esa-sceva/satcom-chunk-collection huggingface dataset:
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 2 Distance Learning  
E-learning is connected with virtualized distance learning through electronic communication devices and mechanisms, with their own functionalities both as a support and as an integral part in the teaching-learning procedures. Virtual courses, supported partly or in full by the distance learning approach, rely on web-based architectures, themselves capable of storing and delivering the so-called Learning Objects (LOs), i.e., various kinds of educational materials (slides, videos, quizzes, tests, etc.) having a single educational objective. LOs are usually encoded into container formats, such as SCORM (Shareable Content Object Reference Model), themselves capable of recording the time spent by the students on the LO itself. These items are part of a so-called Virtual Learning Environment (VLE), a digital framework and web-based portal made of all learning sources, with which students and instructors can interact in a constructivist way. Moodle ([[http://moodle.org/](http://moodle.org/)]([http://moodle.org/](http://moodle.org/))) (Modular Object-Oriented Dynamic Learning Environment) is surely one of the most renowned learning environments, especially thanks to its modularity, its reusability in different contexts and for the constructionist pedagogical principles on which it is based. Concerning the data sources of a typical VLE, they are usually multifaceted and consist of four main layers [4]:  
* VLE-specific sources;
* general application sources;
* middleware sources;
* systems software sources.  
Some examples of the most widespread sources are the following: a user profile, private and shared annotation spaces, SCORM containers, chats, forums, blogs, tests and e-portfolios, wikis, concept maps, and the like.  
Distance learning has a clear advantage compared with classical attendance groups: the higher number of students that can have access to educational contents. This is achieved, for example, through the deployment of MOOC (Massive Open On-line Course) platforms, such as the ones provided by the Khan Academy ([[http://www.khanacademy.org](http://www.khanacademy.org)]([http://www.khanacademy.org](http://www.khanacademy.org))), the EduOpen([[http://en.eduopen.org/](http://en.eduopen.org/)]([http://en.eduopen.org/](http://en.eduopen.org/))) project, the MIT Open CourseWare ([[http://ocw.mit.edu/index.htm](http://ocw.mit.edu/index.htm)]([http://ocw.mit.edu/index.htm](http://ocw.mit.edu/index.htm))), Coursera ([[http://www.coursera.org/](http://www.coursera.org/)]([http://www.coursera.org/](http://www.coursera.org/))), edX ([[http://www.edx.org/](http://www.edx.org/)]([http://www.edx.org/](http://www.edx.org/))), and the like. However, the advantages of e-learning systems are not limited to the aforementioned, but are multifaceted and can address different stakeholders: students, instructors, IT staff, institutions and educational researchers [5].
## 8 Conclusions and Future Work  
In this paper, an urban-based path planning algorithm suitable for campus-environment autonomous driving vehicles is proposed. The path planning algorithm applied to the campus environment, MGPF-Hybrid A*, was proposed to ensure collision-free driving by considering road constraints and multiple obstacles. To consider campus-road constraints, the proposed algorithm used reference path points that considered road lanes. The reference path points are rebuilt as a polynomial equation. This approach solves the lane departure problem. The path polynomial equation and two-dimension obstacle data are applied to the artificial potential field to create attractive and repulsive forces that control the path points that can be generated by the proposed path planning algorithm. If the attractive force is high, it pulls toward the generated path. Otherwise, if the repulsive force is high, it pushes away from the generated path. To evaluate our urban-based path planning algorithm, the MORAI simulator, which mimics the campus of Chungbuk National University, is used. Moreover, to evaluate a real-world environment, autonomous driving algorithms, such as vehicle localization, object recognition, and vehicle control, are implemented. These are applied to the micro electric vehicle that exists within the MORAI simulator. In this simulation, our urban-based path planning algorithm successfully maintains road constraints and avoids multiple obstacles. Although the proposed algorithm is evaluated in a simulated environment, it is also tested on a real road of the CBNU campus using a micro-EV as an autonomous vehicle delivery service. The proposed algorithm could be used to determine a proper solution to static obstacles, which can be used for autonomous vehicle delivery services on a campus, but it does not have a flexible a path and lacks the same decision-making as a human driving. Therefore, further research will be conducted to reduce the burden on the decision logic of the autonomous vehicle driving system through path planning and which behavior decisions are considered for dynamic obstacles.  
Conceptualization, C.P.; methodology, C.P.; validation, C.P.; writing--original draft preparation, C.P.; writing--review and editing, S.-C.K.; supervision, S.-C.K.; funding acquisition, S.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support, program(IITP-2021-2020-0-01462) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).  
Not applicable.  
The source code of the proposed algorithm is available on the site, [[https://github.com/zed654/path_planning](https://github.com/zed654/path_planning)]([https://github.com/zed654/path_planning](https://github.com/zed654/path_planning)) (accessed on 26 April 2021).  
The authors declare no conflict of interest.
## 1 Introduction
Alternative approaches are also used, such as adopting new network models and topologies [11, 12] to make the WSN work effectively with available resources [13, 14]. In the current study, we mainly intended to develop an energy efficient routing mechanism using artificial intelligence to select the optimal path routing in a WSN. Although the existing approaches have attempted to provide routing protocols through cluster formation, most of them fall short in terms of energy consumption and time delay in the transmission, along with security threats in the network. Hence, the use of algorithmic hybridization leads to improvements in the network and improves the overall performance of the routing model used to route data from a sender to a receiver in the network. The aim of the proposed work is to provide an optimal path considering the parameters of computational time, performance, energy consumption, and resource usage. The enhanced system addresses the problem of computational complexities through an artificial intelligence-based model of meta-heuristic and optimization algorithms. For the implementation of this work, we employed an optimization algorithm, GWO, for cluster head selection and a meta-heuristic algorithm, DFO, for path determination. Both algorithms provide efficient transmission of data packets through clustering-based techniques, thereby finding the optimal path for the densely populated network. The proposed hybridization of GWO and DFO focuses on increasing the efficiency in performance in the selection of the optimal path, thereby using minimal energy for transmission. Finally, the outcome of the combined approach can be tested with the QoS metrics for bringing out the advantages of the system. Hence, using the proposed work of artificial intelligence-based optimal path selection in WSNs, the network achieves an extended lifetime through energy consumption and transfers the data reliably with minimum time. This article is organized as follows. Section 2 provides the literature review. Section 3 details the materials and methods of the proposed method. Section 4 provides a comparative analysis and the results of the proposed method with other studies. Finally, Section 5 concludes with conclusions and future research prospects.
# A New, Fast Pseudo-Random Pattern Generator for Advanced Logic Built-In Self-Test Structures
## 3 Classical Method to Design LBIST Circuits with the STUMPS Architecture
### Layout of a DT-LFSR Register  
Any DT-LFSR is designed as an endless ring of D and T flip-flops connected in series. The principal schematic of an \(n\)-bit DT-LFSR register comprising \(k\) flip-flops of the D type and \(t=n-k\) flip-flops of the T type is depicted in Figure 7. In this study, such a structure of a DT-LFSR register shall be symbolically designated as \(\text{D}^{\text{T}}\text{T}^{\dagger}\). Attention should be paid to the fact that such a register contains no XOR gates in the main loop of the linear feedback. Instead, it contains XOR gates in internal linear feedback loops inside T-flip-flops. A T-type flip-flop is actually a D-type flip-flop with an XOR gate in a feedback loop. Therefore, the characteristic polynomial \(p(x)\) that corresponds to the linear feedback of an \(n\)-bit DT-LFSR from Figure 7 adopts the following form:  
\[p(x)=1+x^{k}\cdot(1+x)^{t}, \tag{2}\]  
where \(k+t=n\). It is important to mention that any rearrangement of the register layout with reordering of the D and T flip-flop sequence in the ring register has no effect on the characteristic polynomial \(p(x)\) of that register. Relocation of T-type flip-flops in such registers can be actually considered as a sequence of left shift operations applied to local linear feedback loops. Such a procedure was named in [60] as the EL transformation. According to Theorem 1 disclosed in that study, such modifications of linear register structures are indifferent to the characteristic polynomial of the register.  
It is also worth mentioning that a DT-LFSR register is actually a specific variation of Linear Hybrid Cellular Automata (LHCA) with cyclic boundary conditions, made up of cells with the rules 60 and 240. Each T-flip-flop is a CA cell with the rule 60 while a D-flip-flop is a CA cell with the rule 240. A table summarizing all possible structures of DT-LFSR registers with the length of \(n\in[15,100]\) and a linear feedback loop described by means of a primitive polynomial can be found in some past papers, including [38]. The table content serves as the proof that for the specific length \(n\) of a DT-LFSR register only some combinations of \(k\) and \(t\) parameters lead to such a \(\text{D}^{\text{T}}\text{T}^{\dagger}\) structure of the register that corresponds to a primitive characteristic polynomial. Each of these structures contains at least one D-type flip-flop (i.e., \(k>0\) for every case). In addition, for some specific lengths \(n\) of registers, any DT-LFSR structure associated with a primitive characteristic polynomial may not exist.
## 3 Aerospace Engineering Applications
### Aplication to Wing Morphing
The proposed method consists of two steps: (a) a strong algorithm that can solve the coupled problem of SMA-structure in the context of a finite element analysis software and (b) the coupling of the entire process with an optimization technique to find configurations able to obtain target structures using SMA actuators. To confirm the superiority of the proposed method, numerical analysis was performed on the airfoil-based wing of the A320 model. Table 3 shows various studies on morphing wings performed using an SMA actuator. In addition, details of the morphing wing study are summarized in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Control Type** & **Controller** & **Application** \\ \hline
[86] & & self-tuning fuzzy controller, PID controller & morphing wing (camber) \\ \hline
[87] & & PID with on–off controller & morphing wing (camber) \\ \hline
[89; 90] & & on–off and PI controller & morphing wing (camber) \\ \hline
[91; 92] & & fuzzy-PID controller & morphing wing (camber) \\ \hline
[96] & PID Control & PID controller, PID with robust compensator, and PID with anti-windup compensator & Morphing wing (camber) \\ \hline
[109] & & adaptive control (PID + pulse frequency and pulse with variation) & MAV morphing planar wing \\ \hline
[82] & & passive control (stress to temperature modeling) & morphing wing (camber) \\ \hline
[83] & & passive control (thermo-mechanical modeling) & morphing wing (camber) \\ \hline
[88] & Passive & passive control &
\begin{tabular}{c} morphing wing (sweep angle change and span-wise bending) \\ \end{tabular} \\ \hline
[93] & & passive control & morphing wing (camber) \\ \hline
[94] & & passive control & morphing wing (camber) \\ \hline
[97] & & passive control & morphing wing (camber) \\ \hline
[100] & & passive control & morphing wing (camber) \\ \hline \end{tabular}
\end{table} 
Table 3: Control methods for morphing wings.
## 2 Materials and Methods
### Climatic Data  
#### 2.1.1 Current Scenarios  
In previous studies, three sub-climate types were defined for South Tyrol, according to the similarities and distinctions in climate patterns (climate zones are shown in Figure 1, and the detailed methodology is introduced in [27]). The differences among the three climate zones are explained in Table 1. Climate zone I and II cover the regions with an altitude below 1300 m, where a large part of the population is concentrated. Thus, this study is focused on the performance of buildings in these two zones. For each climate zone, hourly climate data in current and future conditions are used. Essential climatic variables for the indoor climate simulation, including air temperature, solar radiation, precipitation, wind direction, wind speed and humidity, are obtained from the WISKI dataset of the Hydrographic Office of the Autonomous Province of Bolzano-Sudtirol [28]. The stations with longest uninterrupted, hourly observations of all variables are selected. This prerequisite is fulfilled by the weather stations located in Bolzano and Silandro.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 3 Experimental Area and Data Source
### Radar Control Parameter Setting  
The X-band radar was set to track scanning mode with a pulse width of 0.5 \(\upmu\)s, a range resolution of 75 m, a sample size of 128, a single PRF, and the deactivation of clutter filters such as ground clutter. Due to the proximity of the UAV experimental site to the radar, the effects of ground clutter interference were significant in the absence of filters. Therefore, the elevation angle of the radar was set by referencing the volume scan data where the background clutter was relatively low. The specific observation parameters are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators** & **Parameters** & **Indicators** & **Parameters** \\ \hline Wavelength (cm) & 9.4 GHz & Number of pulses & 128 \\ Scanning mode & TRACK & Antenna beamwidth (\({}^{\circ}\)) & 1 \\ Pulse width (\(\upmu\)s) & 0.5 & Elevation angle (\({}^{\circ}\)) & 8.5 \\ Maximum unambiguous velocity (m/s) & 12.8 & Ground clutter & OFF \\ \hline \hline \end{tabular}
\end{table}
Table 4: X-band radar system main configuration parameters.  
Figure 6: The experimental site.
## 7 Results and Discussion
With the Adamax optimizer, the results with greater repetitiveness were presented with the batch size of 256, for the batch size of 128 it had a mean of 16.25 with a variance of 0.5, which was greater than that present in the batch size of 96 maintaining a similar mean. Finally, Figures 12 and 13 show the behaviors of the execution times of each of the tests in the modeling of PM\({}_{2.5}\) and PM\({}_{10}\) particulate matter, respectively. In both Figures, the same behavior is observed regardless of the optimizer implemented. In other words, the execution time was higher as the batch size decreased. This was especially so with a batch size of 96, regardless of the topology implemented. However, the execution times in the modeling of the PM\({}_{2.5}\) particles were higher with the Adamax optimizer, with the slowest execution being that of the LSTM network, followed by the GRU network. The Elman network was the fastest. For the PM\({}_{10}\) particle modeling, Figure 13 indicates that the behavior recorded was practically the same, regardless of the optimizer implemented. This is also consistent with previous results in which the Elman network was the fastest topology, followed by the GRU network and the LSTM network. Summarizing the above, Tables 4 and 5 show the best combination of hyperparameters for each network in the modeling of PM\({}_{2.5}\) and PM\({}_{10}\) particles, respectively, indicating the average of the corresponding metrics obtained. Figure 12: Processing time variability in modeling particle behavior PM\({}_{2.5}\). (**a**) Elman neural network using Adam optimizer. (**b**) Elman neural network using Adamx optimizer. (**c**) GRU neural network using Adam optimizer. (**d**) GRU neural network using Adamx optimizer. (**e**) LSTM neural network using Adam optimizer. (**f**) LSTM neural network using Adamx optimizer. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Neural Network** & **Optimizer** & **Batch Size** & **CC** & **RMSE** & **Times(s)** \\ \hline Elman & Adam & 512 & 0.7977 & 16.6869 & 256.72 \\ GRU & Adam & 96 & 0.8004 & 16.2505 & 2544.90 \\ LSTM & Adamax & 128 & 0.7992 & 16.2492 & 2536.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best hyperparameters for PM\({}_{10}\) particle modeling.
## 4 Discussion
### Explanations of Findings  
There are several factors that may help to explain the results of this experiment.  
In the context of XAI, prior work emphasizes not only that users should be able to build trust toward AI but also should be capable of detecting potentially malfunctioning AI. Building on this recommendation, we see that, by highlighting the AI's performance, users may have become aware of an AI with low predictive power and therefore adapted their strategy. Through transparent potential error communication, the users were able to anticipate when it was worth following an AI recommendation or actively engage in the decision process based on their experienced trust or distrust. Furthermore, our results also highlight that error communication did not affect the _overall_ self-reported trust in the system.  
An additional factor for these findings may be the perception of risk: on one side, we see no effect of risk on the number of checks but, on the other side, we see a significant influence of risk perception on the decision time. An explanation for these results may lie in the perceived level of risk to which the users were exposed. Due to the limited capabilities of a controlled lab environment, we were not able to increase the severity of risk that may have caused the users to perceive not being in a risky situation.  
The findings on the decision time suggest that conveying the potential AI error to the user helps the user make a faster decision on whether to actively check the model's prediction before accepting it or fully delegating it to the AI regardless of the risk level. These results are in line with prior work [70] showing that users required longer to make a decision whenever they felt insecure about the decision. By providing information about the AI's shortcomings, the users anticipated potential risks more easily and were therefore able to make faster decisions [71]. As prior research shows, a higher risk may increase the time required to make a decision [72], which we also observed in the \"not revealed\" condition. However, our results show that, once the AI's performance information was revealed, there was no significant difference in the decision time across risk levels.  
The findings about the total score may reflect the participant's performance improvement when communicating the potential AI error. We consider the total score increase as a product of a more efficient participant's strategy of collaborating with the AI. Note that the risk effect becomes apparent as the score increment and decrement are larger at high risk. Thus, given that the total number of checks of the model's decision did not vary between two conditions and across two risks, the total score difference was substantially bigger at a high risk.
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 5 Conclusions
With technological advances to cloud computing, 3D and virtual Earth, the ideal Big Earth Data processing platform is gradually emerging [100]. Although the amount of research based on EO satellite data has increased rapidly in recent years, there are still difficulties in accessing some types of satellite data. For example, it is clear that institutions in China reference Gaofen data far more often than institutions in other countries. This is not conducive to the development and expansion of the Gaofen satellite mission. From the analysis described in this study, it is clear that there is potential for widespread global use of Gaofen data. However, it is necessary to simplify access to these data and broaden distribution channels in other countries so that scientists from various countries can efficiently conduct research referencing Gaofen satellite data. To varying degrees, similar problems exist in relation to other types of EO satellite data including Cartosat, COSMO-SkyMed, Fengyun, IRS, and Ziyuan data. Open and simplified access to geospatial data is vital to the development of remote sensing applications, spurring the creation of standard products and new approaches to data application [103]. The following are available online at [[https://www.mdpi.com/article/10.3390/rs14081863/s1](https://www.mdpi.com/article/10.3390/rs14081863/s1)]([https://www.mdpi.com/article/10.3390/rs14081863/s1](https://www.mdpi.com/article/10.3390/rs14081863/s1)): Table S1: List of journals published articles referencing EO satellites, Table S2: List of research disciplines of articles referencing EO satellites, Table S3: List of EO satellites discussed, Table S4: List of clusters found for the application of EO satellite data, Table S5: List of keywords in all analyzed papers, Table S6: List of institutions applied EO satellite data, Table S7: List of countries or regions applied EO satellite data. Conceptualization, L.Y. ; Methodology, Q.Z. and L.Y. ; Software, Q.Z. ; Formal analysis, Q.Z., L.Y., Z.D., D.P., P.H., Y.Z. and P.G. ; Resources, L.Y. and Y.Z. ; Writing--original draft preparation, Q.Z. and L.Y. ; Writing--review and editing, Q.Z., L.Y., Z.D., D.P., P.H., Y.Z. and P.G. ; Supervision, L.Y. and Y.Z. ; Project administration, L.Y. ; Funding acquisition, L.Y. and D.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Key R & D Program of China (grant number: 2019 YFA0606601; 2019 YFE0115200), Tsinghua University Initiative Scientific Research Program, (grant number: 2021Z11 GHX002), and the National Key Scientific and Technological Infrastructure project \"Earth System Science Numerical Simulator Facility\" (EarthLab). **Data Availability Statement:** This study does not contain reports on any data. **Conflicts of Interest:** The authors declare no conflict of interest.