esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 3 The Gated High-Low Resolution Frames Network
### Pre-Initial Hidden State Network  
The one-way recurrent network approach allows hidden state information to be retained for the next input. As the hidden state information is passed through the network and updated to provide better long-term temporal dependence, the better the super-resolution reconstruction effect becomes. However, this reconstruction effect is unbalanced in that it accumulates in one direction, i.e., the one-way cyclic approach is a one-way accumulation process. To reduce the effect of this imbalance, we propose to give an initial hidden state information to have better hidden state information at the beginning, and the initially hidden state information can help the network to converge faster and reduce the training time during the cyclical process. In addition, the initial hidden state information can improve the super-resolution reconstruction effect.  
As shown in Figure 3, ISHNet is a neural network consisting of a convolutional layer and an attentional U-shaped module for taking as input the first reference frame in a video sequence with its neighboring frames and transforming it into an initial hidden state H0:  
\[\text{H0}=\text{ISHNet}\ (\text{I}_{\text{t-N}}^{\text{L}},\dots,\text{I}_{ \text{t}}^{\text{L}},\dots,\text{I}_{\text{t+N}}^{\text{L}},\Theta_{\text{ISH }}) \tag{1}\]  
where \(\text{I}_{\text{t-N}}^{\text{L}},\ \dots,\ \text{I}_{\text{t}}^{\text{L}},\ \dots,\ \text{I}_{\text{t+N}}^{\text{L}}\) in \(\text{I}_{\text{t}}^{\text{L}}\) denotes a low-resolution reference frame and the rest are adjacent frames; \(\Theta_{\text{ISH}}\) denotes the set of parameters.  
Specifically, as follows, the first reference frame \(\text{I}_{\text{t}}^{\text{L}}\) and its adjacent frames are input to a weight-sharing convolutional layer, and feature channel stitching is performed. Then the spliced features are input to the U-shaped module of channel attention, and, after generating features through the residual layer, the convolution layer operation and the  
Figure 3: Schematic diagram of the initial hidden state network (IHSNet). CONV3 denotes a convolutional layer consisting of a \(3\times 3\) convolutional kernel and a Relu activation function. RCAB denotes a residual channel attention block. RB denotes a residual cell consisting of 5 layers. CONV is the convolution of a \(3\times 3\) convolutional kernel. Concat denotes channel number stitching fusion. H0 denotes initial hidden state information.  
convolution layer operation containing the activation function are performed to generate o0 and h0, respectively. Finally, the channel number splicing is performed between o0 and h0 to obtain the final initial state hiding information H0.  
In summary, ISHNet generates initial hidden state information by feeding the first reference frame and adjacent frames into the network, using convolutional layers and U-modules, which help to provide a better initial state for subsequent super-resolution reconstruction and reduce the imbalance effect of one-way cyclical frames.
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Ship Arrivals and Departures
#### 4.4.6 Dynamics  
_UC \"ship arrival\"_ is executed as a series of steps described below. Figure 12 shows the sequence diagram for the arrival of the ship at the port.  
_UC: Ship Arrival_  
Summary: a ship arrives at a port.  
Actors: ship master, harbor master, marine pilot, and tugboat pilot.  
Preconditions: ships' times of arrival are known. Assignments of berths to arriving ships, of pilots to ships, and/or of tugboats must be predefined.  
Description:  
1. Ship master contacts harbor master and requests access to the port.
2. Harbor master checks the berthing schedule.
3. Harbor master grants access for ships to berth.
4. Ship master requests a marine pilot to bring the ship to port.
5. Harbor master grants request to send marine pilot.
6. Ship master requests a tugboat for maneuvering vessel in port.
7. Harbor master grants request to send tugboat pilot and tugboat to assist ship.  
Figure 11: Class diagram of ship arrival and departure operations.
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 3 Data Preparation and Data Preprocessing
### Data Preprocessing  
In taxi trajectory big data, there may be a certain amount of abnormal data due to the instability of the equipment. To complete the data mining work more accurately, this study preprocesses the raw data before starting the research. The trajectory data preprocessing can be divided into five core stages: data quality inspection, outlier handling, abnormal and missing value handling, abnormal GPS trajectory processing, and extraction of passenger pick-up and drop-off points [28,29].  
Data quality inspection is a crucial step in preprocessing and is an important foundation for ensuring the correctness of the analysis conclusions. The main task of data quality analysis is to check for the presence of \"dirty data\". Common dirty data in taxi trajectory datasets include missing values, outliers, inconsistent formats, duplicate data, and unexpected abnormal trajectory points outside the study area.
# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 4 Conclusions  
An improved CDBN-based fault diagnosis model was proposed for the effective extraction and learning of quantitative and qualitative features of different bearing fault types and sizes. The proposed model was able to extract deep features of the dataset, effectively implementing the diagnosis of multiple degrees and types of bearing faults. First, a band-pass filter was used to preprocess the original signal to obtain optimized features. Simultaneously, the Adam optimizer was introduced to speed up training and improve the model convergence speed. Finally, in combination with the softmax two-layer connection, multi-layer feature fusion was used to fully enable the feature representation capabilities of each layer of the model. Compared with the standard CDBN, the improved CDBN model displayed higher accuracy. The improved model also ensured the characterization of the learned features and ultimately enabled qualitative and quantitative diagnosis of bearing faults. The experimental results show that the proposed method had reduced training error, a smoother decline, and a higher diagnostic accuracy than SAE, ANN, and DBN. The proposed model had a positive impact on the classification of single and compound fault types of the datasets. Future research will focus on dealing with unbalanced data or small data, since these cases are more meaningful for practical applications. In future investigations, comparative experiments of different optimizers, other networks (such as recurrent neural network and generative adversarial networks), and attention-based mechanisms will be conducted.  
Figure 14: Comparison of second-layer reconstruction error when training the CDBN with different optimizers: (**a**) Adam, (**b**) Gradient Descent, (**c**) Nesterov, (**d**) RMSprop.  
**Author Contributions:** Conceptualization, S.L. and J.X.; Methodology, S.J., C.S. and X.S.; Validation, S.L., J.X. and D.W.; Formal Analysis, S.L. and C.S.; Data Curation, X.S., D.W. and Z.Z.; Writing - Original Draft Preparation, S.L., C.S. and D.W.; Writing - Review & Editing, X.S. and Z.Z.; Supervision, C.S. and Z.Z.; Funding Acquisition, C.S., D.W. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (No. 51875376, 51875375), as well as the Suzhou Science Foundation (No. SYG201802).  
**Conflicts of Interest:** The authors have no conflicts of interest to declare.
## 2 Related Work
The features selected for machine learning process considered three aspects: (1) the information uncertainty level; (2) problem complexity, such as clashing objects' size, priority, materials, type, and clashing volume, and (3) contextual flexibility, such as the location, spatial relationship, and available space [6]. However, their method based on machine learning obtained an average prediction accuracy of 80%, but it required a great amount of labor to preprocess the training data. Some researchers then argued that before a sufficient number of training cases are collected, the prediction accuracy is insufficient [7]. In summary, both rule-based reasoning and machine learning have their own pros and cons to provide a solution to domain problems. The former produces a favorable result no matter how big the training data size is but often requires a great number of efforts to acquire knowledge from human experts. On the contrary, the latter does not require human efforts to prepare and formulate the domain knowledge to produce results. Still, it requires sufficient training data in order to obtain a favorable result. In the field of machine learning, many studies have proved that applying the hybrid method that combines two or more sophisticated algorithms on certain domains can obtain better results than using individual methods [14; 15; 16]. However, most studies combined two or more machine learning algorithms as their hybrid methods, but few have taken advantage of machine learning and rule-based systems from the perspectives of minimal development efforts and maximal predictive performances. Considering the nature of clash detection, in which a large training dataset is not easy and cost-effective to collect and prepare, this study made the best use of expertise from human experts hired by the research team to both prepare the training dataset for machine learning and to be interviewed to acquire their heuristic know-how for rule-based reasoning. Based on the perspective of clash filtering, this study first used rule-based reasoning to preliminarily determine the type of clashes; subsequently, the results of the preliminary classification are added into the dataset of machine learning for training and the testing of classifiers in order to improve the prediction accuracy under a small training dataset.
## 2 Principles of MFL Technique  
The magneto-statics FEA is employed to numerically generate the MFL signals in the study. The corresponding governing equation derived from [PERSON]'s equations is shown in Equation (1):  
\[\
abla\times\left(\frac{1}{\mu}\,\
abla\times\mathbf{A}\right)=0 \tag{1}\]  
where \(\
abla\times\) denotes the curl operation; \(\mu\) represents the magnetic permeability of the ferromagnetic specimen (N/A\({}^{2}\)), and \(\mathbf{A}\) is the magnetic potential vector. Equation (1) is solved under the specified boundary conditions to obtain \(\mathbf{A}\), which is then used to obtain the magnetic flux density \(\mathbf{B}\) as follows [26; 27]:  
\[\mathbf{B}=\
abla\times\mathbf{A} \tag{2}\]  
The MFL technique relies on the measurement principle where a ferromagnetic material demonstrates MFL in the vicinity of a defect, as illustrated in Figure 2[18]. The measurement procedure is as follows: during the ILI process, the MFL tool is propelledthrough the pipeline, and magnets within the tool generate a magnetic field. In a perfect pipe, all magnetic flux remains within the pipe wall [28], resulting in a uniform magnetic field distribution [29]. However, in the presence of defects, some magnetic flux \"leaks\" outside the pipe, forming a magnetic leakage field close to the defect [30]. This leakage field is detected by Hall sensors, generating electrical signals [29]. The strength and distribution of the flux leakage vary depending on the geometry of the defect on the pipeline. By capturing the MFL signal, which typically consists of a three-dimensional magnetic flux density (\(\mathbf{B}\)) vector field, represented by \(B_{x}\) in the circumferential direction of the pipe, \(\mathbf{B}_{y}\) in the radial direction, and \(\mathbf{B}_{z}\) in the longitudinal (axial) direction, it is possible to estimate the size and location characteristics of the corrosion defect.  
Axial (AMFL) and circumferential (CMFL) MFL tools are the two types of MFL tools categorized by the arrangement of the magnets inside the MFL tool. In AMFL tools, the permanent magnets are oriented parallel to the longitudinal axis of the pipe. These tools excel in detecting and measuring defects in the circumferential direction of the pipe but are less effective in sizing features oriented axially [31]. Conversely, CMFL tools have magnets positioned in the circumferential direction of the pipeline. They are more precise in measuring features aligned along the longitudinal direction compared to AMFL tools. The CMFL tool is the main focus of this study because it is more accurate than the AMFL tool for sizing longitudinally-oriented corrosion anomalies, which are perpendicular to the hoop stress resulting from the pipe's internal pressure and, therefore, can have a great influence on the pipeline burst capacity.
# Sensing Hydrogen Seeps in the Subsurface for Natural Hydrogen Exploration
## 3 Results and Discussion
### Effect of Methane Concentration  
Methane is a common gas that is also present in natural seeps and is a possible chemical interfearant when sensing hydrogen. To determine the effect of methane on the hydrogen sensor response, sensor calibrations were performed in the absence and presence of different levels of methane. It is evident from Figure 5 that the hydrogen sensitivity of the GA5000 analyser did not vary significantly with different concentrations of methane. Most critically, the presence of methane did not give any false positive during hydrogen detection and measurement. In other words, the sensor did not respond to methane when no hydrogen was present. This can be seen in Figure 4, as zero hydrogen concentration gives a zero measured concentration, despite the 10,000 ppm methane present. Table 3 summarises the calibration functions/response factors and it is evident that the coefficient of determination in all cases was above 0.99. This study clearly shows that methane does not pose a significant influence on the GA5000 sensor response and this is important in the context of monitoring of hydrogen seeps in geological formations that can also contain high levels of methane. As it is known that electrochemical type hydrogen sensors generally do not have high cross sensitivity to methane, this further confirms our expectation that GA5000 used an electrochemical sensor and that methane does not pose a significant issue for using the GA5000 sensor. The sensitivity of the sensor is related to the slope of the  
Figure 4: Measured hydrogen concentration (GA5000) vs. known hydrogen concentration (mass flow controllers) for experiments containing approximately 10,000 ppm methane (experiments 17–20).  
calibration curve. From Table 3, the calibration slope varied between 1.25 to 1.35. Sensor selectivity is typically the ratio of the targeted analyte sensitivity versus the sensitivity of a potential interferant and is represented by K, where K = (sensitivity-H\({}_{2}\))/(sensitivity-CH\({}_{4}\)). As the sensor did not respond to methane in the absence of hydrogen, we are unable to calculate the K value, but estimate that it is very high (K > 1000).
# Artificial-Intelligence-Based Charger Deployment in Wireless Reshargeable Sensor Networks
## 3 Problem Definition
### Linear Programing Model  
In this paper, we follow the method of finding the position of a candidate charger by our previously proposed method, MCBA. In order to decrease the deployment cost and reduce the chance of falling into the local optimum, we use a metaheuristic algorithm to solve this problem. According to the above-mentioned expected goal, we define a linear programing model for this problem:  
\[\textit{Minimize}\sum_{i=1}^{m}F_{i} \tag{3}\] \[\textit{s.t.}\] \[\sum_{j=1}^{n}P_{i,j}^{S}(d)\geq W_{i}^{S},\] (4) \[\mathbb{P}_{i,j}^{C}>P_{i,j}^{S},\] (5) \[d_{i,j}<E,\] (6) \[\frac{\sum_{j=1}^{n}P_{i,j}^{S}(d)}{n}>0 \tag{7}\]  
The main goal of this paper is to minimize the number of chargers, \(F_{i}\), and make sure that each sensor can continue working. A lot of chargers will lead to a higher deployment cost. Conversely, fewer chargers will lead the power of sensor nodes to become exhausted more quickly. Therefore, the most important thing is to balance these two metrics to make sure that sensor nodes can continue working. We defined some restrictions to make this paper closer to a real scenario. The first restriction represents that sensor nodes' total collected power must be larger or equal to the power consumption. According to the energy conservation law, the chargers' power needs to be greater than the power received by the sensor nodes because power will be lost during the process of power transfer, as described in the second restriction. To guarantee that the sensor nodes will be charged exactly, the third restriction is to restrict the efficient charging distance. This means that the distance between the charger and sensor must less than \(E\). For deployment results, the energy efficiency must be greater than zero as the fourth restriction. Energy efficiency means the sum of the energy received by all sensors divided by the number of chargers.
## 3 Results and Discussions
### Reaction Heat Measurement  
Measurement of the reaction heat was performed by measuring the temperature around the cladding specimen in order to identify the reason for the breakaway time delay. In Figure 4a,b, the history of the temperature escalations during the early stage of the reaction between oxygen and fresh metal in terms of the increasing flow rate are shown. The reproducibility of temperature measurements is very satisfactory when both the lowest and the highest flow rate cases are considered (Figure 4a). Moreover, the peak temperature increases with the increase in the oxygen flow rate due to rise in the oxygen flow rate per the outer surface of the metal [1], as shown in Figure 4b.  
The interesting point is the delay point corresponding to the peak temperature that exceeds 1050 \({}^{\circ}\)C. As reported earlier in [1,4,7], at temperatures above 1050 \({}^{\circ}\)C, dense columnar oxides are favorably formed. This observation is used as a standard to distinguish the breakaway time delay.
# Imogolite Nanotubes: A Flexible Nanoplatform with Multipurpose Applications
## 4 Applications
This dilute liquid-crystal has such low visco-elasticity that the nanotubes are easily aligned in an electric field (Figure 7e), which is a significant advantage for the elaboration of anisotropic nanocomposite materials or the development of electro-optic devices. As far as nanodevices are concerned, it has been suggested that INT can be used for nanoelectronic applications by altering their insulating behavior through surface modifications [98; 99; 104]. Hence, thin film INTs have been proposed as field emission electrodes for use as water sensors [165] or electrocatalytic activity [166] (Figure 8a). Another behavior of imogolite nanotubes is their ability to immobilize metalloids, nanoparticles and dyes on specific sites on the external surface (Figure 8b) [37; 38; 167; 168; 169], which open new ways towards water treatment [69; 81; 105; 170], antimicrobial agents and their use in transparent dressings [171; 172; 173] or for catalysis [174]. Indeed, imogolite nanotubes were suggested as a unique catalyst since 1983 [35], due to their defined porosity, regular internal and external surface and its ease of func  
Figure 8: Examples of nanodevices and catalysis applications using imogolite nanotubes (**a**) I-V curves of an INT film measured in a FET device at different relative humilities for water sensoring. Adapted with permission from [165]. (**b**) Stabilization of modified gold nanoparticles on outer surface of INTs. Adapted with permissions from [167]. (**c**) Selective isomerization of glucose to fructose by INTs. Adapted with permissions from [78]. this field remains less explored compared to the production of polymer/INT nanocomposites. [PERSON] et al. evidenced that Cu-imogolite had higher activity in the decomposition of _tert_-butyl hydroperoxide than unloaded imogolite and Cu-SiO\({}_{2}\) materials [174]. In a similar manner, Cu\({}^{2+}\)-grafted TiO\({}_{2}\)/INT composites exhibited very efficient absorption and photodecomposition of acetaldehyde without being affected by the relative humidity, offering a wide range of uses in different environments [175]. Imogolite nanotubes and their derivatives have also been applied for hydroxylation of olefins [80], catalytic oxidation of aromatic hydrocarbon [176] or azo-dyes [106; 177] and act as a tunable platform for the isomerization of glucose to fructose (Figure 8c) [78].
## 5 Performance Evaluation
### Energy  
For clone detection using the DT approach, another important factor is energy as an advisory needs high power to control and monitor the MWSN.  
Correspondingly, to accomplish a better performance for clone detection in MWSN, the energy level must also be considered. Figure 11 displays the level of energy consumed by using DT concept at multiple phases of detection. It shows that the energy consumption values at first, second and third level detection among 80 mobile nodes are similar. It is also observed that by utilizing fixed batteries types, the third level detection consumes more energy than the first and second level detection.