esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Energy Analysis for the Engine
The pumping losses have a 37% decline at the CDAc mode due to no pumping losses in the deactivated cylinder. The change in exhaust energy \(\rm N_{ex}\) is related to the temperature and the flow rate of exhaust gas from the active cylinders and the deactivated cylinders. At an engine speed of 450 rpm and a load of 1000 N\(\cdot\)m, the flow rate of exhaust gas for the normal, CDAo, and CDAc models is 2877.1, 2910.9, and 1520.6 kg/h, respectively. In comparison with the normal mode, at CDAo mode, the temperature of exhaust gas from the active cylinders rises by 80.4 K, but the temperature of non-combustion air from the deactivated cylinder decreases by 98.6 K, which leads to an 8.76 kW decrease in exhaust power \(\rm N_{ex}\) for the whole engine. In comparison with the normal mode, at CDAc mode, the temperature of exhaust gas from the active cylinders rises by 53.2 K, but the flow rate of exhaust gas is reduced by 47.1%, due to disabling the motion of intake and exhaust valves for the deactivated cylinder. As a result, the exhaust power \(\rm N_{ex}\) is reduced by 15.08 kW at the CDAc mode compared with the normal mode. The mechanical loss changes are comprehensive results of the work change in active cylinders and deactivated cylinders. As shown in Figure 9, the analysis of mechanical losses was carried out for the porotype engine and two CDA modes. Engine mechanical losses consist of friction losses of piston group, friction losses of crankshaft bearings, friction losses of the valvetrain, and auxiliary mechanism losses. In comparison with the normal operation, the mechanical losses for the CDAc mode are reduced from 66.3 kW to 65.5 kW, which are attributed to the reduction in friction losses of the valvetrain. However, with all valves closed in the deactivated cylinders, the frictional losses of piston rings are increased due to the raised gas pressure and the lubricating oil temperature decrease. Friction losses  
Figure 11: The distribution of fuel combustion energy for the engine (**a**) and mechanical loss components of the engine (**b**) under the normal mode and CDA modes at an engine speed of 450 rpm and an engine load of 1000 N\(\cdot\)m (the calculated results were obtained from 1D simulation of GT-SUITE). of the main bearings and big-end connecting rod bearings are decreased due to the overall load drop. Moreover, at a constant engine speed and low loads, the auxiliary mechanism losses almost do not change practically.
## 2 Bending Loss
### Receiving Optical Fiber Slots  
Using the same concept described above. The fiber bend can create a receiving terminal for the light coupled from the surroundings into the fiber. Some of the incident angles  
Figure 4: Experiment Setup.  
Figure 5: Measured Output Power (dBm) vs. Bend Radius(mm), and a Plot of Equation (24) as Dotted Line.  
at the bend allow light to enter the fiber from the surrounding area. These angles satisfy [PERSON]'s law, expressed as follows:  
\[n_{0}\text{sin}(\theta_{0})=n_{1}\text{sin}(\theta_{1})=n_{2}\text{sin}(\theta_{ 2}), \tag{27}\]  
where \(\theta_{0}\) is the incident angle from air into cladding, \(\theta_{1}\) is the incident angle from cladding into core and \(\theta_{2}\) is the transmitted angle in the core that satisfies  
\[\theta_{2}\leq\frac{\pi}{2}-\text{sin}^{-1}\bigg{(}\frac{n_{2}}{n_{1}}\bigg{)}. \tag{28}\]  
Therefore, the values of \(\theta_{0}\) is given by:  
\[\theta_{0}\leq\text{sin}^{-1}\bigg{(}\frac{n_{2}}{n_{1}}\text{sin}(\theta_{2}) \bigg{)}. \tag{29}\]  
Therefore, some of the incident angles will satisfy [PERSON]'s law, and light insertion into the optical fiber is possible.  
In our experiment, \(n_{0}=1\), \(n_{1}=1.48\) and \(n_{2}=1.5\). This will allow angles of less than \(14.13^{\circ}\) to penetrate the fiber. In the following section, we used the effect of bending of the optical fiber to generate transmitting points along the optical fiber. This creates an Optical Leaky Feeder (OLF) array transmitter.
## 3 Results and Discussion
### CO Concentration Experimental Results
#### 3.1.2 Evaluated by the ICOS CO Observations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Type** & \begin{tabular}{c} **CAM-Chem** \\ **(ppb)** \\ \end{tabular} & \begin{tabular}{c} **SIM** \\ **(ppb)** \\ \end{tabular} & \begin{tabular}{c} **DA** \\ **(ppb)** \\ \end{tabular} & \
\begin{tabular}{c} **MOPITT** \\ **(ppb)** \\ \end{tabular} &
\begin{tabular}{c} **ICOS** \\ **(ppb)** \\ \end{tabular} \\ \hline Exp2017 & median & 144.45 & 115.12 & 127.31 & 131.40 & 139.95 \\  & mean & 158.44 & 130.33 & 175.70 & 146.17 & 157.86 \\ Exp2018 & median & 121.39 & 114.77 & 121.37 & 142.63 & 148.25 \\  & mean & 130.51 & 127.07 & 151.01 & 151.41 & 172.99 \\ Exp2019 & median & 114.30 & 110.60 & 114.23 & 131.11 & 143.13 \\  & mean & 125.94 & 123.48 & 134.43 & 138.27 & 168.72 \\ Exp2020 & median & 107.10 & 106.52 & 109.92 & 129.20 & 132.92 \\  & mean & 115.14 & 117.51 & 129.11 & 137.43 & 150.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The mean and median CO concentration values in the boxplots of Figure 13.  
the MBE values were negative, implying that the CO concentrations from the DA experiments and the external CAM-Chem results [48] were, overall, lower than the ICOS observations [47], which is consistent with [PERSON]'s research [87]. The average MBE of the DA experiments was \(-22.43\) ppb, and was \(17.52\)-ppb and \(14.84\)-ppb better than that of the SIM experiments and the external CAM-chem results [48], respectively. The RMSEs of the DA experiments were also the best, the average RMSE was \(4.17\)-ppb and \(13.03\)-ppb better than that of the SIM experiments and the external CAM-chem results [48], respectively.
# Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network
###### Abstract
Furthermore, the MPC based prediction only refers to the current system model state as initial value [15]. Therefore, the strategy based on MPC that only uses an internal system model could be less reliable if external factors occur. Traffic is a complex situation where the EV could be highly influenced by uncertainty of external factors. The study in [16] carried out the significance of future power demand information into HESS performance. Fortunately, the recent development of vehicular networks supported by the internet of things (IoT) has made it possible to obtain future power demand information even the process is not straightforward [17]. The vehicular networks allow information sharing between connected EVs to improve situational awareness and decision-making for an individual EV. For instance, information about vehicle speed, distance, traffic light duration, road signs, and other IoT devices could be well retrieved. Motivated by the advantages of IoT and the limitation of previous works, this paper proposes a power management strategy with two-level control structures. The first level utilizes FLC to control the power split of HESS, while the second level adopts an artificial neural network (ANN) to perform power demand prediction. Thus, when compared with the previous works [13; 14] the proposed strategy does not simply use single direct structure but the combination of two methods to optimize the HESS by performing future power demand prediction as the references input to FLC. Furthermore, the ANN in the proposed strategy could perform direct and simple power demand predictions compared to MPC in [13]. The input data for power demand prediction could be obtained by considering the information from IoT technology. The rest of this paper is organized as follows. Section 2 introduces the scenarios of the system model and description. The proposed power management strategy is formulated in Section 3. Section 4 presents a detailed result of the simulation, and the conclusion is given in Section 5.
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 5 Experiments
### Analysis and Discussions  
To discuss the generalization of the algorithm, we analyze the algorithm visually on the test sequence 00. As shown in Figure 5, the vehicles with IDs 83, 115, 102, 113, 117, and 87 are all well detected and tracked. Although the detection of the vehicle with ID 101 between frames 113 and 117 failed at frame 175, the vehicle with ID 101 with a dark purple bounding box was re-detected in the subsequent 179 frames and retained its original ID. This means that our tracker is extremely robust.  
Figure 4: Qualitative results of our visual-based tracker on KITTI. The color of each bounding box indicates the target identity. The number of frames, fps and tracked objects is shown in the upper left corner of each image.  
Figure 5: Three-frame tracking results on test sequence 00 of the KITTI tracking benchmark.  
In the TBD paradigm, the detection result determines the performance upper bound of the algorithm, and the data association determines whether the upper bound can be reached. To some extent, the data association capability of the proposed method achieves its detection performance. This can be seen from the comparison results in several tables, where the performance of our tracker for data association is relatively high, while the detection results are good. The performance of the vehicle-tracking algorithm is also confirmed in the qualitative analysis. Even if a target is lost in an intermediate frame, subsequent targets can still be assigned the same ID. This is also due to the excellent performance of the motion model and the matching mechanism that incorporates embedding.  
On the KITTI dataset, the frame rate of our algorithm is about 16 fps, which is determined by both the size of the input image and the number of objects.  
In the future, we will consider three technologies to optimize the runtime of the deployment on Nvidia devices. First, the architecture of the detection model can be changed according to its size, depth, and width. Secondly, quantization can be used to change the digital representation of data and network weights. For example, we can replace floating-point numbers with integers. Finally, we will choose the appropriate image resolution. We will conduct hardware acceleration and quantitative experiments with TensorRT on the target platform. For TensorRT reasoning, we can export the trained PyTorch model to ONNX and parse it into the optimized TensorRT runtime engine in a C++ environment on the target system. TensorRT allows us to choose the required quantization when building the engine. There are three options: Float32, Float16 and Int8.
## 4 Simulation Results
### Scenario 3
As shown in the analysis of current waveforms in Figure 10c-f, the proposed DCch + CBFCL-based SVM method improves active power and DC-link voltage control by making current waveforms more stable during LVRT, increases system stability by reducing inverter input current fluctuations, and supports safe operation of the equipment by preventing overcurrent conditions. As a result, it is shown that the proposed method improves the LVRT performance, ensuring more reliable and efficient operation of grid-connected PV systems. Figure 10: _Cont._  
The DCch-based Tree protection method has higher ripples than the DCch + CBFCL-based SVM protection method due to the inherent limitations of the response of the DC chopper during a voltage dip, as shown in Figures 8-10. The rapid fluctuations in voltage and current values occur when it experiences a sharp voltage dip on the grid. The DCch-based Tree cannot completely compensate for these rapid fluctuations on the grid, which causes low-frequency oscillations due to the finite response time and switching characteristics of the DC chopper.
# Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs
## Problem Description and Modeling
### Model Building  
#### 2.3.1 Constraint Conditions  
1. Drone performance constraints: 1. Flight range The distance \(l_{ij}\) from the delivery point \(i\) to the demand point \(j\) should meet the longest flight range \(L_{ij}^{m}\) of the drone \(m\)[27], which is expressed as: \[l_{ij}\leq L_{\max}^{m}\quad\exists m\in M\] (1) (2) Load Only when the weight \(w_{k}\) of the package \(k\) is within the maximum load limit \(W_{ij}^{m}\) of the drone \(m\), can the package be transported by the drone \(m\)[29], which can be expressed as: \[w_{k}\leq W_{\max}^{m}\quad\exists m\in M\] (2) (3) Working hours The working time \(t_{ij}^{m}\) of the drone should not exceed its own working time limit \(T^{m}\). If the working time is too long, its battery pack will be exhausted, and the drone will stop working. The formula is expressed as: \[\sum_{j=1}^{J}t_{ij}^{m}\leq T^{m}\quad\exists m\in M\] (3) (4) Charging duration When the drone \(m\) stops working due to insufficient power, the battery pack needs to be charged, and the drone \(m\) must wait until the charging is completed before continuing to work. The formula is expressed as: \[x_{i}=\left\{\begin{array}{l}0\text{ UAV }i\text{ charging}\\ 1\text{ UAV }i\text{ not charging}\end{array}\right.\] (4) (5) Backup battery Consider that when the power of the drone \(m\) is insufficient, a backup battery \(n\) can be selected from the total backup battery pack \(N\), and then the primary battery pack will be charged. The drone \(m\) can continue to work after replacing the primary battery, but if the backup battery pack is charging and there is no spare battery available, the drone \(m\) will stop working and must be charged. The formula is expressed as: \[y_{i}=\left\{\begin{array}{l}0\text{ Backup battery }n\text{ available}\\ 1\text{ Backup battery }n\text{ not available}\end{array}\right.\] (5)
2. Airspace constraints: 1. Flight altitude As the drone \(m\) flies in the low-altitude airspace, the flight altitude \(h^{m}\) should meet the maximum flight altitude limit \(H_{\text{max}}\) and the minimum limit \(H_{\text{min}}\) allowed in the airspace. This is expressed by the formula:  
\[H_{\text{min}}\leq h^{m}\leq H_{\text{max}}\quad\forall m\in M \tag{6}\]  
(2) Flight speed  
The drone \(m\) flies in the low-altitude airspace, and its flight speed \(v^{m}\) must meet the maximum and minimum allowable flight speeds \(V_{\text{max}}\) and \(V_{\text{min}}\) in the airspace. In this article, we stipulate that the drone has a speed of \(V_{\text{min}}\) when loading goods for distribution and a speed of \(V_{\text{max}}\) when returning empty. This is expressed as:  
\[V_{\text{min}}\leq v^{m}\leq V_{\text{max}}\quad\forall m\in M \tag{7}\]
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 5 Results and Discussion
### Surface Tests  
The first and second surface examples demonstrate the robustness of the second-order algebraic point projection for NURBS surfaces involving discontinuous and non-existent footpoints, respectively. In the first example (Figure 19), the discontinuous projection occurs again when the Newton-Raphson method is applied on a surface segment with high mean curvature. In the second example (Figure 20), the Newton-Raphson method failed in regions where the mathematical footpoints do not exist. Not only does the algebraic point projection overcome those problems, but it also produces an accurate and efficient solution. As listed in Table 4, the computational cost per point of the proposed method is only 26% and 14% of that of the Newton-Raphson method in the first and second surface examples, respectively.  
Figure 17: Relative error \(\frac{|u_{f}^{NR}-u_{f}^{APP}|}{b-a}\) vs. distance of test points \(d(\mathbf{x})\), where \(u_{f}^{NR}\) and \(u_{f}^{APP}\) are parameter values of footpoints obtained using the Newton–Raphson method and the algebraic point projection respectively.  
Figure 18: Illustration of the robustness of the 2D algebraic point projection for the NURBS curve. (**a**) Trace of points that were projected onto target curve. (**b**) Solution parameter of footpoints on target curve vs. parameter of trace curve for the two methods. Parameter discontinuity in Newton–Raphson solution occurs due to non-uniqueness of the footpoint near the local minimum at \(u_{trace}\approx 0.176\)Figure 19: Illustration of the robustness of the 3D algebraic point projection algorithm involving discontinuous footpoints. (**a**) Trace of points that were projected onto bowl-shaped target surface using the proposed algebraic method and the Netwon-Raphson method. (**b**) Parameters of footpoints on the target obtained by both the methods. Discontinuity occurs due to non-unique footpoints for test points near the bottom of the surface.  
Figure 20: Illustration of the robustness of the 3D algebraic point projection algorithm involving test points whose mathematical footpoints do not exist. (**a**) Trace of points which are projected onto mountain-shaped target surface using both methods. (**b**) Parameters of footpoints on target. The solution does not exist near the four mountain ridges of \(\mathscr{G}^{0}\) continuity as shown in the four corner regions of (**b**).
#### 1.1.2 Impacts on High Energy Consumption  
The impacts of high energy consumption on economic and environmental development were also investigated. The investigations include coping with hikes in tariffs amid power shortages in urban households in Ethiopia [6], geothermal heat pumps interacting with low-temperature thermal microgrids and their benefits to the global and local environmental and energy [7], a value-based approach with a focus on creating sustainable alternatives in the built environment [8], developing a residential building's electricity consumption profile using smart meter data grouping [9], renewable energy conservation and the vast fields of the United States [10], combining power and gas in the transition to future urban energy systems [11], and sustainable home service to household services that promote ecological, social and economic sustainability [12].
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 5 Conclusions  
An algorithm for retrieving the SM is proposed in this paper based on the parameterization of some theoretical models. The spatial distribution of SM is estimated over an agricultural area in Hebei Province of China using both the Sentinel-1 and Sentinel-2 data. The encouraging comparison results (R = 0.85, RMSE = 0.032 cm\({}^{3}\cdot\)cm\({}^{-3}\)) between the SM inversion results and the measured values confirm that it is feasible to develop the SM inversion algorithm based entirely on theoretical model simulations instead of the essential support of the in situ measured data.  
Since the performance of the modified ratio model developed in this paper depends entirely on whether the input parameter configuration of the MIMICS model is consistent with the actual situation, it is not suitable for areas with a variety of unknown vegetation types. The differences between the vegetation types are significant, and the model-based simulations are only valid when the model input parameters match the actual vegetation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**28 April**} & **16 April and 28** & **28 April and 10** & **16 April and 28** \\  & & **April** & **May** & **April and 10** \\  & & & & **May** \\ \hline R & 0.56 & 0.79 & 0.77 & 0.85 \\ RMSE/cm\({}^{3}\cdot\)cm\({}^{-3}\) & 0.061 & 0.041 & 0.044 & 0.032 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Inverted SM accuracy comparison under the data combination on different dates.  
Figure 5: The retrieved SM Scatter plot between the retrieved SM and measured SM.  
type. Therefore, the empirical coefficients listed in Table 3 in this paper is only applicable to winter wheat and not to other vegetation types. However, the method proposed in this paper is completely independent of the in situ measured data, and it can well predict the changing trend of SM in the areas where the prior knowledge of the vegetation types has been known. Under the premise of correctly setting the input parameters of the scattering model, the method proposed in this paper can be effectively implemented to retrieve the soil moisture information especially in areas where in situ SM measurements are not easy to carry out.  
Software and writing--original draft preparation, Z.W. and S.S.; data analysis and investigation, Y.J. and S.L.; methodology and writing--review and editing, H.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Shandong province Natural Science Foundation of China, grant number ZR2021 MD067, the Fundamental Research Funds for the Central Universities, grant number 22 CX03011A, and the National Natural Science Foundation of China, grant number 41971292.  
The authors declare no conflict of interest.
## 4 Case Study
### Calculate combined weights  
The integrated weights of the metrics are calculated by using the formula for computing the integrated weights in Section 3, by substituting the subjective weights obtained from the AHP calculation and the objective weights obtained from the entropy weighting method, and the results are shown in Table 10 below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **2010** & **2011** & **2012** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** \\ \hline B\({}_{11}\) & 0.98 & 0.98 & 0.72 & 0.97 & 0.71 & 0.89 & 0.97 & 0.94 & 0.73 & 0.78 & 0.82 & 0.91 \\ B\({}_{12}\) & 0.99 & 0.73 & 0.84 & 0.87 & 0.93 & 0.91 & 0.79 & 0.92 & 0.88 & 0.79 & 0.87 & 0.89 \\ B\({}_{13}\) & 0.83 & 0.98 & 0.92 & 0.81 & 0.76 & 0.9 & 1.00 & 0.99 & 0.76 & 0.82 & 0.89 & 0.93 \\ B\({}_{14}\) & 0.86 & 0.73 & 0.92 & 0.97 & 0.85 & 0.89 & 0.84 & 0.78 & 0.71 & 0.79 & 0.93 & 0.98 \\ B\({}_{15}\) & 0.91 & 0.83 & 0.72 & 0.83 & 0.95 & 0.92 & 0.87 & 0.88 & 0.81 & 0.98 & 0.93 & 0.90 \\ B\({}_{21}\) & 0.9 & 0.93 & 0.86 & 0.81 & 0.87 & 0.79 & 0.74 & 0.89 & 0.98 & 0.97 & 0.92 & 0.83 \\ B\({}_{22}\) & 0.8 & 0.84 & 0.91 & 0.73 & 0.75 & 0.81 & 1.00 & 0.92 & 0.95 & 0.82 & 0.84 & 0.79 \\ B\({}_{23}\) & 0.99 & 0.95 & 0.86 & 0.81 & 0.79 & 0.94 & 0.91 & 0.73 & 0.86 & 0.83 & 0.79 & 0.80 \\ B\({}_{31}\) & 0.93 & 0.71 & 0.75 & 0.72 & 0.79 & 0.81 & 0.85 & 0.97 & 0.92 & 0.82 & 0.9 & 0.81 \\ B\({}_{32}\) & 0.95 & 0.91 & 0.76 & 0.74 & 0.89 & 1.00 & 0.93 & 0.95 & 0.91 & 0.82 & 0.86 & 0.99 \\ B\({}_{33}\) & 0.89 & 0.91 & 0.84 & 0.76 & 0.78 & 0.93 & 0.92 & 0.95 & 0.89 & 0.81 & 0.77 & 0.90 \\ B\({}_{41}\) & 0.88 & 0.85 & 0.98 & 0.92 & 0.91 & 0.78 & 0.77 & 0.83 & 1.00 & 0.94 & 0.9 & 0.85 \\ B\({}_{42}\) & 0.99 & 0.95 & 0.89 & 0.84 & 0.75 & 0.79 & 0.97 & 0.92 & 0.89 & 0.83 & 0.73 & 0.79 \\ B\({}_{43}\) & 0.98 & 0.95 & 0.91 & 0.85 & 0.77 & 0.73 & 0.89 & 0.86 & 0.9 & 0.91 & 0.97 & 0.94 \\ B\({}_{44}\) & 0.83 & 0.89 & 0.85 & 0.79 & 0.82 & 0.94 & 0.90 & 0.91 & 0.98 & 0.85 & 0.84 & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Data for previous years.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Level 1 Indicators** & **Secondary Indicators** & **Objective Weights** \\ \hline  & Equipment load & 0.0888 \\  & Operating temperature & 0.0490 \\  & Viscosity of the lubricant & 0.0886 \\  & Anti-wear properties of lubricants & 0.0671 \\  & Thickness of oil film & 0.0399 \\ \hline  & Failure rate & 0.0521 \\ Fault maintenance & MTBF & 0.0772 \\  & MTTR & 0.0672 \\ \hline Energy saving and reduction & Lubricant replacement intervals & 0.0810 \\  & Lubricant consumption & 0.0578 \\  & Electricity consumption & 0.0795 \\ \hline  & Selection of friction sub-materials & 0.0700 \\  & Selection of lubricants & 0.0736 \\  & Payback period & 0.0462 \\  & Profitability & 0.0655 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of objective weight calculation by entropy weight method.