esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Modeling Methodology
### Gray Wolf Optimization (GWO)  
GWO is a new type of pack intelligence optimization algorithm, which imitates the social hierarchy and hunting strategy of gray wolves in nature. It has been widely used in continuous optimization and combinatorial optimization problems in recent years. In the gray wolf optimization algorithm, the wolf pack is divided into four hierarchies, from high to low as \(\alpha\) wolf, \(\beta\) wolf, \(\delta\) wolf and \(\omega\) wolf. Among them, the gray wolves of higher ranks have absolute dominance over the gray wolves of lower ranks. There is one and only wolf in \(\alpha\) wolf, \(\beta\) wolf and \(\delta\) wolf, and the \(\omega\) hierarchy involves more than one. Where \(\alpha\) wolf represents the optimal solution in the algorithm in each round, the \(\beta\) wolf and \(\delta\) wolf is the suboptimal solution. The \(\omega\) wolves are subject to \(\alpha\) wolf, \(\beta\) wolf and \(\delta\) wolf's guidance to keep searching for new prey, and at the end of each round will re-update the \(\alpha\) wolf's position as the global optimal solution [35].  
Suppose that the position of \(i\) wolf in an n-dimensional space is \(X_{i}=(X_{i,1},X_{i,2},X_{i,3},\ldots,X_{i,n})\), the process of hunting can be divided into surrounding prey and hunting, where surrounding prey means that the \(i\) wolf updates its position information with prey under the guidance of \(\alpha\) wolf, \(\beta\) wolf and \(\delta\) wolf. The formula is as follows:  
\[\begin{cases}X_{\alpha,i}=X_{\alpha}-A_{1}D_{\alpha}\\ X_{\beta,i}=X_{\beta}-A_{2}D_{\beta}\\ X_{\delta,i}=X_{\delta}-A_{3}D_{\delta}\end{cases} \tag{11}\]  
where \(X_{\alpha,i}\), \(X_{\beta,i}\) and \(X_{\delta,i}\) are the position information of \(i\) wolf after being updated by \(\alpha\) wolf, \(\beta\) wolf and \(\delta\) wolf, and \(A\) is the vector coefficients. \(D\) is formulated as follows:  
\[\begin{cases}D_{\alpha}=\left|C_{1}X_{\alpha}-X_{i}\right|\\ D_{\beta}=\left|C_{2}X_{\beta}-X_{i}\right|\\ D_{\delta}=\left|C_{3}X_{\delta}-X_{i}\right|\end{cases} \tag{12}\]  
where \(C\) is the relaxation factor. Thus, the process of \(i\) wolf surrounding the prey is obtained. The hunting process is described by the following formula:  
\[X_{i}(t+1)=\frac{X_{\alpha,i}+X_{\beta,i}+X_{\delta,i}}{3} \tag{13}\]  
where \(X_{i}(t+1)\) denotes the \(i\) wolf's final position information, from Equation (13), GWO algorithm finds the global optimal solution through the guidance of \(\alpha\), \(\beta\) and \(\delta\) wolves to \(\omega\) wolves, not only can dynamically adjust the weight value, but also can effectively reduce the probability of falling into the local optimal.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Respiration Rate Extraction
#### 2.5.6 Combining RGB and NIR  
The combination strategy for the respiration rate measurements based on RGB and NIR is shown in Figure 4, where we use \(\sigma_{RGB}\) and \(\sigma_{NIR}\) to denote the standard deviation of the breathing motion in a segment. First, thresholds \(T_{NIR}\) and \(T_{RGB}\) of the breathing motion metric were selected such that their overall 3 RPM agreement across all patients was 90%. Then, we chose whether to use the RGB- or NIR-based measurement based on the brightness level in the chest ROI; if it was sufficient, the RGB-based measurement was used. Otherwise, the NIR measurement was chosen. We determined the brightness threshold \(T_{brightness}\) by plotting the overall 3 RPM agreement, MAE, and coverage of both modalities and the combined measurements against the threshold and chose the threshold which yielded the best overall performance.  
Finally, after choosing the breathing motion and brightness thresholds, for each patient, we reported a number of evaluation metrics: the 3 RPM agreement, MAE, coverage, largest time gap, and valid time. The largest time gap consists of the longest sequence of discarded measurements in the camera-based respiration rate measurements during the valid time of each patient. The valid time consists of the total time where the cameras are recording and reference measurements are available, excluding annotated moments of privacy when the patient was not present or when the view of the patient was obstructed by staff or equipment. Using these evaluation metrics, we investigated if there were any significant errors in the extraction of the respiration rate. Here, we will not distinguish between RGB- and NIR-based measurements but only interpret the combined performance, since the method of extraction was shared between the two modalities.  
Figure 4: Flowchart of the combination strategy for RGB and NIR respiration rate measurements.
# Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding
###### Abstract  
Aiming at the problem of sparse measurement points due to test conditions in engineering, a smoothing method based on zero-padding in the wavenumber domain is proposed to increase data density. Firstly, the principle of data extension and smoothing is introduced. The core idea of this principle is to extend the discrete data series by zero-padding in the wavenumber domain. The conversion between the spatial and wavenumber domains is achieved using the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). Then, two sets of two-dimensional discrete random data are extended and smoothed, respectively, and the results verify the effectiveness and feasibility of the algorithm. The method can effectively increase the density of test data in engineering tests, achieve smoothing and extend the application to areas related to data processing.  
two-dimensional discrete data; data extensions; data smoothing; zero-padding 2023
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 4 Simulation Results  
Network simulator ns-2.34 [25] was used to conduct simulations in street scenarios (see Table 3). The channel fading was addressed by using Nakagami propagation model (see Table 4) [26]. The average transmission distance was approximately 250 m. The proposed scheme was compared with \"Non-edge\" (the conventional approach without edge computing), \"Edge (BeaconSz = 56 B)\" (Edge-based approach with beacon size of 56 bytes), \"Edge (BeaconSz = 512 B)\", and \"Edge (BeaconSz = 1024 B)\". The protocols were evaluated under broadcast traffic flows, connection-dependent unicast traffic flows, and connection-independent traffic flows. The error bars in the following figures indicate the 95% confidence intervals.
## 3 Results
### Foliage Projected Cover
#### 3.1.2 Field Data  
For each field site, the mean value of the SPOT5 FPC map was calculated from all pixels within a 55 m radius, and compared to the field measurements. Ordinary least squares regression revealed a weak positive linear relationship (\(r^{2}=0.66\)) between SPOT5 FPC values and the field measured overstorey FPC values, which improved to a very strong relationship (\(r^{2}=0.90\)) when the midstorey foliage was included in the field FPC values (Figure 5). In general, the model slightly underestimated overstorey plus midstorey FPC, especially for values less than 40%. The validation of SPOT5 FPC using field data presented here should only be considered preliminary, and the statistics presented may not be representative of the statewide map due to the small sample size and the opportunistic sampling design.
## 3 Related Studies
The nodes collect data, which is then sent to a central node via multi-hop routing channels. The depth of the node determines which CHs are employed. To transport data from the nodes to the SN, the CH gathers the data packets from each cluster member and delivers them to the SN's upper layer [22]. Transmission techniques based on electromagnetic (EM), free space optical (FSO), and acoustic waves are all consistent with [PERSON] and [PERSON]'s recommendation for the ideal clustering for UWSNs. Based on the ideal clustering and energy use, these underwater transmission systems are examined and contrasted for their efficiency [23]. [PERSON] et al. created an Energy Optimization Clustering Scheme (EOCA) for multi-hop underwater cooperative sensor networks since underwater sensors have limited energy. These systems reflect numerous issues, including the remoteness amongst the sensors and the sink nodes, the number of nearby nodes, the Residual Energy (RE) of all the nodes, and the sensor movement brought on by ocean currents [24]. [PERSON] et al. aimed to improve the MCR-energy UWSN's efficiency while addressing issues such as the underwater current, low bandwidth, high water pressure, propagation latency, and error probability. The MCR-UWSN technique seeks to route to the required location by choosing a productive set of cluster heads (CHs). The MCR-UWSN approach uses cultural emperor penguin optimizer-based clustering to produce clusters (CEPOC). The multi-hop routing and grasshopper optimization (MHR-GOA) algorithms are also derived using a variety of input parameters. The MCR-UWSN approach's performance was assessed using various indicators, and the outcomes were examined. The MCR-UWSN approach fared better than the newest cutting-edge techniques, according to the results of the experiments [25]. The IMCMR-UWSN technique was created for underwater wireless sensor networks by improving meta-heuristics-based clustering and multi-hop routing protocols; see [PERSON]. [PERSON] et al. This method's primary goal is to identify the best cluster heads (CHs) and the quickest routes to a particular location. The two fundamental elements of the IMCMR-UWSN approach are SA-GSO multi-hop routing and CKHA clustering. Several variables, such as residual energy, intra-cluster distance, and inter-cluster distance, are considered when the CKHA technique selects CHs. The SA-GSO algorithm's fitness function considers remaining energy, latency, distance, and trust. Energy economy and service life can be considerably improved by using IMCMR-UWSN [26]. The IMCMR-UWSN technique was tested in several simulations, and the results showed that it was more effective in terms of some metrics as shown in Table 1.
## 3 Results and Discussion  
### Cell Disintegration Index  
The cell disintegration index (CDI) was used to determine the structural changes induced by electroporation of the cell membrane due to the action of a pulsed electric field. This index takes values between 0 and 1--CDI = 0 indicates intact cells, while CDI = 1 stands for totally disintegrated cells of the material tissue [27]. Undoubtedly, the values of this index are influenced by the parameters used, such as temperature, the number of pulses supplied, or the intensity of the electric field, but the structure of the treated material is also important [4; 24; 47]. The more energy was supplied to the apples during the PEF treatment, the more the tissue of this plant material was damaged (Table 2). This tendency is consistent with the results obtained by other authors [26; 33]. The supply of a variable amount of energy (1-6 kJ/kg), with the electric field strength \(E\) = 1 kV/cm, resulted in the CDI in the range of 0.08-0.37.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Microwave Power** & **Specific Energy** & **CDI** & **d.m.** & **\(\Delta E\)** \\
**[W]** & **Intake [kJ/kg]** & **Sample Code** & **[-]** & **[\%]** & **[-]** \\ \hline \multirow{4}{*}{100} & 0 & MW-CD100 & - & 92.5 \(\pm\) 0.0 b  & 12.8 \(\pm\) 3.2 abc  \\  & 1 & MW-CD100\_PEF1 & 0.09 & 92.6 \(\pm\) 0.0 b  & 14.2 \(\pm\) 3.0 bed  \\  & 3.5 & MW-CD100\_PEF3.5 & 0.23 & 92.4 \(\pm\) 0.0 b  & 11.8 \(\pm\) 2.6 abc  \\  & 6 & MW-CD100\_PEF6 & 0.36 & 90.7 \(\pm\) 0.1 a  & 14.2 \(\pm\) 1.8 bed  \\ \hline \multirow{4}{*}{200} & 0 & MW-CD200 & - & 94.4 \(\pm\) 0.1 cd  & 13.7 \(\pm\) 4.0 ab  \\  & 1 & MW-CD200\_PEF1 & 0.09 & 94.8 \(\pm\) 0.2 de  & 11.6 \(\pm\) 2.9 ab  \\  & 3.5 & MW-CD200\_PEF3.5 & 0.27 & 94.4 \(\pm\) 0.2 c  & 13.7 \(\pm\) 3.3 bc  \\  & 6 & MW-CD200\_PEF6 & 0.36 & 94.9 \(\pm\) 0.1 e  & 16.1 \(\pm\) 3.1 bed  \\ \hline \multirow{4}{*}{300} & 0 & MW-CD300 & - & 96.5 \(\pm\) 0.1 f  & 16.5 \(\pm\) 4.2 cd  \\  & 1 & MW-CD300\_PEF1 & 0.08 & 96.4 \(\pm\) 0.1 f  & 9.0 \(\pm\) 2.4 a  \\  & 3.5 & MW-CD300\_PEF3.5 & 0.21 & 96.5 \(\pm\) 0.0 f  & 15.9 \(\pm\) 4.3 bed  \\  & 6 & MW-CD300\_PEF6 & 0.37 & 97.1 \(\pm\) 0.0 g  & 17.9 \(\pm\) 3.8 d  \\ \hline \end{tabular}
\end{table}
Table 2: Values of CDI (cell disintegration index), dry matter content (d.m.), and \(\Delta E\) (total colour difference) of obtained dried apples.
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
###### Abstract  
The paper addresses the problem of localization based on hybrid received signal strength (RSS) and time of arrival (TOA) measurements, in the presence of synchronization errors among all the nodes in a wireless network, and assuming all parameters are unknown. In most existing schemes, in fact, knowledge of the model parameters is postulated to reduce the high dimensionality of the cost functions involved in the position estimation process. However, such parameters depend on the operational wireless context, and change over time due to the presence of dynamic obstacles and other modification of the environment. Therefore, they should be adaptively estimated \"on the field\", with a procedure that must be as simple as possible in order to suit multiple real-time re-calibrations, even in low-cost applications, without requiring human intervention. Unfortunately, the joint maximum likelihood (ML) position estimator for this problem does not admit a closed-form solution, and numerical optimization is practically unfeasible due to the large number of nuisance parameters. To circumvent such issues, a novel two-step algorithm with reduced complexity is proposed: A first calibration phase exploits nodes in known positions to estimate the unknown RSS and TOA model parameters; then, in a second localization step, an hybrid TOA/RSS range estimator is combined with an iterative least-squares procedure to finally estimate the unknown target position. The results show that the proposed hybrid TOA/RSS localization approach outperformed state-of-the-art competitors and, remarkably, achieved almost the same accuracy of the joint ML benchmark but with a significantly lower computational cost.  
localization; hybrid positioning; ranging; wireless sensor networks; time of arrival; received signal strength +
Footnote †: journal: Sensor and Actuator Networks
## 2 Design Method of Conventional Switched Type Attenuators
### Simplified \(T-\)Type Attenuator  
When the value of attenuation is small, the resistance of the series resistor \(Rs\) in the T-type structure is also small. Taking the 0.5 dB attenuation bit as an example, we find that the resistance of the series resistor \(Rs\) in the T-type structure is only 1.34 \(\Omega\), which means that the microstrip line can be used to replace the series resistor \(Rs\).  
Figure 3 shows the simplified T-type structure and its equivalent circuits. In this configuration, _Cp,off_ represents the equivalent capacitance of the parallel transistor in its off state, while _Rp,on_ denotes the on resistance during the parallel transistor's on state. The simplified T-type design eliminates both the series resistor \(Rs\) and the series transistor \(Ms\) found in conventional T-type structure. This allows for targeted attenuation by solely adjusting \(Rp\) and \(Mp\)[26; 27]. Notably, this architecture boasts reduced insertion loss and a compact layout area.  
In the reference state, the transistor \(Mp\) is turned off, and the signal to ground shows a high resistance state; at this time, the insertion loss tends to be close to zero. Conversely, in the attenuation state, the transistor \(Mp\) is on, and the signal leaks to the ground to achieve the attenuation. The resistance value of \(Rp\) can still be obtained by (6).
## 4 Discussion
### The SMIKE Model vs. the Uniform Model of Kinematic Excitation  
The results of the simultaneous computations allowed for the comparative analysis of the bridge's dynamic response level obtained by adopting the proposed SMIKE model as well as the uniform model of kinematic excitation. In the SMIKE model, the measured value of the wave propagation velocity \(v=240\) m/s was implemented (see Section 3.3.1). The value of space scale parameter \(\alpha=7\), estimated on the basis of the regression relationships for the USBC region (see Section 3.3.2), was introduced.  
The stress-time histories obtained for both models are compared in Figure 16. Having considered the outcomes gathered for all representative points (see Figure 15), it was resolved to compare stresses at point C1 located over the first column and at points S2 and L3 placed in the middle of the second and the third span, respectively.  
Based on the presented charts, an important conclusion can be drawn. In the case of points C1 and S2, located above the supports and on the stocky span, the maximum stress caused by the tremor is higher with the SMIKE model application (see Figure 16a,b), whereas the opposite relation occurs for point L3, placed on the longest slender span: the maximum stress is greater for the uniform model of excitation (see Figure 16c).  
Figure 15: Representative points chosen for the dynamic analyses.  
In Figure 17a, the stress envelopes, presenting the maximum time from maximal principal stresses at points along the bridge length, computed with both excitation models, are compared. Figure 17b shows the ratio of these envelopes (SMIKE stresses to uniform model stresses).  
Based on Figure 17a, a clear interpretation of the differences in the bridge's dynamic performance under two different models of excitation can be provided. However, in the context of the particular spans' slenderness, the stresses ratio (Figure 17b) seems to be the best way to present and clarify the results. It unquestionably portrays how the SMIKE model application affects the level of the dynamic response of particular spans of the bridge. The response level is up to twice higher under the SMIKE model than under the uniform one as far as the stocky spans are concerned. The quasi-static effects appearing in these structural parts take on values high enough to outweigh the loss in the average excitation amplitudes due to the incoherence effect and lead to an increase in the global dynamic response. The opposite situation can be noticed for the longest slender span. The decrease in the average excitation amplitudes introduced by the SMIKE model causes a reduction in the global dynamic response level.  
Figure 16: Time-stress histories obtained for the SMIKE model with the empirical parameters \(v=240\) m/s and \(\alpha=7\) (red line) and for the classic Uniform Model of Excitation (black line) at representative points: (**a**) C1 (over the first column), (**b**) S2 (in the middle of the second span), (**c**) L3 (in the middle of the third span).
# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks
## 5 Numerical Results  
In this section, we utilize simulations to evaluate the proposed AMCPLS scheme for the IEEE 802.11p V2V communications. The simulation parameters of the considered IEEE 802.11p system are shown in Table 2. We consider channel equalizations that are based on both traditional LS channel estimation [14] and STA channel tracking [13]. Furthermore, we compare the performance of our proposed DCNN based AMCPLS with the performance of other rival machine learning algorithms such as k-NN and SVM. In our evaluations we assume that the set of allowable payload lengths is \(\mathbb{L}=\{100,300,500\}\) Bytes. Furthermore, for the IEEE 802.11p, the set of available MCSs is \(\mathbb{I}=\{0,1,2,3,4,5,6,7\}\). Thus the set of all possible classes is \(\mathbb{A}=\{0,1,2,...,23\}\). In our simulations, we assume that the target FER, \(\epsilon\) is 0.05. Furthermore, we assume a rural LOS environment [9; 26]. In our evaluations, we utilize both the effective throughput and the FER. We have used the FER rather than the bit error rate (BER) because our target is to select both the MCS and the payload length to maximize the effective throughput under FER constraint and the BER is independent on the payload length.
## 4 Discussion
### Non-Carcinogenic Risk  
The THQ values for Mn, Cr, Cu, Cd, Pb, Co, and Ni in sunflower oil were 3.42 \(\times\) 10\({}^{-5}\), 1.19 \(\times\) 10\({}^{-3}\), 8.75 \(\times\) 10\({}^{-4}\), 3.57 \(\times\) 10\({}^{-3}\), 7.65 \(\times\) 10\({}^{-3}\), 1.78 \(\times\) 10\({}^{-4}\), and 1.07 \(\times\) 10\({}^{-4}\); Table 3. Finally, the HI for the heavy metals studied was 0.01360 (1.36 \(\times\) 10\({}^{-2}\)). For grapeseed oil and extra virgin olive oil, the THQ values were in the ranges of 0 and 1.42 \(\times\) 10\({}^{-3}\) and 1.48 \(\times\) 10\({}^{-5}\) and 3.92 \(\times\) 10\({}^{-3}\), respectively, while the HI values ranged between 0.00369 and 0.01047. For the organic extra virgin olive oil, rapeseed oil, and blend oil, the THQ values were in the following ranges: 3.99 \(\times\) 10\({}^{-5}\) and 1.03 \(\times\) 10\({}^{-2}\); 12.28 \(\times\) 10\({}^{-5}\) and 1.78 \(\times\) 10\({}^{-3}\); and 3.4 \(\times\) 10\({}^{-5}\) and 10.1 \(\times\) 10\({}^{-3}\), respectively. The HI values for these oils were 0.01745, 0.01182, and 0.02527, respectively. For the walnut oil, the THQ was in the range between \(2.51\times 10^{-5}\) and \(6.02\times 10^{-3}\), with an HI of 0.009506, and for the rice oil, the HI was 0.02672, while the THQ was in the range between 1.03 \(\times\) 10\({}^{-5}\) and \(22.14\times 10^{-3}\).  
For comparison, the average THQ values for 6387 edible vegetable oils in Chinese supermarkets studied in 2017-2019 were 0.01574 for Cd and 0.9446 for Cr [24], much higher than those obtained in this study (the average THQ was 0.00182 for Cd in all oils). In the case of Pb, a mean THQ of 0.00635 was found in China, whereas in the present study, the THQ values for Pb ranged from 0.00112 to 0.02214, with higher values observed for the organic extra virgin olive oil, blend oil, and rice oils.  
These HI values are <1 and are considered to be at an acceptable level, and not a health risk. The HI values are in the range of 0.00369 and 0.02672. They are <1, indicating that the oils are not cause for concern and can be considered safe for consumption [24; 61].