esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Method
### Functionality Verification Phase
#### 3.3.1 Two-Dimensional FE Model Adjustment  
The 2D FE models used to simulate the two dynamic tests were built upon the 2D FE model developed in Section 3.1.1. The 2D FE model was characterized by speeding up the simulations in the case of geometry optimization, carried out by changing specific geometric parameters. Nevertheless, two-dimensional FE models are less precise than three-dimensional FE models. The first reason is, in the 2D FE model, the width of each part cannot be set as variable, as depicted in Figure 17. The second reason is geometric and concerns the holes, which are not considered in the 2D FE model, which is depicted again in Figure 17.  
The third reason regards the material properties. The orientation of the fibers in composite materials is fundamental, in terms of both stiffness and strength. In the 3D FE model, the fibers were modeled following the curvatures of the components (middle blade and upper blade in MyFlex-\(\gamma\)). The same situation did not occur in the 2D FE model. Therefore, the 2D FE model built as described in Section 3.1.1 was simulated again. The widths of the elastic parts were slightly adjusted to obtain the same _displacement force_ curves obtained in the static mechanical test (Section 3.2).  
Figure 16: ISO 1328 equivalent static tests: actual plantar flexion and dorsiflexion tests and 3D Cad model of the test set up, in plantar flexion test configuration.
## 1 Introduction
[PERSON] et al. (2017) developed a framework, termed Visual Attention Driven Self-Supervised Transfer Learning (VAD-STL) to distinguish between benign and malignant nodules in chest CT scans. This proposed framework suggests utilizing the entire nodule volumes as inputs to extract distinctive features, deviating from conventional methods that rely on 2D image-based transfer learning or building 3D models from scratch. In contrast to employing a single attention mechanism, the VAD-STL architecture incorporates a Multi-View Aggregative Attention (MVAA) module which recalibrates feature maps from multiple perspectives enhancing the resilience against background interference. The authors tested their algorithm on a newly constructed dataset named CQUCH-LND along with the public LIDC-IDRI dataset. [PERSON] et al. (2018) proposed an ensemble learning method named Fuse-Long Short-Term Memory-Convolutional Neural Network (F-LSTM-CNN). Their approach combines visual attributes and deep features to distinguish between benign and malignant nodules. Initially, clinical data are used to derive attribute features, while deep features are extracted from the preprocessed CT images of the nodules. The Fuse-Convolutional Neural Network (F-CNN) model is then introduced to emphasize the importance of attributes in the classification process, integrating deep and attribute features via transposed convolution. Simultaneously, the Fuse-Long Short-Term Memory (F-LSTM) model is introduced to focus on specific deep features for classification utilizing affine transformation of attribute features. Subsequently, the early detection of malignant lung nodules is achieved by merging prediction scores from both F-LSTM and F-CNN models. The effectiveness of this approach was evaluated using the LIDC-IDRI public lung nodule dataset. Within the current body of research surrounding CAD systems, numerous shortcomings have been highlighted: Firstly, some systems overlook the integration of morphological features, depending entirely on raw data for analysis. Secondly, there is a reliance on basic geometric attributes by certain models, which may lead to errors due to segmentation inconsistencies. Furthermore, a portion of these systems do not account for the voxel spatial interconnections, relying only on Hounsfield unit values to depict visual characteristics. In response to the challenges and limitations observed in existing systems, our approach seeks to harness the strengths of both engineered and deep learning features. We introduce a novel technique, the 3D-Local Octal Pattern (LOP), designed to capture and analyze the complex three-dimensional relationships among nodule voxels. In addition, we utilize a 3D-CNN for deep feature extraction directly from pulmonary nodules. The classification probabilities obtained from these two types of features, alongside geometric attributes of the nodules, are integrated and processed through a softmax classifier. This comprehensive methodology culminates in the accurate final diagnosis of pulmonary nodules.
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
## 2 Theory of Clay Deformation under Cyclic Loading
### Deformation Characteristics of Clay under Different Loads  
#### 2.2.1 Deformation Characteristics of Clay under Long-Term Static Load  
Due to the nonlinear creep property of clay, the total strain \(\varepsilon_{s}(t)\) under constant static load consists of both instantaneous and creep strain [35], so that.  
\[\varepsilon_{s}(t)=\varepsilon_{e}+\varepsilon_{c}(t) \tag{4}\]  
where \(\varepsilon_{e}\) is instantaneous strain, and \(\varepsilon_{c}(t)\) is creep strain, which contains the viscoelastic strains \(\varepsilon_{\rm vc.1}\) and \(\varepsilon_{\rm vc.2}\) in the initial creep and steady creep stages of the loading process, respectively, and the viscoplastic strain \(\varepsilon_{vp}\) in the accelerating creep stage. Figure 3 shows the representative creep strain-time curve for clay under long-term static load.  
Figure 1: Diagrammatic sketch of cyclic stress.  
Figure 2: (**a**) Diagrammatic sketch of static stress; (**b**) Diagrammatic sketch of alternating stress.  
During initial creep, we calculate  
\[\varepsilon_{s}(t)=\varepsilon_{e}+\varepsilon_{ve.1}(t)\ \ (0<t\leq t_{1}) \tag{5}\]  
During steady creep, we calculate  
\[\varepsilon_{s}(t)=\varepsilon_{e}+\varepsilon_{ve.1}+\varepsilon_{ve.2}(t)\ \ (t_{1}<t\leq t_{2}) \tag{6}\]  
During accelerating creep, we calculate  
\[\varepsilon_{s}(t)=\varepsilon_{e}+\varepsilon_{ve.1}+\varepsilon_{ve.2}+ \varepsilon_{vp}(t)\ \ (t_{2}<t) \tag{7}\]  
In all, the total representative creep process of clay of consists four parts: instantaneous stage, initial stage, steady stage, and accelerating stage. It is worth noting that the creep properties of clay are associated with stress levels, matric suction, consolidation and other factors [36; 37; 38; 39].
## 2 Background
### Mutual Coherence  
Suppose that the frame size is \(\left|J\right|=N\) and the frame length is \(m\). We can deal with a frame \((f_{j})_{j\in J}\) as a matrix that has the frame vectors as its columns, i.e., \(\mathbf{A}=[f_{1},\ f_{2},\ \cdots,\ f_{J}]\in\mathbb{C}^{m\times N}\). Then, the mutual coherence [8] of \(\mathbf{A}\) is given by  
\[\mu(\mathbf{A})=\max_{1\leq i<j\leq N}\frac{\left|\mathbf{a}_{i}^{H}\cdot \mathbf{a}_{j}\right|}{\left\|\mathbf{a}_{i}\right\|_{2}\left\|\mathbf{a}_{j} \right\|_{2}}, \tag{2}\]  
where \(\mathbf{a}_{i}\) is the \(i\)th column vector of \(\mathbf{A}\). The coherence of a frame is bounded by  
\[\mu(\mathbf{A})\geq\sqrt{\frac{N-m}{m(N-1)}}, \tag{3}\]  
which is called the Welch bound [13], and the right-hand side term of (3) is called the Welch bound equality (WBE).
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Power Module  
This module provides power supply to the system and is responsible for collecting the energy of the environment. The four submodules that form this module are: solar, mechanical, storage and regulation, and debugger. All of them will be described in their corresponding subsections.  
As described in Figure 2, our device has three energy sources: solar, mechanical, and electrical via USB (Universal Serial Bus). These correspond, respectively, to the solar, mechanical and debugger submodules. The collected energy is stored in a supercapacitor, since the batteries, which are the main alternative, have the shortest life. In addition, supercapacitors have a higher power density and faster charge time in comparison to batteries. The outputs of the energy sources are connected to the supercapacitor using Schottky diodes; so, the current of one source follows the correct path and does not interfere with the current of the other sources. After the diodes, the current of the different sources is added by connecting the current lines before they reach the supercapacitor. Finally, the energy stored in the supercapacitor is converted by a regulator into the 1.8 V needed by the processing module.  
The voltage provided by the output of every energy input was set to 4.2 V. The main reason is that batteries normally use this voltage to work and, as this is a prototype, there is an option to connect a battery as an auxiliary source of power for debugging scenarios. Furthermore, 4.2 V is considered a valid voltage because of the capacity of the supercapacitor, less voltage may imply reducing the capacity and the time that the system is on, which is the opposite of what we are searching for.  
To design this module, preliminary tests were developed in order to understand how techniques work in a real environment. The thermal technique was also evaluated in conjunction with the two techniques finally selected, solar and mechanical. The three techniques were tested to charge a capacitor and to turn on a LED. In the case of thermal technique, this was checked with two Peltier modules and thermal paste, but this technique was discarded due to the low voltage obtained in the capacitor. An instantaneous peak of voltage of 10 mV was raised along four days, but, mostly, the voltage was 0 V. For the other two techniques, supercapacitor has voltage values that allow to turn on the LED, continuously in the case of solar energy and occasionally in the case of mechanical energy.  
Figure 2: Power module architecture. Path 1 gets energy from the two energy harvesting modules (solar and mechanical) to charge the supercapacitor and the battery (if connected) to power the regulation module. In this path, the debugger (USB) is not connected and, usually, neither is the battery. Path 2 gets energy from the debugger module to charge the supercapacitor and the battery (if connected) to feed the regulation module. In this path, the debugger is connected and the battery is not usually plugged in. The USB and battery are only connected for debugging cases.
## 1 Introduction  
Accurate tree species identification through bark characteristics is a vital skill for foresters, enabling precise differentiation critical for effective forest management. There are many ways to identify trees, including leaves, fruit, and bark [1]. Traditionally, this expertise demands years of experience and advanced training, limiting its accessibility. Recent advancements in artificial intelligence (AI), particularly deep learning and convolutional neural networks (CNNs), offer the potential to replicate this precision with automated, rapid, and remote identification tools [2]. This project aims to develop AI-based tree bark identification tools to streamline digital forestry and support conservation efforts without requiring extensive professional training.  
Deep learning has enabled human-like pattern recognition by learning from large, labeled datasets [3]. In tree bark identification, CNNs have demonstrated accuracy comparable to or surpassing traditional texture classification methods, with straightforward implementation and end-to-end training [1, 4, 5]. However, existing datasets, such as BarkTex [6],Trunk12 [7], and BarkNet 1.0 [4], which provide labeled bark images of hardwood species across the United States, Canada, and Europe, are limited by variability in scope, species diversity, and accessibility [8]. These constraints hinder comprehensive AI training, resulting in identification systems that currently underperform compared to experienced foresters.  
To address these limitations, this study leverages the CentralBark dataset [9], which includes over 19,000 images of 25 commercially valuable hardwood species from the Central Hardwood and Central Appalachian regions, accompanied by metadata such as diameter at breast height (DBH), bark moisture content (wet or dry), GPS location, timestamp, date, and camera settings [9]. We are focusing on a new, smaller three-species dataset separate from the CentralBark dataset--northern red oak, hackberry, and bitternut hickory. We are calling this new dataset CBDS_Small. This research investigates the impact of three key variables contributing to inaccuracies in AI-based bark identification: (1) time of day (affecting lighting conditions), (2) bark moisture content, and (3) cardinal direction of observation (north, south, east, or west). Identifying primary sources of error will guide improvements in data collection and preprocessing, such as standardizing image capture protocols or augmenting datasets with underrepresented conditions.  
By enhancing the robustness of deep learning models, this work aims to develop reliable AI tools for real-world forestry applications, where environmental variability is a common occurrence. Improved bark identification systems will streamline forest inventory processes, enable precise monitoring of species diversity and health, and support sustainable forest management.
## 3 Materials and Methods
### System Architecture
#### 3.1.3 Squeeze-and-Excitation (SE) Attention Modules  
Squeeze-and-Excitation (SE) blocks are incorporated into the DenseNet-121 architecture to enhance the model's focus on crucial features related to surgical tool detection. SE blocks improve CNN performance by selectively emphasizing important feature channels while diminishing less relevant ones. This is achieved through two main operations: \"squeeze\" and \"excitation\". The squeeze operation compresses the spatial dimensions of the feature maps into a single channel-wise descriptor using GAP [18]. This descriptor \(z\in\mathbb{R}^{C}\) is computed as follows:  
\[z_{c}=F_{\text{sq}}(u_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}u_{c }(i,j) \tag{1}\]  
where \(u_{c}\) is the _c_-th channel of the input feature map \(U\in\mathbb{R}^{H\times W\times C}\).  
Here, \(H\) and \(W\) denote the height and width of the feature map, respectively, and \(C\) is the number of channels. \(z_{c}\) is the global average pooled value of the _c_-th channel, obtained by averaging all the spatial elements in that channel.  
The excitation operation then recalibrates the feature channels by passing this descriptor through two FC layers. The resulting output \(s\) is computed as follows:  
\[s=F_{ex}(z,W)=\sigma(W_{2}\delta(W_{1}z)) \tag{2}\]  
where \(\delta\) refers to the ReLU activation function, \(\sigma\) is the sigmoid activation function, \(W_{1}\in\mathbb{R}^{C/r\times c}\) and \(W_{2}\in\mathbb{R}^{C\times C/r}\) are the weights of the two FC layers, and \(r\) is the reduction ratio used to reduce the dimensionality in the first FC layer.  
The output \(s\), containing the channel-wise recalibrated weights, is used to scale the original feature maps, enabling the network to prioritize more relevant features.  
The reduction factor \(r\) for the SE blocks is set to 16. By integrating these SE modules, the model can better distinguish between critical tool-related features and background information, leading to enhanced classification performance.
## 7 Simulation Results and Discussions  
### Transient Stability Analysis  
Simulation analysis is performed on the model system shown in Figure 11 using PSCAD/EMTDC software. The FORTRAN language is incorporated into PSCAD/EMTDC in order to implement FLC as new component. The simulation time is chosen as 10 s. The triple-line-to-ground (3 LG) fault near bus 11 is considered to be a network disturbance, as shown in Figure 11. The fault occurs at 0.1 s. The duration of the fault is 0.1 s. The circuit breakers (CBs) on the faulted line are opened at 0.2 s in order to isolate the faulty line from the power system. The CBs are recclosed at 1.0 s based on the consideration that the fault has been cleared. The wind speed data applied to each wind turbine  
Figure 16: Low voltage ride through (LVRT) requirement for wind farm (WF).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Doubly Fed Induction Generator (DFIG)** & \multicolumn{2}{c}{**Squirrel Cage Induction Generator (SCIG)**} \\ \hline MVA & 27, 28, 58 and 59 & MVA & 41, 42, 72, 73 \\ \(R_{s}\) & 0.007 pu & \(R_{1}\) & 0.01 pu \\ \(R_{r}\) & 0.005 pu & \(X_{1}\) & 0.1 pu \\ \(L_{\mathrm{ls}}\) & 0.171 pu & \(X_{m}\) & 3.5 pu \\ \(L_{rl}\) & 0.156 pu & \(R_{21}\) & 0.035 pu \\ \(L_{m}\) & 2.9 pu & \(R_{22}\) & 0.014 pu \\ - & - & \(X_{21}\) & 0.03 pu \\ - & - & \(X_{22}\) & 0.089 pu \\ - & - & \(H\) & 1.5 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of wind generators.  
is maintained constant at the rated speed based on the assumption that the wind speed does not change dramatically within this small period of time. Simulation analyses are carried out for both the proposed and conventional rotor-side controllers reported in [23] in order to demonstrate the effectiveness of the proposed control system. The simulation results are presented and discussed in the following subsections.
## 2 Method Description
### Partial Linearization  
In order to improve the numerical reliability of the method, it is convenient to approximate the \(i\)-th eigenfrequency \(\lambda_{i}(\mathbf{\mathrm{q}},\mathbf{\mathrm{y}})\) in a neighborhood of \((\mathbf{\mathrm{q}}_{0},\mathbf{\mathrm{y}}_{0})\) with a polynomial, which is linear in the variable \(\mathbf{\mathrm{y}}\), rather than with the usual Taylor polynomial. The definition of the polynomial employed in this work is  
\[\hat{\lambda}_{i}^{p}(\mathbf{\mathrm{q}},\mathbf{\mathrm{y}}) =\sum_{|\beta|\leq 1}\sum_{|\alpha|+|\beta|=0}^{p}\frac{ \partial_{\mathbf{q}}^{a}\partial_{\mathbf{\mathrm{y}}}^{\beta}\lambda_{i}(\mathbf{ \mathrm{q}}_{0},\mathbf{\mathrm{y}}_{0})}{\alpha!}\delta\mathbf{\mathrm{q}}^{a}\delta \mathbf{\mathrm{y}}^{\beta} \tag{7}\] \[=\sum_{|\alpha|=0}^{p}\frac{\partial_{\mathbf{q}}^{a}\lambda_{i}(\mathbf{ \mathrm{q}}_{0},\mathbf{\mathrm{y}}_{0})}{\alpha!}\delta\mathbf{\mathrm{q}}^{a}+\sum_{| \beta|=1}\sum_{|\alpha|=0}^{p-1}\frac{\partial_{\mathbf{q}}^{a}\partial_{\mathbf{ \mathrm{y}}}^{\beta}\lambda_{i}(\mathbf{\mathrm{q}}_{0},\mathbf{\mathrm{y}}_{0})}{ \alpha!}\delta\mathbf{\mathrm{q}}^{a}\delta\mathbf{\mathrm{y}}^{\beta}\] (8) \[=\underbrace{\sum_{|\alpha|=0}^{p}\frac{\partial_{\mathbf{q}}^{a} \lambda_{i}(\mathbf{\mathrm{q}}_{0},\mathbf{\mathrm{y}}_{0})}{\alpha!}\delta\mathbf{ \mathrm{q}}^{a}}_{l_{i,0}^{p}(\mathbf{\mathrm{q}})}+\sum_{j=1}^{n_{y}}\underbrace{ \sum_{|\alpha|=0}^{p-1}\frac{\partial_{\mathbf{q}}^{a}\frac{\partial_{\mathbf{q}}^{a} \partial_{\mathbf{\mathrm{y}}_{j}}^{\beta}}{\partial_{\mathbf{\mathrm{q}}}^{\beta}}(\mathbf{ \mathrm{q}}_{0},\mathbf{\mathrm{y}}_{0})}{\alpha!}\delta\mathbf{\mathrm{q}}^{a}}_{l_{i, j}^{p}(\mathbf{\mathrm{q}})}\delta\mathbf{\mathrm{q}}^{a}\delta\mathbf{\mathrm{y}}_{j} \tag{9}\]  
where \(\delta y_{j}\) is the \(j\)-th entry of \(\delta\mathbf{\mathrm{y}}\). It can be seen from (9), in which the multi-index notation associated to variables \(\mathbf{\mathrm{y}}\) is unpacked, that \(\hat{\lambda}_{i}^{p}(\mathbf{\mathrm{q}},\mathbf{\mathrm{y}})\) is linear in the variables \(\mathbf{\mathrm{y}}\).  
Evidently, \(\hat{\lambda}_{i}^{p}(\mathbf{\mathrm{q}},\mathbf{\mathrm{y}})\) can be obtained from \(\lambda_{i}^{p}(\mathbf{\mathrm{q}},\mathbf{\mathrm{y}})\) by simply discarding the terms related to \(|\beta|\geq 2\). The function \(\hat{\lambda}_{i}^{p}\) approximates the \(i\)-th eigenfrequency with a larger error than that of the Taylor polynomial of order \(p\), but the numerical simulations carried out show that the approximation is acceptable in a neighborhood sufficiently large for the purpose of eigenfrequency assignment. The assessment of the accuracy of the approximation in a significant example is shown in Section 3.2.
# An Assessment of Anthropogenic CO\({}_{2}\) Emissions by Satellite-Based Observations in China
## 2 Data and Methodology
### Methodology  
The method for estimating anthropogenic CO\({}_{2}\) emission include three major steps as shown in Figure 2.  
Firstly, we enhance the signals of CO\({}_{2}\) from anthropogenic emission in XCO\({}_{2}\) which is described in Section 2.3.1. Secondly, we apply the training datasets of XCO\({}_{2}\) and ODIAC in 2010-2014 to GRNN to get the estimating model of anthropogenic emission which is described in Section 2.3.2 in detail. Thirdly, anthropogenic emissions are estimated by GRNN model using XCO\({}_{2}\) in 2015, and validated by comparing with ODIAC data in 2015.
## 2 Experimental Program
### Test Procedure  
Considering the house environment, the experiments were conducted in an indoor room where the temperature was maintained at 25 \({}^{\circ}\)C by air conditioning. Due to insufficient experimental data on slingshot impacts, this study designed an experiment where a steel ball would strike the glass surface perpendicularly. The experimental protocol was conducted in two distinct stages to determine the damage limit velocity and analyze the resulting damage patterns.  
* Range determination of initial damage limit velocity--An exploratory experiment was performed to identify the approximate damage limit velocity. Given the unknown nature of the glass's damage limit velocity, steel balls were propelled at the glass surface at 5 m/s velocity intervals. This systematic approach allowed for the identification of a preliminary damage limit velocity range.
* Refined analysis of damage limit velocity and pattern--Following the initial assessment from Stage 1, a more precise experiment was conducted to accurately determine the damage limit velocity and characterize the associated damage patterns. Multiple tests were performed within a \(\pm\)10 m/s range of the preliminary damage limit velocity identified in Stage 1. This refined approach facilitated a comprehensive analysis of the damage patterns across a narrower velocity spectrum.  
To quantify the glass damage patterns resulting from steel ball impacts, two primary features were measured: the cone crack and the perforated hole. The maximum and minimum dimensions of the cone crack were denoted as \(C_{max}\) and \(C_{min}\), respectively. Similarly, the maximum and minimum dimensions of the perforated hole were designated as \(P_{max}\) and \(P_{min}\) (Figure 2). These measurements provided a standardized method for characterizing the extent and nature of the impact-induced damage.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Nominal Diamter of**} & \multicolumn{5}{c}{**Diamter (mm) and Mass (g)**} & \multirow{2}{*}{**Average (g)**} \\ \cline{2-2} \cline{4-6}
**Steel Ball** & \#**1** & \#**2** & \#**3** & \#**4** & \#**5** \\ \hline \multirow{2}{*}{8 mm} & 7.99 mm & 7.97 mm & 7.98 mm & 7.99 mm & 7.99 mm & 7.98 mm \\  & 2.11 g & 2.11 g & 2.10 g & 2.13 g & 2.11 g & 2.11 g \\ \hline \multirow{2}{*}{10 mm} & 10.02 mm & 10.03 mm & 10.03 mm & 10.03 mm & 10.03 mm & 10.03 mm \\  & 4.15 g & 4.15 g & 4.16 g & 4.14 g & 4.18 g & 4.16 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured mass of 8 and 10 mm steel balls.