esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of ANN-PM and Medlyn-PM  
Figure 6 shows the scatter plots of measured ET vs. predicted ET by the Medlyn-PM and the ANN-PM model, respectively. At the site scale, the two models differ substantially in performance from each other. Figure 6 shows good correlations between the observed ET and the predicted ET by the two methods (R\({}^{2}\) = 0.75 and 0.83). Figure 6 also illustrates that the R\({}^{2}\) value of the ANN-PM model is 0.08-0.09 higher than that of the Medlyn-PM model and the RMSE and MAE of ANN-PM are 4.26-4.3 and 3.12-3.34 W m\({}^{-2}\) smaller than that of the Medlyn-PM model, respectively. Overall, the ANN-PM model shows relatively high accuracy with smaller RMSE and MAE, and larger R\({}^{2}\) (RMSE = 19.09-19.50 W m\({}^{-2}\) (39.84-40.46%), MAE = 13.27-13.41 W m\({}^{-2}\), R\({}^{2}\) = 0.83) in estimating cropland ET compared to the Medlyn-PM model (RMSE = 23.39-23.76 W m\({}^{-2}\) (49.95-51.14%), MAE = 16.39-16.75 W m\({}^{-2}\), and R\({}^{2}\) = 0.74-0.75), indicating a great advantage in estimating cropland ET using the ANN-PM model.
## 2 Framework Design of Gateway
### Gateway: Functional Design  
Based on the functional requirements analysis, we developed an embedded communication gateway with automated back-up capabilities, as illustrated in Figure 3. The functional design comprises five modules: Ethernet communication, GSM/SMS communication, UART communication, Ethernet/GSM communication backup, and data transfer, each of which is further discussed below.  
Figure 3: Functional design of embedded communication gateway with automatic back-up capabilities.  
1. Ethernet communication: This is the primary communication interface between the gateway and the remote monitoring and control server (RMCS). Its function is to enable data transfer between the gateway and the server in accordance with Ethernet communication protocols.
2. GSM/SMS communication: This is the secondary communication interface between the gateway and RMCS. Its function is to enable data transfer between the gateway and the server in accordance with GSM/SMS communication protocols.
3. UART communication: This is the data transmission interface between the gateway and the WSN Coordinator. Its main function is to transmit control commands and sensor data.
4. Ethernet/GSM communication backup: The purpose of this function is to establish automated communication backup for RCMS. If the Ethernet fails, then the system can automatically switch to GSM/SMS communication.
5. Data transfer: The purpose of this function is to analyze and package data into packets corresponding to the appropriate communication method.
## 3 Proposed Smart Route: IoV-Based Congestion Detection and Avoidance (CDA) Using Rerouting
### Conversion of Area of Interest (AOI) or Road Intersection to Routing Graph  
The intersection points are more susceptible to traffic congestion; for this reason, this paper considers the traffic congestion detection and avoidance in intersection points (i.e., AOI). From the real road intersection point, as shown in Figure 2, a routing graph \(G(V,E,O)\) is constructed, where \(V\) and \(E\) represent the set of junction points (i.e., vertices) and the collection of edges (i.e., roads). The parameter \(O\) indicates the occupancy rate of each road segment (i.e., edge) connecting the junction points. The occupancy rate of a particular road segment refers to the ratio of the current number of vehicles to the total capacity of the road segment. The extracted graph from the real scenario in Figure 2 is shown in Figure 3.  
In this example, the number of junctions and edges in the extracted graph is 11. The symbol \(J_{1}\) to \(J_{11}\) represents the junction points, while \(E_{i,j}\) represents the road connecting junction \(I\) and \(J\). The value of \(I\) and \(J\) ranges from 1 to 11. In the extracted graph, \(J_{1}\) is the entrance junction, while \(J_{5}\) is the proposed destination junction. The direct route to the destination is \(\{J_{1},J_{2},J_{3},J_{4},J_{5}\}\), while \(\{J_{1},J_{2},J_{8},J_{9},J_{11},J_{5}\}\) is the indirect route to the destination. The adjacency matrix of the extracted graph is formed by merely putting one for connected intersections and zero for disconnected, as given below in Equation (3).  
\[A=\begin{cases}1,&\text{if }(v_{i},v_{j})\in E\\ 0,&\text{otherwise}\end{cases} \tag{3}\]  
Figure 3: Extracted graph from a real road map.  
Figure 2: Alwaha (Riyadh, KSA) junction point for IoV-based congestion detection and avoidance.  
where \(v_{i}\) and \(v_{j}\) are the two junction points and \(A\) represents the graph of the road intersection. The adjacency matrix for our selected road intersection is given below:  
\[Adj=\begin{bmatrix}0&1&0&0&0&0&1&0&0\\ 0&0&1&0&1&1&0&0&0\\ 0&0&0&1&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0\\ 0&0&1&0&0&1&0&0&0\\ 0&0&1&0&0&0&0&0&0\\ 0&0&0&0&0&0&1&0\\ 0&0&0&0&0&1&0&0&1\\ 0&0&0&1&0&0&0&0\end{bmatrix}\]  
If \(O\) is the occupancy rate of a given edge, then the graph \(G\) will be  
\[G=\begin{cases}O_{i,j},&\text{if }(v_{i},v_{j})\in E\\ 0,&\text{if }i=j\\ \infty,&\text{otherwise}\end{cases} \tag{4}\]  
where \(O_{i,j}\), represents the occupancy rate of the road segment connecting junction \(v_{i}\) and \(v_{j}\).
## 3 Computational Results
### Computational Cost Optimizations
In the original process, the fundamental matrix needs to be solved by inversing the transition matrix; therefore typically, the simulation must store the whole transition matrix for matrix inversion, which would cause RAM usage related problems (i.e., matrix size exceeds available RAM). However, with the modified inversion process, there is no need to register the complete matrix in the RAM. Vectors can be easily calculated from \(P\left(\theta_{i},\theta_{j},n\right)\) with far less computational cost. Although this approach could  
Figure 12: Modified Markov chain inversion computational cost and stopping scattering order as a function of the OD, in comparison to Monte Carlo simulations. increase the computational cost for such calculations, this method validates the use of the Markov chain approximation with higher dimensions or finer resolutions.
# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
## 3 Proposed Method
### Heterogeneous Hypergraph and Attention Fusion  
#### 3.4.1 Construction of Heterogeneous Hypergraphs  
To more accurately capture the higher-order relationships between students, instructors, and courses, five types of heterogeneous hypergraphs are constructed, respectively: 1 A hypergraph with students as hyperedges and courses as vertices, with the incidence matrix defined as \(\mathrm{H}_{s\to c}\); 2 A hypergraph with courses as hyperedges and students as vertices, with the incidence matrix defined as \(\mathrm{H}_{c\to s\hat{c}}\); 3 A hypergraph with instructors as hyperedges and students as vertices, with the incidence matrix defined as \(\mathrm{H}_{t\to s\hat{c}}\); 4 A hypergraph where courses with similar selections are hyperedges and students are vertices, with the incidence matrix defined as \(\mathrm{H}_{c^{\prime}\to s\hat{c}}\); and 5 A hypergraph with instructors as hyperedges and courses as vertices, with the incidence matrix defined as \(\mathrm{H}_{t^{\prime}\to c^{\prime}}\). These five types of hypergraphs include student nodes, instructor nodes, course nodes, and hyperedges, which further describe higher-order relationships between different types of nodes through hyperedges. First Hypergraph: The subgraph of students and courses represents the collection of course sequences for students' online learning. This can identify connections between students and uncover relationships among them. Second Hypergraph: The subgraph of courses and students represents the collection of students enrolled in a specific online course. This subgraph can identify students' online course learning behaviors, thereby recognizing similarities in their course selections. Third Hypergraph: The subgraph of online instructors and students reveals which students chose courses taught by a particular instructor. It represents the collection of students enrolled in a specific instructor's online courses and indirectly reflects the popularity of the instructor's courses and students' preferences for instructors. Fourth Hypergraph: The subgraph of similar course selections and students explores the similarity in courses taken by students directly connected (e.g., through edges) and those not directly connected (not connected by edges) to a particular student. Fifth Hypergraph: The subgraph of instructors and courses represents the collection of courses offered by online instructors. Traditionally, definitions often use \(2^{k\times j\times 1}-k-j-l-1\) hyperedges. To reduce the complexity of the algorithm, we define \(2\times j+k+l+c+n\), where \(k\), \(j\) and \(l\) represent the number of students, courses, and instructors, respectively. \(c\) and \(n\) represent the number of students who are directly connected and chose similar courses and the number of students who are not directly connected but chose similar courses, respectively. Based on the definition of the hypergraph convolution operator given in Formulas (4)-(6) Section 3.3, the convolution formulas for these five hypergraphs are defined as the following Formulas (7)-(11).
###### Abstract  
The value of leaf photosynthetic capacity (V\({}_{\text{cmax}}\)) varies with time and space, but state-of-the-art terrestrial biosphere models rarely include such V\({}_{\text{cmax}}\) variability, hindering the accuracy of carbon cycle estimations on a large scale. In particular, while the European terrestrial ecosystem is particularly sensitive to climate change, current estimates of gross primary production (GPP) in Europe are subject to significant uncertainties (2.5 to 8.7 Pg C yr\({}^{-1}\)). This study applied a process-based Farquhar GPP model (FGM) to improve GPP estimation by introducing a spatially and temporally explicit V\({}_{\text{cmax}}\) derived from the satellite-based leaf chlorophyll content (LCC) on two scales: across multiple eddy covariance tower sites and on the regional scale. Across the 19 EuroFLUX sites selected for independent model validation based on 9 plant functional types (PFTs), relative to the biome-specific V\({}_{\text{cmax}}\), the inclusion of the LCC-derived V\({}_{\text{cmax}}\) improved the model estimates of GPP, with the coefficient of determination (R\({}^{2}\)) increased by 23% and the root mean square error (RMSE) decreased by 25%. V\({}_{\text{cmax}}\) values are typically parameterized with PFT-specific V\({}_{\text{cmax}}\) calibrated from flux tower observations or empirical V\({}_{\text{cmax}}\) based on the TRY database (which includes 723 data points derived from V\({}_{\text{cmax}}\) field measurements). On the regional scale, compared with GPP, using the LCC-derived V\({}_{\text{cmax}}\), the conventional method of fixing V\({}_{\text{cmax}}\) using the calibrated V\({}_{\text{cmax}}\) or TRY-based V\({}_{\text{cmax}}\) overestimated the annual GPP of Europe by 0.5 to 2.9 Pg C yr\({}^{-1}\) or 5 to 31% and overestimated the interannually increasing GPP trend by 0.007 to 0.01 Pg C yr\({}^{-2}\) or 14 to 20%, respectively. The spatial pattern and interannual change trend of the European GPP estimated by the improved FGM showed general consistency with the existing studies, while our estimates indicated that the European terrestrial ecosystem (including part of Russia) had higher carbon assimilation potential (9.4 Pg C yr\({}^{-1}\)). Our study highlighted the urgent need to develop spatially and temporally consistent V\({}_{\text{cmax}}\) products with a high accuracy so as to reduce uncertainties in global carbon modeling and improve our understanding of how terrestrial ecosystems respond to climate change.  
Keywords:gross primary production; photosynthetic capacity; Europe; land surface greening; terrestrial biosphere model +
Footnote †: journal: remote sensing
## 3 Security and Privacy Challenges
[PERSON] et al. proposed a problem space of EC-based IoT security defined over three distinct classes, i.e., transmission, storage, and computation [6],  
* **Transmission**: Jamming attacks, sniffing attacks, worm propagation, distributed denial-of-service (DDoS), and similar assaults can disrupt data links by choking the network or observing the data flow. * **Storage**: Innumerable sensors and devices produce a gigantic volume of data, which is then stored across various third-party locations. Such an arrangement poses issues like data integrity being seriously challenged due to the distribution of data into many fractions, resulting in data packet losses as well as data corruption. Also, adversaries can modify or abuse stored data at third-party locations, leading to data leakage and other privacy issues. * **Computation**: The relocation of computational tasks from the cloud to edge nodes in EC demands an establishment of trust between edge servers and end devices. Figure 5: Security functionalities in a cloud-edge computing architecture.
## 3 Results and Discussion
### Electron Number Density
The Stark broadening and, consequently, the plasma electron density could be calculated via Equations (28) and (30), respectively. Figure 8b shows the experimental values of the electron density obtained in this way. A constant distribution along the discharge was again obtained. The experimental value of electron density was of the order of \(5\times 10^{14}\) cm\({}^{-3}\). This high value supports the electron kinetics for CO\({}_{2}\) decomposition assumed for this reactor.
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Computational Component Optimization
#### 4.2.2 Dilated Convolution  
Convolution calculation is a common operation used by neural networks to process image data. In contrast to other computer vision tasks, the image Inpainting task requires the network model to pay attention to as large an area outside the missing area as possible,  
Figure 2: Image inpainting classification architecture.  
but the receptive field of a common convolution kernel is limited. Dilated convolution [55] originated from semantic segmentation and achieved good results in many methods in the field of semantic segmentation. [PERSON] et al. [56] introduced dilated convolution to the image inpainting task. Unlike common convolutions, dilated convolutions can adjust the dilation rate to change the size of the receptive field. It ensures that each output pixel has a larger input area and the same number of parameters and computing power. By using dilated convolution on low-resolution images, the model can effectively \"see\" a larger area of the input image when calculating each output pixel. The specific convolution calculation method is shown in Figures 3 and 4.  
Figure 3 illustrates a standard convolution, while Figure 4 depicts a dilated convolution. [PERSON] et al. [57] introduces dilated convolution layers in convolutional encoder-decoder networks with a coarse-to-fine approach to expand the receptive field.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 2 Background
### Theoretical Maximum Number of Connections  
The maximum number of connections that can be sustained for a given BLE configuration can be theoretically estimated given the connection interval, MTU size, and event length. Developers should first note that the event length and connection interval values are often constrained by the development environment. Developers should consult the appropriate documentation for their selected development environment. Additionally, the event length minus 300 \(\upmu\)s (required 150 \(\upmu\)s interframe space plus 150 \(\upmu\)s minimum subevent space) must be greater than the MTU size divided by the BLE data rate (0.25 MB/s for a BLE throughput of 2 Mbps). Then, the theoretical maximum number of connections--based purely on timing and assuming no transmission errors--is the connection interval divided by the event length, rounded down to the nearest integer value. Table 1 contains this theoretical number of connections for the BLE configurations considered in this work. In practice, of course, this theoretical maximum is not always achieved. Our experiments investigate the number of connections achieved for numerous configurations.
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
###### Abstract
The approaches to build the model are carefully selected depending on the nature of the dataset, and the objectives to be achieved. The model parameters are carefully selected through critical analysis, while considering their value and impact  
Figure 1: IoT-enabled use case of smart healthcare system [8]. on optimization. The proposed model, named Protocol-specific and SN-inherited Attack Detection (ProSenAD), is evaluated considering the type of learning paradigm used for building the model, which is classification, and the type of classification considered, which is multiclass classification. The performance of the ProSenAD model is evaluated using the testing set of the self-generated LIoTN-RPL dataset. The results demonstrate that the proposed model outperforms when compared with the benchmarks in terms of: (1) number of attacks detected when compared with ML-LGBM [12] and GRU-DL [13], (2) accuracy, precision, recall in comparison with GAN-C [14], (3) Cross Entropy (CE), Cohen's Kappa (CK), and the Matthews Correlation Coefficient (MCC), when compared with gradient boosting (GB), extreme GB (XGBoost), and light gradient boosting machine (LGBM). Following is the summary of contributions of this research work:  
* The ML-based approaches are analyzed for attack detection in IoT and RPL-based networks to secure the routing mechanisms as well as the network resources. * We intend to improve the ML models for concurrent detection of protocol-specific and SN-inherited attacks. * The proposed model is evaluated using ML-based metrics, such as accuracy, Positive Predictive Values (PPV), sensitivity, CE, inter-rater reliability and agreement through CK, and MCC to determine its performance in comparison with the existing models. * The lack of updated dataset is addressed by generating novel LIoTN-RPL dataset using different network simulation scenarios that is then employed for developing attack detection model using ML approaches. The aim of this research is to contribute to the enhancement of IoT routing and network security for its widespread secure adoption in different domains. The remaining paper is structured as follows: Section 2 describes the protocol-specific and SN-inherited attacks focusing on PS-R and SN-W attacks, followed by the review of existing methods for attack detection. Section 3 presents the proposed ProSenAD model in complete detail along with the process model adopted for different model building phases. The novel LIoTN-RPL dataset generated for this study is also explained in addition to the parameters selected for model development and optimization. Section 4 discusses the results and comparative performance analysis. Section 5 provides the conclusion and future research direction.
## 4 Results
### The Analysis of Measurement Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Question**} & \multirow{2}{*}{**Loading**} & \multicolumn{2}{c}{**Factor**} & \multirow{2}{*}{**CR Value**} & \multirow{2}{*}{**AVE**} \\  & **Item** \({}^{*}\) & & & & & \\ \hline Relative & RA1 & 0.839 & & & & \\ Advantage & RA2 & 0.868 & 0.739 & 0.874 & 0.698 \\  & RA3 & 0.797 & & & & \\ \hline Compatibility & CA1 & 0.853 & & & & \\  & CA2 & 0.852 & 0.857 & 0.868 & 0.687 \\  & CA3 & 0.780 & & & \\ \hline Trialability & TR1 & 0.776 & & & & \\  & TR2 & 0.768 & 0.831 & 0.823 & 0.607 \\  & TR3 & 0.794 & & & \\ \hline Observability & OB1 & 0.809 & & & & \\  & OB2 & 0.880 & 0.837 & 0.833 & 0.714 \\  & OB3 & 0.805 & & & \\ \hline Data Security & DS1 & 0.808 & & & & \\  & DS2 & 0.718 & 0.707 & 0.749 & 0.502 \\  & DS3 & 0.582 & & & \\ \hline Top & TM1 & 0.721 & & & & \\ Management & TM2 & 0.761 & 0.813 & 0.775 & 0.534 \\ Support & TM3 & 0.710 & & & & \\ \hline Employees’ & EK1 & 0.638 & & & & \\ Knowledge & EK2 & 0.824 & 0.775 & 0.771 & 0.531 \\  & EK3 & 0.713 & & & & \\ \hline Absorptive & AC1 & 0.709 & & & & \\ Capability & AC2 & 0.736 & 0.814 & 0.768 & 0.524 \\  & AC3 & 0.727 & & & \\ \hline Competitive & CP1 & 0.841 & & & & \\ Pressure & CP2 & 0.780 & 0.928 & 0.845 & 0.644 \\ \hline Business & BP1 & 0.747 & & & & \\ Partner & BP2 & 0.812 & 0.893 & 0.814 & 0.594 \\  & BP3 & 0.752 & & & & \\ \hline External & ES1 & 0.847 & & & & \\ Support & ES2 & 0.834 & 0.936 & 0.871 & 0.692 \\  & ES3 & 0.814 & & & \\ \hline Government & GS1 & 0.867 & & & & \\ Support & GS2 & 0.911 & 0.944 & 0.870 & 0.693 \\  & GS3 & 0.706 & & & \\ \hline Performance & PE1 & 0.791 & & & & \\ Expectancy & PE2 & 0.676 & 0.840 & 0.701 & 0.541 \\  & PE3 & 0.414 & & & \\ \hline Effort & EE1 & 0.712 & & & & \\ Expectancy & EE2 & 0.753 & 0.708 & 0.761 & 0.515 \\  & EE3 & 0.687 & & & \\ \hline User Habit & UH1 & 0.740 & & & & \\  & UH2 & 0.803 & 0.723 & 0.780 & 0.543 \\  & UH3 & 0.660 & & & \\ \hline Internal & EI1 & 0.679 & & & \\ Users’ & EI2 & 0.726 & 0.199 & 0.778 & 0.540 \\ Intention & EI3 & 0.794 & & & \\ \hline \hline \end{tabular} \({}^{*}\)The list of question item description is in Appendix A, Table A5.  
\end{table} 
Table 3: Measurement of potential variables of loadings, CR value and AVE.
## 4 Results and Discussion
### Deployment Considerations  
The proposed LSTM autoencoder model is well suited for real-time deployment in drilling environments due to its low inference time and minimal computational overhead during prediction. Once trained, the model can be integrated into existing drilling monitoring systems to continuously analyze incoming sensor data and flag anomalies as they occur. This integration can be achieved through API-based streaming frameworks or embedded systems compatible with field instrumentation that enable proactive operational decisions and minimizing NPT without the need for constant human oversight.  
To maintain long-term accuracy and adaptability, the model should undergo periodic retraining using newly accumulated operational data. This approach allows the system to adapt to evolving geological formations, equipment wear, or operational changes. A sliding window retraining schedule or data-driven triggers based on detection drift can be employed to update the model parameters. Such strategies ensure that the anomaly detection system remains reliable under shifting real-world drilling conditions while minimizing the risk of performance degradation over time.