esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The antenna has two resonance frequencies of 3.56 GHz and 5.28 GHz with bandwidths of 65 MHz and 112 MHz, and gains of 4.2 dBi and 2.8 dBi, respectively. The MIMO antenna design achieves a minimum inter-element isolation of 22 dB, with dimensions of 90 \(\times\) 90 \(\times\) 1.57 mm\({}^{3}\). Previous works in the field have mostly focused on either achieving high bandwidth or optimizing for higher gain, so there is a great need for an antenna design that excels in both aspects concurrently. In this study, a rectangular novel-shaped monopole antenna array and its MIMO configuration are presented. The 2 \(\times\) 1 antenna array exhibits dual-band operation, covering the frequency ranges of (2-3.71 GHz) and (5.9-7.54 GHz), thus encompassing the 2.5 GHz, n78, and n96 frequency bands assigned for WiMAX and 5G applications. The array antenna has compact dimensions of 63 \(\times\) 50 mm\({}^{2}\) and a peak gain of 6.47 dBi. In contrast, the two-element MIMO antenna demonstrates resonance behavior comparable to that of the antenna array but achieves a higher peak gain of 7.5 dBi. The MIMO antenna configuration proposed in this work exhibits a new and simple geometric structure that distinguishes it from existing complex structures in the literature. In comparison to previously reported works, the antenna design presented herein achieves a distinct performance of both higher gain and broader bandwidth. The MIMO antenna demonstrates a low envelope correlation coefficient (ECC) of less than 0.0002, a minimal channel capacity loss (CCL) of less than 0.1 bps/Hz, a diversity gain (DG) exceeding 9.991 dB, a total active reflection coefficient (TARC) below \(-\)10 dB, a mean effective gain (MEG) lower than \(-\)6.2 dB, and high radiation efficiency (<99%), making it a suitable option for WiMAX, 5G sub-6 GHz, and 5G sub-7 GHz applications.
## 4 Results and Discussion
### Effect of Temperature
On the other hand, although the static deflection of the structure and the maximum load show a similar magnitude for warmer ice, the load drop involves a significant extrusion process which results in a more continuous,'slow' release of energy; therefore, the process was unable to excite the structure as significantly. This observation is consistent with the results presented in the sensitivity study of ice-induced vibration models discussed by [26]. It is noted here that [27] reported from their analysis of field measurement data from Norstromsgrund lighthouse that ice-induced vibrations were observed to occur more frequently during warmer periods of time in the spring of the year. Further analysis is needed to relate the present work with such observations, since multiple _h\(p\)zs_ act simultaneously during full-scale interactions, and further work is needed to better understand interactions between such _h\(p\)zs_ over larger scales. Moreover, during the spring months in addition to being warmer, sea ice is at its thickest and is more mobile than during the winter. Since it is known that increases in thickness and drift speed are also expected to increase the likelihood of dynamic interactions, further work is needed to better understand the interplay between temperature, thickness, and drift speed in such observations. Figure 10: Ice failure processes in the indentation area for different temperatures.
## 2 Materials and Methods
### Real, Measured Signal  
The proposed method was additionally tested on a real signal. To measure it, a 14-channel Emotiv EPOC+ NeuroHeadset electroencephalograph was used. Emotiv EPOC+ NeuroHeadset is designed for scalable and contextual human brain research. Arrangement of specific electrodes utilize 10-20 standard. The signal was measured between the _O2_ electrode and the Common Mode Sense (_CMS_) electrode [32], which was chosen because alpha waves have biggest amplitude in occipital lobe, especially in the nondominant hemisphere [33]. Signal was sampled with 128 Hz frequency. Obtaining electrode resistance not exceeding 10 k Ohm at the start of the measurement is an essential condition for obtaining a good quality EEG. It depends, to a large extent, on proper preparation of the scalp, which before the electrodes are attached, should be carefully degreased and the superficial calloused layer of the epidermis removed.  
The participant was a 25-year male without any diagnosed neurological disorders. The measurement was taken while the subject was well-rested and without drug usage in prior 6 months.  
After putting on the electroencephalograph, the participant relaxed, closed their eyes, and after some time, opened their eyes again. To avoid rise of frequency and amplitude of the signal, immediately after closing eyes persisting for a few seconds [34], only part of the signal was used for further analysis (seconds 15-25 in Figure 5a,b). This part, with corresponding discrete Fourier transform (DFT), is shown in Figure 5c,d.  
Figure 4: Visualization of the accuracy study. Note, that this process was conducted 100 times, with different reference parameters.  
To minimize the impact of artefacts, including the imperfect adhesion of electrodes to the head skin, the acquired signal was first filtered in the range of 2-20 Hz, so range greater than the typical alpha wave frequencies (8-12 Hz [35]). They are characterized by an amplitude of 3 to about 50 \(\upmu\)V on average [36]. The activity of these waves increases when the subject lay with eyes closed before falling asleep. It is also present when a person is at rest, in a relaxed or meditative state, etc. Alpha waves disappear during increased mental effort [37].  
During the conducted research, the digital bandpass filter with steepness parameter of 0.85 and stopband attenuation of 60 dB was used.
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example
### Classification of Data and Construction of Labels  
As previously mentioned, the problem of predictive maintenance under analysis is a case of Supervised Learning. In order to train a model to predict failures, it is necessary for not only examples of failure but also a time series of observations that led to that failure. Furthermore, the model needs examples of \"normal\" operating periods in order to be able to see the difference between the two. The classification between these two states is binary (stable or without failure/unstable or with failure). With this information available (stable/unstable), the model is only useful if it is able to promptly alert you to the imminence of a failure.  
In order to fulfill this early warning criterion, it is necessary to modify the definition of the failure event label, which occurs at a specific time, to a time interval where the failure event may occur. The time until the failure occurs, which delimits the boundary between the two categories, must be chosen according to operational criteria. Is the knowledge that a failure will occur within 12 h sufficient to prevent it? And what about 24 h? And two weeks? The model's ability to predict a failure will also depend on the duration of this time window.  
This process is illustrated in Figure 10. In order to achieve the reset from unstable to pre-unstable, observations within the time window (represented by \"X\" in Figure 9) before the occurrence of a failure have been labeled as _pre-instable_, while records outside this time interval X have been labeled as stable.  
Figure 10: Data classification and label construction—adapted from [9].  
Figure 9: Detailed analysis of the correlation between features pressuremean_3h and pressuremean_24h.  
The main objective of the Machine Learning models used will be to predict the probability of a failure occurring within this time window. In this case, more specifically, the probability of a machine failure occurring in the next 24 h (duration of the time window chosen for this application) related to one of the components (components 1,2,3 or 4). Thus, a new categorical feature \"failure\" was created, where all records in the 24 h prior to the occurrence of a failure in component 1 have the value failure = comp1 and so on for components 2,3 and 4. Records that do not check these conditions have the value of failure = none. This leads to the problem turning from a binary problem (stable/pre-unstable) to a multi-class classification problem (stable/n pre-unstable component). It should also be noted that, henceforth, due to this redefinition of failure event, when it is mentioned that a certain algorithm predicts a failure, in fact, what is being mentioned is that the algorithm predicts the occurrence of a failure within this time window.  
Table 11 shows examples of failure in component 2. Note that the first 8 records occur in the 24 h prior to the occurrence of the first failure of component 2. The next 8 records in the 24 h prior to another failure of component 2.
## 2 Research Objectives
### Sine Baffle Structure  
The purpose of adding baffles is to change the structure of the flow field, enhance the strength of the fluid convection circulation, provide a stable free liquid surface, and then promote mixing. However, the baffle settings also greatly improve the stirring power consumption, and a circulation dead zone can easily form on the back side of the baffle, which reduces the uniformity of fluid mixing. [PERSON] et al. (2015) explored the influence of the number, length, and installation method of the baffles on particle suspension characteristics and affirmed the superiority of non-standard baffles. As such, an ideal baffle structure can achieve a mixed effect of high quality and low power consumption. From the perspective of energy loss, [PERSON] et al. (2016) analyzed the drag reduction mechanism of the sawtooth guide vanes, which promoted the fluid mixing in the pipe elbow and had a positive effect on reducing the energy dissipation at the leading edge of the guide vane. [PERSON] et al. (2017) introduced U-shaped and V-shaped cut impellers, which improved the turbulent kinetic energy level and energy dispersion in the outflow area of the impeller and reduced the power consumption. Several studies (2018, 2019) have shown that structural cutting was beneficial for breaking the mixing isolation zone, had a key impact on wake vortex and energy dissipation, was conducive to the effective mixing of fluids and medium diffusion, and had a positive effect on reducing power consumption. In the present study, based on the above-mentioned beneficial effects, the standard baffle in the stirred tank was edge-cut, and the profile curve of the cut was a sine function, with the shape thereof being determined by the relative tooth height (\(A/W\)) and relative tooth width (\(\lambda/h\)). In order to facilitate the calculation of the mathematical model and the demonstration of results, several structures similar to Figure 3 were taken as examples in this paper. Table 2 shows the characterization parameters of the sine baffle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **Symbol** & **Value** \\ \hline Tank diameter & \(T\) & 288 mm \\ Impeller diameter & \(D\) & T/2 \\ Off-bottom clearance & \(C\) & T/4 \\ Blade width & \(W_{b}\) & 38 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The stirring component dimensions.  
Figure 2: A schematic diagram of the stirring device. (**a**) The stirred tank; (**b**) PBTD.
## 5 Discussion and Best Practice
### Geometric Processing  
We argue that high absolute geometric accuracy is a requirement for hyperspectral data products acquired by UAVs. The ultra-high spatial resolution of UAV imagery is one of the key benefits for addressing new challenges in environmental and agricultural remote sensing applications. With most hyperspectral UAV sensors offering sub-decimeter spatial resolution, the absolute accuracy of the resulting data products should arguably be better than 10 cm to allow for accurate co-registration with other UAV datasets and the differentiation of small-scale features, such as sub-canopy elements (e.g., individual leaves, branches, stems, etc.). High absolute accuracy requires centimeter-level positioning capabilities on-board the UAV and/or centimeter-level accuracy of GCP coordinates. This requires a local GNSS base station and a differential multi-constellation (GPS, GLONASS, Galileo, Beidou) multi-frequency (L1/L2/L5) GNSS receiver on-board the UAV or for the collection of GCP coordinates. We argue that navigation-grade GNSS receivers, offering an absolute accuracy of 5-10 m, are inadequate for UAV applications. There are four approaches for georeferencing that can be used as a standalone technique or in combination (summarized in Table 3).
## 4 The Proposed Model
### Objective Function
In the original GAN [17], fully connected networks served as the building blocks, which were suited only to low-resolution image datasets such as MNIST and CIFAR-10. Increasingly powerful convolutional neural networks (CNNs) have been used to synthesize high-resolution complex images, providing better image generation performance. Several GAN variants have been proposed in the literature based on the vanilla GAN model [17] for various applications [46; 47; 48]. By conditioning the GAN on auxiliary information, it can be modified to generate images with desired attributes--as opposed to the original GAN, which has no control over the data being generated. A conditional GAN (cGAN) is a type of artificial neural network conditioned on a class label. In this study, we aim to predict future floral scent compounds of Taif rose based on GC-MS fingerprints acquired during the distillation process using a conditional GAN (cGAN) [33]. The chemical components of rose oil are referred to as the visible phenotypes of plants at different locations. Using GC-MS fingerprint analysis of 10 Taif rose samples, we generated images of the future phytoconstituents in rose oil. [PERSON] and [PERSON] [22] were among the first researchers to develop conditional GANs (cGANs). Using \(z\) and \(y\) as inputs, the \(\mathfrak{S}\) represents random noise in the joint representation, and \(\mathfrak{D}\) represents real samples and their labels. A cGAN has the following objective function:  
\[J(\mathfrak{D},\mathfrak{S},y)=\mathbb{E}_{x\sim p_{data}}[\log\mathfrak{D}(x |y)]+\mathbb{E}_{z\sim p_{z}}[\log(1-\mathfrak{D}(\mathfrak{S}(z|y)))] \tag{2}\]
## 4 Performances Evaluation
### Identification Experiments  
In the identification modality, the system performs a one-to-many comparison to recognize an unknown individual searching through templates stored in the database. In this work, identification experiments were executed by comparing each template with all the others in the database. Results were grouped in 64 tables: each table contains 63 scores sorted in descending order.  
The identification rate is calculated as the ratio between successful experiments, i.e., when all genuine scores are higher than all impostor scores and total experiments for any recognition method. Results are summarized in Table 2. As can be seen, the use of the SRAD filter and 3D templates allows us to achieve an identification rate of 100%. It is also appropriate to highlight that 3D performances are worse than 2D ones if the SRAD filter is not used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **2D (\%)** & **3D (\%)** \\ \hline Mean & 95.23 & 93.65 \\ SRAD and mean & 98.41 & 100 \\ Ridler & 95.23 & 93.65 \\ SRAD and Ridler & 95.23 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identification rates for the various recognition methods.  
Figure 12: ROC curves computed for all analyzed methods.
## 1 Introduction
### General Strategies Adopted and the Added Value of the Project
#### 1.4.4 Runtime Online Platform  
It is important to clarify that this platform, devoted to the long-lasting preservation of datasets and their metadata, does not correspond to the runtime online platform, where the web apps and their contents can be played by the common users of the project. In fact, the servers and services for content streaming and runtime web apps have different characteristics from the other repository. Thus, the two services are addressed by two distinct platforms (although they may coexist on the same physical server).  
A web app is an application that can be used via a simple browser; it does not require any third-party software installation, and it is accessible just by typing a URL from any platform.  
For instance, the web app e-Archeo 3D [43] delivers multimedia, virtual reality (VR) and mixed reality (MR) content to users while they are walking through an archaeological site, or even remotely. This platform has been conceived for communication purposes, with a specific user graphic interface (GUI) and user experience design. The software has been centralised on a unique server managed by ALES SpA, together with the contents, making maintenance and updates easier. The following contents and web apps are running on this server:  
(1) \"e-Archeo.it\" website;  
(2) \"e-Archeo 3D\" web app, allowing audio-visual experience (multimedia and VR, 360\" MR);  
(3) \"e-Archeo Voices\" web app, to access podcasts for each archaeological site.  
Audio stories in the podcast may be more rewarding in case the sunlight on the archaeological site hampers the perception of visual content on the display, or the visitor prefers a 'lighter' enjoyment of the content without engaging with the gaze on their device.  
In most cases, the audio stories, VR or MR contents provided by the web apps refer to specific points of interest of the archaeological site; a map is the main interface to start the experience in \"e-Archeo 3D\" (Figure 4).  
Therefore, at the discretion of park directors, these specific points could be indicated along the archaeological visitor routes, by means of QR codes, to let visitors experience them in defined locations using their personal devices. Alternatively, visitors could live an immersive experience, putting their smartphones in a cardboard frame and starting the web app in immersive mode, or renting other head-mounted displays, for instance, the Oculus Quest2, at the ticket office of the site. Three-dimensional content and platforms are compatible with all these solutions.
# Expanding Horizons: Taking Advantage of Graphene's Surface Area for Advanced Applications
## 2 Increasing the Surface Area of Graphene
### Layering  
The increase in surface area achieved through the layering, or stacking, of graphene involves the superposition of multiple sheets or layers atop one another in a manner that generates a three-dimensional structure [33]. This stacking leads to significant augmentation in the effective surface area for several fundamental reasons. Primarily, the superimposed graphene layers generate a stratified, interlocking structure with the po  
Figure 4: A schematic showing the formation of ripples with a higher surface area of graphene.  
tential for creating additional interlayer spacing and, therefore, additional surface area between the layers [34]. This interlayer spacing can be manipulated to accommodate guest molecules, solvents, or ions, further enhancing the overall surface area accessible for interactions. Secondly, the layering of sheets in three dimensions can produce unique geometries, such as mesoporous structures, within the interlayer spaces. These mesopores create an extended surface topology, featuring numerous pores and channels that amplify the surface area available for adsorption, catalysis, and molecular interactions. The layering of sheets can result in the creation of edges and corners where the individual layers meet. These edges and corners provide high-energy sites that are more chemically reactive, thus offering increased opportunities for chemical reactions, binding, and surface-related processes. Consequently, the increase in surface area due to layering is not solely a consequence of the interlayer spacing but also the emergence of these high-energy sites at layer interfaces. The substantial augmentation in surface area by layering makes it particularly advantageous in applications such as gas storage, adsorption, and separation. The three-dimensional structure enhances the capacity for gas adsorption within the interlayer spaces and mesoporous regions, amplifying the storage capabilities for gases, including hydrogen and methane. In applications like filtration and environmental remediation, the layering method provides a larger area of contact for the adsorption of different compounds and ions. This increased surface area is key in enhancing the performance of G-b materials and devices in applications that require gas storage, adsorption, catalysis, and molecular interactions, further solidifying the significance of graphene in multidisciplinary fields of science and technology.
## 4 Results
### Establishing the EASY System Characteristics
#### 4.3.2 System Characteristics Based on User Needs and Requirements
Existing spatial datasets included in a specific topic package may be hosted in other accessible systems such as the National Data Grid [31] or government spatial data libraries, or EASY may provide its own upload and storage functions for contributed datasets. EASY is envisioned to re-publish other publicly accessible datasets, primarily via open standards web mapping services, which are becoming more prevalent in supporting spatial data infrastructures. EASY would provide tools for extension program managers and researchers to create new datasets to which end-users could contribute. Extension staff, consultants and landholders would be able to publish their own local monitoring information and to provide feedback on published spatial datasets and models by contributing to these datasets through a crowd-sourcing approach. Over time these user-contributed datasets would become valuable sources of local information and potentially useful in the validation of research models and published spatial datasets. Figure 3: Users, functions, and topic packages in EASY. Figure 2: EASY functional areas and user groups based on topic package concept established to support industry customisation. For example, topic package for dairy, livestock or horticultural industries.
## 3 Results and Discussion
### Effect of the TX-114 Concentration  
TX-114 was chosen in this study due to its ambient cloud point (23-25 \({}^{\circ}\)C) at different concentrations, low toxicological properties and cost, in addition the high density in surfactant rich phase facilitating phase separation by centrifugation, which was the most popular nonionic surfactant used in the CPE [25; 26; 27]. The effect of TX-114 concentration can be the major factor influencing the extraction. The TX-114 solution can form two phase above the cloud point temperature, a lower phase with high surfactantconcentration, and the other aqueous top phase with low surfactant concentration (slightly above the CMC of TX-114) [28]. Thus, the effect of TX-114 concentration in the range of 1-5 wt. % (weight ratio) was investigated. The phase-separation cannot be achieved at 1 wt. % TX-114 concentration during the extraction of CR and MV. It can be seen in Figure 5 that the extraction efficiencies obviously increased with increase of TX-114 concentration. Because the extraction mainly depended on the TX-114 and the influence of MNPs to extraction efficiency can be concealed at high TX-114 concentration, thus, the 4 wt. % TX-114 concentration was chosen for further studies.  
Figure 4: Effect of the Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) amount added to the AMTPS on the extraction efficiency of dyes. (Each system contained 10 mL dyes solution (1 mg/mL CR, MB and 0.125 mg/mL MV), 4 wt. % TX-114, 40 min vibration time, and incubated at temperature 40 \({}^{\circ}\)C for 10 min). Different letters in the same series indicate significant difference at \(p<0.05\) level.