esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Input Representation
#### Event-based Inputs  
Next-generation vision sensor event cameras with low latency, low energy consumption, and light sensitivity capture visual information by detecting brightness changes at specific pixel points. When the brightness change of a pixel point exceeds a defined threshold, an event is generated.  
The recorded event data are formatted as pixel addresses and event streams, including essential 3D information: the exact pixel location, the brightness change trend (i.e., polarity) and the event timestamp. Each event trigger is represented as \(e=(x,y,p,t)\), where event \(e\) denotes an event at a pixel point \((x,y)\) that occurs at time \(t\) due to changes in scene luminance, and \(\rho(p,c)\) represents the truncation function:  
\[\rho(p,c)=\begin{cases}+1,&p\geq c\\ -1,&p\leq-c\end{cases} \tag{1}\]  
In the event camera, an excitation threshold \(c\) is set to determine the triggering of the event. When the luminance change of a pixel exceeds this threshold \(c\), a positive polarity event is generated; conversely, if the luminance change is below the negative threshold \(-c\), a negative polarity event is triggered. And when the absolute value of the luminance change does not reach the threshold \(c\), the event camera produces no output.  
Each event consists of coordinates \((x,y)\), a timestamp \((t)\), and a polarity \((p)\) indicating the increase or decrease in light intensity. To process these events in SNNs, they are organized into a sparse grid in 3D space, within a spatio-temporal window. The event stream is divided into temporal bins with a constant time window, and mapped into 2D representations resembling images [16]. The network operates over a fixed number of time steps, with the total sequence determined by the product of the number of time steps and the time window size.
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## Appendix A The Rate of Successful Packet Transmission on COG  
### Stay and Switch Mode  
The transmission success rate varies depending on the state of \(Q_{\mathtt{R0}}\) and \(Q_{\mathtt{HY}}\). Let \(v_{j,1,0}\) and \(v_{j,1,1}\) be the success rates of packet transmission for \(j\in\{\mathtt{R0,HY}\}\) with conditions \(Q_{k|k\in\{\mathtt{R0,HY}\},k\
eq j,}=0\) and \(Q_{k}\
eq 0\), then we have  
\[v_{\mathtt{HY,1,0}} = p_{f,\mathtt{HY}}(s_{f_{0},\mathtt{HY}}+(1-s_{f_{0},\mathtt{HY}} )s_{f_{2},\mathtt{HY}})+(1-p_{f,\mathtt{HY}})(s_{f_{0},\mathtt{HY}}+(1-s_{f_{0},\mathtt{HY}})s_{f_{2},\mathtt{HY}} \tag{1}\] \[+(1-s_{f_{0},\mathtt{HY}})(1-s_{f_{2},\mathtt{HY}})s_{f_{1}, \mathtt{HY}}),\]  
\[v_{\texttt{HY},2,1} = p_{d,\texttt{HY}}[s_{f_{0,\texttt{HY}}}s_{f_{2,\texttt{HY}}}(1-(1-p_ {d,\texttt{RD}})s_{f_{2,\texttt{RD}}})+s_{f_{0,\texttt{HY}}}s_{f_{2,\texttt{HY}} }^{I}(1-p_{d,\texttt{RD}})s_{f_{2,\texttt{RD}}}] \tag{12}\] \[+(1-p_{d,\texttt{HY}})[[s_{f_{0,\texttt{HY}}}s_{f_{2,\texttt{HY}} }(1-s_{f_{1,\texttt{HY}}})+s_{f_{0,\texttt{HY}}}(1-s_{f_{2,\texttt{HY}}})s_{f_{ 1,\texttt{HY}}}\] \[+(1-s_{f_{0,\texttt{HY}}})s_{f_{2,\texttt{HY}}}s_{f_{1,\texttt{HY }}}][p_{d,\texttt{RD}}+(1-p_{d,\texttt{RD}})(1-s_{f_{2,\texttt{RD}}})](1-s_{f_{ 1,\texttt{RD}}})\] \[+[s_{f_{0,\texttt{HY}}}s_{f_{2,\texttt{HY}}}^{I}(1-s_{f_{1, \texttt{HY}}})+s_{f_{0,\texttt{HY}}}(s_{f_{2,\texttt{HY}}}-s_{f_{2,\texttt{HY}} }^{I})s_{f_{1,\texttt{HY}}}+(1-s_{f_{0,\texttt{HY}}})s_{f_{2,\texttt{HY}}}^{I} s_{f_{1,\texttt{HY}}}]\] \[\times(1-p_{d,\texttt{RD}})s_{f_{2,\texttt{RD}}}(1-s_{f_{1, \texttt{RD}}})\] \[+[s_{f_{0,\texttt{HY}}}s_{f_{2,\texttt{HY}}}(s_{f_{1,\texttt{HY}} }-s_{f_{1,\texttt{HY}}}^{I})+s_{f_{0,\texttt{HY}}}(1-s_{f_{2,\texttt{HY}}})s_{f _{1,\texttt{HY}}}^{I}+(1-s_{f_{0,\texttt{HY}}})s_{f_{2,\texttt{HY}}}s_{f_{1, \texttt{HY}}}^{I}]\] \[\times[1-(1-p_{d,\texttt{RD}})s_{f_{2,\texttt{RD}}}]s_{f_{1,\texttt{ RD}}}+[s_{f_{0,\texttt{HY}}}s_{f_{2,\texttt{HY}}}^{I}(s_{f_{1,\texttt{HY}}}-s_{f_{1, \texttt{HY}}}^{I})\] \[+s_{f_{0,\texttt{HY}}}(s_{f_{2,\texttt{HY}}}-s_{f_{2,\texttt{HY}} }^{I})s_{f_{1,\texttt{HY}}}^{I}+(1-s_{f_{0,\texttt{HY}}})s_{f_{2,\texttt{HNY}}} s_{f_{1,\texttt{HY}}}^{I}]\] \[+(1-s_{f_{0},\mathtt{HY}})(1-s_{f_{2},\mathtt{HY}})s_{f_{1}, \mathtt{HY}}),\]0  
\[+(1-s_{f_{0},\mathtt{HY}})(1-s_{f_{2},\mathtt{HY}})s_{f_{1}, \mathtt{HY}}),\]1 \[+(1-s_{f_{0},\mathtt{HY}})(1-s_{f_{2},\mathtt{HY}})s_{f_{1}, \mathtt{HY}}),\]2 (14) \[+(1-s_{f_{0},\mathtt{HY}})(1-s_{f_{2},\mathtt{HY}})s_{f_{1}, \mathtt{HY}}),\]3 \[+(1-s_{f_{0},\mathtt{HY}})(1-s_{f_{2},\mathtt{HY}})s_{f_{1}, \mathtt{HY}}),\]4 \[+(1-s_{f_{0},\mathtt{HY}})(1-s_{f_{2},\mathtt{HY}})s_{f_{1}, \mathtt{HY}}),\]5  
_Appendix A.3. A Hybrid SMT and SAS Transmission Mode_  
Let \(v_{j,n,0}\) and \(v_{i,n,1}\) be the respective success rate of \(n\) packets transmitted in a single time slot for \(j\in\{\texttt{RQ},\texttt{HY}\}\) with conditions \(Q_{k|k\in\{\texttt{RQ},\texttt{HY}\},k\
eq j,}=0\) and \(Q_{k}\
eq 0\).
# Analysis of Floral Scent Component of Three Iris Species at Different Stages
## 2 Methods and Materials
### Research Methodology
#### 2.2.2 HS-SPME-GC-MS Analysis  
Headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS, Shimadzu, Kyoto, Japan) was employed in this study. Approximately 1 g of each flowering stage sample was placed in 15 mL headspace solid-phase microextraction bottles. Subsequently, 10 uL of 2-nonanone (0.008 uL-mL\({}^{-1}\)), dissolved in propylene glycol, was added to the bottom of the bottle, and the bottle cap was sealed with tin foil. We utilized the SPME extraction head from the German company Supelco, model DVB/CAR/PDMS, with a diameter of 50/30 um. After equilibrating at 45 \({}^{\circ}\)C for 10 min, a manual headspace sampler was inserted, and extraction was performed for 30 min. Each sample was repeated four times. The manual headspace sampler was then inserted into the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Array Serial Number** & **Sensor Name** & **Performance Description** \\ \hline
1 & W1C & aromatic \\
2 & W5S & broadrange \\
3 & W3C & aromatic \\
4 & W6S & hydrogen \\
5 & W5C & arom-aliph \\
6 & W1S & broad-methane \\
7 & W1W & sulphur-organic \\
8 & W2S & broad-alcohol \\
9 & W2W & sulphur-chlor \\
10 & W3S & methane-aliph \\ \hline \hline \end{tabular}
\end{table}
Table 1: Response characteristics of each sensor of PEN3 electronic nose.  
inlet of the GC-MS at 250 \({}^{\circ}\)C for 2.5 min to completely release the aroma components in the gas chromatography-mass spectrometer [18].  
Chromotographic conditions involved using an HP-5 MS capillary tube (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Shimadzu, Kyoto, Japan) as the chromatographic column, adopting the splitless mode. The inlet temperature was set at 250 \({}^{\circ}\)C, and high-purity nitrogen (99.999%) was used as the carrier gas at a flow rate of 1.0 mL-min\({}^{-1}\). The column temperature followed a heating and cooling program: the initial temperature was 40 \({}^{\circ}\)C, with a holding time of 3 min. The temperature was then raised to 150 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C-min\({}^{-1}\), followed by a further increase at a rate of 10 \({}^{\circ}\)C-min\({}^{-1}\) to 220 \({}^{\circ}\)C, where it was kept for 10 min.  
Mass spectrometry conditions involved the full scan mode, electron ionization (EI) as the ionization mode, an emission current of 10 Ua, electron energy of 70 eV, ion source temperature of 220 \({}^{\circ}\)C, transmission source temperature of 220 \({}^{\circ}\)C, and a mass scanning range of 35-450 amu.  
For GC-MS data identification and analysis methods:  
Qualitative method: The stable peak on the ion peak diagram was identified by corresponding to the first place in the preferred ranking of multiple substances under the same retention time (RT) value, starting from the first stable peak. The selected substances were compared with the four times duplicate tests to ensure they appeared at the same RT value and ranked at the top.
## 2 Vertical Static Load Model Experiment Scheme
### Post-Grouting and Loading  
The study employs a grouting device consisting of various components, such as an air compressor, pressure-regulating valves, a grout storage tank, a grout inlet pipe, and a pressure gauge, as depicted in Figure 7. The pressure-regulating valve is used to maintain a stable pressure to facilitate the injection of a defined volume of cement grout from the grout storage tank into the pile tip or side grouting pipe. Considering the strength of the cement-stabilized soil surrounding the piles and the consistency of cement grout, ordinary Portland cement (P.O.) 42.5 with a water-cement ratio of 0.6 was selected as the grouting material for the study.  
The experimental loading method involves converting the gravitational force of certain weights into a vertical load applied to the pile top by utilizing steel tracks and pulley blocks. The loading device diagram is shown in Figure 8. Dial gauges and strain gauges were used to record the settlement of the pile top and deformation data of the pile body, respectively. The loading process utilized the rapid maintenance load method, which follows the \"Technical code for testing of building foundation piles\" of China (2014). Pile top displacement data were recorded at 0, 5, 10, 15, and 30 min after each level of the load was applied (and every 15 min thereafter). When the difference between the two displacements is less than 0.01 mm, the pile is considered to be stable, and the next level of load continues to be applied. The criterion for stopping loading is that the pile top displacement under one level of load is more than 5 times that of the previous level. The ultimate bearing capacity of the model pile is the load value of the previous stage.  
Figure 8: Loading device.  
Figure 7: Grouting device.
## 3 Results
### Impact of ECIs on EIs at the Annual Scale
In the YGP, the NDVI was influenced by ECIs, standardized indices, and CECIs (Figure 3g), whereas the GPP was primarily affected by ECIs (Figure 3c). Similarly, in the GXB, SU, DTR and GSL were the most important for the NDVI, with extremely significant positive correlations (\(p<0.001\)), along with highly significant correlations for the STI (+, \(p<0.01\)), CDC (+, \(p<0.01\)), CDW (\(-\), \(p<0.05\)), and ETI (+, \(p<0.05\)) (Figure 3h). The temporal stability of the impacts of climate extremes on EIs varied across regions. The GPP in the HDMs and the YGP was more strongly influenced by ECIs, while the SCB was less affected. In the HDMs, the GPP showed significant and stable correlations with several indices, including the ETI (+), SDII (+), CWD (+), RDI (+), GSL (+), SU (+),  
Figure 3: Weights and correlation of ECIs with EIs across different regions of SWC (annual scale). (**a**–**d**) display the weights and correlations of ECIs with the GPP for the HDMs, SCB, YGP, and GXB, respectively. (**e**–**h**) present similar analyses, but with the NDVI as the EI. and FD (\(-\)) (Figure 4a). Additionally, the CDI (\(-\)), DTR (\(-\)), CDD (\(-\)), HPI (\(-\)), and TR (\(+\)) displayed significant correlations over different periods, which remained stable until 2019 (Figure 4a). In contrast, the temporal stability of the indices in the SCB was lower (Figure 4b). In the YGP, most indices impacting the GPP showed strong temporal stability, except for the standardized index, CECIs, and the WDI, CDI, and WSDI (Figure 4c). The temporal stability of the DTR, SU, and TR was higher in the GXB (Figure 4d). For the NDVI in the HDMs and SCB, different indices show a notable change in stability around the year 2000 (1982-2008). The correlations for some indices, such as the SPI, CDW, WDI, and CDI, decreased after 2000, while others, including the ETI, DTR, GSL, FD, and SU, increased (Figure 4e,f). In contrast, the influence of indices on the NDVI in YGP and GXB is more complex than their influence on the GPP owing to the significant temporal variations that occurred over different periods, resulting in a more dynamic overall performance (Figure 4g,h).
## 2 Effect of Floating DC Bus on System Stability
### Basin of Attraction versus Region of Asymptotic Stability  
The numerical continuation analysis is a method for studying the stability of nonlinear dynamic systems [32,33]. On the one hand, it allows identifying the Hopf bifurcation [36], which appears when the unstable limit cycle and the equilibrium point coalesce, thus making the equilibrium point unstable. Such a bifurcation was displayed at \(v_{0}=0.696\) p.u. for the case studied in Reference [33]. On the other hand, the numerical continuation analysis makes it possible to evaluate the BA for several different equilibrium points. In fact, it allows determining the lower voltage stability bound once the magnitude of the possible perturbations is known. In particular, in Reference [33], the BA for \(v_{0}=0.8\) p.u. was recognized as the smallest acceptable region for assuring stability in the presence of an impulse perturbation with a reasonable magnitude [37]. Actually, the defined lower bound allows keeping voltage stability after a perturbation constituted by an instantaneous voltage drop of  
Figure 2: DC microgrid equivalent circuit model.  
up to \(\sim\)30% with respect to the actual working point \(v_{0}=0.8\) and \(i_{0}=1.25\) p.u. (i.e., the system state moved to \(v=0.5768\) p.u. and \(i=1.25\) p.u.). This can be demonstrated by considering Figure 3, where the perturbed state (yellow triangle) is still in the calculated BA for the starting equilibrium point (blue-bounded area). Conversely, if a lower equilibrium point is assumed (e.g., \(v_{0}=0.7\) p.u., resulting in the red-bounded area in Figure 3), the related BA is so small that the system can be considered unstable for any realistic perturbation.  
A different approach to define the stability limits of the system is to establish the region of asymptotic stability (RAS). By applying the Lyapunov theory [38], it is possible to find a conservative region, i.e., the RAS, where a sufficient but not necessary condition for large-signal stability is verified [30]. This, in turn, means that any system state inside the RAS originates a transient that evolves toward a stable equilibrium point. Conversely, system states located outside the RAS are not guaranteed to originate an unstable evolution. Although RAS provides only a subset of the entire BA, its determination is significant. Unlike the BA, which is found numerically, the RAS is defined analytically. Therefore, it is possible to use it in algorithms and control laws able to perform a stability-oriented real-time load management in floating-bus islanded DC microgrids. In particular, the mathematical approach at the basis of the definition of the RAS is used in this paper to define a stability preserving criterion, able to suitably reduce the CPL power when the bus voltage drops. In such a way, the voltage stability is guaranteed even in the presence of low battery voltage output \(e\), i.e., low SoC, assuring the correct supply of the remaining loads.
# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
## 1 Introduction
This could be achieved by designing and developing an online clustering algorithm for handling evolving data streams using the tempo-spatial hyper cube called BOCEDS TSHC. The contributions of this research are as follows:  
1. The proposed BOCEDS TSHC uses a tempo-spatial hyper cube to add more dimensions to the data summarization for more degree of freedom. The hyper-cube is a quantization level that will map various values to one value in each coordinate to reduce the resolution of data storing and eliminate the effect of slight changes that do not influence the meaning of the data (type of filtering). The hyper-cube features heterogeneity to handle heterogeneous data and dynamicity to handle the evolving data according to the values of its features or coordinates. 2. The TSHC algorithm is built using the recursive and dynamic update for adaptive quantization of arrived data when they are projected to the clustering space. 3. The developed TSHC algorithm is an add-on to a single-phase clustering algorithm that assists in generating clusters from evolving data streams in a fully online manner. It can work independently or jointly with an existing clustering algorithm such as BOCEDS. The remaining paper is organized as follows. Section 2 presents a literature review of the latest clustering algorithms. Section 3 discusses the research methodology used in this research, which includes the description of the developed algorithm. Section 4 illustrates the results and discussion of the research followed by Section 5, which presents the findings and summary. Finally, Section 6, concludes the paper.
When integrated with sensor data, DRL enhances autonomous navigation by enabling robots to interpret complex data and adjust their behavior dynamically [10]. This combined approach leverages both sensory information and adaptive learning to equip robots with improved situational awareness and flexibility. This research focuses on integrating radar sensors with DRL methodologies to achieve robust autonomous navigation for wheeled robots. By using radar for dynamic obstacle detection and leveraging DRL for real-time decision-making, the proposed system demonstrates potential as a viable option for ensuring safety and adaptability in complex environments. To enhance autonomous navigation in dynamic and unpredictable environments, a novel memory-augmented deep reinforcement learning (DRL) framework is introduced. This framework employs BiGRU layers to enable the robot to learn from past experiences and make informed decisions. Experimental results validate the efficacy of this approach in real-world scenarios, highlighting its potential to drive advancements in autonomous navigation across diverse applications. The key contributions of this paper are as follows:  
* Integration of radar sensors and DRL: Development of a robust system combining radar-based obstacle detection with deep reinforcement learning for real-time decision-making. * Introduction of a memory-augmented DRL framework: Utilization of BiGRU layers to enable robots to learn from past experiences and improve decision-making in dynamic environments. * Enhanced obstacle detection: Implementation of filtering and tracking algorithms to improve the precision of radar-based dynamic obstacle detection. This paper is structured as follows: Section 2 provides a review of related work in autonomous navigation and sensor fusion. Section 3 introduces the dynamic object detection approach using radar sensors, detailing the methodology and its advantages. Section 4 presents the DRL model, describing its architecture, training process, and contributions to the navigation system. Section 5 discusses experimental results, validating the proposed approach through various tests in varied environments. Finally, Section 6 concludes the paper and outlines future research directions, focusing on expanding sensor fusion techniques and enhancing DRL-based navigation.
The Medium Resolution Spectral Imager (MERSI) and Ocean and Land Color Instrument (OLCI) onboard the FY-3A and Sentinel-3A, respectively, contain some channels covering the spectral range from visible light to long-wave infrared [18]. The near-infrared channels can be used to derive PWV [19]. [PERSON] et al. [20] found that the RMS and Bias of PWV derived from FY-3A/MERSI in China are 2.2-17 mm and 0.8-12.7 mm, respectively. [PERSON] and [PERSON] [21] showed that the PWV derived from Sentinel-3A/OLCI has a good agreement with that from the GNSS. In addition, the RMS of PWV difference between Sentinel-3A and GNSS are 3.03 mm under clear weather conditions. Based on a variety of current PWV-derived technologies, the PWV products with high spatio-temporal resolutions cannot be obtained using a single technique while guaranteeing high accuracy. Therefore, this study establishes a comprehensive PWV retrieval and application platform (CPRAP) to solve the above issues by combining the ground-based (GNSS, RS), space-based (FY-3A, Sentinel-3A) and reanalysis-based (ERA5) techniques. Accordingly, the accuracy of PWV derived from some techniques is firstly validated and statistical results show that the proposed CPRAP has good performance in deriving PWV. Then, CPRAP-derived PWV is applied for drought and rainfall monitoring, which also indicates the potential of the proposed CPRAP for corresponding weather and climate studies.
## 6 Experiments and Discussion
### Preliminary Experiment
#### 6.1.4 Experimental Results  
Table 6 shows all combinations of each material and the accuracy rate of all subjects. The accuracy here was calculated by \"the number of correct answers in all subject experiments/all combinations of each material\".  
As shown in the table, we found that when all combinations were tried, the proportion of materials, other than cloth, that could be correctly discriminated by the subjects, even when sound was presented from the dataset, was less than 80%.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 5 Conclusions  
The GNSS buoy recorded data in very challenging conditions and provided both sea-level and wave data. These GNSS data show that the tidal elevations are a closer match to tide gauge data at Liverpool than at Heysham, although there may be subtle differences especially during the flood tide; however, during the ebb periods the comparison with GNSS 900 s average and Liverpool 15 min data produced a mean difference \(-0.02\) m, standard deviation difference 0.03 m and RMSE difference 0.04 m. In addition, the GNSS buoy recorded almost indistinguishable significant wave heights (differences between 0.0 and 0.3 m; Table 4) and wave periods (differences between 0.1 and 0.2 s; Table 4) to those measured by the Cleveleys wave buoy. It was possible to extract wave data from the poorer positional solutions during the flood tide and when there were large accelerations in the GNSS buoy.  
Overall, the deployment over two tidal cycles showed that the GNSS buoy can perform within the intertidal zone, and provide acceptable sea-level and good quality wave data. In addition, it was demonstrated that these data can also be downloaded remotely which would allow for timely analysis. This approach offers considerable efficacy for determining local hydrodynamics in support of coastal monitoring or defence scheme implementation.  
[PERSON] conceived, designed and built the GNSS buoy, developed software, performed the experiments, analysed the data and wrote the original draft. [PERSON] reviewed and edited the final draft. [PERSON] and [PERSON] provided supervision. [PERSON] and A.S. provided project administration and resources. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Spectral density for tidal cycle A—(**a**) 2 hr period before HW, (**b**) 2 hr period after HW, and tidal cycle B–(**c**) 2 hr period before HW, (**d**) 2 hr period after HW.  
**Funding:** The research is part of a PhD study funded by the \"Low Carbon Eco-Innovatory (LCEI)--Liverpool University\" (www.liverpool.ac.uk/environmental-sciences/working-with-business) (accessed on 1 May 2021), with industrial partner MM Sensors Ltd., Liverpool, UK.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the author.  
**Acknowledgments:** The authors would like to thank Wyre Council for general support, and the Applications Team at the National Oceanography Centre for access to the POLTIPS-3 coastal tidal prediction software. The research is part of a PhD study funded by the \"Low Carbon Eco-Innovatory (LCEI)--Liverpool University\" (www.liverpool.ac.uk/environmental-sciences/working-with-business) (accessed on 1 May 2021), with industrial partner MM Sensors Ltd., Liverpool, UK. This paper contributes to Work Package 4 of the UK NERC-funded BLUEcoast project (NE/N015614/1).  
**Conflicts of Interest:** The authors declare no conflict of interest.