esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Results Achieved  
After the application of all stages of ELECTRE-MOr, with the support of the ELECTRE-MOr Web software, optimistic and pessimistic distributions were obtained for the two forms of standardization (bh and bn), for a cut-off level of 0.8, chosen in conjunction with the experts for presenting good discrimination in the classification of alternatives (Table 11).  
The results after the application of the method present four classifications, which allow for a transparent and robust analysis [70]. For the grouping of alternatives, aiming to obtain the categorization of the officers into three predefined classes, we used the following criterion:  
An alternative that presents a result compatible with a particular class in three or four scenarios is categorized in this class; an alternative that presents a specific classification in two of the four scenarios will be categorized into the lower class.  
The OF1 alternative will be used in class B because it presents a performance in three of the four scenarios. The OF2 alternative will be answered in class B since it presents two classifications corresponding to class A and two to class B, which is the worst classification presented by the alternative. Table 12 shows the consolidated classifications for a \(\lambda\) value of 0.8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Lambda**} \\ \hline  & \multicolumn{3}{c}{**0.8**} \\ \hline  & \multicolumn{3}{c}{**Bh**} & \multicolumn{3}{c}{**Bn**} \\ \hline  & **Pessimist** & **Optimistic** & **Pessimist** & **Optimistic** & **Consolidated** \\ \hline OF1 & C & B & B & B & B \\ OF2 & B & A & B & A & B \\ OF3 & B & A & B & A & B \\ OF4 & B & A & B & A & B \\ OF5 & C & B & C & A & C \\ OF6 & B & A & B & A & B \\ OF7 & C & B & B & B & B \\ OF8 & B & A & B & A & B \\ OF9 & C & A & B & A & A \\ OF10 & C & A & B & A & A \\ OF11 & B & A & B & A & B \\ OF12 & C & A & C & A & C \\ OF13 & C & A & C & B & C \\ OF14 & B & A & B & A & B \\ OF15 & C & A & B & B & B \\ OF16 & C & B & C & A & C \\ OF17 & C & A & B & A & A \\ OF18 & C & A & C & A & C \\ OF12 & B & A & B & A & B \\ OF20 & C & A & B & A & A \\ OF21 & C & A & B & B & B \\ OF22 & C & A & C & B & C \\ OF23 & C & A & B & A & A \\ OF24 & B & A & B & A & B \\ OF25 & B & A & B & A & B \\ OF25 & C & A & B & A & A \\ OF27 & C & A & C & A & C \\ OF28 & B & A & B & A & B \\ OF22 & C & A & C & B & C \\ OF30 & C & B & B & B & B \\ \hline \hline \end{tabular}
\end{table}
Table 11: Result obtained after application of ELECTRE-MOr.  
Analyzing the results, we observed that only 6 of the 30 (20%) officers evaluated presented class A performance, which corresponds to promotion by deserving. Class B has 16 officers (about 53% of the alternatives) who would gain promotion for service time. In comparison, according to the proposed modeling, eight officers (or 26.7%) had the worst possible performance and would not be promoted.  
We emphasize that it is possible to approach the results differently, analyzing only one way to obtain the classes (bh or bn) at the DM's discretion.
## 2 Materials and Methods
### Experimental Setup and Procedures  
The schematic of the system experiment is shown in Figure 3. The physical setting is shown in Figure 4. The flat-plate collector was mounted on the stand, inclined at 4.5\({}^{\circ}\) from horizontal, facing south, corresponding to the location of the test (Seri Iskandar, Malaysia 4.3590\({}^{\circ}\) N, 100.9849\({}^{\circ}\) E), thus focusing on direct solar irradiance at nontime. The system utilized a closed-loop water circulation with an uninsulated 16 L storage tank, powered by a 0.5 hp centrifugal pump. Two valves, at the inlet and outlet of the water tank, were used to maintain a mass flow rate in the range of 0.00318-0.00345 kg\(\cdot\)s\({}^{-1}\) which is consequently measured via flowmeters connected to the inlet and outlet of the flat-plate collector. Initially, the RSioaf tubes were filled with water and connected to the pump and storage tank. As the pump was powered, water flowed from the collector into the pump suction and was discharged towards the storage tank.  
Figure 4: Physical setup of the system.  
Figure 3: Schematic diagram of the experimental setup.  
Solar irradiance data were measured at every 1 min interval using a photodiode pyranometer. The inlet and outlet water temperatures as well as the ambient temperature were simultaneously monitored by k-type thermocouples. The volume flow rate of the water was measured by turbine flowmeters (OMEGA model FLT-40/C/05/W/S/N, Signapore) at the thermal collector inlet and exit. The entire data acquisition process was overseen using a Hioki MEMORY HiLOGGER LR8431 (Ueda, Japan) data logger and subsequently processed on a desktop computer. The experimental period, which took place under partially cloudy skies, spanned from 9 a.m. to 5 p.m. in April 2023 at Universiti Teknologi PETRONAS Solar Research Park. The data were averaged for each hour, reported and analyzed accordingly. Throughout the experiment, the tropical climate exhibited solar irradiance levels ranging between 105 and 1004 W\(\cdot\)m\({}^{-2}\), with ambient air temperatures fluctuating between 27-36 \({}^{\circ}\)C, as illustrated in Figure 5. The ambient temperature remained relatively high even during periods of lower irradiance in the afternoon, attributable to significant cloud shading during those intervals.
# Non-Compliant Behaviour of Automated Vehicles in a Mixed Traffic Environment
## 3 Implementation of Automated Driving Functions in Microscopic Simulation
### Non-Compliant Trajectories  
Automated vehicles must strictly comply with the relevant standards and regulations for automated driving tasks to meet safety and legal requirements in all traffic conditions. The standard for adaptive cruise control systems is ISO 15622, which sets limits on acceleration and deceleration to maintain a safe distance to leading vehicles. As a result, automated driving functions require large gaps for lane-change maneuvers or must decelerate immediately in merging situations to guarantee safety. This impacts traffic flow negatively and makes dense traffic situations challenging for automated vehicles. Therefore, we address this problem by introducing a new concept called **non-compliant trajectories**. The purpose of non-compliant trajectories, is to facilitate the merging process for automated vehicles and to maintain the traffic flow in mixed traffic situations.  
Figure 5: Illustration of case 4.  
**Definition 1**.: _A trajectory is non-compliant, when the distance to a leading vehicle, based on \(\tau_{min}\) and the clearance minimum \(c_{min}\) from ISO 15622, falls below the safe distance \(d_{safe}\) for a certain period of time \(T_{max,nc}\)._
## 4 Vehicle Test
### Vehicle Test
#### 4.2.2 Simulation Test Analysis  
In order to verify the accuracy of the simulation model, the simulation model uses the same conditions as the real vehicle test. The accuracy of the simulation model is verified by comparing the data of the simulation and the real vehicle test. The yaw angle, sideslip angle and hinge angle of the train are selected for comparison. The comparison between the real vehicle test and the simulation model data is shown in Figure 6.  
Figure 6 shows the comparison between the actual test data and the simulation data for the semi-trailer train. At 0-3 s, when the train is running in a straight line state, the simulation and test data only show small changes over time. At 3 s, the driver turns the steering wheel and the train turns. The data of the simulation and the test begin to change significantly. The yaw angle of tractor and semi-trailer increases approximately linearly with time. The lateral acceleration of the tractor has a step change. Due to the interaction between the tractor and the trailer, the lateral acceleration of the tractor reaches the maximum and then fluctuates irregularly. After the fluctuation, the lateral acceleration of the tractor tends to be stable. Due to the influence of inertia, the lateral acceleration of the trailer changes more smoothly than that of the tractor. The jackknife angle of the two  
Figure 6: Comparison of real vehicle test and simulation data of semi-trailer train running on curve. (**a**) Tractor yaw angle comparison. (**b**) Trailer yaw angle comparison. (**c**) Tractor lateral acceleration comparison (**d**) Trailer lateral acceleration comparison. (**e**) Jackknife angle comparison.  
vehicle increases rapidly after the train turns. When the train is in the steady state of the curve, the jackknife angle of the two vehicles tends to be stable. From the above simulation and real vehicle test data, it can be seen that, when the same input is given to the simulation model and the real vehicle test, although the output of the two has some differences in response and data, the overall trend and range of change are in good agreement. The minimum deviation of the yaw angle of the tractor is 2.66%, and the maximum deviation of the jackknife angle is 12.48%. This shows that the established simulation model can reflect the motion characteristics of the real vehicle and can provide reliable support and a theoretical basis for the following research.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 2 The Navigation Filter
### Observation Model
#### 2.2.3 Update  
Equations (16) and (21) allow us to express the observation errors \(\Delta\mathbf{z}=\left[\Delta\hat{\mathbf{\rho}}^{l},\ \Delta\mathbf{B}^{l}\right]^{T}\) as a linear function of the error state \(\delta\mathbf{x}\) as  
\[\Delta\mathbf{z}\cong\mathbf{H}\delta\mathbf{x}+\mathbf{\eta}_{\mathbf{z}} \tag{22}\]  
In (22), \(\mathbf{H}\) is the observation model matrix given by  
\[\mathbf{H}=\left(\begin{array}{ccccc}\mathbf{h}_{\mathbf{r}}&\mathbf{h}_{\mathbf{v}}&\mathbf{0}_{1, 3}&\mathbf{0}_{1,3}&\mathbf{0}_{1,3}&\mathbf{h}_{c,\delta t}&1\\ \mathbf{0}_{3,3}&\mathbf{0}_{3,3}&\left(\widetilde{\mathbf{B}}^{l}\times\right)&\mathbf{0}_{3,3}&\mathbf{0}_{3,3}&\mathbf{0}_{3,1}&\mathbf{0}_{3,1}\end{array}\right) \tag{23}\]  
and \(\mathbf{\eta}_{\mathbf{z}}\) is the observation model noise given by  
\[\mathbf{\eta}_{\mathbf{z}}=\left[\eta_{\hat{\rho}^{l}},\mathbf{R}_{b}^{l}\mathbf{\eta}_{\mathbf{B }}\right]^{T} \tag{24}\]  
The noise covariance, \(\mathbf{R}=cov(\mathbf{\eta}_{\mathbf{z}})\), is a diagonal matrix formed by the variance of the Doppler shift noise and the variances of the three-axis magnetometer noises.  
The predicted state \(\mathbf{x}\) and the covariance matrix \(\mathbf{P}\) are then updated as follows:  
\[\begin{array}{l}\mathbf{x}=\mathbf{x}+\mathbf{K}\Delta\mathbf{z}\\ \mathbf{P}=\mathbf{P}-\mathbf{K}\mathbf{H}\mathbf{P}\end{array} \tag{25}\]  
where \(\mathbf{K}\) is the EKF gain given by \(\mathbf{K}=\mathbf{P}\mathbf{H}^{T}\big{(}\mathbf{H}\mathbf{P}\mathbf{H}^{T}+\mathbf{R}\big{)}^{-1}\).
## 7 Security Analysis
### Attack Analysis
#### 7.2.1 Key Disclosure  
Key Disclosure (KD) is an attack where an internal or outsider attacker can in some way extract the secret variables stored in the tag. The current protocol uses asymmetric cryptography, therefore, the private key is never exposed and always kept protected in the SE.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Threat Type** & **Mitigation** \\ \hline Spoofing & The use of an HSM to generate and store the private keys of the parts ensures the impossibility of credentials spoofing. Furthermore, identification in the smart contract requires a public key of the SE for the correct functionality. These two facts together ensure the impossibility of cloning the parts. \\ \hline Tampering & As a result of the proposed scheme, the ownership of a package can only be modified by someone with physical access to the package. This ensures that no false ownership information about the part can be added to the ledger. Also, the data are stored in the blockchain, which prevents any malicious manipulation of the data once uploaded. \\ \hline Repudiation & All data uploaded to the smart contract are signed, and the decentralized system identifies every contributor before accepting the data. Finally, the peers of the blockchain themselves verify the presence of the real and unique part in the change in ownership process. This means that the information cannot be disputed once it is accepted in the blockchain. \\ \hline Information disclosure & The solution is implemented over a permissioned blockchain. This means that no entity outside the supply chain can access the data. \\ \hline Denial of Service & The SCM is based on blockchain with a decentralized peer-to-peer infrastructure. This architecture provides resilience by the design of the system against DoS attacks. \\ \hline Elevation of Privileges & The solution uses privacy-preserving blockchain, taking advantage of the private collections of Hypledger Fabric. With this solution, the stakeholders’ part of the supply chain cannot freely access all the information, only those that are relevant to them. Also, the ownership change of the asset requires physical access to the part itself, and the information can never be deleted, which avoids any stakeholder using its privileges to insert fake data in the blockchain. \\ \hline System misuse & In order to take digital ownership of the package, it is required to verify its authenticity. This operation forces the verification of the asset at every ownership change in the supply chain. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Threat analysis.
# Data Privacy and Ethical Considerations in Database Management
## 3 Best Practices
### Ethical Implications of Data Minimization  
While data minimization is generally considered a good privacy practice, it is important to consider its potential ethical implications:  
* Loss of context: Excessive data minimization might lead to a loss of important contextual information, potentially affecting the quality of data analysis or decision-making processes [16].
* Bias introduction: If not carefully implemented, data minimization could inadvertently introduce bias by selectively removing certain types of data [37].
* Balancing utility and privacy: There is an ongoing ethical challenge in finding the right balance between minimizing data for privacy protection and retaining enough data for meaningful analysis and use [17].  
DBAs and organizations must carefully consider these ethical implications when implementing data minimization strategies to ensure they protect privacy without compromising the utility and fairness of their data practices.
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
## 4 Parametric Analysis
### Effect of \(L\) for Fixed \(\Delta\) and SLA  
The simulations were carried out to study the effect of the number of antenna elements (\(L\)) with three different cases of \(\theta_{0}\) (0\({}^{\circ}\), 30\({}^{\circ}\) and 60\({}^{\circ}\)). \(\Delta\) was fixed at 0.15\({}^{\circ}\) and SLA was fixed at 21 dB. Figure 7(a) shows the results for \(L\) vs. HPBW. It was observed that the HPBW decreases almost linearly with an increase in \(L\) for all the values of \(\theta_{0}\). The HPBW also increased again if the \(\theta_{0}\) is increased from 0\({}^{\circ}\) to 60\({}^{\circ}\). Figure 7(a) shows the results for \(L\) vs. directivity. The increase in antenna elements linearly increased the directivity for all the cases of \(\theta_{0}\). The directivity for 60\({}^{\circ}\) is slightly lower compared to lower values of \(\theta_{0}\). Figure 7(b) shows the effect of increasing \(L\) on the PSLR and power loss. With an increase in \(L\), the PSLR remained almost flat for fewer antenna elements, beginning to decrease rapidly when the \(L\) exceed 22. A steep decaying variation was observed in the PSLR, from 20.85 to 20.3 dB with an increase in \(L\). Figure 7(b) also shows that, with increasing \(L\), the power loss increases somewhat slowly from 14 to 22 elements but increases rapidly afterwards. It is observed that the power loss is again higher at lower values of \(\theta_{0}\).  
The above analysis and Figures 6-8 show that increasing \(L\) is always beneficial in terms of HPBW and directivity, similar to SULA. However, increasing \(L\) in SAs of SCASS-C causes narrower main lobes, causing a smaller \(\Delta\) to be chosen to ensure reasonable overlapping of the main lobes. Therefore, \(\Delta\) should be chosen according to the widths of the SA1 and SA2 main lobes. For wider SA main lobes, \(\Delta\) can be configured in a wider range, while for  
Figure 7: Performance metrics of the SCASS-C for variable SLA, fixed \(\Delta\) =0.3\({}^{\circ}\) and \(L\) = 14.  
narrower main lobes, \(\Delta\) should be kept smaller. However, in any case, \(\Delta\) should not be set to a value so large that the grating lobes overlap. It is also noteworthy that the widths of SA main lobes are affected not only by a change in \(L\) but also by a change in SLA. Decreasing SLA means narrower SA main lobes causing a smaller \(\Delta\) to be selected and vice versa.
## 4 Experimental Verification
### Experimental Setup  
The experimental model was a steel benchmark structure similar to the numerical model, as presented in Figure 14. Additional details about this structure are provided as follows:  
* The height of each floor was 1.1 m, and the length and width of each floor were 1.5 and 1.1 m, respectively.
* The cross section of each column measured 0.15 m \(\times\) 0.025 m.
* L-shaped steel angles measuring 0.065 \(\times\) 0.065 \(\times\) 0.006 m\({}^{3}\) were installed on the structure along the weak axis for bracing.
* Blocks weighing 500 kg were added to each floor of the structure (Figure 15).  
Damage was simulated for two cases: without the braces and with the braces installed. The complete removal of the brace was only used to simulate the possible loss of stiffness. As the stiffness of the bracing is relative small compared to the total floor stiffness, it can represent a realistic damage scenario. The damage simulation is shown in Figure 16. This study set five destruction modes: undamaged, one-story damage, two-story damage, three-story damage, and multistory damage. The experiment was conducted at night to avoid any unexpected noise interference from the environment. Velocity data obtained under ambient vibration were extracted for each floor. A dataset was measured for 300 s, and the sampling rate was 200 Hz. A total of four datasets were recorded to reduce the possible variance between different measurements. As the ambient vibration can be continuously measured, stability and reliability of the proposed method can be largely improved. The velocity responses observed for each case were examined by the fast Fourier transform (FFT). The fundamental frequencies are presented in Table 12.  
Figure 13: Health index of damage on the high floor.  
Figure 14: A seven-story scale-down benchmark structure.  
Figure 15: Arrangement of velocity meter and mass block.
## 3 Results
### Linking PFAS to AOPs  
We reduced the data complexity by creating a network diagram (see Figure 3) to simplify the identification of links between PFAS exposure and metabolic outcomes and related biomarkers. Several effect biomarkers influenced by PFAS exposure were identified and were related with a value increasing the risk of metabolic disease, such as increased ALT levels, increased gamma-glutamyltransferase (GGT) levels, elevated uric acid levels, elevated TC, elevated TG, elevated LDL, elevated renal glucocorticoid receptor expression, elevated aryl hydrocarbon receptor (AhR) activation, increased reactive oxide species (ROS), or increase in intracellular calcium levels, in addition to decreased renal nephron endowment. Comparing the identified molecular events and AOs to the three AOPs, including (1) AOP72: epigenetic modification of PPARG leading to adipogenesis, (2) AOP149: peptide oxidation leading to hypertension, and (3) AOP226: SSRI (selective serotonin reuptake inhibitor) leading to hypertension, which are related to either obesity or hypertension, showed that some of our identified events matched well to citable AOPs (Figure 3). For instance, the activation of PPARY (KE1028 of AOP72) increased adipogenesis (KE1149 of AOP72), increased intracellular calcium (KE 1339 of AOP226), activated phospholipase C (PLC), or increased ROS, which are (potentially) related to peptide oxidation (MIE209 of AOP149). We identified several studies that reported that certain PFAS, such as PFOS, PFOA, PFHxS, perfluoro-2-methyl-3-oxahexanoic acid (PMOH), and 3H-perfluoro-3-[(3-methoxypropoxy) propanoic acid] (PMPP), act as PPARy agonists [42; 43; 44; 45; 46; 47]. At least one study was identified reporting that PFOS exposure promotes adipogenesis [48], one reported that PFAS exposure increases intracellular calcium levels [49], one reported that PFDA exposure can activate PLC in
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 5 Towards Algorithm Development: Propagation Concepts, Path Loss and Communication Graph
### Air-to-Ship Channel (A2S)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Measure Unit** \\ \hline f & Frequency & Hz \\ d & Geodesic distance between transmitter and receiver & m \\ \(h_{t}\) & Height of transmitter’s antenna & m \\ \(h_{r}\) & Height of receiver’s antenna & m \\ \(h_{d}\) & Height of duct & m \\ c & Light speed in vacuum & m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters involved in Equations (7)–(10): description and measure units. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Step** \\ \hline f & 900 MHz & - \\ d & 1–10,000 m & 50 m \\ \(h_{t}\) (USV) & 0.5–5 m & 0.5 m \\ \(h_{r}\) (UAV) & 5–120 m & 20 m \\ c & \(3\times 10^{8}\) m/s & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Values of the parameters involved in simulations based on Equations (7)–(10): description and measure units. Nevertheless, it would be challenging to treat all the scenarios that may arise and find an optimum (d, \(h_{t}\), \(h_{r}\)) tuple following a non-automate or a traditional mathematical model, as one has to ensure many such communication links, not only one. Figure 4: Path loss, link budget and critical distance for UAV–USV link, when USV antenna height is \(h_{t}=2\) m and the solution in [57] is used. Figure 3: Path loss, link budget and critical distance for UAV–USV link, when USV antenna height is \(h_{t}=0.5\) m and the solution in [57] is used.