esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 2 Materials and Methods
### Environment and Data Description  
The framework of this study is a smart city and its critical infrastructure. A smart city is an urban environment that uses ICT, including IoT, to improve its inhabitants' efficiency, sustainability, and quality of life. In this context, the focus of the research is on protecting the city's critical infrastructure, which comprises essential elements such as the smart electrical grid, water supply system, public transportation, and others.  
The core of this work lies in a network of IoT devices distributed in various parts of the city [24]. These devices are designed to collect real-time data related to multiple aspects of the urban environment. By way of example, they include:  
* Electricity Meters: These devices are installed in homes and businesses and record electricity consumption in real-time. They can identify unusual patterns in energy use, making them a valuable source of data for detecting anomalies in the power grid.
* Water Quality Sensors: These sensors are in water supply networks and monitor water quality regarding contaminants, turbidity, and pH. Any deviation from quality standards is recorded as an anomaly.
* IoT Security Cameras: Strategically placed throughout the city, these cameras capture images and videos in real-time. Machine learning algorithms analyze these images for suspicious activity or unauthorized incidents.
* Traffic and Transportation Sensors: Distributed on roads and public vehicles, these sensors monitor traffic flow, speed, and movement patterns. They record any unusual congestion or abnormal behavior for later analysis.  
The data generated by these IoT devices is integrated into a real-time analytics platform. This platform uses advanced machine learning and fog computing technologies to analyze data at the point of generation, enabling immediate response to events and anomalies [25]. The primary purpose is to develop and evaluate an anomaly detection system based on machine learning algorithms.  
As an application case, the detection of cyber-attacks on the city's smart electrical grid is considered. IoT electric energy meters collect data on electricity consumption and grid stability [26]. Machine learning algorithms are trained to identify standard behavior patterns and detect deviations that could indicate a cyberattack, such as attempts to manipulate the power grid.
# Stability Evaluation of a Simplified Reclaimer Using Fluid-Structure Interaction
## 2 Materials and Methods
### Mathematical Model
#### 2.1.1 Analytical Model  
Figure 1 presents the free-body diagram of the reclaimer. The main forces can be classified as wind and resistance forces. The wind forces may cause the superior part of the equipment to fall over the bottom part, causing a destabilizing moment. The resistance forces are responsible for making the body stable, in a counter-movement moment. The FEM 2-131/13 standard [25], defined by the European Materials Handling Federation (FEM), established that when the destabilizing moment is higher than 1.5 times the stabilizing moment, the structure may fall.  
The destabilizing (\(Dm\)) and stabilizing (\(Em\)) moments are determined based on the wind speed \(V\) and mass of the equipment \(M\), according to Equations (1) and (2):  
\[D_{m}=\left(\frac{\rho V^{2}}{2}\right)Ar_{y} \tag{1}\]  
where \(\rho\) is the density of the air, \(V\) is the wind velocity, \(A\) is the area of wind incidence, and \(r_{y}\) is the distance between the wind load center and the support point.  
\[E_{m}=(Mg)r_{x} \tag{2}\]  
where \(M\) is the mass of the superior part of the reclaimer, \(g\) is the gravity, and \(r_{x}\) is the distance between the mass center and the support point.  
Figure 1: Free-body diagram of the reclaimer.  
The ratio between these moments determines the security operation levels. The FEM standard determines a security level when the ratio of \(Em\)/\(Dm\) is lower than 1.5. When this ratio was lower than 1, the \(Dm\) moments were bigger than the \(Em\) moments.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 5 Application to Wildlife Tracking
### Wildlife Tracking Results  
Twelve solar-powered FastFix tags were attached to nesting adult Royal Albatross [28] (shown packaged and attached in Figure 8). While nesting, the adults carry out feeding flights of up to two weeks of duration. Of the 12 tags attached, eight were recovered undamaged, and from these eight tags, 26,042 positions were calculated. The position tracks recovered from the tags are shown in Figure 9.  
Figure 7: Closeup of FastFix tag electronics. The GPS RF front-end is on the right, and the microprocessor on the left. The SD card for data storage is underneath the PCB. Total tag electronics weight is 2.6 g.  
Figure 8: A solar-powered FastFix tag attached to the back of a Royal Albatross. This tag can store in a 2 GB SD-card approximately a quarter of a million fixes. Photo ©[PERSON]—used with permission.  
Figure 9: Position estimates recovered from 8 tags attached to Royal albatross over a one-month period off the coast of the South Island of New Zealand. There are 26,042 fixes. Each colour represents a different animal, showing the remarkable ability of these animals to navigate over the long distances traveled during feeding flights.
# Safe Functional Modified CuO Nanoparticles?
## 1 Introduction
The rationale behind this was that Fe-doping should decrease the dissolution of Cu ions which were held to be responsible for cytotoxicity. Such a decrease in metal ion dissolution was shown by [PERSON] et al. [39] in several media. [PERSON] et al. [39] furthermore tested Fe-doped and non-doped CuO nanoparticles as to their cytotoxicity in the human cell lines BEAS-2B and THP-1, and regarding their hatching inhibition of zebrafish embryos. Fe-doping was found to be linked to reduced cytotoxicity and reduced inhibition of hatching. [PERSON] et al. [39] concluded that their study demonstrated the safe use of (6-10%) Fe-doped CuO nanoparticles in the environment. [PERSON] et al. [40] focused on the generation of reactive oxygen species (ROS) induced by Mn\({}_{3}\)O\({}_{4}\) nanoparticles and in their conclusions generalized their findings to all metal oxide nanoparticles. [PERSON] et al. [40] have shown that doping with about 5 (molar)% Zn is linked to a substantial reduction in ROS generation compared with non-doped Mn\({}_{3}\)O\({}_{4}\) nanoparticles in exposed BEAS-2B cells. They concluded that the way doping by Zn shifts the Fermi energy edge far away from the valence band energy edge, to reduce pro-oxidant activity, becomes a feasible safe-by-design approach to achieve safe Mn\({}_{3}\)O\({}_{4}\) nanoparticles, and more in general safe metal oxide nanoparticles. This would include CuO nanoparticles. In the next two sections, studies regarding safe(r)-by-design efforts based on the modification of CuO nanoparticles will be reviewed. The central question asked is whether these efforts have led to safe functional CuO nanoparticles, cf. Figure 1.  
The focus will be on two matters. The fist regards the question of whether nanoparticle modifications have been be proven safe by the available studies. Safe is defined here as  
Figure 1: The central question: do doping or coating lead to safe functional Cu nanoparticles? posing no risk. Risk is the chance that negative impacts will occur. This is the subject of Section 2. The second matter, discussed in Section 3, is the compatibility of the modifications studied with the functionality of CuO nanoparticles. Section 4 summarizes the conclusions of this paper.
## 4 Ground Directional Pre-Grouting Reinforcement Technology
### Study on Similar Simulation Test of Pre-Grouting Reinforcement  
The two-dimensional similar simulation test bench was used to study the reinforcement effect of ground directional pre-grouting on the roadway passing through the fault fracture zone. Model 1 simulates the fault fracture zone by increasing the material particle size and adjusting the material ratio during the material laying process. Model 2 constructs the same fault fracture zone as Model 1, and the grouting pipeline is embedded in the laying process. The test steps are shown in Figure 7.  
Model 1 does not take any measures. Model 2 uses a high-pressure air gun to inject the pre-allocated cement slurry into the fault fracture zone through a pre-embedded grouting pipeline. After the cement slurry solidifies, the two models are excavated at the same time for roadway excavation. Since the overlying loading roadway excavation has not been carried out, the two model roadways are complete, and there is no significant difference. After the overlying load is loaded, the roadway in model 1 is completely destroyed, and the surrounding rock is broken, and the integrity of the roadway cannot be guaranteed during the actual excavation operation. Model 2 is reinforced by pre-grouting, and the slurry is fully diffused through the cracks, and the fault fracture zone is effectively reinforced. After overlying load loading, the shape of the roadway is complete, and only a small number of cracks are generated in the surrounding rock. In the actual excavation process, the difficulty of excavation is low, and the subsequent roadway maintenance is simple. Therefore, the ground pre-grouting reinforcement has played a significant role.  
Figure 6: Advantages of ground directional pre-grouting technology.
## 2 The Problem Statement
### Network Intrusion Types  
There are several common types of network intrusions, as categorized within the MITRE ATT&CK framework [38]. In our study, we consider the following main network attacks:  
**Normal Traffic:** This pertains to normal traffic gathered from the network.  
**PortScan (PS)/Network Service Discovery [MITRE ATT&CK ID: T1046]:** In this type of intrusion, the attacker seeks to conduct reconnaissance on the victim's computer, often as a preliminary step to subsequent attacks. PortScan entails dispatching connection requests to multiple ports to pinpoint possible access points for intrusion [39].  
**Denial of Service (DoS)/Network Denial of Service [MITRE ATT&CK ID: T1498]:** This attack type aims to render the target network or system unavailable by inundating it with excessive traffic, causing it to become overwhelmed and inaccessible [35; 36].  
**Brute Force [MITRE ATT&CK ID: T110]:** In this attack, the intruder attempts to gain unauthorized access to the network by systematically trying all possible passwords [36].  
**Web Attack/Initial Access [MITRE ATT&CK ID: TA0001, T1659, T1189]:** These attacks exploit vulnerabilities in web applications to gain initial access to the underlying systems [40].  
**Infiltration/Initial Access [MITRE ATT&CK ID: TA0001]:** This assault entails securing initial entry into a system or application, typically via targeted spear phishing or by exploiting vulnerabilities [41].  
**Botnet/Compromise Infrastructure [MITRE ATT&CK ID: T1584.005, T1059, T1036, T1070]:** Botnets are automated attacks that leverage hijacked devices to execute malicious activities, often orchestrated remotely by the attacker [42].  
**Probe Attack/Network Scanning or Surveillance [MITRE ATT&CK ID: T1595]:** These attacks consist of scanning a network to collect information or identify known vulnerabilities, acting as the initial phase of more extensive attacks [43].  
**Remote-to-Local Attack (R2L) [MITRE ATT&CK ID: TA0001, T1110, T1078]:** R2L attacks occur when an attacker gains unprivileged access to a machine over the network, typically without initially having an account on that machine [43].  
**User-to-Root Attack (U2R) [MITRE ATT&CK ID: TA0004, T1078]:** U2R attacks involve exploiting vulnerabilities to escalate privileges from an unprivileged user account to gain root access to the system [43].  
These various types of network intrusions pose significant threats to the security of computer networks. Understanding and effectively detecting these intrusions are critical for maintaining network integrity and security. In our study, we consider these different intrusion types and aim to develop a framework that can accurately detect and provide explanations for them using black-box AI models. By tackling the lack of explainability in IDSs, our work strives to boost the overall efficacy and reliability of intrusion detection systems in practical applications.
## 3 Results and Discussion
### Dry Mechanical Connections
#### 3.5.1 Rebar Diameter  
This subsection examines the influence of reinforcement bar diameter (\(\Phi\)) on the shear capacity of precast walls. Fixed parameters for this analysis include concrete compressive strength (fck) of 30 MPa and normalised axial force (v) of 0.2.  
Figure 10a shows the global trends for all section heights (hc = 1.5 m, 2.25 m, and 2.5 m) with increasing bar diameters from 20 mm to 30 mm with the fixed scale of 0 to 5000 kN consistent throughout this paper. Figure 10b-d present detailed magnified views for each specific section height. For the smallest section height (hc = 1.5 m), as shown in Figure 10b, increasing the bar diameter from 20 mm to 30 mm results in a nearly linear increase in shear capacity, ranging from approximately 350 kN to 450 kN. The trend shows a consistent rate of increase across the diameter range.  
At the intermediate section height (hc = 2.25 m), Figure 10c demonstrates a similar linear trend but with higher overall shear capacity values, ranging from about 750 kN to 900 kN. The rate of increase appears to be proportional to that observed in the smaller section.  
For the largest section height (hc = 2.5 m), Figure 10d reveals the most significant impact of bar diameter on shear capacity, with values increasing from approximately 900 kN to 1100 kN. Notably, the rate of increase appears to be more pronounced between 25 mm and 30 mm diameter bars compared to the 20 mm to 25 mm range, suggesting a nonlinear relationship at more considerable section heights.  
Figure 9: (a) Wall height curve for fck = 30 MPa, vd = 0.4 (b) magnified view.
## 4 Proposed Transfer Functions Based on DFs
### Estimation of Weight Parameters for NN Transfer Functions  
The DF can provide a flexible NN transfer function family based on various values of the weight parameter. Among the likelihood-based estimation methods, the MLE method was proposed to estimate the optimal weight parameters for NN transfer functions. On the premise that DF values \(\mathbf{d}_{j}\) follow a normal distribution \(\left(\mu,\sigma^{2}\right)\), the distribution of summation \(\mathbf{y}_{j}\) can be inferred.
## 3 Results and Discussion
### Design of Experiment (DOE)
#### 3.2.4 Mathematical Model and Analysis of Variance
and model effects and coefficients are presented in Table 8.  
From the results of Table 9, it can be seen that a first-order linear regression was obtained. The factors [NaCl], T, and the two interactions T * [NaCl] and [NaCl] * [Aliquat-336] had significant effects on the back-extraction performance of Cr(VI), whereas [Aliquat-336] and the interactions [Aliquat-336] * T and [Cr(VI)] * [Aliquat-336] * T had nonsignificant effects. After analysis of variance and by eliminating nonsignificant factors, the mathematical model obtained at the beginning becomes:  
\[\text{R}=43.872+7.945\text{[NaCl]}+5.329\text{T}+3.397\text{[NaCl] * [Aliquat-336]}-2.674\text{[NaCl] * T}. \tag{4}\]  
Figure 13 shows the comparison between the experimental results of the back-extraction performance of Cr(VI) and those predicted. A good agreement between the experimental and predicted results was obtained (first-order linear regression with a determination coefficient of R\({}^{2}\) = 97.89% (R\({}^{2}\)corr = 94.93%)), then the reported model was validated. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Term** & **Effect** & **Coefficient** \\ \hline Constant & & 43.872 \\
[NaCl] & 15.891 & 7.945 \\
[Aliquat-336] & 3.435 & 1.717 \\ T & 10.658 & 5.329 \\
[NaCl] * [Aliquat-336] & 6.794 & 3.397 \\
[NaCl] * T & \(-\)5.348 & \(-\)2.674 \\
[Aliquat336] * T & \(-\)1.842 & \(-\)0.921 \\
[NaCl] * [Aliquat-336] * T & 1.351 & 0.676 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Predicted factor regression coefficients and their effect. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **DF 1** & **Adj SS 2** & **Adj MS 3** & **F-Value** & **P-Value** \\ \hline Regression & 7 & 915.747 & 130.821 & 33.08 & 0.001 \\
[NaCl] & 1 & 505.018 & 505.018 & 127.68 & 0.000 \\
[Aliquat-336] & 1 & 23.597 & 23.597 & 5.97 & 0.058 \\ T & 1 & 227.185 & 227.185 & 57.44 & 0.001 \\
[NaCl] * & 1 & 92.314 & 92.314 & 23.34 & 0.005 \\
[Aliquat-336] & 1 & 57.195 & 57.195 & 14.46 & 0.013 \\
[NaCl] * T & 1 & 6.787 & 6.787 & 1.72 & 0.247 \\
[NaCl] * & 1 & 3.561 & 3.651 & 0.92 & 0.381 \\
[Aliquat-336] * T & \multicolumn{1}{c}{} & & & \\ \hline \hline \end{tabular}  
* \({}^{1}\) DP: Degree of Freedom; 2 SS: Sum of Square; and 3 MS: Mean Square.  
\end{table} 
Table 9: Analysis of variance.
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
###### Abstract  
Cellulose is the most venerable and essential natural polymer on the planet and is drawing greater attention in the form of nanocellulose, considered an innovative and influential material in the biomedical field. Because of its exceptional physicochemical characteristics, biodegradability, biocompatibility, and high mechanical strength, nanocellulose attracts considerable scientific attention. Plants, algae, and microorganisms are some of the familiar sources of nanocellulose and are usually grouped as cellulose nanocrystal (CNC), cellulose nanofbriil (CNF), and bacterial nanocellulose (BNC). The current review briefly highlights nanocellulose classification and its attractive properties. Further functionalization or chemical modifications enhance the effectiveness and biodegradability of nanocellulose. Nanocellulose-based composites, printing methods, and their potential applications in the biomedical field have also been introduced herein. Finally, the study is summarized with future prospects and challenges associated with the nanocellulose-based materials to promote studies resolving the current issues related to nanocellulose for tissue engineering applications.  
nanocellulose; additive manufacturing; nanocomposites; biomedical applications +
Footnote †: journal: selected topics in biomedical applications
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Vulnerabilities
#### 4.2.4 Communication Failures  
These failures cover both disruption or interruption of the smart grid's communication channels and protocols for data transmission and receiving, resulting in network congestion, signal interference, delayed or inaccurate response, and the system's failure to transmit and receive real-time data effectively [5]. This can be mitigated using strong encryption and authentication (multi-factor) protocols, redundant communication paths,fault-tolerant systems, and backup or alternative communication channels [106]. Ineffective power distribution, loss of real-time telemetry and control, and possible blackouts can result from communication failures in smart grids' SCADA systems and IEC 61850 protocols [107]. Furthermore, grid stability is jeopardized by interruptions in data transmission between grid components via cellular, Wi-Fi, or Zigbee networks. This results in deficient load balancing, delays in fault isolation and detection, and a higher risk of cascading failures [108].
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 4 Photonic Implementations in Fog Computing Domains
### Security and Video Surveillance  
The global deployment of high-resolution surveillance and security cameras generates massive amounts of data--up to terabytes per camera per day. Traditional Cloud-based models struggle to scale effectively due to bandwidth limitations and network transport costs, particularly with 1080p and 4K video streams. Real-time security decisions often must be made locally, requiring edge-level processing. Fog computing addresses these challenges by enabling on-site data analysis, reducing latency, and offloading bandwidth-intensive tasks from the Cloud [21; 22; 23]. Applications include smart cities, transportation hubs, retail, and industrial environments. These systems demand secure, dynamic, and low-latency processing for tasks like facial recognition, anomaly detection, and access control. Machine vision accelerators and secure firmware updates are essential for maintaining operational integrity. Photonic signal processors can accelerate edge inference tasks within surveillance networks.  
Example: Edge AI modules integrating photonic accelerators are under development for smart airports and railway stations, where bandwidth and response time are critical [27].
## 2 Materials and Methods
### Secondary Classification  
Our algorithm incorporates two complementary object detection methods to enhance overall performance. Employing multiple detection strategies improves robustness by ensuring that if one method fails to detect an object, the other can serve as a fallback. However, this redundancy may also result in overlapping detections, including both true and false positives.  
A common strategy for handling overlapping detections is Non-Maximum Suppression (NMS). However, in our case, NMS cannot be directly applied because the motion-based detection method does not produce confidence scores--it merely identifies motion regions and filters them based on area. To introduce a consistent confidence framework, all detections from both methods are passed through a secondary classification stage, which assigns a confidence score to each bounding box. Bounding boxes with classification scores below a predefined threshold are excluded from the final output.  
Subsequently, overlapping detections are filtered using an IoU-based rule: if the Intersection over Union (IoU) between two bounding boxes exceeds 0.85, the one with the lower confidence score is discarded; otherwise, both are retained. The IoU threshold of 0.85 was selected empirically through iterative testing to strike a balance between eliminating redundant overlapping detections and preserving multiple objects in close proximity. This setting helps reduce noise without suppressing valid detections in crowded scenes, where people may appear close together.  
The architecture of the custom-built secondary classifier is presented in Figure 7. Despite these postprocessing steps, the final output still contains a non-negligible number of false positives, indicating an opportunity for further refinement in future work.