esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Satellite Data  
Satellite-based observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor were used in this study. We used the MODIS land surface vegetation index product, which was downloaded from the Oak Ridge National Laboratory's Distributed Active Archive Center (DAAC) website [29]. This MODIS vegetation index product (MOD13Q1, 500 m, collection 5) can provide the enhanced vegetation index (EVI) [30] for a 16-day time interval using atmospherically corrected bi-directional surface reflectance. Low-quality observations resulting from cloud contamination and other factors (e.g., snow) were rejected by selecting the quality flag, which ensured that the monthly EVO calculation was of good quality.  
The EVI was quantified by the following equation:  
\[EVI=2.5\times\frac{R_{NIR}\cdot R_{Rad}}{1+R_{NIR}+6\times R_{Rad}-7.5\times R _{Blue}} \tag{1}\]where \(R_{NIR}\), \(R_{Red}\), and \(R_{Blue}\) represent the reflectance at near-infrared, red, and blue wavelengths (nm), respectively. Based on the geo-location information (latitude and longitude) of the flux tower sites, EVI was extracted from the 3 \(\times\)3-pixel area at the center point on the flux tower, similar to the approaches of [PERSON] _et al._[31] and [PERSON] _et al._[32]. This was reasonable due to the large footprints (\(>\)1.5 km) of all sites throughout the year reported by [PERSON] _et al._[33]. The averages of the observations during the month were then used to represent the mean monthly value [34].
# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
###### Abstract  
Multicriteria route planning is a crucial transportation planning issue under the field of GIS-based multicriteria decision analysis (GIS-MCDA) with broad applications. A searching algorithm is proposed to solve the multicriteria route planning problem with spatial urban information and constraints such an existing transit network in operation, certain vertices to be visited in the path, total number of vertices been visited, and length or range for the path. Evaluation of two in-operation mass-transit systems from Chicago and Tainan show that our method can retrieve solutions in a Pareto-optimal sense over comparative methods between profit under queried constraints (the expected passenger flow to be maximized, referring to the social welfare for the public) and cost for construction as well as maintenance (the cost of route to be minimized, referring to the sustainability for the government) with reasonable runtime over comparative methods.  
constrained route planning; multicriteria optimization; GIS-MCDA
## 2 Related Work
### LLMs and OE
The study explores several state-of-the-art models, utilizing a variety of prompting techniques such as Chain of Thoughts (CoT), Graph of Thoughts (GoT), and Decomposed Prompting to assess the ability of LLMs to produce sufficient quality OWL suggestions. The research demonstrates that GPT-4 is particularly capable of generating high-quality modeling suggestions, significantly outperforming other models in generating structurally and syntactically correct OWL files. This paper highlights the importance of carefully selected prompting techniques to leverage the capabilities of LLMs effectively in OE, suggesting a promising direction for automating this traditionally labor-intensive task. [PERSON] et al. [6] explore the potential of fine-tuning GPT-3 to automate the translation of natural language sentences into Description Logic, specifically into OWL Functional Syntax. Their research involves developing a Protege plugin that assists in both developing new ontologies and enriching existing ones by automatically translating domain-specific natural language into formal OWL axioms under human supervision. The authors fine-tuned the GPT-3 model using a dataset of 150 natural languages to OWL translation pairs, covering a variety of instances and relationships. This approach leverages the linguistic prowess of LLMs to streamline the OE process, potentially reducing the technical challenges and costs traditionally associated with manual ontology development. Their work demonstrates a significant advancement in applying LLMs to semantic web technologies, particularly in automating and facilitating the labor-intensive processes of OE. [PERSON] et al. [7] explore the application of LLMs to OE within the context of Search and Rescue (SAR) operations. Their research demonstrates how LLMs can be effectively utilized to automate the construction of ontologies, particularly for complex and dynamic scenarios like SAR missions in wildfire incidents. However, it is important to note that in this work, fine-tuning is not researched or evaluated; the study focuses solely on simple prompting techniques. The paper proposes a novel collaborative OE methodology that harnesses both human expertise and the advanced natural language processing capabilities of LLMs. This hybrid approach facilitates the rapid development of domain-specific ontologies, leveraging the LLMs' ability to process and structure large datasets into ontological knowledge, which is critical for enhancing situational awareness and decision-making in SAR operations. The integration of LLMs into OE representsa significant advancement in making these operations more efficient and effective by providing a structured knowledge framework that supports the dynamic requirements of SAR missions.
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 3 Yaw Position Anomaly Recognition Model
### Model TSA-Informer
\[\alpha_{t}=\alpha\cdot\frac{\hat{m}_{t}}{\sqrt{\hat{v}_{t}+\varepsilon}} \tag{13}\]  
\[\overline{w}(X_{i})=\frac{1}{n}{\sum_{j=1}^{N}SIG(X_{i};X_{j};C)+w(X_{i})} \tag{5}\]0 TSA–Informer model structure. In the formula, \(\varepsilon\) is a constant used to ensure numerical stability. (8) Parameter update: apply the adaptive learning rate to update the model parameters. \[\overline{w}(X_{i})=\frac{1}{n}{\sum_{j=1}^{N}SIG(X_{i};X_{j};C)+w(X_{i})} \tag{5}\]1  
(9) Iteration: repeat steps (2) to (8) until the model performance converges or a predetermined number of iterations is reached. Select the direct-drive unit data of a certain wind farm in service. Its basic parameters are as follows: rated power 4.2 MW; impeller diameter 136 m; cut-in wind speed 2.5 m/s; cut-out wind speed 25 m/s. The model is built using the deep-learning framework PyTorch based on Python 3.10. Some of its parameter settings are shown in \[\overline{w}(X_{i})=\frac{1}{n}{\sum_{j=1}^{N}SIG(X_{i};X_{j};C)+w(X_{i})} \tag{5}\]2  
To verify the effectiveness of the model's prediction results, based on the model performance evaluation indicators of MAE, RMSE, and R\({}^{2}\), this paper uses SGD and Adam optimization algorithms to compare the performance of TSA-Informer with six models such as BPNN, RNN, GRU, CNN, Transformer, and Informer (with unified parameter settings). The performance comparison results are shown in \[\overline{w}(X_{i})=\frac{1}{n}{\sum_{j=1}^{N}SIG(X_{i};X_{j};C)+w(X_{i})} \tag{5}\]3  
As can be seen from Tables 3 and 4, when the prediction step length (pred_len) of the TSA-Informer model is 300, its MAE, RMSE, and R\({}^{2}\) are all better than the other six basic models, and the optimization performance of the Adam algorithm is significantly higher than that of the SGD algorithm, indicating that the Adam optimization algorithm is more suitable for the construction of the diagnostic model in this paper. Specifically, compared with the basic Informer model, the MAE of TSA-Informer is reduced by 26.02%, RMSE is reduced by 25.2%, and R\({}^{2}\) is increased by 4.9%.
# Quantum Neural Networks Approach for Water Discharge Forecast
## 2 Methodology and Data Series
### Quantum Neural Network
#### 2.2.2 Structure of Quantum Neural Networks  
A QNN consists of three core components:  
(1) Quantum Feature Mapping (Data Encoding)  
Before processing, classical input data must be mapped to a quantum state. This process is known as Quantum Feature Mapping, which uses quantum gate operations to embed the input data as quantum state parameters [48]. For example, for a one-dimensional input \(x\), encoding can be performed using rotation gates (\(R_{y}\), \(R_{z}\)):  
\[\ket{\psi(x)}=R_{y}(x)\ket{0} \tag{2}\]In multi-qubit systems, entanglement-based mapping (such as ZZ feature mapping) can be used to enhance data separability [38]:  
\[U_{encode}(x)=e^{ixH} \tag{3}\]  
where \(H\) represents the Hamiltonian, typically constructed using Pauli-Z interaction terms [49].  
Entanglement is a quantum correlation where the particle's state is linked to another particle's state (no matter the distance between them). Therefore, the state of one entangled particle determines the other particle's state [47].  
(2) Measurement and Optimization  
Once QNN computations are complete, quantum measurement is performed to extract the computational results. Common measurement methods include:  
* Pauli-Z measurement: Computing the expectation value \(\langle Z\rangle\) for classification or regression tasks [50]: \[f(x)=\langle\psi(x,\theta)|Z|\psi(x,\theta)\rangle\] (4)
* Shot-based Sampling: Since quantum measurements are probabilistic, multiple measurements are required to obtain a stable output.  
The measured results are used in the loss function, and classical optimization algorithms such as Gradient Descent [51], L-BFGS-B (quasi-Newton method) [52], and COBYLA [53; 54] (constrained optimization) are employed to fine-tune the circuit parameters, improving the model's fit to the target data.
## 5 ESTCube-Luna Preliminary Mission Requirements and Platform Design Solutions
### Attitude and Navigation System (ANS)
#### 5.5.1 Attitude Thrusters (ATs)  
The ESTCube-LuNa spacecraft is designed to have eight attitude thrusters (ATs) for spin rate and spin plane orientation control, as shown in Figure 5. For regular operations before tether deployment, the attitude thrusters can be used together with RWs for maintaining the spin rate and pointing the spin plane. For spin-up and tether deployment manoeuvres, ATs are used directly to provide the angular momentum _between_ periods of tether deployment, as discussed in Section 3.2.  
While a specific AT technology is not selected yet, in the mission design, we have assumed that each thruster produces 8 uN, which has been demonstrated in orbit on board a one-unit cubesat UWE-4 [41] using the highly miniaturised field-emission electric propulsion (NanoFEEP) thrusters from TUD [58], also an ESTCube-LuNa consortium member. When two ATs burn at the maximum thrust, as required by the spin-up for tether deployment, they consume up to 2.5 W.  
The NanoFEEP thrusters are extremely compact, with the specific impulse reaching 4000 s. The extreme efficiency provides the necessary spin-up propulsion budget with just 12.1 g of propellant, spread between two thrusters. The NanoFEEP thruster ionises liquid metal and the ions are accelerated by a strong electric field. The resulting metal plume's half-angle is in the \(\pm 35^{\circ}\) nominal range and can reach \(\pm 50^{\circ}\) at high thrust, as in our case. We assume that a focusing electrode is necessary to limit the nominal plume half-angle to \(<\pm 25^{\circ}\). As shown in Figure 5, the X-configuration deployable panels form a \(45^{\circ}\) angle with the thrust direction. The maximum plume half-angle should be several degrees smaller than \(\pm 45^{\circ}\) in order to avoid coating of solar cells by a safe margin.
# Contact Fatigue State Identification of Specim Based on Heterogeneous Data and Evidence Theory
## 3 Contact Fatigue State Identification
### Contact Fatigue State Identification of Specim
#### 3.4.3 State Identification Results and Analysis
Therefore, the ROC curve should be as close as possible to the (0,1) point and deviate from the 45-degree diagonal. \[TPR=\frac{TP}{TP+FN} \tag{5}\]  
Figure 11:0  
where _TP_ is the number of correct judgments in True, _TN_ is the number of correct judgments in False; _FP_ is the number of incorrect judgments in True, and _FN_ is the number of incorrect judgments in False. The contact fatigue state identification of the specimen is carried out for three states of normal, medium and failure with 100 validation set data, respectively. The ROC curves are shown in Figure 11:1 It is clear that the ROC curve of the method proposed in this paper is close to the (0,1) point and deviates from the 45-degree diagonal. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample Number**} & \multicolumn{2}{c}{**Based on Vibration Signal**} & \multicolumn{2}{c}{**Based on Image**} & \multicolumn{2}{c}{**Vibration +**} & \multirow{2}{*}{**Real State**} \\  & **VGG16** & **ResNet** & & & **Signal** & \\ \hline
1 & 0 & 0 & 0 & 0 & \\
2 & 0 & 0 & 0 & 0 & \\
3 & 0 & 0 & 0 & 0 & \\
**\(\vdots\)** & \(\vdots\)** & \(\vdots\)** & \(\vdots\)** & \(\vdots\)** & 0 \\
98 & 0 & 0 & 1(\(\times\)) & 0 & \\
99 & 0 & 0 & 1(\(\times\)) & 0 & \\
100 & 0 & 0 & 1(\(\times\)) & 0 & \\ \hline
2 & 0(\(\times\)) & 0(\(\times\)) & 1 & 0(\(\times\)) & \\
3 & 0(\(\times\)) & 0(\(\times\)) & 1 & 1 & \\
**\(\vdots\)** & \(\vdots\)** & \(\vdots\)** & \(\vdots\)** & \(\vdots\)** & 1 \\
98 & 0(\(\times\)) & 1 & 1 & 1 & \\
99 & 0(\(\times\)) & 0(\(\times\)) & 1 & 0(\(\times\)) & \\
100 & 2(\(\times\)) & 2(\(\times\)) & 1 & 2(\(\times\)) & \\ \hline
3 & 2 & 2 & 2 & 2 & \\
**\(\vdots\)** & \(\vdots\)** & \(\vdots\)** & \(\vdots\)** & \(\vdots\)** & 2 \\
98 & 2 & 2 & 2 & 2 & \\
99 & 2 & 1(\(\times\)) & 2 & 2 & \\
100 & 1(\(\times\)) & 1(\(\times\)) & 2 & 1(\(\times\)) & \\ \hline Number of & & & & & \\ identification & 14 & 27 & 20 & 4 & / \\ errors & & & & & \\ \hline Accuracy & 95.33\% & 91.00\% & 93.33\% & 98.67\% & / \\ \hline \hline \end{tabular} Note: “\(\times\)” represent the recognition state does not match with the actual state.  
\end{table} 
Table 13: State identification results for validation set data. Therefore, it is feasible and effective to use D-S evidence theory to perform decision-level fusion of state identification results from a single information source of vibration and image, which improve the accuracy of contact fatigue state identification of the specimen.
## 4 System Model
### Model Performance
#### 4.3.1 Smart Attendance  
Manual attendance consumes time and is one of the most boring activities considered. The current school schedule, with 8 periods of 40 min and at least 60 students per class, does not allow manual attendance tracking. The RFID smart card system automates this process by registering the presence and timing of students and teachers as they enter the classroom. The system also informs the teacher and principal about the number of students in each class. The technology operates seamlessly and quietly. The RFID smart card system also notifies parents when their children arrive or depart from school, enhancing their awareness and safety. Additionally, the system records student activity when they access the library, cafe, labs, etc.
## 2 Finite Element Analysis for Bridge Structures with Damage and Deterioration  
### Outline of Eigen-Modal Analysis  
It is known that the dominant vibration frequency of a structure generally decreases when the stiffness of bridge components is reduced with a sort of material and structural deterioration. In this study, a vibration analysis was conducted on slab and pier models with several damage scenarios to reveal the frequency change and modal amplitude change caused by damage. For the slab model, it consisted of six uniform panels, whose material properties were tuned down to model damage. For the pier model, it consisted of four identical panels and the riverbed level was tuned down to model scouring.  
This numerical vibration analysis was conducted using finite element analysis software suite ABAQUS 6.14. After the finite element models' construction, eigenvalue analysis was conducted to obtain the modal properties for each model. Based on the vibration analysis results, generated power is calculated according to the MEH device structure. The eigenvalue problem for a finite element model is involved in solving the eigenequation shown in Equation (5).  
\[Kx\ =\ \lambda Mx \tag{5}\]  
where \(K\) is the global stiffness matrix (sparse positive definite matrix of size \(n\times n\)), \(M\) is the global mass matrix (sparse positive-definite matrix of size \(n\times n\)), \(\lambda\) is the eigenvalue, \(x\) is the eigenvector, and \(n\) is the number of degrees of freedom. In ABAQUS, the Lanczos inverse power method is used to solve the eigenvalue problem, especially in a case where lower vibration modes are of more concern.
# Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information
## 2 Materials and Methods
### Deep Learning Models
#### 2.2.2 Two-Dimensional-Based CNN-Based Transfer Learning Model  
In this study, two-dimensional spectrogram images were trained using VGGish and YAMNet among CNN-based models pretrained through audio data. VGGish is a CNN-based neural network proposed by [PERSON] [18] designed to classify audio classes by training the deep learning neural networks using audio data contained in large-scale video databases. The deep learning network is trained using audio content consisting of more than 2 million YouTube videos, including 527 audio classes. Adult voices, baby babbling, and animal sounds are included in the 527 audio classes [19]. This model is constructed based on the VGG network, one of the CNN-based pretrained models frequently used in the field of computer vision. VGGish consists of four convolution blocks and receives a spectrogram comprising an audio clip with a size of 96 \(\times\) 64 \(\times\) 1 as input. Each block includes a 2D-based convolution layer that performs the feature extractor role, ReLU activation function, and max pooling layer, which reduces the image dimensions while maintaining the image features. Two fully connected layers, embedding layers, and regression output layers, that function as a classifier, are included behind the four convolution blocks.  
The YAMNet model is an audio detection model trained using an audio set with 521 classes, such as laughter, dog barking, or sirens, contained in more than 2 million video databases. The YAMNet model was proposed by [PERSON] and [PERSON] and is a computationally efficient model for the problem of classifying audio events. The VGGish model has a complicated computation because it involves over 72 million parameters. Conversely, the YAMNet model uses only 4.7 million parameters, thereby being computationally more efficient than the VGGish model. A lightweight model was designed using a convolution kernel capable of separating by depth to be used in the computer vision field; this model was designed based on the proposed MobileNet architecture in the study of [PERSON] [20]. The YAMNet model consists of 14 convolution layer blocks, and in this study, the pretrained model was modified by reassigning the number of classes by adding a fully connected layer after the last convolution layer and then replacing the classification layer.
## 2 Materials and Methods
### Optimization of Grey-Green Infrastructure (GGI) Networks
#### 2.3.3 Life Cycle Cost  
The comparison of various GGI configurations was conducted based on their respective LCC, which served as a key metric for evaluating investments in stormwater management systems. LCC encompassed both initial capital expenditures and subsequent operation and maintenance (O&M) costs, expressed as present values (PV). This comprehensive approach ensured a holistic assessment of the economic feasibility of different GGI designs (Equations (3) and (4)),  
\[LCC=Capital_{GGI}+PV_{O\&M-GI}+PV_{O\&M-GREI} \tag{3}\]  
\[PV_{O\&M}=\sum_{1}^{n}O\&M\frac{1}{(1+i)^{n}} \tag{4}\]  
where \(n\) is the expected service life, set at 30 years [8]; \(i\) represents the annual discount rate in China, fixed at 2% [37]; \(PV\) denotes the present value of capital costs; \(PV_{O\&M}\) signifies the \(PV\) of cumulative O&M costs over the service life.  
Capital costs were estimated based on locally sourced materials and construction/project costs [38]. Annual O&M costs for PP, BCs, and GREI were approximated at 4%, 8%, and 10%, respectively, of their initial capital cost, in accordance with findings by [PERSON] et al. [39] and adjusted for the Guangzhou context.
# Impacts of Heat and Drought on Gross Primary Productivity in China
###### Abstract  
Heat and drought stress, which often occur together, are the main environmental factors limiting the survival and growth of vegetation. Studies on the response of gross primary production (GPP) to extreme climate events such as heat and drought are highly significant for the identification of ecologically vulnerable regions, ecological risk assessments, and ecological environmental protection. We got 1982-2017 climatic data from the University of East Anglia Climatic Research Unit, Norwich, England, and GPP data from National Earth System Science Data Sharing Service Platform, Beijing, China. Using Theil-Sen median trend analysis and the Mann-Kendall test, we analyzed trends in temperature and the standardized precipitation/standardized precipitation evapotranspiration indices in the eight vegetation regions of China. Additionally, the response of GPP to the single and combined impacts of heat and drought were analyzed using multidimensional copula functions, and GPP reduction probabilities were estimated under different drought levels and heat intensities. The results showed that the probability of a drastic GPP reduction increases with increasing drought levels and heat intensities. The combined impacts of heat and drought on vegetation productivity is greater than the impacts of either drought or heat alone and presents a nonlinear superposition of the two extremes. The impact of heat on GPP is not evident when the drought level is high. The temperate grassland and warm temperate deciduous broad-leaved forest regions are the most sensitive regions to drought and heat in China. This study provides a scientific basis for the comprehensive evaluation of the risk of GPP reduction under the single and combined impacts of heat stress and drought stress.  
G 2021 13 378 10.3390/rs13030378
## 6 From Single-Point to Multi-Point Equalization
### Prototype Approach
The RRE is based on measuring the sound pressure at the listening position and in at least three other randomly selected positions. The measurement in the listening position holds information on the perceived sound field, while the other room measurements hold information on the energy in the three-dimensional sound field. The information is then used to calculate lower and upper gain limits for the designed equalizer. The prototype response is automatically calculated based on the measurements. At low frequencies, the prototype response is designed to provide the same room gain of a listening room conforming to the IEC 268-13 standard [154], approximating a smooth room gain with a second-order shelving function, which adds 6 dB level smoothly below 120 Hz [152]. The equalizer is minimum phase and designed on basis of the homomorphic technique [151].