esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It is important to note that many of the studies dealing with vegetation changes as a proxy for archaeological presence have relied solely on the use of relatively moderate resolution satellite imagery, such as Landsat (30 m spatial resolution) and Sentinel-2 (10-20 m spatial resolution). However, very high-resolution imagery, with a finer spatial resolution of \(\sim\)30-50 cm, has been shown to be much more reliable in both detecting cropmark anomalies and distinguishing small-scale details [20; 21]. In addition to the utilization of remote sensing techniques, archaeologists have also recently started to turn to 3D modeling as a way to both study and share their results, including data collected from field excavations or large-scale data derived using geospatial technologies [22; 23; 24]. As we live in a three-dimensional world, 3D visual reconstructions are imperative in conceptualizing the information that is often lost in 2D images (e.g., maps and planimetric plans), such as shape, dimension, and physical consistency [25; 26]. The adoption of such 3D technologies has enabled the continued reusability of data by scientists at a larger scale, fostering more collaborative efforts across disciplines [27]. However, 3D modeling is not only crucial for making data more tangible and understandable to scientists and the public alike, but it is also essential in ensuring the digital protection of cultural heritage and the preservation of collective memory for future generations [26].
# Multiple Correspondence Analysis of Emergencies Attended by Integrated Security Services
## 6 Experimental Results
### Combined FRIs Analysis
#### 6.2.6 Transit and Fire Brigades  
We obtain 11 categories (7 of transit and 4 of fire brigades), as seen in Figure 8f illustrates. Related categories are hit by a car (T), motorcycle accident (T), overturned car (T), and rescue (FB), which is logic because in a traffic accident, usually the fire brigades attend and cut the metal structure of the car to rescue the survivors. The less related categories are crashes (T), bad-parked (T) vehicle, vehicular congestion (T), and structural fire (FB).  
We also analyzed the combination of FRIs in a group of three, including health services, transit, and fire brigades. Figure 9a shows the first 6 eigenvectors of the categories, and by applying the selection criteria, the first one is the most representative. Panel (b) shows the 3D plot of 12 categories (5 of health services, 6 of transit, and 1 of fire brigades). Panel (c) depicts two well-differentiated concentrations, where the left one shows a relation between traffic accidents (HS), crashes (T), hit by a car (T), and motorcycle accident (T). In a separate concentration are the points corresponding to fire threat (FB). This distribution is logical and follows the nature of the events of transit and how they are known to be related, whereas hand fire threat does not have any relation with the other four overlapped categories.  
We observed a combination of a group of four FRIs, including fire brigades, risk management secretary, military, and municipal services. In Figure 10a we can observe the eigenvectors, and according to our selection criteria, any of these eigenvectors is representative for this group of categories. In Panel (b) we have 3 groups of events overlapped. When a forest fire of big dimensions overpasses the operative capacity of fire brigades, armed forces (Military) are called to move personnel, vehicles, and aircrafts, and at the same time, potable water service (Municipal Services) is alerted as well to  
Figure 9: Combined emergencies of health services, transit, and fire brigades: (**a**) Eigenvectors; (**b**) All the visible categories; (**c**) Only five categories visible, with two concentrations well defined (health services and transit) separated from fire brigades; (**d**) Eigenvalues and relative relevance of the eigenvectors.  
provide the liquid needed to recharge the bambi-bucket (big capacity recipient) attached to helicopters and tank trucks.
# A Framework for Decentralized Energy Trading Based on Blockchain Technology
## 2 Methodology  
### Blockchain  
Blockchain technology is a relatively new research area. It is first proposed in 2009 by [PERSON] and used for financial safety and digitization of currencies [13]. Blockchains are designed for decentralized storage secured by a cryptographic signature and distributed maintenance. Figure 1 shows an example of a blockchain structure in Bitcoin, which is one of the most famous digital currencies. The hash in one block is calculated according to the content of this block through a cryptographic hash function (Bitcoin uses SHA-256). The hash can determine the input data's integrity, because any changes in the original data will result in extensive changes to the hash [14; 15]. Using the cryptographic hashes here will ensure that any changes to the block will cause mismatching of the hash and content. The hash in this block will be the header of the next block. A sequence of blocks linked by hash numbers is called a blockchain. One advantage of this mechanism is that if anyone attempts to tamper with the transaction information in a past block, all the subsequent blocks also need to be tampered with [16]. The information stored in the ledger is validated and maintained by every participator in the network, and thus no centralized authority is needed. It should be noted that multiple (but not necessarily all) nodes owe a full copy of the entire database.  
Bitcoin uses a proof-of-work consensus mechanism, where the ability to verify and publish a transaction is dependent on the computing power of a node [17]. Once a block is published, some reward will be given as encouragement. In order to publish a block and receive the reward, a node must complete the following steps, shown in Figure 2:  
1. Build a candidate block according to the submitted transaction;
2. Generate a random number, add it to the content of the block and calculate a hash of the block using SHA-256 before comparing it with the target (a special number of leading zeros can be used to adjust the calculation's difficulty);
3. If the hash is not correct, a new random number will be generated repeatedly until a solution is found or the target is changed (in this case, another block is added in the chain);
4. If the hash is correct, the block will be broadcast to the whole network;
5. Once the majority node in the network accepts the block, it will be permanently added in the chain, and the publisher will be rewarded.  
Figure 1: Example of a blockchain.  
Bitcoin's protocol ensures that a block is added to the chain roughly every 10 min by adjusting the difficulty of finding the hash target. An example of one block in Bitcoin is shown in Table 1[17].
## 2 Datasets and Method
### Accuracy Assessment  
In general, the performance of deep learning is based on the evaluation metrics such as accuracy and loss of the DL model training, IoU, recall, F1-score, confusion matrix, etc. Since such metrics are statistical representations of the overall quality by quantitative numbers computed using formal equations, there is a limitation in revealing the local and detailed phenomena. In this regard, visualization of the differences between prediction and ground truth leads to meaningful evaluation and analysis of the results. In particular, the pattern and location of the misclassified features can be identified through visual inspection on the basis of the difference images between prediction and ground truth.  
We applied global accuracy (or overall accuracy (OA)), mean accuracy, mean intersection-of-union (mIOU), weighted IoU, IoU, and recall to evaluate DL model training performance and results of the semantic segmentation. Mean accuracy is the average accuracy of all classes in particular data (i.e., each test and new data). Weighted IOU is for reducing the impact of errors in the small classes by taking number of points into account for each class. The evaluation metrics are as follows:  
\[Global\ accuracy=\frac{Number\ of\ correct\ prediction}{Total\ number\ of\ prediction}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Mean\ accuracy=\frac{\sum(each\ class\ accuracy)}{k} \tag{2}\]  
\[IoU=\frac{Area\ of\ overlap}{Area\ of\ union}=\frac{Area\ (prediction\ \cap\ target)}{Area\ (prediction\ \cup\ target)}=\frac{TP}{TP+FP+FN} \tag{3}\]  
\[mIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{TP}{TP+FN+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
where _TP_, _TN_, _FP_, and _FN_ are true positive, true negative, false positive, and false positive, respectively. \(k\) is number of classes.  
Class imbalance is a common problem in deep learning for multi-class classification or segmentation with a disproportionate ratio of the data in each class. i.e., there is a significant discrepancy in the amount of data across different classes. In this case, the prediction accuracy of the class with greater data is higher than that of the class with less data. Therefore, the ideal case would be for each class to have approximately the same amount of data [28; 29].  
We applied weighted cross-entropy loss among the approaches for correcting class imbalance such as over- or under-sampling, class aggregation, class weighting, focal loss, and incremental transfer learning. The concept of the weight-based class imbalance correction is that the weights are assigned inversely proportional to the number of data of the classes. The weighted cross-entropy weights the losses associated with incorrectly predicting rarer data more heavily than losses associated with more data as follows [30]:  
\[w\ (p,\ y)\ =\ \sum_{t=1}^{C}w_{t}\ p_{t}\ log\ (y_{t}) \tag{6}\]  
where \(p_{t}\) and \(y_{t}\) denote the probability estimate and ground truth of class \(t\), respectively. \(C\) denotes the number of ground truth classes, and \(w_{t}\) denotes the weight for class \(t\).
## 1 Introduction
Thus, in this work, we propose the use of an AHP/TOPSIS hybrid approach. The hybrid approach results in (1) lowering of the computational complexity and (2) easing the manual effort needed for the construction of AHP pairwise comparison matrices. Here, all of the candidate sites are ranked based on their performance scores and the optimal site is identified as the candidate with the highest score. The decision makers are given flexibility to chose the optimal site in the form of top ranked alternatives based on the performance scores of all of the candidates/alternatives. Since the AHP/TOPSIS approach consists of a number of steps, a detailed step-by-step case study is presented to identify an optimal location for a new gas station in New York City by employing four criteria. To assess the effectiveness of the proposed approach, an experimental evaluation is performed to select an optimal location of a convenience store making use of five criteria. The proposed approach presented here is flexible and can effectively incorporate any number of criteria to rank the candidates. This is an extended version of our work published in [15]. The main contributions of the extended work can be summarized as follows:  
* A comprehensive flowchart showing the flow of important steps in the proposed AHP/TOPSIS approach (Section 5);
* An experimental evaluation utilizing real datasets to select an optimal location of a convenience store using the proposed AHP/TOPSIS approach (Section 6); and
* A detailed discussion on the evaluation results and the strength and weaknesses of the proposed AHP/TOPSIS approach (Section 7). The rest of the paper is organized as follows: Section 2 discusses the related work. In Section 3, the problem formulation is presented. Section 4 elaborates two multi-criteria decision-making approaches. In Section 5, a case study is shown for the selection of an optimal location for a gas station. Section 6 presents experimental details for the evaluation of the proposed AHP/TOPSIS approach, while Section 7 discusses the evaluation results and the strength and weaknesses of the AHP/TOPSIS approach. Section 8 concludes the manuscript with the possible future directions.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 8 Simulation Results  
For examining the developed control systems dynamics, a series of simulations was carried out in the MATLAB/Simulink software framework. The system considered was (1), (2) and four control laws, (25), (30), (38) and (40). In what follows, passification-based control (25) law control is denoted as PB law, law (30) is denoted as PD law, (38) as proportional-differential-consensus (PDC) law and (40) as passification-based consensus (PBC) law. One situation was simulated for all control laws: 8 satellites in two PCO orbits.  
The leader's satellite orbit was taken to be the same for all simulation runs. The leader's orbit parameters are as follows: \(m_{F}=10\) kg; \(\mu_{e}=398.600\) km\({}^{3}\cdot\) s\({}^{-2}\); perigee radius \(R_{p}=6971\) km; eccentricity \(e=0.2\); \(\Omega\), \(\omega=i=M=0\) rad/s. Disturbances \(d_{j}(t)\) for \(j=\{x,y,z\}\), acting on (1), (2), and including gravitational perturbations \(\mathrm{J}_{2}\), atmospheric resistance and solar radiation pressure perturbation forces were taken into account. The following disturbances model of [7; 36] was adopted:  
\[\left[\begin{array}{c}d_{x}\\ d_{y}\\ d_{z}\end{array}\right]=1.2\times 10^{-3}\left[\begin{array}{c}1-1.5\sin(nt) \\ 0.5\sin(2 nt)\\ \sin(nt)\end{array}\right]. \tag{41}\]  
It is worth mentioning that model (41) gives the disturbances magnitudes which are slightly higher than the actual ones under the similar conditions.
## 5 Performance Evaluation
### Simulation Results
The gain of the proposed scheme over our previous scheme in Reference [14] is reduced as \(|\Omega|\) increases; for example, a 20% gain with \(|\Omega|=6\) is decreased to 12% with \(|\Omega|=12\).
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Nickel-Doped Zinc Oxide Films  
The nickel (Ni)-doped zinc oxide films were prepared on glass substrates under various concentrations of nickel (1, 3, and 5%) [164]. The Fourier transform infrared analysis confirmed the presence of different functional groups in the obtained films.
## 1 Introduction
By providing actionable recommendations, including dynamic master station assignments and interdisciplinary real-time adaptive strategies, this research clearly contributes to advancing maritime safety and evacuation efficiency by improving certain already existing technologies/solutions and even opening up a path towards their optimization.
###### Abstract  
Over the past fifty years, telecommunications has transformed, in an unprecedented manner, the way we live, work and communicate. For telecommunications, fixed networks are the one pillar and wireless networks the other. The wireless networks and their generations have been extensively investigated in the literature; however, the fixed networks lack a consistent exploration of their evolution. For this purpose, our motivation is to present a review of the evolution of fixed networks in a holistic manner, from various different perspectives, which can assist engineers and students to understand, in a better way, how broadband networks developed and the main features of the current networking environment. Specifically, in this review, we aim to shed light on the most important technologies, standards and milestones of the fixed networks from various perspectives, such as the service perspective, the networking perspective (both access and core part) and the physical layer perspective. Our study follows the timeline of the five generations of fixed networks defined by the European Telecommunications Standards Institute (ETSI) F5G group and discusses the key achievements and limitations of each generation for each different infrastructure layer and segment, designating the most important issues for the most widely-adopted technologies and standards. Finally, our analysis helps to reveal the technical challenges that need to be addressed by the fixed telecom network community, such as energy efficiency, capacity scaling, cost-efficiency, etc., while also revealing potential future directions.  
Fixed networks; F5G; layered approach; access networks; core networks; network services; optical transmission; telecom industry
## 2 Materials and Methods
### Methods
#### 2.2.2 Network Architecture  
Residual layers with skip connections proposed by [28] are used for the encoder to provide stronger gradients and to increase the network depth in order to learn higher level features. Spatial down-sampling is achieved by maximum pooling layers while the ResNet blocks have a stride of 1 and use the implementation from [29]. The full encoder is shown in Figure 2.  
The sampler activations are used as mean \(\mu_{x}\) and lower triangle of the covariance matrix \(\Sigma_{x}^{LT}\) for \(x\) having a multi-variate normal distribution, \(x\sim N(\mu_{x},\ \Sigma_{x})\), with \(\Sigma_{x}=\ (\Sigma_{x}^{LT})\), \(\Sigma_{x}^{LT}\), with prime indicating the matrix transposition. Then, for an input sample \(x\) the encoder and sampler output are  
\[p_{\Phi}(x)=\left[\begin{array}{c}z_{s}\\ z_{m}\end{array}\right]=z,\text{ with }z\gets N_{x}. \tag{9}\]  
The activation functions used are the identity mapping \(f(x)=x\) and the exponential function \(f(x)=e^{x}\) for the mean and covariance components, respectively.  
Following standard autoencoder conventions, the decoder approximately reconstructs the input \(x\) by a series of deconvolution and up-sampling operations without residual skip connections. A kernel size of 2 is used for the first deconvolution to increase the spatial resolution like pooling layers, see Figure 3.  
Both the classifier and the discriminator are single layer dense networks with \(|x|\) weights, since the latent representation is already an abstract high-level representation of the raw data.  
The final model used for the experiments was trained on 10,000,000 image patches of size 64 by 64 pixels for approximately 2,500,000 steps with a batch size of 256. The encoder is comprised of 6 ResNetV2 blocks with 2 units, followed by activation, batch normalization and pooling layers while the decoder has 8 transposed convolution, activation and batch norm layers, as well as 6 unpooling layers in order to restore the initial input shape.  
Figure 2: Architecture of the encoder part, comprising of ResNet v2 blocks with skip connections and information bottleneck layers.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 4 The Methods of Decoupling
### Internal Decoupling  
The internal decoupling provides good solutions to isolation improvement relies on antenna diversity, such as pattern diversity, spatial diversity, and polarized diversity. The relationship between antenna diversity and mutual coupling was recognized in [27]. The isolation enhancement was achieved by constituting some radiation elements with different polarization characteristics, which can be of any type of antenna, such as patch, slot and substrate integrated waveguide. Thus, additional decoupling structures are not needed. Among them, the focus in the open literature was orthogonal polarization.
## 5 Optimization of Fuzzy Fatigue Reliability
### Fuzzy Reliability Optimization Design Based on Genetic Algorithm  
Based on the genetic algorithm, the number of population was set to 20, and the number of iteration was given to 100. After running around 32 min. and 3201 iterations, the optimal values were listed, as in Table 8.  
According to the results that are shown in the Table 8, the optimized Young modulus \(E\) reduced to 199,190 MPa, while the Poisson ratio \(v\) and cyclic strain hardening exponent \(n^{\prime}\) remained unchanged. The increase of thickness of tail plate and roof plate was helpful in lessening the maximum equivalent stress \(S\). The vertical force at front traction joint \(F_{1}\) and lateral force at front lateral stabilizer joint  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Random Variables** & **Lower Limit** & **Upper Limit** \\ \hline x1 (MPa) & 193,198 & 214,500 \\ x2 & 0.275 & 0.325 \\ x3 (MPa) & 660.99 & 1060.9 \\ x4 & 0.1742 & 0.1842 \\ x5 (mm) & 20 & 50 \\ x6 (mm) & 20 & 50 \\ x7 (mm) & 10 & 40 \\ x8 (mm) & 10 & 40 \\ x9 (N) & 54,250 & 100,750 \\ x10 (N) & 15,750 & 29,250 \\ x11 (N) & 15,750 & 29,250 \\ x12 (N) & 43,750 & 81,250 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Boundary values of random variables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Initial Results** & **Optimal Value** \\ \hline x1 (MPa) & 201,000 & 199,190 \\ x2 & 0.3 & 0.282 \\ x3 (MPa) & 860.99 & 782.1 \\ x4 & 0.1792 & 0.180 \\ x5 (mm) & 36 & 39 \\ x6 (mm) & 32 & 39 \\ x7 (mm) & 20 & 18 \\ x8 (mm) & 20 & 18 \\ x9 (N) & 77,500 & 74,772 \\ x10 (N) & 22,500 & 23,582 \\ x11 (N) & 22,500 & 24,839 \\ x12 (N) & 62,500 & 51,619 \\ \(S\) (MPa) & 126.45 & 110.01 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimized results.  
\(F_{4}\) both reduced, and the latter almost decreased 10,000 N. The remaining load was retained. Thus, reducing the driving speed under this serve condition could lead to a decrease for this load.  
Subsequently, based on the optimized results that are mentioned above, the fuzzy fatigue reliability of welded A-type frame was again evaluated by MATLAB software. It rose up to 95.12%, and thus met the engineering requirement. The stress contour of optimized A-type frame under the same condition is shown in Figure 20. The maximum equivalent stress was located at the same place, but the value dropped to 107.81 MPa and the optimized A-type frame presents more uniform stress distribution.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
###### Abstract  
This study aimed to biosynthesize zinc oxide nanoparticles (ZnO-NPs) using extracts from various seaweeds, including _Ullua lactuca_, _Ullua intestinalis_ (Chlorophyta), and _Sagrassum multicum_ (Phaeophycae). The biosynthesized ZnO-NPs were characterized using UV spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction, transmission electron microscopy, and zeta potential analysis. Their antimicrobial activity was assessed using the disk diffusion method, revealing significant efficacy against two bacterial species (_Klebsiella pneumoniae_ and _Escherichia cal_) and two fungal species (_Candidia albicans_ and _Apergillus niger_). Additionally, the antioxidant potential of the ZnO-NPs was evaluated based on the total antioxidant capacity, ferric reducing antioxidant power, and DPPH (2,2-diphenyl-1-picrylhydraz) radical scavenging assays. The antioxidant activity of the ZnO-NPs was confirmed using the three antioxidant assays. The ZnO-NPs of _U. lactuca_ recorded the highest antioxidant activity. The cytotoxicity of the ZnO-NPs was tested on different cell lines using the MTT assay. The ZnO-NPs of _U. lactuca_ showed very weak cytotoxic effects on WI 38 (\(84.98\pm 4.6\)\(\mu\)g/mL) and 23, and this result confirmed its safety on normal cells. The ZnO-NPs of _U. lactuca_ showed moderate cytotoxic effects on the HepG-2 (46.66 \(\pm\) 2.8 \(\mu\)g/mL) and MCF-7 (30.60 \(\pm\) 2.1 \(\mu\)g/mL) cell lines. In an in vivo study, the ZnO-NPs of _U. lactuca_ showed a decrease in tumor volume, weight, and serum malondialdehydedehyde in experimental mice, while the total antioxidant capacity of the serum was increased. Histopathological changes in ZnO-NPs indicated a reduction in tumor size, a lower number of mitosis divisions, and a rise in apoptosis correlated with the ZnO-NPs of the _U. lactuca_-treated groups. In conclusion, biosynthesized ZnO-NPs from seaweed showed potent antimicrobial, antioxidant, and antitumor activities, which can be used in the pharmaceutical industry.  
[PERSON]; ZnO-NPs; antimicrobial; antioxidant; cytotoxic; antitumor