esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Calcite Percentage Results
#### 3.3.2. Calcite Percentage to Hydraulic Conductivity Ratio  
Experimental findings have revealed that the amount of precipitated calcite plays a decisive role in reducing soil hydraulic conductivity. In both injection and mixing methods, an increase in calcite content corresponded with a decrease in hydraulic conductivity. The mixing method facilitated greater calcite accumulation at high SA and pellet ratios due to the homogeneous distribution of additives within the soil. In contrast, the injection method provided effective low hydraulic conductivity in fine-grained soils by promoting more targeted precipitation. However, the success of hydraulic conductivity reduction depends not only on the quantity of precipitated calcite but also on the extent to which this precipitation fills soil pores and its structural uniformity. This relationship has been supported by various studies in the literature. For instance, MICP has been reported to significantly reduce hydraulic conductivity by decreasing soil porosity [42]. Similarly, it has been demonstrated that the effect of calcite precipitation on hydraulic conductivity in sands with different grain sizes is directly related to its pore-filling capacity [43]. Another  
Figure 12: Effect of SA and SA + MICP applications on calcite precipitation under different SA ratios in the mixing method.  
study reported up to a 90% reduction in hydraulic conductivity in MICP-treated sands, with this effect being dependent on both the volume of precipitation and the quality of its micro-distribution [44]. These findings emphasize that not only the amount of calcite, but also its homogeneous distribution and structural integration within the soil matrix, are critical determinants of hydraulic conductivity reduction [39].
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 4 Challenges and Future Work
### Other Future Trends
#### 4.4.2 Applied to Quantum Key Distribution (QKD)  
VBDA technology can be applied to QKD to improve its performance. QKD is a novel cryptographic technique used to establish private encryption keys. One of the challenges of QKD is the loss of photons during transmission, which would reduce the signal-to-noise ratio (SNR) and transmission distance [96; 97]. The use of VBDA can help reduce this loss by adjusting the beam divergence angle based on the communication distance and the link quality, thereby improving the received optical power. This can enable secure distribution of quantum keys over longer communication distances.
# Improved YOLOv7 Algorithm for Small Object Detection in Unmanned Aerial Vehicle Image Scenarios
## 2 Related Work  
### YOLOv7 Algorithm  
Accuracy and speed serve as the basic performance metrics in deep learning-based object detection algorithms [17]. Fast-RCNN [18] and Mask-RCNN [19] are typical two-stage algorithms that can improve the accuracy. In the first stage, the suggested areas are obtained from the input image. In the second stage, these regions are sent to the classifier to determine the category and confidence level of objects. However, the two-stage network requires significant computational resources, making it unsuitable for deployment on UAVs that require real-time detection [20]. [PERSON] et al. [21] firstly proposed the YOLO algorithm with a one-stage model, which used a single neural network to output the predicted bounding box and the corresponding confidence levels of objects. The YOLO algorithms are characterized by a lightweight network architecture that balances speed and accuracy, making them widely used for real-time object detection on edge devices [22; 23].  
Among the studies of YOLO algorithms, [PERSON] et al. [24] proposed the YOLOv7 algorithm and implemented a range of strategies to enhance its performance based on an extended efficient long-range attention network (E-ELAN), max pooling convolution (MPConv), and planned re-parameterized convolution (RepConv) strategies. In particular, the E-ELAN structure adopts group convolution to expand the channel and cardinalityof computational blocks to enhance the learning ability. With the above effective network architectures, YOLOv7 outperformed the existing object detection algorithm in speed and accuracy in a range from 5 FPS to 160 FPS [24]. Due to its significant advantages, YOLOv7 is suitable for deployment on edge devices such as UAVs. Therefore, we aim to improve the performance of small object detection in UAV image scenarios based on the traditional YOLOv7.
## 2 Materials and Methods
### Modeling, GIS Mapping, and Validation
#### 2.3.3 Two-Dimensional (2D) RAS Model Simulations  
The processed DEM was used to create the river analysis model (RAS) model using the HEC-RAS 6.2, a practical river hydraulic simulation and analysis software. The RAS model was processed through unsteady flow analysis, and the boundary conditions used were flow hydrograph in the upstream and normal depth in the downstream which considers both the frictional resistance and slope of the channel. The calibrated outflow in HMS and precipitation were incorporated into the model. Flood depth considering a 100-year return period was regarded as one of the parameters in hazard mapping; this was exported as raster files and translated into spatial data in the GIS.
# Holographic Optical Tweezers: Techniques and Biomedical Applications
## 4 Summary and Outlook  
Optical tweezers have been widely applied in biotechnology for more than 30 years. [PERSON] was awarded the 2018 Nobel Prize in physics for introducing OT and its diverse applications. On the other hand, CGH endows the ability of OT to the purpose of dynamical manipulation for multiple samples. This article reviewed the development and technology of HOT and its application in biological research. Gerchberg-Saxton is the most commonly used interactive algorithm to produce CGH for shaping spatial amplitude distribution in the HOT system. Moreover, 2D-GSA simulates the optical field on multiple planes with a higher diffraction efficiency; however, 3D-GSA calculates the 3D optical field more directly but uses more computing resource. Researchers are still working on developing algorithms with higher computing speed and diffraction efficiency.  
The reconstructed optical field of HOT enables the assembly and arrangement of the microsamples in the crowed and complex fluid. Thus, combining other measurement technologies, HOT provides a platform to study bio-samples or their interactions. In addition to reconstructing the spatial distribution of light, CGH can also shape the polarization [145] of the trapping light or transfer orbital angular momentum to the trapped particles [146]. Thus, it can be used to study bio-samples with polarity, such as RBC. In addition, most bio-applications of HOT have been conducted in vitro. Recently, [PERSON] et al. [147] demonstrated a 3D HOT, using a high-NA soft-glass multimode fiber. Their system provides a possible solution for medical purple in vivo.  
Conceptualization, all authors; methodology: all authors; writing--original draft preparation, H.-C.C.; writing--review and editing, C.-J.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Technology in Taiwan under Grant No. MOST 103-2221-E-030-006.  
Figure 14: 3D refractive index reconstruction [144] of **(a,b)** normal RBCs and **(c,d)** echinocyte, by **(a,c)** conventional optical diffraction tomography and **(b,d)** TOMOTRAP system.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 3 Background
### Semi-Public Charging Infrastructure  
Semi-public charging infrastructure is a combination of public and private charging infrastructure. Practically, it is not possible for all EV users to own a personal charging infrastructure at home and it also increases the charging time of the EVs, which can discourage users from charging in emergency situations. On the other hand, public charging infrastructure seems to be quite a costly choice for EV users for charging their vehicle. Therefore, the high charging cost and high charging time challenges of public and private charging infrastructure lead to the need for semi-public charging infrastructure. Semi-public charging infrastructure can be considered to be managed by the charging service operator in which EVs of several households or apartments can charge their vehicles, which is termed as community charging [49; 50; 51]. For example, [PERSON] et al. [52] analyzed the best option of charging for users by considering the charging infrastructure of Beijing. They discussed the case study of Beijing in relation to the suitability of semi-public and private charging stations for users.  
Therefore, we considered different types of EV charging infrastructures based on the requirement of users or their traveling habits. However, the standardization of EVs community charging infrastructure will help to overcome the issues of public and private charging infrastructure. This means that EVs manufactured by different companies with different specifications and charging standards can opt for charging. However, it is not possible to make available all types of charging types at a charging station. Therefore, we considered the critical issue of standardization of EVs community charging infrastructure in the proposed survey.
# Research on Seismic Signal Analysis Based on Machine Learning
## 2 Application of Machine Learning Method in Seismology
### Machine Learning Methods
#### 2.2.1 Supervised Learning Methods  
Supervised learning is a method that uses an algorithm to map input feature vectors to output label vectors. These algorithms use known training samples (pairs; the number of samples) to optimize the model. Seismic waves are precisely a set of time series data arranged in chronological order, so they can be treated in this way. Convolution is a powerful tool for feature extraction in data mining. Since the establishment of AlexNet, the champion model of ILSVRC challenge in 2012, convolutional networks have developed rapidly. There are AlexNet [32], VGG [33], Inception [34], and ResNet [35]. The above network models all have a multilayer network structure. Although they have a good classification effect, the computational efficiency decreases with the deepening of the network. In addition, these models can only be used for supervised learning tasks, not large unlabeled datasets. The random convolution kernel transformation Rocket, however, uses a large number of random convolution kernels combined with linear classifiers (ridge regression or logistic regression). This method confirms that for relatively simple time series classification, better classification performance can be achieved without the establishment of a deep neural network [36]. Rocket is divided into two parts: the feature extraction part and the linear classification part. From the resulting feature map, Rocket returns the maximum value and a new feature, the proportion of positive values (PPV). Yet, MiniRocket is an improved method based on the Rocket method [36], with further improvements in computational efficiency [24].  
MiniRocket is characterized by using only one-dimensional convolution, which has a good trade-off between classification accuracy and model complexity, and is currently the state-of-the-art for univariate time series, which can compute all kernels at once [37]. Like Rocket, MiniRocket is a transformation that produces some features, which are then used to train a linear classifier [24]. Instead of using both PPV and max (the maximum value of the resulting feature maps), MiniRocket only uses the PPV. So the dimension of the vector is reduced by half (about 10,000 dimensions instead of 20,000). MicroRocket is 75 times faster than Rocket when processing large datasets. To classify feature vectors, both Rocket and MiniRocket use simple ridge regression. In general, MiniRocket uses fewer feature dimensions, and it has significant improvements in reducing the amount of parameters, shortening training time, and improving classification accuracy [37].
# An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM
## 2 Related Work
### Deep Neural Network-Based Methods  
The commonly used deep neural network-based methods for text classification are RNN, LSTM, GRU, and CNN. CNN has been employed in object recognition, question answering, and sentiment analysis [2; 35]. CNN-based models are used in pattern recognition in text. The presentative deep learning-based methods are introduced as follows.  
[PERSON] et al. [36] proposed a CNN-based unimodal disease risk prediction (CNN-UDRP) algorithm that automatically extracts features from the dataset, and predicts heart disease. In their study, they focused on two main objectives: (1) to predict heart disease along with heart disease risk based on structured data, (2) to handle missing values to improve the accuracy of heart disease. The results showed that they achieved an accuracy of 65%.  
[PERSON] et al. [37] proposed an Inception Convolutional Autoencoder Healthcare question Clustering (ICAHC) model to solve existing problems in learning and representing the question corpus, including high dimensionality, sparseness, noise, and nonprofessional expression in Chinese healthcare. Their model can be used to predict patients' conditions and to develop an automatic Health Question Answering (HQA) system.  
[PERSON] et al. [38] used a finetuned CNN model and an enhanced LSTM for natural language inference (ESIM) to predict breast cancer. They developed a dynamic website that has a cancer detection facility, user interface, and chatbot. By finetuning the Visual Geometry Group (VGG) CNN model, they were able to classify hematoxylin and eosin (H&E) breast issue images; while ESIM was used to process text matching in website intelligent question answering.  
The existing researches assume the availability of large and high-quality training datasets. However, in real-world scenarios, obtaining labeled data for medical question classification can be challenging due to privacy concerns or limited expert annotations. The works presented in this section provide the background of QC in the medical domain based on deep learning technology. However, the proposed model differs from existing works. In our proposed model, Word2 Vec is used for word embedding, then, features are extracted from the word embedding matrix based on CNN. Finally, BiLSTM is used to classify questions. Although the CNN method is a commonly used model in extracting features from images, in our work, we combined it with BiLSTM to classify text-based questions. Thus, the reliance on the high-quality training datasets of our proposed model is less than other methods, by leveraging the power of a pre-trained word embedding and a hybrid model.
# Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images
## 3 Results and Discussion
(**b**) Noisy \(\sigma=5\). (**c**) Filter output. (**d**) Noisy \(\sigma=10\). (**e**) Filter output. (**f**) Noisy \(\sigma=20\). (**g**) Filter output. (**h**) Noisy \(\sigma=30\). (**i**) Filter output. Figure 14: Method outputs using test image Parrots contaminated with various intensities of impulsive noise \(p\). (**a**) Parrots. (**b**) Noisy \(p=0.05\). (**c**) Filter output. (**d**) Noisy \(p=0.1\). (**e**) Filter output. (**f**) Noisy \(p=0.2\). (**g**) Filter output. (**h**) Noisy \(p=0.3\). (**i**) Filter output. Figure 13: Method outputs using test image Flower contaminated with various intensities of impulsive noise \(p\). (**a**) Flower. (**b**) Noisy \(p=0.05\). (**c**) Filter output. (**d**) Noisy \(p=0.1\). (**e**) Filter output. (**f**) Noisy \(p=0.2\). (**g**) Filter output. (**h**) Noisy \(p=0.3\). (**i**) Filter output. Finally, we present a summary of results on execution times. Experiments were carried out using MATLAB 2018a in an Intel XEON X5660 processor (2.8 GHz and 48 GB RAM memory). The running time of the proposed FRF-FPGA hybrid algorithm was 2.18 s for denoising Lena image (\(512\times 512\) pixels) deteriorated by Gaussian noise \(\sigma=10\) and impulsive noise \(p=0.1\). The execution time increased from 1.83 s for the FPGA to 2.18 s for the proposed FRF-FPGA. The new approach compared to FPGA, with a small increase of 0.37 s in time, provides a significant improvement in MAE, PSNR, and NCD measures. The computational times for the other methods considered for comparison were: 2.23 s for the ANNF, 15.2 s for the APF, 14.1 for the C-BM3 DF, 2.1 s for the FVMF, 1.63 s for the FWSDF, 2.94 s for the GRF, 1.92 s for the IPGSVF, 9.7 s for the PBTVMF, 1.61 s for the PGF, and 5.18 s for the TF filter. Although the computational time of the proposed filter (2.18 s for a \(512\times 512\) image) makes this sequential approach hardly usable for real-time processing, the method has a skeleton which makes it appropriate for parallel computing. Then, using parallel techniques real-time can be easily achieved.
# Soft Segmentation of Terrestrial Laser Scanning Point Cloud of Forests
###### Abstract  
As the three-dimensional (3D) laser scanner is widely used for forest inventory, analyzing and processing point cloud data captured with a 3D laser scanner have become an important research topic in recent years. The extraction of single trees from point cloud data is essential for further investigation at the individual tree level, such as counting trees and trunk analysis, and many developments related to this topic have been published. However, constructing an accurate and automated method to obtain the tree crown silhouette from the point cloud data is challenging because the tree crowns often overlap between adjacent trees. A soft segmentation method that uses K-Nearest Neighbor (KNN) and contour shape constraints at the overlap region is proposed to solve this task. Experimental results show that the visual effect of the tree crown shape and the precision of point cloud segmentation have improved. It is concluded that the proposed method works well for tree crown segmentation and silhouette reconstruction from the terrestrial laser scanning point cloud data of the forest.  
3D point cloud; forest point cloud; forest segmentation; soft segmentation; tree crown reconstruction
## 3 Results and Discussion
### TSS and pH
During the early stages of fermentation, the yeast initiates growth and utilizes sugars as the main metabolic substrate. The accumulation of  
Figure 2: Changes of TSS (**A**) and pH (**B**) during fermentation. The different lowercase letters indicate significant differences (\(p<0.05\)). organic acids gradually reduces the pH value of the fermentation liquid. In the middle of fermentation, the yeast's metabolic activity reaches its peak. At this stage, the production of organic acids is high, resulting in a further decrease in the pH value of the fermentation liquid. As fermentation progresses, sugars are gradually converted by the yeast into alcohol and other metabolites, and the accumulation of organic acids is also reduced. This results in a gradual increase in the pH of the fermentation solution. The pH change in the cider fermentation process is influenced by various factors, including the acidity of the raw materials, the characteristics of yeast strains, fermentation temperature, and more [25; 26]. Additionally, pH control in cider can be achieved by adjusting the initial pH and adding acidic or alkaline regulators. Ensuring the appropriate pH range helps promote yeast growth and metabolic activity while maintaining the quality and stability of the cider [27].
## 3 Results  
In this section, the main results coming from compression tests are presented and discussed. Some common features of all manufactured foams while subjected to compression tests (stress-strain curve) are reported below:  
1. A nearly linear pseudo-elastic stage in which stress and strain are almost linearly proportional is always present in all foams. However, limited and localized plastic deformations occur in the foam, hence the name \"pseudo\" elastic.
2. The second stage of the curve is characterized by the presence of an extensive \"plateau\", which is a widened part of the curve in which the stress is nearly constant while the material undergoes huge plastic strain. Microscopically, this stage is associated with the progressive plastic collapse of the cell walls.
3. Once the porosities inside the foam totally collapsed, the material turns into the final stage of densification, which is characterized by a fast increase in the compressive load while only small deformation occurs.  
Figure 3: Stress–strain curve for sample 1 (0.2% TiH\({}_{2}\), 2.8% SiC).  
Figure 2: Compression stages of AI foam: **(1)** before the test, **(2)** at the end of the pseudo-elastic stage, **(3)** at the end of the plateau stress, **(4)** after final densification.
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 2 Overview of IEEE 802.11 Physical Layer
### Channel Estimation  
Figure 1 shows the basic OFDM frame structure used in IEEE 802.11 protocol which consists of a preamble, a SIGNAL, and multiple data fields. Specifically, the preamble field contains two types of training formats: short training format (STF) and long training format (LTF). STF is used to detect the beginning of OFDM frame and to obtain the timing synchronization, and LTF is used for channel estimation in frequency domain. LTF consists of two OFDM symbols (\(3.2\times 2=6.4\)\(\upmu\)s) with guard interval (\(0.8\times 2=1.6\)\(\upmu\)s) and is used to repeatedly carry a pre-defined long training symbol.  
The long training symbol consists of 53 subcarriers including the zero value at DC, which are modulated by the elements of the sequence \(L\), given by [10]  
\[\begin{split} L_{-26,26}=&\left\{1,1,-1,-1,1,1,-1,1, -1,1,1,1,1,1,-1,-1,1,1,-1,1,-1,1,1,1,0,\right.\\ &\left.1,-1,-1,1,-1,1,-1,-1,-1,-1,-1,-1,1,1,-1,-1,-1,1,-1,1,-1,1,1,1\right\},\end{split} \tag{1}\]  
where the subscript \(k\) indicates subcarrier index, i.e., \(k\in[-26,+26]\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bandwidth & 20 MHz \\ Operating bandwidth & 16.6 MHz \\ Subcarrier spacing (\(\Delta_{F}\)) & 31.25 KHz (20 MHz/64) \\ FFT period (\(T_{\text{FFT}}\)) & 3.2 \(\upmu\)s (\(=1/\Delta_{F}\)) \\ Guard interval (\(T_{\text{GLI}}\)) & 0.8 \(\upmu\)s (\(=T_{\text{FFT}}/4\)) \\ OFDM symbol duration & 4.0 \(\upmu\)s (\(=T_{\text{GLI}}+T_{\text{FFT}}\)) \\ Data rate & 6/9/12/18/24/36/48/54 Mbps \\ Modulation & BPSK, QPSK, 16 QAM, 64 QAM \\ Coding rate & 1/2, 2/3, 3/4 \\ Total subcarriers & 52 (Freq. index: \(-26\) to \(+26\)) \\ Data subcarriers & 48 \\ Pilot subcarriers & 4 (Freq. index: \(-21\), \(-7\), \(+7\), \(+21\)) \\ DC subcarriers & Null (Freq. index: 0) \\ \hline \hline \end{tabular}
\end{table}
Table 1: OFDM parameters in IEEE 802.11 legacy mode [10].  
Figure 1: OFDM frame structure in IEEE 802.11 [10].  
The long training symbol generated based on \(L_{-26,26}\) in Equation (1) is repeated over two OFDM symbols to improve channel estimation accuracy. Thus, the channel response in frequency domain can be estimated as follows:  
\[\hat{H}(k)=L_{k}Y(k), \tag{2}\]  
where \(Y(k)\) denotes the received signal at \(k\)th subcarrier, which is transformed from the received preamble in time domain. Using IFFT (Inverse Fast Fourier Transformation) operation, channel response in time domain (equivalently, delay domain) can be obtained as follows:  
\[\hat{h}(\tau)=\sum_{k=-26}^{+26}\hat{H}(k)\exp\left(j2\pi k\Delta_{F}\right), \tag{3}\]  
where \(\hat{H}(k)\) in Equation (2) and \(\Delta_{F}\) is defined in Table 1, respectively. Note that we can measure wireless channel for WAIC systems by using IEEE 802.11 OFDM preambles.