esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Comparison of HS-SPME with Solvent Methods  
HS-SPME is a fairly fast technique to determine VOCs in complex matrices, which are undoubtedly specialized metabolites produced by the oil bodies of _Calypogeia azurea_. Using this technique, it was possible to detect 98.17% of the compounds, including 85.84% identified. As shown in Table 3, a richer composition of VOCs can be obtained using this method (SPME). Compared with the solvent methods, the SPME method can detect a percentage of compounds that are lower, but the number of compounds detected is greater than that of SLE or HD.  
For comparison, 66 of the 73 compounds could be detected using HS-SPME. Although only 11 of the 73 compounds were extracted with the SLE method, this may suggest that this method is not a good method for isolating VOCs from _Calypogeia azurea_.  
In the case of HD, 53 compounds were detected for n-hexane (HD1) as a solvent, and in the case of m-xylene (HD2), it was 52 out of the 73 compounds that could be isolated with HS-SPME. During the analyzing sample of extracts resulting from the HD and SLE extraction methods, VOCs appeared, which could not be determined by SPME. Examples of such relationships are ledene (**40**), 4,5,9,10-dehydro-isolongifolene (**51**), (+)-spathulenol (**54**). It is sometimes possible to detect compounds that cannot be extracted by solvent methods using HS-SPME. Such compounds include \(\alpha\)-pinene (**5**), \(\beta\)-pinene (**6**), and limonene (**7**).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \multirow{2}{*}{**HD**} & \multicolumn{3}{c}{**SLE**} & \multicolumn{3}{c}{**SLE**} & \multicolumn{3}{c}{**SLE**} & \multicolumn{3}{c}{**SLE**} & \multicolumn{3}{c}{**SLE**} & \multicolumn{3}{c}{**SLE**} \\  & \multicolumn{3}{c}{**n-Hexane**} & \multicolumn{3}{c}{**Diethyl Ether**} & \multicolumn{3}{c}{**Methylene Chloride**} & \multicolumn{3}{c}{**Ethyl Acetate**} & \multicolumn{3}{c}{**Methanol**} \\ \hline  & 24 h & 48 h & 72 h & 24 h & 48 h & 72 h & 24 h & 48 h & 72 h & 24 h & 48 h & 72 h & 24 h & 48 h & 72 h \\ \cline{2-13}
0.15 & 0.19 & 0.23 & 0.24 & 0.13 & 0.12 & 0.09 & 0.11 & 0.10 & 0.09 & 0.22 & 0.20 & 0.20 & 0.23 & 0.32 & 0.33 \\ \hline \end{tabular}
\end{table}
Table 2: The content of VOCs (mg/kg) depending on the extraction method.  
The MAE1-MAE4 appears to be the least effective method of all the others.  
Using this method, it is possible to extract 56.71% of the total VOCs for the methanol solvent, and up to 70.09% for the diethyl ether solvent.  
Too drastic extraction conditions resulted in the decomposition of some organic compounds, while others, e.g., RI = 1710 (**64**), were extracted in the greatest amount compared to HD, SLE, and MAE. In the case of this method, the conditions were too drastic (exposure to microwave radiation, elevated temperature, and solvent) for a plant with such delicate cell walls.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 1 Introduction
### Challenges and Problem Statement  
The fault detection process in WSNs faces many challenges due to the following reasons:  
* There are very limited means and resources at the node level, which compel nodes to use classifiers [1] because they do not require complex computation.
* The sensor nodes are stationed in dangerous and risky environments, e.g., indoors, war zones, tropical storms, earthquakes, etc.
* The fault detection process [5] should be precise and rapid to avoid any loss, e.g., the process should recognize the difference between abnormal and normal cases, so that it can contain loss in the case of acquiring erroneous data that could lead to misleading results.
## 5 Simulation Results and Discussion
### Effects of Packet Send Rate  
In this section, we compare different protocols' performances with the different packet send rate changes between 1 and 5 Hz in test phase. The node speed is 10 m/s. There are 100 nodes moving in the network and the initial energy is 1000 J. The simulation results are shown in Figures 11-13.  
Figure 11: Performance comparison of average E2E delay among GPSR, QNGPSR, DSEGR, and FEQSAI with different packet send rates.  
Figure 12: Performance comparison of PDR among GPSR, QNGPSR, DSEGR, and FEQSAI with different packet send rates.  
Figure 13: Performance comparison of the time of the first dead node among GPSR, QNGPSR, DSEGR, and FEQSAI with different packet send rates.
# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 5 Experimental Evaluation
### Evaluation Results
#### 5.2.2 Efficiency of the Proposed DOCMA  
We first investigate the efficiency of the designed DOCMA under different combinations of support and confidence. From Figure 5, we can easily find that the time overhead mainly depends on the support rather than the confidence. Furthermore, it will increase significantly if the support decreases slightly. In particular, it takes nearly 4 h to mine all CARs when the proposed DOCMA is not employed and the support is set to 0.2, which is obviously unacceptable in practice. However, once the proposed DOCMA is applied, the time overhead will decrease by more than half. Concretely, compared with the optimized pruning strategy and the pre-pruning strategy, the optimized connection strategy brings  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Combinations** & **ACC** & **SEN** & **SPE** \\ \hline NONE & 78.8\% & 79.6\% & 78.3\% \\ BCSA & 83.8\% & 85.2\% & 83.7\% \\ MWCC & 85.9\% & 87.7\% & 84.6\% \\ BCSA + MWCC & 93.3\% & 93.8\% & 92.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification performance improvements contributed by BCSA and MWCC.  
the largest time savings. In particular, it reduces the time overhead by approximately 44%, 39%, and 47% when the support is set to 0.3, 0.25, and 0.2, respectively. Moreover, if the optimized pruning strategy and the pre-pruning strategy are also employed, an additional 24.4-36.2% time savings can be obtained. In total, C-MWCAR can save 68.3-81.2% of time and the best and worst performance are obtained when support and confidence are set to 0.3 and 0.7, and 0.25 and 0.8, respectively.  
Figure 4: Performance comparison between C-MWCAR and four state-of-the-art baselines [7,8,20,29].  
Figure 5: Time overhead of the mining of the CARs under different combinations of support and confidence. In particular, OCS, OPS, and PPS stand for the optimized connection strategy, the optimized pruning strategy, and the pre-pruning strategy described in Section 4.2, respectively. None means that none of those strategies is employed.  
Next, we compare DOCMA with three state-of-the-art CAR mining methods, i.e., CAR-Miner [12], ECR-CARM [23], and PMCAR [18], with the conffffidence fixed at 0.8. From Table 7, we find that DOCMA spends the least time except for when the support is set to 0.2. Concretely, compared with CAR-Miner and ECR-CARM, DOCMA can save 3.6%, 36.5%, and 69.2%, and 38.1%, 60.7%, and 82.9% of the time when support is set to 0.2, 0.25, and 0.3, respectively. Compared with PMCAR, DOCMA spends 17.7% more time when support is set to 0.2 but saves 15.7% and 47.9% of the time when support is set to 0.25 and 0.3, respectively.
## 2 Sensors
### Inertial Measurement Unit (IMU)
The calibration process is usually performed for different temperature settings, since IMU behavior (especially for MEMS IMUs) could be temperature dependent. Despite the robust nature of the mentioned calibration schemes, they require accessing expensive calibration mechanisms; therefore, other practical calibration methods, such as calibration with fixed location [56], have been developed by researchers.
###### Abstract  
This study focuses on a new identification method for moving loads on bridge structures using field-based displacement data from different measurement points on a continuous rigid-frame bridge. A novel approach has been proposed to make use of the area of the absolute field value derived from the displacement influence line of continuous rigid-frame bridges. Considering the potential presence of other nuisance loads (i.e., noise) on the bridge, this method can significantly mitigate the impact of noise by adopting the absolute area method of influence lines. In addition, the new method combines data from various field measurement points to identify the moving loads, which can in turn minimize the influence of measurement errors. To validate the new method, several numerical simulations varying different noises and parameters have been carried out for benchmarking. The results show that our proposed method achieves an outstanding identification accuracy of over 95% for the simulation cases with the disturbance noise amplitude less than 1.0% and the field data with random noise. This new method enables the identification of moving loads on bridges, thereby providing fundamental data for bridge health monitoring and damage detection. This will help improve predictability of the remaining fatigue life of bridge structures.  
displacement; moving load identification; influence line; continuous rigid-frame bridge; remaining fatigue life Article
## 1 Introduction
### The Extension of Parameter Stability Region  
In order to reduce the conservatism of the PSR obtained by the proposed method, the PSR can be extended by the method introduced in Section 2.2. In the PSR shown in Figure 3, the set of parameters (h\({}_{\text{P3}}\) = 2.41, b\({}_{\text{P1}}\) = 0.12) is the boundary point of the PSR obtained, a PSR area is added via extension, which is the shadow area in Figure 5. The added PSR area is merged with the PSR in Figure 3 to form the extended PSR, which is the shadow part in Figure 6. This process can be further fulfilled to extend the PSR area. Obviously, conservativeness of the PSR is effectively reduced, and the results are shown in Figure 6.  
For stability margin evaluation, the benchmark \(d_{\text{b}}\) is firstly obtained for the parameter equilibrium point (h\({}_{\text{P3}}\) = 1.1, b\({}_{\text{P1}}\) = 0.2), which is 1.5861. Then, the index for any parameter set can be obtained. Take the PSR in Figure 2, for example: for the parameter set (h\({}_{\text{P3}}\) = 2, b\({}_{\text{P1}}\) = 0.2) inside the PSR, the index is F = 0.6757, which is less than 1, indicating that the parameter set (h\({}_{\text{P3}}\) = 2, b\({}_{\text{P1}}\) = 0.2) is closer to the PSR boundary and the small signal stability is not desirable, compared with the parameter equilibrium point (h\({}_{\text{P3}}\) = 1.1, b\({}_{\text{P1}}\) = 0.2). It can be said that the stability margin evaluation index can reflect the stability margin quantitatively and provide visual guidance for  
Figure 4: The PSR obtained by point-wise calculation of eigenvalues and proposed method.
## 5 Discussion
### Limitations and Future Work  
The current study presented a fundamental stage in developing an alternative monitoring modality. Therefore, it also has several limitations and potential areas for improvement. The current study was designed only for the cylinder's grasping purposes, specifically utilizing certain weights during home-based training. Furthermore, the implementation of the proposed method is limited to the selection of weights and cylinder sizes adopted in the current experimental setting. Any modification to the movement type or weight variations requires a thorough adjustment to the system. Additionally, the current study constructed the estimation model utilizing a stretch sensor that provided the kinematic mechanism of the finger. Therefore, an additional sensor that provides force or pressure information may complement the kinematic information to improve the estimation model. The system's ultimate goal is designed for patients in rehabilitation; however, the study included only ten healthy participants to simulate the assumed abnormal finger conditions, which differs from the actual abnormal condition of patients. Consequently, this limits the generalizability of the findings. Therefore, further observation of the estimation performance of a patient's abnormal finger conditions is necessary. In terms of potential for clinical trials, future improvements will be considered, including glove size personalization and recruiting actual patients to obtain actual abnormal grasping postures.
###### Abstract  
The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing a three-dimensional (3D) indoor structure with multiple stories. With this method, the over-segmentation that usually occurs in the reconstruction of long corridors in a complex indoor environment is overcome by morphologically eroding the floor space to segment rooms and by overlapping the segmented room-space with partitioned cells via extracted wall lines. Such segmentation ensures both the integrity of the room-space partitions and the geometric regularity of the rooms. For spaces across floors in a multistory building, a peak-nadir-peak strategy in the distribution of points along the z-axis is proposed in order to extract connected areas across multiple floors. A series of experimental tests while using seven real-world 3D scans and eight synthetic models of indoor environments show the effectiveness and feasibility of the proposed method.  
point cloud; room segmentation; 3D indoor modeling; reconstruction of multistory building
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 1 Introduction
\begin{table}
\begin{tabular}{l l} \hline \hline
**Control and Support** & **Grid Service** \\ \hline Active power control and frequency support & - Primary control or frequency containment reserves (FCR) - Secondary control or frequency restoration reserves (FRR) - Tertiary control or replacement reserves (RR) \\ \hline Reactive power control and voltage support & - Reactive power absorb/supply \\ \hline Rotor angle stability-related control & - Avoiding loss of synchronism - Damping electromechanical oscillations \\ \hline Other & - Power oscillation damping capability - DC power flow control - Black start capability - Loss compensation - DC transmission reserve \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Grid services of voltage-sourced converter (VSC) high voltage direct current (HVDC) for alternating current (AC) systems. In this paper, a method for determining the normal/contingency operation point of embedded back-to-back (BTB) VSC HVDC is proposed. In calculating the operation point during normal conditions, the system total loss minimization (STLM) control to minimize total losses within the grid reliability criterion is conducted using [PERSON]'s direct set theory. At this time, by using the power transfer distribution factor (PTDF), we calculate the lines that are sensitive to HVDC operation beforehand, reduce the number of lines to be monitored, and exclude the investigation of unnecessary lines. In addition, the security-constrained loss minimization (SCLM) control for eliminating the overloads in N-1 contingencies operates additionally. The optimum HVDC operating point is determined by setting the system total loss reduction as the objective function even within the operating range in which the overload can be eliminated by a method different from the existing remedial control. This paper is structured as follows. Section 2 describes the Korean power system briefly and provides a concept of BTB VSC HVDC in a meshed system. The method for finding the operating point of embedded BTB VSC HVDC is described in Section 3. Section 4 shows the simulation results of the BTB VSC HVDC operating point decision process for the Korean power system and demonstrates the validity of the proposed algorithm. The simulations are conducted by Power System Simulator for Engineering (PSS/E) 33.4.0 and the optimization results are implemented in Python code.
## 4 Field Validation Tests and Global Vibration Intensity Prediction
### Global Vibration Intensity Prediction  
Once the vibratory sheet pile driving work was localized, vibration intensities (e.g., PPV values) in the global area could be predicted. Figure 18 illustrates the PPV predictions based on the measured PPV values at various estimated distances from the localized vibratory driving location. It should be noted the estimated plan distances were used as the horizontal axis. The PPV values represent the vertical PPVs measured by six uniaxial accelerometers (in the vertical direction) and four triaxial accelerometers (the vertical component). A linear log-log best-fit line of Equation (1a) (\(n=1.75,k=80\)) was derived to estimate the vibration intensities of the entire field considering the site-specific nature of the tested construction site. The coefficient of determination (i.e., R squared) for the derived empirical relationship was calculated as 0.962 when using all measured data points.  
Figure 17: Localization results using monitoring points in (**a**) a linear layout; (**b**) a right-angle layout.  
Figure 18: PPV (mm/s) predictions based on measured vibration intensities at estimated plan distances \(d\) (m) from the localized vibratory driving source.  
Consequently, the vibration intensities in an extensive surrounding area rather than at a few monitoring points can be conveniently predicted using the derived empirical relationship and the estimated locations of the vibratory sheet pile driving work. In this way, the assessment of vibration impact is no longer limited to a few monitoring points, but rather extends to the entire surrounding environment.
## 4 Numerical Results and Discussion
### Stability Analysis of Large Section Tunnel under Different Excavation Sequences
#### 4.1.2. Lining Deformation Analysis
When the excavation of the target section was completed, the displacement of the measuring point floated around 0 with the alternate excavation of the middle pilot tunnel and the right pilot  
Figure 8: The vertical settlement variation curve of the vault at the target section in different calculation steps. Figure 9: The measuring points of horizontal displacement. tunnel in each condition (Figure 10a). The displacement direction of the measuring points in each condition was changing, and the variances of Conditions 1, 2, and 3 were 0.00231, 0.00823, and 0.0157, respectively. The same phenomenon also existed in Figure 10b, and the variances of the three working conditions were 0.00272, 0.00699, and 0.01129. When the tunnel section was excavated, Condition 1 better controlled the horizontal displacement of the arch waist and would not produce a large horizontal displacement change value. When the tunnel was transfixed, the horizontal displacement values at the arch waist of the target section tended to be consistent, and the sudden increase in the horizontal displacement in the later stages of each working condition was caused by the disassembly of temporary support, which led to the lack of horizontal bearing capacity in the tunnel and the lateral movement of the surrounding rock. In terms of the lining deformation, Condition 1 not only maintained a short period of rapid settlement on the vault settlement but also avoided large horizontal displacement changes at the arch waist, which was beneficial to lining deformation.
# A Scalable Deep Network for Graph Clustering
###### Abstract  
Recently, many models based on the combination of graph convolutional networks and deep learning have attracted extensive attention for their superior performance in graph clustering tasks. However, the existing models have the following limitations: (1) Existing models are limited by the calculation method of graph convolution, and their computational cost will increase exponentially as the graph scale grows. (2) Stacking too many convolutional layers causes the over-smoothing issue and neglects the local graph structure. (3) Expanding the range of the neighborhood and the model depth together is difficult due to the orthogonal relationship between them. Inspired by personalized pagerank and auto-encoder, we conduct the node-wise graph clustering task in the undirected simple graph as the research direction and propose a Scalable Deep Network (SDN) for graph clustering via personalized pagerank. Specifically, we utilize the combination of multi-layer perceptrons and linear propagation layer based on personalized pagerank as the backbone network (i.e., the Quasi-GNN module) and employ a DNN module for auto-encoder to learn different dimensions embeddings. After that, SDN combines the two embeddings corresponding; then, it utilizes a dual self-supervised module to constrain the training of the embedding and clustering process. Our proposed Quasi-GNN module reduces the computational costs of traditional GNN models in a decoupled approach and solves the orthogonal relationship between the model depth and the neighborhood range. Meanwhile, it also alleviates the degraded clustering effect caused by the over-smoothing issue. We conducted experiments on five widely used graph datasets. The experimental results demonstrate that our model achieves state-of-the-art performance.  
graph embedding; deep clustering; auto-encoder; scalable GNN; deep learning +
Footnote †: journal: Applied Sciences
## 2 System Definition
### Components of the Radar System  
A UWB bistatic radar approach was used for determining the respiration and movement of the human. Considering the attenuation characteristics of the building construction materials, such as concrete, brick, marble, and wood, a wideband operating radar that covers the 150-650 MHz frequency band was preferred. The signal attenuates rapidly within dense materials such as concrete, especially at high frequencies. Thus, operation at low frequencies is required to reach lengthy distances. However, for the signal to pass through the iron grids of the building, a higher frequency operation is required. The undetermined and heterogeneous nature of the debris requires the usage of the wideband radar system.  
The radar system consisted of a 1.3 GHz vector network analyzer (VNA), a power amplifier, a receiver, and a transmitter antenna. The dynamic range of the 2-Port VNA was 135 dB (10 Hz intermediate frequency bandwidth; IFBW), and the output power adjustment range was between \(-\)55 dBm and +3 dBm. RF coaxial cables were used for the connections with the antennas. N-type to SMA adapters were used for connecting the coaxial cables to the VNA ports. The wide band amplifier has high output power (+26 dBm)and high flat gain (\(27\pm 2\) dB typ.). The receiving and transmitting antennas were linearly polarized bowtie antennas operating in the 150-650 MHz band. The transmitting antenna was connected to the first port of the VNA, whereas the receiving antenna was connected to the second port. The concept of the human detection radar system is demonstrated in Figure 1. The control interface, which was compiled in MATLAB 2023b, was used to follow the measurements at each step. First, the system determined the ideal operating frequencies automatically by observing the attenuation characteristics of the debris. The transmission coefficient variation was measured as the function of frequency. Three frequencies that exhibit low-loss characteristics were chosen by the system. Afterward, the amplified signal at the chosen frequencies was sent to the physical debris model, which was constructed in the laboratory by the transmitting antenna. The signal received by the receiving antenna was transmitted to the second port of the VNA. During the measurements, a human subject lies within the corrugated pipe along the \(z\)-axis. The measured signal was processed, and a result that provides information about the condition of the human within the corrugated pipe was obtained by the radar control interface.