esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Control for Current Distortion Mitigation in Mining Power Grids
## 2 Preliminaries
### System Description  
The utility power grid is assumed to be an infinite bar with ideal voltage regulation; therefore, it only provides positive sequence voltage at the fundamental frequency to the system. The continuous-time model of utility voltage is defined by (1). The current model, presented in (2), represents the total current demanded by the load. In these continuous-time equations, the subscript \(h\) denotes the \(h\)-th harmonic component, whereas supra-indices \(\alpha\beta\) and \(+-\) represent the real/complex and the symmetric components, respectively. For instance, \(I_{h}^{\alpha+}\) denotes the real-positive sequence component of current harmonic \(h\). Note that, according to (1) and (2), \(\vec{V}\) and \(\vec{I}\) represent vectors and \(V\) and \(I\) represent magnitudes.  
\[\vec{V}(t)=\left[V_{1}^{\alpha+}+\hat{J}V_{1}^{\beta+}\right]=V_{1}\cos(\omega t )+\hat{J}V_{1}\sin(\omega t)\qquad[V] \tag{1}\]  
\[\begin{split}\vec{I}(t)=\sum_{h=0}^{H}\left(\left[I_{h}^{+}+ \hat{J}I_{h}^{\beta+}\right]+\left[I_{h}^{\alpha-}+\hat{J}I_{h}^{\beta-} \right]\right)\qquad[A]\\ \vec{I}(t)=\sum_{h=0}^{H}\left(I_{h}^{+}[\cos(h\omega t+\phi_{h} )+j\sin(h\omega t+\phi_{h})]+I_{h}^{-}[\cos(h\omega t+\phi_{h})-j\sin(h\omega t +\phi_{h})]\right)[A]\end{split} \tag{2}\]  
By using (1) and (2), the real power delivered by the \(h\)-th current component is defined by (3).  
\[\vec{P}_{lh}(t)=V_{1}[I_{h}^{-}\cos((h+1)\omega t+\phi_{h})+I_{h}^{+}\cos((h-1 )\omega t+\phi_{h})]\qquad[W] \tag{3}\]  
Computing \(P_{I(h-1)}\) and \(P_{I(h+1)}\) (see (4)) and reorganizing the \(h\)-th power component, and the power balance necessary condition, are stated in (5) and (6), respectively.  
\[\begin{split} P_{I(h-1)}=V_{1}\Big{[}I_{(h-1)}^{-}\cos(h\omega t +\phi_{h-1})+I_{(h-1)}^{+}\cos((h-2)\omega t+\phi_{h-1})\Big{]}\\ P_{I(h+1)}=V_{1}\Big{[}I_{(h+1)}^{-}\cos((h+2)\omega t+\phi_{h+1}  
\[\sum_{h=0}^{H}\vec{P}_{h}(t)=\sum_{h=1}^{H+1}\vec{P}_{h_{h}}(t) \tag{6}\]  
Equation (6) is the truncated representation of the instantaneous real power balance for \(H\) harmonics components. In this case, the \(h\) = 0 component represents the active power referred to in the classical approach, whereas \(h\
eq 0\) components are excited according to the distortion disturbances. For instance, the 3\(h\) current components, which are excited by three-phase unbalanced loads, are related to \(3h-1\) and \(3h+1\) power harmonic components. Note that, according to (2) and (5), both \(I_{-1}^{-}\) and \(I_{0}^{+}\) do not have physical meaning, and, therefore, without loss of generality, can be assumed to be zero. Although it is not possible to state a one-to-one relation among current and power harmonics components, it is possible to state valid power-related inequalities to bound current components, as presented in the following subsection.
# Electrical Stimulation May Improve the Feeding and Nutritional Status of Children with Dysphagia
## 7 NMES in Children with Dysphagia
\begin{table}
\begin{tabular}{c c c c c} \hline
**Authors/Year** & **Study Type** & **Participants** & **Population** & **Treatment Outcomes** & **Adverse Events** \\ \hline [PERSON] et al., 2022 [85] & Systematic review & 393 & Children with & Improved swallowing & Rare; no serious events \\  & & & including neurological disorders & 0.18 to 1.49 & \\ \hline [PERSON] et al., 2019 [86] & Prospective pilot & 10 & Infants and young children & 100\% improvement in & \\  & & with severe/ dysphagia and & swallowing 5 achieved & Mild skin irritation \\  & & & neurological impairment & oral feeding & \\ \hline \end{tabular}
\end{table} 
Table 2: Studies on NMES therapy in children with dysphagia. The NMES therapy was considered to be safe, but not without potential adverse events. Many authors indicated advantages of NMES in dysphagia therapy. First of all, NMES increases muscle strength and activity. Direct stimulation of facial, pharyngeal, or laryngeal muscles can support their function, even in patients with very limited voluntary movement or low behavioral cooperation [92]. It can be used in patients with severe dysphagia. NMES offers a therapeutic option even when manual massage or exercises are not possible [65; 93]. Next is neuroplasticity. It was shown that repetitive stimulation may induce neuronal reorganization and support the restoration of functions such as swallowing [94]. NMES can be integrated with other methods. It can be effectively combined with functional exercises, enhancing overall therapy effectiveness [92]. This method, however, is not free from limitations. One of these is a lack of conclusive clinical evidence. There are conflicting study results regarding the effectiveness of NMES in dysphagia therapy; many studies have methodological limitations (e.g., small sample sizes, lack of randomization) [90]. There is also a risk of incorrect application. Improper electrode placement may lead to ineffective stimulation or undesirable reactions such as unintended neck muscle contractions or may cause mild skin irritation [95; 96]. Next is cost and limited accessibility. The therapy requires specialized equipment and trained personnel, which may limit its availability in certain facilities [97]. Discomfort or limited cooperation during the NMES session is also indicated. Some patients, especially children, may poorly tolerate the sensation of electric stimulation or show limited cooperation and may exhibit protest behaviors [93]. Advantages and disadvantages are summarized in Table 3.
## 1 Introduction
The design of a reflectarray comprises the following stages: feed design or selection and characterization, geometry design (feed position, focal length to diameter ratio \(f/D\) for maximizing efficiency, separation between array elements, and reflectarray shape), element selection and optimization, and phase distribution calculation [12]. The design of a sectorial beam reflectarray usually requires that the phase distribution is calculated through an optimization process, as there is not an analytical expression for it [13]. In [14], a method for designing a reflectarray antenna for the surveillance channel of a DVB-S-based PR was proposed. That method, employing an optimization for estimating the best positions of several contiguous beams, allows to achieve a sectorial beam for the detection and tracking of ground targets. In this paper, that method is improved with the inclusion of a new variable in the optimization process that controls the influence of the phases driving the central broadside beam over the phases that generate the rest of contiguous beams. The improved method  
Figure 1: PR geometry scheme and operation principle. is employed to design an elliptical reflectarray that could employ the feed, feedarm and aperture holding structure from a commercial parabolic dish antenna, allowing a low cost and fast assembling. The geometrical parameters of the commercial set have been estimated and employed for the reflectarray design, driving to a 22 \(\times\) 16 elements aperture. The feed radiation pattern was characterized in an anechoic chamber and modelled using the Ansys HFSS electromagnetic simulator for being included in the reflectarray antenna design and optimization. The antenna was manufactured and validated through measurements in an anechoic chamber and with radar trials in two real scenarios. Results of anechoic chamber measurements show that the manufactured reflectarray generates a sectorial beam of 8.7\({}^{\circ}\) in azimuth and 5.2\({}^{\circ}\) in elevation with a quite stable measured realized gain greater than 19 dBi in the frequency range [10.5-12] GHz and a maximum of 19.82 dBi at 11.25 GHz. Results of radar trials show that the reflectarray antenna allows to detect and track small ground targets at distances up to 600 m from the PR, providing a great improvement in the PR demonstrator coverage with respect to previous versions that employed commercial parabolic dish antennas.
## 4 Results and Analysis
### Extraction Results of Building Information
#### 4.1.1 Extraction Results of Building Footprint
The effect of the selected model in the verification set is shown in Figure 7.  
Analyzing the results from a qualitative perspective: the accuracy of the results of the U-net model is reasonable, as shown in Figure 7. Most of the buildings labeled in the original dataset are identified as independent elements, but there are some inaccurate pixel identification phenomena, which is due to the low clarity of the original data. Some of the prediction results are relatively clear and complete, but there are more fuzzy adhesions between buildings, as shown in Figure 7, because of the low resolution of remote sensing images, and the buildings are low buildings with small spacing between buildings. These problems have a negligible impact on the earthquake insurance categorization study. According to the above analysis, it can be seen that the model has the ability to extract the  
Figure 5: The example of raw and labeled data. (**a**) Raw data; (**b**) labeled data. Figure 6: Change curve of accuracy rate during training of U-net model. Figure 7: Building footprint areas recognition effect. (**a**) Validation set A; (**b**) Validation set B.  
building footprint, and the extraction results perform well. The final data of the building footprint in the central city of Tangshan City and some examples were obtained through the model identification, as shown in Figure 8. A total of 170,000 buildings were identified, covering an area of 54 million square meters.
## 4 Results  
### Reconstruction Results of Examples  
Figure 4 presents visual results for calcaneus PA signal reconstruction by TR reconstruction and trained AB-U-Net.The cortical bone corresponds to the outer boundaries of the bone region, while the inner bone tissues are all cancellous bone. TR-reconstructed images show that the area of cortical bones is restored. However, there are significantly blurring and missing details at the inner part of the bone. It shows that, although TR tends to compensate for the perturbation caused by inhomogeneous medium to some extent, problems like strong scattering and the variation of sound speed are still unsolved and result in serious artifacts. In contrast, the AB-U-Net method successfully removes artifacts and restores the high-frequency information, such as the micro-structure of the trabecular bone. Compared with TR, the CNN-based network provides significant improvement in PSNR and SSIM, i.e., SSIM of sample 1 increases from 0.62 to 0.88, indicating an accurate modeling of the initial pressure.  
The effect of the attention mechanism has been studied. Two types of CNN frameworks--U-Net and attention U-Net--were added to compare the reconstruction performance, shown in Figure 5. In addition, local details of a selected region were zoomed for a clearer interpretation. In the area with complex bone trabeculae, the initial pressure reconstructed by AB-U-Net (Figure 5d) is the best, as most of the micro-structures are well restored without inaccurate extra information.  
Figure 4: **(a-i)** Visual comparison of the performance in three examples based on TR and AB-U-Net. Each row represents a different bone sample, from top to bottom: sample 1, sample 2 and sample 3. Every column represents different method, from left to right: ground truth, time-reversal reconstructed and AB-U-Net.  
Figure 5: The example of performance comparison based on different convolution neural network (CNN) architectures: **(a)** Ground Truth, **(b)** U-Net, **(c)** Attention U-Net and **(d)** AB-U-Net. A red rectangle area is selected and zoomed to intuitively compare reconstruction effects for the complex bone trabeculae.  
To compare globally, SSIM-maps of results corresponding to three CNN models and TR are utilized, illustrating the SSIM value of each pixel on reconstructed PA images [79], as seen in Figure 6. It is noticeable that yellow areas (low SSIM) of AB-U-Net (Figure 6d) are the smallest, which represents better performance than the others. In addition, Figure 6b,c illustrate that the result of Attention U-Net is also better than that of U-Net, indicating that the added attention module helps to focus bone micro-structure information effectively. For PA imaging of bone tissue, it is suggested to add the attention module into the feature extraction part instead of in the decoder part.
# Study on Temperature Correction of Asphalt Pavement Deflection Based on the Deflection Change Rate
## 5 Deflection Temperature Modelling  
### Temperature Correction Model at the Load Center  
Through the comparative study of the load center deflection under different temperatures, it is found that similarly using the exponential model to correct the load center deflection has a better effect, and the equation is shown below:  
\[d_{20}=10^{\alpha(20-T)}\times d_{T} \tag{6}\]  
where \(d_{20}\) and \(d_{T}\) are deflections at standard and test temperatures, respectively, in mm; \(T\) is the test temperature, in \({}^{\circ}\)C; and \(\alpha\) is the model parameter.  
The fitting results of the load center deflection before and after the correction are shown in Figure 7. The specific temperature correction formula is shown in Equation (7).  
\[\left\{\begin{array}{l}d_{20}=10^{0.004644(20-T)}\times d_{T}\\ \lambda_{d}=\frac{d_{20}}{d_{T}}=10^{0.004644(20-T)}\end{array}\right.,R^{2}=0.874 \tag{7}\]  
where \(\lambda_{d}\) is the temperature correction factor.  
Figure 6: RMSE at different radial distances.
# High Voltage Ride through Strategy of Wind Farm Considering Generator Terminal Voltage Distribution
###### Abstract  
When wind power is transmitted via high-voltage direct current (HVDC), the problem of high-voltage ride-through (HVRT), caused by direct-current (DC) blocking must be seriously taken into account. All the wind turbines in a wind farm are usually equivalent to a single turbine in the existing research on HVRT, which ignores the generator terminal voltage distribution in a wind farm. In view of the fact that the severity of fault voltage felt by each wind turbine in the field is different, an improved HVRT strategy considering voltage distribution is proposed in this article. First, this article analyzes the mechanism of voltage swell failure caused by DC blocking, and the characteristics of the generator terminal voltage distribution in wind farms. Second, the reactive power characteristic equations of the synchronous condenser and the doubly-fed induction generator (DFIG) are derived. Third, based on the extraction of the key node voltage, this article takes the key node voltage as the compensation target, and put forwards a HVRT strategy combining the synchronous condenser and wind turbine. Finally, the simulation is carried out to demonstrate the effectiveness of the proposed strategy in improving the HVRT capability of all wind turbines.  
wind power transmission; DC blocking; doubly fed induction generator; synchronous condenser; high voltage ride through +
Footnote †: journal: Applied Sciences
Common Information Model-Oriented Ontology Database Framework for Improving Topology Processing Capability of Distribution Management Systems Considering Interoperability  
[PERSON]  
expected to exceed 500 GW by 2030 [1]. As the share of distributed energy increases, the related market is also expanding, and substantial investments are being made to enhance grid operation reliability by transitioning to distribution system operators (DSOs). These investments encompass electrification, renewable energy integration, grid modernization, and digitalization. In Europe, investments are projected to exceed approximately 600 billion euros by 2030 [2]. The 10 th Basic Power Supply Plan, as outlined by the Ministry of Industry of the Korean government, forecasts a rise in renewable energy generation to 21.6% by 2030 and to 30.6% by 2036 [3]. Additionally, the Ministry of Environment has unveiled the 2050 Long-Term Low-Carbon Power Generation Strategy, targeting a renewable energy generation share of 65-80% by 2050 [4]. As of 2021, in the Republic of Korea, the cumulative number of registered EVs and EV charging stations has been steadily increasing, reaching approximately 230,000 and 70,000 units, respectively [5]. Furthermore, the necessity of securing MVDC distribution operation technology by 2030 is indicated in the 4 th Energy Technology Development Plan [6]. The increasing complexity and diversity of entities managed within a DMS to cover the medium-voltage grid necessitate an adaptable system capable of accommodating technical and business changes in distribution networks. Developing an advanced DMS with each shift in the power distribution system's business environment is impractical. Furthermore, predicting the development and implementation of power facilities, crucial for ensuring the future efficiency and stability of the distribution system, poses significant challenges. Adapting to evolving business landscapes requires ensuring interoperability among components in the DMS. When a DMS facilitates interoperability among its internal components, it becomes capable of effectively responding to shifts in business dynamics. The flexibility and scalability inherent in DMS components allow for the addition of new features or processes, or modifications to existing ones, with minimal disruption to interactions among system components.
# Vegetarianism Discourse in Russian Social Media: A Case Study
## 6 Experimental Evaluation
### Metrics  
We report the following evaluation metrics across all models to provide a comprehensive assessment of performance.  
Precision quantifies the model's accuracy in its positive predictions, measuring how many predicted positives are actual positives. We compute it as  
\[\text{Precision}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Positives}}\]  
Recall (or sensitivity) gauges the model's ability to identify all relevant positive instances, focusing on minimizing missed positive cases. It is calculated as  
\[\text{Recall}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Negatives}}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Settings** \\ \hline RF & n\_estimators = 200; no maximum depth for each tree; Gini impurity for splitting; square root of the number of features considered for splitting at each node. \\ XGB & learning rate = 0.1; maximum depth = 3 for each tree; logistic loss function for binary classification. \\ LR & L2 regularization penalty; regularization strength (C) = 1.0; lbfgs solver. \\ VC & combined predictions of RF and LR using a majority voting scheme. \\ ruBERT, SBERT & pretrained on masked language modeling (MLM) and next sentence prediction (NSP) objectives; uses byte-level encoding (BPE) tokenization; vocabulary size = 12:10\({}^{4}\) tokens; fine-tuned for sentence classification on relevant data. \\ ruRoBERTa & pretrained on MLM objective; uses byte-level BPE tokenization; vocabulary size = 5:10\({}^{4}\) tokens; fine-tuned for sentence classification on relevant data. \\ DualCL & Dual contrastive learning method; uses RuBERT, SBERT, or ruRoBERTa models for feature representations; trained for 20 epochs; contrastive loss with label-aware data augmentation. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model settings used in experiments.  
When class distributions are unbalanced, the F1 score unifies precision and recall into a single metric that is especially informative. It is described as  
\[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{Recall}}\]  
Accuracy computes the proportion of accurate predictions to the entire dataset, offering a comprehensive evaluation of model performance:  
\[\text{Accuracy}=\frac{\text{True Positives}+\text{True Negatives}}{\text{Total Data Size}}\]  
Since each metric highlights distinct facets of model performance, using them in tandem is crucial for accurate assessment in text categorization. Precision emphasizes the importance of accurate positive predictions, vital in applications where false positives carry a high cost. Recall measures the model's capability to capture all relevant cases, reducing the likelihood of missing critical instances, which is crucial in contexts where overlooking positives is highly undesirable. The F1 score balances recall and precision, offering a robust metric in situations where data classes are imbalanced, thus providing a more comprehensive performance summary.
# Cost-Aware IoT Extension of DISSECT-CF
## 3 Cost Modeling in DISSECT-CF
### Cloud Pricing  
Besides the IoT-side costs we had to investigate how the four providers calculate the cloud side costs. Most providers have a simple method, which is the following: (i) to run an IoT application, one needs at least one virtual machine (VM), container, compute service or application instance, which has a fixed instance price in every month, or (ii) the providers consider the hour per price for every instance the IoT application needs. The pricing scheme of these providers can be found on their websites. We considered the Azure's application service [20], the Bluemix's runtime pricing sheet under the Runtimes section [21], the Amazon EC2 On-Demand prices [22], and the Oracle's compute service [23] together with the Metered Services pricing calculator [24]. The cloud cost is based on either instance prices (Azure and Oracle), hourly prices (Amazon) or the mix of the two (Bluemix) provider uses both type of price calculating. For example, Oracle charges depending on the daily uptime of our application as well as the number of CPU cores used by our VMs.