esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
The distribution of the global score of attitudes and behaviors is depicted in Figure 8, where the values range between 20 and 140 for the behaviors and between 25 and 100 for the attitudes. Low values represent positive behaviours, while high values represent good attitudes. It is possible to observe a positive skewed distribution on the behavior scale, where 75% of the students obtained a global score up to 50, which shows a globally positive behavior attained. Regarding attitudes, a mean (standard deviation) global value of 76 (7.7) was obtained, showing that globally students presented positive attitudes. Figure 6: Findings: Attitudes and behaviors. (**a**) Finding 01—attitudes and behaviors. (**b**) Finding 02—attitudes and behaviors. (**c**) Findings 03 and 04—attitudes and behaviors. The mean value of the global score of behavior (\(t\) = 0.38, \(p\) = 0.704) and attitudes (\(t\) = 0.789, \(p\) = 0.431) between gender were compared and no significant differences were found (Figure 9a,b). In Figure 9a,c the distribution of the global scores attained for attitudes by grade are represented, and concerning both significant differences were observed (global behavior: \(t\) = \(-\)2.731, \(p\) = 0.007; global attitude: \(t\) = 6.035, \(p\) \(<\) 0.001), and it can be observed that students of 6 th grade showed to have higher positive attitudes and behaviors. This fact  
Figure 8: Distribution of global behaviors and attitudes. Figure 7: Findings: attitudes and behaviors. (**a**) Finding 05—attitudes and behaviors. (**b**) Findings 06 and 07—attitudes. (**c**) Findings 08 and 09—behaviors. may infer the need to invest more in cyberawareness in the 9 th grade students. The fact that these students have more information and apparently have more experience with the electronic devices and digital applications may give them a more relaxing attitude regarding cyberawareness. Since significant differences were observed between the global scores of behaviors and attitudes, all the items of both questionnaires between 6 th and 9 th grades were compared. Table 4 presents the questions where significant differences were found and the corresponding \(p\)-value obtained using the Mann-Whitney non-parametric test.
## 3 Results
### Analysis of Variance of Selected Factors  
A division of the sample's population into three subpopulations was made. The division was computed according to several different factors (structural and physical).  
Angle \(\alpha\) (slope of grain) was the first regrouping element. The \(\alpha\) ranges were 0\({}^{\circ}\) to 0.5\({}^{\circ}\), 0.6\({}^{\circ}\) to 1\({}^{\circ}\), and over 1\({}^{\circ}\). The tendency of the L velocities to change according to \(\alpha\) was observed. Multiple comparisons were made by Kruskal Wallis Anova (non-normal distribution of subpopulations). Significant differences among the groups were identified between the first and the third group and between the second and third group. The second element used to regroup the subpopulation was the \(\beta\) angle (cross orthogonal section). The \(\beta\) ranges were 0\({}^{\circ}\) to 15\({}^{\circ}\), 16\({}^{\circ}\) to 30\({}^{\circ}\), and over 30\({}^{\circ}\). The \(V_{RTUS}\) was then analyzed according to \(\beta\). Significant differences among all groups were seen. Furthermore, the density, \(\rho\), was another regrouping element, between ranges of less than 440 kg m\({}^{-3}\), 441 to 513 kg m\({}^{-3}\), and over 514 kg m\({}^{-3}\). L velocities (calculated by both methods) showed the most interesting  
Figure 8: Comparison of the L acoustic radiation in \(US\) and \(FREQ\) methods (**a**) and comparison of the L impedance in \(US\) and \(FREQ\) methods (**b**). The box plots focus on the minimum, first quartile, median, third quartile, and maximum values (from the lowest).  
results related to \(\rho\) (Figure 9). Multiple comparisons showed significant differences between the second and third groups compared to the first one. Velocity reduction increased when the density was above 440 kg m\({}^{-3}\).  
Final analysis focused on the _AWAR_ as a regrouping element with the following ranges: less than 1.80 cm; 1.80 to 2.80; over 2.81 cm. The density (\(\rho\)) was examined using an analysis of variance (normal distribution). As confirmed with the p value, there was consistent variation throughout all groups. \(\rho\) decreased with an increasing _AWAR_. This happened because in coniferous wood the portion of latewood is the same, and the thicker the ring, the more earlywood.
# Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles
## 6 Experimental Results
### Error Rate Experiment  
Experimental Method  
In the experiment, a total three experiment environments were configured, and each one had its own baseline environment map. Therefore, in this experiment, the environment map generated by using only collected data from LRF and the environment map generated by using LRF and the algorithm proposed in this paper were compared to their baseline, respectively, and the error rates were measured.  
The following equation was proposed for the sake of the above experiment:  
\[\begin{array}{l}\text{errorate}(\%)=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}[ cell_{\textit{result}}[[i]]\hat{[j]}-cell_{\textit{base}}[i]]}{n+m}*100\;(\;if\;cell_{ \textit{result}}[i][j]>\\ \text{threshold}\;\textit{then}\;\textit{cell}_{\textit{result}}[i][j]=1\; \textit{else}\;\textit{cell}_{\textit{result}}[i][j]=0)\end{array} \tag{2}\]  
The environment map for the baseline can be indicated as either 1 or 0; 1 represents the cell with obstacles, and 0 represents the cell with no obstacles. However, the environment map generated by the algorithm contains cells with obstacle candidates; therefore, they are encoded into binary numbers as either 1 or 0 by the threshold.  
In Equation (2), each of \(n\) and \(m\) represent the maximum number of rows and columns when they are expressed by a vector represented with rows and columns. Each of \(i\) and \(j\) represent the index of the array. The meaning of \(cell_{\textit{result}}[i][j]\) is the rate of how long the cell had been occupied at row \(i\) and column \(j\) in the environment map, which is the result of the algorithm. However, the value of \(cell_{\textit{result}}[i][j]\) is transferred to binary numbers compared to the threshold. \(cell_{\textit{base}}[i][j]\) represents the occupied rate of the cell on the baseline environment map. The baseline environment map is the most optimized one; therefore, the gap of cells that are obstacles and not obstacles are clearly distinguished.  
Figure 17: Starting point and the destination in experimental environment 1.  
Figure 18: Results of path planning in an environment with transparent obstacles, the red diagonal line represent waypoint and the red cross line represent goal: (**a**) starting point; (**b**–**e**) steps in finding the transparent obstacle and modifying the planned path; (**f**) step in which the robot reaches the destination and stops.  
Table 3 shows the proposed algorithm having a lower error rate in all experimental environments compared to using only data collected from LRF.  
Also, the case of using only data collected from LRF shows the average error rate was 17.74%. On the other hand, the case of using LRF and the proposed algorithm shows the average error was 6.12%. On average, the error rate of using LRF and the proposed algorithm is 11.62% lower than error rate of using only data from LRF.
## 4 Parametric Studies  
This section compares the expected total costs of the proposed method and the conventional method to investigate the applicability of the proposed method and to find the economically optimized design.  
Figure 9 describes how the normalized expected costs change with the initial safety factor (SF). In the conventional method, the expected total cost, \(EC_{tot}\), is a downward convex function of the SF and has a minimal value [50]. It is mostly consistent with the building cost with a high SF, while the expected failure cost accounts for most of the expected total cost with a low SF. In this case, the optimized SF becomes 3.0.  
In the proposed method, \(EC_{tot}\) is also a downward convex function of the SF and has a minimal value, as in the conventional method. \(EC_{tot}\) is minimized at a SF of about 2.4, which is lower than 3.0 in the conventional case, and even when the initial SF is lower, the expected total cost does not significantly increase, indicating that the proposed method is robust. This is because the countermeasure (CM) cost is significantly lower than the failure cost. Note that when the CM cost is too low, the reduction in the building cost exceeds the increment in the cost of the CMs, and the minimal value does not appear, as noted in Section 3.2.3. This is not realistic, and will be excluded from further consideration.  
Figure 8: Graphical models of the parameters for calculating the normalized expected total cost.
# Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays
###### Abstract  
This article deals with the consensus tracking problem for multi-agent systems (MAS) under the influence of unknown time-varying delays. Each agent of the MAS is a quadrotor unmanned aerial vehicle (UAV) represented as a linear continuous-time system. The main objective of this paper is the stabilization of multi-agent systems where the control input is affected by unknown time-varying delays, which are assumed to be upper-bounded, and where these bounds are not required to be known. The proposed observer-based control scheme guarantees the consensus tracking of multi-UAV systems with the desired \(H_{\infty}\) performance, which adds a further level of mitigation of unknown delays present in MAS systems by minimizing the \(H_{\infty}\) norm, which measures the maximum gain from the disturbance to the controlled output of the system. For each UAV agent, an unknown input observer is employed to isolate the unknown time-varying delays in the state estimation process. With the use of an unknown input observer-based consensus tracking control, sufficient conditions are derived to ensure that all follower UAVs can reach a consensus with the leader, despite the presence of distinct unknown time-varying delays. The stability of the proposed scheme is proven using Lyapunov theory for the leader and follower agents. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method.  
multi-agent systems; unknown time-varying delay; consensus control; multi-quadrotors Article
# Energy Sources of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency
## Energy Sources of Mobile Robots  
### Energy Storage and Battery Technologies  
The main mobile robot energy sources are rechargeable batteries which are made from different materials. For the best performance, low weight, high current draw capability, and high capacity are required. Battery voltage is also an important parameter as power losses increase with a square relationship to current; raising voltage allows current to be reduced, and thus considerably reduces power losses and conductor cross-sections while maintaining the same power. However, it must be noted that control electronics usually require a low voltage supply. For large robots, extra energy conversion is not an issue, but in small robots, supply voltage is often shared between control and motors, limiting energy conversions and component count in the design.  
Electric energy can also be stored in supercapacitors. This type of storage is characterized by relatively large volume, but low mass and small energy density. However, it allows energy to be stored and extracted at high rates. Therefore, such storage is employed as an additional power reservoir, mostly for regenerative energy storage and supply of current in short overload states or during motor startup when a high amount of current is required.  
In hydraulic and pneumatic systems, pressure tanks can be used for energy storage. They can serve as the main or supportive storage devices. Their energy storage-retrieval characteristics are analogous to those in supercapacitors in electrical circuits. Both springs and flywheels can also be used to store small amounts of mechanical energy. While spring storage can maintain energy for a long time, flywheels are subject to friction in bearings and air drag, which limits their practical energy storage time, as energy is gradually dissipated.
## 6 Conclusions  
A cluster-based distributed compressed sensing approach that combines spectrum-aware QoS routing is proposed in this work. The purpose is to minimize energy consumption while satisfying QoS requirements in delay and reliability. Based on the correlation degree among adjacent video sensors with the overlapped field of views, the video sensor that observes the object is selected as a member node. Then, SCS is used to determine enough number of collaborative nodes that can meet the requirements of the reconstruction error to participate in DCS. Finally, a QoS-guaranteed routing framework is presented in which each node chooses the optimal next hop that minimizes energy consumption in a distributed manner. Simulation results show that, by integrating FoVs' correlation operations in DCS, CDCS can realize high energy-efficient delivery and reconstruction accuracy of visual information.  
[PERSON] put forward the original ideas and performed the research; [PERSON] performed the experiments and analyzed the data; [PERSON] provided useful comments; [PERSON] raised the research question, reviewed this paper. All authors have read and approved the final manuscript.  
The authors gratefully acknowledge the support and financial assistance provided by the National Natural Science Foundation of China under Grant Nos. 61502230, 61501224 and 61073197, the Natural Science Foundation of Jiangsu Province under Grant No. BK20150960, the National Key R&D Program of China under Grant No. 2018 YFC0808500, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 15 KJB520015, the National Engineering Research Center Program of Communications and Networking under Grant No. GCZX012, and the Nanjing Municipal Science and Technology Plan Project under Grant No. 201608009.  
The authors thank the anonymous reviewers who provided constructive feedback on earlier pieces of this work appearing at WCNA 2018 [42]. The first author is especially grateful to the Jiangsu Government Scholarship for Overseas Studies that assist his research abroad.  
Figure 8: QoS provisioning.
## 2 Materials and Methods
### Image Processing  
The image processing was carried out with MATLAB to acquire the feature data. At first, the acquired RGB image was separated in single R, G and B channel in grayscale mode. Subsequently, each channel grey image was converted to a binary image using [PERSON]'s method [32]. This method converts the grayscale image to a binary image based on image clustering in accordance with a threshold value. This threshold value is optimally determined between 0 and 1 by [PERSON]'s method. The grey level is normalized from 0-255 to 0-1. The method then splits the normalized image into two classes having lower or higher grey level than the threshold value. Each pixel is set to white (1) if the grey level is higher than the threshold value, otherwise, it is set to black (0). Thus, the image segmentation considers the identification of objects within an image using an edge detection algorithm which identifies the boundaries of individual object and labels the centre of each object for further processing. Eventually, each grain's position is fixed and it is tagged according to its position through a segmentation process.  
The noise of each image is then eliminated using a morphological process. It is followed by morphological opening operation [33] were applied with 'disk' type structuring element using 'imopen' function followed by hole filling and clear borders. Morphological opening operations generally smooth the objects of the image. Opening operation eliminates thin protrusions of the objects. Opening operation eliminates the objects which cannot accommodate the structuring element completely. Thus it removes the noise from the image. Then, each object was labelled followed by counting the objects.  
Feature extraction involves the retrieval of quantitative information from the segmented images. Here, extraction of parameters e.g., eccentricity, equivalent diameter, area, perimeter, major axis length and minor axis length have been carried out further with'regionprops' function for differentiating head grain from broken grain. Aspect ratio i.e., major axis length/minor axis length was estimated from object feature set. The schematic processing chain is shown in Figure 2. The similar operation was conducted for other training images of basmati rice sample and for the test image too. Feature dataset for training as well as testing was created from the object properties and object class (Whole grain = 1 or broken grain = 0).  
Figure 1: Schematic diagram of image acquisition system equipped with a camera, illumination source and geometry, and connected PC.
## 4 Experiment, Results and Discussion
### Forensics Analysis: Chrome Application as a Case
#### 4.2.4 Reporting
This is achieved through advanced file header recognition algorithms, making it a powerful addition to the digital forensics toolkit. The first aim of this experiment was to evaluate the efficacy of the FORC tool in retrieving artifacts from a mobile device. The results of our investigation are summarized in Table 3, which provides a comprehensive breakdown of the artifacts retrieved by these tools. As shown, all four tools retrieved a similar set of artifacts from Chrome. However, FORC also retrieved webRTC logs and an HSTS preload list, which are not retrieved by the other tools. WebRTC logs can be used to track the websites that a user has visited using voice or video chat. The HSTS preload list is a list of websites that are configured to use HTTPS only. This information can be used to identify websites that a user has visited that are not secure. Figure 24 shows that FORC is the only tool that can automatically identify and extract all 24 SQLite files. FORC does not require manual searching, which can save time and effort for the investigator and prevents the investigator from forgetting or overlooking any files. Figure 24 also shows the time saved in hours. The time savings is calculated based on the following assumptions:  
* The investigator spends 1 h manually searching for SQLite files;
* FORC can automatically identify and extract all 24 SQLite files in 0.5 h;
* The other three tools require the investigator to manually search for the SQLite files, and they may not be able to find all of the files. As shown, FORC can save the investigator 4 h by automatically identifying and extracting all 24 SQLite files. The other three tools, FinalMobile, Megnet AXIOM, and Belkasoft can save the investigator 2, 1, and 0 h, respectively. The second aim of the experiment is to evaluate the efficacy of the FORC tool in extracting SQLite data. The results we obtained were revelatory. The FORC tool successfully extracted a total of twenty-four SQLite files, each containing invaluable evidence tables, as outlined in Table 4. \begin{table}
\begin{tabular}{c c} \hline
**Tool** & **Artifacts** \\ \hline BelkaSoft & Bookmarks, history, cookies, cache, passwords, extensions, apps \\ \hline Magnet AXIOM & Bookmarks, history, cookies, cache, passwords, extensions, apps, \\  & autofill data, form history, download history, geolocation history \\ \hline FINAL Mobile & Bookmarks, history, cookies, cache, passwords, extensions, apps, \\  & autofill data, form history, download history, geolocation history \\ \hline FORC & Bookmarks, history, cookies, cache, passwords, extensions, apps, \\  & autofill data, form history, download history, geolocation history, \\  & webRTC logs, HSTS preload list \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of artifacts retrieved by mobile forensics tools. Notably, FORC uncovered files No.1 and No.18, which had eluded even commonly used forensic tools like Belkasoft, FINALMobile, and MAGNET AXZIOM. While FINAL
## 5 Experimental Setups and Procedures
### Setup I Experimental Procedure and Observations
#### 5.2.2 Observations  
The results of using experimental setup I to measure the energy consumption of dual connectivity receivers running MPTCP with different congestion control algorithms such as OLIA, LIA, BALIA, Cubic, and wVegas are revealed in this section.  
The energy consumed in the downlink of the receiving nodes when transferring some data volume (60 MB) by the sending node is measured in a scenario where the receiving nodes are varied from one to nine. Figures 3 and 4 show the data transfer throughput and energy consumption obtained with one receiving node for different MPTCP congestion control algorithms, respectively. The figures reveal the direct relation between the throughput and energy consumption. Apparently, the congestion control algorithm capable of transferring with a high throughput achieves lower energy consumption since data transfer happens in a shorter time interval.  
Moreover, from Figure 4, it can be noted that BALIA and Cubic consume less downlink energy over dual connectivity MPTCP connections when compared with the other three  
Figure 2: Experimental setup I.  
congestion control algorithms. Thus, in our further experimentation, we use BALIA and Cubic to measure the performance of the proposed algorithms.  
Furthermore, we analyze the energy behavior of different congestion control algorithms while varying the number of receiving nodes, as depicted in Figure 5. The figure shows that the energy consumption increases gradually with the number of nodes, as shown in Figure 5. This is mainly due to the reduction in average throughput as the capacity of the wireless channel is distributed on a larger number of nodes. From the figure, the MPTCP energy value differences while running different congestion control algorithms, such as OLIA, LIA, BALIA, Cubic, and wVegas, are also visible. Again, it can also be noticed that BALIA consumes the least amount of energy, followed by Cubic, which consumes a slightly higher value.  
Figure 4: MPTCP energy consumption using WiFi and LTE interfaces.  
Figure 3: MPTCP data transfer throughput using WiFi and LTE interfaces.