esa-sceva/satcom-chunk-collection huggingface dataset:
# IPAQ-L and CPET Usefulness in a North-Eastern Romanian Population Undergoing Cardiac Rehabilitation
## 3 Results
### Outcomes  
Age was significantly correlated with total MET-minutes/week at work (r = \(-\)0.28, \(p\) = 0.002), but not with the total activity in the other three analyzed domains or with sitting time (\(p\) > 0.05). Furthermore, age presented a weak borderline correlation with total physical activity MET-minutes/week (r = \(-\)0.18, \(p\) = 0.053), but not with BMI, AC or EF. Maximum tolerated cardiac frequency (MTCF) fitness exhibited a significant positive correlation with total MET-minutes/week at work (r = 0.28, \(p\) = 0.002), and a borderline correlation with total physical activity MET-minutes/week (r = 0.18, \(p\) = 0.053), but not with BMI or AC (Table 4).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Total** & **Males** & **Females** \\ \hline Low level of PA (\%) & 27.07 & 31.79 & 22.40 \\ Moderate level of PA (\%) & 65.35 & 62.62 & 68.04 \\ High level of PA (\%) & 7.57 & 5.58 & 9.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of total physical activity among subgroups.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Age**} & \multicolumn{2}{c}{**MTCF**} \\  & **R-Value** & **\(p\)-Value**\({}^{*}\) & **R-Value** & **\(p\)-Value**\({}^{*}\) \\ \hline Total MET-minutes/week at work & \(-\)0.28 & 0.002 & 0.28 & 0.002 \\ Total MET-minutes/week for transportation & \(-\)0.01 & 0.850 & 0.01 & 0.850 \\ Total MET-minutes/week in domestic and & \(-\)0.01 & 0.852 & 0.01 & 0.852 \\ garden activities & \(-\)0.04 & 0.631 & \(-\)0.04 & 0.631 \\ Total MET-minutes/week in leisure time & \(-\)0.12 & 0.183 & 0.12 & 0.183 \\ Vigorous PA (leisure time) & \(-\)0.18 & 0.053 & 0.18 & 0.053 \\ Total physical activity MET-minutes/week & 0.15 & 0.107 & \(-\)0.15 & 0.107 \\ AC (cm) & 0.10 & 0.295 & \(-\)0.10 & 0.295 \\ LVEF (\%) & 0.10 & 0.338 & \(-\)0.10 & 0.338 \\ \hline \hline \end{tabular} \({}^{*}\): Spearman’s rho. MET: total metabolic equivalent of task; MTCF: maximum tolerated cardiac frequency; PA: physical activity; BMI: body mass index; AC: abdominal circumference; LVEF: left ventricular ejection fraction.  
\end{table}
Table 3: IPAQ questionnaire results.  
Vigorous activity was statistically significant correlated with VO\({}_{2}\) max% (r = 0.52, \(p\) = 0.025) and with AT (r = 0.53, \(p\) = 0.026). However, the correlation between VO\({}_{2}\) max% and total physical activity did not reach statistical significance (r = 0.34, \(p\) = 0.168). Total physical activity presented significant correlations with WR% (r = 0.48, \(p\) = 0.04) and HRR (r = 0.65, \(p\) = 0.003), as determined by CPET. HRR also correlated with vigorous activity (r = 0.61, \(p\) = 0.007), moderate activity (r = 0.63, \(p\) = 0.005), and walking (r = 0.59, \(p\) = 0.009) (Table 5).
## 3 Results and Discussion
### Effect of AC on NO\({}_{x}\)
\begin{table}
\begin{tabular}{c c c c c} \hline Power (kW) & 6 & 9 & 12 & 15 \\ \hline Urea Flow (mL/h) & 146 & 206 & 269 & 345 \\ \hline \end{tabular}
\end{table} 
Table 5: Urea volume flow at different powers (unit: mL/h). Figure 7: NOx concentration of AC reactor outlet (without urea). (**a**) Dynamic change process; (**b**) stable value. At present, the commercial denitration catalyst generally had a high denitration efficiency at temperatures higher than 200 \({}^{\circ}\)C. The exhaust gas temperature was low at low power and urea is generally not sprayed, resulting in a poor denitration effect at low power. Diesel engines usually run in a low-power state when ships enter or depart from ports. At this time, exhaust emissions mainly occur in the ports. The atmospheric pollution caused by this process has a serious impact on the environment. It is beneficial to utilize the better adsorption ability of AC at low temperatures to initially adsorb and store NO\({}_{\mathrm{x}}\). When the ship is at cruising speed, the exhaust gas temperature rises with the power. Excess urea solution can be sprayed for denitration reaction when the temperature is higher than the hydrolysis temperature of urea. One way to improve the denitration efficiency is to modify the activated carbon [33; 34] or load with other metal oxides [35; 36]. In that case, activated carbon could serve as a practical solution for marine diesel engine exhaust denitration.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 5 Results and Evaluation
### Different SNR Environments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Error** & **TSNetIQ** & **ResNetIQ** & **CNN** \\ \hline
0–5\({}^{\circ}\) & 999 & 985 & 977 \\
5–10\({}^{\circ}\) & 1 & 12 & 14 \\ \textgreater{}10\({}^{\circ}\) & 0 & 3 & 9 \\
0–5\({}^{\circ}\) & 999 & 985 & 977 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction error of the three compared models(1000 samples). To further evaluate the stability of model predictions under different SNRs, the standard deviations of RMSE values were calculated based on 10 independent prediction runs for each setting. As shown in Table 5, the standard deviation of RMSE consistently decreases with increasing SNR for all three models, indicating improved prediction stability under cleaner signal conditions. Among the models, TSNetIQ exhibits the lowest standard deviation across all SNR levels, particularly in high-SNR scenarios, where its variance approaches negligible levels (e.g., 0.06 at 20 dB). In contrast, ResNetIQ demonstrates moderate sensitivity to noise, with higher standard deviations in low-SNR conditions but reasonable convergence at higher SNRs. CNN, however, shows the largest variability, especially under  
Figure 8: RMSE results of three models under different SNRs. Figure 6: Error histogram of the three models. Figure 7: Visualization of error lines obtained by the three models. low-SNR conditions (e.g., 6.48 at -20 dB and 2.79 at -5 dB), suggesting lower robustness to noise perturbations. These results confirm that TSNetIQ provides superior consistency and robustness across varying noise levels, underscoring its effectiveness for reliable DOA estimation in noisy environments. Collectively, these results confirm that TSNetIQ not only achieves the lowest RMSE across all tested SNR levels, but also exhibits remarkably consistent and accurate DOA predictions, making it a highly promising solution for practical sound source localization in dynamic and noisy environments.
# Predictive Model for the Optimized Mixed-Air Temperature of a Single-Duct VAV System
## 2 Predictive Model for Optimized Mixed-Air Temperature  
The economizer controls the outside, return, and exhaust dampers of the HVAC system in order to satisfy the mixed-air temperature set point, and generally keeps the mixed temperature set point constant. However, when the mixed-air temperature set value is changed in relation to indoor and outdoor conditions, energy savings are possible compared to the existing control method; so, this study proposed an economizer control method using predictive model-based mixed temperature optimization. To do this, a load prediction model was developed, and an energy prediction model was developed to derive a set value in a scenario where energy use was minimal. In addition, a co-simulation between EnergyPlus and Matlab was established through BCVTB. The detailed control method is as follows, and Figure 1 depicts the optimization concept.  
Step 1. Prediction of the load based on the operating data: Predicted building load based on the operating data of the system.  
Step 2. Prediction of energy use using the operating data, mixed temperature set point, and predicted load: Energy consumption was estimated through iterative prediction using the operating data, predicted load, and mixed-air temperature set point.  
Step 3. Derivation of the economizer optimal mixed-air temperature set value: Based on the result predicted in Step 2, the mixed-air temperature set value for a scenario with minimal energy consumption was derived.  
Step 4. The derived mixed-air temperature set point value was applied as the economizer control set point value of the next time step.
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 5 Analysis of Image Encryption and Decryption
### Statistical Analysis  
The statistical analysis of the algorithm is presented in this section to understand the random distribution of the pixels in the encrypted image and the correlation between the pixels.
## 6 Conclusions  
The proposed methodology considers the design of a MG for university campuses, complying with MG regulations. This approach also includes the definition of critical loads, the analysis of the local resources, and the study of electrical distribution systems where the MG will be installed. This methodology was applied in the campus of the University of Puerto Rico at Mayaguez. As a results, the best options were PV, batteries, and CHP, if the technological advances and availability of resources for Puerto Rico are considerer. Four functional scenarios were designed and simulated to evaluate the best option for a MG in a university campus in MATLAB-Simulink. These models contain the distribution system of the University of Puerto Rico's campus at Mayaguez Campus as a load. For each scenario, the generation resources were modeled.  
After simulation, the CHP was determined as the best option as it allows islanding mode operation and generates additional energy for refrigeration. In addition, the area needed for installation is less than other scenarios. However, the CHP depends on fuel to generate electricity, and in emergency cases the availability of fuel could be affected. Puerto Rico is impacted by this fact since the fuel comes from other places, but the solar and battery resources are not disturbed by this phenomenon. Currently, the PV system has a profitable cost per kWh, and it cannot operate in islanding mode, since it needs a reference voltage. Though, the installation of PV system generates savings in the energy consumption of for a better price than the grid. It is expected that the cost of batteries will decrease, and in the future, it will compete with the CHP cost.  
As future work, different scenarios can be evaluated based on the models created in this investigation, increasing the generation capacity, for example. Other alternatives are variate the scenario number three using greater installed capacity that allows no load interruptions and operate the entire campus in islanding mode. Furthermore, an economical analysis could be done, including the environmental costs and the implications of using the proposed technologies for the different presented scenarios.  
Conceptualization, Y.V.G. and O.G.; methodology Y.V.G., F.A. and A.I.; formal analysis, Y.V.G., O.G., F.A. and A.I.; investigation, Y.V.G., F.A., O.F.R.-M. and A.I.; resources, Y.V.G., F.A. and O.F.R.-M.; writing-original draft preparation, Y.V.G. and O.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the U.S. Department of Energy under Grant DE-SC002028. The authors thankful to the Sustainable Energy Center (SEC).  
This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office Award Number DE-EE0002243-2144.  
The authors declare no conflict of interest.  
Figure 20: Cost in 10 years.
## 2 Related Works
### Machine Reading Comprehension Development  
Early MRC systems can be traced back to the 1970s when [PERSON] proposed the famous QUALM system [10]. However, due to its scale and field limitations, it has not been widely used. In the 1980s and 1990s, research on MRC fell into disuse. In 1999, [PERSON] et al. presented a dataset of 60 stories [11], which brought renewed attention to MRC. At that time, approaches to MRC were limited to rule-based approaches [12; 13]. Although these methods have strong interpretability, they cannot understand the semantic information of the text and primarily rely on the rules and features designed by experts, so they do not achieve good results.  
From 2013 to 2018, researchers began formalizing MRC as a supervised learning task and using manual annotation methods to construct datasets. In 2013, MCTest [14] was released as a multiple-choice reading comprehension dataset, which was of high quality, whereas it was to small to train neural models. In 2015, CNN/Daily Mail [15] and CBT [16] were released. These two datasets are generated automatically from different domains and are much larger than previous datasets. In 2016, SQuAD [17] was launched as the first large-scale dataset with questions and answers written by humans. Then, in the same year, other datasets with high quality were constructed [18]. Within the next three years, various datasets from different domains sprung up [19; 20; 21; 22; 23], making training an end-to-end MRC model possible.  
Since 2018, there has been tremendous change in solving MRC tasks. Along with the release of larger datasets, neural MRC, built by fully developed deep-learning techniques, shows its great superiority over traditional rule-based and machine-learning-based MRC and has gradually become the mainstream in the research community. Neural MRC methods based on deep learning can capture the semantic information of text to a certain extent, such as Math-LSTM [24], BiDAF [25], and QANet [26]. These models go far beyond traditional approaches. In recent years, with the advent of large-scale pre-trained language models such as BERT [27], MRC has made remarkable progress and even surpassed human performance on some datasets.
## 3 The 6G Technology
### Sixth-Generation Key Enabling Technologies  
The 6G is expected to accomplish ubiquitous connectivity after it has been fully deployed. Several technologies ensure that the future wireless network meets some of its demands from users. Researchers are now exploring these technologies and how they can meet the technical requirements of the network [15; 82]. These technologies include Terahertz (THz) communication, reconfigurable intelligent surfaces (RISs), visible light communication (VLC), ultra-massive MIMO, optical wireless communication technology (OWC), holographic beamforming, quantum computing (QC), mobile edge computing (MEC), non-orthogonal multiple access (NOMA), blockchain technology, unmanned aerial vehicles (UAVs), etc. [83]. Researchers are now exploring integrating several of these technologies for optimization applications [84].
# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 3 Results and Discussion
### Text Recognition  
Accuracy is the most commonly adopted metric for the assessment of the text recognizer [1, 5, 6]. We implemented two popular state-of-the-art text recognition models and compared the influence of fine-tuning both with text regions from digital meter images. The experimental results are presented in Table 5.  
Pre-trained Rosetta performs poorly on the UC, compared to CRNN. For the FM, the difference is not abrupt, yet CRNN continues to outperform the first. In the implementation of [PERSON] and Atienza [18], CRNN exhibits average accuracy of 90.9% and 70.8% for ICDAR 2013 (1015 test images), and ICDAR 2015 (2077 test images). Rosetta's implementation in [PERSON] et al. [16] returns accuracy values of 89.2% and 64.2% for the same datasets, each. Such results corroborate our proposed thesis that CRNN outperforms Rosetta.  
Regarding the fine-tuned models, this technique is proved to be beneficial, except for the identification of FM values by the CRNN model, whose accuracy remains unchanged. Although Rosetta returns the top result in FM, CRNN yields the best performance altogether, with average accuracy of 88.45% against 85.61% of the former. Therefore, the CRNN fine-tuned model is selected for incorporation into the pipeline.
## 3 System Implementation
### Feature Extraction  
The MFCC-based acoustic features have been proven effective in detecting various special audio events [27]. MFCC is used as the feature extraction method in this work. The process of extracting the MFCC-based acoustic features is shown in Figure 3. It can be summarized as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Num** & **Time** & **Coords (latitude, longitude)** & **IMEI** & **dB** \\ \hline
158 & 20190702143712 & 25.289270, 110.343036 & 0005 & 56 \\
159 & 20190702143714 & 25.288436, 110.343405 & 0003 & 49 \\
160 & 20190702143715 & 25.289270, 110.342036 & 0004 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data information in MySQL (My Structured Query Language).  
1. Frame the signal into short frames. An audio signal is constantly changing, so to simplify things we assume that on short time scales the audio signal does not change much. We frame the signal into 25 ms frames. The continuous audio signal is blocked into frames of \(N\) samples, with adjacent frames being separated by M. We take the value of M as a half of N.
2. Calculate the power spectrum of each frame by the following: \[S_{i}(k)=\sum\limits_{n=1}^{N}{{s_{i}(n)h(n)e^{-{\left|{2\pi ibn}/{N}\right.}}} \ 1\leq k\leq K}\] (6) where \(h(n)\) is an \(N\) samples long analysis window, and \(K\) is the length of the DFT. \(s_{i}(n)\) is the time domain signal where \(n\) ranges over 1-N and \(i\) ranges over the number of frames. The power spectrum of the \(i\)-th frame is given by: \[P_{i}(k)=\frac{1}{N}{\left|{S_{i}(k)}\right|^{2}}\] (7)
3. Apply the mel filterbank to the power spectra and sum of the energy in each filter. In this step, we calculate the mel frequency by: \[f_{mel}=2595\log_{10}{\left({1+\frac{f}{700}}\right)}\] (8) where \(f_{mel}\) is the mel frequency and \(f\) is the frequency value. The suggested number of filterbank size K is in the range of 20 to 40, and we chose 26 because it is the standard.
4. Take the logarithm of all filterbank energies.
5. Take the DCT of the logarithmic filterbank energies. We keep the DCT coefficients from 2 to 13 and discard the rest.
6. Obtain delta and delta-delta features by the derivative. The following formula is used: \[d_{t}=\frac{\sum\limits_{l=1}^{L}{l(c_{t+l}-c_{t-l})}}{\sqrt{2\sum\limits_{l=1 }^{L}{l^{2}}}}\] (9) where \(d_{t}\) is the delta coefficient of \(t\), and \(c_{t}\) represents the cepstrum coefficient of t, and a typical value for L is 2. Delta-delta coefficients are calculated in the same way, but they are calculated from the deltas, not the static coefficients.
7. The MFCC consists of base features, delta features, and delta-delta features.  
Figure 3: The process of feature extraction.
## 3 Results
### Ablation Experiment  
An ablation experiment was conducted to evaluate the performance of the proposed strategies. The fusion modules of the attention mechanism and the pyramid pooling layer, the channel expansion modules of the first and second stages, and the improved loss function were verified item by item. The corresponding improved networks were trained and compared respectively. The results are shown in Table 2. The experimental results show that each key module had a positive effect on the final rain removal performance of the network.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Advances on the PHY Layer
#### 4.1.1 Flexible Spectrum Usage  
A major drawback of OFDM in Wi-Fi is the fact that only a single user can leverage a channel. For every transmission in OFDM, there is a channel access contention overhead, which is significant for small packets.  
To overcome this limitation, Wi-Fi 6 introduces Resource Units (RUs). Essentially, the bandwidth of a channel is partitioned in frequency and time, yielding time-frequency blocks. The access points assign these blocks to users for their uplink transmissions. The spectrum of such a time-frequency block is an RU and its duration is called a TXOP. A TXOP is big enough to send one or multiple packets and to receive their acknowledgement. To avoid interference, RUs used by different transmitters must not overlap (i.e., they must use disjoint resources).  
The assignment of RUs and TXOPs leads to a time-frequency matrix that enables transmissions between multiple stations and their access point. RUs and TXOPs make bandwidth usage more flexible: a station may use wider (resp. narrower) RUs and shorter (resp. longer) TXOPs. Moreover, multiple parallel transmissions within a single channel are possible.
###### Abstract  
This article proposes a method for the prediction of wide range two-dimensional refractivity for synthetic aperture radar (SAR) applications, using an inverse distance weighted (IDW) interpolation of high-altitude radio refractivity data from multiple meteorological observatories. The radio refractivity is extracted from an atmospheric data set of twenty meteorological observatories around the Korean Peninsula along a given altitude. Then, from the sparse refractive data, the two-dimensional regional radio refractivity of the entire Korean Peninsula is derived using the IDW interpolation, in consideration of the curvature of the Earth. The refractivities of the four seasons in 2019 are derived at the locations of seven meteorological observatories within the Korean Peninsula, using the refractivity data from the other nineteen observatories. The atmospheric refractivities on 15 February 2019 are then evaluated across the entire Korean Peninsula, using the atmospheric data collected from the twenty meteorological observatories. We found that the proposed IDW interpolation has the lowest average, the lowest average root-mean-square error (RMSE) of \(\
abla\)M (gradient of \(\dot{M}\)), and more continuous results than other methods. To compare the resulting IDW refractivity interpolation for airborne SAR applications, all the propagation path losses across Pohang and Heukands are obtained using the standard atmospheric condition of \(\
abla M=118\) and the observation-based interpolated atmospheric conditions on 15 February 2019. On the terrain surface ranging from 90 km to 190 km, the average path losses in the standard and derived conditions are 179.7 dB and 182.1 dB, respectively. Finally, based on the air-to-ground scenario in the SAR application, two-dimensional illuminated field intensities on the terrain surface are illustrated.  
refractivity interpolation; inverse distance weighted (IDW) interpolation; long-range propagation; synthetic aperture radar (SAR); active electronically scanned array (AESA) radar +
Footnote †: journal: Applied Sciences  
1
## 3 Results and Discussion
### Thermal Conductivity of the Hybrid Nanofluids
#### 3.2.2 Effect of Mass Fraction of the Nanoparticle  
Figure 14 shows the influence of mass fraction of nanoparticle on thermal conductivity of the hybrid nanofluid. It indicates that the thermal conductivity of the hybrid nanofluid increased with the mass fraction of the nanoparticles in the range of 0-0.2%. Moreover, the influence of the mass fraction on thermal conductivity enhancement at a lower temperature was more significant than at a high temperature. At 0 \({}^{\circ}\)C, the thermal conductivity of 0.2 wt % hybrid nanofluids increased by 7.2% as compared to the base fluid, and the thermal conductivity enhancement of 0.2 wt % was about 2.1 times larger than that of 0.025 wt %. At a higher temperature, 70 \({}^{\circ}\)C, the thermal conductivity of 0.2 wt % hybrid nanofluid increased by 1.76% as compared to the base fluid, and the thermal conductivity enhancement was 14.7 times that of the 0.025 wt % hybrid nanofluid. It shows that the nanoparticle mass fraction had a significant influence on the thermal conductivity of the hybrid nanofluid, owing to the fact that thermal conductivities of GO and Al\({}_{2}\)O\({}_{3}\) nanoparticles were larger than that of deionized water. The liquid molecules on the surface of the nanoparticles form ordered structure, which acts as a thermal bridge between the fluid and the solid particles, resulted in a higher thermal conductivity and enhanced energy transfer within the system. Meanwhile, Brownian motion of nanoparticles caused microconvection in the liquid, which enhanced the energy transfer.