esa-sceva/satcom-chunk-collection huggingface dataset:
# Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models
## 4 Methodology of the Proposed Three-Layer Granule Models
### Granulating in the Medium Space  
#### 4.2.1 Construction of the Medium Space  
As seen in the description in Figure 1, the medium space is a hidden layer which has no clear definition. Its purpose is to build the connection between input space and output space, and to realize the model-based clustering. Therefore, we could choose some mathematical functions or performance indicators to construct the medium space. The granules of input space not only wipe out the shape influence by dividing original clusters, but can also be used to reconstruct the original clusters with arbitrary shapes. It is seen that  
Figure 3: (**a**) A rectangle information granule is constructed by the prototype, \(z_{i}\), and its size is determined by four elements \([\epsilon_{1}^{+},\epsilon_{1}^{-},\epsilon_{2}^{+},\epsilon_{2}^{-}]\) in vector, \(S\); (**b**) the conflict relation of specificity and coverage when determining the size of granules.  
reconstruction is a perfect bridge connecting the two spaces. Therefore, a reconstruction indicator is proposed for evaluating the performance quantitatively here, as defined in (10):  
\[Rec=\frac{1}{card(X)}\sum_{x\in X}\|x-\hat{x}\|^{2} \tag{10}\]  
where \(Rec\) is the reconstruction error; \(card(X)\) is the function counting data amount in the dataset, \(X\). This indicator requires that a good reconstruction should have a small \(Rec\) value.  
It is seen from (10) that, if the membership matrix and prototypes are given, the reconstructed data point is expressed in (11):  
\[\hat{x}_{k}=\left(\sum_{i=1}^{c}u_{i}^{m}(x_{k})\cdot z_{i}\right)/\left(\sum_ {i=1}^{c}u_{i}^{m}(x_{k})\right) \tag{11}\]  
where \(\hat{x}_{k}\) is the reconstructed data of the original data, \(x_{k}\). Equation (11) is to reconstruct a data point by the weighted average of the prototypes, and the weights are calculated as \(u_{i}^{m}(x_{k})/\sum_{i=1}^{c}u_{i}^{m}(x_{k})\). According to this concept, the reconstructed data will be different when using descriptors of different clusters. If the original data belong to a cluster, the reconstruction error calculated by granules of the corresponding cluster would be small. Therefore, the medium space is constructed by the reconstruction error of all output clusters in this paper.
## 3 Characteristics of the Waveforms  
### Contrasts between the Features of the Typical Waveforms in the Polar Regions  
As can be seen in Figure 4, the rate of the 20 Hz data had been used in this study to show the typical waveforms of the different surface types (seawater and sea ice). The selected waveforms were mainly located in the Chukchi Sea and the Beaufort Sea. The ice in those locations were almost exclusively the first-year ice formations.  
Figure 4: Typical waveforms and distribution of the waveforms: (**A**) Distribution maps of the data, in which the mosaic product of the Sentinel-1a/b (3-day) of the base map shows clear distinctions between the sea ice and seawater; (**B**) normalized typical waveforms (C1 to C4), in which the original waveforms of the different bands along the orbit are distinguishable.  
In the present study, the typical waveforms of the sea ice and seawater from the HY-2A/B ALT were found to be similar of the research result of [PERSON] [6; 7], with the exception the C band. The echo presented a trend of first increasing and then decreasing, as detailed in Figure 4B. The trailing edge slopes of the diffuses surfaces were more slowly reduced than those of the quasi-specular surfaces. In addition, there were spiky waveforms of rapid rising and falling observed for the quasi-specular surfaces. Furthermore, major differences between the sea ice and seawater of the Ku and C Bands of the HY-2A/B had been presented, as shown in Figure 3 (C1 to C4).
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
###### Abstract  
Phenological events are good indicators of the effects of climate change, since phenological phases are sensitive to changes in environmental conditions. Although several national phenological networks monitor the phenology of different plant species, direct observations can only be conducted on individual trees, which cannot be easily extended over large and continuous areas. Remote sensing has often been applied to model phenology for large areas, focusing mostly on pure forests in which it is relatively easier to match vegetation indices with ground observations. In mixed forests, phenology modelling from remote sensing is often limited to land surface phenology, which consists of an overall phenology of all tree species present in a pixel. The potential of remote sensing for modelling the phenology of individual tree species in mixed forests remains underexplored. In this study, we applied the seasonal midpoint (SM) method with MODIS GPP to model the start of season (SOS) and the end of season (EOS) of six different tree species in Slovenian mixed forests. First, substitute locations were identified for each combination of observation station and plant species based on similar environmental conditions (aspect, slope, and altitude) and tree species of interest, and used to retrieve the remote sensing information used in the SM method after fitting the best of a Gaussian and two double logistic functions to each year of GPP time series. Then, the best thresholds were identified for SOS and EOS, and the results were validated using cross-validation. The results show clearly that the usual threshold of 0.5 is not best in most cases, especially for estimating the EOS. Despite the difficulty in modelling the phenology of different tree species in a mixed forest using remote sensing, it was possible to estimate SOS and EOS with moderate errors as low as <8 days (_Fagus sylvatica_ and _Tilia_ sp.) and <10 days (_Fagus sylvatica_ and _Populus tremula_), respectively.  
p 2021  
* +
Footnote †: journal: Computer Science  
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# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 5 Experiments
### Experimental Results and Analysis
#### 5.2.2 Simulation with Different Simulation Times  
In this section, the simulation time was set from 40 to 200 s to test the long time stability of the protocols. In all experiments, the number of nodes was fixed to 30, and the node movement speed was fixed at 5 m/s.  
First, the average end-to-end delay of the three routing protocols is shown in Figure 6. It can be seen from the figure that the average end-to-end delay of the three routing protocols gradually decreased as the simulation time increased, and finally tended to become stable. This is because, at the initial stage, the routing table of each node was empty, the delay of the initial few messages was relatively large, and the average delay was high at the beginning of the simulation. Following the establishment of routes between nodes, the delay of new messages decreased, and the average delay of the network continued to decrease, finally achieving a stable state.  
The survival rates are shown in Figure 7. It can be seen from Figure 7 that the overall survival rate decreased as the simulation time increased. However, as the network gradually converged, the downward trend of the node survival rate gradually slowed down. Undoubtedly, AODV-EOCW showed a better survival rate again. The reason is that the new protocol takes congestion degree, residual energy, and hop count into account when selecting the next hop, and the relative importance of residual energy among the three metrics will increase as the energy is exhausted, which can be explained by Equation (3).  
Figure 5: Comparison of average node survival rates.  
Figure 6: Comparison of the average end-to-end delay.  
Figure 8 shows the variation in the message delivery rate with different simulation times. It can be seen from the figure that as the simulation time increased, the message delivery rate also increased continuously. As shown in the figure, the AODV-EOCW routing protocol outperformed the other two by a large margin. The authors thought that the delivery rates are directly bound up with the nodes' survival rate. If there are more nodes alive, the more messages can be delivered, so Figure 7 explains the results in Figure 8 from another perspective.
## 3 Results
### Kinetics of Photocross-Linking
\begin{table}
\begin{tabular}{c c c c c} \hline
**Resin** & **G’ 1, Pa** & **G’ 1, Pa** & **\(\eta^{*}\)1, mPa-s** & **t\({}_{\text{gel}}\) s 2** \\ \hline AESO & 4.76 \(\times\) 10\({}^{6}\) & 1.47 \(\times\) 10\({}^{6}\) & 7.93 \(\times\) 10\({}^{4}\) & 49 \\ AESO/VDM1 & 2.65 \(\times\) 10\({}^{6}\) & 1.01 \(\times\) 10\({}^{6}\) & 4.51 \(\times\) 10\({}^{4}\) & 98 \\ AESO/VDM2 & 2.70 \(\times\) 10\({}^{6}\) & 9.31 \(\times\) 10\({}^{5}\) & 4.55 \(\times\) 10\({}^{4}\) & 91 \\ AESO/VDM3 & 3.51 \(\times\) 10\({}^{6}\) & 1.18 \(\times\) 10\({}^{6}\) & 5.89 \(\times\) 10\({}^{4}\) & 66 \\ AESO/VDA1 & 5.89 \(\times\) 10\({}^{5}\) & 2.27 \(\times\) 10\({}^{5}\) & 1.01 \(\times\) 10\({}^{4}\) & 782 \\ AESO/VDA2 & 1.11 \(\times\) 10\({}^{6}\) & 3.74 \(\times\) 10\({}^{5}\) & 1.87 \(\times\) 10\({}^{4}\) & 531 \\ AESO/VDA3 & 3.56 \(\times\) 10\({}^{6}\) & 1.17 \(\times\) 10\({}^{6}\) & 5.96 \(\times\) 10\({}^{4}\) & 290 \\ \hline \end{tabular}
\end{table} 
Table 2: The storage modulus (G’), loss modulus (G′′), complex viscosity (\(\eta^{*}\)), and gel time (t\({}_{\text{gel}}\)) of all resins. Figure 2: The dependencies of storage modulus G’, loss modulus G′′, loss factor tan\(\delta\) and complex viscosity \(\eta^{*}\) of AESO on irradiation time. It is known that the acrylic group is more reactive than methacrylic [49]. This explains the increase of the induction period and tgel value during the photocross-linking of the resin series AESO/VDM in comparison to AESO. Additionally, the slope of the G' curve of AESO was steeper than that of the resin series AESO/VDM indicating the quicker formation of the polymer network [50]. High G' values indicate better mechanical properties of polymers caused by the high density of cross-links [41]. Thus, the higher G' values of AESO indicate the higher density of cross-links in this polymer (pAESO). Figure 4: The irradiation time dependencies of the storage modulus G’ of the resin series AESO/VDA. Figure 3: The irradiation time dependencies of the storage modulus G’ of AESO and the resin series AESO/VDM. The irradiation time dependencies of the storage modulus of the resin series AESO/VDA are shown in Figure 4. The photocross-linking kinetics of this resin series strongly depends on the VDA concentration. The higher the amount of VDA was used, the higher the values of t\({}_{\text{gel}}\) obtained (Table 1). The increase of the VDA amount caused the lower G' values obtained to show a lower density of cross-links in these polymers. The induction period of AESO/VDA was longer and the G' values were lower compared to those of the resin series AESO/VDM. Due to these data, especially due to the very high t\({}_{\text{gel}}\) values of the resin series AESO/VDA, further experiments were performed only with AESO and the resin series AESO/VDM.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 6 Conclusions  
This paper proposes an intelligent computing architecture for water quality, based on collaboration between edge servers and remote clouds. A deep learning application aimed to provide real-time monitoring of water quality in WDS. Thus, the datasets from three different areas (Area-B, Area-C, and Area-F) in the WDS were used to develop the deep learning model and then evaluated the framework's performance. The datasets include the main parameters used to indicate the quality of water such as chlorine, turbidity, Ph, TOC, conductivity, temperature, and pressure. Four different scenarios were applied in the proposed framework to investigate the ideal architecture for the used application. We have used Contiki with Cooja simulator to implement and simulate all these scenarios. We have used five different metrics to evaluate the performance of the proposed framework in all these scenarios. These metrics are latency, throughput, packet delivery ratio, power consumption, and deep learning model detection accuracy. The overall performance of the proposed work shows that scenario 1 outperforms the others in all stations (B, C, F) in terms of latency, throughput, and packet delivery ratio obtaining 20.33 mS, 148 Kb/s, 97.47%. On the other hand, scenario 1 shows the worst in terms of power consumption as it records a high value of 2,259,987 mW. In terms of model accuracy detection, Scenario 3 shows the best, and it records 94.43%.  
[PERSON], [PERSON] and [PERSON] conceived of the presented idea. [PERSON], [PERSON], [PERSON] and [PERSON] developed the theory and performed the computation. [PERSON] planned and carried out the simulations. [PERSON], [PERSON] and [PERSON] verified the analytical method. [PERSON] wrote the draft of the manuscript with input from all authors. [PERSON], [PERSON] and [PERSON] revised and edited the manuscript. [PERSON] and [PERSON] supervised the project. [PERSON] funding acquisition. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research is supported by European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie-Innovative Training Networks (ITN)- IoT4 Win-Internet of Things for Smart Water Innovative Network (765921).  
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4
## 3 Proposed Method
### Partial Averaging (PA) Method  
We propose a Partial Averaging (PA) method to address the problem of large RSSI evaluations for the released resources. The PA method evaluates the RSSI only for resources after receiving a packet with \(P_{rspp}=0\). This method enables the UEs to prevent the RSSI from being greatly evaluated by limiting the smoothing range to the resources and be able to make effective use of the released resources.  
Figure 8 shows an example of the RSSI evaluation using the PA method. This figure indicates that UE2 has reselected in subframe 820 and UE1 reselects in subframe 1000. When evaluating the RSSI for \(R_{1,1020}\) in the SPS, UE1 largely evaluates it because of averaging resources before subframe 820 that was used by UE2. In contrast, the PA method evaluates the RSSI only for resources after subframe 920 since the packet with \(P_{rspp}=0\) has been received in subframe 820. In subframe 920, UE2 has not transmitted, and the RSSI for \(R_{1,1020}\) is evaluated to be smaller. As a result, UE1 can make effective use of \(R_{1,1020}\).
## 4 Solution Problem
### UAV-BSs Clustering and Positioning Algorithm (UCPA)  
In UCPA, we calculated the number of UAV-BSs each congested macrocell requires to minimize the associated user flow, in addition to determining the optimal positioning of each UAV-BS in the coverage area of the congested macrocell. The algorithm obtained the database containing the load of the overloaded macrocells defined by the set \(M_{s}=\{1,2,3,...ms\}\). Then, the number of UAV-BS needed to serve each macrocell in a congested situation was estimated based on two strategies (naive and optimized), to study the relationship between cost and quality of service. Once the number needed to support the network was known, the optimal positioning of each UAV-BS was determined.  
To calculate the number of users in the coverage area and centralize the support of the UAV-BSs, the coverage area had to be reduced and the distance between the users and the macrocells calculated, as indicated in Equation (29).  
\[Loc_{(i,j)}=\sqrt{(m_{x}-u_{y})^{2}},\qquad Loc_{(i,j)}<R \tag{29}\]  
Here, \(Loc_{(i,j)}\) is the dataset of users in the new coverage area used by the optimized strategy to determine the number of UAV-BSs and find the optimal location of UAV-BSs in possible candidates for support in the region, and \(R\) is the value of the radius set to calculate the number of users.  
The optimal location is found using the K-means clustering algorithm, which calculates the centroid of each subgroup. The main variable is \(Q_{v}\), which is used by the algorithm to determine the number of subgroups. Each subgroup is the deployment area of a UAV-BS. The second input variable of the algorithm is the database \(Loc_{(i,j)}\), with the locations of the users, which is used to calculate the similarities between the data. In this way, the subgroups are separated and their center is selected, which is the ideal point for a UAV-BS.
## 3 Scattering Parameters
### Oblique Incidence Performance  
The oblique incidence performance was investigated in full-wave simulation under periodic boundary conditions. The Floquet port adds the top and bottom of the transmitter and receiver layers of the unit cell and the air box's walls surrounded by the master/slave periodic boundary conditions on each incidence angle from 0 to 60 degrees. Scanning angles from 0 to 60 degrees with oblique incidences in \(xoz\)-plane and \(yoz\)-plane have been studied. The reflection and transmission coefficients oblique incidence performance from 0 to 60 degrees are shown in Figure 5. The reflection coefficient S\(x_{i2,}x_{i2}\) for the transmitter layer (active patch) is less than \(-10\) dB for each oblique incident angle.  
The bandwidth shifted toward a higher frequency at 500 MHz; however, the bandwidth gets lower by 50-60 degrees and shifts toward the lower frequency at 16 GHz. The transmission coefficient increases with an increase in incidence, which is under \(-1\) dB between 0 and 40 degrees, and the bandwidth of the unit cell increases on higher angles but enough for the central frequency at 16 GHz with low insertion losses. The H-plane oblique incidence performance to reflection coefficient S\(x_{i2,}x_{i2}\) of transmitter plane is under \(-10\) dB until an angle of 40 degrees. It exceeded 50-60 degrees at an angle but was suitable along the center frequency. The transmission coefficient transmission loss exceeds as the angle of incidence increases, but it maintains less than \(-3\) dB for 0-50 degrees. The insertion losses increase abruptly for 60 degrees is shown in Figure 6.
## 5 Conclusions  
In this paper, the communication performances of the \(4\times 4\) indoor MIMO-VLC system are analyzed under different noise constraints. The precoding matrix from the BBD is utilized in the MIMO-VLC transmitter to modify the input data stream before transmission. The interferences such as thermal noise, shot noise and phase noise are mitigated by using BBD-based precoding in communication. Besides, the QAM modulation/demodulation improves the transmission of the indoor MIMO-VLC system. From the performance analysis, it is known that the proposed system has better performance than the MIMO-VLC without any precoding. Additionally, the communication performances of BBD-based MIMO-VLC are compared with the existing MIMO-VLC system in terms of BER. The throughput of the proposed system for scenario 3 is 26 Kbps at 140 dB; it is high when compared to the MIMO-VLC without any precoding. Moreover, the BER of the proposed system of scenario 1 is 0.0126 at 70 dB; it is less when compared to the OAP. Furthermore, the power allocation with an efficient beam-forming technique can be developed to improve the MIMO-VLC system performances.  
**Author Contributions:** Methodology, formal analysis and investigation, P.S.; software, resources and data curation, G.B.R.; data collection and validation, J.S.; original draft preparation--review and editing, P.B.D.; supervision and project administration, R.P.d.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**SNR (dB)**} & \multicolumn{2}{c}{**BER Analysis**} \\ \cline{2-3}  & **NRZ-OOK-LOS [24]** & **Proposed System** \\ \hline
110 & 0.3162 & 0.1259 \\
123 & 0.1259 & 0.0316 \\
130 & 0.0251 & 0.0100 \\
140 & 0.0126 & 0.0063 \\
150 & 0.0025 & 0.0003 \\
160 & 0.0007 & \(6.3\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 6: Comparative analysis for scenario 3.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**SNR (dB)**} & \multicolumn{2}{c}{**BER Analysis**} \\ \cline{2-3}  & **OAP [22]** & **Proposed System** \\ \hline
60 & 0.3548 & 0.1000 \\
70 & 0.1000 & 0.0126 \\
80 & 0.0016 & 0.0010 \\
90 & 0.0001 & \(3.16\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 4: Comparative analysis for scenario 1.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**SNR (dB)**} & \multicolumn{2}{c}{**BER Analysis**} \\ \cline{2-3}  & **FBM [23]** & **Proposed System** \\ \hline
**10** & 0.1585 & 0.0501 \\
**20** & 0.0398 & 0.0100 \\
**30** & 0.0100 & 0.0025 \\
**40** & 0.0050 & 0.0006 \\
**50** & 0.0013 & 0.0001 \\
**60** & 0.0005 & \(3.1\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 5: Comparative analysis for scenario 2.
# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
###### Abstract  
As an important space for disaster prevention, the construction of emergency shelters is crucial for the creation of a complete disaster relief facility network. Based on the goal of the prevention of day and night disaster, short-term fixed shelters are taken as the study object of the present work, and models are designed for evacuation simulation and the spatial optimization of shelters. According to the simulation, 680 of the 2334 demand points were found to be incompletely evacuated, and the average time for everyone to be evacuated was 10.3 min. Moreover, of the 888 short-term fixed shelters, only 218 did not reach their maximum capacity. In the context of short-term fixed sheltering, Haizhu was found to have the largest number of non-evacuated people (1.11 million), and the average number of non-evacuated people in Yuexiu was the largest (2184). According to the spatial optimization data of the shelters, the numbers of target plots for new shelter resources that must be added in Haizhu, Yuexiu, Liwa, and Tianhe are 406, 164, 141, and 136, respectively, the effective shelter areas of which are 2,621,100, 2,175,300, 812,100, and 1,344,600 m\({}^{2}\), respectively. A total of 487 short-term fixed shelters and 360 temporary shelters were newly added, and the recommended scales for Haizhu, Liwan, Tianhe, and Yuexiu were 243, 70, 58, and 116, respectively, with average effective areas of 6169 m\({}^{2}\), 5577 m\({}^{2}\), 8707 m\({}^{2}\), and 12,931 m\({}^{2}\), respectively. Additionally, the recommended scales of newly added temporary shelters in Haizhu, Liwan, Tianhe, and Yuexiu are 163, 71, 78, and 48, with an average effective area of 2706, 2581, 4017, and 6234 m\({}^{2}\), respectively. These findings provide a direct quantitative basis for the spatial optimization of various types of emergency shelters, and the method proposed in this paper supports the planning and layout of emergency shelters, as well as the improvement of the efficiency of urban resource allocation.  
disaster management; emergency shelter; spatial optimization; evacuation simulation +
Footnote †: journal: Applied Sciences