esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
To validate the efficiency of the proposed COA, two practical cases of 250 W PEM stack [26; 27] and NedStack PS6 [43; 44; 45] have been studied. The proposed algorithm is verified by means of the data in the literature [26; 27; 44]. The parameters, technical, and working circumstances of the tested stacks are given in Table 1. Further, Table 2 shows the lower and upper bounds of the decision variables of the model at two different operating conditions, (operating condition #1: 1/1 bar and 343.15 K) for both stacks (250 W and NedStack PS6) and (operating condition #2: 3/5 bar and 353.15 k) for 250 W stack. The simulation results have been compared to other literature works [18; 19; 20; 21; 22]. Moreover, four competitive methods; SSA [46], SCA [47], MFO [48], and PSO [16] have been implemented to test the effectiveness of COA. For a fair comparison, all of populations are set to 100 with 400 iterations. All simulation results were carried out using the MATLAB R2016b software on a PC with Intel (R) Core (TM) i3- CPU [EMAIL_ADDRESS] GHz 3 GB (RAM).  
Herein, the optimization techniques aim to minimize the square deviation between measured V-I data and the estimated data by the stack model. This optimization process results in extracting the optimal values of the decision variables (\(\xi_{1},\xi_{2},\xi_{3},\ \xi_{4},\ R_{c},\ \lambda\ and\ b\)) of the PEM stack model. These variables are used to obtain an accurate model of the PEM fuel cells stack. The simulation results for the two studied cases are explained as follows:
## 3 The Proposed Method
### NeuroTIS+  
#### 3.3.1 Dependency Network Representation  
In this paper, apart from label dependency between CDSs and TISs, we also consider the heterogeneity of negative TISs and introduce frame information that has an influence on the final TIS prediction for the first time. To be specific, we can reformulate Equation (2) as follows:  
\[(z_{\kappa}^{*},f^{*},y^{*})=\arg\max_{z_{\kappa},f,\mathbf{y}}p(z_{\kappa},f,\mathbf{y} |\mathbf{s}) \tag{6}\]  
where variable \(f\in\{0,1,2\}\) denotes which frame that the CDS of an mRNA sequence \(\mathbf{s}\) is located in. NeuroTIS+ also builds a dependency network to represent and simplify dependency relationships among multiple variables. As shown in Figure 2, NeuroTIS+ assumes denser and stronger codon label dependencies compared with NeuroTIS; that is, each codon label is interdependent on all the left codon labels of the mRNA sequence \(\mathbf{s}\). Moreover, frame variable \(f\) is dependent on codon labels, and TIS variable is dependent on mRNA sequence \(\mathbf{s}\), codon variables \(\mathbf{y}\), and frame information. Based on the probability chain rule, the joint inference of TIS and coding regions can be formally transformed into a MAP estimation problem as follows:  
\[(z_{\kappa}^{*},f^{*},\mathbf{y}^{*})=\arg\max_{z_{\kappa},f,\mathbf{y}}p(z_{\kappa}| \mathbf{s},\mathbf{y},f)p(f|\mathbf{y})\prod_{i}p(y_{i}|\mathbf{s},\mathbf{y}_{\
eq i}) \tag{7}\]  
where \(\
eq i\) denotes all the indices of an mRNA sequence except \(i\). It should be noted that Equation (7) constitutes an NP-hard problem where exact inference is computationally infeasible. To address this, we propose a practical greedy inference approach to obtain approximate solutions as follows:  
\[\hat{\mathbf{y}} =\arg\max_{\mathbf{y}}\prod_{i}p(y_{i}|\mathbf{s},\mathbf{y}_{\
eq i}) \tag{8}\] \[\hat{f} =\arg\max_{f}p(f|\hat{\mathbf{y}})\] (9) \[\hat{z}_{\kappa} =\arg\max_{z_{\kappa}}p(z_{\kappa}|\mathbf{s},\hat{\mathbf{y}},\hat{f}) \tag{10}\]  
Using the above three equations, NeuroTIS+ can efficiently infer variables \((z_{\kappa},f,\mathbf{y})\) in three stages: one stage to predict CDSs, the second stage to predict frames of CDSs, and the third stage to combine frame information and coding scores for TIS prediction. In the following sections, we introduce a TCN to estimate the conditional probability distribution \(p(y_{i}|\mathbf{s},\mathbf{y}_{\
eq i})\) and a simple strategy to calculate \(f\) according to \(\hat{\mathbf{y}}\), and, as for the conditional probability distribution \(p(z_{\kappa}|\mathbf{s},\hat{f},\hat{\mathbf{y}})\), we estimate it by utilizing frame-specific CNN.
# Agricultural Evolution: Process, Pattern and Water Resource Effect
## 4 Discussion
### Driving Force of Cultivated Land Changes  
Significant climate warming since the 1950s in the Northeast China has been reported in previous studies [38; 44; 46]. Previous studies reported increasing yearly temperature and decreasing wind speed in the West Songnen Plain [17; 40], which were favorable for agricultural activities especially paddy planting. Population increase and technological development also provided great convenience for agricultural activities [17]. The population in the West Songnen Plain showed an increasing trend since the 1950s, making agricultural expansion easier. Technological development including modern agricultural machinery, water conservancy projects, cold-tolerant seed of rice have provided convenience for large-scale reclamation and the northward movement of rice cultivation [38; 42].  
On the West Songnen Plain, shifts in land-use policies also played an important role in the progress of cultivated land changes. At the beginning of the 1980s, the Chinese government reformed the land tenure policy and introduced a system of household contract responsibility, in which land-use rights were granted to individuals [18]. Under the encouragement of government, farmers reclaimed a large area of grassland, marshland and sandy land for cultivated land. In 1992, market economy was adopted in China [17]. Due to the higher price of rice than other crop such as maize and wheat, more paddy field was reclaimed in the West Songneng Plain, resulting in the conversion from grassland, marshland, and saline-alkali land to paddy field to some extent [18]. Additionally, more cultivated land was reclaimed by local farmers to obtain economic returns. During 1992-1995, the \"to promote dry farmland to paddy\" policy was implemented in the Northeast China [38]. Under this new policy, a large-area of paddy appeared at the expense of dry farmland loss. For example, trajectory code \"DPPP\" (Dry farmland-Paddy-Paddy), \"DDPP\" (Dry farmland-Dry farmland-Dry farmland-Paddy) and \"DDDP\" (Dry farmland-Dry farmland-Dry farmland-Paddy) indicated the changes from dry farmland to paddy. Irrigation projects also contributed cultivated land changes in the West SongnenPlain. The number of reservoirs with areas larger than 1 km\({}^{2}\) in the West Songneng Plain increased from 79 in 1990 to 152 in 2013, which provided great convenience for agricultural irrigation [47]. Some dams were also established to improve irrigation conditions. Some ecological restoration policies nationally or locally also influenced cultivated land changes such as \"Grain for Green\" and \"Wetland restoration\" [44]. On one hand, these policies promoted the conversion from agricultural land to grassland/wetland. On the other hand, they reduced the transformation from natural resources to agricultural land.
# PV-Powered Charging Station for Electric Vehicles: Power Management with Integrated V2G
## 4 Simulation Results and Analyses  
In the DC microgrid system, PV sources include 400 PV panels (40 in parallel and 10 in series), and its power under standard conditions is 100 kW [31]. The storage voltage and capacity are 300 V and 300 Ah, respectively. The public grid is a low-voltage three-phase network with a frequency of 50 Hz. The charging method of lithium-ion batteries in EVs is constant current/constant voltage, while the discharge is constant power. The battery energy of EV is about 24 kWh, namely \(J_{EV\_ERF}\). There are three charging modes in the charging station: fast mode whose maximal charging power is 83 kW, namely \(P_{FAST\_MAX}\); average mode whose maximal charging power is 27 kW, namely \(P_{AV\_MAX}\); slow mode whose maximal charging power is 7 kW, namely \(P_{SLOW\_MAX}\).
# The Limitations of 5f Delocalization and Dispersion
## 2 Experimental Section  
Photoelectron spectroscopy (PES) and inverse photoelectron spectroscopy (IPES) or Bremsstrahlung isochromat spectroscopy (BIS) can be used to measure the energy of the electronic states immediately below (PES) or above (IPES/BIS) the Fermi energy. If the direction of the electrons involved in either process is defined, it is possible to measure the dispersion relations of these states, i.e., energy versus momentum. The variant of PES that allows this is ARPES, angle resolved photoelectron spectroscopy, because the emission direction of the electrons is restricted via angular resolution of the collected electrons. Of course, both the electrons and photons must be monochromatized in some sense, with a small energy range. Generally speaking, the momentum parallel to the surface is conserved and the momentum perpendicular to the surface is determined via energy conservation and the assumption that the final state is plane-wave-like [18; 19; 20; 21; 22; 23].  
The X-ray emission and absorption spectroscopy (XES and XAS) measurements were carried out at the Stanford Synchrotron Radiation Lightsource (SSRL), the particulars of which are described elsewhere [17; 26; 27]. The details of the other experiments and calculations can be found in the source articles, quoted in the text and cited as references below. In general, it should be noted that actinides are highly reactive. The lifetime of an atomically clean metal actinide surface in ultra-high vacuum (10-10 to 10-11 Torr) is on the order of a couple or few hours [28]. Electron-based techniques, with short penetration and/or escape depths, are especially surface sensitive, but even M edge spectroscopy (hv \(\sim\) 3 keV) have been demonstrated to exhibit an enhanced surface sensitivity relative to the higher energy (hv \(\sim\) 17 keV) measurements [29]. However, it is important to differentiate that what is meant as a \"surface\" for the electron-based spectroscopies is typically the first couple of atomic layers, whereas what is meant as a \"surface\" in photon-out spectroscopies, e.g., for M-edge XAS, is on the order of 10 s to 100 s of nm--which is substantially larger, penetrating far deeper into the material [29].
## 5 Application Domain
### As-Built/As-Is Modeling  
During construction, deviations may arise between the as-built structure and the as-planned model of the project due to various reasons. To avoid disputes, the changes must be communicated to all stakeholders in a timely manner [182]. Three-dimensionalreconstruction of the as-built state is useful for measuring those deviations [46; 183]. Three-dimensional reconstruction is creating a 3D model of the building using visual data from it. Capturing visual data from the site using manual means is laborious and time-consuming [184]. Robots can be used to automate the data-capturing process for 3D reconstruction or as-built modeling [185; 186].  
Point clouds are a popular data structure for storing as-built information in the form of physical scans of structures [187]. Point clouds are generated either using laser scanners or image-based photogrammetry [187]. Photogrammetry techniques rely only on RGB cameras to capture 2D images for as-built modeling [188]. Photogrammetry also allows taking volumetric measurements from images [189]. Image processing techniques such as structure-from-motion are used to build a 3D model from 2D images from a moving camera for photogrammetry [190]. However, this requires heavy post-processing of the images. Laser scanners are more accurate, but they require specialized equipment and skilled operators, which is substantially more expensive than conventional RGB image capturing. Photogrammetry using regular RGB cameras is more suitable than bulky laser scanners for UAVs. However, the accuracy of measurements generated from photogrammetry is subject to various factors, such as the choice of lens, sensor resolution, the sensitivity of the sensor, and lighting conditions [191]. LiDAR is also used instead of laser scanners. Being lighter in weight, LiDAR mounted on UAVs has also been used for as-built modeling [192]. Another approach is using Kinect sensors [48]. Kinect measures the distance of objects using structured light projection [48]. Kinect sensors are lighter than laser scanners; therefore, they have also been used with UAVs [48].  
Visual obstructions at construction sites, such as scaffolding, stacks of building materials, and people moving around, create the requirement for multiple scans within a single space [193]. This makes manual laser-scanning time-consuming and labor-intensive. By automating this procedure, the rate of generating as-built information may be raised from weekly inspections to daily data collection. [187].  
Both UAVs and UGVs can be used for as-built data collection [34; 91; 194]. Robots equipped with laser scanners were used for as-built/as-is modeling of construction sites [193]. Drones were used with photogrammetry to create automatic point clouds [187]. [PERSON] et al. [52] used a UAV for collecting as-built information and updating the BIM model by updating the IFC data pre-loaded on its system. [PERSON] et al. [30] used Pix4D to create automatic flight plans to fully automate the capturing of images of a building using a UAV and create a point cloud for inspection.
## 3 Kriging-Based Probability Density Estimation for FCTP
### KDE-Based Life Cycle TVR Analysis  
Commonly used nonparametric estimation methods include kernel density estimation (KDE), K-nearest neighbor (KNN), kernel least squares estimation (KLS), etc. KDE is a method of estimating random variable PDF by smoothing data in data space using the kernel function. KDE is only based on data samples for inference, and the actual situation of the data can be reflected well with fewer overall distributional assumptions. Therefore, in this paper, the KDE method is adopted to estimate the PDF of FCTP.  
Let \(Q_{1},Q_{2},\ldots,Q_{b}\) be samples from one dimensional continuous sample population \(\mathbf{Q}\). The kernel density of PDF \(\rho(q)\) at any point \(q\) is defined as follows:  
\[\rho(q)=\frac{1}{bh}\sum_{i=1}^{b}K\Big{(}\frac{q-Q_{i}}{h}\Big{)} \tag{32}\]  
where \(K(\ )\) is the kernel function; the Gaussian kernel function is used, i.e., \(K(\omega)=\frac{1}{\sqrt{2\pi}}exp\Big{(}-\frac{1}{2}\omega^{2}\Big{)}\); and \(h\) is bandwidth. The bandwidth parameter can have a big impact on the estimation results, but there is no universal way to choose the right bandwidth parameter. In this paper, the optimal bandwidth is determined by mean integrated squared error (MISE). Combined with the Gaussian kernel function, the optimal bandwidth can be determined as follows:  
\[h_{opt}=\bigg{(}\frac{4}{3b}\bigg{)}^{0.2}\sigma(\mathbf{Q}) \tag{33}\]  
where \(\sigma(\mathbf{Q})\) is standard deviation of sample population \(\mathbf{Q}\).  
Based on probabilistic properties \(\mathbf{c}^{initial}\) of the input random variable \(\mathbf{Z}\), \(b\) samples are generated by simulation. The FCTP surrogate model \(\phi^{final}(\mathbf{Z})\) is built using Kriging models and EDF learning function. The FCTPs \(\mathbf{\
u}=[v_{1},v_{2},\ldots,v_{b}]\) are computed for all samples. The PDF of FCTP is obtained by combining the KDE method as follows:  
\[f(\
u)\approx\frac{1}{\sqrt{2\pi}bh_{opt}}\sum_{i=1}^{b}exp\bigg{(}-\frac{( \
u-v_{i})^{2}}{2h_{opt}^{2}}\bigg{)} \tag{34}\]  
where \(h_{opt}=\frac{1.06}{b^{22}}\sigma(\
u)\). Therefore, the failure probability of product over its life cycle is expressed as follows:  
\[P_{f}(\tau)=\int_{0}^{\tau}f(\
u)dv=\int_{0}^{\tau}\frac{1}{\sqrt{2\pi}bh_{opt }}\sum_{i=1}^{b}exp\bigg{(}-\frac{(\
u-v_{i})^{2}}{2h_{opt}^{2}}\bigg{)}dv= \bigg{\{}\frac{1}{b}\sum_{i=1}^{b}\Big{[}\Phi\Big{(}\frac{\tau-v_{i}}{h_{opt}} \Big{)}+\Phi\Big{(}\frac{v_{i}}{h_{opt}}\Big{)}\Big{]}\bigg{\}}-1 \tag{35}\]  
where \(\Phi(\ )\) is the cumulative distribution function of standard normal distribution.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 2 SINS/GPS Fusion Model
### Measurement Model  
Within the tightly coupled integration model, the measurement state vector of KF is constructed by the relation between the INS derived measurements and GPS raw observables. The GPS observables can be basically implemented with three different measurements, such as carrier phase, pseudo-range and Doppler. In order to obtain high accuracy results, the carrier phase measurements have to be used in the filter update. Considering this fact, in this paper the duel-differential (DD) pseudo-range data and single-differential (SD) Doppler data, which can be implemented in the integration system, will be used. The GPS measurement model can be described as  
\[\left\{\begin{array}{l}\Delta\
abla\rho_{GPS}^{e}-\Delta\
abla\rho_{INS}^{e }=H^{e}C_{n}^{e}\delta r+H^{e}C_{b}^{e}\delta L^{b}+\Delta\
abla\varepsilon_{\rho} \\ \
abla D_{GPS}^{e}-\Delta D_{INS}^{e}=\frac{1}{\lambda_{GPS}}H^{e}C_{n}^{e} \delta v+\
abla\varepsilon_{D}\end{array}\right. \tag{3}\]  
in which \(\
abla\) stands for differential value between two adjacent satellites and \(\Delta\
abla\) represents the double differential arithmetic; \(D_{GPS}^{e}\) and \(\rho_{GPS}^{e}\) refer to GPS Doppler and pseudo-range observables, respectively; \(D_{INS}^{e}\) and \(\rho_{INS}^{e}\) indicate the Doppler and pseudo-range data given by INS, respectively; \(\varepsilon_{\rho}\) and \(\varepsilon_{D}\) indicate the pseudo-range and Doppler data of measurement noise, respectively; \(H^{e}\) denotes the geometry transform matrix; \(C_{n}^{e}\) denotes the rotation matrix from navigation frame to earth frame; \(C_{b}^{e}\) denotes the rotation matrix from body frame to earth frame; and \(\lambda_{GPS}\) denotes the wavelength of GPS signal.  
Because of the uncertainty of lever arm, deviation between IMU physical center and GPS antenna should be considered, especially under the conditions that the value of IMU prediction needs to be updated. The IMU position originates from GPS position in the e-frame.  
\[r_{GPS}^{e}=r_{INS}^{e}+C_{b}^{e}L^{b} \tag{4}\]  
in which \(L^{b}\) means the lever arm deviation vector within the body coordinate frame.  
Analogously, the relationship between the IMU speed and the GPS antenna  
\[v_{GPS}^{e}=C_{n}^{e}v_{INS}^{e}+C_{b}^{e}\left(\Omega_{ib}^{b}-\Omega_{in}^{ b}\right)L^{b} \tag{5}\]
## 3 Experimental Results
### Hysteretic Curve  
Figure 10 illustrates the load-displacement relationships of each specimen and Table 2 summarizes the average values of the experimental results including the cracking load (\(F_{cr}\)) and the corresponding displacement (\(D_{cr}\)), the peak load (\(F_{p}\)), and the corresponding displacement (\(D_{p}\)), as well as the theoretical flexural capacity of the test walls.  
Figure 10 shows that both traditional and precast test walls exhibited similar hysteretic behavior under lateral cyclic loading tests. All test walls behaved in a linear-elastic manner at the early stage of the loading process. As the imposed lateral load increased, inelastic deformation of the specimens occurred and the horizontal cracks initiated in the bed joints. As can be seen in Table 2, precast test walls showed a relatively higher cracking load than traditional walls and axial compression also delayed the appearance of horizontal flexural cracks. With the development of inelastic deformation, the outmost vertical rebar in the test walls yielded and the lateral resistance increased to the peak load. At this stage, the area of the hysteresis loops increased significantly, which led to higher levels of energy dissipation. As observed, the second cycle of loading exhibited less resisting force of the shear wall, which indicated the stiffness degradation of the specimen for accumulated damage [23]. Table 3 shows that CMW-1 and PMW-1 with an axial compression of \(\sigma_{n}/f_{m}^{\prime}\approx 0.10\) reached peak load at about 0.6% drift while CMW-2 and PMW-2 with an axial compression of \(\sigma_{n}/f_{m}^{\prime}\approx 0.15\) reached their peak load at a smaller displacement of about 0.4% drift. A detailed discussion on the lateral load capacity of test walls is given in Section 4.1.  
At the post-peak stage, both precast and traditional test walls exhibited ductile flexural failure. Nonetheless, Figure 10 shows that CMW-2 and PWM-2 with an axial compression of \(\sigma_{n}/f_{m}^{\prime}\approx 0.15\)  
Figure 10: Hysteretic curves of test walls: (**a**) CMW-1; (**b**) CMW-2; (**c**) PMW-1; (**d**) PMW-2.  
experienced slightly more distinct strength degradation from the peak point, which proved that axial compression had a detrimental effect on the ductile behavior of the specimens. In addition, the curves of both traditional and precast RMSWs showed obvious pinching behavior and the phenomenon concurs with the report of [PERSON] [24]. Furthermore, abrupt step-like decline of lateral load was observed in the hysteretic curves of test walls due to steel rupture and seriously compressive toe crushing under high loading displacements.
## 3 Computational Results for Windowed and Global \(L_{1}\) Splines
Figure 4: Second-derivative-based global \(L_{1}\) spline  
Figure 3: Second-derivative-based 7-point-window \(L_{1}\) spline  
Figure 2: Second-derivative-based 5-point-window \(L_{1}\) spline  
Figure 5: First-derivative-based 5-point-window \(L_{1}\) spline  
Figure 6: First-derivative-based 7-point-window \(L_{1}\) spline  
Figure 7: First-derivative-based global \(L_{1}\) spline  
Figure 8: Function-value-based 5-point-window \(L_{1}\) spline  
Figure 10: Function-value-based global \(L_{1}\) spline  
Figure 9: Function-value-based 7-point-window \(L_{1}\) spline  
Figure 11: Antiderivative-based 5-point-window \(L_{1}\) spline  
Figure 3:0 Antiderivative-based 7-point-window \(L_{1}\) spline  
Figure 3:1 Antiderivative-based global \(L_{1}\) spline  
We do not claim here that the results presented in Figures 2-13 should yet be broadly interpreted as general descriptions of the shape-preservation capabilities of the various types of \(L_{1}\) splines. However, these results do provide insight into the shape-preservation capabilities of these types of \(L_{1}\) splines as well as a piece of evidence supporting the use of second-derivative-based 5-point-window \(L_{1}\) splines. In these computational results, the performance of second-derivative-based \(L_{1}\) splines, the hitherto most widely used variant of \(L_{1}\) splines is improved by using 5-point windows. The other two \"top performers\" of Figures 2-13, namely, the first-derivative-based and function-value-based global \(L_{1}\) splines of Figures 7 and 10 are computationally much more expensive than second-derivative-based 5-point-window \(L_{1}\) splines. No theory has been published for function-value-based \(L_{1}\) splines. Elementary theory has been published for first-derivative-based \(L_{1}\) splines but only for the global case [15]. Based on these considerations, we choose the second-derivative-based 5-point-window \(L_{1}\) spline as the \(L_{1}\) spline to be used in the remainder of this paper.