esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Depth Estimation of Light Fields Considering Blur Features and Prior Knowledge
#### 3.2.1 Depth Transfer Procedure Considering Blur Features  
The current light field depth estimation algorithms work on each microimage separately, missing out on depth info from overlapping clear areas. This leads to wasted computations and lower accuracy. As shown in Figure 7, the positions of the microlenses in a light field image are fixed and no scale variation is present.  
By performing dense matching on the microimages that exhibit less blur to acquire disparity information, we can map this information as an initial value for more-blurred microimages. This reduces the amount of computation and improves accuracy. Therefore, we propose a method that first uses depth information from adjacent microscopic images in the neighborhood to aid this transfer.  
In the stereo matching field, the traditional initialization method involves randomly assigning disparity and normal vectors to each pixel. However, owing to the limited numbers of pixels contained in microimages, increasing the number of iterations can increase the resulting accuracy but at the expense of significant computational overhead. Therefore, we utilized the depth information derived from the more clearly matched microimages that are located within the neighborhood to initialize the unmatched blurred images, thereby increasing the ratio of correctly estimated disparity planes. This improves the matching accuracy and efficiency of the algorithm while keeping the number of iterations at a manageable level.  
Specifically, for a microimage \(\mathrm{I_{a}}\) with acquired disparity information, we select pixel points with reliable depth information (costs below a certain threshold) and calculate their corresponding points in the neighborhood image \(\mathrm{I_{b}}\), transferring the disparity plane. For the pixel points in \(\mathrm{I_{b}}\) that do not meet the imposed criteria, random initialization is still employed.  
Figure 7: Overlapping regions between microimages. (**a**,**b**) Adjacent microimages with different levels of blur; (**c**) the overlapping region.  
During the random optimization phase, by recognizing that the pixel points with transmitted depth information are close to the optimal solution, we reduce the randomness range for the disparity and normal vectors. This adjustment helps decrease the incurred computational costs and improve the resulting matching accuracy. This method effectively leverages the depth correlations between microimages with different levels of blur, achieving more efficient depth estimation effects.
# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
## 4 Discussion
### Strengths and Weaknesses of the Current Study
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Researcher** & \multicolumn{2}{c}{**Wind Farm Model**} & **Wind** & **Spacing** & **Induction Control Type** & **Power Increase** \\ \hline [PERSON]. [7] & - & Real wind farm & Turbulent & 3.5 RD & - Manual Pitch & +5.00\% @2\({}^{\circ}\) \\  & - & WT model: Full size (2.5 MW) & & & \\ \hline Corten C.P. [8] & - & Wind Tunnel & & & \\ \hline Corten C.P. [8] & - & WT model: Scaled model & & & \\ \hline [PERSON]. [8] & - & [PERSON] wake model & & & \\ \hline \multirow{2}{*}{[PERSON]. [7]} & - & WT model: BEM (no WT. dynamics) & & & \\ \hline \multirow{2}{*}{[PERSON]. [10]} & - & [PERSON] wake model & & & \\ \cline{2-6}  & - & WT model: BEM (no WT. dynamics) & & & \\ \hline \multirow{2}{*}{[PERSON]. [16]} & - & [PERSON] wake model & & & \\ \cline{2-6}  & - & WT model: BEM (include WIT. dynamics \&control) & & \\ \hline \multirow{2}{*}{[PERSON]. [11]} & - & UPMWAKE & & & \\ \cline{2-6}  & - & WT model: BEM (no WT. dynamics) & & & \\ \hline \multirow{2}{*}{[PERSON]. [12]} & - & Middalphan and Rantrer & & & \\ \cline{2-6}  & - & WT model: BEM (with WT. dynamics \&control) & & \\ \hline \multirow{2}{*}{[PERSON]. [17]} & - & Large Eddy Simulation & & & \\ \cline{2-6}  & - & WT model: actuator disk & & & \\ \hline \multirow{2}{*}{[PERSON]. [18]} & - & Large Eddy Simulation & & & \\ \cline{2-6}  & - & WT model: Actuator Line coupled with FAST & & & \\ \hline \multirow{2}{*}{[PERSON]. [19]} & - & Large Eddy Simulation & & & \\ \cline{2-6}  & - & WT model: Actuator disk & & & \\ \hline \multirow{2}{*}{Current Study} & - & Ansile wake model & & & & \\ \cline{2-6}  & - & WT model: BEM (with WT. dynamics \&control including DPPT) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Trend of Active Wake Control Validation (RD: rotor diameter, TSR: tip speed ratio, Pav: available power, WT: wind turbine, DPPT: demanded power point tracking).
## 5 Current Status and Limitations of Data Interoperability with Landsat and Sentinel
### Landsat-8 and Sentinel-2 Burned Area Mapping
Figure 13 shows a time series of Landsat 8 OLI (filled circles) and Sentinel-2A MSI (open circles) surface NBAR data processed as described in the above two paragraphs for a single 30 m location. The near-infrared (865 nm) and short-wave infrared (1610 nm) bands of each sensor are illustrated as these bands are sensitive to fire effects [34; 40; 50]. The pixel is over a Zambian woody savanna location and burned after day 208 and on or before day 214, with evident drops in OLI and MSI surface NBAR between these dates. In Figure 13 it is apparent that OLI and MSI observations were acquired occasionally on the same day or only one day apart but that their surface NBAR values are different, despite the above processing. On the same day different sensor acquisitions were within 15 min of each other and so it is unlikely that the observed differences were due to surface or atmospheric changes. Rather they are due to sensor spectral band pass differences and residual calibration, atmospheric correction and NBAR derivation errors. A statistical comparison of a large amount of OLI and MSI surface NBAR data, similar to the approach applied to derive statistical spectral transformations between Landsat 7 and Landsat 8 data [51], has been undertaken. The MSI surface NBAR MSI data were adjusted to OLI using the spectral linear transformations [52] and evidently (straight lines) make the plotted OLI and MSI surface NBAR time series more coherent with respect to each other.
## 2 Materials and Methods
### Testing and Characterization
#### 2.3.4 Damping Capacity and Young's Modulus  
Samples of 7.5 mm diameter and 60 mm length were subjected to impulse excitation to measure their damping characteristics (Damping Capacity, Loss Rate, Frequency and Young's modulus). The vibrational damping capacity of the materials was determined by using the response frequency damping analyzer (RFDA) from IMCE Belgium. The vibration signal was recorded in terms of amplitude vs. time. The attenuation coefficient was calculated with the help of GetData graph digitizer software, which allowed for a qualitative analysis of the damping capacity of the material.
## 1 Introduction
First, the two-path differential SAR interferometry (D-InSAR) method was applied to three L-band SAR images acquired by ALOS-PALSAR over the study area in the period from 20 January 2007 to 25 January 2009; Second, we further applied the Persistent Scatterer (PS) and Small baseline subset (SBAS)-InSAR technique to 21 C-band images acquired by the Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) for the period 30 September 2003 to 15 June 2010. Moreover, comparisons of different InSAR methods and the comparison of satellite-derived deformation rates with the results of a previous continuous Global Positioning System (GPS) station was conducted. This research is intended to assist with identifying areas experiencing ground deformations, in turn helping local authorities concentrate on the detailed monitoring, investigation and risk mitigation over large areas at relatively low costs.
# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 3 Results and Discussion
### Studies' Characteristics
#### 3.3.4 Spatial and Tactical Analysis
& To develop an automated method for identifying and classifying COD movements in professional tennis using tracking data \\ [PERSON] and [PERSON] (2021) [46] & 17 male tennis players & Tosine hand movement area, ball upwards movement, lower body, area surrounding server, racket, hips, ball release, upper body, ball contact area, and back leg. & To analyze whether visual search strategies can be attributed to the individual server and the returning player during the tennis serve return or return performance \\ [PERSON] et al. (2023) [57] & 6 national first-class and second-class experienced tennis players & 15 Kinematic variables & To study the impacts of batting strength and angle of tennis players on batting results based on DL image processing technology \\  & 14,146 serves were analyzed from 97 full (2023) [49] & 14,146 serves were analyzed from 97 full (2023) [49] & Speed, dL, serve angle, loss of speed, net clearance, vertical projection angle, position, and direction \\  & & Davis Cup & \\  & &  
An analysis of the implemented ML algorithms revealed a diverse methodological landscape. Most of the articles used supervised learning algorithms. [PERSON] et al. [59] utilized decision trees to construct a diagnostic model for player performance evaluation, exploiting a common classification technique. Additionally, unsupervised learning was also employed. [PERSON] and [PERSON] [51] utilized a k-means clustering to pinpoint optimal landing locations for aces. This clustering algorithm groups data points into a predefined number of clusters based on similarities. Additionally, [PERSON] et al. [57] implemented a convolutional neural network to analyze batting strength and angles based on video footage. CNNs excel at image recognition tasks, making them well suited for this application. Finally, [PERSON] and [PERSON] [52] leveraged a Bayesian network to predict the probability of different stances based on court situations. However, while the studies achieved high accuracy levels (e.g., [PERSON] and [PERSON]'s model, with 87% accuracy for serve outcome prediction), further exploration of advanced algorithms, such as deep learning architectures, could potentially yield even higher levels of precision. Furthermore, generalizability across different surfaces, playing styles, and player levels remains a crucial consideration. Investigating how models trained on one dataset perform when applied to a different context is essential for ensuring their robustness and practical applicability.
## 3 WiFi 7 for Industrial Communications
By providing a wider bandwidth, WiFi 7 has the potential to support a large number of industrial devices. Moreover, operating in a less congested frequency spectrum also reduces interference, which can be a challenge in industrial settings where many systems and devices operate in close proximity. * **Multi-AP Operation:** WiFi 7's multi-AP operation allows multiple access points to work together as a single, continuous network. This feature can facilitate seamless handovers between WiFi networks, simplify overall network configuration (e.g., selecting operating channels), and enhance the capacity of the WiFi network [48]. By synchronizing multiple access points, coverage can be extended across the entire factory floor, guaranteeing that all machines and mobile devices maintain a reliable and strong connection. Furthermore, cooperation among neighboring APs through the exchange of crucial scheduling information and channel state information (CSI) is a potential strategy to enhance the utilization of scarce radio resources [43], particularly in an industrial environment with a high density of sensors and actuators, where co-channel interference can reach intolerable levels. * **WiFi Sensing:** Wireless radio sensing is a cutting-edge feature of WiFi that allows WiFi networks to sense and detect the presence of people, objects, and other devices, even when they are not actively transmitting data. In smart manufacturing, WiFi sensing has a range of important applications, including enabling location-based services, asset tracking, and improved safety and security. Location-based services allow for real-time tracking of machines, devices, and personnel across the factory floor. Asset tracking is another significant application of WiFi sensing, ensuring that costly machinery and equipment are not misplaced or stolen. By detecting the presence of these assets, manufacturers can monitor their usage, maintenance schedules, and movements, ensuring they are always in good working condition and ready for use. Furthermore, WiFi sensing can be key to improving the safety and security of the smart factory [49]. By detecting people and machines, WiFi sensing helps ensure a safe and secure work environment for all personnel by preventing accidents. For instance,WiFi sensing can alert workers to dangers in areas where machinery may pose a risk, allowing them to take appropriate measures. The features of WiFi 7 and its usage in smart manufacturing are summarized in Figure 2. These features enable advanced smart manufacturing applications, including collaborative robots, augmented reality, and predictive maintenance. However, WiFi 7 is not without its limitations. Due to its higher modulation and higher carrier frequency, the range of WiFi 7 signals is likely to be reduced compared to previous generations operating at lower frequencies. This could be an issue in certain industrial environments where devices, machines, and systems are spread out over a large area. In addition, since the standard has not yet been ratified, it is unclear when WiFi 7 access points and client devices will be commercially available.
# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 3 Experiments and Results
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image** & **Algorithms** & **Real** & **Correct** & **Missing** & **False alarm** & **False** & **Detection** & **Quality** \\  & **Number** & **Number** & **Number** & **Number** & **Number** & **Rate/(\%)** & **Factor/(\%)** \\ \hline \multirow{4}{*}{**1**} & Log-normal distribution of CFAR & & 5 & 0 & 112 & 112 & 100 & 4.27 \\  & CFAR + EF [28] & & 5 & 0 & 24 & 24 & 100 & 17.24 \\  & CFAR + Contrario [29] & 5 & 4 & 1 & 63 & 64 & 80 & 5.8 \\  & YOLO v5 & & 3 & 2 & 1 & 3 & 60 & 37.50 \\  & Proposed algorithm & & 5 & 0 & 0 & 0 & 100 & 100 \\  & Log-normal distribution of CFAR & & 3 & 0 & 71 & 71 & 100 & 4.05 \\  & CFAR + EF [28] & & 3 & 0 & 0 & 0 & 100 & 100 \\
**2** & CFAR + Contrario [29] & 3 & 3 & 0 & 40 & 40 & 100 & 6.98 \\  & YOLO v5 & & 2 & 1 & 1 & 2 & 67 & 40 \\  & Proposed algorithm & & 3 & 0 & 0 & 0 & 100 & 100 \\  & Log-normal distribution of CFAR & & 5 & 0 & 90 & 90 & 100 & 5.26 \\  & CFAR + EF [28] & & 5 & 0 & 36 & 36 & 100 & 12.2 \\
**3** & CFAR + Contrario [29] & 5 & 4 & 1 & 64 & 65 & 80 & 5.71 \\  & YOLO v5 & & 3 & 2 & 1 & 3 & 60 & 37.50 \\  & Proposed algorithm & & 5 & 0 & 1 & 1 & 100 & 83.33 \\  & Log-normal distribution of CFAR & & 9 & 0 & 40 & 40 & 100 & 18.37 \\  & CFAR + EF [28] & & 9 & 0 & 5 & 5 & 100 & 64.29 \\
**4** & CFAR + Contrario [29] & 9 & 9 & 0 & 27 & 27 & 100 & 25 \\  & YOLO v5 & & 2 & 5 & 1 & 6 & 22 & 13.33 \\  & Proposed algorithm & & 9 & 0 & 0 & 0 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the detection results of the four algorithms.
## 3 Results  
### Flood Hazard Map  
The LiDAR DEM used in the flood hazard map has an accuracy of 1 \(\times\) 1 m pixel size, a vertical of \(\pm\)15 cm, and was based on 5, 50, and 100 years return periods. A higher return period denotes a higher intensity of rainfall but has a low likelihood of happening annually. In this study, a 5-year return period was selected because this kind of event has a 20% probability of happening annually [62]. Shown in Figure 4 are areas colored yellow, orange, and red that illustrate low, moderate, and high flooding hazards, respectively. Areas within low hazard areas experienced a flood height of up to 0.5 m, while those in the medium hazard category were flooded between 0.5 and 1.5 m. High hazard areas experienced flood heights greater than 1.5 m. These flood hazard classifications were based on the capability of people to mobilize for evacuation in scenarios with those flood heights.  
Figure 3: Research Diagram.
## 2 Materials and Methods
### Dosimetry for Therapeutic X-ray Beams
#### 2.2.2 Output Factor  
The OF was determined by comparing the dose per MU received by the central pixel at a specific field size to the dose per MU received at a 10 \(\times\) 10 cm field size. The measurement was conducted with a Varian CLINAC 21 iX at the SCCC by positioning the edgeless array at a depth of 10 cm within a 30 \(\times\) 30 cm\({}^{2}\) solid water phantom and with 10 cm of solid water positioned downstream for backscattering, with an SSD of 90 cm (isocenter).  
The alignment of the central pixel of the field was obtained using a dual-axis stepper motor stage. This stage utilizes Standa 8 MT200 (Slovenia) series stepper motors (Standa Ltd., Vilnius, Lithuania), offering sub-micron 2D positioning accuracy within the field. The beam was aligned with the central diode by conducting a scan using a field size of 5 \(\times\) 5 mm\({}^{2}\). The detector was shifted in the x and y directions using a stepper motor, with steps of 100 \(\upmu\)m, until the sensor's response was maximized. The central diode was then centered in the position of the maximum measurement.  
The integral charge measured by the central pixel was recorded for field sizes ranging from 0.5 \(\times\) 0.5 cm\({}^{2}\) up to 20 \(\times\) 20 cm\({}^{2}\). The measurements were determined based on three repetitions to assess the detector response's uncertainty.  
The performance of the detector was benchmarked against the response of the CC13 IC (IBA Dosimetry, Schwarzenbruck, Germany) with sensitive volume of 0.13 cm\({}^{3}\), for field sizes larger than 3 \(\times\) 3 cm\({}^{2}\), and against the CC04 IC (IBA Dosimetry, Schwarzenbruck, Germany) with sensitivity volume of 0.04 cm\({}^{3}\), for smaller fields to prevent the volume average effects of the CC13 IC. The edgeless array response was also compared to Gafchromic film (EBT3--ASHLAND, Wayne, NJ, USA), extracted from [31]. The measurement in this study was conducted using the same LINAC machine and with the same setup. The 20 \(\times\) 20 cm\({}^{2}\) EBT3 films were scanned with an A3 flatbed scanner (Epson Expression 10000 XL (Epson, Suwa, Nagano, Japan)) in 48-bit RGB color mode. However, only the red channel was utilized for converting optical density to dosage.
### Patient-Ventilator Asynchrony  
Patient-ventilator asynchrony (PVA) occurs when inspiratory and expiratory times of a ventilator do not match the neural control of patient's respiratory effort or when there is a disparity between flow, pressure or volume demands of the patient and the assistance delivered by the mechanical ventilator [13]. The neural inspiratory time can be estimated to describe the demands of a patient's respiratory system by measuring the electrical activity of the respiratory muscles. For this purpose, diaphragm electromyographic recordings and airflow signals of healthy subjects are evaluated according to respiratory protocols on respiratory rate increments and fractional inspiratory time decrements [14]. Modern synchronization algorithms use new approaches such as deep learning [13; 15; 16] to estimate patients' respiratory mechanics and neural activity based on the measured pressure and flow waveforms, esophageal pressure or transdiaphragmatic pressure readings, or diaphragm electromyography [17; 18]. However, errors of bias in the estimations can further contribute to an increased incidence of PVA [17].
# AC Microgrids Protection: A Digital Coordinated Adaptive Scheme
## 2 Proposed Protection Schemes
### DGU Protection
#### 2.4.2 DGU Local Trip Command  
As can be observed from Figure 5, the employed structure for grid-forming power converters protection scheme is utilized as explained in the following. A UVR for detecting voltage sags and solid faults is employed. Then, an OCR is utilized for high impedance and overload conditions. Due to the heating effect of the fault current during high impedance faults or overload conditions, a long delay is considered here, such that for each phase, an OCR and a long enough delay are considered. The proposed OCR per phase module can detect asymmetrical overcurrent and faults as well. The last module presented in the local trip command for DGUs is voltage and current THD blocks, which protects the inverter from distorted voltage and currents. The importance of this block is not only for fault condition, but is also for overload conditions, when the inner current control loop of the inverter limits the current amplitude and leads the DGU to a distorted current and voltage waveforms. Figure 6 shows a complete DGU trip command which includes local and external trip commands.  
Figure 4: The proposed external protection scheme for DGUs. (**a**) sending-end and receiving-end protection commands, and (**b**) upstream and downstream command selection for DGU external command purposes.  
Figure 5: The proposed local DGU protection scheme.
## 3 Results and Analysis
### Results and Analysis of Basic Performance Test of Modified Bitumen
#### 3.1.4 Aging Test  
The results are shown in Figure 4.  
Figure 3: Ductility test results.  
Figure 4 shows that the penetration ratio of Sasobit-modified bitumen increased and then decreased with the increase in Sasobit dosing; however, the values were higher than that of the virgin bitumen, indicating that the Sasobit modifier can effectively improve the anti-aging performance of bitumen with optimal dosing. The penetration ratios of the XT-W3-modified and Evotherm-modified bitumens were higher than those of the virgin bitumen, indicating that in terms of aging resistance, the two modifiers showed certain advantages. However, the penetration ratio of SBS-modified bitumen is the largest among several bitumens after aging, which indicates that SBS-modified bitumen has the best anti-aging performance.