esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Stride Length, Foot Clearance, and Foot Progression Angle Using UWB Sensors
###### Abstract  
Stride length (SL), foot clearance (FC), and foot progression angle (FPA) are the key parameters for diagnosing gait disorders. This study used the distance data between two feet measured by ultra-wideband (UWB) sensors installed on shoes and proposed a method for estimating the three gait parameters. Here, a method of compensating the offset of the UWB sensor and estimating the distances between a base sensor installed on one foot during the stance phase and three UWB sensors on the other during the swing phase was applied. Foot trajectory was acquired in a gait experiment with ten healthy adults walking on a treadmill. The results were compared with those obtained using a motion capture system (MCS). The UWBs sensor displayed average errors of 45.84 mm, 7.60 mm, and 2.82\({}^{\circ}\) for SL, FC, and FPA, respectively, compared with the MCS. A similar accuracy level was achieved in a previous study that used an inertial measurement unit (IMU). Thus, these results suggest that UWB sensors can be extensively applied to sensor systems used to analyze mobile gait systems.  
ultra-wideband; gait parameter; stride length; foot clearance; foot progression angle +
Footnote †: journal: Applied  
0000-0001-5002-4900]B.S.S.Park  
0000-0001-5002-3899]Sh.S.Park  
0000-0002-3899-3899][PERSON]
# Empirical Approach to Defect Detection Probability by Acoustic Emission Testing
## 3 POD Estimation  
Simplified probability estimation algorithms are shown in Figure 2. To assess the probability of making a type I error associated with a false defect detection, long-time realizations of noise should be obtained. For each realization, a processing procedure is carried out to verify the defect detection criterion. The correct completion of the check procedure is the deviation of the criterion fulfillment condition, otherwise, a type-I error occurs. The probability of making type-I error \(\alpha\) is defined as the number of erroneous decisions \(k\) to the total sample size \(N\), i.e., \(\alpha=\frac{k}{N}\).  
The probability of making a type-II error is estimated similarly based on a priori models of the AE data set. The stochasticity of the AE source parameters provides a difference in the AE parameters of the sample. The probability of making a type-II error \(\beta\) is defined as the number of cases when the detection criterion is not fulfilled \(m\) to the total sample size \(N\), i.e., \(\beta=\frac{m}{N}\).  
Figure 1: Experimental setup (a) and loading diagram (b).  
Figure 2: Simplified algorithms for estimating the probability of making a type I (**a**) and type II (**b**) errors.
## 2 Materials and Methods
### Evaluation Metrics and Loss Functions
\[L_{MSE}(\theta)=\frac{1}{N}\sum_{i=1}^{N}\lVert f(x_{i};\theta)-y_{i}\rVert_{F }^{2}. \tag{6}\]  
The Structural Dissimilarity Index Measure (DSSIM) as a loss function is derived from the Structural Similarity Index Measure (SSIM). It focuses on structural dissimilarity rather than pixel-wise error, as calculated in Equation (7). Unlike the MSE, the DSSIM penalizes loss in structural features, like edges and textures, by assessing differences in luminance, contrast, and structural composition. Using the DSSIM as a loss function helps the model preserve anatomical structures, which is crucial for diagnostic accuracy in medical images. It also aligns better with human perception, as small pixel differences matter less than overall structural integrity. However, the DSSIM alone can overlook intensity accuracy and may not perform as well in areas without strong structural patterns, making it useful in combination with other loss terms. \[L_{DSSIM}(\theta)=\frac{1}{N}\sum_{i=1}^{N}(1-SSIM(f(x_{i};\theta)-y_{i}))/2 \tag{7}\]  
Perceptual loss (PL) is calculated using the last four blocks of the VGG16-pretrained network [24]. Unlike the MSE, perceptual loss takes high-level features into consideration, which accurately models the human visual system due to its ability to learn the features. The feature maps, \(\phi_{i}\), are extracted from the last convolutional layer in blocks \(i=1,2,3,4\) of the VGG16 with size \(h_{i}\times w_{i}\times d_{i}\), which can be expressed as  
\[L_{PL}(x,y)=\sum_{i=1}^{N}\frac{1}{h_{i}w_{i}d_{i}}\lVert\phi_{i}(x)-\phi_{i}( y)\rVert^{2} \tag{8}\]The overall proposed loss function can be calculated as follows, where \(\gamma\) represents the contribution of each loss function depending on how the model is being trained. To determine the optimal balance, we conducted ablation studies with different loss weightings, which is discussed in Section 4. \[L(\theta)=\gamma_{1}L_{MSE}(\theta)+\gamma_{2}L_{DSSIM}(\theta)+\gamma_{3}L_{PL} (\theta) \tag{9}\]
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 2 Materials and Methods
### Field Tests for the Wireless Data Relay
\[P_{\rm a}=P_{\rm t}\bigg{(}\frac{\lambda}{4\pi d}\bigg{)}^{2}G_{\rm t}G_{\rm r} \tag{2}\]  
\[P_{\rm r}=4P_{\rm a}\bigg{\{}\sin\bigg{(}\frac{2\pi H_{\rm t}H_{\rm r}}{ \lambda d}\bigg{)}\bigg{\}}^{2} \tag{3}\]  
Figure 10: Schematic showing field tests at a pond for two-ray ground reflection model calculation of communicable distance. Each antenna height in this figure includes its own vertical length. Figure 9: Components of the water quality monitoring system with amphibious drones for wireless data relay tests: 1, SplashDroneTM 3+; 2, SplashDroneTM 4; 3, upper mounter for SplashDroneTM 3+; 4, upper mounter for SplashDroneTM 4; 5, lower mounter for SplashDroneTM 4; 6, side mounter(s) for SplashDroneTM 3+; 7, drone controller for SplashDroneTM 4; 8, laptop computer for data display; 9, receiver; and 9′, contents included in the receiver. where \(P_{\rm a}\) is the received power transmitted through the air (W), \(P_{\rm t}\) is the transmitter power (W), \(\lambda\) is the wavelength (m), \(d\) is the transmitted distance (m), \(G_{\rm t}\) is the gain of transmitter antenna (2.1 dBi = 1.62), \(G_{\rm r}\) is the gain of receiver antenna (2.1 dBi = 1.62), \(P_{\rm r}\) is the received power (W), \(H_{\rm t}\) is the height of transmitter antenna (m), and \(H_{\rm r}\) is the height of receiver antenna (m).
## 2 Materials and Methods
### Urban Growth
#### 2.1.4 Urban Redevelopment  
Redevelopment of existing built-up areas in Bishkek was evaluated using the 1979 KH-9 and 2021 Sentinel-2 data for areas falling within the initial 1979 built-up area classification (Figure 2b). Therefore, capturing buildings present in the 1979 built-up classification that were demolished, and new ones which were built, was visible as an increase in the spectral reflectance of the area. Whitebox Tools v.1.4.0 _Histogram Match_ tool was used to match Sentinel-2 band 3 (green) to the single-band KH-9 data and normalized the outputs in the range 0-1. The KH-9 image was downsampled to match the 10 m resolution of Sentinel-2. The normalized KH-9 data were subtracted from the normalized Sentinel-2 data and extracted the mean digital number (DN) change in 20 polygons, which were digitized in areas where the land cover remained consistent 1979-2021 based on visual inspection (Figure S2a). We used this analysis to conservatively mask changes between values of \(-\)0.1-0.1 as 'unchanged'. The remaining negative values (\(-\)1--0.1) represent a DN decrease, which was associated with vegetation growth. These were reclassified into a'vegetated' class. Conversely, positive values (0.1-1) represent a DN increase. These were associated with built-up area redevelopment and were reclassified into a 'built-up' class. A low DN could be associated with low-reflectivity buildings with tiled roofs, e.g., [52], rather than vegetation. Therefore, the validity of these assumptions was tested by extracting  
Figure 2: Study methodology. (**a**) Built-up area classification and change detection. (**b**) Extraction of building footprints and heights using high-resolution satellite imagery.  
the normalized difference vegetation index (NDVI) values for the two classes using 2021 Sentinel-2 imagery:  
\[\text{NDVI}_{(S2)}=\frac{\text{Band NIR }(8)-\text{Band Red}(4)}{\text{Band NIR }(8)+\text{Band Red}(4)} \tag{1}\]
## 2 Results
### The DAC Activity of DisA Does Not Compromise RuvAB-Mediated ATP Hydrolysis  
DisA might exhaust the ATP pool with its DAC activity, or the c-di-AMP produced might poison RuvAB ATPase activity. To examine this hypothesis, DisA was replaced by the DisA D77N variant, which cannot synthesize c-di-AMP [18; 22]. In the presence of ssDNA or HJ-J3 DNA and DisA D77N, RuvAB-mediated ATP hydrolysis was inhibited to a comparable level to that when _wt_ DisA was used (Figure 4A,B vs. Figure 4E,F, orange, green, and brown vs. dark blue line, Table 1). Therefore, neither the DAC activity of DisA with subsequent consumption of ATP, nor c-di-AMP, are involved in the reduction of RuvAB-mediated ATP hydrolysis. Altogether, these data suggest that DisA inhibits the ATPase activity of the RuvAB-ssDNA complex by its direct interaction with RuvB.
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 4 Results  
As you can see in Figure 3, the clutter matrix summarizes the obtained results. This matrix is used to explain the accuracy of the classification and each house expresses a specific value of perceptron multilayer neural network. In order from the right, each house represents sensitivity, specificity, and accuracy. The results of the validation of the 10-layer neural network used to classify the signal with 95.5% accuracy are shown in Figure 3. Next, wavelet transform with level 8 and decomposition level 4, along with Shannon entropy, were used to diagnose epilepsy with 91% accuracy. Decomposition level 4 in wavelet transform represents dividing the EEG signal frequency by two four times and then calculated Shannon entropy on the decomposed signal. In addition, the validation value and accuracy after wavelet transform conversion are shown in Figure 4. This paper applied the proposed method to differentiate patients from normal subjects by building two individual classifiers using features extracted from the EEG signals. The first classifier is a Support Vector Machine (SVM) implemented with a linear kernel. The results obtained using SVM led to 95.5% accuracy, 98.78% specificity, and 82.60% sensitivity. The second  
Table 5 presents a comprehensive comparison of various studies to validate and reinforce the proposed method. It serves to support the stability and reliability of the proposed approach by highlighting its performance in relation to other relevant studies.  
As you can see in Table 5, the classifiers used in this study are compared with three ANFIS, FAWT, and DWT methods. The results obtained by using two classifiers SVM and ANN show higher accuracy as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Wavelet transform & 91.0\% & 95\% & 87\% \\ \hline Multilayer perceptron neural network & 95.5\% & 97\% & 94\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the performed method.  
Figure 5: The advantages of the presented methodological approach.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy** \\ \hline Proposed method using wavelet Transform & 91.0\% \\ \hline Proposed method using multilayer perceptron neural network & 95.5\% \\ \hline Adaptive neuro-fuzzy inference system (ANFIS) & 92.2\% \\ (21) & 92.2\% \\ \hline Flexible analytic wavelet transform (FAWT) & 89.1\% \\ (23) & 85.9\% \\ \hline Adaptive neural fuzzy network and the energy of discrete wavelet transform (DWT) & 85.9\% \\ (22) & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between different methods.  
To optimize the learning cost and diagnosis performance, it is important to carefully choose the SVM classifier parameters and kernel width. To do this, we divided the training data into train and validation sets and selected the optimum values of the parameters such that the smallest error on the validation data set is achieved. The optimum value of the parameter delta was found to be 1.5.
# Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments
## 3 Experimental Methodology
### Measurement Setup  
With the network architecture configurations defined, the measurement setup used to evaluate them can be presented. Given that this work aims to evaluate the network architectures of the IPS and not the sensing modalities or application layer functions, a pseudo-IPS is used to conduct measurements. This pseudo-IPS, illustrated in Figure 5, consists of a single pseudo-FSN connected (via 1 Gb Ethernet) to a private 5G NR standalone network from DruidSoftware (Wicklow, Ireland--v5.2.4.0-1.P1) [50] with a single gNodeB serving a pseudo-CIV. Windows-based PCs are used to represent both the pseudo-FSNand pseudo-CIV in this measurement setup. Connectivity is provided via a _VirtualAccess GW1400_[51] (Sub-6 GHz, i.e., 3.5 GHz) rugged automotive cellular router featuring a _Quetel RM500Q_[52] modem. To appropriately approximate a sub-urban or urban blind road junction or blind corner scenario, the pseudo-CIV is located \(\sim\)50 m away from the gNobeB and repeatedly traverses a \(\sim\)20 m path, as illustrated in Figure 5. Additionally, given the spectrum allocation changes in 5G NR [53, 54], two connectivity options are investigated for Configuration 2, Sub-6 GHz (FR1) and mmWave (FR2). In addition to the Sub-6 GHz (3.5 GHz) connectivity option, connectivity for mmWave spectrum (26 GHz) is provided by a _MicroAmp_[55] CPE router featuring a _Quetel RM530N_[56] modem.  
Two different software tools, ping and iPerf3 [57], were used to conduct the measurements for this study. A custom application was written to generate ICMP packets at a frequency of 1 Hz and record the round-trip-time (RTT) and thus the end-to-end (E2E) latency. iPerf3 was used to collect throughput measurements. An iPerf3 server was installed on the pseudo-FSN and the iPerf3 client was installed on the pseudo-CIV, where UDP streams were used to more closely align with the CAM [46], DENM [47] or CPM [58] packets used in V2X communications, sent from the pseudo-FSN to the pseudo-CIV. These tools were not run in parallel to avoid the effects of packet collisions or routing delays from the scheduling of the various ICMP and UDP packets.
## 2 Materials and Methods
### HS-SPME and GC/MS Conditions and Analysis  
Aliquots (7 mL) of spirit was transferred to glass vials (20 mL) and closed with a silicone septa. Sampling was performed using DVB/CAR/PDMS fiber (Divinylbenzene/Carboxen/Polydimethylsiloxane), 1 cm long, at 25 \({}^{\circ}\)C and an absorption time of 20 min using the SPME system (Solid Phase Extraction System). Before absorption of volatiles on the SPME fiber, 1 \(\mu\)L of internal standard (1,2-dichlorobenzene, 0.01% solution in methanol) was added to each sample. The fiber was inserted into the GC/MS (gas chromatography--mass spectrometry) injector, where desorption after 2 min took place (splitless mode). The apparatus used in the experiment was GCMS-QP2010 (Shimadzu Corporation, Japan). The separation of aroma compounds was performed with the use of Stabilwax (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, polyethelene glycol) Restek. The carrier gas was helium and the column flow was 0.65 mL/min. The column temperature program was as follows: an initial temperature of 35 \({}^{\circ}\)C (for 1 min) at a rate 5 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C (holding time of 2 min) and 220-240 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min. The total time of the analysis was 40 min. The interface and injector temperatures were 260 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. Ion source temperature was 250 \({}^{\circ}\)C. Mass spectra were acquired in the electron impact mode (70 eV) using a full scan with a mass acquisition range of 30-450 m/z. The internal standard 1,2-dichlorobenzene was used to semi-quantify volatile compounds. Identification of aroma compounds was made on the basis of mass spectra libraries (NIST 2008, [PERSON] 175) and from literature data. All analyses were performed in triplicate.
## 3 Discovering Polysaccharides in Africa
### A Focus on Bioactive and/or Functional Polysaccharides from Arid and Semi-Arid Lands
#### 3.3.4 Plantago  
_Plantago_ spp. are valuable official plants belonging to the family Plantaginaceae. They are distributed in temperate regions, tropical zones [150] but also in drier environments such as deserts and oases [151]. The most dominant polysaccharides in psyllium are described as a complex of heteroxylans [152]. _P. major, P. ovata_ and _P. notata_ are largely described in the literature. _Plantago notata_ Lagasca is a spontaneous plant from Septentrional Algerian Sahara used by the Ghardaa population for its medicinal effects. Water-soluble polysaccharides extracted from its leaves and seeds were mainly composed of neutral sugars (around 80% \(w/w\) for both). Note that the polysaccharides from leaves were richer in uronic acid than for the ones extracted from seeds, with values reaching 20.3% against 4.9%, respectively [28]. [PERSON] et al. studied the monosaccharides composition of a water-soluble polysaccharide from _P. notata_ leaves [153]. The polymer consisted of Gal (44%), Rha (20%), Glc (11%), Ara (10%) and GalA (13%). In the same way, [PERSON] et al. determined the monosaccharide composition of water-soluble polysaccharide from _P. notata_ seeds [28]. The polysaccharide was an heteroxylan with a high molecular weight (2.3 \(\times\) 10\({}^{6}\) Da), composed of Xyl (77%) in addition to Rha (9%), Ara (8%), Gal (3%), Glc (1%) and GalA (2%). The structural analysis revealed that the backbone was a \(\beta\)-(1,3) and \(\beta\)-(1,4)-linked D-Xyl\(p\). Several lateral chains and terminal residues linked in O-2 and O-3 positions were identified, such as \(\alpha\)-L-Araf-(1,3)-\(\beta\)-d-Xyl\(p\), \(\beta\)-D-Xyl\(p\)-(1,2)-\(\beta\)-D-Xyl\(p\), terminal Xyl\(p\) or terminal Araf. As another example, _P. ovata_ Forsk. called Isabgol is growing in drought prone areas. Producing mucilage from this species has an economical importance, especially in India [154]. The analysis of _P. ovata_ Forsk. seeds mucilage revealed the presence of two polysaccharide fractions, one soluble in cold water and the other one in hot water. The first fraction was composed of 46% of d-Xyl, 40% of an aldobiouronic acid, 7% of l-Ara, and 2% of insoluble residue, whereas the second fraction contained 80% of d-Xyl, 14% of l-Ara, 0.3% of aldobiouronic acid and traces of d-Gal [155]. [PERSON] et [PERSON] found that an arabinoxylan (73% \(w/w\)) structure could be extracted from Psyllium husk, composed of a Xyl main chain branched with Ara, Rha and GalA [156]. More recently, [PERSON] et al. described an arabinoxylan structure from _Plantago ciliata_ seeds [23].
# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 5 Improved PSO-Jaya Optimization Algorithm (I-PSO-Jaya)
### Illustration of the I-PSO-Jaya Algorithm  
To generate the initial population (\(P\)), we use a loop that iterates from \(i=1\) to \(S\) and \(j=1\) to \(P\); where \(S=4\). We generate the 5 initial populations randomly to initialize \(X_{i,j}^{c}\), denoted as \(X_{1,1}^{0}\), \(X_{1,2}^{0}\), \(X_{1,3}^{0}\), \(X_{1,A}^{0}\), and \(X_{1,5}^{0}\). The populations are defined as follows:  
\[X_{1,1}^{0}=\{\text{Agent1,Agent2,Agent3,Agent4}\},\] \[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\] \[X_{1,3}^{0}=\{\text{Agent9,Agent10,Agent11,Agent12}\},\] \[X_{1,A}^{0}=\{\text{Agent13,Agent14,Agent15,Agent16}\},\] \[X_{1,5}^{0}=\{\text{Agent17,Agent18,Agent19,Agent20}\}.\]  
Communication cost is calculated using Equation (2). Initial costs are as follows:  
\[f(X_{1,1}^{0})=0.7,\ \ \ f(X_{1,2}^{0})=0.8,\ f(X_{1,3}^{0})=0.65,\ \ \ f(X_{1,A}^{0})=0.75,\ \text{and}\ f(X_{1,5}^{0})=0.6.\]  
For Iteration 1:  
**Initial Velocities (\(v_{i}^{0}\)):** Assume all initial velocities are \(v_{i}^{0}=0.1\). **Updating Velocity. ** For the particle with index \(i\) in iteration \(c+1\), the updated velocity \(v_{i}^{(c+1)}\) is calculated using Equation (3). Let us assume \(\alpha=0.5\) and \(\beta=0.3\). For the current iteration, the calculation is as follows:  
\[\text{For particle 1:}\ \ \ v_{1}^{(1)}=0.1\oplus 0.5\otimes(0.7-0.7)\oplus 0.3\otimes(0.6-0.7)=0.07;\]  
\[\text{For particle 2:}\ \ \ v_{2}^{(1)}=0.1\oplus 0.5\otimes(0.8-0.8)\oplus 0.3\otimes(0.6-0.8)=0.04;\]  
\[\text{For particle 3:}\ \ \ v_{3}^{(1)}=0.1\oplus 0.5\otimes(0.65-0.65)\oplus 0.3\otimes(0.6-0.65)=0.085;\]  
\[\text{For particle 4:}\ \ \ v_{4}^{(1)}=0.1\oplus 0.5\otimes(0.75-0.75)\oplus 0.3\otimes(0.6-0.75)=0.055;\]  
\[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]0  
**Position Update. ** Using Equation (4), the calculation of Position Update is as follows:  
\[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]1  
\[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]2  
\[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]3  
\[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]4  
\[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]5Update using Equation (5), where \(r^{c}_{1,j}=0.6\), \(r^{c}_{2,j}=0.4\), is as follows:  
\[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]6 \[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]7 \[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]8 \[X_{1,2}^{0}=\{\text{Agent5,Agent6,Agent7,Agent8}\},\]9 \[X_{1,3}^{0}=\{\text{Agent9,Agent10,Agent11,Agent12}\},\]0  
**Comparison of Fitness Functions. ** The solution is modified by comparing the fitness function values and noting that \(X^{1}_{1,5}\) is 0.664 and \(X^{(c+1)}_{i}\) is 0.6. Since \(X^{c+1}_{i,j}\) is not less than \(X^{(c+1)}_{i}\), the value of \(X^{(c+1)}_{i}\) remains the same, i.e., 0.6. **Return the Best Solution for Iteration 1:** The acceptable solution at the end of the iteration is \(f(X)=0.6\). For Iteration 2:  
Update Velocity using Equation (3). Let us assume \(\alpha=0.5\) and \(\beta=0.3\).
## 6 Evaluation
### Performance Evaluation
#### 6.2.2 Intra-Cluster Block Allocation Performance Evaluation
Under different block counts, VSPGBAA maintains a storage hit rate comparable to TSGA and significantly outperforms the random allocation strategy. Specifically, compared to TSGA, VSPGBAA incurs a 14.3% higher access cost, and experiences a 46.5 percentage point lower cache hit rate, but achieves a 14\(\times\) shorter runtime, demonstrating a strong efficiency advantage. Meanwhile, as shown in Figure 8c, TSGA's runtime increases significantly with the number of blocks, far exceeding that of VSPGBAA. Additionally, compared to NodeFirst, VSPGBAA reduces total access cost by 11.3%, improves the cache hit rate by 39.1%, but requires 9% more runtime. By effectively balancing storage hit rate, access cost, and computational efficiency, VSPGBAA ensures an optimized storage allocation strategy while significantly enhancing performance. Figure 7: Results of block allocation algorithms with the number of vehicles.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 1 Introduction
### Background
In [42], EE is maximized through a user-pairing approach, utilizing the greedy algorithm and power allocation utilizing SCP. The NOMA-SCP-GA algorithm is proposed to solve the problem.
## 4 Application
### Non-Clinical Application
#### 4.2.5 Living Skin Detection  
Recently, researchers have been using the iPPG technique to detect living skin. For example, [PERSON] et al. [133] proposed a novel unsupervised technique called \"Voxel-Pulse-Spectral\"(VPS) to detect living skin tissue using a CCD camera considering the pulse as a feature. However, the VPS method is time consuming because of the complexity of unsupervised learning for pulse extraction. A fast living skin detection method was introduced in another study [134] where the time variant iPPG signal is transformed into signal shape descriptors using a technique known as multiresolution iterative spectra.