esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Sowing Dates on Fatty Acids and Phytosterols Patterns of _Carthamus tinctorius_ L.
## 2 Materials and Methods
### Fatty Acids and Sterol Contents Determination  
The determination of fatty acid (FA) composition was performed according the solubility of oil in TerButylMethyl Ether (ME0552, Scharlab, Barcelona, Spain) based on the method (Norm ISO 5509, 1990), which allows the transformation of FAs to methyl esters. Seed samples (3 g of each one separately) were ground in 5 mL of TerButylMethyl Ether and then filtered across a GPH filter of 13 mm-diameter. Based on the norm NF EN ISO 12966-3, 100 mL of the filtrate were mixed with 50 mL (at 0.2 M) TriMethylSulphonium Hydroxide in methanol (Macherey Nagel, Hoerdt, France). This allowed obtaining a mix from which 1 mL was injected into GC-3800 chromatograph (Varian 3900, Palo Alto, Milpitas, CA, USA) associated to a Flame Ionization Detector (FID) and equipped with a CP Select CB 50 m capillary column (D: 0.25 mm). Over temperature was initially held at 185 \({}^{\circ}\)C for 40 min. This temperature increased by 15 \({}^{\circ}\)C min\({}^{-1}\) to reach 250 \({}^{\circ}\)C and then maintained there for 10 min. The temperatures of the injector and detector were fixed at 250 \({}^{\circ}\)C. A known standard mixture of Fatty Acid Methyl Ester (FAME) of rapeseed oil (Supelco, Bellefonte, PA, USA) was used as an external standard and served to estimate the FAME of the samples by comparison of the retention times.  
The small-scale sample extraction method [10] was used to extract and determine the sterol content of safflower. Ground seed samples (1.3 g) were added to each tube containing 100 \(\upmu\)g of cholesterol (dihydrocholesterol; Aldrich Chemicals Co., Saint-Quentin-Fallavier, France), used as an internal standard. The extraction method used to extract and determine the sterol content of safflower was based on those developed by [PERSON] et al. [10]. One hundred micrograms of cholestenol (Aldrich Chemicals Co., 38070 Saint-Quentin-Fallavier, France, internal standard) were added to each sample (1.3 g) of ground seeds of safflower. Ethanolic KOH (1 M) (TITRINORM, Prolongo) for 60 min at 75 \({}^{\circ}\)C, were then added in order to perform saponification. Distilled water (1 mL) was added to each sample and the extraction of unsaponifiable fraction was extracted from the saponified lipids by adding 6 mL of iso- hexane (Merck, Lyon, France). The silylation of sterols was released by a mixture of N-methyl-N-trimethylsilyl-heptafluorobutryamide (MSHFBA; [PERSON], Hoerdt, France) and 1-methylimidazole (Sigma, Saint-Quentin-Fallavier, France). The sterol trimethylsilyl ether derivatives (1 mL) were injected into a Perkin-Elmer GC equipped with a CP-SIL 5 CB 30 m column (i.d.: 0.25 mm), and an on-column injector. Detection was done with FID.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 3 Data Acquisition Methods
### Sensor Calibration
#### 3.2.2 TDS Sensor Calibration  
For TDS sensors, we use a standard TDS calibration solution for periodic calibration to ensure that the sensor's measurements are consistent with standard values. After each use, the electrodes are rinsed with deionized water to avoid contamination and deposits, thus ensuring their long-term stability [23]. Our TDS sensor electrodes often use platinum black electrodes, which have a lower polarization effect and a higher surface area. These properties improve measurement accuracy and stability while effectively reducing errors caused by electrode polarization. Over time, the performance of TDS electrodes may change due to contamination or aging, so we regularly adjust the electrode constants according to the manufacturer's guidelines and recalibrate as needed to ensure the high accuracy of the sensor.
# Electochemically Engineered Mesoporous Sn-Oxide Rods for Anode Materials in Lithium-Ion Batteries
###### Abstract  
Sn-based anodes for lithium-ion batteries (LIBs) offer high capacity and low cost; however, significant volume changes during lithiation/delithiation cause mechanical degradation, limiting their practical applications. Microstructural control is a key approach to mitigating these volume changes. This study reports the fabrication of core (Sn rod)-shell (mesoporous Sn-oxide layer) structures through electrodeposition followed by anodization, and their applications to anode active materials for LIBs. First, micro-Sn rods with controlled lengths and diameters were fabricated under various electrodeposition conditions. The electrodeposited Sn exhibited a dendritic structure with short secondary rods branching from a long primary rod. While the primary Sn rod diameters remained constant, the secondary rod diameters varied depending on electrodeposition parameters. Notably, rod coarsening due to secondary rod agglomeration occurred at higher currents and longer deposition durations during galvanostatic electrodeposition. In contrast, potentiostatic electrodeposition prevented agglomeration and increased the quantity of Sn rods with voltage. Subsequently, the core-shell structures were fabricated by anodizing Sn rods, forming mesoporous Sn-oxide layers with different pore sizes and pore wall thicknesses. Electrochemical characterization revealed that the core-shell anode performance for LIBs varied with the Sn-oxide shell's microstructure. These findings provide insights into optimal core-shell structures to improve anode performance for LIBs.  
Keywords: lithium battery; electrodeposition; anodic oxidation; tin oxide; core-shell structure +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### CFD Simulation
#### 2.2.1 Wind Profile  
The investigated structure is subjected to a wind load. Generally, the wind profile of Davenport is described by the following formula [41]:  
\[(z)=v_{\mathrm{g}}\bigg{(}\frac{z}{z_{\mathrm{g}}}\bigg{)}^{\alpha} \tag{1}\]  
where \(\mathrm{v_{g}}\) is the wind velocity measured at the height \(\mathrm{z_{g}}\) over the ground level. The exponent \(\alpha\) takes values of 0.4, 0.28, and 0.16 depending on the type of terrain, namely urban terrain, village terrain, or open terrain, respectively. In the current work, we arbitrarily assumed that in all studied cases \(\mathrm{v_{g}}=6\) m/s at the height \(\mathrm{z_{g}}=10\) m. In Figure 5, the assumed wind profiles are shown.  
Based on the wind profiles depicted above, the reference wind velocity is also computed for the three wind velocity profiles according to the following formula:  
\[v_{\mathrm{ref}}=\frac{\int_{0}^{H_{z}}V(z)dz}{H_{Z}}, \tag{2}\]  
where H\({}_{Z}\) is the total height of the crane, where H\({}_{z}=34.5\) m. The values of the reference wind speed, computed according to Formula (2) for assumed wind profiles, are equal as follows: urban terrain \(\mathrm{v_{ref}}=7.0321\) m/s, village terrain \(\mathrm{v_{ref}}=6.62928\) m/s, and open terrain \(\mathrm{v_{ref}}=6.30482\) m/s. These values will be used to compute the aerodynamic force  
Figure 5: Applied wind profiles.  
and moment coefficients for the whole crane structure. Moreover, it is assumed that the turbulent intensity equals 9% with the turbulent length scale \(\mathrm{L_{TURB}}\) = 3.5 m. These values are assumed to be arbitrary. The choice was made according to the parameters discussed in our previous work.
## 1 Introduction
A method for constant voltage charging of battery-type loads is proposed in this article, while simultaneously achieving maximum efficiency tracking control of the WPT system. It can significantly improve system transmission efficiency. Additionally, the short feedback control loop minimizes the delay time, thus enabling a quick and stable output for the WPT systems. This article is organized as follows. Section 2 presents a coordinated control method of constant voltage output and system transmission efficiency. Section 3 provides the simulation analysis of the proposed method. Section 4 gives the experimental verifications and discussions. Finally, Section 5 presents the conclusion.
###### Abstract  
In view of the centralized operation, high failure rate and large number of harvesters involved in the cross-regional operation of combine harvesters, which has led to a surge in maintenance service demand and a lack of effective maintenance service systems, in order to be able to quickly solve problems arising from failures during the process of cross-regional operation, an operation and maintenance (O&M) service platform for the cross-regional operation of combine harvesters was designed in this research on the basis of data resources, supported by the computing power of a big data platform and centered on an artificial intelligence algorithm. Meeting the demand for maintenance service during cross-regional operation, we built a system platform integrating service order management, maintenance service activity management, and maintenance service resource management, and a technical algorithm for operation and maintenance service resource allocation and service path optimization was developed in order to achieve service function modularization and intelligent monitoring, while early warning and display were realized using multi-dimensional platforms such as a PC, a control screen, and a mobile App. This platform was able to solve problems arising when harvesters break down, maintenance service can be carried out quickly when traditional resource information is blocked and the demand for the service is difficult to meet. The reduction in cost and the increased efficiency for agricultural machinery enterprises was also achieved, while the problem of ensuring continued service was systematically solved during the process of cross-regional operation. Finally, the performance of the software architecture and the effect of path optimization were verified. The results showed that the platform system developed using the three-layer C/S architecture offered more stable characteristics, and the path optimization in the platform system was better able to reduce the maintenance time and distance, thus making it possible to realize the dynamic on-demand configuration and scheduling management of cross-region job service resources.  
cross-regional operation of agricultural machinery; cross-regional maintenance service; resource scheduling optimization; operation and maintenance service platform
## 3 Proposed Methodology
### Data Acquisition
#### 3.1.2 PAMAP2 Dataset  
[PERSON] and [PERSON] [35] introduced the PAMAP2 physical activity monitoring dataset, containing recordings from nine participants, encompassing both male and female subjects. These participants were instructed to engage in eighteen diverse lifestyle activities, ranging from leisure pursuits to household tasks, such as climbing and descending stairs, ironing, vacuum cleaning, lying, standing, walking, driving a car, performing Nordic walks, and more. Household activities included lying, sitting, standing, ascending, and descending stairs.  
Over ten hours, the participants' activity data were collected using three IMUs positioned on the dominant wrist, thorax, and ankle. Each IMU operated at a sampling rate of 100 Hz, while a heart rate sensor collected data at a rate of 9 Hz. Additionally, a thermometer was utilized. A summary of the results obtained from training models on the PAMAP2 dataset is presented in Table 1. In the PAMAP2 dataset, the sensor information underwent sampling within fixed-width sliding intervals lasting 3 s, devoid of overlap. This approach yielded 300 readings per window, given that the data were captured at 100 Hz.
# Biomechanical Rationale for a Novel Implant Design Reducing Stress on Buccal Bone
## 3 Results  
The insertion process of AlfaGate implants took approximately 30% more time as compared to BLT implants, requiring the normalization of data for statistical analysis by setting the duration of full insertion to 1 for each implant type independently. In BLT implants, three fractures of bone specimens occurred due to overstressing and consequently, these samples were excluded from comparative statistical analysis.  
Torque and strain development during implant insertion was characteristic for both implant types used. In BLT implants a continuous increase in strain and torque development occurred during insertion reaching its maximum when the implant was fully seated. AlfaGate implants showed maximum torque and strain development when the bulky portion of the implant body passed the cortical layer followed by a drop in both parameters prior to the implants being fully seated (Figures 3 and 4).  
The mean values and standard deviations for all parameters determined during the experiment are given in Table 2 as well as the results of the t-tests performed for comparing both groups of implants. The mean maximum insertion torque was significantly greater in BLT as compared to AlfaGate (\(p\) = 0.0016) and was reached at a significantly later timepoint of the insertion process, i.e., when the implant was almost fully seated (\(p\) = 0.0001). The final insertion torque was significantly lower in AlfaGate implants as compared to BLT implants (\(p\) < 0.00001).  
The maximum strain development in buccal bone was greater in BLT implants showing high standard deviation in the range of 30% of absolute values resulting in a non-significant difference compared to AlfaGate implants (\(p\) = 0.1069). The maximum strain development in BLT occurred at a significantly later timepoint as compared to AlfaGate implants (\(p\) < 0.00001). The final strain development was almost twice as high in BLT implants as compared to AlfaGate implants (\(p\) = 0.0137). The primary stability of both implant types as measured with the Periodest device did not differ significantly (\(p\) = 0.219).  
Figure 3: Torque development over time during implant insertion for both groups investigated showing an overall torque increase with insertion depth for Straumann BLT while lower maximum torque was required in AlfaGate implants coinciding with the bulky central part of the implant passing the cortical plate.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation
### ML Techniques
#### 4.5.1 Stochastic ML Technique  
Stochastic ML (SML) is the first ML technique with the assumption that signals are stochastic in nature and are stochastically processed. In the case of time-domain smoothing, the array covariance matrix is  
\[\widetilde{\mathbf{R}}_{aa}=\mathbf{Y}^{H}(\Omega)\widetilde{\mathbf{R}}_{ss}\mathbf{Y}(\Omega )+\widetilde{\sigma}_{a}^{2}\mathbf{I} \tag{43}\]  
For time instant \(t_{i}=t_{1},\ldots,\ t_{N}\), the SML probability density function is given as  
\[\mathbf{p}\left(\widetilde{\mathbf{A}}_{N}/\Omega,\widetilde{\mathbf{R}}_{ss},\widetilde{ \sigma}_{a}^{2}\right)=\prod_{i=1}^{N}\frac{1}{\pi^{(N_{h}+1)^{2}}\left| \widetilde{\mathbf{R}}_{aa}\right|}\exp(-\widetilde{\mathbf{q}}^{H}(t_{i})\widetilde{ \mathbf{R}}_{aa}{}^{-1}\widetilde{\mathbf{q}}(t_{i}) \tag{44}\]  
where \(\widetilde{\mathbf{R}}_{aa}\) denotes the modeled covariance matrix of the antenna array, \(\left|\cdot\right|\) is the matrix determinant, and \(\widetilde{\mathbf{A}}_{N}=\left[\widetilde{\mathbf{q}}(t_{i}),\ldots,\widetilde{\mathbf{ q}}(t_{N})\right]\) are equalized SH coefficients in the time domain.  
Usually, in ML techniques, we find the solution from the negative log-likelihood for SML as  
\[log\left(\mathbf{p}\left(\widetilde{\mathbf{A}}_{N}/\Omega,\widetilde{\mathbf{R}}_{ss}, \widetilde{\sigma}_{a}^{2}\right)\right)=log\left|\widetilde{\mathbf{R}}_{aa} \right|+Tr\left\{\widetilde{\mathbf{R}}_{aa}{}^{-1}\widetilde{\mathbf{R}}_{aa}\right\} \tag{45}\]  
The negative log-likelihood solution with respect to \(\mathbf{S}\), \(\sigma^{2}\) ([31] provides the details), results in  
\[\widetilde{\mathbf{R}}_{ss}(\mathbf{\Omega})=\mathbf{Y}^{t}\left(\widetilde{\mathbf{R}}_{aa}- \widetilde{\sigma}_{a}^{2}(\mathbf{\Omega})I\right)\mathbf{Y}^{tH}(\mathbf{\Omega}) \tag{46}\]  
and  
\[\widetilde{\sigma}_{a}^{2}(\mathbf{\Omega})=\frac{1}{\left(N_{h}+1\right)^{2}-U}Tr \left\{P\widetilde{\mathbf{Y}}(\mathbf{\Omega})\hat{\mathbf{R}}_{aa}\right\}, \tag{47}\]  
respectively, where  
\[\mathbf{P\widetilde{\mathbf{Y}}(\mathbf{\Omega})=I-\mathbf{Y}^{H}(\mathbf{\Omega})\mathbf{Y}^{t}(\mathbf{ \Omega})} \tag{48}\]and  
\[\mathbf{Y}^{t}(\mathbf{\Omega})=\Big{(}\mathbf{Y}(\mathbf{\Omega})\mathbf{Y}^{H}(\mathbf{\Omega})\Big{)}^{ -1}\mathbf{Y}(\mathbf{\Omega}) \tag{49}\]  
are the orthogonal projector and the pseudo-inverse of \(\mathbf{Y}(\mathbf{\Omega})\) onto the null space of \(\mathbf{Y}^{H}(\mathbf{\Omega})\), respectively. The SML localization function, as in [31], is  
\[P_{SML}(\mathbf{\Omega})=\log\Big{\{}\Big{|}\mathbf{Y}(\mathbf{\Omega})\mathbf{R}_{\mathbf{s}\mathbf{ \varsigma}}(\mathbf{\Omega})\mathbf{Y}^{H}(\mathbf{\Omega})+\widehat{o}_{a}^{2}(\mathbf{\Omega })\mathbf{I}\Big{|}\Big{\}} \tag{50}\]
## 3 Polar Coded SDF Cooperation
### Polar Encoding and Decoding
In this case, either a higher SNR or a lower data rate should be applied in order to maintain the desired BER performance. From [32],  
Figure 2: Typical polar encoding in Equation (7) with the systematic parameters \((N,K,f)=(8,5,\{1,3,5\})\). the relationship between the block error rate \(P_{e}\) with data rate \(R\) and the capacity of the underlying channel \(I(W)\) are represented as  
\[P_{e}=2^{-2^{\frac{R}{2}+\sqrt{R}Q^{-1}(\frac{R}{I(W)})+\omega(\sqrt{\gamma})}}, \tag{12}\]  
where \(Q^{-1}(x)\) is the reverse function of \(Q(x)\), which is represented as \(Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-\frac{t^{2}}{2}}dt\). Then, the channel capacity \(I(W)=C\) is derived as  
\[C=\underset{|h|}{\mathrm{E}}\{C(|h|\sqrt{\gamma})\}, \tag{13}\]  
where \(\gamma\) is the SNR and \(C(|h|\sqrt{\gamma})\) is defined as  
\[C(|h|\sqrt{\gamma}) =\int_{-\infty}^{+\infty}\frac{1}{\sqrt{2\pi}}e^{\frac{-(y-|h_{i} \sqrt{\gamma})^{2}}{2}}\log_{2}\frac{2}{1+e^{-2\beta|h|\sqrt{\gamma}}}dy \tag{14}\] \[x_{1}^{N}=u_{f^{c}}\mathbf{G}_{N}(f^{c})\oplus u_{f}\mathbf{G}_{N}(f),\]0  
The variable \(P_{e}\) is concave in terms of the data rate \(R\) with a fixed \(I(W)\). With a data rate, increasing SNR does not yield a significant BER performance improvement at high SNR regions. Instead, a proper method is used to decrease the data rate \(R\). By this method, a new polar coding scheme is accomplished to improve BER performance in fading channels. To obtain both good and poor channel indices in polar code construction, a certain amount of channels must be detected. Finally, a detailed polar coding scheme for fading channels is concluded as follows. 1. Input a channel fading vector \(\mathbf{h}=\{h_{1},h_{2},\ldots,h_{N}\}\), the limit of the channel value \(\alpha\), data rate \(R\), code length \(N\), \(M\) and \(Q_{s}\), where \(M=N\times p\) is selected as a tradeoff between BER performance and decreased code rate. Here, \(Q_{s}\) is a channel index matrix chosen according to the quality of the bit channels and \(M\). 2. Count the number of bit channels where \(h_{i}\) is less than \(\alpha\), denoted as \(N_{rm}\). If \(N_{rm}\) is larger than \(M\), the actual number of unreliable channel is represented as \(M_{i}=M\). Otherwise, \(M_{i}=N_{rm}\). 3. The number of new frozen bits is expressed as \(M_{i}\cdot c\), where \(c\) is the calculated channel capacity according to the operation \(\text{SNR}=\gamma\) and the capacity \(C=\underset{|h|}{\mathrm{E}}\{C(|h|\sqrt{\gamma})\}\) in (13). (iv) The indices of information bits are \(Q_{s}(1,(K-M_{i}\times c))\) and those of frozen bits are \(Q_{s}[(K-M_{i}\times c+1),N]\). Both of them are ranked in the natural sequence.
# An Improved Contact Force Model of Polyhedral Elements for the Discrete Element Method
## 4 Comparison between DEM and Experimental Results
### Two Elements under Edge-Edge Contact Mode  
#### 4.1.1 Collinear  
From Figure 9, it can be seen that the trend of the simulated contact force-time curve is roughly consistent with the curve in the experiment, and the opening size and amplitude of the main peak are close. However, the contact force-time curve obtained by the discrete element simulation is shifted upward compared to the experiment--that is, there is no negative value. This is because in the simulation, when the upper specimen is subjected to an impact load, it produces a slight vition, and when it separates from the lower specimen, the contact force returns to zero. In the experiment, the contact force is measured by a thin-film pressure sensor, although the blocks no longer contact each other due to the vition, and the thin-film pressure sensor produces a certain restoring force to restore deformation, resulting in negative values in the experiment. The relative error between the peak contact force obtained by the discrete element simulation and the experimental value is within 3%.
## 2 Wave Input Calculation Model  
Pile-soil dynamic interaction is a classic problem. Research has been carried out for many years. There are mainly two directions of research in this area. One is to combine the Fourier transform in the frequency domain for research. The other is to consider the whole of soil and structure and conduct research in the time domain. There are many studies on the former. In terms of vibration, research and testing in the frequency domain can be achieved. However, because it is difficult to consider nonlinear problems happened in soil, research based on the frequency domain under earthquake action has obvious limitations. The real soil should be simulated into the numerical model. Because of the seismic wave input and calculation amount of the overall model, it is difficult to consider the overall model of soil and structure in the time domain. A soil-structure dynamic interaction system is shown in Figure 1. Generally, when solving such problems, a certain range of soils on the structural foundation is cut out, and then, the artificial boundary elements are introduced into the finite element model to absorb the outer wave [22]. Simultaneously, a force boundary condition (Figure 2) is applied to the node. Therefore, the motion equation of the finite element model of the soil-structure dynamic interaction can be presented as shown in Equation (1).  
Figure 1: Schematic diagram of the soil-structure dynamic interaction system.  
Here, \(U\), \(I\), \(S\), and \(B\) represent the nodes in the structure, the nodes in the soil, the boundary-soil nodes, and the boundary nodes, respectively; \(M\), \(C\), and \(K\) are the mass matrix, damping matrix, and stiffness matrix, respectively; \(\tilde{u}\), \(\tilde{u}\), \(\mathbf{u}\) are the acceleration, velocity, and displacement vector of a node; \(F_{B}\) is the equivalent load acting on the artificial boundary node. Therefore, the equivalent load can be obtained by expanding the last line of Equation (1) as follows:  
\[\mathbf{F}_{B}=\underbrace{\mathbf{M}_{BB}\tilde{u}_{B}}_{\text{Inertia force}}+ \underbrace{\left\{\mathbf{C}_{IB}\tilde{u}_{I}+\mathbf{C}_{BB}\tilde{u}_{B}\right\}} _{\text{Damping force}}+\underbrace{\left\{\mathbf{K}_{BI}\mathbf{u}_{I}+\mathbf{K}_{BB}\mathbf{u }_{B}\right\}}_{\text{Restoring force}}+\underbrace{\mathbf{C}_{B}\tilde{u}_{B}+\mathbf{K}_{ B}\mathbf{u}_{B}}_{\text{Artificial boundary additional force}}+ \tag{2}\]  
Equation (2) indicates that the unknown is the displacement \(\mathbf{u}_{B}\) of all boundary nodes and the node displacement \(\mathbf{u}_{I}\) connected to the boundary directly. Therefore, the equivalent load \(\mathbf{F}_{B}\) can be solved simply by using \(\mathbf{u}_{B}\) and \(\mathbf{u}_{I}\).