esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Privacy and Ethical Considerations in Database Management
## 3 Best Practices
### Data Retention Policies  
Data retention policies have a vital role in upholding data security and confidentiality. Their implementation and upkeep carry considerable weight for businesses, particularly those with data-centric models like research service providers [29]. Without indefinite data retention, there is a risk of losing collective memory, leading to a misleading perception of privacy and freedom [7 ].  
Therefore, some of the best methods to develop data retention policies are the following:  
* Data assessment: Identify and classify the types of data the organization stores, considering the level of risk to which they are subject. Personal data, such as Personally Identifiable Information (PII), should be prioritized and managed rigorously.
* Clear conservation periods: Collaboration with data owners to establish and validate appropriate retention periods for diverse data categories, balancing legal and operational mandates while safeguarding confidentiality.
* Deletion processes: Implement efficient strategies for purging obsolete or irrelevant data within specified retention timelines, mitigating data accumulation and minimizing the risk of privacy breaches or unauthorized disclosures.  
Restricted access control: Ensure strict control over access to sensitive data, permitting only the authorized personnel necessary for organizational functions while maintaining comprehensive monitoring and recording mechanisms to detect and address any potential unauthorized breaches.
* Continuous assessment and improvement: View data retention policies as dynamic, requiring ongoing assessment and enhancement, with data controllers staying up to date on evolving legal frameworks, security protocols, and technological advancements to ensure compliance and maximize effectiveness. While following the above methods, some key principles are needed to follow to build data retention policies:
* Have a precise, legal, and legitimate purpose;
* Establish a precise and time-limited retention period;
* Data must be relevant and necessary;
* Data must be stored for the shortest possible time;
* Data is safe and secure. As with many legal policies, they present several implications regarding the implementation of these policies:
* Data volume management: The surge in data volume needs efficient storage, processing, and deletion approaches to maintain critical organizational data while reducing the threat of excessive accumulation.
* Difficulty in data classification: Ensuring accuracy and consistency in data classification is essential for successfully applying retention policies. However, organizations need help in accurately classifying data.
* Ongoing regulatory changes: Data controllers must stay updated with changes to relevant laws and regulations, as well as security and privacy best practices and technologies. Nonetheless, implementing data retention policies is challenging. Data managers face balancing operational needs with strict compliance requirements [29].
## 4 Simulations
### Setup
There are the same size of the experience buffer for the two algorithms, which are set to \(|\mathcal{D}1|=|\mathcal{D}2|=10000\). The learning rate, including the MADDQN, actor network, and critic network of the P-MADDPG was set as 0.0001. Furthermore, we set the hyperparameters of the prioritized replay buffer in the P-MADDPG \(\mathcal{D}2\) as \(\alpha=1\) and \(\beta=0.1\). In the training phase, both the MADDQN and P-MADDPG used the Adam optimizer to optimize the loss function. The sampling batch size was \(D=128\), and the reward discount factor was \(\gamma_{1}=\gamma_{2}=0.89\). The system began to train the neural network when the memory buffer was full, and it updated the neural parameters at one-step frequency after training. Besides, we set the number of episodes \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Values** & **Description** \\ \hline \(M\) & 7 & The number of cells \\ \(N\) & 4 & The number of users per cell \\ \(P_{\rm min}\) & \(-40\) dBm & The minimum transmitting power \\ \(P_{\rm max}\) & 23 dBm & The maximum transmitting power \\ \(W\) & 2 GHz & The total bandwidth \\ \(TH^{\rm th}\) & 0.15 bit/s & The minimum throughput constraint \\ \(n_{f}\) & \(-174\) dBm/Hz & The power spectral density of noise \\ \(r\) & 866 m & The radius of cells \\ \(h_{\rm b}\) & 10 m & The average height of building \\ \(h_{\rm a1}\) & 10 m & The antenna height of BS \\ \(h_{\rm a2}\) & 1.5 m & The antenna height of user \\ \(\varepsilon\) & 1.72 & Excitation factor \\ \(f_{\rm c}\) & 1 GHz & The center frequency \\ \(v\) & \(3\times 10^{8}\) m/s & The propagation velocity \\ \(l_{\rm w}\) & 20 m & The average road width \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Environmental parameters. to \(I=500\). Note that every episode did not have fixed steps. To determine whether an episode was completed, a done flag was designed, where the done flag was true if the reward \(R\) increased by 200 steps; otherwise, it was false (the learning of this episode was not finished). the other parameters of each neural network are listed in Table 2.  
All experiences were operated by a computer with the 12-th Gen Intel(R) Core(TM) i7-12700F @2.10 GHz, 16-GB RAM. The simulation results were presented using _Numpy 1.21.5_ and _Tensorflow 2.3.0_ on the _Python 3.6 platform_.
## 3 Theoretical Foundations  
The proposed vulnerability assessment framework integrates complex network theory, fuzzy production rule (FPR), and fuzzy Petri net (FPN) theory to enable systematic modeling, vulnerability propagation simulation, and quantitative evaluation. This section delineates the fundamental concepts of these three theoretical pillars and their synergistic application logic.
# Efficient Key-Value Data Placement for ZNS SSD
## 1 Introduction  
Recently, key-value stores have been widely used in emerging technologies (e.g., deep learning, blockchain) [1, 2] due to multiple benefits such as high performance, flexibility and simplicity [3]. In particular, log-structured merge-tree (LSM-Tree)-based key-value stores such as RocksDB and LevelDB can obtain high Input/Output (I/O) performance on NAND flash memory-based Solid-state Drives SSDs, due to sequential write characteristics caused by an out-place update [4, 5]. However, a large number of writes occurs during compaction, which can reduce the performance and lifespan of the SSD [6]. In addition, in the case of legacy block interface SSDs, the garbage collection of flash translation layers further increases write amplification (FTLs) [7].  
ZNS (zoned namespace) SSDs use a new zone-based interface that divides the NAND flash memory area into zones of a certain size and allows the host to directly manage the zones [8]. Therefore, ZNS SSD can minimize garbage collection overhead and write amplification, because data placement is performed directly by the host [9, 10, 11]. Currently, research is being conducted to minimize the write amplification of LSM-Trees using ZNS SSD.  
In the case of RocksDB, which is the most popular key-value store developed by Facebook, ZNS SSDs can be used through ZenFS, a simplified filesystem for ZNS [12]. However, the current ZenFS does not effectively utilize the information of RocksDB; therefore, files with different hotness are recorded in the same zone and space is used inefficiently. Moreover, because the current ZenFS does not support garbage collection, RocksDB cannot manage a large key-value set on ZNS SSDs.  
Therefore, in this paper, we propose a method to quickly secure an empty zone during compaction through zone-based data placement optimized for LSM-Tree-based key-value store. In addition, a garbage collection technique considering the lifetime of the data is proposed to minimize write amplification. The method proposed in this paper was implemented by modifying the ZenFS of RocksDB [13] and the performance was evaluated using an emulated ZNS in Quick Emulator (QEMU) 5.1.0 [14]. As a result of theperformance evaluation, the space efficiency could be improved by up to 75%. We were also able to improve the performance by up to 10% using an improved garbage collection algorithm.  
The rest of the paper is structured as follows: In Section 2, we briefly describe RocksDB and ZNS SSD and, in Section 3, we provide the motivation behind this paper. In Section 4, we propose the zone allocation method according to the hotness and the garbage collection logic considering the lifetime of the file. The implementation issues are discussed in Section 5. Section 6 shows the evaluation results of the proposed methods, and we conclude the paper in Section 7.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 2 BDS Integrity Concept
### Alarming Approach
#### 2.2.2 Satellite Monitoring and Alarming  
The satellite monitoring and alarming approach includes two mechanisms. One is that the satellite sends back the SIS quality monitoring information to the ground, and after the ground segment confirms the fault, the alert notification is sent to the satellite through the inter-satellite link (ISL). According to the topology of BDS ISL and the en-route operation performance requirements of ICAO, the TTA is designed to be 300 s.  
Another mechanism is that the satellite uses the on-board SAIM equipment to monitor the satellite clock, SIS quality, and SIS-URE in real time without the need for ground confirmation. This mechanism can achieve a very fast alarm speed, and the expected TTA for it is 6 s. At present, this alarm approach is in the on-board testing stage, and BDS is working hard to implement this rapid alarm mechanism as soon as possible and has not ruled out the use of non-standard PRN codes and other alert notifications.  
The use of the satellite monitoring and alarm approach is mainly to make up for the limitations for BDS's failure to deploy monitoring stations globally. When BDS satellites are located in China, the system will use the ground monitoring and alarm approach; when BDS satellites are located outside of China, it will mainly rely on the satellite monitoring and alarm approach to ensure the integrity of satellites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**B1C/B2a SIS Health Status** & **HS** & **SIF** & **DIF** \\ \hline Healthy & 0 & 0 & 0 \\ \hline Marginal & 0 & 0 & 1 \\ \cline{2-4}  & 2/3 & 0 & 0 \\ \hline Unhealthy & Any value & 1 & 0/1 \\ \cline{2-4}  & 1 & 0/1 & 0/1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: B1C/B2a SIS health status indications.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 4 Simulation Results and Discussion
### Pair of Turbines with Two Interconnected Turbines
#### 4.3.2 Impact of Direct Lightning  
Figure 17a-c shows that despite reducing peak overvoltages from 26 kV-18 kV at the control equipment, from 40 kV-34 kV on the HV side and from 80 kV-78 kV on the LV side of the first wind turbine by connecting the PMGSs, the values are still above the upper limit and may lead to extreme failures. The situation for the second tower is completely different; in the worst condition, the peak value goes up to about 1 kV at the control system, which is within the normal range of the control circuit's operation (Figure 17d-f).  
Even though the probability of a 200-kA lightning strike is scarce in nature, because of its devastating effect on sensitive electrical equipment, it is necessary to predict the transient voltage peaks in diverse conditions for wind turbine installations. The results are provided in Tables 1 and 2.  
The information shown in the tables indicates that despite the high overvoltages during lightning, the peak rates decrease by half in the PMGS model, which is indeed much more reasonable and safer for the wind turbines' electric and electronic equipment compared to previous models (see Section 2.6) and studies.  
Figure 16: Impact of indirect lightning on overvoltages. (**a**) LV side of the first tower; (**b**) HV side of the first tower; (**c**) control circuit of the first tower; (**d**) LV side of the second tower; (**e**) HV side of the second tower; (**f**) control circuit of the second tower.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indirect Strike** & **LV Side** & **HV Side** & **Control System** \\
**(200 kA)** & **(kV)** & **(kV)** & **(kV)** \\ \hline Single wind turbine & 19 & 14 & 19 \\ Wind farm with separated PMGS & 25 & 75 & 60 \\ Wind farm with connected PMGS & 18 & 60 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Peak voltage values of LV/HV sides of boost transformer and control circuit with indirect strike.  
Figure 17: Impact of direct lightning on overvoltages. **(a)** LV side of the first tower; **(b)** HV side of the first tower; **(c)** control circuit of the first tower; **(d)** LV side of the second tower; **(e)** HV side of the second tower; **(f)** control circuit of the second tower.
# Parametric Analysis for Hybrid-Electric Regional Aircraft Conceptual Design and Development
## 2 Mathematical Preliminaries and Methodology
and allows for the evaluation of also the fraction of electrical supplied power \(\Phi_{k}^{e}\), as the amount of total requested power to fly is known by the mission simulation [36]. The aircraft sizing procedure ends when convergence on the maximum take-off weight (MTOW) is reached. For the present case study, a number of passengers of 40 and a design range of 600 nm, flown at Mach equal to 0.4 and at a height of 6100 m, have been considered as a reference design mission [8]. Specific power values equal to 13 kW/kg, 19 kW/kg, and 352 kWm/kg and efficiency values equal to 0.96, 0.98, and 0.99 have been considered for electric motors, inverters, and wires, respectively [8]. The performance analysis hereafter discussed, having the aim of defining a feasible space in which to develop the regional hybrid-electric aircraft, takes into account the forecasts of battery technology development up to 2035. In this context, a gravimetric battery energy density (BED) sensitivity analysis was carried out to show the current limits of applicability of this technology and highlight the maximum reachable potential. This sensitivity study is conducted by studying the trends of two different figures of merit (FoM): the fuel consumption for the standard mission (i.e., the block fuel \(m_{\beta b}\)), and the payload-range efficiency \(PREE\), defined as:  
\[PREE=\frac{m_{\beta}g\ x\ R}{E} \tag{3}\]  
where \(m_{p}\) is the payload mass; \(g\) is the standard gravity; \(R\) is the range; and \(E\) is the energy, both electrical (stored in the battery) and thermal (stored in the fuel), required to accomplish the mission. Both performance metrics refer to the block mission, i.e., they do not consider the energy and fuel required for diversion and the reserves. The two FoMs, \(PREE\) and block fuel \(m_{\beta b}\), were used as objective functions within two different numerical optimisations, set as follows:  
\[\left\{ \begin{array}{rl}\min(FoM(x))\\ 0\ <\ H_{P}<\ 0.7\\ 250\ <\ W/S\ <\ 325\\ 0\ <\Phi_{ell}^{e}<\ 0.56\\ 0\ <\Phi_{d\ell}^{e}<\ 0.56\\ 0\ <\Phi_{d\ell}^{e}<\ 0.56\\ \end{array} \right. \tag{4}\]The FoM is maximized (or minimised) when it coincides with the _PREE_ (or the \(m_{\text{fp}}\)). The design variables selected are \(H_{P}\), \(W/S\), and \(\Phi_{k}^{t}\) for the climb (\(cl\)), cruise (\(cr\)), and descent (\(de\)) of the standard mission; taxiing is accomplished with only electrical power, take-off is performed by supplying total available power, and diversion is conducted with thermal power only to avoid the need of a large amount of unused battery for standard operations. As taxiing and take-off involve the supply of electrical power, the value of \(H_{P}\) is bound to be larger than zero; hence, the optimiser cannot find full-thermal solutions. The power supply strategy adopted in this work is schematically sketched in Figure 1.  
The optimisation is performed by coupling a multi-start procedure with local gradient-based algorithms [37].
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
###### Abstract  
Reconfigurable intelligent surface (RIS) has been envisioned as one of the promising solutions for enhancing signal transmissions in high-speed communications (HSC). In this paper, we present a time-varying channel model with distance-dependent Rician factors for the RIS-assisted HSC. Our model not only contains Rayleigh components and Doppler shift (DS) terms but also distance-dependent Rician factors, for characterizing time-varying features. In particular, we show that when the vehicle is far from the base station and the RIS, the channel contains only Rayleigh fading. However, when they are close enough, the channel can be considered as a light-of-sight channel. Based on the proposed model, it is proven that using RIS phase shift optimization, the DS of the cascaded links can be aligned with the DS of the direct link; and if the direct link is blocked, the DS can be removed entirely. Furthermore, we derive the closed-form expressions for the ergodic spectral efficiency and the outage probability of the proposed system. Besides, it is observed that the deployment strategy also affects the system performance. Simulation results validate all analyses.
## 5 Results
### Effect of Segmentation Window Size  
The size of the signal's segmentation window is a critical element in the case of inertial sensors-based classification. The importance of this has been highlighted in [35; 64]. The selection of window sizes and step sizes was inspired by [35], where they used the segmentation size of 200 with the same stepping size, i.e., 200 resulting in 0% overlap in two segments. The motivation was to align the step size of 200 samples with the walking frequency of a normal human gait. For our case, we experimented with different segmentation window sizes and step sizes, and the results are given in Table 3. The optimal values are 256 for the window size and 16 for the step size. The experiment for the selection of these parameters was performed only for the case of the magnitude of 3D accelerations.  
Figure 8: Performance report (**a**) and Confusion matrix (**b**) for hot vs. cold emotions classification. The average classification accuracy, precision, recall, and F1-score remain at 98%.  
Figure 9: The accuracy curves (**a**) and the loss curves (**b**) computed with the magnitude of 3D accelerations as input signal using hot-cold mapping. The training and testing curves show a good fit, ruling out over-fitting.
## 4 Performance Analysis
### Safety and Comfort Analysis  
The simulation conditions were set to the WLTC (Worldwide Harmonized Light Duty Test Cycle), which includes a series of operations such as acceleration, deceleration, constant speed driving, emergency braking, etc., to simulate the real driving conditions in the city. This working condition does not consider the impact of road slope, and the total cycle time is 1800 s. When driving under WLTC conditions, the minimum inter-vehicle distance under the action of each controller is shown in Table 6.  
The minimum distance between two vehicles usually occurs when the preceding vehicle brakes to speed 0, and the host vehicle slows down in the process. It can be seen from Table 6 that each controller can meet the preset safety distance, thus ensuring the safety of the driving process. The PID controller has the best followability; however, superior followability is accompanied by a drastic change in torque, and this will reduce energy utilization and lead to a decline in battery life.  
At the initial moment, the vehicle in front was set to 8 m away from the host vehicle. Figure 9 shows the acceleration and impact of the vehicle under the action of each controller under the WLTC driving conditions. We defined passenger comfort as being guaranteed when the jerk is kept at (\(-\)2.5, 2.5). From Figure 9, it can be seen that, apart from the SMC controller which is out of range at the beginning of the simulation phase, the controllers meet the comfort requirements.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Controller** & **Minimum Inter-Vehicle Distance (m)** \\ \hline PID & 5.0000 \\ SMC & 5.0333 \\ MPC1 & 5.0021 \\ MPC2 & 4.8735 \\ AMPC & 4.8679 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Minimum distance between two vehicles under the action of each controller.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters (Unit)** & **Value** & **Parameters (Unit)** & **Value** \\ \hline Prediction horizon \(N\) & 20 & Time headway (s) & 1.5 \\ Sampling time \(ts\) (s) & 0.05 & Minimum safe distance \(d_{0}\) (m) & 4 \\ Minimum acceleration (m/s\({}^{2}\)) & \(-\)5 & Maximum acceleration (m/s\({}^{2}\)) & 3 \\ Minimum jerk (m/s\({}^{3}\)) & \(-\)2.5 & Maximum jerk (m/s\({}^{3}\)) & 2.5 \\ Tracking weight parameter \(\omega_{1}\) & 1 & Comfort weight parameter \(\omega_{2}\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters in PID, SMC, MPC1 controllers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters (Unit)** & **Condition** & **Value** \\ \hline \multirow{2}{*}{Economic weight parameter \(\omega_{3}\)} & \(\
u\) \(<\) 40 km/h & 5 \\ \cline{2-3}  & \(\
u\) \(\geq\) 40 km/h & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Economic weight parameters of MPC2.
## 4 Implementation
### Proposed Modulation Scheme
#### 4.3.2 Undersampling  
Undersampling happens when the captured image rate per second of the receiving end is less than the data transmission rate of the light source. The packet merge function is confused in this instance because the DS is overlooked. It is simple to recover the entire missing payload by appending a specific SN value to every packet. Likewise, each DS derived from a certain data packet will be marked by the value of the SN of that data packet. This allows for the easy detection of lost data packets by comparing consecutive SN values to determine which data packets were missed. In addition, the amount of missed payloads is computed by accounting for the length of the SN. For example, the system can identify up to 15 missed payloads if the bit length of SN is 5. If two nearby payloads that are not adjacent to the SN value are detected by the system (e.g., m and m + 2), the missed payload m + 1 is realized.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 4 Constrained Stochastic Game
### Transition Probability
To sum up, the delay \(L_{1}\) and \(L_{3}\) to deliver the observation to the destination for the first and third cases can be obtained as \(M_{i}+L_{T_{0,R}}^{W}+E[Q]L_{Q}+L_{T_{R,G}}^{W}\) and \(M_{i}+L_{T_{0,R}}^{C}+E[Q]L_{Q}+L_{T_{R,G}}^{C}\), respectively, where \(L_{T_{0,R}}^{W}\) (\(L_{T_{R,G}}^{W}\)) and \(L_{T_{0,R}}^{C}\) (\(L_{T_{R,G}}^{C}\)) denote the latency between observation satellite \(i\) (relay satellite) and the relay satellite (ground station) without and with the compression, respectively. In addition, \(E[Q]\) and \(L_{Q}\) are the expected queue length when the observation arrives at the relay satellite and the unit latency to handle one observation in the relay satellite, respectively. Note that the expected queue length \(E[Q]\) depends on the transmission policies of observation satellites. On the other hand, for the second and fourth cases, because the observation is directly delivered to the ground station, we only consider the latency between observation satellite \(i\) and the ground station. Thus, the delay \(L_{2}\) and \(L_{4}\) to deliver the observation to the destination for the second and fourth cases can be calculated as \(M_{i}+L_{T_{0,G}}^{W}\) and \(M_{i}+L_{T_{0,G}}^{C}\), respectively, where \(L_{T_{0,G}}^{W}\) and \(L_{T_{0,G}}^{C}\) represent the latency between observation satellite \(i\) and the ground station without and with the compression, respectively. Note that observation satellite \(i\) can directly transmit the observation to the ground station only when there is the connection between observation satellite \(i\) and the ground station (i.e., \(G_{i}=1\)). To sum up, \(r(S_{i},A_{i})\) can be defined as (12).