esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of the study was to assess the effects of 8 weeks of resistance training using a flywheel device applied to upper limbs, compared to traditional isotonic training, on strength and shot precision in tennis. Twenty-seven elite senior tennis players (age: 55.78 \(\pm\) 2.69) were randomly divided into an experimental group (EG) using flywheel devices (\(n=13\)) and a control group (CG) performing isotonic training (\(n=14\)). The EG program included forehand, backhand, and one-handed shoulder press movements, while the CG performed seven resistance exercises on isotonic machines. A similar workout intensity was ensured using the [PERSON]'s CR-10 scale. The assessment included a 30s arm curl test, a medicine ball throw test, and forehand/backhand/overhead shot precision tests. A significant time effect was found in the 30s arm curl test for the EG (F\({}_{(1,25)}=13.09;p=0.001\)), along with a time * group interaction (F\({}_{(1,25)}=5.21;p=0.031\)). A significant group difference was observed in the forehand shot precision test, where the EG achieved better scores than the CG and significant interaction time * group (F\({}_{(1,25)}=8.35;p=0.008\)). In the shot backhand precision test, a significant effect of time (F\({}_{(1,25)}=5.01;p=0.034\)) and significant time * group interaction were found (F\({}_{(1,25)}=4.50;p=0.044\)), but there was no significant difference between groups. Resistance training with flywheel devices has shown potential in improving tennis performance. Applying overload to specific athletic movements during both concentric and eccentric phases in the EG has shown enhanced strength and neuromuscular coordination in relation to shot precision, thereby enabling simultaneous improvements in both conditioning and the technical aspects of fundamental tennis shots.  
strength training; eccentric overload; tennis skill; tennis stroke; athletic performance Article
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 5 Publicly Available Datasets for Medical Video Analysis
### CVC-ClinicVideoDB  
This publicly available dataset contains 18 colonoscopy videos with 768 \(\times\) 576 resolution [34]. All videos are of different duration and contain polyp and nonpolyp frames.
# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 3 Geocoding Algorithm Using Machine Learning Techniques
### Address Parsing  
Parsing is the analysis of a sequence of characters and, in particular, breaks given texts into meaningful pieces. In this paper, we used Korean addresses, based on the road name address system implemented in 2014. This system follows a hierarchy of administrative units, optionally beginning with a province name (suffixed by \"do\"), city (\"si\"), district (\"gu\"), that precedes the road name (ending with \"daero\", \"ro\", or \"gil\", depending on the number of lanes) and building number [30]. In the developed algorithm, the parsing (left side of Figure 2 and Algorithm 1) divides the input address into the city, district, road, and building number so that each piece can work adequately in the matching or locating process. Then, the building number goes through the query building number to decide the road segment used to compute the geocoded location. The rest of parsed city, ward, and road goes to the matching component and we match them with the records in the Alias DB.  
Regular expressions, or simply Regex, is a programming tool used in many languages such as Python, Java, Perl, and PHP [31]. Regexes allow expression of patterns and repetitions in texts [32], so they perform well in detecting parts of an address input. In the case of Korean addresses, they may be used to identify address elements more flexibly, compared to purely whitespaces as delimiters (since each element may contain a whitespace in between). Based on the character suffixes used in Korean Addresses, we constructed a regex to detect characters or numerals for each element from the raw input of the user.  
```
1:Input:AddressText
2:Output:City, District, Road, Building Number
3:Algorithm
4:Initialize the parameters of the City, District, Road, Building Number, keys, and match
5:defineregex_dictionary keys:
6:city = [province suffix][space] <alphanum.city>[city suffix]
7:district = [city suffix][space] <alphanum.district>[district suffix]
8:road = [district suffix][space] <alphanum.road>[maj. rd. suffix][num][min. rd. suffix]
9:blgno = [any alphanum][space] <num.blgno>[whitespace]
10:for every key in regex_dictionary:
11:match = Search (AddressText)
12:for every match:
13:ifkey == 'city':
14:City = match
15:elseifkey == 'district':
16:District = match
17:elseifkey == 'road':
18:Road = match
19:elseifkey == 'blgno':
20:BlgNo = match
```  
**Algorithm 1** Parsing process
## 2 Modeling of AUV
### Coordinate System Establishment  
To accurately depict the spatial position and attitude of the AUV, it is imperative to establish appropriate coordinate systems. In this regard, this paper presents the establishment of two coordinate systems: the ground coordinate system, referred to as {G}, and the body coordinate system, denoted as {B}. In order to effectively determine the angular relationship between the {G} and {B} systems, the auxiliary north-east-down coordinate system ({NED}) is selected to represent the angular correlation between the {G} and {B} systems. The Euler angles, representing the angle between the corresponding axes of the {NED} system and the {B} system, are employed to describe the AUV's attitude. It is important to note that the choice of rotation order for the Euler angles can yield varying coordinate system conversion relations.  
The relationship among the three above-mentioned coordinate systems is depicted in Figure 1. The ground coordinate system establishes a reference point, denoted as \(O\) on the Earth's surface, serving as the coordinate origin, with the coordinate axes being stationary relative to the ground. Specifically, the \(x\)-axis aligns with the north pole, the \(y\)-axis aligns with the east direction, and the \(z\)-axis points downwards, normal to the Earth's surface. The body coordinate system is typically established with the center of buoyancy of the AUV as the origin. The \(x_{b}\)-axis points forward, along the AUV's longitudinal axis, the \(y_{b}\)-axis points towards the starboard of the AUV, and the \(z_{b}\)-axis is perpendicular to the \(O_{b}-x_{b}y_{b}\) plane and points downward. The north-east-down coordinate system can be visualized as a ground coordinate system that moves in tandem with the AUV. Its origin remains fixed on the AUV and generally coincides with the origin of the body coordinate system. The orientation of the coordinate axes aligns with that of the ground coordinate system.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 2 Problem Formulation
### Notation and Conventions  
Throughout this paper, the time convention \(\mathrm{e}^{\mathrm{j}2\pi ft}\) is used for raw data, where \(f\) denotes the frequency and \(t\) the time. Let \(\mu_{0}\), \(\epsilon_{0}\), and \(\mathrm{c}_{0}\) denote the permeability, the permittivity, and the speed of light in vacuum, respectively, where \(\mathrm{c}_{0}=1/\sqrt{\mu_{0}\epsilon_{0}}\). The real, the imaginary parts, and the complex conjugate of a complex number \(\zeta\), \(\zeta\in\mathbb{C}\), are denoted by \(\mathrm{Re}\{\zeta\}\), \(\mathrm{Im}\{\zeta\}\), and \(\zeta^{*}\), respectively. Finally, the right and the left complex half-planes are indicated by \(\mathbb{C}_{+}\) and \(\mathbb{C}_{-}\), respectively, where \(\mathbb{C}_{+}=\{\zeta\in\mathbb{C}|\,\mathrm{Re}\{\zeta\}>0\}\) and \(\mathbb{C}_{-}=\{\zeta\in\mathbb{C}|\,\mathrm{Re}\{\zeta\}<0\}\), respectively.
## 3 Results
### P and S Wave Velocity Anomalies  
The main tomography results are the 3D models of P- and S-wave velocity models from a five-iteration inversion used to generate tomographic images, a compromise between computation time and the quality of the solution (nonlinear effect reduction). Table 3 shows the RMS residuals reduction after iterations for the reference 1D model. Figure 12 presents the results for P and S anomalies in horizontal slices at depths of 5, 10, 15 and 20 km and Figure 13 shows the anomalies for P- and S-wave and Vp/Vs ratio in three vertical sections passing through the study area. The P- and S-wave velocity anomalies are expressed as percentage deviations from the optimized 1D reference velocity model. The Vp/Vs ratio was computed by dividing the derived absolute velocities of Vp by Vs.  
Figure 12: P- and S-wave velocity anomalies in horizontal slices, in percent with respect to the optimized 1D velocity model. Black dots depict the relocated sources around the corresponding depth levels, and the main faults are presented with red lines.  
The P- and S-wave velocity structure shows a strong correlation in both horizontal slices and vertical cross-sections. The \(\mathrm{Vp/Vs}\) ratio cross-sections (Figure 13) reveal structural patterns consistent with those observed in the primary P-wave models. The independently derived P- and S-wave models exhibit similar features, supporting the reliability of the inversion results and indicating that the available dataset is adequate for confidently resolving the identified structure.
## 4 Experimental Results
### Performance Evaluation and Discussion
#### 4.3.1 Performance Evaluation on SCUT-FBP
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**AlexNet**} & \multicolumn{3}{c}{**VGG16-Net**} & \multicolumn{3}{c}{**FIAC-NET**} \\ \hline
**Loss** & **Fold \#** & **PC \(\uparrow\)** & **MAE \(\downarrow\)** & **RMSE \(\downarrow\)** & **PC \(\uparrow\)** & **MAE \(\downarrow\)** & **RMSE \(\downarrow\)** & **PC \(\uparrow\)** & **MAE \(\downarrow\)** & **RMSE \(\downarrow\)** \\ \hline \multirow{10}{*}{L2} & Fold 1 & 0.886792 & 0.309923 & 0.387405 & 0.885873 & 0.233409 & 0.288079 & 0.902156 & 0.159987 & 0.229707 \\  & Fold 2 & 0.890809 & 0.387383 & 0.461403 & 0.900324 & 0.272331 & 0.364368 & 0.894374 & 0.30578 & 0.462875 \\  & Fold 3 & 0.894072 & 0.327179 & 0.398048 & 0.896106 & 0.27479 & 0.33756 & 0.900172 & 0.158858 & 0.224796 \\  & Fold 4 & 0.88454 & 0.277123 & 0.360177 & 0.893316 & 0.286573 & 0.358064 & 0.893911 & 0.272233 & 0.359504 \\  & Fold 5 & 0.89616 & 0.237447 & 0.35655 & 0.887568 & 0.251615 & 0.349024 & 0.893666 & 0.174137 & 0.232828 \\  & **Average** & **0.890475** & **0.325402** & **0.392717** & **0.892637** & **0.263744** & **0.339419** & **0.896858** & **0.214199** & **0.301942** \\  & Fold 1 & 0.886594 & 0.268403 & 0.347494 & 0.891489 & 0.188005 & 0.253954 & 0.913914 & 0.179596 & 0.244818 \\  & Fold 2 & 0.889972 & 0.29734 & 0.399723 & 0.090594 & 0.273945 & 0.343632 & 0.89788 & 0.285959 & 0.421616 \\  & Fold 3 & 0.897251 & 0.3151 & 0.389115 & 0.89499 & 0.206798 & 0.265649 & 0.901024 & 0.150231 & 0.221037 \\  & Fold 4 & 0.89355 & 0.291143 & 0.369973 & 0.900606 & 0.28731 & 0.361998 & 0.882645 & 0.250829 & 0.360831 \\  & Fold 5 & 0.905704 & 0.299369 & 0.398775 & 0.885381 & 0.28123 & 0.396588 & 0.890992 & 0.18624 & 0.244843 \\  & **Average** & **0.896414** & **0.294271** & **0.380476** & **0.896412** & **0.247458** & **0.32256** & **0.897291** & **0.102571** & **0.299537** \\  & Fold 1 & 0.883631 & 0.313012 & 0.388109 & 0.904711 & 0.236537 & 0.287591 & 0.917584 & 0.168858 & 0.231028 \\  & Fold 2 & 0.908497 & 0.239261 & 0.343423 & 0.9077 & 0.282616 & 0.352005 & 0.909033 & 0.263164 & 0.345673 \\ Log- & Fold 3 & 0.89916 & 0.294613 & 0.358759 & 0.901113 & 0.162906 & 0.221393 & 0.905177 & 0.141968 & 0.216695 \\ cosh & Fold 4 & 0.896872 & 0.256079 & 0.392367 & 0.900411 & 0.2745 & 0.362309 & 0.896621 & 0.246203 & 0.355604 \\  & Fold 5 & 0.89441 & 0.254158 & 0.369783 & 0.904146 & 0.226115 & 0.303037 & 0.893336 & 0.160434 & 0.236355 \\  & **Average** & **0.896514** & **0.271245** & **0.359868** & **0.093616** & **0.236535** & **0.305321** & **0.902729** & **0.196125** & **0.277071** \\  & Fold 1 & 0.91087 & 0.277514 & 0.349938 & 0.906243 & 0.177149 & 0.242459 & 0.910428 & 0.16176 & 0 Figure 6: A scatter-plot representation of samples of predicted beauty scores against actual beauty scores on SCUT-FBP: (**a**) AlexNet, (**b**) VCG16-Net, and (**c**) FAIC-Net.
## 3 Results and Discussion
### Effect Analysis of Exogenous Substances on PAHs Degradation  
#### 3.3.1 Crude Oil  
During the microbial degradation of PAHs in oily sludge, the content of residual oil in sludge would affect the degradation efficiency of PAHs. Additionally, the residual oil in randomly stacked oily sludge can enter the soil directly or indirectly, affecting the physical and chemical properties and functions of the soil. Toxic substances can inactivate plant enzymes and penetrate plant structure, resulting in the failure of plant growth [46]. Furthermore, excessive residual oil would pollute groundwater after being washed by rain. After horizontal expansion in groundwater, some residual oil can enter surface runoff with runoff sediment, resulting in excessive chemical oxygen demand (COD) and petroleum substances in surface water [47]. The test of crude oil effects was conducted (Figure 6), and its effect on the degradation of NAP, PHE, and PYR by P&A bacterial consortium was explored.  
It is shown that the crude oil content factor did not impact NAP, PHE, and PYR degradation in this experiment, and all of them were completely degraded (Figure 6). Moreover, within the P&A bacterial consortium system, the degradation rate of crude oil reached 80.85% when the addition of crude oil was 5 g/L. Compared with Section 3.2, the possible reason for the complete degradation of NAP, PHE, and PYR caused by the addition of crude oil is that there are short linear and branched alkanes with simple structure in crude oil [48]. They can not only provide a relatively high-quality carbon source for the P&A bacterial consortium, which greatly increased the bioaccumulation in the system in a short time, but also stimulate the P&A bacterial consortium to produce corresponding degradation enzymes to accelerate the degradation rates of NAP, PHE, and PYR. After the easily degradable short chain alkanes and straight chain alkanes were consumed, a large number of bacteria began to degrad the NAP, PHE, and PYR; therefore, its degradation rate was greatly increased.  
The possible reason for the different crude oil degradation rates (Figure 6) is that, under appropriate stimulation, microorganisms can emulsify crude oil by changing the hydrophobicity of cell surface or secreting biosurfactants themselves [49,50]. This effect is limited when crude oil content is insufficient, and the crude oil is difficult to disperse in the solute, resulting in its low degradation rate. With the increase of crude oil content (to 5 g/L), this effect is gradually obvious. However, when the crude oil content increases to 10 g/L, the intermediate products produced by crude oil are toxic to the P&A bacterial consortium (the results of viable bacteria count showed that the number of viable bacteria was lower than others), which lead to the crude oil degradation rate declining.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 6 Measurements
### Usrp  
Two NI USRP-2921 were used for the experiments (Figure 10). These older USRPs use standard Ethernet as an interface between the device itself and the controlling computer. Other than that, USB 2.0 can be also used for communication, although it offers lower bit rates. Two SMA connectors for the transmitting or receiving side were also present. Synchronization could be either software driven, driven by an external clock generator, or via bus by connecting a proprietary PCI-E like cable. It originally offered daughterboards operating at 2.4 to 2.5 GHz and 4.9 to 5.9 GHz, which were not suitable for VLC experiments. Therefore, two boards--Ettus LFTX for the transmitter and LFRX for the receiver--which operated at 0-30 MHz, were used during the experimental measurements.
## 3 Results and Discussion
### Reusability  
For practical applications, the reusability of photocatalysts is important as it is related to the stability of the photocatalyst [29]. The reuse test was conducted for four cycles under visible-light irradiation to investigate the stability of PANI/TiO\({}_{2}\). After the photocatalytic reaction, the catalyst was reused for the next cycle, and the degradation efficiency was determined by analyzing the concentrations of MB residue in the samples. After four reuses, more than 99% of MB was degraded by PANI/TiO\({}_{2}\) in 360 min under visible-light irradiation (Figure 10). PANI/TiO\({}_{2}\) did not show any notable loss of photocatalytic activity, suggesting that it remains stable during the degradation of MB. It also suggests that active sites of PANI/TiO\({}_{2}\) were not occupied by the adsorption of MB molecules because MB might be oxidized by the continuously generated reactive oxygen species on the surface of PANI/TiO\({}_{2}\)[30].  
Figure 9: (**a**) Degradation of bacteriophage MS2 by 4% PANI/TiO\({}_{2}\) under visible-light irradiation, and (**b**) pseudo-first-order kinetics of MS2 degradation (MS2 = 10\({}^{5}\) CFU/mL, catalyst dose: 1.33 g/L).
## 1 Introduction
In [28], a bit allocation scheme is introduced to minimize the power consumption and avoid the interference between subcarriers so that the scheme can guarantee the QoS of each user. According to the studies above, the key factor of the D2D system is an interference management. We propose an improved FFR scheme to gain high throughput and to handle both of the interferences. In the proposed scheme, a cell is separated into six equal portions called zones and the given radio frequency is also divided into six fractional pieces. The proposed scheme allocates each orthogonal  
Figure 1: Cellular link and D2D link in WMSN. frequency to cellular links of six zones one by one. In addition, each D2D link uses resources that do not overlap with the cellular link of the zone. Therefore, interference between the two links can be reduced by the proposed scheme. The contribution of this paper is as follows:  
* We propose a novel interference management scheme by dividing cells into six zones for D2D links. * The efficiency of proposed scheme is mathematically described by a geographical average distance. * We implement the proposed scheme on a computer simulation to show various aspects of uplinks and D2D links. This paper is organized as follows. Section 2 describes the scenarios in which the proposed network can be applied. Section 3 explains the proposed FFR scheme. Section 4 describes performance evaluation results. The conclusion will be discussed in Section 5 and future work is represented in Section 6.
## 5 Conclusions  
In this paper, we propose a practical technique to optimize the display of a captured 3D scene in an integral imaging optical display system by nonuniformly rescaling the depth range along the z-axis and resizing the field-of-view of a captured scene along the x-y axes. Using this approach, synthetically captured elemental images can be computationally regenerated to match the depth range and field-of-view of a specific 3D display system. Additionally, this technique allows focusing on specific parts of the 3D scene to enhance detail visibility. By fully utilizing the capabilities of the display system, both real and computer-generated 3D display experiments have demonstrated the feasibility of the proposed method in generating elemental images that align with the depth range and size requirements of a specified display system for optimal 3D visualization. The proposed method may face limitations when the scaling factor significantly exceeds one, as detailed information of the 3D scene must be computationally synthesized for accurate elemental image generation and 3D optical display. Further research will explore integrating machine learning algorithms to enhance the generation of synthetic elemental images, depth estimation, reduce noise and distortion, as well as extract details from original elemental images for high-resolution 3D optical displays.  
Conceptualization, X.S.; methodology, X.S.; software, N.G., V.H. and X.S.; validation, X.S.; formal analysis, N.G., V.H. and X.S.; investigation, N.G., V.H. and X.S.; resources, X.S.; data curation, N.G., V.H. and X.S.; writing--original draft preparation, N.G. and X.S.; writing--review and editing, N.G., V.H. and X.S.; project administration, X.S.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Real 3D object experimental group: (**a**) front view of the 3D display results showing (i) the original object parameters, (ii) the effect of the depth rescaling process only, and (iii) the combined effect of depth rescaling and scene resizing. (**b**) Display results from multiple perspectives, including front view, top view, bottom view, right view, and left view, after applying both depth rescaling and the field-of-view resizing processes.  
**Funding:** This research was funded by the NASA Connecticut Space Grant Consortium, PTE Federal Award No.: 80 NSSC20M0129.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors gratefully acknowledge the Department of Physics at the University of Hartford for providing the equipment support necessary for the experiments.  
**Conflicts of Interest:** The authors declare no conflicts of interest.