esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Issues on Image-Based SHM
#### 4.4.1 Limited Range  
Monitoring a building has a range limit [76]. In two-dimensional (2D) and three-dimensional (3D) structural displacement monitoring, the device used has a specific range to get a more accurate result. Regardless of the application, whether on infrastructure or element of a structure, captured images and videos must be of the best quality. It is important to acknowledge that image quality can be improved. This can be obtained by considering these factors that influence the picture quality of long-range cameras:  
* Hardware components of a camera. Each lens will vary in how much it magnifies the image (video).
* Atmosphere and weather. Even under ideal conditions, the air you are viewing through contains molecules that impair the image by obstructing light transmission. Moisture, rain, snow, dirt, and fog lessen visual contrast, while air turbulence caused by thermal fluctuations throughout the scene causes image distortions.
* Pixels Per Meter (PPM). A number that indicates the number of identifiable pixels over a 1 m width at a certain distance from the camera. PPM is an objective assessment of detail that takes into consideration the camera's lens, resolution, and sensor size.
* Angle of View (AOV). The focal length of the lens in proportion to the sensor size determines AOV. Longer lenses or smaller sensors generate narrower fields of vision, whereas shorter lenses or bigger sensors produce broader fields of view.
* Sensor resolution. The amount of detail inside a camera's field of vision is determined by this factor.
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 2 SDN-Powered Mobile Edge Computing  
The SDN-powered MEC architecture consisting of multiple MEC servers and multiple end-users is presented in Figure 1. Each MEC server \(s,s\in S\), \(S=[1,...,s,...|S|]\) communicates with the SDN controller towards setting the price \(p_{s}^{(t)}[\$/bits]\) of its computing services per time slot \(t\). The whole operation of the examined system is divided in time slots, where \(T=[1,...t,...,|T|]\) denotes their corresponding set. At each time slot, the SDN controller determines the MEC server selection by the end-users (see Section 3), as well as the optimal price \(p_{s}^{(t)}\) for each MEC server and the optimal data offloading \(b_{u,s}^{(t)}[bits]\) of each end-user \(u\) to the selected server \(s\) (see Section 4). Each end-user \(u,u\in U,U=[1,...,u,...|U|]\) receives the required information by the SDN controller to offload its data \(b_{u,s}^{(t)}\) to the selected server \(s\). Each end-user \(u\) has a maximum amount of data \(I_{u}^{(t)}\) that should be processed to perform a computing task, and part of them are offloaded to the MEC server, i.e., \(b_{u,s}^{(t)}\in A_{u}^{(t)}=[0,I_{u}^{(t)}]\), while the rest of the data are processed locally.
## 3 Implementation of the OAC-DES
### Evolutionary Algorithm in OAC-DES  
In our study, the EA was moved from the disembodied computer system into real hardware in a dynamic environment, where it acts autonomously. In line with this, the fitness of solutions could not be calculated directly from a phenotype of solution, but had to be obtained after observation of how the phenotype reacted to the conditions of the dynamic process governed by the environment [37]. Thus, the behavior of the phenotype was evaluated after the solution was exposed to the dynamic conditions of the environment. Consequently, the traditional three-step evaluation chain genotype-phenotype-fitness was replaced by the four-step genotype-phenotype-behavior-fitness chain [29].  
Moreover, evaluating the behavior of the solution in real-world systems presents another problem due to the need for long-term calculations. Therefore, a so-called surrogate model [29] was used to employ computationally cheaper models in place of full fitness function evaluations. As a result, in our study the behavior of the solution is predicted using ANN-simulation.  
In what follows, the fundamentals of ES are discussed, after which the proposed (1+1)-DES algorithm is illustrated in detail.
## 5 Formal Modeling and Analysis of SAPS
### SAPS Analysis and Verification  
In this section, SAPS is analyzed for secure session key establishment for the patient to access their physicians anonymously for medical prescription, as discussed in section IV. In this scenario, the establishment request is initiated by the patient (A) by transmitting secret credentials generated using secure methodology established through the TS. After the sending operation, an update operation performed by the sender to refresh the security credentials for future sessions is as shown below. * Concat(IDA, MA, N1, MAC(MA), IDB) \(\rightarrow\) QA
* Encrypt((QA,H\({}_{\text{YA}}\))) \(\rightarrow\) P\({}_{\text{A}}\)
* Encrypt((Concat(X\({}_{\text{A}}\), P\({}_{\text{A}}\), T\({}_{1}\)), K\({}_{\text{(A-TS)}}\))) \(\rightarrow\) C\({}_{\text{A}}\)
* Send((ID\({}_{\text{A-T}}\), C\({}_{\text{A}}\))) to TS and Update(ID\({}_{\text{A-T}}\))
* Update()  
To observe, a list of associated factors, messages, nonce numbers, actual key, ciphers, pseudo-dynamic, finite field, and one-way hash values, are kept in a possession set at the sender node, i.e., with the patient (A). In this case, during the authentication for key establishment phase, the following steps are performed. * POSS(A) = (ID\({}_{\text{A}}\), P, R\({}_{\text{P}}\), K\({}_{\text{A-TS}}\), F, PW\({}_{\text{P}}\))
* BEL(A) = {#(ID\({}_{\text{A}}\)), #(P), #(R\({}_{\text{P}}\)), #(K\({}_{\text{(A-TS)}}\)), #(F), #(PW\({}_{\text{P}}\))}
* BL(A) = Mul(Rp, P) \(\rightarrow\) XA, Mul(Rp, F) \(\rightarrow\) YA  
After sending messages to the other party and the authentication request is sent to the trusted authority, the TS decrypts the received message to get the secret credentials necessary for verification and to explore the request message. The freshness of the message is checked by comparing the timestamp threshold value, nonce number, and masked IDs value with set values. If we fulfill the threshold parameter and verify the answer, then further process calculations are processed. Otherwise, the message is discarded. MAC is calculated for message integrity and compared to hash values to ensure integrity. The participant verification and message integrity steps performed at the TS are as follows. * Receive(A, [C\({}_{\text{A}}\)])
* Decrypt((ID\({}_{\text{A-T}}\), C\({}_{\text{A}}\))K\({}_{\text{TS-A}}\)) and Split to get [X\({}_{\text{A}}\), P\({}_{\text{A}}\), T\({}_{1}\)]
* check(T\({}_{1}\)-T\({}_{1}\)) \(\geq\)\(\Delta\)T then abort
* Hash(h(. ); Mul(d, X\({}_{\text{A}}\))) \(\rightarrow\) H\({}_{Y^{\prime}\text{A}}\)
* Decrypt((P\({}_{\text{A}}\))H\({}_{Y^{\prime}\text{A}}\)) and Split to get [ID\({}_{\text{A}}\), M\({}_{\text{A}}\), N\({}_{\text{A}}\), MAC(M\({}_{\text{A}}\)), ID\({}_{\text{B}}\)]
## 2 Review of PV Model and MPPT Control Methods
### Review of MPPT Control Methods
#### 2.2.3 Fuzzy Logic (FL)  
The fuzzy logic (FL) mathematical framework is often used in artificial intelligence and control systems. Fuzzy logic uses linguistic variables and human expert rules, designed from the expert knowledge of system dynamic behavior, to control complex systems [40]. Therefore, FL does not usually need a mathematical model of the system to be controlled. Instead, FL needs a system dynamics behavior expert to devise a set of required membership functions, linguistic variables, and linguistic rules that translate the expert knowledge of the system dynamics.  
In the literature, there are several implementations of the FL-based MPPT algorithm [5,17]. In [5], there are six possible solutions for the fuzzy logic implementation of MPPT.  
The discrete form of \(dP_{PV}/dV_{PV}\), denoted as \(S(k)\), and its discrete time derivative \(\Delta S(k)\), are used to develop an FL MPPT algorithm [5]:  
\[S(k)=\frac{dP_{PV}}{dV_{PV}}=\frac{P_{PV}(k)-P_{PV}(k-1)}{V_{PV}(k)-V_{PV}(k-1 )}\, \tag{7}\]  
\[\Delta S(k)=S(k)-S(k-1), \tag{8}\]  
In this research, the implementation of the FL-based MPPT algorithm in [5] was investigated, in which the duty-cycle variation \(\Delta D\) output is based on \(S(k)\) and on its discrete derivative \(\Delta S(k)\), according to the fuzzy surface control presented in Figure 5.  
The normalized set of input and output values can be described using five membership functions: negative big (NB), negative small (NS), zero (ZE), positive small (PS), and positive big (PB). Figure 6 shows the input and output membership functions within the normalized universe of discourse. Table 1 shows the devised fuzzy rules. The Mamdani method was employed as an inference engine for FL implementation, and the center of gravity algorithm was used in the defuzzification process.  
Figure 5: Fuzzy logic control surface.
## 2 Proposed Method
### Feature Extractor  
Convolutional Neural Networks (CNNs) [25] are a type of feedforward neural network primarily comprising convolutional layers. These layers perform convolution operations to extract deep features and enable parameter sharing, which reduces network complexity and training costs. CNNs effectively extract hierarchical features from PM\({}_{2.5}\) and other variables, thereby improving calibration accuracy. The structure of the CNN is illustrated in Figure 2.  
Convolutional layers are central to CNNs, performing convolution operations on input data through local connections and weight sharing to extract deep features. Given input data \(X=[x_{1};x_{2};...;x_{i};...;x_{n}]\in\mathbb{R}^{n\times m}\), the convolution process is defined by  
\[u_{i,j}^{k}=\sum_{l=1}^{m}\sum_{l=1}^{g}X_{i+l,j+l}W_{i,j}^{k}+b^{k} \tag{1}\]  
\[y^{k}=\varphi(u^{k}) \tag{2}\]  
where \(u^{k}\) represents the feature map obtained by convolving the input data with the \(k\)-th kernel, where \(W^{k}\) and \(b^{k}\) are the kernel's weights and bias, respectively. Here, \(g\) is the kernel size, \(n\) is the sensor sequence length, and \(m\) is the number of sequences. The ReLU activation function \(\varphi\) is then applied to introduce nonlinearity, producing the final features extracted by the convolutional layer.  
Residual Neural Networks (ResNet) are deep learning models that enhance network learning capacity [26]. The basic unit of the ResNet is the residual block, which introduces an identity mapping directly from input to output, as shown in Figure 3. This connection  
Figure 2: The diagram of CNN feature extraction.  
allows original input information [27] to flow through subsequent layers, enhancing feature representation and mitigating the vanishing gradient problem. To ensure consistent data dimensions in the residual connection, a CNN module is used for adjustment. The mathematical expression for the residual block is as follows:  
\[y=F(x)+x \tag{3}\]  
where \(x\) is the original input from the skip connection, \(F(x)\) is the network's output, and \(y\) is the residual block output, defined as \(F(x)+x\).
## 3 Man-In-The-Middle Attacks
### MITM as Message Dropped  
This type of attack also refers to \"black hole\" attacks in VANET, where attacker intentionally drops the received legitimate message (\(M_{G}\)), thus suppressing the further propagation of \(M_{G}\)[38]. Hence, this action of the attacker prohibits the legitimate vehicles to receive any kind of message (safety and non-safety) as the messages never reaches to their destination. Dropping the safety-related messages can have significant impact on the network as it contains sensitive information such as collision avoidance. As an illustration, consider a scenario where legitimate vehicles are broadcasting the messages regarding black-ice on the road. Dropping such information can put the life of vehicular users in danger as they are prohibited from receiving sensitive information by the attacker.  
Figure 3: Illustration of Passive and Active MITM Attacks in VANET.
###### Abstract
Based on our knowledge of the structures of these intermediates, the enzymes responsible for these steps from 10-oxogeranial to catalpol were proposed to be uridine diphosphate glycosyltransferase (UGT), aldehyde dehydrogenase (ALD), oxidoreductase, hydroxylase, dehydratase, decarboxylase, epoxidase, and cytochrome P450 (CYP450) (Figure 1). However, in addition to _epi_-iridoid synthase (_epi_-ISY) and aucubin synthase, no other gene in _route II_ has been identified to date, and the molecular mechanism of catalpol biosynthesis remains unknown [25; 26]. In recent years, comparative transcriptome analysis has been used as an efficient tool for exploring the biosynthesis of natural products. [PERSON] and [PERSON] screened several enzymes, such as flavanone 3-dioxygenase, uroopphyrinogen decarboxylase, and squalene monooxygenase, which is probably involved in the downstream steps of catalpol biosynthesis through sequencing approaches [27; 28]. However, other important tailoring enzymes, including CYP450, oxidoreductase, hydroxylase, dehydratase, decarboxylase, and epoxidase, have never been identified. To elucidate the biosynthesis of catalpol, young leaf transcriptomes were prepared from _R. glutinosa_ Beijing No. 3, Huaifeng, and Jin No. 9 cultivars. The transcriptomes derived from the tuberous and adventitious roots of the Jin No. 9 cultivar were also constructed to narrow down the number of putative genes involved in catalpol formation. Using comparative transcriptome analysis, some candidate genes were identified in this study. To confirm the reliability of the gene screening results, the geraniol synthase, which was proposed to be the first committed enzyme in the formation of catalpol, was isolated and characterized to analyze its biochemical function. The results of this study enrich the gene information for elucidating the biosynthesis of catalpol in _R. glutinosa_ and are beneficial for improving the medicinal quality of _R. glutinosa_.
## 2 Methods
Similarly, normalized 1300 nm \(NI_{SR}\)(1300), 1400 nm \(NI_{SR}\)(1400), 1550 nm \(NI_{SR}\)(1550), and 1650 nm \(NI_{SR}\)(1650) glucose absorption band channels are tested by other metric learning elements to build 3D data (GData 3), which are also optimized using SNE, diffusion maps, and factor analysis algorithms;
3. Second level of metric learning elements: According to the self-comparison effect between 1D tissue absorption data (TData 1) and 3D glucose absorption data (GData 3), these (both TData 1 and GData 3) are further input into the second-level metric learning elements to build 3D multi-wavelength light data (MData 3). Furthermore, these data (MData 3) are also optimized by SNE, diffusion maps, and factor analysis algorithms to remove tissue absorption interference and extract glucose absorption information effectively;
4. Glucose prediction of BP network: The predicted glucose BP network, with 3 initial input variables of MData 3, is constructed using the following method. The architecture of our proposed network consists of three layers: one input, one hidden, and one output layer. The number of input layer neurons is the same as the number of input variables, and the 3D multi-wavelength light data (MData 3) serve as the input layer. The number of hidden layer neurons is adjusted to 36. The output layer has a neuron that represents the predicted concentration of human blood glucose. The network was trained using the Bayesian Regularization algorithm, in which the maximum number of training rounds was set to 20,000. The learning rate was set to 0.01 and the minimum error of the training target was set to 0.0000001. The total 135 samples were split into two sets: 115 samples were randomly selected as the training set for generating and training the network, and the remaining 20 samples were used to evaluate the performance of the trained model. In addition, the performance of the established model was evaluated according to MARD, the root mean square error (RMSE), Bland-Altman analysis, and EGA.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 5 Results
### Case of 5 Clusters  
As seen previously, we had to set a number, k, for this clustering study. We arbitrarily chose k = 5 because the Koppen classification states (empirically) that there are five different climates in Spain. The results of clustering are shown in Figures 12-15. As expected, the results related to k-means and k-medoids are equivalent. Indeed, when there is no or few outliers, these two methods are almost similar. Concerning the two other methods, there are small differences, but it is visually difficult to differentiate them. At this level, it is really difficult to distinguish between these four methods, and specific metrics will have to be used for more objectivity. Tables 3 and 4 are presented the Rand and Jaccard Indices. The Rand and Jaccard approaches for the cluster comparison are really similar in the study. If some differences are visible, the conclusions remain the same. There is no interest in using k-means, k-medoids, and spectral separately since the clusters resulting from these models are similar (similarity higher than 95% for Rand and 91% for Jaccard). The choice to use k-medoids, hierarchical, and Koppen for geographical clustering seems quite justified given this result and given that k-medoids is in theory more robust than k-means and spectral. Let us see now if, by decreasing the number of clusters (3), this conclusion remains the same.  
Figure 12: Using k-means for 5 clusters.  
Figure 14: Using spectral for 5 clusters.  
Figure 13: Using k-medoids for 5 clusters.  
Figure 15: Using hierarchical for 5 clusters.  
Figure 19: Using hierarchical for 3 clusters.  
Figure 17: Using k-medoids for 3 clusters.  
Figure 18: Using spectral for 3 clusters.  
When analyzing the results obtained by k-means and spectral clustering algorithms, it can be said that they present similar results in more than 90% for classes three and five. The two tables (Rand and Jaccard) are similar; this suggests that the methodology is robust and the conclusions are stable. Contrary to case k = 5, the conclusions are slightly different here, and there would be a model to add: k-means or spectral. Because of its totally different foundation from that of k-medoid, our preference is for spectral. Thus, the methodologies to use (and to test) when studying predictions that require geographic clustering are, in this case, k-medoids, hierarchical, spectral, and Koppen.
## 3 Results
This study's results (Figure 8) show that the target archaeological artifacts are scattered underground throughout the entire site, typically at depths of approximately 1-2 m. The Archaeological Authority of Israel (AAI) conducted precise experiential excavations at several suspected points of interest within the site area and a few attempts outside the GPR-investigated area. Two examples of shallow and short trenches, the photos of which are incorporated into Figure 9, were dug to a depth of 2 m. The archaeological findings align with the geophysical survey results, indicating that archaeological artifacts are located at depths estimated by the GPR findings. These artifacts consist of randomly shaped structures made mainly of boulders, with a density higher than the surrounding soil. This correlation reinforces the accuracy and reliability of our geophysical survey in identifying archaeological features within the Hulata solar panel construction site. Figure 7: The map of suspected archaeological findings at the Hulata solar panel construction site indicates ground-based GPR discoveries (small dots) and drone-based GPR findings (large dots). Colors correspond to different surveying groups based on the direction of the GPR lines. Figure 8: Numerically mapped locations of overlapping ground-based and drone-based GPR findings, presenting potential archaeological artificial objects, likely walls or concentrations of pottery. The irregular scattering pattern suggests a connection to the Early Bronze Age construction practices observed in the Hulata region, characterized by structures crafted from irregularly broken limestone and sandstone boulders. Figure 9: Excavation trenches and corresponding archaeological findings at the Hulata solar panel construction site.
## 2 Materials and Methods  
Ni\({}_{1-x}\)Zn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) (x = 0.5, 0.6, 0.7) nano-particles were synthesized by a thermal decomposition method as shown in Figure 2. The raw materials were nickel(II) acetylacetonate (97%), zinc acetylacetonate hydrate (95%), iron(III) acetylacetonate (97%). Oleic acid (90%) and oleylamine (70%) were used as a surfactant, 1,2-hexadecadediol (90%) as a reducing agent, and benzyl ether (98%) as a solvent. Ni, Zn and Fe-acetylacetonate precursors were weighed, respectively according to the chemical form of Ni\({}_{1-x}\)Zn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) (x = 0.5, 0.6, 0.7) and poured into a 500 mL three neck flask that was filled with a mixed solution of 6 mmol oleic acid, 6 mmol oleylamine, 1,2-hexadecandediol(HDD), and 20 mL benzyl ether. The precursor solution was heated to 200 \({}^{\circ}\)C for 1 h, then further heated to 300 \({}^{\circ}\)C and kept there for 1 h; the process was carried out in a N\({}_{2}\) atmosphere with refluxing using cooling water. The solution that finished the reaction was cooled to room temperature, centrifuged for 30 min at a speed of 4000 rpm and washed repeatedly with hexane and ethanol in order to separate organic remains from the ferrite particles. Hexane was used to dissolve fat acids such as oleic acid and oleylamine. Finally, Ni\({}_{1-x}\)Zn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) (x = 0.5, 0.6, 0.7) nanoparticles were collected and dried at 100 \({}^{\circ}\)C for 24 h.  
Toroidal samples of NiZn ferrites were fabricated by uniaxial pressing. The ferrite powder was mixed with a PVA (Polyvinyl alcohol, 5 wt %) binder solution, which was poured into a toroidal  
Figure 2: Experimental procedure for synthesizing Ni\({}_{1-x}\)Zn\({}_{1-x}\)Fe\({}_{2}\)O\({}_{4}\) (x = 0.3, 0.4, 0.5) ferrite nanoparticles.  
shaped mold and applied with a pressure of 1 ton/cm\({}^{2}\) to make green toroidal samples. The green toroidal samples were burned out at 650 \({}^{\circ}\)C for 30 min in air and then sintered at 1250 \({}^{\circ}\)C for 2 h.  
The phase purity of the NiZn ferrites nanoparticles was investigated with an X-ray diffractometer (XRD: D/max 2200 V/PC, Rigaku Co., Akishima, Japan). Microstructures of sintered toroidal ferrite samples were observed with a Scanning Electron Microscope (SEM, JSM 6700F, JEOL, Japan). Magnetic permeability (u\({}^{\prime}\), u\({}^{\prime\prime}\)) was measured from 1 MHz to 1 GHz with an Impedance analyzer (E4991A). Saturation magnetization of the ferrite particles was measured with a Vibrating Sample Magnetometer (VSM).