esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 6 Results
### Result of LUI Instance  
Figure 7 presents the obtained Pareto fronts from eight different algorithms and Table 6 provides the numerical results obtained for all evaluated algorithms. To simplify the table, only two extreme points on both Pareto fronts are shown. The highest achieved coverage is 391 users, which is yielded by the iterative heuristic algorithm, the exact method, and NSGA-II. On the other hand, the lowest distance of 93.5 m is achieved using PolySCIP.  
Overall, results from NSGA-II are debatably superior when both objectives are considered. The results are close to the optimum (106.4 from NSGA-II and distance-focused iterative heuristic and 93.546 m from PolySCIP). In fact, the distance result from NSGA-II is even better than its iterative counterpart as it covers more users. Figure 8 shows the stations and their respective coverage in LUI instance.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Covered Users** & **Maximum Walking Distance (Meters)** \\ \hline PolySCIP (Best coverage) & 391 & 149.528 \\ PolySCIP (Best distance) & 108 & 93.546 \\ NSGA-II (Best coverage) & 391 & 149.528 \\ NSGA-II (Best distance) & 203 & 106.4 \\ Coverage Heuristic & 348 & 148.491 \\ Distance Heuristic & 187 & 112.398 \\ Bi-objective Heuristic & 333 & 144.515 \\ Coverage Iterative Heuristic & 391 & 149.528 \\ Distance Iterative Heuristic & 87 & 106.4 \\ Bi-objective Iterative Heuristic & 358 & 144.401 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical results in LUI instance. Only extreme solutions from the two Pareto fronts are mentioned.  
Figure 7: Results in the validation phase. The higher the value on the x axis (moving toward the right), the better the distance objective is. The higher the value on y axis, the better the user coverage objective is.  
For the IGD indicator (see Table 7), NSGA-II yields 3.02. This value is in accordance to Figure 7 between a true Pareto and an approximated fronts. It can be seen that NSGA-II achieved some of the solutions on the true Pareto front--especially the highest user coverage solution.  
As for the spread indicator, the true Pareto front yields 0.488, while the NSGA-II Pareto front yields 0.525. This means the diversity in the exact method Pareto front is better than NSGA-II's. This was due to the fact that the coverage objective overwhelmed the distance objective leading to a cluster of solutions in the upper right region in Figure 7. The hypervolume of the true Pareto front is 0.449 and the NSGA-II Pareto front yields 0.351. The difference occurs because some solutions of NSGA-II are dominated by PolySCIP's.  
It is essential to note that PolySCIP was applicable for the LU1 instance due to its small size. However due to the FPP complexity, for larger instances like LU2 and MU1, PolySCIP became an enviable approach. This is elaborated in Sections 6.2 and 6.3.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 2 Literature Review
### Advanced Control Techniques  
Computed Torque Control uses the following equations, which represent the fundamental control law applied to a 3-DoF robot:  
* PD Computed Torque Control Law, which can be represented by Equation (4) \[\tau=\mathrm{M}(\mathrm{q})\big{(}\ddot{\mathrm{q}}_{\mathrm{d}}+\mathrm{K}_{ \mathrm{p}}\mathrm{e}+\mathrm{K}_{\mathrm{d}}\dot{\mathrm{e}}\big{)}+\mathrm{C }\big{(}\mathrm{q}\,\ddot{\mathrm{q}}\big{)}+\mathrm{G}(\mathrm{q})+\mathrm{F }\dot{(\mathrm{q})}\] (4)
* PID Control Law, which can be represented by Equation (5) \[\tau=\mathrm{K}_{\mathrm{p}}\mathrm{e}+\mathrm{K}_{\mathrm{d}}\dot{\mathrm{e}} +\mathrm{K}_{\mathrm{i}}\int_{0}^{\mathrm{t}}\mathrm{edt}\] (5)  
For the case of intelligent control, the fuzzy control law can be mentioned. There is the possibility of combining some controls, for example, the PID control plus the fuzzy control, obtaining a hybrid type of control.  
Predictive control [19] is generally designed based on an objective function that is sought to be minimized, for example, optimizing the robot's trajectory to avoid obstacles that appear in its path. As discussed in the state of the art, for the development of the work, the modeling of a 3-DoF robot, the Scorbot model, and the design of the Computed Torque family controllers, PD-PI + fuzzy, and Model Predictive Control are considered, structured, and developed based on the theoretical and technical information indicated in the bibliography.  
The structure of this article is as follows: Section 2 presents a review of the state of the art in robot manipulator control. Section 3 describes the robotic system used in the study. Section 4 details the design of the proposed controllers. Section 5 presents the simulation results. Section 6 discusses the main findings. Section 7 addresses the conclusions of the research, and the projections for future work are raised in Section 8.
## 6 Operations
This would naturally lead the community toward a network of ground stations. There have been some attempts to create such a network, such as the Global Educational Network for Satellite Operations (GENSO), which is a worldwide consortium of amateur ground stations established in 2007 [54]. However, all attempts to communicate with the organization indicate that the consortium is no longer active. Although formation of global networks such as GENSO may be the ultimate long-term approach, teams of CubeSat developers can join together on an ad hoc basis, and sign bilateral agreements for sharing their ground stations during the operations, as was the case for SE01. A Virtual Private Network (VPN) tunnel was established between the remote machines, in order to have a secure remote access to the ground stations without taking much of operator's time from the host station. During the satellite operation, it is crucial to prepare a plan for daily observations, due to their short window. First, simulations should be performed to predict the exact time when the CubeSat appears over the horizon for every observation event. The orbital elements extracted from the TLEs should be updated on a regular basis, since such TLEs deviate over time. The prediction of the acquisition of signal time using a one-week-old TLE set may deviate up to 5 min. Further, to anticipate and store the results of every pass it is recommended that a template be made for the operations report. Such a report can be used in the future for keeping track of the work performed, as well as analyzing the data in case some operations fail. The operations report may include for each event the date, time, used ground stations, operators, a plan with the list of commands to be sent, status of the satellite, reply of the satellite to each command, any deviation of the plan, and other comments or communications to team members. Such communications may include observations from the remote ground stations. Further, voice communications may also be used to speed up the interaction between team members during an observation event. The status of the satellite is summarized in the housekeeping telemetry data, which include parameters such as the satellite operational mode, battery voltage, bus current, temperature of critical subsystems (transceiver, batteries, onboard computer,) and possible rotational speed for each axis. The SE01 operation continued for nearly one year, and the satellite finally reentered the Earth's atmosphere on 26 February 2019.
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
## 3 Conclusions  
In conclusion, this paper proposes an FSR structure with an ultra-wide absorption/transmission band and low infrared emissivity. Firstly, a method of expanding the FSR transmission bandwidth is proposed based on equivalent circuit analysis. This method utilizes RS based on a sinusoidal microstrip structure and cascade with FSS at 1:4 to form UWB FSR. Subsequently, an IRSL with low infrared emissivity and high radar transmittance is designed and combined with FSR. The experimental results demonstrate that after the loading of IRSL, the absorption band of FSR shifts from 2.65-8.80 GHz to 2.66-8.01 GHz, and the transmission band shifts from 9.15-17.71 GHz to 9.16-17.94 GHz, both exhibiting ultra-wideband characteristics. It also shows a low emissivity value (0.26) in 8-14 \(\upmu\)m. Furthermore, the RS, FSS, and IRSL are all flexible, allowing for attachment to curved surfaces to achieve conformal design, and the thickness of IRSL-FSR is only 0.10 \(\lambda_{L}\) (\(\lambda_{L}\) is the lowest frequency on the spectrum and corresponds to the wavelength). In general, this research provides a viable solution to the challenge of radar-infrared bistelalth for satellites or other communication-enabled platforms.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Deep Learning Experiments
#### 4.3.5 Gated Recurrent Units (GRU)  
The proposed GRU model consists of three GRU layers. Each of these three GRU layers has 32 filters. We added a fully connected layer and a dense layer that has activation function Softmax with 16 nodes. Finally, we added a dropout layer, which helps in avoiding overfitting; we set the dropout ratio at 20%.
## 5 Discussion & Future Work
### Limitations & Future Work  
Following up on the discussion about unwarranted on-board access, it is noteworthy to highlight that diving deeply into the vehicle logs represents a real limitation to verifying the evident effects of our pentests. The restrictions also made the entire pentest more complex as it was limited to being pushed in a black-box manner. Therefore, our future efforts are focused on conducting grey and white pentests. More specifically, it is envisioned to target further assets varying from automated driving decision-making units, V2X components, on-demand service applications, and the fleet management system.  
Additionally, considering the continuous upgrades impacting the vehicle operating systems, supplementary tests are foreseen to accomplish future comparative analyses with the present findings. On the same note, for a more granular and uniform analysis, it is planned to complement the attack tree paths with an additional detailed level linking CVEs to each damage scenario. Such a future work would provide consonant comparisons and an evolution of the identified vulnerabilities based on the universal CVE databases [33].  
Another shortcoming to highlight in the present research is the impact of the rapidly evolving technologies on the pertinence of our findings. Being a pilot vehicle under regular emerging changes, the L4V has been subject to several modifications and upgrades. Consequently, our findings reflect the risk analysis and pentests results on the assessed vehicle configuration at the time of the elaboration of our experiments. As a future work, we intend to build an automated TARA framework supporting with continuous assessment of the L4V risks with a possible comparison of current risk values to the historical records. Such a solution aims to help keep risks at an acceptable level while coping with the technological progression.  
To that end, the present work can be considered a valuable path and a starting point advertising the implementation of frequent risk assessments and the importance of penetration testing on approaching the full deployment of L4 and L5.
_Applied_  
_sciences_  
_Article_  
**Effect of Alkaline Artificial Seawater Environment**  
**on the Corrosion Behaviour of Duplex Stainless Steel 2205**  
**[PERSON] 1(r), [PERSON] 1,*(r), [PERSON] 2 and [PERSON] 1**  
1 Energy and Resources Institute, College of Engineering, Information Technology and Environment, Charles Darwin University, Darwin, NT 0909, Australia; [EMAIL_ADDRESS] (T.T.T.T.); [PERSON](S.T.)  
2 Research Institute for the Environment and Livedihoods, College of Engineering, Information Technology and Environment, Charles Darwin University, Darwin, NT 0909, Australia; [PERSON]SS]  
* Correspondence: [EMAIL_ADDRESS]  
Received: 29 June 2020; Accepted: 17 July 2020; Published: 22 July 2020  
**Featured Application: A study about corrosion behaviour of duplex stainless steel in alkaline marine environment containing sulphate reducing bacteria.**  
**Abstract:** Sulphate reducing bacteria (SRB) can be found in alkaline environments. Due to their metabolite products such as hydrogen sulphide, the corrosion behaviour of materials in alkaline environments may be affected by the presence of SRB. This study focuses on the investigation of corrosion behaviour of duplex stainless steel DSS 2205 in nutrient rich artificial seawater containing SRB species, _Desulfoibrio vulgaris_, at different alkaline conditions with pH range from 7 to 10. The open circuit potential value (OCP), sulphide level and pH were recorded daily. Confocal laser scanning microscopy (CLSM) was used to study the adhesion of SRB on the DSS 2205 surface. Electrochemical impedance spectroscopy (EIS) was used to study the properties of the biofilm. Potentiodynamic polarization was used to study the corrosion behaviour of material. Inductively coupled plasma mass was used to measure the concentration of cations Fe, Ni, Mo, Mn in the experimental solution after 28 days. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) were used for surface analysis. The results showed that _D. vulgaris_ are active in an alkaline environment with pH 7-9. However, at pH 10, _D. vulgaris_ activity exhibited an 8-day lag. The corrosion rate of DSS 2205 at pH 9 was higher than at other pH environments due to a higher dissolved concentration of hydrogen sulphide.  
**Keywords:** microbiologically influenced corrosion; sulphate reducing bacteria; seawater; duplex stainless steel
## 2 Related Work
### Lexical Approaches  
In an effort to improve current lexical approaches for ASA, a novel lexical technique for ASA was suggested [21]. The LX was constructed in four stages. In the first step, we selected 300 root words from the SentiStrength site and added synonyms to our LX in the second step. In the third step, a phrase intensity weighting mechanism was deployed to the LX to examine if any terms had been omitted even after passing through the first two phases. The fourth step expanded the vocabulary by including terms from other Arabic dialects. Sentiment analysis was then conducted using this LX and the basic lexical technique by determining the text's polarity without taking negation or intensification into consideration. Precision was measured at 70.05% using multiple LX scalability stages, but it only covered MSA and did not provide any dialects. For example, one of the three systems presented by [22] was built on top of an improved form of an older lexical technique that could accommodate contextual polarization such as negative and intensifying comments. Using these additional variables, the authors were able to achieve an accuracy rate of 91.75%. For unlabeled data, the authors in [23] found that the LX-based approach was frequently used. However, facts are labeled, and polarity is estimated using sentiment lexica. Sentimental terms and phrases from the LX can be used to gauge the tone of a piece of writing (such as a review). The authors in [24] performed LX-based sentiment classification for Arabic Twitter datasets on the Syrian civil conflict and issues. Arabic TTs provided as a \"bag of words\" (BOW) were negatively or positively evaluated by checking the given emotions in an Arabic sentiment dictionary. The findings of this article did not analyze dialectical Arabic or other factors that may influence SA efficiencies, including intensifying and negating.  
ASA effectively uses both lexical and ML methodologies, as evidenced by surveying relevant research. We applied a text-based lexical strategy and evaluated the impact of numerous elements such as strength, light stemming, negating, and emoji on analytic precision in evaluating various lexical parameters.
## 4 Technologies to Support Contextualization
### Building and Interconnecting Formal Models
Finally, resource models describe the physical and functional properties of equipment and instrumentation, its behavior, interdependencies between local control units or modules, and requirements for performance and maintenance. The _integration of these partial models_ is crucial to formally describe the transformation of substances and the abilities of a plant or production line, which is a prerequisite for operation models that are required to derive optimal process parameters and interventions [122; 123]. However, this integration is challenging as the partial models stem from different trades with different perspectives (e.g., machine builders vs. process engineers). Therefore, ontology-based collaboration environments for concurrent process engineering have been developed to facilitate model integration [124], and different ontology-based integration strategies have been compared [121]. How to build the models described above? A promising approach is the use of _Semantic Web Technologies_[125; 126]. These concepts are based on graphs (e.g., ontologies) that describe how different elements are connected (for an example, see Figure 2). Graph-based models can be implemented in specific technologies such as Linked Data [127]. To address industrial requirements, the concept of Linked Data has been extended to Linked Enterprise Data [128]. The power of graph structures such as ontologies lies in the fact that knowledge is interconnected and it is possible to make queries about relations (e.g., What features affect the transport of chocolate bars? What process steps are affected by temperature?). Thus, operators can ask the system to return all elements that have a particular relationship with a concept of interest. To this end, different query languages such as SPARQL [129] are available. Based on an ontological modelling of relationships, knowledge can be generated by drawing inferences from the model (e.g., if chocolate handling operations are affected by bar geometry and the injection into the packing head is a handling operation, the faults of this operation may result from geometrical distortions). Ontologies enable system descriptions on different levels of abstraction (e.g., physics vs. function), can make connections between these levels explicit, and allow operators to switch between them. In this way, ontologies make it possible to inspect the system from different perspectives. Depending on the technology used, they can also make it explicit when information is unavailable or explain why an inference cannot be made. Moreover, different ontologies (e.g., machine and fault ontologies) can be connected to draw conclusions that consider aspects from different knowledge domains. They can also be used to contextualize process data and make them interpretable, which will be explained in the following section.
## 6 Conclusions  
In this paper, we applied a deep convolutional neural network based on transfer learning models to detect crack images using a popular crack dataset named Concrete Crack Images for Classification (CCIC). We utilized four transfer learning models for the experimental setup containing VGG16, ResNet18, DenseNet161, and AlexNet. As a performance metric, we used four terms named accuracy, recall, precision, and f1-score. Among the utilized models, AlexNet outperforms all the cases of performance metrics by achieving the accuracy of 99.90%, P of 99.92%, R of 99.80%, and F1-score of 99.86%. We also showed the training duration per epoch of all models. In this case, AlexNet achieves the first position in less time. In future work, we will conduct further research to provide a robust description of changing knowledge in our model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**SN** & **Reference** & **Base Model or Method** & **Accuracy** & **Dataset** \\ \hline
01 & [14] & VGG16 & 90\% & Beam, column, wall and joint brace images of a building \\
02 & [16] & FF-BLS & 96.72\% & CCIC dataset \\
03 & [17] & VGG16 & 94\%, 98\% & Fatigue cracks in gusset plate joints in steel bridges \\
04 & [12] & SegNet & 99\% & Concrete pavement, asphalt pavement, and bridge deck \\  & & & & cracks images \\
05 & [18] & VGG16 & 92.27\% & Concrete surfaces dataset collected from the Danish \\  & & & & Technological Institute \\
06 & [21] & DCNN model & 97.70\% & CCIC dataset \\
07 & [23] & ResNet18 & 98.80\% & Roads and bridges crack images \\
08 & [24] & GoogLeNet Inception V3 & 97.30\% & Wall images at college of environmental resources of \\  & & & & Fuzhou University \\
09 & [13] & MobileNet & 99.59\% & Wall, pavements, bridge deck images \\
10 & [26] & YOLOv5 & 88.10\% & Asphal crack pavement images \\
11 & Proposed & AlexNet & 99.90\% & CCIC dataset \\ \hline \end{tabular}
\end{table}
Table 8: Summary of publications using CNN-based transfer learning techniques for cracks detection.  
**Author Contributions:** Conceptualization, M.M.I. and M.B.H.; Methodology and software, M.M.I. and M.B.H.; validation and formal analysis, M.B.H., M.M.I. and K.F.H.; resources, M.M.I. and M.B.H.; data curation, M.M.I. and M.B.H.; writing--original draft preparation, M.M.I., M.B.H. and K.F.H.; writing--review and editing, K.F.H. and M.A.M.; visualization, K.F.H. and M.M.I.; supervision, M.N.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** The source code of this research work available at the GitHub repository ([[https://github.com/belal-bh/concrete-crack-detection-CNN-TL](https://github.com/belal-bh/concrete-crack-detection-CNN-TL)]([https://github.com/belal-bh/concrete-crack-detection-CNN-TL](https://github.com/belal-bh/concrete-crack-detection-CNN-TL))) (accessed on 11 August 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Prediction Models  
The SOG value of a ship involves environmental disturbance, which is difficult to model using conventional parametric approaches. In view of this complexity, this section describes the modeling techniques and the general method followed in this study to build potential machine learning models for ship speed prediction, such as DTR, and ensemble models, such as GBR, XGB, RFR, and ETR.