esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Modelling of a Novel Integrated Photonic Device for Nano-Scale Magnetic Memory Reading
## 3 Results and Discussion
As seen from the peak intensities, despite the use of \(TM\)-selective out-couplers, the \(TE\) mode dominates. Undeterrred by the \(TE\) mode dominance, the phase difference plot in Figure 2e indicates a phase of 180\({}^{\circ}\) at the position corresponding to the \(TM\) peak. The phase shift corresponding to the \(TE\) mode reads 0\({}^{\circ}\). Testing the device design further by addition of a noise that described in Section 2.2, Figure 2e,f are obtained. The 'noisy and reduced-performance device' demonstrates that, even though the intensity vs. wavelength plot is dominated by noise and mixed modes, it is still possible to determine the magnetic memory type via the Fourier transform technique. Referring back to Section 2.1 and clarifying the reason for the choice of an unbalanced MZI design over a balanced one, as seen in Figure 2a, if the device is performing at a fixed wavelength, the change in the light intensity upon changing the memory bit type corresponds to only 0.3% of the total light intensity. This observation indicates that the magnetic memory reading functionality of the device can be obstructed by the noise when operating at a single wavelength. Sweeping of a range of wavelengths accompanied by the Fourier transformation method are the key concepts for eliminating sensitivity to noise and increasing memory reading accuracy. Since the wavelength sweep technique is not successful without the specific frequency oscillations that the added delay line provides, an unbalanced MZI is preferred over a balanced one. Note that for an ideal device depicted in Figure 2a, the difference in light intensity between the two memory states is proportional to the strength of the Kerr rotation. Therefore, if a calibration by using a material with known Kerr rotation and optical loss is done, very small Kerr rotations can be measured quantitatively by using the same design.
## 3 Results and Discussion
### Empirical Correlations between the Solid Particle Density, Dry Density, and Void Ratio  
Based on the measured triplets of data \((e,\rho_{d},\) and \(\rho_{s})\) obtained with the air pycnometer, an empirical correlation between the solid particle density, dry density, and void ratio can be obtained. Equation (5) shows the mathematical expression of the best fit correlation obtained using XLSurffFit 3D surface fitting software:  
\[e=\frac{\rho_{s}^{b}}{\rho_{d}^{c}}-a \tag{5}\]  
where \(a\), \(b\), and \(c\) are the following coefficients: \(a=0.966128;\ b=1.003598;\ c=1.005804\). The resemblance between Equations (3) and (5) is obvious. The coefficient \(a\) represents the asymptote at \(e=-0.966128\), while coefficients \(b\) and \(c\) represent the correction factors with respect to the measured solid particle density and dry density values. The resemblance between Equations (3) and (5) reveals that the theoretical Equation (3) is fully applicable to the MBT waste materials for which closed pores are removed due to a heavy treatment process.  
Although there is no significant advantage of the proposed Equation (5) over the theoretical Equation (3), it is interesting to note that Equations (3) and (5) can be rearranged  
\[\rho_{s}=(e+1)\rho_{d} \tag{6}\]  
\[\rho_{s}=\sqrt[b]{(e+a)\rho_{d}^{c}} \tag{7}\]  
Now, by equating Equations (7) and (8) and solving for \(\rho_{d}\), a direct correlation between the dry density and the corresponding void ratio can be obtained.  
\[\rho_{d}=10^{\frac{Log(e+a)-b-Log(1+e)}{b+c}} \tag{8}\]  
The advantage of Equation (8) in comparison with Equation (3) is its ability to evaluate the \(\rho_{d}\) vs. \(e\) relationship of MBT waste material without needing to know the exact solid particle density value. However, the proposed Equation (8) can only be used in cases in which the waste material in question shares similar geotechnical properties and MBT processes as the waste material from which the Equation (8) is derived. Figure 6 reveals that the \(\rho_{d}\) vs. \(e\) relationship obtained with Equation (8) coincides reasonably well with the oedometer test results. It should also be noted that the proposed equation for void ratios larger than 7.6 provides erroneous estimates of dry density values. Therefore, Equation (8) cannot be used for void ratios higher than 7.6 and dry density values below 200 kg/m\({}^{3}\). However, the practical limitations of the proposed Equation (8) are irrelevant because the dry density of waste materials is commonly much higher than the 200 kg/m\({}^{3}\).
## 1 Introduction
[15], OMW solar drying and composting led to a final product of 3.5% N, 1% P, 6.5% K, and 0.29% phenolic compounds, which proved similar to commercial NPK fertilizers for pepper plants' cultivation. However, the case of dark fermentation (DF) has not yet been widely exploited, according to the literature, as only a few papers can currently been found [20; 21; 22]. DF is a biological approach for hydrogen (H\({}_{2}\)) production in the absence of light, through the decomposition and transformation of the organic material, not only to H\({}_{2}\), but also to other metabolites such as volatile fatty acids (VFAs), lactate, and ethanol. The aforementioned metabolites are associated with metabolic pathways that are followed by microflora in the used inoculum and their possible synergistic (or antagonistic) interactions [23; 24]. Additionally, various factors could affect the H\({}_{2}\) and other metabolites' efficiency, such as the pH, the temperature, the organic loading, the inhibitor's concentration, the nitrogen or phosphorus concentration, or the amounts of metals [25]. For instance, pH plays a significant role in the various strains' activation of the inoculum, leading to several metabolic pathways. On the other hand, temperature strongly affects bacteria growth and the substrate conversion efficiency to H\({}_{2}\), and organic loading provides optimum conditions for acid-forming bacteria; however, organic acid production increases to an OLR range and then decreases gradually. Furthermore, compounds such as phenolics, metals, or sulfides may inhibit the overall process and thus influence the H\({}_{2}\) yield. Concerning the macronutrients nitrogen and phosphorus, both are able to significantly affect the process, and for this reason, the optimum wide ranges of C/N (5-200) and C/P (up to 1000) have been proposed in the literature [25; 26; 27]. DF can be considered a waste valorization method, as except the clean energy generation through H\({}_{2}\) production, there is an option for further exploitation of the other metabolites for methane, bio-plastics, or biodiesel production [24; 28]. DF has been widely used for the exploitation and valorization of several waste and wastewater streams such as sugarcane, wheat, rice, corn, and other crop residues, the organic fraction of municipal wastes (usually kitchen and food wastes), wastes of animal origin, and industrial residues such as cheese whey, brewery, and citric acid wastewaters [27; 29]. The current work focuses on OMW valorization through the DF process under thermophilic conditions, as the latter have been proven more efficient [30; 31] in terms of H\({}_{2}\) production. A series of batch experiments were carried out in order to optimize the pH value of the process in the range of 4.5 to 7.5, and a second set of experiments were performed, testing the organic loading effect of the substrate on the optimum pH value of the previous experiment set.
## 3 Operation of the Proposed Converter
### Operation Principle during the SITO Mode  
The operation of the converter under the SITO mode is similar to the SIDO mode with SC in operation, as can be observed from the typical waveforms and current paths shown in Figures 6 and 7.  
Figure 4: Illustration of the current paths (black lines and gray lines indicate the activated and deactivated paths, respectively) during the different steps of operation of SIDO mode: (**a**) Step I, (**b**) Step II, (**c**) Step III, and (**d**) Step IV.  
Figure 5: Relevant voltage and current waveforms during SIDO mode.  
During this mode, the voltage of the SC changes significantly from \(\rm{Vsc}_{\rm{min}}\) up to \(\rm{Vsc}_{\rm{max}}\). Therefore, to prevent the undesired conduction of the switch S1 through its body diode to maintain the controllability of the charging current, the average voltage at point A (shown in Figure 6) should always be less than \(\rm{Vsc}_{\rm{SC}}\). This average voltage (\(\rm{V_{A}}\)) under this mode can be defined as:  
\[\rm{V_{A}}=(1-d_{5})V_{O} \tag{7}\]  
Accordingly, to ensure the controllability of the SC current under the large variation in its voltages during charge/discharge operations, the minimum duty cycle of the switch S5 should satisfy:  
\[\rm{d_{5 min}}\geq 1-\frac{\rm{Vsc}_{\rm{min}}}{\rm{V_{O}}} \tag{8}\]  
where \(\rm{Vsc}_{\rm{min}}\) is the minimum voltage of the SC. It can be observed that the maximum power point tracking (MPPT) function using S5 can be overridden when the required duty  
Figure 6: Illustration of the current paths (black lines and gray lines indicate the activated and deactivated paths, respectively) during the different stages of operation of SITO mode: (**a**) Step I, (**b**) Step II, (**c**) Step III, and (**d**) Step IV.  
Figure 7: Relevant voltage and current waveforms during SITO mode.  
cycle of S5 (d\({}_{5}\)) is less than d\({}_{5\min}\). In this particular case, the switch S5 is be forced to operate with a duty cycle of d\({}_{5\min}\), causing the PV to work away from the maximum power point, which can be accepted for a short time until the SC voltage is increased to be higher than V\({}_{\rm A}\). The output voltage during this mode can be calculated based on (6).
## 4 Analysis of the Origin of Low-Resistivity Oil Reservoirs  
### Theoretical Model Based on the Experimental Law of Additional Electrical Conductivity of Clay  
It is well known that, for pure sandstones, the relationship between the rock resistivity and the porosity or oil saturation can be well described by the Archie formula [13]. However, the presence of clay minerals in the reservoir rock complicates the interpretation of logs based on physical measurements. On the basis of a large number of laboratory petrophysical-chemical measurements made by [PERSON] and [PERSON] and by [PERSON] and [PERSON], [PERSON] and [PERSON] established the well-known model for evaluating the saturation of muddy sandstones, the Waxman-Smits model (abbreviated as the W-S model).  
Initial establishment of the W-S model:  
Through experimental measurements, [PERSON] and [PERSON] found a nonlinear pattern of change in the rock conductivity (\(C_{o}\)) of argillaceous sandstones with an increasing solution conductivity (\(C_{w}\)).  
As shown in Figure 6, [PERSON] and [PERSON] suggested that the sharp increase in the rock conductivity caused by the increase in the solution's electrolyte concentration in the dilute solution range (the low \(C_{w}\) section in Figure 6) was due to an increase in the mobility of the  
Figure 4: Lab experimental work flow.  
Figure 5: Experiment results: (**a**) core porosity, (**b**) core permeability, (**c**) core clay content, and (**d**) overall CEC content of cores.  
exchangeable cations on the surface of the clays in the rock. The conductivity induced by the exchangeable cations proceeds continuously with the help of the migration of cations in the electric field. The migration of cations can be either from one fixed exchange position of a clay particle to another, or from one clay cluster to another exchange position of a cluster via a free electrolyte [14].  
When the clay content is increased, i.e., the number of cation exchange sites that can be used, in the presence of little or no free electrolytes, this will result in a decrease in the energy required for the cations to carry out the conductivity, and therefore cause an increase in the cation mobility. Subsequently, if the salt concentration in the solution is increased, the conductivity of the free ions in solution becomes easier and is accompanied by a significant increase in the mobility of the exchanged cations. After the equilibrium electrolyte solution reaches a certain higher concentration, a further increase in the concentration should result in no further change in the mobility of the exchanged cations; in other words, the mobility of the exchangeable cations should reach a certain constant or extreme value. With further increases in the electrolyte concentration, the sandstone conductivity will increase linearly with increasing solution conductivity [15].  
On this basis, [PERSON] and [PERSON] proposed a general equation for the conductivity of cemented sandstones:  
\[C_{o}=\frac{1}{F*}(BQv+C_{w}) \tag{1}\]
# Influences of Different Factors and Sensitivity Analysis of Permeability of Gassy Coal
###### Abstract  
Influencing factors and sensitivity analysis of coal permeability are significant for reasonably setting coalbed methane (CBM) extraction parameters and increasing CBM output. Seepage tests were conducted on gassy coal using a seepage test system for damaged coal and rock mass under various conditions of axial pressure, confining pressure, and gas pressure. Moreover, the influences of different factors on the permeability of gassy coal and the sensitivity of permeability to these factors were analyzed. Research results show that under the same confining pressure, the relationship between permeability and axial pressure of gassy coal meets the quadratic polynomial function; under the same axial pressure, the permeability changes with the confining pressure as a power function. The permeability of gassy coal is far more sensitive to confining pressure than to axial pressure during axial seepage. Under the same axial pressure and confining pressure (same stress), the permeability of gassy coal reduces at first and then increases in a V-shaped trend with growing gas pressure. There is a turning point in the seepage tests, that is, the critical gas pressure. When the gas pressure is lower than the critical value, the slippage effect plays the leading role in the variation of permeability of the coal; on the contrary, effective stress plays the dominant role. In the non-isobaric deviatoric stress state, the permeability of gassy coal is most sensitive to the confining pressure, followed successively by gas pressure and axial pressure. The research results provide a theoretical basis for precise gas extraction and control in coal seams.  
permeability; gas seepage; influencing factors of seepage; effective stress; sensitivity analysis of coal; 2025
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 7 Dynamic Precision Reliable Analysis of Multilink Structure with Clearance
### Influence of Travelling Speed on Reliability of the Dynamic Accuracy of a Mixed Clearance Mechanism  
This subsection investigates impact of various drive speeds on dynamic accuracy reliability of multilink mechanisms with mixed clearances. Assuming that clearance values are all 0.2 mm, the driving speeds are set to 30 rpm, 60 rpm, and 150 rpm, respectively. The reliability indices and reliabilities of the slider acceleration are shown in Figure 28.  
As illustrated in the Figure 28, when driving speeds are 30 rpm, 60 rpm, and 150 rpm, respectively, the corresponding reliability indices of the minimum acceleration of the slider are 2.9985, 1.7258, and \(-\)0.0569. The figures also indicate that the reliabilities of the minimum acceleration of slider corresponding to various driving velocities are 0.9986, 0.9578, and 0.4772, respectively. The sharp decline in reliability at higher speeds is driven by iner  
Figure 27: (**a**) Reliability indices of slider acceleration; (**b**) reliability of slider acceleration.  
tial effects and reduced time for dynamic stabilization. At 150 rpm, the mechanism's inertia dominates over joint damping, causing chaotic contact forces and resonance phenomena. This leads to unpredictable acceleration spikes that exceed the failure threshold, significantly increasing the probability of dynamic failure. Therefore, for multilink mechanisms with mixed clearances, higher drive speeds result in less reliable dynamic accuracy of the slider. The acceleration reliability is 0.47, which deviates significantly from the ideal acceleration envisioned, which makes this mechanism almost unsuitable for industrial tasks that require strict acceleration control. When the reliability is below 0.90, design changes are crucial and can be improved by optimizing the mechanism design, introducing speed related damping, or using adaptive control measures.
## 5 Conclusions  
Aiming at the abnormal uplift pressure of concrete dams, this essay has discussed the anomaly identification, uplift pressure application method, structural numerical simulation, and structural diagnosis of concrete dams around the data processing and analysis of abnormal uplift pressure. This study has shown that:  
1. An intelligent anomaly identification model of uplift pressure, based on DBSCAN, is constructed. Because the uplift pressure is closely related to the reservoir water level, the DBSCAN can precisely identify the monitoring data clustering of the uplift pressure in the normal and the abnormally stable state. Therefore, the Density-based clustering methods, such as the DBSCAN, have good applicability for the identification of abnormal uplift pressure data.
2. There is still no unified understanding of applying the lifting pressure in the FEM, but the analysis of the structural behavior of concrete dams is partially safe with the face force application method. Then, a measured uplift pressure application method is proposed to accurately reflect the spatial distribution and abnormal position of uplift pressure. Meanwhile, self-written software is compiled, enabling users to complete the uplift pressure application quickly.
3. Comparing the structural behavior, under the designed and measured uplift pressure, the influence extent and scope of abnormal uplift pressure on the concrete dam is clear. For those reservoirs that cannot be stopped immediately and thoroughly, the safety of concrete dams is evaluated accurately and reasonably using this method, which is of great significance to ensure the project's safety and maximize the social benefits.  
Taking a 100 m concrete arch dam in western China as an example, a series of studies on the structural diagnosis of the concrete arch dam was carried out from the anomaly identification, cause analysis, FEM simulation, and influence analysis, verifying the feasibility of the above analysis ideas and calculation methods. This study provides the comprehensive assessment of dam safety under abnormal uplift pressure, which had important scientific research and practical engineering significance for the safe operation of concrete structures.  
Conceptualization, C.M.; methodology, C.M.; software, C.M.; validation, T.Z.; resources, T.Z.; data curation, G.L.; writing--original draft preparation, C.M.; writing--review and editing, A.Z.; supervision, L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [51809212], Joint Funds of Natural Science Fundamental Research Program of Shaanxi Province of China and the Hanjiang-to-Weihe River Valley Water Diversion Project, grant number [2019]LM-55] and the Doctoral Foundation of Xi'an University of Technology, grant number [104-451121003].  
Not applicable.  
The authors are grateful to [PERSON] for the information about the arch dam.  
The authors declare no conflict of interest.
## 2 Literature Review
### Topology Optimization and Lattice Design for Resin-Printed Structures
#### 2.5.2 Use of Lattice and Cellular Structures to Reduce Weight While Maintaining Strength  
The implementation of lattice structures in resin printing establishes a mode of construction that aligns strength with flexibility while addressing weight concerns. Structures designed with cellular architectures benefit from distribution patterns that enable stress diffusion and enhance impact resistance [13; 59]. This methodology has a significant impact on various applications, particularly in aerospace engineering, where weight savings are paramount to energy efficiency [25; 60].  
Research into optimizing cell geometries further emphasizes the connection between lattice structuring and mechanical performance. By varying void ratios and strut dimensions, it is now possible to adjust mechanical outputs tailored to specific application demands, resulting in enhanced performance across various service conditions [16; 37].
## 2 Methodology
### Saliency Detection in Frequency Domain  
A novel frequency-based saliency detection method for floating objects in complex scenes is proposed. Different from the mainstream saliency methods such as BMS [22], the Multi-task Sparsity Pursuit method (MBP) [37], RC [20], MSS [25], SR [23], and FT [24], our method can better solve the problems caused by waves, reflections, and uneven illumination. Algorithm 2 is the framework of our saliency detection algorithm for each video frame.  
```
Input:\(I\) Output:\(B_{2}\) Stage 1: Initial Saliency Map
1:\(G\leftarrow\) GammaCorrection(\(I\))
2:\(H\leftarrow\) GaussianHighpassFitering(\(G\))
3:\(P\leftarrow\) PhaseReconstruction(\(H\)) Stage 2: Redundancy Removal
4:\(P_{g}\leftarrow\) MatrixDecomposition(\(P\))
5:\(N_{1},N_{2},\ldots,N_{t}\leftarrow\) CreateBlocks(\(P\))
6:for\(i=1\to k\)do
7:\(M_{i}\leftarrow\) MatrixDecomposition(\(N_{i}\))
8:\(S_{i}\leftarrow\) ComputeNorm(\(M_{i}\))
9:\(N_{i}\leftarrow\) Suppression(\(N_{i}\), \(S_{i}\))
10:endfor
11:\(P_{i}\leftarrow\) Merge(\(N_{1}\), \(N_{2}\),..., \(N_{k}\)) Stage 3: Ultimate Saliency Map
12:\(P_{f}\leftarrow\gamma Pg+(1-\gamma)P_{I}\)
13:\(B_{2}\leftarrow\) BoundBox(\(P_{f}\))
14:return\(B_{2}\)
```  
**Algorithm 2** Saliency Detection  
In saliency detection, as shown in Algorithm 2, the processing steps fall into three stages. Aiming at obtaining an initial saliency map with obvious foreground and background distinctions, the first stage includes gamma correction, Gaussian high-pass filtering, and phase spectrum reconstruction. The second stage aims to remove the scattered redundancies in the initial saliency map, and obtain the global and local saliency maps. At this stage, splitting and merging image blocks are needed for local low-rank decompositions. The third stage is to obtain the ultimate saliency map by fusing global and local saliency maps, and output the bounding box vectors as the final results.