esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Although the above methods all achieved effective load prediction, they did not consider the specific characteristics of the load, and they focused more on the decomposition of the signal's high-frequency and low-frequency components, resulting in poor interpretability. In reference [15], a method is introduced that utilizes the seasonal and trend decomposition using a local regression (STL) algorithm to decompose the load sequence based on seasonality and trend. It then uses the Light Gradient Boosting Machine algorithm to predict the components. This method fully considers the trend, seasonality, and volatility of the load. However, it uses the same model to predict components with different characteristics and does not consider the matching between component characteristics and prediction methods, resulting in insufficient feature extraction for the load. In response to the above-mentioned problem, this paper introduces a novel short-term load combination forecasting method that utilizes STL. The approach integrates Loess decomposition technology with hybrid model forecasting methods. Through the decomposition of the original load data into a trend component, seasonal component, and residual component via STL, appropriate models are then applied to predict the characteristics of each component. Specifically, the trend component is forecasted using the LSTM model, the seasonal component utilizing the CNN model, and the residual component using the Gaussian process regression model. The forecast results from these models are then aggregated to derive the final load forecast. By presenting specific examples and conducting a comparative analysis with existing methods such as LSTM, CNN, GPR, STL-LSTM, STL-CNN, and STL-GPR, the efficacy and superiority of the proposed method in practical applications are highlighted. This method ensures a more precise capture and prediction of the intricate dynamics of power load compared to traditional standalone models or methods that overlook component characteristics, thereby enhancing prediction accuracy. By introducing STL decomposition and hybrid model techniques, this research offers a fresh perspective and tool for load forecasting studies, particularly in handling load data exhibiting complex seasonal and trend patterns.
###### Abstract  
The Haiyang 2B (HY-2B) satellite requires precise orbit determination (POD) products for geodetic remote sensing techniques. An improved set of reduced-dynamic (RD) orbit solutions was generated from the onboard Global Positioning System (GPS) measurements over a 14-month period using refined strategies and processing techniques. The key POD strategies include a refined empirical acceleration model, in-flight calibration of the GPS antenna, and the resolution of single-receiver carrier-phase ambiguities. In this study, the potential periodicity of empirical acceleration in the HY-2B POD was identified by spectral analysis. In the along-track direction, a noticeable signal with four cycles per revolution (CPR) was significant. A mixed spectrum was observed for the cross-track direction. To better understand the real in-flight environment, a refined empirical acceleration model was used to cope with the time variability of empirical accelerations in HY-2B POD. Three POD strategies were used for the reprocessing for superior orbit quality. Validation using over one year of satellite laser ranging (SLR) measurements demonstrated a 5.2% improvement in the orbit solution of the refined model. Reliable correction for the GPS antenna phase center was obtained from an over-420-day dataset, and a trend in radial offset change was observed. After application of the in-flight calibration of the GPS antenna, a 26% reduction in the RMS SLR residuals was achieved for the RD orbit solution, and the carrier phase residuals were clearly reduced. The integer ambiguity resolution of HY-2B led to strong geometric constraints for the estimated parameters, and a 15% improvement in the SLR residuals could be inferred compared with the float solution.  
HY-2B; precise orbit determination; empirical accelerations model; satellite laser ranging: GPS antenna phase center; single-receiver ambiguity resolution Article  
Reduced-Dynamic Precise Orbit Determination of Haiyang-2B Altimetry Satellite Using a Refined Empirical Acceleration Model  
[1][PERSON]. [PERSON]  
1 GNS Research Center, Wuhan University, Wuhan 430079, China; [EMAIL_ADDRESS] (Y.W.);  
kc:[EMAIL_ADDRESS] (K.J.);  
[EMAIL_ADDRESS] (Q.Z.)
# Numerical Validation of the Radiative Model for the Solar Cadaster Developed for Greater Geneva
## 2 Materials and Methods
### Tools Considered
#### 2.2.2 DIVA-for-Rhino  
DIVA-for-Rhino is a highly optimized daylighting and energy modeling plug-in for Rhinoceros. This software uses ray-tracing and light-backwards algorithms based on the physical behavior of light in a 3D volumetric model. For hourly solar radiation, the Daysim interface is used. Daysim has been validated by several studies to be accurate in modeling visible-wavelength natural light for multiple sky conditions [17].  
The daylight coefficient approach and the all-weather sky luminance model according to [18] are used here. In this approach, the irradiance received on an element of surface \(x\) is calculated as the sum of all sky segments visible from this element of the surface. For the diffuse component, 145 sky segments are used [19] concomitantly with three ground segments [20]. For the direct component, Daysim uses 65 sun positions; therefore, at a specific time \(t\), Daysim will use one of the 65 positions that is closest to the real sun position at time \(t\).  
To calculate the complete set of daylight coefficients, two ray-tracing runs are performed:  
* One for each of the 148 diffuse and ground divisions;
* The second ray-tracing run uses 65 direct solar positions that are distributed along the annual solar path to calculate the contribution of the direct component. For that, 65 angular light sources with a solar cone opening of 0.53 are assigned for the building model. The positions of the light sources correspond to the representative sun positions of the building site.  
The default Daysim simulation parameters were chosen. Up to two reflections from direct solar irradiation and one reflection from diffuse sky irradiation from the environment were considered.  
Similarly to the solar cadaster, the irradiance calculated by Diva-for-Rhino for each surface element corresponds to the average of those calculated at its vertices.
## 3 Results and Discussion
### Quality Loss Rate  
The changes in the mass of the original specimen and the specimens after each aging period were recorded, and the results are shown in Figure 5. As observed in the figure, when the aging times were 2 and 5 days, the rate of mass loss increased with the aging duration. This may be attributed to thermo-oxidative aging at high temperatures, which alters the internal chemical composition or causes the volatilization of surface substances. At 7 days of aging, the rate of mass loss decreased significantly. This was likely due to the application of putty powder on the internal platform of the thin-film oven (TFO) to prevent adhesion between the specimen and the platform during the thermo-oxidative aging test. As the aging time increased, the viscosity of the specimen's surface also increased, causing putty powder to adhere to the specimen's surface, which could not be completely removed during post-processing, thus resulting in an increase in the specimen's mass. After 7 days, the rate of mass loss increased again, but more slowly than during the 2-5-day period, for the same reasons as the initial mass loss.
## 5 Development of the Execution of the Proposed Algorithm
### Simulation Findings  
To validate the performance of the proposed algorithm, an experimental setting was implemented. Six categories of benchmark mathematical functions were deployed to test the algorithm's efficiency (Table 2). The variants were coded in MATLAB 2018a on a Core i5, 3.1 GHz system, and the configuration was performed using the same personal computer (PC) for all of the simulation experiments. To ensure a fair comparison between the metaheuristics, a comparison was also made between the hybrid-to-hybrid metaheuristics, namely, hybrid PSOABC, PSOGWO and PSOGSA.
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 5 Implementing the Proposed Model
### Technical Considerations
#### 5.1.2 Data Management  
Data management exploits a robust database schema that includes treatment cycle identifiers for each entry. This maintains data integrity and prevents the mixing of cycle information, with strict validation protocols and regular audits ensuring the accuracy and reliability of patient records. The data management concern has been considered in two axioms.  
**Database Schema:** The database schema is designed to include a column for the treatment cycle identifier, ensuring that each data entry is associated with the correct cycle. An example of the mentioned schema is as follows.  
As demonstrated in Table 1, the database schema includes columns for the patient ID, form ID, data, and treatment cycle. This structure ensures that each data entry is accurately linked to its treatment cycle. The patient with Patient ID 12004 has undergone two distinct treatment cycles in this example. In the first treatment cycle, forms F1 and F2 were used. Hence, both were tagged with \"Cycle 1\". This tagging ensures that all data related to the first cycle are easily identifiable and can be retrieved or analyzed without confusion.  
Subsequently, the same patient began a second treatment cycle, during which forms f1 and f3 were used, now tagged with the \"Cycle 2\" identifier. This demonstrates the model's flexibility in reusing forms across different cycles while maintaining a clear separation between the data entries of each cycle.  
**Data Integration:** The model is designed to integrate seamlessly with existing EMR systems, ensuring that the tagging process does not disrupt current workflows. In this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Patient ID** & **Form ID** & **Data** & **Treatment Cycle** \\ \hline
12004 & F1 &... & Cycle 1 \\ \hline
12004 & F2 &... & Cycle 1 \\ \hline
12004 & F1 &... & Cycle 2 \\ \hline
12004 & F3 &... & Cycle 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Database structure in the cycle model.  
regard, interoperability standards are adhered to, facilitating the exchange and management of tagged data across different healthcare information systems.  
The flexibility of the cyclical model, allowing physicians to modify forms, directly impacts data integration. This adaptability necessitates dynamic data mapping and integration processes. When physicians can alter the structure or content of forms, it implies that the data structure is dynamic, meaning that the data model must be adaptable to accommodate changing form layouts and data elements.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 4 CBIR Method in the AUV Collaborative Search Mission
### Construction of Behavior Models Based on Fuzzy Inference  
Fuzzy inference is used in this study to establish the AUV behavior model, essentially serving to unify the decision-making method of the AUV swarm. The core idea is to ensure that the same or similar information consistently leads to the same decision outcome, thereby achieving decision-making consensus among the AUVs. We applied Gaussian membership functions and triangular membership functions to fuzzify the inputs and outputs, respectively. Through multiple tests, we established a comprehensive fuzzy rule  
Figure 2: The data flow of the system.  
table. The Mamdani-type fuzzy implication operator and the \"max-min\" fuzzy composition operation were used to compute the inference result for each rule. The weighted average defuzzification method was then applied. The complete process is illustrated in Figure 3.  
Fuzzy inference is a method based on fuzzy logic, used to handle situations involving uncertainty and vagueness [45]. Fuzzy inference allows for the processing of fuzzy information in a system, simulating the way humans handle ambiguous and complex decisions to draw reasonable conclusions. Fuzzy inference relies on fuzzy set theory and fuzzy logic. Traditional binary logic can only represent \"true\" or \"false\", but fuzzy logic allows for continuous values between \"true\" and \"false\", commonly using numbers between 0 and 1 to represent the degree of membership of an element. Fuzzy inference consists mainly of four components: fuzzification, fuzzy rules, fuzzy inference, and defuzzification.
# Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique
## 5 Conclusions  
This research proposes a kernel principal component analysis (KPCA)-particle swarm optimization (PSO)-extreme learning machine (ELM) model to predict the external corrosion rates in buried pipelines, particularly under data-limited conditions. The results underscore several key advantages:  
1. KPCA is effective in extracting principal components. Four principal components containing 91.33% of the original information were extracted, reducing the input dimensions and simplifying the model.
2. PSO optimized the parameters of the ELM model to improve the prediction accuracy, and the KPCA-PSO-ELM model achieved excellent prediction results on 60 datasets, with an MAE of 0.475% and an MRE of 6.394%, and the standard deviation of its prediction data was also close to the actual values. In contrast, the R\({}^{2}\) of the ELM model was 88.67%, with relatively low prediction accuracy. This indicates data degradation and global parameter optimization can effectively improve the prediction accuracy.
3. The optimized ELM model outshined the other models in generalization on the validation set, underscoring its significant utility in pipeline corrosion monitoring and early warning.  
In summary, this paper introduces an advanced, small-sample-appropriate model for external corrosion rate prediction. The synergistic integration of KPCA and PSO augments both its accuracy and generalization capabilities, promising substantial applications in pipeline safety management. Future efforts aim to further validate the model's effectiveness across varied datasets and enhance its predictive robustness.  
Conceptualization, Z.R. and D.Y.; methodology, Z.R. and D.Y.; software, Z.R. and D.Y.; validation, D.Y. and Z.W. and K.C.; formal analysis, K.C. and D.Y. and W.Q.; investigation, K.C. and D.Y. and Z.R.; resources, K.C. and Z.W. and W.Q.; data curation, W.Q. and D.Y.; writing--original draft preparation, Z.R. and D.Y.; writing--review and editing, Z.R. and D.Y.; visualization, Z.R. and D.Y. and W.Q.; supervision, K.C. and Z.W. and W.Q.; project administration, K.C. and Z.W.; funding acquisition, K.C. and Z.W. All authors have read agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Chongqing (Grant numbers: CSTB2023 NSCQ-LZX0036 and CSTB2022 NSCQ-MSX0051) and the Scientific and Technology Research Program of Chongqing Municipal Education Commission (Award number: KJZD-K201901501).  
Not applicable.  
The data used in this study can be found in the main article and the Appendix A.  
Author [PERSON] was employed by the company Chongqing Gas District, Petro China Southwest Oil and Gasfield Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Experimenting and Results
### Evaluation of Results  
The results of the prediction of oil spill penetration into groundwater using the chosen ML algorithms and the proposed ANFIS-based approach are shown in Table 6. The proposed approach for the prediction of the consequences of oil spills using small sets of data with ANFIS showed the lowest NRMSE (1.0%). It also had a better oil penetration predictive performance in comparison with the analysed ML algorithms (i.e., Linear Regression, Decision Trees, SVR, Ensembles, and GPR). However, the GPR method also shows sufficiently accurate results. Thus, the proposed ANFIS-based approach can be considered an efficient oil penetration into groundwater prediction model, which can be further utilised with more data and other oil spill cases.
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods
### Annual Ice Velocity Mosaics  
Yearly mosaics are formed with all acquisitions spanning from 1 July to 30 June of the next year (centered on 1 January). We use this convention because previous SAR campaigns (2000-2010) from ALOS/PALSAR, ENVISAT/ASAR or RADARSAT-1&-2 were mostly acquired between October and March. Here, we focus on the mosaics generated for years 2013-2014, 2014-2015 and 2015-2016. These yearly mosaics are generated in 3 steps as follows:  
1. If the number of acquisitions (per pixel) is greater than two, we compute the median \(\tilde{v}_{x,y}\) and the standard deviation \(\sigma_{x,y}\) for each pixel individually over all the acquisitions made during the year.
2. We remove measurements that exceed \(2\times\sigma_{x,y}\) from the median: \(\|v_{x}-\tilde{v}_{x}\|>2\sigma_{x}\ \.\  
same as in the case of Landsat with the addition of Wilkes Land, East Antarctica, known for strong katabatic winds. Standard deviations for the different mosaics are between 10 and 20 m/year.
# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 1 Introduction
### Feature Pyramid  
Feature pyramids can fuse extracted features across different scales, thereby enhancing algorithm performance. Commonly used feature pyramids include FPN (Feature Pyramid Networks) [35], PANet (Path Aggregation Network) [36], AFF (Adaptively Spatial Feature Fusion) [37], and BiFPN [20]; the first three architectures are illustrated in Figure 2. The left part of Figure 2a depicts the architecture of FPN, which has lateral connections and a top-down pathway, enabling the extraction of semantic information at all scales. However, the unidirectional flow of information may lead to feature loss. The right part of Figure 2a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Model** & **Box mAP** & **Params (M)** \\ \hline
1 & Co-DETR & 66 & 348 \\
2 & InterImage-H & 65.4 & 2180 \\
3 & Focal-Stable-DINO (Focal-Huge, no TTA) & 64.8 & 689 \\
4 & Co-DETR (Swin-L) & 64.8 & 218 \\
5 & InterImage-XL & 64.3 & 602 \\
6 & Relation-DETR (Focal-L) & 63.5 & 214 \\
7 & SwinV2-G (HTC++) & 63.1 & 3000 \\
8 & YOLOX-X (Modified CSP v5) & 51.2 & 99.1 \\
9 & LeYOLO-Large & 41 & 2.4 \\
11 & YOLOX-Tiny (\(416\times 416\), single-scale) & 32.8 & 5.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model comparison on COCO test-dev dataset.  
shows the architecture of PANet, which augments FPN by adding a bottom-up path aggregation, but this augmentation only fuses features from the middle portion of the network; by omitting the initial sampling process, fine-grained information might be lost. Moreover, Figure 2b presents the architecture of ASFF, in which each ASFF module integrates features from all of the previous layers in the bottom-up path; it resolves conflicts between features from different layers, but is limited in generalization and information redundancy.
## 1 Introduction
However, the long-term climate changes often differ between seasons, so the vegetation responses to climate also vary significantly between seasons. In recent years, some studies have explored the characteristic patterns and driving factors of vegetation growth from the perspective of seasonal dynamics. For example, a study has shown that the seasonal change of temperature can regulate the spatial heterogeneity of deciduous broadleaf forests greening [26]. A previous study reported that the decrease in vegetation greenness in the Northern hemisphere after 1997 was mainly due to the decrease in vegetation cover in the early parts of the growing season and the slowdown increase in vegetation cover in the peak of the growing season [27]. Since the seasonal dynamics of vegetation in different regions and ecosystems are different, it is not accurate to study the seasonal changes of vegetation in a certain region by simply using meteorological seasons based on a constant calendar (for example, spring is defined as March-May). Therefore, it is more universal to define seasonality based on local vegetation phenology information. Analyzing the interseasonal relationship of vegetation growth will help to better understand the climate-vegetation interaction. In this study, we first assessed the vegetation indexes change from three different datasets to detect and characterize vegetation growth trends in the upper and middle of the YRB from 2000-2019. The satellite-derived NDVI, GPP, and LAI were regarded as indicators to reflect vegetation dynamics and productivity. Secondly, we divided the growing season into three active seasons based on phenological metrics [17; 28]. Then, we assessed the effect of vegetation carryover on vegetation growth across the phenology-based season using partial correlation analysis and compared the size of this effect against that of the immediate and lagged impacts of climate factors. Temperature (TEM), precipitation (PRE), and sunshine duration (SSD) were used to represent the climate conditions. Finally, the individual contributions of climate variability and the vegetation carryover on vegetation greening were calculated. Our research is expected to increase understanding of vegetation-climate interactions at the seasonal scale and provide insight on how to better manage vegetation under global climate change in the future.
## 5 Conclusions  
In order to construct a universal prediction model for SSC of multiple blueberry varieties, this study first applied S-G, MSC and SNV processing methods to spectral data to eliminate noise interference and reduce spectral differences between samples. In the establishment of the PLSR universal prediction model, different preprocessing methods resulted in significant differences in the prediction performance of the model. Through comparative analysis, the PLSR model based on the combined treatment of S-G, MSC and SNV had the best prediction effect, with an \(R_{p}^{2}\) value of 0.94, RMSEP of 0.33% and RPD of 3.94. In order to further optimize the performance of the model, the UVE algorithm was used to screen the spectral variables, which effectively reduced the complexity of the model and maintained a high predictive ability. On this basis, SPA was used to further extract 12 characteristic wavelengths significantly related to blueberry SSC, and a simplified model was constructed by integrating the MLR algorithm. The results show that the UVE-SPA-MLR model significantly improves the detection efficiency under the premise of ensuring a certain prediction accuracy, and shows its potential application value in spectrometers with limited wavelengths. In summary, the multi-variety universal model based on hyperspectral imaging technology can effectively overcome the limitations of a single-variety model, and meet the rapid and non-destructive testing requirements in actual production while ensuring detection accuracy.  
Conceptualization, L.M., G.C. and D.L.; methodology, L.M. and G.C.; software, L.M. and N.T.; writing--original draft preparation, L.M.; investigation, G.C.; writing--review and editing, D.L.; visualization, L.M. and N.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 32202147), China Postdoctoral Science Foundation (No. 2021M690573), and Fundamental Research Funds for the Central Universities (No. 2572020 BF05).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors sincerely thank the editor and the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflicts of interest.