esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison with Machine Learning Methods
The confusion matrices of all models are given in Figure 8. The number of correct and incorrect classifications for all classes can be seen more clearly from these matrices. These machine learning methods and XGBoost were combined with the developed CNN model and their classification success was compared. The results show that the XGBoost method classified images with higher accuracy based on the features extracted by CNN when compared with these methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Std** \\ \hline CNN + SVM & 0.0168 \\ CNN + KNN & 0.0150 \\ CNN + DT & 0.0066 \\ CNN + NB & 0.0167 \\ CNN + RF & 0.0262 \\ CNN + CatBoost & 0.0165 \\
**CNN + XGBoost** & **0.0025** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy standard deviations of CNN + ML models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Class Type** & **Precision** & **Recall** & **F1 Score** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{CNN + SVM} & G & 0.986 & 0.950 & 0.967 & \multirow{4}{*}{98.03} \\  & M & 0.951 & 0.967 & 0.959 & & \\ \cline{1-1}  & N & 0.992 & 1.0 & 0.996 & & \\ \cline{1-1}  & P & 0.987 & 0.996 & 0.991 & & \\ \hline \multirow{4}{*}{CNN + KNN} & G & 0.982 & 0.953 & 0.967 & \multirow{4}{*}{97.95} \\  & M & 0.954 & 0.964 & 0.959 & & \\ \cline{1-1}  & N & 0.992 & 1.0 & 0.996 & & \\ \cline{1-1}  & P & 0.983 & 0.993 & 0.988 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification performance comparison of CNN + ML models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Class Type** & **Precision** & **Recall** & **F1 Score** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{CNN + DT} & G & 0.993 & 0.983 & 0.988 & \multirow{4}{*}{99.16} \\  & M & 0.980 & 0.983 & 0.982 & \\  & N & 1.0 & 1.0 & 1.0 & \\  & P & 0.990 & 0.996 & 0.993 & \\ \hline \multirow{4}{*}{CNN + NB} & G & 0.982 & 0.950 & 0.966 & \multirow{4}{*}{97.80} \\  & M & 0.951 & 0.957 & 0.954 & \\ \cline{1-1}  & N & 0.992 & 1.0 & 0.996 & \\ \cline{1-1}  & P & 0.980 & 0.996 & 0.988 & \\ \hline \multirow{4}{*}{CNN + RF} & G & 0.955 & 0.933 & 0.944 & \multirow{4}{*}{95.66} \\  & M & 0.903 & 0.933 & 0.918 & \\ \cline{1-1}  & N & 0.987 & 0.969 & 0.978 & \\ \cline{1-1}  & P & 0.976 & 0.988 & 0.982 & \\ \hline \multirow{4}{*}{CNN + CatBoost} & G & 0.979 & 0.950 & 0.964 & \multirow{4}{*}{97.86} \\  & M & 0.948 & 0.964 & 0.956 & \\ \cline{1-1}  & N & 0.992 & 1.0 & 0.996 & \\ \cline{1-1}  & P & 0.986 & 0.990 & 0.988 & \\ \hline \multirow{4}{*}{**CNN + XGBoost**} & G & 0.993 & 0.997 & 0.995 & \multirow{4}{*}{**99.77**} \\  & M & 0.998 & 0.993 & 0.995 & \\ \cline{1-1}  & N & 1.0 & 1.0 & 1.0 & \\ \cline{1-1}  & P & 1.0 & 1.0 & 1.0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._  
Figure 8: _Cont._
## 2 Materials and Methods
### Monte Carlo Model  
The present Monte Carlo model performed two separate tasks, namely, to model the interaction between the incident photons with (i) the EBT3 film active layer and (ii) the IOC sensitive volume. The setups for irradiating the EBT3 film and the IOC are schematically shown in Figures 2 and 3, respectively. The energies deposited from photons and the generated secondary electrons were scored. The thickness of the EBT3 film active layer was 28 \(\upmu\)m and the surface area was 1 \(\times\) 1 cm\({}^{2}\).  
Figure 1: Photon energy distribution of the X-ray beam used to irradiate the EBT3 film [10].  
The photon doses absorbed in the targeted domains (i.e., EBT3 film active layer and IOC sensitive volume) were determined using the track length estimate of energy deposition (tally F6:P). The mass energy-absorption coefficients (\(\mu_{em}(E)/\rho)_{m}\) of the EBT3 film active layer and air were used to determine the absorbed photon dose (\(D\)) as  
\[D=\int\psi(E)E\left(\frac{\mu_{em}(E)}{\rho}\right)_{m}dE \tag{1}\]  
where \(\psi(E)\) was the differential photon fluence for photon energy \(E\), and integration from minimum to maximum energies in the photon energy spectrum was needed. In the present MCNPX computations, the mass energy-absorption coefficients in the MCPLIB04 database were employed. Data for cross-sections, form factors, and fluorescence were all derived from the ENDF/B-VI.8 data library. The photon-coupled-electron transport mode was used to track the propagation of the incident photons and the generated secondary electrons in both EBT3 film active layer and IOC sensitive volume. The coupled transport mode was essential due to the dose deposited by the secondary electrons. For the pure photon transport mode, local energy deposition would be assumed for electrons at their creation sites, which was not desirable for precise determination of energy deposition in the EBT3 film active layer and IOC sensitive volume.
## 4 Performance Evaluation
### Numerical Results
The network lifetime improvement of the location-based model compared to the periodic model is proportional to the request interval. In particular, the improvement increases from only 4% to more than two times when the request interval increases from 10 s to 6 min. Similar to Figure 4, Figure 5 shows the network lifetime improvement of the location-based model under various cluster sizes. The stronger the data correlation among sensors in a network, the greater the network lifetime improvement that the location-based model can achieve. Figure 4: Network lifetime improvement vs. request intervals. Figure 3: Average energy consumption under various cluster sizes. **Scenarios with High Error Localization Systems:** In this paper, we assume mobile devices have GPS or use location tracking services like StarTrack. The accuracy of the current localization techniques is good enough for our model as well as similar existing location-based applications like mobility pattern monitoring of moving objects in large cities [22], continuous multi-dimensional context and activity recognition [23], criminal tracking, autonomous car navigation and obstacle avoidance [24; 25]. Real-world data from the Federal Aviation Administration show that their GPS attains better than 2.168 m accuracy with a 95% confidence level [26]. Higher accuracy is attainable by using GPS in combination with augmentation systems that enable real-time positioning to within a few centimeters. In cases of a high error localization system, the model can still ensure the region of interest of a user by compensating for the localization error into the region of sensor data collection. We conduct a simple analysis for illustration. Given a sensor network deployment area has a number of sensors. The average distance between two sensors is 8 m and the transmission range is 10 m. The average cluster size is 3. The region of interest of a user has a radius of 50 m.  
We assume the error of the used localization technique is 10 m. To ensure the user's region of interest, the model collects sensor data in a region of 60 m (i.e., \(50\) m + \(10\) m). In this case, more nodes are required to be involved in providing sensing data collection. As a result, energy efficiency of the proposed model is reduced by 12%, compared to the case of an accurate localization system. Although the improvement ratio is reduced due to a high localization error, energy efficiency of the proposed model is still significantly better than the conventional model.
# Improved Electrochemical-Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models
## 3 Experiment  
Three commercial LFP/graphite prismatic LIB samples undergo electrical, thermal, and mechanical characterization to evaluate their performance. The tests consist of measuring voltage, temperature, and deformation responses under different charge and discharge currents. Charge rates vary from C/20 to C/2, while discharge rates range from C/20 to 3C, in accordance with manufacturer specifications. Each charge/discharge test is conducted within a state of charge (SOC) range of 0-100% and repeated five times on the same battery sample. A dedicated testing setup with laser sensors is used to monitor battery deformation during operation by measuring thickness variations, as reported in a previous study by the authors [33]. The measured voltage and thickness change, averaged over the tested samples, constitutes the dataset used for parameter estimation through the optimization algorithm.  
Differential voltage analysis was performed on a voltage profile recorded during low-rate charge/discharge cycles. Similar results were obtained from charge and discharge measurements. The voltage signal was filtered before computing derivation to mitigate the noise amplification. The Gaussian-weighted moving average filter and the Savitzky-Golay filter were found to be the more effective filtering strategies. The Gaussian filter was applied with the \"smoothdata\" function in Matlab, activating the option \"Gaussian\". Using the window length and a value of 1/20 of the total data points was found to be effective. The Savitzky-Golay filter was applied using the \"smooth\" function in MATLAB with the \"Sgolay\" option activated. This filter utilizes the Savitzky-Golay method, which requires specifying both the polynomial degree and the number of data points. It was determined that a third-order polynomial and 1/6 of the total number of data points provided optimal results.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 4 Materials and Methods  
The goal of this research described was to estimate the locations of pedestrians, detect the groups of pedestrians, and visualize the possible forming of groups. The proposed experimental solution is divided into three individual parts, visualized in Figure 2. The first component is an unmanned aerial vehicle (UAV), which is responsible for providing video recordings and additional data about the location and time of the recording. The second component is the application written in Python language for processing the input from the UAV and calculation of the pedestrians' positions. The method is further discussed in Section 4.1. The data file from the second component is input to the last, the web application, being a graphical user interface (GUI), which implements clustering algorithms to visualize the formation of groups; more is discussed in Section 4.2.  
Figure 1: Two clusters (red and blue) with non-circular shapes.  
The approaches mentioned in the YOLO Section 2.2 of the analysis presume that detection is done on the UAV. In the case of this research, we provide the option to separate the detection from the UAV to the separate machine. This approach still allows a near real-time detection process or later post-processing. The Python application may also be applied to the UAV.  
As mentioned, the video and data file from a UAV is processed by the designed Python application. The YOLOv3 algorithm [8] is used to detect pedestrians. This algorithm provides faster speeds at the cost of some accuracy. Our method to calculate the real-world location of pedestrians based on their positions in an image frame is proposed to achieve visualization of the pedestrians' positions and visualize the formation of possible groups.  
The data file produced by the designed Python application is further utilized by a web application. The data are provided by a database management module. This means that the data can be stored and analyzed over several video recordings and long periods. Clustering analysis algorithms, such as K-Means, DBSCAN, and OPTICS, were implemented for visualization.
# Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar-Communication System
## 2 System Model and Problem Formulation
### Radar Part  
Assuming that a far-field target is located at the \(\theta\) direction, the transmitted signal in the \(\theta\) direction can be given by  
\[\textbf{y}(\theta)=\textbf{a}_{T}^{\text{T}}(\theta)\textbf{X}, \tag{6}\]  
where \(\textbf{a}_{T}(\theta)=\left[1,e^{-j\pi\sin\theta},\ldots,e^{-j\pi(N-1)\sin \theta}\right]^{\text{T}}\in\mathbb{C}^{N\times 1}\) denotes the steering vector. Thus, the transmitted power at \(\theta\) can be expressed as  
\[\begin{array}{ll}P(\theta)&=\left\|\textbf{y}(\theta)\right\|_{2}^{2}\\ &=\textbf{x}^{\text{H}}\big{(}\textbf{I}_{L}\otimes\textbf{a}_{T}^{\text{T}} (\theta)\big{)}^{\text{H}}\big{(}\textbf{I}_{L}\otimes\textbf{a}_{T}^{\text{ T}}(\theta)\big{)}\textbf{x}\\ &=\textbf{x}^{\text{H}}\textbf{R}(\theta)\textbf{x},\end{array} \tag{7}\]  
where \(\textbf{x}=\text{vec}(\textbf{X})\), and \(\textbf{R}(\theta)=\big{(}\textbf{I}_{L}\otimes\textbf{a}_{T}^{\text{T}}( \theta)\big{)}^{\text{H}}\big{(}\textbf{I}_{L}\otimes\textbf{a}_{T}^{\text{ T}}(\theta)\big{)}\). The MIMO radar transmission pattern design mostly adopts the pattern matching design principle. In the least squares framework, the waveform is designed to match the transmission pattern with the expected pattern and minimize the cross-correlation pattern [19]. This paper uses weighted least squares as the cost function, which can be expressed as  
\[f(\textbf{x})=\frac{1}{J}\sum_{j=1}^{J}\left|\alpha^{2}D(\theta_{j})-\textbf{ x}^{\text{H}}\textbf{R}(\theta_{j})\textbf{x}\right|^{2}, \tag{8}\]where \(\alpha\) is a scaling parameter to be optimized, \(D(\theta_{k})\) represents the desired beampattern, and \(J\) means the number of sampled angles over the region of interest. To simplify notations under CM, Equation (8) can be expressed as  
\[\begin{split}&\min_{\mathbf{z}}f(\mathbf{x})=\frac{1}{\int\limits _{j=1}^{J}\left|\mathbf{z}^{\mathrm{H}}\mathbf{A}(\theta_{k})\mathbf{z}\right| ^{2}\\ & s.t.\left|x_{n,l}\right|=\sqrt{\frac{P_{0}}{NL}},n=1,2,\ldots,N;l= 1,2,\ldots,L,\end{split} \tag{9}\]  
where  
\[\mathbf{z}\triangleq\binom{\alpha}{\mathbf{x}}, \tag{10}\]  
\[\mathbf{A}(\theta_{k})\triangleq\binom{D(\theta_{k})}{\mathbf{0}}\quad\mathbf{ 0}^{\mathrm{T}} \tag{11}\]  
Typically, the problem under CM is also a nonconvex fourth-order polynomial. By optimizing the problem of (9), we could obtain the desired \(\mathbf{X}_{0}\in\mathbb{C}^{N\times L}\) as the referenced waveform.
# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 2 Materials
### Data Used  
Three types of satellite data in 2016 were used in this study: (1) Sentinel-2 L1C images (10 m), (2) Landsat-8 OLI images (30 m), and (3) NPP VIIRS nighttime light data (about 500 m). The Sentinel-2 and Landsat-8 images are fused to generate the time series at a spatial resolution of 10 m, and the NPP VIIRS nighttime light data is used to obtain the boundary that divides urban and rural areas. All Landsat-8 and Sentinel-2 images used in this study are summarized in Table 2. Details of each dataset are introduced in the following sub-sessions.  
Sentinel-2 that consists of two twin satellites, provides multi-spectral images with spatial resolution from 10 m to 60 m and temporal resolution of 5 days. The onboard Multi-Spectral Instrument (MSI) can collect 13 spectral bands including four bands (BLUE, GREEN, RED and Near Infrared) at 10 m; 6 bands (vegetation RED Edges and SWIR) at 20 m for vegetation classification and snow, ice and cloud discrimination; and 3 bands (0.443, 0.945 and 1.375 \(\upmu\)m) at 60 m for aerosol detection, water vapor and cirrus monitoring. In this study, Sentinel-2 L1C images (Tile Number: T50 TMK) in 2016 were downloaded via USGS Earth Explorer ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))). This dataset has been geometrically corrected and converted to top of atmosphere (TOA) radiance [36].  
Landsat-8 OLI collects eight spectral bands (Coastal Aerosol, BLUE, GREEN, RED, Near Infrared, SWIR-1, SWIR-1, and Cirrus) at 30 m [37]. In this study, nine Landsat-8 OLI surface reflectance images (Path: 123, Row: 032) in 2016 with cloud cover lower than 60% were collected via USGS Earth Explorer ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))). These images can cover the entire Sentinel-2 scene (Tile Number:  
Figure 1: (**a**) Location of study site, Beijing; and (**b**) study area covered by the Sentinel-2 scene T50 TMK (red box).  
T50 TMK). Landsat-8 images with a spatial resolution of 30 m are fused with Sentinel-2 images (10 m) by the spatiotemporal data fusion algorithm [38].  
Monthly composites of the daily Day/ Night Band (DNB) imagery of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) satellite provide reliable nighttime light data in 15 arc-second geographic grids (about 500 m). According to the location of our study area, the Tile 3 (75N060E) of NPP VIIRS nighttime light data in 2016 was collected from the Earth Observations Group (EOG) at NOAA/NCEI ([[https://ngdc.noaa.gov](https://ngdc.noaa.gov)]([https://ngdc.noaa.gov](https://ngdc.noaa.gov))). Previous studies have demonstrated the high accuracy and good performance of this nighttime light dataset for extracting rural-urban boundaries [24; 39].
## 4 Results and Discussion
### Optimization Results of Urban Spatial Form  
Figure 9a shows the distribution of the urban spatial form in the feasible solution schemes during the optimization process. As shown, the feasible solutions tend to place high-rise buildings, such as P-2, on the northern and western sides of the urban block, particularly in land units 0, 1, 2, and 4. In contrast, the southern part of the block mainly features medium-rise buildings, such as C-3, while the eastern side, in blocks like 2 and 8, is predominantly composed of low-rise buildings. Overall, the spatial form in the feasible solutions presents a layout with higher buildings in the northwest and lower buildings in the southeast.  
Figure 9b shows the distribution of urban spatial forms in the Pareto solutions during the optimization process. Similarly to the conclusions in Figure 8a, the spatial forms in the Pareto solutions also show a general northwest-high, southeast-low layout. However, unlike the feasible solutions, the proportion of high-rise buildings in the western land units decreases, while the proportion of medium- and low-rise buildings increases.  
This layout may be due to the prevailing wind direction in Beijing, where the summer winds predominantly come from the southeast and the winter winds come from the northwest. By maintaining high-rise buildings on the north side while reducing the height  
Figure 9: Three-dimensional (3D) bar charts of spatial morphology optimization results. (**a**) Data distribution of feasible solutions in the design space. (**b**) Data distribution of Pareto solutions optimized by the algorithm.  
of buildings on the west side, this layout not only ensures that high-rise buildings block the cold winds in winter but also allows the cool summer air to enter the block, carrying away heat and lowering the outdoor temperature. Additionally, considering that Beijing is located in the Northern Hemisphere, the northwest-high, southeast-low layout helps increase morning sunlight exposure in the block.  
The enclosed building types C-2, C-1, and C-3 appear most frequently in both the feasible and Pareto solutions. According to previous studies, enclosed buildings have a smaller form factor, which can reduce the impact of cold winds on the outdoor environment in winter in cold northern regions and can create shaded areas in the summer by providing self-shading, improving the outdoor thermal environment.  
In conclusion, the optimized urban spatial form can adapt to the unique climate conditions in Beijing, providing a comfortable thermal environment in both winter and summer while ensuring ample sunlight, thus creating a comfortable outdoor environment for the elderly.
## 4 Discussion
### %EMG Peak Amplitude  
No significant increase in %EMG peak amplitude was observed for the feedback conditions.  
However, significant differences in %EMG peaks between exercises were noted: biceps brachii for Biceps Curls, lower trapezius for Reverse Flys, and triceps brachii for Shoulder Extensions. These findings align with the expected muscle activation patterns for these exercises. For instance, Biceps Curls target the biceps brachii, along with other anterior upper humeral muscles, resulting in higher %EMG peaks. The stable upper attachment sites at the scapula during this movement lead to forearm movement toward the shoulder joint, with the triceps brachii and lower trapezius contracting to stabilize the humerus and scapula [40], respectively. Given that the biceps brachii primarily drives the action, it is expected to be the dominant contracting muscle [41].  
Reverse Flys showed greater lower trapezius %EMG peaks, indicating more activation in the lower trapezius. Perhaps this was because of the position of the humerus. The starting position of the movement is with the arms raised 90\({}^{\circ}\) to the torso. This position in itself challenges the scapular position--one must work to keep the scapula down when raising the arms. As the arm moves posteriorly, the primary movers are latissimus dorsi and teres major, while the lower trapezius continues to maintain control of the scapula [33]. While the biceps and triceps act as stabilizers in this movement, the triceps showed slightly greater %EMG than the biceps, which is congruent with expected results--posterior movements would recruit posterior muscles.  
The Shoulder Extension is similar to Reverse Flys in that the humerus is rotating backward in the glenohumeral joint while the scapula remains stable. In the Shoulder Extension, the triceps brachii had greater %EMG peaks. In this movement, the triceps are responsible for stabilizing the scapula while simultaneously adding stability to the humerus. Although the primary mover is not the triceps (latissimus dorsi), because the humerus is moving backward, posterior muscles will most likely contract more to assist in movement (and stability). The lower trapezius should assist primarily in stabilizing the scapula as the humeral head rotates backward within the joint capsule [33]. The lower trapezius, however, did not show %EMG peak during this movement, which supports the vertical displacement results for Shoulder Extensions, which showed more displacement. In other words, there was more movement in the shoulder during Shoulder extensions due to the lack of lower trapezius activation.