esa-sceva/satcom-chunk-collection huggingface dataset:
Because most of the sensor nodes have already been overcharged in the first stage, only a minor proportion of the total sensor nodes must further be charged to bring them up to the target energy level. The main contributions of the present work are summarized as follows. A novel hybrid scheme for charging distributed sensor nodes by a mobile charger employing a directional antenna is proposed. For comparison of charging performance, an omnidirectional antenna is also considered. The total charging time and the total amount of energy required for all the sensor nodes to reach the target energy level are reduced compared with those of the scheme in [10]. The sequential movement of the charger with the omnidirectional antenna is determined by a golden section search (GSS), while that of the charger with the directional antenna is decided by a sector-based search. Different optimization methods are used because of the different modalities of power received by the cluster heads from the omnidirectional and directional antennas. Moreover, dependence of the total charging time on the antenna gain is discussed with simulation results. 2. A sector-based approach is employed to provide energy-efficient charging. The interior angle of the sector is determined by the beamwidth of the radiation pattern of the directional antenna. This sector-based charging reduces the charging time before energy trading. To this end, the shape of the sector is defined according to the radiation pattern. 3. The difference in time taken for energy trading, which depends on the radiation pattern of the directional antenna, is also presented. The higher directivity of the antenna is found to reduce the charging time, taken by the mobile charger, and the number of overcharged member nodes, other than cluster heads. This reduction in the number of overcharged member nodes causes an increase in the time needed for energy trading. As a result, the proportion of time taken for energy trading is larger with a higher antenna gain. The organization of this paper is as follows. Section 2 provides the details of the hybrid charging scheme. Section 3 presents the simulation results for the proposed scheme. For comparison, the results of another charging scheme are also presented. Finally, Section 4 concludes this paper.
###### Abstract  
Fibromyalgiagia (FM) is characterized by persistent widespread pain that severely impacts quality of life. Immersive virtual reality-based exercise (iVRE) is emerging as a therapeutic modality for chronic pain management. However, research on iVRE in FM patients has primarily focused on perceived pain intensity (PI), with limited exploration of underlying analgesic mechanisms. This study aims to explore the effects of iVRE on PI, considering risk of poor outcomes (RPO) stratification, and on mechanical pain sensitivity (MPS) in FM. A single-arm, uncontrolled, pre-post-test exploratory study was conducted in subjects with FM. The intervention included 2 weekly 15-min iVRE sessions for 6 weeks. PI (numeric rating scale [NRS]) and MPS (pressure pain thresholds [PPTs] at the upper trapezius, lumbar spine, and knee) were assessed at baseline, after the first session (to assess exercise-induced hypoalgesia), and postintervention. RPO was assessed using the Keele StarT MSK Tool. Eleven participants completed the study. No adverse effects were reported. Clinically important reductions were observed in PI (mean difference [MD]: \(-\)2.36, 95% CI: [\(-\)4.15, \(-\)0.58], d = 0.89; \(p\) < 0.05) with this effect being associated with baseline RPO. No observable changes were found in PPTs (all 95% CIs included 0, \(p\) > 0.05). In this sample, iVRE appears to reduce PI but not PPTs, suggesting the persistence of MPS and limitations in activating endogenous pain inhibitory mechanisms. Further randomized controlled trials with larger samples are needed to corroborate these results.  
Keywords:fibromyalgiagia; virtual reality; exercise; numeric rating scale; pressure pain threshold; risk of poor outcomes +
Footnote †: journal: Applied sciences
## 3 Materials and Methods
### Karst Features Delineation  
Apart from identifying fractures and faults in the field, karst features such as caves, doclines/sinkholes, karst depressions, and speleothems were found and examined. Sinkholes or dolines are the most common karst features [60]. A DEM combined with a topographical map delineated the karst depression [60; 61]. As one of the prevalent features of karstification, closed depressions were categorised as potential (future) karst depressions. However, the coarse resolution of the DEM in the study area was insufficient to distinguish fine-scale karst features (less than several metres). On the other hand, ALOS-DSM could identify larger sinkholes indicated by SRTM-DEM. Thus, SRTM-DEM and ALOS-DSM data may be used for the large-scale mapping of karst features in the Subis Limestone, ranging from metres to kilometres.
# Long-Term Renewable Energy Planning in the Indonesian Context: A Lombok Island Study Case
## 3 Input Data
### Gili Trawangan Island  
Gili Trawangan Island is located to the west, north of Lombok. Its geographical location concerning Lombok and the neighbouring islands, Gili Meno and Gili Air, is depicted in Figure 2. The last island left is Gili Trawangan.  
An electrical SLD for the Gili Islands is represented in Figure 3. Ampenan and Tanjung substations, which accumulate and deliver the total power flow to the Gili Islands, are located on Lombok Island. These two substations ensure a stable connection for the Gili Islands with the MV electricity network. Also, in Figure 4, the Gili Trawangan SLD is shown. The route of the Gili Trawangan feeder from a substation to local loads is visible in Figure 4.  
However, as previously described, the estimated peak power demand in Gili Trawangan is 3.3 MW, and the maximum local power production from an intermittent NRE generation unit is facilitated by the PV power plant, reaching 600 kW.  
The projected peak power demand in Gili Trawangan from 2019 to 2030 is equivalent to a 315% increase [17]. The average yearly increase in peak power consumption is about 14%. Furthermore, the accumulated power capacity of the submarine cables is 36 MW [17; 18].  
Figure 1: Geographical location of the Mandalika areas and Gili islands in Lombok.  
Figure 2: Geographical location of Gili islands (Gili Trawangan, Gili Meno, Gili Air).  
The accumulated submarine cable capacity of 36 MW can easily accommodate future peak power demands. Even considering that Gili Meno and Gili Air would experience the same percentage increase in peak power demand, the submarine cables will not be exposed to congestion within the next few decades.  
If the solar PV plant capacity of 600 kW is not expanded by 2030, when the projected peak power demand is 13.7 MW, a power deficit of 13.1 MW would be present [17; 18]. To  
Figure 4: Distribution network of Gili Trawangan 20 kV SLD.  
Figure 3: Electrical single line diagram (SLD) of the Gili islands and their connection to the Lombook electricity system.  
avoid such a situation, the construction and commissioning of dispatchable generating units is a prerequisite for the deployment of an islanded MG to cover the residual demand.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results  
### Datasets  
Based on previous work [22; 56; 57; 58; 63], the widely used SR dataset DIV2K [64] is selected as our training dataset. The DIV2K dataset contains 800 high-quality RGB training images and 100 validation images. For testing, we test the reconstruction performance of the model using two remote-sensing datasets proposed by FeNet [46], RS-T1 and RS-T2. RS-T1 and RS-T2 are remote-sensing datasets used for land use studies collected from the UC Merced [65] dataset, and both contain 120 images covering 21 complex ground-truth remote-sensing scenes. To further test the robustness of the model comprehensively, we use four SR benchmark datasets: Set5 [66], Set14 [67], BSD100 [68], and Urban100 [69].
## 5 Experimental Test
### High-Frequency Signal  
In this case, in order to prove the performance of the proposed approach, a faster reference is applied as, for an unknown reference, it is better appreciated that the error limits are lower for the fault-tolerant regulator than for the normal one, in addition to the fact that the number of oscillations is reduced, as can be seen in Figures 16 and 17. To enable a quantitative comparison, we employ the Mean Square Error (MSE). The results for the normal case [24] are MSE = 71.9577 and for the fault-tolerant regulator are MSE = 29.7233. Because the MSE is lower than that obtained using the control given in [24], it can be concluded that our proposal has a better performance in following the trajectory and maintaining the vertical position of the link.  
As in the previous case, the stabilization action is improved by rejecting the disturbances caused by actuator failures, thereby reducing the oscillations of \(x_{2}\), as shown in Figure 18.  
Note that, although the control strategy has been designed for the linear approximation, both in numerical simulations (where the nonlinear equations have been used) and in real-time experiments, the Furuta pendulum has been stabilized, and its horizontal beam effectively tracks an unknown reference signal. In contrast, the vertical beam remains in the vertical position.  
Figure 16: Comparison between \(x_{1}\) and \(w_{1}\) when using control law given in [24] for high frequency signal.  
Figure 15: Difference between virtual fault and actual fault of the system.  
Finally, Tables 2 and 3 summarize the results obtained from the simulations and real-time experiments for the low- and high-frequency signals. Based on the results reported in Tables 2 and 3, it can be concluded that the proposed approach achieves better performance compared to [24].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Low-Frequency**} \\ \cline{2-3}
**Aspect** & \multicolumn{2}{c}{**Signal**} \\ \cline{2-3}  & **Simulation** & **Experiment** \\ \hline Reference & \(\Psi_{1}(t)\) & \(\Psi_{1}(t)\) \\ \hline Tracking performance & Good tracking of \(w_{1}\) by \(x_{1}\) & Improved synchronization over method in [24] \\ \hline HGO convergence & Accurate and fast & Effective via \(w1\) estimation \\ \hline \(x_{2}\) stabilization & Minimal oscillations, good control & Reduced oscillation amplitude \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the simulation and experimental results for low-frequency signals.  
Figure 17: Comparison between \(x_{1}\) and \(w_{1}\) when using the proposed control strategy for high frequency signal.  
Figure 18: Comparison of \(x_{2}\) between the control given in [24] and the proposed control strategy for high frequency signal.
## 3 Implementation
### Data Sending and Receiving  
Figure 8 presents the flow diagram for the DTC codes Arduino-based emitter. In it, once Bluetooth connection among the OBDII reader and the SIM900 shield is established, the phone call begins. Later on, DTC error codes transmission starts in case the vehicle generates them.
## 3 Materials and Methods
### The Drone-LoRa Layer  
This layer is responsible for collecting data from wireless server gateway and transmitting them to the cloud using LoRa technology. Since at remote areas such as farms, there might be no network deployment, and/or the installation of a LoRa gateway might impose a significant cost without guaranteeing the interconnection of all the installed devices, this proposal used a drone as a mobile gateway to collect data from time to time from various sensors located in the farm. Besides the drone mobility and flexibility, it allows accessing remote and hard locations, which is represented as a flying LoRa gateway. The drone-LoRa gateway is a multi-radio device that is endowed with at least two different wireless interfaces for communication between the IoT devices and the cloud. Dragino, a manufacturer of IoT systems, created a LoRa gateway device called LG02, which was used for this system. LG02 is an open-source dual-channel LoRa Gateway. It lets you bridge LoRa wireless network to an IP network via WiFi, Ethernet, 3G, or 4G cellular, with a frequency range of 862-1020 MHz.  
Figure 7 shows the drone configurations of the experiment, where the drone is presented, along with its components including electronic speed controllers (ESCs), motors, propellers, flight controller (Pixhawk-4), BME280 sensor, battery, and Raspberry Pi 3 microcontroller, and the LoRa module. This module, which is located onboard the drone, uses a 4G transceiver as a channel to establish communication between the ground server gateway and the cloud, as seen in Figure 8. Of note, the gateway server represents the interface between a large area with a significant number of nodes and the cloud.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specifications** & \multicolumn{3}{c}{**Short Range**} & \multicolumn{3}{c}{**Long Range**} \\ \hline  & Bluetooth & ZigBee & WiFi & LoRaWAN & SigFox & Ingenu \\ \hline Modulation & GPSK/DQPSK/DPSK & BPSK/OQPSK & various schemes & Chirp Spread & DBPSK(UL) & RPMA-DSSS(UL) \\  & & & & & \begin{tabular}{c} SPetrum (CSS) \\ \end{tabular} & GFSK(DL) & CDMA(DL) \\ \hline MAC & FDMA/TDMA & CSMA/CA & CSMA/CA & unslotted MAC & \begin{tabular}{c} UNslotted \\ ALOHA \\ \end{tabular} & CDMA-like \\ \hline Data rate & 3 Mbps & 250 kbps & 7 Gbps & 0.3 kbps-50 kbps & 100 bps(UL) & 78 kbps(UL) & 19 kbps(DL) \\ \hline Coverage & up to 30 m & up to 100 m & up to 100 m & up to 100 m & \begin{tabular}{c} up to 5 km \\ (urban) \\ \end{tabular} & \
\begin{tabular}{c} 10 km (urban) \\ 50 km (rural) \\ \end{tabular} &
\begin{tabular}{c} up to 15 km \\ (urban) \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 2: Comparison between short-range and long-range technologies [7, 8].  
Figure 6: Block diagram of wireless server gateway.
## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Based on the Traditional Approach  
The multi-level tree for online packets (MULTOPS) and the large-scale automated DDoS detection system (LADS) are the two most prevalent traditional DDoS attack detection methods. In [21], the authors demonstrated the use of MULTOPS on a software router with simulated attacks. MULTOPS permits routers to identify bandwidth attacks when there is a significant difference in the rates of incoming and outgoing packets from the victim or the attacker. As a result, MULTOPS triggers an attack alarm, since such packets are identified as malicious. When running on a 700 MHz Pentium III PC, routing software with MULTOPS may handle up to 340,000 packets per second [21]. This method has a number of significant flaws, one of which is its inability to detect attacks that leverage several proportional flows to disrupt a victim server. LAD uses a pre-defined bandwidth attack threshold to determine whether a specific incident needs to be reported as a potential DDoS attack or if no other DDoS-related features are present, such as high volumes of SYN, ICMP, or RST packets. This threshold was established in [48] at 26 Mbps. SYN, ICMP, and RST traces were reported in this study when demonstrated on a Tier-1 ISP network.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 2 Background
### Datasets and Assessment Metrics
#### 2.4.2 Assessment Metrics for Super-Resolved Images  
In the quality assessment of SR images, there are two primary approaches: one is subjective evaluation based on human perception, and another one is object evaluation using quality metrics. Subjective evaluation aligns more closely with practical needs but has drawbacks, such as being influenced by personal preference and being costly and difficult to automate. On the other hand, objective evaluation is more convenient and
# Experimental and Numerical Dynamic Identification of an Aerostatic Electro-Spindle
## 4 Numerical Results
### Static Stiffness of Journal Bearings  
The stiffness of journal bearings at zero speed was obtained from static simulations. A given displacement was imposed on the shaft (1 \(\upmu\)m on radius), and the reaction forces in the bearings were computed in order to estimate the radial stiffness, \(k_{f}\) and \(k_{r}\), of the front and rear bearings, respectively. The stiffness values are listed in Table 5, considering minimal and maximal radial clearances and taking into account the manufacturing tolerances, as shown in Section 2.1. Of course, stiffness values increase with supply pressure and decrease with increasing air gaps. The front bearing shows an increment of stiffness with the air gap only with \(p_{s}=0.3\) MPa. The following explanation can be given after investigating the pressure distribution: the reason is that the front bearing is near the saturation condition; that is to say that the pressure drop through the input orifices is quite small (less than 0.03 MPa).  
The tilting stiffness of each journal bearing, calculated with respect to its center, is also evaluated in order to investigate whether its contribution to the overall tilting stiffness of the rotor bearings system is negligible or not. Table 6 compares the contribution of each bearing to the tilting stiffness obtained from the radial stiffness (left column) with the tilting stiffness of each bearing with respect to its center (right column). From this comparison, it is evident that in this case, this last bearing can be neglected so the overall tilting stiffness can be estimated from the radial stiffness of each journal bearing.  
Figure 10: Air flow vs. number of iterations obtained with different grid numbers along the circumferential direction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{\(p_{s}\) (MPa)} & \multicolumn{2}{c}{\(h_{f}\) = 16 \(\upmu\)m, \(h_{r}\) = 20 \(\upmu\)m} & \multicolumn{2}{c}{\(h_{f}\) = 19 \(\upmu\)m, \(h_{r}\)=23 \(\upmu\)m} \\ \cline{2-5}  & \(k_{f}\) (N/\(\upmu\)m) & \(k_{r}\) (N/\(\upmu\)m) & \(k_{f}\) (N/\(\upmu\)m) & \(k_{r}\) (N/\(\upmu\)m) \\ \hline
0.3 & 4.51 & 4.57 & 5.53 & 4.22 \\
0.5 & 12.6 & 9.37 & 11.8 & 7.45 \\
0.7 & 20.3 & 13.0 & 16.7 & 9.5 \\
0.9 & 27.0 & 15.8 & 20.6 & 10.9 \\ \hline \end{tabular}
\end{table}
Table 5: Radial stiffness of journal bearings at different supply pressures and radial clearances.
# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 3 Results
### Evaluation Metrics  
Evaluation metrics are metrics used to measure the difference between the final segmentation results and the true labeling and are important for outcome diagnosis, as well as subsequent treatment options. The experiment finally needs to segment three regions: the enhanced tumor region (ET), tumor core region (TC), and whole tumor region (WT). Therefore, in order to evaluate the network model proposed in this paper, the Dice coefficient (Dice coefficient) and the Hausdorff distance (HD) are used to assess the accuracy of the final segmentation results. The two measures are the two most important metrics for most of the current brain tumor segmentation methods.  
The Dice coefficient measures the degree of overlap between two sets, and the range for its value is [0,1]; the larger the Dice coefficient is, the more accurate the segmentation result is. In the field of medical images, the Dice coefficient is widely used as the main index to judge the degree of conformity between a predicted segmented region and the ground truth, which is calculated based on Equation (14), where \(X\) denotes the set of values in the ground truth and \(Y\) denotes the set of predicted values; \(X\cap Y\) denotes the intersection of the predicted result with the ground truth.  
\[D(X,Y)=\frac{2*|X\cap Y|}{|X|+|Y|} \tag{14}\]  
The Hausdorff distance (HD) is used to measure the distance between two point sets and evaluate the distance between a predicted edge and a real edge. A smaller value for HD usually suggests higher segmentation accuracy. In addition, the result of the 95 th quantile, HD95, is used as the value of HD. Equation (15) is used to calculate HD, where \(d(x,y)\) denotes the Euclidean distance between \(x\), \(y\). As shown in Figure 7, \(x\) and \(y\) are two points from the predicted edge and the corresponding real edge, respectively.  
\[HD(X,Y)=\max\Bigl{\{}sup_{x\in X}inf_{y\in Y}d(x,y),sup_{y\in Y}inf_{x\in X}d (x,y)\Bigr{\}} \tag{15}\]
## 3 Results and Discussion
### Analysis of Variance
#### 3.1.2 Analysis of Wear Volume  
Similarly, Table 7 shows the ANOVA result on S/N of wear volume. The 0.014 _p_-value and 79.39% contribution from SiC powder ratio indicated its significant influence on wear volume during laser cladding process with 316L/SiC powder mixture, while the other processing parameters presented minimal impact.  
Table 8 and Figure 4 exhibit the S/N of wear volume increased with the increasing SiC powder ratio. Since the S/N of wear volume was converted using Equation (2), in which the wear volume was expected to be the smaller the better, the S/N of wear volume was negatively correlated with the actual wear volume. In Figure 4, the increasing SiC powder ratio resulted in the increasing S/N of wear volume, which in fact represented the decreasing of the actual wear volume on the clad. It is generally accepted that the wear resistance is related to the hardness of material [39]. According to Archard wear equation, the wear volume is negatively related to hardness [40]. Because of the larger SiC powder ratio promoted a larger micro-hardness, which improved the capability of the clad in resisting deformation by reducing the actual contact and rubbing area during scratching. Hence, the wear volume was decreased.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **DF** & **Seq SS** & **Adj SS** & **Adj MS** & **F Value** & **Contribute** & _p_**Value** \\ \hline LP & 3 & 99.44 & 33.15 & 7.769 & 2.63 & 8.84\% & 0.224 \\ SS & 3 & 59.17 & 19.72 & 7.553 & 1.57 & 5.26\% & 0.360 \\ GF & 3 & 35.45 & 11.82 & 0.466 & 0.94 & 3.15\% & 0.520 \\ PR & 3 & 893.12 & 297.71 & 293.140 & 23.66 & 79.39\% & 0.014 \\ Error & 3 & 37.75 & 12.58 & 6.549 & & 3.36\% & \\ Total & 15 & 1124.92 & & & & 100\% & \\ \hline \hline \end{tabular}
\end{table}
Table 7: ANOVA analysis on S/N of wear volume.  
Figure 3: Main effects plot for the S/N of micro-hardness.
# Corrected Tilt Calculation for Atmospheric Pressure-Induced Seismic Noise
## 3 Results
As depth increases, the effective horizontal displacement becomes smaller and eventually changes sign at a certain depth. The dips in the curve are where the sign changes occur. Beyond this depth, the horizontal and vertical displacements decay in a similar fashion. At very long periods (Figure 4e), it can be observed that the total horizontal displacement can be greater than the vertical displacement for a considerable depth (\(\sim\)275 m). Beyond 275 m, the vertical displacement is larger than the horizontal displacement. Even if the model parameters are changed, the results do not change dramatically from the result presented in Figure 4 because the attenuation ratio does not contain the shear modulus and the ratio \(\frac{\lambda+2\mu}{\lambda+\mu}\) does not vary greatly for a given elastic medium. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model No.** & \(\alpha\) **(km/s)** & \(\beta\) **(km/s)** & \(\rho\) **(g/cm\({}^{3}\))** & **Geology** \\ \hline
1 & 0.3 & 0.1 & 1.6 & Unconsolidated sediments \\ \hline
2b & 2.9 & 1.0 & 2.3 & Sedimentary rock \\ \hline
4 & 5.8 & 3.25 & 2.85 & Granites \\ \hline \end{tabular}
\end{table} 
Table 1: Model parameters used in the computation of total horizontal and vertical displacement at the surface caused by a plane pressure wave moving at a speed of 5 m/s.