esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicular Alarm System Using mHealth Data and Lightweight Security Algorithms
## 4 mHealth Integrated Vehicular Alarm System
### Emergency Situation Determination  
The Emergency Situation Determination is required to determine emergency situations when it occurs, which can be caused by various situations such as deteriorated health conditions, distractions, and unexpected changes in the weather or road conditions. [PERSON] et al. (2016) found that health-related symptoms are related to difficulties of driving. They found that functions related to both the spine (42%) and lower body (29%) were among the most commonly reported symptoms and resulted in difficulties of driving function involving these parts of the body. This result makes sense since long distance drivers maintain the same posture for the upper body, which affects the spine as well as the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Types**} & \multicolumn{2}{c}{**Acceptable**} & \multicolumn{2}{c}{**Warning**} & \multicolumn{2}{c}{**Severe**} & \multicolumn{2}{c}{**Critical**} \\ \cline{2-9}  & **Low** & **High** & **Low** & **High** & **Low** & **High** & **Low** & **High** \\ \hline
**BP** & 91 & 169 & 90 & 170 & 80 & 185 & 65 & 220 \\
**HR** & 51 & 139 & 50 & 140 & 40 & 180 & 32 & 210 \\
**BT** & 34.1 & 37.9 & 34 & 38 & 32 & 40 & 30 & 42 \\
**RR** & 11 & 29 & 10 & 30 & 8 & 36 & 6 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Alarm threshold of sensed data suggested. These data are provided by a medical practitioner for a general purpose [19].  
lower body when sitting. When sensors recognize a health situation or distraction, the previously mentioned actions will be taken such as sending warnings, alarm notification, and/or an actuation.  
A comprehensive set of health-related symptoms need to be included in the HD of the inference system. Some of the commonly used health related symptoms are: musculoskeletal, cardio/pulmonary, neurological, endocrine, visual, sleeping disorders, upper/lower body discomfort, central nervous system, heart attack, sudden collapse, and unconsciousness.  
In addition to health-related symptoms, other factors that could lead to an accident include distractions, which should be avoided. Some of the possible distractions are: talking, texting, handling a GPS device, adjusting music and controls, not looking at the road, and zoning out.  
The data collected using our proposed mHealth integrated vehicular system is distributed over various servers and networks in the cloud. In such an interdisciplinary domain covering healthcare, sensor technologies, vehicles, and road traffic, data privacy is of the utmost importance and laws in different countries regulate data privacy. To address these privacy and security issues, data must be tagged to the right driver and encrypted, authenticated, and made accessible to the authorized people. We present a novel and trust-based security framework that could be applied to our proposed mHealth integrated vehicular notification system.
# Reducing GPS Error for Smart Collars Based on Animal's Behavior
## 2 Materials and Methods  
### GPS Based Smart Collars  
In a previous study, we introduced the use of a smart collar, which can be worn around the neck of a goat, and for the convenience of the user, we introduced a smart virtual fence application to monitor and control the movement of goats. Communication applications \"smart collar\" and \"virtual fence\" were implemented in the form of a centralized client-server architecture, where the server component is responsible for determining the virtual fence zones using a decision-making algorithm.  
Figure 1a represents the components of the center shield of a smart collar. These are Arduino Nano (Arduino, Ivrea, Italy) as a central microcontroller, XBee module (Digi International Worldwide Headquarters, Hopkins, MN, USA) for communication with the server, GPS receiver, MPU-6050 accelerometer and gyro sensors (InvenSense, Technology Dr. San Jose, CA, USA), for the stimuli it is  
used MP3 player (developed by the DFRobot Electronics in Shanghai, China) and high voltage electric shocker. Figure 1b illustrates a fully assembled smart collar around the neck of a goat.  
We used the Adafruit GPS device to determine the current position of the goats. This device was connected to a serial port (TX1, RX0) and 3v power port. Adafruit GPS module is a high-quality GPS module capable of tracking up to 22 satellites through 66 channels. The device is able to receive up to 10 location updates per second for high speed, high sensitivity of recording or tracking [27].  
The MPU-6050 was used to obtain accelerometer and gyroscope data; get the current activity behavior of the animal. The MPU-6050 devices combined a triaxial gyroscope and a triaxial accelerometer on a single chip along with an integrated Digital Motion Processor, which processed complex six-axis algorithms MotionFusion. In addition, there was a built-in temperature sensor which had an I2C bus interface for communicating with microcontrollers, such as an Arduino Nano.
# Digital-Twin-Based System for Foam Cleaning Robots in Spent Fuel Pools
## 5 Obstacle Detection and Localization  
To facilitate human operations, temporary devices such as floating platforms, floating barriers, and buoys may be intermittently placed on the surface of the fuel-depleted pool. These devices, potentially positioned along pre-planned operational routes, pose a safety risk, as they are prone to collisions with the robot. Due to the limited computational capacity of the robot itself and the need to minimize its energy consumption, obstacle detection, and localization were conducted using a digital twin system platform. This platform provided effective feedback regarding obstacle positions relative to the robot's status. Initially, RGB images and depth maps of the pool environment were obtained through infrared and depth cameras positioned above the four corners of the pool. Subsequently, an obstacle detection algorithm based on YOLOv5 was employed to identify obstacles in the RGB images and obtain pixel coordinates of the obstacle's location. The three-dimensional coordinates and depth information of the obstacle's location in the camera coordinate system were computed by combining depth information. Following a target transformation, the absolute coordinates of the impediment to the water surface coordinate system were derived. These coordinate data were then transmitted to the robot, enabling it to autonomously detect, locate, and navigate around obstacles in the spent fuel pool.
# NOMA-Based VLC Systems: A Comprehensive Review
## 1 Introduction
### Scope and Contributions  
Readers interested in reading NOMA-based VLC systems should read this review. This paper offers an in-depth analysis of existing research works on NOMA-based VLC systems. The purpose of this review is to assist readers with a thorough understanding of the NOMA-based VLC systems from a variety of perspectives. It provides a comprehensive discussion of MISO/MIMO techniques for NOMA-based VLC systems. It also explains the integration of emerging technologies such as IRS, OFDM, MIMO, UVLC, PLS, ML, etc. into NOMA-based VLC. Finally, it explains a number of potential challenges and future research areas for further breakthrough novelties.
###### Abstract  
In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly.  
backpropagation neural network (BPNN); battery management system (BMS); dSPACE 1104; energy storage; PV-battery integration; state of charge (SOC) +
Footnote †: journal: Applied Sciences
## 2 Results
### Expression Dynamics of Transcription Factors  
Transcription factors (TFs) are key elements that regulate the spatiotemporal expression of bioactive-metabolite biosynthesis [28]. Overall, 33,040 transcripts encoding 58 TF families, with significant abundances of bHLH (3386), NAC (2439), MYB related (2398), ERF (1998), WRKY (1655), C2H2 (1456), B3 (1328), C3H (1243), FAR1 (1121), and MYB (1113), were identified in _A. glauca_ (Figure 2F). Among these, 2764 TFs exhibited significant spatial expressions in the leaf (L), stem (S), and root (R), wherein bHLH (L: 65, S: 47, R: 19), NAC (L: 33, S: 54, R: 16), MYB related (L: 42, S: 46, R: 24), ERF (L: 77, S: 71, R: 22), and WRKY (L: 18, S: 20, R: 4) were the most abundant (Tables 4 and S6). Additionally, the Trihelix, TCP, YABBY, WOX, SBP, and SRS TF families also recorded tissue-specific expressions, with higher enrichments of WRKY, TCP, and YABBY in the leaf tissue, while Trihelix, WOX, SBP, and SRS showed significant enrichments in the root tissue in _A. glauca_ (Figure 8A).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No.** & **TF Family** & **Leaf (L)** & **Stem (S)** & **Root (R)** \\ \hline
1 & bHLH & 65 & 47 & 19 \\
2 & NAC & 33 & 54 & 16 \\
3 & MYB related & 42 & 46 & 24 \\
4 & ERF & 77 & 71 & 22 \\
5 & WRKY & 18 & 20 & 4 \\
6 & C2H2 & 20 & 21 & 9 \\
7 & B3 & 33 & 14 & 9 \\
8 & C3H & 16 & 23 & 2 \\
9 & FAR1 & 21 & 16 & 14 \\
10 & MYB & 17 & 21 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tissue-specific expressions of abundant TFs encoding transcripts in _A. glauca_.  
Figure 8: Heatmap visualization of differentially expressed transcripts of: (**A**) transcription factors (in leaf, stem, and root); (**B**) CYP; (**C**) UGT; (**D**) transporters in three combinations (L vs. S; L vs. R; S vs. R), of _A. glauca_.
## 1 Introduction
Three examples are given to illustrate the ground deformation monitoring and its application in tunnel construction: the Jubilee line extension of the London metro, lines 2 and 3 of the Athens Metro and the 9 km long Kallidromo railway tunnel in Greece. Another study [11] used lidar to measure tunnel deformation. The abovementioned study has shown that this method can greatly improve measurement accuracy. Using different monitoring methods, [PERSON] [10] studied the changes in the size and shape of the plastic zone during the whole excavation process. [PERSON] and [PERSON] [12] found that deformation was affected by different factors, such as stress adjustment, tunnel direction, shape of excavation section and excavation method, by microvelocity probes and borehole cameras. Other studies [13; 14; 15] found that clear changes in plastic deformation and wave velocity decrease were observed in the surrounding rock mass area near the tunnel sidewall. Studies [16; 17; 18] evaluated changes in the mechanical properties of the surrounding soil when using the drill bit and blasting excavation method. [PERSON] et al. [19] studied the reinforcement method and reinforcement parameters outside the deep foundation pit of a subway station and proposed a response scheme for the construction of foundation pits under high water levels. The deformation influence of the diaphragm wall under different retaining structure stiffness and anti-overturning stability coefficients was analyzed, and the grouting reinforcement parameters were analyzed and optimized according to the law of economic marginal decline. Studies [20; 21; 22] have found that the excavation of deep rock tunnels leads to the rearrangement of geological stress, resulting in a large deformation of the surrounding rock. Currently, there are many discussions on the type selection of foundation pit support design and the verification of the application of foundation pit composite support structures in complex environments. However, there are few studies on the deformation law of its own retaining structure caused by deep and large foundation pit construction of composite support systems in complex environments, the law of surface settlement and the impact on the safety of adjacent subway stations. To ensure the safe operation of adjacent subway vehicles during the excavation of deep and large foundation pits, it is particularly important to study the influence of deep and large foundation pit construction of composite support systems on the deformation of adjacent subway stations. Based on the deep and large foundation pit with a composite support system of a new building project in Beijing, this study analyzes the deformation law of the supporting structure of the foundation pit itself in the process of foundation pit construction and its influence on the deformation of adjacent subway stations and tracks through numerical simulation, on-site monitoring and data fitting to provide some project support for the construction of similar foundation pit projects in the future.
# Uncertainty Representation of Natural Frequency for
## 6 Numerical Examples
### Construction of Accurate Kriging Model  
Due to a series of errors during manufacturing and installation, some uncertain factors are generated. In Section 2, the longitudinal modulus of elasticity \(E_{1}\), the transverse modulus of elasticity \(E_{2}\), the main and secondary Poisson ratio \(\mu_{1}\) and \(\mu_{2}\), and in-plane shear modulus \(G\) are considered to be uncertain, The material of laminated composite cylindrical shells is T300/QY8911. The length \(X\), radius \(R\), thickness \(h\) and density \(\rho\) of laminated composite cylindrical shells are 1000, 1000, 100 mm and 1380 kg/m\({}^{3}\). The uncertainty parameters \(Q_{ij}\) of composite laminated cylindrical shells can change the matrix coefficients in Equation (3), which can affect the subsequent DNF calculations. Therefore, the uncertain parameters are classified, and then the interval variables are converted into the corresponding CDFs by using the four statistical moments method, which can facilitate the subsequent calculation. The available data for the uncertain input variables are listed in Table 3, and these data are the partial data from Ref. [57].  
According to the available uncertainty data of laminated composite cylindrical shells, the probabilistic variables are \(\mu_{1}\) and \(G\), and the interval variables are \(E_{1}\) and \(E_{2}\). The classification details and methods of uncertain variables are found in Ref. [57]. The Poisson ratio \(\mu_{1}\) obeys normal probability distribution, where the mean and variance are 0.329 and 0.146, respectively. The in-plane shear modulus \(G\) also obeys normal probability distribution, where the mean and variance are 5.145 and 0.3, respectively. Due the longitudinal modulus of elasticity and the longitudinal modulus of elasticity not being able to constitute an accurate PDF, it is necessary to convert the available data into interval variables \([\underline{z},\overline{z}]\) for calculation. The upper bounds \(\overline{z}\) and lower bounds \(\underline{z}\) of the interval variables can be solved by  
\[\left\{\begin{array}{l}\underline{z}=z_{\min}-\sigma_{z}\times\varphi\\ \overline{z}=z_{\max}+\sigma_{z}\times\varphi\end{array}\right., \tag{26}\]  
where \(z_{\max}\) and \(z_{\min}\) are the maximum and minimum values of all the sampling points, \(\sigma_{z}\) is the standard deviation, and \(\varphi\) is the tolerance coefficient. Generally, the standard deviation \(\sigma_{z}\) is set as 0.4 [58]. Therefore, the interval variables of \(E_{1}\) and \(E_{2}\) are \([125.54,133.56]\) and \([8.55,9.85]\), respectively. Then the interval variables are transformed into cubic normal distribution PDF by the four statistic moments method, and the CDF of interval variables \(E_{1}\) and \(E_{2}\) are shown in Figure 2.
## 3 Extraction Techniques
### Emerging Extraction Technologies
#### 3.2.3 Pressurized Methods  
Another interesting technique is the pressurized liquid extraction (PLE), which is also named accelerated solvent extraction (ASE) or pressurized hot water extraction (PHWE) when water is used [51]; also, pressurized solvent extraction (PSE) presents important benefits compared to conventional extractions [52].  
[PERSON] et al. [53] studied the efficiency of cyclically pressurized solid-liquid extraction using a Naviglo Extractor for the recovery of acidic compounds from hops flowers in comparison with supercritical fluid extraction (SFE). The results showed that the cyclically pressurized solid-liquid extraction was more effective for the extraction of \(\alpha\) acids and iso-\(\alpha\)-acids, while SFE exhibited a greater potential for the isolation of \(\beta\) acids. The authors concluded that both technologies can be used to obtain high yields of hops extracts, confirming the possibility of adjusting the experimental parameters of both methods to make it selective for specific kinds of compounds.  
[PERSON] [51] assessed the suitability of PHWE for the selective extraction of isoxanthohumol (IX) against xanthohumol (XN) from hops. Water extraction was performed using 1500 psi at 150 \({}^{\circ}\)C and an extraction time of 30 min (5 min per cycle for 6 cycles). Ethanol extraction at 150 \({}^{\circ}\)C during 30 min (5 min per cycle for 6 cycles) was carried out for comparative purposes. Besides, sequential extractions were performed using a solvent in increasing polarity order (hexane, ethanol, and water). Based on the results, PHWE showed high selectivity toward isoxanthohumol against xanthohumol (2.34 mg/g and 0.11 mg/g, respectively, with a ratio IX/XN of 21) in comparison with PLE using EtOH as a solvent, after using hexane (5.15 mg/g and 2.57 mg/g, respectively, with a IX/XN ratio of 2) [51].  
PSE has been proposed to extract \(\alpha\)-acids and \(\beta\)-acids from hops and hop products [52]. The authors studied the sample preparation method influence and the parameters that affect the extraction efficiency to find an alternative method to the EBC 7.7 extraction method (used for bitter acids determination in hop products) to save time and facilitate the laborious extraction. The most important parameters were optimized (1) temperature, (2) extraction solvent type, (3) process of sample preparation, and (4) number of cycles. The PSE method saved solvent, it was less arduous and time-consuming, and according to the statistical evaluation carried out by the authors, the developed PSE process can be considered comparable to the EBC 7.7 extraction method.
## 5 The Convergence of the QP Interpolations by the Modified Fourier System
: _Let \(f\) be an odd function and \(f^{(2p+2m+2)}\in AC[-1,1]\) for some \(p\geq 0\), \(m\geq 1\) and_  
\[\mathcal{R}_{N,m}^{1}[f](x):=f(x)-\mathcal{I}_{N,m}^{1}[f](x). \tag{121}\]8  
_Then, the following estimate holds:_  
\[\mathcal{R}_{N,m}^{1}[f](x):=f(x)-\mathcal{I}_{N,m}^{1}[f](x). \tag{121}\]9  
_where_  
\[f^{(k)}(-1)=f^{(k)}(1)=0,\quad k=1,\dots,2p. \tag{122}\]0  
_and functions \(\mu_{m,j}\) and \(\
u_{m,j}\) are defined in Lemmas 2 and 5, respectively._Proof. : We define \(f^{*}\) (see (75)) for \(\alpha=2p+2m+2\) and decompose \(\mathcal{R}^{1}_{N,m}[f](x)\) as follows:  
\[f^{(k)}(-1)=f^{(k)}(1)=0,\quad k=1,\dots,2p. \tag{122}\]1  
Then,  
\[f^{(k)}(-1)=f^{(k)}(1)=0,\quad k=1,\dots,2p. \tag{122}\]2  
Lemmas 2 and 5 lead to  
\[f^{(k)}(-1)=f^{(k)}(1)=0,\quad k=1,\dots,2p. \tag{122}\]3  
We obtain the required estimate by tending N to infinity and replacing the sums with the corresponding integrals. A similar estimate can be proven for even functions on \([-1,1]\). **Theorem 8**. : _Let \(f\) be an even function and \(f^{(2p+2m+2)}\in AC[-1,1]\) for some \(p\geq 0\), \(m\geq 1\) and_  
\[f^{(k)}(-1)=f^{(k)}(1)=0,\quad k=1,\dots,2p. \tag{122}\]4  
_Then, the following estimate holds:_  
\[f^{(k)}(-1)=f^{(k)}(1)=0,\quad k=1,\dots,2p. \tag{122}\]5  
_where_  
\[f^{(k)}(-1)=f^{(k)}(1)=0,\quad k=1,\dots,2p. \tag{122}\]6  
_and functions \(\mu_{m,j}\) and \(v_{m,j}\) are defined in Lemmas 2 and 5, respectively._  
These theorems collectively demonstrate that QP interpolations provide superior convergence properties both within the interval and at the endpoints compared to traditional interpolations and Fourier expansions. The comparison of Theorems 1, 2, and 5, 6 highlights the advantages of the QP interpolations, particularly their improved convergence within the interval \(|x|<1\). According to Theorems 5 and 6, the exact convergence rate of the QP interpolations is \(O(N^{-2p-2m-3})\) as \(N\to\infty\). For example, when \(p=0\), the convergence rate is \(O(N^{-2m-3})\). Hence, comparedto the traditional interpolation, the improvement is of order \(O(N^{-2m})\), \(m\geq 1\). Compared to the Fourier expansion by the system \(\mathcal{H}_{1}\), the improvement is of order \(O(N^{-2m-1})\), \(m\geq 1\). The comparison of Theorems 1, 3, and 7, 8 reveals the convergence properties at the points \(x=\pm 1\). All theorems show the same convergence rate \(O(N^{-2p-1})\). However, as our experiments show, the QP interpolations for \(m\geq 1\) are more accurate compared to the Fourier expansions. However, for a fair comparison, we must remember that a higher accuracy requires more smoothness from the function on interval \([-1,1]\).