esa-sceva/satcom-chunk-collection huggingface dataset:
# Near to Mid-Infrared (1.3-5 um) Widely Tunable, High Power Picosecond Pulsed Laser
## 4 Conclusions  
We have investigated the operation of a high-power, widely tunable, synchronously pumped picosecond pulsed OPO generating emission in the near to mid-infrared wavelength ranges. The OPO is pumped using a Nd:YVO\({}_{4}\) picosecond pulsed laser(Wuhan Huazu Laser Technology Co., Ltd, in Wuhan, Hubei Province, China). By using a Z-shaped, signal singly resonant cavity, widely tunable OPO field outputs were generated with a total wavelength tuning range of 1.3-5 \(\upmu\)m being demonstrated. This OPO design is simple and offers wide wavelength tunability. Critical to achieving this broad tunability was period and temperature heating of the MgO:PPLN crystal. When the pump power reached 19 W, the signal field output power was 8.67 W at 1.74 \(\upmu\)m, and the corresponding idler field power was 5.61 W at 2.73 \(\upmu\)m. We anticipate that, with such high power, widely tunable picosecond pulsed outputs will be utilized in ultrafast processes, time-domain  
Figure 6: Plots showing (**a**) the signal field power; and (**b**) the idler field power, both as functions of wavelength, input power set as 19 W.  
spectroscopy, and laser micromachining, this being due to wavelengths in this spectral band corresponding with the characteristic vibrational absorption of many organic materials.  
Conceptualization, M.A. and T.Y.; methodology, M.A. and X.Y.; software, M.A.; validation, T.Y. and X.Y.; formal analysis, M.A.; investigation, M.A.; resources, D.J.; data curation, M.A., D.J. and Z.L.; writing--original draft preparation, M.A., Z.L. and T.Y.; writing--review and editing, T.Y. and X.Y.; visualization, M.A.; supervision T.Y. and X.Y.; project administration and funding acquisition, T.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 12264049, 11664041), and the Foundation of Xinjiang Normal University Young Outstanding Talent Programmer (Grant No. XJNUQB2022-17).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
###### Abstract  
The process of rock damage and failure is accompanied by the dissipation of energy and an increase in plastic strain. This study attempts to determine the relationship between dissipated energy and plastic strain in sandstone during the damage and failure process. A conventional triaxial cyclic loading and unloading test was conducted on sandstone samples to analyze the energy evolution and plastic strain characteristics of rock during the mechanical performance degradation and deformation failure process. The analysis results indicate that the evolution law of rock energy dissipation and plastic growth during the whole stress-strain process is highly consistent. Before the peak stress, dissipated energy and plastic strain increased linearly with input energy and axial strain, respectively. In the residual stress stage, there is an approximate linear evolution relationship between dissipated energy and plastic strain. Based on the correlation characteristics of energy dissipation and plastic growth, a modified damage model was established by characterizing plastic deformation by damage. In addition, a numerical program was developed using the Finite Volume Method (FVM) based on the damage model. The rock damage model has been validated by experimental results and numerical test. The research findings may provide valuable insights into the correlation mechanism between energy dissipation and plastic growth.  
energy dissipation; plastic growth; cyclic loading and unloading; correlation characteristics; FVM 2020
## 3 MPTCP Congestion Control Background
### Linked Increases Algorithm (LIA)  
This algorithm utilizes the RTT and congestion window size of all subflows to determine the congestion window of a subflow, which increases until congestion is detected. When congestion is detected, the transmission rate is reduced to alleviate the issue. The LIA algorithm strikes a balance between the rate of increasing and decreasing the congestion window to ensure a stable transmission rate. It is highly suitable for fluctuating network conditions as it facilitates smooth and stable data transfer while making the most of available resources. LIA utilizes the same algorithms as TCP-Reno, including slow start, fast retransmit, and fast recovery. During the congestion avoidance phase, LIA also employs an additive increase multiplicative decrease (AIMD) algorithm, which increases the congestion window (\(CW\)) proportionally to the sum of the congestion windows of all subflows upon receiving an acknowledgment (ACK) on each subflow. The congestion window size increase for MPTCP subflows is calculated using  
\[\min(\delta_{1},\delta_{2}) \tag{1}\]  
where \(\delta_{1}=\frac{\alpha}{CW_{\text{total}}}\) and \(\delta_{2}=\frac{1}{CW_{i}}\)[39]. The congestion window size for each MPTCP subflow and the combined size of all subflows' congestion windows are represented by the variables \(CW_{i}\) and \(CW_{total}\), respectively. The formula calculates the disparity between the increase in congestion window size for the MPTCP subflow and the increase that a typical TCP flow would experience in comparable conditions, where \(\alpha\) is given by (2). This
###### Abstract  
Polarization sensitive optical coherence tomography (PSOCT) makes use of the birefringence information of the sample to compensate for the lack of internal tissue-specific contrast in conventional optical coherence tomography (OCT). Circularly polarized light is always used as an incident beam in PSOCT, but it is difficult to have perfect in practice. The manual calibration method of circularly polarized light suffers from the problems of complicated calibration operation and lack of stability. This study proposes a simple method to enhance the imaging of PSOCT without altering the system. A numerical calibration of circularly polarized light can be implemented using the original system setup, ensuring that the system's complexity remains unchanged. Enhancements in imaging quality can be achieved through an algorithmic analysis of the captured interference fringe data. This calibration is applied in the field map of interference data before being quadrature-assembled. Notably, the proposed approach achieves high sensitivity in PSOCT. The birefringence image shows a more obvious layered structure. Improvements in the signal-to-noise ratio (SNR) were demonstrated for chicken breast, pork, and beef imaging at about 20 dB.  
Keywords:optical coherence tomography; polarization sensitive optical coherence tomography; circularly polarized light +
Footnote †: journal: Applied
## 6 Simulation Results
### Simulation Results Analysis
LBMSA always saves more network resources, compared to the other two algorithms and is particularly prominent in small-scale networks. When the size of the network is small, insufficient spectrum resources are the only reason for blocking. In addition, as can be seen in Figure 8, the percentage of traffic requests using MFC is above 75% for different traffic load cases. The high frequency of using MFC is also an important reason for the efficiency of LBMSA. The use of MFC tends to decrease as the traffic load increases. In all three network topologies, the use of MFC is minimal when the total traffic rate is 350 Tb/s. This is because when the network traffic load increases, the LBMSA algorithm is more inclined to carry all traffic requests rather than efficient transmission. Figure 7: **(a) Utilization of the modulation formats in the simple mesh network; (b) Utilization of the modulation formats in the Japan network; (c) Utilization of the modulation formats in the NSF network. **
# Innovation in Company Labor Productivity Management: Data Science Methods Application
## 3 Experimental Results
### Exploratory Data Analysis and Visualization
The presence of chronic diseases and the self-assessment of one's health are not strong enough reduce labor productivity by an average of 449 rubles per hour, and in married men this effect increases by another 162 rubles per hour. Furthermore, in sample of married men with chronic diseases there is a decrease in labor productivity by an average of 584 rubles per hour. We also mention that there are some shortages of the described regression model. Firstly, some of its parameters are statistically insignificant, and secondly, many qualitative predictors negatively affect the results of model interpretation, as well as interpretation complexity. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Variable** & **Age** & **Gender** & **Mar** & **Edu** & **Child** & \begin{tabular}{c} **Chron\_** \\ **dis** \\ \end{tabular} & \begin{tabular}{c} **Bad\_** \\ **health** \\ \end{tabular} & \begin{tabular}{c} **Health\_** \\ **self-ass** \\ \end{tabular} & **Nutt** & \
\begin{tabular}{c} **Bad\_** \\ **hab** \\ \end{tabular} &
\begin{tabular}{c} **Labor\_** \\ **pert** \\ \end{tabular} \\ \hline age & 1.00 & \(-\)0.23 & 0.06 & \(-\)0.22 & 0.30 & 0.35 & \(-\)0.01 & 0.18 & 0.17 & 0.20 & \(-\)0.32 \\ gender & \(-\)0.23 & 1.00 & 0.20 & 0.07 & 0.03 & 0.04 & 0.18 & \(-\)0.03 & \(-\)0.10 & 0.01 & 0.34 \\ mar & 0.06 & 0.20 & 1.00 & 0.07 & \(-\)0.00 & \(-\)0.02 & 0.18 & 0.09 & \(-\)0.04 & 0.07 & 0.03 \\ edu & \(-\)0.22 & 0.07 & 0.07 & 1.00 & \(-\)0.08 & \(-\)0.28 & \(-\)0.00 & \(-\)0.05 & 0.00 & \(-\)0.15 & 0.02 \\ child & 0.30 & 0.03 & \(-\)0.00 & \(-\)0.08 & 1.00 & 0.13 & \(-\)0.14 & 0.20 & 0.13 & 0.26 & \(-\)0.02 \\ chron_dis & 0.35 & 0.04 & \(-\)0.02 & \(-\)0.28 & 0.13 & 1.00 & 0.26 & 0.26 & 0.25 & 0.77 & \(-\)0.27 \\ bad\_health & \(-\)0.01 & 0.18 & 0.18 & \(-\)0.00 & \(-\)0.14 & 0.26 & 1.00 & 0.48 & 0.21 & 0.31 & 0.02 \\ health\_self-ass & 0.18 & \(-\)0.03 & 0.09 & \(-\)0.05 & 0.20 & 0.26 & 0.48 & 1.00 & 0.54 & 0.41 & \(-\)0.31 \\ nutr & 0.17 & \(-\)0.10 & \(-\)0.04 & 0.00 & 0.13 & 0.25 & 0.21 & 0.54 & 1.00 & 0.18 & \(-\)0.20 \\ bad\_hab & 0.20 & 0.01 & 0.07 & \(-\)0.15 & 0.26 & 0.77 & 0.31 & 0.41 & 0.18 & 1.00 & \(-\)0.25 \\ labor\_pert & \(-\)0.32 & 0.34 & 0.03 & 0.02 & \(-\)0.02 & \(-\)0.27 & 0.02 & \(-\)0.31 & \(-\)0.20 & \(-\)0.25 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Partial correlations of factors (significant parameters are marked in red).
# Deep-Learning Based Positron Range Correction of PET Images
## 1 Introduction
### Neural Networks in Medical Imaging  
Machine learning (ML), and more specifically the area of ML known as deep learning, are having a huge impact on many areas, including medical imaging and PET [33]. Deeplearning methods are able to create accurate mappings between inputs and outputs by means of artificial neural networks (NNs) with a large (deep) number of layers. The fact that the same framework can connect many different inputs, such as measurements, raw images, outputs such as labels, and reference images, allows its application in a large variety of problems and disciplines. A recent overview on ML and deep learning for PET imaging can be found in Reference [34].  
The components of the NN are learned from example training datasets. In the case of supervised learning, the example inputs are paired with their corresponding desired outputs. After the training, the NN can then be used on new input data to predict their outputs.  
Various studies based on convolutional neural networks (CNNs) have been proposed for medical image generation, especially for segmentation, many of them using the U-Net structure [35]. However, to the best of our knowledge, such networks have not yet been applied for PRC.
## 7 Results and Discussion  
In this section, the proposed method is validated using the United Kingdom's Disaster Monitoring Constellation (UK-DMC) satellite data, United Kingdom's Technology Demonstration Satellite-1 (UK-TDS-1) data over the ocean surface and the simulated data.
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Data from Geographic Parameters (DGPs)  
Table 2 presents the performance evaluation of three machine learning algorithms (Gradient Boosting, XGBoost, and Random Forest) applied to data from geographic parameters. The results show that:  
Gradient Boosting achieves the best accuracy (51.53%) and the best F1 score (50.75%), while Random Forest has the shortest execution time (2.58 ms). On the other hand, XGBoost presents the lowest performance in terms of accuracy (47.78%) and MCC (35.72%). These results allow us to compare the algorithms' effectiveness in speed and prediction quality for this specific application.
# Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
## 4 Evaluation  
In order to prove the validity of the proposed RAID scattering and adaptive ECC, we implemented the proposed schemes inside FlashSim [34; 35] that is widely used in the storage system evaluation. This open source simulator includes the simulated NAND flash controller and NAND flash memories that can be set for page size, block size, etc. The simulator also implements well-known FTLs and NAND flash operations, including read, write, and erase, as well as garbage collection. We have added the RAID parity management, compression-based adaptive ECC, and RAID-scattering schemes in this simulator. The simulation configuration set for performance evaluation is as follows. The page-level FTL was used for an FTL mapping management scheme, in which 8 KB was used for page size, and a 1 KB sub-page was used. The compression module uses the zlib [36] compression module. The BCH ECC model is used with 1024 BCH16, which is an ECC module that can correct 16 bits per 1024 bytes. That is, 1024 BCH16 is applied for the 1 KB sub-page. For the bit error generation and simulation, the BER is sampled and an error is injected according to its P/E cycles. The samples from BER versus P/E cycle data are taken from a previous study on NAND flash devices [24]. The simulated BER sample is depicted in Figure 2.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 3 Method
### Network Architecture  
U-Net [28] stands as a well-established architecture in image enhancement, renowned for its multiscale encoder-decoder structure with skip connections that strike a balance between global and local information, preserving fine details. In our approach, a lightweight U-Net with seven convolutional layers is employed, containing only 10.564 K trainable parameters and 38.626 M flops for 256 \(\times\) 256 \(\times\) 3 input images. The initial six layers consist of 32 convolution kernels of size 3 \(\times\) 3, followed by the Rectified Linear Unit (ReLU) activation function. The final layer utilizes the same convolution kernels with the Tanh activation. To prevent data detail loss and maintain relationships between adjacent pixels during downsampling operations, inspired by WaveCNet [29], we substitute downsampling with Discrete Wavelet Transform (DWT). This allows transmission of the low-frequency information to later layers for generating robust high-level features, while the high-frequency information remains unaltered and is directly used for data reconstruction during InverseDWT (IDWT). The channel attention mechanism is also introduced in the decoding stage to enhance feature representation further.  
Furthermore, the low-light image is inserted into the network to obtain a parameter map having the same dimensions as the input. Consequently, it iteratively enhances the low-light image using this parameter map. To sum up, it can be expressed as follows:  
\[f(I)=I+\mathcal{A}*I(1-I). \tag{8}\]  
where \(I\) is a low-light image, \(\mathcal{A}\) is a parameter map of the network output, \(*\) is pixel-by-pixel multiplication, and \(f(I)\) represents the enhancement result after one iteration. In our experiments, a great improvement is achieved by setting the number of iterations to eight. Specifically, \(I\) is the input of each iteration, \(f(I)\) is the output of each iteration, and the input of each iteration is the output of the previous iteration. The main purpose of using the iterative method to enhance low-light images is to realize gradual fine-tuning and avoid the local area of insufficient or excessive enhancement caused by one enhancement.
# An Energy-Efficient Convolutional Neural Network Processor Architecture Based on a Systolic Array
## 2 System Architecture  
### Overall Architecture of the Processor  
The CNN processor architecture proposed in this paper can accelerate the computation of the convolutional layer or fully connected layer of CNNs, which is essentially equivalent to a coprocessor. The external main processor configures the instruction registers inside the processor, and direct memory access (DMA) completes the data interaction between the CNN processor and external memory.  
Figure 1 illustrates the proposed CNN processor architecture, including data buffers, the computing processing element (PE) array, the activation module, the central controller and the data interface. The data buffer includes the image buffer, filter buffer and bias buffer, which are used to store image data, weight data and bias data respectively. The scatter is used to distribute the data transferred from DMA through the AXI-Stream interface to each buffer. The PE array consists of 128 processing units (PUs), and each PU is composed of a systolic array of three PEs, which is the core unit of the processor used to perform convolution and fully connected computation. The main processor configures instruction registers in the central controller through the AXI-Lite interface. The controller controls the reading and writing of each data buffer and the operation of the PE array according to the obtained configuration parameters.  
In this design, the bias buffer also serves to store the intermediate values of the computing process or the final results, eliminating the need of the output buffer and saving storage resources. Such a simplified design is made possible by the systolic array computation strategy, which also makes it easier to expand the proposed processor to support parallel computing between more output feature map channels, as shown in the later sections. The first-input-first-output (FIFO) storage structure is adopted in the bias buffer to ensure first-in-first-out dataflow, and the stored computation results are carried to the external memory by DMA after the rectified linear unit (ReLU) activation.
## 4 Results and Discussion
### Challenges
#### 4.2.4 Awareness Challenges  
The lack of knowledge and awareness was highlighted as a challenge in digital payment technologies in 10 studies. For example, in order to curb the black money business, India encouraged electronic payments and enabled the nation to move towards a cashless society. The shift in the payment system from cash-based transactions to digital was part of the Digital India campaign, launched for the welfare of the public. Therefore, the move was anticipated to be fast and smooth [57]. However, studies showed that people pay through cash more than they do electronically. Although trust was one of the reasons behind skepticisms, comparatively, it was found that educated people are using more cashless transactions, e.g., out of 100 digital payment technology users, 79 are in the educated group (12 th standard to graduate-level degree) [57]. This shows that the lack of knowledge and awareness about digital payment technologies affects people's willingness to shift from cash to digital payments [57]. Furthermore, it was highlighted that the 'lack of literacy or digital literacy' is one of the key challenges of using digital financial services [12]. Additionally, other studies emphasized that the lack of digital literacy, language barrier and awareness of digital payments discouraged people from using digital payment systems [13]. Thus, it was suggested to establish digital literacy kiosks or help desks in rural areas [13].  
Another big challenge was the 'inability to reach a critical mass of consumers' and the issues concerning financial inclusion. [PERSON] and [PERSON] [92] highlighted that mostly low income, unemployed, disabled and illiterate people and women are excluded from formal financial services due to emerging digital payment technologies. Therefore, the study advised that an integrated approach should be adopted for achieving trust on the usage of digital payment technologies. It was further stated that people should be educated with information relevant to the application of digital payment systems and their security concerns. Further, policymakers and regulatory bodies must make clear guidelines in an easily understandable format for the usage of digital payment technologies and attached benefits [92].
# Correlation Study of Auroral Currents with External Parameters During 10-12 October 2024 Superstorm
## 2 Methods  
The European Space Agency Swarm satellites operate in near-polar orbits with inclination angles of about 87.5\({}^{\circ}\). They orbit at altitudes of around 500 km, completing each orbit in roughly 93 min. The orbital constellation for the first mission phase was finalized on 17 April 2014. Swarms A and C fly in close formation with a 1.4\({}^{\circ}\) longitudinal separation at approximately 450 km altitude. Swarm B maintains an altitude of around 510 km.  
FACs are obtained from vector magnetic field measurements from Swarms A and C using [PERSON]'s integral law:  
\[j_{\parallel}=\frac{1}{\mu_{0}Asin(i)}\oint Bdl\]  
where the integral is calculated around a closed loop enclosing the measurement quadrilateral defined by the orbit tracks of Swarms A and C (e.g., please refer to [PERSON] et al., 2020 [13] for more details of the inversion method). \(B\), the disturbance magnetic field in the horizontal plane, is derived by subtracting the CHAOS-6 magnetic field model; \(dl\) is the line element along the integration path; \(A\) is the closed quadrilateral area; \(i\) is the inclination angle; and \(\mu_{0}\) is the vacuum permeability.  
For Swarm B, FACs are computed using the following formula:  
\[j_{\parallel}=\frac{1}{\mu_{0}v_{x}sin(i)}\frac{\partial B_{y}}{\partial t}\]  
where \(B_{y}\) is the disturbance magnetic eastward component parallel to the current sheet, and \(v_{x}\) is the velocity perpendicular to the current sheet in the mean-field-aligned system [14]. The large-scale FACs from Swarms A/C and B are low-pass-filtered for 20 s to suppress small-scale FAC structures smaller than 150 km [13]. Thus, the variation in small-scale FACs (below 150 km) is not within the scope of this study. Positive (negative) FACs represent currents flowing out of (into) the ionosphere. FACs with absolute values less than 50 uA/m\({}^{2}\) were considered in the present work to exclude outliers.  
PEJs were calculated from the scalar magnetic field observed by Swarms A and B by using the line current method [15; 16; 17]. This method approximates auroral PEJs as infinite line current elements in the E region (at about 115 km altitude), separated from each other by 1\({}^{\circ}\). The residuals of the total field were inverted using the least-squares fitting approach to determine every line current density (please refer to [15] for more details of the inversion method). Here, positive (negative) values indicate eastward (westward) currents. To ensure reliable results, the absolute PEJ density should be between 0.03 and 3 A/m. In this work, we used apex latitudes for FACs and quasi-dipole MLat for PEJs [18].