esa-sceva/satcom-chunk-collection huggingface dataset:
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 2 Capabilities
### SMAP-R Spatial Resolution
Finally, the coarser resolution is obtained over ocean, where 44.8 km diameter resolution is achieved. For an ocean scenario, the size of the scattering zone can expand over hundreds of kilometers depending on its roughness, i.e., wind speed, but SMAP-R is observing the scattering power filtered by its antenna. The SMAP antenna's -3 dB beamwidth footprint is estimated to be 40 km, therefore SMAP measures scattering power from slightly further away from its antenna's -3 dB beamwidth. The incidence angle of the GPS reflections has not been accounted for in Table 1, but it is an important parameter to consider. Derived from an analysis similar to the one shown in Table 1, a look-up-table (LUT) linking the size of the scattering area (semi-major and semi-minor axis of an ellipse) to the delay that accounts for the N% of the total scattering observed with the incidence angle can be computed. This LUT would be used reduce the computational cost of the calculation of iso-delay and iso-Doppler lines for each measurement. In other words, the DDM provides the delay that represents N% of the total power observed as well as the incidence angle of the measurement. From these two variables, the LUT provides the size of an ellipse representative of the scattering area. The LUT approach is not surface-dependent, and therefore is valid for any type of class such as sea ice, wetlands, rivers, lakes, bare soils, vegetated soils, desserts, and oceans. Sea ice DDMs contain the N% of the power within short delays, while ocean DDMs contain the N% of the power within longer delays. Future studies will include analyzing the selection of N to be representative of the scattering area accounting for the incidence angle. Another approach might include selecting the DDM samples over the noise level for each DDM instead of a fixed N% value. We also need to consider how the SMAP antenna pattern will constrain the DDM footprint in some instances (e.g., oceans or highly vegetated areas). Further discussion of the impact of the SMAP antenna pattern on the DDM footprint is discussed in Section 3.2.
# Recognition of Customers' Impulsivity from Behavioral Patterns in Virtual Reality
###### Abstract
Moreover, these methods can be applied to low-cost, noninvasive signals, which can be accessible through VR headgear such as 3D eye movement, gaze information [16], eye blink rate [64], head movement and orientation, controller (hand) movement, and orientation [19]. These signals can be highly informative. For instance, head and eye movement imply macro and micro levels of attention allocation, respectively [4], and eye-tracking represents visual perception and processing, which are related to human behavior, cognitive status, and psychological activities [33]. By tracking the head position in 2D, the navigational pattern of the shopping travel can be developed. This is an important signal since--based on the literature--up to 80% of a shopper's in-store time is allocated to navigating [65]. Recent studies have been conducted in the VR environment to study impulsivity behavior. For instance, [PERSON] et al. investigated impulsive buying by using a virtual grocery store implemented through an HMD. In this study, the analysis was performed based on post-interviews conducted after the VR experience [17]. A second study explored the impact of impulsivity on risky decisions through an immersive virtual reality game and physiological signals [66]. A third used self-assessment to investigate the impact of risky environments on the emerged level of impulsivity and sensation-seeking [67]. Although using VR in studies of consumer behavior is trending, to our knowledge, no research has been made to investigate and recognize the impacts of impulsivity on shopping behavior using virtual behavior tracking. Either the other works did not investigate behavioral signals or they did not focus on consumer behavior. The current study has been conducted to examine the relationship between customers' behavior while shopping and their impulsivity. Moreover, it has explored which kind(s) of behavior(s) could represent different subscales of impulsivity. To achieve this, it was necessary to define some tasks that could elicit certain types of behavior so customers could be distinguished based on their impulsivity levels. Hence, the research questions for this study are:  
To what extent can shoppers' impulsivity be predicted by their behaviors while shopping in virtual shops? Which types of behaviors and signals are more informative for predicting shoppers' impulsivity? Which types of tasks elicit the discriminating human impulsivity behaviors? Which types of feature sets can distinguish subscales of impulsivity?
# High-Precision Pointing and Tracking System Design for Near-Space Balloon-Based Optical Observation
## 2 Methods and Analysis
### Secondary Precise Tracking System
#### 2.3.3 Accuracy Analysis  
Tracking accuracy refers to the deviation in the line of sight of a target with certain motion characteristics that reflects the stable tracking of the platform. Tracking accuracy mainly depends on the reliability of the servo control system and tracking algorithm.  
The servo control factors that affect tracking accuracy mainly include the tracking loop correction algorithm and the stability loop. The main evaluation indicators of the tracking loop correction algorithm are bandwidth and static error. The wider the tracking loop bandwidth, the better the stable performance, and the smaller the static error, the better the tracking performance [33].  
At present, the volume and weight of this system are relatively large, and the interference torque of the shaft system is controlled below 0.5 N\(\cdot\)m. The gyroscope noise is controlled below 7\({}^{\circ}\)/h (the gyroscope noise index is 5\({}^{\circ}\)/h, and according to experience, the noise may be increased due to power supply noise, reading noise, etc.). The oscillation frequency and amplitude of the balloon are very low (the balloon is unpowered, the high-altitude wind direction and wind speed are fixed, and the oscillation frequency and amplitude of the balloon are not high). According to the response curve of the stable circuit to the interference torque (0.5 N\(\cdot\)m, 1\({}^{\circ}\), 0.1 Hz), the peak stability accuracy is 23 \(\upmu\)rad, and the root mean square value is 9 \(\upmu\)rad, which can meet the accuracy of \(\leq\)25 \(\upmu\)rad, providing a guarantee for improving tracking accuracy.  
The purpose of an automatic tracking loop is to ensure the framework to track targets within a certain speed and acceleration range and achieve the required steady-state and dynamic performance. The main factors affecting the steady-state and dynamic performance are the bandwidth and open-loop gain of the tracking loop system.  
The image tracker can be regarded as a sampling and holding stage, where the tracker outputs an error signal once per frame and holds it for one frame, with a sampling frequency equivalent to a frame rate of 80 Hz. Due to the 12.5 ms delay in each error signal output of the image tracker and the error signal containing a certain high-frequency component,  
Figure 8: Diagram of the stable pointing and tracking function.  
Figure 7: Diagram of the pitch axis system.  
the tracking loop is usually calibrated to an I-type system to improve the stability of the system and achieve better tracking results.  
Considering the impact of the interference torque caused by balloon oscillation, under the interference torque of frequency of 0.1 Hz, amplitude of 1\({}^{\circ}\), and 0.5 N-m, the target motion speed is input under conditions such as 4\({}^{\prime}\)/s (Earth's rotation speed; the sun is basically stationary), and the tracking accuracy simulation result is 0.02 mrad, about 4\({}^{\prime\prime}\), which can realize the 10\({}^{\prime\prime}\) tracking accuracy.
## 3 Fault Diagnosis Method
### DBN Modeling  
In 2006, a DBN model with an efficient learning algorithm proposed by [PERSON]. This algorithm becomes the main framework of the deep learning algorithm later. It can extract the required features from the training set automatically [32; 33]. The typical model is the restricted Boltzmann machine (RBM). The features extracted automatically solve the careless factors in the manual extraction, and initialize the weights of neural network. Then Softmax function can be used to classify, and contributes good experimental results. DBN can be composed of multi-layer RBM. A typical DBN model with double-layer RBM is shown in the Figure 2.  
The process of DBN training model can be mainly divided into two steps.  
Step 1: Unsupervised pretraining. Each layer of RBM network is trained independently and unsupervised to ensure that as much feature information as possible is preserved when the feature vectors are mapped to different feature spaces. The greedy method is adopted between the layers training, and the process is as follows:  
1. The input layer \(V_{0}\) of the first RBM is also the input layer of the entire network. It typically involves training the first layer RBM by applying contrastive divergence. \(W_{0}\) is the weight in the first RBM.
2. The hidden layer \(H_{0}\) of the previous layer RBM can be seen as the visible layer \(V_{1}\) of the back layer RBM, followed by iterative training remaining RBM. \(W_{1}\) is the weight in the back layer RBM.  
Step 2: Classification decision. Set the feature vectors from unsupervised reconstruction as the input feature vectors of Softmax function, which is set at the last layer of DBN to supervised train and classification decision.  
Figure 2: Deep belief network (DBN) model with double-layer restricted Boltzmann machine (RBM).
# Measurement Grid Optimization for OTA Testing of 5G Smart Watches
## 3 Reference Patterns for MU Analysis of Different Grid Configurations  
In this paper, a variety of coarse grid configurations are analyzed to compare their accuracy in reconstructing the reference patterns and subsequently to determine the related MUs. The reference patterns used in this research are passive patterns measured from a 5G commercial smart watch at different bands, with very fine angular resolution. The rationale for this operation is based on the following considerations:  
1. Based on the radiation behavior investigation conducted in CTIA [19; 20], radiation properties among different smart watches from different manufacturers are highly similar, owing to their very limited space and working volume, especially the nearly identical usage scenario of wrist-worn devices. This has become a consensus in the industry. Therefore, in the CTIA Small Form Factor Device Inter-Lab Comparison Test Exercise, only one Vowor watch was used as the reference device. Different labs performed measurements using this reference device, and their testing results were compared. Furthermore, radiation behaviors of smartphones from different vendors are also considered highly similar by CTIA and 3 GPP due to their highly similar usage scenario of handheld devices. Accordingly, only one or two smartphones were used as the reference devices in the CTIA and 3 GPP Inter-Lab Comparison Test Exercise for smartphones [21; 22; 23];
2. These patterns can represent the general radiation behaviors of typical 5G commercial smart watches, including the directivities, variations over angles, etc.  
Accordingly, instead of analyzing the grid influence using individual radiation patterns from various devices and manufacturers case by case, the MU analysis for grid influence is performed using a single set of reference patterns. This ensures that the analysis and related conclusions are applicable to all cases while simultaneously reducing the workload and duration of the analysis.  
The reference patterns are measured in wrist-worn condition, i.e., with the smart watch worn on the forearm phantom, as illustrated in Figure 3. During the measurement, both the E-field amplitude and phase values are measured and stored in the near field using the WPTC-L chamber from [PERSON] and Schwarz, while the reference patterns are obtained through a near-field to far-field transformation (NFTF). The adopted reference patterns for various bands under wrist-worn condition are illustrated in Figure 4, featuring a very fine angular resolution of \(\Delta\theta=\Delta\phi=1^{\circ}\).  
The measured directivities at different bands are listed in Table 2 for reference.
## 4 Dataset Acquisition
### Convolutional Neural Networks  
Convolutional Neural Networks (CNNs), a class of feed-forward artificial neural networks, are essential for the processing and analysis of digital images. Comprising main layers, including the convolutional layer and the Pooling Layer, CNNs identify patterns in the image and reduce the dimensionality of feature maps, respectively [50]. As image data progress through the layers of the CNN, the network begins to recognize elements of the object, revolutionizing computer vision by offering a scalable approach to image classification and object-recognition tasks [13; 51].  
Fine-tuning in CNNs is a technique that adapts pre-trained models to specific tasks, reconfiguring them instead of starting training from scratch. This approach saves resources by using pre-trained weights, such as those from the ImageNet dataset [52], and offers knowledge transfer, leveraging the models' prior knowledge of general image features for tasks such as object classification or disease detection [53; 54].  
In this study, we adopted a hybrid training approach. Initially, we removed the FC layers from the pre-trained model and added new fully connected layers, randomly initialized in our case as Transformers, to fit the new task. The previous convolutional layers were frozen during the process to preserve previously learned features. We then commenced training, focusing only on the Transformers in a shallow training methodology. However, during the second training stage, we unfreeze all convolutional layers of the two CNNs to learn from the new problem, updating their kernels only after the entire model has absorbed knowledge from the new dataset, reserving the second stage to deepen the learning process [13].  
Figure 9: Example of Polydactyly data augmentation: (**a**,**e**) original frontal and lateral images; (**b**–**d**) examples of synthetic samples generated from (**a**) using random data augmentation techniques; (**f**–**h**) examples of synthetic samples generated from (**b**) using random data augmentation techniques.  
In this study, we opted for the Inception-V3 CNN architecture for image feature extraction, motivated by the availability of pre-trained weights on the ImageNet dataset, enabling resource-efficient learning from millions of images. Inception-V3, a deep neural network employing deep convolutions and Inception modules, captures information at various scales and abstraction levels, facilitating complex object and feature representations in images [34]. This is crucial for tasks like image-to-text conversion, aligning with our goal of automating medical reports for Polydactyly radiographs. This architecture has a total of 21,802,784 parameters (83.17 MB), with 21,768,352 trainable parameters (83.04 MB) and 34,432 non-trainable parameters (134.50 KB). This is crucial for tasks like image-to-text conversion, aligning with our goal of automating medical reports for Polydactyly radiographs.
## 2 Distribution System Simulation
### Effect on Voltage Level in Case of Distribution System with DG Placements  
This simulation started from the various DG placement and measured the voltage value on that position on time. The simulation results will be followed by Table 4. The voltage results in case of no DG installation do not impact the lower voltage standard by 100  
system. The simulation evaluation is compared on both situations with DG and without DG connections. The loads demand will be applied between 100% and 80% of power consumption. The details of simulation result are shown in Table 5.  
From the table, it can be seen that the DG can reduce power loss in the system when compared to the case without DG. The amount of power loss reduction depended on the distance from the substation; as the DG placement was further away from the substation, the power loss in the system is decreased. The DG placement at 12 km from the substation or the middle point of the distribution line achieved the lowest power loss. The reason is power generated from DG can be provided to load instead of received from the substation, thus reduce power loss in the distribution line. The benefit of DG placement at the middle line is getting the lowest power loss. However, the further away from the substation the DG placement is, the effectiveness in reducing power loss is decreased, but the power loss is still lower than the case without DG.
## 2 Research Method and Material
### Sample Preprocessing  
After collecting the soil samples, they were taken to the laboratory where each sample was placed in a plastic tray and any impurities such as weeds, twigs, and other materials were removed. The soil samples were air dried at room temperature over a week. The dried samples were then pulverized into fine powder and sieved with a 10-mesh sieve (2.0 mm). The sieved dry samples were assessed with respect to soil properties including pH, electrical conductivity (EC), and organic matter (OM). The measuring method followed standard methods [15]. Soil texture was determined using the sieve method. A 10 g soil sample was sieved through a 2 mm sieve. The material retained on a 325-mesh sieve (0.47 mm) was classified as sand (0.47-2 mm). The material that passed through the sieve was washed into a sedimentation cylinder and water was added to make the volume 1000 mL. The settling time required by particles to fall 10 cm was calculated using Stokes' law. A 25 mL pipette was used to extract 25 mL of clay suspension (<2 \(\upmu\)m) at the 10 cm mark. This sample was dried and weighed to determine the clay content. The silt content was calculated by subtracting the sand and clay content from the total weight.  
The soil properties are listed in Table 1. The average pH, EC, and OM were 7.20 \(\pm\) 0.62, 165 \(\pm\) 88 \(\upmu\)S/cm, and 7.67 \(\pm\) 3.52%, respectively. The soil textures exhibited a wide variation. The percentage ranges were 4.7-83.8%, 5.0-59.1%, and 11.2-66.4% for sand, silt, and clay, respectively. The average percentage order was: sand (47.4 \(\pm\) 18.7%) > clay (35.0 \(\pm\) 14.5%) > silt (17.6 \(\pm\) 10.2%). The soil samples were categorized as clay, sandy clay, sandy clay loam, loamy sand, and sandy loam according to USDA classification.  
Figure 1: The schematic location map of the sampling sites and the corresponding sample codes.
# Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning
## 4 Experimental Analysis
### Implementation Details  
We conduct all experiments using the PyTorch 2.5.1 framework on a NVIDIA Tesla V100-PCIE-32 GB GPU. For model training and testing, we first split the data into training and validation sets using an iterative data splitting technique [44], based on the information of the chest retraction signs. The dataset of 153 patients (86 ARD and 67 non-ARD) is divided into 70% for training (60 ARD and 47 non-ARD) and 30% for testing (26 ARD and 20 non-ARD), ensuring balanced group allocation. The training dataset is further expanded by segmenting each video into 13 overlapping clips of 6.4 s, yielding a total of 1498 clips: 780 from ARD patients and 718 from non-ARD patients. We then spatially crop the videos to the shorter side to maintain the aspect ratio and resize them to 256 \(\times\) 256 pixels. Additionally, we temporally sub-sample the videos to 10 frames per second (fps). We normalize the RGB and depth videos to a 0-1 pixel range. All data processing techniques are similar for both modalities, except for depth modality videos, which undergo spatial normalization (0-1), as described in Sections 3-3.2. We use stochastic gradient descent with a fixed learning rate of 0.0005 and a momentum of 0.9. The batch size is set to 64, using gradient accumulation techniques and binary cross-entropy loss is employed. We train the model for 40 epochs, saving the best checkpoints by monitoring the validation loss. During training, we apply temporal and spatial data augmentation techniques such as random spatial cropping to \(224\times 224\), temporal jittering, random rotation (\(\pm 30\) degrees), and horizontal and vertical flipping. During inference, we divide each video into four non-overlapping clips of 6.4 s, applying similar data pre-processing techniques as during training. The final prediction for each patient is determined by averaging the scores of the four clips.
# Evolution of Flow and Streaming in Exponential Variable Cross-Section Resonators
## 4 Results and Discussion
### Effect of Driving Strength
#### 4.3.2 Transient Flow Fields
Figures 8 and 9 indicate that the DC flow is very sensitive to the intensity of the flow field because the pressure differential, wave amplitude, and the corresponding vortex propagation speed are different. Here, a detailed analysis is given for Case E, with large driving amplitudes. The instantaneous velocity distributions at eight different instants (\(\theta~{}=~{}0\), \(\pi/4\), \(\pi/2\), \(3\pi/4\), \(\pi\), \(5\pi/4\), \(3\pi/2\), and \(7\pi/4\).) during one cycle, along with the streamlines, are shown in Figure 10; the whole flow fields are depicted in the figure for better flow visualization. The flow field and streaming structure in Figure 10 are very different from those in Figures 8 and 9. A very strong pressure differential occurred between positive and negative half periods that broke the vortex, distorted the flow direction, and generated turbulence. Thus, no vortices can be observed in Figure 10, and the process of energy storage and release is also unclear. Meanwhile, the changes in the velocity and temperature from one moment to another are gentler than those in the previous cases, implying that the bigger the harmonic distortion is (see Figures 6 and 7), the more homogeneous the distributions of the flow and temperature fields inside the resonator are [32]. Moreover, chaos and irregularity of the streaming overlaid on the main flows can speed up the uniformity distribution of the flow field in space and time. The turbulent motion also causes the time-averaged pressure, velocity, and temperature to fluctuate irregularly [27], which will consume the energy and prevent its storage and release. Figure 10: _Cont._  
The numerical simulation of velocity field with turbulent motion for high-amplitude cases is also reported in References [27; 33] for cylindrical resonators, and the critical Reynolds numbers for the transition to turbulence reported are 374 and 400, respectively. In this work, however, the Reynolds number for the \(m=2.2\) exponential resonator in Case E is 177, which is smaller than that for cylindrical resonators. This may be because the streaming in the exponential resonator is hydrodynamically asymmetric.