esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, the accuracy of VMF model interpolation into the zenith troposphere delay is experimentally analyzed, and a ZTD height change grid model is constructed based on ERA5 reanalysis data. A ZTD height change grid model is proposed to correct VMF interpolation model, and the accuracy of the model is verified.  
1. Using the VMF grid model to interpolate the tropospheric delay, and using the function of the troposphere delay changing with the elevation to correct the tropospheric delay will produce a large error. The greater the elevation difference, the greater the error. The elevation difference between the URUM station and its surrounding grid points is the largest (about 1000 m), and the resulting tropospheric delay error is also the largest (about 100 mm).
2. The tropospheric delay is obtained using ERA5 reanalysis data integration. According to the analysis, the ZTD below 10 km in China clearly shows an exponentially decreasing form with the altitude change.
3. The least squares exponential fitting method is used to establish a ZTD height change grid model in China, and the ERA5 integration is used to obtain ZTD delay to verify the accuracy of the model. The root mean square error is less than 12 mm. The fitting accuracy is related to the latitude. The higher the latitude, the higher the fitting accuracy. The reason for the lower fitting accuracy at low latitudes is that the air at low latitudes contains more water vapor and the climate is relatively wetter, making it difficult to estimate ZWD accurately.
4. Through experimental analysis, using the ZTD height change grid model as the ZTD height reduction method can effectively reduce the error caused by the tropospheric delay of VMF interpolation when the height difference is large. The VMF interpolation accuracy improvement at URAM is the largest, with an improvement of 63.5 mm in root mean square error.  
Conceptualization, M.Z. and H.G.; methodology, M.Z.; software, M.W.; validation, H.G., J.X. and J.H.; formal analysis, M.Z.; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 41764002; 62263023; 62161022).  
Not applicable.  
We thank International GNSS Service for the reference station data sets, Vienna University of Science and Technology for Vienna Mapping Functions data (VMF), and the European Centre for Medium-Range Weather Forecasts (ECMWF) for the integration of the fifth generation of global climate in the analysis data set (ERA5).  
The authors declare no conflict of interest.
## 3 Cutting-Edge Online Fault Diagnosis Techniques for Al-Caps
### Buck Converter  
A buck converter was employed to validate the proposed TML-DB-ON-FDT due to its wide use and high efficiency in reducing the input voltage [53]. This topology is well suited for applications that require low voltages and high output currents, such as DC power networks and microgrids [54], photovoltaic (PV) systems [55], electric vehicles (EVs) battery chargers [56], internet of things (IoT) applications [57], and implantable medical devices (IMDs) with wireless power transfer (WPT) [58], among others.  
Figure 3 illustrates the schematics of a buck converter, which includes two semiconductor devices, a diode (Dio) and a transistor (Mos) that regulate the current flow along with two energy storage elements, an inductor (L), and a capacitor (Cap) that stores energy temporarily. In the conducting state, the Mos is ON and the Dio is OFF. During this phase, current flows through the inductor (L) and the capacitor (Cap), with both storing energy, and also passes through the load resistor (R). In the non-conducting state, the Mos turns OFF, causing the Dio to conduct. Therefore, the current flows through the L, Cap, Dio, and R.  
Figure 3 displays the raw data, which include the converter input and output currents (\(\mathrm{i_{in}}\) and \(\mathrm{i_{out}}\)), the output voltage (\(\mathrm{v_{out}}\)), the SPM, the IPM, and the alarm mechanism. Initially, the SPM processes the raw data into N primary features, which are then analyzed by the IPM to assess the condition of the al-cap. If the capacitor is in good health, the green LED is activated; otherwise, the red LED turns ON.
## 5 Discussion on Sensitivity Parameters
### Effect of \(R_{w}\)  
To focus on the effect of \(R_{w}\), the groundwater drawdown \(S_{h}\)/\(H_{0}\) and the thickness of the aquifer \(H_{0}\) were set to 0.375 and 32 m, while other parameters were unchanged as described above. As shown in Figure 10, the variation curve of drawdown difference is obtained by the calculation formula with the radius of the pit. As the radius of the pit increases from 2 m to 40 m, corresponding to an increase of \(R_{w}\)/\(H_{0}\) from 0.063 to 1.250, the drawdown difference shows a decreasing trend. When \(R_{w}\)/\(H_{0}\) is in the range from 0.063 to 0.781, the drawdown difference decreases quickly with the increase of \(R_{w}\)/\(H_{0}\). When \(R_{w}\)/\(H_{0}\) is larger than 0.781, the effect of radius of the foundation pit on the groundwater drawdown outside the pit is very slight.  
When \(L\)/\(H_{0}\) decreases from 0.844 (Figure 11a) to 0.531 (Figure 11c), the drawdown difference gradually decreases. Figure 11a shows that as \(R_{w}\)/\(H_{0}\) increases from 0.063 to 1.250, \(L\)/\(H_{0}\) = 0.531 causes the largest range of drawdown difference, and as \(\Delta\)/\(H_{0}\) decreases from 0.357 to 0.029 when \(t\) = 192 h, the \(\Delta\)/\(H_{0}\) decreases by 0.328. If the larger drawdown difference is maintained, it is recommended to adopt the scheme of small radius of the foundation pit.
# Internet Video Delivery Improved by Super-Resolution with GAN
## 6 Video Quality Assessment Metrics
### Pixel-Wise Quality Assessment  
The peak signal-to-noise ratio (PSNR) assesses the quality of videos, calculating the ratio of the maximum value of a signal and the power of distortion noise in decibels. The PSNR highest value indicates the best video quality; see Equation (8).  
\[PSNR=\frac{1}{N}\sum_{i=1}^{N}20\log_{10}\frac{\text{max}(f_{i}^{HR})}{\sqrt{MSE( f_{i}^{HR},f_{i}^{SR})}}, \tag{8}\]  
where \(N\) is the total number of frames. The MSE is given by  
\[MSE=\frac{1}{WH}\sum_{x=1}^{H}\sum_{y=1}^{W}\left(f_{x,y}^{HR}-f_{x,y}^{SR} \right)^{2}, \tag{9}\]  
where \(W\) and \(H\) are the dimensions of the frames.  
Although the PSNR inconsistently captures human visual perception [21; 61], it has been applied to the video quality assessment revealing the difference between the two signals.
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 5 Research Gaps and Future Directions
### Production Phase  
The trajectory of research concerning digital technologies within the production phase of industrialized construction is through the amalgamation of advanced manufacturing technologies and digital tools. On the one hand, emerging technologies like 3D printing and CNC enable the fabrication of customized precast elements with intricate geometries and textures. On the other hand, digital tools such as digital twins and human-computer collaboration find an extensive utilization in improving production flexibility, to contend with uncertainties and fluctuating demands. Furthermore, advanced measurement technologies have been seamlessly integrated to automate the inspection of element dimensions and surface quality. Subsequently, potential future research directions can be delineated as follows:  
* While 3D printing technology substantially economizes on manpower, enhancing efficiency and curbing material wastage, its comparative slowness and higher cost vis-a-vis mass production pose challenges. Additionally, the necessity for the manual assembly of customized parts at construction sites further escalates construction cost. Hence, research endeavors can focus on synergizing robotics with 3D printing to automate the assembly of individual components to facilitate mass customization.
* Human-robot interaction technology within the production stage remains primarily experimental, with limited participation, predominantly from individuals possessing experience [53; 54]. Concerning kinematics, the precision of robot movements and their maneuverability directly influence production process efficiency. Therefore, the kinematic performance of robots should be further investigated, and the application scope of human-robot interaction should be expanded in industrialized construction.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 4 Proposed Protocol  
The proposed protocol CAEL is based on two mechanisms that work together, i.e., the suppression of the communication region and expiration details of the links to improve the performance. Therefore, the information about the process of creating a trilateral region and the mechanism is mentioned for the identification of any node whether it lies inside or outside the said zone is discussed and then the mechanism of RF, RET and LET is also explained.
## 4 Analysis and Discussion of Results
### Interpretation of the Effects
#### 4.2.3 Comparison of Optimal Conditions  
According to the experimental analysis, the optimal temperature (_B_: high) aligns for both responses (\(G_{TR}\) and _h_), indicating that increasing temperature consistently improves system performance. However, the power supply (\(A\)) shows conflicting behaviors. A low level minimizes \(G_{TR}\), but a high level maximizes h. This reflects a trade-off between these variables. The flow rate (\(C\)) has a limited influence on both responses, but for h, the high level is preferred.  
For a complementary analysis, where the focus is on maximizing the convective heat transfer coefficient (_h_), the optimal configuration involves all factors at their high  
Figure 11: Heat transfer coefficient and ambient temperature at the power level of 65 W.  
Figure 10: Heat transfer coefficient and ambient temperature at the power level of 35 W.  
levels: Power Supply (High, 65 W), Temperature (High, 29 \({}^{\circ}\)C), and Flow Rate (High, 5 L\(\cdot\)min\(-\)1). This setup provides the highest \(h\), indicating enhanced heat exchange efficiency. However, this is less critical for operational and commercial applications compared to thermal resistance. In addition, the volumetric flow rate, despite not presenting statistical significance, had a positive contribution to the heat transfer coefficient, evidencing that the increase in turbulence can be explored for additional thermal gains, as highlighted in the literature [28; 38].
## 3 Materials and Methods
### Procedure
#### 3.3.2 Study 2: Cognitive Walkthrough
For instance, too many options to the system user interface are available to select in the first task, confusing the user. Therefore, it is a type of \"information overload\" problem related to topic 2 (see Table 1). Moreover, the same interface is developed in two different languages of English and Arabic, which raises the misunderstanding among the users concerning the roles and consequences of various system functions. We found a similar issue in the heuristic evaluation study. Furthermore, some basic functionality is missing that can facilitate the users to input new values into the system when required, which is a type of \"missing information\" related to topic 5 (see Table 1). Moreover, inadequate or unclear feature labeling may also cause problems to users. Additionally, it was found that the edit function could not perform the function as expected; thus, users were not allowed to amend or edit any information entered, which is the problem of \"customization\" and related to topic 4 (see Table 1). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tasks** & **Problems** & **Usability Problem under the Topics in Table 1** \\ \hline \multirow{4}{*}{Task 1} & There are many options makings the users confused. & 2 (Unstructured data and **information overload**) \\ \cline{2-3}  & Some of the titles are written in Arabic, and some of them are in English on the same interface. & 6 (Contextual data and **information organization issues**) \\ \cline{2-3}  & When the user opens sub-tabs, it is confused with the primary tabs. & 3 (**Difficulty in searching and finding desired item/information in interface and error handling**) \\ \hline \multirow{4}{*}{Task 2} & A few tasks are restricted, and no permission to perform them. & 4 (**Interface customization problems**) \\ \cline{2-3}  & The edit icon option does not allow editing anything. & 4 (**Interface customization problems**) \\ \hline \multirow{4}{*}{Task 3} & This task can be completed in one step, but currently, it is required to complete in multiple steps. & 3 (**Difficulty in searching and finding desired item/information in interface and error handling**) \\ \cline{2-3}  & Categories and sub-categories are not clear. & 5 (**Missing data and information**) \\ \cline{2-3}  & Labels’ names are not clear. & 5 (**Missing data and information**) \\ \hline \multirow{4}{*}{Task 4} & There is no option available to select the employee’s name first. & 4 (**Interface customization problems**) \\ \cline{2-3}  & There is no button to click on the system to send the new amount for the system. & 5 (**Missing data and information**) \\ \cline{1-1} \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The ERP usability problems found in the cognitive walkthrough study.
## Abbreviations  
* [1]**Key Acronyms Used in this Paper**
* [2]**HOG** **Oriented Gradient Histogram**
* [3]**V2V** **Vehicle-to-Vehicle**
* [4]**V2I** **Vehicle-to-Infrastructure**
* [5]**V2X** **Vehicle-to-Everything**
* [6]**VLC** **Visible Light Communication**
* [7]**I2V** **Infrastructure-to-Vehicle**
* [8]**ITS** **Intelligent Transportation System**
## 5 Experiment
### Experimental Results  
Table 2 shows the evaluation results of our proposed DSTAGCRN model and the main baseline methods on three VPS datasets. Our method (DSTAGCRN) consistently achieves the best performance and is highlighted in bold. HA and LSTM only consider the temporal correlation of parking data, resulting in large prediction errors. These models ignore the spatial correlation among parking lots, and hence, their accuracy is lower than that of the graph neural network-based models, which also demonstrates the importance of considering spatial correlation for VPS prediction. Among the graph neural network models, the proposed DSTAGCRN model achieves the best performance. Predefined graph-based methods such as ASTGCN exhibit good performance as they can capture the spatial dependencies present in the VPS data. Additionally, methods such as AGCRN that utilize adaptive adjacency matrices can better extract complex dependency relationships present in VPS data and therefore exhibit good performance. Compared with dynamic graph networks such as DGCN and DGCRN, our proposed STDGCRN model explores the complex spatio-temporal dependency relationship in parking data more robustly by considering factors such as daily and weekly cycles. The three parking datasets used in the experiment have large differences in node numbers and temporal span, indicating that our proposed STDGCRN is capable of handling prediction tasks for different parking network scenarios. Note that according to the different roles of the training set, validation set, and test set in the model development process, all evaluation metrics (MAE, RMSE, MAPE) presented in this article are calculated based on the test set. This is because the training set is used for learning model parameters, the validation set is used for hyperparameter adjustment and model selection, and the test set is used to evaluate the model's performance on unseen data, thereby verifying its generalization ability. Therefore, displaying the results of the test set can better reflect the actual application effect of the model. To provide a comprehensive evaluation of the models, we present the 15 min, 30 min, and 60 min prediction accuracies for all models on the three parking datasets in Table 2.  
It can be observed that, except for the Singapore parking dataset, the DSTAGCRN model achieves the best prediction performance regardless of the time step variations in the prediction task. Overall, our proposed STDGCRN model effectively captures the complex spatio-temporal features present in the parking data. Across all time ranges, the error rate of DSTAGCRN is significantly lower than that of the other baselines, which is crucial for real-world VPS prediction. Figure 6 illustrates the results of ASTGCN, MegaCRN, AGCRN, DCRNN, DGCRN, and our proposed DSTAGCRN model at different prediction horizons on the three VPS datasets. It can be observed that the prediction results of all models decrease as the prediction range expands, but the DSTAGCRN model demonstrates higher prediction accuracy and smaller prediction error fluctuations compared with these models.
## 4 Results
### Comparison of GPP Estimation  
The overall estimation accuracy of PAR-LUE GPP was better than that of MODIS GPP, EC-LUE GPP, and D-VPM GPP (Figure 5). Compared with MODIS GPP, EC-LUE GPP, and D-VPM GPP at the 8-day scale, the R\({}^{2}\) between PAR-LUE GPP and observed GPP increased by 12.07%, 1.56%, and 8.33%, and the RMSE decreased by 0.18, 0.09, and 0.41 g C m\({}^{-2}\) MJ\({}^{-1}\) d\({}^{-1}\), respectively. At the annual scale, R\({}^{2}\) increased by 29.41%, 2.33%, and 12.82%, and the RMSE decreased by 0.36, 0.14, and 0.34 g C m\({}^{-2}\) MJ\({}^{-1}\) d\({}^{-1}\), respectively. Although the GPP estimation accuracies of PAR-LUE and EC-LUE were closer in R\({}^{2}\) and RMSE, EC-LUE obviously underestimated the high GPP. Overall, PAR-LUE showed better performances than the reference LUE models in GPP estimation, especially in reducing the underestimation of high GPP. From the performances of LUE models in different vegetation types (Figure 6), the GPP estimation accuracy of PAR-LUE was generally comparable to that of EC-LUE and D-VPM in most types.  
Figure 5: Accuracy comparison of GPP estimated from different LUE models. (**a–d**) show the 8 daily results, and (**e–h**) show the yearly results.  
Figure 6: Accuracy comparison of GPP estimated from different LUE models in different vegetation types. (**a**) Comparison of RMSE at the 8-day scale, (**b**) comparison of R\({}^{2}\) at the 8-day scale, (**c**) comparison of RMSE at the yearly scale, and (**d**) comparison of R\({}^{2}\) at the yearly scale.  
The comparison at typical sites in the Northern Hemisphere showed that PAR-LUE, EC-LUE, and D-VPM were all in good agreement with the observed GPP in characterizing seasonal dynamics (Figure 7). In the majority of vegetation types, the PAR-LUE GPP was in better agreement with the observed GPP, which is evidenced by a smaller RMSE and a closer regression slope to 1. For example, at the evergreen coniferous forest site (Figure 7c), although the GPP estimated using the three LUE models showed very high agreement with the observed GPP (R\({}^{2}\geq\) 0.93), the PAR-LUE-estimated GPP was closer to the observed GPP throughout the growing season (smaller RMSE and closer slope to 1); at the closed shrub site (Figure 7g), the PAR-LUE GPP was closer to the observed GPP during the rising and falling phases of the growing season.  
Figure 7: Comparison of GPP seasonal variation at typical sites. Eight site-year samples (**(a–h)**, Latitude; Lon: longitude; see Figure 1 for vegetation types) with similar or concurrently higher R\({}^{2}\) values were selected to exhibit the better performance of the PAR-LUE model in further reducing the RMSE.
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 4 Performance Analysis  
In an RFID system, the total number of slots for tag identification, system efficiency and communication complexity are important parameters in the anti-collision algorithm. Here, the performance of the algorithm is analyzed from these three aspects.
## 5 Validation of the Modal-DBD Analysis  
In conclusion, to check the accuracy of the full Modal-DBD process, a Push-Over (PO) analysis was carried out for the examined Blockhaus system of Figure 4, based on a geometrically simplified but still reliable 3D numerical model. In order to further validate a calculation tool in use for design practice, in addition, such a FE model was developed in SAP2000 [35].  
As shown in Figure 8, the FE building model was thus fixed at the base, and assembled in a series of Equivalent Energy Elastic-Plastic (EEEP) springs representative of the actual in-plane response of P1-to-P4 log-walls. Careful consideration was spent for the mechanical connection of these springs with the other FE components, as well as for the calibration of their constitutive law (Table 3). The EEEP springs were in fact located at the mid-height of each log-wall, and properly oriented so that they could work in the longitudinal direction only. No additional stiffness was taken into account for the description of the out-of-plane behavior of each log-wall, given that the EEEP constitutive laws of each spring were already able to include the effect of the orthogonal logs. All the springs were linked together by means of rigid elastic frames. A set of in-plane rigid diaphragms with lumped masses, moreover, was taken into account to represent the floors.  
The modal load distribution was separately investigated in the primary (X, Y) directions, thus comparing the base-shear load and the top displacement predictions (at the centre of mass of the roof) with the corresponding Modal-DBD estimates. Figure 9 reports the so-obtained PO curves (X- and Y-principal directions), with evidence of the corresponding limit states.  
Figure 8: Axonometric view of the simplified 3D model for the Push-Over (PO) analysis of the case-study Blockhaus system (SAP2000 [35]).  
The analysis of Modal-DBD method and PO estimates was performed in terms of shear base forces and top displacements, in both the principal directions, at the CP, LS and IO levels. In particular, see Tables 10 and 11, the comparative study proved that the Modal-DBD procedure could offer reliable estimates for Blockhaus timber structures (as far as torsional phenomena can be disregarded). The Modal-DBD shear forces were generally found in close correlation with PO predictions. Similarly, the corresponding top displacements were found to be only partly overestimated by the Modal-DBD approach, up to +14% the PO analyses for the selected case-study building.