esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge Graph Completion Based on Entity Descriptions in Hyperbolic Space
## 2 Related Work  
### Knowledge Representation Learning  
From the perspective of development, knowledge representation learning has mainly dealt with translation models and bilinear models that use the idea of vector translation, neural network models that mine deep information, rotation models that treat relations as rotations between head entities and tail entities, and hyperbolic models that use the isometric idea of hyperbolic space.  
Euclidean embeddings. The translation models [4; 5] and the bilinear models [6] are basic models of knowledge representation learning, using the idea of vector translation to infer the relationship between entities. Compared with neural network models [7; 8; 9] and rotation models [10; 11], these methods are relatively simple, and have fewer parameters; however, they failed to encode logical properties such as symmetry, anti-symmetry, inversion, and combination at the same time [10]. For example, the translation model TransE [4] cannot encode symmetry, and the bilinear model DisMult [6] cannot encode anti-symmetry, inversion and combination, and the limitations of these models will affect the results of link prediction. Due to the prominent applications of neural network models in other aspects, they are now also applied to knowledge graph embeddings. However, using neural network models often requires pre-training knowledge base embeddings as input, resulting in excessive computation [28; 29; 30]. For rotation models RotatE [10], it theoretically proves to be able to solve symmetry, anti-symmetry, inversion, and combination problems, but these embeddings require high-dimensional space, which leads to high memory cost.  
Hyperbolic embeddings. From the translation models to the graph neural network models to the hyperbolic models, the knowledge graph embedding models are from simple to complex, and the embedding effect is continuously improved. In hyperbolic space, [PERSON] et al. [31] demonstrate that the hysteresis of translation models relative to bilinear models can be unconstrained in hyperbolic space. In addition, RotationH [26] provides a key concept of sufficiently rich representation for complex logical patterns, which can be used to model logical patterns in hyperbolic space. Based on RotationH [26], AttH [27] uses attention mechanism to capture hierarchical and logical information in knowledge graph. In order to have the advantages of different spaces, some models [32; 33; 34] apply hyperbolic space, Euclidean space, and spherical space to obtain knowledge representation with spatial advantages.
## 2 Results and Discussion
### Amino Acids and Related Compounds  
Dry-cured hams contain a large number of free amino acids and derivatives resulting from extensive proteolysis, which is characteristic of all types of hams, though the extent of amino acid release depends on the processing time [14,15]. The responsible enzymes are aminopeptides that act on the N-terminal of peptides and proteins [14]. Table 1 shows that lysine, followed by others such as valine, isoleucine, leucine and phenylalanine, are the amino acids determined in higher amounts. The values of amino acids are, in general, higher in Iberian hams, which explains that all the amino acids can be used to distinguish Iberian from non Iberian hams with the exception of tryptophan and asparagine. Thus, results show leucine, isoleucine and lysine are higher in Iberian hams whose drying step is longer, which agrees with [PERSON] _et al._[15].  
[PERSON] [3] described that glutamic acid, aspartic acid, histidine, arginine, valine, methionine, isoleucine, leucine, tryptophan and lysine were strongly correlated with the length of the drying process [3]. Our results agree with those results, except for tryptophan, when hams were classified into four groups: 7 months, 8-11 months, 12-17 months and more the 18 months (Iberian hams). Those amino acids were positively correlated (\(p<0.001\)) to the drying length with adjusted-R\({}^{2}\) regression coefficients higher than 0.70. Coefficient values between 0.65 and 0.70 were also determined for tyrosine, asparagine, taurine, serine, and glycine.
The action of the tool in the perpendicular position on the machined surface can have a negative effect, such as plowing, high values of roughness, tearing dimples, scratches, or plastic deformation. For a better understanding of surface characteristics, we need to obtain more knowledge about the relationship between the perpendicularity of the tool position and machined surface contact [30; 31]. The authors [PERSON] et al. [32] and [PERSON] [33] describe the machined surface texture as a group of continuous grooves. Different methods are used in the evaluation of machined surfaces. Their disadvantage is that they are time- and money-consuming [34]. Customers require ever more precise results in production. One possibility is using the equipment as a coordinate measuring machine, where it is possible to measure different geometric deviations. [PERSON] et al. [35] investigated roundness deviation, where for pocket shape evaluation, diameter, center coordinate, and form deviations were measured. Also examined was the effect of the measuring points on the roundness measurement. The result was that the lowest points were not adequate for the quality evaluation, and a high number of points did not make the measurement more precise. In many cases, circles are measured using 4 points, and hardly ever with more than 16 points. The performed investigations proved that 4-point measurements are not enough, especially as the measured detail is to be combined with another one in narrow tolerance. [PERSON] [36] analyzed the cylindricity deviations using an online method. Another work focused on this issue [37] investigated flatness deviations of stainless steel. [PERSON] [38] examined the relationship between measured roughness and flatness. Twelve techniques of point feature sampling strategies were used for the experiment. For free-form surfaces, a ball end mill is used because, in the production process, the contact between the axis of the tool and the part is changing, but the question remains what the effect is when a ball end mill for the planar surface is used [39; 40], and to what extent it can negatively affect the surface of the component. Currently, it is possible to find a lot of articles describing the influence of the tool path on surface quality, but only a few studies investigate the tool's effect on the surface texture. Upon reviewing the previous articles, this article focuses on the influence of the finish milling strategies when a pocket shape is milled by using a ball end mill in the perpendicular position in the machining process. The article compares the most common milling strategies that are applied when milling pockets. Consequently, obtained results are compared and evaluated in terms of geometric deviations, surface texture and roughness. The results also show a possible negative effect on the surface texture when the ball end mill is used on the planar surface at the bottom of the pocket. This combination of results can give us more information about the ball end mill usage in the machining process.
## 4 Results and Discussion
### On the Distribution Used for Sampling Random Inputs  
In a GAN based model, the \(z\) signal presented to the generator (recall Figure 4) can be drawn from any distribution. However, for this particular application, some distributions are better than others for the training process. Figure 16 shows the classification results for RF-GAN when (a) \(z\) is sampled from the standard normal distribution (0 mean and variance 1) and (b) a uniform distribution in [\(-\)1,1]. Undoubtedly, the former outperforms the latter.
## 1 Introduction
For example, in a case where the class ratio is 50:1 positive to negative samples, classifier accuracy can reach 98% even if all the negative samples are misclassified as positive samples; however, the true identification rate for the negative samples is zero. In addition, as long as the majority class can be correctly identified, even if the minority class is largely  
Figure 1: Class imbalance distribution. misclassified, the accuracy metric still obtains a high score, which misleads assessments. As a result, instances belonging to a minority class are more likely to be misclassified than those belonging to a majority class. This undesirable effect makes it very difficult to predict different classes accurately. One study found that the proportion of spam on Twitter is approximately 3.75% [14], and approximately 8.7% of the accounts on Facebook are fake accounts created by attackers [1]. In another study, [PERSON] et al. [2] found that approximately 5% of tweets are spam. On the Microblogging platform, approximately 10% of users are spammers [15]. [PERSON] et al. [11] reported that when the class imbalance ratio (IR) in the Twitter dataset increases from two to 20, the spam detection rate drops by 33%, and the error rate for non-spam drops by 5% because traditional classifiers are biased toward the non-spam class. When faced with class imbalance problems, it is difficult to obtain satisfactory classification performances using traditional classification methods [16; 17]. In this paper, we propose a two-level heterogeneous stacking-based ensemble learning framework to address the problem of class imbalance of spam detection in social networks. First, the framework utilizes various machine learning algorithms as base classifiers to automatically extract effective features from the original data. These prediction results are combined into metadata with new features, forming the input data to the next learning stage. Then, a deep neural network (DNN) is used as a metaclassifier to capture the deep information hidden in the output of the basic classifiers. In addition, we set the misclassification costs based on cost- sensitive methods to improve the classification performance. Finally, we compare the proposed method with existing methods. The experimental results show that our method effectively improves the classification performance on data with imbalanced classes. The remainder of this paper is organized as follows: In Section 2, we provide an overview of the related works; in Section 3, we present the process of the proposed approach in detail; in Section 4, we report an experiment using a real-world dataset to demonstrate the validity and robustness of our method; and in Section 5, we conclude the paper and suggests future work directions.
## 4 Results
### Path Convergence with Smooth Steering
#### 4.2.3 Return to Lane  
The convergence performance of the controller can be assessed by evaluating its ability to execute the return-to-lane path. The return-to-lane path refers to the vehicle's process of returning to a straight line from an offset posture, which frequently happens during normal vehicle operation.  
Compared to the other methods, our proposal demonstrates superior overall performance in terms of fast convergence and avoidance of overshoot. While achieving rapid path convergence, it also maintains smooth and minimal steering as illustrated in Figure 12. The numerical comparison is summarized in Table 10, where delay time is defined as the time required for the cross-track error to reach 50% of its steady-state value, settling time is defined as the time required for the cross-track error to enter the \(\pm\)5% range of its steady-state value and overshoot is defined as the percentage difference between the peak value of the cross-track error and its target value, relative to the target value.
# RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis
## 2 Related Works
### Deep Learning Approaches
The hybrid model consisted of an embedding layer using the pretrained word2 vec model, a convolutional layer, a max pooling layer, an LSTM layer, a dropout layer, and a classification layer. The model achieved an accuracy of 75.01% on the self-collected dataset. In [PERSON] (2018) [16], the author proposed a Recurrent Neural Filter-based Convolutional Neural Network (RNN-CNN) and LSTM model for sentiment analysis. In this model, the RNN was utilized as the convolutional filter. The experiments were conducted using the Stanford Sentiment Treebank dataset, which was transformed into word embeddings using the GloVe word embedding model. The model consisted of an embedding layer using the pretrained GloVe model, a pooling layer, and an LSTM layer. The Adam optimizer and early stopping were used to prevent overfitting. The proposed RNN-CNN-LSTM model achieved an accuracy of 53.4% on the Stanford Sentiment Treebank dataset. The study conducted by [PERSON] et al. (2020) [17] aimed to compare the performance of both machine learning and deep learning methods in sentiment analysis. To conduct the experiments, the authors utilized two datasets, namely the Sentiment140 and Twitter US Airline Sentiment datasets. Before the analysis, the datasets were preprocessed to remove noise, such as stop words, URLs, hashtags, punctuations, etc., and tokenized to make the analysis easier. Five methods were used in the comparison, including Multinomial Naive Bayes, Logistic Regression, Support Vector Machine, Long Short-Term Memory, and an ensemble of Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine using majority voting. The results showed that Long Short-Term Memory achieved the highest accuracy of 82% on the Sentiment140 dataset. On the other hand, Support Vector Machine recorded the highest accuracy of 68.9% on the Twitter US Airline Sentiment dataset. Various studies that employed deep learning approaches for sentiment analysis are presented in this subsection. Most studies used MLP architecture and a hybrid model comprising CNNs and LSTM or Bi-LSTM. Preprocessing the dataset included tokenization,case folding, stemming, and the removal of numbers, stop words, punctuations, and other irrelevant symbols. The datasets used in the experiments were in different languages such as Turkish, French, English, and Korean, and included tweets, restaurant reviews, news articles, and movie reviews. The accuracy of the models ranged from 53.4% to 93.73%. Some studies also utilized pretrained models for text embeddings, such as Word2 vec, GloVe, and Doc2 vec, and while text embedding is a crucial component of sentiment analysis, it is surprising that only a few studies utilize Transformer models for this task. The benefits of using Transformers for text embedding include their ability to capture contextual relationships, handle long-range dependencies, and produce highly expressive representations that outperform traditional embedding techniques. A summary of the related works is presented in Table 1.
## 2 Materials and Methods
### Computational Techniques
#### 2.2.3 Genetic Algorithm and Multi-Objective Optimization  
The objective functions generated from the ANN models were used for optimization using the Genetic Algorithm (GA). One form of the stochastic search algorithms that have evolved from nature is the GA. GA helps to arrive at feasible solutions in a population. It is a subset of AI, much alike other search algorithms that help to find a region of feasible solutions to eventually attain one that resolves the given problem. The feasible solutions are denoted as 'genes'--a series of characters from a certain script. Fresh solutions can be created by'mutating' participants of the present population, and also by 'breeding' two solutions. The enhanced solutions are nominated to mate and mutate, removing the inferior ones. GA is a probabilistic method of searching, in which the states investigated are not defined exclusively by the properties of the issues [72]. This GA-based optimization includes a function called objective function, in a distinct or a multi-objective manner, by incorporating or neglecting the constraints.  
In case of multi-objective problems, the genetic search is performed following the concept of Pareto optimality [38,71]. Due to the presence of more than one conflicting objective function, the idea of optimal solution changes from the unique solution to a set of solutions, which lead to compromises between the objectives. This set of solutions  
Figure 1: Configuration of ANN.  
is called a Pareto front. In a Pareto front, no solution exists that is not as good as some member of the Pareto set in terms of all the objectives, and definitely better in terms of one objective. As genetic algorithms can process a number of solutions in parallel, it can easily find the solutions in the Pareto front. In GA, the selection of the solutions is done using a process called the non-domination principle. The important aspects of multiple-objective optimization using GA are as follows: (1) selection based on a non-domination principle; and (2) niching in objective and/or decision variable space. This method of search optimization is utilized here to build UHMWPE hybrid composites with multiple micro/nano particles to attain feasible values of the tribo-mechanical properties. The optimization was performed using the GA multi-objective optimization toolbox in MATLAB(r) with different parameters, as shown in Table 2.
## 3 Results and Discussions
### Comparison of the Differently Pretreated Lignocellasic Biomass in the AD Process
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Feedstock/Lignin Content**} & \multirow{2}{*}{**Pretreatment/Operation Type**} & **Operating Condition and** & \multirow{2}{*}{**Reference**} \\  & & **Gas Production** & \\ \hline _Potamogeton mackianus_ & Alkaline: 80 \({}^{\circ}\)C 3 h & \({}^{\text{b}}\) CH\({}_{4}\) = 219.2 (HRT: 40 d) & \\ Type: Aquatic & 0.2 g NaOH/1 g TS & \({}^{\text{a}}\) OLR: 1 & \\ Lignin = 20.7\%TS & Semi-continuous & & \\ \hline Wheat straw & & Biogas, OLR: 8\%TS & \\ Type: Terrestrial & Sun dried & \({}^{\text{b}}\) R1 = 55.2 (HRT: 20 d) & [39] \\ Lignin = 7.5 \(\pm\) 0.3 * & Semi-continuous & \({}^{\text{b}}\) R2 = 94.3 (HRT: 40 d) & * [38] \\  & & \({}^{\text{b}}\) R3 = 105.2 (HRT: 60 d) & \\ \hline Olive mill solid waste & Thermal & HRT: 25 d & \\ Type: Terrestrial & 170 \({}^{\circ}\)C, 60 min & Day 0–75 = \({}^{\text{b}}\) CH\({}_{4}\): 119 \(\pm\) 30, \({}^{\text{a}}\) OLR: 2 & [65] \\ Lignin = 20.3 * & Semi-continuous & Day 75–175 = 172 \(\pm\) 60, \({}^{\text{a}}\) OLR: 1 & * [66] \\  & & Day 175–275 = 2, \({}^{\text{a}}\)OLR: 2 & \\ \hline Ensiled sorghum forage & Alkaline: 40 \({}^{\circ}\)C 24 h & \multirow{2}{*}{CH\({}_{4}\) = 346 \(\pm\) 9 (HRT: 21 d)} & \multirow{2}{*}{[58]} \\ Type: Terrestrial & 10 g NaOH/100 g TS & & \\ Lignin = 20\(-\)23\%TS & Semi-continuous & & \\ \hline Olive mill solid waste & Steam explosion & HRT: 25 d & \\ Type: Terrestrial & 200 \({}^{\circ}\)C, 5 min & Day 0–75 = \({}^{\text{b}}\) CH\({}_{4}\): 151 \(\pm\) 21, \({}^{\text{a}}\) OLR: 2 & \\ Lignin = 20.3 * & Semi-continuous & Day 75–175 = 163 \(\pm\) 28, \({}^{\text{a}}\) OLR: 1 & \\  & & Day 175–275 = 85 \(\pm\) 59, \({}^{\text{a}}\) OLR: 2 & \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{CH\({}_{4}\) = 133.2} \\ \(\text{Day}\) 0–42 = 148.24, \({}^{\text{a}}\) OLR: 0.9 \\ Type: Aquatic & (HRT: 30 d) \\ Lignin = 25.22 \(\pm\) 4.6\%TS & Semi-continuous & Day 42–49 = 144.32, \({}^{\text{a}}\) OLR: 0.5 \\  & (HRT: 50 d) \\ Day 49–98 = 113.0, \({}^{\text{a}}\) OLR: 0.7(HRT: 40 d) & \\ \hline Steam-exploded citrus waste, & Steam explosion: \\ municipal solid wastes & 150 \({}^{\circ}\)C, 20 min & \({}^{\text{b}}\) CH\({}_{4}\) = 560 \(\pm\) 15 (HRT: 21 d) \\ Lignin = Not specified & Semi-continuous & \({}^{\text{a}}\) OLR: 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the lignocellulosic feedstock with different pretreatment on the AD conditions. The SE of citrus waste was performed (150 \({}^{\circ}\)C, 20 min), and it was co-digested with municipal solid waste [26] (Table 4), producing a methane yield of 560 \(\pm\) 15 mL g-VS\({}^{-1}\). This clarifies that for further optimising the process for a large industrial scale, co-digestion can be a vital way to improve the overall stability in the reactor.
## 3 Algorithms Descriptions
### State-of-the-Art (Earliest Finish Time) EFT-ILS
#### 3.3.1 EFT-ILS Local Search  
The Local Search (LS) in EFT-ILS is based on the first improvement pivoting rule, see Algorithm 6. This algorithm evaluates the tasks in the execution order \(O\) (see line 2). The algorithm generates neighbors \(s^{\prime}\) assigning machines \(m_{j}\in M\) to the current task \(t_{current}\), if a neighbor improves solution \(s\) then \(s\) is updated (see line 7), as consequence the search is reinitialized in line 8. Finally, the algorithm verifies an auxiliary external stopping criterion before continuing with the neighbor generation process to avoid exceeding the maximum CPU time or objective function evaluations.  
```
0: Solution to improve \(s\), and an order execution of the tasks \(O=\{o_{1},...,o_{|T|}\}\)
0: s
1:for\(i=1\) to \(|T|\)do
2:\(t_{current}\gets o_{i}\)\(\triangleright\) Assigns the task \(o_{i}\) in the execution order as the current task
3:for\(j=1\) to \(|M|\)do
4:\(s^{\prime}\gets s\)
5: Assign \(t_{current}\) in \(s^{\prime}\) to the machine \(m_{j}\)
6:if\(f(s^{\prime})<f(s)\)then
7:\(s\gets s^{\prime}\)
8:\(i=1\), \(j=1\)
9:endif
10:endfor\(\triangleright\) If the external stopping criterion is reach stop the Local Search
11:endfor
```  
**Algorithm 6** EFT-ILS Local Search procedure.