esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Optimization of the FRM Design  
Multi-objective optimization most fully meets the requirement of designing the three-phase high-speed FRM for a real application [9]. Some multi-objective methods are aimed to build the Pareto front, that is, the set of the solutions in which no objective can be improved without worsening other objectives. In this case, the engineer chooses such a solution among the Pareto front in which the objectives reach their values according to their merits in the given task. Usually, such an approach requires a large number of function calls (3000 as reported in [9]).  
Another approach to multi-objective optimization is implemented on the basis of the one-criterion methods of optimization. In this case, the objectives merits should be set to construct the optimization criterion as a function of these objectives. It is possible to use both encouraging and fining components of this function [10].  
Therefore, for any multi-objective optimization to be implemented, the objectives' merits must be set either at the optimization beginning or at its end.  
To reduce the calculation efforts, the multi-objective optimization implemented on the basis of the one-criterion Nelder-Mead method is used in the paper. Such an approach for the optimization of the high-speed single-phase FRM is developed in [5]. In contrast to the approach described in [9] and requiring a large number of function calls (3000), the optimization based on the Nelder-Mead method requires only 115 function calls [5], which is 26 times lower.  
Since the Nelder-Mead algorithm is well known and it is also implemented in the Matlab function \"fminsearch\", we do not include a detailed description about the algorithm in the paper.  
As for the number of individuals, the Nelder-Mead algorithm can be considered as the algorithm of the group behavior. In contrast to a genetic algorithm where the most adapted individual wins, the Nelder-Mead algorithm improves the weakest individual. The number of vertexes in the simplex (the number of individuals) is \(n+1\), where \(n\) in the dimension of the parametric space. In our case, \(n=7\), and the number of individuals is 8.  
The three-phase high-speed FRM is optimized for a fan load in which the load torque of the machine is proportional to the rotational speed in a quadratic way. Herein, the optimization process is performed according to several criteria, and thus, the objective function is formulated based on such criteria. The Nelder-Mead method used for the optimization [11] is a gradient-free method of unconditional optimization of a function of several variables. The method is easily applicable to non-smooth and/or noisy functions [12]. This method is widely used for non-linear optimization and is well suited for problems in which the derivatives of functions may be unknown. The Nelder-Mead method can be more effective than gradient-based methods, as well as some other gradient-free methods; in particular, the Powell method [12].  
Four optimization criteria are discussed in detail below. To add, the merits of the criteria are set, which is necessary to determine the objective function.
The residual norm and the condition number can be interpreted as parameters representing the uniqueness and stability of the solution, respectively. The commercial hyperspectral cameras have limited choices in the available spectral bands and do not have a spectral resolution as high as the broadband spectroscopy devices used by [PERSON] et al. and [PERSON] et al. Therefore, the optimal spectral bands identified in these studies may not be available with the commercial cameras. Moreover, the more spectral bands are used, the more time is needed to compute functional brain maps. Since time is the key factor in intraoperative imaging, the smallest number of spectral bands must be acquired while ensuring minimal quantification errors. First, we propose in this study a method to define the optimal spectral combinations of a commercial hyperspectral camera for intraoperative hemodynamic and metabolic brain mapping. This method could be used with any hyperspectral or standard RGB camera to evaluate its ability to compute accurate hemodynamic (\(\Delta C_{HbO_{2}}\) and \(\Delta C_{Hb}\)) and/or metabolic (\(\Delta C_{oxCCO}\)) brain maps following neuronal activation. The method is based on the Monte Carlo simulations of the acquisition of the intrinsic signal acquired by a camera. All spectral combinations of the hyperspectral camera are tested to evaluate the optimal spectral configuration that minimize the quantification errors in \(\Delta C_{H\text{O}_{2}}\), \(\Delta C_{Hb}\), and \(\Delta C_{oxCCO}\). In this work, we also show that a spectral correction [10] of the reflection spectra acquired by a mosaic hyperspectral sensor is mandatory to minimize the chromophores' quantification errors. Finally, we compare standard RGB imaging and hyperspectral imaging for hemodynamic and metabolic brain mapping. We demonstrate that RGB imaging is a low cost, but not an accurate solution to identify the functional areas in a patient brain based on the analysis of the cortical hemodynamics. Hyperspectral imaging is the ideal solution for an accurate computation of hemodynamic and metabolic brain maps.
###### Abstract  
In recent decades, solving complex real-life optimization problems has attracted the full attention of researchers. Dam and reservoir operation rules are considered one of the most complicated optimization engineering problems. In fact, the operation rules of dams and reservoirs are multisystematic and highly stochastic and have highly nonlinear system constraints due to the direct influence of environmental conditions: Therefore, these rules are considered highly complex optimization problems. Recently, metaheuristic methods inferred from nature have been broadly utilized to elucidate the way optimal solutions are provided for several complex optimization engineering applications, and these methods have achieved interesting results. The major advantage of these metaheuristic methods over conventional methods is the unnecessity to identify a particular initial condition, convexity, continuity, or differentiability. The present study investigated the potential of using a new metaheuristic method (i.e., the crow algorithm (CA)) to provide optimal operations for multireservoir systems, with the aim of optimally improving hydropower generation. A multireservoir system in China was considered to examine the performance of the proposed optimization algorithm for several operation scenarios. The results obtained the average hydropower generation by considering all examined operation scenarios based on the operation rule achieved using the CA, which outperformed the other metaheuristic methods. In addition, compared to other metaheuristic methods, the proposed CA lessened the time required to search for the optimal solution. In conclusion, the proposed CA has high potential for achieving optimal solutions to complex optimization problems associated with dam and reservoir operations.  
metaheuristic methods; optimization algorithms; crow algorithm; multireservoir operation; hydropower generation 2019; 9, 2280; doi:10.3390/app9112280
## 3 Three-step WLS Localization Algorithm for Hybrid Positioning
### Second-Step WLS  
After achieving the initial position \(u^{\prime}=\left(x^{\prime},y^{\prime}\right)\), the distance \(r_{i}{}^{\prime}\) from ANT \(i\) to \(u^{\prime}\) can be calculated. Inserting \(u^{\prime}\) and \(r_{i}{}^{\prime}\) into Equation (18), the weighting matrix \(W\) could be rewritten as \(W^{\prime\prime}\):  
\[W^{\prime\prime}=\left[\begin{array}{ccccc}\frac{1}{k^{2}}&0&0&\ldots&0\\ 0&\frac{1}{r_{2}{}^{\prime 2}}&0&\ldots&0\\ 0&0&\frac{1}{r_{3}{}^{\prime 2}}&\ldots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\ldots&\frac{1}{r_{M}{}^{\prime 2}}\end{array}\right]\text{.} \tag{21}\]  
Inserting matrix \(W^{\prime\prime}\) into Equation (16) in the same way as Equation (20), the solution \(w^{\prime\prime}=\left(x^{\prime\prime},y^{\prime\prime},r_{1}{}^{\prime \prime}\right)^{T}\) can be obtained.
## 4 Results  
### Classification of User Sentiment Information and Extraction of User Concern Information  
#### 4.1.1 Training and Classification of User Sentiment Model  
To improve the accuracy of the user sentiment classification results from the public opinion texts, this study used the weibo_senti_100k sentiment dataset with sentiments labels applied to train the Bert-BiLSTM model. The model was implemented based on the Pytorch framework. The initial learning rate for model training was set to \(2\times 10^{-5}\), and the model was trained for 10 epochs. Experimental loss and accuracy curves are shown in Figure 6.  
According to the training results, the accuracy rate of the sentiment analysis model when used on the test set is 98.32%, the recall rate is 97.95%, and the F1 score is 98.32%, indicating that the model has a good classification effect on the dataset.  
Then, the trained Bert-BiLSTM model was employed to predict the sentiments expressed in the online public opinion texts after the preprocessing performed in Section 3.2. In total, 21,288 positive sentiments and 5768 negative sentiments were obtained, with positive sentiments accounting for 78.68% of the total.
# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 7 Experimental Setup  
### Training Details  
Figure 2 illustrates the neural network's schematic diagram. The model was trained on a 24 GB NVIDIA RTX 3090, (Palit, Munich, Germany) using the PyTorch-lightning Python library. To ensure reproducibility, the standard techniques for fine-tuning as described by [PERSON] et al. (2014) were utilized, along with a constant seed of 0. A 10% proportion of the training data was used as a warm-up alongside the Adam optimizer with a \(1\times 10^{-5}\) learning rate. Each run of 5-fold cross-validation utilized a batch size of eight. The experiment was terminated early when the validation loss did not improve after three iterations. A [CLS] token was extracted from the encoder and passed through a fully connected network (FC1) three-class softmax layer for each training instance. The encoder's output is routed through a second fully connected network (FC2) and then a five-class softmax layer. The encoder's features were discarded with a probability of 0.2. We sampled a mini-batch from each of the datasets for each training step, namely four English samples and four Bulgarian samples. The instances were then sent through the network, and the error was calculated separately for five and three classes before being summed. To update the parameters, the calculated error was back-propagated throughout the network. In cases where the lengths of the two datasets did not match, the smaller dataset was duplicated to match the larger length. Slovene was the only language for which we performed any preprocessing, replacing user mentions with placeholders and removing all URLs from the text. To compute the effect of having two classifiers, a simple three-class classifier was added to the pre-trained language model as a baseline. No additional hyperparameters were altered. The results are displayed in Table 4. We observe that cascading classifiers lead to improvements over our baseline in all languages except Polish.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Source Languages**} \\
**1 st** & **2 nd** & **3 rd** \\ \hline Bulgarian (Latin) & & \\ Russian (Latin) & & \\ Bulgarian (Latin) & Russian (Latin) & \\ Russian (Latin) & Croatian & \\ Bulgarian (Latin) & Croatian & \\ Russian (Latin) & Slovak & \\ Bulgarian (Latin) & English & \\ Russian (Latin) & English & \\ Bulgarian (Latin) & Polish & \\ Bulgarian (Latin) & Czech & \\ Russian (Latin) & Czech & \\ Bulgarian (Latin) & Slovene & Slovak \\ Russian (Latin) & Slovene & Slovak \\ \hline \hline \end{tabular}
\end{table}
Table 3: Language pairs with Latin Bulgarian and Latin Russian.  
Figure 2: A neural network diagram showing the multi-task fine-tuning process on the pre-trained language model (PLM).
# Laplacian Eigenmaps Dimensionality Reduction Based on Clustering-Adjusted Similarity
## 4 Results  
### Results on Different Datasets  
Figure 2 (for FMI's performance), Figure 3 (for F-measure's performance) and Figure 4 (for PU's performance) show the results with respect to different fixed target dimensionality after applying different dimensionality reduction solutions and with the same \(k\)-means clustering. To avoid random effects, we repeated the experiment for 30 times and calculated the mean and variance of the results of LE-CAS and comparing methods with respect to four evaluation metrics on respective datasets, which is shown on Table 2.  
According to the table and figures, we can observe that the performances of all methods increases with the target dimensionality rising and LE-CAS always outperforms other comparing methods across all the evaluation metrics and six datasets. LE-CAS and LE both uses Laplacian Eigenmaps, while LE-CAS achieves better performance on four evaluation indices than that of the LE. That is because LE-CAS changes the similarity calculation method of the instances on the decision boundary, and the adjacent instances are still close enough and the clusters they belong to remain the same after dimensionality reduction. The fact indicates the clustering similarity adjustment (CAS) can improve the performance of GEDR methods. LE-CAS and GGE both uses Gaussian kernel function (LE-CAS calculated the similarity between clusters and instances by Gaussian kernel function, GGE measures similarity between instances via different generalized Gaussian kernel functions), while the performance of GGE is much lower than that of LE-CAS. This fact shows that CAS used by LE-CAS can better reflect the real relationship between the instances than generalized Gaussian kernel functions used by GGE. The performance of LE-CAS is better than that of S-LE and SCPLE, which use the ratio of Euclidean distance to geodesic distance between two instances adjusts the number of nearest neighbors. That is because the clustering-adjusted similarity can more well explore the geometric structure of instances than adaptive adjustment of neighborhood parameters. SCPLE performs better than S-LE, which verifies that SCPLE is an improved method based on S-LE via concerning neighborhood parameter of instances from same or different clusters. As for stability, LE-CAS performs well, the variance is basically stable and its effect of cluster can maintain good stability in different target dimensions, while the variance of other improved LE methods fluctuates and its effect of cluster has a complex turbulence into different target dimensions at the same time. That is because the similarity without CAS can be easily destroyed by noisy featured.
## 5 Conclusions  
In this study, 21 remote sensing metrics were assessed to determine their relationships with the richness distribution of three animal classes. The correlation between the species richness of the three animal classes and 21 biodiversity metrics was evaluated based on regression models, and ET exhibited the strongest correlation with the global distribution of the three animal classes. The metrics with high explained variance were mainly in the energy/productivity category. The ecosystem texture category exhibited a higher correlation with species richness at regional scales. For a single class, we found radiance and temperature had a larger impact on the distribution of birds than the other two animal classes. Three commonly used machine learning models were evaluated to determine their capabilities in developing a multivariate integration index, and the RF model was selected as the optimal model for multivariate index integration. Having taken DHI-min, DHI-cum, DHI-sea, GPP, CV, ET, and LST as the major metrics, the multivariate integration index exhibited a 20% higher explained variance of the global distributions of mammal, bird, and amphibian richness, compared to the univariate metrics. However, the in situ biodiversity data were still insufficient. With improvements in the availability of these datasets, further studies can consider more factors and offer higher-accuracy metrics for the assessment of global biodiversity.  
Supplementary materials are available online at [[http://www.mdpi.com/sl](http://www.mdpi.com/sl)]([http://www.mdpi.com/sl](http://www.mdpi.com/sl)). Figure S1: Correlations of all remote sensing metrics; Figure S2: Scatterplots between species richness and power-law metrics; Figure S3: Correlation between predicted species richness and in situ species richness; Table S1: The explained variance of remote sensing metrics in different biomes.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] contributed to the analysis of the results; all of the aforementioned contributed towards writing the manuscript.  
**Acknowledgments:** This work was partially supported by the National Key Research and Development Plan of China (Grant No. 2016 YFA0600102) and the Natural National Science Foundation of China (grant No. 41331173).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Theory
Using Equation (5) with the prior probability, it is possible to evaluate the posterior probability that observation \(x\) is from class \(k\) by using the Bayes rule shown below:  
\[\hat{P}\left(k\middle|x\right)=\frac{P\left(x\middle|k\right)P\left(k\right)}{P \left(x\right)} \tag{6}\]  
This is a component of Equation (4) that required minimization as part of the classification decision-making process. The second type of classifier that was applied was the RF process [43; 44], which is a supervised machine learning algorithm that uses a series of binary decisions configured in a tree form which allow for the categorization of a given input to a series of classes. A decision tree is formed by considering all possible split combinations which are available from all feature samples, and then the optimum splits are defined using an optimization criterion. A random component of the decision tree process is introduced by utilizing a random subset of the given data to construct a series of decision trees in an iterative way. The purpose of this randomization is to minimize the error in the classification process by testing the numerous different generated decision trees. The error that is being minimized is the Gini Diversity Index (GDI), which is defined as:  
\[g\left(n\right)=1-\sum_{k}p^{2}\left(k\right) \tag{7}\]  
where \(g\) is the GDI, \(k\) is the possible classes and \(p\) is the fraction of classes with the correct classification that have reached a given node within a RF decision tree. An example RF tree using a three class problem (car, person, bike) and four features (x1, x2, x3, x4) can be seen in Figure 4. At each fork of the tree a decision is made on each sample based on a greater than or less than aspect of some of the selected features. At the leaf end of each branch the decision for each sample is indicated by the class. For example, if feature x3 is \(<\) 2.45 the sample will immediately be classified as a car by the first fork of the tree.
# Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things
## 7 Results
### Performance Comparison with Centralized Methods
#### 7.1.1 Hamming Loss Evaluation  
Table 3 and Figure 3 compare the Hamming loss of the KNN algorithm, the OML algorithm and the DOML algorithm for different datasets.  
We can see that the three algorithms have comparable performance in most datasets. In this experiment, we simulated DOML with three independent nodes, i.e., each node has  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Number of Instances** & **Number of Features** & **Number of Labels** & **Domain** \\ \hline CAL500 & 502 & 68 & 174 & music \\ Corel5k & 5000 & 499 & 374 & images \\ Emotions & 593 & 72 & 6 & music \\ Enron & 1702 & 1001 & 53 & text \\ Medical & 978 & 1449 & 45 & text \\ scene & 2407 & 294 & 6 & image \\ yeast & 2417 & 103 & 14 & biology \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of multi-label benchmark datasets.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **KNN** & **OML** & **DOML** \\ \hline CAL500 & 0.1325 & **0.1310** & 0.1359 \(\pm\) 0.0007 \\ Corel5k & **0.0093** & 0.0094 & 0.0114 \(\pm\) 0.0003 \\ Emotions & 0.2937 & 0.2979 & **0.2851 \(\pm\) 0.0161** \\ Enron & 0.0522 & 0.0610 & **0.0479 \(\pm\) 0.0006** \\ Medical & 0.0251 & 0.0292 & **0.0242 \(\pm\) 0.0050** \\ scene & **0.0989** & 0.1820 & 0.2294 \(\pm\) 0.0375 \\ yeast & **0.1210** & 0.1254 & 0.1362 \(\pm\) 0.0008 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hamming loss of different algorithms with full benchmark datasets.  
KNN algorithms generally have higher F1 scores in all datasets, both Macro F1 scores and Micro F1 scores. This is because the KNN method is a batch lazy learning algorithm. Compared to online learning, the training of KNN methods always considers all the data in the dataset, so KNN methods should have better performance than online algorithms. Compared with the KNN method, the traditional OML method shows a significant disadvantage in terms of F1 scores. However, our proposed DOML method does not lose performance in F1 scores in most datasets but also has better training results in COREL5K and ENRON datasets. Our proposed algorithm improves the recall and precision of the model through the mechanism of distributed communication compared to the traditional OML.