esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Damping Processes and Homeostatic Balance Equation
The strong disturbance in the balance equation creates a cliff-like effect which is observed on the right side of Figure 5.  
An analogical (but clearer) effect can be observed in the shape of the \(\mu(D)\) function, which can be obtained after some calculation from Equations (3) and (11) (where \(\theta^{-1}=(\beta_{H}\,D-\,\alpha_{H})/\alpha_{C}\)) (see Figure 6). This function, which connects radiation dose and damping factor, shows an interesting thing: the damping factor is slightly negative for the lowest doses, and positive for medium ones. This area is analogical to the plateau from Figure 5. However, the mentioned negative value for the lowest doses seems to be unexpected but has a strong basis: too low a radiation stressor (e.g., below natural back ground) is not natural, and makes the \(\mu\) factor slightly negative. Indeed, many experimental findings showed that a loo-low background radiation makes organisms weaker [14; 15]. Figure 4: The analogy to Figure 2 but with conditions from Figure 3. The system reaches the exact conditions due to the damping process. Figure 3: The conditions presented originally in Figure 1 but with additional damping parameters \(\mu_{C}=\mu_{H}=0.1\) (see Equation (7)). In this exact case, the \(P_{C}\) finally dominates, which generates higher risk of cancer induction in the organism. \[P_{H}=\frac{\mu_{C}\varepsilon_{H}+k_{H}\varepsilon_{C}}{k_{H}k_{C}+\mu_{C} \mu_{H}} \tag{8}\]0 The visualization of Equation (11) for exemplary value \(\mu=0.1\). Each point from the presented surface corresponds to the homeostatic balance conditions (see Equation (1)). The rapid decrease on the right side represents the instability around the critical point of \(k_{H}=\mu\). Please note that \(\theta<1\) for \(k_{H}>\mu\). \[P_{H}=\frac{\mu_{C}\varepsilon_{H}+k_{H}\varepsilon_{C}}{k_{H}k_{C}+\mu_{C} \mu_{H}} \tag{8}\]1 The damping factor (parameter), \(\mu\), in the function of radiation dose, \(D\) (given in arbitrary units). The \(\mu(D)\) function is calculated from Equation (3) and (11) for exemplary parameters: \(k_{C}=k_{H}=1\), \(\alpha_{C}=2.1\), \(\alpha_{H}=3.1\) and \(\beta_{H}=1\). Please note that the damping factor is a response of the cell/organism to a given stressor (here: radiation), which allows the homeostasis (equilibrium) state. However, for high doses, the system is unstable (critical point), which causes an irreversible transformation of the irradiated cell/organism. On the contrary, as one can see on the right side of Figure 6, the damping factor incidentally grows up for higher doses, which means that the system is near the critical point above which the equilibrium is no longer possible; this is an irreversible transformation of an irradiated cell (e.g., cell death, cancer transformation, stable mutation, etc. ).
## 3 Results and Discussion
### Color Values of Shrimp  
The changes in color-related values (\(L^{\star}\), \(a^{\star}\), \(b^{\star}\), and \(W\)) of peeled shrimps processed by a scaled-up MAIH machine at 130 \({}^{\circ}\)C for 80-120 s and at 90 \({}^{\circ}\)C for 110-150 s are shown in Table 3. The raw shrimp body had an \(L^{\star}\), \(a^{\star}\), \(b^{\star}\), and \(W\) of 25.23, 1.90, \(-\)1.06, and 25.20, respectively. After the shrimp was processed by scaled-up MAIH under 130 \({}^{\circ}\)C, the \(L^{\star}\) (lightness) values increased significantly from 51.78 in the 80 s sample to 58.09 in the 120 s sample (\(p\) < 0.05). Similar to 130 \({}^{\circ}\)C, heating of samples at 90 \({}^{\circ}\)C increased the \(L^{\star}\) values from 52.79 in the 110 s sample to 55.66 in the 150 s sample (\(p\) < 0.05). The increase in lightness is because of the decrease in pigment activity and protein coagulation (denaturation), because protein coagulation changes the characteristics of the sample surface, increases light reflection, and produces a white appearance [19]. Furthermore, after heating at 130 \({}^{\circ}\)C, the \(a^{\star}\) value (redness) of samples increased significantly, from 6.79 in the 80 s sample to 8.82 in the 120 s sample of (\(p\) < 0.05), whereas that of samples heated at 90 \({}^{\circ}\)C increased from 6.86 in the 110 s sample to 8.69 in the 150 s sample (\(p\) < 0.05). When alive, the body surface of crabs and shrimps shows a brown or dark brown color. This color is because of a protein known as crustacyanin, which contains a red pigment (i.e., astaxanthin). During the thermal process, this protein is denatured and releases astaxanthin (red pigment), which alters the color of the body surface to red [20]. In contrast, the \(b^{\star}\) value (yellowness) had the opposite trend, which decreased with an increase in heating time and reached the lowest value of \(-\)2.29 with heating at 130 \({}^{\circ}\)C for 120 s, and \(-\)2.30 with heating at 90 \({}^{\circ}\)C for 150 s. In addition, \(W\) (whiteness) values of the samples heated at 130 \({}^{\circ}\)C increased from 51.51 in the 110 s sample to 57.48 in the 120 s sample, whereas that of the samples heated at 90 \({}^{\circ}\)C tended to increase significantly (\(p\) < 0.05) to 55.24 for the 150 s sample with increased heating time (Table 3). It was found that the color of peeled shrimp turns increasingly white with the increase in heating time, which may be because of the denaturation and degeneration of proteins (e.g., myofibrillar and sarcoplasmic proteins), and the whitening phase is accompanied with the production of the cooked seafood flesh [18].
## 2 Application of BRBS in the Detection of Diabetic Nephropathy Biomarkers
### Creatinine (Crn)
A molecularly imprinted polymer (MIP) @ GO-Fe\({}_{3}\)O\({}_{4}\) film was constructed on the surface of an indium tin oxide (ITO) electrode using Crn as the template, tetraethyl orthosilicate (TEOS) as the cross-linking agent, and magnetic graphene oxide (GO-Fe\({}_{3}\)O\({}_{4}\)) as the modifier and embedded nickel nanoclusters (NiNCs). Due to the excellent conductivity of GO-Fe\({}_{3}\)O\({}_{4}\), the MIECS retained the high specificity of the MIP @ GO-Fe\({}_{3}\)O\({}_{4}\) template for Crn, while significantly improving its sensitivity. In addition, NiNCs have strong and stable ECL with co-reactant TPA, whereas Crn has a quenching effect on NiNCs' ECL, causing the ECL signal intensity to decrease as Crn concentration increases. The detection limit for Crn in serum and urine samples is 0.5 nmol/L (Table 1). Traditional imprinting methods prepare MIPs that embed most of the binding sites and nanoscale sensitizing materials, making it difficult for TM to wash off from the highly cross-linked heterogeneous rigid polymer structure, and at the same time, obstructing the electronic transport of nanoscale sensitizing materials, which leads to poor sensitivity of ECS. [PERSON] et al. immobilized bovine serum albumin (BSA) templates on the surface of SiO\({}_{2}\) nanoparticles and synthesized surface-modified MIPs [32]. The results showed that surface MIT can reduce the \"embedding\" phenomenon, and the MIPs prepared by controlling the template location on the material surface or near the surface have high specificity, high affinity, good reproducibility, as well as advantages such as high imprinting efficiency, high binding capacity, and fast speed, providing a good analytical and detection platform for the imprinting of large molecules such as proteins, cells, and viruses. [PERSON] et al. [33] reported a novel graphene nanoplatelet (GNP)/polydopamine (PDA)-MIP-biosensor for ultra-trace detection of Crn in a range of body fluids (Figure 2). Under the catalytic action of ammonium persulfate (AP), dopamine hydrochloride (DA) monomer was polymerized on the surface of GNP forming a thin PDA-MIP layer with high-density Crn recognition sites. This novel surface MIP technology avoids overly thick MIP layers, thereby allowing more TM to anchor on or near the polymer surface during the polymerization process. This ensures that the TM can be more thoroughly removed, leading to a broader detection range (Table 1).
## 3 Numerical Experiments
### Lorenz Signal Simulation Research  
Lorenz equation is one kind of typical nonlinear dynamic problem. The following signal is used in the simulation research, all variables of which are dimensionless.  
\[\begin{array}{l}\frac{dx}{dt}=\delta(y-x)\\ \frac{dy}{dt}=rx-y-xz\\ \frac{dz}{dt}=-bz+xy\end{array} \tag{21}\]  
where \(x\) denotes convection intensity, \(y\) denotes convection caused by horizontal temperature difference, \(z\) represents variables related to vertical temperature difference, \(\delta\) is the Prandtl number, \(r\) is the Rayleigh number, and \(b\) represents parameters related to container size. A Lorentz signal with parameters of \(\delta=10\), \(b=8/3\), \(r=28\) is constructed. The time series \(x(t)\) obtained from 100 Hz sampling frequency  
Figure 7: The proposed method based on smooth local subspace projection and permutation entropy (PE).  
is used to analyze the effectiveness of the method under the condition of superimposing Gauss white noise. Gaussian white noise [52] amplitudes accord with Gaussian distribution, and its power spectral density uniformly distributes, so it is very appropriate for simulation analysis. Here the magnitude of superimposed Gaussian white noise is 5 dB. The first 40 s time domain plot of \(x(t)\) component of Lorenz signal is shown in Figures 8 and 9.  
After calculating the fractal dimension of the main attractor of Lorenz signal, \(d=6\) and \(q=3\) are selected as the embedding dimension and local embedding dimension, respectively [34]. Then the SOD method is conducted to denoise this Lorenz signal with noises. The domain plot of the \(x(t)\) component of the denoised signal is shown in Figure 10. The phase space reconstruction of original signal, superimposed noises signal, and 7 times iteration denoised signal, are shown in Figure 11.  
The SNR, C\({}_{m}\), and MSE values change along with iterations, and can be shown in Figures 12-14 respectively.  
It can be seen from various parameters' value changes of the above figures, that the SOD has a significant effect towards a typical nonlinear signal, such as the Lorenz signal, in denoising. After 7  
Figure 8: \(x(t)\) of Lorenz signal.  
Figure 9: \(x(t)\) of Lorenz signal with noises.  
times SOD noise reduction algorithm iterations, SNR changed from 5 to 16.2009, Cm changed from 0.8726 to 0.9886, MSE changed from 21.4739 to 1.6129, so the effectiveness of the method can be regarded as good. It can be seen from the simulation researches that this SOD algorithm is suitable for denoising nonlinear and nonstationary signals. Since mechanical vibration signals have strong nonlinear and nonstationary characteristics, it can be concluded that the proposed smooth local subspace projection method based on SOD can be applied in denoising the mechanical vibration signals.  
Figure 11: The original signal, superimposed noises signal, and 7 times iteration denoised signal.  
Figure 10: \(x(t)\) of denoised Lorenz signal with noises.  
Figure 14: MSE values along with iterations.  
Figure 12: SNR values along with iterations.  
Figure 13: Cm values along with iterations.
# Influence of Xanthan Gum-Based Soil Conditioners on the Geotechnical Properties of Soils
###### Abstract  
The impact of climate change has become increasingly severe in forests, where droughs and strong winds on the one hand and extreme rainfall events on the other hand can damage forest ecosystems. To mitigate the effects of drought and enhance soil water retention capacity, three types of soil conditioners (SCs), labeled SC_R, SC_CG, and SC_ZZC, were developed as part of the European project ONEforest. All the conditioners are based on Xanthan gum and have different types and amounts of fillers with diverse cellulose fiber lengths. These can offer the potential to optimize the SC characteristics, e.g., water absorption, water retention, and mechanical stability. This paper focuses on the influence of fillers in the SCs on the geotechnical properties of forest soils from Ljubel in the Alpine part of Slovenia (S1), Catalonia, northeastern Spain (S2), and Heldburg, Germany (S3). The results show an increase of 53% to 100% in the water absorption of treated soil. A less favorable impact of the SCs was found on the drained shear strength and the compressibility. The drained shear strength of untreated forest soils in a saturated state was S1 \(c^{\prime}\) = 4.4 kPa, \(\varphi^{\prime}\) = 33.5\(\lx@math@degree\); S2 \(c^{\prime}\) = 1.4 kPa, \(\varphi^{\prime}\) = 30.0\(\lx@math@degree\); and S3 \(c^{\prime}\) = 12 kPa, \(\varphi^{\prime}\) = 28.0\(\lx@math@degree\). The addition of SCs results in a reduction in the drained shear strength of saturated mixtures. The reduction depends on the dosage of added SC--whether it is a low (L) or a high (H) dosage. For instance, when the soil S1 was treated with a low dosage of the soil conditioner SC_R, it demonstrated a cohesion (\(c^{\prime}\)) of 11 kPa and a friction angle (\(\varphi^{\prime}\)) of 27.0\(\lx@math@degree\). However, increasing the dosage of the SC_R led to a decrease in both the cohesion and the friction angle for the same soil (\(c^{\prime}\) = 7.7 kPa, \(\varphi^{\prime}\) = 25.0\(\lx@math@degree\)). Additionally, the type of soil conditioner also impacts the drained shear strength. Among the mixtures with a high dosage of the SC_R, SC_CG, or SC_ZZC, those containing the SC_CG with the longest fibers stand out, demonstrating the highest friction angle. Therefore, longer fibers can be a promising component of the SCs to reduce the negative influence of XG on the mechanical properties of treated soils.  
soil conditioners (SCs); xanthan gum; forest soils; mixtures; compressibility; shear strength; liquid limit; plastic limit; water absorption; hydraulic conductivity +
Footnote †: journal: Applied Sciences
## 3 Design of Data Collaboration Mode Based on Improved Framework
### Data Collaboration Method Based on ITS
#### 3.1.1 Collaboration between BIM and IoT in Real-Time  
With the rapid development of IoT technology, digital twins can play a role in the field of intelligent transportation, and IoT devices can provide professional services for cloud computing, cloud storage, etc. [29]. BIM is a digital expression of the physical and functional characteristics of facilities [30]. The integrated application of BIM and the IoT can more effectively manage and use a large amount of data, visualize heterogeneous data from multiple sources, establish consistent links between data in real time, and improve the practicability and interactivity of historical data.  
Based on the above statement, this paper used the method of multiple BIM collaboration to collect data, model the traffic system, and prepare for subsequent analysis. This paper used the Unreal Engine4 (UE4) engine of Epic Games to create 3D visualization data using BIM model. The main steps are as follows: First, data preprocessing, which calculates the correlation between UE4 scene coordinates and actual space coordinates, and maps  
Figure 3: Data interaction between multiple physical entities. Building information data, UAV video data and environmental sensor data belong to physical entity data. Expert knowledge, professional standards, etc. belong to knowledge data. Algorithm models, processing methods and so on belong to services data. Emulator data belongs to virtual entity data. Then the four kinds of data are preprocessed and integrated to acquire the fusion-derived data. User feedback is used as the basis for data structure adjustment. Efficient data transmission using 5G wireless networks.  
them to virtual space by rotating and scaling the coordinates of actual space, and converted the relationship to:  
\[(-\mathrm{R})(-\mathrm{T})\left(\begin{array}{c}x\\ y\\ z\end{array}\right)=\left(\begin{array}{c}x^{\prime}\\ y^{\prime}\\ z^{\prime}\end{array}\right) \tag{2}\]  
where, \(o(x,y,z)\) is a point in real space, \(O^{\prime}(x^{\prime},y^{\prime},z^{\prime})\) is a point in virtual space, \(x,y,z\) are the coordinates of real space and \(x^{\prime},y^{\prime},z^{\prime}\) are the coordinates of virtual space. R is the rotation matrix, and T is the translation and scaling matrix. Second, import the UE4 engine to generate 3D scene models of traffic elements such as roads, terrain and buildings. Third, obtain a new 3D interactive traffic model with strong fidelity and immersion.
## 6 Experimental Results
### Performance Evaluation
#### 6.3.4 Needham-Schroeder Public Key Protocol  
Within the NSPKT protocol (Figure 6), we assigned the following delay values: \(D_{1}=2\), \(D_{2}=3\), \(D_{3}=4\). Over the course of our analysis, we executed four scenarios, encompassing two instances of Intruder interventions, two [PERSON]'s attacks, and two instances of Man-in-the-Middle behaviour. It is important to note that, as outlined by the protocol structure, we were required to employ two distinct lifetime values, which play a crucial role in the protocol's operation.  
[PERSON]'s attacks were found to be possible at locations 23 or 55, while Man-in-the-Middle behaviour could manifest at locations 47 or 71. Honest executions were feasible at locations 3 or 7. Intruder interventions were observed at locations 11, 15, 27, 31, 39, 43, 59, or 63.  
Our investigation revealed that for the NSPKT protocol and the stipulated delays, the minimum values of \(L_{1}=L_{2}=9\) ensured honest executions without enabling the Man-in-the-Middle behaviour. To allow for the occurrence of the Man-in-the-Middle behaviour at locations 47 and 71, the minimum required values were \(L_{1}=L_{2}=12\). These  
Figure 5: Denning-Sacco Protocol.  
Figure 6: NSPKT Protocol.  
minimum values for honest executions and specific attacks are crucial for understanding the behaviour of the NSPKT protocol.
# Research and Development of Inland Green and Smart Ship Technologies in China
## 3 Current Inland Green and Smart Ship Technology in China
### Inland Ship Technology  
Due to the requirements of the large-scale shipping market, inland ships are characterized by being wide, shallow, and full form, with a large breadth draft ratio (B/T) and a large block coefficient (\(\mathrm{C_{b}}\)), and generally navigating at low speeds. To achieve excellent performance of inland ships, it is necessary to develop a ship characterized by a large capacity, low fuel consumption, environmental-protection capabilities, and high efficiency. Such a ship should be developed from wide, flat, and large-capacity ship types. It should also be maneuverable in shallow water and structurally safe and reliable.
# Development of a 3-DOF Cylindrical Ultrasonic Motor Based on Non-Standard Modes
## 4 Simulation and Analysis
### Harmonic Response Analysis  
Frequency analysis provides the maximum displacement occurring on the stator surface. A voltage with an amplitude of 100 V\({}_{\text{P-P}}\) is applied to the stator, and the voltage frequency range is 50-80 kHz. The harmonic response analysis of group A of bending PZTs is collectively referred to as upward bending frequency response analysis. In contrast, the frequency response analysis of group B is referred to as downward bending frequency response analysis.  
Figure 10 demonstrates the results of the upward-bending harmonic response. With frequencies between 62.5 and 70 kHz, the stator can be effectively excited in a third-order bending oscillation state, oscillating and remaining in phase. The maximum amplitude point is 67 kHz, with an amplitude value of approximately 1.6 \(\upmu\)m.  
Downward bending frequency response analysis is shown in Figure 11. In the frequency range of 62.5 to 70 kHz, the stator can be effectively excited in a third-order bending mode. The maximum amplitude is 1.42 \(\upmu\)m, which gives a frequency of 67.3 kHz. Group B bent-vibrating ceramics excite a pattern that intersects with group A and has a lower amplitude than group A.  
Figure 11: Downward bending frequency response analysis: (**a**) stator oscillation diagram at 67.3 kHz, and (**b**) amplitude as a function of frequency.  
Figure 10: Upward-bending frequency response analysis: (**a**) stator oscillation diagram at 67 kHz, and (**b**) amplitude as a function of frequency.  
As can be observed from Figure 12, the frequency response of the longitudinally vibrating PZT appears at a frequency point of 64 kHz, with an amplitude value of 1.43 \(\upmu\)m.  
The stable operation of the motor depends on a suitable drive frequency range. To select the frequency range of the motor, the maximization of the amplitude needs to be considered. The motor motion is in a mixed mode of longitudinal and bending vibration, so the drive frequency range cannot have a bending resonance frequency point. Consequently, the suitable drive frequency range for the designed motor is 64-67 kHz, as obtained from the simulations.
# Parasitic Motions of 3-PRS Parallel Mechanisms with Two Different Branch Chain Arrangements
## 2 3-PRS PM I
### Parasitic Motion Analysis  
The structural parameters of the 3-PRS PM I in Figure 1 are given in Table 2. The ranges of the kinematic parameters \(\psi\) and \(\theta\) are \([-60^{\circ},60^{\circ}]\), which fully satisfies the rotation angles required by the PM.  
When the structural parameter \(r\) of the moving platform \(S_{1}S_{2}S_{3}\) is given as 250 mm, 175 mm, and 100 mm and the ranges of the kinematic parameters \(\psi\) and \(\theta\) are all as \([-60^{\circ},60^{\circ}]\), the parasitic motions \(x\) and \(y\) can be obtained through MATLAB, as shown in Figures 5 and 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Equivalent circumradius of the fixed platform \(B_{1}B_{2}B_{3}\), \(R\) [mm] & 125 \\ Equivalent circumradius of the moving platform \(S_{1}S_{2}S_{3}\), \(r\) [mm] & 80 \\ Range of the moving distance of the pairs \(P_{1}\), \(d_{1}\) [mm] & [105, 195] \\ Ranges of the kinematic parameters \(\psi\) and \(\theta\) & \([-60^{\circ},60^{\circ}]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural parameters of 3-PRS PM I.  
Figure 4: Diagram of branch chain distribution.  
The CPU running times of the parasitic motions x with the moving platform m given as 250 mm, 175 mm, and 100 mm through MATLAB are 2.3400 s, 1.9968 s, and 1.9032 s, while the CPU running times of the parasitic motions \(y\) are 1.4820 s, 1.4976 s, and 1.5600 s.  
When the circumradius \(r\) of the moving platform \(S_{1}S_{2}S_{3}\) is given as 250 mm, the kinematic parameters \(\psi\) and \(\theta\) are given as from \([-60^{\circ},\,60^{\circ}]\) to \([-5^{\circ},\,5^{\circ}]\). The motion parameter \(\phi\) obtained through MATLAB is shown as in Figure 7.  
The CPU running times of the motion parameter \(\phi\) with kinematic parameters \(\psi\) and \(\theta\) given as \([-60^{\circ},\,60^{\circ}]\), \([-30^{\circ}\,\,30^{\circ}]\), and \([-5^{\circ},\,5^{\circ}]\) through MATLAB are 1.9032 s, 0.4524 s, and 0.0936 s.  
According to Figures 5 and 6, the parasitic motions \(x\) and \(y\) are symmetrically distributed and the value of \(x\) and \(y\) at \(\psi\) and \(\theta\) at \([0,\,0]\) is 0, that is, there is no parasitic motion occurring.  
Figure 5: Variations in the parasitic motion \(x\) with different moving platform circumradius \(r\). (**a**) \(r\) = 250 mm, (**b**) \(r\) = 175 mm, and (**c**) \(r\) = 100 mm.  
Figure 6: Variations in the parasitic motion \(y\) with different moving platform circumradius \(r\). (**a**) \(r\) = 250 mm, (**b**) \(r\) = 175 mm, and (**c**) \(r\) = 100 mm.