esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Soil Texture Factor
#### 3.2.2 Statistical Analysis and Hypothesis Testing of the Soil Texture Factor  
We employed two types of statistical hypothesis tests. The first one is based on categorical data and their corresponding frequencies of soil texture classes, while the second one is based on the numerical values of Sand and Clay, which represent the two major factors of soil texture properties.  
The rationale behind this approach is twofold; it is focused on the finest detail of soil texture classes and observed coarser differences based on numerical values. For the former approach, a Freeman-Halton exact test was applied with significance a = \(1\times 10^{-3}\)[24], and a significance level correction scheme based on the methodology described by [PERSON] [25]. The latter approach is based on [PERSON]'s T-squared test [26] in combination with a similar significance level correction.  
Inference based on categorical data:  
The null hypothesis (\(H_{0}\)) is: There is no correlation of mechanical class frequency (i.e., columns of contingency table), with the subregion (i.e., rows of contingency table).  
By applying the Freeman-Halton exact test on all combinations of subregions, and considering the significance level correction scheme, statistically significant results (i.e., rejection of \(H_{0}\)) were observed, as shown in Figure 9. Note: A conservative significance level of a = \(1\times 10^{-3}\) was chosen, due to the abrupt boundaries of mechanical classes. The full data of the contingency tables and \(p\)-values are given in Appendix A (Figure A1 and Table A1).  
Inference based on numerical data:  
The null hypothesis (\(H_{0}\)) is: There is no difference between the mean values of Sand and Clay measurements in the two subregions.  
Data normality and covariance matrix equality are two prerequisites of this test. Both of them are verified by using Henze-Zirkler [27] and [PERSON]'s test [28], respectively. Afterwards, the application of [PERSON]'s T-squared test provided the following statistically significant results in Figure 10. The full data and \(p\)-values are given in Appendix B (Tables A2-A5).  
Figure 10: Statistically significant results of soil texture (numerical data; Clay and Sand).  
Figure 9: Statistically significant results of soil texture (categorical data; soil texture classes).
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 6 On the Optimization of the Time/Frequency Allocation of the Spreading Code  
One of the key point of the proposed modem is to use spreading in time and frequency to improve the robustness and create a flexible design that uses time and frequency diversity. However, choosing the best time/frequency allocation can be a challenging task.  
Many approaches can be used to optimize/adapt the parameters of the modem to the channel. Usually, some kind of channel knowledge is supposed at the transmitter. This knowledge is often obtained through a feedback link [21; 22; 23; 24]. Although this approach can be efficient, it is energy costly. Furthermore, for some underwater communications applications, a feedback link can be hard to implement, expensive, and might not be feasible over long range communication [25]. In the proposed application, acoustic communication over tens of km are envisaged. For our use case, the hypothesis of some kind of channel knowledge by the use of a feedback link is not appropriated and will not be considered.  
Another approach consists of simulating a channel based on information on the communications environment, such as the communication range, the relative speed between the transmitter and the receiver and the water depth. This approach was used in [26]. Currently, AUVs are used for planned missions. In that case, the needed information to simulate the channel can be easily obtained beforehand. Therefore we will assume the knowledge of the following information:  
* Communication range;
* Depth of water;
* Depth of transducers;
* Relative speed between emitter and receiver.
## 4 Conclusions
The time needed for obtaining the consolidated diagnosis decision varied from 2.5 h to 16.5 h following the moment of the fault appearance. It is noteworthy that data used for building the training data matrix for all faulty classes, i.e., data from days 2 to 6, did not contain the data of the first day. The latter was only used for testing the promptitude and efficacy of the identification methodology. The complexity of the diagnosis increases when the number of fault types subject to investigation expands or the sensor faults produces comparable effects on the process variables. The results showed that the WWTP effluent quality performance index depreciated during all of the DO sensor faults. From the EQ index perspective, the most detrimental cases were the complete failure maximum, fixed value and loss of accuracy faults, while the less affected case was the wrong gain fault type. The WWTP normal and faulty operation modes were further investigated to assess the environmental effect of the on-site and off-site emissions of CO\({}_{2}\) and N\({}_{2}\)O GHG and the economic impact of spent aeration and pumping energy. On-site emissions have the most significant GHG contribution, being responsible for about three-quarters of the total emissions, both for CO\({}_{2}\) and N\({}_{2}\)O. The complete failure maximum, fixed value and loss of accuracy were the DO fault-affected operation modes that had the most undesired impact on the amount of GHG released emissions. The identification of simultaneous acting fault types needs specific investigations, and this is the subject of future research work. The comprehensive evaluation of effluent quality, energy costs and GHG emissions is a useful quantitative assessment basis for the control and safety systems design aimed at satisfying multiple objective targets and the overall plant sustainability goal. Instrumentation maintenance tasks, metrological calibration or verification services and designers of intelligent DO sensors may benefit from the presented results for promoting the safe, efficient and environmentally friendly operation of the WWTP. Conceptualization, A.-V.L. and V.-M.C. ; Methodology, A.-V.L. and V.-M.C. ; Software, A.-V.L., M.S.-V. and N.-B.M. ; Formal analysis, A.-V.L. and V.-M.C., Investigation, A.-V.L. and V.-M.C. ; Resources, M.S.-V. and N.-B.M. ; Data curation, A.-V.L., M.S.-V. and V.-M.C. ; Writing--original draft preparation, A.-V.L. ; Writing--Reviewing and Editing, V.-M.C. ; Supervision, A.-V.L. and V.-M.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. No data availability. The present work has received support from the project: Entrepreneurship for innovation through doctoral and postdoctoral research, POCU/380/6/13/123886, co-financed by the European Social Fund through the Operational Program for Human Capital 2014-2020. \"The author [PERSON] is an employee of MDPI, however she does not work for the journal Applied Sciences at the time of submission and publication.\" The authors declare no conflict of interest.
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 4 Experiments
### Results
The reason why the DPC-MS algorithm performs poorly on the E. coli dataset is that there is a large density difference in the E. coli dataset. When the DPC-MS algorithm searches for the true number of clusters by calculating density, it is affected by the \(MinDistance\) parameter, resulting in a discrepancy between the calculated results and the actual results. Table 2 presents a comparison of the Rand Index (RI) evaluation metrics. The results demonstrate that, in the majority of cases, our proposed approach yields the most favorable outcomes. Additionally, the experiments compared methods that adjust the Rand Index (ARI). Based on these results, it is evident that the performance of the DPC-MS algorithm significantly surpasses that of other contemporary clustering techniques. The reason for the particular RI and ARI evaluation metrics of the DPC-MS algorithm in the Thyroid dataset is the high degree of crossover between the various clusters in the Thyroid dataset, and there are many mutually inclusive situations between the clusters, which leads to them being mistaken by DPC-MS for noise or being misclassified. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **SNN-DPC** & **DPC-KNN** & **DPC-MS** \\ \hline Seeds & 0.8117 & 0.8177 & **0.8469** \\ Thyroid & 0.7395 & 0.6369 & **0.7566** \\ Iris & 0.854
# Research on Narrow Linewidth External Cavity Semiconductor Lasers
###### Abstract  
Narrow linewidth external cavity semiconductor lasers (NLECSLs) have many important applications, such as spectroscopy, metrology, biomedicine, holography, space laser communication, laser lidar and coherent detection, etc. Due to their high coherence, low phase-frequency noise, high monochromaticity and wide wavelength tuning potential, NLECSLs have attracted much attention for their merits. In this paper, three main device structures for achieving NLECSLs are reviewed and compared in detail, such as free space bulk diffraction grating external cavity structure, waveguide external cavity structure and confocal Fabry-Perot cavity structure of NLECSLs. The Littrow structure and Littman structure of NLECSLs are introduced from the free space bulk diffraction grating external cavity structure of NLECSLs. The fiber Bragg grating external cavity structure and silicon based waveguide external cavity structure of NLECSLs are introduced from the waveguide external cavity structure of NLECSLs. The results show that the confocal Fabry-Perot cavity structure of NLECSLs is a potential way to realize a lower than tens Hz narrow linewidth laser output.  
narrow linewidth; external cavity; FSBDG; FBG; silicon-based waveguide; confocal F-P cavity +
Footnote †: journal: _crystals_
## 3 Results and Discussion
### Comparison of Neural Computer Results to Clinical Data  
#### 3.2.1 Neural Computer Results and Neonatal State  
The normal and pathological outcome probabilities obtained by the NNW computer were compared to the neonatal states evaluated by the Apgar score, clinical condition, and umbilical cord blood pH, where the analysis time was as long as 50 minutes and the neural computer training reference data was also obtained in 50 minutes. The neonatal states matched outcome probabilities in 25 out of 29 cases (86 %) [23].  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{Example No.} & \multicolumn{5}{c}{Probability} \\ \cline{2-5}  & name & normal & intermediate & pathologic \\ \hline
1 & svd* & 0 & 0.001 & 0.999 \\
2 & mvd** & 0.001 & 0.999 & 0 \\
3 & normal & 0.998 & 0.001 & 0.001 \\
4 & normal & 0.998 & 0.001 & 0.001 \\
5 & ssp*** & 0 & 0 & 1 \\
6 & bdc**** & 0.001 & 0 & 0.999 \\
7 & svd* & 0 & 0.001 & 0.999 \\
8 & ld**** & 0 & 0 & 1 \\
9 & lv *6 & 0 & 0 & 1 \\
10 & normal & 0.998 & 0.001 & 0.001 \\
11 & mvd** & 0.001 & 0.999 & 0.001 \\
12 & mvd** & 0.001 & 0.998 & 0.001 \\
13 & lv *6 & 0 & 0 & 1 \\
14 & lv *6 & 0 & 0 & 1 \\
15 & ld**** & 0 & 0 & 1 \\
16 & ld**** & 0 & 0 & 1 \\
17 & bdc**** & 0.001 & 0 & 0.999 \\
18 & bdc**** & 0.001 & 0 & 0.999 \\
19 & ssp*** & 0 & 0 & 1 \\
20 & svd* & 0 & 0.001 & 0.999 \\ \hline \hline \end{tabular}  
* svd: Severe variable deceleration; ** mvd: Mild variable deceleration; *** ssp: Sinusoidal patter; **** bdc: Bradycardia; **** ld: Late deceleration; *6: lv: Loss of variability.  
\end{table}
Table 3: Recognition Probability.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Training with More Features  
One of the most important aspects of training a classifier successfully is the selection of the correct features, which hold the data the model is trained on. If too few features are selected, there is not enough data for the training process. The created HMM will therefore not have enough information to generalize the datasets ideally.  
Apart from position data, the recording framework also captured accelerometer data, when the user was performing input gestures and timestamps for the corresponding recording timeframes. When including those features as part of the model training process, it has to be considered that they also have to be brought into a normalized form. For example, the accelerometer value range differs largely from the range of the position input. Furthermore, we have to consider whether to use the same classifier for all features, or to create separate classifiers for each. Moreover, the number of hidden states and mixtures, which are utilized by the HMM may change depending on the number of used features since more complex inter-connections have to be handled by it. It is, however, also important to keep the number of hidden states and mixtures as low as possible. If a model with fewer states and mixtures achieves a similar or slightly worse result, it should be evaluated if the added complexity, of the model with the high amount of parameters, is worth it. This has to be done in order to prevent overfitting of the training data.  
Figure 5: ROC multiple gestures for position data with gesture lengths bigger than 50—3 hidden states and 2 mixtures.
# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
###### Abstract  
This paper studies the usage of orthogonal frequency division multiple access (OFDMA) for uplink transmissions in IEEE 802.11 ax networks. OFDMA enables simultaneous multi-user transmissions in Wi-Fi, but its usage requires efficient resource allocation algorithms. These algorithms should be able to adapt to the changing channel conditions, including the frequency-selective fading. This paper presents an OFDMA resource allocation algorithm for channels with frequency-selective fading and proposes an approach to adapt the user transmission power and modulation and coding schemes to the varying channel conditions, which is efficient even in the case when the access point has outdated channel state information. The proposed scheduling algorithm and power allocation approach can double the goodput and halve the data transmission time in Wi-Fi networks even in dense deployments of access points.  
Wi-Fi; 802.11 ax; scheduling; frequency-selective fading; power allocation; CSI estimation
# Natural Virtual Reality User Interface to Define Assembly Sequences for Digital Human Models
## 2 Materials and Methods
### User Study
#### 2.3.2 Measures  
In this study, we captured objective, as well as subjective, criteria. To measure the subjective workload of defining an assembly sequence for a digital human model with the WIMP and the VATS, we used the NASA-TLX questionnaire [30]. The questionnaire addresses the following dimensions: mental demand, physical demand, temporal demand, performance, effort, and frustration. With this questionnaire, we want to investigate the subjective workload users have when using the different user interfaces to define the assembly sequence. Furthermore, to investigate the learnability, we use the corresponding part of the standardized questionnaire for dialogue design [31; 32]. The questionnaire is based on the ISONORM 9241/110 and has seven dimensions. The dimension we focused on was \"suitability for learning.\" This dimension contains five items: the time required to conduct the task, encouragement users have to test it, and the memorization of details, memorability, and learning without help. The average of these is the value for suitability for learning. The scale is between -- until ++++. Additionally, we created a questionnaire with ten questions. The aim was to get feedback on which system (VR vs. WIMP) the participants liked more, and if they would use the VATS and why. Two questions also focused on technical issues, like if the participants can imagine using gloves instead of a Leap Motion and using an HMD at their daily workplace. The scale ranged from 1 (do not agree at all) to 6 (fully agree).  
As objective data, we also measured two different values. The task completion time (TCT) and the amount of errors users did during the task.
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 2 Wiener Entropy Measurements for SHM Purposes
### Uses for Damage Localisation  
The effects of damage fade moving away from its location. This represents a limitation for single-output damage detection, since the acquisition channel may be too far from the damage to detect it correctly. However, with a properly placed array of sensors (thus, with a multi-output acquisition system), this spatial variability can be exploited to locate the (single or multiple) source(s) of inconsistency.
## 3 Conclusions  
The proposed equivalent composite control structure in this paper achieves a balance between tracking performance and robustness by adjusting the time coefficient \(a_{2}\) of the compensation loop. Smaller values of \(a_{2}\) enhance tracking performance but also lead to the displacement of the system's dominant zeros away from the imaginary axis and the convergence of dominant poles towards the imaginary axis. This diminishes system robustness, deteriorates dynamic response, and exacerbates the waterbed effect. Optimal compromise between performance and robustness is attained when the time constant satisfies \(a_{2}/b_{2}\in[2,4]\). The influence of equivalent composite control on the original velocity loop is significant. If inertial stability is desired post-implementation of equivalent composite control, the feedforward gyroscope speed needs to undergo differential processing and be reduced by a factor of \(a_{2}\). This imposes considerable demands on the resolution of the velocity gyroscope. Moreover, the system becomes more sensitive to gyroscope noise, Hall sensor noise, and torque disturbances. Compared to the existing method referenced in [11], the method proposed in this paper simultaneously focuses on system performance and robustness. It not only intuitively reflects the improvement in system performance but also highlights the reduction in robustness, making it easier for users to adjust and achieve a more suitable control system. Therefore, when designing equivalent composite control or coaxial tracking for electro-optical equipment, comprehensive analysis, evaluation, and trade-offs among various factors such as effective system inputs, uncertainties, noise, and disturbances are necessary to achieve superior tracking performance.  
Conceptualization, Y.L.; methodology, Y.L. and W.W.; software, Y.L.; validation, Y.L. and J.C.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, J.C.; writing--review and editing, Y.Z.; visualization, M.C.; supervision, W.W.; project administration, Y.L.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
Supported by the National Key Research and Development Program of China (NO. 2021 YFB3601402).  
No applicable.  
The data presented in this study are available on request from the corresponding author.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & & \\ \hline Equivalent composite control & peak pointing deviation (pixel) & root mean square (pixel) \\ \hline Azimuth angle tracking error & 4 & 1.7 \\ Elevation angle tracking error & 4 & 1.22 \\ \hline Without compensation loop & peak pointing deviation (pixel) & root mean square (pixel) \\ \hline Azimuth angle tracking error & 24 & 13.59 \\ Elevation angle tracking error & 15 & 8.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error analysis.
# Evolving Strategies and Materials for Scaffold Development in Regenerative Dentistry
## 5 Conclusions  
### Overall Conclusions  
1. Multi-Material Scaffolds are Key: The most impactful research emphasizes the need for combining various materials in biocompatible scaffolds to achieve tailored properties and optimal biological responses in hard tissue regeneration.
2. Focus on Stem Cell Interaction: Studies with the highest impact explore how scaffold materials influence stem cell proliferation, differentiation, and behavior. Understanding these material-cell interactions is crucial for developing successful therapies.
3. Novel Modifications are Promising: Advancements in nanotechnology, 3D bioprinting, and surface modification techniques have the potential to revolutionize scaffold design, increasing their efficiency and customization for regenerative dentistry.
# A Simple Experimental Stand for Studying the Network Security Level of Power Electronic Devices
## 4 Proposed Experimental Testbed and Methodology to Study the Network Security Level of a PED
### Developed Experimental Stand  
Figure 8 shows the topology of the proposed experimental setup used to study the network security level of a PED. The experimental setup consists of the following devices:  
* **Studied PED:** this is the PED to be studied. It can be any kind of PED as long as it has the capability to connect to an IP network;
* **Managed switch:** the managed switch is needed because the port mirroring functionality is required for the bench purposes. By using this functionality, all traffic from a specific port or from all ports is copied to one specific port to which a **monitoring station** connects. In this way, the traffic generated by the studied PED is not \"mixed\" with the other traffic and it is known exactly what the traffic generated by the PED is;
* **Monitoring station:** this is a workstation on which special tools are installed to monitor the traffic. Through this machine only the generated traffic from the PED is monitored and recorded;
* **Kali Linux:** this is a workstation with Kali Linux version 2024.4 installed on it. Some of the operating system tools are used to perform the PED network security level study.