esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{4}{c}{**HIIT without Cognitive Load**} & \multicolumn{4}{c}{**HIIT with Cognitive Load**} \\ \hline
**Variables** & **Pre (1)** & **Post (2)** & **Pre-Post** & **Pre (3)** & **Post (4)** & **Pre-Post** & **Pre-Post Change** \\ \hline \multicolumn{4}{c}{Hooper and Mackinnon wellness questionnaire} \\ \hline General & 3.97 \(\pm\) 0.89 & 2.81 \(\pm\) 0.78 & \textless{}0.001 \({}^{\star}\) & 4.06 \(\pm\) 0.98 & 2.97 \(\pm\) 0.78 & \textless{}0.001 \({}^{\star}\) & 0.87 \\ Fatigue (a.u.) & & & & & & \\ Muscle & 3.72 \(\pm\) 1.08 & 3.50 \(\pm\) 0.98 & 0.132 & 3.84 \(\pm\) 1.11 & 3.47 \(\pm\) 1.07 & 0.105 & 0.67 \\ soreness (a.u.) & 3.84 \(\pm\) 1.11 & 3.56 \(\pm\) 1.08 & 0.197 & 3.97 \(\pm\) 1.06 & 3.69 \(\pm\) 0.85 & 0.150 & 0.95 \\ Mood state (a.u.) & 4.25 \(\pm\) 0.80 & 3.94 \(\pm\) 0.88 & 0.039 \({}^{\star}\) & 4.25 \(\pm\) 0.76 & 4.25 \(\pm\) 0.91 & 1.00 & 0.15 \\ \hline VAS for MF (a.u.) & 23.44 \(\pm\) 17.20 & 29.69 \(\pm\) 19.67 & 0.014 \({}^{\star}\) & 25.78 \(\pm\) 17.97 & 41.40 \(\pm\) 21.48 & 0.001 \({}^{\star}\) & 0.04 \({}^{\star}\) \\ \hline \multicolumn{4}{c}{Physiological indicator of fatigue} \\ \hline RMSSD (ms) & 47.94 \(\pm\) 12.68 & 30.66 \(\pm\) 7.24 & \textless{}0.001 \({}^{\star}\)+ & 51.18 \(\pm\) 10.84 & 32.72 \(\pm\) 8.08 & \textless{}0.001 \({}^{\star}\)+ & 0.53 + \\ \hline \multicolumn{4}{c}{\({}^{\star}\): \(p\) < 0.05; \(t\): Due to distribution requirements, the \(p\)-value was obtained from a paired sample \(t\)-test. \(p\)-values reported in the table were adjusted to Bonferroni corrections for multiple comparisons. HIIT—High-intensity interval training; VAS—Visual analogue scale; RMSSD—Root mean square of successive differences between normal heartbeats; MF—Mental fatigue; \(a.u.\)—arbitrary units; ms—miliseconds.} \\ \end{tabular}
\end{table} 
Table 2: Analysis of subjective and physiological indicators of mental fatigue. Comparison between different protocols. mental fatigue was observed in HIIT without cognitive load (\(p\) = 0.014) and HIIT with cognitive load (\(p\) = 0.001), with a significant higher increase in the HIIT with cognitive load than in the HIIT without cognitive load (\(p\) = 0.04). Meanwhile, only HIIT without cognitive load caused poorer levels of mood state reported from pre- to post (\(p\) = 0.039). No significant differences from pre- to post- nor inter-protocol were observed for muscle soreness or stress level.
## 1 Introduction
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Reference** & **Date** & **Covered Methods** & **Covered Datasets** \\ \hline Systematic review and critical appraisal of prediction models & March 2020 & 6 & - \\ for diagnosis and prognosis of COVID-19 infection [3] & & & \\ Mapping the landscape of artificial intelligence applications & March 2020 & 6 & - \\ against COVID-19 [13] & & & \\ Artificial Intelligence in the Battle against Coronavirus & April 2020 & 12 & - \\ (COVID-19): A Survey and Future Research Directions [14] & & & \\ Detection of Covid-19 From Chest X-ray Images Using & April 2020 & 5 & - \\ Artificial Intelligence: An Early Review [15] & & & \\ The Role of Imaging in the Detection and Management & April 2020 & 23 & - \\ of COVID-19: A Review [16] & & & \\ Novel coronavirus (COVID-19) diagnosis using computer & June 2020 & 21 & 7 \\ vision and artificial intelligence techniques: a review [17] & & & \\ A Survey on the Use of AI and ML for Fighting the & August 2020 & 35 & - \\ COVID-19 Pandemic [18] & & & \\ Songraphic Diagnosis of COVID-19: A Review of Image & September 2020 & 12 & - \\ Processing for Lung Ultrasound [19] & & & \\ Survey of the Detection and Classification of Pulmonary & December 2020 & 39 & 26 \\ Lesions via CT and X-ray [20] & & & \\ Medical imaging and computational image & June 2021 & 51 & - \\ analysis in COVID-19 diagnosis: A review [21] & & & \\ Deep neural networks for COVID-19 detection and diagnosis & August 2021 & 27 & 10 \\ using images and acoustic-based techniques: a recent review [22] & & & \\ Diagnosis of COVID-19 Using Machine Learning and & October 2021 & 52 & - \\ Deep Learning: A review [23] & & & \\ \hline (Ours) Let AI Perform Better Next Time—A Systematic Review of Medical & April 2022 & 179 & 15 \\ Imaging-based Automated Diagnosis of COVID-19: 2020–2022 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summarizing existing review or survey papers related to automated diagnosis of COVID-19.
## 6 Simulation Results
### Performance of the Simplified Model  
The last set of experiments estimates the performance of the average number of communication group members with the PM model, as detailed in Section 4. With the simplified model and 500 typical runs, Figure 20 depicts the mean of the communication group size of the source vehicle considering random uniform deployment with varying the cone angle of Region A (\(\theta_{A}\)) and the parameter \(\gamma\). Referring to Figure 20, we have the average group size \(|G_{u}|^{(\mathrm{PM})}\approx|G_{u}|^{(\mathrm{HDTC})}\) with \(\theta_{A}=65^{\circ}\) and \(\gamma=0.6\), \(\theta_{A}=60^{\circ}\) and \(\gamma=0.7\), and \(\theta_{A}=55^{\circ}\) and \(\gamma=0.8\), respectively, which suggest that in order to improve the estimation accuracy, a larger value of parameter \(\gamma\) may be used in a smaller Region A (_i.e._, a smaller \(\theta_{A}\)), such that the similarity measures in Region B are emphasized. The variation of the prediction achieves \(||G_{u}|^{(\mathrm{PM})}-|G_{u}|^{(\mathrm{HDTC})}|\leq 2\).  
Notice that with the probabilistic model (PM), the performance of the HDTC is roughly predicted, due to flexibility and similarity measures of the vehicles within the observation cone, suggesting that the PM model may provide a good approximation to the HDTC. Thus, the proposed simplified model may reasonably abstract the network behaviors and measure the system performance with the settings of the cone angle of Region A (\(\theta_{A}\)) and the parameter \(\gamma\).  
Figure 20: The average group size with varying \(\theta_{A}\) and the parameter \(\gamma\).
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 3 An Algorithmic Metatheorem for Logspace and Its Applications
### Applications: Special Fruits  
Before we proceed to the really advanced applications, there are some \"special\" applications of the logspace version of [PERSON]'s Theorem that concern problems to which one normally does not apply algorithmic metatheorems: problems inside P, the class of problems solvable in polynomial time. Problems such as perfect-matching or reach are normally considered \"easy\" since they already lie in P, and, thus, applying an algorithmic metatheorem to them will not move them from \"unsolvable\" into the realm of \"solvable\", but, at best, from \"solvable in quadratic or cubic time\" to \"solvable in linear time with huge hidden factors\"--at the expense of making heavy restrictions on the input concerning the tree width. Generally, the expense is considered too high a price to pay and one does not even bother to formulate the resulting statements. However, from the perspective of space complexity, problems like the matching problem belong more to the realm of \"unsolvable\" problems, and the logspace version of Courcelle's Theorem does give us useful applications such as the following:  
**Lemma 5**.: _For each \(k\), the set \(\{G\mid G\) has tree width at most \(k\) and \(G\) has a perfect matching\(\}\) can be decided in logarithmic space._  
**Lemma 6**.: _For each \(k\), the set \(\{(G,s,t)\mid G\) has tree width at most \(k\) and there is a directed path from \(s\) to \(t\) in \(G\)\(\}\) can be decided in logarithmic space._  
There is a whole paper ([18]) mainly devoted just to proving the first lemma directly, which shows nicely how powerful algorithmic metatheorems can be. In both cases, the lemmas follow from the fact that we can describe the graph properties \"has a perfect matching\" or \"there is a path from \(s\) to \(t\)\" using monadic second-order logic. (Astute readers may have noticed that monadic second-order logic as defined in this paper does _not_ allow us to describe the perfect matching problem directly since we would need to quantify existentially over a subset of the graph's _edges_ rather than its _vertices_. However, this can be fixed easily for instance by subdividing each edge and marking the new vertices using one of the \(P_{i}\) predicates. Now, existentially quantifying over a subset of these new vertices is essentially the same as quantifying over a subset of the original edges.)
## 7 Conclusions and Future Work  
Today, it is essential to classify particular land cover types to support environmental protection, water management, and sustainable development. This article is focused on UAV-borne very-high-spatial-resolution monitoring of the ice surface.  
UAVs can provide very-high-spatial-resolution data on demand, of course, with respect to the legal and weather conditions. On the other hand, cheaper UAVs may be equipped with a standard RGB camera only. In this case, not all standard remote sensing methods can be used for data processing; e.g., many spectral indices require other bands; RGB bands are insufficient.  
The article provides a comparison of various pixel-based classifications, which were used to classify RGB imagery. The classification aimed to identify and distinguish the ice surface from the other land cover types.  
The direct focus on identifying only ice surfaces represents a fundamental limitation of this study. We did not distinguish between different types of ice (e.g., dry and wet). RGB bands do not allow the utilization of many spectral indices and the full range of the reflectance curve.  
Future work can expand on this work by including object-based classification methods, e.g., OBIA or an object-based version of deep learning, and comparing these methods.  
Next, there is space to improve the deep learning training phase to prevent classification mistakes, e.g., based on frozen edges.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app132011400/s1](https://www.mdpi.com/article/10.3390/app132011400/s1)]([https://www.mdpi.com/article/10.3390/app132011400/s1](https://www.mdpi.com/article/10.3390/app132011400/s1)), File S1: The results of searches in the WoS and Scopus databases during the literature review phase.  
Conceptualization, J.K.; Methodology, J.J. and D.B.; Software, J.J.; Validation, J.J. and D.B.; Formal Analysis, J.J.; Investigation, J.J.; Resources, J.J. and D.B.; Data Curation, J.J.; Writing--Original Draft Preparation, J.J. and J.K.; Writing--Review and Editing, J.K. and D.B.; Visualization, J.J.; Supervision, J.K.; Project Administration, J.K. All authors have read and agreed to the published version of the manuscript.  
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this article was supported by the Student Grant Competition of the University of Pardubice (grant No. SGS_2023_013).  
Not applicable.  
Figure 11: Results of all classifications for both areas of interest.
# Guard Band Protection Scheme to Facilitate Coexistence of 5G Base Stations and Radar Altimeters
## 3 Numerical Results
### Rural Areas  
Figure 8 displays the simulation results for different antenna arrays, i.e., 4-by-4, 8-by-8, and 16-by-16 antenna arrays. The solid blue line represents the scenario without a guard band, indicating no protection. The solid red line corresponds to the case with a 50 MHz guard band, while the solid yellow line represents the scenario with a 150 MHz guard band. The simulation results are compared with the protection threshold, set as \(-6\) dB and indicated by the red-dotted line. This threshold serves as a reference point, functioning as the interference limit.  
Figure 8 shows a clear trend that as the guard band width increases, the received INR values are significantly reduced, resulting in a leftward shift of the CDF curves. As expected, the protection threshold of the radar altimeter is not exceeded when using a 150 MHz guard band for the 4-by-4 and 8-by-8 antenna arrays. In this case, the maximum received interference is \(-12.2302\) dB and \(-7.9293\) dB with the 4-by-4 and 8-by-8 antenna arrays, respectively. In contrast, with the 16-by-16 antenna array, the maximum received interference reaches 2.0897 dB, with a threshold-exceeding probability of 0.03%. When the 50 MHz guard band is used, the maximum received interference is 8.4409 dB, 16.7643 dB, and 24.5277 dB with the 4-by-4, 8-by-8, and 16-by-16 antenna arrays, respectively. The probabilities of exceeding the protection threshold are 98.9%, 96.6%, and 94.8%, with the  
Figure 8: CDF of interference-to-noise ratio in the rural environment with: (**a**) 4-by-4; (**b**) 8-by-8; and (**c**) 16-by-16 antenna arrays.  
4-by-4, 8-by-8, and 16-by-16 antenna arrays, respectively. Furthermore, the maximum received interference without the protection method is 33.6218 dB, 38.4036 dB, and 44.1662 dB with the 4-by-4, 8-by-8, and 16-by-16 antenna arrays, respectively. These values closely align with those summarised in Table 4. The probabilities of exceeding the protection threshold are 76.8%, 63.3%, and 49.9% with the 4-by-4, 8-by-8, and 16-by-16 antenna arrays, respectively. The probability of exceeding the threshold decreases with an increase in the number of antenna arrays and a decrease in the guard band span. In summary, the 150 MHz guard band effectively protects radar altimeters when 5G base stations use 4-by-4 and 8-by-8 antenna arrays, keeping the received interference levels below the \(-6\) dB threshold. However, the proposed guard band protection method may not be entirely effective for larger antenna arrays, such as the 16-by-16 antenna array, in rural environments. Given the importance of ensuring aviation safety, deploying 5G base stations with 16-by-16 or larger antenna arrays near airports in rural areas is not recommended under the proposed guard band protection scheme.
## 3 Data and Empirical Results
### Data  
The annual datasets of GPP and TRADE time-series data were obtained from the Bank of Thailand database, while the FDI data were acquired from the Thai Board of Investment over the period 1995-2016. Before that time, those statistical datasets were incomplete. The GPP was counted at chain volume measures (the reference year at 2002), the TRADE was measured at the FOB prices and the FDI was counted at current prices. Figure 1 shows Songkhla economic performance. The GPP has been increasing for the manufacturing and trade sectors, while the agriculture sector has been slowing down due to the agricultural price fluctuation especially in rubber, which is the most important cash crop in the south (Figure 1a-c). In 2005, Thailand Board of Investment started to approve inward Thai direct investment and FDI in southern Thailand. Afterwards, the FDI volumes seemed surge and stop in neighboring countries such as Vietnam and Indonesia, seeking cheaper material resources or labor offshore.
## 4 Results and Discussion
### Mechanical Properties  
The calculated Hill elastic moduli, Poisson ratio, and fracture toughness are listed in Table 5 in comparison with those obtained by other authors. A comparison of our data with the few available experimental results shows that good agreement is observed between the calculated and experimental characteristics.  
Figure 8: DFT results of total energy (in ev/atom) versus atomic volume in Å\({}^{3}\) for \(\beta\)-FeSi\({}_{2}\), \(\alpha\)-FeSi\({}_{2}\), and \(\Theta\)-FeSi\({}_{2}\), and their bcc- and fcc-based approximant counterparts. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).  
Regarding the elastic properties, the small value of G for \(\gamma\)-FeSi\({}_{2}\) indicates that this phase can be unstable against a small shear strain. Given the calculated elastic constants for the orthorhombic \(\beta\)-FeSi\({}_{2}\) phase, the anisotropic factors are estimated [61] for the (100) facet (A\({}_{100}\) = 0.960); for the (010) facet (A\({}_{010}\) = 0.943); and for the (001) facet (A\({}_{001}\) = 1.013). The value A\({}_{\rm i}\) = 1 suggests completely isotropic properties. In the case of \(\beta\)-FeSi\({}_{2}\), we observe anisotropy for each facet. Judging from the calculated anisotropic factors, most likely the (010) and (100) facets will be the main sliding planes. Based on the elastic constants, the linear bulk moduli along the different axes were also estimated: along the a-axis, B\({}_{\rm a}\) = 464.8 GPa; along the b-axis, B\({}_{\rm b}\) = 621.8 GPa; and along the c-axis, B\({}_{\rm c}\) = 557.9 GPa. This finding indicates that the compressibility of \(\beta\)-FeSi\({}_{2}\) is anisotropic.  
In Table 5, the estimated fracture toughness is presented for the three phases of FeSi\({}_{2}\) in comparison with available experimental values. For \(\alpha\)-FeSi\({}_{2}\), the predicted value is in good agreement with the experiment. The fracture toughness of the \(\gamma\)-phase is the smallest compared to the values of KIC for the \(\alpha\)- and \(\beta\)-phases. The latter phases exhibit fracture toughness comparable to that of TiC (2-3 MPa\(\cdot\)m\({}^{1/2}\)) [46].
## 1 Introduction
References [12, 13, 14] presents control methods to provide frequency regulation service for power grid by adjusting the charging and discharging power of EVs. Since the number of EVs in these methods [12, 13, 14] is not large, and as the number of EVs increases, the control complexity will increase exponentially, which presents a new challenge to the design of frequency regulation strategies, due to the different SoC and different expected time length of gird access of each electric vehicle. To realize V2G, the bidirectional inverters, which are the interface between the electric vehicle and the power grid is the key energy exchange point and should be well controlled. At present, the inverter control method mainly considers the 'p/q method'. However, inverters controlled by p/q method in the power grid behave like the controlled current source [15], which does not have the ability to maintain voltage and frequency stability, so p/q control does not provide effective power support in the event of a power grid failure. With the increasing penetration of distributed power sources, virtual synchronous machine (VSM) technology [16,17] have received increasing attention, due to its ability to improve grid stability. VSM is a grid-friendly inverter control technology. The operating mechanism of the VSM is to simulate the inherent electromagnetic transient characteristics of the synchronous motor, so that the inverter has the same internal synchronous operation mechanism and external adjustment characteristics as the conventional synchronous generator, so that the inverter operates closer to the real synchronous generator. It can provide the necessary damping and inertia support to the grid [18], which is of great significance for future power systems with the increase of penetration rate of power electronic enabled devices in the power grid [19, 20, 21]. In this paper, VSM is utilized as the power interface between EVs and the power grid. A control method for EVs participating in grid Frequency regulation is proposed, which is based on the clustering algorithm and automatically adjust VSM parameters. Firstly, the K-means clustering algorithm is applied to cluster EVs with different battery SoC and with different average vehicle daily travel miles. Then according to the clustering results, a multi-objective optimization problem is proposed to optimize the set power and the droop coefficient of the VSM. The rest of this paper is organized as follows: In Section 2, K-means algorithm is introduced firstly; and then the EV group self-classify strategy is proposed; The VSM based optimal EV charging strategy is proposed in Section 3; Simulations are carried out in Section 4, and the conclusion is stated in Section 5.
# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 1 Introduction
In this paper, we propose two kinds of methods that can improve the performance of the weakly supervised SED, as well as reduce the time and space complexity. The proposed model is a modification of the U-Net structure [16; 26] suited for sound event detection. The first method is a limited upsampling from the lower layers to the higher ones in the decoder part of U-Net. Because SED performs temporal segmentation only, we did not apply upsampling along the frequency axis in the U-Net decoder, and thus, the output map sizes were reduced greatly without performance loss. According to the experimental results, a total of 40% of the convolution output map size and 12.5% of the fully connected layer output were required to provide slightly better SED performance. The second proposed method is a global pooling technique called global threshold average  
Figure 1: Examples of strong and weak labels. A strong label consists of a sequence of sound event types and onset and offset times in the given recording. A weak label consists of sound event types only. pooling (GTAP). The conventional GWRP requires sorting the total output map, and its time complexity grows with the growth of the output map size. The proposed method uses a fixed threshold to determine the set of the output map units for the average pooling. The threshold computation requires the mean and the standard deviation of the map values, which is much faster than the sorting operation. Moreover, higher SED performance can be obtained by adjusting the threshold with a single hyperparameter. The major contribution of the proposed methods is the reduction of the computation time without any performance loss. The size of the bottleneck layer of U-Net is the same for the proposed LUU-Net, so the amount of information for the audio events transferred to the final output is the same. The proposed LUU-Net limits the expansion along the frequency axis only, and the time resolution in the time domain is kept unchanged. While doing so, the network size of the encoder is reduced by up to \(\frac{1}{8}\), resulting in a 40% reduction in the number of parameters. The proposed GTAP further improves the computation time by pruning unnecessary output map components, with the SED and AT performances being improved over the conventional CNN and U-Net. The rest of this paper is organized as follows. Section 2 describes the conventional SED method based on U-Net and GWRP. Section 3 explains the proposed U-Net with limited upsampling and the proposed global threshold average pooling (GTAP). Section 4 gives the experiments' setup and the results with a detailed analysis. Finally, Section 6 summarizes our contribution.