esa-sceva/satcom-chunk-collection huggingface dataset:
# Defect Prediction for Capacitive Equipment in Power System
## 3 Method
### Comparison Models  
* Liner Classifier: Linear classifiers categorize targets by linearly combining features. The model facilitates decision making by summing the product of each feature and its corresponding weight [32].
* MLP: Multilayer perceptron (MLP) is a forward-structured artificial neural network characterized by its layered structure. It can be conceptualized as a composition of multiple single-layer perceptron. The output layer of one perceptron serves as the input layer for the subsequent perceptron, with the final output layer representing the overall output of the MLP [33].
* SVM: Support vector machines (SVMs) are algorithms rooted in statistical theory, proficient in solving classification and regression problems with small-scale data. SVM addresses inner product operations in high-dimensional spaces by employing a kernel function, facilitating the effective implementation of nonlinear classification [34].
* XGBoost: Extreme gradient boosting (XGBoost) algorithm employs ensemble thinking to combine multiple weak learners into a strong learner through specific methodologies. XGBoost comprises multiple classification and regression trees (CARTs) and can handle diverse problems, including classification and regression [35].
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 3 Sample Results
### TC-016: SBAS Switch  
The test aims to check the correct switch between SBASs corresponding to different service provider IDs. The test results are shown in Figure 8. The figure shows the periods in which EGNOS (PRN S126) and GAGAN (PRN S127) are enabled/disabled throughout the test. It can be observed that, when the EGNOS signal is switched off at 00:20, the receiver successfully switches to GAGAN and keeps providing an SBAS PVT throughout the test.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Comparison of Different FD-Physical Layer-Based UWAC Systems
#### 2.5.5 Data Rate  
It is discovered that AUA-CCS system [40] has achieved high data rate values ranging 7.8467 kb/s to 7.9226 kb/s as compared to the A-DTWRS [19], because of its high spectral efficiency. In digital modulation-based FD-UWAC systems, the FSK-based UWAC system [24] offers a higher data rate and greater energy efficiency than RLS-adaptive [25] and ANE [60] systems, as well as longer battery life. To transmit 10 bps data between FD modems, a spread spectrum (SS) based Hybrid-SS system [33] uses DSSS and code shift keying (CSK).
## 3 System Model and Problem Formulation  
In this section, we generalize the proposed privacy-friendly task-offloading model in satellite-terrestrial edge computing networks and then represent mathematically the process of finding a task-offloading strategy that minimizes the overhead.
## 2 Materials and Methods
### 3D De-Blurred Structures from Limited Angle of View  
Figure 5 shows the methodology underpinning the 3D reconstruction of de-blurred structures derived from a de-blurred image. As delineated in Figure 5, if images that span a complete rotation of \(360^{\circ}\) can be procured, a corresponding set of \(360^{\circ}\) de-blurred images can be achieved. These de-blurred images act as pivotal projection sources. Building upon this foundation, the well-established filtered back-projection (FBP) technique facilitates the generation of cross-sectional views and an encompassing 3D representation of the absorbing structures. Notably, this technique retains its efficacy even when the available viewing angles are constrained.  
However, in the case of only one blurred image or with few angles of view, 3D reconstruction with the filtered-back-projection method cannot be done. In this case, with the depth information matrix, 3D de-blurred of absorbing structures can be done to make one view angle or multi-view angle at different angles as shown in Figure 6.  
Scattering de-blurring: The initial phase involves transforming the blurred image into a sharp and clear 2D representation. Using the FCN (Attention Res-UNet) model, a systematic de-blurring process is applied to the image [31; 32]. The model processes the  
Figure 5: Principle of 3D de-blurred structures reconstruction from a blurred image. The red arrow shows the scatter deblurring method using the CNN model  
Figure 6: Principle of 3D de-blurred structures reconstruction from a single 2D blurred image.  
blurred image through a \(256\times 256\) matrix using a pixel-by-pixel scan method. The zero-padding technique is implemented to ensure comprehensive processing of the entire image, including its edges, enhancing the 2D image to better illuminate the absorbing structures. Furthermore, the CNN model, combined with a pixel-to-pixel scanner method, is used for concurrent depth estimation and scatter de-blurring, as indicated by the red arrow in Figures 5 and 6. This method utilizes a depth matrix derived from the depth estimation model and a pixel scanning method. A threshold-based approach is used, where the intensity of pixels is set to 1 if the depth matrix value is below a predetermined threshold; conversely, it is set to 0 if it exceeds the threshold. This facilitates the reconstruction of a 2D image from the depth matrix. Using the 2D image and its associated depth map, the method enables the reconstruction of a 3D image from the original 2D representation.  
Depth estimation: Once the 2D image is clarified, determining the spatial depth of each pixel becomes essential. The CNN (DenseNet-169) model designed for this task employs a \(224\times 224\) pixel estimation matrix to analyze the de-blurred image, ascertaining the depth at each pixel [31; 34]. This process yields an extensive depth map, which accentuates the spatial positioning of the absorbing structures.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 4 Proposed LSTM-Based Recovery Scheme
### Network Architecture  
The architecture of the used LSTM network is now illustrated in Figure 4, which depicts the different stages of the deep learning network, which is composed of the input sequences, four recurrent LSTM layers, a dense layer and an output layer. The input layer is connected to the LSTM layer. The recurrent connections in the LSTM layer are directly from the cell output units to the cell input units, input gates, output gates and forget gates. The cell output units are connected to the output layer of the network. The use of four layers is to enhance the efficiency and accuracy of the proposed algorithm. Meanwhile, a dense layer is used (matrix vector multiplication) for outputting a prediction. Here, each layer is composed of 50 cells (neurons), where each cell consists of an input, modulation gates, with operations and interconnections, as illustrated in Figure 5, i.e., input \(g_{t}^{in}\), input modulation \(g_{t}^{mod}\), forget \(g_{t}^{f}\) and output \(g_{t}^{out}\) gates that determine information entering the cell state.  
The output of the last LSTM layer is fed as the input of the dense layer, which is composed of a linear activation function. The output of the dense layer presents the output  
Figure 4: Proposed LSRM network architecture.  
Figure 5: LSTM cell structure.  
of the proposed scheme, which is a beam index prediction at the (\(t+1\)) th time step. The process of this LSTM-based beam prediction algorithm is now presented.
## 2 Sensors
### Global Positioning
Additionally, a multi-antenna GNSS integrated with an IMU has been reported to provide azimuth estimation quality comparable to that of differential GNSS/IMU configurations [40]. A particularly efficient setup, utilizing three antennas with a double differencing (DD) technique, demonstrated a standard deviation of 0.02 degrees for the heading angle [41]. These advancements highlight the potential of multi-antenna GNSS systems in enhancing navigation accuracy in various MMS applications.
## 4 Common/Unifying Theoretical-Conceptual Framework
### qEEG Phenotype and Neurophysiological, Cognitive, and Behavioral Traits
#### 4.2.6 Anxiety Tendency (Anxious Arousal vs. Anxious Aprehension)  
Anxiety is one of the best-known and oldest evolutionary systems evolved in humans. It results from a set of information-gathering reactions that allow the individual to face uncertainty and danger and survive. Despite being adaptive, since it helps us avoid dangerous situations and to achieve our goals, anxiety also causes significant suffering and, in its extreme forms, can be debilitating.  
Biologically, there are two subcomponents of anxiety: _somatic_ and _cognitive_[643]. Somatic anxiety (or _anxious arousal_) is a physiological component of anxiety, characterized by autonomic arousal and somatic tension (high blood pressure, bounding heart, sweating, dryness of mouth, difficulty breathing) [643, 644, 645]. Cognitive anxiety (or _anxious aprehension_) refers to the mental component of anxiety and consists of expectations about and anticipations of a difficult task or threat, success or self-evaluation, worrying, negative self-talk, and disrupted attentional processes [643, 645, 646]. It is important to stress that each of the two components of anxiety is associated with different psychopathologies.  
A person with an anxious arousal tendency is usually alert during the distraction-free resting-state period and experiences rest periods as more aversive and anxiety-inducing. Extreme anxious arousal is typical for neurosis, panic or phobic disorders, and PTSD [647, 648].  
A person with an anxious apprehension tendency usually has (a) higher expectation for the perceptual events, which reflects higher nonselective readiness for perception and action, even in the absence of any goal-directed task and (b) excessive worry for the future and verbal rumination about those expectations. Highly expressed anxious apprehension is typical for generalized anxiety disorder, obsessive-compulsive disorder, and separation anxiety disorder [647; 648].  
Again, converging evidence suggests that both components of anxiety are reflected in the characteristics of qEEG phenotypes [497; 649; 650; 651; 652; 653].
## 5 Discussion
### Limitations and Potential Usability of the Proposed Method  
Despite the promising results, it is essential to acknowledge the limitations and areas for improvement encountered in this study. One limitation lies in the sensitivity of the logarithm background subtraction method to variations in image resolution, as indicated by the observed errors in the detection results. Additionally, the algorithm's performance may be influenced by changes in environmental conditions, such as weather and lighting, which were not explicitly addressed in this research.
# Vibration of Rotating and Revolving Planet Rings with Discrete and Partially Distributed Stiffness
## 3 Vibration Analysis
### Forced Response  
In a planetary gear set, the rotating speed and revolving speed of a planet ring are proportional. In this work, the ratio is set to \(i_{0}=0.1825\), according to a practical application. Therefore, the excitation of rotating and revolving movement to the planet ring can be expressed by rotating or revolving speed. Here, the rotating speed is chosen; thus, the amplitude of excitation is \(\Omega^{2}R_{0}/R\), and the excitation angular frequency is \(\omega\). It should be noted that the excitation only acts on the first order component, as shown in \(\widetilde{\mathbf{Q}}\) in Equation (26), after carrying out the orthogonal operation. In addition, Equation (26) represents a nonlinear dynamic system, since \(\theta_{bi}\) and \(\theta_{mi}\) are time dependent, as described in Section 2; then the stiffness matrix, \(\mathbf{K}\), will change with respect to time, \(t\). Therefore, Equation (26) will be solved by the classical numerical method.  
Figure 9 shows the forced responses of the planet ring under the excitation of rotating and revolving movement at the rotating speed \(\omega=1\). Here the forced responses of radial direction at different positions are illustrated. It is obvious that, although the forced responses are nonlinear, as shown in Figure 9e, the forced responses at different positions exhibit some periodic behavior with a quasi-period of rotating speed, \(\omega\), as shown in Figure 9a-d. The regions with small amplitudes in Figure 9a-d mean that the partially distributed stiffnesses are passing through this position, and the regions with large amplitude mean that the partially distributed stiffnesses are out of the position. These phenomena imply that the forced responses of the planet ring will vary with a quasi-period of its rotating speed, and the partially distributed stiffnesses will suppress its vibrations.  
Figure 8: Influences of (**a**) partial distributed stiffness, (**b**) discrete stiffness to natural frequencies, (**c**) veering parameter \(\xi_{mn}\) for (**a**), and (**d**) veering parameter \(\xi_{mn}\) for (**b**) at \(\omega=1\).  
Figure 10 shows the forced responses of the planet ring in different moments in a period, \(T\), of rotating speed at \(\omega=1\). It can be found that the vibration of the planet ring is small in partially distributed stiffnesses area, and the vibration in other areas is large and irregular, that is, nonlinear. The forced responses of the planet ring almost rotate with its rotating movement.
# A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks
## 2 Related Work
### Linear Coding  
Linear coding (LC) [9] is a matrix decomposition technique that represents the original data as a combination of a set of base vectors and their corresponding \"compressed\" representations. Its data representation form is  
\[X\approx UZ, \tag{4}\]  
where \(X=(x_{1},x_{2},\ldots,x_{n})\in R^{m\times n}\), \(U\in R^{m\times p}\), and the column vectors of \(U\) are the basis vectors. \(Z\in R^{p\times n}\) and the column vectors of \(Z\) represent p-dimensional vectors with respect to the base vectors of \(U\). Therefore, the matrix decomposition of LC is defined as the following optimization function problem:  
\[\operatorname*{arg\,min}_{U,Z}||X-UZ||^{2}. \tag{5}\]  
LC adds sparse constraints to each column of \(Z\) in the optimization problem described in Formula (5). LC implements a data-sparse representation based on matrix decomposition, where each data point is a linear combination of a few base vectors. This approach allows for a more accurate interpretation of each data point.
###### Abstract  
The prospect of harnessing wind energy in urban areas is not promising owing to low wind speeds and the turbulence caused by surrounding obstacles. However, these challenges can be overcome through an improved design of wind turbine that can operate efficiently in an urban environment. This paper presents a novel design of a building integrated cross axis wind turbine (CAWT) that can operate under dual wind direction, i.e., horizontal wind and vertical wind from the bottom of the turbine. The CAWT consists of six horizontal blades and three vertical blades for enhancing its self-starting behavior and overall performance. The study employed a mock-up building model with gable rooftop where both of the developed CAWT and the conventional straight-bladed vertical axis wind turbine (VAWT) are mounted and tested on the rooftop. The height of the CAWT and the VAWT above the rooftop was varied from 100 to 250 mm under the same experimental conditions. The results obtained from the experimental study showed that there is significant improvement in the coefficient of power (\(C_{p}\)) and self-starting behavior of the building integrated CAWT compared to the straight-bladed VAWT. At 100 mm height, the \(C_{p,max}\) value of the CAWT increased by 266%, i.e., from 0.0345 to 0.1263, at tip speed ratio (TSR) (\(\lambda\)) of 1.1 and at wind speed of 4.5 m/s. Similar improvements in performance are also observed for all condition of CAWT heights above the rooftop where the CAWT outperformed the straight-bladed VAWT by 196%, 136% and 71% at TSR of 1.16, 1.08, and 1.12 for \(Y=150\), 200, and 250 mm, respectively. Moreover, the CAWT performs better at \(10^{\circ}\) pitch angle of the horizontal blade compared to other pitch angles.  
cross axis wind turbine; gable roof shape; wind energy; renewable energy; urban wind energy system; building integrated wind turbine
## 3 Proposed System Model
### Proposed Fog-Cloud Architecture
It will be emphasized that the fog layer reduces latency and enhances system security by performing data processing closer to the source. Additionally, the potential for this model to be expanded across the campus in the future and adapted to different use scenarios will be discussed.
## 7 Performance Evaluation  
In this section, we present four main simulation results to show the effect of DRRF in both non real-time and real-time traffic: (1) DRRF, (2) DRRF+GWF, (3) DRRF Extensions, and (4) EDF-DRRF. The first simulation configuration is used to show system performance of DRRF in terms of system throughput, percentage of overheads, and fairness index of DRRF compared to GPS and DRR. The second configuration is to show if DRRF can be used to achieve GWF criteria. The third configuration is to show whether DRRF can support other QoS parameters for artPS. Finally, the fourth configuration is to show system performance of EDF, DRRF, and EDF-DRRF in terms of system throughput, delay, delay jitter, and fairness.
## 3 Results
### Stair- and Gait-Cycle Kinematic Parameters  
The kinematic descriptive statistics of the stair and gait cycles are summarized in Table 3.  
Regarding lower-limb kinematics, in the stair cycle, there is a tendency towards existing statistically significant differences between older adults with and without FoF in the ipsi FC and the contralateral hip angle at the moment of FP. Both were reduced in the FOF group when compared to the NFOF group.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Spatiotemporal Parameters**} & **NHOF** & **HOF** & **NFOF** & \multicolumn{4}{c}{**Between-Subject Comparison**} \\  & **(_n = 38_)** & **(_n = 22_)** & **(_n = 41_)** & **FOF** & **_(n = 19)_ & **FOF** & **_(Observed Power)_ \\ \hline Task Velocity & 0.87 & 0.81 & 0.88 & 0.79 & 0.488 & 0.257 & 0.417 \\ (m/s) & \(\pm\)0.15 & \(\pm\)0.28 & \(\pm\)0.22 & \(\pm\)0.16 & (0.106) & (0.203) & (0.127) \\ Task Time & 1.13 & 1.18 & 1.09 & 1.26 & 0.689 & **0.009** & 0.586 \\ (s) & \(\pm\)0.18 & \(\pm\)0.25 & \(\pm\)0.18 & \(\pm\)0.22 & (0.068) & **(0.764)** & (0.084) \\ Double-Support Time & 0.28 & 0.28 & 0.26 & 0.30 & 0.705 & **0.047** & 0.873 \\ (s) & \(\pm\)0.06 & \(\pm\)0.09 & \(\pm\)0.06 & \(\pm\)0.09 & (0.066) & **(0.513)** & (0.053) \\ Single-Support Time & 0.70 & 0.71 & 0.68 & 0.77 & 0.710 & **0.009** & 0.476 \\ (s) & \(\pm\)0.11 & \(\pm\)0.16 & \(\pm\)0.11 & \(\pm\)0.14 & (0.066) & **(0.755)** & (0.109) \\ Peak downward CoM Velocity & \(-\)0.54 & \(-\)0.49 & \(-\)0.54 & \(-\)0.47 & 0.287 & **0.043** & 0.668 \\ (m/s) & \(\pm\)0.09 & \(\pm\)0.14 & \(\pm\)0.11 & \(\pm\)0.11 & (0.185) & **(0.530)** & (0.071) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Task’s spatiotemporal parameters’ group mean (\(\pm\)SD) differences in older adults with (HOF) and without history of fall (NHOF) and with (FOF) and without fear of falling (NFOF) when descending Steps 2 and 1, reaching level ground, and walking. Statistical significant differences are identified with bold.  
Figure 3: Graphic of mean and standard deviation of time to complete (T), velocity (V), double-support (DS) and single-support (SS) time, peak downward CoM velocity (PeakCoM), and joint angle of the ankle at heel-strike (HS) and toe-off (TO) in the ipsi (I) and contralateral (CL) limb. * Statistically significant difference.  
In the gait cycle, older adults with HoF exhibited significantly reduced ipsi ankle angles at TO than older adults without HoF (\(p\) = 0.015). The FOF group presented a significantly reduced ipsi ankle angle at HS (\(p\) = 0.041) and TO (\(p\) = 0.026) when compared to the NFOF group. Older adults with FoF exhibited significantly reduced contralateral ankle angles at TO when compared to older adults without FoF (\(p\) = 0.022). There was no interaction between HoF and FoF for any dependent variable.
# SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios
## 4 Experiments
### Performance Evaluation Standard
#### 4.2.4 Compare Experiments with Advanced Detectors  
This paper compares the detection accuracy and speed with the five most advanced detectors to verify the performance of the proposed pedestrian detection method, including YOLOv4-Tiny, YOLOv3, YOLOv4, SSD, and SlimYOLOv3. All of the target detectors use the COCO dataset as the pre-training dataset and are trained based on the VisDrone dataset. As shown in Figure 9, compared with the original YOLOv4, the volume of the SMYOLO model is reduced by 76.9%, mAP is reduced by 12.65%, and FPS is increased by 33.25%. After the scale matching, the accuracy is increased by 9.49%. Compared with YOLOv4-tiny, the proposed pedestrian detection algorithm mAP increased by 24.27%. Compared with YOLOv4, the overall FPS increased by 33.33%, achieving the accuracy of mAP and the speed FPS balance, reaching 68.09% and 54.4. As shown in Table 3, the table shows the detailed detection results of different target detectors in the pedestrian dataset extracted by VisDrone. The SSD detector uses a deeper network model, so the accuracy is high. Nevertheless, the complexity of the calculation increases, resulting in real-time performance being inferior. Although YOLOv4 minimally achieves the best real-time performance, it has a poor detection effect on pedestrian targets and low performance. SlimYOLOv3 is an excellent model to improve the YOLO series because this model reduces the complexity of the model through pruning, but the accuracy is not ideal. If deployed on a drone, it cannot achieve the balance of accuracy and delay. In general, the method proposed in this paper improves the detection accuracy while ensuring real-time performance in pedestrian target detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**BN Layer** & **Multi-Scale Training** & **mAP/\%** & **FPS** \\ \hline Y & Y & 58.6 & 57.72 \\ Y & N & 54.32 & 59.3 \\ N & N & 50.69 & 62.25 \\ N & Y & 55.41 & 58.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experimental results of SMYOLO using BN and multi-scale training.  
Figure 8: Comparison of the detection results of SMYOLO without scale matching and after scale matching. **(a)** Before scale-match. **(b)** After scale-match.