esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Spectral Analysis
#### 3.2.2 Classification Based on Data of the Mixture of Maize Kernels Orientated Germ Up and Down
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pretreatment** & \multirow{2}{*}{**MAS**} & \multirow{2}{*}{**SGS**} & **MAS +** & **SGS +** & **MAS +** & **SGS +** & **MAS +** & **SGS +** \\
**Methods** & & & **NMZ** & **NMZ** & **SNV** & **SNV** & **MSC** & **MSC** \\ \hline PCs & 8 & 7 & 9 & 9 & **6** & 5 & 6 & 6 \\ Calibration (\%) & 88.54 & 84.90 & 100 & 100 & **95.83** & 89.58 & 80.21 & 79.17 \\ Validation (\%) & 79.17 & 75.00 & 67.71 & 70.83 & **84.38** & 81.25 & 77.08 & 76.04 \\ \hline \hline \end{tabular} MAS: moving average smoothing (width = 7); SGS: Savitzky-Golay smoothing (width = 7); NMZ: normalization; SNV: standard normal variate; MSC: multiple scatter correction.  
\end{table} 
Table 3: Comparison of classification models with different pretreatment methods based on the mixture of kernel samples orientated with germ up and down. kernel curvature, shape and texture, on spectral variation of NIR hyperspectral images. Loading line plot for PC\({}_{4}\) to PC\({}_{6}\) was shown in Figure 8. Five key wavelengths, ascribed to meaningful chemical functional groups, were pointed out. They were 1145, 1408, 1935, 2103, and 2383 nm. The wavelength of 1146 nm ascribed to the 2 nd overtone (OT) of aromatic -CH- group, showed the existence of the benzene ring, which can be attributed to some kinds of the few amino acids such as tyrosine [16]. The wavelength of 1405 nm, with an O-H stretch first overtone attributed to an ROH structure, was associated with starch [26; 29]. Furthermore, 1940 nm was ascribed to the O-H bend second overtone of water [29]. A wavelength of 2100 nm was associated with carbohydrate [29]. In addition, 2382 nm was ascribed to the 2 nd overtone of C-H stretching from aliphatic compounds [16; 29]. These indicated that variation in different groups was mainly caused by changes of ingredient contents of maize germ due to the fungal growth.
## 4 Clustering, Classification, and Quantification of Coal Based on Machine Learning
### Regression Models  
When the output is no longer labels but continuous variables, the regression model can be used for quantitative analysis. Regression models study the relation between independent variables and dependent variables and then establish a possible function to predict the trend of the dependent variables. The regression model can predict the proximate analysis of coal, such as ash, volatile content, and fixed carbon.
## 4 Numerical Model
### Influence of Grouting Reinforcement Layer Thickness
#### 4.4.2 Vault Displacement Analysis  
Figure 15 shows the displacement of seven monitoring points near the vault of the left tunnel. Monitoring point 4 was the center of the vault, monitoring points 1-3 were on the left side of the center of the vault, and monitoring points 5-7 were on the right side of the vault. The closer to the vault, the greater the displacement. When h = 0 m, 0.5 m, and 1.0 m, the maximum displacement was at monitoring point 3. When h = 1.5 m, 2.0 m, and 2.5 m, the maximum displacement was at the center of the vault. With the increasing grouting reinforcement layer thickness, the area with the maximum displacement gradually shifted to the center of the vault.  
Figure 16 shows the relationship between the maximum displacement of the vault monitoring points and grouting reinforcement layer thickness. When the thickness of the reinforcement layer was h = 0 m, 0.5 m, 1.0 m, 1.5 m, 2.0 m, and 2.5 m, the maximum displacement of the vault was 59.7 mm, 43.4 mm, 27.8 mm, 21.7 mm, 19.6 mm, and 18.9 mm, respectively. Compared with the simulation result of the previous reinforcement layer,  
Figure 14: Relationship between the maximum ground settlement and the grouting reinforcement layer thickness.  
Figure 15: Displacement of 7 monitoring points near the vault of the left tunnel.  
the variation range of the maximum displacement of the vault was 16.3 mm, 15.6 mm, 6.1 mm, 2.1 mm, and 0.7 mm. With the increase of grouting reinforcement layer thickness, the maximum displacement of the vault decreased, and the reduction amplitude also decreased. When the thickness of the reinforcement layer was small (h = 0 m, 0.5 m, 1.0 m), increasing the thickness of the reinforcement layer obtained an obvious reinforcement effect. However, when the thickness of the reinforcement layer was large (h = 1.5 m, 2.0 m, 2.5 m), the reinforcement was not obvious by increasing the thickness of the reinforcement layer. Considering the maximum vault displacement and its variation range, when the thickness of the grouting reinforcement layer was h = 1.5 m, the maximum vault displacement was small and the reduction was large. The reinforcement effect was better and the benefits were higher.
# Biodegradable Polymeric Architectures via Reversible Deactivation Radical Polymerizations
### Reversible Deactivation Radical Polymerizations  
In addition to ionic and coordination ring-opening polymerization [11; 12], free radical polymerization RDRPs have been exploited extensively to generate multi-armed structures with predetermined molecular weights and narrow molecular weight distributions. ATRP [13; 14], nitroxide mediated radical polymerization (NMRP) [15] and reversible addition fragmentation chain transfer (RAFT) polymerization [16; 17; 18; 19; 20] are the most explored RDRPs (Figure 1). ATRP is one of the most studied RDRPs and many articles have been published about this topic since its development in 1995 by [PERSON] [21; 22]. ATRP is usually initiated by a halogenated organic species in the presence of a metal halide. The metal has a number of different oxidation states that allows it to attract a halide from the organohalide, creating a radical that then starts free radical polymerization. ATRP is an excellent tool for the synthesis of well-defined polymers, however the low solubility of metal halides may limit the catalyst availability and the residual catalyst among the as-prepared polymers may limit the applications in biological field and electronic devices [23]. RAFT polymerization was discovered by [PERSON] et al. only two decade ago, but has also been well-explored and -employed to synthesize polymers with predetermined molecular weight and narrow molecular weight distributionsover a wide range of monomers. RAFT technique is suitable for polymerizing versatile monomers in different media, where solution (either in organic or aqueous media), emulsion and suspension polymerizations can be carried out for purposely generating functionalized polymers. These functional groups can also be exploited for further polymerization or further reaction to form complicated architectures. RAFT polymerization, in comparison with ATRP, can be undertaken without the introduction of metal ion catalysts, therefore, it will be a secure tool particularly in biological and electrical applications [17; 24; 25].
## 4 Simulation Results and Discussion
### Displacements at the Tops of the Drilled Grouting Piles  
Displacements at the tops of the drilled grouting piles are closely related to settlement of the surrounding ground, thus making the latter an important parameter for evaluating the stability of a foundation pit at different excavation stages. Considering that the foundation pit was excavated in separate sections from south to north, the displacements at the tops of the drilled grouting piles corresponding to different sections of the foundation pit at different excavation depths were also calculated, including the south end, north end, standard section, and transfer section. The displacements at the tops of the drilled grouting piles corresponding to different sections at different excavation depths are illustrated in Figure 12.  
It can be obtained from Figure 12 that the displacements at the tops of the drilled grouting piles corresponding to the four typical sections gradually increased with an increasing  
Figure 12: Displacements at tops of the drilled grouting piles at different sections.  
exavation depth. Meanwhile, when transverse supporting scheme D was adopted, the maximum displacements at the tops of the drilled grouting piles corresponding to the four typical sections were the smallest, which was because prestressed anchorages set at 8 m underground could effectively restrain the movements of the upper weathered limestone layer and thus reduced displacement of the surrounding rocks. When transverse supporting Scheme C was adopted, the maximum displacements at the tops of the drilled grouting piles corresponding to the four typical sections were 1.63 mm, 1.68 mm, 1.71 mm, and 1.73 mm, which all meet the displacement requirements of 20 mm stipulated in GB50007-2011 [10].
## 3 Tracking Control Laws
### Derivation of Control Laws
: _Consider the case where the vehicle moves with a constant velocity in the full cavity state. As we deflect two rudders by the identical angle \(\delta_{f}\), then the vehicle rolls with angular acceleration of \(2F_{y}^{2}/\left(mL\right)\). Here, \(m\) is the mass of the vehicle._  
Figure 3: We change \(u_{0}\) slightly to generate a moment, and the body rotates in the clockwise direction. **Proof. ** Consider the case where the vehicle moves straight in the full cavity state. As the vehicle is in the full cavity state, the vehicle speed is sufficiently fast (85 m/s), as presented in Section 4. Thus, buoyancy effect on the cavity can be ignored in this analysis. Since the vehicle moves straight, the principal axis of the vehicle coincides with the cavity center line using the Logvinovich independence principle, see Figure 4. Thus, the immersion depth of fin 2 is identical to that of fin 4. Since two fins are deflected by the identical angle, we have \(\mathrm{F}_{\mathrm{x}}^{2}=\mathrm{F}_{\mathrm{x}}^{4}\), \(\mathrm{F}_{\mathrm{y}}^{2}=-\mathrm{F}_{\mathrm{y}}^{4}\), \(\mathrm{F}_{\mathrm{z}}^{2}=-\mathrm{F}_{\mathrm{z}}^{4}\)  
The moment generated by \(\mathrm{F}_{\mathrm{x}}^{4}\) is nullified by that generated by \(\mathrm{F}_{\mathrm{x}}^{2}\). In other words, \(\mathrm{LF}_{\mathrm{x}}^{2}=\mathrm{LF}_{\mathrm{x}}^{4}\), since \(\mathrm{F}_{\mathrm{x}}^{2}=\mathrm{F}_{\mathrm{x}}^{4}\). Similarly, the moment generated by \(\mathrm{F}_{\mathrm{z}}^{4}\) is nullified by that generated by \(\mathrm{F}_{\mathrm{z}}^{2}\), since \(\mathrm{F}_{\mathrm{z}}^{2}=-\mathrm{F}_{\mathrm{z}}^{4}\). The moment generated by \(\mathrm{F}_{\mathrm{y}}^{4}\) is \(\mathrm{LF}_{\mathrm{y}}^{4}\). This moment \(\mathrm{LF}_{\mathrm{y}}^{4}\) is identical to \(-\mathrm{LF}_{\mathrm{y}}^{2}\) (the moment generated by \(\mathrm{F}_{\mathrm{y}}^{2}\)), since \(\mathrm{F}_{\mathrm{y}}^{2}=-\mathrm{F}_{\mathrm{y}}^{4}\). \(2\mathrm{LF}_{\mathrm{y}}^{2}\) (the sum of two moments) makes the vehicle roll in one direction. Hence, the net force to roll the vehicle is \(2\mathrm{F}_{\mathrm{y}}^{2}\). Since the vehicle's mass is m, the vehicle rolls with angular acceleration of \(2\mathrm{F}_{\mathrm{y}}^{2}/(\mathrm{mL})\). \(\square\)
## 5 Insertion Loss Reduction Techniques (ILRTs)
### Low-Pass Inductive Compensation Structure  
When cascading attenuation bits, the parasitic capacitance of transistors causes mismatch, which results in reflected signal power and causes unnecessary loss. Since the transistor parasitic capacitance forms a high-pass structure, we aim to construct a low-pass filter to compensate for its tendency to change; thus, we connect inductors in series at the input and output of the \(\pi\)-type structure to realize the low-pass filtering function, and the \(\pi\)-type topology of the structure employing low-pass inductive compensation is shown in Figure 10a.  
Figure 10b shows the equivalent circuit of Figure 10a in its reference state. _Llow_, _Cp_,_off_, _Rs_,_on and _Rs_ form a low-pass filter.  
Figure 11 shows the insertion loss before and after using low-pass inductive compensation structure to the conventional 8 dB \(\pi\)-type attenuator. Low-pass inductive compensation structure reduces the insertion loss from \(-1.36\) dB to \(-0.92\) dB, effectively compensating for the circuit's capacitive losses. This paper uses microstrip lines instead of inductors to achieve compensation functions.  
Figure 10: (**a**) \(\pi\)-structure with low-pass inductive compensation and its equivalent circuit. (**b**) Reference state.  
Figure 9: Comparison of insertion loss between T-type and simplified T-type attenuators.
# Dynamic Modeling and Full-Process Simulation of the Core Engine Test Rig Main Test System
## 3 Modularization Modeling Approach
### Gas Valve Model
#### 3.1.2 Valve-Spool Throttling Models  
The injector orifice model of IEC60534-1998 standard-based expansion coefficient method is established for Vanessa 30,000 pneumatic zero leakage butterfly valve. The spool mass-flow-rate equation as follows is obtained by converting to the international unit and converting volume flow rate into mass flow rate:  
\[Q_{\text{m}}=\left\{\begin{array}{cc}\frac{\rho_{1}+\rho_{2}}{2}\frac{2460 }{3600}p_{1}Y_{\text{eff}}K_{\text{v}}\sqrt{\frac{X}{Y_{1}\text{MwZ}}}&X= \frac{p_{1}-p_{2}}{p_{1}}<F_{\text{k}}X_{\text{t}}\\ \rho_{1}\frac{1390}{3600}p_{1}K_{\text{v}}\sqrt{\frac{KX_{\text{t}}}{Y_{1} \text{MwZ}}}&X=\frac{p_{1}-p_{2}}{p_{1}}\geq F_{\text{k}}X_{\text{t}}\end{array} \right. \tag{3}\]  
In the case of reverse flow, that is, when the equation meets \(0\leq p_{1}<p_{2}\), the positions of \(\mathit{Var}_{1}\) and \(\mathit{Var}_{2}\) are exchanged and a minus sign is added in front of the mass flow-rate  
Figure 2: Finite control volume grids of gas valve.  
result, where \(Var\in\{\rho,p,T\}\), \(F_{\rm k}=K/1.4\), \(Y_{\rm ef}=1-X/(3F_{\rm k}X_{\rm t})\). The relation formula of \(X_{\rm t}\) and \(\tau\) (\(\theta=90\tau\) is butterfly valve-spool rotation angle) is:  
\[\begin{array}{ll}X_{\rm t}=&-2.56410\times 10^{-10}\theta^{5}+6.64336\times 10^{-8 }\theta^{4}-5.97902\times 10^{-6}\theta^{3}\\ &+1.94522\times 10^{-4}\theta^{2}-3.72121\times 10^{-3}\theta+0.55333\end{array}\]  
Besides 0-7 eight calculation schemes of flow coefficient [14; 24], the ninth scheme can be obtained by fitting the flow-rate characteristic curve of the Vanessa 30,000 series valves provided by the manufacturer:  
\[\left\{\begin{array}{ll}K_{\rm v}=0.53215\times 10^{-2}\frac{\tau}{t_{\rm subcr }}\ K_{\rm vRating}&0\leq\tau<\tau_{\rm subcr}\\ K_{\rm v}=K_{\rm vRating}\ K_{\rm vPercent}&\tau_{\rm subcr}\leq\tau\leq 1 \end{array}\right. \tag{4}\]  
\[\begin{array}{ll}K_{\rm vPercent}=&-76.58189\tau^{8}+293.22451\tau^{7}-457.8 0741\tau^{6}+368.54586\tau^{5}-161.22735\tau^{4}\\ &+37.87258\tau^{3}-3.57322\tau^{2}+0.53803\tau+2.62161\times 10^{-4}\end{array}\]  
The error sum of squares between the calculated results of the flow coefficient percentage \(K_{\rm vPercent}=K_{\rm v}/K_{\rm vRating}\) and the characteristic data is \(5.4356\times 10^{-4}\), and the standard error of the estimate (SEE) is \(2.0141\times 10^{-3}\), where \(K_{\rm vRating}\) is the flow coefficient rating, that is, the value of \(K_{\rm v}\) in the case of \(\tau=1\). In case of small opening, the linear interpolation of two points, \(K_{\rm v}(\tau)=\tau\cdot K_{\rm v}(\tau_{\rm subcr})/\tau_{\rm subcr}\), \(\tau_{\rm subcr}=0.01\), is used to supplement the formula near zero-opening zone and ensure \(Q_{\rm m}=0\) when \(\tau=0\).
# GPU-Accelerated Fock Matrix Computation with Efficient Reduction
## 2 Background
### GPU Programming Model  
Our GPU implementation is developed in CUDA, which is a general-purpose parallel computing platform offered by NVIDIA. Graphics Processing Units (GPUs) facilitate high-performance parallel processing by decomposing substantial computational tasks into smaller, autonomous units handled by CUDA blocks. These blocks are then distributed among a multitude of streaming multiprocessors (SMs), each comprising a large number of processing cores. An SM has the capacity to process multiple CUDA blocks concurrently. Each block is further subdivided into units termed warps, composed of 32 individual threads. A thread represents the fundamental unit of sequential instruction execution. The 32 threads within a warp are concurrently scheduled onto the CUDA cores. Because each thread in a warp executes a single instruction on different data simultaneously, this architecture is characterized as Single Instruction, Multiple Threads (SIMT). Although individual threads within a warp can branch and execute independently, different instructions branched by if-else statements are executed sequentially. This phenomenon is known as warp divergence, leading to a decrease in parallel execution efficiency.
## 3 The Human Role in Teleoperation
### Field of View
This \"cognitive tunneling\" effect present in egocentric visualization approaches contrasts with exocentric systems in which the frame of reference (FOR) remains unchangeable, requiring less mental transformation. In navigation tasks, operators usually focus their attention on a destination point without worrying much about the surrounding environment. Either in navigation or manipulation tasks, a limited FOV can cut crucial distance cues and limit user depth perception [88]. However, [PERSON] [21] proved that an HMD's limited field of view has no effect on perceived distance, reported in virtual environments. Navigation performance research has identified several problems related to restricted FOV: drivers with difficulties in judging vehicle speed, object perception, time to collision, obstacle location, and start procedures to curve. The peripheral vision contributes to speed perception, lane following, and lateral control avoidance. Wider FOVs are common solutions in teleoperation indirect navigation. This broadens the view of the scene either with wide angle cameras or using extra cameras on-board to cover the surrounding space. Nevertheless, such FOV increments, particularly when based on wide angle cameras, might induce a faster perception of speed. This effect is related to scene compression and quite often leads the operators reducing their speed. Researchers have pointed out that the scene distortion and resolution decrements, related to scene compression, may increase operator cognitive workload, degrade object localization tasks, and cause motion sickness symptoms [85]. These authors conducted a direct viewing driving and an indirect video driving of a military car. Three internal tiled LCDs provided a 100\({}^{\circ}\) panoramic view returned by a camera array mounted in the front roof of the car. They tested three sets of camera lenses, providing 150\({}^{\circ}\) (near unity), 205\({}^{\circ}\) (wide), and 257\({}^{\circ}\) (extended) camera FOVs. They found that map planning performance and spatial rotation improve with a wider FOV. Wider FOVs had an effect on spatial cognitive functions similar to peripheral cues for direct viewing. The best performances were achieved using vision displays with the FOV close to direct vision, using systems that enabled electronic adjustment of the FOV.
## 5 Discussion  
Tables 3 and 4 summarize the studies of assimilating GNSS RO and GNSS-R data, respectively. In summary, both GNSS RO data and GNSS-R data have positive impacts on TC forecasting, although they influence different aspects of forecasts. We cannot compare the assimilation results between RO data and GNSS-R data directly as there are no data aiming at the same TC, and the type or size of data are not comparable either. However, in a comparison of Tables 3 and 4, we can find that RO data contribute to moisture and precipitation forecasts more effectively than GNSS-R data. Only combined with other observations, GNSS-R winds will positively impact precipitation forecasting. For the TC track and intensity, both can improve the accuracy of forecasts, but RO data perform better in track forecasting, while GNSS-R data contribute more to the TC intensity forecasting. This is because RO data have a high resolution in vertical and can characterize atmospheric circulation in more detail, and GNSS-R data convey more ocean surface information, such as the major factor of intensity, the surface wind speed. Figure 16: TC intensity in time series for (**a**) MSLP and (**b**) MSW from JTwC best track data, CTRL (a WRF regional simulation), DA_cyg_fd (assimilating CYGNSS fully developed seas wind speed), DA_cyg_If (assimilating CYGNSS young seas/limited fetch wind speed), and DA_com (assimilating combined IMERGE rainfall, AST vector wind, and CYGNSS wind speed) at 0000 UTC on 6–9 January 2018 [90]. Figure 17: (**a**) IMERG (Integrated Multi-satellite Retrievals for GPM data), (**b**) CTRL, and (**c**) DA_com precipitation around the TC center at 1500 UTC on 7 January 2018; (**d–f**) as in (**a–c**), but at 2100 UTC on 8 January 2018 [90]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Title** & **NWP Model and DA System** & **Simulated or Real Observations** & **Assimilation Method** & **Impact** \\ \hline Impact of Assimilating CYGNSS Data on Tropical Cyclone Analyses and Forecasts in a Regional OSSE Framework [89] & WRF, 3 DVAR & Simulated & Only wind speed was assimilated, thinning to the grid & CYGNSS surface wind data could improve the analyses of the TC position, structure, and intensity. \\ Impact of CYGNSS & & & \\ Ocean Surface Wind Speeds on Numerical Simulations of a Hurricane in Observing System Simulation Experiments [88] & HWRF, 3 DVAR & Simulated & Only wind speed was assimilated & CYGNSS winds had great potential to improve \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of studies assimilating GNSS-R ocean surface wind data.
## 4 Experimental Analysis
### Ablation Study
#### 4.5.2 Effectiveness of Decoder  
As shown in Table 6, we evaluated the performance of our decoder with previous advanced works, including LegoFormer [15], 3D-RETR [14], and LRGT [11]. All experiments were performed using the same encoder as WGA-SWIN, and IoU was used for comparing the results on the ShapeNet dataset. The results demonstrate that our decoder performs better than others across every input view configuration. For instance, our decoder obtains an IoU of 0.7636 at three input views, which is higher than compared to 3D-RETR (0.7592) and LRGT (0.7581). However, our decoder achieves the highest IoU of 0.7827 at 20 input views, which is 0.68% higher than LegoFormer and 0.36% higher than LRGT.  
Finally, the superior performance of our decoder stems from its progressive design with SWIN 3D LR and SWIN 3D HR, which gradually increases voxel resolution through distributed swin transformer blocks at every stage. On the other hand, previous methods demonstrate different steps in applying simple structures, such as many fully connected layers. In addition, our approach allows better feature representation by combining window attention mechanisms for global refinement and convolution layers for local detail preservation. The results validate the effectiveness of our WGA-SWIN decoder, which achieves consistent improvements over prior methods, particularly with a large number of input views.