esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance of the GPP Prediction Model
Overall, the GPP prediction model demonstrates high accuracy and robustness, making it a valuable tool for assessing rubber GPP under drought conditions. The bar chart in Figure 7 compares the GPP results from prediction models, flux observations, and MOD17A3 HGF inversions at four sites: DR, XR, NR, and SR. At the DR site, in 2014, the model-estimated GPP was 2148.65 g C m\({}^{-2}\) year\({}^{-1}\), very close to the flux observation of 1952.67 g C m\({}^{-2}\) year\({}^{-1}\), while the MOD17A3 HGF value was 2286 g C m\({}^{-2}\) year\({}^{-1}\). In 2015, the model-estimated GPP was 2231.03 g C m\({}^{-2}\) year\({}^{-1}\), the flux observation was 2036.39 g C m\({}^{-2}\) year\({}^{-1}\), and the MOD17A3 HGF value was slightly higher at 2382 g C m\({}^{-2}\) year\({}^{-1}\). In 2016, the model prediction, MOD17A3 HGF estimation, and flux observation of GPP were very close, with errors less than 5%. At the XR site, in 2011, the model prediction, MOD17A3 HGF estimation, and flux observation of GPP were also very close, with MOD17A3 HGF having a smaller error than the model prediction. In 2012 and 2013, the model-estimated GPP was closer to the flux observation results, particularly in 2013, where the model estimation error was only half that of MOD17A3 HGF. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **NDVI** & **EVI** & **NIRv** & **SIF** & **PAR** \\ \hline NDVI & 0.24 & & & & \\ EVI & 0.50 & 0.45 & & & \\ NIRv & 0.44 & 0.52 & 0.12 & & \\ SIF & 0.62 & 0.61 & 0.61 & 0.56 & \\ PAR & 0.39 & 0.51 & 0.20 & 0.60 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Interaction between remote sensing index factors and GPP. Figure 6: GPP simulation training and validation based on remote sensing indices. At the NR site, from 2015 to 2017, the GPP estimates from MOD17A3 HGF were significantly lower than both the model predictions and flux observations. At the SR site, in 2017, the model-estimated GPP was consistent with the MOD17A3 HGF estimates, about 15% lower than the flux observation. In 2018, the model-estimated GPP was 1801.57 g C m\({}^{-2}\) year\({}^{-1}\), the flux observation was 2020.32 g C m\({}^{-2}\) year\({}^{-1}\), and the MOD17A3 HGF value was 2096 g C m\({}^{-2}\) year\({}^{-1}\), closer to the flux observation. However, observing the trend from 2017 to 2018, the flux observed GPP decreased, whereas the MOD17A3 HGF estimates increased. The model prediction also showed a decrease, more accurately reflecting the trend. Overall, compared to flux observations, the developed model's GPP estimates demonstrated high accuracy and consistency across all sites and were more reliable than the MOD17A3 HGF estimations.
## 3 Dynamic Characteristics and Comfort Analysis
### Human-Induced Vibration Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Conditions** & _f/Hz_ & **Participants** & **Excitation Source** \\ \hline \multirow{6}{*}{Number of people test} & 1 & 2 & A & Walk \\  & 2 & 2 & A, B & Walk in line \\  & 3 & 2 & A, B, C & Walk in line \\  & 4 & 2 & Ten people & Walk in line \\  & & & (70 kg/person) & \\ \hline \multirow{6}{*}{Walk frequency test} & 5 & 1 & \\  & 1 & 2 & A & Walk \\  & 6 & 3 & \\ \cline{1-1}  & 7 & 1 & \\ \cline{1-1}  & 2 & 2 & A, B & Walk in line \\ \cline{1-1}  & 8 & 3 & \\ \cline{1-1} \cline{2-5}  & 9 & 1 & \\ \cline{1-1}  & 3 & 2 & A, B, C & Walk in line \\ \cline{1-1}  & 10 & 3 & \\ \cline{1-1} \cline{2-5}  & 11 & 1 & Ten people & \\ \cline{1-1}  & 4 & 2 & (70 kg/person) & Walk in line \\ \cline{1-1}  & 12 & 3 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Conditions information. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Current Codes** & \multicolumn{2}{c}{**Evaluation Standard of Human-Induced Vibration Comfort**} \\ \cline{2-3}  & **Vertical** & **Lateral** \\ \hline CJ69-95 (China) & \(f_{\text{vertical}}\geq 3\) Hz & - \\ BS5400 (Engind) & \(a_{\text{max}}\leq 0.5f_{\text{v}1}{}^{0.5}\) m/s\({}^{2}\) & - \\ EN03(2007) (Germany) & \(a_{\text{max}}\leq 1.0\) m/s\({}^{2}\) & \(a_{\text{max}}<1.0\) m/s\({}^{2}\) \\ Bro2004 (Sweden) & \(a_{\text{rms}}\leq 0.5\) m/s\({}^{2}\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Current codes on human-induced vibration at home and abroad. Table 3 and Figure 10 show that the self-reported walking experience was relatively poor. For MTTS, even if the driving comfort is good, there is still a problem of poor pedestrian comfort. The lateral fundamental frequency of the structure should be controlled by considering the factors of pedestrian comfort. However, the current domestic specifications do not consider the impact of pedestrians on the lateral dynamic response of the structure, and there are hidden dangers in comfort and safety. Therefore, it is important to clarify the corresponding design parameters.
## 1 Introduction
To ensure the robustness of the detector, a large database of ground-truth images is required. The major innovation of this work is the preprocessing of orthophoto generation, merging individual citizen's image data in its workflow as a preprocessing. Graffiti markings are generally on long or large-size walls that cannot be captured in a single image; the orthophoto will improve the detection performance and enable quantification on large-scale structures. The capability of the system to estimate the sizes and locations of graffiti autonomously could help in remotely determining how much time and money should be invested in cleaning a specific section. On the other hand, the system allows the incorporation of images taken from ordinary citizens (people without the intention of doing visual inspection), which helps update the system without the requirement of scheduling visits to the site by experts or community staffs. In this study, we developed a vision-based method to detect and quantify graffiti damage using images. We leveraged a ground-truth graffiti database generated by a recent European Union H2020 project, entitled \"Safeguarding Cultural Heritage through Technical and Organisational Resources Management\" (STORM). STORM has gathered a large volume of images of damaged or contaminated structures to be used for studying the community's long-term preservation strategies. Labeled graffiti images were utilized to build a robust graffiti detector. A key idea in the proposed technique was to generate a high-resolution orthophoto before the graffiti detector. To collect images periodically, various crowdsourcing strategies were used, such as a game applications on smartphones, harvesting from social media, promotions, advertisements to engage tourists, commuters, residents, and any other type of citizen. Once images over target structures in a community (TOCs) were collected, each facade was automatically estimated from the full set of images, yielding the appropriate locations and number facades within the TOC. An orthophoto for each facade of the structure was then generated by geometrically stitching multiple images collected from the TOC using a structure-from-motion (SfM) algorithm [26; 27; 28; 29; 30]. Then, the graffiti detector was applied to each orthophoto, which provided the relative size and location of each graffiti marking on the corresponding facade, thus localizing and quantifying the damage. All of these processes, including orthophoto generation and graffiti detection, were automated once we built the detector in advance. The capability of the technique was demonstrated using a cultural heritage site, the Church of Agios Nikolaos (Leontariou), Kantza, Greece [31].
# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 6 Comparisons
### Summary of the Findings and Open Issues
Open issues remain when depth and color information fail to be complimentary. As an example, it has been shown that an object moving on a wall can be detected based on its color, rather than its camouflaged depth. However, what if the object has the same color of the wall? Future research directions should certainly investigate these cases.
# A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks
## 5 Simulation Results  
### V2X Service Classification  
V2X services are divided into many types and collected from multiple sources, as presented in Table 1. According to the further classification of various services in the V2X communication scenario, and combining the vehicle communication modes corresponding to the services, it can be seen that each service also includes multiple vehicle communication modes. For example, V2V communication between vehicles includes both V2V communication and V2R communication between the vehicle and the base station. In other words, related information such as communication and location between vehicles needs to be reported in real time and shared within a certain area. The V2X service is mainly carried by the V2T. Therefore, the traffic flow control and management of the V2X service can further optimize the communication efficiency of the V2T terminal.
## 5 Conclusions and Way Ahead  
### Conclusions  
In order to estimate grain size variations in the order of 1 \(\upmu\)m, the wave velocity must be known in the order of a few meters per seconds. In a similar fashion, an estimation of phase ratio variations in the order of 10%, requires a wave velocity accuracy of a few tens of meters per second. These accuracies are not feasible for practical use, since they lead to constraints towards temperature accuracy in the order of a few tens of degrees Celsius or less, and a sample thickness accuracy of less than 10 \(\upmu\)m.  
The attenuation coefficient is much less sensitive to temperature and sample thickness than the velocity and is considered a more promising alternative. The accuracy of 1 \(\upmu\)m in grain size or 10% in phase composition can be obtained by measuring the attenuation with an accuracy in the order of a few hundredths. This leads to constraints to the temperature accuracy in the order of 70 \({}^{\circ}\)C.
# A New Method for Constructing the Static-Voltage-Stability Boundary with a Conversion Strategy
## 3 Analysis of Boundary Tracking Methods  
### The Solving Process of the Parameter Tracking Method  
Equation (4) is usually solved using the predictor corrector technique, which is mainly divided into two stages: the tangent estimation variable \(\mathbf{S}^{pre}\) and normal hyperplane correction to the exact value \(\mathbf{S}^{cor}\). It is mainly described as follows:  
(1) Tangent estimation variable \(\mathbf{S}^{pre}\).  
To obtain the tangent vector of \(\mathbf{\Psi}\) at the \(\mathbf{S}_{0}\) point in Equation (4), the incremental equation can be supplemented as follows:  
\[\varepsilon\cdot\Delta\alpha|_{\mathbf{s}_{0}}=d, \tag{7}\]  
where \(\varepsilon\) is a known numerical constant representing the relative magnitude of change in \(\Delta\alpha\), and \(d\in\{1,-1\}\) is the direction indication factor specifying the direction of change in \(\Delta\alpha\).  
Simultaneously using Equations (4) and (7), we have the following:  
\[\left\{\begin{array}{l}\mathbf{\Psi}(\mathbf{S})=\mathbf{0}\\ \mathbf{e}\cdot\Delta\mathbf{S}=\mathbf{D}\end{array}\right., \tag{8}\]  
where \(\mathbf{e}\) and \(\mathbf{D}\) are appropriate dimension vectors, that can be written as follows:  
\[\left\{\begin{array}{l}\mathbf{e}=[0,\cdots,0,1]\\ \mathbf{D}=[0,\cdots,0,d]^{\mathrm{T}}\end{array}\right.. \tag{9}\]  
Figure 3: The schematic of constructing SVSRB. The green lines are P-V cure with CPF method, while the red point is the boundary point.  
Therefore, the tangent vector \(\Delta\)S can be written via inversion operation from Equation (8) as follows:  
\[\Delta\)S = \(\left[\begin{array}{c}\frac{\partial\mathbf{\Psi}}{\partial S}\\ \mathbf{e}\end{array}\right]^{-1}\cdot\mathbf{D}\).  
Hence, the estimated value in the predictor stage can be obtained as follows:  
\[\mathcal{S}^{\text{pre}}=\mathcal{S}+\sigma\cdot\Delta\mathcal{S}, \tag{11}\]  
where \(\sigma\) is the step-size control factor used to manipulate the relative distance between the estimated value and the initial value in the predictor stage.  
(2) Accurate value of hyperplane correction  
To obtain the corrected value, the hyperplane equation passing through the \(\mathcal{S}^{\text{pre}}\) point and perpendicular to vector \(\Delta\)S can be written as follows:  
\[\Delta\mathcal{S}^{\text{T}}\cdot(\mathcal{S}-\mathcal{S}^{\text{pre}})=0. \tag{12}\]  
Simultaneously using Equations (4) and (12), we can obtain the following:  
\[\mathbf{\mathcal{G}}(\mathcal{S})=\left\{\begin{array}{c}\mathbf{\Psi}(\mathcal{S}) =\mathbf{0}\\ \Delta\mathcal{S}^{\text{T}}\cdot(\mathcal{S}-\mathcal{S}^{\text{cor}})=0 \end{array}\right.. \tag{13}\]  
Notice that Equation (13) is a nonlinear system with multiple coupled variables. Hence, the Newton-Raphson method can be applied to solve it with \(\mathcal{S}^{\text{pre}}\) as the initial value. Specifically, the iterative scheme can be written as follows:  
\[\mathcal{S}^{\text{cor}}=\mathcal{S}_{0}-\left[\frac{d\mathbf{\mathcal{G}}}{d \mathcal{S}}\right]^{-1}\mathbf{\mathcal{G}}(\mathcal{S}_{0}). \tag{14}\]
## 2 Related Work  
STAP has been studied extensively in previous works. Initially, the fully adaptive STAP, where adaptive weight is calculated using all elements and pulses, was considered [12; 13]. Afterwards, a variety of STAP approaches have been developed to reduce the computational burden and enhance the convergence speed. To improve the detection ability in heterogeneous environments, [PERSON] et al. proposed a knowledge-aided STAP technique in which the prior information was incorporated within the conventional training strategies [14]. A similar idea was used in other works [15; 16; 17; 18]. The reduced-rank and reduced-dimension STAP, such as subspace projection and extended factored algorithms, have been widely considered since the training requirements were reduced to twice the degrees of freedom or the clutter rank [19; 20; 21; 22; 23; 24; 25; 26]. To further reduce the training requirements, [PERSON] et al. proposed the sparse representation STAP [27]. By utilizing intrinsic sparsity and compressive sensing techniques to recover the clutter, estimating the accurate clutter spectrum with minor training support is achievable. Similar conclusions were also obtained in other works [28; 29; 30; 31; 32]. The deep learning technique provides a new idea for STAP design. Authors in [33] and [34] used deep convolution networks to offer the high-resolution profile, and simulations demonstrated that deep-learning STAP reduced the computational load compared with sparse representation STAP.  
These previous studies for STAP provide some important insights into clutter suppression and target detection for airborne passive radar; however, the common assumption is that the reference signal is pure. In a realistic environment, this assumption is violated. To enable the target detection ability of STAP, the question is how to develop the MP clutter suppression approach before STAP.  
In [9], [PERSON] et al. used a blind equalization approach to construct the constraint optimization model and restore the pure DP signal based on the prior information. To overcome the dependence on prior knowledge, some sparse reconstruction algorithms were proposed for MP clutter suppression [8; 10; 11]. In our previous work [8; 10], we derived the sparse reconstruction algorithms (SRAs), in which the sparse model of MP clutter was designed and several optimization algorithms based on the \(L_{1}\)-norm were exploited to estimate the profile. In our previous work [11], under the framework of sparse models in SRAs, we used the local search algorithm (LSA) to select optimal atoms to construct the range-Doppler dictionary and address the off-grid issue. However, the presence of ICM disturbs the sparse model of MP clutter and corrupts the MP clutter suppression performance of SRA and LSA. To the best of our knowledge, very little literature addresses the ICM problem in MP clutter suppression. Although the ICM problem also arose in STAP [35; 36; 37; 38], these methods only focused on the DP clutter cancellation. In this paper, we propose a novel MP clutter suppression method to mitigate ICM effects while also taking off-grids into account.
## 3 DTN Structure and Implementation Issues  
This section describes the disaster environment assumed in this paper and our proposed DTN structure. It also identifies the target characteristics of the DTN, and implementation issues that must be solved.
## 3 Results and Discussion  
### Reliability Analysis  
The stability of the cavity internal pressure is important to the test accuracy. For this reason, the internal pressure of the cavity during the experiment is recorded, as shown in Figure 3. The pressure in the cavity can be regarded as the working pressure around the cooling surface. The working pressure around the cooling surface is maintained by controlling the opening of the inlet and outlet valves of the cavity. The internal pressure of the cavity is maintained around the preset pressure.  
For the purpose of obtaining the conclusions with high creditability, repeated tests are carried out. The selected working condition is \(T_{\text{a}}\) = 20 \({}^{\circ}\)C, \(P_{\text{v}}\) = 935 Pa, \(T_{\text{w}}\) = \(-\)38 \({}^{\circ}\)C, and \(P_{\text{a}}\) = 1.50 \(\times\) 10\({}^{5}\) Pa. The frost thickness of the two experiments is shown in Figure 4. In the initial period, there is almost no difference between the frost thickness, while it increases to 0.07 mm at 60 min. The overall deviation is 3.2%, which is within the acceptable range.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Set No.** & **Partial Pressure of Water Vapor \(P_{\text{v}}\)** & **Temperature of Air \(T_{\text{a}}\)** & **Temperature of Cooling Surface \(T_{\text{w}}\)** \\  & **[Pa]** & **[\({}^{\circ}\)C]** & **[\({}^{\circ}\)C]** \\ \hline \multirow{3}{*}{A} & 350 & \multirow{3}{*}{20} & \multirow{3}{*}{\(-\)38} \\  & 935 & & \\  & 1400 & & \\ \hline \multirow{3}{*}{B} & \multirow{3}{*}{350} & 15 & \multirow{3}{*}{\(-\)38} \\  & & 20 & \\  & & 25 & \\ \hline \multirow{3}{*}{C} & \multirow{3}{*}{350} & \multirow{3}{*}{20} & \multirow{3}{*}{\(-\)20} \\  & & & \\ \end{tabular}
\end{table}
Table 2: Test conditions in the three sets of frosting experiments.  
Figure 3: Transient working pressure in the cavity during the experiment.
## 3 Results and Discussion
### Analysis of Microstructure and Chemical Composition  
For the purpose of image analysis enabling microstructural classification of ceramics, 20 images were taken from each sample. The procedure for recording the images of samples involved randomly selecting fields distributed over the entire surface of the tested ceramics. The selection of magnifications was made separately for each tested sample, taking into account its grain size as well as image readability. Images of the microstructure and gain size distribution diagrams of PBZT-25/70/30 ceramics doped with La\({}^{3+}\) at concentrations of \(x=1\) at.%, \(x=2\) at.%, \(x=3\) at.%, and \(x=4\) at.% are shown in Figure 3. The microstructure is characterized by a compact structure with well-developed grains. There is visible variation not only in grain size but also in their orientation throughout the volume.  
It can be seen that the lanthanum concentration has a significant effect on the grain size. Grain size distribution charts, created by analyzing multiple images for each concentration at the same magnification, are also shown in Figure 3. Based on the results, it can be observed that as the concentration of the La\({}^{3+}\) dopant increases, the grain size decreases from \(d=17.83\) um for \(x=1\) at.% La to \(d=1.83\) um for \(x=4\) at.% La. Lanthanum doping causes a reduction in grain size compared to undoped PBZT [33], where the average grain size of undoped ceramics was larger than 15 um. The grain structure refinement and homogenization of grain sizes under the influence of lanthanum doping are also observed in other materials with a perovskite structure, including barium titanate [34; 35] and lead titanate [36].  
Figure 4 shows an example of an EDS spectrum for PBZT \(x=1\) at.% La ceramics. The graph shows the peaks originating from the spectral lines of the elements in the obtained ceramics. EDS studies confirmed the purity of the examined samples and ruled out the presence of other elements. It can be concluded that the produced ceramics maintain the intended chemical composition.  
Figure 3: SEM image and gain size distribution diagrams of PBZT-25/70/30 ceramics doped with La\({}^{3+}\) at concentrations \(x=1\) at.%, \(x=2\) at.%, \(x=3\) at.%, and \(x=4\) at.%.
## 3 Results
### Denoising Effect of the Entire-Tunnel-Section Point Cloud  
The denoising results of the selected 40 m long tunnel point-cloud data using the Huber loss function-based ellipse-fitting method are shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation** & \multirow{2}{*}{**Section 1**} & \multirow{2}{*}{**Section 2**} & \multirow{2}{*}{**Section 3**} & \multirow{2}{*}{**Section 4**} & \multirow{2}{*}{**Section 5**} & **Average** \\
**Metrics** & & & & & & & **Value** \\ \hline MSE(m\({}^{2}\)) & 0.00006 & 0.00022 & 0.00004 & 0.00013 & 0.00029 & 0.00015 \\ RMSE(m) & 0.00806 & 0.01504 & 0.00667 & 0.01149 & 0.01690 & 0.01163 \\ MAE(m) & 0.00642 & 0.01185 & 0.00477 & 0.00878 & 0.01444 & 0.00925 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metric calculation results for the tunnel-top (60\({}^{\circ}\)-120\({}^{\circ}\)) point cloud of an individual tunnel section.  
Figure 15: Schematic diagram of the point-cloud spatial distribution after denoising for the entire-tunnel section (unit: meters). (**a**) XYZ coordinate system (**b**) XY plane (**c**) XZ plane.  
For the entire-tunnel point cloud, to quantitatively evaluate the denoising effect of the point cloud, the denoising evaluation metrics for all sections of the tunnel are calculated and shown in Table 4. Additionally, for all sections of the tunnel, this study selects the local point cloud at the tunnel top (60\({}^{\circ}\)-120\({}^{\circ}\)) to compute the evaluation metrics, as shown in Table 5.  
According to the evaluation metrics in Tables 4 and 5, the denoising performance of the Huber loss-based point-cloud denoising method applied to the entire-tunnel point cloud shows no significant difference compared to that on a single tunnel cross-section point cloud. This indicates that the method exhibits a certain degree of robustness and can effectively denoise point clouds from various tunnel cross-sections.  
Similar to the case in Tables 2 and 3, the results in Table 5 show a significant improvement over those in Table 4, further demonstrating that the original point cloud--containing noise such as tunnel pipelines--introduces errors in the evaluation metrics that misrepresent the denoising performance. In contrast, the evaluation metrics computed for the tunnel-top point cloud directly assess the denoising effect on the tunnel-lining-structure point cloud, thereby providing a more accurate reflection of the denoising performance on the tunnel lining structure.  
In summary, the ellipse-fitting denoising method based on the Huber loss function demonstrates good denoising performance for the entire-tunnel point cloud. It effectively preserves the geometric features of the tunnel lining structure and removes non-structural facilities, such as pipelines, from the point cloud.