esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 4 Proposed Model
### Feature Engineering  
The historical wind power signal is decomposed using WPT. The WPT is a general form of the wavelet decomposition, which performs a better signal analysis. WPT was introduced in 1992 by [PERSON] and [PERSON] [43]. Unlike DWT, the WPT waveforms or packets are interpreted by three different parameters: frequency, position and scale (similar to the DWT). For every orthogonal wavelet function, multiple wavelet packets are generated, having different bases (Figure 2). With the help of these bases, the input signal can be encoded in such a way that the global energy of signal is preserved and the exact signal can be reconstructed effectively. Multiple expansions of an input signal can be achieved using WPT. The most suitable decomposition is selected by calculating the entropy (e.g., Shannon entropy). The minimal representation of the relevant data based on a cost function is calculated in WPT. The benefit of the WPT is its characteristic of analyzing signals in different temporal as well as spatial positions. For highly nonlinear and oscillating signal such as wind power DWT does not guarantee good results [44]. In WPT, both the approximation and detail coefficients are further decomposed into approximation and detail coefficients as the wavelet tree grows deeper. Wavelet packet decomposition operation can be expressed by Equations (1) and (2). For a signal \(a\) to be decomposed, two filters of size \(2N\) are applied on \(a\). The corresponding wavelets are \(h(n)\) and \(g(n)\).  
\[W_{2n}(a)=\sqrt{2}\sum_{k=0}^{2N-1}h(k)W_{n}(2a-k) \tag{1}\]  
\[W_{2n+1}(a)=\sqrt{2}\sum_{k=0}^{2N-1}g(k)W_{n}(2a-k) \tag{2}\]  
where the scaling factor is \(W_{0}(a)=\phi(a)\) and the wavelet function is \(W_{1}(a)=\psi(a)\).  
The past wind power signal is decomposed into 36 signals and the best representation of the input signal is selected through Shannon entropy.  
Figure 1: Overview of proposed system for wind power forecasting.  
Figure 2: Wavelet packet tree with three levels.  
After decomposing the past wind signals, the engineered features along with NWP variables (dew point, dry bulb, and wind speed), lagged wind power (w-24 and w-25) and time are input to the proposed forecasting model. The proposed forecasting model is discussed in the next section.
Micro-electro-mechanical-systems (MEMS) are based on designing and manufacturing sensors and actuators using micromachining techniques (e.g., surface and bulk micromachining) which allow mass manufacturing with repeatable intrinsic characteristics. MEMS have enabled the design of sensors and systems using micromachining techniques since 1990s [2]. The application of the MEMS concept into AE sensing was first introduced by [PERSON] and [PERSON] in 1998 [3]. Since then, there are several examples of piezoresistive, capacitive and piezoelectric MEMS AE sensors reported in the literature. With the introduction of a thin-film piezoelectric layer into MEMS manufacturing, potential towards to a commercial success for piezoelectric MEMS AE sensors increased. The first example of piezoelectric MEMS AE sensor was reported by [PERSON] and [PERSON] in 2010 [4].  
In this review article, the physical factors inherent to AE sensing and contributing to the design of MEMS AE sensors are discussed. The main characteristics of MEMS AE sensors such as resonant frequency, bandwidth/quality factor, sensitivity and other specifications pertinent to the transduction principles are presented. Transduction principles commonly used in MEMS AE sensing, including piezoresistive, capacitive (i.e., electrostatic) and piezoelectric, are discussed. Common MEMS geometries in the forms of beam or diaphragm and their design equations to tune to different frequencies are discussed. The history of MEMS AE sensors reported in the literature is presented. Conclusions and future outlook are discussed in the final section.
## 3 Results  
### XRD Results  
Figure 2 shows the XRD patterns of all the obtained PSOO@NaMt, (Figure 2a) PScoMA/OO@NaMt, (Figure 2b), and PS/OO@OrgMt (Figure 1c) films. The XRD patterns of NaMt and OrgMt as received nanoclays, as well as of OO@NaMt and OO@OrgMt modified nanostructures are also included in this figure for comparison reasons. It is obvious from Figure 2a that the major diffraction peak of NaMt clay is located at around 20 = 7.3\({}^{\circ}\) which corresponds to a d-spacing value of 1.21 nm. The OO@NaMt modified nanostructure exhibits a broader peak at 20 = 6.8\({}^{\circ}\).  
According to a previous report [14], the modification of NaMt with EO molecules took place in the external surface of the NaMt layers and resulted in a partial exfoliation of clay platelets. XRD plots of the NaMt clay and OO@NaMt nanostructure blended with  
Figure 2: XRD plots of all PS/OO@NaMt, PSOO@OrgMt, PScoMA/OO@NaMt (**a**) films, OO@NaMt, OO@OrgMt nanostructures and, NaMt (**b**), and OrgMt clays (**c**).  
PS chains exhibited no basal peak in the 2 theta range of 2 to 6\({}^{\circ}\) (see Figure 2a). Thus, it seems that PS chains cannot intercalate in the hydrophilic interlayer space of NaMt, which is something expected. The same happens in the case of OO@NaMt clay platelets where the modification of the NaMt with OO leads to the formation of a partially exfoliated nanocomposite structure which does not help the PS to further intercalate the clay. This result agrees with others in literature [28] where PS/OO@NaMt nanocomposites were prepared via an in situ polymerization method.  
XRD plots (see Figure 2b) of PScoMANaMt and PScoMAOO@NaMt films exhibit a broad basal space peak at around 20 = 5.2\({}^{\circ}\) which corresponds to d-spacing value of 1.70 nm. This broad peak indicates that hydrophilic modified PScoMA chains intercalated partially the NaMt and OO@NaMt clays platelets. In Figure 1c the basal space of OrgMt clay is located at around 20 = 3.5\({}^{\circ}\) which corresponds to a d-spacing value of 2.52 nm. In the case of OO@OrgMt nanostructure, a clear shift of basal space at 20 = 2.9\({}^{\circ}\) value is observed. This observation is consistent with the results of a previous work [14] where it was reported that in the case of the modification of OrgMt clays with EO, the EO molecules intercalate the interlayer space of OrgMt platelets. Basal space of PS3 OrgMt, PS3 OO@OrgMt, and PS5 OO@OrgMt films are located at 2 theta values of 2.8\({}^{\circ}\), 2.6\({}^{\circ}\), and 2.6\({}^{\circ}\) correspondingly. These values result in an increase of d-spacing at 3.15 nm for PS3 OrgMt and at 3.40 nm for both PS3 OO@OrgMt and PS5 OO@OrgMt films. Moreover, indicate that PS was further intercalated the OO@OrgMt nanostructure, and a final secondary intercalated nanostructure is obtained. This result agrees with previous publications where the OO@OrgMt nanostructures favour the formation of nanocomposite structures intercalated with LDPE [19] and PS [28] matrixes.
# A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers
## 3 Models and Simulation Setup  
Figure 1a,b illustrate the queueing architectures. The global and local models follow the M/M/K and M/Mi/K queuing distribution notations. Tasks arrive at a \(\lambda\) rate, each allocated to one worker. The systems examined in the current study are differentiated in the queue architecture, scheduling policies, and the characteristics of the resources (homogeneous and heterogeneous).
# Uni-Planar MIMO Antenna for Sub-6 GHz 5G Mobile Phone Applications
## 2 Proposed Uni-Planar MIMO Antenna  
The proposed sub-6 GHz MIMO antenna's design is shown in Figure 1. A low-cost FR-4 substrate is used for MIMO antenna design. The thickness of the substrate is chosen to be 1.6 mm and has a relative permittivity of 4.4. To save space on the PCB, a loop-shaped structure was used for the antenna design [19]. At 3.5 GHz, the electrical length of a radiator is approximately equal to \(1\lambda_{g}\), where \(\lambda_{g}\) is the guided wavelength. Therefore, the optimized electrical length of an antenna element is noted to be \(\approx\)45.5 mm, while the line width is equal to 1 mm. It is worth noting that a single antenna element can occupy a maximum area of 14.5 \(\times\) 12 mm\({}^{2}\).  
The MIMO antenna, shown in Figure 1, comprises four loop-shaped radiators placed at each corner of the mobile phone PCB. The ground plane is placed on the same plane as the radiation elements. The design parameters of the proposed MIMO antenna are illustrated in Figure 1, while the optimized values are given in Table 1.
# Fatigue Life Prediction of Radial Tire Bead Using a Maximum Strain Energy Density Range Method
## 3 Experiment  
### Fatigue Test Method  
The material distribution of a 175/75R14 tire is shown in Figure 1. Based on the actual damage of the radial tire, rubber compounds of carcass, apex, and abrasion are selected to perform uniaxial cyclic tensile tests to test their fatigue life.  
To evaluate the fatigue characteristics of the three rubber compounds, the specimens' dimension and shape conformed to ASTM D4482-07 (a standard test method for rubber property-extension cycling fatigue) [12]. For the rubber fatigue test, the dumbbell-shaped specimen has the advantages of a shorter test period and more convenient test [13], so it is widely used. The test specimen is shown in Figure 2. Figure 3 shows the specimen cutter. According to the ASTM D4482-07, this test used a mean value of 10 specimens under different strain levels to reduce error.  
After the specimens were installed, the fatigue tests were carried out using displacement control to apply the load. In the study of [PERSON] et al. [14], frequency and waveform had little influence on fatigue testing, so the fatigue tests were conducted with a sinusoidal waveform of 5 Hz, as in Figure 4, and the minimum strain was maintained at zero. Tests were performed in an ambient temperature (23 \({}^{\circ}\)C). The maximum strain of each rubber compound is shown in Table 1.  
Figure 1: Material distribution of a 175/75R14 tire.  
Figure 3: Specimen Cutter.  
Figure 2: Dumbbell specimen.  
Under normal conditions, the crack propagation life accounts for nearly 90% of the total lifespan of rubber components [15]. Thus, the fatigue failure life was defined as the number of cycles from the initial state to the complete fracture of the specimen. The number of cycles of the specimen until failure was measured by an electromagnetic counter. In order to avoid sudden failures, a camera was used to monitor the test process.
# Assessment of Water Consumption Behavior in Single Households Using Smart Water Meters
###### Abstract  
Smart meters monitor hourly water consumption patterns while reducing the cost and inconvenience of traditional meters. This study comprehensively analyzes 1871 households that previously used traditional meters from the distribution point to the distribution area. All the households were equipped with smart meters and the data collected were used for analysis. On the basis of the total estimated water consumption, 227 households were classified as single households. These households were further classified into single-worker and -nonworker households. This study analyzed smart meter data to evaluate the timings and amounts of water consumption peaks. The results indicate that worker households peaked at 8:00, with 29 L/h of consumption on weekdays, and peaked again on evenings at 20:00-21:00, averaging 32 L/h. For nonworker households, the peak occurred at 9:00, with 20 L/h, with no major changes in the afternoon, and a second peak occurred at 19:00-20:00 in evening, with an average of 19 L/h. Moreover, worker households peaked at 8:00 and 20:00 on weekdays, and at 9:00 and 19:00 on weekends. It was revealed that worker households consume 10% more on weekends than on weekdays, and 262% more from 13:00 to 16:00. These findings may assist in water supply planning by supporting distribution schedules on the basis of peak household consumption, leading to more helpful resource management.  
single household; smart meter; water consumption peaks; water distribution +
Footnote †: journal: Applied
# FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints
## 3 Method
### Local Refinement Module  
In this section, we employ a dual-branch structure consisting of the local refinement module to learn the corresponding fine-tuning mapping functions for high and low frequencies, respectively. Each local refinement module consists primarily of FAGCN [27] in order to allow the neural network to learn to adaptively filter parameters of different frequencies. The FAGCN operators are as follows:  
\[x_{i}^{\prime}=\varepsilon x_{i}^{(0)}+\sum_{j\in\mathcal{N}_{i}}\frac{a_{ij} }{\sqrt{d_{i}d_{j}}}x_{j} \tag{1}\]  
\[a_{ij}=\tanh(a^{T}([x_{i}||x_{j}])) \tag{2}\]  
In Equation (1), \(x_{i}^{(0)}\in\mathbb{R}^{l_{dim}\times 1}\) is the initial feature of node \(i\), \(l_{dim}\) is the dimension of the hidden layer, \(\mathcal{N}_{i}\) is the neighborhood of node \(i\), \(d_{i}\) is the degree of node \(i\), and \(\varepsilon\) is a scaling hyper-parameter limited to \([0,1]\). Equation (2) shows the calculation method of the attention coefficient \(a_{ij}\), where \(\mid\mid\) represents the concatenation operation, and \(a^{T}\) is a trainable parameter.  
The structure of the local refinement module is shown in Figure 5b. Initially, we map 1283-dim features to 1024-dim features using two linear layers with ReLU. To avoid the negative effect caused by the entanglement of filters and weight matrices, we use a linear layer with ReLU before the FAGCN operator to convert the initial features to 256 dimensions. Multiple cascading filters may result in a narrow passband. Therefore, we add skip connections to ensure that the network can take advantage of features at various frequencies. Since we learn on small-scale local graphs, we use 4 residual blocks instead of a deeper GNN structure, which is sufficient to meet the requirements, thereby improving the over-smoothing issue caused by a too deep GNN. Finally, we use a linear layer of  
Figure 5: Local refinement module. (**a**) the base patch and the extended patch; (**b**) the structure of the local refinement module.  
Tanh layers to output the offsets of vertex positions. The high and low curvature parts are treated differently by the two branches, resulting in different parameters learned by the two branches. The low-curvature branch learns a low-pass filter to filter noise and maintain smoothness, while the high-curvature branch learns a high-pass filter to restore details. Notably, the loss at the two branch ports is calculated during training, but during inference, the two types of patches are stitched to the original mesh using mean pooling, resulting in a smoother mesh seam.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 2 Related Work  
This section delves deeper into pivotal advancements in natural disaster detection technology, with an emphasis on mudslide identification. A myriad of recent publications underscore the integration of machine and deep learning techniques to refine mudslide and geological hazard detection. Specifically, [PERSON] et al. (2016) underscore the transformative role of convolutional neural networks (CNNs) in interpreting remote sensing data for landslide identification, setting a foundational framework for subsequent research [11]. Building on this, [PERSON] et al. (2022) demonstrate the strategic application of deep learning and transfer learning to swiftly evaluate mudslide-impacted zones, showcasing the pre-trained models' role in boosting detection precision [12]. This paper's novelty lies in harnessing the YOLOX-Nano algorithm, enriched with an attention mechanism and optimized network modules, to eclipse prior methods in detecting mudslide hazards swiftly and accurately. Additionally, we draw upon a broader spectrum of literature to anchor our innovations firmly within the evolution of disaster detection technologies, thereby ensuring a comprehensive understanding of our contribution against the backdrop of existing research.
## 4 Results and Discussion
### Porosity  
Porosity is a crucial property in ceramic materials as it can provide valuable insights into the behavior and characteristics of the samples. For the ancient pottery, the porosity of the ceramic samples can offer significant information about the firing temperature, type of clay, and tempering materials used, as well as the manufacturing and forming techniques employed by the ancient pottery. Different textured samples with varying levels of porosity can reflect the processes involved in the creation of the pottery, the type of raw materials utilized, and the level of craftsmanship exhibited. By analyzing the porosity of ancient pottery, a better understanding of the cultural and technological aspects of the civilizations that produced these artifacts could be obtained.  
All these vessels have been used for storing or boiling various liquids. A high degree of porosity is observed, due both to the relatively low temperature and to the characteristics of ceramic vessels to have their own porosity. By repeated use, when storing certain products, a decrease in this porosity was observed by clogging the external pores with particles of these products, which penetrated deeply through the capillary absorption process.  
Textural measurements are essential for studying the composition-dependent changes in properties of materials. In the case of ceramic samples, the specific surface area plays a significant role. A larger specific surface area, such as 42 m\({}^{2}\)/g, can suggest a lower firing temperature of the material, as an indicator about the conditions in which the ceramic was formed.  
In addition, the presence of calcite in the ceramic composition can impact the shrinkage behavior during firing. The amount of calcite can influence the porosity and texture of the final ceramic product. By analyzing the mesopore size distribution, it is observed that the pore diameters in the samples vary between 4 and 10 nm. This variation in pore sizes can affect the overall properties of the ceramic, such as its strength, density, and thermal properties.  
Overall, textural measurements, specific surface area, and pore size distribution are crucial parameters in understanding the composition and firing conditions of ceramic materials. They provide valuable information about the processing steps involved in the production of ceramics and can help researchers interpret the technological aspects of ancient pottery, as can be seen in Table 3.  
A surface and pore structure analysis for the two ceramic samples were performed using N\({}_{2}\) physisorption. Table 3 shows that all the sample P1 show large pores, while sample P2 reveals a partially blocked mesoporous structure, with very small pores. The value of the pore diameter is relevant for the above-mentioned assumption.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
### The Goal of the Review  
To the best of our knowledge, there is no paper that incorporates and integrates the specific research results to extract some general precipitation features from all existing studies. Therefore, the goal of this review is to seek an overall understanding of tropical precipitation by investigating common achievements among the isolated studies, and exploring the problems and future prospects in using the space-borne precipitation radar data.  
In this review, we summarize features of precipitation and the related physical phenomena in existing studies mainly using the PR data. While introducing the selected studies, we lay emphasis on abstracting general and reliable conclusions, rather than presenting the research aims, processes or significances, regarding it being difficult to give full treatment and cover details of numerous studies in a usual review article. Although many studies used older versions of PR products, the revealing conclusions in them are independently meaningful. Nevertheless, we still give information of PR product versions, study periods, and study regions for reference.  
The approach of this review is to identify categories of the mentioned contents in the studies, single out the agreement and disagreement, reveal general precipitation features that may come to light in the context of multiple studies, and analyze possible mechanisms yielding the conclusions. One advantage of this review is the aggregation of information leading to a higher statistical power and more robust conclusion than is possible from the specific measure derived from the individual studies, and provide useful reference and inspiration for studies relating to the next generation of space-borne precipitation radar.  
In the remainder of the paper, the content is structured as follows. To illustrate the reliability of PR data, Section 2 introduces comparison studies between precipitation measures obtained by the PR with that observed by ground-based radars or rain gauges. Sections 3-5 elaborate the PR measures on, respectively, lightning analysis, latent heat retrieval and rainfall observation. In Section 6, the conclusion, existing problems, future prospects in using space-borne precipitation radar data as well as the review limitation are presented. A conceptual scheme of the paper is shown in Figure 2.  
Figure 2: A conceptual scheme of the article.
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 2 Traffic Data Collection and Status Prediction Modeling Methods
### Traffic Data Collection on Highways  
To collect traffic flow data on highways in Taiwan, the Highway Administration Bureau has deployed various types of detection equipment. These include loop detectors, roadside microwave radar vehicle detectors (VDs), image-based vehicle detectors for the automatic vehicle identification (AVI) system, and electronic tag detectors for the Electronic Toll Collection (E-TAG) system, as illustrated in Figure 1[39; 42]. Data collected from these detectors are transmitted to the Traffic Control Center via communication networks. The center processes this data using various algorithms in conjunction with real-time road conditions and provides drivers with up-to-date traffic information for each highway segment [1]. This information also includes predicted travel times between system interchanges for drivers' reference.  
Providing real-time traffic information for every driver on each road segment through the Traffic Control Center involves complex and time-consuming computations for data analysis and outcome generation. In addition, the continuous collection of large volumes of traffic data from ETC, AVI, and VD detectors demands substantial memory and storage capacity. Generating specific traffic information, such as highway travel-time predictions for individual drivers, often requires further data processing. Therefore, developing an efficient scheme to address these challenges is essential--not only to reduce the system's computational load but also to enable faster and more effective processing and delivery of traffic information.