esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evolution of HCI Mode
### Eye Tracking  
Eye tracking is an interactive method that realizes HCI by analyzing eye motion information. Eye-tracking interaction can enhance the user experience by minimizing external intervention [59] and simplifying operations [60], increasing freedom [61], reducing communication costs and mental load, assisting the surgeon in concentrating on critical operations. A representative product is the Senhance Surgical System, which provides surgeons with a well-centralized surgical view through eye movement control [33]. Current research in related fields is focused on maintaining the level of user attention and optimizing the operational experience, primarily concentrating on the following two aspects:  
1. Improving the usability of the interaction system through eye-tracking technology: [PERSON] et al. [62] proposed an HCI model based on eye motion control that can control the movement of a robotic arm by gaze, reducing the frequency of surgery interruptions and ensuring the surgeon's focus while simplifying the surgical procedure. [PERSON] et al. [63] used SVM and PNN to develop an interaction system to control an endoscope robotic arm based on pupil changes, and verified through experiments that this method has more advantages over traditional methods. [PERSON] et al. [60] developed a system based on eye tracking for needle positioning during surgery, which reduced the frequency of switching between control devices and surgical tools, and verified through experiments that this method effectively improves operation smoothness.
2. Optimizing calibration methods to improve recognition accuracy: [PERSON] et al. [64] proposed a new online calibration method for gaze trackers, which improves the gaze-tracking accuracy. [PERSON] et al. [65] proposed a calibration method based on robot-generated 3D space continuous trajectories, which improves the reliability of gaze point estimation.  
Although eye-tracking interaction has been applied in some products, there is still much room for improvement in control accuracy [60]. The high postural requirements of eye trackers for wearers [62] have become a key factor limiting the large-scale promotion of eye-tracking interaction in relevant fields.
## 5 Conclusions  
This paper focuses on parameter calibration in the DEM and explores a universal parameter calibration method based on deep learning. We improve upon the multi-head attention mechanism by proposing a sparse multi-head attention mechanism implemented using a sparse mask matrix. This paper also establishes an error quantification format for parameter calibration. By utilizing the Taylor expansion form of the difference function, an adaptive differencing method is introduced to establish a quantitative analysis of errors  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Neural Networks** & **Task** & **Standard Error** \\ \hline
**PartsNet** & Triaxial test & **6.863** \\ GRU+TransE & Triaxial test & 26.622 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error quantization data for PartsNet and GRU with Transformer encoder.  
in discrete sequences. PartsNet is an innovative end-to-end solution, which efficiently calibrates constitutive parameters by integrating attention and retention mechanisms. The conclusions of this study mainly reveal two aspects:  
1. The attention retention fusion with the deep learning mechanism, PartsNet, addresses long-term dependencies in calibrating parameters using high-dimension sequential dynamical information on wet sand. The microscale parameters calibrated through PartsNet excellently describe the macroscopic mechanical behavior and nonlinear mechanical properties of elastoplastic wet sand, surpassing the results achieved by existing methods. The performance of PartsNet in wet sand within the triaxial compression test and its verification tests highlight its robust generalization capacity.
2. PartsNet demonstrates the capability to accurately obtain the EEPA model's constitutive parameters of elastoplastic wet sand, surpassing seven other deep learning approaches in the ablation test. In the triaxial compression calibration test, PartsNet demonstrates the best predictive accuracy (91.29%) and surpasses the Transformer encoder-GRU architecture by 1.12% while achieving a 21.25% faster convergence. Moreover, the standard error of PartsNet is only one-fourth of that of the other method.  
Conceptualization, Z.H. and X.W.; Methodology, Z.H. and X.W.; Software, Z.H., X.Z., S.B. and Z.Z.; Validation, Z.H., J.Z., Z.Z. and X.W.; Formal analysis, [PERSON], J.Z., Z.Z. and X.W.; Investigation, [PERSON], J.Z., S.B., Z.Z. and X.W.; Resources, Z.Z. and X.W.; Data curation, Z.H., X.Z. and S.B.; Writing--original draft, Z.H., S.B. and X.W.; Writing--review & editing, [PERSON], J.Z., Z.Z. and X.W.; Visualization, [PERSON], X.Z., S.B. and Z.Z.; Supervision, X.W.; Project administration, X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Two-Photon Excitation of Azobenzene Photoswitches for Synthetic Optogenetics
## 3 Photomodulating Cellular Activity  
One prototypical example of a synthetic optogenetic tool is SPARK (Synthetic Photoisomerizable Azobenzene-Regulated K\({}^{+}\)-channel) [30]. SPARK was designed to control neuronal excitability. This channel was designed to tether MAL-AZO-QA. MAL-AZO-QA includes an azobenzene (AZO) flanked by a cysteine-reactive maleimide (MAL) and a potassium channel blocker (quaternary ammonium; QA). For the maleimide to specifically tether SPARK, the channel had a cysteine residue inserted in one of its external loops (as water-accessible cysteines are not common in proteins [31]). Prior illumination, the photoswitch is found in its elongated _-trans_ form; spanning 17 A. The authors designed the length of the photoswitch to equal the distance between the cysteine residue and the pore so that the QA drug would easily reach the pore and block the channel. The functional outcome of blocking this potassium channel is robust neuronal excitability and action potential firing. This effect was completely reversed by near-UV illumination (\(\sim\)400 nm), pushing the photoswitch back to _-cis_, a much shorter form (end-to-end distance \(\sim\)10 A), thereby physically removing the blocker away from the pore leading to immediate silencing of the neuron [30]. This could be repeated many times, by quickly toggling the photoswitch back to _-trans_ by \(\sim\)500 nm light (rather than slow thermal relaxation in the dark). Similar schemes have been adopted to photocontrol a variety of channels and receptors with the, almost exclusive, use of visible light [9; 12].
## 3 Performance Analysis of WCwEH
### Exact Analysis
 \tag{11}\]In Equation (11), the first equality originated from the statistical independence of \(M\) random variables while the second equality is comprehended from their identical distributions. Furthermore, each MGF is represented as  
\[ \begin{split}\Psi_{g_{P}}(v)&=\int\limits_{0}^{\infty} f_{g_{P}}(y)e^{xy}dy\\ &=\int\limits_{0}^{\infty}\left(\sum\limits_{n=0}^{N}\frac{Q_{n}} {\mathrm{Y}_{p}^{\chi_{n}}\Gamma(\chi_{n})}y^{\chi_{n}-1}e^{-\frac{y}{Y_{p}}} \right)e^{xy}dy\\ &=\sum\limits_{n=0}^{N}\frac{Q_{n}}{\mathrm{Y}_{p}^{\chi_{n}} \Gamma(\chi_{n})}\int\limits_{0}^{\infty}y^{\chi_{n}-1}e^{-\left(\frac{1}{Y_{p }}-y\right)y}dy\\ &=\sum\limits_{n=0}^{N}Q_{n}\big{(}1-\mathrm{Y}_{p}\big{)}^{- \chi_{n}}.\end{split}  \tag{12}\]  
By substituting (12) into (11) and applying the multinomial expansion, one obtains the MGF of \(H\) as  
\[ \begin{split}\Psi_{H}(v)&=\left[\sum\limits_{n=0}^{N }Q_{n}\big{(}1-\mathrm{Y}_{p}v\big{)}^{-\chi_{n}}\right]^{M}\\ &=\sum\limits_{\sum\limits_{n=0}^{N}b_{n}=M}\frac{M!}{\prod \limits_{n=0}^{N}b_{n}!}\prod\limits_{n=0}^{N}\left[Q_{n}\big{(}1-\mathrm{Y}_ {p}v\big{)}^{-\chi_{n}}\right]^{b_{n}}\\ &=\sum\limits_{\sum\limits_{n=0}^{N}b_{n}=M!}\left\{\prod \limits_{n=0}^{N}\frac{\left(Q_{n}\right)^{b_{n}}}{b_{n}!}\right\}\big{(}1- \mathrm{Y}_{p}v\big{)}^{-\theta},\end{split}  \tag{13}\]  
where \(\theta=\sum\limits_{n=0}^{N}b_{n}\chi_{n}\). Using the PDF-MGF mapping in [32], one infers the PDF of \(H\) to be  
\[f_{H}(z)=\sum\limits_{\sum\limits_{n=0}^{N}b_{n}=M}\left(\prod\limits_{n=0}^{ N}\frac{\left(Q_{n}\right)^{b_{n}}}{b_{n}!}\right)\frac{M!z^{\theta-1}}{ \mathrm{Y}_{p}^{\theta}\Gamma(\theta)}e^{-\frac{z}{\mathrm{Y}_{p}}}. \tag{14}\]  
Subsequently, the CCDF of \(H\) is expressed from its definition as \(\bar{F}_{H}(z)=\mathbb{P}\{H\geq z\}=\int\limits_{z}^{\infty}f_{H}(r)dr\). Plugging (14) into \(\bar{F}_{H}(z)\) yields  
\[\bar{F}_{H}(z)=\sum\limits_{\sum\limits_{n=0}^{N}b_{n}=M}\frac{M!}{\mathrm{Y} _{p}^{\theta}\Gamma(\theta)}\left(\prod\limits_{n=0}^{N}\frac{\left(Q_{n} \right)^{b_{n}}}{b_{n}!}\right)\int\limits_{z}^{\infty}r^{\theta-1}e^{-\frac{ z}{\mathrm{Y}_{p}}}dr. \tag{15}\]  
With the aid of ([30], Equation (3.351.2)), one reduces (15) to  
\[\bar{F}_{H}(z)=\sum\limits_{\sum\limits_{n=0}^{N}b_{n}=M}\frac{M!}{\Gamma( \theta)}\Gamma\left(\theta,\frac{z}{\mathrm{Y}_{p}}\right)\prod\limits_{n=0}^ {N}\frac{\left(Q_{n}\right)^{b_{n}}}{b_{n}!}. \tag{16}\]
## 4 Results and Discussion
### High-Water Event Dynamics of Suspended-Sediment Concentration
The suspended-sediment dynamics in water-courses rely on various factors, including energy conditions determined by discharge levels, sediment availability, precipitation intensity, land use, and vegetation cover. These factors influence the typical hysteresis effects that are observable when studying the relationship between runoff and sediment concentration. Figure 9 displays the correlation between the concentration of suspended sediments and discharge for a specific stretch of the River Drava during a high-water event in October 2018. A counter-clockwise hysteresis loop was observed, indicating that sediment sources were located at the area's far end, contributing to surface runoff, which suggests that sediments reached the stream during the falling limb [38]. However, the hysteresis loop may also have occurred due to material eroded in a catchment that did not reach the stream during the previous flood event and settled on the slope. The surface runoff then transported these sediments during the next high-water event. Reports of high values of suspended-sediment concentration during a falling limb are associated with bank collapse [45]. To determine the main factors contributing to the increase in concentration of suspended sediments during the falling limb, additional land use and precipitation intensity data must be analysed. Figure 9: The relationship between the concentration of suspended sediments (C) and water discharge values (Q) was investigated. Time evolution of the water discharge Q and concentration of suspended sediments C for the high-water event in October 2018 (based on [38]). From the results, it is clear that on this part of the River Drava, most of the material is transported during high-water events, which can lead to various problems and damages in the flooded areas, e.g., the pollution of agricultural land and reduced performance of returning waves. Special attention and additional measures to protect these areas are required.
## 5 Research Progress on Membranes for Redox Flow Batteries
### Other Perfluorinated Membranes (PVDF as Backbone)  
A solution-grafting method was employed to prepare a poly (vinylidene fluoride)-graft-poly (styrene sulfonic acid) (PVDF-g-PSSA) cation exchange membrane. The PVDF membrane was pre-treated with KOH and grafted with styrene via radical polymerization, followed by sulfonation. These membranes exhibited high conductivity and dramatically low vanadium ion permeability when compared to Nafion 117. The VRB with the low-cost PVDF-g-PSSA membrane exhibited a higher performance than that with Nafion 117 under the same operating conditions; its energy efficiency reached 75.8% at 30 mA/cm\({}^{2}\). The performance of VRB with the PVDF-g-PSSA membrane was found to be stable even after 200 cycles at a current density of 60 mA/cm\({}^{2}\)[78].  
Another partially fluorinated cation exchange membrane was prepared by radiation grafting of styrene and maleic anhydride onto PVDF membrane followed by sulfonation. The grafted membrane showed a much lower permeability than Nafion 117. Moreover, the OCV was maintained above 1.3 V after 33 h, which was much longer than Nafion [91].
# LDPC Coded Massive MIMO Systems
## 1 Introduction
However, there do not exist many research works on developing a systematic way to determine the JDD strategy improving the BER performance with a given amount of computational complexity and an efficient way to design LDPC codes depending on the structure of JDD strategy. Thus, there exist strong needs for a systematic and efficient design procedure of LDPC codes and JDD strategy for coded massive MIMO systems. In this paper, we design the LDPC coded massive MIMO system with an iterative JDD algorithm, where the suboptimal FG-GAI BP detection is considered. We represent the LDPC coded massive MIMO system by a factor graph composed of observation nodes, middle nodes, variable nodes and check nodes connected through edges, and we define iterative updating rules for messages flowing over the factor graph of JDD. We propose an analysis tool for investigating the EXIT behavior of JDD, by which the density evolution of messages is analyzed and a 3-D (three-dimensional) EXIT chart visualization is obtained. Based on the proposed EXIT analysis, we design jointly irregular LDPC codes and the JDD strategy for the coded massive MIMO system to attain the lowest BER with a given amount of computational complexity. In the LDPC code design, we include an additional constraint regarding the placement of edges between variable nodes and check nodes in a practical point of view. It is observed that the coded massive MIMO system equipped with the proposed LDPC codes and the proposed JDD strategy has a lower BER performance than that equipped with conventional LDPC codes and conventional JDD strategy. The performance gain of the proposed scheme over conventional schemes are noticeable, especially when a low amount of computational complexity is allowed for the receiver of the coded massive MIMO system. This paper is organized as follows. In Section 2, we present the model for coded massive MIMO system. In Section 3, we introduce the operation of JDD, propose the EXIT analysis tool for JDD, and analyze the EXIT behavior of JDD of the LDPC coded massive MIMO system. In Section 4, we design LDPC codes and the JDD strategy by using the proposed EXIT analysis tool. In Section 5, we present BER performances of the proposed LDPC coded massive MIMO system in various points of view and compare those with conventional ones. Finally, we conclude this paper in Section 6.
###### Abstract  
We report an all-solid-state continuous wave (CW) tunable orange laser based on cascaded single-pass sum-frequency generation with fundamental wavelengths at 1545.7 and 975.2 nm using two quasi-phase-matched (QPM) MgO-doped periodically poled lithium niobate (MgO:PPLN) crystals. Up to 10 mW of orange laser is generated in the cascaded module corresponding to a 10.4%/W nonlinear conversion efficiency. The orange output showed a temperature tuning rate of \(\sim\)0.05 nm/\({}^{\circ}\)C, and the beam quality (M\({}^{2}\)) value of the orange laser was about 2.0. We use this technique to combine the high efficiency offered by uniformly poled crystals and the broad input wavelength acceptance characteristic of step-chirped structures.  
Keywords:lithium niobate; sum frequency generation; quasi phase-matching; lasers +
Footnote †: journal: Applied
An idea of the surge in interest in this field is evidenced by the SCOPUS(r) search \"polymerization in confinement\" as a function of the year of publication, as shown in Figure 1.  
Nevertheless, although there have already been a few reviews published on polymerization reactions conducted in other nanoporous materials (CPG, MS, or MOF), to our knowledge there is no state-of-the-art review that provides a general overview within nanoporous AAO geometries. Therefore, our focus is placed on the use of anodic aluminum oxide (AAO), which is one of the most widely used nanoporous materials in the study of confinement effects on polymerization. The scope of this review on Polymerization reactions within Nanoporous Anodized Alumina Oxide Templates is to provide a general overview of the state-of-the-art of basic aspects of the fabrication of polymeric nanostructures via the \"in situ\" polymerization of monomers in spatially restricted geometries, using a diverse range of polymerization mechanisms and monomers in particular, an up-to-date account of the progress of different aspects of polymerization in confinement including monitoring of reaction conversion; kinetic studies; modeling and polymer characterization (Mn, D, Tg, etc.). The review starts with a brief description of the particular AAO geometry. The main part of the review is concerned with polymerization reaction in confinement in AAO geometries. Although it is not easy to establish the classification of polymerization reactions, as a first approach, polymerization works summarized the electrochemical polymerization and then polymerization reactions are categorized based on the polymerization mechanisms: free radical polymerization (FRP); step polymerization; ring-opening polymerization; ATRP polymerization and other mechanisms. The studies are further subdivided based on the chemical nature of monomer. The review also highlights a number of potential applications arising from polymerization in AAO geometries addressed to energy, sensing and electronic applications, and surface properties such as wetting and adhesion.
## 2 Materials and Methods
### Anti-Demineralization Properties  
The anti-demineralization tests used to assess the remineralization effect of the GaMBN-containing resin (CF, CharmFill Flow, Dentkist, Seoul, South Korea) were based on methods previously reported in the literature, and were reviewed and approved by the Institutional Review Board of Pusan National University Dental Hospital (PNUDH-2018046) [8; 9; 10; 11]. Each group of experiments used ten healthy premolar teeth--untreated enamel with no WSLs or other enamel defects--that had been extracted for orthodontic purposes.  
Each tooth was embedded in acrylic resin (Caulk Orthodontic Resin, Dentsply Caulk, York, PA) using a mold, and etching and adhesive application were conducted following the same method as the bracket retention tests. GaMBN (1, 3, and 5 wt%) was mixed with resin (CF, CharmFill Flow, Dentkist, Seoul, South Korea) in 2 mL black microcentrifuge tube and the mixtures were mixed twice for 10 s using a mixer (TORNADO SHM-ALM00, Shinhung, Seoul, South Korea).  
Photopolymerization was performed for 5 s after applying resin (CF, CharmFill Flow, Dentkist, Seoul, South Korea) containing 0, 1, 3, and 5 wt% GaMBN to the tooth surface and the teeth were stored in distilled water for 24 h. The following precipitation cycle was repeated for 14 days: 6 h in 500 mL demineralizing solution (Biosesang, Kyeonggi-do, South Korea) followed by 18 h in 500 mL remineralizing solution (Biosesang, South Korea). The solutions were replaced with fresh ones every week and the teeth were washed--1 min in distilled water and gently air-dried--before they were moved between solutions.  
The teeth were monitored using micro-CT (InspeXio SMX-90 CT Plus Benchtop Micro Focus X-ray, Shimadzu, Japan) at 90 KV and 109 \(\upmu\)A, and the measured micro-CT data was analyzed using ImageJ (National Institutes of Health, Bethesda, MD, USA) (Figure 1). Brightness was used to determine whether enamel had been remineralized, with 87% brightness relative to control enamel set as the threshold; when brightness dropped below this threshold, the distance from the end of the orthodontic bonding primer coating was the remineralization length (Figure 1, Table 1).
## 5 Simulation Results
### Channel Parameter Estimation  
We first examine the scheme for estimating channel parameters as described in Section 4.4. During each simulation, the channel parameters \(\beta\) and \(\sigma_{dB}\) are specified in the TCL script file and used in modeling log-normal shadowing channels for each transmission [33]. We assume that the proposed MAC protocol is not aware of the values, and uses the scheme described in Section 4.4 to estimate the parameters based on received signal powers.  
We consider a network with a chain topology, where eight nodes are placed in a row with 20 m separation between adjacent nodes. The average transmission range is 26.9 m. The 8-node chain network has two CBR sessions: one from Node 1 to Node 8 with 1,000-byte frames and the other from Node 8 to Node 1 with 700-byte frames. We trace the estimated value of \(\beta\) and \(\sigma_{dB}\) at an intermediate node every 0.01 second for 45 seconds after the sessions are started.  
The simulation results are plotted in Figure 5. We observe that in general the estimation schemes are quite effective. The estimated values quickly converge to the actual values set in the TCL scripts. Specifically, the estimation of path exponents \(\beta\) is more accurate than that of the standard deviation \(\sigma_{dB}\) for a given number of samples of received power. Furthermore, the convergence of \(\beta\) estimation is much faster than that of \(\sigma_{dB}\). The convergence is also slower when \(\sigma_{dB}\) becomes larger. This is because when the channel exhibits high randomness (as indicated by the larger \(\sigma_{dB}\) value), more samples are needed to get an accurate estimate. We define the _convergence ratio_ for \(\sigma_{dB}\) estimation as:  
\[R_{\sigma}=1-\left|\frac{\sigma_{dB}-\hat{\sigma}_{dB}}{\sigma_{dB}}\right| \tag{18}\]  
For the worst case when \(\sigma_{dB}\) = 6, the convergence ratio at 30 seconds is 95.31%. After 45 seconds, the convergence ratio becomes 97.83%. On the other hand, the convergence ratios for the cases of \(\sigma_{dB}\) = 2 dB and \(\sigma_{dB}\) = 4 dB become very close to 1 after a few seconds of estimation, due to the lower randomness of the channels.  
Similar results are observed when other topologies are used, but are omitted for brevity. The estimation algorithms can be continuously executed due to its low complexity. The estimated values can be updated for every frame received. We conjecture that the proposed approach is also applicable to the case of time-varying channels where the parameters vary over time, as long as they conform to the log-normal slow fading channel model.
## 3 Results and Discussion  
### Yield and Properties of Cellulose Nanocrystals  
In this study, the yield of cellulose nanocrystals (CNC) extracted from oil palm trunk (OPT) is approximately 34%. The yield was within the expected efficiency range of 21-44% when a high sulfuric acid concentration (64-65%) was used [29; 31; 32]. Despite the lower yield, acid hydrolysis is a commonly used method for CNC extraction as CNCs with a higher stability and pure crystalline region can be obtained [32; 33]. Figure 4 presents the TEM image of the CNC after the extraction process. The CNC exhibited typical rod-like structures with an average diameter and length of around 7 \(\pm\) 2.4 and 113 \(\pm\) 20.7 nm, respectively. Generally, CNCs have a broad distribution of geometric dimensions, and the size of nanocrystals are varied, with a diameter of around 2-20 nm and a length of around 100-600 nm [33; 34] depending on the preparation methods and the cellulose source.  
Figure 4: TEM image of CNC extracted from oil palm trunk (OPT) after acid hydrolysis treatment with magnification of (**A**) 40,000\(\times\) and (**B**) 20,000\(\times\).