esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Sensitivity Analysis for Improving Steering Feel  
### Difference Analysis between the Competitor Vehicle Model and Our Vehicle Model  
After obtaining the simulation model for predicting the performance of the competitor vehicle, the factors affecting the steering feeling performance could be analyzed through a sensitivity analysis with respect to the difference between our vehicle and the competitor's. We analyzed the effects of the difference between the two vehicles with respect to the steering system, suspension, and tire (Figure 19).  
Figure 17: Steering performance at various steering angular velocities.  
Figure 18: Comparison of steering returnability during high-speed maneuver.  
For reference, the lateral acceleration level appears different in Figure 20, which is the result of the same steering angle input, due to a change in the understeer characteristics.  
* Case Study Model 1: Steering  
Applying the steering system of our vehicle to the target vehicle (Figure 20, red line vs. black line) revealed an overall decrease in steering torque due to excessive power  
Figure 19: Effect analysis of vehicle characteristic factors.  
Figure 20: Effect analysis of factors compared between two vehicles.  
assist. In addition, a decrease in the gradient was observed in the off-center region, which did not provide proper feedback to the driver.
* Case Study Model 2: Suspension  
When applying the suspension (Figure 20, red line vs. green line) and our chassis (including the steering system and suspension) to the target vehicle (Figure 20, red line vs. dotted red line), the steering wheel torque in the off-center area was reduced. This is because the caster trail (\(l_{t}\) in Figure 6) of our development vehicle is smaller, which can be compensated for to a certain extent by reducing the assist torque of the steering system. Moreover, passing through the on-center section, the hysteresis width of the steering wheel torque increased. This is attributable to the friction and damping of the suspension increasing, and compensation to some extent being achieved by changing the damping logic of the steering system.
* Case Study Model 3: Tire  
Applying the tire of our vehicle to the target vehicle (Figure 20, red line vs. yellow line) yielded almost the same result. This is because tire cornering stiffness is a factor that determines the lateral acceleration response characteristics by the steering angle input and, thus, has little effect on the steering wheel torque characteristics for lateral acceleration. Therefore, to determine the influence of tire cornering stiffness changes, the lateral acceleration response according to the steering angle input and the resulting steering wheel torque response must be analyzed by accounting for the complexity involved, which will be covered separately in the next section.
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 2 Methodology
### Operation Principles
#### 2.2.2 Voltage Gain  
By applying the voltage-second balance to \(L_{in}\), we can obtain  
\[\frac{1}{T_{s}}\int_{t}^{t+T_{s}}v_{Lin}(\tau)d\tau=V_{in}(D+\alpha)+(V_{in}-V _{c})(1-D-\alpha)=0 \tag{31}\]  
By rearranging Equation (31), we can obtain  
\[\frac{V_{c}}{V_{in}}=\frac{1}{1-D-\alpha} \tag{32}\]  
where  
\[\alpha=\frac{T_{6}+T_{7}}{T_{s}} \tag{33}\]  
By applying the voltage-second balance to \(L_{r}\), we can obtain  
\[\begin{array}{l}\frac{1}{T_{s}}\int_{t}^{t+T_{s}}v_{L_{r}}(\tau)d\tau\\ =(V_{c}-V_{o})(1-D-\alpha)+(-V_{o})(\alpha+\beta)=0\end{array} \tag{34}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Interval** & **Stage** & **Type** \\ \hline \(t_{3}\leq t\leq t_{4}\) & 4 & \(S_{2}\) ZVS turn-on \\ \(t_{7}\leq t\leq t_{8}\) & 8 & \(S_{1}\) ZVS turn-on \\ \(t_{8}\leq t\leq t_{0}+T_{s}\) & 9 & \(D_{o}\) ZCS turn-off \\ \hline \end{tabular}
\end{table}
Table 1: Soft switching type. ZVS, zero voltage switching; ZCS, zero current switching.  
Figure 12: (a) Current path for stage 9; (b) equivalent of (a).  
By rearranging Equation (34), we can obtain  
\[\frac{V_{o}}{V_{c}}=\frac{1-D-\alpha}{1-D+\beta} \tag{35}\]  
where  
\[\beta=\frac{T_{8}+T_{9}}{T_{s}} \tag{36}\]  
Substituting Equation (35) into Equation (32) yields  
\[\frac{V_{o}}{V_{in}}=\frac{1}{1-D+\beta} \tag{37}\]  
It can be seen from Equation (35) that the active clamp voltage \(V_{c}\) is larger than the output voltage \(V_{o}\), whereas it can also be seen from Equation (37) that this voltage gain is smaller than that of the conventional boost converter in CCM.
## 5 Discussion
### Limitations in Satisfying Security Requirements for Offline Payment Function
#### 5.1.4 Tracking Flow of Money  
The methods of tracking transactional information covered in [PERSON] [42] and [PERSON] et al. [72] are also difficult to apply to offline payments. Unlike online situations where transactional information can be recorded and tracked on a central server, users do not have access to the main server in offline situations. Thus, transactional information should be recorded in the device's storage rather than on the central server.  
Reinforcing security by tracking the flow of money is also discussed in CBDC [106; 109]. However, in this offline situation, it is difficult to monitor whether the fraudulent activity has occurred in the transactions based on the information recorded in the storage; forgery/falsification can occur freely. Due to these problems, tracking the flow of money in offline payments of CBDC is hard to implement.
## 3 Results and Discussion  
### Single-Factor Experimental Analysis  
In this experiment, the main UAE parameters influencing the polysaccharide yields, namely, ultrasonication temperature, power, and time, were evaluated. As shown in Figure 1A,the polysaccharides yield increased with an increase in the ultrasonication temperature in the range of 40 to 60 \({}^{\circ}\)C, and reached a maximum yield at 60 \({}^{\circ}\)C. However, there was a decrease in the yield of PAP when the temperature exceeded 60 \({}^{\circ}\)C, which might have been due to polysaccharide decomposition at higher temperatures. Therefore, the optimum extraction temperature was set at 60 \({}^{\circ}\)C. Alterations in the ultrasonication power generated a certain increase in yield, with the maximum yield being at 200 W. Above this level, the yield was not significantly changed; therefore, 200 W was chosen as the optimal ultrasonication power setting (Figure 1B). A similar trend was observed with the ultrasonication times. The yield rapidly increased from 1 to 2.5 h; therefore, 2.5 h was set as the optimal time (Figure 1C). High temperatures increased the solubility of the polysaccharides, which was conducive to the diffusion of polysaccharides from cells within a certain extraction period. With the increase of ultrasonic power, the intensity of acoustic cavitation increased, which may have promoted the wall-breaking effect and mass transfer. However, ultra-high temperatures, extended extraction times, and excessive ultrasonic power would lead to degradation of the polysaccharides [14].
## 3 O-MAC Protocol Design Goals and Architecture
### Interior Communication
#### 3.1.1 Optimized Data Frame Format Model
Thus, the retry transmission time can be calculated as follows:  
\[T_{retry}\ =T_{s\Delta}\times\ \beta \tag{4}\]  
The possible data rate can be determined using an ideal scenario and a worst-case scenario. In an ideal situation, the ODFF-based approach finds the free channel for sending the data on a non-beacon-enabled network. The ODFF also improves the data payload and reduces the acknowledgement time. In the worst-case scenario, it should be assumed that 25% of the data packets require a retry transmission. Thus, the possible total time in the ideal and worst-case scenarios can be calculated as follows:  
\[T_{ti}=C_{at}+T_{f}+T_{turn}+\ T_{ack} \tag{5}\]  
Equation (5) shows the total time consumed for transmitting the data frame in an ideal scenario. \[I_{data}=\frac{\left(D_{p}\ \times\ \omega\ \right)}{T_{ti}} \tag{6}\]  
Equation (6) shows possible data rate in an ideal situation using the ODFF approach. \[T_{tw}=C_{at}+T_{f}+T_{retry}\ +C_{at}\ +\ T_{f}+T_{turn}+\ T_{ack} \tag{7}\]  
Equation (7) shows the total time consumed for transmitting the data frame in the worst-case scenario. We have already calculated the total time in ideal scenario \"\(T_{ti}\)\" consumed by the 75% of the data frame packets that do not require a retry transmission; 25% of the data frame packets are assumed to experience a retry transmission. Thus, the data rate in the worst-case scenario can be calculated as follows:  
\[T_{time}=(T_{ti}\times 75\%)\ \ +(T_{tw}\times 25\%)\]  
Figure 3: Acknowledgement data frame format. \[W_{data}=\frac{\left(D_{p}\,\times\,\omega\,\right)}{T_{time}} \tag{8}\]  
When a specific amount of data is transmitted on network using ODFF approach, the total time can be calculated as follows:  
Figure 3.0  
Based on this mathematical formulation, we have validated the important parameters of our proposed ODFF and the existing IEEE802.15.4 data frame formats. The statistical data are shown in Figure 3.1  
Based on the obtained statistical results, the ODFF is considerably better than the existing IEEE 802.15.4 protocol in terms of each parameter; therefore, the ODFF can extend the network lifetime and improve the QoS. The details of the metrics used in the ODFF are provided in Figure 3.2
# A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection
## 3 Training of CESDD
### Training Sample Categorization  
In this work, samples from vehicle category and background category consist of several deliberately specified types, to take care of the variousness of image contents during vehicle detection. Samples in vehicle category consist of three types with different occlusion levels: FV vehicles, PO vehicles, and LO vehicles; samples in background category consist of two types: GB and VP. The constitution is illustrated in Figure 3. These types constitute almost all sorts of image contents in urban street views in KITTI2017 dataset [29] (the 2D object detection subset included in KITTI's \"3D Object Detection Evaluation 2017\" channel).  
Figure 1: Flowcharts of training CESDD (**a**) and detection with CESDD (**b**). (**a**) Training of CESDD. All sparse-and-dense dictionaries (SDDs) of both levels in the cascade are obtained through ESDD learning: training samples are firstly grouped into subcategories; then the SDD ensemble is learned with one pair of dictionaries (A, B) for each subcategory; next, downstream AdaBoost classifier is learned from the residue combinations from the ESDD; (**b**) Detection with CESDD. Through levels in the cascade, ESDD-based window classification is performed: a window patch from sliding-window scan process is passed to each SDD in the ensemble for coding, and the resulting reconstruction residues are then transformed into the input to the downstream AdaBoost classifier to give prediction.  
Figure 2: Initial detections of our approach with cascade vs. without cascade. (**a**) Initial detections without cascade; (**b**) Initial detections with cascade.  
Moreover, to be suitable for subsequent ESDD learning, each type of vehicle samples are further divided into subcategories according to their orientations, as illustrated in Figure 4. Background types are further divided into subcategories through clustering process for which we adopted K-means.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 4 Towards Time-Slotted LoRa Communications
### Time Slots, Frames, and Synchronisation  
The current work uses the same frame design as TS-LoRa [31]. TS-LoRa is a time-slotted protocol for industrial IoT using LoRa as the physical layer. According to TS-LoRa, the time is divided into repeated frames where each frame consists of a number of time slots. Each time slot accommodates the transmission of one node. Assuming packets of equal size and the fact that the transmission time increases with higher SFs, the length of the time slots is the same only for packets transmitted with the same SF. This allows TS-LoRa to have 6 parallel frames where each frame accommodates transmissions of nodes using the same SF. The frames have a minimum allowed size as this is dictated by the radio duty cycle rules, however, additional slots can be added and expand the frame size in order to accommodate more transmissions per frame. Apparently, the longer the frame, the sparser the transmission rate, and thus, the data collection time.  
As with all synchronised protocols, the nodes need to be periodically synchronised by the gateway according to a global clock. In order to accommodate the nodes' clock drift over time, guard times are added between successive time slots to ensure that adjacent transmissions do not overlap. Assuming the propagation delay negligible, the guard time depends on the crystal clock accuracy and on how often the gateway communicates with the nodes to synchronise their clocks. In TS-LoRa, this is performed using a novel mechanism called \"SACK\" (Synchronisation and ACKNOWLED) which merges sync and (optional) ack data into a single downlink packet. The frame size and the guard time are adjusted automatically according to the number of registered nodes. An example of the frame structure is given in Figure 3a. Detailed information of the time-slotted operation and the synchronisation can be found in [31; 32].
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 3 Results and Discussion
### Antioxidant Acitivity Changes During Blackcurrant Postharvest Storage  
Both DPPH and FRAP assays (Supplementary Table S3) indicated that blackcurrant postharvest treatments had no significant impacts on antioxidant activities. The DPPH  
Figure 3: Microbial population measurements on blackcurrants during storage. Four treatments are included in the postharvest treatments. Different letters correspond to mean values significantly different (_p_ < 0.05) across storage by analysis of variance (ANOVA) and Tukey post hoc test. Each treatment at each storage days has three replicates.  
assay detected that antioxidant activities barely changed, and the range was from 95 to 105 TEAC \(\upmu\)M/\(\upmu\)L on average. The FRAP assay showed antioxidant activity ranging from 61 to 77 \(\upmu\)M/\(\upmu\)L on average during storage. These two assay principles were different: the FRAP assay was based on the electron transfer of antioxidants, whereas the DPPH assay detected hydrogen atom abstraction, electron transfer between polyphenols, or a combination of these two [44]. However, both indicated that postharvest treatments did not cause significant differences in blackcurrant antioxidant activities. Chitosan coating's effects on fruit antioxidant capacities depend on fruit cultivars [45]. In the range of 15 days of storage, there were no significant differences in antioxidants influenced by chitosan or UVA.  
Our study focused on exploring the effects of UVA and chitosan on general fruit quality during postharvest storage of blackcurrant fruit. Additional topics to further understand this study include understanding the treatment's effects on oxidative stress through reactive oxygen species (ROS) production and enzymatic antioxidant changes during postharvest storage. Although in this study, there were no significant changes in antioxidant capabilities detected in the blackcurrant fruit under different treatments, chitosan was reported to function in delaying oxidative stress during fruit postharvest storage [46; 47]. Oxidative stress was highly associated with postharvest fruit quality, specifically impacting senescence or damage during storage [48; 49]. Investigating the oxidative stress during blackcurrant storage may explain the UVA or chitosan functional mechanisms better.
## 4 Numerical Results and Comparisons
### Independent Channels  
In Figures 1 and 2, we present the results for implementations of the SDC and LM05 for independent channels compared to the asymmetrical BB84 in terms of protocol efficiency against errors. In this case, the error rates for SDC and LM05 in EM are given by \(2q/2(1-q/2)\) and \((2q-q^{2})/4\) respectively [5].  
We can observe that there are particular regions where LM05 outperforms both SDC and BB84. Generally, this can be understood when considering the competing terms of the extra \(\sqrt{nk}\) contributing to LM05's raw key versus the amount of bits to be discarded due to error correction. For LM05, this is given by \(h_{2}[2q/2(1-q/2)]\), SDC's \(h_{4}[(2q-q^{2})/4]/2\) while for BB84, it is simply \(h_{2}(q/2)\) where  
\[h_{2}[2q/2(1-q/2)]\geq h_{4}[(2q-q^{2})/4]/2\geq h_{2}(q/2), \tag{26}\]  
with equality only at \(q=0\) or \(q=1\). The term \(\sqrt{nk}\) is most dominant for relatively small values of \(n\), hence LM05's best performance of the three in Figure 3 where \(M(n,k)=10^{4}\). In the same figure we see instances when the errors are big enough and outweighs the contribution from \(\sqrt{nk}\), LM05 performs poorly compared to BB84. The error correction term for SDC is only very slightly better than that of LM05 in the infinite key regime, explaining why it still does not exceed LM05's \(\sqrt{nk}\) advantage in the finite key scenario here. Thus LM05 exceeds BB84 up to an error rate of about \(2.7\%\). However, in Figure 4 where the number of qubits used, \(M(n,k)=10^{7}\), LM05 exceeds BB84 only up to about \(1\%\) and SDC's up to \(3.8\%\). The plot of the protocols' efficiency against \(M(n,k)\) for the error of \(0.01\) in Figure 5 exhibits the convergence of the efficiencies as the number of qubits used increases up to \(10^{7}\). These results clearly emphasizes LM05's determinism over the asymmetric BB84's. It is worth recalling the fact that while LM05 claims deterministic status in terms of the absence for bases mismatch in EM, BB84 only approximates this (in the infinite key regime). Figure 6 exhibits the case for the three protocols in the infinite key regime.  
Figure 3: Efficiency for Asymmetrical BB84 (blue), LM05 (red) and SDC (green) against error rate \(q/2\) given independent channels for total qubit used as \(M=10^{4}\).  
Figure 4: Efficiency for Asymmetrical BB84 (blue), LM05 (red) and SDC (green) against error rate \(q/2\) given independent channels for total qubit used as \(M=10^{7}\).  
Figure 5: Efficiency for Asymmetrical BB84 (blue), LM05 (red) and SDC (green) against total qubit used, \(M\) (for SDC, it is to be understood as total pairs used) for error rate \(0.01\). The horizontal dashed lines represents the infinite key regime.
# Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
## 2 Methodology
### Horizontal Displacement Selection  
The correlation and variance texture features have hyperparameters to tune. From Equations (11) and (12), its evaluation depends on horizontal displacement \(d\). This displacement \(d\) controls the number of values to compare. Figure 4 shows an empirical analysis from different horizontal displacements allowing select \(d=1\) as the best parameter. For the values, \(d=2\) and \(d=3\) the separation is minimum, making the creation of a decision boundary using the SVM difficult. For the horizontal displacement \(d=4\), the samples are completely superimposed, preventing the generation of a hyperplane using the SVM.
# The Combined Compact Difference Scheme Applied to Shear-Wave Reverse-Time Migration
## 3 Analysis of CCD and CSCD
### Time Dispersion Suppression Comparison  
Seismic waves propagate in time and space. The numerical dispersion caused by grid discretization includes both spatial dispersion and time dispersion. The accuracy of time extrapolation of the traditional high-order difference (2 m, 2) scheme is only second order. When a large time step is adopted, there will be obvious time dispersion.  
We use the same model to evaluate time dispersion as used for spatial dispersion. To evaluate time dispersion, it is necessary to eliminate the influence of numerical dispersion caused by the spatial gird size. Therefore, the spatial grid size is set to 5 m, and we increase the time step to 4 ms. The calculated seismic records of CCD and CFD are shown in Figure 6a,b.  
The seismic record simulated using the CFD scheme shows serious and obvious dispersion, while the result of the CCD scheme shows almost no such dispersion. As the CCD scheme proposed in this paper uses the fourth-order difference operator to approximate the time partial derivative, it has better stability than the CFD scheme, which can only use the second-order difference operator to approximate the time partial derivative. The CSCD scheme, by contrast, uses the same fourth-order difference operator to approximate the time partial derivative, which shows even higher accuracy than the CCD scheme.  
Figure 6: (**a**) CFD is used to simulate the numerical simulation of seismic records in the 1D homogeneous medium model, \(\Delta\)t = 0.004 s; (**b**) CCD is used to simulate the numerical simulation of seismic records in the 1D homogeneous medium model, \(\Delta\)t = 0.004 s.  
Figure 7a,b compare the seismic records using the fourth- and second-order difference operators to approximate the time partial derivative from the CCD scheme. It can be seen that when using the same finite difference scheme to suppress the numerical dispersion caused by the spatial gird size, the fourth-order operators show smaller numerical dispersion caused by increasing time step size than the second-order operators, which provides a theoretical basis for the use of large spatial gird size and large time step size for shear-wave simulation.