esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
Microscopic identification and statistical analysis revealed palynofossils in 25 palynological samples collected from the Cretaceous Madongshan Formation in the Liupanshan Basin. Samples with more than 100 palynofossils were considered statistically significant. Among them, 11 samples had spore and pollen counts ranging from 100 to 200 grains, whereas the remaining contained less than 100. A total of 42 species from 37 genera with predominantly gymnosperm pollen were identified (Figure 4). Based on this, the proportions and distributions of spore and pollen genera and species were calculated, and a representative percentage table of spore and pollen genera and species was established for the Madongshan Formation in the Liupanshan Basin (Figure 5).  
Figure 4: Photographs of representative Cretaceous palynomorphs recovered from the Liupanshan Basin. (**A**) _Anruaciatites australis_, sample HJZ-15, slide H5/1, England Finder coordinates K55(3), (**B**) _Erlianpollis_ sp., HJZ-21, H21/1, W58(1), (**C**) _Punctatisporites incognatus_, HJZ-6, H6/2, Q31(2), (**D**) _Cycadopites_ sp., HJZ-21, H21/1, J24(3), (**E**) _Ginkgoretectina_ sp., HJZ-2, H2/1, V16(4), (**F**) _Jiaheppollis_  
scutellatus_, HJZ-12, H12/2, W28(4), **(G)**_Jiaheppollis verus_, HJZ-15, H15/1, R58(0), **(H)**_Jiaheppollis flexuous,_ HJZ-6, H6/1, O52(0), **(I)**_Jiaheppollis beltus_, HJZ-19, H19/2, L55(3), **(J)**_Cadripietes desinreticulatus_, HJZ-24, H24/2, M64(1), **(K)**_Pinnspollenites_ sp., HJZ-19, H19/1, N46(0), **(L,M)**_Classopollis annularus_, HJZ-23, HJZ-25, H23/2, H25/1, Y43(2), H29(4), **(N)**_Classopollis classoides_, HJZ-24, H24/1, R45(0), **(O)**_Inapertureopellites limbatus_, HJZ-10, H10/2, U38(3), **(F)**_Retivernicipora_ sp., HJZ-6, H6/2, V37(1), **(Q)**_Callialasporites_ sp., HJZ-22, H22/1, T41(4), **(R)**_Punctatisporites leigensis_, HJZ-2, H2/2, O43(4), **(S)**_Appendisporites macrorbizus_, HJZ-6, H6/2, U62(2), **(T)**_Sphaupmsupporites apolaris_, HJZ-2, H2/2, O32(1), **(U)**_Vertucosisporites rotundus_, HJZ-10, H10/1, P44(2), **(V)**_Undulatisporites_ sp., HJZ-6, H6/2, H25(1), **(W)**_Cicatricossiporites hallei_, HJZ-10, H10/2, D33(3), **(X)**_Cicatricossiporites australiensis_, HJZ-19, H19/2, O53(2).  
Among the gymnosperm pollen, the extinct _Classopollis_, related to the Coniferaceae and Cheirolepidiaceae families, occupied the dominant position (72.90-81.48%, avg. 78.60%); the most abundant species was _Classopollis annualatus_, followed by _Classopollis classoides_ and _Classopollis meyeriana_. _Araucariacites_ (3.82-12.80%, avg. 9.43%) were the second most abundant. Other gymnosperm pollen genera had percentages below 10%, and the contents of _Psophosphaera_ (0-5.61%, avg. 2.33%) and _Jiahepollis_ (0.62-3.82%, avg. 1.96%) were relatively high. In addition, _Callialsporites_, _Pinusopellites_, _Cedriytes_, _Protopinus_, _Perinopolenties_, and _Cycadopites_ occurred sporadically. Fern spores, with species such as _Verrucospirites_, _Retivernicispora_, _Concavisporites_, and _Cicatricosisporites_, were scarce.
## 2 Results and Discussion
### Linear Rheological Behaviors  
The morphology of polymer blends may affect their linear viscoelastic response at the low frequency \(\omega\) region [33]. Small amplitude oscillatory shear (SAOS) tests were carried out at 100 \({}^{\circ}\)C (\(\gamma\) = 0.5%) for NR/SBR blends within the linear viscoelastic regime before large amplitude oscillatory shear (LAOS) tests. Figure 4 shows dynamic storage moduli \(G^{\prime}\) and loss moduli \(G^{\prime}\) as a function of \(\omega\) for NR/SBR blends with various weight ratios. The NR, SBR, and NR/SBR blends all exhibit nonterminal flow behavior (\(G^{\prime}\) > \(G^{\prime\prime}\)) in the experimentally achieved \(\omega\) range at 100 \({}^{\circ}\)C. The values of \(G^{\prime}\) and \(G^{\prime\prime}\) of the NR/SBR blends increase with the increasing content of NR within the whole investigated \(\omega\) region. At high \(\omega\)s, the values of \(G^{\prime}\) and \(G^{\prime\prime}\) of NR/SBR lie between the moduli values of NR and SBR. To distinguish the contribution of morphology and components of NR/SBR blends to \(G^{\prime}\) and \(G^{\prime\prime}\) at low \(\omega\)s, three kinds of NR + SBR physical blends without internal mixing are prepared, as shown in Figure 1. Figure 5 shows the \(\omega\) dependence of \(G^{\prime}\) and \(G^{\prime\prime}\) for the NR/SBR blends and NR + SBR physical blends at 100 \({}^{\circ}\)C. The dynamic moduli of NR/SBR blends at low \(\omega\)s are obviously higher than those of the NR + SBR physical blends, which may be attributed to the presence of the heterogeneous gel-like structure in NR/SBR blends, as shown in Figure 3. Hence, the enhancement of the dynamic viscoelastic response for NR/SBR blends is attributed to their morphology evolution and interfacial tension between two phases [30]. The modulus deviation of the NR/SBR (25/75) blend is less marked than those of the NR/SBR (75/25) and (50/50) blends, resulting from the low deformability of the dispersed NR phase with high viscosity under small strain. Furthermore, the more remarkable modulus increment of the NR/SBR (50/50) blend may be attributed to the formation of interpenetrating gel-like networks. Here, the effect of morphology (droplet-matrix or co-continuous) for NR/SBR blends on the \(G^{\prime}\) at low \(\omega\)s is similar to that for some other binary blends of poly (methyl methacrylate) (PMMA)/poly (styrene-co-maleic anhydride) (SMA) [34] and fluorescently labeled polystyrene (FLPS)/poly (styrene-co-acrylonitrile) (SAN) [35]. Here, the samples are all in the linear viscoelastic regime, and the linear rheological behaviors of NR + SBR physical blends are just related with the contribution from two components.
# Noise Suppressed Image Reconstruction for Quanta Image Sensors Based on Transformer Neural Networks
###### Abstract  
The photon detection capability of quanta image sensors make them an optimal choice for low-light imaging. To address Possion noise in QIS reconstruction caused by spatio-temporal oversampling characteristic, a deep learning-based noise suppression reconstruction method is proposed in this paper. The proposed neural network integrates convolutional neural networks and Transformers. Its architecture combines the Anscombe transformation with serial and parallel modules to enhance denoising performance and adaptability across various scenarios. Experimental results demonstrate that the proposed method effectively suppresses noise in QIS image reconstruction. Compared with representative methods such as TD-BM3D, QIS-Net and DPIR, our approach achieves up to 1.2 dB improvement in PSNR, demonstrating superior reconstruction quality.  
Keywords: quanta image sensors; photon counting; image reconstruction; image denoising
# Biocompatibility of Membranes Based on a Mixture of Chitosan and _Lythri herba_ Aqueous Extract
## 3. Results
### Membrane Evaluation  
#### 3.2.1 Membrane Percentage of Swelling (%)  
When membranes come into contact with an alkaline buffer solution, they suffer a loss of rigidity and become more flexible, making them easier to fold. Furthermore, they do not undergo distortion, and upon drying, they regain their original characteristics. Following the first 10 min of immersion in PBS solution, membranes combined with 0.5 g/L and 1 g/L aqueous extracts of _Lythi herba_ (Figure 2A) reached approximately 60% degree of swelling (Figure 2A) and up to 80%, respectively (Figure 2B), and afterwards the uptake level stabilized in the next 20-30 min. At CS 1, without extract, the membranes had a swelling rate close to that of membranes with Ly, in the first 10 min. Following 30 min of immersion in PBS solution, the process changed, with a decrease in the swelling percentage, possibly induced by the hydrophobicity of some poly-GlcNAc groups [33]. At CS 0.5%, membranes' swelling percentage varied from 120% to 140% (Figure 2B). The swelling measurements of CS 0.5-Ly were around 80% for all concentrations, and remained stable after the first 10 min (Figure 2B). This study suggested the significance of chitosan concentrations employed and the extract role in maintaining the stability of the chitosan matrix. Another significant aspect related to the preservation of the original color and the absence of increased deformity, indicating that the plant extract engaged in chemical bonding with the chitosan matrix, thereby providing notable stability.  
In other studies in which chitosan is associated with plant extracts, this swelling process is correlated with the replacement of the extract in the spaces created by the chitosan matrix. The extract leaves the gaps in the polymer matrix passing into the aqueous solutions, consequently leading to an escalation in the rate of swelling [13; 14].  
Figure 1: Plant–chitosan membranes (**a**) CS 1 membrane, (**b**) CS1–Ly 0.5 (1% standard chitosan with 0.5 g/L _Lythi herba_ extract) membrane, (**c**) CS 1–Ly 1 (1% standard chitosan with 1 g/L _Lythi herba_ extract) membrane, (**d**) CS 1–Ly 2 (1% standard chitosan with 2 g/L _Lythi herba_ extract) membrane (representative images).  
Figure 2: Membrane percentage of swelling tested as a function of time (min) and chitosan concentrations. (**A**) CS 1 and mixture with 0.5 g/L, 1 g/L, 2 g/L _Lythi herba_ extract (CS 1–Ly 0.5, CS 1–Ly 1, CS 1–Ly 2). (**B**) CS 0.5 and mixture of 0.5 g/L, 1 g/L, 2 g/L _Lythi herba_ extract (CS 0.5–Ly 0.5, CS 0.5–Ly 1, CS 0.5-Ly 2).
# Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform
## 4 Related Work  
Synchronization of chaotic systems is an interesting scheme and is studied in many research engineering applications. The first savant studied the synchronization between two identical systems without the same initial condition by [PERSON] (PC) in 1990 [21]. Several control methods have been devoted to the chaotic signal in order to make the synchronization between two systems (master-slave), such as [PERSON] et al. who, in 1990, first presented the scheme of chaos control [22], sliding mode control method [23], adaptive control method [24], impulsive control method [25], back-stepping design method [26], sampled-data feedback method [27], and other methods for control on synchronization of the chaotic signal [28]. This method and other methods have been proposed to make different chaotic systems stabilized around the unstable orbit. One example is finite-/fixed-time synchronization of memristor chaotic systems (MCSs), which are addressed via the sliding-mode control method in image encryption applications [29].  
On the other hand, [PERSON] et al., in [4], introduced chaos-based frequency modulated (CBFM) waveforms for joint monostatic and bi-static radar-communication systems, and the wideband radar imaging uses short-duration pulses created by chaotic oscillators, with the information contained in the pulses using chaos shift keying (CSK). The information is decoded at the communication receiver, and the transmitted waveform is reconstructed at the bistatic radar receiver, using a self-synchronization technique for chaotic systems, and it is shown that the CBFM waveforms closely track the theoretical Bit-Error Rate (BER) associated with bipolar phase-shift keying using a nonlinear detection approach (BPSK). At the bistatic radar receiver; the same nonlinear detection approach is used to optimize target detection. In both the monostatic and bistatic examples, the ambiguity function resembles a thumbtack ambiguity function with a pseudo-random side lobe distribution. In addition, [PERSON] et al. [2] proposed a direct monostatic radar model using a chaotic time-delayed system in the presence of self-synchronization to recover the echo signal for long-wave anti-stealth applications. To overcome the problems encountered in VLF chaos radar design, such as parameters mismatch of chaotic system in chaos synchronization, in this paper, we developed the FM-based chaotic radar system (0.3 GHz). The Phase-Locked Loop (PLL) is used in place of Pecora and Carroll (PC) or an adaptive-observer scheme in order to achieve chaos synchronization and then recover the original signal correctly. The master chaotic signal (source) is transmitted via the VCO to convert the baseband signal to a microwave signal; after that, it is transmitted through a wireless channel with echo delay and under various values of noise levels. Then, the received echo signal is demodulated by the analog Phase-Locked Loop (PLL) to recover the reference signal, and the correlation is used to measure the performance of the proposed mono-static radar system.
## 5 Deformation Control Measures
### Comparative Analysis of Deformation Results
#### 5.2.2 Comparative Analysis of Ground Surface Settlement  
Figure 14 presents a line chart illustrating the maximum ground surface settlement points of the foundation pit at different excavation depths. A comparison with Figure 13 indicated the existence of consistent variation trends in the horizontal displacement of the diaphragm wall and the ground surface settlement. Plan 3 exhibited the best deformation control, with a maximum ground surface settlement of \(-37.81\) mm after excavation. Plan 2 provided the second-best performance, with a maximum ground surface settlement of \(-41.55\) mm, while the original scheme demonstrated the least effective deformation control. Compared to the excavation method of the original scheme, Plan 3 reduced the maximum ground surface settlement by \(13.49\) mm (approximately \(26.29\%\)), and Plan 2 achieved a reduction of \(9.75\) mm (about \(19.01\%\)). Furthermore, the analysis of diaphragm wall deformation revealed that the range horizontal displacement was consistently greater than the ground surface settlement under the same construction scheme. This observation indicated that the diaphragm wall deformation is more sensitive to the construction scheme than the ground surface settlement. Compared with Plan 2, the maximum horizontal displacement of Plan 3 is reduced by \(4.64\) mm, or \(12.76\%\). The deformation control effect does not increase significantly.  
Figures 13 and 14 revealed that increasing the number of segments did not result in the horizontal displacement of the diaphragm wall or the ground surface settlement reaching zero; instead, they converged toward a certain fixed value. During construction, factors such as the operation of construction machinery must be considered, and the segmented storey height should be comprehensively evaluated to determine the optimal construction scheme.
## 2 Modeling and Simulation Method
### Modeling of the Modified MHD ARS Combing with Coriolis Effect (Abbreviated as C-MHD ARS)
#### 2.1.3 Basic Governing Equations of C-MHD ARS  
The governing equations can be expressed as Equations (3)-(6). With the knowledge of magnetohydrodynamics, the MHD coupling is achieved by additional source terms to the fluid momentum equation. The Navier-Stokes equation for an electrically conducting and incompressible Newtonian fluid can be written as Equation (3). The additional source term in the Navier-Stoke  
Figure 2: Description of the fluid channel in C-MHD ARS.  
equation is the Lorentz force given as \(\mathbf{F}=\mathbf{J}\times\mathbf{B}\). In addition, the conductive fluids such as mercury and Galinstan are almost heavy fluid, so the gravity \(\rho\cdot\mathbf{g}\) should be taken into account. According to the law of conservation of mass, the velocity of homogeneous Newton fluid can be given as Equation (4). Besides, the governing equations include the magnetic diffusion Equation (5) and the expression of magnetic induction intensity in passive field (6) [28; 29; 30].  
\[\rho\frac{d\mathbf{u}}{dt}=-\
abla p+(\
u\cdot\rho)\cdot\
abla^{2}\mathbf{u}+ \frac{1}{\mu}(\
abla\times\mathbf{B})\times\mathbf{B}+\rho\cdot\mathbf{g}+ \mathbf{J}\times\mathbf{B} \tag{3}\]  
\[\
abla\cdot\mathbf{u}=0 \tag{4}\]  
\[\frac{\partial\mathbf{B}}{\partial t}=\frac{1}{\mu\sigma}\
abla^{2}\mathbf{B}+ \
abla\times(\mathbf{u}\times\mathbf{B}) \tag{5}\]  
\[\
abla\cdot\mathbf{B}=0 \tag{6}\]  
Magneto-conductivity, total magnetic field, the electric current density, the pressure field, and the velocity field are respectively denoted by \(\mu\), \(\mathbf{B}\), \(\mathbf{J}\), \(\mathbf{p}\) and \(\mathbf{u}\).
# Shoot Phenology in _Bambusoideae_: A Review
## 2 Phenological Studies in Bamboo Species
### Phenological Studies in Temperate Bambos
#### 2.2.4 Fargesia  
_Fargesia_ genus contains more than 100 species. In total, 104 species and 1 variety are distributed in China, except for _F. racemosa_. _F. racemosa_ is distributed in Eastern Nepal and India, while _F. fansipanensis_ is native to Northern Vietnam; some species possibly exist undescribed in Northern Myanmar. Many species are planted in gardens in Europe, the USA, Japan and other countries. In general, new _Fargesia_ shoots arise from summer to autumn [41], while other sources mention that the new shoots of this genera occur from May to September [47].  
_F. yuanjiangensis_ grows its new shoots in July [41]. The phenology of three different bamboo taxa, including _F. yuanjiangensis_, was observed in 2022 [48]. The samples were taken from the cultivated bamboo garden of Southwest Forestry University in Yunnan Province, China. _F. yuanjiangensis_ started to shoot in July. The average daily temperature recorded in the tested area was 20 \({}^{\circ}\)C in July. According to their results, the shooting time of this species did not change during the observed 6 years. The branching started simultaneously on the 1-year culms from May to July. Their new finding was that the branching and leafing stages of monopodial bamboos do not overlap as they do in sympodial bamboo.
## 3 Features Selection and Preprocessing
### Features Selection  
(1) Carrier-to-Noise Ratio (\(C/N_{0}\)): The \(C/N_{0}\) is a basic indicator of the quality of radio frequency (RF) signals such as BDS, indicating the strength of the received signal. Signals with stronger \(C/N_{0}\) possess better positioning performance. However, in urban forest environments, signal strength is reduced due to LOS superimposed on NLOS such as reflection and refraction. It can be calculated using the following equation:  
\[C/N_{0}=A_{d}^{2}+A_{r}^{2}+A_{m}^{2}+2A_{d}^{2}A_{r}^{2}A_{m}^{2}\cos\Delta\phi \tag{1}\]  
where \(A_{d}\), \(A_{r}\), and \(A_{m}\) are the amplitudes of the LOS, NLOS, and MP signals, respectively; \(\phi\) is the phase; and \(\Delta\phi\) is the phase shift of the NLOS. As shown in Equation (1), the strength of the BDS signal is closely related to the strength of the NLOS and MP signals.  
Here, we used \(C/N_{0}\) instead of the signal-to-noise ratio (SNR). This is because SNR is the ratio between the power of a signal or carrier and the power of noise in a given specific bandwidth. Hence, we need to have the same bandwidth to have a fair SNR comparison between different signals. \(C/N_{0}\) is the ratio of the carrier power to the noise power per unit of bandwidth, independent of receiver bandwidth, and can be used directly to indicate the quality of the received signal.  
(2) Pseudorange Residual (PR): The PR represents the geometric distance between the satellite and the ground user reception position, and it is usually calculated using the weighted least squares (WLS) method.  
\[\Delta x=\left(H^{T}WH\right)^{-1}H^{T}W\Delta\rho \tag{2}\]  
where \(\Delta x\) denotes the receiver state, which consists of the 3D position difference between the receiver and the satellite and the receiver clock error. \(H\) is the observation matrix of the satellite, \(\rho\) is the pseudo-distance measurement vector, and \(W\) is a weighting matrix. According to Equation (2), the PR for each satellite can be calculated as follows:  
\[PR=\Delta\rho-H\Delta x \tag{3}\]  
In the case of static receivers, the PR for LOS is slightly jittery around zero, and the \(PR\) for MP and NLOS is usually large.  
(3) Elevation Angle (EA): The satellite elevation angle can be used as one of the important metrics for signal identification. EA can be calculated using Equation (4):  
\[EA=\sin^{-1}(\rho_{U}/\|\rho\|) \tag{4}\]where \(\hat{r}\in R^{3}\) is the estimated distance between the satellite and the receiver in the northeast-up (ENU) coordinate system. \(\hat{r}_{U}\) is the \"Up\" component of \(r\).  
There is a strong correlation between the occlusion of GNSS signals and the corresponding satellite EA. As shown in Figure 4, satellite signals at lower elevations are more likely to be NLOS satellites. On the contrary, satellite signals at higher elevations are more likely to be LOS satellites. In addition, according to the law of reflection, signals at higher elevations are more difficult to reflect because they require the building to be high enough to have an effective reflection point.
## 2 Proposed Fault Feature Extraction Methodology for the Fault Diagnosis of Rolling Bearings
### Preview of the ITD Method  
ITD can accurately extract the instantaneous features of the signal in real-time and decompose the nonstationary signal to be analyzed into a series of proper rotation components (PRCs) and a residual trend component. The steps are as follows [11]:  
Define \(X_{t}=[x_{1},x_{2},\cdots,x_{n}]\) as a set of original signals; L is defined as a baseline extraction operator. The mean value curve of the signal is expressed as \(LX_{t}\) or can be abbreviated as \(L_{t}\); then, \(X_{t}=L_{t}+H_{t}\) and \(H_{t}=X_{t}-L_{t}\) can be obtained from the signal \(L\) via ITD decomposition, and \(H_{t}\) is defined as a reasonable PR component.  
1. Determine the extreme value \(X_{k}\) and the corresponding time \(\tau_{k}\) of the original signal, \(X_{t}\), wherein \(k\) is the number of extreme points; the piecewise linear baseline extraction operator \(L\) of the signal is defined as follows: \[LX_{t}=L_{t}=L_{k}+\frac{L_{k+1}-L_{k}}{X_{k+1}-X_{k}}(X_{t}-X_{k}),\] (1) \[L_{k+1}=\alpha\bigg{[}X_{k}+\bigg{(}\frac{\tau_{k+1}-\tau_{k}}{\tau_{k+2}- \tau_{k}}\bigg{)}(X_{k+2}-X_{k})\bigg{]}+(1-\alpha)X_{k+1},\] (2)where \(t\in(\tau_{t},\tau_{t+1})\) and \(\alpha\) is the gain control parameter for extracting the amplitude of the intrinsic rotation component for control, which satisfies \(0<\alpha<1\) and is typically set to 0.5 [7].
2. By subtracting the baseline component \(L_{t}\) from the original signal, the inherent rotation component \(H_{t}\) can be calculated as follows: \[H_{t}=X_{t}-LX_{t}=X_{t}-L_{t}.\] (3)
3. Regarding \(L_{t}\) as the original signal of the next step, repeat the above steps to decompose until the baseline component is monotonous.
4. After several ITD operations, the original signal is decomposed into several PR components and the sum of a monotone trend; the decomposition result is expressed as follows: \[\begin{split}& X_{t}=LX_{t}+HX_{t}=(H+L)LX_{t}+HX_{t}\\ &=\left[H(1+L)+L^{2}\right]X_{t}=\left(H\sum\limits_{k=0}^{p-1}L_ {t}^{k}+L_{t}{}^{p}\right)X^{t}\\ &=H_{t}^{1}+H_{t}^{2}+H_{t}^{3}+\cdots+H_{t}^{p}+L_{t}^{p}\end{split}\] (4) where \(HL_{t}^{k}\) is the PR component of layer \((k+1)\), \(L_{t}^{p}\) is the baseline component of layer \((p+1)\), and \(H_{t}^{1}\) is the proper rotation component that is obtained via decomposition. \(L_{t}^{p}\) is the residual signal.