esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controllers Design
### Controllers Design for Stage 1  
A Lyapunov function is built with the stage 1 control objective as follows:  
\[V_{1}(X)=\sum_{i=1}^{m}\left(\frac{P_{i}}{2}(x_{i}-x_{id})^{2}+\frac{1}{2}x_{ m+1+i}^{2}\right) \tag{14}\]  
where \(x_{id}=q_{id}\) denotes the corresponding target angles of the ALs and \(P_{i}\) denotes positive constants.  
Derived from \(V_{1}(X)\) is  
\[\tilde{V}_{1}(X)=\sum_{i=1}^{m}x_{m+1+i}(P_{i}(x_{i}-x_{id})+f_{i}+G_{i}\tau_{ i}) \tag{15}\]where \(G_{i}=[g_{i1}\ g_{22}\,\ldots,\ g_{im}]\).  
To ensure that \(\dot{V}_{1}(X)\leq 0\) is achieved, let  
\[\left\{\begin{array}{l}\tau_{i}=-\big{(}P_{i}(x_{i}-x_{id})+f_{i}+D_{i}x_{m+1+ i}+T_{j}\big{)}S_{ii}^{-1}\\ T_{j}=\sum\limits_{j=1,j\
eq i}^{\infty}g_{ij}\tau_{j}\end{array}\right. \tag{16}\]  
where positive constants \(D_{i}\) and \(P_{i}\) are present.  
Clearly, Equation (16) guarantees the following:  
\[\dot{V}_{1}(X)=-\sum\limits_{i=1}^{m}D_{i}x_{m+1+i}^{2}\leq 0 \tag{17}\]  
The closed-loop system is obtained when the controllers (16) are substituted for (2)  
\[\dot{X}=F_{a}(X) \tag{18}\]  
Setting up \(\Psi_{1}\) as the invariant set of (18), which is  
\[\Psi_{1}=\Big{\{}\,X\in R^{2m}\big{|}\dot{V}_{1}=0\Big{\}} \tag{19}\]  
For (19), the biggest invariant set is the following:  
\[M_{1}=\{\,X\in\Psi_{1}|x_{i}=x_{id},x_{m+1+i}=0\} \tag{20}\]  
[PERSON]'s invariance theorem states that the solution \(X\) of (18) converges to the largest invariant set \(M_{1}\) as \(t\to\infty\)[42].  
The system is, therefore, simplified to the PVP when the following \(S_{a}\) requirements are met:  
\[S_{a}\text{:}\left\{\begin{array}{l}|x_{i}-x_{id}|\leq e_{1}\\ |x_{m+1+i}|\leq e_{2}\end{array}\right. \tag{21}\]  
where \(e_{1}\) and \(e_{2}\) are small positive constants.  
When stage 1's control goals are met, stage 2 control objectives are adopted. We currently note the time, \(t=t_{1}\).
## Materials and Methods
### Sample Measurements  
#### 2.3.1 Stable Isotope Analysis  
Isotopic values are reported in conventional \(\delta\) notation (isotopic composition or signature) versus international standards: Vienna Pee Dee Bellemite (V-PDB) for \(\delta^{13}\)C and Vienna Standard Mean Ocean Water (V-SMOW) for \(\delta^{2}\)H and \(\delta^{18}\)O, according to Equation (1) [13]:  
\[\delta^{i}X\ \ =\ \ \left(\frac{R_{sample}}{R_{standard}}-1\right)*1000 \tag{1}\]  
where \(i\) is the mass number of the heavier isotope of the element \(X\) (\({}^{13}\)C, \({}^{2}\)H, and \({}^{18}\)O), \(R_{sample}\) is the isotope number ratio of a sample (\({}^{13}\)C/\({}^{12}\)C; \({}^{2}\)H/\({}^{1}\)H; and \({}^{18}\)O/\({}^{16}\)O), and \(R_{standard}\) is that of the international standard. The delta values are multiplied by 1000 and are expressed in units \"per mil\" (%).  
An isotope ratio mass spectrometer (Delta V Advantage, Thermo Scientific, Waltham, MA, USA) in line with a dual inlet system was used to determine the carbon isotope ratios of CO\({}_{2}\). All meat samples were measured in duplicate. One working standard was measured daily before beginning meat sample analyses. This working standard was calibrated against NBS-22 oil certified reference material from IAEA Vienna (International Atomic Energy Agency), whose isotopic composition is \(\delta^{13}\)C\({}_{VPDB}=-30.03\)%. The uncertainty was \(\pm 0.3\)%.  
A liquid-water isotope analyzer (DLT-100, Los Gatos Research) was used to measure the isotopic signatures of \(\delta^{18}\)O and \(\delta^{2}\)H. A set of five working standards was used (working standard 1, \(\delta^{18}\)O = \(-19.57\pm 0.1\)% and \(\delta^{2}\)H = \(-154.1\pm 1\)%; working standard 2, \(\delta^{18}\)O = \(-15.55\pm 0.1\)% and \(\delta^{2}\)H = \(-17.0\pm 1\)%; working standard 3, \(\delta^{18}\)O = \(-11.54\pm 0.1\)% and \(\delta^{2}\)H = \(-79.0\pm 1\)%; working standard 4, \(\delta^{18}\)O = \(-7.14\pm 0.1\)% and \(\delta^{2}\)H = \(-43.6\pm 1\)%; and working standard 5, \(\delta^{18}\)O = \(-2.96\pm 0.1\)% and \(\delta^{2}\)H = \(-9.8\pm 1\)%. For \({}^{18}\)O/\({}^{16}\)O, the uncertainty was \(\pm 0.2\)%, while for \({}^{2}\)H/\({}^{1}\)H, it was \(\pm 1\)%.
###### Abstract  
To identify potent plant extracts with strong antioxidant activity, we evaluated the free radical scavenging activity of 184 plant extracts obtained from the Freshwater Bioresources Culture Collection (FBCC) of Nakdonggang National Institute of Biological Resources (Republic of Korea), as various plant extracts have been used therapeutically to prevent chronic diseases associated with oxidative stress. From them, three plant extracts (FBCC-EP858 from _Anmaminia multiiflora_, FBCC-EP920 from _Anmaminia coccinea_, and FBCC-EP1014 from _Salix gracilistyla_) were selected based on their abilities to scavenge the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical with more than 80% efficiency. We found that these extracts had in vitro half maximal inhibitory concentration (IC\({}_{50}\)) values ranging from 11.89 to 14.26 \(\upmu\)g/mL and strong total antioxidant activity (corresponding to approximately 0.18, 0.22, and 0.23 mM Trolox, respectively). We also studied the effect of these extracts on RAW 264.7 macrophages and found that FBCC-EP920 significantly downregulated relative cell viability at a concentration of 100 \(\upmu\)g/mL. However, the other two extracts, FBCC-EP858 and FBCC-EP1014, did not affect cell viability at the same concentration. Additionally, all three extracts inhibited hydrogen peroxide (H\({}_{2}\)O\({}_{2}\))-induced reactive oxygen species (ROS) production and depolarization of mitochondrial membrane potential in RAW 264.7 macrophages. An additional experiment in zebrafish larvae showed that the three extracts reduced 2\({}^{\prime}\),7-dichlorohydrofluorescein diacetate (DCFDA) fluorescent intensity induced by H\({}_{2}\)O\({}_{2}\). The extracts also upregulated the expression of nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) expression, and an HO-1 inhibitor, zinc protoporphyrin (ZnPP), attenuated the extract-induced antioxidant activity both in vivo and in vitro. Taken together, these findings suggest that the extracts from _A. multiiflora_, _A. coccinea_, and _S. gracilistyla_ have potential free radical scavenging and antioxidant capacities both in vivo and in vitro by activating the Nrf/HO-1 signaling pathway. These results could be useful for the prevention and treatment of various oxidative stress-mediated human diseases.  
R 264.7 macrophages; zebrafish larvae; H\({}_{2}\)O\({}_{2}\); ROS; bioactive activity +
Footnote †: journal: Applied Sciences
## 7 Conclusions  
Several tools are available to detect and/or prevent cyber-attacks. These include firewalls and Intrusion Detection Systems (IDSs). Practically, such tools cannot be directly applied to protect a CI since the attack surface of an ICS is different from that of the classical IT, i.e., network-based systems. Thus, designing a generic defense mechanism against most, if not all, ICS attacks is a challenging task. AlCrit is intended to detect process anomalies. Unlike other available solutions that attempt to detect network traffic anomalies, AlCrit monitors the physical processes controlled by an ICS and monitors the correctness of the interactions among the devices. From the study reported here, AlCrit was found to be effective in detecting cyber-attacks that lead to process anomalies. Also, from Table 9, it is observed that AlCrit supports the plant operator in (i) localizing the anomalies, (ii) identifying the area of impact, and (iii) suggesting recovery actions to restore the normal plant operation.  
In CISS2021-OL, a total of ten red teams and six blue teams participated and six anomaly detectors were deployed. The event served as an opportunity for researchers to know how an adversary designs and launches attacks against an operational CI and also enables the evaluation of defense mechanisms. Details of all attacks launched, together with the dataset, are made public and available in [7; 11]. In addition, it is worth noting that differentiating the reasons for anomalies, whether they result from cyber attacks or the malfunctioning of plant components, remains an open research problem that needs further investigation and exploration.  
Conceptualization, G.R.; Methodology, G.R. and A.M.; Validation, G.R.; Investigation, A.M.; Writing--original draft, G.R.; Writing--review & editing, A.M.; Funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by the National Research Foundation, Singapore, under its National Satellite of Excellence Programme \"Design Science and Technology for Secure Critical Infrastructure: Phase II\" (Award No: NRF-NCR25-NSOE05-0001). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.  
Not applicable.  
The data presented in this study are available in [[https://itrust.sud.edu.sg/itrust-labs_datasets/dataset_info/](https://itrust.sud.edu.sg/itrust-labs_datasets/dataset_info/)]([https://itrust.sud.edu.sg/itrust-labs_datasets/dataset_info/](https://itrust.sud.edu.sg/itrust-labs_datasets/dataset_info/)).
## 2 Materials and Methods
### Governing Equations and Discretization Schemes  
The conservation equation of mass, momentum and energy can be written as follows:  
Figure 1: Schematic view of a cross section of the investigated domain with indication on the numbering of passes, discs and channels.  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\mathbf{u})=0 \tag{1}\]  
\[\rho\frac{\partial\mathbf{u}}{\partial t}+\rho\mathbf{u}\cdot\
abla\mathbf{u} \ =-\
abla p+\
abla\cdot\left\{\mu\left[\
abla\mathbf{u}\ +(\
abla\mathbf{u})^{\mathbf{T}}-\frac{ \mathbf{2}}{3}(\
abla\cdot\mathbf{u})\mathbf{I}\right]\right\}+\rho\mathbf{g} \tag{2}\]  
\[\frac{\partial\big{(}\rho c_{\mathrm{p}}T\big{)}}{\partial t}+\
abla\cdot(\rho c _{\mathrm{p}}\mathbf{u}T)=\
abla\cdot(k\ \
abla T) \tag{3}\]  
To account for the temperature distribution inside the discs of windings, the heat conduction equation in a solid is solved:  
\[\frac{\partial\big{(}\rho c_{\mathrm{p}}T\big{)}}{\partial t}=\
abla\cdot(k\ \
abla T)+\mathrm{S}_{\mathrm{E}} \tag{4}\]  
The source term \(\mathrm{S}_{\mathrm{E}}\), which represents the energy losses in the windings, is imposed in the copper conductors of the discs.  
The above equations were numerically solved under the hypothesis of laminar flow via the open-source OpenFOAM computational toolbox [18], which is based on the Finite Volume method. A transient conjugate heat transfer solver (chtMultiRegionFoam, from OpenFOAM v7) was adopted, implementing the PIMPLE algorithm, i.e., a combination of the PISO (Pressure-Implicit with Splitting of Operators) algorithm, which handles time advancement, and the SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm, which is used to iteratively solve the coupled pressure and velocity fields within a single time step. Second-order schemes were used for both spatial and temporal discretization.
## 2 Background
### Code-Based GNSS Inter-User Ranging Techniques
#### 2.1.1 Pseudorange Measurements  
In GNSS systems, the receivers recover their position by trilateration of the measured distances to multiple visible satellites [14; 49]. These distances are derived from an estimated time of transmission and are called _pseudorange measurements_. As explained in [50], the pseudorange measurements between GNSS receiver \(i\) to satellite \(s\) at epoch \(k\) are affected by various sources of errors and can be decomposed into:  
\[\rho_{i,k}^{s}=r_{i,k}^{s}+b_{i,k}-c\cdot\delta t_{k}^{s}+d_{atm,k}+\epsilon_{ i,k}^{s} \tag{3}\]  
where \(r_{i,k}^{s}\) is the range between receiver \(i\) and satellite \(s\), \(b_{i,k}=c\cdot\delta t_{i,k}^{r}\) is the range offset caused by the receiver's local clock bias \(\delta t_{i,k}^{r}\) to the system time, \(\delta t_{k}^{s}\) is the modeled part of the satellite clock offset with the system time, which includes the correction calculated using the values broadcast in the navigation messages (for SPP) as well as a small relativistic correction caused by the orbital eccentricity and \(d_{atm,k}\) are the atmospheric delays, standing both for tropospheric and ionospheric delays. Finally, \(\epsilon_{i,k}^{s}\) represents the unmodeled errors including receiver noise, multipath signals, orbital errors and other effects [50].
## 4 Method
### Transmission Estimation Based on Saturation Line Prior
Upon establishing the saturation line, the transmission for the corresponding local block can be derived via Equation (18):  
\[t_{sl}(x)=1+\frac{k}{b}. \tag{18}\]  
To construct a reliable saturation line, we adopted the approach outlined in [32]. While the saturation line prior works well for most areas, it fails when the local saturation approaches zero or when there is minimal color variation. To recalculate the transmission in regions where the saturation line prior fails, we introduce the boundary constraint in [41], as shown in Equation (19):  
\[t_{b}(x)=\max_{c\in\{R,G,B\}}\left(\max\left(\frac{A_{adj}^{c}(x)-I_{cc}^{c}(x)}{ A_{adj}^{c}(x)-B_{0}^{c}},\,\frac{A_{adj}^{c}(x)-I_{cc}^{c}(x)}{A_{adj}^{c}(x)-B_{1 }^{c}}\right)\right), \tag{19}\]  
where \(B_{0}^{c}\) denotes the lower boundary and \(B_{1}^{c}\) represents the upper boundary of the color channel. When \(A_{adj}^{c}(x)<I_{cc}^{c}(x)\), the restored image exhibits localized blue artifacts and white patches. We assume that \(A_{adj}^{c}(x)\) must not be less than \(I_{cc}^{c}(x)\). Consequently, cases of \(A_{adj}^{c}(x)<I_{cc}^{c}(x)\) yield a transmission value of zero, thereby eliminating chromatic artifacts and white patches. After improving the boundary transmission calculation, we applied a guided filter [35] for smoothing, yielding the final transmission map \(t_{f}(x)\). In the guided filter, the window radius is set to 30, and the regularization parameter is set to 0.01. Finally, we substituted the obtained atmospheric light map and transmission map into the atmospheric scattering model to obtain the restored clear image, as illustrated in Equation (20):  
\[J(x)=\frac{I_{cc}(x)-A_{adj}(x)}{\max(t_{f}(x),t_{min})}+A_{adj}(x). \tag{20}\]  
The parameter \(t_{min}\) denotes the minimum transmission threshold, calculated as the mean of the bottom 5% of the transmission values. In Algorithm 1, the main steps of the proposed method are outlined. ```
Input: Sand-dust image \(I(x)\) Parameter setting:\(patch\_size=15\), \(B_{0}=[20,20,20]\), \(B_{1}=[275,275,275]\) Begin  Step 1: Compensate color channels using Equations (1)-(3)  Step 2: Correct the color shift using Equation (4)  Step 3: Stretch dynamic range using Equation (5)  Step 4: Estimate local atmospheric light \(A_{I}(x)\) via DCP [23]  Step 5: Obtain \(A_{rl}(x)\) using Equation (7)  Step 6: Adjust the illumination of the atmospheric light map using Equation (8)  Step 7: Calculate \(t_{sl}\) using Equation (18)  Step 8: Calculate \(t_{b}\) using Equation (19)  Step 9: Restore the dust-free image \(J(x)\) using Equation (20)  End Output: Clear image \(J(x)\)
```  
**Algorithm 1** The Main Steps of the Proposed Method
## 3 System Architecture and Service Process
### System Services
#### 3.2.2 Image Monitoring Service  
The image monitoring service is a service to provide image information on the outdoors to users through the CCTV installed on the server system. The CCTV sends the collected images on the outdoors to the image information manager, and the image information manager provides the received images to a GUI by means of real-time streaming service via a DVR server and classifies the streamed data to store it in the database. In addition, when users request image data stored in the database through the GUI, it would be provided to them. Figure 3 shows the operating process of the image monitoring service.  
Figure 2: Operation Process of the Environment Monitoring Service.
## 3 Dual Boundary Element Method (DBEM)  
### Outer Solution  
The outer solution \(\phi^{(O)}\) in the region \(x\leq-L\), which is sufficiently far away from the wave absorber, can be expressed by the sum of the incident wave approaching from the left and the reflected wave toward the left:  
\[\phi^{(O)}\left(x,z\right)=-\frac{igA}{\omega}\left[e^{ik(x+L)}+Re^{-ik(x+L)} \right]\frac{\cosh\ k(z+h)}{\cosh\ kh} \tag{14}\]  
where \(R\) is the complex reflection coefficient associated with the amplitude of the reflected waves at \(x\rightarrow-\infty\).  
Matching the velocity potential of the outer and inner solutions at \(x=-L\), we obtain  
\[R=\frac{i\omega}{gA}\frac{\cosh kh}{N_{0}}\int_{-h}^{0}\phi(-L,z)\cosh k(z+h) dz-1 \tag{15}\]  
where \(N_{0}=\int_{-h}^{0}\cosh^{2}k(z+h)dz=\frac{h}{2}\left(1+\frac{\sinh 2 kh}{2 kh}\right)\).  
If substituting Equation (15) into Equation (14), and then applying the remaining matching condition that the normal flux in each region is continuous at \(x=-L\), we obtain the following boundary condition applied at \(x=-L\).  
\[\frac{\partial\phi}{\partial n}-ik\phi=-\frac{2 kgA}{\omega}\frac{\cosh k(z+h) }{\cosh kh},\ \ x=-L \tag{16}\]
# AIS Data Vulnerability Indicated by a Spoofing Case-Study
## 4 Case Study on Spoofing Large Amounts of AIS Fake Targets Nearby Elba Island and Its Impact on VDL
### VDL Load and Spoofing Source Analysis
This may indicate a naval ship conducting an electronic warfare exercise using two MMSIs. However, two transmitters sending AIS with the same power can be received with the same RSSI, even if they are in entirely different positions, i.e., because they can be more or less the same distance from the receiver BS. However, it should be noted that it is highly likely that warships in such activities wish to keep a shallow profile due to the particular sensitivity of these activities. Of course, we cannot rule out the possibility that fishing vessels performed the spoofing; but usually, they prefer to avoid attention rather than attract it. Also, they tend to use simpler manipulation methods on the AIS, though they are becoming increasingly adept at manipulating. When looking at the received signal analysis (Figure 12), rapid changes are visible between one transmission and the other, which may indicate that the transmitting and receiving AIS stations are not in the line of sight and are therefore subject to signal fading: a ship at sea rolling on the waves may contribute. There is a large and abrupt drop at the end of the first burst that we do not quite understand. It could be a blind sector for the BS, or some sort of obstruction. There is a slight and constant decrease in the average RSSI from the middle to the end of the second burst, which could indicate that the distance between the transmitting and receiving AIS stations has increased, again indicating a moving spoofer. Figure 11: Three-month ship trajectory of MMSI 9999999.
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### Off-Chain Data Storage Methods
#### 2.2.2 Decentralized Systems (P2P)
* _Off-chain Data Storage with Swarm_: Swarm is an Ethereum-based P2P data storage protocol designed to store large datasets off-chain in a decentralized manner. It fragments and distributes data across network nodes, ensuring security and scalability. Fully integrated with the Ethereum ecosystem, Swarm enables secure storage of large files, while encryption mechanisms protect data confidentiality. It provides ease of access and flexibility, although performance limitations may arise due to network constraints [87]. * _Off-chain Data Storage with Storj_: Storj is a P2P-based decentralized cloud storage solution that allows users to rent out unused storage space, contributing to the network. Data are encrypted, fragmented into smaller pieces, and distributed across multiple nodes, minimizing the risk of data loss. With a strong security infrastructure, Storj ensures data traceability and verifiability through blockchain integration. However, coordination challenges among network participants may arise [88]. * _Off-chain Data Storage with Sia_: Sia is a blockchain-based data storage platform that enables users to rent storage space via a decentralized marketplace. It offers a low-cost, encryption-supported model that enhances data security. Data are sharded into smaller fragments and stored across multiple providers, with access restricted to the user's private key. Additionally, smart contracts facilitate secure agreements between storage  
Figure 4: Blockchain network interaction with IPFS network [10; 78]. Encrypted data are stored off-chain via IPFS, with its content identifier recorded on-chain. This ensures data integrity and verifiability while reducing on-chain storage overhead. Encryption is applied before IPFS storage for confidentiality, and the blockchain records the reference for secure retrieval. providers and users. However, compared to traditional cloud storage solutions, Sia presents scalability and usability challenges [80].
### Contributions of the Work  
The main contributions of this work are listed below:  
* A Secured Optimal Path-Routing (SOPR) protocol for improving the performance of wireless body-area networks is proposed.
* This protocol offers improved packet-delivery ratio, enhanced security, and reduced attack-detection overhead, detection time, energy consumption, and delay compared to existing protocols.
* In addition, this protocol can be applied to different wireless body-area networks, such as those used in healthcare applications or environmental monitoring, to ensure secure and energy-efficient routing.
## 1 Motivation
### Technology Asset Groups  
The main focus of the paper is on the technology asset groups, which include various technologies with electricity consumption needs, production potential or storage capability (Note, only the electricity part of the energy vector is considered) located in a bounded area (e.g., municipality, selected part of a distribution grid) [3; 4; 5; 6; 7]. These assets are connected to distribution grids and are capable of coordinated operation or can work independently. An energy community is a typical case where individual technologies can be operated individually according to user needs or managed according to a selected community operational strategy. Such operational arrangements can be considered coordinated decentralized power systems exchanging information among particular assets to ensure system stability  
Figure 3: Illustration of the connection relationship between power plants, transmission system, and demand side—where the idea of decentralized resources is shown.  
and reliability. This decentralized approach is suitable for heterogeneous environments with various stakeholders and offers resilience and economic savings compared to centralized systems [3; 8; 9]. Such information flows are drafted in Figure 3 by blue lines among individual assets and create the information topology of the considered network, which defines the communication scheme among individual agents operating a given asset.  
The presented work is divided into several parts: Section 2 focuses on analyses and a literature review of the state of the art. Section 3 brings the mathematical formulation of the problem and its boundaries. Next, Section 4 is devoted to the algorithm design, including the construction of models for renewable sources and battery systems. Apart from that, all uncertainties are incorporated into the algorithm. Six simulation examples will be introduced in Section 5. This is followed by Section 6, which deals with Future works and other possible improvements. All outcomes and results are concluded in Section 7.