esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
## 6 Results
### Packet Delivery Ratio (PDR)  
Firstly, we analyzed the results for PDR. In Figure 8, Figure 9, and Figure 10, regardless of the protocol and topology, LL unicast outperformed LL broadcast. One of the characteristics of WSNs is the unreliability of the links formed between the nodes. LL unicast has a retransmission mechanism in place that tackles this problem. LL broadcast does not benefit from such a mechanism, which explains its inferior results.  
Looking again at Figure 8a,b, Figure 9a,b, and Figure 10a,b, it can also be observed that for TSCH, the PDR did not always reach 100%, not even at the first hop. We used TSCH in combination with the Orchestra scheduler that allocated a slotframe for every traffic plane. At runtime, the slots from different traffic planes were combined, and even if the lengths of the slotframes were co-prime, two slots could overlap. In the case of schedule collisions, the slot belonging to the slotframe with the highest priority was selected. RPL control traffic and EBs responsible for synchronizing the network had higher priority than application messages.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **TSCH Default** \\ \hline Operation & Sending: 0\% \\  & Listening: 2\% \\  & Sending: 2.9 ms \\  & Listening: 1 ms \\  & Sending: 1 ms \\  & Listening: 4.3 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: The theoretical time a node’s radio is on while operating the TSCH RDC protocol and sending/receiving packets.  
Figure 8b,d, shows that on topologies without interference between same rank nodes, ContikiMAC in the broadcast mode outperformed TSCH in the same mode when looking at PDR. When using ContikiMAC with the LL broadcast, a sender transmits a packet during an entire channel check interval (CCI) to ensure that each mode within its transmit range wakes up and receives the packet, hence the superior performance of ContikiMAC (more details about ContikiMAC can be found in Appendix A). The reason TSCH underperformed compared to ContikiMAC was linked to the already discussed combination of the different traffic planes at runtime and their respective priorities.  
Simulating LL broadcast on topologies with interference between same rank nodes resulted in comparable PDR performance for the two protocols (Figure 9b,d). Increasing the level of interference by simulating topologies with random placement of nodes resulted in superior performance for TSCH (Figure 10b,d). Having other nodes transmitting in the vicinity would create competition for using the shared medium, which would be detrimental to ContikiMAC.  
Figure 8: Packet delivery ratio (PDR) comparison between LL unicast and LL broadcast and between ContikiMAC and TSCH on topologies without interference between same rank nodes. (**a**) Results for LL unicast in BMRF over TSCH. (**b**) Results for LL broadcast in BMRF over TSCH. (**c**) Results for LL unicast in BMRF over ContikiMAC. (**d**) Results for LL broadcast in BMRF over ContikiMAC.
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 2 Background
### DGNSS in the Range Domain  
The range domain DGNSS provides users with pseudorange corrections based on the reference station located at a known location \((x_{m},\ y_{m},\ z_{m})\). The range between the reference station and the satellite \(i\) can be computed as  
\[R_{m}^{i}\ =\ \sqrt{\left(x_{m}\ -\ x^{i}\right)^{2}\ +\ \left(y_{m}\ -\ y^{i}\right)^{2}\ +\ \left(z_{m}\ -\ z^{i}\right)^{2}}. \tag{11}\]  
Subtracting the pseudorange \(\rho_{m}^{i}\) from the calculated range \(R_{m}^{i}\) yields the pseudorange correction (_PRC_)  
\[PRC^{i}\ =\ R_{m}^{i}\ -\ \rho_{m}^{i}. \tag{12}\]  
In the user receiver, the correction \(PRC^{i}\) is then employed to reduce various errors  
\[\rho_{c}^{i}\ =\ \rho^{i}\ +\ PRC^{i}, \tag{13}\]  
where \(\rho_{c}^{i}\) is the corrected pseudorange. Using the corrected pseudorange, one can calculate the position using the iterative least squares method discussed in the previous subsection.  
Because the satellite clock error is the same for the reference station and the user, the above differencing process can remove all of the satellite clock errors. Moreover, because the propagation errors and the orbit errors are correlated over space, most errors due to the satellite orbit and the propagation delay can be reduced significantly. The corrected pseudorange is then used to determine user position. This process is usually achieved based on the iterative method presented in the previous subsection.
## 5 Challenges and Future Pathways  
Several high-cost processes involving surfactants are generally used to make commercial silica nanoparticles. Furthermore, the use of acid washing at high temperatures and pressures increases the cost of production, while also contributing to concerns related to environmental pollution and global warming. Thus, a simple and economically effective waste utilization and waste reduction technique used to produce nano-silica particles that are both inexpensive and less energy intensive than commercially available nano-silica appears to be a viable solution. As a result, a new initiative of using agricultural waste as a precursor for nano-silica production was undertaken in the present investigation. While converting agricultural waste into a sophisticated technology for environmental applications may appear to be a compelling circular economy solution, there are considerable knowledge gaps. The ability to preserve the material's properties and quality consistency is a big concern. Chemical approaches for the production of silica nanoparticles remain attractive due to their simplicity of control and high-purity product. However, only a few researchers have examined the synthesis and growth mechanisms that lead to the production of various forms of silica nanoparticles. There are currently no broad rules that can be used to guide the design and synthesis of products that fulfill specific functional needs. As a result, better knowledge and control of the synthesis process are required to assure product repeatability. Agricultural waste collection in large quantities can also be challenging, as these wastes are not always immediately available and there are costs associated with storing, transporting, and sorting, as farmers and producers would normally dispose of them at the farm waste site. Using the procedure, outlined here, the nanoparticles can be produced at the laboratory scale, since most nanoparticles begin to cluster during the production chain because of mechanical coupling, charge transfer, or elevated interfacial charge. Finding a way to prevent nanomaterial agglomeration, especially during the functionalization, would also facilitate the transition from the lab to the market. The ease of the synthesis process, ecologically benign silica precursor, and cheap cost of raw material are all key characteristics of using biodegradable materials to generate sustainable and eco-friendly silica nanoparticles. The cost-benefit analysis of commercially producing nano-silica is beyond the scope of the current investigation, however, is planned as an independent investigation.
# A Distance-Encoded Bloom Filter for Fast NDN Name Lookup
## 3 Proposed Scheme
### A Distance-Encoded Bloom Filter  
A distance-encoded Bloom filter provides the range of descendant prefixes when the membership test result is positive. This range, represented by two distance values, determines the next search range.  
**Definition 1**.: _(Shortest Distance): For a given node \(t\) in a trie, sd (t) is defined as the distance to its nearest descendant prefix._  
**Definition 2**.: _(Longest Distance): For a given node \(t\) in a trie, ld (t) is defined as the distance to its farthest descendant prefix._  
For a given membership query on node \(t\), if the distance-encoded Bloom filter returns a positive result, the next search range is derived from \(sd\) (\(t\)) and \(ld\) (\(t\)). In Figure 7, the nearest descendant of N\({}_{3}\) is P\({}_{3}\), with a distance of 2. Similarly, the farthest descendant of N\({}_{3}\) is P\({}_{4}\), with a distance of 3. Figure 7 presents a table showing \(sd\) and \(ld\) values for all nodes. If the membership test for N\({}_{3}\) returns positive, the next search range is given by the interval [4 + \(sd\), 4 + \(ld\)], where 4 represents the level of N\({}_{3}\), resulting in the interval [6; 7]. In contrast, the binary search approach would produce the interval [5; 7], which is wider. Additionally, for any prefix node, \(sd\) is always 0, and for any leaf prefix node, \(ld\) is also 0.  
In a basic Bloom filter of \(m\) bits, the membership checking is performed by examining the bits indexed by the \(k\) hash functions: \(h_{1}\) (\(t\)), \(h_{2}\) (\(t\)), \(\ldots\), \(h_{k}\) (\(t\)). If all \(k\) bits are set to 1,  
Figure 7: \(sd\) and \(ld\) values in a distance-encoded Bloom filter.  
the membership check returns a positive result. [PERSON] et al. proposed a shifting Bloom filter that utilizes an offset to represent element counters [PERSON] et al. (2018). The distance-encoded Bloom filter also uses an offset but extends this approach by incorporating both negative and positive offsets to encode two distance values: \(sd\) and \(ld\), respectively. Figure 8 illustrates how membership and distance values are represented in the distance-encoded Bloom filter, providing a comparison with the conventional Bloom filter.
# A Performance-Based Airspace Model for Unmanned Aircraft Systems Traffic Management
## 3 Urban Airspace Model
### CNS Risk Protection Volume Generation
#### 3.1.1 Navigation Uncertainty  
GNSS range measurement accuracy is well described by aviation standards [38], with Gaussian overbound distributions accounting for various sources of measurement error. These include:  
* Satellite Clock and Ephemeris Errors (\(\sigma_{\text{clk\&eph}}\))
* Ionospheric Residual Error (\(\sigma_{\text{iono}}\))
* Propospheric Residual Error (\(\sigma_{\text{tropo}}\))
* Thermal noise and interferences (\(\sigma_{\text{noise}}\))
* Multipath (\(\sigma_{\text{multipath}}\))  
Figure 6: UAS fundamental protection volume.  
The User Equivalent Range Error (UERE) \(\sigma_{\text{UERE}}\) is the root sum square of these independent error sources:  
\[\sigma_{\text{UERE}}{}^{2}=\ \sigma_{\text{clkkeph}}{}^{2}+\sigma_{\text{iono}}{}^{2 }+\sigma_{\text{tropo}}{}^{2}+\sigma_{\text{noise}}{}^{2}+\sigma_{\text{ multipath}}{}^{2} \tag{2}\]  
In addition to the errors in range measurement, accuracy is also dependent on the geometry of the satellites relative to the receiver. The effect of geometry on the accuracy of the solution is parameterized through a number of scalar factors termed the Dilution of Precision (DOP). The Position Dilution of Precision (PDOP) is the most commonly used factor and is presented in greater detail in [39]. The navigation position error is expressed in Equation (3), showing the relation of \(\sigma_{\text{UERE}}\) and PDOP. The maximum errors are chosen to capture the worst performance, representing the conservative case. The covariance matrix of the navigation position uncertainty is presented in Equation (4).  
\[\sigma_{\text{NAV,GNSS}}{}^{2}=\text{PDOP}.\sigma_{\text{UERE}}{}^{2} \tag{3}\]  
\[\sigma_{\text{GNSS}}^{2}=\left[\begin{array}{ccc}\sigma_{\text{xNAV}}^{2}&0 &0\\ 0&\sigma_{\text{yNAV}}^{2}&0\\ 0&0&\sigma_{\text{zNAV}}^{2}\end{array}\right] \tag{4}\]  
In general, the components of \(\sigma_{\text{UERE}}\) are well described by long-standing models in the literature that conservatively bound the error [39]. The only exception is the multipath error component \(\sigma_{\text{multipath}}\), which is a site-dependent source of error that is difficult to model. However, recent work in the domain is focussing on developing conservative multipath models for UAM given a terrain map and satellite ephemerides [40]. Threshold values on the positioning uncertainty are standardized in conventional aviation operations through so-called 'Alert Limits' [41]. These specify the maximum allowable positioning uncertainty. Since no standards are currently defined for UAM navigation systems, this value is conservatively bounded based on urban canyon width dimensions (since this represents the most stringent performance requirements for UAM use-cases). Therefore, the navigation performance is declared insufficient for \(\sigma_{\text{GNSS}}\geq\text{W}\), where W is the smallest canyon width being traversed for a given planned operation (e.g., this corresponds to W = 25 m for most Australian city centres).
## 4 Methodology
### Image Preprocessing  
Orthorectification was applied to the WorldView-2 data, using a small number of ground control points combined with the satellite model to correct spatial and geometric distortions in the image. Remote sensing images generally record information in digital quantization values (DN) without quantization scales, and we need to use radiometric calibration to convert DN values into specific physical quantities (radiance, reflectance). Radiometric calibration establishes a quantitative relationship through the linear equation L\({}_{\lambda}\) = Gain \(\times\) DN + Offset (where Gain is the sensor gain value, and Offset is the offset value) [55]. Then, the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) atmospheric correction algorithm was used to eliminate or reduce the influence of atmospheric scattering on the reflectance of the land surface, to obtain the true reflectance of the land cover. It is based on the MODTRAN5 radiation transfer model and is currently the most accurate atmospheric correction method under ENVI software. In many studies, it has been proven to be significantly effective in removing atmospheric effects [56; 57; 58; 59; 60; 61]. The fusion method used the super-resolution Bayesian fusion algorithm, namely, Gram-Schmidt Pan Sharpening, which is currently one of the best remote-sensing fusion algorithms in terms of fusion effect and color fidelity. Its advantages lie in automatic alignment, retaining spectral information, and increasing spatial resolution to enrich land cover information [62; 63; 64].
## 5 Discussion
### Effect of Rock Step Height on Shear Characteristics  
Figure 14 illustrates the effect of rock step height on the shear characteristics of samples with a size of 200 mm, summarising three typical shear stress curves (Figure 14a) and failure modes (Figure 14b). When the rock step height is small, the stress curve transitions from strain hardening to strain softening following the elastic stage. The characteristics of the curves and failure modes for moderate rock step height were discussed in Section 4 and are not reiterated here. With large rock step height, a plastic stage emerges prior to the peak, and the strain softening stage occurs immediately after the peak.  
During the shear process of the samples with a small rock step height, the degree of stress concentration is high due to the limited contact area. After failure, the reduction in the contact area exacerbates the stress concentration, which is the primary cause of strain hardening. Once the steps have broken to a certain extent, shear resistance is primarily provided by particle friction, leading to shear contraction within the sample and resulting in strain softening. Conversely, when the rock step height is large, the increased contact area mitigates the degree of stress concentration, making the shear behaviour of the specimen more prone to plastic deformation. Shear dilation is less noticeable near the beginning of the steps, where failure usually starts. It is evident that the rock step height regulates the failure mode and the characteristics of the stress curve by influencing the level of stress concentration and the degree of shear dilation.  
Figure 13: Size effect on shear mechanical parameters: (**a**) 1 MPa; (**b**) 2 MPa; (**c**) 4 MPa.  
a step height of 15 mm), with amplitude reductions and amplifications of 23.73% and 116.94%, respectively.
# Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA
## 4 Simulation Experiments and Result Analysis
Particularly, when SNR was 10, some interference components with frequencies of 0.34 Hz, 0.66 Hz, 1.32 Hz, and 1.56 Hz appeared due to noise interference, and the denoising performance was also poor. The detection results of the frequency, amplitude, and phase angle of the eccentricity signal by the SFFT-IAA and FFT algorithms are presented in Table 1.  
Figure 6: The results obtained by SFFT-IAA (SRN = 30). The red circles indicate estimated frequencies. (**a**) Frequency band [0.37 Hz 0.43 Hz]; (**b**) Frequency band [0.76 Hz 0.82 Hz]; (**c**) Frequency band [1.12 Hz 1.19 Hz]; (**d**) Frequency band [2.27 Hz 2.36 Hz]. The analysis of the data in Table 1 indicated that compared with FFT, SFFT-IAA can obtain more accurate parameters of each harmonic component of the eccentricity signal at different SNRs, thus helping to build a more accurate roll eccentricity signal model. In contrast, the harmonic parameters obtained by the FFT algorithm deviated significantly from the original signal and the accuracy was poor. According to the above simulation results, under the same noise condition, the proposed SFFT-IAA algorithm achieves significantly higher accuracy for frequency estimation than the FFT algorithm and high frequency resolution in the case of close frequencies. Moreover, with the SFFT-IAA algorithm, the required frequency points can be obtained clearly without band overlapping and the algorithm can overcome the disadvantages of frequency spectral leakage and fence effect of the FFT algorithm, thus showing better overall performance than the FFT algorithm. With a high computational speed and high resolution, the SFFT-IAA algorithm can be applied to reconstruct a more accurate roll eccentricity signal model and can well meet the requirements of roll eccentricity signal detection. Figure 8: The amplitude–frequency characteristics using FFT. The red circles indicate estimated frequencies. (**a**) The results at SNR = 30; (**b**) The results at SNR = 10. Figure 7: The results obtained by SFFT-IAA (SRN = 10). The red circles indicate estimated frequencies. (**a**) Frequency band [0.37 Hz 0.43 Hz]; (**b**) Frequency band [0.76 Hz 0.82 Hz]; (**c**) Frequency band [1.12 Hz 1.19 Hz]; (**d**) Frequency band [2.27 Hz 2.36 Hz].
# Asymmetric Measurement-Device-Independent Quantum Key Distribution through Advantage Distillation
## 3 Results
As can be seen from Figure 4, the AD method improves the performance of the asymmetric MDI-QKD protocol under various finite-size effects. Even though there is a large statistical fluctuation when the number of pulse pairs \(N=10^{10}\), the AD method can still tolerate transmission losses of more than 5 dB, which means that AD method can also be more adaptable with finite-size cases. Figure 3: Results of the value \(L_{asy}\) versus the improved percentage. The improved transmission loss is the difference of the maximum transmission loss of the asymmetric MDI-QKD with and without the AD method, and we define the improved percentage as the difference divided by the latter. With the increasing degree of asymmetry, the improved percentage also becomes better.
## 2 Materials and Methods
### RNA Isolation and Droplet Digital Reverse Transcription-PCR (ddRT-PCR)  
For relative quantification of the _isaA_, _isaB_, and _hglB_ expression by ddRT-PCR, the following primers were designed: _isaA_-for: 5'-AACCTGAAGCACCTGATGGG-3', _isaA_-rev:5'-CTGCAGGTGGTAC TGGTTCA-3', _isab_-for: 5'-CCGCCTGTCTTTGATG-3', _isab_-rev: 5'-GTGCAGCAACGACATGACA-3', _hlgB_-for: 5'-TGGCGACTTAACGGAACA-3', _hlgB_-rev: 5'-TGTGTTCTACACCCA ACC-3') were designed using Primer3 Plus [34] and synthesized by Metabion GmbH. For total RNA isolation, the PL8 h, PL96 h, EDBF_24 h, and SDBF_24 h cells were harvested in RNAlater (Qiagen, GmbH, Hilden, Germany) and the RNA was extracted using the RNeasy Mini Kit (Qiagen, GmbH) with DNase (RNase-Free DNase set; Qiagen, GmbH) treatment according to the manufacturer's instructions. To remove contaminating DNA, the isolated total RNA was subjected to an additional DNase treatment using the RNase-Free DNase (Qiagen, GmbH) kit followed by an additional purification with RNeasy@ Mini Kit (Qiagen, GmbH). First-strand synthesis of cDNA was performed using the iScript Advanced cDNA Synthesis Kit for RT-qPCR (Bio-Rad, Hercules, CA, USA). Equal amounts of RNA samples were used in control reactions without reverse transcriptase. Reverse transcription (RT) reactions were incubated for 30 min at 46 \({}^{\circ}\)C and diluted 1:200, and 5 \(\upmu\)L was used in ddPCR reactions (22 \(\upmu\)L) using 1 \(\times\) QX200 ddPCR EvaGreen Supermix (Bio-Rad, Hercules, CA, USA). For droplet generation the Automated Droplet Generator (Bio-Rad, Hercules, CA, USA) was used and the thermal cycling protocols were as previously described [35]. The fluorescence of each droplet was measured by a QX droplet reader (Bio-Rad, Hercules, CA, USA), and the results were analyzed using QuantaSoft software (version 1.7.4.0917; Bio-Rad), and normalized to the total RNA used in cDNA synthesis. The ddRT-PCR assay was conducted with two biological replica samples.
## 2 Experimental Section
### GlobAlbedo-Derived LAI and fAPAR
#### 2.1.1 Two-Stream Model  
The TIP is an inversion scheme explicitly developed for rapid, robust retrieval of surface variables (including LAI and fAPAR) from observations such as albedo, particularly at large spatial scales. The TIP approach makes use of effective parameters explicitly, in particular the LAI is intended to be consistent with the 1D RT solution for deriving observed fluxes, rather than to be the most \"realistic\" estimate possible (unlike the MODIS LAI/fAPAR RT model scheme). One reason for taking this approach is that it is consistent with how radiation is treated in large-scale Earth System Models of climate and carbon cycle [22]. A second reason for using this approach is that it is driven entirely by the underlying observations, unlike 3D model schemes that account for variability in clumping and background properties by specifying biome-specific prior structural information to separate grasses, deciduous and evergreen tree types for example.  
The approach proposed by [20] uses a standard two-stream approach allowing decomposition of the directional hemispherical reflectance field (DHR, \(R_{coupled}^{total}\left(z_{toc},\mu_{0}\right)\)) emerging from the top of a canopy layer at depth \(z_{toc}\) into three terms:  
\[R_{coupled}^{total}\left(z_{toc},\mu_{0}\right)=R_{reg}^{Coll}\left(z_{toc}, \mu_{0}\right)+R_{bgd}^{UnColl}\left(z_{toc},\mu_{0}\right)+R_{bgd}^{Coll} \left(z_{toc},\mu_{0}\right) \tag{1}\]  
where \(\mu_{0}\) is the solar zenith angle. The terms on the RHS represent, respectively: \(R_{reg}^{Coll}\left(z_{toc},\mu_{0}\right)\), radiation that has interacted with the vegetation canopy elements only; \(R_{bgd}^{UnColl}\left(z_{toc},\mu_{0}\right)\) radiation that has interacted with the soil only _i.e._, travelling uncollided both downwards and upwards through gaps in the canopy; and \(R_{bgd}^{Coll}\left(z_{toc},\mu_{0}\right)\) that has collided multiple times with both canopy and background. The major advantage of this approach (over a more detailed 3D approach for example) is that it can be solved analytically. The use of effective variables (for the canopy) guarantees the correct simulation of the scattered, transmitted and absorbed radiant fluxes. The authors of [20] derive values of the surface variables by inverting a 1D radiant flux model against fluxes generated by a 3D model. This guarantees accurate simulations of the three radiant fluxes when using, in direct mode, these effective values. In addition it does not require explicit consideration of other canopy elements (woody material) as these are considered implicitly in the diffuse (multiple scattered) fluxes. The resulting approach agrees well with 3D model simulations across a range of structural configurations [20].