esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
## 2 The Preliminaries
### A Brief Introduction to ELM  
Extreme learning machine (ELM) is a generalized single hidden-layer feedforward network. In ELM, the hidden-layer node parameter is mathematically calculated instead of being iteratively tuned; thus, it provides good generalization performance at thousands of times faster speed than traditional popular learning algorithms for feedforward neural networks [31].  
As a powerful classification model, ELM has been widely applied in many fields. For example, in [32], ELM was applied for plain text classification by using the one-against-one (OAO) and one-against-all (OAA) decomposition scheme. In [31], an ELM-based XML document classification framework was proposed to improve classification accuracy by exploiting two different voting strategies. A protein secondary prediction framework based on ELM was proposed in [33] to provide good performance at extremely high speed. The work in [34] implemented the protein-protein interaction prediction on multi-chain sets and on single-chain sets using ELM and SVM for a comparable study. In both cases, ELM tends to obtain higher recall values than SVM and shows a remarkable advantage in computational speed. The work in [35] evaluated the multi-category classification performance of ELM on three microarray datasets. The results indicate that ELM produces comparable or better classification accuracies with reduced training time and implementation complexity compared to artificial neural network methods and support vector machine methods. In [36], the use of ELM for multiresolution access of terrain height information was proposed. The optimization method-based ELM for classification was studied in [37].  
ELM not only tends to reach the smallest training error, but also the smallest norm of weights [6]. Given a training set \(D=\{(\mathbf{x}_{i},\mathbf{t}_{i})|\mathbf{x}_{i}\in\mathbf{R}^{n},\mathbf{t }_{i}\in\mathbf{R}^{m},i=1,\ldots,N\}\), activation function \(g(x)\) and hidden node number \(L\), the pseudocode of ELM is given in Algorithm 1. More detailed introductions to ELM can be found in a series of published literature [6; 37; 38].  
```
Input: DB: dataset; HN: number of hidden layer nodes; AF: activation function Output: Results
1 for\(i=1\)to\(L\)do
2 randomly assign input weight \(w_{i}\);
3 randomly assign bias \(b_{i}\);
4 calculate \(\mathbf{H}\);
5 calculate \(\mathbf{\beta}=\mathbf{H}^{\mathsf{T}}\)
```  
**Algorithm 1**ELM
## 3 Detailed Analysis of Maximum Power Point Tracking (MPPT) Methods
### ANN (Artificial Neural Network)-Based MPPT Method  
Artificial Neural Networks (ANNs) are machine learning methods inspired by the working principles of the human brain. ANN-based MPPT algorithms are used to adapt to the complex and dynamic nature of PV systems. This method learns the relationship between environmental variables (e.g., solar irradiance and temperature) and the output power of the PV system to predict the maximum power point.  
* Highly accurate predictions.
* Excellent adaptability to environmental changes.  
* The training process is time-consuming.
* The performance depends on the quality of the training dataset.
# HI-Sky: Hash Index-Based Skyline Query Processing
## 3 Hash Index-Based Skyline Query Processing
### Hash Index-Based Skyline Query Processing  
To increase the processing efficiency of skyline computation, we propose a HashGrid-based skyline query method called hash index-based skyline (HI-Sky). HashGrid is an index structure that manages the given data by dividing the data space into uniformly sized partitions. In this section, we discuss the basic background, data space pruning step, and skyline computation step of HI-Sky.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 2 Methods
### Data Management Based on the Pixel-Level Grid
#### 2.2.2 Product Definition and GiST Index  
ODC provides definition files and organization scripts for EO data such as Landsat and Sentinel, and configuration files and image metadata files in the YAML format (YAML Ain't Markup Language, a highly readable format used to express data serialization) [41] can be automatically generated from the image files. Access methods and tools supporting GF-1 satellite imagery data are still needed. To this end, we set and achieved the goal to acquire spectral information, band information, and spatial reference information from GF-1 satellite imagery files and automatically generated metadata files in YAML format that conform to ODC standards. The specific processes included imagery data product definition, imagery data format conversion, and index construction, as shown in Figure 3. In the imagery data product definition phase, shown in Figure 3(I), a unified GF-1 imagery data product definition file type was formulated (e.g., GF-1 Product.yaml). In the imagery data format conversion stage, as shown in Figure 3(II), the imagery data were evaluated to determine whether single-band separation was necessary, as shown in Figure 3(II)-(1), before converting their metadata files in formats such as XML (Extensible Markup Language) into the YAML format, as shown in Figure 3(II)-(2). In the index construction phase, shown in Figure 3(III), an index file (metadata. yaml) with a UUID (Universally Unique Identifier) was generated for each scene of imagery data, and its relevant information was stored in the database. The details are as follows:  
1. The imagery data product definition focused on defining and describing the type of prestored imagery data. Considering the characteristics of GF-1 imagery metadata, the defined information included product name, basic metadata information, spatial reference information, and band information, which were saved in YAML format.
2. The image data format conversion was mainly the imagery data single-band extraction and YAML format metadata generation. Since ODC is based on single-band storage and the GF-1 imagery data are in a composite band format, a single-band separation operation is required to extract single-band data from the original images. To avoid the case of NoData in metadata documents of XML and other formats, we unified the NoData values of the original images to the parameters defined in YAML documents using the Geospatial Data Abstraction Library (GDAL) tool.
3. Index construction focused on storing the above YAML format data products in the PostgreSQL database with the GiST indexing method. GiST is a balanced tree structure access method, which can be used as a basic template to implement arbitrary indexing patterns such as b-trees and r-trees.  
Figure 3: The process from source data to indexed data.
## 4 Firmware Design  
The firmware controls the localization module and runs on the SBC. In order to obtain a position estimate of the mobile receiver unit from RSS values, the firmware executes all required tasks, e.g., power spectrum computation or Bayesian filtering. The open source firmware can be obtained at [[https://github.com/[PERSON]/RF_Localization](https://github.com/[PERSON]/RF_Localization)]([https://github.com/[PERSON]/RF_Localization](https://github.com/[PERSON]/RF_Localization)) or [[https://github.com/[PERSON]/RFLoc](https://github.com/[PERSON]/RFLoc)]([https://github.com/[PERSON]/RFLoc](https://github.com/[PERSON]/RFLoc)).  
The Python programming language is chosen for the firmware implementation, because it is a widely-available cross-platform open-source language. The DVB-T dongle is interfaced via the librtlsdr library ([[https://github.com/librtlsdr/librtlsdr](https://github.com/librtlsdr/librtlsdr)]([https://github.com/librtlsdr/librtlsdr](https://github.com/librtlsdr/librtlsdr))), which provides drivers for the RTL2832U chip-set. The wrapper pyrtlsdr ([[https://github.com/roger-/pyrtlsdr](https://github.com/roger-/pyrtlsdr)]([https://github.com/roger-/pyrtlsdr](https://github.com/roger-/pyrtlsdr))) is included, as well, as it conveniently wraps functions of the librtlsdr library to make them accessible within the Python software architecture. These functions include setting the center frequency, i.e., \(433\,\mathrm{MHz}\), on the DVB-T dongle and the access to the complex eight-bit I/Q samples, which are provided as an array for further signal processing. The communication chain is shown in Figure 13.  
The localization process and, thus, the firmware are split into two main workflows: first, the calibration, which performs the parameter identification of the URSM, and second, the localization, which contains the localization algorithm via Bayesian filtering. Both steps are part of the super class RfEar-provided functionalities, which are commonly used for its subclasses CalEar and LocEar.
## 2 Method Development
### Feature Extraction  
The features of each paper were extracted from the bibliographic information and citation network of the obtained paper. The features referred to here were learning data for predicting emerging papers. These were used as explanatory variables. The features in this study can be classified into four categories: network macro features; cluster features; network centrality features; citing paper features. A network macro feature is a typical feature of the target citation network. These include: the maximum value (NW MAXQ) of the number of papers (NW NODES); the total number of citations (NW EDGES); the modularity (\(Q\) value) of the clusters in the network. Cluster features describe the cluster to which the target paper belongs to and includes the maximum \(Q\) value of the cluster (CL QMAX), the number of nodes in the cluster (CL NODES), and the order of the cluster (CL RANK) to which the subject paper belongs. The network centrality features indicate the central position of a given paper in a cited network. Specifically, these include: the centrality degree (CNT DEGRE) [43]; the betweenness centrality (CNT BETWE) [43]; the closeness centrality (CNT CLOSE) [43]; the eigenvector centrality (CNT EIGEN) [44]; the network constraint (CNT NETWO) [45]; the clustering coefficient (CNT CLUST) [46]; the page rank (CNT PAGER) [47]; the hub score (CNT HUBSC) [48]; the authority score (CNT AUTHOR) [48]. Reference paper feature values were calculated for the cited papers, and representative statistical values such as the maximum, minimum, average, and total were used as the feature values. For each of the papers, 15 features (network, cluster, and centrality) can be calculated directly. In addition, three cluster features and nine centrality features were calculated for each reference in the paper, and the maximum, minimum, average, and total values were considered to be the features. Thus, the number of reference paper features was 48 (=12 * 4). The number of all features was 63 (=15 + 48) for all papers. Table 1 summarizes all 63 types of features in the four abovementioned categories. These features calculated the maximum connected components among the cited networks in the target field, which were used as explanatory variables in the prediction model.  
Figure 1: Overview of the method.
## 3 Development of the Proposed Computational Unit  
A universal integrated logic device, the computational unit performs arithmetic and logical computations. The computational unit is an essential microprocessor component due to its versatility. The computational unit proposed in this research is illustrated using the block diagram shown in Figure 2. The computational unit takes two operands, A and B, as inputs and produces an output by applying predefined operations to the inputs, as presented in Table 1. The selection input S(2) decides the arithmetic or logic operation. The inputs S(1) and S(2) decide the operations to perform within the arithmetic or logic block. The arithmetic computational unit comprises an ET adder and a low-power multiplier. An OR, AND, XOR, and NOT logical gate comprises the logic unit using operand isolation.  
The computational unit is built by integrating the ET adders, the low-power multipliers, and the logic unit for 8-bit, 16-bit, and 32-bit computations. The computational unit has been configured so that the logical module activates when S(2) is 1, and the arithmetic module activates when the select input S(2) is 0. Depending on the S(0) input, addition using the ET adders or multiplication utilizing the low-power multipliers is performed. When the input S(2) is 1, the inputs S(1) and S(0) determine which logical operation has to be carried out. The different combinations of designs are mentioned in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**S(2)** & **S(1)** & **S(0)** & **Operation** & **Function** \\ \hline
0 & 0 & 0 & A + B & Addition using ET Adders \\ \hline
0 & 0 & 1 & A \(\times\) B & Multiplication using LP Multipliers \\ \hline
1 & 0 & 0 & A \(\mid\)B & \\
1 & 0 & 1 & A \(\oplus\) B & \\
1 & 1 & 0 & \(\sim\)A & Logical operation using logic unit \\
1 & 1 & 1 & A \& B & \\ \hline \end{tabular}
\end{table}
Table 1: Operation Table for Proposed Computational Unit.  
Figure 2: Proposed Computational Unit Block Diagram.
# Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model
## 4 Experiments
### Generated Quality Evaluation
#### 4.3.2 Sentiment Evaluation
Comparing the above metrics results for all models on different datasets, we can observe that our model has stable advantages over both ASTE-Data-V2 and MAMS-ASTA, which indicates that our AISeCond model has stronger adaptability. Additionally, Figure 7 presents the learning curves for fine-tuning all models with the labeled dataset, which also demonstrates the strong capabilities of our model compared to baselines. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Dataset** & **Models** & **Cov-a** & **Cov-o** & **Cov-ao** & **Acc.** \\ \hline \multirow{8}{*}{ASTE-Data-V2} & PPLM & 0.3597 & 0.3642 & 0.1094 & 0.1761 \\ \cline{2-6}  & HTT & 0.7689 & 0.7773 & 0.6050 & 0.6328 \\  & T5-base & 0.9563 & 0.9764 & 0.9403 & 0.7812 \\  & T5-large & 0.9633 & 0.9839 & 0.9508 & **0.7948** \\  & UniLM & 0.9513 & 0.9568 & 0.9182 & 0.7450 \\  & BERT-Gen & 0.9352 & 0.9343 & 0.8886 & 0.7521 \\  & UniLM-v2 & 0.9438 & 0.9488 & 0.9087 & 0.7475 \\ \cline{2-6}  & AISeCond & **0.9824** & **0.9849** & **0.9734** & 0.7771 \\  & w/o sentiment loss & 0.9633 & 0.9649 & 0.9468 & 0.7683 \\  & w/o query-hint & 0.9412 & 0.9313 & 0.8966 & 0.7443 \\  & w/o unlabeled dataset & 0.8158 & 0.8841 & 0.7556 & 0.6306 \\ \hline \multirow{8}{*}{MAMS-ASTA} & HTT & 0.7203 & 0.5123 & 0.3800 & 0.4532 \\  & T5-base & 0.9610 & 0.9147 & 0.9042 & 0.5734 \\  & T5-large & 0.9738 & 0.9453 & 0.9416 & 0.5698 \\  & UniLM & 0.9251 & 0.7821 & 0.7590 & 0.5883 \\  & BERT-Gen & 0.9438 & 0.8009 & 0.7807 & 0.6048 \\  & UniLM-v2 & 0.9341 & 0.7515 & 0.7305 & **0.6310** \\ \cline{2-6}  & AISeCond & **0.9798** & **0.9588** & **0.9558** & 0.6267 \\  & w/o sentiment loss & 0.9318 & 0.8952 & 0.8825 & 0.6050 \\  & w/o query-hint & 0.8338 & 0.6811 & 0.6257 & 0.5447 \\  & w/o unlabeled dataset & 0.7829 & 0.7095 & 0.6325 & 0.5157 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for the sentiment evaluation. Note that Accuracy (Acc.) is a rate indicating how many fine-grained sentiments are accurately expressed in the sentence, and it is automatically evaluated by an external classifier. Figure 7: Learning curves for fine-tuning models with the labeled dataset. **(a)** illustrated the learning curves for BLEU-4 changing with fine-tuning steps. **(b)** illustrated the learning curves for Cov-ao changing with fine-tuning steps.
## 3 Results and Discussion
### Optimization of Purge and Trap (P&T) Preparation  
#### 3.3.1 Effect of Sample Purge Temperature  
The influence of sample purge temperature on detection accuracy were investigated at 25, 30, 35, 40, 45, 50 and 55 \({}^{\circ}\)C, respectively with the concentration of individual analyte at 20 \(\upmu\)g/L. The effects of purge temperatures on the sample purge efficiency are presented in Figure 2a,b.  
For TBA, the signal intensity reached maximum at purge temperature 55 \({}^{\circ}\)C. This phenomenon reflects that high temperature could increase dissociation energy of TBA from water to strength the mass transfer process, since appropriate sample temperature during sample purge process could promote the purge of odorous compounds from water sample [31]. Hence, high purging temperature was favorable for TBA extraction. However, excessively high preparation temperature may cause water vapor transfer into the pyrolysis system, disturbing the gasification and the following adsorption-desorption process. Water vapor is bad for the exclude of interference of impurities in environmental water, due to the interference with water vapor and other substances in water. Hence, during the GC detection process, the sample purging temperature is generally set at about 40 \({}^{\circ}\)C [32]. For this reason, the sample purging temperature of 40 \({}^{\circ}\)C was selected for the P&T method.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Designing an Energy-Conscious Algorithm
#### 5.1.3 Sparsity-Aware Arithmetic  
Sparsity-aware arithmetic reduces the number of active computations by leveraging the sparsity (presence of many zero or near-zero values) to optimize the computations. This approach can significantly reduce the number of operations and memory usage, leading to more efficient processing and thereby reducing computational resources and power consumption. In [82], the authors used sparsity-aware arithmetic in a BioCNN-based emotion detector. They exploited the sparsity generated by weight pruning to avoid unnecessary computations in zero-operand cases, thereby reducing the number of computations and the power consumption to 150 mW. Sparsity-aware arithmetic not only enables faster processing times but also allows for the efficient use of FPGA resources, facilitating the deployment of larger or more complex models on the same hardware. Additionally, it can reduce latency by decreasing the number of operations per inference. However, it is crucial to carefully manage sparsity-aware techniques to prevent significant accuracy loss.
## 4 Results and Discussion
### Land Use and Land Cover Simulation  
Table 10 reports the results of binary logistic regression analysis, which was carried out to identify the LULC type distribution according to the driving factors of LULC change after the multicollinearity test. The top three significant driving factors for the allocation of specific LULC types in the study area are the distance to water bodies, elevation, and distance to roads. The significant driving factors identified for each LULC type for LULC allocation were further used in the CLUE-S model during the simulation process. In addition, the area under the curve (AUC) values derived for allocating each LULC type using binary logistic regression analysis are between 0.65 and 0.94, which means that the difference between simulated and actual LULC is acceptable, and the model has fair to outstanding discrimination ability [56]. The simulated LULC data between 2020 and 2029 and their distribution are summarized and presented in Table 11 and Figure 7. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Driving Forces**} & \multicolumn{8}{c}{**LULC Type**} \\ \cline{2-11}  & **UR** & **PA** & **FCH** & **PO** & **AQ** & **ID** & **EF** & **MF** & **SF** & **WA** & **MI** \\ \hline Constant & \(-\)3.33699 & \(-\)6.69685 & \(-\)3.73240 & \(-\)0.44283 & \(-\)2.14060 & \(-\)1.63215 & \(-\)3.43759 & \(-\)1.92855 & \(-\)2.46648 & \(-\)3.21996 & \(-\)5.21909 \\ Elevation (X\({}_{\text{J}}\)) & \(-\)0.03065 & n. s. & n. s. & \(-\)0.98929 & \(-\)0.00804 & 0.00549 & \(-\)0.12154 & n. s. & \(-\)0.02983 & \(-\)0.02009 \\ Slope (X\({}_{\text{J}}\)) & 0.03082 & n. s. & n. s. & n. s. & 0.09641 & 0.08299 & n. s. & 0.12170 & n. s. & 0.03006 & 0.01965 \\ Distance to & n. s. & n. s. & n. s. & 0.00117 & 0.00154 & n. s. & 0.00072 & 0.00270 & n. s. & n. s. & n. s. \\ Distance to road (X\({}_{\text{J}}\)) & \(-\)0.00274 & n. s. & n. s. & \(-\)0.00068 & n. s. & \(-\)0.00070 & 0.00075 & 0.00213 & n. s. & 0.00138 & 0.00099 \\ Distance to water & n. s. & \(-\)0.00362 & n. s. & 0.00350 & \(-\)0.00101 & \(-\)0.00056 & 0.00065 & \(-\)0.00192 & \(-\)0.00082 & n. s. & 0.00055 \\ bodies (X\({}_{\text{J}}\)) & n. s. & n. s. & 0.0026 & n. s. & n. s. & n. s. & n. s. & 0.00007 & \(-\)0.00002 & n. s. & n. s. \\ Population density at sub-district level (X\({}_{\text{J}}\)) & 0.00025 & n. s. & \(-\)0.00165 & \(-\)0.00075 & \(-\)0.00075 & \(-\)0.00014 & n. s. & n. s. & \(-\)0.00015 & n. s. & n. s. \\ The average income & & & & & & & & & & & \\ The error capita at the & 0.00037 & n. s. & n. s. & n. s. & n. s. & n. s. & n. s. & n. s. & n. s. & n. s. \\ sub-district level (X\({}_{\text{J}}\)) & & & & & & & & & & & \\ \hline Area Under Curve & 0.883058 & 0.668109 & 0.717422 & 0.799695 & 0.879755 & 0.689687 & 0.888607 & 0.935244 & 0.65199 & 0.731248 & 0.695395 \\ (AUC) & & & & & & & & & & \\ \hline \hline \end{tabular} Remark: All explanatory variables are significant at \(p<0.05\) error level; n. s. is not significant at 0.05 level; AUC, area under the curve.  
\end{table} 
Table 11: Area of simulated LULC data between 2020 and 2029.
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
## 4 Simulation Study and Results
### Collision Avoidance in a Complex Environment
In the static environment, the minimum and average of the distance \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Start Point (m)** & **Start Time (s)** & **Velocity (m/s)** & **Speed (kn)** \\ \hline Dynamic Obstacle 1 & (300, 500) & 15 & (6, \(-\)6) & 16.5 \\ Dynamic Obstacle 2 & (600, 700) & 40 & (0, \(-\)10) & 19.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The information of the dynamic obstacles. Figure 5: Demonstration diagram of the USV in a static environment. (**a**) Time 1 s; (**b**) Time 18 s; (**c**) Time 83 s; (**d**) Time 93 s; (**e**) Time 113 s; (**f**) Time 126 s; (**g**) Time 143 s; (**h**) Time 168 s.  
are 12.2 m and 60.2 m, and they are 14.9 m and 72.8 m in the dynamic environment. It can be seen that the system is safe in the two tests. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Environment** & **Index** & \begin{tabular}{c} **Speed of** \\ **USV (m/s)** \\ \end{tabular} & \begin{tabular}{c} **Thruster Speed** \\ **(r/min)** \\ \end{tabular} & \begin{tabular}{c} **Propulsion Angle** \\ **(\({}^{\circ}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Computation Time** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Distance** \\ **(m)** \\ \end{tabular} \\ \hline \multirow{3}{*}{static} & maximum & 11.1 & 4000 & 8.0 & 0.22 & 111.5 \\  & minimum & 0.0 & 0 & \(-\)16.0 & 0.09 & 12.2 \\  & average & 8.3 & 3375 & \(-\)0.5 & 0.14 & 60.2 \\ \hline \multirow{3}{*}{dynamic} & maximum & 11.1 & 4000 & 10.0 & 0.21 & 135.5 \\  & minimum & 0.0 & 0 & \(-\)16.0 & 0.09 & 14.9 \\ \cline{1-1}  & average & 8.4 & 3389 & 0.4 & 0.14 & 72.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the evaluation index comprised of two tests in the simulation environment. Figure 6: Demonstration diagram of the USV in the dynamic environment. **(a)** Time 15 s; **(b)** Time 39 s; **(c)** Time 55 s; **(d)** Time 62 s; **(e)** Time 78 s; **(f)** Time 93 s; **(g)** Time 96 s; **(h)** Time 104 s; **(i)** Time 117 s; **(j)** Time 174 s.