esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Optimal Control Strategy of Four Drive Motors for an Electric Vehicle
## 2 Simulation Platform and Optimal Energy Management
### Electric Vehicle Simulation Platform
#### 2.1.1 Driving Cycle Model  
An important factor for consumers when purchasing an electric vehicle is its driving range. The driving range of an EV is determined by the total distance it can travel on a full charge until the battery is depleted. Currently, the mainstream energy consumption test standards for electric vehicles worldwide include the NEDC (New European Driving Cycle) and FTP-75 (Federal Test Procedure 75).  
The NEDC is a commonly used automotive testing standard in Europe for assessing a vehicle's fuel efficiency and emissions performance. This test cycle simulates urban and suburban driving conditions, as well as some high-speed situations [28]. The NEDC testing process includes fixed driving modes, speeds, and idle times, using these parameters to calculate the vehicle's energy consumption. The total driving time is 1180 s, with a  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Item** & \multicolumn{1}{c}{**Specification**} \\ \hline \multirow{4}{*}{Drive Motor} & Type & Permanent Magnet \\  & & Synchronous \\  & & 95 kW (Single)/380 kW \\  & & (Four Units) \\  & & Maximum Output Torque \\  & & Lithium-ion Ternary \\ Energy Storage Battery & & Battery \\  & & Rated voltage \\  & & Maximum Capacity \\  & & Vehicle Mass \\  & & Gravitational Acceleration \\  & & Aerodynamic Drag \\  & & Coefficient \\ Vehicle Parameters & & Density of Air \\  & & Frontal Area \\  & & Tire Radius \\  & & Rolling Resistance \\  & & Coefficient \\  & & Road Grade \\  & & Final Drive Ratio \\  & & Drive Friction Coefficient \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the electric vehicle.  
Figure 1: Four-motor drive configuration for the electric vehicle.  
maximum vehicle velocity of 120 km/h. The overall vehicle velocity simulation is shown in Figure 2.  
The FTP-75 is a standardized driving test cycle used for testing vehicle emissions, fuel economy, and energy consumption, established by the United States Environmental Protection Agency (EPA) [29]. This driving cycle is widely used in laboratory emission testing to simulate typical urban and highway mixed driving scenarios, and it provides experimental data to evaluate the environmental performance of vehicles. The driving time is 1370 s, with a maximum vehicle velocity of about 90 km/h. The overall vehicle velocity simulation is shown in Figure 3.
## Materials and Methods  
### Study Area  
This study focuses on the flood-prone rivers in Guangdong Province in southern China; namely, the Beijing and Dongjiang River Basins of the Lower Pearl River, which are the main water supplies for the Guangdong-Hong Kong-Macao Greater Bay Area (Figure 1).  
The Beijinga River, located in southern China within 111\({}^{\circ}\)51\({}^{\prime}\)-114\({}^{\circ}\)42\({}^{\prime}\)E and 23\({}^{\circ}\)12\({}^{\prime}\)-25\({}^{\circ}\)41\({}^{\prime}\)N, is 468 km long with a mean gradient of around 0.026%; the total drainage area of the basin is 38,363 km\({}^{2}\) above the Shijiao hydrological station. It is the second-largest  
Figure 1: Maps of the DEM, river network, rain gauge distributions, and streamflow stations over the Beijing and Dongjiang River Basins.  
tributary in the Pearl River Basin and is an important water source for the Pearl River Delta region. Most areas of the basin are hilly and mountainous, with elevations ranging from 1719 m (in the north) to 0 m above sea level. Climatically, under the influence of summer monsoons and tropical cyclones, the mean annual precipitation and temperature of this river basin are ~1785 mm and ~21 \({}^{\circ}\)C, respectively. The rainfall regime displays a notable summer maximum during the wet season (from April to September), since over 75% of the annual total amount occurs during this period. Due to its complex topography and the associated effect of the east Asian monsoon climate, rainstorms and floods are frequent in the Beijing River Basin. For instance, the recent extraordinary flood that occurred in June 2022 had a peak flow of up to 18,500 m\({}^{3}\)/s at the Shijiao station; this exceeded the historically measured maximum and caused enormous damage to local society.  
The Dongjiang River, adjacent to the Beijing River in the east is located within 113\({}^{\circ}\)58\({}^{\prime}\)-115\({}^{\circ}\)52\({}^{\prime}\) E and 22\({}^{\circ}\)35\({}^{\prime}\)-25\({}^{\circ}\)12\({}^{\prime}\) N; it is 562 km long with a mean gradient of about 0.039%; the total drainage area of the basin is 25,325 km\({}^{2}\) above the Boluo hydrological station. As the third tributary of the Pearl River Basin, it is crucial not only for the local region but also for Hong Kong City, as around 80% of Hong Kong's water supply comes from this river through cross-river transference. The hilly mountains with an elevation of >200 m account for 49.1% of the basin, with elevations ranging from 1325 m in the north to 0 m above sea level from northeast to southwest. The predominant land use type is forest, occupying ~70% of the basin, but the urban city is expanding rapidly, particularly in the low-lying areas. Under the influence of the subtropical monsoon climate, the mean annual precipitation and temperature of this river basin are ~1795 mm and ~21 \({}^{\circ}\)C, respectively. Approximately 80% of the annual precipitation and runoff occur during the rainy season from April to September.
# High-Speed, High-Performance DQPSK Optical Links with Reduced Complexity VDEE Equalizers
## 1 Introduction
As the hardware implementation circuitry of a digital equalizer is proportional to the computational complexity and the memory requirements [2; 10; 12], the proposed approach offers reduced cost solutions that can be viably applied for the equalization of high-speed, high-performance optical transmission systems. A comparative study of the performance of the proposed equalization schemes is performed by means of computer simulations, using a typical 40 Gb/s optical transmission setup.
# Axial Bearing Mechanism of Post-Grouted Piles in Calcareous Sand
## 3 Analysis of the Loading Test Results
### Modified Shaft Resistance of a Single Pile  
Figure 6 shows the distribution of the average unit shaft resistance of each single pile under ultimate loading. The shaft resistance is a process that gradually develops from the upper soil layer to the lower soil layer. For ungrouted piles, the shaft resistance of the pile gradually increases with the increase in buried depth when the load increases, while the grouted piles obviously increase the horizontal effective stress of the surrounding soil due to the grouting at the side of the pile, and the shaft resistance of the pile increases significantly; therefore, the extreme point of shaft resistance occurs approximately 0.1 m from the surface. Figure 6 shows that the average unit shaft resistances of single piles MP1, MP2, MP3, MP4, MP5, and MP6 under ultimate loads are 28.23 kPa, 167.33 kPa, 123.87 kPa, 44.26 kPa, 121.86 kPa, and 177.65 kPa, respectively. The shaft resistance of the ungrouted pile in calcareous sand is less than that in siliceous sand, which is mainly caused by the decrease in shaft resistance due to the particle breakage of calcareous sand during pile driving. In addition, compared with that of the ungrouted pile, the shaft resistance of the grouted pile improves substantially, which indicates that the injected cement grout effectively improves the physical and mechanical properties of the undisturbed surrounding soil. In addition, the effective stress of the soil around the pile increases, thereby enhancing the strength of the surrounding soil. After grouting at the side of the pile, the cement grout and calcareous sand particles condense to form stable cement-stabilized soil; therefore, the pile-soil interaction system forms a new structure, which greatly increases the shaft resistance of a single pile in calcareous sand, and the mobilization of side friction in calcareous sand basically demonstrates identical characteristics as those of side friction in siliceous sand.  
The relationship between total shaft resistance and pile head settlement for each pile, in two types of sandy soil, are plotted in Figure 7. Figure 7 shows that the variation in total shaft resistance of each pile is roughly identical to the displacement of the pile head, while the total shaft resistance of the grouted pile is significantly improved. The injected cement grout fills the pores, condenses into cement-stabilized soil with high strength, increases the resistance and roughness of the shear interface at the pile side, enhances the interaction between pile and soil, and improves the shaft resistance mobilization. Thus, the single pile with post-grouting has greater shaft resistance than the single pile without post-grouting, which effectively improves the stability of the bearing performance of the pile.  
Figure 6: Distribution of the average unit shaft resistance of each pile under ultimate loading.
## 4 Results  
The K-mean clustering algorithm combined with the Elbow method was employed to determine the optimal number of clusters. The analysis considered solutions ranging from 2 to 6 clusters. The Within-Cluster Sum of Squares (WCSS) values for different cluster solutions were as follows: K = 2 (0.761), K = 3 (0.605), K = 4 (0.578), K = 5 (0.44), and K = 6 (0.372). The Elbow method indicated that the most significant reduction in WCSS occurred between K = 2 and K = 3. After evaluating two- and three-cluster solutions, the two-cluster solution was deemed most acceptable regarding segment stability and interpretability, as shown in Figure 5. Namely, given the relatively small sample (N = 84), increasing the number of clusters would result in groups with few cases, potentially reducing statistical power and making both the finding interpretation and applicability to the educational context less meaningful [81]. The first segment, designated as _Conformists_, constituted 22.6% of the sample. This group primarily comprised students who consistently scored below the mean (negative z-scores) across all three items of perceived immersion. The second group, identified as _Inmersionists_, constituted 77.4% of the total sample. Within this group, the z-scores for all the perceived immersion items were positive (i.e., above the total mean). A \(t\)-test of independent samples demonstrated that the segments' PIMM mean scores differed significantly at the probability level of \(p<0.001\), as shown in Table 2.  
Figure 5: Distribution of z-scores by segments.  
The differences between segments in the perceived learning benefits, future behavioural intention, and overall experience are illustrated in Table 3. The results of the \(t\)-test analysis showed that _Immersionists_ exhibited statistically significantly higher mean scores for all the perceived learning benefits, future behavioural intentions, and overall experience items. Among them, the most significant discrepancy among segments was recorded for learning benefits related to comprehension of previously acquired knowledge (t (82) = 4.25, \(p\) < 0.001) and willingness to use VR in future (t (82) = 3.83, \(p\) = 0.001).
# Research on Control Technology of Large-Section Water-Bearing Broken Surrounding Rock Roadway
## 2 Engineering Summary and Analysis of Surrounding Rock Failure Characteristics  
### Engineering Summary  
The focus of this research paper is the Y1# belt conveyor roadway located at the 830 working face of the deep underground mining area of the Yingping mining site, which is part of the Gaoping mining area within the Wengfu phosphate mine. The stratum in which the Y1# belt transport roadway is situated belongs to the Sinian Qingshuijiang formation, characterized by gray-black slate with an argillaceous structure. The layers are thin to medium-thick, and the core predominantly exhibits long columnar and columnar forms. The rock is mechanically fragmented along existing cracks, and the joints are well developed. This formation contains bedrock fissure water and acts as a relative aquiclude, with exposure ranging from 266.06 to 381.89 m. The average core recovery rate for the entire section is 98.32%, while the average rock quality designation (RQD) is 65%. The Y1# roadway features numerous folds, small faults, and weak interlayers, resulting in an unstable rock structure. The total length of the Y1# belt conveyor roadway is 1200 m, with the research segment of this paper covering 853 m. The burial depth is 385 m, the excavation cross-sectional area is 15.45 m\({}^{2}\), and the net section measures 10.63 m\({}^{2}\). The shape of the roadway cross-section is a straight wall arch, with excavation dimensions of 4.6 \(\times\) 3.85 m. The surrounding rock is primarily classified as grade IV, and the excavation method employed is artificial drilling and blasting.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 2 NDN and QoS
### Issues to Consider for Implementing QoS Services for NDN  
* Congestion control in an NDN is aimed at preventing network overload, preventing the starvation of a particular class of traffic, and implementing fairness in resource allocation;
* Leveraging the NDN name hierarchy is beneficial for traffic classification, rather than framing a separate definition. The network may use CS as an instrument for implementing temporary re-transmission buffers and avoiding content request load on the producers;
* The resources which can be managed to implement QoS in an NDN are bandwidth, content stores, and PITs [15];
* A content name entry into the PIT ensures sufficient bandwidth is allocated to the content in the inverse path towards the consumer, but this time-invariable interest entry may have to wait for a long time to become satisfied by the corresponding data packet, causing inefficient PIT space and bandwidth reservation;
* For managing NDN resources, policies for the identification of traffic equivalence classes and their corresponding treatment must be specified;
* As the consumer-requested content may be satisfied by multiple sources, the effect of topology plays an insignificant role for QoS;
* QoS mechanisms of IP cannot be directly ported to NDN because hop-to-hop transmission is not confined to a single path for an NDN, thereby restricting the ability of advanced resource allocation during the process of network admission control.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Netflix** & **Show1-Prompt** & **Frame ID** & **\#Segment** \\ \hline Regular name &
\begin{tabular}{c} ECNCT-encoded \\ name \\ \end{tabular} & Regular name & Regular name \\ \hline \hline \end{tabular}
\end{table}
Table 2: An example of an ECNCT mechanism.
## Data and Methods  
### Field Data  
#### 3.1.1 Eddy Covariance Gross Primary Productivity  
The eddy covariance (EC) systems at all three sites are equipped with LI7500 open path infrared CO\({}_{2}\)/H\({}_{2}\)O analyzers (LI-COR Inc., Lincoln, NE, USA) and GILL R3 triple-axis sonic anemometers (GILL Instruments, Lymington, UK). Eddy covariance data were recorded at 20 Hz and averaged over 30-min periods. The EC method measures net ecosystem exchange of CO\({}_{2}\) (NEE) and following the approach in [PERSON] et al. [41], NEE was partitioned into GPP and ecosystem respiration (R\({}_{\text{eco}}\))  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site, Country** & **Longitude, Latitude (DD)** & **V Vegetation Type** & **Soil Type** & **MAP (mm)** & **MAT (\({}^{\circ}\)C)** & **Measurement Years** & **Rel. Hum. DS/GS (\%)** & **Ref.** \\ \hline Demokeya, Sudan & 30.47, 13.28 & Sparse acacia savanna & Cambic Arenosols & 320 & 30 & 2007–2009 & 16/57 & [36] \\ \hline Wankama, Niger & 2.63, 13.64 & Fallow shruland & Sandy & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline Dahra, Senegal & \(-\)15.43, 15.40 & Open woody & & & & & \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic site characteristics including location in decimal degrees (DD), vegetation and soil types, mean annual precipitation (MAP), and mean annual air temperature at 2-m height (MAT). The DS and GS in the relative humidity column refer to Dry Season (November-June) and Growing Season (July–October), respectively.  
using a non-linear asymptotic regression with incoming photosynthetically active radiation and vapor pressure deficit as independent variables. Gaps in the time series of NEE, GPP and \(\mathrm{R_{eco}}\) were consequently gap-filled, again following the method outlined in [PERSON] et al. (2017).
## 2 Material and Method
### Analytical Method
#### 2.1.1 Residual Stresses after Autofrettage  
The residual stresses developed as a result of the autofrettage process are examined for two regions of the cylinder: the region experiencing plastic deformation in the inner part of the cylinder and the elastic region outside the plastic zone. These regions are schematically represented in Figure 2. In this schematic depiction, \(a\) represents the inner radius of the cylinder, \(b\) represents the radius where the elastic region ends and the plastic region begins, and \(c\) represents the outer radius of the cylinder [18].  
Residual stresses developed in the elastic part of the cylinder (\(b\leq r\leq c\))  
\[\sigma_{re}^{R}=\frac{1}{\sqrt{3}}S_{y}b^{2}\left(\frac{1}{c^{2}}-\frac{1}{r^{ 2}}\right)-P_{a}\frac{a^{2}}{c^{2}-a^{2}}\left(1-\frac{c^{2}}{r^{2}}\right) \tag{1}\]  
\[\sigma_{ge}^{R}=\frac{1}{\sqrt{3}}S_{y}b^{2}\left(\frac{1}{c^{2}}+\frac{1}{r^{ 2}}\right)-P_{a}\frac{a^{2}}{c^{2}-a^{2}}\left(1+\frac{c^{2}}{r^{2}}\right) \tag{2}\]  
\[\sigma_{ze}^{R}=\frac{1}{\sqrt{3}}S_{y}\frac{b^{2}}{c^{2}}-P_{a}\frac{a^{2}}{c ^{2}-a^{2}} \tag{3}\]  
where \(S_{y}\) and \(P_{a}\) represent the yield stress and autofrettage pressure, respectively. The autofrettage pressure \(P_{a}\) is expressed by the following equation.  
\[P_{a}=\frac{1}{\sqrt{3}}\left[2 Aln\frac{b}{a}+\big{(}S_{y}-A\big{)}b^{2}\left( \frac{1}{a^{2}}-\frac{1}{b^{2}}\right)-S_{y}b^{2}\left(\frac{1}{c^{2}}-\frac{1 }{b^{2}}\right)\right] \tag{4}\]  
Residual stresses developed in the plastic part of the cylinder (\(a\leq r\leq b\))  
\[\sigma_{rp}^{R}=\frac{2}{\sqrt{3}}Aln\frac{r}{a}+\frac{b^{2}}{\sqrt{3}}\big{(} S_{y}-A\big{)}\left(\frac{1}{a^{2}}-\frac{1}{r^{2}}\right)-P_{a}-P_{a}\frac{a^{2}}{c ^{2}-a^{2}}\left(1-\frac{c^{2}}{r^{2}}\right) \tag{5}\]  
\[\sigma_{gp}^{R}=\frac{2}{\sqrt{3}}A\left(1+ln\frac{r}{a}\right)+\frac{b^{2}}{ \sqrt{3}}\big{(}S_{y}-A\big{)}\left(\frac{1}{a^{2}}+\frac{1}{r^{2}}\right)-P_ {a}-P_{a}\frac{a^{2}}{c^{2}-a^{2}}\left(1+\frac{c^{2}}{r^{2}}\right) \tag{6}\]  
\[\sigma_{zp}^{R}=\frac{1}{\sqrt{3}}A+\frac{2}{\sqrt{3}}Aln\frac{r}{a}+\frac{1}{ \sqrt{3}}\big{(}S_{y}-A\big{)}\frac{b^{2}}{a^{2}}-P_{a}-P_{a}\frac{a^{2}}{c^{2 }-a^{2}} \tag{7}\]  
The term \(A\) in the above equations is a constant expressed by the following equation.  
\[A=S_{y}-T\times\varepsilon_{y} \tag{8}\]  
Figure 2: Schematic representation of elastic and plastic regions after autofrettage pressure.  
where \(T\) and \(\varepsilon_{y}\) are tangent modulus and yield strain, respectively.
# Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine
## 3 HGCELM
### Computation Complexity and Connection to Existing Methods  
We give a theoretical analysis for the computation complexity of our method. Ignoring the hypergraph generation procedure and assuming \(L\ll N\), the time complexity of HGCELM is approximately \(O\big{(}N^{3}\big{)}\). Compared with ELM, HGCELM has a higher time complexity, which is determined by the hypergraph convolution. Fortunately, the augmented normalized incidence matrix can be precomputed and is sparse. Thus, the hypergraph embedding can be efficiently implemented. HGCELM is more efficient than GCN since it does not use iteration.  
Our HGCELM is closely related to the classic ELMs. First, HGCELM maintains the advantages of ELMs, i.e., fast learning speed achieved by a closed-form solution. Second, our HGCELM will degenerate to be the classic ELM by casting away \(\mathbf{S}\). Consequently, the classic ELM is a special case of the HGCELM model in the regular Euclidean domain. Furthermore, our HGCELM remedies the drawback that traditional ELMs cannot deal with structured data.
## 4 OGVs Parameterisation and Its Optimisation Method  
### OGVs Parameterisation  
The analysis of the flow field above shows that the overall performance of the fan is up to standard, but that there is a significant separation vortex in the area of the rear guide vane, as well as further reflux in the abdomen of the OGVs This model has a large number of motorised parts and OGVs and a high grid consistency. Changing the chord length of the OGVs will alter the original grid consistency, so the chord length is set to a constant value during the parameterisation process.  
To harmonise with the engineering guide vane type line design, the following method was chosen to describe the OGVs type line. In Figure 15, \(O(x,y)\) is the centre of the OGVs type line circle, \(A(x_{1},y_{1})\) is the start of the type line, and \(B(x_{2},y_{2})\) is the end of the type line. \(R\) is the radius of the OGVs type line, \(\sigma_{\rm a}\) is the axial distance between the moving blades and the OGVs, and \(\theta\) is the OGVs incidence. The starting point \(A\) of the type line is determined by changing the axial distance \(\sigma_{\rm a}\). In the case of keeping the chord length unchanged, by changing the centre position \(O\) and radius \(R\) counterclockwise to draw a starting point for \(A\), chord length is a fixed value of the arc, namely the type line, the endpoint is point \(B\). The angle between the chord and the \(y\)-axis formed by connections \(A\) and \(B\) is the incidence \(\theta\).  
Some range control of the variables is required to reduce the calculation time. The variable range of some parameters was adjusted, i.e., \(y_{1}\) for a constant value, \(R=164\)\(\sim\)\(264\) mm, \(\sigma_{\rm a}=40\)\(\sim\)\(160\) mm, \(-20\leq x\leq 20\) mm, and \(-20\leq y\leq 20\) mm. Some of the values are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Point** & \(\sigma_{\rm a}\) **(mm)** & \(R\) **(mm)** & \(x\) **(mm)** & \(y\) **(mm)** \\ \hline
1 & 100.00 & 214.10 & 0.00 & 0.00 \\
2 & 114.1 & 205.08 & \(-7.06\) & \(-5.67\) \\
3 & 85.13 & 212.64 & 14.76 & \(-2.92\) \\
4 & 131.26 & 187.35 & \(-6.99\) & \(-11.41\) \\..... &..... &..... &..... &..... \\ \hline \hline \end{tabular}
\end{table}
Table 4: OGVs structure parameters summary table.  
Figure 15: Geometric parameters of OGVs.