esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation Results and Discussion  
This section provides implementation details for the proposed addition structures. We implemented 32-bit versions of the three algorithms as this size is commonly needed in cryptographic algorithms such as SALSA20. Note that adaptions to other widths are trivial. All designs were specified in VHDL and VERILOG and were synthesized for a Xilinx Spartan 6 XC6 SLX75 Field-Programmable Gate Array (FPGA) with speed grade \(-3\) using Xilinx ISE 14.7. In order to assure the correct realization of masked gadgets the relevant signals were exclude from optimizations and the design hierarchy was preserved. The implementation results are presented in Table 4.
## 3 Results
### Results for the IEEE 14-bus system  
Figure 6 shows the total real (\(Pa\)) and reactive (\(Pr\)) power losses, both desired (targets-\(\mathbf{Y_{des}}\)) and obtained by the ANN (\(\mathbf{Y_{ob}}\)). In Figure 6a, the results during the training phase with 80% of the samples (173) are presented, showing a high similarity between the obtained output and the desired one, and the same pattern is observed for \(Pr\). During the validation, in which \(Pa\) and \(Pr\) losses were predicted, Figure 6b displays the results for the 40 samples (20%) that were not used in training, for both \(Pa\) and \(Pr\). A similar behaviour is noticed between the outputs in both curves, indicating that the network managed to approximate \(\mathbf{Y_{ob}}\) very closely to \(\mathbf{Y_{des}}\). Finally, Figure 6c shows the total losses of \(Pa\) and \(Pr\) desired (\(\mathbf{Y_{des}}\)) and the outputs obtained (\(\mathbf{Y_{ob}}\)) by ANN for 100% of the samples (213 samples), where a similar behaviour is also observed between the outputs on both curves.  
Figure 4: ANN performance for training and validation: (a) MSE; (b) error histogram (\(\mathbf{Y_{des}}-\mathbf{Y_{ob}}\)).  
Figure 7 shows the total real (_Pa_) and reactive (_Pr_) power loss curves, both desired and obtained, as a function of the loading factor (\(\lambda\)) for the IEEE 14 bus system. In the study of continuation power flow (CPF) in electrical power systems, reaching the critical point (CP) with a minimum error is extremely important, since this point defines the stability and instability of the system. In this work, the CP value for _Pa_ obtained via ANN was (\(\lambda\), _Pa_) = (1.7680, 0.8008), while the desired value is (\(\lambda\), _Pa_) = (1.7680, 0.7855). This resulted in an error of 0.0153 for _Pa_ in CP. As for _Pr_, the CP obtained value was (\(\lambda\), _Pr_) = (1.7680, 3.1245), while the desired value is (\(\lambda\), _Pr_) = (1.7680, 3.1321), with an error of 0.0076, according to Table 2 presented at the end of the results.  
Figure 5: Correlation between Y\({}_{\text{des}}\) and Y\({}_{\text{ob}}\) outputs: (**a**) training phase (80% of samples); (**b**) validation phase (20% of samples); (**c**) 100% of samples (training and validation).  
Figure 6: _Pa_ (**Y\({}_{\text{des}}\)** and **Y\({}_{\text{ob}}\)**) _Pa_ (total real power losses) and _Pr_ (total reactive power losses) as a function of the samples from the IEEE 14-bus system: (**a**) training (80% of the samples); (**b**) validation (20% of the samples); (**c**) 100% of the samples (2 network phases).
# Diversity of Collagen Proteins and Their Biomedical Applications in Drug Delivery
## 2 Diversity of Collagens
### Marine Collagen  
Marine organisms consist of both invertebrates (such as sponges, jellyfish, cuttlefish, prawns, and other similar species) and vertebrates (such as fish and marine mammals). These multicellular eukaryotes are significant sources of collagen. Marine collagens exhibit greater bioavailability and absorption efficiency in the body compared to terrestrial animal collagens, such as bovine and porcine collagen. Among the 29 types of collagens that have been discovered, type I collagen is the most abundant type of collagen and can be found in fish scales, skin, and bone (Table 1), whilst type II collagen can be found in the fishbone cartilage. In addition, invertebrates, the vast majority of marine organisms, contain collagen types IV, XV, XVIII, and some fibrillar collagens, as well as some fibril-associated collagens with interrupted triple helices (FACITS). The evolutionarily most ancient collagen among these is type IV collagen [23; 24].  
Figure 1: Crystal structures of the CLPs from prokaryotic organisms. (**a**) SclB (or Scl2) from _Streptococcus pyogenes_ (PDB ID:4 NSM); (**b**) the CLP from bacteriophage T4 of _E. coli_ (PDB ID:1 NAY); (**c**) the collagen triple helix model of Saimirine herpesvirus 2 (PDB ID:1K6F).  
Figure 2: Comparison of domain constitution of collagens from eukaryotes and prokaryotes. NTD = N-terminal Domain; CTD = C-terminal Domain; V = Trimerization Domain; SS = Signal Sequence; OEP = Outer Membrane Efflux Protein; I = Interruption; LPXTG = LPxTG-motif protein (LMP).  
Glycine is the most abundant amino acid in marine collagen, while proline and hydroxproline are less abundant than in mammalian collagen. However, there are more serine and threonine residues in the collagens of cold-water fish. These variations in amino acid composition are generally responsible for the properties of collagen, such as rigidity, temperature stability, and denaturation temperature [25]. For instance, fish collagen has a low concentration of glycine, proline, and hydroxproline, which results in a low denaturing temperature of marine collagen [26], but it has a higher concentration of serine and threonine amino acid than mammalian collagen [27]. In general, a high level of proline and hydroxproline is necessary for collagen stability [28]. On the other hand, collagen from warm water fish has a similar amino acid composition, rheological properties, and thermostability to those of mammalian collagen. All in all, the denaturation temperature of marine collagen is lower than that of mammalian, and this is due to the lower extent of hydroxylation of proline and lysine residues in the marine collagen [29; 30].
# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
###### Abstract  
Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model's performance was evaluated on big data of Maine wind farm ISO NE, USA.  
big data; data analytics; wind power; demand side management; energy management; forecasting; convolution neural network; deep learning +
Footnote †: journal: Applied Sciences
# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 6 Conclusions  
In the paper, a methodology is presented for modeling the availability of services in multi-tiered cloud-fog-edge networks, which are becoming pervasive in the evolution towards the future internet. As interconnected systems spanning satellites, aerial platforms, terrestrial infrastructure, and oceanic components continue proliferating, quantifying end-to-end service availability becomes critical.  
The proposed Markov modeling approach provides an adaptable tool to assess and optimize reliability across the heterogeneous networks and devices that will comprise the future internet. By evaluating individual tiers and their composition into overall availability, system architects can set quantitative availability targets during design based on application requirements. Operators can benchmark availability to guide enhancements in real-world deployments.  
As the scale and complexity of networks grow exponentially, availability modeling using techniques like that developed here may be useful in delivering the ubiquitous, reliable connectivity that users expect. By identifying high-risk points across multi-tier systems, availability modeling helps reinforce the future internet's fault tolerance and resilience.  
As the future internet emerges, reliability modeling will rapidly increase in importance across both research and practical engineering for networks like space-air-ground-ocean architectures. The methodology presented in the paper offers a foundation to support the availability demands of the complex, mission-critical applications to come.  
While the proposed Markov chain modeling approach provides a useful methodology for assessing availability in multi-tier networks, there remain opportunities to further enhance the technique and address its limitations. Here are some potential future research directions in this area:  
* The standard Markov chain assumes time-homogeneous transition probabilities. Extension to semi-Markov or time-dependent Markov models could capture temporal variations in failure and repair processes. This could improve the accuracy in non-stationary environments.
* Individual tier Markov models could be integrated into larger system-level availability models to account for cascading failures between interdependent tiers. Hybrid modeling approaches could also couple Markov chains with complementary simulation, machine learning, or network science techniques.
* As multi-tier systems evolve, the Markov model should be dynamically updated to reflect changes in transition probabilities and topology. Adaptive Markov chains and reinforcement learning methods could enable self-reconfiguring availability models.
* While the methodology is generalizable, adapting the Markov model with reliability data and attributes tailored to specific applications and sectors (e.g., telecom, power grid, healthcare) could improve its fidelity.
* Availability and security are interlinked. Extending the model to account for the impact of threats like cyber attacks on availability could be highly relevant.
# Review of Energy Management Systems in Microgrids
## 2 Energy Management Systems in Microgrids
### Stochastic and Powerful Programming Approaches-Based Energy Management Systems  
Energy management systems based on stochastic (variable) and powerful programming approaches use estimated values of electricity price, solar irradiation, wind speed, and load power. As a result, the overall operating cost, including the cost of energy trading with the grid and the operating cost, is minimized. This energy management approach is considered to deal with uncertainty by estimating variable parameters in the system through programming techniques. A controller is also used in the system to reduce fluctuations in the bus voltage and control the battery current. Stochastic programming models are being developed to optimize microgrid energy management that takes into account uncertainties such as wind speed, solar irradiation, and load demand in renewable energy resources [28; 29; 30].  
Optimal operation of the microgrid and optimization of investment costs can also be achieved through energy management and variable programming methods. Scenario creation methods are used for microgrid scenarios and the probabilities of these scenarios occurring, and problems caused by disruptions or malfunctions in the main grid or microgrid. The goal is to reduce the anticipated running costs as much as possible. These costs include load shedding and the running costs of renewable energy resources like solar, wind, and battery systems.
# Design of a Bionic Spider Robot with a Two-Degrees-of-Freedom Leg Structure and Body Frame
## 3 Design and Analysis of Structure
### Design and Analysis of Leg Structure
#### 3.1.4 Inverse Kinematics  
Using Equation (16), the coordinates of the foot tip \((x_{B},y_{B},z_{B})\) are obtained. Based on the relationships between the various links, the rotational angles of the two drive motorsduring operation are analyzed. By rearranging and expanding Equation (16), we derive Equation (17).  
\[\left\{\begin{array}{l}\frac{x_{B}-P_{Ax}^{i}}{(l_{4}+l_{6})\cos(\theta_{1}- \theta_{3})-l_{7}\cos\theta_{1}+(d_{1}+l_{1})+\Delta d}=\cos(\phi_{i}+\gamma_{ 1})\\ \frac{y_{B}-P_{Ay}^{i}}{(l_{4}+l_{6})\cos(\theta_{1}-\theta_{3})-l_{7}\cos \theta_{1}+(d_{1}+l_{1})+\Delta d}=\sin(\phi_{i}+\gamma_{1})\\ \frac{z_{B}-l_{7}\sin\theta_{1}-P_{Az}}{l_{4}+l_{6}}=\sin(\theta_{1}-\theta_{ 3})\end{array}\right. \tag{17}\]  
For Equation (17), algebraic substitutions are performed, letting  
\[a=l_{7}\cdot\cos\theta_{1}-d_{1}-l_{1},b=l_{4}+l_{6},c=x_{B}-P_{Ax}^{i},d=y_{ B}-P_{Ay^{\prime}}^{i},e=z_{B}-P_{Az}^{i}-l_{7}\sin\theta_{1},e=z_{B}-P_{Az}^{i} -l_{7}\sin\theta_{1} \tag{18}\]  
Then, we substitute Equation (18) into Equation (17) and obtain Equation (19).  
\[\Delta d=a-\sqrt{b^{2}-e^{2}}+\sqrt{c^{2}+d^{2}} \tag{19}\]  
The rotation of the leg lift motor is transmitted through a nut and screw mechanism, resulting in the movement distance of the slide block. Consequently, the operational rotation angle \(\varphi_{2}\) of the leg lift motor can be determined as shown in Equation (20).  
\[\varphi_{2}=2\pi\cdot\Delta d/P \tag{20}\]  
Substituting Equation (19) into Equation (17) yields Equation (21).  
\[\frac{d}{\sqrt{c^{2}+d^{2}}}=\sin(\phi_{i}+\gamma_{1}) \tag{21}\]  
Due to the transmission of the swing motor through a worm gear and worm shaft mechanism, the motor rotation angle \(\varphi_{1}\) can be analyzed from the transmission ratio, as shown in Equation (22).  
\[\varphi_{1}=i_{1}\cdot(\arcsin\frac{d}{\sqrt{c^{2}+d^{2}}}-\phi_{i}) \tag{22}\]  
Through the kinematic analysis of the legs, it can be seen that the movement trajectory of the legs is determined not only by the operating parameters of the motor, but also by the design parameters of different components. The detailed analysis is shown in Section 4.
# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 7 Discussion  
The proposed hybrid CNN + GRU model attained the highest accuracy value in Turkish and English SMS spam detection upon the comparison of the obtained results with other results in the literature. The CNN algorithm's near-perfect performance in extracting n-gram features and the GRU algorithm's ability to capture long-term dependencies are the main determinants for using a hybrid model. This high accuracy value attained by the proposed hybrid model in a dataset containing Turkish words will serve as a reference for future studies that are conducted in this field. The hyperparameters selected in the study and the fine-tuning of these hyperparameters increase the performance rate of the hybrid  
Figure 9: Occurrence frequencies of words in the Turkish SMS Collection dataset.  
model in spam detection. The number of neurons and the activation function used directly lead to an overfitting problem being encountered by the model. The generation of datasets involving Turkish spam is projected to provide convenience for many researchers in their studies. The limited amount of data used in this study also limits the performance attained by the study in real life. In spite of the fact that a sufficient amount of datasets of English spam messages are available, it is challenging to find datasets of Turkish spam messages. In this respect, it is our intention to put the proposed model to the test on a big dataset generated by ourselves in the future. Data used in this section could not be expanded due to the fact that there is an insufficient amount of data, particularly considering Turkish SMS spam.  
Conceptualization, Z.A.; methodology, H.C.A.; software, H.C.A.; data curation, Z.A.; writing--original draft preparation, Z.A.; writing--review and editing, Z.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data is contained within the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 2 Methodology
### Search Strategy  
In this study, we conducted the search and collection process for relevant studies and articles until April 2022 on eight different databases and data sources to extract and collect related studies from the literature. The databases comprise Google Scholar, IEEE Xplore(r), ScienceDirect, Scopus, Springer, Wiley, MDPI, and the PubMed database. The electronic links for databases and data sources searched are as follows:  
* IEEE Xplore [[https://ieeexplore.ieee.org/Xplore/home.jsp](https://ieeexplore.ieee.org/Xplore/home.jsp)]([https://ieeexplore.ieee.org/Xplore/home.jsp](https://ieeexplore.ieee.org/Xplore/home.jsp)); accessed on 23 April 2022;
* Scopus Database [[https://www.scopus.com/search/form.uri?display=basic#basic](https://www.scopus.com/search/form.uri?display=basic#basic)]([https://www.scopus.com/search/form.uri?display=basic#basic](https://www.scopus.com/search/form.uri?display=basic#basic)); accessed on 23 April 2022;
* Science Direct [[https://www.sciencedirect.com/](https://www.sciencedirect.com/)]([https://www.sciencedirect.com/](https://www.sciencedirect.com/)); accessed on 25 April 2022;
* Springer [[https://www.springer.com/gp](https://www.springer.com/gp)]([https://www.springer.com/gp](https://www.springer.com/gp)); accessed on 25 April 2022;
* Google Scholar [[https://scholar.google.com/](https://scholar.google.com/)]([https://scholar.google.com/](https://scholar.google.com/)); accessed on 25 April 2022;
* Wiley Online Library [[https://onlinelibrary.wiley.com/](https://onlinelibrary.wiley.com/)]([https://onlinelibrary.wiley.com/](https://onlinelibrary.wiley.com/)); accessed on 26 April 2022;
* MDPI [[https://www.mdpi.com/](https://www.mdpi.com/)]([https://www.mdpi.com/](https://www.mdpi.com/)); accessed on 27 April 2022;
* PubMed [[https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/)]([https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/)); accessed on 29 April 2022.  
This study used both broad and specific keywords to obtain an appropriate number of studies on the research topic of interest. In particular, broad keywords are used to represent the domain such as \"SDN security\", while specific keywords are used to refer to specific topics within the search domain such as \"LDDoS attack detection techniques\". The search terms used are: \"Software Defined Network\", \"SDN\", \"DDoS\", \"Low-Rate DDoS\", \"LDDoS\", \"Machine Learning\", \"Deep Learning\", and \"OpenFlow\".  
In addition, we reviewed the titles and abstracts of the articles using Mendeley [18] to identify studies relevant to SDN research and to exclude unrelated areas. We then inspected these research papers to identify studies that fell within the scope of our study. For example, we only considered studies that used machine learning mechanisms to detect LDDoS attacks in SDN. Last but not least, we need to exclude studies that are not accessible due to restricted access to databases.  
Figure 1: Publications on LDDoS detection mechanisms: SDN vs. traditional IP networks.
# Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure
## 1 Introduction
In order to evaluate the distinguish ability of two areas similar to the SNR, the contrast-to-noise ratio (CNR) is a commonly used quantity [28; 29]. Since the contrast between a defect and its surroundings is key for the defect detection, the calculation of the CNR to assess the image quality concerning a subsequent defect detection is preferred here. Despite the variety of research work already conducted in the field of active thermography on rotor blades and on GFRP components, no experiments have been carried out on the leading edge of the rotor blades so far. Mostly flat GFRP panels, which are predominantly uncoated, have been investigated. Investigations on curved GFRP specimens are rare and have only been performed on specimens with relatively large radiation, which tend to be found in the root region of rotor blades rather than in the tip area, where the most intense load exist. Therefore, the existing findings are only transferable to a limited extent to the examination of a coated leading edge of a wind turbine rotor blade. In particular, investigations of test samples with a coating and with a defect diameter of less than 4 mm are necessary in order to gain insights into the influence of initial defects on the premature occurrence of erosion. In addition, the resolvable depth-to-diameter ratio as well as the achievable CNR for coated and strongly curved GFRP test specimens need to be clarified. The aim of this work is to introduce active thermography for the special application of examining a rotor blade leading edge regarding initial production defects and material defects. In order to investigate the detectability of different defects, coated test specimens with a strong curvature, which correspond in shape, material, and structure to a rotor blade leading edge tip section of a real wind turbine, are modified with drillings in the lower single-digit millimeter range to imitate small hidden voids in the material. For the examination of the test specimen, long-pulse thermography in combination with a PCA is used. In Section 2, the measuring principle of long-pulse thermography as well as the image evaluation approach are described in detail. The description of the test specimens and the experimental test set-up including the test procedure follows in Section 3. Section 4 deals with the presentation of the experimental results and their discussion, while the important findings and conclusions are summarized together with an outlook in Section 5.
## 3 Target Response Model and Analysis
### Residual Errors
It quantifies the reliability of the response characterization over azimuth. The coherence is close to one for clean portions of the target response and decreases towards zero where the response is perturbed by residual clutter, system noise or Radio Frequency Interference (RFI). It is denoted \(w_{pq}^{s}(az)\), as it is principally used as a weight when deriving calibration corrections, and is defined as  
\[w_{pq}^{s}(az)=\left|e^{j\delta\phi_{pq}^{s}(az)}\otimes S(az)\right| \tag{19}\]  
where \(\otimes\) denotes convolution and \(S(az)\) is a low-pass filter that is narrow enough to provide an unbiased coherence estimate. The convolution may, for example, correspond to a moving average window with a size on the order of \(\sim\)100 samples.