esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks
## 7 Conclusions and Future Work  
This research finds that the loss of network persistence is the main drawback for WSNs, which can be improved with the use of renewable charge cycles and an efficient routing scheme. The suggested methodology results in maximizing the vacation time over the charge cycle time and optimizing the path for the WPCD with the help of a shortest path algorithm. The important properties of WSN are addressed, i.e., minimizing the recharge time, energy consumption, and maximizing the remaining energy of the node. The numerical and simulation results show that grid-based clustering is the much better energy conservation technique for extending the life of the WSN. The research model posits the best possible renewable cycle and grid clustering strategies and decides a better WSN layout for the efficiency of energy.  
Probing deeper, the simulated results raise some points to be addressed in future work. One main point is the scalability, i.e., a wide area of the network should be taken for deployment of the WSN by reducing its phases. Another intention is to develop more economically feasible and energy saving strategies in the renewable charge cycle of a WSN. This strategy enables the WPCD to gather data from each node while charging the battery of that node concurrently. Further exploration is still required to investigate the issues mentioned above in the present work.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Single-Hop Routing** & **Grid Clustering Routing** \\ \hline Total Distance covered by WPCD & 6502.826120 (m) & 6761.404616 (m) \\ Traveling Time for WPCD by Physical Path P & 1300.565224 (s) & 1352.280923 (s) \\ Overall Time consumed in the renewable charge cycle & 1666.658984 (s) & 1393.098968 (s) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Entire simulation results for a wireless rechargeable network.  
Figure 14: Remaining energy after first charge cycle.  
**Acknowledgments:** Our research work is funded by National Natural Science Foundation of China (No. 51208168), Natural Science Foundation of Hebei Province (No. 2016202341) and Natural Science Foundation (No. 13 JCYBJC37700) of Tianjin as well.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; and finally, [PERSON] wrote the paper. All the authors have contributed substantially to the work reported.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Performance Characteristics of an Orthopter-Type Vertical Axis Wind Turbine in Shear Flows
## 2 Experimental Approach
### Torque and Power Coefficients  
Due to the difficulty of evaluating the mechanical losses of the bearings, the sprockets and the chains, this study considers only the aerodynamic torque generated by the blades as the rotor torque of the O-VAWT. The aerodynamic torque generated by the blades was computed by:  
\[T_{B}=T_{wb}-T_{woB}, \tag{5}\]  
where \(T_{wb}\) was the measured aerodynamic torque generated by the rotor when the blades were not removed; and \(T_{woB}\) was the measured aerodynamic torque generated by the rotor when the blades were removed. It is worth noting that, generally, when the blades were not removed from the rotor, the rotor generated positive torque while the motor acted as a load to keep the value of \(\omega\) constant. Conversely, at all tip speed ratios, when the blades were removed from the rotor, the rotor generated negative torque while the motor acted as the driving force of the rotor revolution. Therefore, at all tip speed ratios, \(T_{B}\) was higher than \(T_{wb}\).  
The power coefficient describes that fraction of the power in the wind that may be converted by the turbine into mechanical work [30] and is defined in this study as:  
\[C_{P}=\frac{T_{B}\omega}{0.5\rho A\U_{0}^{3}}, \tag{6}\]  
and the torque coefficient is defined as:  
\[C_{T}=\frac{T_{B}}{0.5\rho A\U_{0}^{2}R}. \tag{7}\]  
Here, the \(U_{0}\) is the time-mean stream-wise velocity, \(\overline{u}(x,y,z)\), averaged over the projected rotor's swept area at \(x=0\) and is computed by:  
\[U_{0}=\frac{\int_{-(R+0.5c)}^{R}\overline{u}(0,y,0)dy}{2R+0.5c}. \tag{8}\]
# GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera
## 9 Conclusion and Future Work  
In this paper, we presented GazeGuide, a novel framework that controls UAV camera maneuvering through the eye-gaze of a remote user. We described the implementation of GazeGuide and reported on an experimental study. A surveillance and monitoring application-based experimental set up was prepared in an outdoor environment, and a user study with 15 participants was conducted through both quantitative and qualitative measures. The objective of the evaluation was to compare the efficiency of the proposed GazeGuide against that of the RC-based control method. The quantitative measure showed that the participants using GazeGuide performed significantly better than those using the RC-based control. In addition, the qualitative measure provided clear evidence that GazeGuide performed significantly better than the RC-based control. Overall, these measures revealed that the GazeGuide performed well under all the selected conditions as an input modality in HRI.  
The gimbal camera used in this research was mounted on a UAV for a specific reason. The camera behaved like a human eyeball, and the UAV behaved like the human head while tracking an object. The current research focused on the maneuvering of the camera with the UAV fixed in a predefined direction. In the future, we intend to explore how GazeGuide would behave when both the camera (eyeball) and the UAV (head) are moving. The future work will also include enhancements in providing more degrees-of-freedom to control the camera by integrating interactions using eye blinks and other combinations.  
Conceptualization and methodology: P.K.B.N., A.B., and Y.H.C.; software, validation, formal analysis, and investigation: P.K.B.N., A.B., C.B., and A.K.P.; writing-original draft preparation: P.K.B.N.; writing-review and editing: P.K.B.N., A.K.P., and A.B.; data curation: P.K.B.N., A.B., A.K.P., and C.B.; supervision, project administration: A.K.P. and Y.H.C.; funding acquisition: Y.H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science & ICT of Korea, under the Software Star Lab. program (IITP-2018-0-00599) supervised by the Institute for Information & Communications Technology Planning & Evaluation and the Chung-Ang University research grant in 2020.  
We would like to thank [PERSON] and [PERSON] for their time and support in conduction of the experiments.  
The authors declare that they have no conflicts of interest to report with respect to this paper.  
The following abbreviations were used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned Aerial Vehicle \\ HMD & Head-Mounted Display \\ HRI & Human-Robot Interaction \\ HCI & Human-Computer Interaction \\ ToI & Target of Interest \\ ToIs & Target of Interests \\ RC & Remote Controller \\ \end{tabular}
## 3 Models Design
### Design of CNN+Transformer Hybrid Model  
In this study, we apply a hybrid concept called CNN-Transformer, which combines the CNN and Transformer models, to design the basic structure of the CNN+LSTM hybrid, CNN, LSTM, and ELM models. Furthermore, we measure various performance metrics, such as accuracy, precision, recall, F1 score, and RMSE, to evaluate the predictive capabilities of each model and compare their performance across these metrics. The CNN-Transformer hybrid model offers the advantage of incorporating both the CNN's convolutional operations and Transformer's attention mechanism, potentially capturing both local and global patterns in the input data. This combination aims to enhance the model's ability to extract relevant features and capture long-term dependencies, leading to improved performance in odor analysis tasks.  
The CNN-Transformer hybrid neural network architecture in Figure 1 defines the input data format through the Input layer. The CNN layer extracts features from the sequence data by performing convolutional and pooling operations, followed by applying layer normalization. The output of the CNN layer serves as an input to the Transformer layer, which consists of attention layers and feed-forward neural network layers, followed by global average pooling. In this architecture, the output layer is a dense layer with two nodes, utilizing the sigmoid activation function.
## 1 Introduction
### Description of the Issue  
According to the literature review, five main challenges were found:  
1. A microgrid planning approach that continues to be on the supply side. The microgrid models found in the literature assume that the energy resources belong to a single party, usually the public service companies or grid operators, and ignore community interests, such as the need to improve reliability indices and participate in the energy markets.
2. The need to incorporate reliability requirements in the planning of microgrids, which favors their islanded operation during a minimum period of autonomy. The solutions found in the literature, even though they consider elements that provide reliability, such as energy storage, do not guarantee that users can protect themselves against different levels and durations of faults in the main grid.  
3. Consideration of the stochastic dimension in the planning of microgrids, so that a community can prepare for different fault scenarios. In some works, microgrid planning is based on deterministic optimization models that consider a single operational scenario, generally without fault [17; 18; 21]. Other investigations, while they take into account the intermittency of renewable sources, the interruptions of the energy supply, or the growth in demand [20; 27; 28; 29], do not present solutions that clearly quantify the advantages of having microgrids which satisfy reliability requirements.
4. Development of planning models considering a long-term analysis horizon, with hourly resolution of the data. Some planning works include the use of data samples with hourly load and generation resolution, with which they seek to represent annual meteorological conditions, in time windows of weeks of a month, or even a typical day [30; 31; 32]. Using this type of representation of the study horizon, the results could be extended to a full year (365 days), while the calculation time of the optimization problem is considerably reduced [33]. In this research, the stochastic and random behavior of hundreds of load profiles, with 8760 h, was considered.
5. The conclusions of this research are obtained from the optimal development of hundreds of microgrids with reliability requirements. In contrast, previous investigations on microgrid planning base their conclusions on a few case studies, or even particular cases (a specific microgrid planning) [34; 35; 36], which make it impossible to extract generalized conclusions.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 2 Proposed Chaos-Based Cryptosystem
### Circular Substitution Process Based on the S-Box and Their Inverse Processes
#### 2.2.1 S-Box Construction  
Mathematically, an \(n\times n\) S-box is a nonlinear mapping \(S:\{0,\ 1\}^{n}\rightarrow\{0,\ 1\}^{n}\), where \(\{0,\ 1\}^{n}\) represents the vector spaces of elements from GF (2). Since the key-dependent S-boxes do not offer any specific properties to the attackers, then, they are more secure than fixed S-boxes. Therefore, many methods have been proposed to design and create key-dependent S-boxes in conventional cryptography, such as SEAL [39], which used a Secure Hash Algorithm (SHA) and especially in chaos-based cryptography [40; 41; 42; 43; 44; 45; 46]. All of these later methods are based on iterating discrete chaotic maps to generate a 1-D table of size 256, containing unique chaotic values belonging to 0 and 255. The proposed S-box is based on our PRNG-CS and uses the approach of [PERSON] et al. [42].  
The block diagram of the \(8\times 8\) S-box design algorithm is given in Figure 4.  
The design steps of the S-box generation algorithm are as follows:  
1. Specify a one-dimensional empty array called 1-D S-box \(S[\ ]\).
2. The eight-bit \(V\)-value is obtained from the PRNG-CS output sequence \(X(n)\).
3. Check for the existence of the last obtained \(V\)-value in the content of the 1-D S-box \(S[\ ]\).
4. If this value exists, it is rejected. Otherwise, it is added to the S-box 1-D \(S[\ ]\).
5. Afterward, new 8-bit values are generated and the whole process is repeated until all 256 values are completed in the form of a sequence \(S[t]=\{S[0],\ \ldots,S[255]\}\), where each value \(\in[0,\ 255]\) is unique to others in it, so the constructed \(8\times 8\) S-box is obviously bijective (by construction).  
The number \(n\) of needed samples to construct the S-box is the size of the dynamic key \(K_{c}\): \(|K_{c}|=n\times 32\)-bit.  
Figure 4: Block diagram of the proposed chaos-based \(8\times 8\) S-box construction.  
In Table 2, we give an example of an S-box obtained by our proposed algorithm, presented in its usual form, a 16\(\times\)16 matrix of byte values in hexadecimal presentation.
## 6 Discussion  
### Baseline vs. Automatic Hierarchy Method  
The automatic hierarchy method proposed in this study has shown a positive trend in enhancing the efficiency of searching creation records, as well as in usability and ease of learning compared to the baseline. Our findings confirm that the automatic hierarchy can reduce the number of interactions and shorten the time required for searches, thus increasing efficiency for users. Preference for future use also showed higher favorability compared to the baseline, and participants found it easy to use. Interestingly, although participants mentioned that becoming accustomed to the automatic hierarchy method requires more learning and repetition than the baseline, they could intuitively understand the information-searching method of automatic hierarchies. We believe that familiar components like the navigation bar provided at the top of the automatic hierarchy method, a common feature in traditional GUIs, contributed to its intuitive usability.
## Appendix A
For continuous spectrum X-rays that pass through a homogeneous material, using an energy-integrating detector, the value of the transmitted signal after passing through the object is:  
\[T=\int_{0}^{E_{in}}N(E)P_{d}(E)Ee^{\mu(E)t}dE \tag{10}\]  
where \(E\) is energy of X-ray photons, \(E_{in}\) is the highest energy of the input continuous spectrum, \(N\) (\(E\)) is the number of X-ray photons between \(E\) and \(dE\), \(P_{d}\) (\(E\)) is the probability (efficiency) of photon detection with energy \(E\), \(B\) (\(E\)) is the accumulation factor, \(\mu\) (\(E\)) is the linear attenuation coefficient. For dual-energy X-ray transmission with maximum energy \(E_{H}\) and \(E_{L}\), there are the following nonlinear integral equations:  
\[\left\{ \begin{array}{l}T_{L}=\int_{0}^{E_{L}}N(E)P_{d}(E)Ee^{\mu(E)t}dE\\ T_{H}=\int_{0}^{E_{H}}N(E)P_{d}(E)Ee^{\mu(E)t}dE\end{array} \right. \tag{11}\]  
Similarly, the R_value of continuous spectrum DE-XRT is defined as:  
\[R=\frac{\ln(T_{L}/T_{L0})}{\ln(T_{H}/T_{H0})} \tag{12}\]  
where \(T_{L0}\) and \(T_{H0}\) are, respectively, the measured values of the low-energy and high-energy integral of the transmission signal before attenuation. \(T_{L}/T_{L0}\) and \(T_{H}/T_{H0}\) are defined as low-energy and high-energy transparency, respectively. The following R_values are defined by Equation (12). Because of the influence of integration, both the coefficient of the integration term and the linear attenuation coefficient in exponential terms in Equation (11) are functions of energy \(E\) of integration variable, so the calculation of R_value in Equation (12) cannot remove the influence of thickness \(t\), that is, the R_value is not only related to the atomic number \(Z\), relative atomic mass \(A\) and density \(\rho\), but also related to the thickness \(t\) of the material. Therefore, DE-XRT method cannot directly identify the atomic number and the material according to the R_value calculated by Equation (12), and the influence of thickness needs to be removed. For a homogeneous compound or mixture, if the percentage content of each element constituting the object is \(\omega_{i}\), the corresponding mass attenuation coefficient of each element is \(\mu_{mi}\), the corresponding atomic number of each element is \(Z_{i}\), and its effective atomic number is \(Z_{eff}\), one of the calculation methods of the effective atomic number and its mass attenuation coefficient are as follows [40]:  
\[Z_{eff}=\sum_{i}\omega_{i}Z_{i} \tag{13}\]  
\[\mu_{m}=\sum_{i}\omega_{i}t_{mi} \tag{14}\]  
Therefore, the above Equation (12) is also applicable to homogeneous compounds or mixtures, that is, for known mixtures or compounds, the DE-XRT method based on the R_value of Equation (12) can also be used for material identification.
# Digital Twin and Internet of Things--Current Standards Landscape
## 2 Classification Schema
### Resource Description
#### 2.1.7 Kinds of Data  
The great variety of different use cases and application domains of IoT and DT require coping with different kind of data. The Big Data Value Association (BDVA) identified six different types of data, including time series, geo-spatial, temporal, and multimedia data, as well as the fact that different types of data require different query capabilities [30]. Adding explicit support for a special kind of data is something that is beyond the absolute core of a standard, i.e., it can be seen as adding a cross-domain main model according to Figure 2 below the meta-model (which is part of the absolute core). As most IoT and DT standards considered in this paper are either rather new or still being defined, they are still focusing on the core of the standards and often did not yet add explicit support for different kinds of data. In this paper, we, therefore, consider only the support for geo-spatial, historical, and temporal data as these are the most important aspect that are already covered at least by some platforms.  
Geo-spatial data is typically needed when there is any interaction with the physical world, e.g., a resource with a physical location, typically somewhere on earth but in some scenarios, this could also be somewhere in space. Historical and temporal data is quite similar, as both require data to be considered in the context of time. Historical data represent the history of a resource over time with a focus on the timely order of different states. Temporal data is considered simply data in the context of time. It can comprise data about the past but also the future of different resources at once, e.g., time-series data or results of prediction algorithms.  
If standards support these different kinds of data is often determined by the paradigm followed. Probably most common is a device-centered paradigm where the virtual resource representing a physical device is considered a proxy reflecting the current state of the physical device as-is. Therefore, historical and temporal data is typically not available following this paradigm. Less common is an observation-based paradigm where observed changes in the environment are not only represented as changes in the state but also documented as an observation that was made by a sensor.
# Carbon Dioxide Uptake by Cement-Based Materials: A Spanish Case Study
## 6 Results and Discussion
### Carbon Dioxide Indicators for Concretes  
Figure 14 collects the results of the kg\({}_{\text{cement}}\)/(m\({}^{3}\)\(\times\)MPa) indicator for 40 commercial concretes made in Spain [31]. This indicator, among others, is proposed to measure the environmental impact of the construction materials [29]. The results range from 5 to 14 kg\({}_{\text{cement}}\)/(m\({}^{3}\)\(\times\)MPa). Most of the CEM I and half of the CEM II/A-V cements [28] range between 5 and 8 kg\({}_{\text{cement}}\)/(m\({}^{3}\)\(\times\)MPa). As consequence, the higher the 28-day compressive strength, the lower the carbon dioxide indicator defined as kg\({}_{\text{cement}}\)/(m\({}^{3}\)\(\times\)MPa). In addition, high-strength and high-performance concretes led to the design of slender concrete elements than using normal concrete. Given that, a lower amount of concrete is necessary. Consequently, it can be underlined that the global environmental impact of using this type of concrete is lower.  
Blended cements, which are recommended for aggressive environments because they are able to provide a longer service life of the concrete structures, exhibited higher carbon dioxide indicator values. Therefore, we share the view that the scientist should better suggest more adequate carbon dioxide indicators in which the concrete durability would be seen as a capital aspect. In addition, new cement types [32] and concrete designs [33] are developed in order to progress in accordance with the climate change actions [34].  
Figure 13: Carbon dioxide uptake by the cement-based materials produced with the Spanish cement manufactured between 2005 and 2015 considering the IVL simplified methodology [26].  
Finally, it is believed that the net assessment with regard to the anthropogenic CO\({}_{2}\) emissions of the cement sector should be taken into account in future climatic models due to the great significance of the linker production. For instance, in 2016, the estimated contribution of the cement industry to the anthropogenic CO\({}_{2}\) emissions was 7.4% (2.9 Gtons CO\({}_{2}\)) of world carbon dioxide emissions (39 Gtons CO\({}_{2}\)). However, if we take into account the CO\({}_{2}\) uptake by applying the simplified methodology, such contribution is now estimated to be 6.3%, as shown in Equation (7).  
\[\text{Net CO}_{2}\text{ emissions}=(\text{Total CO}_{2}\text{ emissions})-(0.23\times\text{Process CO}_{2}\text{ emissions}) \tag{7}\]  
where total CO\({}_{2}\) emissions in 2016: 2.9 Gtons CO\({}_{2}\); and process CO\({}_{2}\) emissions in 2016: 0.64 \(\times\) 2.9 Gtons CO\({}_{2}\) = 1.86 Gtons CO\({}_{2}\).  
In this case, it is assumed that 64% of the total carbon dioxide emissions are due to the calcination process. Therefore, the net CO\({}_{2}\) emissions in 2016 are 2.47 Gtons CO\({}_{2}\).