esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Key Concepts and Enablers for 5G Private Networks
### Data Analytics and AI/ML-Based Network Optimization  
Data collection and analysis are of primary importance in 5G networks (both public and private) due to the increasing need for efficient monitoring and management of the network and underlying infrastructures. At the same time, technologies like data analytics and AI/ML are investigated as a means to facilitate network automation and result in enhanced utilization of the network resources [31]. Network automation is particularly important for the cost-efficient deployment of NPNs since it has the potential to significantly reduce management costs. Towards this direction, AI/ML techniques are of the utmost importance for the deployment of an intelligent, cognitive, and flexible 5G system, primarily due to the memorization and accurate decision-making capabilities [32]. As a matter of fact, ML algorithms have already been proven to represent a powerful tool with many potential applications to enhance and facilitate wireless communication networks in several areas, such as radio channel modelling, channel estimation, signal detection, network management and performance improvement, access control, and resource allocation.  
In our proposed architectural approach, as will be described in the following section, the system incorporates modules that are in charge of gathering information from all architectural layers and provide this information either to the registered services or to AI/ML-based modules for network and service optimization. In this context, cross-layer data gathering is supported. In addition, network and management related data are processed by AI/ML algorithms in order to optimize specific parameters and key performance indicators (KPIs). Typical examples include network configuration (RAN planning and deployment, scheduling), cross-slice optimization for improved quality of service (QoS), energy consumption by each UE, as well as mobility management.
## 3 Operating Characteristics Analysis and Parameter Design
### Comparison with Existing Multi-Output Inverters  
Table 1 presents a comparison between the proposed inverter and existing multi-output inverters in terms of the number of power switches/diodes, number of capacitors/inductors, number of outputs, maximum gain, control method, and independent controllability. As can be seen from the table, references [13; 20], and [21] required fewer power switches/diodes than the proposed inverter. However, they have only two outputs that cannot be independently controlled. Moreover, the output gain of reference [13] was lower than that of the proposed inverter, and the output voltage of references [20; 21] was load-dependent, resulting in poor stability. Regarding the number of capacitors/inductors, references [15; 16] have advantages over the proposed inverter. However, they are inferior to the proposed inverter in terms of the number of power switches/diodes, number of outputs, and maximum gain. In terms of the number of outputs and maximum gain, the proposed inverter outperforms all the literature participating in the comparison in the table. Among the existing multi-output inverters, there are two main control methods: Pulse Width Modulation (PWM) and Phase Shift (PS), with PWM being the most common and PS mainly used in bridge-based inverters. However, the output of reference [24] is fixed and non-adjustable, and therefore does not have independent controllability. In addition, almost all PWM-controlled inverters in Table 1, including the proposed inverter, integrate DC-DC conversion. This means that the voltage stress of the components is usually positively correlated with the duty cycle. Therefore, in practical applications, it is necessary to avoid excessively high duty cycles as much as possible, otherwise excessive voltage stress will increase system costs. Overall, compared with existing multi-output inverters, the proposed inverter can generate more controllable outputs with fewer components and guarantee output capability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Number of Power** \\ **Switches/Diodes** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Capacitors/Inductors** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Outputs** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Gain** \\ \end{tabular} & \
\begin{tabular}{c} **Control** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Independent** \\ **Controllability** \\ \end{tabular} \\ \hline
[13] & 2/0 & 1/1 & 2 & 1.39 & PWM [2] & No \\
[14] & 4/0 & 1/1 & 2 & 2.25 & PWM & Yes \\
[15] & 8/0 & 0/0 & 3 & 0.9 & PS [3] & Yes \\
[16] & 6/0 & 0/0 & 2 & 0.9 & PS & Yes \\
[17] & 8/0 & 3/1 & 3 & LD [1] & PWM & Yes \\
[18] & 9/0 & 3/1 & 3 & LD & PWM & Yes \\
[19] & 4/4 & 3/4 & 3 & LD & PWM & Yes \\
[20; 21] & 2/0 & 3/0 & 2 & LD & PWM & No \\
[24] & 4/0 & 1/1 & 2 & 0.45 & Fixed & No \\ Proposed & 4/0 & 1/1 & 4 & 2.25 & PWM & Yes \\ \hline \hline \end{tabular}  
* Load-dependent. 2 Pulse Width Modulation. 3 Phase Shift.  
\end{table}
Table 1: Comparison with existing multi-output inverters.
# Slug Regime Transitions in a Two-Phase Flow in Horizontal Round Pipe. CFD Simulations
## 1 Introduction  
### Two-Phase Gas-Liquid Flow in Horizontal Round Pipe Section  
The two-phase gas-liquid flow with the slug flow pattern in round macro- and microchannel or pipes is frequently detected in many technical systems; for example, in the oil and gas industry, in power plants, sewage systems, and metallurgy [1; 2; 3; 4; 5].  
Two-phase flows and mass porosity are intensively studied for the cases of flow in macro- and microchannels. A detailed review of microchannel flows is given in [6]. Flow in microchannels was not considered in this article.  
The simplest cases of two-phase flows in a round pipe are vertical upward two-phase flow and vertical downward two-phase flow. A feature of two-phase gas-liquid flow in inclined pipes [7; 8] is the position of the gas and liquid phases of the flow asymmetric relative to the pipe axis as a result of the effect of gravity on the flow. In this case, the upward and downward flows in the inclined pipe have different characteristics. A feature of horizontal two-phase flow is also an asymmetrical distribution of two phases.  
Horizontal two-phase gas-liquid flow modes are mainly classified as [1; 9]:* Stratified flow: In this mode, the phases are separated along the channel height. Layered flow is observed at low velocities of gas and liquid.
* Wavy flow: As the velocity of the gas phase of the flow increases, waves are formed at the gas-liquid interface. Waves propagate in the direction of the flow.
* Bubbly flow: The gas phase is distributed in the form of individual bubbles in a liquid medium. Most often, bubbles are mixed with liquid at the top of the pipe.
* Plug flow: This flow is an intermittent flow, observed at low flow rates of gas and liquid. The mode is characterized by elongated gas bubbles (plugs).
* Slug flow: This flow is an intermittent flow. The flow is characterized by chaotic formation and movement of gas bubbles and a less distinct gas-liquid interface. Plug and slug flow patterns are often classified as intermittent flow.
* Churn flow: Occurs when the flow rate increases. The bubble shells break, and an emulsion appears. The flow regime is transitional between the slug and annular.
* Annular flow: Higher gas velocities create liquid films on the channel walls.  
The visual patterns of horizontal gas-liquid flow modes are presented in [10]. The velocity difference between the two phases increases at a higher gas quantity [11]. The flow pattern in corrugated pipes is significantly more complex than flow in smooth ones [12]. In this paper, only the flow in smooth pipes is considered.
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 4 Results
### Monte Carlo Analysis  
To consolidate the landing scenario, the sensitivity of the filter performance and the robustness of the two controllers are assessed with a Monte Carlo analysis on a set of 500 trajectories. These sample trajectories are generated by perturbing both the initial condition and the start time of the landing phase, \(t_{5}\). In particular, by changing \(t_{5}\), the visibility condition of the LNS constellation is slightly changed as well, modifying the  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**CTRL** & \(\delta r_{\text  
observability level accordingly. On the other hand, the error in the initial condition is added to perturb the nominal scenario and test the robustness of the GNC chain. The landing starting time, \(t_{S}\), is sampled from a uniform distribution in the range \(3000\div 5000\,\mathrm{s}\), recalling that the nominal scenario sees a \(t_{S}\) equal to \(3400\,\mathrm{s}\). In practice, this is imposed by shifting the initial true anomaly of the orbit backward or forward in time, such that, in the nominal case, at \(t=t_{S}\), the lander is at the periselene of its orbit when the thrusting begins. The error dispersion in the initial condition is instead introduced as additive zero-mean white Gaussian terms with a standard deviation of \(100\,\mathrm{m}\) and \(0.1\,\mathrm{m}\,\mathrm{s}^{-1}\) in magnitude per each position and velocity component, respectively. This small perturbation of the state at the beginning of the simulation becomes quite high when propagated forward up to \(t=t_{S}\). This directly impacts the control error with respect to the prescribed guidance that the PID regulator will face at the beginning of the thrusted arc, hence making convergence more and more difficult. A summary of the simulation settings is reported in Table 2, where both stochastic variables and Monte Carlo dispersed parameters are summarised. When the \(1\,\mathrm{km}\) altitude threshold is reached, the simulations are stopped and the final position errors with respect to the prescribed guidance trajectory are recorded. In order to evaluate the quality of the provided GNC architecture and compare the two controllers, each sample of the Monte Carlo analysis is labelled as a _successful_ or _failed_ sample in accordance with its final error. Considering a threshold of \(500\,\mathrm{m}\) of control error, all samples with a larger final control error are classified as _failed_ samples, while the remaining ones as _successful_. Figures 11 and 12 provide the logarithmic evolution of the navigation (upper plot) and control (lower plot) position errors for the successful samples during the thrusted arc only, for case A and B, respectively. The colour of the lines reflects the value of \(t_{S}\), from the lowest in dark violet to the highest in yellow. The cyan lines represent the average (solid) and the \(3\sigma\) upper bound (dashed), considering the represented lines. Table 3 presents the aggregated results for navigation and control errors in the two cases.
# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 3 Basic Concept and Initial Settings
### Threat Model  
Assuming that the adversaries work as a group, driven by interests and rational in economic interests. Its purpose is divided into two types, to obtain its own economic interests by various means or to ruin the security of the system. Means include but are not limited to internal attacks, external attacks, malicious attacks, selfish attacks, etc. Depending on the means and purpose, common attacks against VANET include: false information injection, on-off attacks, new comers, Sybil attacks, collusive attacks, inconsistency attacks, network jamming, etc. [44; 45]. In our model, the following three possible attack modes against the reputation model are studied.  
1. _direct attack_: Adversaries show the same behavior as other normal vehicles in the first part of the activity cycle to accumulate reputation value. Following a certain point in time, they start to perform malicious behaviors;
2. _on-off attack_: Adversaries alternately behave normally or maliciously throughout the activity cycle to confuse other vehicles and RSUs;
3. _collusive attack_: Different from the first two attack modes that are marked by time, adversaries against a certain event or a certain vehicle act out by communicating each other's status. The attack content not only includes giving lower trust scores to normal nodes, but also includes high trust values given from each other.  
In the DBREMM, the trust value of the malicious node will decline rapidly after the malicious node commits evil. When the trust value of the node is lower than the preset threshold, the node will be detected by the TA, and then be immediately stored in the observation area, and its subsequent network activities will be rejected. Following a second verification, the TA will give a final result concerning whether the node is malicious or misjudged. Information about this node is retained permanently on the chain. In Section 5, we will describe in detail how the DBREMM detects and defends against those types of attacks.
## 3 Queueing Analysis  
The queueing analysis techniques we suggest are based on open models whose use is computationally very fast due to the much simpler structure of their normalizing constants, whereas earlier work focused on closed queueing network models [22]. The closed model approach is based on a representation where an evacuation indefinitely repeats itself at random under identical stochastic conditions, so that the evacuees are \"virtually\" re-entering the building after having been evacuated.  
Figure 3: Graph-based algorithms’ result comparison.  
On the other hand, the open model approach that we use is useful for situations where the overall evacuation time is very long compared with the individual evacuation steps that evacuees are taking, and during this time evacuees are progressively leaving their offices or other parts of the area being considered to create the \"arrival flow\" of people into the evacuation process.  
We associate with each location \(L\) from which evacuees may originate (e.g., a room in an office building) an \"arrival rate\" \(\lambda_{L}\) of evacuees. Here \(L\) does not represent a segment of a corridor or of a passage area (e.g., area in front of a lift) where evacuees arrive from other locations; \(L\) is just a specific room that they will leave during the evacuation.  
Specifically let \(n(L)\) be the number of people who are present at location \(L\) before the evacuation begins. Note that \(n(L)\) itself may be random because we actually do not always know how many people are present at each location in the building. Then if \(W(L,E)\) is the expected \"best case\" evacuation time from location \(L\) based on no congestion and no impediments on the known normal evacuation path, we will take \(\lambda_{L}\approx\frac{n(L)}{W(L,E)}\). Note that this will provide a larger or pessimistic value of \(\lambda_{L}\) because in fact the real evacuation time will be larger than the best case value.  
Consider any path from some starting in location \(L\) to some location \(i\) on the evacuation path from \(L\), and denote it by \(\pi(L,i)\); note that there may be many such paths. Let us denote by \(p(\pi(L,i))\) the probability that the path \(\pi(L,i)\) is taken by an evacuee starting at \(L\). Then the total arrival rate of evacuees into the location \(i\) will be:  
\[\Lambda_{i}=\sum_{L}\sum_{All\ \pi(L,i)}p(\pi(L,i))\lambda_{L} \tag{1}\]
# Experimental Study on Physical and Rheological Properties of Trinidad Lake Asphalt Modified Binder
## 3 Results and Discussion
### TLA Content Effect on Rheological Properties of Modified Binders
#### 3.2.3 Comparison of Two Methods of Rheological Testing  
The rheological testing carried out in this study evaluated two methods for determining the ZSV of modified binders. The values determined in the creep test versus the values calculated using the Cross method are shown in Figure 15. A good correlation is revealed between both methods (the difference between the two methods is within 10%), and, thus, the Cross method can be reliably used to obtain the ZSV for the TLA-modified binders tested in this study. There are no significant outliers in the entire range of ZSV values.
## 3 Implementation and Experiments
### Heuristic vs. Mondrian
\begin{table}
\begin{tabular}{l l l l l l l l l l l l}  & \multicolumn{6}{c}{Greedy Heuristic} & \multicolumn{6}{c}{Mondrian} \\ \hline \(\mathbf{k}\) & \(\mathbf{u}\) & \(\mathbf{r}\) & \(\mathbf{\#h}\) & \(\mathbf{h_{\text{avg}}}\) & \(\mathbf{h_{\text{max}}}\) & \(\mathbf{k}\) & \(\mathbf{u}\) & \(\mathbf{r}\) & \(\mathbf{\#h}\) & \(\mathbf{h_{\text{avg}}}\) & \(\mathbf{h_{\text{max}}}\) \\ \hline
2 & 2.062 & 5.502 & 14,589 & 2.232 & 16 & 2 & 3.505 & 2,789.4  
Figure 1: Heuristic _vs_. Mondrian: Diagrams comparing running time and usefulness for the Adult dataset. \begin{table}
\begin{tabular}{l l l l l l} \multicolumn{5}{c}{Greedy Heuristic} \\ \hline \(\mathbf{k}\) & \(\mathbf{u}\) & \(\mathbf{r}\) & \(\mathbf{\#}\mathbf{h}\) & \(\mathbf{h}_{\text{avg}}\) & \(\mathbf{h}_{\text{max}}\) \\ \hline  
\end{tabular}
\end{table} 
Table 2: Heuristic _vs_. Mondrian: Results for the Adult-2 dataset. CanadaAgain, our heuristic outperforms Mondrian in terms of efficiency (at least three times faster). However, for this dataset, the quality measures are contradictory. Whereas the usefulness of the heuristic results is always slightly better and the number of output row types of the heuristic results is at least four times the number of output row types of Mondrian results, the measures concerning the size of the output row types are significantly better for Mondrian. The reason seems that our heuristic always produces one block of at least 2,448 identical rows. See Table 5 for details. Conclusions for Classical \(k\)-AnonymityWe showed that our greedy heuristic is very efficient, even for real-world datasets with more than 100,000 records and \(k\leq 100\). Especially for smaller degrees of anonymity \(k\leq 10\), Mondrian is at least ten times slower. Altogether, our heuristic outperforms Mondrian for all datasets, except Nursery, in terms of the quality of the solution. There is no clear winner for the Nursery dataset. Hence, we demonstrated that even when neglecting the feature of pattern-guidedness and simply specifying _all_ possible pattern vectors, our heuristic already produces useful solutions that can at least compete with Mondrian's solutions.
# Data Privacy and Ethical Considerations in Database Management
Database designs and management practices must incorporate mechanisms to efficiently and completely remove an individual's data upon request, while maintaining the integrity of the remaining data and complying with any legal retention requirements. This paper presents a practical application of data privacy principles in managing a patient health dataset that mimics real-world healthcare data. The dataset was acquired from Kaggle [11], a popular open-source platform that presents datasets, machine learning competitions, tools, and tutorials for data science enthusiasts and additional areas. The results obtained from implementing data minimization, anonymization, pseudonymization, encryption, and access control practices in the \"Healthcare Dataset\" demonstrate significant improvements in the security and privacy of stored health information, although with a slight performance impact. Data minimization reduces the stored sensitive information, lowering the risk of exposure and privacy breaches. Anonymization and pseudonymization add a layer of protection while reducing data granularity, making detailed analysis challenging. Encryption using the Advanced Encryption Standard algorithm, facilitated by the _pgcrypto_ extension in PostgreSQL, effectively safeguard sensitive data from unauthorized access, although at the cost of increased processing time. Lastly, access control measures enhance system security by carefully assigning permissions to database users. In addition to encryption, this paper will also discuss hashing techniques, which are crucial for verifying data integrity. We will explore various hashing algorithms and their applications in database management, complementing our discussion on encryption methods. This paper is structured as follows. Section 2 provides a literature review on works related to database management, data privacy, and ethical considerations. In Section 3,we introduce specific techniques and rules that database administrators should follow regarding ethical issues such as minimization, anonymization, pseudonymization and data encryption, access controls, and transparent communication with stakeholders. Section 4 presents a real-world database scenario that shows how to implement ethical implementations and best practices. Section 5 discusses the results and analyses obtained by applying ethical considerations and best practices. In Section 6, we provide recommendations for more research in this area, critically evaluate the effects and implications of our findings, and summarize the study's findings. Throughout this paper, we aim to provide a comprehensive overview of data privacy and ethical considerations in database management, addressing not only technical aspects but also the broader ethical implications of data handling practices.
## 2 Materials and Methods
### Data Analysis  
Based on Signal Detection Theory, we computed d' scores for each participant in the VR Goalkeeper task using the hit rate (i.e., % of targets responded to from target-present trials) and the false alarm rate (i.e., % of distractors responded to from target-absent trials). Similarly, hit rates and false alarm rates were used to compute a d' score for each participant in the Whack-a-Mole task. Following the procedure reported in previous studies using the ANT (e.g., [7]), we computed three scores for each participant using their response time data. To compute the Alerting score, we subtracted the response time for double cue trials from that of no cue trials. For the Orienting score, we subtracted the median response time for spatial cue trials from that for central cue trials. Finally, to compute the Conflict score, we subtracted the response time for congruent trials from the response time for incongruent trials. We computed subscores for focusing and attentional shifting, as well as a total score, from participant responses to the ACS. Additionally, we computed scores for inattentiveness (Part A of the scale) and hyperactivity/impulsive behavior (Part B of the scale) for each participant from responses to the ASRS scale. Statiscial analyses were carried out using Jamovi.1.6 (www.jamovi.org, (accessed on 4 October 2021)).