esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Drcn  
DRCN [10] uses a recursive convolutional network. Increasing the depth of the network will add more parameters, which will lead to two problems. One problem is that it is easy to overfit, and the other issue is that the model is too large to be stored and reproduced. Therefore, a recurrent neural network is used.  
The structure of DRCN is similar to SRCNN [6], and it is also divided into three layers. The first layer is the embedding network. The function of this layer is the same as the first convolution layer of SRCNN. It is the feature extraction layer. The second layer is the inference network. Non-linear mapping is implemented. The third layer is the reconstruction network, which is the image reconstruction layer. In the non-linear mapping part, it is formed by \(d\) convolutional layers, and each convolutional layer shares parameters. This layer is equivalent to the network being cyclic. In the reconstruction layer part, the input is the output result of each convolutional layer. Therefore, \(d\) reconstructed images can be obtained. Then the reconstructed images are weighted and averaged to obtain the final output image \(H_{d}\). It also uses a skip-connection to add the LR feature map and the output of \(H_{d}\) to obtain high-frequency information of the image. Although this algorithm optimizes the SRCNN algorithm, it is easy to cause network degradation.
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
## 2 Sensing WASH
### Sanitation  
Several studies have used the Passive Latrine Use Monitor (PLUM) first developed at the University of California at Berkeley and commercialized by SweetSense Inc. The sensor uses a simple passive infrared motion detector to identify warm-bodied movement in a latrine stall. These binary movement data are recorded at three-second intervals, and either logged locally on the sensor device or transmitted by cellular or satellite data networks for online processing. Structured observation-validated algorithms process these movement data into discrete use estimates. These estimates have been shown to be more reliable in individual households than in community latrines where lines may form. In some cases, the sensors include radio-frequency identification (RFID) readers to enable latrine-servicing requests, as demonstrated by Sanergy Inc. in Nairobi, Kenya. Hence, sensing systems can be designed to collect data in a cost-efficient manner, through careful maintenance and reuse (even if the cost of individual sensors is significant).  
In one recent study in Bangladesh, these sensors demonstrated a significant overreporting of latrine utilization. Across 1207 households randomly selected from 52 village committees in Bangladesh, the mean four-day self-reported latrine use was 32.8 uses, while sensor analysis suggested 21.7 uses on average, indicating a more than 50% exaggeration of latrine use. At the low end of the regression model, the intercept suggests that many households report using latrines when in fact no use is detected [47].  
Similarly, in a recent cross-sectional study among 292 households in 25 villages in India, these sensors were installed for two weeks and compared with household surveys. The mean reporteddaily use was nearly twice that of the sensor-recorded use, with moderate agreement between daily reported use over the previous 48 h [48].  
Remote sensing technologies can also be applied to larger sanitation infrastructure networks. For instance, some utilities are using remote sensing to optimize routes for meter collection data. The remote sensing information is helping large and complex sanitation systems to identify spatially intelligent routes for infrastructure expansion in densely populated areas, consequently reducing money and time based on spatially integrated information.
# Analysis of the Effect of Pulse Length and Magnetic Field Strength on Nonlinear Thomson Scattering
## 2 Materials and Methods  
First, it should be noted that all the definitions of the following formulae are normalized separately with \(k_{0}^{-1}\) for spatial coordinates and \(\omega_{0}^{-1}\) for time coordinates, where \(\omega_{0}=2\pi c/\lambda_{0}\) corresponds to the laser frequency, \(k_{0}=2\pi/\lambda_{0}\) corresponds to the laser wavenumber, and \(\lambda_{0}\) is the laser wavelength, specifically \(1\)\(\upmu\)m, with \(c\) being the speed of light. The normalized vector potential of a focused Gaussian pulsed laser electric field with an applied magnetic field can be expressed as follows:  
Figure 1: Schematic diagram of nonlinear Thomson scattering. \[\overset{\rightharpoonup}{a}=a_{0}exp\left(-\frac{\eta^{2}}{L^{2}}-\frac{\rho^{2}} {{{b_{0}}}^{2}\bigg{(}1+\frac{z^{2}}{Z_{f}^{2}}\bigg{)}}\right)\frac{1}{\sqrt{1+ \frac{z^{2}}{Z_{f}^{2}}}}\Big{[}cos(\varphi)\overset{\rightharpoonup}{x}+ \delta sin(\varphi)\overset{\rightharpoonup}{y}\Big{]}+B_{0}x\overset{ \rightharpoonup}{y} \tag{1}\]  
where \(a_{0}\) is the normalized laser amplitude by \(m\frac{c}{c}\); \(m\) and \(e\) denote the electron rest mass and net charge; \(L\) and \(b\) denote the pulse length and beam waist radius of the laser; and phase \(\varphi\) can be expressed as follows: \(\varphi=\eta+\varphi_{0}-\theta+\frac{\rho^{2}}{2}\times R(z)\). \(\eta=z-t\). \(\rho^{2}=x^{2}+y^{2}\). \(\theta=arctan\frac{\tilde{z}}{Z_{f}^{2}}\). \(Z_{f}=\frac{{b_{0}}^{2}}{z}\). \(R(z)=\frac{z}{Z_{f}^{2}+z^{2}}\). \(b=b_{0}\sqrt{1+\frac{z^{2}}{Z_{f}^{2}}}\). We set  
\[a_{L}=\text{a}_{0}\frac{\text{b}_{0}}{\text{b}}\exp\left(-\frac{\eta^{2}}{ \text{L}^{2}}-\frac{\rho^{2}}{b^{2}}\right); \tag{2}\]  
then, there is  
\[ \begin{cases}a_{x}=a_{L}cos\varphi\\ a_{y}=\delta a_{L}sin(\varphi)+\text{B}_{0}x\end{cases}  \tag{3}\]  
Relativistic electron motion in a strong laser field is described by the Lorentz equation:  
\[d_{t}p=\partial_{t}p+(u\cdot\
abla)p=-E-u\times B, \tag{4}\]  
where \(p=\gamma u\) is the electron momentum normalized by \(mc\); \(u\) is the electron velocity normalized by the speed of light \(c\); \(\gamma\) is the relativistic factor of the electron; and the time and space coordinates are normalized by \(\omega^{-1}\) and \(k^{-1}\) (not explained below). Combined with the above equations, the phase and vector potentials can be partially differentiated as follows:  
\[\left\{ \begin{array}{c}\partial_{t}\varphi=-1\\ \partial_{x}\varphi=xR(z)\\ \partial_{y}\varphi=yR(z)\\ \partial_{z}\varphi=1-\frac{Z_{f}}{Z_{f}^{2}+z^{2}}-\frac{\rho^{2}\left(z^{2 }-Z_{f}^{2}\right)}{Z_{f}^{2}+z^{2}}\end{array} \right. \tag{5}\]  
\[\left\{ \begin{array}{c}\partial_{t}a_{L}=\frac{2\eta}{L_{d}}a_{L}\\ \partial_{x}a_{L}=\frac{-2x}{2x}a_{L}\\ \partial_{y}a_{L}=\frac{-2y}{2x}a_{L}\\ \partial_{z}a_{L}=a_{L}\left(-\frac{\partial_{z}b}{b}+\frac{2\left(x^{2}+b^{ 2}\right)\partial_{z}b}{b^{3}}-\frac{2\eta}{L^{2}}\right),\end{array} \right. \tag{6}\]  
\[\left\{
# On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
## 2 5G as the Network of the IoE
This way, the packets could be processed in a data centre located in the same energy plant. In order to dynamically commission and activate one function, the software (network function) is containerized in one instance (the hypervisor of a system-level virtualization platform is responsible for operating different instances developed on one common-to-all-instances Operating System to work simultaneously on a single server). Even if the 5G has achieved high flexibility and high technical capacities, a specific implementation such as IoE could require specific functions to be executed in the network, and this may require the Network Equipment Supplier to deploy such energy sector-specific functions. For this scope, the operators could develop the network based on the Open RAN (Radio Access Network) approach. Open RAN consists of defining high (higher than 5G standardization) interfaces in the radio part of the network so that the elements can be separated and interconnected through well-defined interfaces. The scope of the Open RAN model is that smaller elements can be deployed separately, by different vendors. This increases the concurrence and flexibility (for example, one vendor could specialize in some functionalities for the energy sector). In the next section, we present Open RAN as one of the keys for the deployment of the network addressed to the implementation of IoE.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### ASTER and OLI Band Combinations  
Band combinations and false color composites (FCCs) are frequently employed for mineral and lithological mapping in order to highlight spectral characteristics between the  
Figure 4: MNF of ASTER.  
target and background materials. This also reduces the impacts of changes in albedo and topographic slope [53; 54; 55; 56].
# ndInIoT-FC: IoT Devices as First-Class Traffic in Name Data Networks
## 5 Performance Evaluation
### Use Case 1--An ICN with Pub-Sub Only  
The first use case refers to a small scenario in which an application, a Cloud service, a single subscriber to a single service installed on a single DCU. Results on the amount of sent and received Interests and Data packets are illustrated in Figure 10(a), for both pub-sub and native NDN approach. The results show that both strategies were able to deliver the same amount of information, namely Data packets, with the pub-sub mechanism presenting a significant decrease in the number of transmitted packets required to search for the content, as the result of the pub-sub mechanism.  
The previous scenario was extended to include three additional ICN nodes (DCUs), each one hosting a different pub-sub service subscribed by the same ICN node, the ICN consumer node (cloud node). Similar behavior to the previous case was experienced. As depicted in Figure 10(b), despite the expected increase in the number of Interest packets, the ratio between the base version and our pub-sub implementation remains. The pub-sub version maintains the efficient delivery of Data packets using only 18% of the Interest packets observed in the native version.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 7 Conclusions and Future Work  
This work presents a novel agent-based load-balancing approach (ALBA) that dynamically balances loads while accounting for host heterogeneity, VM resource utilization heterogeneity, and co-resident attacks. ALBA enables agents to balance loads in a distributed form while mitigating security risks from co-resident attacks by utilizing VM live migration. The empirical evaluation in Section 6 shows that agents without global knowledge of cloud computing can balance loads, outperforming central managers with complete knowledge. Furthermore, agents adopting ALBA exhibited higher security performance (in terms of Coverage and CVR) than simplified ALBA while achieving slightly lower load-balancing performance, which is an acceptable trade-off to improve the security of the cloud computing.  
This research contributes an Agent-based VM migration architecture that combines load balancing with co-resident attack mitigation and allows for the trade-off between load balancing and co-resident attack mitigation. It also presents a novel agent-based load-balancing mechanism (ALBA) that can balance workloads across hosts cooperatively, considering heterogeneity in both CPU and memory, as well as security levels. Additionally, this work enables the Agents to decide the initial allocation of VMs, the timing of VM migration, the migration destination and whether to accept the migration requests.  
Future research will focus on other issues that can be solved with VM migration, such as zero-day exploit and physical security (such as theft or unauthorized access to a physical host; live VM migration can be used to move VMs to other secure hosts). While this work demonstrates the desirable properties of an Agent-based VM migration approach (e.g., load balancing, security), more security and reliability issues that can be solved by VM migration can be studied in the same framework, and these issues can be solved eventually through comprehensive trade-offs.  
Conceptualization, B.X.; methodology, B.X.; programing, B.X.; validation, B.X.; formal analysis, M.L.; writing--original draft preparation, B.X.; writing--review and editing, M.L.; visualization, B.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the still unpublished papers using the same experiments.  
The authors declare no conflict of interest.
# A Multi-Parameter Comprehensive Optimized Algorithm for MPTCP Networks
## 5 Result Analysis
### Optimized Solutions  
Table 5 presents the optimized solutions for three scenarios. We can see that the different suitable PM and CC are used for different scenarios. In heterogeneous networks, such as the scenarios in Sections 5.1 and 5.2, PCDC is more suitable than FullMesh. However, the scenario in Section 5.3 actually is a homogeneous network, therefore PCDC is equivalent to the FullMesh algorithm, FullMesh is a special case of PCDC. Considering the complexity of PCDC, it is recommended to choose FullMesh directly in homogeneous networks, and choose PCDC in heterogeneous networks.  
The results of the above three scenarios have exhibited the dynamic relationship of mutual influence and action from ISP, impact factor \(\Omega\), \(SP\), \(SP\)*, \(OCC\) and \(OTP\) as well as \(OBS\) multi-parameters in a running MPTCP system, and the MPCOA algorithm can capture the relationship correctly, and make effective judgments and provide the optimized outputs consistent with the reality. Meanwhile, for comparison, we also calculated the traditional solutions of buffer size using Equation (1) and list the comparison results in Table 5. It is very clear that the OBSs given by MPCOA algorithm are much lower than traditional solutions, only 3.76% of traditional solutions for SRL-HU, 17.07% for UNIS-HiN and 40.98% for UiT-HAW. That is, MPCOA can save at least 50% of buffer resources by sacrificing 7% of throughput.
## 2 Preliminary
### Reachability Graph (RG) Analysis  
In reachability graph (RG) analysis, all reachable markings can be divided into two main groups: legal and illegal markings. Illegal markings include deadlock and quasi-dead markings. In the above, the legal, illegal, deadlock, and quasi-dead markings can be defined as follows:  
**Definition 1**.: _The set of deadlock markings \(\mathcal{M}_{D}=\{M\in R(N,M_{0})|\) at \(M\), no transition can be enabled}._  
Further, the quasi-dead marking is defined as follows:  
**Definition 2**.: _The set of quasi-dead markings \(\mathcal{M}_{Q}=\{M\in R(N,M_{0})|\)\(M\) must eventually evolve to a dead one regardless of transition firing sequences}._  
The reachable markings except quasi-deadlock and deadlock markings are called legal markings. The set of legal markings \(\mathcal{M}_{L}\) in conventional deadlock prevention means the maximal number of reachable markings of one system, from which the initial marking \(M_{0}\) is reachable without leaving \(\mathcal{M}_{L}\). However, in this paper, it just represents the group of legal markings outside deadlock and quasi-deadlock markings. The set of legal markings \(\mathcal{M}_{L}\) can be defined as follows:  
**Definition 3**.: \(\mathcal{M}_{L}=\left\{M\in R(N,M_{0})\wedge M\
otin\left(M_{D}\cup M_{Q}\right)\right\}\)__  
In fact, \(\left(M_{D}\cup M_{Q}\right)\) of Definition 3 means the set of illegal markings. Therefore, the set of illegal markings \(\mathcal{M}_{l}\) can be defined as:  
**Definition 4**.: \(\mathcal{M}_{l}=\left\{M\in R(N,M_{0})\wedge M\in\left(M_{D}\cup M_{Q}\right)\right\}\)__  
Besides,  
**Definition 5**.: _A zone consisting of all dead markings is called a dead zone, denoted by \(Z_{D}\)._  
**Definition 6**.: _A zone consisting of all quasi-dead markings is called a quasi-dead zone, denoted by \(Z_{Q}\)._  
**Definition 7**.: _A zone consisting of all quasi-dead and dead markings, i.e., \(Z_{I}=Z_{D}\cup Z_{Q}\), is called an illegal zone._  
All markings except quasi-dead and dead markings are legal ones. Once a legal marking is enforced into the illegal zone, the net will eventually become deadlock.  
**Definition 8**.: _A zone consisting of all legal markings is called a legal zone, i.e., \(Z_{L}=R(N,M_{0})\) - \(Z_{I}\)._  
Please notice again, that in this paper, deadlock markings and legal markings are used in the proposed deadlock recovery policy. Besides, all markings will be identified in reachability graph analysis stage. Further, in the traditional deadlock prevention domain, the maximal permissive or optimal control means the maximal number of legal markings in a live system. In our recovery policy,our controllers will make all markings live including deadlock and quasi-dead markings. In other words, our designed policy recovers one deadlock system.
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 3 System Model
### Computation Model
#### 3.4.3 Server Execution
##### 3.4.3.2 Offloading from \(U\!E\) via Relay \(UH_{i}\) to MEC Server  
The UE first sends task \(k\) to the relay \(\mathrm{UH}_{i}\), which then forwards the task to the server \(S_{m}\). The delay consists of three steps for transmissions and computation:  
\[T_{k}^{s_{i,m}}=\frac{b_{k}}{r_{k}^{\mathrm{UH}_{i}}}+\frac{b_{k}}{r_{k}^{S_{m,i}}}+\frac{b_{k}c_{k}}{f_{k}^{S_{m}}}, \tag{12}\]  
whereby \(r_{k}^{\mathrm{UH}_{i}}\) is the transmission rate from the UE to \(\mathrm{UH}_{i}\), and \(r_{k}^{S_{m,i}}\) is the transmission rate from \(\mathrm{UH}_{i}\) to server \(S_{m}\). The UE and UH battery energy consumption is \[E_{k}^{s_{i,m}}=P_{k}^{tr}\Bigg{(}\frac{b_{k}}{r_{k}^{\text{UH}_{i}}}\Bigg{)}+P_{k} ^{\text{wait}}\Bigg{(}\frac{b_{k}}{r_{k}^{\text{S}_{m}}}+\frac{b_{k}c_{k}}{f_{k} ^{\text{S}_{m}}}\Bigg{)}+P_{k}^{trj}\Bigg{(}\frac{b_{k}}{r_{k}^{\text{S}_{m}}} \Bigg{)}, \tag{13}\]  
where \(P_{k}^{trj}\) is the transmission power of \(\text{UH}_{i}\). We assume that the computation result is sent back directly from server \(S_{m}\) to the UE and that the transmission delay for the result is negligible. We define the selection variables \(s_{k}^{i,m}=1,s_{k}^{i,m}\in\mathcal{HS}\) for offloading task \(k\) via \(\text{UH}_{i}\) to \(S_{m}\), whereby the set \(\mathcal{HS}=\{s_{k}^{i,m}|k\in\mathcal{K},i\in\mathcal{I},m\in\mathcal{M}\}\) contains all relays and MEC server selection variables. Since a given task \(k\) can only be serviced via a single helper node \(\text{UH}_{i}\) by a single server \(S_{m}\), the offloading selection algorithm should follow the constraint  
\[\sum_{m\in\mathcal{M}}\sum_{i\in\mathcal{I}}s_{k}^{i,m}\leq 1\ \ \forall k\in \mathcal{K}. \tag{14}\]
# Amaranth Seed Polyphenol, Fatty Acid and Amino Acid Profile
## 3 Results and Discussion
### Fatty Acid Composition  
The fatty acid composition of the amaranth seed was found based on the oil extracted from the seeds (7.01% oil content in amaranth seed). In Table 4, the fatty acid composition of the amaranth seed is presented. As can be observed, the fatty acid in the highest amount was reported to be oleic acid (23.78 mg/100 g amaranth), representing 42.68% of the total fatty acids, followed by _cis,cis-_9,12-linoleic acid (C18:2), palmitic acid C16:0 and _trans,trans_-9,12-linoleic acid (C18:2). The saturated fatty acids represented 16.54%, while unsaturated fatty acids represented 83.45% (poly-unsaturated fatty acids represented 37.56%). In a previous study, a higher concentration of saturated fatty acids (26.72-27.28%) and lower concentration of unsaturated fatty acids (72.72-73.28%) in amaranth seed were reported [32]. [PERSON] et al. reported that the predominant saturated fatty acid in amaranth seed was palmitic acid, while the predominant unsaturated fatty acids were linoleic acids and oleic acids [32]. According to [PERSON] et al., the major fatty acids in the oil from 11 genotypes of 4 grain amaranth species were palmitic (19.1-23.4%), oleic (18.7-38.9%) and linoleic (36.7-55.9%) acids [33]. The linoleic acids represented in amaranth oil are essential fatty acids which cannot be synthetized by the human body and play an important role due to their antiadipogenic, antidiabetogenic, anti-carcinogenic and anti-therosclerotic activities [34]. Based on the fatty acid profile of the amaranth oil, amaranth can be considered a promising food and an important raw material for pharma.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Phenolic** & **mg/kg Amaranth Seed** \\ \hline Gallic acid & ND \\ Protocatechuic acid & ND \\
4-hydroxybenzoic acid & ND \\ Vanillic acid & 10.33 \\ Caffeic acid & 3.37 \\ Chlorogenic acid & 4.00 \\ \(p\)-coumaric acid & 3.63 \\ Rosmarinic acid & 7.68 \\ Myricetin & 105.05 \\ Luteolin & 39.15 \\ Quercitin & ND \\ Kaempfer & 1041.50 \\ Total & 1214.71 \\ \hline \hline \end{tabular} ND = not detected.  
\end{table}
Table 3: Phenolic profile of amaranth seed.
reports, impersonation, unauthorized access, the disclosure of customer information, meter compromise, false data injection, etc., that damage infrastructures and steal energy.  
Blockchain technology provides a promising solution to overcome these security issues. In a blockchain, users can interact with each other in a verifiable manner without needing a trusted intermediary. All network participants maintain the public ledger instead of central managers. Moreover, blockchain is comparably more robust considering a single point of failure. Thus, being a decentralized, tamper-proof, and trustworthy technology, blockchain has become an incredible innovation for the key management of smart grid systems. It paves the way for privacy-preserving communication between SMs and MDMS too. Especially, the Merkle trees or hash trees, which are well-known for secure and efficient transaction validation and integration, can also be used to provide authentication between SMs and MDMS [6; 7]. Therefore, we propose a blockchain-enabled distributed AMI secure communication scheme combining these two technologies to achieve an efficient, secure, and low-cost security protocol in this work. The main contribution of this work is as follows:  
* We integrate decentralized blockchain technology in smart meter secure communication to remove the need for conventional trusted authorities;
* We eliminate single point of failure and regulate communication in a flexible, trustworthy, and automated way;
* The proposed scheme takes less time and expends less communication bit than other existing works;
* Finally, by an informal security discussion, we confirm that the proposed scheme is secure enough against the most common types of attacks.  
The rest of the paper is organized as follows: Section 2 discusses the research works regarding smart meter secure communication and blockchain-based security frameworks; and Section 3 provides details on blockchain, smart contracts, and the consensus algorithm. Section 4 provides the system overview and a detailed workflow of the proposal. Section 5 includes the implementation and performance analysis of the proposed scheme along with a comparative study. Section 6 concludes the paper.