esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Gas Flow Velocity Distribution  
Figure 5a,b present the flow optimal velocity distribution for the two gases utilized in our simulations. Notice that the scales are not the same to enhance images. In both figures, the voltage between the anode and cathode is 20 kV, the ballast resistance is 500 M\(\Omega\), and the block capacitor is 1 pF. For the argon thruster, Figure 5a, the cylindrical cathode has a height of 10.5 mm and an inside diameter of 24 mm, while in Figure 5b, for the xenon thruster, the dimensions of the cylindrical cathode are 14.7 mm in height, and the inside diameter is 40 mm.  
The axial distance between the anode tip and the cathode inlet is about 30 mm on both thrusters in Figure 6a,b. A higher speed can be seen for argon (maximum value of 52.3 cm/s) than for xenon (maximum speed of 23 cm/s). Comparing the velocity distribution of gas flow for both thrusters, we observe for argon that the higher speeds can be found at points close to the entrance surface of the cathode, where the ions are more accelerated [24], and in the middle region of its output cross-section area.  
For the xenon thruster, it is observed a similar behavior, although less pronounced, in the cathode inlet region, but with a more exuberant extension in the central region  
Figure 5: Influence of the secondary electron emission coefficient in the thruster’s performance in the thruster’s performance: (**a**) variation of the output thrust \(T\) in (\(\mu\)N) for several \(\gamma_{i}\); (**b**) variation of the thrust-to-power ratio \(T/P\) in (mN/kW) for several \(\gamma_{i}\)  
Figure 6: Optimal fluid velocity distribution (cm/s): (**a**) thruster with argon as propellant gas; (**b**) thruster with xenon as propellant gas. Notice the different scales necessary to reinforce the contrast in both pictures.  
of cathode outlet cross-section. Another essential difference in these distributions at the cathodes operating with argon and xenon is that, in the latter case, the velocity distribution is higher only in a ring close to the entrance wall of the cathode, not forming the cylindrical configuration of high velocity existing when the working gas is argon.  
Figure 7 shows the fluid velocity components (total, axial, and radial) as a function of the cathode diameter at the exit of the EHD thruster, where the similarity of the profiles for argon and xenon can be seen, as well as difference in the maximal values. We note that the values of the radial components are much lower than the axial.
# Impact of Coil Misalignment in Data Transmission over the Inductive Link of an EV Wireless Charger
## 4 Analysis of the Results
For the joint vertical and horizontal misalignments, the effects are revealed with a shorter distance (\(xm\ \cong 15\) cm), as the increase in the separation between the coils reduces the magnetic flux traversing the secondary coil earlier. In conclusion, these results indicate that the communication channel capacity is more sensitive to vertical misalignment than to horizontal displacement. The variations in the channel capacity cannot be neglected, especially when symmetrical compensation networks are used in the charger. Consequently, the data rate of the source should be carefully selected and spread-spectrum techniques, such as the one proposed in [11], may not be suitable because of coil misalignment.
## 4 Experimental Results
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Operation** & \multicolumn{2}{c}{**DeFog**} & \multicolumn{2}{c}{**DeRain**} & \multicolumn{2}{c}{**DeSnow**} & \multicolumn{2}{c}{**Ave Value**} & \multicolumn{1}{c}{**Evaluation**} \\ \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**AOD-NET**} & \multirow{2}{*}{**GridDehazeNet**} & \multirow{2}{*}{**Efficient DeRain**} & \multirow{2}{*}{**ADMM**} & \multirow{2}{*}{**USENet**} & \multirow{2}{*}{**USENet**} & **All in** & **IDP-YOLOV9** & \\  & & & & **ResNet** & & **Net** & **One** & **(Ours)** & \\ \hline \multirow{2}{*}{VOC} & 15.06 & 18.80 & 16.36 & 17.58 & 17.14 & 17.96 & 19.80 & PSNR \(\uparrow\) \\ \cline{2-8}  & 0.805 & 0.814 & 0.809 & 0.832 & 0.816 & 0.841 & 0.847 & SSIM \(\uparrow\) \\ \hline \multirow{2}{*}{HAZE} & 14.76 & 16.13 & 17.56 & 18.02 & 15.52 & 16.96 & 18.24 & PSNR \(\uparrow\) \\ \cline{2-8}  & 0.763 & 0.799 & 0.784 & 0.773 & 0.688 & 0.756 & 0.736 & SSIM \(\uparrow\) \\ \hline \multirow{2}{*}{FTOD} & 20.15 & 22.36 & 18.86 & 19.36 & 19.52 & 21.08 & 21.97 & PSNR \(\uparrow\) \\ \cline{2-8}  & 0.727 & 0.805 & 0.655 & 0.714 & 0.739 & 0.814 & 0.809 & SSIM \(\uparrow\) \\ \hline \end{tabular}
\end{table} 
Table 2: Evaluation of defogging, deraining, and desnowing methods on different datasets against benchmarks by IDP-YOLOV9. \(\uparrow\) indicates that our method achieves higher scores for this metric. Figure 4: Comparison of different image processing algorithms for severe weather conditions in power scenes captured from drones’ perspective: (**a**) Snow image; (**b**) AOD-net algorithms; (**c**) GridDehazeNet algorithms; (**d**) IDP-YOLOV9. Figure 5 illustrates how the small CNN network predicted the DLIE module's image enhancement parameters (WB, gamma, contrast, tone) in three examples. The small CNN network learned a set of parameters for each image based on specific information. Information about brightness, color, and hue and weather feature parameters for each image with fog, rain, and snow were considered. After filtering out weather factors, the images were processed through the DLIE module, improving the visual effects, enhancing image clarity, supplementing detailed information, and benefiting subsequent object detection.
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 3 Results
### Prediction of Pile Bearing Capacity  
The bearing capacity of tested piles based on CPTu output was calculated using prediction methods, the UniCone, LCPC, and EC 7--2 methods as described above. The bearing capacity for pile shaft \(Q_{s}\), pile tip \(Q_{b}\), and overall pile bearing capacity \(Q_{p}\) are listed in Table 3.
# Electromagnetic Imaging of Uniaxial Objects by Two-Step Neural Network
## 2 Formula  
This section presents the direct problem formulation for TM and TE waves via electromagnetic imaging. Figure 2 shows the dielectric scatterer located in the free space. We configure the transmitter and receiver around the domain of interest area for measurement. The position, shape, and size of the scatterers can be known from the measured scattered field. We define a Cartesian coordinate system to describe the dielectric coefficient distribution of the object by a diagonal matrix as follows:  
\[\overline{\epsilon}_{r}= \begin{bmatrix}\epsilon_{x}(x,y)&0&0\\ 0&\epsilon_{y}(x,y)&0\\ 0&0&\epsilon_{z}(x,y)\end{bmatrix} _{xyz} \tag{1}\]  
In the TM case, we assume the incident wave to be \(e^{i\omega t}\) and the scattered field along the z-direction. The material of the scatterer is related to the x- and y-directions, so the irradiation of the TM-polarized waves will produce only TM-scattered waves. We define the incident wave as follows:  
\[\overline{E}^{i}\left(\overrightarrow{r}\right)=E_{z}^{i}(x,y)\hat{z}=e^{-i \int_{0}(xcos\varnothing+ysin\varnothing)}\hat{z} \tag{2}\]  
where \(k_{0}\) represents the wave number in free space, and \(\varnothing\) denotes the angle of incidence. The TM wave contains the z-component scattered field only. Equations (3) and (4) represent the total field \(\overline{E}_{z}=E_{z}\)\(\hat{z}\) and the external scattered field \(\overline{E}^{z}=E_{z}^{s}\)\(\hat{z}\), respectively. \[E_{z}(\overline{r})=\int_{S}G\left(\overrightarrow{r},\overrightarrow{r}^{ \prime}\right)\left(\epsilon_{z}\left(\overrightarrow{r}^{\prime}\right)-1 \right)E_{z}\left(\overrightarrow{r}^{\prime}\right)ds^{\prime}+E_{z}^{i} \left(\overrightarrow{r}\right),\ \overrightarrow{r},\overrightarrow{r}^{\prime}\in S \tag{3}\]  
\[E_{z}^{s}(\overline{r})=\int_{S}G\left(\overrightarrow{r},\overrightarrow{r }^{\prime}\right)\left(\epsilon_{z}\left(\overrightarrow{r}^{\prime} \right)-1\right)E_{z}\left(\overrightarrow{r}^{\prime}\right)ds^{\prime},\ \overrightarrow{r}\
otin S,\ \ \overrightarrow{r}^{\prime}\in S \tag{4}\]  
In 2-D free space, the Green's function is \(G\left(\overrightarrow{r},\overrightarrow{r}^{\prime}\right)=-\frac{i\int_ {0}^{2}}{4}H_{0}^{(2)}\left(k_{0}\left|\overrightarrow{r}-\overrightarrow{r }^{\prime}\right|\right)\). \(H_{0}^{(2)}\) represents the zero-order Hankel function of the second kind. Figure 2: Schematic of dielectric scatterer. In the case of TE, the material properties of the scatterer are related to the x- and y-directions.
## 2 Study Area and Data  
### Study Area  
The study area was located in the central part of the Yunnan-Guizhou Plateau, which is characterized by higher elevations surrounding a lower central region (Figure 1). It can be divided into three main parts: the Dianchi Lake, the plains along the shores of Dianchi, and the mountainous areas at the edge of the plains. Most of the urban area lies in the transitional zone between the medium-low mountains eroded by tectonic forces and the slopes along the lake basin. This region features flat terrain dominated by depositional landforms, with an elevation ranging from 1500 to 2800 m and an average elevation of 1891 m in the urban area. The city is surrounded by mountains on three sides and borders Dianchi Lake.  
The Kunming Basin, where the study area was located, is a fault basin at the southern end of the North-South Tectonic Belt in China, with predominantly north-south-oriented structures and a complex geological environment. The basin is primarily characterized by lacustrine landforms, with the widespread distribution of loose sedimentary layers and soft soils typical of plateau lacustrine deposits, resulting in weak foundationsThe study area experiences a subtropical highland monsoon climate, with an average annual temperature of around 15 \({}^{\circ}\)C, mild weather, and a year-round spring-like climate. The area receives approximately 2200 h of sunshine annually, with an average annual precipitation amount of about 1000 mm. The rainy season occurs from May to October, accounting for roughly 85% of the yearly precipitation. Rainfall is concentrated in the summer, with frequent heavy rains and thunderstorms, peaking in August and reaching a minimum in January. As shown in Figure 1, the specific geographical coordinates of the study area are 24\({}^{\circ}\)37\({}^{\prime}\)48\({}^{\prime\prime}\) to 25\({}^{\circ}\)10\({}^{\prime}\)48\({}^{\prime\prime}\) N latitude and 102\({}^{\circ}\)32\({}^{\prime}\)24\({}^{\prime\prime}\) to 102\({}^{\circ}\)58\({}^{\prime}\)48\({}^{\prime\prime}\) E longitude, covering an area of approximately 2721 square kilometers, which essentially includes the main urban area of Kunming.
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 4 Experimental Data Analysis
### Spectral Analysis  
The FFT method enables the examination of a signal's frequency spectrum, which is essential for identifying frequency tags associated with flickering visual stimuli. In this study, we focus on the tag frequencies corresponding to the two perceived orientations of the Necker cube: \(f_{L}\) for the left-oriented cube and \(f_{R}\) for the right-oriented cube.  
To ensure optimal analysis resolution, we implemented an FFT algorithm designed to operate on power-of-2 segmentations. The EEG signals were segmented into 30 s trials based on the duration of each task (MI, MLV, or MRV). Following preprocessing and artifact removal, the FFT was computed for 20 s frames with a 1 s overlap to reduce noise. This configuration maximized spectral resolution, allowing for efficient and precise signal decomposition in the frequency domain. As a result, we could efficiently identify the spectral characteristics associated with each cube orientation when analyzing EEG signals.  
The FFT spectra of the MI, MLV, and MRV tests are shown in Figure 3. Distinct spectral peaks can be observed near the modulation frequencies within the ranges \(6.15<f_{1}<6.75\) and \(8.00<f_{2}<8.60\). The brain does not respond on discrete frequencies due to two factors: (i) the modulation signal is not harmonic, and (ii) slight variation in the modulation signal occurs throughout the experiment, influenced by computer load and memory fluctuations.  
By comparing the spectrum obtained for involuntary attention (MI) shown in Figure 3a with the spectra for voluntary attention to the left-oriented cube (MLV), shown in Figure 3b, and to the right-oriented cube (MRV), shown in Figure 3c, it can be observed that the spectral amplitude at \(f_{1}\) dominates the amplitude at \(f_{2}\) when the subject perceives the cube as left-oriented (Figure 3b), and the spectral amplitude at \(f_{2}\) dominates the amplitude at \(f_{1}\) when the subject perceives the cube as right-oriented.
# A Psychoacoustic-Based Multiple Audio Object Coding Approach via Intra-Object Sparsity
## 3 Performance Evaluation
### Subjective Evaluation
It is demonstrated in Figure 8 that our proposed approach at 105 kbps possess the similar perceptual quality as separate encoding approach using MPEG-2 AAC. Yet the complexity of separate encoding is much higher than our proposed approach. Furthermore, both our proposed method and separate encoding approach attained a better performance compared with SAOC. The last test devotes to evaluate the quality of the spatial soundfield generated by positioning the decoded audio objects in different spatial locations, which stands for the real application scenario. Specifically, for each eight-track audio, which are positioned uniformly in a circumference with a center at the listener, i.e., the locations are 0\({}^{\circ}\), \(\pm\)45\({}^{\circ}\), \(\pm\)90\({}^{\circ}\), \(\pm\)135\({}^{\circ}\), \(\pm\)180\({}^{\circ}\), respectively. A binaural signal (test audio data) is created by convoluting each independent decoded audio object signal with the corresponding Head-Related Impulse Responses (HRIR) [31]. The MUSHRA listening test is employed with 6 conditions, namely, Ref, Pro-105, SPA-128, AAC-30, SAOC and Anchor, which are the same as previous tests. Here, Sennheiser HD600 headphone is used for playing the synthesized binaural signal. It can be observed from Figure 9 that our proposed method can achieve a higher scores compared to all the rest encoding approaches. The results (Figures 8 and 9) also show that the proposed approach achieves a significant improvement over separate encoding method using MPEG-2 AAC for binaural rendering but not in the independently playback scenario. This is due to the spatial hearing theory, which reveals that in each frequency only a few audio objects located at different positions can be perceived by the human ear (i.e., not all audio objects are sensitive at same frequency). In our proposed codec, only the most perceptually important time-frequency instants (not all time-frequency instants) of each audio object are coded with a higher quantization precision, while these frequency components are important for HAS. The coding error produced by our codec can be masked by spatial masking effect to a great extant from the last experiment. However, MPEG-2 AAC encodes all time-frequency  
Figure 8: MUSHRA test results for separate AAC (Advanced Audio Coding) encoding at 30 kbps, SAOC (Spatial Audio Object Coding) and our proposed approach at 105 kbps with 95% confidence intervals. instants with a relatively lower quantization precision at 30 kbps. When multiple audio objects were encoded separately by MPEG-2 AAC, there are some coding error that cannot be reduced by spatial masking effect. Hence, the proposed approach shows significant improvements over condition 'AAC-30' for binaural rendering. From a series of objective and subjective listening test, we prove that the proposed approach can adapt to various bitrates conditions and it is suitable for encoding multiple audio objects in real application scenarios.
## 2 Materials and Methods
### Circuit Design  
#### 2.3.1 Design Ideas  
In order to achieve the pressure-sensing and luminescent interactive function of the POF fabric, the circuit module needs to convert the resistance value changes in the fabric caused by pressuring the surface into different colors of the LED light source coupled with POFs. The circuit module required for interactive luminescent fabric is shown in Figure 5, including signal conditioning circuit and light control circuit. In signal conditioning circuits, the resistance signal \(R_{X}\) from the fabric's conductive yarns is input to the signal conversion module, which converts \(R_{X}\) into a voltage signal \(U_{0}\) and outputs \(U_{0}\) to the STM32 microcontroller (STMicroelectronics Investment Co., Ltd., Shanghai, China). In the microcontroller, \(U_{0}\) is further converted into a standard digital signal \(VOLTAGE\_AO\). In the light control circuit, the STM32 microcontroller determines the value of \(VOLTAGE\_AO\) and outputs a corresponding pulse width modulation (PWM) pulse signal to the WS2812B module (Shenzhen Magic Lamp Technology Co., Ltd., Shenzhen, China). This module then outputs an RGB-LED light signal, ultimately controlling the color of the POF fabric's  
Figure 4: Laser engraving on the fabric surface: (**a**) Pattern design; (**b**) after laser engraving effect (unlit).  
light source. The design of the circuit part mainly includes the signal conversion module, printed circuit board (PCB) design, and software development.
## 2 Materials and Methods
### Assumptions  
The analytical model used in the software generalises the GNSS measurement campaign planning process. It is not dedicated to any particular application. Since the model was designed to be as universal as possible, it is not dependent on the inspection vehicle type (e.g., a car, an aerial or surface vehicle, a pedestrian, or a rail vehicle) or on the linear object type (e.g., high-voltage lines, pipelines, or railway rails). The model is not linked to the measurement method applied or the instruments by which it is performed.  
The software was developed based on a number of assumptions about the process of performing GNSS measurements which must be satisfied for the use of the program to be reliable:  
1. A measurement of a single position is carried out instantaneously, which means that the duration of the performance is negligibly short in relation to the duration of travelling the consecutive route sections;
2. The test linear object is continuous (uninterrupted);
3. The inspection vehicle can stop at any point of the linear object;
4. The linear object is covered in one direction, without going back to the places already visited;
5. The vehicle moves in a uniform motion, at a constant velocity for the entire campaign, and is also capable of starting or stopping relatively instantaneously.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 3 Consideration of Local Scour
### Scour Effect
#### 3.3.1 Morphological Features Equivalence  
On the basis of the scour characteristics of large diameter monopile, this paper takes the volume of erosion and deposition as an equivalent criterion of morphological features, and converts it into the change of equivalent scour depth in a certain horizontal range. Assuming that the radius of scour  
Figure 10: Scour patterns of large diameter monopile from Dafeng offshore wind farm in Jiangsu Province. (**a–f**) represent different monopiles.  
range is \(A\), the erosion or deposition volume is \(V\), and the pile diameter is D, the equivalent scour depth \(S_{d}\) can be calculated according to the following equation:  
\[S_{d}=\frac{V}{\pi(A^{2}-D^{2})} \tag{10}\]  
From the conclusion above, erosion and deposition are mainly concentrated in the center radius of 3D, then Equation (10) can be rewritten as follows:  
\[S_{d}=\frac{V}{8\pi D^{2}} \tag{11}\]  
Taking pile 1#, 7#, 19#, 37#, 40#, and 57# as examples, equivalent scour depths are calculated by erosion and deposition volume, and the results are shown in Table 3. Compared with the maximum scour depth, the absolute error is 2.88 m\(\sim\)4.03 m, the minimum relative error is 40.9%, and the maximum relative error can reach 103.6%.
## 4 Method  
This research employs anonymized video data from the \"MeltdownCrisis\" dataset, comprising recordings of 13 autistic children aged 5 to 9 years, exhibiting behaviors from typical states to pre-meltdown and complete meltdown episodes. The ethical approval consists of a signed agreement with the Assaada Association for Autists, accompanied by a commitment executed with legal counsel, ensuring that all data have been anonymized to safeguard participant identities. The dataset comprises 623 video sequences classified into normal, pre-meltdown, and meltdown states, with this study concentrating on pre-meltdown behaviors marked by high-frequency SMMs including hand flapping and body rocking. Utilizing the Kinect SDK V2.0, body movements are recorded through the tracking of skeletal joints, facilitating accurate motion analysis. Preprocessing steps include frame extraction, bounding box cropping to isolate child movements, caregiver frame removal, and sequence annotation as 0 (No-SMM) or 1 (SMM). To improve the dataset, data augmentation techniques such as random rotation, scaling to 224 \(\times\) 224 pixels, horizontal flipping, and color jitter, are used. The proposed hybrid model combines a 3D-ResNet backbone to extract local spatiotemporal features with a Swin-3D-b Transformer to simulate global temporal relationships. This design uses a deep classification layer to evaluate input frame sequences and categorize behaviors as No-SMM or SMM. To provide robustness and prevent overfitting, the model is trained and tested using a 5-fold cross-validation approach, with hyperparameter adjustment resulting in optimal performance. In the following sections, we describe in detail each step of our proposed method.
###### Abstract  
Protected agriculture is a field in which the use of automatic systems is a key factor. In fact, the automatic harvesting of delicate fruit has not yet been perfected. This issue has received a great deal of attention over the last forty years, although no commercial harvesting robots are available at present, mainly due to the complexity and variability of the working environments. In this work we developed a computer vision system (CVS) to automate the detection and localization of fruit in a tomato crop in a typical Mediterranean greenhouse. The tasks to be performed by the system are: (1) the detection of the ripe tomatoes, (2) the location of the ripe tomatoes in the XY coordinates of the image, and (3) the location of the ripe tomatoes' peduncles in the XY coordinates of the image. Tasks 1 and 2 were performed using a large set of digital image processing tools (enhancement, edge detection, segmentation, and the feature's description of the tomatoes). Task 3 was carried out using basic trigonometry and numerical and geometrical descriptors. The results are very promising for beef and cluster tomatoes, with the system being able to classify 80.8% and 87.5%, respectively, of fruit with visible peduncles as \"collectible\". The average processing time per image for visible ripe and harvested tomatoes was less than 30 ms.  
image processing; greenhouse; automatic tomato harvesting; precision agriculture +
Footnote †: journal: Applied Sciences