esa-sceva/satcom-chunk-collection huggingface dataset:
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
## 2 Background
### ICS Reference Model  
The ICS is a general term that covers numerous control systems, including SCADA systems, distributed control systems, and other control system configurations [3]. An ICS consists of combinations of control components (e.g., electrical, mechanical, hydraulic, pneumatic) that are used to achieve various industrial objectives (e.g., manufacturing, transportation of matter or energy). Figure 1 shows an example of an ICS reference model [4].  
As can be seen from Figure 1, the ICS model is divided into four levels, 3 to 0. Level 3 (corporate network) consists of traditional information technology, including the general deployment of services and systems, such as file transfer, websites, mail servers, resource planning, and office automation systems. Level 2 (supervisory control local area network) includes the functions involved in monitoring and controlling the physical processes and the general deployment of systems such as HMIs, engineering workstations, and history logs. Level 1 (control network) includes the functions involved in sensing and manipulating physical processes. For example, receiving the information, processing the data, and triggering outputs, which are all done in PLCs. Level 0 (I/O network) consists of devices (sensors/actuators) that are directly connected to the physical process.  
As shown in Figure 1, Level 3 is composed of the traditional IT infrastructure system (Internet access service, file transfer protocol server, Virtual Private Network (VPN) remote access, etc.). Levels 2, 1, and 0, represent a typical SCADA system which is composed of the following components:  
* HMI: Used to observe the status of the system or to adjust the system parameters for processes control and management purposes.
* Engineering workstation: Used by engineers for programming the control functions of the HMI.
* History logs: Used to collect the data in real-time from the automation processes for current or later analysis.
* PLCs: Act as slave stations in the SCADA architecture. They are connected to sensors or actuators.
## 3 Results and Discussion
### Simulation Results for Diabetes Detection  
The presence of glucose at different concentrations in the environment results in a resonance shift, as shown in Figure 9. This is due to the change in the effective refractive index of the waveguides with different concentrations of the analyte. The change in the effective refractive index will then trigger a resonance shift towards a longer wavelength, as shown in Equation (4) [33].  
\[\lambda_{\text{res}}=2\pi\text{Rn}_{\text{eff}}/\text{m} \tag{4}\]  
Figure 10 shows a linear relation between the resonance shifts from MRR resonance pulses and changes in the environmental refractive index. This linearity was similar to previous studies of MRR-based sensors by [PERSON] et al. and [PERSON] et al. [8; 25]. The linear curve has a positive, linear slope, which means that the relationship between changes in the environmental refractive index are triggered by an increase in glucose concentration and the resonance shifts. This demonstrates that the addition of glucose around the MMR system will shift the resonance towards a larger wavelength, as theoretically described in Equation (4). The sensitivity of the MRR-based sensor obtained in this study was 69.44 nm/RIU.  
Figure 10: The linear curve of the relationship between resonance shifts is relative to changes in the environmental refractive index and sensitivity of the sensor based on MRR.  
Figure 9: The resonance pulses obtained using the 3D Finite Difference Time Domain (FDTD) method approach showing resonance shifts for each glucose concentration.  
Figure 11 shows a response curve simulation of a sensor based on MRR on changes in glucose concentration. The changes are indicated by the shift in resonance that occurs. The tested glucose concentrations were based on the glucose level in the blood of diabetics, as shown in Table 1. A resonance shift was observed, which showed that MRR-based sensors were able to respond to changes in glucose concentration, which is very low in people with diabetes. This shows that MRR-based sensors have a great potential to be developed as an effective diabetes detection device. Despite small variations in the glucose concentration and resonance shift, the changes can still be detected optimally due to the sensitivity value of the sensor.
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 5 Discussion
### Analysis of the Applicability of the Method to Real Case  
A space information system with MEO satellites is chosen as an example to demonstrate the effectiveness of the method. The core of the method is to introduce three factors based on orbital dynamics. Since each space information system has its own independent orbit dynamics, the only difference is the expression of the three factors when using the method in this paper. Therefore, the effectiveness of the method does not depend on the case, whether it is real or simulated, or on the number of cases. It is undoubtedly that the method will lead to more properly conclusions with a more precise input. And the real satellite orbit could be taken as a factor to correct the constellation configuration which is the input condition of this method.
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 4 System Model
### Network Scenario  
To be specific, we model the scenario using a Poisson Cluster Process, namely the Thomas cluster process (TCP) [28], as proposed in [1] and conventionally done in the literature (see, e.g., [29]). The TCP is a stationary and isotropic Poisson cluster process generated by a set of offspring points independently and identically distributed (i.i.d.) around each point of a parent Poisson Point Process (PPP) [28]. In particular, the locations of parent points are modeled as a homogenous PPP, with intensity \(\lambda_{\text{P}}\), around which offspring points are distributed according to a symmetric normal distribution with variance \(\sigma^{2}\) and mean value \(m\). As a conquence, the intensity of the offspring points can be written as \(\lambda=\lambda_{\text{P}}\cdot m\). In our scenario, offspring points represent the IoT nodes asking for service, while parent points are only reference coordinates for cluster centers.  
We simulate a square area of size \(L\times L\) m\({}^{2}\), where offspring points are located according to the description above. We picture a sample scenario together with possible UAB trajectories in the following (see Figures 2-4 below). We consider a single UAB to decrease capital and operational expenditures, and to simplify the final numerical evaluation. Please note that, in this model, the extension to the case of multiple UABs does not necessitate additional complex settings, and therefore it is not a major focus of this work. However, for the sake of completeness, it will be discussed in the following.  
We assume the UAV starts its flight from a fixed position, which can be considered as a recharge station, where it has to come back at the end of the trajectory. In this way, it can recharge or change its battery for the next flight. In this scenario, we assume that the capacity of the UAV battery is sufficient to enable a full round trip over any trajectory. Provided that the UAB has no heavy payload other than the RF equipment and the flight time is no longer than half an hour (that is always our case), this is reasonable [30; 31]. The UAB is assumed to fly at a constant altitude from the ground between 200 m and 300 m (not violating the regulations in EU--[32]).
###### Abstract  
This paper presents a multi-parameter ionospheric disturbance analysis of the total electron content (TEC), density (Ne), temperature (Te), and critical frequency foF2 variations preceding two significant earthquake events (2015 Mw 7.5 and 2023 Mw 6.3) that occurred in Afghanistan. The analysis from various ground stations and low-Earth-orbit satellite measurements involved employing the sliding interquartile method to process TEC data of Global Ionospheric Maps (GIMs), comparing revisit trajectories to identify anomalies in Ne and Te from Swarm satellites, applying machine learning-based envelope estimation for GPS-derived TEC measurements, utilizing the least square method for foF2 data and ionograms obtained from available base stations in the Global Ionosphere Radio Observatory (GIRO). After excluding potential influences caused by solar and geomagnetic activities, the following phenomena were revealed: (1) The GIM-TEC variations displayed positive anomalies one day before the 2015 Mw 7.5 earthquake, while significant positive anomalies occurred on the shock days (7, 11, and 15) of the 2023 Mw 6.3 earthquake; (2) the Swarm satellite observations (Ne and Te) for the two earthquakes followed almost the same appearance rates as GIM-TEC, and a negative correlation between the Ne and Te values was found, with clearer appearance at night; (3) there were prominent positive TEC anomalies 8 days and almost 3 h before the earthquakes at selected GPS stations, which were nearest to the earthquake preparation area. The anomalous variations in TEC height and plasma density were verified by analyzing the foF2, which confirmed the ionospheric perturbations. Unusual ionospheric disturbances indicate imminent pre-seismic events, which provides the potential opportunity to provide aid for earthquake prediction and natural hazard risk management in Afghanistan and nearby regions.  
multi-parameter ionospheric disturbances; earthquake; total electron content (TEC); GPS; Swarm; Afghanistan 2024  
6
## 5 Experiment and Analysis
### Experimental Setting  
We design a formation generation scenario based on the simulation platform designed by [49]. A \(V\)-shape formation is used in the experiments. In the formation generation scenario, \(N\) USVs are moving in the two-dimensional maritime surface, which is considered as a square with a side length of 2. Only the kinematics model of USVs is considered. In a formation generation scenario, the formation generation is to control the team of USVs to form a predefined formation \(F_{s}\) by following the formation generation policies \(\mathbf{\mu}=\left\{\mu_{\theta_{1}},\mu_{\theta_{2}},\ldots,\mu_{\theta_{N}}\right\}\). The input of the formation generation policy of each USV \(u_{i}\) is a row vector \(s_{u_{i}}=\left(p_{u_{i}},p_{u_{i}},re_{point},re_{eth}\right)\), including \(1\times 2\) position vector \(p_{u_{i}}\), \(1\times 2\) velocity vector \(v_{u_{i}}\), \(1\times 2N\) relative position vector \(re_{point}\) between USV \(u_{i}\) and \(N\) formation points, and \(1\times 2(N-1)\) relative position vector \(re_{eth}\) between USV \(u_{i}\) and \(N-1\) other USVs. The output \(a_{u_{i}}=\Delta v_{u_{i}}=\left(\Delta v_{u_{i}}^{x},\Delta v_{u_{i}}^{y}\right)\) is the velocity change of USV \(u_{i}\). The main hyperparameters of the generation policies are shown in Table 1. The cumulative discounted return and the average length of the movement path of the team are used to evaluate the performance of the policies. However, for the formation maintenance task, all the followers follow the leader's movement while maintaining the whole formation geometry. In a formation maintenance scenario, the goal is to minimize the error between the current formation shape and the expected formation shape by following the decentralized formation maintenance policies \(\mathbf{\mu}^{fm}=\left\{\mu_{\theta_{1}}^{fm},\mu_{\theta_{2}}^{fm},\ldots,\mu_{ \theta_{N}}^{fm}\right\}\). The input of the formation generation policy of each USV \(u_{i}\) is a row vector \(s_{u_{i}}=(p_{u_{i}},v_{u_{i}},re_{leader})\), including the \(1\times 2\) position vector \(p_{u_{i}}\), \(1\times 2\) velocity vector \(v_{u_{i}}\), and \(1\times 2\) relative position vector \(re_{leader}\) between USV \(u_{i}\) and its immediate leader. The output is the change of velocity \(a_{u_{i}}=\Delta v_{u_{i}}=(\Delta v_{u_{i}}^{x},\Delta v_{u_{i}}^{y})\). The cumulative discounted return and the average of the error are utilized to measure the performance of the maintenance policies.
## 3 Theory  
In this section, we briefly recall the basic concepts of _spatial resolution_ and _point spread function_ (PSF), and later, we report the main quantities for a _Gaussian beam_. These calculations will be the fundamentals to understand the experimental results found in our work.
# Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack
## 4 Consensus Mechanism Weaknesses
### Proof of Work (PoW)  
Proof of work (PoW) is a consensus mechanism that is based on solving a mathematical equation. PoW was first initiated by bitcoin, and then it was rapidly implemented by many major  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Consensus** & **Energy Cost** & **Decentralization** & **Processing Speed** \\ \hline PoW & High & High & Low \\ PoS & Low & High & High \\ DPoS & Low & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main features of consensus mechanisms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Consensus** & **51\%** & **Long Range** & **DDoS** & **P + Epsilon** & **Sybil** & **Balance** & **BGP** \\
**Mechanisms** & **Attack** & **Attack** & **Attack** & **Attack** & **Attack** & **Attack** & **Hijacking** \\ \hline PoW & ✓ & ✗ & ✓ & ✓ & ✓ & ✓ & ✓ \\ PoS & ✓ & ✓ & ✓ & ✗ & ✓ & ✗ & ✗ \\ DPoS & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Consensus mechanisms that are vulnerable to various attacks.  
cryptocurrencies [26]. Miners are the main pillar of PoW consensus, and the process of authorizing and recording the transaction is approved by the miners. PoW consensus requires the miners to put a considerable amount of effort to mine a block. The process involves a random method of attempting answers on a trial and error basis. Hence, it may take one or even thousands of attempts to solve the equation. In order for the \"target hash\" to be recognized, it must contain a lower number than the hash of the block [27].  
PoW consensus assumes that half of the network nodes are always honest miners; hence, obtaining more than half the hashing power makes this consensus vulnerable. One of the significant drawbacks of PoW is the cost of energy and hardware requirement. Research has shown that the amount of electricity consumption of the bitcoin mining process is a lot more than 159 countries [28]. However, the mining requirement and mining time may vary due to the algorithms used by individual cryptocurrencies.  
The mining process of PoW is comparatively slow compared to that of other consensus protocols. Since a few mining pools dominate with a large amount of mining power, attacks to those pools can cause severe disruptions to the bitcoin network. Recent attacks proved that PoW is vulnerable to the 51% attack. Low hashing crypto-coins utilizing PoW consensus are more vulnerable to 51% attack, as the required hash can be acquired easily. Thr P + epsilon attack can be executed at no cost while in possession of the required budget [29].  
In our analysis, we indicate that the Sybil attack can successfully exploit the PoW by forming a large number of malicious nodes. The Ethereum protocol and private blockchains are vulnerable to the balance attack [22; 30]. Moreover, the DDoS attack and BGP hijacking can also be used to interrupt the regular stream of this consensus mechanism [23]. AntPool, BW.com, NiceHash, CKPool, and GHash.io are a few mining pools which have already been hit by DDoS attacks [31].
# Multipath Interference Impact Due to Fiber Mode Coupling in C+L+S Multiband Transmission Reach
###### Abstract  
Multiband transmission is, nowadays, being implemented worldwide to increase the optical transport network capacity, mainly because it uses the already-installed single-mode fiber (SMF). The G.654E SMF, due to its attributes (e.g., low-loss, and large-effective area in comparison with the standard G.652 SMF), can also increase network capacity and can also be used for multiband (MB) transmission. Nevertheless, in MB transmission, power mode coupling arises when bands with wavelengths below the cut-off wavelength are used, inducing multipath interference (MPI). This work investigates the impact of the MPI, due to mode coupling from G.654E SMF, in the transmission reach of a C+L+S band transmission system. Our results indicate that for the S-band scenario, the band below the wavelength cut-off, an approximately 25% reach decrease is observed when the MPI/span increases to \(-26\) dB/span, considering quadrature phase-shift keying (QPSK) signals with a 64 GBaud symbol rate. We also concluded that if the L-band were not above the wavelength cut-off, it would be much more affected than the S-band, with an approximately 52% reach decrease due to MPI impact.  
G.654E fiber; multipath interference; multiband transmission; optical networks; physical layer impairments 2025  
3 July 2025  
Revised: 25 July 2025  
Accepted: 28 July 2025  
Submitted: 30 July 2025  
Multiband Transmission Reach  
**Proceedings:**: 2025, 12, 770  
**Copyright:**: 02 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
_Phoenix_**2025, 12, 770  
[[https://doi.org/10.3390/photonics12080770](https://doi.org/10.3390/photonics12080770)]([https://doi.org/10.3390/photonics12080770](https://doi.org/10.3390/photonics12080770))**
## 1 Introduction  
Owing to the increased demand for storage of digital data in the information age, conventional magnetic data storage systems have reached their storage density limit because of physical and engineering problems, such as the superparamagnetic limit. To satisfy the increasing demand, overcome the aforementioned limitation, and expand the storage density to 4 Tb/in\({}^{2}\), bit-patterned media recording (BPMR) has been recognized as one of the promising technologies [1]. In BPMR, each data bit is stored in a single-domain magnetic island that is distributed in both the down- and cross-track directions. Because nonmagnetic regions exist between single-domain magnetic islands, BPMR can reduce track edge and transition noise, nonlinear bit shift, and simplified tracking [2]. High storage density in BPMR can be achieved by reducing the distances between islands in both the down- and cross-track directions. However, this reduction results in major challenges of intersymbol interference (ISI) and intertrack interference (ITI) in the down- and cross-track directions, respectively, as such, two-dimensional (2D) ISI must be considered [3]. These error factors disrupt signal detection and degrade BPMR system performance. In addition, when the read head cannot continue at the center of the main data track, the system performance is degraded owing to track misregistration (TMR). To overcome performance degradation from these error factors, signal processing schemes such as signal detection, error control coding, and modulation coding schemes must be employed [4; 5; 6; 7]. To mitigate ISI and ITI effects, a 2D equalizer and one-dimensional (1D) partial response (PR) target based on the minimum mean square error (MMSE) criterion was proposed for the BPMR system [4]. In recent years, signal processing techniques using multilayer perceptron (MLP), which is a class of feedforward artificial neural networks, have been employed to estimate and recover data sequences [8; 9; 10]. Herein,we propose an iterative signal detection scheme between the Viterbi detector and MLP. In the proposed detection scheme, to improve the BPMR system performance, the modified output of the MLP is used as a priori information to improve equalization and extrinsic information to cancel the effect of ITI. The rest of this paper is organized as follows: In Section 2, the BPMR channel model is briefly introduced; in Section 3, we explain the proposed iterative signal detection scheme using MLP; in Section 4, we discuss simulation and results; and in Section 5, the conclusions of this study are stated.
## 3 The Proposed Method  
Due to the CNN powerful adaptive feature extraction ability for a raw signal, the combination of raw data and CNN has been employed in machinery fault diagnosis. However, the operation process of mechanical equipment is complex, and signal acquisition is often disturbed by the noise, so, the model may not perform well. Therefore, it is necessary to process a raw signal to reduce the sensitivity to the noise. It is worth considering that some information can be lost after the signal is processed; thus, the choice of signal processing algorithm is very important.  
To solve the problems listed above, [PERSON] et al. [37] proposed a multiscale convolutional neural network (MSCNN) architecture to learn the high-level robust and useful fault features at different time scales automatically directly from the complex raw vibration signals in a parallel way through the hierarchical learning. To a certain extent, the influence of high-frequency vibration and random noise on the model was reduced. However, there were some drawbacks, a raw signal was processed through low-pass filtering, the number of layers had the opposite effect on the training time and precision, and data distribution was imbalanced in the fault diagnosis field.  
In order to give full play to the ability of CNN model to extract signal features, we combine the advantages of raw data and processed data and improve the anti-jamming ability, proposing a variant of the 1D-CNN called the MDI-CNN for fault diagnosis based on the intelligent fusion of multi-input layer. The structure of the developed fault diagnosis network is shown in Figure 1, wherein it can be seen that at the front end, multiple input layers are constructed to fuse different data types. The input dataset can contain both raw data and processed data, which provides rich fault features at different scales.  
In the MDI-CNN network model, the ReLU function is applied as an activation function, the maximum pooling function is used to realize the reduction of a feature map, and the Softmax classifier is used to output the target category. In addition, cross entropy is used as a loss function to optimize network parameters.  
Generally, the proposed model consists of three stages: A multiple data fusion stage, a feature extraction stage, and a classification stage. In the multiple data fusion stage, multiple data types are entered into the model through multiple input layers, and data fusion is performed at the first convolutional layer. In the feature extraction stage, the fused data is processed into a feature map through continuous convolutional and pooling operations, which can be regarded as signal fault characteristics. Finally, the feature maps are classified by classifier functions in the classification stage. The data fusion stage, which is an innovation point of the proposed model, is introduced in the following.  
Figure 1: The structure of the Multi-Dimension Input convolutional neural network. C1 and C2 represent the convolutional layer 1 and convolutional layer 2, respectively, and P1 and P2 represent the pooling layer 1 and pooling layer 2, respectively.