esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Antenna
### Design  
The use of a microstrip patch appeared to be the most attractive option to achieve a low profile and reduced complexity and cost. Alternative types of antennas would increase the cost and/or complexity. A dipole antenna, for example, would additionally require a balun for mode transformation from a differential feed to an unsymmetric microstrip line, and it would also need to be \(\lambda/4\) apart from the underlying ground plane for constructive interference with the ground. As a second example, although it offers a high gain, an array design based on leaky-wave antennas fed via a slotted waveguide [23] would result in a bulky structure and pose limitations on the antenna's beam-steering capabilities.  
The single element was designed using electromagnetic full-wave simulations in CST microwave studio [24], with the dimensions mentioned in Figure 2a,b. In this paper, [25] was taken as the starting point for the design of the antenna. Significant changes were made in order to adapt it to our intended application; for example, the feeding structures were optimized, a vertically stacked parasitic patch was included to enhance the impedance bandwidth in addition to other bandwidth-enhancing techniques, as mentioned later, and a low-loss substrate, a Rogers RO4350B (\(\varepsilon_{r}\) = 3.66, \(tan\delta\) = 0.0037 at 10 GHz) [26], which is suited for high frequencies, was chosen. The feeding was accomplished via aperture coupling, using two microstrip lines that coupled to the two orthogonal H-shaped slots in the antenna ground, exciting dual polarization. In comparison to other techniques like microstrip line feeding, proximity coupling, et cetera, aperture coupling offers the widest bandwidth [27; 28].  
Figure 1: Number of elements required for an antenna array to fulfill the gain and data rate requirements in downlink.  
The antenna described above would offer a typical single-element realized gain \(G_{SE}\)\(\approx\) 6 dBi, but if we would like to create an array with a reduced size, this value should be increased without compromising the surface area, i.e., a higher gain per unit area should be realized. A highly efficient way to achieve this was proposed in [29] and is based on the suspension of a superstrate above a patch antenna. When a relatively large-sized superstrate (>\(\lambda\)) is used over a small radiator with a large ground plane, significant gain enhancement is achievable when the superstrate is suspended at approximately a half-wavelength above the antenna ground. Therefore, in the second step, a superstrate was added which is approximately six times the size of the main patch, as shown in Figure 2c. The superstrate was separated from the parasitic patch by an air gap of 0.27 \(\lambda\), corresponding to a distance of 8.1 mm at a reference frequency of \(f\) = 10 GHz. It has a relative permittivity and loss tangent of \(\epsilon_{r}\) = 11.1 and \(tan\delta\) = 0.0022 at 10 GHz. The thickness \(t_{S}\) of the superstrate was 2.5 mm, which is around 0.08 \(\lambda\). These values were determined through the parametric optimization of the gain achievable at the frequencies of interest.
## 3 Method
### Classification Model Based on Triplet-EfficientNet  
In this study, we propose Triplet-EfficientNet, a modified version of EfficientNet-B7 that incorporates the triplet attention mechanism, and establish the rock classification model based on Triplet-EfficientNet. The classification model is shown in Figure 9, and its backbone is Triplet-EfficientNet, which is similar to EfficientNet-B7. The difference is that Triplet-EfficientNet consists of Triplet-MBConv, an improved MBConv module with  
Figure 8: Structure of compound pooling layer.  
stronger characterization capability. The Triplet-MBConv module is shown in Figure 10, it was improved by replacing the SE module in the original MBConv module with the triplet attention module. The classification model based on Triplet-EfficientNet can not only capture the long-term dependence between network channels but also retain the precise location information, so as to further improve the accuracy of rock image classification.  
The process of rock-type classification using the classification model based on Triplet-EfficientNet is as follows: First, the input rock images are pre-processed using methods of data enhancement, and converted into images of size 600 \(\times\) 600 \(\times\) 3 as the input of the classification model. Then the first convolution layer of kernel size 3 \(\times\) 3 will downsample the input image to achieve space squeeze and channel expansion. Subsequently, the seven stages of Triplet-MBConv layers containing the triplet attention modules will further extract high-dimensional features from the rock images. The high-dimensional feature maps are then squeezed into a two-dimensional tensor through a convolution layer of kernel size 1 \(\times\) 1, a pooling layer, and a fully connected layer. Finally, through the Softmax function, the model will output the prediction probabilities, which represent the probability value of the input rock image belonging to each type. The type corresponding to the maximum probability is the final classification result.
## 2 Materials and Methods
### Data
#### 2.2.3 Built Environment and Sociodemographic Features  
To examine the mediating effects of various factors between aggressive driving behavior and elderly pedestrian traffic accidents, we construct a built environment and sociodemographic features as independent variables. First, we achieved a 10 m resolution land use/land cover (LULC) of Seoul city, from the Environmental Geographic Information Service ([[https://egis.me.go.kr/](https://egis.me.go.kr/)]([https://egis.me.go.kr/](https://egis.me.go.kr/)) (accessed on 23 December 2024)). LULC patterns are understood as one of the most significant factors associated with aggressive driving behavior [22; 23] and elderly pedestrian traffic accidents [24]. Specifically, we utilized 18 types of intermediate level LULC features, including built-up area, agricultural area, forest and grass areas, and bareland and water areas, separating the nature of the land-cover types that are associated with aggressive driving behaviors and elderly pedestrian traffic accidents efficiently [25; 26].  
Second, this study collects the datasets of several urban facilities that have been found to be highly associated with elderly pedestrian traffic accidents. The existing studies found that the existence of public transit stations including bus stops and subway stations serve as key transit access points, often leading to high concentrations of elderly pedestrians [3]. Other facilities such as schools, hospitals, traditional markets, and tourist attractions were also found to be highly associated with elderly pedestrian populations and the possibility of crashes [2; 4]. From the Seoul Open Data Platform ([[https://data.seoul.go.kr/](https://data.seoul.go.kr/)]([https://data.seoul.go.kr/](https://data.seoul.go.kr/)) (accessed on 23 December 2024)), the spatial locations of the public transit (bus, subway) stops, elementary/middle/high schools, hospitals and traditional markets, and tourist attractions in Seoul were selected as urban facility variables.  
Third, we elaborate a number of road infrastructure variables to figure out their effects on the association between risky driving behavior and elderly pedestrian crashes. The existence of road infrastructure including crosswalks, crossings, traffic lights, and speedbumps are some of the significant obstacles that control aggressive driving behaviors [27; 28]. The child and elderly protective zones (also known as school and silver zones) are designated areas aimed at enhancing pedestrian safety for children and the elderly, and have been found to noticeably decrease aggressive driving behaviors in Korea [29; 30]. The variables for road facilities were constructed using the data provided by the Korea Transport DataBase (KTDB, [[https://www.ktdb.go.kr/](https://www.ktdb.go.kr/)]([https://www.ktdb.go.kr/](https://www.ktdb.go.kr/)) (accessed on 23 December 2024)), and those related to vehicle speed limits were constructed using data from the Seoul Open Data Platform. Lastly, to control population exposure to traffic accidents, we adopted the number of the total, elderly, and working populations within the city.
## 2 Theoretical Basis and Related Work
### The Framework of CS
#### 2.1.2 Low Dimensional Measurement  
Select the measurement matrix \(\boldsymbol{\varphi}\in\mathbf{R}^{M\times N}\) unrelated to sparse basis to obtain measurements. \(\boldsymbol{Y}\in\mathbf{R}^{M\times 1}\) is the \(M\)-dimensional measurement vector and can be acquired by linear random projections:  
\[\boldsymbol{Y}=\boldsymbol{\varphi}\cdot\boldsymbol{X}=\boldsymbol{\varphi} \cdot\boldsymbol{\psi}\cdot\boldsymbol{\theta}=\boldsymbol{A}\cdot\boldsymbol {\theta}, \tag{2}\]  
where \(\boldsymbol{A}\in\mathbf{R}^{M\times N}\) denotes the sensing matrix with \(M\geq K\cdot\log(N/K)\)[1].  
Due to the signal \(\boldsymbol{X}\) is sparse, it can be reconstructed precisely through the measurement vector provided that \(\boldsymbol{A}\) satisfies the restricted isometry property (RIP), i.e., for all \(K\)-sparse signals \(\boldsymbol{X}\), there exists a restricted isometry constant (RIC) \(\delta_{k}\) (\(0<\delta_{k}<\)1) that holds:  
\[1-\delta_{k}\leq\frac{\|\boldsymbol{AX}\|_{2}^{2}}{\|\boldsymbol{X}\|_{2}^{2}} \leq 1+\delta_{k}, \tag{3}\]  
where \(k\) denotes the order of RIP [26].  
The measurement matrix has a direct influence on compression and reconstruction performance. Conventional measurement matrixes include Gaussian matrix, Bernoulli matrix, etc.
## 3 Materials and Methods
### Image Restoration Algorithm through Ray Tracing  
Component \(V\) of the imaging sensor views the airborne scene through the wavy WAI, as shown in Figure 4. As the shape of the WAI is known, according to the principle of 3D camera imaging, this section proposes an image restoration algorithm based on inverse ray tracing. The principle of the algorithm is shown in Figure 7.  
An internal coordinate system of \(V\) consists of its optical axis and the lateral pixel coordinates of the image plane. The origin of the coordinate system of camera \(\mathbf{v}\) is at the optical center \(\mathbf{o}^{\mathrm{lab}}\). The optical axis intersects the image plane at location \(\mathbf{c}\). The focal length of camera \(\mathbf{v}\) is \(f\). In this coordinate frame, the 3D location of the pixel \(\mathbf{u}\) is  
\[\mathbf{u}^{\mathrm{cam}}=[\mathbf{u}^{\mathrm{T}},-f]^{\mathrm{T}}, \tag{14}\]  
Figure 7: The principle of reverse ray tracing.  
where \(\mathbf{u}\) is the 2D location of the pixel on the image plane, and T represents transposition. Relative to the global coordinate system, the pose of camera \(\mathbf{v}\) is defined by a rotation matrix \(\mathbf{R}\) and a translation vector \(\mathbf{t}\). In the global coordinate system, the 3D location of the pixel \(\mathbf{u}\) can be expressed as  
\[\mathbf{u}^{\text{lab}}=\mathbf{R}^{\text{T}}(\mathbf{u}^{\text{cam}}-\mathbf{ t}). \tag{15}\]  
Let \(\mathbf{u}^{\text{cam}}=0\) in Equation (15), and the origin of camera \(\mathbf{v}\) in the global coordinate system is  
\[\mathbf{o}^{\text{lab}}=-\mathbf{R}^{\text{T}}\mathbf{t}. \tag{16}\]  
In the global system, according to inverse ray tracing [31], the back-projected ray from \(\mathbf{u}^{\text{lab}}\) through \(\mathbf{o}^{\text{lab}}\) can be given by  
\[I^{\text{w}}(\mathbf{u})\equiv\left\{\mathbf{o}^{\text{lab}}+\hat{\mathbf{v} }_{\text{w}}l\right\}_{\forall l>0}, \tag{17}\]  
where the ray direction vector is  
\[\hat{\mathbf{v}}_{\text{w}}=(\mathbf{u}^{\text{lab}}-\mathbf{o}^{\text{lab}} )/\left\|\mathbf{u}^{\text{lab}}-\mathbf{o}^{\text{lab}}\right\|, \tag{18}\]  
where \(l\) denotes the propagation length along the ray. In a perspective system, the inverse projected ray intersects the water-air interface at  
\[\mathbf{q}(\mathbf{u})=\text{WAI}\cap I^{\text{w}}(\mathbf{u}), \tag{19}\]  
where the WAI estimated in Section 3.2 is known. The WAI normal vector is \(\hat{\mathbf{N}}\). According to the vector form of [PERSON]'s law, the direction vector of the airborne observing ray is given by  
\[\hat{\mathbf{v}}_{\text{a}}=n\hat{\mathbf{v}}_{\text{w}}+\hat{\mathbf{N}}\,[ \sqrt{1-n^{2}+n^{2}(\hat{\mathbf{v}}_{\text{w}}\cdot\hat{\mathbf{N}})^{2}}-n \hat{\mathbf{v}}_{\text{w}}\cdot\hat{\mathbf{N}}], \tag{20}\]  
where \(n\) is the refractive index of water, \(n=4/3\). \(\hat{\mathbf{v}}_{\text{a}}\) is the undeflected direction vector pointing to the object. Supposing that the airborne rays \(\{\hat{\mathbf{v}}_{\text{a}}\}\) of all of the pixels in the distorted image intersect with a plane in the air \(\prod^{\text{object}}\), we can easily recover the distorted image through perspective projection.
# Aerodynamics Analysis of Speed Skating Helmets: Investigation by CFD Simulations
## 5 Wind Tunnel Measurements  
Wind tunnel measurements were performed with a skating mannequin in the Open Jet Facility of the Aerodynamics Laboratories of TU Delft. The measurements were conducted at 15 m/s free-stream velocity for both the bare-head case as for the Omega helmet case (notice that the other helmets were not manufactured, hence no wind tunnel measurements with those could be performed). However, it should be noticed that the mannequin represented only the upper body, and not the full body, of the skater, as seen in Figure 7.  
Infrared thermography measurements were conducted on the head and helmet of the mannequin to determine the location of the separation line with and without the helmet. The camera OP PI-640 of the company Optris Infrared Thermometers was used. The camera was located at \(\Delta x=0\) m, \(\Delta y=-1.5\) m and \(\Delta z=0.15\) m taking the skater's head as the reference point. The recording was performed in wind-off conditions (null free-stream velocity), as well as at 15 m/s free-stream velocity. Each recording retrieved 50 frames that were averaged to attenuate the noise in the data. By computing the difference in the averaged recorded data between 0 m/s and 15 m/s cases, the convection of the flow over the mannequin surface was captured.  
Force balance measurements were conducted to evaluate the drag reduction between Omega helmet and bare-head configurations. As the experimental model differed from the numerical model, the comparison was carried out based on the relative results with respect to bare-head configuration of each case (numerical or experimental). A six component balance designed, manufactured and calibrated by the Dutch Aerospace Laboratory was used [33]. It was able to measure loads up to 250 N in the streamwise direction with an uncertainty of 0.06% of the full scale value [33]. The balance was placed under a ground plate that was shielded from the air stream.  
Figure 7: Skater model used in the wind tunnel measurements for (**a**) bare-head and (**b**) Omega helmet cases.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 5 Discussion
### Comparative Performance Analysis  
At Table 5, a detailed listing of the performance of state-of-the-art-models in terms of effectiveness (expressed by the mIoU metric) and efficiency (expressed by the FPS metric) for both \"Cityscapes\" and \"CamVid\" datasets. Concerning the results of the \"Cityscapes\" dataset, STDC1-50 presents the best result in terms of inference speed with 250.4 FPS and 71.9% mIoU in terms of accuracy. Moreover, FasterSeg achieves 163.9 FPS in terms of inference speed and 71.5% mIoU in terms of accuracy. Finally, BiSeNet V2 and Fast-SCNN achieve 156 FPS and 123.5 FPS with 72.6% and 68% mIoU, respectively. The results of these models are exceptional and really promising about the future of real-time semantic segmentation. As far as the results on the \"CamVid\" dataset are concerned, FasterSeg presents the outstanding results of 398.1 FPS in terms of inference speed and 71.1% mIoU in terms of accuracy. Moreover, DDRNet-23-slim achieve 230 FPS and 78% mIoU, while STDC1-Seg achieved 197.6 FPS and 73% mIoU.  
After the aforementioned objective evaluation, we examined the presented models by taking into account the trade-off between speed and accuracy which can be shown in Figure 27a,b. In particular, in Figure 27a, where experimental results for the \"Cityscapes\" dataset are considered, we can observe that STDC1-50 [11] presents a satisfactory trade-off between mIoU and FPS, compared to the other models. Although other models such as U-HardNet70, BiSeNet v2 Large, SwiftNetRN-18 and ShelfNet18 achieve better performance in terms of accuracy, they lag behind in terms of frame rate. Moreover, in Figure 27b, where experimental results for the \"CamVid\" dataset are considered, it is shown that FasterSeg [27] can be considered to be an appealing choice for real-time semantic image segmentation, by taking into account the trade-off between accuracy and efficiency compared to all other models.
## 3 1-D CNN Single-Measurement Point Diagnosis Model  
The vibration signal of the reciprocating compressor is complex and difficult to get the features that have good characterization capacity. In this paper, a 1D CNN single-measuring point diagnosis model is proposed and its structure is shown in Figure 4. The end-to-end model using the original vibration signal as an input to 1D CNN to perform feature self-extraction and fault diagnosis.  
Figure 3: M-P neuron model.  
Figure 2: Fully connected layer structure.  
The fault diagnosis model of 1D CNN single measuring point for reciprocating compressor valve includes 5 1D convolutional layers (Conv1D), 4 maximum pooling layers (MaxPooling 1D) and 2 fully connected layers (Dense). Among the model, the size of the convolutional layer and the convolution kernel both are 3 and the convolution step size is 1. Besides, the size of the pooling layer is 2 and the pooling step size is 2. Because this classification problem belongs to the 4 classifications problem, the output of the last layer is 4 neurons and uses softmax as the activation function. Therefore, the corresponding output is the probability value of each category and the category with the highest probability is the type of fault diagnosed. The specific parameters of the network are given in Table 1.  
In order to improve the generalization performance of the model, the model applies Dropout regularization, batch normalization and data augmentation technology. Data augmentation is to intercept part of the original signal by moving the interception window. When the moving window intercepts the original signal once, a new sample is obtained. Among them, 6964 samples were obtained after data augmentation and 70% of which were used as the training set and 30% as the test set.
## 3 Novel Approach EKF Tuning
### Mathematical Modeling  
In this subsection, the mathematical equations required for the implementation of the algorithm are discussed. Detailed explanations of the methods and equations used to ensure its proper functioning are provided.  
For the input data, ideal inertial sensor data are used, which can be obtained through the trajectory generators proposed by [PERSON] [43] or tools like MATLAB's Navigation Toolbox. The IMU errors are derived from the manufacturer's datasheet, and the provided data are implemented accordingly. The next topic will discuss the IMU errors in detail and explain how they are integrated into the system.
## 5 Discussion
This framework included three essential components: (1) periodic manual audits, conducted regularly on randomly selected subsets of extracted data to identify systematic errors or misinterpretations by individual models; (2) defined human-in-the-loop checkpoints, strategically positioned after initial data extraction and prior to consensus formation, ensuring reviewers could systematically review and resolve ambiguous or conflicting outputs; and (3) automated triggers for human intervention, activated by pre-defined thresholds such as low-confidence scores, significant discrepancies among model outputs, or parsing anomalies. Our pipeline was intentionally structured into three distinct processing stages (trial retrieval, initial filtering, and structured data extraction) with automated halts designed explicitly to facilitate careful human validation before advancing. This structured integration of human oversight addresses critical ethical considerations, enhances data reliability, and bolsters the practical validity of the high \(\kappa\)/ICC scores reported, underscoring the real-world applicability of our results in systematic reviews and clinical decision-making.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 4 Algorithms for Multiple GWs Placement  
We assume there are \(N\) SNs \(\{s_{1},s_{2},\cdots,s_{N}\}\) with known locations, and there are number of \(M\) GWs \(\{g_{1},g_{2},\cdots,g_{M}\}\) to be installed in this scenario to maximize PDR. We also assume \(z\leq\frac{1}{\tau^{2}}\), as explained in Section 3.3, which implies that decoding crescents exist. Finally, without loss of generality, we set the location margin \(\delta=0\).  
Our general approach is to superimpose the decoding crescents for all possible pairs of sensors, and place the GWs so that jointly, they serve the maximum number of sensor pairs. We propose two greedy algorithms that differ in the way they discretize the set of possible locations. The weight bipartite graph (WBG) approach uses intersection points of decoding crescents and the PGL approach limits potential GW locations to points or \"pixels\" in a rectangular grid.
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 3 Algorithm to Detect Vacant Parking Spaces
### Parking Space Detection Stage
#### 3.2.1 Feature Extraction  
As shown in Figure 4, parking space markings typically comprise a lane boundary line (the red line in Figure 4) and multiple divider lines (the yellow lines in Figure 4) that are parallel to the lane boundary line. Using this characteristic, we can construct an entire parking space by detecting the junctions between the lane boundary line and the divider lines. For corner detection, we chose the features from accelerated segment test (FAST) detector [24] instead of the traditional Harris corner detector. The key difference between the FAST corner detector and other corner detectors is that the FAST detector is an accelerated method that does not need to calculate image pixel gradients; instead, it only needs to determine the relationships between a specific pixel and 16 pixels in a circular mask. Because the circular mask around the corner candidate in the segment test of FAST is operated by examining only four pixels (the four compass directions) for rejecting non-corner points, through sampling, four of the masked pixels are chosen to predict the result. Therefore, this method is substantially faster.  
Figure 5 shows the results using the FAST detector; green dots indicate corners. In addition, to reduce outliers (i.e., corners that are not part of the parking space) and to reduce the processing load in the next stage of feature classification, we defined a region of interest to limit the corner detection field (the area outlined in brown in Figure 5).  
Figure 4: Characteristics of parking space markings.  
Figure 3: Algorithm flowchart.