esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Finite Element Simulation
#### 2.1.2 Moving Heat Source  
The heat source is simulated as a 2D mesh with a \"radiative heating Simulation Object\" in order to mimic the heating region of a line heater. The size of the heated region is derived from the heating region of a commercially available line heater: Mehler Engineering Services [16]. The heated region has a width of 10 mm at a height of 15 mm above the sample to be inspected, as measured in [17].  
There exist multiple approaches to simulate Dynamic Line Scan Thermography inspections. One method relies on the movement of the sample underneath the heat source. The opposite approach is to move the heat source and thermal camera over the sample to be inspected. This second method is used in the research as it is less computing intensive.The 2D mesh representing the heating region has less mesh elements than the meshed flat bottom hole plate. The movement is accomplished by using a \"Solid Motion Simulation Object\" on the 2D mesh in the longitudinal direction of the flat bottom hole plate.  
\begin{table}
\begin{tabular}{c c} \hline Radiation & Simple radiation to environment: GBVF 1 (Gray Body View Factor) \\ Convection & Convection to environment: Inclined Plate, Top, Multiplier 1 \\ Thermal Diffusivity PVC & 0.08 mm\({}^{2}\)/s \\ \hline \end{tabular}
\end{table}
Table 1: Settings used in finite element simulation.  
Figure 2: Visualisation of the finite element simulation. (**a**) A 3D representation from the top in order to clearly see the flat bottom hole plate and the 2D mesh for the moving heat source. (**b**) Bottom view of the components. The circular void is placed in the middle of the flat bottom hole plate.
## Materials and Methods
### Mechanical Testing Setup  
The radial strengths of the three stent types shown in Figure 2 were evaluated by compressing them under load after balloon expansion. The stents were expected to exhibit sufficient radial force and minimal radial recoil when deployed in animal models. In our present study, we fabricated Zn stents with the three different designs (rhombus, U, and Omega) shown in Figure 2a-c. The lengths of each unexpanded stent design were 19.5, 21 mm, and 25 mm, respectively, for rhombus, U, and Omega stents. These stents were crimped onto a Percutaneous Transluminal Coronary Angioplasty (PTCA) balloon catheter delivery system. Once expanded, the radial force of each was evaluated by performing a compression test using an instrument called a UniVert (Cell Scale Biomaterial Testing, Canada) with a 10N load cell. The stents were compressed between two flat plates parallel to each other. This tool recorded the displacement and the resistive force by compressing the stent longitudinally. Resistance force generated by the stents was acquired in loading and unloading modes during the experiment.  
For the compression test, stents with rhombus, Omega, and U designs were dilated with identical balloon catheters to their maximum outer diameters from the initial diameter of 4 mm each. Dilated stents were left for a few minutes to allow plastic deformation and the measured outer diameters before compression (\(d_{i}\)) were 7.4, 5.68, and 6.203 mm, respectively. The system's zero measurements were set to the diameter (\(d_{i}\)) of the stents. Step reduction was set to 0.04 mm and then the plates of the tool were allowed to reduce the gap between them, reaching a final position corresponding to 50% of the initial stent diameter (50% (\(d_{i}\))). Figure 4 shows the compressive loading of a Zn stent. The resistive force exerted by the expanded stent was recorded during the loading and unloading cycles of the experiment. A hysteresis curve was obtained illustrating the crush resistance of the pure Zn stents. The resistive force was calculated using the formula below.  
Radial force \(\left(\frac{\text{N}}{\text{mm}}\right)=\frac{\text{The maximum resistive force exerted }(\text{N})}{\text{The total length of the stent }(\text{mm})}\)
# Analysis of Fatigue Performance of Spot-Welded Steel T-Profiles Under Cyclic Torsional Loading
## 3 Results and Discussion
### Influence of the Diameter of Spot Welds  
One of the variables that could have an impact on the assembly's torsional performance is the spot weld's diameter. This investigation looked at three different scenarios in which \(D\) was adjusted from 4.5 mm to 6.5 mm with a 1.0 mm increase and \(t\) was maintained at 1.5 mm. The corresponding \(N_{f}\) and crack propagation rate (\(da/dN\)) values are shown in Figure 7. When \(D\) increased from 4.5 mm to 6.5 mm, there were 11,539, 37,290, and 79,283 cycles recorded, respectively, where \(N_{f}\) grew by 3.23 and 2.12 times while \(D\) climbed by 1.22 and 1.18 times, respectively. The average crack propagation rate was calculated for each configuration using \((a_{f}-a_{0})/N_{f}\), where \(a_{0}\) and \(a_{f}\) were the crack lengths at the end of static cycle and at \(N=N_{f}\), respectively. It was observed to decrease from 6.79 \(\times\) 10\({}^{-4}\) mm/cycle to 1.19 \(\times\) 10\({}^{-4}\) mm/cycle and around 5.7 times when \(D\) was increased from 4.5 to 6.5 mm.  
Figure 8 shows the resulting crack patterns for each configuration when \(N\) reaches 400,000 cycles. It was observed that regardless of the size of \(D\), the crack emerged and expanded at the identical position in Part 1 around the spot weld connecting Parts 1 and 2 on the lower left side. After 400,000 cycles, the crack size increased to 34.01, 29.20, and 22.69 mm when \(D\) equaled 4.5, 5.5, and 6.5 mm, respectively. It was determined that when the nugget's diameter shrank, the crack propagated more quickly because the smaller spot-welding size could not assemble the parts adequately. Here, it was determined that the size of the spot weld significantly affected the fatigue performance of the structure. This is in line with what was shown in [16; 17], where longer fatigue lives were achieved by larger nugget diameters because of the notch effect. This resulted in lower strains and stresses developing close to the spot-welding site, which decreased the likelihood that fractures would emerge.  
Figure 7: Numerically determined \(N_{f}\) and \(da/dN\) for various spot-weld diameters.
## 2 Mathematical Model
### Numerical Solution Method  
The Lagrangian equation of ice crystal motion is expressed as a first-order ordinary differential equation for velocity [21; 22], which is expressed as:  
\[\begin{array}{l}\left\{\begin{array}{l}\frac{d\overset{\rightarrow}{u}}{ dt}=f\Big{(}t,\overset{\rightarrow}{u}\Big{)}\\ \overset{\rightarrow}{u}(t_{0})=\overset{\rightarrow}{u}_{0}\end{array} \right.,\end{array} \tag{12}\]  
The initial position and the initial velocity of ice crystals and the velocity distribution of the airflow are used as the initial conditions and the equations are solved using the fourth order Runge-Kutta method.  
\[\overset{\rightarrow}{u}_{d}^{n+1}=\overset{\rightarrow}{u}_{ic}+\frac{1}{6 }\Delta t\cdot(\overset{\rightarrow}{k}_{1}+\overset{\rightarrow}{k}_{2}+ \overset{\rightarrow}{k}_{3}+\overset{\rightarrow}{k}_{4}), \tag{13}\]  
where the coefficient can be expressed as:  
\[\begin{array}{l}\overset{\rightarrow}{k}_{1}=\Delta t\cdot\overset{ \rightarrow}{f}\big{(}\overset{\rightarrow}{r}_{n}\overset{\rightarrow}{u }_{ic}^{n}\big{)}\\ \overset{\rightarrow}{k}_{2}=\Delta t\cdot\overset{\rightarrow}{f}\big{(} \overset{\rightarrow}{r}_{n}+\frac{1}{2}\Delta t\cdot\overset{ \rightarrow}{u}_{ic}^{n}\overset{\rightarrow}{u}_{ic}^{n}+\frac{1}{2} \overset{\rightarrow}{k}_{1}\big{)}\\ \overset{\rightarrow}{k}_{3}=\Delta t\cdot\overset{\rightarrow}{f}\big{(} \overset{\rightarrow}{r}_{n}+\frac{1}{2}\Delta t\cdot\overset{\rightarrow}{ u}_{ic}^{n}+\frac{1}{2}\Delta t\cdot\overset{\rightarrow}{k}_{1}\overset{ \rightarrow}{u}_{ic}^{n}+\frac{1}{2}\overset{\rightarrow}{k}_{2}\big{)}\\ \overset{\rightarrow}{k}_{4}=\Delta t\cdot\overset{\rightarrow}{f}\big{(} \overset{\rightarrow}{r}_{n}+\overset{\rightarrow}{u}_{ic}^{n}+\frac{1}{2} \Delta t\cdot k_{2}\overset{\rightarrow}{u}_{ic}^{n}+\overset{\rightarrow}{ k}_{3}\big{)}\end{array}\, \tag{14}\]  
After obtaining the velocity of the ice crystal movement \(u_{ic}^{n+1}\), \(v_{ic}^{n+1}\), then the position of the ice crystal at the step \(t^{n+1}=t^{n}+\Delta t\) can be expressed as:  
\[\begin{array}{l}x_{n+1}=x_{n}+0.5\times(u_{n}+u_{n+1})\cdot\Delta t\\ y_{n+1}=y_{n}+0.5\times(v_{n}+v_{n+1})\cdot\Delta t\end{array}\, \tag{15}\]  
In the solution process, as shown in Figure 1, the initial conditions for the ice crystal motion trajectory calculation are the initial position and the initial velocity, while the computational grid and the velocity distribution of the airflow are determined in the airflow field calculation. The velocity and position of the ice crystal after a given time step can be obtained by solving the control equation of the ice crystal; that is, the trajectory of the ice crystal. During the calculation, the geometric relationship between the ice crystal and the object surface is judged. If the ice crystal hits the object surface or goes beyond the calculation boundary, the calculation is stopped. The impact position of the ice crystal can be obtained by determining the intersection of the ice crystal trajectory with the object surface.
## 3 Findings for Each of the Three Layers
### Technologies Layer
#### 3.3.1 Distributed Energy Resources  
From the perspective of residential smart grids the following technologies can be distinguished: DER systems in the form of micro-generators and energy storage, smart appliances, smart meters, energy monitoring, and home automation (Table 2).  
\begin{table}
\begin{tabular}{l l} \hline
**Category** & **Example** \\ \hline  & Electricity: \\  & - Photovoltaic solar systems \\  & - Wind turbines \\ Distributed energy resource & Electricity and heat: \\ systems: micro-generators & - Micro cogeneration units \\  & - Fuel cells \\  & - Hybrid and fuel cell electric vehicles (FCEVs) \\  & - Solar heating and cooling \\ \hline  & Electricity: \\  & - Batteries (household or neighborhood size) \\  & - Electrolyzers \\  & Heat: \\ Distributed energy resource & - In home hot water storage \\ systems: energy storage & - Storage heaters \\  & - Shared storage in buildings or neighborhoods \\  & - Ground, aquifers, phase-change materials, thermochemical materials, etc. \\ \hline \end{tabular}
\end{table}
Table 2: Technologies layers in a smart energy system (adapted from [9; 36]).  
Figure 3: Stakeholder overview showing the flow of flexibility.  
With reference to the technologies layer, mainly capabilities regarding the flexibility of specific technologies or residential applications are of interest. In addition to the above, hydrogen as a storage medium has received much attention in the last few years because of the flexibility it can provide [53; 54]. Electrolyzers can provide wind/solar peak shaving by splitting water with renewable energy sources electricity and producing hydrogen. This bulk energy storage process is known as power-to-gas. At peak demand, fuel cells can use hydrogen to quickly respond to the load demand. Hydrogen energy technologies are complementary to batteries, which can supply day-to-day electricity, whilst hydrogen can be used for long-term energy storage, particularly seasonal storage (summer-to-winter).
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 2 Materials and Methods
### Correlation Analysis Method  
To detect the shoot beetle stress using TIR and LiDAR data, the correlations between SDR and temperature at tree and plot scales were analyzed using Origin software (Origin-Lab, Northampton, MA, USA). At the individual tree scale, the data of trees (15 samples)  
Figure 3: The segmentation of individual trees in the plot from LiDAR point cloud with the specific viewing angles (45\({}^{\circ}\), 45\({}^{\circ}\)). Gray color means background, white color means no points, and other colors present segmented trees.  
with different LAIs were selected from the plots to calculate their LAI from the LiDAR point cloud dataset and evaluate the impact of LAI on the correlation between SDR and temperature. After weakening the impact of LAI, a linear regression method with a 95% level of confidence was applied to build the correlation between SDR and temperature.  
To further analyze the correlation between SDR and temperature on a large scale, a total data of 50 small plots were analyzed with the same method, which was the same as the tree scale. The coefficient of determination (R\({}^{2}\)) between SDR and temperature was used to assess the accuracy of the regression model. The Pearson correlation coefficient (r) was used to indicate the relationship between LAI and temperature.
## 5 Conclusions  
This paper proposed a new VP-approach to increase the peak power coefficient of a straight-blade VAWT. The new approach was designed for the blade at the optimum TSR, and focuses mainly on widening the azimuthal zone of the largest AoA, instead of increasing it. A two-blade H-type VAWT was studied using the DMST model to validate the effect of the new approach, in which the tip loss was evaluated by Prandtl's mathematics. The data of the middle height of the VAWT was adopted to analyze and compare the FP-blade and the VP-blade from six different characteristics, namely the AoA, lift, drag, resultant velocity, torque, and power output. The conclusions drawn from the study are as follows.  
1. Through the application of the new approach, a large increase in the AoA is produced in the upwind half-cycle of the VP-blade. A large increase in the AoA in the downwind half-cycle is also achieved when the TSR = 4, 4.5, and 5, but the increase becomes smaller as the TSR increases, so that a negative growth is shown when the TSR = 5.5.
2. The new approach greatly enhances the lift of the blade and distributes the maximum lift in a wider zone in the upwind region. In the downwind region, two new and larger maximum lifts are created when the TSR = 4.5 and 5, but a smaller lift is obtained when the TSR = 5.5.
3. The increased AoA leads to an increase of the drag of the blade in most of the positions. The trend of the distribution of the drag is similar to that of the lift.
4. The resultant velocity experiences little changes in the upwind region, while in the downwind region, the changes are obvious, and become more obvious as the TSR increases.
5. Influenced by the lift and drag, the ultimate effect of the torque shows that a large increase is obtained in the upwind region and downwind region at most of the TSRs. The distribution trend is also much like that of the lift.
6. The new VP-approach also enlarges the azimuthal zone of the blade with the highest power output. Consequently, an 18.9% increase of the peak power efficiency of the VAWT is achieved when the TSR = 4.5. Additionally, the turbines are capable of working with high efficiency in a wider TSR zone. The new VP-approach designed at the rated TSR is suitable to enhance the peak efficiency of VAWTs.  
[PERSON] and [PERSON] conceived and designed the research; [PERSON] performed the calculations; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] and [PERSON] wrote the paper. [PERSON] revised the paper.  
This paper was supported by the project of National Natural Science Foundation of China, Project No. 11502070 and 51607058; the Fundamental Research Funds for the Central Universities, Project No. 2018B24914; National Basic Research Program of China (\"973\" Program), Project No. 2014 CB046200.  
The authors declare no conflict of interest.
For the simplest case (a rectangular-like barrier), the main goal is to determine the transmission and reflection coefficients through a potential barrier [17; 18]. However, there are more complex barriers that involve combinations of potentials--for instance, a triangular-like potential coupled to a rectangular-like potential or a rectangular-like potential combined with a parabolic-like potential, i.e., potentials that approach a more realistic physical problems [19; 20]. Again, [PERSON]'s theorem can also be used to address the corresponding asymmetric-like potentials, which also reduces the computational costs. In solid state physics, the Kronig-Penney model stands out as the simplest and most widely known periodic potential to explain band theory [3; 12], unlike other potentials, such as linear-like or parabolic-like potentials [21; 22; 23; 24]. It should be noted that the Kronig-Penney model has an analytical solution of time-independent SE, and the solutions are real exponential-like functions. On the other hand, other periodic potentials require the use of special functions, such as the Airy, Bessel, parabolic cylinder, or Mathieu functions [25; 26; 27; 28; 29]. Therefore, the solution of the secular determinant, applying the boundary and continuity conditions using [PERSON]'s theorem, leads to the transcendental energy equation [3; 12]. In addition, the question of how the group velocity and effective mass are obtained from this transcendental energy equation for coupled potentials together with a Dirac-delta potential is a problem that still needs further study. In this work, a general solution to the transcendental energy equation (SE in k-space) for the rectangular-like barrier combined or not with other types of potential shapes was proposed. Under this condition, asymmetric \(V=V(x)\) potentials were generated along with the rectangular-like potential. As a consequence, the transcendental energy equation, group velocity, and effective mass were obtained for a periodic potential in the form of a rectangular barrier combined with any \(V=V(x)\) potential. Moreover, the generalization of the energy equation, group velocity, and effective mass for N-coupled potentials along with a zero-like or rectangular-like potential was calculated, this research being more general than that described in Ref. [30].
## Abbreviations  
The following abbreviations are used in this manuscript.  
\begin{tabular}{l l} Abbreviation & Meaning \\ MVDS & Multipoint video distribution systems \\ GEO & Geostationary Earth orbit \\ C/N & Carrier-to-noise ratio \\ dBW & Decibel watt \\ FSS & Fixed satellite services \\ ES & Earth stations \\ ASI & Adjacent satellite interference \\ IMI & Intermodulation interference \\ XPI & Cross-polarization interference \\ ACI & Adjacent channel interference \\ ITU & International Telecommunication Union \\ IS1002 & Intelsat 1002 \\ C/ASI & Carrier-to-adjacent satellite interference \\ dBi & Decibels relative to isotropic \\ MHZ & Megahertz \\ C/IM & Carrier-to-intermodulation interference \\ C/XP & Carrier-to-cross-polarization interference \\ ATDI & Automated spectrum management solutions \\ ICS & Integrated communication systems \\ LNA & Low-noise amplifier \\ RF & Radio frequency \\ CST & Computer simulation technology \\ \end{tabular}
# Fivadmi: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation +
## 4 Implementation of FIVADMI
### Monitoring Scheme
#### 4.3.1 Rules for Security Properties  
Figure 13 shows rules expressed in EC, to monitor injection attack.  
Rule IR1 signifies the creation of the fluent _CAN_Data(dID, dMsg, dTS)_ with empty values at the beginning of the monitoring process. This fluent will be used to store CAN frame source ID, the message contained in the CAN frame and the time stamp of receipt.  
Rule IR3 is used to update the fluent _CAN_Data_. Each time a CAN frame is received (signified by the predicate _Happens(CAN_Frame(ID, cMsg), cTS)_), the values of _dID_, _dMsg_ and _dTS_ in the fluent _CAN_Data_ are updated which is signified by the predicates _Initiates(CAN_Frame(clD, cMsg), equalTo(dID, cID),cTS)_, _Initiates(CAN_Frame(clD, cMsg), equalTo(dMsg, cMSg), cTS)_ and _Initiates(CAN_Frame(clD, cMsg), equalTo(dTS, cTS), cTS)_.  
Rule IR2 is used to detect IVN injection attacks. FIVADMI applies a frequency based analysis approach. By design, most CAN messages are transmitted at regular frequencies. These frequencies are nearly constant, even during transitions between vehicle driving modes [2; 7; 22]. FIVADMI checks the difference of arrival times between two successive CAN frames from the same source. If the difference is less than a predefined threshold, it raises an alarm as an injection attack might have occurred. The CAN frame that has arrived (signified by the predicate _Happens (CAN Frame(clD, cMsg), cTS)_) contains the ID and time stamp of the current CAN frame. The fluent _CAN_Data_ (signified by _HoldsAT (CAN Data (dID, dMsg, dTS), cTS)_) contains the ID and time stamp of the previous frame. The difference of arrival time stamps of two successive CAN frames from the same source is checked. The rule checks that both CAN frames have valid data and different time stamps. The rule also checks that both frames are from the same source and that the difference  
Figure 13: EC Rules—Injection Attack Detection.  
between time stamps is less than a threshold (e.g., 50). If these conditions are violated, then an alert is raised.
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 3 Key Technologies
### Digital Beamforming Technology
The beam directions of different satellites are calculated based on the a priori information, the beam-forming parameters of the corresponding satellites are read through the look-up table, and the beam-forming parameters are assigned to different channels, ultimately verifying the effectiveness of beamforming. If it is invalid, determine whether the satellite has transited. If it has transited, stop the beam-forming process [12; 15; 17]; if it has not transited, jump to beamforming based on airspace search. Initially, complete the beamacquisition of different satellites according to the preset beam pointing parameters; then each satellite is searched within a certain angle range from the direction of the captured beam, and then spread spectrum tracking verification and power comparison are performed. The beam direction with the highest power is selected as the tracking result and assigned to the beam-forming channel of the corresponding satellite [17; 18; 19]. Considering that each communication time of low-orbit satellites is short and the beam changes quickly, the beamforming in the second stage mainly uses the look-up table method [12; 18]. The weighting coefficients in different directions are calculated through simulation analysis and the coefficients are solidified in the memory. Then, extract the weighting coefficient according to the specific pointing parameter for beam synthesis. The advantages of this method are less calculation, fast running speed, and simple algorithm design [15; 19]. In the scheme, each unit of the beam is optimized with amplitude weighting, such as Taylor distribution or Chebyshev distribution, to achieve low sidelobe characteristics of the array beam [17], while reducing the beam gain, taking both low sidelobe and constant gain performance into account.