esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation
### AF Protocol  
For AF protocol, in the second time slot, the \(i\)-th R forwards the received signal with a forward gain \(G\), then the received signal at \(S_{j}\) from the \(i\)-th R is given by  
\[y_{ji}\left(t\right)=\mathbf{w}_{i}^{H}\mathbf{h}_{ji}\left(\sqrt{k_{3}P_{r}}G \mathbf{y}_{r}\left(t\right)+\eta_{3}\left(t\right)\right)+n_{j}\left(t\right),j\in\left\{1,2\right\}, \tag{2}\]  
where  
\[G=\sqrt{\frac{P_{r}}{\left|\mathbf{w}_{i}^{H}\mathbf{h}_{1i}\right|^{2}P_{1}+ \left|\mathbf{w}_{i}^{H}\mathbf{h}_{2i}\right|^{2}P_{2}+\delta_{i}^{2}}}, \tag{3}\]  
Figure 1: Illustration of the system model.  
\(k_{3}\) represents the impairments level at the \(i\)-th R, \(P_{r}\) the transmitted power of the \(i\)-th R. \(\eta_{3}\) denotes the distortion noise due to HIs with power of \(\eta_{3}\left(t\right)\sim\mathcal{CN}\left(0,\left(1-k_{3}\right)P_{r}\right)\), \(n_{j}\) the AWGN at the \(j\)-th source obeying \(n_{j}\left(t\right)\sim\mathcal{CN}\left(0,\delta_{j}^{2}\right)\).
# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 2 Materials and Methods
### Methodology--Model Development
#### 2.3.3 Band Ratio Methodology
An unsupervised classification methodology was then adopted in order to model bathymetry over various seabed compositions and by implementing as criteria the main IOPs and AOPs (i.e., the total backscattering coefficient, the total absorption coefficient, and the diffuse attenuation coefficient (\(K_{d}\))). The unsupervised classification of the entire study area resulted in five classes, namely, turbid waters, sparse seaweed coverage, dense seaweed coverage, shallow offshore waters without any flora, and deep waters without any flora (Figure 8). Figure 8: Seabed classification results for the Sentinel-2A imagery for (**a**) suspended sediments, (**b**) dense flora, (**c**) sparse flora, (**d**) shallow offshore, and (**e**) deep offshore.
# Approximate Networking for Universal Internet Access
## 4 Case Study: Approximate 5G Networks in Rural/Low Income Areas
With the current developments in approximate computing and the diverse use cases of NPUs, the time is right to apply these concepts to the development of network hardware, such as routers, switches, network interface cards (NICs), as well as to RF components, digital and analog processors, and the various hardware blocks of RANs. Further studies considering the affects of area and power efficient approximate hardware on cost and efficiency of cellular networks will provide a more detailed overview of the true potential of approximate networking. The time is now ripe to design cost efficient accelerated approximate memory and processors considering the demands of cellular systems. For example, the use of approximate digital blocks to design content addressable memory, widely used for packet classification and forwarding in routers, and the impact of new errors on system level performance will provide deep insights on the feasibility of approximate networking. Similarly, self organizing networks (SON) proposed for future cellular networks [70] use machine learning algorithms for various self organization phases. Cost, area and power efficient hardware at the cost of 100 percent accuracy are proposed for machine learning and deep learning applications [12]. The use cases for SONs using approximate hardware can reduce the cost of futuristic cellular systems. However, a detailed and careful analysis of the resultant performance degradation and cost benefit is necessary to understand the benefits and penalties of approximate networking. Furthermore, the co-design of approximate hardware and software stack can help to extract maximum benefits of approximate networking.
## 5 Structural Seismic Performance Analysis
### Structural Collapse Performance Considering All Uncertainties  
The collapse vulnerability curves of the four structures with total collapse uncertainties considered are illustrated in Figure 8a-d in blue dashed lines. The total collapse uncertainties include all the mentioned uncertainties, i.e., the record-to-record variability, model uncertainties \(\sigma_{MDL}\), design requirement uncertainties \(\sigma_{DR}\), and test data uncertainties \(\sigma_{TD}\).  
The blue collapse vulnerability curves have a comparatively gentler slope because the increasing collapse uncertainties raise the collapse probability of the observed data points before the median collapse intensity, whereas the MCE and median collapse intensities remain the same. The low-ductility structure NS-1 is lowest in seismic performance, but its collapse earthquake intensity at MCE remains less than the median earthquake intensity of collapse. Therefore, with an increase in collapse uncertainties, the collapse probability at MCE increases simultaneously. This finding implies the considerable influence of structural collapse uncertainty on the result of collapse performance assessment.  
The collapse probabilities at MCE of NS-1, RS-1, RS-2, and S-1 become 35%, 13%, 6.4%, and 9.7%, respectively, after considering total collapse uncertainty. These values exceed the acceptable collapse probability at MCE set by the Chinese code CECS 392 for Category-C buildings. Strict seismic strengthening measures should be adopted to meet the requirements of Chinese specifications.  
Figure 8d proves that with all uncertainties considered, the collapse probability at MCE of the structure design according to GB 50011-2010 is 9.7%. This value is larger than 5%, which is the limit of CECS 392.  
Discussions can be made from two aspects. On the one hand, the obtained collapse probabilities at MCE satisfy the requirements of CECS 392 when only record-to-record variability is considered but fail when other uncertainties are considered. The effect of collapse uncertainties on the assessment outcome must thus be controlled in the process of collapse performance assessment. On the other hand, if IDA is used to calculate structural collapse probability at MCE, then a relatively small change in total collapse uncertainty could induce an alternation in the found collapse probability at MCE and the failure to remain below the suggested limit value in CECS 392. The question of whether the acceptable collapse probability at MCE in Chinese specifications is excessively strict is raised: Should a more appropriate acceptable collapse probability in the design reference period or an acceptable average annual collapse probability be proposed under the background of Chinese codes?
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 4 Evaluation Framework
### Datasets
#### 4.1.6 SYNTHIA  
SYNTHIA dataset [60] contains 9400 road scenes captured from a simulation of a city environment. It employs 13 classes. The resolution of the images is 1280 \(\times\) 960.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 4 Discussion
### Rotor Blades  
Regarding the rotor blades, the issue is more complicated. As can be seen in Figure 3a, even very simple aerodynamic shapes cause patterns that cannot be predicted by simpler means other than a complete numerical simulation. An RCS peak to the leading edge, towards the top and slightly forward as well as towards the bottom and slightly backwards, is common between the investigated models. If this can be generalized, however, is a question that needs to be investigated. In the meantime, RCS analysis of rotor blades should be part of the type-approval process.  
Due to the electrically conductive properties of CRP, a PEC model can serve as a baseline prediction of the expected RCS.  
The same, unfortunately, cannot be stated for FRP. While the PEC models do not exhibit polarization dependence, the FRP models do, sometimes significantly (see, e.g., Figure 4b). Furthermore, the sandwich structure common in lightweight construction cannot be omitted from the simulations as the layered structure causes interference, both constructive or destructive, depending on the angle of incidence. Simulations with dielectric losses considered showed no significant change. This makes finding a baseline \"upper bound\"/worst-case estimate for the RCS infeasible. It is to be assumed that features present in the FRP simulations but not in the PEC models are subject to change as more details of the internal structure (e.g., spar and trimming elements) are added. This might be alleviated in the future, as CRP is currently gaining market share in all domains of lightweight construction.  
Considering the RCS values, the signal return of all investigated rotor blades is quite high, ranging from about 20 dB m\({}^{2}\) in average to 50 dB m\({}^{2}\) in peaks.  
As with the nonrotating parts, using special radar-absorbing coating should be considered in order to reduce the radar backscattering.
## 3 Configuration of the System and Conditions
### Computation Process  
Based on previous sections, we established a robust data reconciliation framework for the proposed schematic S-CO\({}_{2}\) system. The computation process, from condition generation to data correction, is shown in Figure 5. It should be noted that solution failure may still happen under the IPM solver, and to ensure that 2000 conditions could be acquired in the final results, backup conditions were generated for those failed cases. All computations were carried out on a personal computer with Intel Core i7-10700 CPU. MATLAB parallel pools [39] were enabled to accelerate the computations.
)1 Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan; m10302338@mail. the establishment of an optical wireless communication link has been proposed [8], recent performance presented have shown that only fewer Kb/s can be achieved [9]. In contrast to using a CMOS optical camera as an optical terminal, an optical terminal based on micro-projection architecture of PCD seems more attractive, since it is widely expected that the micro-projector integrated with VLC technology will be a vital part of a new generation PCD [7]. In particular, a micro-projection enabled SRC system based on VLC technology providing a nearly gigabit data transmission rate has experimentally demonstrated and proposed to serve as an alternative communication approach for PCD application [10]. However, the work that was presented in [10] was based on one-way transmission, and multi-wavelengths were not transmitted simultaneously. This encouraged the development of an optical terminal based on micro-projection architecture of PCD to establish a bidirectional optical wireless communication link for M2M or D2D applications. In this research, optical terminal based on micro-projection architecture of PCD for bidirectional multi-wavelength visible light communication application is proposed and experimentally demonstrated.
## 3 Methodology
### Feature Extraction Methods  
The following hand-crafted feature extraction methods were applied to the tiled images: Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), Gray Level Co-occurrence Matrix (GLCM), and Discrete wavelet transform (DWT). Combinations of GLCM features with other hand-crafted features were considered, as well as a combination of the hand-crafted features with CNN features. The feature extraction methods used are described as follows:
## 3 Results
### Application in Smart Structures
\begin{split} q_{u}(t)=\begin{bmatrix}\bar{q}(t)\\ \hat{q}(t)\\ q(t)\end{bmatrix}\\ \widetilde{D}=-\big{[}M_{0}m_{p}&K_{0}k_{p}\big{]}\begin{bmatrix}I_{2n \times 2n}\delta_{M}&0_{2n\times 2n}\\ 0_{2n\times 2n}&I_{2n\times 2n}\delta_{K}\\ &=G_{1}\cdot\Delta\cdot G_{2}\end{bmatrix}\begin{bmatrix}I_{2n\times 2n}&0.0005I_{2n \times 2n}&0_{2n\times 2n}\\ 0_{2n\times 2n}&0.0005I_{2n\times 2n}&I_{2n\times 2n}\end{bmatrix}= \end{split}  \tag{14}\]  
Equation (7), when expressed in state-space form, generates  
\[ \begin{split}\dot{x}(t)=\begin{bmatrix}0_{2n\times 2n}&I_{2n\times 2n}\\ -M^{-1}K&-M^{-1}D\end{bmatrix}x(t)+\begin{bmatrix}0_{2n\times 2n}\\ M^{-1}f_{e}^{*}\end{bmatrix}u(t)+\begin{bmatrix}0_{2n\times 2n}\\ M^{-1}f_{d}\end{bmatrix}d(t)+\begin{bmatrix}0_{2n\times 6n}\\ M^{-1}G_{1}\cdot\Delta\cdot G_{2}\end{bmatrix}q_{u}(t)\\ &=Ax(t)+Bu(t)+Gd(t)+G_{u}G_{2}q_{u}(t)\end{split}  \tag{15}\]  
In this method, we considered the uncertainty of the original matrices as an additional uncertainty parameter. Incorporating uncertainty into the equation for a smart structure involves modeling the uncertainty in the parameters of the system. In our paper, we introduce uncertainty in the matrices of smart structures and robust control theory, such as \(\mu\)-analysis and Hifoo control.
## 4 Conclusions
It is also interesting to note that for both Doppler- and TDCP-based velocity, the knowledge of the receiver position could affect the accuracy of the estimated velocity. To prove that, a simulated approach considering an increasing error in the estimated position is needed, and this could be an interesting analysis to address in further studies. The issue related to the impact of the adopted setup, as discussed in Section 3.1, could be addressed in further empirical analyses in order to assess the possible benefits related to the adoption of a ground plane. Additional future works will include the performance evaluation of the considered velocity estimation methods using kinematic data. Conceptualization, A.A. and G.C. ; methodology, A.A. and C.G. ; software, A.A. and G.C. ; validation, C.G. and S.G.; formal analysis, C.G. and S.G.; investigation, A.A. and C.G. ; resources, S.G.; data curation, A.A. and G.C. ; writing--original draft preparation, A.A., G.C. and C.G. ; writing--review and editing, A.A., G.C., C.G. and S.G.; visualization, A.A. and C.G. ; supervision, S.G.; project administration, G.C. and A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The used software tools are available at [[https://link.springer.com/article/10.1007/s10291-014-0425-1](https://link.springer.com/article/10.1007/s10291-014-0425-1)]([https://link.springer.com/article/10.1007/s10291-014-0425-1](https://link.springer.com/article/10.1007/s10291-014-0425-1)) (accessed on 11 October 2023) and at [[https://geodesy.noaa.gov/gps-toolbox/](https://geodesy.noaa.gov/gps-toolbox/)]([https://geodesy.noaa.gov/gps-toolbox/](https://geodesy.noaa.gov/gps-toolbox/)) (accessed on 11 October 2023). The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.
## 2 Results
### Identification of Allium Species Non-Volatile Metabolites via UPLC/PDA/qTOF-MS
#### 2.3.3 Identification of Fatty Acids and Oxylipins  
MS spectra of several fatty acids eluting mostly in the late elution part of the chromatogram (\(R^{l}\) 400-600 s), were identified including linoleic (L48), palmitic acid (L49), oleic acid (L50), and stearic acid (L51) from their respective molecular ion masses at \(m/z\) 279.2324 (C\({}_{18}\)H\({}_{31}\)O\({}_{2}\))\({}^{-}\), \(m/z\) 255.2329 (C\({}_{16}\)H\({}_{31}\)O\({}_{2}\))\({}^{-}\), 281.2485 (C\({}_{18}\)H\({}_{33}\)O\({}_{2}\))\({}^{-}\), and 283.2638 (C\({}_{18}\)H\({}_{35}\)O\({}_{2}\))\({}^{-}\). A few hydroxylated fatty acids were identified from extra loss of water molecule (\(-\)18 amu) in peak (L34) [\(m/z\) 305.0709, (C\({}_{12}\)H\({}_{17}\)O\({}_{7}\)S)\({}^{-}\)], peak (L39) [\(m/z\) 329.2337, (C\({}_{18}\)H\({}_{33}\)O\({}_{5}\))\({}^{-}\)], and peak (L47) [\(m/z\) 295.229, (C\({}_{18}\)H\({}_{31}\)O\({}_{3}\))\({}^{-}\)]. A novel hydroxylated fatty acid present in both _Allium_ species was annotated as 9,12,13-trihydroxy octadeca-7-enoic acid (\(m/z\) 329.232, [M - H]\({}^{-}\), L39) based on fragment masses at \(m/z\) 311, 229, and 171, later resulting from cleavage at the C9 position, (Supplement Figure S6). Hydroxy fatty acids are recognized for their anti-inflammatory, antimicrobial, and cytotoxic activities [17];whether they contribute to _Allium_ effects has yet to be determined. A sulphated oxylipin was tentatively identified as jasmonic acid-hydroxy-\(O\)-sulfate in peak L34 (\(m/z\) 305.0709, C\({}_{12}\)H\({}_{17}\)O\({}_{7}\)S, [M - H]\({}^{-}\)) and exhibiting the loss of 80 amu for the sulphate moiety. Another sulphated fatty alcohol was detected in peak (42) [\(m/z\) 265.1477, (C\({}_{12}\)H\({}_{25}\)O\({}_{4}\)S)\({}^{-}\)] assigned as trimethyllnonanol sulphate. Both sulphate peaks L34 and L42 are reported for the first time in _Allium_ and suggest occurrence of sulphur is not limited to peptides in _Allium_.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 4 System Model
### Contention Window Size Adjustment  
In addition, we adjust the contention window size (_CWS_) by _QoS_ factors: distance, _RSSI_ and residual energy. This process reduces the end-to-end delay and energy consumption for transmitting emergency packets. Table 3 shows the data traffic specification. All the data are prioritized based on health parameters such as temperature, heart rate, blood pressure, respiration, ECG, etc. Based on these parameters, the data traffic type is classified into three types, as mentioned in Table 3. Depending upon the values of the health parameters, the data are queued to any one of the three queues.
## 2 Novel LEO Augmented GNSS Receiver System Structure
### Tracking Method in the LEO-Augmented GNSS Receiver
Ignoring high-frequency information items, the output of the integration and dump circuit by integrating the signal within a bit interval can be written as [22]:  
\[s(t)=AD(t)sinc(\pi f_{e}T_{coh})e^{i[2\pi f_{e}(t+\frac{T_{coh}}{2})+\phi_{e}]}+n (t) \tag{3}\]  
where \(T_{coh}\) represents the coherent integration time, and the \(sinc(x)\) function equals \(sin(x)/x\). In a single bit, the Differential Amplitude (DA) frequency discriminator estimates the standard frequency error \(f_{e}\) by the following equation:  
\[f_{e}=\Delta f\cdot\frac{A_{s}-A_{f}}{\left[A_{s}-2 cos(\pi\Delta fT_{coh})A_{p }+A_{f}\right]}. \tag{4}\]  
In Equation (4), \(A_{s}=|Asinc((f_{e}-\Delta f)T_{coh})|\), \(A_{f}=|Asinc((f_{e}+\Delta f)T_{coh})|\), and \(A_{p}=|Asinc(f_{e}T_{coh})|\). \(\Delta f\) stands for the interval of three local carrier frequencies. When \(\Delta f=2/(3T_{coh})\), this frequency discriminator can obtain the optimal frequency estimation error and reach the lowest tracking threshold. After the frequency error \(f_{e}\) is calculated, it is filtered through a second-order loop filter, which finally is fed back through the carrier NCO. Significantly, in the LEO-assisted tracking loop, the navigation bit \(D(t)\) in Equation (3) could be removed according to the information from the LEO, resulting in a longer integration time than a bit duration.
## 6 Conclusions  
The paper has described a methodology for secure cooperation within a network of autonomous mobile underwater sensors connected through an acoustic communication network. A cooperative algorithm based on the behavioural paradigm has been illustrated. Each mobile sensor solves simple parallel subtasks responding to local rules based on the available information to perform the mission and maintain the communication links within the network. The algorithm has been designed to be intrinsically robust, in the sense that with loss of communication among the vehicles the coverage performance (_i.e._, the mission goal) is degraded but not lost. Moreover, the algorithm attempts to minimize the information exchange among the vehicles prior to the local decision. Area coverage performance of the proposed adaptive approach has been discussed and compared with those obtainable with optimal geometrical solutions. It was also shown how, in dependence with the type of information transmitted but without introducing additional data exchange or algorithmic complexity, the proposed algorithm may instigate additional and more complex team behaviors. Crucial for the agents cooperation is the trustworthiness of the messages among the sensors. To ensure it, a security suite based on the group communication paradigm has been designed, at the communication middleware level. The security suite has been specifically oriented to the underwater scenario, and in particular to the goal of minimizing message size overload and computational and message exchange increase, while still guaranteeing security. Details on the implementation of the given methodologies have been given, describing the architecture developed to integrate the cooperative algorithm into the security suite. Furthermore, we reported statistics and figures of merit on the performance of the UAN project underwater acoustic network as tested in May 2011, in the Trondheim area, Norway. The UAN experimental testing was focused on the general validation of the network architecture and it included all the security mechanisms described in this work and the autonomous adaptation and cooperation of the nodes.
# Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
## 4 Results
### Peak Electron Density Height Analysis  
The NEDM2020 model is employed to determine the height at which the maximum electron density peak \(Hm\) is observed along the paths of both the incident and reflected rays. This altitude is significant as it represents the point of maximum ionization within the ionosphere that the signals traverse.  
From a geometrical standpoint within the grazing GNSS-R configuration, variations in elevation angles directly correspond to changes in the segment of the signal ray that travels along the ionosphere. Furthermore, throughout the diurnal cycle, electron densities within the E and F layers exhibit greater magnitudes during daylight hours compared to nighttime, with the F layer generally obtaining higher electron concentrations. These fluctuations are examined to comprehend the intricate ionospheric interactions that the signals undergo  
Figure 14: Distribution of Doppler shift depending on elevation and rising or setting event using NEDM2020 STEC retrievals with F10.7 = 75.  
Figure 13: Distribution of Doppler shift depending on elevation, daytime (DT), and nighttime (NT) using NEDM2020 STEC retrievals with F10.7 = 75.  
during their propagation. This phenomenon results in variations in the height of the maximum electron density peak, as depicted in Figure 15 for both day and nighttime.  
The overall average of the \(Hm\) during the LSA period is 270 km. Nevertheless, noticeable variations are evident with respect to daytime and nighttime. In general, during nighttime, the \(Hm\) is on average 10% higher than during daytime. The tropics region stands out as one of the most dynamically changing areas within the ionosphere. In this zone, the distribution of \(Hm\) during daytime exhibits a spread ranging from 236 to 326 km, lacking a distinct peak value. However, during nighttime, \(Hm\) reaches its maximum value at approximately 305 km. This highlights the substantial variations in electron density within this region, particularly during daytime. During HSA, the \(Hm\) exhibits a consistent increase of 21% across all scenarios.