esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Dual Beam Cascaded Schemes for 346 GHz Harmonic-Enhanced TWTs
## 4 Pre-Amplified HHTWT
### SWS Design Methodology of PA-HHTWT  
For the PA-HHTWT, the pre-amplification section plays a role in amplifying the fundamental wave to several-watts level. The pre-amplification section utilizes the same structural parameters with modulation section. The length of pre-amplification section is set as 51 mm. The power versus axial length curve and the output signal are shown in Figures 18 and 19, respectively. The input power is set at 200 mW and then 4.5-W output power is obtained.  
The length of the modulation section is chosen to make the energy modulate the beam as deeply as possible, rather than amplify the EM wave. Figure 20 depicts the input and output signal of the modulation section at 86.5 GHz. When the length of the modulation section is 20.48 mm, the output power is barely higher than the input power, because the energy of electron beam is almost not changed within the modulation process. The behavior mentioned above is also validated in Figure 21. The electron beam is modulated more deeply than HHTWT and CE-HHTWT. Meanwhile, there is no obvious nonlinear characteristic.  
Figure 17: The SWS of dual beam PA-HHTWT.  
Figure 18: Power curve of the pre-amplification section.  
The method to determine the length of the drift tube and the radiation section is similar to that in the HHTWT and CE-HHTWT. Eventually, the length of the drift tube and the radiation section are chosen at 4 mm and 10.2 mm, respectively. For convenience, the parameters of PA-HHTWT are shown in Table 4.  
Figure 19: Output signal of pre-amplification section.  
Figure 20: Input (red) and output (black) power of the modulation section at 86.5 GHz.
## 5 Discussion
### Two Main Directions of Anisotropy Varying with Depth
#### 5.2.3 Electrical Anisotropy Direction Related to the Plates Motion  
In others tectonic contexts such as the Asal rift, geoelectrical strike and electrical anisotropy direction were correlated and electrical anisotropy direction was interpreted to be linked directly or indirectly to plate motion direction, as discussed next. For instance, [50] found consistent correlation between shear wave splitting (SKS) direction and geoelectric strike direction in the same direction as the plate motion direction, and interpreted these observations as an upper lithosphere that contain thin anisotropic layers related to the paleo-subduction and a uniform and ductile anisotropic lower lithosphere. Moreover, consistent phase splitting and low magnitude induction vectors with a geoelectric strike direction oblique to the present day Indian plate-movement were associated with an anisotropic mantle [51]. The discrepancy between the geoelectric strike and the actual Indian plate-movement direction was interpreted by the effects due to the heterogenous and complex convection of the mantle or a resistance to mantle flow by the actual stress field of plate motion [51]. In active extensional at the Great Basin province of north-central Nevada, geoelectrical strike direction parallel to the initial direction of extension was found with small values of magnitude induction vectors that was interpreted as consequence of electrical anisotropy direction [52]. Furthermore, high conductivity direction which is consistent with the shear wave splitting was found to be close to the direction of plate motion of the western Europe [53]. Seismic activity and surface deformation produced by injection of magma into dykes are often occurring during rifting episodes where the velocity of plate opening is mainly controlled by the magmatic intrusion [54]. And intrusive activity mainly controls the faults system activity where magma plumbing system is key of primary control on the rifting process [55].
## 3 Results
### Block Diagram of the Simulator
#### 3.1.3 Configuration Trajectory Generator  
This block computes the desired positions and velocities of the leg extensions. The position is shown in the third row of Figures 7 and 8. Since the robot home position corresponds to contracted legs or zero effective extension, which does not belong to the virtual spherical constraint defined to perform satellite tracking path, then configuration space coordinates trajectories are computed in three different phases:  
1. Initialization Phase: This phase consists of reaching initial pose based on inverse task kinematics of (18) for open-loop (27) or closed-loop (28). This pose corresponds to a point at the virtual spherical constraint from the secure home position of the system. Point to point approximate path is smoothed with a second order filter tuned for a settling time of 30 s.
2. Point-to-point Phase: System is pointing in the vicinity of initial horizon appearance of the satellite, This phase is set to start at simulation time of \(t=60\) s.
3. Go-to-home Phase: With that second order filter, and for 30 s, it tracks via points to the home position.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 2 Methodology
### Orbit Model  
The orbit and the following attitude, irradiance, and thermal models generalize the previous work presented in [17]. An inertial Cartesian coordinate system called Earth Reference Frame (ERF) is centered on the Earth, and a non-inertial reference system named Orbit Reference Frame (ORF) is at the satellite's center of mass. The y-component of the ERF points to the Sun (\(\vec{S}=[0,1,0]\)), the z-component to the celestial north pole, and the x-component is the cross product between them. The vector \(\vec{R}_{sat}\) connects ORF to the origin of ERF, and defines the satellite's position as a function of the angles in Figure 1.  
The rotation matrix in Equation (1) is used to rotate a vector over one arbitrary axis.  
\[[G]=\begin{bmatrix}b_{11}&b_{12}&b_{13}\\ b_{21}&b_{22}&b_{23}\\ b_{31}&b_{32}&b_{33}\end{bmatrix} \tag{1}\]  
where:  
\[b_{11} =\cos(\theta)+u_{1}^{2}(1-\cos(\theta)) \tag{2}\] \[b_{12} =u_{1}u_{2}(1-\cos(\theta))-u_{3}\sin(\theta)\] (3) \[b_{13} =u_{1}u_{3}(1-\cos(\theta))+u_{2}\sin(\theta)\] (4) \[b_{21} =u_{2}u_{1}(1-\cos(\theta))+u_{3}\sin(\theta)\] (5) \[b_{22} =\cos(\theta)+u_{2}^{2}(1-\cos(\theta))\] (6) \[b_{23} =u_{2}u_{3}(1-\cos(\theta))-u_{1}\sin(\theta)\] (7) \[b_{31} =u_{3}u_{1}(1-\cos(\theta))-u_{2}\sin(\theta)\] (8) \[b_{32} =u_{3}u_{2}(1-\cos(\theta))+u_{1}\sin(\theta)\] (9) \[b_{33} =\cos(\theta)+u_{3}^{2}(1-\cos(\theta)) \tag{10}\]  
\(\theta\) is the angle of rotation around the unitary vector with components \(\vec{u}=[u_{1},u_{2},u_{3}]\). For a satellite with spin, \(\theta=\omega t\), where \(\omega\) and \(t\) are the angular speed and time, respectively. The satellite's position (\(\vec{R}_{\text{sat}}\)) results from the combination of different rotation matrices in sequence.  
Two matrices will be used here, representing the orbit inclination (\([M_{i}]\)) and the translational progress in the orbital plane (\([M_{j}]\)), respectively, as in the next equation:  
\[\vec{R}_{\text{sat}}=\begin{bmatrix}M_{j}\end{bmatrix}[M_{i}]\vec{R}_{\text{ sat,0}} \tag{11}\]  
The subscript 0 represents the first satellite's position, given by \(\vec{R}_{\text{sat,0}}=[0,h+R_{e},0]\) in the ERF, where \(h\) is the altitude and \(R_{e}\) the Earth's radius (6378 km). The unit vector inside the matrix \([M_{j}]\) must be perpendicular or co-linear to the solar vector \(\vec{S}\) to reproduce orbits with or without eclipse, respectively. The angle to mimic the translation of the satellite around the Earth is given by:  
\[\theta_{j}(t)=\omega_{e}t=\sqrt{\frac{m_{e}K}{\left(R_{e}+h\right)^{3}}}t, \tag{12}\]  
Figure 1: Systems of reference.  
From this equation, valid for circular orbit, \(\omega_{e}\) is the satellite's angular speed around the Earth, \(K\) is the gravitational constant (\(6.674\times 10^{-11}\) m\({}^{3}\)/kg s\({}^{2}\)), and \(m_{e}\) is the Earth's mass (\(5.972\times 10^{24}\) kg) [26].
###### Abstract  
The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average.  
VANET; VANET routing protocol; traffic-aware; ACO algorithm; multiple junction 2024242913390/s24092913
# Calibration Method of Projectional Geometry for X-ray C-arm Fluoroscopy Using Sinogram Data
## 2 Materials Furthermore, Methods
### Detection of Fiducial Markers  
The sinogram is in the form of stacked data of the projection images at various angles. The fiducial markers in the images were detected at each angle, and the detected markers were divided into upper and lower layer markers. Since the X-ray attenuation coefficient in the fiducial marker is much higher than in the plastic material of the phantom, the fiducial markers could be distinguished using the threshold-based segmentation method [12].  
Figure 3: Coordinate system used in the calibration; the red axes show the world coordinate system, and the blue axes represent the orientation of the phantom.  
Therefore, by applying the threshold value to the CT sinogram data, the markers could be segmented, and the layer to which the fiducials belonged could be classified. Then the centers of markers were determined by successively applying the Hough circle transformation [13] and findContours function [14] of the OpenCV library. Since the upper and lower fiducial markers have different heights and radii, they could be divided into two groups based on the sizes of the detected markers.  
Since some markers (close to each other or superimposed) could not be detected well, a detection algorithm that considers these cases is developed. The detected markers were divided into four sections--top, bottom, left, and right--and a rectangle was formed using the detected markers. Four straight lines were drawn connecting both corner points for each divided section. The corners of the detection rectangle were formed by the four intersection points of the four lines. Consequently, even if a few markers are missed during the detection process, a consistent rectangle can be generated using the detected markers.  
Calibration is performed as shown in Figure 4 based on the position of the marker detected in the sinogram data.
## 4 Evaluation Scenario
### Simulation Models  
OMNeT++, an open-source, modular, component-based C++ simulation library and framework [11], was used to implement the simulation models of the unslotted CSMA/CA scheme of IEEE 802.15.4 and variants of the eLPRT protocol. Each simulation run ended after the base station (PAN coordinator) received 100,000 data packets from the nodes.  
The Gilbert-Elliot model [PERSON] and Elliot (1964) was used to model the occurrence of burst errors, which are typical in wireless channels. The model considers a channel alternation between a good state with low bit error rate (\(BER_{good}\)) and a bad state, with high bit error rate (\(BER_{bad}\)), with mean dwelling time \(T_{good}\) for the good state and \(T_{bad}\) for the bad state. The values of the parameters used in simulations, unless otherwise stated, are presented in Table 2. The chosen values are intended to model fast fading, which typically occurs on timescales of milliseconds to tens of milliseconds D.  
The channel state for the different nodes was made symmetrical and independent, which means that at any moment the channel for some nodes can be in the bad state while for others it can be in the good state.
# Supply-Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model
## 3 Modeling of the SDM Problem Based on the Hypernetwork Model
### Problem Description  
In CMfg platforms, dynamic SDM poses a critical challenge that directly impacts the efficient utilisation of resources and the effective allocation of services. Traditional initial matching schemes often struggle to maintain long-term effectiveness due to continuous fluctuations in market demand and resource supply. To address these challenges, this paper adopts a matching process that integrates the initial matching stage with the dynamic adjustment stage to ensure continuous optimisation of supply-demand relationships in a dynamic environment, as illustrated in Figure 1.  
The initial matching stage establishes initial supply-demand relationships to complete allocating services and tasks. Meanwhile, the dynamic adjustment stage continuously optimises the matching scheme by monitoring and responding to environmental changes in real time, adapting to the supply and demand fluctuations. This matching process not only offers a solution to current dynamic SDM issues but also lays the foundation for subsequent research on the impact of network resilience on matching effectiveness.  
In traditional SDM processes, the lack of capturing latent connections and interaction patterns between participants often limits matching efficiency, affects resource allocation,  
Figure 1: Dynamic matching method basic step.  
and reduces the system's ability to adjust to dynamic changes promptly. Relying solely on traditional methods may overlook the significant impact of network characteristics on the matching process. Supply-demand relationships are typically embedded in complex network structures, and insufficient consideration of these structures can lead to incomplete information and inaccurate matching results.  
This study aims to improve resource allocation and adjustment mechanisms in dynamic SDM processes by leveraging network characteristics and proposes a network-based dynamic-matching model. First, a hypernetwork model is established to clarify the relationships between manufacturing platform participants. Additionally, the clustering coefficient of network nodes is used to assess the degree of connection, while load balancing is employed to optimise resource allocation. Most importantly, network features empower the matching system with robust dynamic adjustment capabilities, updating network relationships based on real-time environments to achieve more stable matching outcomes, improve matching efficiency, and enhance system resilience.  
In summary, integrating network characteristics into SDM can significantly improve matching performance, efficiency, and dynamic adaptability. Therefore, adopting this innovative matching strategy in CMfg platforms is crucial for managing complex supply-demand relationships in dynamic environments.
## 2 Materials and Methods
### LGE Extent Reference and Variability  
LGE extents (LGE\({}_{3\sigma,\text{k,i,e}}\) and LGE\({}_{\text{FWHM,k,i,e}}\)) were calculated for each slice (k), repetition (i), and specialist (e), separately for both mathematical quantification methods. LGE\({}_{3\sigma,\text{k,i,e}}\) was calculated by using ROI\({}_{\text{myoc,k,i,e}}\), ROI\({}_{\text{ref}3\sigma,\text{k,i,e}}\), and ROI\({}_{\text{excl3\sigma,\text{k,i,e}}}\), and LGE\({}_{\text{FWHM,k,i,e}}\) was calculated by using ROI\({}_{\text{myoc,k,i,e}}\), ROI\({}_{\text{ref}3\sigma,\text{k,i,e}}\), and ROI\({}_{\text{excl}\text{FWHM,k,i,e}}\).  
One of the goals of the study was to evaluate the contribution of a single ROI delineation imprecision to the LGE extent, which required keeping the other two ROIs unchanged. To achieve this, we determined a reference LGE extent value as the average of all possible combinations and then compared it with the LGE extent influenced by individual ROIs.  
To construct the reference LGE extent, we combined the results of three repetitions of the same specialists (e) for the same slice (k). For example, ROI\({}_{\text{myoc}}\) of the first repetition (ROI\({}_{\text{myoc,k,1,e}}\)) was combined with all three repetitions of ROI\({}_{\text{ref}3\sigma,\text{k,1-3,e}}\) and all three repetitions of ROI\({}_{\text{excl3\sigma,\text{k,1-3,e}}}\), which gave us 9 LGE\({}_{3\sigma,\text{k,1,e}}\) values, and 27 altogether for all three repetitions of ROI\({}_{\text{myoc3\sigma,\text{k,1-3,e}}}\) itself. The same was reiterated for the remaining two ROIs, giving us 81 LGE extent values per specialist per slice and separately for the 3\(\sigma\) and FWHM methods.  
The reference LGE extent was calculated as the average of 81 values obtained per specialist, per slice, and per quantification method. Then, a variable LGE extent for a given ROI, repetition, slice, and specialist was calculated as the average of the nine  
Figure 3: Workflow diagram illustrating the image processing procedure where the ROI matrices used for the pathology calculations were obtained by multiplying the binary mask by the original grayscale image. An illustrative example of ROI\({}_{\text{myoc}}\) is provided. Abbreviations as in Figures 1 and 2.  
values obtained previously; for example, nine LGE extents assigned to ROI\({}_{\text{myoc3}\sigma,\text{k,1,e}}\) were averaged to obtain \(\text{LGE}_{\text{myoc3}\sigma,\text{k,1,e}}\).  
The \(\text{LGE}_{\text{3}\sigma}\) extent variability for ROI\({}_{\text{myoc3}\sigma}\), ROI\({}_{\text{freq3}\sigma}\), and ROI\({}_{\text{excl3}\sigma}\) was calculated as the mean absolute deviation between \(\text{LGE}_{\text{myoc3}\sigma,\text{k,1-3}\sigma}\), \(\text{LGE}_{\text{freq3}\sigma,\text{k,1-3}\sigma}\), and \(\text{LGE}_{\text{excl3}\sigma,\text{k,1-3}\sigma}\), and the corresponding reference values. Finally, we calculated the total LGE extent variability for each ROI by averaging the variability results across all observers and slices. We calculated the corresponding standard errors by using the general formula for the propagation of uncertainties [15].
# An Overview of Bioactive Flavonoids from _Citrus_ Fruits
## 6 _Citrus_ Flavonoids and Chronic Diseases
### Cardiovascular Effects  
Cardiovascular disease is a general term for conditions affecting the heart and blood circulation, including coronary artery diseases, such as angina and myocardial infarction. This may be caused by high blood pressure, diabetes, obesity, high blood cholesterol... etc. Diabetes leads to increased inflammation, and oxidative stress also deteriorates endothelial cell dysfunction. Flavonoid-rich foods such as _Citrus_ fruit can promote cardioprotector effects mainly derived from their antioxidant and anti-inflammatory activities [77]. Hesperidin exerts an anti-obesity activity and hypoglycemic activity by regulating glucose metabolism [78]. Didymin inhibits the release of various inflammatory cytokines and chemokines from high glucose-treated human umbilical vein endothelial cells [79]. Studies on mice showed potential vasorelaxant effects of hesperetin, hesperidin, naringenin and naringin by the inhibition of different phosphodiesterase isoenzymes [80; 81]. Another effect of flavonoids on the vascular system is the inhibition of platelet aggregation and reducing clot formation [63]. In another study on mice fed with a cholesterol-rich diet, naringenin showed a reduction in plasma cholesterol and hepatic triacylglycerols concentrations [82].
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### SAR Sensors Used for Detecting Snow  
Figure 2 presents an overview of commonly used spaceborne SAR sensors including their operation time span and band information. C-band SAR sensors stands out due to the longest available time series of continuous observations since 1992, which mainly thanks to the ESA's and Canadian Space Agency (CSA)'s missions, featuring ESA's pioneering ERS-1/2, Envisat, CSA's Radarsat-1/2, and the ESA milestone missions of Sentinel-1. The pioneering Active Microwave Instrumentation (AMI) C-band instrument equipped on oceanographic research-aimed ERS-1 satellite starts the era of C-band SAR for the following decades, although it was only aimed at aiding the scatterometer for deriving ocean wind and wave information [123]. Together with the subsequent ESA's C-band missions, there have been a seamless time series of C-band imagery for more than 20 years. This time series became the most commonly used dataset for analyzing snow cover from SAR as the characteristics of C-band SAR is suitable for snow detection and the interchangeability of each C-band SAR sensor is guaranteed. Accordingly, the majority of published snow cover studies relying on SAR data are based on C-band sensors, with X- and L-band following on second and third position, respectively (see Figure 3 for a detailed overview).  
Figure 2: The operating periods of available satellites equipped with SAR sensors and their band as well as revisit time configurations (number in brackets referring to the revisit time in days). L-, C-, and X-band SAR refers to frequency of 1–2 GHz, 4–8 GHz, and 8–12 GHz; wavelength of 30–15 cm, 7.5–3.75 cm, and 3.75–2.5 cm, respectively [62].  
Due to the longer wavelength of L-band SAR and the subsequently deeper penetration of the L-band signal into the snowpack, the snowpack remains nearly invisible in the L-band data [124]. Therefore, studies about the implementation of L-band data to analyze snow cover properties are scarce although it has a longer history than X-band SAR. X-band SAR has been facilitated more often than L-band, which is due to the higher sensitivity of the X-band signal to the snowpack even when compared to C-band [115; 125]. Moreover, multi-satellite constellations are available such as COSMO-SkyMed operated by the Italian Space Agency (ASI) and the TerraSAR-TanDEM-X twin satellite employed by Airbus Defense and Space and the German Aerospace Center (DLR).
## 5 Experiment Verification
### Experiment Equipment
#### 5.1.1 The Computer System:  
1. One computer equipped with Signal Express Software to compute the captured signal.
2. Signal capture software:  
Software, in combination with the data capture system designed by National instruments, provides real-time record and capture of signals generated by the system. Settings can be adjusted for capture speed, temperature range, capture time, sensor type, cold point compensation method, capture channel, and record format.