esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Evaluation of NIMBY Phenomenon with Fuzzy Analytic Hierarchy Process and Radar Chart
## 2 Risk Evaluation System of NIMBY Phenomenon
### Evaluation Index System  
The most important step of the risk model lies in the selection of evaluation indexes whose accuracy and comprehensiveness directly affect the reliability and scientificity of the evaluation results. By analyzing, extracting and summarizing the cases of transmission and transformation projects with the NIMBY phenomenon in Zhejiang Province, Chongqing Municipality, and Xi'an City, and following the laws on transmission and transformation projects formulated by China, the risk model of the NIMBY phenomenon is constructed as shown in Figure 2. Meanwhile, the risk-evaluation indexes include the power-grid corporation, the local residents, the ambiguity of relevant scientific standards and changes in national laws.  
The risk model of the NIMBY phenomenon consists of six units and thirty-one elements, as shown Figure 2. In more detail, the six units identify the main risk of the NIMBY phenomenon, and thirty-one elements point to specific influences. The subject and impact aspects of each unit are listed below [40; 41; 42]. In this figure, U1 to U6 are the abbreviations of the units. A1 to A5, B1 to B6, C1 to C7, D1 to D6, E1 to E5 and F1 to F6 are the abbreviations of the elements.  
U1: Anti-risk ability of the citizen. The units focus on the resident's family situation, family income, children's status, parental status and residential property, and these factors reflect the acceptability of financial compensation to residents.  
Figure 2: Risk model of NIMBY phenomenon.  
U2: Residents' understanding of transmission and transformation projects and whether they accept the construction of transmission and transformation projects.  
U3: The impact of transmission and transformation projects on the lives and livelihoods of neighboring residents.  
U4: Whether the location of transmission and transformation projects can reduce the probability of the NIMBY phenomenon.  
U5: Whether the power-grid corporation's promotion and communication are in place to facilitate public acceptance of the construction of transmission and transformation projects.  
U6: Measuring the acceptance of local residents and the power-grid corporation in economic terms.  
The above units and elements are not isolated from each other, but are closely related. In the specific practice process, the elements can be adjusted appropriately according to the actual transmission and transformation project. The thirty-one elements in these units can be used as the risk-evaluation indexes of the NIMBY phenomenon in transmission and transformation projects.
###### Abstract  
The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.  
adaptive network-based fuzzy inference system; stone matrix asphalt; genetic algorithm; particle swarm optimization; support vector machine
## 1 Introduction
(**b**) Application of phase space representations (PSR) to digital holograms. Aided by this PSR, we discuss a variety of different applications in DH--some of which may only be most easily addressed with visual interpretations. By doing so, we hope to raise awareness of the reader to this valuable tool in the context of DH. Furthermore, we aim to provide a concise collection of all relevant details required to perform this analysis. After a short literature review, we will motivate the use of phase space analysis for DH in general in Section 2. Section 3 will then introduce visually interpretable PSRs and outline the S-method. In Section 4, we utilize PSRs to discuss the interference of object and reference wavefields. The main part of our paper will discuss different applications of PSRs to DH in Section 5. We will briefly explain in Section 5.1 what one can learn through phase space analysis about a given macroscopic DH, for which no knowledge on its scene composition is available. Thereafter, we elaborate more on details concerning the space-bandwidth product of DHs in Section 5.2 and on scene depth estimation in Section 5.3. In Section 5.4 the S-method is demonstrated in the analysis of DH processing artifacts. The effects of common operations applied to DHs are provided in Section 5.5. In Section 5.6, we show how the degree of surface roughness influences the PSR footprint of a DH. We close with an outlook to three advanced applications where visually interpretable PSRs aid conceptual understanding. Namely, we touch on multi-object holographic scene segmentation (Section 5.7), perceptual quality assessment of DH (Section 5.8), and provide some thoughts on design and understanding limitations of DH setups (Section 5.9). We conclude our work in Section 6. This paper is predominantly written as a tutorial for researchers that are unfamiliar with thinking about DH in terms of phase space analysis. Any expert reader, interested only in the novelties, might skip ahead and read about the S-method, newly proposed for DH analysis, in Sections 3.3-3.5, as well as about advantageous applications thereof in Sections 5.3-5.9.
# Study on the Ground Pressure Manifestation Patterns of Roof Cutting and Pressure Relief
## 4 Ground Pressure Monitoring Data Analysis
### Cable Bol Load Analysis  
The load-monitoring data of roof cable bolts in the retained roadway section were collected, with the average load curve plotted in Figure 9. Monitoring results indicate that cable bolt stress begins to significantly increase approximately 30 m ahead of the face due to front abutment pressure. The stress stabilizes at about 185 m behind the face, indicating that the rock surrounding the roadway reaches relative stability.  
Load analysis reveals that intense deformation occurs within 0-70 m behind the face (caving zone influence area). High-strength cable bolts in this zone provide active support, effectively controlling roof deformation and ensuring stability.  
Figure 8: Characteristics of surrounding rock deformation vs. distance from working face in retained roadway.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Monitoring Station Name**} & **Road–Floor** & **Floor Heave** & **Rib-to-Rib** \\  & **Convergence** & **(mm)** & **Convergence** \\  & **(mm)** & **(mm)** & **(mm)** \\ \hline Advance & 640 & 460 & 200 \\ monitoring section & 580 & 390 & 70 \\ Central monitoring section & 600 & 480 & 240 \\ Average & 607 & 443 & 170 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surrounding rock deformation data at monitoring stations in retained roadway.
## 2 Baseline Algorithm, SAR-SIFT: A SIFT-like Algorithm for SAR Images
### Feature Descriptor Construction  
Feature descriptor construction involves computing feature descriptors for each orientation. SAR-SIFT diverges from the original SIFT feature descriptor, which employs square neighborhoods and \(4\times 4\) square sectors, by utilizing circular regions (radius is \(12\sigma\)) and logarithmic polar sectors. Similarly, the feature descriptor is constructed based on the computed gradient direction. The generated feature descriptor is referred to as the ratio feature descriptor. This feature descriptor is constructed in the same manner as the circular SIFT feature descriptor by connecting the number of orientation histograms corresponding to the logarithmic polar sector. The only difference lies in the use of GR instead of differential gradients. The GR method for gradient computation can be directly applied to other spatial configurations of sectors, such as the spatial configuration of the original SIFT.
# Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality Assurance
## 2 Basic System Architecture
The _workflow recognition_ extracts the 3D body pose of the current worker from the video feed and the 3D point cloud of the depth camera. The worker's current activity is estimated using the 3D body pose. With constant observation and a graph-based model of the assembly process, this component is able to tell at any time which step of the process the worker is currently doing and what the next steps need to be. It recognizes if the worker diverges from the optimal path, and it can recognize points of interaction with the workpiece. The component is able to differentiate critical steps that need to be documented from normal steps using the assembly model. When recognizing a critical step, the video and critical data of that assembly step is sent to the storage. The _documentation & video storage_ consists of a file storage for the raw video data of the assembly steps and a database for the recordings and corresponding metadata. The database associates the assembled part with the video data and the worker. The _authentication_ component tells the documentation storage which user is logged into the system so that it can associate the current recordings to the currently active worker as well as the unique workpiece ID. Workers can authenticate by holding their personal smartcard near a reader mounted next to the assembly station. The authentication  
Figure 1: A collaborative manual assembly station. is performed using certificates retrieved from an external user directory service accessed over the industry standard Lightweight Directory Access Protocol (LDAP). Besides classical smartcards, a smartphone can also serve as an authentication token, if it supports near-field communication (NFC). Using a smartphone for this purpose gives workers access to more advanced security features offered by the documentation system, which we will cover in the following sections.
## 7 Discussion
### Efficiency Table Analysis
Finally, DBP removal was not quantified, since ozonation is also more likely to produce these compounds than to remove them. Next, non-thermal plasma was analysed. It displayed good oxidative abilities with a 98% removal of phenol in 50 s. While this may seem on par with chlorination (which also required one minute), the plasma only required electricity, whereas the fast chlorination process required large amounts of chlorine and a dechlorination stage. Relative to ozonation, plasma was faster at phenol degradation. Plasma's potential was highlighted by the removal of carbamazepine (a persistent organic pollutant (POP)) because it was faster and achieved greater degradation than both ozone and chlorine. A similar trend applies to the removal of PFOS. Next, plasma's removal of oestrone was sufficient (83%), but not as effective or fast as ozone. For metal oxidation, the literature data were insufficient due to the relative immaturity of plasma technology. Plasma's pathogen degradation proves that it is equally as effective as ozone and chlorine. It degraded _Cryptosporidium_ significantly faster than chlorine, but is probably not as quick as ozone. Another interesting observation is that there is balance between chlorine and plasma: chlorine is more effective and faster at _E. coli_ removal, but plasma is more effective at norovirus removal. Lastly, plasma only removes the DBPs of chlorination, and not for ozonation. Furthermore, it removes this DBP (chloroform) to a large extent (80 %). Considering the above, it is clear that if the objective is to provide a community with water that is free of endocrine-disrupting chemicals (EDCs), no single technology is sufficient. Combining ozone with plasma will be effective, since both are powerful oxidants. However, plasma would be unable to remove the DBPs of ozonation and will likely increase the bromate concentration. The most feasible option would be to combine chlorination with plasma. Both are powerful oxidants, with plasma offering good EDC removal where chlorine fails, and with chlorine offering good inorganic oxidation where data for plasma are unknown. Furthermore, chlorination offers more effective and faster _E. coli_ reduction, while plasma offers more effective norovirus and _Cryptosporidium_ reduction. To further reinforce this pairing, plasma is able to remove the DBPs associated with chlorination. However, other factors are yet to be considered, including costs, chemical demand and energy efficiency.
# Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs
## 2 Related Work
Then, the scoring mechanism is used to evaluate each road according to the collected information. Finally, a routing path is determined by the data route construction mechanism based on the scoring results. Table 1 shows the characteristics of some routing protocols. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Routing Protocol** & **Intersection Based** & **Multi-Factor Decision** & **Main Forwarding Strategy** & **Recovery Strategy** & **Advantage** & **Drawback** \\ \hline GPSR & No & No & Greedy & The right hand role & Easy and cost least & Can only be used in city scenarios & Can only be scenarios & Can only be scenarios \\ GPCR & Yes & No & Restricted Greedy & The right hand role & Easy and suitable for city & Anchor node redundancy at intersections \\ GSR & No & No & Greedy & Carry-and-forward & Sensitive to dynamic topology changes & High overhead and easy to lost packets \\ EGSR & No & No & Greedy & Carry-and-forward & No additional hardware & High routing overhead \\ STAR & Yes & No & Traffic lights & Carry-and-forward & More applicable for city & High cost in \\ GTLQR & Yes & Yes & Traffic lights and queue aware & Carry-and-forward & Suitable for urban high-density vehicle & High computing overhead \\ iCAR-II & Yes & Yes & Integrate VANET, cellular network, and location centers & Reschedules the transmission & Provide an efficient and dynamic data routing & Highly dependent on infrastructure \\ TAROC & Yes & Yes & Construct a data delivery path & Carry-and-forward & Safety and efficiency & Spend time on data collection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related routing protocols.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 5 Future Research Issues
### Multimedia Communication Routing  
UAVs can help people accomplish tasks in the air by using radio-controlled devices and their own programmed control equipments. Nowadays, UAVs have been widely used in aerial photography, agriculture, express delivery, and other fields. However, with the improvement of people's requirements, simple image information can no longer meet the task requirements. Real-time transmission of video and audio information has become a major application trend. In order to meet the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ISSUES** & **RESARCH PROBLEM** & **RESARCH DICTION** \\ \hline \multirow{2}{*}{**Multimedia communication routing**} & Multiple video streams need to be & Routing protocol with high transmission rate, \\  & transmitted in FANET. & low delay, and low distortion. \\ \hline \multirow{2}{*}{**Path planning**} & High altitude and rapid flight of UAVs may & Trajectory optimization method. \\  & deviate from the path and hit obstacles. & \\ \hline \multirow{2}{*}{**Dynamic topology control**} & Instability of topology will cause link asymmetry. & Dynamic topology control mechanism. \\ \hline \multirow{2}{*}{**Energy collection of UAVs**} & Limited energy will influence flight time & \multirow{2}{*}{Energy collection mechanism.} \\  & and distance of UAVs. & \\ \hline \multirow{2}{*}{**Security**} & UAVs may suffer hijacking, privacy disclosure, & Nodes’ privacy protection; \\  & or damage by enemy. & Anti-interference routing protocol. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Research issues and directions for FANET.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \begin{tabular}{c} **PAKCET** \\ **DELIVERY** \\ **RATIO** \\ \end{tabular} & \begin{tabular}{c} **SEQUENCE** \\ **LENGTH** \\ \end{tabular} & \begin{tabular}{c} **LINK** \\ **QUALITY** \\ \end{tabular} & \begin{tabular}{c} **GEOGRAPHIC** \\ **LOCATION** \\ \end{tabular} & \begin{tabular}{c} **RESIDUAL** \\ **ENERGY** \\ \end{tabular} & \
\begin{tabular}{c} **BUFFER** \\ **STATE** \\ \end{tabular} &
\begin{tabular}{c} **NEIGHBOUR** \\ **INFORMATION** \\ \end{tabular} \\ \hline
**XLinGO [51]** & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & x & x \\
**CABR [52]** & x & x & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of metrics used in beaconless opportunistic routing protocols.  
high efficiency, low frame loss rate, and low delay of multimedia video information transmission [56] in the network, we need to pay attention to the design of routing protocol to satisfy the user's demand for quality of service.
# Assessment of Strength Development at Hardened Stage on High-Strength Concrete Using NDT
## 3 Results and Discussion
### Discussion on Regression Equation of Concrete Strength Based on NDT
Figure 11: Regression equation of compressive strength of concrete based on rebound value. Consequently, we proposed a regression equation of the compressive strength of concrete based on the ultrasonic pulse velocity for strengths of 40 MPa or higher, as per Equation (10). \[\text{AJJ: F}_{\text{U,\,\,etm}}=(215\text{V}_{\text{p}}-620)/10.2 \tag{6}\]  
\[\text{[PERSON]: F}_{\text{U,\,\,etm}}=(92.5\text{V}_{\text{p}}{}^{2}-508\text{V} _{\text{p}}+782)/10.2 \tag{7}\]  
\[\text{[PERSON]: F}_{\text{U,\,\,etm}}=0.000012\text{-}(1000\text{V}_{\text{p}})^{1.7447} \tag{8}\]  
\[\text{[PERSON]: F}_{\text{U,\,\,etm}}=1.19\text{e}0.715\text{V}_{\text{p}} \tag{9}\]  
Figure 9:0  
where F\({}_{\text{R,\,\,etm}}\) denotes the estimated compressive strength according to the ultrasonic pulse velocity (MPa) and V\({}_{\text{p}}\) the ultrasonic pulse velocity (km/s). The regression equation proposed in this study can ensure reliability in estimating strengths close to the measured strengths for concrete specimens in the strength range of 24-60 MPa. However, the models may have limitations, because our study utilized concrete mixes containing only cement; the model reliability can decline depending on the material properties used and the curing method. Therefore, it is necessary to acquire experimental data considering influence factors, such as aggregate and mineral admixture and the curing condition, to improve the reliability of the proposed compressive strength regression equation based on NDT.
## 3 Results
### Major Variables Related to the FSV Data  
Figure 4 shows the results of the numbers of selected variables using the RF algorithms provided by the VSURF package. First, no variables were eliminated based on the VI mean and VI standard deviations. Based on the OOB error, eight variables were selected: B2, B3, B4, B5, B12, TCW, TCB, and NDVI_B5. The PercentIncMSE (percent increase in the mean square error) and IncNodePurity (increase in the NodePurity) estimated from the RF OOB data were used to rank all predictor variables according to their abilities to estimate the FSV; the higher the value, the more important the variable (Figure 5).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Descriptive Statistics** & **Training Data** &
\begin{tabular}{c} **Transformed** \\ **Training Data** \\ \end{tabular} & **Test Data** & **Transformed Test Data** \\ \hline Mean & 121.11 (m\({}^{3}\) ha\({}^{-1}\)) & 3.98 & 120.53 (m\({}^{3}\) ha\({}^{-1}\)) & 3.95 \\ Median & 103.33 (m\({}^{3}\) ha\({}^{-1}\)) & 4.08 & 98.37 (m\({}^{3}\) ha\({}^{-1}\)) & 4.02 \\ Minimum value & 1.42 (m\({}^{3}\) ha\({}^{-1}\)) & 1.11 & 4.25 (m\({}^{3}\) ha\({}^{-1}\)) & 1.55 \\ Maximum value & 577.49 (m\({}^{3}\) ha\({}^{-1}\)) & 6.87 & 450.11 (m\({}^{3}\) ha\({}^{-1}\)) & 6.37 \\ Variance & 9019.13 & 1.15 & 9053.02 & 1.24 \\ Kurtosis & 2.73 & \(-\)0.23 & 0.22 & \(-\)0.89 \\ Skewness & 1.40 & \(-\)0.18 & 0.89 & \(-\)0.11 \\ Number of sample plots & 321 & 321 & 138 & 138 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary statistics for the FSVs of the training data and test data.  
Figure 3: Training and test data distributions before and after transforming. The green and red histograms represent the distribution before and after data transformation, respectively; the red and green curves represent the distribution before and after data transformation, respectively.  
For the MLR algorithm, 14 out of the 24 variables were eliminated by the \"redun\" function to avoid multicollinearity; these eliminated variables were NDVI_B8, EVIZ, SAVI, NDVI_B7, B8A,  
Figure 4: The variable selection based on the VSURF package. The top graphs illustrate the removal of the negative importance variables’ threshold based on the VI mean (left, the horizontal red solid line represents the threshold position) and VI standard deviation (right, the green piece-wise line represents prediction values given by a CART model and the horizontal red dotted line represents the minimum prediction value), and the bottom graphs are related to the interpretation (left, the vertical red solid line represents the minimum error position) and prediction (right) and show the number of variables selected according to the OOB error.  
Figure 5: Plots of the selected variable importance. (left) %IncMSE, and (right) IncNodePurity.  
TCB, NDVI_B6, B6, B3, B11, TCG, NDVI_B8A, and B4. The remaining variables were reselectedusing the \(P\) significance in linear modeling. Finally, B5 and MSI were selected for the MLR model.