esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
## 2 Related Work
### Energy Consumption in WSNs Model  
Unequal clustering in a WSN with hundreds or thousands of sensor nodes is an efficient way to organize such a vast number of nodes, uniformly distribute load, and prevent hot spot problems [33]. However, the equal clustering approach often has cluster size which is the same throughout the network. Admittedly, in unequal clustering, the cluster size is determined based on the distance to the BS. The cluster size is smaller when the distance to the BS is shorter, and the size increases as the distance to the BS increases.  
The wireless radio transceiver in a WSN depends on the various parameters, e.g., distance and energy consumption. The definition is related to the power consumption and the ranges of the nodes referred to in [16]. The distance between the transmitter and receiver obeyed on the attenuated trans-receiving power decreases exponentially with increasing distance. A threshold separates the free space model and the multipath model.  
\[d_{0}=\sqrt{\left(\left(\varepsilon_{fs}\right)/\varepsilon_{mp}\right)} \tag{1}\]  
where \(\varepsilon_{fs}\), \(\varepsilon_{mp}\) are power loss for free space and multipath models, respectively, and \(d_{0}\) is a threshold of space model. The transmitting \(l\) bits for dissipated energy over distance \(d\) is formulated as:  
\[E_{Tx(lA)}=E_{Tx-elex}(l)+E_{Tx-amp}(l,d)=\left\{\begin{array}{l}l\times E_ {elc}+l\times\varepsilon_{fs}\times d^{2},\ d>d_{o}\\ l\times E_{elc}+l\times\varepsilon_{mp}\times d^{4},\ d\geq d_{o}\end{array}\right. \tag{2}\]where \(E\) is the power consumption of the node, \(Tx\) is for transmitting subscript _elec_, _amp_ indicate electronic, and amplify for digital coding, modulating, filtering, and spreading signal. The energy consumption for receiving the messages can be expressed as:  
\[E_{Rx}(l)=\ E_{Rx}\_{dec}(l)=l\times E_{dec} \tag{3}\]  
WSN assumed implemented \(N\) nodes in the two-dimensional area of \(M^{2}\) with \(k\) clusters. Let \(d\) be the distance member node to the CH, and \(D\) be the distance of the CHs to the BS. The consumed energy of a cluster for a data frame is modelled as:  
\[E_{cluster}=E_{CH}+\left(\frac{N}{k}-1\right)\times E_{member} \tag{4}\]  
where \(N/k-1\) is the averaged member nodes in a cluster. \(E_{CH}\) and \(E_{members}\) are the dissipated energy for CH and members. The calculated energy consumptions for cluster members and CHs are as follows:  
\[E_{member}=l\times E_{dec}+l\times\varepsilon_{fs}\times\ d_{toCH}^{2} \tag{5}\]  
\[E_{CH}=l\times E_{dec}\times\left(\frac{N}{k}-1\right)+l\times E_{DA}\times \frac{N}{k}+l\times E_{dec}+l\times\varepsilon_{mp}\times D_{j}^{2} \tag{6}\]  
The total consumed energy for a WSN could be formulated from the energy process and power transceiver as:  
\[E_{total}=E_{p}+E_{frame}=\ E_{p}+k\times E_{cluster} \tag{7}\]  
where \(E_{p}\) is the power consumption of the microcontroller. It does not affect the optimizing processes. Thus, the power consumption of \(E_{frame}\) optimized is based on only the distance for clustering optimization.
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
###### Abstract
The Issue of Data (IOD), including the IOD of SSR (IOD SSR), IOD of pseudo-random noise mask (IODP), IOD of navigation (IODN), and the IOD of orbit and clock corrections (IOD Corr), is employed as a matching indicator to ensure the correct matching between corrections and broadcast ephemeris. Correction messages can be matched for use only if the IODs retrieved from these messages are consistent and the received PPP-B2b correction message is within the nominal validity period, as shown in Table 1. It is important to note that the nominal validity period recommends the time range to use each type of message, and the quality of the data cannot be guaranteed for the message beyond the nominal validity period. Over the last few years, several studies on the PPP-B2b service have been carried out. [PERSON] et al. [23] developed a homemade receiver to collect and decode real-time correction messages to evaluate the PPP-B2b service. [PERSON] et al. [24] proposed a PPP method using PPP-B2b signal and verified the replaceability of the PPP-B2b service for IGS RTS. [PERSON] et al. [25] conducted an all-round evaluation of the PPP-B2b service using B1I/B3I and B1C/B2a ionospheric-free (IF) observations. They concluded that the precision of the orbit and clock offset is greatly improved by the PPP-B2b, and that real-time PPP after convergence can reach positioning accuracy up to the centimeter. This has the advantages of being highly flexible and independence from the ground communication network and analysis center. However, we note that their research on the PPP-B2b has mainly focused on positioning; the performance of real-time PPP time transfer based on the PPP-B2b service (hereinafter referred to as B2b-RTPPP) remains unknown. To fill this gap, it is important to fully investigate the B2b-RTPPP time transfer performance. The paper starts with the quality evaluation of the PPP-B2b products. Then, the B2b-RTPPP time transfer with combinations of different GNSS systems is studied. The detailed organization of this paper is as follows. We briefly describe the recovery strategies of precise orbits and clock offsets and introduce the principle of B2b-RTPPP time transfer in Section 2. In Section 3, experimental PPP-B2b data and its processing strategies are introduced. We assess the quality of the PPP-B2b orbit, clock offset, and DCB compared with the CNES products from and the CAS DCB, respectively in Section 4. The performance of B2b-RTPPP time transfer is also analyzed. Finally, the conclusions and prospects of this paper are given in Section 5.
# A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method
## 3 BDS-5G Signal Fusion Location Algorithm
### Multiobjective Particle Swarm Optimization (MOPSO)
Specifically, this is achieved in the following ways: First, use each objective function in the multiobjective optimization problem to find the global extremum \(g_{best}[i](i=1,2\ldots n)\) and individual extremum \(p_{best}[i,j](j=1,2\ldots n)\) of each particle corresponding to each objective function. Second, when updating the speed of each particle, use the \"mean\" of each \(g_{best}[i]\) as the global extremum. The \(p_{best}[i,j]\) of each particle is determined by judging the dispersion degree of \(p_{best}[i,j]\) relative to \(g_{best}[i]\) to decide whether to take the mean value of \(p_{best}[i,j]\) or randomly select in \(p_{best}[i,j]\).
# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 2 Methodology  
Our proposed approach encompasses two major computer vision tasks, namely image classification and localization. The first task outputs the diagnosis result (normal or glaucomatous) and the diagnostic confidence. Upon diagnosis of glaucoma, the second task outputs the most suspicious area in the given fundus image.  
At the core of the classification task is a CNN architecture that acts as a feature extractor, returning a predicted output \(y_{i}\) for the class \(i\). The softmax function \(\sigma=e^{y_{i}}/\sum_{k}e^{y_{k}}\) is then applied to calculate the probability of each class \(k\in K\). We used the resulting probability of the predicted output as a measure of the diagnosis confidence, given that the softmax function normalizes the output into a distribution of \(K\) probabilities.  
In our research, through a thorough comparison of various CNN architectures and datasets, we focused on obtaining a high diagnosis accuracy and a localization technique that is clinically meaningful. More details about the different CNN architectures used and the localization technique employed can be found below.
## 3 Results and Discussion  
### Comparative Analysis of Optical Sensor Products and In Situ Data  
#### 3.1.1 Temporal Variations of Soil Moisture and Comparison with Ground Observation  
As mentioned in Section 2.4, [PERSON] et al. (2019) and [PERSON] et al. (2019) presented a method for soil moisture (SM) estimation using optical sensor products. In this study, SM was calculated based on this estimation method (hereinafter referred to as SM\({}_{\text{MODIS}}\)) and compared with soil moisture (hereinafter referred to as SM\({}_{\text{fluor}}\)) measured at the SMC and CMC validation points. Figure 2 shows the comparison of SM\({}_{\text{MODIS}}\) and SM\({}_{\text{fluor}}\) from 2014 to 2015 and compares them with the precipitation values observed in the same period. Because the entire country was under a period of extreme drought status, the occurrence of rainfall in that period was less than the average year. However, when rainfall occurred, the value of SM also increased. Both sites show relatively low values in the period of September to October 2014 and after July 2015.  
Figure 2: Temporal variations of average daily soil moisture at (**a**) Seolmacheon (SMC) and (**b**) Cheongmicheon (CMC) site.  
Figure 3 and Table 3 show the statistical analysis results of SMMODIS and SMfluxtower at two validation points. Figure 3 illustrates a one on one plot of the two results, revealing a high correlation at both sites.  
Results of the bias and RMSE showed that SMMODIS results in CMC were better than in SMC, while the correlation in SMC was somewhat higher. Results from both sites were statistically significant (95% confidence interval). The reason that bias is relatively higher in SMC is its geographical properties. SMC is in the mixed forest area of a mountainous region, where the land surface albedo value is heterogeneous. This heterogeneity leads to bias when measured by remote sensing [31].
## 3 Results and Discussion
### Frequency Responses for Red and Green Light
For GH and BH fibers, differences between wavelengths reduced with increasing length. In fact, at 50 m, they are similar to those obtained with the SPD, because the filtering was less effective as the power distributions were close to equilibrium with nearly complete mode mixing. In Figure 8, we represent the 3-dB bandwidths calculated from all the frequency responses obtained in the two sessions. Figure 8: Evolution of the 3-dB bandwidth with length for the three fibers and the two receivers. Upper graphs: results obtained with the SPD; lower graphs: results obtained with the SPA; from left to right, data for the MH, GH and BH fibers, respectively. Figure 7: Normalized frequency responses obtained with a pigtail launch and the two laser diodes using the SPA receiver: (**a**) fiber length of 15 m; (**b**) fiber length of 20 m; (**c**) fiber length of 50 m.  
This figure confirms that the fiber bandwidths were better with red light for the two high-NA fibers, and that the differences decreased with fiber length, being practically negligible at 50 m. On the other hand, the figure reveals only insignificant differences in the bandwidths at red and green wavelengths for the MH fiber and all tested lengths. Therefore, we conclude that, at least for the high-NA fibers, modal dispersion is higher for green than for red light. We propose that one of the causes underlying the higher dispersion is the larger number of modes that the fiber is able to transmit at shorter wavelengths. For a SI fiber, the number of modes N is given by: \(\mathrm{N}=V^{2}/2\), with the normalized frequency, \(V\), being defined as:  
\[V=\frac{2\pi}{\lambda}\mathrm{a}\;\mathrm{NA} \tag{2}\]  
where a is the fiber's radius (approximately 500 \(\upmu\)m for the PMMA POFs considered) and NA its numerical aperture. Thus, the number of modes is inversely proportional to the squared wavelength, implying an increase of 1.6 times when using the 520 nm laser diode. As the number of modes is also proportional to the square of the fiber NA, this explanation is consistent with the different behavior found for the low-NA MH fiber. In addition, we suggest that another contribution to the increase of modal dispersion can be the higher diffusion for shorter wavelengths. Diffusion in PMMA fibers has been attributed to Rayleigh and Mie scattering [15], whose dependence with wavelength is consistent with our findings. As Rayleigh scattering varies with \(\lambda^{-4}\), and Mie scattering approximately with \(\lambda^{-2}\), both effects are expected to be greater at 520 than at 650 nm [15; 24]. Therefore, the combination of the increased number of modes and the stronger diffusion for the 520-nm wavelength explains the higher modal dispersion that was behind the behavior of our measured frequency responses.
# Computational Test for Conditional Independence
## 5 Discussion
### Assumptions and Limitations  
There are two key implicit assumptions in the CCI testing procedure using XGBoost. Firstly, the XGBoost classification modeling must be able to unravel potentially complex relationships between the variables. Secondly, the XGBoost classification algorithm must generalize well to unseen data. Of course, there is no way to ensure this, but a few steps can increase the likelihood of good performance. Include transformation of the original features; we include squared and cubed terms in our example, but a richer set is possible. As mentioned above; if sufficient data is available, setting aside a portion for tuning the model before testing can enhance its ability to utilize all information effectively when testing for CI.  
The computational method is inherently more computationally intensive than most other CI testing methods, except KCI testing with a large sample size. For instance, with 2000 observations from the Interaction simulation, creating a null distribution with 1000 iterations on a standard laptop with 8 GB RAM and a 1.60 GHz processor using a single core required approximately 13 min. For larger datasets, especially those exceeding 10,000 observations, employing methods such as subsampling may help mitigate computational demands. Furthermore, optimizing computation time will also depend on settings in the XGBoost classification model; hence, adopting specific strategies informed by expert knowledge is recommended for efficiency improvements in practical applications.  
No matter how many simulation examples are applied, computational methods can only be shown to work for the specific datasets and scenarios provided. We have attempted to provide a range of difficult testing scenarios to show that computational testing is viable in cases where one needs to establish CI between categorical variables, even when the underlying data structure is complex. However, computational methods are usually less efficient and less generalizable compared to asymptotic methods.
## 5 Experimental Design
### Evaluation of Classifiers  
The selected classifiers were assessed in the same consistent way across experiments, using accuracy (ACC) and [PERSON]'s kappa (\(\kappa\)) scores to measure the agreement between classified and reference data. The ACC score, defined as the fraction of correctly labeled samples, is widely used; however, it is difficult to interpret for imbalanced datasets (see Section 2.3). In such a case, [PERSON]'s kappa score [60] is more robust as it takes into account the possibility of agreement occurring by chance. Its values range from \(-1\) to \(1\), where \(1\) is perfect agreement between predicted and reference labels, \(0\) denotes (dis)agreement that would be obtained by a completely random classifier, whereas negative values denote (dis)agreement that is worse than random. The formula for [PERSON]'s kappa score is given by Equation (3).  
\[\kappa=\frac{P_{o}-P_{e}}{1-P_{e}} \tag{3}\]  
where:  
\(P_{o}\)--empirical probability of agreement between predicted and reference labels (same as ACC);  
\(P_{e}\)--hypothetical probability of agreement between randomly assigned and reference labels.  
The randomly generated synthetic datasets introduced stochastic variations of derived classification quality metrics. Therefore, the experiments with the synthetic datasets were repeated 30 times for each combination of three variables: number of samples, class imbalance and noise level. In the case of the daytime cloud detection over water surfaces using the MODIS/CALIPSO/CPR collocations, the analysis was run once with the training dataset collected in 2007 and the validation dataset from 2006.  
Ultimately, the numerical performance of the classifiers in terms of the execution times of the training and retrieval processes separately was investigated. The machine used for this analysis was based on the AMD EPYC 7401 24-Core Processor with 256 GB RAM, running the Ubuntu version 18.04.5 operating system with a Python 3.9.6 environment containing the NumPy version 1.21.1, Scipy version 1.7.14. and scikit-learn version 0.24.2 libraries. For the sake of comparability, the multicore/hyper-threading support for the selected classifiers (including the VOR) was deactivated in order to measure the single-core numerical performance. For this analysis, the linear synthetic dataset with an increasing number of samples (\(10^{2}\), \(10^{3}\), \(10^{4}\), \(10^{5}\), \(10^{6}\)) was used, and each analysis was repeated five times to generate mean execution times. In the case of the Gaussian process classifier, training of more than \(10^{4}\) samples was computationally too expensive and the execution times for those datasets were not measured.
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
## 4 Experimental Results and Analysis
### Comparative Analysis of Different Speed of Peristaltic Pump  
A TEG module, consisting of five semiconductor thermoelectric plates coated with thermal grease on their surfaces, is assembled in series and sandwiched between aluminum alloy deflectors. This TEG module is then placed in a system that allows it to be heated by hot oil and cooled by tap water. The temperature control system ensures the accuracy of the hot end temperature in real time and maintains a stable temperature difference.  
Figure 10: The experimental platform.  
Figure 9: Schematic diagram of the experimental device.  
Since the maximum speed of the peristaltic pump is 300 r/min, in order to prevent the pump from running at full load and resulting in reduced service life, the speed of the pump is set as 260 r/min, 270 r/min, 280 r/min and 290 r/min. After the set target temperature value is stabilized, maintain the same temperature difference and change the speed of the pump to measure its open circuit voltage. According to the measurement results, the open circuit voltage diagram at different peristaltic pump speed is shown in Figure 11.  
From Figure 11, it can be seen that with the increase of the peristaltic pump's speed, the open-circuit voltage also increases. The increase of the speed of peristaltic pump increases the heat dissipation efficiency, which indirectly increases the temperature difference. As a result, the TEG module produces an amplified electromotive force. To accommodate the extended operational duration of the double-headed peristaltic pump and the lifespan of the silicone tube, the pump's velocity is established at 280 r/min.
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 5 Proposed Authentication and Billing Scheme
### Impersonation Attack  
Another common concern during wireless communications is that anyone can pretend to be the sender and encrypt the messages using the receiver's public key. This is acknowledged in the proposed model by using digital signatures, implemented using ECDSA. The message is signed using the signing key of the sender, which cannot be generated by any other entity, and the receiver has to verify the signature before establishing the communication link.
## 4 Conclusions
Within the publicly accessible infrastructure, EV/CS ratio results into higher values, spanning from 8:1 to 27:1 depending on scenarios and time period; these values were also higher than AFI Directive suggestions for a 10:1 ratio and Italian PNIRE suggestions for an average figure of a 7:1 ratio. Even with these high EV/CS ratios, the global use ratio of the infrastructure did not rise over 17% of its theoretical maximum potential, showing an almost constant trend over the period, thus highlighting the possible need for policies supporting CS profitability, at least during the first transition period. The impact of the planned infrastructure on the available public stalls results were less pronounced than that of the ICEV and showed a decreasing trend over the period, as a result of the increasing EV/CS ratio. From a grid perspective, the average amount of daily energy required by xEV fleet charged on public infrastructure was expected to reach more than 80 MWh for Florence and almost 245 MWh for Bruxelles by 2030, for the most demanding scenario. As a comparison, the private residential charging infrastructure, was expected to reach 50 MWh for Florence and 155 MWh for Bruxelles; these results were strictly related with the assumptions on the share of xEV using private infrastructure, as defined in the model. Based on the assumptions made for the daily charging windows duration, energy requests translate into average continuous power requirements for the public infrastructure of 4 MW in Florence and 12.3 MW in Bruxelles; the same analysis made on the private residential charging infrastructure, gave as a result 6.3 MW for Florence and 19.4 MW for Bruxelles. Finally, results shown in Section 3.2 made evident that optimal charging infrastructure configuration was obtained with a higher share--well beyond 90% of the total at 2020--of slow and medium CS, compared to fast ones. This situation was due to the worst charging events/cost ratio of the fast CS--if compared to the slow and medium ones--that is obtained from the input used in this study. It is worth noting that this result was quite different from the suggestion given by PNIRE of a 25% to 50% share of fast CS over the total on year 2020 [5]. Overall, therefore, more than the additional power demand by EV and the associated costs, the critical issue for developing a charging infrastructure able to meet the EV fleet on the coming years will most likely lie on the actual implementation of the civil works at urban level, and the ability to implement these vis-a-vis the EV fleet growth, so to achieve comparable development rates. This assuming a reasonable incremental rate of EVs in the next decades, thus excluding unrealistic and excessively optimist or pessimistic assumptions about car renewal rates. Conceptualization and Methodology, D.C.; Model Development, Methodology, Validation, Investigation, Formal Analysis, Writing and Editing: G.T. ; Reviewing: D.C.; Supervision, D.C. and F.G. This research received no external funding  
Authors declare no conflict of interest.