esa-sceva/satcom-chunk-collection huggingface dataset:
# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
## 2 Global Path Planning
### Improved A* Algorithm  
#### 2.3.1 Path Pruning  
In an environment with known obstacles, the A* algorithm can accurately avoid obstacles and compute the shortest path. However, the generated paths often contain excessive turns, making it unsuitable for application to intelligent courier vehicles. To address these issues, this paper introduces a pruning strategy to smooth the path by eliminating redundant nodes and minimising unnecessary turns.  
As shown in Figure 2, the traditional A* algorithm plans the path (S, n\({}_{1}\), n\({}_{2}\), n\({}_{3}\), n\({}_{4}\), n\({}_{5}\), n\({}_{6}\), n\({}_{7}\), n\({}_{8}\), n\({}_{9}\), T), which exhibits excessive turns and low smoothness. The specific optimisation steps of the pruning strategy are as follows:  
When the connection distance between non-adjacent nodes is shorter than the planned path distance and no collision with obstacles occurs during the path connection process, these intermediate nodes can be regarded as redundant nodes and deleted. Therefore, all nodes except the start node, the goal node and the turning points in the path can be considered as redundant and removed. For example, if n\({}_{1}\) and T can be directly connected to each other and will not collide with obstacles, some of the redundant points in the path can be removed, thus simplifying the path to (S, n\({}_{1}\), T). The optimised path retains only the start node, the middle important node and the goal node, forming a more concise path.  
After removing the redundant nodes, the smoothness of the path is further optimised. During the optimisation process, starting from the target node T, each turning point along the path is examined in reverse to assess whether sharp turns can be reduced by adjusting the path connections. By removing redundant turning points, a more optimal path (S, n\({}_{10}\), T) is finally obtained.
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 3 Automated Driving Risk Assessment
### Risk Assessment for Drive Mode Decision  
A decision sight distance (DSD) [10] is used to extend the human-centered risk assessment algorithm and it allows to determine the intervention timing of a drive mode (lane change) by considering safety and smoother operation of vehicle. The experimentally acquired and fitted data for the decision sight distance with a range of speed is described in Table 2.  
Figure 5: Human Centered Risk Assessment-based Safe Triangle Analysis.  
The DSD is the distance required for a driver to handle the dangerous situation, select a proper velocity and path, and start and complete the required maneuver safely. A 14 s of human driver reaction time for decision sight situations incorporates the abilities of most drivers, including those of older drivers. This sight distance is for the recommended urban driving condition. As seen in Table 2, a DSD formula is described incorporating driver perception-reaction time with deceleration distance to derive the design speed.  
\[\begin{array}{ll}DSD(t)&=\text{Driver perception -- reaction distance}+\text{braking distance}\\ &=0.278\cdot v_{x}(t)\cdot T_{r}+0.039\cdot\frac{v_{y}(t)^{2}}{a}\end{array} \tag{3}\]  
where,  
\(DSD=\text{required decision sight distance }[\text{m}]\)  
\(v_{x}=\text{longitudinal Speed }[\text{m}/\text{s}]\)  
\(T_{r}=\text{brake reaction time}=14\) [s]  
\(a=\text{deceleration rate}=3.4\) [m/s\({}^{2}\)]
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AC & Analysis Center \\ APC & Antenna Phase Center \\ AR & Ambiguity Resolution \\ BNC & BKG NTRIP Client \\ CLAS & Centimeter-Level Augmentation Service \\ CNES & Centre National d'Etudes Spatiales \\ CODE & Center for Orbit Determination in Europe \\ DoY & Day of Year \\ EKF & Extended Kalman filter \\ GNSS & Global Navigation Satellite System \\ GPT & Global Pressure and Temperature \\ GPS & Global Positioning System \\ HAS & High Accuracy Service \\ IERS & International Earth Rotation and Reference Systems Service \\ IGS & International GNSS Service \\ NWP & Numerical Weather Prediction \\ OSB & Observable-specific Signal Bias \\ PCO & Phase Canter Offset \\ PCV & Phase Center Variations \\ PPP & Precise Point Positioning \\ PWV & Precipitable Water Vapor \\ QZSS & Quasi-Zenith Satellite System \\ RTK & Real-Time Kinematic \\ RTS & Real-Time Service \\ VMF & Vienna Mapping Functions \\ TGD & Time Group Delay \\ ZTD & Zenith Tropospheric Delay \\ \end{tabular}
# Improving Congestion Control of TCP for Constrained IoT Networks
## 2 Related Work
### Studies on Congestion Control for CoAP
Like the current version, the early version adopted the weak estimator and computed the overall RTO estimate, which is an exponentially weighted moving average computed of the strong and the weak estimator. However, unlike the current version, it computed SRTT (smoothed RTT), RTTVAR (RTT variance), and RTO according to RFC (Request for Comments) 6298 [29] and used a binary exponential backoff mechanism instead of using the variable backoff factor. In Reference [30], the authors applied the idea of an Eifel retransmission timer [31] to the early version of CoCoA to prevent making RTO too aggressive or too conservative. The existing estimator gains (i.e., the constant coefficients \(\alpha\) and \(\beta\)) used for computing SRTT and RTTVAR can make RTO aggressive. To address the problem, the authors proposed replacing the constant coefficients with a dynamically changing parameter defined as the ratio between the current RTT sample and the RTO value. The authors also pointed out that negative mean deviation can cause undesirable effects; an RTT decrease can increase RTTVAR, thereby increasing RTO as opposed to an indication of the RTT decrease. The authors addressed this problem by skipping the rule of updating RTTVAR when RTT-SRTT is negative. Comparing the proposed scheme of Reference [30] to our proposed scheme, the two schemes are similar in that SRTT, RTTVAR, and RTO are computed using the same algorithm for both the strong and the weak estimator and a binary exponential backoff mechanism is used. However, when taking a weak RTT sample, the scheme of Reference [30] measures the time from the initial transmission, whereas ours measures the time from the latest transmission. Moreover, while ours computes SRTT, RTTVAR, and RTO according to the classical rule defined in RFC 6298, the scheme of Reference [30] computes SRTT and RTTVAR using a more dynamic parameter.
# Grouting Process Simulation Based on 3D Fracture Network Considering Fluid-Structure Interaction
## 3 Case Study  
Hydropower station X is located in the upper reaches of Lancang River. It is a large-scale hydropower project which mainly generates electricity and takes into account the comprehensive  
Figure 3: The principle of fluid–structure interaction.  
utilization benefits of irrigation and water supply. The hydropower project is mainly composed of a gravelly soil core rockfill dam, left bank slope spillway, water diversion and power generation system, ground workshop, and so on. The installed capacity of the hydropower station is 1400 MW, the maximum dam height is 139.80 m, and total dam crest length is 576.68 m. The dam area exposed strata are mainly from the Middle Jurassic flower group (J2h) and Quaternary strata (Q). The layout of the dam's foundation grouting curtain and the project profile are shown in Figure 4. The dam foundation curtain grouting project is divided into several continuous grouting units. In this study, a typical grouting unit is taken as a case study to simulate a three-dimensional random fracture network. The location of the study area is shown in the red wireframe in Figure 4b. This area is the foundation curtain grouting unit of the river bed dam section and is also the main area of the dam foundation seepage control of this project. In the study area, there are 41 grouting boreholes including 21 in the upstream row and 20 in the downstream row with the hole spacing of 1.5 m, the diameter of the grouting borehole is 75 mm, and the total depth is 60 m.
## 3 Results and Discussion
### Results of the Optimisation of the CPM Mechanism Links
#### 3.3.2 Results of Optimising Link 4 of the CPM Main Positioner
When MG-AL ALLOY material was used to generate the structures, the most favourable result was obtained after optimisation of link 4 at a limit volume of 40%. The mass of the resulting structure was 0.5670 kg, being 0.32685 kg lower than the original mass. The displacement parameter value was 3.7318 mm, with the von Mises stress set at 202.90 MPa (originally 255.53 MPa). The result obtained at the 50% limit volume is less favourable from a mass point of view, although there is a significant improvement in the displacement parameter, which has been reduced to a value of 2.1634 mm. It can therefore be concluded that the search for an optimum solution should be limited to carrying out further retail simulations in the 40% to 50% range, where more favourable results can be obtained in terms of further mass reduction and improvement of the displacement parameter.
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 2 Methods
### Machine Learning for Aquatic Vegetation Classification
#### 2.2.1 Data Preprocessing  
The acquired.sl2 data, being in a binary format with no manufacturer-provided tools or guidelines for opening and reading the contents, could not be directly used for machine learning classification algorithms. To convert this.sl2 data into a series of standard digital images (.jpg format) with geotagging, a two-step approach was used. First, the.sl2 file was opened using the Reefmaster Sonar Viewer (Reeff Master Software Ltd., West Sussex, U.K.), a software program that enables the display of sonar data (hydroacoustic imagery and corresponding geographical location) in a continuous video form. Figure 11 is a screenshot of hydroacoustic data recorded at Lake Raleigh viewed using the Reefmaster Sonar Viewer software. Primary and DownScan(tm) images are displayed along with a map indicating the scanning location. Both images show the ground surface with aquatic plants. DownScan(tm) (bottom right in Figure 11) was selected since it provides a relatively clearer view of the ground and vegetation, compared to Primary scan. Imagery display options, including contrast and brightness, horizontal scaling, and color scheme, were selected within Reefmaster for maximum visual clarity.  
Next, still hydroacoustic images were acquired at fixed time intervals using the MATLAB Toolbox 'Screen Record' by [PERSON] [26], which captures PC monitor contents in real-time. Playback of imagery at nine times the original speed expedited the capture process. The image capture rate was selected to achieve 20%-30% spatial overlap between consecutive images. The toolbox code was modified to incorporate clock time instead of CPU time, creating images with consistent overlap by eliminating dependence on CPU load. Images were digitally masked and cropped to remove unnecessary information and facilitate the machine learning process (Figure 12). The top left portion of the image contains the GPS coordinates, while DownScan(tm) covers the remaining portion.  
Figure 11: Screenshot from Reefmaster Sonar Viewer software illustrating hydroacoustic imagery acquired on Lake Raleigh: left—map location corresponding with imagery (indicated by boat icon) and traversed path (with increasing depth, path color changes from red to blue); top right—Primary scan sonar; bottom right—DownScan(tm) sonar.
## 2 Data Collection
### RT Surveying  
RT solutions were obtained from both Phase 1 and Phase 2. The RT solutions in Phase 1 were performed for newly installed marks to test various factors, such as occupation times, baseline lengths, number of reference stations, number of epochs etc. During the RT session of Phase 1, independent of the static session, the receiver/antenna combinations remained the same to preserve the surveying conditions.  
On the other hand, in Phase 2, existing GPSBMs with various baseline lengths from seven reference stations were used to verify the relevance of using GLONASS constellation, baseline length, improvement by averaging window, and the effect of the multiple reference stations on the RT solutions. Figure 3 shows the number of observations for each RT type used in this study. RTK solutions were obtained from both Phase 1 and Phase 2. Most of the solutions were based on GNSS, and some GPS-only solutions were obtained in Phase 2. The number of long baseline (\(\sim\)30 km) solutions is very limited, as compared to the count of short baselines (\(\sim\)2 km), and various lengths of baselines were analyzed as well. Unless stated otherwise, all comparisons were accomplished for the ellipsoidal heights in this study.  
Figure 3: Number of observations by RT types (Phases 1 and 2). SHORT/LONG/MIXED refer to baseline lengths.
## 3 Results
### Resonances on the Si-Rod Metasurfaces  
Figure 4 shows a series of \(R\) spectra for the Si-rod metasurfaces shown in Figure 2. Figure 4a,b show numerically calculated and measured \(R\) spectra, respectively, for the metasurface of a SOI of 200 nm thickness and \(P\) = 330 nm.  
In Figure 4a, the simulated \(R\) spectra at incident angles of 0\({}^{\circ}\), 5\({}^{\circ}\), 10\({}^{\circ}\), and 20\({}^{\circ}\) are shown with red dotted, black solid, yellow solid, and gray solid curves, respectively. The incident polarization was set to be p polarization. We note that the \(R\) spectra at 0\({}^{\circ}\) and 5\({}^{\circ}\) are quite similar and almost overlap. The simulation was implemented to model the actual metasurface in Figure 2a by assuming that the Si rod is a square-shaped rod from the top view.  
In Figure 4b, the \(R\) spectra were measured at incident angles of 5\({}^{\circ}\), 10\({}^{\circ}\), and 20\({}^{\circ}\), shown similarly to Figure 4a. It is verified in Figure 4a,b that the highest-\(R\) band appears at 5\({}^{\circ}\) in the longer wavelength range around 800 nm, and the other two high-\(R\) bands appear at 5\({}^{\circ}\) in shorter wavelength range than the highest-R band.  
A comparison between the measured \(R\) spectra and the simulated \(R\) spectra in Figure 4a,b revealed a qualitatively good agreement. The three high-\(R\) bands with \(R\) > 50% appear in both spectra. The high-\(R\) bands at the longest wavelength exhibit the widest band width among the three bands. As incident angles increase, the \(R\) tends to decrease in the wavelength range from 550 to 800 nm. Thus, several agreements are readily found. We note that the small deviation in the spectral shapes probably comes from structural difference between the actual metasurface in Figure 2a,c and the modeled metasurface of ideally rectangular Si rods in the simulation.  
The numerically calculated and measured \(R\) spectra are respectively shown in Figure 4c,d for the metasurface of a SOI of 200 nm thickness and \(P\) = 400 nm. A qualitative good agreement is also found in this case. For example, (i) almost perfect \(R\) at the edge of long wavelength side appears in the calculated and measured spectra of incident angles of 10\({}^{\circ}\) and less. (ii) A high-\(R\) band around 650 nm in Figure 4c includes two asymmetric modes that appear as two \(R\) dips at 5\({}^{\circ}\) and more; the asymmetric modes were measured as shown in Figure 4d. We point out that the asymmetric modes do not contribute to the FL enhancement in Figure 3c,d. This is probably because the asymmetric modes do not match the symmetry of electric-dipole transitions resulting in the FL, and cannot couple with the electronic transitions.  
The agreement between the simulation and measurement was found to be qualitatively better in Figure 4c,d than that in Figure 4a,b. This is partially because the actual metasurface is more similar in structure to the modeled metasurface in the simulation. Indeed, it is easily verified in the SEM images of Figure 2 that the Si rods of \(P\) = 400 nm resemble ideal squares in shape, compared with the Si rods of \(P\) = 330 nm.
# Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety
###### Abstract  
A smart helmet-based wearable personnel proximity warning system was developed to prevent collisions between equipment and pedestrians in mines. The smart helmet worn by pedestrians receives signals transmitted by Bluetooth beacons attached to heavy equipment, light vehicles, or dangerous zones, and provides visual LED warnings to the pedestrians and operators simultaneously. A performance test of the proposed system was conducted in an underground limestone mine. It was confirmed that as the transmission power of the Bluetooth beacon increased, the Bluetooth low energy (BLE) signal detection distance of the system also increased. The average BLE signal detection distance was at least 10 m, regardless of the facing angle between the smart helmet and Bluetooth beacon. The subjective workload for the smartphone-, smart glasses-, and smart helmet-based proximity warning system (PWS) was evaluated using the National Aeronautics and Space Administration task load index. All six workload parameters were the lowest when using the smart helmet-based PWS. The smart helmet-based PWS can provide visual proximity warning alerts to both the equipment operator and the pedestrian, and it can be expanded to provide worker health monitoring and hazard awareness functions by adding sensors to the Arduino board.  
m line safety; smart helmet; personnel proximity warning system; Bluetooth beacon; Arduino +
Footnote †: journal: Applied  
18 April 2021  
Accepted: 10 May 2021  
Submitted: 11 May 2021
## 3 An Interactive Learning Network That Maintains Sentiment Consistency
### Maintaining Sentiment Consistency  
The issue of sentiment inconsistencies for aspect items arises from end-to-end modeling using sequence annotation. In order to deal with this problem, this paper constructs a consistent sentiment component with a gate mechanism; in this component, the input is a preliminary sentiment prediction result \(S\) for each word. Use Equation (10) to calculate the threshold value \(g_{t}\), which represents the importance of the current moment prediction result, then, combine the prediction result of the previous moment through Equation (9) so that the prediction of the current moment will inherit the characteristics of the previous moment, to reduce the situation of different sentiment polarities in the same aspect item. The internal structure of this component is shown in Figure 3. In Equations (9) and (10), \(S_{t}\) represents the preliminary prediction result at the current moment, \(S^{\prime}_{t-1}\) represents the final prediction result at the previous moment, \(W_{g}\) is the weight matrix, \(b_{g}\) is the bias term, and \(\sigma\) is the Sigmoid function.  
\[S^{\prime}_{t}=S_{t}g_{t}+S^{\prime}_{t-1}(1-g_{t}) \tag{9}\]  
\[g_{t}=\sigma(W_{g}S_{t}+b_{g}) \tag{10}\]
# Practical Considerations in the Design of Passive Free Piles in Sliding Soil
## 6 Strength of a Free-Head Pile Restraining a Translational Landslide  
The current design approach for a translational landslide stabilisation with free-head flexible single piles involves the following steps:  
1. [label=()]
2. an evaluation of the integrative shear strength along the sliding surface of the ground volume pertaining to the single pile to obtain a given safety factor;
3. a sizing of the single pile to give the requested design shear strength.  
The total strength offered by a single pile at the slip surface level is \(\mathrm{R_{d}}\) (Figure 13) and results from the composition of the design normal stress, \(\mathrm{N_{d}}\), and the design shear strength, \(\mathrm{V_{ult,d}}\). This is because, as indicated by the results of the numerical analyses, a significant amount of axial load is mobilised by the pile in proximity to the sliding surface, even for limited soil displacements along the slope.  
The component of \(\mathrm{R_{d}}\) along the sliding surface is:  
\[S_{d,N}=N_{d}\ sen\beta+V_{ult,d}\ cos\beta \tag{4}\]  
If, however, the contribution of the axial load is ignored, the design strength offered by a single pile along the slip surface becomes:  
\[S_{d}=V_{ult,d}\ cos\beta \tag{5}\]  
Figure 13: Design strengths, \(\mathrm{S_{d,N}}\) and \(\mathrm{S_{d}}\), provided by a free-head single pile along the slip surface regardless of whether or not normal stress, \(\mathrm{N_{d}}\), is considered in combination with shear strength.  
The ratio, \(\mu\), between the two previous strengths, indicates the gain in strength offered by the pile along the slip surface if the axial load is taken into account:  
\[\mu=\frac{S_{d,N}}{S_{d}}=1+\frac{N_{d}}{V_{ult,d}}tan\beta \tag{6}\]  
For example, the pile of Model 1 of Figure 7a shows an axial load N\({}_{\text{d}}\) = 1900 kN when the design yield moment, M\({}_{\text{y,d}}\), is reached and the shear stress approaches V\({}_{\text{ult,d}}\) = 1770 kN. This axial load can be combined with the design shear strength to give a strength ratio of \(\mu\) = 1.19 (Equation (6)), that is a gain in strength of about 19%. For this combination of forces, Figure 14 shows the variation in the \(\mu\) with the slope inclination variable just around the design value \(\beta\) = 10\({}^{\circ}\).
# Research on Lateral Deformation Control Criteria of Metro Shield Tunnels with Excessive Ellipticity
###### Abstract  
In recent years, excessive lateral deformation of subway shield tunnels has been observed due to adjacent engineering activities. This study examines the monitoring of excessive lateral deformation of the shield tunnel and the special steel plate reinforcement process to enhance the safety and stability of the operating subway tunnel structure. It uses a three-dimensional refined finite-element model of the shield tunnel for parametric structural loading simulation analysis to propose a structural deformation limit value suitable for the subway shield tunnel. This study's findings indicated the following: (1) as observed from the engineering examples, a tunnel with significant elliptical deformation increases the likelihood of cracking and other structural issues in the adjacent subway shield tunnel segment; (2) as observed from the post-reinforcement monitoring data, the steel plate reinforcement method effectively enhances the load-bearing stability of the damaged tunnel structure; (3) based on the finite-element simulation results and the comprehensive review of practical conditions, the standard warning value for lateral deformation, using ellipticity evaluation of the subway shield tunnel, is established at 20%, with a control value of 25%. The outcomes of this research offer valuable insights into the operation, maintenance, and health monitoring of subway shield tunnels.  
shield tunnel; ellipticity; numerical simulation; deformation control criteria +
Footnote †: journal: Applied sciences