esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
###### Abstract
Most importantly, this study aims to minimize the out-of-sample issues that commonly occur during model construction by comparing and verifying the actual observed traffic volume data against those obtained using the proposed methodology, thereby ensuring reliable traffic volume information. Additionally, this study focuses accurately predicting traffic volume, which has various potential applications in the field of road traffic. Specifically, it aims to provide more reliable traffic volume information by addressing the issue of missing data owing to the failure of traffic information collection equipment, thereby contributing to improved travel reliability and enhanced transportation planning and management. In this study, a power-function-based long short-term memory (LSTM) model algorithm is proposed to enhance the model's ability to recognize uncertainty and long-term trend patterns that accurately reflect the characteristics of traffic volume.
## 4 Discussion
### Implications  
Even though a few studies have proven the effectiveness of crop rotation in DSM, they mostly focused on SOM or SOC and hardly mentioned soil pH [31; 58; 65]. Our study implies that cropping information should be included in the DSM of soil pH in agricultural landscapes. Moreover, the results highlight the great potential of the soil sample grouping strategy, considering differences in crop rotation, for obtaining more accurate soil pH estimates (Table 5, Figure 5b), which may also be valid for other soil properties and similar regions. Testing this grouping approach in other regions with different soil types and cropping systems is meaningful and necessary but also challenging in terms of gathering remote sensing data of cropping systems. Previous studies have explored the effects of the grouping strategy for DSM. Yet, they mainly adopted remote sensing, soil spectra, or soil type to group soil samples, while grouping by crop rotation has not yet been investigated over complex agricultural landscapes. In addition, we proved the usefulness of Sentinel-1 SAR images in predicting soil pH. Therefore, when mapping soil pH over cloudy regions (such as Southern China) where optical sensors are limited, radar data are particularly valuable and should be considered. A few historical satellite composites were selected by the models (Figures 6 and 7), suggesting the usefulness of using long-term satellite images in predicting soil pH.  
Our study also has substantial implications for agricultural and environmental management. For example, it can support decision making for remediating and preventing soil acidification by redistributing cropping systems [8]. The statistical analysis revealed average soil pH values for different crop rotation systems, according to which the paddy system had the highest pH value (Figure 10c). Therefore, farmers could consider converting very acid soils which grow vegetables or orchards into paddy systems. Alternatively, inter-annual rotations between paddy and vegetable/orchard systems and the intra-annual rotations between vegetables and paddy rice, which balance economic output and soil conservation, could be promoted. Soil pH maps could also be used to support soil heavymetal pollution control and remediation, because soil pH closely relates to the activity and availability of heavy metals. For example, maps of the tolerable threshold concentration of heavy metals in arable land have been previously derived based on produced soil pH maps and national standards [30; 37].
## 5 Results
### Flows over a Hemispherical Headform  
It is pointed out that the above relatively accurate results of the flows over the airfoils are obtained with the help of the curved boundary treatment presented in Section 3. In order to have a view of the effect of curved boundary treatment on the numerical solution, a typical flow over a hemispherical headform [35; 36; 37; 38; 39] at the low Mach number 0.01 with zero angle of attack is simulated. Following the computational domain defined in the Reference [39], a relatively coarse mesh, having 27,976 tetrahedral elements (see Figure 11), is used in the present third-order (\(p=2\)) accurate simulation. As shown in Figure 12a, a meaningful high-order accurate solution can be obtained with the present curved boundary treatment, while it seems that the DG method fails to obtain the correct solution without any curved boundary treatment, judging by the poor smoothness of the Mach contours evidenced in Figure 12b. Such a phenomenon can also be observed in the distribution of corresponding surface pressure coefficients against the ratio of distance along the boundary of the head diameter, as shown in Figure 13. Additionally in Figure 13 are the experimental data [40] and other researchers' results [35; 39], which are included in order to have a view of agreement. In particular, the deviation between the numerical results with or without curved boundary treatment can be noted, particularly on the surface with relatively coarse meshes, which indicates that an appropriate curved boundary treatment is most helpful for seeking high-order accurate numerical solutions.  
Figure 11: Computational mesh of the hemispherical headform.  
Figure 10: Comparisons of convergence histories. (**a**) Vs iteration; (**b**) Vs time.
## 4 Numerical Simulation Analysis
### Soil Properties in the Seismic Response of the Underground Substation
#### 4.2.3 Internal Force Responses  
The peak internal forces of each section on surface-6 under different seismic motions are shown in Table 9 and the peak internal forces of different points on surface-6 are shown in Figure 21.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**M1**} & \multicolumn{3}{c}{**M2**} & \multicolumn{3}{c}{**M3**} \\ \cline{2-7}
**Section** & \begin{tabular}{c} **Static** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Dynamic** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Amplification** \\ **Amplification** \\ \end{tabular} & \begin{tabular}{c} **Dynamic** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Amplification** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Dynamic** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Amplification** \\ **Amplification** \\ \end{tabular} \\ \hline \(-1\) Floor & 3.97 & 4.95 & 24.66 & 4.80 & 20.89 & 5.08 & 27.89 \\ \(-2\) Floor & 3.61 & 4.22 & 16.90 & 4.13 & 14.40 & 5.28 & 46.23 \\ \(-3\) Floor & 3.90 & 4.43 & 13.74 & 4.65 & 19.38 & 5.51 & 41.39 \\ Side wall 1 * & 1.99 & 2.27 & 14.09 & 2.10 & 5.44 & 2.23 & 11.98 \\ Side wall 3 & 2.93 & 3.32 & 13.38 & 3.26 & 11.26 & 3.58 & 22.16 \\ Side wall 4 & 5.43 & 6.56 & 20.80 & 6.11 & 12.59 & 6.51 & 19.97 \\ Side wall 5 & 7.72 & 8.54 & 10.70 & 8.36 & 8.33 & 9.04 & 17.08 \\ Side wall 6 & 4.15 & 4.70 & 13.14 & 4.65 & 12.36 & 5.41 & 30.23 \\ Side wall A & 4.14 & 4.71 & 13.88 & 4.40 & 6.48 & 4.73 & 14.49 \\ Side wall B & 4.09 & 4.14 & 1.09 & 4.10 & 0.25 & 4.22 & 3.07 \\ Side wall C & 3.73 & 5.19 & 39.08 & 4.79 & 28.28 & 5.14 & 37.69 \\ Side wall D & 2.48 & 2.60 & 4.70 & 2.59 & 4.43 & 2.81 & 13.19 \\ \hline \hline \end{tabular}  
* Side wall 1 means the number of the wall, i.e., located at the cross of axis 1. The rest have similar meanings.  
\end{table}
Table 8: The peak stresses and stress amplitudes under Lanzhou motion.  
Figure 20: Stress nephograms of the underground substation.  
From Table 9 and Figure 21, it can be inferred that the shear forces of the underground substation gradually increase with the increase in the elastic modulus of the surrounding soil, while vertical pressure and bending moments change little under the same condition. The reason is that soil shear deformation reduced with the increase in the soil elastic modulus, and the corresponding displacements of the structure decreased as well.  
The change of the soil's physical properties has a great effect on the seismic response of the underground substation. Therefore, the interaction between the underground substation and soil should be taken into account in the seismic design.
# A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids
## 4 Experiments and Discussion
### Loudness Compensation Experiment
#### 4.3.1 Objective Evaluation
The frequency responses of these are shown in Figure 6. The group of six filterbanks corresponds to 12, 78, 22, 34, 50, and 104 audiograms, respectively. The audiograms of patients with moderate hearing loss were mainly classified into groups 2 and 5, while severe hearing loss mainly corresponds to groups 4 and 5. The hearing impairment of the profound grade generally belongs to groups 2 and 6. In addition, we compared the average HASPI and HASQI indices of the output signals after the loudness compensation based on the 4-channel gammatone filterbank and gammachirp filterbank. Figure 7 shows a comparisons of the two indices computed using the output signals based on the 4-channel gammachirp filterbanks and gammatone filterbanks. A significance analysis indicated that the difference between the various indices has a statistical significance (\(p<0.0001\)), and the speech intelligibility and quality indices of the system based on the gammachirp filterbank output are better than those of the system based on the gammatone filterbank. Furthermore, a comparison was made between the HASPI and HASQI indices of the output signals of the 16-channel and 4-channel loudness compensation systems. Figure 8 shows the comparisons of the two indices that were derived using the output signals that were obtained from the 16-channel and 4-channel gammachirp filterbanks. The average index values of the 50 output signals are shown on the vertical \(y\)-axis, and the serial numbers of the audiograms are shown on the horizontal \(x\)-axis. It can be observed from the two curves that the performance of the 4-channel gammachirp filterbank was better than that of the 16-channel filterbank. Therefore, the effectiveness of the new channel combination method can be verified, considering the hearing characteristics of patients and reducing the number of channels. A significance analysis shows that the HASPI and HASQI of the 4-channel compensation system are better than those of the 16-channel compensation system, and the difference between the various indices has statistical significance (\(p<0.0001\)). Figure 6: Amplitude–frequency response of six groups of 4-channel gammachirp filterbanks. (**a**) Group 1; (**b**) group 2; (**c**) group 3; (**d**) group 4; (**e**) group 5; (**f**) group 6.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 3 Methods
### Cluster Routing Implementation
#### 3.3.2 Agreement Vector  
In cluster routing, the weighting coefficients \(c_{i}\) in Equation (6) can be computed efficiently using the softmax function in Pytorch. However, when we substitute Equation (3) into Equation (6), the \(c_{i}\) can be simplified into the following form,  
\[\mathbf{c}_{i}=\frac{\exp(\mathbf{a}_{i})}{\sum_{j=1}^{C}\exp(\mathbf{a}_{j})}= \frac{\frac{1}{\sigma}_{i}}{\sum_{j=1}^{C}(\frac{1}{\sigma_{j}})} \tag{7}\]  
where \(\sigma_{i}\) is the standard deviation of the vote cluster.  
The agreement vector in Equation (6) now can be redefined as \(\mathbf{a}_{i}=\frac{1}{\sigma_{i}}\). This can be explained by stating that the agreement is inversely proportional to the standard deviation of the vote cluster: with a higher standard deviation, less agreement exists between the votes, resulting in less weight being assigned to the centroid of the cluster.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 6 Simulation Results and Discussions  
This section assesses the suggested clustering scheme under various performance metrics and experimental setup. We performed multiple experiments under specific traffic and network simulation setups. The overall tests are presented with the help of a traffic simulator and network simulator. In our case, the traffic simulation was performed with SUMO 0.25.0, while network simulation (i.e., routing) was performed with the help of MATLAB R2015b. SUMO is an open source traffic simulator licensed under General Public License (GPL) collaboratively developed by the Center for Applied Informatics Cologne (ZAIK) and the ITS at the German Aerospace Center (DLR). We extracted real road traces through SUMO and deployed vehicle traffic with the help of randomTrips.py application [25; 26]. To assess the impact of connectivity over the performance of our proposed clustering scheme, we compared the proposed scheme with a reliability-based clustering scheme [3] as well as with the triple cluster-based routing protocol (TCRP) [1]. We performed several experiments, including temporal assessment of the dynamic formation of clusters, assessment of the link connectivity, and assessment of overhead (i.e., route request messages and average packet loss). The traffic and network simulation setups are shown in Tables 1 and 2, respectively. The definition of our selected performance metrics such as overhead/RRMs, average packet loss, number of clusters, and link connectivity are given below:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Length of road & 2 km \\ Number of lanes & 3 \\ Length of vehicle & 5 m \\ Mean velocity & [60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70;  
Overhead/RRMs: The overhead in our context is related to the magnitude of route request messages in the discovery of a particular destination. The performance of a network will be worse if an excessive number of messages disseminates in the discovery of a strongly connected route.  
Average packet loss: Packet loss is measured as a percentage of dropped packets from all active connections with respect to all sent packets. In our case, we considered the mean packet drops of all clusters.  
Number of clusters: The number of clusters explains how dynamic and evolving our disclosed clustering scheme is under various temporal densities. The term temporal density refers to the number of vehicles at different time stamps. The dynamic selection of clusters handles the highly evolving structure of VANET. This metric reduces the computational cost as well as improves the connectivity of routes by a dynamic number of clusters.  
Route connectivity/link connectivity: Route connectivity refers to the probability of a route connectivity under a specific transmission range and node density. It represents the stability of a route. A strongly connected route (i.e., high probability of connectivity) will be more stable compared to the loosely connected route.
## 2 Case Report
### Treatment Progress  
After extraction of the third molars, the transpalatal arch was delivered on the maxillary first molars, and 0.022 \(\times\) 0.028-in edgewise appliance were placed in both arches, and leveling began with a 0.014-in nickel-titanium (NiTi) and 0.018-in and 0.018 \(\times\) 0.025-in stainless steel archwires. Two maxillary interradicular miniscrews (1.6 mm in diameter, 8 mm in length; Jeil Medical, Seoul, Republic of Korea) were placed between the maxillary second premolars and first molars. Class III intermaximillary elastics were applied between the maxillary posterior miniscrews and the mandibular amines, and vertical elastics were applied between the maxillary and mandibular amines. Six months into treatment, the anterior open bite was improved by extrusion of the incisors and rotation of the occlusal planes (Figure 6).  
Figure 5: Posterior available spaces for distalization of the mandibular posterior teeth on CBCT axial images. (**A**) At root apex level. (**B**) At cementoenamel junction (CEJ) level.  
At eight months into treatment, a mandibular miniscrew was placed on the mandibular left buccal shelf area between the first and second molars. A closed coil spring was applied between the anterior hook and buccal shelf screw to correct the mandibular dental midline deviation and the Class III relationship by unilateral distalization of the mandibular dentition on the left side (Figure 7A). At eleven months of treatment, the dental midline was corrected, and positive overbite and overjet were obtained. Class III and vertical intermaximillary elastics were additionally applied to improve the occlusion (Figure 7B). At thirteen months of treatment, a closed coil spring in the anterior maxillary teeth and elastomeric chains in the mandibular arch were applied for space closure and detailing (Figure 7C).  
At fifteen months into treatment, fixed appliances were removed. Maximillary and mandibular anterior teeth were stabilized with canine-to-canine fixed lingual retainers, and wraparound retainers were delivered in both arches. A scalloped labial bow along the cementoenamel junction of the anterior maxillary teeth with a tongue rib was additionally placed in the maxillary retainer to prevent a relapse of the open bite. Also, a positioner was delivered to improve the anteroposterior and vertical dental relationships. The patient was instructed to wear the wraparound-type retainers during the daytime and the positioner at night (Figure 8).  
Figure 6: Treatment progress (6 months) photographs, schematic diagram of biomechanics, and cephalometric radiographs and superimposition.  
Figure 7: Treatment progress intraoral photographs ((**A**) 8 months; (**B**) 11 months; (**C**) 13 months).
## 3 Materials and Methods
### Overall Flow of the Architecture
\[\overset{\rightarrow}{D}=\left|\overset{\rightarrow}{C}\times\overset{ \rightarrow}{X}_{p}(i)\right.\ -\ \overset{\rightarrow}{X}(i)\right| \tag{11}\]  
\[\overset{\rightarrow}{A}=2\overset{\rightarrow}{A}\times\overset{ \rightarrow}{r}_{1}\ -\ \overset{\rightarrow}{b} \tag{12}\]  
The variables \(\overset{\rightarrow}{A}\) and \(\overset{\rightarrow}{D}\), used in Equation (10), may be written as Equations (11) and (12), respectively. The variables \(\overset{\rightarrow}{C}\) in Equation (11) can be found in Equation (13). \[\overset{\rightarrow}{C}=2\overset{\rightarrow}{r}_{2} \tag{13}\]  
Figure 1: Overall flow of the architecture. \(\stackrel{{\rightarrow}}{{r_{1}}}\) and \(\stackrel{{\rightarrow}}{{r_{2}}}\) are the random vectors for the Equations (12) and (13). The \(\stackrel{{\rightarrow}}{{b}}\) reduces from (2 to 0) with respect to the number of iterations. Equation (14) updates the position of the wolf. \[\stackrel{{\rightarrow}}{{Q}}\ (i+1)=\frac{\stackrel{{ \rightarrow}}{{Q_{1}}}+\stackrel{{\rightarrow}}{{Q_{2}}}+ \stackrel{{\rightarrow}}{{Q_{3}}}}{3} \tag{14}\]  
After calculating fitness, Equation (15) updates the position by the DA position function:  
\[Q_{t+1}=Q_{t}+\Delta D_{t+1} \tag{15}\]  
The parameter in \(\stackrel{{\rightarrow}}{{b}}\) GWO is controlled to maintain the exploration process for the proposed model. \[\stackrel{{\rightarrow}}{{b}}=2-i\times\frac{2}{Max_{i}ter} \tag{16}\]  
In Equation (16), the maximum number of iterations is given by \(Max_{i}ter\). \[N=\sum_{j=1}^{x}\sum_{i=1}^{k}\parallel Q_{i}^{j}-c_{j}\parallel^{2} \tag{17}\]  
Equation (17) gives the cluster formation for the nodes. One node acts as a cluster head. To select the cluster head,  
\[\overset{\rightarrow}{X}(i+1)=\overset{\rightarrow}{x}_{p}(i)+\overset{ \rightarrow}{A}\times\overset{\rightarrow}{D} \tag{10}\]0  
Cluster head \(CH_{i}\) selection is based on Equation (18). The program is executed and the results are obtained. The GWO can find search for path that are faster, more precise and security to transfer data, which makes it easier to search for the global solution in a WSN. The hierarchy-based decision models could easily identify the cluster. Additionally, GWO has the slowest convergence, which makes it inefficient in finding the local optimal solution. Thus, it would be effective to apply GWO in a global search [25]. In summary, GWO takes an exploration approach to identify the optimized route for the data transfer and DFO assists in finding the local optimal solution to select the appropriate node. With help of the cluster head, the nodes that require data transfer could be easily detected for subsequent processing. The average residual energy with the number of rounds was measured for further comparison.
# A Micro-Processor-Based Feedback Stabilization Scheme for High-Q, Non-Linear Silicon Resonators
## 2 Materials and Methods  
### \(\pi\)-Phase Shifted Bragg Grating Design and Fabrication  
To demonstrate the effects of active stabilization on high-Q cavity resonators this work takes a \(\pi\)-phase shifted Bragg grating geometry as an example, though the effects are generally applicable across resonator types. \(\pi\)-phase shifted gratings have demonstrated high-Q factors [16], whilst maintaining moderate cavity lengths and single resonance line operation. The grating presented here is realized as a sidewall modulation of the silicon ridge waveguide [17]. This scheme allows simple  
Figure 1: Measured transmission spectrum of a silicon \(\pi\)-phase shifted grating demonstrating resonantly enhanced thermal refractive index detuning effects.  
integration with single mode waveguides with direct control over the grating spectral characteristics through modulation of the grating period and modulation amplitude [18]. The \(\pi\)-phase shift is fabricated as a half period delay in the grating modulation at the centre of the device. This is easily created as a section of straight waveguide. The grating delay of \(\Lambda_{0}/2\) is realized at the midpoint of the perturbation amplitude in order to create a \(\pi\)-phase shift with a geometric length of half a grating period. Placing the delay at either the peak or trough of the perturbation would create a different effective modal index and therefore modify the phase delay. A schematic of the device is shown in Figure 2a, with a scanning electron microscope (SEM) image of the \(\pi\)-phase shift section in Figure 2b.  
The cavity wavelength tuning mechanism used in this work is achieved by fabricating a thermal tuning element above the grating device, as shown in Figure 2a. By applying a voltage across the heater element, localized temperature variations can be applied to the device and therefore control exerted over its resonance wavelength [11; 14]. The devices fabricated in this work were produced on a SOI wafer (SOITEC, Bernin, France) with a 220 nm thick silicon core. Gratings were defined using e-beam lithography with Hydrogen Silsequivoxane as a mask. The waveguides were nominally 500 nm wide with a 5 nm perturbation amplitude, \(d\). The grating was 628.5 periods long. The devices were clad with a Plasma Enhanced Chemical Vapour Deposition (PECVD) silica layer and a spin-on-glass coating (DOW corning, Midland, MI, USA), and finally a resistive heater was fabricated directly above the grating as an 800 nm wide, 50 nm thick layer of NiCr. The NiCr heater was contacted using low resistance gold tracks.