esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### Reprogramming G56R Fibroblasts to iPSCs  
We isolated dermal fibroblasts from a skin biopsy of an _NR2E3_-associated adRP patient in order to develop a CRISPR/Cas-mediated knockout approach for the G56R mutation. We reprogrammed the fibroblasts using non-integrative Sendai virus (SeV) vectors carrying [PERSON]'s transcription factor cocktail _KLF4_, _OCT3/4_, _SOX2_, and _c-MYC_[27]. Emerging iPSCs showed a typical morphology that consists of tightly packed cells with a distinct border (Figure 1A). Sanger sequencing confirmed the presence of the c.166G\(>\)A mutation in the G56R iPSCs as compared to WT iPSCs (Figure 1B). At passage (P) 12, RT-PCR analysis of the exogenous pluripotency genes determined the clearance of the SeV vectors in comparison to non-transduced (negative control) and SeV-transduced (positive control) fibroblasts (Figure 1C). We observed a normal karyotype of the G56R iPSCs at P11, being indicative of genetic stability (Figure 1D). By qPCR analysis, we detected the expression of the host pluripotency genes _NANOG_, _OCT 3/4_, and _LIN28A_ in comparison to the lack of expression in non-transduced and SeV-transduced G56R fibroblasts (Figure 1E); NANOG expression was already detectable in SeV-transduced fibroblasts, consistent with our previous observations [28; 29]. Pluripotency was further confirmed by immunofluorescence (IF) staining for NANOG, OCT3/4, and SOX2 (Figure 1F). In addition, we validated the ability of the G56R iPSC to differentiate into the three germ layers using an embryoid body (EB) differentiation assay and IF staining to detect specific markers: Alpha Fetal Protein (AFP) for endoderm, Smooth Muscle Actin (SMA) for mesoderm, and Nestin for ectoderm (Figure 1G). Taken together, the generated G56R iPSC line passed all of the quality controls assessing pluripotency and integrity.  
Figure 1: _Cont._In conclusion, we generated a pluripotent and genetically stable G56R iPSC line from an _NR2E3_-associated adRP patient, which could be used for therapeutic development.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 2 Materials and Methods
### MAFANet Network  
The extraction accuracy of UGS feature plays a crucial role in the final segmentation accuracy. To optimize the MAFANet model performance, we modify the input image size and channel number during the encoding part, increasing information richness. During the decoding part, we restore the image size and reduce channels to the input size, preserving image details. This work employs ResNet50 as the backbone network, complemented by an MSPA module for multiscale contextual awareness and deep spatial channel information extraction, aiding UGS segmentation based on contextual cues. Furthermore, to retain essential information while filtering out noise, we built a novel decoder module by designing a DE and a BFF module. The DE module is mainly used for information fusion between the encoding and decoding layer. The BFF module enhances communication between channels, facilitating mutual guidance in feature mining between adjacent layers of the ResNet50 network, promoting feature fusion, and providing richer feature information for upsampling. The use of four upsampling modules progressively integrates features from high to low and reinstates the details of HRS image, significantly enhances the MAFANet model segmentation performance.
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 4 Simulation Results
### Performance of Models Comparison  
The recognition accuracy of different deep neural network models is compared at first. the four kinds of networks are trained at the SNR of \(-10\) dB-\(10\) dB. The training was carried out on a data set of 8 million samples and each input sample length was 8192. The Simple RNN, the improved RNN and the typical CNN, networks performed better in the SNR of 4 dB-\(10\) dB, as shown in the Figure 10.  
When the SNR exceeds 5 dB, the recognition accuracy of Simple RNN and improved RNN can reach 99%, while that of typical CNN can reach 90%. Then, the overall recognition rates were stable. However, compared with the traditional RNN network, the improved RNN has fast convergence and large rise rate and has basically reached a high recognition rate of 99% at 0 dB. As the Figure 10 shows, regardless of the high SNR or low SNR, the overall performance of the improved RNN is superior to that of traditional CNN and Simple RNN. At the same time, the Figure 10 also summarizes the recognition performance of MLP and SVM. First proposed by [PERSON] and [PERSON] in 1995, SVM has shown many unique advantages in solving small samples, nonlinear and high-dimensional pattern recognition and can be extended to other machine learning problems such as function fitting. SVM method is based on statistical learning theory based on VC dimension theory and structure risk minimum principle, according to the limited sample information in the complexity of the model and learning ability to seek the best compromise between, in order to get the best generalization ability. MLP is also called Artificial Neural Network (ANN), including input layer, hidden layer, output layer. Layer to layer is fully connected state. All models were compared used the same training and test data. On the condition that there is high signal-to-noise ratio, SVM can reach about 95% but MLP can just reach about 43%. The  
Figure 9: Recognition performance of the improved RNN in different sizes of training set (S).  
performance of the SVM model is similar to the RNN model and the latter is difficult for fully connected network to complete type recognition for forward error correction coding.
###### Abstract  
This study investigated if non-thermal atmospheric pressure plasma (NTAPP) treatment of root dentin surfaces promotes human dental pulp stem cell (hDPSCs) adhesion. Freshly extracted human single-rooted teeth (\(n=36\)) were decorated and cut (first vertically, then horizontally) into root dentin slices (3 mm thick). Primary hDPSCs cultures were seeded onto slices randomly assigned to pretreatment groups (\(n=9\)/group): NaOCI (1.5%), EDTA (17%) then NTAPP (Group I); NaOCI then NTAPP (Group II); NaOCI then EDTA (Group III); and NaOCI alone (Group IV). Cell viability and proliferation were measured using MTT assay with log-linear statistical analysis. Cell attachment and spreading morphologies on dentin slices (\(n=3/group\)) were examined through scanning electron microscopy. Early cell adhesion events and subcellular activities were observed in real time by live-cell imaging through holotomographic microscopy. Cell viability and proliferation were significantly higher on NTAPP-treated dentin (\(p<0.05\)), without interactions with EDTA (\(p>0.05\)). The attachment, spreading, extensions and multiple layers of hDPSCs were heightened on NTAPP-treated dentin. Cell adhesion, spreading, and dental tubule penetration were hastened on NTAPP-treated dentin surfaces in real-time, with elevated subcellular activities and intracellular lipid droplet formation. NTAPP-treated root dentin surfaces support enhanced cellular responses, potentially promoting pulp-dentin regeneration.  
denttin; human dental pulp stem cells; live-cell imaging; MTT assay; non-thermal atmospheric pressure plasma; scanning electron microscopy
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 5 Evaluation and Results
### All Missions Combined
Finally, the F1-score, illustrated in Figure 15(d), had a mean of 99.821%, reaching a maximum of 99.898% and a minimum of 99.736%, with an STD of \(\pm\)0.03. As a consequence, all the models achieved high performance and demonstrated stable behavior. However, the random forest model delivered the highest overall results compared to the other models. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature** & **Decision Tree** & **Random Forest** & **XGBoost** \\ \hline PRN & 0.089573 & 0.101541 & 0.218030 \\ DO & 0.099101 & 0.151993 & 0.163281 \\ PD & 0.527503 & 0.321745 & 0.258517 \\ DELTA\_RX & 0.000000 & 0.000000 & 0.000000 \\ DELTA\_TOW & 0.000035 & 0.000027 & 0.001258 \\ CP & 0.162943 & 0.157375 & 0.117846 \\ EC & 0.000010 & 0.007641 & 0.001126 \\ LC & 0.000054 & 0.007857 & 0.001150 \\ PC & 0.000127 & 0.014292 & 0.015329 \\ PIP & 0.000000 & 0.002072  
Figure 14: Performance metrics of the decision tree model on all-missions dataset. Figure 15: Performance metrics of the random forest model on all-missions dataset.
## 2 Materials and Methods
### Volatile Compounds  
Volatile compounds were extracted by headspace solid-phase microextraction (SPME) from 5 g of minced and homogenised cheese samples. The samples were deposited in 50 mL vials and heated while stirring at 40 \({}^{\circ}\)C. A fibre of divinylbenzene/carboxon/polydimethylsiloxane (DVB-CAR-PDMS; 1 cm long \(\times\) 110 \(\upmu\)m diameter; Supelco, Bellefonte, PA, USA) was fixed in the headspace of the vial for 10 min. The volatile compounds were desorbed into the split-splitless injector of the gas chromatograph (GC) system set at 250 \({}^{\circ}\)C for 5 min. The volatile compounds were analysed in a GC Thermo-Scientific Trace 1300 connected to an ISQ mass spectrometer (MS) using a VF-42 WAXms column (30 m x 250 um i.d. \(\times\) 0.50 um film thickness) with helium as the carrier gas. The chromatographic conditions were as follows: the oven temperature began at 45 \({}^{\circ}\)C for 4 min, increased to 150 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, remained at 150 \({}^{\circ}\)C for 3 min, increased to 250 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min, and remained at 250 \({}^{\circ}\)C for 5 min; the transfer line temperature was 280 \({}^{\circ}\)C. The MS worked in electron impact mode. The electron impact energy was 70 eV and the equipment recorded data at a rate of 1 scan/s. The relative abundance of the volatile compounds in the chromatograms was calculated by considering the area units under each peak. A series of n-alkanes was used to obtain retention index (RI) values for each volatile compound under the same conditions. Compounds were tentatively identified by comparing their mass spectra with those contained in the National Institute of Standards and Technology (NIST; Gaithersburg, MD, USA) library or in previously published literature.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 2 Received Signal and Proposed HCTC Model  
### Received Signal  
The modulation recognition problem can be outlined as a multiclass classification problem. Generally, the received signal from the transmitter is defined, as in [25], as follows:  
\[r(t)=m(t)*h(t)+n(t) \tag{1}\]  
where \(m(t)\) is the modulated received signal, \(h(t)\) is the impulse response, and \(n(t)\) is the additive Gaussian noise (AWGN). More commonly, signal \(r(t)\) is expressed in the in-phase form, i.e., \(I=A.cos(\varnothing)\), and the quadrature components are \(Q=A.\sin(\varnothing)\). \(A\) denotes the amplitude of \(r(t)\), and \(\varnothing\) denotes the phase of \(r(t)\).  
In this study, the modulation recognition problem was investigated in the popular RadioML2016a and RadioML2016b datasets [53]. These were synthetic datasets generated by GNU Radio [54]. The RadioML2016a dataset included 11 modulation classes: 64 QAM, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QPSK, WBFM, 16 QAM, and 8 PSK. The RadioML2016b dataset consisted of 10 modulation cases: 64 QAM, AM-DSB, BPSK, CPFSK, GFSK, PAM4, QPSK, WBFM, 16 QAM, and 8 PSK. Both datasets consisted of (i) I/Q signal components, where the dimensions of each signal were 128 and, collectively, 2 \(\times\) 128; (ii) modulation signals, where each signal comprised a particular modulation listed above, with signal-to-noise ratios (SNRs) of \(-\)20 dB, \(-\)18 dB, \(-\)16 dB, \(-\)14 dB, \(-\)12 dB, \(-\)10 dB, \(-\)8 dB, \(-\)6 dB, \(-\)4 dB, \(-\)2 dB, 0 dB, 2 dB, 4 dB, 6 dB, 8 dB, 10 dB, 12 dB, 14 dB, 16 dB, and 18 dB. However, the number of samples in each dataset varied; the RadioML2016a dataset consists of 220,000 signal samples, whereas RadioML2016b contained 1,200,000 signal samples.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### In-Office Therapeutic Treatments of Dentin Hypersensitivity
#### 6.2.3 Sodium Fluoride  
The impact of fluorine compounds on DH reduction has been extensively investigated [110; 111; 112]. Fluorine compounds lead to a reduction in calcium fluoride (CaF\({}_{2}\)) crystal presence within dentin tubules, subsequently decreasing dentin permeability [113]. These crystals are almost insoluble in saliva. The 2% sodium fluoride (NaF) varnish commonly employed in dental practice induces a reduction in calcium salts; however, these are readily eliminated from the dentin surface by saliva and brushing. To address this challenge, an acidified formulation, namely acidulated phosphate fluoride (APF 1.23%, 12,300 ppm F, pH 3.2) is available as solution, gel, or thixotropic gel, and as foam, is employed to prompt the reduction of salts deep within dentin tubules [65]. If fluoroapatite forms after the treatment, it exhibits improved stability against the effects of saliva, brushing, and dietary substances [114]. Fluorine compounds can also be employed in conjunction with iontophoresis, a method that employs an electric current to enhance ion diffusion [115; 4].  
Products containing fluorine compounds for in-office use are applied to exposed dentin usually in varnish forms.
# Neural Multivariate Grey Model and Its Applications
## 3 NMGM Model
### Modeling Procedure  
In the NMGM model, we assume that \(X_{i}^{(0)}(t)\) represents the system's characteristic sequence when \(i=1\), and the pertinent factor sequences when \(i=2,3,\cdots,N\). This assumption is based on the definition of GM(1, N) in Section 2. Subsequently, the 1-AGO (accumulating generation operator) sequences of \(X_{i}^{(0)}\) are denoted as \(X_{i}^{(1)}(t),j=1,2,\cdots,N\). Considering that \(f_{\theta}\) is a function of \((X_{1}^{(1)}(t),X_{2}^{(1)},\cdots,X_{N}^{(1)})\), \(t\), the coefficient \(\theta\) is the dependent variable. Then, the function \(f_{\theta}\) is defined by the deep neural network, which can represent a series of transformations on \(t\) and the 1-AGO sequence \(X_{1}^{(1)}(t),X_{2}^{(1)}(t),\cdots,X_{N}^{(1)}(t)\). A deep neural network can be understood as a function that executes a series of operations on its input data. We define \(z(t)\) as the hidden state of the neural network. The NMGM model's whitening equation can be represented as a NODE:  
\[\frac{dz(t)}{dt}=f_{\theta}(z(t),t), \tag{15}\]  
where \(\theta\) denotes the associated weights for neural networks. Once the neural network is defined, employing the initial value \(z(0)\), time span \(t_{s}\), and \(\theta\), we can proceed to invoke the ODESolver, enabling the computation of the forecast sequence Z:  
\[\begin{split} Z&=\Big{[}z(0),\hat{z}(1),\cdots, \hat{z}(n-1)\Big{]}\\ &=ODESolver\Big{(}z(t),t,f_{\theta}(z(t),t),z(0),t_{s},\theta \Big{)},\end{split} \tag{16}\]  
where \(t_{s}=[0,1,2,\cdots,n-1]\). After constructing the model, we can use forward propagation and backpropagation to optimize parameters to obtain the optimal prediction sequence. Based on the above theoretical explanations, the flowchart for the new proposed model can be seen in Figure 2. The computational steps are summarized below:  
1. Collecting the raw data set \(X_{1}^{(0)},X_{2}^{(0)},\cdots,X_{N}^{(0)}\);
2. The 1-AGO series \(X_{1}^{(1)},X_{2}^{(1)},\cdots,X_{N}^{(1)}\) of raw data are computed and used as input to the NMGM model;
3. Constructing the NMGM model, using forward propagation and backpropagation to optimize parameters;
4. Computing the simulated values and errors where the predictive value can be generated by the IAGO;
5. Predicting the future value and analyzing the development trend of the system.
# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
## 3 Proposed Signal Processing Methodology  
The proposed algorithm consists of two stages; firstly it implements pre-processing of raw data for the de-noising purpose and followed by blind feature extraction for defect detection. The pre-processing algorithm is implemented by eliminating spikes in data which could make extracting features difficult due to the fluctuation. After the initial cleaning process, the second stage of feature extraction algorithm using the proposed BSS technique is performed to detect damage sensitive features.
## 4 Results and Discussion of Load Mutation  
### Simulation and Analysis of Pulsing Load Mutation  
As shown in Figure 7, a pulse with a pulse width of 2 s and amplitude of 6.3% Pe is superimposed on the power output of the CCPP model, which is regarded as the input entering the load controller and participates in the closed-loop control of the CCPP model. Figure 7 shows the curves of the single-shaft CCPP and steam turbine (ST) during the normal state and the pulsing load mutation was obtained by simulation. The load command generates a large deviation from the actual load of the unit when there is a pulse in power  
Figure 5: Reheat system model.  
Figure 6: Fault simulation model of single-shaft CCPP.  
output. The CCPP and ST were adjusted to the amount of fuel in the combustor or steam flow in the ST to smooth the deviation. The CCPP regulates more quickly and corrects the power output simultaneously. However, the deviation between the actual power output and the load command of the CCPP is larger than that of the ST when the pulse signal recovers to the normal state, which increases the regulation of the CCPP. Because regulating the frequency of some components, that is, the fuel control valve, directly affects its chronological age, the pulsing load mutation caused by electrical interference can ultimately damage the service life of the critical equipment of the CCPP [23].  
Further study was conducted on how the pulse amplitude influences the load correction of the CCPP and ST, as shown in Figure 7. The load correction increased with the pulse amplitude. Compared to ST, CCPP has a larger load correction for the same pulse amplitude condition.
## 3 Results and Discussion
### Surface Observation  
#### 3.1.1 Microscope Observation  
Figure 2A,B shows the appearance variation of T/P blends and T/P/MA blends as related to the post-2 nd and post-3 rd recycling, respectively. In Figure 2A, blends that are composed of a greater T content appear in dark yellow, while blends that are composed of a greater P content appear in lighter yellow, according to the comparison between the images of T90 and T70. The yellowing phenomena can be ascribed to the oxidation reaction of thermoplastic polyurethane during the recycling processes [31; 32]. Based on the results of melt-recycling, T/P/MA blends are more easily and efficiently hot-pressed than T/P blends (i.e., MA-free blends). As MA serves a bridging role that can improve the original interfacial property between T and P, it well attenuates the interfacial tension. It has been proven that a proper compatible agent for a multiple-polymer system is desirable, where the original system without a compatible agent was with poor performance and limited application [27]. Figure 2B shows that P100 and T100 waste-blended samples do not show distinct defects in the post-3 rd-recycling process and still maintain good formability. It is a good piece of evidence that the thermoplastic materials can be well reformed within an appropriate treatment. At the same time, the MA-containing blends also have stable formability and can be hot-pressed efficiently. With a greater number of melting-recycling cycles, MA provides T/P matrices with significant compatibility and stability. As for MA-free blends, the images of T90, T70, and T50 in Figure 2B exhibit distinctive pores and defects. The phenomenon results from melting-recycling cycles, in which the variation of thermal energy may escalate the incompatibility and immiscibility between T and P. Moreover, phase separation becomes apparent and forms larger defects on the T/P blends with increasing content of polypropylene. Furthermore, the larger the content value difference between T and P, the more significant the differentiation effect. On the other hand, the MA-containing groups (T90/MA, T70/MA, and T50/MA) show a reverse result, where the presence of MA makes a contribution to stable formability regardless of multiple melting-recycling cycles, which suggests that the feasibility of multiple utilization is increased considerably.
## 1 Introduction
In the present study, we summarized nearly six years of consecutive measurement data of NEE, ET, and environmental factors such as air temperature (T\({}_{\rm a}\)), soil temperature (T\({}_{\rm s}\)), relative humidity (RH), wind speed (W\({}_{\rm s}\)), precipitation (P), soil water content (SWC), VPD, photosynthetically active radiation (PAR), and net radiation (R\({}_{\rm n}\)), covering the period from September 2004 to June 2010, which included six winter wheat seasons. The main objectives were to: (1) analyze seasonal and interannual variations in GPP, NEE, ET, and WUE in a winter wheat ecosystem (with a single crop from September to the next June, observed at a 2 m height, denoted as WWE), and an agroforest ecosystem (with both cropland and apple orchard, observed at a 32 m height, denoted as AFE); (2) to explore the applicability of sinusoidal function in the simulation of interannual ET; and (3) to quantify the correlation between environmental factors and the GPP, NEE, and WUE of the two different agroecosystems using the Pearson correlation.
## Abbreviations  
The following abbreviations are used in this manuscript:  
MFI Multi-frequency MFI Multi-frequency integration  
PBR Passive bistatic radar  
DVB-S Digital video broadcasting--satellite  
RCM Range cell migration  
DFM Doppler frequency migration  
MF-DFM MF-based DFM  
RFT Radon--Fourier transform  
DFB Doppler filter bank  
ME Minimize entropy  
CPI Coherent processing interval  
PSO Particle swarm optimization  
CFAR Constant false alarm rate  
UEL Unequal-length CPI  
EL Equal-length CPI  
SNR Signal-to-noise ratio  
RDM Range-Doppler mapping  
NCI Non-coherent integration  
QCI Quasi-coherent integration
## 5 Computational Experiments
### Comparison of Machine Learning Method and Inverse Optimization Method  
The results of machine learning and inverse optimization are compared. Table 11 shows the comparison between machine learning and inverse optimization for each problem scale. The machine learning value is the one with the better accuracy when the feature is the objective function value, or the feature selected by SFS.  
In all cases, inverse optimization is more accurate than machine learning. In the 2-objectives case, inverse optimization is significantly more accurate. In the 2-objectives case, the change in the objective function value varies monotonically with the weighting factors. The gradients of the weighting factors are easy to compute. In the case of the 3-objectives, the gradients of the weighting factors were not calculated properly in some cases.