esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 6 GNSS Meta-Signal RTK Performance Results  
So far, we have discussed the ultimate time-delay and phase MLE performance obtained with both meta-signals and HO-BOC signals and the meta-signal-based SPP performance considering both good and bad satellite geometries. The remaining open point is the impact of the phase MLE behavior on positioning techniques that exploit such phase information. To address this point, we considered the RTK positioning problem in Section 3.4. Again, we considered the two scenarios depicted in Figure 7. In this case and taking into account the three-step RTK solution in (20), we wanted to assess the performance of the float solution (denoted as RMSE\({}_{real}\)), the fixed solution considering all the estimates (denoted as RMSE\({}_{mixed}\)), and the fixed solution considering only the estimates that were declared as a correct ambiguity fix (denoted as RMSE\({}_{correct\ amb}\)), the latter only shown for the bad GDOP scenario. The position RMSEs for the RTK performance were obtained from \(10^{4}\) Monte Carlo realizations.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 2 Preprocessing Chain for SoNaR Signals
### Two-Pass Split Window  
The TPSW is a noise-whitening technique [20; 24]. Let \(\mathbf{X}=\begin{bmatrix}X(1),&X(2),&\ldots,&X(N)\end{bmatrix}\), where \(X(i)\) represents the absolute value on the \(i^{\text{th}}\) frequency beam of the Lofar and \(N\) is the length. The TPSW estimates the noise level of interest \(X(\gamma)\), where \(\gamma=1,2,\ldots,N\), using the spectral lines inside the two neighboring windows, as illustrated in Figure 3.  
The indices of the samples in Figure 3, taking \(A\) as the center of the window, that is, (\(X_{A-G}\) to \(X_{A-E}\)) in the left window and (\(X_{A+G}\) to \(X_{A+E}\)) in the right window, are \(X_{A}=\{X_{i}|i=A+k,k\in\{-G,-G+1,\ldots,-E,E,E+1,\ldots,G\}\}\). Note that when index \(i\) is close to the beginning or near the end of the data support, the corresponding window is truncated at the boundaries of the sequence. The local average of the elements inside the two windows is  
\[\mu(A)=\sum_{i\in X_{A}}\frac{\mathbf{X}_{i}}{A}. \tag{1}\]  
The TPSW takes advantage of the average noise \(\mu_{A}\), which is a function of the neighborhood of the frequency bin \(A\). To estimate \(\mu_{A}\) to properly filter the noise, the sequence  
\[\Phi(A)=\begin{cases}X(A)&\text{if }X(A)<r\widehat{X}(A)\\ \widehat{X}(A)&\text{if }X(A)\geq r\widehat{X}(A)\end{cases}, \tag{2}\]  
is used, where \(r=1+C[\left(4/\pi-1\right)/A]^{1/2}\), \(C\geq 1\) is a constant, and \(A=G-E+1\) is the window length. The normalized TPSW is given by  
\[Z_{\text{TPSW}}(A)=\frac{X(A)}{\mu(A)}. \tag{3}\]  
After this chain of procedures, the feature data were used to feed the classifier.
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
## 8 Experimental Results
### Area and Performance Benchmarks  
We perform the benchmarks of the optimized versions of SDR applications by targeting the Xilinx Spartan-6 xc6 slx150t FPGA device. The non-optimized solutions are excluded as they are all not synthesizable on the FPGA. The FPGA area use comprises the number of Registers, LUTs and occupied Slices. The total number of individual resources which are available on the FPGA are as follows, Registers = 184,304, LUTs = 92,152 and Slices = 23,038. It needs to be noted that the percentage of used registers and LUTs in our results are in terms of those available in the occupied slices (i.e., not in terms of the total available registers and LUTs on the device). The total average resource usage for each application using optimizations _opt1_, _opt2_, _opt3_ and _opt4_ is shown in Figure 16 as a the percentage of  
Figure 14: The total number of VHDL code lines for each SDR application.  
Figure 15: The total time elapsed for SDF-AP analyses, VHDL code generation and RTL synthesis for each SDR application.  
available FPGA resources. The FPGA uses less registers as shown in Figure 16a, followed by the LUTs in Figure 16b and the slices in Figure 16c. Generally, _opt3_ and _opt4_ use less resources than _opt1_ and _opt2_. Please note that the results for non-synthesizable designs are not shown in which case the rectangular bars are skipped and this happens in large OFDM applications that are based on the IEEE 802.22.  
Figure 16: The benchmark results of resource use for each SDR application.  
Moreover, we carry out the performance benchmark experiments using the metrics of power consumption and maximum frequency for each SDR application where the results are shown in Figure 17. The results of the average power consumption by each of the SDR applications when the four optimizations are used are as shown in Figure 17a. In most cases, the applications with the largest area consume more power than the ones with the smallest area footprint on the FPGA. On average, _Opt4_ consumes 16%, 15%, and 1% more power than _opt1_, _opt2_ and _opt3_ respectively. Similarly, _Opt3_ consumes 15%, and 13% more power than _opt1_ and _opt2_ respectively. Lastly, the _opt2_ power consumption is 1% more than that of the _opt1_. Our final performance benchmark results include the maximum frequency that is achieved for each SDR application as shown in Figure 17b. For each application, the frequency results are the same in all design variants. The optimizations _opt1_ and _opt2_ have the same frequency results in each application, likewise _opt1_ and _opt2_ also result in similar maximum frequencies for each application. Both optimizations _opt3_ and _opt4_ have highest maximum achievable frequency by an average factor of 1.093 in comparison to _opt1_ and _opt2_. This happens because _opt3_ and _opt4_ operate without FIFO channel instances where the allocated buffer size is 1.
## 3 Results and Discussion
### Effect of coolant Electrical Conductivity on HER
This effect, known as ohmic polarization, is the dominant factor and the responsible for decreasing HER in the diluted coolants, as shown in Figure 5[35]. On the other hand, on ICE coolants, the current is no longer solely governed by the ohmic polarization. Instead, the activation energy and the concentration polarization become more significant and contribute to determining the final current density, resulting in a non-linear relation, as presented in Figure 6.  
Figure 6: Absolute value of current measured at \(-5\) V (vs Ag/AgCl) by the electrical conductivity of different BEV and ICE coolants dilutions with their respective best fit. These results show that solutions with reduced electrical conductivity considerably decrease the HER and highlight the importance of using low electrical conductivity coolants as a strategy to mitigate the risk associated with hydrogen formation in BEVs.
## 2 Materials and Methods
### In Vitro Digestion of Starch and Determination of pGI  
A method by [PERSON] et al. [15] with some modifications was used to determine the pGI value of banana flour samples. Banana flour (0.25 g) was placed into a 100 mL conical flask and 30 mL distilled water was inserted. The sample was incubated at 37 \({}^{\circ}\)C for 30 min with no stirring, and the pH adjusted to 2.5 by 1 M HCl if necessary. Then 0.8 mL of 2% pepsin (P7012, \(\geq\)2500 units/mg) was added and the sample was digested with slow constant stirring (130 rpm) to simulate gastric digestion conditions. After 30 min, 1 mL digestion solution and 4 mL absolute ethanol were pipetted into another 100 mL conical flask and mixed evenly. The pH of the mixed solution was adjusted to 6.2 with the addition of 1 mL 1 M NaHCO\({}_{3}\), followed by 5 mL solution containing porcine pancreatin (8\(\times\)USP specification) and amyloglucidase-2 (A7095, \(\geq\)260 U/mL) (\(\varpi\): 1:1). The final volume was increased to 55 mL with distilled water. After that, aliquots of 1.0 mL were withdrawn at 0, 10, 20, 60, 120, and 180 min, added to centrifuge tubes containing 4 mL absolute ethanol, and centrifuged (3000 rpm, 10 min). The supernatant (0.1 mL) was taken to measure the amount of glucose by a glucose oxidase-peroxidase (GOPOD) kit.  
A standard reference was created by taking 0.25 g of white bread and following the same procedure as indicated above. The percentage of hydrolysis of the banana starch samples was calculated using Equation (1):  
\[\text{Starch hydrolysis}\ (\%)=\frac{\text{At}\ \times 0.9}{\text{B}}\ \times 100\% \tag{1}\]  
where At is the glucose content produced at t (h) and B is the total starch content. A conversion factor of 0.9 was used to convert molar mass from glucose to starch monomer units.  
The starch hydrolysis curve was drawn with starch hydrolysis rate (%) as the ordinate and time (h) as the abscissa. In general, the curve followed the first-order equation and could be used to calculate the area under the hydrolysis curve (AUC). By dividing the AUC of starch samples by the AUC of a reference sample (white bread), the hydrolysis index (HI) of the samples was obtained. Then the predicted GI (pGI) was estimated using Equation (2) reported by [PERSON] et al. [16].  
\[\text{pGI}=0.862\ \text{HI}+8.192 \tag{2}\]
# A Security and Privacy Scoring System for Contact Tracing Apps
## 2 Methodology
### Motivation of Methodology  
A widely used method to assess and compare the severity of a computer system vulnerability is the common vulnerability scoring system (CVSS). The CVSS assigns severity scores to vulnerabilities based on a formula, which depends on several metrics. The metrics examine the ease of implementation and impact of an exploit on the system and the users. The equations used by the CVSS were developed by the CVSS Special Interest Group (SIG), who built a lookup table by assigning scores and a severity (low, medium, high, critical) to previously known vulnerabilities. Having defined numeric ranges for each severity level, the SIG then collaborated with Deloitte & Touche LLP to adjust the formula parameters to fit the metric scores to the SIG's proposed severity ratings. Thus, the equations were created through qualitatively ranking known vulnerabilities, then working backward to create a quantitative method that could be applied to future vulnerabilities [20].  
The CVSS was not directly used for the scoring of the vulnerabilities in this paper. This is because the CVSS is designed to compare general vulnerabilities and does not consider privacy, which is a large part of the contact tracing discussion. A metric that is more specialized to the unique requirements of contact tracing will provide more actionable information. For the rubric of the security assessment, some metrics are similar to those of the CVSS. Specifically, attack complexity, required privileges, scope, confidentiality, and the division of the rubric into exploitability metrics and impact metrics are taken and adjusted for contact tracing from the CVSS.
## 1 Introduction
In [18], the fundamental actuation properties such as force-moment decoupling, and fail-safe robustness were investigated. It follows from the cited literature that the fully actuated configurations were suitable for performing more precise and complex maneuvers in 3D space, and thus imposed themselves as favorable platforms in missions involving interaction and manipulation. In [19], 6D interaction control with a fully actuated hexarotor platform was experimentally tested. In addition to configurations with passively tilted rotors, a number of papers dealt with design, modeling, and the control of fully actuated configurations with various geometric arrangements, the so-called omnidirectional aerial robots [20; 21]. In this paper, an approach to the performance analysis of fully actuated multirotor UAV configurations is presented. The approach is based on a structured mathematical model consisting of a control allocation scheme that describes the multirotor configuration. The implementation of the model in the software package enables a performance analysis for configurations with arbitrary parameters and is also the first step in determining the feasibility of the configuration. This step is important before designing the aircraft because it allows for the selection of system components and the parameters of the geometric arrangement of the rotors. A procedure for the analysis of the configuration parameters is proposed and the analysis of the distribution of the aerodynamic forces of EPUs on the force vector of the propulsion subsystem is presented. The results of the analysis, based on the characterized EPUs for fully actuated configurations with passively tilted rotors, are presented. Configuration parameter analysis is a necessary step that allows parameters to be optimized with respect to the requirements and constraints associated with the mission profile. Fully actuated hexarotor UAV control experiments are performed in order to validate configuration feasibility, and to set up a framework for experimental testing.
## 8 Conclusions and Future Work
### Three Protocols  
As shown in Table 6, this section provides a comprehensive summary and recommendations for the three protocols discussed in this paper. The unbalanced PSI-CA protocol based on Cuckoo filter offers high security but involves significant computational and storage demands, making it suitable for clients with strong computational and storage resources. The unbalanced PSI-CA protocol based on single-cloud assistance, while being the fastest and offloading computational burdens to the cloud, poses security risks as it cannot withstand collusion attacks, making it appropriate for scenarios where the cloud is fully trusted. The unbalanced PSI-CA protocol based on dual-cloud assistance offers an ideal balance of runtime, security, and efficiency, making it the most versatile and practical option.
## 3 Results and Discussion  
### Conditions for Numerical Simulation of Comparison with the Conventional Method  
A large-angle rest-to-rest maneuver simulation verifies the control performance of the proposed control system consisting of SISM and BSDW. Additionally, by comparing the proposed control system with the conventional steering law [10], we verify the usefulness of the proposed method from the viewpoint of the agility of attitude maneuvering and the performance of singularity avoidance/escape and gimbal angle guidance. The conventional steering law can also provide singularity avoidance/escape and gimbal angle guidance by torque producing and null motion. In addition, an SDRE is adopted as the attitude controller to Equation (19), and all design variables of the conventional method are selected so as to maneuver in the shortest time.  
The settling condition of the satellite attitude is set to \(\pm\) 0.003 degrees, and the parameters are listed in Table 2.
# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 2 Methodology
### Pose Estimation Network  
The pose estimation network illustrated in Figure 5 employs a CNN-based model to regress the six degrees of freedom (6-DOF) pose transformation information between two adjacent frames. This network extracts the pose transformation matrix by generating an input feature map through the superimposition of the two frames. Initially, the pose estimation network increases the number of feature map channels sequentially from 6 to 128, 256, 512, and finally 1024. It then reduces the number of feature map channels to 256 and applies two convolutional layers to extract high-level semantic features pertinent to pose transformation. Ultimately, a convolutional layer is utilized to convert the feature map into a pose transformation matrix.
## 4 Experiments and Results
### Sample Base Characterization though STE Local Analysis  
Once we have detected different levels of response in each feature, the following questions might come up. First, depending on the type of transaction, are some features more relevant than others, and can we explain the decision process? Alternatively, on the contrary, have all the features the same relevance and can they produce some, or wrong, interpretability? From the results shown in Figure 7, we can observe how the fraud and non-fraud classes tend to occupy different regions in the latent space once we use the encoder with fine-tuning. Figure 7 was generated with the the encoder defined in Section 3.6. As can be seen from Figure 7, there are different regions with a concentration of instances in latent space for fraud and the non-fraud classes, which we can consider as different transaction profiles. For this purpose, we proposed to perform the analysis in a local environment and for each transaction. Following the model described previously in Section 3.7.2, we only incorporate the features that have been shown to consolidate the relevant information in the previous experiments and in previous work [1]. We start from the autoencoder model applied in the previous section. Additionally, with the intention of studying the behavior in the local environment for each and every transaction using STE, and continuing with what is described in the methods section, we use the VAE to generate a set of viable samples sufficiently close to the transaction under study. Finally, for each transaction under study and together with the samples generated by the VAE, the result of the STE will propose the linear regression model that best approximates the score of the classifier implemented for this dataset. For this dataset, the coefficients of the regressor will be considered as the weights that summarize the contribution of each feature of this transaction. This approach therefore allows us to formalize a linear model, consistent with the previous experiments and specific, that should be valid both for the transaction under study and for its environment. The generalization of this experiment over all the transactions will give rise to a set of feature weights of the transaction one by one, which we will refer to as STE.  
Figure 6: Score distributions obtained in the latent spaces when small variations are applied for PaySim Dataset.
## 3 TIES Method
### Problem Definition  
The TIES method systematically identifies and evaluates new technologies in eight steps. In the first step, it requires the identification of the problem and determination of the target values for the design. The target values for the PAV design are presented in Table 1. The rationale for the target values is as follows.  
The maximum takeoff weight (MTOW) is considered as one of the most important factors in aircraft design because it affects not only aircraft performance metrics such as the stall speed but also the cost analysis via the weight data. In this study, the target value for the PAV MTOW was determined by considering an existing fixed-wing PAV developed by AeroMobil [17].  
The wing span must be considered as one of design variables with length constraint in a fixed-wing PAV design, as the vehicle should be able either to take off from or land on a road if necessary. In this study, it was assumed that PAVs take off from and land on a four-lane road. In general, a fixed-wing PAV requires a wing-folding mechanism. The wing root chord was determined by considering the minimum width of the vehicle, because the wing root chord has the greatest impact on the vehicle's width when the vehicle is in the driving mode [18].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Objective Parameters** & **Values** & **Units** \\ \hline Maximum takeoff weight & \(\leq\)2500 & lbs \\ Wing span & \(\leq\)40 & ft \\ Wing root chord & \(\leq\)5.3 & ft \\ Wing loading (W/S) & \(\leq\)17 & lb/\(ft^{2}\) \\ Power-to-weight (P/W) & \(\leq\)0.07 & hp/lb \\ Fuel efficiency & maximum & mpg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design objective parameter target values.  
Figure 1: Procedure and composition of the technology identification, evaluation, and selection (TIES) method. RSE, Response Surface Evaluation; QFD, Quality Function Deployment; IPT, Integrated Product Team; FPI, Fuzzy Performance Index; MADM, Multi Attribute Decision Making; EIS, Enterprise Investment Scheme; FLOPS, Flight Optimization System Program; ALCCA, Aircraft Life Cycle Cost Analysis.  
Wing loading (W/S) plays a key role in determining the wing area when designing an aircraft. It is also considered as a significant factor affecting the lift coefficient, wetted area, and wing span. In addition, the aircraft performance is typically influenced by W/S, meaning that it must be considered during the design of an aircraft. Due to the lack of information available, the authors decided to determine a target value for W/S by investigating statistical data for general aircraft of a size similar to that of a fixed-wing PAV [19].  
The power-to-weight ratio (P/W) is also considered as a highly important factors affecting the aircraft performance. In particular, it is considered as a significant variable during the engine selection process. The P/W ratio was also estimated using available statistical data [19].
# Virtual Reality and Digital Twins for Catastrophic Failure Prevention in Industry 4.0
## 3 Workflow
### Validation
#### 4.1.2 Questions and Answers: Simulator Accuracy Section  
In the first instance, the survey sought to delve into the perception of the students regarding the accuracy of the simulation, focusing mainly on aspects such as the quality of detail and clarity in the interpretation of the signals, among others.  
The results reflect a highly positive evaluation by the respondents, especially highlighting the clarity in the interpretation of emerging alerts, with 88% of responses being \"Excellent\" and \"Good\", which can be verified by observing Figure 13; in the same way, the detailed graphic representation of the engine can be seen to have a value of 78% of responses between the same ratings, while the similarity of the simulated values with respect to the real operation is close to 98%. Although most of the evaluated aspects were well received, small opportunities for improvement were identified in the accuracy of the response to operational changes and in the accuracy with which certain common faults were reflected. Overall, the proposed simulator meets high standards of technical and graphical accuracy, proving effective for practical applications in industrial tasks related to maintenance and training.  
Figure 12: A pop-up alert activation in the simulation environment. When a monitored parameter reaches its critical threshold, the system triggers a red-colored alert and displays a pop-up window with specific maintenance or inspection instructions, allowing users to respond immediately within the virtual training scenario.