esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerospace
## 7 Venus Small Satellite Constellation
### SmallSat Targeting and Orbiter Deflection Maneuvers  
Table 14 shows the maneuver plan to accomplish the B-plane targeting shown in Figure 29. \(\Delta V_{1}\) is performed four days prior to entry (E-4 days), followed by subsequent maneuvers every 12 h. \(\Delta V_{5}\) is the orbiter deflection maneuver performed after the final small satellite is released. The maneuver schedule is chosen so as to keep the total targeting and deflection maneuver \(\Delta V\) within the 45 m/s set in the mission requirements.  
Figure 30 shows the approach directory of the five small satellites until entry and the approach trajectory of the host orbiter.  
Though the analysis presented in Sections 6 and 7 is for the small satellites as secondary payloads on a host orbiter, it is also applicable to a standalone mission using a dedicated cruise stage delivering the small satellites instead of an orbiter. The cruise stage can be a Rocket Lab Photon upper stage, which will be launched to LEO or GTO as a secondary payload, with just enough propellant to perform a propulsive burn to escape the Earth's vicinity and perform the targeting and deflection maneuvers on arrival. This opens up the possibility of dedicated standalone small satellite constellation missions to Mars and Venus. On approach, the spacecraft will perform small maneuvers to release the small satellites on their approach trajectories for aerocapture, after which the cruise stage can be targeted to fly by the planet, or enter the atmosphere and burn up. Figure 31 shows the orbits of the constellation of five near-polar small satellites and the host orbiter.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Maneuver** & **Type** & **Time, Days** & \(\Delta V\)**, m/s** \\ \hline \(\Delta V_{1}\) & Targeting & E-4 & 4.71 \\ \(\Delta V_{2}\) & Targeting & E-3.5 & 5.37 \\ \(\Delta V_{3}\) & Targeting & E-3 & 6.18 \\ \(\Delta V_{4}\) & Targeting & E-2.5 & 7.29 \\ \(\Delta V_{5}\) & Orbiter deflection & E-2 & 18.2 \\ \hline \end{tabular}
\end{table}
Table 14: Venus small satellite constellation targeting and orbiter deflection maneuvers.  
Figure 30: Approach trajectory of the five Venus small satellites with various inclinations (green) until atmospheric entry and the host orbiter (magenta) until periapsis.  
The orbits in Figure 31 intersect because all orbits have the same apogee and perigee, and only the inclination is different. In practice, each satellite can be allowed to have a slightly different apogee and perigee to avoid the risk of collisions between satellites.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
###### Abstract  
In this paper, the improvement of the cascaded prediction method was presented. Three types of main predictor block with different levels of complexity were compared, including two complex prediction methods with backward adaptation, i.e., extension Active Level Classification Model (ALCM+) and extended Ordinary Least Square (OLS+). Our own approach to implementation of the effective context-dependent constant component removal block is also presented. Additionally, the improved adaptive arithmetic coder with short, medium and long-term adaptation was presented, and the experiment was carried out comparing the results with other known lossless audio coders against which our method obtained the best efficiency.  
Keywords:adaptive arithmetic coder; cascaded prediction; context-dependent constant component removing; extended active level classification model; Least Mean Square +
Footnote †: journal: Applied Sciences
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 2 Theory  
### Time-Frequency Domain Analysis  
The hydrophone captures a time-varying discrete signal that represents the radiated noise data of the underwater target, which shows the time-varying information of sound pressure. The single time domain information can not reflect the time-varying signal's characteristics; so, the signal's frequency domain characteristics need to be analyzed. Ifthe radiated noise signal is denoted as \(p(n)\), the formula in the frequency domain after performing the discrete Fourier transform (DFT) is given by [22]:  
\[P(\omega)_{k}=\sum_{n=0}^{N-1}p(n)e^{-j\frac{2\pi}{N}nk} \tag{1}\]  
In this formula, \(P(\omega)_{k}\) is the frequency, \(0\leq k\leq N-1\) and \(N\) is the total length of the signal. The discrete Fourier transform is capable of transforming the sound pressure signal \(p(n)\) into its corresponding representation in the frequency domain, denoted as \(P(\omega)\). Because of the intricacy involved in computing the discrete Fourier transform and the limited ability to accurately resolve low-frequency components, the fast Fourier transform has been developed. Compared to the traditional Fourier transform algorithm, the FFT algorithm can significantly reduce the computational load and improve the computational efficiency [23].  
The hydrophone captures the radiation's noise signal, which is then divided into several frames for processing. Then, each data frame is windowed to improve the spectral characteristics of the signal and reduce the spectral leakage. FFT obtains the spectrum characteristics of each frame of a windowed signal, and then the signal is processed in the time and frequency domains. The frequency is shown on the vertical axis, while the horizontal axis represents time. The signal's amplitude is represented by color filling, and the Time-Frequency characteristics are obtained after the above processing. Figure 1 shows the specific Time-Frequency domain acquisition process. The Time-Frequency feature can show the frequency band of the signal that does not change with time, which is an essential reference for target identification.
# Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
## 2 Hybrid Network Expressway Scene Modeling  
### Dynamic Expressway Hard Shoulders Opening  
The expressway hard shoulder refers to the pavement area with a certain strength adjacent to the carriageway. Various countries place differing emphasis on the shoulder-setting. In typical countries such as the United Kingdom and the Netherlands, both the right and left hard shoulders can be utilized for HSR. According to the Federal Highway Administration (FHWA), expressway use is increasing at a rate of roughly 2% per year, and this trend is expected to continue. The survey also highlighted that traffic congestion has not only increased, but has also become more unstable over the past 20 years [41]. Relevant research has shown that in congested road sections during peak hours, temporarily allowing traffic to run on hard shoulders and implementing coordinated speed control can increase the traffic capacity by 15% to 30% without the need for road expansion [42; 43; 44], thereby alleviating regular traffic congestion problems.  
According to the relevant standards, the hard shoulder will only be opened when it is safe and with no obstructions. Drivers must obey the signs and speed limits to ensure their own safety as well as that of other road users, and the hard shoulder is applied as an extra lane only when it is declared open. If there is no effective signal on the hard shoulder, drivers should not use it as it is illegal and unsafe, and whether an accident occurs on the original road or the open hard shoulder, all hard shoulders should be closed to avoid affecting emergency vehicles. Due to the increasing popularity of CAVs, the composition of expressway traffic flow is expected to change in the future [45]. As shown in Figure 1, while considering setting up a hard shoulder opening strategy, it is also vital to fully consider the characteristics of connected vehicles and non-connected vehicles.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Link Faults
#### 3.2.6. LIVRD Algorithm  
To conclude the simulations, the LIVRD algorithm is analyzed under different fault conditions. In link faults, the \(\mathrm{V_{bus}}\) shows a higher value than the \(\mathrm{V_{link}}\). As a result, the VR takes a value between zero and one. On the other hand, the VRD demonstrates a negative value. Figure 14,b show a pole-to-pole fault at 0% of link L34 and a pole-to-ground fault in 100% of link L24. The pre-selected thresholds for the LIVRD algorithm in link faults are \(\mathrm{THR_{VR}}=0.90\) and \(\mathrm{THR_{VRD}}=-1500\).  
It can be seen that as the fault resistances increase, the VR and VRD have lower values. However, this has no effect on the detection time. Nevertheless, the distance from the fault to the measuring relays has an effect on the fault detection time, but in absolute terms, the detection times in these two scenarios remains being very fast.  
Tables 17 and 18 show all the registered detection times for the LIVRD algorithm for faults at link L24 and link
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 4 Performance Evaluation
### HetNet Deployment with Small Cells near the Macro Cell eNodeB  
In this section, the performance analysis for the second HetNet deployment scenario is presented. The uniform macro/small cell deployment described in Section 4.1 was considered as the reference case. Figure 3 shows that there was only one interfering ring of small cells around the central small cell. The \(P_{\text{max}}\) value varied for the small cell uplink from 16 dBm to 23 dBm.  
To see the results of the interference offered to the central small cell from the interfering ring as well as the macro cell, Equation (22) was plotted in Figure 14. It is observed that due to the absence of one interfering ring as compared to the previous scenario, the interference offered to the central small cell decreased. This was because the second interfering ring, which was at a distance of \(4R\) from the central cell eNodeB, was not present. The macro cell interference was present, but the dominant interference was the one offered by the first interfering ring of small cells. By comparing Figures 8 and 14, it can be observed that the interference level decreased by 0.9 dB on average. Due to the decrease in interference, the transmit power in the small cells could be increased. In Figure 14, it can clearly be seen that for the small cells, \(P_{\text{max}}=18.03\) dBm corresponded to the same interference level as compared to the reference cases with \(P_{\text{max}}=23\) dBm and \(P_{\text{max}}=18\) dBm in uniformly distributed macro and homogenous small cell deployments, respectively.  
Figure 13: Spectral efficiency for the small cell as a function of distance and \(\alpha_{\text{small}}\) in the HetNet deployment with uniformly distributed small cells.
# Correlating Disorder Microstructure and Magnetotransport of Carbon Nanowalls
## 3 Results and Discussion
### Magnetoresistance
#### 3.2.2 Sample II, High Disorder  
Sample II shows a linear positive MR only below 2 T on approximately all ranges of temperature (40-75 K and 120-250 K) and a negative MR for the higher magnetic field (see Figures 8 and 9a). The MR varies fundamentally with the magnetic field and slightly with the temperature.  
Figure 7: **(a)** Three-dimensional image of the MR in function of the temperature and the magnetic field of Sample I. **(b)** Isoline map of the MR.  
Figure 8: The MR measurement of Sample II.  
Figure 9: (**a**) Three-dimensional image of the MR in function of the temperature and the magnetic field of Sample II. (**b**) Isoline map of the MR.  
The transition from WL to WAL is suppressed for Sample II. The linear positive MR at low field (below 2 T) could be associated with in-plane graphene transport via a hopping mechanism due to the large number of defects. This is in agreement with the results obtained by [PERSON] and coworkers [48] in graphite with disorder and [PERSON] and collaborators [49] in irradiated monolayer. The process is based on the spin polarization and the electron-electron interaction [50]. At a low magnetic field, the spin polarization of localized electrons into a single graphene layer dominates rather than magneto-orbital effects. As the magnetic field increases, more localized sites present a spin aligned to the field, and the hops are suppressed through Pauli blockade, leading to a positive MR [51]. This positive MR has no limitation for the observation at high temperatures, which is consistent with our result. Above 2 T, the hops are canceled due to the Pauli blockade, and the transport is dominated by diffusion of electrons associated with the negative and quadratic magnetic field dependence of the MR. The electronic charges could be scattered among graphene nanocrystallites that reduce the carrier mean free path. [PERSON] and collaborators [52] also reported the effect on the graphene monolayer. The negative diffusion magnetoresistance, \(MR_{Ds}\), is given by the following:  
\[MR_{Ds}=-\frac{\Delta l}{l_{0}}=-KB^{2} \tag{5}\]  
where \(\Delta l\) is the increment of the mean free path of the charge carriers caused by the magnetic field, \(l_{0}\) is the mean free path of the carrier without magnetic field and \(K\) is a constant [5]. The lengthening of the mean free path with greater magnetic field leads to a negative \(MR_{Ds}\).
## 5 Performance Analysis
### Performance of Caching in the LTE Network  
In our proposed architecture, we showed the cellular network (i.e., LTE network) whereby the content providers were in the Tier 4 and the PGW is situated in Tier 3. The eNBs are in Tier 2 and the end-users of the LTE network were available in Tier 1 devices. The end-users had several network interfaces (e.g., cellular, Wi-Fi, etc.); those were used to access the contents in different scenarios. There were several transmission modes in the LTE network. From those LTE transmission modes, we used two transmission modes in our simulation environments. One was single input single output (SISO) and the other one was multiple input multiple output (MIMO). Figure 14 shows the simulation result for the LTE network. The simulation result shows that MIMO provides almost double throughput compared to SISO at 10 MHz channel bandwidth, but our main objective is to show the performance of caching in different Tiers. Although the simulation result is for the same transmission mode, the content access time is less based on the location of the content providers. In the LTE network, multiple eNBs were connected to the PGW that was in Tier 3. The rest of the core network and Tier 4 devices were connected to the LTE network through the PGW. Therefore, PGW plays an important role in the LTE network to reduce the content access time and to increase the probability of content access from nearby devices. Moreover, the PGW was the part of the clustered devices and it could be the cluster head of a cluster. As a result, the direct communication among the cluster head also increases the probability of content access from nearby devices where the contents are cached.  
The types of EC were the most important factor for the cellular network because it has caching and computing capabilities including other cloud-related characteristics. We did not show the performances of the combined situation of the eNB's and the types of EC mechanisms. But, surely, the presence of any types of EC increases the caching and computing capabilities in the edges of the core and access network devices. Therefore, the probability of the more popular content caching increases within the one-hop distance of the end-users. Therefore, the end-users can directly access the most popular content from the one-hop distance and the content access time reduces a lot. The coexistence of EC and ICN concepts also ensures the low latency communication in the cellular network.  
Figure 13: Impact of duplicate content in caching.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 2 Background and Motivation
### Related Works
This means that for Trust-ND to continually work by calculating high-scoring trust between nodes, both nodes will need to remain at fixed IP addresses and link-layer identifiers, because these are the identifiers used by Trust-ND trustors to identify trustees. If nodes cannot change their addresses freely, then they will be subject to the privacy concerns of tracking and activity correlation. Finally, NDPsec was introduced by [PERSON] et al. in [15] to augment Secure Neighbor Discovery practices and guarantee improved performance among a litany of other proposed SEND improvements (algorithm changes, new options, etc.). While a significant leap forward for NDP security, this proposal still lacks privacy preservation, flexibility, and simplicity in one way or another thanks to its associations with SEND and CGA. One might imagine that adding things to SEND or CGA would certainly not reduce their complexities. However, NDPsec offers a drastically improved baseline by which SEND and CGA can be evaluated in a more recent context. Other analyses of these proposals and how they affect the efficacy of SEND and CGA--as well as analyses for SEND and CGA themselves--can be found in [16; 17; 18; 19; 20]. Related endeavors striving to prevent neighbor spoofing capture an exceptional amount of time, effort, and ingenuity to close what security researchers rightfully understand as a very important gap in local IPv6 network security. Of any existing proposal, one might be a more viable candidate than another depending on the context of the target network being secured. That is precisely why this work does not desire to express that these related proposals are by any means inadequate to solve NDP security issues. Rather, it is left to adopters to determine which is best suited for their own applications. This research proposes an alternative to securing NDP against neighbor spoofing attacks that relies on the idea of caching being a crucial point of interception. It identifies and applies what should be considered three crucial aspects for the adoption of Neighbor Discovery amendments: simplicity, flexibility, and privacy preservation. Voucher-Based Addressing (VBA) is thus introduced as a possible candidate to mitigate the neighbor spoofing threat in local IPv6 networks. Compared to the given related works which do not satisfy these three ideals, VBA does so by introducing a simple software-level shim during address resolution processes, by having a dynamic operating mode for each interface, and by generating outwardly random addresses for use at any scope.
## 4 Results  
### Identifying Which Sensor or Sensor Combination Results in the Highest Mapping Accuracies  
Our results on the overall accuracy of the map showed that models combining optical and SAR data outperform those using single-sensor data (Table 5). The Optical Fusion + Sentinel-1 dataset achieved the highest classification accuracy (\(F1\) = 0.85), whereas the combination of Sentinel-1 and Sentinel-2 performed slightly worse (\(F1\) = 0.82), and the model using only the PlanetScope and Sentinel-1 combination showed poorer performance, only achieving an \(F1\) accuracy of 0.75. Among the single optical data sources, PlanetScope data achieved the best classification performance (\(F1\) = 0.8), which is not only better than Sentinel-2 data (\(F1\) = 0.69) but also better than Optical Fusion data (\(F1\) = 0.74), which integrates PlanetScope's geometric information on Sentinel-2. In contrast, the model using only Sentinel-1 data yielded the lowest classification performance (\(F1\) = 0.49). For mapping most land cover categories, models integrating optical and SAR data performed well, with the Optical Fusion + Sentinel-1 model in particular achieving the highest F1 scores for the Amazon forests, Cerradao, Water, and Other categories (Table 6). Forests were mapped with the highest level of accuracy, achieving \(F1\) scores > 0.8 for all data combinations, except PlanetScope + Sentinel-1. However, Cerradao was often confused with the Amazon forest (Figure 6b,c), and only combinations that utilized SAR could accurately identify Cerradao (\(F1\) \(\geq\) 0.65; Table 6). Including SAR also yielded higher \(F1\)_scores_ for mapping Savanna (\(F1\) \(\geq\) 0.76; Table 6), as opposed to using optical data alone (\(F1\) \(\leq\) 0.74; Table 6), particularly where the boundaries between pastures and the Savanna were unclear (Figure 6a). When only optical imagery was used, high-resolution PlanetScope data proved best for distinguishing the Savanna (\(F1\) > 0.7; Table 6; Figure 6a). PlanetScope imagery alone, or in combination with SAR, was also useful for mapping Agriculture class (\(F1\) > 0.90; Table 6), possibly due to the high brightness of agriculture fields (Figure 6d). Confusion matrix heatmaps for all six models are provided in Appendix A (Figures A1-A7), offering a detailed overview of classification errors. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Date Type** & \
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **\(F1\)_Score_** \\ \end{tabular} \\ \hline \multirow{3}{*}{Optical and SAR} & Optical Fusion + Sentinel-1 & 85\% & 0.85 \\  & Sentinel-2 + Sentinel-1 & 83\% & 0.82 \\  & PlanetScope + Sentinel-1 & 76\% & 0.75 \\ \hline \multirow{3}{*}{Optical} & Optical Fusion & 76\% & 0.74 \\  & Sentinel-2 & 73\% & 0.69 \\  & PlanetScope & 80\% & 0.80 \\ \hline SAR & Sentinel-1 & 62\% & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall accuracy results of land cover type classification for different datasets, including overall accuracy and Weighted Average \(F1\)_score_.
## 3 Results
### Interfacial Tension and Contact Angle  
IFT was collected between oil and either of the two brines. Figure 3 shows that for 100% Na\({}_{2}\)SO\({}_{4}\) IFT value starts around 30 mN/m and plateaus at 15.8 mN/m. While for the 1% Na\({}_{2}\)SO\({}_{4}\) system, IFT starts at 28.5 mN/m and plateaus at 12.3 mN/m. This is shown to corroborate that IFT is not expected to play such a significant role in recovery differences in this case.  
Each oil drop created in the pendant-drop system was given a stabilization time of 30 min, and the contact angle was tracked during this period, without finding any significant changes in the measured value. Dimethyldichlorosilane produces a hydrophobic behavior on treated surfaces, due to the absorption process on the solid surface [53; 54]. Contact angle provided information to verify the surface changes to a hydrophobic response, confirming an oleophilic behavior and therefore the wettability alteration within the three components system.  
Results in Table 1 confirm that untreated glass slides are water-wet, while treated ones turned out oil-wet. The contact angle behavior reflects the effect of salinity, which suggests that when surfaces are water-wet, the increment in salinity goes along with an increment in the angle (122.6 \(\pm\) 2.8\({}^{\circ}\) for 1% Na\({}_{2}\)SO\({}_{4}\) and 139.6 \(\pm\) 2.5 \({}^{\circ}\) for 100% Na\({}_{2}\)SO\({}_{4}\)). On the other hand, for treated glass beads, low-salinity brine leads to an increase in the contact angle (33.1 \(\pm\) 2.1\({}^{\circ}\) for 1% Na\({}_{2}\)SO\({}_{4}\) and 21.4 \(\pm\) 7.3 \({}^{\circ}\) for 100% Na\({}_{2}\)SO\({}_{4}\)). This shows the direct effect on the surface, as the liquid-liquid interaction are the same for both treated and untreated slides.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Salinity** & **Condition** & **Contact Angle** \\ \hline
1\% & Untreated & \(122.6\pm 2.8^{\circ}\) \\  & Treated & \(33.1\pm 2.1^{\circ}\) \\ \hline
100\% & Untreated & \(139.6\pm 2.5^{\circ}\) \\  & Treated & \(21.4\pm 7.3^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Oil contact angle for treated and untreated glass slides, in the presence of Na\({}_{2}\)SO\({}_{4}\) brine.  
Figure 3: Interfacial tension of 1% Na\({}_{2}\)SO\({}_{4}\) with oil (plateau at 12.3 mN/m) and 100% Na\({}_{2}\)SO\({}_{4}\) with oil (plateau at 15.8 mN/m).