esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Application of Mesh Opportunistic Networks
## 3 Materials and Methods
### Participants  
This experiment was conducted with 14 participants. The low number of participants was due to the lack of mesh communication devices available for this study. The participants (n = 14) were students (11) at the UAB and at the UCLM and audiovisual professionals from RTVE (3). There were seven women and seven men. Of the 11 students, there were 3 students of Audiovisual Communication studies, 3 were second-year Journalism students and the remaining 5 studied History, Teaching, Psychology or Sociology. The eight UAB participants received a 6 h course (at the RTVE Institute in Barcelona), which included a workshop on mobile journalism techniques, metadata protocols and configuration practices, and on video uploading to dedicated servers to broadcast audiovisual material from mobile devices. The three UCLM students received a similar course at their own university, coordinating the content and protocols. The three RTVE professionals who participated in the study were two RTVE journalists and the technological innovation manager at the RTVE Institute, the coordinator of this study and one of its authors. In all cases, informed consent was requested for participation in the experiment.
# Radial Structure of OAM-Carrying Fundamental X-Waves
## 2 Results  
### Localized Waves  
We start our analysis by considering a scalar, monochromatic solution of the Helmholtz equation  
\[(\
abla^{2}+k^{2})\psi(\mathbf{r},k)=0, \tag{1}\]  
where \(k=2\pi/\lambda\) is the wave number in a vacuum. In a cylindrical reference frame \(\{R,\theta,z\}\), the solution can be given in terms of Bessel beams:  
\[\psi(\mathbf{r},k)=J_{m}(kR\sin\theta_{0})e^{im\theta}e^{ikz\cos\theta_{0}}, \tag{2}\]  
where \(R=\sqrt{x^{2}+y^{2}}\), \(\theta=\arctan(y/x)\), \(J_{m}(x)\) is the Bessel function of the first kind, \(m\) is the OAM index, and \(\theta_{0}\) is the Bessel cone angle, which is the beam's characteristic parameter [31]. Notice that \(\psi(\mathbf{r},k)\) is a propagation invariant, as the \(z-\)dependence is contained only in the plane-wave term \(\exp\left(ikz\cos\theta_{0}\right)\), and thus the intensity distribution of a Bessel beam does not change during propagation, i.e., \(\partial_{z}|\psi(\mathbf{r},k)|^{2}=0\). With this solution at hand, we can construct an exact solution of the wave equation:  
\[\left(\
abla^{2}-\frac{1}{c^{2}}\partial_{t}^{2}\right)\phi(\mathbf{r},t)=0, \tag{3}\]  
as a polychromatic superposition of monochromatic solution in the following way:  
\[\phi(\mathbf{r},t)=\int\mathrm{d}k\,g(k)\,e^{-ickt}\psi(\mathbf{r},k), \tag{4}\]  
where \(g(k)\) is an arbitrary spectral function. If \(\phi(\mathbf{r},t)\) is constructed from Bessel beams, then the \(z-\) and \(t-\) dependence of each spectral component will be of the kind \(\exp\left[ik(z\cos\theta_{0}-ct)\right]\), which is the distinctive characteristic of localized waves [17]. For nondiffracting pulses, in fact, regardless of the explicit form of \(g(k)\), it can be demonstrated that they are propagation-invariant with respect to both propagation direction and time, or, alternatively, with respect to the co-moving propagation direction \(\zeta=z\cos\theta_{0}-ct\)[17].  
In this work, we concentrate our attention on one spectral function in particular, namely the generalized exponentially decaying spectrum:  
\[g_{exp}(k)=\Theta(k)\,k^{n}\,e^{-ak}, \tag{5}\](where \(n\in\mathbb{N}\) defines the order of the spectrum, \(\alpha>0\) accounts for the width of the spectrum, and \(\Theta(k)\) is the Heaviside step function [32]), which gives rise to the so-called fundamental X-waves,. The form of the exponentially decaying spectrum for various values of the spectral index \(n\) is depicted in Figure 1.
## 2 Materials and Methods
### CT Data Analysis  
Prior to the further processing of the extracted histogram data, it is necessary to remove the characteristic periodic spikes in the histograms that are induced due to the finite incremental steps of both wall thickness methods. To achieve this, a search algorithm was implemented to separate the histograms into \"standard\" data and periodic spikes. After the periodicity of the spikes is determined, a sliding window locally determines the mean value around the spike and resets the value in the particular histogram bin to the determined mean value. To obtain \(d_{mean}\), \(d_{eq}\) and _msth_ from the adjusted histograms, two different approaches were implemented. The first approach applies a Gaussian fit function to approximate the leading slopes of the peaks corresponding to either the macroscopic channel dimensions, i.e., the diameter, or the _msth_ in microscale features below 200 \(\upmu\)m, where the centre position of the Gaussian peak is considered \(d_{mean}\), \(d_{h}\) or _msth_. The second approach is based on a search algorithm to determine characteristic peaks in the histograms, whereas the histograms are approximated by applying a non-linear least-squares spline approximation to suppress local variations throughout the different histogram bins. After the peaks were determined in the approximated histogram profile, the corresponding histogram bin positions in the unaltered data were determined to again obtain \(d_{mean}\), \(d_{eq}\) or _msth_. To illustrate, Figure 3 shows an example of the adjusted histogram data (black dots) and the corresponding approximation by the non-linear spline. In addition, the two red rectangles indicate the data ranges of small wall-thickness values that were evaluated to obtain _msth_ and of large wall-thickness values that were evaluated to obtain \(d_{mean}\) and \(d_{eq}\). Within these two ROIs, the values are slightly smoothed with an averaging window function prior to the fit of the Gaussian function.
## 4 Experimental Results
### Experiment Results and Analysis
#### 4.3.2 Results on the AG News Dataset  
We introduced MPNet [50] and other methods on the English dataset to continue the experiments. APTrans was also compared with these advanced methods on the AG News dataset following the same principle of training rules to maintain fairness. Table 2 shows the results of the comparison. On this long-text dataset, our method still achieves advanced results, reaching an accuracy of 94.68%. SHGCN [54] connects the relationships among the text, entities, and words to complete the information transfer among each other by building a heterogeneous graph convolutional network, which also achieved results on this dataset. However, since no pre-training model was introduced, the model could still be further improved and developed. MPNet achieves good results on this news dataset by integrating training strategies and adding auxiliary location information. Our approach goes a step further in harnessing the diversity of information in the text, with location information proven to improve accuracy. The ability of the T-PTLM approach to achieve expressive performance compared with traditional models is primarily due to the Transformer's ability to leverage long-term dependent semantic relationships hidden between all patches. Overall, APTrans performs most efficiently among the methods listed in Table 2. Our approach improved the accuracy by 0.11% compared with the pre-trained baseline architecture. This result shows that our model effectively extracts and fuses a small number of core association relationships and semantic locational association representations in the text.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Pad Size** & **Dev Acc (\%)** & **Test Acc (\%)** \\ \hline BigBird [52] & & 96.85 & 95.73 \\ ALBERT [47] & & 96.95 & 96.64 \\ DeBERTa [53] & & 97.15 & 96.33 \\ ELECTRA [49] & & 97.45 & 97.32 \\ GPT-2 [36] & & 97.80 & 97.51 \\ BART [46] & & 97.85 & 97.10 \\ RoBERTa [11] & & 98.00 & 97.11 \\ BERT [10] & & 98.30 & 97.67 \\ ERNIE [48] & & 98.30 & 97.15 \\ XLNet [12] & & 98.45 & 97.67 \\ \hline APTrans (ours) & **98.55** & **97.89** \\ \hline \hline \end{tabular} The top performing values are shown in **bold**, and those following the top values by the underlining.  
\end{table}
Table 1: A performance comparison among the APTrans approach and Transformer-based methods on the THUCNews dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Pad Size** & **Acc (\%)** \\ \hline SHGCN [54] & None & 88.38 \\ \hline ERNIE [48] & & 90.79 \\ DeBERTa [53] & & 91.38 \\ ALBERT [47] & & 93.13 \\ MPNet [50] & & 93.22 \\ GPT-2 [36] & & 94.20 \\ ELECTRA [49] & 512 & 94.28 \\ RoBERTa [11] & & 94.42 \\ XLNet [12] & & 94.51 \\ BERT [10] & & 94.57 \\ \hline APTrans (ours) & **94.68** \\ \hline \hline \end{tabular} The top performing values are shown in **bold**, and those following the top values by the underlining.  
\end{table}
Table 2: A comparison on the AG News dataset between APTrans and other typical methods.
# Towards a Digital Twin for Open-Frame Underwater Vehicles Using Evolutionary Algorithms
## 4 BlueROV2 Case
### Real Tests
#### 4.2.1 Propeller Modeling  
As the only part introducing energy over the system, the thrusters need to be measured and adequately characterized. There are individual characterizations of the BlueROV2 thrusters available [46] that could be used to generate an estimated curve for the vehicle based on the number of thrusters and their orientation. However, the propeller flow when mounted on the vehicle is partially interrupted by its structure, cables, etc.  
To adequately measure the real thrust, a series of tests were performed using an AXI 9195 DELTA IP68 triaxle load cell to measure the empirical curve of the thrusters mounted on the vehicle. The tests consisted of thrust and torque measurements with the thrusters of each axis actuated for a few seconds with different turning speeds. The thruster rotation speed was controlled using an electronic speed controller (ESC) with a pulse width modulation (PWM) input signal.  
Figure 3 shows the measured and estimated thrust for the x axis (\(X\)) at different PWM values of the input signal (I). The first curve was obtained directly from the measurements, while the estimated one was obtained from the individual curves and the geometry configuration. In this specific case, assuming the same response for the four horizontal thrusters and \(T\) as the individual thrust of each one, obtained from [46], we could express the estimated \(X\) as:  
\[X=4\ T\cos\frac{\pi}{4} \tag{12}\]  
There are noticeable differences between the two curves, especially in the region with high PWM signals. The same phenomenon appears on the \(y\) axis thrust, with similar intensity. On the \(z\) axis, whose thruster flow is unimpeded, the estimated and the empirical curves are almost identical.
## 3 Research Model and Methodology
### Methodology
#### 3.2.2 Data Collection Method  
Before the actual data collection could begin, a data collection sheet had to be prepared, and this was a straightforward task. The data collection sheet had two sections: The first was the entry details, which required the collector to record whether a car changed lanes as approaching the roundabout as well as whether they used the turn signal indicator while entering the roundabout and finally, whether they entered the roundabout using the correct lane. The second section, which is the exit details, required the collectors to record whether the driver used their turn signals while exiting the roundabout as well as whether they used the correct lane while exiting. All recordings consisted of \"Yes\" or \"No\" answers, making it a very simple task for the data collectors.  
Once briefed on the data collection sheet, the data collectors visited the selected sites (mentioned in Section 4.1) and stationed themselves at an area where they could safely and clearly observe the incoming cars from all entry lanes. The data collectors also ensured that the data were collected in a way where they were not noticed by the drivers in order to not influence the behavior of the drivers. The data collectors picked any cars approaching the roundabout at random and observed them as they traversed the roundabout while recording all the details. Any government car, learning, drivers, and motorcycles were  
Figure 4: (**a**) Signalized roundabout. (**b**) Non-signalized roundabout.  
avoided. The data collectors repeated this procedure until they had 100 cars total for each roundabout. The data were thoroughly checked for any errors and logged into a spreadsheet ready for analysis.
## 2 Models and Assumptions
### Receiver's Dynamics and Kinematics  
For this work, an accurate MTO trajectory of the receiver was generated using the Astrogator Tool of system tool kit (STK) [11], and after provided as reference trajectory in our Spirent simulator, as user motion text (UMT) (user motion) file.  
The considered MTO is illustrated in Figure 3, where is identified by the yellow curve in LEO and the blue curve in HEO with perigee at the Moon altitude. In this trajectory, a GNSS receiver experiences all signal characteristics that it would experience orbiting at different altitudes in space (from LEO to the Moon).  
Figure 3: Assumed MTO for the receiver.  
The initial time of investigation is 18:10:00 on 1 July 2005, while the launch of the vehicle was performed earlier. The investigated part of this study is propagated until 05:11:12 on 6 July 2005, which corresponds to the end of the blue curve (Moon altitude); it experiences an impulsive acceleration of 3.14217 km/s\({}^{2}\) in the \(x\)-axis, at time 18:41:37:154 on 1 July 2005, to change the orbit from LEO to HEO with a perigee at Moon altitude. The initial earth centered inertial (ECI) position and velocity coordinates of the investigated trajectory are (\(-6188.944987\); \(1742.455236\); \(1155.838277\)) km and (\(2.484942\); \(-6.510775\); \(3.563661\)) km/s, respectively.
## 3 Results and Discussion
### Chlorophyll a and b Concentrations  
The photosynthetic activity of _H. pluvialis_ cells during the 30-d cultivation under different _rac_-GR24 concentrations (2.0-8.0 \(\upmu\)M) was estimated based on the concentrations of chlorophyll \(a\) and \(b\), the main light-harvesting pigments [24] (Figure 4). Similar to the cell number density results (Figure 2), the chlorophyll \(a\) and \(b\) concentrations of the _rac_-GR24-treated algal cultures were higher than those of the untreated control and also showed an increasing trend with increasing _rac_-GR24 dosage. The highest chlorophyll \(a\) and \(b\) concentration (19.6 \(\pm\) 0.9 mg/L) at the 8 \(\upmu\)M _rac_-GR24-added culture significantly improved by 50.3% compared to the control (13.1 \(\pm\) 0.1 mg/L). The enhanced cell number density of the _rac_-GR24-treated algal cells might be mainly due to the improved chlorophyll \(a\) and \(b\) concentrations along with the earlier germination-promoting effect (Figure 2c). [PERSON] et al. [13] observed increased chlorophyll \(a\) content in _C. vulgaris_ FACHB-8 at 0.1 \(\upmu\)M _rac_-GR24. [PERSON] et al. [25] observed the stimulating effect of _rac_-GR24 on carbonic anhydrase activity during microalgae-fungi-bacteria symbiont cocultures, such as _C. vulgaris_-_Ganoderma lucidum_-endophytic bacteria. Similarly, _rac_-GR24 enhanced the chlorophyll content and photosynthesis rate of plants, such as _Vitis viinifera_ L. [26] and _Brassica napus_ L. [27], under stressful drought and salinity conditions, respectively.  
Figure 3: Cell diameter of _H. pluvialis_ after 30-d photosynthetic cultivation at different _rac_-GR24 concentrations (2.0–8.0 \(\upmu\)M). Over 200 algal cells at each concentration were measured under light microscopy.  
In Figure 4, the chlorophyll \(a\) and \(b\) concentrations of all _H. pluvialis_ cultures showed a decreasing trend after peaking around day 18, similar to the cell number density results (Figure 2a). This may be related to the physiological change of _H. pluvialis_ cells from chlorophyll-synthesizing vegetative growth to the dormant encystment with astaxanthin induction as the nitrogen nutrient was depleted (see Figure 2c). In the absence of additional photosynthetic growth, chlorophyll pigments can be degraded and reallocated to support the metabolic activity of algal cells exposed to adverse culture conditions. [PERSON] [28] reported that during the photosynthetic cultures of _H. pluvialis_, _Chlamydomonas reinhardtii_, and _C. zofingenisis_, down-regulation of chlorophyll synthesis was associated with subsequent upregulation of astaxanthin and lipid synthesis.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
###### Abstract  
Reconfigurable antennas have gained tremendous interest owing to their multifunctional capabilities while adhering to minimalistic space requirements in ever-shrinking electronics platforms and devices. A stark increase in demand for flexible and conformal antennas in modern and emerging unobtrusive and space-limited electronic systems has led to the development of the flexible and conformal reconfigurable antennas era. Flexible and conformal antennas rely on non-conventional materials and realization approaches, and thus, despite the mature knowledge available for rigid reconfigurable antennas, conventional reconfigurable techniques are not translated to a flexible domain in a straight forward manner. There are notable challenges associated with integration of reconfiguration elements such as switches, mechanical stability of the overall reconfigurable antenna, and the electronic robustness of the resulting devices when exposed to folding of sustained bending operations. This paper reviews various approaches demonstrated thus far, to realize flexible reconfigurable antennas, categorizing them on the basis of reconfiguration attributes, i.e., frequency, pattern, polarization, or a combination of these characteristics. The challenges associated with development and characterization of flexible and conformal reconfigurable antennas, the strengths and limitations of available methods are reviewed considering the progress in recent years, and open challenges for the future research are identified.  
flexible antennas; frequency reconfigurable; microfluidic antennas; pattern reconfigurable; polarization reconfigurable antennas; wearable antennas +
Footnote †: journal: Electronics Electronics
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### Embedding Domain
#### 2.1.2 Discrete Wavelet Transform  
DWT technique is applied for many different applications, which include the signal processing domain. The main idea of DWT technique is to split objects into a number of frequency channels. Nonetheless, the bandwidth is preserved through the technique according to a logarithmic scale, i.e., the 'Multi-resolution analysis' [39]. Thus, several steps are considered when applying the conversional process. Different approaches such as the diagonal or High-High (HH), vertical or High-Low (HL), approximation or Low-Low (LL), and horizontal or Low-High (LH) entirely represent different sub-bands related to an object that is decomposed by using the 1-level DWT technique (see Figure 4). In the rows of the DWT, the first letter represents the high or low-pass frequency. In the columns of the DWT, the second letter represents the filter. The first three sub-bands, which include the HL, LH, HH and sub-bands, are based on the best scale of wavelet coefficients.  
Figure 3: The Watermark Embedding process.  
The last sub-band, LL, is based on rough scale wavelet coefficients. The LL functions efficiently once an additional level is gained during the decomposition procedure where it is persistently analysed for a determined objective until the required number within the level of decomposition is provided [40].
### Context
#### 1.1.1 Resilient Distributed Systems  
The earliest attempts at remotely controlling computers before the post-war era [6] aside, the concept of geographically distributed computers collaborating was introduced in the 1950s. These early computer systems emerged in the military domain and were coordinated by transmitting generalised digital data over standard phone lines [7]. While issues of reliability were of concern in these early designs, these were met by basic error control techniques, like parity checking of input and output data, only [8]. Notably, these early systems assumed that participating computers were fully trusted and secured via military procedures. Formalisations of computer networks and their failure characteristics were put forward alongside the inception of multi-process computing in the 1960s [9]. Back then, Kleinrock [10] laid the foundation for data networking theory by proposing to research the effects of messaging delays and message prioritisation in 'large communication nets'. These theoretical foundations led to the practical implementation of formal message protocols for wide-area communication [11] and subsequently to the implementation of the Advanced Research Projects Agency Network (ARPANET) [12], the predecessor of the modern Internet, that brought with it the notion of various autonomous systems clustered in hierarchical layers [13]. These advances, however, focussed mostly on providing stable means of connecting network members in principle and did not govern higher protocol layers.