esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 2 Experimental Setup and Theoretical Analysis
### Laser Confocal Feedback Tomography  
The basic schematic of the laser confocal feedback tomography is shown in Figure 4. The optical path before the AOMs is the same as that of the laser feedback interferometer as shown in Figure 1. Then the modulated light with the shift frequency of \(\Omega/2=1\Omega_{1}-\Omega_{2}\,|\) is expanded by the beam expander (BE), reflected by the reflector (R), and finally, focused by the objective (Obj) onto the sample (SA). The reflected or scattered light by the SA returns back to the laser cavity along the same path, working as the measuring light with the shift frequency of \(\Omega\).  
By sending the signal of the PD to the lock-in amplifier (Lock-in) as the measurement channel, generating the electrical signal with the \(\Omega\) frequency from the radio-frequency generator (RG) as the reference channel, the intensity of the measuring light can be demodulated by the Lock-in. The optical power modulation of the measuring light can be induced as [38; 39]:  
\[\frac{\Delta I(\Omega)}{I_{s}}=I(u)\kappa G(\Omega)\cdot\cos(2\pi\Omega t-\phi+ \phi_{s}) \tag{8}\]  
\(I(u)\) is the light intensity response function of the traditional confocal system, and is given as [27; 43]:  
\[I(u)=K_{r}\int\limits_{0}^{v_{d}}\left|\int\limits_{0}^{1}\exp(iu\,\rho 2)J_{0}(v_{2}\rho)\rho d\rho\right|^{2}v_{2}dv_{2} \tag{9}\]  
where \(u\), \(\rho\), and \(v_{d}\) are the normalized de-focus parameter, radial coordinate, and diameter of the pinhole, respectively, and \(K_{r}\) is the normalized constant.  
Figure 4: Schematic diagram of the laser confocal feedback tomography. LD: laser diode; ML, microchip Nd:YVO\({}_{4}\) laser; BS, beam splitter; PD, photodiode; L, lens; AOM\({}_{1}\), AOM\({}_{2}\), acousto-optic modulators; BE, beam expander; R, Reflector; Obj, objective; SA, sample (phantom); RG, radio-frequency generator; Lock-in, lock-in amplifier; PC, computer.  
Figure 3: The relationship between the amplification factor \(G\) and the shift frequency \(\Omega\).  
It is noted that there is no pinhole in Figure 4 because the laser in the setup is not only the light source but also the detector; therefore, the laser waist can be utilized as the pinhole filter. Thus, the structure of the laser confocal feedback tomography greatly simplified compared with the traditional confocal system.
# Dynamics and Control of Typical Orbits around Saturn
###### Abstract  
This paper investigates the dynamics of some typical orbits around Saturn, including sun-synchronous orbits, repeating ground track orbits, frozen orbits, and stationary orbits, and corresponding control methods mainly based on the mean element theory. The leading terms of Saturn's aspheric gravitational field, \(J_{2}\) and \(J_{4}\) terms, are used when designing the orbits around Saturn. Two control methods of sun-synchronous orbits, including initial inclination-biased method and periodic inclination-biased method, are used to damp the local time drift at the descending node, which is caused by solar gravitation and atmospheric drag. The compensation of semimajor axis and maneuver period to maintain the recursive feature of repeating ground orbits are calculated. While only \(J_{2}\) and \(J_{3}\) terms are taken into account, we examine the argument that the perigee of frozen orbits around Saturn should be 270 deg to promise meaningful eccentricity. The perturbations of inclination and eccentricity of stationary orbits due to solar gravitation and solar radiation pressure are presented. Meanwhile, the preliminary control strategies of inclination perturbation and eccentricity perturbation are naturally introduced.  
S +
Footnote †: journal: Appl. Sci.  
eurm; mean element theory; orbital dynamics; orbital maintenance
# The Impact of an Office Fire Combined with the Stack Effect in a Multi-Story Building
## 2 Modeling and Parameter Adjustment
### Boundary Conditions and Initial Conditions  
Regarding boundary conditions, the four lateral boundaries and the upper boundary, specifically, were defined as open boundaries. This setting allowed for natural air circulation within the domain, without the airflow being restricted by the physical boundaries of the domain, consistent with approaches in other studies [9; 12]. The environmental parameters where characteristic of the winter season, with the outdoor temperature set to 0 \({}^{\circ}\)C, typical for Romanian winters based on historical weather data. Inside, we set the temperature to 20 \({}^{\circ}\)C, a standard for human comfort and building operations. For a typical residential building with a level height of less than 3 m, the temperature gradient is negligible, and the same temperatures are found at each level of the building. This 20 \({}^{\circ}\)C difference between the indoor and outdoor temperatures was chosen to study the impact of thermal gradients on air exchange, enhancing the stack effect that influences air movement and fire behavior. The open boundary and the domain size are sufficiently large and well-proportioned to optimally reproduce the airflow and the stack effect. This ensures that natural ventilation is not adversely impacted, maintaining a pronounced stack effect.  
In the entire simulation domain, the following environmental parameters were defined as follows:  
* Initial oxygen concentration was set to 20.8%.
* Indoor temperature in the building was set to 20 \({}^{\circ}\)C.
* Initial carbon dioxide concentration was set to 0.04%.
* Initial visibility, at all points, was set to 30 m.
* Atmospheric pressure was set to 101,325 Pa.  
To simulate the influence of wind on the building, the Monin-Obukhov similarity theory [36; 37; 38] was utilized, being incorporated into PyroSim 2023.3.1312 according to the following relationship:  
\[L=\frac{-u_{*}^{3}}{k\cdot\omega} \tag{1}\]  
where  
* \(L\) (m) is the turbulence length (Obukhov length).
* \(u\)* (m/s) is the reference turbulence velocity.
* \(k\) (-) is the von Karman constant.
* \(\omega\) (s\({}^{-1}\)) is the Earth's rotational frequency.  
The values of \(k\) and \(\omega\) were set to the predefined values by simulation software.  
The only parameters input into the simulation program were the wind speed of 1 m/s at the reference height \(Z_{\text{REF}}\) = 2 m, as well as the direction Y (180\({}^{\circ}\)), perpendicular to the main entrance (XOZ plane). The other variables remained at the default values set by the simulation program.  
After establishing the similarity conditions and input parameters, we will proceed to present the details of the simulation scenarios and the numerical setup.
###### Abstract  
Farmland abandonment monitoring is one of the key aspects of land use and land cover record, as well as being an important prerequisite for ecological environmental protection and food security. A Normalized Difference Vegetation Index (NDVI) time series analysis is a common method used for farmland abandonment data extraction; however, extracting this information using high-resolution data is still difficult due to the limitations caused by cloud influence and data of low temporal resolution. To address this problem, this study used STARFM for GF-6 and Landsat 8 data fusion to enhance the continuity of high-resolution and cloudless images. A dataset was constructed by combining the phenological cycle of crops in the study area and then extracting abandoned farmland data based on an NDVI time series analysis. The overall accuracy of the results based on the NDVI time series analysis using the STARFM-fused dataset was 93.42%, which was 15.5% higher than the accuracy of the results obtained using only GF-6 data and 28.52% higher than those obtained using only Landsat data. Improvements in accuracy were also achieved when using SVM for time series analysis based on the fused dataset, indicating that the method used in this study can effectively improve the accuracy of the results. Then, we analyzed the spatial distribution pattern of abandoned farmland by extracting the results and concluded that the abandonment rate increased with the increase in the road network density and decreased with the increase in the distance to residential areas. This study can provide decision-making guidance and scientific and technological support for the monitoring of farmland abandonment and can facilitate the analysis of abandonment mechanisms in the study area, which is conducive to the sustainable development of farmland.  
multi-sensor data fusion; farmland abandonment; STARFM; NDVI time series; Mianchi County +
Footnote †: journal: Applied sciences  
0000-0002-0002-0002-338X][PERSON]  
0000-0002-338-0002-38X][PERSON]  
0000-0002-0002-388X][PERSON]  
0000-0002-0002-388X][PERSON]  
0000-0002-388-0002-38X][PERSON]. [PERSON]  
0000-0002-388X][PERSON]
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
## 3 System Architecture
### OFDM Transceiver  
An OFDM based transceiver model is demonstrated in Figure 1. The input binary stream \(b_{i}\) is randomly generated, having a value of 0 or 1 where \(i=0,1,2,\ldots\). The serial stream is then block encoded using a \(\mathrm{BCH}(n,k)\) encoder.  
The output from the BCH encoder then undergoes XOR encryption. In a typical BCH(\(n\), \(k\)) code, the value of \(n\) is a prime number, while the number of sub-carriers \(N\) in an OFDM system are in powers of two. Therefore, a single bit is appended to each of the encoded sequence. Then, the bits pass through the modulator block where MPSK, QAM or DPSK mapping takes place. Typically, the output of the modulator is a complex number. Next, the serial stream of complex numbers is converted into a parallel stream that can be represented in vector form as:  
\[C_{p}=\left[c_{p,0},c_{p,1},c_{p,2},\ldots c_{p,N-1}\right]^{T} \tag{1}\]  
where \(C_{p}\) is a vector of complex numbers for \(p\)th OFDM symbol and \(c_{p,q}\) is the complex number for \(p\)th OFDM symbol and \(q\)th subcarrier. These signals are then converted to their discrete time-domain complex equivalents using an IFFT block of size \(N\), and are represented as:  
\[X_{p}=\left[x_{p,0},x_{p,1},\ldots,x_{p,N-1}\right]^{T} \tag{2}\]  
where \(X_{p}\) is a vector representing the \(p\)th OFDM symbol and \(x_{p,q}\) is the \(q\)th sample of the \(p\)th OFDM symbol. A parallel to serial operation is performed and a cyclic prefix (CP) is attached at the start of the OFDM symbol by copying the tail samples of the symbol.  
The discrete-time OFDM symbols are then converted into continuous-time signals using the digital to analog conversion block followed by a high-power amplifier block. The final output of the transmitter is then given by:  
\[x_{p}(t)=\frac{1}{\sqrt{T}}\sum_{q=0}^{N-1}c_{p,q}e^{j2\pi f_{q}t} \tag{3}\]  
where \(x_{p}(t)\) is the continuous-time equivalent signal of the \(p\)th OFDM symbol and the duration of an OFDM symbol is \(T\). The symbol \(x(t)\) is used for the OFDM signal that has been transmitted for all the symbols. The transmitted signal then passes through the underwater acoustic channel. On the receiver, all the steps that were performed at the transmitter are performed in the reverse order. The received signal \(\hat{x}(t)\), which is the degraded form of the transmitted signal, is converted back to discrete-time symbols using an analog to digital convertor. The CP is discarded and a serial to parallel operation is performed. The FFT block then produces the discrete frequency domain symbol vector given as:  
\[\hat{C}_{p}=\left[\hat{c}_{p,0},\ \hat{c}_{p,1},\ \hat{c}_{p,2},\ldots\hat{c}_{p,N-1}\right]^{T} \tag{4}\]  
Figure 1: Proposed ciphered BCH OFDM architecture.  
A zero-forcing equalizer is employed, assuming the channel state information (CSI) is known. The symbols are de-mapped after parallel to serial conversion. This is followed by an XOR decryption block and, finally, BCH decoding takes place, giving the estimated binary sequence \(\hat{b_{i}}\).
# Modification and Optimization of Cycloidal Gear Tooth Profile Based on Machining Error Compensation
###### Abstract  
The rotary vector reducer presents high precision and load capacity characteristics. The shape of the cycloidal gear tooth profile in the rotary vector reducer significantly affects its performance. Meanwhile, the effect of the machining error on the error between the designed and theoretical tooth profiles cannot be ignored. Thus, this paper analyzes the machining error items that affect the shape of the cycloidal gear profile from the machining process perspective. Due to the random characteristics and different distribution rules inherent in various machining errors, this paper proposes a cycloidal gear machining error compensation and modification model based on the Monte Carlo simulation method, providing a new theoretical method for compensating cycloidal gear machining errors. While compensating for machining errors, considering the impact of cycloidal gear modification on the carrying capacity, the NSGA-Ioptimization algorithm is utilized to optimize the cycloidal gear modification parameter and finally, to solve the modification parameters with a more comprehensive performance.  
rotary vector reducer; cycloidal gear tooth profile modification; machining error; Monte Carlo simulation method; multi-objective optimization
# To Cache or Not to Cache
## 3 Design
### Miss-History  
**FOMO's Miss-History** is an LRU structure that primarily holds recently missed items. The Miss-History is set to the size of the cache. Whenever **FOMO** encounters a cache miss, **FOMO**'s Miss-History is updated. Should an item that causes a cache hit also exist in the Miss-History, this item will be removed from the Miss-History. When **FOMO** is in the _Insert_ state, as shown in Figure 2, the item is inserted into both the Cache and the Miss-History. The Miss-History is straightforward, as any item incurring a cache miss, including those already within the Miss-History, are moved to the MRU part of the Miss-History, removing the LRU item as needed. However, when **FOMO** is in the _Filter_ state, as shown in Figure 3, **FOMO** will treat the Miss-History as a method to track the filtered items.When **FOMO** is in the _Filter_ state, the Miss-History will treat items that incur a cache miss and exist within the Miss-History the same as a cache hit, removing the item from the Miss-History. Despite the differences in their function, **FOMO** in both states is utilizing the Miss-History to discover items with reuse, with the state as context. Since the _Insert_ state has these items already entering the cache without knowing if they have reuse, the Miss-History is used to verify the reuse of the item _and_ that the cache is seeing this reuse. With the _Filter_ state, only the Miss-History is seeing this reuse, after which it inserts the item into the cache, passing it along to the underlying cache replacement algorithm, believing that it will see further reuse. In the next section, we go into further detail about **FOMO**'s states and how they are determined.  
Figure 3: Example of **FOMO** working in _Filter_ state handling the request stream: \(X\), \(Y\), \(X\). (**a**) Shows the starting state of the cache and Miss History. (**b**) Has \(X\) missing and being inserted only in the Miss History. (**c**) Has \(Y\) missing and being inserted only in the Miss History. (**d**) Has \(X\) miss in the cache, but hit in the Miss History. Due to \(X\) showing reuse, it is then inserted into the cache and removed from the Miss History.  
Figure 2: Example of **FOMO** working in _Insert_ state handling the request stream: \(X\), \(Y\), \(X\). (**a**) Shows the starting state of the cache and Miss History. (**b**) Has \(X\) missing and being inserted into both the cache and the Miss History. (**c**) Has \(Y\) missing and being inserted into both the cache and the Miss History. (**d**) Has \(X\) hitting in the cache and being removed from the Miss History.
# Experimental Research on the Load Transfer Mechanism of Tie Plates for 400 km/h High-Speed Turnouts
###### Abstract  
Based on the research and development demand of the 400 km/h high-speed turnout, a new type of elastic iron tie plate based on stiffness optimization is proposed. The new elastic iron tie plate is expected to reduce the stiffness unevenness and effectively solve the problems in the use of existing elastic iron tie plates. However, the new plate uses rubber bosses instead of rubber washers. There are certain differences in the force transmission mechanism from the original tie plate because of the change in structure of the new tie plate. In this paper, a series of experiments are designed to quantitatively compare and analyze the overall stiffness characteristics of different tie-plate structures and the differences in bolt force transmission and distribution laws. The main conclusions are as follows. The laws of vertical load transfer and distribution in the new tie plate are remarkably different from those for the original tie plate because of the differences in their structures. Under the rated bolt torque, the force transferred to the upper surface of the tie plate through the rubber boss in the new tie plate is less than that in the original tie-plate structure by 5.18 kN in the assembled state. Further, the new structure also has lower tie plate precompression; hence, the influence on the stiffness characteristics of the tie plate is less than that in the original structure. However, the steel sleeve deflection in the new tie plate is greater than that in the original tie plate; that is, a larger proportion of the bolt preload force is borne by the steel sleeve, thus making the vulcanized rubber under the steel sleeve more vulnerable to shear damage. If put into actual use, the tie plate bolt torque will be somewhat different from the rated torque; because of the difference in the torques coupled with the different vertical load transfer mechanisms between the new and the original tie-plate structures, the precompression and the proportion of the bolt preload force borne by the steel sleeves of the two tie plates will also show different degrees of deviation corresponding to different deviations of the tie plate bolt torque. Therefore, the new tie plate structure needs to be further optimized to make the transfer mechanism of preload force of bolt as consistent with the original tie plate as possible. In addition, the vibration and deformation characteristics and long-term service performance of the new type of plate under the condition of vehicle passing will be studied through a small-scale field trial and systematic dynamic tests.  
high-speed railway turnout; elastic iron tie plate; force transmission mechanism; stiffness; laboratory test +
Footnote †: journal: Applied Sciences
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 4 Revisit the Regulatory Mechanisms of tRNA and tsRNA
### tRNA: Focus on Both Main and Side Businesses
#### 4.1.3 Interacting with Proteins  
Research has shown that tRNA can also bind to specific proteins, thereby influencing the function of proteins and exerting an effect. For instance, tRNA has been identified as a starvation sensor. In prokaryotes, deacylated tRNAs accumulate under stress conditions, and RelA interacts with these uncharged tRNAs in the vacant ribosomal A-site, triggering the synthesis of (p)ppGpp, a critical signaling molecule that initiates the bacterial stringent response [141]. In eukaryotes, deacylated tRNAs bind to the protein kinase general control nonderepressible 2 (GCN2), which downregulates translation and activates its downstream functions [142]. In addition, the binding of mitochondrial and cytoplasmic tRNA to cytochrome c prevents the interaction between cytochrome c and Apaf-1, blocking Apaf-1 oligomerization and caspase activation, thereby inhibiting cell apoptosis [143]. Additionally, research has indicated that the capsid binding membrane protein pp150 on human cytomegalovirustivn (HCMV) binds to tRNA, and one proposed biological function of this binding is to enhance the stability of the capsid protein, allowing the virus to withstand its massive genome [144]. Specifically, several studies have highlighted the role of tRNA in immune regulation. For instance, research has shown that tRNA can interact with interferon-stimulated gene (IgG) proteins (e.g., Schlafen11(SLFN11), SLFN13, interferon 5 (IFIT5)), potentially regulating protein translation [145; 146; 147]. Additionally, the Gm18 modification of tRNA can inhibit the activation of 7 toll-like receptor 7 (TLR), which may represent a strategy employed by bacteria to weaken host defense responses through tRNA modification [148]. These protein-tRNA interactions are crucial for regulating cellular responses to stress, apoptosis, and immune challenges, showing that tRNA's role in the cell extends far beyond translation. Understanding these interactions provides deeper insight into the complex regulatory networks within cells, highlighting the multifaceted roles of tRNA in modulating protein function and influencing key cellular processes.
# Design and Experiment of High-Resolution Multispectral Polarization Imaging System
## 4 Research on Multi-Dimensional Information Fusion and Enhancement Processing Algorithm  
### Principle of Two-Dimensional Discrete Wavelet Transform  
Figure 2 shows the filter bank representation of the two-dimensional Mallat fast algorithm. As can be seen from Figure 2a, the specific process of two-dimensional wavelet decomposition is as follows [10; 11; 12; 13]: firstly, one-dimensional wavelet decomposition is performed on the one-dimensional data composed of each line in the image, and then one-dimensional wavelet decomposition is performed on the one-dimensional data of each column of low and high-frequency information formed by decomposition, and finally, four sub-band images are obtained: \(\mathrm{c}_{j-1}\), \(\mathrm{d}_{j-1}^{1}\), \(\mathrm{d}_{j-1}^{2}\) and \(\mathrm{d}_{j-1}^{3}\). \(\mathrm{c}_{j-1}\) is obtained by row low-pass and column low-pass, so it contains the low-frequency approximate information of the image; it is often referred to as \(\mathrm{d}_{j-1}^{\mathrm{LH}}\) (Here \(\mathrm{I}\) stands for low-pass filtering and \(\mathrm{H}\) stands for high-pass filtering), corresponding to the high-frequency components in the vertical direction, that is, the horizontal edge detail information. \(\mathrm{d}_{j-1}^{2}\) is obtained by row high pass and column low pass, denoted as \(\mathrm{d}_{j-1}^{\mathrm{HL}}\) and corresponds to high-frequency components in the horizontal direction, that is, vertical edge detail information. \(\mathrm{d}_{j-1}^{3}\) is obtained by row high pass and column high pass, denoted as \(\mathrm{d}_{j-1}^{\mathrm{HH}}\); it contains high-frequency components in the focus direction, that is, diagonal edge detail information.  
Figure 1: Schematic diagram of the micro-polarizer array system.