esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Description
It involves some pre-processing of raw seismic data and padding or trimming to maintain a uniform vector length across the three components of East, North, and Vertical, followed by their concatenation into one feature vector; this vector then gets normalized and turned into a stable input for the succeeding autoencoder network. It acts as a nonlinear feature extractor that first compresses the data from the input in the encoder to a 16-dimensional latent space representation, then reconstructs it in the decoder phase. The decoder output provides embeddings that summarize the most striking features of the data for classification (Figure 3). We used an autoencoder with the following properties: learning rate: 0.01; optimizer: Adam; batch size: 32; loss function: Mean Squared Error (MSE); and number of training epochs: 50. Figure 5 depicts the training and validation loss across 50 epochs. The training loss (shown in green) regularly stays somewhat lower than the validation loss (depicted as a red dashed line). The minimum difference between the two curves implies that the model learns successfully and generalizes well to unknown validation data. These embeddings are input for the stacked ensemble model, an architecture that includes carefully chosen powerful base models: a multi-layer perceptron and an XGBoost classifier. This ensures that the model leverages complementary strengths from both algorithms. A meta-model combines these model outputs, and another MLP merges the base model predictions to provide an overall classification output. The resulting ensemble technique overcomes overfitting and, thus, adapts to complex, imbalanced datasets with  
Figure 4: t-SNE visualization of the 16-dimensional latent space learned by the model. robust performance. The model performance is evaluated using measures such as accuracy and the area under the receiver operating characteristic (ROC) curve (AUC), with applications in seismic hazard assessments, site characterization, and predictive geophysical modeling [39; 40; 41; 42]. This work presents an innovative combination of deep learning and classical ensemble techniques, proposing a flexible and scalable model for different seismic applications. It describes a sophisticated mixture of signal processing, unsupervised feature extraction, and ensemble learning to conduct the difficult task of discriminating liquefaction potential classes in highly imbalanced datasets.
## 4 Scenario Analysis
### Scenario Description  
In order to test the applicability of our model and algorithm, we took JD distribution in the main city of Chongqing as an example and analyzed the effect of the algorithm in the actual distribution environment. Here, distribution maps of JD distribution centers and customer locations were obtained through Baidu maps, the map marking tool and other tools, which are shown in Figure 2.  
As shown in Figure 2, there were 10 customer nodes and one distribution center in the scenario of JD. In the scenario, the distribution center needed to complete the relevant delivery within the specified time because each customer had its own delivery time window. Meanwhile, the main area of Chongqing has an advanced transportation network, which provided JD with more route choices. Therefore, the distribution center needed to select the optimal travel route to complete the distribution operation in consideration of the delivery time window of each customer node, so as to improve the distribution efficiency of JD logistics.
## 5 Data Products, Processing and Distribution  
### Data Products  
NASA's Earth Science Data Systems program references data products based on their level of processing, ranging from Level 0 to Level 4 [20]. Level 0 (L0) data products include unprocessed instrument data; Level 1 (L1) products are time-referenced data that have been processed to at-sensor radiometric units; Level 2 (L2) products are geophysical variables derived from L1 products; and Level 3 (L3) products are geophysical variables mapped on a space-time grid scale. G-LiHT data products (L1 through L3) are listed in Table 2 for each of the instruments. Many of these products will help end users create additional higher-order products (e.g., solar and view angles are needed for computing bidirectional corrected reflectance; terrain and canopy heights are needed for orthorectification) and facilitate scientific data analysis (e.g., cloudiness and solar illumination conditions affect canopy photosynthesis) [21].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Instrument** & **L1** & **L2** & **L3** \\ \hline
**Oxford RT-4041 GPS-INS** & Trajectory data & \(\star\): Aircraft elevation & \(\star\): Aircraft elevation \\
250 Hz measurement rate & (coordinates, roll, pitch, yaw) & \(\star\): Aircraft altitude AGL & \(\star\): Aircraft altitude AGL \\  & & pitch, yaw) & \(\star\): Geographic Look-Up Table & \(\star\)View angle \\  & & (GLT) & \(\star\): View azimuth \\ \hline
**Riegl VQ-480 Scanning** & Return data & \(\star\): Classified return data (ground, & \(\star\): LiDAR returns (”point \\
**Lidar** & (coordinates, scan & non-ground) & clouds”) \\
1550 nm laser & angle, return & \(\star\): AGL heights & \(\star\): DTM \\ discrete returns (\(\leq\)8 pulse\({}^{-1}\)) & number, apparent & & \(\star\): CHM \\
150 kHz measurement rate & reflectance) & & \(\star\): LiDAR metrics \\ \hline
**Headwall Hyperspec** & At-sensor radiance & \(\star\): At-sensor reflectance computed & \(\star\): At-sensor reflectance \\
**Imaging Spectrometer** & spectra & with observed irradiance & computed w/observed \\
417 to 1,007 nm & (W\(\cdot\)m\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\)) & \(\star\): Surface reflectance computed & irradiance \\
402 bands, \(\leq\)5 nm FWHM & & with atmospheric correction & \(\star\): Surface reflectance computed \\
1,004 pixels per line & & \(\star\): Fluorescence [experimental] & w/atmospheric correction \\
50 Hz measurement rate & & & \(\star\): Common vegetation indices \\  & & & \(\star\): Fluorescence [experimental] \\ \hline \hline \end{tabular}
\end{table}
Table 2: G-LiHT data products for public distribution. FWHM, full width half maximum; PAR, Photosythetically Active Radiation; DTM, Digital Terrain Model; CHM, Canopy Height Model.
# Physical and Respiratory Rehabilitation in Spinal Muscular Atrophy: A Critical Narrative Review
## 4 Discussion
### Respiratory Interventions  
Respiratory interventions primarily targeted people with SMA I, recognising their need for timely pulmonary care to extend their life expectancy. People with SMA commonly exhibit combined inspiratory and expiratory muscle weakness, while the diaphragm is partially spared. This weakness compromises the cough mechanism, leading to recurrent infections and subsequent nocturnal hypoventilation, hypercapnia, desaturation, and paradoxical breathing [46]. In recent years, pulmonary treatments have shifted from a reactive to a proactive approach, particularly with the introduction of early treatments [50]. Chronic proactive respiratory management comprehends various strategies, including airway clearance through manual or mechanical physiotherapy techniques, cough assistance, and NIV (BiPAP) support during sleep. In severe cases, tracheostomy may be necessary [50].  
NIV (BiPAP) was associated with improved sleep quality, reduced hypercapnia, and facilitated hospital discharge in SMA I [44; 46]. [PERSON] et al. suggested timely and appropriate use of pulmonary aids (among which especially NIV), rather than immediate tracheostomy, can ensure survival, although instances like thoracic infections may lead to temporary periods of intubation and hospitalisation [44]. Polysomnography conducted during BiPAP in people with SMA I and II has revealed differences in thoracic-abdominal movement coordination (measured by phase angle, transcutaneous oximetry, and SpO2 desaturation rate), suggesting that nottrume NIV (BiPAP mode) might normalise various parameters such as AHI, SpO2, and inspiratory muscle synchronisation [56]. Another study on NIV supported its potential role in enhancing muscle synchronisation during sleep and improving sleep quality [46]. Proper facial mask positioning has been highlighted as crucial, as incorrect positioning may negatively affect the hemodynamic status of people with SMA undergoing long-term treatment (14-72 months) [53]. However, further research with long-term follow-ups is recommended to fully understand the effects of BiPAP therapy on hemodynamics, disease progression and motor function.
###### Abstract
To achieve precise segmentation of the kidney region, an efficient kidney segmentation method is proposed. The classic ResNeXt50 network [13] is utilized for downsampling, extracting multi-scale information containing kidneys at different network layers, and integrating deep feature information into shallow feature maps. The multi-scale fusion features contain rich texture and semantic features, addressing the problem of blurry kidney contours in abdominal CT images and optimizing feature representations at different network layers;
2. Utilizing residual full-attention convolutional modules to recalibrate multi-scale features can expand the information contained in features and further fit features to improve the output results. Adopting a multi-path parallel approach to obtain four sets of average segmentation outputs enhances the segmentation performance of the model. The model addresses the problem of large variations in kidney position by introducing a complete attention mechanism to handle multi-scale fusion features and obtain correlated features between different features, thereby resolving attention deficits;
3. Excellent segmentation results are achieved on the Kits19 dataset, with a Dice coefficient of 0.972 for kidney segmentation. Compared to other kidney models, this performance is superior, demonstrating outstanding segmentation capability, particularly surpassing the classic U-Net [14] model by 5.3%.
# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 4 Experimental Results
### Efficiency Analysis  
To investigate the time efficiency of our proposed method, we record the detection time on a PC computer with Intel(R) Xeon (R) CPU E5-2630 (2.2 GHz), 128 GB of RAM, and GPU Tesla K40C. Results show that it takes about 3.2 milliseconds for our algorithm to detect one fingerprint image, which is fast enough to be favored in real-time applications.
# Electromagnetic Assessment of UHF-RFID Devices in Healthcare Environment
## 3 Results
### Simulations of E-Field Distribution from AG-RFID Reader Using In-House 3D-RL Algorithm  
E-field distribution within cut plane heights is presented in Figure 11. The maximum values are obtained inside the room in which the AG-RFID reader is located. It was positioned on the center of a room ceiling, with a height around 3.5 m, covering an estimated zone of 8 \(\times\) 8 m. As height increases, so do the average E-field levels, as the measuring points are closer to the reader antenna.  
To validate the E-field results obtained with the 3D RL approach, they have been compared with those from the ward scenario in the real case, considering different angle values (with orientations of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\) and 135\({}^{\circ}\)) and different cut-plane heights (h = 1 m and h = 1.7 m) inside the room. Some results are displayed in Figures 12 and 13, showing good agreement between the simulation and measured levels, within a linear radial distribution, 0\({}^{\circ}\) orientation, and cut plane height of 1 m. Deviations between the simulation and measurement results were in the range of 2-5% in E-field value estimation, and are given by differences between the topo-morphological description of the simulation scenario as compared to the real one, as well as deviations in material parameters.  
Figure 11: E-field distribution within the hospital ward simulation scenario [13].  
Figure 12: E-field estimation within a linear radial distribution, 0\({}^{\circ}\) orientation, cut plane height 1 m [13].  
A concise and precise description of the experimental work is presented here. Wide and detailed information may be found in [13].
# Research on Automatic Recognition Method of Artificial Ground Target Based on Improved HED
## 3 Research on Automatic Target Recognition Algorithm Based on Geometric Primitives of Line Groups  
Geometric primitives usually refer to the basic elements that constitute geometric figures. Common geometric primitives include corner, line segment, circle, ellipse, etc. Geometric primitives' extraction plays an important role in model-based computer vision. [PERSON] et al. used the arc geometric primitive to match the workpiece [14]. [PERSON] et al. used points to extract architectural plane features [15]. [PERSON] et al. used geometric primitives to characterize the scattering characteristics [16]. Common artificial ground target are mostly composed of straight lines. The ratio of geometric primitive lines to the triangle area formed by their combination is used as the feature quantity to describe the group of lines, so as to characterize the artificial ground target. In this paper, we searched for geometric primitives that could reflect the characteristics of the structure of the target, and then used these characteristics to characterize the target.  
Firstly, line segments were extracted by Hough transform in the image. According to the spatial proximity relationship, multiple line segments with common intersection points between line segments were formed into the geometric primitive of the common point line. Hough transform is a common method to recognize line features in image processing, it can transform line coordinate space into parameter space. When combining line segments, first combine the line segments in pairs, determine the threshold value of the neighborhood of the line segment according to the length of the line segment, combine the adjacent line segments in space and calculate the intersection point of the line segments. Finally, combine the line segments with the common intersection points into the primitives of the common point line. The method is shown in Figure 7.  
In this paper, four lines on the target image were selected as the affine invariant feature of the geometric primitive, and this feature was used to represent the measured target. The similarity between the two targets was measured by the distance between the feature quantities of the two line groups. After finding the line group that matched the most, the homonymous points between the target template and the real-time image were found, the affine transformation matrix was obtained, the target positioning in the real-time image was completed and the target was automatically identified. The main process is shown in Figure 8.  
Figure 7: Triangle formed by line group.  
When target detection was carried out in the real-time image, it was necessary to detect lines in the real-time image first, and four different line combinations from all lines were selected as the geometric primitives of the candidate targets. Because the real-time image was easily interfered with by noise, it needed to be preprocessed. The preprocessing included filtering, edge extraction, line detection and other steps.
# Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment
## 5 Conclusions  
This paper presents a methodology based on a surrogate model (PCE method) to improve the microwave transmission link calculation in a biomedical application involving an implanted antenna. Firstly, the transmission scenario and the embedded antenna design are presented. Then the PCE method and the construction of a surrogate model based on PCE are discussed in details. With this method, the surrogate model of the transmission link is established. The trustworthiness of the model is firstly validated by LOO error and the impact of the different variables on the results is compared by calculating the Sobol indices of these variables. The result of finding that the implantation depth is the most important factor is also theoretically reasonable since the muscle tissue has a higher permittivity and thus leads to more loss. Finally, a surrogate model that is based on limited number of samples is used to estimate the results of other variable combinations. The average relative error could be easily controlled within 2% by using less than 1/4 of all samples, which proves the possibility of saving largely the calculation time by using this method when the model is relatively complex and a great number of calculation is needed. Such approach is an engineering oriented tool which may improve the design of wireless power transfers in a biomedical scenario.  
Conceptualization, S.D.; L.P.; methodology, L.P.; software, S.D.; validation, S.D.; L.P.; formal analysis, S.D.; investigation, S.D.; resources, S.D.; data curation, S.D.; writing--original draft preparation, S.D.; L.P.; writing--review and editing, S.D.; L.P.; visualization, S.D.; supervision, L.P.; project administration, L.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Samples** & & & & \\ (\(B\), \(R\), \(F\), \(M\), \(I\)) (mm) & **Simulated Gain (dB)** & **Estimated Gain (dB)** & **Absolute Error (dB)** & **Relative Error (\%)** \\ \hline (10; 30; 10.5; 37; 15) & \(-\)29.7364 & \(-\)29.4675 & 0.268919 & 0.9043 \\ (10; 36.67; 10.5; 37.5; 11.5) & \(-\)29.0338 & \(-\)28.6122 & 0.421595 & 1.4521 \\ (3.3; 30; 10
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 4 UTM System Architecture
### Data Base  
The function of the Data Base module is to handle a digital data base with the required information to represent the situation of the current UAS operations, in the airspace managed by the UTM system. Basically, this information is made up of active geofences and UAS operations. The DB works as a server for the rest of the UTM system and hence, other modules can read the database in order to carry out their tasks (e.g., the Monitoring module uses the UAS predicted trajectories to detect events of lack of separation); or they can write the database to update the airspace situation (e.g., the USM can notify new accepted flight plans and the new EM geofences).  
The DB manages two types of objects internally: geofences and UAS operations. Tables 2 and 3 depict the data structures for each of these objects. A geofence is a 4D portion of the airspace (a 3D geometrical space with an activation period of time) which has special restrictions for UAS, like flight prohibition. In the UTM context, the term _dynamic_ geofence is used for those created during the UAS operation, while the _static_ geofences are set in a pre-flight phase. The DB stores for each geofence in the airspace the following information: a unique identifier, its type (cylindrical or polygonal), its geometry definition, its minimum and maximum altitude, and its starting and finishing time instants. Besides, the DB stores each UAS operation, which consists of the following data: a unique identifier for the UAS, given by its ICAO (_International Civil Aviation Organization_) address; the priority level of the operation; its associated flight plan; the next waypoint assigned to the UAS; the predicted trajectory of the UAS; a brief description of the UAS operation; and the sizes of the Flight Geometry and the Operational Volume.  
\begin{table}
\begin{tabular}{c c c} \hline
**Attribute** & **Data Type** & **Description** \\ \hline Identifier & Integer & Unique number for geofence identification \\ \hline Type & Enum & Cylindrical or polygonal \\ \hline Geometry & List of 2D waypoints & Definition of the horizontal shape, defined by a circle or a polygon \\ \hline Min/max altitude & Float & Altitude range where the geofence is active \\ \hline Start/end time & Float & Time period in which the geofence is active \\ \hline \end{tabular}
\end{table}
Table 2: Attributes of a geofence object.
## 3 Results and Reports  
### Case Study: Blood Pressure  
We distinguished between two different readings of BP, the systolic (S) and diastolic (D) values. We could also use the mean arterial pressure (MAP) as an intermediate value that represents S and D. In each simulation process, we generated multiple datasets representing different realistic medical cases. Table 2 shows related medical rules and acronyms on which our study was based.  
In order to apply the data processing described in this paper, three datasets for three different patients were generated, where \(\mu\_\)Sys, \(\sigma\_\)Sys, \(\mu\_\)Dias and \(\sigma\_\)Dias represent the mean of Systolic measures, standard deviation of Sys measures, mean of Diastolic measures and standard deviation of Dias measures respectively, as shown in Table 3. Since our data source structure was a transactional database, each Dts_i belonged to a time interval varying from T\({}_{i0}\) to T\({}_{if}\), where 'i0' and 'if' were the initial and final time entries of each dataset respectively.  
These datasets were as follows:  
* Dataset1 (Dts_1) corresponding to (Patient1): suffering from high blood pressure (HBP);
* Dataset1 (Dts_2) corresponding to (Patient2): suffering from pre-high blood pressure (PHBP);
* Dataset1 (Dts_3) corresponding to (Patient3): suffering from low blood pressure (LBP).  
Figure 10 shows two box-plot representations of generated BP values. It gives a detailed view of the different values contained in our datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Blood Pressure** & **Systolic (S)** & **Diastolic (D)** & **(MAP)** \\ \hline HBP (High Blood Pressure) & 141 \(\leq\) S & 91 \(\leq\) D & 107 \(\leq\) M \\ PHBP (Pre-High Blood Pressure) & 121 \(\leq\) S \(\leq\) 140 & 81 \(\leq\) D \(\leq\) 90 & 94 \(\leq\) M \(\leq\) 106 \\ IBP (Ideal Blood Pressure) & 91 \(\leq\) S \(\leq\) 120 & 61 \(\leq\) D \(\leq\) 80 & 71 \(\leq\) M93 \\ LBP (Low Blood Pressure) & S \(\leq\) 90 & D \(\leq\) 60 & M 70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the international blood pressure standards.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & \(\mu\_\)**Sys** & \(\sigma\_\)**Sys** & \(\mu\_\)**Dias** & \(\sigma\_\)**Dias** \\ \hline Dts\_1 & 110 & 10 & 73 & 6 \\ Dts\_2 & 107 & 7 & 74 & 7 \\ Dts\_3 & 98 & 11 & 67 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical summary of the generated datasets (Dts_i).  
Figure 9: Proposed anomaly classification using the normal distribution.