esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Computational Results and Analysis
### IQABC Features Analysis  
For the first set of experiments, we undertook a sensitive performance analysis of our newly proposed algorithms IQABC by varying different parameters. These parameters were experimentally found to be good and robust for the problems tested. We chose a full factorial design in which all possible combinations of the following ABC factors were tested:  
* Population size _PopSize_: three levels (50, 70, and 100);
* Onlooker bees percentage _onlook_%: three levels (20%, 30%, and 40%);
* Abandonment number of trials _limit_: three levels (5, 10, and 50);
* Number of iterations _max_iterations_: three levels (100, 150, and 200).  
All the cited factors resulted in a total of \(3\times 3\times 3\times 3=81\) different combinations run on a set of \(n\times m\) Taillard instances with \(n\in\{20;70;120;170;220\}\) and \(m\in\{5;10;15;20\}\). The job processing times of the instances were generated uniformly in the interval \([1,99]\), and maintenance durations were generated according to the first maintenance mode (M1) described in Section 5.1. Each instance was executed five times, which meant a total of 16,200 executions. The ARPD was calculated as a response variable. The resulting experiment was analyzed by means of a multifactor analysis of variance (ANOVA) technique [81] with the least significant difference (LSD) intervals at the 95% confidence level.  
Each parameter was set to its most adequate level as follows: _PopSize_ = 70; _onlook_% = 40%; _Limit_ = 5; and _max_iterations_ = 200.  
For the QL factors, namely the Q-learning index \(\alpha_{ql}\), the discount factor \(\gamma\), and the probability for the QL mechanism \(\epsilon\), based on the study of [82], four typical combinations for the QL algorithm were tested:  
* C1: \(\alpha\) = 0.1, \(\gamma\) = 0.8, \(\epsilon\) = 0.2;
* C2: \(\alpha\) = 0.1, \(\gamma\) = 0.8, \(\epsilon\) = 0.1;
* C3: \(\alpha\) = 0.1, \(\gamma\) = 0.9, \(\epsilon\) = 0.1;
* C4: \(\alpha\) = 0.1, \(\gamma\) = 0.9, \(\epsilon\) = 0.2.  
After executing different experiments for each combination, we decided to keep the second combination, as it was able to report better results. Finally, the stagnation factor was set at 80% since a large number of convergence curves for multiple executions showed a stabilization of the results up to this value.
## 1 Introduction
### Atomic Migration as a Failure Mechanism  
Among the potential failure mechanisms at temperatures higher than 1000 \({}^{\circ}\)C, atomic migration phenomena is crucial. Atomic migration refers to the movement of atoms within a material, often driven by external forces like temperature gradients, stress, or electric fields. The following types of atomic migration can be distinguished: electromigration (driven by an electric field), thermomigration (driven by temperature gradients), and stress-induced migration (driven by hydrostatic stress gradients) [19; 20; 21]. These migration processes can lead to void formation or material accumulation, leading to open-circuit or structural failure, especially in metallic components such as metallic interconnects or heating elements commonly used in thermal MEMSs [19; 21; 22]. Prior studies at lower temperatures have shown that such atomic migration mechanisms significantly influence device reliability [6; 7; 13], but their behavior in MEMS devices operating at \(T>\) 1000 \({}^{\circ}\)C remains mostly underexplored. Adapting and transferring models of failure modes observed in low-temperature MEMSs to high-temperature MEMSs operating at \(T>\) 1000 \({}^{\circ}\)C, like atomic migration caused by an electric field, hydrostatic stress gradient and temperature gradient, can potentially show that these failure modes are also relevant in high-temperature tungsten MEMSs. Such high-temperature MEMS devices can be specifically designed for use as IR sources. These type of applications inherently require significantly elevated operating temperatures in order to achieve thermal emission in the infrared spectral range. According to [PERSON]'s law, emission in the mid- to long-wave IR region demands high blackbody temperatures, which places constraints on material selection, device design, and reliability [3; 16].
## Results and Discussion
### Electrical Properties of the Electrolyte BCGCuO Film Formed on Various Cathode Substrates
The film obtained on the LBNO\({}_{\mathrm{ss}}\) substrate, according to the impedance spectroscopy data, possessed the highest electrical conductivity among the films deposited on the various cathode substrates, amounting 1.6 mS/cm at 600 \({}^{\circ}\)C. This value is lower than that for the compact BCGCuO sample (6.2 mS/cm) [16]; however, it is higher than that derived from the impedance spectroscopy data for the BCGCuO electrolyte deposited on the anode substrate (1.1 mS/cm) [7]. Electrolyte conductivity in the cell with the supporting cathode substrate can be increased via activation of the  
Figure 9: Electrical properties of BCGCuO electrolyte deposited at various cathode substrates: Examples of the spectra collected in wet and dry air atmospheres for the Pt|BCGCCuO/LNFO\({}_{\mathrm{P}}\)|Pt cell **(a)**; Arrhenius plots of the electrical conductivity for the thin BCGCuO electrolyte films calculated from the electrochemical impedance spectroscopy data obtained using various half cells (shown in the legend) in comparison with the BCGCuO compact conductivity **(b)**. thick cathode's performance, used for the functional sublayer Ba-containing cathode materials with a higher level of MIEC conductivity [36] and/or by the introduction of electrochemically active additives in the porous cathode substrate structure [37], as well as by applying the electrochemically active anodes [38]. According to the XRD data (Figure S6), the orthorhombic structure (sp. gr. Pmcn) of the BCGCuO compact sample was preserved after the sintering at 1450 \({}^{\circ}\)C. The values of the cell's parameters were a = 8.7901(26) A, b = 6.2227(25) A, and c = 6.2238(21) A, which are close to those obtained for the base powder before sintering (Table 1). Nevertheless, the conductivity of the BCGCuO compact sample was lower than those of current state-of-the-art proton-conducting electrolytes such as BaZr\({}_{0.1}\)Ce\({}_{0.7}\)Y\({}_{0.1}\)Yb\({}_{0.1}\)O\({}_{3\text{-}8}\) (\(\sim\)13-15 mS/cm at 550 \({}^{\circ}\)C in wet air [39; 40]) or BaCe\({}_{0.6}\)Zr\({}_{0.2}\)Dy\({}_{0.2}\)O\({}_{3\text{-}8}\) (\(\sim\)35 mS/cm at 600 \({}^{\circ}\)C in wet air [41]). Thus, future studies should be oriented on the development of the EPD technology for these or similar materials, taking into account the features observed in the present study.
# Synthesis, Structure, and In Vitro Biological Evaluation of Semi-Synthetic Derivatives of Betulin
###### Abstract  
Betulin and \(\alpha\)-lipoic acid are naturally occurring substances with different biological properties. Combining two phytochemical units into a conjugate is a frequently used method to obtain new compounds with better pharmacokinetic parameters. This research concerned the preparation of lipoate derivatives of betulin using the Steglich method. Experimental lipophilicity values were determined for target compounds **6-10** by reversed-phase thin-layer chromatography. In silico methods were used to calculate the physicochemical parameters and lipophilicity of new derivatives and to determine the probable directions of biological activity. \(\alpha\)-Lipoic acid, betulin, and lipoate derivatives **6-10** were tested for antiproliferative activity against MV4-11, A549, MCF-7, PC-3, HCT116, MaiPaca-2, and Hs294T cancer cells. 3-(5-(1,2-Dithiolan-3-yl)pentanoyl!))betulin **10** showed moderate anticancer activity against MV4-11, PC-3, and HCT116, with IC\({}_{50}\) values in the range of 39.8-76.7 \(\mu\)M. The introduction of a dithiolate substituent at the C3 position in 28-acetylbetulin gave compound **9** the highest activity (IC\({}_{50}\) = 37.9 \(\mu\)M), in the ratio of biphenylotropic B myelomonocytic leukemia cells (MV4-11). All lipoate derivatives were inactive towards normal cells.  
Keywords:triterpenoids; anticancer activity; betulin +
Footnote †: journal: Applied sciences
## 2 Experimental Measurement System  
Scholars and engineers attach great importance to the method used to measure the electromagnetic properties of magnetic materials. At present, there are essentially three kinds of measurement methods: the single-slice measurement method, the standard Epstein square ring method, and the ring sample method [13; 14; 15; 16; 17]. Among them, the more traditional measurement methods are the Epstein square ring method and the ring sample method. The basic principle of the Epstein square ring method is to form a box of silicon steel sheets joined by double tower joints, form four beams with equal lengths and cross-sections, and insert them into four coils. To conduct the square ring sample test, we used the MPG-200D soft magnetic measurement system (including an arbitrary waveform generator, power amplifier, control program, etc.) made by the Brockhaus Messtechnik GmbH & Co. KG in Germany (Ludenscheid, Germany). The Epstein square coil consists of a primary coil (magnetization winding) in the outer layer, a secondary coil (induction voltage winding) in the inner layer, and a sample as the core [18]. A physical diagram of the Epstein square ring is shown in Figure 1.  
The 0.10 mm UTGO silicon steel was selected as the research object, and the magnetic field strength and loss value of the magnetic material were measured using the Epstein square ring method at 0.1-1.7 T under sinusoidal excitation. The sample's dimensions are 300 mm \(\times\) 30 mm, and the number of primary- and secondary-side turns is 100. An experimental schematic diagram is shown in Figure 2. The electromagnetic properties of the FBA alloy materials measured using the ring sample method are then compared. The TEM images of the microstructure and corresponding SAD pattern for the Fe-based amorphous alloy sample are shown in Figure 3. A single diffraction ring means this sample is an amorphous structure.  
Figure 1: Physical diagram of the Epstein square ring.  
Figure 2: Schematic diagram of the experiment.
# Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking
## 3 The Improved Spherical Simplex-Radial Cubature Rule
### Spherical Simplex Rule  
The proposed third-degree spherical rule and spherical simplex rule to calculate Equation (10) are listed below, respectively.  
The third-degree spherical rule is  
\[S_{\mathrm{s}}(r)\approx\sum_{i=1}^{N_{\mathrm{s}}}\omega_{s,i}\mathbf{g}(\mathbf{r} \mathbf{y}_{i})=\frac{A_{n}}{2n}\sum_{i=1}^{n}[\mathbf{g}(\mathbf{r}\mathbf{e}_{i})+\mathbf{g}(- \mathbf{r}\mathbf{e}_{i})] \tag{13}\]  
where \(A_{n}=2\sqrt{\pi^{n}}/\Gamma(n/2)\) denotes the surface area of the unit sphere, and \(\Gamma(z)=\int_{0}^{\infty}e^{-t}t^{z-1}dt\) is the Gamma function. The vector \(\mathbf{e}_{i}\) represents the unit column vector with the \(i\)th element being \(1\). The number of cubature points needed is \(N_{\mathrm{s}}=2n\).  
The third-degree spherical simplex rule [31] consisting of \(N_{\mathrm{s}}=2(n+1)\) points is  
\[S_{\mathrm{ss}}(r)=\frac{A_{n}}{2(n+1)}\sum_{i=1}^{n+1}[\mathbf{g}(\mathbf{r}\mathbf{a}_{i })+\mathbf{g}(-\mathbf{r}\mathbf{a}_{i})] \tag{14}\]  
where \(\mathbf{a}_{j}=\left(a_{j,1}\quad a_{j,2}\quad\cdots\quad a_{j,\mu}\right)^{ \mathrm{T}},j=1,2,\cdots,n+1\) denote the vertices of the \(n\)-dimensional simplex, and the elements are defined as follows.  
\[a_{j,i}=\left\{\begin{array}{l}-\sqrt{\frac{n+1}{n(n-i+2)(n-i+1)}},i<j\\ \\ \sqrt{\frac{(n+1)(n-j+1)}{n(n-j+2)}},i=j\\ \\ 0,i>j\end{array}\right. \tag{15}\]  
Although the aforementioned two spherical rules share the same accuracy of the third degree, through extensive simulations, it has been proven that spherical simplex rules have higher accuracy than spherical rules [31], and that is the reason the spherical simplex rule is chosen to approximate \(S(r)\) in this paper. For more details, please refer to the related references.
## 2 Partial Discharge Signal
### Evaluation Criteria for Noise Reduction Effect  
In order to evaluate the noise reduction effect of different algorithms, three indexes are introduced for the simulated signal [22] and two indexes are introduced for the field-measured signal [13].  
(1) Signal-to-noise ratio (SNR) reflects the ratio of the effective signal to noise. The larger the SNR value, the smaller the residual noise, which is expressed as (5).  
(2) Normalized correlation coefficient (NCC) reflects the degree of relatedness of the noise-reduced signal and the pure signal. An NCC value closer to 1 signifies a higher degree of correlation, indicating that the noise-reduced signal closely resembles the pure signal, which is expressed as (6).  
(3) Root mean square error (RMSE) reflects the deviation between the denoised signal and the pure signal. The smaller the RMSE value, the smaller the error in amplitude and frequency between the noise-reduced signal and the pure signal, which is expressed as (7).  
\[\text{SNR}=10\text{lg}\frac{\sum\limits_{i=1}^{N}S_{signal}\ ^{2}(n)}{\sum \limits_{i=1}^{N}\left[S_{denoised}\ (n)-S_{signal}\ (n)\right]^{2}} \tag{5}\]  
\[\text{NCC}=\frac{\sum\limits_{n=1}^{N}S_{\text{signal}}\ (n)S_{\text{ denoised}}\ (n)}{\sqrt{\left(\sum\limits_{n=1}^{N}S_{\text{signal}}\ ^{2}(n)\right)\left(\sum\limits_{n=1}^{N}S_{\text{ denoised}}\ ^{2}(n)\right)}} \tag{6}\]  
\[\text{RMSE}=\frac{\sum\limits_{i=1}^{N}\left|S_{\text{signal}}\ -S_{\text{ denoised}}\ \right|^{2}}{N} \tag{7}\]  
In (5)-(7), \(S_{\text{signal}}\) is the pure signal, \(S_{\text{denoised}}\) is the denoised signal and \(N\) is the number of sampling points.  
\begin{table}
\begin{tabular}{c c c c c c} \hline Narrowband interference & 1 & 2 & 3 & 4 & 5 \\ \hline Amplitude (\(A_{i}/\text{mV}\)) & 0.08 & 0.2 & 0.2 & 0.1 & 0.12 \\ Frequency (\(f_{i}/\text{MHz}\)) & 1.11 & 1.53 & 2.26 & 4.14 & 6.23 \\ Initial phase (\(\varphi_{i}\)) & \(\pi/6\) & \(\pi/4\) & \(-\pi/4\) & \(-\pi/6\) & \(-\pi/3\) \\ \hline \end{tabular}
\end{table}
Table 2: Specific parameter settings.  
(4) In the field detection, since the pure PD signal is unknown, the noise reduction effect cannot be judged by SNR, NCC and RMSE. Therefore, in this paper, the Noise Reduction Ratio (NRR) is applied to assess the effectiveness of noise reduction, which is expressed as:  
\[\text{NRR}=10\cdot\left(\log_{10}\sigma_{1}^{2}-\log_{10}\sigma_{2}^{2}\right) \tag{8}\]  
In (8), \(\sigma_{1}\) and \(\sigma_{2}\) represent the standard deviations of the field-measured signal and the denoised signal.  
(5) ARR evaluates the noise reduction effect from the amplitude of the PD waveform, which is expressed as  
\[\text{ARR}=\frac{A_{c}-A_{d}}{A_{c}}\times 100\% \tag{9}\]  
In (9), \(A_{c}\) and \(A_{d}\) are the amplitude of the field-measured signal and the noise reduction signal, respectively.
# Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning
## 2 Method
### MixUP  
MixUP is employed for semi-supervised learning, distinguishing itself from prior approaches that solely mix images. MAC-SSL involves mixing both labeled samples with ground-true labels and unlabeled samples with pseudo-labels (generated as described in Section 2.2). For a pair of samples with their respective label predictions \((x_{1},q_{1})\) and \((x_{2},q_{2})\), when \(x_{1}\) is a labeled sample, \(q_{1}\) represents the true label. When \(x_{1}\) is an unlabeled sample, \(q_{1}\) represents the generated pseudo-label. The same applies to \(x_{2}\) and \(q_{2}\). The MixUP technique employed can be described as follows:  
\[\lambda\,\sim\text{Beta}(\alpha,\alpha), \tag{4}\]  
\[\lambda^{\prime}=\max(\lambda,1-\lambda), \tag{5}\]  
Figure 2: Process of pseudo-label generating. \[(x^{\prime}_{1},q^{\prime}_{1})=\lambda^{\prime}(x_{1},q_{1})+(1-\lambda^{\prime} )(x_{2},q_{2}),\text{ and } \tag{6}\]  
\[(x^{\prime}_{2},q^{\prime}_{2})=\lambda^{\prime}(x_{2},q_{2})+(1-\lambda^{\prime })(x_{1},q_{1}), \tag{7}\]  
where \((x^{\prime}_{1},q^{\prime}_{1})\) represents the new sample generated by mixing with \(x_{1}\) as the base (\(\lambda^{\prime}\) is a value close to 1), \((x^{\prime}_{2},q^{\prime}_{2})\) represents the new sample generated by mixing with \(x_{2}\) as the base, and \(\alpha\) is a hyperparameter. The specific calculation steps for Equation (6) are as follows:  
\[x^{\prime}_{1}=\lambda^{\prime}x_{1}+(1-\lambda^{\prime})x_{2}\text{ and } \tag{8}\]  
\[q^{\prime}_{1}=\lambda^{\prime}q_{1}+(1-\lambda^{\prime})q_{2}, \tag{9}\]  
and the specific calculation steps for Equation (7) are as follows:  
\[x^{\prime}_{2}=\lambda^{\prime}x_{2}+(1-\lambda^{\prime})x_{1}\text{ and } \tag{10}\]  
\[q^{\prime}_{2}=\lambda^{\prime}q_{2}+(1-\lambda^{\prime})q_{1}. \tag{11}\]  
The mixing process in the algorithm is presented in line 15 of Algorithm 2. ```
1:Input: Batch of labeled examples \(\mathcal{X}=((x_{p},q_{b});b\in(1,\dots,B))\), batch of unlabeled examples \(\mathcal{U}=(u_{b};b\in(1,\dots,\mu B))\), ratio of sample size \(\mu\), sharpening temperature \(T\), Beta distribution parameter \(\alpha\) for MixUp, unlabeled loss weight \(\lambda_{u}\), contrastive loss weight \(\lambda_{c}\).
## 1 Introduction
The sensors include global navigation satellite systems (GNSS), such as the Beidou global navigation satellite system and global positioning system [21; 22], and in situ sensors, such as crack meters, moisture meters, and inclinometers [2; 6; 23]. Automated landslide monitoring has three characteristics: first, high collection frequency and large amounts of observation data; second, data fluctuations and outliers, as the sensors are easily affected by the environment; third, the automation of the data analysis process makes it difficult to conduct artificial early warnings, especially when the number of landslides under observation is very large. Due to these features, many traditional methods are slightly insufficient when they are applied to these data. Firstly, some related studies focus on the critical sliding early warning [5; 11; 13; 14] and ignore the acceleration process that often occurs in the evolution of landslides before reaching the critical sliding stage. But such early warning is also very important, because every acceleration may be the prelude to the failure of the landslide. Secondly, data fluctuations and outliers in traditional differential calculations often cause false early warnings. Finally, some early warning methods are based on prior parameters. For example, some early warning methods based on the normalized tangent angle require the average speed of the landslide in the constant deformation stage (CDS), which cannot be obtained at the beginning of the monitoring. Furthermore, obtaining these parameters needs experienced assistance and cannot be fully automated. Therefore, the problem of how to provide early warnings of landslide acceleration processes based on automated monitoring data characteristics remains unsolved. This paper proposes a hybrid warning method that can both accurately identify the landslide acceleration process and reduce false warnings based on the characteristics of automated monitoring data from landslides. The method includes two parts: conventional early warning and critical sliding early warning. For conventional early warning, the deformation process is divided into the normal zone, initial attention zone, and key attention zone, according to the cumulative displacement. Then, the initial attention zone and key attention zone are divided into different warning levels based on the velocity and acceleration calculated by the least-squares method. When the displacement of the landslide enters the key attention zone, the average velocity since the beginning of deformation is calculated artificially, and the critical sliding early warning based on the normalized tangent angle is enabled. Then, the two early warning methods are combined according to different strategies to take into account the requirements of computing resources, the number of false warnings, and immediacy. The proposed hybrid warning method not only ensures a precise understanding of the landslide deformation acceleration process while reducing false alarms, but it also makes full use of monitoring data and requires almost no human intervention, making it suitable for automated landslide warning systems.
# Multiple Genetic Rare Variants in Autism Spectrum Disorders: A Single-Center Targeted NGS Study
## 3 Results  
### Gene Panel  
We found 147 variants in 39 out of 40 patients (36 males, 4 females) analyzed by NGS (which fall into 60 out of 120 genes): only one patient (H16/18) showed no significant variant (Online Supporting Information, Supplementary Table S2 and Table 4).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Group** & **Alle Frequency (AF)** \\ \hline
** ** & (AF) \(<\) 1/10,000 (0.01\%) \\ \hline
** ** & 1/10,000 (0.01\%) \(<\) AF \(<\) 1/5000 (0.02\%) \\ \hline
** ** & 1/5000 (0.02\%) \(<\) AF \(<\) 1/1000 (0.1\%) \\ \hline
** ** & 1/1000 (0.1\%) \(<\) AF \(<\) 1/500 (0.2\%) \\ \hline
** ** & 1/500 (0.2\%) \(<\) AF \(<\) 1/100 (1\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequency subcategories of rare variants.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Gene** & \begin{tabular}{c} **SFARI** \\ **Score** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Variants** \\ \end{tabular} & **Patients** & \begin{tabular}{c} **Same** \\ **Variants** \\ \end{tabular} & \begin{tabular}{c} **ASD** \\ **Frequency in** \\ **Our Cohort** \\ \end{tabular} & \
\begin{tabular}{c} **GnomAD** \\ **Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Statistical Significance Exact** \\ **_p_-Value (1-Tailed)** \\ \end{tabular} \\ \hline FAT1 & 3S & 12 & 11 & none & 0.15 & 0.000061 & exact \(p\) \(<\) 0.0001 \\ \hline VPS13B & S & 12 & 10 & 1 & 0.15 & 0.000062 & exact \(p\) \(<\) 0.0001 \\ \hline ANK2 & 1 & 6 & 6 & none & 0.075 & 0.000046 & exact \(p\) \(<\) 0.0001 \\ \hline RAI1 & 3S & 6 & 6 & 1 & 0.075 & 0.000049 & exact \(p\) \(<\) 0.0001 \\ \hline SHANK3 & 1S & 5 & 5 & none & 0.0625 & 0.000056 & exact \(p\) \(<\) 0.0001 \\ \hline ANKDD11 & 2S & 5 & 5 & none & 0.0625 & 0.000040 & exact \(p\) \(<\) 0.0001 \\ \hline CSMD1 & 4 & 5 & 5 & none & 0.0625 & 0.000038 & exact \(p\) \(<\) 0.0001 \\ \hline DLGAP2 & 4 & 5 & 5 & none & 0.0625 & 0.000024 & exact \(p\) \(<\) 0.0001 \\ \hline ZNF804A & 3 & 5 & 4 & none & 0.0625 & 0.000052 & exact \(p\) \(<\) 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Most frequently mutated genes in our cohort.  
+++ ==ERROR: No output for this page==
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Patient**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Internal**} &  
The variants taken into consideration have a GnomAD frequency \(<\) 1%. Those with a greater frequency were discarded.  
We found 137 missense, 2 synonymous (predicted to alter normal splicing), and 8 indel variants, all in a heterozygous state, except for one missense variant in the MET gene, which was carried by both alleles. Two variants of the DMD gene and one in the NRXN3 gene, located on Chromosome X, are present in male patients in a hemizygous state.