esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling of UAHW Target Recognition Algorithm Based on SMOTE-Stacking
### Active Acoustic Signal Detection System of ULAHW
#### 3.1.2 Adaptive Threshold  
The purpose of signal detection is to extract as many useful signals as possible from noise-polluted signals. For signal detection, there are two hypotheses: \(H_{0}\) contains no signal and \(H_{1}\) contains a signal, which can be expressed as:  
Figure 4: Time-varying gain control diagram.  
Figure 5: Time-domain signal after TVG processing result.  
Figure 6: (**a**) Frequency-domain signal before TVG processing result. (**b**) Frequency-domain signal after TVG processing result.  
\[H_{0}=n(t)\quad H_{1}=s(t)+n(t) \tag{9}\]  
where \(s(t)\) and \(n(t)\) are the signal and noise, respectively.  
The detection system requires the best criterion to be met under the given assumptions. For active sonar systems, the optimal detection is typically based on the Neiman-Pearson criterion, and the likelihood ratio is calculated via a matched filter. A matched filter is an optimal linear filter. When the input signal is known, and the background noise is white noise, then the output SNR of the matched filter reaches its maximum. The optimal detection system of active sonar can be simplified as the structure presented in Figure 7. In fact, the matched filter was implemented through the cross-correlation. As presented in Figure 8, the input signal is \(s(t)\) and the background noise is \(n(t)\). Assume that \(n(t)\) is white noise, and its power spectrum density is \(\frac{N_{0}}{2}\). Then, the max output SNR of the matched filter is \(\frac{2E}{N_{0}}\), where \(E\) is the energy of \(s(t)\).  
TVG can effectively implement dynamic range compression. However, due to the non-stationary characteristics of the background, their normalization of the background can not reach constant variance. Another important measure to achieve constant false alarm processing is adaptive threshold processing. The calculation of adaptive thresholds is closely related to the signal form and signal processing methods used in homing. Dynamic compression, background normalization, and adaptive thresholds constitute constant false alarm processing for homing signal detection. Adaptive threshold processing is the ability to adaptively adjust the detection threshold as the interference background changes, thereby enabling more accurate detection of targets. We adopt an adaptive threshold generation method based on low-pass filters. This adaptive threshold generation method assumes that after low-pass filtering, the relevant peaks of the signal will be filtered out. Therefore, the design of low-pass filters is crucial and must be determined based on the shape of the signal's relevant peaks. In addition, when generating the detection threshold in the final step, a fixed offset can be added to the entire threshold as needed, which should be related to the current background.
## 3 Results and Discussion  
### Validation of the Multi-Mycotoxin Determination Method in Vegetable Drinks  
The described method was validated following European Commission Decision 2002/657/EC [21] to observe the performance and extraction behavior for each of the mycotoxins under study and for four types of beverages: rice, oats, soy and almonds. For this, the stock solutions of all mycotoxins were prepared individually, and working solutions with the calibration lines in the solvent and the calibration lines with the matrix were prepared for the quantification of the samples to evaluate the matrix effect and for the fortification of the samples for the study of intraday and interday recovery (reproducibility and repeatability). The solutions until their use were stored frozen at \(-20\)\({}^{\circ}\)C and prepared periodically to maintain their stability.  
The concentrations of the working solutions were 10 ug/mL for AFs OTA, AME, AOH and TENT; 40 ug/mL for T-2; 5 ug/mL for ENA, ENA1, ENB, ENB1 and BEA; 15 ug/mL for ZEA and HT-2; and the starting point solutions (P1) to prepare the calibration curves  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Analyte**} & \multicolumn{6}{c}{**MS/MS Detection Parameters**} \\ \cline{2-7}  & **Precursor Ion** & **Product Ion** & **DP** & **CEP** & **EC** & **CXP** \\  & **Q1 (m/z)** & **Q3 (m/z)** & **(V)** & **(V)** & **(V)** & **(V)** & **(V)** \\ \hline HT-2 & 442.2 & 262.8  & 22 & 22 & 4 \\  & [M + NH4] + & 215.1 & & & 29 & 4 \\  & 319.0 & 301.0  & 116 & 33 & 27 & 10 \\  & [M + NH4] + & 244.1 & & & 39 & 6 \\  & 699.4 & 210.1  & 21 & 22 & 37 & 8 \\  & [M + NH4] + & 214.2 & & & 59 & 10 \\  & 657.3 & 196.1  & 51 & 28 & 59 & 10 \\  & 671.2 & 214.1  & 313.1  & 46 & 18 & 39 & 4 \\  & [M + H] + & 241.1 & & & 41 & 4 \\  & 315.1 & 286.9  & 76 & 18 & 39 & 6 \\  & 329.1 & 313.1  & 76 & 18 & 39 & 6 \\  & 329.1 & 313.1  & 61 & 18 & 39 & 6 \\  & 404.1 & 239.0  & 128 & 32 & 16 & 60 & 3 \\  & [M + H] + & 228 & & & 40 & 3 \\  & 259 & 128  & 312 & 32 & 29 & 2 \\  & [M + H] + & 256 & & & 29 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MS/MS detection parameters for the analysis of the studied mycotoxins.  
were 0.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for AFs OTA, AME, AOH and TENT; 2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for T-2; 0.25 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for ENA, ENA1, ENB, ENB1 and BEA; and 2.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for ZEA and HT-2.  
The validation of the method was carried out for the parameters of linearity, matrix effect, sensitivity, reproducibility and repeatability.
## 3 Machinery Monitoring
### Filtering and Normalization of Collected Signals  
The signals collected from accelerometers are filtered to remove noise prior to feature value calculation. To deal with the various forms of noise found in different machinery, we use the main frequency of the carrier signal for the selection of filter parameters. For example, if the frequency of the carrier were around 10 Hz, then a low-pass filter would be employed for the removal of noise.
## 3 Experimental Results and Analyses
### Experiment in Waters with Complex Seabed Topography  
In an underwater environment featured by complex topography, the water depth experiences significant variations, spanning from over 10 to 500 m and covering a distance of 800 m from east to west. Notably, the overall slope of this terrain exceeds 20\({}^{\circ}\), and the presence of densely clustered rocks near the platform reef results in a rugged and uneven landform, as depicted in Figure 18. Given the AUV's limitations in maintaining a specific altitude under its constant-altitude mode, the experiment experienced frequent interruptions. The AUV repeatedly triggered alarms and initiated premature surfacing during the guidance phase before reaching the specified altitude. Nevertheless, the constant-depth-based experiment was successfully conducted upon several attempts in various modes, including a combination of constant-altitude and fixed-point modes and a blend of constant-altitude and fixed-direction modes. As such, the performance of current AUV models cannot support sustained operations in the constant-altitude mode.  
Figure 17: Position deviation of anchor nuts.
# Magnetic Vortex and Hyperthermia Suppression in Multigrain Iron Oxide Nanorings
###### Abstract  
Single-crystal iron oxide nanorings have been proposed as a promising candidate for magnetic hyperthermia application because of their unique shape-induced vortex-domain structure, which supports good colloidal stability and enhanced magnetic properties. However, the synthesis of single crystalline iron oxide has proven to be challenging. In this article, we showed that chemically synthesized multigrain magnetite nanorings disfavor a shape-induced magnetic vortex-domain structure. Our results indicate that the multigrain Fe\({}_{3}\)O\({}_{4}\) nanorings with an average outer diameter of \(\sim\)110 nm and an inner to outer diameter ratio of \(\sim\)0.5 do not show a shape-induced vortex-domain structure, which was observed in the single-crystal Fe\({}_{3}\)O\({}_{4}\) nanorings of similar dimensions. At 300 Ks, multigrain magnetite nanorings showed an effective anisotropy field of 440 Oe, which can be attributed to its high surface area and intraparticle interaction. Both calorimetric and AC loop measurements showed a moderate inductive heating efficiency of multigrain magnetite nanorings of \(\sim\)300 W/g at 800 Oe. Our results shed light on the magnetic ground states of chemically synthesized multigrain Fe\({}_{3}\)O\({}_{4}\) nanorings.  
multigrain; nanorings; magnetic vortex-domain; hyperthermia +
Footnote †: journal: Applied Sci. 2020, 10, 787; doi:10.3390/app10030787
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 6 Results and Discussion
### Performance Metrics  
In HTTP evaluation, the page load time (PLT) has been used as the primary indicator of layer-7 protocol performance. It summarizes many of the factors that are contributed by the network, transport and application protocols. However, due to the complex interaction and intrinsic features of the modern protocols such as HTTP/2, HTTP/3 and QUIC, the PLT does not always provide a sensible performance measure. Therefore, we need other performance metrics to study the interaction between clients and web servers as seen by the end user.  
To assess the relative user experience, we used two additional user-level performance measures: the First Contentful Paint (FCP) and the Largest Contentful Paint (LCP) [29]. The FCP indicates the first response from the server and reveals what the user perceives first from the web page. The LCP expresses the perceived load speed as the arrival time of the most significant object (largest object). All the key performance metrics used in the evaluation are defined in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **HTTP Version** & **Web Page Structure** & **Transport** & **Congestion Control** \\ \hline Effect of parallel streams & All & All & TCP, QUIC & BBR \\ Effect of congestion control & All & Uniform & TCP, QUIC & Cubic, BBR \\ Effect of web page structure & HTTP/2, HTTP/3 & Uniform, triangular & TCP, QUIC & BBR \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment scenarios.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Metric** & **Description** \\ \hline Page Load Time (PLT) & The time when the entire page is loaded including text, images, scripts, etc. [29]. \\ First Contentful Paint (FCP) & The time when the first object is loaded in the browser [29]. \\ Largest Contentful Paint (LCP) & The time when the largest object is loaded in the browser [29]. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance metrics used in the evaluation.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 2 Structure and Production of Pectins
### Structure
#### 2.1.1 Homogalacturonan  
Partially C-6 carboxylated and O-2 or O-3 acetylated HGs are the most abundant forms in pectin [2,18] as they represent between 57% to 70% of them [19]. The methyl esterification of the homogalacturonan regions partly determines the extent of industrial applications of pectins and their capacity for interaction [20]. This methyl esterification corresponds to the degree of methylation (DM) as a percentage. HGs form the smooth zone of pectins.
# Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions
## 1 Introduction
Thus, the organization of this paper is as follows: we devote Section 2 to compile some related works from the points of view of both the application in the communication environment and the use of Fuzzy Logic. In Section 3, we describe the fuzzy model of the communication network and we proceed with the definition of various types of fuzzy numbers and their possible interpretation. Section 4 introduces our proposal of Fuzzy Dijkstra Algorithm applied on a network with fuzzy costs and using the Total Integral Method proposed in [9] as the ranking procedure for fuzzy numbers. Section 5 presents the application of these theoretical concepts to a communication network with a description of the communication network used, the different cost functions used in the comparison as well as the different strategies to search the optimal path (with a particular interest in Strategy 8). Section 6 deals with our experimental study, where the reader can find the simulation results and their discussion. Finally, Section 7 summarizes the main and conclusive ideas of this work and we also outline future research lines of work.
## 3 Materials and Methods
### Methodology
#### 3.2.4 Statistical Measurements  
Sensitivities of all five vegetation parameters were measured individually in respect to PDSI using three different statistical methods: (1) agreement between parameters using [PERSON]'s r, (2) error estimation between parameters, and (3) uncertainty estimation through Root Mean Squared Error (RMSE). [PERSON]'s r showed the relationship between the response variable (i.e., vegetation parameters) and the explanatory variable (i.e., PDSI) and helped to understand the agreement between response and explanatory variables ranging between -1 (negative agreement) to +1 (positive agreement). Standard Error of the estimate (SEE) derived from [PERSON]'s simple linear regression analysis was used for error estimation between the PDSI and the other vegetation parameters, as this statistic allows to construct a confidence interval within which the true population correlation will fall [23]. Hence, smaller values indicate better sensitivity. Lastly, the Root Mean Squared Error (RMSE) was used to assess the standard deviation of the prediction errors and to measure the uncertainty between variables. The lower values of RMSE indicated the low uncertainty between variables and vice versa. Open-source computing (R studio, version: 3.6.1) was used for statistical calculations.
## 3 Bursting Liability Indices
### Calculation Principle  
The calculation principles of bursting liability indices are summarized in Figure 6. The elastic strain energy index \(W_{ET}\) is the ratio of elastic strain energy \(\Phi_{SE}\) to plastic strain energy \(\Phi_{SP}\), which is determined from uniaxial loading-unloading compression tests. The elastic strain energy index \(W_{ET}\) is a parameter related to the elastic hysteresis loop of rocks, and the unloading point is set approximately to 80-90% of the uniaxial compression strength \(\sigma_{c}\), as shown in Figure 6a.  
With the increase of the permanent (irreversible) strain \(\varepsilon_{d}\), the elastic strain energy index \(W_{ET}\) decreases, and the accumulation ability of elastic strain energy \(\Phi_{SE}\) of rocks decreases, which makes the bursting liability decrease. The bursting energy index  
Figure 5: Proportion statistics of bursting liabilities of coal and non-coal rocks using the classification standard by uniaxial compressive strength of coal.  
Figure 6: Calculation methods of bursting liability indices. Notes: \(\Phi_{SE}\) is the elastic strain energy, and it is the area under the unloading curve; \(\Phi_{\text{C}}\) is the total strain energy, and it is the area under the loading curve; \(\Phi_{SP}\) is the plastic strain energy, and it is the area enclosed by the loading curve and the unloading curve; \(\varepsilon_{c}\) is the total stain in loading-unloading curve; \(\varepsilon_{d}\) is the permanent non-elastic strain; \(\varepsilon_{p}\) is the total stain in comprehensive loading curve; \(\varepsilon_{F}\) is the stain before pre-peak strength in comprehensive loading curve; \(A_{S}\) is the accumulated deformation energy; \(A_{X}\) is the dissipate deformation energy; \(\sigma_{c}\) is the uniaxial compressive strength; \(\sigma_{t}\) is the uniaxial tensile strength.  
is the ratio of the accumulated deformation energy \(A_{S}\) to the dissipate deformation energy \(A_{X}\), which is determined from the complete stress-strain curve of rocks in uniaxial compression tests.  
The area of pre-peak curve is defined as the accumulated deformation energy \(A_{S}\), and the area of post-peak curve is defined as the dissipate deformation energy \(A_{X}\), as shown in Figure 6b. The duration time of post-peak segment of rocks in uniaxial compression tests is defined as the dynamic fracture duration time \(DT\), as shown in Figure 6c. [PERSON] et al. [25] defined the strength brittleness index \(B\) as the ratio of uniaxial compressive strength \(\sigma_{c}\) to uniaxial tensile strength \(\sigma_{t}\), as shown in Figure 6e.
## 4 SDP Solution Method for the FFP-IM
### The SDP Solution Method
#### 4.2.4 SDP Method for Solving FFP-IM  
The SDP method for estimating variance-based IM can obtain all the first-order terms in the response function HDMR at one time with only a few sets of input-output samples, which in turn yields the principal variance contribution \(Var[E(Y|Z_{i})](i=1,2,\ldots,n)\) of the input variable. This method is versatile, as it applies to continuous functions, non-smooth functions, and discontinuous functions alike. For the failure domain indicator function \(I_{F}=\left\{\begin{array}{ll}1&M(\mathbf{\alpha})\leq 0\\ 0&M(\mathbf{\alpha})>0\end{array}\right.\), it can be viewed as a function of the membership levels, denoted as \(\mathbf{\alpha}\), of the fuzzy variables \(\mathbf{Z}\), there is an implicit functional relationship between \(I_{F}\) and the membership level \(\mathbf{\alpha}\), represented by \(I_{F}=\varphi(\mathbf{\alpha})\). Employing the membership level \(\mathbf{\alpha}\) as the input and the value of the failure domain indicator function as the output, the SDP method is utilized to estimate the first-order HDMR of the indicator function \(I_{F}=\varphi(\mathbf{\alpha})\). This estimation facilitates the determination of the principal variance contribution \(Var[E(I_{F}|Z_{i})](i=1,2,\ldots,n)\) of all input fuzzy variables to the failure domain indicator function, subsequently deriving the FFP-IM index for all input fuzzy variables \(\tau_{i}(i=1,2,\ldots,n)\).  
In this paper, since the membership level of the fuzzy variable \(Z_{i}\) is taken as a realized value \(\alpha_{i}^{*}\), the fuzzy variable is degraded to an interval variable, which eliminates its fuzziness. Then, the IM of the fuzzy variable on the FFP is established by considering the uniform change in the membership level \(\alpha_{i}\) in the range of \([0,1]\). The proposed IM model can well evaluate the average impact of the input fuzzy variable on the FFP and directly guide the fuzzy reliability design.  
Before conducting example studies to validate the rationality of the proposed FFP-IM index and its efficient SDP-based solving algorithm, it is imperative to systematically elucidate the anticipated advantages of the proposed index and methodology in the field of fuzzy structural importance measures. The FFP-IM index proposed in this study, computed through the SDP-based approach, enables efficient evaluation of the impact of input fuzzy variables on structural systems from the perspective of failure probability. This method not only ensures the accuracy of computational results but also significantly reduces the computational complexity of the algorithm, thereby offering a novel solution for the reliability analysis of complex structural systems. This innovative approach enhances computational efficiency while maintaining a high level of precision, providing a new technical pathway for research in the field of fuzzy structural importance measures.  
To enhance the accessibility of the proposed FFP-IM method, a flowchart summarizing the key derivation steps is provided in Figure 3.
## 4 Experimental Data and Discussion
Data-driven modelling and machine learning was already successfully applied, e.g., to materials science [8; 56; 57], failure of concrete [58] or geology [59]. Using numerical data, [PERSON] et al. [60] showed that artificial neural networks can be trained to predict an FCG's path and life, and they reckon that experimental data is required to improve training. Considering that FCG can be described with physical laws, physics-informed data-driven approaches [61; 62] can be exploited in analyses involving mixed-mode FCG; however, those approaches require significant experimental data for model training, which is not yet widely available.
# LADRC-Based Magnetic Field Measurement Method for a Nuclear Magnetic Resonance Rotation Sensor
## 3 LADRC-Based Synchronous Control Method
### Analysis in Frequency Domain  
By some deduction-based literature [24], the closed-loop transfer function of the phase loop and the frequency loop of LADRC are given by the expressions:  
\[\Phi_{Y}(s)=\frac{Y(s)}{D(s)}=\frac{bs(s+2\omega_{o})}{C(s)} \tag{31}\]  
\[\Phi_{F}(s)=\frac{\hat{X}_{2}(s)}{D(s)}=\frac{b\omega_{o}^{2}(s+K_{c})}{C(s)} \tag{32}\]where  
\[\begin{array}{ll}C(s)=(s+p_{1})(s+p_{2})(s+p_{3})\\ =a_{0}s^{3}+a_{1}s^{2}+a_{2}s+a_{1}\\ =s^{3}+(b(2\omega_{o}+\Gamma)+K_{c})s^{2}+(2\omega_{o}\Gamma b+2K_{c}\omega_{o}+ \omega_{o}^{2})s+K_{c}\omega_{o}^{2}\end{array} \tag{33}\]  
From the Routh stability criterion, systems in Equations (31) and (32) are always stable because \(a_{1}a_{2}-a_{0}a_{3}>0\).  
According to the analysis in Section 2, a monotone nondecreasing step response without overshoot is required. Based on theorem in [24; 25], a specific combination of control parameters is presented to have monotone nondecreasing step responses.  
Let \(b=\frac{K_{c}}{K_{c}-\Gamma}>0\), and \(0<K_{c}<\omega_{o}\), then the system in Equation (32) has a monotone nondecreasing step response.  
Proof of the corollary.: The precondition means that the absolute values of the three real poles are  
\[\begin{array}{ll}p_{1}=K_{c},p_{2}=w_{o}\sqrt{1-1/b},\\ p_{3}=w_{o}\sqrt{1+1/b}\end{array} \tag{34}\]  
Thus, Equation (32) can be decomposed as  
\[\Phi_{ADRC-Z2}=\frac{bw_{o}^{2}}{(s+p_{2})(s+p_{3})} \tag{35}\]  
The damping ratio of the system in Equation (35) is \(\xi=\sqrt{b}>1\); therefore, a monotone nondecreasing step response can be achieved. \(\square\)
###### Abstract  
Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10-20 m for Bluetooth and 50-100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the energy required by the drone to use the technology. A finite number of states have been designed to include a larger range of wireless network techniques, enabling the drones to be recognized while they are further away and in remote areas. This is achieved by including other means of RF channels, such as 4G/5G, Automatic Dependent Surveillance-Broadcast (ADS-B), long range Internet of things (IoT), and satellite communications, in the suggested ER-ID algorithm of this study. The introduced algorithm is tested via a case study. The results showed the ability to detect drones using all types of available radio frequency communication systems (RF-CS) while also minimizing the consumed energy. Hence, the authorities can control the licensed drones by using available RF-CS devices, such as Bluetooth and Wi-Fi, which are already widely used for mobile phones, as an example.  
drones; lifetime data automation; drone electronic remote identification; radio frequency communications; Internet of things; global system for mobile; automatic dependent surveillance broadcast; satellites; 4G/5G wireless communication +
Footnote †: journal: Applied  
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