esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles
## 3 Methodology
### Type Approval  
This subsection combines the assessment results \(y_{m}^{a}\) of the simulation at the data-driven application scenarios \(\mathbf{x}^{a}\) from Section 3.2 with the estimated model uncertainties \(I(\tilde{e}^{pa})\) from the preceding subsection. It treats each application scenario individually, since there are no test repetitions compared to the coverage-based validation experiments. We use the uncertainties to expand the minimum distance to line to an interval-valued prediction  
\[I(\tilde{y}_{s}^{a}) =y_{m}^{a}+I(\tilde{e}^{pa})=[y_{m}^{a}+\varepsilon^{pa},y_{m}^{a }\ +\tilde{e}^{pa}] \tag{20}\] \[I(\tilde{y}_{s}^{a}) =[y_{m}^{a}-\tilde{e}_{l}^{pa}-g_{pJ}(\mathbf{x}^{a}),y_{m}^{a}+ \tilde{e}_{r}^{pa}+g_{pJ}(\mathbf{x}^{a})]\]  
of the unknown true minimum distance to line from the real vehicle. As shown in Figure 7, the lower bound of the left area metric estimate shifts the nominal simulation result \(y_{m}^{a}\) to the left, and the upper bound of the right area metric estimate shifts it to the right. It is important to note that we use this interval prediction including uncertainties for type approval instead of the nominal simulation results, which are imperfect by the nature of the inherent models.  
Figure 7: Type approval at the exemplary application scenario \(v_{x}=81\,\mathrm{km/h}\) and \(a_{y,ref}=0.35\,\cdot\,a_{y,smax}\). The shift to the left results from the error model of the left area metric, parameterized over all validation scenarios and inferred to this application scenario, plus its prediction interval. The shift to the right results analogously from the right error model and its prediction interval.  
In the end, the minimum distance to line must exceed the threshold of zero to pass the type approval. In the exemplary application scenario in Figure 7, this is the case for both the nominal simulation and more importantly for the interval-valued prediction, including the estimated model uncertainties. Figuratively speaking, exceeding the threshold value means that the outer edges of the vehicle plus a buffer for the modeling errors do not cross the lane markings. Since the distance requirement has only a lower threshold and since we defined the minimum distance as the minimum over the left and right distances in Section 3.4, we must only look at the lower bound \(\varepsilon^{va}\) and the left edge of \(I(\hat{y}_{s}^{a})\) from a worst case safety point of view. Nevertheless, the methodology works in analogy for quantities with upper thresholds.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 3 Performance Evaluation
### Two Detectors
#### 3.1.2 Consistency Check of Doppler  
Doppler shift is generated by the relative motion of the satellite and receiver. The carrier Doppler shift of the GNSS signal must be consistent with its code Doppler shift. However, this is not satisfied in some cases of the spoofing attack. Thus, the consistency check of Doppler can be used for spoofing detection. A brief introduction of this method is given herein.  
As a consequence of the formula of Doppler shift, the pseudo-code Doppler of the GNSS signal is proportional to the carrier Doppler; the proportionality coefficient is given as follows:  
\[\alpha=\frac{f_{D_{code}}}{f_{D_{carrier}}}=\frac{f_{code}}{f_{carrier}} \tag{29}\]where \(f_{D\_code}\) is the Doppler shift of pseudo-code and \(f_{D\_carrier}\) is the Doppler shift of the carrier and \(f_{D\_code}\). \(f_{code}\) is the code rate of the pseudo-code, and \(f_{carrier}\) is the carrier frequency. For example, the coefficient of the GPS L1 C/A signal is:  
\[\alpha=\frac{f_{D\_code}}{f_{D\_carrier}}=\frac{1.023\times 10^{6}}{1575.42\times 1 0^{6}}=\frac{1}{1540} \tag{30}\]  
Not just the Doppler of the pseudo-code and carrier, but a similar relationship exists between signals of different frequencies from one satellite, so the consistency check can apply to them. Based on Equation (29), define:  
\[x=f_{D\_code}-\alpha f_{D\_carrier} \tag{31}\]  
The equivalent hypothesis testing of the consistency check of Doppler can be written as follows:  
\[\left\{\begin{array}{ll}H_{0}:&x[k]=w[k]\\ H_{1}:&x[k]=A+w[k]\end{array}\right. \tag{32}\]  
\(A\) is a non-zero bias between carrier Doppler and code Doppler, and \(w[k]\) represents the noise and error of measurements. Assuming that \(w[k]\) conforms to a zero mean Gaussian distribution, \(x[k]\) can be described as:  
\[x\ \sim\ N(\mu,\ \sigma^{2}) \tag{33}\]  
where \(\mu\) and \(\sigma^{2}\) represent the mean value and variance. Hypothesis testing of Equation (32) is equivalent to:  
\[\left\{\begin{array}{ll}H_{0}:&\mu=0\\ H_{1}:&\mu\
eq 0\end{array}\right. \tag{34}\]  
As \(\sigma^{2}\) is unknown, define the test statistic as follows:  
\[T=\frac{\sqrt{K}\cdot\bar{x}}{\hat{\sigma}} \tag{35}\]  
where \(\bar{x}\) is the mean value of the observed data of \(x[k]\) with data length \(K\), and \(\hat{\sigma}\) represents the maximum likelihood estimation (MLE) of \(\sigma\); they can be obtained as Equation (25). The test statistic conforms to Student's \(t\) distribution with \(K-1\) degrees of freedom [17] under hypothesis \(H_{0}\) as follows:  
\[T\ \sim\ t(K-1) \tag{36}\]  
Therefore, the detection as Equation (27) can be used for the consistency check of Doppler, as well as the threshold is obtained by a similar way as Equation (28).
# A Study on Wireless Charging for Prolonging the Lifetime of Wireless Sensor Networks
## 5 Plan Charging Path  
In this section, we plan the moving path of the MC, which is a charging sequence, denoted as \(V=\{v_{1},v_{2},\cdots,v_{n}\}\). The MC moves starting from the base station and comes back to replenish its energy. We set the base station as \(v_{0}\), the charging sequence can thus be denoted as \(V=\{v_{0},v_{1},v_{2},\cdots,v_{n},v_{0}\}\).  
The problem when planning a moving path is equivalent to the charging sequence problem of sensor nodes. Therefore, the moving path plan problem can be transferred to the TSP problem. The traditional weight of a TSP considers the distance factor. In this work, we consider the distance and the remaining energy of sensor node as the weights to calculate the optimal path, and then transform it into a TSP problem.  
We denote \(V=\{v_{0},v_{1},v_{2},\cdots,v_{n},v_{0}\}\) as the sensor nodes. The sensor network is represented in the form of a graph \(G=(V,E;v,e)\), where \(V\) is the set of charging nodes, \(E\) is the edge between sensor nodes, \(v\) is the weight of each sensor node, and \(e\) is the weight of the edge between sensor nodes.  
Our goal is to find the best path in \(G\). We make the weight of node be the remaining energy of the sensor node, and the weight of the edge to be the distance between the sensor nodes as follows:  
\[\omega=\alpha\times v+(1-\alpha)\times e \tag{12}\]  
In Equation (12), \(\omega\) is the weight, \(v\) is the remaining energy of sensor node, and \(e\) is the distance between the sensor nodes. \(\alpha\) is a constant between 0 and 1.  
When \(\alpha=0\), the path we figured out is the shortest path. When \(\alpha=1\), the path figured out is the ascending sequence by remaining sensor node energy. Equation (12) can be represented as:  
\[\omega=\alpha\times\frac{\lambda_{i}}{c_{i}}+(1-\alpha)\times\frac{d_{i}}{v} \tag{13}\]  
In Equation (13), \(\lambda_{i}\) is the remaining energy of sensor node \(i\), \(c_{i}\) is the energy consumption rate of sensor node \(i\), \(d_{i}\) is the distance between sensor nodes, and \(v\) is the speed of the MC. Therefore, we transform the two different dimensions into one weight. After determining the weight, we can transform the path problem into a TSP problem.
## 2 Material and Methods
### Site Condition  
In the current paper, two full-scale maintained static load experiments on bored-piles are conducted. The first experiment (BP# 1) was performed at Cadangan Pembangunan, Lorong Stonor, Kuala Lumpur, Malaysia. The test pile was initially loaded up to 2 times to pile structural capacity, therefore, regarding BP# 1 with the structural capacity of 22,200 kN, the nominal diameter of 1,800 mm with a penetration depth of 36.95 m from the current piling platform level is RL 36.25 m. The pile was initially examined by up to 44,400 kN (2 x working load) in two loading cycles. The second test (BP# 2) was applied  
Figure 8: The electrical lead wires from the sensors come to the top.  
Figure 7: VWSG attached to the steel cage.  
at Utama Lodge, Jalan Senangria, Kuala Lumpur, Malaysia. The summary of soil types, besides the SPT-N values measured near the pile location, are illustrated in Table 2. Noting that the soil stratum was classified according to the unified soil classification system.  
The gauges were investigated prior and after installation, after cage placement in the borehole, and after concreting. The strain gauges' signal cables were reserved for testing approximately after 28 days, allowing for the concrete to achieve the design strength. Therefore, on test day, the strain gauges' cable was linked to the switch box connected to the data logger to ensure the functional sequences. Regarding the rod extensometer, galvanized iron (GI) pipes were tied to the main reinforced cage with steel wires at each terminating depth (Figure 9). A mild steel rod (10 mm) was inserted untill it touched the bottom of the pipe. In addition, a steel plate was welded to the rod's end for the plunger to sit on along the experiment (Figure 10).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Test Pile** & & **Soil Stratum** & **Depth (m)** & **SPT-N Values \({}^{*}\)** & **Average SPT-N** \\ \hline BP\# 1 & L\({}_{1}\) & Stiff Sandy Silt with little gravel & 0–8 & 3–16 & 15.50 \\  & L\({}_{2}\) & Very Stiff Sandy Silt with little gravel & 8–10 & 16–50 & 27.5 \\  & L\({}_{3}\) & Hard Yellowish Sandy Silt with little gravel & 10–17 & 50–111 & 110 \\  & L\({}_{4}\) & Hard Yellowish Sandy Silt with little gravel & 17–24 & 111–150 & 122 \\  & L\({}_{5}\) & Fractured limestone & 24–36.95 & 143–150 & 150 \\ \hline BP\# 2 & L\({}_{1}\) & Sandy SILT & 0–12 & 4–30 & 30 \\  & L\({}_{2}\) & Sandy CLAY & 12–17 & 19–39 & 39 \\  & L\({}_{3}\) & SILT & 18–23 & 54–125 & 122 \\  & L\({}_{4}\) & Weathered Sandstone & 25–31.65 & 176–200 & 195 \\ \hline \end{tabular}  
* Note: SPT-N is the number of SPT driven into the ground (e.g., at the bottom of a borehole) by blows from a slide hammer with a mass of 63.5 kg.  
\end{table}
Table 2: Soil profile for tested bored pile after the standard penetration test (SPT).  
Figure 9: global strain extensometer pipe for tell-tales extensometer is pre-installed at VWSG.
# III-Nitride Multi-Quantum-Well Light Emitting Structures with Selective Carrier Injection
## 2 Methods  
For carrier transport simulation, the mobile electron and hole subsystems were further split into the drift-diffusion and high-energy parts, each assigned with different kinetic characteristics, such as carrier relaxation times, mobility, etc. [5]. The drift-diffusion carrier subsystems are described by equations of the standard drift-diffusion transport model complemented with rate equations for confined dynamic (non-equilibrium) QW populations, separately for each quantum well and for each type of carrier [6]. The high-energy electron subsystem includes Auger electrons supplied by intra-QW Auger recombination. High-energy electrons are assumed to fully relax into drift-diffusion electron subsystems within the span of the LED active region with characteristic relaxation time of 1 ps. Due to the much faster hole relaxation, the high-energy hole subsystem has not been included into the simulation. Low-energy drift-diffusion mobile carriers were assumed to be the only source of capture  
Figure 1: Band profiles of a blue-emitting LED with (**a**) a plain-barrier and (**b**) example intermediate carrier blocking layer (IBL) active region designs calculated at a nominal injection current of 50 A/cm\({}^{2}\). Dashed lines show the quasi-Fermi levels for electron (blue) and hole (green) drift-diffusion subsystems. The dashed-dotted line represents the internal built-in potential distribution across the active region.  
for QW-confined carriers; direct capture from the high-energy subsystem was ignored. Still, in the presence of barriers, high-energy Auger electrons can play a noticeable part in inter-QW transport, and therefore, affect MQW injection distribution [7, 8]. Quasi-ballistic QW overshoot was incorporated into the drift-diffusion transport simulation by combining the QW capture characteristics with mobile low-energy carrier transit and relaxation times [14]. The final model equation system was solved by standard COMSOL Multiphysics(r) numerical solver without use of any specific application modules.
# Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion
## 2 SGM-MSIF Algorithm  
Similar to the classic SGM algorithm, the proposed SGM-MSIF algorithm consists of four steps: cost computation, cost aggregation, disparity computation and disparity optimization. Figure 1 shows the flowchart of the proposed SGM-MSIF algorithm, and the detailed steps are described as follows:  
1. Matching cost computation. The initial cost volumes at \(S+1\) scales are calculated by the combination of census transform and color information of images.
2. Improved cross-scale multi-path cost aggregation. The initial cost volumes at different scales are aggregated by multi-path cost aggregation to obtain the aggregated cost volumes at each scale. Then, the aggregated cost volumes of all scales are fused to obtain the final cost volume containing multi-scale information.
3. Disparity computation. The winner-take-all (WTA) principle is adopted to calculate the initial disparity map.
4. Disparity optimization. The initial disparity map is optimized by internal left-right consistency check, weighted median filter and median filter.
## 3 Methodology
### Simulation of AV Maneuvering  
To ensure the reality of the simulated AV-driving behavior we were required to conduct effective and reliable simulation experiments. Actual AVs perform the process of recognizing surrounding situation using various vehicle sensors and communication functions to determine vehicle maneuvering. Simulating all these functions requires a lot of time and effort. Because the core of this study is not to fully implement the driving behavior of an AV on the simulation, but to understand under what conditions the vehicle interaction between AV and MV becomes dangerous, we proposed a method for abstracting the behavior of AVs. The abstraction refers to simplifying and condensing the essential concepts of autonomous driving systems and technologies. This study established three requirements of AV driving behavior to develop an AV maneuvering control algorithm based on an assumption that there exists a difference in driving behavior between AVs and MVs. The first requirement of AV maneuvering is'safety'. This requirement means that an AV does not create a dangerous situation by itself when there is no external stimulus such as the cutting in or cutting out of a nearby vehicle [19; 20; 21]. The second requirement is 'deterministic'. The AVs determined their path, acceleration, and steering according to a predefined AV driving algorithm as defined in the previous studies [22; 23; 24]. The characteristics of a deterministic maneuver is that the AV always moves the same way under the same conditions. The last is'smoothness'. AVs show the smoothness driving characteristic unlike MVs because the longitudinal and lateral control functions of AVs maintain a constant driving speed and acceleration and adjust their lane offset to support stable driving [25; 26]. Autonomous driving, to reflect the requirements of AV behavior, includes four functions: maintaining driving speed, following a vehicle, changing lanes, and merging in this study. Figure 2 illustrates the process of implementing each drivingfunction algorithm, using the script editor module of SCANeRTM STUDIO.  
Figure 2: Framework for simulating AV driving behavior.
# T-Type Vertical Wall for Decoupling and Pattern Correction of Patch Antenna
## 4 Mechanism and Equivalent Circuit Analyses  
As shown in Figure 10b, the current concentrates on the vertical wall's position, indicating that the corresponding surface current, surface wave, and radiated wave, will be induced in this proposed structure. The mixture of these three influencing factors will form a specific current distribution at the desired frequency on the T-type metal. Figure 11 shows the current distribution of the desired 5.80 GHz of the proposed structure. This indicates that a resonant structure is formed if the dimensions of the vertical and parallel strips are set properly.  
The formed resonator by the induced current makes the vertical wall a bandstop filter, thus preventing the current from flowing to the other antenna. This can easily be explained by the corresponding equivalent circuit model, as shown in Figure 12. Here, the conventional patch antenna is equivalent to the parallel RLC circuit and they are placed symmetrically on the T-type vertical wall, while the corresponding parallel RLC circuit also indicates the T-type vertical wall.  
When the patch is excited, there is a voltage difference between the patch and the T-type vertical wall, which means that capacitive coupling exists. Thus, we use the capacitance C3 to indicate this coupling. If the vertical wall induces the coupling, the symmetrical T-type metals form the corresponding resonance, which causes the parallel resonant circuit form, as indicated by R2, L2, and C2, respectively.  
Subsequently, the corresponding values for these components are extracted using the ADS 2020 and listed in Table 3. The responding comparison between the simulated results by HFSS and ADS software are shown in Figure 13. From this, we know that the S11 and S21 agree well with each other, thus validating the equivalent circuit.  
Figure 11: Resonator of the T-type metal.  
Figure 12: The equivalent circuit for the proposed structure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Component** & **R1** & **L1** & **C1** & **R2** & **L2** & **C2** & **C3** \\ \hline Value & 60 \(\Omega\) & 0.07 nH & 10.8 pF & 620 \(\Omega\) & 0.29 nH & 2.6 pF & 40 pF \\ \hline \end{tabular}
\end{table}
Table 3: Values of the components.
# Conserved Plastid Genomes of _Pourthiaea_ Trees: Comparative Analyses and Phylogenetic Relationship
## 3 Results
### Phylogeny Inference  
In this study, 78 shared plastid protein-coding sequences and 29 nrDNA sequences were used to investigate the phylogenetic relationships of 13 _Pourthiaea_ species, with _Photinia glomerata_ as the outgroup (Table S9). The aligned matrix of 78 shared plastid protein-coding sequences and nrDNA sequences were 68,085 bp and 6250 bp long, with 451 and 301 parsimony informative sites, and proportions of 0.66% and 4.82%, respectively. Two methods (ML and BI) produced the same topologies with generally high support values using the 78 shared plastid protein-coding sequences, while two methods (ML and BI) yielded slightly inconsistent topologies using the 29 nrDNA sequences (Figure 7). The monophyly of _Pourthiaea_ was strongly supported based on our analyses (ML, BS = 100; BI, PP = 1) in both plastome and nrDNA phylogenies (Figure 7). _Pourthiaea_ was sister to _Malus_ (ML, BS = 100; BI, PP = 1) in plastome phylogenies, while it was sister to the remaining genera in nrDNA phylogenies (Figure 7). Significant cytotoxic disadvantage was observed between our plastome phylogenies and nrDNA phylogenies within _Pourthiaea_ (Figure 7). The monophyly of the three sections (sect. _Amphidoxae_, sect. _Impressivenae_, and sect. _Pourthiaea_) was supported in nrDNA phylogenies, whereas none of the three sections formed a monophyletic group in plastome phylogenies (Figure 7).  
Figure 5: Comparison of the _Pourthiaea_ plastome sequences using mVISTA with _P. villosa_ as the reference.
## 3 Data-Collection Platforms and Instruments
### Sensor Deployment and Operation over Vegetation Canopies: A Basic Procedural Philosophy
The end of the boom has a self-righting frame which holds the upwelling and downwelling light-collecting fibers, a GPS antenna, a down-looking infrared thermometer (IRT), a downwelling global-irradiance pyranometer, a downwelling PAR quantum sensor, and a down-looking camera to photograph the scene in the upwelling fiber field of view (25\({}^{\circ}\)). The fibers and electrical interconnections to the instruments are routed from the frame to the HIB, mounted on the boom just below the frame. A ruggedized power cable is the only physical electrical connection running up the boom. The electronics inside the HIB are shown in Figure 5. As described for the backpack, a serial terminal server provides physical serial ports within the HIB and corresponding virtual ports via a TCP/IP network to the controlling computer. In addition, the HIB contains a USB terminal server that performs an analogous function for devices using USB connectivity. Application software cannot distinguish these virtual ports from the conventional hardware ports. Hence, no modifications are required to instrument-control and data-acquisition software in order to remotely connect to instruments. The terminal server provides USB connectivity for the down-looking digital camera. The serial terminal server supports several instruments including Ocean Optics hyperspectral field radiometers, GPS, and Campbell data logger (Logan, UT, USA). The data logger collects information sent from the pyranometer, quantum sensor, and IRT. The terminal servers are connected via short Ethernet cables to an inexpensive wireless router. Two external antennas connect to the router in order to assure good signal strength to the laptop computer (normally operated in the Hercules cab). The HIB also includes a 12-volt distribution block and voltage converters for 6 and 5 volts. These lower voltages are required to power the radiometers and the digital camera. Three fans provide cooling air flow to keep the instruments within the limits of their operating temperatures. The digital camera, while primarily used as a scene capture and documentation device, also helps position the Hercules boom. A software reticule is superimposed on the computer-screen display of an image captured by the nadir-looking boom camera to help set the boom height. The reticule has rotatable and positionable parallel lines spaced so that when the lines overlay the rows of a crop, the upwelling fiber on the boom is 16 feet above the canopy. The reticule spacing may be adjusted by the operator if other heights are required or if crop row spacing varies.