esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Datasets  
The Moderate Resolution Imaging Spectroradiometer (MODIS) Net Primary Production (MOD17A3 HGF) was chosen to analyze the long-term NPP dynamics, which was applied to explore the contribution of climate and human activities on forest dynamics during 2000-2019. This dataset was provided by the United States Geological Survey (USGS) ([[https://lpdaac.usgs.gov/products/mod17a3](https://lpdaac.usgs.gov/products/mod17a3) hgfv006/]([https://lpdaac.usgs.gov/products/mod17a3](https://lpdaac.usgs.gov/products/mod17a3) hgfv006/), accessed on 15 September 2021). The NPP of MOD17A3 was calculated by the BIOME-BGC model with 500 m spatial resolution and 1a temporal resolution [19; 20]. NPP dataset was widely used to explore  
Figure 1: Location of the study area.  
vegetation ecosystem variation [9; 19; 21]. Hence, we applied the nearest neighbor method to resample the NPP dataset to 1 km, with the same spatial resolution as the forest dataset.  
Monthly meteorological data consists of precipitation, temperature, and solar radiation from 2000 to 2019, which are available in the National Earth System Science Data Center ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), accessed on 15 September 2021) [22; 23]. With a 1 km spatial resolution, the average temperature, total precipitation, and solar radiation during the growing season (from April to October) (2000-2019) were calculated. To evaluate the impact of climate on forest NPP more comprehensively, annual values of the Standardized Precipitation Evapotranspiration in Index (SPEI) were applied [24]. Annual SPEI of different time scales were considered such as 1, 3, 6, 9, and 12 months. The SPEI data were obtained from the website [[https://digital.csic.es/handle/10261/202305](https://digital.csic.es/handle/10261/202305)]([https://digital.csic.es/handle/10261/202305](https://digital.csic.es/handle/10261/202305)) (accessed on 15 September 2021). Its spatial resolution is 0.5\({}^{\circ}\).  
To attain the distribution of old and new forests, 2000 forest data and 2017 forest data were applied. The forest dataset was retrieved from the National Earth System Science Data Center ([[http://loess.geodata.cn/index.html](http://loess.geodata.cn/index.html)]([http://loess.geodata.cn/index.html](http://loess.geodata.cn/index.html)), accessed on 15 September 2021). In this study, Fold and Fnew were separated based on the 2000 and 2017 forest images. The old and new forest distributions are illustrated in Figure 2. The forests include the coniferous forest, broadleaf forest, and coniferous and broad-leaved mixed forest. Forests are mainly distributed in two climate zones: subtropical zone and plateau climate zone. Coniferous forests are characterized by drought-tolerant and barren-tolerant and are dominated by Masson pine, larch, and fir species. And broadleaf forest has the characteristics of high-temperature resistance and are dominated by camphor wood, oak and cypress species. In addition, forest volume stock and forest areas of different stand ages were obtained from the China Forest Inventory data (2014-2018).
## 4 Discussion
Thus, such research with total RNA usually involves the employment of own total RNA from liver cells [45]. Nevertheless, the most common clinically or at least experimentally methods for liver regeneration do not comprise HSCs or their derivatives at all. For instance, there exist such methods as hepatocyte grafting, gene editing, tissue engineering, liver organoid development, and siRNA treatment for certain genes, and all of them find their application [46]. This excellent review of [PERSON] and [PERSON] also outlines that stem cell therapy is appropriate for liver regeneration with bone marrow MSCs or induced pluripotent stem cells used as well as HSCs. However, it is more difficult to obtain MSCs or induced pluripotent stem cells than HSCs. Therefore, our finding proposes an option seldom considered earlier, and its machinery should be elucidated in further research. In liver regeneration, there are plenty of pathways for metabolic and cellular crosstalk [47]. The most efficient way is regeneration due to the proliferation of cholangiocarcinoma-cytes recruiting the Notch pathway [48]. The Notch pathway is suppressed in senescent cells, is dependent on macrophages, and is sensible to molecular signals from HSCs, including their protein and RNA signals [49]. Thereby, the Notch pathway may be an important target of HSC-derived total RNA in the liver. Undoubtedly, the activity of total RNA and aHSCs in the liver may have an impact on oncogenic potential. The role of this impact remains unclear and requires further elucidation paying attention to a close interplay between proliferation in the liver and abdominal malignancies [50]. _Limitations and Perspectives_  
1. The study aimed to compare different HSC-originating modes to enhance regeneration. Thus, the methodology applied is not relevant for revealing all the mechanisms of HSC communication, and the ratio between paracrine, direct, and immune-mediated machinery remains unclear. An analysis of the RNA sequence in new proliferating cells in the liver after HSC-derived total RNA would help to elucidate the problematics. 2. The study has not implied assessment of specific markers' content by cellular and molecular methods, e.g., immunoblotting. These approaches are planned for future experiments in the field. 3. The tumorigenic potential of aHSCs and total RNA is challenging. The experiment is to be widened for rat follow-up for several consecutive months after the liver recovery to estimate the rate of oncological risks before extrapolation to humans. 4. Clinical implementation of the study results is limited due to legislative procedures. For instance, human stem cells are restricted for usage in certain countries' jurisdictions, and the studies should absolutely respect the restrictions before planning any human extrapolation.
## 7 Limitations and Future Work  
Although the results of the evaluation were rather positive, it is clear that the study presented has some limitations. The main limitation refers to the approach utilized for evaluating the effectiveness of the game. The results regarding the player experience and the scenario of SpAI War can be considered to be solid, since they refer to subjective aspects of a game, and all the participants are experienced game players. Furthermore, it is promising that the participants considered that they learned through game playing, but this has to be confirmed with a different experimental design for an objective assessment of actual learning. Specifically, a control and experimental group study could be designed in the future, while a pre-test and post-test could be utilized for investigating potential statistically significant differences in students' performance. Moreover, incorporating learning analytics capabilities to the game [8; 9] would give us the chance to investigate the interactions of players with the game elements and draw even more interesting conclusions both in terms of player experience and learning. Learning analytics could help us record data: that indicate how often, when and for how long players resorted to the educational material (theory, examples) and if this affected the learning outcomes; how many playedsome levels more than once, and if they improved; which levels/tasks, and consequently AI algorithms, caused more difficulties to players and so on.  
Furthermore, taking into account that the available games for AI [27; 28; 29] and SpAI War adopt a different game-based learning approach for familiarizing players with AI concepts and algorithms, it would be interesting to carry out further research for: investigating the actual long-term learning in all four games; comparing the learning effects in these games and drawing conclusions on the most effective game-based learning approaches and types of games for AI. This would also give the chance to compare the game-based learning approach with the traditional approach for the field of AI in terms of learning effectiveness, which is currently not possible due to the lack of related research.
# Possibility of Bridge Inspection through Drive-By Vehicles
## 2 Research Objectives  
This study focused on the drive-by inspection of bridges through numerical simulations. 2D and 3D vehicle-bridge-interaction models in ABAQUS were created first and validated through literature results. Different bridge damage conditions, simulated as stiffness reductions, were embedded in the model, and the connection between drive-by vehicles with damage details was sorted. A parametric study was then conducted to evaluate damages through the FFT and STFT method. Finally, road roughness was included in the 3D vehicle-bridge-interaction model to study the possibility of bridge damage detection through the suggested drive-by method. This study's contribution lies in providing a new STFT energy spectrum method to identify bridge damages, evaluate the correct entering conditions for VBI simulations, and predict the possibility of identifying damages of multi-span bridges through drive-by vehicles.
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
###### Abstract
During the 2020 edition of the DCASE Challenge, we introduced an approach that increased the performance of a SED system based in convolutional-recurrent neural networks (CRNN) by using several time-frequency resolutions in the process of mel-spectrogram feature extraction, and then combining the outputs obtained with up to five different time-frequency resolution points. The idea behind this method is that each time-frequency resolution is more adequate for the detection of certain event categories, depending on their temporal and spectral characteristics. Therefore, the combination of resolutions leads to systems that show better overall performance. In this paper, we offer an analysis of the performance of single-resolution and multiresolution SED systems when facing adverse acoustic scenarios that critically affect the spectra of the acoustic signals (high-pass and low-pass filtering) or their dynamic range (clipping and dynamic range compression), as well as situations in which the acoustic events are noticeably overlapped in time. For this purpose, we process the audio segments of the Public Evaluation set of DESED to achieve the mentioned acoustic conditions, and then compute SED metrics over the resulting datasets. Through this study, we aim to determine whether the improvement on performance obtained by the multiresolution approach is robust to the proposed types of acoustic degradation. These adverse settings represent plausible scenarios that could be found when applying the detectors in other data. For instance, audio from web sources can be recorded using a wide variety of capturing devices that affect their quality and their acoustic characteristics. In the case of a real life application in which sound events are detected in a stream of recorded audio, similar kinds of channel distortions could appear due to the malfunction of the capturing device. The application of artificially generated acoustic conditions to DESED evaluation data is inspired by some of the scenarios described in the DESED Synthetic evaluation set, in particular, high- and low-pass filtering, dynamic range compression, and clipping. Whereas this dataset was already used to analyze the robustness of state-of-the-art SED systems [29] using synthetically generated audio segments, in this work we apply different degradations to the DESED Public evaluation set, which is formed by real audio recordings obtained from YouTube videos. The rest of the paper is organized as follows. Section 2 explains the materials used in this work and the methodology of the experiments: the Sound Event Detection task of the DCASE Challenge 2020 (Section 2.1) and its baseline system (Section 2.2), our multiresolution approach (Section 2.3), and the motivation of this analysis and the different acoustic scenarios that we are considering (Section 2.4). In Section 3, the results of the experiments are provided and discussed. Finally, the conclusions of this work are highlighted in Section 4.
## 4 Experimental Results
### Results of TIR Ship Detection
#### 4.2.3 Quantitative Comparison to TIR Ship Target Detection Baseline Methods  
To evaluate the performance of TIR ship target detection methods in the experiments, misclassification error (ME) [3], relative foreground area error (RAE) [3], missing alarm ratio (MAR) [4] and false alarm ratio (FAR) [4] are utilized as quantitative evaluation metrics. ME denotes the percentage of pixels that are wrongly classified, that is, the background misclassified as foreground and the foreground misclassified as background. RAE represents the detected area accuracy between the detected image and the ground-truth image. MAR reflects the probability of missed targets among which ship targets truly exist and FAR illustrates the rate of detected targets where ship targets do not exist. Hence, smaller ME, RAE, MAR, and FAR imply the better results, and defined as:  
\[ME=1-\frac{|B_{O}\cap B_{T}|+|F_{O}\cap F_{T}|}{|B_{O}|+|F_{O}|} \tag{28}\]  
\[RAE=\begin{cases}\frac{A_{O}-A_{T}}{A_{O}},&A_{T}<A_{O}\\ \frac{A_{T}-A_{O}}{A_{T}},&A_{T}\geq A_{O}\end{cases}, \tag{29}\]  
\[MAR=\frac{MT}{MT+DT}, \tag{30}\]  
\[FAR=\frac{FD}{DT+FD}, \tag{31}\]  
where \(B_{O}\) and \(F_{O}\) are the background pixels and ship target pixels in the ground-truth image (manually labeled), respectively. \(B_{T}\) and \(F_{T}\) are the background pixels and ship target pixels of detected image, respectively. \(|\cdot|\) represents cardinality of a set. \(A_{O}\) is the area of true target (manually labeled), and \(A_{T}\) is the area of detected ship target. \(MT\) is the number of missed targets, \(DT\) is the number of detected targets, and \(FD\) is the number of false detection results. The average values of ME, RAE, MAR, and FAR of different methods for various sequences are listed in Table 4. Table 4 shows that the proposed method obtains smaller average values for ME, RAE, MAR, and FAR on all nine sequences than the other compared eight methods. Consequently, the experimental results verify that the proposed method not only has better ship target detection capability compared with other classical and state-of-the-art methods, but also can work stably for different complex maritime scenarios.  
Figure 14: The detection results of different methods for the Figure 12. (**a1–a9**) show the representative frames of the nine TIR sequences, respectively, (**b1–b9**) are the detection results of THF/BHF, (**c1–c9**) are the detection results of 2 DME, (**d1–d9**) are the detection results of 2 DO, (**e1–e9**) are the detection results of MSS, (**f1–f9**) are the detection results of IMFS, (**g1–g9**) are the detection results of IFCM, (**h1–h9**) are the detection results of CVM, (**i1–i9**) are the detection results of HCST, (**j1–j9**) are the detection results of proposed method.
## 4 Experiments
### The Effect of Different Modules on the Detection Results
#### 4.5.4 The Impact of ROI Quantity  
The number of ROIs affects the inference speed and detection performance of the model. A large number of regional proposals generated in RPN stage will be sorted according to their confidence levels, and only a few high-confidence region proposals will be used to extract ROI features. More ROI features will gain better detection results, but it will also slow down the inference speed of the model. As shown in Table 5, we performed ablation experiments with the number of ROIs as a variable. It can be seen that as the number of ROIs increases, the detection performance of the model shows an upward trend, while the detection speed gradually decreases. When the number of ROIs is increased from 500 to 1000, the detection speed will be damaged to a certain extent and the performance improvement will be limited. Additionally, when the number of ROIs is reduced to 50, the best detection performance is still 96.7%, which proves the robustness of our proposed model. In actual industrial production, the steel strip defect detection model needs to achieve a balance between detection performance and inference speed. Therefore, we set the number of ROIs to 500.
###### Abstract  
In this study conducted in the Shendong mining area, this paper tackles the challenge of estimating mining subsidence parameters in the absence of empirical values. The study employs a tailored pattern recognition method specifically designed for mining subsidence in a specific working face. The goal is to determine a globally approximate optimal solution for these parameters. Subsequently, this study utilizes the approximate optimal solution as an initial exploration value and harnesses the modular vector method to obtain stable, accurate, optimal solutions for the parameters. The results of the study demonstrate the effectiveness of the improved modular vector method. In simulation tests involving the subsidence coefficient, the main influence angle tangent value, the propagation angle of mining influence, and the deviation of the inflection point, the relative errors do not exceed 1.2%, 1.9%, 1.7%, and 7.9%, respectively. Furthermore, when subjected to random errors of less than 20 mm, the relative errors for each parameter remain below 2%. Even in conditions with 200 mm gross error, the relative error for each parameter does not exceed 5.1%, indicating high precision. In an engineering example, the root mean square error of the improved modular vector method's fitting result is 64.31 mm, constituting a mere 1.79% of the maximum subsidence value. This performance surpasses that of the genetic algorithm (70.47 mm), particle swarm algorithm (72.82 mm), and simulated annealing algorithm (75.45 mm). Notably, the improved modular vector method exhibits superior stability and reduced reliance on the quantity of measured values compared to the three aforementioned algorithms. The inversion analysis of predicted parameters based on the improved modular vector method, coupled with the probability integral method, holds practical significance for enhancing the accuracy of mining subsidence prediction.  
mining subsidence; parameter inversion; modular vector method; pattern recognition; improved algorithm +
Footnote †: journal: Applied Sciences
## 2 Heterogeneous Systems: TiO\({}_{2}\) Photocatalysis
### The Role of Oxygen  
According to the well-known mechanism for TiO\({}_{2}\) photocatalysis, the presence of oxygen plays a key role in the oxidizing process by reacting with photogenerated electrons and reducing electron/hole recombination according to the general scheme:  
\[TiO_{2}\overset{hv}{\rightarrow}h^{+}+e^{-} \tag{21}\]  
\[O_{2}+e^{-}\to O_{2}^{\bullet-} \tag{22}\]  
\[A+h^{+}\to A_{ox} \tag{23}\]  
However, most microchannels are closed systems with respect to oxygen. As a result, oxygen concentration and, therefore, reaction rate can decrease in the channel as the reaction occurs, depending on the adopted conditions. In the field of photo-microreactors for pollutant degradation in water, very few paper address the role of oxygen in intrinsic kinetics determination. [PERSON] et al. [12] ensure that oxygen consumed in the reaction is replaced by using an oxygen-permeable surface of PDMS, a commercial polymeric coat. In the above-mentioned study, [PERSON] et al. [44] ensure oxygen overpressure in a batch system used as a benchmark, without mentioning how oxygen constant concentration is ensured in the microreactor system.  
One of the first studies assessing the role of gaseous oxygen in photocatalytic microreactors for contaminant removal was presented by [PERSON] et al. [48]. In this work, they take advantage of the high surface-to-volume ratio that can be realized in photo-microreactors and the consequent enhancement of mass transfer between phases to saturate oxygen concentration by generating alternating plugs of gas and liquid in the reactor. The results show that the addition of gaseous oxygen dramatically enhanced the removal of methylene blue, demonstrating that there are conditions under which oxygen consumption in the liquid phase does not allow to consider zero-order kinetics with respect to its concentration. More recently, [PERSON] et al. [61] presented a more detailed fluid dynamics modeling of such a system. In their study, it is evident that alternated oxygen bubbles can both increase oxygen concentrations in proximity of the catalytic layer and change the fluid dynamics of the stagnant liquid film covering the solid, increasing its velocity and mixing.  
As a general conclusion, oxygen consumption within closed-system microdevices must be properly assessed, and the resulting assumption verified, in order to consider investigations on intrinsic kinetics to be reliable and transferable across scales.
## 2 Proposed Antenna Design  
A monopole UWB-MIMO antenna system with triple notching features for C-band, WLAN, and satellite networks is presented. The notching characteristic is achieved by etching Complementary Split-Ring Resonators (CSRR) at the patch elements and ground side, and the third notch is achieved with the C-shape. The 4 \(\times\) 4 MIMO antenna system is arranged orthogonally to achieve pattern and polarisation diversity. Because of the symmetric positioning of the antenna port components, as well as the mutual coupling of Complementary Split-Ring Resonators, which block the RF signal between the port elements, the MIMO antenna obtained good isolation. In this article, the etching of CSRR achieves both notching qualities and excellent isolation in the MIMO system. The proposed antenna is fabricated on an FR-4 substrate with a dielectric constant of 4.4, loss tangent of 0.02, and a thickness of 1.6 mm. The geometry and dimensions of the monopole, 4 \(\times\) 4 MIMO, CSRR, and Arch shape or C-shape are illustrated in Figure 1. The antenna design is modeled and simulated using ANSYS HFSS-21. The physical prototype of the proposed antenna is depicted in Figure 1d. The leading edge of the patch is crescent-shaped and incorporates a CSRR in the feeding line, while another CSRR is embedded in the ground plane. The research work is carried out in the following steps:  
1. The UWB design achieves a bandwidth of 2 GHz to 13 GHz by feeding a rectangular patch with a ground plane.
2. Notching characteristics are introduced in the UWB frequency range by placing a CSRR at the feeding point of the patch and another CSRR in the ground plane. Additionally, a curve shape is employed to create a notch for the satellite network band. The CSRR in the feeding line rejects the C-band, while the CSRR in the ground plane rejects the WLAN bands. Both CSRR elements increase the coupling effects and filter high-traffic bands.
3. The 4 \(\times\) 4 MIMO configuration is achieved by arranging the monopole antennas in an orthogonal configuration, providing pattern diversity and polarization diversity.
4. High isolation within the MIMO system is achieved by utilizing the mutual coupling of CSRR elements at the patch and ground plane, effectively reducing coupling in the MIMO design system.  
Interference poses challenges in wireless communication reliability. To address this issue in UWB technology, a notching approach is proposed. The UWB frequency range, designated by the FCC, spans from 3 GHz to 10 GHz. However, WLANs, WiMAX, and satellite links utilize different portions of the radio spectrum, resulting in potential interference when heavily utilized. Interactions between the UWB and occupied narrow bands lead to interference. To mitigate this interference, various parasitic slots are introduced at specific positions, and CSRR elements are incorporated into the final design to reduce interference between the UWB and narrow bands.