esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Maintenance Strategy Optimization for Railway
However, few studies on maintenance strategy optimization consider the impact of external shocks on wheelsets. [PERSON] [34] et al. modeled within the framework of the semi-Markov decision-making process, considering wear and external shocks and optimizing the repair strategy. [PERSON] [35] optimized the periodic inspection interval for high-speed train wheelsets, which are subject to internal degradation and external shocks. The wear of wheels during running is mainly caused by the interaction between rails. It is assumed that the degradation of wheelsets is caused by external shocks. The continuous action of external conditions leads to the wear of wheel and needs to be reprofiled. Combined with practical applications, a joint maintenance strategy that combines periodic inspection and preventive maintenance is proposed to improve the existing maintenance mode. For the proposed strategy, the decision variables of periodic inspection intervals and reprofiling thresholds are optimized to prolong service life and reduce maintenance cost rates. The main contributions of the work are summarized as follows: (1) A novel joint maintenance strategy is proposed by analyzing the wear of wheel flanges and diameters and the effect of maintenance according to the actual maintenance mode of daily inspection and periodic inspection. (2) Three typical failures are comprehensively considered, which are wheel flange wear, wheel diameter wear and tread failure. The age-reduction factor is introduced to describe the effect of imperfect maintenance, and the joint maintenance probability model is established based on the cumulative shock model. (3) The inspection interval and reprofiling control strategy are optimized with the objective of achieving a minimum cost rate and with the wheel flange thickness failure as the failure risk constraint. Detailed steps are provided to illustrate the effectiveness and necessity of the proposed maintenance strategy in the case study. The paper is organized as follows. Section 2 describes the cause of wheelset wear, analyzes the effect of maintenance and proposes a new maintenance strategy. In Section 3, the joint maintenance probability model and failure risk constraint of the wheelset are constructed. Numerical calculations and an analysis of the results are given in Section 4. The final section summarizes the work and suggests future research directions.
## Materials and Methods  
### Measurement Site  
The study here presented was carried out at the University Institute of Research in Environmental Studies of the University of A Coruna (43\({}^{\circ}\)20\({}^{\prime}\)11\" north (N,) 08\({}^{\circ}\)217\" west (W); Galicia, Spain). Specifically, the Institute is located in Oleiros (Figure 1), in the northwest part of the autonomous region of Galicia (NW of Spain). It is a commuter town with a low population density characterized by (i) medium traffic density, especially in summer due to tourism caused by its beach area [25], and (ii) by single family homes with heating systems based on wood combustion. The nearest populations are two small villages at 2 and 4 km distance. The closest large population is the city of A Coruna (around 250,000 inhabitants) at a distance of 10 km. There are industries close to the sampling site that can influence air quality: three power plants (10 km W, 25 km southwest (SW) and 60 km northeast (NE)), one factory for the production and transformation of metals (9 km W), an industrial area (petrochemical refinery, primary aluminum manufacturing and graphite electrode manufacturing) (10 km W), one solid waste incinerator (25 km SW), several port areas (2-11 km NW) and one airport (4 km SW). There are also other significant activities in a radius of about 15 km: paper manufacturing and processing, food and beverage plants, ports, hospitals, funerary homes, printers and laundries.  
The meteorological parameters here used were measured at Alvedro Airport (43\({}^{\circ}\)18\({}^{\prime}\)00\" N, 08\({}^{\circ}\)22\({}^{\prime}\)59\({}^{\prime\prime}\) W; NOAA code: 080020-99999), and related data was accessed through the worldmet (v0.9.2; [PERSON], 2020) package for R.  
Figure 1: Map of the sampling site showing relevant issues. The prevailing wind direction in Galicia is northeast (NE) in summer and southwest (SW) in winter. The local wind pattern in the sampling site is mainly conditioned by the land–sea breeze because of its proximity to the sea (1 km). Northwest (NW) winds are dominant, although other wind directions are also recorded, and generally carry in relatively clean air from the sea. However, the main industrial sources (including A Corüña city) are located in this direction.
# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 3 Results
### Identification of Input Variables  
Table 2 demonstrates the feature importance distributions across different models for each case, where each row corresponds to an individual case and each column represents a feature parameter. In this study, we progressively increased the number of input parameters to evaluate their impact on model performance: Case 1 exclusively utilized two sea surface parameters (Tx and Ty) as input variables; Case 2 expanded to three sea surface parameters (Tx, Ty, and SSS); this progression continued until Case 9 incorporated all 10 parameters. By sequentially inputting each case into the models, we systematically assessed the influence of diverse parameter combinations on the model inversion capability, ultimately identifying the optimal parameter set. Figure 4 further compares the model performance metrics across cases, providing a quantitative basis for parameter selection.  
To precisely evaluate and select parameters as input variables, this study adopted \(RMSE\), \(MAE\), and \(R^{2}\) as key evaluation metrics. Comparative analysis of model performance enables clear identification of performance disparities across cases in these metrics. As illustrated in Figure 4, Case 1 exhibited the highest \(MAE\) (0.14 m/s) and \(RMSE\) (0.18 m/s) values among all configurations, accompanied by the lowest \(R^{2}\) (0.55), indicating its maximum inversion error and weakest correlation. In contrast, Case 9 demonstrated superior  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Variables** & **Tx** & **Ty** & **SSS** & **MLD** & **D20** & **SLA** & **Curlz** & **SST** & **LON** & **LAT** \\ \hline Case 1 & \(\surd\) & \(\surd\) & & & & & & & & & \\ Case 2 & \(\surd\) & \(\surd\) & \(\surd\) & & & & & & & & \\ Case 3 & \(\surd\) & \(\surd\) & \(\surd\)  
performance with the lowest _MAE_ (0.06 m/s), minimal _RMSE_ (0.08 m/s), and optimal \(R^{2}\) (0.9), signifying the highest reconstruction accuracy. Based on this comprehensive analysis, Case 9 was selected as the optimal model configuration, with its parameter set established as the definitive input variables for the model.
## 2 Instruments and Methods
### Evaluation of the Atmospheric Opacity from Global Atmospheric Models
The coefficient of determination \(R^{2}\) on the validation part of the dataset was 0.8 for kNN and 0.86 for ANN, with symmetrical deviations from the trend line [60]. The result of this findings is that the PWV may be consistently determinated from the sky dip data using the statistical approach. In our study, we also employ an approach based on building statistical models, but we utilize a more complex neural network topology by incorporating an LSTM layer. The input data consist of MIAP-2 measurements \(2\times 6\times 3\) vector (bands, angles, \(n_{m}=t_{\text{MERRA}}/t_{\text{MIAP}}\), 243 measurements of MIAP per MERRA-2 timestamp). The MIAP-2 datasets for the Badary (2016-2017) and Svalbard (2018-2019) territories comprise 53,449 and 51,680 measurements, respectively. The target values are based on hourly PWV data from the MERRA-2 model. After aligning the MIAP-2 and MERRA-2 data to a common temporal grid, the datasets for the Badary and Svalbard territories consist of 8233 and 8345 measurements, respectively. The normalized dataset undergoes Independent Component Analysis (ICA) dimensionality reduction procedure with 10 components. Random partitioning was applied to create training and testing datasets with a ratio of 75% to 25%, respectively. The topology of the ANN consists of six layers: an input fully connected layer (Dense), a long short-term memory (LSTM) layer and four fully connected layers. The layers are composed of 350, 200, 17,500, 700, 10 and 1 neurons, respectively. In our study, we found that PWV estimations based on WVR data are biased from MERRA-2 data in dry conditions (Figure 1). The resulting \(R^{2}\) was 0.98 on the validation dataset trained on Badary data only and 0.96 for the Svalbard and Badary combined dataset. The time series and scatter plot are presented in Figures 1 and 2. The results show a good agreement between the predicted and the PWV MERRA-2-based and GNSS-based values. The lower \(R^{2}\) value is probably caused by the different impact of the absorption in O\({}_{2}\) and LWP in different sites that cannot be represented by the single regression model. Still, the predictive ability of the model is very high at the full variety of the atmospheric conditions during the sessions. This approach can be applied to different types of the data-acquiring methods and should produce statistically coherent results. The \(R^{2}\) traces the predictive ability of the statistical model and can test the validity of the different nondirect methods testing astroclimatic measurements or atmospheric model predictions in specific places. We used the MERRA-2 model as the reference, but it can also be tested by different direct PWV drone-based measurements or aerosolude, which are too rare to serve as training sets.
# An Anti-Interference Method Based on Energy Residual Searching in GNSS Positioning Applications
## 3 Signal Model  
The signal transmitted by the satellite includes navigation messages, PRN codes, and a carrier wave. The signals from different pseudorandom noise (PRN) codes are superimposed, to form the signal received by the receiver. LFM interference spreads across the entire spectrum and masks the true signal, thereby degrading the receiver's performance. The receiver utilizes the signal received by the antenna, which is mixed to an intermediate frequency (IF) through the RF front end. At this stage, the IF signal consists of the true signal, LFM interference, and noise, and it can be expressed as follows:  
\[\begin{split} r(nT_{s})=&\sum_{i\in\kappa}A_{i}C_{i} (nT_{s}-\tau_{i})D_{i}(nT_{s}-\tau_{i})e^{i2\pi(f_{IF}+f_{i})nT_{s}+j\phi}\\ &+j_{\text{R}}F(nT_{s})+N(nT_{s})\end{split} \tag{1}\]  
where \(A,C\), and \(D\) represent the amplitude of the satellite signal, the PRN code, and the navigation message, respectively; \(e^{i(\cdot)}\) represents the complex carrier, with its real and imaginary parts corresponding to the in-phase and quadrature components of the signal; \(n\) denotes the discrete-time variable; \(\tau_{i},f_{IF},f_{i}\), and \(\phi\) correspond to the code phase delay, intermediate frequency, carrier frequency, and initial signal phase, respectively; \(\alpha\) represents the set of all satellite signals, and the subscript \(i\) denotes the \(i\)-th satellite in the satellite set; \(N\) denotes Gaussian white noise with a mean of zero; and \(j_{RF}(nT_{s})\) represents the non-stationary GNSS LFM interference signal generated by the jammer, which can be expressed as  
\[j_{RF}(nT_{s})=A_{j}cos[2\pi(f_{0}nT_{s}+\frac{1}{2}kn^{2}T^{2}+\phi_{j}]e^{ 2\pi(f_{IF}+f_{i})nT_{s}+j\phi} \tag{2}\]  
where \(A_{j}\) denotes the amplitude of the LFM interference signal; \(f_{0}\) denotes the initial frequency; \(k\) denotes the frequency sweep rate; \(\phi_{0}\) denotes the initial phase of the LFM signal; \(T_{s}\) denotes the the sampling period; \(f_{IF}\) denotes the intermediate frequency; \(f_{i}\) denotes the carrier frequency; and \(\phi\) denotes the initial signal phase.
## 4 Experimental Results  
### Experimental Setting  
Both rain streak removal and rain accumulation removal stages were trained separately. However, they were tested progressively. The rain streak removal network (PRN) was trained on 1800 images of the Rain100H dataset, while the rain accumulation removal network was trained on a synthetic set of hazy images generated from the NYU dataset and augmented real hazy data generated from the NTIRE 2018 dataset. Both networks used the ADAM algorithm optimizer for training with an initial rate of 0.001. The full proposed network was tested and evaluated using a computer equipped with NVIDIA GTX 1050 Ti GPU.
# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 4 Methodology
### Initialization
#### 4.1.2 Intra-Session Graph  
We define an intra-session graph to be a directed graph \(\mathcal{G}_{s}^{l}=\langle\mathcal{V}_{s}^{l},\mathcal{E}_{s}^{l}\rangle\) for representing the inner relations in a session \(s\) as in SR-GNN [12], where the superscript \(l\) stands for _local_. The vertex set \(\mathcal{V}_{s}^{l}=\{v|\forall v\in s\}\) is items appeared in \(s\). The edge set \(\mathcal{E}_{s}^{l}=\left\{\langle v_{(s,l)},v_{(s,i+1)}\rangle|\forall v_{(s,i)}\in s\right\}\), where \(v_{(s,i)}\) is the \(i\)-th item in session \(s\). That means that each edge in \(\mathcal{E}_{S}^{l}\) starts from the previous item and points to the next item in the sequence of session \(s\). Take \(s=\{v_{1},v_{2},v_{3},v_{4},v_{2}\}\) in the lower-left portion of Figure 1 as an example. The vertex set \(\mathcal{V}_{s}^{l}=\{v_{1},v_{2},v_{3},v_{4}\}\). The edge set \(\mathcal{E}_{s}^{l}=\{\langle v_{1},v_{2}\rangle,\langle v_{2},v_{3}\rangle, \langle v_{3},v_{4}\rangle,\langle v_{4},v_{2}\rangle\}\).
# Clustering Approach for Reliable Wireless Communication
## 3 System Model and Set-Up  
The implemented uplink wireless communication system employs CD-NOMA for multiple access in a multiple-cell system (with \(K\) users having one individual antenna), with one base station (BS) using two or four antennas. Rayleigh fading or Rice fading with a K-factor equal to 5 dB was assumed to affect all channels [43; 44]. The MMSE and MMSE-SIC detectors were deployed at the base station [41; 42], and users were transmitting OFDM symbols. Thus, the system is less susceptible to interference and uses the channel bandwidth more efficiently.  
Other aspects of the system setup consist of the following:  
* The number of users varies from 20 to 70.
* The length of data transmitted by each user is 700 or 7000 bits.
* Digital modulation: BPSK and QPSK;
* Noise on the communication channel: AWGN;
* Simulation iterations: 10.  
After completing the following procedures, an OFDM symbol was produced [56]:  
Step 1. Modulation: Data symbols are created by mapping the input bits. This study employed BPSK or QPSK modulation.  
Step 2. Different users' symbols are spread using the codes presented in Section 2.  
Step 3. Serial-to-parallel conversion: Each data symbol has a subcarrier associated with it.  
Step 4. Inverse fast Fourier transform (iFFT): A translation into the frequency-domain occurs.  
Step 5. Parallel-to-serial conversion: The subcarriers' transmission time expansion occurs.  
Step 6. A cyclic prefix (CP)-guard interval is added to each symbol to avoid inter-symbol interference (ISI). The aim is to extend the symbol's length such that the various copies of a symbol arrive at reception in a multipath propagation, fall within a symbol period, and thus maintain the orthogonality between subcarriers.  
The OFDM setup parameters complying with the LTE standards are provided in Table 2.
# MZAP--Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports
## 6 MZAP Android Application--Adoption and the Effects of Digitalization
### Working Time Reduction and Decrease in Error Rates  
Working time reduction results were obtained through a survey in which officials were asked to rate how much the use of the MZAP application has reduced their working time. A scale was created ranging from 0% to 50% in 5% increments, with intermediate options included to represent percentages that fall between two main steps. These intermediate options were denoted with a plus sign next to the main step--e.g., \"5+%\" represents a percentage greater than 5% but less than 10%. The survey results regarding the reduction in working time are presented in Table 4.  
Survey results indicate that the MZAP application has led to a reduction in basketball officials' working time, highlighting the over 40% reduction in the majority of cases while significantly surpassing the anticipated 30% reduction in 87.82% of cases. Table 4 shows that the majority of basketball officials (169 officials, or 62.36% of those surveyed) reported a working time reduction of 40% or more. When responses indicating a reduction between 30% and 40% are also included, the total number of officials who experienced a minimum 30% decrease in working time rises to 238, or 87.82%. This surpasses the initial goal of achieving at least a 30% reduction. Notably, all officials reported a reduction in working time, with only two mentioning the smallest reduction of 10%.  
A primary contributor to this efficiency gain is the application's ability to facilitate real-time error detection and correction. During games, multiple officials can monitor the input of one official and immediately identify discrepancies. Previously, substantial time was allocated for checking the report and post-game revisions, and scanning and uploading finalized match reports. With the implementation of the MZAP application, all data are instantly available to online viewers upon finalization of the match report.  
Survey results also indicate that, due to improved control measures, the likelihood of an official making an error when using the MZAP application is now estimated between 3% and 7%, compared to an estimated 12% to 21% error rate when using the manual (paper scoresheet) system. This improvement is attributed to the constant display of the match report on a large screen, allowing multiple officials to monitor and verify the data being entered. Nonetheless, occasional input errors persist, often due to the rapid pace of in-game events.  
\begin{table}
\begin{tabular}{c c c} \hline
**User Group** & **SUS Score** & **SD (Standard Deviation)** \\ \hline Coaches & 85.45 & 1.97 \\ Delegates & 86.40 & 2.92 \\ Referees & 77.06 & 2.80 \\ Statisticians & 81.62 & 2.15 \\ Other & 67.21 & 4.54 \\ \hline \end{tabular}
\end{table}
Table 3: SUS scores for user groups.
## 4 Soil Moisture Retrieval from Reflection Measurements: A Background on the Discipline
### LHCP + RHCP-Based Soil Moisture Retrieval
 \tag{11}\]  
The functions \(f_{1}\) and \(f_{2}\), can be described through proper scattering models that take into account various other physical parameters involved in the reflection phenomena, other than the satellite elevation angle \(\theta\), assumed to be known, and the dielectric constant \(\epsilon_{r}\) to be estimated. Different scattering models have been proposed in the literature [31, 35, 37]; for instance, applying the so-called small perturbation method (SPM), the RCS components can be expressed as a function of \(\epsilon_{r}\) and \(\theta\)[35]. To do that, for simplicity, the variables \(\alpha_{hh}\) and \(\alpha_{vv}\) can be introduced as:  
\[ \begin{cases}|\alpha_{hh}|=\sqrt{\sigma_{hh}^{o}}\\ |\alpha_{vv}|=\sqrt{\sigma_{vv}^{o}}\end{cases}  \tag{12}\]  
Then, the relationship between \(\alpha_{hh}\), \(\alpha_{vv}\) (_i.e._, \(\sigma_{hh}^{o}\), \(\sigma_{vv}^{o}\)) and \(\epsilon_{r}\), \(\theta\) can be expressed as follows [35]:  
\[ \begin{cases}\alpha_{hh}=\dfrac{1-\epsilon_{r}}{\left(\cos\theta+\sqrt{ \epsilon_{r}-\sin^{2}\theta}\right)^{2}}\\ \alpha_{vv}=\dfrac{(1-\epsilon_{r})(\epsilon_{r}-\sin^{2}\theta-\epsilon_{r} \sin^{2}\theta)}{\left(\epsilon_{r}\cos\theta+\sqrt{\epsilon_{r}-\sin^{2} \theta}\right)^{2}}\end{cases}  \tag{13}\]  
Thus, combining the expressions in Equations (12) and (13) with Equation (10) and determining the value of \(C^{{}^{\prime}}\) through the calibration phase, the dielectric constant \(\epsilon_{r}\) can be numerically solved.
# XCO\({}_{2}\) Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker
## 1 Introduction
[PERSON] used the Fast atmOSpheric traCe gAs retrievaL (FOCAL) algorithm to obtain the results of XCO\({}_{2}\) from GOSAT and GOSAT-2 radiation, but after calculating the collected CO\({}_{2}\) concentration data, they still did not obtain a data product with high precision, high coverage, and high temporal and spatial resolution [23]. In this study, the method of data fusion was selected to combine multiple satellite data, and the uncertainty of data was the key factor to be considered in the process of data fusion. It was necessary to assign reasonable weight to each dataset according to the random error of each dataset. When multi-source data is fused, the random error of data is often weakened according to the best combination scheme, so as to generate an independent dataset with better accuracy. Weighted arithmetic average is a common method to combine multiple datasets. Ideally, the uncertainty evaluation of data needs absolute \"truth\" as a reference. However, it is difficult to obtain the global truth XCO\({}_{2}\) within the scope of a satellite footprint, and the previous verification is only limited to a few TCCON ground sites. In 1998, [PERSON] [24] developed a data quality evaluation method, triple collision (TC), which can avoid obtaining the \"truth value\". This method can objectively estimate the random error of three parallel datasets and is applicable to regional and global scales. It has been widely used in various disciplines. In view of this, this study used the Kriging interpolation method to fill the no-data regions of XCO\({}_{2}\) data products from the GOSAT, OCO-2, and carbontracker models, respectively, measured the uncertainty, and integrated the multi-source products to improve the accuracy, coverage, and spatio-temporal resolution through the TC algorithm.
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 3 HSI Classification Method in Explainable Two-Layer Mode
### Front-End Learning Layer for Data Re-Expression
#### 3.1.1 Segment HSI with Superpixel Segmentation  
When interpreting HSI, the same land cover type is affected by geographical location, growth conditions, and other factors, presenting different spectral curves in regions, resulting in the same object with a different spectrum. The sensor resolution may yield the same spectral curve containing spectral information of multiple land cover types simultaneously.  
Entropy-rate superpixel segmentation is used to reduce the impact of the above issues. It constructs a graph for the HSI image and defines the objective function for superpixel segmentation with two components: the entropy rate of a random walk on the graph and a balance term. The entropy rate favors compact and homogeneous clusters, while the balancing term encourages clusters of similar size. The superpixel segmentation provides spatial support for region-based features [32; 33].  
Given the HSI cube \(H\in\mathbb{R}^{m\times n\times b}\), where m and n represent the spatial width and height, respectively, and \(b\) denotes the number of bands, superpixel segmentation cuts an image into \(N\) parts, where each piece is called a superpixel. We divide \(H\) into \(N\) non-overlapping parts and obtain \(N\) pixel set \(S_{i}\), where \(S_{i}\) is the index set of the \(i\) th superpixel and \(i=1,2,\ldots,N\). Each superpixel is a group of adjacent pixels that share similar characteristics.