esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Optimal Scale of Spatial Grid and Optimal Data Discretization Method  
#### 4.2.1 Optimal Grid Scale  
We used python code [18] to calculate the NSR, and the result is shown in Figure 6. The optimal grid scale of the pick-up ridership in the morning peak hours of working days in Chengdu was 1100 m, the optimal scale of the drop-off ridership in the morning peak hours and the pick-up ridership in the evening peak hours was 900 m, and the optimal scale of the drop-off ridership in the evening peak hours was 1100 m. The first local minimum value should be taken as the best measure for two reasons: first, according to the elbow theory [66], after the elbow point, increasing the number of clusters will produce new clusters that are very close to the existing clusters, and the rate of cost decline will slow down. Secondly, as a choice of people's travel modes, the research on the influencing factors of ride-hailing should be based on a more detailed scale to better analyze the relationship between the built environment and the ridership of ride-hailing, since an excessive grid scale may obscure some of the spatial heterogeneity of the ridership of ride-hailing.  
The results calculated by the OPGD model are shown in Figure 7. During the morning peak hours, the optimal grid scale for studying the impact of the built environment on the pick-up ridership was 1000 m, the optimal grid scale for the morning peak hour and evening peak hour drop-off ridership was 1300 m, and the optimal grid scale for the pick-up ridership in the evening peak hours was 1200 m.  
Figure 6: Comparison of size effects and NSR of explanatory variables. (**a**) Pick-up ridership in the morning peak hours; (**b**) drop-off ridership in the morning peak hours; (**c**) pick-up ridership in the evening peak hours; and (**d**) drop-off ridership in the evening peak hours.  
The comparative analysis of the impact of the built-environment explanatory variables on the pick-ups and drop-offs of ride-hailing in the morning and evening peak hours needed to be carried out under a unified grid scale. We suggested taking the average value of these optimal grid scales just mentioned for the next analysis. The suggested grid scale for exploring the impact of the built environment on the ridership of ride-hailing in Chengdu was 1100 m.
## 4 Results and Discussion  
### Sedimentation Profiles  
The properties of dispersions are dependent on the particles size distribution, on the particle shape, and on the net interaction forces between the particles. In order to monitor such factors, sedimentation measurements are performed both on samples mixed with polyacrylamide and on samples polymer free. For each sample the height, \(H(t)\), of the particle-rich phase as a function of time is measured. To compare the sedimentation profiles, the heights were first normalized to the initial height, as shown in Figure 3.  
Normalized sedimentation profiles of samples flocculant-free, for a large range of solids concentration, are shown in Figure 3a. As one can see these profiles are characterized by slow or non-existent settling for a finite waiting time. Furthermore, it is evident that the individual profiles are distinct from each other. The concentration range width is  
Figure 3: Normalized sedimentation profiles. (**a**) Normalized sedimentation profiles of clay material polymer free. Dispersions from Occhito reservoir at different solids concentration (kg m\({}^{-3}\)), indicated by numbers in the plot. (**b**) Normalized sedimentation profiles of dispersion mixed with polyacrylamide. Material from Occhito reservoir at different solids concentration, indicated by numbers in the plot.  
picks up the material at different depths. Figure 3b displays normalized sedimentation profiles of samples mixed with polyacrylamide, for different solids concentrations. Interestingly, these profiles can be scaled together onto a single curve. It is worth mentioning that organic polymeric flocculants, such as polyacrylamide, have a remarkable ability to aggregate even when added in small quantities. High molecular mass polymer with long chain is absorbed on solid particles attaching on _dangling_ polymer segments onto other particles. Briefly, flocculation brought about by simultaneous co-adsorption of polymer molecules on two or more particles, i.e., _bridging flocculation_, generates denser clusters (_flocs_) with better settling characteristics [13]. In other words, a uniform particle-size distribution is generated, which gives a unique response to sedimentation. The high degree of \"monodispersion\" makes the system more easily manageable, therefore, the agglomerating function of the flocculant assumes a crucial role for optimizing of the dredging plant.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 3 Locking Detection, Assessment, and Resolution
### Calculation of the Optimized Value of \(p\)
#### 3.2.1 The Idea  
The idea lies in solution of the sequence of the local problems for each element \(e\) of a thin- or thick-walled structure or such a part of a complex structure. The sequence can be characterized with  
\[\overset{\epsilon}{B}(\overset{\epsilon}{u}^{HP_{l}Q},\overset{\epsilon}{v}^ {HP_{l}Q})-\overset{\epsilon}{L}(\overset{\epsilon}{v}^{HP_{l}Q})-\int_{S_{e \setminus\{P\cup W\}}}(\overset{\epsilon}{v}^{HP_{l}Q})^{T}\ \langle\overset{ \epsilon}{r}(\overset{\epsilon}{u}^{hpq})\rangle\ dS_{e}\ \ =\ \ 0 \tag{33}\]  
where \(P_{j}=P_{1},\ldots,P_{max}-1\), with \(P_{1}=p\) and \(P_{max}=8\). The solutions of the above sequence are compared with the solution to the problem potentially free of locking phenomena, i.e.,  
\[\overset{\epsilon}{B}(\overset{\epsilon}{u}^{HP_{l}Q},\overset{\epsilon}{v}^ {HP_{l}Q})-\overset{\epsilon}{L}(\overset{\epsilon}{u}^{HP_{l}Q})-\int_{S_{e \setminus\{P\cup W\}}}(\overset{\epsilon}{v}^{HP_{l}Q})^{T}\ \langle \overset{\epsilon}{r}(\overset{\epsilon}{u}^{hpq})\rangle\ dS_{e}\ \ =\ \ 0 \tag{34}\]  
where \(P_{2}=P_{max}=8\). The comparisons are based on the summary criterion  
\[\sum_{e=1}^{E}\frac{1}{2}\overset{\epsilon}{B}(\overset{\epsilon}{u}^{HP_{l} Q},\overset{\epsilon}{u}^{HP_{l}Q})\geq a\sum_{e=1}^{E}\frac{1}{2}\overset{ \epsilon}{B}(\overset{\epsilon}{u}^{HP_{l}Q},\overset{\epsilon}{u}^{HP_{l}Q}) \tag{35}\]  
where, as above, one may set \(a=0.1\). For all particular orders of approximation \(P_{k}\), fulfilling the above criterion, the adaptive algorithm can obtain the global adapted solution with the standard \(hp\)-adaptive algorithm. We choose the solution \(P_{l}\) fulfilling (35), such that:  
\[\sum_{e=1}^{E}\frac{1}{2}\overset{\epsilon}{B}(\overset{\epsilon}{u}^{HP_{l} Q},\overset{\epsilon}{u}^{HP_{l}Q})=\min_{P_{k}}\left\{\ldots,\sum_{e=1}^{E} \frac{1}{2}\overset{\epsilon}{B}(\overset{\epsilon}{u}^{HP_{k}Q},\overset{ \epsilon}{u}^{HP_{k}Q}),\ldots\right\} \tag{36}\]  
The solution corresponding to \(P_{l}\) is enough and the most optimal for removal of the phenomenon. Note that for \(P_{l}=P_{1}\) removal of the locking is not necessary, as the standard \(hp\)-adaptation can be performed without changing the longitudinal order of approximation. This order is equal to its value from the assessed global problem, i.e., \(P_{1}=p\). For \(P_{l}=P_{max}\), one obtains the same result as for the detection criterion (32). Note that the search for the optimized value of \(p\) should be performed if and only if the bending dominance criterion (29) is fulfilled.
## 2 Materials and Methods
### Experiments
#### 2.2.2 Experimental Apparatus and Method  
The capacitance sensor used in this study was a parallel plate capacitor manufactured according to the design example in Section 2.1, and the capacitance was measured by the designed measurement circuit board. The calibration experiment of the field experiment was also conducted at Zhencheng Farm. The target moisture content of corn drying was 14.5% w.b. The online moisture measurement device equipped with the capacitance sensor was installed between the grain discharge bucket and the elevator bucket of 5 HP-50 double tower dryer, as shown in Figure 3. The corn was let out of the dryer through the grain discharge bucket, and flowed into the measurement device by free falling. After the measurement, the corn was discharged from the bottom into the elevator bucket to recover the material. The corn in each position of the drying bed was collected in a discharge bucket, which represented the average discharging grain state. The samples collected here reflected the overall drying process. A few corns were sampled manually at grain outlet of the online moisture content measurement device every 30 min. The corn temperature was measured using an infrared thermometer (Model: MT4 MAX, \(-\)30 \({}^{\circ}\)C to 350 \({}^{\circ}\)C, accuracy 0.1 \({}^{\circ}\)C, Fluke Testing Instruments (Shanghai) Co., Ltd., Shanghai, China).  
In the lab experiment, the porosity of the experimental sample was measured using a self-developed porosimeter, as shown in Figure 4. The relevant indicators of the porosimeter are provided in the paper by [PERSON] et al. [26]. The capacitor was placed into the measuring chamber of the porosimeter to measure the porosity of the capacitor without corn filling. Then, the capacitor was filled with the corn sample using a free-falling style at a certain height. The corn porosity was changed by applying different pressures to the upper surface of the capacitor. The excess sample was scraped off with a scraper to ensure constant volume filling. Finally, the porosity of the sample was calculated using Equation (21):  
\[p=p_{t}+p_{0}-\frac{V_{c}-V_{0}}{V_{c}}-1, \tag{21}\]  
where \(p\) is the porosity of corn sample, \(p_{0}\) is the porosity of the capacitor without material filling, \(p_{t}\) is the porosity of the capacitor material filling, \(V_{c}\) was the chamber volume of the porosimeter, and \(V_{0}\) is the volume of the capacitor.  
Figure 4: Experimental apparatus for measuring the porosity.  
Figure 3: Installation position of the online moisture content device in the drying process.
# Enhancing SERS Intensity by Coupling PSPR and LSPR in a Crater Structure with Ag Nanowires
## 3 Results and Discussion  
Finding a Metal with Excellent Surface-Enhanced Raman Scattering (SERS) Properties When Combined with AgNWs  
First, to determine which metal film has high SERS properties when combined with AgNWs, we prepared 2 nm thick Au and Ag films on GaAs (100) substrates, and 4 cm\({}^{3}\) of AgNWs/ethanol dispersion was spray-coated on each sample. The average diameter and length of the AgNWs were approximately 70 nm and 50 \(\upmu\)m, respectively. Raman measurements were performed to compare the SERS characteristics according to the metal films, and the schematic is shown in Figure 1a.  
Figure 1b shows a comparison of the SERS intensities of AgNWs deposited on Ag/GaAs, Au/GaAs, and GaAs substrates. For the samples, the Raman peaks of pyridine were observed at the same positions of 1003 cm\({}^{-1}\) and 1034 cm\({}^{-1}\). Comparing the SERS intensities at 1003 cm\({}^{-1}\) showed that the intensity of the substrate with the Ag film was the largest, and the SERS intensity of AgNWs/Agfilm/GaAs was approximately 2.23 times that of the AgNWs/GaAs. This indicates that the electric field is enhanced by the LSPR generated in the gap between the AgNPs and Ag film. In addition, the SERS intensity of AgNWs/Agfilm/GaAs was approximately 1.6 times stronger than that of AgNWs/Aufilm/GaAs. This may be explained by the longer SPPs propagation length of Ag compared to that of Au and, thus, a greater electric field enhancement was observed in the gap between the Ag film and AgNWs and among the AgNWs [31]. To investigate the electric field distribution resulting from the metal film, FDTD simulations were performed for each condition, and the results are shown in Figure 1c-e. The diameter and length of the AgNPs were set to approximately 70 nm and 50 \(\upmu\)m, respectively, and the gap between the AgNPs was set to 50 nm. As shown in Figure 1c, there was a weak electric field enhancement between the AgNPs-GaAs substrate. Conversely, a strong local field was confirmed between the AgNPs and metal film, as shown in Figure 1d,e. In particular, the largest electric field enhancement was observed in the AgNWs-Ag, implying that the surface plasmon coupling between the AgNPs and Ag films is superior to that of the other films.
## 3 Controller Design and Stability Analysis
### Simulation Results for HSTs with Different Magnitudes of Actuator Faults  
To demonstrate the fault-tolerant tracking capacity of the proposed controller, simulations for HSTs with different magnitudes of loss of actuator effectiveness faults are performed. Different magnitudes of actuator faults can be simulated by changing the value of actuator effectiveness. Suppose that the actuator faults occur in 150 s and last until the end of the simulation. A 10%, 30%, and 50% loss of actuator effectiveness, i.e., \(k_{c}=0.9\), \(k_{c}=0.7\), and \(k_{c}=0.5\), are considered in the simulation. The simulation results are shown in Figure 6.  
Figure 6: Control performance for HSTs with different magnitudes of actuator faults: (**a**) position tracking errors, (**b**) velocity tracking errors, (**c**) actual control forces.  
Along with the decrease of \(k_{c}\) value, loss of actuator effectiveness faults becomes more and more serious and the impact of actuator faults on train tracking operation is growing. However, as can be seen from Figure 6, the position tracking error and the velocity tracking error do not increase significantly along with the severity of actuator faults and remain small all the time. It can also be seen that there is still no obvious chattering and the anti-actuator saturation is effective. Figure 6 illustrates that the proposed controller can accommodate loss of actuator effectiveness faults, handle actuator output saturation constraint, and has good fault-tolerant tracking capacity.  
**Remark 6**.: _As described in Assumption 2, \(\tau_{c}\) is supposed to be bounded, thus \(k_{c}\) would be larger than a certain value, which means that the proposed controller in this paper is applicable to a certain limit of loss of actuator effectiveness faults. Through simulation experiments, when the loss of actuator effectiveness exceeds 65%, that is, when \(k_{c}\leq 35\%\), the proposed controller in this paper will not work._
## 2 Governing Equations  
Based on the inviscid fluid consideration, there is an assumption of \(p=c^{2}\rho\), where \(p\), \(c\), and \(\rho\) denotes the static pressure, the sound speed, and the density of the fluid, respectively. The fluid-structure interaction equation can be employed below [29], relying on the assumption that the amplitude of the cylindrical structure vibrating in the radius direction is relatively small compared to its radius. The geometry model is shown in Figure 1. Considering the rotationally symmetric deformation of this structure [30; 31; 32], the dynamic pressure on the boundary of the fluid affects the structure simultaneously. The governing equations include both the structure dynamics equation as Equation (1) [33; 34] and the fluid dynamic equation as Equation (2) [35; 36; 37], namely:  
\[m\frac{\partial^{2}U}{\partial^{2}t}+k\cdot\
abla^{4}U+\Delta p=0 \tag{1}\]  
\[\left\{\begin{array}{l}\frac{\partial\rho}{\partial t}+\frac{\partial\mu} {\partial t}+\frac{\mu u}{r}=0\\ \frac{\partial u}{\partial t}+u\frac{\partial u}{\partial r}+\frac{c^{2}}{ \rho}\frac{\partial\rho}{\partial r}=0\end{array}\right. \tag{2}\]  
Equation (1) is the typical structural dynamic mechanical equation, where \(m\) is the areal density of the structure, \(U\) is the displacement, \(k\) is the bending stiffness, and \(\Delta p\) is the pressure of the flow field boundary acting upon the structure. The initial conditions can be written as follows:  
\[\left.U\right|_{t=0}=0,\left.\frac{\partial U}{\partial t}\right|_{t=0}=u_{s}(0) \tag{3}\]  
Equation (2) is the fluid control equation, which is also called the Naiver-stokes equation. The first equation of Equation (2) is the mass conservation equation and the second one is the conservation of momentum equation. In Equation (2), \(\rho\) is the fluid density, \(p\) the static pressure in the fluid field, \(c\) is the sound speed, and \(u\) is the fluid velocity generated by the vibration of the boundary. On introducing the symbol:  
\[\frac{\partial U}{\partial t}=u_{s}(t)=u \tag{4}\]  
The boundary conditions can be written as follows:  
\[u|_{r=r_{s}}=u_{s}(t),\left.p\right|_{r=R}=p_{0},u|_{r=R}=0 \tag{5}\]  
where \(r_{s}\) expresses the boundary of the fluid and is a function of the time, thus:  
\[r_{s}=r_{s}(0)+U \tag{6}\]  
On the assumption that the amplitude is very small compared to the radius, \(r_{s},r_{s}\) can be considered as a constant value. The initial conditions can be written as shown below (Equation (7)):  
\[u|_{t=0}=0,\left.\rho\right|_{t=0}=\rho_{0},\left.p\right|_{t=0}=p_{0} \tag{7}\]
## 3 Results
### Missing Data Prediction  
In the application of GCN-LSTM for missing traffic flow prediction, we treat the missing traffic flow as the value to be predicted and use the time periods with complete traffic flow records as the training set. The entire dataset is divided into an 80% training set and a 20% test set. The training and evaluation process is based on the PyTorch framework, and the model loss function adopts Mean Squared Error (MSE). The model loss reduction and prediction results are shown in Figure 7.  
As shown in the Figure 8, the purple curve represents the actual values of the test set, and the red curve represents the predicted values of the model. Whether it is during peak hours or off-peak periods, the GCN-LSTM model is able to predict the missing traffic volume values accurately. Therefore, the predicted results can be used to calculate the  
Figure 6: Selected correlation heatmap between independent and dependent variables.  
Figure 7: Loss in train and valid datasets.  
input variable \"the ratio of traffic volume at the time of the accident to the average traffic flow during the same period of the month\", which can then be used to predict the traffic conditions and the duration of the accident.
# Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
## 5 Results and Discussion
### Accuracy of the Machine Learning Algorithms Model  
We applied 90% of the WCSI data to train a model that classifies wrong and correct posture and 10% of the WCSI data to validate the trained model. We used five-fold cross-validation, which protects against overfitting. The following five well-known algorithms were selected to train the extracted features.  
* Fine decision tree (FDT)  
Figure 8: Result of wrong activity using 64-subcarriers.  
Figure 9: The comparison between the correct and wrong activities.  
* Linear discriminant analysis (LDA)
* Linear support vector machine (LSVM)
* Fine k-nearest neighbor (FKNN)
* Ensemble boosted trees (EBT)  
Chart 1 shows the performance of each algorithm applied. The performance of fine KNN is best among all the five algorithms applied and can classify 99.6% of the post-surgery posture. The accuracy of the classifier is high due to binary classification. The result shows that the trained model is not over-fit to the training data set. The accuracy measurement depends on the operating system and can vary slightly. To confirm the accuracy of the model, it is necessary to place your device in the same way as described for collecting the training data. On the basis of the results achieved, we conclude that a non-contact sensing testbed can be implemented for post-surgery monitoring by exploiting artificial-intelligence techniques. This research can be carried out for multiclass, more complex post-surgery monitoring by using this testbed.
# An Online Multicriteria--Spatial Decision Support System for Public Services Planning
## 4 Prototypical Implementation
### User Interface and Parameter Control  
At this point, we would like to present the setting options mentioned in the previous sections using a potential application example with the user interface. The MCDA runs through the following eight steps. The corresponding tab in the user interface can be seen in Figures 2 and 3:  
* Step 1--Start analysis or load existing analysis: If the user has already performed an analysis, the parameters of the performed analysis can be reloaded. The parameters are then reloaded into the analysis. Otherwise, they can perform a new analysis (Figure 4, left).
* Step 2--Select study area: The user can select their study area via area selection in the map window (Figure 4, right).
* Step 3--Select relevant infrastructures: According to their decision problem or analysis objective, the user can add the relevant infrastructures/criteria to the analysis. This forms the supply side mentioned in Section 3.2 (Figure 5, left). In this analysis example, only the infrastructure types relevant for the purposes of the study are selected from the categories local supply, health, and education (user and analysis preference).
* Step 4--Select age groups of the population to be considered: The user selects the population groups that need to be considered in the analysis (age of people in family formation phases and the age of children). This selection might not be relevant for all decision problems (Figure 5, right).
* Step 5--Accessibility calculation: The user specifies the mode of temporal distance calculation over the road network (car, public transport, foot), defines the distance of undersupply (for simplicity we have preset recommendations), and the weighting of the supply line as a function of distance. All values are preset and can be adjusted by the user (Figure 6, left).  
Figure 4: Step 1 and step 2 of the user interface.  
Figure 5: Step 3 and step 4 of the user interface.  
Figure 6: Step 5 and step 6 of the user interface.  
Step 6--Weighting: The user sets the relative importance of the selected infrastructures to each other by sliders with values between 0 and 100 (Figure 6, right).
* Step 7--Summary: The user receives an overview of the settings made. After checking and possibly adjusting the parameters, the analysis can be started and the parameters are passed on to the backend (Figure 7, left).
* Step 8--Results: After a calculation time of a few seconds (depending on the size of the study area; Figure 7, right) the calculation results of the entire supply situation are displayed in a 100 \(\times\) 100 m grid on the map (Figure 8). It is also possible to view the map results as a heat map instead of a grid and the results of the individual infrastructures with the individual spatial weighting limits from step 5. It is easily conceivable to go back to change parameters and run the analysis again. The possibility to export parameter settings makes it possible to keep an overview of the different calculations.
## 3 Results and Discussion  
### Sampling Sites and Sample Collection  
The field experiment was performed in Hefei, Anhui Province, China (117.12\({}^{\circ}\)E, 31.85\({}^{\circ}\)N), from 29 July to 5 August 2021. To verify the feasibility of applying CWML to vehicles, calibration experiments, comparison experiments, and continuous observation experiments were carried out successively. According to the characteristics of Hefei In  
Figure 11: Adaptive local peak-retrieval results of the measured power spectrum.  
novation and Entrepreneurship Industrial Park (IEIP), three distinct sampling sites were selected, including the residential district (RA), commercial district (CD), and industrial park (IP). These sites are representative of the different atmospheric wind field data, and reflect Hefei's overall three-dimensional wind field information. The RA site is located west of IEIP, surrounded by residential buildings with substantial forested areas and low industrial activity. Likewise, the CD site represents the mixed emissions of commercial activities and vehicle exhaust. In contrast, the IP site is far from the IEIP, where the industrial activity likely strongly affects air quality.