esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Object Behavioral Feature Extraction
Vehicle acceleration (VA): Vehicle acceleration is also an important factor to determine the potential risk for pedestrian injury. In general, while vehicles pass over a crosswalk with a pedestrian nearby, they reduce speed (resulting in negative acceleration values). If many vehicles maintain speed (zero value) or accelerate (positive values) while nearby the crosswalk or pedestrian, it can be considered as a risky situation for pedestrians. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Object ID in First Frame** & **Object ID in Second Frame** & **Distance (m)** & **Result** \\ \hline A & D & 4.518 & Over threshold \\
**A** & E & **1.651** & **Min. dist.** \\ A & F & 2.310 & \\ \hline B & D & 3.286 & \\ B & E & 2.587 & \\ B & F & **1.149** & **Min. dist.** \\ \hline C & D & 7.241 & Over threshold \\ C & E & 5.612 & Over threshold \\ C & F & 3.771 & Over threshold \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Result of tracking and indexing algorithm using threshold and minimum distance. Vehicle acceleration is the difference between vehicle velocities in the current frame (\(v_{0}\)) and in the next frame (\(v\)):  
\[Acceleration=\frac{v-v_{0}}{F}\ m/s^{2} \tag{2}\]  
The unit is finally converted into km/h\({}^{2}\). Distance between vehicle and pedestrian (DVP): This feature refers to the physical distance between each vehicle and pedestrian. In general, if this distance is short, the driver should slow down with additional caution. However, if the vehicle has already passed the pedestrian, accelerating presents less risk than when the pedestrian ahead of the car. Therefore, we measure DVP to distinguish between these types of situations. If a pedestrian is ahead of the vehicle, the distance has a positive sign, if not, it has a negative sign:  
\[DVP=\left\{ \begin{array}{ll}+\frac{object\ distance}{P}(m),&\text{if the pedestrian is in front of the vehicle}\\ -\frac{object\ distance}{P}(m),&\text{otherwise}\end{array} \right. \tag{3}\]  
Distance between vehicle and crosswalk (DVC): This feature is also extracted by calculating the distance between vehicle and crosswalk. We measure this distance from the crosswalk line closest to the vehicle; when vehicle is on the crosswalk, the distance is 0:  
\[DVC=\left\{ \begin{array}{ll}\frac{object\ distance}{P}(m),&\text{if a vehicle is out of crosswalk}\\ 0,&\text{otherwise}\end{array} \right. \tag{4}\]  
As a result of feature extraction, we can obtain a structured dataset suitable for various data mining techniques, as seen in Table 2.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 3 Background
### Random Forests  
Random forests [23] are an ensemble learning method used for both classification and regression tasks. They work by constructing multiple decision trees during training and aggregating their results. For classification, the final prediction is the mode of the classes predicted by individual trees. For regression, the final prediction is the mean of the predictions from all trees. This method leverages the power of multiple models to improve accuracy and control overfitting.  
The main idea is to combine the predictions of several base estimators to reduce variance and enhance generalization. Each tree in a random forest is trained on a different random subset of the training data, using a technique called bagging (bootstrap aggregating). This approach creates diverse trees, which reduces the risk of overfitting to the training data.  
First, bootstrap sampling is applied to the training dataset \(\mathcal{D}=\{(\mathbf{x}_{i},y_{i})\}_{i=1}^{n}\), generating \(B\) bootstrap samples \(\mathcal{D}_{b}\) by sampling with replacement.  
Each bootstrap sample \(\mathcal{D}_{b}\) is used to train a decision tree \(h_{b}(\mathbf{x})\). During the training of each tree, at each node, a random subset of features \(\mathcal{F}_{b}\subseteq\mathcal{F}\) is selected, and the best split among these features is found.  
For aggregation, in classification tasks, the final prediction \(\hat{y}\) for an input \(\mathbf{x}\) is obtained by majority voting:  
\[\hat{y}=\text{mode}\{h_{b}(\mathbf{x})\}_{b=1}^{B}\]  
In regression tasks, the final prediction \(\hat{y}\) for an input \(\mathbf{x}\) is obtained by averaging:  
\[\hat{y}=\frac{1}{B}\sum_{b=1}^{B}h_{b}(\mathbf{x})\]  
Here, \(B\) denotes the number of trees, \(h_{b}(\mathbf{x})\) represents the prediction of the \(b\)-th tree, and \(\mathcal{F}\) is the set of all features.  
By averaging multiple decision trees, each trained on different parts of the training data and different features, random forests utilize the law of large numbers to improve the stability and accuracy of the model.
# Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals
###### Abstract  
This study introduces a novel methodology for classifying cognitive states using convolutional neural networks (CNNs) on electroencephalography (EEG) data of 41 students, aimed at streamlining the traditionally labor-intensive analysis procedures utilized in EEGLAB. Concentrating on the 30-40 Hz frequency range within the gamma band, we developed a CNN model to analyze EEG signals recorded from the inferior parietal lobule during various cognitive tasks. The model demonstrated substantial efficacy, achieving an accuracy of 91.42%, precision of 71.41%, and recall of 72.51%, effectively distinguishing between high and low gamma activity states. This performance surpasses traditional machine learning methods for EEG analysis, such as support vector machines and random forests, which typically achieve accuracies between 70-85% for similar tasks. Our approach offers significant time savings over manual EEGLAB methods. The integration of event-related spectral perturbation (ERSP) analysis with a novel CNN architecture enables capture of both fine-grained and broad spectral EEG features, advancing the field of computational neuroscience. This research has implications for brain-computer interfaces, clinical diagnostics, and cognitive monitoring, offering a more efficient and accurate alternative to current EEG analysis methods.  
EEG; CNN; gamma band; deep learning Article
###### Abstract  
Task offloading in satellite networks, which involves distributing computational tasks among heterogeneous satellite nodes, is crucial for optimizing resource utilization and minimizing system latency. However, existing approaches such as static offloading strategies and heuristic-based offloading methods neglect dynamic topologies and uncertain conditions that hinder adaptability to sudden changes. Furthermore, current collaborative computing strategies inadequately address satellite platform heterogeneity and often overlook resource fluctuations, resulting in inefficient resource sharing and inflexible task scheduling. To address these issues, we propose a dynamic gradient descent-based task offloading method. This method proposes a collaborative optimization framework based on dynamic programming. By constructing delay optimization and resource efficiency models and integrating dynamic programming with value iteration techniques, the framework achieves real-time updates of system states and decision variables. Then, a distributed gradient descent algorithm combined with Gradient Surgery techniques is employed to optimize task offloading decisions and resource allocation schemes, ensuring a precise balance between delay minimization and resource utilization maximization in dynamic network environments. Experimental results demonstrate that the proposed method enhances the global optimizing result by at least 1.97%, enhances resource utilization rates by at least 3.91%, and also reduces the solution time by at least 191.91% in large-scale networks.  
dynamic satellite networks; resource allocation; task offloading; collaborative optimization; gradient descent +
Footnote †: journal: _Mathematics_  
0000-0001-5610-788X][PERSON]. [PERSON]  
1 School of Cyber Science and Engineering, College of Information Science and Engineering,  
Xinjang University, Urumqi 830046, China; [EMAIL_ADDRESS]
## 3 Framework Statement
### Problem Definition  
The task of trajectory anticipation is to model a vehicle movement sequence that simultaneously embeds temporal and spatial characteristics. The general proposal is to predict the potential position of ego-vehicle for the \(T\) upcoming time steps based on historical information. Specifically, our approach not only considers history attributes but also substantially integrates multi-agent interaction mechanisms. The interaction features are derived by the interaction graph \(G\), where the relationships of vehicle-vehicle and vehicle-traffic rules are aligned at the same time. Thus, the prediction problem input \(I\) can be extended to the following:  
\[I=[X_{t},G_{t}] \tag{1}\]  
where  
\[X_{t}=[\mu_{t}^{1},\mu_{t}^{2},\cdots,\mu_{t}^{n}] \tag{2}\] \[\mu_{t}^{i}=[(x_{(t-\tau+1)}^{i},y_{(t-\tau+1)}^{i}),\cdots,(x_{t- 1}^{i},y_{t-1}^{i}),(x_{t}^{i},y_{t}^{i})] \tag{3}\]  
where \(X_{t}\) denotes the historical status \(\mu\) of \(n\) vehicles before moment \(t\). The \(\tau\) is the trace-back horizon, and the \(\mu_{t}^{i}\) normally shows the coordinates of vehicle \(i\).  
The output \(O\) presenting future potential trajectory in prediction length \(T\) after observed time \(T_{obs}\) can be defined as shown below:  
\[O=[Y_{(T_{obs}+1)}^{i},Y_{(T_{obs}+2)}^{i},\cdots,Y_{(T_{obs}+T)}^{i}] \tag{4}\]  
where \(Y_{(t)}^{i}\) is the vehicle \(i\) anticipation position at moment \(t\).  
As a result, the crucial challenge of the problem is how to effectively learn the rule function \(f(\cdot)\) according to the prior knowledge extracted from \(X\) and \(G\) in time windows [\(T_{obs}-\tau\), \(T_{obs}\)]. Once the \(f(\cdot)\) is determined, the potential position of the vehicle in the short term can be obtained by the following:  
\[\begin{split}&[Y_{(T_{obs}+1)}^{i},Y_{(T_{obs}+2)}^{i},\cdots,Y_{(T_ {obs}+T)}^{i}]\\ &=[f(G,X_{(T_{obs}-\tau)}^{i},\cdots,X_{(T_{obs}-1)}^{i},X_{(T_{ obs})}^{i})]\end{split} \tag{5}\]
# Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning
## 2 Global System Overview  
The LOCATE-US permits locating and tracking a mobile target with high accuracy in a reduced region, in the range of centimetres for 2D deployments when the mobile targets are supposed to navigate on the floor. In this case, to position a mobile target in an extended area, a larger set of ultrasonic beacon units installed at the ceiling and oriented to the floor is typically deployed to cover the full region of localization and navigation [24].  
For other types of applications that require 3D positioning, this work proposes hereinafter the use of various ULPS units, which cover the scanned region from diverse points of view in order to obtain an accurate estimation of the receiver's position in the common coverage volume. Three ULPSs are installed in three perpendicular planes, which define the most common indoor room shapes, pointing to the centre of the volume, whereas other non-central areas are still scanned by at least one of them. Figure 1 depicts the general aspect of this beacon distribution in a normal ordinary-size room. The first ULPS is installed at the ceiling. The other two units are installed on two perpendicular walls [25].
## 3 Results and Discussion
### Characterization of Hydration Products
#### 3.3.3 SEM Analysis  
At the 28-day curing age, the SEM image of the hydration products from the cement paste (H\({}_{\text{YMO2}}\)) is presented in Figure 11. The samples primarily consist of [PERSON]'s salt, ettringite, portlandite, as well as C-S-H and C-A-S-H gel phases [69; 70]. At high magnifica  
Figure 10: TG-DTG curves for hydration products of the cement pastes at a curing age of 28 days.  
Figure 9: XRD patterns of hydration products of produced cement at 28 days. 1: Ca(OH)\({}_{2}\), 2: C\({}_{2}\)S/C\({}_{3}\)S, 3: C\({}_{3}\)A, 4: C\({}_{4}\)AF, 5: Na\({}_{2}\)SO\({}_{4}\).  
tion, it is observed that [PERSON]'s salt exhibits a typical layered sheet structure, suggesting that Cl\({}^{-}\) may react with cement hydration products to form the AFm phase [71]. Ettringite forms columnar crystals as an early hydration product, demonstrating the completeness of the reaction between C\({}_{3}\)S and gypsum. Ca(OH)\({}_{2}\) is distributed as regular flake crystals, and the formation of by-products during the hydration process indicates that the hydration reaction of the sample is more complete [72]. As the primary binding products, C-S-H and C-A-S-H gels exhibit an irregular gel structure, filling pores and demonstrating high compactness, indicating the completeness of the hydration reaction and the effective pore filling [73]. The low magnification image reveals the distribution characteristics of hydration products at the macro scale. The widespread presence of the gel phase suggests that the internal structure of the sample becomes denser after hydration, thereby enhancing the strength of the material. This observation is consistent with the excellent mechanical properties of the cement samples mentioned above. This further confirms that biomass fuels have minimal impact on cement hydration at low substitution ratios.
## 3 Evaluation Method
### Local Metrics
Interpreting \(N\) works as follows: the decision-map pixels \(\mathbf{p}\), which are close (in 2D) to the projections \(P(\mathbf{x})\) of the training set points \(\mathbf{x}\in D_{t}\), should represent data points that are close to such \(\mathbf{x}\). If this is not the case, i.e., if we see high \(N(\mathbf{p})\) values for pixels close to the training set projection, it means that the decision map has issues with extrapolating from the training set, that is, it takes classifier values from points _far_ from the training set and depicts them _close_ to the projection of the training set. **Class stability map:** As outlined in Section 3.1, the class stability map \(S\) can act as a local metric. As explained, pixels with high \(S\) values will have the same class label, even after multiple round-trip \(P\) and \(P^{-1}\) iterations. When a pixel is close to a decision boundary, \(S\) is likely to be lower since the chance that a point there 'jumps' to the other side of a decision boundary due to these round trips increases. However, if the pixel is far from a decision boundary, its \(S\) value should be higher. If this is not the case, then the decision map may be unreliable in such areas. As such, visualizing how \(S\) matches the distances to the depicted boundaries in a decision map indicates how confident we are about the quality of that map.
# Analysis of the Delamination Process with Nitric Acid in Multilayer Composite Food Packaging
## 3 Results
### Packaging Structure and Products after Delamination
Polymer 1, the outermost layer of the packaging, has a thickness of 25 \(\upmu\)m. Subsequent layers following Polymer 1 consist of adhesive and colored ink resin. Polymer 2, situated in the coil, possesses a thickness of merely 5 \(\upmu\)m. It exhibits remarkable elasticity and can be effortlessly electrified, stretching to filament thickness. The layers succeeding Polymer 2 are composed of aluminum and adhesives. Ultimately, Polymer 3, with a thickness of 15 \(\upmu\)m, is the innermost polymer layer of the packaging material that directly comes into contact with food. The total thickness of the packaging material before delamination measures 60 \(\upmu\)m, and after subtracting the thickness of the polymers, the remaining layer, consisting of residue, adhesive, and aluminum, has a thickness of 15 \(\upmu\)m. A thickness gauge was employed to measure the thickness of each recovered polymer, enabling us to quantify the thickness of each polymer present in the packaging material. Figure 3 shows a visual representation of the analysed packaging structure. Our findings offer valuable insights into the composition and structure of packaging material, underscoring the significance of understanding packaging materials concerning food safety. Further research on the interaction between packaging materials and food is imperative to ensure food products are safe for consumption and adhere to regulatory requirements.
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
## 4 Simulation
### Simulation for Different Terminal Angles  
The proposed multivariable barrier function-based super-twisting guidance law is expected to have a nice performance under different desired terminal angles. In order to test this property, a simulation with various desired terminal angles is investigated.  
Let the desired terminal angles \(\phi_{d}=35^{\circ},30^{\circ},25^{\circ}\) and \(20^{\circ}\), when the azimuth angle \(\theta_{d}=45^{\circ}\); \(\theta_{d}=55^{\circ},50^{\circ},45^{\circ}\) and \(40^{\circ}\), when the elevation angle \(\phi_{d}=25^{\circ}\). Other parameters are set up in Section 4.1.  
Figures 3 and 4 show the simulation results. Figures 3a and 4a illustrate the relative trajectories between missile and target in the inertial coordinate system when the desired terminal LOS angles are \(\phi_{d}=35^{\circ},30^{\circ},25^{\circ},20^{\circ}\) and \(\theta_{d}=55^{\circ},50^{\circ},45^{\circ},40^{\circ}\), respectively. All the trajectories under the simulation conditions could converge to zero, which testify that missiles can catch up with the target in finite time utilizing the designed guidance law in this paper. Figures 3b and 4b show the change of elevation angle and azimuth angle during the flight time under the two designed scenarios, respectively. The missile with the proposed guidance law can reach the desired terminal angle before meeting the target and then keep on it. This implies the proposed guidance law has the nice property of terminal angle constraint. The elevation and azimuth angular rates are shown in Figures 3c and 4c. One can conclude that a wider margin between the initial LOS angle and the desired terminal angle will lead to a larger LOS angular rate and shorter convergence time. Remarkably, when the missile approaches the target, or \(r\to 0\), \(\ddot{\theta}\) and \(\ddot{\phi}\) will change significantly due to the form of Equation (4). Hence there are drops at the end of the flight time in Figures 3b,c and 4b,c. Figures 3d and 4d illustrate the trends of extraneous LOS angles and LOS angular rates of the two scenarios, respectively. There are no distinct gaps between the trends. The acceleration histories are shown in Figures 3e and 4e, from which we can conclude that there is no distinct chattering during the flight time in these simulation conditions. Moreover, Figures 3f and 4f demonstrate the adaptive gains. The time variables reach the switch point \(t=\tilde{t}(s_{0})\) around 2 s, which results in leaps of the gains at that time.
###### Abstract  
To study the influence of electrochemically modified mediums on the physical and mechanical parameters of mudstone samples, focusing on electrolyte solutions and electrode materials, this paper combines theoretical analysis and experimental research. It analyzes the modification mechanism of mudstone through electrochemical techniques, clarifying that the main factors improving the strength of mudstone are electro-osmotic drainage consolidation and electrochemical reaction cementation. The mudstone was electrochemically modified using the controlled variable method. The mudstone sample's hydraulic properties and shear strength were measured before and after modification. The study compared and analyzed the effectiveness of different modified materials. The results indicated that the liquid limit of the modified mudstone samples decreased by 7.874%, while the plastic limit increased by 9.499%. The type of ions introduced by the electrolyte solution influenced the cementation strength of the mudstone. AlCl3 solutions with a 10% mass fraction and CaCl2 solutions with a 25% mass fraction both effectively modify the reinforcement; however, the AlCl3 solution with a 10% mass fraction is the most effective for modifying mudstone samples. The electrochemical modification of mudstone samples with the three electrode materials (graphite, iron and aluminum) revealed that the samples modified with graphite electrodes had the highest shear strength, while those modified with aluminum electrodes had the lowest shear strength. The internal friction angle of graphite electrode-modified mudstone specimens was 26.7\({}^{\circ}\), compared to the original value of 23.9\({}^{\circ}\), and the cohesion was 34.4 kPa, compared to the original value of 12.3 kPa, nearly three times the original value. It is recommended to use graphite electrodes and a 10% mass fraction of AlCl3 for the electrochemical modification of this type of mudstone in engineering applications.  
electrochemical modification; mudstone; electro-osmotic drainage consolidation; electrochemical reaction cementation; graphite +
Footnote †: journal: Applied Sciences  
Article  
Preferred Chemical Agent for Electrochemical Modification of Physical and Mechanical Parameters of Mudstone
# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 4 Experiments and Results
### Data Set  
This experiment uses the VisDrone2019 [33] dataset. The VisDrone2019 dataset is a large-scale dataset and benchmark constructed to promote research in drone visual perception. This dataset focuses on various visual tasks captured from the drone's perspective, including object detection, object tracking, single-object tracking, and crowd counting. The VisDrone2019 dataset was collected and annotated through a collaborative effort by international computer vision community researchers to provide a unified platform to evaluate and compare the performance of different algorithms when processing drone data.  
The VisDrone2019 dataset contains videos and images captured by drones in various environments, at different times and locations, covering many scenes such as city streets, rural roads, parks, and residential areas. These data are highly diverse regarding the complexity of the backgrounds and factors such as different weather conditions, lighting conditions, shooting angles, and altitudes.  
Each video and image in the dataset is accompanied by detailed annotation information, including, but not limited to, object bounding boxes, object categories, and tracking IDs. This annotation information is crucial for training and evaluating computer vision algorithms. VisDrone2019 provides researchers with a challenging testing platform that can be used to test and improve their visual perception algorithms, especially in drones.