esa-sceva/satcom-chunk-collection huggingface dataset:
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
###### Abstract  
Road intersections are shared among several conflicted traffic flows. Stop signs are used to control competing traffic flows at road intersections safely. Then, driving rules are constructed to control the competing traffic flows at these stop sign road intersections. Vehicles must apply a complete stop with no motion in front of stop signs. First to arrive, first to go, straight before turns, and right then left are the main driving rules at stop sign intersections. Drivers must be aware of the stop sign's existence, the architecture of the road intersection, and traffic distribution in the competing traffic flows. This is to make the best decision to pass the intersection or wait for other conflicted flows to pass according to the current situation. Due to bad weather conditions, obstacles, or existing heavy vehicles, drivers may miss capturing the stop sign. Moreover, the architecture of the road intersection and the characteristics of the competing traffic flows are not always clear to the drivers. In this work, we aim to keep the driver aware ahead of time of the existing stop signs, the architecture of the road intersection, and the traffic characteristics of the competing traffic flow at the targeted destination. Moreover, the best speed and driving behaviors are recommended to each driver. This is based on his/her position and the distribution of the existing traffic there. A driving assistance protocol is presented in this paper based on vehicular network technology. Real-time traffic characteristics are gathered and analyzed of vehicles around the intersections. Then, the best action for each vehicle is recommended accordingly. The experimental results show that the proposed driving assistant protocol successfully enhances the safety conditions around road intersections controlled by stop signs. This is by reducing the percentage of accident occurrences. Fortunately, the traffic efficiency of these road intersections is also enhanced; the accident percentage is decreased by 25% upon using the proposed protocol.  
road intersection; stop sign; driving assistance; safety conditions; competing traffic flows Article
_applied sciences_  
_Technical Note_  
**Three-Dimensional Subsurface Pipe Network Survey and Target Identification Using Ground-Penetrating Radar: A Case Study at Jilin Jianzhu University Campus**  
**[PERSON] 1,2\({}^{\copyright}\)**, [PERSON] 1,*\({}^{\copyright}\)**, [PERSON] 1, [PERSON] 1, [PERSON] 3,*\({}^{\copyright}\)**, [PERSON] 1 and [PERSON] 1,3\({}^{\copyright}\)**  
1 School of Geomatics and Prospective Engineering, Jilin Jianzhu University, Changchun 130118, China; [EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (Q.X.); [EMAIL_ADDRESS] (L.Y.); [EMAIL_ADDRESS] (Z.S.)  
2 College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China  
3 Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China  
* Correspondence: [EMAIL_ADDRESS] (J.P.); [EMAIL_ADDRESS] (H.Z.)  
**Abstract** This study focuses on the application of ground-penetrating radar (GPR) in conducting field surveys and data processing at the northern campus of Jilin Jianzhu University. The research site's geographical location and overall conditions are described. A detailed layout of the survey lines for 3D surveys is presented. The collected data undergo basic processing and interpretation, identifying multiple target bodies and their associated electromagnetic responses. Advanced analyses such as 3D imaging, common attribute analysis, and time-varying centroid frequency attribute analysis are employed to investigate underground features and potential pipe networks. The case study in this research demonstrates that the integration of 3D GPR surveys and time-varying centroid frequency analysis can effectively assess the attenuation characteristics of subsurface media and structures, thereby enhancing the overall prospecting and data interpretation capabilities of GPR.  
**Keywords**: ground-penetrating radar (GPR); attribute analysis; time-varying centroid frequency; GPR data processing; case study
# Particle and Particle Agglomerate Size Monitoring by Scanning Probe Microscope
## 4 Detection of Particles Using the Surface Curvature Function  
For particle detection, the curvature function of surface or plane curves can be used. However, conventional methods for the curvature computation use derivatives; thus, the field of their application is limited to smooth surfaces [40]. Therefore, the development of new methods for the computation of the curvature of the SPMimages considering their peculiar features is of high practical value. Let us consider one of the possible realizations of the method for computing the surface curvature [41; 42] and particles sizes in the SPMimages.  
To determine the curvature the neighbourhood of each point in each line of the SPMimage Z(x) is approximated by the segment of the adjacent circle. The segment height \(\varepsilon=AB\) is a constant value for the entire image, and the chord length \(H=P_{1}P_{2}\) is a variable (Figure 2).  
The radius \(R\) of the particle osculating circle is computed from the figure \(\Delta P_{1}OB\):  
\[\left(R-\varepsilon^{2}\right)+\frac{H^{2}}{4}=R^{2},\ R=\frac{H^{2}+4 \varepsilon^{2}}{8\varepsilon} \tag{1}\]  
The algorithm of the curvature computation is as follows. The points \(P_{1}\) and \(P_{2}\) are incrementally moved away from A with the step of 1 pixel along the \(x\) axis until \(AB<\varepsilon\). After each step, the coordinates of points \(P_{1}\) and \(P_{2}\) are determined and the general equation  
Figure 2: Schematic view of the curvature detector operation.  
of the line passing through these points is computed. The value \(AB\) is determined by the following formula:  
\[AB=\left|\frac{ax+by+c}{\sqrt{a^{2}+b^{2}}}\right|;\ AB=\left|\frac{c}{\sqrt{a^{2} +b^{2}}}\right|_{(x=0,y=0)} \tag{2}\]  
where \(a\), \(b\), and \(c\) are the coefficients of the general equation of the straight line drawn through \(P_{1}\) and \(P_{2}\). If the inequality \(AB<\epsilon\) ceases to hold, then the chord length is calculated. The \(H\) values for the image lines and columns form the functions \(H(x)\) and \(H(y)\).  
For the images with a low level of distortions, the centre of a particle is the point of the coincidence of the local extrema \(H(x)\) and \(H(y)\). The particle radius is computed by Formula (1). In the presence of distortions, the particle centres can be determined by the local extrema of the function:  
\[W^{+}(x,\ y)=\left\{\begin{array}{cc}H_{y}(x)+H_{x}(y),&(H_{y}(x)\geq 0) \wedge(H_{x}(y)\geq 0),\\ C,&\text{otherwise},\end{array}\right. \tag{3}\]  
where \(C\geq\max\) (\(H_{y}(x)\) +\(H_{x}(y)\))is a constant. In the presence of several close-spaced extrema \(W^{+}(x,y)\),the dominant extremum is chosen. The curvature detector is sensitive to image noise. Therefore, preliminary smoothing is required.
## 4 Implementation Results of a Fully-Connected ANN and CNN  
### Influencing Factors of Bridge Maintenance Costs  
The random forest method in Section 3.1 was used to calculate the VIM values of eight influencing factors (GDP growth rate, maintenance time, bridge grade, bridge technical condition, bridge location, bridge age, superstructure, and highway grade). The calculation results are shown in Figure 7.  
It can be seen from Figure 7 that the importance values of the four indicators of maintenance time, technical conditions, highway class, and GDP growth rate are all greater than 0.2. The importance of these four indicators is significantly higher than the other indicators, and the importance value of the other indicators is less than 0.1. Therefore, we select GDP growth rate, bridge technical conditions, highway grade, and maintenance time as the input parameters of the prediction models.  
The scatter graph matrix was adopted to conduct an exploratory data analysis on the database, to test the correlation between the four input parameters selected in the previous section and bridge maintenance costs. The probability histogram is on the diagonal line of the matrix, which represents the frequency of occurrence of a corresponding label for a certain feature. The scatter diagram is on the non-diagonal line of the matrix, which represents the correlation between labels, corresponding to different maintenance cost characteristics.  
A total of five characteristics of the four selected indicators, plus maintenance costs, were involved in the exploratory data analysis. Thus, the matrix consists of 5 probability histograms and 20 scatter plots. As can be seen from Figure 8, there is a certain correlation between the selected indicators and maintenance costs, while ensuring certain independence.
## 1 Introduction
### Reactive Power Support for Voltage Regulation
#### 3.4.3 Costs and Benefits  
The costs involved with this AS are the operational costs as mentioned above and the investment cost due to converter oversizing. The oversizing factor generally depends on the \(R\)/\(X\) ratio of the feeder on which the DRES is connected [78]. In addition, the operation of a DRES with a PF as low as 0.7, which implies an oversizing of the order of \(\sqrt{2}\) does not significantly affect its overall efficiency [22]. Thus, we may assume an oversizing of the order of \(\sqrt{2}\) as a worst case. As can be seen in Figure 7, such an oversizing does not lead to a proportional increase of the inverter purchase cost. Figure 7 shows the per-unit retail prices of commercially available PV inverters in Greece in July 2020 from four different manufacturers. It can be noticed that the per-unit price falls exponentially as the nominal power of the inverter increases. As an example, let us consider the case of \(S_{n1}=17.5\) kVA and \(S_{n2}=25\) kVA which are related with \(\sqrt{2}\). The unit cost of the former is approximately 125 \(\upxi\)/kW, while that of the latter is 93 \(\upxi\)/kW. Therefore, there is an increase in purchase cost by \(\upxi\)138 in total, which is 6.3% of the cost of the 17.5 kVA inverter.  
The benefits associated with this AS are mainly the deferral of investments the DSO would otherwise do in order to accommodate the increasing penetration of DRES. Such investments are the upgrade of feeders, the use of advanced distribution transformers with OLTC capability, the use of coil banks to mitigate the capacitive reactive power at the POI of the distribution grid with the transmission system, etc.  
Some indicative costs of feeders for the Spanish distribution system are: the cost of the overhead LV feeders is in the range of 16-26 \(\upxi\)/m depending on the cross section and the kind of terrain, [42], while their operational and maintenance costs are 170-270 \(\upxi\)/km/annum, [42]. In the case of cables, the respective costs are 48-98 \(\upxi\)/m and 500-1000 \(\upxi\)/km/annum, [42]. In the case of overhead MV feeders the respective costs are 58-110 \(\upxi\)/m and 600-1100 \(\upxi\)/km/annum while for MV cables 118-316 \(\upxi\)/m, and 1200-3200 \(\upxi\)/km/annum, [42].  
Some indicative costs of distribution transformers with OLTC [42]: According to the Slovenian DSO, a 400 kVA distribution transformer with OLTC costs approximately 28 k\(\upxi\) while the conventional one just 10 k\(\upxi\).  
Some indicative costs of coil banks for reactive power compensation [42]: The installation cost of coil banks in the Spanish distribution systems is of the order of 800 \(\upxi\)/MVAr with the operational cost of the order of 22 \(\upxi\)/MVAr/annum.
## 4 Analysis and Results
### Path Analysis  
Path analysis is a statistical method used to analyze causal relations between variables. It is the way to find and verify path coefficients that meet the model assumptions. Table 6 shows the path coefficient results of the research model. Intrinsic motivation (\(b=0.251\), \(p<0.001\)) and extrinsic motivation (\(b=0.231\), \(p=0.003\)) impact attitude significantly. Then, learning attitude (\(b=0.912\), \(p<0.001\)) impact well-being significantly. Moreover, learning attitude provides 50.9% of coefficient  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Bias-Corrected**} & \multicolumn{2}{c}{**Percentile Method**} \\ \cline{4-7}  & & \multicolumn{2}{c}{**Pearson Correlation**} & \multicolumn{2}{c}{**Lower**} & **Upper** & **Lower** & **Upper** \\  & & & & **Bond** & **Bond** & **Bond** & **Bond** \\ \hline IN & \(\leftrightarrow\) & EX & 0.750 & 0.581 & 0.857 & 0.583 & 0.858 \\ WBE & \(\leftrightarrow\) & IN & 0.544 & 0.351 & 0.685 & 0.338 & 0.678 \\ ATT & \(\leftrightarrow\) & IN & 0.428 & 0.193 & 0.640 & 0.205 & 0.656 \\ WBE & \(\leftrightarrow\) & EX & 0.513 & 0.321 & 0.682 & 0.303 & 0.668 \\ ATT & \(\leftrightarrow\) & EX & 0.431 & 0.195 & 0.679 & 0.195 & 0.679 \\ ATT & \(\leftrightarrow\) & WBE & 0.700 & 0.418 & 0.914 & 0.452 & 0.940 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bootstrap correlation coefficient under 95% confidence interval.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Fit** & **Criteria** & **Model Fit of Research Model** \\ \hline \(\chi^{2}\) & The small the better & 959.33 \\ DF & The large the better & 770 \\ Normed Chi-sqr (\(\chi^{2}\)/DF) & 1 \(<\)\(\chi^{2}\)/DF \(<\) 3 & 1.25 \\ RMSEA & \(<\)0.08 & 0.02 \\ TLI (NNFI) & \(>\)0.9 & 0.98 \\ CFI & \(>\)0.9 & 0.98 \\ GFI & \(>\)0.9 & 0.91 \\ AGFI & \(>\)0.9 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model fit.  
determination of well-being. Then, intrinsic motivation and extrinsic motivation can predict 32.9% variance of attitude.
## 3 Architectural Overview
### Route Cache in MMS-DSR  
The Route Cache in MMS-DSR has been enhanced to dynamically store and manage the most efficient routes based on real-time and predictive analytics. This module now works closely with the Beamforming Optimization Unit to ensure that the cached routes are not only optimal in terms of path metrics but also in terms of communication quality and reliability. The operation principle of Route Cache is briefed by Algorithm 2 and consists of the following phases:  
* Dynamic Caching: the Route Cache dynamically stores routes that are scored highest by the Multi-metric Scoring Engine. It takes into account not only the traditional route metrics but also the beamforming efficacy, ensuring that the stored routes are optimal under current and predicted network conditions. This dynamic caching is key for reducing routing overhead and improving responsiveness in the fluctuating urban environment.
* Predictive Updates: based on feedback from the Machine Learning-Based Prediction Unit, the Route Cache updates its entries to preempt potential degradations in route quality. This includes adjusting stored routes based on predicted changes in node mobility and channel quality, ensuring that the cache reflects the most current and anticipated network states, and enhancing the system's ability to handle urban dynamics.
* Beamforming Information: each route in the cache includes detailed beamforming vector information for every link in the path. This ensures that when a route is retrieved from the cache, each node along the path can quickly adjust its beamforming vectors to the optimal settings, facilitating a coherent and coordinated approach to maintaining route quality.
* Eviction and Maintenance: the cache follows an intelligent eviction policy where less optimal routes are replaced by newer, higher-quality routes. This policy considers route age, frequency of use, and predictive quality scores to maintain a balance between route freshness and historical efficacy, which is crucial for ensuring that the network can quickly adapt to changes in the urban context.
* Support for Fast Route Recovery: in case of rapid topology changes, which are common in urban environments, the Route Cache supports fast route recovery by providing alternative paths that can be quickly evaluated and deployed, minimizing downtime and packet loss.  
The enhancements to the Route Cache enable MMS-DSR to maintain a high-performance routing table that is adaptive and predictive, significantly reducing the need for frequent route discoveries and improving overall network efficiency in the complex and dynamic environments of smart cities.  
```
1:procedureUpdateRouteCache(NewRoute, PredictedQuality)
2:ifRouteCache.IsFull()then
3:EvictLeastOptimalRoute(RouteCache)
4:endif
5:AdjustBeamformingVectors(NewRoute)
6:RouteCache.Store(NewRoute, PredictedQuality)
7:endprocedure
```  
**Algorithm 2** Dynamic Route Caching and Maintenance Algorithm
## 2 Materials and Methods
### Workflow and Validation  
In this study, we presented two cases: case 1 consisted of the ANFIS modeling and prediction of the temperature difference output. It started by dividing the dataset (input-output data points) into training and testing sets. The training set was used for the ANFIS model construction and the testing set for evaluating the ANFIS model on the data yet unseen by the model. In k-fold cross-validation, the original dataset was randomly partitioned into \(k\) equal sized sub partitions. The \(k-1\) sub partitions were used as training data and a single partition was used for the testing of the ANFIS structure. During the cross-validation procedure, all external ANFIS input parameters, such as number of membership functions (MF), type of MF, number of epochs, learning step, increase in learning step and optimization method, were kept fixed. The whole cross-validation procedure was then repeated \(k\) times, with each of the \(k\) sub partitions used exactly once as testing data. In the majority of cases presented in this paper, the leave one subject out (LOSO) validation scheme (\(k=35\)) was adopted, partitioning the whole dataset into 35 equal parts and performing 35 iterations of learning with 34 folds and testing on the remaining fold (yet every iteration a different fold). The LOSO procedure promised better overall generalization of the model's performance and enabled the use of the whole dataset for both the model's learning and its testing. The root mean squared error (RMSE) was used as a fitness function and represented the quantitative evaluation of the ANFIS's performance.  
Case 2's procedure was structurally the same as that of case 1, except for an additional part represented by the PSO algorithm, which was set to optimize the external ANFIS input parameters. As the PSO fitness function, the RMSE testing error was adopted. The PSO algorithm was set to minimize the RMSE testing error and consequently find optimal ANFIS input parameter settings (Figure 5).
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 2 Materials and Methods
### Evaluation Metrics  
The FCN module shown in Figure 2 is a single-class object classifier, and it produces only one change map for each input image pair. After the change map is normalized between 0 and 1 using the sigmoid/softmax function, we need a binarization threshold defining whether a certain pixel in it is changed or not. In order to maximize precision and recall at the same time or keep a balance between them, here, the threshold is manually set to 0.70 or so.  
By comparing the binarized change map and ground truth, four metrics were used to validate the effectiveness and accuracy of the proposed methods, which are: precision, recall, F1 score, and IOU. Their formulas [1] are respectively given below:  
\[\text{precision}=\frac{TP}{TP+FP} \tag{13}\]\[\text{recall}=\frac{TP}{TP+FN} \tag{14}\] \[\text{F1}=2\frac{precision*recall}{precision+recall}\] (15) \[\text{IOU}=TP/(FP+TP+FN) \tag{16}\]  
where \(TP\), \(FP\), and \(FN\) denote all classes' total number of true positives, false positives, and false negatives, respectively.
# Two-Dimensional Digital Beam Steering Based on Liquid Crystal Phase Gratings
###### Abstract  
Electrically tunable phase gratings are able to steer an incoming light beam without employing movable parts. Here, we present the design and implementation of a 2D beam steering device by cascading two orthogonal 1D liquid crystal (LC) based phase gratings, each having an array of 72 rectangular individually controlled pixels and driven by a custom 12-bit Pulse-Width Modulation (PWM) electrical driver. High-resolution structures in glass wafers coated with transparent Indium-Tin Oxide (ITO) have been prepared using Direct Laser Writing (DLW) techniques. With DLW, a high number of pixels can easily be drawn with an interpixel space of less than 3 \(\upmu\)m, leading to devices with a high fill factor. The active area of the cascaded device is 1.1 \(\times\) 1.1 mm\({}^{2}\). We present a 72 \(\times\) 72 point efficiency map corresponding to a maximum diagonal steering angle of 1.65\({}^{\circ}\). Special attention has been paid to make the device compatible with space application by avoiding electronics in the active area.  
beam steering; liquid crystal; blaze grating; direct laser writing +
Footnote †: journal: Applied Sciences  
0
Footnote 0: _Applied Sciences_
## 3 Results  
### Correlation between Conditioning Factors and Gully Occurrence Using the Frequency Ratio Method  
We used the frequency ratio method to calculate the probabilistic relation between gullies as a dependent variable and conditioning factors as independent variables. Figure 5 presents FR values for the classes of each conditioning factor. In the case of rainfall, the 148-159 mm class has the highest FR value (2.15), followed by the 159-171 mm (0.29) and 171-192 mm (0) classes. The \(>\)1000 m distance-from-road class had the highest FR value (1.63), followed by the 200-500 m (1.10), 500-1000 m (0.96), 100-200 m (0.77), and 0-100 m (0.73) classes. In the case of NDVI, the 0.12-0.37 class has the highest FR value (3.48). Bare land areas have the highest FR values in the land-use class (1.21). In the case of drainage density, high FR values are associated high drainage density. The 1.62-2.46 km/km\({}^{2}\) class, for example has a value of 4.32. For lithology, the Ekgy has by far the largest FR (4.43), followed by Qft2 (1.02), PCK (0.34), and PCmt2 (0.08). No gullies are present on the other lithologies; therefore, their values are 0. Areas located \(<\)500 m from rivers have a FR value of 1.63; the more distant classes have 0 values. In the case of profile curvature, the highest FR value (1.18) is associated with the \(>\)0.25 class. Flat areas have a FR value of 1.09, which is higher than the values for convex and concave areas (0.72 and 0.53, respectively). The highest FR value for the slope factor is 1.23 (5-10% class). Values for the 0-5% and 10-20% classes are, respectively, 1.07 and 0.76; the 20-30% and \(>\)30% classes are 0. Slopes with an eastern aspect have the highest FR value (1.30), following by slopes with northeastern (1.17), southeastern (1.13), northern and flat (1.05), southwestern (0.94), northwestern (0.88), southern (0.70), and western (0.68) aspects. Finally, all gullies are located in areas with an elevation range of 1800-2000 m a.s.l. (FR = 1.16).  
Figure 5: Frequency ratios for factors related to gully erosion.