esa-sceva/satcom-chunk-collection huggingface dataset:
# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 3 Wide-TSNet  
In this chapter, we delve into the details of our proposed method, Wide-TSNet, a novel hybrid approach for time-series classification. Wide-TSNet was designed to predict Bitcoin prices from time-series data, leveraging the power of image-based convolutional neural networks (CNNs) and a series of transformations and enhancements. We begin by discussing the data processing layer. Thereafter, we describe the image generation layer, including the process of converting time-series data into a Markov transition field (MTF) image. This unique transformation visually encapsulates the dynamic relationships between past price points.  
The enhancement layer follows the image generation layer, which plays a crucial role in improving the quality of the input data. This layer employs histogram equalization (HE), a powerful image processing technique that enhances the contrast, thus making the patterns in the data more discernible. This is achieved by redistributing the pixel intensities of the image, such that they are uniformly distributed across the entire available intensity range. This uniform distribution of intensities can significantly improve the image's visual quality, revealing details that may have been obscured in the original data.  
In our model, this enhancement layer pre-processes the input data, preparing it for the subsequent layers of the network. By improving the contrast and enhancing the details of the input data, the enhancement layer aids in extracting meaningful features, which is vital for the model's performance.  
Following the enhancement layer, we have the classification layer. This layer utilizes the Wide ResNet framework--a CNN variant known for its performance in image classification tasks. Wide ResNet was designed to utilize a wide range of complex patterns and structures within the data, making it an excellent choice for our model.  
The classification layer analyzes the enhanced data, identifies the key features, and classifies the data based on these features. Wide ResNet's strength lies in its ability to handle high-dimensional data and extract complex patterns, which is crucial for financial time-series prediction tasks.  
The Wide-TSNet framework diagram shown in Figure 1 visually represents these layers and their sequence in the model. This diagram serves as a roadmap, guiding the flow of data through the various layers of the network from the initial input to the final output.  
Figure 1: Wide-TSNet framework diagram.
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 3 Materials and Methods
### Data Collection  
It was important for the exercise to establish a model that could be used in the specific local conditions in Sicily, Italy. Given this challenge, it was necessary to obtain a collection of images from the Sicilian region. To overcome this challenge, the application, MyCityReport [58], was utilized to capture images from a smartphone which was mounted in a car driven along the Sicilian urban roadways. This setup is depicted in Figure 4. The application has the ability to capture images at an approximate distance of 10 m ahead of the positioned phone with photographs taken every second. The application also has the ability to classify 8 types of distresses as previously mentioned. This option was not utilized however as the application was relied on solely for data collection purposes. For the purpose of this study, several trips were made across urban road networks in Sicily generating over 7000 images. The weather and area were diverse within the dataset so as to offer a robust training dataset of urban road networks within the region. However, only the images that were taken during the day and when there was no rain were used. This does identify a limitation of the process as it is difficult to accurately identify distresses during increment weather conditions. Additionally, only images of flexible pavements were used. The same camera phone, the Google Pixel 2 XL, was utilized for all trips to ensure all of the images had similar image qualities and dimensions.
# Study on the Effect of Rock Mass Structure on CO2 Transient Fissure Excavation
## 3 Influence of Angles between Different Structural Surfaces (Test 1)
### Analysis of Cracking Effect
#### 3.3.2 Effective Cracking Square Amount  
Effective cracking square amount, deemed an important index to characterize the rock cracking effect, means the total square amount of rock block that is crushed and completely keeps away from the original rock body under the transient cracking role of carbon dioxide.  
As shown in Figure 5, No. 1 hole's effective cracking square amount is 4.53 m\({}^{3}\); No. 2 hole's effective cracking square amount is 20.80 m\({}^{3}\); No. 3 hole's effective cracking square amount is 14.60 m\({}^{3}\); No. 4 hole's effective cracking square amount is 8.64 m\({}^{3}\). No. 1 and No. 4 holes adopt the vertical hole layout, in which No. 1 fails to give full play to the cracking effect because its free face height is less than 4m, corresponding to the minimum square amount. No. 2, 3, and 4 holes adopt the attitude hole layout of the consequent rock stratum, vertical hole layout, and attitude hole layout of the inverse rock stratum, respectively. The No. 2 cracking square amount is 1.42 times the cracking square amount of the No. 3 cracking hole and 2.41 times the cracking square amount of the No. 4 cracking hole. Thus, it can be seen that, in terms of the cracking square amount, the consequent rock stratum presents the optimal hole layout and cracking effect.  
Combined with the above theoretical analysis, the hole layout of the consequent rock stratum is conducive to the high-pressure swelling gas generated by cracking acting on the structural surface of the rock and beneficial to the collapse and unloading of upper layer rock along the rock stratum under the double action of gravity and high-pressure gas expansion.  
Figure 4: Demarcation of effective area of cracking and explosion range.
## 6 Earthquakes Consistent with the IBC and the EC8 Design Spectra  
By following the two-step procedure illustrated above, five suites of earthquakes consistent with the IBC design response spectra (see Figure 3b) and five other suites of earthquakes consistent with the EC8 design spectra (see Figure 4) were derived. It is noted that all the obtained accelerograms are baseline corrected. Figure 6 shows the spectra of the IBC-spectrum-consistent earthquakes in the two main directions compared with the target design spectrum for each of the five site classes. Figure 7 displays the accelerograms of the matched earthquakes. Similarly, the spectra and the accelerograms of the earthquakes consistent with the EC8 design spectra are provided in Figures 8 and 9 for the two main directions. It is worth noting that the matching procedure was carried out by considering the range 0-4 s according to Assumption 3 of Section 3. However, for a better comparison in the highest acceleration range, a shorter range (from zero to 2 s) was considered in the diagrams of Figures 6 and 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Mismatch Tolerance** & **Max Iterations** & **Scale Factor** & **Min Eigenvalue** & **Max Number of Waves** & **Additional Waves** & **Off Diagonal Reduction** & **Group Size** \\ \hline
0.1 & 200 & 1 & 0.1 & 10 & 20 & 0.7 & 250 \\ \hline \end{tabular}
\end{table}
Table 9: Values of the matching parameters used in SeismoMatch.  
Figure 6: Target design spectra and response spectra of the suites of IBC-spectrum-consistent earthquakes in the \(X\) and \(Y\) direction (left and right diagrams) for the five soil types.  
Figure 7: Accelerograms of the suites of IBC-spectrum-consistent earthquakes referred to in Figure 6.  
Figure 8: Target design spectra and response spectra of the five suites of EC8-spectrum-consistent earthquakes in the \(X\) and \(Y\) direction (left and right diagrams) for the five soil types.  
Figure 9: Accelerograms of the suites of EC8-spectrum-consistent earthquakes referred to in Figure 8.  
Labelled as they are in Figures 6-9, the accelerograms are freely downloadable from a repository that is hosting them, as indicated at the end of the paper. They can be used for time-history (linear or non-linear) analyses of Lebanese structures. As derived from SeismoMatch, the records have a rather long time-length. A procedure to suitably reduce the time-length of spectrum-consistent accelerograms is proposed in Section 6.
# Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
## 2 Positioning Model and Inequality Constraints
#### 2.2.1 Vertical Velocity Constraint
Additionally, we obtain the following inequality:  
\[\mathbf{C_{0}}\mathbf{X}\leq\mathbf{d_{0}} \tag{12}\]
# Effect of Bentonite Content and Hydration Time on Mechanical Properties of Sand-Bentonite Mixture
###### Abstract  
The bentonite is commonly used mixed with soils for groundwater retention and waste contaminant facilities. The incorporation of bentonite could significantly reduce hydraulic conductivity. In this study, the effects of bentonite content, hydration time and effective confining pressure on the static properties of a sand-bentonite mixture were studied using experimental and numerical methods. Firstly, a large number of drainage static triaxial tests on the sand-bentonite mixture with various bentonite contents were conducted. The test results show that the increase in bentonite content and hydration time leads to a slight decrease in shear strength and initial tangent modulus of the sand-bentonite mixture. The presence of bentonite reduces the shear shrinkage and dilatancy trend of the mixture. The cohesion of the mixture increases with the increase in bentonite content and hydration time, but the internal friction angle decreases correspondingly. The hydration of bentonite on the surface of sand particles changes the contact form between particles. The bentonite slurry between pores of the sand skeleton also affects the mechanical behavior of the sand-bentonite mixture. Then, a series of 3D discrete element models were established for numerical simulations of drainage static triaxial tests. The numerical model parameters were calibrated by experimental results. The meso-mechanism of bentonite content affecting the mechanical behavior was revealed according to the contact force distribution between particles. The research results are helpful to understand further the mechanism of bentonite on the mechanical properties of the sand-bentonite mixture.  
sander-bentonite mixture; bentonite content; hydration time; mechanical behavior; discrete element method (DEM) 2021
# Surface Texture Measurement on Complex Geometry Using Dual-Scan Positioning Strategy
## 4 Surface Tracing Strategy for Long Scan Length
### Correction of Misalignment  
The misalignment of the internal and external scan needed to be calibrated. Instead of physically parallelizing the two moving axes, a pre-test on an optical flat (Figure 6a) was conducted to determine the misalignment. With the data collected from all sections plotted into one coordinate system, the measured profile is illustrated in Figure 6b.  
Discontinuity between neighboring sections can be corrected by introducing a vertical shift \(\Delta d\) to compensate for the misalignment. The corrected height values can be expressed as  
\[\left\{\begin{array}{l}z_{i,0}^{\prime}=z_{i,0}+0;\\ z_{i,1}^{\prime}=z_{i,1}+\Delta d;\\ z_{i,2}^{\prime}=z_{i,2}+2\Delta d;\\ \ldots\\ z_{i,m-1}^{\prime}=z_{i,m-1}+m\Delta d.\end{array}\right. \tag{3}\]  
where \(m\) is the number of data sections. These equations can be generalized as  
\[z_{i,j}^{\prime}=z_{i,j}+j\cdot\Delta d. \tag{4}\]  
where \(z_{i,j}\) is the height value captured by the LT9011 at the \(i\)th position in the \(j\)th section, and \(z_{i,j}^{\prime}\) is the value after misalignment correction. When the section length was set as 1.1 mm with a sampling spacing of 2 um, and the external positioning interval was set as 1 mm, there was an overlapping of 0.1 mm with 50 sampling points between neighboring sections.  
The least-squares principle can be applied to determine the value of \(\Delta d\). The optimal \(\Delta d\) should minimize the residual sum of squares (\(RSS\)) of the deviation between the neighboring overlaps:  
\[\begin{array}{ll}RSS&=\sum\limits_{j=0}^{m-1}\sum\limits_{i=0}^{n}{\left(z_ {i+(n-N),j}^{\prime}-z_{i,j+1}^{\prime}\right)^{2}};\\ &=\sum\limits_{j=0}^{m-1}\sum\limits_{i=0}^{n}{\left[z_{i+(n-N),j}+j\cdot \Delta d-z_{i,j+1}-(j+1)\Delta d\right]^{2}};\\ &=\sum\limits_{j=0}^{m-1}\sum\limits_{i=0}^{n}{\left[z_{i+(n-N),j}-z_{i,j+1}- \Delta d\right]^{2}}.\end{array} \tag{5}\]  
where \(m\), \(n\) and \(N\) represent the number of sections, the number of data points in each section and the number of the data points in each overlap. In order to obtain the minimum value of \(RSS\), \(\Delta d\) should comply with the following condition:  
\[\frac{\partial RSS}{\partial\Delta d}=0. \tag{6}\]  
With Equation (5) taken into Equation (6):  
Figure 6: Profile data misalignment illustration: (**a**) optical flat; (**b**) measured surface profile.  
\[-2\sum\limits_{j=0}^{m-1}\sum\limits_{i=0}^{N}{(z_{i+(n-N),j}-z_{i,j+1}-\Delta d )}=0. \tag{7}\]  
\[\Delta d=[\sum\limits_{j=0}^{m-1}\sum\limits_{i=0}^{N}{(z_{i+(n-N),j}-z_{i,j+1}) }]/{[N/(m-1)]}. \tag{8}\]
## 2 Study Area and Datasets  
### Study Area and In Situ Data  
The TRHR (89 deg31'-102 deg14'E, 31 deg38'-36 deg20'N) is situated in the backland of the Qinghai-Tibet Plateau, and it is the headwater source of the three famous rivers in China: the Yellow River, the Yangtze River, and the Lancang River (Figure 1). The total area is 302,500 km2. The total grassland area is 209,897 km2, while the area of usable grassland is 178,744 km2. The area has an average elevation of over 4000 m and an annual temperature of \(-\)4.4 degC. Its annual precipitation is 262.2-772.8 mm and occurs mostly from June to September each year [54]. The grass growth period is less than 3 months when there is no absolute frost-free period. The major types of grassland include alpine meadow steppe and alpine steppe, which account for approximately 76% and 23% of the total grassland area, respectively [55].  
Figure 1: Maps showing the location of the study sites. (**a**) The distribution of land cover types. The black dots are training sites, and the red dots are validation sites. (**b**) The location of the Tibetan Plateau (TP) and Three-River Headwaters Region (TRHR) in China. (**c**) The elevation data of the TRHR. The study area consists of 17 counties and cities delineated by the Ecological Protection and Restoration Program, including Zeku, Tongde, Henan, Xinghai, Maqin, Gande, Jüzhi, Dari, Banma, Maduo, Chengduo, Yushu, Nangqian, Qumalai, Zaduo, Zhiduo, and Tanggulashan.  
Ground observations of grassland AGB were used to train the models and evaluate the accuracy of the AGB estimates. AGB ground observations were derived from the Qinghai Eco-Environment Monitoring Center, including a total of 125 effective AGB observations for the period of June to September, the period of grass growth in 2009. The 125 AGB observation sites were divided into two groups: 75 training sites and 50 validation sites. Each grassland sampling site consisted of a 1000 m\({}^{2}\) area with an approximate dominant grass type. Each site included 9 grass sample plots that were 1.0 m \(\times\) 1.0 m in size. All the observation sites were all-natural grasslands with no human interaction during the study period. The data recorded from each sampling site included latitude, longitude, elevation, grass type, coverage, grass height, fresh weight, and dry weight (oven-dried at 65 \({}^{\circ}\)C for 48 h in the lab) [30]. The average of AGB observations from nine grass sample plots represented the AGB of each grassland sampling site.
## 4 Experimental Results and Analysis  
### Dataset and Experiment Setup  
We adopted various datasets in our experiment to evaluate the effectiveness of our proposed model. In addition, we conducted five cross-validation experiments on each dataset with a ratio of 5:1 between the training and validation sets, and the experimental results were averaged.  
To evaluate the effectiveness of SLPF and LA, we conducted experiments on Ci-far100 [39] and Tiny-ImageNet [40] datasets. The experiments were carried out on a hardware platform with an NVIDIA GeForce RTX 2080 Ti. The initial learning rate was set to 0.1, with a batch size of 128. We applied a weight decay of \(5\times 10^{-4}\) and a Nesterov momentum of 0.9 during the training process, which lasted for 200 epochs. The learning rate was reduced by a factor of 5 at the 60 th, 120 th, and 160 th epochs.  
To evaluate the ability of our mask-wearing status classification model, we collected 3399 photos of correctly worn masks, 3298 photos of incorrectly worn masks, and 3913 photos of no masks. The parameter settings were the same as for the ablation experiments evaluating SLPF and LA. In addition, we deployed the model on Android phones and conducted real-time monitoring testing, with the device collecting 1583 photos for testing. Android operating environment configuration: Redmi K30 pro with 8 GB of running memory and Qualcomm Snapdragon 865 processor.  
To evaluate the ability of our mask-wearing status detection model, it was first trained with a set of 853 face photos capturing individuals wearing masks from various angles. The experiments were carried out on a hardware platform with an NVIDIA GeForce RTX 2080 Ti for conducting the trials. Stochastic gradient descent was employed as the optimizer with an initial learning rate of 0.01, a weight decay of 0.01, and the Nesterov momentum set to 0.95. The models underwent training for a duration of 300 epochs.  
Figure 12: The YOLOv5 model with the spatial-frequency fusion module.
## 1 Introduction
### Literature Review
#### 1.2.3 Collection of Traveling Time in Relevant Studies  
In conventional travel behavior theory and existing empirical studies, travel time is considered a fundamental factor in the selection of travel mode [31; 35]. However, the majority of studies employing this indicator have relied on inaccurate travel time approximations, particularly in the case of public transportation. Further investigation is warranted to enhance the accuracy of travel time calculations to align more closely with real-world scenarios.  
In current efficiency studies, traffic data are primarily sourced from the following four outlets: (1) The sources involve utilizing cell phone signaling data [36], which necessitates a thorough process of cleaning, mining, and extraction to acquire high-quality data. This process is relatively complex. (2) Public historical data can be acquired in large quantities directly from institutions or websites [26; 37]. However, it frequently lacks timeliness and may have gaps in information. (3) The process entails performing calculations or simulations utilizing algorithms such as Dijkstra and Floyd, following the acquisition of accurate travel location details [28; 38; 39]. (4) The real-time intercity navigation data across various modes of transportation are obtained using online mapping technology [5; 16]. This approach offers advantages because of the continuous updates of real-time data provided by different mapping software such as Baidu Map and Tencent Map. The system efficiently mirrors the real transportation network operational status and computes interchange durations among various modes by leveraging extensive data from public transportation systems. Consequently, it offers highly accurate estimations of travel time [14]. This method has garnered growing popularity in research applications due to its advantages in data accuracy, availability, and timeliness.
# Coverage-Based Classification Using Association Rule Mining +
## 4 Discovery of Class Association Rules  
The step of finding frequent itemsets in associative classification is the most important and computationally expensive step [10; 11]. Several different approaches to discover frequent rule items from a dataset have been adopted from association rule discovery. For example, some AC methods [15; 50] employ the \"APRIORI\" candidate generation method. Other AC methods [8; 51; 52] mainly apply the FP-growth approach [45] to generate association rules. The CPAR algorithm utilizes a greedy strategy produced in FOIL [53]. \"Tid-lists\" methods of vertical data layout [54; 55] are used in References [9; 13]. This section focuses on discovering class association rules from frequent itemsets.  
Association rule generation is usually split up into two main steps:  
1. In the first step, the minimum support threshold is applied to find all frequent itemsets from the training dataset.
2. A minimum confidence constraint is applied to generate strong class association rules from the frequent itemsets.  
Figure 1: Associative Classification working procedure.  
In the first step, we use the \"APRIORI\" algorithm to find frequent itemsets. \"APRIORI\" utilizes the 'downward-closure' property to speed up the searching process by reducing the number of candidate itemsets at any level. The main feature of 'downward-closure' is that all subsets of a frequent itemset must be frequent. Infrequent itemsets found at any level are removed, because, it is not possible to \"make\" a frequent itemset from an infrequent one. \"APRIORI\" uses this process to prune infrequent itemsets before computing their support at any level. This reduces the time complexity of computing the support for all combinations of items in a dataset.  
Once all frequent itemsets are found from a learning dataset in the first step, it is a straightforward approach to generate strong class association rules that satisfy both minimum support and minimum confidence constraints from these frequent itemsets in the second step. This can be done using the following equation for confidence:  
\[confidence(X\to C)=\frac{support\_count(X\cup C)}{support\_count(X)} \tag{1}\]  
The Equation (1) is expressed in terms of itemsets support count, where \(X\) is antecedent (itemsets that is, left-hand side of the rule), \(C\) is a consequence (a class label that is, right-hand side of the rule), \(support\_count\) (\(X\cup C\)) is the number of transactions containing the itemsets \(X\cup C\), and \(support\_count\) (\(X\)) is the number of transactions containing the itemsets A. Based on this equation, CARs can be generated as follows:  
* For each frequent itemsets \(L\) and class label \(C\), generate all nonempty subsets of \(L\).
* For every nonempty subset \(S\) of \(L\), output the strong rule \(R\) in the form of \"S\(\rightarrow\)C\" if, \(\frac{support\_count(R)}{support\_count(S)}\geq min\_conf\), where \(min\_conf\) is the minimum confidence threshold.