esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**EyePaCS**} & \multicolumn{2}{c}{**DDR**} & \multicolumn{2}{c}{**Messidor-2**} \\ \hline
**Metrics** & \begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **TPR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **TNR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **TPR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **TNR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **TPR** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **TNR** \\ **(\%)** \\ \end{tabular} \\ \hline DLGP-DR in [14] & - & - & - & - & - & - & 87.87 & 72.37 & 86.25 \\ \hline The proposed method & 86.40 & 82.80 & 90.90 & 89.62 & 86.39 & 92.84 & 90.15 & 89.28 & 91.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._Both self-supervised learning and semi-supervised learning have the potential to learn from unlabeled data, and there should be more research in this field in the future. Studies in self-supervised learning have used interclass balanced benchmark datasets and have not yet considered the problem of imbalanced learning. Although it is not possible to categorize features learned from unlabeled data, there is no doubt that the learned features must be more representative of the majority class if imbalanced datasets are used to train models through self-supervised learning. Therefore, future research on self-supervised learning using imbalanced data will be initiated, while there should be improvements for imbalanced learning in semi-supervised deep learning algorithms. In addition, the DR diagnostic system not only discerns the presence or absence of symptoms, but also needs to grade the disease so that patients can understand the status of their illness in more detail, and thus take appropriate measures. This study is limited to binary classification. Using self-training semi-supervised learning, which is not an efficient method, is also one of the limitations of our work. The current semi-supervised learning method with relatively better performance is the consistency regularization paradigm; however, since it also suffers from imbalanced learning, models have been restricted to training using balanced datasets. In this study, since we only focused on the learning problem arising from the use of imbalanced labeled datasets, only the self-trained method of iteratively generating balanced data using the reference provided by pseudo labeling was adopted. As can be seen from the data, the quantity of unlabeled data we used in the work did not reach a hundred times higher than the labeled data, which was limited by the availability of fundus data and limited computational resources. This study focused on how to utilize the unlabeled data in the method rather than improving the performance by the huge amount of data.
## 2 Related Work
### Sensing Scheduling Strategies
Most schemes strive to attain an energy balance [28], for example, the distributed self-spreading algorithm (DSS) [26] and intelligent deployment and clustering algorithm (IDCA) [27]. In the DSS, the sensor nodes are initially deployed randomly and move because of the influence exerted by nearby nodes. In the IDCA, however, the remaining energy level of the node determines whether it moves or not. The idea behind DSS and IDCA is to reduce the residual capacity differential between nodes. Many studies envisage network connectivity with sensing coverage, for example, [29; 30; 31]. When the transmission range of the sensor node is at least twice its sensing range, \(k\)-coverage leads to \(k\)-connectivity [29; 30]. Typically, high connectivity ensures high robustness, but one result of high connectivity is that data conflicts between nodes can seriously affect data transfer rates. The results of recent research presented in [32] are not based on the assumption that the transmit range of the sensor nodes is not less than twice their sensing range. [PERSON] et al. [31] considered a tiered sensor network that includes sensors that could fail and they discussed the sensing coverage, network connectivity, and network diameter. In [19], the authors proposed an optimal deployment strategy to achieve two connections that fully cover all communication and sensing ranges. The design objectives of the reviewed studies are summarized in Table 2.
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 2 Related Work
### GNSS Simulators  
In the literature, several works have been proposed focusing on architectural and implementation aspects of GNSS simulators. In [15] the authors design a simulator of digital Intermediate Frequency (IF) signal based on the mathematical model of digital IF GPS signals. Their experiments show that the structure of simulated signals is closer to real signals. In [16] the authors propose a GPS I Galileo receiver called TUTGNSS. It provides a platform for testing and developing new algorithms for GNSS allowing developers to have full control over its further development. The open source Global Navigation Satellite System based on software-defined receiver, called GNSS-SDR, is proposed in [17]. The authors provide details about the software architecture design and implementation, for a multiband, multisystem GNSS receiver. They have built a testbed for customizable GNSS signal processing, able of permitting signal sources and signal processing algorithms management, guaranteeing systems and output format interoperability. In [18], the authors provide details about a GNSS software receiver implementation able to operate with Galileo E1B and E1C signals. They provide descriptions of the used algorithms that manage signals for navigation acquisition and tracking. An open source software implementation of a GPS receiver as a simple modification of a commercial-off-the-shelf (COTS) receiver is shown in [19]. In the paper, the authors describe the hardware and software architecture, the features added to allow debugging of the code and carrier tracking loops.
## 4 Results and Discussion
### Results for EIS
#### 4.1.3 Creating Transfer Function Models from EIS Data and Comparative Analysis
The variable domain obtained through the estimated transfer function model provides a framework to evaluate the concentration variation at a given operating condition during the antisolvent crystallization. This is an essential task that could lead to the design of a process control unit. Figure 8: Bode plot comparisons of acquired data with transfer function models for sucrose 33% _w/w_ and (**a**) 0% ethanol, (**b**) sucrose to Ethanol ratio 1, (**c**) sucrose to Ethanol ratio 0.5, (**d**) sucrose to ethanol ratio 0.33. Table 3 shows effective sucrose to ethanol ratios for all the solutions. Figure 9: Changes of Model 1 parameters. (**a**) Gain constant K; (**b**) system time constant \(\tau\), for different working conditions. According to the current experimentation, the ternary chemical solution system can be modelled into a set of transfer functions containing poles and zeros based on the electrical impedances obtained within the frequency range. However, the present approach requires further investigation in order to increase the interpretability by limiting the variable space and obtain a fully usable system model. Such a system can be obtained using various methods such as principal component analysis [59] or development of a plausible equivalent electrical circuit via electrochemical modelling [31, 60].
## 4 Experiment and Results
### Networking Measures
#### 4.2.2 Energy Consumption  
The energy consumption is presented in Figure 13. We found that the solution provided by SC-MOPSO generated a higher level of energy consumption with an increasing pattern than NSGA-II and NSGA-III. This is consistent with the self-conflicting nature of objectives.
## 2 Materials and Methods
### Synthesis of Results and Statistical Analysis  
Studies were eligible for inclusion in the meta-analysis if they implemented a structured physical training intervention in an experimental group and included a control group that received no additional training. Furthermore, only trials that reported pre- and post-intervention measurements of performance outcomes were considered. Those that did not meet these methodological criteria were excluded from the quantitative synthesis. To assess quality and interpret bias risk, version 5.4 of Review Manager (Copenhagen, Denmark: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) was used. The same software was used to conduct both descriptive and inferential analyses within the meta-analysis. For each included study, effect sizes were computed by comparing outcomes between the experimental group, which received a structured physical training intervention, and the control group. Standardized mean differences (SMDs) were calculated using Hedges' g [38], with corresponding standard errors. The overall pooled effects and 95% confidence intervals (CIs) were estimated using inverse variance weighting. In parallel, unstandardized effect sizes (ESs) were derived by subtracting post-intervention means between groups and normalizing by the pooled standard deviation. The magnitude of effects was interpreted based on [PERSON]'s thresholds: <0.2 (trivial), 0.2-0.5 (small), 0.5-0.8 (moderate), and >0.8 (large) [39]. Furthermore, for each pooled outcome, heterogeneity metrics were extracted and reported: Tau\({}^{2}\), Cochran's Q, degrees of freedom, exact \(p\)-values, and I\({}^{2}\) percentages.
## 2 Results and Discussion
### Volatiles Isolated by Ultrasonic Solvent Extraction
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Area percentage (\%)**} \\ \cline{4-7}
**No.** & **Compound** & **RI** & \multicolumn{2}{c}{**sample I**} & \multicolumn{2}{c}{**sample II**} \\  & & & **A** & **B** & **A** & **B** \\ \hline
1. & 3-Methylbutanoic acid & \(<\) 900 & 0.1 & - & - & - \\
2. & 2-Furanmethanol & \(<\) 900 & 1.7 & 0.3 & 3.0 & 3.7 \\
3. & 1,3-Dimethylbenzene\({}^{**}\) & \(<\) 900 & 0.1 & - & - & 0.2 \\
4. & 3-Hydroxy-pentan-2-one & \(<\) 900 & 0.2 & - & 0.5 & - \\
5. & 1-(2-Furanyl)-ethanoneone & 914 & 0.3 & - & - & - \\
6. & Hexane-2,5-dione & 931 & 0.2 & 0.1 & - & - \\
7. & 5-Methylfurfurlat & 969 & 1.5 & 0.5 & 0.1 & 0.1 \\
8. & Hexanoic acid\({}^{\text{a}}\) & 974 & 0.1 & - & - & - \\
9. & 2-Formylpyrrole & 1018 & 0.2 & - & 0.1 & 0.4 \\
10. & \(p\)-Cymene\({}^{\text{a}}\) & 1031 & 0.1 & - & 0.2 & - \\
11. & 2-Hydroxy-3-methyl-cyclopent-2-en-1-one & 1034 & 0.3 & 0.1 & - & 0.1 \\
12. & Benzyl alcohol\({}^{\text{a}}\) & 1037 & 1.2 & - & 1.4 & 0.6 \\
13. & Pantoic lactone & 1046 & - & 0.5 & - & 0.8 \\
14. & Phenylacetaldehyde\({}^{\text{a}}\) & 1048 & 0.1 & - & - & - \\
15. & 2-Acetylpyrrole & 1063 & 0.2 & 0.1 & 0.2 & 0.1 \\
16. & _trans_-Linalool oxide & 1076 & 0.7 & - & 0.2 & - \\
17. & 4,5-Dimethyl-2-formylfuran & 1078 & 0.4 & - & - & - \\
18. & 6-Methyl-2-pyrazinylmethanol & 1084 & - & 0.3 & - & 0.2 \\
19. & 1-(2-Furyl)-2-hydroxyethanone & 1087 & 5.5 & 4.6 & 5.1 & 8.8 \\
20. & _cis_-Linalool oxide & 1091 & 0.4 & - & 0.2 & - \\
21. & Linalool\({}^{\text{a}}\) & 1101 & 0.3 & - & 0.2 & - \\
22. & 2-Phenylethanol\({}^{\text{a}}\) & 1116 & 0.7 & - & 0.9 & - \\
23. & 3-Hydroxy-2-methyl-4H-Pyran-4-one & 1119 & - & 0.5 & - & 1.3 \\
24. & 2-Formyl-1-methylpyrrole & 1139 & 1.1 & 0.5 & 0.3 & 0.8 \\
25. & 2,3-Dihydro-3,5-dihydroxy-6-methyl-4H- & 1145 & 1.3 & 0.3 & 2.8 & 4.3 \\
26. & Benzoic acid\({}^{\text{a}}\) & 1162 & 4.5 & 1.8 & 4.3 & 3.5 \\
27. & 3,5-Dimethylphenol\({}^{**}\) & 1181 & 0.3 & 0.1 & 0.4 & 0.2 \\
28. & 3,7-Dimethyl-octa-1,5-dien-3,7-diol & 1191 & 0.4 & 0.1 & 0.5 & 0.8 \\
29. & Dodecane\({}^{\text{a}}\) & 1200 & 0.2 & - & 0.1 & 0.5 \\
30. & 1,2-Benzenedi & 1219 & 0.3 & - & 0.2 & - \\ \hline
31. & 2,5-Di(hydroxymethyl)-furan\({}^{\text{a}}\) & 1229 & 2.2 & - & 10.9 & 17.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Oak honey volatile organic composition isolated by USE. Another abundant group of identified compounds from Table 2 were aliphatic alcohols, acids and hydrocarbons such as (_Z_)-octadec-9-en-1-ol, hexadecan-1-ol, octadecan-1-ol, hexadecanoic acid, tetradecane, hexadecane, tricosane and others. These compounds can be connected with bees-wax composition [11].
## 3 Results and Discussion
The comparison of the XRD spectra of pristine ZrO\({}_{2}\) NPs and the Au-ZrO\({}_{2}\) are reported in Figure 3. XRD indicates the cubic phase of ZrO\({}_{2}\) in Au-ZrO\({}_{2}\), as diffraction peaks perfectly matched with the reported data (JCPDS No. 27-0997). For instance, several prominent peaks observed at 2\(\theta\) values of 30.46\({}^{\circ}\), 34.54\({}^{\circ}\), 50.45\({}^{\circ}\), 60.37\({}^{\circ}\), 74.56\({}^{\circ}\), and 81.55\({}^{\circ}\) correspond to the (111), (200), (220), (311), (400), and (331) planes of crystalline zirconia, respectively. In addition, due to the high loading of gold nanoparticles (25% of ZrO\({}_{2}\)), sharp and prominent peaks belonging to the cubic _fcc_ phase of Au are also clearly visible in the XRD pattern of Au-ZrO\({}_{2}\). The peaks at 2\(\theta\) values of 38.31\({}^{\circ}\) (111), 44.46\({}^{\circ}\) (200), 64.67\({}^{\circ}\) (220), 77.45\({}^{\circ}\) (311), and 82.30\({}^{\circ}\) (222) are matched with the standard gold metal (Au\({}^{\circ}\)) with JCPDS data card No. 04-0784 [27]. Figure 3: XRD diffractograms of Au-ZrO\({}_{2}\) and pristine ZrO\({}_{2}\) NPs depicting the cubic phase of both Au and ZrO\({}_{2}\) nanoparticles in Au-ZrO\({}_{2}\). Figure 2: Comparison of the FT-IR spectra of Au-ZrO\({}_{2}\) with pristine ZrO\({}_{2}\) NPs stabilized with benzyl alcohol. Figure 4 shows the corresponding SEM and TEM images of Au-ZrO\({}_{2}\) nanoparticles. Figure 4a shows a representative electron micrograph. Although it is difficult to conclude the exact size of gold domains, a dark contrast could be due to the presence of high-density metal (Au) present on the surface of ZrO\({}_{2}\) nanoparticles. Figure 4b shows the SEM micrographs which demonstrated that the Au NPs are deposited on the ZrO\({}_{2}\) NPs in the Au-ZrO\({}_{2}\) hybrid and shows the distribution of elements present therein, while the EDX elemental mapping spectra confirm the presence of representative elements (Figure 4c).
# Fast Ferroelectric Liquid Crystal Based Optical Switch: Simulation and Experiments
## 3 Optical Switching in FLC Modulator
### Simulation of Switch Performance Parameters  
In multimode optical switching, the most challenging problem is the attainment of low cross-talk and faster response time. For the potential application of a FLC switch, the most significant performance parameter cross-talk (ratio of power leaked through the wrong output to the right one) is characterized theoretically and experimentally. Low crosstalk indicates high signal quality or small signal interference. The light transmittance through the FLC material between the crossed and parallel polarized state can be expressed as:  
\[T_{+}^{\pm}=\sin^{2}(45^{\circ}\pm 2\theta)\cdot\sin^{2}(\pi\Delta nd/\lambda) \tag{2}\]  
where \(\theta\) is the tilt angle and \(\pi\cdot\Delta\mathbf{n}\cdot d/\lambda\) is the optical retardation, in which \(\Delta\mathbf{n}\) is the refractive index anisotropy, \(d\) is FLC layer thickness, and \(\lambda\) is the wavelength. \(T^{+}\) and \(T^{-}\) are the maximum and minimum transmitted light intensity, which depend on the phase shift introduced by the FLC material. The superscripts (\(\pm\)) correspond to the polarity of applied voltage and subscripts (+ and \(\parallel\)) represent the crossed and parallel polarizer geometry. Therefore, the transmittance between parallel polarizers \(T_{\parallel}^{\pm}\) complements \(T_{+}^{\pm}\), so that:  
\[T_{\parallel}^{\pm}=1-T_{+}^{\pm} \tag{3}\]  
From the transmittance measurements, it is possible to calculate the contrast ratio as:  
\[K_{+}=\frac{T_{+}^{+}}{T_{+}^{-}} \tag{4a}\] \[K_{II}=\frac{T_{\parallel}^{-}}{T_{\parallel}^{+}} \tag{4b}\]  
The \(K_{+}\) and \(K_{\parallel}\) are the contrast ratio of both crossed and parallel polarized states. As cross-talk is 10 log (1/K), we have simulated the cross-talk (\(CT\)) using Equations (5a) and (5b), also shown in Figure 5.  
\[CT_{+}=10\cdot\log(T_{+}^{-}) \tag{5a}\] \[CT_{\parallel}=10\cdot\log(T_{\parallel}^{+}) \tag{5b}\]  
and \(\Delta\mathbf{n}\)(T) of crossed and parallel polarized states.  
Figure 5: Simulated cross talk as a function of temperature dependent parameters \(\theta\) (T) and \(\Delta\mathbf{n}\) (T) of (**a**) crossed and (**b**) parallel polarised states.  
Figure 5 reveals the possible conditions for better cross-talk in both crossed and parallel polarized states. In the former state (\(K_{+\max}at,\sin^{2}(45^{\circ}-2\theta)=0\) or \(\theta=22.5^{\circ}\)) this depends only on molecular tilt angle, but in the parallel polarized state it depends on birefringence in addition to the molecular tilt angle (\(K_{\parallel\max}at,\Delta nd/\lambda=1/2,3/2\dots\) and \(\theta=22.5^{\circ}\)). The simulation clearly explains the cell thickness dependencies of cross-talk. As FLC optical switching is wavelength-dependent, it implies certain restrictions for wavelength division multiplexing (WDM) applications. Nevertheless, this effect is not as strong as the other cross-talk factors.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 2 Exponential Monitoring Schemes for TBE Data
### One-Sided Exponential EWMA Schemes for TBE Data  
In the context of the exponential EWMA scheme, the implementation of two independent one-sided control charts is recommended, detailed below:  
* To detect an increase in the process parameter \((c>1)\), the upper-sided exponential EWMA schemes monitoring statistic for the \(t\)th sample is \[Q_{t}^{+}=\max(B^{+},(1-\lambda^{+})Q_{t-1}^{+}+\lambda^{+}Y_{t}),\quad t=1,2,\ldots,\] (5)where the smoothing parameter \(\lambda^{+}\in(0,1]\) is a fixed value. The reflecting boundary \(B^{+}\) and starting value \(Q_{0}^{+}\) are suggested to be set to 0.5 and 1, respectively [24].
* To detect a decrease in the process parameter \((c<1)\), the lower-sided exponential EWMA schemes monitoring statistic for the \(t\)th sample is \[Q_{t}^{-}=\text{min}(B^{-},(1-\lambda^{-})Q_{t-1}^{-}+\lambda^{-}Y_{t}),\quad t =1,2,\ldots,\] (6) where the smoothing parameter \(\lambda^{-}\in(0,1]\) is a fixed value. The reflecting boundary \(B^{-}\) and starting value \(Q_{0}^{-}\) are suggested to be set 2.0 and 1, respectively [24].  
An out-of-control signal is alarmed when \(Q_{t}^{+}>UCL^{+}(Q_{t}^{-}< LCL^{-})\), where \(UCL^{+}( LCL^{-})\) is the upper (lower) control limit of the scheme. When the smoothing parameter \(\lambda^{+}(\lambda^{-})\) is larger, it indicates that the current sample is given more weight. In particular, when \(\lambda^{+}(\lambda^{-})=1\), the scheme simplifies to a Shewhart-type scheme.
# SAM-PAY: A Location-Based Authentication Method for Mobile Environments +
## 1 Introduction
As noted in [22; 23], although gathering and maintaining a rich profile of an individual and his or her transactions might seem antithetical to privacy interests, in some transactions or contexts (such as for m-payments), it might actually help protect the individual's privacy by raising a red flag about suspected identity theft. In our case, if the UT used to perform payments in the service at a gas station is suddenly being used to make purchases in other locations (e.g., cities) where it has not been used before, this could indicate that an attacker is fraudulently using the identity of a legitimate individual. **Organization. ** The paper is organized as follows. In Section 2, we present the related work and the location-authentication problem. In Section 3, we present the architecture and the functionality of its main components, in Section 4 we describe our SAM-PAY authentication method, and in Section 5 we present the design and implementation of the proposed SAM-PAY-based service at the self-service gas stations. In Section 6 we present the test-bed used to experiment with the proposed SAM-PAY-based service at the self-service gas stations. Finally, in Section 7, we indicate possible future developments and extensions for our work, and we conclude our paper in Section 8.
## 2 Dataset and Proposed Model
### Method for Correction of Sensor Orientation
Normalize the vector \(\mathbf{e}_{i}=(e_{i,1},e_{i,2},...,e_{i,8})\) by dividing each of its values by the length \(k\) of each channel of \(\mathbf{E}_{i}\), obtaining in this way the new vector of normalized energies \(\mathbf{e}_{i}^{*}~{}=~{}(e_{i,1}/k,e_{i,2}/k,...,e_{i,8}/k)\). 2. Compute the component-wise sum of the normalized vectors of energies to obtain the vector \(\mathbf{e}^{*}=\sum_{i=1}^{m}\mathbf{e}_{i}^{*}\). 3. Find the index \(i^{*}\) of the component of the vector \(\mathbf{e}^{*}\) with the largest value. 4. Compute the new sequence \(\widetilde{S}=\widetilde{s}_{1},\widetilde{s}_{2},...,\widetilde{s}_{8}\) in which the pods of the EMG sensor should be arranged, where \(\widetilde{s}_{j}=mod(s_{j}+i^{*},8)\), where \(s_{j}\in S\), with \(j=1,2,...,8\). Selecting the gesture for synchronization is a key aspect for the correction of orientation to be successful. In this work, we selected the gesture for synchronization by testing each of the five gestures considered here using 20 users from the training set. For each of these 20 training users and for each candidate gesture for synchronization, we selected randomly four EMGs out of the 25 training EMGs of that candidate. With these four EMGs for synchronization, we followed the procedure stated for experiment 4, which is described in the next section. In this way, we estimated the recognition accuracy for each of the five candidate gestures for synchronization over 20 training users: _wave in (83.68%), wave out (96.76%), \(fist\) (91.80%), \(fingers\) spread_ (83.96%), and _double tap_ (84.12%). Therefore, based on these recognition accuracies, we selected _wave out_ as the synchronization gesture for this work. The way how, in practice, the recognition model combined with the algorithm for correcting the orientation is as follows. Here, we assume that the recognition model is already trained. After wearing the bracelet, a user has to perform the synchronization gesture a given number of times. Then, the algorithm for correcting the orientation finds an arrangement of the pods based on the synchronization EMGs. After this, the recognition systems use this new arrangement of the pods to rearrange the channels of an EMG before it passes to the recognition system. Note that the process of acquiring the synchronization EMGs must be performed each time a user wants to use the recognition system.