esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Verification and Discussion
The experimental waveforms with the CC&OV-PT and PWM methods are shown in Figure 15a,b, respectively, when the input voltage changes from 20 V to 15 V. It can be seen that, with the CC&OV-PT control method, the transient time is 5 ms and the output overshoot voltage \(v_{\text{o}}\) is 1.3 V. However, with the PWM method, the transient time is 170 ms and the output overshoot voltage \(v_{\text{o}}\) is 1.7 V. Therefore, the proposed CC&OV-PT method can provide better performance as compared to PWM method for two stage WPT system. These experimental results are consistent with the simulation verification conducted earlier. As mentioned in Section 5, the conventional PT control is not suitable for the two-stage coupled resonant WPT system. These have shown the superiority and applicability of the proposed double-side CC&OV-PT control approach.
## 5 Proposed Quadrature Bandpass Continuous-Time Delta-Sigma ADC
### Quadrature Bandpass Continuous-Time Delta-Sigma ADC Circuit Design
#### 5.2.1 Four-Bit Feedback Current Digital-to-Analog Converter Circuit
Without DWA, the system achieves a mean IRR of \(-\)68 dB with a standard deviation of 4.6 dB. When DWA is implemented, the mean IRR slightly decreases to \(-\)70.2 dB with a standard deviation of 4.7 dB, indicating a minor degradation in image rejection performance. The IRR values range from approximately \(-\)78 dB to \(-\)58 dB across all simulations. The timing structure of the system is carefully designed, with the feedback DAC's final injection occurring at 0.5 T after the quantizer's sampling edge. This timing allocation ensures sufficient processing time for three critical operations: SAR ADC subtraction, conversion, and DWA data transmission. Performance testing through 20 Monte Carlo post-simulations reveals impressive overall results, with the system achieving an SFDR greater than 80 dB and a signal-to-quantization-noise ratio (SQNR) exceeding 77 dB. The worst-case IRR of approximately 60 dB is deemed sufficient to make the ADC's contribution to image rejection negligible in the overall system performance. In the bias circuit design shown in Figure 9, the system operates with a 1 V supply voltage provided by an internal regulator, supporting a mismatch range of approximately \(\pm\)10%, from 0.9 V to 1.1 V. Due to limited voltage headroom, source degeneration resistors  
Figure 8: Twenty Monte Carlo post-simulations without DWA and DWA for (**a**) SFDR and (**b**) IRR. are excluded from the design. The low-IF receiver design minimizes sensitivity to flicker noise, with thermal noise from the devices being the predominant noise source. To achieve the required dynamic range of over 60 dB, the transistors are designed with increased length and decreased width, resulting in lower transconductance (g\({}_{\rm{m}}\)) and higher saturation voltage (V\({}_{\rm{DSAT}}\)). Noise from the bandgap and bias generator circuits degrades dynamic range due to the receiver's 2 MHz wide BW; the bias circuit implements an RC filter with a cutoff frequency of 48 kHz. To mitigate potential long settlement times caused by the RC filter, the design includes a fast-settle shorting switch activated by a loop filter reset signal. This approach achieves a settlement time of less than 30 \(\upmu\)s, including the bandgap, bias generator, and loop filter reset timing, meeting the BLE system specification of 90 \(\upmu\)s.
## 6 Results of Computational Experiment
### The Transistors 2P771A Dataset  
The use of the entire set of features in FCA leads to a huge number of concepts that make further analysis difficult. The work combines two approaches. Using the test theory algorithm, the smallest dead-end test (the support set of features) is found. FCA is then applied to the features of the dead-end test.  
Application of the support set search algorithm (dead-end test) revealed four features sufficient to separate observations of different classes: T4, T6, T10, and T12.  
For these features, the following boundary values were found:  
T4 = 2.925;  
T6 = 0.03425;  
T10 = 0.6975;  
T12 = 0.04065.  
A computational experiment was carried out using two approaches:  
* A concept formation algorithm, CloseByOne (FCA);
* A conceptual clustering algorithm, COBWEB.  
The results obtained are presented in Appendix A and Figures 12 and 13. It should be noted that class labels were used only to validate the results, but not to build sets of concepts. Figure 13 shows part of the formal concept lattice for 2P771A with a breakdown according to class (from CloseByOne).  
When displaying the results, the following encoding of binary features was used in accordance with the cut-points:  
1(+): x\({}_{1}\) = 1, which corresponds to T4 \(\geq\) 2.925;  
1(-): x\({}_{1}\) = 0, which corresponds to T4 < 2.925;  
2(+): x\({}_{2}\) = 1, which corresponds to T6 \(\geq\) 0.03425;  
2(-): x\({}_{2}\) = 0, which corresponds to T6 < 0.03425;  
...  
As a result of the concept formation algorithm based on the selected features, 64 formal concepts were found. For each of them, an estimate was made using the normalized intracluster distance (\(D\)) and the silhouette index (\(S\)).  
Examples of the most informative subgroups obtained by FCA are shown in Table 1.  
After imposing class labels, one can determine that among them there are pure-positive, pure-negative, and mixed concepts. The StandardQF score was used to evaluate compliance with known labels.  
By analyzing the results obtained, the following conclusion can be drawn: If the silhouette index is high, say, more than 0.1, and the number of objects to be distinguished is large, for example, more than 20, then the distinguished subgroup is compact and at the same time corresponds to labels.  
The study of the tree of concepts obtained with the help of Cobweb shows that the division into homogeneous subgroups (according to labels) occurs on the top branch. However, the description and interpretation of such concepts are difficult since they are based on the probabilities of accepting a certain value according to the features (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Concept**} & **Class According to Labels** & **Coverage** & **Homogeneity** & **Intracluster Distance, D** & **Silhouette Index, S** & **StandardQF** \\ \hline x\({}_{3}\) = 0 & neg & 64 & 0.97 & 0.482 & 0.247 & 0.317 \\ \hline x\({}_{1}\) = 1, & & & & & & \\ x\({}_{3}\) = 0 & neg & 50 & 1 & 0.292 & 0.352 & 0.297 \\ \hline x\({}_{2}\) = 0, & & & & & & \\ x\({}_{3}\) = 0 & neg & 57 & 1
## 2 Materials and Methods
### Theory of Filtering over Vertical Horizontal (HOVE) Angular Grid  
The method of HOVE grid offers a wide range of possibilities for effective point filtering. Normally, in the case of surface computation, the points are plotted in an \(X\), \(Y\) coordinate field. Interpolation then takes place with the inclusion of the \(Z\)-coordinate (height). Since the point cloud of terrestrial laser scanners results in a very irregular \(X\), \(Y\) coordinate distribution, large areas have to be interpolated or estimated during the generation of height-grid.  
A terrestrial laser scanner measures the terrain with a constant angle step in a strip-like manner. If instead of the \(X\) coordinate, we use the horizontal angle (\(\phi\)) values and instead of the \(Y\) coordinate, use the vertical angle (\(\theta\)) values, an almost perfect, gap-free raster is normally obtained (Figure 1A). This approach is very similar to the method described by [PERSON] et al. (2013). An interpolation involving the recording length has several advantages:  
* The output grid contains no gaps.
* It is easier to find terrain structures (e.g., terrain edges).
* Quick recalculation of adjacent points is easily computable.
* 3D filtering is possible.  
All information on the search area of the adjacent points can be made in the form of grid spacing, which is normally constant. The grid spacing corresponds to the angle step (\(\theta\) and \(\phi\)) of the laser scanner.  
The HOVE grid has been used in the following ways.
## 3 Results
### Advantages of the Analytical Hierarchy Process (AHP)  
The analytical hierarchy process (AHP) offers several advantages, such as:  
* **Structured Decision-Making**: AHP provides a structured framework for decision-making by breaking down complex problems into a hierarchical structure of criteria, sub-criteria, and the alternatives. This helps decision-makers systematically evaluate and compare different options.
* **Pairwise Comparisons**: AHP allows decision-makers to make pairwise comparisons between the criteria and the alternatives based on their relative importance. This enables a more nuanced assessment of the criteria and facilitates the consideration of multiple factors in the decision-making process.
* **Consistency Checking**: AHP incorporates a consistency checking mechanism to ensure that decision-makers provide reliable and consistent judgments during the pairwise comparisons. This helps to enhance the credibility and validity of the decision-making process.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Alternatives** & **Weighted Sum of Alternatives** \\ \hline
**A1 “Machine Learning (ML)”** & 0.453 \\
**A2 “Deep Learning (DL)”** & 0.635 \\
**A3 “Natural Language Processing (NLP)”** & 0.312 \\
**A4 “Genetic Algorithms (GA)”** & 0.148 \\
**A5 “Fuzzy Logic”** & 0.324 \\
**A6 “Expert Systems”** & 0.247 \\
**A7 “Swarm Intelligence”** & 0.405 \\
**A8 “Bayesian Networks”** & 0.531 \\
**A9 “Hybrid Approaches”** & 0.574 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Weighted sum of alternatives.  
* **Flexibility and Adaptability**: AHP can accommodate changes in the decision problem by allowing decision-makers to revise their judgments and priorities. This flexibility enables the adjustment of criteria weights and the reassessment of alternatives as new information becomes available.
* **Transparency and Communication**: AHP provides a clear structure for decision-making and facilitates transparent communication among decision-makers. The hierarchical representation and pairwise comparisons enable stakeholders to understand the decision rationale and engage in meaningful discussions.
# ERMOCTAVE: A Risk Management Framework for IT Systems Which Adopt Cloud Computing
## 4 Case Study with Microsoft Azure Cloud
### Applying ERMOCTAVE Phase 1
* Orders table and customers table: it contains private information. Its exposure may affect reputation of the company. Which assets will largely impact the organization if they are modified without authorization? (integrity)
* Order applications: if the order's application is modified without legal authorization, all orders made by customers may be wrongly directed and the result of delivery from transport partners would be a disaster. * Azure SQL: it is a database service fully supporting SQL-based operations. If some changes are made without valid authorization, this would create malfunctions on orders table, customers table, and products table. Which assets will largely impact the organization if access to them is interrupted? (availability)
* Orders table: the orders application reads the order table in order to display the current orders list and to send delivery information to users. If access to the order table is interrupted, orders cannot be consulted by orders application and customers would not receive what they ordered. * Transport partners: if an access to transport partners is interrupted, delivery systems would not work. * Describe security requirements for critical assets: from P1.6, critical assets are listed, and security requirements are enumerated as follows. * Server on premises: confidentiality, integrity, and availability. * Orders table: confidentiality and availability. * Customers table: confidentiality. * Order's application: integrity. * Azure SQL: integrity. * Transport partners: availability. Integrity is not requirement for the tables because it is guaranteed by applications' integrity. * Create threat profiles for critical assets: which potential threats can be non-negligible to the critical asset? * Intruder willing to access the orders application. * Insufficient quality of outsourcing services and transport partners. * Unauthorized people physically intruding the server on premises.
## 4 Markov Detection and Prediction (MDP) Model
### MDP Model  
The MDP model is designed to proactively identify and mitigate DDoS attacks within LTE-M networks in an IoT environment. In an era where IoT connectivity plays an essential role, the MDP model emerges as a vital safeguard, leveraging advanced predictive analytics to detect and preemptively thwart Malicious activities. This model promises to enhance the security and reliability of LTE-M networks, ensuring uninterrupted IoT operations and safeguarding critical data and services against the ever-evolving security threat landscape. In this section, we introduce the MDP model, exploring its architecture, functionality, and real-world applications.  
The proposed MDP system delves into the intriguing realm of data transmission through IoT devices and communication towers, while proposing a comprehensive classification framework that aids in detecting the DDoS attacks and predicting the system's behavior. The MDP system involves three steps:  
* Defining states using Markov chains.
* Generating equilibrium equations.
* Solving the linear system.
## 3 Specimens and Experimental Techniques
### Preparation on Piezoelectric Ceramics  
To establish complete piezoelectric ceramic material constants, we used RM to perform measurements of five specimens with specific geometric shapes, aspect ratios, polarization orientations, and electrode surface orientations. As shown in Table 1, the specific mode shapes include the TE, LE, TS, LTE, and RAD modes.  
We procured piezoelectric ceramic specimens that fit the specifications of the TE and RAD modes in RM from American Piezo Ceramics (APC) International, Ltd. and the Fuji Ceramics Corporation. The piezoelectric specimens for the LE and LTE modes were cut to the required aspect ratios using ultrasonic cutting. Finally, for the TS mode, we ground our own specimen, removed the electrodes, and redistributed the electrodes in the direction normal to that of polarization so that the aspect ratio, polarization orientation, and the direction of the electric field created by the electrodes would meet specifications for TS mode testing.
## 5 Simulation Results and Discussion
### Impact of the CV Penetration Ratio  
Next, let us investigate the impact of the CV penetration ratio. Figure 10 shows the simulation results when using _Proposed_Full_ for various values of the CV penetration ratio. We assume that the NCVs follow _Manual_.  
Overall, all metrics tend to become worse as the CV penetration ratio declines. However, traffic throughput and comfort level tend to improve above 50% the CV penetration ratio. These characteristics show that when the CV penetration is around 50%, obstacle  
Figure 10: Simulation results showing impact of CV penetration ratio under different traffic loads, \(d_{\text{avoid}}=300\,\text{m}\), \(d_{\text{prelim}}=100\,\text{m}\), \(d_{\text{decel}}=500\,\text{m}\).  
avoidance is less smooth than when either CVs or NCVs are dominant. This can be explained as follows. As the CV penetration ratio becomes smaller, more vehicles in the closed lane have difficulty in completing a lane change, resulting in more frequent rapid deceleration and traffic jams around the obstacle, thereby degrading the traffic throughput and comfort level. On the other hand, as the CV penetration ratio becomes smaller, that is, as more vehicles do not adjust their time headway, they tend to maintain a high speed, and, thus, the traffic throughput increases. Even if some vehicles have a high discomfort level, if many vehicles overtake the obstacle at high speed, the average discomfort will be low. As a result of these two phenomena, as the CV penetration ratio approaches 50%, traffic throughput and comfort level deteriorate.  
We can see that the 10% CV penetration ratio improves the fairness of the traffic throughput compared with the 0% CV penetration ratio in Scenario 30. This is because even with a 10% ratio, the CVs act to accommodate lane-change vehicles by adjusting their time headway, thereby restraining chronic traffic jams in lane 0 (closed lane) and lane 1.
# GNSS Accuracy Under White Gaussian Noise Jamming +
## 5 Conclusions  
In this paper, the effect of an AWGN jammer on the pseudo-range accuracy of a GNSS receiver was investigated. First, the theoretical background was provided, after which the theory was compared to actual measurements. The major impact of a jammer on a GNSS receiver is a reduction in the \(C/N_{0}\). The model provided to predict the impact on the \(C/N_{0}\) showed a close match with the actual measured impact. From this, it can be concluded that Equation (3) predicts the suppression of the \(C/N_{0}\) by a AWGN jammer very well. Next, the prediction of the standard deviation of the pseudo-range tracking error, given by Equation (1), was compared to the standard deviations of the measured pseudo-range. For GPS C/A, the prediction matches the observation reasonably well. For Galileo E1, the predictions show some differences, but the general trend and order of magnitude are similar. This shows that the standard deviation of the pseudo-range measurements can also be predicted, although not as accurately as the \(C/N_{0}\). From this, we can conclude that the theory can be used to predict the performance of a GNSS receiver under jamming. This will allow us to determine the effective range of a jammer, in this case, an AWGN jammer, for applications where the accuracy of the position solution has certain requirements. This effective range is likely to be much larger than the range at which the receiver loses its lock on the signals.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The author declares no conflicts of interest.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 5 Orbiting Microgravity Facilities  
### CubeSats  
The CubeSat Project started in 1999 as a way of increase accessibility to Space, and now involves hundreds of groups, in both the public and private sector. A CubeSat is a modular miniaturized satellite for Space research. Each unit is a 10 \(\times\) 10 cm cube with a mass of no more of 1.33 kg and each CubeSat can be constituted of up to 12 units (Table 3, Figure 4a). The CubeSat technology is normally applied on experiments that can be miniaturized. CubeSats are employed to demonstrate spacecraft technologies intended for larger satellites and equipment for biology experiments [64,65]. In biology research, CubeSats have been employed to study the effect of the Space environment in the spore-forming bacterium _Bacillus subtilis_[66] and, soon, in yeast [67] and plants [64], and the stability of organic molecules.  
The technology utilised in the CubeSat is based on a standardised satellite design that uses commercial off-the-shelf (COTS) components for electronics and structure, reducing costs and development time. About 75% of CubeSats orbit Earth at an altitude between 350 and 700 km [68]. They are normally powered by solar panels and can contain a control habitat for biological samples and cameras or spectrometers for image data collection [66]. CubeSats are commonly launched as secondary payloads on launch vehicles, further reducing the costs of the mission, or put in orbit by deployers on the ISS [69; 70]. CubeSats, therefore, suffer from the same restrictions of other experimental opportunities on orbit, like early late access and safety standards. Moreover, if issues were to arise during orbit, they could only be managed remotely, hardware permitting, and this could be detrimental for the experiment [66]. Samples on the CubeSat are normally not retrievable.  
CubeSats can also be used in missions to the Moon and Mars [71]. In May 2018, the two Mars Cube One (MarCO) provided a real-time communications link to Earth to the InSight Mars lander mission, while proving the resilience of CubeSats. Moreover, both NASA and ESA are planning to use CubeSats to support lunar exploration objectives in the next decade.
## 3 Results and Discussion
### HPLC-MS Method Validation  
The full method validation involved the assessment of linearity, limit of detection (LOD), limit of quantification (LOQ), intra-day variability (repeatability), inter-day variability (intermediate precision), and recovery (trueness). Although the lack of use of the internal standard may seem to be a limitation of the method, the validation results, presented below, demonstrate that the method is still suitable and reliable for the use proposed in this work. Linearity. Linearity was verified on both standard and spiked sample solutions. Five dilutions of a mixture of stock standard corticosteroid solutions (BM 17-V, BC, BCDP, MP, BD, FN, FM, and DM) in a mixture of 0.1% formic acid in acetonitrile/0.1% formic acid in water 20:80 at the following concentrations: 0.1, 0.3, 1.0, 5.0, 10.0, 20.0 ng/mL and spiked samples (cream A, cream B, and serum) at the same final concentration were prepared. The solutions were injected in HPLC, and calibration curves were obtained for each steroid by using the least-squares method. The linear correlation coefficients (R\({}^{2}\)) were between 0.99 and 0.98, and the results are reported in Table S2. The limit of detection (LOD) and limit of quantitation (LOQ) were determined by lowering the concentration of steroid solutions or spiked sample solutions to reach signal/noise = 3 and signal/noise = 10 for LOD and LOQ, respectively. The results for sensitivity (LOQ) can be found in Table 1.  
Recovery precision and matrix effect. Recovery values and the matrix effect were evaluated in order to assess the applicability of the methods on real samples. Recovery measures the ability of a method to extract an analyte from a matrix and it is expressed as the percentage of the known amount of the analyte carried through the sample extraction and processing steps of the method. Four concentrations (low, medium, and high levels) of a mixture of standard steroids were added to commercial samples (cream A, cream B, and serum). The samples were treated as described in Section 2.2. The final solution of the samples containing the spiked steroids were analyzed twice along with the standard solutions to compare the results and determine the recovery percentage. The results are reported in Table 1. The mean values for cream A, cream B, and serum were 87.3%, 88.7%, and 93.1%, respectively.
## 3 System Description
### System Model
#### 3.2.3 Channel Model
As explained earlier, the effects of transmitter locations on a system's performance can either be determined by the study of \(P_{r}\) or illuminance. The former is a radiometric quantity and can be obtained from the power transmitted by LEDs, whereas the latter is a photometric quantity that can be derived from the luminous intensity of an LED. All of the parameters described so far are radiometric, which are valid for VLC and IR; however, since VLC performs the dual purposes of communication as well as illumination, some photometric quantities must also be considered as defined in the next subsection.