esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
This paper presents a novel method to measure the impact of amplitude ionospheric scintillations of GNSS trans-ionospheric signals, based on statistical considerations on the distribution of the samples at the output and on open loop architecture. Despite being one of the major sources of error affecting high accuracy GNSS applications, ionospheric scintillations are an important tool to study the behavior of this layer of the atmosphere. However, strong events prevent the receiver from correctly tracking the signal, thus producing erroneous scintillation measures. The quality of traditional indices can also be questioned by the detrending operation. To overcome such problems, an alternative measure is derived, exploiting an assisted open loop approach specifically designed for this task in a GNSS software defined radio receiver. The mathematical and technical details of the receiver implementation are given, along with the description of the alternative amplitude scintillation index \(R_{4}\). The validity of the proposed approach is confirmed by the results obtained processing real GNSS data affected by amplitude scintillations, and by comparing them with the traditional \(S_{4}\) index based on the closed loop approach. In addition, the paper reports the case of strong scintillation that produces a LoL, completely preventing the traditional closed loop to track the signal and to produce a valid measure of \(S_{4}\); the proposed technique, instead, shows a valid and reliable amplitude scintillation measure.  
Conceptualization, F.D. and N.L.; Methodology, F.D. and N.L.; Software, N.L.; Validation, F.D. and N.L.; Formal Analysis, F.D.; Investigation, F.D. and N.L.; Data Curation, N.L.This research received no external funding. The authors would like to thank [PERSON] for the contribution he provided to this work. The authors declare no conflict of interest.  
Figure 11: Comparison of \(S_{4}\) and \(R_{4}\) parameters after filtering, for a LoL case.
## 2 Materials and Methods
### Features Extraction  
The purpose of feature extraction was to select the significant features existing in the received signals, which are required for proper sorting and grading. The input signal in the feature extraction block indicates the digital signal in the time domain, and the output signal indicates the features vector. The values of signal range in the time domain and signal values in the wavelet domain were considered as input features (Figure 10) [17].  
Wavelet analysis, similar to Fourier analysis, is a modified and scaled version of the original wavelet. In fact, the wavelet theory was introduced to overcome the Fourier transform problems. In wavelet analysis, the signals are subdivided into different segments and solved using scale factor and function transformation, and the results are obtained as a set of argument frequency data. In this case, a fully scalable modular window is used to solve the signal cut-off problem. The spectrum is calculated for each situation when the window moves along with the signal. This process is repeated with a window slightly shorter or longer for each new cycle. Finally, a set of time-frequency signal time-frequency representations will have resulted in different resolutions. A discrete wavelet transform is a series of wavelets that results from continuous wavelet transform. In discrete wavelet transforms, different frequencies are analyzed by transmitting signals from the high pass and low pass filters. In addition, in this transform, the signal resolution is controlled by the performance of the filters, and the scale is changed by down-sampling or up-sampling [17; 18].
## 3 Applicability of Standard Solutions
### Dhcp-Pd  
_Dynamic Host Configuration Protocol - Prefix Delegation_ (DHCP-PD) [8] is an option derived from DHCPv6 [9] which provides a mechanism for the delegation of the IPv6 address prefixes and lets one of these be assigned automatically. For this, if a node wishes to get an IPv6 address, it sends a DHCP message with the activated _prefix delegation_ option to obtain a prefix from a DHCP server in the network.  
Its applicability is limited in mobile _ad hoc_ networks because it is based on DHCP, so it assumes that all the nodes can connect to a DHCP server, whether directly or through several hops, and due to the MANET topology, the direct connection to DHCP server is not usually frequent with the consequence that the connection through several hops can make the server unreachable.
# Enhancing Impact Localization from Fluid-Pipe Coupled Vibration under Noisy Environment
## 2 Materials and Methods
### Time-Delay Estimation for Coupled-Wave Localization  
To localize an impact source is, it is necessary not only to reduce noise but also to enhance the target signal. Based on observation of the characteristics of coupled-wave signals and the noises that we face in practice, CWT is able to effectively discriminate the signal and noises by selecting featured frequency bands different from those of white and colored noises. The burst noises are, however, not easy to distinguish in the time-scale domain because they possibly have localized peaks, like the signal, with comparable or even larger intensity.  
In an effort to enhance the signal from the wavelet transform and also to remove burst noises, it is necessary to take more steps to find the time delay accurately. Suppose that we have a coupled wave signal that has a dominant scale band with lower limit of \(a_{low}\) and upper limit of \(a_{high}\). As shown in Figure 3b, for instance, the signal contains most of the signal power in a specific scale band, and the maximum modulus is well localized throughout the scale at a specific time. The burst noise is also localized at a time, like the signal, but the noise has a larger value on a higher scale as shown in Figure 5b. This observation motivated us to propose the following noise removal and signal enhancement steps.  
Step 1: At each time (\(b_{j}\)), all wavelet transform coefficients are set to zero if the maximum coefficient outside the upper scale (\(a_{high}\)) is larger than the maximum value in the band (\(a_{low}\leq a\leq a_{high}\)), that is  
\[W_{x}(a_{k},b_{j})=\begin{cases}x*\psi_{k,j}&if\max_{a_{low}\leq a_{k}\leq a_ {high}}\left|W_{x}(a_{k},b_{j})\right|>\max_{a_{k}>a_{high}}\left|W_{x}(a_{k}, b_{j})\right|\\ 0&otherwise\end{cases} \tag{7}\]  
Step 2: At each time (\(b_{j}\)), all wavelet transform coefficients are summed within the band of scale:  
\[W_{x}(b_{j})=\sum_{a_{k}=a_{low}}^{a_{high}}W_{x}(a_{k},b_{j}) \tag{8}\]  
The first step ensures the removing of burst noises by comparing the maximum magnitude in the band of interest and that outside the band. This step reinforces the removing of noises further after the following reduction in band noises. The second step aims at improving signal detectability by summing all the signal power in the band.
## 1 Introduction
It is generally acknowledged that quantitative calibration of the imaging system is one of the critical steps associated with in vivo activity assessment in nuclear medicine. Calibration of the imaging system consists in converting reconstructed voxel values to absolute activity or activity concentration. However, contrary to positron emission tomography (PET) that was developed as a quantitative tool SPECT has traditionally been considered as non-quantitative. This is because quantitative analysis using SPECT data is a time-consuming process requiring accurate algorithms and techniques that correct for several degrading factors, among which are attenuation, scatter, dead time, and partial volume effects. However, recent advances in image processing software and the advent of hybrid SPECT/CT systems have made quantitative SPECT viable in a manner similar to quantitative PET. The aim of our research was to perform quantitative analysis of \({}^{99m}\)Tc-MAA scintigrafic imaging in a novel anthropomorphic set-up. Against this backdrop, we developed a workflow that relies on SPECT/CT data to assess the accuracy of quantitative analysis using a 3D-printed anthropomorphic phantom (Abdo-Man) provided with a number of spherical inserts contained in the liver volume. Recent work [15] has demonstrated that Abdo-Man can be successfully used for assessing the quantitative accuracy in RE usingdifferent imaging modalities. The use of such a phantom, along with up-to-date technology and optimization acquisition protocols has the potential to improve outcome for patients.
# An Investigation into Audio-Visual Speech Recognition under a Realistic Home-TV Scenario
## 4 Experiments and Analysis  
### Experimental Setups  
We evaluated the use of different methods on the MISP2021-AVSR corpus [35], which contained about 122 h of audio-visual data collected from 30 real-life living rooms. The dataset includes 376 sessions, with each session consisting of an approximately 20 min discussion under the real home TV scenario. There were 95 male speakers and 153 female speakers. The collected data were partitioned into three distinct subsets for training (about 101 h), development (about 9.83 h), and evaluation (about 9.94 h). Each subset was comprised of unique sets of speakers and recording rooms that do not intersect with any other subset. In order to be close to the realistic home TV scenario, we only used far-field data for training (about 100 h), which included videos from the wide-angle cameras and six-channel audios from the linear microphone arrays. The evaluation corpus was collected by far-field devices. Because low-resource training data (about 100 h) contain simple context and spectral information, cutting continuous short segments (CAT) [45] was used to augment the original audio-visual data. We obtained a new 300 h CAT dataset for training. Furthermore, it is difficult for speed perturbation to align audio and video time series. We only employed speed perturbation (SP) for audio with ratios 0.8, 1.0 and 1.2. For the hybrid ASR/AVSR system, we utilized the baseline system in the MISP2021 challenge [35]. For the E2E-AVSR framework, we utilized combining the main task of attention and the sub-task of CTC as the acoustic model [24]. The two tasks shared the same encoder, and the \(\alpha\) and \(\beta\) in Equation (3) were set to 0.8 and 0.2, respectively. In the E2E-AVSR framework, we used two layers of VGG as the audio extractor, like in [45], and the Resnet18 as the video extractor. After VGG and Resnet, audio and video hidden representations were concatenated along time series as inputs of the conformer [38], where the number of layers \(M\) was set to 12. In the decoder part, we utilized a six-layer transformer [39] (i.e., \(N=6\)). The number of attention heads was eight. All networks were implemented using Fairseq [46]. We applied SpecAugment [47] to improve the robustness. In addition, gradient clipping was adopted  
Figure 6: Audio–visual fusion in the decoder part.  
with a value of 10 to avoid an exploding gradient. All models were trained with a batch size for 8 k frames and a transformer [39] learning-rate schedule with 3 k warm-up steps. The max learning rate was set to 0.0007. Character error rate (CER) was employed as the evaluation metric as in [35], which is defined as follows:  
\[\text{CER}=\frac{S+D+I}{N}\times 100 \tag{8}\]  
where \(S\), \(D\), \(I\) and \(N\) represent the number of substitutions, deletions, insertions and characters in the ground truth, respectively.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 5 Compressive Sensing
### Compressive Signal Reconstruction  
The signal reconstruction problem requires the recovery of signal \(\mathbf{x}\), or equivalent of the weighted coefficients \(\mathbf{s}\), given the sensing matrix \(\mathbf{\Phi}\), basis matrix \(\mathbf{\Psi}\) and compressed signal \(\mathbf{y}\). The ultimate goal of compressive sensing is to find the possible sparsest representation of the signal. Therefore, the ideal reconstruction problem can be expressed via the \(l_{0}\) norm as follows:  
\[\min ||\mathbf{s}||_{0}\] (42) s.t. \[\mathbf{\Theta}\mathbf{s}=\mathbf{y}.\]  
Unfortunately, such a formulation of the problem is both numerically unstable and computationally complex (NP-complete) [45]. For simplicity, problem (44) can be reduced to minimize the \(l_{2}\) norm of \(\mathbf{s}\)  
\[\min ||\mathbf{s}||_{2}\] s.t. \[\mathbf{\Theta}\mathbf{s}=\mathbf{y}, \tag{43}\]  
which leads to the closed form solution \(\delta=\theta^{T}(\theta\theta^{T})^{-1}y\). However, this method will almost never yield a \(K\)-sparse solution. Even worse, usually a full representation solution (no zero elements) will be generated by using the \(l_{2}\) norm.  
Surprisingly, the optimization based on the \(l_{1}\) norm is the most promising recovery technique used so far for sparse signals. In this case, the recovery problem is formulated as follows:  
\[\min ||\mathbf{s}||_{1}\] s.t. \[\mathbf{\Theta}\mathbf{s}=\mathbf{y}. \tag{44}\]  
In some cases, the \(l_{1}\) minimization yields the same result as the ideal \(l_{0}\) minimization [46]. Moreover, the \(l_{1}\) minimization can be efficiently solved via linear programming which presents a computational complexity of \(\mathcal{O}(N^{3})\). For the last decade, a series of papers [39; 43; 47] explained why \(l_{1}\) minimization can be approximated as an \(l_{0}\) minimization and why it can recover a sparse signal. Until now, most of the works dedicated to compressive sensing, see, e.g., the works of [PERSON], [PERSON], [PERSON] and [PERSON] [39; 40; 41; 42; 43] assume the \(l_{1}\) norm formulation.
## 6 Conclusions  
Nonlinear and non-stationary detection of complex signals is usually a prerequisite for further developing time-varying system identification methods. In this paper, the framework of the combination of signal decomposition methods with statistical RP and RQA is adopted to solve the difficulty of original RP's application in bridge engineering. The following concluding remarks can be drawn from this study.  
1. By introducing a new white noise assistance way by considering the uniformity of white noise probability density function, and cluster analysis to original EEMD, an improved EEMD method is proposed to alleviate the mode mixing issues of the EEMD method. The decomposition and reconstruction of a numerical simulation signal has proven the superiority of IEEMD.
2. By combining the surrogate techniques and hypothesis-testing scheme of nonstationary and nonlinear synchronization, a data-driven RQA method is performed on the single-mode signal components and the true indicator pairs, which contain the statistical RQA measures median and a binary RQA index are set up. The results of the conventional time-frequency analysis method, nonlinear test method, and energy analysis are separately used to prove the effectiveness of the nonstationary, nonlinear RQA measures and binary RQA index. Based on the effective implementation of a model cable-stayed bridge, the frequency-evolving RQA of the given signal indicates that the apparent dynamics are mainly controlled by its medium-frequency and low-frequency components.  
Conclusively, although the novel method that combines signal decomposition with statistical RP and RQA was applied to detect the nonlinearity and non-stationarity of signals from a model cable-stayed bridge, this method has the potential to be applied to bridges in real complex operating environment. The difference is that the level of noise contained in those signals is generally high  
Figure 19: Time-varying diagram of the first two-order frequency, damping ratio of the curved cable-stayed model bridge: (**a**) first order, (**b**) second order, (**c**) first order, and (**d**) second order.  
and some effective signal processing technologies such as adaptive noise reduction methods may be needed to improve the SNR.
## 3 Results and Discussion
### Soil Leaching by Inorganic Acids
#### 3.1.1 Effects of Inorganic Acids on the Lead Removal from Protogenetic Soil
\[2\text{H}^{+}+\text{Pb}(\text{OH})_{2}=2\text{H}_{2}\text{O}+\text{Pb}^{2+} \tag{3}\]  
\[2\text{H}^{+}+\text{Pb}\text{CO}_{3}=\text{H}_{2}\text{O}+\text{Pb}^{2+}+ \text{CO}_{2} \tag{4}\]  
\[6\text{H}^{+}+\text{Pb}_{3}(\text{CO}_{3})_{2}(\text{OH})_{2}=4\text{H}_{2} \text{O}+3\text{Pb}^{2+}+2\text{CO}_{2} \tag{5}\]  
\[2\text{Cl}^{-}+\text{Pb}^{2+}=\text{PbCl}_{2} \tag{6}\]  
\[\text{PbCl}_{2}+\text{Cl}^{-}=\left[\text{PbCl}_{3}\right]^{-} \tag{7}\]  
\[\left[\text{PbCl}_{3}\right]^{-}+\text{Cl}^{-}=\left[\text{PbCl}_{4}\right]^{ 2-} \tag{8}\]  
\[\text{SO}_{4}^{2-}+\text{Pb}^{2+}=\text{PbSO}_{4} \tag{9}\]  
\[2\text{PO}_{4}^{3-}+3\text{Pb}^{2+}=\text{Pb}_{3}(\text{PO}_{4})_{2} \tag{10}\]  
Figure 1: Remediation efficiency (\(\eta\)) of four inorganic acids and deionized water.
## 3 Bioactivity of _Chlorella vulgaris_ Compounds
### Antiviral and Antimicrobial Activity  
Research indicates that _C. vulgaris_ has antiviral properties, particularly against enveloped viruses such as herpes simplex virus, hepatitis C virus, and HIV. Sulphated polysaccharides from _C. vulgaris_ have been shown to prevent viral entry into host cells by interfering with viral attachment mechanisms [21; 67]. This antiviral activity suggests _C. vulgaris_ could be used as a supplementary treatment for viral infections, though further studies are needed to confirm its clinical efficacy [4].  
In addition to its antiviral effects, _C. vulgaris_ has shown notable antimicrobial activity against a wide range of bacterial and fungal pathogens. Certain bioactive compounds in _Chlorella_, including peptides, fatty acids, and polysaccharides, have been demonstrated to inhibit the growth of Gram-positive bacteria such as _Staphylococcus aureus_ and _Bacillus subtilis_, as well as Gram-negative bacteria like _Escherichia coli_ and _Pseudomonas aeruginosa_[84]. This antimicrobial potential makes _C. vulgaris_ an appealing natural alternative to synthetic antibiotics, especially in an era of increasing antibiotic resistance.  
Moreover, _C. vulgaris_ has demonstrated antifungal properties, particularly against pathogenic fungi such as _Candida albicans_ and _Aspergillus niger_, which are responsible for various infections in humans and animals [85]. The antifungal effect is attributed to bioactive polysaccharides and fatty acids in _Chlorella_, which interfere with fungal cell wall integrity, leading to cell death.  
The antimicrobial properties of _C. vulgaris_ have significant implications for its use in the food industry as a natural preservative. Its ability to prevent the growth of spoilage microorganisms can extend the shelf life of food products, while its safety profile makes it suitable for use in functional foods and nutraceuticalals [21].
### Outline  
This article follows a structured outline, starting with an introduction (Section 1) that establishes the research motivation (Section 1.1), existing studies (Section 1.2), knowledge gaps (Section 1.3), research questions (Section 1.4), methodology overview (Section 1.5), and practical implications (Section 1.6). In the methodology section (Section 2), the research approach and the methodology employed are described in detail. The application of the analytical hierarchy process (AHP) as a decision-making framework (Section 2.2) for evaluating cybersecurity options in smart grids is explained, emphasizing its ability to consider multiple criteria and prioritize options. The results section (Section 3) presents the key findings obtained through the application of the methodology described earlier. The discussion section (Section 4) delves deeper into the implications and significance of the research findings. It examines the advantages and limitations of the methodology employed, addressing potential challenges and areas for improvement. The conclusion section (Section 5) summarizes the key findings, insights, and implications discussed throughout the article.
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 3 Preservation Technologies and Optimization
### Freezing  
Frozen storage is a widely used method for preserving meat over long periods. However, it can negatively affect muscle properties by increasing free fatty acids (FFAs) and lipid oxidation products [53; 54]. Freezing chambers, whether air-blast tunnels or cryogenic spray systems, feature adjustable nozzles and airflow grids to achieve rapid velocity (3-5 m/s) and uniform \(-40\)\({}^{\circ}\)C conditions; in plate freezers, fish are sandwiched between refrigerated plates exerting consistent pressure for optimal heat transfer. Freezing slows microbial and enzymatic activity, preserving flavor and nutritional value better than chilling [55]. The size of ice crystals formed during freezing is crucial; larger crystals can damage texture and increase oxidation [55; 56]. Faster and more uniform freezing results in smaller ice crystals [57]. Slow freezing produces larger ice crystals, causing significant muscle fiber damage [55]. Rapid freezing techniques like cryogenic freezing might cause intracellular ice crystallization and cell cracking [55]. Maintaining a stable temperature is essential to prevent ice crystal growth during storage [57]. In frozen salmon, ice crystals grow larger over time due to recrystallization, becoming more spherical [58]. Fast freezing tech niques include high-pressure freezing, pressure shift freezing, cryogenic freezing, liquid immersion freezing, plate freezers, and air-blast freezers [57; 59; 60]. Plate freezers work well for regular-shaped fish products, operating at around \(-40\)\({}^{\circ}\)C. Air-blast freezers use cold airflow, effective for various shapes, at \(-20\)\({}^{\circ}\)C to \(-40\)\({}^{\circ}\)C. Immersion freezing uses liquid media like brine or glycol for fast freezing [60]. High-pressure freezing creates uniform ice crystals quickly, preserving texture and microstructure better [55; 57; 61]. Injecting or dipping fish in antifreeze proteins helps form small ice crystals and prevent recrystallization [57]. Freezing at \(-40\)\({}^{\circ}\)C minimizes unfrozen water, reducing primary oxidation [62; 63].
## 3 Model Construction
### Data Pre-Processing  
Traffic flow data is the basis for traffic prediction research. High-quality data can effectively ensure better training results and prediction accuracy. Some characteristics differ greatly in magnitude, which will expand the effects of large-scale features on the model such that the model depends on these features, worsening the model's performance. At the same time, the difference in the orders of magnitude will slow down the model training, and so the data is normalized before being fed into the network model.  
\[{x_{i}}^{*}=\frac{x_{i}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{6}\]  
where \(x_{i}\) is the data in the same row vector at a moment in the input data, \(x_{\text{min}}\) is the minimum value in the row vector, and \(x_{\text{max}}\) is the maximum value in the row vector.
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
## 2 OCDM Basics
### Underloaded OCDM System  
The concept of underloaded OCDM consists of modulating a useful number \(N_{u}\) of chirp waveforms that is inferior to the total number \(N\) of available waveforms. Under this situation, the vector of information data \(\mathbf{x}_{k}\) in (6) can be expressed as:  
\[\mathbf{x}_{k}=\sqrt{\frac{N}{N_{u}}}\left[x_{k}^{0}\ \ \ \mathbf{0}_{1\times N/N_{u}-1} \ \ \ \ldots\ \ \ x_{k}^{N_{u}-1}\ \ \ \mathbf{0}_{1\times N/N_{u}-1}\right]^{T} \tag{9}\]  
Figure 1: OCDM chirp waveforms for \(N=16\).  
For computation simplicity, \(N_{u}\) is set to an integer divisor of \(N\); for example, \(N_{u}=\frac{1}{2}N\) indicates that half of the total waveforms are being used. Note that the coefficient \(\sqrt{N/N_{u}}\) is introduced in order to keep the same signal energy whatever \(N_{u}\). As depicted in Section 4, the underloaded approach leads to Signal-to-Noise Ratio (SNR) gain, but at the expense of the decreasing data rate. The particular case when \(N_{u}=1\) corresponds to CSS modulation. The same underloaded approach can also be applied to OFDM systems.