esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sorption Study
#### 3.1.3 Effect of Adsorbent Dose  
To attain the influence of AHIIBC mass on Y(III) adsorption efficiency, a series of experiments were performed on the adsorbent dose of AHIIBC in the range between 10 and 400 mg under constant conditions at pH of solution 6.0, contact time of 60 min, and 50 mL volume of 200 mg\(\cdot\)L\({}^{-1}\) of Y(III) solution at room temperature.  
The effect of adsorbent dose was stated in terms of maximum uptake of Y(III) (q\({}_{\mathrm{e}}\)). The obtained result, shown in Figure 1c, exposes that the Y(III) adsorption efficiency was  
Figure 1: Influence of adsorption parameters on the uptake capacity of Y ions (**a**) pH (200 mg\(\cdot\)L\({}^{-1}\) yttrium ions concentration, 50 mL volume, 60 min contact time, 0.05 g adsorbent dose, room temperature), (**b**) contact time (pH 6.0, 50 mL volume, 200 mg\(\cdot\)L\({}^{-1}\) yttrium ions concentration, 0.05 g adsorbent dose, room temperature), (**c**) adsorbent dose (50 mL volume, 200 mg\(\cdot\)L\({}^{-1}\) yttrium ions concentration, 60 min contact time, pH 6.0, room temperature), (**d**) initial yttrium ions concentration (50 mL volume, 60 min contact time, pH 6.0, room temperature, 0.05 g adsorbent dose), and (**e**) temperature (200 mg\(\cdot\)L\({}^{-1}\) yttrium ions concentration, pH 6.0, 50 mL volume, 60 min contact time, 0.05 g adsorbent dose).  
171.32 mg\(\cdot\)g\({}^{-1}\) as the mass of the AHIBC adsorbent boosts from 10 to 50 mg. It was obvious that all exposed active sites were fully covered, whereas the adsorption capacity of AHIBC reduced from 171.32 to 122.20 mg\(\cdot\)g\({}^{-1}\) at 75 mg and continued to decrease progressively to 22.98 mg\(\cdot\)g\({}^{-1}\) with increasing the adsorbent dose of AHIBC to 400 mg. The subsequent decrease is due to the presence of more active sites on the surface of the adsorbent. The utmost adsorption efficiency of AHIBC was 171.32 mg\(\cdot\)g\({}^{-1}\). At a higher dose of the AHIBC, more active sites became accessible for ion exchange procedures due to the increase of surface area. Thus, the 50 mg adsorbent dose was selected for the next experiments. It was obvious that the adsorption capacity of AHIBC was higher than the adsorption capacity of other adsorbent materials reported in earlier studies, as listed in Table 1 below.
# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
## 3 The Solution Algorithm for the Hybrid Sensor Deployment  
In the model proposed above, Equation (7) is a nonlinear form to express the capacity constraint, which makes the model unable to be solved in polynomial time. In Equation (7), the term \(\sum_{j\in N,k\in\mathcal{S}}\alpha_{k}x_{ij}^{k}\) denotes the expected computational load of cell \(i\). If it is pre-determined, the optimization model can be easily solved with commercial solvers. This value is highly related to variable \(x_{ij}^{k}\). It is obvious that the computational load of cell \(i\) can be estimated only when the multi-sensor deployment plan is given. Under this condition, the valuable solutions can be determined by respectively finding cells to install sensors and servers through the optimization model. The basic idea is to divide the solving process into two steps. In step I, the algorithm will first determine variable \(x_{ij}^{k}\) and then explore \(z_{u}\) with budget and capacity constraints. In step II, the algorithm will enumerate potential locations for servers and then solve a sub-model with the service scope constraint to obtain variable \(x_{ij}^{k}\). Since the expected budget is another critical constraint for each step, the algorithm intends to search for more feasible solutions by adjusting the total budget allocation. The framework of the algorithm is depicted in Figure 3. A detailed explanation is introduced as follows.
## 4 Examples
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**M** & **Design Method** & **Filter Order** & **Number of Nonzero Taps** & **Amplitude Distortion** & **Total Aliasing Error** \\ \hline \multirow{3}{*}{4} & WCLS & 80 & 80 & \(3.10\times 10^{-3}\) & \(1.06\times 10^{-6}\) \\  & QBPSO & 80 & 80 & \(7.01\times 10^{-4}\) & \(8.49\times 10^{-8}\) \\  & Proposed Method & 140 & 62 & \(1.70\times 10^{-3}\) & \(5.06\times 10^{-7}\) \\ \hline \multirow{3}{*}{8} & WCLS & 144 & 144 & \(1.80\times 10^{-3}\) & \(3.90\times 10^{-7}\) \\  & GI & 132 & 132 & \(2.14\times 10^{-5}\) & \(1.30\times 10^{-5}\) \\  & Proposed Method & 160 & 98 & \(5.20\times 10^{-4}\) & \(9.85\times 10^{-6}\) \\ \hline \multirow{3}{*}{16} & WCLS & 224 & 224 & \(1.30\times 10^{-3}\) & \(9.03\times 10^{-7}\) \\  & QBPSO & 256 & 256 & \(4.73\times 10^{-4}\) & \(2.34\times 10^{-6}\) \\ \cline{1-1}  & Proposed Method & 254 & 166 & \(5.21\times 10^{-4}\) & \(4.24\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons for Several Parameters of the Designed Filters Derived from Different Methods. Figure 3: \(M=8\) channel cosine-modulated filter banks in example 2. (**a**) The amplitude distortion function magnitude \(e_{am}(\omega)\); (**b**) The total aliasing transfer function magnitude \(e_{a}(\omega)\); (**c**) Amplitude response of sparse prototype filter. **Example 3**. : _This example is concerned with the design of NPR cosine-modulated filter banks with \(M=16\). The filter order of the FIR linear phase prototype filter is \(N=254\). The numbers of discretized samples for pass band, transition band and stop band are \(L_{p}=4\), \(L_{t}=7\) and \(L_{s}=94\), respectively. Choose \(\delta_{p}=\delta_{t}=\delta_{s}=1\times 10^{-3}\). The amplitude distortion function magnitude \(e_{am}(\omega)\), the total aliasing transfer function magnitude \(e_{a}(\omega)\), and the amplitude response of the sparse prototype filter derived from the proposed scheme are shown in Figure 4. The amplitude distortion \(e_{am}\) and total aliasing error \(e_{a}\) of the corresponding cosine-modulated filter bank are \(e_{am}=5.21\times 10^{-4}\) and \(e_{a}=4.24\times 10^{-6}\). It takes 83 iterations for proposed method to converge, with the CPU time equals to 0.1108 seconds in each iteration._  
We compare the performance of the proposed scheme with the WCLS [6] scheme and QBPSO [7] scheme. At the similar reconstruction performances, the number of nonzero tap weights of the prototype filter yielded from the proposed method is 58 less than that of the WCLS scheme, the number of nonzero tap weights of the prototype filter yielded from the proposed method is 90 less than that of the QBPSO scheme. In Table 1, we compare the filter order, the number of nonzero tap weights, the amplitude distortion \(e_{am}\) and the total aliasing error \(e_{a}\) corresponding to the proposed scheme, the WCLS scheme and the QBPSO scheme.
# RF Exposure Assessment by Drone-Based Technology
## 2 Materials and Methods  
The study was conducted at the Polytechnic School of the University of Extremadura, located on the university campus in the city of Caceres, Spain (see Figure 1). It consists of seven buildings dedicated to teaching and research in the fields of telecommunications, computer science, building construction, and civil engineering. The study area has pedestrian pathways between each building that allow for user mobility and is surrounded by vegetation. It covers an area of approximately 32,000 m\({}^{2}\) in a rectangular shape, including the perimeter encompassing all buildings, as well as the parking lots located to the north.  
The central building, located on the southern side, has a mobile-phone base station on the roof, situated 17 m above the ground. The base station includes three sector antennas that cover a 120\({}^{\circ}\) angle each (see Figure 1) with a tilt angle of 4\({}^{\circ}\). The antennas operate in different frequency bands associated with specific communication technologies. These bands are as follows: 842-852 MHz (4 G), 949.9-959.9 MHz (2 G/3 G), 1825-1845 MHz (2 G/4 G), and 2140-2155 MHz (3 G) [15]. The 700 MHz band (5 G) was not active at this site when the sampling was carried out in 2018, and will therefore not be considered in this study.  
The geographic location of this base station makes it a strategic point for providing coverage to the entire Caceres campus and its surroundings. However, it also exposes the study area to significant electromagnetic radiation emitted by the mobile base station antennas.
# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF
## 3 Results and Discussion
### Measurement and Prediction of Wind Turbine Wake
The reason being that the turbine spacing in actual wind farms is always larger than 3D, and, in most cases, the streamwise spacing is larger than 7D [38]. Figure 21: Horizontal profiles of normalized velocity deficit at the hub height level and at various downstream locations of the wind turbine wake. Top figure shows horizontal profile of mean wind speed 1 D upstream from the turbine. LiDAR measurement data from 28 September 2016 from 16:00 to 22:45.
## 3 Results and Discussion
### Definition of Maximum FSR Function of CA50
higher compared to the previous case, and its trend can be considered as constant. Further increments were not possible due to ringing and noise limitations. Furthermore, due to a lower fraction of diesel injected longer combustion duration was observed as remarked in Table 5.  
Figure 4 compares the HRR profiles at 4 and 14 deg of CA50. As seen, for more advanced combustion phasing (4 deg) the HRR profile shows two characteristic peaks, the first one was related to the burning of the pilot diesel fuel and the second to the burning of the ethanol as the main energy carrier. HRR profiles lose the two peaks previously observed retarding combustion phase and increasing the ethanol fraction. The progressive loss of the first diesel peak was due to the high heat of vaporization of alcohol injected; hence its cooling effect became more significant on the combustion rate [27]. The HRR profile comparison indicates a transition to a premixed, homogeneous ethanol-dominated combustion [22]. Related to the previous point, the cooling effect of ethanol lowered the HRR peak.
This raises questions about which living environments should be developed further, and what level of development is necessary in each environment. If these questions are answered using appropriate measures, local governments will be able to solve the concrete problems in each region, and U-turn migration would become more practicable for improving population disparities. To address these questions, it is necessary to clarify which living environments and conditions heighten people's motivations for U-turn migration. Numerous studies to this end have been conducted in Japan [6; 14; 15; 16; 17]. The subjects of these studies were people (especially younger adults) who moved back to rural and depopulated areas. The researchers found several critical reasons that influenced these people's decisions. However, the current population disparity in Japan is not only a problem for depopulated rural villages but also for provincial cities that flourished in the past. Moreover, when people migrate, two regions are involved (in the case of U-turn migration, individuals' current residence and the hometown). Therefore, it is obvious that people compare the living environments of both locations to make a decision. Nonetheless, few studies have quantitatively measured the influence of people's current residences on U-turn motivations. This study introduces quantitative measures of the relationship between individuals' U-turn motivations and their evaluations of the living environments. Based on the results, we clarify the structures of people's U-turn motivations that would improve the population disparities in Japan. In addition, we propose which living environments are necessary to develop. Then, we provide helpful information for local governments that want to conduct the appropriate development of living environments to promote these people's U-turn migration. The target subjects of this study are people of various attributes living in various places in Japan. Data from a questionnaire survey were analyzed from three perspectives. First, we used decision tree analyses to describe the multilayered relationships between the U-turn motivations and the evaluations of living environments. We also determined which living environments are deciding factors for U-turn migration, and what level of development is required for each factor. The influence of the living environments of the current residence was considered in the analysis. Secondly, we assessed the demographic attributes and urban characteristics of the current residences and hometowns of respondents' groups that were categorized in the decision tree. This enables local governments to clarify which living environments they should develop according to their target populations and estimate the contribution of people's U-turn migration in the improvement of population disparities in Japan. Finally, we examined whether respondents' evaluations of important factors varied on the basis of the urban features of the current and possible future residences. The results help predict the likelihood of U-turn migration for individuals, using information about their current residences and hometown.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 5 Simulation Results
### Time Distribution of Fully Charged Devices  
The last experiment shows the time distribution for fully charged devices. Figure 21 shows the number of fully charged devices in different iterations for different charging ratios, where the number of devices is 30, the transmission angle is 90, and the offset is 10. The results for Fix, RAN, and RR are not included because these methods need a longer charging time. We can observe that the DE algorithm can efficiently distribute the charging completion time of devices to different iterations. In Figure 21b, the fully charged devices of the greedy algorithm only occupy 8 iterations, but those of the DE algorithm occupy 17 iterations. Both Figure 21a,c show similar trend. This is because the DE algorithm postpones the charging completion time of a device by decreasing the charging time for the devices of each sector and increasing the charging time for the devices of the subsequent sectors. The DE algorithm can thus evenly distribute the fully charged devices to different iterations.  
We further show the average data rate of fully charged devices in each iteration, where each device periodically transmits data to the AP when it is fully charged. The configuration of data transmission is based on our previous work [31]. It includes 10 MHz bandwidth and 100 mW transmission power. The background noise is \(-100\) dbm and the channel gain is defined as \(d_{i}^{-\alpha}\), where \(d_{i}\) is the distance between device \(i\) and the AP. The path loss factor, \(\alpha\), is set to four. However, there is only one wireless channel considered in this work since these sensor devices can only transmit data when they are fully charged.  
As shown in  
charged devices, the date rate of a device would be degraded because of the interference from more devices. As a result, the DE algorithm can improve the data rate of a fully charged device.  
Figure 23 further shows the total data rate of fully charged devices in each iteration. The results reveal that the DE algorithm outperforms the proposed greedy algorithm by providing a higher total data rate of fully charged devices in most iterations. In particular, for the charging ratio 1, the DE algorithm can improve the average data rate by 160%. For the charging ratios, 0.6 and 0.8, the average data rate is further improved by 800%. In summary, although the DE algorithm requires a slightly longer charging time than the proposed greedy algorithm, it can effectively improve the performance of data transmission.  
Figure 21: The number of fully charged devices in different iterations. (**a**) Ratio = 1, (**b**) Ratio = 0.8, (**c**) Ratio = 0.6.  
Figure 23: _Cont._  
Figure 22: The data rate of fully charged devices in different iterations. (**a**) Ratio = 1, (**b**) Ratio = 0.8, (**c**) Ratio = 0.6.
# Image Mosaicing Applied on UAVs Survey
## 3 Stitching Methods
### Feature-Based: Global Single Transformation
#### 3.1.1 Harris Corner  
The Harris Corner Detector [62] was one of the first feature detection methods and it is based on the Moravec Corner Detector. This method uses a small window to scan in different directions for changes in the average light intensity of the image; then, the center point of the window is extracted as a corner point, shifting the window. Should there be a flat region, it will show no change of intensity in all directions. If an edge region is found, then there will be no change of intensity along the edge direction. However, if a corner is found, then there will be a significant change of intensity in all directions [68].  
The corresponding eigenvalues provide the actual value amounts of these increases. \(\lambda_{1}\) and \(\lambda_{2}\) are the eigenvalues of matrix \(M\). Then, the corner, edge, and flat area of the image can be computed from the eigenvalues as follows:  
* Flat area: both \(\lambda_{1}\) and \(\lambda_{2}\) are very small.
* Edge: one of \(\lambda_{1}\) and \(\lambda_{2}\) is smaller and the other is bigger.
* Corner: both \(\lambda_{1}\) and \(\lambda_{2}\) are bigger and are nearly equal.
## 3 Results and Discussion
### GFET Based Flexible Receiver
#### 3.5.3 Flexible Graphene Speaker  
Graphene along with its outstanding electrical and mechanical properties has very good thermal-acoustic characteristics. It offers very small heat capacity per unit area making it suitable as a thermo-acoustic speaker [23,24]. These graphene speakers are becoming quite attractive owing to its potentially low manufacturing cost, light weight, low power consumption and also high flexibility. Recently, [PERSON] _et al._ demonstrated flexible graphene based speaker on a paper substrate [23,24]. In this light, we have designed and fabricated a flexible mono layer graphene speaker on PI substrate as shown in Figure 11. CVD grown monolayer graphene (1 cm\({}^{2}\)) is transferred onto a smoothened PI substrate. Gold/Silver metal contacts are deposited at the two edges of graphene. Baseband demodulated signal from receiver is applied to the two contacts. These signals create thermal oscillations at the graphene-air interface, which are then converted to sound waves.  
The measurement of the sound waves from this speaker is performed using a microphone and signal analyzer (See Figure 11b). The distance between the microphone and speaker is kept at 3 cm. This speaker performance is as shown in Figure 11c. It also shows the comparison with commercial speaker.  
Figure 11: **(a)** Side view of graphene speaker (grey—monolayer graphene, yellow—gold contacts, brown—PI substrate); **(b)** Fabricated graphene speaker and microphone test setup; **(c)** SPL _vs._ frequency plot comparing flexible graphene speaker with a commercial speaker. (adapted from [25], Copyright 2014, IEEE)  
Figure 10: **(a)** GFET Demodulator **(b)** AM input and **(c)** Demod output (adapted from [17], Copyright 2013, permission granted by [PERSON]).  
These speakers provide a sound pressure level (SPL) of 25-50 dB in the audio frequency range. The speaker power at high frequencies is given by Equation (6) [23]. It mainly depends on frequency of sound (f), heat capacity of air (\(\gamma\)), velocity of sound in air (\(v_{g}\)) thermal effusivity of PI substrate (\(e_{s}\)) and air (\(e_{a}\)) [23].  
\[\mathbf{P_{speaker}}=\frac{\mathbf{Ro}}{\sqrt{2}\mathbf{r_{o}}}\frac{\mathbf{\mathrm{y}}-\mathbf{ \mathrm{1}}}{\mathbf{v_{g}}}\frac{\mathbf{e_{g}}}{\mathbf{e_{s}}+\mathbf{a_{c}}+\mathbf{e_{g}}}\bm {q_{o}} \tag{6}\]  
Future research towards monolithic graphene nanosystems requires integration of the speaker with the AM radio receiver. The performance of this speaker at low frequency can be improved by high quality graphene film, waveguide design and fabrication of suspended graphene speakers.
# Two Medoid-Based Algorithms for Clustering Sets
## 3 Medoid-Based Approaches
### K-Medoids Algorithm for Sets  
Algorithm 1 describes the steps of the algorithm in pseudo-code which differs from the Lloyd's classical K-means only in the _Update_ step, where instead of computing the mean of the points associated with a cluster, the medoid of the cluster is identified.  
The two steps, Steps 2 and 3, are repeated until the medoids stop moving. The computational cost of the algorithm is \(O\big{(}N^{2}KT\big{)}\) where \(T\) is the number of iterations. The cost is dominated by the \(O\big{(}N^{2}\big{)}\) cost for all pairwise distances calculation. The matrix of all pairwise distances can be built for tiny datasets in the initialization step. More in general, though, distances between data objects are computed on demand. Therefore, implementation of a parallel algorithm can be required to reduce the computational needs (see later in this paper).
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
###### Abstract  
In this work it is considered a circular Wireless Sensor Networks (WSN) in a planar structure with uniform distribution of the sensors and with a two-level hierarchical topology. At the lower level, a cluster configuration is adopted in which the sensed information is transferred from sensor nodes to a cluster head (CH) using a random access protocol (RAP). At CH level, CHs transfer information, hop-by-hop, ring-by-ring, towards to the sink located at the center of the sensed area using TDMA as MAC protocol. A Markovian model to evaluate the end-to-end (E2E) transfer delay is formulated. In addition to other results such as the well know energy hole problem, the model reveals that for a given radial distance between the CH and the sink, the transfer delay depends on the angular orientation between them. For instance, when two rings of CHs are deployed in the WSN area, the E2E delay of data packets generated at ring 2 and at the \"west\" side of the sink, is 20% higher than the corresponding E2E delay of data packets generated at ring 2 and at the \"east\" side of the sink. This asymmetry can be alleviated by rotating from time to time the allocation of temporary slots to CHs in the TDMA communication. Also, the energy consumption is evaluated and the numerical results show that for a WSN with a small coverage area, say a radio of 100 m, the energy saving is more significant when a small number of rings are deployed, perhaps none (a single cluster in which the sink acts as a CH). Conversely, topologies with a large number of rings, say 4 or 5, offer a better energy performance when the service WSN covers a large area, say radial distances greater than 400 m.  
Wireless Sensor Network; Markov process; protocol; Frame Slotted ALOHA; TDMA; E2E delay +
Footnote †: journal: Information  
Article