esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Data-driven health management methods are widely used in the O&M of spacecraft, but there are still factors such as a limited number of sensors, a lack of fault data, and nonmeasurement and control areas that restrict the application of data-driven methods. In this paper, a satellite constellation model library is developed in MWorks and a PHM framework for satellite constellation health based on model-data fusion is proposed. The purpose is to use high-precision models to compensate for the deficiencies of pure data methods in various stages of testing, O&M. The work can be summarized as follows:  
* A satellite constellation system model library has been developed in MWorks, which integrates the constellation orbit model, satellite multidomain model and communication link model. Based on this model library, a satellite constellation system can be built for ground coverage characteristic calculation and orbit control simulation, satellite energy balance simulation, link selection and margin calculation simulation.
* A PHM architecture for satellite constellation based on the Modelica model combined with a telemetry-data-driven model is proposed, which can also provide guidance for the design of a general spacecraft health management platform.
* Extracting the orbital parameters of the Iridium NEXT constellation and designing a verification example, it verifies the feasibility of building the constellation orbit model, satellite multidomain model and communication link model in a unified manner under the Modelica language and proves that the simulation model has a positive effect on the enhancement of telemetry data in the PHM of satellite constellation.  
As for the outlook for further research, the focus will be on how to use a model-data hybrid for the training of fault diagnosis algorithms and how distributed solutions can improve the efficiency of satellite constellation system model simulation.  
Conceptualization, C.L., D.S. and L.C.; methodology, C.L. and L.C.; validation, C.L. and J.D.; writing--original draft preparation, C.L.; writing--review and editing, J.D.; visualization, C.L.; supervision, L.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Key R&D Program of China (Grant No. 2019 YFB1706501 00905) and Research on Model-Driven Design Method of Complex Product System (Grant No. 0270100065).  
**Data Availability Statement:** Data supporting the findings of this study are available from the corresponding author upon request.  
**Acknowledgments:** Thanks for the support of the members of the spacecraft digital escort team in Huazhong University of Science and Technology CAD Center and Suzhou Tongyuan Software and Control Tech. Co.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 3 Experiment and Results
### Experimental Evaluation Indicators  
To evaluate the effectiveness of ST-YOLOv8, mean average precision (mAP) [52], precision, recall [53], and F1-score [54] were used as evaluation metrics. The definitions of these metrics are as follows:  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP(i) \tag{8}\]  
\[AP=\int_{0}^{1}P(R)*dR \tag{9}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[F_{1}=2*\left(\frac{Precision*Recall}{Precision+Recall}\right) \tag{12}\]  
In these formulas, mAP stands for mean average precision, which is calculated as the average of \(AP(i)\) (the average precision of the \(i\)-th instance or class) over \(N\) instances or classes. \(AP\) (average precision) is defined as the integral of \(P(R)\) (precision at a given recall \(R\)) with respect to \(dR\) (the differential of recall) from 0 to 1. _Precision_ represents the ratio of true positives (\(TP\) the number of correctly predicted positive samples) to the sum of true positives and false positives (\(FP\), the number of incorrectly predicted positive samples). _Recall_ is the ratio of true positives (\(TP\)) to the sum of true positives and false negatives (\(FN\), the number  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **SSDD** & **SSDv0** \\ \hline Sample quantity & 1160 & 43,819 \\ The number of samples for distant & 932 & 37,246 \\ sea scenes & Dense ship scene, & Multi-scale ship scene, \\ Scene type & Multi-scale ship scene & Strong clutter background scene \\ The number of ships & 2540 & 59,535 \\ The number of small ships & 1529 & 35,695 \\ The number of medium-sized ships & 935 & 23,660 \\ The number of large ships & 76 & 180 \\ Image size & 214 \(\times\) 653 - 190 \(\times\) 526 & 256 \(\times\) 256 \\ Resolution & 1 m 10 m & 3 m 25 m \\ \multirow{2}{*}{Location} & Yantai, China, & Yantai, Shanghai, China \\  & Visakhapatnam, India & Busan, South Korea \\ \multirow{2}{*}{Sensor} & Radar Sat-2, Terra SAR-X, & Tokyo, Japan, etc. \\  & Sentinel-1 & Gaofen-3, Sentinel-1 \\ Polarization mode & HH, VV, VH, HV & Single, Dual, Full \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information of the dataset used in this article.  
of incorrectly predicted negative samples). \(F_{1}\) is the harmonic mean of precision and recall, calculated as 2 times the product of precision and recall divided by the sum of precision and recall, combining the two metrics to evaluate the model's performance comprehensively.
## Materials and Methods
### Methods  
The methods used herein consist of the following steps: (a) preprocessing, (b) urban area extraction, (c) spatial evaluation, and (d) temporal plausibility analysis. The preprocessing includes the (automated) acquisition of historical maps, manual georeferencing for some of the study areas, and mosaicking, as well as calibration of the map data and other gridded datasets used in this study (i.e., the transformation of the datasets into common grids) (Section 2.2.1). Then, we extract urban areas from the historical maps using the GHSL as ancillary data (Section 2.2.2). This extraction process includes an unsupervised, color-based map segmentation step, the integration of map signals with built-up areas from the GHSL, a rule-based decision mechanism to identify historical urban areas, and a spatial refinement step including spatial constraining and morphological cleaning (Figure 5). We evaluate the resulting historical urban areas _across space_, by quantifying the spatial agreement with built-up density distributions from the HISDAC-US (Section 2.2.3), and evaluate our results _across time_, by assessing the agreement to the HYDE data, and by testing the plausibility of our results with respect to the GHSL-based development trajectories in each of the study areas (Section 2.2.4).  
Figure 5: Flow diagram illustrating the historical urban area extraction and validation steps.
## 3 Results and Discussion  
The different OA schemes introduce rather important differences in modeled POA and SOA values (Figure 1). In January 2019, the OA represented a high share of the total PM\({}_{2.5}\) mass in agreement with previous atmospheric composition studies for the urban background locations in the GAA [20], and the OA modeled with VBS decreased by 20-35%than those with SOAP in the urban and suburban areas of the GAA (Figure 1a). The lower OA-VBS are related to POA reductions (Figure 1b), which can reach the order of 50% since POA are treated as volatile and undergoing aging processes in VBS. In July 2019, the contribution of OA to the total PM\({}_{2.5}\) mass was less pronounced than in January, and the OA-VBS increase, mainly between 20 and 30%, with respect to OA-SOAP in the urban and suburban areas of the GAA (Figure 1c). This increase is configured by the decrease of POA (Figure 1d) and the increase of SOA levels (Figure 1e).  
The evaluation metrics' average values for PM\({}_{10}\) and PM\({}_{2.5}\) are presented in Table 1 by the different OA schemes used in CAMx runs. The overall performance of the modeling system can be considered quite satisfactory since most of the evaluation metrics take average values that fall within the acceptance criteria limit values [21, 22, 23]. The agreement between modeled and measured PM\({}_{10}\) and PM\({}_{2.5}\) is better with the VBS scheme than with SOAP since the metrics values are improved. For example, mean reductions of NMB and NMSE are estimated to be 35% (41%) and 4% (7%), respectively, for PM\({}_{10}\) (PM\({}_{2.5}\)) in January 2019. NMB and NMSE decreased on average by 7% (12%) and 9% (12%), respectively, for PM\({}_{10}\) (PM\({}_{2.5}\)) in July 2019. These results are indicative of larger improvements with the use of the VBS OA scheme in wintertime than in summertime.  
The comparison of simulated organic carbon (OC) concentrations (scaled from simulated OA using a factor equal to 1.6) with OC observations at the PANACEA stations also reveals that the VBS scheme outperforms the SOAP one since it reduces: (a) the model overestimation in the cold period studied (NMB is reduced by 38% in Thissio station) and (b) the model underestimation in the warm period studied (NMB is reduced by 11% in Thissio and by 18% in Demokritos stations) (Figure 2). Similarly, the warm period results for OA predictions in Finokalia (Greece) were improved when the original SORGAM (OA aging excluded) was replaced by the VBS scheme [8].  
Figure 1: Percentage difference in simulated monthly concentration values between VBS and SOAP schemes for (**a,b**) OA, POA in January 2019 and (**c–e**) OA, POA, SOA in July 2019.
# Effect of Surface Roughness on Early Stage Oxidation Behavior of Ni-Base Superalloy IN 625
## 3 Results
### Post-Exposure Analyses
#### 3.2.3 Oxidation Kinetics  
The oxidation kinetics were determined using SEM images of the cross-sections of polished and ground alloy after exposure. The results are shown in Figure 5. The results show that the average value of the constant parabolic rate after 24 h of air exposure at 950 \({}^{\circ}\)C obtained for polished alloy is 2 \(\times\) 10\({}^{-12}\) cm\({}^{2}\cdot\)s\({}^{-1}\), while for ground one it is 9 \(\times\) 10\({}^{-13}\) cm\({}^{2}\cdot\)s\({}^{-1}\). The obtained results show that grinding decreased the oxidation kinetics of about twice.  
Figure 4: SEM/BSE images of (**a**) polished (1 \(\upmu\)m); (**b**) ground (220 grit) IN 625 after isothermal oxidation performed at 950 \({}^{\circ}\)C for 24 h in laboratory air.  
Figure 5: Parabolic rate constant k\({}^{\prime}\)p calculated based on the oxide scale thickness measurements on the studied alloy during isothermal exposure to laboratory air at 950 \({}^{\circ}\)C for 24 h.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods  
### Challenges of Grid-Level Power Load Forecasting Integration  
Grid-level load forecasting presents unique integration challenges beyond those encountered in single-region forecasting. The primary challenge stems from multi-source data heterogeneity across diverse provinces and cities. Regional dispatch centers must contend with forecasts generated using different methodologies, data formats, and update frequencies, making direct aggregation problematic.  
Economic development disparities and varying resource investments across provinces result in significant differences in forecasting systems and capabilities. These differences manifest in non-uniform data collection methods, inconsistent data structures, variable update frequencies, and transmission delays. Such heterogeneity necessitates sophisticated data alignment and integration techniques that can account for these variations while preserving forecast accuracy.  
Additionally, effective grid-level forecasting requires comprehensive understanding of the distinct load characteristics across provinces and their relative impact on the overall grid. A robust integration framework must not only handle complex multi-source data but also adaptively weight provincial forecasts based on their reliability, significance, and forecasting complexity.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 1 Introduction
Snow cover onset and melt dates are shifting, generally leading to shorter snow cover seasons with later onset and earlier melt [53] although these general patterns can vary  
Figure 1: Illustration of different snow types and snow line (deep blue font), the importance of snow (black font), synthetic aperture radar (SAR)-related characteristics (font in italics), factors influence snow (green font) and snowpack parameters (red font). on a regional scale [54; 55]. On a global scale, winter precipitation has been shifting from snow to rainfall, which is particularly evident in regions with a more maritime climate [47]. In mountain regions, the effects of climate change on snow cover are even more significant, leading to an apparent decrease of SCE and duration [23; 56; 57]. Studies predicted that in the highest altitudes of the European Alps, SWE may be reduced to less than 20% of the present level by 2100 and that they may become totally snow-free in summertime [58; 59]. As a result of all these aspects, continuously monitoring of snow cover is crucial. Considering the wide areal coverage, temporal variability, inaccessibility and remote location of many snow covered regions, remote sensing is an ideal data acquisition technique for monitoring snow cover and its trends and developments on both spatial and temporal scales. Although the utilization of optical/multispectral remote sensing data for monitoring SCE has a long history compared to synthetic aperture radar (SAR) data [60; 61], as data from optical sensors can be affected by cloud cover as well as (polar) darkness, spaceborne SAR data offers a valuable alternative for monitoring snow cover as it is independent from clouds and illumination conditions. Due to the availability of new SAR satellites during the recent decades together with the developments of the SAR-based SCE detection algorithms, more studies have tried to detect SCE based on SAR imagery instead of optical sensors. However, there has been no comprehensive discussion of current SAR-based SCE detection approaches' theories, technical limitations, critical auxiliary data, and the so far developing trajectory as well as future possibilities. Thus, in the following sections we therefore summarize the currently available techniques to detect snow cover utilizing SAR data and thoroughly compare their advantages and drawbacks.
## 2 Review of Conventional Path Loss Models
### ABG Model  
The ABG model is a simple extension to the alpha-beta (AB) model used in the 3 rd Generation Partnership Project (3 GPP) by adding a frequency-dependent parameter [5]. It is also one of the standard 3 GPP models and is currently widely used in 5G applications [6]. The ABG model can be expressed as [5]:  
\[PL^{ABG}=10\alpha\log_{10}R+\beta+10\gamma\log_{10}f_{c} \tag{2}\]  
where \(\alpha\) is the path loss exponent, \(\beta\) is an offset term, and \(\gamma\) represents the dependence of path loss on log-frequency. The optimal model parameters are typically achieved by performing linear regression with the measurement data [18].
## 2 Materials and Methods
### Porosity Characteristics and Classification  
The volumetric models produced by CT scanning were analysed for porosity characteristics using the metrology software package Volume Graphics StudioMax version 3.3 [38]. This software package has a porosity-analysis software module specifically used to analyse and visualise internal defects from scans of manufactured objects. The pore analysis module presents the results in a tabulated spreadsheet with the pore characteristics including the location, volume, sphericity, compactness, and many more details of the pores. These traits were then utilised for pore classification.  
The identification and classification of pores using CT and other post-manufacturing analytical methods has been studied extensively by other authors and has been covered in the literature review of this paper. This literature provides the basis for classifying porosity in the CT analysis to produce a dataset to train and test ML algorithms.  
The procedure for classification was as follows:  
* A voxel was first classified as porous or non-porous depending on whether there were any pores whose centre of volume lay within it. The procedures for data alignment and voxelisation are described in Sections 2.5 and 2.6;
* Porous voxels were then classified as KH or LOF based on the average sphericity of the pores. Sphericity greater than 0.6 was considered KH and less than this was considered LOF. The sphericity was calculated according to Equation (2). This method has been corroborated in many research papers that were reviewed for this purpose [25; 27; 39; 40].  
The compactness (C) and sphericity (\(S\)), as calculated in Equations (1) and (2), have been previously used to define the difference between different types of pores (keyhole or LOF) [27]. Equation (2) provides the sphericity formula by utilising the knowledge of the volume (\(V\)) and surface area (\(A\)) of the pore being analysed. These values are obtained from the StudioMax software package.  
\[C=\frac{36.\pi.V^{2}}{A^{3}} \tag{1}\]  
\[S=(C)^{\frac{1}{3}}=\bigg{(}\frac{36.\pi.V^{2}}{A^{3}}\bigg{)}^{\frac{1}{3}} \tag{2}\]  
Figure 4: Schematic of \(\mu\)CT scanning.
## 4 Results and Discussion
### Limitations of the Proposed Approach  
In this subsection, we outline some limitations of the proposed approach with respect to the DL-based ones.  
We underline that the DNN methods can produce a spectrum of a posterior probabilities that may be, in turn, used as indexes of the reliability of the taken decision. This is possible only by the prior learning performed by such approaches. In our approach, instead, the reliability is directly measured by the value of the inter-histogram distance used in the discrimination phase. Although this distance may be used as (coarse) reliability index, nevertheless, it is not more informative than the corresponding spectrum of posterior probabilities nor is it related to the probability spectrum in a direct way.  
As highlighted in previous numerical results, we have identified some datasets where the proposed approach is able to attain the unit-valued accuracy. However, the performance obtained on other datasets could be not so significant, since the effectiveness of the histogram comparison depends on the quality of the scanned images and the way used to be recorded. DNNs, due to their prior learning phase, are expected to be less sensitive, indeed, to the image characteristics.  
To recap, DNN-based approaches are expected to be more flexible, powerful, and generalizable than the proposed approach, but at the cost of larger datasets, higher computational resources, and longer training time. After stressing this, we point out that the goal of this paper is not the introduction of a general methodology but, rather, showing that, under some operating conditions, a simple and computationally efficient approach can produce good results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **\# param.** & **Training Time CT** & **Training Time CXRs** \\ \hline
**Proposed** & 1 & 0.31 & 0.087 \\
**AlexNet** & 58 M & 223 & 62 \\
**GoogLeNet** & 6 M & 258 & 73 \\
**ResNet18** & 11 M & 290 & 84 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Computational complexity of the tested models (“M” stands for millions of parameters). The training time, in minutes, refers to data sets composed of images of size 300 \(\times\) 200 pixels for the CT dataset and 320 \(\times\) 390 pixels for the CXR dataset. The number of images is presented in Table 2.  
Figure 6: The confusion matrices obtained by the considered state-of-the-art supervised approaches: (**a**) AlexNet for CT, (**b**) AlexNet for CXRs, (**c**) GoogLeNet for CT, (**d**) GoogLeNet for CXRs, (**e**) ResNet18 for CT, and (**f**) ResNet18 for CXRs.
## 3 Results and Discussion
### Analysis of Word Frequency  
In our analysis of the word frequency from the responses provided by ChatGPT and Gemini, we focused on identifying the terms most frequently utilized by each AI model in discussing LLPS (Figure 2a,b). The ten most frequently used words in the ChatGPT-generated responses were LLPS (243), proteins (129), condensates (100), phase separation (77), cellular (50), molecules (33), cells (26), dynamic (24), RNA (23), and diseases (20). Similarly, the Gemini-generated responses were LLPS (225), phase separation (220), cellular (100), condensates (93), proteins (87), interactions (65), dynamic (45), cells (27), disease (25), and organelles (24). The word \"LLPS\" appeared most frequently in responses from both ChatGPT and Gemini. Additionally, out of the top 10 most-used words, 5 were common to both models and appeared with similar frequencies, highlighting a significant overlap in the vocabulary used by the two chatbots. However, there was considerable variation in the number of appearances of the other words. An observation of the total word frequency reveals that the responses are scientifically relevant to the topic of the queries.  
Word frequency analysis reveals that both ChatGPT and Gemini prioritize core LLPS-related concepts, including 'LLPS', 'proteins', 'phase separation', and 'cellular'. Our findings expose distinct patterns in language utilization and provide a clear indication of strengths and weaknesses inherent to each model. For instance, where one model may excel at detailing complex interactions, another might better summarize overarching themes or principles, thereby suggesting a complementary utility in scientific inquiry. The measurement of word frequency offers a practical metric for refining AI models and highlights the potential areas of enhancements.
## 2 Back-End Server  
The back-end server processes student requests and forwards them to the core LLM module for analysis and response generation. It also integrates a learning behavior data collection module and a database for storing the following information:  
* Submitted program code.
* Execution results and comparison outcomes with test data.
* Usage statistics of auxiliary functions, including the following:
* Number of times each function (Hint, Debug, User-defined Question) is selected.
* Timestamps for auxiliary function usage.
* Feedback messages generated by each auxiliary function.
* Content of custom questions submitted by students.  
The back-end is built using a Node.js framework, ensuring efficient and stable data transmission.  
1. Core LLM Module  
ITS-CAL integrates GPT-4 as a natural language processing engine to generate educationally relevant feedback, but it does not directly generate executable program code for students. The compile functionalities of ITS-CAL were independently developed and are not derived from AI-generated code. Instead, GPT-4 serves as an assistance mechanism to interpret user queries and provide conceptual explanations, debugging guidance, and structured hints. Key features include the following:  
* Adaptive Feedback: the module generates technically accurate responses to student queries, avoiding the direct provision of answers to encourage deeper engagement.
* Example Explanations: it provides relevant examples and learning directions while ensuring example code is unrelated to the specific question to prevent direct copying.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 4 Simulation Results for PVCS with V2G Service
### Case 1: Sunny Day  
For case 1, a day with high irradiance was considered, specifically, 29 June 2019 in Compiegne. The real and predicted PV power are shown in Figure 4, where it can be observed that the predicted PV power is slightly higher than the real PV power and follows the same trend. As a result, this uncertainty has an impact on the optimization results, which will potentially lead to supplying energy from the public grid instead of discharging energy from the stationary storage. In this case, two scenarios are conducted, which involve constant and variable charging/discharging powers.