esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Driven Leakage Detection and Classification of a Boiler Tube
## 1 Introduction
For this reason, a preprocessing setup is required that can extract features from the recorded AE signals. The extracted features act as descriptors of the health state of the boiler tubes. The better the descriptors are, the higher quality the classification results will be. Therefore, selection of an appropriate signal processing technique for feature extraction is very important. In this regard, wavelet packet decomposition of AE signals was carried out in the current study. The relative energy and the entropy of the subbands are used as the descriptors to describe the health state of the boiler tubes. The features extracted are classified into these classes using a deep neural network (DNN). A DNN is a variation of an artificial neural network with more than one hidden layer. Due to its deep architecture, a DNN can obtain high accuracy with satisfactory classification performance. The remainder of this research article is as follows. Section 2 describes the boiler tube leakage detection model developed. In Section 3, the data acquisition and experimental setup are explained. In Section 4, the results of the current study are presented in detail. The conclusions are given in Section 5.
# Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm
## 2 Materials and Methods
### Grid Modeling  
Based on the driving characteristics of DDMR that can be turned in place, the grid method is used to establish the environment map. The work space is projected as a two-dimensional plane and discretized into a square grid of uniform, continuous, and non-intersecting. According to the environmental information, the grids with obstacle projection  
Figure 1: Analysis of DDMR’s turning motion.  
can be set as obstacle grids, represented by 1; the grids without obstacle projection can be set as free grids, represented by 0. A Cartesian coordinate system is established in the grid map, with the lower left corner as the origin of coordinates, the horizontal axis for the \(X\) axis, with values increasing from left to right. The vertical axis for the \(Y\) axis, with values increasing from bottom to top. The position of each grid is represented by the coordinate \(p(x_{i},y_{j})\) in the upper right corner of the grid, where \(i,j=1,2,3,\ldots,n\).  
The environmental information in the grid maps will be different from the actual maps, which mainly depends on the size of the grids. If the grids are too large, the resolution of the environment maps will be reduced, which may make the planned paths deviate from the theoretical optimal paths seriously, or even fail to search for feasible paths. If the grids size are too small, the resolution of the environment maps will be too high, which will occupy a lot of computational resources. In order to simplify the planning problem, DDMR is regarded as a square with side length \(l_{edge}=1\), and the side length \(l\) of the grid is also set as 1. In order to improve the safety of the path, DDMR should keep away from obstacles during the driving process. Therefore, the obstacles are puffed up as shown in Figure 2, the grids with only partial obstacles are also set as obstacle grids.
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
## 5 Algorithm Verification and Simulation Results
### Comparison of Double Outlier Combinations with a Fixed Bias  
The status of the visible satellites at the beginning of the simulation is shown in Figure 8. BDS satellites are numbered from PRN 1 to 30 and GPS satellites are numbered from PRN 41 to 71.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Error** & **Function** & **Parameter** \\ \hline
\begin{tabular}{c} \(\text{Tropospheric}\) \\ \(\text{delay}\) \\ \end{tabular} & \(\sigma_{\text{tropo},i}=0.12\cdot 1.001/\sqrt{0.002001+\sin\left(\theta_{i} \right)^{2}}\) & \(\theta_{i}\) is the elevation angle \\ \(\text{User error}\) & \(\sigma_{\text{user},i}=\sqrt{\sigma_{MP}^{2}+\sigma_{noise}^{2}}\sqrt{f_{1}^{ 4}+f_{2}^{4}}/(f_{1}^{2}-f_{2}^{2})\) & \(f_{L1}=1575.42\) MHz, \(f_{L2}=1227.60\) MHz \\ Multiple path & \(\sigma_{\text{MP}}=0.13+0.53\exp(-18\theta_{i}/\pi)\) & For BDS: \\ Noise & \(\sigma_{noise}=0.15+0.43\exp(-180\theta_{i}/6.9/\pi)\) & \(f_{B1}=1561.098\) MHz, \(f_{B2}=1207.14\) MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error model.  
Figure 7: (a) Comparison of the position dilution of precision (PDOP); (b) reduction of subsets after subset selection.  
At the initial epoch of simulation, there were 19 visible satellites and 171 double outlier combinations were obtained. Bias was fixed to 10 m and the positioning errors of the four RB RAIM detectors after outlier elimination are shown in Figure 9.  
Compared with detector 1, detector 2 produced a better detection effect for individual but not all combinations of double outliers as shown in Figure 9a,b. This showed that the M estimator could be  
Figure 8: The status of satellites at the beginning of simulation.  
Figure 9: Positioning errors of four detectors for all combinations in Case 1: (**a**) detector 1, (**b**) detector 2, (**c**) detector 3 and (**d**) detector 4.  
robust but without a high breakdown point. Figure 9c,d showed that there was no obvious difference between the positioning errors of detector 3 and detector 4. The numerical results extracted from the detectors (Table 3) included the RMSE of the positioning error, maximum and 95% confidence level errors and the simulation time.  
The simulation time of detector 4 reduced by 80.71% compared with detector 3 and there was no significant difference in the accuracy of the final parameter estimation with obvious advantages compared with detectors 1 and 2.
## 2 Materials and Methods
### Isolation and Characterization of Compounds  
EtOAc leaf extract from the voucher specimen of _P. maurtianum_ (3.3 g) was used to carry out a compound isolation. Solid phase extractions were carried out on 10 g reversed phase C18-E, 55 \(\upmu\)m, 70 A Strata cartridges (Phenomenex, Torrance, CA, USA). The dark-green extract (3.3 g) was subjected to three successive reverse phase SPE (1.1 g per cartridge) to yield eight fractions (F1-F8) of decreasing polarity: F1 (ACN/water, 3:7, 290.4 mg), F2 (ACN/water 1:1, 101.8 mg), F3 (ACN/water 65:35, 83.2 mg), F4 (ACN/water 75:25, 85.8 mg), F5 (ACN/water 85:15, 99.9 mg), F6 (ACN, 183.0 mg), F7 (MeOH, 302.3 mg), and F8 (DCM, 393.3 mg). Each fraction was evaporated. Preparative HPLC separations were performed using a 150 \(\times\) 21.2 mm, 5 \(\upmu\)m reversed phase C18 preparative column (Gemini, Phenomenex) on an Autopurif instrument (Waters, Milford, CT, USA) equipped with a PDA 2998 detector (Waters, Milford, CT, USA) coupled to a 2424 evaporative light scattering detector (Waters, Milford, CT, USA) piloted by MassLynx 4.2 software.  
F2 was dissolved (ACN/water, 3:7, 10 mg/mL) and purified by repetitive preparative HPLC. 500 \(\upmu\)L per injection, gradient of ACN/water, 2:8 to 7:3 over 26.31 min, followed by 5 min of isocratic elution (ACN). A constant flow rate was set at 20 mL/min. F3 was dissolved (ACN/water, 65:35, 10 mg/mL) and purified by repetitive preparative HPLC. 500 \(\upmu\)L per injection, gradient of ACN/water, 1:1 to 8:2 over 23.43 min, followed by 6.57 min of isocratic elution (ACN). A constant flow rate was set at 20 mL/min. F5 was dissolved (ACN/water 85:15, 10 mg/mL) and purified by repetitive preparative HPLC. 500 \(\upmu\)L per injection, gradient of ACN/water, 3:1 to 1:0 over 20.15 min, followed by 5 min of isocratic elution (ACN). A constant flow rate was set at 20 mL/min.  
Fifteen compounds were retrieved in this process. They were characterized by extensive NMR and HRMS/MS. NMR spectra were performed on a 600 MHz Bruker AvanceTM II+ spectrometer equipped with a TCI cryoprobe at 300 K. Compounds were solubilised in 80 uL of MeOD, introduced in a 2 mm o.d. Match NMR tube and centrifuged prior to analysis. Proton (\({}^{1}\)H) and carbon (\({}^{13}\)C) signals from the deuterated solvent (i.e., 49.00 ppm for \({}^{13}\)C and 3.31 ppm for \({}^{1}\)H for MeOD) were used as references. Optical rotation was measured on an MCP 200 [PERSON] modular circular polarimeter at 25 \({}^{\circ}\)C with a 589 nm wavelength in a 10 \(\times\) 5 mm, i.e., 0.2 mL sample cell (Na D-line). HRMS spectra were acquired on a SYNAPT G2 HDMS (Waters, Guyancourt, France) equipped with an API oven and a TOF analyser. See supplementary information for NMR data of isolated known compounds. The characterization of one new compound will be further detailed in the results.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 5 Result Analysis  
### Domestic Cargo  
The forecasting results and comparisons with other models for domestic cargo flow are presented in Table 1 and Figure 5. From the results, it can be concluded that:  
1. The performance of the EMD-SARIMA model is superiorto that of the SARIMA model, the Holt-Winters model, and the naive model, especially for the local maximum and minimum values;
2. The performance of the Holt-Winters model is the second best, and the performance is close to the SARIMA model; this is consistent with previous research, which claimed that the Holt-Winters model and the SARIMA model are comparable based on situations [41];
3. The hybrid model improves the forecasting accuracy of the traditional SARIMA model; the MAPEs of the EMD-SARIMA model for the six-month and twelve-month forecasting horizons are 1.42% and 4.64%, respectively, compared with the MAPEs of the SARIMA model for the six-month and twelve-month forecasting horizons, which are 4.62% and 9.94%, respectively;
4. By comparing with other models, the EMD-SARIMA model forecasts with better accuracy consistently regardless of whether it is single- or multi-step forecasting.
# An Algorithm for Finding Optimal \(k\)-Core in Attribute Networks
## 4 Experiments and Results
### Parameter Sensitivity  
This section evaluates the impact of query vertices and query attributes on algorithm efficiency and effectiveness through experiments. As mentioned earlier, after the forest construction is complete, the Advanced query algorithm only includes two stages: LCA (Algorithm 2) and Expand (Algorithm 3). This section will illustrate how parameters Q and S affect the efficiency of the Advanced algorithm and the quality of the target community.  
As shown in Figure 6, the larger the query attribute set, the longer it takes to calculate the score, as there are more primary keys to calculate. The attribute density of a dataset also affects query time. In Flickr, \(\overline{A(v)}=24.5\), while in F1684, \(\overline{A(v)}=7.85\), which leads to a significant difference in the number of representative attributes of the ground-truth  
Figure 5: Community quality on different datasets.  
community. The higher the attribute density of the dataset, the more representative it contains, and the longer the average query time. The more representative attributes a query attribute set contains, the more accurately the community can be queried.  
Figure 7a shows that the more query vertices there are, the more related tree nodes can be located, and the distribution may be broader as well. More importantly, the depth of their common ancestors may be shallower, which to some extent reduces the query time. Nevertheless, the core number of vertices in the dataset has a significant impact on query time. For example, despite its smaller size, F1912 has a much higher \(k_{max}\) and average degree than Twitter, resulting in more query iterations and longer query times. Too many query vertices can lead to excessive dispersion of related tree nodes, large community size, and mixing with low-quality vertices. Especially in loosely structured networks, such as F1684 in Figure 7b, LCA is too close to the tree root, resulting in a loss of community quality.  
Overall, the algorithm proposed in this article can quickly find the globally optimal \(k\)-core for the target community. But the experiment conducted in this article is based on high-quality, large-scale communities in datasets. If applied to development scenarios, the model's accuracy will be affected. Additionally, it is necessary to design stable and efficient algorithms to maintain attribute forests in dynamic network graphs, which will be the model's future improvement direction.  
Figure 6: Varying query attributes. (**a**) Time with different attributes (**b**) F1 score with different attributes.  
Figure 7: Varying query vertices. (**a**) Time with different vertices (**b**) F1 score with different vertices.
# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 2 Background
### Autonomous Ground Vehicle Convoys  
Autonomous ground vehicle convoys are composed of a leader vehicle and follower vehicles. Follower vehicles follow the path of the leader at a given offset distance. While there are various levels of autonomy employed by the autonomous ground vehicle convoys, such as MM, PU, and FA [4], the fundamental design of autonomous convoys remains consistent. The leader vehicle sets the path, either through human driving or autonomous navigation, and utilizes V2V communications to send information to the follower vehicles. The information sent varies depending on system design, but typically includes path points, vehicle pose, vehicle speed, and other sensor information to help follower vehicles track their leader. The follower vehicle uses onboard sensors to estimate its own state, and produces a state estimate of its leader using the information received from V2V and various onboard sensors. A path planning system uses the state information and leader path points to generate a motion plan, and the vehicle is actuated accordingly [3]. Figure 1 lays out the high-level architecture as described.  
The V2V communications sent between vehicles can contain a broad range of information from a wide suite of sensors. Information relevant to the vehicle's speed and position, such as sensor data (cameras, GPS, Light Detection and Ranging [LiDAR], wheel encoders, etc.), vehicle kinematics and intended maneuvers, would all need to go through the V2V communications to allow follower vehicles to reach the desired speed and formation for leader following [2]. Various V2V communication topologies can be utilized in autonomous ground vehicle convoys, depending on the needs of the system. Figure 2 shows some common leader-follower topologies used, including predecessor following, predecessor-leader following, bidirectional, and bidirectional-leader [21].
# Cybersecurity Vulnerabilities in Off-Site Construction
## 4 Targetable Entities and Vulnerabilities
### Primary Production
#### 4.1.3 Onsite Assembly  
Onsite assembly services are responsible for the administration, supervision, data handling, and real-time feedback at the prefabrication assembly points. Onsite, the administration is crucial for managing resources and onsite workers. Through RFID, each unit on the construction site can be identified, and site management is optimized to allocate resources where there are needed to shorten the assembly time.  
Furthermore, this information is helpful for onsite safety management by identifying potential hazards and risks in advance. It has been reported that an 80% improvement can be achieved in emergency assessment [35; 39]. Real-time tracking is also enabled to allow remote participants to know the project's progress. A model is shown in Figure 5 of real-time feedback implemented in a system that generates information for efficient supervision, control, and accurate decision-making from all involved parties [24].  
Services, such as virtual reality (VR) presentation [43], have been used to relay real-time information regarding assembly status, labor assignments, and material utilization for concerted decision making. From the above-described value chain, it is clear that digital technologies are immensely beneficial as summarized in Table 2[39].  
Assessing the value chain of OSC, it is evident that cyber-physical interactions are necessary for accurate and real-time monitoring. In the OSC value chain, cyber-physical systems encompass a network of interconnected systems that can interact, monitor and control IoT-related devices with additional abilities to manipulate the physical environment [44]. Cyber-physical systems have a specific taxonomy comprised of three layers, and each layer is dependent on another [45]. The first layer, the perception layer, is responsible for interacting with the physical system and generating organized data based on its receiving feed.  
The perception layer comprises sensory devices and actuators, RFID tags, and GPS that collect real-time data, and secure control of these data is critical to secure feedback and control loops [38; 46]. This layer can be identified in the previously discussed OSC value chain's primary production, transportation, and installation stages. Before analysis of information garnered in the perception layer, an intermediary layer, called the transmission layer, assists in exchanging and processing data obtained from the perception layer.  
Cloud-computing services facilitate the transmission of data. Hence, it is crucial to secure the transmission before outsourcing the data to avert unwanted incursion and unauthorized access [47; 48; 49]. Finally, the transmitted information is passed into an executable command in the Application layer, which uses decision-making algorithms that allow automated design [41; 50]. Data leaking is a possibility at this layer because of the handled volumes of data [41].
## 2 Materials and Methods
### Statistical Methods  
Subjects' demographic characteristics were reported as means \(\pm\) SD. The CBGs and HbA1c were expressed by means and 95% confidence intervals (CIs). All statistical analyses were done with an alpha error of 0.05.  
To analyze the HIIT effects on glucose control, mean glucose and the time spent at each glucose range (euglycemia (70-180 mg/dL), level 1 hypoglycemia (54-70 mg/dL), level 2 hypoglycemia (\(<\)54 mg/dL), level 1 hyperglycemia (180-250 mg/dL) and level 2 hyperglycemia (\(>\)250 mg/dL)) were evaluated with isCGM during 6 h, 12 h, and 24 h following any exercise session during stage 0, 1, and 2. In addition, glycemic variability was evaluated by using the coefficient of variation (CV).  
\[CV_{i}=\frac{SD(FLS_{i})}{Mean(FLS_{i})} \tag{1}\]  
where \(SD(\cdot)\) stands for the standard deviation, \(FLS_{i}\) is the isCGM data with FLS for patient \(i\), and \(Mean(\cdot)\) is the mean value.  
The isCGM data from FSL were formatted and pre-processed in Matlab R2018a (Mathworks Inc., Natick, MA, USA). Statistical significance was calculated using R and Rstudio IDE v1.2.5001. All the above defined metrics were calculated for each subject, observation window, stage, and exercise session. Shapiro-Wilk tests were applied to the data to demonstrate a normal distribution. If the null hypothesis of normality of the data could not be rejected, a linear mixed effects analysis was performed, considering the stage variable as a fixed factor and the patient ID as a random effect. If the normality test failed, a generalized Friedman test was used to determine the influence of the stages on each of the metrics.  
In addition, immediate effects of HIIT on CBGs were analyzed by one-factor repeated-measurements ANOVA. Post-hoc Bonferroni procedures were also used to examine isolated differences following significant ANOVAs for CBG performed before, immediately post-session, and 1 h later. CBG values from the first two and the last two weeks during the 12-week study period were examined to assess the subjects' adaptation to exercise.  
Finally, paired t-tests were used to analyze changes in HbA1c after the HIIT intervention, with a level of significance \(p<0.05\). However, in light of the small sample size of this study, normality tests for HbA1c were performed from the pre-post differential value using the Shapiro-Wilk test.
# Performance of a Mid-Size Net-Zero Energy Solar House
## 2 Previous Research Studies on Net-Zero Energy Buildings
The HVAC system with solar thermal collector, air source heat pump, and waste heat recovery saved electrical energy for water heating. Their design house consumed 14 times less energy due to its high thermal insulation, the heat recovery system, and the solar thermal system. This design house won a second place in the Comfort conditions contest in the Solar Decathlon. A NZEB test facility was constructed at the National Institute of Standards and Technology (NIST) in order to demonstrate that a house similar in size, and amenities to those in the surrounding communities could serve as a NZEB while serving the needs of a four-member family [11]. The living area was 252 m\({}^{2}\) (2712 ft\({}^{2}\)). The house incorporated various renewable energy and energy efficient technologies, such as air-to-air heat pump systems, solar photovoltaics, and a heat recovery ventilator. Their study addressed exterior above-grade walls, basement walls/floor, roof structure, electrical distribution system, air-distribution systems, air-to-air heat pump system, photovoltaic system, solar hot water system, heat pump water heating system, plumbing system, ventilation system, etc. The energy performance of the house was monitored over a one-year period that began on 1 July 2013. During the seven-month period when air conditioning was required, the sensible to total load ratio varied from 0.58 to 0.78. The photovoltaic and thermal solar systems were significantly impacted by snow cover. During the study period, the house generated 13,523 kWh of electricity using the 10.2 kW solar photovoltaic system. The house consumed 13,039 kWh energy while meeting the electrical and comfort needs of a four-member family, resulting in a net energy export of 484 kWh. The solar photovoltaic system converted 16.8% of the incident solar radiation into useful electrical energy. The solar thermal hot water system provided 54% of the energy to meet the domestic hot water load.