esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability and Control of Surrounding Rock of a Trapezoidal Roadway Retained with Hard Roof Cutting
## 5 Discussion
### Practical Implications and Future Research  
The technical solution proposed in this study offers a comprehensive approach for managing surrounding rock stability in trapezoidal top-cutting roadways. The successful implementation of pre-splitting, high-strength support systems, and effective gangue protection ensures that the roadway remains stable throughout the mining process. The real-time monitoring of roof deformation, including subsidence and delamination, further confirms the validity of the proposed control techniques.  
However, while the results obtained from the 21,100 panel demonstrate the feasibility of these control technologies, further research is needed to explore the long-term effects of top-cutting on surrounding rock stability. The development of more advanced monitoring systems and the integration of real-time data analysis could improve the prediction of deformation patterns, allowing for more proactive control measures.
# AI-Enabled Compact and Efficient Dynamic Light Scattering System for Precise Microparticle Sizing
## 2 Materials and Methods
### Algorithms  
The ACF calculation was performed using the Pandas library (version 2.3.3) functions within the PyCharm Community (version 2022) software environment. The computed ACF values were normalized to their maximum and minimum, and particle size was used as the corresponding label for the ACF. The resulting dataset was then used to train the models using a 5-fold cross-validation strategy. The DNN, 1D-CNN, and 2D-CNN models were implemented and trained in the TensorFlow 2.4 framework, also within the PyCharm Community environment. For the DNN model, a batch size of 2 and 700 epochs were used; for the 1D-CNN model, a batch size of 2 and 1200 epochs were applied; and for the 2D-CNN model, a batch size of 8 and 400 epochs were employed. The MSE was selected as the loss function for all models, while the MAE was chosen as the evaluation metric to assess model performance. All processing was conducted on a desktop computer equipped with an Intel i7-13700 KF CPU @ 3.4 GHz, with 64 GB RAM sourced from Intel Corporation, Santa Clara, CA, USA, and an NVIDIA GeForce RTX 4080 GPU sourced from NVIDIA Corporation, Santa Clara, CA, USA.  
Our dataset exclusively comprises integer-sized microspheres (100-1500 nm) due to their ease of acquisition and testing convenience. All data in this dataset are thus integers. In machine learning, the test set and the training set are two non-overlapping subsets derived from the same whole, so the results on the test set can be considered as a reflection of its actual performance. While our dataset is relatively small compared to large-scale ones, its effectiveness lies in its quality and representativeness. Our study prioritizes striking a balance between dataset size and achieving accurate results.
## 5 System Outline
### Receive Hardware Description
A traditional Costas loop will treat the additional carrier frequencies as an error signal and try to remove them [50; 51]. Thus, we take inspiration from the structure of a Costas loop (but do not use the loop itself) to remove the phase offset still present in the signal. We first form the region \(b_{1}\leq n\leq b_{2}\) as shown in Figure 11. Within this region, we create two sums based on the sign of the real component of the signal  
\[ \begin{split} S_{1}&=\sum_{n=0}^{N_{\
u}-1}s[n] \cdot e^{i\phi_{off}},\ \ \ \text{Re}(s[n])>0\\ S_{2}&=\sum_{n=0}^{N_{\
u}-1}s[n]\cdot e^{i\phi_{ off}},\ \ \ \text{Re}(s[n])<0\end{split}  \tag{21}\]  
The desired compensation angle \(\phi_{off}\) is such that \(\angle S_{1}\) and \(\angle S_{2}\) are driven as close to the desired values \(\angle S_{1}\to 0\) and \(\angle S_{2}\to\pi\) as possible. This output of this process is shown in Figure 12. Note that it is possible to lock \(\pm\pi\) out of phase, but this can be corrected by checking the sign of a Barker code correlation. To create the receive bitstream, we follow Figure 2 of [43]. The procedure is as follows:  
1. Identify the bit transitions based on a zero-crossings vector. 2. Sum 10 samples of \(\text{Re}(s[\cdot])\), incrementing by 10 samples for each sum. 3. Choose 1 vs. 0 based on the sign (\(+1\) for 1 and \(-1\) for 0) of the summation. Figure 8: Similarities in the structure of an OFDM CP (**left**) and the BPSK FDA CP (**right**). Figure 11: Captured baseband data with CFO removed (**left**) and constellation behavior of the modulation region \(b_{1}\leq n\leq b_{2}\) (**right**). Figure 10: Captured baseband data with CFO removed (**left**) and phase information from the correlation of regions \(b_{1}\leq n\leq b_{2}\) and \(b_{3}\leq n\leq b_{4}\) (**right**). Figure 9: Captured baseband data before receiver synchronization (**left**) and phase information from the correlation of regions \(b_{1}\leq n\leq b_{2}\) and \(b_{3}\leq n\leq b_{4}\) (**right**).
# Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity
## 3 Proposed Scheme
### Phase 3: Post-Processing  
Once the embedding process of our enhanced scheme is completed, the resulting embedded image exhibits a concentration of pixel values towards even numbers. To address this, we perform a further adjustment to transform the embedded images into stego-images that exhibit a normal distribution of numbers across the three color channels. This is achieved through the following two-step process. Firstly, a PRNG is employed to generate a randomly arranged bitstream. Secondly, for each of the three color channels, a random bit is selected and mapped to a corresponding number. While this process may lead to increased image distortion, it is an acceptable trade-off to achieve a higher level of security, as the eavesdropper is unable to obtain the original or cover images. Moreover, the distortion introduced by our scheme is imperceptible to the human eye. The aforementioned two steps are further elaborated below.  
Step 1. A random bit stream is generated by a pseudo-random number generator.  
Step 2. Pixel values are modified according to the following cases.  
Case 1: Given a pixel with max\((R,G,B)=127\), the maximum values are increased by adding 1 to become 128.  
Case 2: Given a pixel with max\((R,G,B)=254\), if the corresponding randomly generated bit is \"0\", the maximum value remains unchanged. However, if the bit is \"1\", the maximum value is incremented by 1 to become 255. Similarly, the values of the remaining two color channels are modified based on their respective randomly generated bits. If a bit is \"1\", the value remains the same. It is worth noting that the maximum value of the embedded image is limited to 254 to avoid overflow during post-processing.  
The secret message extraction process is made straightforward. A stego HDR image is provided, and every pixel is examined. The homogeneity value, HV\({}_{K}\), for each stego pixel (e.g., \(K\)) that is inspected, is computed. If HV\({}_{K}\) is less than or equal to 1, no secret message is conveyed by this pixel. Alternatively, if HV\({}_{K}\) is greater than 1, the homogeneous representation group, HRG\({}_{K}\), for this stego pixel is produced, and the number of concealed secret message bits in the cover pixel \(K\) is calculated using Equation (3). The homogeneity index of the cover pixel, HI\({}_{K}\), is determined by comparing the cover pixel \(K\) with all elements in HRG\({}_{K}\), and the status of the stego pixel, S(HV\({}_{K}\), HI\({}^{\prime}\)\({}_{K}\)), is generated. Ultimately,by referring to the homogeneity index table (Table 2) and S(HV\({}_{K}\), HI\({}^{\prime}\)\({}_{K}\)), the bits of the secret message can be extracted.
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 4 Conclusions  
This study investigated the potential of GLCM-based texture information for crop classification with time-series UAV images and machine learning algorithms. The main focus was on the evaluation of the benefit of utilization of texture features along with spectral information when using a single UAV image. A case study of crop classification in the high-hand Kimchi cabbage cultivation area demonstrated the most accurate classification of multi-temporal UAV images with GLCM-based texture features. However, the utilization of texture features with spectral information from multi-temporal images did not lead to a significant improvement in classification accuracy. In contrast, when only a single UAV image was used, the utilization of texture features could significantly improve the classification accuracy. Therefore, when only one UAV image should be used for crop classification due to a difficulty in constructing a time-series UAV dataset, the information deficiency in spectral information can be complemented by structural information from texture features. Furthermore, the impact of texture information on classification accuracy was dependent on the kernel size of GLCM. Texture information extracted from the GLCM with larger kernel size improved classification performance in the study area. Therefore, proper kernel size selection is critical for the extraction of GLCM-based texture features. This indicates that both spatial resolution of input UAV images and shape characteristics of individual crops of interest should be considered in selection of optimal kernel size. However, these findings may be specific to this study area with particular crop types and not applicable to other areas. Therefore, more experiments on other areas with different combinations of crops should be carried out to strengthen the potential benefit of texture information from UAV images for crop classification. Experiments regarding determination of the minimum number of UAV images in crop classification with texture features, and comparison with deep learning algorithms will also be carried out in the future to extend key findings and recommendations presented herein.  
**Author Contributions:** Conceptualization, G.-H.K. and N.-W.P.; methodology development and experiments, G.-H.K.; manuscript writing, G.-H.K. and N.-W.P.; supervision, N.-W.P.  
**Funding:** This work was supported by INHA UNIVERSITY Research Grant.  
**Acknowledgments:** The authors thank [PERSON], [PERSON], and [PERSON] at the National Institute of Agricultural Sciences for providing UAV images and ground-truth data used in this study. Constructive comments from the three anonymous reviewers are gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Methods
### Simpson's Rule Integration  
In order to find the displacement spectrum, it is necessary to integrate the vibration as a function of the time twice. The two most common integration methods take place in the time and frequency domain. In the frequency domain integration, the original signal is transformed into the frequency by the Fourier transform, then it is integrated either once or twice to finally return the results to the time domain to obtain the speed and displacement. In the time domain integration, the original signal is directly integrated in the same time domain; however, in the time domain some integration trends are generated due to the cumulative errors of the method and the discretization of the data. These trends must be  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Step** & **Integration Process** \\ \hline
1 & Calculate the _IMFs_ from the acceleration signal \\ \hline
2 & Obtain the main frequency and amplitude for each _IMF_ \\ \hline
3 & Determine the weight for the amplitude correction \\ \hline
4 & Apply the phase shift with \(\tau/4\) for each _IMF_ \\ \hline
5 & Compute the new amplitudes of the corrected _IMF_ \\ \hline
6 & Add the new _IMFs_ corrected to obtain the velocity \\ \hline
7 & Repeat the steps 1–6 to calculate the displacement \\ \hline \hline \end{tabular}
\end{table}
Table 1: Steps for the proposed integration method.  
corrected for the results to be reliable. There are several methods used to correct effects of the trends by extracting the curve that represents the trend obtained by a polynomial fit, and EMD has been commonly applied in recent years. For simple functions, a second-order polynomial usually solves the problem; however, for complex vibrations, the degree of the polynomial increases and becomes somewhat subjective. In this article, the trend correction is performed with the EMD method. In addition to dealing with trends, there is a lack of knowledge of the initial conditions of speed and displacement, which are usually defined as zero. This consideration generates errors of vertical and horizontal displacements in the results, which must also be handled to minimize integration errors. Some of the most common integration methods are the trapezoid and the Simpson's rule. In this work, the integrals will be performed using the Simpson's 1/3 rule shown in Equation (11) because it has a reasonably small truncation error.  
\[I_{1/3}=\int_{t_{0}}^{t_{1}}f(t)dt\approx\frac{t_{1}-t_{0}}{6}\bigg{(}f(t_{0} )+4f\bigg{(}\frac{t_{1}-t_{0}}{2}\bigg{)}+f(t_{1})\bigg{)}-\frac{(t_{1}-t_{0}) ^{5}}{2880}f^{(4)}(\xi) \tag{11}\]  
where the truncation error is:  
\[e(f)=\frac{(t_{1}-t_{0})^{5}}{2880}f^{(4)}(\xi) \tag{12}\]
## 2 Configuration of Hybrid Tramway  
### Hybrid Tramway Description  
In this work, the hybrid tramway configuration is composed of the PEMFC, the BAT, the SC, the unidirectional DC/DC converter, the bidirectional DC/DC converter, the DC/AC inverter, the traction motor drives, the auxiliary services module, the braking resistor, and the power management controller as described in Figure 1. In this system, the PEMFC serves as a primary energy source and is connected to a unidirectional DC/DC converter to boost the low voltage of the PEMFC to the required voltage of the DC bus. Meanwhile, rechargeable BAT and SC are utilized as ESDs to supplement the insufficient power of PEMFC during the acceleration and cruise processes and absorb the regenerative energy during the braking process. Moreover, the SC is equipped to compensate or consume the peak power of the traction load that the PEMFC and BAT cannot accommodate in a short time owing to its characteristic of fast dynamic response. The PEMFC and BAT are interconnected to the DC bus through unidirectional and bidirectional DC/DC converters, respectively, for energy delivery to the load power demand and recovery tasks to the BAT. The auxiliary devices such as fans, lighting, air conditioning system, etc. are described as the auxiliary services module of the hybrid tramway system. Besides, the braking resistor is also installed to dissipate the energy during regenerative braking if necessary. In order to distribute the power demand, the power management controller is designed to coordinate the power of energy sources by regulating DC/DC converters with charge/discharge mechanisms.  
Figure 1: Configuration of hybrid tramway system.  
The dynamic model of each component in the hybrid tramway system is defined to simulate the system behavior and evaluate the effectiveness of the proposed EMS. The following subsections outline the specific features of energy sources, DC/DC converters, auxiliary services module, braking resistor, and traction load.
## 3 Results and Analysis  
### Spatiotemporal Evolution of Carbon Sink and SDE Analysis  
#### 3.1.1 Time Change Analysis of Carbon Sink  
To investigate the time evolution trend of the carbon sink in the 40 key SOFRs in Heilongjiang Province during the study period, ArcGIS software was used to calculate the annual carbon sink value of each forest region and draw it as a line graph (Figure 2).  
From the perspective of time, the carbon sink of the SOFRs showed an upward trend from 2001 to 2019, with a total growth rate of approximately 20.17%, and the fitting formula for annual growth was y = 1.5042x \(-\) 2809. It reached the maximum value in 2018, approximately 233.78 million tons, but the overall fluctuation was relatively large. The reasons for the overall increase in the carbon sink are as follows. According to the data from the fifth to eighth forest resource surveys, Heilongjiang has a relatively large proportion of natural forests and a relatively small proportion of artificial forests. The carbon storage per unit area of planted forests is approximately twice that of natural forests. Since 2000, the implementation of the NFPP and the increase in the area of planted forests have increased  
Figure 2: Time change of carbon sinks in the SOFRs in Heilongjiang Province.  
the intensity of forest management and protection. Therefore, the carbon sink function of forest stands has been enhanced, and the overall trend is increasing.
# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 3 System Framework and Method  
An intelligent algorithm is designed to complete radar signals deinterleaving with high accuracy. The first step is to extract the TOA sequence of the radar signals, make a self-correlation difference of the TOA sequence to obtain the PRI transformation matrix, and count the frequency of potential PRI to obtain the FCM. Then, normalize the elements in the FCM and feed them to the network of Sep-RefineNet for model training. Finally, the position of the prediction results is decoded to obtain that each pulse belongs to its own radar emitter, and the signal interleaving is completed. The system framework is shown in Figure 2.  
The one-dimensional pulse sequence is transformed into a two-dimensional matrix through the coding of FCM, and the FCM can extract the PRI features of pulse streams. The semantic segmentation network named Sep-RefineNet can better identify the contextual semantic features of FCM and finally complete pixel-level segmentation and position decoding. The proposed method can accurately complete the task of the deinterleaving radar signals in the interference of noise.
## 2 Materials and Methods  
### Related Works  
Currently, many tools have been proposed for analyzing DEGs and the corresponding GO terms. They can be categorized into three groups according to their purpose: (1) identifying a DEG from microarray data; (2) visualizing the corresponding GO from the DEG; and (3) exploring information related to a GO term.  
Figure 1: Comparison between previous DEG analysis and the proposed DegoViz tool. (**a**) Previous analysis process; (**b**) Proposed DegoViz tool.  
First, for identifying DEGs from the microarray data, some tools are available to obtain the desired result, including a single image heatmap visualizing the expression of the genes and organism [5; 6; 7]. Because they focus on statistical techniques, they do not provide a graphical user interface (GUI) for modifying the heatmap. Second, web-based applications for determining GO terms exist [8; 9; 10]. When there is user provided DEG input data, applications provide the corresponding GO terms with a visualized graph. However, the majority of these applications, such as GOrilla (see Figure 2) [10], are based on the graph drawing software graphViz [11], which only provides a single static image of the drawn graph. Force-directed graphs for visualization were proposed [12]. Several applications such as biNGO [13] are implemented based on Cytoscape [14], an effective tool for visualizing network relationships and providing user interaction. However, it does not provide the interactivity between integrated visualizations. Therefore, when users require additional information regarding a certain GO term, they must use another application for searching the GO library, such as QuickGO [15] or AmiGo [16]. These GO term searching applications provide specific information and relationships between GO terms.  
In the process of biological research, from the data analysis of DEGs to viewing the related GO graph and specific terms, the three types of applications mentioned above must be utilized. Despite the importance of this ongoing process, due to its existence in distinct applications, there is a lack of efficiency and inconvenience in research and a low probability of finding new observations. Further, as previously mentioned, the technique in graph visualization depending on graph visualizing software [11; 14] is limited to drawing static images, which decreases the possibilities of application development for other purposes.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.2. Deep Convolutional Neural Network (DCNN)  
In 2018, [PERSON] et al. conducted a retrospective clinical study utilizing deep learning to stage liver fibrosis from CT images. This pilot study included 496 CT scans from 286 patients, leveraging a development set of 396 portal phase images and a test set of 100 images, with both sets sorted by fibrosis stage. A DCNN was trained with augmentation techniques such as rotation and noise addition. Their method achieved AUROC for diagnosing significant fibrosis, advanced fibrosis, and cirrhosis of 0.74, 0.76, and 0.73, respectively [13].
## 2 Materials and Methods
### Variable Selection  
#### 2.3.1 Spectral Regions Selection  
We divided the Raman spectra into different regions for modeling to explore the impact of different spectral regions on the model's performance. Twenty different spectral regions containing Raman fingerprint region were selected, labeled as FP1, FP2, FP3, FP4, FP5, FP6, FP7, FP8, FP9, FP10, FP11, FP12, FP13, FP14, FP15, FP16, FP17, FP18, FP19, and FP20, respectively. There are seven different spectral regions involving the C-H stretching region, labeled as CHS0, CHS1, CHS2, CHS3, CHS4, CHS5, and CHS6, respectively. The corresponding spectral ranges of each spectral region are shown in Figure 1. CHS0 does not contain any spectral variables, and FP20 plus CHS6 represents the whole range of spectra. The Raman-based models were developed by using different Raman fingerprint regions plus C-H stretching regions. In total, there were 7 \(\times\) 20 = 140 different spectral regions used for modeling. These spectral regions involved different Raman fingerprint regions, Raman fingerprint regions plus C-H stretching regions, and the whole range of spectra, which could represent the spectral regions selected by most researchers.