esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Governing Equations for the Flow Field and the Improved MLBFS
#### 2.2.3 The New Flux Reconstruction Scheme  
In previous studies [40; 42], the distribution function \(f_{\alpha}^{\wedge}\) is approximated by:  
\[f_{\alpha}^{\wedge}=f_{\alpha}^{eq}(\mathbf{r},t)-\left(\tau-\frac{1}{2}\right) \left[f_{\alpha}^{eq}(\mathbf{r},t)-f_{\alpha}^{eq,m}(\mathbf{r}-\mathbf{e}_{\alpha} \delta_{t},t-\delta_{t})\right], \tag{26}\]  
\[f_{\alpha}^{eq}(\mathbf{r},t)=w_{\alpha}[p+\rho\Gamma_{\alpha}]=w_{\alpha}\left[p +\rho c_{s}^{2}\left(\frac{\mathbf{e}_{\alpha}\cdot\mathbf{u}}{c_{s}^{2}}+\frac{(\bm {e}_{\alpha}\cdot\mathbf{u})^{2}-(c_{s}|\mathbf{u})^{2}}{2c_{s}^{4}}\right)\right], \tag{27}\]  
\[f_{\alpha}^{eq,m}(\mathbf{r}-\mathbf{e}_{\alpha}\delta_{t},t-\delta_{t})=w_{\alpha}[p +\rho(\mathbf{r},t-\delta_{t})\Gamma_{\alpha}], \tag{28}\]  
It can be seen that \(f_{\alpha}^{eq}(\mathbf{r},t)\) is the original equilibrium particle distribution function obtained by applying the LBM after the collision, \(f_{\alpha}^{eq,m}\) is the modified equilibrium particle distribution function, \(\tau\) is the single relaxation parameter, \(\mathbf{r}\) is the mid-point of the interface between two adjacent control volumes, and \(f_{\alpha}^{eq}(\mathbf{r},t)\) is used for calculating the convective fluxes. It may be noted that, due to large density ratios at the phase interface in some multiphase flows and the nonlinearity of the convective terms, numerical instability can occur since not much numerical diffusion is introduced by only considering the distribution function before the collision. Inspired by the previous studies on supersonic compressible flows with shock waves in gas kinetic methods [43; 44], the equilibrium particle distribution function before collisions is also applied to calculate the inviscid fluxes:  
\[f_{\alpha}^{\wedge}=(1-\eta)f_{\alpha}^{eq}(\mathbf{r},t)+\eta f_{\alpha}^{eq,m}( \mathbf{r}-\mathbf{e}_{\alpha}\delta_{t},t-\delta_{t})-\left(\tau-\frac{1}{2}\right) \left[f_{\alpha}^{eq}(\mathbf{r},t)-f_{\alpha}^{eq,m}(\mathbf{r}-\mathbf{e}_{\alpha}\delta _{t},t-\delta_{t})\right] \tag{29}\]  
Here, \(\eta=\min\ (\lambda,\ 0.025)\), \(\lambda=\tanh(|C_{L}-C_{R}|/(2(C_{L}+C_{R}+0.2)))\), \(C_{L}\) is the order parameter of the left control volume for a surface, and \(C_{R}\) is the order parameter of the right control volume. As mentioned earlier, the new scheme includes the particle distribution functions both before and after the collision. A local switch function is also introduced to adjust their contributions in different flow regions. In most of the flow fields without any phase interfaces, the particle distribution after collision contributes more to the inviscid fluxes, as usual, whilst in the interfacial zone, the particle distribution function before collision has a greater effect. Therefore, the numerical diffusion can be controlled and the fluxes can be evaluated according to the characteristics of the local multiphase flows.
## 4 Analysis of Methods for Forecasting Transportation Flow
### Combined Algorithm Model Based on ANN  
Although the flow prediction method based on a BPNN has high accuracy, it is still easy to fall into the local minimum rather than the global minimum when dealing with highly nonlinear problems. This shortcoming may cause the problem of good data fitting with the deviated prediction results [69]. A single traffic flow prediction model may not achieve the best accuracy due to its shortcomings, so the combined model comes into being. [PERSON] used a CNN to extract traffic flow characteristics and SVR to predict short-term traffic flow [70]. Compared with the single model, the performance improved by 11%. [PERSON] used the principal component analysis method to extract the main features affecting the network traffic as the input vector of the BPNN and then predicted the network traffic in the future through the BPNN [71]. According to the research result, the mean absolute error (MAE) and normalized mean square error (NMSE) of the combined model were smaller than those of the single prediction model. Next, [PERSON] et al. [72] applied the attention-based spatial-temporal graph convolution network to improve the traffic flow prediction accuracy of a certain section in a road. Compared with the traditional benchmark algorithm, the prediction performance is better. [PERSON] et al. used an ant colony algorithm to optimize the initial weights and thresholds of the BPNN to predict the short-term traffic flow of roads [73]. Compared with the traditional single model, the MAE decreased by more than 4%. [PERSON] et al. obtained the optimal weight threshold by introducing the PSO algorithm to optimize the BPNN [74]. Through the coupling analysis of the space-time characteristics of the subway passenger flow through three time granularities and two spatial granularities, the prediction accuracy and calculation speed of the combined model were significantly improved. Then, [PERSON] proposed a subspace learning method based on a CNN, solving the prediction problem of fuzzy asymmetric data and improving the prediction ability of the model by converting data into images for spatial feature extraction [75]. Finally, [PERSON] et al. established the traffic flow prediction model in holiday expressways through the LSTM-SVR. Compared with the single SVR and LSTM models, the MAE decreased by 70.93% and 15.74%, confirming the effectiveness of the combined model [76]. According to this study, the combination of different algorithms could overcome the shortcomings of a single algorithm, form complementary advantages, and improve the prediction accuracy of the model.
# Protection of Personal Data in the Context of E-Commerce
## 3 Protection of Personal Data on the Internet
### Collecting Confidential Information Online  
The Internet makes global connectivity and access to numerous services possible and serves as a crucial platform for gathering and analyzing personal data, regardless of the provider's location. Organizations often collect data through various methods, such as requesting it for service delivery, tracking behaviors using cookies, or obtaining data from external sources. Personal data, such as names and dates of birth, are often requested by online services like free email and social networks to grant access to their platforms. Although these services may seem free, the price is paid with personal data, which companies utilize to generate profits, frequently without consumers fully comprehending the worth or consequences of the data they supply.  
Data collection methods encompass two main types: \"first-party\" data, which involves companies directly gathering information from users, and \"third-party\" data, which involves aggregating information from multiple sources and subsequent sales. Generally, companies view \"first-party\" data as more reliable due to their knowledge of its source and collection methods, which helps ensure compliance with regulations. However, risks arise when dealing with \"third-party\" data, including data overlap, uncertainty regarding collection methods, and potential regulatory violations. There has been a significant shift in data collection approaches, driven by a growing understanding and regulatory measures such as the GDPR. This has led companies to decrease their dependence on third-party data and prioritize protecting data privacy. Personal information is stolen for malicious purposes through social engineering techniques alongside legitimate data collection practices. Phishing, vishing, and smishing are commonly used to obtain sensitive information through deceptive emails, fraudulent phone calls, and text messages. Victims are often manipulated into revealing personal data by attackers who assume false identities, resulting in significant financial and reputational damage for both individuals and businesses. These attacks, which fall under the umbrella of social engineering, are a severe concern. Additional methods of data theft involve the creation of fraudulent websites, unauthorized acquisition of databases, and disseminating malware and spyware through unreliable sources.
## Results  
### Climate and Soil Conditions  
Average air temperature during the growing season of winter wheat during the study year was 1.3 degC higher than the long-term (1981-2010) average of 11.5 degC (Figure 1a). In particular, air temperature during early growth of winter wheat (October-November) was 2.6 degC higher than the long-term averages. Additionally, air temperature during February-March was 3.4 degC higher than the long-term air temperatures in those months.  
Figure 1: Mean monthly (**a**) temperature and (**b**) total precipitation during the study period (October 2016 to May 2017; grey bars), compared to the long-term (1981–2010) mean in the study area (black circles).  
Total precipitation received during the 2016-2017 growing season of wheat (563 mm) was slightly greater (34 mm) than the long-term average of 529 mm (Figure 1b). However, precipitation was not well distributed through the growing season. Only 53 mm precipitation was recorded during October-December, which was 130 mm below the long-term average of 183 mm for those months. In contrast, precipitation received during February and April 2017 was higher than the long-term averages.  
Soil temperature at 0-5 cm depth was similar for both systems of tillage, which ranged from 0 to 20 \({}^{\circ}\)C during flux measurements (Figure 2a,b). Likewise, the average PAR in a measurement date was similar for both systems of tillage. As the measurements were undertaken under sunny, partially cloudy or overcast conditions, large difference in PAR in the successive measurement dates were frequently recorded (Figure 2c,d). As expected, soil VWC increased after rainfall events and declined during dry spells (Figure 2e-h). Dynamics and magnitude of soil VWC was mostly similar under both tillage systems throughout the study periods.  
Figure 2: Dynamics of (**a,b**) soil temperature, (**c,d**) photosynthetically active radiation (PAR), and (**e,f**) volumetric water content (VWC) during flux measurements at plots managed by no-till and conventional-till. (**g.h**) Daily average precipitation during the study period. Data (panels a-f) are presented as averages (\(n=12\)) and standard errors across all N treatments under a tillage system. Unidirectional error bars are shown for clarity.
###### Abstract
The digital planning of the restorations can then be 3D printed to obtain a physical model of the restorations. From digital and traditional wax-ups, a silicone guide can be produced to be used as a guide to print a mock-up in the patient's mouth, which is an essential part of the treatment plan in order to mimic and validate the planned restorations [21]. Digital imaging and designing are useful for communicating directly with the patient and showing visual feedback of the possible treatment outcomes of rehabilitations in the aesthetic area, and also improve communication between clinicians who can access the images remotely [22,23]. These digital techniques allow the patient to be involved in the design process of the dental elements, which results in a more humanistic and confident smile [22]. [PERSON] et al., in their article, reported that the duration of procedures appears to be an important issue concerning the opinion of patients about their care. The perception of a faster procedure was found to be the aspect that pleased most of the patients involved in the study, which reinforces the necessity to pursue further analysis of different types of workflows in terms of working times [12]. With all this in mind, focused on the purpose to make digital aesthetic planning available in an open-source way and, because various types of workflows can be used to obtain a final mock-up of anterior aesthetic restorations nowadays, the objectives of this experiment were:  
* Evaluation of the aesthetical opinion of the patient and of a group of external people regarding the three mock-ups obtained with three different types of workflows;
* Comparison of the working times of the three workflows;* Comparison of the symmetry between gingival curves and dental elements in every mock-up.
## 4 Application Scenario: Step-Down Converter
### Comparison with a Taylor Model-Based Solution Approach  
In general, there exist two different options to account for the uncertain parameter vector \(\mathbf{p}\in[\mathbf{p}]\) defined in Equation (48) when using a general-purpose verified ODE solver. In this section, the solver verifyode[34; 43] is employed to obtain a representative comparison. When appending the uncertain parameters to the state vector \(\mathbf{x}(t)\), introduced in (45), and accounting for the time invariance of these parameters by the so-called integrator  
Figure 4: State enclosures for the step-down converter using the novel procedure according to Section 3 in combination with the real-valued transformation approach of Section 2.3. Figure 5: Partitioning of the parameter domain according to Section 3 for two different parameterizations of the multi-sectioning strategy. Figure 3: State enclosures in terms of their lower and upper bounds for the step-down converter after backward transformation into the original state-space using the procedure in Section 2.4.  
disturbance model \(\hat{\mathbf{p}}=\mathbf{0}\) in the state equations, the solver verifyode does not at all succeed in computing guaranteed state bounds. This results from a division by zero when substituting the Taylor model representations of both uncertain parameters into the system model (46). This problem is independent whether identical Taylor model orders are chosen for all four state variables or if the Taylor model order for the interval parameters is reduced to its minimum value 1 as described in [43]. A simulation of this augmented state-space representation becomes possible--even with the default setting of identical Taylor model orders for all state variables--if the uncertainty in both parameters \(\mathbf{p}\) is reduced significantly. This, however, changes the system dynamics and can therefore not be used for a fair comparison with the proposed cooperativity-enforcing simulation approaches. Successful simulations with this ODE solver become possible if the uncertain parameters are not appended to the state vector but if they are specified instead within the state equations as interval variables (datatype intval). Then, manually specifying the initial step size of the solver as \(h_{0}=10^{-4}\), the minimum step size as \(h_{\min}=10^{-6}\), the Taylor model orders as either 10, 12 (the default setting), or 30, performing either a QR preconditioning or a curvilinear preconditioning with or without blunting (a strategy of widening the angles between the column vectors of an ill-conditioned preconditioning matrix to reduce overestimation [44]) and/or shrink wrapping (aiming at an elimination of additive error intervals and incorporating them in the Taylor model coefficients [45]), it is possible to simulate the system model.
# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
## 3 Method
### Sparsity-Robust Radar Pillar Feature Fusion Network  
Unlike the original PointPillars network, our Radar Pillar Feature Fusion Network uses the unprocessed and sparse radar point clouds in the form of \(N\) points \(\times\) 6 as input, as shown in Figure 2. Here, 6 refers to the amount of information recorded for each point. Each point can be described by \(p=[x,y,z,v_{r},v_{rc},RCS]\), where \((x,y,z)\) are the spatial coordinates, \(v_{r}\) is the radial Doppler velocity, \(v_{rc}\) is the radial velocity compensated with ego-motion, and \(RCS\) is the radar cross-section. We provide comprehensive details on the data used in Section 4.1. These sparse irregular point clouds are first converted into voxels via Pillar-Voxel Feature Extraction (Pillar-VFE) [30]. Subsequently, these voxels are used to generate a two-dimensional pseudo-image of size \(160\times 160\times 64\), which serves as input to a CNN backbone for generating deep semantic features. Note that a large number of pixels in the pseudo-image are empty, as voxels are not occupied. The backbone, depicted in Figure 2, consists of three stages, each spatially reducing the pseudo-image by a factor of 2 per stage and doubling the number of channels. We use resolutions \(i\in\{1,2,3\}\) as \((H_{i},W_{i})\in M_{i}\times M_{i}\) for each stage, with \(M=\{160,80,40\}\), respectively, and the corresponding feature depths \(d_{i}=\{64,128,256\}\). Each layer in the 2D backbone is composed of a \(3\times 3\) convolution, batch normalization [31], and a non-linear activation in the form of a ReLU function. The final features of each backbone stage are fused using our novel Sparsity-Robust Feature Fusion (see Section 3.2). These fused features are processed again with a \(3\times 3\) convolutional layer and upsampled to the desired resolution. The resolution depends on the corresponding network configuration, and we test the resolutions \(\{64,128,256\}\). Our experiments in Section 4.3 show that a higher resolution leads to better detection of small objects, which is why the final resolution is preferably set to \(160\times 160\). In the final step, we use two convolution layers as an anchor-based detection head, supporting the high resolution of input features.  
We choose an anchor-based RPN head following our SRFF neck for all experiments.
## 3 Results  
### Spatial Trends in the Climate Indices at the Site Scale  
Spatial variations of the trends in climate extremes for the 52 meteorological stations across Hubei Province are shown in Figure 2. Summer days (SU25) show increasing trends at almost all stations in Hubei Province and its surrounding areas. Spatially, the statistically significant increasing trends mainly vary between 0.3 and 0.5 days year\({}^{-1}\) at the 0.05 significance level, except for a few stations in the southwest regions, which have relatively higher elevations. Consequently, the warm-related indices of tropical nights (TR20) and WSDI also exhibit significant increasing trends, mainly in the eastern regions, with the ranges of 0.2-0.4 days year\({}^{-1}\) and 0.1-0.4 days year\({}^{-1}\), respectively. Significant changes are shown at night, with increases during warm nights (TN90p) and decreases during cool nights (TN10p) at most stations. In contrast, changes during daytime (as expressed by warm days (TX90p) and cool days (TX10p)) are generally not significant at the 0.05 significance level. Trends in these indices at most stations are all consistent with climate warming, which is characterized by increases in warm-related indices and decreases in cold-related indices.  
As for the precipitation indices, the number of stations with significant trends at the 0.05 significance level is generally less than that for temperature indices. Based on the spatial patterns of the trends in precipitation extremes for extremely heavy precipitation days (R50), very wet days (R95p) and extremely wet days (R99p), the eastern areas show increasing trends in extreme precipitation while western areas show the opposite. The difference between the western and eastern regions of the study area is also demonstrated by the moderate precipitation days (R10) and consecutive dry days (CDD) indices. This difference might relate to the increases in annual total wet-day precipitation (PRCPTOT) in the east and decreases in PRCPTOT in the west. The decline of wind speed across the globe and China [55; 56] is likely the key factor for the differences. As shown in Figure 1, there is a clear gradient in altitudes from the west to the east within the study area. As a result, decreased east wind speed in summer fails to bring moisture to the west of Hubei Province and consequently leads to increased precipitation in the east and decreased precipitation in the west.
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 4 Results of the Analysis of the Selected Works
### Analysis of the Device Layer  
The main objective of the communications infrastructure is to interconnect nodes and generate a way to access the data generated in different domains of the city.
## 4 Simulation and Results
### Results of the Proposed Model without the TMR Effect and Media Noise  
In Figure 9, the 'best proposed model' is the BER of the signal \(a[j,k]\), the 'proposed model with the 2-way GPR target' represents the BER of the signal \(a_{\text{2 way}}[j,k]\), which is similar to the performance of the model in [15], and the 'proposed model with the 4-way GPR target' is the BER of the signal \(a_{\text{4 way}}[j,k]\). It can be observed that the BER performance was improved through each detection layer. Finally, the BER performance nearly matches the ideal case. In addition, we compared our proposed model with the iterative row-column soft decision feedback algorithm (IRCSDFA) [24], hybrid 2D equalizer and modified Viterbi detector (H2 DEMV) [17], ITI cancellation method [19], serial soft detection [23], and 2D  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & \\ \hline Size of square magnetic island & 11 nm \\ Thickness of square magnetic island & 10 nm \\ Thickness of read head & 4 nm \\ Width of read head & 15 nm \\ Gap distance of read head & 6 nm \\ Fly height of read head & 10 nm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical parameters.  
patterned dependent noise prediction (2D PDNP) [29]. The results indicate that, in each model, the 8-way GPR target improves the gain by \(\sim\)1 dB at a BER of 2 \(\times\) 10\({}^{-5}\).
## 3 Results and Discussion
The SEM micrographs illustrate the structural effects of each stabiliser on the soil microstructure and the impacts of the chemical reactions which have taken place in each specimen. Figure 8 shows selected micrographs that illustrate the noticeably rougher soil microstructure in the specimens prepared with the pozzolanic stabilisers, along with the presence of heavier elements (denoted by light specs on the images, also indicated by arrows). Interestingly the soil microstructure of specimens prepared with HL and BFS/HL is similar, indicating that hydrated lime proportion in the blend may dominate the stabilisation mechanism. Furthermore, the micrographs of specimens treated with cement show the formation of large crystals of hydrous calcium aluminium sulphate or ettringite. While the HL and BSF/HL also show the presence of these type of crystals, they are much smaller by comparison. For each sample, an Energy Dispersive X-ray Spectroscopy (EDS) was also conducted. The EDS spectrum show the distribution of different elements in the soil (Table 3), which can indicate the presence and increase in the elements associated with  
All specimens treated showed substantial increase in the calcium content, along with smaller noticeable changes in the levels of carbon, iron and aluminium. The increase in iron (Fe) in the BSF/HL blends is not surprising as ground granulated blast furnace is a waste product derived from the steel making industry and the prevalence of these element in the material is likely to be high, albeit in small amounts (Table 2).