esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 3 Chemical Modification and Functionalization of Nanocellulose  
For a particular application, the surface functionalization of cellulosic nanostructure offers a possible great platform. The surface properties of cellulose-based materials can be successfully modified. Various chemical alteration methodologies are performed to improve the effectiveness of the isolation method and to modify the hydrophobicity of nanocellulose materials. These alterations in the nanocellulose materials enhance the degradability and compatibility of nanocellulose [63].  
Figure 2: The attractive characteristics of nanocellulose, including its biocompatible nature, high precision printing, biodegradability, low toxicity, lightweight, and flexibility, are crucial for synthesizing composites and hydrogel fabrication. Adapted with permission from Refs. [56; 57; 58; 59; 60; 61; 62].
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 3 Preliminaries
### Associated Grids  
Based on the grid structure and an issuing user's query distance, we can find out a certain number of grid cells that cover the issuing user's query distance. Thus, associated grids are the smallest set of grid cells that cover the query distance of an issuing user. Suppose that there is a user who wants to search for nearby users (e.g., friends or strangers) and submits a request with the query distance \(l\) through base station \(B\). If base station \(B\) locates in grid cell \(GID_{a}\), then we can lock the smallest set of grid cells that need to be searched based on the query distance \(l\). The specific steps are as follows.  
1. Based on the grid cell which base station \(B\) locates in, the four coordinates (i.e., upper-left, lower-left, upper-right, and lower-right) of \(GID_{a}\) can be found.
2. Four circles can be formed by taking the user's query distance \(l\) as the radius and four coordinates (i.e., upper-left, lower-left, upper-right, and lower-right) of \(GID_{a}\) as the center.
3. The grid cellsthat cover the above four circles can be figured out. Then these grid cells are named the associated grids of the user. Figure 1b shows the associated grids (i.e., green grid cells) when the user's query distance is 2 km.
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 5 Conclusions  
Up to now, far too little attention has been paid to the fusion of the camera and flash LiDAR system. In this paper, we present a novel method of calibrating the camera and flash LiDAR system, as the traditional calibration method has limited accuracy, restricted by mechanical scanning LiDAR. Making use of dense point cloud from flash LiDAR, the proposed method has state-of-the-art accuracy. We design a calibration target that is an arbitrary triangular pyramid with three chessboards on its planes. With abundant 2D information of the chessboards, we can obtain the intrinsic parameters of the camera, and then utilize three planes of the triangular pyramid to obtain the extrinsic parameters of the system. The extrinsic parameters can be optimized by minimizing the distance between points in the LiDAR coordinate system and chessboards in the camera coordinate system. Through the proposed method, one pair capture of the camera and LiDAR is enough to calibrate the intrinsic parameters of the camera and extrinsic parameters of the system. More pairs of frames can increase the accuracy. Emulation experiments demonstrate that our method is robust to sensor noise. Moreover, we use RMSE to evaluate the accuracy and the calibration result is shown by incremental verification experiments. In the future, we will make efforts to increase the resolution of flash LiDAR to obtain a more accurate result.  
Conceptualization, Z.B.; methodology, Z.B.; software, Z.B.; validation, Z.B.; formal analysis, H.D.; writing--original draft preparation, Z.B.; writing--review and editing, C.S. and P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology on Electro-optic Control Laboratory (201951048002).  
Not applicable.  
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
The authors declare no conflict of interest.
# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
## 1 Introduction
Another approach is to select a new candidate nodes set with the same reliability as the previous strategy. In this way, the improvement of transmission power results in farther transmission distance, which means fewer hops are needed to reach the sink. Consequently, it satisfies the need of reducing delay and improving reliability. (3) We provide an extensive theoretical analysis to confirm the strong effectiveness of our scheme. In our experimental comparison, for medium scale sensor networks, our scheme can improve the reliability by 36.62% and reduce delay by about 21.09%. The rest of the paper is organized as follows: Section 2 reviews related works compared with our scheme. Preliminary knowledge and research motivation are given in Section 3. Section 4 describes the network model and defines problem statements of this paper. In Section 5, we give the design of Cross-layer Optimized Opportunistic Routing (COOR) scheme for loss-and-delay sensitive WSNs. Section 6 proposes experimental results and comparison of COOR scheme. We conclude this paper in Section 7.
## 4 Results and Discussion
### Instance Segmentation Results
In Figure 6d, AFPM can assist the instance segmentation algorithm to detect different sizes and categories of objects more precisely, which aids the network to exactly segment two people on the left edge of the first \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AP (\%)** & **AP\({}_{50}\) (\%)** & **Parameters(M)** \\ \hline Baseline & 28.3 & 51.7 & 32.79 \\ Baseline + CTAM & 28.7 & 52.0 & 32.79 \\ Baseline + CBAM & 30.2 & 52.5 & 33.32 \\ Baseline + CBAM + CTAM & 30.7 & 53.3 & 33.32 \\ Baseline + AFPM & 31.0 & 54.1 & 33.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of instance segmentation on the Cityscapes validation set. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Person** & **Rider** & **Car** & **Truck** & **Bus** & **Train** & **Motorcycle** & **Bicycle** \\ \hline Baseline & 25.5 & 21.5 & 48.4 & 26.3 & 48.1 & 24.4 & 16.0 & 16.0 \\ Baseline + CTAM & 25.7 & 22.0 & 49.0 & 26.7 & 49.1 & 24.8 & 16.4 & 16.0 \\ Baseline + CBAM & 28.9 & 24.6 & 49.3 & 26.4 & 51.2 & 25.2 & 16.5 & 19.5 \\ Baseline + CBAM + CTAM & 28.9 & 24.8 & 49.8 & 26.9 & 52.3 & 26.5 & 17.0 & 19.6 \\ Baseline + AFPM & 29.2 & 24.8 & 50.9 & 27.0 & 52.5 & 26.9 & 17.3 & 19.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of AP (%) for each instance class on the Cityscapes validation set. image, distant objects in the second image and the white truck similar to the background in the third image. As shown in Figure 7, we also qualitatively compare the instance segmentation results of ground-truth and the proposed module. The proposed method which combined attention modules and a multi-level feature network can not only recognize the object correctly, but also detect and segment the instance object, which is contained in the input image but not labeled in the ground-truth. More intuitively, it can be seen from the first two rows of images in Figure 7 that the instance object in the input image can be accurately segmented by exploiting the proposed module. Furthermore, as shown in the last two rows of images in Figure 7, our method can also predict more correct objects that are not annotated in the ground-truth. Figure 6: Comparison of instance segmentation results of different methods. (**a**) Original images; (**b**) the instance segmentation results of baseline; (**c**) the instance segmentation results of baseline with CBAM and CTAM; (**d**) the instance segmentation results of baseline with AFPM. Figure 7: Comparison of instance segmentation results between ground-truth and the proposed method. (**a**) Original images; (**b**) the instance segmentation results of ground-truth; (**c**) the instance segmentation results of baseline with AFPM.
## 7 Clinical Assessment
### Questionnaire
#### 7.1.2 Toronto Alexithymia Scale 26 (TAS-26)  
It is a subjective self-evaluation questionnaire with 26 items that allows for the depiction of alexithymia features. The items response is based on a five-point Likert scale. The sum score of the TAS-26 determines the extent of alexithymia, which is classified in three subscales. Therefore, it allows for differentiating which of the three characteristic aspects of alexithymia (difficulty in identifying emotions (scale 1), difficulties in describing emotions (scale 2) and externally oriented thinking style (scale 3)) dominate. It has demonstrated good internal consistency (Cronbach's alpha = 0.67 to 0.84). A participant is diagnosed with alexithymia when a score of 30 and higher is recorded [120].  
7.1.3. Tampa Scale for Kinesiophobia for Temporomandibular Disorders (TSK-TMD-S)  
It is an instrument to assess fear of movement in TMD patients. It has been translated into several languages [121; 122; 123] but was firstly developed and validated in Dutch [124]. This version is composed of 12 items and two subscales: activity avoidance and somatic focus. This two-factor solution of the Dutch TSK-TMD has generally good reliability and convergent validity [119]. When the validation to the Spanish was performed, the statistics reduced it to 10 items, probably due to language differences [121].
## 4 Literature Review: Other Research on CWSN
### Energy Efficiency
#### 4.2.2 Clustering for Energy Efficiency  
As described in Section 3, the routes with more hops and shorter hop distances can be more power efficient than those with fewer hops and longer hop distances. Therefore, clustering is one of the solutions for power conservation in CR-WSNs. Clustering schemes for WSNs cannot be used directly in CR-WSNs because to form a cluster, the sensors need to communicate and decide the cluster head in a different manner than the conventional WSNs. [PERSON] _et al._[80, 81] proposed distributed spectrum-aware clustering schemes considering the energy efficiency for CR-WSNs. The authors modeled the power consumption and derived the optimal number of clusters as follows:  
\[K_{opt}=\left\lfloor\frac{N}{r_{\max}\sqrt{3\rho}}+0.5\right\rfloor \tag{5}\]  
where \(K_{opt}\) is the optimal number of clusters, \(N\) is the number of CR wireless sensor nodes, \(r_{max}\) is the maximal transmission range of CR wireless sensor nodes, and \(\rho\) is the CR wireless sensor nodes density. These schemes have low complexity and rapid convergence under dynamic PU activity.
# Area-Efficient Universal Code Generator for GPS L1C and BDS B1C Signals
## 2 Background  
To analyze the structure of code generators, it is necessary to fully understand signal configuration and code construction. In this section, signal configuration and code con  
\begin{table}
\begin{tabular}{c c c c} \hline
**GNSS** & **Frequency Band a **Signal** & **Code Structure** \\ \hline \multirow{8}{*}{GPS [4]} & L1 & L1C/A & LFSR \\  & L1 & L1C & Legendre sequence \\  & L2 & L2C (L2 CM/L2 CL) & LFSR \\  & L5 & L51 & LFSR \\  & E1 & E1 & Memory code \\ GALILEO [5] & E5 & E5a (E5a-I/E5a-Q) & LFSR \\  & E5 & E5b (E5b-I/E5b-Q) & LFSR \\  & L1 & L1 OF & LFSR \\  & L1 & L1 OC (L1 OCd/L1 OCp) & LFSR \\ GLONASS [6] & L2 & L2 OF & LFSR \\  & L2 & L2 OC (L2 OCp) & LFSR \\  & L3 & L3 OC (L3 OCd/L3 OCp) & LFSR \\  & B1 & B1 & LFSR \\  & L1 & B1C & Legendre sequence \\  & B2 & B2I & LFSR \\  & L5 & B2A (B2 ad/B2 ap) & LFSR \\  & B3 & B3I & LFSR \\ \hline \end{tabular}  
* L1:1575.42 MHz, L2: 1227.60 MHz, L3: 1381.05 MHz L5: 1176.45 MHz; B1:1561.098 MHz, B2: 1207.140 MHz, B3:1268.520 MHz; E1: 1575.420 MHz, E5: 1191.795 MHz.  
\end{table}
Table 1: Summary of GNSS signals.  
struction are mathematically described to clarify the structure of code generators in the following sections.
## 3 The Proposed Traffic Flow-Prediction Model
### The Attention Mechanism Layer
Then \(K\) is used as a key matrix to participate in the calculation of the attention matrix. Inspired by the design of the BiDAF network [40], the decoder includes a bidirectional spatiotemporal attention module designed to handle both the historical data (\(E_{m}\)) and predicted data (\(Q_{de}\)). To combine the spatial correlation features from the location attention module (LAM) with temporal correlation features from the temporal attention module (TAM), it computes a set of attention feature vectors. Following that, the spatiotemporal bidirectional attention features are computed at each timestamp, based not only on the features from the previous time step but also the interactions of all traffic flow situations. In this module, the input consists of the spatial correlation features \(E_{en}=[e^{0},\cdots,e^{i},\cdots,e^{k-1}]^{T}\in R^{k\times U\times V}\) and \(Q_{de}=[q^{0},\cdots,q^{j},\cdots,q^{l-1}]^{T}\in R^{l\times U\times V}\). We compute the attention scores from both directions, \(E_{en}\) to \(Q_{de}\) and \(Q_{de}\) to \(E_{en}\), for the calculations, i.e., \(H_{de}^{1}=[h_{UV}^{0},\cdots,h_{UV}^{i},\cdots,h_{UV}^{k-1}]^{T}\in R^{k \times UV}\) the historical spatiotemporal awareness vector and the predicted spatiotemporal awareness vector \(U_{de}^{1}=[u_{UV}^{0},\cdots,u_{UV}^{i},\cdots,u_{UV}^{k-1}]^{T}\in R^{l\times UV}\). The multi-output strategy  
The MRWG BiAtten model can be used to predict the future traffic demand for all regions, thus maintaining the spatiotemporal correlations between regions and prediction time intervals. Inspired by the design of the BiDAF network [40], the integrated attention vectors \(H_{de}^{1}\), \(U_{de}^{1}\), the processed decoder states \(H_{de}\), \(U_{de}\), and the encoder states \(E_{en}\) are all combined together,  
\[G=[E_{en};U_{de};E_{en}\circ U_{de};E_{en}\circ H_{de}]\in R^{4I\times UV} \tag{10}\]  
where \"\(\circ\)\" denotes the element-wise multiplication and [;] represents the concatenation of the cross columns. \(G\) is then transformed to obtain the final result \(Q_{out}\in R^{l\times UV}\) via a one-dimensional CNN, where each column of \(Q_{out}\) can be assumed as the predicted representation of each input of the historical regional traffic data.
###### Abstract  
_Fusarium_ wilt (caused by _Fusarium oxysporum_ f. sp. _Lilii_) is one of the most damaging diseases in lily (_Lilium sargentiae_ [PERSON]). Although some _F. oxysporum_-resistant lily varieties have been identified and are being utilized in resistant breeding, the regulation network of the resistance-associated mechanisms is yet to be studied due to the lack of reliable reference genes for qRT-PCR (quantitative reverse transcription PCR) normalization. The reliability of results by qRT-PCR relies mainly on the stability of the reference genes. This study investigated the reliability of nine candidate reference genes (_CYP_, _EF1-a_, _GAPDH_, _TUB_, _UBQ_, _AQP_, _HIS_, _PGK_, and _RPL13_) for qRT-PCR analysis of _F. oxysporum_-resistant genes. Expression stability analysis via common programs GeNorm, BestKeeper, and NormFinder, at different time points post-inoculation of _F. oxysporum_, revealed that all nine genes met the basic requirements of reference genes. Amongst them, _HIS_ and _GAPDH_ displayed the highest and the lowest expression stability, respectively. The reliability of _HIS_ was further validated by analyzing the expression levels of four resistance-related candidate genes. The expression patterns of the four target genes were consistent with their responses to pathogenic fungi in other plants. Our results show that _HIS_ is the most suitable reference gene for accurately normalizing _F. oxysporum_-resistant genes' expressions in _L. sargentiae_.  
Lilium sargentiae [PERSON]; _F. oxysporum_ inoculation; gene expression; stability
## 4 Semantic Information Extraction
### IPM-Based Hybrid Semantic Edge Extraction
The algorithm of the polyline-approximation-based edge segmentation method is shown in Algorithm 1. ```
1:Input: Synthesized surround view image within the passable area \(\mathbf{I_{edge}}\). 2:Output: Valid edge image after segmentation \(\mathbf{I_{seg}}\). 3:Initializing: The maximum allowable error of the polyline approximation \(D_{max}\), the maximum allowable angle threshold for the line and polyline between the midpoint of the effective polyline and the camera optical center \(\theta_{max}\), reject range \(r\). 4: Extracting the contours of all edges in \(\mathbf{I_{edge}}\) to form the edge profile set \(\mathcal{C}\);
5:for\(c\in\mathcal{C}\)do
6: Computing the polyline set \(\mathcal{L}\) by taking advantage of Douglas-Peucker operators and \(D_{max}\) to approximate the contours with polylines;
7:for\(l\in\mathcal{L}\)do
8: Computing the camera center note position \(C\) of the field of view based on the endpoint of the polyline \(l\);
9: Computing the connecting line \(l_{C}\) between the middle point of \(l\) and the camera optical center \(C\);
10: Computing the angle \(\theta\) between \(l\) and \(l_{C}\);
11:if\(\theta<\theta_{\text{max}}\)then
12: Removing all the edge points covered by \(l\) along with reject range \(r\) from \(\mathbf{I_{seg}}\) ;
13:endif
14:endfor
15:endfor
```  
**Algorithm 1** Polyline-approximation-based edge segmentation algorithm.
## 3 Results
### The Best Spectral Resolution of Soil Moisture  
The influence of different spectral resolutions on the estimation accuracy of soil moisture content was explored. The reflectance spectra of soils with different moisture contents were resampled at sensitive wavelengths with a sampling interval of 1 nm. Next, based on the soil moisture content inversion model derived in [20], the soil moisture content was estimated by using the spectral data with spectral resolution set to 1 nm, 2 nm, 3 nm, 4 nm, 5 nm,..., 100 nm, respectively, so as to obtain the best spectral resolution for soil moisture content inversion. As shown in Figure 6, the RMSEP of soil moisture content first decreases and then increases with the increase of spectral resolution. The best spectral resolution for soil moisture content inversion is 34 nm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number** & **RMSEP (g kg\({}^{-1}\))** \\ \hline SA-SPA & 5 & 12.1 \\ ICO-SPA & 2 & 13.1 \\ MWSE-CARS-SPA & 8 & 12.7 \\ MWSE-GA-SPA & 8 & 12.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results between SA-SPA and 3 traditional empirical wavelength selection methods.  
Figure 5: First-order sensitivity analysis of the input parameters. (**a**) Original. (**b**) Amplified.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Wavelength (nm)** & **RMSEP (g kg\({}^{-1}\))** \\ \hline
1010 & 12.1 \\
1068 & 11.6 \\
1915 & 10.4 \\
1946 & 11.9 \\
1988 & 12.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensitive wavelengths of soil moisture.
# Design and Analysis of an O+E-Band Hybrid Optical Amplifier for CWDM Systems
###### Abstract  
Broadband amplification in the O+E-band is very desirable nowadays as a way of coping with increasing bandwidth demands. The main issue with doped fiber amplifiers working in this band such as the bismuth-doped fiber amplifier is that they are costly and not widely available. Therefore, a wideband and flat-gain hybrid optical amplifier (HOA) covering the O+E-band based on a parallel combination of a praseodymium-doped fiber amplifier (PDFA) and a semiconductor optical amplifier (SOA) is proposed and demonstrated through numerical simulations. The praseodymium-doped fiber (PDF) core is pumped using a laser diode with a power of 500 mW that is centered at a wavelength of 1030 nm. The SOA is driven by an injection current of 60 mA. The performance of the HOA is analyzed by the optimization of various parameters such as the PDF length, \(\mathrm{Pr}^{3+}\) concentration, pump wavelength, and injection current. A flat average gain of 24 dB with a flatness of 1 dB and an output power of 9.6 dBm is observed over a wavelength range of 1270-1450 nm. The noise figure (NF) varies from a minimum of 4 dB to a maximum of 5.9 dB for a signal power of 0 dBm. A gain reduction of around 4 dB is observed for an O-band signal at a wavelength of 1290 nm by considering the up-conversion effect. The transmission performance of the designed HOA as a pre-amplifier is evaluated based on the bit-error rate (BER) analysis for a coarse wavelength-division multiplexing (CWDM) system of eight on-off keying (OOK)-modulated channels, each having a data rate of 10 Gbps. An error-free transmission over 60 km of standard single-mode fiber (SMF) is achieved for different data rates of 5 Gbps, 7.5 Gbps, and 10 Gbps.  
hybrid optical amplifier; O+E band amplifier; praseodymium-doped fiber amplifier; semiconductor optical amplifier 2022
## 4 Results and Analysis
### Analysis of Compliance Behavior
#### 4.1.7 Actual Behavior and Compliance
According to a literature review done by [35], protection motivation and sanctions have significant effects on attitude, and attitude determines behavioral intent; moreover, perceived behavioral control and subjective norms also significantly affect behavioral intent. These researchers indicated that behavioral intent carves the actual behavior of an individual. Reference [91] also analyzed the TPB predictors with four types of dysfunctional security behaviors (a naive mistake, dangerous thinking, harmful misuse, and intentional destruction); it has been shown that the TPB elements have different effects and the intentions of employees vary among multiple dysfunctional security behaviors, and intention is the strongest predictor of actual behavior. Reference [92] discussed the fact that intention is not the only predictor of actual behavior, and not all intentions become actions. Their result shows that not all misuse behaviors are intended; sometimes, it is an unreasoned action. In the same way, they have argued that security managers are more interested in actual behaviors than intention. Reference [93] presented a TPB, GDT (i.e., sanction severity, sanction certainty), and SCPT based framework to mitigate insiders' information security misbehavior. This framework was tested upon a total of 444 employees from different organizations. This study concluded that all GDT and SCPT significantly affect employees' negative attitudes towards misbehavior except reducing provocations and remove excuses; furthermore, all of TPBs constructs have significant effects on employees' actual ISB. Table 10 summarizes the actual compliance studies.