esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### BMP--Inorganic Nitrogen Addition  
In the first BMP series, the effect of steam explosion pre-treatment together with the addition of inorganic nitrogen was evaluated. Figure 1 depicts the cumulative methane production in BMPs with NH\({}_{4}\)Cl addition to untreated and steam-exploded wheat straw samples. After 30 d of BMP digestion, untreated WS presented an average cumulative methane production of 280 mL CH\({}_{4}\)/g VS. Slightly higher methane production values (304 mL CH\({}_{4}\)/g VS) for untreated WS in BMPs have been reported elsewhere [14], attributed to higher culture temperatures (55 \({}^{\circ}\)C) compared to the mesophilic system (42 \({}^{\circ}\)C) used in the present study. The methane production from SE samples, without nitrogen addition, reached values of up to 332 mL CH\({}_{4}\)/g VS after the end of the 30-day digestion period, which was significantly higher (\(p<0.05\)) than the yields offered by the untreated WS without the addition of nitrogen (280 mL CH\({}_{4}\)/g VS; Figure 1). This increase was equivalent to almost 15% (_w/v_) compared to WS. In addition, the biomethane yields offered by SE BMPs after NH\({}_{4}\)Cl supplementation (SE + N) reached values of 387 mL CH\({}_{4}\)/g VS, which were significantly higher (13% (_w/v_)) than those from SE without N\({}_{2}\) addition (332 mL CH\({}_{4}\)/g VS; Figure 1). It is possible that higher gas yields were the result of an enhanced microbial activity due to the pre-treated straw offering increased accessibility to available carbon and with the concomitant increased availability of nitrogen in the culture.
###### Abstract  
For nonlinear systems, both the cubature Kalman filter (CKF) and square-root cubature Kalman filter (SCKF) can get good estimation performance under Gaussian noise. However, the actual driving environment noise mostly has non-Gaussian properties, leading to a significant reduction in robustness and accuracy for distributed vehicle state estimation. To address such problems, this paper uses the square-root cubature Kalman filter with the maximum correlation entropy criterion (MCSRCKF), establishing a seven degrees of freedom (7-DOF) nonlinear distributed vehicle dynamics model for accurately estimating longitudinal vehicle speed, lateral vehicle speed, yaw rate, and wheel rotation angular velocity using low-cost sensor signals. The co-simulation verification is verified by the CarSim/Simulink platform under double-lane change and serpentine conditions. Experimental results show that the MCSRCKF has high accuracy and enhanced robustness for distributed drive vehicle state estimation problems in real non-Gaussian noise environments.  
non-Gaussian noise; distributed drive vehicle state estimation; maximum correntropy criterion; square-root cubature Kalman filter
# Comparison of the Chemical and Sensorial Evaluation of Dark Chocolate Bars
## 3 Results and Discussion
### Headspace Compositions
Pyrazines were detected as the most abundant VOC chemical class in BIO samples, as well, where they accounted for 52.0% and 76.4% in BIO1 and BIO2, respectively. With the only exception of sample SM13, pyrazines were detected as the most abundant chemical class of VOCs in all SM samples, too, accounting for up to almost 90% in SM10. In SM4 headspace (HS), however, their relative content was only slightly higher (31.2%) than that of oxygenated sesquiterpenes (28.8%). Among this chemical class, tetramethylpyrazine and 2,3,5-trimethylpyrazine were the most quantitatively relevant in all samples. Pyrazine are heterocyclic volatiles produced during the Maillard reaction, whose aroma contribution is of the utmost importance in chocolate, as they are responsible for its typical pleasant flavor [1; 24; 25]. Tetramethylpyrazine was found as the most abundant compound in all HSs, with relative abundances always higher than 35%. Its odor is described as reminiscent of coffee, with a green and roasted aroma [2; 12; 15]. 2,3,5-Trimethylpyrazine followed; its aroma contribution is similar to that of tetramethylpyrazine, but with a slightly earthier and roasted-nuts like flavor [15; 26; 27]. Non-terpene esters followed as the second most abundant VOC chemical class in HD3 to HD8 samples, with relative abundances varying between 9.4% and 22.1% in HD7 and HD8, respectively. Their quantitative importance was also evidenced for BIO (33.3% in BIO1, 8.8% in BIO2) and SM (from 2.6% in SM5, up to 36.8% in SM13) samples, although with great variation based on the bar brand. Among them, ethyl acetate and 2-phenylethyl acetate were the most represented. Pyrazines decrement in SM13 and BIO1 were coupled with the increment of non-terpene esters, in particular of that of ethyl acetate. Ethyl and acetate esters are indeed reported as important VOCs involved in chocolate aroma [12; 13]. In SM4, instead, their decrement was coupled with an increment in sesquiterpene hydrocarbons, which confer green and spicy notes to the product [28]. Among HD bars, HD1 HS demonstrated the highest relative content of non-terpene aldehydes, of which nonanal was the most quantitatively important. These compounds also exhibited a quantitatively relevant presence in SM4 and SM12, as they reached up to 18.1% and 13.8%, respectively; among them, the former was mainly rich in tetradecanal, while pentanal reached 13.5% in the latter. In BIO bars, they were detected with a 2.5% relative abundance in BIO1, while in BIO2 they only accounted for 0.3%. Fruity and flowery notes are associated to aliphatic volatile aldehydes [15], among which the most abundant detected in the present study were nonanal and pentanal. The former is associated with citrus-like aroma notes [29], while the latter is more pungent and bitter [1]. A citrus peel-like flavor is also described for tetradecanal [29], which was only detected in sample SM4 but with a significant relative abundance. In this study, benzaldehyde followed among the
# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 5 Analysis of Privacy Protection Effect of the Algorithm  
**Theorem 1**.: _SSKM_DP algorithm satisfies the \(\varepsilon\)-differential privacy._  
Proof of Theorem 1.: Given two adjacent data \(U_{1}\) and \(U_{2}\), the output of the SSKM_DP algorithm is \(A\) (\(U_{1}\)) and \(A\) (\(U_{2}\)), respectively, and \(\widetilde{U}\) is the differential privacy data. According to the definition of differential privacy, the following equation is proven to be true:  
\[\frac{P_{r}(A(U_{1})\in S)}{P_{r}(A(U_{2})\in S)}\leq\exp(\varepsilon) \tag{20}\]Assume that the query results of \(U_{1}\) and \(U_{2}\) are \(f(U_{1})\) and \(f(U_{2})\), respectively, and \(f(\widetilde{U})\) is the query results of \(\widetilde{U}\).  
\[P_{r}(A(U_{1})\in S)\propto\exp\left(\frac{\varepsilon\big{|}f(\widetilde{U})- f(U_{1})\big{|}}{\Delta f}\right)\]  
then  
\[\begin{array}{ll}\frac{P_{r}(A(U_{1})\in S)}{P_{r}(A(U_{2})\in S)}=&\frac{\exp \left(\frac{\varepsilon\big{|}f(\widetilde{U})-f(U_{1})\big{|}}{\Delta f} \right)}{\exp\left(\frac{\varepsilon\big{|}f(\widetilde{U})-f(U_{2})\big{|} }{\Delta f}\right)}\\ &\leq\exp\left(\frac{\varepsilon\big{|}f(U_{1})-f(U_{2})\big{|}}{\Delta f} \right)\\ &\leq\exp\left(\frac{\varepsilon\big{|}f(U_{1})-f(U_{2})\big{|}}{\Delta f} \right)\\ &\leq\exp\left(\varepsilon\right)\end{array} \tag{21}\]  
In the SSKM_DP algorithm, there is no intersection among the \(m\) clusters generated. According to the parallel combinatorial property of differential privacy, the privacy budget \(\varepsilon_{i}\) allocated by the SSKM_DP algorithm for each cluster is the overall privacy budget \(\varepsilon\) of SSKM_DP.  
The conclusion is that the SSKM_DP algorithm satisfies \(\varepsilon\)-differential privacy. \(\square\)
# Fatigue Life Assessment in Bainitic Steels Based on The Cumulative Strain Energy Density
###### Abstract  
Carbide-free bainitic steels are an example of high-strength steels that show an excellent combination of strength, ductility, toughness and rolling fatigue contact resistance and are progressively being introduced in the production of railways, crossings and automotive components. Although there are Mn-free approaches able to produce carbide-free bainitic steels, those based on the addition of Mn are less expensive. Therefore, it is important to fully understand the mechanical behavior of such materials to develop reliable engineering products. In this paper, three low-carbon bainitic steels, differing in Mn content, namely 0%, 2.3% and 3.2%, designated as steel A, B and C, respectively, were studied in a systematic manner. Low-cycle fatigue tests were conducted under symmetrical strain-controlled conditions for different strain amplitudes (0.6%, 0.7%, 0.8% and 1%). Independent of Mn content, a strong relationship between cumulative strain energy density and number of cycles to failure was found. Based on this relationship, a new predictive model, capable of estimating the fatigue lifetime, was developed. Predictions based on the new model were close to the experimental lives and were more accurate than those computed via the well-known Smith-Watson-Topper (SWT) and Liu criteria.  
high-strength bainitic steels; Mn effect; strain energy density; fatigue life predictions +
Footnote †: journal: Applied Sciences
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 2 Material and Methods
### The UWB Life-Detection Radar Hardware System Design
#### 2.1.3 The Design of Single Golay-Coded Radar
To understand the orderly collaboration between radars, the workflow timing is presented in Figure 3. The scanning operating detection work starts from the radars and control host netted. After receiving the \"START COMMAND,\" the control host orders radars' run network clock synchronization and relative position measurement, then calculates the topology's rationality and allocates the time slice. The radars perform electromagnetic detection and upload data within their time slice. Finally, after 16 s of detection, the host calculates the target's position and displays the results via human-machine interface. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Equivalent sampling frequency (\(F_{S}\)) & 16 GSPS * \\ Real-time sampling frequency & 125 MSPS * \\ Sampling points (\(N\)) & 16,384 \\ Average times (\(N_{A}\)) & 32 \\ ADC * Resolution & 16 bits \\ \hline \hline \end{tabular}  
* GSPS: Gigabit samples per second, MSPS: million samples per second, ADC: analog to digital converter.  
\end{table} 
Table 2: Key parameters of the proposed radar.
## 3 Data Preparation and Analysis
### Selection of Evaluation Factors
In the application of geohazard deformation monitoring, the displacement condition and spatial distribution of the deformation zone can be recognized by the InSAR deformation rate (Figure 5l,m). Thirteen factors, including elevation, slope, slope direction, curvature, average annual rainfall, vegetation normalization index, stratigraphic lithology, distance from rupture, isokinetic line, distance from river, distance from road, deformation rate of descending rail, and deformation rate of ascending rail are selected as the evaluation factors of landslide susceptibility. The continuity data in the 13 evaluation factors are graded and processed. The discrete data are graded in accordance with the actual determination of the state, and the distribution map is shown in Figure 5.
## 3 Sub-Problem 1
### The Batch Algorithm  
For each measurement, the residual \(y\) is expressed as:  
\[y_{i}=Y_{oi}-Y_{ci} \tag{6}\]  
where the subscript '\(c\)' indicates the calculated value, and the subscript '\(o\)' indicates the measured value. The subscript \(i\) is the serial number of measurement. \(Y\) is the measurement data. Here, it is the same as Equation (3). For a constellation of two spacecraft, the state vector (denoted as \(\mathbf{X_{0}}\)) to be determined is a 12-dimensional vector which includes the position and velocity of both spacecrafts. The error in the initial state vector is denoted as \(\hat{x_{0}}\). The equation of orbit determination becomes  
\[y_{i}=H_{x}\hat{x}_{0}+\epsilon \tag{7}\]  
where  
\[H_{x}=\frac{\partial Y_{i}}{\partial X_{i}}\frac{\partial X_{i}}{\partial X_{ 0}}=\widetilde{H_{x}}\Phi\ ;\ \Phi=\frac{\partial X_{i}}{\partial X_{0}} \tag{8}\]  
\(\Phi\) is the system transition matrix calculated by integrating the following equation  
\[\dot{\Phi}(X,t)=\frac{\partial\dot{X}}{\partial X}\Phi(X,t) \tag{9}\]  
Using the batch algorithm [22], the solution of \(\hat{x}_{0}\) can be estimated as  
\[\hat{x}_{0}=(H_{x}^{T}H_{x})^{-1}H_{x}^{T}y \tag{10}\]  
By substituting Equation (10) into the initial estimate of the state vector \(\mathbf{X}_{0}\), we can start the iteration process until the correction \(\hat{x_{0}}\) is small enough. This is a common practice in orbit determination and further details are omitted.  
Detailed expression of \(\widetilde{H_{x}}\) in Equation (8) depends on the type of the measurement data. For the inter-satellite range data, it can be expressed as  
\[\widetilde{H_{x}}=\frac{\partial Y}{\partial X}=[\frac{x}{\rho},\frac{y}{\rho },\frac{z}{\rho},0,0,0,-\frac{x}{\rho},-\frac{y}{\rho},-\frac{z}{\rho},0,0,0] \tag{11}\]  
where \(x,y,z\) are given in Equation (2).
# Experimental Study of Radiated Emission Due to Secondary Common-Mode Current in Buck Converters
## 4 Noise Measurements in the Experimental System
### Common-Mode Currents
First, Figure 10 shows that the PCM current is almost suppressed to the background noise level over the measured frequency range by connecting the P-CMI near the power converter. This result is because the P-CMI has high impedance for PCM as shown in Figure 9b. On the other hand, since the S-CMI has an insignificant impedance for PCM due to high coupling between the windings, the PCM current was not reduced when the S-CMI was connected. A peak of PCM current is observed at 110 MHz when the CMI is not connected which is possibly caused by the standing wave of the PCM current. The 3/4 wavelength of the 1.5 m power cable is 150 MHz. Considering that the shortening coefficient of the wavelength depends on the cable material, 110 MHz is almost equal to 3/4 wavelength of the power cable. Moreover, an increase in the PCM current at around  
Figure 10: Measured primary common-mode currents. Figure 9: CM impedance measurements of the fabricated CMIs. (**a**) Measurement configurations of the S-CMI; (**b**) measured CM impedances. 110 MHz is confirmed when S-CMI is connected. This is possibly due to the resonance of the leakage inductance and winding stray capacitance of the S-CMI. As shown in Figure 4, the power converter side is open to the ground plane and has a significant SCM impedance. Therefore, the standing wave of SCM current is generated in the three-core cable with the power supply side as the antinode and the power converter side as the node. In this case, even if the S-CMI is connected near the power converter, insignificant attenuation is obtained on the SCM current at the frequencies where the power converter side is the node of the SCM current. Therefore, even when the S-CMI is connected near the power converter, as shown in Figure 12, a standing wave of the SCM current similar to those shown in Figure 4 is generated in the three-core cable. Furthermore, the power cable functions as a monopole antenna for the SCM. Therefore, the radiated emission is possibly very large at the frequencies where the standing wave peaks of the SCM current appear. Based on the above discussion, the position of the S-CMI installed and the radiated emissions from the power cable are strongly related. This relationship will be studied in detail in future work. Since the length of the three-core cable connecting the power converter and the LISNs is 1.5 m, the SCM current near the LISN is at maximum at 50 MHz (1/4 wavelength) and 150 MHz (3/4 wavelength). These frequencies are almost equal to 45 MHz and 110 MHz, where large SCM currents were observed in Figure 11, considering the cable's shortening coefficient of wavelength. As the cable length increases, peaks of SCM current may shift to lower frequencies. Thus, when the buck converter is connected to LISNs with a long cable, the measured peaks of radiated emission may be shifted to lower frequencies.
# Lower Limb Muscle Activation in Young Adults Walking in Water and on Land
## 3 Results
### Interactions
#### 3.2.1 Kinematics  
Post hoc comparisons revealed that stance time was significantly longer in water than on land at 2.5 mph (\(p\) < 0.001), with no difference observed between environments at 3.5 mph (\(p\) = 0.936). Stance time was also significantly longer for the 2.5 mph water condition compared to all other conditions (\(p\) < 0.001; Table 4). Swing time was significantly longer for the 2.5 mph water condition compared to all other conditions (\(p\) < 0.001) and for the 3.5 mph water condition compared to both speed conditions on land (\(p\) < 0.001). No significant difference was observed between the 2.5 mph and 3.5 mph land conditions (\(p\) = 0.221; Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Measure** & **Land 2.5 mph** & **Land 3.5 mph** & **Water 2.5 mph** & **Water 3.5 mph** \\ \hline Stance time (s) & 0.75 (0.04) & 0.64 (0.04) a  & 0.81 (0.06) a,b  & 0.63 (0.04) a,c  \\ Swing time (s) & 0.38 (0.03) & 0.36 (0.03) & 0.62 (0.07) a,b  & 0.57 (0.05) a,b  \\ GM RMS Stance (\(\mu\)V) & 116.9 (42.4) & 144.9 (64.8) a  & 56.4 (24.3) a,b  & 110.4 (41.5) b,c  \\ Co-A Stance (\%) & 59.7 (27.8) & 79.2 (38.1) a  & 135.3 (75.0) a,b  & 110.4 (56.8) a,b  \\ TA RMS Swing (\(\mu\)V) & 97.5 (31.9) & 134.8 (65.3) a  & 112.1 (37.8) a  & 217.4 (81.0) a,b  \\ GM RMS Swing (\(\mu\)V) & 18.5 (17.8) & 17.9 (13.9) & 8.7 (7.4) a,b  & 18.1 (7.8) c  \\ Co-A Swing (\%) & 19.6 (16.1) & 14.4 (9.8) a  & 8.7 (8.7) a,b  & 9.1 (5.3) a,b  \\ \hline \end{tabular}
\end{table}
Table 4: Post hoc comparisons on significant environment \(\times\) speed interactions.  
Figure 1: Speed \(\times\) environment interaction on tibialis anterior muscle activation during the swing phase of walking gait.
## 2 Related Work
### Multi-Objective Optimization Methods  
Multi-objective optimization is a subfield of mathematical optimization that simultaneously addresses the optimization of multiple objective functions. In multi-objective problems, objectives are considered conflicting if the improvement of one objective results in the deterioration of others. A solution is defined as Pareto optimal, Pareto efficient, or non-dominated if no alternative solution exists that can improve one objective without compromising at least one other. Non-dominated solutions serve as a fundamental criterion for evaluating the quality of the solution set. Specifically, the Pareto optimal set consists of solutions for which no objective can be further improved without compromising another [PERSON] (1988). In practical applications, multi-objective optimization is widely employed in areas such as engineering design, resource allocation, and scheduling optimization. It provides decision-makers with a diverse and flexible set of solutions, enabling effective trade-offs among competing objectives.  
The Pareto front partitions the objective function space into feasible and infeasible regions in multi-objective optimization problems. It is defined as the boundary encompassing all feasible points that satisfy the constraints, representing the set of optimal trade-off solutions [10; 11]. Depending on the number of objectives, the Pareto front can manifest as a continuous curve in two-objective problems or as a multidimensional surface in higher-dimensional spaces. Constructing and analyzing the Pareto set facilitates the effective balancing of conflicting objectives, thereby providing a rigorous scientific foundation for decision-making.  
Multi-objective evolutionary algorithms (MOEAs) have been widely applied to shortest path problems, exhibiting strong capabilities in maintaining solution diversity while managing computational complexity [12]. To overcome challenges such as the limitations of the Pareto dominance criteria, methods including quartile filtering combined with penalty mechanisms have been proposed to achieve a balance between convergence and diversity [13]. Furthermore, enhanced [PERSON]'s and [PERSON]'s algorithms have been applied in emergency logistics transportation to model practical constraints, enabling Pareto front generation and providing real-time decision support [14].
## 4 Simulation Results and Analysis  
### Validation of the DS/SS Module  
In the DS/SS module, the time-domain waveforms of the data code, PRN code and the communication ranging code obtained after DS/SS are shown in Figure 12. The spectrum of the communication ranging code is presented in Figure 13. Since DS/SS effectively performs an XOR operation between the data code and the PRN code, the resulting communication ranging code will be as follows: when the data code is 1 and the PRN code is 0, the resulting communication ranging code will be 1; when the data code is 1 and the PRN code is 1, the resulting communication ranging code will be 0; when the data code is 0 and the PRN code is 1, the resulting communication ranging code will be 1; and when the data code is 0 and the PRN code is 0, the resulting communication ranging code will be 0. In other words, when the data code is 1, the communication ranging code should be the inverse of the PRN code; when the data code is 0, the communication ranging code should be identical to the PRN code. Meanwhile, spreading is essentially the process of expanding the frequency spectrum. Based on the inter-satellite communication signal system designed in Figure 10, it is clear that since the PRN code rate is 1 Mbps, after spreading, the main lobe bandwidth of the resulting communication ranging code in the frequency spectrum should be twice the PRN code rate.  
As shown in Figure 12, when the data code is 1, the communication ranging code inverts the PRN code; when the data code is 0, the communication ranging code remains identical to the PRN code. This confirms that the data code and PRN code are subjected to XOR operation, consistent with the DS/SS principle. In Figure 13, the main lobe bandwidth of the communication ranging code spectrum is twice the PRN code rate, matching the theoretical spectral bandwidth after spreading. Therefore, the spreading functionality in the designed DS/SS module is correct.