esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Mass Loss and Weight Percentage Gain of Wood  
The _ML_ of specimens after TM at 150 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, and the _WPG_ of the 150 \({}^{\circ}\)C TM specimens after WI are presented in Table 2. It can be seen that the _ML_ was small in the moderate temperature of the 150 \({}^{\circ}\)C condition. The _ML_ was only 0.79% after 2 h TM, and 1.45% after 6 h TM. However, the _ML_ became severe at 200 \({}^{\circ}\)C TM conditions. The _ML_ of specimens after 200 \({}^{\circ}\)C/2 h TM was 2.2 times as much as that after 150 \({}^{\circ}\)C/6 h TM, indicating that the _ML_ was affected more obviously by temperature. The ratios of _ML_ of specimens after 6 h TM to after 2 h TM were 1.83 times and 1.32 times for 150 \({}^{\circ}\)C and 200 \({}^{\circ}\)C conditions, respectively; this suggests that the effect of modification time was greater in the moderate temperature ranges, and became weak at higher temperature conditions. The _ML_ increased obviously with temperature and modification time, while the temperature played a critical role in the _ML_ of the wood. The weights of the 6 h and 2 h TM specimens after WI increased 13.57% and 10.61%, respectively. The _WPG_ of the 6 h TM specimens increased by about 27.90% compared with the of 2 h TM specimens. These findings indicate that wax was successfully impregnated into the TM specimens, and that TM time affected the _WPG_ of TM specimens.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Temperature(\({}^{\circ}\)C)** & **Time(h)** & **ML (\%) after TM** & **WPG (\%) after WI** \\ \hline
150 & 2 & 0.79(0.19) & 10.61(0.26) \\
6 & 6 & 1.45(0.18) & 13.57(0.78) \\
200 & 2 & 3.16(0.40) & - \\ \hline \end{tabular} Note: - means no wax impregnation.  
\end{table}
Table 2: Mass loss (_ML_) and weight percentage gain (_WPG_) of wood after TM and WI.
# A Comparison of Dental Arch Width and Length on 3D Digital and Plaster Models
###### Abstract  
The aim of the study was to determine potential deviation in measurements of commonly used orthodontic variables, including internormal width (IMW), intercamine width (ICW), and arch perimeter (AP), when measured on three-dimensional images compared to measurements performed on plaster models obtained from alginate impressions using a hand-held digital vernier caliper. A total of 120 sets of models were divided into four groups: plaster models made from an alginate impression, digitized plaster models made from an alginate and silicone impression, and digital models obtained via intraoral scan. The repeated measurements analysis of variance (ANOVA) with Sidak post-hoc test was applied to test differences in measurements between the four methods. The results show better agreement for internormal and intercanine width (\(\tau\) = 0.468-0.967) and smaller measurement error (ME = 0.53-1.07) compared to arch length (\(\tau\) = 0.422-0.477; ME = 2.04-2.89). Better agreement levels were more frequently observed between plaster models with intraoral scans than between digitized plaster models. The deviations in internormal and intercanine width measurements between the hand-held digital vernier caliper and digital analyses were found to be clinically insignificant. The observed difference in arch perimeter suggests that curved measurements may not be clinically acceptable.  
orthodontics; three-dimensional models; intraoral scanner; plaster models Article
## 6 Conclusions  
In silty clay, the feasibility of substituting slurry pressure balanced shield muck for bentonite in single slurry synchronous grouting was explored via uniform tests, regression, and 3D response surface analysis. The effects of the water-binder, fly flash-cement, binder-sand, soil-binder ratios, and water-reducing admixture content on the slurry performance were systematically analyzed. The optimal slurry ratio for a slurry pressure balanced shield in silty clay formation was determined. The conclusions are as follows:  
(1) Considering the chemical similarity between shield muck and bentonite, refined shield muck particles can substitute bentonite in silty clay synchronous grouting, reducing transportation needs, maximizing resource utilization, and saving costs. The optimized slurry mixture ratio comprises water-binder (0.559), binder-sand (0.684), fly flash-cement (2.080), soil-binder (0.253) ratios, a particle size < 0.075 mm, and water-reducing admixture (0.06).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Density** & **Setting Time** & **Bleeding** & **Fluidity (cm)** & **Consistency** \\  & **(g/cm3)** & **(h)** & **Rate** & **Fluidity (cm)** & **(cm)** \\ \hline Model results & 1.73 & 600.00 & 0 & 24.61 & 12.00 \\ Test results & 1.70 & 534.07 & 1.2 & 25.23 & 13.12 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The optimal proportion slurry performance.  
(2) The muck content and particle size significantly affect the slurry properties. A higher muck content reduces the bleeding rate, fluidity, and consistency. A smaller particle size also decreases the bleeding rate. When the muck particle size is less than 0.075 mm, it tends to stabilize slurry gradually.  
(3) Below a soil-binder ratio of 0.6, the water-binder ratio's impact on the slurry density, bleeding rate, and setting time intensifies inversely. The fly flash-cement ratio inversely correlates with a bleeding rate below 0.6, shifting to a positive correlation above. Larger muck particles amplify the water-binder ratio's effect on the bleeding and the binder-sand ratio's on the density. The fly flash-cement ratio inversely correlates with the density for a <0.2 mm particle size, directly for >0.2 mm.  
Conceptualization, J.Y.; methodology, J.Y. and M.L.; software, J.Y. and J.J.; validation, M.L. and S.M.; formal analysis, J.Y.; investigation, J.J.; resources, M.L. and S.M.; data curation, J.Y.; writing--original draft preparation, J.Y.; writing--review and editing, J.Y., M.L., M.X. and J.J.; visualization, M.L. and M.X.; supervision, S.M. and M.X.; project administration, M.L. and S.M.; funding acquisition, M.L. and M.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Fund of Taishan Industry Leading Talents Project, grant number tscx202306015.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 1 Introduction
Two DER electrical models are considered: the approximate model of the synchronous machine for considering the INIDER effect, and the inverter model operating in limiting current to consider the IIDER effect. These models are used to estimate the current contribution from DER units to the fault point. In this work, the fault resistance was considered to be constant over time, and the problem of multiple estimation of FL was not addressed. The remaining of this paper is organized as follows. Section 2 presents the proposed fault location equations for ground and phase faults. Section 3 presents the proposed adaptive impedance-based fault location algorithm for active distribution systems. Section 4 presents the case study, and Section 5 the results. Section 6 presents the discussion of the results and finally, in Section 7, the main conclusions of this work are presented.
## 3 Numerical Modeling of the Filling Phase  
### Governing Equations  
The filling phase employs a full volumetric approach, utilizing three-dimensional spatial discretization of the cavity. Both the cavity volume and the governing Navier-Stokes equations are discretized using the finite volume method (FVM), which has been established as standard for computational fluid dynamics (CFD) [6; 9; 15; 16]. During the filling phase, the melt flow is assumed to be incompressible and, due to the relatively short process duration, isothermal [17]. [PERSON] and [PERSON] [17] demonstrated that isothermal models provide accurate and computationally efficient results for simulating the mold filling phase in injection molding, particularly when modeling three-dimensional isothermal flow of incompressible, highly viscous fluids. Furthermore, [PERSON] [18] demonstrated that in thin-walled cavities, temperature differences within the polymer are negligible, confirming the accuracy of isothermal simplifications. The results showed that isothermal models are sufficient for characterizing the essential flow characteristics without introducing significant  
Figure 1: Illustration of the basic structure of an injection molding machine.  
inaccuracies in the simulation. In Ansys CFX, the cavity filling can be modeled using either a homogeneous multiphase model or an inhomogeneous model. The homogeneous model assumes that both phases (air and polymer melt) are transported in the same ratio and share the same flow fields, such as velocity and temperature, allowing the transport equations to be combined [6; 9]. Nevertheless, the findings of [PERSON] [19] demonstrate that the homogeneous model does not produce accurate results because the same boundary conditions are applied to both fluids. [PERSON] et al. [20; 21] recommend the implementation of no-slip boundary conditions for the melt phase. The inhomogeneous model, as described by [PERSON] and [PERSON] [9], employs distinct flow fields for each phase, allowing the application of a free-slip boundary condition for the air phase. Interphase transfer terms are employed to regulate the interaction between phases. In addition, [PERSON] et al. [10] describe a model incorporating a suitable rheological model for polymers, which enhances the accuracy of the simulation with Ansys CFX.  
The following equations describe the two phases, designated as \(m\) and \(a\), respectively (melt and air). The mass balance equation for each respective phase is as follows, as presented in [9; 10; 19; 22]:  
\[\frac{\partial}{\partial t}(r_{m}\rho_{m})=\
abla(r_{m}\rho_{m}\mathbf{U_{m}}) \tag{1}\]  
\[\frac{\partial}{\  
both the drag coefficient \(C_{D}\) and the interface length scale \(d_{ma}\), can be described by the following equation:  
\[\mathbf{M_{ma}}=\frac{C_{D}r_{m}r_{a}}{d_{ma}}(\rho_{m}r_{m}+\rho_{a}r_{a})| \mathbf{U_{a}}-\mathbf{U_{m}}|(\mathbf{U_{a}}-\mathbf{U_{m}}) \tag{9}\]
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 4 The 3D-LUNA Localization Framework  
To establish a global coordinate system for underwater networks, anchor nodes with predetermined positions must be deployed. Conventionally, these anchors can either be tethered underwater nodes or floating surface nodes. However, as noted in Section 1, deploying these anchors could be impractical in many underwater applications. This  
Figure 3: Location estimation of an underwater node using airborne VLC beam intensity in the absence of depth information.  
paper overcomes this serious shortcoming and proposes a more efficient approach utilizing air-to-water cross-medium communication. This section outlines how VLC can be applied to this task.
## 6 Conclusions  
This paper shows the security proof of the recent work for the CCA conversion method in [12] has a flaw and proposes the corrected proof for the method. The CCA conversion method [12] from OW-CPA-REM to IND-CCA-KEM is a tightly secure conversion and is based on the Random Oracle model. Without changing the basic conversion method, this paper proposes the new corrected security proof of the conversion method. If the security proof for the newly designed cryptography scheme is not correct, there may exist some vulnerability in the system using the scheme. Therefore, this report on the flaw of security proof of the conversion method [12] and the fixed security proof should be considered by developers that use this conversion method. With the revised CCA conversion method, one can obtain an IND-CCA-KEM scheme from an IND-CPA-KEM scheme, and an IND-ID-CCA-IBKEM scheme from an IND-ID-CPA-IBKEM scheme, respectively. This paper provides the revised applications of the CCA conversion method which are the practical encryption schemes and the ID-based encryption schemes. As a result, information systems such as software tools, image processing, and sound transmission [25; 26; 27] that require information security can use those applications of the CCA conversion method to enhance data privacy.  
However, this work has the limitations that the new security proof assumes the stronger condition that an underlying CPA-KEM is secure in terms of IND instead of OW. As future work, we note that proving the security of the conversion method with a weaker assumption that an underlying CPA-KEM is secure in terms of OW is an interesting open question.  
Conceptualization, J.H.P.; Formal analysis, Y.L. and J.H.P.; methodology, Y.L. and J.H.P.; validation, D.H.L. and J.H.P.; writing--original draft preparation, Y.L. and J.H.P.; writing--review and editing, Y.L., D.H.L., and J.H.P.; supervision, J.H.P.; project administration, D.H.L.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean Government (MSIT) (No. 2016-6-00600, A Study on Functional Encryption: Construction, Security Analysis, and Implementation.  
Not applicable.  
The authors declare no conflict of interest.
# A Model for Simulating the Upward Flow of a Viscous Fluid in a Fracture Network
## 2 The Upward Flow of a Viscous Liquid in a Single Fracture
### Model
#### 2.1.2 The Numerical Scheme, Boundary and Initial Conditions, and Parameters  
To capture the evolution of the fracture aperture and the flow dynamics, we build the governing equation by combining Equation (17) with Equation (9). We then employ an implicit time-stepping scheme and the standard second-order central difference method to discretize the governing equation. More details of our numerical scheme are presented in Appendix A.  
In all computations in this study, we impose a constant pressure, \(p_{\text{in}}\), at the inlet of the fracture as  
\[p\mid_{z=0}=p_{\text{in}}\, \tag{18}\]  
At the outlet of the fracture, however, we define two types of boundary conditions, as illustrated in Figure 2. The first one is an \"open\" outlet, for which we impose a constant outlet pressure \(p_{\text{out}}\) as  
\[p\mid_{z=L}=p_{\text{out}}\, \tag{19}\]  
For the computation shown in the results section, we set the outlet pressure, \(p_{\text{out}}\), to be equal to the original pressure, \(p_{0}\). The second option is to assume a \"closed\" outlet. We impose a constant speed, \(u_{\text{out}}\), at the \"closed\" outlet in order to allow the outlet of fracture to propagate. Hence, the gradient of the pressure at the outlet is  
\[\left.\frac{\partial p}{\partial z}\right|_{z=L}=-\frac{12\mu(L,t)u_{\text{ out}}}{h(L,t)^{2}}\, \tag{20}\]  
where \(\mu(L,t)\) and \(h(L)\) are, respectively, the liquid viscosity and fracture aperture at the outlet.  
In the simulation with either an open or closed outlet, we define the initial pressure to vary linearly from \(p_{\text{in}}\) to \(p_{\text{out}}\).
###### Abstract  
Fungi infection in maize kernels is a major concern worldwide due to its toxic metabolites such as mycotoxins, thus it is necessary to develop appropriate techniques for early detection of fungi infection in maize kernels. Thirty-six sterilised maize kernels were inoculated each day with _Aspergillus parasitic_s from one to seven days, and then seven groups (D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\), D\({}_{5}\), D\({}_{6}\), D\({}_{7}\)) were determined based on the incubated time. Another 36 sterilised kernels without inoculation with fungi were taken as control (DC). Hyperspectral images of all kernels were acquired within spectral range of 921-2529 nm. Background, labels and bad pixels were removed using principal component analysis (PCA) and masking. Separability computation for discrimination of fungal contamination levels indicated that the model based on the data of the germ region of individual kernels performed more effectively than on that of the whole kernels. Moreover, samples with a two-day interval were separable. Thus, four groups, DC, D\({}_{1-2}\) (the group consisted of D\({}_{1}\) and D\({}_{2}\)), D\({}_{3+4}\) (D\({}_{3}\) and D\({}_{4}\)), and D\({}_{5-7}\) (D\({}_{5}\), D\({}_{6}\), and D\({}_{7}\)), were defined for subsequent classification. Two separate sample sets were prepared to verify the influence on a classification model caused by germ orientation, that is, germ up and the mixture of germ up and down with 1:1. Two smooth preprocessing methods (Savitzky-Golay smoothing, moving average smoothing) and three scatter-correction methods (normalization, standard normal variate, and multiple scatter correction) were compared, according to the performance of the classification model built by support vector machines (SVM). The best model for kernels with germ up showed the promising results with accuracies of 97.92% and 91.67% for calibration and validation data set, respectively, while accuracies of the best model for samples of the mixed kernels were 95.83% and 84.38%. Moreover, five wavelengths (1145, 1408, 1935, 2103, and 2383 nm) were selected as the key wavelengths in the discrimination of fungal contamination levels. In general, near-infrared hyperspectral imaging can be used for early detection of fungal contamination in maize kernels.  
hyperspectral imaging; maize kernel; _Aspergillus parasitic_; early detection
# Optics Based Label-Free Techniques and Applications in Brain Monitoring
## 2 FNIRS in Human Brain Imaging
### Current Clinical Applications of fNIRS
According to recent studies, the protein accumulation is caused by a failure of the glymphatic brain clearance system and the memory decline in demetined mice has been successfully treated by increasing the protein clearance [87]. In this research, RINRS has the potential to provide information on the glymphatic activity of the brain [88]. Monitoring blood-brain barrier (BBB) opening is of great interest in terms of brain drug delivery. Although proper opening of the BBB is crucial for successful treatment, there is no clinically approved method for monitoring it. [PERSON] et al. [89] used fNIRS combined with EEG to monitor primary central nervous system lymphoma patients during BBB opening aiding the chemotherapy. FNIRS detected a remarkable multiphasic response reflecting BBB opening and gradual closing, making the method potentially applicable for monitoring BBB opening during brain chemotherapy.
## 4 Proposed Neural Networks for GEQ Design
### Training the Neural Network for the Third-Octave GEQ Design (NSGE3)  
The training dataset for NSGE3 was created with the novel SGE3 least-squares gain optimization technique proposed in the previous section. A total of 1500 input-output gains were created, where seven pairs consisting of extreme cases--all zeros, all up to +12 dB, and zigzag cases--were set manually, while the remaining 1493 input-output pairs were randomized automatically. Essentially, the input gains of the training data were the same as those used in [20], and the output gains were recalculated using the SGE3 method.  
The actual training of NSGE3 was conducted in several steps. Since the structure and operation of the NGEQ3 neural network [20] was similar to that of the new NSGE3, the NSGE3 was trained based on the previous network in order to save time in the training. The NGEQ3 neural network was retrained using a training dataset with the same input gains as previously, but where the output gains were calculated using the SGE3 algorithm. The resulting neural network already provided accurate predictions of the optimized filter gain values. After the initial training, the SGE3 routine was further optimized to produce even more accurate results, after which the network was retrained using yet another set of training data, consisting of the same inputs, but updated output gains from the  
Figure 9: Group delays of (**a**) the third-octave GEQ and (**b**) the Bark GEQ corresponding to the magnitude responses shown in Figure 7.  
optimized SGE3. The second round of training also converged and turned out to be even more accurate than the first version of the neural network. The accuracy of both proposed methods, SGE3 and NSGE3, is discussed in Section 5.1.
# Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11 Technology
## 2 State of the Art on IEEE 802.11-Based Wake-Up Radio Solutions
After the legacy preamble, the WuC is generated, activating/deactivating the central 13 subcarriers of a 20 MHz IEEE 802.11's OFDM symbol, thus reducing the effective bandwidth to 4 MHz. The WuC contains a synchronization sequence intended to allow frame detection and synchronization by the WuRx, and the WuC data, which is Manchester encoded. Two different encodings are defined: 1 WuC bit per IEEE 802.11 OFDM symbol (i.e., 1 bit per 4 \(\upmu\)s), yielding 250 kbps, and 1 WuC bit per 4 OFDM symbols (i.e., 1 bit per 16 \(\upmu\)s), offering 62.5 kbps. Our goal is to offer an IEEE 802.11-based WuR solution that is compatible with the existing ecosystem of Wi-Fi devices at the cost of a minimum software update. The only requirement for a Wi-Fi transmitter to work as WuTx is the support of channel bonding (available since IEEE 802.11n-2009 [23]). Therefore, our proposal is based on the first type of system, that is, WuR systems based on the transmission of standard IEEE 802.11 frames, increasing over the effective bit rate provided by [10; 15], and not requiring the manipulation of OFDM symbols at the PHY as the one shown in [11]. As detailed in the following sections, our solution minimizes the limitations of these types of systems by (i) using the shortest possible frame to maximize the effective WuC bit rate, and (ii) transmitting the sequence of frames comprising the WuC within one TXOP, hence protecting the whole sequence against interruptions by other Wi-Fi STAs in the area.
## 2 Preliminaries  
### Awg  
An \(N\times N\) AWG is associated with a set of \(N\) wavelengths {0, 1,..., \(N-1\)}. A signal from input port \(i\) to output port \(o\), using wavelength \(w\), is denoted by [\(i\), \(w\), \(o\)], where \(w\in\{0,1,\ldots,N-1\}\). An AWG has different cyclic wavelength routing properties, which lead to contention-free wavelength assignments [8; 23; 27; 28]. One cyclic wavelength routing property, a relationship among parameters \(i\), \(o\), and \(w\), in an \(N\times N\) AWG is given by Equation (1) [27; 28]:  
\[o=(i+w)\bmod N. \tag{1}\]  
An AWG is a passive device. Equation (1) implies that given a wavelength \(w\), the wavelength routing of a signal [\(i\), \(w\), \(o\)] in an AWG is determined and unique. Equation (1) also implies that each input of an \(N\times N\) AWG can send \(N\) signals, each destined to a different output, of \(N\) different wavelengths simultaneously. Thus, an \(N\times N\) AWG can send all \(N^{2}\) signals simultaneously without blocking. An example of the routing of nine signals in a \(3\times 3\) AWG is given in Figure 1.