esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Effect of Gender and Age of the Participants  
The effect of participants' gender on RT and on error was preliminarily analyzed using the \(t\)-test for the results of the male and female groups. The test gives the probability (\(p\)-value) that the difference in the means of the two groups (men and women) could have been due to chance or other effects. If the probability is below the adopted significance level (\(\alpha\) = 0.05), the difference can be considered statistically significant.  
Relatively high values of \(p\) for each individual IC indicate that the gender of the participant has no significant effects on RT (\(p\) = 0.255...0.913) and on reading error (\(p\) = 0.167...0.960) in the conducted tests.  
The effect of the participant's age on the mean RT is described by the correlation coefficient. The correlation coefficients are 0.338 and 0.557 in the first and second sessions, respectively. The overall correlation coefficient is 0.473. These values indicate a moderate positive correlation; that is, RT could be longer with the age of the participants. Because IC design also affects RT, the correlation coefficients for each IC are given in Table 2.  
A moderate correlation (about 0.5) between participant age and error magnitude was observed on IC03 and IC04, indicating that older participants made more reading errors than younger participants. The correlation between age and error on other ICs was weak.
# Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance Criteria
## 3 Methodology
### Text Preprocessing
#### 3.3.3 Stemming and Lemmatization  
Stemming and Lemmatization are text normalization techniques in NLP (natural language processing). Usually, a word has multiple meanings based on its context in the text; in the same way, different words convey different meanings. We use different word forms in our sentences based on the grammar rules to convey the complete and correct message. However, in the field of NLP, ML models do not work on such different forms of words; the models treat such multiple forms of words as different individual entities that cause more extensive storage and high computation with no use. The ML model isalso affected by such variations of words, so we converted all different forms of words into their root word in data preprocessing activities.  
* _I liked dining here._
* _The Asian guy working was very respectful._
* _The phone seems to be disconnected and hans't opened up in weeks._  
In the above short reviews, users use 'liked', 'working','respectful', 'disconnected'. These are the extended forms of words. These can also be liking, likeness, worked, respected, respectfulness, respected, disconnection, etc. Therefore, in NLP, we convert such words into their root forms: 'like', 'work','respect', 'disconnect', using both stemming and lemmatization techniques by SnowballStemmer and WordNetLemmatizer, respectively.  
Although both techniques are used to obtain root words, the difference is that stemming works by cutting the end or beginning of the word and extracting the common word form among all its variants as the final root word. Most of the time, it works successfully, but not always. For example, the words'study','studying','studies','studied' stemming would extract the word'stud' as the root word, because changes are after the letter 'd', but the word'stud' is not the correct word and is wrong in this case. In contrast, lemmatization extracts the root word based on its dictionary meaning. For the sample example above, lemmatization will extract the word'study' as the root word. However, the drawback of lemmatization is that it is significantly slower than stemming as it has to look up the word in the dictionary for the correct root.  
The existing techniques used for recommendation are collaborative filtering and content-based filtering. However, these approaches have data sparsity, cold start problems, and scale-ability issues. Our work revolves around graph-based service and hybrid service recommendations. We get the hybrid service recommendation by combining these two approaches using a knowledge graph.  
To better understand the process of this recommendation using knowledge graph framework, a flow diagram is given in Figure 4, which demonstrates the end-to-end flow of the recommendation framework.
## 3 Performance Metrics  
The accuracy of the forecasting technique is evaluated by the performance metrics of the root mean square error (RMSE), normal root mean square error (NRMSE), mean absolute error (MAE), [PERSON]'s  
Figure 6: Flowchart of proposed hybrid firefly and particle swarm optimization (HFPSO).  
index of agreement (WIA) [41] and Legate's coefficient of efficiency (LCE) [41]. All performance metrics are shown in Equation (17) to Equation (21):  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{(\text{I}_{\text{f}}-\text{ I}_{\text{m}})^{2}}} \tag{17}\]  
\[\text{NRMSE}=\frac{\left(\frac{\text{I}}{\text{n}}\sum\limits_{i=1}^{n}{( \text{I}_{\text{f}}-\text{I}_{\text{m}})^{2}}\right)^{1/2}}{\frac{1}{\text{n}} \sum\limits_{i=1}^{n}{(\text{I}_{\text{m}})}} \tag{18}\]  
\[\text{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}{\left|\text{I}_{\text{f}}-\text{I} _{\text{m}}\right|} \tag{19}\]  
\[\text{WIA}=1-\frac{\left(\sum\limits_{i=1}^{n}{(\text{I}_{\text{f}}-\text{I}_ {\text{m}})^{2}}\right)}{\left(\sum\limits_{i=1}^{n}{(\left|\text{I}_{\text{ f}}-\overline{\text{I}_{\text{m}}}\right|+\left|\text{I}_{\text{m}}-\overline{ \text{I}_{\text{m}}}\right|)^{2}}\right)} \tag{20}\]  
\[\text{LCE}=1-\frac{\left(\sum\limits_{i=1}^{n}{(\text{I}_{\text{f}}-\text{I}_ {\text{m}})^{2}}\right)}{\left(\sum\limits_{i=1}^{n}{(\text{I}_{\text{m}}- \overline{\text{I}_{\text{m}}})^{2}}\right)} \tag{21}\]  
where I\({}_{\text{f}}\) denotes the forecasted values, I\({}_{\text{m}}\) represents the actual values, \(\overline{\text{I}_{\text{m}}}\) signifies the mean of actual values, \(\overline{\text{I}_{\text{f}}}\) indicates the mean of forecasted values and \(n\) is for a number of observations.  
Estimation of the RMSE provides the variability measurement of forecasted and actual values [42]. It is one of the standard metrics used to identify the efficiency of the forecasting model [43]. Furthermore, the MAE offers an absolute gap between the forecasted and actual values [44]. The forecasting process is becoming more precise for a smaller value of the RMSE, NRMSE and MAE [43; 44]. Besides, the WIA and LCE metrics are the indexes used to specify the relationship between the actual and forecasted values. These metrics give a value between 0 and 1 where the value of 1 implies a robust positive relationship, and the value of 0 denotes zero relationships between actual and forecasted values. An accurate forecasting model provides the WIA or LCE with a value closer to 1. All above-mentioned performance metrics are the added advantages that indicate the performance of the proposed hybrid model.
## 5 Conclusions  
In conclusion, this comprehensive review has focused on research efforts related to the fundamentals of vibration and damage detection in plate structures using DR analysis, especially for the challenge of crack location and path identification. It has been established that damage can indeed be detected through vibration tests because the existence of cracks in structural elements results in the introduction of local flexibility, which modifies the vibration responses of the members. However, several research gaps and promising directions for future investigation have been identified:  
* Most research on damage detection in plates has focused on the forward problem, which is to study how crack damage affects the DR of plates. This work has provided valuable insights and developed various models for different structures. However, there is a need to apply these models to the inverse problem, which is to detect damage from vibration measurements.
* Additionally, most research on the inverse problem has focused on plates with the UT, even though plates with VT are important in many applications. This oversight needs to be addressed to comprehensively address the challenges of damage detection in the VT plates.
* Moreover, many studies on the inverse problem have not been able to accurately identify crack parameters such as size, length, and propagation rate. They often only pinpoint the damage location in plates with UT based on changes in vibration responses. Future research should focus on improving the accuracy of these parameters.
* Finally, crack path identification, a critical area of research for beam structures, has yet to be studied in plate structures. This important gap in the literature needs to be addressed.  
In summary, there is a need for more research on damage detection in VT plates and crack path identification. This research is important to improve the efficiency and cost-effectiveness of structural inspections in engineering applications.  
Conceptualization, Y.L.A.A., F.H. and M.K.; Methodology, Y.L.A.A., F.H. and M.K.; Formal analysis, Y.L.A.A.; Investigation, Y.L.A.A.; Resources, Y.L.A.A.; Data curation, Y.L.A.A.; Writing--original draft preparation, Y.L.A.A.; Writing--review and editing, Y.L.A.A., F.H., A.A.A., and M.K.; Visualization, Y.L.A.A.; Supervision, M.K. and F.H.; Project administration, Y.L.A.A.; Funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 10: Damage location vector (DLV) method error [237].  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 4 Evaluation  
In this paper, for performance evaluation, performance is measured and evaluated on a 2020 MacBook Pro 13-inch equipped with Apple M1, which is the same environment as the profiling analysis. The 2020 MacBook Pro is equipped with the Apple-designed processor M1, one of the latest ARM processors. The Apple M1 processor is a very high-performance processor with a SoC (System on Chip) that integrates CPU, GPU, DSP and neural network engine into one chip [19].  
To compare performance, the operations of the existing implementation and the optimized implementation were compared by calculating the average cycles measured after 1000 repeated operations with the optimization option -O3. The measured results are shown in Table 11.  
The performance measurement results for Hawk1024 show identical performance in the signing and verification processes. However, in the key-generation process, which is the focus of this paper, the optimized method shows a difference of 1,941,798 cycles compared to the reference implementation. This means that applying the optimization method results in a 4% performance improvement in the key-generation process compared to the existing implementation. Skipping a single instruction in the overall process may seem trivial, but as more functions skip instructions, the performance improvement becomes more significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **KeyPair** & **Sign** & **Verify** & **Total** \\ \hline Hawk512-ref & 8,352,475 & 180,094 & 158,098 & 8,690,667 \\ Hawk512-Ours & 8,125,339 & 179,441 & 157,985 & 8,462,765 \\ \hline Hawk1024-ref & 48,259,761 & 377,498 & 329,483 & 48,966,742 \\ Hawk1024-Ours & 46,317,963 & 377,788 & 329,533 & 47,025,284 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance result of HAWK (unit: cycle; loop: 1000).  
Figure 8: Comparison of Reference and Parallel NTT: This graph compares the performance of the Reference NTT (blue) and the “Parallel NTT (red) based on the average number of cycles over 100,000 loops. As \(\log n\) increases, the Parallel NTT consistently shows lower cycle counts compared to the Reference NTT, demonstrating its efficiency, especially at higher values of \(\log n\).  
Specifically, by reducing a single instruction in a specific task process, a substantial 4% performance improvement is achieved. This is accomplished by profiling the key-generation process to identify tasks generating significant overhead and implementing optimized functions for these identified tasks. As a result, greater performance improvements can be realized.  
In conclusion, when considering the entire key-generation, -signing and -verification processes of the HAWK algorithm, the overall performance improvement through Totalresults shows that a performance gain of approximately 2.5% for Hawk512 and 4% for Hawk1024 is possible. These results demonstrate that performance enhancement can be achieved through profiling analysis and optimized implementation.
## Materials and Methods
### Eddy Covariance and Meteorological Measurements
#### 2.2.2 EC Data Processing
During the stable conditions, when the stratification is stable and the wind speed is low, turbulence may not be fully developed. In such situations, the hypothesis underlying eddy covariance method cannot be met [42; 45]. These occasions occurred mostly at night, when the ecosystem acts as a sourceof carbon. Eddy covariance method underestimates CO\({}_{2}\) flux under the stable conditions, which lead to the overestimation of \(NEE\) at the annual scale [42]. To check the reliability of night-time flux measurements at Jastrebarsko site, flux data was divided into 20 classes with respect to friction velocity \(u^{*}\) (from 0 to 1 m s\({}^{-1}\) with increment of 0.05 m s\({}^{-1}\)) and grouped according to the season (leaf-off, leaf-on; in order to account for differences in surface roughness), and air temperature class (2 \({}^{\circ}\)C wide temperature classes). For the each \(u^{*}\) class average and median of \(NEE\) was calculated. Neither the average nor the median of night-time \(NEE\) showed significant trends with \(u^{*}\) (data not shown). Therefore, we could not select a fixed \(u^{*}\) threshold for the \(u^{*}\) filtering, which is used for identifying low turbulence conditions. Instead, we used the value for the u\({}^{*}\) threshold calculated by the online gap-filling tool by the Department Biogeochemical Integration at the Max Planck Institute for Biogeochemistry in Jena, Germany [17; 46]. The fluxes and meteorology data for Jastrebarsko oak forest (half-hourly eddy covariance and meteorological measurements, corrected daily precipitation, variable description, and processing codes) are provided in the S1 supplementary material online.
# Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction
## 7 Conclusions  
In this study, we investigated resource allocation in the context of cloud computing. To match the resource requirements with changes in workload, we proposed a proactive cloud resource allocation method to schedule the cloud resources elastically and on demand. First, to improve the prediction accuracy, we proposed a new workload prediction model, which we called the adaptive two-stage multi-neural network based on LSTM (ATSMNN-LSTM). The proposed method can adaptively classify the workload into two categories (i.e., uphill and downhill), according to the workload trend. Next, according to the workload classification results, the workload prediction process is routed to the corresponding LSTM prediction sub-model. Furthermore, to avoid the cost of manual annotation in building the training data set, we use the first-order gradient feature and the unsupervised \(k\)-means algorithm to cluster and label the original training data into two training subsets (i.e., uphill and downhill data sets) for training of the NNs. Based on the queueing theory, the cloud service was modeled using a comprehensible mathematical description. With the target of the maximum cloud service profit, VM system load, and QoS requirements, we proposed a cloud resource scheduling algorithm to find an optimal number of VMs for the cloud center, in accordance with the predicted workload. The experimental results demonstrated that the proposed cloud resource allocation method not only achieves a high workload prediction accuracy but can also elastically allocate and schedule the cloud resources to match the workload requirements under QoS and VM system load requirements.  
Conceptualization, L.L.; Methodology, L.L.; Software, L.L.; Writing--original draft, L.L.; Writing--review and editing, X.G.; Funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Guangdong Science Technology Project (GDSTP: 2017A010101027) and the National Natural Science Foundation of China (NSFC: 61936003).  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
# Collapse Potential of an Existing Reinforced Concrete Bridge Structure
###### Abstract
[18] proposed a method to evaluate the seismic damage of bridges incorporating both the maximum drift and residual drift of columns as the demand parameter. They applied the proposed method to assess the seismic resilience of a two-span reinforced concrete bridge and observed that the residual drift has a significant effect on the probability of collapse. They reported that seismic resilience decreases in the case of taking both maximum drift and residual drift into consideration. [PERSON] et al. [19] investigated the effect of structural parameters on seismic resilience, such as deck radius, pier height irregularity, and seismic wave angle under short- and long-period records by using fragility curves and the resilience surfaces. They observed that the angle of incidence is the most effective parameter when long-period records are applied in one direction, and the vulnerability of bridges increases when the angle of incidence approximates \(0^{\circ}\). [PERSON] et al. [20] carried out a study to quantify the seismic damage of RC bridges under the effect of ground motions and proposed a new definition for the damage index and damage states. They performed a seismic vulnerability evaluation, developed fragility curves, and concluded that the proposed damage model is effective. [PERSON] et al. [21] examined the earthquake performance of a highway bridge with RC multi-column bents, whose span number is five, under the effect of earthquakes and chloride-induced corrosion with the help of fragility surfaces. They concluded that elastomeric bearings have the highest probability of damage. [PERSON] et al. [22] used fragility curves to examine the efficiency of a new type of hybrid pier with replaceable components to guarantee that structural damage remains repairable following earthquakes. They derived the fragility curve of the new type of pier and showed that its fragility curve is slightly higher than the fragility curve of a conventional RC pier. They also noted that, when the hybrid pier experiences damage, the decreased stiffness affects its seismic performance, leading to a reduction in the seismic forces acting on the structure and enhancing its seismic resilience. [PERSON] et al. [23] developed a numerical model to simulate the response of two large-scale RC columns, which had been repaired with different techniques, and implemented it into a prototype bridge model. Incremental dynamic analyses and a fragility analysis of numerical models were carried out to investigate the effectiveness of the repair techniques and their post-repair seismic performance. [PERSON] et al. [24] aimed to make an investigation on the fragility of reinforced concrete bridges by incorporating the vertical component of ground motions. They obtained fragility surfaces by taking pile-cap displacement and drift ratio as damage indicators into consideration. They showed the considerable effect of vertical spectral acceleration on the increase in damage probability for both slight damage and collapse state.
###### Abstract  
The phase change microcapsule (mPCM) is one of the primary candidates in the fields of energy storage and thermal regulation. In this study, electro-spraying, as a green, high-efficiency electrohydrodynamic atomization technology, is applied to the microencapsulation of two phase change materials (PCM) (\(n\)-hexadecane and \(n\)-eicosane) with three loading contents (30%, 50%, and 70% by weight) in a polycaprolactone matrix. Ethyl acetate (EA) and chloroform (Chl) were chosen as solvents to prepare the working solutions. The objective of this study is to clarify the microencapsulation process during electro-spraying and to optimize the structure and properties of the electro-sprayed mPCM. The structures, morphologies, and thermal properties of the mPCM were characterized by optical microscopy (OM), scanning electron microscopy (SEM), differential scanning calorimeter (DSC), thermogravimetric analysis (TGA), and fourier transform infrared spectroscopy (FT-IR). Electro-sprayed spherical and non-porous mPCM have been successfully prepared. The mean diameter and the particle size distribution depend mainly on the choice of the \(n\)-alkane, as well as the solvent used to prepare the working solutions. Meanwhile, the structure formation of electro-sprayed mPCM and the loading content of PCM were mainly influenced by the evaporation of the solvent and the phase separation between PCM and poly(caprolactone) (PCL) matrix. During the shell formation or PCL solidification, the control of the PCM leaching out of the matrix allows improving the loading content. Finally, based on a high latent heat and simple formation process, the electro-spraying route of PCM is a green, non-toxic, and high-efficiency direction for energy storage and heat regulation.  
**Keywords:** phase-change microcapsules; electrospray; poly(caprolactone); paraffin wax; formation mechanism; structure and morphology; thermal properties