esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Problem Statement  
This study aims to (i) increase and guarantee the sustainability of the R-WSN to create a high-efficiency network; (ii) rapidly detect the failed nodes in the sensing field by utilizing \(BS\) capabilities for collecting and analyzing data to achieve the highest performance in replacing the failure nodes using Air-R; and (iii) minimize the Air-R effort and movement by applying the new field-dividing mechanism that leads to fast replacement. We propose the sector-based replacement (SBR) by utilizing \(BS\) features to divide the monitoring field into sectors, and analyze the incoming data from the nodes to detect the failed nodes. We then send the Air-R to perform a replacement task. To the best of our knowledge, this is the first paper to discuss the problem when a failed or damaged node is found in R-WSN and the replacing technique in R-WSN. We make the following assumptions in this study:  
* Sensors are randomly and uniformly distributed in the field. We assume that all nodes can detect their own location in the initial deployment process. The \(BS\) then starts to divide the field into sectors, and \(CH\)s are selected for each sector (the same as in [24]).
* The Air-R travels at a constant speed, and it is attached with a GPS to locate and replace the failure nodes.
* Through continuous synchronized information exchange between \(BS\) and Air-R, the Air-R can determine whether it can serve additional sectors (replacing the failed node in the sectors) after completing the first mission (replacing sector nodes that are already planned before the Air-R departure).
* The \(BS\) manages the coverage inside the sectors before the node replacement process to keep the coverage area and data transfer in the sectors [14].  
The rest of this paper is organized as follows: Section 2 introduces the problem formulation; Section 3 presents the experimental results; and finally, Section 4 concludes the paper and future work.
## 2 Conventional Channel Estimation Methods
### Blind Channel Estimation Method  
Unlike the pilot-based channel estimation, the blind channel estimation does not require any pilots. The core concept of this method is to interpret the CSI. This is achieved by leveraging the inherent statistical characteristics of both received and transmitted signalswhile adaptively tracking the dynamic changes of channels [32]. In general, blind channel estimation may be performed using the subspace algorithm, the higher-order statistics (HOS) algorithm, and the maximum likelihood (ML) algorithm.
## 6 Reviewed Papers on Machine-Learning-Based Path Loss Models
### Evaluation of Indoor Machine Learning Path Loss Models  
Several researchers focused on investigating the performance of machine learning models in an industrial environment for both mid-band and high-band frequencies. Two different models have been tested for mid-band frequencies, as in the case of ANN and LR in [91], where ANN outperformed with the minimum MAE value of 5.37 dB at 2.5 GHz, and KNN and GPR in [92], where the proposed GPR outperformed the KNN with the minimum MSE value of 4.25 dB at 2.3 GHz, as shown in Figure 8.  
Figure 8: Comparative analysis of models in the mid-band and high-band frequency spectrums for indoor environments.  
Figure 7: Comparative analysis of models in the high-band frequency spectrum for outdoor urban environments.  
Similarly, for the high-band scenario, the CNN model performed excellently well at 28 GHz with an RMSE value of 1.65 dB, while in the context of RF and DL models at 28 GHz, the RF model outperformed the DL model, having the lowest RMSE value of 1.15 dB.
## 2 Materials and Methods
### Spectral Measurements  
The measurements were performed during a field campaign (27 April 2022 to 10 May 2022). The reflectance was obtained between 350 and 2500 nm using a portable spectroradiometer (FieldSpec(r) 4 ASD, USA) with a field of view of 25\({}^{\circ}\). First, a reflectance calibration was performed using a Spectralon(r) disk before plant measurement. For the measurements, the optical fiber was positioned at nadir, 10 cm above the canopy of target plant. On each of the 3 individuals per plot, 3 reflectance measurements were performed. To check for sensor stability, each measurement was repeated 3 times. The data acquisition was performed between 11:00 and 15:00 in similar synoptic conditions. A total of 1215 data were recorded: monospecific (189); bispecific (324); trispecific (486); and tetraspecific (216).
## 7 Simulation Results
### Performance Evaluation in a MANET Scenario
#### 7.2.1 Gain for I and P Frames  
To better see separately the gain obtained for I and P frames, we define the following parameters:  
\[\%GainI_{i}=\left(\frac{ILNG_{i}-ILG_{i}}{ILNG_{i}}\right)\cdot 100 \tag{51}\]  
\[\%GainP_{i}=\left(\frac{PLNG_{i}-PLG_{i}}{PLNG_{i}}\right)\cdot 100 \tag{52}\]  
Figure 12: Delay jitter through the medium-quality path.  
Figure 13: Peak Signal to Noise Ratio (PSNR).  
where  
* \(i\): 2, 3, 4 and 5 sources (players).
* \(ILNG_{i}\): percentage of packet losses for I frames when the game-theoretical scheme is not used.
* \(ILG_{i}\): percentage of packet losses for I frames when the game-theoretical scheme is used.
* \(PLNG_{i}\): percentage of packet losses for P frames when the game-theoretical scheme is not used.
* \(PLG_{i}\): percentage of packet losses for P frames when the game-theoretical scheme is used.
* \(GainI_{i}\): Gain obtained for I frames using the game-theoretical scheme with respect to not using it.
* \(GainP_{i}\): Gain obtained for P frames using the game-theoretical scheme with respect to not using it.  
Figure 14 shows the average of all the simulation results for \(\%GainP_{i}\) and \(\%GainI_{i}\). We can see that when the game-theoretical scheme is applied, the gain is 11%, 18%, 25%, 32% for I packets (dark bars) and 95%, 83%, 75%, 69% for P packets (light bars), for 2, 3, 4 and 5 players, respectively. As we can observe, \(\%GainP_{i}>\%GainI_{i}\) in all the cases. This is because there are much more P frames than I frames (see Figure 1) per player so the benefits are more noticeable for P frames. When \(N\) increases, the difference is lower, since the number of I frames increases.  
In addition, we observe a slightly increment for \(\%GainI_{i}\) (dark bars) as \(N\) increases. This is because as \(N\) increases, the number of I frames increases and this makes the game-theoretical scheme benefits more noticeable. On the other hand, \(\%GainP_{i}\) decreases as \(N\) increases. The reason is that as \(N\) increases, there are too many P frames (more collisions), higher loads to be balanced and as a result we obtain a lower benefit. Nonetheless, \(\%GainP_{i}\) is still high (around 70% for 5 sources).
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 5 Analysis of Image Encryption and Decryption
### Statistical Analysis
#### 5.1.3 Correlation Coefficients  
Correlation in general is used to study the strength of relationships between any two variables. The higher the correlation, the greater their change in the same direction. The correlation lies between [\(-\)1,1] with negative values representing changes in the variables occurring in opposite directions. In the case of image encryption, if the correlation between any two pixels is high, it is simple to track them down with simple probability. Therefore, for safe and secure information transmission it is necessary to have as little correlation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & **Watermarking Level** & \(\chi^{2}\) **Test** \\ \hline Peppers & Level 1—Lena & 269.1328 \\
256 \(\times\) 256 & Level 2—Fingerprint & 269.1328 \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(\chi^{2}\) test of encrypted peppers image with level 2 decomposition.  
Figure 11: Histogram analysis of the peppers image with watermarking in the level 1 and 2 sub-bands. (**a**) Input image, (**b**) encrypted image, and (**c**) decrypted image.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 3 Optimization-Based Point Cloud Upsampling
For each point set, to promote piece-wise smoothness in reconstructed 2D surfaces while preserving the original point coordinates, the GTV target adjacent to the surface normal was designed and the point cloud upsampling problem was defined as the minimum GTV problem. The authors used part of the Stanford 3D scanning repository data to verify the algorithm, and selected two evaluation criteria, point-to-point and point-to-plane, to quantitatively evaluate the algorithm model. In general, although optimization-based methods can achieve the purpose of upsampling point clouds to a certain extent, they are not data driven and have significant limitations. They rely on priors, such as normal estimation and the hypothesis of smoothness surfaces with fewer features. These methods also struggle with the preservation of multiscale structures.
## 2 Materials and Methods  
### Experimental Data  
Experimental data from this study were collected from a previous study determining the quantitative levels of _Listeria monocytogenes_ and _Salmonella enterica_ at four sites representing two types of irrigation water sources (streams and ponds) [28]. More details on the sites can be found in previous work in which water from those sites was analyzed for Shiga-toxicie _E. coli_[30] and _E. coli_, _Enterococcus_ spp., and _Aeronomas_ spp. [31]. Detailed descriptions of the quantitative recovery and isolation of _Salmonella_ spp. and _Listeria monocytogenes_ can be found in [28]. The frequency of the collection and the total number of water samples collected, along with more specific information about their location, can be found in previously published work [28; 30; 31]. Briefly, 0.1 L, 1 L, and 10 L of water (in triplicate) were filtered through modified Moore swabs (MMS) at each sampling event. MMS of each triplicate volume were analyzed for the presence of _S. enterica_ or _L. monocytogenes_. Levels of _S. enterica_ and _L. monocytogenes_ at each site on each date were quantified using a Most Probable Number assay (MPN/L), leading to a total of 9 samples for each MPN/L determination. The description of the culture methods used to recover presumptive _L. monocytogenes_ and S. enterica_, along with the polymerase chain reaction (PCR) methods used for confirmation of these pathogens, were published previously in detail [28]. The dataset collected was also published as supplementary material in the previous publication [28].  
More details on land use and cover were obtained from the USDA-NASS database [32]. The Cropland Data Layer (CDL) is a crop-specific land cover data layer that is created every year for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth. LU/LC (land use/land cover) data were collected for each sampling site as both percentages and area (in square kilometers). For stream samples, the LU/LC data were collected for the watershed; for ponds, the LU/LC data were collected for areas within 1 km surrounding the pond. The land use dataset is characterized in the Supplementary Table S1.  
Cumulative precipitation amounts for one-day (Rain1) and seven-day (Rain7) periods before sampling were retrieved from weather underground (www.wunderground.com, accessed on 29 May 2022) for weather stations within 5 km of the sampling sites. Supplementary Table S2 shows the summary of rainfall from the four sampling sites during the two-year period of water collection and analysis.
## 3 Results and Discussion
### Volatile Aroma Compounds
#### 3.2.7. Esters  
Volatile esters, which are well-known contributors to the formation of the aroma and flavor character of wine, are mostly formed during fermentation and storage [44]. The results for esters identified in this study are presented in Table 8.  
Ethyl esters are formed through several biosynthetic pathways, and it is considered that their concentrations in wine depend more on the precursor availability than on the activity of genes encoding the corresponding enzymes [46]. \(LEV\) wine had higher concentration of particular ethyl esters, including the ester of pyruvate, an important product of glycolysis and intermediate/precursor for the synthesis of volatile compounds [47], which could point to particular differences between \(L\). _therntolerans_ and \(S\). _cerevisiae_ in the expression of genes that participate in the initial steps of yeast metabolism. \(LEV\) wine also contained increased amounts of certain esters with unknown sensory relevance, such as ethyl 3-hydroxyhexanoate, ethyl 9-decenoate isomers I and II, ethyl 3-hydroxybutyrate, ethyl 3-acetoxyoctanoate, ethyl hexanoate I and II, ethyl 2-butenoate, and ethyl 2-hexenoate II, as well as ethyl isobutyrate, an important contributor to wine aroma with its fruity odor. The increase in ethyl isobutyrate corresponded to several previous studies on \(L\). _therntolerans_[8; 10; 48] and was in line with the higher concentrations of its precursor formed in the Ehrlich pathway, isobutyric acid, in \(LEV\) wine (Table 7). The concentration of ethyl lactate, which is formed via the esterification of ethanol and lactic acid, was almost four times
## 2 Indoor Positioning System in 433 MHz ISM Band
### Algorithm for Indoor Position Estimation
For each repeater, specific satellites are chosen based on their CNR levels and locations. Each satellite's position, clock bias, and distance from the corresponding repeater are calculated using the satellite's ephemeris data. In the third step of the algorithm, the pseudo-range (PR) from each repeater to the selected satellite for the corresponding repeater is cleaned by subtraction operation on the right-hand side of Equation (1). Thus, the indoor distance from the receiver to each repeater is calculated. The calculated indoor distances also include the receiver clock bias. The indoor distances between each repeater and the indoor receiver is calculated using Equation (1). \[d_{i}+t_{bias}^{receiver}=PR_{Ri}^{Sj}-\left(D_{Ri}^{Sj}+\tau_{i}\times c+t_{ bias}^{Sj}\times c\right) \tag{1}\]  
In Equation (1), \(i\) is the index of the repeaters (\(i=1,2,3\)), \(j\) is the index of the satellites (\(j=1,2,\ldots,n\)), \(Ri\) represents the \(i^{\text{th}}\) repeater, \(Sj\) represents the \(j^{\text{th}}\) satellite, \(d_{i}\) represents the indoor distance from repeater \(Ri\) to the receiver (with a clock bias of \(t_{bias}^{receiver}\)), \(D_{Ri}^{Sj}\) stands for the distance from ith repeater (\(Ri\)) to the \(j^{\text{th}}\) satellite (\(Sj\)), \(\tau_{i}\) is the propagation delay of the \(i\)th repeater, \(t_{bias}^{Sj}\) is the satellite clock bias of the \(j\)th satellite, and \(c\) represents the speed of the light. When more than one satellite is chosen for a repeater, the \(d_{i}+t_{bias}^{receiver}\) value is calculated by averaging the calculations from each satellite selected for that repeater. Thus, all the distances in Figure 2 can be solved with the proposed system. The final step of the proposed algorithm is using the LSAAV algorithm to calculate the 2D indoor position for each sample collected. The indoor distances between the receiver and each repeater with receiver clock bias are calculated using Equation (1). Then, these calculated values and the repeater positions are given as the inputs to the LSAAV algorithm. It is important to note that the calculated indoor distances still have a receiver clock bias. Since the receiver clock bias (\(t_{bias}^{receiver}\)) is the same in all indoor distances, it is removed by the LSNAV algorithm by subtracting them from each other, and the indoor position is calculated. This algorithm is run for each sample. The obtained results are filtered with a moving average filter, and the indoor position is estimated as an average of these filtered points. More details regarding the estimation are provided under Section 4.
# AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals
###### Abstract  
This study explores the use of accelerometer signals as the predictors of Rate of Torque Development (RTD) using an artificial neural network (ANN) prediction model. Sixteen physically active men participated (\(29\pm 5\) years), performing explosive isometric contractions while acceleration (ACC) signals were measured. The dataset, comprising ACC signals and corresponding RTD values, was split into training and testing (70-30%) sets for ANN training. The trained model predicted the peak RTD values from the ACC signal inputs. The measured and predicted peak RTD values were compared, with no significant differences observed (\(p=0.852\)). A strong linear fit (R\({}^{2}=0.81\)), ICC = 0.94 (\(p<0.001\)), and a mean bias of 30.8 Nm/s demonstrated almost perfect agreement between measures. The study demonstrates the feasibility of using accelerometer data to predict peak RTD, offering a portable and cost-effective method compared to traditional equipment. The ANN prediction model provides a reliable means of estimating RTD from ACC signals, potentially enhancing accessibility to RTD assessment in sports and rehabilitation settings. The findings support the use of ANN models for predicting RTD, highlighting the potential of AI in developing performance analysis tools.  
rate of force development; artificial neural network; accelerometer; sports performance 2024 (1), 5137. [[https://doi.org/10.3390/app14125137](https://doi.org/10.3390/app14125137)]([https://doi.org/10.3390/app14125137](https://doi.org/10.3390/app14125137))
## 2 Materials and Methods
### Liver Transient Elastography (TE)  
TE offers a non-invasive means of quantifying hepatic steatosis through the assessment of the Controlled Attenuation Parameter (CAP) and measuring liver stiffness using Fibroscan(r) 502 by Echosense SASU, Paris, France [44; 45]. The CAP and stiffness scores were obtained from the same volume of liver parenchyma simultaneously. All individuals underwent examination with the standard 3.5 MHz M probe applied to the right lobe of the liver, accessing intercostal spaces while the subjects were in a supine position. The right arm was placed behind the head to facilitate access to the upper right quadrant of the abdomen. The probe transducer's tip was positioned on the skin between the ribs, at the level of the liver's right lobe. The same investigator conducted all of the scans. The hepatic stiffness was determined by using the median value (in kPa) derived from 10 measurements taken within a depth range of 25 to 65 mm. Only data with ten valid images and a liver stiffness ratio represented by a 30% interquartile range (IQR) over the median were considered for inclusion. Given the device's unique CAP algorithm, we exclusively employed the M probe to calculate the CAP score. Each patient underwent 10 successful measurements, and only individuals with all 10 measurements deemed successful were included in this study. The success rate was determined by dividing the total count of successful measurements by the overall number of measurements. We calculated the IQR-to-median ratio of liver stiffness (IQR/MLSM) as an indicator of variability. The ultimate CAP score, within a range of 100 to 400 decibels per meter (dBm-), was computed as the median of individual measurements. A CAP score equal to or greater than 248 dB/m was utilized to diagnose NAFLDD. Distinct cutoff values were applied to determine the grade of steatosis: a CAP score of 248 dB/m or higher for the diagnosis of S1 grade, a CAP score of 269 dB/m or higher for the diagnosis of S2 grade, and a CAP score of 281 dB/m or higher for the diagnosis of S3 grade, indicating severe steatosis [50].
## 1 Introduction
### Background and Motivation
Recently, the idea of sidelobes self-refining in the uniform antenna arrays by beamspace processing has achieved deep SLL for linear one-dimensional arrays as in [32; 33; 34] where single or multiple secondary mainlobes are formed and their patterns are weighted and subtracted from the original pattern to suppress the intended sidelobes. The sequential sidelobe damping (SSD) algorithm in [32] has optimized linear arrays using single beam subtraction which is sequentially repeated to damp or suppress the remaining sidelobes in the radiation pattern, so after several iteration cycles, the SLL could be effectively reduced. The SSD algorithm, although have stable convergence profiles and faster convergence time compared with evolutionary optimization techniques, suffers from slower convergence where more than 1000 iteration cycles are required to achieve deep SLL of \(-50\) dB or less for planar arrays. The SSD algorithm has been improved by simultaneous sidelobe reduction (SSR) algorithm in [33] and adaptive SSR in [34] for massive linear arrays. The convergence speed in these two algorithms has been improved and could find the optimum weights five times faster than the SSD algorithm where the optimization of massive linear arrays formed by several hundreds of antenna elements could be achieved in few iterations as in the adaptive SSR algorithm. However, these three algorithms were proposed to mainly optimize linear arrays which can be extended or modified to work with planar arrays efficiently after investigation and controlling its performance. Therefore, in this paper, an optimization technique for planar arrays beamforming and SLL reduction is proposed and analyzed using multibeam pattern subtraction (MPS). The proposed beamforming technique is extensively investigated to achieve complete algorithm convergence over wide range of planar array sizes and angular scanning range.
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 4 Retinal Artery Occlusion
### Current Imaging Techniques for Central Retinal Artery Occlusion  
Multimodal imaging can be used to describe characteristics of acute retinal ischemia. Acute retina ischemia on SD-OCT appears as thickening of the inner retina layers and loss of structure down to the outer plexiform layer. On fundus autofluorescence photos (FAF), darker areas correspond to thickened ischemic tissue with speckled regions occurring at the posterior pole in general hypoperfusion. Peri-arterial regions show normal FAF signal while peri-venous regions show decreased FAF signal intensity, which can create a 'frosted branch' appearance [67].  
FA reveals important information regarding large vessel flow and is useful in demonstrating the delay in arterial filling and decreased capillary density corresponding to areas of occluded artery. Acutely, there are areas of hypofluorescence of the involved segment due to the blockage of background fluorescence by inner retinal swelling [63]. However, studies have shown that FA provides incomplete assessment of the SCP and even less information on the DCP. Therefore, FA is limited in studying microvascular histopathological processes [68].