esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Responses to a Marine Heat Wave in Five Fringing Reefs of Southern Taiwan
## 2 Materials and Methods
### Image Analysis  
For each 35 \(\times\) 35-cm image (Figure S2), 50 random points were overlaid with CoralNet [28], and each of the 366,800 random points was assigned one of the following 83 benthic categories. First, scleractinian corals were identified to genus level (except for _Gontipora_ & _Alveopora_, which could not be differentiated by eye or CoralNet), and 34 genera were identified. For several of the more common genera, morphology was considered; montiporids were scored as branching, encrusting, or foliose, acroporids as branching or tabulate, and pavonids as massive or encrusting (\(n\) = 39 groupings upon including an additional category for \"unknown hard coral species\"). Unless otherwise specified, _Tubastrea_ spp. (sun coral), _Millepora_ spp. (fire coral), and _Heliopora_ spp. (blue coral) were considered in the stony coral analyses (despite not being scleractinians) since they construct reef framework in Taiwan (\(n\) = 42 stony coral groups). For certain analyses, bleached corals (methods described below) in each of these groups were considered their own categories; as 13 genera were observed on the transects as bleached (+one category for \"unidentifiable bleached hard corals\"), there were 14 bleached groupings. Note that other genera may have bleached though were not sampled by CoralNet's artificial intelligence. In total there were 56 coral benthic groupings. Algae were scored as one of seven bins: crustose coralline algae (CCA) on hard substrate, CCA on rubble, turf growing on hard substrate, turf growing on rubble, blue-green macroalgae, encrusting macroalgae, or upright/erect macroalgae. Seven soft coral and seven non-coral invertebrate groupings were also considered (see Appendix S2 for details), and the following six abiotic categories were quantified: dead hard coral (tissue-free skeleton that had not yet been colonized by turf algae), fine sediments (e.g., mud & silt), sand, hard substate, transect hardware (e.g., rebar), and unclassified/unknown. Point data were first converted to feature areas within each \(\sim\)0.1 m\({}^{2}\) quadrat (e.g., 20% algal cover) and then to percentages of the total \(\sim\)10 m\({}^{2}\) transect area, and the transect served as the unit of replication; this involved first calculating an average across the \(\sim\)90 images taken from each of the three transects (3-6 in the case of OL) at each site for each of the five survey dates (resulting in 15, 15, 26, 15, & 15 mean transect data points for HJ, HBH, OL, ILI, & ILO, respectively; \(n\) = 86 pooled observations used in the statistical analyses outlined below for the 83 benthic groupings). All cover data are given as a percentage of the total benthos, though for certain analyses, they are instead presented as relative to total coral cover to emphasize differences in bleaching sensitivity among genera.
# Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs
## 3 Performance Analysis
### Asymptotic Analysis
Since the QN is coupled with the transmit power, the SINRs of both common and private messages saturate at high-SNR and -QN._
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 5 Framework Design for 5G Mm-Wave Backhaul
### Scheduling
#### 5.3.1 Optimization Model
Suppose that the link from \(b_{1}\) to \(b_{3}\) may interfere with the one from \(b_{5}\) to \(b_{4}\), so that they cannot be scheduled concurrently. Therefore, In every scenario in Figure 4b, link \(b_{1}\to b_{3}\) and link \(b_{5}\to b_{4}\) are allocated into differenttime phases. The time demands for the original flows, coloured in blue, red and green, are 4, 7 and 5 \(\upmu\)s, respectively. The wired link between \(b_{1}\) and \(b_{4}\), depicted by dotted rectangles, is excluded from the constraints of hybrid beamforming. For example, in (b4), as long as the number of concurrent wireless links transmitted from \(b_{1}\) does not exceed the number of RF chains, wired link \(b_{1}\to b_{4}\) is able to be allocated in the same time period. Thanks to full duplexing, consecutive hops in a new flow are able to be allocated into the same phase. For example, as for the new flow \(b_{3}\to b_{2}\to b_{1}\to b_{4}\) in (b2) where full duplex is allowed, the first two hops, \(b_{3}\to b_{2}\) and \(b_{2}\to b_{1}\), are allocated into the first phase. The last hop \(b_{1}\to b_{4}\) is transmitted in the following phase, which is not allowed to be allocated in the previous phase of the first two hops due to relay sequencing. It is undoubted that the backhaul system with the capability of the both two new physical layer techniques ((b4) in Figure 4b) greatly reduces the total required transmission time and attains the best throughput performance, the throughput of which exceeds three times that of the STDMA scheduling scheme without using any new physical layer techniques ((b1) in Figure 4b).
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 6 Hardware Simulation
### The Test for the Trajectory with Higher Dynamic Condition  
For the higher dynamics scenario, the velocity, acceleration and jerk of the vehicle increase gradually after a 60 s stationary period, and each of them changes with time in a sine form. After ninety seconds, the amplitudes of the acceleration and jerk reach the stable maximums of 50 g and 20 g/s, respectively, meanwhile the same noise as the Trajectory 1 is added into the GPS IF data. The sky plot is shown in Figure 21, and the Doppler and \(C/N_{0}\) variations of the satellites are shown in Figure 22.  
The sinusoidal variation of Doppler means there are not only acceleration and jerk, but also much higher order dynamics in this scenario. The tracking performance of SV10 is shown in Figure 23, from which it can be seen that the tracking sensitivity of the adaptive method is increased about 5 dB compared with the other two methods. The result is consistent with that of the software simulation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Position Error (m)**} & \multicolumn{3}{c}{**Velocity Error (m/s)**} \\ \hline
**Method** & **Adaptive Method** & **Method 1** & **Method 2** & **Adaptive Method** & **Method 1** & **Method 2** \\ \hline RMS & 4.9493 & 6.4867 & 5.9646 & 0.1926 & 0.3034 & 0.3227 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSs of the position and velocity errors in Trajectory 1.  
Figure 21: Sky plot of the satellites in Trajectory 2.  
Figure 22: The Doppler (**left**) and \(C/N_{0}\) for satellites (**right**) in Trajectory 2.  
The position and velocity errors are shown in Figure 24, and their RMSs are given in Table 3. We can see that the navigation performance of the adaptive tracking method is also better than others, with the velocity error decreased by 50% and 36%, and position error decreased by 6% and 7%.  
In summary, the hardware simulation results demonstrate that the adaptive INS-aided PLL tracking method can improve the tracking ability and navigation performance significantly under harsh environment, even the environment with complex high dynamic and signal attenuation.
## 4 Discussion
### Influence of Feature Variables on Remote Sensing Classification
When the RF algorithm was used, the participations of the terrain features and radar features had a greatly positive effect on the PAs of the wetlands and construction lands. However, the participation of the spectral indices had a small negative effect on the PAs of some land cover types, including the shrublands, wetlands, and construction lands, with an average decrease of 2-7.83%. When the different classification algorithms were used for the classification, the participation of the different feature variables also had different impacts on the UA (Table A3 (Appendix A)). When the SVM classification algorithm was used for the classification, the participation of most of the feature variables had a positive effect on the improvement of the UA values of the land cover types. Moreover, the most significant effect was that the UAs of the wetlands and construction lands increased by 8.88% and 19.86%, respectively, when the radar features were inputted into the classification process. In contrast, there were also cases in which the feature variables had a negative effect on the UAs of some of the land cover types. Specifically, the participations of the spectral indices and texture features resulted in a significant decrease in the UA of the wetlands (16.67% and 28.01%). When the terrain features participated in the classification process, the UA of the construction lands decreased by 27.8%. For the CART classification algorithm, in general, the participation of most of the feature variables had a positive effect on the improvement of the UAs of the land cover types. The most substantial effect was that the UAs of the croplands and shrublands, which increased by an average value of 12%, and the UA of wetlands increased by 23.02%. When the spectral indices, terrain features, and radar features were added to the classifier in turn, the UA of the construction lands gradually increased by 11.78%. However, when the spectral indices were added to the classifier, the UAs of the croplands and grasslands decreased by 8.56%. In addition, the participation of the radar decreased the UAs of the croplands, shrublands, and wetlands by an average value of 5.46%. For the RF classifier, the different land cover had higher UA values, and the feature variables had positive effects on the increase of the UA values, except for the wetlands. Specifically, when the terrain features, radar features, and texture features were added to the classifier in turn, the UA of the wetlands gradually decreased by 2.14%.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 4 Methodology
### System Validation  
This section summarizes the validation metrics used in system validation. The proposed system was validated using four performance measures: sensitivity, specificity, accuracy, and reliability. The first measure is sensitivity, which reflects the ability of the system to detect the rock correctly, calculated as [53]:  
\[Sensitivity=\frac{TP}{TP+FN}\times\ 100\% \tag{8}\]  
The second measure is specificity. This reflects the ability of the system to confirm the absence of a rock-fall event, which is defined as:  
\[specificity=\frac{TN}{TN+FP}\times\ 100\% \tag{9}\]  
The third measure is accuracy. This reflects the accuracy of the system in detecting a rock-fall event, which is defined as:  
\[Accuracy=\frac{(TP+TN)}{(TP+FN+TN+FP)}\times\ 100\% \tag{10}\]  
The last measure is reliability, which reflects the degree of measure consistency when repeating result \(N\) times under the same conditions, which is defined as:  
\[Reliability=\frac{1}{N}\sum_{i=1}^{N}(Accuracy\text{ at Test(i) }) \tag{11}\]  
where true positive (TP) means all events were true detected, false negative (FN) means some events occurred but were not detected, true negative (TN) means events were absent and the system reports an absent event, and false-positive (FP) means the events were absent but the system reports it as present.
## 5 Numerical Experiments and Discussion
### Case Study-1  
As a toy example, let us consider the case where a defender can pick between Phantom Routing (PR) and Multi-path Routing (MR). On the other hand, the adversary can pick between Traffic Analysis (TA) and Tracing Back (TB). From the literature, the following payoff matrix (illustrated in Table 1) can be constructed. Furthermore, the average values of energy and latency presented in Table 2 can be attributed to the above routing mechanisms.  
Clearly, the \(2\times 2\) game presented in Table 1 has no _pure strategy_ Nash equilibrium. In fact, this game is similar to the famous _Matching Pennies_ game [47], which is a cornerstone example for the notion of mixed strategy Nash equilibrium and the concept of steady state analysis of the games. Indeed, Tables 1 and 2 are not constructed from a comprehensive literature survey, and the studies in the literature may involve other parameters that may result in biased comparisons. Nevertheless, the sole purpose of Case Study-1 is to establish the following points:  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **TA** & **TB** \\ \hline PR & Long [44] & Medium [1, 10, 45] \\ MR & Medium [21] & Long [46] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Survival time payoffs for the defender \(2\times 2\) game.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PR** & **MR** \\ \hline
**P** & High & Medium \\ \hline
**q** & Long & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy (**p**) and Latency (**q**) for each routing mechanism of the \(2\times 2\) game.  
* \(\mathbf{H},\ \mathbf{p},\ \mathbf{q}\) can be estimated beforehand from either simulation or emulation study of the actual networks.
* Mixed strategy Nash equilibrium is strongly applicable to the proposed defender and adversary game.
## 2 Electrical Variable Transmission (EVT)-Equipped Hybrid Electric Vehicles (HEV) Architecture  
Due to their unique electromechanical features, electrical variable transmission (EVT) devices offer opportunities as a substitute solution to conventional transmission systems. The EVT encompasses two concentric rotors, an inner and outer, which are nested in a stator frame for mechanical, electrical, and electromagnetic energy exchange and transfer [46; 47]. This makes EVT a suitable choice for use in applications where continuously variable and electric drive are combined with power generation components to provide improvement in overall efficiency and functionality. Example applications include but are not limited to transmission systems in hybrid electric vehicles (HEV), constant speed power take-off with mechanical drive and electric capability, and combined mechanical and electric drive for auxiliaries, electric clutches, and torque converters.  
The architecture of an EVT-equipped HEV consists of an inner rotor connected to ICE and an outer rotor connected to the wheels through their corresponding shafts. The possibility of decoupling the wheel and engine speed can enhance the vehicle performance as the engine can operate at desired operating points [48; 49]. In addition, there would be advantages of shorter maintenance intervals by preventing the mechanical losses of the gears' involvement since an electromagnetic approach can be used to split the power needless to a planetary gear system. The net power generated by the engine can partially supply the power required by the wheels while converting the remaining part to electrical form to supply the stator or be stored in the battery for propulsion [37]. In an EVT-based HEV topology, two electrical ports at the inner rotor and stator are connected to two back-to-back inverters. Figure 2 illustrates a schematic for an EVT-based passenger HEV topology.
# An Improved FFIP Method Based on Mathematical Logic and SysML
## 2 Functional Failure Logic and Propagation Model
### The Extension of Functional Failure Logic  
In the modeling process, we need to identify the functions of products and components first, and then complete the system design according to the mapping relationship of function-behavior-structure. However, the function and structure of components are not always a correspondence of one-to-one. As shown in Figure 1, one function might correspond to multiple structures, and one structure might participate in the implementation of multiple functions. Under the design idea of V-model [35], in the design phase, the personnel need to allocate the equipment according to the function corresponding to the  
Figure 1: System design based on function–behavior–structure model [34].  
structure. In the verification phase, the personnel need to verify and integrate equipment for the target function. Therefore, the functional requirements of the single component should be considered in the early design stage of the system. One of the purposes of MBSA is to use a unified modeling method in the whole life cycle of equipment [36]. It is incomplete to consider only FFL in the safety system modeling.  
Failure logic describes the relationship among the causes, processes, and consequences of failure. FFL is the functional highlights of failure logic. To improve the deficiency that the FFIP method has, namely, that it is mainly suitable for the early design stage of the system, this paper extends the FFL in FFIP and introduces the concept of deviation. The deviation is the degree to which the real situation deviates from the normal situation. Based on the magnitude of the deviation, the state of the components can be classified to infer the extent of the component exception. We can also classify failure deviations and determine failure types based on observed values. In this way, the FFIP method not only stays on the function analysis, but also extends to the specific failure. By analyzing the specific failure of the system, the FFIP method can be applied to the design stage and other stages of the system operation, including the decision-making and formation stage, and the use and maintenance stage [37].  
The extension of the concept will lead to a change of the FFIP framework. Firstly, the extended FFIP method needs more detailed modeling of the behavior and function of the system to cover the whole life cycle of the system. It is because the typical FFIP method is suitable mainly for the early design stage, and the function description of the system and components is unspecific. Secondly, the extended FFIP method is changed from analyzing FFL to analyzing failure logic, which makes the level of analysis higher. Additionally, it is necessary to classify failure logic to facilitate subsequent failure logic analysis.
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 2 Theory of MIMO System
### Single Link MIMO System
When the parameters of the varactor loaded on the two parasitic units are the same (\(P_{1}=P_{2}\)), it means the two directions have achieved a state of balance. A diagonal function can be constructed on the premise that the \(\mathcal{G}_{1}(\theta,\varphi)\) and \(\mathcal{G}_{2}(\theta,\varphi)\) are mirror symmetry. \[\mathcal{B}_{\Sigma}(\theta,\varphi)=\frac{1}{\sqrt{2}}(\mathcal{G}_{2}( \theta,\varphi)+\mathcal{G}_{1}(\theta,\varphi)) \tag{4}\]  
\[\mathcal{B}_{\Delta}(\theta,\varphi)=\frac{1}{\sqrt{2}}(\mathcal{G}_{2}( \theta,\varphi)-\mathcal{G}_{1}(\theta,\varphi)) \tag{5}\]  
The correlation between \(\mathcal{B}_{\Sigma}(\theta,\varphi)\) and \(\mathcal{B}_{\Delta}(\theta,\varphi)\) can be expressed as:  
\[ \begin{array}{lll}\rho_{\Sigma\Delta}&=&\frac{1}{4\pi\sqrt{P_{1}P_{2}}} \iint\mathcal{B}_{\Sigma}(\theta,\varphi)\mathcal{B}_{\Delta}^{*}(\theta, \varphi)\sin(\theta)d\theta\;d\varphi\\ &&=\frac{1}{8\pi\sqrt{P_{1}P_{2}}}\iint(\mathcal{G}_{2}(\theta,\varphi)+ \mathcal{G}_{1}(\theta,\varphi))\big{(}\mathcal{G}_{2}^{*}(\theta,\varphi)- \mathcal{G}_{1}^{*}(\theta,\varphi)\big{)}\sin(\theta)d\theta\;d\varphi\\ &&=\frac{1}{4\sqrt{P_{1}P_{2}}}\mathcal{B}_{2}\big{(}P_{2}-\sqrt{P_{1}P_{2}} \rho_{12}^{*}+\sqrt{P_{1}P_{2}}\rho_{12}-P_{1}\big{)}\\ &&=\frac{1}{4\sqrt{P_{1}P_{2}}}\big{(}P_{1}-P_{1}\rho_{12}+P_{1}\rho_{12}-P_{ 1}\big{)}=0\end{array}  \tag{6}\]  
Therefore, when the known direction map \(\mathcal{G}_{1}(\theta,\varphi)\) and \(\mathcal{G}_{2}(\theta,\varphi)\) have mirror symmetry, the free space will naturally form a pair of mutually orthogonal angle functions, \(\mathcal{B}_{\Sigma}(\theta,\varphi)\) and \(\mathcal{B}_{\Delta}(\theta,\varphi)\). In the independent fading environment, the two signals emitted from the transmitting antennas will be independent from each other, which is a breakthrough in the traditional MIMO system for achieving the miniaturization design of the transmitter. In Figure 1, the first input binary code \(b_{1}\) after BPSK modulation is denoted as \(S_{1}\), then the antenna radiation patterns in the far field can be expressed as Equation (7) or Equation (8):  
\[\mathcal{G}_{T}(\theta,\varphi)=\mathcal{G}_{1}(\theta,\varphi)s_{1}=\frac{s _{1}}{\sqrt{2}}[\mathcal{B}_{\Sigma}(\theta,\varphi)-\mathcal{B}_{\Delta}( \theta,\varphi)] \tag{7}\]  
\[\mathcal{G}_{T}(\theta,\varphi)=\mathcal{G}_{2}(\theta,\varphi)s_{1}=\frac{s _{1}}{\sqrt{2}}[\mathcal{B}_{\Sigma}(\theta,\varphi)+\mathcal{B}_{\Deltathe two binary signal \(b_{1}\) and \(b_{2}\) XOR results can form two states, and each state will correspond to far field radiation map of a switch parasitic antenna.
## 3 Results
### Industrial Tests  
Once different methodologies to study the oxidation were tested at laboratory scale in nuts, the active packaging was produced at industrial scale and used in other foods. TBARS method in combination with tasting (for ready-to-eat foods) were the methodologies selected for this work. The analysis of fatty acids was discarded, since TBARS offered sufficient results to compare active and blank packaged food, and TBARS was also simple and quicker.  
To begin with, a laboratory scale study was done with the industrial multilayer to conduct an additional study of the active packaging with real food prior the industrial scale. The industrial active multilayer was prepared following the same steps as in the laboratory. Firstly, nanoparticles were prepared and incorporated into the adhesive. This active adhesive was then applied between the two plastic films to form the multilayer. This preliminary test was designed to save money and food coming from a large test at industrial scale in a food company. Once the antioxidant properties of the industrial multilayer were confirmed, the industrial tests with different food produce were carried out. In the preliminary study, potato chips, which are susceptible to oxidation, were selected. Once the results of this study were obtained, industrial trials were prepared with ham, vegetables seasoned with butter, and fresh chicken.