esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
### Underground Facility Digital Twins and Structure Performance Analysis  
Digital twin technology is a cutting-edge technique that creates virtual models of physical processes, products, or services [9; 10]. This model enables a real-time data analysis and system monitoring, offering the opportunity to optimize efficiency, identify problems preventively, and test potential solutions [11]. The applications of digital twin technology in various sectors include the following:  
* Manufacturing [12; 13; 14]: Digital twins create virtual models of machinery to anticipate failures, schedule maintenance, and enhance quality control.
* Construction [15; 16; 17]: Through digital twins, buildings are virtually replicated to optimize designs, reduce waste, and enhance safety by predicting hazards.
* Transportation [18; 19; 20]: Digital twins mirror real-life transport systems to optimize traffic flow, reduce congestion, and improve safety by recreating accident scenarios.
* Healthcare [21; 22]: Digital twins mimic patients' body parts to formulate personalized treatment plans and educate healthcare professionals for various scenarios.  
In particular, digital twins are extremely useful for underground facilities where conventional visual inspections might be challenging or unfeasible. They offer the capacity to track the structural integrity of these structures, monitoring aspects such as the stress and strain on walls and various other elements. This is possible by setting up sensors that can identify shifts in the structural activities, such as vibrations or movements. The information obtained from these sensors is integrated into the digital twin, delivering a precise depiction of structural dynamics.  
Technologies such as imaging sensors and LiDAR are employed to assess the sturdiness of structures such as buildings, bridges, and subterranean utility tunnels [23]. These sensors are capable of identifying structural deformations and movements that may suggest instability while also tracking alterations in structures over time to flag possible issues. Digital twins facilitate the visualization and analysis of the data gathered from these sensors, enabling the prompt identification and prevention of potential structural collapses.
## 2 Radio Communication Technologies
### The Main Characteristics of LPWAN Technologies
#### 2.2.4 Latency  
The latency is another important factor, which indicates the needed time for a request to be sent from the sender to the receiver and for the receiver to process that request. The aim is to keep this time as close to zero as possible; however, in practice, there are limitations that prevent the latency time reaching zero. The latency is an essential parameter for real-time IoT applications to be kept as low as possible. LPWAN generally does not perform well in terms of latency due to its network characteristics [13]. The measurement results for one laboratory experiment, where the end-to-end latency time was measured for LoRaWAN and NB-IoT, indicated that there is still a need for latency reduction techniques to fulfill many LPWAN use cases. The mentioned results showed an average latency of 5 s in the case of LoRaWAN and 0.072 s for NB-IoT [7].
#### 3.1.2 Maintenance Cost  
The second factor to consider within the framework of sustainability assessment for the economic pillar is maintenance costs (Table 5). For natural ventilation operating systems, the economic investment required is almost negligible. This is because, unless major renovations are undertaken, the doors and windows of educational buildings typically withstand their entire useful life without significant interventions beyond hinge lubrication or knob and lock adjustments. Additionally, for the same reasons as in the case of initial costs, the score obtained when comparing these systems among themselves is also 1 in all cases.  
The expenditure associated with the maintenance of the hybrid system can be expressed as the sum of the allocations directed towards the upkeep of doors and windows, augmented by the resources allocated to mechanical devices. Consequently, this maintenance cost exceeds that required for natural components. Nonetheless, it is imperative to note that maintenance for devices such as fans remains exceedingly minimal. Consequently, when examined collectively, the overall maintenance cost of the hybrid system is also relatively low. Therefore, the comparative evaluation of natural systems in relation to this hybrid solution merits a rating of only 2 points.  
Conversely, the centralized mechanical system once again occupies the opposing end of the spectrum due to its considerably more substantial maintenance requirements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Initial Investment Cost**} \\ \hline  & **N-SSV** & **N-CRV** & **N-AAC** & **H-NMR** & **M-CVS** & **Results** \\ \hline
**N-SSV** & 1 & 1 & 1 & 3 & 9 & 0.290 \\ \hline
**N-CRV** & 1 & 1 & 1 & 3 & 9 & 0.290 \\ \hline
**N-AAC** & 1 & 1 & 1 & 3 & 9 & 0.290 \\ \hline
**H-NMR** & 1/3 & 1/3 & 1/3 & 1 & 3 & 0.097 \\ \hline
**M-CVS** & 1/9 & 1/9 & 1/9 & 1/3 & 1 & 0.032 \\ \hline \hline \end{tabular}
\end{table}
Table 4: AHP Matrix for Initial Investment Cost.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Maintenance Cost of Ventilation Systems**} \\ \hline  & **N-SSV** & **N-CRV** & **N-AAC** & **H-NMR** & **M-CVS** & **Results** \\ \hline
**N-SSV** & 1 & 1 & 1 & 2 & 9 & 0.278 \\ \hline
**N-CRV** & 1 & 1 & 1 & 2 & 9 & 0.278 \\ \hline
**N-AAC** & 1 & 1 & 1 & 2 & 9 & 0.278 \\ \hline
**H-NMR** & 1/2 & 1/2 & 1/2 & 1 & 4 & 0.136 \\ \hline
**M-CVS** & 1/9 & 1/9 & 1/9 & 1/4 & 1 & 0.032 \\ \hline \hline \end{tabular}
\end{table}
Table 5: AHP Matrix for the Maintenance Cost.  
Consequently, when juxtaposed with natural and hybrid systems, its relative ratings are 1/9 and 1/4, respectively.
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 4 Experiments
### Experimental Results  
Figure 2 presents the recommendation accuracy of different approaches on the two metrics. As the results show, our approach exhibits improvements over all competing methods for different values of K. In particular, we have the following observations:  
* LDA and Doc2 vec methods only exploit textual similarity to match mashup requirements and candidate services, and ignore the existing mashup-service composition patterns. Therefore, they perform worse than the other methods that utilize both content similarity and composition relation. Moreover, Doc2 vec performs better than LDA, indicating that learning the semantics of content with a distributed representation is better than the traditional bag-of-words model.
* CF and CMF methods achieve better performance than LDA and Doc2 vec, due to the simultaneous use of mashup-service composition history and textual relations between mashups and services. Moreover, CMF performs better than CF. The reason could be that CMF implicitly considers the semantic relations between mashups and services through the shared topics, while only the semantic similarities between mashups are considered in CF.
* PaSRec is a CF-based method that evaluates the similarities between mashups. Different from the plain CF model, it considers diverse kinds of relations (captured as meta-paths) between mashups including mashups with similar contents, tags as well as services, and combines them with a different set of weights learned from data. It unsurprisingly achieves a better performance than CF.  
* DHSR is a state-of-the-art method for service recommendation. It uses a deep neural network to combine the mashup and service features learned from the CF component and content component. Its performance improvement could be attributed to the use of deep networks to characterize the complex relations between mashups and services.
* Finally, our method MRGCN outperforms all these models in both Recall and NDCG. For instance, the Recall@5, NDCG@5, Recall@10, and NDCG@10 of our model were higher than DHSR by 9.9%, 13.1%, 7.3%, and 9.5%, respectively. On the one hand, it effectively combines different kinds of relations between both mashups and services, as opposed to PaSRec that only considers the relations between mashups; on the other hand, it can propagate and aggregate higher-order features of mashups and services with GCN, which can effectively deal with the problem of data sparsity. Moreover, MRGCN_D2V outperforms MRGCN_LDA, which is consistent with the baseline models and shows that empirically doc2 vec has an advantage in modeling service functionality.
## 4 Experimental Evaluation
### Scenario 3: Multiple STAs
#### 4.4.3 Results of Wireless Network Layer in Scenario 3  
Figure 17 shows the RSSI of two STAs and an AP. The graphs show the same trend for both TCP and UDP communications, and as described above, it is difficult to adequately detect degradation in communication quality. Therefore, although the RSSI is sensitive to environmental changes such as obstacles, it is not sensitive to an increase in traffic, such as an increase in STAs.  
Table 8 shows the invalid RSSI values in the _iw_ results. There were no invalid values in the experiments, but there were missing values on the STA1 side. The occurrence increases as the distance from STA2 increases.  
Figure 14: Throughputs of TCP and UDP communications (Scenario 3).  
Figure 15: Jitter of UDP Communication (Scenario 3).  
Table 9 indicates the percentage of the Tx bitrates used at both STAs. Note that the values in bold in the table indicate the mode of the Tx bitrate at each distance. While the Tx bitrate value of STA2 decreases as the distance of STA2 increases, the Tx bitrate of 2310.0 Mb/s is used above 90% on STA1. This result indicates that using a lower bitrate affects the throughput of other STAs because the wireless medium is a shared medium.
# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 5 Results and Discussion
Moreover, when we looked at the classification errors that were calculated using the confusion matrix, it was noted that RF caused negligible classification errors as shown in  
Figure 4: The classification accuracy for all attacks obtained by the proposed classifier (RF), DT, AdaBoost, MLP, and KNN. Figure 3: The classification accuracy rates achieved by all classifiers when using the developed balanced dataset with an information gain feature selection method (BCICIDS2017-GI). The research findings confirm that the proposed model, which is based on RF and employs the information gain feature selection method and a balanced dataset, has achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It has achieved an accuracy of 99.8%, outperforming all benchmark classifiers:  
Figure 8: ROC for KNN classification model. Figure 9: ROC for RF classification model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attack** & **FN** & **FP** \\ \hline Botnet ARES & 0.0000 & 0.0005 \\ \hline Brute Force & 0.0000 & 0.0001 \\ \hline Dos/DDos & 0.0027 & 0.0126 \\ \hline Infiltration & 0.0000 & 0.0000 \\ \hline PortScan & 0.0005 & 0.0008 \\ \hline Web Attack & 0.0000 & 0.0002 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Classification errors of the proposed classifier, RF. AdaBoost, DT, KNN, and MLP. In addition, to the best of our knowledge, it outperforms all the existing relevant classification techniques, as indicated in the Related Work section. The proposed classifier consumed the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. The processing cost is an important factor for VANETs, which require fast and real-time actions to avoid damage and ensure safe human life. Any delays can cause considerable damage.
###### Abstract  
Internet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to be completed quickly within the time constraints of IoT devices. To address this challenge, researchers have proposed computation offloading, where computing tasks are sent to edge servers instead of being executed locally on user devices. This approach involves using edge servers located near users in cellular network base stations, and also known as Mobile Edge Computing (MEC). The goal is to offload tasks to edge servers, optimizing both latency and energy consumption. The main objective of this paper mentioned in the summary is to design an algorithm for time- and energy-optimized task offloading decision-making in MEC environments. Therefore, we developed a Lagrange Duality Resource Optimization Algorithm (LDROA) to optimize for both decision offloading and resource allocation for tasks, whether to locally execute or offload to an edge server. The LDROA technique produces superior simulation outcomes in terms of task offloading, with improved performance in computation latency and cost usage compared to conventional methods like Random Offloading, Load Balancing, and the Greedy Latency Offloading scheme.  
offloading decision; Lagrange duality optimization; mobile edge computing; real-time IoT; resource allocation +
Footnote †: journal: Medical Computing  
Article
# Rock Mass Structure Classification of Caves Based on the 3D Rock Block Index
## 5 Classification of Rock Mass Structure Based on 3D Rock Block Index
### Three-Dimensional Rock Block Index  
The rock block index (RBI) is defined as the cumulative value of the product of the measured core lengths in flat caves or boreholes in terms of core acquisition rates of 3-10 cm, 10-30 cm, 30-50 cm, 50-100 cm, and greater than 100 cm as weights, multiplied by their respective corresponding coefficients [19]:  
\[RBI=3\times C_{r3}+10\times C_{r10}+30\times C_{r30}+50\times C_{r50}+100\times C _{r100} \tag{8}\]  
where \(C_{r3}\), \(C_{r10}\), \(C_{r30}\), \(C_{r50}\), and \(C_{r100}\) are the core acquisition rates for cores 3-10 cm, 10-30 cm, 30-50 cm, 50-100 cm, and greater than 100 cm in length, expressed as percentages and considered as weights.  
Figure 11: Network simulation window trace diagram.  
Figure 10: Measured window trace diagram.  
Because the excavation of the flat cave was carried out via blasting operations, it was not possible to directly obtain the complete core take rate, so the above 3D joint network model was used instead and virtual survey lines were arranged in different sidewalls to represent the boreholes. According to the intersection and tangency between the joint and survey lines, the spacing between the joint and survey lines was measured, and the rock block indexes of the different boreholes were obtained through the calculation of Formula (8). The RBI value of rock block index obtained using one borehole could only determine the structural characteristics of the exposed wall in the direction of the borehole or a certain excavation in the flat hole, and it was not possible to evaluate the three-dimensional structural characteristics of the rock mass. Therefore, the concept of a 3D rock block index was proposed. It is the expected value of borehole RBI in different orthogonal planes.  
In the model, the XOZ surface, which is parallel to the excavation surface of the flat cave, is defined as the front surface of the model; the vertical YOZ surface, which is perpendicular to the excavation surface, is defined as the side surface of the model; and the XOY surface, which is perpendicular to both the XOZ surface and the YOZ surface, is defined as the top surface of the model. The above three orthogonal planes were selected for the study of rock structure characteristics, and in each orthogonal plane, 36 virtual survey lines were arranged at equal angles centered on the form center and at 5\({}^{\circ}\) intervals, for a total of 108 survey lines (see Figure 12). After counting the RBI values corresponding to the 36 virtual survey lines on different orthogonal planes, the RBI values on different orthogonal planes are obtained (see Table 5). It can be seen that the distribution of RBI values is different for each orthogonal plane.  
Figure 12: Intersection relationship diagram between survey lines and joints.
# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 1 Introduction
These findings indicate the broad application potential of electronic noses in soil pollution monitoring, including pesticide residue detection [24]. Machine learning plays a crucial role in soil information and pollution recognition, which use electronic nose technology. The choice of machine learning methods directly impacts recognition accuracy. Unlike other fields, soil pesticide residue detection requires the careful selection of machine learning techniques. Firstly, the selected method should be able to detect pesticide contamination in soil, regardless of whether the pesticide has been previously detected. This step is essential for subsequent actions. Secondly, the precise identification of pesticide types is necessary for effective soil management and remediation. However, due to the limited availability of training samples covering all pesticide types, achieving accurate identification through supervised learning using an electronic nose is challenging [25; 26; 27]. It is difficult to obtain training samples covering all pesticide types. This can easily lead to contaminated samples being misjudged as healthy samples and missing the best opportunity for soil management. Currently, most research has overlooked the distinction between a contaminated soil that did not participate in training and a healthy soil. We proposed a model which combines supervised and unsupervised learning in this paper. A hybrid model based on a one-class SVM and supervised learning was used to realize the reliable identification of soil health and the accurate identification of pesticide types. The proposed hybrid model adopts a distributed framework, which includes two steps: pollution detection and pesticide identification. An unsupervised learning system called a one-class SVM is used to model healthy soil in the first step. It can realize the real-time detection of soil pollution and obtain healthy soil sample information. Even if there are pesticides that have never appeared in the soil, the pollution can be accurately identified. In the second step, a supervised learning classifier is used for accurately identify the types of pesticide residue in the soil. This model addressed the issue in current research where contaminated soil that did not participate in training may be incorrectly identified as healthy soil. In addition, the effects of different feature extraction methods and supervised learning classifiers on the recognition of soil pesticide residues are studied in this paper.
# Cellular and Virtualization Technologies for UAVs:
## 4 Testbed Experimentation
### Communication Performance Evaluation
On the other hand, RTT experiments (Figure 3(b)) reveal that all devices and ISPs fulfill 3 GPP KPI requirements. Please, take note that the error bars correspond to the standard deviation. This experiment also reveals that the Movistar network has balanced downlink and uplink data rates; meanwhile, Yoigo prioritizes downlink data rates. For selecting a final prototype, it is important to consider that UAVs potential applications will overload mostly  
Figure 4: Performance analysis using Ookla speed test. the uplink (i.e., the highest data rate will be generated on the UAV and sent in the uplink). However, all the combinations (mini PCIe and ISPs) comply with the 3 GPP KPIs. In the second set of experiments, we have measured the performance from the RPi upon the UAV-controller, enabling and disabling the VPN service to quantify the performance penalty that the VPN may introduce. Throughput results are similar in the second experiment to those obtained with the Ookla speed test as represented in Figure 4(a). As expected, there is a slight decrease in the performance since data packets are not only traversing the VPN server but also have to include the VPN headers (decreasing the data payload size). However, the values are still much higher than those required for C2. On the other hand, the RTT results (Figure 4(b)) are considerably worse, although they are within the requirements set by the 3 GPP KPIs. This phenomenon occurs because of the VPN encryption and decryption process. The VPN relay is not greatly affecting the performance since the VPN server is located on the same network as the UAV-controller. More details about the performance decrease of introducing a VPN service can be found in [43]. Reliability tests were also performed by sending a 200 Kb/s stream as required in the KPIs (both downlink and uplink), and no packets were lost during this process. It can be concluded that all the devices fulfill the management requirements. Other services can be provided using this communication solution, such as transmitting periodic High-Definition (HD) images or real-time video streaming (HD 720p). Still, some challenges may arise when using this technology for complex multimedia applications or multiple services simultaneously.