esa-sceva/satcom-chunk-collection huggingface dataset:
# Decoding _Scn2a_ Variants: Bridging Genetics and Phenotypes in Autism Spectrum Disorder
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline
**Reference** & **Observations** & **N (Total)** & **N (ASD)** & **N (ASD \(\leftrightarrow\)** & **ASD Diagnosis** & **Ethnicity** & **Race** & **M** & **F** & **Age (Range)** & **Consanguality** \\ \hline [PERSON] et al. [90] & Inductively dysheric & 1 & 1 & 1 & NR & African & Black & 1 & 14 & NR \\  & & & & & & & & & & & & \\ \hline [PERSON] et al. [63] & Subset includes & 65 & 45 & 10 & ADI-R & NR & NR & 4 & 6 & 5-21 & NR \\  & & & & & & & & & & & \\ \hline [PERSON] et al. [98] & Inductically destined & 119 & 119 & 2 & DSM-V-TR & NR & NR & 2 & 13-23 & NR \\  & & & & & & ADI-R, ADOS-G & NR & NR & 1 & 1.75 & NR \\ \[PERSON] et al. [60] & SCEN2 and SCNA & 1 & 1 & 1 & M-CHAT-R & NR & NR & 1 & 1.75 & NR \\ \[PERSON] et al. [61] & Case report, detailed phenotype & 1 & 1 & 1 & Clinical diagnosis & NR & NR & 1 & 29 & NR \\ \[PERSON] et al. [57] & Explores sensory & 39 & 24 & 24 & NR & 33 White, 5 & Mixed (Asian), African & 19 & 20 & 3-22 & NR \\  & & & & & & & & & & \\ \[PERSON] et al. [64] & Focuses on monitor & 22 & 14 & 7 & DSM-V & White, African American, Asian & NR & 14 & 8 & 3-15 & NR \\  & & & & & & & & & & \\ \hline [PERSON] et al. [65] & Detailed phenotypic & 22 & 3 & 3 & Not-specified & White, Asian, & NR & 10 & 12 & 2-S2 & NR \\  & & & & & & & & Middle Eastern & NR & 0 & 1 & 3.5 & NR \\ \hline [PERSON] et al. [62] & Vietnamese cohort & 100 & 100 & 1 & DSM-V & Vietnamese & NR & 0 & 1 & 3.5 & NR \\ \hline [PERSON] et al. [49] & Large cohort with & 71 & 23 & 10 & NR & NR & NR & NR & NR & NR & NR \\  & & & & & & & & & & \\ \hline [PERSON] et al. [56] & Pipeline analysis for & 354 & 354 & 3 & DSM-V & NR & NR & 279 & 75 & NR & NR \\ \hline \multicolumn{8}{c}{NR Not reported.} \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of included studies. Summary of studies reporting SCEN2A variants in ASD cohorts, including sample size, diagnostic tools, and demographic details.
## 3 Results and Discussion
### Adsorption Kinetics
#### 3.4.2 Freundlich Isotherm  
The Freundlich isotherm [49], utilized for heterogeneous systems with non-equivalent binding sites, is given by Equation (10):  
\[\ln q_{e}=\ln K_{F}+\frac{1}{n}\ln C_{e} \tag{10}\]  
where \(K_{F}\) (L/g) and \(n\) are the Freundlich constants that indicate the capacity and intensity of the adsorption process, respectively [47]. Figure 8c gives the fitting curves for the Freundlich isotherm.  
From Table 1, comparison of correlation coefficients of Langmuir and Freundlich isotherms shows that the Freundlich model is not appropriate for describing the adsorption process. The values of \(n\) ranged between 1 and 10, which indicates favorable sorption of P.
## 1 Introduction
This information will be useful to engineers concerned to solar energy capture systems and energy efficiency who can therefore take knowledge of the local radiation levels. High accuracy measurements of direct normal irradiances can be obtained by pyrhelometers, but these are costly and therefore are too scarce for proper temporal or spatial coverage. Then, the direct horizontal irradiance can be easily calculated, and the diffuse component can be estimated from the difference of global and direct horizontal irradiances. The second objective of this work is the statistical analysis of the downward shortwave solar radiation components and the implementation of a BRL diffuse fraction model (Boland-Ridley-Lauret), which is a multiple predictor logistic model developed by [PERSON] et al. (2010) [23] and can be used to estimate the diffuse, and later, the direct radiation component. The model is a function of the clearness index (\(k_{I}\)), the apparent solar time (AST), the solar altitude (\(\alpha_{s}\)), the daily clearness index (\(K_{T}\)) and a persistence parameter \(\psi\), which is an average of both a lag and lead of the clearness index. Furthermore, the levels of the shortwave components are estimated based on the classification of four categories of clearness index. The third objective of the work concernes the estimation of total energy received by an inclined surface, based on measurements on horizontal surfaces. In practice, photovoltaics are installed with a slope with respect to a horizontal surface and in a southern direction. For the estimation of solar radiation on an inclined surface, there are three types of solar radiation, namely, (i) beam; (ii) diffuse; and (iii) reflected solar radiation from the surfaces surrounding it. Hence, there are three conversion factors, i.e., for beam (\(R_{B}\)), diffuse (\(R_{D}\)), and reflected (\(R_{R}\)) solar radiation [24]. These conversion factors convert the beam and diffuse solar radiation of a horizontal surface to those of an inclined surface. The above methodology of analysis will give valuable information concerning the application of solar radiation in renewable energy resources projects.
# Enhancing 3D-Printed Clay Models for Heritage Restoration Through 3D Scanning
### Research Aims and Structure  
The present research focuses on the integration of 3D scanning and 3D printing technologies with clay for the conservation of cultural heritage, as well as for artistic and architectural creation. 3D scanning allows for the precise capture of the geometry and conservation status of historical artefacts and structures, facilitating the planning of conservation interventions and the creation of digital replicas for analysis and virtual exhibitions. 3D printing with clay enables the reproduction of historical pieces as detailed as required according to the function and context of the element, while also exploring new creative possibilities in art and architecture. The objectives of the research aim to integrate both technologies into restoration processes and to explore creative applications of 3D printing with clay. The research develops methodologies and protocols that can be adopted by cultural heritage professionals and artists, demonstrating how these technologies can transform conservation practices and open up new creative possibilities while reducing costs and time in the production of identical elements that can be manufactured in series. In the scanning and digital processing stages, this method allows for precise control over the level of detail, as well as the adjustment of scaling parameters to meet the specific requirements of each piece. Notably, this includes accounting for the shrinkage that occurs during the clay firing process. This flexibility in regulating both detail and size ensures that the reproduction remains faithful to the original design, making it an innovative approach in handling the intricacies of clay-based restoration work.
# A pH Monitoring Algorithm for Orifice Plate Culture Medium
## 3 Algorithm Principle and Result Analysis
### Analysis of HSV and pH Function Model  
#### 3.2.1 HSV Characteristic Quantification Value Selection  
After extracting the target image and removing the interference image processing, the centroid coordinates were obtained, the target image was clipped, and the HSV feature quantification value of the clipped image was calculated. The quantified values of HSV characteristics and corresponding pH values of the in-hole images in this experiment are shown in Table 2 below.  
The trend graph corresponding to the color moment in the HSV color space is shown in Figure 7.  
According to the relationship between the pH of the orifice plate and the color moment of the image shown in Figure 7, only the value of the average hue and pH are inversely related among the quantified values of color moments of images. The larger the pH value (the color of the culture fluid is biased towards red), the smaller the average hue of the solution image inside the well. The greater the pH value (the color of the culture fluid is biased towards yellow), the greater its average of hue. According to this law, the average hue as a function of pH is established.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**H-Mean** & \begin{tabular}{c} **H-Standard** \\ **Deviation** \\ \end{tabular} & **H-Skewness** & **S-Mean** & \
\begin{tabular}{c} **S-Standard** \\ **Deviation** \\ \end{tabular} & **S-Skewness** & **V-Mean** &
\begin{tabular}{c} **V-Standard** \\ **Deviation** \\ \end{tabular} & **V-Skewness** & **pH** \\ \hline
0.079131 & 0.004208 & 0.003359 & 0.250748 & 0.029091 & 0.033622 & 0.590624 & 0.030555 & 0.032838 & 7.03 \\
0.069579 & 0.004518 & 0.002654 & 0.229366 & 0.01
# Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT
## 3 IoT Monitoring Solution
### General Architecture
protocols is also described in [27] for remote monitoring conditions. In this case, to provide greater reliability and synchronicity to the data, a DS1302 RTC was incorporated. Thanks to this module, the sending of packets to Pybytes was controlled every minute as were the deep-sleep mode periods for suitable energy saving. This module provided us with a temporary time stamp, helping us when drawing conclusions from the measured data. In addition, and due to the constant disconnections of the Pycom LTE network, different solutions were tested by the authors. The connection to the network was configured by following the official documentation. The script was set to send packets every minute over NB-IoT. In parallel, the firmware of the FiPy microcontroller used was developed to perform a ping to the Pybytes platform and thus verify correct communication. If that ping was not answered due to any connection drop, then it would wait 10 min for the next ping. If this second ping was not answered either, then the watchdog timer was triggered, and the system rebooted to re-establish communication. Consequently, under a communication failure event, 10 min of information would be lost as the microcontroller remains on hold. This operation cannot be modified in the program, but it is a matter of firmware configuration.
## 5 Conclusions  
This study investigates the effectiveness of the flight load regression analysis method using PCR and PLSR. The results of numerical experiments demonstrate that the regression analysis based on PCR and PLSR achieves high accuracy. Through the analysis of various indicators, such as relative error, mean absolute error, mean square error, root mean squared error, and R-square between predicted values and true values for shear force, bending moment, and torque moment, it is confirmed that the regression analysis methods based on PCR and PLSR are suitable for flight load analysis, with PLSR slightly outperforming PCR.  
Additionally, by adopting numerical analysis methods based on regression analysis, the need for complex finite element solving processes is eliminated, resulting in shorter solution times and improved computational efficiency. This provides an alternative approach for enhancing traditional flight load analysis methods.  
In conclusion, the flight load regression analysis methods based on PCR and PLSR are applicable for predicting and analyzing flight loads while ensuring accuracy and significantly improving computational efficiency. Future research will focus on studying more complex flight conditions, such as roll maneuvering and yaw maneuvering, to achieve the rapid calculation of severe load scenarios within the flight envelope.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PCR** & **PLSR** \\ \hline Shear force & 0.0220 & 0.0217 \\ Bending moment & 0.0277 & 0.0276 \\ Torque moment & 0.6408 & 0.6406 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RMSE of different methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PCR** & **PLSR** \\ \hline Shear force & 0.999512 & 0.999519 \\ Bending moment & 0.998976 & 0.998988 \\ Torque moment & 0.990028 & 0.990082 \\ \hline \hline \end{tabular}
\end{table}
Table 7: R-square of different methods.  
Author Contributions:Conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, and writing were conducted by Q.Y. Funding acquisition was carried out by Z.W. and C.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Thanks to [PERSON] for her guidance on this paper.  
The authors declare no conflict of interest.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 4 Experimental Validation  
### Experimental Design Based on the IALO-GRU Model  
The transmission oil temperature warning model based on the IALO-GRU method consists of two parts. For gearbox normal data processing, first, eliminate the invalid data and incorrect data from the historical normal SCADA data set, and select reasonable input parameters with the Pearson correlation coefficient. Second, the test residual of gearbox oil temperature in a healthy state is obtained by the IALO-GRU prediction method. Since the residuals generally conform to the normal distribution principle under the predicted and real data, about 99.7% of the data will fall within \(\mu\pm 3\sigma\). Thus, residuals beyond this range are generally considered outliers. For gearbox abnormal data processing, first, the real-time SCADA data are preprocessed, and then, the input parameters are obtained through the correlation coefficient analysis. Second, the predicted value was obtained from the model prediction of abnormal data trained on the IALO-GRU normal data. Finally, to determine whether the moving window residue is the threshold calculated during the hypermodel procedure, if the set threshold is exceeded, the monitoring system will issue a warning signal. If neither exceeds the threshold, the transmission is running well and then continues to monitor the running status of the transmission.
###### Abstract  
We investigate modulational instability (MI) in asymmetric dual-core nonlinear directional couplers incorporating the effects of the differences in effective mode areas and group velocity dispersions, as well as phase- and group-velocity mismatches. Using coupled-mode equations for this system, we identify MI conditions from the linearization with respect to small perturbations. First, we compare the MI spectra of the asymmetric system and its symmetric counterpart in the case of the anomalous group-velocity dispersion (GVD). In particular, it is demonstrated that the increase of the inter-core linear-coupling coefficient leads to a reduction of the MI gain spectrum in the asymmetric coupler. The analysis is extended for the asymmetric system in the normal-GVD regime, where the coupling induces and controls the MI, as well as for the system with opposite GVD signs in the two cores. Following the analytical consideration of the MI, numerical simulations are carried out to explore nonlinear development of the MI, revealing the generation of periodic chains of localized peaks with growing amplitudes, which may transform into arrays of solitons.  
Keywords:modulational instability; asymmetric nonlinear fiber couplers; linear stability approach; coupled nonlinear Schrodinger equations +
Footnote †: journal: Applied Physics Letters
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 2 Related Work
### Multi-Task Learning  
Multi-task learning [28], which is applied in the present study, is a machine learning method based on shared representation that combines multiple related tasks to improve the model generalization ability, thereby yielding superior overall performance to that of a single task [29]. In contrast to single-task learning, multi-task learning uses multiple related tasks for simultaneous parallel learning and gradient backpropagating. Multi-task learning is a type of transfer learning that uses prior knowledge to aid learning of related or more complex tasks; regarded as being inspired by the human learning behavior. It aims to leverage useful information contained in multiple learning tasks to learn a more robust learner, helping alleviate this data sparsity problem [30], what is facing in the field of spacecraft anomaly detection.  
Multi-task learning is widely used in natural language processing, speech recognition, drug discovery, and other fields [31]. With regard to anomaly detection, a video anomaly detection method based on self-monitoring and multi-task learning has been proposed [32], which integrates multiple self-monitoring proxy tasks and knowledge distillation algorithms, achieving good results in three benchmarks. To train a robust anomaly detector for Railway Track Inspection from an insufficient data and detect multiple types of anomalies, multiple tasks such as material classification and fastener detection are combined within a multi-task learning framework, resulting in improved performance [33]. Similarly, both high-scale shape features and low-scale fine features are combined by using two complementary tasks of jigsaw puzzle and geometric transformation recognition in a multi-task framework, which greatly improves fine-grained anomaly detection performance over simple geometric tasks [34]. The work above decomposes the anomaly detection problem into multiple related but different proxy tasks, and then train them jointly within a multi-task framework to get better performance or detect more failure modes than single task. However, these works mainly focus on video, image and other fields rather than spacecraft anomaly detection.
## 2 Materials and Methods
### Histological Preparation and Histomorphometry Analysis  
Using a randomization protocol, 50% of the tissue blocks were processed for ground sectioning according to the methods described by [PERSON] and [PERSON] (1982), while the remaining were decalcified following the modification of the \"fracture technique\" [18]. The histological outcomes of the undecalcified ground sections have been reported in a prior publication [16].  
The present manuscript reports the histological and immunohistochemical results of the decalcified specimens processed through the modified \"fracture technique\". These samples were first partially decalcified in ethylenediamine tetra acetic acid (EDTAA, Laboratoriumdiscounter, Velsen-Noord, Netherlands) and then sectioned into 4 portions, respectively: mesio-bucal, mesio-lingual, disto-bucal and disto-lingual. These tissue portions were then dissected from the implant surface and independently processed via complete decalcification in EDTA, dehydration in serial steps of ethanol concentrations, secondary fixated in OsO4 and immersed in EPON (EPON Fluka chemie, Buchs, Switzerland) [19]. The resulting tissue blocks were sectioned with the microtome set at 7 \(\upmu\)m and stained in Mallory's trichrome or hematoxylin/eosin.  
For each implant, the most representative decalcified buccal section was selected for the histological study and examined with a Nikon Eclipse Ti microscope (Nikon, Heidelberg, Germany) equipped with a dedicated image analysis software (Q-500 MC; Nikon). All measurements were evaluated by a single experienced examiner (DP). Intraclass correlation coefficient tests were performed to estimate the intraexaminer reproducibility, resulting in an intraclass correlation coefficient of 0.997. Sixty-two section preparations were analyzed, resulting in 31 vestibular and 31 lingual sections.  
In addition, measurements of the soft tissue dimensions were performed in the undecalcified ground sections.  
The following vertical and horizontal linear distances, expressed in mm, were measured on the buccal aspects of both decalcified and undecalcified sections (Figures 3 and 4):  
Height of the Peri-implant mucosa (PM-B)  
Height of the supra-crestal soft tissues (PM-Bc)  
Height of the barrier epithelium (PM-alE)  
Height of the connective tissue (alE-B)  
These measurements used the following landmarks:  
Shoulder of the implant (I)  
Most coronal bone to implant contact (B)  
Most coronal bone crest (Bc)  
Margin of the peri-implant mucosa (PM)  
Apical border of the junctional epithelium (aJE)From the ground sections, the thickness of the peri-implant mucosa was measured on a horizontal plane, perpendicular to the long axis of the implant, at five different reference levels: (i) at the level of the implant platform and 1 mm apically; and (ii) 1, 2, and 3 mm apical to the free gingival margin (Figure 3).
###### Abstract  
Pseudo-Random Binary Sequence (PRBS) phase modulation is an effective method for suppressing the stimulated Brillouin scattering (SBS) effect generated by narrow-linewidth fiber lasers during amplification. We noticed that backward time-domain pulses were generated when using PRBS modulation signals in fiber amplification. In this paper, the time-domain dynamic characteristics of the forward output laser and the backward Stokes light after PRBS phase modulation were studied theoretically. Through analyzing the transient SBS three-wave coupling theory and combining it with the SBS accumulation time constant, we knew that the forward and backward high-intensity pulses were caused by the long dwell time of the PRBS. For this purpose, we provided a new method for suppressing high-intensity pulses caused by a long dwell time; namely, we modified the maximum length sequence (MLS) of PRBS signals to eliminate the long dwell time, took the PRBS-9 signal at 1 GHz as an example, and then used MLS1 modulation and MLS2 modulation to compare them with unoptimized PRBS modulation. The output laser peaks of the MLS1 and MLS2 signals were reduced from \(\pm\)55% to \(\pm\)25% and \(\pm\)10% relative to the original PRBSs, respectively, and the peaks of Stokes light were reduced from 39% to 19% and 11%, respectively. Additionally, we experimentally verified that the rational optimization of the sequence did not reduce the SBS threshold. The results provided a new method for suppressing high-intensity pulses during the amplification of a PRBS phase-modulated laser, which played an important role in the output stability of high-power narrow-linewidth fiber amplifiers.  
n
## 3 Fault Model and SH and GH Node Generation Mechanism
### Satellite Network Fault Model under SEU
One type of SDC error may cause false links in the RIB (Detailed in Section 3.2). In the subsequent route planning, the wrong RIB will be used as the input of the path planning algorithm, and further affect the satellite  
Figure 2: Satellite network fault model under SEU. node to make it believe that there is a false link. When the path planning algorithm adopts the false link, it can be considered that the satellite node is deceived and a SH node appears in the satellite network. (2) In the route planning phase, the satellite calculates the optimal path through the path planning algorithm, obtains the next hop node ID, and generates a routing table. SEU may affect the execution of the path planning program, cause path planning errors, and further cause SDC errors in the generated routing table. SDC errors include data loss in the routing table, a change in destination nodes, or unreachable next hops (Detailed in Section 3.3). When any of the above SDC errors appear in the wrong routing table, it can be considered that there may be GH nodes in the satellite network. In the subsequent data forwarding process, the satellite node queries the wrong routing table, and may not be able to find the next hop and discard the data packet. To explain the generation mechanism of the SH and GH nodes and the detection and protection framework proposed in this paper in detail, we construct a satellite network scenario based on the Iridium network structure [13] and DHRP [31]. DHRP is a typical routing protocol based on link delay. The programs executed by satellites in the route discovery phase and the route planning phase are shown in Table 2. Based on this scenario, we further discuss the impact of SEU on the satellite network routing mechanism and the generation mechanism of SH and GH nodes induced by SDC errors.