esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimized Differential Step-Size LMS Algorithm
## 6 Conclusions  
Starting from the LMS algorithm, we developed an optimized differential step-size algorithm in order to update the adaptive filter coefficients. Our approach is developed in the context of a state-variable model and follows an optimization criterion based on the minimization of MSD. We derived a general algorithm valid for any input signal, ODSS-LMS-G, together with a simplified version for white Gaussian input signal, ODSS-LMS-W. The ODSS-LMS-G algorithm presents a high complexity (e.g., \(O(L^{3})\)) due to matrix manipulations, while the complexity of the ODSS-LMS-W algorithm is \(O(L)\). Nevertheless, they can be considered as benchmark versions for performance analysis. Their performance is highlighted in Section 5, emphasizing the features of the optimized step-size. An important parameter of the algorithm is \(\sigma_{w}^{2}\), which should be tuned to address the compromise between low misalignment and fast tracking. In this context, it would be useful to find a variable parameter. Experiments demonstrate that the ODSS-LMS-G algorithm can be a benchmark in system identification, while the ODSS-LMS-W algorithm could be a possible candidate for field-programmable gate array (FPGA) applications, if the input signal is less correlated.  
Conceptualization, A.-G.R.; Formal analysis, S.C.; Software, C.P.; Methodology, J.B. This research received no external funding.  
The authors declares no conflict of interest.
# A 40 Mb/s VLC System Reusing an Existing Large LED Panel in an Indoor Office Environment
## 1 Introduction
The residual current ripple from the LED driver introduces unwanted low-frequency light intensity modulation and, hence, reduces the modulation depth of VLC signals. Optimised LED driver circuits for VLC have been proposed to address the flicker issue [34; 35]. In this paper, we still use the existing LED driver with the flicker problem in order to fully reuse the existing illumination system and keep the modification within a minimum degree. The detrimental effect of flicker on a binary phase modulation (BPM) VLC system is investigated in [36] under the condition that the flicker is treated as a random interference. In this paper, we treat the flicker as an underlying stationary and predictable signal and, therefore, we can use a low-pass finite impulse response (FIR) filter to filter out the low-frequency components as an estimate of the flicker. After subtracting the estimated flicker from the received raw signal, the adverse effects of flicker can be greatly alleviated, offering a different approach to addressing the flicker at the receiver. Because the estimation of the flicker can be realised by a programmable FIR filter in the digital domain, this digital signal processing (DSP)-based solution can be universally applied to deal with a variety of flicker specifications. Our demonstration system can support a data rate of 40 Mb/s at a distance of up to 2 m with a minimum illuminance level of 300 lux [37]. The VLC functionality is added to the existing light infrastructure without any degradation in the lighting level, presenting a usable VLC/LiFi system in an indoor office environment. The remainder of this paper is organised, as follows. In Section 2, the demonstration system is described in detail. Section 3 discusses the key challenges in the implementation of the proposed system and presents the experimental results. Finally, conclusions are made in Section 4.
## 2 Materials and Methods
### Batch, Repeated Batch and Fed-Batch Cultivation  
Batch, repeated batch and fed-batch cultivations were conducted in 2 L working volume stirred tank bioreactors (BioFlo 310 Benchtop Fermentor/Bioreactor, New Brunswick Scientific, Eppendorf AG, Hamburg, Germany) with process control for pH, dissolved oxygen (DO), agitation, temperature and pump feed. All cultivations were inoculated with 10% (\(v/v\)) of the inoculum's cultures with initial biomass concentration in the bioreactors ranging from 0.10 to 0.15 g DCW/L. The initial operational conditions were as follows: initial pH 7.0 (not controlled during the process); temperature 30 \({}^{\circ}\)C; agitation 250 rpm; dissolved oxygen (DO) 90-98% saturation and aeration rate 0.8 L/min.  
In batch cultivation, the BSM described above was supplemented with 20 g/L glycerol and 1.3 mM DMSO. Cultivation volume was 2 L. Repeated batch cultivation experiments involved the replacement of a part of the culture broth with fresh sterile medium at an appropriate time during the batch cultivation process. More specifically, when cells reached the end of the exponential growth phase, 80% (\(v/v\)) of the culture broth was removed and replaced with fresh sterile medium. Glycerol and DMSO concentrations after medium replacement were maintained at 20 g/L and 1.3 mM, respectively. Cultivation volume was also maintained at 2 L. Two batches of cultivation were performed.  
Fed-batch cultivation was carried out by feeding sterilized glycerol + DMSO solutions into the bioreactor. The concentration of glycerol and DMSO in the feed was 40 g/L and 1.3 mM, respectively. Initially, the bioreactor was filled with 900 mL of the medium supplemented with 5 g/L glycerol and inoculated with 100 mL of the inoculum culture prepared as described above (initial volume 1000 mL). The fed-batch operation was initiated approximately 21 h after inoculation. Pulse feeding was applied. DO was maintained above 20% saturation adjusting the aeration and agitation rate in the bioreactor. Feeding was terminated when cell concentration between two successive samplings was constant.  
Samples were withdrawn periodically to monitor cell growth, glycerol concentration and cells' desulfurization activity (CDA) following the methods described below. All experiments were conducted in duplicate.
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 4 Experiment and Evaluation
### Security Analysis  
The security of VidBlock's blockchain is fundamentally influenced by the number of decentralized nodes and the distribution of computational power across the network. To address potential vulnerabilities, such as the creation of counterfeit blocks by malicious actors, we conducted a probabilistic analysis and explored preventive mechanisms.
## 5 Conclusions  
The main objective of this study was to numerically investigate the pull-out behavior of a tunnel-type anchorage based on the geometric and rock joint conditions, such as the enlarged part, anchorage spacing, joint orientation, spacing, and the shear strength of the rock joints. The following conclusions can be drawn from the study:  
1. When there was no enlarged part in the tunnel-type anchorage, the anchorage showed a pull-out failure mode; however, if there was an enlarged part, it showed a wedge-shaped failure mode. Additionally, it was confirmed that the pull-out resistance of the anchorage decreased as the spacing between the anchorages became narrower, similar to the group effect of the pile.
2. It was found that the lowest resistance was shown when the tunnel-type anchorage was constructed on the rock with the joints in the direction perpendicular to the cable  
Figure 14: Load–horizontal displacement curve with joint cohesion.  
Figure 13: Load–horizontal displacement curve with the internal friction angle of joint.  
load. The reason for this could be that the joint direction is similar to the wedge shape, which is a typical failure mode of the tunnel-type anchorage.
3. It was found that the ultimate load increased as the joint spacing became wider because the weight of the rock between the joints increased, and the number of joints decreased. In the pull-out behavior of the tunnel-type anchorage, the internal friction angle of the joint was more significant than the cohesion between the joints.  
Conceptualization, M.C.; methodology, M.C., S.S., J.K., and H.L.; software, H.L., and S.S.; formal analysis, M.C.; writing--original draft preparation, H.L.; writing--review and editing, H.L., J.K., and S.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (21 SCIPB119947-06 and 22 SCIP-C151438-04) from the Construction Technology Research Program funded by the Ministry of Land, Infrastructure and Transport of Korean government.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Experiment
### QPM in LPPs
There are four factors that contribute to the phase mismatch: atomic dispersion, Gouy phase shift, intensity-dependent dynamical phase shift in nonlinear dipole moments, and plasma dispersion. The first one refers to the variation in the refractive index of the neutral components of plasma, which shows considerably less influence than the dispersion induced by the presence of free electrons (fourth factor) in the plasma in the spectral ranges far from the resonance transitions, especially in the case of low-density plasma medium, thus allowing us to exclude the influence of atomic dispersion on the phase mismatch. Gouy phase shift could be also dismissed when the confocal parameter of the focused radiation exceeds the sizes of plasma, which was the case of our experiments (correspondingly 8 and 5 mm). The Gouy phase mismatch [33] at our conditions was 0.6 cm\({}^{-1}\). The intensity of NIR femtosecond radiation used in the plasma area did not exceed \(3\times 10^{14}\) W cm\({}^{-2}\). At these conditions the phase mismatch between the harmonics emitted at different ends of the 5-mm-long plasma medium can be estimated as 8 cm\({}^{-1}\)[34]. Finally, the phase mismatch due to plasma electrons [35] was considerably higher (\(\sim\)50 cm\({}^{-1}\) for the 34 th harmonic) compared to other components. Thus, at our conditions, the only component influencing the phase mismatch is related to the presence of electrons in the laser-produced plasma. Our studies confirmed that the above-mentioned formula (\(L_{\mathrm{coh}}\approx 1\times 10^{18}/H_{\mathrm{qpm}}\times N_{\mathrm{e}}\)) for the coherence length can be used at the conditions of low-density plasma (\(\leq\)10\({}^{18}\) cm\({}^{-3}\)), relatively long confocal parameters (\(b>l_{\mathrm{plasma}}\)), and relatively low intensities of the driving pulse (\(<\)\(5\times 10^{14}\) W cm\({}^{-2}\)).
# A Quantum-Secure Cryptographic Algorithm Integrating Fractals and Prime Numbers
## 3 A Multiscale Prime Generator Sub-Key  
The other sub-key that we give as input to the quantum F&NIF algorithm is a product of primes that we obtain through the generation of two prime numbers, with each one  
Figure 6: Bamsley fern.  
Figure 7: Vicsek fractal.  
generated thanks to a multiscale sieve, using the rules in [21]. We randomly generate a number \(n\in\) N, and the sieving algorithm will return either \(n\) if it is prime or the prime number closest to \(n\). The sieving algorithm is based on an automated procedure based on a proved theorem in [3], which asserts that each prime number can be written as the difference between the product of the first m primes and a prime obtained in the previous level of gridding. So, using a multiscale analysis, each prime number can be written as  
\[p_{ij}=\left(\prod_{j=1}^{m}p_{j}\right)k-p_{i},\qquad with\ k\in N\]  
where \(k\) is the counting index, \(p_{i}\) is a prime number obtained at the level \(m-1,j\) is the level of decomposition, and \(p_{ij}\) the prime number obtained. So, the algorithm stores the candidate prime numbers useful for multiscale generation gradually, up to the nearest prime number of the input. Therefore, the key elements to the algorithm are the following:  
* \[\left(\prod_{j=1}^{m}p_{j}\right)\] is the product of the first \(m\) primes that represents the multiscale level and is the basis for generating the prime candidates.
* At each multiscale level, k can range from 1 to a value \(k_{max}\) that changes at every level, and specifically, the last \(k_{max}\) takes the value of \(p_{m+1}\) that is the prime number that serves to generate the next multiscale level.
* After obtaining the product between the multiscale level and \(k\), we subtract \(p_{i}\), which is each prime number obtained at the previous multiscale level.  
As shown in the recent work [21], the multiscale algorithm proposed here is not only applicable but even more efficient than other sieves.
## 6 Performance Evaluation
### Simulation Results
#### 6.4.2 Sensor Network Density Test (Second Simulated Scenario)  
In this scenario, we fixed the number of source communicating nodes to 20 and varied the network size from 100 to 1000 nodes in step of 100 nodes. As can be observed from Figure 5a-e, the ASAART protocol provides the highest packet delivery rate or success rate compared with the SHR and SSR protocols. When the number of sensor nodes reaches 800 nodes, the success rate becomes stable with 100% packet delivery shown by ASAART protocol. However, at 400 node network size, SSR outperforms SHR protocol. This is because of its continuous back-off delay scheme that allows more flooding of network packets. However, at higher network density, both SSR and SHR protocols are competing achieving a stable success rate with over 95% delivered network packets.  
We can observe that the end-to-end delay reduces significantly when the source nodes are set to 20 compared with the third simulated scenario with source equal to 40 nodes. We can also observe that between 100 to 400 nodes, the end-to-end delay is higher in all three routing protocols, with a maximum of 1.4 s exhibited by the SSR protocol. However, at a higher network density, _i.e._, from 400 to 1000 nodes, the end-to-end delay reduces significantly with the ASAART protocol, showing better reduction in end-to-end delay.  
Figure 5: _Cont._  
From Figure 5c, we can observe that the energy consumption is minimal compared with the SSR and SHR protocols at different network densities. This is because there is a reduced number of delivered MAC layer packets that reduce network congestions and save energy consumption. In addition, in Figure 5e, we can observe that PER for the three routing protocols at higher network density becomes stable particularly, between 600 to 1000 network sizes. Any further increase in network density has no impact on performance.
## 3 Materials and Methods
### RFI Mitigation Technique  
The existing signals in the channels are treated as noise that needs to be suppressed to deploy other valuable signals in the same frequency band. The signals can be represented in the time-frequency domain. Short-time Fourier transform (STFT) and inverse short-time Fourier transform (ISTFT), a Fourier transform pair, have been used for the analysis. This agrees with the methods used by [PERSON] [54, 55].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S/N.** & **Procedure** & & **Specific Task** \\ \hline \multirow{4}{*}{1.} & & (a) & Specify the sample rate frequency: \\  & & (b) & Specify the offset frequency: \\  & & (c) & Specify the number of averages: \\  & & (d) & Determine the type of noise at the site; \\  & & (e) & Specify the power of the noise; \\  & & (f) & Generate signals at specified frequencies, as shown in the scans.: \\  & & (g) & For multiple scan results on a graph, specify the number of input ports. \\ \hline \multirow{4}{*}{2.} & & (a) & Generate the histogram of the site scan data: \\  & & (b) & Observe the distribution of the generated histogram and then generate an approximate distribution for the data: \\  & & (c) & For the site data, obtain the mean, median, standard deviation, variance, interquartile range, skewness, and Kurtosis; \\  & & (d) & For the estimated distribution, obtain the negative loglikelihood, lower and upper confidence intervals for the standard deviation: \\  & & (e) & Generate a line graph connecting the centers of the histograms; \\  & & (f) & Generate a smooth line approximating the generated line graph using 9 th order polynomial. \\ \hline \multirow{4}{*}{3.} & & (a) & Obtain the following statistical parameters between the actual site data and each of the estimated distribution and generated smoothed lines: \\  & & & Mean Absolute Error (MAE): \\  & & & Root Mean Square Error (RMSE): \\  & & & Mean Absolute Percentage Error (MAPE): \\  & & & Mean Squared Error (MSE): \\  & & & Relative Absolute Error (RAE). \\  & & (b) & Generate the probability distribution function and the cumulative distribution functions. \\ \hline \multirow{4}{*}{4.} & & & The step-by-step process of the signal denoising is described as follows: \\  & & (1) & Using a suitable window, obtain the short-time Fourier transform (STFT) of the noisy signal; \\  & & (2) & Square the absolute values of the results obtained in (1): \\  & & (3) & Sort the results obtained in (2) in descending order: \\  & & (4) & Obtain the cumulative sum of the results obtained in (3): \\  & & (5) & Identify the values of (4) that have more than 30\% of the power. Set this as the threshold value: \\  & & (6) & Find the values of (2) that are greater than the threshold value: \\  & & (7) & The next step is the denoising of the signal through multiplying (1) by (6): \\  & & (8) & Recover the signal without noise through inverse short-time Fourier transform (ISTFT). \\ \hline \hline \end{tabular}
\end{table}
Table 4: Step-by-step procedure for measured data processing and analysis.
# 3D-Printed Accessories and Auxiliaries in Orthodontic Treatment
## 4 Discussion
### Contemporary Use of 3D-Printed Accessories in Orthodontics
#### 4.1.3 Bone-Anchored Maximilary Protraction (BAMP) Devices  
Bone-anchored maxillary protraction (BAMP) is an orthodontic technique used for the treatment of skeletal Class III malocclusion with maxillary deficiency. The method is based on the modification of the growth of the maxilla while minimizing dental side effects [40]. This can be performed with simultaneous rapid palatal expansion (RPE) [40; 41; 42]. While it could be challenging to place conventional protraction plates into the preferred exact location, the using of preoperative simulation and individualized 3D-printed titanium BAMP plates can be more precise [43]. It was demonstrated that satisfying results of maxillary protraction with digital protocol improved the speed and safety of the treatment, while also improving patients' comfort [40; 41; 42; 43].
# Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming
## 1 Introduction
The high levels of mortality, the need for better data monitoring and analysis technologies, and the considerable decline in labor intensity in all aquaculture facilities dictated by COVID-19 require an urgent innovation in current production monitoring technologies for this industry. In order to develop an innovative approach that can indicate the current state of the fish hatchery, avoiding high mortality rates, and to assist in the construction of positive changes in aquaculture practices, we report in this paper a Gaussian distribution model for detecting dangerous operating conditions in these aquaculture facilities. To present this approach, this paper is organized as follows. In Section 2, the acquisition of fish hatchery water quality parameters employed to build the approach is presented. Then, the mathematical formalism of our model is described along with the main steps for its implementation. Subsequently, Section 3 describes the results of the proposed method referring to weaning and pre-fattening development phases of Sengealese sole. Next, we discuss the results obtained in Section 4. Finally, the closing remarks are addressed in Section 5.
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## Appendix B Bimodality Check  
AUTOWADE checks the bimodality of the histogram of the pixel values by evaluating the Ashman's D coefficient [43], the bimodality coefficient [44; 45], and the ratio between the modes of the distribution.  
The Ashman's D coefficient quantifies how well two Gaussian distributions are separated, thus assuming a Gaussian mixture model for the distribution of the corresponding populations. It is defined as [56]:  
\[D=\sqrt{2}\frac{|\mu_{1}-\mu_{2}|}{\sqrt{\sigma_{1}^{2}+\sigma_{2}^{2}}} \tag{1}\]  
where \(\mu_{1}\) and \(\mu_{2}\) are the mean values of the distributions and \(\sigma_{1}\) and \(\sigma_{2}\) are the corresponding standard deviations. A distribution has two peaks if \(D>2\).  
The bimodality coefficient is defined as:  
\[BC=\frac{\gamma^{2}+1}{k+\frac{3(P-1)^{2}}{(P-2)(P-3)}} \tag{10}\]  
where \(P\) is the population size, \(k\) is the kurtosis, and \(\gamma\) is the skewness of the distribution. Values of BC greater than 5/9 (\(\sim\)0.555) indicate a bimodal distribution.  
A further check assumes that a minimum percentage of the total data should be in each mode of a bimodal distribution. This constraint is made explicit by introducing the weight ratio defined as [56]:  
\[W=\min_{i,j}\left(\frac{M_{i}}{M_{j}}\right)\;i,j=1:2 \tag{11}\]  
where \(M_{i,j}\) are the modes of the distributions.  
The bimodality test is assumed as passed if \(D>2\)[43], \(W>0.2\) (each mode should contain at least 10% of the total population of pixels), \(BC>0.4\). Note that a conservative choice has been done for \(W\) because in [24; 56] a value of 0.1 is suggested. Conversely, although a threshold value of 5/9 is commonly suggested for \(BC\) (corresponding to the value of \(BC\) for a uniform distribution), this constraint has been relaxed considering that a Gaussian mixture model has been assumed and that the value of \(BC\) for a Gaussian distribution is around 0.33. Note that \(D\), \(W\) and \(BC\) are evaluated considering the Gaussian functions fitting the histograms of water/flooded and non-water/non-flooded pixels (Section 3.2.2).