esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, due to their small size and thus light weight, they tend to drift. Large drops, on the other hand, do not drift as easily; however, their size makes their distribution on the plant surface uneven. Testing the spectrum of droplets produced can be done using a variety of testing methods [19; 20; 21; 22]. A widely used method is to measure the droplet size using water-sensitive paper [23; 24; 25]. In their work, [PERSON] et al. designed and constructed a spray nozzle analysis station, in which the stream of liquid under test is directed at Petri dishes filled with silicone oil of appropriate viscosity and density [26; 27]. Typical non-invasive methods include high-speed image analysis [28; 29; 30; 31], laser diffraction (Malvern Spraytec) [32], and laser Doppler anemometry (2D LDA/PDA) methods [16; 33]. Each of the methods mentioned has its advantages but also disadvantages and limitations. The PDA method works best for homogeneous and spherical droplets, while laser diffraction uses the matching method and so all droplets deviating from the assumed shape are excluded [34]. Therefore, it is very important to choose the right test method. The comparative studies conducted give information as to the advantages and disadvantages of the various measurement methods [35; 36]. In their work, [PERSON] et al. compared four research methods: Liquid Immersion, laser diffraction, Phase Doppler Particle Analysis, and Shadowgraphy, which were used to study flat-fan nozzles [37]. The use of Doppler anemometry to study the spray stream provides information on, among other things, droplet velocity distribution; average diameter: D10, D20, D21, D30, D32; and average diameters in terms of volume: Dv0.1, Dv0.5 (VMD), Dv0.9, and Dv0.98 (\(\upmu\)m). In the conducted study, a 2D laser analyzer LDA/PDA was used to determine selected micro and macro parameters defining the quality of operation of flat-fan spray nozzles. The main parameters characterizing the spray flux were determined. The tests were carried out for different measurement times (from T = 120 to T = 10 s). Based on the specified volume diameters (D30), the volume distribution characterized by the coefficient of variation for a single sprayer was determined. In the final stage of the study, the results of the volume distribution obtained for individual nozzles were combined to evaluate the value of the coefficient of variation for a group of nozzles. The measurement results were compared with those obtained using a groove table. The genesis of the research was an attempt to develop a method of measuring selected micro and macro parameters using a single measuring station. The aim of the study is to apply the 2D Laser Doppler Analyzer LDA/PDA in the research in the context of obtaining information about the characteristics of the atomized liquid with the simultaneous possibility of assessing the lateral distribution of the jet volume as a function of measurement time.
# Study of the Relationship between Mode I Fracture Toughness and Rock Brittleness Indices
## 1 Introduction
Significant correlations with only two indices, represented as (0.5\"UCS\"TS) and ((UCS\"TS)[7; 7]), were reported. Using a probabilistic approach, a new brittleness index was suggested, which provided a more accurate K\({}_{\text{IC}}\) estimation [24]. The grain size effect on a strength-based rock brittleness index, i.e., (0.5\"UCS\"TS), was investigated for three granite types with small, medium and large grain sizes in [45]. A proportional trend between the brittleness index and the grain size was reported. In these studies, only strength-based brittleness indices were considered in addition to the literature data used in [22; 23], which were identical, and CB was the dominant K\({}_{\text{IC}}\) test method in [22; 23; 24]. So, it would be interesting to investigate the correlation between K\({}_{\text{IC}}\) and strength-based indices using an extended and updated dataset considering various K\({}_{\text{IC}}\) test methods and rock types (i.e., different properties) in an attempt to generalize the K\({}_{\text{IC}}\)--rock brittleness relationship as suggested in [23]. Furthermore, elastic-based brittleness indices, i.e., in terms of Young's modulus and Poisson's ratio, can provide a more accurate relationship when considering other, less brittle rock types, such as shale rocks [43]. This paper investigated the correlation between rock brittleness and the apparent K\({}_{\text{IC}}\), i.e., obtained from tests, using literature data in order to obtain a general relationship between rock brittleness and K\({}_{\text{IC}}\). Literature data were collected for different rock types and core test methods under level I and static testing conditions. Only core test methods were considered, because the core sample is the most obtainable sample geometry particularly at great depths. Rock brittleness was assessed using ten indices based on strength and pre-peak elastic parameters. The correlation between K\({}_{\text{IC}}\) and elastic-based indices has not been considered before in the literature, to the best of the authors' knowledge. We believe that investigating the relationship between K\({}_{\text{IC}}\) and rock brittleness could be helpful in assessing the stability of rock structures and/or the performance of rock engineering operations. Furthermore, such a relationship can estimate K\({}_{\text{IC}}\) with a good accuracy, using more common rock properties, where testing is not feasible.
## 6 Conclusions  
In this paper, we have successfully modified the posterior encoder of the original VITS model to incorporate the pre-trained HuBERT model, achieving enhanced extraction of prosodic features during speech synthesis. This innovation retains the WaveNet block architecture while significantly improving the naturalness, intonation, and prosodic features of synthesized speech. Comparative experiments and subjective evaluations confirm the superior performance of our approach, demonstrating the effectiveness of integrating pre-trained models like HuBERT into speech synthesis frameworks.  
Our work contributes to the theoretical understanding of prosodic feature extraction and their integration into end-to-end speech synthesis models. Practically, the proposed method paves the way for more realistic and natural-sounding speech synthesis, which is crucial for applications in intelligent speech interaction systems, such as virtual assistants and automated customer service. This study is distinct in its application of self-attention pooling and HuBERT for enhancing prosodic feature extraction in VITS models. It represents a novel approach to leveraging pre-trained models in speech synthesis, showcasing the potential for improved performance through cross-domain knowledge transfer.  
While our findings are promising, it is important to acknowledge several limitations. These include the reliance on a specific dataset for training and evaluation, which may not be generalizable to all speech synthesis scenarios. Additionally, the computational cost associated with integrating HuBERT might pose challenges in real-time applications. Future research could explore the scalability of our method to different languages and dialects as well as investigate the impact of various pre-trained models on speech synthesis quality. Moreover, optimizing the computational efficiency of the integrated model would be beneficial for deployment in resource-constrained environments.  
In summary, this research offers a novel strategy for enhancing speech synthesis technologies by integrating pre-trained models. It underscores the importance of leveraging linguistic knowledge from large-scale textual data to improve speech synthesis performance and realism. Our findings lay a solid foundation for the advancement of intelligent speech interaction systems, opening new avenues for creating more intelligent and natural speech synthesis systems.  
Conceptualization, Z.W. and M.S.; Methodology, Z.W. and M.S.; Software, Z.W. and M.S.; Validation, Z.W. and M.S.; Writing--original draft, Z.W. and M.S.; Writing--review & editing, Z.W., M.S. and D.Z.; Visualization, Z.W. and M.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The datasets we used are from reference [34, 35].  
The authors declare no conflict of interest.
# Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques
## 3 Research Methodology  
Given the importance of a refactoring prediction scheme in this analysis, the predominant emphasis has been placed on exploiting key structural metrics or software code metrics and their relationship with refactoring proneness to perform class-level refactoring classification. In other words, this research first intends to identify the optimal set of code metrics and their association with refactoring proneness to perform each class classification as refactoring prone or non-refactoring. This can help developers design highly efficient, reliable, and cost-efficient software solutions. This research uses a multi-phased optimization paradigm where different enhancements, including data enhancement, feature enhancement, and classification enhancement, are performed. Though the principal goal is to design a robust and automatic refactoring prediction system, this research employs different techniques such as data sampling, data normalization, heterogeneous ensemble learning, and multi-phased feature selection to achieve an eventual goal. Thus, with the above-stated methods, a few research questions have been defined, which intend to assess whether the proposed method efficiently yields targeted refactoring prediction purposes. The identified research questions are given as follows:  
**RQ1:**_Can software code metrics characterizing different traits, including inheritance, size, complexity, cohesion, coupling, etc., be an efficient identifier to classify a code as refactoring-prone or non-refactoring?_
**RQ2:**_Can a multi-phased feature selection method (Wilcoxon significant text, Pearson correlation test, and PCA) be used to help perform accurate software refactoring prediction?_
**RQ3:**_Can data sampling and normalization help alleviate the data imbalance issue in refactoring Prediction?_
**RQ4:**_Can a heterogeneous ensemble structure with ANN classifiers, extreme learning methods, decision trees, and regression methods yield optimal software refactoring prediction for software quality optimization?_
**RQ5:**_Can a heterogeneous ensemble classifier with MVE ensemble structure yield more efficient software refactoring prediction than the BTE method?_
## 3 Results and Analysis
### Tool Wear  
In this study, two RUM tools were used to machine all pockets. The first tool is used until the dimensional errors of the machined pockets reach 5%. Figure 15 shows a comparison between the new and the used tool. Sharp cutting edge along with uniform inner and outer radii can be easily observed in the new tool in Figure 15a,b. On the other hand, as depicted in Figure 15c,d bond fractures, tool edge rounding, and plastic deformation can be detected, these types of tool wear were also documented in [34; 35]. The plastic deformation, which happened due to the high pressure applied to the tool end face (see Figure 15d), can partially block the internal coolant. It should be noted that the calibration system built in the DMG was used to measure the tool length after fabricating each pocket. Moreover, the tool rounded edge can affect the pocket's geometry. Therefore, in this study, the dimensional accuracy of the machined pockets was taken into consideration.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Tool** & **Design** & **Actual** & **Depth** & **Design** & **Actual** & **Width** & **Tolerance** & **Depth** & **Width** \\
**Path** & **Depth** & **Depth** & **Tolerance** & **Width** & **Width** & **Tolerance** & **Error (\%)** & **Error (\%)** \\ \hline Uni-X & & 0.3138 & 0.0138 & & 6.3 & 0.3 & 4.6 & 5.0 \\ Uni-Y & & 0.3141 & 0.0141 & & 6.27 & 0.27 & 4.7 & 4.5 \\ Zigzag-X & & 0.3105 & 0.0105 & & 6.234 & 0.234 & 3.5 & 3.9 \\ Zigzag-Y & & 0.3102 & 0.0102 & & 6.282 & 0.282 & 3.4 & 4.7 \\ Cross & & 0.3087 & 0.0087 & & 6.246 & 0.246 & 2.9 & 4.1 \\ Spiral & 0.3 & 0.3114 & 0.0114 & 6 & 6.222 & 0.222 & 3.8 & 3.7 \\ Mix-1 & & 0.315 & 0.015 & & 6.258 & 0.258 & 5.0 & 4.3 \\ Mix-2 & & 0.3117 & 0.0117 & & 6.234 & 0.234 & 3.9 & 3.9 \\ Mix-3 & & 0.3126 & 0.0126 & & 6.288 & 0.288 & 4.2 & 4.8 \\ Machine & & 0.3111 & 0.0111 & & 6.27 & 0.27 & 3.7 & 4.5 \\ \hline Average & & 0.3119 & 0.0119 & & 6.2604
## 1 Introduction  
Good handling qualities of an aircraft are key to ensuring safe flight. The problem of predicting the dynamic stability of an aircraft can be solved by flight testing a dynamically scaled aircraft model or by testing an aircraft prototype. This technique is most often used to definitively verify aircraft behavior and confirm that airworthiness regulations are met. These types of tests are performed at the end of the design process to confirm previous analyses.  
The ability to determine the dynamic characteristics of the aircraft at an early stage of the project is very desirable, especially when we are dealing with an unconventional configuration where the experience and intuition of the designer may not be sufficient [1]. The problem of taking into account the dynamic stability of the aircraft has been the subject of more than one project, e.g., the SimSAC project [2]. There have also been numerous attempts to incorporate multidisciplinary optimization as a tool to shape aircraft configuration [3; 4; 5; 6; 7]. The key problem in this type of issue is the quality of the data, and in particular the fidelity of the aerodynamic characteristics. This problem has been raised in a number of works that examined the impact of aerodynamic data fidelity on the results of dynamic stability analyses [8; 9] or the quality of simulations [10; 11].  
The aerodynamic characteristics necessary for dynamic analyses can be determined using engineering methods based on ESDU [12] or Datcom [13] reports, semi-empirical methods [14], experimental methods (wind tunnel) [15] or with the help of CFD (Computational Fluid Dynamics) methods of various classes. Nowadays, computational methods are widely used. However, despite the strong increase in computing power of modern computers, the issue of computational cost (time) is still important. Therefore, it is important to answer whether it is necessary to use higher fidelity methods (e.g., Euler) or whether simpler methods can be used, i.e., potential ones, the computational cost of which is significantly lower. This is even more important because, nowadays, design is strongly supported by a multidisciplinary optimization process [4; 16], where aerodynamic calculations of complex configurations are performed many times during subsequent iterations. To ensure good handling qualities of the aircraft, it is necessary to check whether the computation method used is sufficiently reliable. Therefore, the research problem addressed in this article is to investigate the influence of aerodynamic fidelity on the dynamic characteristics of aircraft.  
To address this problem, an aircraft with an unconventional configuration was selected, as it was particularly sensitive to the quality of the results. The rocket plane shown below was chosen as an example of an unconventional configuration designed to fly over a wide range of angles of attack and Mach numbers, which means it can be very useful as a case for this type of analysis.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 7 Evaluation Results
### Result Representation of the Three Evaluated Classifiers Using the Confusion Matrix
#### 7.2.1 Comparative Analysis of the Two Test Scenarios for SVM Classifier  
An insightful comparison of the two test scenarios based on the confusion matrix for SVM is presented in Figure 8. The result showed that the overall accuracy of the SVM significantly dropped, i.e., from 82.8% to 75.4%, when the tested dataset was different from the training state. We observed that this was the cause of a significant decrease in the identification process of the MP condition. By comparing the two test scenarios in Figure 8a,b, the predicted accuracy of the MP conditions in SVM declined from 28.6% to 19.0%. Meanwhile, there existed no sharp deviations in the predicted accuracy of the LOS and NLOS conditions in both test scenarios. This increased the misclassification rate of the LOS and NLOS conditions as an MP in the SVM classifier.  
To be precise, a significant misclassification rate of the MP condition as an NLOS was detected in the evaluated results, i.e., the value rose from 0.6% (when the training and test were in the same condition) to 5.8% (when the training and test were in different conditions), as provided in Figure 8. The misclassification rate of LOS as MP was also quite high, i.e., it increased from 4.2% to 8.5%. The main reason could be the data collected for MP conditions when the two transceivers were too close to each other. In that case, it was acceptable to interpret the received signal in MP condition as an LOS.  
Regarding the NLOS condition in both test scenarios, we observed quite a good outcome in the predicted accuracy, precision, and recall of the SVM classifier (Figure 8). This result was  
Figure 8: Comparison of the multi-label classification results for SVM using the confusion matrix in two different scenarios: (**a**) the test dataset was obtained from the same environments as in the training state; (**b**) the test dataset was collected in the environments different from the training state. In the evaluation, the radial basis function was used as the kernel for the SVM classifier.  
crucial because the main impact on the performance of the UWB localization algorithm was the NLOS condition (Section 2). The misclassification of the LOS as an NLOS did not produce a severe consequence on the overall performance of the UWB system. The reason was that the location algorithm assigned different weights to the classification results, and giving LOS as a smaller weight did not hurt the system performance.
# A Survey of Internet of Things (IoT) Authentication Schemes +
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref\#** & **IoT Layer** &
\begin{tabular}{c} **Identity Context** \\ **Credentials** \\ \end{tabular} & **Token Based?** & **Proc.** & **Arch.** & **HW.** & **Strength(+)/Weakness(–)** \\ \hline
[145] & A & Encryption & x & 2 & C/H & I &
\begin{tabular}{c} +Encapsulation is used to efficiently benefit from resources. \\ \end{tabular} \\ \hline
[69] & A + N & Encryption / RSA & x /DTLS & 2 & C/H & I &
\begin{tabular}{c} +Low overhead regarding computation and communication and high interoperability \\ –Unreliability due to the use of UDP over DTLS leads to \\ \end{tabular} \\ \hline
[108] & P & \
\begin{tabular}{c} Encryption/ Symmetric \\ asynchronous One Time \\ Password (OIT) \\ \end{tabular} & x/TLS & 2 & C/H & I &
\begin{tabular}{c} +Resistance to some DoS and replay \\ attacks. \\ –Performance analysis is not considered. \\ \end{tabular} \\ \hline
[109] & N & Encryption/Symmetric & \
\begin{tabular}{c} ✓(NodeID, Indices \\ of space, and Seed) \\ \end{tabular} & 3 & D/H & I &
\begin{tabular}{c} +Resistance to node capture attack \\ –Energy cost is not efficient. \\ \end{tabular} \\ \hline
[110] & N & Encryption/Symmetric & ✓(polynomialID) & 3 & D/H & I &
\begin{tabular}{c} +Resistance to node capture attack \\ +Efficient with respect to communication \\ overhead \\ \end{tabular} \\ \hline
[143] & A + N & Encryption/Asymmetric & ✓(Information) & 2 & D/H & I &
\begin{tabular}{c} +Resistance to Malicious entity by using \\ PKI \\ –Performance analysis is not considered \\ \end{tabular} \\ \hline
[144] & A + N & Encryption/Asymmetric & ✓(FormId) & 2 & C/H & I &
\begin{tabular}{c} +Compatibility problems are solved \\ –Performance analysis is not considered. \\ \end{tabular} \\ \hline
[153] & A + N + P & Encryption/Symmetric & ✓(IP/EPC) & 2 & C/F & I &
\begin{tabular}{c} +Resilience to attacks, access control, client \\ privacy and data confidentiality \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of IoT Authentication Schemes.
## 4 Discussion  
To the best of our knowledge, only one previous study in the literature has addressed the safety of using ELF-MFs on the head in stroke patients [16]. However, that study did not compare magnetotherapy on the head and the pelvic area, and it employed a different method of stimulation and parameters, with a gradual increase in dose. Our pilot study is the first to include a group receiving magnetotherapy on the head, a group receiving therapy on the pelvic area--both using a solenoid-shaped coil applicator--and a control group, which did not receive any magnetotherapy. In our study, similar to the study by [PERSON] et al., no adverse effects of therapy using ELF-MFs were observed. There were no adverse effects of magnetotherapy, regardless of the application area. During the study, none of the patients experienced epileptic seizures, nor did they report other symptoms such as discomfort, headaches, nausea, heart palpitations, breathing difficulties, or paresthesia. Except for one individual from the control group who developed pneumonia and had to be withdrawn from the study, the remaining participants in the treatment groups received all treatments using ELF-MFs and completed the study. This indicates a high acceptance rate for this type of procedure among this group of patients. Our preliminary study results support the implementation of full-scale research in this area, which could significantly advance knowledge on adjunctive therapies in the rehabilitation of post-stroke patients. Identifying tools that enable the modulation of inflammatory processes may greatly improve outcomes for stroke patients. The modulation of inflammatory processes in stroke patients appears to play a crucial role in the recovery and rehabilitation process [20]. Inflammation significantly influences brain plasticity, tissue regeneration, and overall neurological recovery, which are critical factors in determining the effectiveness of post-stroke rehabilitation strategies. It is noted that elevated CRP is typically associated with poorer functional outcomes three months post-stroke [27]. By addressing and potentially mitigating these inflammatory responses, it may be possible to enhance the brain's capacity to adapt and reorganize, thereby improving functional outcomes for patients. Given the central role of inflammation in the pathophysiology of strokes, exploring therapeutic approaches that target these processes could offer substantial benefits in optimizing recovery and maximizing the success of rehabilitation efforts [12; 20]. As the scientific literature on the use of ELF-MFs primarily focuses on examining the effects of magnetotherapy applied to the pelvic area [13], the findings of this study