esa-sceva/satcom-chunk-collection huggingface dataset:
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
## 6 Conclusions and Future Directions  
The key conclusion of this paper is that SD-WAN is the most effective WAN technology for enhancing the availability of OT infrastructure, particularly in ICS and CPS environments. This conclusion is supported by the proposed risk assessment approach, where SD-WAN achieved the lowest risk score of 0.3522, outperforming MPLS at 0.3625 and Leased Line at 0.3685, both widely recognized WAN connectivity options. Other WAN technologies exhibited moderate to high-risk values, ranging from 0.4321 to 0.5950, indicating that SD-WAN provides the best risk-to-mitigation ratio. The findings emphasize SD-WAN's superior mitigation capabilities. It can prevent vulnerabilities such as Denial of View (Technique T0815), Denial of Control (Technique T0813), and Denial of Service (DoS) in OT WAN environments by selecting the WAN technology with the lowest risk score, thus minimizing exposure to threats.  
This paper demonstrated that by assessing potential SD-WAN risks alongside its integrated mitigation features, a risk score can be calculated. This scoring aligns with NIST and ISA/IEC risk and mitigation prioritization frameworks, offering an evaluation of how effectively SD-WAN can prevent OT WAN vulnerabilities. Additionally, this paper conducted a comprehensive OT risk analysis of various WAN technologies, including Satellite WAN, LoRaWAN, LTE/5G, MPLS, leased line, DMVPN, IPSec VPN, and VPLS, providing a detailed approach to assessing WAN technology risks.  
By calculating the risk score for each WAN technology, considering their inherent risks and mitigation capabilities, the exploitable vulnerabilities of these technologies when applied in OT infrastructure are clearly identified. As a result, the probabilities of Denial of View (DoV), Denial of Control (DoC), and Denial of Service (DoS) can be estimated and minimized.  
A limitation of this paper is the absence of a comprehensive network simulation for the proposed designs in Figures 7 and 8. Therefore, it would be beneficial for future studies to test the proposed design by implementing it within an actual OT infrastructure. This would allow for the fine-tuning of IPS/IDS anomaly detection settings, as well as monitoring and tagging network traffic over SD-WAN. Simulations should specifically involve actual OT devices, particularly focusing on unidirectional traffic from OT to IT, and examine the behavior of SD-WAN multi-VPN during secure remote monitoring (such as ICS and CPS status updates) and redundancy backup drills for ICS and CPS data (including historical logs and configuration backups).  
Conceptualization, V.J.A. and F.M.; methodology, V.J.A. and F.M.; software, V.J.A.; validation, V.J.A. and F.M.; formal analysis, V.J.A.; investigation, V.J.A.; resources, V.J.A.; writing--original draft preparation, V.J.A.; writing--review and editing, F.M.; visualization, V.J.A. and F.M.; supervision, F.M.; project administration, V.J.A. and F.M. All authors have read and agreed to the published version of the manuscript.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 2 Model Description
### NGSO Satellite System Model
#### 2.3.3 Satellite Antenna Pattern  
The antenna model proposed in ITU-R S.1528 [28] is used to analyse the interference from the NGSO satellite operating in the fixed-satellite service below 30 GHz. The logarithm of antenna gain \(G_{t}(\theta)\) is shown below  
\[[G_{t}(\theta)]=\left\{\begin{array}{cc}G_{max}-3(\theta/\theta_{b})^{\alpha }&0<\theta\leq a\theta_{b}\\ G_{max}+L_{N}+25\log(z)&a\theta_{b}<\theta\leq 0.5\theta_{b}\\ G_{max}+L_{N}&0.5\theta b_{b}<\theta\leq b\theta_{b}\\ X-25\log(\theta)&b\theta_{b}<\theta\leq Y\\ L_{F}&Y<\theta\leq 90^{\circ}\\ L_{B}&90^{\circ}<\theta\leq 180^{\circ}\end{array}\right., \tag{13}\]  
where \(G_{max}\) and \(\theta_{b}\) are the maximum gain in the mainlobe (dBi) and the one-half 3 dB beamwidth, respectively, \(X=G_{max}+L_{N}+25\log(b\theta_{b})\), and \(Y=10^{0.04(G_{max}+L_{N}-L_{F})}\). The far-out side-lobe level and the back-lobe level of the main beam are denoted as \(L_{F}\) and \(L_{B}\). For \(L_{N}=-25\), the values of \(a\), \(b\), and \(\alpha\) are \(2.58\sqrt{1-0.6\log z}\), 6.32 and 1.5. The value of \(z\) is 1 for circular beam.
## 3 Case-Study Building and Retrofit
### Renewable Energy  
Renewable energy in the retrofitted building comes from solar power generation installed on the rooftop of the building to generate electricity and provide a reduction in total energy consumption. Concerning the renewable energy system in the retrofitted case, a PV was installed on the rooftop to compensate for the total energy consumption. The building rooftop has an installation area for the PV of approximately 630 m\({}^{2}\) because of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Currently Used Alir Condition**} \\ \hline
**Type of Air Conditioning Unit** & **Cooling Capacity (BTU/h)** & **Power Consumption (kW)** & **SEER (BTU/h/W)** & **Quantiity** \\ \hline  & 9000 & 0.700 & 12.850 & 38 \\ Fixed speed & 12,000 & 1.065 & 11.267 & 36 \\  & 18,000 & 1.570 & 11.460 & 42 \\ \hline  & \multicolumn{4}{c}{**Replacement Luminaire**} \\ \hline
**Type of Air Conditioning Unit** & **BTU/h** & **Power Consumption (kW)** & **SEER (BTU/h/W)** & **Quantiity** \\ \hline  & 9000 & 0.600 & 24.00 & 38 \\ Variable speed & 12,000 & 0.930 & 22.50 & 36 \\  & 18,000 & 1.470 & 20.81 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Specifications of air conditioning employed in the case-study building. SEER: seasonal energy efficiency ratio.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Currently Used Alir Condition**} \\ \hline
**Type of Air Conditioning Unit** & **Cooling Capacity (BTU/h)** & **Power Consumption (kW)** & **SEER (BTU/h/W)** & **Quantiity** \\ \hline  & 9000 & 0.700 & 12.850 & 38 \\ Fixed speed & 12,000 & 1.065 & 11.267 & 36 \\  & 18,000 & 1.570 & 11.460 & 42 \\ \hline  & \multicolumn{4}{c}{**Replacement Luminaire**} \\ \hline
**Type of Air Conditioning Unit** & **BTU/h** & **Power Consumption (kW)** & **SEER (BTU/h/W)** & **Quantiity** \\ \hline  & 9000 & 0.600 & 24.00 & 38 \\ Variable speed & 12,000 & 0.930 & 22.50 & 36 \\  & 18,000 & 1.470 & 20.81 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Specifications of luminaires employed in the lighting systems of the case-study building. CFL: compact fluorescent.  
space required for maintenance and the avoidance of shadows on the panel itself. Thus, the total generating capacity of the installed PV system was 70 kWp. A monocrystalline PV panel was selected because of its high efficiency. In addition, the panel was installed with a 15-degree tilt angle and faced the south owing to its potential to receive the highest solar radiation and generate electricity at the highest possible capacity [25]. The specifications of the installed PV system are summarized in Table 12.
# Study of Rail Squat Characteristics through Analysis of Train Axle Box Acceleration Frequency
## 2 Description of System  
### Squats  
Squats, which generally start from a surface, are rolling contact fatigue (RCF) defects of rails. Squats are caused by the repeated loading on the narrow contact surface in the middle of the rail heads by the wheels on the straight sections of the rails. Figure 1 shows the three types of squats, which are light, moderate, and severe squats [25].  
Generally, squats are not so deep as to touch the wheel (deeper than the depth of compression of the rail by the wheel load (0.05 mm)). Some squats cause cracks and become rusty.  
Squats are expanded by the dynamic contact between the wheel and the rail. The squat length is generally 20 to 40 mm, but it can increase to 60 mm depending on the rail strength [1]. Squats are reflected to the wheel vibration characteristics through dynamic wheel-rail interaction, and thus can be detected by an accelerometer.  
Not all the defects generated on the rail surface develop into squats. A rail surface defect with a size greater than a threshold value may develop into a squat. The threshold  
Figure 1: (a) Light squat. (b) Moderate squat. (c) Severe squat [25].  
defect size is 6 to 8 mm in both the longitudinal and transverse directions when the train traction and braking forces are at maximum. A defect smaller than the threshold size is insignificant, and the defect may be worn out and disappear.  
Realization of Measurement System and Data Verification  
As shown in Figure 2, accelerometers installed on the EM-140K (detection speed 80 km/h) were used to measure the train axis acceleration. The EM-140 is a vehicle that inspects Korea's track infrastructure. Three accelerometers were mounted on the axis of the bogie, two in the vertical direction and one in the transverse direction. The DAQ measurement equipment (NI), which is reliable for frequencies higher than 1 kHz, was used in the system to measure both the vertical and longitudinal signals. The vertical signals are effective for detecting light squats because of the high signal-to-noise ratio. The sampling frequency of the accelerometers was 2 kHz. Figure 2 shows the DAQ system that was employed to measure the axis acceleration of the EM-140K. The system measures the coordinates through the GPS receiver, and the train velocity can be calculated based the coordinates. The GNSS system used in this study had an accuracy of 0.01 m + 1 ppm CEP as a high-precision GNSS RTK receiver. The maximum speed of the test vehicle was about 140 km/h and vehicle speed over the study period was about 80 km/h. Speed measurement was determined as the correct value because it produced the coordinate measured by the tachometer and the GNSS receiver attached to the train. In addition, the accuracy of the position measurement by the field inspection is less than \(\pm 5\) m.  
A visual inspection was performed to acquire the details of the rail over a short interval. The valid data acquired by visual inspection included GPS coordinates of defects, images, welds, and joints.
###### Abstract
Near-infrared (NIR) diffuse optical tomography (DOT) is a convenient and cost-effective optical imaging modality, and has the potential to become a powerful tool in biomedical research and clinical studies [26; 27]. Due to the use of an effective model-based reconstruction algorithm, DOT is able to provide accurate visualization and quantification of the three-dimensional (3D) distribution of intrinsic tissue absorption and scattering properties in deep turbid tissue with good spatial and temporal resolution. Over the last several decades, DOT has been widely applied to many clinical areas, including the detection of breast cancer [28; 29], arthritis [30; 31] and functional brain activities [32; 33; 34]. Recently, we have developed and reported a powerful fast multispectral DOT imaging system which allows for a full set of 3D data that is collected utilizing 3 NIR wavelengths (660, 780 and 850 nm) for the purpose of real-time imaging in 14.4 Hz [35; 36] (pages 7 and 8 in reference [35], and pages 3462-3465 in reference [36]). Extensive phantom and clinical experiments have demonstrated and verified that this fast DOT imaging system is able to recover both structural and functional information from both animal and human brains. The goal of this study is to present a new approach based on DOT to monitoring the heating process during HIFU treatment and to investigating the feasibility of an alternative approach to predict the temperature change of tissue. Using a fast DOT imaging system, the effects of temperature-dependent optical property (i.e., reduced-scattering coefficient, \(\mu_{s}^{{}^{\prime}}\)) on temperature distribution within tissue during HIFU treatment were investigated by extensive experimentation on both porcine and chicken breast muscle specimens.
###### Abstract  
Retrospective dosimetry is one of the most important tools of accident dosimetry for environmental dose estimation when large-scale radiological incidents and nuclear mass-casualty events occur. Electron paramagnetic resonance (EPR) dosimetry is a physical method for the retrospective assessment of absorbed dose based on the measurement of stable radiation-induced radicals in materials. Different from the fast disappearance of radials in aqueous systems, the radials can persist indefinitely in some organized matrices. Therefore, environmental materials contained in creatures from sea or land can be potentially used as environmental dosimeters for a retrospective dose analysis. This study aims to assess the EPR signals of free radicals from environmental biological samples, potentially for the retrospective dose estimation. The evaluated samples involve ox bone, cyclina shell, clam shell, chitin from squid, and human tissue (enamel and fingernail). First, we dehydrate and grind these materials to the powder with different sizes. Subsequently, all materials were irradiated with different doses ranging from 5 Gy to 50 Gy using 6 MV linear accelerator, and EPR spectra of these materials were obtained from the calculation of peak-to-peak amplitudes. The dose-response curve of EPR signals versus irradiated dose for the six materials shows good linearity (R\({}^{2}\)-0.99). For the grain-size experiment, the ox bone and tooth with 0.5 mm, the chitin with 0.1 mm, and the others with 1 mm have the strongest signal. For the storage temperature experiment, the optimal temperature of storage is at \(-20\)\({}^{\circ}\)C for tooth, fingernail, ox bone, and chitin, at 45 \({}^{\circ}\)C for clam shell and cyclina shell where the signal fading is minimal. In conclusion, the developed dose-response curves of the six materials may potentially help a fast, rough retrospective dose reconstruction under the environment when radiation accidents occur.  
a Article  
e lectron paramagnetic resonance; radiation accident; enamel; chitin; cyclina shell; clam shell
## 3 Results
### Loads for Steady Propeller Disc Pitch (1P Hub Loads)
Methods that do not account for unsteady lift lag (quasi-steady time-domain and Houbolt/Reed quasi-st. methods) only predict the in-phase force terms, and the off-axis force term (\(F_{y}\)) is zero. The same applies for the off-axis moment term (\(M_{y}\)), although the quasi-steady time-domain method predicts a small value due to the airfoil torque arising from the offset between lift reference point at quarter chord and the y-axis, which is neglected in the Houbolt/Reed formulation. In general, the relative variation among the methods is higher for the off-axis terms, as their prediction is based on the unsteady lift model employed and not mainly on the kinematics. The prediction by the mid-fidelity methods (top four rows in each plot) are all within the same range, deviating by only about 5% with respect to each other (for \(M_{z\theta}\)). Only predictions in \(M_{y\theta}\) deviate a bit more, as this component is influenced by the chord-wise distribution of the lift and thus the offset moment between lift vector and y-axis, which is different among the mid-fidelity methods. For the low-fidelity methods, the quasi-steady time-domain method over-predicts the in-phase loads compared with the mid-fidelity codes (e.g., by 47% in \(M_{z\theta}\)). This is due to the lack of the lift deficiency (unlike the Wagner method, which over-estimates \(M_{z\theta}\) by 28%), as well as induction and tip loss effects (a further difference from BEM+Wagner). Because, additionally, no off-axis terms are predicted, this method is not further used in the following analysis. The equivalent quasi-steady Houbolt/Reed formulation incorporates the aspect ratio correction (see Equation (7)), explaining the better match in amplitude of the z-force and -moment due to the reduction in lift due to tip loss effects. The same applies to the comparison between the unsteady [PERSON] method and the baseline Houbolt/Reed  
Figure 8: Comparison of the four in-plane hub load derivatives under steady disc pitch angle. The top row shows the y- and z-forces, and the second row, the y- and z-moments. results, where the differences can also be mainly attributed to the missing tip loss correction in the [PERSON] method. The baseline Houbolt/Reed results for the 1P hub loads match well with the mid-fidelity predictions, even though the method lacks an aerodynamic wake model. The results from BEM+Wagner (the most detailed low-fidelity method) show good agreement with the mid-fidelity codes, with slight over-predictions of \(F_{2\theta}\) and \(M_{y\theta}\).
## 3 Longitudinal Driver Model
### MPC-Based Driver Model Design  
To design a driver model that considers human driving characteristics and various constraints using the simplified vehicle model, it is required that a controller should reflect future information and satisfy constraints. A model predictive control (MPC) algorithm is a well-known optimal controller design method to consider future states and constraints. The MPC algorithm is based on the discrete-time state-space model, and it calculates sub-optimal control inputs to minimize the cost function with various potential optimization methods [46]. The driver model of the proposed longitudinal motion scenario design method is designed by the MPC algorithm that minimizes the cost function (\(J(u,\ x_{0})\)) by satisfying the constraints in Equation (6).  
\[\begin{array}{l}J(u,x_{0})=x_{N}^{T}Px_{N}+\sum\limits_{k=0}^{N-1}\left(x_{ k}^{T}Qx_{k}+u_{k}^{T}Ru_{k}\right)\\ s.t.\ x_{k+1}=A_{d}x_{k}+B_{d}u_{k},\ k=0,\ldots,\ N-1\\ x_{k}\in\mathcal{X},\ u_{k}\in\mathcal{U},\ k=0,\ldots,\ N-1\\ x_{0}=x(0),\ P=P^{T}>0,\ Q=Q^{T}>0,\ R=R^{T}>0\end{array} \tag{6}\]  
The continuous time error dynamics model shown in Equation (4) is discretized by the Euler method, denoted by the discrete-time state-space model \(A_{d},\ B_{d}\) to construct the MPC problem. \(\mathcal{X}\), \(\mathcal{U}\), and \(N\) are the constraints on the state, input, and prediction horizon, respectively. In this paper, a quadratic cost function is used, where P, Q, and R are symmetric and positive definite. The control input \(u^{*}\) that minimizes the cost function \(J(u,x_{0})\) and satisfies the constraints can be calculated using a quadradic programming (QP) solver, such as \"quadprog\" in MATLAB, which is used in this paper. The driver model is set to 0.2 s for the sampling time and 60 m for the prediction horizon, which means the prediction time is about 12 s to consider the time that a vehicle traveling at 30 m/s can stop with a maximum deceleration of \(-2.5\) m/s\({}^{2}\). Since this paper focuses on deriving a human driving characteristics analysis and longitudinal motion scenario design, the process for solving the MPC problem is omitted, and the existing MPC-related literature is referred to for the solution process [46].  
The driver model in this study is used to simulate longitudinal driving situations, such as acceleration/deceleration. It is designed using the fixed weights and constraints in Equation (6). The driving tendency of human drivers can vary, mainly depending on the driving situation [47; 48]. Accordingly, the weights and the constraints of the driver model for imitating a specific driver can be changed. Therefore, it is limited to expressing the acceleration/deceleration situation, in which the longitudinal driving characteristics of  
Figure 2: The scheme of error dynamics for the car-following model.  
human drivers are well expressed, and it is globally optimal in this situation through the optimal control problem in Equation (6).
## 2 Material and Methods
### Gel Slices Proteolysis and Mass Spectroscopy Analysis  
The gel slices containing the specific DNA-bound proteins were reduced with 10 mM DTT, incubated at 60 \({}^{\circ}\)C for 30 min, carboxamidomethylated with 40 mM iodoacetamide at room temperature for 30 min, and trypsinized overnight at 37 \({}^{\circ}\)C [modified trypsin (Promega)] at a 1:100 enzyme-to-substrate ratio. The tryptic peptides were resolved by reverse-phase chromatography on \(0.075\times 200\)-mm fused silica capillaries (J&W, Wilmington, DE, USA) packed with Reprosil reversed phase material (Dr Maisch GmbH, Germany). The peptides were eluted with a linear 90 min gradient from 5 to 45% acetonitrile containing 0.1% formic acid, followed by a wash of 95% acetonitrile for 15 min. The flow rate was 0.25 \(\upmu\)l/min and the peptides were electrospray into an ion-trap mass spectrometer (Orbitrap, Thermo Fisher Scientific Inc., USA). MS was performed in a positive mode using repetitively full MS scans followed by collision induced dissociation (CID) of the 7 most dominant ions selected from the first MS scan. The MS data was clustered and analyzed using the Sequest software (Finnigan, San Jose, CA, USA) and Pep-Miner [39] searching against International Protein Index (IPI) protein IDs (part of the NR-NCBI database).  
Figure 2: Silver staining of IGF-IR promoter-bound proteins identified by DNA affinity chromatography. Nuclear extracts of MCF7 cells were prepared as described under _Materials and Methods_ Section. Proteins bound to IGF-IR promoter DNA (10 \(\upmu\)g) were electrophoresed through 10% SDS-PAGE, fixed, and stained with silver (Bio-Rad, Hercules, CA, USA). Lane 1, M.W. marker; lane 2, starting material (MCF7 nuclear extract, 4.2 \(\upmu\)g); lane 3, unbound material (MCF7 nuclear extract that did not bind to DNA, 4.2 \(\upmu\)g); lane 4, eluted material bound to Biotin-520 bp (MCF7 nuclear proteins that bound to IGF-IR promoter, 10 \(\upmu\)g); and lane 5, negative control (MCF7 nuclear proteins bound to streptavidin magnetic beads, 10 \(\upmu\)g).