esa-sceva/satcom-chunk-collection huggingface dataset:
# Linked Data Triples Enhance Document Relevance Classification
## 1 Introduction
_Topic-Entity Triples_: a novel way to generate triples from raw text with topics and named entities is proposed. _Semantic enrichment to enhance document representations using linked-data triples_: we conduct several experiments on a dataset of our domain focus, the Energy-Hub news articles, and then validate our findings on two separate standard datasets, namely the Reuters ([[http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html](http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html)]([http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html](http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html)), accessed on 5 February 2021) and 20 News groups ([[https://www.kaggle.com/crawford/20-n-ewsgroups](https://www.kaggle.com/crawford/20-n-ewsgroups)]([https://www.kaggle.com/crawford/20-n-ewsgroups](https://www.kaggle.com/crawford/20-n-ewsgroups)), accessed on 5 February 2021) datasets. Results indicate that documents enriched with linked-data triples improve classification performance over and above those without enrichment. The paper is organized as follows: Section 2 introduces existing literature on document enrichment with semantic features, and Section 3 provides a discussion on natural language (NL) features extracted for enrichment; the Energy-Hub section (Section 4) describes an experimental framework with data source, as well as its corpus; Learning Models (Section 5) discusses the proposed architecture; Experiments (Section 6) presents the experimental results against in-house Energy-Hub dataset and standard Reuter's 21,578 and 20 News groups datasets. Discussion (Section 7) provides an overall discussion of the results, including future work. Concluding Remarks (Section 8) provides conclusions and final comments.
# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 3 The Proposed Intrusion Detection System
### Generate Grey Hole and Rushing Attacks  
In order to analyse the security system performance, we designed two types of scenarios identified in the previous stage, i.e., the normal and abnormal behaviours. Numerous researches consider that creating grey hole attacks is hard [11] in VANETs due to the fact that these attacks exhibit two behaviours. Thus, the vehicle may be considered normal at time t = 0 and become abnormal at t = n.  
This condition makes creating grey hole attacks a difficult challenge in ad hoc networks. The abnormal behaviour that caused the dropping of some or sometimes all received packets was implemented in ns-2 using the Object-oriented programming and Object Tool Command Language (OTCL) script. However, we were required to create a new routing protocol in order to generate grey hole attacks in VANETs. Additionally, we need to adapt the AODV routing protocol so as to create rushing attacks. Two functions were incorporated into the standard AODV routing protocol to simulate selective packet dropping. There are 40 vehicles and nine RSUS [33] in our simulation environment, with two rushing vehicles and two grey hole vehicles. Detection is focused on identifying which vehicle drops packets received from RSU or vehicles in coverage radio area.
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 3 Background
### Network Model  
Unlike terrestrial sensor networks, the data transmissions of UWSNs are not limited to 2D region and some applications are aimed at forwarding data packets from seafloor to the sea surface. So the underwater network model is a 3D model as shown in Figure 1. The nodes involved in data transmissions are classified into three categories: the sink nodes, the sensor nodes and the relay nodes. The sink nodes are mainly located on water surface and are responsible for collecting data from the child nodes. Then they forward these data to other gateways or shore-based processing platform through wireless or wired communications. Generally, these sink nodes are equipped with solar panel and have great capacity in data processing. The sensor nodes are equipped with sensors and transmitters. The main functions of sensor nodes are sensing and sampling information obtained from monitored area. The information collected in sensor node is packaged and sent to other nodes. The relay nodes, which are distributed between sink nodes and sensor nodes, are responsible for forwarding data packets to sink nodes. These nodes usually do not have sensors but can decode received data packets and encode data into new packets. The general process of underwater data transmissions is: The sensor nodes collect information from monitored area and generate data packets. These packets are forwarded to chosen relay nodes according to the routing protocols. After multi-hop transmissions, data packets reach the sink nodes and are aggregated or delivered to terrestrial processing platform for further analysis. Most routing protocols define a maximum communication range \(R_{max}\), nodes inside \(R_{max}\) can communicate with each other and data packets cannot be sent to nodes outside \(R_{max}\). Here we redefine a reliable communication range \(R\), nodes inside \(R\) can communicate with each other under low PER (\(\leq\)10%) but other nodes which are outside \(R\) can also receive and decode packets successfully under high PER (\(\geq\)10%). This assumption corresponds to real underwater environment and is helpful for joint decoding process. Underwater nodes might move passively with water currents, so we must take routing maintenance into consideration. We assume that each underwater node knows its location because many underwater applications require node position information. The media access control (MAC) protocol we use here is CW-MAC [26], a MAC protocol which uses a random slot window to transmit data packets if the channel is sensed busy. In this paper, we mainly focus on the design of transport protocol and routing protocol.  
Figure 1: 3D network model.
## 3 Methodology
### Optimal Control
#### 3.3.2 Full State Compensator  
Full State Feedback measures each state using sensors. These sensors often contribute to Full State Feedback's main shortcoming; its large hardware requirement to measure each of the internal states of the RUAV. This is not only expensive in terms of hardware, but is also computationally expensive for the controller to have constant feedback from all states of the system (eight sensors required in the case of the RUAV model).  
Instead, the values of the states of the system are estimated so less emphasis is placed on highly accurate, constant sensor data. The Full State Feedback gain is used with a Luenberger Observer predicting the internal states of the system to create a Full State Compensator. The controller architecture of the Full State Compensator is shown in Figure 7[25]. The main change from Full State Feedback is that the feedback gain \(K\) is multiplied by the predicted state \(\hat{x}\) from the Observer instead of the actual internal states of the system. By using the Luenberger Observer, a smooth transient response can be achieved even when the real internal states may be compromised. This is particularly applicable to Aerial Vehicles where weight comes at a premium, by using an observer component, the internal states of the vehicle do not need to be measured by bulky sensors.  
The State Feedback gain is calculated using the same process as the Full State Feedback discussed in Section 3.3.1. The Observer gain is calculated in the same four steps as Full State Feedback with slight modifications. The A and B matrices are replaced with transposed A and transposed C matrices while the \(Q\) matrix comprises the product of B and B transposed matrices. The \(R\) matrix remains the same as the Full State Feedback.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 3 Faults in WSNs
### Data Loss Fault  
A data loss fault [2] occurs when the sensed data have missing values from the series of a particular node. This could happen because of hardware failure, calibration fault, or battery failure. Equation (6) represents the data loss fault.  
\[f(t)=\Phi,\ t>\tau \tag{6}\]  
where \(\tau\) is the maximum time that is required for sensing data and \(\Phi\) is a null set.
###### Abstract  
Profiling groups of patients in clusters can provide meaningful insights into the features of the population, thus helping to identify people at risk of chronification and the development of specific therapeutic strategies. Our aim was to determine if spectral clustering is able to distinguish subgroups (clusters) of tension-type headache (TTH) patients, identify the profile of each group, and argue about potential different therapeutic interventions. A total of 208 patients (\(n=208\)) with TTH participated. Headache intensity, frequency, and duration were collected with a 4-week diary. Anxiety and depressive levels, headache-related burden, sleep quality, health-related quality of life, pressure pain thresholds (PPTs), dynamic pressure thresholds (DPT) and evoked-pain, and the number of trigger points (TrPs) were evaluated. Spectral clustering was used to identify clusters of patients without any previous assumption. A total of three clusters of patients based on a main difference on headache frequency were identified: one cluster including patients with chronic TTH (cluster 2) and two clusters including patients with episodic TTH (clusters 0-1). Patients in cluster 2 showed worse scores in all outcomes than those in clusters 0-1. A subgroup of patients with episodic TTH exhibited pressure pain hypersensitivity (cluster 0) similarly to those with chronic TTH (cluster 2). Spectral clustering was able to confirm subgrouping of patients with TTH by headache frequency and to identify a group of patients with episodic TTH with higher sensitization, which may need particular attention and specific therapeutic programs for avoiding potential chronification.  
tension-type headache; spectral clustering; pain; groups; sensitization
## 2 Numerical Simulation
### Material Model
#### 2.2.2 Mohr-Coulomb (M-C) Model  
In this study, a three-dimensional analysis model was developed, assuming a specific scenario in which an existing urban gas station will be converted into a hydrogen refueling station. Figure 6 schematically illustrates the geographical information and the borehole layout used in the model. In the figure, 'NT-21' indicates the locations where borehole investigations were conducted. The composition of the stratum reflects the actual drilling results of the site and was found to have stratigraphy in the order of silty sand, gravel, silty sand, soft rock, hard rock, and soft rock layers at a depth of approximately 28 m from the ground surface. Based on the results of this drilling investigation, a three-dimensional simulation model was constructed, as shown in Figure 7. The gradient of the stratum surface within the site was assumed to be a uniform horizontal plane.  
Figure 5: Validation results of the simulation model using the TNT equivalent method (Experimental data were obtained from [PERSON] et al. (2018)).  
Figure 6: Location of the gas station and the drill log for the borehole.  
The ground property information is listed in Table 2. The mechanical properties of each geological material were determined using results from indoor tests such as soil property tests and triaxial compression tests. Furthermore, dynamic properties were estimated based on the compressional wave velocity and shear wave velocity obtained from seismic exploration for each depth. The equation for calculating dynamic properties is as follows:  
\[E_{d}=2\times\rho{V_{s}}^{2}\times(1+v_{d}) \tag{6}\]  
\[v_{d}=\frac{1}{2}\times\frac{(\frac{v_{p}}{v_{s}})^{2}-2}{(\frac{v_{p}}{v_{s}} )^{2}-1} \tag{7}\]  
where \(E_{d}\) represents the dynamic elastic modulus (MPa) and \(v_{d}\) represents the dynamic Poisson's ratio (\(-\)). \(V_{p}\) and vs. are the compressional wave velocity (m/s) and shear wave velocity (m/s), respectively.
## 4 Result and Discussion
### Recovery of Ancient Gas Content  
#### 4.1.1 Direct Cap Thickness  
The thickness of the cap rock has a direct impact on the capacity to capture shale gas. Previous research [2,5] has clearly identified the segmentation of shale gas cap. In the Weiyuan area of Southern Sichuan, the Longmaxi Formation's top and bottom are uninterrupted deposits with a significant thickness, consistent distribution, impermeable lithology, high breakthrough pressure and provide excellent isolation. The L1\({}_{1}\) well's principal pay layer is L1\({}_{1}\), overlaying L1\({}_{2}\) and L2, which are 250 m tall cap layers. The first and second members of the Longmaxi Formation show an average porosity and permeability of 2.4% and 0.0016 \(\times\) 10\({}^{3}\)\(\upmu\)m\({}^{2}\), indicating the formation's strong isolation properties and that it has great potential as the L11 direct cap rock.
## 2 Seismic Analysis of Liquid Storage Tanks
### Hydrodynamic Analysis
#### 2.2.1 Pressure Components  
The total pressure \(p\) exerted on the tank walls during seismic events comprises three components: hydrostatic pressure \(p_{h}\), due to the liquid's weight; hydrodynamic pressure made up of impulsive pressure \(p_{i}\), associated with the rigid motion of the liquid;and convective pressure \(p_{c}\), induced by sloshing. These are added up as follows:  
\[p=p_{h}+p_{i}+p_{c} \tag{4}\]  
Figure 2: Schematic of the two-degree-of-freedom system (2 DoF) representing impulsive and convective components of the tank.  
The impulsive pressure is associated with the acceleration of the tank and the inertia of the attached liquid, while the convective pressure arises from surface waves and has a sinusoidal distribution over the height of the tank.
## 3 Shearer Positioning System Based on SINS
### Experimental Design  
To demonstrate the applicability of the shearer positioning system and the effectiveness of the data processing method, the actual movement of the shearer was simplified and the movement route of the experimental device was planned. The movement process of the shearer on the working face is simulated, the data output from the gyroscope and accelerometer is collected for data processing, and the characteristics of the motion path are recorded. Combined with the data output from the odometer, the positioning accuracy of the shearer positioning system was studied experimentally.  
Under THE current conditions of mining technology, the length of a fully mechanized mining face is usually 200 m. The designed experimental route length is 2 m, and the length of the curved section of the scraper conveyor is 0.2 m. Because of different coal mining processes, the inclined sumping can be located in the middle or at both ends of the working face. Therefore, four routes were designed for testing, as shown in Figure 6.  
Figure 5: Motion characteristics of the shearer: (**a**) the shearer is at the end of the working face, the left drum is on the top, the right drum is at the bottom, and the scraper conveyor is in a bent state; (**b**) the shearer is inclined-sumping coal cutting, the left drum is at the bottom, the right drum is on the top, and the scraper conveyor is in a straight state; (**c**) the shearer returns to the end of the working face, the left drum is on the top, the right drum is at the bottom, and the scraper conveyor is in a straight state; (**d**) the shearer keeps cutting coal and runs towards the other end of the working face, the left drum is at the bottom, the right drum is on the top.  
When the shearer does not cut coal, the scraper conveyor is usually in a straight line shape. For this reason, a straight line running experiment is designed, as shown in Figure 6a, where the positioning device runs from A to B over a running distance of 2 m. In the experiment, four sets of experiments were performed using different motion velocities to test the time effect of the positioning effect. According to the different sumping positions on the working face, three experimental paths are designed, as shown in Figure 6b-d. Each moving path tests two sets of data at motion speeds to compare and analyze the positioning accuracy of different data processing methods.
## 4 Analysis
### Security Analysis
#### 4.2.4 Relay Nodes Collusion  
In this case, the adversary may attempt to disrupt the normal delay estimation process, using adjacent compromised nodes to launch a collusion attack, in which the adversary deceives the verifier into mistaking the extra computing time for the network delay. Nonetheless, only the adjacent nodes compromised by the adversary could manipulate the delay \(D_{i}\).  
For an \(n\)-hop path, if the adversary takes control of \(m\) consecutive nodes adjacent to the destination node, the increased delay can be divided into \(m\) parts, which will be added to the report of each compromised node. In order to avoid being eliminated by the Pauta criterion [28], the time difference reported by compromised nodes should be:  
\[\Delta^{\prime}T_{n-m+i}=\Delta T_{n-m+i}-i\cdot\frac{2\upalpha T_{if}}{m} \qquad(1\leq i\leq m) \tag{46}\]  
where the \(T_{if}\) is the increased computing time caused by the extra if statements and \(\upalpha\) is the scale factor.  
Furthermore, the manipulated delay between the adjacent nodes is:  
\[D_{i}^{\prime}=\frac{\Delta T_{i}^{\prime}-\Delta T_{i-1}^{\prime}}{2}=D_{i}+ \frac{\upalpha T_{if}}{m}\qquad(n-m+1\leq i\leq n) \tag{47}\]  
To avoid being regarded as outliers by the verifier, the manipulated network delay should meet the following condition:  
\[\begin{array}{l}\min\\ s.t.\end{array}\left\{\begin{array}{l}\mid D_{i}^{\prime}-\overline{D}^{ \prime}\mid\leq 3\upsigma\qquad(n-m+1\leq i\leq n)\\ \mid D_{i}-\overline{D}\mid\leq 3\upsigma\qquad(1\leq i\leq n-m)\end{array}\right. \tag{48}\]  
where the \(\bar{D}\) is the normal sample mean and \(\bar{D}^{\prime}\) is the sample mean after some reports  
0.001, \(T_{if}\) is 0.3 and \(\alpha\) is 0.5). Notice that when the remote node is far from the verifier, the adversary needs to control more nodes, and a successful attack becomes harder.
# Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification
## 2 Materials and Methods
### Feature Extraction
* **Euclidean distances (4 features)**: the Euclidean distance (sample, amplitude) between the R spike peak and four points of the beat segment are obtained as described in [25]. These points correspond to the maximum amplitude value between 250 ms and 139 ms before the R spike, the minimum value between 42 ms and 14 ms before the R spike, the minimum value between 14 ms and 42 ms after the R spike, and maximum value between 139 ms and 250 ms after the R spike. * **Normalized heart rate features (6 features)**: \(RR_{0}\) divided by the average of the last 32 beats (\(RR_{0}/avgRR\)), \(RR_{-1}\) divided by the average of the last 32 R-R intervals (\(RR_{-1}/avgRR\)), \(RR_{+1}\) divided by the average of the last 32 R-R intervals (\(RR_{+1}/avgRR\)), \(RR_{-1}\) divided by \(RR_{0}\) (\(RR_{-1}/RR_{0}\)), \(RR_{+1}\) divided by \(RR_{0}\) (\(RR_{+1}/RR_{0}\)), and the t-statistic of \(RR_{0}\) (\(tRR_{0}\)) defined by the difference between \(RR_{0}\) and \(avgRR\) divided by the standard deviation of the last 32 R-R intervals. * **Normalized QRS temporal characteristics and amplitude differences (12 features)**: the same QRS temporal characteristics and amplitude differences previously specified, except that they are divided by their average value in the last 32 heartbeats. For the extraction of features, all the records of the datasets are evaluated sequentially one heartbeat after another simulating a real-time scenario. For this reason, normalized features use statistics calculated only from precedent heartbeats instead of statistics over an entire record (patient-wise) as is performed in related works [24; 31]. Since only the features related to \(RR_{+1}\) need a future value, the classification of each heartbeat can be done once the following beat is detected.
## 5 Discussion
### Weight Assignment Strategy  
In a multi-branch network, optimizing different loss functions requires balancing the contributions of each branch to ensure optimal performance on the final task. To evaluate the effectiveness of the weight coefficients in the proposed DBS-NET, we compared it with other weight allocation strategies, including softmax auto-learning weight coefficients, exponential weight decay [33], and learning based on uncertainty loss functions [41], as shown in Table 14.  
The results indicate that our proposed strategy outperforms the other three methods in terms of accuracy, precision, recall, and F1-score. The main reason behind this superior performance is that our fixed weight assignment strategy eliminates to adjust weights frequently during training, making the learning process more stable and controllable. By setting fixed weights, the contribution of each task to the total loss can be clearly defined, allowing the model to focus more effectively on optimizing the most important tasks.  
In contrast, softmax and uncertainty-based loss functions tend to introduce frequent weight fluctuations during training. These fluctuations can interfere with the stability and convergence of the model, making training more unstable. The exponential decay strategy assumes that the importance of a task gradually decrease over time, but it fails to fully capture the nature of tasks that require consistent attention throughout the training process.  
The use of the relatively fixed loss weights ensures that each branch maintains a consistent contribution during training, which facilitates smooth convergence. This consistent  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Methods** & **Accuracy** & **F1-Score** \\ \hline \multirow{8}{*}{Birdsdata} & AemNet & 81.98 & 82.01 \\  & EAT-S & 86.66 & 86.62 \\  & ESResNeXt & 87.78 & 87.74 \\  & ERANN & 92.18 & 92.11 \\  & CMS-H & 87.78 & 87.74 \\  & SDFIE-NET & 94.10 & 94.05 \\  & ESResNeXt & 93.81 & 93.59 \\  & EfficientnetV1\_b0 & 95.02 & - \\  & EfficientnetV2\_s & 95.30 & - \\  & SIAlex & 93.66 & - \\  & Transound & 94.70 & - \\  & LRM-MVSR & 94.80 & - \\  & LDFSRE-NET & 96.46 & 96.39 \\  & MFF-ScEnet & 96.66 & - \\  & DBS-NET(OURS) & 97.09 & 97.17 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparative analysis with other methods on the Birdsdata dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{Birdsong30} & EMSCNN & 91.49 & 90.73 & 90.07 & 90.30 \\  & DBS-NET(ours) & 97.54 & 97.47 & 97.23 & 97.22 \\ \hline \multirow{4}{*}{Birdsong16} & RMSC-BLS & 92.37 & 92.61 & 91.59 & 91.99 \\  & MVC\_CNN & 95.76 & 95.94 & 95.19 & 95.50 \\ \cline{1-1}  & MDF-NET & 97.29 & 97.32 & 97.29 & 97.28 \\ \cline{1-1}  & DBS-NET(ours) & 97.01 & 97.05 & 96.74 & 96.88 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparative analysis with other methods on BirdSong30 and BirdSong16 dataset.  
weighting leads to better overall performance, as shown in Table 14, where DBS-NET achieves the highest values on all evaluation metrics.
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
## 3 Results
### Results of Data Anomaly Prediction
The results also suggest that the model demonstrates stability in its predictions across different data subsets (folds), as evidenced by the small difference between the average metric and \(MSE_{\text{min}}\). However, it is crucial to note that the average metric offers a more generalized view of performance across the entire training set, while \(MSE_{\text{min}}\) highlights the optimal result obtained from a single data subset. Further analysis could investigate whether additional folds or variations in data distribution might further enhance the model's accuracy.