esa-sceva/satcom-chunk-collection huggingface dataset:
# SnowMamba: Achieving More Precise Snow Removal with Mamba
## 4 Conclusions  
This paper proposes a novel Mamba-based network architecture for image desnowing, called SnowMamba. This method represents the first application of Mamba in image desnowing. The design combines CNN's local detail processing with Mamba's long-range feature capturing capabilities to achieve higher-quality images. Specifically, the paper designs the SCM, a CNN-Mamba desnowing module, as the core component, using a four-level encoder-decoder structure combined with a multi-scale residual network for image desnowing. SE modules are added during upsampling and the initial input stages to enhance the model's ability to distinguish and focus on different image features. Extensive experiments demonstrate that the proposed method achieves better desnowing results compared to existing approaches.  
Conceptualization, G.W. and F.S.; methodology, G.W.; software, G.W.; validation, G.W. and Y.Z.; formal analysis, G.W., F.S. and Y.Z.; investigation, G.W. and F.S.; resources, F.S.; data curation, G.W.; writing--original draft preparation, G.W.; writing--review and editing, G.W., F.S., Y.Z. and Z.J.; visualization, G.W.; supervision, F.S.; project administration, Z.J.; funding acquisition, F.S. and Z.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of XinJiang province under Grant 2022D01C60, National Natural Science Foundation of China (No. 62261053), Tianshan Talent Training Project--Xinjiang Science and Technology Innovation Team Program (2023 TSYCTD0012), Tianshan Innovation Team Program of Xinjiang Uygur Autonomous Region of China (2023D14012), and Scientific Research Plan of Universities of Xinjiang under Grant XJEDU(2019Y006).  
Not applicable.  
The datasets used in this study are all publicly available. The specific dataset information is as follows: the access link for the Snow 100K dataset is [[https://sites.google.com/view/yunfuliu/desnownet](https://sites.google.com/view/yunfuliu/desnownet)]([https://sites.google.com/view/yunfuliu/desnownet](https://sites.google.com/view/yunfuliu/desnownet)), the access link for the CSD dataset is [[https://github.com/weitingchen83/ICCV2021-Single-Image-Desnowing-HDCWNet?tab=readme-ov-file](https://github.com/weitingchen83/ICCV2021-Single-Image-Desnowing-HDCWNet?tab=readme-ov-file)]([https://github.com/weitingchen83/ICCV2021-Single-Image-Desnowing-HDCWNet?tab=readme-ov-file](https://github.com/weitingchen83/ICCV2021-Single-Image-Desnowing-HDCWNet?tab=readme-ov-file)), and the access link for the SRRS dataset is [[https://github.com/weitingchen83/JSTASR-DesnowNet-ECCV-2020?tab=readme-ov-file](https://github.com/weitingchen83/JSTASR-DesnowNet-ECCV-2020?tab=readme-ov-file)]([https://github.com/weitingchen83/JSTASR-DesnowNet-ECCV-2020?tab=readme-ov-file](https://github.com/weitingchen83/JSTASR-DesnowNet-ECCV-2020?tab=readme-ov-file)).  
The authors declare no conflicts of interest.  
Figure 6: The visual comparison results with state-of-the-art methods on real-world datasets.
## 5 Results and Discussion  
The experimental results for the proposed IGWOHHO algorithm are presented. A time complexity analysis is conducted to assess computational efficiency. Benchmark test functions are then used to evaluate the algorithm's convergence, global search ability, and robustness, with comparisons to multiple heuristic algorithms under identical conditions. Furthermore, a case study involving a 35-node intermodal transport network analyzes the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Highway–Railway** & **Railway–Waterway** & **Waterway–Highway** \\ \hline \(t_{i}^{bh}\) (h/TEU) & 0.14 & 0.20 & 0.26 \\ \(c_{i}^{bh}\) (CNY/TEU) & 150 & 275 & 200 \\ \(f_{i}^{bh}\) (kg/TEU) & 2.16 & 1.89 & 1.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data related to the modal shift in transport.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Node** & **Soft Time Window** & **Node** & **Soft Time Window** & **Node** & **Soft Time Window** \\ \hline
1 & (1, 3) & 12 & (12, 19) & 23 & (26, 35) \\
2 & (1, 3) & 13 & (13, 20) & 24 & (27, 36) \\
3 & (2, 5) & 14 & (14, 23) & 25 & (28, 37) \\
4 & (3, 6) & 15 & (17, 25) & 26 & (28, 37) \\
5 & (3, 7) & 16 & (18, 27) & 27 & (30, 39) \\
6 & (4, 8) & 17 & (20, 29) & 28 & (31, 39) \\
7 & (4, 9) & 18 & (21, 30) & 29 & (32, 40) \\
8 & (5, 12) & 19 & (22, 33) & 30 & (34, 41) \\
9 & (7, 15) & 20 & (22, 33) & 31 & (34, 43) \\
10 & (8, 17) & 21 & (24, 34) & 32 & (36, 43) \\
11 & (10, 18) & 22 & (25, 35) & 33 & (36, 46) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time windows for different nodes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Highway** & **Railway** & **Waterway** \\ \hline \(v_{k}\) (km\(\cdot\)h) & 80 & 65 & 25 \\ \(c_{ij}^{k}\) (CNY/km\(\cdot\)TEU) & 6.22 & 3.45 & 1.87 \\ \(e_{k}\) (kg/km\(\cdot\)TEU) & 0.89 & 0.16 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data relating to the mode of transport.  
model's performance, focusing on total cost, transport time, and carbon emissions under various operational settings, and carbon tax policies.
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 3 Realization of the Improved Model
### Impact of the Sampling Interval of Input Data  
Sampling interval reflects the intensive extent of input data, and it is another key parameter in a GRNN network. If the sampling interval is very small, a larger quantity of data joins in the  
Figure 5: Clock differences between true (German Research Centre for Geosciences (GFZ) precise clock products) and prediction, using different lengths of input data.  
GRNN network construction, and usually, it will lead to better predictions. However, the computation complexity increases dramatically with the number of neurons raising rapidly. With increasing sampling interval, sparse data may cause loss of short-term characteristics and decreasing the prediction accuracy. Therefore, sampling interval selection is worth to investigate for a better balance.  
In this experiment, the K-fold cross method is employed to investigate how the sampling interval affects the prediction accuracy, and therefore, the sampling interval of input data is set up to 30 s, 60 s, 120 s, 180 s, 240 s, and 300 s, respectively. The clock differences between true and prediction for two satellites of each type are presented in Figure 6. Overall, the sampling interval, less than 300 s, has little effect on the prediction accuracy. Additionally, the clock differences increase along with prediction time increasing but are still within \(\pm\)1 ns for all fourteen BDS-2 satellites. In order to reduce the computing load, 300 s is choosing as the sampling interval in accordance with IGS 5 min clock products.  
Figure 6: _Cont._
###### Abstract  
Image registration is crucial in multimodal longitudinal skeletal muscle Magnetic Resonance Imaging (MRI) studies to extract reliable parameters that can be used as indicators for physio/pathological characterization of muscle tissue and for assessing the effectiveness of treatments. This paper aims at proposing a reliable registration protocol and evaluating its accuracy in a longitudinal study. The hips of 6 subjects were scanned, in a multimodal protocol, at 2 different time points by a 3 Tesla scanner; the proposed multi-step registration pipeline is based on rigid and elastic transformations implemented in SimpleITK using a multi-resolution technique. The effects of different image pre-processing (muscle masks, isotropic voxels) and different parameters' values (learning rates and mesh sizes) were quantitatively assessed using standard accuracy indexes. Rigid registration alone does not provide satisfactory accuracy for inter-sessions alignment and a further elastic step is needed. The use of isotropic voxels, combined with the muscle masking, provides the best result in terms of accuracy. Learning rates can be increased to speed up the process without affecting the final results. The protocol described in this paper, complemented by open-source software, can be a useful guide for researchers that approach for the first time the issues related to the muscle MR image registration.  
image registration; accuracy evaluation; skeletal muscle; multimodal MRI +
Footnote †: journal: Applied Sciences
# Design and Experimental Development of
## 1 Introduction
Based on the concept of maintaining multi-point contact on uneven ground, a foot capable of providing stable contact on convex and concave surfaces was developed. The foot sole was equipped with four rigid spikes and corresponding locking mechanisms, each of which had an optical sensors to detect the ground height. According to the sensor values, the foot landing pattern was modified to guarantee a support polygon on uneven terrain with three or four spikes [24]. In [25], a flexible foot with 12 degrees of freedom was designed. Connected by four independently-actuated parts, the foot sole could maintain multi-point contact on complicated terrains. However, the adaptation was also actively controlled, which increased the complexity of the control system. In [26], [PERSON] et al. developed a completely passive foot by mimicking the longitudinal arch of human foot. The foot was able to vary its shape to comply with uneven terrains as a function of the exerted forces. However, its adaptation capabilities in the coronal plane and push-off movement were limited. If we observe the human foot closely, we will find that when walking on the ground with trivial obstacles, the human foot sole can adapt to the irregularities passively without adjusting walking patterns or any active control. This is partly due to the intrinsic adaptivity of the soft tissues, such as the plantar fascia underneath the foot sole, which can soften and stiffen accordingly under different contact conditions. In this paper, we aim to design a new stiffness adjustable foot system for biped robots which is adaptable to small-sized bumps and obstacles on the ground on the basis of the inspirations from the human foot. Specifically, the foot can exert high stiffness to provide sufficient support and propulsion to the robot body during walking, and can also exert low stiffness to adapt to the bumps on the ground. The rest of this paper is organized as follows: Section 2 introduces the working mechanism of the stiffness-adjustable foot system. Section 3 presents the design of the major components of the foot system in detail. Some preliminary experiments are performed to validate the functions of the foot system, and the results are shown in Section 4. Finally, some discussions and conclusions are given in Section 5.
## 2 Materials and Methods
### Characteristics  
The crystal structure of red mud and the adsorbents were determined by an X-ray powder diffraction (XRD, RAPID-S, Rigaku, Japan). The saturation magnetization of red mud and the adsorbents were detected by a superconducting quantum interference devices magnetometer (SQUID-VSM, Quantum Design, San Diego, CA, USA). X-ray photoelectron spectroscopy (XPS) measurements were performed with a spectrometer (ADES 400, VG-ADES, UK). The composition of red mud and the adsorbents was analyzed by XRF spectroscopy. The morphology of red mud and the adsorbents were recorded by field-emission scanning electron microscopy (SEM, FE-SEM, FEI Co., Hillsboro, OR, USA).
# Instability Risk Assessment for Deep Excavation of Soil-Rock Combinations Containing Groundwater
## 2 Models and Methods
### Rationale
#### 2.2.3 Grey Relational Analysis  
Grey relational analysis is an analytical method that quantitatively describes the trend of a system by comparing the geometrical similarity of the reference data columns with the comparison data columns to determine their degree of correlation. It has the capability to reflect the degree of correlation between different sequences, which allows quantitative analysis of the impact of each risk assessment indicator on the assessment results.  
The steps of grey relational analysis are as follows:  
1. Determination of the analytical sequence  
The risk assessment grade is defined as a reference sequence reflecting the characteristics of the system, and the risk assessment index is defined as a comparative sequence affecting the system, resulting in the following matrix:  
\[[X]=\left[\begin{array}{ccc}x_{10}&\cdots&x_{1n}\\ \vdots&\ddots&\vdots\\ x_{m0}&\cdots&x_{mm}\end{array}\right], \tag{21}\]  
where the first column of the matrix is the parent sequence and columns 2 through \(n\) are the subsequences;  
2. Dimensionless processing of data  
Due to the different dimensions of each risk assessment index, it is difficult to reach a correct conclusion due to errors when analyzing and comparing. In order to reduce the analytical error caused by the difference in dimensions, the original data are made dimensionless using the initial value method. The initial value method is formulated as follows:  
\[X^{\prime}=\frac{x_{ij}}{\frac{1}{n}\sum_{i=1}^{n}x_{ij}}, \tag{22}\]  
3. Calculation of correlation coefficients  
\[\xi_{ij}=\frac{min\Big{\{}\Big{|}x^{\prime}_{ij}-x^{\prime}_{i0}\Big{\}}+\rho max \Big{\{}\Big{|}x^{\prime}_{ij}-x^{\prime}_{i0}\Big{|}\Big{\}}}{\Big{|}x^{\prime }_{ij}-x^{\prime}_{i0}\Big{|}+\rho max\Big{\{}\Big{|}x^{\prime}_{ij}-x^{\prime }_{i0}\Big{|}\Big{\}}}, \tag{23}\]  
where \(\xi_{ij}\) represents the correlation coefficient between the parameter \(i\) of the subsequence \(j\) and the parameter \(j\) of the parent sequence, \(\rho\) is the distinguishing coefficient whose value characterizes the variability between correlation coefficients with a range of \([0,1]\). In this paper, \(\rho\) is calculated with a value of 0.5.  
4. Correlation calculation  
The mean value resulting from homogenization of the correlation coefficient sequence is the correlation degree, as follows:  
\[\gamma_{0i}=\frac{1}{n}\sum_{i=1}^{n}\xi_{ij}. \tag{24}\]The closer \(\gamma_{01}\) is to 1, the higher the correlation between the two.
## 3 Finite Element Analysis
### Cross Partition Curved Cut Reinforcement  
Under the action of the wheel load, there is a stress concentration area near the arc-shaped incision, which is located inside the arc of the arc-shaped incision. This is located in the crack initiation area, which is referred to as the CI area in this article.  
1 For short cracks, the arc notch optimization scheme shown in Figure 3a is used. The diameter of the arc notch is 70 mm, the length of the straight-line section tangent to the arc notch is 84 mm, and the other side of the straight-line section is also tangent to the arc chamfer. The stress cloud before and after the optimization of the arc notch is shown in Figure 6. The Mises equivalent stress amplitude of the finite element before reinforcement is 154.2 MPa, and the equivalent stress amplitude is reduced to 63.5 MPa after optimization of the arc notch. The stress reduction is more than 50%, which shows that the effect of stress reduction after optimization of the arc notch is obvious. Furthermore, the comparison of stresses in the curved cut of the cross-sectional plate is shown in Table 2. As can be seen from Table 2, the calculated values of ABAQUS finite elements are close to the measured values of the test, and the error does not exceed 10%, especially after reinforcement, the two stress values match very well, which proves that the ABAQUS model can better simulate the stress distribution at the curved cutout of the diaphragm under the action of vehicle load. Moreover, the FEM calculated value is slightly smaller than the measured value, which is due to the influence of the actual open lane during the test. Comparing Tables 1 and 2, it can be seen that both the measured values and the finite element simulation values are much smaller than the yield strength of Q345 qD steel. This indicates that the material is in an elastic state when it is stressed, which does not fall into the scope of material nonlinearity consideration.  
Figure 5: Commonly used truck wheelbase (unit: m).  
Figure 6: Stress cloud diagram before and after optimization of arc-shaped incision: (**a**) Before reinforcement, (**b**) after optimization of short crack arc cut.  
2 For longer cracks, the reinforcement scheme of arc notch optimization + steel plate reinforcement is used, as shown in Figure 3b. The stress cloud diagram after the arc notch reinforcement is shown in Figure 7, and the Mises equivalent effect force amplitude on the pressurized side after the steel plate reinforcement is reduced to 43.2 MPa, which is a reduction of about 71.9%. The peak Mises equivalent force is 20.3 MPa smaller for the reinforcement solution with steel plate reinforcement than for the optimized reinforcement solution with the curved notch, so for long cracks, \"arc notch optimization + steel plate reinforcement\" is more beneficial to reduce the stress amplitude. From the stress amplitude, it can be judged that the risk of fatigue cracking is basically eliminated after reinforcement. In addition, the ABAQUS calculated value compared with the measured value, the two results match and the stress reduction is very close.
# Research on Waveform Adaptability Based on Lunar Channels
## 2 Lunar Communication Environment  
We investigate the H-ITM lunar radio wave propagation fading model, analyzing the path loss of radio wave propagation [14]. It analyzes the typical radio wave propagation path loss prediction model, ITM, and studies the process of predicting path loss in H-ITM lunar radio wave propagation [15]. The different communication environments on the lunar surface are divided into free space segment, dual-path loss propagation segment [16], and spherical diffraction propagation segment, as shown in Figure 1.  
Figure 1: Propagation path of lunar electromagnetic waves.  
Using the ITM model, the path propagation loss is calculated and divided into three parts based on the path distance: line-of-sight loss, diffraction loss, and scattering loss. The corresponding calculation formulas are as follows:  
\[A_{\text{ref}}=\left\{\begin{array}{l}\max[0,A_{cl}+k_{1}d+k_{2}\ln(d/d_{Ls})],d_{\min}\leq d<d_{Ls}\\ A_{cl}+m_{d}d,\text{d}_{Ls}\leq d<d_{x}\\ A_{cs}+m_{s}d,\text{d}\geq\text{d}_{x}\end{array}\right., \tag{1}\]  
\[A_{cs}=A_{cl}+(m_{d}-m_{s})d_{x}, \tag{2}\]  
where the distance for the communication path is represented by \(d\), while \(d_{LS}\) stands for the smooth ground distance corresponding to the transmitting and receiving antennas. This distance is also the boundary point between line-of-sight propagation and diffraction propagation, which is given by  
\[d_{Ls}=0.75d_{0}+0.25d_{L}, \tag{3}\]  
\[d_{0}=\min[0.5d_{L},1.9 kh_{t}h_{r}], \tag{4}\]  
where \(d_{0}\) is the assumed distance for line-of-sight propagation, \(h_{t}\) is the height of the transmitting antenna, \(h_{r}\) is the height of the receiving antenna, \(k\) is the wave number, \(d_{L}\) is the distance from the transmitting and receiving antennas to the effective reflecting surface.  
For the path loss, \(d_{x}\) is the minimum starting distance for calculating scattering loss, where the scattering loss equals diffraction loss, \(A_{cl}\), \(A_{cl}\), and \(A_{cs}\) represent the propagation losses at a specific point in free space, diffraction, and scattering, respectively. \(K_{1}\) and \(K_{2}\) are propagation loss coefficients. \(m_{d}\) and \(m_{s}\) are diffraction and scattering loss coefficients. \(A_{i}\) and \(d_{i}\) are coefficients related to antenna height and permittivity.  
After the above derivation and calculation, in line-of-sight propagation, i.e., the dual-path loss segment, the channel loss calculation is formulated as  
\[L_{R}=-101\lg\Big{|}1+R_{\text{e}}\mathrm{e}^{j\delta}\Big{|}^{2}, \tag{5}\]  
where \(\delta=\frac{4\pi h_{t}h_{r}}{\lambda d}+\pi\), \(R_{\text{e}}\) represents the equivalent reflection coefficient.
## 4 Results and Discussion
### Comparison with Previous Studies  
To our knowledge, this is the first study to identify vulgar words in the Chittagonian dialect of Bangla. For that, we could not find any previous research to compare with our study. Since there was a study [35] to find vulgar words in the Bengali language, we compared it to our approaches. Below are the main differences and findings compared to the previous study to facilitate our research evaluation.  
1. Our research language domain is the Chittagong dialect of Bangla, while previous work focused on Bengali/Bangla. Working with dialects has many challenges such as data collection, data annotation, data processing, dataset validation, model creation, etc. By overcoming all these challenges, we successfully completed the research.
2. We carried out the research in two steps. Firstly, we reported the performance of the three keyword-matching baselines. No previous research has tried this type of method.
3. Then, we built machine learning and deep learning models and compared them with baseline methods. We observed that our models gave comparatively better results then previous studies [35].
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 2 Scenarios
### Illicit Trafficking of SNM and Radioactive Materials--Scenario B
In scenario B, the main goal of MRD systems is to detect, quantify, and localize SNM sources and other radioactive material resultant of illicit tracking or inadvertent movement. Since these sources are normally weak (e.g., SNM) and the detector-source distance can be high (up to 100 m), normally, high efficiency detection systems are necessary. To distinguish threat sources from NORM or other medical isotopes, identification of the source is also needed. In the next subsection, the existent standards for the detection systems used in security applications are described. When new detection systems are developed, they should first be analyzed accordingly to existing standards, specifying performance requirements and test methods. On the other hand, when novel technology has a significant impact on the instruments performance or introduces new features, it is necessary to revisit the existing standards, e.g., the first use of scintillator material for neutron detection led to the revision of standards [19]. The International Electrotechnical Commission (IEC) and the American National Standards Institute (ANSI) have published some important standards for mobile radiation detection equipment, listed in Table 1, which describes not only radiological requirements and test methods, but also requirements, like mechanical, electrical, and magnetic related properties. Both IEC63121:2020 [29] and ANSI N42.43-2016 [30] standards are only applied to vehicle-mounted mobile systems which travels predominantly on public roads (e.g., car, van or trailers). Therefore, these documents do not apply to detection systems mounted in other types of vehicles, like air-based platforms (e.g., planes and helicopters), trains, or boats. In order to keep the same data formats among the detection systems, there are important standards that must be considered: IEC 62755:2012 [31] and ANSI N42.42-2012 [32], for radiation instruments used in the detection of illicit trafficking of radioactive materials; and IEC 63047:2018 [33], which defines the data format for list-mode digital data acquisition used in radiation detection and measurement. The latter allows the collation of data from one or more detectors and one or more data acquisition devices (e.g., time-correlated data) [19].