esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
## 6 Conclusions and Future Works  
In this paper we studied the optimization of surveillance trajectories in different scenarios for a differential mobile robot using three heuristic algorithms: GA, PSO, and PS. To do so, the calculation and simulation of the robot's model and movements were simulated using Matlab and Python. Each optimization technique was tested for a given time (eight hours). During this time, it was verified that the algorithms were able to avoid obstacles in the different scenarios, cover the largest possible percentage of the space, and, finally, optimize the trajectory in terms of time. These processes were carried out with [PERSON]'s Hussar robot using the LiDAR multiScan100. However, the study presented in this work can be used for any type of robot with any type of surveillance system (vision, gas detector, etc.).  
Having analyzed the results, it can be concluded that all three algorithms give excellent results, but PS stands out from the other two as it was able to find solutions faster and, except in the case of the more complex room, offer better final solutions.  
For future lines of research, we intend to investigate the hybridization between PSO and PS. This method works as follows: PSO approaches a good solution and when it has done so, PS refines the results. In addition, in the future we will test other metaheuristic techniques and intelligent techniques such as reinforcement learning.  
M.P.: conceptualization, methodology, software, validation, writing--original draft preparation, writing--review and editing. J.E.S.-G.: conceptualization, methodology, formal analysis, supervision, software, writing--original draft preparation, writing--review and editing. M.S.: conceptualization, validation, writing--review and editing, supervision. I.M.: conceptualization, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the European Commission, under European Project CoLLaboratE, grant number 820767.  
Not applicable.  
Data Availability Statement:Data are available upon reasonable request.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Fitness Function Adapted from [36]**} & \multicolumn{2}{c}{**Fitness Function of This Work**} \\ \hline
**Technique** & **Trajectory Time (s)** & **Best Dist.** & **Best \%** & **Trajectory Time (s)** & **Best Dist.** & **Best \%** \\ \hline GA & 85.04 & 21.82 & 100\% & **56** & **10.59** & 100\% \\ SP & 80.19 & 16.21 & 100\% & **41.1** & **10.28** & 100\% \\ PS & 51.97 & **7.94** & 100\% & **49** & 14.41 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of results obtained with the fitness function proposed in this work and adapted from [36]. Best results have been boldfaced.
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 3 Motivation for OWL vs. XML
### Web Ontology Language (OWL)  
OWL descriptions can be viewed as consisting of two parts--descriptions of the concepts and then facts that are _instances_ of these concepts. Concepts are analogous to database schema, while instances are analogous to tuples in database tables. The concepts are expressed in OWL, while the instances are expressed as RDF triples. OWL representations are called _ontologies_. The term ontology emerges from artificial intelligence and conveys the syntax and semantics of concepts and their relationships in a formal, declarative, and computer-understandable way [39]. An ontology for a radio communication domain serves as the base for representing device descriptions. Data are stored in, and retrieved as, triples in an RDF store, also called a _triple store_. Application requests are expressed in SPARQL. SPARQL is a set of specifications that provide languages and protocols to query and manipulate RDF graph content on the Web or in an RDF store [6]. Due to the fact that OWL has formal semantics, the data annotated in OWL can be processed by any _inference engine_ (or _reasoner_) that conforms with the OWL semantics to derive the facts that are implicitly contained by the explicitly encoded facts. Thus querying is applied to the extended set of facts after the inference step.
## 3 Proposed Methodology
### Cluster Formation  
BS initiates cluster formation and cluster head (CH) is selected based on residual energy. It is assumed that the energy level of the node does not change after the cluster is formed. In total, 5-8% of the cluster nodes are selected as CH with the criteria given below: (1) Two cluster heads should not be neighbors; (2) every CH has not less than 7-10% nodes as a neighbor. Then BS unicasts CH INT to CH along the route from CH to BS as illustrated in Figure 2. By considering that the node is next hop in route, the format of the packet CH INT is given by Equation (9).  
\[BS\to CH:CH\_INT|ID_{bs}|ID_{i}|E(K_{ibs},PATH|seq_{no})\big{|}MAC(k_{chbs}, CH\_INT|ID_{ch}|PATH|seq\_no) \tag{9}\]  
Every node that receives the packet performs the following: (1) ID of the next hop is checked, if identical to its own, routing path (PATH) is decrypted and next hop from PATH is identified or else packet is dropped. (2) The sequence number is checked in the receiver packet table, then store packet type and sequence number if the sequence number did not exist in the table, and the changes needed are made, or else it is stopped. (3) Preceding hop is assigned its ID, while the subsequent one with the ID is found in PATH. (4) Routing table is stored in memory along with the previous one, as well as next hop, such that it helps in transmitting the data to BS. (5) PATH, as well as the sequence path for the subsequent hop, are encrypted with a pairwise key and the updated packet is forwarded. While CH is  
Figure 2: Clustered architecture.  
receiving the CH INT packet, decryption of PATH takes place and the data are verified by the MAC; then acknowledgement (ACK) is sent to BS via the same path.  
When ACK is not received by BS within certain time from CH, the path is recomputed and the CH INT packet is sent again. A few criteria are involved in determining the routing path including the path having residual energy and power consumed. A path is selected with more residual energy and small hops count. To form a cluster, a CH ADV packet is sent by CHs to publicize their will.  
ID and CERT are present in CH and the ADV packet, such that the receiving node ensures authentication. Nodes receiving several CH and ADV select CH based on two factors: (1) checking if the pairwise key is present in the ID advertised and (2) signal strength of advertisement forwarded. Once CH is selected, the will of the nodes is forwarded by CH JOIN packet with ID as well as MAC with pairwise key and a nonce. Once entire CH JOIN is received, information of the cluster members is sent to the BS by CH, and the TDMA schedule is created based on the total nodes in the cluster and unicast to every member. The packet has the following format by Equations (10)-(12).  
\[CH\rightarrow*:CH\_ADV|ID_{ch}|CERT_{ch} \tag{10}\]  
\[x\to CH:CH\_JOIN|ID_{x}|MAC(K_{xch},CH\_JOIN|ID_{x}|nonce_{x} \tag{11}\]  
\[CH\to x:CH\_SHED|ID_{x}|E(k_{xch},t_{x})MAC\Big{(}CH_{SHED|ID_{x}|E(K_{xch},t _{x})nonce_{x}}+1\Big{)} \tag{12}\]
# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 2 Literature Review
### Challenges in Cost and Schedule Integration  
For cost-schedule integration, a match in hierarchies for data associated with them is essential to store, manage, and use the data in the same environment. However, cost data and schedule data are managed by different information units at different levels [8]. According to [PERSON] et al. [4], the lowest unit of typical cost items in project cost control focuses on the _Element and Operation_ level (i.e., Floor, Formwork) in line with the cost breakdown structure (CBS). Regardless of location, these items are synthesized at the _Operation level_ (i.e., Formwork) for cost estimation reports and at the _Work Section_ (i.e., Concrete) level for project budget reports. On the other hand, the lowest unit of typical schedule items refers to deliverable works in locations at the _Zone and Operation_ level, without regard to an element (i.e., Area B-3, Formwork). The item is based on the work breakdown structure (WBS); they are grouped in a master schedule by the _Location_ or _Operation_ level (i.e., 4 th Floor, Formwork or Area B-3, Concrete).  
In construction management, cost and schedule data show large differences in types and levels of detail (LOD). Although both data share operation data, it is used at different LODs in different types of cost and schedule reports. On the other hand, element and zone data are related to both cost data and schedule control; however, they show various LODs according to reporting purposes, likewise operation data. This variety within cost and schedule data leads to the independent use of multiple information hierarchies with complicated parameters. It results in challenges in the cost-schedule integration with interoperable data hierarchies and the dynamic cost and schedule tracking.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 5 Performance Evaluation
### Attack-Based Network
#### 5.1.4. Multiple Attackers  
* _Attacker A1 and A4:_ further experiments were conducted by simultaneously subjecting the network to the optimally positioned attackers, A1 and A4. From Figure 6, it is shown that DWSIGF-P was barred completely from routing due to its selection strategy as A1 was chosen in all attempts.  
Figure 7. Possibility of attacker selection.  
DWSIGF-R achieved 7% packet delivery with a 92% chance of selecting the attacker. FuGeF, however, succeeded in achieving 60% packet delivery with 40% chance of selecting the attacker.
* _Attacker A2 and A3_: Both malicious nodes in the modal positions were simultaneously tested. DWSIGF-P, DWSIGF-R, and FuGeF were able to achieve 67%, 65% and 79% packet delivery with 20%, 24% and 14% possibility in choosing an attacker respectively, as shown in Figures 6 and 7.
## 3 Experiment and Analysis
### Sensitivity Analysis of the Proposed DBSCAN-Based Alternative Model's Parameters  
#### 3.4.1 Sensitivity Analysis of DBSCAN's Proximity Threshold  
The sensitivity of the proposed DBSCAN-based alternative model to the proximity threshold \(\varepsilon\) is evaluated using \(\lambda=0.250\) m, \(\delta=0.110\) m, \(\alpha=0.080\), and \(\tau=0.001\), aligning with the configurations used in the proposed model's sensitivity analysis in Section 3.2. As shown in Figure 22, the model achieves the highest recall of \(88.9\%\) when \(\varepsilon=0.250\) m. Although this configuration does not yield the highest precision, it results in the highest F1 score of \(92.1\%\).  
When \(\varepsilon\) is small, the model delivers low performance, as DBSCAN classifies many actual matches as outliers. Once \(\varepsilon\geq 0.081\) m, the model achieves recall above \(70\%\). Recall continues to improve until \(\varepsilon\) reaches the best value of \(0.250\) m. Beyond this value, performance slightly fluctuates until \(\varepsilon\) reaches a value of \(0.740\) m, where the model stabilizes at a recall of \(84.4\%\).
## 3 The Study Area and Spatial Datasets
### Configuration and Training of the BE-LMTree Model  
Configuration of the BE-LMTree model consists of two steps: (i) Determining the minimum number of samples (NS) that are used for growing the LMTree; and, (ii) Determining the number of bootstrap subsets (BS) used for BE. Because at least five samples are required to build a logistic regression model at a tree node [52], we varied NS from 5 to 100 with a step size of 1, and then, estimating the classification rate of the corresponding LMTree model on both the training dataset and the validation dataset. As a result, minimum of 10 samples is the best for the data at hand; therefore, NS of 10 was selected. For the case of determining the number of the bootstrap subsets, since no thumb rule is available, an empirical test was carried out by varying BS from 2 to 100, and then, compute their classification rates of the LMTree model both on the training dataset and the validation dataset. The test result revealed that the BE-LMTree with 50 tree-based classifiers provided the highest classification accuracy for the data at hand; therefore, BS of 50 is selected. Once the BE-LMTree model had been configured, the training process was carried out to derive the final BE-LMTree model.
# The EGNOS Augmentation in Maritime Navigation +
## 2 EGNOS Background
#### EGNOS in Maritime
Indeed, where latitudes do allow, GEO satellites are well-suited to support many users, especially shipping activity, given the need for mobility and the absence of land upon which to deploy terrestrial communications infrastructure. These communication gaps are therefore particularly relevant to the Polar Regions, where users often require near real-time delivery of information to ensure safety of life and efficient operations. In both Polar Regions, the geostationary satellites are used for many connectivity requirements, but physical limitations associated with visibility of these satellites at higher latitudes undermine their viability. Due to the location of GEO satellites directly above the equator and the curvature of the Earth, they have very low inclination 'look-angles' at high latitude parts of the globe. As a result, visibility of these satellites from the ground reduces to zero from latitude of approximately \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Navigation Scenario**} \\ \cline{2-3}  & **Coastal,** & **Inland-Water,** & **Ocean** \\  & **Hardour Navigation** & & **Waters** \\ \hline Horizontal Accuracy 95\% & 10 m & 100 m \\ Signal Availability & 99.8\% & 99.8\% \\ Continuity (over 15 min) & 99.97\% & NA \\ Position update rate & 2 s & 2 s \\  & & As soon as \\ Time to Alarm & 10 s & practical by \\  & & Maritime safety information \\ Coverage & Local & Global \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Requirements for a general maritime navigation established in the IMO Resolution A.1046 (27) [5; 8]. \(70^{\circ}\) to \(79^{\circ}\) (north and south). This restricts the number of corrections available and leads to a decrease in accuracy in the border areas of the coverage.
## 4 Discussion
### Effect of OT on Gingial Recession  
The advantage of orthodontic tooth movement is that it moves the entire attachment apparatus, including the osseous structure, periodontal ligaments, and soft tissue components, together with the tooth [52]. The challenge in orthodontics is to complete treatment with minimal effects on the root and periodontium [53; 54]. The impact of OT on gingival atrophy is a topic that has elicited mixed opinions. Some scholars argue that proper OT can improve gingival recession [55; 56; 57] while others suggest it can cause or worsen gingival recession [58; 59; 60]. In one study by [PERSON] et al., the prevalence of gingival recession in patients with infraversion and an open bite was higher after OT [60]. Scientific evidence suggests that orthodontic tooth movement does not cause a gingival recession but could create an environment that makes patients more prone to this condition [59]. This risk is especially increased when the teeth are repositioned in a facial direction. Longitudinal assessments of clinical attachment levels are the most reliable method to determine patient periodontal status changes [61; 62]. The systematic review indicates that comprehensive OT with fixed appliances has a minimal or not clinically noteworthy detrimental effect on clinical attachment levels [61].
## 4 Conclusions  
In this study, based on a fluid model, the influence of gap width on temporal nonlinear behaviors in DBDs with CO\({}_{2}\) as the plasma forming gas under Martian pressure is investigated by applying the tailored sinusoidal voltages. The simulation results found that, by increasing the gap width, discharge evolves from P1 discharge into P2 discharge, then converts into chaos, and finally evolves from the RP2 discharge to the RP1 discharge through an inverse period-doubling bifurcation. The simulation data show that, after the CO\({}_{2}\) discharge is extinguished, due to the larger mobility of electrons and the electronegativity of CO\({}_{2}\) gas, a rapid decrease in the electron density is obtained, and the heavy CO\({}_{3}^{-}\) and CO\({}_{2}^{+}\) ions become the dominant residual charged particles in the discharge gap. In the evolution from P1 discharge to P2 discharge, the increase in the gap width prolongs the time for the CO\({}_{3}^{-}\) and CO\({}_{2}^{+}\) ions to reach the surface of dielectric barriers, thus these heavy ions cannot be dissipated during the power-off phase and remain in the sheath region, which makes CO\({}_{2}\) gas easy to be ignited at a lower voltage, effectively reducing the breakdown voltage of the next discharge. Then, the second discharge is weaker and fewer charged particles are produced; these charged particles can be completely dissipated during the power-off phase without affecting the third discharge. When the gap width increases to 5.0 mm, more charged particles can stay in the discharge region at the beginning of the voltage period, and the negative charges are mainly electrons. When the applied voltage begins to rise, these residual charged particles are rapidly driven to the surface of the dielectric barriers to build a reverse electric field, which enhances the memory voltage and reduces the gas voltage, resulting in a weak positive discharge and a strong negative discharge. As the gap width continues to increase, the density and spatial distribution of the residual ions in the sheath region for two consecutive voltage periods in the RP2 discharge are gradually equal, resulting in the evolution from RP2 discharge to RP1 discharge. This study contributes to a deeper insight into the effects of gap width on nonlinear behavior in CO\({}_{2}\) DBDs under Martian conditions, and lays the groundwork for optimizing CO\({}_{2}\) discharges.  
Writing--original draft preparation, X.W.; writing--review and editing, X.W., S.G. and Y.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 11975142). Not applicable. Not applicable. Not applicable. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.
## Appendix D Area of Stability of QRWS as Function of \(\alpha\) and \(\phi\)  
In Figure A4, the probability \(p(\phi,\alpha,m)\), when coin is constructed by a generalized HR and a phase multiplier (Equation (12)), and there is a relation between phases as in Equation (18), is shown. The walk coin depends on the phase \(\phi\), the parameter \(\alpha\), and the size of the coin \(2\leq m\leq 10\). Lighter colors correspond to higher \(p\). The contours on the color plots show the probability \(p\) in the range between 5% and 95% of \(p_{max}\). The contour interval is 5%.  
The more important case from practical point of view, analogically to Appendix C, is when we search for areas with probability \(p\) greater than a fixed value. The images in Figure A5 show probability \(p\) as function of \(\phi\) and \(\alpha\). However, contrary to Figure A4, here the contours are positioned at the same values of \(\mathrm{p}\) for all coin sizes \(m\in[2,10]\). Contour values are between 0.037 and 0.37.  
Figure A4: (Color online) Probability to find solution \(p\) as function of \(\phi\) and the parameter \(\alpha\) in the non-linear Equation (18). The solid green line corresponds to \(\alpha=\alpha_{ML}(m)\), the dash-doted blue—to \(\alpha=-1/(2\pi)\) and dashed teal—to \(\alpha=0\). The lighter colors show higher probability and darker—lower. The scale is between \(0\) and \(p_{max}\), and the contours are at intervals \(5\%\times p_{max}\). The probabilities larger than \(95\%\times p_{max}\) are depicted with the lightest color. Each picture corresponds to a different value of \(m\in[2,10]\). In the first row from left to right are given the images for \(m=2,3,4\) (**a–c**), on the second—\(m=5,6,7\) (**d–f**), and on the last—\(m=8,9,10\) (**g–i**).  
Appendix E Robustness of the Modified Quantum Random Walk Algorithm for Different Size of the Walk Coin  
In Section 4.2, the stability of our proposed modification of the QRWS algorithm was investigated. A quantitative description of the root-mean-square deviation of the probability to find solution and the relative improvement in the stability have been graphically presented in the cases of walk coin size \(5\) and \(9\).  
Here, in Figure 11, we give a complete set of results for the robustness of the QRWS algorithm, as defined in Equation (19), for coin of size \(2\leq m\leq 10\). It can be seen that, in all the images, there is a wide central area (darker colors) where the r.m.s. deviation of the probability \(\sigma_{p}\) is extremely low for small changes in the parameters \(\phi\) and \(\alpha\).  
Analogously, in Figure 12 the numerical calculations' results for the ratio of the relative stability of the modified QRWS algorithm proposed by us are presented, to the one with standard construction of the walk coin. By increasing the coin size from \(m=2\) to \(m=10\), an expansion of the central dark area can be observed. This indicates that with increase of the coin size, the algorithm's relative stability continues to improve.
# A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain
## 6 Conclusions  
Based on the mathematical characteristics of SVD, a robust image watermarking scheme is proposed in this paper. Unlike the common SVD domain watermarking schemes, a binary watermark is inserted into the largest singular values of the selected image blocks in the spatial domain. Before watermark embedding, the watermark image is encrypted by the Arnold transform, which is used to ensure the security of the watermark. To verify the feasibility and performance of the proposed scheme, several experiments are conducted. From the experimental results, it is proved that the proposed watermarking scheme achieves better watermark invisibility and robustness under different attacks. Since the watermark embedding process is performed in the spatial domain, the proposed method avoids the false positive problem existing in traditional SVD-based watermarking and reduces the computational complexity. However, due to the defect of spatial domain watermarking, this method has poor robustness in resisting some other attacks, like image rotation. On the receiving end, some side information is needed to extract the watermark. In other words, the watermark extraction process of the proposed method is not blind. For the future work, we will address these problems and extend the proposed scheme for video copyright protection.  
This work was supported by the National Natural Science Foundation of China (No. 61201371), the Research Award Fund for Outstanding Young and Middle-Aged Scientists of Shandong Province, China (No. BS2013 DX022), and the Natural Science Foundation of Shandong Province, China (No. ZR2015 FP004).  
[PERSON] and [PERSON] conceived the algorithm and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the results; [PERSON] drafted the manuscript; [PERSON] revised the manuscript. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.