esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
## 4 Experimental Design
### Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Month** & **Algorithms** & **PA(\%)** & **MAE(MW)** & **MAPE(\%)** \\ \hline \multirow{4}{*}{1} & DWT\_AE\_BiLSTM & 84.75 & 29.27 & 13.30 \\  & AE\_BiLSTM & 81.42 & 30.41 & 13.82 \\  & LSTM & 80.30 & 31.51 & 14.32 \\  & BP & 79.45 & 33.19 & 15.09 \\ \hline \multirow{4}{*}{4} & DWT\_AE\_BiLSTM & 82.65 & 30.31 & 13.78 \\  & AE\_BiLSTM & 81.71 & 33.94 & 15.43 \\  & LSTM & 80.45 & 34.23 & 15.56 \\  & BP & 80.35 & 34.47 & 15.67 \\ \hline \multirow{4}{*}{7} & DWT\_AE\_BiLSTM & 84.11 & 28.17 & 12.80 \\  & AE\_BiLSTM & 83.65 & 29.36 & 13.35 \\  & LSTM & 83.23 & 30.38 & 13.81 \\  & BP & 82.95 & 32.42 & 14.74 \\ \hline \multirow{4}{*}{10} & DWT\_AE\_BiLSTM & 84.35 & 28.18 & 12.81 \\  & AE\_BiLSTM & 81.57 & 29.49 & 13.40 \\ \cline{1-1}  & LSTM & 81.31 & 32.07 & 14.58 \\ \cline{1-1}  & BP & 80.25 & 34.94 & 15.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of each prediction algorithm in Wind Farm #2. Figure 5: Comparison among different wind power short-term prediction models of Wind Farm #2 in April 2019.  
analysis of the short-term predictions of three wind farms, the prediction algorithm based on the deep learning frameworks LSTM, AE_LSTM, and DWT_AE_BiLSTM had a better prediction effect than the traditional neural network prediction algorithm BP. In summary, it is clearly seen that the proposed model DWT_AE_BiLSTM shows the best prediction ability in three different wind farms. The performance of the BP prediction algorithm is extremely unstable; it oscillates up and down, and the prediction is either too large or too small. The proposed prediction algorithm shows better prediction performance than other algorithms. All deep learning models have better prediction performance than traditional neural networks. The prediction model using LSTM and an autoencoder for feature extraction is better than the LSTM model. Bidirectional LSTM is better than LSTM. Experiments show that a discrete wavelet transform can effectively remove the nonstationary noise data in wind power prediction, and an autoencoder can capture more abstract and hidden nonlinear features. The proposed DWT_AE_BiLSTM algorithm model has good universality and generalization ability in all situations.
## 3 Results
### Bibliometric Analysis
#### 3.1.2 Publishing Framework  
After reviewing the literature and following the analysis protocol described above, the results were categorised to define the editorial framework of the texts examined. Figure 3 shows the yearly distribution of the articles selected for the study regarding real-time monitoring technologies and building safety. At first glance, there is a clear upward trend in the number of articles on this topic, even taking into account that the data for 2024 and 2025 are incomplete at the time of this writing.  
Scientific collaborative networks can help identify the leading research groups in a particular field, providing researchers with insights into the research frontiers [32]. Figure 4 illustrates the collaborative network of 15 countries based on research contributions identified through co-authorship analysis in this study, with larger node sizes for China, South Korea, and Australia, indicating their more significant contributions to real-time security monitoring research.  
Figure 5 shows the 23 journals publishing research on real-time fall monitoring, with Automation in Construction and the Journal of Construction Engineering and Management emerging as the leading journals, as shown by the node sizes and connection strength in the network analysis.  
Figure 2: The process of selecting studies using the PRISMA methodology.  
The main content of the published documents and the types of areas studied in a particular domain are indicated by keywords, and the proximity between keywords is indicated by their co-occurrence [33]. Following the suggestions of [PERSON] et al. [34], in this study, \"author keywords\" and \"fractional counting\" were used in the VOSViewer  
Figure 4: Networking and global reach of countries participating in real-time safety monitoring.  
Figure 5: Network of citation sources for the journals publishing the included articles.  
Figure 3: Annual distribution of articles.  
analysis. The minimum number of keyword occurrences was set to 2. Out of a total of 142 keywords, 22 met the threshold. Some other keywords with the same semantic meaning, such as 'falls' and 'fall', were combined, and finally, 17 keywords were selected, as shown in Figure 6.
## 1 Introduction
Microorganisms that use organic carbon compounds are the most common denittifiers in nature [11]. The heterotrophic biological denitrification is considered more economical, implementable on a large scale, and allowing for the ultimate reduction of nitrate to nitrogen gas with high selectivity [12]. The presence of organic compounds in wastewater significantly affects nitrification and denitrification, with a low organic content promoting effective nitrification [13] and a low C/N ratio significantly reducing denitrification [4]. The denitrification rate is also determined by the availability of nitrification products (nitrites and nitrates). Furthermore, the rate of biochemical conversion and the microbial activity are directly affected by the temperature of the wastewater treatment. Some of these parameters may be regulated easily, but due to the high specific heat capacity of water, it is nearly impossible to influence temperature. Nitrification and denitrification are elements of the N-cycle critical to the removal of nitrogen from the treatment system. While the removal of organic compounds and nitrogen from municipal wastewater has been the subject of much research and is relatively well explored, there is limited knowledge on how to eliminate such pollution from airport wastewater. The previous authors' research has shown that biofilters LECA filling could be an effective method for removing nitrogen and organic compounds from wastewater containing airfield deicing fluids. The nitrogen compounds were removed as a result of the simultaneous process of nitrification and denitrification, where the organic compounds present in the treated wastewater served as a carbon source [5,14]. To that end, the present paper provides the findings of a study aimed to determine the transformation kinetics of pollutants generated from airport maintenance, providing a means to estimate the retention time for de-icing wastewater in filters located near airports or in individual, local systems for removing such pollutants. The study was conducted under four different temperature profiles (0 \({}^{\circ}\)C, 4 \({}^{\circ}\)C, 8 \({}^{\circ}\)C, and 25 \({}^{\circ}\)C) and at 3 loading rates of de-icing agents used for winter maintenance of airport pavements, with C/N ratios of 0.5, 2.5, and 5.0.
## 4 Discussion
### Practical Implications  
The findings from our study can draw a complex picture of balance control in the elderly and the interplay between different aspects of motor function.  
Firstly, our results pinpointed that postural balance is not simply about minimizing the movement, but rather the efficient and adaptive control of the movement. The findings regarding the area under the acceleration curve and the RMS angular velocity suggested that excessive or poorly controlled movements indicated balance issues. Meanwhile, the total path length and volume metrics showed that a larger range of controlled movement can be indicative of better balance. Thus, balance evaluation should incorporate measures that capture sufficiently diverse measures.  
Secondly, the differences observed between static and dynamic tasks emphasized that balance control strategies are highly task-specific, which is aligned with the observations by [PERSON] and [PERSON] (2018) [41]. Therefore, comprehensive balance assessments should include a variety of tasks to capture the full spectrum of an individual's balance capabilities. Also, given the task-specific nature of balance control, interventions should be tailored to address individual deficits in specific types of tasks or movements.  
Thirdly, as we found varying contributions of the IMUs placed at different body locations, it might be necessary to use several sensors together in balance assessment.  
The measurement of each body part, i.e., lower back, torso, and head, could uniquely give insights about balance control, and their interactions could be important, as also mentioned by [PERSON] et al. (2013) ([PERSON] et al., 2013). Using IMUs in multiple body locations, if possible, could be a promising approach for monitoring postural balance in daily life and for the early detection of balance issues for timely intervention.  
Lastly, the positive associations between larger movement volumes, longer path lengths, and better balance scores supported [PERSON]'s ([PERSON] et al., 2013) theories on the importance of confidence and motor competence in balance control. This suggests that balance interventions such as training should focus on not only stability but also encourage the exploration of movement to build confidence and competence.
## 3 Result and Discussion
### Surface Morphology  
Surface morphology is one of the crucial factors that influence the performance of solar selective absorber (SSA) surface coatings [36]. The morphology of single-layered green synthesized CuO nanocoatings was characterized using SEM and AFM. Figure 3a-d shows SEM images of CuO nanocoatings on SS substrate deposited using a spin coater at 700, 800, 900, and 1000 rpm, respectively. From the SEM images, nanorod-like structures are observed. The surface structures are densely covered and compacted at lower RS of 700 and 800 rpm, as shown in Figure 3a,b. When the RS increase to 900, and 1000 rpm, a nanorod-like surfaces with lower density is observed, as shown in Figure 3c,d. The particle size of CuO nanocoatings are found to be 57.6, 51.2, 43.1, and 36.7 nm at 700, 800, 900, and 1000 rpm, respectively, these results confirm that the thickness of the coating decrease with an increase in RS of the spin coating [37; 38].  
Figure 4a-d depicts the 3D AFM images of green synthesized CuO nanocoatings deposited by spin-coating on SS substrate at RS of 700, 800, 900, and 1000 rpm, respectively. The AFM images confirm the presence of vertically aligned nanorod-like structures on the CuO nanocoating surfaces. From Figure 4a,b at RS of 700 and 800 rpm, growth of vertically aligned nanorod-like structures with relatively uniform distribution and better coverage of nanorod surfaces is observed, and these are characterized by higher surface roughness of 28.3 and 24.5 nm, respectively. From Figure 4c,d, a further increase in the RS to 900 and 1000 rpm complicates the uniform distribution of the particles, and nanorod-like surfaces are reduced as well as appeared smooth surfaces with a surface roughness of 21.2 and 17.6 nm, respectively. As the RS increases the surface homogeneity of vertically aligned nanorod-like surfaces and grain size are reduced, indicating that the crystallization quality and thickness of the coatings are decreased. Moreover, the surface roughness of CuO is gradually decreased with an increase in RS due to the lowering of the coating thickness and surface uniformity of the coatings [39; 40], as shown in Figure 4a-d. It should be noted that surface roughness has a key role in the absorber surface, as the incident light can be trapped efficiently in the absorber surfaces. When the incident light encounters the absorber surface of the coatings, some of them are absorbed, and some are reflected. The suitable surface roughness creates optical traps between nanosized particles, which can increase the light scattering of short wavelengths and reflection of long wavelengths [40; 41]. Moreover, the distribution of peaks in the surface structure traps more light in the inner structure of the coatings by multireflection; consequently, the intrinsic absorption of CuO nanocoatings can be enhanced, which contributes to better solar absorption properties [42; 43].  
Figure 3: SEM images of CuO nanocoatings (**a**) 700 rpm, (**b**) 800 rpm, (**c**) 900 rpm, and (**d**) 1000 rpm.
## 2 Fundamentals of Aerial Wireless Sensor Networks for Oceanographic Monitoring.
### A-WSN General Architecture  
Figure 2 shows a general architecture of an A-WSN for oceanographic monitoring. There are two main types for inter-node communication: point-to-point _vs._ multihop. There are almost always one or more nodes that communicate directly with the base station. These act as sinks and may not have a role in the monitoring process.  
The differences between deployments are essentially determined by decisions concerning:  
(1) the network topology;  
(2) the dimensions of the area to be monitored and the number of nodes used in the deployment;  
(3) the communication devices/protocols used and the radio frequencies chosen;  
(4) facilities for accessing the nodes for repair or removal (maintenance);  
(5) the flotation and mooring systems used;  
(6) the types of oceanographic sensors considered;  
Figure 1: General scheme of a sensor node for oceanographic monitoring.  
(7) the tools for monitoring the network developed for real-time visualization of the data gathered; and (8) the electronics used for autonomous sampling of the requisite parameters and for wireless transmission to a data server.  
The component elements of an A-WSN are generally common, irrespective of where they are used (wireless sensor nodes, a communications protocol, a monitoring application, _etc._). But even so, there are major differences depending on the characteristics of the deployment. One of the most obvious such differences, as we shall see in detail later on, is the type of sensors that are to be used, which will be very specific to the environment it is proposed to monitor and the end in view.
## 2 Materials and Methods  
### Synthesis of Free-Stand Two-Layer Graphene and Gr/AgNW/Gr Nanocomposite  
Figure 1 shows the low preassure chemical vapor deposition (LPCVD) system for graphene synthesis. The mass flow controller (MFC) controls the quantity of gas that flows into the quartz tube for reaction. A 125 \(\upmu\)m thick copper foil (99.9%, Alfa Aesar 13380, Alfa Aesar, Ward Hill, MA, USA) was pretreated in acetic acid (99.7%, Sigma Aldrich 695092, Sigma Aldrich, St Louis, MO, USA) for 48 h and then cleaned with DI water, IPA and acetone. Monolayer graphene was synthesized on the copper foil in the reaction chamber (quartz tube) with 5 scccn (standard cubic centimeter per minute) hydrogen flow for 30 min and then 5 sccm methane flow for 10 min under 1030 \({}^{\circ}\)C at \(\sim\)100 mTorr vacuum pressure. The as-grown graphene on copper was then cooled down to room temperature for next step usage.  
Figure 2 shows the procedure of free-stand two-layer graphene and Gr/AgNW/Gr nanocomposite synthesis process. As-grown graphene on copper was spin coated with Poly (methyl methacrylate) (PMMA, Sigma Aldrich 182265, M\({}_{\mathrm{w}}\) = 996,000) in chlorobenzene (46 mg/mL) solution at 4000 RPM for 30 s. The PMMA/Gr/copper was heated under 170 \({}^{\circ}\)C for 10 min to evaporate the chlorobenzene solvent and then floated on the copper etchant (Sigma Aldrich 667528) for 2 h to dissolve the copper substrate. The floated PMMA/Gr film was cleaned with deionized (DI) water twice and flipped over to make the graphene layer on the top. The Gr/PMMA film was transferred on a square meshed TEM grid (EMS 400-Ni, hole size 50 \(\upmu\)m). A 3 \(\upmu\)L solution of AgNW dispersed in isopropyl alcohol (IPA) (ACS Materials, Agnw-L100, diameter = 30 nm, length = 100-150 \(\upmu\)m) with five different concentrations (0.2, 0.4, 0.6, 0.8, and 1.0 mg/mL) was applied on the Gr/PMMA surface through a micro syringe with needle. After the IPA dried at room temperature, another top layer PMMA/Gr film was transferred onto the AgNW/Gr/PMMA sample. The Gr/AgNW/Gr composite was free-stranded on the TEM grid after a 30 min annealing process in vacuum (\(\sim\)50 mTorr) at 380 \({}^{\circ}\)C to evaporate the PMMA film. For comparison, a two-layer free-stand graphene film was also prepared by the similar procedures without applying the AgNW. The average thickness of the Gr/AgNW/Gr nanocomposite is \(\sim\)31 nm based on the diameter of the AgNW (30 nm) and thickness of monolayer graphene (0.335 nm).  
Figure 1: (**a**) Schematic illustration and (**b**) setup of the low pressure chemical vapor deposition (LPCVD) system. MFC, mass flow controller.  
Figure 2: Schematic procedures of free-stand Gr/AgNW/Gr composite. PMMA, poly (methyl methacrylate).
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 4 Discussion and Research for the Future  
At present, ExtractEO is mature enough to be a key rapid-mapping production tool, especially in the context of climate change, food security and disasters that are becoming more frequent, intense, and widespread. The processing time, the thematic quality, the robustness, and the multi-sensor approach are major assets of EEO, enabling SERTIT to respond to the growing rapid-mapping production requests from CEMS, the International Disaster Charter, or any other domain, including food security. Fires and floods are the most common events, and EEO is valuable in dealing with these. In fact, the thematic quality assessment performed through comparing EEO automatic results with published CEMS vectors gave very good results, with mean F1 score values of 0.91 and 0.88 for burnt areas and flooded area detection, respectively. Algorithm reliability and IT infrastructure facilitate ramping up to cover very large areas, in the case of mega-events or long time series that emphasize dramatic changes to water resources. The automatic results were not perfect, but considerably limited any manual refinement. The architecture of EEO enables the addition of any thematic chain. Clouds and landslide mapping are also managed. The perspectives can be encapsulated in three main points. First of all, there is the need to keep increasing the integration of new satellite constellations as they appear and are used in CEMS. Then, there is the need to improve computational speed by going deeper into clustering and parallelization. Tools like Dask, Apache Airflow, Apache Spark are currently being considered. Finally, there are several deep learning methods in the testing phase, awaiting the approval for production work, such as urban flood extraction, or landslide and fire mapping with fewer spectral bands.  
Conceptualization, R.B., M.C., J.M.; Methodology, J.M. and M.C.; software, R.B.; validation, J.M.; writing--original draft preparation, S.C.; writing--review and editing, J.M. and R.B. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by SERTIT and in part linked to SERTIT's Copernicus EMS Rapid-mapping mega-disaster product development.  
The authors declare no conflict of interest.  
Figure 9: Annual water occurrence (2020) expressed as a percentage of total annual observations (from 1% in light blue to 100% in dark blue).
# Free Vibration Analysis of Curvilinearly Tapered Axially Functionally Graded Material Beams
## 2 AFGM Beams with Variable Cross-Section
### Graded Materials of the Beams  
We suppose that the materials of the beams with variable thickness are homogeneous in the thickness direction but graded along the axial direction. Consequently, the material properties, such as mass density and Young's modulus, vary with the position along the \(x\)-axis. Specifically, we analyze the following types of material distributions:  
_Type 1_. The effective mass density and Young's modulus are power law functions of \(x\) written as follows:  
\[\rho(x)=\rho_{0}\sum_{i=0}^{m}a_{i}\Big{(}\frac{x}{L}\Big{)}^{i},\ \ \ \ \ \ E(x)=E_{0}\sum_{i=0}^{n}b_{i}\Big{(}\frac{x}{L}\Big{)}^{i}, \tag{3}\]  
where \(\rho_{0}\) and \(E_{0}\) are the mass density and Young's modulus at \(x=0\), respectively; \(m\) and \(n\) are power coefficients that account for arbitrary variations of the material parameters along the beam's length; and \(a_{i}\) and \(b_{i}\) constant coefficients.  
_Type 2_. The effective density and Young's modulus follow exponential law functions of \(x\), given as follows:  
\[\rho(x)=\rho_{0}V_{e},\ E(x)=E_{0}V_{e}, \tag{4}\]  
where the exponential function \(V_{e}=e^{\gamma_{e}(1-a(1-\frac{x}{L}))}\) is determined by the parameter \(a\) and gradation index \(\gamma_{e}\), [29].  
Figure 1: The geometries of AFGM beams with variable thickness: (**a**) Convex upward shape; (**b**) Concave downward shape, as noted in [29].  
_Type 3._ The beams with variable thickness are composed of a metal-ceramic mixture. The effective material properties are calculated based on the rule of mixtures in the following form:  
\[\rho(x)=\rho_{c}+(\rho_{m}-\rho_{c})V_{c},\ E(x)=E_{c}+(E_{m}-E_{c})V_{c}, \tag{5}\]  
where the subscripts '\(m\)' and '\(c\)' refer to the properties of metal and ceramic constituents, respectively; \(V_{c}\) denotes the ceramic volume fraction, expressed as \(V_{c}=\big{(}1-b\big{(}1-\frac{x}{L}\big{)}\big{)}^{\gamma_{c}}\) with the parameter \(b\) and gradation index \(\gamma_{c}\), [29].  
[PERSON]'s ratio \(\
u\) is assumed to be constant throughout this study.
## 2 Study Area and Data  
### Study Area  
This study focuses on the Sierra Nevada Mountains and the surrounding areas in the western United States (Figure 2). Situated between California's Central Valley and the Great Basin, the Sierra Nevada Mountains stretch approximately 640 km from north to south, with a width ranging from 80 to 130 km from east to west. The elevation of the mountain ranges gradually increases from about 150 m in the Central Valley to around 4300 m at the peaks as it extends eastward. The variation in altitude results in diverse surface cover types in the region, including tree cover, shrubland, grassland, cropland, bare land, and so on. Due to the terrain's elevation, significant snowfall and precipitation occur annually in the mountains, with maximum snow depths exceeding 5 m. The snow cover period mainly extends from November to July of the following year, with March to April mainly being the snowmelt period [35; 36]. The snow cover in the study area serves as a primary water source and an essential source of hydroelectric power for California.  
Figure 2: Location and land cover type of the study area.