esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Dynamic Multi-Stage Path Planning Experiments
#### 4.3.1 Task Completion Rate  
Figure 8 shows the average task completion rate of the algorithm in different environments. ID A* and CBS perform well in low AGV scenarios, but fail as the AGV count increases, due to an exponential growth in computation time. PER-D3 QN-EDAgger maintains a high task completion rate of more than 95% when the number of AGVs is below 200 and the shelf density is below 0.5, demonstrating strong adaptability. In environments with a high AGV count, PER-D3 QN-EDAgger achieves a success rate that is at least as high  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{1}{c}{**AGV Count**} & **1–10** & **1–10** & **11–20** & **11–20** \\  & **Shelf Density** & **0.05–0.45** & **0.50–0.90** & **0.05–0.45** & **0.50–0.90** \\ \hline \multirow{3}{*}{Average} & ID A* & 0.420 & 0.422 & 0 & 0 \\  & CBS & 0.982 & 0.854 & 0.638 & 0.488 \\ \multirow{3}{*}{Success Rate} & PER-D3 QN-EDAgger & 0.994 & 0.956 & 0.984 & 0.888 \\  & PRIMAL2 & 0.975 & 0.943 & 0.939 & 0.891 \\ \hline \multirow{3}{*}{Average} & ID A* & 41.634 & 41.386 & 60 & 60 \\  & CBS & 3.415 & 13.691 & 29.632 & 41.987 \\ \multirow{3}{*}{Response Time(s)} & PER-D3 QN-EDAgger & 0.012 & 0.010 & 0.012 & 0.013 \\  & PRIMAL2 & 0.026 & 0.024 & 0.025 & 0.026 \\ \hline \multirow{3}{*}{Average Path} & ID A* & 164.758 & 165.506 & 256 & 256 \\  & CBS & 20.517 & 49.265 & 106.266 & 149.899 \\ \multirow{3}{*}{Cost (steps)} & PER-D3 QN-EDAgger & 26.094 & 40.038 & 45.247 & 79.576 \\ \cline{1-1}  & PRIMAL2 & 39.698 & 43.524 & 60.636 & 73.311 \\ \hline \hline \end{tabular}  
* The table divides the environments into four categories based on high/low AGV counts and high/low shelf density, then reports each algorithm’s performance in these environments based on the experimental results and calculates their average values.  
\end{table}
Table 5: Experimental results.  
Figure 7: A larger warehouse-like simulated grid map (\(100\times 100\)).  
as PRIMAL2, while PRIMAL2 performs better under high shelf density conditions. This advantage may be attributed to PRIMAL2's corridor structure optimization.
# Few-Cycle, \(\mu\)J-Class, Deep-UV Source from Gas Media
## 2 Experimental Setup and Methods  
A 30 fs, 780 nm NIR laser was loosely focused to a 1 m-long hollow-core fiber (HCF) with 400 \(\upmu\)m of the inner diameter, which was filled with 3 bar Ne gas. The generated supercontinuum laser was temporally compressed by a set of chirped mirrors and a fused silica wedge pair. Consequently, a 5 fs, 0.25 mJ NIR laser with a mean photon energy of 1.7 eV was obtained. The laser was operated with a 3 kHz repetition. The mean photon energy was defined by the median of the spectral intensity distribution. The dispersion scan (Dscan) method was used to measure the pulse duration [24].  
An f = 500 mm spherical mirror was used to focus the few-cycle laser into the quasi-static gas cell (3 mm thick inner diameter, 300 \(\upmu\)m hole diameter), as shown schematically in Figure 1. The beam size at the focus was measured to be \(\sim\)100 \(\upmu\)m in diameter (full width at 1/e\({}^{2}\) maximum). The estimated peak intensity at the focus was on the order of \(\sim\)1.2 \(\times\) 10\({}^{15}\) W/cm\({}^{2}\). The gas cell was placed on a motorized XYZ stage so that the central location of the gas cell with respect to the laser focus could be controlled in vacuum. Using a UV-grade fused silica prism and an aperture, the generated third-order harmonics were separated from the fundamental NIR laser and collected by a lens to an absolutely calibrated UV photodiode for energy measurements, or to a UV-NIR (200-1100 nm) spectrometer for spectrum measurements. The tightest focus position (z\({}_{0}\) = 0 mm) was determined by measuring the transmitted NIR spectra as a function of the cell position when 0.2 bar Ar gas was supplied to the cell (Figure 2a). The gas cell position in the laser propagation direction was changed by the motorized linear stage on which the entire gas cell unit was mounted. The absolute focus position (z\({}_{0}\) = 0) was judged as 18.25 mm (Figure 2b) at which the largest spectral blue-shift was observed. Then, the generated DUV energies were measured by changing the cell position (z\({}_{0}\)). We note again that the gas cell at z\({}_{0}\) = 0 mm means that the gas cell is positioned at the laser focus, and a negative z\({}_{0}\) value indicates the gas cell location being in front of the laser focus.
# Identification of Complicated Lithology with Machine Learning
## 2 Model Principles
### Support Vector Machine Algorithm Principles  
Support vector machine (SVM) is a powerful supervised learning model primarily used for classification problems. Its core principle is to find an optimal hyperplane in a high-dimensional space that maximizes the margin between the data points of different classes. The support vectors are the data points closest to this hyperplane, and they determine the position and orientation of the hyperplane [32].  
When selecting an appropriate kernel function, the following empirical guidelines are typically followed: If the dataset has a large number of features with high dimensionality, and the data points can be linearly separated, a linear kernel or polynomial kernel is a suitable choice. For such datasets, especially when the number of features far exceeds the number of samples, a linear or polynomial kernel often yields good results. Additionally, these kernels generally perform faster during training compared to other nonlinear kernels. Conversely, when the input data has lower dimensionality and a small sample size, and the data exhibits significant nonlinear characteristics, the Gaussian kernel (a type of radial basis function) is particularly well suited for handling nonlinear problems. Similarly, the sigmoid kernel function is also applicable to datasets with nonlinear characteristics. Although it is not as flexible as the Gaussian kernel, the sigmoid kernel tends to perform better in terms of generalization on unseen data, which helps prevent the model from overfitting [33].  
Commonly used kernel functions include  
\[linear,k(x,y)=x^{\prime}*y \tag{1}\]  
\[poly,k(x,y)=\left(x^{\prime}*y+c\right)^{d} \tag{2}\]  
Figure 1: Schematic Diagram of the Principle of Decision Tree.  
\[gauss,k(x,y)=\exp\left(-0.5*\left(\frac{norm(x-y)}{s}\right)^{2}\right) \tag{3}\]  
\[sigmoid,k(x,y)=\tanh\left(g*x^{\prime}*y+c\right) \tag{4}\]  
When applied to regression, the variant of support vector machine (SVM) is called support vector regression (SVR). The goal of SVR is to find a function that ensures the predicted values are as close as possible to the actual data points within a given tolerance range. The main difference between SVR and SVM is that in SVR, there is no need to classify samples. The objective is not to find an optimal hyperplane that maximizes the distance between all sample points, as in SVM, but rather to find a hyperplane that brings all sample points as close as possible to it. In SVR, the sample points are located between two tolerance lines, with the distance between these lines and the hyperplane representing a predetermined threshold. This kind of hyperplane minimizes the regression error, similar to the margin maximization in SVM [34].
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 3 PS-OCT Applications
### PS-OCT in Cancerous Tissue  
Cancer alters tissue microstructure. This alteration can change the optical properties of affected tissues. PS-OCT has been applied for imaging cancerous tissues in several organs. Altered birefringence  
Figure 7: PS-OCT of skin. (**a–e**) Cross-sectional PS-OCT image of human skin (adapted with permission from [176], Optical Society of America, 2013). (**a**) Scan location at the proximal interphalangeal joint of middle finger; (**b**) Reflectivity image. SC stratum corneum, D dermis, ED epidermis; (**c**) Retardation image. CLS “column” like structure; (**d**) Axis orientation image; (**e**) DOPU image. (**f–j**) Birefringence and vascular imaging of a hypertrophic scar and adjacent normal skin (adapted with permission from [106], [PERSON], 2014). (**f**) Photograph. Locations of PS-OCT scans are indicated by two blue squares; (**g.h**) Vasculature maximum intensity projections of the normal skin and scar, respectively; (**i,j**) En face birefringence maps of the normal skin and scar, respectively.  
and depolarization characteristics enabled imaging and identification of skin lesions such as basal cell carcinoma (Figure 8a-d) [24; 182; 183].  
Further promising results of PS-OCT based cancer imaging were reported in larynx, ovaries, and bladder [32; 184; 185]. Several groups also successfully studied PS-OCT for imaging breast cancer [112; 186; 187]. Figure 8 shows exciting results of PS-OCT imaging, which enabled the differentiation of tumor from surrounding tissue. Using intraoperative scanning of excised tissue or in situ needle-based imaging (cf. Figure 8e-j), PS-OCT could represent a promising method for reliably demarking malignant breast tumors, thus reducing the re-excision rate due to positive margins.
## 3 Results and Discussion
### Analysis of Adsorption Isotherm Model  
We used the Langmuir and Freundlich models to fit the adsorption process. The thermodynamic model parameters are shown in Table 7. The Langmuir and Freundlich isotherm models showed a good linear relationship in Figure 10. It can be seen from Table 7 that R\({}^{2}\)L was greater than R\({}^{2}\)F. Obviously, the determination coefficient of the Langmuir isotherm equation was closer to 1, and the fitting effect was good. The adsorption of the ions by manganese sand was a single-layer surface chemical adsorption. In the quasi-second-order kinetic model, the adsorption reaction was assumed to be a single-layer adsorption system, and the adsorption mechanism was chemical adsorption, which was consistent with the fitting result of the adsorption isotherm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**First-Order Kinetic**} & \multicolumn{3}{c}{**Second-Order Kinetic**} \\  & \multicolumn{3}{c}{**Equation**} & \multicolumn{3}{c}{**Equation**} \\ \cline{2-7}  & \(\mathbf{Q_{e}}\) & \(\mathbf{k_{1}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{Q_{e}}\) & \(\mathbf{k_{1}}\) & \(\mathbf{R^{2}}\) \\ \hline Unmodified manganese sand & 0.1026 & 0.4298 & 0.9819 & 0.126 & 3.307 & 0.992 \\ Modified manganese sand & 0.1261 & 0.4961 & 0.9790 & 0.150 & 3.687 & 0.996 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Kinetic fitting parameters of the adsorption of manganese ions by manganese sand.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**First-Order Kinetic**} & \multicolumn{3}{c}{**Second-Order Kinetic**} \\  & \multicolumn{3}{c}{**Equation**} & \multicolumn{3}{c}{**Equation**} \\ \cline{2-7}  & \(\mathbf{Q_{e}}\) & \(\mathbf{k_{1}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{Q_{e}}\) & \(\mathbf{k_{1}}\) & \(\mathbf{R^{2}}\) \\ \hline Unmodified manganese sand & 0.0941 & 0.4701 & 0.9839 & 0.110 & 5.059 & 0.995 \\ Modified manganese sand & 0.1189 & 0.5564 & 0.9772 & 0.139 & 4.828 & 0.998 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Kinetic fitting parameters of the adsorption of iron ions by manganese sand.  
Figure 9: The adsorption kinetic model of iron (**a**) and manganese (**b**) ions.
## 1 Introduction  
In recent decades, due to its excellent intrinsic mechanical properties (Young's modulus \(\approx\) 1000 GPa and tensile strength \(\approx\) 130 GPa) [1, 2] and electrical properties (electrical conductivity of 10\({}^{7}\)-10\({}^{8}\) S/m) [3, 4], graphene has received attention in lightweight structural and electrically conductive materials. Graphene is a two-dimensional nanomaterial with single atomic thickness, so it should be properly assembled at bulk scale for practical use. To realize a bulk graphene assembly, graphene oxides (GOs) have been widely used as starting materials. GOs have many functional groups and can be dispersed in various solvents [5, 6]; because of this, a bulk graphene assembly can be produced by various solution processes such as vacuum filtration [7], wet spinning [8], casting [9], and the electrospray roll-to-roll process [10].  
Currently, most graphene assemblies exhibit significantly lower properties than monolayer graphene due to the presence of defects, which are mainly located in the interconnection regionsbetween each graphene. To obtain a graphene assembly with better properties, defects within assemblies should be reduced by realizing more ordered graphenes. Recently, a nematic liquid crystal behavior of GOs in which GO flakes are aligned in a colloidal dispersion has been reported [6; 11; 12]. By subsequent solution processing of such dispersion, a highly-ordered graphene assembly can be obtained [13; 14; 15].  
It is known that the liquid crystal behavior of GOs is highly affected by various factors such as concentration, lateral size and functional group of GO flakes [11; 12]. More systematic studies are required to better understand the liquid crystal behavior of GOs and to obtain a more ordered GO assembly.  
In this study, using liquid crystal GO dispersions, we developed bulk-scale graphene films (cm scale in lateral size with 3.96 \(\upmu\)m thickness), which consisted of highly ordered graphene flakes. The liquid crystal dispersion of 150 \(\pm\) 29 \(\upmu\)m sized GO flakes showed a phase transformation of liquid crystal from isotropic to nematic at a concentration of 4 mg/mL, which is much lower than the liquid crystal dispersion of the 28 \(\pm\) 24 \(\upmu\)m sized GOs flakes (20 mg/mL). As a result, the larger-sized GO flakes showed a more ordered state than the smaller sized GO flakes at the same concentration. After film preparation from these liquid crystal dispersions and subsequent reduction by hydroiodic acid (HI), graphene-based films with larger-sized GO flakes showed better mechanical and electrical properties because there were fewer defects in the films.
## 4 Experimental Results and Performance Analysis  
To practically demonstrate the efficiency of this application in selecting the best satellites, tests with real GPS L1 C/A were carried out. Two uBlox LEA-6T were used as RS1 and RS2 to collect series of measurements in order to assist the MS. Both reference receivers were in good condition and set to receive satellite signals at spots with open sky. RS1 was placed in a parking lot (N 45\({}^{\circ}\)49\({}^{\prime}\)64.29\({}^{\prime\prime}\), W 73\({}^{\circ}\)56\({}^{\prime}\)42.78\({}^{\prime\prime}\), 83.61 m), and RS2 in the middle of a green park (N 45\({}^{\circ}\)49\({}^{\prime}\)17.66\({}^{\prime\prime}\), W 73\({}^{\circ}\)56\({}^{\prime}\)16.07\({}^{\prime\prime}\), 83.59 m). A bit grabber USRP B210 was used to collect raw data as MS in a narrow street in downtown Montreal (N 45\({}^{\circ}\)29\({}^{\prime}\)34.8\({}^{\prime\prime}\), W 73\({}^{\circ}\)33\({}^{\prime}\)49.4\({}^{\prime\prime}\), 83.60 m) where acquiring satellite signals is generally difficult due to concentrated building structures.  
The position of the two reference receivers (RS1 and RS2) that assist the MS in downtown Montreal is shown in Figure 5 and they were in a good sky visibility. Figure 6 shows the location of the MS raw data acquisition where the GPS L1 C/A signals are degraded because of the high buildings around and poor sky conditions.  
The measurement campaign was carried out for 4 h and the values are taken every 1 h. During this observation period, the data received changes (\(C/N_{0}\) level, elevation angle, forming GDOP) as time progresses. The received satellites during the entire observation period by each receiver are summarized in Table 1 (\(C/N_{0}\) level in (dB-Hz) and elevation angle in (\({}^{\circ}\))). Note that the real distances between the receivers are: RS1-RS2 = 816.46 m, RS1-MS = 412.03 m, and RS2-MS = 424.66 m.  
Figure 5: Position of MS and both RSs during satellite signals recording in downtown Montreal.  
Figure 6: Location of MS in Montreal downtown (GPS antenna placed inside a car).  
The horizontal uncertainty range was set to 20 km to reflect a realistic application scenario as shown in Table 2. Collective acquisition was performed by three iterations, with the search space refined at each iteration until an accurate estimate of the MS position could be obtained as we can be seen in Table 2.
## 3 Experimental Results and Analyses
### Comparison of YOLOv3-Lite with Three Modern Methods  
To better verify the superiority of the improved method, we compare YOLOv3-Lite with YOLOv3, YOLO-Tiny and MobileNet-SSD. These models are evaluated in terms of detection of average precision (AP) and offline detection time. The results of the contraction experiment are shown in Table 4. The parameter quantity of YOLOv3-Lite is about 31 million, YOLOv3 is twice that of ours, at about 61 million, and YOLO-Tiny is minimum. The number of parameters of SSD-mobilenet is roughly the same as ours. However, the parameters of YOLOv3-Lite are small, the detection speed is faster than most of the algorithms, and its average precision is the highest. We adopt deep separable convolution, which reduces the number of parameters by 8-9 times, on average, with an effect similar to that of standard convolution. Therefore, the detection speed of YOLOv3-Lite is 50% faster than that of YOLOv3. As YOLO-Tiny only uses 13 standard convolution layers, its feature extraction ability is poor and AP is very low. Although we have fewer network layers, the proposed method is still much better than MobileNet-SSD as we use the way of connecting feature maps at different levels, each layer of  
Figure 7: The experimental results of YOLOv3-Lite.  
feature pyramid containing not only high-level semantic information, but also low-level information. Therefore, YOLOv3-Lite not only has high detection accuracy but also achieves the goal of being lightweight, which greatly improves the network detection speed.  
Although the detection time of YOLO-Tiny is the shortest, its accuracy is too low to detect cracks at all. The accuracy of YOLOv3 is the highest, but its detection speed is 50% lower than YOLOv3-Lite, so it is not a good choice. We compare the detection results of YOLOv3-Lite and SSD-Mobilenet, which are shown in Figure 8. The bounding boxes of YOLOv3-Lite can accurately frame the location of cracks, while SSD-Mobilenet not only shows a large error in the location of the boxes, but also has a very low confidence level. In addition, there are many cracks that can not be detected by the SSD-Mobilenet method. Therefore, YOLOv3-Lite performs best in these modern methods.
# Effects of Cement Treatment on Mechanical Properties and Microstructure of a Granite Residual Soil
## 1 Introduction
For clayey soils with dual-porosity, a reduction in the intra-aggregate pores and an increase in the inter-aggregate pores were revealed as the cement content increased [23; 26]. It can be inferred that changes in the pore structure with the cement content depend on the clay content of the origin soil. In the case of high clay content, an increase in the cement content may not produce an increase in the strength and stiffness of the treated soil because of the increase in the large pores [23]. Hence, a proper treatment of soils requires both macro- and micro-scopic evaluations. This study investigates the mechanical properties and microstructure of a GRS treated with Portland fly-ash cement. The effects of cement treatment on the strength and stiffness of the treated soil are discussed based on the results of uniaxial and oedometric compression tests. Prediction formulas for the uniaxial compressive strength and secant modulus incorporating the porosity after curing, cement content, and curing time are also established. The presence and types of C-S-H and the changes in the pore structure following cement treatment are identified using XRD, SEM, and MIP tests. The effects of the pore structure change on the mechanical properties of the treated soil are discussed, and an appropriate cement dosage for treating the GRS is suggested based on mechanical and microstructural characteristics of the treated soil.
# Interclass Interference Suppression in Multi-Class Problems
## 5 Experiments
### Comparison Among Different Interclass Interference Suppression Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Conventional**} & \multicolumn{3}{c}{**OVA**} & \multicolumn{3}{c}{**OVO**} \\ \cline{2-10}  & **Algorithm** & **OVA\_V** & **OVA\_C** & **OVA\_PS** & **1 vR\_NS** & **OVO\_V** & **OVO\_C** & **OVO\_S** \\ \hline zoo & 0.9400 & 0.9509 & 0.9409 & 0.9509 & 0.9309 & 0.9509 & 0.9509 & 0.9009 \\ lymphography & 0.8186 & 0.8252 & 0.8324 & 0.8319 & 0.8319 & 0.8257 & 0.8257 & 0.7786 \\ wine & 0.9389 & 0.9444 & 0.9389 & 0.9441 & 0.9219 & 0.9549 & 0.9549 & 0.9157 \\ flags & 0.5937 & 0.5884 & 0.5837 & 0.5984 & 0.5982 & 0.6495 & 0.6287 & 0.4700 \\ autos & 0.7438 & 0.7731 & 0.7833 & 0.7636 & 0.7590 & 0.7633 & 0.7586 & 0.5221 \\ machine & 0.6552 & 0.6267 & 0.6505 & 0.6457 & 0.6410 & 0.6886 & 0.6886 & 0.5074 \\ images & 0.8667 & 0.8667 & 0.8714 & 0.8429 & 0.8524 & 0.8667 & 0.8667 & 0.6238 \\ glass & 0.6955 & 0.6771 & 0.6768 & 0.6675 & 0.5781 & 0.6823 & 0.6777 & 0.5799 \\ audiology & 0.7615 & 0.7447 & 0.7490 & 0.7316 & 0.7708 & 0.7800 & 0.7800 & 0.5002 \\ heart & 0.5216 & 0.5246 & 0.5246 & 0.5244 & 0.5051 & 0.5544 & 0.5443 & 0.5841 \\ solar & 0.8671 & 0.8609 & 0.8607 & 0.8578 & 0.8483 & 0.8761 & 0.8731 & 0.7865 \\ soybean & 0.8462 & 0.8960 & 0.9033 & 0.8843 & 0.8814 & 0.9254 & 0.9298 & 0.4802 \\ vehicle & 0.6631 & 0.6855 & 0.7020 & 0.6843 & 0.6962 & 0.7175 & 0.7210 & 0.6192 \\ anneal & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 0.9989 & 0.9989 & 0.9232 \\ Mean & 0.7794 & 0.7832 & 0.7870 & 0.7805 & 0.7725 & 0.8024 & 0.7999 & 0.6565 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy of OVA and OVO approach.