esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical and Fatigue Behavior of Cellular Structure
## 2 Material and Methods
### Finite Element Model  
#### 2.1.1 Porous Microstructure  
Square pores microstructure three-dimensional (3D) models with various volumetric porosities ranging from 10% to 90% were designed and simulated under compression loading using ABAQUS 6.17 as illustrated in Figure 1 and presented in Table 1. The square pores' structure was selected based on the results of a previous fatigue study carried out by [PERSON] et al. [26] on different Ti-4 Al-6V alloy porous structures. The experimental results show the superiority of the cubic porous structures over the other structures such as diamond shape structures. None of the cubic porous structure specimens failed under fatigue testing even at a maximum load of 80% of the relevant porous structure yield strength (0.8\(\sigma_{\text{Ys}}\)). These porous cubic structures reached 10\({}^{6}\) loading cycles without signs of failure. This is attributed to the vertical struts performing better than the diagonal and horizontal ones in compression loading conditions [26; 27].  
Different sizes of square pores with minimum strut thicknesses of 3.0 mm [26] were fitted within 15 mm \(\times\) 15 mm \(\times\) 15 mm 3D cubes. The volumetric porosity percentages were calculated based on the volume of the square pores introduced to the 3D cubes based on Equation (1) generated in our previous study [11]. The top and bottom sides of the cubes were fitted with plates; a displacement boundary condition was assigned to the top plate; and a encastre boundary condition was used to tie up the bottom plate. The designed structures were assigned with bulk Ti-4 Al-6V material properties. C3D10 quadratic tetrahedral meshing elements had been used to mesh the assembly using a mesh size of 0.2 mm based on a sensitivity study determined from stiffness convergence of dense cube [11].  
\[Vp=L_{2}^{2}N^{2}\ (2 Nt+2t) \tag{1}\]  
Figure 1: Porous structure designs sample for finite element model to study the mechanical properties of different volumetric porosities.  
where:  
\(V_{p}\): Volumetric porosity  
\(L_{2}\): cubic pore inner dimension  
\(N\): Number of pores in a single cube face  
\(x\): Full cube dimension  
\(t\): Strut thickness  
The upper plate was assigned with axial displacement to attain the yield points using 0.05 increment size to generate more accurate values of the yield points, modulus of elasticity, and ultimate tensile strength. Mass properties in ABAQUS were used to compare the designed volumetric porosity for each designed cube.
## 4 Conclusions  
In this study, novel aquafaba-based egg-free mayonnaies (CM) enriched with bioactive compounds extracted from by-product (BCC) and synthesized phenolipid (OC) were prepared. The proposed mayonnaise samples without and with antioxidants and stored in contact with active film incorporating BCCE had higher antioxidant properties than commercially available plant-based mayonnaise (CPBM). Antioxidant potential increased with increasing the concentration of BCCE in the mayonnaise samples. Moreover, BCCE  
Figure 3: Biplot of the first two principal components (PC1 and PC2) of six various mayonnaise samples (CPBM, CM, BCCM0.1%, BCCM3%, OCM, and AFM) at 0, 1, 2, 3, and 4 weeks of storage.  
inhibited the formation of free fatty acids and primary and secondary oxidation products in mayonnaise samples (BCCM0.1% and BCCM3%), improving their total oxidative status. Interestingly, the presence of OC or an active film in a jar and the extension of storage time positively affected the antioxidant properties of OCM and AFM. Although the OCM revealed the highest AC, the OC presence had low activity in preventing oil phase oxidation under storage conditions. However, the use of active film slightly slowed down the oxidation and hydrolysis reactions in the AFM sample during the storage period.  
In conclusion, the addition of BCCE-enriched active film and BCCE added directly to mayonnaise samples had a beneficial effect on oxidative status and prolonged the shelf life of aquafaba-based egg-free mayonnaise. Thus, natural antioxidants extracted from fat industry waste can be used as a potential alternative to synthetic antioxidants in food emulsions to extend their shelf life.  
Conceptualization, K.W., A.T., D.R.-K. and A.S.-C.; methodology, K.W., A.T., D.R.-K. and A.S.-C.; software, A.S.-C. and K.W.; validation, A.S.-C. and K.W.; formal analysis, K.W., A.T. and D.R.-K.; investigation, K.W., A.T., D.R.-K. and A.S.-C.; data curation, A.S.-C., K.W., A.T. and D.R.-K.; writing--original draft preparation, K.W., A.T. and D.R.-K.; writing--review and editing, A.S.-C.; visualization, K.W. and A.S.-C.; supervision, A.S.-C.; project administration, A.S.-C. and K.W.; funding acquisition, A.S.-C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All of the data is contained within the article.  
The authors declare no conflict of interest.
###### Abstract  
Thermodynamic modeling helps to reveal insights into the basic chemical kinetics of dissolution and precipitation in cementitious materials, but relevant applications to cement-stabilized soils have seldom been reported. Based on the thermodynamic database of Cemdata18 and the pore solution composition of cement-stabilized soils, this study formulated a specialized thermodynamic model, using essential thermodynamic constants for soil minerals that were calculated to ensure the model's accuracy. Two commercial admixtures of alkaline activator and polynaphthalene sulfonate were selected for the different modification mechanisms and plain and modified cement-stabilized soils were prepared. Compressive strength was tested to determine the specimens for pore solution analysis and the influences of the admixture type and dose on dissolution and precipitation were investigated by modeling the ionic activity products and saturation indexes. An X-ray diffraction (XRD) analysis was performed to verify and complement the thermodynamic results. The major research findings were that (1) thermodynamic modeling can be reliably applied to cement-stabilized soils by providing the essential thermodynamic data and an appropriate product model, (2) the pozzolanic reaction is accelerated by increasing the OH\({}^{-}\) concentration in the pore solution, while the cement hydration is highly dependent on the dissolution of Ca(OH)\({}_{2}\) and the relevant complexes and (3) the dissolution equilibrium of Ca(OH)\({}_{2}\) is directly affected by the alkaline activator dissolution and is indirectly affected by the polynaphthalene sulfonate adsorption of the reactants.  
thermodynamic modeling; cement-stabilized soil; dissolution and precipitation; alkaline activator; polynaphthalene sulfonate Article  
An Investigation on Mineral Dissolution and Precipitation in Cement-Stabilized Soils: Thermodynamic Modeling and Experimental Analysis [PERSON] et al. (2022), [PERSON] et al. (2022), [PERSON] et al. (2022), [PERSON] et al. (2022) and [PERSON] 2
## 5 Discussion and Future Research Trends
### Discussion  
Figure 14 shows the total percentage division of DL approaches listed in Figure 6 to solve the problems for target detection. Figure 15 describes all problems, DL approaches, and the aggregate of the articles related to each problem in target detection. In addition, we specified the major research findings obtained from Section 4.  
1. _One-Stage Detectors_: In 55% of the papers, the one-stage detectors have been manipulated in contrast to other DL approaches as presented in Figure 14. From Figure 16, we can notice that small-size objects are a trendy problem tackled by one-stage detectors and YOLOv3 is commonly utilized as one-stage detector in target detection. Figure 16 also presents the classification of one-stage detectors in problems related to target detection.  
Figure 14: Comprehensive percentage division of DL approaches for handling problems in target detection.  
Figure 15: Applicability of DL approaches for handling problems in target detection.  
2. _Two-Stage Detectors:_ From Figure 14, it is observed that 45% of the papers used two-stage detectors to tackle the problems in target detection. Two-stage detectors have been commonly employed for small-size objects and joint issues as demonstrated in Figure 17. The classification of two-stage detectors for problems related to target detection is shown in Figure 17.
# Ultra-Compact Digital Metasurface Polarization Beam Splitter via Physics-Constrained Inverse Design
## 4 Conclusions  
We have demonstrated an ultra-compact polarization beam splitter utilizing a metasurface layer formed on SOI based on the physics-constrained DBS algorithm. The simulation shows that at 1550 nm, the insertion losses of the TE and TM modes are 0.706 dB and 0.809 dB, and the extinction ratios of the TE output port and TM output port are 12.59 dB and 10.76 dB, respectively. The low insertion loss (<0.731 dB for TE mode and <0.873 dB for TM mode) over a large wavelength range of 1510 nm to 1590 nm (covering the C-band) indicates that the device has a high performance under broadband inputs. Furthermore, our study demonstrates that the device has good tolerance to fabrication errors. We hope this novel device can be used as a highly-functional nanophotonic element for densely integrated PDM systems.  
Conceptualization, H.S.; Data curation, Z.L. and L.W. ([PERSON]); Formal analysis, L.W. ([PERSON]) and L.Z.; Investigation, R.T.; Methodology, H.S. and L.Z.; Resources, Y.W. and R.T.; Software, H.S., L.W. ([PERSON]) and Z.L.; Supervision, L.Z. and Y.W.; Writing--original draft, H.S.; Writing--review & editing, H.S., L.Z. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2017 YFA0206002, 2017 YFA0403400.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: (**a**) Device performance under irregular etching conditions for TE mode and (**b**) TM mode. (**c**) Device performance when the metasurface layer suffers a 1 \(\upmu\)m deviation for TE mode and (**d**) TM mode.
## Measurements Results
### Methods for Joining Evanohm-S Wire To Terminals  
The results for the three methods for joining Evanohm-S to a terminal are shown in Figure 6. It is evident that the tin soldering method does not achieve a proper joining, causing changes in the measured resistance values up to 90 \(\upmu\Omega/\Omega\) in less than one day. The method of introducing the wire in a copper tube and crimping it manifested a 0.25 \(\upmu\Omega/\Omega\)/hour drift. The drift could be due  
Figure 5: Calculated frequency performance of the bifilar, quadrifilar, and octofilar CRs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Type** & **Nominal Value** & **NMI** & **Wire/Dielectric** \\ \hline Haddadd1000 1201 & Coaxial & 1 k\(\Omega\) & METAS & Evanohm/air \\ \hline RC-1k-D & Bifilar & 1 k\(\Omega\) & CENAM & Isaohm/air \\ \hline RC-10k-D5e & Octofilar & 10 k\(\Omega\) & CENAM & Isaohm/air \\ \hline \hline \end{tabular}
\end{table}
Table 3: Standards involved in the frequency evaluation of the CRsto the mechanical stress of the tight section of the wire that changes the shape of the wire in the junction. For this reason, the methods that involve mechanical stress in the wire should be avoided. Finally, joining with a spot welder does not present drift or changes in the measured resistance value. The standard deviation of the measurements with the last method is less than 0.5 \(\upmu\Omega/\Omega\) and is due to the measurement system in the 100 \(\Omega\) range.  
Through the results, the method of joining the wire by a spot welder was chosen in this work for the development of CRs. This method also has the advantage of using it with an Evanohm terminal to avoid EMFs in the terminal-wire junction.
## 9 Conclusions  
The holographic display systems that combine the MEMS SLMs and the scanning systems were developed to enlarge the screen size and viewing zone angle. The use of the galvano mirror increased the screen size to 2.0 inches and the viewing zone angle to 40.0\({}^{\circ}\). When the circular scanner was combined, the viewing zone was enlarged to 360\({}^{\circ}\) with a screen diameter of 100 mm. By use of the high-speed operation of the MEMS SLM and R, G, and B lasers, color images were generated using a single MEMS SLM based on the time-multiplexing technique. The two-channel scanning system increased the screen size to 7.4 inches using the space-multiplexing technique. The short pulse illumination on the MEMS SLM and the precise control of the pulse generation timing enabled the removal of the mechanical scanning system from the holographic display system, and the viewing zone angle was enlarged to 42\({}^{\circ}\). The measurements of the accommodation responses of human eyes to the 3D images generated by the screen scanning system showed that the visual fatigue problem caused by the vergence-accommodation conflict could be solved.  
The holographic display techniques described in this article used commercial MEMS SLMs, which were developed for the video projectors. The development of MEMS SLMs specialized for holographic displays would effectively increase viewing zone angles and screen sizes of holographic displays.  
This research was funded by Japan Society for the Promotion of Science (JSPS) KAKENHI, grant number 15H03987 and 19H02189, and also funded by CASIO Science Promotion Foundation.  
The author declares no conflict of interest.
## Materials and Methods
### Classification
#### 2.3.2 Random Forest Classifier  
The main idea of random forest is to use bootstrap re-sampling method to select multiple sub-samples from original samples and conduct decision tree modeling one by one [46]. After each tree is classified separately, the final output classification result is obtained by voting. Random forests do not need to estimate the distribution of data, which is very meaningful for input variables of different types or different scales, and random forests have interpretability for results [39]. These excellent properties make random forest very suitable for forest-type recognition of remote-sensing image processing [47].  
In this study, we adopted object-oriented random forest classification [47, 48]. Object-oriented classification includes two main processes: Image segmentation and feature extraction. Firstly, the image is segmented in multiple scales and the optimal segmentation scale is selected. This algorithm is a bottom-to-top segmentation algorithm, which gradually merges up from a single pixel until the threshold is reached [49]. Then, since the object is composed of multiple pixels, in addition to the spectral features, other features can be added to participate in the classification to achieve better classification accuracy. In this study, minimum noise fraction (MNF) [33, 48] and some vegetation index features, such as normalized difference vegetation index (NDVI) [33, 50], enhanced vegetation index (EVI) [27], and ratio vegetation index (RVI) [33, 51], are selected to participate in classification. However, when all features (including spectral features) are involved in classification, it will lead to many problems, such as high dimension [2, 52], slow computation speed, and data overload [49, 53]. Therefore, feature extraction is of great significance for the processing of hyperspectral data. Finally, these extracted features are input into the random forest classifier for training, and the results of classification can be obtained.
## 4 Series Expansions
### Cross-Over Frequencies  
The leading-order terms in Equations (14) and (21) are power-laws in \(\omega\). In a plot of \(\ln Q_{\beta}\) or \(\ln V_{\beta}\) versus \(\ln\omega\), these power-law asymptotes are straight lines that intersect at  
\[\omega_{Q}:=\left(\frac{\beta\Gamma(1+\beta)\sin(\beta\pi/2)}{\Gamma(1/\beta )}\right)^{1/(1+\beta)} \tag{27}\]  
and  
\[\omega_{V}:=\left(\frac{\beta}{\Gamma(2/\beta)}\right)^{1/2} \tag{28}\]  
For \(\beta\to 0\), both cross-over frequencies go rapidly to zero, with a leading singularity  
\[\omega_{Q,V}\sim\beta^{1/\beta} \tag{29}\]  
This explains why the limiting case \(\beta\to 0\) has no practical importance, and it also explains why previously published tables [1] of \(Q_{\beta}(\omega)\) and \(V_{\beta}(\omega)\) are useless for small exponents \(\beta\lesssim 0.3\): As these tables employ the same linear \(\omega\) grid for all \(\beta\), for small \(\beta\) they only cover the asymptotic large-\(\omega\) power-law regime, and not the nontrivial cross-over regime for which alone a table would be needed.  
For \(\beta\to 2\), \(\omega_{Q}\) goes to zero because of the sine term in Equation (27). This regime, probably of little practical importance, will be dealt with in Section 5.6.
## 1 Introduction  
Primary industries, such as agriculture, construction, fisheries, and forestry, are vital to global economies [1, 2, 3]. However, these industries are subject to a complex web of regulations [4, 5, 6, 7], often involving multiple jurisdictions and agencies [8]. Compliance with these regulations is crucial for ensuring product safety, environmental sustainability, and fair-trade practices [9, 10, 11]. Traditional compliance methods can be time-consuming, costly, and prone to human error [12, 13, 8, 14]. This is where regulatory technology (RegTech) can streamline compliance checks and documentation with an online process enhanced by modern computing and Internet of Things (IoT) tools [15]. RegTech uses such technology to improve regulatory efficiency, reduce compliance costs, and enhance regulatory oversight [16; 17; 18]. The primary objective of applying RegTech solutions in primary industries is to release regulatory burdens for all stakeholders by streamlining compliance processes, reducing administrative burdens, and improving decision making.  
In this paper, we consider the agriculture sector, which is a major primary industry, as a case study. It involves the typical stakeholders of RegTech: _assessors_, including regulators; _assesses_, including individual farmers; and _artificial intelligence_ (AI) tools like large language models (LLM) and deep learning services. The main aim of this system is compliance checks.  
Despite its potential, implementing RegTech in agriculture faces significant obstacles. Technically, limited connectivity and access to necessary technologies and digital skills [19], coupled with the ever-changing landscape of agricultural regulations [15], pose substantial challenges. Integrating disparate agricultural systems and addressing cybersecurity and data privacy concerns [20; 21] further complicate matters. Economically, the high cost of developing and deploying RegTech solutions [22], combined with a lack of awareness and understanding among farmers [21], creates significant barriers to adoption [23]. Socially, resistance to change from those accustomed to traditional farming methods can also hinder progress [24]. Finally, the political landscape, with its complex global supply chains, multiple jurisdictions, and diverse stakeholders, makes standardizing regulatory compliance a particularly difficult undertaking. LLMs can mitigate many of these problems by providing a continuous, easy-to-use interface to technology.
## 2 Modeling of EDS  
EDS was created to solve the turning issue for four-wheel drive electric vehicles. Many successful studies have since been proposed [12; 13; 14; 15; 16]. Figure 2 shows a basic scenario of a turning vehicle. It shows the kinematic model of the proposed system in a left turning maneuver, where \(\delta\) is the turning angle command, \(\delta_{1}\) and \(\delta_{2}\) are the turning angle of the front left and right wheels, \(L\) is the wheelbase, \(D\) is the rear gauge, \(\omega_{\delta}\) is the rotational speed of turning, \(R\) is the radius of turning, \(V\) is vehicle speed, \(V_{1}\), \(V_{3}\) are the tangential speed of the front and rear left wheels, and \(V_{2}\), \(V_{4}\) are the tangential speed of the front and rear right wheels. Generally, the speed of both the front and rear wheels should be the same in order to keep the vehicle traveling in a straight track. However, if the vehicle makes a turn, the speed differential must be constructed by a mechanism that entails less wire slipping and scrapping. Clearly, for a left turn scenario, if \(\omega_{0}\) is the preferred rotating command, then \(V=\omega_{0}R\) and  
\[V_{1}=V\left(1-\frac{D}{R}\right) \tag{1}\]  
\[V_{2}=V\left(1+\frac{D}{R}\right) \tag{2}\]  
\[V_{3}=V\left(\frac{R-\frac{D}{2}}{R}\right) \tag{3}\]  
\[V_{4}=V\left(\frac{R+\frac{D}{2}}{R}\right) \tag{4}\]  
where the radius of the turning curvature can be found as  
\[R=\frac{L}{\tan\delta}. \tag{5}\]  
Figure 1: Steering responses while taking a curve.  
The aforementioned studies solve the EDS for speed control in a four-wheel drive vehicle. However, usually the vehicle is operated by users under a pedal command (acceleration command) for torque distribution. Consequently, the aforementioned approaches are not so straightforward. Figure 3 shows the scheme of the proposed EDS. Figure 4 illustrates its corresponding control block diagram. Clearly, the actual vehicle speed is set as follows considering cost-effectiveness:  
\[V=\frac{V_{3}+V_{4}}{2} \tag{6}\]  
Figure 3: Proposed electronic differential system (EDS).  
Figure 2: Kinematic model of proposed system in left turning maneuver.  
For acquiring the vehicle speed in practice without expensive sensors, studies [17; 18] provide solutions. Figure 5 shows the \(K\) gain adjusting philosophy. Basically, sufficient \(K\) gain falls into an acceptable region as  
\[K^{*}_{\text{min}}<K<K^{*}_{\text{max}}. \tag{7}\]  
Note that the vehicle pretends to over-steer if the \(K\) gain is larger than \(K^{*}_{\text{max}}\). Hence, the steering angle will become sensitive and cause the turning radius to get smaller. Finally, the speed difference between the left and right wheels increases. Conversely, if the \(K\) gain is smaller than \(K^{*}_{\text{min}}\), the steering angle will become insensitive and cause the turning radius to decrease. Consequently, the speed difference between the left and right wheels also decreases.