these resulting responses, we obtain the predicted 20 cycles, which are almost indistinguishable from actual cycles. The B-spline with the number of knots learned by the training data is applied to these predicted measurements.  
1. Using the eigenfunctions obtained from the training phase, the fPC scores are extracted from each predicted curve.
2. The prediction of \(\hat{g}_{i},\,\,i=101,\,\,\dots,\,120\) are obtained by LASSO model obtained in (d) and compared with the true battery capacity values to evaluate the model performance.  
The functional data explorer from the software JMP Pro 14 [33] was used to implement the tasks mentioned above, except (e), which provides useful features to deal with functional data provided as discrete measurements. Task (e) was conducted using R [34].
# Building Equi-Width Histograms on Homomorphically Encrypted Data
## 3 Evaluation Results
### First Configuration Use Case (UC-1)
As previously described, in our case, the length of the input is determined by the number of buckets in the histogram and the number of IoT data samples we want to process. We measured the variation of different types of operations with the size of the inputs. As expected, the number of operations performed increases when the size of the data input increases. However, since we propose an additive algorithm operating on encrypted data, encrypted addition is the most frequently performed operation. The number of programmable bootstrap operations, used to reduce the noise and to allow repeated computations on the same ciphertext, also significantly increases with the size of the inputs as the number of values that must be repeatedly operated on grows. A synthetic view of the operation-based complexity variation is depicted in Figure 8b. We consider the total number of operations as a measure of the complexity of the model. In this global view, the complexity of our prototype in UC-1 doubles when either the number of buckets or the number of measurements doubles, resulting in an almost linear variation. We evaluate the performance of our prototype by measuring the compile time, which is the time needed to train the model using a set of plain input samples, the time taken by the model to generate the keys, and the time taken to generate the encrypted labels associated with the input encrypted measurements. Figure 8c depicts the variation of all time components considered for UC-1 configuration, with the size of inputs. The time required for key generation is significantly lower than both the compilation and detection (execution) times. Meanwhile, compilation and detection times follow a similar pattern, increasing exponentially as the input size grows.
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 5 System Limitations
### Across-Track Baseline Effects
However, just supposing \(\Delta q\) small and linearizing it, we have  
Figure 7:2  
Representing with \(\sigma_{h}\) the dispersion of the height error, the total power of the ambiguity is:  
Figure 7:3  
as there are \(R\) ambiguities to be removed. Hence, we have:  
Figure 7:4  
Considering as an example, \(L\) band, r = 600 km, \(N=4,\ R=3,\ \theta=30^{0}\), we have:  
Figure 7:5  
If we wish to keep the AASR better than 20 dB:  
Figure 7:6  
If \(\sigma_{h}=10\) m, then \(\sigma_{b}<66\) m that appears to be indeed very difficult unless we mitigate the effect recurring to autofocus methodologies to unmix, as to be briefly discussed in the following. If the DEM has lower resolution, then the number of unknown heights would decrease and one could try to estimate them with unmixing but still, as common to all unmixing problems [22; 28], any solution would be acceptable unless the higher order moments of the amplitude statistics are used. Furthermore, in the case of full resolution, two degrees of freedom only (the real and imaginary part of the reflectivity) are available for each unknown (the height error) and thus hardly sufficient for any reliable estimate of the height. For lower and lower resolution of the DEM (say just a slope), a solution might be found but only in the case of a very active texture of the data. The obvious cure is to keep \(q\) high, the cross track baselines small and thus the formation short. However, then, if the positions of the satellites are random and the adaptation of the PRF becomes insufficient, the additional antennas become mandatory to reduce the ambiguities. For small height errors, hypothetically, an autofocusing code could be considered that maximize the contrast in each one of the \(R\) affected positions by modifying the height of the contributing target. However, then, the targets in the affected positions would already be disturbed by the other \(R-1\) ambiguities. The solution would only be obtained with a code that considers \(M\) points mutually ambiguous and thus distanced of a footprint, and finds their \(M\) heights by maximizing the \(M\) contrasts and /or their reciprocal independence. It is not impossible that such a code could work, but indeed highly unlikely, also because the maximum contrast need not to be the correct solution. Furthermore, in the case of targets with Gaussian distributions with the same variance, the unmixing would be utterly impossible. Still, as said, things get easier and maybe doable if the DEM is lowpass.
## 3 Material and Methods
### Dimensionality Reduction
#### 3.2.1 Feature Selection  
Feature selection methods select relevant features for model construction [34]. Widely used feature selection methods are filter methods, wrapper methods, and embedding methods. Filter methods evaluate the features based on their statistical properties, wrapper methods assess a subset of features using machine learning algorithms, and embedding methods learn feature importance as part of the model training process. In this research, to reduce the feature space, the most useful features are selected using the Recursive Feature Elimination technique, which ranks the features based on their predictive power. This method falls under the wrapper methods category.  
Recursive Feature Elimination: Recursive Feature Elimination recursively reduces the feature space. RFE uses an RF classifier to assign feature weights in this research. Initially, it builds a model using all features, ranks the features based on their importance, and removes the smallest-ranked feature. Then, it builds the model again, using the remaining features, ranks them, and removes the least important feature [35].  
Random Forest: A Random Forest is an ensemble of decision trees that can perform classification using a majority vote. Each decision tree uses a randomly selected sample of m features from the full set of p features. This technique ensures that trees are uncorrelated with each other hence their average result is less variable and more reliable. In addition, each tree uses a different sample of data, like the bagging approach. RF can successfully model high-dimensional data where features are non-linearly related and do not assume the data follow a particular distribution.
# VDMS: An Improved Vision Transformer-Based Model for PM\({}_{2.5}\) Concentration Prediction
## 2 Data  
### Ground-Level PM\({}_{2.5}\) Data  
Ground-based station data were obtained from the China National Environmental Monitoring ([[http://www.cnemc.cn/](http://www.cnemc.cn/)]([http://www.cnemc.cn/](http://www.cnemc.cn/))), covering the period from 1 January 2017, to 31 December 2022, with a temporal resolution of one hour (Figure 1). To enhance spatial coverage and validation robustness, we integrated annual global PM\({}_{2.5}\) estimates for 2017-2022 from the Atmospheric Composition Analysis Group (ACAG) website. This study targeted Beijing as China's capital and a typical large city with high population density, heavy traffic and multiple pollution sources (industrial emissions, vehicle exhaust, and regional transport). Its extensive air-quality monitoring network provided continuous, high-quality PM\({}_{2.5}\) data for robust model training and validation, while pronounced seasonal PM\({}_{2.5}\) fluctuations allow thorough testing of predictive accuracy. To better capture the spatiotemporal variations in PM\({}_{2.5}\) concentrations, near-surface daily average PM\({}_{2.5}\) data were selected as the primary research variable. Compared with hourly PM\({}_{2.5}\) data, daily averages contain substantially less noise, providing greater stability during processing and reducing the interference of short-term meteorological fluctuations or transient factors. This approach ensures a more accurate depiction of overall PM\({}_{2.5}\) variation trends. Moreover, as reported by [PERSON] et al. [31], daily average PM\({}_{2.5}\) levels tend to receive more attention than hourly data. Using hourly measurements would limit satellite-based PM\({}_{2.5}\) retrievals to specific times, which could affect the practical applicability of the model. Additionally, according to the National Ambient Air Quality Standards (NAAQS) established by the U.S. Environmental Protection Agency (EPA), PM\({}_{2.5}\) standards and attainment criteria are generally based on daily or annual averages rather than hourly measurements. To ensure data quality, station records with fewer than 12 h of observations per day due to equipment problems were excluded. After data cleaning, the daily average PM\({}_{2.5}\) concentrations were calculated for each monitoring station.
# Modeling Public Transportation Use Among Short-Term Rental Guests in Madrid
###### Abstract  
Urban tourism has experienced significant growth driven by platforms such as Airbnb, yet the relationship between short-term rental (STR) location and guest mobility remains underexplored. In this study, a structured survey of STR guests in Madrid during 2024 was administered face-to-face through property managers and luggage-storage services to examine factors influencing public transport (PT) use. Responses on bus and metro usage were combined into a three-level ordinal variable and modeled using ordered logistic regression against tourist demographics, trip characteristics, and accommodation attributes, including geocoded location zones. The results indicate that first-time and international visitors are less likely to use PT at high levels, while tourists visiting more points of interest and those who rated PT importance highly when choosing accommodation are significantly more frequent users. Accommodation in the central almond or periphery correlates positively with higher PT use compared to the city center. Distances to transit stops were not significant predictors, reflecting overall network accessibility. These findings suggest that enhancing PT connectivity in peripheral areas could support the spatial dispersion of tourism benefits and improve sustainable mobility for STR guests.  
short-term rentals; Airbnb; urban tourism; tourist mobility; public transport; accommodation location; sustainable tourism Article
## 2 Differential Pulse Spectrum Analysis and 3D Forward Modeling  
### Differential Pulse Spectrum Analysis  
Figures 1a and 2a show waveforms of different pulse widths and their corresponding spectra. The pulse widths of the square wave and differential pulse are 0.6 ms, 1.0 ms, 2.5 ms, 5.0 ms, and 10.0 ms, respectively. Figures 1b and 2b present the normalized spectra of the square wave and differential pulse, where hollow circles represent the cutoff frequencies, and solid circles represent the dominant frequencies. As shown in Figure 1, the square wave is a broadband waveform, but its harmonic energy is mainly concentrated in the low-frequency range, lacking high-resolution harmonic components. In contrast, Figure 2 shows that the differential pulse consists of two square waves with identical pulse widths but opposite polarities, with its harmonic energy concentrated near the dominant frequency. Compared to the spectrum of a square wave with the same pulse width, the differential pulse exhibits a higher cutoff frequency, indicating a richer content of high  
frequency harmonics. As the pulse width of the differential pulse decreases, both the dominant and cutoff frequencies shift toward the high-frequency range. Given the varying dominant frequencies and the concentrated harmonic energy of differential pulses with different pulse widths, this study uses multiple differential pulses to scan subsurface spaces, enabling multi-resolution imaging from shallow to deep layers.  
To investigate the relationship between differential pulse width, dominant frequency, and detection depth, this paper presents approximate curves for pulses with widths ranging from 10 \(\upmu\)s to 100 ms, along with their corresponding dominant frequencies and detection depths. Figure 3a shows the dominant frequency curve for differential pulses of different pulse widths, where it is evident that as the pulse width increases, the dominant frequency decreases. Figure 3b displays the approximate curves of pulse width versus detection depth, derived using the skin depth formula under different uniform half-space conductivity conditions. It can be observed that detection depth increases with pulse width, and the smaller the conductivity of the uniform half-space, the greater the detection depth.  
Figure 1: Square waves of different pulse widths and spectra: (**a**) waveform; (**b**) normalized spectrum.  
Figure 3: The relationship between differential pulse width, dominant frequency, and detection depth: (**a**) pulse width and dominant frequency; (**b**) pulse width and detection depth.  
Figure 2: Differential pulses of different pulse widths and spectra: (**a**) waveform; (**b**) normalized spectrum.
## 3 Research Results
### Analysis of Power Supply and Demand at Each Facility
#### 3.1.2 Development of Load Pattern of Always-in-Operation Line  
As shown in Figure 11, in the always-in-operation line, the operating power load fluctuates even during 24 h operation. A field survey of this factor revealed that the equipment of analysis of the power load during operation of each always in operation line are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Line Name**} & **Operation Power,** & **Non-Operation** & **Prediction Error,** \\  & **kW** & **Power, kW** & **\%** \\ \hline Crushing facility, A & 265 & 0 & 13.68 \\ Manufacturing & 746 & 2 & 9.13 \\ facility B & & & \\ Manufacturing & 158 & 8 & 12.41 \\ facility A & 230 & 6 & 19.33 \\ Bolidification facility & 162 & 0 & 16.84 \\ Container packaging & 379 & 35 & 11.66 \\ Crushing facility B & 73.5 & 0 & 18.27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Operating power and error rate of each facility.  
Figure 10: Operation planning line (Manufacturing facility A) load pattern.