esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
## 2 Materials and Methods
### The Improved Dijkstra Algorithm  
#### 2.1.1 Dijkstra Algorithm Analysis  
The fundamental concept underlying [PERSON]'s algorithm is that of a greedy algorithm. This procedure involves selecting a designated starting point and generating an array that captures the shortest distances from this starting point to all other points. Subsequently, the algorithm iteratively identifies the closest point to the current position, updates the current point to the closest point, and recalculates the distances from the starting point through the current point to all other points. This process continues until the shortest distance data are updated appropriately. The selected points are those that are identified as determining the shortest path and are not included in subsequent calculations. The ultimate outcome is the most concise route from the initial location to each of the remaining nodes.  
The flow of the traditional [PERSON]'s algorithm is as follows:  
1. Define the set sum \(S\) and \(H\), perform initialization, let \(S=\{V_{0}\}\) where \(S\) is the shortest path target node set, \(H\) is the set of nodes excluding \(S\), \(V0\) is the initial node, and \(VP\) is the target node.
2. Select the node \(Vk\) closest to the initial node \(V0\) in set \(H\), so that \[L[V_{m},V_{n}]=min\{L[V_{m},V_{k}]+L[V_{k},V_{n}]\}\] (1) where \(L[V_{m},V_{n}]\) is the distance matrix, and then the distance between any two nodes can be expressed as \[L[V_{m},V_{n}]=l_{mn}(m,n=1,2,3\cdots)\] (2)
3. Update set \(S\) and \(H\) as \[S=S+\{V_{k}\},\;V_{k}\in H\] (3) \[H=H-\{V_{k}\},\;V_{k}\in H\] (4) 4. Correct path length if \[L[V_{m},V_{n}]>L[V_{m},V_{k}]+L[V_{k},V_{n}]\] (5) Then, \(L\) is amended as \[L[V_{m},V_{n}]=L[V_{m},V_{k}]+L[V_{k},V_{n}]\] (6) 5. Judge whether all the target nodes are added to the shortest path set \(S\); if not, jump to step 2; otherwise, output the shortest path node set \(S\).  
The traditional Dijkstra algorithm has the disadvantage of ignoring the time factor and is prone to in-orbit conflicts. To solve this problem, the real-time occupation of the site is introduced into the path weight function of the Dijkstra algorithm.
## 5 Results
### Full Multispectral Band Features  
#### 5.2.1 Classification Performance Evaluation  
It can be seen from Figure 4 that, in addition to the commonly used RGB bands, other bands (e.g., NIR, SWIR) can also provide vital discrimination information for land cover classification and therefore all multispectral band features are also assessed for the three machine learning based models in this subsection. Similar to the case of RGB band features in Section 5.1, the results of Bayesian hyperparameter optimization for full multispectral band features are displayed in Figure 7 including the minimum objectives over time and the estimated objective function values over different combinations of _mls_ and _npts_. The optimized hyperparameters vector shows that \(mls=1\) and \(npts=10\) (default parameters: \(mls=1\), \(npts=6\)). In addition, the out-of-bag error over the number of trees is also displayed in Figure 8. The smaller the out-of-bag error is, the more accurate the classifier will be. It can be seen that the error using Bayesian optimization is smaller than that of the RF with default parameters when the same number of trees is used. Therefore, the RF with Bayesian optimization possesses better performance over the one with default hyperparameters. Under this hyperparameter setting, the confusion matrix of the optimized RF is shown against the ones for SVM and RF with default parameters in Figure 9.  
Figure 8: The out-of-bag error over the number of grown trees using different methods.  
Figure 9: Confusion matrix of the classifiers (**a**) SVM, (**b**) Random forest, (**c**) Random forest with Bayesian optimization) with full multispectral band features.  
The confusion matrices for the three methods are shown in Figure 9. It can be seen that incorporating more band information in the range of NIR and SWIR of the Sentinel-2A/B satellite can significantly improve the land cover classification performance. For example, the classification accuracy change of SVM is 46.1%---93.2%, RF is 87.4%---96.5%, and RF with Bayesian optimization is 87.9%---98.3%. This observation clearly demonstrates that incorporating more related band information can significantly improve the classification performance, as it can be seen from Figure 4 that, in addition to RGB bands, NIR bands and SWIR bands also have a strong discrimination capability. On the other hand, in comparison with SVM and the RF with default parameters, the RF with Bayesian hyperparameter optimization shows the best performance in terms of Precision (user's accuracy), Recall (producer's accuracy), and Overall Accuracy [33]. The comparison between different methods is displayed in Table 4. This again shows the advantages of optimizing the hyperparameters of RF classifiers.  
In addition, the curvature test [34] is capable of evaluating feature scores to reflect their contribution and usefulness in the classification task. The curvature test result for the RF with Bayesian optimization is displayed in Figure 10, where the usefulness of different bands is shown with a high value meaning a higher predictor importance.
## 3 Method Proposal to Analyze AST n-Tools Combinations
### Metrics Calculation
#### 3.4.2 2-Tools and 3-Tools Metrics Calculation  
The rules showed in Table 5 have been considered for computing TP, TN, FP and FN metrics in n-tools combinations. We use the 1-out-of-N (1 ooN) strategy for combining the results of the AST tools. The process proposed to calculate the combined results for two or more tools is based on a set of automated steps. 1 ooN SAST for true positives detection: Any \"TP alarm\" from any of the n-t \begin{table}
\begin{tabular}{c c} \hline
**Tool** & **Elapsed Time** \\ \hline FSB & 22 min \\ Fortify & 19 min and 86 s \\ ZAP & 36 h \\ Arachni & 13 h and 10 min \\ CCE & 2 min and 43 s \\ CxIAST & 4 min and 10 s \\ \hline \end{tabular}
\end{table} 
Table 4: Execution times of each tool against OWASP Benchmark project. \begin{table}
\begin{tabular}{c c c c c} \hline  & **FP** & **FN** & **TN** & **TP** \\ \hline CCE & 0 & 0 & 160 & 160 \\ FSB & 107 & 3 & 53 & 157 \\ CxIAST & 0 & 15 & 160 & 145 \\ Fortify & 92 & 21 & 68 & 139 \\ ZAP & 8 & 118 & 152 & 42 \\ Arachni & 0 & 124 & 160 & 36 \\ \hline \end{tabular}
\end{table} 
Table 2: Metrics applied to individual tools. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Rec** & **FPR** & **Prec** & **F-Mes** & **F\({}_{0.5}\)S** & **F\({}_{1.5}\)S** & **Mark** & **Inf** \\ \hline CCE & 1 & 0 & 1 & 1 & 1 & 1 & 1 \\ FSB & 0.99 & 0.67 & 0.59 & 0.74 & 0.65 & 0.82 & 0.56 & 0.32 \\ CxIAST & 0.91 & 0 & 1 & 0.95 & 0.98 & 0.93 & 0.91 & 0.91 \\ Fortify & 0.87 & 0.58 & 0.6 & 0.71 & 0.64 & 0.76 & 0.37 & 0.29 \\ ZAP & 0.29 & 0.01 & 0.98 & 0.45 & 0.67 & 0.37 & 0.56 & 0.29 \\ Arachni & 0.23 & 0 & 1 & 0.37 & 0.59 & 0.3 & 0.56 & 0.23 \\ \hline \end{tabular}
\end{table} 
Table 3: Metrics applied to individual tools. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Tool A** & **Tool N** & **N-Tools** \\ \hline \multirow{4}{*}{Positives cases (P)} & TP & or & TP & TP \\  & TP & or & FN & TP \\  & FN & and & FN & FN \\ \hline \multirow{4}{*}{Negative cases (N)} & FP & or & FP & FP \\  & FP & or & TN & FP \\ \cline{1-1}  & TN & or & FP & FP \\ \cline{1-1}  & TN & and & TN & TN \\ \hline \end{tabular}
\end{table} 
Table 5: Logic applied to the combination of two tools. detection will lead to a TP alarm in a 1 ooN system. If no tool in a combination detects a TP in a test case, it is a FN result for the 1 ooN system. Additionally, we use the 1-out-of-N (1 ooN) strategy for combining the results of the AST tools. The process proposed to calculate the combined results for two or more tools is based on a set of automated steps. 1 ooN SAST for true positives detection: Any \"FP alarm\" from any of the n-tools in a test case for FP detection will lead to an FP alarm in a 1 ooN system. If no tool in a combination detects a FP in a test case, it is a TN result for the 1 ooN system. Table 6 shows the metrics calculations for 2-tools combinations. In any 2-tools combination can be included tools of the same type. Table 7 shows the metrics calculations for 3-tools combinations. In any 3-tools combination tools of the same type can be included.
# BOPVis: Bridge Monitoring Data Visualization for Operational Performance Mining
###### Abstract  
Bridges are fundamental facilities in the transportation system, and their operational performance is crucial for economic and social development. Many large bridges are now equipped with structural health monitoring (SHM) systems that collect various types of real-time data. However, our user study found that despite the accumulation of massive amounts of monitoring data, current analysis methods cannot efficiently process large-scale, high-dimensional data. To address this, we have developed BOPVis, a visualization system for bridge monitoring data. BOPVis allows users to intuitively locate sensors and extract corresponding data from a 3D digital model of a bridge. It also provides convenient and flexible interactions for examining trends over time and correlations across hundreds of monitoring channels. A real-world long-span suspension bridge in China is used as a case study to demonstrate the advantages of the BOPVis system for operational performance mining. Through BOPVis, the global temperature deformation behaviors of the bridge are explored and found to align with the physical mechanism documented in the SHM literature. The BOPVis system, with its interactive visualization analysis capabilities, offers a new method for analyzing bridge monitoring data.  
bridge monitoring; data visualization; operational performance; data mining 2024 2 In this paper, a bridge monitoring data visualization system for operational performance mining, called \"BOPVis\", is proposed. This system intuitively locates sensors and extracts corresponding data from a 3D digital model of the bridge. It allows users to easily view trends and correlations over time across hundreds of monitoring channels through flexible interactions. By incorporating human-computer interaction technologies, BOPVis integrates bridge engineering expertise into the data mining process. This enables engineers to analyze data more intuitively and efficiently, and to better understand the bridge's behavior during operation [9]. The advantages of BOPVis are demonstrated using a real-world example of a suspension bridge in China. The main contributions include:  
* A complete interactive visualization system called BOPVis is developed based on user studies, which greatly improves the efficiency of bridge engineers in analyzing bridge monitoring data.
* A real-world long-span suspension bridge in China is used as a case study to demonstrate the advantages of the BOPVis system for operational performance mining. Using BOPVis, we explore the global deformation patterns and dominant factors, which conform to the physical mechanisms of suspension bridges documented in the SHM literature.  
The rest of the paper is organized as follows. Section 2 reviews related work while Section 3 covers the user studies. Section 4 describes the bridge monitoring data visualization system. Section 5 presents two case studies illustrating the operational performance mining results using BOPVis. Finally, Section 6 concludes the paper.
## 3 Proposed Method
### KNN Algorithms  
KNN is a simple algorithm that stores all the received data and classifies them based on their similarity. New input is classified by comparing the similarity (or distance) of the new input to all the neighboring data. The KNN can use various methods to calculate the similarity, two of which are described below.  
1. Euclidean distance  
The length of the line between two points is considered the distance. It is calculated by the following equation.  
\[\sqrt{\sum_{i=1}^{k}(x_{i}-y_{i})^{2}}\]  
where \(x\) is the new input and \(y\) is the neighboring point.  
A plethora of algorithms exists for classifying and analyzing data. Among them, the KNN algorithm stands out for its simplicity and efficiency. Being a lazy algorithm, KNN requires no preprocessing, making it particularly suitable for implementation on fog layer nodes. Hence, we opted for KNN as the data mining algorithm for our proposed method.  
2. Cosine similarity  
Cosine similarity measures the similarity between two non-zero vectors by calculating the cosine of the angle formed between them. The equation below illustrates this calculation.  
\[similarity=cos(\theta)=\frac{A\cdot B}{\parallel A\parallel\parallel B\parallel} =\frac{\sum_{i=1}^{n}A_{i}B_{i}}{\sqrt{\sum_{i=1}^{n}A_{i}^{2}}\sqrt{\sum_{i=1 }^{n}B_{i}^{2}}}\]  
where \(A\) is the new input and \(B\) is the neighboring point.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**1** & **Time** & **Code** & **Blood Glocose** \\ \hline
2 & 9:09 & 58 & 100 \\ \hline
3 & 17:08 & 62 & 119 \\ \hline
4 & 22:51 & 48 & 123 \\ \hline
5 & 7:35 & 58 & 216 \\ \hline
6 & 16:56 & 62 & 211 \\ \hline
7 & 7:25 & 58 & 257 \\ \hline
8 & 17:25 & 62 & 129 \\ \hline
9 & 7:52 & 58 & 239 \\ \hline
10 & 17:10 & 62 & 129 \\ \hline
11 & 22:09 & 48 & 340 \\ \hline
12 & 7:29 & 58 An important consideration in utilizing KNN algorithm is selecting an appropriate value for K, representing the number of nearest neighbors to consider. Two common approaches for this are arbitrary selection and cross-validation, which determines the optimal. In our proposed method, cross-validation was employed to ascertain an optimal.
# Grid-Connected PV Systems Controlled by Sliding via Wireless Communication
## 2 The Grid-Connected PV System Configuration
### The DC/DC Converter  
Regarding the DC/DC converter, the buck-boost configuration [17; 19], has been chosen, instead of the boost, which is the most used in the technical literature. The main reason is that the buck-boost allows the decreasing of the voltage, as well as the increasing. Thus, they can be used in applications with loads which works at low voltages, as for example those related to communications. The configuration of a buck-boost converter is presented in Figure 3, where \(v_{PV}\) is the PV output voltage and the voltage at the input of the converter, in V, \(i_{PV}\), in A, is the solar array output current, \(i_{L}\), in A, is the inductor current and \(v_{0}\), in V, is the buck-boost converter output voltage, which is the same as the voltage at the input of the DC/AC converter. The DC/DC converter output current is \(i_{0}\), in A. \(L\) is an inductor with a value of 20 mH, \(C1\) is the filtering capacitor with a value of 1000 uF and \(C\) is the output capacitor which has a value of 5700 uF. The used diode is a MBR10200 and the MOSFET is a CSD19536 KCS device driven by an FOD3180 driver. The values of all these elements are constants.  
Figure 3: Buck-boost converter connected between PV array and the load.  
Figure 2: I-V and P-V characteristic curves of a PV module.  
The switch ON/OFF commutation generates the duty cycle \(D=t_{ON}/t_{C}\), being \(t_{ON}\) the time ON (\(u=1\)) and \(t_{C}\) the switching period (\(0<D<1\)). The voltage output/input ratio is as expressed in Equation (1).  
\[\frac{v_{O}}{v_{PV}}=\frac{D}{1-D} \tag{1}\]  
The transistor commutation is established by means of a pulse width modulation (PWM) method. The input and output voltages present opposite polarity. The input and output voltages corresponding to different \(D\) values are presented in Figure 4.
## 3 Results
### Time-Frequency Analyses
#### 3.2.1 Alpha Effects: Across Participants  
Average alpha activity waveforms are presented in Figure 4; illustrative FFT plots are shown in Figure 5. The alpha attenuation effect was significant in the slow calibration for both signal sources (\(p\) values \(\leq\) 0.003). Among the experimental conditions, however, the alpha attenuation effect never quite reached statistical significance. At most, the difference between target (\(Mdn\) = 0.190) and non-target (\(Mdn\) = 0.230) alpha activity at Pz in the small jitter condition was trending toward significance, \(Z\) = \(-\)1.857, \(p\) = 0.063. The same trend was also evident between target (\(Mdn\) = 0.029) and non-target (\(Mdn\) = 0.187) stimuli at Pz in the large condition, \(Z\) = \(-\)1.771, \(p\) = 0.076. Friedman tests revealed no significant changes in the alpha attenuation distributions among the no jitter (\(Mdn\) = \(-\)0.042), small (\(Mdn\) = \(-\)0.120), or large jitter (\(Mdn\) = \(-\)0.130) conditions at Pz, \(\chi^{2}(2)\) = 0.750, \(p\) = 0.687, and no changes in alpha attenuation distributions across the no jitter (\(Mdn\) = \(-\)0.051), small (\(Mdn\) = \(-\)0.085), or large jitter (\(Mdn\) = \(-\)0.100) conditions for the pooled signal, \(\chi^{2}(2)\) = 0.333, \(p\) = 0.846.  
Figure 4: Alpha attenuation responses, averaged across all participants and separated by experimental condition and signal source. Highlighted windows correspond to the baseline (\(-600\) to \(-100\) ms) and effect (\(300\) to \(800\) ms) windows used to z-score the time series data and quantify alpha attenuation, respectively; variance shading illustrates standard error. The first three columns illustrate the experimental test conditions, while the final column offers an example of non-overlapping attenuation responses for visual comparison. A pronounced alpha attenuation effect is visible in the non-overlapping column, but either greatly reduced or completely absent from the experimental test conditions.  
Figure 5: FFT plots of real voltage amplitude (y-axis) over frequency (x-axis) in the pooled signal. Panel (**A**) shows spectral activity in the slow calibration (solid) compared to output from the no jitter calibration (dashed line). The SSVEP signature is clearly visible over 5 Hz in the no jitter signal. As well, the 1 st harmonic of the SSVEP is visible at 10 Hz, overlapping with alpha activity. (**B**) Comparing the same no jitter condition as in panel (**A**) (bold) to large jitter (dashed) shows that the SSVEP and associated harmonic are greatly reduced. Indeed, the FFT average for the large jitter condition in panel (**B**) looks very similar to that of the slow calibration in panel (**A**).
###### Abstract  
Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models.  
unmanned aerial vehicle (UAV); path loss (PL); agricultural scenario; artificial neural network (ANN); ray tracing (RT); channel measurement +
Footnote †: journal: Advances in Neural Networks
## 6 Characterization, Identification, and Purification of MAAs
The water fraction, called the crude MAA fraction, was identified through LC/MS. Hartmann et al. [118] also used a Dowex column (50 WX H\({}^{+}\) form, 100-200 um mesh) to obtain a MAA-enriched fraction, as a pre-purification step. The purification was realized by semi-preparative HPLC with a Lichrosorb C18 100 A column (7 um, 200 \(\times\) 10 mm) and a mobile phase containing 0.1% acid acetic in water (A) and acetonitrile (B). Boulhoho et al. [136] also used CPC for fractioning of UV-B absorbing molecules. More recently, Orfanoudaki et al. [122] described the purification of 11 MAAs, using a combination of silica gel column chromatography, flash chromatography, semi-preparative HPLC on diverse stationary phases, and fast centrifugal partition chromatography (FCPC). Geraldes et al. [124] purified porphya-334, shinorine and mycoposrine-glycine-alanine using a semi-preparative HPLC with a Luna C18 column (5 um, 250 \(\times\) 10 mm) and a fraction collector. As mobile phase the authors used a gradient of: (A) 0.2% (\(v/v\)) formic acid solution + ammonium hydroxide (pH 3.15); (B) Milli-Q water/acetonitrile/methanol (8:1:1) + formic acid (pH 2.20); and (C) acetonitrile/methanol (1:1) + 0.1% (\(v/v\)) formic acid (phase C). The MAAs were quantified and the method validated by LC-MS/MS. Semi-preparative and preparative HPLC are the methods associated with scaling up the purification of small molecules. With bigger columns and a higher flow than analytic HPLC, it is possible to obtain a higher amount of these molecules with a high degree of purity. However, the cost of the equipment still limits its widespread use [137]. Alternatively, liquid-liquid chromatography (such CPC) is a very attractive method used for separation of different natural molecules, due to its capacity to be scaled up. This technique presents various advantages in comparison with HPLC, including: (1) no use of columns with solid phase; (2) high loading capacity; (3) total recovery of the injected sample; (4) low risk of sample denaturalization; (5) low solvent consumption [138; 139].
# Influence of Silane-Modified Coal Gangue Ceramsite on Properties of Ultra-High-Performance Concrete
## 2 Materials and Methods
### Hydrophobic Modification of Aggregate  
The silane coupling agent (SCA) KH-560 (\(\upgamma\)-propyl trimethoxysilane) used in this experiment was purchased from Shanghai Yuanye Biotechnology Co., Ltd. in Shanghai, China. KH-560 is a colorless transparent silane, which contains epoxy functional groups and is soluble in organic solvents (such as acetone, benzene, and ethylene) and water. In order to promote the dispersion of the SCA on the surface of the CGC, it is necessary to carry out hydrolysis in the solution of water and alcohol (ethanol, isopropanol, etc.) in  
Figure 3: Particle size distribution of raw materials.  
advance. If the amount of SCA is too small, the linking effect with the aggregate surface is poor, which affects the hydrophobic effect of the surface; however, when the amount of SCA is too large, the thick coating may affect the bond strength between the aggregate and cementitious materials. Therefore, when preparing the SCA solution, the mass fraction of KH-560 (2.5 wt% and 5 wt%) is taken as the control variable, and the appropriate dosage of KH-560 is determined according to the test numbers KH0, KH2.5, and KH5.  
Firstly, water and alcohol were mixed and stirred according to the mass ratio of 4:6 [22], and a certain amount of acetic acid was added to adjust the PH of the solution to 4, which is conducive to a better hydrolysis of SCA in an acidic environment, and then KH-560 with a certain mass ratio was added. The prepared solution was placed on a magnetic stirrer and continuously stirred for 20 min to achieve hydrolysis. After hydrolysis, CGC was added and the mixture was left to soak for 24 h. The soaked CGC was then dried so that the hydrophobic modified aggregate needed for the experiment was obtained. Then the hydrophobic effect of different concentrations of SCA was tested using a contact angle test, and the changes of surface morphology and functional groups of modified aggregate were observed through infrared analysis and by scanning electron microscope, so as to confirm the required mass ratio of KH-560.