esa-sceva/satcom-chunk-collection huggingface dataset:
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Common Vulnerabilities  
Analysing the attacks to which systems are vulnerable, several key reasons for confidentiality breaches were identified. First, many systems rely on sensors and resource-constrained IoT nodes that store sensitive information but lack sufficient built-in security. As a result, adversaries with the capability to corrupt internal system components (such as \(\mathcal{A}_{middle}\) or \(\mathcal{A}_{strong}\)) can access sensor data, disclose it, or leverage it for future attacks [28; 34; 39; 47]. Second, user or client confidentiality is often compromised through fraudulent schemes in which users are tricked into voluntarily providing their credentials. This allows adversaries to impersonate legitimate users, effectively performing spoofing attacks. Third, insecure communication channels and transport protocols are another major vulnerability, as they are susceptible to eavesdropping, allowing adversaries to intercept sensitive data during transmission [15; 40; 62; 92].  
Integrity violation attacks often occur as a consequence of successful spoofing. Once an adversary has deceptively gained access to a user's resources, they can modify or manipulate those resources by leveraging the user's privileges [21; 28; 37]. Furthermore, many integrity breaches result from the use of insecure communication channels, enabling the adversary to launch MitM attacks. In addition, sensors are frequent targets of tampering due to their limited protection mechanisms. This allows the adversary to falsify sensor readings, which can have critical implications, particularly in healthcare systems, where accurate data are crucial for patients [42; 43; 56].  
The majority of system availability violations are related to DoS attacks, which are often feasible even for the weakest adversary (\(\mathcal{A}_{weak}\)) [93; 94]. For example, attacking sensors can disrupt the availability of their data. In some cases, this may not lead to critical issues, but in domains such as healthcare, data from devices like heart rate or oxygen monitors are crucial and must be available on demand [86]. More resourceful adversaries, with either indirect (through accomplices) or direct access to the system, are capable of targeting more critical components, e.g., databases or servers, via DoS attacks. Often, such attacks can lead to SPoF, causing a complete system outage. Additionally, powerful adversaries (\(\mathcal{A}_{strong}\)) that are insiders may be able to restrict legitimate user access depending on their position within the system, thereby compromising availability [85; 87; 95].  
Summarising this section, it can be concluded that systems across all application domains are susceptible to similar types of attacks. The most common issues related to breaches of confidentiality and integrity are caused by the disclosure of credentials by users or clients, often as a result of social engineering or fraudulent schemes. Regarding system availability, the most frequent cause of disruption is a DoS attack targeting critical system components that lack redundancy, resulting in SPoF.
# Robust Control with Uncertain Disturbances for Vehicle Drift Motions
## 2 Vehicle Drift Motion Mechanism Analysis
### Motion Mechanism Analysis in Steady States  
The vehicle motion in the steady state is in a straight line or in a constant circle with a constant velocity, while the vehicle status parameters including the sideslip angle, the yaw rate and the roll angle also are constant:  
\[v=v^{s},\ \gamma=\gamma^{s}=\frac{v^{s2}}{v_{x}{}^{s}{}^{R}{}^{s}}=\frac{v^{s}} {R^{s}\cos\beta^{s}},\ \beta=\beta^{s},\ \phi=\phi^{s}\]  
Then the derivative parameters of the above, including vehicle accelerated velocity, equal zero:  
\[\dot{v}=0,\ \dot{\beta}=0,\dot{\gamma}=0,\ \ddot{\Phi}=\dot{\phi}=0\]  
It is clear that vehicle dynamics model equations in steady states are derived as:  
\[0=F_{x}{}_{f}{}^{s}\cos\delta_{f}{}^{s}-F_{y}{}_{f}{}^{s}\sin\delta_{f}{}^{s} +F_{x}{}^{s}-F_{d}{}^{s}+mv^{s}\gamma^{s}\sin\beta^{s} \tag{6}\]  
\[0=F_{x}{}_{f}{}^{s}\sin\delta_{f}{}^{s}+F_{y}{}_{f}{}^{s}\cos\delta_{f}{}^{s} +F_{y}{}_{f}{}^{s}-mv^{s}\gamma^{s}\cos\beta^{s} \tag{7}\]  
\[0=m_{b}\dot{g}h_{b}\sin\phi^{s}-K_{\phi}\phi^{s}+m_{b}h_{b}v^{s}\gamma^{s}\cos \beta^{s} \tag{8}\]  
\[0=l_{f}\Big{(}F_{x}{}_{f}{}^{s}\sin\delta_{f}{}^{s}+F_{y}{}_{f}{}^{s}\cos \delta_{f}\Big{)}-l_{r}F_{y}{}_{f}{}^{s} \tag{9}\]  
Figure 2: Roll dynamic model should be listed as: (**a**) before wheel lift-off; (**b**) after wheel lift-off.  
In the following, the rear and front wheel slip ratios in drift conditions are derived, which needs to satisfy in order for the vehicle to maintain a steady-state condition.
## 2 Theoretical Analysis
### The EFS of the Shaft Segment with the Contact Interface  
The presence of the contact interface makes the calculation of the flexural stiffness of the shaft segment different from the corresponding integral rotor. Many factors can influence the flexural stiffness of the shaft segment, such as the preload, the structural parameters, and the contact state of the contact interface, and the length of the shaft segment.  
When the \(i\)th flexible shaft segment of the rotor has a contact interface, as shown in Figure 1, \(l_{1},l_{2}\) are the lengths of the shaft segments on both sides of the contact interface, respectively. \(\left(EI\right)_{i}\) is the flexural stiffness of the shaft segment and \(T_{0}\) is the axial preload. When the contact surfaces have sufficient lateral friction to ensure that the contact interface does not experience lateral slippage, the contact can be further equivalent to a torsional spring-hinge structure in which the torsion stiffness of the torsion spring is equal to the EFS \(G_{r}\) of the contact interface.  
Since the flexible shaft segment and the torsion spring are connected in series, EFS of the entire shaft segment is written as:  
\[\left(EI\right)_{iag}=\frac{l_{1}+l_{2}}{\frac{l_{1}}{\left(EI\right)_{i}}+ \frac{1}{G_{r}}+\frac{l_{2}}{\left(EI\right)_{i}}}=\frac{1}{\frac{\left(EI \right)_{i}}{\left(EI\right)_{i}}+\frac{1}{G_{r}L}}=\frac{1}{1+\frac{\left(EI \right)_{i}}{G_{r}L}}\left(EI\right)_{i} \tag{3}\]  
where \(L\) is the total shaft length \(L=l_{1}+l_{2}\).  
From Equation (3), it can be seen that the flexural stiffness of the elastic shaft segment with the contact interface is always smaller than the bending stiffness of the corresponding integral shaft segment. The greater the EFS of the contact interface \(G_{r}\), and the longer shaft segment will have a smaller influence on EFS of the shaft segment. That is to say, in this case, EFS of the shaft segment with the contact interface will be close to the flexural stiffness of the corresponding integral shaft segment.
## 2 Materials and Methods
### Battery Lifetime Estimation  
When the node is powered by a lithium-polymer (Li-Po) battery, the state of charge (SOC) of the battery is estimated and used as the energy consumption metric. There are several techniques for estimating the SOC of a battery. They include the coulomb counting method, voltage method, Kalman filter method, impedance spectroscopy, etc. [42]. However, we used the voltage method because the battery voltage can be read directly from one of the ESP32 pins without needing extra circuitry. From the SOC-versus-voltage-discharge curve obtained from the 3.7 V 2000 mAh Li-Po rechargeable battery's datasheet [43; 44], we used polynomial interpolation to derive the analytic relationship between SOC and the voltage of the battery given by Equation (5).  
\[SOC=\mathrm{pV^{4}-qV^{3}+rV^{2}-sV+t} \tag{5}\]  
where V is the battery's voltage, with p = 2808.3808, q = 43,560.9157, r = 252,848.5888, s = 650,767.4615, and t = 626,532.5703 Now that the experimental testbed and power characterization tools have been introduced, the next section will discuss the experimental scenarios that were investigated in this study.
## 4 Results and Discussion
### Impact Resistance  
Figure 14 shows the number of blows for the initial and final impact resistance of the concrete mixtures incorporating various rubber sizes (R1, R5 and R10) and their dosages (10%, 20% and 30%) at 28 days under a repeated drop weight test. Each result reported showed a CV of less than 0.25%. For all the tested mixtures, the impact resistance increased with larger size and the higher dosage of rubber. For example, the difference was 13 blows in the case of the 30% replacement as compared to 5 blows for the control mixture without rubber. The mixture incorporating R5 rubber exhibited 33% and 66% increases in the first crack impact strength and 31% and 81% increases in the ultimate impact strength for 20% and 30% rubber, respectively. Similarly, a higher impact strength was also reported by [PERSON] et al. [17] for a mixture with rubber. The increase in impact strength with a higher replacement of rubber was mainly attributed to the flexibility and energy absorption capacity of rubber particles. On the other hand, the joining capacity of rubber with concrete ingredients was also affected by a higher rubber replacement; therefore, an optimized dosage of rubber is recommended for desired properties. The change in impact resistance was found to be more in the case of the R10 size as compared to R1 and R5. For instance, there were almost 44% and 94% increases in impact resistance for the mixture incorporating R10 rubber as compared to R1 and R5, respectively (Figure 15). This increase in impact strength was mainly attributed to the enhanced ductility and crack arresting property of larger-sized rubber particles [46]. Figures 16-19 show the initial and final crack patterns for mixtures incorporating various sizes of rubber and their dosages. The failure mechanism was cracking, crushing and compaction in the direction of impact loading.  
Figure 14: _Cont._  
Figure 14: Number of blows for the initial and final cracks under the impact load for various dosages of rubber: (**a**) R1, (**b**) R5, (**c**) R10.  
Figure 15: Impact energy for the mixture incorporating various rubber sizes and dosages.  
Figure 16: Control specimen under impact loading: (**a**) initial crack, (**b**) final crack.  
Figure 17: Cracking pattern for specimens with various dosages of R1 rubber: (**a**) initial crack for 10% R1, (**b**) initial crack for 20% R1, (**c**) initial crack for 30% R1, (**d**) final crack for 10% R1, (**e**) final crack for 20% R1, (**f**) final crack for 30% R1.  
Figure 18: Cracking pattern for specimens with various dosages of R5 rubber: (**a**) initial crack for 10% R5, (**b**) initial crack for 20% R5, (**c**) initial crack for 30% R5, (**d**) final crack for 10% R5, (**e**) final crack for 20% R5, (**f**) final crack for 30% R5.
# Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles
## 3 CAN Bus Attack Mitigation Schemes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Methodology** & & **Merits** & & **Limitations** \\ \hline \multirow{6}{*}{Cryptographic algorithms} & Usage of cryptographic encryption to guarantee the & \(\bullet\) & Guarantee message & & \\  & & & confidently. & \(\bullet\) & Change to all CAN nodes. \\  & confidentially of CAN messages, & \(\bullet\) & Guarantee message & \(\bullet\) & Increased latency. \\  & and of cryptographic MACs and digital signatures to guarantee the & \(\bullet\) & Stop sniffing, injection, ECU & \(\bullet\) & Change to the CAN \\  & authenticity of the sender. & \(\bullet\) & Tops sniffing, injection, ECU & \(\bullet\) & bus behavior. \\  & & & impersonation, and frame & \(\bullet\) & Reliance on key secrecy. \\  & & & falsifying attacks. & \(\bullet\) & \\ \hline \multirow{6}{*}{Intrusion detection systems} & Usage of machine learning and deep learning algorithms to detect anomalies in CAN messages. & \(\bullet\) & Detect fuzzing, injection, ECU impersonation, DoS, and frame falsifying attacks. & \(\bullet\) & Lack of mitigating actions. \\  & Usage of machine learning and deep learning algorithms to detect anomalies in CAN messages. & \(\bullet\) & Able to learn from & \(\bullet\) & Not all attacks are detected. \\  & & & new attacks. & \(\bullet\) & Minor deviation may not be detected. \\  & & & \(\bullet\) & new attacks. & \(\bullet\) \\  & & & \(\bullet\) & Albe to predict new attacks. & \(\bullet\) \\ \hline \multirow{6}{*}{Proposed secure boot scheme} & Usage of cryptographic integrity algorithms to detect the presence of a malicious code and perform the necessary mitigation actions to prevent the code from affecting the CAN bus. & \(\bullet\) & Prevents all attacks that are due to malicious \\  & & & code injection. & \(\bullet\) & Change to all CAN nodes. \\ \cline{1-1}  & & & & \(\bullet\) & Cannot detect attacks due to added malicious ECUs. \\ \cline{1-1}  & & & & not affect the CAN bus. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the CAN bus attack mitigation schemes.
# Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety
## 2 Design of the Proximity Warning System (PWS) Based on Bluetooth Beacons and Smart Helmets  
The design of PWS based on Bluetooth beacons and smart helmets is summarized in Figure 1. The smart helmet worn by the worker receives a BLE signal transmitted from the Bluetooth beacon and provides a visual alert when it comes close to the beacon. The Bluetooth beacon can be attached to heavy equipment, a management vehicle, or a dangerous area at the mine site, and the attached beacon continuously transmits the BLE signal. The smart helmet can warm wearers of access to heavy equipment or vehicles and access dangerous areas and warm drivers that there are workers nearby. Visual proximity alerts are received through a smart helmet while working on the spot; therefore, both workers and drivers can quickly detect and respond to dangerous situations.
## 3 Results  
### Selection of Extraction Solvent  
In the IL-SFODME method, the selection of an appropriate extraction solvent is crucial and must fulfill several criteria. The solvent must have a lower density than water, allowing it to remain at the top of the aqueous phase. Crucially, its freezing point should be near ambient temperature, roughly 25 \({}^{\circ}\)C, to ensure it solidifies post-extraction. To prevent loss of the organic phase, which is minimal, the solvent should exhibit low volatility and minimal to no solubility in water. Moreover, it should efficiently extract the target  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & **Temperature** & **Ramp Time** & **Hold Time** & **Gas Flow** \\  & (\({}^{\circ}\)C) & **(s)** & **(s)** & **(mL/min)** \\ \hline \hline Drying 1 & 110 & 1 & 30 & 250 \\ \hline Drying 2 & 130 & 15 & 30 & 250 \\ \hline Pyrolysis & 250 & 10 & 20 & 250 \\ \hline Atomization \({}^{\mathrm{a}}\) & 1300 & 0 & 5 & 0 \\ \hline Clean-Out & 1700/2450 \({}^{\mathrm{b}}\) & 1 & 3 & 250 \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\) Reading step. \({}^{\mathrm{b}}\) Set for reference and real samples.  
\end{table}
Table 1: Furnace heating program.  
Figure 1: Procedure: Step 1: Standard/sample solution containing Hg prepared; step 2: pH adjustment satisfied; step 3: ionic liquid and 1-dodecanol addition performed, respectively; step 4: at proper conditions, solution prepared; step 5: micro-drop solidified; step 6: solidified micro-drop transferred to vial; step 7: with alcohol addition, drop melted; step 8: micro-liquid introduced to graphite tube for analysis.  
analyte. Aligning with the principles of green chemistry and environmental preservation, the solvent's toxicity and quantity used should be minimal. Potential solvents include 1-bromohexadecane, 1,10-dichlorodecane, 1-dodecanol, 1-undecanol, 2-dodecanol, and n-hexadecane. Table 2 presents the melting points and extraction efficiencies of these solvents. 1-dodecanol stands out with an extraction efficiency of approximately 98.8% (Table 2). Given this high efficiency, along with its cost-effectiveness and availability, 1-dodecanol was chosen as the extraction solvent.
## 2 Materials and Methods
### Pathogen Removal  
The concentration of _E. coli_ in the incoming wastewater was 3.75-4.49 log\({}_{10}\) CFU mL\({}^{-1}\) (Figure 6). The HFF effluent contained lower numbers of _E. coli_ (1.35-2.97 log\({}_{10}\) CFU mL\({}^{-1}\)) than the VFF effluent (0.49-1.86 log\({}_{10}\) CFU mL\({}^{-1}\)) (Figure 6A). On average, the reduction in _E. coli_ in the VFF was 1.53 log\({}_{10}\) units, while the overall reduction in the entire system was 2.95 log\({}_{10}\) units. With increasing HLR from 23 to 31 and then 39 L m\({}^{-2}\) day\({}^{-1}\), no significant effects were observed on the performance of the VFF and HFF in terms of _E. coli_ removal. The VFF achieved a 1.09-2.1 log\({}_{10}\) unit reduction (\(p>\) 0.05) and the HFF achieved a 2.48-3.39 log\({}_{10}\) unit reduction (\(p>\) 0.05) at the HLR values investigated.  
After spiking with _Salmonella_ spp., the wastewater contained 7.29-9.40 log\({}_{10}\) CFU mL\({}^{-1}\). The VFF and HFF did not effectively remove _Salmonella_ spp., but rather attenuated the concentration of this microorganism and slowly released it over time (Figure 6B). Following addition of _Salmonella_ spp. to the wastewater at HLR-23, the effluent of HFF released _Salmonella_ spp. for 20, 5 and 21 days at HLR 23, 31 and 39 L m\({}^{-2}\) day\({}^{-1}\), respectively, and the effluent contained 1.6 log\({}_{10}\) CFU mL\({}^{-1}\) at HLR 39 L m\({}^{-2}\) day\({}^{-1}\).  
Figure 4: Scatter plots of (**A**) concentration of nitrate (NO\({}_{3}\)-N) in effluent of the vertical flow filter (\(\blacktriangle\)) and horizontal flow filter (\(\blacktriangle\)), and (**B**) efficiency of nitrification in the vertical flow filter (\(\blacktriangle\)) and denitrification in the horizontal flow filter; (\(\blacktriangle\)) during 126 weeks of operation.  
Figure 5: Boxplots of (**A**) nitrification efficiency in the vertical flow filter (VFF), (**B**) denitrification efficiency in the horizontal flow filter (HFF) and (**C**) overall removal of total nitrogen (Tot-N) in the entire system (combined VFF and HFF sections). The middle line (\(-\)) of the box is the mean, the cross (\(+\)) is the median, the box is the confidence interval, the whiskers are the non-outlier range and the red dots are the outliers and extreme values.
## 2 Active Systems
### Magnetorheological Fluids  
In the late 1940s, inspired by the work of [PERSON], [PERSON] discovered the magnetorheological effect (MRFs) [33]. Similarly to their ERFs counterpart, MRF's typically consist of micron-sized magnetizable particles suspended in a non-magnetic carrier fluid, such as mineral or silicone oil, whose rheological properties change rapidly and reversibly from liquid-like to solid-like with yield stress under the influence of an external magnetic field [34]. Again, as in ERF's, the change in the rheological behavior is due to a change in the microstructure. Upon the application of a magnetic field, the particles magnetize, attracting to each other along the field lines and forming anisometric aggregates, as in ERFs [35]. MRFs typically possess several kPa of yield stress and a maximum yield stress above 50 kPa [36; 37; 38; 39] depending on their composition, particle concentration and magnetic field strength. For this reason, despite that both types of complex fluids were discovered almost simultaneously, MRFs are currently preferred for commercial applications over ERFs [32], with the exception of giant electrorheological fluids [40]. Nevertheless, there is still work to be done in the formulation of MRFs, since they may not satisfy all the requirements demanded in some commercial applications. [PERSON] [41] highlighted some important issues inherent to magnetic materials and to the use of coils: (i) the saturation magnetization of the magnetic particles limits the maximum magnetorheological effect; (ii) the ratio between the inductance of the coils and their resistance restricts the response; (iii) the magnetic phase frequently exhibits remanece, resulting in a smaller relaxation and redispersibility issues. Nevertheless, more important are the problems related to sedimentation, irreversible aggregation and poor redispersibility.  
Inverse ferrofluids, also known as magnetic holes [42], arised more recently as another type of MRFs consisting of micron-sized non-magnetizable particles dispersed in a ferrofluid. The nonmagnetic particles are typically several orders of magnitude larger than the magnetic nanoparticles constituting the ferrofluid. On application of a magnetic field, dipolar interactions are induced between the nonmagnetic particles, whose strength can be varied by changing the magnetic field or the saturation magnetization of the ferrofluid [43]. Thus, the control of their mechanical properties can be done by varying either the saturation magnetization of the ferrofluid, the strength of the magnetic field, or both of them simultaneously. This sort of MRF can be interesting to use because non-magnetizable particles are commonly available with different sizes, shapes and functionalities [35]. Additionally, they may produce viscosity enhancements comparable to traditional MRFs [44; 45], with the advantage of reducing the density and improving the anti-corrosion properties of the magnetic particles in a conventional MRF [46].
## 3 Privacy and Security Threats in IoT Domain
### Homomorphic Encryption
#### 3.1.1 Homomorphic Encryption Schemes  
HE schemes describe cryptographic protocols for performing computation on encrypted data without the need to first decrypt it. This promising approach maintains data privacy and confidentiality while enabling meaningful operations, making it highly useful in secure data processing, cloud computing, and machine learning that preserves privacy. The most prevalent of the many HE schemes are the BFV [24], BGV [25], and CKKS [26] schemes. These schemes vary depending on the technique used to manage arithmetic operations over encrypted data and handle noise in a way that balances precision with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper Title** & **Encryption Method** & **Key Results** & **Limitations** \\ \hline \multirow{3}{*}{Federated Learning and HE [10]} & CKKS & Achieved highest accuracy of & \multirow{3}{*}{\begin{tabular}{c} High computational overhead, \\ with significant encryption \\ and aggregation time \\ \end{tabular} } \\  & (Cheon–Kim–Kim–Song) & 97.3\% while maintaining data & & \\  HE [10] & algorithm integrated with & privacy in distributed ML & \multirow{3}{*}{\begin{tabular}{c} High computational overhead, \\ with significant encryption \\ and aggregation time \\ \end{tabular} } \\  & federated learning & & \\ \hline \multirow{3}{*}{Lattice-based HE [18]} & Lattice-based fully & \multirow{3}{*}{\begin{tabular}{c} Efficient smart meter data \\ encryption and aggregation; \\ allows computations on \\ encrypted data \\ \end{tabular} } \\  & & & \\ \hline \multirow{3}{*}{Homomorphic WiSARDs [13]} & TFHE (Fast Fully & Demonstrated effective & \multirow{3}{*}{\
\begin{tabular}{c} High computational \\ requirements for large \\ datasets and complex \\ operations \\ \end{tabular} } \\  & Homomorphic Encryption) & encrypted RAM unit & & \\ \cline{1-1}  & for WiSARD model & generation with lookup tables & & \\ \hline \multirow{3}{*}{Random Forest using  Multi-Key HE [22]} & Multi-Key Homomorphic & \multirow{3}{*}{
\begin{tabular}{c} Achieved 97.0\% precision \\ with encrypted Random \\ Forest models; minimal \\ performance degradation \\ \end{tabular} } \\  & & \\ \cline{1-1}  
computational efficiency. While both the BGV and BFV schemes perform exact arithmetic on encrypted integers, the former offers greater flexibility in managing noise. On the other hand, CKKS aims to approximate real and complex arithmetic, catering to applications where a slight loss of precision can significantly enhance performance. Table 2 provides a detailed comparison of HE approaches.