esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 1 Introduction
When nodes are increased in the network and randomly deployed with partial nodes in the interference range, the network experiences a long delay in communicating to the border router. In time-sensitive applications such as forest fire where data are critical, it is very much needed in sending the data with less delay and more packet delivery ratio for real-time analysis. Additionally, the energy consumption of sensor nodes in energy-efficient RPL increases with a varying number of nodes for different transmission range. So, towards obviating the issue of minimal delay and packet delivery ratio with reduced power consumption in forest fire networks, we have proposed here a compression-aware aggregation-based approach for energy-efficient RPL. The aggregation scheme has been proposed in energy-efficient RPL for balancing the load, minimizing the delay, improving the scalability of the network, and reducing the power consumption. In addition to aggregation, data compression is also included at the aggregator for reducing the number of packets to be transmitted to the border router and, in turn, optimize the power consumption. The proposed work is implemented in Contiki OS with a Cooja simulator. The major contributions of the paper are as follows:  
* Region formation and aggregator selection in CAA-ERPL. * Data aggregation with data compression at the aggregator. * Data reconstruction at the border router. * Comparative analysis of the performance of CAA-ERPL versus E-RPL. The paper is organized in the following way. Section 2 gives a detailed review of related research works in RPL routing in IoT. Section 3 explains the energy-based RPL routing for the forest environment. Section 4 details the proposed compression-based data aggregation scheme. Section 5 provides the simulation setup, results, and discussions on CAA-ERPL. Section 6 provides the concluding remarks and future work.
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 2 System Block Diagram  
This chapter describes the overall operation procedure of the magnetic field based tunnel navigation system. The overall system operation is presented in Figure 1, and the block diagram of the proposed system is illustrated in Figure 2. This system does not use any external devices apart from a smartphone to determine the vehicle position inside the tunnel. In addition, no infrastructure installation cost is required as the system utilizes a magnetic field. Moreover, the system is expected to consume less battery power than alternative RF-based position estimation systems. However, two concerning issues must be resolved for estimating the position using the magnetic field. The first issue is regarding sensor calibration, and the second issue concerns the attitude estimation of the smartphone for comparing the current magnetic field strength with the sensor and the magnetic field measurement stored in the DB. This study aims to resolve these two issues by utilizing the specific situation of vehicle navigation. An accelerometer and gyroscope were utilized to estimate the attitude of the smartphone, and this attitude estimation value was used to convert the magnetic value measured in the body frame of the smartphone into a DB frame, where the DB frame refers to the smartphone attitude to collect magnetic field values at the reference point (RP) and is described in detail in Section 3. Outside the tunnel, the vehicle position can be estimated using GNSS, and the fingerprint DB stores the magnetic field values for each known position. As the current position is known, the magnetic field value for the current position can also be determined from the DB. In addition, the calibration was performed before entering the tunnel by comparing the magnetic sensor value and the magnetic field value stored in the DB. The calibration and vehicle positioning were performed after the axes of the smartphone were converted from the body frame to the DB frame. This calibrated value of the user's smartphone is stored in the user compensation data document. As the vehicle moved, the measured magnetic field values were periodically stored in the user buffer, and the accumulated magnetic field values were used to estimate the vehicle position. Since the entire tunnel section is very long, a minor DB is generated for efficient position calculation. The minor DB represents a temporary DB partially extracted from the entire DB using the previous estimated position. The minor DB generator fetched only the RPs corresponding to the expected position of the current vehicle for calculation efficiency. As the vehicle started from the tunnel entrance, all the RPs in the DB were not required for estimating the position. Furthermore, subsequence DTW was applied to compare candidate RPs and user buffers.  
Figure 1: Tunnel navigation using a magnetic field.
## 3 Methodology
### Pre-Processing  
The images are first pre-processed after acquisition to prepare the images for feature extraction. The input image is scaled down to 128 \(\times\) 128 to minimize the computational complexity [22]. All the input images are normalized by dividing the pixel intensities of each image by 255.
# Studies to Overcome Brain-Computer Interface Challenges
## 2 Studies to Overcome BCI Limitations
### Prediction of User State  
Although we can predict arm movement with high accuracy using our method, there are still problems. The research team of the University of Pittsburgh revealed that severe movement prediction errors could occur during the resting state [27]. It implies that the brain-controlled robot can operate in a dangerous manner if the BCI user does nothing or sleeps. Therefore, the user should be able to turn on or off the BCI system according to their needs. Moreover, different kinds of BCI have different pros and cons. It is challenging to type characters or control a wheelchair by controlling a robot arm. Thus, BCI should be able to predict a user state and apply a suitable BCI mode to the system.  
Figure 1: Arm movement prediction from brain signals and compensation based on image processing. (**A**) We developed technology to predict three-dimensional arm movement using MEG signals without surgery. (**B**) We proposed a novel prediction method, FPA, to increase accuracy using image information.  
We developed a technology that could predict the user state to change the BCI type as shown in Figure 2[28]. The user state could be estimated based on the brain's functional connectivity and a convolutional neural network (CNN). Common average reference (CAR) and band-pass filtering are applied to EEG signals. After filtering, the system calculates mutual information (MI) among EEG signals as functional connectivity. The MI is used as the CNN input. The CNN predicts the user state into four states (resting, speech imagery, leg-motor imagery, and hand-motor imagery). Five-fold cross-validation was applied to evaluate the feasibility. The mean accuracy of 10 subjects for state prediction was 88.25 \(\pm\) 2.34%. It implies that predicting user state and changing BCI mode are possible using functional connectivity and CNN.
## 3 Results and Discussion
### Concrete and Steel Strains  
The placing of the strain gauges is such that the concrete strains are on the top surfaces of the beams in the longitudinal direction whereas the steel plate and longitudinal reinforcement strains are in the bottom rebar. The strain progress through the loading of each beam is showed in Figure 9. The load-strain behavior for concrete (\(\epsilon_{\text{c-avg}}\)) for the CB beams is shown in Figure 9a. The average concrete strain was found to be 0.0026 based on the linear regression analysis of the loading data up to spalling (crushing of concrete cover). A full failure of the cross-section was observed for the CB beam, quickly after the spalling load. The failure was very brittle in nature.  
Linear regression was not fitted for determining average concrete strain for the steel plate for the BCSP-6, BCSP-10, and BCSP-15 beams. As shown in Figure 9b-d, concrete stain at failure was found to be 0.003, 0.0035, and 0.0038 respectively. These values are quite consistent with the assumed maximum compressive strain values of 0.003 and 0.0035 according to ACI and CSA [3; 34], respectively. For these beams, the tension reinforcement strains from the tests at the spalling load exhibited elastic behavior without failure until the peak load was reached. After the crushing of the beams, the tension reinforcement yielded as shown in Figure 9. At higher strains on steel bars of the BCSP beams, they withstood more loading. This means that the beams can carry higher loads at the same strain of steel bar without failure. At much higher applied loads, yielding of the steel-plates occurred and was localized under the flexural loading area. The steel plate exhibited plastic strain until the point of peak loading and yielding of the steel plates was initiated before the crushing of the concrete progressed as shown in Figure 9.
## 4 Semantic Interoperability Provision in Heterogeneous Healthcare Service Environments
### Semantic Interoperability in Healthcare Data Models
#### 4.2.3 Semantic Ontology Alignment using Deep Representation Learning
Moreover, for learning the description of entities and their correlation, the model minimizes loss function for VO ontology set of class entities \(Loss_{f}(w,\,b)^{n}\)\({}_{Clss}~{}=~{}\sum_{k=~{}1}^{n}~{}Diff(ClssVO_{O}^{(i)},\,ClssVO_{O}^{(j)})\), given the weights (w) and bias (b), where the custom build function \(Diff\) minimizes the loss between VO class descriptions of \(ClssVO_{O}^{(i)}\) and \(ClssVO_{O}^{(j)}\) as per standard procedure [50; 52]. A similar operation is performed for the data properties (\(Loss_{f}(w,b)^{n}_{Dp}\)) and individual entities (\(Loss_{f}(w,b)^{n}_{Ind}\)). Here, \(VO_{O}^{(i)}\), \(VO_{O}^{(j)}\) is the vector representation of the (B-B-Ws) of entities in the VO source and target ontologies. When the vectors of B-B-Ws of VO ontology entities are provided to the network, it generates the representation at diverse levels. Based on multi-layers of learning, representations have been learned. In this model, the last layer or representation as the final output of the VO ontology description are used to calculate the semantic alignment of concepts. The two AuEs have been used, where the last AuE creates the final representation. Using the learned representations, the semantic distance based on cosine similarity measure is computed similarly used in [49], and the similarity matrix is formulated. Likewise based on the method used in [54; 55], the algorithm of stable marriage has been utilized to  
Figure 10: Representation of the learning model. find out the similarity among entities based on the similarity matrix of VO source ontology and VO target ontology.
# Bandwidth-Enhanced Circularly Polarized Crescent-Shaped Slot Antenna via Circular-Patch Loading
## 4 Conclusions  
A microstrip-fed broadband CP crescent-shaped slot antenna loaded by a circular patch was presented, fabricated, and tested. The proposed antenna was easily fabricated on a single Taconic RF-35 substrate. The experiment results demonstrated that the proposed antenna has a broad 3 dB AR bandwidth of 84.40% (1.89-4.65 GHz), which is entirely enclosed by a broad \(-\)10 dB reflection bandwidth of 92.33% (1.79-4.86 GHz). In addition, the proposed antenna has a compact size of only  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & \(f_{c}\) **(GHz)** & **CP Bandwidth \({}^{*}\) (\%)** & **Antenna Area (\(\lambda_{c}^{2}\))** & **Peak Gain (dBic)** \\ \hline [8] & 2.22 & 30.63 & 0.44 \(\times\) 0.44 & 4.1 \\ [9] & 2.30 & 36.01 & 0.71 \(\times\) 0.63 & 3.9 \\ [10] & 2.75 & 48.82 & 0.55 \(\times\) 0.55 & 4.2 \\ [11] & 3 & 50 & 0.62 \(\times\) 0.62 & 4.32 \\ [12] & 4.03 & 58.56 & 0.730.55 \(\times\) 0.55 \(\lambda_{c}^{2}\). Therefore, the proposed antenna can feasibly support various wireless-communication systems in which a broadband CP antenna element is necessary.  
The presented work was carried out in collaboration of all authors. [PERSON] performed the simulations. [PERSON], [PERSON], [PERSON], and [PERSON] participated to the conception, fabrication and experiment. [PERSON] wrote the paper which was edited by all co-authors.  
This research was funded by Institute for Information and Communications Technology Promotion (IITP), grant funded by the Korean Government (MSIP) (10079984, Development of Nonbinding Multimodal Wireless Power-Transfer Technology for Wearable Devices).  
The authors declare no conflict of interest.
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing
## 4 Numerical Simulations and Analyses
### Transient Development of Cement Hydration Degree  
The heat generation during the cement hydration process is the key reason for annular pressure. Using the models established above, the cement hydration degree of cement at different depths is simulated as shown in Figure 6. The degree of cement hydration shows a slow increase at the beginning and then a rapid increase. Finally, the cement hydration degree approaches a constant slowly. In addition, the cement hydration process is obviously affected by temperature. The higher temperature in deep part of wellbore leads to the faster hydration rate of cement. The cement in the well bottom takes only 5.2 h to reaching the hydration degree of 0.5. However, the cement at the depth of 500 m takes 15.7 h to reach the hydration degree of 0.5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Cement density & 2.03 & g/cm\({}^{3}\) \\ Total heat released by cement & 267 & kJ/kg \\ Surface temperature & 20 & \({}^{\circ}\)C \\ Geothermal gradient & 2.07 & \({}^{\circ}\)C/m \\ Specific heat capacity of cement & 2000 & J/kg-K \\ Thermal expansion coefficient of cement & \(1.5\times 10^{-5}\) & \({}^{\circ}\)C\({}^{-1}\) \\ Thermal conductivity of cement & 0.72 & W/k-m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic data of the simulation well.  
Figure 5: Structure of the simulation well.  
As shown in Figure 7, the transient distribution of cement hydration in the wellbore is also simulated. As a result of the geothermal gradient, the cement hydration degree increases with depth. Therefore, the waiting time for cement thickening is mainly dependent on the hydration process in the shallow part of the wellbore.
# Cross-Section Shape and Asymmetric Support Technology of Steeply Inclined Thick Coal Seam Roadway
## 2 Engineering Background  
The Dayuan Coal Industry is currently mining the 5# coal seam, with a thickness of 5.5 m and a dip angle ranging from 38\({}^{\circ}\) to 44\({}^{\circ}\) (average 42\({}^{\circ}\)). The 1502 level roadway, driven along the floor, is located at a depth of approximately 300 m. The immediate roof of the 5# coal is sandy mudstone with a thickness of 2.7 m, and the immediate floor is siltstone with a thickness of 4.0 m. The characteristics of the roof and floor rock layers of the 5# coal seam are shown in Figure 1.  
Field investigations found that the 1502 level roadway in the Dayuan Coal Industry and its adjacent roadways exhibit the following main characteristics (as shown in Figure 2):  
1. Obvious asymmetric deformation and failure occur in the roadway roof and sidewalls. The deformation of the lower side of the roadway is significantly greater than that of the upper side, and the deformation of the upper side of the roof is significantly greater than that of the lower side.
2. There are localized anchor bolt failures at the middle position of the lower side of the roadway and the roof on the upper side (where the roadway deformation is the largest).  
Figure 1: Rock structure drilling column chart of 1502 level roadway in Dayuan Coal Industry.  
Deformation and Failure Characteristics and Selection of Surrounding Rock in Roadways with Different Cross-Sectional Shapes  
Through investigations on the Dayuan Coal Industry and adjacent steeply inclined coal seam mines, it was found that the main cross-sectional shapes of roadways used are arch, rectangular, and inclined roof trapezoidal sections. Therefore, the influence of the above three roadway cross-sectional shapes on the asymmetric deformation of steeply inclined thick coal seam roadways is explored through a mechanical analysis and numerical simulation, and the optimal roadway cross-sectional shape is selected.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 4 The Taxonomy of Malware Analysis and Detection Approaches
### Malware Detection Approaches
#### 4.2.2 Behavioral-Based  
After monitoring the executable files in an isolated environment and collecting the exhibited behaviors, features extraction techniques have been developed to extract the sensitive features by which the developed model can classify the known malicious behaviors as well as any behavior that seems to be similar to them with respect to false positive behaviors. The ability to identify novel malware behaviors in addition to the known ones based on collecting behaviors during run-time has made this approach more valuable than the signature-based approach. As a result, the majority of the studies in the literature review focused on using behavioral-based approaches in the form of continuous, sequential, and common behaviors to increase malware detection ratios.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Ref.** & **Date** & **Sting** & **PE-Header** & **Opcode** & **API Calls** & **DLL** & **Machine Activities** & **Process Data** & **File Data** & **Registry Data** & **Network Data** & **Derived Data** & **Accuracy** \\ \hline \multicolumn{11}{|c|}{**Static Analysis**} \\ \hline  
✓ & ✓ & & & NA \\
[68] & 2016 & ✓ & & ✓ & & & & 98.17\% \\ \hline \multicolumn{11}{|c|}{**Dynamic analysis**} \\ \hline
[43] & 2015 & & & & & ✓ & & & 97.8\% \\
[44] & 2016 & & & ✓ & & & & 97.19\% \\
[15] & 2016 & & ✓ & & & & & 98.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The relation between analysis approaches and data types.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Ref.** & **Date** & **String** & **PE-Header** & **Opode** & **API Calls** & **DLL** & \begin{tabular}{c} Machine \\ Activities \\ \end{tabular} & \begin{tabular}{c} Process \\ Data \\ \end{tabular} & \begin{tabular}{c} File Data \\ Data \\ \end{tabular} & \
\begin{tabular}{c} Network \\ Data \\ \end{tabular} &
\begin{tabular}{c} Derived Data \\ \end{tabular} & \multirow{2}{*}{**Accuracy**} \\ \cline{1-1} \cline{8-11}
[45] & 2016 & & ✓ & ✓ & & & ✓ & & 96.00\% \\ \hline
[52] & 2016 & & & & ✓ & ✓ & & & NA \\ \hline
[69] & 2017 & & & ✓ & & & & & 98.54\% \\ \hline
[47] & 2018 & & ✓ & ✓ & & & & NA \\ \hline
[49] & 2019 & & & ✓ & & & & & 92.00\% \\ \hline
[54] & 2019 & & ✓ & & & & & & 97.22\% \\ \hline
[48] & 2019 & & ✓ & & & & & & 94.89\% \\ \hline
[46] & 2020 & & ✓ & & & & & & 97.28\% \\ \hline
[53] & 2019 & & ✓ & & ✓ & ✓ & & & 75.01\% \\ \hline
[70] & 2020 & & ✓ & & ✓ & & & & 98.43\% \\ \hline
[51] & 2020 & ✓ & & ✓ & & ✓ & ✓ & ✓ & 99.54\% \\ \hline
[71] & 2017 & & & ✓ & & & & & NA \\ \hline  & & & & & & & & & NA \\ \hline  & & & & & & & & & NA \\ \hline
[72] & 2014 & ✓ & & ✓ & & & & & 98.71\% \\ \hline
[55] & 2016 & & ✓ & & & & ✓ & & 99.99\% \\ \hline
[73] & 2019 & & ✓ & & ✓ & & & & 99.70\% \\ \hline
[58] & 2021 & ✓ & & & ✓ & & & & 94.70\% \\ \hline
[74] & 2020 & & ✓ & & ✓ & ✓ & & & 93.92\% \\ \hline
[75] & 2020 & ✓ & ✓ & ✓ & ✓ & & & & 96.30\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._
# Position Control of a Mobile Robot through Deep Reinforcement Learning
## 2 Reinforcement Learning
### Deep Q-Network (DQN)  
The DQN algorithm is an application of the Q-learning algorithm that uses neural networks. Through interaction with the environment, it is sought that the agent can maximize the future accumulated reward, which is achieved by occupying neural networks to optimally approximate the action-value function (Equation (1)), which is responsible for maximizing the sum of the reward \(r\) discounted by \(\gamma\); thus, as the time step \(t\) increases, future rewards are increasingly less likely to occur.  
\[Q^{*}(s,a)=\max_{\pi}E\Big{[}r_{t}+\gamma r_{t+1}+\gamma^{2}r_{t+2}+\ldots \Big{|}s_{t}=s,\;a_{t}=a,\pi\Big{]} \tag{1}\]  
where \(a\) is an action, \(s\) is the current state and \(\gamma\in[0,1]\) and \(\pi\) are policies that map the agent's behavior \((\pi=P(a|s))\). A pseudoexplanatory code of the algorithms is presented in Algorithm 1. To address the effect of the \"Exploration/Exploitation trade-off\", Epsilon Greedy can be used; Epsilon refers to the probability of choosing between exploring or exploiting, where most of the time the probability of exploring is low.  
```
Initialize replay memory \(D\) to capacity \(N\) Initialize action - value function \(Q\) with random weights \(\theta\) Initialize target action - value function \(\tilde{Q}\) with weights \(\theta^{-}=\theta\) for\(\text{episode}=1,\;M\)do  Initialize sequence \(s_{1}=\{x_{1}\}\) and preprocessed sequenced \(\phi_{1}=\phi(\text{s}_{1})\) for\(t=1,\;T\)do  With select a random action \(a_{t}\) Otherwise, select \(a_{t}=argmax_{a}Q(\phi(s_{t}),a;\theta)\)  Execute action \(a_{t}\) in simulation and observe reward \(r_{t}\) and image \(x_{t+1}\)  Set \(s_{t+1}=s_{t},a_{t},x_{t+1}\) and preprocess \(\phi_{t+1}=\phi(s_{t+1})\)  Store transition \((\phi_{t},a_{t},r_{t},\phi_{t+1})\) in \(D\)  Sample random minibatch of transitions \(\left(\phi_{j},a_{j},r_{j},\phi_{j+1}\right)\) from \(D\)  Set \(y_{j}=\begin{cases}r_{j}&if episode terminates at step \(j+1\\ r_{j}+\gamma\max_{a^{\prime}}\tilde{Q}\left(\phi_{j},a_{j};\theta\right)& otherwise\end{cases}\)  Perform a gradient descent step on \(\left(y_{j}-Q\left(\phi_{j},a_{j};\theta\right)\right)^{2}\)  with respect to the network parameters \(\theta\)  Every steps, reset \(Q=Q\) endfor endfor
```  
**Algorithm 1** Deep Q-Learning algorithm with Experience Replay [44]