esa-sceva/satcom-chunk-collection huggingface dataset:
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Ministatellites
#### 3.2.4 Micro XSAR (135 kg)  
JAXA/ISAS, University of Tokyo, and Keio University are working on the 100 kg class X-band SAR project whose proto-flight model (PFM) was completed in 2019 and is currently ready for launch [101]. A novel signal processing scheme as well as an efficient waveguide feeder design, with an increased chirp bandwidth of 300 MHz, is capable of providing 1-m resolution at nadir off-set angle of 30\({}^{\circ}\)[102; 103; 104]. Figure 17 (left) illustrates the concept of operations where active or wide-area sensing is made during the day and passive Earth-oriented sensing is made at night. Figure 17 (right) shows possible daytime observation modes, which include underground mapping.  
To supply SAR power more than 1.3 kW for several minutes, during the daytime, the solar cells of Micro XSAR charge their lithium-ion battery whose discharge specification is rated at 3 C or the speed of depleting the whole battery in 1/3 h [105]. The estimated battery energy is 400 Wh and the ampere-hour capacity is expected to be similar to or less than TECSAR whose design specifications are summarized in Table 6. Amongst the other satellites in Table 6, Radar In a CubeSat (RaInCube) provides the lower capacity bound of about 10 Ah (similar to an electric scooter) for smaller SAR satellites discussed in the next subsection [106; 107; 108]. Further advances in power source densities could bring both the cost and footprints down to intensify the advantages of small SAR satellite systems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mission**} & **Battery** & **Solar Array** & **Payload Peak** & \multirow{2}{*}{**Payload Mass**} & **Total Launch** \\  & **Capacity** & **Power** & **Power** & & **Mass** \\ \hline RaInCube & \(\sim\)10 Ah & 45 W & 22 W & 5.5 kg & 12 kg \\ TECSAR & 45 Ah & 1600 W & 850 W & 100 kg & 260 kg \\ HJ-1C & 80 Ah & 1100 W & - & 200 kg & 890 kg \\ SAR-Lupe & 108 Ah & - & 1800 W & 394 kg & 1230 kg \\ TSX & 132 Ah & - & - & - & 770 kg \\ Cosmo-SkyMed & 336 Ah & 40,000 W & 7000 W & - & 1700 kg \\ \hline \hline \end{tabular}
\end{table}
Table 6: Power and mass of different SAR missions from nanosatellites to medium satellites.  
Figure 17: Mission concept of Micro-XSAR: (**left**) Attitude control in orbit. (**right**) Observation modes.
## Appendix B Sample Delay Calculations  
We can first inspect the best-case one-hop average delay in a scenario where there is a minimum contention delay (\(\rho_{WI}=0,\rho_{WL}=0\)) and the average queue size is 1 (\(\overline{\mathcal{R}_{i}}=1\)). Such a scenario is possible when routing is scheduled with a large time gap. In such a scenario, the average DSRC and Ethernet one-hop delay can be calculated as shown in Equations (A4) and (A5) by substituting in Equations (32) and (33), respectively:  
\[\overline{\mathcal{D}_{iWL,min}} =\overline{\mathcal{R}_{i}}\times(t_{trans,i}+4\overline{t_{prop, i}}+4t_{proc,i}+t_{trs}+t_{cts}+t_{ack}+DIFS+\frac{CW_{min}T_{slot}}{8})\] (A4) \[=1\times(((204\times 8\ b)/27\ \text{Mbps})+4\times(333\ \text{m}/3\times 1 0^{8}\ \text{ms}^{-1})+4\times 12\ \text{\mu s}\] \[+20\times 8\ b/27\ \text{Mbps}+(14\times 8\ b/27)\ \text{Mbps}+(14\times 8\ b /27)\ \text{Mbps}+50\ \text{\mu s}+\frac{15\times 20}{8}\ \text{\mu s})\] \[=60.4\ \text{\mu s}+4.44\ \text{\mu s}+48\ \text{\mu s}+5.92\ \text{\mu s}+4.15\ \text{\mu s}+4.15\ \text{\mu s}+50\ \text{\mu s}+37.5\ \text{\mu s}\] \[=214.5\ \text{\mu s}\]  
Note that, in Equations (A4) and (A5), the total encapsulated routing packet size is 204 bytes; the DSRC data rate is 27 Mbps; the maximum transmission distance is 333 m; the Ethernet data rate is 1000 Mbps; the processing delay is considered to be 12 \(\upmu\)s, which is usually between 10 and 16 \(\upmu\)s [97]; and the Ethernet link distance is 1000 m.  
\[\overline{\mathcal{D}_{iWL,min}} =\overline{\mathcal{R}_{i}}\times(t_{trans,i}+\overline{t_{prop, i}}+t_{proc,i}+IFG)\] (A5) \[=1\times((204\times 8\ b)/1000\ \text{Mbps})+(1000\ \text{m}/(0.64\times 3 \times 10^{8}\ \text{ms}^{-1}))+12\ \text{\mu s}+9.6\ \text{\mu s})\] \[=1.632\ \text{\mu s}+5.21\ \text{\mu s}+12\ \text{\mu s}+9.6\ \text{\mu s}\] \[=28.4\ \text{\mu s}\]
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Specifications of Current UAM Aircraft
This is while fixed-wing, helicopter, and lighter-than-air aircraft are mostly fuel-powered. The fact that batteries are less efficient compared to fuel regarding their energy \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Configuration**} & \multicolumn{1}{c}{**VITOL**} & \multicolumn{1}{c}{**VITOL**} & \multicolumn{1}{c}{**VITOL**} & \multicolumn{1}{c}{**VITOL**} & \multicolumn{1}{c}{**VITOL**} & \multicolumn{1}{c}{**VITOL**} \\ \hline HTOL & Fixed wing & 4211 & 277 & 888 & 10,112 & 2196 & 9.25 & fuel & large \\ \hline VTOL & Helicopter & 658 & 249 & 250 & 5075 & 953 & 6.75 & fuel & medium \\  & Multirotor & 198 & 65 & 142 & \textless{}1000 & 283 & 2.8 & electricity & medium \\  & Lighter than air & 2795 & 1320 & 110 & 2774 & 10,742 & 15.75 & fuel & large \\ \hline Hybrid & Dual system & 249 & 95 & 218 & 2350 & 378 & 3.57 & electricity & medium \\  & Tilt-wing & 193 & 40 & 262 & 1524 & 333 & 4.25 & electricity & medium \\  & Tilt-rotor & 313 & 64 & 251 & 1045 & 429 & 5.2 & electricity & medium \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Average specification of different configurations in UAM vehicles. Figure 22: Distribution of passenger capacity among the studied configurations. to-mass ratio, and the payload capacity of multirotor and hybrid configurations is less than fuel-powered aircraft regarding their number of passengers. The reason is that a considerable share of payload capacity is dedicated to the batteries. Another interesting point is that multirotor, tilt-wing, and tilt-rotor aircraft have less than 1 h of average endurance. By increasing the max cruise speed, hybrid ones have tried to increase the max range and compensate for this weakness. However multirotor aircraft do not have relatively high speed and consequently high ranges. This makes them suitable only for short flights. The concentration of multirotor aircraft on 1-2 passengers is a consequence of this limitation. It should be noted that in VTOL category the majority of aircraft with 8 and more passengers belong to lighter-than-air configuration.
# Stereo Digital Image Correlation in MATLAB
## 3 Implementation
### Temporal Matching Implementation
#### 3.4.2 ImgCorr Function  
ImgCorr, outlined in Table 12, makes use of a nested for loop to perform temporal matching. The outer loop cycles through the images of an image series, starting at the second image, preparing data for correlation. This includes loading the appropriate reference and deformed images; filtering the images; determining the interpolation coefficients of \(G\); determining the reference image gradient; shifting the reference subset positions by the displacement SFPs determined in the previous correlation run (these displacement SFPs are rounded down such that the reference subset need not be interpolated [75]); and using PCM to determine initial estimates of the SFPs for the current correlation run. In the case of the absolute reference image strategy, the reference subset positions are not shifted and PCM is only used to obtain an initial estimate of the displacement SFPs for the first correlation run. For subsequent iterations, the SFPs of the previous correlation run are used as initial estimates.  
The inner for loop cycles through the subsets calling SubShapeExtract and SubCorr to perform correlation of the subsets. The if statement of line 19 ensures that only subsets which pass stereo matching are analyzed within the for loop.
# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 3 Results
### Experimental Results
#### 3.3.2 Experiment 2  
Figure 7 shows the results of average heartbeat and subject identification accuracy of 10 experiments with six different methods, which are TT-CNN, TRT-CNN, QRS-CNN, TT-PCANet, TRT-PCANet, and the proposed method. Here the subject identification accuracy was obtained after all heartbeats of each signal voted. With TT-CNN and TT-PCANet, the average heartbeat (subject) identification rates were 71.51% (89.44%) and 77.22% (89.72%), respectively. They had the worst performance among the tested methods since it preserved the least information during heartbeat processing. The average accuracies increased to 77.86% (93.88%) and 85.93% (95.27%) with TRT-CNN and TRT-PCANet, in which the R point alignment was additionally taken into account. The proposed method and QRS-CNN showed performance of 83.44% (94.4%) and 89.41% (97.78%), respectively. It was found that when the feature extraction manner was set, methods with the proposed QRS-centered resampling strategy obtained the best heartbeat and subject identification, indicating that the preservation of the temporal period and morphological information of the QRS did benefit for identification accuracy. Also when compared with the CNNs, the PCANets achieved better performance using different heartbeat resampling manner. Furthermore, the CNNs (6c-2s-12c-2s) used in our work needed complicated iterative process for 468 (\(5\times 5\times 6+6+5\times 5\times 12+12=468\)) parameters optimization, while the number of PCANets parameters is six (\(k_{1}\), \(k_{2}\), \(l\), \(\ell\), \(\eta\), \(v\)) here. Based on the results, we confirmed that our proposed method could achieve good performance under HR variability conditions.  
Figure 6: Comparison of different classifiers using the proposed method.  
Figure 7: The accuracy comparison of the six identification methods. (**a**) Heartbeat identification comparison; (**b**) Comparison of mean subject identification accuracy of six methods.
# Efficient Preamble Design Technique for Millimeter-Wave Cellular Systems with Beamforming
## 4 Simulation  
In this section, the performance of the proposed preamble design technique is evaluated by computer simulation with a simple setup of an OFDM-based mmWave cellular system, as shown in Figure 2. The center frequency, bandwidth, FFT size, CP size, and subcarrier spacing are set to 28 GHz, 250 MHz, 1024, 128, and 270 kHz, respectively [17]. A Rician channel model consisting of one LOS path and one NLOS path is used for an mmWave channel [3] and is programmed with a 3-dimensional spatial channel model (3D-SCM) [18]. The number of rays in the NLOS path is set to 20, and the K-factor is set to 10 dB in our simulation. In the simulation, only BS1 is considered for a one-cell environment and both BS1 and BS 2 are considered for a two-cell environment. The cell radius is set to 500 m. In a two-cell environment, it is assumed that the MS with a switched beam is located 400 m away from BS1. The residual STO (\(\chi\)) is generated uniformly in the range 0-4. A uniform linear array (ULA) with eight antenna elements is used at the BS. A uniform circular array (UCA) with eight antenna elements is used at the MS to cover a 360\({}^{\circ}\) azimuth angle around the MS. The parameters for the preamble generation, beamforming, and the CBID information used in the simulation are listed in Table 2.  
Different notations are used for the length of ZC sequence, \(L\), to distinguish between the parameters used for each technique. The notation, \(\overline{L}\), is used for the previous technique [9] and \(\widetilde{L}\) is used for the proposed technique. Here, the previous technique refers to the case where the CBID information is mapped to the preamble sequence generated by a single ZC sequence. In the proposed technique, a preamble sequence is generated by a combination of two ZC sequences. Here, \(\overline{L}\) and \(2\widetilde{L}\) are set to 1021 and 1018, respectively. The number of CIDs, \(N_{C}\), is set to 1020 (\(\overline{L}-1\)) for both the previous and the proposed techniques. The values of \(L_{P}\) and \(\{Q_{0}^{\rm{o}},O_{1}^{\rm{o}}\}\) are selected such that \(\widetilde{d}_{s}^{\rm{o},b}\) satisfies the conditions described in Section 3 and are listed in Table 2. In the simulation, two types of the proposed preamble sequences (Type-1 and Type-2) with different values of \(N_{r}\) are considered. The value of \(N_{r}\) is set to 255 for Type-1 and 86 for Type-2, respectively. The value of \(N_{g}\) is set to 255 for both Type-1 and Type-2. Figure 4 shows the correlation properties of the proposed preamble sequences (Type-1 and Type-2) when CID is 278 and BID is six. It can be seen from Figure 4a that the preamble sequence of Type-1 has a good correlation property because the number of side-peaks is zero. Note that the number of side-peaks (satisfying the condition in Property 3) is given by \(2\,(w-1)\), where \(w=\big{[}N_{g}/N_{r}\big{]}\). In the case of the Type-2 preamble, there are side-peaks with a value of 0.5 at four different CIDs because the value of \(2\,(w-1)\) is four, as can be seen in Figure 4b.
# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 2 Disassociation and Utility Awareness for Associations
### Data Clustering  
Clustering by definition is the process of grouping objects with similar characteristics together within a cluster. There exists no unified solution to solve all the clustering problems and it has been proven to be NP-hard [11; 12], thus the problem consists of finding an optimal or near-optimal solution. In what follows, we present briefly general techniques used for data clustering.  
Figure 3: Example of a utility driven disassociation.  
Classical clustering algorithms attempt to optimize a fitness function in order to minimize the dissimilarity between items within a cluster and maximize it between the clusters. Clustering can be either fuzzy or partitional. For fuzzy clustering, data items may belong to multiple clusters with a fuzzy membership grade like the \(c-means\) algorithm [13]. For partitional clustering, clusters are totally disjoint and data objects belong to exactly one cluster like the \(K\)-means algorithm [14]. In this work and in accordance with the disassociation principle, we are only interested in partitional clustering.  
Bio-inspired algorithms model processes existing in nature for optimization problems. Example of bio-inspired systems used for data clustering are: ant colony system [15], particle swarm optimization [16], artificial bee colony [17]. Those algorithms draw inspiration from the collective behavior of decentralized, self-organized natural social animals. Even though particles of a swarm may have very limited individual capabilities, they can perform very complex jobs, vital for their survival, when acting as a community. Choosing the right bio-inspired algorithm to cluster data relies on the comparability of the given problem's background with the behavior of the bio-particles.  
Ant clustering algorithm (ACA) is a population-based stochastic search process, modeled after the social behavior of ants searching for food, sorting larval and cleaning corpse. To pick and drop items, ants action is influenced by the similarity and the density of the data within the local neighborhood. From this behavior, researches introduced many variation of clustering algorithms applicable in a wide range of problems [18; 19; 20; 21; 22].  
In the next section, we define a variant version of the ant-based clustering algorithm to cluster records that are supersets of the predefined utility rules, for a more utility-guided disassociation.
## 3 Methodology
### Data Acquisition and Model Parameter Settings
The parameter \(M\) for judging the hotspot is set to 720, indicating that the user should stay in the hotspot for more than 1 h. The tolerance distance constraint \(\gamma\) used to determine if a hotspot is a receiving point is set to 50 m. The initial weight ratio \(RT_{W}=0.5\), the volume ratio \(RT_{V}=0.5\), and the urgency parameter for parcel delivery, \(\alpha\), is set to 2. Bandwidth (\(h\)) is determined based on [PERSON]'s empirical rule. Below, \(\sigma\) represents the sample standard deviation, IQR is the interquartile range, and n denotes the number of trajectory points. \[\text{h}=0.9*\min\left(\sigma,\frac{\text{IQR}}{1.34}\right)*\text{n}^{-1/5} \tag{28}\]  
The reasons for selecting [PERSON]'s rule are as follows: (1) Adaptability to data distribution: The spatial distribution of trajectory points in this study exhibits multi-modality (as shown in Figure 3a). [PERSON]'s rule effectively avoids over-smoothing or under-smoothing through the trade-off between the interquartile range (IQR) and standard deviation [30]. (2) Computational efficiency: Compared to cross-validation methods, [PERSON]'s rule has a complexity of O(n), making it suitable for processing large-scale trajectory data. (3) Domain generality: This rule has been widely applied in traffic trajectory analysis [33] and urban hotspot detection [41] ([PERSON] et al., 2016), with its reliability extensively validated.
## 4 The Proposed Model
### ABT-RRMAC Formulation  
In light of the previous considerations, our ABT-RRMAC approach modifies the original round-robin algorithm formulation regarding the evaluation of the sector time without affecting the plane sectorization and thus the width of the sectors. Basically, the idea is to assign a portion of time for each sector that is proportional to the size of the queues, that in this case is not fixed for each sector and can vary dynamically.  
Figure 7: Uniform node distribution vs. non-uniform using round-robin approach. (**a**) Uniform node distribution. (**b**) Non-uniform node distribution.  
In Figure 8, an example of the ABT-RRMAC application is shown. The plane is normally sectorized into equalamplitude sectors as well as the round-robin MAC. However, in this case, we consider a traffic pattern that is not uniformly distributed among nodes; in Figure 8, it is assumed that, two particular sectors denoted \(S_{x}\) and \(S_{y}\), respectively, have different sizes of frame waiting queues; in this regard, the term \(size_{q_{i}}\) denotes the size of the waiting queue related to the \(i\)-\(th\) sector. While considering the situation in Figure 8, assuming that \(T_{X}\) and \(T_{Y}\) are the beamforming (sector) times related to the sector \(x\) and \(y\), respectively, the ABT-RRMAC assigns the beamforming times as follows:  
\[\left\{\begin{array}{c}T_{1}=\frac{size_{q_{i}}}{\sum_{i=1}^{N}size_{q_{i}}} \times\overline{T}\
eq T_{s}\\ \ldots\\ \ldots\\ T_{N}=\frac{size_{q_{N}}}{\sum_{i=1}^{N}size_{q_{i}}}\times\overline{T}\end{array}\right.\]  
where  
\[\overline{T}=\sum_{i=1}^{N}\frac{T_{i}}{N}. \tag{6}\]  
The term \(\overline{T}\) denotes the mean beamforming time averaged by all sectors. Initially, the beamforming times are the same for all sectors and set to \(T_{s}\) (RR-MAC); after a training phase set to 10 s (the time of convergence of the RR-MAC), the mean beamforming time and then the sector times are updated periodically so that each sector is assigned a quantum of time that is proportional to the size of the queue of that sector multiplied by the mean beamforming time. In this way, sector times can be different and unbalanced, and the major fraction of time is assigned to the sector having the biggest queue size. For instance, if we consider the example of Figure 7, because \(size_{q_{x}}>size_{q_{y}}\), the ABT-RRMAC will assign sector times such that \(T_{X}>T_{Y}\). The motivation of this choice is, due to the fact that \(size_{q_{x}}>size_{q_{y}}\), the serving queue rate of \(S_{X}\) is lower than the serving queue rate of \(S_{Y}\); consequently, \(S_{X}\) needs a beamforming time higher than \(S_{Y}\) in order to empty its queue. Among other things, this choice will optimize the energy consumption of nodes in the network; in fact, the undesired waste of energy caused by the classic round-robin due to the static model is limited by the dynamic beamforming time assignment.
## 1 Introduction
An important property of imaging radar is its ability to penetrate dry desert sand and produce images of the shallow subsurface terrain [15]. For radar wave penetration, the sand cover needs to be fine-grained, physically homogeneous, and extremely dry with a moisture content of less than 1%. The low dielectric constant of the Sahara sand results in a low attenuation and maximum penetration for the radar waves [18]. These criteria are all satisfied in the sandy desert of North Africa. Remote sensing has contributed to the discovery of several suspected impact structures in the Sahara region. Out of the 13 confirmed structures cited in North Africa, about five impact craters were found in the eastern sector of the Great Sahara clustered in Southeastern Libya, Southwestern Egypt, and Northern Chad. Yet, no circular structure has been cited in the neighboring Sahara area in Northwestern Sudan. With vast areas of desert, Sudan still holds high potential for the discovery of impact craters. This work, therefore, is aiming at exploring a new large crater-like structure of possible impact origin in the Northwestern Sudan desert using optical and radar satellite data. A thorough examination of the morphology and the regional influence exerted by the conceivable impact event is considered in this work. The discovered Rimaal circular structure is centered at 21\({}^{\circ}\)34\({}^{\prime}\)10\({}^{\prime}\) N and 20\({}^{\circ}\)50\({}^{\prime}\)15\({}^{\prime}\) E, about 280 km south of Uweinat Mountain. It is located in the prefecture of the State of Northern Darfur in Sudan about 654 km north of Al-Fashir City. As there are no prominent landmarks or geographic features in the immediate vicinity of the structure, Rimaal, meaning \"sand\" in Arabic, is given as the name for this structure since it is largely concealed beneath Aeolian sand cover. The subsurface geology of the region is not very well known; nevertheless, the region, in general, is mapped as Mesozoic Nubian Sandstone [19] (Figure 2). The thickness of the Nubian Sandston in the eastern Sahara region is unknown, but it can generally reach a maximum thickness of 1700 m in some parts [20]. Figure 2: A simplified geological map of the region (map modified after [21] showing the Rimaal structure (marked as a circle) residing in the Cretaceous Nubian Sandstone formation.