esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation Study
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{3}{*}{**Pharmaceutical or Metabolite**} & \multirow{3}{*}{\
\begin{tabular}{l} **LOD** \\ **(\(\upmu\)g mL\({}^{-1}\))** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{l} **LOQ** \\ **(\(\upmu\)g mL\({}^{-1}\))** \\ \end{tabular} } & \multicolumn{3}{l}{**Recovery (RSD) (\%)**} & \multirow{3}{*}{**U (\%)**} & \multirow{3}{*}{**ME (\%)**} \\ \cline{3-3} \cline{5-7}  & & & & & **0.001** & & & **0.1** \\  & & & & & **(\(\upmu\)g\(\cdot\)mL\({}^{-1}\))** & & **(\(\upmu\)g\(\cdot\)mL\({}^{-1}\))** & \\ \hline Atenol & 0.05 & 0.15 & 96 (8) & 97 (4) & 98 (4) & 11 & 4 \\ Bisoprolol & 0.06 & 0.20 & 71 (8) & 70 (4) & 75 (6) & 14 & \(-\)2 \\ Candesartan & 0.20 & 0.65 & 85 (5) & 86 (5) & 84 (7) & 11 & 4 \\ Ciprofloxacinoxin & 0.09 & 0.30 & 81 (4) & 80 (9) & 79 (11) & 13 & 8 \\ Doxycycline & 0.15 & 0.50 & 97 (16) & 98 (5) & 95 (5) & 11 & \(-\)10 \\ Duloxetine & 0.13 & 0.45 & 96 (12) & 94 (13) & 96 (12) & 20 & \(-\)1 \\ Escitalopram & 0.15 & 0.50 & 90 (6) & 88 (9) & 90 (9) & 21 & 7 \\ Fluoxetine & 0.10 & 0.35 & 95 (6) & 96 (8) & 99 (4) & 14 & \(-\)2 \\ Indapamide & 0.15 & 0.50 & 75 (6) & 73 (3) & 75 (6) & 11 & 12 \\ Levonogestrel & 0.15 & 0.50 & 87 (6) & 84 (6) & 87 (9) & 17 & 15 \\ Metformin & 0.10 & 0.35 & 85 (10) & 84 (13) & 82 (6) & 26 & 8 \\ Metoprolol & 0.08 & 0.25 & 84 (7) & 73 (9) & 74 (6) & 20 & \(-\)9 \\ Prografenone & 0.08 & 0.25 & 82 (5) & 80 (5) & 82 (5) & 8 & \(-\)7 \\ Ramipril & 0.09 & 0.30 & 72 (7) & 70 (3) & 73 (5) & 9 & \(-\)11 \\ Sertraline & 0.13 & 0.45 & 72 (10) & 74 (7) & 76 (6) & 15 & 7 \\ Torsemide & 0.19 & 0.65 & 90 (11) & 92 (4) & 94 (6) & 14 & 2 \\ Valsartan & 0.15 & 0.50 & 97 (6) & 95 (11) & 93 (8) & 22 & \(-\)6 \\ Venlafaxine & 0.22 & 0.70 & 74 (9) & 73 (5) & 74 (6) & 9 & 2 \\ Venlafaxine N-oxide & 0.20 & 0.65 & 73 (8) & 71 (6) & 73 (4) & 20 & 12 \\ Venlafaxine-O-desmethyl & 0.07 & 0.25 & 83 (11) & 82 (3) & 84 (6) & 7 & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average recoveries, LODs, LOQs, and expanded uncertainties U, % (k = 2, confidence level 95%) of optimized procedure.
## 5 Discussion
### Implications for Practice  
Failure prediction of components depends on the quality of the data. In practice, massive volumes of data are usually collected, and the failures are sparse compared to this data. This leads to the challenge of imbalanced data [8; 51; 52]. This challenge was addressed in [10] by using suitable scores. Using suitable scores helps to catch the effect of the imbalance challenge on predictive results. However, we employed the SMOTEENN method to handle this challenge, which combines both over-sampling and under-sampling, providing many advantages. Moreover, in [10] the results are evaluated based on one fold approach, where the data are split into training and test data. However, to increase the plausibility of the results, we employed the time series cross-validator introduced in [48]. Last but not least, the feasibility of the introduced framework could motivate the application of the MCS perspective into PdM, which could help enhance research results on the topic. However, an evaluation in this regard remains as future work.
# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
## 3 Results and Discussion
### Recurrent Neural Network (RNN) Model Training  
As mentioned in Section 2.4.1, multiple network configurations were proposed in order to find an optimal LSTM-RNN architecture for the classifier. A total of six LSTM-RNN models were trained, Figure 7 shows the learning curves of the six LSTM-RNN models, presenting their loss during the training process. It is important to note, that during the manual search in the hyper-parameter tuning process, it was found that setting the learning rate \(\alpha\) to 0.0001, the batch size to 64 and the number of training epochs to 1000, allowed a better performance and stability during training and validation.  
Analyzing Figure 7 it can be seen how models 1, 2 and 3 under-perform when compared to the rest of the LSTM-RNN models. It can also be noted how models 2 and 3 present high instability during training, thus making it difficult for them to converge to an optimal minimum; this instability can be due to their inability to generalize and learn the patterns in the data. In contrast, models 4, 5 and 6 presented stable convergence to a minimum in the loss function, especially model 6, which had the most stable training process and its loss value was the lowest of all models.  
The final performance of the LSTM-RNN models across the training and validation sets can be seen in Table 1. This table contains in its first column the LSTM-RNN model number; the second column presents the architecture of the LSTM-RNN model indicating its layers and number of units following the format _<layer name_ (_number of units_)>; the third and fourth column presents the final accuracy and macro F1-Score on the training set, and the fifth and sixth column presents the final accuracy and macro F1-Score on the validation set.  
As Table 1 shows, models 1 and 2 under-performed (have high bias or Under-fitting) when compared to the rest of the LSTM-RNN models, they also suffered high variance (Over-fitting), thus, making them not suitable for the classifier. In the case of model 3, even though it presented an accuracy superior to 95%, it was not close to the accuracy presented by models 4, 5 and 6. This can also be validated looking at the macro F1-Score. Conversely, models 4, 5 and 6 presented an accuracy superior to 98% across both training and validation  
Figure 7: Evolution of different LSTM-RNN architectures losses during the training training process.  
sets. It was the same case for their macro F1-Score; making them suitable for testing the classifier in real time.
# Dynamic Malware Analysis Based on API Sequence Semantic Fusion
## 3 Methodology
### Overview  
The overall structure of the Mal-ASSF model is shown in Figure 1. The input of the Mal-ASSF framework is a well-labeled API sequence (for the stage where the trained model is used) or an unknown sample (for the stage where the trained model is used for classification). API sequences are extracted from the operation analysis report of executable file samples. API sequences are processed in parallel by the sequence feature module and the TextRNN feature module. The API sequence is converted into two-dimensionally reduced sequences. Then, the classification results will be drawn through the fusion module, the attention module, and the final MLP classifier module.  
In the _preparation process_, we run the executable file samples in a security sandbox environment to obtain the operation analysis report in JSON format. We extract the API call information, which is recorded in order, from the report.  
In the _sequence feature module_, to represent API behavior, we adopt API2 Vec embedding and BiLSTM. Through dimensionality reduction and convolution operations, functional behavior with different lengths can be captured. The APIs are then converted into a sequential chain.  
In the _TextRNN feature module_, the API semantic chain of function names is transferred into a vectorized representation containing operation type encoding and operation object encoding by the embedding layer, and the implicit semantic features are extracted by the BiLSTM layer.  
In the _fusion module_, the outputs of the API embedding module and the API implicit semantic extraction module are concatenated together to obtain all the features of the API sequences.  
In the _attention module_, the attention mechanism is used to calculate the weight corresponding to each positioning element so that the model can focus on some malicious segments.  
In the _MLP classifier module_, we apply a fully connected layer MLP for classification. Dropout is adopted to reduce overfitting. Softmax is used to calculate the probability of malware classification. The module uses an Adam optimizer with binary cross-entropy as the loss function.  
Figure 1: Overall architecture of the Mal-ASSF framework.  
The subsequent paragraphs of this section will provide a detailed account of the data processing mechanism.
## 7 Summary and Conclusions  
Planetary gearbox vibration signals have complex and multiple AM-FM features, and the modulating frequency links to the gear fault. Thus, amplitude and frequency demodulation are expected to be effective in gear fault feature extraction. VMD is effective in decomposing complex modulated signals into their constituent AM-FM components, thus providing a potential approach to demodulation analysis. In this paper, considering the modulation feature of planetary gearbox vibration signals and the merits of VMD, we propose an amplitude and frequency demodulation analysis method for planetary gearbox fault diagnosis. In order to make VMD efficient in real complex signal analysis, the number of IMFs to be separated can be set as how many meshing frequency harmonic orders is covered in the effective frequency band from 0 to half sampling frequency, according to the spectral distribution characteristics around carrier frequencies of modulated signals. Furthermore, to enable accurate instantaneous frequency estimation via Hilbert transform, IMFs can be preprocessed via empirical AM-FM decomposition, thus avoiding contamination of AM on the carrier signal, particularly negative frequency values. To extract gear fault feature effectively, sensitive IMF can be selected as the one with a center frequency equal to meshing frequency or its harmonics, based on the wavelet packet like property of VMD and the modulation characteristics of gear vibrations. In the amplitude and frequency demodulated spectra, gear fault feature can be identified by matching present peaks with theoretical gear characteristic frequencies. We validate the proposed method through both numerical simulated and lab experimental signal analyses. Localized fault on all the three types of gears (sun, planet and ring) are successfully diagnosed.  
This work is supported by the National Natural Science Foundation of China (51475038 and 51375078), and the Natural Sciences and Engineering Research Council of Canada (RGPIN-2015-04897). The authors would like to thank the reviewers for their valuable comments and suggestions.  
[PERSON], [PERSON] and [PERSON] proposed the idea and designed the experiments. [PERSON] performed the experiments and analyzed the data. [PERSON] drafted, [PERSON] and [PERSON] revised the paper.  
The authors declare no conflict of interest.
## 4 Engineering Application
### The Hybrid Model Verification
#### 4.2.2 Evaluation Indicators and Training Settings  
This paper illustrates the proposed system tool with displacement monitoring data collected from 25 December 2019. The sensors were monitored at a frequency of 1 day, thus providing seven monitored values per week. The series of monitored values were divided into two subsets. The first subset comprised 70% of the data, with a total of 748, and was used to train the model. The second subset comprised 30% of the data and was applied to validate the model. For the setup of the model training process, the root-mean-squared error (\(RMSE\)), mean-squared error (\(MAE\)), maximum absolute error (\(MAXE\)), and coefficient of determination (\(R^{2}\)) were used as training metrics, with the following equations:  
\[RMSE=\sqrt{\sum\limits_{i=1}^{n}\left(y_{f,i}-y_{m,i}\right)^{2}/n} \tag{15}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\Big{|}y_{f,i}-y_{m,i}\Big{|} \tag{16}\]  
\[MAXE=max\Big{|}y_{f,i}-y_{m,i}\Big{|} \tag{17}\]  
\[R^{2}=\frac{\Big{[}\sum\limits_{i=1}^{n}\Big{(}y_{f,i}-\overline{y_{f}}\Big{)} \big{(}y_{m,i}-\overline{y_{m}}\big{)}\Big{]}^{2}}{\sum\limits_{i=1}^{n}\Big{(} y_{f,i}-\overline{y_{f}}\Big{)}^{2}\sum\limits_{i=1}^{n}\big{(}y_{m,i}- \overline{y_{m}}\big{)}^{2}} \tag{18}\]  
where \(n\) is the number of datasets fitted, \(y_{f,i}\) is the fitted value at step \(i\), \(y_{m,i}\) is the measured value at step \(i\), and \(\overline{y_{f}}\) and \(\overline{y_{m}}\) are the mean of the predicted and monitored values, respectively. The model was trained when the \(RMSE\) between the fitted and measured values of the model was minimized. In addition, using the Adam optimizer, the batch size was set to 100. Two indicators of model completion were set, namely the maximum number of training iterations (epochs = 150) and an \(RMSE\) of less than 1 \(\times\) 10\({}^{-5}\). The training was immediately terminated when the RMSE was less than \(1\times 10^{-5}\), or the maximum number of iterations was reached.
# HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities
## 5 HEAD Metamodel: Development Approach to Access Control in Dynamic and Heterogeneous Environments
### Policy Analysis and Assessment
#### 5.4.1 Analysis of Quality Requirements of a Policy  
This step consists of assuring the quality of the obtained AC policies and making sure that they are consistent, relevant, minimal, complete, and correct with respect to the actions needed to be performed by subjects on some objects [13, 36].  
* Consistency means investigating that the obtained policies do not include both an allow and deny decision to the same subject for the action of an object.
* Relevance means checking if the obtained policies do not contain rules that do not apply to any action performed by subjects. In other words, making sure that subjects do not have any authorization to access object(s) and perform action(s) that they are not expected to execute.
* Minimality refers to investigating that the obtained policies do not include redundant or unnecessary policies.
* Completeness means that, for a given access request to access an object, there must be a corresponding policy, and any action to be executed by the subjects on any object the default decision that should be taken by AC model is to deny the access.
* Correctness refers to the process of checking that the policies conform with their intended goals and are in compliance with the system requirements.  
Figure 22: A sample of Eclipse Xrend notation.  
Hence, this phase is of major importance while implementing AC rules in highly dynamic and heterogeneous computing environments, especially IoT environments. Unfortunately, although several methods for policy analysis and assessment are proposed [13, 38], they have major shortcomings. For example, they do not address all the quality requirements, e.g., they only address consistency and minimality, they analyze and assess the obtained policies of only some AC models (e.g., RBAC policies), and they also require all possible AC requests as input [13]. Hence, the proposed methods are not suitable for dynamic and distributed environments. Moreover, developing an approach for analyzing the quality requirements of a policy at runtime is of major importance, especially for today's environments such as IoT and industry 4.0 where data are generated in real-time basis.
## 2 Materials and Methods
### Statistical Analysis  
The power of the sample size was calculated by G*Power software (version 3.1). Since the aim was to compare the results of two trials of the same dynamometers and also the measurements of old-new dynamometers, we applied a two-tailed paired Student's \(t\)-test (difference between two dependent means) type power analysis. Analysis was conducted with a 95% confidence interval, 80% power, and 0.50 effect size values, and the minimum number of athletes was found as \(n=34\)[17, 18].  
Statistical analyses were performed using the IBM SPSS Statistics version 23.0 (IBM Corp., Armonk, NY, USA) and \"BlandAltmanLeh\" package in R Studio [19]. A 5% type-I error level was used to infer statistical significance.  
Forty-two athletes were involved at the beginning of the study. Four of their measures were incomplete. By considering the differences between the trials and dynamometers of each measure, the box-plot was used to detect the outliers, and one of the athletes was removed because the calculated differences were out of the Q1-1.5 \(\times\) IQR or Q3 + 1.5 \(\times\) IQR in most of the measures. Each measure for 37 athletes was evaluated for normality with the Shapiro-Wilks Test and demonstrated that all data had a normal distribution (\(p>0.05\)).  
An Intraclass Correlation Coefficient (ICC) with a 95% confidence interval was used as a measure of test-retest (intra-rater) and inter-rater reliability. For the intra-rater reliability, the reliability of trails, a single-measurement, absolute-agreement, two-way mixed-effect model ICC (ICC 3.1) was used [20]. ICCs were calculated for old and new dynamometers, separately. For the inter-rater reliability, the reliability of two dynamometers, a single-measurement, absolute-agreement, two-way random-effect model ICC (ICC 2.1) was used and calculated for two trials, separately. ICC 2.1 results can be generalized to other raters [21]. ICC values are interpreted according to [PERSON] (2008) intervals: 0.75 is considered good, 0.75 to 0.50 is moderate, and below 0.50 is poor [22].  
For assessment of absolute reliability, standard error of measurement (SEM) and smallest detectable change (SDC) were used. SEM was calculated as \(SEM=SD_{diff}/\sqrt{2}\), where \(SD_{diff}\) was the standard deviation of the difference scores for the two trials or two dynamometers. SDC was calculated as SDC95 = SEM \(\times\) 1.96\(\surd\)2, where 1.96 is the z score for a 95% CI [23]. SDC% was calculated as SDC95/mean\(\times\)100%, presenting the relative amount of random measurement error [24].  
[PERSON] (1986) plots were also created, and 95% limits of agreement (LoA) were calculated to evaluate the intra-rater reliability [25]. A paired \(t\)-test was performed to detect the difference between test and re-test values. The paired \(t\)-test and 95% LoA were used to assess systematic bias between trials and dynamometers. The regression analysis between the difference of trials or dynamometers and their means were used to detect the proportional bias.
## 4 Experiments and Results
### Experiment Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Experiment 1**} \\ \hline
**Case** & **AP** & **ACR** & **AIR** & **UIR** & **Similarity** & **Popularity** & **Coverage** & **Personalisation** & **Diversity** \\ \hline Without clustering & 0.707 & 0.036 & 0.223 & 0.161 & 0.785 & 0.779 & 0.039 & 0.654 & 0.220 \\ With clustering & 0.735 & 0.039 & 0.197 & 0.484 & 0.791 & 0.826 & 0.172 & 0.733 & 0.208 \\ \hline  & & & \multicolumn{6}{c}{**Experiment 2**} \\ \hline Without clustering & 0.712 & 0.038 & 0.223 & 0.164 & 0.784 & 0.780 & 0.041 & 0.676 & 0.210 \\ With clustering & 0.762 & 0.040 & 0.181 & 0.543 & 0.794 & 0.806 & 0.206 & 0.753 & 0.191 \\ \hline  & & & \multicolumn{6}{c}{**Experiment 3**} \\ \hline Without clustering & 0.705 & 0.035 & 0.206 & 0.142 & 0.788 & 0.794 & 0.038 & 0.684 & 0.213 \\ With clustering & 0.742 & 0.039 & 0.178 & 0.519 & 0.792 & 0.822 & 0.199 & 0.758 & 0.199 \\ \hline  & & \multicolumn{6}{c}{**Experiment average**} \\ \hline Without clustering & 0.708 & 0.036 & 0.217 & 0.156 & 0.786 & 0.785 & 0.039 & 0.671 & 0.214 \\ With clustering & 0.746 & 0.039 & 0.185 & 0.516 & 0.792 & 0.818 & 0.192 & 0.748 & 0.199 \\ Difference (diff) & 0.038 & 0.003 & \(-\)0.032 & **0.360** & 0.006 & 0.034 & **0.153** & **0.077** & \(-\)0.015 \\ \hline  & & & \multicolumn{6}{c}{**Experiment 3**} \\ \hline Without clustering & 0.708 & 0.036 & 0.217 & 0.156 & 0.786 & 0.785 & 0.039 & 0.671 & 0.214 \\ With clustering & 0.746 & 0.039 & 0.185 & 0.516 & 0.792 & 0.818 & 0.192 & 0.748 & 0.199 \\ Difference (diff) & 0.038 & 0.003 & \(-\)0.032 & **0.360**Concerning category recall, the box plots show that, although the concentration of the test set did not result in any differences in both cases, there were more outliers with good scores when clustering was used. In the case of item recall, the performance increased without clustering because a larger pool of POIs was considered, thereby creating the chance to have more POIs recommended that could have been visited by users. The diversity measure was the only one with better scores for all quartiles in all experiments when clustering was not used. To summarise, we evaluated the performance of the proposed system to quantify the benefit of a social media-based clustering step in the recommendation process. When clustering was applied, the performance scores increased across all metrics except diversity and item recall. The clustering process helped to refine the pools of POIs considered for recommendations.
## 7 Conclusions  
Biosensors face severe issues, such as unreliable data transmission and short life spans, because of poor communication efficiency caused by the complexity of the body tissues and unpredictable body movements in BASNs. Therefore, reliable and sustainable transmission of the instant data  
Figure 6: Residual energy vs number of rounds.  
collected from these body sensors to the observers (e.g., doctors, nurses, etc.) is extremely challenging, which results in a poor monitoring picture of the health condition of the patients. Thus, a more advanced data transmission mechanism is greatly needed to optimize resource utilization and to overcome network reliability and instability issues for BASN-based health monitoring applications. To this end, this research proposed a novel, multiobjective routing protocol for BASNs-based IoT healthcare applications. The proposed scheme, by employing a novel, multiobjective lion optimization mating architecture, reduced local search problems when finding the best routing paths between the source and the destination in BASNs. Extensive simulation studies were performed through a network simulation tool, namely MATLAB 9.5 (R2018b), to validate the performance of the proposed scheme against the existing routing protocols. The study results show that the SARP scheme performed best in terms of the high packet delivery ratio with low latency, packet error rates, and network energy consumption, at the expense of data redundancy, for BASN-based health monitoring applications. As future work, research may focus on a security-aware routing mechanism in BASN-based health monitoring applications.  
Conceptualization, Writing\(-\)Original Draft, M.F.; Formal Analysis, R.A.B.; Project Administration, Validation, Visualization, B.R.; Funding acquisition, Software, H.A.; Investigation, Methodology, M.Z.A.; Supervision, Investigation, Methodology M.A.N.; Writing\(-\)Review and Editing, V.C.G.  
This research was funded by the Deanship of Scientific Research at Majmaah University, Saudi Arabia.  
The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work.  
The authors declare no conflicts of interest.