esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Experimental Results
Table 2 shows the accuracies achieved by PCANet using time-frequency representations on PTB-ECG. Table 3 presents the accuracies achieved by the different models based on CNN using time-frequency representations on PTB-ECG. Considering MFCC on PTB-ECG, CNN failed to train VGGNet-19. The best accuracies achieved by the time-frequency representations on PTB-ECG were 98.99% for DenseNet-201 in MFCC, 98.85% for Xception in spectrogram, 97.16% for Xception in log spectrogram, 96.92% for Xception in mel spectrogram, and 97.83% for ResNet-101 in scalogram. This paper did not consider a potential issue from the variability from the selection of the ECG sensor devices. A different ECG sensor device (or product) would change the shape of the ECG signals and accuracy. The MFCC input to the DenseNet-201 model achieved the best accuracy for the PTB-ECG dataset. Figure 12 shows the training processes that achieved the best accuracies for the time-frequency representations applied to PTB-ECG. The CU database directly constructed for this study was acquired at 500 kHz. The dataset was resampled to \(1\) kHz because it was too large for data processing. The CU database included ECG data from 100 people. However, the data from 99 people were used to construct CU-ECG as the ECG data from one subject had very few R peak points. The R peak points from 99 people were compared and adjusted to ensure that the maximum number of data per class was 300. The size of the CU-ECG database was \(784\times 29\),700 (300 data/class \(\times\) 99 classes). The row represents the dimension of the data, while the column indicates the number of data. To train the CNNs, CU-ECG was divided into the training, validation, and test datasets with ratios of 0.45, 0.05, and 0.5, respectively. The sizes of the training, validation, and test datasets were \(784\times 13\),365, \(784\times 1485\), and \(784\times 14\),850, respectively. Figure 13 shows the structure of CU-ECG. The CNNs were trained using a mini-batch size of 30, an \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Type.**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{3-4}  & & **Training** & **Test** \\ \hline MFCC & & 99.72 & 98.56 \\ \hline Spectrogram & \multirow{2}{*}{PCANet} & 99.72 & 98.15 \\ \hline Log spectrogram & & 99.70 & 98.74 \\ \hline Mel spectrogram & & 99.78 & 98.37 \\ \hline Scalogram & & 99.78 & 98.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracies achieved by PCANet using time-frequency representations on PTB-ECG.
## 3 Fuzzy Incremental State Model  
Nonlinear systems can be modeled using the T-S fuzzy model, based on a set of measurable system variables \(\{z_{1}(k),z_{2}(k),\dots z_{m}(k)\}\). If \(\{r_{1},r_{2},\dots r_{m}\}\) are chosen as fuzzy sets for these variables, the system can be defined through IF-THEN rules as follows [15]:  
\[\begin{split}\text{S}^{(i_{1}\dots i_{m})}\text{: If }z_{1}(k) \text{ is }M_{1}^{i_{1}}\text{ and }\dots\text{ and }z_{m}(k)\text{ is }M_{m}^{i_{m}} \text{, then}\\ y_{i}(k+1)=\\ a_{i0}^{(i_{1}\dots i_{m})}+a_{i1}^{(i_{1}\dots i_{m})}y_{i}(k)+a_{i2}^{( i_{1}\dots i_{m})}y_{i}(k-1)+\dots+a_{im_{i}}^{(i_{1}\dots i_{m})}y_{i}(k-n_{i}+1) \\ +\sum_{j=1}^{p}\left(b_{ij1}^{(i_{1}\dots i_{m})}u_{j}(k)+b_{ij2}^ {(i_{1}\dots i_{m})}u_{j}(k-1)+\dots+b_{ijn_{i}}^{(i_{1}\dots i_{m})}u_{j}(k-n _{i}+1)\right)\end{split} \tag{14}\]  
where \(j\) is the index of each fuzzy variable, and \(i_{m}\) is the index of each fuzzy rule associated with that fuzzy variable. Starting from the result obtained in Equation (11), the fuzzy incremental state model can be derived:  
\[\begin{split}\text{S}^{(i_{1}\dots i_{m})}\text{: If }z_{1}(k) \text{ is }M_{1}^{i_{1}}\text{ and }\dots\text{ and }z_{m}(k)\text{ is }M_{m}^{i_{m}} \text{, then}\\ \left[\begin{array}{c}y(k+1)\\ \Delta x(k+1)\end{array}\right]=\left[\begin{array}{cc}I&CA^{(i_{1}\dots i_{ m})}\\ 0&A^{(i_{1}\dots i_{m})}\end{array}\right]\left[\begin{array}{c}y(k)\\ \Delta x(k)\end{array}\right]+\left[\begin{array}{c}CB^{(i_{1}\dots i_{m})}\\ B^{(i_{1}\dots i_{m})}\end{array}\right]\Delta u(k)\\ y(k)=\left[\begin{array}{cc}I&0\end{array}\right]+\left[\begin{array}{c}y(k )\\ \Delta x(k)\end{array}\right]\end{split} \tag{15}\]  
where \(A^{(i_{1}\dots i_{m})}\in\Re^{n\times n}\), \(B^{(i_{1}\dots i_{m})}\in\Re^{n\times p}\) y \(C\in\Re^{n\times n}\). Moreover, its matrix form can be expressed as follows:  
\[\begin{split}\text{S}^{(i_{1}\dots i_{m})}\text{: If }z_{1}(k) \text{ is }M_{1}^{i_{1}}\text{ and }\dots\text{ and }z_{m}(k)\text{ is }M_{m}^{i_{m}} \text{, then}\\ x_{a}(k+1)=A_{a}^{(i_{1}\dots i_{m})}x_{a}(k)+B_{a}^{(i_{1}\dots i_{m})} \Delta u(k)\\ y(k)=C_{a}x_{a}(k)\end{split} \tag{16}\]  
where \(A_{a}^{(i_{1}\dots i_{m})}\in\Re^{((q+n)\times(q+n))}\), \(B_{a}\in\Re^{((q+n)\times p)}\) y \(C_{a}\in\Re^{(q\times(q+n))}\).
## 5 Simulation Experiments, Verification, and Comparative Analysis
### Comparative Analysis  
The comparative analysis between the three architectures, which is shown in Figure 1, has been presented in Tables 1 and 2. The first table shows the efficiencies of the architectures under three scenarios, which are based on a number of active converters. It can be confirmed from Table 1 that the proposed architecture always maintains high efficiency (\(\geq\)90%) as compared to the other two architectures. It is because of two reasons: (1) it activates the converters of only those modules that are under the influence of partial shading, and (2) it employs the basic buck topology for each module. Table 2 describes the comparison of three architectures in the context of their respective features and other engineering aspects. The table indicates that apart from higher efficiency, the proposed architecture operates with low control complexity and its switching devices do not suffer from high voltage stress. Clearly, the proposed architectures exhibit better engineering features as compared to other architectures. Figure 11 shows the penta-modeling graph of three architectures, which is based on five parameters. It can be seen that the horizon of the proposed architecture is quite spread as compared to other architectures.  
Figure 10: Response of the proposed architecture when the PV array is under the influence of the Figure 7c shading case, with sub-graphs: (**a**) \(P_{pv}\) curves, (**b**) \(I_{pv}\) curves, (**c**) \(V_{pv}\) curves, (**d**) \(D\) signals of buck converters, and (**e**) DC-link voltage controlled by the centralized MPPT inverter.
## 3 Materials and Methods
### Classification Algorithms
#### 3.5.2 k-Nearest-Neighbor (k-NN)  
k-NN is a non-parametric estimation method that implements refinement where the feature environment is high resolution in regions with dense training and low resolution in variance. This algorithm assigns labels to previously unsampled points, usually with lower efficiency as the data size increases [23].  
Classification with k-NN is based on the constant k defined by the user, where the new case will be assigned to the most common class among its k-nearest neighbors measured using a distance metric such as Euclidean, Manhattan, Minkowski, or Hamming.  
Most k-NN classifiers use the Euclidean metric to measure differences between examples represented as vector inputs [24]. The Euclidean distance is defined as:  
\[d\big{(}x_{i},x_{j}\big{)}=\sqrt{\sum_{r=1}^{n}W_{r}\big{(}a_{r}(x_{i})-a_{r} \big{(}x_{j}\big{)}\big{)}^{2}} \tag{34}\]  
where an example is defined as the vector \(x=(a_{1},a_{2},a_{3},\ldots a_{n})\), \(n\) is the dimensionality of the input vector (number of attributes of an example), \(a_{r}\) is the \(r\)th attribute of the example, \(W_{r}\) is the weight of the \(r\)th attribute--\(r\) is from 1 to \(n\)--and \(d\big{(}x_{i},x_{j}\big{)}\) represents the smaller of the two most similar examples [25]. The class label assigned to a test example is determined via the majority vote of its k-nearest neighbors using:  
\[y(d_{i})=\underset{k}{\arg max}\sum_{x_{i}\in kNN}y\big{(}x_{j},C_{k}\big{)} \tag{35}\]  
where \(d_{i}\) is a test example, \(x_{j}\) is one of its k-nearest neighbors in the training set, and \(y\big{(}x_{j},C_{k}\big{)}\) indicates whether \(x_{j}\) belongs to class \(C_{k}\).
## 3 Results
### Variation in Productivity  
The relationship between gross primary production (GPP) and light intensity across varying salinity levels showed distinct patterns of photosynthetic responses in the _Spirulina_ strain (Figure 2a). GPP remained almost steady at low salinity (10 ppt) when light intensity increased. On the other hand, GPP increased with increasing light intensity at higher salinity levels (17 ppt to 45 ppt).  
GLM was also used to assess the effect of salinity on the photosynthetic efficiency of the _Spirulina_ strain (\(\theta\), mg O\({}_{2}\cdot\)L\({}^{-1}\cdot\)d\({}^{-1}\) per \(\upmu\)mol photons\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\))--estimated by the slope of the linear relationship between GPP and light intensity. The analysis of deviance showed that salinity produced a statistically significant effect on photosynthetic efficiency (F(7, 16) = 3.08, \(p\) < 0.05). The model explained a large part of the deviance (\(\Delta\)Deviance = 0.0004), reducing the residual deviance by 66.67%.  
The results of [PERSON]'s HSD test for post hoc pairwise comparisons showed significant differences in photosynthetic efficiency (Figure 2b). Significantly higher mean estimates were observed at 27, 40, and 45 ppt, which were \(\sim\)0.01 mg O\({}_{2}\cdot\)L\({}^{-1}\cdot\)d\({}^{-1}\) per \(\upmu\)mol photons\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) greater than the mean estimates at 10 ppt (\(p\) < 0.05). There was no significant difference between treatments in all other pairwise comparisons, showing that photosynthetic efficiency improved most at higher salinity levels.  
\begin{table}
\begin{tabular}{c c c} \hline
**Salinity Treatments** & **Growth Rate (\(r\), d\({}^{-1}\))** & **Carrying Capacity (\(K\), a.u. 1)** \\ \hline
10 ppt & 0.44 \(\pm\) 0.05 & 0.18 \(\pm\) 0.01 \\
17 ppt & 0.19 \(\pm\) 0.05 & 0.20 \(\pm\) 0.01 \\
22 ppt & 0.31 \(\pm\) 0.08 & 0.18 \(\pm\) 0.01 \\
27 ppt & 0.39 \(\pm\) 0.03 & 0.16 \(\pm\) 0.01 \\
31 ppt & 0.33 \(\pm\) 0.07 & 0.17 \(\pm\) 0.01 \\
36 ppt & 0.35 \(\pm\) 0.08 & 0.19 \(\pm\) 0.01 \\
40 ppt & 0.17 \(\pm\) 0.02 & 0.29 \(\pm\) 0.01 \\
45 ppt & 0.16 \(\pm\) 0.01 & 0.24 \(\pm\) 0.01 \\ \hline \end{tabular} \({}^{1}\) arbitrary unit.  
\end{table}
Table 1: Average maximum specific growth rate (\(r\), d\({}^{-1}\)) and carrying capacity (\(K\), arbitrary unit) of _Spirulina_ strain across salinity treatments. All values are reported as mean \(\pm\) standard error (\(n\) = 4).
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 3 Experimentation
### Training Details  
We trained the semantic segmentation model on 2975 training data using the Adam [37] optimizer with a batch size of 4 and set the learning rate to be \(1\times 10^{-4}\). In addition, we used a polynomial decay strategy to adjust the learning rate of the model, and the formula is as follows:  
\[lr=r\cdot\left(1-\frac{t}{T}\right)^{power} \tag{8}\]  
where \(lr\) is the current learning rate, \(r\) is the initial learning rate, which is set to \(1\times 10^{-4}\), \(power\) is the attenuation coefficient, generally set to 0.9, \(t\) is the number of current iterations and \(T\) is the maximum number of iterations. Due to the limitations of the semi-global matching algorithm, the left and bottom halves of the depth images are not applicable, so these pixels needed to be cropped, and bilinear interpolation up-sampling was used to adjust the image to the original resolution. To accelerate the training, the input images were down-sampled to 768 \(\times\) 768 to remove the blank boundaries or invalid regions caused by data augmentation. Our semantic segmentation model was trained on one NVIDIA Titan Xp GPU for approximately 200 epochs.  
We show the detailed visual results of the Cityscapes datasets in Figure 7. We found that our method was more accurate in the segmentation of large target objects such as cars, trees, and buildings, and worked well in the segmentation of smaller object categories such as billboards and poles.
Sensor-System-Based Network with Low-Power Communication Using Multi-Hop Routing Protocol Integrated with a Data Transmission Model  
[PERSON]  
1 Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Electronics and Communication Engineering, Vardhaman College of Engineering, Hyderabad 50125, India; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Computer Science and Engineering, Narasaraapeta Engineering College, Narasaraapeta 522601, India; [PERSON]S]  
[PERSON]  
4 School of Computing, SASTRA Deemed University, Thanjavur 613401, India; [EMAIL_ADDRESS]  
[PERSON]  
5 Department of Civil Engineering, Faculty of Engineering, Arak University, A  
various applications. Depending on the application, the WSN might have hundreds to thousands of nodes, all of which may be connected in a network. Each node comprises a radio transceiver with an internal or external antenna, microprocessor, electronic circuit for sensor interface, and power source, such as batteries [3]. The nodes utilized in a WSN are typically small and powered by batteries or solar panels [4]. There are network lifetime extensions and energy-efficient data transmission. In most applications, batteries are powered by batteries, which utilize little energy due to their limited energy supply [5].  
The aims of this work are described as follows:  
1. Design a sensor system and network based on low power communication with data transmission.
2. Integrate multi-hop communication with swarm intelligence to achieve low-power communication.
3. Cluster the nodes and calculate optimal cluster heads with data transmission.
4. Apply the adaptive clustering-based locative data transmission algorithm for data transmission, where the exact locations of the sink node and base station are known.  
The rest of this research is organized as follows. Section 2 discusses existing WSN techniques based on a sensor system with a network model. Section 3 explains the proposed system method, including the energy model, clustering and cluster head selection evaluation, data transmission model, and low power transmission model. Section 4 presents the experimental results based on parametric data transmission analysis and low power consumption evaluation. Finally, Section 5 concludes the research and describes the direction of future work.
# _Sci_
## 2 Materials and Methods
### History of Laser Employment as Link Source
#### 2.1.3 Satellite Laser Range Finding  
This section describes satellite range finding in accordance with [8; 20; 33; 54; 55; 56]. European Space Agency's Copernicus Program includes the Earth observation satellite constellation called the Sentinel-3, comprised of two spacecraft: Sentinel-3A and Sentinel-3B with two more (subsequently named) yet to be delivered [57]. Instruments dedicated to the precise measurement of sea and land surface temperatures, ocean and land surface color, and surface topography are onboard Sentinel-3A/B. Laser retroreflectors for the satellite laser ranging instrument SLR and the Doppler Orthography and Radio positioning Integrated by Satellite (DORIS) antenna aid global positioning system navigation. [PERSON] [9] investigated using these instruments for global geodesy (including measurement of motion of the geo-center) in addition to realization of the terrestrial reference frame. Earth rotation parameters were also investigated using 2016-2018 data. SLR site range bias estimates preceded analyzed solutions using disparate constraining tests of the network and a different number of orbital arcs.  
_\"The repeatability of SLR station coordinates based solely on SLR observations to S3A/B is at the level of 8-16 mm by means of interquartile ranges even without network constraining in 7-day solutions. The combined S3A/B and LAGEOS solutions show a consistency of estimated station coordinates better than 13 mm, geocenter coordinates  
Figure 2: NASA’s First Space Laser Communication System Demonstration [43].  
with a RMS of 6 mm, pole coordinates with a RMS of 0.19 mas and Length-of-day with a RMS of 0.07 ms/day when referred to the IERS-14-C04 series.\"_[9]  
To aid fiducial reference systems, the rotation of the earth, and basic physics, such as modeling the gravity field, planning for geodetic satellites with retro-reflectors and high mass-to-area ratios was underway by the early 1970s.  
_\"Early geodetic-tic satellites were Starlette, launched in 1975 by Cen-tre National d'Etudes Spatiales (CNES), and LAGEOSin 1976 by the National Aeronautics and Space Administration (NASA). Recent geodetic satellites include LARES, launched in 2012, and LARES-2 under development, both by the Italian space agency (ASI). Today a complex of these 'geodetic satellites' from low to high altitude Earth orbits supports many space geodesy requirements. This manuscript will discuss the evolution of the geodetic satellites from the early days, through current programs and out to future needs as we approach our goal for millimeter accuracy.\"_[9]
# Deep Learning-Based Postural Asymmetry Detection Through Pressure [PERSON]
## 4 Results  
### Patient Classification  
The first use of deep learning algorithms has been to classify patients: do they have postural asymmetries or do they not have postural asymmetries? It may seem that all postural asymmetries are visually evident; however, this is not the case. The idea then was to determine if there were key features that an algorithm could identify, such as increased movement during data collection or non-uniform pressure distribution on the blanket.  
To do this, a CNN was trained to identify whether a patient has asymmetries or not. The workflow is as follows: a video of the patient is collected using a pressure platform. From each data collection session, a video with pressure images is generated, with a duration of approximately 30 s. Since the data collection platform records data at a rate of 5-6 frames per second (FPS), approximately 150 images are obtained for each user. These images correspond to the size of the platform's sensor array (in our case, 48 \(\times\) 22), which are single-channel greyscale images with values ranging from 0 (no pressure) to 1500 (maximum pressure), which corresponds to the value collected for each sensor.  
To feed the convolutional neural network, a set of 100 consecutive images extracted from the video is used (20 s). Each of these images has dimensions of 48 \(\times\) 22 pixels, corresponding to the sensor array of the pressure platform. Therefore, the input to the neural network has dimensions of 48 \(\times\) 22 \(\times\) 100, where the first two values represent the spatial dimensions of each image, and the third one corresponds to the temporal sequence of 100 images. The idea is shown in Figure 6.  
The generated dataset has 4580 images, which are distributed for training (80%) and validation (20%) as shown in Figure 7.  
Figure 6: Convolutional neural network input array representation.  
The trained model is a simple convolutional neural network with 73,089 parameters. However, the results show that patient classification has 100% accuracy (see Figure 8).  
The fact that a CNN can detect asymmetry when it is not visually obvious is an interesting aspect. It suggests that DL could offer a new diagnostic and preventive tool that provides objective, data-driven insights into postural asymmetry. This could assist in early detection and intervention if data of the same patient over time is analyzed, especially in cases where asymmetry may not yet be obvious but may become worse (or generate other deformities) in the future. Moreover, early detection may allow interventions (e.g., physical therapy, orthopedic treatment, and other corrective measures) that could prevent the asymmetry from progressing, generating deformities and even pain or other functional consequences.
## 3 Discussion
### Terpenoid Synthesis and Transport in A. Argyi Leaves  
Prior studies have primarily employed genetic or biochemical approaches to explore the mechanisms governing terpenoid biosynthesis in _A. argyi_. Generally, terpenoid biosynthesis relies on DMAPP and IPP as precursors that are generated by the MVA and methylerythnitol phosphate (MEP) pathways [7; 32], with the MEP pathway being primarily involved in generating isoprenoids for the synthesis of monoterenes [33],iterpenes [34], and carotenoids [35]. This suggests that the MEP pathway may enhance the accumulation of volatile oils by increasing the supply of precursors compounds in _A. argyi_.  
HMGR is the first enzyme in the MAV pathway that plays an essential regulatory role [36; 37]. In _Ginkso biloba_, for example, the expression of this gene is correlated with terpene accumulation [13], and similar results have been observed in _Solanum tuberosum_ and _Glycyrrhiza uralensis_[38; 39]. The HDS and HDR genes function in the final two steps of the MEP pathway; HDS has previously been studied in _Solanum lycopeneum_, _Heeva brasiliensis_, _Arabidopsis thaliana_, and _G. biloba_[40; 41; 42; 43], functioning as a regulator of the carbon flux of the MEP pathway and plant defense mechanisms [37], with HDR being of particular importance [44]. HDR regulates IPP and DMAPP biosynthesis in the context of andrographide production. Herein, we conducted SMRT sequencing of _A. argyi_ samples at different developmental stages and found that HDS7, HDR1, and HDR3 expression levels were positively correlated with the total volatile oil levels in analyzed leaves. As such, these genes may play an essential role in regulating the MEP pathway flux in _A. argyi_. The unigene expression levels of the MEP-pathway-related genes were higher than those for the MVA-pathway-related genes, which is consistent with the high levels of monoterenes. Crosstalk between these two different terpene skeleton pathways has been documented, and the relative contributions of each pathway to the process of terpene biosynthesis remains uncertain [45].  
The expression patterns of the TPS2 and TPS5 genes can regulate the distribution of monoterpene- and sesquiterpene-type terpenoids in the _A. argyi_ samples collected during different periods. Many TPS are prolific enzymes that can produce mixtures of different proportions of the same compounds [46]. Sequence similarity alone cannot predict the specific biochemical function of a single TPS family member, because only a few amino acids can cause dramatic changes in the terpenoid structures produced by a given TPS enzyme [47; 48]. Therefore, it is difficult to judge the specific functions of genes based on sequence similarity alone. The specific functions of these two genes, thus, require further verification. The future functional validation and utilization of these monoterenoids should focus on these genes.