esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Device Discovery Mechanism for Device-to-Device Communications in 5G Networks +
## 4 Performance Analysis of Proposed Hybrid D2D Discovery Mechanism
Thus, the collision or contention probability \(p_{f}\) in (3) is given by  
\[p_{f} = \sum_{n=1}^{\infty}\Big{[}1-(1-p)^{n-1}\Big{]}\frac{e^{-\sigma/3 }(\sigma/3)^{n}}{n!} \tag{4}\] \[= 1-\frac{e^{-\sigma p/3}-pe^{-\sigma/3}}{1-p}.\]  
As in [32], only collisions are considered and bit errors are ignored here. Finally, using Equations (3) and (4) the transmission probability \(p\) can be solved using the numerical approach suggested by [32]. Figure 5: System model for analysis. Subsequently, the probability \(p_{s}\) that a discovery beacon can be successfully transmitted at any time slot is derived as follows. \[p_{s} = \Pr\{\text{transmission successful}\ |X>0\} \tag{5}\] \[\sigma = \frac{\lambda_{UE}\pi d^{2}(r_{1}+r_{2})P_{S}}{(3\sqrt{3}R^{2})\mu} \tag{1}\]0 \[\sigma = \frac{\lambda_{UE}\pi d^{2}(r_{1}+r_{2})P_{S}}{(3\sqrt{3}R^{2})\mu} \tag{1}\]1  
According to the binary exponential backoff algorithm, after the \(j\)-th collision, the random backoff time is chosen among these integers \(\{0,1,\cdots,2^{j}{-}1\}\). It leads to the result that the average backoff time between the \((j{-}1)\)-st and the \(j\)-th retransmissions equals \((2^{j}{-}1)/2\) slots. Then the average discovery delay can be expressed by \(\sum_{j=1}^{i}\left(1{+}\frac{2^{j-1}{-}1}{2}\right)\) when a discovery beacon is transmitted successfully at the \(i\)-th transmission (including \(i{-}1\) retransmissions). Notably, a target UE can be discovered only when it is located within the probing range \(d\) of the source UE. Hence, the probability that a target UE can be successfully discovered at the \(i\)-th transmission equals \((1{-}p_{s})^{i-1}p_{s}p_{c}\), where \(p_{c}\) is the probability that two UEs of a D2D link are located within the probing range \(d\) of Wi-Fi signals and is derived in the Appendix A. Consequently, the average discovery delay \(D\) (in slots) is derived as follows. \[\sigma = \frac{\lambda_{UE}\pi d^{2}(r_{1}+r_{2})P_{S}}{(3\sqrt{3}R^{2})\mu} \tag{1}\]2 \[\sigma = \frac{\lambda_{UE}\pi d^{2}(r_{1}+r_{2})P_{S}}{(3\sqrt{3}R^{2})\mu} \tag{1}\]3 \[\sigma = \frac{\lambda_{UE}\pi d^{2}(r_{1}+r_{2})P_{S}}{(3\sqrt{3}R^{2})\mu} \tag{1}\]4 \[\sigma = \frac{\lambda_{UE}\pi d^{2}(r_{1}+r_{2})P_{S}}{(3\sqrt{3}R^{2})\mu} \tag{1}\]5  
Using a similar approach, the average number \(N\) of transmitted discovery beacons can be estimated as  
\[\sigma = \frac{\lambda_{UE}\pi d^{2}(r_{1}+r_{2})P_{S}}{(3\sqrt{3}R^{2})\mu} \tag{1}\]6 \[\sigma = \frac{\lambda_{UE}\pi d^{2}(r_{1}+r_{2})P_{S}}{(3\sqrt{3}R^{2})\mu} \tag{1}\]7  
Finally, one can obtain the discovery success rate \(P_{S}\) as follows. \[\sigma = \frac{\lambda_{UE}\pi d^{2}(r_{1}+r_{2})P_{S}}{(3\sqrt{3}R^{2})\mu} \tag{1}\]8  
Related performance metrics can be iteratively computed, as given by Algorithm 1. Table 2 shows the computation results of the example with parameters \(R=200\) m, \(d=100\) m, \(\lambda_{UE}=0.5\) s\({}^{-1}\), \(1/\mu=300\) s, \(r=0.1\), \(r_{1}=0.9r\), and \(R_{T}=5\) using Algorithm 1.
###### Abstract  
The human opportunistic networks (ONs) formed by hand-held smart devices can facilitate peer-to-peer communication when humans are on the move, despite contemporaneous end-to-end paths rarely existing. In some scenarios, where network resources, especially power, are scarce and the traffic is large, the data delivery is prone to poor user experience and unbounded delay, although the best effort mechanism \"store-carry-forward\" is used. To cope with that, most transport/routing schemes obtain an acceptable latency at the cost of energy resources. In real-life human ONs, however, excessive energy consumption will trigger passive participation of the relays in message forwarding, so as to save their limited energy resource. Thus, the reliability of these schemes may get worse in real-life human ONs. In this paper, a reliable transport scheme is developed by making an optimal trade-off between the file round-trip delay and the energy consumption of relays. We make use of acknowledgements and coding at the source to enable successful file delivery. When setting up the network model, the cache management rule referred as \"full-duplex\" strategy is formulated, and then a mathematical model is established to analyze the proposed scheme. This model describes the evolution of packet dissemination and allows both the mean file round-trip delay and the energy consumption up to the reception of the last acknowledgement by the source to be expressed. Subsequently, a new function based on these two metrics is proposed to reflect the number of files that can be delivered under time and energy constraints. Through optimization procedure, the configurations that can maximize the function are obtained; thus, the optimization of these two metrics is achieved. Numerous simulations and comparisons are conducted and the results verify the accuracy of the analytical model. Comparison results show that with limited energy and passive relays, the proposed transport scheme can significantly reduce the energy consumption of file delivery, which obviously alleviates the selfish behavior of nodes. Therefore, the reliability and stability of the communication service in human ONs are enhanced.  
human opportunistic networks; reliable transport; analytical models; energy consumption 1
## 6 Application of the Method on Two Earthquakes in Greece
### Efpalio Earthquake
#### 6.1.3 Comparison with Seismological Results  
The EYPA position changes derived from the GNSS observations have been seismically interpreted (see [13]). The seismic model, consisting of the centroid moment tensor solution and a simple planar fault with a constant slip, has been processed with the COULOMB v. 3.1 software (by colleagues from Charles University in Prague [13]). The results from the GNSS measurements (co-seismic displacement was calculated after both earthquakes) and from the static model (only for 18 January event) are summarised in Table 1. The difference of both displacements (+0.18 cm, +0.44 cm, \(-0.98\) cm) is below the RMSE of GNSS (2 nd approach). This again shows that the 2 nd approach for computing the RMSE gives a more realistic estimate than the RMSE from the 1 st approach. [PERSON] et al. [13] states that there is a relatively good agreement between the results of anharmonic analysis and the accuracy given by the 2 nd approach. The difference of both displacements may also be caused by post-seismic displacements that occurred shortly after the earthquake, which the static seismic model does not consider.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 4 Discussion and Conclusions  
In this study, 13 different cracks were simulated using finite element models of an arch dam. It was determined how they affected the displacement recordings at 27 monitoring points. Based on these displacements, a methodology was defined and tested to detect cracks and estimate their dimensions. This is an exploratory study intended to assess the potential of the proposed methodology.  
The primary conclusions drawn from this research are limited to the context of the case study under investigation. In order to generalize the results beyond this setting, further supporting evidence or comparative analysis is recommended. The conclusions are as follows.
## 10 Conclusions and Future Work  
In this work, the impact of the Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in the NOMA cell is investigated, where the LSTM network is developed for complex data processing. In the proposed DL algorithm, the DNN model is trained online based on both the normalized channel statistics and the relationship between successive training sequences. The validity and efficiency of the proposed DL channel estimation scheme is emphasized by inspecting the proposed DNN model using the Rayleigh fading channel and Rician fading channel. Furthermore, we introduce a framework that investigates how the proposed channel estimation based on the DL and the power optimization scheme are jointly utilized for multiuser detection in the PD-NOMA system. To maximize the sum rate of the system users, we optimize the power coefficients allocated for each user on the basis of the overall power transmitted and the QoS constraints. A systematic mathematical analysis for the optimization problem is introduced and the Lagrange function and KKT conditions are employed to deduce the optimal power factors. The simulation results in terms of the BER, outage probability, sum rate, and individual capacity have verified that the proposed DL model-assisted NOMA can realize reliable performance compared to the conventional NOMA scheme, even when cell capacity is increased.  
In future work, the performance of the proposed DNN model can be further explored in terms of single input, multi output (SIMO) or multi input, single output (MISO) for different types of fading channels and modulation schemes.  
**Author Contributions:** Methodology, M.G.; Project administration, A.A.; Resources, A.A.; Supervision, M.A.; Writing--original draft, M.G.; Writing--review & editing, M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
Figure 15: Individual capacity vs. power for DL-based optimized and FPA power schemes (Rayleigh Channel).
## Material and Methods
### Data Analysis  
Images were captured with a GenePix 4000B (Axon, Molecular Devices, San Jose, CA, USA) and spots quantified using GenPix software (Axon, Molecular Devices, San Jose, CA, USA). Background correction and normalization of expression data were performed using the methods normexp and loess in LIMMA, respectively [20]. The expected False Discovery Rate (FDR) was controlled to be less than 5%. Genes were considered as differentially expressed (DE) when nominal \(p\)-values were \(<\) 0.05 and had a fold change (FC) equal or higher than \(\pm\) 1.5 FC was calculated as the average of the fold change between significantly regulated probes. Hybridizations and statistical analysis were performed by the Genomics Facility at the National Center for Biotechnology, CSIC, Madrid, Spain.  
Finally, genes showing a 1.5-fold or greater FC (\(p\) \(<\) 0.05) were used to conduct a functional enrichment analysis according to their GO and KEGG's pathway terms by using DAVID [21] ([[https://david.ncifcr.gov](https://david.ncifcr.gov)]([https://david.ncifcr.gov](https://david.ncifcr.gov))). Terms with \(p\)-values less than 0.1 were considered as significant.
# Securing SDN-Based IoT Group Communication
## 4 Implementation and Evaluation
### Private Advertisements Using ZKPs  
_Zero-knowledge proofs_ (ZKP) are a class of proofs in which a _prover_ can prove to a _verifier_ the knowledge of a value without revealing what the value is [19; 20]. In the context of our system, ZKPs can be used by a user in order to generate a WoT-TD that reveals only affordances that are accessed through CoAP group communication. In order to achieve this, the proof of the TD signature must have been generated using an appropriate signature algorithm. In our implementation, we used the BBS+ linked signature algorithm for this purpose [21]. This signature scheme makes use of BLS12-381 pairing-friendly keys [22].  
In a WoT-TD that contains a BBS+ signature, a subset of the affordances can be hidden by \"framing\" the original WoT-TD in a JSON-LD frame [23]. A _JSON-LD frame_ can be seen as a filter that, when applied to a JSON-LD document (e.g., a W3C-compliant VC), it outputs a new JSON-LD document that contains only a subset of the fields of the original document. Table 2 shows the time required to generate and verify an LDP proof, using the same endpoints as in Section 4.1, for a WoT-TD file that includes four affordances, three of which are hidden. For this purpose, we used node.js and the jsonld-bbs library, ([[https://github.com/mattrglobal/jsonld-signatures-bbs](https://github.com/mattrglobal/jsonld-signatures-bbs)]([https://github.com/mattrglobal/jsonld-signatures-bbs](https://github.com/mattrglobal/jsonld-signatures-bbs)) accessed on 5 August 2021), which handles JSON-LD objects and uses BLS12-381 keys to generate BBS+ ZKPs. The size of the corresponding base64-encoded LDP is 891 bytes.  
As can be seen from this Table, LDP generation and verification in the Raspberry Pi requires approximately 1 s. Nevertheless, we are using an old device and an implementation written in node.js; therefore, there is significant space for improvement. Additionally, these signatures have to be calculated every time a new WoT description file is advertised; this process does not have to take place often--its frequency can be in the order of hours.
## 2 Data Acquisition and Pre-Processing
### Test Procedure  
The highway driving scene was designed to collect the driving data in a car-following situation. All the participants first gave their informed consent and signed the test information book before participating in the study in the actual test process. Before the formal testing, the driver practiced for 30 min to become familiar with the test procedures and equipment operation. In order to eliminate random errors, the experiments were repeated three times. The testing procedure is shown in Figure 3. Testing data were collected including the driver style questionnaire (DSQ) (6-level Likert scale) [29], risk perception questionnaire (RPQ), the state of the ego vehicle, and the state of the preceding vehicle. The physiological signals of drivers were also collected. All information used for data collection is shown in Table 1.  
Figure 2: Data collection system architecture.  
The primary task was following the preceding vehicle, while the secondary task was answering N-Back questions. When the driver performed the primary driving task, he/she heard a series of random numbers broadcast from 0 to 9; the interval between the two numbers was 2.5 s. For instance, when performing the 0-Back task, if the driver hears that two adjacent numbers are the same, they need to answer \"Yes\". Figure 4 is a schematic diagram of the N-Back task. The red boxes mean the drivers should say \"Yes\".
# Energy-Efficient Device Discovery Mechanism for Device-to-Device Communications in 5G Networks +
## 6 Conclusions  
This article proposes a two-phase hybrid D2D discovery mechanism which combines the network-assisted and direct discovery techniques. In Phase 1, the BS sifts D2D candidates from all UEs to enhance the D2D discovery performance. In Phase 2, the Wi-Fi Direct is used to discover the target UE. However, the search and listen channel for the UEs of a D2D link is assigned by the BS and the binary exponential backoff algorithm without frozen backoff time is used to resolve collisions. The energy consumption, discovery delay, and discovery success rate of the proposed D2D discovery mechanism are analyzed. Evaluation results demonstrate that the proposed two-phase hybrid D2D discovery mechanism significantly outperforms the conventional direct D2D discovery and inverse proportional transmission probability schemes. Additionally, evaluation results validate the accuracy of our theoretic analyses and show that the energy consumption is a linear function of retry limit \(R_{T}\) and the discovery delay is an exponential function of \(R_{T}\). There exists a tradeoff between the discovery success rate and discovery delay (or energy consumption). However, using the analytic results the optimal retry limit \(R_{T}\) for maximizing the discovery success rate, under the constraint of energy consumption or discovery delay, can be properly designed. In the near future, we aim to construct the eNB and EPC systems in our laboratory using the 5G software packages supported by the open air interface (OAI) software alliance [33], and test our proposed mechanism on this real testbed.  
[PERSON] designed the D2D discovery mechanism, analyzed the performance, and wrote the paper; [PERSON]-L.T. implemented the D2D discovery algorithms and wrote the simulation programs. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the Ministry of Science and Technology of Taiwan under Grant MOST106-2221-E-182-012-MY2.  
The authors declare no conflict of interest.
## 4 Results
### Landslide Susceptibility Analysis  
#### 4.3.1 Slide Susceptibility  
Figure 8 depicts the slide susceptibility. The study area was divided into four categories based on normalized values: low susceptibility (<0.36), medium susceptibility (0.36-0.54), high susceptibility (0.54-0.71), and extremely high susceptibility (>0.71) (Figure 8a). In total, 75.4% of the area has low slide susceptibility. Only 19.5% of the region was very susceptible to slides, which were all located below the snow line (Figure 8b). Statistically, 82.6% of the slides were distributed in areas with medium, high, and very high susceptibilities. The accuracy for the slide susceptibility was 0.92 (Figure 8c), which was estimated using the remaining 20% of slides.  
Figure 7: Predicted temperature, rainfall, and snowfall under the climate scenario of SSP1-2.6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Slides**} & \multicolumn{6}{c}{**Debris Flows**} \\ \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{\(\mathbf{Z_{CF}}\)} & \multirow{2}{*}{\(\mathbf{\beta}\)} & \multirow{2}{*}{**S.E**} & \multicolumn{3}{c}{**95\%CL of Exp(B)**} & \multirow{2}{*}{**Factor**} & \multirow{2}{*}{\(\mathbf{Z_{CF}}\)} & \multirow{2}{*}{\(\mathbf{\beta}\)} & \multirow{2}{*}{**S.E**} & \multicolumn{3}{c}{**95\%CL of Exp(B)**} \\ \cline{5-10}  & & & & **Lower** & **Upper** & & & & **Lower** & **Upper** \\ \hline Elevation (m) & 0.74 & \(-\)0.78 & 0.19 & \(-\)1.37 & \(-\)0.42 & Elevation (m) & 0.87 & 0.55 & 0.22 & 0.13 & 1.06 \\ Slope (\({}^{\circ}\)) & 0.78 & 0.21 & 0.25 & \(-\)0.28 & 0.87 & Slope (\({}^{\circ}\)) & 0.67 & 0.14 & 0.31 & \(-\)0.50 & 0.82 \\ Hydrogeology & 0.14 & \(-\)0.09 & 0.27 & \(-\)0.70 & 0.46 & Relative relief & 0.43 & \(-\)0.01 & 0.25 & \(-\)0.56 & 0.54 \\ LS factor & 0.52 & \(-\)0.11 & 0.23 & \(-\)0.70 & 0.39 & Aspect & 0.59 & \(-\)0.09 & 0.07 & \(-\)0.25 & 0.06 \\ TWI & 0.82 & 0.07 & 0.13 & \(-\)0.23 & 0.34 & LS factor & 0.32 & 0.05 & 0.18 & \(-\)0.35 & \(-\)0.45 \\ Valley depth & 0.47 & \(-\)0.32 & 0.11 & 0.15 & 0.59 & Distance to & 0.79 & \(-\)0.43 & 0.14 & \(-\)0.76 & \(-\)0.21 \\ \(P_{\text{max}}\) (mm) & 0.80 & 0.25 & 0.22 & \(-\)0.16 & 0.79 & \(T_{\text{max}}\) & 0.91 & 0.32 & 0.21 & \(-\)0.08 & 0.78 \\ Constant & \(-\) & \(-\)0.94 & 1.09 & \(-\)3.13 & 1.42 & Constant & \(-\) & \(-\)3.69 & 2.08 & \(-\)8.18 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Z_{CF}\) and \(LR\) regression coefficients of key triggering factors for slide and debris flow. \(\beta\) denotes the regression coefficient, S.Emeans standard error, and CI indicates confidence interval.  
Figure 8: Susceptibility of slides. (**a**) The distribution result of the susceptibility in each grade by the LR model. (**b**) The statistical proportion of slide area and each susceptibility grade area. (**c**) The evaluated accuracy of slide susceptibility. ROC curve: receiver operating characteristic curve; AUC: area under curve.
# Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
## 3 Results and Analysis  
### Gaussian Capacity Modeling Results  
Using the above Gaussian flux model equation, land surface temperature and air temperature images captured on 5 September 2020 were used in this study. The SUHI and CLHI distribution maps at typical times of day and night were drawn, and their Gaussian capacity model parameters (at 11:00 in the day and 02:00 at night as examples) were determined. The results are shown in Figures 1 and 2.  
In the heat island distribution map, red indicates a higher temperature, while blue indicates lower temperatures. To facilitate a more intuitive understanding of the inversion results of the Gaussian capacity model, the heat island center \((x_{0},y_{0})\) is taken as the center, \(a_{x}\) and \(a_{y}\) are the major semi-axis (red line) and minor semi-axis (green line), and \(\varnothing\) is the major semi-axis. The heat island footprint ellipse is drawn in the heat island distribution diagram at an angle with the horizontal direction. Figures 1 and 2 show that (1) the intensity of the SUHI and CLHI is strong during the daytime and relatively weak at night, and this pattern is also consistent with the suburban background temperatures of the SUHI and CLHI at the same transit time. (2) The centers of the heat islands of the SUHI and CLHI are closer at night and differ more during the day; the elliptical areas of the heat island footprints of the two also show the same pattern, which are larger during the day than at night. (3) The directions of the heat islands of the SUHI and CLHI are closer at the same transit time because the temperature mainly comes from the surface radiation. Although there are some differences in the parameters of the Gaussian capacity models of the SUHI and CLHI, the two are not very different and are close to each other, which is in consistent with the general rule.  
Figure 1: Distribution map of SUHI **(a)** in the day and **(b)** at night in Beijing on 5 September 2020.
## 3 Results and Discussion
### Differentiation and Identification of Five-Year Boundaries  
In consideration of the difference between food and medicine, the misunderstanding is mainly caused by confusion between those at 5-year growth and those at 6-year growth; thus, we carried out discrimination identification with 5 years as the boundary (<5 or \(\geq\)5). The test accuracies of the 10-time experiment results are all 100%, and the importance of the spectral band is shown in Figure 7. By jointly considering the food and medicine identification results, we can conclude that 1-, 2-, 3-, 4-, and 7-year-old ginseng are easy to identify with spectral reflectance, but this is not the case with 5- and 6-year old ginseng.  
Over the spectrum in 400-2500 nm, VNIR accounted for 98.1% of the importance, while SWIR accounted for 1.9%; we can regard SWIR as irrelevant. In Figure 2, we can also find spectrum aliasing in SWIR bands and clear year-to-year changes in VNIR bands. The 400 nm to 650 nm range accounted for 67% of the importance. For identification with a five-year boundary, the SWIR band can be ignored and only VNIR hyperspectral equipment needs to be used. The spectral reflectance characteristics of ginseng changed greatly in the fifth year. The chemical analysis of ginseng should pay attention to the changes of active components in the fifth year.