esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### DBN Establishment
#### 3.1.2 Network Structure and Parameter Determination  
The factors and logical relationships in the proposed risk evolution mechanism were mapped onto a DBN, as shown in Figure 6. According to their dependencies, the nodes in the DBN are divided into three categories: (i) root nodes, (ii) intermediate nodes, and (iii) leaf nodes. The solid arrows represent risk transfer between different nodes, and the dashed arrows represent risk transfer within the same node. Figure 5: Risk evolution mechanism of cable fires in the power distribution room. The assessment states for each node have been systematically categorized based on cable performance and real-world operational conditions (see Table 2). In particular, each root node is divided into three states, S0, S1, and S2, which indicate that the basic risk factors represented by the root nodes are in the optimal, relatively unfavorable, and most unfavorable states, respectively. Moreover, within this set of eight root nodes, the assessment states for X1 to X4 are delineated based on quantitative monitoring data, whereas those for X5 to X8 are determined using expert evaluations that employ a scale ranging from 0 to 10. The DBN segments continuous time into discrete time slices. However, the evolution degree of the risk is different for time slices with different lengths. Therefore, before determining the CPT of the DBN, the length of the time slice must first be determined. Generally, the cable (60227 IEC 05 (BV)) reaches a balanced temperature increase within 2 min. To proactively mitigate risks at the earliest opportunity, the time slice length of the DBN in this case was defined as 1 min, serving as the foundation for determining the CPT. The CPT in a DBN can generally be determined using parameter learning or expert knowledge, and parameter learning is based on a large amount of statistical data. Due to a lack of available data, the CPTs of the nodes were primarily determined by expert knowledge and referred to the CPT data from [PERSON], [PERSON] [16]. For example, the CPT of leaf node \(Z\) is listed in Table 3. The system model's factors correspond to the various represented root node factors. Based on the structure and parameters of the DBN, we can instantiate the DBN. With the principles of DBN introduced in Section 2, we performed dynamic inference using the GeNle software, and a modeling schematic is shown in Figure 7. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Node** & **Description** & **States** & **Node** & **Description** & **States** \\ \hline \multirow{3}{*}{X1} & \multirow{3}{*}{Voltage} & S0: safety (0\(\sim\)300 V) & \multirow{3}{*}{Y1} & \multirow{3}{*}{Insulating capacity} & S0: good \\  & & S1: mild overload (300\(\sim\)345 V) & & & S1: general \\ \cline{1-1}  & & S2: severe overload (\(>\)345 V) & & & S2: bad \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The state division of each node. Figure 6: DBN structure of cable fire in the power distribution room.
# Vlog-Based Multimodal Composing: Enhancing EFL Learners' Writing Performance
## 2 Literature Review
### SNS in EFL Writing  
Social technologies can be utilized to support EFL teaching and learning through SNS educational activities, such as Facebook, Instagram, wiki, etc. In foreign language pedagogy, these can bring flexible support to the process of language learning to ease academic communication, provide the sharing of ideas, and fully utilize learning resources [34]. There were many studies that were conducted to promote the application of SNS in language teaching. For instance, [PERSON] and [PERSON] [35] indicated that Facebook had the potential for classroom language teaching and learning. [PERSON] et al. [36] found that most of the candidates in the study agreed that Facebook could offer great promises to facilitate English learning and improve their confidence and motivation in using English.  
In order to investigate exactly how SNS aid in teaching EFL writing, several scholars also spent much effort in studying them. [PERSON] [37] showed that students can be put into an interesting environment to express themselves to audiences with their writing, and learners were actively involved in it. Another similar example is that Facebook was proven to improve learners' motivation and enhance writing ability in a social context [38]. [PERSON] [39] provided empirical evidence that participants recognized the usefulness of Facebook and the interactive nature of the incorporation of Facebook in writing classes. [PERSON] and [PERSON] [40] found that Facebook can be a useful tool in collaborative writing for EFL learners by improving English writing quality. [PERSON] [41] discovered that learners' writing accuracy has been improved by receiving instruction of grammar points through SNS (Telegram APP). Additionally, several studies have reported learners' positive perceptions towards using SNS in EFL writing classes. [PERSON] et al. [42] demonstrated that students hold high positive perceptions and views towards using Instagram in writing skill improvement, but moderate attitudes. [PERSON] et al. [43] indicated that participants in the study felt they had a sense of purpose and became more engaged in the writing tasks, and they were interacting more confidently in Google+ writing classes compared with conventional writing lessons.  
Numerous studies indicate that EFL learners benefit from using SNS to develop writing skills and other aspects. However, despite the advantages that SNS brought, the application of vlogs was rarely found in these studies. It is true that SNS can provide opportunities for the teaching and learning of EFL writing. Nevertheless, different types of SNS can serve different purposes [44]. Vlogs, as one form of SNS, receive less attention to the application to EFL writing classes. Therefore, there is a need to investigate the application of vlogs to teaching English writing, as well as its effects on learners' writing performance.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 3 Storage and Access of Remote Sensing Data in MongoDB
### Archiving Model for Metadata  
Remote sensing metadata plays an essential role in correlative studies of earth-observation, and managing the metadata effectively can contribute to the application and sharing of remote sensing data [25, 26]. In our research, remote sensing metadata is the descriptive information of the remote sensing image, which is generated to store attribute information. However, the contents of different metadata files are different, which brings difficulties to the unified management of remote sensing metadata. In the Landsat metadata file, for example, the \"SENSOR_ID\" field is the sensor identifier of the satellite, and the \"SPACECRAFT_ID\" field is the satellite identifier of the image, while in the ZY-3 metadata file, the corresponding fields are SensorID and SatelliteID. Therefore, constructing a unified archiving model for the management of remote sensing data is urgent. Based on the investigation and survey of current metadata standards containing ISO (International Organization for Standardization) 19115 geographical information metadata standard and CSDGM (Content Standard for Digital Geospatial Metadata), as well as the metadata structures of multiple remote sensing data sources, the archiving model for remote sensing metadata is established, and its fields are determined: SatelliteID, SensorID, ReceiveDate, geographic coordinates, and so forth. These fields are shown in Table 1.  
Figure 2: The distributed cluster architecture for remote sensing data. The remote sensing metadata is shown in the form of document in the MongoDB database. When inserting data into MongoDB database, if the \"_id\" field does not exist in the document, MongoDB will generate the field automatically. Taking one Landsat8 metadata file as example, the standard storage in the MongoDB database is achieved through data processing. The storage mode of the remote sensing metadata is as follows:  
\(\{\)  
\"_id\": ObjectId(\"5 cd393629e11f33380453591\"),  
\"ImageName\": \"LC80010762013365 LGN01\",  
\"SatelliteID\": \"LANDSAT_8\",  
\"SensorID\": \"OLT_TIRS\",  
\"ReceiveDate\": \"2013/12/31\",  
\"StartTime\": \"2013:365:14:38:11.868608\",  
\"StopTime\": \"2013:365:14:38:43.6386040\",  
\"ProductLevel\": \"L1\",  
\"ProductFormat\": \"GEOTIFF\",  
\"Resolution\": 30,  
\"CloudPercent\": 4.5,  
\"Image Quality\": 9,  
\"CenterLatitude\": -23.11263,  
\"CenterLongitude\": -69.68594,  
\"TopLeftLatitude\": -22.06363,  
\"TopLeftLongitude\": -70.38825,  
\"TopRightLatitude\": -22.43484,  
\"TopRightLongitude\": -68.5718,  
\"BottomRightLatitude\": -23.78778,
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### [PERSON]'s SADRL Approach for a Hierarchical Multi-Agent Environment
#### 6.4.1. [PERSON]'s SADRL Approach with Actors and Critics  
In [77], [PERSON] et al. embedded SADRL with actors and critics in distributed agents, which are the users, to select their resources (i.e., channels with transmission opportunities) (A.1) based on their own observations without knowing the channel states of other agents and exchanging information with other agents, while reducing the signaling overhead (O.4). The agents maximize their individual rewards by selecting the best possible channels in a competitive (X.2.1) manner. The proposed approach addresses the challenge of ultra-densification (C.1) caused by a large number of node pairs, and high dynamicity (C.2) due to the dynamic condition of the operating channels.  
Each user \(i\) employs a separate set of actor and critic networks. The user \(i\) represents state \(s^{i}_{t}\) with the availability of its own channels. The action \(a^{i}_{t}\) represents the selected channel. The reward \(r^{i}_{t+1}(s^{i}_{t+1})\) is based on the SINR of the selected channel and whether a collision has occurred or not. The SADRL with actor and critic networks approach has been shown to increase throughput (P.3).  
A similar approach has been applied in the literature [70]. In [70], [PERSON] et al. embedded this approach in distributed agents (i.e., users) to select their resources (i.e., operating channels) (A.1) based on the the successful and unsuccessful (e.g., collision) transmissions, while addressing the challenges of ultra-densification (C.1) caused by a large number of users and high dynamicity (C.2), in order to increase throughput (P.3). Distributed agents store important (or prioritized) observations, which have less noise in the channel as estimated by a channel estimator, in the replay memory to increase the learning speed (O.2). Long Short Term Memory (LSTM) is used to predict the next state based on historical information (i.e., historical states and actions).
## 5 Results
### Comparison of Average Classification Times Between CNN and Transformer-Based Classifiers  
The average classification times for CNN-based classifiers are shown in Table 7.  
The following can be observed:  
* AlexNet, DarkNet53, and ShuffleNet were the fastest among the CNN-based classifiers, with an average classification time of 13.00 milliseconds.
* VGG16 had the longest average classification time of 124.60 milliseconds, which was approximately 9 times slower than AlexNet.
* Models like DenseNet201 and ResNet50 exhibited moderate classification times (108.00 and 69.10 milliseconds, respectively) but typically offered higher accuracies, as shown in previous analyses. This indicates a trade-off between computational efficiency and accuracy.
* All models except VGG16 and DenseNet201 had classification times under 70 milliseconds, making them suitable for near-real-time applications.  
The average classification times for the Transformer-based classifiers are shown in Table 8.  
The following can be seen:  
* The LeViT and CvT exhibited the lowest and the highest classification time, respectively.
* Models like SwinT and PvT struck a balance between accuracy and computational efficiency.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Average Class. Time (ms)** \\ \hline AlexNet & 13.00 \\ \hline DarkNet53 & 13.00 \\ \hline ShuffleNet & 13.00 \\ \hline VGG16 & 124.60 \\ \hline DenseNet201 & 108.00 \\ \hline ResNet50 & 69.10 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average classification times for CNN-based classifiers.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Average Class. Time (ms)** \\ \hline LeViT & 9.00 \\ \hline CvT & 21.00 \\ \hline SwinT & 15.00 \\ \hline PvT & 17.00 \\ \hline TNT & 18.00 \\ \hline ViT & 19.00 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average classification times for Transformer-based classifiers.  
* The majority of the Transformer-based models, such as the TNT, ViT, and CvT, were computationally efficient and suitable for real-time applications.  
In summary, the CNN-based models generally showed comparable or slightly better computational efficiencies than the Transformer-based models, with AlexNet, DarkNet53, and ShuffleNet being the fastest classifiers overall. Both AlexNet and ShuffleNet were strong candidates due to their low classification times and reliable performances.
## 3 Results
### Physical-Based AVS Model Parameter Optimization  
To create an appropriate biosensor model, the extracted data from NEGF modeling are used to determine _AVS_ model parameters that are compatible with GFET characteristics. In the first step, the training data file provided by the proposed NEGF approach is formed in a three-column format. The first column corresponds to the drain-source bias, \(V_{ds}\), the second column corresponds to the gate-source bias, \(V_{gs}\), while the third column is the measured drain-source current, \(I_{ds}/W\), in Amperes per meter of the device width. Then, MATLAB's built-in routine least-square curve-fitting routine, _Isqcurvefit_, _is used_ to optimize the parameters in the AVS model according to the training file. In the used AVS v1.0.0 circuit model [34], the fixed parameters are shown in Table 1, and a total of eight parameters can be optimized as shown in Table 2. In order to extract a realistic and physically meaningful way, all the optimized parameters are considered with appropriate lower and upper bounds, including a robust initial guess. Table 2 shows the extracted parameters with their lower and upper bounds and initial guess values used in the nonlinear parameter extraction routine. After optimizing AVS parameters, a good agreement is yielded with NEGF results; Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Meaning** & **Values** \\ \hline w & Device width (m) & \(20\times 10^{-6}\) \\ \hline Lg & Device Length & 140,300,650 \(\times 10^{-9}\) \\ \hline cg & Gate capacitance (F/m\({}^{2}\)) & \(3.6\times 10^{-3}\) \\ \hline \(n_{0}\) & Non-ideality factor (unit-less) & 2; \\ \hline alpha & Shift in threshold voltage (unit-less) & 6; \\ \hline Tjun & Junction temperature & 298 \\ \hline zeta & Channel ballisticity parameter (unit-less) & 0.2 \\ \hline mc & Relative effective mass of carriers (kg) & 0.04 \\ \hline cofs & Outer-fringing capacitance for the source—terminal (F/M) & 0 \\ \hline cofd & Outer fringing capacitance for the drain—terminal (F/M) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fixed parameters in the AVS v1.0.0. model.  
Figure 8: (**a**) Transfer characteristics. (**b**) Output characteristics of GFET, proposed method (solid line), and low’s approach (circles).
## 5 Simulation and Experimental Results  
For implementing the idea of the research Ubuntu 16.04, NS-2.35 and SUMO-1.1.0 are used. To construct real-time traffic, mobility files are generated using open street mapping and SUMO. The coverage area taken for the simulation environment is 1500 \(\times\) 1500 m with the varying speed from 15 to 35 (m/s). The other essential parameters that are involved in the process of simulation are given in Table 6. The analysis is conducted using three different scenarios, which are a varying number of vehicles, varying speeds, and the presence of malicious vehicles in the network. The parameters which are considered for this analysis are energy efficiency, packet delivery ratio, overhead, and packet loss. For comparison, the considered earlier research for the purpose of performance analysis is S-AODV [26], ES-AODV [32], and ECC-ACO-AODV [37]. S-AODV is a security-based routing protocol that is mainly used to detect the blackhole attack using enhanced route request and route reply transmission in the routing protocol. Additionally, security-based encryption and decryption are included with it. The major parameters which are concentrated are packet delivery ratio, delay, and throughput. The results are moderate, and it is given the analysis. ES-AODV provides enhanced security to overcome the blackhole attack, which uses a flag status-based technique that includes route discovery, route reply, detection, and prevention process. The simulation results of this model mainly concentrate on packet delivery ratio, delay, routing overhead, and throughput. This kind of security is suitable for a network with a low density of vehicles which is less than 100, and it is not suitable for a network with huge density mobility and high density. In the ECC-ACO-AODV protocol, both the ECC algorithm and ACO algorithm are combined with the AODV protocol to achieve effective performance. The secured optimal path selection is performed using this method. The simulation results include packet delivery ratio, throughput, delay, and overhead. The results are moderate and need improvement.
## 2 Background  
### PRESENT Block Cipher  
PRESENT [18] is a lightweight block cipher based on the Substitution Permutation Network (SPN) structure standardized in ISO/IEC in 2012. PRESENT supports 64-bit input data blocks and key sizes of 80 and 128 bits. The input key is processed internally to generate a round key for each of a total of 31 rounds. The cipher is composed of a structure that repeats the following three basic operations for a state, as shown in Figure 1 The steps addroundkey, sboxLayer, and pLayer are repeated for each round.  
* AddRoundKey: Adding state to the 64-bit word of the round key using finite field arithmetic.
* sBoxLayer: Using an S-box (replacement box) with 16 values to replace 4-bits to 4-bits in the state.
* pLayer: Applying a bit-level shift to the state.  
Based on the key schedule in the 80-bit version, the key schedule first processed the 80-bit key by rotating it to the left by 61-bits. Next, the leftmost 4-bits are passed through the S-box, and the round_counter value \(i\) is XORed with the least significant bit of \(K\).
## 3 Results and Discussion
### Visualization of Detection Results  
This section presents a visual evaluation of the WD-YOLO model's predictions on the PlantDoc dataset, highlighting its commendable accuracy and reliable performance. Figure 9 presents a comparison of partial prediction results from YOLOv10n, YOLOv10l, and WD-YOLO against the true labels of multiple images, illustrating the accuracy of the models.  
Comparing the prediction results of these three models, it was observed that both YOLOv10l and WD-YOLO showed high detection accuracy and stability. Figure 10 displays samples of some detection results, indicating that although YOLOv10l performs commendably in plant disease detection tasks, the refined WD-YOLO outperforms it, highlighting the performance advantages of our proposed model. Furthermore, the parameter count and complexity of WD-YOLO are significantly lower than those of YOLOv10l, underscoring the lightweight advantages of our proposed WD-YOLO model.  
Figure 8: Visual comparison of detection performance between YOLOv10l and WD-YOLO. Despite fewer parameters, WD-YOLO achieves more precise detections.  
Figure 9: Pictures with labels and pictures of detection results.
## 3 Advances in Control Strategies
### Cybersecurity in Robot Control  
The rapid proliferation of hyper-connected, 5G-enabled industrial robots has widened the cyber-physical attack surface in manufacturing systems. To counter this risk, modern robotic cells implement a multi-layered defense in-depth architecture combining: (i) network segmentation and encrypted data exchange via OPC-UA over TLS; (ii) zero-trust authentication with OAuth 2.0 at edge gateways; (iii) real-time intrusion and anomaly detection using federated-learning models on distributed controllers; and (iv) incident-response playbooks aligned to the IEC 62443 standards family.  
[PERSON] et al. [21] introduced SESAME, an automated security assessment framework for multi-robot systems, achieving \(\sim\)20% improvement in threat-detection accuracy. [PERSON] and [PERSON] [24] showed that AI-enhanced intrusion-prevention pipelines reduced  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dimension** & **KPI** & **Typical Value** & **Source** \\ \hline Material circularity & Material reuse intensity (kg year\({}^{-1}\) per robot) & 800–1200 kg; 60–80\% raw material savings vs. new build & [83] \\ \hline Operational emissions & Carbon intensity (kg CO\({}_{2}\)-eq h\({}^{-1}\), amortized 10 yr) & \(\sim\)2.2 & [85] \\ \hline Resource efficiency & Energy–payload–cycle factor (\% savings) & up to 15\% vs. fixed speed & [86] \\ \hline Macro impact & National CO\({}_{2}\) reduction per +10k robots (\%) & 0.8–1.4 vs. pre-automation & [87; 88] \\ \hline AI leverage & \(\Delta\) carbon intensity per +1\% AI adoption (\%) & \(-\)0.04 vs. static MES & [89] \\ \hline Circular-economy adoption & Metric standardization gap (\%) & 10–15\% adoption shortfall & [90; 91; 92] \\ \hline Regional implementation & Secondary materials utilization gap (\%) & 32\% (Spain) & [93] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proposed KPI set for industrial robotics sustainability reporting.  
successful attacks by ~15% across robotic applications. [PERSON] et al. (2015) increased safety by 18% relative to trajectory-only monitors. [PERSON] et al. (2016) leveraged digital-twin replicas to mitigate cyber vulnerabilities, yielding ~15% fewer exploitable vectors in cyber-physical production cells ([PERSON] et al., 2015; [PERSON] et al., 2016). [PERSON] et al. (2016) applied deep learning to sensor streams, improving the intrusion-detection performance by 12%. [PERSON] et al. (2016) nevertheless identified ~20% residual vulnerability in networked robotic systems, underscoring the need for resilient control--especially in ultra-low-latency 5G infrastructure ([PERSON] et al., 2016; [PERSON] et al., 2017). [PERSON] et al. (2017) further hardened edge security by ~15% over non-micro-segmented designs.