esa-sceva/satcom-chunk-collection huggingface dataset:
# The Biosynthesis and Medicinal Properties of Taraxerol
### Distribution of Taraxerol in the Plant Kingdom  
Members of the Asteraceae family comprise the greatest number of taraxerol-containing taxa, followed by the Euphorbiaceae and Malvaceae families (Table 1). It should be noted that within the Euphorbiaceae family, species in the Euphorbiena genus have shown considerable accumulation of taraxerol. The most prominent source of taraxerol was found to be chiefly concentrated in the leaves for most taxa [11; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37], followed by the roots [9; 12; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52] and finally the stems [53; 54; 55; 56; 57; 58; 59]. Some literature has also managed to isolate taraxerol from flowers (Table 1). However, the distribution of taraxerol is highly diverse in plants, and taraxerol content differ in different parts of plants and across different plant species. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Family** & **Genus** & **Species** & **Parts Extracted** &
\begin{tabular}{c} **Taraxerol** \\ **Accumulation** \\ \end{tabular} & **Authors, [Ref.]** \\ \hline \multirow{3}{*}{Acanthaceae} & \multirow{3}{*}{_Stroblanthes_} & _S. callosus_ & Aerial parts & 0.69\% for 5.0 Kg of plant \\  & & & & material & [13] \\ \cline{3-6}  & & _S. crispus_ & Leaves & N/A 1 _ & [11] \\ \hline \multirow{3}{*}{Anacardiacaceae} & \multirow{3}{*}{_Lamrea_} & _L. schimperi_ & Stems, bark, and roots & 299 mg/Kg dry weight & [38] \\ \cline{2-6}  & & _M. indica_ & Leaves & 0.4–0.9\% yield 2 & [18] \\ \cline{3-6}  & & _M. persiciformis_ & Not specified & N/A & [60] \\ \hline \multirow{3}{*}{Annonaceae} & \multirow{3}{*}{_Ulavria_} & _U. microcarpa_ & Not specified & N/A & [61] \\ \cline{3-6}  & & _U. macrophyulla_ & Not specified & N/A & [62] \\ \cline{3-6}  & & _U. hoekeri_ & Bark of the roots & 75 mg/Kg dry weight & [39] \\ \cline{3-6}  & & _U. narum_ & Bark of the roots & 0.04 mg/g dry weight & [39] \\ \hline Apocynaceae & _Gomphocarpus_ & _G. frutiicosus_ & Aerial parts & 80 mg/Kg dry weight & [63] \\ \hline Araliaceae & _Schefflera_ & _S. octophylla_ & Bark of the roots & N/A & [40] \\ \hline Araliaceae & _Acanthapmax_ & _A. tryldiatus_ & Leaves & N/A & [19] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The distribution of taraxerol isolated from different plant taxa. Figure 1: The proposed structure of taraxerol. Adapted from [PERSON] et al. (1955) [17].
# Seismic Performance Assessment of Wide Pile-Supported
## 2 Methods
### Finite Element Modeling
The piles had a yield strength of 2.4 \(\times\) 10\({}^{5}\) kN/m\({}^{2}\) and a Young's modulus of 2.0 \(\times\) 10\({}^{8}\) kN/m\({}^{2}\). The out-of-plane pile spacing was 6 m. Lateral resistance (\(T_{lat}\)) was calculated using Equation (6), using the correlation between undrained cohesion (\(Cu\)) and \(\O\)[34]. Axial (\(F_{max}\)) and skin (\(T_{skin}\)) resistance were calculated based on Decourt [35]. \(T_{skin}\) in Equation (7) was calculated considering pile base friction coefficient (\(\beta\)) [36] average N-SPT value at embedded pile depth (\(NS\)), pile cross-sectional area (\(A_{p}\)), and embedded pile depth (\(L\)). \(F_{max}\) (Equation (8)) was calculated considering pile base coefficient (\(\alpha\)) [36] average N-SPT value in between 4\(\O\) below and 4\(\O\) above pile tip (\(NP\)), characteristic soil coefficient (\(K\)) [36], and \(A_{p}\). In this model, \(T_{lat}\) and \(T_{skin}\) values were set to linearly increase from the smallest at the pile head to the largest at the pile tip. \[T_{lat}=9\;Cu\;\O \tag{6}\]  
\[T_{skin}=\beta\;(NS/3+1)\;A_{p}\;L \tag{7}\]  
\[F_{max}=\alpha\;NP\;K\;A_{p} \tag{8}\]  
The reinforced concrete decks are modeled as plate elements, and their material properties are outlined in Table 4. The elements were modeled as an elastoplastic-isotropic material with a compressive strength of 35 MPa, a Poisson's ratio of 0.2, and a Young's modulus of 2.7 \(\times\) 10\({}^{7}\) kN/m\({}^{2}\). The wharf deck had a larger dimension and capacity than the trestle deck due to the greater load that a wharf must withstand. For the wharf, the live  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Unit** & **Clay 0** & **Clay 1** & **Clay 2** & **Clay 3** \\ \hline \(\gamma\)_sat_ & kN/m\({}^{3}\) & 16.24 & 16.31 & 16.40 & 16.47 \\ \(\gamma\)_unstst_ & kN/m\({}^{3}\) & 13.35 & 14.11 & 14.29 & 14.53 \\ \(C_{c}\) & & 0.0297 & 0.0319 & 0.0320 & 0.0319 \\ \(C_{s}\) & & 0.00595 & 0.00638 & 0.00640 & 0.00637 \\ \(e_{init}\) & & 0.77 & 0.88 & 1.10 & 1.00 \\ \(C_{ref}\) & kN/m\({}^{2}\) & 2.5 & 5 & 10 & 20 \\ \(\phi^{\prime}\) & & 23 & 25 & 27 & 35 \\ \(\Psi\) & & 0 & 0 & 0 & 0 \\ \(\gamma^{0.7}\) & & 0.00021 & 0.00022 & 0.00022 & 0.00021 \\ \(Gload per unit width and depth was set at 20 kN/m/m, while for the trestle, it was set at 10 kN/m/m.
# Effects of \"S\"-Type Bowed Guide Vanes on Unsteady Flow in 1.5-Stage Axial Compressors
## 2 Methods  
### The 1.5-Stage Axial Compressor with the \"S\"-Type Bowed Guide Vane  
In this study, based on a civil axial compressor, a numerical calculation model of a 1.5-stage full three-dimensional axial compressor including a guide vane, moving vane, and stator vane was established. The structural parameters of the compressor model are shown in Table 1.  
The guide vane of the original model is straight. We fix the endwall sections and ensure that the profiles at different blade height positions are the same. The stacking line is bent into the \"S\"-type in the form of a sine function in the tangential direction and its position in the axial direction is unchanged. The stacking line of the \"S\"-type bowed curved guide vane can be expressed as shown in Equation (1) in the cylindrical coordinate system:  
\[c=a\sin\left(\frac{2\pi}{R_{1}-R_{0}}(r-R_{0})\right) \tag{1}\]  
where \(a\) is defined as the guide vane bending coefficient, which represents the maximum distance of the \"S\"-type bowed guide vane from the original vane in the tangential direction. \(R_{1}\) is the outer diameter of the guide vane, \(R_{0}\) is the inner diameter of the guide vane, \(r\) is the radial coordinate of the stacking line, and \(c\) is the tangential coordinate of the stacking line. Figure 1 shows the guide vanes with different bending coefficients.  
In this study, the 1.5-stage axial compressor with different bending coefficient \(a\) is numerically simulated under the design condition and near-blockage condition, respectively. The boundary conditions of the two working conditions are shown in Table 2.
## 4 Experimental Results and Analysis  
### Data Set Introduction  
The data set used in this study includes high-frequency trading data of crude oil and hot-rolled coil futures. The time granularity is recorded every 5 min, and the time span is from January 2019 to April 2024. The data set records the core transaction data such as the opening price, the highest price, the lowest price, the closing price, and the trading volume in each 5 min period, which fully reflects the price fluctuation and trading activity of future contracts. The timestamp field accurately records the transaction time of the data, which provides a rich information basis for financial market analysis. The time span of the data set is more than 5 years, covering the price fluctuations of the market under the influence of different economic cycles and events. The characteristics of high-frequency data make the data set particularly suitable for short-term price volatility forecasting tasks, which can capture subtle changes in prices in a short period of time and provide important support for the development of quantitative trading strategies. At the same time, the regular time granularity of data is helpful for constructing an effective time series data model and revealing the inherent law and trend characteristics of price data over time. In order to evaluate the performance of the model, we divide the data set into training set and test set. The training set accounts for 80% of the total data and is used for model training. The test set accounts for 20% of the total data volume and is used to evaluate the generalization ability of the model. This division method ensures the continuity of the training set and the test set in time and simulates the prediction ability of the model for new data in the actual transaction scenario.
# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
###### Abstract  
Every year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.  
remote sensing; time series; Sentinel-2; Sentinel-1; Fuel Management Zones; machine learning +
Footnote †: journal: Geo-Informational Journal of Geo-Informational
## 3 Results
### Doppler Shift Tolerance  
When a communication device is mounted on a satellite, it must have a sufficient reception bandwidth with respect to the Doppler shift caused by high-speed movement to avoid reception performance degradation. Thus, a Doppler shift tolerance test was performed. As the experimental conditions of this test, the satellite was assumed to receive a signal at 400 km altitude; the receiving frequency at the satellite was fixed, and a signal with a slightly shifted frequency was sent. Table 9 lists the Doppler shift test parameters, and Figure 12 shows the apparatus.  
Figure 11: LET–SEU upset cross-section characteristic curve (microcomputer: H8-3048. Reprinted from [21]).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Frequency at receiver & 437.425 MHz \\ Receiving bandwidth & 100 kHz \\ (satellite side) & 100 kHz \\ \hline Frequency at transmitter & 437.325–437.525 MHz \\ (Earth-station side) & Sweep with 1 kHz step \\ \hline Success criterion & Five continuous receivals \\ \hline \multicolumn{3}{c}{10 ms interval at the same frequency} \\ \multicolumn{3}{c}{500 ms interval for the frequency change} \\ Test method & 9600 bps \\ \multicolumn{3}{c}{2400 Hz shift FSK} \\ \multicolumn{3}{c}{18 bytes/times} /times \\ \hline \hline \end{tabular}
\end{table}
Table 9: Frequency shift tolerance test parameters.  
The test results confirmed that reception was possible in the range of \(\pm 38\) kHz from the center frequency (437.387-437.463 MHz). The frequency shift was determined using the following equation:  
\[f=\frac{1}{1+\frac{v}{c}\cos\beta}\,f_{0} \tag{3}\]  
where \(f\) is the received frequency; \(f_{0}\) is the transmitted frequency; \(c\) is the velocity of light; \(v\) is the satellite velocity; and \(\beta\) is the angle between the satellite velocity vector and Earth-based transmitter position vector relative to the satellite [11]. From Equation (3), the frequency shift in the 430 MHz band for a satellite in LEO at 400 km altitude is approximately \(\pm 10\) kHz, which confirms that communication is possible regardless of the relative velocity at the time of passage.
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 3 The Microsoft Kinect
### Kinect Software Tools  
At the time of publication, Microsoft initially planned to use the Kinect only for video games. However, Adafruit launched a hacking contest to make Kinect functional on Windows with an increased bonus of up to USD 3000 [36]. Hacker groups have successfully exploited Kinect data on the PC, providing free libraries and unofficial software development kits (SDKs) such as the CL NUI platform, Libfreenect, OpenNi, and PCL. To act against hacker initiatives and open-source libraries, Microsoft launched the first official SDK in July 2011 [37]. The official SDK allows users to develop applications that encourage gesture and voice recognition. It provides direct access to Kinect data. This SDK includes drivers for deploying the Kinect on a computer running the required operating system. Thus, application programming interfaces (APIs), device interfaces, and samples codes are available in the development kit delivered by Microsoft. Indeed, research laboratories were the first users of Kinect outside consoles. Most of the previous libraries offer basic functionalities such as camera calibration, automatic body calibration, skeleton tracking, face tracking, and scanning. Each library has advantages and disadvantages. OpenNi and Microsoft SDK are the most used libraries in the development community [38]. OpenNI's most compelling benefit is its flexibility for multiple operating platforms. Moreover, it is most beneficial to use OpenNi to provide the best results in finding colored points clouds. However, the official Kinect SDK is more stable in terms of the accuracy of the original image and the preprocessing technology. Moreover, it is more convenient to use when it requires skeletal monitoring and audio processing. Each version of Kinect can detect and interpret the movements of a specific number of individuals in the scene (see Figure 2 and Table 2). It exploits a person's skeleton by generating 3D data (position and orientation) for particular joints [39]. The depth data are used with a random forest classifier to establish the skeleton in each acquired frame [40];the random forest categorizes each pixel into the background or part of the body, then the pixels allocated to each body part are clustered to create the skeletal model. The ease of obtaining the skeleton has made the Kinect one of the most suitable technologies for fall detection.
## 3 Background
### Literature Review Methodology
#### 3.1.2 DDoS Attacks in IoT/Cloud Environments  
The literature indicates that IoT architectures are particularly vulnerable to DDoS attacks. As [20] explains, the large number of devices connected to the network, often with insufficient security measures, make them easy targets for attackers to exploit their vulnerabilities, compromise the devices, and launch DDoS attacks from them to other targets in a coordinated and massive manner.  
On the other hand, cloud-based environments also present challenges in threat detection, precisely because of what is considered an advantage of such architectures: their decentralized and distributed nature. Ref. [21] addresses this challenge from the perspective of using ML, which, according to the author, is a key tool for taking robust security measures to protect against this type of attack, which can disrupt services and compromise the integrity of data stored in the cloud.  
The integration of both architectures (IoT/Cloud) together can further complicate the security landscape because IoT devices often rely on cloud-hosted services for data processing and storage, creating additional vectors for attackers [22]. This interconnection requires robust security measures to address both the vulnerabilities inherent in IoT devices and those associated with cloud services that can effectively mitigate DDoS attacks.  
The literature suggests that the effectiveness in mitigating these types of attack lies in a combination of anomaly detection systems, in conjunction with ML techniques. The focus is on the architectural implications rather than the specific algorithms used, and how these can be integrated into broader security systems, so that they address vulnerabilities in these types of system holistically, rather than in isolation. The implementation of advanced traffic monitoring and management systems is crucial to achieve success in detecting and mitigating DDoS attacks in IoT/Cloud environments, as presented by [21], in order to ensure that the distributed nature of cloud services does not become an additional liability for network administrators.
# Maximum Safe Parameters of Ships in Complex Systems of Port Waterways
## 2 Methods
The ship speed in individual waterway sections is calculated from this formula: \[T_{i}=h_{i}-\Delta_{i}\] (16)The underkeel clearance is a function of ship speed and other parameters, fairway parameters and the performed maneuver:  
\[\Delta_{i}=f(V_{i}) \tag{17}\]  
The maximum safe draft of the ship in the considered \(j\)-th section of the waterway to be assumed:  
\[T_{i}=\underset{i}{\text{min}}T_{i} \tag{18}\]  
* Determination of maximum parameters of two-way traffic in each fairway section. * Determination of the conditions of safe operation of ships passing through the waterway system between the examined turning basins: \[L_{co}max,\ldots,\ L_{co}min\]0 (19)  
The algorithm of the parameters determination is shown in \[L_{co}max,\ldots,\ L_{co}min\]1  
\[L_{co}max,\ldots,\ L_{co}min\]2 The algorithm of the process of determining maximum parameters of the ship in complex port waterways systems. Notably, safe parameters of ships (\(L_{c}\), \(B\)) maneuvering in turning basins and fairway bends can be determined by simulation or empirical methods [3; 14; 15]. Simulation methods using full mission bridge simulators are more accurate than empirical methods. In straight sections of the fairway, empirical methods are sufficiently accurate.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 6 The Inter-Cluster Communication
### On the Output Process in the Inter-Cluster Communication  
It is straightforward to see that, from Equation (12), the departure process in a given CH, in short \(D(z)\), i.e., the number of customers served during one transmission slot (TDMA sub-frame), can be expressed as, in terms of its PDF,  
\[D(z)=\sum_{i=0}^{N_{msT}}d_{i}z^{i}=z^{N_{msT}}-\sum_{i=0}^{N_{msT}-1}\pi_{T,i} \big{(}z^{N_{msT}}-z^{i}\big{)} \tag{15}\]  
Clearly,  
\[D^{\prime}(1)=N_{msT}-\sum_{i=0}^{N_{msT}-1}\pi_{T,i}\big{(}N_{msT}-i\big{)}=F^ {\prime}(1) \tag{16}\]  
The last equality in Equation (16) comes from the fact that under the stead-state condition, the service rate and the arrival rate per Combi-Frame are coincident.  
Then, back to Equation (14) and to Table 6 we have, for instance, CH \(\langle 3,1\rangle_{h}\) with pattern \(\mathcal{TR}\)-\(\mathcal{SCS}\)-\(\mathcal{SRSSS}\)- receives information from CH \(\langle 4,1\rangle_{h}\) at time slot 1 and from CH \(\langle 4,2\rangle_{h}\) at time slot 6; the local information (mode \(\mathcal{C}\)) is captured at time-slot 3, see also the bottom of Figure 5. That is, in hexagonal coordinates, the \(A_{i}(z)\) associated with CH \(\langle 3,1\rangle_{h}\) are,  
\[A_{1}(z) =R_{\langle 4,1\rangle_{h}}(z)=p_{\langle 4,1\rangle_{h}- \langle 3,1\rangle_{h}}D_{\langle 4,1\rangle_{h}}(z)+\overline{p}_{\langle 4,1 \rangle_{h}-\langle 3,1\rangle_{h}}\] \[A_{3}(z) =L(z),\quad\text{local traffic}.\] \[A_{6}(z) =R_{\langle 4,2\rangle_{h}}(z)=p_{\langle 4,2\rangle_{h}- \langle 3,1\rangle_{h}}D_{\langle 4,2\rangle_{h}}(z)+\overline{p}_{\langle 4,2 \rangle_{h}-\langle 3,1\rangle_{h}}\] \[A_{i}(z) =1,\quad\text{elsewhere}.\]  
with \(\overline{p}_{\langle x,y\rangle_{h}-\langle u,v\rangle_{h}}=1-p_{\langle x, y\rangle_{h}-\langle u,v\rangle_{h}}\).  
The previous expressions imply that the routing decision is taken slot by slot. Alternatively, we could decide packet by packet, which means that, in \(A_{1}(z)\) and \(A_{6}(z)\), \(pD_{\langle x,y\rangle}(z)+\overline{p}\) must be replaced by \(D_{\langle x,y\rangle}(pz+\overline{p})\). The first option seems more appropriated from the point of view of energy efficiency, since after detecting an empty mini-slot the CH can enter into the mode \(\mathcal{S}\), remaining in this state at least until the end of the current time slot.
# Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation
## 2 The Fingerprinting Localization Model  
Fingerprinting localization method is a database matching approach. As Figure 1 shows, the fingerprinting localization method uses the position information and the related features measured in the target region to establish the positioning database. In the actual positioning, the signal captured by the positioning system will match with the samples in the positioning database, and the samples most similar to the target signal are selected to accomplish the position estimation.  
As what has been introduced in our previous work [16], fingerprinting acoustic localization method requires an offline phase to construct the positioning database and an online phase for acoustic target location [30].  
In the offline phase, the positioning database can be constructed by the coordinate of position marks and the corresponding features. The coordinates of the position marks are usually determined according to the site environment of the positioning area and the location accuracy requirements of the task. As the location-related feature in this work, time difference of arrival (TDOA) is widely used in real-time acoustic positioning applications for its low computational complexity and small data size [31; 32; 33; 34; 35]. Finally, samples, also known as position fingerprints, are formed by the coordinate of position marks and their corresponding features. The fingerprints in each position mark are collected and the location fingerprint database is established.  
In the online phase, the feature vector of the observed target signal is matched with each sample of the positioning database. A specific number of samples are selected as the adjacent RPs according to the similarity with the target. Finally, the position of the target can be calculated by the specific position estimation algorithm based on the adjacent RPs according to the matching result. Exactly the same estimation algorithm used in the RADAR system, weighted-nearest neighbor (WKNN) [36] is usually used for the fingerprinting localization process.