esa-sceva/satcom-chunk-collection huggingface dataset:
# Cellulose Nanofibril (CNF) Films and Xylan from Hot Water Extracted Birch Kraft Pulps
## Materials and Methods
### Chemical and Molecular Analyses of Pulps and Hydrolysates  
The carbohydrate and lignin content of the pulps, before and after the hot water extractions, were determined after a two-stage acid hydrolysis, according to the analytical method NREL/TP-510-42618 issued by the US National Renewable Laboratory (NREL). Neutral monosaccharides were determined by high-performance anion exchange chromatography with pulse amperometric detection (HPAEC-PAD) in a Dionex ICS-3000 (Thermo Fisher Scientific, Sunnyvale, CA, USA) system. Based on the amount of neutral monosaccharides, the cellulose, xylan and glucomannan fractions in the pulps were calculated using the [PERSON]'s formulas [30]. With these formulas, cellulose is defined as the content of anhydroglucose in the sample after subtracting the contribution of glucose to glucomannan, and xylan is defined as the content of anhydroxylose with uronic acid substituents. The amount of acid insoluble (Klason) lignin was quantified gravimetrically, and the amount of acid soluble lignin (ASL) was determined in a Shimadzu (Kyoto, Japan) UV-2550 spectrophotometer at a wavelength of 205 nm, using an adsorption coefficient of 110 L/(g-cm). The carboxylate content of the pulps before and after TEMPO-mediated oxidation was determined according to the SCAN-CM 65:02 method.  
The intrinsic viscosity of the pulps was determined in cupriethylenediamine (CED) according to the SCAN-CM 15:99 method. The molar mass distribution (MMD) of the pulps was obtained by size exclusion chromatography (SEC) using 2 \(\times\) PL gel MiniMixed A columns with a pre-column in 0.8% LiCl/DMAc as eluent (0.36 mL/min, T = 80 \({}^{\circ}\)C). The elution curves were detected using a Waters 2414 Refractive index detector, and the MMD was calculated against eight pullulan standards (6.1-624 kDa), using Waters Empower 3 software.  
The total number of monosaccharides in the hydrolyses was quantified by HPAEC-PAD after total hydrolysis in an autoclave at 121 \({}^{\circ}\)C for 60 min, according to the analytical method NREL/TP-510-42623 issued by the US NREL. The molar mass of dissolved compounds was determined SEC in a PSS (Mainz, Germany) instrument. Prior to the analyses, the samples were dissolved overnight in 1 M NaOH and filtered through 0.45 \(\upmu\)m filters. The SEC measurements were performed in 0.1 M NaOH as eluent. The MMD of the dissolved compounds was calculated against eight pullulan standards (6.1-624 kDa), using Waters Empower 3 software.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 2 State of the Art
The results showed that drone last-mile delivery is suitable for small and lightweight packages, which can effectively reduce the impact on the environment and society and safeguard the profit of the enterprise [14]. In this paper, a model of ground and UAV co-distribution is innovatively established and a solution method is provided. This model can give full play to the advantages of the two distribution methods and ensure that the economic benefits are maximized under the premise of the shortest distribution time in the region.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 6 Conclusions  
In this paper, we have proposed a novel on-demand node-disjoint multipath routing approach called NDMR. The proposed approach finds two node-disjoint paths between a source and a destination in a wireless network using the request-reply mechanism. By using a two-way or three-way handshake route discovery process and discarding duplicates of request messages at nodes in the network, NDMR significantly reduces the control overhead as well as decreasing the time needed for discovering routes when compared with existing node-disjoint route discovery approaches, such as AODVM and MP-AODV. Numerically, for simulation networks used in this paper, NDMR-2 HS reduced the received control messages by 42% compared with MP-AODV and NDMR-3 HS and by 85% compared with AODVM. Additionally, NDMR and MP-AODV can discover and establish similar node-disjoint paths, but NDMR-2 HS reduced the route discovery time by 50% and NDMR-3 HS reduced it by 25% when compared with MP-AODV. These features make NDMR suitable for applications that require fault tolerance in MANETs and WSNs.  
Our future work is to develop and implement the proposed approach in hardware devices to evaluate and compare its performance in the real environment of wireless networks.  
**Acknowledgments: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (grant number: 2017R1A2B4003964), and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number: 2016R1D1A1B03933315).**  
**Author Contributions: Authors [PERSON], [PERSON], and [PERSON] conceived and developed the ideas behind the research. [PERSON] carried out the performance analysis and simulations and wrote the paper under supervision of [PERSON]. [PERSON] and [PERSON] supervised the research and finalized the paper.**  
**Conflicts of Interest: The authors declare no conflict of interest.**
# Assessing Traffic-Flow Safety at Various Levels of Autonomous-Vehicle Market Penetration
## 1 Introduction  
SAE International classifies autonomous vehicles into six levels based on the driver's intervention and the vehicle's capabilities. It is predicted that, by 2023, autonomous driving technology will have reached Level 2 or Level 3. With the advancement of autonomous driving technology, various studies have been conducted on the effects of introducing autonomous vehicles, including potential accident collisions [1], accident severity [2, 3], economic analysis [4], and traffic-control system design [5]. Accordingly, several studies have been conducted to investigate the safety of traffic flow when autonomous vehicles are introduced using micro-traffic simulations such as VISSIM. However, there is a lack of research that analyzes the safety of traffic flow in mixed traffic that reflects the driving behavior of general-purpose vehicles.  
This study analyzes the safety of traffic flow due to changes in the introduction rate of autonomous vehicles using the driving behavior of general vehicles analyzed based on actual data and the input variables of autonomous-vehicle driving behavior used in previous studies for sections that include highway grade separations and examines the operation of lanes dedicated to autonomous vehicles.
_Algorithms_**2010**, \(3\), 276-293; doi:10.3390/a3030276  
_article_  
**Universite Cubic \(L_{1}\) Interpolating Splines: Analytical Results for Linearity, Convexity and Oscillation on 5-Point Windows**  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{1,2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Industrial and Systems Engineering Department, North Carolina State University, Raleigh, NC 27695-7906, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Mathematical Sciences Division, Army Research Office, Army Research Laboratory, P.O. Box 12211, Research Triangle Park, NC 27709-2211, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +01-919-513-1909; Fax: +01-919-515-5281.  
_Received: 9 June 2010; in revised form: 11 July 2010 / Accepted: 20 July 2010 / Published: 30 July 2010_  
**Abstract:** We analytically investigate univariate \(C^{1}\) continuous cubic \(L_{1}\) interpolating splines calculated by minimizing an \(L_{1}\) spline functional based on the second derivative on 5-point windows. Specifically, we link geometric properties of the data points in the windows with linearity, convexity and oscillation properties of the resulting \(L_{1}\) spline. These analytical results provide the basis for a computationally efficient algorithm for calculation of \(L_{1}\) splines on 5-point windows.  
**Keywords:** convexity; cubic \(L_{1}\) spline; 5-point window; interpolation; linearity; locally calculated; oscillation; second-derivative-based; univariate  
**Classification: MSC** 65D05, 65D07
## 3 Results and Analysis  
### Effect of Moisture Content on Recovery Coefficient of Wheat  
The experimental data corresponding to various moisture content levels are presented in Table 3. From this information, a trend figure (Figure 3) illustrating the factors influencing single-factor water content on the recovery coefficient can be derived. The analysis reveals that under the test premise of employing Q235 steel as the collision material, maintaining material thickness at 4 mm, and setting the drop height at 180 mm, the recovery coefficient of wheat is significantly impacted by moisture content. As moisture content increases, the recovery coefficient progressively declines.
## 3 Doppler OCT for Otology Applications  
### Preclinical Simulations Using a Phantom Membrane  
To evaluate the Doppler OCT system performance and characterize the effects of the external sound pressure on the membrane, phantom-based simulations are required. As an approach of phantom simulation in otology, the latex membrane, which mimics TM, was utilized as a representative sample with external stimulating speakers. As an initial approach to measure the vibrating pattern of the phantom membrane, [PERSON] et al. [45] captured the rapid periodic oscillating motions of latex membrane using OCV, with a sub-nanometer scale for the motion sensitivity and a micro-second scale for the temporal resolution. By using the experimental configuration shown in Figure 1a, a homogeneous vibrating pattern was measured at opposite vibration phases (0 and \(\pi\)) and a cutaway view (Figure 1b,c), respectively. This study demonstrated the feasibility of Doppler OCT to visualize the vibrating mode according to the external sound stimulation.  
To evaluate the performance of the developed Doppler OCT, [PERSON] et al. [66] preliminarily measured the vibrating patterns of the latex membrane, while the obtained results were compared with in vivo assessments. Before the in vivo experiment, a preliminary study using a latex drum was conducted to evaluate the performance of the Doppler OCT system. Additionally, this result was employed for analysis data to improve the objectivity of the obtained experimental results.  
As a different approach to compensate the equivalent anatomical structural characteristics of the latex membrane, a set of two speakers were utilized to make a localized sound distribution to evoke distinct characteristics of the phantom membrane by external sound stimulation by [PERSON] et al. [46]. Measured phase differences of each cross-sectional image and combined enface phase map by Doppler OCT are shown in Figures 2a-d, respectively. According to the increasing applied frequency of sound (2, 2.2, 2.8, 3.1, and 3.2 kHz), an escalation of the order of circular mode was measured as shown in Figure 2e-h. This study showed the feasibility of Doppler OCT for measuring a complex micro-vibration of the membrane.  
Figure 1: (**a**) Schematic of the setup with a latex drum. (**b**) Vibrography snapshot images (0 and \(\pi\) motion phases) of the vibrating drum at the fundamental frequency of 800 Hz. (**c**) A cutaway image showing the motion in a cross-section of the membrane. Reprinted with permission from Ref. [45]. © The Optical Society.  
Figure 2: Process to obtain 2D-ODT enface image representations. (**a–d**) Process of 2D-ODT enface image extraction. (**a–c**) depict acquisition of uppermost cross-sectional information. (**d**) Total ODT-enface representation at 2 kHz frequency. (**e–h**) show the ODT enface image variations according at 2.2 kHz, 2.8 kHz, 3.1 kHz and 3.2 kHz frequencies, respectively; ODT, optical Doppler tomography. Reprinted with permission from Ref. [46].
## 2 The System Model of LDPC
### The Mechanism of Addition Operation  
Since the wtd-AlgB algorithm has a large number of multiplication operations with \(I(\sigma_{i,j}^{(k)})=\theta_{\vec{d}(z_{j}^{(k)},\sigma_{i,j}^{(k)})}n( \sigma_{i,j}^{(k)})\), it leads to an increase in resource consumption and energy consumption. To reduce the hardware resource on the satellite, the addition operation of \(\theta_{d(z_{j}^{(k)},\sigma_{i,j}^{(k)})}\), \(\rho(\sigma_{i,j}^{(k)},w_{j})\), and \(n(\sigma_{i,j}^{(k)})\) can be expressed as:  
\[M_{j}(\sigma_{i,j}^{(k)})=[r-\vec{d}(z_{j}^{(k)},\sigma_{i,j}^{(k)})]+\rho( \sigma_{i,j}^{(k)},w_{j})+n(\sigma_{i,j}^{(k)}) \tag{9}\]  
In Equation (9), it is worth mentioning that each indicator is an integer and is positively correlated. The larger the total reliability \(M_{j}(\sigma_{i,j}^{(k)})\), the more likely it is to be judged as \(\sigma_{i,j}^{(k)}\).  
For the \(j\)-th variable node, the updated variable node of the MRAI-LDPC decoding algorithm is shown in Figure 2. Bidirectional information transfer is carried out between the variable node \(V_{j}\) and the check node \(C_{i}\). \(z_{j}^{(k)}\) is passed to \(C_{i}\) through \(V_{j}\), and \(C_{i}\) feeds back the total reliability \(M_{j}(\sigma_{i,j}^{(k)})\) to \(V_{j}\).  
Therefore, for the \(j\)-th variable node, the updated variable node of the MRAI-LDPC decoding algorithm is:  
\[\mathbf{z}_{j}^{(k+1)}=\left\{\begin{array}{ll}\arg&\max\!M_{j}(\sigma_{i,j }^{(k)}),&\max\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!
## 4 Project Examples
### Experimental Analysis
#### 4.3.2 Prediction Results  
This experiment uses the PyTorch1.4 deep learning framework to build the Bi-Mamba model and obtain relevant results. The specific implementation is based on the Python3.6 programming language and is completed through the VSCode development platform in the Ubuntu 22.04 operating system environment. The experimental hardware configuration is a NVIDIA RTX 4090 graphics card, Intel (R) i7-13700k processor, and 60 GB memory. The network training uses the Adam optimizer, with mean square error loss (MSELoss) as the loss function, and the learning rate is set to 0.001. This paper uses the Bi-Mamba model to predict and analyze the monitoring point: L1-1. The prediction results are shown in Figure 10.  
Figure 10: (**a–d**) respectively show the comparison of L1-1 prediction results at step sizes of 16, 32, 64, and 96.
## 2 C.F.R.P. Material Analysis
### Failure Criteria
#### 2.1.2 Tsai-Hill Failure Criterion  
The maximum work failure theory, also widely known as the Tsai-Hill failure criterion, is based on a modification of the [PERSON] distortion energy criterion for isotropic materials. According to this criterion, a lamina that displays orthotropic behavior is bound to fail if its plain stress state violates Equation (6). The maximum work failure criterion differentiates itself with respect to other failure theories, because it takes into account the interaction between the three strength parameters that exist in a unidirectional lamina. However, one of its inherent drawbacks is the absence of distinguishment on whether the stress state under examination concerns compressive or tensile loading. Additionally, it should be noted that the maximum work failure criterion does not provide information regarding the mode of failure, a fact that leads to the need for further examination of the loads by calculating the absolute values of the ratios of \(\alpha_{1}\) to X\({}_{1}\), \(\sigma_{2}\) to Y, and \(\tau_{12}\) to S and selecting the greatest value [1].  
The Tsai-Hill failure criterion was also applied to offer an alternative view on failure initiation in CFRP laminates. Based on the maximum work theory and analogous to the [PERSON] criterion, it enabled an analysis of stress interactions among tensile, compressive, and shear strengths. Although it does not differentiate tension from compression or specify failure modes, it provides a practical estimate of combined loading effects.  
\[\left[\frac{\sigma_{1}}{X_{1}}\right]^{2}-\left[\frac{\sigma_{1}}{X_{1}}\frac {\sigma_{2}}{X_{2}}\right]+\left[\frac{\sigma_{2}}{Y}\right]^{2}+\left[\frac{ \tau_{12}}{S}\right]^{2}<\ 1 \tag{6}\]  
where  
\[\begin{array}{l}\text{X}_{1}=\left(\sigma_{1}^{\text{T}}\right)_{ult^{\prime }}\text{, if }\sigma_{1}>0\ \ \text{X}_{2}=\left(\sigma_{1}^{\text{T}}\right)_{ult^{\prime}}\text{, if }\sigma_{2}>0\ \ \text{Y}=\left(\sigma_{2}^{\text{T}}\right)_{ult^{\prime}}\text{, if }\sigma_{2}>0\\ \text{X}_{1}=\left(\sigma_{1}^{\text{C}}\right)_{ult^{\prime}}\text{, if }\sigma_{1}<0\ \ \text{X}_{2}=\left(\sigma_{1}^{\text{C}}\right)_{ult^{\prime}}\text{, if }\sigma_{2}<0\end{array}S= \left(\tau_{12}\right)_{ult} \tag{7}\]
## 6 Comparison Analysis and Research Challenges
### Research Challenges  
#### 6.2.1 Multiple Sender  
OR may frequently encounter a situation wherein a few senders try to transmit data at the same time, thus experiencing transmission collisions, which lead to energy waste. A conventional approach to deal with the situation is the receiver-initiated ORs [32; 33]. This method is better for detecting collisions and recovering data packets, even though the latency is increased. In addition to the routing layer's approach, we can consider a MAC layer's approach to properly control the asynchronous duty-cycling (ADC) of the sender node. In other words, if the wakeup times of each sender node are not overlapped, the number of simultaneous senders is going to be minimized. PW-MAC may be an example, in that it used the information from the previous data transmissions in order to control its duty-cycle, which can avoid collisions.
## 5 Conclusions  
This study first proposed SRN based on Siamese neural network for the visual reconstruction, inspired by humans' inherent ability to recognize one image while using few samples by comparing between images. There are always less instances in fMRI measurement, while these supervised learning models need large amounts of labelled data to train the large number of parameters, so common deep learning methods that are applied to the visual reconstruction have a poor effect due to the over-fitting. To make full use of data, the proposed method takes the relationship of sample pairs into account through changing the distributions in feature space by clustering the feature vectors of similar classes and enlarging the distance from the feature vectors of different classes during learning. Given the high dimensionality of limited data instances, it is essential to select voxels to reduce the dimensionality of fMRI data and learn the mapping from fMRI data to extract features in visual reconstruction. First, we employed the extracted features based on trained classification networks to estimate the correlation of each voxel to accomplish the selection of voxels. Second, we trained the reconstruction network with the selected voxels. Based on qualitative and quantitative results, the proposed Siamese reconstruction net performs considerably better in all two datasets in the most important SSIM metric, providing 8% and 10% higher accuracy in Dataset 1 and Dataset 2 as compared to the state-of-the-art methods. These results have demonstrated that our proposed Siamese reconstruction net architecture plays a role in clustering the characters of the same class and it showed effectiveness in visual reconstruction. To the best of our knowledge, this study is the first to propose a mechanism of comparison for dramatically clustering the characters based on Siamese neural network in visual image reconstruction. Visual reconstruction was realized by an end-to-end method instead of mapping the voxel responses to the feature space. In this work, we did not explore the full range of possibilities that Siamese reconstruction net potentially enables. Our future work includes applications of our method to fMRI mental illness classification problem.  
Conceptualization, L.J., H.B. and B.Y.; Formal analysis, L.W.; Methodology, L.J., K.Q. and C.Z.; Software, L.J. and J.C.; Validation, J.C. and L.Z.; Writing-original draft, L.J.; Writing-review & editing, K.Q., L.W., H.B. and B.Y.  
This work was funded by the National Key R&D Program of China under grant 2017 YFB1002502 and National Natural Science Foundation of China (No. 61701089 and No. 61601518).  
Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Ultra-Wideband Analog Radio-over-Fiber Communication System Employing Pulse-Position Modulation
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ECC**} & **Pulse Duration** & **Time Bin** & **Number of** & **20 km Fiber BER** & **Back-to-Back** \\  & \(\tau_{\tau}\)**ps** & **Duration \(\Delta_{\tau}\)ps** & **Positions \(M\)** & & **BER** \\ \hline \multirow{8}{*}{**No ECC**} & 500 & 200 & 128 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 500 & 100 & 256 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 500 & 50 & 512 & \(2.578\times 10^{-2}\) & \(9.169\times 10^{-3}\) \\  & 200 & 200 & 128 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 200 & 100 & 256 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 200 & 50 & 512 & \(3.848\times 10^{-2}\) & \(1.068\times 10^{-2}\) \\  & 100 & 200 & 128 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 100 & 100 & 256 & \(5.936\times 10^{-3}\) & \(3.781\times 10^{-3}\) \\  & 100 & 50 & 512 & \(7.436\times 10^{-2}\) & \(6.931\times 10^{-2}\) \\ \hline \multirow{8}{*}{**Gray code**} & 500 & 200 & 128 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 500 & 100 & 256 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 500 & 50 & 512 & \(9.433\times 10^{-3}\) & \(4.572\times 10^{-3}\) \\  & 200 & 200 & 128 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 200 & 100 & 256 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 200 & 50 & 512 & \(2.518\times 10^{-2}\) & \(6.317\times 10^{-3}\) \\  & 100 & 200 & 128 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 100 & 100 & 256 & \(3.524\times 10^{-3}\) & \(2.924\times 10^{-3}\) \\  & 100 & 50 & 512 & \(4.427\times 10^{-2}\) & \(4.318\times 10^{-2}\) \\ \hline \multirow{8}{*}{**Gray code**} & 500 & 200 & & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 500 & 100 & & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\  & 500 & 50 & & \(9.285\times 10^{-3}\) & \(4.356\times 10^{-3}\) \\ \cline{1-1}  & 200 & 200 & & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\ \cline{1-1}  & 200 & 100 & 256 & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\ \cline{1-1}  & 200 & 50 & & \(1.584\times 10^{-2}\) & \(5.267\times 10^{-3}\) \\ \cline{1-1}  & 100 & 200 & & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\ \cline{1-1}  & 100 & 200 & & \(<\)\(8.17\times 10^{-7}\) & \(<\)\(8.17\times 10^{-7}\) \\ \cline{1-1}  & 100 & 100 & & \(1.514\times 10^{-4}\) & \(1.875\times 10^{-5}\) \\ \cline{1-1}  & 100 & 50 & & \(4.226\times 10^{-2}\) & \(3.804\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the TR-PPM transmission over ARoF experiments, using PRBS for back-to-back setup and setup with 20 km SMF. existing research on RoF systems, where waveforms were captured after the optical transmission network.