esa-sceva/satcom-chunk-collection huggingface dataset:
# Status of Pure Electric Vehicle Power Train Technology and Future Prospects
## 4 Electric Propulsion
### Switch Reluctance Motor  
SRMs, also known as doubly salient motors, are synchronous motors and they are driven by unipolar inverter-generated current. SRM motors work on the principle of variable reluctance. SRMs is mostly suitable for high-speed operation without mechanical failure. Due to their high mechanical integrity, they are also suitable for driving EVs as in-wheel drive systems. However, they have the disadvantages of lower torque density, higher torque ripple, and larger acoustic noise [108; 109; 110].  
The simple rotor structure is and does not require windings, magnets, commutators, or brushes, so it has rapid acceleration and immensely high-speed operation. Making it suitable for gearless operation in EV propulsion [111; 112; 113]. The current copping control (CCC) and the advance angle control (AAC) are the two main control systems for SR motor control systems. The speed boundary between these two control schemes is called the base speed, \(\omega\)b, at which the back EMF is equal to the DC source voltage.  
The back EMF is lower than DC voltage below the base speed control system, the phase current can be regulated at the rated value, hence offers the constant-torque operation by regulating the phase current at rated speed. When the back EMF is higher than the DC source voltage above the base speed, the torque drops. When the back EMF increases with rotor speed decreasing, the phase current torque drops inversely to the rotor speed, in the AAC system running the SRM in constant power region, phase advancing is not possible beyond the critical speed of the motor, thus it operates in natural operation mode.  
The Figure 12 shows the torque-speed profile in all three operating regions: constant torque operation, constant power region, and natural operating region application of SRMs in electric vehicles. The earliest first SR motor recorded one was built by [PERSON] in Scotland in 1838 for propelling a locomotive. Also, a lot of research and development projects are being carried out with application of SRM drive systems in electric propulsion systems [114; 115].  
Two major types of SR motor drives are illustrated for driving EVs. The first one is a SR with high-speed capable motor internally coupled with a planetary gear for speed reduction. The second one is a low-speed SR motor for in-wheel direct-drive configuration [62; 113; 116].  
(a) Planetary-Geared SR Motor Drive  
During single-motor architecture system, the design of SR motor drive is focused for high-speed operation using a planetary gear synchronize the motor speed to wheel speed [62].  
(b) Outer-Rotor In-Wheel SR Motor Drive  
To avoid the transmission gear and differential gear, in driving EVs the SR with an outer-rotor topology for low-speed operation are designed so as to directly drive each wheel.  
Figure 12: Torque–speed capability of the SR motor [78].
## 3 System Design
### Scene Recognition Model  
To achieve scene recognition, we built a lightweight ShuffleNet network, whose key design philosophy is based on two principles. First, a group convolution (GC) is used as the basic unit with a pointwise convolution, which replaces the standard \(1\times 1\) convolution and effectively reduces computational and parametric model costs. Second, a channel shuffle is used to solve the problem of the lack of shared feature information between groups caused by GCs and to enhance the model's feature representability by facilitating channel information exchange. The overall ShuffleNet structure is illustrated in Figure 3.  
The model consists of several basic stages, and each is composed of multiple essential units. Unit_A has a stride of one, and Unit_B requires downsampling with a stride of two. Conv, GC Conv, DW Conv, GAP, and FC denotes regular convolutional layer, group convolution, depthwise convolution, global average pooling, and the fully connected layer, respectively. SoftMax is set in the classifier layer to output the probability distributions of the \(11\) scene labels.
## 3 Experimental Studies  
The experimental setup is shown in Figure 8, consisting of a KUKA KR500-2830 MT industrial robot (KUKA Robotics China Co.,Ltd., Shanghai, China) controlled by a Siemens 840 Dsl numerical control system (Siemens Ltd., Berlin and Munich, Germany) and a Reckerth RF170D motor spindle (Hugo Reckerth GmbH, Filderstadt-Bonlanden, Germany). The positioning errors are collected by a Faro VantageE laser tracker (FARO Technologies Inc., Lake Mary, FL, USA). In order to reduce the error caused by repetitive fitting base frame \(\{BR\}\) and the possible temperature drift and zero drift produced by the measuring equipment in the sampling process, three reference targets are set on the platform, and the platform frame \(\{P\}\) is calibrated through these three targets. The transformation relationship between \(\{P\}\) and \(\{BR\}\) is \({}^{\mathrm{P}}\)\(T_{\mathrm{BR}}\). During the regionalized TCP calibration and error sampling process, only \(\{P\}\) is re-calibrated, and the actual base frame \(\{BR\}\) can be obtained by \({}^{\mathrm{World}}\)\(T_{\mathrm{BR}}=^{\mathrm{World}}\)\(T_{\mathrm{P}}\cdot^{\mathrm{P}}\)\(T_{\mathrm{BR}}\).  
When executing the NC program in the robot control system, auxiliary code has been added in advance by means of offline programming to indicate that a certain line of command needs to be calibrated. The user-defined HMI interface and R parameters can be used to interact with NC programs so as to transmit the desired TCP frames and compensated TCP frames. Hence, the PCS is developed by an HMI interface and communicates with the 840 Dsl CNC system through Ethernet.  
The drilling task of typical aeronautical curved surface parts faced by the robotic machining system is shown in Figure 9. For the purpose of verifying the regionalized compensation method proposed in this paper, the drilling area in the range of \(-110^{\circ}\)--\(40^{\circ}\) and \(40^{\circ}\)-\(110^{\circ}\)are divided into Region 1 (\(\Omega_{1}\)), Region 2 (\(\Omega_{2}\)), Region 3 (\(\Omega_{3}\)) and Region 4 (\(\Omega_{4}\)). The material of the parts to be processed is plexiglass. The spindle speed and feeding speed of the drilling process are 3000 rpm and 1 mm/s, respectively.  
Figure 7: Robot error compensation process.
## 2 Experimental Part
### Adsorption Tests  
A certain amount of sorbent suspension, uranium stock solution, NaNO\({}_{3}\), and a multi-ion solution were simultaneously placed into a group of 10.0 mL PE tubes to attain the desired concentrations. Experimental equilibrium adsorption data of PVK and PVKAP were obtained under the condition of \(C\) U(V)initial = 12.8-79.4 mg L\({}^{-1}\), m/V = 0.1 g L\({}^{-1}\), pH = 5. The pH values of the suspension were adjusted by adding a negligible volume of NaOH or HNO\({}_{3}\) solutions (0.1-1.0 mol L\({}^{-1}\)) and measured by a pH meter. Then, the suspensions were oscillated in the reciprocating shaker for a specific time and temperature, and each PE tube was centrifuged to separate the solid phase from the liquid phase. The concentrations of U(VI) in the supernatants were determined by a kinetic phosphorescence analyzer (KPA-11, Richland, WA, USA). The concentrations of U(VI) mixed with other competitive absorption metal cations were measured by an inductively coupled plasma atomic emission spectroscopy (ICP-AES, Optima 7300 DV, PerkinElmer, Waltham, MA, USA). The adsorption capacities (\(q_{\text{e}}\)) of U(VI) and adsorption percentage (\(R\%\)) were calculated by the following equations [40]:  
\[q_{e}=\frac{(C_{0}-C_{e})\ V}{\text{m}} \tag{1}\]  
\[R\%=\frac{C_{0}-C_{e}}{C_{0}}\times 100\% \tag{2}\]  
where \(C_{0}\), \(C_{\text{e}}\), \(V\) and \(m\) represent the initial concentration of U(VI), the residual concentration of U(VI), the volume of uptake systems, and the mass of adsorbent, respectively. (In this work, \(C_{0}\) = 0.5 mmol L\({}^{-1}\) for all cations, pH = 5.0 \(\pm\) 0.1, m/V = 0.05 g L\({}^{-1}\)). The adsorption selectivity related to U(VI) (\(S_{\text{U}}\)) was measured to illustrate the effectiveness of selectivity of the adsorbent to U(VI):  
\[S_{U}=\frac{q_{e}-U}{q_{e}-tol}\times 100\% \tag{3}\]  
where \(q_{\text{e-U}}\) and \(q_{\text{e-tol}}\) are, respectively, the adsorbed amount of uranium and all cations in multi-ion solution.
## 4 Results
### Linear Case
#### 4.1.3 Step Function  
After testing our architecture to recover the piece-wise forcing function in the previous section. In the present section, we attempt to solve a problem that is much more challenging. We try to determine if our architecture can recover functions involving discontinuities.  
Figure 5: Figure shows the agreement between neural network predictions and actual forcing functions: (**a**) sinusoidal (**b**) combination of parabolic, linear and cubic (**c**) triangular (**d**) step for spring mass damper system.  
To answer this question we train the neural network on the data generated by simulating the spring mass damper system with \(x_{0}=-3.4,\dot{x}_{0}=-2.4\) and step function below,  
\[f(t)=\begin{cases}0&0\leq t<5\\ 10&5\leq t<10\\ -10&10\leq t<30\\ 0&30\leq t\leq 50\end{cases} \tag{12}\]  
A step function is marked by constant values on specific intervals followed by sudden jumps in values at the point of transition.  
Figure 5d shows our findings. It can be observed NN was able to recover the majority of the function from the data. The constant values of the step function are complemented with oscillatory predictions. These oscillations resemble the Gibbs phenomenon, which is observed when approximating functions with jump discontinuities using the Fourier series.
# Incremental Construction for Scalable Component-Based Systems
## 4 Incremental Construction in CBD
### EX-MAN Component Model  
The conventional and fundamental X-MAN component model was defined with encapsulated components and exogenous connectors [53; 54]. Pre-defined exogenous connectors [55] is a unique set of connectors which play a vital role for the suitability of X-MAN for incremental system construction. The concept of exogenous connectors are also proposed to compose web services [56]. However, being an abstract component model, X-MAN does not define many features precisely. For this reason, there are many different definitions of some exogenous connectors and these are implemented in many different ways in the supporting tools (used in [57; 58; 59; 60; 61; 62]) of X-MAN which is not re-producible. In order to overcome these limitations of X-MAN, without violating the fundamental concepts of the model, we extended the model by addressing the limitations of X-MAN; this extended model is referred as EX-MAN [11; 12; 63]. With regards to the strongest feature of X-MAN, the exogenous connectors are precisely defined to be functioning with fixed behavior with the help of flow constraint language (FCL) [63] defined in EX-MAN.  
In EX-MAN, components have one provided interface and components do not call other components' services directly. Components are composed by exogenous composition connectors. The functionality of a component is the set of services exhibited by its interface. To construct the calculator example, we assume three atomic components _Calc_ (with a service to add two numbers), _sCom_ (with a service to square a number) and _cCom_ (with a service to cube a number). By using instances of these components, we compose the system in two constructional steps, as shown in Figure 23. In the first step, we compose _sCom1_ (\(S_{0}\)) with _cCom1_ (\(inc_{0}\)) by a sequencer connector (SEQ1). In composite \(S_{1}\) (Figure 23a) SEQ1 executes a service from _sCom_ and then a service from _cCom_. In the second step, we add _Calc1_ (\(inc_{1}\)) with the current system \(S_{1}\) by means of a pipe connector (PIPE1). In this composite \(S_{2}\) (Figure 23b), the computed results from SEQ1 are passed as input values to _Calc1_ by PIPE1. After each incremental step, the functionality of the incremented system (\(B_{S_{i+1}}\)) contains the behavior of the previous system (\(B_{S_{i}}\)). To avoid cluttering, we only shown the minimum feature of the EX-MAN system which is necessary for this study.
# A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem
## 2 Review
(2018) studied the role of HF in reverse network recycling, and the results also indicated that the main parameters of HF reduce CO\({}_{2}\) emissions. To reduce CO\({}_{2}\) emissions, [PERSON] et al. (2018) considered two types of heavy vehicles with different emission models and analyzed the impact of multi type and homogeneous fleets on CO\({}_{2}\) emissions. As a result, they showed that HF has enormous potential to reduce logistics costs and carbon emissions. Traffic congestion is one of the main factors affecting the emission of greenhouse gases, such as CO\({}_{2}\) and fuel conversion efficiency ([PERSON] et al., 2018). [PERSON] et al. (2018) believed that greenhouse gas emissions, such as CO\({}_{2}\) and fuel consumption, will increase rapidly and non-linearly when the vehicle speed is below 30 mph. For example, when the speed drops from 30 to 12.5 mph, or when the speed drops from 12.5 to 5 mph, greenhouse gas emissions and fuel consumption per mile of CO\({}_{2}\) will double. Therefore, in recent years, many scholars have devoted themselves to analyzing the impact of real-time speed (or uncertainty of travel time) caused by traffic congestion on costs or CO\({}_{2}\) emissions. The method adopted is generally to use the segmentation function of speed to simulate traffic congestion (Poothal  
Let \(\lambda=\xi/k\psi\) (\(\psi\)is the conversion factor), \(\gamma=\frac{1}{1000}n_{t}\eta\), \(\beta^{\text{th}}=0.5C_{d}^{h}A^{h}\) and \(\alpha=\tau+g\sin\theta+gC_{r}\cos\theta\). Then, Equation (4) is further simplified:  
\[F^{h}=\lambda\big{(}k^{h}N^{h}V^{h}d/v+M^{h}\gamma ad+\beta^{h}\gamma dv^{2} \big{)}, \tag{5}\]  
where \(k^{h}N^{h}V^{h}d/v\) is the engine module, which is proportional to the running time; \(M^{h}\gamma ad\) is the load module, which is proportional to the mass and distance of the whole vehicle; and \(\beta^{h}\gamma dv^{2}\) is the speed module, which is proportional to the square of the distance and speed. Figure 1 shows the relationship between fuel consumption and speed when the vehicle travels 100 km. Figure 1a shows the relationship between the fuel consumption and the vehicle speed when the vehicle's own weight is not loaded, and Figure 1b shows the relationship between the fuel consumption and the vehicle speed when the vehicle's own weight is loaded. In the Figure, L1, L2, and M represent three vehicles with different parameters. It can be seen from Figure 1 that the relationship between fuel consumption and velocity exhibits a U-shaped curve and the optimal velocity exists to minimize fuel consumption, that is, \(v_{best}=\big{(}k^{h}N^{h}V^{h}/2\beta^{h}\gamma\big{)}^{1/3}\). The CO\({}_{2}\) emissions of vehicles are linearly proportional to fuel consumption; the larger the fuel consumption is, the larger the carbon emission. The conversion coefficient \(\eta_{T}\) of gasoline and carbon dioxide is 2.32 [17], that is, 2.32 kg of CO\({}_{2}\) is produced for every 1 L of gasoline consumed.
# Effects of Melatonin Treatment of Postharvest Pear Fruit on Aromatic Volatile Biosynthesis
###### Abstract  
Aroma affects the sensory quality of fruit and, consequently, consumer satisfaction. Melatonin (MT) is a plant growth regulator used to delay senescence in postharvest fruit during storage; however, its effect on aroma of pear fruit remains unclear. In this study, we assessed the effects of 0.1 mmol L\({}^{-1}\) MT on volatiles and associated gene expression in the fruit of pear cultivars 'Korla' (_Pyrus bestschneideri_ Rehd) and 'Abbe Fetel' (_Pyrus communis_ L). MT mainly affected the production of \(C_{6}\) aromatic substances in the two varieties. In 'Korla', MT inhibited expression of _PbHPL_, and reduced hydroperoxide lyase (HPL) activity and content of hexanal and (_E_)-hex-2-anal. In contrast, MT inhibited activity of lipoxygenase (LOX), reduced expression of _PbLOX1_ and _PbLOX2_, promoted _PbAAT_ gene expression, increased alcohol acyltransferase (AAT) activity, and increased propyl acetate, and hexyl acetate content in 'Abbe Fetel' that similarly led to the reduction in content of hexanal and (_E_)-hex-2-anal. Content of esters in 'Abbe Fetel' pear increased with increasing postharvest storage period. Although mechanisms differed between the two varieties, effects on aroma volatiles mediated by MT were driven by expression of genes encoding LOX, HPL, and AAT enzymes.  
pear; melatonin; aroma; enzyme activity; gene expression Article
# Seismic Blind Deconvolution Based on Self-Supervised Machine Learning
## 1 Introduction
[23] established a basic framework of self-supervised learning method for deconvolution problem with given wavelet (the SSL-D method for short). This framework incorporates the convolution model into a loss function, then the loss function guides the neural network to produce physically consistent results by minimizing the error between the observed data and the reconstructed data generated by a deep neural network. [PERSON] et al. [21] integrated the sparsity of reflectivity and lateral structural constraint as specific priors into the training process, thereby enhancing the performance of the self-supervised machine learning network to recover more structure details. As we know, seismic deconvolution is typically a blind process. Seismic blind deconvolution means that both wavelet and reflectivity are unknowns, which leads to high ill-posedness. To overcome this issue, the existing classical non-machine learning studies usually apply quite a few constraints to both wavelet inversion and reflectivity inversion. Excessive constraints make the objective function challenging to solve, and the parameters of the regularization terms are also difficult to determine. As for the machine learning studies, the SSL-D methods mentioned above both need a given wavelet. To our knowledge, self-supervised machine learning has not been explored to solve the problem of seismic blind deconvolution. Therefore, in this paper, we propose a new blind deconvolution method based on self-supervised learning aiming to reduce the reliance on a given wavelet and improve the resolution of reflectivity inversion results. Blind deconvolution is an ill-posed and underdetermined system. We take two main measures to overcome this issue. The first one is estimating a zero-phase wavelet from the seismic data. The second one is applying a relative sparsity process to the reflectivity during the joint inversion of wavelet phase and reflectivity. The steps of the proposed method are briefly described as follows. First, we smooth the spectrum of the averaged seismic data and perform inverse Fourier transform on the smoothed spectrum to obtain an initial zero-phase wavelet. Then, we use a residual neural network and a fully connected convolutional neural network to simultaneously invert reflectivity and wavelet phase (the amplitude spectrum of the wavelet has been obtained at the first step). After that, the loss function for self-supervised optimization is taken as the error between the observed data and the reconstructed data that come from the convolution of phase-rotated wavelet and reflectivity predicted by the network. The remainder of this paper is organized as follows. Section 2 describes the methodology, which includes the proposed algorithm framework, the network architectures, and the loss function. In Section 3, the experimental results of three datasets are presented. We discuss the limitations of the proposed method in Section 4. Conclusions are given in Section 5.
## 2 Materials and Methods
### Analytical Methods
#### 2.3.2 Regression and Agreement Analyses (Temporal Dynamics)  
The analyses for the eight-day interval data used the half-hourly GPPec aggregated to eight-day periods (g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\)) and the GPPm eight-day period data (g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\)). All the variables in the eight-day period dataset were tested for normality by tower and year, tower, and biome type using the Kolmorogov-Smirnov test (\(\alpha=0.05\)).  
The relationship between the MODIS estimated eight-day GPP (GPPm) with EC-derived eight-day GPP (GPPec) was explored by linear regression for the sites which met the normality assumption. Model 1 was adjusted by ordinary least squares (OLS) linear regression between GPPec and GPPm per tower and per year. Model 2 was adjusted by OLS linear regression per tower (GPPec _vs._GPPm), without separating the data by year. A similar procedure was followed by [2, 13, 18, 29, 32] for the validation of MODIS-GPP on an eight-day period basis. In order to determine which other variables (besides GPPm) help to explain the temporal dynamics of C, Model 3 was adjusted by stepwise linear regression per tower, with GPPec as the dependent variable and GPPm and the following variables measured at the EC tower as independent variables: air temperature (Ta) (\({}^{\circ}\)C), soil temperature (Ts) (\({}^{\circ}\)C), global radiation (Rg) (W\(\cdot\)m\({}^{-2}\)), vapour pressure deficit (VDP) (hPa), soil water content (SWC) (\(\%\) volume). The collinearity between the variables in the final model was tested before choosing the definitive model, so models with non-collinear predictors were preferred. For each regression, the r\({}^{2}\) coefficient of adjustment and the SEE were calculated.  
When the data were not normally distributed, the relationship between GPPm and GPPec was explored by calculating the Spearman rho coefficient of correlation (\(\rho\)), which is more robust than linear regression when the assumption of normality is not met [41]. In order to compare these results to the results obtained by linear regression (r\({}^{2}\)), \(\rho^{2}\) were included in the tables (Model 1 and Model 2). For Model 3, the correlation (\(\rho\)) between GPPec and each variable was examined, pointing out the variable that was most correlated with GPPec. The SEE could not be calculated in those cases.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 5 Design of Data Transmission Protocol
### Dynamic Routing Strategy
```
1:neighbor table T
2:optimal selection strategy \(S_{n}\)
3:Find forwarding set \(F_{n}\) from \(T\), size of \(F_{n}\) is \(j\)
4:Create \(j*j\) adjacency matrix \(E\)
5:Create Vector \(Counter\)
6:Find all combinations of the elements in \(F_{n}\), push into Candidate set \(Can\), size of \(Can\) is \(\epsilon\)
7:for\(i=0\rightarrow\epsilon\)do
8:if\(Can_{i}\) does not satisfy Equation (7) then
9:Delete \(Can_{i}\) from \(Can\)
10:endif
11:endfor
12:for\(i=0\rightarrow\epsilon\)do
13: Find all competitors of \(Can_{i}\), push into competitor set \(Com_{i}\)
14:\(item1\gets Can_{i}\)
15:\(item2\gets Com_{i}\)
16:\(Counter_{i}\gets 0\)
17:while\(sizeof(item1)=0\)do
18: Find Max element in \(item2\) and Delete it
19:Delete element in \(item1\) which has the found Max element
20:\(Counter_{i}++\)
21:endwhile
22:endfor
23:Find Max element in \(Counter\) and Delete other corresponding elements of \(Can\)
24:Find Max \(sizeof(Can_{i})\) and Delete other elements
25:Find Min \(sizeof(Com_{i})\) and Delete other corresponding elements of \(Can\)
26:\(S_{n}\gets Can_{0}\)
```  
**Algorithm 1** Dynamic Routing Strategy  
After selecting an optimal selection strategy, node \(n\) will insert the strategy into the packet head and then broadcast it out. Only the nodes in the strategy have the permission to forward the packet, others will simply drop the packet when the packet is received. After holding the packet for a while, the nodes in the strategy spontaneously choose the best node to which to forward the packet. In the next section, we will discuss how to calculate the holding time and the spontaneous selection strategy of nodes.