esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Controlled Composting Tests  
#### 2.4.1 Aerobic Biodegradability  
The aerobic biodegradability of the monolayers and multilayers was determined under composite conditions by measuring the amount of CO2 generated according to ISO 14855 [30]. The ripe compost was mixed with vermiculite (compost/vermiculite ratio: 3:1) to avoid compaction of the compost and to ensure good oxygen access. Glass flasks (2L) containing two PP flasks (60 mL) were used as reactors. One of the flasks contained 3 g of dry compost mixed with 1 g of vermiculite and an amount of sample (previously cut into 2 mm\({}^{2}\) squares) equivalent to 50 mg of carbon, while the other flask contained deionized water to ensure 100% RH. The bioreactors were closed and incubated for 45 days at 58 \(\pm\) 2 \({}^{\circ}\)C. One reactor containing only compost was used as blank and one reactor containing MCC mixed with the compost were used as reference sample. The percentage of CO2 generated inside the reactors was measured, in triplicate, using a CO2 analyzer (Gaspace Advance Micro GS3, Systech Illinois, Thame, UK) throughout the biodegradation process. The percentage of biodegradation was calculated using the following Equation (8), assuming that all the carbon in the sample was converted into CO2:  
\[B(\%)=\frac{\sum CO_{2S}-\sum CO_{2B}}{CO_{2}^{Th}S}\times 100 \tag{8}\]  
where \(\Sigma CO_{2S}\) is the accumulative amount of CO2 produced in the sample bioreactor, \(\Sigma CO_{2B}\) is the accumulative amount of CO2 produced in the blank bioreactor, and \(CO_{2}^{Th}S\) is the theoretical amount of CO2 that the test material can produce.
###### Abstract  
Near-infrared (NIR) spectroscopy has become a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on urea content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes--Safe and Unsafe--based on their urea content. A comprehensive NIR dataset comprising 11,960 spectra collected from eight distinct positions within the fish body was obtained from 299 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their urea content exceeded the permissible limit of 1000 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques, as well as ML algorithms, were explored. The results demonstrated that utilizing NIR data specifically obtained from the outer skin of the stomach yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their first derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers, achieving an accuracy of up to 83.9%.  
urea detection; fish safety assessment; non-destructive analysis; rapid methods; convolutional neural network
# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 4 Experiments and Results
### Comparative Experiments with Different Models
Among lightweight models, although EfficientNet-B0 has a parameter count and FLOPs close to that of MobileNetV2, its more complex compound scaling mechanism increases the actual computational workload, resulting in a longer inference time. ResNet-50 benefits from well-optimized convolutional computations, allowing the GPU to process convolutions efficiently, making its inference time lower than that of EfficientNet-B0 despite having a higher parameter count and FLOPs. MobileNetV2, which employs depthwise separable convolutions, reduces FLOPs compared to partial convolution. However, frequent memory access results in a lower floating-point operations per second (FLOPS), leading to an increased actual inference time [37]. This explains why MobileNetV2, despite having fewer FLOPs than FMFVNet, exhibits a longer inference time per image. The proposed FMFVNet achieves an inference time of 1.75 ms per image, reducing the inference time by \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method (ms)** & **Params (M)** & **FLOPs** \\ \hline MobileNetV2 & 3.47 & 300.84 M \\ EfficientNet-B0 & 5.24 & 385.88 M \\ ResNet-50 & 24.81 & 4.13 G \\ Swin-T & 28.27 & 4.32 G \\ FMFVNet & 3.53 & 358.46 M \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Params and FLOPs across different models. 20.81% compared to MobileNetV2, achieving a speedup of approximately 26%. Compared to Swin-T, FMFVNet reduces inference time by 62.7%, achieving a speedup of approximately 168%. These results demonstrate that FMFVNet offers significant advantages in inference efficiency. This improvement makes FMFVNet more suitable for deployment on embedded devices or portable identity authentication terminals, where rapid and efficient identification is required under limited hardware resources. It fully meets the real-time and performance requirements of finger vein recognition systems.
# Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem
## 3 Sampling Class Imbalance Approaches
### Hybrid Sampling Class Imbalance Strategies  
The aim of the hybrid methods is first to deal with the class imbalance problem, and next, to clean the training dataset, in order to reduce the noise or overlap region. Hybrid methods generally employ SMOTE to compensate the class imbalance, because this method reduces the possibilities of over-training or over-fitting [1]. They use methods based in nearest neighbor rule to reduce overlap or noise in the dataset [25].  
Recently, hybrid methods have increased in popularity in the machine learning community, as it can be observed in several references [23; 25; 66], which have studied SMOTE+TL, SMOTE+CNN and SMOTE+ENN, among others methods, to deal with class imbalance problem and to eliminate samples in the overlapping region, in order to improve the classifier performance.  
SMOTE+ENN technique consists of applying the SMOTE; then, the ENN rule is applied [64]. SMOTE+TL combines SMOTE and TL [64], SMOTE+CNN performs SMOTE and then CNN [65]. Figure 1a describes the operation of these hybrid approaches, which have been widely applied to deal with the class imbalance problem [22; 25; 29; 33; 50; 63; 64; 65; 67].  
The effectiveness of an additional hybrid approach is studied in this work, in which the training dataset is cleaned or reduced, and after, the number of samples by class in the training dataset is balanced. As a first stage, TL, OSS, ENN and CNN are used to clean the training dataset; in the second stage, SMOTE is applied to balance the class distribution.  
SMOTE+OSS is the combination of SMOTE and OSS, in that order. TL+SMOTE first applies TL, and then SMOTE. In CNN+SMOTE, CNN is performed, and then SMOTE. Figure 1b shows the operation of these hybrid methods.
###### Abstract
The approach aimed at studying the wave propagation in this geometry lets us advance in our main goal to develop the NDE system to identify the type of damage in the problem described above and illustrated in Figure 1b. In the present study, the frequency range of interest is [0, 100 kHz], this frequency interval is within the range of wave-guided commercial equipment commonly applied in ducts in NDT applications. The attenuation due to the energy dissipation is proportional to the square of the frequency of the excitation. This fact influences the definition of the frequency range of work. Within the elastic wave propagation fields, there are the guided waves (GW), present in slender structures, where one or two geometric dimensions are smaller than the others (waveguide). Examples of this type of structure are ducts, train rails, and plates. GW-based experiments have changed the nondestructive techniques drastically because they allow the evaluation of large areas from point  
Figure 1: (**a**) The different layers of a typical riser, (**b**) the region of the riser that could be monitored using techniques based on guided wave propagation. transducers [6]. Knowing the propagation modes in the waveguides is fundamental to the success of the NDT based on GW. The dispersion curves of the cited structure allow us to build by using the analytical and semi-analytical methods. The rod dispersion curves are also computed using experimental results. The comparison between the different methodologies used not only let verified the limitation and vantages of the method used but also understand how the waves travel in this kind of elements. Two examples were present to illustrate the curve dispersion applications: (i) The wave displacement detection using the fiber grating Bragg (FGB) technique was applied to show this not conventional technique in the present context and (ii) the simulation of a rectangular waveguide with a pre-fissure is loaded to induce characteristic acoustic emission events shows how the dispersion curves knowledge could to improve to understand the characteristics of the signal emitted from the source and also the potential possibilities to use a non-conventional model that combines the finite element method and a version of the discrete element method in this context.
# A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration
## 1 Introduction
It is known that a wrong estimation of \(L_{h}\) can lead to drastic noise-like errors in the restorations [13]. Furthermore, utilizing deblurring techniques for industrial purposes requires real-time, reliable, and unsupervised methods. To satisfy these requirements, this article proposes a Wiener filter that is monitored by a fuzzy inference system. A Wiener filter is selected due to its simplicity, real-time execution, and superior performance in the restoration of linear motion blur [6]. The fuzzy inference system deals with the uncertainty of the deconvolution by monitoring the entire restoration process. This FIS is comprised of three crisp inputs that included the PSF length or probe size (\(L_{h}\)) deviation, attenuation of the vanishing frequencies, and deconvolution residue. However, probe size deviation is an optional input, which is based on a previous rough knowledge of \(L_{h}\). If \(L_{h}\) is roughly known, it serves as a reference point from which the PSF length deviation is calculated. Therefore, unlike Reference [6], prior knowledge of \(L_{h}\) does not limit the inaccuracy compensation to \(\pm 15\%\). It is demonstrated in Reference [6] that the spatial domain of \(h\) has a sharper transition compared to the EB distribution (\(f\)). This is due to the semi-Gaussian distribution of \(f\) compared to \(h\). Therefore, vanishing frequencies of \(h\) are expected to have higher attenuation or lower magnitude compared to \(f\). Hence, the normalized magnitude of the detected null frequencies in \(G\) are the second crisp input to the fuzzy inference systems. The last input of the system is the quantified deblurring artifacts that are introduced during the restoration of \(f\) from \(g\). The restored beam distributions are denoted as (\(\hat{f}\)). These residual artifacts are inevitable and they increase as the \(h\) deviates from its mathematical definition. Extraction of residues from \(\hat{f}\) is explained in section II. The output of the fuzzy system (\(E_{i}\)) is defuzzified to represent the quality of the restorations. This output is generated based on the definition of the fuzzy rules that are explained in the next section. The rest of this paper is arranged as follows: Section 2 illustrates the details of FIS implementation. This includes specifying the crisp inputs and fuzzifying them, defining the membership functions, and formulating the fuzzy sets. The section continues by identifying the fuzzy rules and making an inference to generate the output. Section 3 presents the practical results of the proposed method and the ability of the system to distinguish the correct deblurring results. The values of membership functions parameters are provided and a comparison is made between implementing the fuzzy system with and without the knowledge of probe size (\(L_{h}\)).
## 2 Materials and Methods
### HPLC-MS/MS System  
The Agilent 1200 HPLC system (Santa Clara, CA, USA) coupled with the AB SCIEX triple quadrupole 5500 (AB Sciex, Thornhill, ON, Canada) was used for quantification of Fingolimod and Fingolimod-P in human plasma.  
Chromotographic separation was made using a Luna C18 column (150 \(\times\) 2 mm, 3 \(\mu\)m particle size; Phenomenex Inc., Torrance, CA, USA) with Security Guard C18 4 \(\times\) 2 mm (Phenomenex Inc.). Mobile phases (MP) were 0.1% formic acid in water (MP-A) and acetonitrile (MP-B), and flow rate was set at 200 \(\mu\)L/min. Autosampler and column oven temperatures were set, respectively, at 8 \({}^{\circ}\)C and 35 \({}^{\circ}\)C. Elution started with an 8-min linear gradient from 40% to 99% of MP-B held for 3 min and followed by a 1-min linear gradient for returning to the initial condition that was kept for 5 min for column equilibration. The total run time was 17 min. Retention times were 4.68 min (Fingolimod-D4), 4.69 min (Fingolimod) and 5.60 min for both Fingolimod-P and its internal standard Fingolimod-P-D4.  
The mass spectrometer was equipped with an electrospray ionization source (ESI) operating in positive ion mode. The mass spectrometer analysis was made in multiple reaction monitoring (MRM) measuring the fragmentation products of the protonated molecular ions of analytes and their internal standards. For each analyte, two ion mass transitions were acquired, one for peak quantification (quantifier ions) and one for analyte confirmation (qualifier ions). The optimized ion transitions and the corresponding collision energy (CE) are show in Table 1.  
The optimized mass spectrometric parameters for curtain gas pressure, collision gas pressure ion spray voltage, probe temperature, declustering potential, entrance and collision exit potential were, respectively, 32.0 psi, 7.0 psi, 400 \({}^{\circ}\)C, 5500 V, 50 V, 11 V and 16 V. The turbo ion spray source was set at 350 \({}^{\circ}\)C. The Analyst Software 1.6.1 (AB Sciex) controlled the HPLC-MS/MS system, and the same software was used for data collection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Analyte** & **Precursor Ion, \(m/z\)** & **Quantifier/Qualifier Ions, \(m/z\) [CE, V]** \\ \hline Fingolimod & 308.2 & 143.1 [27]/255.2 [21] \\ Fingolimod-P & 388.2 & 255.2 [23]/290.2 [18] \\ Fingolimod-D4 & 312.2 & 147.1 [27]/259.2 [21] \\ Fingolimod-P-D4 & 392.2 & 259.1 [23]/294.2 [18] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass precursor-to-fragment ion transitions (with collision energy, CE) and retention times of analytes.
## 3 Numerical Algorithms and Computational Methodologies  
Equations (1)-(7) are solved numerically using Finite Volume Methods (FVM). The FVM numerical algorithms are implemented on the OpenFOAM software platform. The OpenFOAM computational solvers that are developed in the present work are adapted and modified from both the _viscoelasticFluidFoam_ and the _rheoMultiRegionFoam_ solvers, which pre-exist on OpenFOAM, see for example [41; 42]. These two pre-existing solvers are well adapted to viscoelastic flow simulations, and hence, they are well suited as starting points for the development of the viscoelastic computations required in the present work.  
The computational mesh for the double-pipe geometry, Figure 2, is created using the OpenFOAM mesh generation functionality called _blockMesh_. In OpenFOAM language, this mesh is implemented over a _blockMeshDict_ file, in which the geometry is conveniently defined and results are viewed using the _Paraview_ software.  
Viscoelastic flow computations are prone to numerical difficulties which arise at high Weissenberg, _We_, or alternatively at high Deborah, _De_, numbers, leading to the so-called High-Weissenberg-Number-Problem (HWNP). The higher the Weissenberg (or Deborah) number, the more pronounced would be the elastic effects, in which case the viscoelastic fluid largely exhibits solid-like behaviour leading to the numerical instabilities for the fluid-based computations. Both the Discrete-Elastic-Viscous-Stress-Splitting (DEVSS) [42; 51] and the Log-Conformation-Reformulation (LCR) techniques [52; 53] are employed to mitigate against numerical instabilities arising from the HWNP.
# An Efficient Library for Reliability Block Diagram Evaluation
## 3 RBD Computation Library-librbd
### Design  
The two goals of portability and optimization tend to be in contrast. Interpreted languages, for example, are portable by nature, but they often lack performance; compiled languages, on the other hand, offer greater performance, but they are less portable when an interaction with the OS is required [46]. We decided to implement librbd in C language, at the cost of introducing small parts of conditional compilation when an interaction with the OS is needed. Furthermore, librbd is available both as a dynamic and static library.  
In order to minimize numerical errors, all computations are performed using double-precision floating-point format (_double_) compliant with binary64 format [47].  
Both the uncertainties and the numerical errors are due to the chosen format and, since the reliability is a real number in range \([0,1]\), they are limited to the maximum resolution of floating-point numbers in the same range as described in [47].  
We decided to implement both formulas for RBD blocks with identical components and for RBD blocks with generic components. This choice implies doubling the Application Programming Interfaces (APIs), thus almost doubling the size of the library itself, but it allows for the achievement of higher performance in the identical case, especially for Koo\(N\) blocks.
## 2 Materials and Methods
### E-Nose Analysis
#### 2.2.2 Construction of the Sensor Array  
Various volatile components were emitted during the refrigeration of oysters, including significant amounts of aldehydes, amines, hydrocarbons, lipids, acids, ketones, and others. The volatility of these components changed systematically during the refrigeration process, reflecting the progression from freshness to spoilage. Considering the unique characteristics of the sensors and the gas emission patterns of oysters during refrigeration, this study selected ten types of metal oxide sensors with varying sensitivities to construct a sensor array. All sensors were sourced from the TGS series of FIGARO and the MQ series of Weisheng. This sensor array effectively leveraged the cross-sensitivity of the sensors to generate distinct fingerprints for each gas or odor. Consequently, this monitoring method was preferred over conventional single sensors, which often suffer from inadequate cross-sensitivity or selection issues. Table 1 provides detailed descriptions of the sensors along with their main characteristics.  
Figure 1: Electronic nose monitoring system. (**a**) General description of the human olfactory system and how it works with an electronic nose. (**b**) Electronic Nose development platform. (**c**) Thermo-static chamber.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No** & **Sensor Types** & **Volatile Compounds** & **Detection Range (ppm)** \\ \hline S1. & TGS2602 & \begin{tabular}{c} Ammonia, hydrogen sulfide, \\ and toluene \\ \end{tabular} & 1\(\sim\)30 \\ S2. & TGS2603 & \begin{tabular}{c} Amine series, hydrogen \\ sulfide, etc. \\ Methane, propane, \\ isobutane, etc. \\ Refrigerant gas \\ Ammonia and \\ amine compounds \\ Ammonia gas, sulfide, benzene \\ series vapor \\ S7. & TGS2611 \\ \end{tabular} & \
\begin{tabular}{c} 1\(\sim\)10 \\ Methane, propane, \\ isobutane, etc. \\ Refrigerant gas \\ Ammonia and \\ amine compounds \\ Ammonia gas, sulfide, benzene \\ series vapor \\ Ethanol, hydrogen, \\ isobutane, methane \\ Ethanol, hydrogen, methane, \\ isobutane/propane \\ Organic solvents, alcohol, etc. \\ Carbon monoxide, hydrogen \\ \end{tabular} &
\begin{tabular}{c} 10\(\sim\)10 \\ 10\(\sim\)10 \\ 500\(\
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 4 Theoretical Basis of Performance Metrics
### Maximum Range and Range Resolution  
As signified by (15), \(f_{IF}\) is directly proportional to the range of the target. Therefore, it can only be increased to a limit beyond which the cut-off frequency of the IF filter becomes a bottleneck. Being a low-pass filter, the cut-off frequency is essentially the IF filter BW. Per the general specifications of an LRR for the cruise-control use case, the IF filters in automotive radars are designed to serve a few hundred meters of range [50]. Rearranging (15) for the maximum range of an FMCW radar gives us the following:  
\[R_{max}(IF_{max},S)=\frac{cIF_{max}}{2S}. \tag{16}\]  
The rationale of this article is to highlight the hardware limitations in automotive radars and the constrained IF BW is one such limitation alluded to in the introductory part of the text. However, a reduced chirp slope \(S\) may compensate for this constraint. It is imperative that we introduce the range resolution at this stage before further discussion on the intertwined relation of the performance metrics. The range resolution is the ability of a radar to distinctly identify two closely spaced objects. According to the properties of the Fourier transform [51], a time-domain signal with a larger duration has a better frequency resolution. FMCW is a continuous wave radar with an observation window equal to the duration of the chirp signal, \(T_{c}\). The expression for frequency resolution can be expressed as  
\[\Delta f_{IF}>\frac{1}{T_{c}}. \tag{17}\]  
where \(\Delta f_{IF}\) is the minimum allowable difference between beat frequencies corresponding to two closely spaced objects at a minimum possible distance, \(\Delta R\), away from each other. Modifying (16) accordingly and plugging into (17) gives us the following:  
\[\frac{2S\Delta R}{c}>\frac{1}{T_{c}}, \tag{18}\]  
\[\Delta R>\frac{c}{2 ST_{c}}. \tag{19}\]  
Using (3), Expression (19) can be rewritten as  
\[\Delta R(B)>\frac{c}{2B}. \tag{20}\]  
(20) is the expression for the range resolution of an FMCW radar as a function of only the chirp BW.
# Mobile Broadband Performance Evaluation: Analysis of National Reports
## 4 5G Communication Status
### Metrics for 5G  
As indicated above, 5G consumers as well as applications are diverse and should be treated differently by the eNBs. Not only this, but service quality and performance evaluations for each class should also be carried out separately. Furthermore, applications might have behaviors of multiple classes, in such case, the evaluation can include metrics from both. For instance, AR requires low latency and faster speed, which means metrics from URLLC and eMBB can be applied to evaluate the service provided for this application. In addition, different performance requirements are also set out for each class. Therefore, metrics suitable for each class should be discussed separately.  
* **eMMB metrics**\(\rightarrow\) The traditional metrics that are discussed above, which were used to evaluate the performance of mobile broadband communication, can be harmonized, and new methodologies such as the usage of UAVs can be incorporated to evaluate eMBB
* **mMTC metrics**\(\rightarrow\) Research on metrics used to assess the quality of IoT is presented in later sections, which mostly can also be applied for mMTC.
* **URLLC metrics**\(\rightarrow\) As discussed, the unique behavior of applications requires new and innovative metrics to evaluate applications and services under this class.