esa-sceva/satcom-chunk-collection huggingface dataset:
# Volterra Black-Box Models Identification Methods: Direct Collocation vs. Least Squares
## 6 Conclusions  
Two numerical approaches to solving the problem of identification of the Volterra model were proposed in the paper. As can be seen from the presented results, both methods showed stable convergence. Convergence here can be interpreted only as the dependence of the residual on the increase in the number of terms in the expansions of kernels by Chebyshev polynomials (5). And this dependence is presented in numerical results.  
It is to be noted, from the point of view of the arithmetic complexity of calculations, the collocation method turns out to be less expensive. And this factor is more pronounced the more parameters of the model are to be determined. This is due to the need to calculate a significantly larger number of integrals proportional to the square of the number of measurements being processed.  
\begin{table}
\begin{tabular}{c c} \hline \(\delta\) & \(\varepsilon_{N}\) \\ \hline \(10^{-2}\) & \(0.00628\) \\ \(10^{-3}\) & \(5.11\times 10^{-4}\) \\ \(10^{-4}\) & \(6.02\times 10^{-5}\) \\ \(10^{-5}\) & \(5.36\times 10^{-6}\) \\ \(10^{-6}\) & \(2.64\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 6: Stability results for LSM.  
Figure 14: Residual for \(m=7\) and \(k=(m+m^{2})\times 7\).  
\begin{table}
\begin{tabular}{c c c c} \hline \(\delta\) & \(\varepsilon_{N}\) & \\ \hline \(10^{-2}\) & \(0.00628\) & \\ \(10^{-3}\) & \(5.11\times 10^{-4}\) & \\ \(10^{-4}\) & \(6.02\times 10^{-5}\) & \\ \(10^{-5}\) & \(5.36\times 10^{-6}\) & \\ \(10^{-6}\) & \(2.64\times 10^{-6}\) & \\ \hline \end{tabular}
\end{table}
Table 6: Stability results for LSM.  
Further development of research suggests an increase in the number of terms \(n\) in the model (1) to identify a more accurate functional relationship between the input and output signals. It is also planned to develop special methods for approximating integrals (12) for the case of using input signals of a more complex structure, including fast oscillating signals.  
Conceptualization, D.S., A.T. and E.Y.; methodology, D.S., A.T. and E.Y.; software, V.M. and A.T.; validation, D.S. and A.T.; formal analysis, D.S.; investigation, D.S.; data curation, D.S.; writing--original draft preparation, D.S. and A.T.; writing--review and editing, D.S., A.T., V.M. and E.Y.; visualization, V.M. and A.T.; supervision, D.S. All authors have read and agreed to the published version of the manuscript.  
The research was carried out within the state assignment of the Ministry of Science and Higher Education of the Russian Federation (project code: FZZS-2024-0003).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
###### Abstract  
With the increase of natural gas consumption year by year, the shortage of urban natural gas reserves leads to the increasingly serious gas supply-demand imbalance. It is particularly important to establish a correct and reasonable gas daily load forecasting model to ensure the realization of forecasting function and the accuracy and reliability of calculation results. Most of the current prediction models are combined with the characteristics of gas data and prediction models, and the influencing factors are often considered less. In order to solve this problem, the basic concept of multiple weather parameter (MWP) was introduced, and the influence of factors such as the average temperature, solar radiation, cumulative temperature, wind power, and temperature change of the building foundation on the daily load of urban gas were analyzed. A multiple weather parameter-daily load prediction (MWP-DLP) model based on System Thermal Days (STD) was established, and the genetic algorithm was used to solve the model. The daily gas load in a city was predicted, and the results were analyzed. The results show that the trend between the predicted value of gas daily load obtained by the MWP-DLP model and the actual value was basically consistent. The maximum relative error was 8.2%, and the mean absolute percentage error (MAPE) was 2.68%. The feasibility of the MWP- DLP prediction model was verified, which has practical significance for gas companies to reasonably formulate and decide peak shaving schemes to reserve natural gas.  
correlation analysis; daily gas load; forecasting model; MWP; genetic algorithm 2025  
30 May 2025  
31 May 2025  
2025 June 2025
# Precise Measurement of Hyperfine Structure of Cesium 7S\({}_{1/2}\) Excited State
## 3 Results
### OODR Spectroscopy  
The OODR measurement is a popular technique for high-resolution spectroscopy and has been widely employed for investigating the hyperfine structure of the excited state and transitions between excited states of atoms or molecules [18; 19]. The details of OODR are shown in [12]. For comparison with EIS measurements above, we also present OODR spectroscopy measurements of the hyperfine interval of \(|\mathcal{7}S_{1/2}\rangle\) state. The experimental setup of OODR measurement is indicated by the red square in Figure 2. The first step excitation comes from the 852 nm laser with a power 5 \(\mu\)W before entering the vapor cell, with related waist \(\omega_{852}\simeq 150\)\(\mu\)m. The first step laser power was larger than that of the EIS experiments to populate enough atoms in the intermediate \(6P_{3/2}\) state. The second step excitation was driven by 1470 nm laser with power \(\sim\)16 \(\mu\)W and waist \(\omega_{1470}\simeq 400\)\(\mu\)m in the cell center. The 1470 nm laser passed through the dichroic mirror (DM2) and was detected with a balanced photodiode detector (BPD). To avoid the power fluctuation during the frequency scanning, the 1470 nm laser was separated into two parts, where one was measured by OODR spectroscopy and the other propagated through a similar route as the first one but without overlapping with first step beam. We used the difference of the two 1470-nm parts as the final OODR spectroscopy.  
In Figure 4, we present the OODR spectroscopies for the first step excitation laser locked to \(|6S_{1/2}(F=4)\rangle\rightarrow|6P_{3/2}(F^{\prime}=3,4,5)\rangle\) hyperfine transitions. For the first step locked to \(F=4-F^{\prime}=4\) (Figure 4b) and \(F=4-F^{\prime}=3\) (Figure 4c) cases, we clearly observed five double resonant peaks,corresponding to the five possible transitions in Figure 1. For the \(F=4-F^{\prime}=5\) case (Figure 4a), only three double resonant peaks were attained due to the selective rule. We defined the OODR peak of 4-5-4 as the zero-detuning of second step excitation. The Lorentz fittings to the OODR peaks yielded the peak positions and the hyperfine splitting of \(7S_{1/2}\) (see solid lines in Figure 4). We used a similar method to extract the hyperfine splitting of \(|7S_{1/2}\rangle\) state 2183.46 \(\pm\) 0.04 MHz (Figure 4a), 2183.46 \(\pm\) 0.18 MHz (Figure 4b), and 2183.53 \(\pm\) 0.10 (Figure 4c). The average of the three cases yielded the hyperfine splitting 2183.48 \(\pm\) 0.04 MHz of excited \(|7S_{1/2}\rangle\) state. Similar to EIS, we also show the residuals of the Lorentz fits and spectra at the bottom of Figure 4a-c.
## 3 Matching Satellite and GPS IWV Measurements
The lower amount of matched cases for MODIS during the wintertime can be attributed to a higher amount of cloudy to overcast cases, which are not considered, as well as snow-filled pixels misidentified as cloudy. Figure 2: Temporal sampling of the IASI, MIRS and MODIS instruments at all GPS stations combined for one year, assuming for each satellite overpass a maximum distance of 20 km and a maximum time difference of 7.5 min between satellite pixel and GPS station.
## 1 Introduction
By constructing a wind-train-bridge dynamic coupled device, the train's dynamic responses in the tunnel-bridge-tunnel infrastructure in the canyon wind environment, including displacement and acceleration parameters, wheel-rail response, and traffic safety indices, were investigated [5]. [PERSON] discovered that when trains pass into a tunnel under crosswind, transient changes in the flow field structure and aerodynamic characteristics occur, reducing running safety [18]. Although these studies are focused on a dynamic model based on a moving train, most of them are concerned with the train's transient degradation of aerodynamic performance as it passes through various surrounding scenes. However, the safety of the train operation rather than the aerodynamic performance of the train should be the ultimate concern. In addition, as previously described, the wind barrier plays a vital role during the operation of the train on the bridge, and the height of a wind barrier has a significant impact on its sheltering ability [19]. In different environments, wind barriers of different heights are used, and their effect on the dynamic running posture of the train is still unclear. For this consideration, a study on the height of the wind wall based on the dynamic analysis model of the wind-train-bridge coupling should be involved to evaluate its impact on the train's dynamic performance and safe operation. Based on the Unsteady Reynolds-Averaged Navier-Stokes (URANS) turbulence model, a three-dimensional (3D) Computational Fluid Dynamics (CFD) model of the train-bridge-wind barrier is proposed in this study to measure the transient aerodynamic load of the train. The transient aerodynamic load was fed into the wind-train-bridge coupling dynamic system to perform dynamic analysis of running safety. From the perspective of the train's aerodynamic coefficient and flow field structure, the process and effect of the wind barrier height on the sudden change in aerodynamic performance of high-speed trains (HSTs) running on the bridge were investigated. Based on the changing characteristics of the train derailment coefficient (DC) and wheel load reduction rate (WLRR), the effect of wind barrier height and the running speed on running safety were addressed. The time-varying changes of the train's running posture and acceleration in five degrees of freedom were also compared and analyzed. The CAD model, methodology, computational domain, boundary conditions, and meshes are discussed in Section 2, followed by a summary of the findings in Section 3, and finally a conclusion in Section 4.
## 3 Results
### Diurnal Variation in NEE  
Throughout the crop growth period, NEE was negative during the daytime (uptake) and positive during the nighttime (release) (Figure 2). A higher negative NEE was observed during the day in wheat as compared to rice. The average diurnal NEE varied between +0.21 to \(-\)0.70 in rice and between +0.20 to \(-\)0.97 mgm\({}^{-2}\)s\({}^{-1}\) in the wheat crop. The daytime NEE decreased with the progress of the crop season in both rice and wheat, reaching a peak average NEE of \(-\)0.7 mgm\({}^{-2}\)s\({}^{-1}\) in rice (booting-flowering) and \(-\)0.97 mgm\({}^{-2}\)s\({}^{-1}\) in wheat (heading-ripening). The nighttime average NEE increased marginally during the booting to ripening stages in rice, however, in wheat it showed a steady increasing trend from the early vegetative stage, reaching a peak at the heading-ripening stage. A distinct diurnal pattern in NEE was observed from the early vegetative to ripening stage in rice and wheat, which followed a reverse pattern to that of the PAR during the daytime (Figure 3A,B).  
Figure 1: Variations in (**A**) relative humidity and soil moisture (**B**) air temperature and soil moisture during the study period.  
Figure 2: Daytime and nighttime net ecosystem exchange (NEE) in rice and wheat over the crop season.  
Figure 3: _Cont_.  
Figure 3: **(A)**. Diurnal variation in NEE (empty circle) and incident photosynthetically active radiation (PAR) (filled circle) at half hourly interval, averaged for different rice growth stages. **(B)**. Diurnal variation in NEE of CO\({}_{2}\) (empty circle) and incident PAR (filled circle) at half hourly interval, averaged for different wheat growth stages.
# Speciation of the Removed Pollutants in Bioremediation of Hydrocarbon-Contaminated Soil
## 2 Materials and Methods  
### Experimental Set-Up  
This study was carried out in three Plexiglas columns. Each column, with a diameter equal to 19 cm, contained 20 cm of soil, corresponding to 8.4 kg of soil. According to the USDA classification, the soil is loam-sand. The grain size of the soil was in the range between 0.15 mm and 2 mm. Moreover, a value of organic carbon content with mass equal to 1.2% and pH equal to 6.8 was measured.  
The soil was artificially polluted with commercial diesel oil by manually mixing soil and diesel oil. Moreover, the first mesocosm was hydrated with tap water to simulate natural attenuation (NA). The others were hydrated by adding a nutrient solution suitable to biostimulate the bacterial growth and reach the optimal value of carbon to nitrogen ratio (C/N), as per the results of a previous study carried out in microcosmos [9].  
The biostimulated mesocosms had, respectively, no inoculum (BS) and inoculum deriving from a soil rich in indigenous hydrocarbon-degrading microorganisms (BS + IN) deriving from previous runs carried out at a smaller scale, on the same type of soil and with the same diesel oil. Each column was equipped with a time-domain reflectometry (TDR) probe to monitor the electrical conductivity and electrical permittivity of the soil. TDR sends a low-voltage pulse into the probe; the pulse propagates along two metallic rods and the impedance change at the end of the rods provides a reflection of the pulse. TDR measures the time between the release and return of the pulse from the reflection. By measuring the time and knowing the length of the rods, the propagation velocity of the soil is calculated, and the electrical permittivity is then computed. The electrical conductivity is estimated by the drop in the pulse amplitude (voltage) along the rods. The length of the rods was 0.12 m. Each probe (spacing between the rods = 32 mm) was completely immersed in the soil. Figure 1 shows the studied mesocosms.  
Figure 1: Experimental setup of the three mesocosms with the sensors for monitoring electrical conductivity and permittivity and the connection with the datalogger and the laptop.  
The run with the NA mesocosm was stopped after 193 days, having seen that the monitored geophysical parameters did not change anymore, demonstrating the absence of biological activity, while the test on the biostimulated mesocosms was prolonged to 228 days. At the end of each run, soil samples were collected according to the Italian Standards for Soil Sampling [10], as representative of the average amount of soil and of the bottom of the mesocosm.
# Quality Improvement of Green Saba Banana Flour Steamed Cake
## 3 Results and Discussion
### Cake Analysis
#### 3.3.2 Texture Profile Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Texture Profile Parameters**} \\ \cline{2-5}  & **Hardness (kg)** & **Cohesiveness** & **Springiness** & **Chewiness (kg)** \\ \hline Standard & 2.517 \(\pm\) 0.129 \({}^{\text{a}}\) & 0.674 \(\pm\) 0.018 \({}^{\text{f}}\) & 0.880 \(\pm\) 0.009 \({}^{\text{c}}\) & 1.493 \(\pm\) 0.086 \({}^{\text{d}}\) \\ Control & 3.288 \(\pm\) 0.224 \({}^{\text{c}}\) & 0.400 \(\pm\) 0.014 \({}^{\text{e}}\) & 0.715 \(\pm\) 0.163 \({}^{\text{b}}\) & 0.943 \(\pm\) 0.240 \({}^{\text{c}}\) \\ O0P10 & 3.454 \(\pm\) 0.211 \({}^{\text{c}}\) & 0.380 \(\pm\) 0.022 \({}^{\text{d}}\) & 0.689 \(\pm\) 0.137 \({}^{\text{ab}}\) & 0.898 \(\pm\) 0.162 \({}^{\text{bc}}\) \\ O0P15 & 3.784 \(\pm\) 0.177 \({}^{\text{d}}\) & 0.374 \(\pm\) 0.011 \({}^{\text{d}}\) & 0.666 \(\pm\) 0.151 \({}^{\text{ab}}\) & 0.944 \(\pm\) 0.228 \({}^{\text{c}}\) \\ O35P0 & 2.473 \(\pm\) 0.159 \({}^{\text{a}}\) & 0.383 \(\pm\) 0.018 \({}^{\text{d}}\) & 0.740 \(\pm\) 0.070 \({}^{\text{b}}\) & 0.702 \(\pm\) 0.099 \({}^{\text{a}}\) \\ O35P10 & 2.605 \(\pm\) 0.188 \({}^{\text{a}}\) & 0.371 \(\pm\) 0.008 \({}^{\text{d}}\) & 0.665 \(\pm\) 0.061 \({}^{\text{ab}}\) & 0.629 \(\pm\) 0.075 \({}^{\text{a}}\) \\ O35P15 & 2.768 \(\pm\) 0.167 \({}^{\text{ab}}\) & 0.354 \(\pm\) 0.015 \({}^{\text{c}}\) & 0.591 \(\pm\) 0.116 \({}^{\text{a}}\) & 0.596 \(\pm\) 0.102 \({}^{\text{a}}\) \\ O7P0 & 2.405 \(\pm\) 0.140 \({}^{\text{a}}\) & 0.369 \(\pm\) 0.009 \({}^{\text{cd}}\) & 0.726 \(\pm\) 0.072 \({}^{\text{b}}\) & 0.646 \(\pm\) 0.092 \({}^{\text{a}}\) \\ O7P10 & 2.875 \(\pm\) 0.231 \({}^{\text{b}}\) & 0.335 \(\pm\) 0.012 \({}^{\text{b}}\) & 0.687 \(\pm\) 0.135 \({}^{\text{ab}}\) & 0.655 \(\pm\) 0.116 \({}^{\text{a}}\) \\ O7P15 & 3.674 \(\pm\) 0.196 \({}^{\text{d}}\) & 0.309 \(\pm\) 0.008 \({}^{\text{a}}\) & 0.663 \(\pm\) 0.112 \({}^{\text{ab}}\) & 0.751 \(\pm\) 0.128 \({}^{\text{ab}}\) \\ \hline \hline \end{tabular}  
Means (\(n=3\)) in a column with identical alphabet indicate insignificant difference (\(p>0.05\)). Standard—cake made of WE; control—cake made of GSBF without SPI and Ovalette.  
\end{table} 
Table 4: Texture profile of wheat flour cake (standard) and different formulations of Saba banana flour cakes. energy to chew with no difference among them (\(p<0.05\)). The use of SPI and Ovalette was incapable of improving the cohesiveness and chewiness of GSBF cake, instead causing an appreciable reduction in these parameters.
## 3 Technologies Supporting Intelligent Acoustic Monitoring
### Audio Signal Processing Techniques
Classical noise reduction techniques involve bandpass filtering, like low-pass, high-pass, or band-stop filters, which remove specific frequency ranges that cause unwanted noise. More advanced methods employ algorithms such as adaptive filtering (for example,Wiener or Kalman filters) [74], which adjust their settings according to the varying sound conditions surrounding them. Contemporary methods based on artificial intelligence, such as deep neural networks (DNNs) [75; 76], autoencoders [77; 78], or Generative Adversarial Networks (GANs) [79; 80], allow for very effective noise reduction by training models on large datasets containing clean and noisy audio samples. Popular solutions, such as Deep Speech Enhancement (DSE) or those using Wave-U-Net models, can successfully separate the sounds we want from background noise, making the audio more discernible even in challenging situations [81; 82]. Audio signal processing relies on feature extraction to obtain essential sound information while removing superfluous data. The traditional audio processing techniques, MFCCs, LPC, and PLP, are designed to mimic human auditory perception. The speech recognition field primarily uses MFCCs because they accurately represent human frequency perception; however, LPC and PLP are useful for voice and music signal analysis. Modern AI techniques utilise unsupervised feature extraction methods, such as autoencoders and principal component analysis (PCA), to reduce data dimensions while preserving critical information. The transformer-based models Wav2 Vec2 and HuBERT automatically discover sound features that enhance the effectiveness of classification and signal recognition [83; 84]. The fundamental components of sound analysis in modern AI systems are noise reduction and feature extraction. Combining these methods yields clean and structured signals that can be applied to various speech recognition applications, including security sound detection, factory defect identification, and healthcare breath sound analysis for the diagnosis of lung diseases.
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
## 3 Methodology  
In this section, we develop a novel window selection approach for moving window kriging in which the clusters are quantitatively classified through the K-means clustering technique. We start by covering a background of moving window kriging and then window selection based on K-means clustering.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 1 Introduction
This omission, in turn, results in distorted parameter estimations and outcomes of questionable stability [24; 25]. Therefore, a straightforward and efficient methodology that incorporates the spatial effects of variables into the modeling process is required to ensure precise and reliable outcomes. To mitigate the impact of spatial autocorrelation, we employed an eigenvector spatial filter (ESF) approach by incorporating spatial factors that reflect the spatial interdependencies among distinct locations [26]. The core of the ESF methodology revolves around leveraging the neighborhood context of sites to construct the spatial weights matrix that captures the spatial correlation between different sites [27; 28]. These weights can be derived from considerations such as distance, orientation, and similarities among sites. Once the spatial weights matrix is formulated, it is used to calibrate parameter estimations within the model, effectively incorporating spatial effects into the estimation model. It has been demonstrated that the application of the ESF approach to machine learning provides more precise predictions [29]. Therefore, this study introduces a GPP remote sensing estimation model that accounts for spatial autocorrelation, denoted as SA-LGBM. SA-LGBM is characterized by its consideration of autocorrelation in the spatial distribution of GPP. Specifically, this work performed three primary tasks: (1) Based on the influencing factors of photosynthesis, a set of model variables capturing GPP changes was selected from remote sensing observations. Using the first law of geography, the spatial weights matrix was constructed to represent relationships between American flux sites, and feature vectors were created and extracted as spatial factors. (2) GPP estimation models were constructed using various machine learning algorithms, evaluating the effect of spatial autocorrelation on the models. The model's performance was quantitatively evaluated, and comparisons were made with other GPP products. (3) The capacity of the model to be utilized for various vegetation types and the dynamics of GPP were analyzed.
## 3 Results and Discussion
### Comparison of Results at Various QP Values  
In order to verify the improvement effect of the improved method under the influence of different QP values, additional inference experiments with QP values of 37 and 35 were selected, and the results obtained are as follows in Tables 5 and 6.  
According to the results in the table above, the improved method improves the PSNR by an average of 6.4462% and reduces the LPIPS value by an average of 6.4725% at QP = 37. At QP = 35, the improved method improves the PSNR by an average of 10.9875% and reduces the LPIPS value by an average of 10.7312%. Combined with the results at QP = 32 in Section 3.3, it shows that for image restoration after processing with different QP values, the improved method can achieve good results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Processing** & **Attention** & **Loss** & **Training** & **PSNR** & **LPIPS** \\
**Method** & **Mechanism** & **Function** & **Method** & **PSNR** & **LPIPS** \\ \hline CycleGAN & ✗ & ✗ & ✗ & 27.6238 & 0.2946 \\ Method1 & ✓ & ✗ & ✗ & 28.1762 & 0.2857 \\ Method2 & ✓ & ✓ & ✗ & 28.0459 & 0.2766 \\ Method3 & ✓ & ✓ & ✓ & 30.1147 & 0.2639 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of ablation results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Video Sequences** & **Pre-Improved** & **Improved** \\ \hline Akiyo & 0.2822 & 0.2527 \\ \hline Bus & 0.2796 & 0.2504 \\ \hline Bridge & 0.2845 & 0.2548 \\ \hline Foreman & 0.2866 & 0.2567 \\ \hline Flower & 0.2891 & 0.2589 \\ \hline Container & 0.2818 & 0.2524 \\ \hline Tempete & 0.2758 & 0.2470 \\ \hline Basketball & 0.2839 & 0.2542 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of LPIPS values before and after improvement.