esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Urban Traffic Incident Detection Based on Vehicle Cameras
## 3 Data and Methods
### Neural Networks  
In the current work, Since our application scenario is at the edge of the vehicle with limited computing power, we need to choose a more lightweight model. YOLOv5s model has the advantages of high detection accuracy, small number of parameters and fast detection speed among the lightweight models. We utilize an improved model based on the initialized YOLOv5s algorithm for traffic incident detection. Using the Pytorch framework, [PERSON] has suggested the high-precision, single-stage YOLOv5 object identification model. It transforms the object identification job into a regression problem by generating the bounding box coordinates and associated probabilities for each class. The images are fed into the network and the samples are enhanced using a mosaic technique to make the model training better. Then, the focus operation is performed to slice the image to reduce the loss of information. A CSPNet [39] structure is added to the backbone network to connect convolution layers at different stages to reduce information loss. Downsampling is carried out using the GhostNet [40] module, reducing the number of parameters. The network's ability to extract effective features is enhanced by adding a fused attention mechanism module at the end of the neck. In the feature detection stage, multiple feature maps are extracted, and the path aggregation network is utilized to shorten the information path by utilizing the shallow layer's localization information to facilitate the extraction of feature information at different scales and enhance the detection efficiency.  
Realistic scenarios often include dense arrangements of vehicles and pedestrians, multi-scale targets, irregular geometry of traffic accidents, and complex backgrounds, among others. This greatly hinders the accuracy of detection, leading to missed reports and false target predictions. The goal of this study is to address the above challenges by proposing an improved and optimized YOLOv5s algorithm based on the characteristics and complexity of the traffic event dataset to improve the efficiency and accuracy of traffic event detection in complex situations and to achieve real-time detection speed. As shown in the Figure 4, the model network design is divided into three parts: the backbone for feature extraction, the neck for extracting the semantic representation of the features, and the head for prediction.  
Figure 3: After directional data enhancement, the number of labels of different categories.
# Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm
## 4 Results and Discussion
### Prediction of Midspan Deflection by the DCWPSO-SVM Model
While the additional computational time required for optimization is a consideration, the overall reduction in prediction time underscores the practicality and effectiveness of this approach. While the DIWPSO-SVM model's initialization phase is computationally intensive (approximately 4\(\times\) longer than the standalone SVM due to PSO-based parameter optimization), this overhead is confined to offline training and does not affect the online prediction efficiency. Consequently, the model may not be suitable for strict real-time applications requiring sub-second responses (e.g., early earthquake warning systems), where latency is critical. However, for near-real-time structural monitoring tasks--such as periodic assessments conducted every 10-30 min--the enhanced accuracy and generalization of DIWPSO-SVM justify the initial delay. Standalone SVMs, while faster to  
Figure 8: Comparison of prediction results. train, often struggle with suboptimal parameter selection in noisy bridge monitoring data, leading to reduced reliability. In contrast, DIWPSO-SVM's dynamic inertia weight PSO algorithm ensures robustness by escaping local optima and improving long-term predictive performance. This trade-off aligns with industry practices in bridge health monitoring, where minute-level delays are typically acceptable to prioritize reliability over strict latency constraints. Thus, while the increased initialization time limits DIWPSO-SVM's use in ultra-low-latency scenarios, its advantages in accuracy and generalization make it feasible for near-real-time structural assessments.
# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 3 Materials and Methods  
### Construction of Disaster Factor Dataset  
After natural disasters, the main causes of secondary disaster events include fires, electric bicycles, flammable gas storage tanks, strong alkali, acid diluents, pesticides, people trapped in dangerous environments in need of rescue, damaged or cracked buildings that may collapse, etc. In general, there are six types of secondary disaster sources. In this study, these sources are divided into two types: category I includes fire, and category II includes flammable objects, explosive objects, toxic substances, trapped personnel, and dangerous buildings, as shown in Figure 1.  
Because the luminous phenomena generated by fires are easily confused with the four types of similar non-fire phenomena in life, such as sunsets, welding, strong light, and weak light, to distinguish them from bright characteristics of fires, this study combined these four phenomena with fire to constitute the category I secondary disaster factor dataset. Fire is 'factor 1' in the dataset (it consists of pre-fire smoke, field fire, building fire, and indoor fire images), and the other four phenomena do not cause the occurrence of secondary disaster events and are only used in the dataset to assist the exclusive extraction of features of fire phenomena using the deep learning model.  
The category II secondary disaster factors include flammable objects (consisting of electric bicycles and battery images); explosive objects (consisting of liquefied petroleum gas, acetylene, and oxygen tanks that are high-pressure combustibles, and combustion-supporting gas images); toxic substances (consisting of corrosion-resistant plastic drums, strong alkali, strong acid, and pesticides); 'dangerous chemicals' (warning signs are added to the images to enhance the Convolutional Neural Network's ability to learn complex features); trapped persons (consisting of images of adults and children); and dangerous buildings (consisting of characteristic images of cracks in buildings that can cause them to collapse). Among these, flammable objects are disaster 'factor 2', explosive objects are disaster 'factor 3', toxic substances are disaster 'factor 4', trapped persons are disaster 'factor 5', and dangerous buildings are disaster 'factor 6'.  
In this study, to better extract the characteristics of relevant factors and distinguish them from common disaster factor analogs in everyday life, five object analogs, namely, bicycles, iron containers, garbage cans, animals, and brick cracks, were set to correspond with disaster factors 2, 3, 4, 5, and 6, respectively, to assist in training the deep learning model. The dataset contains secondary disaster factors, and similar scene objects are 15 classes used for feature extraction of secondary disaster factors, each class consisting of 1000 images, comprising 800 in the training set, 100 in the verification set, and 100 in the test set.
## 2 Road Traffic Situation Assessment Model
### Traffic Situation Assessment Indicator Analysis
#### 2.2.3 Analysis of Traffic Situation Prediction Indicators
\[h(\mu)=x^{T}\beta \tag{10}\]  
\[\text{where }x^{T}= \begin{bmatrix}1&x_{11}&x_{12}&\ldots&x_{1m}\\ 1&x_{21}&x_{21}&\ldots&x_{2m}\\ 1&x_{31}&x_{32}&\ldots&x_{3m}\\ \ldots&\ldots&\ldots&\ldots&\ldots\\ 1&x_{n1}&x_{n1}&\ldots&x_{nm}\end{bmatrix} \text{ is the Design Matrix, }\beta= \begin{bmatrix}\beta_{1}\\ \beta_{2}\\ \beta_{3}\\ \beta_{n}\end{bmatrix} .\]  
The Poisson regression model applied to the analysis of the association between emergencies and urban roads consists of a combination of Equations (7) and (9). After establishing the regression model, the regression model needs to be tested. The degree of matching between the predicted indicators of road traffic situation and the sample data is checked to determine whether the regression model can effectively explain the association between urban road emergencies and the indicators, and if the results of the model have high consistency with the sample data, the established model can be considered to have high reliability. In this paper, the regression model is evaluated from two aspects: \(\chi^{2}\) statistical test and goodness-of-fit test. Usually, the statistical significance of an estimated regression model is evaluated by a likelihood ratio test. The likelihood ratio statistic approximately obeys the \(\chi^{2}\) distribution, and the \(\chi^{2}\) statistic of the regression model is shown in Equation (11). \[\chi^{2}=2 LL-2 LL_{0} \tag{11}\]  
where \(LL\) is the natural logarithm of the estimated maximum likelihood of the regression model; \(LL_{0}\) is the natural logarithm of the maximum likelihood of the null hypothesis model. After calculating the \(\chi^{2}\) statistic of the regression model, the significant level of the regression model can be obtained by checking the \(\chi^{2}\) distribution table. The Akaike Information Criteria (\(AIC\)) statistic and the Bayesian Schwartz Criteria (\(BIC\)) statistic are the commonly used goodness-of-fit tests. The smaller the value of the AIC statistic, the better the fit of the model. Similarly, the smaller the value of the BIC statistic, the better the fit of the model. The expressions for AIC and BIC are shown, respectively, in Equations (12) and (13). \[AIC=-2 LL+2m \tag{12}\]\[BIC=-2 LL+m\ln(n) \tag{13}\]  
where \(LL\) is the natural logarithm of the estimated maximum likelihood value of the regression model, \(m\) is the number of estimated parameters in the regression model and \(n\) is the number of data samples.
## 2 Materials and Methods
### Data Processing and Analysis  
Statistical analyses including normality tests and Spearman correlation analysis of HMs were performed using SPSS 23 (IBM, Armonk, NY, USA). The coefficient of variation (CV) is a statistical measure that reflects the relative dispersion of data [45]. It is categorized into three types: low variability (CV \(<\) 15%), moderate variability (15% \(\leq\) CV \(<\) 36%), and high variability (CV \(\geq\) 36%) [46]. Geospatial visualizations depicting the study area location, sampling site distribution, and HM concentration patterns were constructed through the application of ArcMap 10.2. Origin 2021 (OriginLab Corporation, Northampton, MA, USA) was used to generate bar charts, box plots, and pie charts. Quantitative source apportionment analysis was conducted utilizing the PMF 5.0 receptor model. Probabilistic modeling through Monte Carlo simulations was implemented using Oracle Crystal Ball software ([[https://www.oracle.com](https://www.oracle.com)]([https://www.oracle.com](https://www.oracle.com))).
# Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar-Communication System
## 4 Numeric Examples
### Radar Waveform Design and Analysis for Beampattern Matching
The goal of the optimization problem is to maximize or minimize the value of the function to be optimized, and the goal of the beam-matching problem is to minimize the difference between the designed waveform and the standard waveform, that is, the final optimization function converges to a certain value. Figure 6 describes the relationship between the number of iterations of the manifold-ADMM algorithm and the ADMM algorithm and the value of the objective function under the same convergence conditions. From the number of iterations, the number of iterations of the WAPM algorithm is 35 times, and the number of iterations of the ADMM algorithm is 25 times. The number of iterations of the WAPM algorithm is about 1.5 times that of the ADMM algorithm. From the function trend, the algorithm designed by the WAPM algorithm makes the objective function curve approximate to an S-shaped curve, and the WAPM algorithm can make the function objective value have a better change trend. Figure 5: Comparison of only one main-lobe beampattern in the case of \(\Delta\theta=60^{\circ}\). Figure 6: Objective function values under different algorithms. (**a**) WAPM algorithm; (**b**) ADMM algorithm.
# Development of an Adaptive Force Control Strategy for Soft Robotic Gripping
## 2 Materials and Methods
### Force Sensing
#### 2.2.2 Model Training  
An autoencoder is an unsupervised learning method to train encoder and decoder models to recreate the encoder's input at the output of the decoder. The reconstruction at the decoder's end is compared with the desired output, often the same image as the input,  
Figure 4: Ansys deformation simulation using fin ray finger and 50 mm round object.  
Figure 5: Ansys deformation simulation using fin ray finger and 10 mm round object.  
and is used to calculate the loss and train the network. Binary cross-entropy was used as the loss function to train the network. Binary cross-entropy is shown in Equation (1) [36]:  
\[loss_{BCE}=-[y\cdot log(y_{pred})+(1-y)\cdot log(1-y_{pred})] \tag{1}\]  
where \(y\) is the ground truth, in this case, the pixel value of the image, and \(y_{pred}\) is the predicted pixel value of the reconstructed image. This network structure is further broken down into two blocks: down-convolution and up-convolution. These blocks contain four layers: a convolution layer, batch normalization layer, rectified linear unit activation function, and max pool layer for the down-convolution block. The up-convolution block is similar, with the convolution replaced with a transpose convolution layer and the max pool replaced with upsampling. The structure of these blocks is illustrated in Figure 6.  
The encoder network was built from four down-convolution blocks in series, outputting a compressed feature matrix of dimensions 256, 2, and 2. This matrix was flattened into a vector of 1024 features before sending it to the regression model. The original compressed features were then passed into the decoder network built from four consecutive up-convolutional blocks, followed by a sigmoid layer. The entire autoencoder network is shown in Figure 6.  
A random forest regression algorithm was trained on pairs of encoded images and force data were collected from a load cell to estimate the force applied by the gripper from the compressed and flattened feature vectors. The size of the forest and other hyper-parameters were chosen through testing.  
The gripping force is in the direction perpendicular to the contact between the fin and the object and normal to the surface of the finger. For this work, the ejection force created by the gripper pushing the object along the finger was not considered but could be added in the future using a 3-axis load cell and multi-output regression models.
## 3 Proposed Method
### Receiver Calibration Mode
 \tag{17}\]  
Step (3) Considering Equation (7), compute \(\Phi_{l}(\text{q}),\Lambda_{k}(\text{q})\);  
Step (4) Use Equation (15) to compute \(\hat{\mathbf{a}}_{i}(\text{q})\) as follows:  
\[\hat{\mathbf{a}}_{i}(\text{q})=\dfrac{(\overline{\mathbf{P}}^{H}(\Phi_{l}( \text{q}),\Lambda_{k}(\text{q}))\overline{\mathbf{C}}^{H}\overline{\mathbf{C} }(\Phi_{l}(\text{q}),\Lambda_{k}(\text{q})))^{-1}\overline{\mathbf{P}}^{H}( \Phi_{l}(\text{q}),\Lambda_{k}(\text{q}))\overline{\mathbf{C}}^{H}\overline {\mathbf{y}}_{i}}{\left\|(\overline{\mathbf{P}}^{H}(\Phi_{l}(\text{q}), \Lambda_{k}(\text{q}))\overline{\mathbf{C}}^{H}\overline{\mathbf{C}}(\Phi_{ l}(\text{q}),\Lambda_{k}(\text{q})))^{-1}\overline{\mathbf{P}}^{H}(\Phi_{l}(\text{q}), \Lambda_{k}(\text{q}))\overline{\mathbf{C}}^{H}\overline{\mathbf{y}}_{i} \right\|}\]  
Step (5) Set \(\text{q}=\text{q}+1\) and return to Step 2 (next recursion). It can be observed that estimating the uncertainties in two stages (the constant part and the compensation look up tables) enhances the robustness of the current steering vector estimation method. In fact, the initialization from the input measurement and constant matrix \(\overline{\mathbf{C}}\) leads to a rough estimate of the steering vector and then, after a few iterations, a proper compensation matrix for an accurate estimate of the steering vector can be obtained. Without this initialization, the method would not necessarily converge. Since \(\overline{\mathbf{C}}\) and \(\mathbf{T}\) are constant matrices and \(\overline{\mathbf{P}}\) is a diagonal matrix, one can easily verify that matrix inverses are not required in the loop. For example \((\mathbf{T}^{H}\mathbf{T})^{\ast}\mathbf{T}^{H}\) and \((\overline{\mathbf{C}}^{H}\overline{\mathbf{C}})^{-1}\overline{\mathbf{C}}^{H}\) can be calculated beforehand. These features make the proposed method low-complex and fast-converging, which is suitable for real-time applications employing a precisely calibrated antenna array.
# Physics-Informed Neural Networks for Modal Wave Field Predictions in 3D Room Acoustics
## 3 Physics-Informed Neural Networks for Wave-Based Room Acoustics
### Initial Setup [5]  
The wavelength is selected to be \(\lambda=1/2\) m. The PINN is setup using DeepXDE 1.12.2 (Python 3.10, Cuda 12.2) and trained on a Nvidia Tesla T4 GPU (NVIDIA, Santa Clara, CA, USA). There are 10 random collocation points per wavelength along each direction for training and 30 used for validation are defined. This amounts to \(n_{\text{train}}=20\) data points per direction and a total \(|\mathcal{T}_{\text{train},\Omega}|=n_{\text{train}}^{3}=8000\) points inside the domain. On the surface, a total number of \(|\mathcal{T}_{\text{train},\Gamma}|6n_{\text{train}}^{2}=2400\) is given. A fully connected neural network consists of \(L=4\) layers and a layer width of \(W=180\) neurons. A sinus activation function is used with Glorot uniform bias and weights initialization [40]. The loss term is defined in Equation (15), with \(w_{\text{NBC}}=5\), and \(w_{\text{PDE}}=1\). The wave field pressure inside the room on a \(z=0.5\) m plane is illustrated in Figure 2. It shows that the real part of the pressure spatially varies between \(-1\) Pa and \(+1\) Pa, and one can see the wavelength. Figure 2a shows the exact solution of the acoustic pressure (\(s\rightarrow\infty\)). Figure 2b shows a visualization of the predicted acoustic pressure by the PINN. The network is trained over 30,000 iterations by the ADAM optimizer with a learning rate of \(\eta=0.001\), resulting in a validation loss of 0.1248. After 200,000 iterations by the ADAM optimizer, the validation loss decreased further to 0.0952 and the relative error \(e_{\text{rel}}\) to 0.36% compared to the analytic solution. Figure 2c shows the result after finetuning the results from Figure 2b with the L-BFGS optimizer for 2000 iterations. It brings down the test loss to 0.0609 (see Figure 2c) and \(e_{\text{rel}}\) to 0.35%. Visually, there are no differences between these plots, and the method seems to be promising.
## 3 Results and Discussion
### Effect of pH in Pure EDTA Solution  
EDTA has four carboxylic acid groups and two amine groups (Scheme 1), which enable chelation of various metal ions. EDTA has four pKa values, i.e., 2.0, 2.7, 6.2, and 10.3 [32]. As a result, adsorption of EDTA by AERs can be affected by the pH of the solution. The EDTA removal efficiency by AERs was examined at various pHs (Figure 4). The adsorption efficiency of EDTA on AERs decreased significantly below pH 4, mainly because protonation of the carboxylic acid groups might hinder the chelation of metal ions. At pH values above 4, the EDTA removal efficiency approached 100%. Similar to the removal efficiency, the equilibrium adsorption capacity (\(q_{\mathrm{e}}\)) of AERs toward EDTA also increased with the increasing pH of the solution, suggesting that EDTA was adsorbed on AERs via a charge interaction caused by deprotonation of EDTA.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 6 Evaluation
### Results and Discussion
#### 6.2.4 Impact of PQC Algorithms on Performance in the Chat-Based Use Case
In the PQC standalone protocol, a lower delay was observed for most of the PQC algorithms, with the exception of CRYSTALS-Kyber, compared to the hybrid protocol. Figure 10c confirms that the average RTT decreased with the increase in the KEM security level for most of the PQC algorithms, ranging from 0.4 ms to 1.7 ms, except for K768-F512, which had a higher average RTT of 2.10 ms, slightly closer to that of AES-256 (2.44 ms). As shown in Figure 10d, a similarly lower average RTT trend, decreasing as the security level increased, was observed. However, there was a slight variation in the pattern for the BIKE algorithms compared to the hybrid protocol. It was confirmed that BIKE-L5-F1024  
Figure 9: Chat total processing times: (**a**) PQC–AES-256 (with F512). (**b**) PQC–AES-256 (with F1024). (c) PQC standalone (with F512). (**d**) PQC standalone (with F1024). increased slightly to 0.99 ms. It was further confirmed that HQC-256-F1024 achieved the lowest average RTT of 0.28 ms.
## 3 Remote Sensors at the SAS
### Ionosonde: Vertical incidence soundings of the ionosphere
[18] tracked a complete night-time Weddell Sea Anomaly (WSA) - the WSA in the ionosphere is characterized by higher plasma densities at night than during the day in the region near the Weddell Sea - and strong horizontal plasma flows that registered the high-conductivity regions of the South Atlantic Magnetic Anomaly (SAMA), and revealed that the strong east-west hemispherical difference was due to the SAMA. [19] showed that the WSA occurs only in the southern summer hemisphere for low solar activity but the WSA occurs in all seasons except for winter when solar activity is high, being most prominent during the December solstice and still strong during both equinoxes, and they conclude that WSA appears to be an extreme manifestation of the longitudinal variations. [20] also reported these anomalous diurnal variations of the ionospheric peak density NmF2 related to the WSA but not found in the diurnal variations of the ionospheric peak height hmF2. [21] showed experimental evidence that the traveling atmospheric disturbances (TADs) play significant role in the ionospheric daytime effects of geomagnetic storms and demonstrate a systematic uplifting of the ionosphere during storms. [22] found changes in the critical frequency (foF2), density (NmF2), and height (hmF2) occurring after the onset of magnetospheric convection associated with high-speed solar wind streams (HSSs) arrival at the Earth's magnetosphere. [23] suggested that impulsive Joule heating of the polar thermosphere, which was accompanied by the generation of the large-scale gravity wave packet that propagated to mid latitudes, was the most probable such source for large-scale ionospheric inhomogeneities, vertical winds monotonously increasing with increasing altitude, and upward plasma transfer. [24] presented an HF selection tool (EDAM533) that has been implemented thanks to the MUF(3000) predictions generated by real-time assimilative electron density model. Also [15] have developed an HF propagation program that takes advantage of the availability of a real-time global model of the ionosphere to specify in real time the range of frequencies that would be supported on a given HF communications circuit which favorably validates against several months of oblique propagation observations on mid latitude circuits, although its day-to-day variability does not match the observations very well, and it tends to underestimate the MUFs. The future ionospheric research at the SAS would benefit with installation of new sensors, as a high sampling rate receiver of Global Navigation Satellite System (GNSS) signals. This type of sensor would provide information about the wave front distortion of a GNSS signal caused by electron density irregularities, which giving rise to highly transient and a random amplitude and phase modulation of the GNSS signal. Such systems have been adopted by several groups for both scientific purposes and space weather applications (e.g., [17; 25]).