esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Case Study 1: Paintings
#### 3.1.3 XRF Analysis  
The XRF analysis performed on the original pictorial surfaces allowed us to identify, for both paintings, the typically pure pigments or mixtures that were used in the XVIII century (Figure 5) [29]:  
* Lead white [(PbCO3)2 \(\cdot\) Pb(OH)2], a natural pigment used since ancient times. This was found both in a mixture in the pictorial layers and in the ground (deducible from the high counts of lead in the dark areas of the pictorial surface and the response obtained in all XRF spectra);
* Vermillion (mercury sulfide, or HgS) has also been used since ancient times. We identified small amounts of it in the mixture for the preparation of the flesh tones, in the red portion, and in the ground or thin primer (probably present in a mixture in very small quantities in the reddish-brown preparation layer, where it was visible even when under the microscope; see Figure 3 and Painting 1 in particular);
* Ochres (iron oxides/hydroxides with clays) are pigments associated with impurities of potassium and manganese oxides typical of natural earth [29]. They are used for brown ground and mixed in red and brown pictorial layers.  
A copper-based pigment was detected in Painting 2. The elemental analysis did not allow the unequivocal identification of a blue chromophore; however the simultaneous presence of a high count of copper lines and the high IR absorption of this pictorial layer suggested the use of azurite (2 CuCO3 \(\bullet\) Cu(OH)2), a mineral that has been used as a blue pigment since ancient times.  
The results obtained by XRF analysis were supported by the evidence provided by the previously discussed diagnostic imaging investigations (DOM, UVF and IRR). Uniform spectral responses showed that the above-mentioned pigments were used for both paintings. This confirmed that the pigment _palettes_ were typical of the historical period (i.e., the XVIII century).
## 3 Results and Discussion
### pH and Electrolytic Conductivity  
The pH and EC in the hydroponic effluent were 5.35 \(\pm\) 0.42 and 7.57 \(\pm\) 0.48 mS/cm, respectively. The pH in wastewater after treatment decreased with increasing J, from 8.57 \(\pm\) 0.72 (R1) to 5.58 \(\pm\) 0.80 (R4). This results from the production of larger amounts of H\({}^{+}\) ions at higher electric current densities and a decrease in the heterotrophic denitrification process share, in which nitrate reduction results in the use of acid equivalent (H\({}^{+}\)) [11]. The solution pH does not change in bio-electrochemical systems with electro coagulation. This is attributed to \"ion buffering\", and neutralisation of H\({}^{+}\) produced on the anode by OH\({}^{-}\) ions generated on the cathode [31]. However, as OH\({}^{-}\) ions are consumed for precipitation reactions, the accumulation of H\({}^{+}\) results in a pH decrease, which particularly manifests at high current densities and with the use of the solution buffer capacity [16; 32]. The pH decrease in the R3 and R4 reactors in this study is attributed to the exhaustion of the effluent buffer capacity.  
The lowest EC was found in the R2 reactor--6.03 \(\pm\) 0.63 mS/cm. The highest levels were found in the R3 and R4 reactors--6.70 \(\pm\) 0.79 mS/cm and 6.64 \(\pm\) 0.79 mS/cm, respectively. A greater conductivity decrease at lower current densities results from larger amounts of removed NO\({}_{3}\)\({}^{-}\) and precipitation of calcium, magnesium and phosphorus ions as deposits. Similar findings were observed by [PERSON] et al. [5] when they treated hydroponic effluent with alternating current (J = 8.8 A/m\({}^{2}\)) and sodium acetate at C/N-1. [PERSON] et al. [21] treated hydroponic effluent in an SBR with methanol and found EC to decrease, as a result of denitrification, from 4 mS/cm to 2.3 mS/cm.
# An Architecture for the Performance Management of Smart Healthcare Applications
## 5 Performance Evaluation
### Results
However, the network slices and network traffic management used in PRIMUS will assist in network traffic concurrence since, in our results, the slice isolation improved traffic management. Therefore, the results represent success in interrelating the set of network components by automatically analyzing and managing the network resources. This work contributes to the state-of-the-art offering a virtualized scenario implementation with network slicing, network traffic management, and improvements on performance requirements to support s-health and 5G networks. However, there are open challenges, such as reducing latency, improving reliability, and validating the scalability considering different combinations of various s-health applications and more devices.
# Operation State Evaluation of Miter Gate Based on On-Line Monitoring and Finite Element Analysis
## 3 Finite Element Analysis of the Miter Gate  
### Model Establishment and Grid Division  
The gate size is large, and the ratio of the length to width to thickness of the plate is too large. Therefore, to obtain a high-quality grid, the grid size must be reduced, which will increase the number of grids, resulting in doubling the calculation amount. Therefore, it is necessary to simplify the model.  
The plate shell structure means that the thickness t of the plate is much smaller than the dimensions in the other two directions. Whether the structure can be simplified as a plate and shell problem requires determining the thickness ratio to other azimuth dimensions. If \(1/80\leq\) t \(\leq\) 1/10, it can be attributed to a plate (thin shell) problem. If it is between 1/10-1/5, it belongs to a thick shell problem. If it exceeds 1/5, it does not belong to a plate and shell structure problem. The mechanical model of the plate and shell element is taken as the neutral surface of the structural element. Each neutral surface is represented as a combination of plate or shell elements with different thicknesses to simulate the structural body, as shown in Figure 4.  
A miter gate comprises a gate leaf structure, supporting part, water stop part, and other auxiliary parts. The gate leaf structure includes a panel, main beam, secondary beam, diaphragm, vertical diaphragm, back tie rod, door shaft column, and miter column. The panel, main beam, secondary beam, diaphragm, and vertical diaphragm are plate parts with  
Figure 3: Layout of sensors on miter gate.  
a large ratio of length, width, and thickness. We set them as plate elements in modeling, and the rest are solid elements, including 200 plate elements and 19 solid elements. The coordinate system selects the \(x\)-axis as the axial direction of the main beam of the gate, the \(y\)-axis from the upstream to the downstream is perpendicular to the axial direction of the main beam, and the \(z\)-axis is vertical upward. The grid type is mainly a hexahedral grid. Finally, the finite element analysis model of 137,296 elements and 160,573 nodes is generated. The finite element model is shown in Figure 5. In the analysis process, the model's material is 16 Mn, the elastic modulus is 206,000 MPa, the Poisson's ratio is 0.3, the density is 7850 kg/m\({}^{3}\), and the gravity acceleration is 9800 mm/S\({}^{2}\). Other attribute parameters adopt default values.
## 4 Proposed Empirical Models for Weather Factors
### Rain Model  
Comparing the three models in Table 2 yields a result which is not very intuitive at first sight: for the same precipitation rate, the model for continental rain results in the highest extinction coefficient and tropical rain the lowest (see Figure 5). The root cause for this result is the very different particle density distributions, which assume that tropical rain has many more large droplets. As the volume of a sphere rises with the third power of the radius, a larger quantity of water is bound in a few large droplets instead of many small ones, as is the case for continental rain. Thus, if a light wave is traveling through tropical rain, it is subject to fewer scattering events, compared with traveling through continental rain, which results in a lower extinction coefficient [41]. As continental rain delivers the largest coefficient, it can be considered as the worst-case scenario for lidar experiments.  
Since no empirical model for the backscattering coefficient could be found, the same approach as for the backscattering coefficient of fog was used, i.e., to assume that the ratio \(\alpha/\beta\) is an invariant. Here, the particle density function of continental rain from [9] was used to calculate the ratio:  
\[\beta_{\text{rain}}\approx\frac{\alpha_{\text{rain}}}{0.60} \tag{11}\]  
It is noticeable in the equation above that the backscattering coefficient of rain is larger than the associated extinction coefficient. Figure 9 shows the cumulative efficiencies \(Q_{\text{ext}}\) and \(Q_{\text{back}}\) for the particle density function used. This shows clearly that the backscattering efficiency dominates the scattering behavior.
## 3 Materials and Methods
### LBS Missing Path Completion
#### 3.5.1 Completion of Missing Paths for LBS Sparse Trajectory Points
Next, the absolute difference between the travel time of all possible travel paths obtained from the simulation and the actual travel time is calculated, shown as follows:  
\\[\\Delta\\mathrm{T}_{\\mathrm{O}_{i},\\mathrm{D}_{k},n}=\\left|\\mathrm{T}_{\\mathrm{O}_ {i},\\mathrm{D}_{k}}-T^{\\prime}_{\\mathrm{O}_{i},\\mathrm{D}_{k},n}\\right| \\tag{11}\\]  
where \\(\\Delta\\mathrm{T}_{\\mathrm{O}_{i},\\mathrm{D}_{k},n}\\) is the absolute difference between the actual travel time and the simulated travel time between the origin \\(\\mathrm{O}_{i}\\) and the destination \\(\\mathrm{D}_{k}\\), \\(T^{\\prime}_{\\mathrm{O}_{i},\\mathrm{D}_{k},n}\\) is the travel time of the \\(n\\)-th travel path. Then, we select the travel path \\(p_{i,k,n}\\) corresponding to the minimum absolute difference in travel time \\(\\Delta\\mathrm{T}_{\\mathrm{O}_{i},\\mathrm{D}_{k},n}\\) as the path completion result for the travel demand, and the travel mode \\(mode_{(n)}\\) corresponding to this path is used as the travel mode completion result when the travel mode is missing, shown as follows:  
\\[\\Delta\\mathrm{T}_{\\mathrm{O}_{i},\\mathrm{D}_{k}}=\\ \\min\\bigl{(}\\Delta\\mathrm{T}_{ \\mathrm{O}_{i},\\mathrm{D}_{k},1},\\ \\Delta\\mathrm{T}_{\\mathrm{O}_{i},\\mathrm{D}_{k},2},\\cdots,\\ \\Delta\\mathrm{T}_{\\mathrm{O}_{i},\\mathrm{D}_{k},n}\\, \\bigr{)} \\tag{12}\\]  
Furthermore, after the completion, we reassessed the average speed of the target links and calculated a new NRMSE value. If the path completion deteriorates the calibration results (the new error exceed the preset threshold), then we return to the calibration process and adjust OD demand again. This iterative and feedback mechanism ensures the continuous optimization and precision of the calibration and completion work in this study. Moreover, this study compared the changes in the average speed of the target road segments in the calibration model and found that the complementation and correction of Type B data had a minimal impact on the error between the traffic simulation model and the actual situation. In other words, the error of the adjusted model remained within the threshold set by this study (below 30%). In theory, our method of completing missing paths ensures a high degree of matching between the complemented travel paths and the actual missing travel data in terms of the departure and arrival times, as well as locations. The completion process also strictly adheres to the real-world average road speeds. Therefore, the completed result will be more realistic and credible.
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 6 Discussion
It remains necessary to explore and determine if it is possible to estimate congestion and get realistic and more accurate information about the performance of the traffic flow based on congestion indexes fused with multi-sensor technologies. Multisource DF enables getting information about the state of traffic on network links with higher resolution, allowing it to be incorporated into the overall picture of a traffic network. For example, data collected from point detectors (radar, video, inductive loops) can measure traffic flow in each traffic lane separately. The traffic volume from one traffic lane to another can significantly differ, and that is the information that point-to-point or area-wide sensors cannot provide. This means that only combination of sensors can provide such detailed or high-resolution information of the traffic flow performance on a network link. This detailed information can be used in advanced ITS applications, such as dynamic traffic management (DTM), advanced traveler information systems, adaptive intersection control, routing, and traffic information services.
## 4 Results  
### Coupled Parameter Identification  
In previous work by the authors, the hydro-elastic modelling and validation of the AAUE-TLP without a nacelle attached has been made [24]. Therefore, a new experimental validation of the stiffness and damping of the coupled TLP and tower with the nacelle attached has been conducted. These experiments are out of the scope for this paper, therefore only the results will be given.  
The stiffness of the coupled TLP in surge was experimentally found by exerting a range of known forces to the TLP and measuring its displacement. The experiments showed that the mean coupled TLP stiffness in surge was 567.1 N/m, which is a reduction by approximately 10% compared to the previous found stiffness in [24].  
The stiffness of the coupled tower in surge was experimentally found by fixating the TLP, and then exerting a range of known forces to the nacelle and measuring its displacement. The experiments showed that the mean coupled tower stiffness in surge was 1253.0 N/m, which is a reduction by approximately 20% compared to the previous found stiffness in [24]. The damping ratio of the coupled tower in surge was experimentally found by fixating the TLP and conducting a series of free decay tests. The experiments showed that the mean coupled tower damping ratio in surge was 2.2% which is an increase of approximately 65% compared to previous found damping ratio found in [24]. The coupled damping ratio was set to be 2.5%, as this showed a better comparison of the tower top displacement between the simulations and experiments in Section 4.4.
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 2 Data and Methods
### Triple-Frequency Positioning Model of BDS-3
#### 2.2.3 UC123  
For frequencies \(i\), \(j\) and \(k\), after correcting for the satellite clock offset, we can obtain the UC123 code and phase observation equation after combining information with Equation (1).  
\[\left\{\begin{array}{l}p^{s}_{r,i}=u^{s}_{r}\cdot x+dt_{r,I\,F_{ij}}+m^{s}_{ r}\cdot Z_{r}+\overline{l}^{s}_{r,i}-\overline{b}^{s}_{UC_{i}}+\epsilon^{s}_{r,i} \\ p^{s}_{r,j}=u^{s}_{r}\cdot x+dt_{r,I\,F_{ij}}+m^{s}_{r}\cdot Z_{r}+\mu_{j}\cdot \overline{l}^{s}_{r,i}-\overline{b}^{s}_{UC_{j}}+\epsilon^{s}_{r,j}\\ p^{s}_{r,k}=u^{s}_{r}\cdot x+dt_{r,IF_{ij}}+m^{s}_{r}\cdot Z_{r}+\mu_{k}\cdot \overline{l}^{s}_{r,i}+IF_{BU_{IC123}}-\overline{b}^{s}_{UC_{i}}+\epsilon^{s}_{ r,k}\\ l^{s}_{r,i}=u^{s}_{r}\cdot x+dt_{r,IF_{ij}}+m^{s}_{r}\cdot Z_{r}-I^{s}_{r,i}+ \overline{N}^{s}_{r,j}+\epsilon^{s}_{r,j}\\ l^{s}_{r,j}=u^{s}_{r}\cdot x+dt_{r,IF_{ij}}+m^{s}_{r}\cdot Z_{r}-\mu_{j}\cdot \overline{l}^{s}_{r,i}+\overline{N}^{s}_{r,j}+\epsilon^{s}_{r,j}\\ l^{s}_{r,k}=u^{s}_{r}\cdot x+dt_{r,IF_{ij}}+m^{s}_{r}\cdot Z_{r}-\mu_{k}\cdot \overline{l}^{s}_{r,j}+\overline{N}^{s}_{r,k}+\epsilon^{s}_{r,k}\end{array}\right. \tag{12}\]  
with:  
\[\left\{\begin{array}{l}\overline{l}^{s}_{r,i}=\overline{l}^{s}_{r,i}+\beta_ {ij}\cdot(DCB_{r,ij}-DCB^{s}_{ij})\\ IFB_{UC123}=\beta_{ij}/\beta_{ik}\cdot(DCB_{r,ij}-DCB^{s}_{ij})-(DCB_{r,jk}-DCB^ {s}_{ik})\\ \overline{N}^{s}_{r,i}=\hat{N}^{s}_{k}+B_{r,i}-B^{s}_{i}+b^{s}_{IF}-b_{r,IF}+ \beta_{ij}\cdot(DCB_{r,ij}-DCB^{s}_{ij})\\ \overline{N}^{s}_{r,j}=\hat{N}^{s}_{k}+B_{r,j}-B^{s}_{i}+b^{s}_{IF}-b_{r,IF}+ \mu_{j}\cdot\beta_{ij}\cdot(DCB_{r,ij}-DCB^{s}_{ij})\\ \overline{N}^{s}_{r,k}=\hat{N}^{s}_{k}+B_{r,k}-B^{s}_{k}+b^{s}_{IF}-b_{r,IF}+ \mu_{k}\cdot\beta_{ij}\cdot(DCB_{r,ij}-DCB^{s}_{ij})\end{array}\right. \tag{13}\]  
where \(\overline{b}^{s}_{UC_{i}}\), \(\overline{b}^{s}_{UC_{j}}\) and \(\overline{b}^{s}_{UC_{k}}\) represent the satellite UCDs after correcting for the satellite clock offset for frequencies \(i\), \(j\) and \(k\), respectively; \(\overline{l}^{s}_{r,i}\) is the ionospheric delay that absorbs the DCB; \(IFB_{UC123}\) is the IFB parameter of UC123; \(\overline{N}^{s}_{r,i}\), \(\overline{N}^{s}_{r,j}\) and \(\overline{N}^{s}_{r,k}\) represent the floating ambiguity at frequencies \(i\), \(j\) and \(k\), respectively.
## 2 Materials and Methods
### Device Installation  
The monitoring-bait stations were buried underground without concrete or stone foundations at each field test site. A cylindrical pit with a depth of 25 cm and a diameter of 15 cm was excavated in the ground initially. Afterwards, a monitoring-bait station was settled into the pit with the circuit board facing upwards. It is crucial to make sure that the top of the station is aligned well with the ground surface when the pit is filled with soil, as this ensures that the handheld reader is able to detect the monitoring-bait station underground. After installation, the station was marked. The distance between each installed monitoring-bait station was 5-10 m.
## 3 Model Design  
### Overall Structure of the Model  
The EBLCS model (Figure 2) is structured into three main layers: the input encoding layer, the feature fusion layer, and the output layer. The primary components of the model include the ERNIE module, the BiLSTM module, the multi-scale attention module, and the classification module.  
Initially, the ERNIE pre-training model processes the word embedding of the goods text to obtain the vector representation \(E\) of the text. These vectors are then input into the BiLSTM module, which further captures the global contextual features of the text. To fully utilize the local and global information of the text and prevent the loss of feature information, the text representation vectors \(E\) are spliced with the global context feature vectors to obtain the feature vectors \(F\).  
Next, within the multi-scale attention module, the importance of different word embedding dimensions in the feature vectors is dynamically adjusted using channel attention. Subsequently, spatial attention is employed to integrate the feature weights of key words in the text, resulting in the final global contextual feature vector \(F^{\prime\prime}\) of the text. This final feature vector is then processed by the classification module to output the HS code to which the goods belong.
## 2 Models and Principles
### Sk_sam
#### 2.3.2 The Convolution of Selective Kernel  
SK Net [32] relies on the convolution of the selective kernel, which can use different receptive fields for convolution kernels used for different inputs. Finally, different parameter weights are obtained and the output is processed adaptively. The convolution of the selective convolution kernel consists of three parts: Split, Fuse, and Select. Their functions are to generate different size branches of convolution kernel, aggregate different branch information to obtain a global representation of selection weights, and combine feature maps from kernels of various sizes based on selection weights.  
The overall process is shown in Figure 6. In the Split phase, two groups of dilated convolution with different convolution kernel sizes are used to extract features from the input which, respectively, represent the receptive fields of different sizes. In the Fuse phase, the information for the two branches, \(U_{1}\) and \(U_{2}\), is added to integrate by using the feature and then using the global max pooling layer to embed the global information \  
gathered at the expense of global information. Therefore, better feature extraction ability will be obtained if the receptive field during network training can be adjusted adaptively. Generally speaking, the size of the receptive field is more directly reflected in the size of the convolution kernels. Therefore, if we want to extract features more accurately, the convolution of the selective convolution kernel may obtain better experimental results.  
In this article, we replace the \(7\times 7\) convolution in the SAM with the convolution of the selective kernel to form SK_SAM. The structures of SAM and SK_SAM are shown in Figure 7: convolution of the selective kernel of two convolutional branches, one of which has three sequential convolutions of size 3, and the combined receptive field is equal to the convolution of size 7. In contrast, the other convolution branch has two sequential convolutions of size 3, and their combined receptive field is equal to the convolution of size 5. The receptive field scales of the two convolutional branches are different. By weighted processing of the feature information of channels with different scales, the network pays more attention to the feature information of WTB surface damage. This makes the network carry out adaptive selection and fusion for features with different scales, which is conducive to establishing a more efficient and accurate model.