esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaotic Evolutionary Programming for an Engineering Optimization Problem
## 4 Proposed Algorithm
### Chaotic Evolutionary Programming  
In chaotic evolutionary programming, a pre-selected chaotic sequence is used to replace the conventional random number generator. The Gauss map and the tent map are used in the iterative process. Therefore, in an \(N_{D}\)-dimensional search space, an \(i\)th individual vector \((x_{i},\eta_{i})\) is a possible solution of the problem. Mathematically, the evolution concept of chaotic EP to generate offspring is illustrated as follows:  
\[x_{ij}^{t+1}=x_{ij}^{k}+\eta_{ij}\phi_{j}(0,1)\quad(i=1,2,\dots,N_{P},j=1,2, \dots,N_{D}) \tag{9}\]  
\[\eta_{ij}^{t+1}=\eta_{ij}^{k}exp(\chi^{t+1}\phi(0,1)+\chi\phi_{j}(0,1))\quad( i=1,2,\dots,N_{P},j=1,2,\dots,N_{D}) \tag{10}\]  
where \(\phi_{j}(0,1)\in[0,1]\) is a chaos generated random number for the \(j\)th individual.  
In this work, the Gauss map and the tent map sequence are used as these have the advantage of a uniform distribution and are one-dimensional maps. These maps generate numbers that help the algorithm converge faster.  
A Gauss map based chaotic sequence is represented as [20]:  
\[\phi_{n+1}=\begin{cases}0&;\phi_{n}<0\\ \frac{1}{\phi_{n}}-[\frac{1}{\phi_{n}}&;otherwise\end{cases} \tag{11}\]and the tent map chaotic sequence follows [20]:  
\[\phi_{n+1}=\begin{cases}\frac{\phi_{n}}{0.7}&;\phi_{n}<0.7\\ \frac{10}{3}(1-\phi_{n})&;otherwise\end{cases} \tag{12}\]  
The time series plot of the tent map and the Gauss map are shown in Figure 1a,b, respectively. The selected maps generates chaos numbers that are well in the acceptable range of the EP algorithm. The correctness of the range of chaotic sequences is another deciding factor for the selection of the chaotic map [24]. After the offspring generation at the \(k\)th generation, the combined population (parents and offspring) compete with each other to survive in the \((k+1)\)th generation. For the selection, an individual's score \(\zeta_{i}\) in the stochastic competition is given by:  
\[\zeta=\sum_{n=1}^{N_{P}}w_{n}\qquad\qquad(i=1,2,\ldots,N_{P}) \tag{13}\]  
with:  
\[w_{n}=\begin{cases}1&;\phi_{1}<\frac{f_{m}}{f_{i}+f_{i}}\\ 0&;otherwise\end{cases} \tag{14}\]  
where \(f_{m}\) is the fitness of the \(m\)th randomly selected competitor in the combined population; \(m=int(2L\phi_{2}+1)\); \(f_{i}\) is the fitness value of the \(i\)th individual; \(\phi_{1},\phi_{2}\in[0,1]\) are random numbers generated by the chaotic sequence.
## 4 Experimental Results
### Experimental Results
#### 4.2.3 Time Performance Evaluation  
Due to the tight real-time constraints of AVs tasks--in our case, object detection--any additional processing should not impose high computational complexity that may affect the real-time performance. Therefore, deraining process should take this into consideration. Moreover, another challenge imposed by the nature of AVs, is that both Avs and the surrounding objects are in a dynamic/moving state. Since computational efficiency is crucial for autonomous driving which requires real-time performance. Table 5 lists the running time of different methods based on a computer equipped with an NVIDIA GTX 1050 Ti GPU. Our proposed model achieves competitive performance compared with other models.  
Authors of these SOTA deraining algorithms have evaluated the inference time in their research on different GPUs; therefore, we had to re-evaluate their running times on the same image sizes on a unified computer to produce valid comparisons.
# Improved Combined Metric for Automatic Quality Assessment of Stitched Images
## 5 Conclusions  
We proposed a novel quality assessment metric by combining two models based on the weighted product and weighted sum of features, which was an extension of the state-of-the-art Stitched Image Quality Evaluator [20]. The proposed metric was very effective in the automatic quality assessment of stitched images due to the novel combination of two models based on the weighted product and weighted sum of features. Considering the obtained high prediction accuracy, which linearly correlated with the subjective quality evaluation, it outperformed the state-of-the-art metric. Analyzing the correlation with the MOS values in the ISIQA database, high prediction monotonicity was also obtained, which was measured by two rank-order correlation coefficients (SROCC and KROCC). The conducted experiments confirmed the relevance of the SIQE metric, which played a fundamental role in the designed solution. Nevertheless, the additional features were useful as extensions of this metric, particularly due to the different \"nature\" of the image data. Contrary to the SIQE metric itself, by using the additional features it was also possible to obtain a significantly better correlation with the MOS values provided in the ISIQA dataset for images containing ghosting artifacts or small details as illustrated in Figure 5.  
Considering the directions of further research, attempts to aggregate the proposed metric with some other metrics are planned. The additional verification of some other databases would be helpful in the design of an even more universal solution that is useful for the assessment of stitched images. Conceptualization, K.O. ; methodology, K.O. ; software, K.O. and M.K. ; validation, K.O. and M.K. ; formal analysis, K.O. ; investigation, K.O. ; resources, K.O. and M.K. ; data curation, K.O. and M.K. ; writing--original draft preparation, K.O. ; writing--review and editing, K.O. ; visualization, K.O. ; supervision, K.O. All authors have read and agreed to the published version of the manuscript. Figure 5: Sample images from the ISIQA database representing the outliers on the scatter plot for the SIQE metric presented in Figure 4**a**—from (**a**–**h**): no. 177, 182, 193, 168, 178, 194, 61, and 229. **Funding:** This research is partially supported by the ZUT Highfliers School (Szkola Orlow ZUT) project within the framework of the program of the Minister of Education and Science (Grant No. MNiSW/2019/391/DIR/KH, POWR.03.01.00-00-P015/18), co-financed by the European Social Fund. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. **Abbreviations**  
The following abbreviations are used in this manuscript:
## 5 Conclusions  
In this study, to the best of authors' knowledge, it has been addressed for the first time in the literature that high performance is achieved for the OTFS waveform transmission with an SDF cooperative communication scenario. In this context, by establishing a cooperative communication model consisting of a base station/source and a traffic sign/relay, end-to-end BER expressions for a smart vehicle/destination moving at a constant speed has been derived. BER analysis was performed with this SDF scheme and it was shown that a superior SNR-BER performance was achieved compared to the point-to-point SISO-OTFS model. With the proposed SDF-OTFS model, the door to better communication quality, low probability of outage and high energy efficiency has been opened for IoV applications that will be widely used in the future.  
As future works, in order to reduce total power consumption which is a challenge for our method, an optimum power allocation method for base station/source and a traffic sign/relay can be developed to minimize the probability of end-to-end BER in the data (internet) communication of a smart vehicle moving at a constant speed. In this way, we can indirectly contribute to many areas such as reducing traffic congestion and accidents and saving fuel/energy by increasing the communication quality of smart vehicles, which would be an IoT or IoV element.  
S.K. designed the research. S.K. and M.S. performed the simulations. M.S. drafted the article. S.K. revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Scientific and Technological Research Council of Turkiye (TUBITAK ARDEB) under the grant number 123E381. The authors thank to TUBITAK for their supports.  
Data Availability StatementData sharing is not applicable.  
All the authors declare that they have no conflicts of interest.
# Synergetic Synthesis of Nonlinear Laws of Throttle Control of a Pneumatic Drive
## 1 Introduction
### Adaptive and Robust Control  
The task of synthesizing effective control laws with adaptive properties to external disturbances is quite relevant. This is confirmed by a large number of scientific papers devoted to the development of various methods for adaptive and robust control of a pneumatic actuator. Methods of classical control based on typical linear controllers as applied to parametrically uncertain models show unsatisfactory results. Therefore, unique robust and adaptive control methods are used to study models with undefined parameters under conditions of external disturbances [32; 33; 34; 35; 36].  
In [37], an optimal adaptive control approach is established for solving robust control problems for nonlinear systems with internal and input uncertainties. First, the robust control is transformed into an optimal control solution containing a nominal or auxiliary system with a predetermined efficiency index. Then, based on [PERSON]'s principle of optimality, online algorithms are proposed for calculating robust controllers for consistent and noncoincident non-defined systems. It is shown that the optimal control law ensures the global asymptotic stability of the system under consideration under all admissible uncertainties.  
A robust adaptive control strategy for synchronizing a class of uncertain chaotic systems with unknown time delays was proposed in [38]. Based on the proposed adaptation rules, an adaptive controller has been developed for stable synchronization of uncertain systems, which proves the robust stability of the proposed control mechanism using the Lyapunov theorem.  
In this paper, we consider the synthesis of nonlinear synergistic laws for controlling the position of the pneumatic drive piston. Synthesis of control laws is carried out using the ADAR STC method. One of the indisputable advantages of the ADAR STC method is that, in the synthesis of the control law, an initially nonlinear model is used, which most fully describes the processes in the system compared to linearized models, which are often tested in many other works discussed above.
## 2 Materials and Methods  
### Study Area  
Six sites (three grasslands, two shrublands and one evergreen forest) located in temperate and dry climatic regions were selected to carry out this work, corresponding to the location of six EC carbon flux towers (Figure 1, Table 1). The time series of available data for these sites were long and the quality of the corresponding MODIS data was flagged as high in the image quality reports.  
Four of these sites (USAUD, USFPe, USVAR, USSO4) are located in water deficient areas (according to the Koppen-Geiger classification system: Bsh, Bsk, Csa, [33]). One site (USKS2) is located in a non-water deficient area, in order to compare the results between water limited and non-water limited sites. In addition, one extra site (USME3) was located in an evergreen needle leaf forested area in a Mediterranean climatic region (water deficient during summer). This last site was selected to compare the results obtained for the grasslands/shrublands sites with a forested site.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site Name** & **ID** & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **Elev. (m)** & **IGBP** & **CLIM** & **EC Data** & **EC Data Used** \\ \hline Audubon & USAUD & 31.59 & \(-\)110.51 & 1469.0 & GRA & Bsh & 2002–2005 & 2003, 2004, 2005 \\ Fort Peck & USFPe & 48.30 & \(-\)105.10 & 634.0 & GRA & Bsk & 2000–2006 & 2004, 2005, 2006 \\ Vaira Ranch-Ione & USVAR & 38.41 & \(-\)120.95 & 129.0 & GRA & Csa & 2001–2006 & 2004, 2005, 2006 \\ Kennedy Space & & USKS2 & 28.60 & \(-\)80.67 & 3.0 & CSH & Cfa & 2000–2006 & 2005, 2006 \\ Center (scrub oak) & & USSO4 & 33.38 & \(-\)116.64 & 1429.0 & CSH & Csa & 2004–2006 & 2004, 2005, 2006 \\ Sky Oaks-new stand & & USME3 & 44.31 & \(-\)121.60 & 1005.0 & ENF & Csb & 2004–2005 & 2004, 2005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the data set. Notes: ID (Fluxnet site code), Elev. (elevation in metres above sea level), IGBP (MODIS International Geosphere-Biosphere Programme (IGBP) classification): GRA: grasslands, CSH: Closed Shrublands, ENF: Evergreen Needleleaf Forest, CLIM (climatic classification according to Koppen-Geiger classification system [33]: Bsh: Hot semi-arid, Bsk: Cold semi-arid, Csa: Mediterranean, Csb: Cool-summer Mediterranean, Cfa: humid subtropical), EC data (data available from the EC Tower), EC used data (data used from the EC Tower).
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 9 Lessons Learned, Suggestions, and Recommendations
### Suggestions and Recommendations
#### 9.2.7 Universal Collaboration  
Government agencies and cybersecurity groups must collaborate universally to provide effective smart grid security. As part of this collaboration, information is continuously shared for threat intelligence analysis and developing best practices for smart grid system security. It also requires researching new technologies, such as edge computing and 5G, particularly in relation to smart grid security, and evaluating how security measures impact system performance. Furthermore, resilience research concentrates on protecting vital smart grid infrastructure against terrorism, natural disasters, armed conflict, and other possible threats (i.e., military operations or acts of terrorism). This also includes user education and training for smart grids. However, improving cybersecurity resilience for smart grids requires user education and training. This entails educating employees and other relevant parties on cybersecurity best practices, such as creating and maintaining secure passwords, recognizing phishing scams, and promptly reporting any suspicious activity. Cybersecurity procedures unique to smart grid operations, as well as new threats, should be covered in regular training sessions. Users can lower the risk of cyber events and guarantee the dependable operation of smart grid systems by actively safeguarding sensitive data and infrastructure and cultivating a culture of cybersecurity awareness. Figure 8 presents a safeguard based on an in-depth examination of the smart grid architecture.
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 3 Proposed Method
### Procedure of Restoration  
The proposed method has two options for restoration as shown in Figure 7. First, it can perfectly retrieve an original image by using a normal restoration process consisting of data extraction and decryption (see Figure 7a). With this option, we first extracted the threshold \(L\) from a marked encrypted image. The image is divided into blocks, and the payload is extracted from them using \(L\). We turned back the histogram of the encrypted image, and then decrypted and integrated the blocks. Lastly, the RGB color components were restructured, and the original image was obtained.  
Our method has another model for omitting data extraction as shown in Figure 7b. With this option, we could obtain a marked image \(\tilde{I}\) that still contains a payload after decryption. This means that the proposed method could decrypt a marked encrypted image without revealing the payload to other users.
## 3 Integration of Volunteered Geographic Information
### Methods and Tools for VGI Data Collection  
In this section, we introduce the main methods and tools that are used to acquire real-time information collected directly on-site in order to overcome the limits of conventional monitoring frameworks and to improve their timeliness and accuracy.  
A major requirement is to include data with precise geographic information, originating from reliable sources, yet from the widest community, so as to guarantee good coverage of critical areas. For these reasons, it has been decided to use nomadic devices (e.g., smartphones and tablets), which have now a ubiquitous diffusion around the globe; since most of them are equipped with a Global Navigation Satellite System (GNSS) receiver, they can register the exact location of sighting and, then, share these data using the cellular network interacting with the MIS. With the aim of having an easy and intuitive interface and of fostering the submission of precise data, a dedicated app, codenamed ARGO Sentinel, has been designed and developed. The app makes transparent to the users the retrieval of their location and the interaction with the MIS. By tapping one of the alert buttons (see Figure 2) according to the severity of the sighted spill, the app gathers the current position and codifies a text message that is sent via Short Message Service (SMS) to a receiving station managed by a service unit of the MIS. The use of SMS is a convenient solution since it suffices for sending the required information (consisting only of a few bytes) and because 2G/3G/LTE networks are not commonly available at sea, even in coastal areas.  
Figure 1: Architectural design of the Marine Information System (MIS).  
Besides the app, that can be used on common smartphones and tablets and is suited for the general public, it has also been decided to develop a device dedicated to special communities of enrolled users (e.g., nautical clubs, fishermen networks). This device, codenamed WhiteBox, is an embedded system equipped with a GNSS receiver and a Global System for Mobile Communications (GSM) modem. Its user interface consists of three physical buttons that, besides controlling device powering, allow one to communicate the sighting of pollution events with two different severity grades. This minimal interface makes it possible to use the WhiteBox with just one hand, thus not compromising any sailing activities.
## 5 Results and Discussion
### Effects of Using Different Activation Functions  
In order to compare the influence of using different activation functions in our model, we conducted an experiment. Here, we used some common activation functions as our baseline, such as Sigmoid, Tanh ReLU, PReLU, as shown in Table 2.  
As shown in Table 2, compared with PReLU and ReLU, the Swish activation function could obtain a higher accuracy. Because Swish activation function uses a smooth curve to compress negative features, unlike PReLU and ReLU, which use a sharper curve, it can make the distribution of positive and negative features smoother.  
In addition, we could see that PReLU could obtain better results compared with ReLU, since PReLU uses a linear function to compress negative features, avoiding feature information loss. However, the ReLU activation function outputs all negative features as 0, resulting in the loss of all negative features.  
Finally, ReLU could obtain better results than Sigmoid and Tanh, as it solves the problem of gradient disappearance during the training process of deep networks.
## 3 Results
### Ablation Experiments  
In this section, we report on ablation experiments conducted on the same test set to analyze the effect of instance normalization and the sub-pixel convolution layer on image dehazing in our method. Three designs are considered: (1) introducing sub-pixel \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **Image** & **AOD-NET [26]** & **MSCNN [6]** & **FFA-Net [11]** & **D4 [27]** & **Ours** \\ \hline \multirow{6}{*}{\(e\)} & Image 1 & 2.38 & 0.46 & 1.30 & 1.77 & **2.03** \\  & Image 2 & 5.41 & 0.76 & 1.40 & 5.47 & **3.47** \\  & Image 3 & 1.75 & 0.30 & 0.86 & 1.56 & **1.31** \\  & Image 4 & 1.39 & 0.26 & 0.63 & 1.23 & **1.82** \\  & Image 5 & 2.00 & 1.16 & 1.40 & 1.09 & **0.64** \\  & Image 6 & 2.94 & 0.68 & 0.72 & 1.73 & **0.52** \\ \multirow{2}{*}{Average} & & 2.65 & 0.60 & 1.05 & 2.14 & **1.63** \\ \hline \multirow{6}{*}{\(\bar{\
u}\)} & Image 1 & 1.70 & 1.22 & 1.34 & 1.49 & **4.45** \\  & Image 2 & 2.18 & 1.29 & 1.45 & 1.60 & **5.34** \\  & Image 3 & 1.73 & 1.20 & 1.43 & 1.53 & **3.82** \\  & Image 4 & 1.69 & 1.19 & 1.35 & 1.52 & **4.03** \\ \multirow{2}{*}{\(\bar{\text{Image 5}}\)} & Image 5 & 1.74 & 1.37 & 1.48 & 1.44 & **2.28** \\  & Image 6 & 1.53 & 1.15 & 1.26 & 1.36 & **1.79** \\ \multirow{2}{*}{Average} & & 1.76 & 1.24 & 1.22 & 1.49 & **3.62** \\ \hline \multirow{6}{*}{\(NIQE\)} & Image 1 & 4.31 & 4.06 & 2.92 & 3.99 & **3.17** \\  & Image 2 & 3.55 & 3.85 & 2.76 & 3.41 & **2.86** \\ \multirow{2}{*}{\(NIQE\)} & Image 3 & 3.78 & 3.67 & 2.91 & 3.54 & **3.03** \\  & Image 4 & 3.56 & 3.52 & 2.76 & 2.32 & **2.95** \\ \multirow{2}{*}{\(\bar{\text{Image 5}}\)} & Image 5 & 3.25 & 3.01 & 3.01 & 2.99 & **2.54** \\  & Image 6 & 4.24 & 3.84 & 3.43 & 3.84 & **3.11** \\ \multirow{2}{*}{Average} & & 3.78 & 3.66 & 2.97 & 3.35 & **2.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of results of deep learning methods on the test set. Figure 7: Comparison of six real foggy images in an icing wind tunnel with existing deep learning methods. (**1,2**) LWC = 1.31; (**3,4**) LWC = 1.0; (**5,6**) LWC = 0.5.  
convolution; (2) introducing the instance normalization layer; and (3) using the whole method. All the methods are trained with the same settings as our algorithm for comparison. The experimental results and result images are shown in Table 4 and Figure 8, respectively. Combining Table 4 and the comparison results in Figure 8, it can be seen that the addition of sub-pixel convolution can better preserve the shape of the ice on the wing edge, while the addition of instance normalization has better dehazing effect. Moreover, Design 2 achieves good results in terms of \(NIQE\) value, and has the best dehazing effect in the case of thin fog with an LWC of 0.5. However, in general, \(e\) and \(\overline{r}\) can more objectively show the number of edges recovered in the image after dehazing. The \(e\) and \(\overline{r}\) values of design 2 are