esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 SFs Impact
### Monitoring SF  
We have studied the amplitude and phase data, obtained by monitoring VLF radio signals emitted by NAA/24.00 kHz transmitter during solar-induced SIDs. During SIDs, a regular method for signal examination and the determination of ionospheric parameters rely on the comparison between the registered variation of amplitude and phase and the matching values acquired with simulations by the Long-Wave Propagation Capability (LWPC) numerical software package [23; 24], as explained in [20; 25; 26].  
As an instructive example of a monitored active day, we present 7 September 2017 (see Figure 1). Three M-class solar flares erupted from sunspot AR2673 of magnitudes M2.4, M1.5, and M7.3, followed by a huge X1.3-class SF. Energy and particulates hurled at the Earth while the proton flux velocity was two times greater than normal. The aurora borealis was also seen.  
In Figure 1 we present measured amplitude (lower panel) and phase (upper panel) for NAA/24.00 kHz signal on high solar activity during M1.5 (\(I_{x}=1.5\times 10^{-5}\), Wm\({}^{-2}\) at peak time, 09:54 UT), M7.3 (10:15 UT), C3 (12:14 UT) and X1.3 (14:36 UT) events on 7 September 2017 as a function of time. There were visible changes in the VLF daily signal during the C, M and X flares (see signal peaks on panels). From Figure 1, one can see that these VLF and GOES peaks during the daytime happened almost simultaneously (with a time delay of the order of minutes).  
In this work we have calculated the amplitude increase \(\Delta A_{rec}\), defined as the difference between the maximum amplitude \(A_{\text{max}}\) registered during the flare and the regular amplitude during quiet condition \(A_{quiet}\) as:  
\[\Delta A_{rec}=A_{\text{max}}-A_{quiet} \tag{1}\]  
In the same way, phase delay increase \(\Delta P_{rec}\) has been calculated as:  
\[\Delta P_{rec}=P_{\text{max}}-P_{quiet} \tag{2}\]  
The time delay \(\Delta t\) can be defined as: \(\Delta t\) = \(t_{\text{Amax}}\) \(-\)\(t_{\text{Imax}}\) where \(t_{\text{Amax}}\) is the time of maximum of VLF amplitude \(A_{\text{max}}\) and \(t_{\text{Imax}}\) is time of maximum of X-ray irradiance \(I_{\text{max}}\)[20]. The time delay is nearly similar to the D-region sluggishness [13] and is an important quantity that can be used to study the ionospheric response to the flares [27]. The quantity \(\Delta t\) depends on flare intensity and other factors, and usually takes values of about a few minutes [16; 27].  
For the representative quiet days, i.e., conditions, we have chosen the days under low solar activity. The conditions were that the daylight maximum of X-ray flux had to be lower than \(10^{-6}\) Wm\({}^{-2}\) in the 0.1-0.8 nm XL band.
## 3 Results  
Table 2 presents the trueness values while Table 3 presents the precision values.  
Regarding the trueness values, the Kruskal-Wallis test indicated that there is a statistically relevant difference between the groups (\(p<0.001\)). The post-hoc analysis (Conover test) was also executed. The Medit i700 group displayed the best level of trueness with the mean value of 23.25 \(\upmu\)m, closely followed by the PrimeScan group which displayed the mean of 25.55 \(\upmu\)m. The Omnicam group showed a decrease in trueness with a mean of 32.3 \(\upmu\)m while the PlanScan group displayed the lowest level of trueness with a mean value of 75.8 \(\upmu\)m. This translates into roughly 2.3 \(\upmu\)m difference in trueness between MedIT i700 and Cerec PrimeScan while Cerec Omnicam shows differences of 10 \(\upmu\)m and Planmeca Planscan presenting a difference in trueness up to 53 \(\upmu\)m (Figure 3).  
Regarding the precision values, the Kruskal-Wallis test returned a value of \(p<0.001\), rejecting the null hypothesis and showing that the groups differ significantly. The post hoc analysis (Conover test) concluded that the PrimeScan group shows the best precision, the Medit i700 group being second in precision performance, the Omnicam group shows a decrease in precision and the PlanScan group being the least precise (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Primescan** & **i700** & **Omnicam** & **PlanScan** \\ \hline M1 & 4.9 μm & 7.6 μm & 15.4 μm & 28.2 μm \\ M2 & 6.6 μm & 11.3 μm & 17.1 μm & 26.7 μm \\ M3 & 6.5 μm & 13.8 μm & 37.9 μm & 73.5 μm \\ M4 & 6
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 7 Performance Evaluation
### Evaluation in Real Systems
#### 7.2.5 Evaluation of Energy Consumption  
We also evaluate the energy efficiency of LESS and compare it to the standard CTP protocol. We evaluate CTP and LESS with simulation, and we select the number of beacon packets as an alternative indicator of energy consumption considering the difficulty of reading residual energy in deployed systems. The Trickle algorithm in CTP [37] increases the beacon interval exponentially from 64 ms to 1 h with good links, and resets it to 64 ms when link quality degrades severely. The details of the Trickle algorithm can be found in [40]. We take an 80-h real trace from the George Town system and regard the energy cost of CTP as the baseline, and then compute the energy consumption of LESS with all algorithms. We test all RS, K-S and K-T with different sampling rates from 0.2 to 0.8 and the result is shown in Figure 13. Generally, all three strategies of LESS can decrease energy consumption. Specially, the energy efficiency is nearly linearly related to the sampling rate, e.g., LESS can achieve a 60% energy efficiency with a 0.6 sampling rate. Another conclusion is that both K-S and K-T can further improve the energy efficiency by taking the spatial and temporal knowledge into account. Note that energy consumption of sampling rate updates and link quality information collection is also considered.
# The Method of Multi-Camera Layout in Motion Capture System for Diverse Small Animals
## 3 Experimental Verification
### Experimental Results
#### 3.2.2 The Precision for Different Layout Patterns under Different Depth of Field  
(1) Calibration test of different sampling areas under different layouts  
In the arched, annual and half-annular layout patterns, the values of \(ASDR\) raised with the increase of the length of sampling area (\(L\)). The \(ASDR\) of the annular layout was significantly larger than that of the half-annular layout (Table S3 for details). When the sampling area was guaranteed to be within the depth of field (\(L<ADF\)), the arched layout and the half-annular layout achieved the highest precision. In addition, the precision of the half-annular layout was higher than that of the arched layout in the same sampling area (Figure 7b).  
(2) Calibration test of different pitch angles in a semi-annular layout  
When the pitch angles \(\alpha\) were 30\({}^{\circ}\), 45\({}^{\circ}\) and 60\({}^{\circ}\) respectively, the working distance \(WD\) and the camera angle \(\theta\) were set to the same. The actual depth range \(DF\) of the system were 173.2 mm, 212.1 mm and 300 mm, respectively (Table S4 for details). The corresponding \(ASDR\) of calibration bar were 1.05%, 0.77% and 0.62%, respectively, which decreased with the increase of depth of field. The \(ASDR\) was the highest when \(\alpha\) was 30\({}^{\circ}\) and it was 26.7% larger than that at 45\({}^{\circ}\) and it was even about 41% larger than that at 60\({}^{\circ}\) (Figure 7c and Table S4). As shown in the Figure 7, the intersecting volume at the pitch angles of 30\({}^{\circ}\) and 45\({}^{\circ}\) in the half-annular layout was the same, about 0.063 m\({}^{3}\), which was 42.8% larger than the intersecting volume (0.036 m\({}^{3}\)) at 60\({}^{\circ}\); however, when the pitch angle was 30\({}^{\circ}\), the actual depth of field was 324 mm, while it was 377 mm when the pitch angle was 45\({}^{\circ}\) the same as 60\({}^{\circ}\). The sampling area length \(L\) in all three cases was greater than the length (300 mm) of the FMA in order to ensure that the motion range was within the depth of field range; the result showed that the \(ASDR\) was about 0.82% at the \(\alpha\) of 60\({}^{\circ}\), which was 25% higher than that at 30\({}^{\circ}\) and about 35% higher than that at 45\({}^{\circ}\) and it was found that the \(ASDR\) at \(\alpha\) of 30\({}^{\circ}\) was 13.3% higher than that at 45\({}^{\circ}\).
# The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC [PERSON]
###### Abstract  
A void coalescence term was proposed as an addition to the original void nucleation and growth (NAG) model to accurately describe void evolution under dynamic loading. The new model, termed as modified void nucleation and growth model (MNAG model), incorporated analytic equations to explicitly account for the evolution of the void number density and the void volume fraction (damage) during void nucleation, growth, as well as the coalescence stage. The parameters in the MNAG model were fitted to molecular dynamics (MD) shock data for single-crystal and nanocrystalline Ta, and the corresponding nucleation, growth, and coalescence rates were extracted. The results suggested that void nucleation, growth, and coalescence rates were dependent on the orientation as well as grain size. Compared to other models, such as NAG, Cocks-Ashby, Tepla, and Tonks, which were only able to reproduce early or later stage damage evolution, the MNAG model was able to reproduce all stages associated with nucleation, growth, and coalescence. The MNAG model could provide the basis for hydrodynamic simulations to improve the fidelity of the damage nucleation and evolution in 3-D microstructures.  
void nucleation; growth and coalescence; damage; Tantalum; molecular dynamics Article  
The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC Ta _Appl. Sci._**2021**, _11_, 3378. [[https://doi.org/10.3390/app](https://doi.org/10.3390/app)]([https://doi.org/10.3390/app](https://doi.org/10.3390/app))
# Application of Internet of Things in a Kitchen Fire Prevention System
## 2 Literature Review  
According to the Exploration of Causes of Housing Fires in Taiwan conducted by the Architecture and Building Research Institute, Ministry of the Interior [6], the total number of fire incidents in 1999 in Taiwan was 16,389, 36.1% of which were building fires, comprising 5,913 cases. In addition, among the building fire incidents, 61.3% of those were housing fire incidents, totaling 3,626 cases, indicating a high percentage of housing fire incidents among all fire incidents in Taiwan. The term \"Smart Sensors\", one of the most strategic devices of Micro System Technologies (MST), refers to sensors that contain both sensing and signal processing capabilities with objectives ranging from simple viewing to sophisticated remote sensing, surveillance, search/track, weapon guidance, robotics, perceptron and intelligence applications [7]. Many services and applications integrating these technologies into daily life have come to form an Internet of Things (IoT) [8].
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 4 Experiment Design
### Dataset  
A diversified collection of 3000 images in jpg format, as source test cases, is taken from www.kaggle.com (accessed on 2 July 2021) for our study. The dataset comprises 1500 images with no brain tumor and 1500 images with brain tumor with multiple characteristics such as horizontal and vertical dimensions, resolution in dpi, image type (T1-weighted, T2-weighted, flair), and bit depth.
# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
## 3 Results  
### Relationship Between Maximum Safe Tilt Angle and SSF  
Figure 6 illustrates the relationship between Equation (1)'s static stability factor (SSF) and the maximum safe tilt angle. The simulation data indicate that the four-axle truck models, _Prima_ and _Xcient_, exhibited similar tilt angles due to their comparable specifications. Although the _New Power Truck_ had a lower curb weight than _Prima_ and _Xcient_, it also had fewer axles, resulting in a maximum safe tilt angle similar to that of the two models.  
The five-axle truck model, _540S_, had a lower curb weight than _Prima_ and _Xcient_, but the additional axle count enhanced roll stability, resulting in a higher maximum safe tilt angle. The two-axle truck model, _Arcos_, had fewer axles than the other models but demonstrated high rollover stability due to its curb weight being over 30% lower than the other trucks.  
Figure 6: Maximum tilt angle analysis for various heavy vehicle types and SSF.
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 3 Vessel Data Preprocessing  
AIS data often comes with various issues during the collection process, such as missing values, noise points, and missing coordinate information, etc. To ensure complete and accurate trajectory data, it is essential to perform trajectory cleaning operations. On this basis, trajectory extraction is performed by statistically analyzing the time span between different trajectories associated with the same MMSI. Therefore, the specific tasks in the pre-processing stage of trajectory data are: removing invalid values, detecting and cleaning outliers, and extracting trajectories.
## 6 Performance Evaluation
### Evaluation with Different MAC Protocols  
We next examine how the underlying MAC protocol layer affects the performance of QDAR and QELAR in terms of total energy consumption. The slotted floor acquisition multiple access (SFAMA) MAC protocol [26] and underwater power control (UPC) MAC protocol [24] are used for QDAR and QELAR, respectively. The results are shown in Figure 7, where the total energy consumption is normalized to (0, 1). As QDAR restricts routing decision with end-to-end delay, some packets may detour with more hops in QELAR than that of QDAR. More nodes forwarding packets means more total energy consumption. Thus, QELAR consumes more energy than QDAR regardless of the MAC protocol used. Besides, we can see that routing algorithms with UPC can achieve lower energy consumption than those with SFAMA. The reason is that UPC can reduce the transmission power level to achieve a better energy efficiency as well as smaller interference range. Moreover, as UPC can deal with the noise, interference and spatial channel reuse, QDAR do not need to pay much attention to these problems. Therefore, using UPC as the underlying MAC protocol, QDAR can prolong the network lifetime more efficiently.
# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 2 Related Works  
### Wireless Power Transfer Technology  
In this section, we mainly introduce three major technologies of WPT [15; 16]: inductive coupling, electromagnetic radiation and magnetic resonant coupling.  
Inductive coupling is driven by magnetic field induction [15]. Due to its simplicity and high-efficiency, it has been used in many applications and devices (e.g., medical implants, RFID tags, electric toothbrush). But it can't be used to charge a sensor node in WRSN because it needs short charging distance and accurate alignment in charging direction.  
Electromagnetic radiation [17], which is a radiative technology that transfers power on a radio frequency (RF), has the advantages of long distance power transfer. It can support the unidirectional and omnidirectional radiation. However, in unidirectional radiation, it requires Line-Of-Sight (LOS) and complex tracking mechanisms, leading to large scale of devices. And in omnidirectional radiation, its power transfer efficiency is very low.  
Magnetic resonant coupling [3] works by having magnetic resonant coils operating at the same resonant frequency and generating a magnetic resonant induction for efficiently power transferring from a source coil to a receiver coil. Due to its high-efficiency and medium transfer distance, it becomes the most used WPT technology in WRSN [5; 6; 7; 13; 15; 16]. For this reason, in the rest of this paper, we mainly focus on the magnetic resonant charging.
## 4 Proposed FGPQ Scheme
### Privacy-Preserving LBS Query
#### 4.2.2 LBS Searching  
After receiving the encrypted query request \(\{E(m_{*}),E_{Q}(\widehat{L}^{\prime})\}\), the fog node \(id_{j}\) completes the LBS searching to find the most matched LBS item, which not only meets both the searching content and spatial range, but also has the shortest distance from the user's location. Specifically, suppose the fog node \(id_{j}\) receives \(k_{j}\) encrypted LBS items from the LBS provider, denoted as \(\{(E(m_{1}),E_{T}(\widehat{W^{(1)}})),(E(m_{2}),E_{T}(\widehat{W^{(2)}})), \ldots,(E(m_{k_{j}}),E_{T}(\widehat{W^{(k_{j}})}))\}\). For each \((E(m_{i}),E_{T}(\widehat{W^{(i)}}))\), where \(i=1,2,\ldots,k_{j}\), the fog node \(id_{j}\) executes the following operations.  
1. For \(E_{T}(\widehat{W^{(i)}})\), the fog node \(id_{j}\) first computes\[R_{i} = E_{T}(\widehat{W^{(i)}})\cdot E_{Q}(\widehat{L}^{\prime})=( \widehat{W^{(i)}_{a}}M_{1},\ \widehat{W^{(i)}_{b}}M_{2})\cdot(M_{1}^{-1}\widehat{L}^{\prime}_{a},\ M_{2}^{-1} \widehat{L}^{\prime}_{b})\] \[= \widehat{W^{(i)}_{a}}M_{1}\cdot M_{1}^{-1}\widehat{L}^{\prime}_{ a}+\widehat{W^{(i)}_{b}}M_{2}\cdot M_{2}^{-1}\widehat{L}^{\prime}_{b}\] \[= \widehat{W^{(i)}_{a}}\widehat{L}_{a}+\widehat{W^{(i)}_{b}} \widehat{L}^{\prime}_{b}\] \[= \widehat{W^{(i)}}\cdot\widehat{L}^{\prime}\] \[= \beta((X_{*}-X_{i})^{2}+(Y_{*}-Y_{i})^{2}-T_{*}^{2}+\omega_{8}r_ {8}+\cdots+\omega_{a^{\prime}-1}r_{a^{\prime}-1}-\sum_{j=8}^{a^{\prime}-1} \omega_{j}r_{j})\] \[= \beta((X_{*}-X_{i})^{2}+(Y_{*}-Y_{i})^{2}-T_{*}^{2}).\]  
Then, it checks whether \(R_{i}\leq 0\) holds. If not, it means that the LBS item \((E(m_{i}),E_{T}(\widehat{W^{(i)}}))\) does not satisfy the searching spatial range.
2. If \(R_{i}\leq 0\) holds, similarly to [33], the fog node \(id_{j}\) checks whether \[e(g^{ah(m_{i})r_{i}},g^{r_{*}})=e(g^{ah(m_{*})r_{*}},g^{r_{i}})\] (7) holds. If not, it means that the service content \(m_{i}\) does not satisfy the given searching content \(m_{*}\). Otherwise, the LBS item \((m_{i},(X_{i},Y_{i}))\) satisfy the searching condition given by mobile users.  
Eventually, the fog node \(id_{j}\) sends the matched result \(E_{T}(\widehat{W^{(i)}})\) to the user. Note that if the service \(m_{*}\) has multi-matched locations, the fog node \(id_{j}\) always returns the nearest location from the user (the location with the smallest \(R_{i}\)).
## 7 Applications and Future Research Directions of RIS
### RIS-Aided Millimeter (mmWave) MIMO Systems
#### 7.2.2 Coverage Improvement of mmWave Communications  
There are a lot of barriers to interacting at millimeter-wave frequencies. The channel is slightly more aggressive at these frequencies than it is at sub 6 GHz frequencies. This implies that shadowing has a significant negative impact on the average received power to create a link. While adaptive beam steering techniques can enhance the reliability of millimeter-wave connections, communication at these frequencies remains very demanding. Therefore, the capacity of millimeter-wave channels for spatial multiplexing is limited. In several examples, e.g., LOS, there is only a single feasible direction of propagation, and spatial multiplexing is not viable. By exploiting the strength of RISs, the above difficulties can be overcome. The RISs will act as a centralized beamformer in low-received power scenarios to increase channel gains. The power coverage of mmWave communications has gotten a lot of attention in recent years due to its large spectrum availability. The RIS is proposed as a promising novel solution to address the issue of power coverage in mmWave communication systems. To improve the coverage area of RIS-assisted mmWave, a variety of strategies have been proposed in the literature as stated below:  
1. A component of the Blockages: The primary concept behind this method is to provide several helpful system levels. It can improve the coverage probability of the cellular network by providing additional indirect line-of-sight (LOS) links to objects that operate as communication link blockers, such as buildings or trees [107].
2. An achievable rate region: It derives a capacity region outer bound for centralized deployment and a capacity region in closed form for distributed deployment using RIS. The main idea is to investigate the capacity region of a multiple access channel (MAC) with two users sending independent messages to an access point (AP) [108].
3. Probability of Reflection: Authors in [109] proposed a new analytical framework that provides the probability of reflection of a reconfigurable metasurface. The RIS is regarded as a reflector for a given transmitter and receiver, given environmental objects modeled with the modified random line process of fixed length and random orientations and locations.  
Further, the usage of mmWave bandwidth is one of the primary enablers for 5G and beyond cellular systems to deliver significant data rates. However, due to their high directivity and sensitivity to blockages, mmWave signals suffer from severe route loss, which limits their use in small-scale deployments. In order to enhance the coverage in Millimeter-Wave Cellular Networks, a large number of RISs are deployed, which passively reflect mmWave signals towards desired directions and therefore improve the range of mmWave communication.