esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Field Application  
In order to illustrate the application effect of the IPSO-FNN model, we and developed built an online system based on the proposed approach and applied it in the field of actual, fully-mechanized coal face, as shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{3}{c}{**Training Phase**} & \multicolumn{3}{c}{**Testing Phase**} \\ \cline{2-6}  & **MSE** & **MAPE (\%)** & **TT (s)** & **MSE** & **MAPE (\%)** \\ \hline PSO & 0.0056 & 2.24 & 8.67 & 0.0081 & 2.51 \\ QPSO & 0.0051 & 1.91 & 9.11 & 0.0076 & 2.09 \\ OLB-QPSO & 0.0039 & 1.81 & 10.24 & 0.0069 & 1.98 \\ LPSO & 0.0042 & 1.83 & 10.67 & 0.0071 & 2.01 \\ Proposed IPSO & 0.0047 & 1.86 & 8.96 & 0.0074 & 2.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance indices of five algorithms for optimizing the FNN model.  
Figure 9: Field application example based on the proposed method.  
In this field application, the four variables of the shearer were collected and transmitted into the explosion-proof computers via wireless Ethernet in the coal mining face. The procedure of the proposed algorithm was loaded in the explosion-proof computers to process the collected signals and estimate the health condition of the shearer. Synchronously, the results were displayed on the ground monitoring center through the communication station. When the shearer was cutting the coal seam from 50 m to 65 m, the corresponding estimation results were plotted, as shown in Figure 10.  
As Figure 10 shown, the coal seam from 55 to 65 m could be divided into three categories, among which the coal seam of \"Coal 1\" was harder than \"Coal 2\", and \"Coal 3\"contained some gangues. When the shearer was working in the first coal seam, the evaluation results of the shearer health condition were mostly between health grades 2 and 3. For coal seam \"Coal 2\", the evaluation results of the shearer health condition could decrease between health grade 1 and 2. For the coal seam with some gangues, the shearer health condition became more undesirable and the evaluation results were around health grade 4.  
Based on above analysis, the evaluation results of shearer health condition were consistent with actual coal seam conditions and the proposed system was proved feasible in the evaluation of shearer health condition.
# An Intelligent Warning Method for Diagnosing Underwater Structural Damage
## 3 Experiment and Result Analysis
### Establishing a Standard Sample Label  
For the purpose of obtaining the standard performance parameters of the SOM diagnostic model, the standard sample label was established using the DFS (digital feature screening) method under the optimal generative criteria (\(d_{5}\)-\(\theta\)-\(\delta\)-\(\delta\)-\(\delta\), among them, \(\theta\) takes \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\)). The DFS method establishes screening criteria for screening parameters by observing the trend of the characteristic parameters of different diagnostic types. The screening criteria are determined according to the size of the intersection interval of different types of damages: The smaller the crossover interval, the better the screening results, and the parameters are suitable for establishing a diagnostic model; otherwise, the parameter is not suitable for diagnosing underwater structural damage. Figures 9-11 shows the screening results.  
Figure 10: Parameters with ordinary screening results.  
Figure 9: Parameters with preferable screening results.  
In Figure 9, the characteristic parameters occupy their respective intervals with preferable screening results. In Figure 10, the two types of damage have a small interaction interval, the screening results are ordinary. In Figure 11, each of the three characteristic parameters is interlaced with each other and each has no independent interval.  
The angular second moment, P1, entropy, P2, variance of grayscale, P3, correlation, P4, inverse matrix, P5, variance, P6, significant clustering, P7, and sums of mean, P8, were screened. Therefore, the DFS method could effectively solve these problems of characteristic parameter selection. The standard values of each parameter are given in Table 2.  
Table 2 gives the standard value of the characteristic parameters corresponding to each kind of damage, which were obtained according to the average value of 200 images.
## 3 Materials and Methods
### Combining the Space Syntax Model with Volunteered Geographic Information (VGI) Data  
To understand the interplay between the spatial configuration of the campus and people's mapped perceptions, we combined and categorised the space syntax results with the VGI perceptual data using geographical information systems (GIS). Two values were attributed to the segments map, representing the campus's spatial structure: (1) the NACH R800m values, representing the potential for  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Question** & **Definition** \\ \hline Indicate on the map the public space(s) that provide(s) possibilities of **spontaneous encounter** & Spontaneous encounters are unexpected findings of valuable ideas, people and things influenced by the built environment [5]. \\ \hline Indicate on the map the public space(s) that provide(s) possibilities of **socialisation** & Tacit knowledge is shared, transferred and accumulated through socialisation between creative individuals [5; 10; 97] \\ \hline Indicate on the map the public space(s) that provide(s) possibilities of **meeting people from outside the campus (MPFOC)** & Campuses should be activity hubs that serve not only students and staff but the wider population of a town and region [98]. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Map-based questionnaire questions and definitions.  
pedestrian movement and co-presence; and (2) the average result of the VGI perceptual data, indicating the frequency of _spontaneous encounters, socialization, and possibilities for meeting people from outside the campus (MPFOC)_. The attribution of VGI values to the segments was calculated using'spatial join', in accordance with the average frequency of the polygon responses that intersected the segments. In other words, every street segment has a space syntax value (NACH) and a VGI value. This procedure enabled us to locate and visualise public spaces with a high or low 'potential for creativity'. The public spaces that we describe as having a high 'potential for creativity' are those shown to have high VGI values and concurrent high NACH R800m values.  
The NACH R800m results, representing a 10-min walk, were combined with VGI, since the observations confirmed that people tend to walk up to 10 min between key destinations on campus, such as classrooms, offices, cafeterias, the library, the supermarket, and the Sports Centre. Furthermore, a scatterplot analysis was conducted in accordance with the following categorisation values: (1) high VGI and high NACH R800m, (2) high VGI and low NACH R800m, (3) low VGI and high NACH R800m, and (4) low VGI and low NACH R800m. This method represents geo-referenced 'patches' of human interactions, rather than networks of communication between people. The categorisation and scatterplot analyses formed the basis for the non-participatory observation analysis.
# Regularized Instance Embedding for Deep Multi-Instance Learning
## 2 Related Work
### Deep MIL Methods  
Before the rise of deep learning, [PERSON] and [PERSON] [41] first proposed the idea of using a traditional neural network to solve multiple-instance learning and called it a multiple-instance neural network. This network learns and predicts instance labels, and obtains bag labels by aggregating the predicted instance labels. Their ideas are undoubtedly forward-looking, but, at that time, deep learning and neural networks had not yet had a breakthrough, and this research direction has not attracted too much attention; nonetheless, it is still a pioneering work.  
In recent years, deep learning has made a breakthrough in the field of artificial intelligence, and has been widely used in many fields, such as speech recognition and computer vision. Many classical neural-network architectures, such as AlexNet [42], VGG [43], GoogleNet [44], and ResNet [45], have become common means for people to solve machine-learning problems. Therefore, for multiple-instance learning, many good studies based on neural networks have emerged in recent years and instance-embedding learning can be done on a neural network due to its good capacity. Recently, [PERSON] et al. [5] re-examined multiple-instance neural networks and achieved the most advanced bag-classification performance. [PERSON] et al. [6] proposed a 3D subconcept layer combined with a deep network to perform multi-instance multi-label classification in various domains. In these works, instance probability was regarded as a latent variable; then, instance probabilities were aggregated in bag probability through a pooling function such as max-pooling. Since the bag-level ground truth was given, back-propagation could be used to adjust instance probability. Because the pooling process omits some instance-level information, it is not necessary for all instances to have the correct labels. As pointed out in [7], in positive bags, some false-negative and false-positive instances hardly affect bag classification. However, learning instance embedding depends on instance probabilities, and wrong instance probabilities are likely to reduce bag-classification quality.  
We used the relationship between instances so that those from the same class would have higher similarity, while those from different classes smaller similarity, which is similar to the goal of metric learning. Since instance probability is a hidden node in the neural network, we could use it to determine the instance category. In practice, given a set of instances in a bag or training batch, the regularizer we designed considers that instances with similar labels should have similar embeddings; otherwise, their embeddings should be different. In other words, the regularizer maximizes the correlation between the embedded similarity matrix and the instance-label similarity matrix.
## 1 Introduction  
Due to fast economic development affected by industrialization and globalization, energy consumption has been steadily increasing [1]. The three main economic sectors that consume a significant amount of energy are industry, transportation and buildings, with buildings accounting for the largest proportion. For example, in the USA, primary energy consumption in buildings is around 40% of total energy consumption, with 35% of the primary energy and 45% of electricity being consumed by Heating, Ventilating and Air Conditioning (HVAC) systems [2].  
Model-Based Predictive Control (MBPC) is the technique of HVAC control [3; 4; 5; 6; 7] that offers the largest potential for energy savings. Despite the large number of publications on this topic, only a few report MBPC applications for existing buildings under normal occupancy conditions; one of the first was a previous work by the authors [8]. To the best of our knowledge, the first attempt towards a marketable MBPC HVAC solution was presented in our recent work [9].  
Despite the large economic advantages of MBPC for HVAC control, predictive control requires the use of additional sensors, namely for measuring variables related to the room's thermal comfort, and possibly external weather variables. Additionally, an Internet of Things (IoT) platform is required to implement all the necessary operations. The main objective of this paper is to present the design, implementation and testing of Self-Powered Wireless Sensors (SPWS) specially designed for home energy management applications, as well as the IoT platform developed, and their use in a real application of the Intelligent Model-Based Predictive Control scheme [9].  
This paper is organized as follows: in Section 2, an overview of the author's proposal for MBPC HVAC control, coined Intelligent MBPC HVAC (IMBPC HVAC) is presented. Section 3 discusses the design, prototyping and validation of the SPWSs, and their characterization in terms of energy consumption and autonomy. The IoT platform is introduced in Section 4. Experimental results are given in Section 5. A discussion section concludes the paper.
# Prediction of Ship-Unloading Time Using Neural Networks
###### Abstract
In response to this research gap, this study proposes a method that employs three neural network-based approaches: a Backpropagation Neural Network (BP), a Random Vector Functional Link Network (RVFL), and a Stochastic Configuration Network (SCN) model to predict unloading time at a steel plant raw materials terminal. There are several compelling reasons for selecting this prediction method:  
Neural networks have been widely applied to prediction and classification and exhibited a great deal of success. Figure 1: The ship-unloading system of a steel plant. BP as a mature neural network and the ensuing learning method has been widely applied [17; 18]. * RVFL introduces an efficient way to find out the optimal network parameters and represents a richer structure variety of neural networks [19]. * SCN exhibits a concise network structure and demonstrates a better application potential [20]. * The primary contributions of this paper are:
* 1. Three neural network models-Backpropagation (BP), Random Vector Functional Link (RVFL), and Stochastic Configuration (SCN) have been developed for predictive applications. * 2. Three prediction models are comprehensively evaluated based on their predictive accuracy, convergence, and stability, and are applied to a real-world scenario involving unloading-time prediction. The paper is organized as follows. Section 2 covers the relevant studies. Section 3 includes a description of the relevant methods. Section 4 elaborates on the experimental results produced with the proposed methods. Section 5 includes the conclusions and identifies some future directions.
###### Abstract  
Background and Objective: the aim of this study is to develop and validate an automated image segmentation-based frame selection and stitching framework to create enhanced composite images from otoscope videos. The proposed framework, called SelectStitch, is useful for classifying eardrum abnormalities using a single composite image instead of the entire raw otoscope video dataset. Methods: SelectStitch consists of a convolutional neural network (CNN) based semantic segmentation approach to detect the eardrum in each frame of the otoscope video, and a stitching engine to generate a high-quality composite image from the detected eardrum regions. In this study, we utilize two separate datasets: the first one has 36 otoscope videos that were used to train a semantic segmentation model, and the second one, containing 100 videos, which was used to test the proposed method. Cases from both adult and pediatric patients were used in this study. A configuration of 4-levels depth U-Net architecture was trained to automatically find eardrum regions in each otoscope video frame from the first dataset. After the segmentation, we automatically selected meaningful frames from otoscope videos by using a pre-defined threshold, i.e., it should contain at least an eardrum region of 20% of a frame size. We have generated 100 composite images from the test dataset. Three ear, nose, and throat (ENT) specialists (ENT-I, ENT-II, ENT-III) compared in two rounds the composite images produced by SelectStitch against the composite images that were generated by the base processes, i.e., stitching all the frames from the same video data, in terms of their diagnostic capabilities. Results: In the first round of the study, ENT-I, ENT-II, ENT-III graded improvement for 58, 57, and 71 composite images out of 100, respectively, for SelectStitch over the base composite, reflecting greater diagnostic capabilities. In the repeat assessment, these numbers were 56, 56, and 64, respectively. We observed that only 6%, 3%, and 3% of the cases received a lesser score than the base composite images, respectively, for ENT-I, ENT-II, and ENT-III in Round-1, and 4%, 0%, and 2% of the cases in Round-2. Conclusions: We conclude that the frame selection and stitching will increase the probability of detecting a lesion even if it appears in a few frames.  
**Keywords:** computer-assisted diagnosis; convolutional neural networks; eardrum abnormalities; image stitching; otoscope; semantic segmentation
## 6 Role of Free Carbon in Si-Based Anode Materials
### Enhancement of Structural Stability  
Free carbon plays a crucial role in polymer-derived ceramics (PDCs) anode materials, especially in improving structural stability and mitigating volume changes. [PERSON] believes that free carbon improves the cycling stability of materials by providing the main host sites for lithium-ions, such as the edges, interstitial spaces, and defect sites of single-layer graphite [69]. In addition, the presence of free carbon helps to mitigate the volume change of lithium-ions during charging and discharging. Since lithium-ions will cause the volume expansion and contraction of the material during the intercalation and expulsion process, the high conductivity and flexibility of free carbon can effectively buffer this volume change, reduce the mechanical stress of the electrode material, and thus improve thecycling stability of the battery. [PERSON] et al. [26] found that the presence of free carbon in 3D Si/C/SiOC nanocomposites significantly improves the electrochemical stability and cycle life of the materials.  
Furthermore, the disordered nature of free carbon makes it exhibit excellent electrical conductivity during electrochemical processes, which is essential for maintaining the structural stability of PDCs anode materials. [PERSON] et al. [69] pointed out that as the heat treatment temperature increases, the microstructure of free carbon tends to be ordered, which reduces the storage site of lithium-ions, but also enhances the structural stability of the material. At high temperatures, the presence of free carbon also contributes to the thermal stability of the material. [PERSON] et al. [36] showed that although excess free carbon may lead to a decrease in thermal stability, the structural stability of the material is significantly enhanced in the presence of the right amount of free carbon. This enhancement is mainly attributed to the stable structure formed by free carbon at high temperatures. [PERSON] et al. [27] found that the increase of free carbon can significantly improve the initial charge-discharge capacity and cycling stability of the materials by studying SiCN composites with different carbon content. They point out that free carbon not only provides more lithium-ion storage sites, but also significantly improves the electrochemical properties of the material through its excellent conductivity and structural stability.
# Prediction Model for the Performance of Different PV Modules Using Artificial Neural Networks
## 3 Proposed ANNs Model for Predicting the Performance of the Six PV Modules
Before the testing process, another \"for loop\" is used to create the testing data input. The inputs are the same as those used during the training process. The last two inputs are calculated by stepping down \(\mathrm{I_{sc}}\) and \(\mathrm{V_{oc}}\). As shown in Figure 5, the first GRNN is utilized as an input for the second GRNN, which predicts the relationship between the \(x\)-axis and \(y\)-axis in the IV curve. The second GRNN, as shown in Figure 5, predicts the current and extracts the IV and PV curves. Finally, after repeating the same process with the same test conditions for the six different PV modules, the output data from each test condition are used to calculate the efficiency and FF to compare how each PV module performs under the same test conditions in Malaysia. Figure 6 shows the flowchart of the proposed model process. Figure 5: The second proposed GRNN flowchart to predict the real current. Figure 6: Flowchart of the proposed model process. For this research, the accuracy of the predicted performance was evaluated by using three types of errors: first, the mean absolute deviation (MAD). This error is an indication to measure the dispersal of a specific set of data and is calculated using Equation (8):  
\[\mathrm{MAD}=\frac{1}{n}\sum_{i=1}^{r}\left|\mathrm{x_{P}}-\mathrm{m}(\mathrm{X })\right| \tag{8}\]  
where r is the number of the value, xi is the predicted value from the proposed model, and m(X) is the dataset's average. Secondly, the mean absolute percentage error (MAPE) was used, which can be given by Equation (9):  
\[\mathrm{MAPE}=\frac{\mathrm{Experimental~{}Value~{}-~{}Predicted~{}Value}}{ \mathrm{Experimental~{}Value}}\times 100\% \tag{9}\]  
This is usually used for calculating the accuracy of the new value (predicted value). Lastly, the root mean square error (RMSE) was used, which indicates the short-term performance and is calculated by using Equation (10):  
\[\mathrm{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{r}\left(\mathrm{x_{P}}-\mathrm{x_{ e}}\right)^{2}} \tag{10}\]  
where xe is the experimental value. MAPE was used to calculate the percentage of error of the predicting result to the experimental result. However, the results of the MAPE cannot always indicate the real error if the value of the prediction is too small. Therefore, types of errors such as RMSE and MAD are useful for this research.
## 3 Numerical Results and Discussion
### Critical Speeds Analysis  
The Campbell diagram of the rotor system is presented in Figure 2. Two critical speeds, 1588 rad/s and 3276 rad/s, can be identified from the Campbell diagram. The corresponding mode shapes for the critical speeds are shown in Figure 3. From Figure 3, it can be seen that the first mode, Figure 3a, is a cylindrical mode, while the second one, Figure 3b, is a conical mode. Furthermore, Figure 3 implies that the rear end of the rotor would have a greater amplitude of vibration than the front end when passing through the first critical speed, while the reverse is true when passing through the second one.