esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 4 Experimental Analysis
### Error Corrected Positioning Results and Evaluations
#### 4.4.3 Evaluations  
To further evaluate the performance of each method, the cumulative distribution function (CDF) and probability density function (PDF) of positioning errors were used as evaluation indicators. The CDF is used to describe the probability that the random variable X falls within a certain range and PDF is a function that describes the probability of the output value of the random error, near a certain point of determination [37; 38]. The CDF and PDF of four different methods in a static scene are presented in Figure 16. It can be seen from the CDF that the error at 95% confidence of Kalman-LSTM is obviously better than the other methods, the WLS-LSTM is also better than Kalman filter and WLS, while the Kalman filter and WLS are almost the same. As shown in Figure 16, the PDF of the position error in Kalman-LSTM is the most concentrated, especially most position errors appear within 1 m and only a few position errors are above 2 m. The PDF of the position error in WLS-LSTM is also more concentrated than Kalman filter and WLS: most position errors concentrate within 2 m and a few are distributed 4 m away. The position errors of Kalman filter and WLS are more dispersed, and Kalman filter is a little more concentrated than WLS. In addition, the position errors of Kalman filter appear within 7 m while WLS appear within 9 m. One can draw the conclusion from the results of CDF and PDF that the LSTM-based has a better performance than Kalman filter and WLS method on integrated BDS/GPS positioning for the higher accuracy and more concentrated position errors.  
The CDF and PDF results in a dynamic scene are presented in Figure 17. It can be seen from the CDF that the corrected error (within 1.5 m) at 95% confidence is significantly improved compared to before correction (above 5 m) and better than the effect in the static scene. As shown in Figure 17b, the dynamic errors before correction are all above 3 m, concentrated between 3 and 4 m, and the maximum error is less than 7 m. Most of the corrected errors are within 1 m, and the maximum error is less than 5 m, which is better than the WLS-LSTM method in the static error. Compared with the Kalman-LSTM method, the overall effect is better, but the proportion of errors above 2 m is higher. It can be concluded that the proposed error improvement method also has a good effect in dynamic scenes and may even be better than the effect in static scenes.  
Figure 15: The corrected dynamic positioning results.
## 2 Related Work
### Methods Based on Deep Learning  
The methods based on deep learning usually build some complex deep neural network frameworks. The most common auto-encoder (AE) is to compress and reconstruct data to obtain reconstruction losses and to judge anomalies by reconstruction loss. The variation auto-encoder (VAE) [17; 18] is a stochastic generative model that provides calibrated probabilities computed by reconstructing the probability density. The long short-term memory-based variational auto-encoder (LSTM-VAE) [19] aims to replace the feedforward network in VAE with LSTM [20; 21].  
Some newer methods, such as the multivariate anomaly detection with GAN (MAD_GAN) framework [22; 23], have a basic architecture similar to the previous generative adversarial network (GAN) [24]. MAD_GAN adopts a long- and short-term recurrent neural network as the basic model of GAN learning to analyze time-dependent multivariate time series data. Its advantage is that it does not need to introduce a lower bound for likelihood estimation and only performs anomaly detection by modeling the nonlinear associations between multiple time series. OmniAnomaly [25] proposes a stochastic recurrent neural network that establishes an explicit time dependency between random variables and uses a stochastic recurrent neural network and a planar normalized flow to generate reconstruction probabilities. It also proposes a method for dynamically selecting the threshold (POT). Ref. [26] leverages a novel threshold to detect cyber attacks. USAD [4] improves the basic AE, adds a decoder, processes the sequence data through three simple auto-encoders, and judges the abnormality based on the reconstruction error; however, the most basic linear changes sometimes cannot effectively extract sequence features. The multivariate time series anomaly detection via graph attention network framework (MTAD-GAT) builds the model jointly by combining the prediction and reconstruction methods using a graph attention network to model the feature and temporal correlation [27]. The deep transformer network for anomaly detection (TranAD) model [28] is a typical representative of anomaly detection combined with a transformer. It uses an attention mechanism to learn temporal trends and model-agnostic meta-learning to guarantee its performance even with limited datasets. It still judges anomalies by the reconstruction error.
## Abbreviations  
* AID Application ID
* CAM Cooperative Awareness Message
* CBR Channel Busy Ratio
* CCH Control Channel
* C-ITS Cooperative Intelligent Transport Systems and Services
* CPM Cooperative Perception Message
* C-V2X Cellular V2X
* DCC Decentralized Congestion Control
* DENM Decentralized Environmental Notification Message
## 5 Evaluation
### Expert Assessment
The expert panel comprised 12 professionals, a sample size consistent with Delphi studies in construction and management research, where a range between 10 and 18 participants is often sufficient to reach consensus and derive meaningful insights [77; 78]. Moreover, this sample size is considered adequate for applying non-parametric tests such as the Wilcoxon signed-rank test and parametric procedures like the one-sample \(t\)-test in expert-based framework evaluations [79].
## 5 Conclusions  
The performances of six big-leaf and one two-leaf LUE-GPP models were evaluated in a humid agroforestry ecosystem for GPP simulations. All the big-leaf LUE-GPP modelswell tracked the seasonal changes in \(GPP_{\text{EC}}\), while the differences in performance owed to the different quantification methods for \(T_{\text{s}}\) and \(W_{\text{s}}\) and their combination in the models. The EF-LUE model outperformed the other five big-leaf LUE-GPP models due to the more accurate quantification of \(T_{\text{s}}\) and \(W_{\text{s}}\). The big-leaf LUE-GPP models overestimated \(GPP\) on sunny days and underestimated it on cloudy days. Meanwhile, the two-leaf LUE-GPP model performed better than the big-leaf LUE-GPP models because the former considered the transmission of radiation in the canopy and the differences in the photosynthetic capacity of canopy leaves; however, under/overestimation of the GPP of shaded/sunlit leaves still occurred under different weather conditions in two-leaf LUE-GPP models. The consistent performance and the accuracy of the traditional big-leaf LUE-GPP model and two-leaf LUE-GPP under different weather conditions were improved by constructing \(K_{\text{r}}\)-based dynamic _LUE_ with exponential form and power form, respectively. These results showed that LUE-GPP models with constructing \(K_{\text{t}}\)-based dynamic _LUE_ have the potential to serve as the basis for optimizing agroecosystem GPP estimates.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16193679/s1](https://www.mdpi.com/article/10.3390/rs16193679/s1)]([https://www.mdpi.com/article/10.3390/rs16193679/s1](https://www.mdpi.com/article/10.3390/rs16193679/s1)), Figure S1: The location, boundary, and topographic features of the experimental orchard station.  
Funding acquisition, N.C. and S.J.; Methodology, W.Z. and S.J.; Software, M.W.; Validation, L.Z.; Visualization, J.L.; Writing--original draft, Z.H.; Writing--review and editing, N.C., D.G. and S.J. 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 (grant numbers 52309055, 52279041, 52179053, and 52109060), the Fundamental Research Funds for the Central Universities (grant number YJ202259), the Sichuan Province Science and Technology Support Program (grant numbers 2022 YEQ0028, 2022 YFN0021, 2023 YFN0024, and 2023 YEQ0025), and the Chengdu Municipal Financial Technology Project (grant number 2022-YF05-01008-SN).  
The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.  
[PERSON] and [PERSON] contributed equally to this work. The authors declare no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Results and Discussion
In the present study, we only show a sample of results to illustrate how our formalism can be used and what physical observables can be obtained. By way of example, in the following, we discuss the calculation of the total heat injected into the assembly at time \(\tau\), which may be defined as (\(V_{s}\) is the volume of the sample):  
\[\Delta Q(\tau)\equiv Q(t)-Q(0)=k_{\mathrm{B}}T_{0}\times\frac{1}{V_{s}}\int \limits_{V_{s}}d\mathbf{r}\,\theta(\mathbf{r},t). \tag{27}\]  
In Figure 5, we compare the two situations with fixed boundary conditions (BCs) and free BC. We show both the \(2D\) (relative) temperature field and \(\Delta Q\). The plot of the latter clearly shows that the maximum heat is reached much more quickly in an assembly with fixed temperature \(T_{0}\) (i.e., \(\theta=0\)) at the boundaries. As is corroborated by the \(2D\) temperature field, in the case with fixed BC, the heat cannot propagate freely but is confined in a smaller region. However, this depends on the heat capacity and thermal conductivity of the system, together with other physical parameters such as the concentration and spatial arrangements of the NMs. This is shown in the sequel. Indeed, in Figure 6, we show the results of \(\Delta Q(t)\) for a monodisperse assembly of NMs with a diameter \(D\) and two size-distributed assemblies with mean diameter smaller or larger than \(D\). It is seen that globally the generated heat increases with the mean diameter, and this is due to the fact that the SAR (the heat source) also increases. The plots also show that the initial slope of \(\Delta Q(t)\) depends on the size distribution of the assembly, and this is related to the fact that the diffusion of heat depends on the size and spatial distributions of the assembly. Figure 5: Left: 2D temperature field for both fixed BC and free BC. Right: Time profile of the total heat for fixed boundaries and free boundaries (right). In Figure 7, we compare assemblies with different values of the thermal conductivity of the host (\(\kappa_{m}\)) (see Equations (14) and (25)). The first observation for such randomly distributed assemblies is that the result strongly depends on the time interval, and for long times we see that in a host with higher conductivity, the maximum heat is reached much faster.
## 5 Future Directions
### Legal Dimension  
With the expansion of LLMs across various domains, the legal implications of using personal data, content generation, and automated decision making have gained increasing importance. These models are often trained on datasets that may contain sensitive information or data protected by intellectual property rights, which raises significant challenges in areas such as data ownership, digital consent, and compliance with existing regulations (such as GDPR, HIPAA, and others). Therefore, future research should focus on developing intelligent, transparent, and enforceable legal frameworks that protect users without hindering innovation. Establishing international legal standards, identifying the role of accountability in automated decisions and designing mechanisms to control access to and use of data are essential steps toward responsible progress in this field [230].  
A central legal concern in the development of language models is the structured protection of personal data across all phases of training, inference, and storage. As LLMs are increasingly deployed in sectors such as education, healthcare, business, and media, ensuring the privacy and security of user data has become a fundamental challenge. These models are typically trained on a vast corpora of real-world data, which may contain identifiable information, private conversations, digital records, or user-generated content. Even in cases where the data has been anonymized, there remains a risk of re-identification through subtle language patterns or by correlating information from multiple sources.  
In response to these risks, future research and regulatory strategies should aim to integrate privacy-preserving mechanisms directly into model design [184]. Prominent approaches include federated learning methods, which allow models to be trained without the need to transfer raw data to centralized servers. Similarly, differential privacy-based learning can help reduce information leakage by introducing statistical noise into model outputs. Furthermore, developing algorithms that enhance decision making transparency, creating legally binding frameworks for data access, and ensuring that users provide informed digital consent are essential measures for responsible model governance. The principle of privacy-by-design where privacy protections are embedded from the earliest stages of system development rather than added later should serve as a foundational philosophy in this domain. Finally, the establishment of international privacy standards tailored to LLMs will be crucial for the long-term, responsible, and sustainable advancement of this technology [231].
## 4 Results and Discussion
### Structure of the LSTM Model  
Table 2 summarizes the structure of the LSTM model used to generate the synthetic Vp. The LSTM model was composed of one input layer, one hidden layer, and one output layer, to simplify the learning-based model given the training data size. Adam (Adaptive Moment Estimation) was adopted as a neural network optimizer. The dropout rate for preventing overfitting was 0.25 [54]. Total data were categorized into training, validation, and test datasets: data from nine wellbores in the training dataset (70%), data from two wellbores in the validation dataset (15%), and data from two wellbores in the test dataset (15%). The loss function for model training was the mean square error (MSE). Early stopping was activated to prevent overfitting if the loss function stagnated for 20 epochs.
# Study on the Control of Torque Distribution of 4 WD Corn Harvester Operation Drive
## 3 Simulation and Result Analysis
### Simulation of Double Rear Wheels Slip with the Same Axle  
The initial state of the simulation was the slipping of the rear axle double drive wheels, after the inter-axle torque control. Since both wheels of the rear axle were in the slipping state, the inter-wheel torque control failed to operate, and the adjustment process was controlled by the engine output to eliminate the slipping. The simulation results are shown in Figure 9a,b.  
Figure 8: Simulation results of the harvester with only the front left wheel slipping: (**a**) Harvester speed; (**b**) slipping wheel speed.  
Figure 9: Simulation results of the harvester with the double rear wheels of the same axle are slipping: (**a**) Harvester speed, (**b**) average speed of slipping wheels.  
From the simulation results, when no control was applied to the harvester, within 0.6 s, the average speed of the slipping wheels first rose sharply to the maximum value due to slippage and then fell sharply to below 3 km/h due to the increase in attachment, during which the speed of the harvester showed a decreasing trend due to wheels slippage. After 0.6 s, the speed of the harvester rose with the speed of the slip wheels and finally reached the uniform speed at the same time. The harvester speed and the average speed of the slip wheels was overshoot, the maximum overshoot was 3.57%; after the control was applied, as shown in the solid line in Figure 9, the harvester speed and the average speed of the slip wheels were in a steady rise during the whole adjustment process, and the adjustment time to reach the uniform speed was 11.1% shorter than that when the control was not applied.  
In summary, active torque control distribution could effectively eliminate double rear wheels slip with the same axle, and improve the passing ability as well as driving stability of the harvester during operation.
## 2 Methods and Materials
### Detail of Dataset and Analysis Method
#### 2.1.3 Machine Learning-Based Analysis Scheme
Furthermore, multiple regression analysis assumes that the explanatory and objective variables follow a normal distribution. However, for non-normal data, this can affect the interpretation of results and predictive performance. Therefore, SVR was used in this study. The hyperparameters required to train the SVR are determined by cross validation on a total of 15,750 pattern presented by grid search, insensitivity coefficient \(\epsilon\) in the range \(2^{-20}\) to \(2^{9}\), regularization coefficient \(C\) in the range \(2^{-10}\) to \(2^{10}\), and RBF kernel function \(\gamma\) in the range \(2^{-15}\) to \(2^{9}\). The combination with the highest coefficient of determination was used as the adequate hyperparameter. As with LASSO, standardization was used to smooth the orders of the explanatory and objective variables when performing SVR, and the explanatory variables ranked by importance in LASSO were changed to search for regression results with the highest coefficient of determination.
# Research on Optimal Voltage Control of Distribution Network with the Participation of EVs and PVs
## 1 Introduction
Fourth, the current literature normally considers only one unique type of voltage support resource in the research on distribution voltage regulation, and the multi-time scale coordination potential among diversified voltage support resources is rarely investigated. To fill the research gaps in the current literature, a two-stage voltage control scheme is proposed in this paper, with the major contributions summarized below: (1) a multi-time scale coordination scheme is proposed for the collaborative operation of different types of voltage-support resourcesincluding OLTC transformers, CBs, EVs, and PVs; (2) a rolling optimization-based control scheme is proposed for the EV charging/discharging scheduling which adaptively adjusts the control period based on the states of EVs and EV chargers without having to foresee the EV charging demands; (3) the impact of EV transport type on the aggregate EV charging demands is investigated. The paper is organized as follows. It starts with the impact analysis of the grid penetrations of EVs and PVs, followed by a discussion of the grid voltage support capability of PV systems and the energy management scheme of EV charging. Later, a two-stage voltage regulation scheme is proposed for distribution networks having PVs and EVs connected, with its performance evaluated via comprehensive case studies performed on the IEEE 123-node test feeder.
## 3 Aerial Images Capture and Data Processing  
### Preimage Data Acquisition  
The first aerial images obtained from the research target were captured from 19 to 22 February 2017. The actual working time was 3 days, and the image spatial resolution was better than 10 cm. The acquired image data were processed into an orthophoto map by software 32 Gbit capacity. As ArcGIS software was used for image processing, we were able to add place names and administrative boundaries to the images, enabling us to make initial grid blocks according to streets and the jurisdictions of community organizations. After that, we divided the image data into 81 areas to make secondary grid blocks, then printed out 82 maps, one of which was of the total area. The map scale for each block was about 1:1000. These results could be used by the grid managers to \"see from the sky\".
## 3 Case Study: Interactive Modelling Implemented Using Vario XR-4 and Ultraleap Motion Controller
### Preliminary Usability Assessment  
The case study has been shared with 20 users, between the ages of 22 and 50, 10 men and 10 women. The users were chosen from among the students of our university with a basic knowledge of geometric entities, feature-based geometric modelling, and 3D transformations. They also have experience in organic modelling based on subdivision surfaces, but using mouse and keyboard interfaces. They were asked to model an arch shape represented on a sheet of paper, wearing the AR headset and acting on the initial control cage through natural interaction based on gesture recognition. At the end of the experience of about 5 min in duration, they were asked to answer a questionnaire giving numerical score (from 0 to 10) to 7 characteristics of the experience. The results of the questionnaire were reported in Table 1 in the form of mean and standard deviation.  
The usability study results show that the quality scores of graphical renderings are very high. The scores are also very good with regard to the displacement of the virtual entity, the depth occlusion, and an overall judgement on the experience. On the other hand, the judgements regarding the recognition of gestures and the selection of entities that required a minimum of training are discreet. Also, the judgements given to comfort are more than discrete mainly due to the need to position the helmet correctly to benefit from the advanced eye-tracking features provided by the Varjo XR-4.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Feature** & **Score (0-10)** \\ \hline
3D rendering quality (realism and graphics) & 9.0 \(\pm\) 0.5 \\ \hline Overall comfort & 7.5 \(\pm\) 1.2 \\ \hline Accuracy in pose recognition & 7.2 \(\pm\) 1.6 \\ \hline Easiness of selecting entities & 7.2 \(\pm\) 1.6 \\ \hline Easiness of moving entities & 8.5 \(\pm\) 0.9 \\ \hline Spatial awareness (depth occlusion) & 8.6 \(\pm\) 1.1 \\ \hline General judgement on natural interaction and modelling procedure & 8.1 \(\pm\) 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Usability assessment through the evaluation of 7 features.  
Figure 8: Finalising the modelling with the conversion of the SubD surface (on the **left**) into NURBS patches (in the **middle**) and then a perfect hole is subtracted from the resulting B-Rep solid (on the **right**). The final modelling is achieved into Rhinoceros.