esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Road Surface Grade Identification
### Feature Selection  
Different road grades affect the acceleration signals of the vehicle during operation, and analyzing these signal features can effectively assess the road's surface quality [18]. Therefore, twelve features from the frequency domain, time domain, and nonlinear domain are selected for comprehensive analysis, including the RMS value, kurtosis (\(\kappa\)), mean (\(\mu\)), standard deviation (\(\sigma\)), dominant frequency (\(f_{do}\)), spectral energy (\(E_{sp}\)), energy ratio of the frequency bands (BER), maximum power spectral density (Max PSD), wavelet decomposition energy ratio (WER), mean instantaneous frequency (IFM), sample entropy (SE), and Hurst exponent (H). Table 3 summarizes the relevance of each feature in distinguishing pavement classes. By combining these features, road surface grades can be comprehensively evaluated, and different road conditions can be accurately identified.  
Figure 3: Road surface grade identification process.
## 3 Routing in Satellite Networks
### Routing Protocols in LEOs
#### 3.3.3 Predictive Routing Protocol (PRP)  
PRP is a recommended routing protocol for providing deterministic QoS guarantees, such as delay jitter in LEO systems. The fact that satellites have different characteristic structures than ground networks, such as limited onboard processing capabilities, makesit mandatory to use ground gateways in the routing process. Gateways perform the task of storing network information, such as the available bandwidth of connections between satellites, the location of users, and traffic patterns. The PRP protocol uses the predictable information of the satellites and the user location information to predict the traffic load that will be between inter-satellite connections in the future for a restricted interval of time. Thanks to this forecast, it determines multiple alternative road recommendations so that congestion can be avoided in the future. The purpose of this routing protocol is to maintain both QoS requirements and maximize the total number of users [106].
## 5 Case Study: An AHP/TOPSIS-Based Optimal Gas Station Site Selection  
This section presents a case study to identify an optimal site for a new gas station. The case study makes use of the AHP/TOPSIS hybrid approach to compute the criteria weights and to choose the best option from among a finite set of decision alternatives. The four criterion values are computed using four real geospatial datasets. Although there could be several criteria affecting the new business site selection for an optimal gas station site, we followed the results of [7]. The authors presented a multi-criteria factor evaluation model for a gas station site selection. Their study was mainly based on a questionnaire/survey and they identified several important and crucial factors for site selection. Figure 1 presents a flowchart depicting the flow of important steps in the proposed AHP/TOPSIS approach. Among the factors they identified, we adopted the following four important factors in this study, listed with respect to their significance from top to down:  
* Competitors in the area (competition);
* Traffic near the location (traffic);
* Popularity of the close-by businesses (area-popularity); and
* Approximate number of vehicles nearby residents own (vehicle-owners).  
The appraoch in this study is empirical in contrast to the appraoch by [7] for the identification of a feasible location for a new gas station. In the Section 5.1, all of the datasets used in the study along with the prediction criteria are listed.
# Limited-Angle Computer Tomography with Truncated Projection Artifacts Removal
## 3 Results
### Analysis  
The full angle DBT images are used as ground truth. The similarity between the ground truth and the image reconstructed by our algorithm and the similarity between the ground truth and the image reconstructed by SART are evaluated by calculating structural similarity index measurement (SSIM) to verify the effectiveness of our algorithm [23]. We use the simplified SSIM equation as follows:  
\[SSIM\big{(}G,G^{\prime}\big{)}=\frac{\big{(}2\mu_{G}\mu_{G^{\prime}}+C_{1} \big{)}\big{(}2\sigma_{GG^{\prime}}+C_{2}\big{)}}{\big{(}\mu_{G}^{2}+\mu_{G^{ \prime}}^{2}+C_{1}\big{)}\big{(}\sigma_{G}^{2}+\sigma_{G^{\prime}}^{2}+C_{2} \big{)}} \tag{10}\]  
Figure 8: (**a**–**c**) Image reconstructed by SART, diffusion method and our proposed algorithm with 35 mm high. (**d**–**f**) Image reconstructed by SART, diffusion method and our proposed algorithm with 51 mm high. (**g**) The selected data marked by a line in (**d**). (**h**) The artifact area in (**g**).  
where \(H_{G}\), \(\mu_{G^{\prime}}\), \(\sigma_{G^{\prime}}^{2}\), \(\sigma_{G^{\prime}}^{2}\), and \(\sigma_{GG^{\prime}}\) are the local means, standard deviations, and cross-covariance for images \(G\), \(G^{\prime}\). \(G\) is the artifact area in the ground truth image; \(G^{\prime}\) is the artifact area in the image used to calculate the similarity with ground truth [24]. The regularization constants \(C_{1}\) and \(C_{2}\) are taken as 0.0001 and 0.0009 in our research.  
This experiment used 20 sets of human breast data to calculate SSIM of the artifact area and the non-artifact area separately and find their ratio. The outcome is shown in Figure 9. Through the similarity comparison, it can be found that our algorithm improves the similarity between the reconstructed image and the ground truth. which suggests that our algorithm can reconstruct the objects more accurately and closer to the real objects. At the same time, our algorithm improves the similarity ratio between artifact areas and non-artifact areas, which means that our algorithm reduces the difference between the regions of the reconstructed image and decreases the effect of TPA.
## 3 Localization Algorithm
### The Implementation of DRSL Algorithm  
In this section, we will illustrate the implementation of DRSL algorithm in detail.  
Settings:  
(1) We apply TDMA (Time Division Multiple Address) protocol to reduce the transmission interference in DUANs during the localization process.  
(2) We assume that nodes in the network have been well synchronized, so that TOA (Time of Arrival) ranging method can be well adopted.  
(3) There are \(M\) ordinary nodes and 3 anchor nodes in the network which is not sufficient for the localization procedure.  
(4) Each node has a unique ID and can communicate with each other.  
The implementation procedure is as follows:Step 1: Each node in the network broadcasts \"hello\" message with the sending time stamp one by one to trigger the localization procedure;
* Step 2: After receiving the \"Hello\" message, each ordinary node calculates the distance between each other according to TOA ranging method;
* Step 3: Based on the distance information obtained in step 2, each ordinary node can calculate its least square localization error according to Equation (4) and then send the localization error to anchor 1;
* Step 4: Based on the receiving least square error, anchor 1 selects an initial optimal reference node according to Equation (5), and then broadcasts the initial optimal reference node's ID to the network;
* Step 5: Based on the selected optimal reference node's information, other unknown ordinary nodes in the network predict their mobility patterns and mobile positions according to Equations (9) and (12) ;
* Step 6: After getting the predicted position results of the network, anchor 1 updates the reference selection dynamically as shown in Equation (13), and then sends the update information to the network;
* Step 7: The network changes the anchor-reference group dynamically to adapt to the varying topology of DUANs.
* Step 8: If the reference selection remains unchanged, the previous predicted mobility patterns will still be used to estimate the drifted nodes' positions continually. If the reference selection changes, we will re-estimate the mobility patterns and re-predict the drifted nodes' positions again according to Equations (9) and (12).  
The implementation of DRSL algorithm can be described as in Figure 5.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 3 Results
### Comparison of the Improved Model with Other Models  
To test the effect of the improved GhostNetV2, we compared it with five lightweight models: GhostNet, MobileNetV3-large [35], EfficientNet-b0 [36], ShuffleNetV2 [37] and EfficientNetv2-S. Figure 15 is the training curve of the model. The training loss of the six models has the same convergence speed as the training ACC curve, and the training curve of the Ghost model fluctuates greatly. The improved GhostNetV2 model performs best in both training loss and training accuracy.  
Table 4 shows the details of various model training, among which the accuracy of GhostNet is 92.56%, the number of parameters is 3.69 M, FLOPs is 0.15 G, and the training time is 1467 s. GhostNet has fewer parameters and FLOPs than other lightweight convolutional neural networks, but the accuracy is also lower. This table also lists the performance data of several other lightweight convolutional neural networks. ShuffleNetV2 has very few parameters and calculations, and the training time is at least 992 s, but the accuracy is low. The accuracy rates of MobileNetV3-large, EfficientNet-b0, and EfficientNetV2-S are similar. The training time of MobileNetV3-large is the shortest at 1263 s, and the amount of calculation is 0.23 G. The number of parameters and the amount of calculation of EfficientNetV2-S are larger, and the amount of calculation is equivalent to 7 times that of EfficientNet-b0, and the training time is 2.3 times that of EfficientNet-b0. Compared with other models, the effect shown by EfficientNet-b0 can only be regarded as quite satisfactory. The improved GhostNetV2 shows the best accuracy of 95.44%, the model training time is 1197 s, slightly higher than MobileNetV3-large, and the model parameters are 5.00 M, slightly lower than EfficientNet-b0. However, in terms of calculation, except for EfficientNetV2-S, the improved GhostNetV2 has the highest number of parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Training-Time** & **Param** & **FIOPs** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(s)** & **(M)** & **(G)** \\ \hline GhostNet & 92.56 & 92.42 & 92.50 & 92.46 & 1467 & 3.69 & 0.15 \\ MobileNetV3-large & 94.96 & 94.92 & 94.81 & 94.87 & 1263 & 5.56 & 0.23 \\ EfficientNet-b0 & 94.40 & 94.19 & 94.27 & 94.23 & 1851 & 5.38 & 0.40 \\ ShuffleNetV2 & 93.44 & 93.25 & 93.28 & 93.26 & 992 & 3.50 & 0.30 \\ EfficientNetv2-S & 94.08 & 94.09 & 93.87 & 93.98 & 4264 & 21.46 & 2.87 \\ Improved GhostNetV2 & 95.44 & 95.33 & 95.27 & 95.30 & 1197 & 5.00 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training results of seven models.  
Figure 14: Confusion Matrix.
## 3 Results
### dNDVI Change Thresholding to Create Hybrid Change Detections  
As described in the methodology section, a hybrid classification option was applied, whereby the random forest post-classification change detection was supported by a secondary classification of pixels as being potential areas of forest loss based on a measured change in NDVI of at least dNDVI \(<-0.2\) relative to the baseline image composite.  
Figure 8 shows the results of the post-classification change detection. The hybrid change detection method removes some agricultural areas that have been misclassified from the change detection maps and results in cleaner forest loss maps with less noise.
# Experimental Investigation on Self-Excited Thermoacoustic Instability in a Rijke Tube
## 4 Results and Discussions
### Flame Response and Pressure  
The flame can stretch regularly and the intensity of chemiluminescence changes greatly during thermoacoustic instability. Figure 9 presents the flame response to thermoacoustic instability in about one period. What is clearly seen is that the flame front has jagged edges due to the perturbations of pressure and velocity. The intensity of chemiluminescence fluctuated periodically and was most apparent at the exit of the burner. At t = 0 ms, the intensity was strongest and then began to grow weaker, reaching the bottom at around 1.98 ms. Subsequently, the strength underwent enhancement and peaked at approximately 4.29 ms. Immediately after that, the intensity declined and the \"decline and ascent\" process continued to repeat.  
Figure 7: Measurements of temperature acquired at 5 kHz using single-ended TDLAS during thermoacoustic instability. (**a**) is the temperature measured within 1 s and (**b**) is the zooming in on the green square region of (**a**). The bule and red dot lines denote temperature measured by TDLAS and thermocouples, respectively.  
Figure 8: Spectrogram depicting the frequency content of the temperature measured by single-ended TDLAS as a function of time.  
Figure 9: Instantons images of chemiluminescence intensity of flame obtained by a high-speed camera with 3000 fps. Red square is the fixed visual area.  
In order to describe the chemiluminescent intensity quantitively, only the intensity exceeding a certain threshold is reserved via binarization. The relative area is defined as the reserved intensity spatial distribution area within a fixed visual area (shown in the last subfigure of Figure 9). Figure 10a illuminates the fluctuations of a relative area. It is observed the area ranges from 1% to 4% periodically. The dominant frequency is near 230 Hz, as well as the appearance of the second and higher harmonics seen in Figure 10c. This suggests that the chemiluminescent intensity oscillates at a specific frequency.  
The measurements of pressure at the exit of the Rijke tube are shown in Figure 10b. As we can see, the pressure varies from \(-\)20 Pa to 20 Pa, which approximates a standard sinuous waveform. This may result from the fact that no other perturbations were exerted on the acoustic propagation except the unsteady heat release. The dominant frequency of pressure coincides with that of relative area.
## 4 Simulation Model
### Environment Settings  
As shown in Figure 5, we assume a situation where an obstacle arises on a one-way straight highway road with two or three lanes. We assume that the road length is 2 km, and a vehicle playing the role of the obstacle enters the road at 20 s and stops at a point 1950 m from the starting point. Vehicles enter the road in accordance with a Poisson arrival model, and their entry lane is chosen randomly. Vehicles start at an initial speed of 16.7 m/s and move at a speed limit of 33.3 m/s. The V2V communication range is assumed to be 300 m, considering that the dedicated short range communications (DSRC) communication range in real environments is around 300 m [24]. Moreover, it is assumed that CVs frequently broadcast driving information messages (e.g., position and speed) and they know the positions of other CVs within this range. In the proposed scheme, the number of vehicles in each lane is counted within this range. If a vehicle detects an obstacle, it broadcasts a notification message at 0.2 s intervals until it passes the obstacle.  
The communications are simplified in the simulations. It is assumed that obstacle detection notification messages are immediately delivered to all following CVs within 1.5 km from the source of the message by multi-hop communication. Table 3 summarizes the simulation parameter values.  
Figure 5: Traffic flow simulation in SUMO.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Simulation time & 500.0 s \\ Time step length & 0.05 s \\ \(d_{\text{avoid}}\) (avoidance zone size) & 10–500 m \\ \(d_{\text{prelim}}\) (preliminary avoidance zone size) & 0–300 m \\ \(d_{\text{decel}}\) (gap adjustment zone size) & 0–1000 m \\ \(a_{\text{comfort}}\) for adjusting time headway & \(-2.94\) m/s\({}^{2}\) (\(-0.3\) G) [25] \\ Threshold \(L\) for avoiding congested lanes & 0.6 \\ Available sensing range & 50 m [26] \\ V2V communication range & 300 m [24] \\ Traffic load input value for 2-lane scenario & 720–3600 vehicles/h \\ Traffic load input value for 3-lane scenario & 720–5400 vehicles/h \\ CV penetration ratio & 0.0–1.0 \\ Broadcast interval & 0.2 s \\ Validity period of the obstacle information & 60.0 s \\ Vehicle length, vehicle width & 4.47 m, 1.795 m \\ Min. inter-vehicular distance & 2.5 m \\ Regular inter-vehicular gap (\(\tau\)) & 2.0 s \\ Gap open ratio & 2.0 \\ Lane change duration & 3.0 s \\ LC2013 lane change mode & 1621 \\ Initial speed, road speed limit & 16.7 m/s, 33.3 m/s \\ Max. acceleration, max. deceleration & 2.9 m/s\({}^{2}\), \(-7.5\) m/s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
## 4 Mapping Algorithm
### Policy Network  
During the virtual network mapping process, we use the policy network to choose a substrate node for each virtual node that needs to be mapped. As illustrated in Figure 2, we construct a policy network in this article. It is made up of an input layer, a convolutional layer, a softmax layer, and a filter. The feature matrix is input, and the probability of mapping the virtual node to the substrate node is ultimately output. Each layer in the strategy network has its specific role:  
1. Input layer: responsible for receiving feature matrix as input.
2. Convolutional layer: through convolutional operation, it extracts local features from the input feature matrix and performs linear transformation on them: \[h_{k}^{c}=\omega\cdot v_{k}+b\] (19) where \(\omega\) represents the convolution kernel weight vector and \(b\) is the bias.
3. Softmax layer: the likelihood that the virtual node will map to the underlying node is produced by nonlinearly transforming the convolutional layer's output. The \(k^{th}\) node has the following probability of being chosen: \[p_{k}=\frac{e^{h_{k}^{c}}}{\sum_{i}^{\left|N^{S}\right|}e^{h_{i}^{c}}}.\] (20)
4. Filter: the filter is mainly used to screen out candidate nodes with insufficient computing resources, leaving only nodes with sufficient CPU capacity.
## 3 Results and Discussion
### Antibacterial Activity and QSAR Studies
aureus_ strain (in terms of log 1/MICs = pMICs) well correlated with those towards both Gram-negative bacterial strains (Pearson's R \(\sim\) 0.9). As noted above, the structural requirements for the cytotoxic/therapeutic activity of HNOX compounds includes the presence of a \(N\)-oxide moiety as a key functional group governing their reductive conversion, and the electron-accepting potency of the compounds is recognized to be the major driving force for their reductive activation. The enzymatic reactivity as well as the cytotoxic (antitumor or antimicrobial) activity of HNOX compounds, including quinoxaline/benzotriazine di-\(N\)-oxides, have been defined to correlate to varying extents with their electron-accepting potencies expressed in terms of the experimentally estimated redox potentials and/or quantum mechanically calculated electrophilic potencies([10; 22; 35; 36; 37; 38; 39; 40; 41; 42], and refs. therein). Table 2 provides the electron-accepting potency of TPZs in terms of LUMO energies (E\({}_{\text{LUMO}}\)) along with their cDFT-based global reactivity indices (electronegativity (\(x\)), the chemical hardness (\(\eta\)), and the electrophilicity (\(\omega\))) [23; 24; 25; 26]. The \(\omega\) index has been used as a suitable descriptor for the prediction of the single- as well as two-electron (hydride) reduction potentials of the electrophilic/redox-active agents, including quinones, nitroaromatics and HNOX compounds. Note that for the neutral systems, this index has been found to be virtually insensitive to the solvent effects [26; 43; 44; 45; 46]. As shown in Table 2, the electron-accepting potency of TPZs in terms of their \(\omega\) values fell within a markedly wider range compared to that of E\({}_{\text{LUMO}}\) (i.e., \(\Delta\omega\) = 0.91 eV _vs._\(\Delta\)E\({}_{\text{LUMO}}\) = 0.56 eV). All the derived compounds were assessed to possess a higher electron-accepting potency relative to that of triapazamine. For the whole set of tested TPZs, we were not able to define reliable dependences between pMICs and the electrophilic potency of the compounds (R = 0.3-0.36); nonetheless, the activity of TPZ **(1)** together with the most active derivatives bearing relatively high homology in terms of their substituents (compounds **2-6**), showed a clear tendency to increase with an increase in their \(\omega\) values towards _E. coli_ (pMIC = \(-\)9.20 \(\pm\) 2.62 \(+\) (2.15 \(\pm\) 0.68) \(\omega\); R = 0.84, F = 9.82), and to a lesser extent against _S. enterica_ (R = 0.77, F = 5.60 ) or _S. aureus_ (pMIC = \(-\)8.30 \(\pm\) 3.21 \(+\) (2.00 \(\pm\) 0.84) \(\omega\); R = 0.77, F = 5.51). Virtually the same situation has previously been reported for the activities of the diverse structure quinoxaline di-\(N\)-oxide compounds against _Mycobacterium tuberculosis_[38; 39; 40], in which no apparent
## 3 Proposed Method
### User Preference Extraction  
In this paper, the method in reference [26] extracts user preferences based on POI-type information and user history check-in data. The details are as follows:  
Figure 1: Overview of personalized scenic tourism route planning model.  
The user-POI sign-in matrix \(I-C_{m\times n}\) is obtained from the user-POI historical sign-in data, where \(m\) is the number of users and \(n\) is the number of POIs. The values of each element in the matrix \(I-C\) are shown in Formula (11): \[Ic_{i,j}=\left\{\begin{array}{ll}c_{i,j},&\text{sign-in times of user $i$ to $POI_{j}$}\\ 0,&\text{user $i$ has not signed in to to $POI_{j}$}\end{array}\right.\] (11) where \(Ic_{i,j}\) represents the number of sign-in times of user \(i\) to \(POI_{j}\), and \(i\in(1,2,\cdots,m)\) and \(j\in(1,2,\cdots,n)\) are subscript variables.
2. The POI-type matrix \(I-T_{n\times l}\) is obtained from the POI basic information data, where \(n\) is the POI number and \(l\) is the POI-type number. The expression of the values of each element in the matrix \(I-T\) is shown in Formula (12): \[It_{j,k}=\left\{\begin{array}{ll}1,&\text{POI}_{j}\text{ has type $T_{k}$}\\ 0,&\text{POI}_{j}\text{ does not have type $T_{k}$}\end{array}\right.\] (12) where \(It_{j,k}\) indicates whether \(POI_{j}\) has type feature \(T_{k}\). \(j\in(1,2,\cdots,n)\) and \(k\in(1,2,\cdots,l)\) are subscript variables.
3. The user-type sign-in matrix \(T-C_{m\times l}\) is obtained from the user-POI sign-in matrix \(I-C_{m\times n}\) and the POI-type matrix \(I-T_{n\times l}\), where \(m\) is the number of users and \(l\) is the number of POI types. The values of each element in the matrix \(T-C\) are shown in Formula (13): \[tc_{i,k}=\left\{\begin{array}{ll}m_{i,k},&\text{sign-in times of user $i$ for type $T_{k}$}\\ 0,&\text{user $i$ has not signed in type $T_{k}$}\end{array}\right.\] (13) where \(tc_{i,k}\) represents the number of sign-in times of user \(i\) for type feature \(T_{k}\), that is, the number of times the user has been to POI with type feature \(T_{k}\), and \(i\in(1,2,\cdots,m)\) and \(k\in(1,2,\cdots,l)\) are subscript variables.
4. After obtaining the \(U-T\) matrix, take the value in the matrix as the parameter, use the linear regression model to obtain the type weight, and use the gradient descent to optimize the weight value. Finally, take the \(N\) types with the largest weight value as the user's preferred type, and code the user preference vector.