esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Data Sources
#### 4.2.2 Remote Sensing and Climate Dataset  
Grassland types were used to classify and estimate the AGB of natural grasslands in Xinjiang. The grassland-type data were based on the 1995 1:1 million grassland-type map of the National Earth System Science Data Center, which was obtained using MSS and  
Figure 9: Geographic location (**a**), sampling point distribution (**b**), and grassland-type map (**c**) on Xinjiang, China.  
TM satellite remote sensing data from the mid-1980s and early 1990s, as well as various geographic data sources.  
Seven variables, including temperature (TMP), precipitation (PRE), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), gross primary productivity (GPP), leaf area index (LAI), and evapotranspiration (ET), were used to construct a random forest algorithm for grassland types in combination with measured data. The meteorological and digital elevation model (DEM) data were collected from the Data Center for Resources and Environmental Sciences at the Chinese Academy of Sciences. MODIS-NDVI, MODIS-EVI, MODIS-GPP, MODIS-LAI, and MODIS-ET were obtained from the National Aeronautics and Space Administration. The data acquisition time was the same as the collection time for the sample plot data, which was unified in July of the same year. The data sources are listed in Table 3.  
Temperature and precipitation data from 105 meteorological stations in Xinjiang with complete references and completeness were selected based on the requirements of the study area. Additionally, temperature and precipitation data were interpolated using ANUSPLIN4.4 to obtain meteorological remote sensing data with a spatial resolution of 1 km, which were the driving factors.
## 3 Results
### Spatial-Temporal Distribution of Chl-a
#### 3.2.2 Temporal Variations of Chl-a  
The Chl-a concentrations in the four seasons in 2019 are presented in Figure 8.  
Figure 8: Seasonal changes of Chl-a.  
Figure 7: Distribution of Chl-a concentrations in Beibu Gulf in Guangxi.  
The thermal and dynamic structures of the sea surfaces of the Beibu Gulf of Guangxi are affected by subtropical monsoons. The spatial and temporal differences in the Chl-a concentration in the Beibu Gulf were caused by the transport of nutrients. The concentration of Chl-a in the coastal sea surface exhibited clear seasonal changes. The average concentration of Chl-a in the summer was the highest (8.312 \(\upmu\)g/L), while it was moderate during autumn (7.714 \(\upmu\)g/L) and spring (6.954 \(\upmu\)g/L) and lowest in winter (6.680 \(\upmu\)g/L).
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 6 Performance Evaluation
### Minimum Number of Hops at Route Construction  
For our comparison of communication efficiency, we discuss the minimum number of hops required for route construction.  
The compared methods prohibit any intermediate nodes from creating an RREP, as these methods require a signature from the destination node. Thus, in all three methods, an RREQ and RREP must always begin from the end nodes. Therefore, the compared methods require \(2(n+1)\) hops for any route construction.  
However, in the proposed method, if an intermediate node has an existing route to the destination, it can behave as a responder instead of a destination node. Herein, we define the number of intermediate nodes between the source node and responder as \(N\) (\(0\leq N\leq n-1\)), an example of which is illustrated in Figure 2. In the proposed method, the number of hops for route construction can be expressed as \(2(N+1)\), implying that, in the best case, the route can be constructed with two hops for any \(n\). Thus, the proposed method can significantly reduce the number of redundant hops required for route construction.
## 2 Algorithms  
### Elliptic Curve Cryptography  
In 1985, [PERSON] and [PERSON] first proposed the new cryptographic algorithm of elliptic curve cryptography, called ECC. The elliptic curve--\(E_{p}(a,b)\)--is defined by the equation below:  
\[y^{2}=x^{3}+ax+b\ (mod\ p), \tag{1}\]  
where \(p\) is the prime value.  
Because of the advantage of ECC, which has a shorter key length and faster calculation speed compared with RSA algorithm[14], as solving the elliptic curve discrete logarithm problem (ECDLP) is more difficult than factoring an integer, the application of it has become a very popular research topic in cryptography. To be specific, the elliptic curve must be a cyclic group over a finite field \(GF(p)\), given a base point \(g\) called a generator with order \(r\), and given \(Q=tg,\ t\in Z_{r}\). It is tough to find an integer \(t\) with \(g\) and \(Q\). ECC can be generally divided into the four steps below.  
1. Set up The plain text is first mapped to a point \(M\) on the elliptic curve, and A and B then agree on the same elliptic curve \(E_{p}(a,b)\) with a generator \(g\).
2. Key generation A selects an integer \(t_{a}\in Z_{r}\) as its private key and computes \(g_{a}=t_{a}g\) as the corresponding public key. B also selects an integer \(t_{b}\in Z_{r}\) as its private key and computes \(g_{b}=t_{b}g\) as the corresponding public key.
3. Encryption A randomly selects an integer \(k\in Z_{r}\) and computes \(C_{1}=kg\) and \(C_{2}=M+kg_{b}\), and then transmits both to B.
4. Decryption After B receives \(C_{1}\) and \(C_{2}\), B then computes \(C_{2}-t_{b}C_{1}\) to get point \(M\). Finally, \(M\) can be mapped back to the original plain text. \[\begin{array}{ll}C_{2}-t_{b}C_{1}&=(M+kg_{b})-t_{b}kg\\ &=(M+kt_{b}g)-kt_{b}g=M\end{array}\] (2)  
Besides, ECC can define bilinear pairing such as Weil pairing and Tate pairing, which has been widely used in Identity-Based Encryption (IBE) [15] and ABE.
## 2 Related Works  
In this section, we briefly review our previous research on the CGAU. The CGAU is an improved convolution-attention structure based on the convolution-augmented transformer (conformer) [22]. In this paper, we used the CGAU to form the basic unit of the low-band speech enhancement network LB-Net.  
The conformer adopts a Makaron-style structure and constructs the conformer block by inserting deep convolution modules between the multi-head self-attention (MHSA) module and the feed-forward module [20]. The combination of convolution and self-attention can capture the global information and local features of speech at the same time [20]. However, due to the use of a large number of MHSA structures and feed-forward modules in multiple stacked conformer blocks, the computational complexity of the network has increased [20]. The structure of the conformer is shown in Figure 1.  
Figure 1: Conformer architecture.  
In our previous work, we proposed a novel architecture called the CGAU, which is shown in Figure 2. The CGAU consists of a single-head self-attention (SHSA), a gated linear unit (GLU) and a convolutional module. Unlike the Makaron-style structure used in the conformer, the CGAU replaces the two feed-forward modules with a GLU. As shown in Figure 2, U is the output of one of the branches in GLU. Z is a shared rep-resentation which can be converted into the query and key in the attention mechanism. V represents the value in the attention mechanism. The GLU's powerful performance reduces the dependence of CGAU blocks on the self-attention structure, thereby allowing us to use SHSA instead of MHSA and reduce network complexity. Moreover, the CGAU integrates the convolution module and GAU effectively by inserting a deep convolution module into the network to capture local speech information, thus significantly reducing the network's computational complexity.  
When we use the CGAU to extract speech features, we need to capture time dependence and frequency dependence, respectively. Therefore, we concatenate two CGAUs to build a CGA block, as shown in Figure 3. Through the stacking of the CGA blocks, we can gradually extract the features of different levels of speech. The specific architecture of each CGA block is shown in the figure below. The shape of the feature map we input into the CGA Block is \(D\in\mathbb{R}^{B\times T\times F\times C}\), where \(B\) represents the batch size, \(T\) represents the number of frames, \(F\) represents the number of frequency bins of the complex spectrogram, and \(C\) represents the number of the channel. The input feature map \(D\) is reshaped to \(D^{T}\in\mathbb{R}^{BF\times T\times C}\) and input into the first CGAU to capture the time dependence. Then, the output \(D_{o}^{T}\) and \(D^{T}\) are element-wise added, reshaped to \(D^{F}\in\mathbb{R}^{BT\times F\times C}\), and input into the second CGAU to capture the frequency dependence. Finally, the output \(D_{o}^{F}\) and input \(D^{F}\) are element-wise added and reshaped to the final output, \(D_{o}\in\mathbb{R}^{B\times T\times F\times C}\).
## 5 Discussion  
### Reconstructing Non-Symmetrical Designs  
This study focuses on the 3D reconstruction of symmetrical designs, exemplified by objects such as eyeglass frames, which are prevalent in industrial and architectural domains. Symmetry is a defining feature in designs ranging from automobiles and monitors to iconic architectural structures like the Louvre Pyramid. However, non-symmetrical designs are equally pervasive in these fields, characterized by their unique geometries and deliberate asymmetry. Such designs often embody a designer's intention to create dynamic, organic forms that deviate from traditional symmetrical patterns [52]. In industrial design, non-symmetrical examples include gaming mice designed for one-handed use, while architectural masterpieces like the Guggenheim Museum illustrate the aesthetic and structural appeal of asymmetry.  
Building on our approach for the 3D reconstruction of symmetrical designs, we propose extending deep learning techniques to address the challenges associated with reconstructing non-symmetrical 3D shapes. Specifically, our method for symmetrical designs can be adapted to handle non-symmetrical ones by incorporating advanced computational solutions. Deep learning algorithms, which have demonstrated robust performance in reconstructing symmetrical 3D shapes, can be further refined to manage the irregularities and complexities of non-symmetrical geometries. For example, adding specialized layers to the neural network architecture could enhance its capacity to process the variations intrinsic to non-symmetrical designs. Additionally, leveraging Generative Adversarial Networks (GANs) can improve the model's ability to infer and reconstruct missing or ambiguous components, facilitating the creation of accurate 3D models even from incomplete or imprecise sketches. By integrating these strategies, our approach becomes more versatile, enabling the effective reconstruction of both symmetrical and non-symmetrical 3D shapes while accommodating diverse design features.
## 7 Research Challenges, Open Issues, and Future Directions
### Lack of Interpretability/justification  
FL models' fairness and interpretability/justification are critical to making humans understand why the learning model has made a decision and improving the learning process. The higher the interpretability, the easier it is to understand why certain decisions or predictions have been made. Most models are considered black-box since they only make a decision; however, they do not interpret them. Therefore, this issue can be handled by converting the black-box model to a white box. White box models are interpretable and justify how a decision is made. One such example of a white-box model is the decision tree (DT), a rule-based approach. Features are split based on information gain from root to leaf nodes, where the leaf nodes are the labels or classes to predict.
# Predicting Location of Tweets Using Machine Learning Approaches
## 4 Methodology
### Location Prediction Using Tweet Text, Home Location, and Named Entity  
After we evaluated the predictability of each two sets of features separately, we evaluated it by combining the three feature sets. We prepared and represented the features exactly as we did previously. This experiment was supposed to yield the best predictability performance compared with the previous ones due to consolidating the three sets of features together.
## 2 Materials and Methods
### In Vitro Drug Release  
After encapsulating PGS (5 mg/mL or 8 mg/mL) or PTX (5 mg/mL or 8 mg/mL) in their blend of PLGA-CS-PEG polymer, a thirty-day in vitro drug release study was carried out in a bid to analyze the kinetics and thermodynamics of in vitro drug release. The in vitro drug release experiments were carried out at three temperatures: 37, 41, and 44 \({}^{\circ}\)C. The first temperature (37 \({}^{\circ}\)C) corresponds to human body temperature while the later (41 \({}^{\circ}\)C and 44 \({}^{\circ}\)C) correspond to hyperthermic temperatures.  
For each formulation, 10 mg of lyophilized PLGA-CS-PEG microparticles were suspended separately in a centrifuge tube containing 10 mL of PBS (pH = 7.4) with 0.01% (\(v\)/\(v\)) Tween 80 to maintain sink condition. This was done in triplicate for each formulation. Then, for in vitro drug release process, the centrifuge tubes containing samples were positioned back into the orbital shaker (Innova 44 Incubator, Console Incubator Shaker, New Brunswick, NJ, USA) rotating at 80 rpm and maintained at the respective set temperatures (37, 41, and 44 \({}^{\circ}\)C). At the predetermined time duration, 1 mL of the centrifuged supernatant was obtained for drug content analysis and then replaced with 1 mL PBS (pH 7.4). The samples in the centrifuge tubes were swirled gently and returned into the shaker incubator to continue the drug release study.  
A UV-visible spectrophotometer manufactured by UV-1900 Shimadzu Corporation, Tokyo, Japan, was employed to evaluate the absorbance of each 1 mL supernatant sample released at set temperatures after 24 h. To measure the absorbance for prodigiosin (PGS) analysis, the wavelength of the UV-visible spectrophotometer was fixed at 535 nm and 229 nm for PGS and PTX, respectively. The concentrations of drug (PGS or PTX) released from their respective PLGA-CS-PEG microparticles loaded with drugs were determined from a standard curve [42].  
In addition, to determine the drug encapsulation efficiencies of drug-loaded PLGA-CS-PEG microspheres, a predetermined amount of drug-loaded PLGA-CS-PEG microparticles was dissolved in DCM. The UV-visible spectrophotometer was used to determine the concentration of drug in the suspension.  
Equation (1) was used to determine the _Drug Encapsulation Efficiency_ (\(DEE\)) of drug-loaded PLGA-CS-PEG microparticles:  
\[Drug\ Encapsulation\ Efficiency\ (DEE)=\frac{M_{X}}{M_{Z}}\times 100 \tag{1}\]  
where \(M_{x}\) represents the amount of drug that was encapsulated and \(M_{z}\) represents the amount of drug used for preparing the PLGA-CS-PEG microparticle.
## 3 Results
### Phase Analysis  
The X-ray diffraction (XRD) patterns presented in Figure 3 provide valuable insights into the crystal structure of the selective plated copper coatings created using both the acidic and alkaline electrolyte solutions. The patterns exhibited well-defined diffraction peaks corresponding to specific copper crystal planes, which were successfully indexed and matched with the cubic Cu crystal structure. These findings align well with the information provided by the JCPDS copper file number 04-0836, further validating the composition and crystal structure of the coatings.  
For a more detailed characterization, peak parameters derived from the XRD profiles can be found in Tables 1 and 2. These parameters provide information about the peak positions, peak intensities, and peak widths, contributing to a more comprehensive understanding of the copper coatings' crystallographic properties.  
Overall, the XRD analysis confirmed the formation of a single-phase solid solution of pure copper in the selective plated coatings, regardless of the substrate material and electrolyte used, highlighting their consistency and quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Intensity [\%]** & **d-Spacing [Å]** & **2 Theta [\({}^{\circ}\)]** & **HKL** & **FWHM** \\ \hline
1 & 100.00 & 2.08820 & 50.727 & 1 1 1 & 0.312 \\
2 & 46.00 & 1.80819 & 59.300 & 2 0 0 & 1.152 \\
3 & 20.00 & 1.27810 & 88.833 & 2 2 0 & 0.336 \\
4 & 17.00 & 1.09009 & 110.286 & 3 1 1 & 1.152 \\
5 & 5.00 & 1.04363 & 117.987 & 2 2 2 & 0.090 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Peak parameters in terms of intensity, distance between the crystallographic planes (d-Spacing), angle between the transmitted and reflective beam (2 Theta), Miller indices of the crystallographic plane (HKL), and full width at half maximum of the peak (FWHM) derived from the diffractogram obtained from the selective plated copper coatings using Cu acid solution.  
Figure 3: X-ray diffraction patterns of the selective copper coatings using either acidic (red) or alkaline (blue) electrolyte solutions.
## 3 Data and Methods
### AI Model Training  
To improve the performance of the AI model under specific tectonic and observation conditions, we rapidly constructed a sample dataset for the local region and retrained the AI model. The methodology for curating the AI training dataset has been delineated in the preceding section. Before feeding samples into the AI model, data augmentation techniques were employed on the original dataset to enhance its robustness. This included temporal adjustments of the sampling window and the infusion of varying degrees of white noise. Specifically, the P-wave arrival served as the temporal anchor around which five random shifts within a 15 s interval were executed, each accompanied by the introduction of white noise ranging from 0 to 40%.  
The hyperparameters, notably the learning rate and batch size, are pivotal in the AI training process, influencing model convergence, the risk of overfitting, and computational efficiency. An inordinately high learning rate can precipitate non-convergence, while an excessively low learning rate may unduly protract the convergence timeline. Similarly, a minuscule batch size could prove inadequate in counterbalancing the stochastic influence on gradient estimation, whereas an excessively large batch size could lead to protracted iteration durations. For the training executed in this study, we utilized hardware equipped with a GeForce RTX 3090 GPU, boasting 24 GB of memory (Nvidia Corporation, Santa Clara, CA, USA). A learning rate of 0.001 was established for both CNN and RNN training,  
Figure 3: The workflow of earthquake detection and location. The blue boxes represent important processing steps, while the black boxes indicate input and output data.  
with a batch size of 512. The training dataset was apportioned into training, validation, and test sets following a 7:2:1 ratio.  
Detection accuracy was quantified as the proportion of correct predictions derived from the training dataset during the CNN training epoch, while validation accuracy was ascertained using the validation dataset. An observed increment in detection accuracy concomitant with a decrement in validation accuracy typically denotes the phenomenon of overfitting within the AI training regime. The 30 s sampling window was dissected into multiple time steps with a granularity of 0.5 s. The accuracy of these time steps, along with the validation rate, was defined as the likelihood of accurately predicting the P- and S-wave phases within these temporal increments during the RNN training phase. The picking uncertainty is characterized as the temporal precision in identifying the P- and S-wave arrivals within the training/validation datasets during the RNN training phase. The trajectories of detection/validation accuracy, picking uncertainty, and time step accuracy are graphically represented in Figure 4.
## 4 Experiment Results
### EfficientNet Optimal Parameter Search
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Width** & **Depth** & **Resolution** & **Train Accuracy** & **Train Loss** & **Validation Accuracy** & **Validation Loss** \\ \hline
2.2 & 1 & 640 & 0.999188 & 0.002179 & 0.965856 & 0.124817 \\
2.2 & 1.5 & 640 & 0.998289 & 0.047659 & 0.956548 & 0.141712 \\
2.2 & 1.8 & 640 & 0.976717 & 0.070334 & 0.955969 & 0.118954 \\
2.2 & 2.2 & 640 & 0.989583 & 0.011818 & 0.958929 & 0.133038 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Grid search to find initial appropriate parameters–depth. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Width** & **Depth** & **Resolution** & **Train Accuracy** & **Train Loss** & **Validation Accuracy** & **Validation Loss** \\ \hline
1.4 & 1 & 640 & 0.998375 & 0.015735 & 0.952546 & 0.154295 \\
1.6 & 1 & 640 & 0.996971 & 0.003325 & 0.958912 & 0.165796 \\
1.8 & 1 & 640 & 0.99867 & 0.013629 & 0.958912 & 0.143445 \\
2.0 & 1 & 640 & 0.999705 & 0.002596 & 0.959491 & 0.154562 \\
2.2 & 1 & 640 & 0.999188 & 0.002179 & 0.965856 & 0.124817 \\
2.4 & 1 & 640 & 0.999778 & 0.004105 & 0.953704 & 0.150497 \\
2.8 & 1 & 640 & 0.999777 & 0.005866 & 0.967857 & 0.142077 \\
3.3 & 1 & 640 & 0.999851 & 0.0
# A Machine Learning Predictive Model to Detect Water Quality and Pollution
## 4 Methodologies
### Data Imputation  
The data we collected has a large percentage of missing data, around 53%, which is problematic for the data-driven machine learning model. Therefore, a well-performing data imputation method is necessary to standardise and clean them to enhance the performance of machine learning models. We design an experiment to examine the efficiency of different imputation models, which includes: (i) simple imputation, (ii) k-nearest neighbour (KNN) imputation, (iii) singular value decomposition (SVD) imputation, and (iv) iterative imputation.  
In our experiment, we select a dataset as the target domain. Then, experimentally, we evaluated the performance of various imputation methods. We performed the imputation methods under different missing rates and repeated the experiment ten times to obtain a statistically significant result. We randomly drop values with a missing rate ranging from 0.35 to 0.65, with an increment step of 0.05. Then we compared the mean of the symmetric mean absolute value (SMAPE) between the imputed and the actual values. The highest performing imputation method is selected in this research.  
Tables 2 and 3 illustrate the performance differences in-between several data imputations methods. Simple imputation refers to using the mean across each feature column to fill missing values. Data standardisation refers to rescaling the value of each feature to the same scale to let all features contribute equally to the developed model. Several ways to standardise include min and max normalisation, z-score standardisation, and centralisation. Standardisation rescales the value into zero means and one unit standard deviation. Normalisation is to rescale the value into 0 and 1. Centralisation is to rescale the value to be centred at 0.  
In classification and clustering algorithms, z-score standardisation performs when the distance is calculated to measure the similarity, or dimensionality reduction technologies, such as principal component analysis (PCA), are applied. In our dataset, the models we propose to develop, such as support vector machines (SVM) and KNN, highly rely on distance calculation, so we adopt the z-score standardisation technique.