esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Collection
### The Survey of Drivers' Psychological Characteristics  
Given the behaviors of humans are determined by intentions to act in a certain way, the design of the questionnaire of drivers' psychological characteristics when facing CAVs conformed to the theory of planned behavior (TPB), including five latent psycho variables (i.e., familiarity, trust, attitude, risk perception and perceived behavior control). The operational definitions for each of the five latent variables are listed in Table 3, and the 16 measurement items are listed in Table 4. All items employed a 5-point Likert scale, ranging from 1 = 'totally disagree' to 5 = 'totally agree'.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Description** & **Frequency** & **Distribution** \\ \hline \multirow{2}{*}{Gender} & Female & 169 & 49.85\% \\  & Male & 170 & 50.15\% \\ \hline \multirow{4}{*}{Age} & 18–23 & 54 & 15.93\% \\  & 24–30 & 153 & 45.13\% \\  & 31–40 & 85 & 25.07\% \\  & 41–50 & 32 & 9.44\% \\  & 51 or older & 15 & 4.43\% \\ \hline \multirow{4}{*}{Monthly income} & Less than \(\yenx\)5000 & 238 & 40.71\% \\  & \(\yenx\)5000 to \(\yenx\)9999 & 112 & 33.04\% \\  & \(\yenx\)10,000\(\sim\)Y29,999 & 85 & 25.07\% \\  & \(\yenx\)30,000 or more & 4 & 1.18\% \\ \hline \multirow{4}{*}{Highest Level of education} & High School Graduate or GED & 23 & 6.78\% \\  & Junior college & 40 & 11.8\% \\  & College & 160 & 47.2\% \\  & Masters or Doctorates & 116 & 34.22\% \\ \hline \multirow{4}{*}{Driving experience} & Less than one year & 55 & 16.23\% \\  & 1–5 & 145 & 42.77\% \\  & 6–10 & 107 & 31.56\% \\  & \(\yenx\)10 & 32 & 9.44\% \\ \hline \multirow{4}{*}{Annual driving mileage} & Have driver’s license but never driving & 86 & 25.37\% \\  & Less than 10,000 km & 96 & 28.32\% \\ \cline{1-1}  & 10,000–30,000 km & 84 & 24.78\% \\ \cline{1-1}  & 30,001–50,000 km & 29 & 8.55\% \\ \cline{1-1}  & \(\yenx\)50,000 km & 44 & 12.98\% \\ \hline \multirow{2}{*}{CAV experience} & Yes & 71 & 20.94\% \\ \cline{1-1}  & No & 268 & 79.06\% \\ \hline \end{tabular}
\end{table}
Table 2: Summary of sample’s socio-demographic characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Latent Variable** & **Operational Definition** \\ \hline Familiarity & The understanding of the performance and advantages/disadvantages of CAV. \\ Trust & The trust in CAV technology, performance and driving behavior. \\ Attitude & Personal positive or negative continued evaluation about sharing the road with CAVs. \\ Risk perceptions & Negative beliefs and fears of potential dangers from sharing the road with CAVs. \\ Perceived behavior control & Personal control over the vehicle when interacting with CAVs. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Operational definition of five latent variables.
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 3 Modeling
### Model Construction

Table 2: _Cont._and  
\[F_{2}=\sum_{k\in K}\sum_{m\in M}\sum_{t\in T}\frac{V_{km}^{t}}{L_{k}^{t}W_{k}^{t} \overline{H}_{k}^{t}}\leq 1. \tag{5}\]  
In Equation (4), \(c^{d}\sum\limits_{k\in K}\sum\limits_{i,j\in I}\sum\limits_{t\in T}u_{iz_{ij}}^{ t}d_{ij}\) denotes the distribution cost of the vehicle \(k\) from node \(i\) to \(j\) in service period \(t\), \(UC\sum\limits_{k\in K}u_{k}^{t}\) denotes the usage cost of the vehicle, \(\sum\limits_{k\in K}\sum\limits_{i,j\in I}\sum\limits_{t\in T}c_{i}^{d}\cdot \left[\max\left\{E_{j}-AT_{kj}^{t},0\right\}\right]\) denotes the time cost of waiting when the vehicle arrives at the unloading site too early, and \(\sum\limits_{k\in K}\sum\limits_{i,j\in I}\sum\limits_{t\in T}c_{i}^{t}\cdot \left[\max\left\{AT_{kj}^{t}-L_{j},0\right\}\right]\) denotes the time cost to be borne when the vehicle arrives at the unloading site too late. Equation (5) represents the average loading rate of all distribution vehicles. The volume of the pallet \(m\) of the vehicle \(k\) after loading in service time \(T\). \(v_{km}^{t}\) is expressed as follows:  
\[v_{km}^{t}=v^{p}+\sum_{k\in K}\sum_{m\in M}\sum_{t\in T}\sum_{n\in N}u_{kmn}^{ t}\cdot x_{kmn}^{t}y_{kmn}^{t}z_{kmn}^{t}. \tag{6}\]  
The objective function is  
\[F_{3}=\frac{(1-\beta)}{\phi}(F_{1}+\xi)-\frac{\beta}{\phi}\left(\frac{F_{2}}{ \max F_{2}-\min F_{2}}+\xi\right), \tag{7}\]  
where \(0\leq\beta\leq 1\) is the weighting factor, which is taken as 0.5 in this study, implying that the loading rate and the total cost are equally important. \(\xi\) is the parameter that ensures that the objective function is non-negative, considering the actual loading problem, and \(\xi=0\), \(\phi\) and \(\varphi\) are generally taken as the regularization factors of the multi-objective function, which is theoretically obtained through the optimization process of Equation (8).
## 4 Conclusions  
In this paper, we presented for the first time a rigorous discussion on the use of free access GIS data and ad hoc RS data for urban 2D-1D stormwater modeling. The coupled 2D-surface and 1D-Sewer TRENOE model was applied to a small urban catchment near Paris (Le Perreux sur Marne, 0.12 km\({}^{2}\)). Four different types of RS and GIS data have been applied in the modeling platform, including (i) high-resolution topographic data derived from LiDAR; (ii) low-resolution DTM data obtained from the free access database; (iii) detailed land-use data proceeded from multiple data sources; and (iv) coarse land-use information extracted from the free access database. According to the results, the detailed land-use information derived from multiple data sources is a crucial factor for accurate simulations; however, using the very high resolution LiDAR data is not equally significant for water flow simulations at sewage outlets. Using only free access GIS topographic data with artificially drug streets seems to be a reasonable compromise to obtain acceptable simulations. Although this study resampled different types of data to the same resolution (5 m) for the model inputs, this paper provided a new approach trying to quantify the gains of using different sources of RS and GIS data on urban stormwater modeling. Moreover, the results of this study indicated that the precise description of manhole locations (sewer inlets, sewer drain grills, etc.) is also an important factor for the urban stormwater 2D-1D modeling. Consequently, using the free access GIS data accompanying the detailed urban sewer network design should be an acceptable low-cost solution for accurate urban 2D-1D stormwater modeling during moderate rainfall events. Depending on different management/research objectives, managers and decisionmakers are suggested to question the necessity of expensive ad hoc data before considering modeling applications. Further studies of urban stormwater modeling should focus on the development of \"suitable\" models with \"enough\" input data in order to solve operational/research issues in an efficient way.  
The research work of student [PERSON] was financed by ANR-Trafpollu project (ANR-12-VBDU-0002) and Ecole des Ponts ParisTech. The authors would like to thank the experimental team of ANR Trafpollu project for all collected necessary for this work, in particular [PERSON] (CEREMA), [PERSON] (LESU) and [PERSON] (LEEU). This study was designed by [PERSON], [PERSON], [PERSON] and [PERSON]. The manuscript was prepared by [PERSON] and revised by [PERSON], [PERSON] and [PERSON]. The authors declare no conflict of interest.
# Computational Investigation of a Novel Box-Wing Aircraft Concept
## 3 Aero-Structural Characterization of CRM Aircraft
### Computational Results and Analysis (Incl. Validation)
#### 3.3.1 Aerodynamic (CFD) Results
The good agreement of the present simulations with reference data is further highlighted in Figure 8, which depicts the lift and drag coefficients obtained under the S\({}_{1}\) flight condition for various angles of attack (\(\alpha\sim 0^{\circ}\) to \(3^{\circ}\)), comparing them with those coming from [48]. The agreement is fairly good, with the present and reference simulations almost collapsing while closely matching the experiments. An additional and more qualitative comparison is provided in Figure 9, which displays the chordwise distribution of the pressure coefficient (C\({}_{\rm P}\)) around the main wing, as obtained under the S\({}_{1}\) flight condition and a higher angle of attack (\(\alpha=5.5^{\circ}\))--following [48]. As observed in the latter reference, beyond a limit angle of attack of \(\alpha=3^{\circ}\), non-linear effects mostly drive the lift creation (and, thus, the structural deformations) experienced by the wings. This goes along with the formation and upstream migration of a shock that initially extended towards the wing tip. One can see that the shock presence is captured by the simulations and the experiment, as revealed by the respective C\({}_{\rm P}\) distributions that are plotted here at three relative positions from the root (\(\eta=(y-y_{\rm root})/(y_{\rm tip}-y_{\rm root})\)). Similar to the reference computation [48], the present simulation agrees reasonably well with the experimental data [48], despite some noticeable deviations around the shock location. At this stage, it is worth underlining the inherent difficulty of capturing shocks using standard CFD approaches such as the one used here (e.g., classical 2 nd order schemes), which makes it usually challenging to simulate such types of transonic configurations. Overall, the present results agree fairly well with the reference data, whether computational or experimental--which validates the CFD methodology and set-up employed here.
# Experimental Tests and Aeroacoustic Simulations of the Control of Cavity Tone by Plasma Actuators
## 2 Methodologies
### Numerical Simulations  
#### 2.4.1 Governing Equations and Finite-Difference Schemes  
To clarify the fluid-acoustic interactions in the cavity flows, the flow and acoustic fields were simultaneously simulated by solving the three-dimensional compressible Navier-Stokes equations with mass and energy conservation laws in conservation form:  
\[\mathbf{Q}_{t}+\left(\mathbf{E}-\mathbf{E}_{\text{v}}\right)_{x}+\left( \mathbf{F}-\mathbf{F}_{\text{v}}\right)_{y}+\left(\mathbf{G}-\mathbf{G}_{\text {v}}\right)_{z}=\mathbf{D}, \tag{1}\]  
\[\mathbf{D}=\left(\begin{array}{c}0\\ s_{x}(x,y,z,t)\\ s_{y}(x,y,z,t)\\ s_{z}(x,y,z,t)\\ D_{\text{c}}(s_{x}u+s_{y}v+s_{z}w)\end{array}\right), \tag{2}\]  
where \(\mathbf{Q}\) is the vector of the conserved variables, \(\boldsymbol{E},\boldsymbol{F},\boldsymbol{G}\) are the inviscid flux vectors, and \(\boldsymbol{E}_{\text{v}},\boldsymbol{F}_{\text{v}},\boldsymbol{G}_{\text{v}}\) are the viscous flux vectors. To reproduce the body force produced by the actuator, the term \(\mathbf{D}\) representing the body force and the power added by it to the unit volume are included on the right-hand side of the equation [9]. Details of the body forces are described in next subsection.  
Figure 6: Comparison of the sound-pressure level SPL of the cavity flow both with and without a flush-mounted actuator.  
The spatial derivatives were evaluated using a sixth-order-accurate, compact, finite-difference scheme (fourth-order accurate on the boundaries) [10]. Time integration was performed with a third-order-accurate Runge-Kutta method.  
The flow in the cavity became turbulent under the given conditions, although the incoming boundary layer was laminar. To reduce the computational cost, large-eddy simulations (LES) were performed in the present study. No explicit subgrid-scale (SGS) model was used. The turbulent energy at the grid-scale (GS) that should be transferred to SGS eddies was dissipated by a 10 th-order spatial filter described below. A number of studies [11; 12; 13] have shown that this approach, which combines low-dissipation discretization schemes with filtering, correctly reproduces turbulent flows. This filter, which also removes numerical instabilities, is given by [14]:  
\[\alpha_{f}\hat{\psi}_{i-1}+\hat{\psi}_{i}+\alpha_{f}\hat{\psi}_{i+1}=\sum_{n=0 }^{5}\frac{a_{n}}{2}(\psi_{i+n}+\psi_{i-n}), \tag{3}\]  
where \(\psi\) is a conserved quantity, and \(\hat{\psi}\) is the filtered quantity. For the coefficients \(a_{n}\), the same values as Gaitonde and Visbal [15] were utilized, and the value of the parameter \(\alpha_{f}\) was set to be 0.45. Cavity flows with incoming laminar and turbulent boundary layers can be predicted with high accuracy using the methods described above [16].
# Lattice Fracture Model for Concrete Fracture Revisited: Calibration and Validation
## 4 Validation
### Three-Point Bending Test
#### 4.3.2 The Influence of Notch Ratio  
Three-point bending tests investigating the effect of notch length to beam depth ratio [52] are simulated to evaluate the LBM ability to predict the flexural fracture behavior with respect to the final crack patterns and load-displacement curve. The mechanical properties of lattice elements are calibrated using the sample with the notch equal to 0.1 (Table 7). These specimens are characterized by a span of 400 mm, height of 100 mm, and depth of 100 mm, respectively. The models with different notch ratios used in their experiments are 0.05, 0.1, 0.3, and 0.5, respectively. The load is applied in the middle of the top side and the vertical displacement is measured (see Figure 10a).  
Figure 10b shows the final crack patterns for the numerical models with different notch ratios. Regardless of the notch ratio, the same fracture behavior can be found, in which the initial crack occurs from the top of the notch and propagates throughout the ligament until the system loses the bearing capacity. A good agreement, in terms of the load-displacement curve before the peak load, was found between the experimental and numerical results in Figure 10c. A clear deviation during the post-peak still exists, with the model showing much more brittle behavior compared to the experiments.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Size**} & \multicolumn{3}{c}{**Local Mechanical Property**} & \multirow{2}{*}{**Cell Size**} & \multirow{2}{*}{**Notch Ratio**} \\ \cline{2-2} \cline{5-8}  & **Span** & **Height** & **(mm)** & & & & & \\ \hline No.1 & & & & & & & 2.5 & 0.05 \\ No.2 & 400 mm & 100 mm & 100 mm & \(f_{\text{c}}=65\) MPa & & 2.5 & 0.1 \\ No.3 & & & & & \(f_{\text{t}}=6.5\) MPa & & 2.5 & 0.3 \\ No.4 & & & & & & 2.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Values of input parameters in the numerical analyses about notch ratio on three-point bending test.  
Figure 10: _Cont._It is also worth mentioning that for models with different notch ratios, the crack patterns and the corresponding displacement for the peak load did not change significantly, which is in accordance with the experimental data [52].
# Pulsed Thermography Applied to the Study of Cultural Heritage
## 3 Results
### 3D Thermographic Representation of the Capitoline Brutus  
In recent years, 3D modelling techniques have often been used in the study of cultural heritage artefacts [55; 81] to integrate them with other imaging techniques in order to improve the display of the obtained results. In this regard, hereafter, an application of a method [54], based on the integration of pulsed IRT imaging and 3D laser scanning technologies [82; 83; 84; 85], is presented and used to obtain a 3D thermographic representation of the Capitoline Brutus, so as to observe the structural and subsurface elements of the artefact in their geometrical contest. Concerning the procedure, it consists of the simultaneous use of a 3D scanner, a thermal camera, and a system for the thermal perturbation. Together with the acquisition of the sample geometrical data by the 3D scanning device, the IRT set-up enables the acquisition of thermograms. This provides the possibility to translate and rotate the position of the points captured by the IR camera into the geometrical space defined by the 3D scanner. Knowing the relative position of the IR camera and of the geometric sensors (for the 3D scanner), the thermographic features can be matched to the spatial coordinates obtained by the 3D scanning technique. The result is a 3D display of the features revealed by the thermograms [48; 49]. For instance, in Figure 13, 3D thermographic imaging of the Capitoline Brutus is shown. In this representation, surface and sub-surface features, like the junction line and the skull cup (indicated by the arrow in Figure 13), appear in their position on a 3D geometrical reconstruction which can be freely reoriented to provide the most suitable viewing angle. Moreover, different 3D-IRT reconstructions, obtained at different delays with respect to the heating pulse, provide information corresponding to different depths in the observed object. In this regard, an important perspective of the 3D thermography concerns the possible integration with other kinds of imaging techniques to provide a single 3D reconstruction where images from different diagnostics can be simultaneously displayed. Such a reconstruction could be virtually explored by browsing along the surface and through the layers beneath it, providing a tool, not only for scholars, but for any kind of end user, able to easily show surface and subsurface features as revealed by IRT and other NDT imaging techniques [86; 87].  
Figure 12: Commedia, XIV century, _Biblioteca Angelica_ of Rome. (**A**) Photograph of the illumination of _folio_ 15 _verso_; (**B**) thermogram where the dashed circle marks the demon.  
Figure 13: 3D representation of the thermographic results obtained for the Capitoline Brutus. The arrows indicate the junction line on the neck and the skullcap.
## 6 Discussion on the Results  
The findings outlined in the previous section underscore the significant impact of the ACPH system on crucial seismic performance metrics across different structural arrangements. In order to place these results within the larger framework of engineering goals, the ensuing analysis examines their consequences for structural efficiency, scalability, practical applicability, and inherent limitations. Additionally, this segment delves into the role of the ACPH system in fulfilling performance-based design objectives, notably in improving reparability, managing damage, and facilitating resilient recovery in the aftermath of substantial seismic occurrences.
## 4 Experimental Section
### Results  
This section presents the training results of the WGAN-GP model proposed in this paper. The proposed model effectively improved color and texture reproduction through the training process using reference images. The reference images were only used during the model training phase and are not required for the model's actual deployment.  
As training progressed, the color and texture of the generated images improved significantly. Initially, the color distribution was irregular, but with continued training, this irregularity gradually decreased, and eventually, the model was able to produce images with increasingly uniform color distribution. Figure 5 shows that the model effectively learned color consistency, and the images generated at each stage of the training gradually became more similar to the ground truth.  
Furthermore, the texture representation in the generated images was significantly improved compared to the initial training phase. In terms of texture reproduction, some unclear areas were observed in the early stages, but as training progressed, more sophisticated and detailed textures emerged. Figure 6 demonstrates that the WGAN-GP-based color correction model effectively learned and reproduced the features of the ground truth texture, showcasing an important result.  
To facilitate a clearer understanding of these visual enhancements, Figures 5 and 6 have been annotated with additional labels and captions. Each stage of training is marked to indicate specific improvements in color distribution and texture details, such as reduced color inconsistency, smoother texture transitions, and the emergence of finer details. These annotations guide the viewer through the model's progressive learning, illustrating the effectiveness of the WGAN-GP approach in refining both color and texture throughout the training process. Therefore, the WGAN-GP-based color correction model proposed in this study demonstrates strong performance not only in maintaining color consistency but also in the precise reproduction of textures.  
Figure 5: Color matching results of BigEarthNet-S2 RGB images and BigEarthNet-S2 preprocessing images. (**a**) Generated image by model at the early stage of training, (**b**) generated image by model at the mid-stage of training, and (**c**) generated image by fully trained model.
## 3 Data and Method  
### Index System and Data Sources  
Having summarized and integrated the existing research results [51; 52; 53] and considered China's actual situation, the present study develops a measurement index system to quantify carbon emission efficiency from three aspects; namely, input variables, output variables, and external environmental variables (Table 1). Additionally, through following the principles of comprehensiveness, effectiveness, systematicness, representativeness, and independence and by analyzing the existing research results [54; 55; 56], a three-dimensional coordination degree assessment index system is further developed, which reflects the relationship between carbon emissions, economic development, and environmental protection on the basis of carbon emission efficiency (Table 2). The present study uses 2009-2018 as the research period. The original data come from the \"China Statistical Yearbook\", \"China Environmental Statistics Yearbook\", \"China Energy Statistical Yearbook\", and \"China Employment Statistical Yearbook\", the Statistical Yearbook of all provinces, with municipalities and autonomous regions included, the National Economic and Social Development Statistical Bulletin, Environmental Status Bulletin, and other relevant statistical materials and literature.
## 4 Experiments and Results
### Experimental Environment and Parameters  
The experiments were performed on an Ubuntu 18.04 operating system workstation with a Xeon w3-2435 processor, 128 GB of memory, and two NVIDIA RTX 3090 GPUs (24 GB each). The proposed G-UNETR++ was implemented using PyTorch. Five-fold cross-validation was used for training our model. Adam optimizer [45] was used to optimize the loss function. The initial learning rate was set to \(5\times 10^{-4}\). The learning rate decay strategy in this study was the same as used in nnFormer [19]. The model was trained with 1000 epochs with a batch size of 64. L2 regularization with a factor of \(3\times 10^{-5}\) was applied to avoid overfitting during training.