esa-sceva/satcom-chunk-collection huggingface dataset:
# Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling--An Oil and Gas Sector Case Study
## 2 Background and Related Work
### Industrial Control System (ICS)
Therefore, it is recommended to use a framework that employs a combination of methods and techniques [10]. This research aims to apply a hybrid threat modeling approach, providing a comprehensive and proactive cybersecurity assessment methodology for legacy ICS in the oil and gas sector. The approach has been employed: PASTA, STRIDE & Attack trees, and CVSS. The following list explains the rationale behind the application and advantages of each TMM in this study. * PASTA's structure [16], risk-centric approach is suitable for complicated systems like legacy ICS. It prioritizes threats based on their potential business impact, which aligns with the importance of oil and gas infrastructure. PASTA promotes collaboration between security specialists and operational stakeholders, resulting in a more thorough understanding of vulnerabilities to legacy ICS. * STRIDE [14] is an acronym for a popular technique to categorize common attacks. This technique serves as a platform for discussing potential threats unique to legacy ICS functionality. Thus, STRIDE helps identify a wide range of risks across several categories, ensuring a full assessment of legacy ICS vulnerabilities. * Attack trees [15] can help discover potential attack vectors and critical control points in legacy ICS. Attack trees provide a clear picture of how attackers can exploit vulnerabilities in legacy ICS, allowing for targeted mitigation approaches. * CVSS [17; 18] provides an established scoring technique for prioritizing vulnerabilities based on their severity and exploitability. This is vital for allocating resources and prioritizing the most critical vulnerabilities in legacy ICS. Thus, CVSS provides a uniform way to examine the security flaws discovered in legacy ICS, resulting in the most efficient use of resources for problem resolution. Section 2.2 further elaborates on the four threat modeling methods that serve as the foundation for the proposed framework, ensuring the self-containment of the research paper.
## 3 Results and Discussion
### Imaging at Night  
Next, the measurements were made in night conditions, which can play the best performance of the LiDAR system due to the high SNR data acquisition. Here, as shown in Figure 5, we present four long-range imaging results at distances of approximately 9000 m  
Figure 4: Imaging in bright daylight. (**a**) Microwave radar antenna at 150 m. (**b**) Baluster at 1900 m. (**c**) House at 2577 m. (**d**) Tower crane at 4000 m.  
Figure 3: Imaging on a hazy day. (**a**) Hazy condition captured by the camera. (**b**) Roof platform at 870 m. (**c**) Logo of the building at 4665 m. (**d**) Building at 6000 m. (**e**) TV tower at 6570 m.  
(building), 13,000 m (TV tower head), 14,000 m (building), and 19,200 m (building). These distances correspond to locations that are accessible and offer unobstructed views from the hillside of the Qinling Mountains. Similar to the previous section, TOF is employed for measuring these distances with an image pixel resolution of 128 \(\times\) 128. However, each pixel's acquisition time is set to 1 ms. As depicted in Figure 5, the reconstructed image enables extraction of profiles and typical details. Given that the building at 19,200 m is the farthest target visible within the field of view, we contend that this system has the capability to detect even longer distances under nighttime conditions.
## 5 Experimental Study
### Route Constructure in Consideration of a Trade-off between Energy Consumption and Lifetime
Furthermore, we study the impact of \(\varepsilon\) on the performance of our algorithm. By varying \(\varepsilon\) from 1 to 1.5, we can observe the change of network lifetime obviously. As shown in Figure 9, with the increase of \(\varepsilon\), the network lifetime becomes longer. As we see, a small increase of \(\varepsilon\) may lead to a huge lifetime extension. We further compare our algorithm with others. As shown in Figure 10, with the increase of \(\varepsilon\), the lifetime ratio of our algorithm to other algorithms increases drastically. These algorithms include not only SPT, MST, SLT and Greed Steiner, but also our algorithm adopted in the first simulation. While other algorithms use consistent nodes throughout to transmit or aggregate data, despite the fact that some nodes possess little energy and other nodes have vast energy remaining, resulting in some nodes' premature death, our algorithm can adjust itself to select an optimal route which can balance the total energy consumption and nodal remaining energy, effectively extending the network lifetime.
# Sustainable Approach of Generating Water and Energy:
###### Abstract  
Cogeneration of energy and clean water by incorporating direct contact membrane desalination (DCMD) and photovoltaic hybrid thermal system (PVT) into a residential building is a promising technology for addressing water and energy shortage in distant places. In this study, a microgrid integration between PVT, DCMD, and a residential building is proposed, with an end goal to meet partial electric load in the building and provide a clean water supply. A mathematical model was developed and validated to assess the system's performance. Artificial Neural Network (ANN) and optimization techniques have been used. The performance of the proposed system was studied under the meteorological conditions of Riyadh, Saudi Arabia, and under several design and operation parameters. The optimal performance of the system is found as functions of the inlet brackish water temperature to the PVT, capital and installation cost, and the desired water productivity. Results reveal that the specific cost of water (SCW) is 23.6 \(\mathrm{\SIUnitSymbolMicro m}^{3}\) achieved with a renewable energy penetration of 25%, depending on the cost of PVT and electricity price. Thus, the proposed system meets 25% of the electric demand for the residential building, while the rest is imported from the grid. In addition, the proposed system reduced the annual greenhouse gas emission by 4300 kg for a single building. This study will contribute to a better understanding of incorporating innovative clean energy and water systems such as PVT and DCMD into a residential house.  
sustainable energy; membrane distillation; photovoltaic hybrid thermal system 2022
###### Abstract  
Impedance or admittance measurements are a common indicator for the condition of lead-acid batteries in field applications such as uninterruptible power supply (UPS) systems. However, several commercially available measurement units use different techniques to measure and interpret the battery impedance. This paper describes common measurement methods and compares their indication for the state of health (SoH) to those of electrochemical impedance spectroscopy (EIS). For this analysis, two strings consisting each of 24 valve-regulated lead-acid (VRLA) batteries with a rated voltage of 12 V and about 7 Ah capacity were kept under standard UPS conditions in float charge for over 560 days. They were monitored continuously with a LEM Sentinel 2 and went into regular check-ups with impedance measurements by a Hioki BT3554 as well as electrochemical impedance spectroscopy (EIS) measurements with an impedance meter (\(\mu\)EIS). Today it is widely expected that solely the relative increase of the impedance reading is sufficient for the estimation of the available capacity. However, it can be shown that the measured relative increase deviates for different frequencies and therefore the choice of the excitation signal and measurement frequency does make a difference for the calculation of the available capacity. Finally, a method for a more decisive monitoring in field applications is suggested.  
Keywords:state of health estimation; battery diagnostics; impedance measurements; electrochemical impedance spectroscopy; UPS; lead-acid battery +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion  
### Penetration  
The assessment of asphalt binder characteristics involves multiple tests to determine their properties. Figure 4 illustrates the penetration values of various asphalt binders at 25 \({}^{\circ}\)C, which serve as an indicator of their hardness or softness. A lower penetration value in CR asphalt binders corresponds to increased hardness. Previous research [40] highlighted that introducing crumb rubber into asphalt binders led to a significant reduction in penetration values, contingent on CR content. Among WMA additives, Cecabase exhibited minimal impact on asphalt binder hardness, with the modified binder's penetration value aligning closely with that of the virgin binder. This was confirmed by [PERSON] et al. [41], who noted Cecabase's limited influence on penetration values. In contrast, Sasobit and Rediset exerted more substantial effects on asphalt binder hardness than Cecabase. The liquid form of Cecabase had minimal impact on the hardness of the CECR asphalt binder. The SECR asphalt binder demonstrated the lowest penetration value due to the combined influence of Sasobit and crumb rubber, which augmented the binder's hardness. [PERSON] et al. [37] reported comparable findings regarding the combined effect of crumb rubber and Sasobit on penetration values, consistent with our current study.
# Investigation of Inner Lining Loss and Correlation with Steel Structure Quality in Rotary Kilns
## 3 Results  
### Loss of Inner Lining  
The loss of the interior lining was determined by measuring the difference between the ideal undamaged condition and the actual measured condition using a laser scanner. The initial hypothesis was that the interior lining of the perpendicular section of the rotary kiln wore evenly. Greater wear occurs close to the flame, whereas the risk of wear decreases with distance. Most of the lime has been removed from the interior of the kiln. Unfortunately, it was not possible to remove all the residue, so the lime remains on the bottom of the kiln and in areas where it has adhered. To obtain optimal results for comparing the laser scanning procedure with boreholes, the loss was statistically determined within a perpendicular section. By stationing, the rotary kiln was divided into dozens of 1-metre sections. The median values of the declines in a given section were used to define the decline from the difference model. The section's utmost value defined the loss in the joint between the bricks. On the other hand, the minimum value consisted of contaminated lime (Figure 6).  
When the difference model and the measured numbers from the wells were compared, they were about the same. It was found that, after 42 m of stationing, the managers stopped measuring the loss of the liner and just assumed that the liners were always worn out. However, it was found that the loss of brick covering was less than that reported from the wells (Figures 7 and 8). Due to not cleaning the lime off the bricks well enough, the median loss went from 6 metres to 66 metres of staging. Most of the brick lining in the part from 66 metres to stopping was made of lime. At the same time, it was not expected that the brick lining would show much wear along this length. Due to the high frequency of lime in the brick lining, it was not further examined from 74 m from the stationing.  
The original idea that there was constant wear in the vertical part of the furnace was also shown to be wrong. Between 42 and 46 m of rotary kiln length, there were large differences in the inner loss. At the brick joint, the loss was up to 52 mm. At the brick face, it was 35 mm, and at another spot, it was 12 mm. The big difference could be because the steel frame is oval-shaped (Figure 9).  
Figure 6: Inner lining loss inside the rotary kiln.
## 3 Illustrative Example
### Adaptive State Estimation
#### 3.3.1 Adaptive Control Scheme Without a Feedback Filter (\(aL=0\))  
The control scheme illustrated in Figure 1 is implemented with a fixed gain \((K)\) and the adaptive law outlined in Equation (56), where \(\gamma_{y}=I\) and \(aL=0\). As illustrated in Figures 3 and 4, both internal and external state errors converge to a radius around zero. Although the proof establishes that the internal and external error states will converge to a neighborhood around zero, it only assures that the variance matrix \((\Delta L)\) will remain bounded. In this particular example, numerical results indicate as \(\Delta L\) collapses such that \(L\xrightarrow[t\to\infty]{}L_{*}\), shown in Figure 5. Consequently, as \(L\xrightarrow[t\to\infty]{}L_{*}\), the internal error radius further reduces to a smaller neighborhood around zero, as seen in Figure 3b.  
Due to the stochastic variations in the true system, values of \(L\) will show variability as time approaches infinity. In order to compare the estimated plant with the true system, the last 20 s of \(L\)'s simulated data were averaged, denoted by \(L_{avg}\). Consequently, the \(L_{avg}\) for this example is as follows:  
\[L_{avg}\approx\begin{bmatrix}-3&4\\ -1.02&-4.95\end{bmatrix}. \tag{77}\]  
Therefore, the estimated plant \((A_{est})\) can be expressed as follows:  
Figure 2: True \((y)\) and reference \((y_{m})\) system’s output response given a unit-step input \((u)\).  
\[A_{est}\approx A_{m}+BL_{agg}C\approx\begin{bmatrix}-13.46&2&1.74\\ 0.32&-1&0.60\\ 4.62&2&-5\end{bmatrix}, \tag{78}\]  
where the eigenvalues of \(A_{est}\) are \(\sigma\{A_{est}\}\approx\{-0.51,-4.64,-14.32\}\). Observe the similarities in internal interactions and constitutive constants between true and estimated plants, as shown in Equation (74) and Equation (78), respectively.  
Figure 4: External state error (\(\delta_{y}\)) converging to a radius around zero with the use of the fixed gain and \(aL=0\).  
Figure 5: Adaptive Gain \(L(t)\) converging to \(L_{*}\) with the use of the fixed gain \((K\
eq 0)\) and \(aL=0\).  
Figure 3: Both sub-figures (**a**,**b**) illustrate the internal state error \((e_{x})\) converging to a radius \((R_{*})\) around a neighborhood of zero; however, they do so at two different time histories. The implemented control scheme used a fixed gain and \(aL=0\).
## 1 Introduction
Even though the sizing optimization can draw simple and efficient antenna update [31], the technique forces the antenna to maintain its original shape, which interrupts a fundamental solution regarding the narrow bandwidth. Furthermore, topology optimization with solid isotropic material with penalization or the level set method has been exploited to address the issue [32], but the efficiency of the suggested design is only identified when it is fabricated by special additive manufacturing process, which intensely increases the fabrication burden. Assorted studies have also suggested unique shape modification such as conical, pentagon, or even fractal geometry [33; 34], but such modifications refuse to preserve an initial outline of the MSP antenna that has advantages of cheaper and easier fabrication process and diverse applicability, so their efficiency is only meaningful in a specialized example. To address these issues of the existing design approaches for the stretchable antenna, a design optimization process that includes a structural shape optimization is proposed in this study. In comparison with the existing approaches, the proposed design process not only inherits the advantages of the MSP antenna, but also derives an innovative antenna shape to enhance functionality in the multi-physics domain. Owing to the mechanical flexibility and electrical radio frequency behavior, the stretchable MSP antenna for strain gauge requires a meticulous multidisciplinary design optimization. Thus, the proposed design maximizes the frequency stability and antenna performance, referring to a return loss under unexpected structural deformation. The optimum design is garnered in accordance with the basic shape of the MSP antenna, so that it can truncate an intense fabrication burden. The design also operates at dual bandwidth to keep the benefit of UWB, but it induces less design intricacy. For the system response prediction, the proposed framework employs the artificial neuron network (ANN)-based surrogate model under geometric uncertainties stemming from the stretchability and fluctuating antenna substrate. Finally, the reliability of the proposed design is evaluated by the probabilistic neuron network (PNN) and Monte Carlo simulation (MCS). This paper is structured as follows. Section 2 introduces backgrounds of the R\({}_{\text{f}}\) fluctuation under structural deformation and the DR methods. Section 3 introduces the proposed design framework employing the DR methods and shape optimization to determine the optimal new design of the stretchable antenna-based strain sensor. In Section 4, an example of the MSP antenna employed as a stretchable strain gauge is provided to represent the efficacy and merits of the proposed framework.
## 3 Analysis of BER for SISO System
### Scenario 2  
Similar to scenario 1, ABER under jamming for scenario 2 can be obtained as  
\[\begin{split} P_{e2a}\Big{(}\overline{\gamma}_{JD}\Big{)}& =\frac{A_{0p}\xi_{p}^{2}}{\alpha_{p}\beta_{p}\Gamma(x_{p})\Gamma(\beta_{p})} \frac{\xi_{q}^{2}}{\Gamma(x_{q})\Gamma(\beta_{q})}\frac{\rho\sqrt{\rho \overline{\gamma}_{JD}}\xi_{q}^{2}}{2\Gamma(x_{q})\Gamma(\beta_{q})}\\ &\times\bigg{[}1-\exp\left(-\frac{\phi_{pda}^{2}}{2\sigma_{pda}^{2}} \right)\bigg{]}\frac{\alpha_{p}\beta_{q}\xi_{q}^{2}}{A_{0j}\Gamma(x_{q}) \Gamma(\beta_{j})}\\ &\times\mathcal{G}_{9,9}^{7,6}\left(\begin{array}{c}\alpha_{p}\beta_{ j}A_{0p}\sqrt{\rho\overline{\gamma}_{JD}}\\ \alpha_{q}\beta_{p}\alpha_{p}\beta_{p}\end{array}\right)\frac{\Theta_{1},\Theta_{2}}{ \Theta_{3},\Theta_{4}}\end{split} \tag{27}\]  
where \(\Theta_{1}=-\xi_{p}^{2},-\alpha_{p},-\beta_{p},-\xi_{q}^{2}\), \(\Theta_{2}=-\alpha_{q},-\beta_{q},\xi_{q}^{2},\xi_{q}^{2},0\), \(\Theta_{3}=-1,\xi_{q}^{2}-1,\alpha_{q}-1\), \(\beta_{q}-1,\xi_{q}^{2}-1\), and \(\Theta_{4}=\alpha_{f}-1,\beta_{f}-1,-1-\xi_{p}^{2},-1-\xi_{q}^{2}\).  
When the jamming is idle, the system and channel models in scenario 1 and scenario 2 are identical. Therefore, the BER between scenario 2 and scenario 1 is the same when the jamming is idle. Consequently, we can obtain the ABER in scenario 2 as follows  
\[\overline{P}_{e2a}=\rho P_{e2a}\left(\overline{\gamma}_{ID}\right)+(1-\rho)P_{ e1i}(\overline{\gamma}_{D}) \tag{28}\]
## 3 Results and Discussion
### Surface Resistivity  
In light of the study by [PERSON] et al., the electrostatic charge of electret exhibits stability that is dependent on the charge storage of particles, space charge of polymers, and the conductivity, the latter of which may also affect the storage of electric charges [21]. The relationship between the TiO\({}_{2}\) ratio and the surface resistivity is shown in Table 3. An increase in TiO\({}_{2}\) adversely affects the surface resistivity of melt-blown fabrics. Namely, a lower surface resistivity means equivalently a greater electrical conductivity, which dissipates electric charges efficiently. Subsequently, with 4 wt% of TiO\({}_{2}\), the surface resistivity of melt-blown fabrics descends in a short time and eventually resembles that of melt-blown fabrics consisting of 1 wt% of TiO\({}_{2}\) as per Figure 3c. In addition, [PERSON] et al. found that significant variations were not presented until TiO\({}_{2}\) exceeded 5 wt% [22]. By contrast, in this study, 1 wt% of TiO\({}_{2}\) instantly causes a difference, which is ascribed to the difference in the membrane and fiber structures. The electric charges of membranes are freely transmitted along any direction, whereas the electric charges of a fibrous structure are only allowed to be transmitted along the fiber direction (i.e., the direction that is perpendicular to the fiber diameter). Subsequently, melt-blown fabrics exhibit a difference in the surface resistivity when the TiO\({}_{2}\) ratio is low.
## 4 30 cm Model Cavity Simulation  
### Electromagnetic Field Simulation  
We plan to construct a 30-centimeter-long cavity for a vertical test, aiming to measure the quality factor \(Q\) of the cavity under SC conditions at different stored energies. This test is essential to validate the performance of the SC RFQ cavity after surface treatments, cooling, and evacuation. The model cavity is depicted in Figure 12. On the one hand, the operating frequency and EM field mode of the RFQ are affected by the cavity's dimensions. As long as we choose the right size, the 30 cm cavity can produce the same resonant frequency and mode as the full-length cavity [38]; on the other hand, we have selected a section of the electrodes with \(m\) near its average value for the electrodes of this 30 cm cavity. As a result, the performance of this short cavity is basically consistent with that of the full-length cavity.  
The simulated cavity frequency given by CST MWS is 162.52 MHz (refer to Figure 13), and the calculated normalized \(E_{sp}\) is 53.13 MV/m, which is close to the simulated values of the full-length cavity. However, the \(B_{sp}\) is 47.29 mT, which is higher than the simulated value of the full-length cavity. This discrepancy can be attributed to the emergence of the peak surface magnetic field at the undercut location, as indicated by the red arrow in Figure 14. The size of the undercut in the short cavity differs from that of the long cavity, resulting in inconsistent magnetic field values.  
Figure 11: The EM field mode of the cavity: (**a**) electric field; (**b**) magnetic field.  
Figure 12: The 30 cm testing cavity: (**a**) closed-cavity model; (**b**) the structure of the vanes.  
While the electromagnetic (EM) field values of this 30 cm cavity exhibit slight variations from those of the full-length cavity, their excitation mode and distributions remain consistent. Importantly, all peak values fall within the established safe limits. This proves that the results obtained from the vertical test of this cavity can also demonstrate the performance of the full-length cavity.  
Figure 14: The magnetic field distribution of the 30 cm cavity.  
Figure 13: The simulated operating frequency and electric field distribution of the 30 cm cavity.
## 5 Analytical Methods
### Other Techniques
[91] for the detection of 4(5)MEI. The researchers fabricated THz metamaterial with a metal array, using an electric-field-coupled inductor-capacitor resonator structure, and a finite-difference time-domain simulation. The THz spectrum of 4(5)MEI was measured in the frequency range of 0.2-2 THz, utilizing the THz transmission mode spectroscopy at concentrations of 0-20 mg L\({}^{-1}\). This technique was able to detect the presence of 4(5)MEI at a few mg L\({}^{-1}\). Nevertheless, the researchers concluded that the sensitivity of the metamaterial must be improved by the enhancement of the interactions among the over-layered materials or using a higher sharp resonant peak. [PERSON] et al. [59] developed a fluorescent probe based on carbon dots, which were prepared by ultrasound in a metallic deep eutectic solvent. This method was implemented for the detection of 4(5)MEI in soft drinks. The synthesis of carbon dots-chelated metals was performed in situ in the presence of 4(5)MEI, which enables a kinetically fluorescence emission, thereby avoiding the multi-step analysis. The LOQ of the method was 0.6 ng mL\({}^{-1}\) and the recoveries were in the range of 89-108%. This fluorescent probe along with the previously described method of triple quadrupole paper spray mass spectrometry provided the most promising results compared to the other methods. Therefore, it can be considered more effective for the analysis of 4(5)MEI.