esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Energy Management and Control Techniques for EV Systems
### Control Techniques for EV Charging Systems  
The widespread adoption of EVs has the potential to raise load demand, boost system losses, and lower grid voltage. Overloading service transformers, reducing their lifespan, and increasing system losses are all possible outcomes of the increased load demand caused by EV loads. The charging of EVs causes new load peaks that may exceed the service transformer's rated capacity, hastening the aging process. The daily expansion and contraction of the transformer can be mitigated if EVs are largely charged during off-peak hours, which is good for the transformer's life [114]. In addition to this, the increased adoption of EVs in our daily lives will give rise to numerous challenges. In order to address the issues, it is imperative to implement effective control techniques throughout the entire process, starting from the grid and extending to the vehicles. In an EV system, various control techniques are employed to manage different aspects of the vehicle's operation [115]. In this section, we will elucidate several prevalent control techniques employed in EV systems. Motor Control: EVs utilize electric motors for propulsion. Motor control techniques include:  
Field-Oriented Control (FOC): FOC is a technique that accurately controls the torque and speed of the motor by decoupling the torque and flux components. It maximizes motor efficiency and performance. Direct Torque Control (DTC): DTC is a control method that directly controls the torque and flux of the motor without needing to decouple them. It provides fast and precise control response. Pulse-Width Modulation (PWM): PWM is used to control the motor drive by adjusting the duty cycle of the voltage pulses applied to the motor. It regulates the motor's speed and torque output.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 4 Production Performance of Single Well Pair
### Sensitivity Analysis of Inter-Well Connectivity
#### 4.3.2 Width of Permeable Channels  
Figure 10 shows the variations in the inter-well connectivity coefficient with time under four different channel widths (7.1 m, 14.1 m, 21.1 m, 28.3 m). The connectivity coefficient experiences a corresponding increasing trend with the increased channel width. The reason is that a wider channel means a larger effective conductive area, which enhances the fluid flow capacity and thus improves fluid production, increasing inter-well connectivity. In particular, channel width is more significant in the medium water cut stage, during which the oil and water flow pathways may be altered frequently and become complex due to the applied pressure impulses.  
Nevertheless, with the passage of time, the connectivity coefficients of each width channel tend to be stable. This convergence may mean that in the later production stages, as the water phase dominates, the difference in the influence of different widths of high permeable channels on inter-well connectivity is reduced. Therefore, the width of the high permeable channel may affect the initial flow in the reservoir, and the effect decreases gradually with the increase in water saturation.  
The time-lag coefficients for all channels with different widths show a steady downward trend subjected to the pressure impulses, implying an improvement in fluid transfer efficiency, as shown in Figure 11. With the increase in the width of permeable channels, the time-lag coefficient decreases obviously. It reaches the minimum for a wider permeability channel, indicating that the higher the width of the permeable channel, the faster the fluid dynamic response. Previous studies also confirmed that a wider permeability band could more effectively promote the rapid flow of oil and water and shape the two phases' contact surface, thus accelerating the speed of fluid dynamic response [43; 44].  
From the long-term run, the time-lag coefficients at different widths of permeable channels tend to be a relatively close-set, which may mean that with the continuous development of the reservoir, the overall fluid transfer efficiency for different permeable channels tends to be consistent.  
Figure 10: Inter-well connectivity coefficients for different high permeable channel widths.
# The Behavior of a Thread-Bar Grounded Anchor in Soils from Local Strain Monitoring
## 4 Discussion
### Load Distribution during Tests  
The strain gauge measurements gave the strain distribution along the thread-bar during each loading step of the loading test. By knowing the gauge factor of the devices, the area of the bar and the Young modulus of the steel, it is possible to turn the strains into the tensile load along the anchor. In Figure 6, the load distribution along the anchor is presented for the different loads applied at the head, which was directly measured by a load cell. Note that the load increase apparent at 9 m from the head during the first loading cycle is due to experimental error, as the load along the bar cannot be greater than the applied load. However, such behavior is admissible after unloading and reloading, due to the redistribution of mobilized shear stress along the ground-anchor interface.  
Figure 5: Strain readings during loading test.  
By observing the continuous curves of the first loading cycle in Figure 6, it is fairly clear that at the middle of the free length (i.e., 9 m from the anchor head) the load is close to the applied load, confirming the absence of any bond between the bar and the soil along the free length. Moving from the first to the second device, at about 20 m from the anchor head, the load reduces, thus indicating that a fraction of the load has been transferred to the ground. Moving further along the anchor, the progression of the load transfer to the ground is clearly shown by the load-distribution curves at any load step.  
The slope of the load-distribution curves represents the rate at which the load is transferred from the anchor to the ground. Slope trends suggest an almost homogeneous collaboration of the entire foundation at the low loads, while the load transfer becomes more effective in the deepest part of the foundation as the applied load increases.  
It is interesting to observe the differences in the load distributions for unloading and reloading steps. By focusing on the curve representing the unloading step at 480 kN, it appears that for a long segment of the foundation, there is not any load transfer to the ground so that the major part of the reaction takes place between 30 to 36 from the head. A more uniform load transfer occurs along the anchor foundation only when higher reloading values are reached (see curves at 705 and 920 kN), so that even the initial foundation segment is involved in the load transfer process.
# Polynomial Cancellation Coded DFT-s-OFDM for Low-PAPR Uplink Signaling
## 4 Simulation and Discussion
### Discussion  
The simulation results show that the BER enhancement is evident even for low-order modulations (i.e., \(\pi/2\)-BPSK and QPSK) if both PA nonlinearity and inter-user interference are considered. Furthermore, the proposed method can effectively improve PSD performance and the immunity of the frequency-selective fading channel for all modulations in DFT-s-OFDM systems that benefit from the transformed-domain data localization of PCC symbols. The extent of the improvement for PAPR, PSD and BER performances depends on the value of \(d\), which would decrease data rate \(2^{d}\) times. Indeed, this might be the main drawback of the proposed method. However, aiming to improve the flexibility on the trade-off between spectral efficiency and the other performances for DFT-s-OFDM, the proposed architecture creates the opportunity for further improvement in terms of PAPR, PSD and BER performances when the channel condition allows bandwidth-greedy transmission. Otherwise, one can reduce to the conventional DFT-s-OFDM for the transmission by using \(d=0\).
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Neural Network Model Performance
#### 5.3.8 Ablation Study  
To understand the contribution of different feature groups, we conducted an ablation study by training the model with different subsets of features: (a) QE metrics only; (b) Biomechanical features only; (c) All features (full model).  
Table 7 shows the results of the ablation study.  
The ablation study demonstrates that while both QE metrics and biomechanical features contribute to the model's performance, the combination of both feature sets yields the best results, highlighting the importance of integrating visual attention and motor control data in predicting shot accuracy. Below are salient points on the results in Table 5.  
1. QE metrics only: This model, using only Quiet Eye metrics, achieved good performance with an accuracy of 79.3% and an F1 score of 0.792. This suggests that QE metrics alone are strong predictors of shot accuracy.
2. Biomechanical features only: When we stripped the model down to just biomechanical data, it clocked in at 77.8% accuracy with an F1 score of 0.777. While not quite matching the QE-only model, these numbers show that body mechanics play a crucial role in predicting where the shuttlecock will land.
3. All features (full model): Combining QE metrics and biomechanical data bumped our accuracy up to 85.7% with an F1 score of 0.856, which gives us a significant edge over using either set of features alone.  
Our findings highlight the complementary nature of QE metrics and biomechanical features in predicting shot accuracy. While each set of data provides independent insights, their integration yields a markedly more effective predictive model. By combining visual processing and physical execution data, we built a more comprehensive and accurate method for anticipating badminton shot outcomes.  
The ablation study reinforces the importance of considering both visual strategies (represented by QE metrics) and physical execution (represented by biomechanical features) in understanding and predicting athletic performance in badminton.
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## 4 Numerical Experiment
### Synthetic Data
In contrast, this difference is larger in the \(\ell_{1}\) regularization, indicating that GMC sparse regularization (14) outperforms the \(\ell_{1}\) and \(\ell_{1/2}\) regularizations in terms of prediction performance when choosing an appropriate \(\lambda\) value. The conclusion of our study highlights the exceptional predictive performance of our model (2) in estimating sparse vectors, surpassing the \(\ell_{1}\) regularization approach employed by the Lasso model. Additionally, although the Lasso model with \(\ell_{1/2}\) regularization also performs well in prediction tasks, its non-convexity poses challenges for the algorithms solving the objective function. In contrast, our model not only has excellent predictive ability but also ensures the overall convexity of the objective function, thereby providing convenient conditions for the design and implementation of optimization algorithms. In summary, the model proposed in this study has significant advantages in both predictive performance and optimization characteristics, effectively avoiding the computational difficulties brought by non-convex regularization methods. Figure 1: Regularization paths. (**a**) F1 score, (**b**) RMSE. The red solid line, black solid line, and blue solid line represent the Lasso model (14) with GMC, \(\ell_{1/2}\), and \(\ell_{1}\) regularization, respectively. The red dotted line, black dotted line, and blue dotted line correspond to the values of \(\lambda/\lambda_{max}\) when the maximum F1 score and the minimum RMSE are achieved in the support recovery process for the Lasso model with GMC, \(\ell_{1/2}\), and \(\ell_{1}\) regularization, respectively.
# Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
## 4 Experiment  
### Data Set and Data Preprocessing  
The Drishti-GS1 dataset [38] contains a total of 101 fundus images: 31 normal and 70 diseased. The training set contains 50 images and the test set contains 51 images. All image OD and OC regions were marked by four ophthalmologists with different clinical experience. In this paper, the average area marked by four experts is used as the standard optic disc and cup area for training, and the pictures have different sizes, as shown in Figure 4a,b. This paper draws on the pre-processing method in [39] to pre-process the original image through polar transformation, which can effectively improve the segmentation efficiency. Due to the small sample size of the Drishti-GS1 data set, in order to prevent the model from overfitting and improve the accuracy and robustness of the model, this paper augments the data of the training set, including the use of a method based on a video disc center point image detection [40] method to extract 10 different sizes of pictures by performing a random horizontal flip, random vertical rotation, and random angle rotation. However, when training the network, the size of the input image is expanded or scaled to the standard \(512\times 512\), the batch size is 4, and the iteration period is 400. Only \(700\times 700\) pictures are extracted on the test set, then scaled to \(512\times 512\) and input into the network, and then the generated predicted image is filled to the original image size.  
Figure 4: Original images and real labels in Drishti-GS1 and the DRIVE dataset. (**a**) is the image number 11 in the Drishti-GS1 dataset; (**b**) is the ground truth corresponding to (**a**); (**c**) is the image number 22 in the DRIVE dataset; (**d**) is the ground truth corresponding to (**c**).  
The DRIVE dataset consists of 40 retinal fundus blood vessel images, corresponding real labeled images, and corresponding masks images. The images originate from a diabetic retinopathy screening program from the Netherlands, and each image has a size of \(565\times 584\). As shown in Figure 4c,d, for the DRIVE data set, during the training process, \(10,480\) image patches of size \(128\times 128\) are randomly extracted for the training set. At the same time, \(10,480\) real label patches with a size of \(128\times 128\) are extracted from the corresponding real labels at the same location to calculate the loss and train the network. The input image batch size is 16 and the iteration period is 200. In the test phase, this article extracts image patches for each test picture of each dataset in a sliding window order. The size of the sliding window is \(128\times 128\), the sliding step is 5 pixels, and the part of the sliding window beyond the picture is filled with 0. Similarly, the batch size of the image input to the network is 16.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 3 The Second-Generation Italian Smart Meter
### Validated Metering Data on \"Chain 1\"  
The energy meter acquires consumption data and, in case of customers with Distributed Generation (DG), production data as well. The DSO collects these measurements and makes them available to the retailer through the so-called \"data management hub\". This dataflow is called \"chain 1\" and has the main goal of providing suppliers with validated consumption data for billing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metering Data** & **SM-2G** & **SM-1G** \\ \hline Active energy withdrawn & 15 min & 3 values per month \\ Active energy injected & 15 min & 3 value per month \\ Reactive energy withdrawn & 15 min & 3 values per month \\ Reactive energy injected & 15 min & 3 values per month \\ Active power withdrawn & 15 min (peak) and instantaneous value (1 s) & 15 min (peak) \\ Active power injected & 15 min (avg) & No \\ Min/max voltage & 1 per week & Only occasionally \\ Voltage in limits & Yes, compliant with EN50160 &
\begin{tabular}{c} Only occasionally and not \\ compliant with EN50160 \\ Implemented but not used \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Second generation Smart Meter (SM-2G) data comparison with respect to the first generation (SM-1G).  
Figure 1: Electricity value chain for second generation Smart Meters (SM-2G).  
The validation process ensures that collected data are sufficient and consistent for the billing phase using advanced data reconstruction algorithms.  
One of the main differences with respect to the SM-1G is the mandatory provision of a backup technology for chain 1 with the goal of reaching those meters that cannot be properly read by the concentrators due to PLC interferences (in Italy, around 2% of the total [28]). The most common backup technology is expected to be the wM-Bus operating at 169 MHz radiofrequency, which is suitable for long-range communications for deep-indoor cases (half of SMs are located in basements) and for the power consumption. It is also worth noting that the Italian standard UNI/TS 11291 for remote gas metering suggests the same technology. It has yet to be verified whether and how the two systems could share the telecommunication infrastructure, thereby avoiding costly network duplications, taking into account that the 169 MHz frequency is non-licensed and that almost everywhere gas and electricity distribution are operated by different companies.  
Another important difference is the presence of a data management hub [29], which acts as a central switch hub in a star network and is in charge of exchanging metering data between DSOs and retailers, avoiding direct exchange of information.
## 2 Materials and Methods
### Mri  
We applied a 3-T MRI system (MAGNETOM Skyra, Siemens Healthineers, Erlangen, Germany), which was one of the medical imaging instruments in the Fusion Medical Imaging Suite (FMIS) of the K-MEDI hub, to acquire noninvasive and transient macroscale brain images after tACS. Specifically, rats were inserted into a 15-channel knee coil (Siemens Healthineers), and T1- and T2-weighted images of the rat brains were obtained immediately and 48 h after tACS. The head of the rat was placed at a specific position in line with the control point of the knee coil to acquire brain image slices in the same horizontal and vertical axes. The detailed specifications for T1-weighted imaging are as follows: TE (time of echo) = 7.36 ms, TR (time of repetition) = 343.00 ms, and imaging resolution = 0.5 \(\times\) 0.5 mm. T2-weighted imaging has parameters as follows: TE = 99.00 ms, TR = 710.00 ms, and imaging resolution is the same as the resolution of T1-weighted images. Post-processing and analysis of DICOM-formed images were performed using a Java-based program (Image)TM to compare the T1- and T2-weighted intensities of regions where tACS was applied and on the other side. Specifically, the mean and standard deviation of the intensities were calculated for the 11 \(\times\) 11-pixel region subjected to transcranial stimulation and the contralateral region in each magnetic resonance brain image.
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 3 Experiments
### Experiments Conducted on Manned Airborne SAR Images  
#### 3.2.1 Experimental Data  
The X-band manned airborne SAR sensor was developed by the Aerospace Information Research Institute of the Chinese Academy of Sciences. This data set was acquired in 2012 and constitutes early Chinese airborne SAR experimental data. Rigorous indoor and outdoor geometric calibrations before flying and imaging were not undertaken for  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**GCP/CP Number** & **Classical Model** & **One-Parameter Model** & **Three-Parameter Model** & **Four-Parameter Model** & **Six-Parameter Model** \\ \hline
0/21 & 1.88 & 1.88 & 1.88 & 1.88 & 1.88 \\ \hline
1/20 & 1.25 & 1.21 & / & / & / \\ \hline
3/19 & 0.74 & 0.82 & 0.75 & / & / \\ \hline
6/15 & 0.40 & 0.67 & 0.41 & 0.45 & 0.47 \\ \hline
12/9 & 0.33 & 0.59 & 0.32 & 0.30 & 0.29 \\ \hline
20/21 (LOOCV) & 0.33 & 0.58 & 0.32 & 0.31 & 0.31 \\ \hline \end{tabular}
\end{table}
Table 1: Geolocation accuracy in a TerraSAR-X image for the five models with different GCPs (unit: meters).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**GCP/CP Number** & **Classical Model** & **One-Parameter Model** & **Three-Parameter Model** & **Four-Parameter Model** & **Six-Parameter Model** \\ \hline
0/12 & 42.42 & 42.42 & 42.42 & 42.42 & 42.42 \\ \hline
1/11 & 3.11 & 3.13 & / & / & / \\ \hline
3/9 & 2.78 & 2.83 & 2.80 & / & / \\ \hline
6/6 & 1.88 & 2.01 & 1.90 & 1.95 & 2.03 \\ \hline
9/3 & 1.59 & 1.81 & 1.55 & 1.56 & 1.59 \\ \hline
11/12 (LOOCV) & 1.40 & 1.64 & 1.41 & 1.38 & 1.37 \\ \hline \end{tabular}
\end{table}
Table 2: Geolocation accuracy with respect to the GF-3 image for the five refinement models with different GCPs (unit: meters).  
the sensor. The system was equipped with a high-precision POS (POS510) capable of obtaining highly accurate sensor position and attitude parameters. The test area is located in Gaocheng Town, Dengfeng, Henan Province, China, with an elevation ranging from 230 to 350 m. It is an area characterized by hilly terrain. Surveying flights were conducted in both east-west and west-east directions. Two scenes of images with a resolution of 0.15 m--namely, the airSAR-X-1 image from the east-west flight and the airSAR-X-2 image from the west-east flight--were selected for geometric positioning experiments. There are 20 and 17 reference points in the two images, respectively. Their distribution and parts of the two images are shown in Figure 4.
## 3 Experiment Setup  
### Public Environment Sound Datasets  
In our manuscript, we have strategically chosen the widely accessible ESC-50 [33] and UrbanSound8K [34] datasets as the standpoints for training process and evaluating our model. These two datasets are publicly available and can be used for sound classification tasks, providing different sound samples suitable for training and evaluating various sound classification models, along with their corresponding ground truth labels.  
The ESC-50 dataset contains 2000 five-second sound clips from different environments. These samples are divided into five major categories: animal sounds, natural soundscapes and water sounds, non-speech human sounds, indoor/domestic sounds, and outdoor/urban noises. These 5 major categories are further subdivided into 50 subcategories, including typical urban environment sounds such as car horns, city noise, sirens, and helicopter sounds. The ESC-50 dataset is formally divided into five folders, each containing an equal number of sound samples, and these samples are balanced by type. The label set of ESC-50 is defined as \(label=\{0,\ 1,\ 2,\ \ldots,49\}\), where each element corresponds to a distinct category.  
The UrbanSound8K dataset contains 8732 labeled urban sound samples from 10 categories, with each sound sample no longer than 4 s. The 10 categories include air conditioner noise, car horn, children playing, dog barking, drilling, engine idling, gunshot, jackhammer, siren, and street music. While the UrbanSound8K dataset is vividly divided into 10 dedicated folders, it is noteworthy that the distribution of sound samples within each folder is not uniformly balanced in terms of both category representation and quantity. So that it brings us a more realistic and challenging condition for the training process and evaluation classification models. The label set of UrbanSound8K is defined as \(label=\{0,\ 1,\ 2,\ \ldots,9\}\), with each element corresponding to a distinct category.  
In the experiments, all audio datasets are resampled at a 44.1 kHz sampling rate. Since the audio samples in the UrbanSound8K dataset have uneven lengths due to the sound clipping process and inconsistencies in the channels used for sound signal collection, the audio length is standardized to 4 s during data preprocessing. For stereo channels, the Patch-Mix method is used to obtain a unique representation.