esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study developed an on-orbit relative sensor normalization method for satellites and applied this method to the radiometric correction of the BNU-1 satellite. This method uses the radiometric probability density distribution of the BNU-1 panchromatic band as a reference, first correcting the probability density distribution of the blue band radiance. Then, the mean adjustment is used to correct the mean of the blue band radiance after PDF correction, obtaining the corrected radiance in the blue band. This method is applied to correct the BNU-1 images. After correction, the corrected BNU-1 images have better visual effects. The color misrepresentations in the uncorrected BNU-1 images are corrected. Comparisons with ground measured data and Landsat8 image data revealed the following: (1) The radiances of snow surfaces also have good consistency with ground observations and Landsat-8 images in the red, green, and blue bands. (2) The radiance errors of the uncorrected BNU-1 images are reduced. The RMSE decreases from 80.30 to 32.54 W/m\({}^{2}\)/\(\upmu\)m/sr. All these results indicate that the on-orbit relative sensor normalization method proposed in this study can effectively reduce the radiance errors of the BNU-1 images.  
Conceptualization, X.C.; methodology, X.S.; validation, S.Z.; data curation, Y.Z.; writing--original draft preparation, S.Z.; writing--review and editing, S.Z., F.H., X.W. and H.H.; visualization, L.L. and S.Z.; supervision, X.C.; project administration, X.C.; and funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China (Grant No. 2021 YFC2800705), the Guangdong Basic and Applied Basic Research Foundation (2021B1515020032), National Natural Science Foundation of China (Grant No. 41925027) and Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. 311021008).  
The data presented in this study are available on request from the corresponding author.  
We greatly thank [PERSON] for her help with the preprocessing of BNU-1 images.  
The authors declare no conflict of interest.
# New Developments and Progress in Absorption Chillers for Solar Cooling Applications
## 2 Solar-Powered Absorption Cooling Systems  
### System Description  
Natural gas or industrial waste heat-driven absorption chillers are usually deployed in large buildings for space cooling applications [10]. However, in recent years, the potential of absorption chillers to use solar energy has been shown in several demonstration projects around the world. A typical solar-driven absorption cooling system (see Figure 1) has four main components: namely, a solar thermal collector, an absorption chiller, a backup heater, and a thermal energy storage unit (e.g., hot water storage tank).  
The solar thermal collector converts incident solar radiation to heat, which is transferred to the thermal energy storage medium via a heat transfer fluid (usually water or thermal oil) circulated using a circulation pump (i.e., CP1 in Figure 1). The collected heat then drives the absorption chiller to provide the required cooling effect, which is carried by the chilled water loop to the cold distribution unit available at the end-user (e.g., fan coils). The heat rejected from the absorption chiller (i.e., the sum of the driving heat and end-user thermal load) is typically dissipated through a cooling water loop to the atmosphere by using a cooling tower. Because of the intermittent nature of solar energy, the system is equipped with a backup heater and thermal energy storage unit (Figure 1). These units are needed to operate the solar cooling system smoothly by providing residence time in the chiller when solar radiation varies (e.g., due to the cloud cover). They are particularly crucial for absorption chillers which have slow startup times and a dilution cycle when going into shutdown [12]. For the system's specific components and operation strategies, there are many options that impact the system's performance and operation boundary.  
Figure 1: Generic layout of a solar absorption cooling system [10]. Note: CP = circulation pump.
## 3 Vibration Signal Preprocessing and Fault Feature Extraction  
To extract effective fault features from the non-stationary gearbox vibration signals containing noise for fault classification, a three-step signal processing procedure is presented in this paper as follows.  
Step 1. GWO-VMD-based vibration signal decomposition.  
To reduce the effect of mode mixing during signal decomposition on the final fault classification accuracy, VMD is adopted to decompose the vibration signal, and the GWO algorithm is used to optimize the parameters of VMD for a better noise-elimination effect.  
Step 2. Correlation-based vibration signal reconstruction.  
After the vibration signal is decomposed by GWO-VMD, correlation coefficients of IMFs are calculated, and only highly correlated IMFs are retained for vibration signal reconstruction.  
Step 3. Reconstructed signals-based fault feature extraction.  
With the reconstructed vibration signals, 18 parameters, including 16 selected time-domain parameters, energies, and permutation entropies, are calculated to form fault feature vectors for the subsequent fault classification.  
The above vibration signal preprocessing and feature extraction process is shown in Figure 1.
###### Abstract  
Power transformers are vital and costly components in power systems, essential for ensuring a reliable and uninterrupted supply of electrical energy. Their protection is crucial for improving reliability, maintaining network stability, and minimizing operational costs. Previous studies have introduced differential protection schemes with harmonic restraint to detect internal transformer faults. However, these schemes often struggle with computational inaccuracies in fault detection due to neglecting current transformer (CT) saturation and associated uncertainties. CT saturation during internal faults can produce even harmonics, disrupting relay operations. Additionally, CT saturation during transformer energization can introduce a DC component, leading to incorrect relay activation. This paper introduces a novel feature extracted through advanced wavelet transform analysis of differential current. This feature, combined with differential current amplitude and bias current, is used to train a deep learning system based on long short-term memory (LSTM) networks. By accounting for existing uncertainties, this system accurately identifies internal transformer faults under various CT saturation and measurement uncertainty conditions. Test and validation results demonstrate the proposed method's effectiveness and superiority in detecting internal faults in power transformers, even in the presence of CT saturation, outperforming other recent modern techniques.  
internal fault detection; power transformer; LSTM neural network; wavelet transform
# AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse
## 1 Introduction  
The optimization of order picking process in the warehouses is a well-studied problem in literature; retrieving stock keeping units to fulfil orders, in fact, is among the most critical and expensive activities [1]. In modern warehouses, the automated component is increasingly replacing manual material handling tasks, with the aim of minimizing the time spent by the human operator for non-value-added activities. For this reason, the order picker is often accompanied by an automated cart (typically AMR or AGV, Automated Guided Vehicles) in its activity of picking stock keeping units [2].  
In this paper, we study a particular hybrid order picking system, defined AMR-assisted picker-to-parts system [2], according to which every human operator, assigned to a specific aisle of the warehouse, manually picks the articles of its zone, and then load them onto an AMR in a dedicated location at the end of each corridor [2]. The AMR, after having visited all the corridors and collected all the items, takes care of transporting those from the storage area to the depot.  
The contribution of our study is to specialize known mathematical programming models to a given warehouse layout based on two-blocks of sub-aisles, with the aim of planning picking operations and the coordination between pickers and AMRs in an optimal way. A second contribution is represented by the comparison between alternativeimplementation of the AMR system in the given context, with an evaluation of the efficiency in terms of minimization of the total tardiness. Our mathematical models are finally applied to a real-world dataset and in close cooperation with a German brick-and-mortar grocery retailer.  
The paper is structured as follow: in Section 2 we review the literature on AMR-assisted order picking; in Section 3 we describe the characteristics of the warehouse subject of our investigation; in Section 4 we define two possible implementation of the AMR-assisted Picker-to-Parts system for the given warehouse configuration and we present mathematical models; numerical experiments on the impact of speed and size of the AMR fleet into efficiency are carried out in Section 5, together with a comparison of the two possible implementations of the AMR system; conclusions are finally drawn in Section 6.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow  
The launch of SeaSat in 1978 initiated the era of utilizing spaceborne SAR sensors for global snow cover monitoring. While some snow-related studies were published with SeaSat's L-band SAR [119; 120], studying snow cover with SAR data has become more mature since the launch of the first continuously revisiting ERS-1 (C-band) in 1991 as ERS-1 providing the opportunity to use repeat-pass multi-temporal observations, which minimizes the influence of topographic effects on the backscattering by ensuring similar sensing geometry [121] and thus produces more reliable snow cover estimations.  
Consequently, the following literature review includes the results and findings from 96 snow cover studies mapping SCE with spaceborne SAR sensors after the launch of ERS-1, selected from commonly used academic search engines including Google Scholar, Web of Science, and Scopus. Studies focusing on SWE and snow depth (SD) have been excluded from the review. Airborne-based studies are also excluded from this compilation as their temporal and spatial scopes are often limited by the mission design and thus, their insights are often not universally transferrable. The number of available publications since 1992 clearly has shown an upward trend. This implies that the present status of SAR-based snow cover monitoring is still in a developing stage [106; 122].
## 2 Materials and Methods
### Imputation Methods
#### 2.2.3 Machine Learning (ML) Methods
The ML models implicitly learn these temporal relationships through the feature patterns. In all imputation scenarios, once a column is predicted and imputed, its values are incorporated as predictors for subsequent imputations. To optimize the use of available information, the gap-filling process prioritizes columns with the highest number of missing values, imputing these first. This approach forms the baseline method, referred to as Progressive (**P**). Beyond this, three additional prediction strategies were evaluated: (1) Stabilized (**PS**)--this method involves iteratively imputing the full dataset until a stable state is reached, defined either by the median of the changes between consecutive imputations falling below a predefined threshold or by reaching a maximum number of iterations; (2) Progressive Duplicate (**PD**)--an extension of the Progressive method, where imputed columns are also used as predictors during subsequent imputations; and (3) Progressive Duplicate Stabilized (**PDS**)--a combination of the progressive duplicate approach with the stabilization procedure applied to ensure convergence. The presented and evaluated imputation methods were applied for prediction of missing data values and through this for reconstruction of hydrological data series, making them suitable for subsequent calculations. There are many rivers around the world that, in the early years of hydrometric measurements (1930s-1960s), did not have systematic measurements, and in some months, there were no monthly averages (especially in less developed countries). In addition, there were no measurements of other hydrometeorological elements (e.g., precipitation or evaporation) in neighbouring basins, so methods that rely on reference data for reconstruction could not be applied. There are many rivers across the world that, in the early years of hydrometric measurements (1930s-1960s), did not have systematic measurements, and in some months there were no monthly averages (especially in less developed countries). In addition, there were no measurements of other hydrometeorological elements (e.g., precipitation or evaporation) in neighboring basins, so methods that rely on reference data for reconstruction could not be applied.
# Crowd Evacuation Guidance Based on Combined Action Reinforcement Learning
## 4 Conclusions  
This study analyzed crowd evacuation guidance using dynamic guidance signs. This paper proposed a crowd evacuation method based on combined action space deep rein  
Figure 7: Period reward with varying number of humans.  
forcement learning to overcome the disadvantages of existing methods. Previous methods required the manual design of topological graph models or grid models and independent path planning algorithms, resulting in a large manual workload and easily introducing human errors. Through end-to-end deep learning, the agent explores and learns building structure and path planning methods on its own during the training process, and automatically corrects cognitive errors through environmental feedback, thereby identifying the optimal evacuation guidance strategy.  
To overcome the \"dimension disaster\" problem caused by the number of output guidance signs when the typical DQN method in deep reinforcement learning is applied to crowd evacuation problems, this paper proposed the CA-DQN method. It reduces the network structure complexity of the output action dimension from exponential growth to linear growth, and improves the usability of reinforcement learning methods in complex scenarios and large-scale crowd evacuation problems. According to the different definitions of sample priority, the CA-DQN method is divided into CA-DQN-max and CA-DQN-mean methods.  
The experiments on crowd simulation based on the social force model showed that the proposed method effectively improved the crowd evacuation efficiency, and reduced the evacuation time compared with static guidance signs. Moreover, the results of the proposed method are comparable to those of the dynamic shortest path method based on manual modeling. Between the CA-DQN-max and CA-DQN-mean methods, CA-DQN-mean showed advantages in training stability and result strategy's efficiency, which indicates that it is more appropriate to define the sample priority as the average of absolute TD errors in each dimension.  
Future work will further improve the training efficiency of reinforcement learning agents in complex scenarios, including more rooms, multi-layer building scenes, and obstacles. Moreover, restrictions will be imposed on the frequency of output signal changes, making it easier to understand in real scenarios.  
Conceptualization, Y.X.; methodology, Y.X. and R.W.; software, Y.X.; validation, Y.X., R.W. and J.L.; resources, J.L. and X.T.; writing--original draft preparation, Y.X. and J.L.; writing--review and editing, Y.X., R.W., J.L. and X.T.; visualization, Y.X.; supervision, R.W. and X.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 61672190.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Federated Kalman Filter Design
### Kalman Filter Algorithm for Inertial/Odometer Integrated Navigation
The satellite positioning system data update rate was 1 Hz, and the filtering period of the Kalman filter was set to 1 s.  
Algorithm Structure of Federated Kalman Filter for the Inertial/GNSS/Odometer Integrated Navigation System
## 2 Method  
To evaluate the current advancements in remote sensing of salinization, a literature review was conducted on articles published between January 2016 and December 2023. We queried the Web of Science and Scopus databases using several search terms related to the topic (i.e., title, abstract, and keywords). The search included expressions such as \"soil salt (g/kg)\", \"soil salinization (dS/m or g/kg)\", \"soil salinity (dS/m)\", \"remote sensing\", \"digital soil mapping\", and \"temporal and spatial\". The inclusion criteria were as follows: (1) Articles focused on regional soil salinity predictions using satellite data; (2) Articles that addressed soil salinity sampling and model construction; (3) Articles that reported a coefficient of determination (R\({}^{2}\)) from a validation or testing phase as a measure of model performance. We selected R\({}^{2}\) over other accuracy measures because it is the most commonly reported metric, resulting in a larger sample size of articles; (4) Only articles published in English-language journals were included. The study will be organized and summarized around the following areas: innovations in the literature, key variables necessary for remote sensing models of soil salinity in various geographic environments, the impact of metadataon model accuracy, and trends in soil salinity across different regions of the world, current challenges, and some considerations on the direction of future research.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed in Python 3.6 (Anaconda Inc., Austin, TX, USA), with packages statsmodels (v0.10.0) and scipy (v1.2.1). Categorical variables are presented as proportions and continuous variables as mean \(\pm\) standard deviation. The normality of distribution was verified using the Shapiro-Wilk test. Paired data were compared using Student's paired \(t\)-test or [PERSON]'s exact test, while unpaired data were compared using the Student's independent sample \(t\)-test or [PERSON]'s exact test. Multiple hypothesis testing was corrected using the Holm-Sidak procedure. A mixed-effect linear model with a random effect controlling inter-individual variability was used to predict the relationship between pre-/post-season, field position, and the interaction between the temporal variable and field position (introduced as fixed effects in the model) and the imaging parameters (the dependent variable in the model). A multivariate stepwise analysis model was used to examine variables associated with RV dilatation. The mixed linear model analysis and the multivariate stepwise analysis are detailed in the Supplementary Materials. The intra-and inter-observer reproducibility of the RV volumetric segmentation of the 3D-echocardiographic images processed using TOTEC was assessed via the mean absolute and percent error. Statistical significance was defined as \(p<0.05\).
## 2 Design Method of Feedback Controller
### Secondary Path Uncertainty Constraint  
The uncertainty discussed in this paper is described by multiplicative uncertainty. The multiplicative form of the secondary path can be expressed as  
\[G(j\omega)=G_{0}(j\omega)[1+\Delta(j\omega)] \tag{11}\]  
where \(G_{0}(j\omega)\) is the initial secondary path transfer function, and the expectation of \(\Delta(j\omega)\) represents the degree of multiplicative uncertainty:  
\[\rm E[\Delta(j\omega)]=E\bigg{[}\frac{G(j\omega)}{G_{0}(j\omega)}-1\bigg{]}= \frac{E[G(j\omega)]}{G_{0}(j\omega)}-1 \tag{12}\]  
From a statistical point of view, the degree of uncertainty is minimal when the initial secondary path \(G_{0}(j\omega)\) is exactly the expected value of the secondary path \(\rm E[G(j\omega)]\). In practical applications, we should test as many secondary path transfer functions as possible to improve the scope of system stability. In the case of limited numbers of measurement, using the mean value of the secondary path transfer function obtained from all tests helps to reduce the  
In the Nyquist plot, noise rising is more than \(1/\chi\) in the red disk with center \((1,j0)\) and radius \(\chi\), which is defined as the waterbed suppressing factor. The open-loop transfer function is restricted in the black disk. When these two disks are non-intersected, the noise rising caused by uncertainty is less than \(1/\chi\); hence, an uncertainty constraint is proposed as below:  
\[\frac{|L_{0}(j\omega)w_{3}(\omega)/\delta|}{|L_{0}(j\omega)-1|-\chi}<1 \tag{14}\]  
Equation (14) considers the constraint conditions of secondary path uncertainty. In order to prevent the bandwidth of noise reduction being too narrow, \(\chi\) should be less than \(w_{2}(\omega)\). When \(\chi=0\), the constraint has only met the Nyquist stability but not limited the waterbed lift for the secondary path uncertainty.
# Research on Blockchain Data Availability and Storage Scalability
## 4 Data Availability Measurement Model for Blockchain
### Factors Affecting Data Availability  
Based on the data synchronization process and the full transaction lifecycle, it can be seen that the factors affecting data availability mainly include the following four aspects:  
1. Node type: In the transaction verification stage, the data acquisition time of a full node refers to the time taken for the target data to be retrieved locally; the data acquisition time of a degraded node is the time interval between the time the data synchronization request is made and the receipt of all target data. The probability of obtaining target data varies with different node types and data acquisition times.
2. Network transmission performance: The target data obtained by degraded nodes needs to be transmitted through P2P networks, and factors such as bandwidth, latency, and network congestion during the data transmission process affect the probability of obtaining target data within a time limit.  
3. Data redundancy: Data redundancy refers to the number of copies of data stored. The higher the data redundancy, the greater the probability of obtaining the target data within a time limit, and vice versa.
4. Other factors: These include the failure of the node storing the target data, high frequency of target data requests requiring waiting, and different routing algorithms causing data transmission paths to become longer, all of which can prevent the target data from being obtained within a time limit.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 2 Data and Analysis Method
### Ionospheric Observation Data
#### 2.1.3 Observation from COSMIC Occultation  
The occultation system of COSMIC has advantages of high accuracy and vertical resolution in global coverage. For COSMIC data, VTEC at the position of maximum electron density of every occultation event can be obtained in the 'ionPrf' products provided by the COSMIC Data Analysis and Archive Center (CDAAC). The DCBs in COSMIC systems have already been eliminated, therefore further processing is unnecessary.