esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 2 LIDAR Theory
### WindCube Coordinate System and Calculated Quantities  
The WindCube v2(r) emits laser pulses into five fixed directions, four inclined at 28\({}^{\circ}\) and one vertical. The directions are defined by the angles \(\theta\), and \(\varphi\), respectively the azimuth angle measured clockwise from the north of the LIDAR, and the elevation angle measured from the zenith position (Figure 1) In the LIDAR reference frame, the angles defining the five line-of-sight (\(LOS\)) directions \(D_{LOS}=(N,E,S,W,Z)\) are (in degrees): \(\theta(D_{LOS})=\left(0,90^{{}^{\circ}},180^{{}^{\circ}},270^{{}^{\circ}},0^{ {}^{\circ}}\right)\) and \(\varphi(D_{LOS})=\left(28^{{}^{\circ}},28^{{}^{\circ}},28^{{}^{\circ}},0^{{} ^{\circ}}\right)\).  
The \(LOS\) radial wind speed measurements from the five beam directions are combined to define the wind vector at several altitudes \(h\) as:  
\[V(h)=\left(\begin{array}{c}u(h)\\ v(h)\\ w(h)\end{array}\right)^{T} \tag{1}\]  
We note that the components \(u(h)\), \(v(h)\) and \(w(h)\) of the wind vector \(V(h)\) are specified in the coordinate system defined in the LIDAR reference frame and are not referred to an absolute coordinate system. At each altitude, the horizontal wind speed \(V_{hor}\) and direction \(W_{dir}\) are deduced from the wind vector \(V(h)\):  
\[V_{hor}(h)=\sqrt{u(h)^{2}+v(h)^{2}} \tag{2}\]  
Figure 1: WindCube v2® pulsed LIDAR principle and the six degrees of freedom of buoy-induced motion.  
\[W_{dir}(h)=mod(2\pi+\theta_{0}+atan2(v(h),u(h)),2\pi) \tag{3}\]  
with \(\theta_{0}\) being the offset between the LIDAR north and the magnetic North. The turbulence intensity (\(TI\)) is calculated from the statistical study of horizontal wind speed measurements:  
\[TI(h)=\frac{\sigma_{V_{thr}}(h)}{V_{har}(h)} \tag{4}\]  
The coordinate system is defined by the direction of the beams \(D_{LOS}\) (Figure 1). The x-component is oriented from N to S, the y-component is oriented from E to W and the z-component points towards the ground at the zenith position.
## 4 Results and Discussion
### Quantitative Interpretation
#### 4.2.2 Radially Power Spectrum Analysis  
This analysis was conducted at 88 location points of 10 km \(\times\) 10 km interval all over Kedah focusing on the Muda River basin, as shown in Figure 10. Each location point has a midpoint of a 20 km horizontal line except for four points in the southern part that were used to enhance the data. To control and validate the data, each profile was set to have 13 data points with the center of the lines representing the depth of deep and shallow sources. Figure 11 shows selected spectral analysis curves of point 1 to 8 with the natural logarithm of the power spectrum against the wavenumber that describes the deterioration or decrease in the value of the power spectrum by observing in line with all other points. The depth due to gravity sources for each profile was measured from the slope of the curves based on the radial power spectrum. The low wavenumber power spectra curves pattern was interpreted as representing a deeper source and vice versa.  
Figure 12a,b shows maps of shallow to deep gravity source of the study area, respectively. In general, the depth of the shallow gravity source increases from east to west with a maximum depth of 0.76 km from the surface located in the southwestern part of the Muda River basin. A similar increasing trend pattern can also be observed on the map of deep gravity source with a maximum depth of 1.2 km from the surface. Figure 13 shows an overview of the shallow and deep gravity sources' trend with the topography. The basin area has a bigger radius and is flatter in the shallow source at \(-\)400 m as compared to the deeper source at \(-\)1190 m, resembling a typical basic structure which is depressing downward. The red curtain shows the downward extension of Main Range Granite that is associated with the Bok Bak Fault. Figure 14 shows four profiles across the topography with shallow and deep source maps. Based on these profiles, the maximum depth area, with an average of 1.1 km, is located almost in the middle of the profile where most of the formations, lithological contact and weak zones were located. The trend of the graphs also showed the thinning of sediments towards the eastern part of the study area (coastal), if the granite bodies were not considered. These indicate that subsurface structures play a significant role in establishing groundwater basin, basement depression and such sedimentary thickness may alarm further groundwater accumulation investigation in the area.  
Figure 9: Two Euler solutions of the Bouguer anomaly map of structural index = 0.0, with a window size of (**a**) 20 km \(\times\) 20 km; (**b**) 5 km \(\times\) 5 km.  
Figure 11: _Cont_.  
Figure 10: Power spectrum location map.  
Figure 11: Log of spectra power vs. wavenumber of (**a**) Point 1; (**b**) Point 2; (**c**) Point 3; (**d**) Point 4; (**e**) Point 5; (**f**) Point 6; (**g**) Point 7; (**h**) Point 8.  
Figure 12: (**a**) Depth to shallow source map; (**b**) Depth to deep source map.
# Effect of _Tinospora cordifolia_-Derived Phtocomponents on Cancer: A Systematic Review
###### Abstract  
The major cancer therapeutic modalities include surgery, chemotherapy and radiotherapy. Although these treatment regimens have played a significant role in effectively inhibiting cancer, their associated morbidity reduces the overall quality of life. Thus, researchers are striving to identify any alternate therapeutic approach capable of inhibiting cancer without eliciting the added morbidity. Among the alternate cancer therapeutics being researched, much importance is being given to the use of plants due to the presence of a wide variety of anti-carcinogenic compounds. _Tinospora cordifolia (Tc)_ is one such plant and has shown to exhibit anti-carcinogenic properties. The present review aimed to systematically analyze published data on the effect of _Tinospora cordifolia_-derived phytocomponents on cancer. PubMed, Scopus, Web of Science, Embase and Cochrane library were searched using the keywords _Tinospora cordifolia_; anticancer; phytocomponents until March 20, 2019. In vivo and in vitro original studies in the English language were included. Of the 342 articles identified, only 25 articles met the selection criteria and were included in the review. Significant anti-carcinogenic properties were exhibited by _Tinospora cordifolia_-derived phytocompounds including palmative, berberine, new clerodane funanditerene glycoside, arabinogalactan, phenolic compounds and epoxy cleodane diterpene. No significant side effects have been elicited with its use. Based on the data from the included studies, _Tinospora cordifolia_ could be a natural therapeutic agent for cancer, provided its anti-carcinogenic properties can be elicited consistently at a large scale in clinical trials.  
cancer; phytocomponents; _Tinospora cordifolia_
# Design and Implementation of Inverse Kinematics and Motion Monitoring System for 6 DoF Platform
## 4 Monitoring System Design
### Laser Displacement and Angular Meters  
Three three-section brackets (Figure 7a) were used to adjust the height of the x, y, and z axes of the measurement platform, while three measurement reference plates (Figure 7b) were magnetically adhered to the x, y, and z axes of the six-axis platform. The positions of the brackets or reference plates were adjusted for a red laser dot fired out of the laser displacement meter to hit near the center of the reference plate. Because the six-axis platform had a hexagonal structure design when viewed from above, the x axis was adjusted for the measurement reference plate to magnetically adhere to the x direction, and the x direction displacement was immediately read when the laser hit the measurement reference plate. The y axis was not perpendicular to any plane. Therefore, the reference plate was installed on the side of the six-axis platform, and y direction displacement was obtained through angle correction. The correction method is shown in Figure 7c.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 5 Resource Optimization Process
### Server Interference  
To obtain the WCRT of all messages we need to extend Equation (4), to consider the server interference, represented by the intermediate term (the summation of _Interf_P_) and the error signaling (the summation of _Err_S_) in Equation (16). The first term depends on the errors that occurred in the previous SW and on the replication level (as explained at the end of the next section) and the second one depends on the errors in the current SW. The response time must be calculated for each error scenario and server interference, accounted by the \(l\) variable.  
\[R_{i}^{n+1}(l)=C_{i}^{E}+\sum_{j=1}^{EC_{number}(R_{i}^{n}(l))}(Interf_{-}P(l,\,j )\times C_{MAX}^{E}+Err_{-}S(l,\,j)\times C_{error}^{E})+\sum_{k=1}^{i-1} \left\lceil\frac{R_{i}^{n}(l)}{T_{k}}\right\rceil C_{k}^{E} \tag{16}\]  
The \(EC_{number}(R_{i}^{n}(l))\) variable, obtained with Equation (17), is the number of ECs that need to be analyzed in iteration \(n\) for message \(i\).  
\[EC_{number}(R_{i}^{n}(l))=\left\lceil\frac{R_{i}^{n}(l)}{LEC}\right\rceil \tag{17}\]
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 2 Systematic Review Methodology
### Search Approach  
This systematic review of the literature covers publications from 2000 to 2022, utilizing databases such as Scopus, IEEE Xplore, and PubMed. To accommodate variations in methodological terminology, a flexible search string is employed. By amalgamating synonyms for the keywords \"human\", \"posture\", \"estimation\", and \"method\" a comprehensive search string is constructed as follows: TITLE-ABS-KEY (human AND (posture OR pose) AND Sitting AND (estimation OR classification OR recognition)). Additional key terms are incorporated: \"video\", \"vision\", and \"Kinect\". While irrelevant subareas are excluded following random sampling. Language restriction is applied, limiting results to English-language publications.
# Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
## 2 Materials and Methods
### Proposed Method
#### 2.2.3 Computational Complexity Analysis  
The computational complexity of the DAS method, the IAA method [1; 21; 42], the traditional LASSO method, and the proposed method are compared, as shown in Table 1.  
From Table 1, it is seen that the proposed methods reduce the computational complexity by an order of magnitude compared with the IAA and LASSO method. In addition, the complexity of the proposed method can reach the order of magnitude of the RLS method (\(O(K^{2})\)). Table 2 shows the computational complexity for the methods with \(K=4M_{r}M_{t}\) and varying \(M_{r}\times M_{t}\). Typically, when \(M_{r}\times M_{t}=1000\), the proposed method reduces the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **Calculation Times of** & **Computational Complexity** \\  & **Multiplication and Division** & **Computational Complexity** \\ \hline DAS & \(K\log_{2}K\) & \(O(K\log_{2}K)\) \\ IAA [1] (per iteration) & (per iteration) & \(O\Big{(}(M_{r}M_{t})^{3}\Big{)}\) \\ LASSO [43] & \(K^{3}+M_{r}M_{t}K^{2}\) & \(O(K^{3})\) \\ Proposed method & \((Q+1)K^{2}+\Big{(}9Q+{M_{r}}^{2}+M_{r}\Big{)}K\) & \(O\big{(}(Q+1)K^{2}\big{)}\) \\ (per recursion) & \((Q+1)K^{2}+\Big{(}9Q+{M_{r}}^{2}+M_{r}\Big{)}K\) & \(O\big{(}(Q+1)K^{2}\big{)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity.  
Figure 3: Flowchart of the online sub–aperture update.  
required computational time by 194 times and 37 times, compared with the LASSO and IAA, respectively.
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 3 Detailed Description of the Proposed Method
### System Joint Calibration
According to Formula (7):  
\[ \begin{bmatrix}-\mathbf{P}&u_{1}\mathbf{P}\\ -\mathbf{P}&v_{1}\mathbf{P}\end{bmatrix}\begin{bmatrix}\mathbf{H}_{1}\\ \mathbf{H}_{2}\\ \mathbf{H}_{3}\end{bmatrix} =\mathbf{0} \tag{8}\]  
where \(\mathbf{H}_{1}=[a_{11},a_{12},a_{13},a_{14}]\), \(\mathbf{H}_{2}=[a_{21},a_{22},a_{23},a_{24}]\), \(\mathbf{H}_{3}=[a_{31},a_{32},a_{33},a_{34}]\), \(\mathbf{P}=[X_{\text{p}},Y_{\text{p}},Z_{\text{p}},1]\). Formula (9) is then acquired:  
\[ \begin{bmatrix}-X_{\text{p}}&-Y_{\text{p}}&-Z_{\text{p}}&-1&0&0&0&0&u_{1}X_{ \text{p}}&u_{1}Y_{\text{p}}&u_{1}Z_{\text{p}}&u_{1}\\ 0&0&0&-X_{\text{p}}&-Y_{\text{p}}&-Z_{\text{p}}&-1&v_{1}X_{\text{p}}&v_{1}Y_{ \text{p}}&v_{1}Z_{\text{p}}&v_{1}\end{bmatrix} \cdot\mathbf{B}^{\text{T}}=\mathbf{0} \tag{9}\]  
\[\text{where }\mathbf{B}= \begin{bmatrix}a_{11}&a_{12}&a_{13}&a_{14}&a_{21}&a_{22}&a_{23 }&a_{24}&a_{31}&a_{32}&a_{33}&a_{34}\end{bmatrix} .\]  
Since there are twelve variables in matrix A, at least six pairs of matching points are required to achieve the linear solution to all variables. The objective function is constructed based on the sum of the reprojection errors of the three-dimensional circle center coordinates, which can be expressed as:  
\[E(\mathbf{R}_{\text{pc}},\mathbf{T}_{\text{pc}})=\text{argmin}\sum_{i=1}^{n}\|\mathbf{z}_{ 1}^{i}-\frac{1}{s_{i}}\mathbf{R}_{\text{kl}}(\mathbf{R}_{\text{pc}}\mathbf{P}_{i}+\mathbf{T}_ {\text{pc}})\|_{2}^{2} \tag{10}\]  
where \(\mathbf{z}_{1}^{i}=[u_{1}^{i},v_{1}^{i}]\), \(s_{i}\) is the homogeneous coefficient in the process of coordinate transformation, i.e., the circular center depth, \(Z_{\text{c}}\). \(\|\cdot\|_{2}\) represents the L2 norm of the matrix. The Levenberg-Marquardt (LM) algorithm [25] is used to optimize the reprojection function to minimize the error function and obtain the optimal solution of the joint calibration.
# Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review
## 3 Techniques
### Machine Learning
#### 3.1.1 Supervised Learning
Nevertheless, the accuracy of the prediction systems can be improved through careful study and implementing different algorithmic features. Thus, preprocessing techniques have been applied together with classification algorithms (SVM, DT and NB) to improve prediction results [19]. A different focus on students' performance can be found in [20], where the main characteristics for observing performance are deduced from students' daily interaction events with certain modules of Moodle. For this purpose, RF and SVM developed the prediction models, and the best results were obtained by RF. With a similar focus, other SL algorithms analyzed datasets directly from websites to evaluate students' performance [21]. Also software platforms in e-learning made it possible to analyze and take advantage of the results of DM and ML algorithms in order to make decisions and justify educational approaches [22]. A data analysis approach to determine next trimester's courses was proposed in [23]. Here, different ML techniques predicted students' performance, which was used to build transition probabilities of a Markov Decision Process (MDP). The Jacobian Matrix-Based Learning Machine (JMLM) was used to analyze the students' learning performance [24], and AdaBoost assembly algorithm was proposed to predict student classification and showed best performance against techniques as DT, ANN, and SVM [25]. Adaboost was also the best meta-decision classifier for predicting student results [26]. SL algorithms are useful for a wide variety of predicting purposes. Predicting whether a student can successfully obtain a certificate was tackled by LR, SVM, NB, KNN, and BN [27]. Predicting graduation grade point averages was tackled by ANN, SVM, and Extreme Learning Machine (ELM) [28], where SVM gave the highest accurate prediction (97.98%). Student performance in the previous semester along with test grades from the current semester were used as input attributes for a series of algorithms (SVM, NB, RF and Gradient Boosting) that predict student grades [29]. Finally, other SL approaches were satisfactorily applied for predicting students' performance. Bayesian Additive Regressive Trees (BART) was used to predict the final grade of students in the sixth week [30]. A model based on SVM weekly predicted the probability of each student belonging to one of these three types: high, medium or low performance [31]. Latent Dirichlet Allocation (LDA) predicted student grades according to how the students described their learning situations after each lesson [32].
# Experimental and Numerical Analyses of Timber-Steel Footbridges
## 4 Laboratory Testing of the Structural Detail of Interest  
### Description of Laboratory Specimens  
Due to the unsatisfactory results of the experimental in situ measurements and numerical analysis of the bridge superstructure presented in Table 2, additional laboratory tests on specially designed and produced test specimens were carried out alongside more sophisticated numerical analyses. To confirm the actual behaviour of the steel-to-timber connection, nine laboratory specimens were designed and manufactured. The basic geometrical parameters of the specimens are presented in Figure 8.  
Every specimen was manufactured using two 200 \(\times\) 500 \(\times\) 500 mm blocks made of glued laminated timber with strength class GL24h, which modelled the main timber girders. A hot-rolled steel beam made of IPE 140, steel grade S235, approximating a cross-girder, was connected to the timber blocks at their bottom edges. The timber blocks were simply supported to allow for movement in the direction of the cross-girder. In order to investigate the steel-to-timber connection only, the rotation of the timber blocks was eliminated using L60 \(\times\) 60 \(\times\) 6 mm hot-rolled angles fixed at all corners of the blocks. The upper angles performed the function of spacers; the angles in the lower part acted as tie rods.  
The connections between the steel cross-girders and the timber blocks were constructed using 10 mm thick end plates and steel bolts of strength class 5.6 with a diameter of 12 mm.  
Three variants of bolt arrangements were designed for the steel-cross-girder-to-timber-block connections; they were manufactured according to the joint stiffnesses designated for variants \"A\", \"B\", and \"C\". Individual specimen variants differed in terms of the number of bolts situated on the cross-girder's upper flange and the height of the end plate, depending on the height of its stiffener. Therefore, three specimens were manufactured for each of the previously mentioned variants. During laboratory tests, the cross-girder's stress-strain response to transverse acting load was observed by means of discrete forces.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Value Source**} & \multicolumn{3}{c}{**Under the Outer Stringer**} & \multicolumn{3}{c}{**At Joint to Main Girder**} & \multicolumn{3}{c}{**In the Middle**} \\  & **T9** & **T10** & **T11** & **T12** & **T13** & **T14** & **T15** & **T16** & **T17** & **T18** & **Deflection** \\ \hline Measurement & \(-11.0\) & \(-17.5\) & \(18.5\) & \(14.4\) & \(6.9\) & \(-0.4\) & \(-1.0\) & \(-0.4\) & \(-42.1\) & \(42.3\) & \(2.8\) \\ Numerical calculation & \(-14.8\) & \(-13.5\) & \(18.2\) & \(19.1\) & \(5.1\) & \(-8.7\) & \(5.5\) & \
## 3 Results and Discussion
### Colour Analysis  
Figure 2 shows the results of colour analysis of WFSP and WFP. It was observed that WFSP and WFP showed the maximum L* (lightness) value of 81.29 \(\pm\) 0.41 and 76.85 \(\pm\) 0.08, respectively. Similarly, the b* (yellowness) value of 12.48 \(\pm\) 0.08 and 17.84 \(\pm\) 0.05 was noted for WFSP and WFP, followed by an a* (redness) value of 1.47 \(\pm\) 0.08 and 1.66 \(\pm\) 0.38,  
Figure 1: The representation of (**a**) proximate composition and (**b**) total energy of white-fleshed sweet potato (WFSP) and white-fleshed potato (WFP). NFE: Nitrogen-free extract.  
respectively. The results showed that the lightness (L*) value was prominent in both samples (i.e., WFSP and WFP), while WFSP presented the best results compared to WFP regarding lightness. In contrast, WFP showed the highest yellowness colour compared to WFSP. Our results are in agreement with the findings of [PERSON] et al. (2018). They experimented on twenty-five different varieties of Chinese-cultivated sweet potatoes and revealed that Xinxiang, Mixuan No.1, Nongdabai, Zheshu 75 and Jishu No. 7 varieties showed a flesh colour of yellow-white, while Qinshu No. 7, XS 15-11-15, Yanshu 25, Sushu 16, Xiangshu 2, Yizi 138, Longshu 515 and Zheshu 13 varieties showed a yellow-orange colour, respectively.
###### Abstract  
The Future Railway Mobile Communication System (FRMCS) has emerged as a worldwide standard for railway communication. This technology enables the operational efficiency and safety of railways to be improved by providing mission critical communications, machine-type communication for the railway system on board, in addition to trackside telemetry and broadband connectivity for passengers. Different equipment types, users, and functional identities can be involved in communication, and each of them is uniquely identified. Identity management is an important part of the security functions provided by the FRMCS system. This paper presents a service-oriented approach to identity management functionality, enabling service composition for railway applications and service virtualization. This paper studies functionality for the initial registration and subsequent deregistration of railway devices, users, and their functional identities, in addition to the transfer of the registered identities between different FRMCS serving areas while the train moves. Two FRMCS services that follow the principles of representational state transfer architecture are proposed. Services' functionality is illustrated by use cases, data types, and application programming interfaces that enable services to be interacted with. Identity registration status models are developed, formally described, and mathematically verified. Discussion of the applicability of the proposed services for the implementation of FRMCS security and safety functions is provided. The presented service-oriented approach features a satisfactory level of flexibility and versatility.  
Keywords:railway equipment: user: functional identity: registration management: service-oriented architecture: application programming interfaces; state machines +
Footnote †: journal: Applied