esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Predictive Modelling of Sound Design Elements and User's Emotional Evaluation
### QTTI Prediction Model Construction and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{\(\boldsymbol{Y_{1}}\) **Annoyance-Comfortable**} & \multicolumn{2}{c}{\(\boldsymbol{Y_{2}}\) **Inexpensive-Premium**} & \multicolumn{2}{c}{\(\boldsymbol{Y_{3}}\) **Weak-Powerful**} \\ \cline{3-11}  & & **C** & **P** & **S** & **C** & **P** & **S** & **C** & **P** & **S** \\ \hline \multirow{3}{*}{\(\begin{array}{c}\text{L}_{A}\\ \text{X}_{1}\end{array}\)} & 60 \(\leq\) x \(>\) 65 & 0.544 & \multirow{3}{*}{3 rd} & 0.290 & \multirow{3}{*}{5 th} & \(-\)0.317 & \multirow{3}{*}{4 th} \\ \cline{2-11}  & 65 \(\leq\) x \(>\) 70 & \(-\)0.012 & & & & & & & & & & \\ \cline{2-11}  & 70 \(\leq\) x \(>\) 75 & \(-\)0.121 & & & & & & & & & & \\ \hline \multirow{3}{*}{\(\begin{array}{c}\text{Loudness}\\ \text{X}_{2}\end{array}\)} & 10 \(\leq\) x \(>\) 25 & 0.332 & \multirow{3}{*}{1 st} & 0.223 & \multirow{3}{*}{0.748} & \multirow{3}{*}{2 nd} & \(-\)0.268 & \multirow{3}{*}{0.845} \\ \cline{2-11}  & 25 \(\leq\) x \(>\) 40 & \(-\)0.245 & & & & & & & & & & \\ \cline{2-11}  & 40 \(\leq\) x \(>\) 55 & \(-\)0.296 & & & & & & & & & & \\ \hline \multirow{3}{*}{\(\begin{array}{c}\text{Tonality}\\ \text{X}_{4}\end{array}\)} & 0.1 \(\leq\) x \(>\) 0.6 & 0.074 & \multirow{3}{*}{0.375} & 0.129 & \multirow{3}{*}{0.608} & \multirow{3}{*}{4 th} & 0.133 & \multirow{3}{*}{3 rd} \\ \cline{2-11}  & 0.6 \(\leq\) x \(>\) 1.2 & \(-\)0.071 & & & & & & & & & & \\ \cline{2-11}  & 1.2 \(\leq\) x \(>\) 1.7 & \(-\)0.083 & & & & & & & & & & \\ \hline \multirow{3}{*}{\(\begin{array}{c}\text{Sharpness}\\ \text{X}_{4}\end{array}\)} & 2.2 \(\leq\) x \(>\) 4.4 & 0.371 & \multirow{3}{*}{0.712} & \multirow{3}{*}{2 nd} & 0.462 & \multirow{3}{*}{0.809} & \multirow{3}{*}{1 st} & 0.289 & \multirow{3}{*}{0.708} & \multirow{3}{*}{2 nd} \\ \cline{2-11}  & 4.4 \(\leq\) x \(>\) 6.5 & \(-\)0.186 & & & & & & & & & & \\ \cline{2-11}  & 0 \(\leq\) x \(>\) 0.5 & 0.078 & \multirow{3}{*}{0.419} & \multirow{3}{*}{4 th} & 0.156 & \multirow{3}{*}{0.704} & \multirow{3}{*}{3 rd} & \(-\)0.054 & \multirow{3}{*}{0.352} & \multirow{3}{*}{5 th} \\ \cline{2-11}  & 0.5 \(\leq\) x \(>\) 1.2 & \(-\)0.136 & & & & & & & & & & \\ \hline \multicolumn{3}{c}{Constant term} & 3.714 & \multirow{3}{*}{0.948} & \multirow{3}{*}{0.948} & \multirow{3}{*}{1 st} & 0.246 & \multirow{3}{*}{0.932} & \multirow{3}{*}{5 th} \\ \cline{2-11}  & Multiple correlation coefficient (\(R\)) & & & & & & & & & & & & \\ \cline{2-11}  & 0.952 & & & & & & & & & & & \\ \hline \multicolumn{3}{c}{Coefficient of determination (\(R^{2}\))} & 0.891 & \multirow{3}{*}{0.899} & \multirow{3}{*}{0.859} & \multirow{3}{*}{0.859} \\ \hline \hline \end{tabular}  
\end{table} 
Table 4: Kansei impact analysis for each item and category in the QTTI prediction model.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 4 Commercial UWB Modules and Chips for IPSs
### Qorvo DW1000 UWB Family  
The Qorvo (Previously DecaWave) DW1000 (Qorvo Inc., Greensboro, NC, USA) is one of the first ultra-wideband units that contains all basic circuits for the transmission and reception process in one chip. It began to spread in the middle of the last decade and is still widely used today, despite the emergence of modern competing products. This product provides different attractive features that enable its widespread use, such as low cost, measurement accuracy of up to 10 cm in line-of-sight conditions, low power consumption compared to cellular radio modules, working on six channels within the radio frequencies3.5 to 6.5 GHz, data transfer capacity of up to 6.8 Mbps, and the ability to communicate over distances of more than 200 m in the case of line-of-sight (LoS) communication. It can be used for both two-way ranging and TDoA algorithms for IPSs and can be hosted by any processor with an SPI interface [29; 30; 31].
# GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation
###### Abstract  
One of the basic possibilities of orientation in forest stands is the use of global navigation satellite systems (GNSS). Today, these systems are used for pedestrian orientation and also for off-road vehicle navigation. This article presents the results of research aimed at measuring the quality of GNSS signal in different types of coniferous and deciduous vegetation for the purpose of optimizing the navigation of off-road vehicles. To determine the structure (density) of the forest stand, tachymetry was chosen as the reference method. The Trimble Geo 7X cm edition device with Tornado for 7X antenna devices using real time VRS (virtual reference station) method was used to measure GNSS signal quality. This article presents the results of recorded numbers of GNSS satellites (GPS, GLONASS, Galileo and BeiDou) during the driving of a terrain vehicle in two different forest locations. Significant presented results include the deviations of vehicle positions determined by GNSS from tachymetrically precisely measured and marked routes along which the vehicle was moving. The authors of the article focused on the accuracy of determining the position of the vehicle using GNNS, as the most commonly used device for off-road vehicle navigation. The measurement results confirmed the assumption that the accuracy of positioning was better in deciduous forest than in coniferous (spruce) or mixed vegetation. This research was purposefully focused on the possibilities of navigation of military vehicles, but the achieved results can also be applied to the navigation of forestry, rescue and other types of off-road vehicles.  
GNSS signal quality; navigation in forest; cross-country mobility; terrain vehicles +
Footnote †: journal: Applied Sciences  
_Article_  
GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation
## 5 Performance Evaluation
### Performance Evaluation of ICN Hierarchical Cache System
#### 5.1.4 Control Overhead  
The extra control overhead in a cache system brought from the mechanism of content addressing based on the NRS includes the signaling overhead of resolution, register, and logout. We estimated the impact of control overhead in terms of link load, which is the average number of packets transmitted on a link per second. Figure 9 reports a comparison of average link load in an overall network between three caching systems and different caching strategies. We observed that the hierarchical cache system saved more network bandwidth than the others did, because it helped users find content copies faster. However, LCE was a special case whose control overhead was much higher than the other strategies due to each node in the network actively participating in caching at the same time.  
Figure 8: Comparison of cache-hit ratios with cache-to-population ratios {0.01, 0.02, 0.05, 0.1, 0.25, 0.5, and 1}.  
Figure 7: Comparison of latencies with cache-to-population ratios {0.01, 0.02, 0.05, 0.1, 0.25, 0.5, and 1}.  
As shown in Figure 10, we estimated the average access load of each level RN in different cache systems (HCS and FCS). We noted that the RN under the LCE strategy faced 243% more service traffic than the other strategies, while the difference between the loads of other strategies was small. This result showed that we should avoid caching a large amount of copies in one transport process. Moreover, the HCS lessened the service pressure of the L3 RN by directing the service traffic to different level RNs.
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 4 Minimum Cross-Entropy by EO for MRIs  
This section describes the application of the equilibrium optimizer (EO) combined with minimum cross-entropy (MCE) for prostate MRI segmentation. One of the key challenges in diagnosing prostate cancer is the subtlety of early-stage abnormalities, which are often difficult to detect with conventional imaging techniques. EO's ability to balance exploration and exploitation during optimization enables it to identify optimal threshold values, enhancing image clarity and precision, especially on older imaging equipment. This improves the delineation of prostate boundaries and helps detect small lesions. Early detection of prostate cancer is critical, as it significantly improves treatment outcomes by enabling less invasive interventions and better patient prognoses. EO's contribution to providing clearer, more accurate segmentations aids radiologists in distinguishing between soft tissues, leading to more informed clinical decisions. From a practical perspective, incorporating EO and MCE thresholding as a preprocessing step in computer-aided diagnostic tools can streamline workflow efficiency in radiology departments.  
The key elements of the proposed algorithm are detailed in the following subsections.
## 2 Materials and Methods  
### Analysis Area  
The coastal waters of Korea, located in northeastern Asia, are the study's analysis area. As a result of Korea's geographical characteristics, the maritime transportation industry plays an important role, and the development of the Korea Autonomous Surface Ship is being carried out actively by the 4 th industrial revolution [29]. This type of industrial development has been combined with artificial intelligence (AI), big data, and the Internet of Things to develop safe navigation and maritime transport networks for ships [30]. Maritime traffic routes should be adequately required for MASS's autonomous operation. This is why a route based on actual AIS data from the nearby Korean Sea is required. Furthermore, recent efforts have been made to develop new and renewable energy sources in Korea's coastal waters. In particular, the composition of the offshore wind farm (OWF) is rapidly increasing, which is a dangerous factor in the customary route of ships [8]. It is even expanding to distant seas with good wind conditions with floating offshore wind power. At a time when securing passageways is becoming increasingly important due to the increasing size of ships, the subject of different marine uses collides. As shown in Figure 1, the neighboring waters of Korea were chosen as the analysis area to solve this problem and find an acceptable compromise point.  
In Korea, the area under analysis was divided into a total of 16 sections to effectively analyze the vast amount of AIS data and understand the overall maritime traffic route. The West Sea region was divided into 4 areas, the South Sea region into 6, and the East Sea region into 6. The coordinate system used to divide regions is depicted in Table 1.  
Figure 1: Analysis of Korean maritime traffic route location and area division details.
## 3 3D-VWZCC Code Construction  
The three-dimensional code, 3D-VWZCC, is built from two sequences of one-dimensional and two-dimensional 1D-VWZCC, 2D-VWZCC codes, respectively. To construct this code, there are three essential parameters, which are presented as follows: code length (\(L\)), code weight (\(W\)), and cross-correlation value (\(\lambda_{c}\)). The 1D-VWZCC code is characterized by the property of zero cross-correlation ZCC; as a result, the code length and the value of cross-correlation are given by the following equations:  
\[\left\{\begin{array}{c}\lambda_{c}=0\\ L=K\times W\end{array}\right. \tag{1}\]  
where \(K\) is the number of users; further, due to the ZCC property the PINN being totally suppressed, the construction is described by four steps:
## 4 Results and Discussions
### Axial Distribution of Heat Release Oscillation
The overall HRR oscillation and the normalized velocity fluctuation with different phase angles under excitation amplitudes of 0.1 and 0.5 are presented in Figure 14. Under a low-amplitude excitation (\(u^{\prime}/\bar{u}=0.10\)), the positive oscillation of region number 5 decreased noticeably, while the negative oscillation of region number 4 remained unchanged. As a result, there was a diminishing trend in HRR during phase interval of 0-\(120^{\circ}\). Following the \(120^{\circ}\) phase, a decline in the negative oscillation of region number 4 was observed while the positive oscillation of region number 3 intensified, leading to an early rise in HRR compared to the phase of velocity fluctuations. In contrast, when the excitation amplitude was high at 0.5, both the positive oscillation of region number 4 and the negative oscillation of region number 5 experienced attenuation. Meanwhile, the positive oscillation of region number 3 increased initially, decreased subsequently, and dominated the value after the \(60^{\circ}\) phase. As a result, the overall HRR first increased, then decreased within the phase range of 0 to \(120^{\circ}\). After the \(120^{\circ}\) phase, the negative oscillation of region number 2 emerged and gradually intensified, causing a continued decrease in overall HRR until a phase angle of \(300^{\circ}\) was reached. At a phase angle of \(360^{\circ}\), an increase in HRR was observed due to an increase in the positive oscillation of region number 3. Consequently, there was a delay of approximately \(90^{\circ}\) between the lowest HRR and the minimum velocity fluctuation. Figure 13: The axial distribution of heat release oscillation with variation of phase angles at\(f_{e}=20\) Hz and \(u^{\prime}/\bar{u}=0.10\). The relative change trend of the oscillation region varies depending on different excitation amplitudes, resulting in a considerable phase difference. This shift is primarily due to the proximity of the positive and negative oscillation amplitude, causing the overall HRR to be more sensitive to variations within a single oscillation region. These conditions are susceptible to changes in the excitation frequency or amplitude, leading to considerable phase shifts.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 3 System Overview and Method
### Camera Calibration  
This section primarily focuses on the analysis and rectification of lens errors. The camera lens consists of a combination of concave and convex lenses, representing the essence of a pinhole camera model. However, inherent distortions caused by the camera lenses often result in radial and tangential distortions in images. Therefore, prior to image processing, it is essential to calibrate the camera and correct these distortions. In this paper, we employ the checkerboard calibration method to obtain both internal and external parameters of the camera. The checkerboard contains multiple reference points for calibration purposes, with each point's coordinates being subject to identical noise disturbance. Consequently, an objective function is established accordingly \(\gamma\):  
\[\gamma=\sum_{i=1}^{n}\sum_{j=1}^{m}\left\|p_{ij}-\hat{p}(A,R_{i},t_{i},P_{i}) \right\|^{2}. \tag{1}\]  
where \(p_{ij}\) is the coordinates between the calibration reference points \(j\) and its corresponding projected point on image \(i\). While \(R_{i}\) and \(t_{i}\) represent the rotation and translation vectors of image \(i\), respectively. Here, \(P_{i}\) denotes the three-dimensional coordinate of the reference point \(i\) in the world coordinate system, whereas \(\hat{p}(A,R_{i},t_{i},P_{i})\) represents its two-dimensional coordinates in the image coordinate system. The rotation matrix and translation matrix between binocular camera lenses can be expressed as follows:  
\[R=R_{r}(R_{l})T,\ T=T_{r}-RT_{l} \tag{2}\]  
Figure 1: The system for binocular instance segmentation and simultaneous localization.  
where \(R\) represents the rotation matrix and \(T\) denotes the translation matrix.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Multi-Agent Reinforcement Learning for UAV Trajectory  
In [70], a collection of drones must work together to manage blocks of land users who make uplink requests in a dynamic and unpredictable manner in this paradigm. The UAVs must move in a coordinated and cooperative manner in order to provide the maximum coverage demanded by consumers. The purpose of this trajectory's design is to maximize the number of people served by each individual drone, which is expressed as an optimization problem (successful service rate). However, the volatility of user requests makes the optimization goal for drones random. Due to this, the traditional optimization strategies, such as branch and bound, are ineffective for this task. As a result, VD-MARL, a new MARL that combines the concepts of the value decomposition network, the agnostic meta-learning model, and the gradient method to optimize the trajectories of all UAVs, has been developed. This technique allows each UAV to assess the expected values of all the other UAVs' successful service rates in order to choose the best possible route. By using the VD-MARL algorithm, each UAV is required to share its rewards with other UAVs, considerably lowering the amount of data exchanged.
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
###### Abstract  
Induction motors are mainstay power components in industrial equipment. Fault diagnosis technology of induction motors can detect the incipient fault and avoid the unplanned shutdown. The broken rotor bar is a significant fault mode of induction motors. Classical fault diagnosis methods always have complex principles and high computational costs, which leads to difficulties in understanding and calculation. In this paper, a method of broken rotor bar diagnosis based on the sum-of-squares of current signals is proposed. This method can eliminate the fundamental frequency and extract the signature frequency components by calculating the sum-of-squares of three-phase current signals. The signature frequency components are more apparent in the spectrum of the sum-of-squares of current signals. The effectiveness of the proposed method under different load levels and rotation motor speeds has been validated by two experiments. Compared with the classical diagnostic methods, the proposed method has better effectiveness and lower computation cost.  
Keywords:induction motors; broken rotor bar; sum-of-squares; diagnosis; motor current signature analysis +
Footnote †: journal: Applied Sciences
# A Feasibility Study of the Drive-By Method for Damage Detection in Railway Bridges
###### Abstract  
Damage identification and localization in railway bridges is a widely studied topic. Strain, displacement, or acceleration sensors installed on the bridge structure are normally used to detect changes in the global behavior of the structure, whereas approaches like ultra-sonic testing, acoustic emission, and magnetic inspection are used to check a small portion of structure near localized damage. The aim of this paper is to explore another perspective for monitoring the structural status of railway bridges, i.e., to detect structural damage from the dynamic response of the train transiting the bridge. This approach can successfully be implemented in the case of resonant bridges, thanks to the high level of acceleration generated, but its application becomes more challenging when the excitation frequencies due to train passage do not excite the first mode of vibration of the bridge. The paper investigates the feasibility of the method in the latter case, through numerical simulations of the complete train-track-bridge system. Accelerations on axleboxes and bogies are processed through suitable algorithms to detect differences arising when the train crosses a defective bridge or a healthy one. The results outline the main operational parameters affecting the method, the best placement for sensors, and the best frequency range to be considered in the signal processing, also addressing the issues that are related to track irregularity. Good performance can be achieved in the case of short bridges, but a few practical issues must be tackled before the method could be tested in practice.  
bridge diagnostics; train-track-bridge modeling; damage detection method; drive-by method
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 3 Proposed Machine Learning Models  
To predict the self-healing capability of ECC, four individual ML models including LR, SVR, BPNN, and CART and three ensemble methods including bagging, AdaBoost, and stacking were proposed. Ensemble models were constructed using individual models as the base estimators. To establish a baseline for comparison, the modeling parameters were set to be the same in both individual models and ensemble models. The main reason for choosing these techniques was due to their popularity, and some of them are even recognized as the top data mining algorithms in related fields of concrete [34]. The proposed individual and ensemble techniques are described in the following subsections.
## 1 Introduction
[20] successfully predicted the freshness of salmon by integrating improved gas sensors with support vector machines. The results of gas chromatography-mass spectrometry (GC-MS) provide detailed information regarding flavor, including the names and proportions of volatile compounds. though it is more time-consuming and requires skilled operation. In contrast, the electronic nose (E-nose) offers an overall assessment of aroma by utilizing a sensor array and pattern recognition algorithms. This allows for rapid and non-destructive aroma analysis, making it suitable for assessing food freshness. Consequently, the analysis of volatile compounds using GC-MS in conjunction with E-nose has been extensively documented [21; 22; 23; 24]. While the E-nose and GC-MS have been used to assess the freshness of various aquatic products, their combined application to oysters remains unexplored. Given the unique composition and storage characteristics of oysters, applying these techniques presents specific challenges, such as accurately detecting subtle changes in volatile compounds during storage. The present study systematically analyzed the changes in volatile compounds in oysters during various stages of storage. Qualitative and quantitative analyses of relevant volatile compounds throughout different storage periods were conducted using solid-phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Data on gas changes related to the deterioration of oyster freshness at temperatures of 4 \({}^{\circ}\)C, 12 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 28 \({}^{\circ}\)C were collected using a self-designed electronic nose, leading to the development of a predictive model for oyster freshness. In the present study, the changes in volatile compounds in oysters during storage were systematically analyzed. The correlation between the content of amines, alcohols, sulfur compounds, and aldehydes representative markers associated with the freshness of aquatic products and the intensity of the electronic nose signals was examined. This research aims to provide insights into the integration of electronic nose technology with GC-MS for predicting oyster freshness during storage.