esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 3 Data Preparation and Technical Resources
### Image Labeling  
For labeling the images of the dataset, the platform makesense.ai [23] was used, a free online tool that allows for manual marking of objects in images. After completing the annotation process, the labels were exported in the format specific to the YOLO network [24], widely used in object detection tasks.  
The labeling consisted of manually drawing a bounding box around each license plate present in the image. These boxes mark the exact position of the plate, being essential for the algorithm to learn how to locate them automatically.  
The YOLO format [24] assumes a simplified and efficient representation of annotations (see Figure 3), where each line in the label file corresponds to an object (in our case, a license plate), with the following structure:  
* class_id--an integer indicating the class of the object (e.g., 0 = license plate);
* X--the position of the center of the box on the X axis, normalized (divided by the width of the image--see Formula (1));  
Figure 2: Structure of Romanian license plate.  
Y--the position of the center of the box on the Y axis, normalized (divided by the height of the image--see Formula (2));
* width--the width of the box, normalized;
* height--the height of the box, normalized.  
**The formula for the normalized \(X\) coordinate of the bounding box center is given by**  
\[X=\frac{X_{c}}{Img_{w}}=\frac{X_{1}+X_{2}}{2\cdot Img_{w}} \tag{1}\]  
**The formula for the normalized \(Y\) coordinate of the bounding box center is given by**  
\[Y=\frac{Y_{c}}{Img_{h}}=\frac{Y_{1}+Y_{2}}{2\cdot Img_{h}} \tag{2}\]  
where  
* \(X_{1},Y_{1}\) are the coordinates of the top-left corner of the bounding box (in pixels);
* \(X_{2},Y_{2}\) are the coordinates of the bottom-right corner of the bounding box (in pixels);
* \(X_{c},Y_{c}\) represent the center coordinates of the bounding box, computed as the average of the two corners;
* \(Img_{w},Img_{h}\) are the width and height of the image (in pixels).  
After completing the image labeling process, two main directories were generated: images and labels. Inside the image directory, there are three subdirectories: train, val, and test, each containing the corresponding image sets for training, validation, and testing. The structure is mirrored in the labels directory, which contains the annotation files associated with each image, in YOLO format.  
The connection between these resources and the YOLO detection algorithm is made through the config.yaml file (Figure 4), which specifies the paths to the train and val directories, as well as the labeled object class, in this case \"car_plate\".  
Figure 3: Structure of YOLO format file.
## 4 Experimental Results
### Results and Analysis of Threat Intelligence Data
Most attackers attempt to exploit TCP and UDP services in order to obtain unauthorized access, infiltrate systems, or conduct other forms of cyber-attacks;
* Attackers use a variety of techniques to mask their true locations/identities, including proxies, VPNs, and hacked resources belonging to reputable Internet service providers;
* The bulk of the cyber-attacks came from unknown sources; however, some were already recognized due to the originating IP address;
* The bulk of the username and password attempts by attackers included default usernames and passwords specified by device manufacturers/solution providers;
* Attackers choose to target certain network ports because of their widespread use and associated vulnerabilities. From the threat intelligence data gathered during the research, it is evident that regardless of whether it is a cloud or direct internet-facing network infrastructure, if proper security measures are not taken, are both highly vulnerable to cyber-attacks. Port scanning with tools like Nmap and Masscan, which aid in locating and interacting with open ports on a network, is a method by which cybercriminals exploit and identify open ports on a global scale [35; 36]. Furthermore, Censys and Shodan, which are search engines for Internet-connected devices, can be misused by cyber attackers to find vulnerable devices, discover open ports, gather detailed device information, identify devices with default configurations, map networks, and locate misconfigured sensitive devices on a global scale [37; 38; 39; 40]. While intended for legitimate security purposes, they can be exploited for malicious activities. Once an open port is identified, the services that are being utilized on it are identified, and any known vulnerabilities are exploited [37; 38; 39; 40]. Frequently, these tactics are combined with additional attack vectors, such as opportunistic attacks on inadvertently exposed ports or the exploitation of weak credentials or software vulnerabilities. This highlights the criticality of implementing proper cyber security measures, such as consistent port monitoring and robust network security measures.
## 2 Related Work
[5] designed a priority-based MAC frame depending on the energy efficiency of the healthcare system. This research employed Fuzzy interference system to accomplish an optimization method for individual sensor nodes which can have independent environments, such as radio status, data length, or transmission interval derived from divergent sensor types. The main contribution of this work is to consider the different characteristics from individual node to find the optimization of ideal energy efficiency. In addition, [PERSON] et al. [6] developed an intelligent management method for WBAN with game theory. The authors endeavored to find an appropriate way in MAC layer between contention access approach and contention free approach. In this work, a network has dynamic strategies considering the application requirements and radio channel status so that the proposed scheme supports flexible energy resource allocation. This research also has an optimized method for energy efficiency to satisfy the requirements from various networks. Other researches tried to consider WBAN routing environment [9] and to design low power MAC [10].
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 3 Preliminaries  
### UE Authentication in 5G Networks  
In this section, we introduce the details about connection establishment process in the 5G AKA protocol. When a new UE is first connected to the 5G cellular network, the UE initializes the authentication process that is managed by the 5G core. The 5G authentication framework supports both 3 GPP access and non-3 GPP access networks with Extensible Authentication Protocol (EAP) that is also in use for IEEE 802.11 (WiFi) standard [20]. Such a security structure is convenient for IoT devices and UAVs. In this regard, the 5G EAP authentication protocol supports both EAP-Transport Layer Security (TLS) and EAP-AKA protocols, where authentication process is executed between the UE (a client device) and the Authentication Server Function (AUSF)/Unified Data Management (UDM) (i.e., HN) through the Security Anchor Function (SEAF)/Access and Mobility Management Function (AMF) (i.e., SN) as an EAP authenticator [40].  
In Figure 1, we provide the details of the AKA security standard. The 5G core network SEAF function triggers the authentication process upon receiving any access-request message from a UE, including the UE 5G-Globally Unique Temporary ID (GUTI) temporary identifier. If the UE is not provided with a 5G-GUTI, a Subscription Concealed Identifier (SUCI) can be used. The SUCI is an encrypted version of the Subscription Permanent Identifier (SUPI) provided to each UE using the public key of the home network, the SUPI should never be sent in plaintext to ensure UE's privacy. Then, the SEAF sends an authentication request to the AUSF in the core network, which verifies that the serving network request is authorized. If it is a legitimate request, the AUSF proceeds with the authentication procedure by sending an authentication request to the UDM. Next, the Subscription Identifier De-Concealing Function (SIDF) validates the SUCI by decrypting the SUCI and obtains the corresponding SUPI and selects the authentication method configured for the corresponding subscriber, which is 5G-AKA for our case. The UDM then sends an authentication response to the AUSF including an AUTH token, an \(XRES\) token, the key \(K_{AUSF}\), and the SUPI if not using the 5G-GUTI. \(K_{AUSF}\) is the primary authentication key that can be further used to derive subsequent keys for different purposes.
## 5 Experimental Results and Discussion
### Characterization of SEB Arrays  
For this type of experiments, a common source of SEBA is connected to MA and the gates are connected to gate voltage sources as schematically shown in Figure 4b. The source wires in the SEBA are bunched together on chip within a short distance from the SEB junctions (Figure 9a-c). The SEM micrograph of the SEBA composed of 8 SEBs is shown in Figure 9a; two gates are shown. Here \(V_{g}\) is applied to the \"tuning gate\" to choose an appropriate operating point and \(V_{p}\) is the voltage applied to the \"sensing gate\". For a sensing application this electrode will be connected to a point at which the voltage needs to be measured. The micrograph in Figure 9b shows an SEB array designed to feature thousands of SEBs situated atop of source lines and Figure 9c illustrates a 9 SEB self-aligned array with an extremely small footprint yet strongly coupled to the gate.
## 4 Results
### Main Phase
#### 4.2.3 Confidence Interval for Test and Predicted Values of Phycocyanin  
To illustrate a method for quantifying the confidence of our model results, we also used the randomly sampled testing dataset (CSLAP data with phycocyanin concentrations above 8 ug/L) to validate the MPH linear regression model for phycocyanin estimation from satellite data by determining the prediction and confidence intervals. Figure 6 shows the predicted and in situ values of phycocyanin, as well as the prediction and confidence intervals with a 0.05 level of significance (95% confidence) for the MPH model based on (a) concurrent data and (b) data with a one-day time window. One observation was removed from the analysis when visual interpretation confirmed that cloud contamination produced an outlier that was outside the feasible prediction range. The lower and upper lines of the prediction interval shown in Figure 6 mean that we are 95% sure that the predicted value for phycocyanin falls in this range, while the confidence interval limits say that we are 95% sure that average phycocyanin concentrations are within that range. As an example, in Figure 6a, for a selected location with an in situ phycocyanin observation of 80.91 ug/L (highlighted with a red circle), the Sentinel-2-based MPH value of 0.033 corresponds to a predicted phycocyanin concentration of 89.78 ug/L (marked with a red cross). By extending vertical lines from the cross in Figure 6, the corresponding prediction interval range demonstrates that we are 95% confident that the phycocyanin concentration for that point is in the range (59.88, 119.68 ug/L).  
Figure 6: The prediction and confidence intervals (95%) for the MPH model based on (**a**) concurrent data model and (**b**) a one-day time window model. The \(X\)-axis shows the computed values from the  
satellite image (the spectral index) while the \(Y\)-axis shows the phycocyanin concentrations for the corresponding predicted values (blue line), the confidence and prediction interval limits (gray and green lines, respectively), and the in situ values, corresponding to the test data points (orange points). For example, an in situ point (highlighted with a red circle) corresponds to an MPH value of 0.033, which connects to an estimated phycocyanin value of 89.78 \(\mathrm{\SIUnitSymbolMicro g}\)/L (red cross).
## 2 Models and Methods
### DP Algorithm with Emission Constraints  
The EREV driving history of RDE Test B is considered a time control domain discretized into 5598 grid nodes at a time step of 1 s. Meanwhile, the SOC of the battery is used as a variable to describe the energy system state of the EREV; SOC takes the value range from 0.2 to 0.8, and 6000 grid nodes are divided in the state control domain at the discrete step of 0.0001. In addition, the engine feasible operating domain is also considered an action control domain, where the engine speed range is from 1000 to 4200 r/min and the engine torque range at each speed state is from zero to the maximum value under the wide-open-throttle operating condition. The action control domain is divided into 61 \(\times\) 51 (speed \(\times\) torque) grid points with an additional engine shutdown point, so a total of 3316 action discrete points are generated, and each action discrete point corresponds to two engine operating parameters, namely, engine speed (\(n_{\text{e}}\)), and torque (\(T_{\text{e}}\)), respectively. The optimization of the energy management strategies is the selection of the best engine operating point from the 3316 action discrete points and the corresponding battery SOC in state space so that the value loss function in the control time domain is minimized as the vehicle state is updated from the moment \(k\)th to (\(k+1\))th:  
\[J=\text{min}\sum_{k=1}^{N}L(k) \tag{1}\]  
where \(J\) is the value loss function in the time control domain, and \(L(k)\) is the value loss function at the kth time step, which is determined by SOC, \(n_{\text{e}}\), and \(T_{\text{e}}\) at that time step. In the case where the state of charge at the start and end points of the control time domain is determined (i.e., the consumption of externally charged electrical energy remains constant), the fuel consumption can be taken as a value loss, and Equation (1) is transformed into  
\[J_{\text{fuel}}=\text{min}\sum_{k=1}^{N}m_{\text{fuel}}(k) \tag{2}\]  
where \(J_{\text{fuel}}\) is the value loss function scaled by fuel consumption; \(m_{\text{fuel}}(k)\) is the fuel consumption at the kth moment, which is determined by \(n_{\text{e}}\) and \(T_{\text{e}}\) associated with SOC at this time step. When the hazards of pollutant emissions are taken into account, the superior choice of state and action variables is constrained by the emission limits:  
\[\sum_{k=1}^{N}m_{\text{CO}}(k) \leq e_{\text{CO}}D_{\text{trip}} \tag{3}\] \[\sum_{k=1}^{N}m_{\text{NOx}}(k) \leq e_{\text{NOx}}D_{\text{trip}}\] (4) \[\sum_{k=1}^{N}m_{\text{PN}}(k) \leq e_{\text{PN}}D_{\text{trip}} \tag{5}\]  
where \(m_{\text{CO}}(k)\), \(m_{\text{NOx}}(k)\), and \(m_{\text{PN}}(k)\) are the CO, NOx, and PN emissions at the kth moment, respectively; \(e_{\text{CO}}\), \(e_{\text{NOx}}\), and \(e_{\text{PN}}\) are the CO, NOx, and PN specified-distance emission limits, respectively; and \(D_{\text{trip}}\) is the distance traveled by the EREV. Here, \(m_{\text{CO}}(k)\), \(m_{\text{NOx}}(k)\), and \(m_{\text{PN}}(k)\) are also determined by \(n_{\text{e}}\) and \(T_{\text{e}}\), which are associated with SOC at the kth time step.
## 2 Materials and Methods
### Feature Importances Determination Logic  
For the resulting models, the feature importances were determined through a systematic two-phase approach using feature engineering (FE) and polynomial features (PFs). Initially, scikit-learn's polynomial features (up to the 2 nd degree) [32] were employed to generate interaction terms such as \"laser power \(\times\) scanning speed\" from the selected process parameters. Those terms were then integrated into the model pipeline when PFs were enabled, followed by a recursive feature elimination [33] with Elastic Net [34] to select key importances only. After training the XGBoost model with this extended set, the feature importances (FIs) were extracted using the \"gain\" metric that quantified the average contribution of each feature. These values were then normalised so that they summed to 1. They were assigned to the corresponding feature and expressed as percentages for direct interpretability. In addition, SHAP (SHapley Additive exPlanations) values were calculated to interpret the model's predictions and depict feature importances [35].
## 6 Conclusions
### Limitations and Further Improvements  
This research has some limitations and provides an opportunity for further improvements. First of all, to avoid the interference of other factors, only videos were used in the experiments, and the simulation of real online shopping was lacking. When shopping online, consumers will not only watch videos but also search for product specifications, similar products, and other buyers' comments. Second, the selected products for the experiments had limitations. Only a smart phone and juice extractor were selected. Thus, it is unknown whether our results can be extended to all digital household appliances. Further experiments are needed. Finally, the experimental data have some limitations. A total of 124 valid datasets were collected, which is a small amount of data. More people should be invited to participate in similar experiments in the future to ensure the reliability of experimental results.  
Further researchers are encouraged to do further study on online product presentations, such as applying the fuzzy-set qualitative comparative analysis (fsQCA) method to study more complex situation. Referring to the application of fsQCA in consumer purchase, [PERSON] et al. combined trust, privacy, emotion, and experience to explain consumers' purchasing behavior in personalized online shopping [43]. According to this study, online product presentation videos show various elements. Maybe we can combine the products' appearances, features, brands, usage scenario information, tutorial information, production process, and host and explore how to have a more positive impact on consumers' purchase intention under different combinations. This is no longer limited to studying the relationships between variables in a single context but can also explore their impact on outcomes [44].  
Conceptualization, B.S. and R.H.; methodology, R.M.; software, R.H.; validation, R.H., R.M. and B.S.; formal analysis, R.H.; investigation, R.H.; data curation, R.H.; writing--original draft preparation, R.H. and B.S.; writing--review and editing, B.S. and R.H.; funding acquisition, R.M. and B.S.  
This research was funded by the Chinese National Funding of Social Sciences (No.14 AGL023) and the Fundamental Research Funds for the Central Universities (No.2019 CDJSK 02 PT 19).  
Thanks to all commenters for their valuable and constructive comments.  
The authors declare no conflict of interest.
## 3 Methodology
### LULC Classification in Google Earth Engine  
The landscape metrics evaluation was undertaken based on the prepared LULC map. A vital stage in every LULC classification is the preparation of the basis dataset. The creation of this dataset for the Landsat 7 and Sentinel-2 (Figure 2) data in this application begins in GEE with a filtered, cloud-masked image collection, and the median bands were computed. The LULC classification is based on a supervised technique, which, as is customary, requires gathering the essential information from the training points in order to  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Software and Cloud Platform** & **Script** & **Use** & **Source** & **Websites** \\ \hline Google Earth Engine (GEE) & Java & Google Cloud Platform & Open & [[https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)]([https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)), accessed on 8 October 2024 \\ \hline RStudio (4.1.1) & R script & Desktop & Open & [[https://www.rstudio.com/products/rstudio/](https://www.rstudio.com/products/rstudio/)]([https://www.rstudio.com/products/rstudio/](https://www.rstudio.com/products/rstudio/)), accessed on 8 October 2024 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Used software and cloud platform.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**S. No.** & **Packages** & **Description** \\ \hline
1 & landscape metrics & Landscape metrics calculation \\ \hline
2 & raster & Spatial raster data reading and handling \\ \hline
3 & sf & Spatial vector data reading and handling \\ \hline
4 & dplyr & Data manipulation \\ \hline
5 & bench & High-precision timing of R expressions \\ \hline \hline \end{tabular}
\end{table}
Table 3: R packages used for evaluating landscape metrics.  
Figure 2: Methodology chart.  
train the classifiers. The random forest (RF) classifier was trained and subsequently applied to the filtered, cloud-masked image collections from Landsat 7 and Sentinel-2, with median composite bands computed to optimize classification accuracy. Further, after collecting the samples and merging the class names, code running was performed, and finally, a classified map of the study area was generated, and this classified image was used for the accuracy assessments. For validation and to determine the accuracy of the classified map, training samples were taken again. For the purpose of LULC classification, four key LULC classes were identified: cropland, waterbodies, built-up, and other. Major crop-sown areas and cultivable lands were included in the layout of cropland. Natural waterbodies, small to large reservoirs, and canals were among the waterbodies. Others were fallow and barren ground, bare soil with very little flora, and drylands. Settlements, roads, railways, and industrial sites were mostly included in the built-up area. The final map layout, including legends and spatial details, was created using ArcMap 10.8 to enhance the presentation of the classified outputs.
## 3 Simulation Results
### As a DC-DC Converter  
The proposed hybrid converter can be used in solar energy systems, as illustrated in Figure 7. The system comprises PV panels as a power supply, the proposed hybrid converter, and two power flow directions. As shown in Figure 7, the proposed converter can supply two different loads simultaneously. The positive energy flow (red arrow) represents the energy flow to the electrical grid through an inverter and/or transformer. In contrast to that, the other negative energy flow (green arrow) is used to recharge the energy storage system.  
The two outputs of the proposed hybrid converter are illustrated in Figures 8 and 9. Figure 8 shows the negative voltage, which is responsible for recharging the energy storage system.  
In contrast to that, Figure 9 depicts the output voltage, which is used to supply the inverter (red arrow in Figure 7).  
The controlled switch M has a drain source voltage, as seen in Figure 10. As illustrated in Figure 10, the rated switch voltage is around 300 V. In addition to that, the switch current
## 3 System Model and Problem Formulation
### Heterogeneity and Behavior Modeling  
Due to the inherent heterogeneity of edge devices, we model client states from three aspects: Statistical heterogeneity: Differences in data distributions between \(\mathcal{D}_{i}\) and \(\mathcal{D}_{j}\) such as label imbalance and sample shift.  
System heterogeneity: Variations in computational capabilities \(\rho_{i}\) and communication latency \(l_{i}\) across clients.  
Behavioral heterogeneity: Behavioral anomalies such as unstable participation, gradient manipulation, or strategic uploads.  
To capture dynamic behavioral characteristics, we introduce a Trust-Aware Incentive Mechanism to enable behavior perception and adaptive regulation during training.