esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 4 Proposed Algorithm
### Comparison of Network Lifespan  
In order to analyze the network performance, we compare our proposed algorithm with three other different methods: MMSR [37] which uses a mobile sink to collect data packets and the Random-Movement technique where two MSs move randomly throughout the network and LEACH which uses one static sink. We define network lifespan as the time when the first CH exhausts its energy supply [25]. Figure 9 depicts the comparison of network lifespan of MMSR, Random-Movement, LEACH, and CMS2V2. As shown in Figure 9, despite the fact that MMSR and Random-Movement techniques use MSs, they do not bring a remarkable improvement in comparison with LEACH. As can be seen, the network lifespan in CMS2V2 is longer than in other strategies. This is because the CMS2V2 algorithm takes the waiting time of CHs into account to compensate for the extra energy consumption of CHs. The network lifespan in our model is six, seven and eight times more than the MMSR, Random-Movement, and LEACH, respectively. Related parameters are listed in Table 3.  
Figure 8: Total energy consumption in our proposed method, DBS and corona-based WSN without applying clustering.  
Figure 9: Network lifetime.  
From Figure 10, it can be seen that CM2V2 has better performance than the Random-Movement, MMSR and LEACH methods in terms of residual energy of the network. The residual energy of the LEACH and Random-Movement approach decrease more sharply than CM2V2 and MMSR, which means these two consume more energy than the CM2V2 technique during the routing process. Furthermore, full energy depletion of CM2V2 occurs later than with the three other methods, therefore, CM2V2 outperforms these strategies in this aspect. This stems from lower energy consumption of CMS2V2 in comparison with the other ones. Figure 11 shows the number of alive nodes as the number of rounds increases. As can be seen from Figure 11, MMSR doesn't have any alive sensor nodes around 1800 rounds, while the number of alive nodes of CM2V2 is 300 nodes in 2000 rounds. Then, we can see that our CM2V2 technique has better performance and longer network lifetime in comparison with MMSR, Random-Movement and the algorithm using one static sink. This is because the energy consumption is completely balanced among CHs located in the same sector and different layers in CMS2V2 algorithm.
## 1 Introduction
In particular, by adding virtual nodes, the network model does not only account for static metrics, but also for both the cumulative characteristic of the EMF exposure, and for the dynamic network. The optimum paths are finally obtained by means of a modified version of the CCA solution. Afterwards, we strictly define the proxies for the EI that will be used to assess the performance of the proposed protocol. The operation of EA-AODV relies on the theoretical models of the aforementioned EI proxies. Its performance is studied by means of an extensive simulation campaign carried out over the NS-3 framework, where we compare EA-AODV to more traditional approaches. Throughout the analysis, we assume that nodes can dynamically adapt their transmission power, based on the characteristics of the links they have with their corresponding neighbors. Altogether, this work makes the following contributions:  
* Definition of a general exposure model for wireless multi-hop networks;
* Introduction of an optimization problem to include the EMF exposure in routing schemes;
* Proposal of a novel EMF-aware routing protocol based on AODV, coined EA-AODV, that practically solves the aforementioned optimization problem;
* Implementation of EA-AODV protocol in the NS-3 framework;
* Evaluation of the EA-AODV operation in comparison with legacy solutions. The paper is structured as follows. Section 2 discusses some related works, and places the contributions that are presented herewith in the state of the art context. Then, Section 3 presents our proposal for EMF-aware routing scheme. To this end, it first depicts the corresponding optimization problem, introducing an algorithm that uses graph theory to solve it. Afterwards, an analytical model for EMF exposure in wireless multi-hop networks is presented. This model is then used to define the operation of the EA-AODV protocol. Then, in Section 4 we assess the exposure model presented before and analyze the performance of the proposed solution, comparing it with legacy approaches. Finally, Section 5 concludes the paper, outlining possible future research directions.
## 3 Results and Discussion
### Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound** & \multicolumn{2}{c}{**Compound Content**} & \multicolumn{2}{c}{**Odor Mescriptor \({}^{*}\)**} & \multicolumn{2}{c}{**Odor Threshold \({}^{**}\)**} & **OAV** \\  & **(ppb)** & & & **(ppb)** & \\ \hline Alcohols & & & & \\ Ethanol & 447.3 \(\pm\) 8.03 & & Fruity [29] & 100,000 [29] & \textless{}0.01 \\ Isoamyl alcohol & 459.5 \(\pm\) 9.95 & Fusel, whiskey, malt, burnt [30] & 30,000 [30] & 0.02 \\
1-Dodecanol & 5.2 \(\pm\) 0.61 & Raw carrot, medicinal [38] & 730 [39] & 0.01 \\
1-Tridecanol & 8.3 \(\pm\) 1.82 & - & - & - \\ Pentadecanol & 5.0 \(\pm\) 0.62 & - & - & - \\ N-Tetadecanol & 57.4 \(\pm\) 2.90 & Cinnamon [31] & - & - \\ Hexadecanol & 24.1 \(\pm\) 4.46 & flower, wax [31] & 750 [40] & 0.03 \\
1-Octadecanol & 30.6 \(\pm\) 4.40 & - & 870 & 0.04 \\
1-Eicosanol & 5.6 \(\pm\) 1.35 & & - & - \\ \(\alpha\)-Methylbenzyl alcohol & 77.6 \(\pm\) 4.01 & & Floral, honey, & 750 [41] & 0.10 \\ \hline Aldehydes and ketones & & & & \\ Nonanal & 2.0 \(\pm\) 3.39 & Floral [42] & 1 [42] & 2.0 \\ Decanal & 6.7 \(\pm\) 0.52 & Orange skin-like, floral [43] & 2 [43] & 3.3 \\ \(\alpha\)-Hexylcinnamaldehyde & 9.9 \(\pm\) 0.61 & Fresh, floral, herbal [44] & 50 [44] & 0.2 \\
5,9,13-Trimethyl-4,8,12-tetradecatritical & 29.7 \(\pm\) 7.22 & & - & - \\ Dodecanal & 1.0 \(\pm\) 1.74 & Fatty, citrus-like [43] & 2 [43] & 0.5 \\ Hexadecanal & 1.3 \(\pm\) 2.18 & leather, & 75 [40] & 0.02 \\ (4\(E\),8_,12_,16_)-4,8,13,17,21- & & & & \\ Pentamethyl-4,8,12,16,20- & 24.0 \(\pm\) 4.60 & - & - & - \\ docosspentaemal & & & & \\
3-Octanone & 3.1 \(\pm\) 5.41 & & Buttery, herbal, & \\ Naryl acetone & 40.5 \(\pm\) 3.34 & - & - & - \\ \hline Esters & & & & \\ Ethyl acetate & 364.5 \(\pm\) 5.59 & - & 5000 [29] & 0.07 \\ Diethyl succinate & 85.4 \(\pm\) 3.17 & Fruity, watermelon [45] & 200 [45] & 0.4 \\ Ethyl octaonate & 24.8 \(\pm\) 3.20 & Burnt, beer [32] & 5 [32] & 5.0 \\ Phenethyl acetate & 4.9 \(\pm\) 0.46 & Rose, floral, fruity, sweet [45] & 250 [45] & 0.02 \\ Ethyl decanoate & 8.8 \(\pm\) 0.74 & Grape, floral, soapy [30] & 6300 [29] & \textless{}0.01 \\ Ethyl 3-methylbutyl butanedioate & 19.8 \(\pm\) 2.39 & & - & - \\ Ethyl cinnamate & 4.6 \(\pm\) 0.56 & Strawberry, fruity, honey, & 1.1 [46] & 4.2 \\ Ethyl dodecanoate & 5.0 \(\pm\) 0.85 & - & 5900 [29] & \textless{}0.01 \\ Methyl palmitate & 7.4 \(\pm\) 0.36 & Ester-like [31] & 2000 [47] & \textless{}0.01 \\ Isopropyl myristate & 8.9 \(\pm\) 1.53 & Faintly oily, fatty [48] & - & - \\
2-Octyl benzoate & 4.1 \(\pm\) 3.61 & - & - & - \\
2-Methylbutyl salicylate & 7.8 \(\pm\) 0.35 & - & - & - \\
1-(4-Isopropylphenyl)-2-methylpropyl & 25.2 \(\pm\) 2.06 & - & - & - \\ Nonyl 2-methylpropanoate & 2.8 \(\pm\) 4.85 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Volatile organic compounds profile and odor features of jaboticaba wine. Odor descriptors, odor threshold, and odor activity value of jaboticaba wine extracted by SPME-CG/MS using PDMS-DVB fiber.
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 3 Simulation and Analysis
### Simulation Experiments  
Assuming a mixed pulse stream is interleaved by four TOA sequences arriving simultaneously. The parameter setting of the mixed pulse stream is referred to [11; 19; 31], which are the latest results for staggered signal de-interleaving. Pulse Sequence 1 is a staggered PRI signal, and the staggering number is five; Pulse Sequence 2 is a jittered signal; the remaining two signals are fixed PRI signals. The parameters of the TOA model are shown in Table 1. The jamming pulses are not shown in the table. The de-interleaving process of the proposed algorithm is introduced as follows:  
In the initial de-interleaving round, no PRI is detected in the first two-grade SSWI function. A PRI is detected in the third-grade SSWI function, but the corresponding pulse sequence is not searched. The FFT of the fourth-grade SSWI function is shown in Figure 4a, the estimated PRI value is 165.9 \(\upmu\)s. The second through eighth rounds of de-interleaving are similar to the first round. The results are shown in Figure 4b-h.  
After the eighth round of de-interleaving, the number of residual pulses is seldom. All pulse sequences have been separated. Then, extract the staggered pulse sequence and estimate the sub-PRIs. The frame period of the staggered pulse sequence is \((165.9*2+165.5*2+165.1)/5=165.6\)\(\upmu\)s, and the sub-PRIs are 11 \(\upmu\)s, 20 \(\upmu\)s, 32.8 \(\upmu\)s, 45 \(\upmu\)s, and 56.5 \(\upmu\)s, respectively. The simulation of the sub-PRIs estimation is shown in Figure 5.  
Define \(m_{error}\) as follows:  
\[m_{error}=\frac{1}{n}\sum_{i=1}^{n}\frac{|P_{e_{i}}-P_{i}|}{P_{i}} \tag{26}\]  
where \(P_{e_{i}}\) is the estimated PRI value, \(P_{i}\) is the setting PRI value, and \(n\) is the number of radar radiation sources.  
The \(m_{error}\) of the simulation is as follows:  
\[\begin{split} m_{error}&=\frac{1}{4}\left(\frac{|1 65.6-166|}{166}+\frac{|442.8-441|}{441}\right.\\ &\left.+\frac{|780.2-780|}{780}+\frac{|1150-1151|}{1151}\right) \\ &=0.21\%\end{split} \tag{27}\]  
The simulation showed that the proposed algorithm can de-interleave staggered PRI signals and estimate sub-PRIs of the staggered signal with high estimation accuracy.  
Figure 4: The de-interleaving results: (**a**) The first round; (**b**) The second round; (**c**) The third round; (**d**) The fourth round; (**e**) The fifth round; (**f**) The sixth round; (**g**) The seventh round; (**h**) The eighth round.
## 2 Study Area, Data Sources, and Methodology  
### Study Area  
Guangzhou city is the capital of Guangdong Province, China (Figure 1). It is located on the northern edge of the Pearl River Delta in southern China, typically with a subtropical and marine monsoon climate [40]. The annual average temperature is about 21.5\({}^{\circ}\)C. The annual precipitation is relatively high, reaching nearly 2000 mm, most of which is between April and September [40]. Apart from a dense urban core area which is dominantly covered by urbanized land (approximately 15% out of the total area), the greater Guangzhou Area (GGA) is mainly covered by larger suburban and rural areas. Forests, urbanized/settlement lands, water bodies, croplands, and rangelands are the main land cover types. Vegetation in the land cover types plays an important role in providing ecosystem services, including carbon sequestration. Forests contribute the most to carbon sequestration and consist of subtropical mountain evergreen broad-leaved forests in the north, subtropical evergreen seasonal rainforests in the middle, and tropical evergreen seasonal rainforests in the south [40]. Croplands are dominated by farmlands for crop production. In the urbanized area, road-side trees, gardens, and residential green spaces also play an important role in carbon sink for GGA. Rangelands are open areas covered in homogenous grasses or other plants with little to no taller vegetation.  
GGA has been observed to undertake rapid urbanization in the past three decades. During that period, the urbanized/built-up area showed an average annual growth of 41 km\({}^{2}\) while urban vegetation was reduced by an average of 40 km\({}^{2}\) per year [41]. The decrease of vegetation cover imposed a direct effect in degrading carbon sequestration from urban vegetation. In addition, previous studies showed that human activities have significantly reduced vegetation productivity in GGA and recommended continuous efforts to improve the intensity of protection and management in the urban environment [42]. We hypothesize that the optimized land management of forests, croplands, rangelands, and urbanized areas helps to increase carbon uptake from the vegetation in the urban ecosystem of GGA. This work tries to answer questions about how to locate optimal LMPs and the spatial patterns in terms of possible extra carbon uptake capacity from optimal LMPs in an urban context.
## 4 Measurement Results  
For the KNN classification in the preliminary study, only intersubject classification is used, incorporating data from all participants. It is important to note that in this paper, for the baseline system as well as Models 1 and 2, the intersubject classification is conducted without using leave-one-out testing. This means that a separate test set from one participant, which is left out of both training and validation, is not used. However, given the satisfactory performance of the ML-CSP-OVR model, the test paradigm is extended to include leave-one-out intersubject classification. For the GoogLeNet classification, both intrasubject and intersubject classifications are utilized. In the baseline system, intrasubject classification is performed for all participants. In the three variant models, intrasubject classification is conducted for three participants: P1, P5, and P12. Intersubject classification uses data from participants 1 to 11, excluding participant 7 as previously mentioned.
## 3 Experiments and Comparisons
### Experiment 2  
We compared the proposed selected multi-stage feature outcomes to select the most suitable feature combination set for vehicle detection. From the above experiment, the feature set dimensionality was set to 1440. Then, we selected one feature from each of the Maxpool_1, Conv_4, and Conv5_layers (as shown in Figure 10) and constructed the feature sets by concatenating them. Thus, there were 27 possible combinations of the feature set, as shown in Figure 14. We trained the AdaBoost algorithm using the 27 feature set cases and compared their performance using the DET curves, as shown in Figure 15.  
The best performance was obtained by combining the Maxpool_1 45 th, Conv_4 38 th, and Conv_5 55 th feature maps. All the outcomes that included the Maxpool_1 45 th feature exhibited good performance because this feature map characteristic information included the vehicle head lamps. Thus, feature map inclusion or otherwise of characteristic information regarding the head lamp considerably influenced the performance of the proposed selective multi-stage feature fusion method.  
Based on the experimental results, the feature set for the proposed vehicle detection framework was finally selected as the concatenation of the Maxpool_1 45 th, Conv_4 38 th, and Conv_5 55 th feature maps.  
Figure 13: Positive (**left**) and negative (**right**) sample image feature map visualizations.
# Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
## 2 Description and Early Warning Framework of the Safety Potential Field in the Bridge Area  
### Description of the Safety Potential Field in the Bridge Area  
The _Measures for the Safe Management of Water Traffic in Bridge-Area Waters_ define bridge-area waters as the waters within a certain range on each side of the bridge axis, which is the critical navigation area around the bridge. Vessels navigating in bridge-area waters need to cope with the influence of a variety of factors, such as bridge structures, underwater obstacles, vessel traffic flow and channel boundaries.  
From a physical perspective, a field is a condition in which an object with specific properties exerts an interaction force on other objects within a certain spatial range around it without direct contact. The magnitude of this interaction force varies with the relative positions of the objects. Due to this interaction force, the objects possess potential energy related to their relative positions. Therefore, the potential field can be used to describe the interaction capacity within the entire space surrounding the object [30]. In the bridge area, similar to the physical field described above, ships need to maintain a certain distance to avoid moving too close to navigational aids and bridge facilities during navigation. This behaviour can be regarded as the ship being influenced by the potential field formed by bridges and beacons. Each factor affecting the safety of navigation in the bridge area can be considered a field source, and the safety potential field can be seen as a physical field reflecting the impact of navigational factors on ship safety in the bridge area. Therefore, this paper applies the theory of safety potential fields to study the overall navigational risk in bridge-area waters.
## 3 Results
### The Influence of Response Period on \(r\) of SIF with P, VPD, ES and RSM
The improvement of the \(r\) of SIF with P and ES were the largest and the smallest, respectively, which suggested the response period affected the correlation of SIF to P the most and the correlation to ES was the least affected among the four factors. The response period, or time lag effect, also has different degrees of influence on evaluating and qualifying the relationship between vegetation growth or ecosystem productivity and the climatic factors. All of these indicated that the correlation of SIF with P, VPD, ES, and RSM were impacted by the time lag effect. In addition, though we corrected the spatial \(r\) of SIF with the four variables, there were poor \(r\) values in some areas; the reason for this may be that vegetation greenness and productivity are affected by multiple variables, and a single variable cannot fully explain the vegetation greenness and productivity dynamics. Figure 6: The spatial Pearson’s correlation coefficient extremum (\(r_{e}\)) and corresponding significance test (\(p_{e}\)) of deseasonalized solar-induced chlorophyll fluorescence (SIF\({}_{\rm ds}\)) with deseasonalized precipitation (P\({}_{\rm ds}\)), vapor pressure deficit (VPD\({}_{\rm ds}\)), evaporative stress (ES\({}_{\rm ds}\)), and root zone soil moisture (RSM\({}_{\rm ds}\)). (**a–d**) respectively represented the corrected spatial \(r\) of SIF\({}_{\rm ds}\) with P (\(r_{e}\) (SIF\({}_{\rm ds}\), P\({}_{\rm ds}\))), VPD\({}_{\rm ds}\) (\(r_{e}\) (SIF\({}_{\rm ds}\), VPD\({}_{\rm ds}\))), ES\({}_{\rm ds}\) (\(r_{e}\) (SIF\({}_{\rm ds}\), ES\({}_{\rm ds}\))), and RSM\({}_{\rm ds}\) (\(r_{e}\) (SIF\({}_{\rm ds}\), RSM\({}_{\rm ds}\))). (**e–h**) respectively represented the spatial \(p\) of SIF\({}_{\rm ds}\) with P\({}_{\rm ds}\) (\(p_{e}\) (SIF\({}_{\rm ds}\), P\({}_{\rm ds}\))), VPD\({}_{\rm ds}\) (\(p_{e}\) (SIF\({}_{\rm ds}\), VPD\({}_{\rm ds}\))), ES\({}_{\rm ds}\) (\(p_{e}\) (SIF\({}_{\rm ds}\), ES\({}_{\rm ds}\))), and RSM\({}_{\rm ds}\) (\(p_{e}\) (SIF\({}_{\rm ds}\), RSM\({}_{\rm ds}\))).
## 4 Experimental Validation
### Ground Simulation Facility
#### 4.1.2 Electric Charge Generated by UV LED
The radiant power of light that passes through the pin-hole lens, denoted by \(P_{\text{hole}}\), is computed using the solid angle of the pin-hole lens,  
\[R_{\text{hole}}=I_{\text{LED}}\Omega_{\text{hole}}, \tag{5}\]  
where the effective solid angle of the pin-hole lens is calculated as follows,  
\[\Omega_{\text{hole}}=\frac{\pi^{2}\phi^{2}}{4\pi D_{\text{LED}}^{2}}. \tag{6}\]  
It follows that the radiant power received by the MaPMT is computed using Equation (2) and Equations (4)-(6),  
\[P_{\text{hole}}=P_{\text{LED}}\frac{\Omega_{\text{hole}}}{\Omega_{\text{LED}} }=\frac{\phi^{2}P_{\text{LED}}}{4D_{\text{LED}}^{2}\tan^{2}\frac{\theta}{2}}. \tag{7}\]  
The amount of electric charge caused by the radiant power \(P_{\text{hole}}\) over the sampling interval \(t_{s}\) is calculated as follows,  
\[C_{\text{LED}}=P_{\text{hole}}St_{s}G_{\text{min}}, \tag{8}\]  
where \(G_{\text{min}}\) is a specific gain value of the MaPMT used for the ground simulation. It should be noted that the gain of the MaPMT is set to be minimum to avoid the output saturation of the MaPMT during the ground simulation. Because the electron count is converted into electric charge by multiplying the elementary electron charge \(e_{v}=1.602\times 10^{-19}\) in coulomb, the electric charge output of the MaPMT due to the actual TLE is derived from Equation (1) such that \(C_{\text{TLE}}=e_{\text{TLE}}e_{v}\). Thus, in order to generate  
Figure 11: Geometry of UV LED and detector in the ground simulation facility. the equivalent charge due to both the actual TLE and the UV LED, the power injected to the pin-hole lens is calculated as follows,  
\[P_{\text{hole}}=\frac{e_{\text{TLE}}e_{v}}{St_{s}G_{\text{min}}}. \tag{9}\]  
Subsequently, from Equations (7) and (9), the desired radiant power of the UV LED is calculated as follows,  
\[P_{\text{LED}}=\frac{4D_{\text{LED}}^{2}\tan^{2}\frac{\theta}{2}}{\phi^{2}St_{ s}G_{\text{min}}}\varepsilon_{\text{TLE}}e_{v}. \tag{10}\]  
The distance between the UV LEDs and the pin-hole lens is chosen to be \(D_{\text{LED}}=0.54\) m by taking into account the field of view angle of the TLE observation system. The detailed sizing of the experimental facility is described in Section 4.1.3. With the given parameters listed in Table 3 and \(\theta=30^{\circ}\), the desired power is determined by 1.22 \(\mu\)W for the limb case and 31.7 \(\mu\)W for the nadir case. Because the nominal radiant power of the UV LED is specified by 40 mW with the forward current of 15 mA [37], in order to achieve the specified emitting power, a current limiting resistor in the UV LED driver circuit is utilized to limit the forward current between 0.46 \(\mu\)A and 11.9 \(\mu\)A. As a result, when the UV LED operates with the forward current ranging from 0.46 \(\mu\)A to 11.9 \(\mu\)A, the UV LED in the ground simulation facility is assumed to imitate the TLE emission while enabling the MaPMT to generate the equivalent charge to the actual TLE.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 2 Materials and Methods
### Traditional Image Processing Algorithms Description
#### 2.5.2 Otsu's Method  
It is based on the gray level image's histogram. It aims to find the threshold value which minimizes the intraclass variance [38] of the thresholded black and white pixels. The main advantage of this method is its unparametric nature which avoids the need for configuring it according to the image noise level.