esa-sceva/satcom-chunk-collection huggingface dataset:
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 2 Interference in Hotspot Scenarios
### Classic Solutions for Interference in Hotspot Scenarios  
#### 2.1.1 MIMO Systems  
Multiple-Input Multiple-Output (MIMO) is a technology that uses multiple antennas for transmitting and receiving signals to mitigate negative aspects of the channel and/or multiplex data transmission. The first goal, for example, can be attained by increasing the diversity of transmitted signals [5]. The unpredictability of the wireless channel is used as a tool to improve system performance [6] by using multiple antennas to exploit the advantages of spatial diversity [7]. Furthermore, MIMO can also explore spatial multiplexing, using multiple antennas to transmit data through various channels.  
Given a minimum spacing between antennas, it is possible to obtain independent or weakly correlated channels on each Tx/Rx (transmission and reception) pair. This concurrent transmission in the same frequency band can increase data rates or improve reliability without compromising spectral efficiency.  
A scenario with hotspots is prone to have some channels suffering with less interference than others. Thus, if an appropriate combination method is applied for the different transmissions, it is possible to improve performance. For example, choosing the path with better SINR [5] is a simple solution that can lead to better results. Additionally, multiple antennas can also control the signal amplitude and phase to direct beams towards certain users or regions and avoid users suffering from interference [8].  
Recent wireless communication systems point MIMO as an efficient approach to increase data rates, especially when suffering from small-scale multi-path, such as systems with hotspots. For instance, the use of multiple antennas has been widely discussed in the literature as a core feature of 5G systems [9], through massive MIMO. The use of Millimiter Waves (mmWaves) enables antenna arrays with a high number of elements [10]. However, MIMO is a solution implemented in the physical layer, which requires investments in hardware and it often demands channel estimation.  
Nevertheless, this work does not intend to indicate MIMO as a technique to be replaced. Still, it introduces FFR as an appropriate technology to address the specific problems noted in the last section. In mobile networks, MIMO often co-exists with FFR techniques.
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 2 Theoretical Background
### Modelling Structure  
The training mechanism of MTC-GAN is significantly different from traditional GANs. Traditional GANs usually combine generated high-quality samples with real samples for additional classifier training after the model training is completed, as a way to achieve data enhancement and improve model performance. However, MTC-GAN adopts a different strategy. Its discriminators are directly applied to the classification task after training is completed. It does not admit any additional generated samples outside the training process. Therefore, MTC-GAN requires data augmentation during the training process. In this process, additional generated samples are internally and dynamically input into the training. This dynamic input strategy adjusts the timing and quantity of inputs based on the real-time distance between samples generated by the model's generator and the real samples. This allows the model to continuously adapt and optimize during training. The input of high-quality samples begins after the model is able to generate them consistently. MTC-GAN ensures that the generated samples used can effectively support model training. This significantly improves overall classification performance. The implementation of thistraining strategy not only enhances the model's adaptability to complex data environments but also introduces new challenges, placing higher demands on the optimization of the training strategy and model performance.  
The general framework of the methodology proposed in this paper is shown in Figure 3, and the main steps are as follows:  
Step 1: Acquire acceleration vibration signals of bearings from different categories and correct the signals to eliminate negative values.  
Step 2: Design the length and size of individual samples based on physical guidelines and convert them into 2D grayscale maps.  
Step 3: Feature extraction of grayscale images using the Local Binary Pattern operator to capture small variations in the signal and texture features. A fault feature signature with high recognition capability is constructed.  
Step 4: Use the obtained fault feature signature to train the MTC-GAN model.  
Step 5: Extract the discriminator part from the trained MTC-GAN model and apply it to the fault diagnosis task.
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
###### Abstract  
Sheath blight (ShB), caused by _Rhizoctonia solani_ AG1-I, is one of the most important diseases in rice worldwide. The symptoms of ShB primarily develop on leaf sheaths and leaf blades. Hyperspectral remote sensing technology has the potential of rapid, efficient and accurate detection and monitoring of the occurrence and development of rice ShB and other crop diseases. This study evaluated the spectral responses of leaf blade fractions with different development stages of ShB symptoms to construct the spectral feature library of rice ShB based on \"three-edge\" parameters and narrow-band vegetation indices to identify the disease on the leaves. The spectral curves of leaf blade lesions have significant changes in the blue edge, green peak, yellow edge, red valley, red edge and near-infrared regions. The variables of the normalized index between green peak amplitude and red valley amplitude (\(\mathrm{Rg-Ro}\))/(\(\mathrm{Rg+Ro}\)), the normalized index between the yellow edge area and blue edge area (\(\mathrm{SDy-SDb}\))/(\(\mathrm{SDy+SDb}\)), the ratio index of green peak amplitude and red valley amplitude (\(\mathrm{Rg/Ro}\)) and the nitrogen reflectance index (NRI) had high relevance to the disease. At the leaf scale, the importance weights of all attributes decreased with the effect of non-infected areas in a leaf by the ReliefF algorithm, with \(\mathrm{Rg/Ro}\) being the indicator having the highest importance weight. Estimation rate of 95.5% was achieved in the decision tree classifier with the parameter of \(\mathrm{Rg/Ro}\). In addition, it was found that the variety degree of absorptive valley, reflection peak and reflecting steep slope was different in the blue edge, green and red edge regions, although there were similar spectral curve shapes between leaf sheath lesions and leaf blade lesions. The significant difference characteristic was the ratio index of the red edge area and green peak area (\(\mathrm{SDr/SDg}\)) between them. These results can provide the basis for the development of a specific sensor or sensors system for detecting the ShB disease in rice.  
rice sheath blight; spectral response; \"three-edge\" parameters; hyperspectral imaging; narrow-band vegetation index; remote sensing +
Footnote †: journal: Sensors
# Early Performance Prediction in Bioinformatics Systems Using Palladio Component Modeling
## 2 Background
### Bioinformatics Systems  
Bioinformatics combines the knowledge of biology, computer science, mathematics, and statistics to analyze biological data. Bioinformatics, for the most part, are responsible for developing the systems; they must have both biological and programming skills. The results of bioinformatics tools and frameworks are achieved gradually and they are less reliable than others. Software engineering does not appear in these tools and frameworks, which might result in major performance problems. Bioinformaticians do not have the needed software engineering services to avoid these problems. By using software engineering standards--preferably devised by software engineers or bioinformatics engineers instead of bioinformaticians--these problems can be avoided [35].  
As mentioned previously, one of the most important fields of bioinformatics is sequence alignment. Sequence alignment can be used in defining protein functions, autoimmune diseases, and in homology and phylogeny. Sequence alignment can be used in protein functions to estimate the final function based on the amino acid sequence by comparing the sequence to a database of defined proteins and their functions. In this case, the protein's conduct and function are determined by its three-dimensional folds along with the amino acid composition [36]. Sequence alignment is being used to discover the genetic origin of autoimmune diseases. It can be used to find similarities between protein sequences in an autoimmune disease patient's body cells and defined viruses and bacteria. When major similarities are discovered, a treatment can be created to change the body's genetic code [37]. Homology is a branch of biology concerned with the formation of evolutionary trees. It can be carried out by aligning significant sections of DNA from different life forms. Humans, for example, have several types of DNA in common with monkeys, ages, and other primates. On a human-only scale, this could be useful in identifying the genes that cause genetic diseases [5]. Phylogeny is a branch of biology that studies the evolutionary history of all living things on the planet in order to build a phylogenetic tree. Multiple sequence alignment is a common practice in phylogenetic analysis and includes determining and organizing the evolutionary history of various organisms into a hierarchy, in which closely related species are physically positioned near one another [38].  
In this study, a sequence alignment system is employed to test the performance enhancement when using the PCM model in the early stages of development. Sequence alignment is selected based on the importance of the field, as discussed earlier; moreover, it serves as a basis for the incorporation of software engineering principles with bioinformatics, meaning that the study can be further expanded to other areas of bioinformatics.
## Materials and Methods
### Penalized Maximum Likelihood (PML) Method in Multi-Locus Screening Stage
#### 2.3.2 Iterative Method for Parameter Estimation  
The parameter vector \(\mathbf{\theta}\) and \(\mathbf{\delta}\) are estimated by the penalized maximum likelihood simultaneously, and the solution of PML needs an iterative method to implement. For the interested parameter vector \(\mathbf{\theta}\), find the first-order partial derivatives of the elements in \(\mathbf{\theta}\) and then make them equal to 0.  
Via \(\frac{\partial}{\partial\mathbf{b}}L_{p}(\mathbf{\theta},\mathbf{\delta})=0\), \(\frac{\partial}{\partial\beta_{i}}L_{p}(\mathbf{\theta},\mathbf{\delta})=0\), and \(\frac{\partial}{\partial\omega_{n}^{2}}L_{p}(\mathbf{\theta},\mathbf{\delta})=0\) and \(i=1,\cdots,t\), it can obtain a closed-form expression on \(\mathbf{b}\), \(\beta_{i}\), and \(\sigma_{i}^{2}\), respectively.  
\[\mathbf{b}=\left(\mathbf{F}^{\mathrm{T}}\mathbf{F}\right)^{-1}\mathbf{F}^{\mathrm{T}}\left( \mathbf{y}-\sum\limits_{i=1}^{t}\mathbf{x}_{i}\beta_{i}\right) \tag{18}\]  
\[\beta_{i}=\left[\mathbf{x}_{i}^{\mathrm{T}}\mathbf{x}_{i}+\sigma_{n}^{2}/\sigma_{i}^{ 2}\right]^{-1}\left[\mathbf{x}_{i}^{\mathrm{T}}\left(\mathbf{y}-\mathbf{F}\mathbf{b}-\sum \limits_{\begin{subarray}{c}k=1\\ k\
eq i\end{subarray}}^{t}\mathbf{x}_{k}\beta_{k}\right)+\mu_{i}\sigma_{n}^{2}/ \sigma_{i}^{2}\right] \tag{19}\]  
\[\sigma_{n}^{2}=\frac{1}{n}\Bigg{(}\mathbf{y}-\mathbf{F}\mathbf{b}-\sum\limits_{i=1}^{t} \mathbf{x}_{i}\beta_{i}\Bigg{)}^{\mathrm{T}}\Bigg{(}\mathbf{y}-\mathbf{F}\mathbf{b}-\sum \limits_{i=1}^{t}\mathbf{x}_{i}\beta_{i}\Bigg{)} \tag{20}\]  
For the nuisance parameter vectors, \(\mathbf{\delta}\), \(\mu_{i}\), and \(\sigma_{i}^{2}\) are acquired in the same way. Via \(\frac{\partial}{\partial\mu_{i}}L_{p}(\mathbf{\theta},\mathbf{\delta})=0\) and \(\frac{\partial}{\partial\sigma_{i}^{2}}L_{p}(\mathbf{\theta},\mathbf{\delta})=0\), \(\mu_{i}=\beta_{i}/(\tau+1)\) and \(\sigma_{i}^{2}=\frac{1}{2}\left[(\beta_{i}-\mu_{i})^{2}+\tau\mu_{i}^{2}\right]\) can be obtained.  
Set the initial values for \(\mathbf{\theta}\), \(\mathbf{\delta}\), and \(\tau\) and update the values of \(\mathbf{b}\), \(\beta_{i}\), \(\sigma_{n}^{2}\), \(\mu_{i}\), and \(\sigma_{i}^{2}\) until convergence. In order to reach convergence quickly, a criterion according to the experience needs to be considered. After the parameter estimation, \(\left|\beta_{i}\right|>10^{-4}\) are selected to further conduct a likelihood-ratio test. The logarithm of the odds (LOD) score is used to determine the final identification.
# Normalized Weighting Schemes for Image Interpolation Algorithms
## 1 Introduction and Background
Of all my works on image interpolation, the most recently presented, in [47], examined the effects of stochastic rounding in nearest-neighbor interpolation for interpolated scan conversion operations in cardiac ultrasound. It is important to note that, unlike the non-extra pixel, the extra-pixel approach category has attracted many researchers to date due to the potential of leading to visually realistic image details after upscaling. In the extra pixel category, there are adaptive and non-adaptive interpolation techniques [8; 48; 49]. Moreover, among adaptive and non-adaptive, some techniques depend on traditional techniques, such as bilinear, to achieve improved outcomes relevant to the targeted problem or application of interest [48; 49; 50; 51; 52; 53; 54; 55]. Here, the main contribution to the digital image processing field is the introduction of new weighting schemes for linear image interpolation algorithms inspired by the geometric shapes and related normalized areas when their sums exceed a unit square size. Here, it is important to remember that image interpolation algorithms belonging to the linear or non-adaptive category remain the most efficient among other interpolation algorithms. It is also important to remember that methods presented in this paper can be extended to 3D image interpolation operations to solve the specific problem of interest (in generating 3D models) by using them in simultaneous multidimensional interpolation operations [56; 57]. This paper is organized as follows: Part 1 presents the introduction and background. Part 2 presents the material and methods. Part 3 presents the results and discussions. The conclusion is presented in part 4.
## 3 The Optimized Agglomerative Approach
### Extension from CLOPE  
As the CLOPE algorithm moves only one transaction from one cluster to another, it is required to extend the definitions and the corresponding data structures to support cluster merge operation.  
**Definition 6** (Extension of Cluster). _A cluster \(c\) is a quadruple \(<\)id,\(\overline{T},\overline{H},\overline{L}\) > where id is the unique identification of \(c\), \(\overline{L}\) is a list of ids from other clusters related to \(c\), and the rest are the same as those in_ Definition 1.  
**Definition 7** (Extension of Profit). _The profit value of a cluster \(c\) is defined as follows_:  
\[\mathrm{Profit},(c)=\frac{S(c)}{W(c)^{\gamma}}\times\left|c\right| \tag{5}\]  
Then the profit of a clustering \(C\) would be:  
\[\mathrm{Profit}_{,}(C)=\frac{\sum_{i=1}^{p}\mathrm{Profit}_{,}(c_{i})}{\sum_{k =1}^{p}\left|c_{i}\right|} \tag{6}\]  
**Definition 8** (Delta): _Suppose there are two clusters \(c\)i and \(c\)i, and \(c\)i, and \(c\)i, is the merge of these two clusters. Then the Delta value of \(c\)i and \(c\)i is defined as follows_:  
\[\mathrm{Delta}(c_{i},c_{j})=\mathrm{Profit}(c_{i,j})-(\mathrm{Profit}(c_{i})+ \mathrm{Profit}(c_{j})) \tag{7}\]  
We use symbol \(\mathrm{D}_{i,j}\) short for Delta(\(c\)i,\(c\)i). Obviously the Delta function is symmetric that we have \(\mathrm{D}_{i,j}=\mathrm{D}_{j,i}\). We also define \(\mathrm{D}_{i,i}=0\) for the relationship of the same cluster. To maximize the profit value in each merge operation, the Delta value for a certain cluster should be maximal. Suppose there are totally \(p\) clusters, the maximum Delta value for cluster \(i\) is denoted as:  
\[\mathrm{M}(c_{i})=\max(\left\{D_{,i,k}\mid k\in\left[1,\cdots,p\right]\right\}) \tag{8}\]  
If \(c\)i could maximize the Delta value of \(c\)i, then \(c\)i is the **merge candidate** of \(c\)i denoted as \(c_{i}\Rightarrow c_{j}\). A cluster might have more than one merge candidates, and all the unique _id_s of those merge candidates are stored in \(\overline{L}\) according to Definition 6 for further process.
## 2 Materials and Methods
### Measurements
#### 2.5.2 Balance Control Ability  
Balance control was assessed using the BioRescue System (RM Ingenierie, Rodez, France), which consists of a pressure-sensitive platform connected to a computer for real-time postural analyses and training. Force platforms, such as the one used in this study, are considered the gold standard for reliably quantifying postural stability and balance control [25]. The system measures the center of pressure path, area (cm\({}^{2}\)), length (cm), and average velocity (cm/s) during specific balance tasks.  
Two tests were conducted: the Romberg test in a standing posture and the limits of stability (LOS) test. During both assessments, participants received verbal instructions and demonstrations. Each test was performed before and after tape application, with a 60 s rest in between the trials. Each test was performed three times, and the average values were used for the analysis to ensure reliability.  
In the Romberg test, participants stood quietly on the platform for 30 s with feet shoulder-width apart and eyes closed. For the LOS test, participants stood on the platform with their feet approximately shoulder-width apart and heels 3 cm apart, maintaining a forward gaze. They were positioned 1 to 1.5 m in front of a monitor in a quiet room to minimize distraction. A trained assistant stood nearby during the test to ensure safety, without physical contact unless required.  
Figure 2: Athletic Tape methods. (**a**) Low-dye taping—sole. (**b**) Low-dye taping—side. (**c**) Anti-pronation spiral taping elastic (Lat.). (**d**) Anti-pronation spiral taping elastic (Med.). (**e**) Anti-pronation spiral taping inelastic (Lat.). (**f**) Anti-pronation spiral taping inelastic (Med.).
## 4 Conclusions  
In this paper, we proposed and evaluated a novel PWC-based _m_CAP-DM scheme with 2D chaotic encryption for secure, bandlimited VLC systems. The use of PWC coding can efficiently mitigate the SNR imbalance caused by the low-pass characteristic of LEDs, and the application of 2D chaotic encryption can successfully guarantee the security of the transmitted useful information in the VLC system. Simulation results validated the excellent BER performance of 4 CAP-DM in comparison to 4 CAP-IM, achieving the same SE of 2.5 bits/s/Hz over the AWGN channel. Furthermore, experimental results showed that PWC-based 4 CAP-DM achieves much better BER performance than the benchmark schemes, while the physical-layer security of the system can be substantially enhanced by applying the 2D chaotic encryption approach. Therefore, the proposed PWC-based _m_CAP-DM scheme with 2D chaotic encryption can be a promising candidate for practical VLC systems.  
Conceptualization, C.C.; methodology. W.Z., Y.Y., X.D. and M.L.; validation, Y.Y.; formal analysis, W.Z. and X.D.; investigation, Y.N.; resources, Y.Y.; data curation, Y.N.; writing--original draft preparation, Y.N.; writing--review and editing, W.Z., X.D. and C.C.; supervision, M.L.; project administration, M.L.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61901065), the Natural Science Foundation of Chongqing (cstc2021 jcyj-msxmX0480), and the Fundamental Research Funds for the Central Universities (2021 CDJQY-013).  
Not applicable.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 1 Introduction  
With the recent development of sensors and information technology (IT), conditions in railway facilities can be monitored continually by using sensors installed in the rolling stock and in areas adjacent to the track. This, in turn, has spurred the interest in using this type of monitoring to create maintenance plans or schedule condition-based maintenance when the track conditions indicate deterioration.  
The condition of railway tracks is an important factor in ensuring the safe operation of trains. To ensure that railway travel is safe and comfortable, it is necessary to maintain and manage tracks properly; this includes preventive maintenance. Further, it is desirable to monitor tracks frequently. The deformations that occur in tracks, referred to as track irregularities, are closely connected to the riding quality and safety of railway vehicles. Urban railway operators use track inspection cars to verify track irregularities.  
Track condition monitoring systems that observe car-body vibrations in in-service vehicles have already been developed for regional railways and have exhibited the capability to oversee track conditions [1]. Additionally, recent studies proposed employing a Kalman filter to predict track irregularities from the acceleration measurements of car-body vibrations of railway vehicles [2].  
Moreover, on regional railway lines, many operators are unable to implement adequate inspections owing to problems such as cost and personnel constraints. Hence, low-cost monitoring systems have been developed based on on-board sensing devices and global navigation satellite system (GNSS) data. In this system, the sensing device is installed in an in-service vehicle to measure car-body vibrations. The diagnosis is performed by rating the track conditions based on the measurement data. Presently, however, the rating needs to be calculated manually. It is difficult to address a large volume of automatically collected data. To enable the efficient computational processing of collected data, the diagnosis and prediction of track conditions must be automated.  
In the present study, we adopted machine learning techniques and developed an algorithm to automatically diagnose track conditions from car-body vibrations to automatically detect track irregularities and identify them by type. We subsequently performed a simulation to verify the effectiveness of the developed algorithm. In addition, we examined the possibility of diagnosing track conditions for an actual regional railway line.