esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Fatty Acid**} & \multicolumn{1}{c}{**Redcurrant**} & \multicolumn{1}{c}{**Blackberry**} & \multicolumn{1}{c}{**Blueberry**} & \multicolumn{1}{c}{**Raspberry**} & \multicolumn{1}{c}{**Grapeseed**} & \multicolumn{1}{c}{**Pomenagrate**} & \multicolumn{1}{c}{**Sour Cherry**} \\ \multicolumn{1}{c}{**(g/100 g)**[36]} & \multicolumn{1}{c}{**(g/100 g)**[37]} & \multicolumn{1}{c}{**(g/100 g)**[38]} & \multicolumn{1}{c}{**(g/100 g)**[38]} & \multicolumn{1}{c}{**(g/100 g)**[39]} & \multicolumn{1}{c}{**(g/100 g)**[40]} & \multicolumn{1}{c}{**(g/100 g)**[41]} \\ \hline Palmitic acid & 4–5 & 3.71 & 4.98 & 2.73 & 7.40 & 3.90 & 5.30 \\ (C16:0) & & & & & & & \\ Stearic acid & 1–2 & 2.18 & 1.47 & 0.87 & 3.90 & 2.60 & 1.50 \\ (C18:0) & 14–16 & 14.72 & 18.00 & 11.76 & 15.60 & 6.60 & 63.90 \\ Oleic acid & 41–42 & 61.22 & 35.84 & 54.27 & 72.20 & 6.90 & 27.00 \\ (C18:20\(\mathfrak{C}\)6) & & & & & & & \\ \(\alpha\)-linolenic acid & 29–31 & 17.60 & 36.08 & 26.68 & 0.24 & - & 0.10 \\ (C18:3\(\mathfrak{C}\)3) & & & & & & & \\ \(\Sigma\) Saturated fatty acids & 6.00 & 5.89 & 6.45 & 3.30 & 11.30 & 6.50 & 6.80 \\ \(\Sigma\) Unsaturated fatty acids & 94.00 & 94.11 & 93.55 & 96.70 & 88.70 & 93.50 & 93.20 \\ \(\mathfrak{C}\)6:3 & 1.40 & 3.50 & 0.90 & 2.00 & \(>\)100 & - & \(>\)100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Seed oil fatty acid content in different fruit species. detected: 0.004 and 0.023 g/100 g, respectively. Considering the other active ingredients, squalene (19.3 mg/g) should play a positive role in the anticarcinogenic properties of the skin and it is the only terpene detected. The content of \(\beta\)-sitosterol (2.0 mg/g), which is chemically similar to the cholesterol molecule, can potentially interfere with its absorption and contribute to reducing its content in the human body.
## 3 Algorithm  
### Event Density and Event-Density Matrix  
Before introducing the algorithm, we first define two concepts: the event density matrix and event density. The output event of a DVS can be represented by three parameters, \(e(x,y,t)\), which include the \(x\) and \(y\) coordinates of the image plane, where the event is located, and the timestamp, respectively. The timestamp is added to the output by the field programmable gate array (FPGA) driving the DVS. According to the spatiotemporal correlation principle, real events are related to events adjacent to each other in time and space; hence, a spatiotemporal neighborhood is set for each event, as shown in Figure 2. The red point indicates the newly arrived event at \(t_{1}\), in the center of a spatial neighborhood sized \(L\times L\); \(L\) is odd, the time neighborhood is \((t_{1}-\Delta t,t_{1})\), and the spatiotemporal neighborhood is expressed as \(\Omega_{\Delta t}^{L}\). The \(L\times L\) pixel output events in \(\Omega_{\Delta t}^{L}\) are respectively accumulated, and the result is placed in the corresponding position in an \(L\times L\) sized matrix, which is the density matrix, \(D\); the matrix element, \(D_{i,j}\), is expressed as Equation (4),  
\[D_{i,j}=\sum_{t=t_{0}-\Delta t}^{t_{0}}\gamma(x_{0}-\frac{L-1}{2}-1+i,y_{0}- \frac{L-1}{2}-1+j,t)\quad 1\leq i,j\leq L, \tag{4}\]  
where \(x_{0},y_{0}\) are the spatial coordinates of the newly arrived event. \(\gamma(x,y,t)\) is a binary function:  
\[\gamma(x,y,t)=\left\{\begin{array}{ll}1&\quad\text{if there is an event }e(x,y,t)\\ 0&\quad\text{otherwise}\end{array}\right.. \tag{5}\]  
After obtaining the density matrix, define the event density, \(d\), as the L1 norm of the density matrix:  
\[d=\|D\|_{1} \tag{6}\]
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 3 True Random Number Generators Design and Evaluation on FPGA Technology
### Candidate Solutions  
#### 3.2.1 Transition Effect Ring Oscillator (TERO)  
To test the TERO presented in Section 2.2.2 it is necessary to evaluate the number of oscillation the latch performs every time the ctrl signal toggles. To do so, a 10-bit asynchronous counter has been placed at the output of the device. Using a 10-bit counter instead of a single T Flip flop, not only we determine if the number of oscillations is odd or even: it is possible to check the exact number of oscillations. The expected outcome is to find a distribution of the number of oscillations similar to the ones showed in [25; 26]. The actual output random variable is a binary variable which equals one if the amount of oscillation  
Figure 7: Fibonacci-Galois Ring Oscillator (FiGaRO) four elements architecture.  
is an odd number or zero if it is an even number. Samples can be regarded as independent since TERO outputs a single bit for every restart, zeroing possible dependencies between successive samples. Instances, where numbers of oscillations are more spread, provide lower differences between ones and zeros, i.e., a higher entropy. We placed multiple TEROs on the FPGA with the same internal placing and routing, constrained into a single group of 10 Adaptive Logic Modules (ALMs). This saved element has been positioned multiple times, to test whether the observed behaviour depended or not from placing inside the FPGA device. From each TERO a sequence of 10,000 numbers of oscillations has been extracted and histograms of these values have been plotted. Figure 8 reports the number of oscillations occurred before having the output of the latch stable, in 8 different TERO elements. As shown, the histograms of the number of oscillations are very different from one element to another: some, like TERO 5, almost have a fixed number of oscillations, while others, like TERO 4, show a more complex plot. This variability was also found in [26]. The authors in [26; 27] claim that routing and placing dependency is the main disadvantage of TERO. Techniques for overcoming this issue have been implemented in [27] on a Xilinx FPGA. However, the feasibility of such techniques strongly depend on the target platform and prevents the realization of a TRNG portable on different FPGAs.
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 4 Methodology
### User Traffic Prediction Module
#### 4.1.1 Long Short-Term Memory Variant
With \(t\) and \(T\) we symbolize the current and final time step and with \(n\) and \(N\) the sequence index of the sliding window sequence and the length of the sliding window sequence. \[L_{d}=\frac{\sum_{n}^{N}\sum_{t}^{T-1}M(D_{a}(n,t),D_{p}(n,t))}{N*(T-1)}\]  
The volatility-based loss is calculated as shown in the equation below. With \(Diff_{a}\) we symbolize the actual distance of one element of the series to the next and with \(Diff_{p}\) the predicted distance. With \(\Delta(S)\) we symbolize an operator that calculates the standard deviation of a set of set of distances. With \(\epsilon\) we symbolize a very small non-zero number that assists with avoiding division with 0. \[L_{p}=\frac{\sum_{n}^{N}\left|1-\frac{\Delta(Diff_{a}(n)}{\Delta(Diff_{p}(n)+ \epsilon}\right|}{N}\]  
Figure 2: The architecture of our LSTM user traffic prediction module.
## 3 Testing Results and Analysis
### Unconfined Compression Test
[19] used cement and fly ash to improve clay and reported that for the unsoaked sample, E\({}_{50}\) = 90 q\({}_{\rm u}\) and R\({}^{2}\) = 0.9890, whereas for the soaked sample, E\({}_{50}\) = 60 q\({}_{\rm u}\) and R\({}^{2}\) = 0.9894. In 2021, [PERSON] et al. [26] indicated that the density control group had a formula of E\({}_{50}\) = 131.1 q\({}_{\rm u}\) and R\({}^{2}\) = 0.89, whereas the cementation control group had a formula of E\({}_{50}\) = 60.4 q\({}_{\rm u}\) and R\({}^{2}\) = 0.84. The difference in the formula coefficients was dependent on the added paste material and specimen preparation methods. Figure 8: Unconfined compression strength with respect to dry density in soil samples for untreated and treated samples: (**a**) mudstone; (**b**) mudstone–cement. Figure 7: Deviate stress–strain curves of untreated and treated samples of unconfined compression test: (**a**) mudstone; (**b**) mudstone–cement.
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 4 Performance Analysis  
### Configuration  
To compare the performance of the proposed technique, experiments were conducted using a Python-based 6 TiSCH simulator developed by members of the 6 TiSCH working group. The 6 TiSCH simulator implements the 6 TiSCH protocol stack and the trickle timer, as shown in Table 2.  
The implementation of the mobility model in the 6 TiSCH simulator changes the position of the communication node at the end of each time slot. We assign cells based on the MAC address of each node and use the MAC hash expression used by the minimal scheduling function (MSF) technique to allocate the automatic cells of each node.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary
### Selection of Coefficient \(k_{i}\)  
Before introducing the improved algorithm, we need to clarify the use of the coefficient \(k_{i}\). The detailed selection scheme of \(k_{i}\) is described as follows:  
1. [PERSON] chooses two large prime numbers \(P\) and \(Q\), and sets \(N=P\cdot Q\). She generates a large integer \(d<N\) randomly.
2. Calculate \(A=T_{d}(x)(modN)\), where \((x,\ A,\ N)\) are the public key and \(d\) is the private key.
3. [PERSON] randomly selects \(r<N\), calculates \(T_{r}(A)(modN)\), and selects \(k_{i}\) based on Equation (20).  
Calculate \(B=T_{r}(x)(modN)\) and send it to [PERSON].
5. [PERSON] calculates \(T_{d}(B)(modN)\) based on the private key \(d\) and selects \(k_{i}\) based on Equation (20). The selection rule of \(k_{i}\) is as follows: \[k_{i}=\begin{cases}k_{1},\ \ 0\leq T_{r}(T_{d}(x))modN=T_{d}(T_{r}(x))modN\leq \frac{\delta}{n}\\ k_{2},\ \ \frac{\delta}{n}\leq T_{r}(T_{d}(x))modN=T_{d}(T_{r}(x))modN\leq\frac{2 \delta}{n}\\ \vdots\\ k_{i},\ \ \frac{(i-1)\delta}{n}\leq T_{r}(T_{d}(x))modN=T_{d}(T_{r}(x))modN\leq \frac{i\delta}{n}\\ \vdots\\ k_{n},\ \ \frac{(n-1)\delta}{n}\leq T_{r}(T_{d}(x))modN=T_{d}(T_{r}(x))modN\leq \delta\end{cases}.\] (20)  
In Equation (20), \(\delta\) is a large number and \(n\) is the number of \(k_{i}\). It is easy to see from the semi-group property of Chebyshev that \(T_{r}(T_{d}(x))modN=T_{d}(T_{r}(x))modN\).  
\((AmodN)(modP)=A(modP)\), where \(A\) represents the Chebyshev polynomial and \(N=P\cdot Q\), \(P\) and \(Q\) are two large prime numbers.
## 1 Introduction
showed that SRT in ramp mode, combined with fast algorithm-based SD-OCT treatment termination, could provide reliable SRT dosing and prevent overtreatment. Recently, [PERSON] et al. were able to show the same effect of abrupt signal changes with a slightly different OCT acquisition scheme, which utilized a wavelength scanning radiation source termed \"swept-source\" (SS-OCT) [29]. These authors also aimed to extract local temperature information from speckle variations, to support the hypothesis that this occurred due to strictly localized heating of melanosomes without major temperature increases in neighboring regions [29]. With this tool for monitoring stress at the cellular scale, the optimal pulse parameters leading to the desired cellular damage can be explored in more detail. To enable automated, highly reliable, selective RPE-level laser treatment, with maximum clinical control, it is necessary to further analyze how well the OCT signal can predict different biological outcomes. The aim of the current work was therefore to investigate the range of pulse durations (from 2 to 20 \(\upmu\)s) in more detail with respect to the threshold radiant exposure for RPE cell damage. A fluorescence-based vitality stain (calcein-AM-assay, median effective dose for RPE cell damage: \(ED_{50}^{RPE}\)) was used directly after laser irradiation. The fluorescence change index of cell damage was compared with fringe washouts observed in SD-OCT M-scans (median effective dose for fringe washouts: \(ED_{50}^{OCT}\)). If the threshold for fringe washouts equals the threshold for RPE cell damage, then the damage can be assumed to be primarily thermomechanical, and thus likely to result in the desired selective RPE damage. Such knowledge may allow the pulse duration regime for SRT to shift to higher pulse durations. To test the hypothesis that SD-OCT has the potential to be used for real-time dosimetry of RPE cell damage, a specially designed system [30] was employed to investigate OCT M-scan fringe washouts during microsecond laser irradiation. Successively, the therapeutic window required to achieve a selective effect in the RPE was examined to investigate if it could be expanded from 1.7 \(\upmu\)s--as previously used in Q-switched laser systems (i.e., RGEN, Lutronic, Gyeonggi-do, Korea)--to longer pulse durations, which would allow the use of smaller, better controllable and more flexible SRT laser sources.
# Enhancement of the Purcell Effect by the Wire Metamaterials Formed by the Hexagonal Unit Cells
## 3 Results
Dependencies of the real (**a**) and imaginary (**b**) parts of the propagation constant, upon frequency. of an infinite wire metamaterial. Two amplitudes of the Purcell factor were located and kept for a different geometry of the sample. Moreover, the Purcell factor enhancement was dramatically affected by replacing the host medium of the wire metamaterial with a vacuum, as seen in Figure 6.  
It can be observed in Figure 6a that a stronger resonance was caused by the growth in the radius of the wires. It is worthwhile noting that, in this case, the lattice constant was fixed. Figure 2b demonstrates the maximum in the lossy factor, \(\left|\text{Im}(\varepsilon_{\perp})\right|\), occurring at a frequency that was, to some extent, lower than that of the band, in which Re\((\varepsilon_{\perp})\) was negligible. Consequently, the growth in the diameter of the wires (\(d\)) gave rise to the second resonance of the Purcell factor. It is worthwhile noting that the second topological transition might have been lost by decreasing the distance between the nanowires. Thus, in a predetermined lattice constant, there might be the best possible estimate for the nanowire radius. Thus, Figure 6 demonstrates the optimal sets of the parameters that aim to achieve two topological transitions in the investigated frequency range. It is worthwhile noting that Figure 6 also demonstrates a comparison in the results obtained for the host medium being either dielectric or air. The decrease in the Purcell factor can be observed in the case of the air host medium, due to the higher absorption and penetration of the electric field in the metamaterial. The values of the Purcell factor for a conventional interface plasmon have also been demonstrated. Figure 7 presents the dependence of the maximum value of the Purcell factor versus the filling ratio. Lowering the LDOS peak frequency towards the low-absorption range was proven to be effective. The value of the Purcell factor can be easily increased ten-fold. Importantly, a decrease in the value of the filling factor increased the value of the Purcell factor for an ordinary plasmon. For the very small values of the filling ratio, the maximum Purcell factor approached zero, as there can be no surface plasmon on a dielectric/dielectric boundary. An increase in the filling ratio caused a decrease in the Purcell factor. Figure 6: Dependence of the Purcell factor upon frequency. (**a**) \(S=70\) nm; (**b**) \(d=30\) nm.
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 3 Proposed Solution
### Bluetooth Low-Energy Interface  
As far as the wireless link is concerned, a standard protocol in the 2.4 GHz Industrial, Scientific, and Medical (ISM) band was preferred rather than a proprietary one, which would be more thought to integrate with other devices and systems. Among the other standard protocols operating in the same frequency band, the Bluetooth Low-Energy (BLE) was chosen because of its elevated spread, which results in low-cost radio transceivers, several compatible devices, and its good radio performance (10 m range). The fundamental entity that can be transferred between two BLE devices is the called characteristic, and it is at least 16 byte longer [30]. As the read (the write) of a single characteristic requires a significant overhead from the reader (writer) point of view, the number of characteristics should definitely be minimized. Taking into account such two aspects, a set of four characteristics, i.e., configuration, command, ranges set-up, and over-threshold peaks, was adopted. The configuration characteristic allows one to control \(f_{s}\), \(N_{s}\), and the windowing function, the command one to set the module in idle state, to force a signal acquisition and the postprocessing, or to perform the fault detection cyclically. The parameters of the ranges to be monitored, as well as their number, can be modify through the ranges characteristic, and finally the amplitudes of the frequency spectrum that exceed the given thresholds, and the corresponding frequencies, are pointed out in the over-threshold peaks characteristic. The list of parameters accessible through the BLE interface are listed in Table 1, and the size of transferred data in Table 2. Moreover, the exploitation of the notification feature allows the proposed module to notify the remote controller only when the over-threshold peaks characteristic changes, meaning that the remote controller is not required to continuously read such characteristic.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sampling frequency (\(f_{s}\)) & 100 Hz to 50 kHz \\ Number of samples (\(N_{s}\)) & 256–65832 \\ Windowing function & No window, Hann-2, Hann-4, top flat, Blackman \\ How often acquire and process signal & 30 s to 100 min \\ Parameters of i-th range & \(TH_{i},f_{s,1},f_{e,i}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurable parameters via Bluetooth Low-Energy.  
Figure 4: The i-th and the j-th range are shown, alongside their thresholds (\(TH_{i}\), \(TH_{j}\)) and frequency intervals. In such a case, a fault is detected in the j-th range.
## 3 Results
### Comparison of Impression Time  
The comparison was based on four key procedures: implant position scanning, opposing arch scanning, centric occlusion scanning, and total impression time. Digital impression techniques were significantly faster than conventional impression methods at all steps (\(p\) < 0.001). For implant position scanning/rubber impression, conventional impressions took 373.9 \(\pm\) 44.2 s, while digital impressions took 214.8 \(\pm\) 47.0 s. In opposing arch scanning/opposing arch impression, conventional impressions took 180.2 \(\pm\) 22.1 s, compared to 102.3 \(\pm\) 25.2 s for digital impressions. For centric occlusion scanning/bite registration, conventional impressions took 86.8 \(\pm\) 14.7 s, while digital impressions took 36.4 \(\pm\) 10.2 s. In total impression time, conventional impressions took 640.8 \(\pm\) 52.8 s, while digital impressions took 353.5 \(\pm\) 62.6 s, with a time difference of 287.3 s (\(p\) < 0.001) (Table 1).
# Methodology for the Assessment of Multi-Hazard Risk in Urban Homogeneous Zones
## 2 Methodology
### Risk Assessment of Homogenous Zones  
At the higher level of analysis, which is the intermediate level, the previously-analyzed objects are grouped into spatial units (assessment area) that are called \"homogeneous zones\" [47]. The process of creation of homogenous zones for the pilot site is presented in Figure 9. In this case, three different layers are intersected: a layer of specific urban characteristics, a layer of areas surrounded by the main roads and a layer of terrain height.  
Figure 8: Distribution of critical zones most prone to extreme sea waves exposure.  
Figure 7: Distribution of critical zones most prone to flood due to impact of climate changes on sea level rise: scenario for year 2100.  
The seismic vulnerability indexes of individual buildings are used to calculate the seismic vulnerability index of a homogeneous zone. In the same way, the other vulnerabilities indexes are evaluated. However, it is important to highlight that the additional criteria are very important at this level of analysis. Since homogeneous zones have complex characteristics, additional criteria are used for both single-hazard and a multi-hazard approach, beside vulnerability and hazard.  
The literature review shows that the level of risk to the community depends on a number of other parameters whose activation in a particular hazard reduces the resilience to extraordinary events. Each area due to difference in size requires a special approach in identifying the relevant parameters. For example, the most common parameters (criteria) for seismic hazard are grouped into area characteristics (geology, soil, slope, historical earthquake events, fault line, etc.), the characteristics of human intervention in space (land use, built communal infrastructure and roads, etc.), and social characteristics (housing density, social purpose of buildings, social structure, etc.).  
For the pilot site at the intermediate level, additional parameters that can be quantified are detected, different for each homogeneous zone. They represent additional criteria for the risk assessment of homogenous zones: communal infrastructure, road network, construction density (distance between buildings), inhabitation density, importance factor (public building, school, etc.), and historical buildings.
# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
## 2 Materials and Methods
### Selection Criteria  
In this study, the PRISMA principles [14] were adhered to throughout the process of selecting and reviewing studies. The results of the previously mentioned searches were imported into the Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia, accessible at www.covidence.org). Duplicates were removed, and the remaining studies were screened based on title and abstract. An additional check was performed on the references of the retrieved articles. The full texts of the articles were evaluated to determine whether they met the inclusion and exclusion criteria outlined previously (see below). Two authors independently reviewed each article at both the title/abstract and full-text stages, blinded to each other's ratings. Disagreements were resolved through consensus meetings among all six reviewers.  
The inclusion criteria were as follows: (i) studies involving adult human participants (\(\geq\)18 years); (ii) studies with participants having a clinically diagnosed nasal obstruction, regardless of their etiology (e.g., septal deviation, nasal polyposis, chronic rhinosinusitis); (iii) studies that presented data about at least one acoustic parameter of voice [15; 16; 17].  
Exclusion criteria comprised the following: (i) studies lacking original empirical data (e.g., reviews, commentaries); (ii) studies with less than five participants; (iii) studies that did not evaluate any acoustic parameter (eg. voice analysis relied exclusively on perceptual evaluation) or without correlation between voice and nasal obstruction, including studies using only a nasometer, which measures relative sound energy but not acoustic parameters; (iv) studies evaluating exclusively hypernasality; (v) studies that did not include any specific assessment of nasal obstruction; (vi) pediatric populations or animal studies. The list of 27 excluded studies is available in the Supplementary Materials (File S1). We have included the PRISMA checklist as Supplementary File (S3).  
This review was not registered in a protocol database (e.g., PROSPERO) due to its narrative scope. It excluded grey literature sources, such as conference proceedings, dissertations, preprints, and unpublished studies. Approval from the ethics committee was not required, as this review did not directly involve human or animal subjects.