esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background and Preliminaries
### Requirements of Anomaly Detection in Wireless Sensor Networks
Another factor that should be considered for distributed structure is how often the local reference models should be sent to the cluster head and therefore how often the global reference model should be reconstructed from the local reference models. _Adaptability with Dynamic Data Changes_: due to dynamic streaming of sensor data measurements, the model that represents the normal data behavior becomes rigid over time. Thus, updating the normal model is crucial for effective anomaly detection. A variety of updating mechanisms were proposed in the literature [55, 56, 57], but their adoption for WSN is governed by their compatibility with the resource restriction demands of sensors. Moreover, the required updating mechanism should be lightweight and efficient to meet the resource restriction demands of sensor devices. _Data Correlation Exploitation_: sensor data measurements of close neighborhoods are characterized by high attribute, spatial and temporal data correlations. The attribute/feature correlation can be exploited to improve the efficiency of detection models through data dimension reduction. Temporal correlation means that the readings collected at one time period are related to the readings collected at the previous time period. Spatial correlations means that the readings of nodes that are geographically close to each other are expected to be correlated [48]. Exploiting such data spatial and temporal correlations altogether increase the effectiveness of anomaly detection.
# Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction
## 2 Principles and Methods
### Principles of Deep-Learning Reconstruction  
Deep learning simulates the interference and diffraction of light in digital holography to establish the mapping relationship between holograms and intensity and phase information [26]. The process of interference between the reference beam and the object beam to form a hologram is regarded as the forward-propagation process of the digital holographic imaging system. For deep-learning networks, once the mapping function \(R:I\to O\) is established, the reverse propagation for reconstruction from \(O\) to \(I\) can be completed. Therefore, the reconstruction process can be represented as:  
\[O(x,y)=P\{I(x,y)\}, \tag{5}\]  
In Equation (5), \(P\) represents the mapping from the hologram to the object wavefront. In the network, \(P\) includes not only the process of removing conjugate images but also the process of diffraction propagation. Therefore, the objective function of the network model is:  
\[\phi_{0}=\arg_{\phi}\min\sum_{n=1}^{N}L\big{(}O_{n},P_{\phi}\{I_{n}\}\big{)}, \tag{6}\]  
In Equation (6), \(\phi_{0}\) represents the optimal solution to the network weight parameters, \(L\) is the loss function, and \(N\) is the total number of training samples. In deep learning, the establishment of the mapping function is achieved through the process of fitting the data with a neural network \(P_{\phi}\) defined by the weight parameters \(\phi\), gradually obtaining a mapping model that closely approximates the true mapping function. The implementation steps of this process are usually as follows: first, obtaining many digital holograms along with their corresponding intensity and phase maps as the dataset, then constructing a neural network to fit this dataset. The network applies multi-layer convolutional operations to learn the image features of many intensity images, phase images, and their corresponding digital holograms. Ultimately, it establishes a (nonlinear) mapping relationship between the intensity and phase images of the optical field and the hologram images, thus achieving the reconstruction of intensity and phase information from digital hologram images.
## 2 Materials and Methods
### Sar-Sift  
SAR-SIFT proposes an improved SIFT method that enhances the matching accuracy of SAR images by refining SIFT in terms of feature scale space and feature description. SAR-SIFT computes the image gradient using ROEWA and generates scale space via Gaussian blurring. The response function of scale space is as follows [13]:  
\[\left\{\begin{array}{l}\sigma_{i}=\sigma_{0}\cdot k^{i}\\ t_{i}=\frac{1}{2}\sigma_{i}^{2}\end{array}\right.,k=2^{\frac{1}{3}},i\in[0 \ldots S-1] \tag{1}\]  
where the first layer's scale parameter is represented by \(\sigma_{0}\), \(S\) represents the quantity of scale layers, \(k\) represents the constant scale ratio. In SAR-SIFT and this paper, the parameter \(\sigma_{0}=2\), \(S=8\), \(k=\sqrt[3]{2}\).  
Figure 1: Algorithm flowchart.  
The SAR-HARRIS response function and its corresponding response value at each scale level are computed as follows [19]:  
\[C_{SH}=g_{\sqrt{2}\sigma}*\left[\begin{array}{cc}\big{(}G_{x,\sigma}\big{)}^{2 }&\big{(}G_{x,\sigma}\big{)}\big{(}G_{y,\sigma}\big{)}\\ \big{(}G_{x,\sigma}\big{)}\big{(}G_{y,\sigma}\big{)}&\big{(}G_{y,\sigma}\big{)} ^{2}\end{array}\right] \tag{2}\]  
\[R=det(C_{SH}(x,y,\sigma))-t\times tr(C_{SH}(x,y,\sigma)) \tag{3}\]  
where \(C_{SH}\) denotes the SAR-HARRIS response matrix, \(g_{\sqrt{2}\sigma}\) is the Gaussian kernel with standard deviation \(\sqrt{2}\cdot\sigma\), \({}^{*}\) represents the convolution operation, \(G_{x,\sigma}\) and \(G_{y,\sigma}\) are the responses of the ROWEA filter in the horizontal and vertical directions, respectively; det denotes the determinant of the matrix, tr denotes the trace of the matrix, t is an empirical value usually between 0.04 and 0.06, and R is for the SAR-HARRIS corner point detector.  
After constructing the scale space, feature points are determined using a response function. First, a threshold is set, and corner points with response values greater than this threshold are selected as candidate feature points. Then, local extrema are identified within a 3 \(\times\) 3 neighborhood around each candidate point at the same scale to verify whether the response value is the maximum in the neighborhood. Finally, bilinear interpolation is applied to precisely determine the position of the extremum, yielding subpixel-accurate corner points.  
Based on the positions and scale information of the feature points identified in the previous step, a corresponding descriptor is constructed using the GLOH method. The final matching points are obtained using the NNDR matching method and RANSAC outlier rejection.
# An Insight into the Chemistry of Cement--A Review
## 3 Hydration Step Involving OPC  
Hydration refers to chemical reactions between anhydrous compounds with water molecules, giving a plethora of hydrated compounds. The hydration phenomenon occurring in cement consists of the chemical reaction between non-hydrated cement and water that allows for important physicochemical transformations and mechanical changes. In this context, a distinction should be made between the partial hydration by means of the moisture contained in an air atmosphere, and the hydration achieved by the direct mixing of the cement with water [47]. In this last case (i.e., direct mixing), several chemical reactions take place, inducing the conversion from a workable paste with plastic features to a hard solid.  
Figure 1: Schematic representation of the process of cement manufacturing. Reprinted with permission from [43].  
Furthermore, since the hydration of OPC consists of a multitude of simultaneous single chemical reactions involving the different main components forming the cement powder with water, the resulting pathway is rather complex and non-obvious, and its degree of complexity is strongly influenced by the starting composition of the clinker that directly affects the transformation occurring within the cementitious matrices.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 2 Methodology
### Measurement Distortions Analysis
#### 2.3.2 Carrier Phase Measurements Analysis  
Similar to pseudorange measurement bias estimation, the carrier phase measurement bias can also be estimated by performing single differencing between the reference antenna and beamformed carrier phase measurements.  
The carrier phase measurements of the \(k\)th satellite after beamforming in the presence of interference can be expressed as  
\[\phi^{k}_{array}=\rho^{k}+d\rho^{k}+c\left(dt^{k}-dT\right)+\lambda N^{k}-d^{ k}_{iono}+d^{k}_{tropo}+\phi^{k}_{bias}+\epsilon^{k}_{\phi} \tag{20}\]  
where \(\epsilon^{k}_{\phi}\) is the receiver carrier phase noise, \(N^{k}\) is the integer ambiguity, and \(\phi^{k}_{bias}\) is the possible carrier phase bias introduced by antenna array processing.  
The carrier phase measurement from the \(k\)th satellite for the reference antenna without interference can be expressed as  
\[\phi^{k}_{ref}=\rho^{k}+d\rho^{k}+c\left(dt^{k}-dT\right)+\lambda N^{k}-d^{k} _{iono}+d^{k}_{tropo}+\epsilon^{k}_{\phi} \tag{21}\]The single difference between the pseudorange measurements obtained from the reference antenna and after beamforming is  
\[\Delta\phi^{k}=\phi^{k}_{ref}-\phi^{k}_{array}=\phi^{k}_{bias}+\varepsilon^{k}_{ \Delta\phi} \tag{22}\]  
where \(\varepsilon^{k}_{\Delta\phi}\) is the single differenced receiver carrier phase noise.  
After single differencing, atmospheric errors cancel out. There is no receiver clock error since a single receiver is used. Integer ambiguity is also removed since a coherent-phase multi-channel front-end using a single oscillator is used which makes receiver to initialize ambiguity to the same value for the single channel and multi-antenna receiver process. As in the pseudorange case, carrier phase noise is ideally zero mean and therefore any bias observed using single differencing can be attributed to array processing in the presence of interference.  
As this research focuses on high precision applications, antennas with good phase centre stability and low mutual coupling are considered. Hence, the effect of phase centre and mutual coupling are not considered in the pseudorange and carrier phase measurement formulations (Equations (17)-(22)).
## 1 Introduction
We formulate a new measure of the reliability of the fire service system, the collapse index, to reduce the overlap of the actions of the resources in the incidents. This is a territorial measure at the pixel level calculated from the historical fire service data for the Madrid Region. A specific maximum value of this index is determined and considered as a restriction in the optimisation process;
2. We propose a new fire station (FS) optimum location model by applying the simulated annealing method in combination with geographic information system algorithms. This model balances the workload between FS when new FS are optimally located. The use of a cost function that minimises the standard deviation of the FS resource workload is a novelty as compared to other studies in the literature on FS locations;
3. The optimisation process calculates the effects on the workload of an old FS due to the replacement of one of its resources in an incident by a resource from a new FS and updates the collapse index for the study area. All types of incidents and resources are considered in the model. The results show that the maximum collapse index in a pixel decreases from its initial value of 10,485 min to 2500 min when five new FS are built. A specific computer application was developed for this study, which includes historical data in the Madrid Region fire service, namely all types of incidents in all vehicle types, as well as the calculation of the collapse maps for each configuration of the FS locations and the optimisation process. Maps of the collapse index can, thus, be obtained to determine its distribution in Madrid and identify the areas with the highest risk of collapse for use in the decision-making process. The decision makers also have information on the effects of adding one to five fire stations on the reliability of the system and where these new FS should be placed. This paper is organised as follows. Section 2 presents the related studies in the literature. Section 3 introduces the study area, while the new optimisation model is described in Section 4. Section 5 contains the results obtained by applying the proposed methodology in the Madrid Region, which are then discussed in Section 6. Finally, the conclusions drawn from our study are explained in Section 7.
## 4 Results
### RQ5: Network Security Attack in IoT and WSN Layers
#### 4.5.1 Denial of Service Attacks (DOS)  
A Denial-of-service (DoS) attack is a malicious attack in which attackers make the victim's system unresponsive and difficult to reach for the legitimate user by making many requests to the expected URL than the server can handle [75; 76]. DoS attacks typically occur when authenticated clients have not been granted access to the information or service [77]. A distributed denial of service (DDoS) attack is a type of DoS attack that uses multiple users or infected systems to attack a victim's system or to attack a website so that it becomes unresponsive. This attack also prevents the website from functioning properly and disrupts regular traffic. In WSN, a DDoS attack changes the routing protocol information DSR, resulting in a huge amount of unauthorized traffic and making the network/website unavailable. On the other hand, a low-rate denial-of-service (LDoS) attack is another type of DoS that penetrates the WSN's routing protocol, thus compromising the security and trust mechanisms. An LDoS attack is difficult to detect due to its non-stationary nature and low signal strength52. In this attack, illegitimate traffic affects the operational capability of a network. It causes severe outages and monetary losses.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sr. No.** & **Layer Name** & **Attacks** \\ \hline
1 & Physical layer & Interception, radio interference, jamming, tempering, Sybil attack. \\ \hline
2 & Data link layer & Replay attack, Spoofing, altering routing attack, Sybil Attack, collision, traffic analysis, and monitoring, exhaustion. \\ \hline
3 & Network layer & Black hole attack, wormhole attack, sinkhole attack, grey hole attack, selective forwarding attack, hello flood attack, misdirection attack, internet smurf attack, spoofing attack. \\ \hline
4 & Transport layer & De-synchronization, transport layer flooding attack. \\ \hline
5 & Application layer & Spoofing, alter routing attack, false data ejection, path-based DoS. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Network Security Attacks on IoT and WSN Layers.
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 5 Experiment and Evaluation
### Dataset and Evaluation Matrix  
#### 5.1.1 Dataset  
Our proposed method's iced transmission line identification performance is evaluated on the collected simulated PTLI scene dataset. A series of simulated PTLI scenes were independently generated to facilitate the collection of icing image data. Long cylindrical pearl cotton (Expandable Polyethylene, EPE) is used to simulate the transmission line; polystyrene foam (Expanded Polystyrene, EPS) is attached to its surface as a simulated ice coating; and pearl cotton is used to simulate the background of the iced transmission line. So far, the iced transmission line scene is built and shown in Figure 19. The image pairs of the simulated PTLI dataset are collected using a Daheng binocular camera. It is huge and challenging work to do pixel-level labeling on collected images. Thus, the proposed method's measurement results of ice thickness are directly compared with the manual measurement results.  
Figure 18: An illustration of the ice thickness calculation using 3D measurements.
## 6 Conclusions  
With this paper, we proposed a communication method for RevAuc-based MCS that can reduce the amount of cellular traffic between a server and mobile hosts. The proposed method reduces cellular traffic by recruiting REPs to forward bid invitations and bids between the server and their one-hop neighbors via D2D communication. We confirmed the effectiveness of the proposed method through simulation. With the proposed method, a successful bidder can obtain data at almost the same location for almost the same reward within almost the same amount of time as in the existing method while reducing the cellular traffic by more than half in simulation cases with 750 mobile hosts. In addition, the proposed method can mitigate the effects of misbehaving mobile hosts that fail to fulfill the role of REP. We also confirmed that the number of D2D communication messages can be reduced by changing the configuration parameter \(T_{b}\) from 1 to 3 s with few changes in the successful bids.  
There are several issues for further study. The first issue is to improve the selection of data requests in bid invitations sent to REPs by considering the predicted trajectory of mobile hosts. The second issue is to perform additional evaluations of the proposed method with other simulation scenarios, such as the case where mobile hosts change their trajectory to obtain data. We hope that the proposed method will enhance the viability of the deployment of services for the collection of sensing data in near real time when D2D communication technologies, such as vehicle to vehicle (V2V), are widely adopted in our society.  
Figure 21: Number of different successful bids between \(T_{b}=3\) and \(T_{b}=1\).  
**Author Contributions:** Conceptualization, T.M. and S.I.; methodology, T.M. and S.I.; software, T.M.; validation, T.M. and S.I.; investigation, T.M.; data curation, T.M.; writing--original draft preparation, T.M.; writing--review and editing, S.I. and T.I.; visualization, T.M. and S.I.; supervision, S.I. and T.I.; project administration, T.I.; funding acquisition, T.I. and S.I. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Analyzing Network Decentralization  
An off-cited benefit of public blockchains is the decentralization of control from single authorities in favor of distributed governance [64]. However, recent studies indicate growing oligopolies in mining pools and increasing centralization risks from protocols favoring stronger validators [65]. Graph analysis provides effective techniques to track such trends over time. The nodes and edges would correspond to actual network participants and their interactions, which could be analyzed for decentralization aspects such as centralization risks, power distribution, or collaboration dynamics.
## 5 Feasibility and Evaluation
### Architectural Comparison
#### 5.5.4 Latency and Throughput  
Architectures designed for end users, _i.e._, host and endpoint architectures, perform analysis locally where the events of interest occur and, thus, have low latency. The other listed architectures, including EDAS, process events away from the point of occurrence and are subjected to delays caused by the network and communication. However, the EDAS autonomous adaptation property decreases any response management delays caused by a human in the loop. Thus, it results in maximum throughput as compared to the rest of the architectures.
# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
Farmers are very knowledgeable about these diseases, but they mostly do not know of early prevention methods to protect them against further loss. This ultimately leads to significant loss in guava production [12]. These different diseases are caused by different factors of guava plants; for example, canker is caused by algae and was first discovered by [PERSON] [13]. Similarly, Dastur is another guava disease that is caused by dry rot [14]. These types of diseases affect guava production, which leads to economic and environmental loss [15]. Environmental loss is any kind of loss, including energy, water, clean air, and land loss, where as far as the economic loss is concerned, this results in financial loss in production. Pakistan is a country in the Asian Pacific whose economy is mostly based on agricultural production. The agricultural significance of Pakistan can be analyzed from its gross domestic product (GDP), with agriculture being 25% of its annual GDP [16]. Many agricultural countries produce guava as a domestic product, and Pakistan is globally fourth in guava production, as it annually produces 1,784,300 t [17]. To diagnose guava diseases in a timely manner, accurate detection is necessary, as false detection may lead to the poor production of guava species. Manual observation may be time consuming and lead to the wrong interpretations. This led us to produce an automatic system for guava disease detection [18], as the production of guava fruits creates severe issues in developed and underdeveloped countries [19; 20]. The automation of disease detection is currently the fastest, least expensive, and most accurate solution [21]. It could cost more, but it can lead to a colossal time reduction by automating the disease detection process [22]. For prediction models, the RGB color channel images are primarily used, which are visually distinguishable by color. The color features could be strong descriptors to distinguish different diseases. However, obtaining deep feature-based models could be more robust, as this covers many other aspects such as geometry, pattern, texture, and other local features. For this, a local guava disease-based RGB image dataset was collected by a high-display-quality camera here. It contains four types of disease, namely canker, dos, rust, and mummification, with the fifth category as the healthy class. Further details of the dataset are discussed in Section 3. The proposed study was inspired by deep learning (DL), and a comparative analysis of various pretrained models is proposed. The main contributions are as follows:  
1. Augmented data cover different aspects of view to provide more real-time data visualization and big data usage for DL;
2. The first local Pakistani guava disease detection dataset using DL;
3. State-of-the-art DL models used to validate Pakistani guava disease detection. The rest of the manuscript is divided into three sections. Section 2 presents the related work. Section 3 is the methodology. Section 4 outlines the results and discussion.