esa-sceva/satcom-chunk-collection huggingface dataset:
# Coal Dust and Methane as a Hazard in Coal Preparation Plants
## 2 Coal Dust Control in Hard Coal Preparation Plants
In fact, this trend is particularly visible in case of the number workplaces in zone 20 (high risk) since the Pearson coefficient shows a negative value of -0.592, indicating that the number is increasing despite the coal production shrinking. On the other hand, Pearson coefficient for zones 21 + 22 has a value of 0.591, indicating decrease of low risk zones with decreasing production trend. Figure 4: Share of workplaces in explosive zone 20 (high risk) and zones 21 + 22 (lower risk) in Poland in 2003–2022. Figure 3: Total number of workplaces in explosive dust atmosphere zones 20, 21, and 22 plotted against total coal production in Poland in 2003–2022 [13; 14; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. To evaluate trends in the Maximum Allowable Concentration (MAC) for dust exposure in coal preparation plant workplaces, the MAC values were analyzed in relation to annual coal production. The proposed IMAC index is calculated by dividing the reported number of workplaces exceeding dust MAC by the respective coal production for each year. The calculated IMAC index for 2003-2022 is presented in Figure 6. In this case, no clear trend is observed, which is further reflected in the Pearson correlation coefficient of 0.145, indicating a weak relationship between production and reported number of workplaces in dust MAC. To assess the risk of methane Explosive Atmospheres in the workplace in coal preparations plants, the number of workplaces in zone 2 was compared at first with annual production resulting in the IMC index. Analysis of the data shows that, during the analyzed period, no zone 0 methane explosion hazard was found at workplaces in the processing plant. Zone 1 occurred at 24 workplaces in 2007, at two workplaces in 2009, 2011, and 2012, and at one workplace in 2015, 2016, 2017, 2021, and 2022. Therefore, occurrences of zones  
Figure 5: Calculated indexes \(\text{IEL}_{20}\) (high risk) and \(\text{IEL}_{21+22}\) (low risk), indicating dust Explosive Atmospheres at workplaces in hard coal preparation plants in Poland in relation to total coal production from 2003 to 2022. Figure 2.0 Changes in calculated IMAC index for 2003–2022. 0 and 1 were neglected from the analysis. In the case of zone 2, there is a clear increasing trend showing index growth despite the decrease in coal production. This comparison does not provide any logical explanation as methane content in coal depends on the coal origin, mining depth rather than production rates. Therefore, it was decided to compare the number of workplaces in methane explosive atmosphere with data on the annual average methane content in coal provided in the reports of State Mining Authorities starting from 2000 to 2022 [21]. Results of the comparison are shown in Figure 2.1  
There is a clear correlation between increasing methane content in coal and the IMC index for coal preparation plant which is reflected in Pearson coefficient of 0.71 for the analyzed set of data.
###### Abstract  
This paper compares the potential for building energy saving of various passive and active strategies and on-site power generation through a grid-connected solar photovoltaic system (SPVS). The case study is a student welfare unit from a university campus located in the tropical climate (Aw) of Guayaquil, Ecuador. The proposed approach aims to identify the most effective energy saving strategy for building retrofit in this climate. For this purpose, we modeled the base line of the building and proposed energy saving scenarios that were evaluated independently. All building simulations were done in OpenStudio-EnergyPlus, while the on-site power generation was carried out using the Homer PRO software. Results indicated that the incorporation of daylighting controls accounted for the highest energy savings of around 20% and 14% in total building energy consumption, and cooling loads, respectively. Also, this strategy provided a reduction of about 35% and 43% in total building energy consumption, and cooling loads, respectively, when combined with triple low-e coating glazing and active measures. On the other hand, the total annual electric energy delivered by the SPVS (output power converter) was 66,590 kWh, from where 48,497 kWh was supplied to the building while the remaining electricity was injected into the grid.  
energy savings; daylighting; photovoltaic system; EnergyPlus; Homer PRO; Net Zero Energy Buildings +
Footnote †: journal: Applied Sciences
## 5 Summary and Conclusions  
Ground penetrating radar integrated with a Real-Time Kinematic (RTK) Global Navigation Satellite System (GPR-RTK/GNSS) successfully determined drainage pipe patterns at three agricultural field sites. At one site, a rectangular pattern of drain lines was mapped, at the second site, a herringbone pattern was delineated, and at the third site, random drain lines were found. A novel field data collection approach was developed, taking advantage of GPR-RTK/GNSS, by which spiral or serpentine GPR measurement transects (or spiral/serpentine segments of GPR measurement transects) were employed to establish the presence and direction of drain lines. Using GPR-RTK/GNSS to map drain lines can therefore provide useful information needed for retrofitting or repair of agricultural subsurface drainage systems. Furthermore, in areas where GPR can detect drainage pipes, GPR-RTK/GNSS is a substantial improvement (faster, better able to resolve drain line trends, and causes no pipe damage) over the traditional tile probe and trench excavation methods for finding drainage pipes.  
Conceptualization, B.A., D.W., H.S., S.M., J.E.; Methodology, B.A., D.W., L.M.; Validation, B.A.; Formal Analysis, B.A.; Investigation, B.A., D.W., L.M., G.M.; Resources, H.S., S.M., J.E., C.C.; Writing-Original Draft Preparation, B.A.; Writing-Review & Editing, B.A., D.W., L.M., H.S., S.M., G.M., J.E., C.C.; Supervision, B.A.; Project Administration, B.A.  
This research received no external funding.  
The authors declare no conflict of interest.  
The use of equipment manufacturer and software developer names in this article are provided solely for informational purposes and does not imply endorsement by the authors or the organizations they represent.
## 1 Introduction
To this end, a data-oriented approach was used in this work to investigate the capabilities of four Machine Learning (ML) algorithms to predict the mechanical properties of soil-cement-fibre mixtures, particularly the elastic modulus, E\({}_{u.50}\): namely, Artificial Neural Networks (ANNs) of type Multilayer Perceptron (MLP) [25], Support Vector Machines (SVMs) [26] and Random Forest (RF) [27]. Multiple Regression (MR) has been used as the basis of comparison for the ML algorithms above. Therefore, such advanced algorithms are extensively used in diverse areas of learning [28; 29] with very encouraging results by leveraging on consolidated experience. In engineering, these tools have been used successfully to solve complicated geotechnical problems associated with slope stability and mechanical properties of soil-cement mixtures [30; 31; 32; 33; 34; 35]. Given the prior application to UCS and ITS of unreinforced and fibre-reinforced soil-cement mixtures [4; 35; 36], the emphasis and main originality of this work is to predict the elastic modulus (E\({}_{u.50}\)) of soil-cement mixtures reinforced with short fibres using ML algorithms. The elastic modulus or Young's modulus of soil-cement mixtures is usually defined as the undrained Young's modulus evaluated for 50% of the unconfined compressive strength (UCS), E\({}_{u.50}\).
## 5 Discussion
### Limitations, Future Research, and Implications  
The study has several limitations that should be taken into consideration in future research. The limitations of this study include the following:  
* Mainly English literature in the theoretical background section;
* Unexplained variance in the results, which indicates that there may be other factors that affect the dependent variable (this is expected with social studies);
* Pie charts and histograms were not developed;
* Analysis was conducted only on enterprises within Serbia. However, transitional economies tend to have the same issues when it comes to competitiveness.
* Extensive equations were not included (the methods derive from well-established equations).  
Considering the noted limitations, the study still contributes to the existing body of the literature in the domain of statistical methods in data analysis/data mining.  
For future research, the following is recommended:  
* Consider the limitations noted above;
* Use a structured approach to future research;* Introduce additional predictor factors such as human resource management, employee productivity, and marketing management;
* Analyze datasets consisting of enterprises from other countries and compare them to this current paper's findings;
* Conduct meta-analysis when similar studies are published;
* Apply CHART, CHAID, and other classifiers in future studies. Practical implications of the results presented in this manuscript include:
* Governments (increasing strategic incentive programs for ICT adoption across industries would improve national competitiveness).
* Schools and universities can address this paper by presenting the necessity and imperative role of ICTs in the modern global economy.
* Fellow scholars can address this study for their own research. This paper provides an adequate basis and guidelines for future analysis in this domain.  
Overall, the study expands the existing body of literature from two significant aspects. Firstly, it applies statistical methods to a robust dataset. Secondly, it addresses trending aspects of the modern business environment. No other similar studies were conducted, and the findings can be used in future research.
# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 3 The Proposed Gesture Recognition Algorithms and Systems
### Residual PairNet with Inception  
The employment of short-cut connections and inception module may have the advantages of both high classification accuracy and low computational complexities. An example of the design is shown in Figure 9, where both the inception module and short-cut connection are located at layer 5 of the network. Figure 10 shows the architecture of the inception module with short-cut. There are three paths in the module, where one of the paths serves as the short-cut. This allows layer 4 to be directly connected to layer 6. The remaining two paths contain convolution modules and a zero-padding module.  
Figure 8: The parameters of the Inception module, which consists of four paths. Each path is associated with convolution modules, average pooling module, and/or a zero-padding module.  
Figure 9: The parameters of Residual PairNet with inception. The input \(X_{t}\) has the same dimension and number of channels as those of the input of PairNet. The network contains seven layers, where the inception module with short-cut connection is located at layer 5. The architecture of inception module is revealed in Figure 10. The number of gestures to be classified is \(Q=11\). Layer 7 is a fully connected layer producing 11 outputs.  
By comparing Figures 5, 7 and 9, it can be observed that the major difference among PairNet, Pairnet with Inception, and Residual PairNet with Inception is at layer 5. For the PairNet, only a single convolution module is employed at that layer. By contrast, inception module is adopted at layer 5 for Pairnet with Inception and Residual PairNet with Inception. Therefore, the PairNet may have inferior classification accuracy with larger size of weights as compared with the other two. Furthermore, because the Residual PairNet with inception contains short-cut connections, it may has highest classification accuracy among the three algorithms.
## 4 Simulation Result
### Comparison with Conventional WT & ESS for LVRT  
Next, we considered LVRT performance with an ESS. In this case, the ESS will help the WT fulfill LVRT operation. Normally, the ESS can effectively take on the entire burden of LVRT operation. However, the ESS control is more efficient when it is utilized for multiple objectives. If the ESS is used for multiple objectives, it is hard to assume its SoC is near the value of 0.5 pu. To highlight the effectiveness of the proposed method, we considered the case when the ESS SoC is 0.8 pu. Conventional methods control the ESS by regulating the dc link voltage. From Figures 13-19, the comparison of the performance between the conventional WT and ESS LVRT control and the proposed coordinated LVRT method is illustrated. As shown in Figure 13, the WT produced zero active power from the GSC and a reactive power of 0.2 pu during the fault period as shown in Figure 14. During the grid fault, the GSC was controlled to produce zero active power. After the grid fault, the GSC was controlled to regulate the dc link voltage. In Figure 15, the RSC was controlled to regulate the dc link voltage during the grid fault. The active power from the RSC oscillated greatly because of the variation in the dc link voltage as shown in Figure 16. This was because of the ESS SoC limit; the ESS SoC approached its limit at around 1.4 s as shown in Figure 17. Moreover, the rotor speed was violated in the conventional method; this violation is another weakness of the conventional method as shown in Figure 18. Figure 19 shows the power coefficient variation during the grid fault. Therefore, we conclude that the conventional method can not effectively handle the operational constraints of the WT rotor speed and the ESS SoC especially when the wind speed is high during the grid fault. We validated the benefits of the proposed method considering two case studies which illustrate the constraints violations during grid fault. Previous coordinated LVRT methods [22; 23] using an ESS can result in constraints violation or can require additional device when there is high wind speed and the ESS SoC is close to 1 pu.  
Figure 14: Grid reactive power during a balanced voltage sag (80%).  
Figure 13: Grid active power during a balanced voltage sag (80%).  
Figure 16: dc link voltage during a balanced voltage sag (80%).  
Figure 17: SoC during a balanced voltage sag (80%).  
Figure 15: Generator active power during a balanced voltage sag (80%).
## 4 Experiments
### Ablation Study  
#### 4.4.1 Quantitative Ablation of Modules  
To systematically validate the effectiveness of each module proposed in this paper, we conducted comprehensive ablation studies on the NRGD and Flare7K++ dataset. The L-Downsampling and L-Upsampling strategies used in our method are collectively referred to as L-Sampling. We evaluate the contribution of each component to the overall performance by individually removing the GPA and L-Sampling modules. Here, removing L-Sampling indicates replacing it with the original convolution-based downsampling and transposed convolution-based upsampling modules.  
Table 3 presents the results on the NRGD. It can be observed that when the GPA module is removed (w/o GPA), the PSNR decreases to 24.2491, SSIM drops to 0.9114, while LPIPS increases to 0.1361 and MSE rises to 508.977. This indicates that removing GPA degrades the model's ability to capture global structures and brightness distributions, leading to noticeably lower de-glared image quality. When the L-Sampling strategy is removed (w/o L-Sampling), the PSNR decreases to 25.1757, SSIM drops to 0.9317, LPIPS increases to 0.1015, and MSE increases to 454.0037. Although this shows clear improvements compared to the removal of GPA, there is still a noticeable performance gap compared to  
Figure 8: Visual comparison with state-of-the-art glare suppression methods on the Flare7K++ dataset.
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 3 Results
### Number of Publications (Sensor)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sensor** & **Agency** & **Satellite** &
\begin{tabular}{c} **Operating Data or** \\ **Launch Data** \\ \end{tabular} \\ \hline \multirow{4}{*}{Historical} & CZCS & NASA (USA) & Nimbus-7 (USA) & 24/10/78–22/6/86 \\ \cline{2-5}  & MERIS & ESA (Europe) & ENVISAT (Europe) & 1/3/02–9/5/12 \\ \cline{2-5}  & OCM & ISRO (India) & IRS-P4 (India) & 26/5/99–8/8/10 \\ \cline{2-5}  & OCTS & NASDA (Japan) & ADEOS (Japan) & 17/8/96–29/6/97 \\ \cline{2-5}  & SeaWiFS & NASA (USA) & OrbView-2 (USA) & 01/08/97–14/02/11 \\ \hline \multirow{4}{*}{Current} & GOCI & KARI/KIOST (South Korea) & COMS & 26 June 2010 \\ \cline{2-5}  & MODIS-Aqua & NASA (USA) & Aqua (EOS-PM1) & 4 May 2002 \\ \cline{2-5}  & MODIS-Terra & NASA (USA) & Terra (EOS-AM1) & 18 Dec 1999 \\ \cline{2-5}  & MSI & ESA (Europe) & Sentinel-2A & 23 June 2015 \\ \cline{2-5}  & MSI & ESA (Europe) & Sentinel-2B & 7 March 2017 \\ \cline{2-5}  & OCM-2 & ISRO (India) & Oceansat-2 (India) & 23 Sept 2009 \\ \cline{2-5}  & OLCI & ESA/EUMETSAT & Sentinel 3A & 16 Feb 2016 \\ \cline{2-5}  & OLCI & ESA/EUMETSAT & Sentinel 3B & 25 April 2018 \\ \cline{2-5}  & VIIRS & NOAA (USA) & Suomi NPP & 28 Oct 2011 \\ \cline{2-5}  & VIIRS & NOAA/NASA (USA) & JPSS-1/NOAA-20 & 18 Nov 2017 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of historical satellites operating data and current satellites launch data. Adapted from: [[https://ioccg.org/resources/missions-instruments/](https://ioccg.org/resources/missions-instruments/)]([https://ioccg.org/resources/missions-instruments/](https://ioccg.org/resources/missions-instruments/)). Figure 6: Temporal evolution of peer reviewed articles published according to sensor.
## 4 Evaluation
### Hardware-in-the-Loop Simulation  
A hardware-in-the-loop (HIL) simulation is a bridge between numerical simulation and a field experiment. A HIL simulation is more realistic compared with numerical simulation for the participation of hardware and a more realistic flight simulator. Compared with field experiment, HIL simulation is more efficient, safe, and cost effective. HIL simulation was conducted to validate the proposed method.  
Figure 7: Simulation results of the UAV path following and obstacle avoidance using different methods.
## 2 Materials and Methods
### Laboratory Experiment
(**c**) Mineralogical composition of the fine-earth material by the X-ray power diffraction (XRPD). (**d**) Elemental composition of the fine-earth material by the X-ray fluorescence (XRF). The soils were tested in a wind tunnel for dust emission--overall 108 runs were performed by the wind tunnel (9 samples, 3 replicates, 4 wind velocities). These experiments were performed in the portable boundary-layer wind tunnel of the Aeolian Simulation Laboratory at BGU [3; 22]. Boundary-layer wind tunnels enable simulations under standardized quasi-natural wind conditions and provide quantitative information on dust emission rates from the soils. The wind tunnel has a cross sectional area in the order of \(0.5\times 0.5\) m with open-floored working sections of up to 10 m length. Air push or air suction flow in the tunnel is generated by an axial fan up to maximum velocity of 18 m s\({}^{-1}\). Instruments installed in the test section of the tunnel enable quantification of wind characteristics and dust (PM) concentrations. The tunnel operated under four wind velocities, 4.0, 6.5, 8.0, and 9.5 m s\({}^{-1}\), representing a range of common natural winds that are associated with dust emission [13]. Note that the frequency of a specific wind velocity should be considered for calculation of the total dust emission in a period of time (month, year). Each run of specific wind velocity lasted 30 s to record the trend of dust emission. Dust concentrations (\(\mu\)g m\({}^{-3}\)) PM10 were recorded by a light-scattering device, DustTrak DRX 8534 (www.tsi.com), in the range of 0.001-150 \(\mu\)g m\({}^{-3}\) (\(\pm 0.1\%\) of reading) at 1-s intervals, was placed at 25 cm above the tunnel bed. The recorded PM10 concentrations were converted into mass flux emitted from the soil surface (mg m\({}^{-2}\) s\({}^{-1}\)) based on the wind tunnel dimensions, the area of the soil bed, and the wind velocity (for in depth description refer to [PERSON] et al. 2016 [23]). The PM10 flux from the soil surface can be used in parameterization of dust emission and transport models [12; 23].
## 5 Conclusions  
As discussed in the previous paragraphs, increasing the efficiency of photovoltaic modules and extending the service life of photovoltaic modules are important drivers of current photovoltaic technology, although their effect is weakening with increasing levels. However, further technological improvements must be implemented to support the decrease in the LCOE of energy generated by PV systems and to play an important role in generating electrical energy. In the current development of photovoltaics, only crystalline silicon wafer-based technologies meet the limits [19] of long service life (more than 20 years), efficiency (more than 16%), and costs (less than 0.3 USD/W\({}_{\text{p}}\)). Such technologies are not significantly limited in resources, although with the increase in prices of some materials such limitations can be expected. Therefore, crystalline silicon modules will remain a working horse for at least the next five years, accounting for almost 95% of total module production. A gradual transition from the current PERC technology to higher efficiency cell technologies (HJT, TOPCon) can also be expected. This transition will depend on the extent to which savings in area-proportional costs outweigh the costs associated with producing modules of higher efficiency. If problems with the operational reliability of perovskite modules can be solved, then it would be possible to assume a more significant application of this technology in energy applications as well.  
Figure 16: Development of material intensiveness of polycrystalline silicon.  
**Author Contributions:** Conceptualization, V.B.; formal analysis, V.B. and L.C.; validation, V.B. and L.C.; writing--original draft preparation, V.B. and L.C.; writing--review and editing, V.B. and L.C.; visualization, V.B. and L.C.; project administration, L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technology Agency of the Czech Republic, grant number TK03020144.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Single Antenna Design
When Slot-D is small, the reflection coefficient at higher frequencies does not match well; as a result, the second pole of the S11 exceeds the desired frequency range, as can be observed in Figure 3b. When the slot distance is greater than 5 mm, it has less impact on the reflection coefficient, and the dual frequency generates a better matching to 50 Ohm. In those cases, they introduce an additional resonance frequency in the desired range from 7.1 GHz to 7.9 GHz. Figure 3c shows that when the slot width (Slot-W) increases, the two resonant frequencies are still ensured. Nevertheless, the resonant frequencies shift to a lower range, and a better match can be reached when the Slot-W = 0.3 mm, implying that the slot width is also useful for adjusting coupling  
Figure 2: Comparison of simulated reflection coefficients of three single patch antennas. to acquire a good match. The length of the inset feed (inset-L) coming into the patch antenna controls the input impedance. As exhibited in Figure 3d, the impedance matching is obtained when the inset-fed length is 3.5 mm. Finally, the four scanning parameters are listed: Slot-D = 5 mm, Slot-L = 4.3 mm, Slot-W = 0.3 mm, and inset-L = 3.5 mm. Table 1 shows the performance comparison of the bandwidth, the gain, and the HPBW of each single antenna type in E- and H-planes. According to the table, the final bandwidth of the inset-fed and two-slot single patch is expanded three times from the conventional patch (from 0.36 GHz to 1.08 GHz). The large HPBW of the single antenna is greater than 90\({}^{\circ}\) in both H- and E-planes.