esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 4 Experiment and Analysis
### Fusion of Improved A* and DWA Algorithm Experiment  
In this system, the panel settings for maximum/minimum linear velocity, maximum angular velocity, and maximum linear acceleration directly determine the velocity bounds available to DWA at each frame, ensuring that the algorithm never issues commands beyond the differential-drive platform's capabilities. The linear velocity resolution, angular velocity resolution, and time step together control the discretization of the velocity interval and the fidelity of short-term trajectory simulation. The three cost weights--goal cost weight, obstacle cost weight, and deviation cost weight--adjust the priority of moving toward the subgoal, maintaining clearance from obstacles, and adhering to the global path when evaluating feasible trajectories. Thus, the GUI parameters are tightly coupled with the kinematic model of the differential-drive robot and the trajectory evaluation. Specifically, in Chapter 3, we set these parameters as follows: maximum linear velocity 1 m/s; minimum linear velocity 0 m/s; maximum angular velocity 1 rad/s; maximum linear acceleration 0.2 m/s2; linear velocity resolution 0.1 m/s; angular velocity resolution 0.1 rad/s; time step 0.1 s; goal cost weight 3.0; obstacle cost weight 5.0; and deviation cost weight 3.0. This configuration enables the robot to perform local planning efficiently and safely in complex environments, ensuring both consistent global-path tracking and robust dynamic obstacle avoidance.
## 2 Theory of Full Waveform Inversion
### Full Waveform Inversion
In this case, they can be classified as a successive-frequency inversion and a simultaneous multi-frequency inversion. In this research, the successive-frequency inversion is applied, as shown in Figure 1.
## 3 Proposed System
### Dataset
This dataset served as the foundation for training and evaluating the proposed learning system. Labels with higher frequencies, such as \"Calculation Errors\" and \"Concept Deficiency\", were instrumental in learning major error patterns, while low-frequency la \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Label** & **Description** & **Data Count** & **Proportion (\%)** \\ \hline Concept Deficiency & Errors due to insufficient comprehension of concepts & 7996 & 26.65\% \\ Calculation Error & Mistakes occurring in basic arithmetic operations & 10,001 & 33.34\% \\ Problem Comprehension Deficiency & Difficulty in understanding problem conditions or context & 5002 & 16.67\% \\ Error in the Solution Process & Incorrect approach in specific problem-solving steps & 2389 & 7.96\% \\ Unit Conversion Error & Errors occurring during unit conversion & 1597 & 5.32\% \\ Inequality Usage Error & Incorrect application of inequality symbols & 800 & 2.67\% \\ Graph Interpretation Error & Misinterpretation of data in graphs and charts & 601 & 2.00\% \\ Condition Omission & Failure to incorporate given conditions into solutions & 402 & 1.34\% \\ Other & Miscellaneous errors that do not fit other categories & 212 & 0.71\% \\ Total & - & 30,000 & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of TF-IDF processing results. bels like \"Condition Omission\" and \"Others\" were valuable for identifying uncommon learning issues. Beyond model training and evaluation, the dataset plays a crucial role in comprehensively assessing students' individual learning challenges and generating personalized feedback. The structured label distribution enhances the accuracy and efficiency of the learning system, demonstrating its practical applicability in real educational settings.
# Effect of Mix Proportion Parameters on Behaviors of Basalt Fiber RPC Based on Box-Behnken Model
## 4 Conclusions  
The effects of four process parameters (sand/binder ratio (\(A\)), water/binder ratio (\(B\)), basalt fiber content (\(C\)), silica fume to cement ratio(\(D\))) on the fluidity, compressive strength and flexural strength of BFRPC were investigated by the BBD experimental design method, the following conclusions can be drawn:  
(1) Three calculation models of fluidity, flexural strength and compressive strength were established through the Box-Behnken method. An analysis of variance (ANOVA) was performed to prove that the three models had enough precision to provide certain reference for the design of basalt fiber reactive powder concrete.  
(2) The three models established by an ANOVA showed that water/binder ratio (\(B\)) and silica fume to cement ratio (\(D\)) had significant influences on all the three reaction variables. Among them, the water/binder ratio (\(B\)) was the most important model item on the performance of BFRPC. The fluidity of the mixture was positively correlated with the water/binder ratio, but the flexural and compressive strength decreased with the increase of the water/binder ratio. In addition, the flexural strength of specimens was significantly affected by the basalt fiber content (\(C\)). For compressive strength, the four process variables in this study all caused the varying degree to it.  
(3) The analysis of the influence of parameters on response variables revealed that the silica fume to cement ratio (\(D\)) was a remarkable factor for BFRPC, which had interaction with other factors. It was established that the enhancement of basalt fiber on RPC was affected by the silica fume to cement ratio. For the flexural test, when \(D=0.2\), the addition of basalt fiber could significantly improve the flexural strength; when \(D=0.4\), the increase of basalt fiber content was not conducive to the flexural strength. For the compression test, the silica fume to cement ratio (\(D\)) interacted with both quartz sand content (\(A\)) and basalt fiber content (\(C\)). The optimal compressive strength could be obtained at \(A=0.9\), \(B=0.18\), \(C=0.15\), \(D=0.2\) or \(A=1.1\), \(B=0.18\), \(C=0.3\), \(D=0.4\).  
[PERSON] conceived of and designed the experiments. [PERSON] analyzed the data and wrote the paper. [PERSON] investigation. [PERSON] performed the experiment. [PERSON] audited the content. This research was funded by the Industrial Technology Research & Development Special Project of Jilin Province (2018C042-1); the Transportation Science and Technology Program of Jilin Province (2018-1-9); and the Science Technology Development Program of Jilin Province (20180201026 SF). The authors would like to appreciate anonymous reviewers for their constructive suggestions and comments to improve the quality of the paper. The authors declare no conflict interest.
## 2 Common-Mode Noise Suppression Filter Design  
The MDCRP structure filter design uses a PCB material of TU-933 (\(\varepsilon_{\mathrm{r}}\) = 3.09, tan\(\delta\) = 0.002) for high frequency, from the Taiwan Union Technology Corporation (TUC) to support up to 20 GHz. The filter dimension is 10 mm \(\times\) 10 mm of a 4-layer PCB. A three-dimensional view of the MDCRP structure filter is shown in Figure 1a. There are 2 coupled microstrip lines on the top layer, and the trace width, length, and spacing between the 2 coupled lines in the differential pair are 0.163 mm, 10 mm, and 0.3 mm, respectively. There are two ground planes and a mushroom structure under the two coupled lines of a differential pair. There are eight vias to connect two ground planes, and there are two vias to connect the mushroom structure and second ground planes that form the MDCRP structure filter. The via side view is shown in Figure 1b. The total thickness of the PCB is 0.77 mm, which can be divided into 4 metal layers (T1, T2, T3, and T4) and 3 insulating layers (H1, H2, and H3). There are differential pairs and via pads in layer 1 (T1), the first ground plane is in layer 2 (T2), the second ground plane is in layer 3 (T3), and there are only via pads in layer 4 (T4). The via diameter is 0.2 mm, the via pad diameter is 0.406 mm, and all vias traverse every layer of the PCB. The entire filter only uses three PCB board layers.  
The first ground plane is shown in Figure 2a. It cuts a 5 mm \(\times\) 10 mm slot to become the 2 ground sheets for GL1a and GL1b. The GL1a and GL1b of the ground sheet is connected to the second ground using four vias, respectively. There is a 3 mm \(\times\) 1.8 mm mushroom sheet between the GL1a ground sheet and the GL1b ground sheet, which uses 2 vias to connect the second ground plane. The second ground plane is shown in Figure 2b. The size is 5.8 mm \(\times\) 10 mm. There are two opposite C-shaped slots on the second ground plane. There are four vias on each side connected to the GL1a ground sheet and the GL1b ground sheet, respectively, and there are two vias connected to the mushroom sheet in the middle. These structural changes give us an MDCRP structure filter. The relevant dimensions in the first and second ground planes are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter** & **GL1a** & **GL1b** & **GW1** & **SL1** & **SW1** & **ML** \\ \hline Length (mm) & 2.5 & 2.5 & 10 & 5 & 10 & 3 \\ \hline
**Parameter** & **MW** & **GL2** & **GW2** & **SL2** & **SW2** & **LL1** \\ \hline Length (mm) & 1.8 & 5.8 & 10 & 5 & 6 & 2.1 \\ \hline
**Parameter** & **LW1** & **LL2** & **LW2** & **LL3** & **LW3** & **—** \\ \hline Length (mm) & 0.2 & 1.8 & 0.4 & 2.1 & 0.2 & **—** \\ \hline \end{tabular}
\end{table}
Table 1: The geometric parameters of the MDCRP structure filter.  
Figure 1: The proposed MDCRP filter structure. (**a**) Three-dimensional view. (**b**) Via side view.
## 2 Related Works
### Adaptive Update Schemes  
Moreover, in order to ensure long-term tracking, the correlation filter should be updated robustly. In the process of losing for the object, the updating rate must be reduced to avoid updating much harmful confusion into appearance model. Several researches have focused on long-term components for failure tracking strategies. [PERSON] et al. [46] propose an online random fern classifier as re-detection component for long-term tracking (LCT). [PERSON] et al. [45] introduce an adaptive learning rate by calculating the ratio between a value of current frame and a desire result based on the Peak to Sidelobe Ratio. However, the above schemes neglect the interframe deviation, which can measure the degree that appearance changing is alleviating or deteriorating.  
In this paper, we propose an adaptive context-aware and structural correlation filter for object tracking. Firstly, we propose a novel context selecting strategy to obtain negative samples, instead of sampling on fixed locations. Secondly, to gain robustness against partial occlusion, we construct a structural correlation filter by learning both the holistic and local models. Finally, we introduce an adaptive update scheme by using a fluctuation parameter.
# Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset
## 1 Introduction
Consequently, the main image-to-image conversion problem--the difficulty in obtaining target images with spatial correspondence to input images--was effectively tackled. In addition, CycleGAN produces efficient training results using relatively small amounts of data. An HDR image conversion algorithm takes images of various exposure values (EVs) or expresses all image information that can be visually recognized by humans using a local area [16]. However, after the training module is developed, a GAN can perform HDR tone mapping even on single image inputs. The model proposed in this study creates an HDR image using a CycleGAN with information obtained from a single image. However, the case of HDR image tone mapping is not straightforward. The conventional CycleGAN trains on the RGB channel [17; 18], and thereby distorts the color and degrades the image quality. Therefore, correction is required when using a conventional GAN module, resulting in difficulty in HDR image generation. The proposed model mitigates this problem by introducing several techniques. First, the proposed model exclusively trains on a luminance channel to reduce color distortion or noise; then, a dataset is augmented with a gamma shift to learn various tone levels. Second, a regional weighted loss (RWL) performs training for tone compression in the local area of images. The proposed weight map uses a Gaussian filter, which helps in distinguishing the dark and bright areas of an image. As the dark and bright areas vary depending on the value of the gamma shift, the weight function can be used differently based on the gamma value. Finally, we propose the use of a regional fusion training (RFT) method designed for providing a difference in restoration speed according to the brightness of a region [17; 18]. The restoration speed is the degree of improvement in the local bright and dark areas of a training image. This method learns a target image's appropriate luminance range value and can enhance image quality more than existing methods. RFT, similar to RWL, is affected by the gamma shift value, resulting in the function being modified, and the input images' color values are retained as the resulting images' color values. In addition, when inputting images into the completed module, global luminance-level tuning is used to learn the biased average luminance level from that of the target image. The proposed model's local contrast restoration performance is better than that of conventional models. A positive effect is also observed on the results of foggy, dark surround, and high-contrast images. The proposed model can incorporate the proposed HDR GAN into Mask R-CNN for improved detection performance in dark environments [19]. Moreover, closed-circuit television (CCTV) surveillance systems using this model can enhance their real-time image detection.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 3 Proposed Method
### Quantum Circuit for PRESENT Block Cipher
#### 3.1.4 Keyschedule of PRESENT Block Cipher  
The PRESENT block cipher can use 80-bit key or 128-bit key. Since they are similar, this paper only describes the 80-bit key schedule version.  
The key is rotated by 19 bits to the right and it can be done using only Swap gates. After the rotation operation, the Sbox of Algorithm 2 is performed on the leftmost 4 qubits (\(k_{79},k_{78},k_{77}\), and \(k_{76}\)). Finally, the round \(i\) is exclusive-ored to \(k_{19},k_{18},k_{17},k_{16}\), and \(k_{15}\) and the leftmost 64 qubits are extracted and used as round key. XORing round \(i\) can be done with \(X\) gates at the position, where the \(i\)-th bit is set to 1. For example, when \(i=1\), an \(X\) gate is performed on \(k_{15}\), when \(i=2\), an \(X\) gate is performed on \(k_{16}\), and when \(i=3\), an \(X\) gate is performed on \(k_{15}\) and \(k_{16}\). The detailed process for Keyschedule of PRESENT-80 is given in Algorithm 3.  
```
0:80-qubit key \(K(k_{0},...,k_{79})\).
0:64-qubit round key \(RK(rk_{63},...,rk_{0})\).
1:\(k\gets k\gg 19\) using Swap gates
2:\(k_{79},k_{78},k_{77},k_{76}\leftarrow\)Sbox (\(k_{79},k_{78},k_{77},k_{76}\))
3:\(k_{19},k_{18},k_{17},k_{16},k_{15}\leftarrow\)X (\(k_{19},k_{18},k_{17},k_{16},k_{15}\)) according to round \(i\)
4:return\(K(k_{79},...,k_{16})\)
```  
**Algorithm 3** Quantum circuits for Keyschedule of PRESENT-80
# Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN
## 3 Methodology
### DNN Classifier  
In fact, we can use the trained generators \(G_{A}^{*}\) and \(G_{V}^{*}\) to generate any number of new multimodal data. However, not all generated data can benefit audio-visual emotion recognition. This may be because some of the generated data does not have sufficient emotional information, thus failing to perform data augmentation. Therefore, inspired by [44; 90], we design a strategy to evaluate the generated multimodal data. We first learn a DNN classifier with the original training set \(D\), which is trained with the cross-entropy loss  
Figure 2: The structure of our multimodal conditional GAN. The audio modality and the visual modality are generated by \(G_{A}\) and \(G_{V}\), respectively. Category information is used as their shared input to generate new data of different categories. Additionally, in the generated multimodal data, the correlation between the audio modality and the visual modality is modeled to approximate the real multimodal data.  
function. Then, we use the trained classifier to predict the emotion labels of the multimodal data generated by our multimodal conditional GAN. If the predicted probability exceeds a certain threshold, we select this data as the final generated data.  
After evaluating the generated data, we use the final generated data to supplement the original training set \(D\) as the augmented training set. On this set, we train a DNN classifier with the same architecture as the above DNN classifier. Then, we evaluate its classification performance on the test set.  
```
Input: Coefficient \(\lambda\), number of training iterations \(T\), number of steps for discriminators \(K_{d}\), number of steps for generators \(K_{g}\). for\(t=1,\cdots,T\)do for\(k=1,\cdots,K_{d}\)do \(\bullet\) Sample \(m\) noise samples \(\{\mathbf{z}^{(1)}_{A},\ldots,\mathbf{z}^{(m)}_{A}\}\) and \(m\) noise samples \(\{\mathbf{z}^{(1)}_{V},\ldots,\mathbf{z}^{(m)}_{V}\}\) from two independent prior distributions. \(\bullet\) Sample \(m\) real examples \(\{(\mathbf{x}^{(1)}_{A},\mathbf{x}^{(1)}_{V},y^{(1)}),\ldots,(\mathbf{x}^{(m) }_{A},\mathbf{x}^{(m)}_{V},y^{(m)})\}\) from the training set \(D\). \(\bullet\) Update the discriminators \(D_{A}\) and \(D_{V}\) in Equations (1) and (2). endfor for\(k=1,\cdots,K_{g}\)do \(\bullet\) Sample \(m\) noise samples \(\{\mathbf{z}^{(1)}_{A},\ldots,\mathbf{z}^{(m)}_{A}\}\) and \(m\) noise samples \(\{\mathbf{z}^{(1)}_{V},\ldots,\mathbf{z}^{(m)}_{V}\}\) from two independent prior distributions. \(\bullet\) Update the generators \(G_{A}\) and \(G_{V}\) in Equations (1)-(3). endfor endfor
```  
**Algorithm 1** Update the parameters of our multimodal conditional GAN.
## 1 Introduction
However, since October 2014, the Sentinel-1 (S-1) satellite constellation has continuously and systematically captured the earths surface with C-band SAR data at short repetition time, enabling the use of SAR multi-temporal data for systematic and operational flood monitoring. Using this data source, [PERSON] et al., [8] recently showed a time series approach for the detection of TFV. This study aims to show the potential of the SAR time series approach proposed in [PERSON] et al., [8] regarding the extraction of the entire flood extent with the focus on TFV for two independent study areas in Greece/Turkey and China. The main focus of the study is to demonstrate the impact of the time series features on the classification results and to show their potential for operational use. The objectives in detail are as follows:  
* to investigate the relevance of polarization and time series features for the derivation of TFV with respect to vegetation types in both study areas;
* to examine if the relevant time series features for the analyzed study areas correspond to the relevant time series features of the previous study area (Namibia) in [8], despite the occurrence of different vegetation types and
* to identify a single time series feature that is relevant for the extraction of different TFV types and for all study areas in order to demonstrate the potential for the transferability and operational use of this time series approach.
# Potential of Plant Stem Cells as Helpful Agents for Skin Disorders--A Narrative Review
## 4 Mechanistic Insights into the Skin Aging of Plant-Derived Stem Cells  
Because of its strategic placement, human skin is susceptible to intrinsic and extrinsic aging. Internal/endogenous factors, such as genetic makeup, hormone changes, or inflammation, often impact intrinsic aging, resulting in stem cell failure and senescent cell accumulation. Extrinsic aging is caused by exogenous causes such as pollution, sunlight exposure, lifestyle, stress, and cellular oxidative stress [73; 74]. Figure 3 depicts the external and endogenous factors that contribute to skin aging.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Plant Sources** & **Participants (n; Age Range)** & **Formulation/Application** & **Clinical Effects** & **Ref** \\ \hline _Crisium eriophorum_ & 40 (20–40) & & & \(\bullet\) reduced the sebum production \(\bullet\) reduction in the number of visible pores \(\bullet\) reduced transepidermal water loss \(\bullet\) enhanced epidermal moisture level & [53] \\ \hline _Daplme odora_ & 20 (18–65) & & \(\text{Cream (0.002\%)}/\text{twice daily for 4 weeks}\) & \(\bullet\) increased hydration \(\bullet\) decreased transepidermal water loss & [21] \\ \hline _Leontopodium_ & 21 (48) & & \(\bullet\) decreased roughness parameters of \(\bullet\) preferential wrinkles \(\bullet\) increased skin elasticity parameters & [22] \\ _alpinum_ & 21 (48) & & \(\bullet\) increased dermal density and skin thickness & [22] \\ \hline _Malus domestica_ & 20 (37–64) & & \(\text{Cream (2\%)}/\text{twice daily for 4 weeks}\) & reduction in wrinkles & [72] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clinical applications of plant stem cells against skin aging.  
Figure 3: Schematic representation of exogenous and endogenous factors that contribute to skin aging.  
The mechanisms of action of natural sensoltics (eliminate senescence cells) or senomorphics (adjust characteristics of senescent cells to mitigate senescence symptoms, without cell elimination) are different and must be fully understood. The role of plant stem cell extracts in preventing cellular senescence is related mainly to oxidative stress, inflammation, and autophagy.