esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis
### Markov Processes (Uncertainty Modeling)
#### 4.1.2 Transition Probabilities  
The transition probabilities represent the likelihood of moving from one state to another in a given time frame. These probabilities are derived from historical data and real-time observations as shown in Table 4. Below are the transition probabilities for the different states:  
Figure 2 shows cargo movement probabilities between key states: Port, Transit, Destination, and Delayed. Arrows indicate transitions with probabilities, highlighting major flows and potential bottlenecks.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**From State** & **To State** & **Transition Probability** & **Interpretation** \\ \hline \(S_{1}\) & \(S_{2}\) & \(P(S_{1}\to S_{2})=0.70\) & 70\% chance that cargo will be dispatched from the port to transit. \\ \hline \(S_{1}\) & \(S_{3}\) & \(P(S_{1}\to S_{3})=0.20\) & 20\% chance of delay at port due to congestion or equipment failure. \\ \hline \(S_{1}\) & \(S_{1}\) & \(P(S_{1}\to S_{1})=0.10\) & 10\% chance that cargo remains at port for another time period. \\ \hline \(S_{2}\) & \(S_{3}\) & \(P(S_{2}\to S_{3})=0.10\) & 10\% chance that cargo in transit gets delayed due to road/rail issues. \\ \hline \(S_{2}\) & \(S_{4}\) & \(P(S_{2}\to S_{4})=0.80\) & 80\% chance that cargo in transit reaches its destination on time. \\ \hline \(S_{2}\) & \(S_{2}\) & \(P(S_{2}\to S_{2})=0.20\) & 10\% chance that the cargo continues in transit. \\ \hline \(S_{3}\) & \(S_{4}\) & \(P(S_{3}\to S_{4})=0.50\) & 50\% chance that delayed cargo gets resolved and reaches the destination. \\ \hline \(S_{3}\) & \(S_{3}\) & \(P(S_{3}\to S_{3})=0.40\) & 40\% chance that cargo remains delayed for another time period. \\ \hline \(S_{3}\) & \(S_{1}\) & \(P(S_{3}\to S_{1})=0.10\) & 10\% chance of returning to port (e.g., rerouting or returning shipment). \\ \hline \hline \end{tabular}
\end{table}
Table 4: Probabilities for the different states.  
Figure 2: A cargo movement transition diagram.
# Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center
## 4 Proposed Multi-Level Traffic Load Balancing MDTLB  
### Basic Concept  
The existing Hermes applies the thresholds and parameters to depict the path, identify the best possible path through the confirmation of the lower round trip time (RTT) measurement, and lower the rate of congestion mismatch. Hence, the congested path can be identified while there are higher RTT and ECN. It is obvious that the higher RTT can be lowered due to the stack latency of the network, and the higher ECN fraction comes from some of the inaccurate samples. Else, the path is stated to be gray in all of the other cases. This characterization approach is sensitive to the parameters setting. It causes a non-steady performance that may increase the congestion mismatch effect on the performance instead of decreasing it due to the wrong threshold values. However, a multi-level path judging with dynamic thresholds-based heuristic characterization of the path is more realistic regarding reducing a congestion mismatch issue than a simple threshold-based approach.
## 5 Results and Discussion
### Isotherms
At higher values of \(\widetilde{\text{Ri}}\), the \(\widetilde{\text{Ha}}\) effect attempts to create isotherms in a parabolic shape around the base wall, which results in a reduced temperature distribution in the transverse direction due to increased heat transfer in that direction. As \(\widetilde{\Phi}\) increases for all values of \(\widetilde{\text{Ha}}\) and \(\widetilde{\text{Ri}}\), the percentage of reduction in maximum temperature inside the cavity also increases. The effect of \(\widetilde{\Phi}\) becomes more remarkable as \(\widetilde{\text{Ri}}\) increases. At lower \(\widetilde{\Phi}\) values, the percentage of decrease in maximum temperature value due to adding more nanoparticles is greater than at higher \(\widetilde{\Phi}\) values, as shown in the following equation:  
\[\text{  
_Case-2:_ The same effect of case-1 is noted here for all values of \(\widetilde{\mathrm{Ri}}\) and \(\widetilde{\Phi}\). However, when \(\widetilde{\mathrm{Ri}}\) is large (i.e., \(\widetilde{\mathrm{Ri}}=100\)), increasing \(\widetilde{\mathrm{Ha}}\) from 0 to 75 does not have a noticeable impact on the isotherm values. The isotherm shapes are non-symmetrical due to the non-symmetrical boundary conditions with Y-axis as shown in Figure 4. As mentioned, the movement of the right-moving wall in the negative direction helps to spread the isotherms in the Y-direction more than the left-moving wall. The non-symmetrical isotherms are more remarkable at low values of \(\widetilde{\mathrm{Ri}}\). At \(\widetilde{\mathrm{Ri}}=100\) and \(\widetilde{\mathrm{Ha}}=75\), the isotherms tend to be symmetrical due to increased fluid movement and \(\widetilde{\mathrm{Ha}}\) tends to distribute the heat in the transverse direction. The effect of \(\widetilde{\mathrm{Ha}}\) on the isotherm shape is significant for all values of \(\widetilde{\mathrm{Ri}}\) and \(\widetilde{\Phi}\) (Figure 4). At \(\widetilde{\mathrm{Ri}}=1\), the isotherms are concentrated near the right wall and the heat sources, resulting in an unsymmetrical shape. However, a symmetrical shape of the isotherms is observed at high values of \(\widetilde{\mathrm{Ha}}=75\) and \(\widetilde{\mathrm{Ri}}=100\). Table 11 points out the percentage change in maximum isotherms for some cases due to an increase in \(\widetilde{\Phi}\) and \(\widetilde{\mathrm{Ha}}\).
# Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems
## 1 Introduction
Hence, we tune the weight of the WA through driving condition analysis to adapt to the dynamic conditions including the center of gravity and inertia. The driving condition is divided into tracking and braking. Between them, data of the braking has different patterns from the data seen during tracking because wheel slip happens frequently during braking. If we ignore the characteristics of data with different patterns representing these conditions and a single static weight with a constant value is applied to the braking condition in order to estimate the speed, our speed estimation yield the unexpected and inaccurate results. Furthermore, the front wheels operate differently from the rear wheels during braking. In order to explore the characteristics of the front and rear wheels while braking and tracking, we compare each Mean Squared Error (MSE) value of the pairs when the weights with a certain value are given on individual pairs of the front wheels and the rear wheels, respectively. According to these MSE values, each weight is determined for the front wheels and the rear wheels during braking. The determined different weights by the defined classes and divided conditions are used to estimate vehicle speeds. Although this paper concentrates on attacks on wheel speed sensors to enhance the safety of the automotive CPS, we aim to provide a diagnosis system and present direction of attack detection and data recovery methods in all of the industry so that a controller can receive exact information from sensors and control an actuator stably for safety and security. The contributions of this paper by solving the two research issues above are as follows. 1. In order to detect and identify attacks that simultaneously happen on the majority of sensors, we present how RNN learns and predicts data obtained from a real vehicle. Each state of the sensors is defined as 15 classes and an architecture is designed by repeated experiments. 2. We propose a speed estimation method based on modified WA using data of only normal sensors. Each driving situation is analyzed to increase the performance of speed estimation. We divide the situations into traction and braking and provide how to assign different weights depending on the driving situations and classes in automotive CPS. 3. There are very few studies to address both attack detection and identification and vehicle speed estimation at the same time. In this work, the process of attack detection is connected to that of vehicle speed estimation to satisfy data recovery property. The remainder of the paper is organized as follows. In Section 2, we provide the information about sensor data and describe how sensor attack detection system is developed. Section 3 puts forward how to estimate vehicle speeds by applying some algorithms. Our system is validated and the results are analyzed in Section 4. Finally, we draw conclusions and discuss future works in Section 5.
## 1 Introduction
### Motivation and Related Work
Our results indicate that the required optical power can vary significantly over an orbital period in links between satellites in adjacent orbits. These calculations are useful in evaluating adaptive optical satellite network strategies, where link parameters such as the transmission power are dynamically adjusted. Such adaptive approaches can lead to significant savings in energy consumption, minimize thermal stress, and potentially prolong the operational lifetime [27]. The model's source code is publicly available [28] on the web to foster collaboration and facilitate the utilization of our research outcomes within the scientific community. The model is easily extendable to a wide selection of constellation types, modulation formats, and detection schemes. The rest of the paper is organized as follows: in Section 2, we start by outlining how to account for constellation dynamics. In addition, we discuss how the initial satellite position can be optimized in order to minimize the chance of collisions. We also outline the satellite connectivity requirements assumed in our work. Section 3 describes the link budget model adopted to estimate physical layer performance. We discuss channel gain and how beam divergence can be optimized in the presence of pointing errors. We also calculate the noise power estimation, the bit error rate (BER), and the receiver sensitivity. Appendix A discusses some implementation details of the Python module. Section 4 presents the results obtained from our model, emphasizing the estimated power requirements for various constellation architectures. Some concluding remarks are given in Section 5.
## 3 Results and Discussion
### Magnetic Structure of a-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\)

Table 4: Calculated total energy \(E_{tot}\) (eV per primitive cell), total magnetic moment \(M_{tot}\) (Bohr magnetons \(\mu_{\text{B}}\) per primitive cell), and local magnetic moments \(M_{\text{Cu}}\), \(M_{\text{P}}\), and \(M_{\text{O}}\) (\(\mu_{\text{B}}\)) on the respective atoms in \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\). Previous theoretical data are also quoted for comparison [23]. _Ab initio_ determination of mechanical properties of anisotropic crystalline materials is a formidable multi-scale problem. In this connection, it is common to make simplified qualitative judgments about the mechanical properties of materials using empirical relationships formulated in terms of directionally-averaged (polycrystalline) elastic moduli such as bulk modulus (\(B\)) and shear modulus (\(G\)). In our work, the elastic moduli of \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) have been calculated using the Voigt-Reuss-Hill theory [52; 53; 54]. More details are presented in the Supporting information. The average Young's modulus (\(Y\)) and Poisson ratio (\(\
u\)) are calculated through \(Y=9 BG/(3B+G)\) and \(\
u=(3B/2G)/[2(3B+G)]\). The calculated averaged moduli and other indicators of mechanical properties are listed in Table 6. The averaged bulk modulus measures the resistance of a polycrystalline material to a volume change. The calculated Voigt-, Reuss-, and Hill-averaged bulk moduli \(B\) are 93.298 GPa, 80.624 GPa, and 86.961 GPa, respectively. Thus, \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) has a relatively weak volume deformation resistance, which is consistent with the conclusions from the single-crystal elastic constants. The averaged shear modulus \(G\) reflects the shear deformation resistance of a polycrystalline material. The obtained values of Voigt-, Reuss-, and Hill-averaged shear moduli are close to each other, around 35 GPa, which shows that \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) has relatively weak shear deformation resistance. The calculated averaged Young's moduli are 120.468 GPa, 80.112 GPa, and 100.533 GPa, indicating low elastic stiffness. In addition, the \(B/G\) ratio is a common parameter to judge the ductility of a material [55; 56]. If the value of \(B/G\) is larger than 1.75, the material is expected to exhibit ductile behavior; otherwise, the material is expected to exhibit brittle behavior. It can be seen that the calculated \(B/G\) ratio for \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) is greater than 1.75, indicating that the material should be ductile. [PERSON]'s ratio is another indicator of the ductility of a material, predicting the material to exhibit ductile behavior if \(\
u>0.26\). The obtained values of \(\
u\) indicate that \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) should be ductile, in agreement with the analysis based on \(B/G\) ratio.
## 2 Concept of Operations
### Robustness and Integrity  
As an early initiative to develop low-latency and discreet clock data transfer, the shift toward a non-dedicated pLEOT time scale enhances programmability and flexibility. This involves the efficient allocation of radio resources for communication and clock data transports and the superimposition of both communication and clock signal energies. Achieving the desired integrity, robustness, and security properties for the ICaC signal, as described in (1), requires careful power allocation. This approach ensures that the proposed ICaC paradigm remains transparent and unobtrusive to the orbital platform equipment that may be unaware of its operation.  
To achieve deep integration of communication and clock radio functions at the physical layer, the local agent \(i\) located at the \(i\)th orbital platform asset in the non-dedicated pLEOT constellation will independently and autonomously respond to transient power allocations for communication and clock data transfer requirements.  
In the framework of dynamic power allocation based on integral control, the allocated powers \(p^{i}_{c}(k+1)\) and \(p^{j}_{clk}(k+1)\) for both communication and clock data transfer services at the time instant \(k+1\) will be adjusted in proportion to the current shares \(\alpha^{i}_{c}(k)\) and \(\beta^{i}_{clk}(k)\), along with the adjustment amounts \(u^{i}_{c}(k)\) and \(u^{i}_{clk}(k)\) for communication rates and clock accuracy levels  
\[p^{i}_{c}(k+1) =(\alpha^{i}_{c}(k)+u^{i}_{c}(k))p^{i}_{total}\] \[=p^{i}_{c}(k)+p^{i}_{total}u^{i}_{c}(k) \tag{6}\]  
provided that \(p^{i}_{c}(k)=\alpha^{i}_{c}(k)P^{i}_{total}\) and \(\alpha^{i}_{c}(k)+\beta^{i}_{clk}(k)=1\) for \(0<\alpha^{i}_{c}(k),\beta^{i}_{clk}(k)<1\).  
Similarly, it follows that  
\[p^{i}_{clk}(k+1) =(\beta^{i}_{clk}(k)+u^{i}_{clk}(k))p^{i}_{total}\] \[=p^{i}_{clk}(k)+P^{i}_{total}u^{i}_{clk}(k)\,, \tag{7}\]  
where \(p^{i}_{clk}(k)=\beta^{i}_{clk}(k)P^{i}_{total}\) and \(P^{i}_{total}\) is the total power available at the \(i\)th-orbital platform.  
Considering Equations (6) and (7), it seems feasible that the powers of received signals can be customized for both communication and clock data service solutions. The adjustment of interoperable CN0 at the \(i\)th orbital platform is expressed as  
\[CN0^{i}_{c}(k+1)=CN0^{i}_{c}(k)+CN0^{i}_{total}u^{i}_{c}(k) \tag{8}\]  
and  
\[CN0^{i}_{clk}(k+1)=CN0^{i}_{clk}(k)+CN0^{i}_{total}u^{i}_{clk}(k)\,, \tag{9}\]  
where in the context of orbital platforms, \(CN0^{i}_{total}\) refers to the total power rate of the \(i\)-th orbital platform. It is important to note that \(CN0^{i}_{clk}(k)\) and \(CN0^{i}_{c}(k)\) are critical parameters for ensuring integrity and robustness in communications. These parameters contribute to reliable communication through sufficient CNR and facilitate unobtrusive clock data transfers with an appropriate CIR.
## 2 Analysis and Simulations of SEB Arrays for Probing Using RF Reflectometry
It is apparent that a larger gate capacitance increases the sensitivity because (a) the peak value of both resistive and reactive components of admittance \(Y\) in (2) are proportional to \(\alpha^{2}\), and (b) the larger \(C_{g}\) value results in stronger coupling to the gate and thus the values of the derivative \(dY/dV_{g}\) increase. From these, it is clear that for both cases (with large and small gate capacitances) the peak sensitivity scales proportional to \(N^{1/2}\). However, searching across a wider \(V_{g}\) range gives more of an opportunity to find a high sensitivity region, resulting in a higher sensitivity for all array sizes. For the smallest arrays, those less than around 5 SEBs, the scaling is proportional to \(N\) when measuring a wide \(V_{g}\) range. This indicates that for small arrays, it is likely to find a region where all \(N\) boxes converge in the \(V_{g}\) ranges investigated. The concept of a capacitance budget, a fixed total island capacitance that can be divided among junctions and gates, also provides a metric to compare SETs and SEBs. Figure 3e compares SEBAs of varying size to the SET with the same 42.9 aF total capacitance budget (\(C_{j}~{}=~{}20\) aF for both junctions of the SET, \(C_{g}=2.9\) aF). Blue horizontal dashed line indicates the value of \(d|Y|dV_{g}\) for such SET biased at a maximum sensitivity point. From this we conclude that optimized SEBAs could compete with SETs for \(N>8\). However, if average admittance of the array needs to approach 1/\(Z_{0}\) (similar to [21]), respectively larger number of SEBs in the array (\(>\)1000) will be required.
## 3 Numerical Simulation and Its Verification
### Grid Generation and Independency  
The fluid computational domain was divided into structured quadrilateral elements using ICEM. Three grid quality levels, coarse (15,664), medium (62,199), and fine (127,093) were used to identify the grid dependency by simulating the Mach number along the steam ejector central axis. The results of grid independence are shown in Figure 3.  
The Mach number of the medium mesh was similar to that of the fine grid, but there was a large difference between medium grid and the coarse grid. Finally, the medium grid was selected to balance the calculation time and accuracy. In addition, mesh self-adapting technology based on the velocity gradient was implemented to capture more detailed internal flow characteristics and is illustrated in Figure 4. After the mesh self-adaptation, the total grid number was 104,327.
## 3 Results
### AI and TI  
The Atherogenicity Index (AI) serves to quantify the potential of dietary fatty acids to promote atherogenesis by comparing specific saturated fatty acids (SFAs) with unsaturated fatty acids (UFAs). Stearic acid (C18:0) is excluded from this index due to its metabolic conversion into oleic acid in the human body, which renders it non-atherogenic. In contrast, lauric (C12:0), myristic (C14:0), and palmitic (C16:0) are recognized for enhancing lipid accumulation in vascular and immune cells, fostering plaque formation, and diminishing levels of phospholipids and esterified fatty acids. Compared to broader indices like the PUFA/SFA ratio, AI provides a more targeted and refined measure of a food's atherogenic potential.  
The Thrombogenicity Index (TI) evaluates the tendency of fatty acids to contribute to thrombus (blood clot) formation. It classifies fatty acids based on their derived eicosanoid activity into pro-thrombogenic components--primarily C12:0, C14:0, and C16:0--and anti-thrombogenic agents such as monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs), particularly from the n-3 (omega-3) and n-6 (omega-6) series. However, emerging research suggests that certain n-6 PUFAs may also play a role in promoting thrombogenesis, calling into question their exclusively protective classification.  
Figure 3 shows that values obtained for AI and TI in octopus are very low, indicating positive effects on health. In our hands, AI-obtained value is lower than 0.5 and TI is lower than 0.01. There were no significant differences in any case.
# Economies of Scale and Efficiency in the Wastewater Treatment Sector: A Decision Tree Approach
## 4 Results
### Potential Operational Cost Savings in WWTP by Group
and has an annual flow of 165,973 m\({}^{3}\) at a cost of EUR 0.75/m\({}^{3}\). The rest of the facilities have similar characteristics; however, by sharing services, they could achieve lower operational costs, around EUR 0.320/m\({}^{3}\), which is the cost associated with larger facilities (Group 2). This represents a potential saving of more than 50%. This example can be extended to larger facilities (Group 2). Table 8 shows four facilities that treat between 10,000 and 20,000 P.E., with operational costs between EUR 0.30 and 0.41/m\({}^{3}\). The total costs amount to EUR 2,405,650/year, showing the potential to share services, integrating these facilities into a larger WWTP (Group 3). This situation would achieve lower operational costs on average, of approximately EUR 0.276/m\({}^{3}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & **Current** & & **Potential** & **Potential** \\
**WWTP L.D.** & **P.E.** & **Flow Treated** & **Operational** & **Total Costs** & **Operational** & **Operational** \\
**(m\({}^{3}\)/Annual)** & **Costs** & **(EUR/m\({}^{3}\))** & **(EUR/Year)** & **(EUR/m\({}^{3}\))** & **(EUR/Year)** \\ \hline
1 & 650 & 57,786 & 0.81 & 46,807 & & 18,492 \\
2 & 652 & 165,973 & 0.75 & 124,480 & & 53,111 \\
3 & 655 & 76,240 & 0.78 & 59,475 & & 24,397 \\
4 & 674 & 50,005 & 0.65 & 32,503 & & 16,002 \\
5 & 683 & 63,721 & 0.81 & 51,614 & & 20,391 \\
6 & 719 & 199,265 & 0.71 & 141,478 & & 63,765 \\
7 & 754 & 88,260 & 0.69 & 60,899 & & 28,243 \\
8 & 787 & 76,143 & 1.15 & 87,564 & & 24,366 \\
9 & 789 & 104,157 & 0.77 & 80,201 & & 33,330 \\
10 & 790 & 115,293 & 0.69 & 79,552 & & 36,894 \\
11 & 868 & 79,247 & 0.85 & 67,360 & & 25,359 \\
12 & 872 & 114,875 & 0.63 & 72,371 & & 36,760 \\ \hline  & & & & 904,305 & & 369,708 \\ \hline \end{tabular}
\end{table} 
Table 7: Potential cost savings for Group 1. WWTP 68 treats a total of 732,958 m\({}^{3}\) annually, with associated costs of EUR 0.30/m\({}^{3}\); the total annual costs amount to EUR 219,887. WWTP 69 treats a total of 1,573,368 m\({}^{3}\) with an operational cost of EUR 0.31/m\({}^{3}\). Similarly, WWTPs 70 and 71 incur costs of EUR 0.41/m\({}^{3}\) and EUR 0.37/m\({}^{3}\), respectively. The possibility of integrating these services into a larger facility (Group 3) would achieve potential savings ranging between 8% and 32%, depending on the WWTP evaluated, placing the cost at around EUR 0.276/m\({}^{3}\). In this regard, a larger facility that consolidates geographically close populations would reduce the operating costs of smaller WWTPs. The economic quantification of these benefits allows decision makers to plan new investments while considering their cost-related impact. However, with any investment aimed at centralizing services, the construction of pipelines to transport wastewater to a single point must be considered. In this context, geographic proximity can justify such investments, prioritizing areas that require lower investment costs.