esa-sceva/satcom-chunk-collection huggingface dataset:
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 2 The Proposed New One-Dimensional Chaotic Map
### Tent Map (TM)  
The mathematical model of the tent map (TM) [11] system is shown in Equation (3):  
\[x_{n+1}=\left\{\begin{array}{c}\frac{b}{2}x_{n},ifx_{n}<0.5\\ \frac{b}{2}(1-x_{n}),ifx_{n}\geq 0.5\end{array}\right.,b\in[0,4]. \tag{3}\]  
In Equation (3), \(x_{n}\) represents the state values of the system at the \(n\)-th discrete time point (\(n=0\), \(1\), \(2\),... ). \(x_{0}\) represents the initial state value of the system, and \(b\) is the control parameter of the system. Figure 2 shows the variation of the Lyapunov exponent and bifurcation diagram of the tent map with parameter \(b\). Figure 2a is the Lyapunov exponent curve of the tent map with a system parameter \(b\). When \(b=4\), the maximum Lyapunov exponent occurs, which is \(LE_{\max}=0.6931\). Figure 2b is the state value bifurcation diagram of the tent map with system parameter \(b\). As can be seen from Figure 2, when \(b\in(2,4]\), system (3) enters the chaotic state.  
The disadvantages of the above two systems are as follows: First, the parameter interval of the system where chaos occurs is relatively narrow (the chaotic parameter interval of LM system is \(b\in(3.57,4]\), and the chaotic parameter interval of TM system is \(b\in(2,4]\)). Second, when \(b<4\), the value of the system state cannot fill the whole space range of \((0,1)\).  
Figure 1: Lyapunov exponent and bifurcation diagram of logistic map. (**a**) The graph of Lyapunov exponent versus parameter \(b\); (**b**) bifurcation diagram of system state quantity versus parameter \(b\).
## 3 Results
### MDD Subtypes  
Mean scores for the four MDD subtypes were depressed Mood = 1.736 (SD = 0.653; range = 1.00 to 3.67), Anhedonia = 1.850 (SD = 0.657; range = 1.00 to 3.75), Cognitive depression = 2.183 (SD = 0.810; range = 1.00 to 4.00), and Somatic depression = 1.618 (SD = 0.511; range = 1.00 to 3.50) using the 4-point scale described above. These means were used as cutoffs to identify the most depressed from the least depressed participants in each SDS subtype. All four SDS subtype scores were significantly correlated with each other (all \(\rho\geq 0.646\), \(p\) < 0.001).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Location** & **MNI (X, Y, Z)** \\ \hline \multirow{4}{*}{Dorsal Attention Network} & left frontal eye field & \(-\)25, 12, 55 \\  & right frontal eye field & 28, \(-\)10, 53 \\  & left posterior intraparietal sulcus & \(-\)22, \(-\)68, 46 \\  & right posterior intraparietal sulcus & 20, \(-\)67, 51 \\ \hline \multirow{4}{*}{Ventral Attention Network} & left middle frontal gyrus & \(-\)47, 14, 32 \\  & right middle frontal gyrus & 47, 14, 32 \\  & left supramarginal gyrus & \(-\)57, \(-\)43, 34 \\  & right supramarginal gyrus & 57, \(-\)43, 34 \\ \hline \multirow{4}{*}{Salience Network} & dorsal anterior cingulate & 0, \(-\)21, 36 \\  & left anterior prefrontal cortex & \(-\)35, 45, 30 \\  & right anterior prefrontal cortex & 32, 45, 30 \\ \cline{1-1}  & left insula & \(-\)41, 3, 6 \\ \cline{1-1}  & right insula & 41, 3, 6 \\ \cline{1-1}  & left lateral parietal lobule & \(-\)62, \(-\)45, 30 \\ \cline{1-1}  & right lateral parietal lobule & 62, \(-\)45, 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Neural networks, brain sites, and MNI coordinates of centroid for each ROI.
## 3 Ice-Templating of Nanocellulose Suspensions
### Effect of the Suspension Formulation
#### 4.2.2 Colloidal Stability of Nanocellulose Suspensions  
Electrostatic stabilisation is the main mechanism for stabilisation of cellulose nanoparticles against aggregation and flocculation [90; 92]. The colloidal stability of nanocellulose suspensions depends on the chosen extraction processing route that confers to CNFs or CNCs different surface charge density [18; 26]. The destabilisation of nanocellulose suspension is also often accomplished by the addition of salt or changing the pH to effectively neutralise or screen the (repulsive) surface charge of the particles [90; 144; 145; 91]. [PERSON] et al. [56] investigated the effect of the CNF type on the resulting microstructures of ice-templated foams. The authors prepared CNF foams using two types of CNF suspensions, namely enzymatic and TEMPO-oxidised CNF suspensions. Enzymatic CNF suspensions exhibited spatially heterogeneous mesostructures such as flocs (50-100 \(\upmu\)m). In contrast, TEMPO-oxidised CNF suspensions were electrostatically stabilised suspensions and thus did not exhibit flocculated textures. Noticeable structural differences between enzymatic and TEMPO-oxidised CNF foams were observed. As shown in Figure 13, the geometry of TEMPO-oxidised cell foams was much more regular compared with that of enzymatic foams. These observations show the role of the colloidal stability of nanocellulose suspensions on the phenomena of particle segregation, entrapment, and redistribution that occur during ice growth. Because of their strongest colloidal stability, TEMPO-oxidised CNFs are presumably more homogenously redistributed by the moving ice front, leading to more regular structures [56]. The situation is probably more complex and chaotic with enzymatic suspensions since they are more polydisperse, less stable and thus exhibit mesoscale heterogeneities in the form of flocs or floc chains. These flocs could be encapsulated or torn by the moving ice front, instead of being homogeneously redistributed. These complex mechanisms are potentially at the origin of the structural defects and heterogeneities, such as the numerous dangling masses and torn cell walls (Figure 13).  
Figure 13: Photographs obtained with a black background of a 2-wt% enzymatic CNF suspension (**a**), and a 0.75-wt% TEMPO-oxidised CNF suspension (**c**). SEM micrographs of enzymatic CNF foams (\(\rho\simeq 23\) kg m\({}^{-3}\)) (**b**) and TEMPO-oxidised CNF foams (\(\rho\simeq 11\) kgm\({}^{-3}\)) (**d**). (Adapted from [PERSON] et al. [56] with permission from Elsevier).
## 3 Compression Standards
### Tested Image Sets  
The MicroZip, Yeast, ApoA1 and ISREC sets used for benchmarking in the literature have been already described in Section 2.8. In addition, we have employed the Stanford set and the Arizona set. The Stanford set contains 20 images with sizes over \(2000\times 2000\), and up to \(2200\times 2700\), obtained from the Stanford Microarray Database public FTP at [[ftp://smd-ftp.stanford.edu/pub/smd/transfers/[PERSON]](ftp://smd-ftp.stanford.edu/pub/smd/transfers/[PERSON])]([ftp://smd-ftp.stanford.edu/pub/smd/transfers/[PERSON]](ftp://smd-ftp.stanford.edu/pub/smd/transfers/[PERSON])). The Arizona set has been kindly provided by [PERSON] and [PERSON] from the University of Arizona, and contains 6 images, each of size \(4400\times 13800\). All images are \(16\) bits per pixel. In Table 4, we summarize all the information about the different sets used in this work, including average image entropy.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm** & **Year** & **MicroZip** & **Yeast** & **ApoA1** & **ISREC** & **Unspecified** \\ \hline \hline \multicolumn{1}{l}{SLOCO [13]} & 2002 & — & — & 8.556 & — & — \\ [PERSON] [12] & 2003 & — & — & — & — & 9.091 \\ [PERSON] [15] & 2004 & — & — & — & — & 6.985 \\ MicroZip [14] & 2004 & **9.843** & — & — & — & — \\ PPAM [35] & 2005 & 9.587 & 6.601 & — & — & — \\ MACE [17] & 2006 & — & — & — & — & 7.070 \\ [PERSON] [37] & 2006 & 8.840 & — & **10.280** & 10.199 & — \\ [PERSON] [18] & 2007 & 8.856 & — & 10.250 & **10.202** & — \\ [PERSON] [19] & 2009 & **8.369** & — & 9.52 & **9.49** & — \\ [PERSON] [38] & 2009 & 8.619 & — & 10.194 & 10.158 & — \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of lossless microarray-specific schemes on 16 bpp images. All results are expressed in bits per pixel (bpp), so lower is better. All results have been adopted directly from the references specified in the table. Best results for each specified image set have been highlighted in **green**, and worst results in **red**.
# Protein Dynamics in F-like Bacterial Conjugation
## 2 Dynamics of Proteins Involved in the Regulation of Bacterial Conjugation
### TraR  
The role of the transcription factor TraR in the process of conjugation is unconfirmed as the protein is dispensable for plasmid transfer; however, it is known to bind RNA polymerase (RNAP) to affect gene transcription [48; 49; 50; 51; 52]. TraR is in the class of proteins that binds the secondary channel of RNAP, which results in transcription inhibition of ribosomal RNA (rRNAA) genes and activates transcription of amino acid (aa) biosynthesis genes [52; 53]. DskA is the best studied member of this class as it is a highly conserved protein in proteobacteria; DskA only binds RNAP in a manner similar to TraR when in the presence of guanosine tetraphosphate (ppGpp), a cell stress-related nucleotide [49; 52]. Speculation has been made to whether TraR confers indirect fitness advantages to the host, thus increasing the survivability of the plasmid [49; 50]. DskA and ppGpp have been shown to modulate auto-aggregation and motility genes; both functions can lead to new successful conjugation events [52]. Genes found on pathogenicity islands are also upregulated by the presence of DskA and ppGpp, and, therefore, TraR is likely to increase production of these virulence genes as well. As ppGpp is not required for TraR activity, gene upregulation normally associated with stress-response occurs without the associated environmental stressors. The function of F TraR may be masked by constitutive expression of the F T4 SS due to the mutation of _fintO_, or perhaps it plays a more notable regulatory role in conjugation when the natural conditions of a competitive microenvironment are present [49; 50].  
A notable structural feature of TraR is its small size at 73 aa, and when compared to DskA it shares 29% sequence homology, all of which lies in the C-terminal region [52]. Structures of TraR bound to RNAP show that the N-terminal \(\alpha_{1}\) helix is analogous to the 2 nd helix of the DskA coiled-coil domain, both of which fit into the secondary channel of RNAP [48; 54]. Conserved TraR residues Asp6 and Ala8 have analogous residues in DskA; Asp6 binds the nucleotide triphosphate (NTP) binding site, and Ala8 fits into the cavity of the bridge helix, and when mutated, function is severely impaired [48; 49; 54]. The N-terminal helix of TraR binds the same residues of RNAP as the coiled-coil domain of DskA when ppGpp is also bound; however, the conformation of TraR allows for more direct interaction with RNAP (Figure 2D) [48; 54]. The hydrophobic residues Ile23 and Ile27 of TraR occupy the space filled by ppGpp; thus, providing a structural justification for the activity of TraR on RNAP function despite a lack of signaling nucleotide. TraR is a well-structured protein and induces a conformational change upon binding to RNAP; however, three major conformational states of TraR bound to the RNAP have been discovered by cryo-EM with a continuous distribution of conformations between them, indicating the inherent flexibility of the small protein when bound to its target [54].
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 3 Traditional Feature-Based Mosaic Process for SAR Images
### Parallel Registration  
With the rapid development of modern radar technology, the image processing scale of spaceborne ScanSAR has increased continually. Parallel operation is becoming an attractive solution when traditional serial operation cannot meet the growing application requirements. Thus, we developed an efficient registration method. First, perpendicular to the direction of the mosaic, the downsampled overlapping areas were divided into multiple equally sized parts. Then, as shown in Figure 6, the keypoints of different parts could be simultaneously detected and matched to achieve parallel registration.  
Figure 5: The relationship of image scaling and homography matrix scaling.  
Directly calculating the homography matrix using the matched keypoints obtained from the downsampled divided overlapping areas was not enough. It should be noted that to ensure the accuracy of \(\mathbf{H}_{0}\) and the mosaic relationship between block and block, the coordinates of the matched keypoints must be corrected due to the offsets caused by overlapping area extraction, downsampling and division. Taking transverse stitching as an example, the row coordinate offset of the overlapping area's \(k^{\text{th}}\) part in \(I_{\alpha}\) is:  
\[x_{e}(k)=\frac{h_{\alpha}}{nM}(k-1),k=1,\ldots,M \tag{14}\]  
where \(h_{\alpha}\) stands for the number of rows in \(I_{\alpha}\), \(n\) represents the multiple of downsampling, and \(M\) is the number of divided parts in overlapping areas, which can depend on the number of central processing unit (CPU) cores on the experimental device. The column coordinate offset in \(I_{\alpha}\) is:  
\[y_{e}=\frac{1}{n}(c_{\alpha}-c_{\alpha}r_{\alpha}) \tag{15}\]  
here, \(c_{\alpha}\) is the number of columns in \(I_{\alpha}\) and \(r_{\alpha}\) is the overlapping rate of \(I_{\alpha}\). Therefore, the coordinate of any matched keypoint in the overlapping area's \(k^{\text{th}}\) part of \(I_{\alpha}\) should be corrected as:  
\[(x_{c},y_{c})=(x,y)+(x_{e}(k),y_{e}) \tag{16}\]  
where \((x_{c},y_{c})\) are the corrected coordinates and \((x,y)\) are the coordinates before correction. After extraction, downsampling and division, the keypoints were detected and matched in the \(M\) parts of the overlapping areas at the same time. Then, the coordinates of the matched keypoints in different parts were simultaneously corrected. The matched keypoints of all blocks were selected randomly to calculate the homography matrix. Thus, the proposed parallel registration can significantly improve the efficiency of the image mosaic.
## 3 Proposed Method
### Weibull FEA Stress Data Analysis
#### 4.1.3 Stress Random Behavior  
Because the determination of the cycles to failure is based on the random behavior of the \(\sigma_{2}\) stress value, in this section the random behavior of the stresses \(\sigma_{1}\) and \(\sigma_{2}\) values are determined by Equations (18) and (19) (see fifth and sixth columns in Table 4). First, by using the \(\beta_{s}\) and the \(Y_{i}\) values in Equation (17), the basic Weibull elements \(t_{0i}\) defined for each one of the \(Y_{i}\) elements are determined, as shown in Table 4. Additionally, by using the \(\eta_{s}\) and the \(\sigma_{1}\) values in Equation (20), the expected Weibull \(t_{01}\) value, from which the \(\sigma_{1}\) and \(\sigma_{2}\) stress values are both reproduced, is determined as \(t_{01}=301.455469/491.75=0.617339\), and it is included in Table 4 as well.  
Then, by using the \(\beta_{s}\) value in Equation (21), the \(Y_{1}\) value that corresponds to the \(t_{01}\) value is determined as \(Y_{1}=ln(0.617339)*(2.248519)=-1.084545\). By using the \(Y_{1}\) value into the Equation (22), the reliability index that corresponds to the \(t_{01}\) element is \(R(t)=exp[-exp[-1.084545]]=0.713156\).  
**Note 4**. Observe that \(R(t)=0.713156\) is not the reliability of the design component; it only represents the reliability of the \(t_{0 max}\) element in the Weibull analysis. The \(R(t)\) of the element is that determined by using the \(Y_{1}\) value that corresponds to the \(S_{y}\) value in Equation (22). It is located between rows 2 and 3 in Table 4.  
The procedure to find the reliability of the design element by using the \(S_{y}\) value as \(\sigma_{1}\) is as follows:  
From Equation (20), the \(t_{0 Sy}\) element that corresponds to the \(S_{y}\) value is \(t_{0 Sy}=301.455469/827=0.364517\) and from Equation (21) the corresponding \(Y_{S_{y}}\) value is \(Y_{Sy}=ln(0.364517)*(2.248519)=-2.269166\). Thus, from Equation (22), the reliability index for the \(Y_{Sy}\) value is \(R(t)=exp[-exp[-2.269166]]=0.901768\). From the above it can be concluded that the reliability of the design element is \(R(t)=0.901768\).  
Bold data highlight: (1) the \(R(t)\) index that corresponds to the used \(S_{y}=\sigma_{1}=827\) MPa value; (2) the reproduced principal stresses values and (3) the stress Weibull eta parameter.  
Based on the fact that in the Weibull analysis the reliability of the element is given by either the applied stress or the corresponding life [14], then the \(t_{oi}\) elements are used to determine the corresponding random behavior of the expected cycles to failure. The analysis is as follows.
## 3 Results and Discussion
### Fusion of _At_SUS1 and _Ugt76G1_ by _Rigid and Flexible Linkers  
Efforts have been made to clarify the properties of linkers in various fusion proteins [39,40]. Empirical linkers of a fusion construct are mainly divided into flexible, rigid, and in vivo cleavable linkers [41]. Rigid linkers and flexible linkers are the most popular applied linkers for fusion constructs [42, 43, 44]. To investigate the potential of fusion protein linkers in the UGT-SuSy reactions, three rigid linkers (EAAAK)n (n = 1, 2, 3) and three flexible linkers (GGGGS)n (n = 1, 2, 3) were fused between the C-terminal of _At_SUS1 and \(N\)-terminal of UGT76G1 based on the above results. The new fusion constructs were named R1, R2, R3, F1, F2, and F3, respectively.  
As is shown in Figure S3b and Figure 2, each fusion protein was expressed and had the ability to catalyze stevioside into RebA. Generally, the rigid linkers worked better than the flexible linkers, and the longer rigid linker worked better than the shorter one. RebA yield of R3 was about 2.4 times of R1 after reaction for 2 h (data not shown). At 16 h, the RebA yield of R3 reached 68.9%. We also compared the R3 with At1-P3-76G1 (Table 4) and found they almost had the same RebA productivity in 3 h. The linker PPP belongs to the rigid linkers as well [45]. As a result, the rigid protein linkers tending to maintain the respective conformations of two fusion partners are more applicable in the fusion construct linking the C-terminal of _At_SUS1 and the \(N\)-terminal of UGT76G1 for RebA synthesis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Reaction**} & \multicolumn{2}{c}{**RebA (g/L)**} \\ \cline{2-3}  & **1 h** & **3 h** \\ \hline At1-P3-76G1 & \(3.5\pm 0.2\) & \(9.2\pm 0.5\) \\ At1-R3-76G1 & \(3.3\pm 0.1\) & \(8.8\pm 0.5\) \\ \hline \hline \end{tabular} Note: The reaction mixture (20 mL), consisting of approximately 5 mg/mL of the crude extract, 20 g/L stevioside, and 60 g/L sucrose, was incubated at pH 7.2 and 30 \({}^{\circ}\)C for 3 h.  
\end{table}
Table 4: The influence of P3 and R3 linkers on RebA production.  
Figure 2: The influence of different types of linkers on the yield of RebA. The reaction mixture (20 mL), consisting of approximately 3 mg/mL of the crude extract, 20 g/L stevioside, and 60 g/L sucrose, was incubated at pH 7.2 and 30 \({}^{\circ}\)C for 16 h.
## 3 Results and Discussion
### Case Study: Effect of the Great East Japan Earthquake  
Enormous amounts of debris and anthropogenically derived chemicals were released from damaged areas to surrounding coastal areas by the tsunami that followed the Great East Japan Earthquake [57; 58; 59]. Related areas were exposed to heavy metals and dioxins. For this reason, we expected that the damaged areas site 2 (HAC), 5 (ISH), 9 (GAM), 10 (SAS), 11 (ONA), and 12 (MAT) had high concentrations of these pollutants. However, no significant difference in total concentration of heavy metals was observed between the damaged areas and others (177-299 \(\mu\)g/g-dw and 241-377 \(\mu\)g/g-dw) (Wilcoxon rank sum exact test: \(p\) = 0.08). Heavy metal pollution in these areas occurred immediately after the earthquake and tsunami, but this was not a serious problem [60]. The total TEQs of the dioxins in the damaged areas were significantly higher than others (4.21-29.64 pg-TEQ/g-dw and 0.97-5.27 pg-TEQ/g-dw) (Wilcoxon rank sum exact test: \(p\) <0.01). Wharf roaches from GAM and HAC contained higher levels of dioxins (29.64 and 12.44 pg-g/g-dry, respectively). [PERSON] et al. [61] reported a significant elevation of PCB concentration in  
Figure 6: Proportion of PCDD/Fs and DL-PCBs on total TEQ at 11 sampling sites.  
blue mussel _Mytilus galloprovincialis_ from the Miyagi prefecture after the tsunami (collection date: June 2011). Additionally, marine sediment around sampling sites contained a higher level of dioxins compared with the surrounding sea area [62]. Current results showed a relatively higher level of dioxin pollution in these areas; therefore, one pollution exposure factor was the earthquake and ensuing tsunami.  
A highly polluted area was not included as a sampling site in this study. Therefore, in the future, the wharf roach should be used as an environmental indicator of multi-class pollution for polluted areas, such as developed ports, discharge ports of industrial waste, and sewage treatment plants.
## 2 Materials and Methods  
### Apparatus  
The listening tests took place in a semi-anechoic room. Figure 2A presents the experiment set-up. Each participant performed the test individually; he/she was seated in a chair in front of a computer screen showing the test instructions. In the first experiment, a blurred image of an urban environment was projected onto a large screen located behind the computer, in order to have a realistic and comfortable luminosity in the room, without providing too much visual information, which could influence the judgments; however, the stimuli was only comprised of audio files. In the second experiment, a video sequence was added to the sound, in order to enhance the sensation of immersion.  
Figure 2: (**A**) Experimental setup and (**B**) graphical interface for continuous assessments.  
The sound sequences were transaurally reproduced; using a system composed of two loudspeakers (Tannoy) and a high quality sound card (RME Fireface 400, Audio AG, Haimhausen, Germany). The listening position was located at \(\pm 30^{\circ}\) from the loudspeakers. The transaural listening technique has the advantage of minimizing front/back confusions, which are known to appear with headphone listening when individual HRTFs and head-tracking are not available, while preserving the perceptual characteristics of a diffused sound field [38]. The fact that participants are not using headphones improves the realism of the simulation technique.  
For both experiments, the audio files were recorded using two high quality microphones (DPA 4060, Alleroed, Denmark), inserted into the operator's ears using specific ear clips. Prior to each recording, a calibration tone (1 kHz/94 dB) was recorded by the microphones, such that the sound level reproduction in the laboratory experiments could be calibrated. For experiment 2, the video sequences were simultaneously recorded with the sound recordings, with a small action camera carried by the hand of the operator at the eye's level. During both experiments, participants had to rate the pleasantness of the soundscape on the computer screen, while an urban picture (experiment 1) or a motion picture (experiment 2) was projected onto a large screen behind.  
All of the statistical tests presented in this paper were realized with the Statistics and Machine Learning Toolbox(tm) from Matlab(r) (Natick, MA, USA).