esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals
## 3 Results
### Q-IF Software Flow Description
#### 3.2.5 Statistical Computations  
Subsequently, a bar plot is automatically generated with the mean of ID/Area (\"Fluorescence intensity\") as the Y-component, also highlighting the SD. Furthermore, the Q-IF tool is able to automatically apply the statistical test and calculate the \(p\)-value, providing useful information to evaluate the statistical significance of the result obtained. A \(p\)-value is defined as the probability, under a given statistical model, of obtaining a result equal to or more extreme than what was observed, assuming that the null hypothesis is true. It is commonly used in hypothesis testing to determine the statistical significance of an experimental result [16].  
To compute the \(p\)-value, the Student's \(t\)-test is commonly employed to determine if there are significant differences between two sets of data. Differently, to compare more than two groups, [PERSON]'s post hoc is employed as described before, which is a statistical method used to determine if there are significant differences between the means of multiple groups in an experiment while controlling for the family-wise error rate.  
A low \(p\)-value (typically \(<0.05\)) suggests statistical significance, indicating a significant difference between the groups. Conversely, a high \(p\)-value suggests a lack of statistical significance, indicating that the observed disparity is not substantial [17]. In detail, after calculating the \(p\)-value using the \(t\)-test, the tool can automatically show the statistical significance through the conventional representation with asterisks, from 0 to 3 based on the numerical value obtained. Conversely, as depicted in Figure 5, given the undefined number of groups to be analysed with [PERSON]'s post hoc, the tool reports a table summarising the results, also providing several metrics related to the carried analysis, such as _adjusted p-value_ and mean; furthermore it shows the rejection of the _null hypothesis_ among every pair of the experimental group. A rejection of the _null hypothesis_ indicates a statistically significant difference or effect in the data, contrary to what the null hypothesis suggests.  
The graphs deriving from the automatic mathematical process of the Q-IF tool are shown in the following Section, where we propose the software validation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Image** & **ID/Area of the Control Set** & **Image** & **ID/Area of the H\({}_{2}\)O\({}_{2}\) Set** \\ \hline
1.tif & 1.1382 & 1.tif & 2.3117 \\ \hline
2.tif & 1.4573 & 2.tif & 3.0640 \\ \hline
3.tif & 1.2178 & 3.tif & 2.5454 \\ \hline
4.tif & 1.1388 & 4.tif & 2.8217 \\ \hline
5.tif & 0.7090 & 5.tif & 3.3326 \\ \hline
6.tif & 0.6627 & 6.tif & 2.7193 \\ \hline
7.tif & 0.9480 & 7.tif & 2.4994 \\ \hline
8.tif & 0.8511 & 8.tif & 2.9567 \\ \hline
9.tif & 1.1093 & 9.tif & 3.1022 \\ \hline
10.tif & 0.7677 & 10.tif & 2.9592 \\ \hline \end{tabular}
\end{table}
Table 2: Dataset obtained with ID/area ratios of control and H\({}_{2}\)O\({}_{2}\)-treated SH-SYSY neuronal cells.
# Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
## 2 AE Source Localization
### TDOA Method  
The source localized by the TDOA method is illustrated in Figure 1. In the TDOA algorithm, the equations of the source localization are obtained as  
\[R_{i}-R_{j}=\big{(}A_{i}-A_{j}\big{)}v_{p}, \tag{1}\]  
where \(R_{i}\) and \(R_{j}\) are the lengths of the ray paths from the localized source to sensors \(i\) and \(j\); \(A_{i}\) and \(A_{j}\) are the arrival times at \(i\)th and \(j\)th sensors; and \(v_{p}\) is a propagation velocity. Moreover, \(R_{i}\) is defined as  
\[R_{i}=\left\|r_{i}-r_{o}\right\|_{2}, \tag{2}\]  
where \(r_{i}\) and \(r_{o}\) are the position vectors of the \(i\)th sensor and the source \(o\). It is noted that the components of \(r_{o}\) are the variables. In Equation (1), \(v_{p}\) is assumed to be homogeneous in the model because the ray path is defined as the straight line. Therefore, the TDOA method requires classifying the straight lines from measured waves to consider the diffraction and the refraction waves if the sources are localized in the heterogeneity.  
The numbers of Equation (1) are determined by subtracting one from the number of sensors. Although Equation (1) requires two equations in the two dimensions, the several sensors should be applied to the heterogeneous velocity distributions because used sensors are selected by classified waves. Thus, it implies that the number of equations is larger in comparison with the number of the variables. In order to compute the large number of equations, the least squares method is applied. However, the particular locations of the sensors used in Equation (1) have potential to obtain a determinant of 0 if the used sensors are only located in an axis. Therefore, it is possible that the TDOA method does not localize the source in the application of the classification.  
Figure 1: The illustration of the TDOA method.
# Research on an Improved SOM Model for Damage Identification of Concrete Structures
## 4 Experiments and Results Analysis
### Parameter Setting of RPC Bending Fatigue Damage SOM Network Model  
Figure 7 shows the structure of the network model for the flexural fatigue damage identification of RPC.  
As shown in Figure 7, the selected six feature parameters are used as the SOM network input vector \([P_{1},P_{2},P_{3},P_{4},P_{5},P_{6}]\), the SOM network competition layer is set to \(8\times 8=64\) neurons and the network model output is four categories of damage.  
In the parameter setting of the SOM network model for concrete structure damage, the number of training steps directly affects the network clustering performance. In order to improve the clustering efficiency, the optimal number of training steps is obtained. After determining the structure of the network model, select different steps for training and observe the performance changes of the network model. Using the step increment as a variable, analyze the clustering results of the network model. The statistics are shown in Table 2 for the clustering results under different training steps. Selecting the training steps with the fewest steps can not only satisfy the sample classification, but also ensure the clustering speed.  
\begin{table}
\begin{tabular}{c c c} \hline
**Input Sample** & **Sample Name** & **Characterized Properties** \\ \hline \(P_{1}\) & ASM & Uniformity \\ \(P_{2}\) & ENT & Complexity \\ \(P_{3}\) & INM & Stability \\ \(P_{4}\) & COR & Correlation \\ \(P_{5}\) & IDM & Volatility \\ \(P_{6}\) & VAR & Circularity \\ \hline \end{tabular}
\end{table}
Table 1: Damage texture properties represented by the input sample.  
Figure 7: The structure of the network model.  
When the number of training steps is set to 10, 50, 100, 200, 500 and 1000, the classification effect of the network model is shown in Table 1. When the number of training steps is 10, the damage diagnosis model is initially established, and the damage is divided into two categories; as the number of training steps increases, when the number of training steps is 50 and 100, the recognition accuracy is further improved, and the injuries are divided into three categories; when the number of training steps reaches 200, the four injury types are completely distinguished; continue to increase the number of training steps to 500 and 1000, and the damage classification results are the same, which is not practical. Therefore, 200 training steps were chosen as the optimal value for the damage identification model.
## 2 Literature Review
### Financial Volatility Prediction Using Machine Learning in Finance  
#### 2.2.1 Machine Learning for Commodity Market Volatility Prediction  
The impact of COVID-19-related news on crude oil futures volatility has been investigated using a model combining Genetic Algorithm (GA), Regularization with Forgetting Online Sequential (RFOS), and Extreme Learning Machine (ELM) to address the nonlinearity and nonstationarity of crude oil prices [23]. The results indicate that COVID-19 news plays a crucial role in predicting short-term volatility in crude oil futures, outperforming traditional forecasting methods. These findings provide valuable insights for investors and policymakers in understanding and responding to market volatility driven by pandemic-related information. Additionally, this model can be applied to other commodity markets.  
The relationship between financial markets and oil futures volatility has been analyzed using traditional, machine learning, and combination models [24]. The results indicate that machine learning models, especially ensemble and neural network models, outperform others in forecasting oil futures volatility. Combination models consistently show the best performance across various prediction and evaluation methods. Additionally, the interpretability of prediction models and the contribution of each indicator have been discussed. This study provides new insights into the application of machine learning for predict ing futures market volatility, offering valuable information for academics, policymakers, and investors.  
A deep learning CNN model, Group Neural Network (GroupNet), has been proposed to forecast oil price volatility using gold, silver, platinum, palladium, and rhodium prices [25]. Leveraging group blocks and the GroupNet architecture, it outperforms seven machine learning and three traditional models. The Waikato Environment for Knowledge Analysis (WEKA) tool's correlation-based feature selection reduces input dimensions and bias. The CNN model excels in MAE, MAPE, and RMSE. Future work aims to enhance learning, reduce overfitting, and apply transfer learning and optimization techniques.  
Various machine learning models have been applied to forecast crude oil and natural gas volatility from 2003 to 2023, with performance varying across forecast periods [26]. Random Forest Regression and Extreme Gradient Boosting (XGBoost) consistently excel in daily, weekly, and monthly forecasts, while Huber Regression and ANN show period-specific strengths but lack consistency. SVM and LSTM underperform in long-term forecasts. This study offers insights for policymakers and investors, highlighting the need for a transition to renewable energy to enhance economic resilience and stability.
# Effectiveness of Augmented Reality in Stroke Rehabilitation: A Meta-Analysis
## 3 Results and Discussion
### AR Technique Overview
#### 3.1.1 AR Technique for Upper Limb Rehabilitation
Although the mirror therapy systems-based AR technology utilizes image processing instead of the real mirror, these systems also include all the functions of the conventional method. For example, [PERSON] et al. proposed a system that includes all of the benefits of traditional mirror treatment with the addition of AR technology [30,31]. The component of the AR-mirror therapy system consists of a screen, a webcam, and a PC. The patients do exercises with their unaffected hand, which is recorded using a camera and mirrored on a screen by image processing. This AR system not only provides the same user perception as traditional mirror therapy but also allows a wider range of computer-mediated visual illusions and exercise possibilities than conventional mirror therapy. Another type of AR mirror therapy is proposed by [PERSON] et al. [32]. Their system is called the NeuroR system, which provides motor imagery for patients. Two visual tracking methods apply to this system: (1) glove and physical markers are used to identify the patients' injured arm; (2) the system detects the injured arm using markerless tracking. During the training of the flexion-extension exercises, the system showed the movement of a virtual 3D arm instead of the movement of the paralyzed arm for the patient. Users face the projection screen to observe the movement of the virtual arm while users perform impaired arm movements by electromyographic signals (EMG)-based human-computer interface triggering. The final type of AR system provides the exercise related to activities of daily living such as finger flexion-extension, wrist flexion-extension, and elbow flexion-extension based on the tabletop system. [PERSON] et al. proposed this system by projecting a virtual environment onto the tabletop which then reacts according to users' movement using the markless tracking technology (a Kinect depth sensor) [33]. This system provides hands-on multi-touch interaction of the tangible objects for the patients. Furthermore, this system supports the audio-visual feedback of the user performance.
## 4 Experimental Simulation Analysis
### Comparison of Bit Error Rates of Different Systems  
As shown in Figure 9, for the SR-DCSK system and SR-ODBR-DCSK system BER performance comparison figure can be seen in Figure 9a,b. Under the condition of equal control half spread spectrum factor, the BER performance of the SR-ODBR-DCSK system is better than that of the SR-DCSK system. Figure 10 is the error performance comparison chart of the DCSK system and SR-ODBR-DCSK system. As is shown in the figure, when the semi-spread spectrum factor is equal to 100 and 256, the error performance of the SR-ODBR-DCSK system is better than that of the DCSK system.  
Figure 8: The BER perform of different half-amplification factor. (**a**) Sine mapping; (**b**) tent mapping; (**c**) circle mapping; (**d**) improved logistic mapping.  
The above simulation is only aimed at comparing the error performance of the two systems. The error performance comparison of the four systems is analyzed below. Figure 11 shows the comparison of the error rates among the DCSK system, SR-DCSK system, ODBR-DCSK system, and SR-ODBR-DCSK system with the Gaussian white noise channel. It can be seen from the diagram that when the semi-spread spectrum factor is equal to 100, the error rate of the ODBR-DCSK system is the highest, and the error performance of the DCSK system and SR-DCSK system is similar. The error performance of the SR-ODBR-DCSK system is better than other systems. When the semi-spread spectrum factor is equal to 200, the error performance of other systems is worse than that of the SR-ODBR-DCSK system. When the signal-to-noise ratio is greater than 13 dB, the BER performance of the DCSK system is better than that of the SR-ODBR-DCSK system.  
Figure 10: Comparison of BER of the DCSK system and the SR-ODBR-DCSK system. (a) \(\beta=100\); (b) \(\beta=256\).  
Figure 9: Comparison of BER of the SR-DCSK system and the SR-ODBR-DCSK system. (a) \(\beta=100\) ; (b) \(\beta=256\).  
Figure 12 shows the relationship between the number of semi-spread spectrum factors and the bit error rate of the SR-ODBR-DCSK system and DCSK system when the signal-to-noise ratio is 10 dB in the Gaussian white noise channel using the circle mapping with the initial value of 0.6. Overall, with the increase in semi-spread spectrum factors, the bit error rate performance of the two systems first improves and then deteriorates. When \(\beta~{}<\) 40, the BER performance of the DCSK system is better than that of the SR-ODBR-DCSK system, but when \(\beta=40\), the performance of the two systems is almost the same. When there is an intersection of about \(\beta=40\) in the diagram, it is obvious that the error performance of the DCSK system is better. The SR-ODBR-DCSK system has better BER performance when it is greater than this intersection point.  
Figure 11: Comparison of the BER of four different systems. (**a**) \(\beta=100\); (**b**) \(\beta=200\).  
Figure 12: Relationship between semi-spread spectrum factor and bit error rate.
# Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives
## 2 Topologies of Electric Drives
### Multiphase Electric Drive Topologies
#### 2.3.1 Classification of Multiphase Drives  
Regardless of the machine type (permanent magnet synchronous machine--PMSM, induction machine--IM, switched reluctance machine--SRM) and the stator winding (distributed/concentrated), the \(n\)-phase machines can be classified into two groups: (a) machines with a prime number of phases (e.g., 5, 7, 11,...) having \(360^{\circ}/n\) spatial displacement between successive phases and (b) machines with a composite number of phases (4, 6, 8, 9, 10, 12,...). In theory, two winding layouts are possible, depending on the displacement between the magnetic axes of two adjacent stator phases: symmetrical with a spatial displacement \(360^{\circ}/n\) and asymmetrical with a spatial displacement of \(180^{\circ}/n\) (Figure 8). However, the availability of this option strongly depends on the specific machine design, as not every slot and pole combination allows this degree of freedom.  
An extra number of phases in a multiphase machine expands options for interconnecting the windings of individual phases; however, star-connected windings are most common (Figure 9). Obvious solution is to connect winding ends to a common neutral point. In addition, the machines with a composite number of phases can also form \(k\) sets of windings, each with \(m\) phases and \(k\) neutral points. Regardless of the number of phases, any \(n\)-phase drive can also be realized with \(n\) neutral points. In this case, the phase windings are strictly speaking not star-connected anymore, and every phase is now driven by their own converter, usually full-bridge with common or isolated DC-links. Since both phase ends are available, this configuration is better known as an open-end winding drive (more in the following).  
Figure 8: Classification of multiphase drives according to number of phases: (**a**) prime number (5,7,11,13,...) of phases with electrical spatial displacement \(360^{\circ}/n\) and \(n\) magnetic axes, (**b**) composite number of phases with electrical spatial displacement \(360^{\circ}/n\) and \(n/2\) magnetic axes (symmetrical layout), and (**c**) composite number of phases with electrical spatial displacement \(180^{\circ}/n\) and \(n\) magnetic axes (asymmetrical layout).
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 5 HD Maps
### Patterns for Updating Data of HD Maps  
The overall flow of information from the vehicle to the complete updating of HD maps depends on the determined strategy of processing the obtained data. The expected process begins with obtaining source data from the vehicle's sensors, followed by data processing and analysis. In the last phase, HD maps are updated and information is sent back to the Vehicle (Figure 8) [28].  
Figure 8: The flow of information from the vehicle to the complete updating of HD maps based on [28].  
Figure 7: HD maps layers based on [28].  
There are several types of ways to implement the HD Map Update Process. The most basic are:  
* Pattern A--we have three functional blocks in the vehicle, as in Figure 9. The collected data from the sensors are processed and the HD map is updated directly in the vehicle. The change information is sent (Uplink) to the MSP Edge/Central Server where the central map is updated for other vehicles nearby.
* Pattern B--there are two functional blocks in the vehicle. The process is conducted by sent data from sensors (Uplink) to the MSP Edge/Central Server where the central map is then processed and updated. The updated map is resent to the vehicle (Downlink) [28].  
Based on Figure 9, Pattern A obtains data from its surroundings and sends it to a module in which the collected data are analyzed and processed. Before performing an in-vehicle HD map update, the vehicle needs to receive confirmation information from a central server. This consists of information that an incident on the road infrastructure was recorded by several vehicles. After receiving the information, the HD map is updated directly in the vehicle. The advantage of this pattern is that bidirectional data transmission is not required. After updating the HD map in the vehicle, the changes made will be sent via the Internet to the MSP Edge/Central Server, where the HD maps stored on the servers will be updated. Even according to [28], network traffic volume is not large, because the single flow of information is directed from the vehicle to the central server.  
On the contrary, Pattern B operates in a two-way data stream and has comparably higher data transmission requirements [28]. In Pattern B, the vehicle contains only two function blocks. The first one is used to obtain data from the environment and the second one is used to update the HD map. When the vehicle detects an event that is not in the HD map, it sends the acquired data via the Internet to the MSP Edge/Central Server. An analysis and update of the HD map is performed in the central server. The updated maps are sent back to the vehicle via the Internet, where the HD map is updated.  
Figure 9: Patterns A and B of updating data of HD maps based on [28].
# An Efficient and Robust Topology Optimization Method for Thermoelastically Damped Microresonators
## 1 Introduction
Furthermore, the iterative optimization of thermoelastics requires an eigenvalue analysis in each iteration; as a result, the consumption of computational time and resources could be unbearable. Although an efficient design method of thermoelastic structures by topology optimization is proposed in our previous research [31], it could not resolve thermoelastic problems in the framework of the phase-lag heat conduction law and may also suffer from an iterative procedure oscillation. Thus, an effective and robust design method is urgently needed for fully-coupled thermoelastic microstructures under non-Fourier phase-lag heat conduction law. To address the need for an efficient design method for thermoelastic microstructures with phase-lag heat conduction law, and considering that existing design methods are not qualified to solve large-scale problems with complex geometry, this paper proposes an efficient and robust topology optimization method for minimizing the thermoelastic damping of large-scale microresonators with phase-lag heat conduction law. This article is organized as follows. First, a state-space method is developed for large-scale microresonators with phase-lag heat conduction law in the framework of commonly-used numerical techniques, and the corresponding optimization method is defined to enlarge the quality factor of the large-scale microresonators. Then, to efficiently reduce the computational cost of the iterative process of optimizing process, a model reduction method is developed based on the projection-based model reduction method whose reduced basis is generated based on the Neumann series expansion technique. However, the model reduction method may be unstable when topology modifications are made during an iteration optimization process. Thus, to ensure robustness, a modal validation technique is first implemented in the iterative process to stabilize the iteration and narrow down the search domain, and a posterior evaluation of the Neumann series expansion is then developed to retain the convergence of the parameterized model reduction method. Finally, several numerical results are discussed to validate the efficiency and accuracy of the proposed method.
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 2 Precision Irrigation Control System
### Process Identification and Model Validation
region, and, at the end of week 3, the lack of irrigation shifted the values to the unavailable region. The model output was not fed with measured data to avoid any correction during the four weeks. The correlation results (\(R^{2}=0.8616\)) showed that the identified model adequately captured the irrigation dynamics for the pecan crop.
## 10 Discussion and Significance
### Deep Learning (DL)  
Large amounts of data are processed by DL to evaluate the behavior of IoT devices in WSNs and provide accurate predictions about channel quality, congestion, and other factors. As a result, DL algorithms improve the accuracy of congestion prediction and help make congestion mitigation measures more effective.