esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vitro Estimation of Relative Compliance during High-Frequency Oscillatory Ventilation
###### Abstract
Reactance is tied to compliance (\(\mathbb{C}\)), inertance (\(L\)), and frequency of the pressure oscillations (\(f\)) according to the equation:  
\[X_{\text{rs}}=2\cdot\pi\cdot f\cdot L-\frac{1}{2\cdot\pi\cdot f\cdot\mathbb{C}}. \tag{1}\]  
Inertance represents the tendency of a gas to remain in motion, and depends on the characteristics of the used ventilation gas mixture and geometric characteristics of the proximal airways [32]. The frequency of the pressure oscillations determines the sensitivity \(X_{\text{rs}}\) to the changes of the lung mechanics in different areas of the respiratory system [33]. The reactance \(X_{\text{rs}}\) measured at \(f=5\) Hz is a sensitive indicator of the changes of the mechanics in peripheral areas of the lungs [33, 34, 35, 36]. Assuming a constant ventilation frequency and proximal airways with constant dimensions and stable shape, we consider the changes of inertance \(L\) to be minimal. The first term of Equation (1) is therefore considered to be constant as well. On this assumption, the value of \(X_{\text{rs}}\) defined by the equation (1) is inversely proportional to the negative of the value of compliance \(C\) of the ventilated system. A higher value of \(C\) also means a higher value of \(X_{\text{rs}}\), i.e., \(X_{\text{rs}}\) is less negative, which is thus called relative compliance further in this study. In a standard configuration of FOT, high-frequency oscillations are generated by a device connected to the inspiration branch of the patient circuit, and the flow is measured at the entrance to the respiratory system. Nonetheless, it has been shown that pressure oscillations generated by the ventilator during HFOV can be also used for the relative compliance measurement [20,28]. The method has been, however, successfully used in small animals (rabbits and preterm lambs), whose parameters of the respiratory system correspond to neonatal patients, but its use has not been verified in larger physical or animal models that would correspond to pediatric and adult patients. The aim of this study is to verify whether it is possible, under laboratory conditions, to use pressure oscillations generated by the high-frequency oscillatory ventilator to determine relative compliance of the respiratory system from the measured proximal airway pressure and flow as a method that could be used for optimization of HFOV in pediatric and adult patients.
# Analysis of Scintillation Effects on Free Space Optical Communication Links in South Africa
## 5 Intensity Distribution
In certain instances where \(\sigma_{SI}^{2}(0,L)<1\) but \(\alpha\beta\leq 10\) or \(\Gamma(\alpha)\Gamma(\beta)\leq 50\), then the Gamma-gamma distribution is employed. In other situations, where \(\sigma_{SI}^{2}(0,L)<1\) and \(\Gamma(\alpha)\Gamma(\beta)\geq 100\) or \(\alpha\beta\geq 20\), some computations involving the Gamma-gamma distribution would produce undefined results [43]. Thus, in this work, when \(\sigma_{SI}^{2}(0,L)<1\) and \(\alpha\beta>10\) or \(\Gamma(\alpha)\Gamma(\beta)>50\), the Lognormal distribution is used.
## 5 Conclusions  
Non-intrusive load monitoring (NILM) is a significant tool of modern smart grid systems and smart load metering devices. Instead of using multiple sensors for measuring load quantities (voltage, current and power) of multiple electrical appliances, single-point sensor measurement yields more efficient and cost-effective solutions. Therefore, NILM emerges as a very important concept for recognizing individual appliances from the accumulated energy data.  
This article proposes time-series current shapelets obtained from the normalized current data of devices as feature components to classify electrical appliances. The fundamental concept is to investigate the unique shape of a current at the starting phase (turn on state) of a device. The envelopes are derived from the original normalized current signatures by scaling and connecting the local maxima of the samples. Then, time-series subsequences are derived from windowed envelopes to form shapelets. A novel method to extract and analyze the characteristics of current envelopes is presented in this article. A multi-class classification model is trained by these shapelets. In this paper, five supervised learning methods are employed and a comparative analysis is carried out in terms of training accuracies, testing accuracies and cross-validation losses. Performance assessments are executed for different test scenarios. The performance evaluations affirm the efficacy of the proposed NILM solution.  
[PERSON] determines the current shapelets, evaluates the performance of the classifiers and revises the article. [PERSON] proposes the technical concept, evaluates the performance of the classification model and documents the manuscript. [PERSON] supervises the work.  
This research work has not received any external financial assistance.  
Authors declare no conflict of interest.
## 3 SOA-Based WDM Cross-Connects  
The schematic of the optical wavelength, space, and time cross-connect switch is illustrated in Figure 3. The non-blocking optical cross-connect has N inputs, and each input carries M different wavelengths generated by the interfaces/TORs. The modular cross-connect processes the N WDM inputs in parallel by the respective optical modules, and forwards the individual wavelength channels to any output ports according to the switching control signals provided by the control plane. A possible solution for fast (sub-microseconds) controlling of the switch is reported in [16]. Each optical module consists of a 1:N splitter to broadcast the WDM channels to the N wavelength selective switches (WSS). The outputs of the N WSSs are connected to the N wavelength combiners of the respective N output ports. Each WSS can select one or more wavelength channels and forward the channels to the output ports according to the control signals. The WSS consists of two AWGs (acting as a demultiplexer and a multiplexer) and M SOA based optical gates. The first 1 xM AWG operates as wavelength demultiplexer. Turning on or off the M SOA optical gates determines which wavelength channel is forwarded to the output or is blocked. The second Mx1 AWG operates as a wavelength multiplexer. Multicast operation is also possible with this architecture. The broadband operation of the SOA enables the selection of any wavelength in the C band. Moreover, the amplification provided by the SOA compensates the losses introduced by the two AWGs. It should be noted that the amount of SOAs is NxNxM, but typically only NxM will be turned on. Moreover, during the switching operation, the modules operate in an independent and parallel way to each other, which introduces important  
Figure 2: Optical metro node architecture exploiting the Wavelength Division Multiplexing (WDM) cross-connects.  
features such as distributed control for the optical switch, leading to the following advantages. Firstly, the overall performance of the optical switch can be evaluated by testing a single module. Secondly, the parallel and independent operation of the module make the control complexity and the switching time (latency) of the entire switch independent of the port count and equal to the switching time of a single module. Furthermore, scaling the port count leads to a linear increase in components and energy consumption, by employing copies of the identical modules.
# Natural Rubber/Styrene-Butadiene Rubber Blend Composites Potentially Applied in Damping Bearings
## 2 Materials and Methods  
### Materials  
Natural rubber (RSS1; number-average molecular weight (M\({}_{\rm n}\)): 9.8 \(\times\) 10\({}^{5}\) g/mol; ML (1 + 4) 100 \({}^{\circ}\)C: 65 MU) was supplied by Yunnan Natural Rubber Industry Group Co., Kunming, Yunnan, China. SBR 1502 (M\({}_{\rm n}\): 1.1 \(\times\) 10\({}^{5}\) g/mol; styrene content: 23.5%; ML (1 + 4), 100 \({}^{\circ}\)C: 48 MU) was supplied by Guangzhou Rubber Industry Research Institute, Guangzhou, China. Carbon blacks N330 (reinforcing filler, particle size: 26-30 nm) and N774 (reinforcing filler, particle size: 80-170 nm) were supplied by Cabot (Tianjin, China) Limited. Zinc oxide (ZnO, vulcanizing activator), hydrophobic silica (reinforcing filler), stearic acid (SA, vulcanizing activator), rubber protective wax (Wax, physical antioxidant), N-(1,3-dimethylbutyl)-N-phenyl-p-phenylene diamine (4020, chemical antioxidant), 1,2-dihydro-2,2,4-trimethyl-6-ethoxyquinoline (AW, chemical antioxidant), bis(3-(triethoxysilyl)) protysulfide (Si-69, coupling agent), rubber plasticizer (naphthene base mineral oil, for reducing interactions between rubber macromolecules), N-cyclohexylbenzothiazole-2-sulphenamide (CZ, vulcanizing agent), dithiocaprolactame (DTDC, vulcanizing agent) and sulfur (S) were purchased from the chemical reagent shop of Beijing. All materials were used as received.  
The pure NR was first plasticated in a two-roll mill (XK-150, Guangdong Zhanjiang Guangyi Machinery, Co., Ltd., Zhanjiang, China) for better processing fluidity, and then the SBR was added to obtain the NR/SBR compound. To obtain NR/SBR composites, the ingredients were added into the compound through the two-roll mill in an orderly manner and were then cured in a standard mold using a hydraulic hot press (XLB-D 350, Huzhou Dongfang Machinery, Co., Ltd., Huzhou, China) at 150 \({}^{\circ}\)C under 15 MPa. It is worth mentioning that we added 5 phr hydrophobic silica at the early stage of two-roll mixing process, which helped increase the strength of the NR/SBR mixture, to facilitate the further addition of other components. In contrast, if no (or less than 5 phr) silica is added, the mixing of NR and SBR becomes very difficult as the mixture will be in a crumbly state and can de-roll during the mixing process.  
In this work, NR/SBR composites with various proportions of NR and SBR were prepared and marked as NR/SBR-x (x represents the proportion of SBR in the rubber matrix). The recipe (phr) for the NR/SBR-x composites was as shown in Table 1.
## 3 SCH Nonlinear Mathematical Model (NMM)
### Nonlinear Model Modification
#### 3.2.1. Hover Modification
The x-force in the nonlinear model can be modified by using the following equations:  
\[\Delta X_{u}=X_{u}(_{IDM})-X_{u}(_{NMM}) \tag{40}\]  
\[X(_{NMM})=X(_{NMM\_original})+m\cdot\Delta X_{u}\cdot u \tag{41}\]  
For example, from Tables 4 and 5, we can get \(\Delta X_{u}=-0.041\). Then according to Equation (41), the updated x-force \(X=X(_{NMM})-0.041\cdot m\cdot u\). If we linearize this equation and calculate the updated forward velocity derivative \(\Delta X_{u}(_{NMM})\), the updated \(\Delta X_{u}(_{NMM})=-0.0192\,-\,0.041\)\(=-0.0602=\Delta X_{u}(_{IDM})\). Therefore, the updated \(\Delta X_{u}(_{NMM})\) coincides with \(\Delta X_{u}(_{IDM})\) in hover condition. This method guarantees a good accuracy of x-force modification in hover, while the high-speed features should be checked in the following validations. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Previous** & **Updated** & **Parameter** & **Previous** & **Updated** \\ \hline \(a_{0}\) & 6.28 & 5.75 & \(\Delta X_{u}\) (hover) & 0 & \(-0.041\) \\ \(K_{col}\) & 0.3011 & 0.2932 & \(\Delta Y_{v}\) (hover) & 0 & 0.0485 \\ \(K_{\beta}\) & 157.54 & 160.57 & \(\Delta X_{u}\) (30 m/s D) & 0 & 0.0102 \\ \(I_{xx}\) & 0.412 & 0.396 & \(\Delta X_{u}\) (30 m/s WD) & 0 & 0.0291 \\ \(I_{yy}\) & 0.676 & 0.653 & \(\xi\) & 1 & 0.8762 \\ \hline \end{tabular}
\end{table} 
Table 5: Key modified parameters. The lateral direction was influenced by three parts: main rotor, airframe and tail rotor. \[Y_{v}=Y_{vmr}+Y_{vf}+Y_{vtr} \tag{42}\]  
Then, the y-force was modified as:  
\[\Delta Y_{v}=Y_{v}({{}_{IDM}})-Y_{v}({{}_{NMM}}) \tag{43}\]  
\[Y({{}_{NMM}})=Y({{}_{NMM\_original}})+m\cdot\Delta Y_{v}\cdot v \tag{44}\]
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 6 Architecture of WSN vs. Architecture of IDS
### Hierarchical WSN Architecture for Distributed or Cooperative IDS  
A WSN's hierarchical architecture is a variation of centralized architecture, which can be implemented as cluster-based or tree-based. In a cluster-based architecture all sensor nodes are partitioned into clusters, whereas in a tree-based architecture the nodes are partitioned into trees according to their topographical area. The nodes in a tree-based architecture are organized into a routing tree rooted at the BS. Cluster-based architectures can be static or dynamic. In static clustering the sensors are divided proactively into several clusters at the time of network deployment, while in dynamic clustering the formation of clusters is triggered reactively by detecting the event of interest. In a distributed IDS, the detection model is placed in every sensor network node, allowing nodes to collaborate in order to detect possible intrusions. The clear advantage of implementing a combined hierarchical and distributed architecture is that the communication overhead is significantly lower than in other approaches, as both hierarchical and distributed architectures involve less communication exchange between nodes [58]. A disadvantage of this approach is the need for each network node to have sufficient energy, processing, and storage capacity. Studies have considered using multi-layer instead of distributed IDS, with heterogeneous detection models placed only at the BS and CHs.
## 5 Design of Prediction Model
### Linear Regression Using Meteorological Data  
Based on earlier analysis of data, it was observed that the difference between the lowest and highest measured speed in a given hour represents could not be explained by the day of the week or by the hour of the day only. Therefore, it makes sense to try to integrate other explanatory variables into the predictions, such as the weather.  
We tried various ways to extend the linear regression using the weather. We tried instantaneous values, as well as their moving average values for the past 3 to 12 h. Unfortunately, the results were very weak. It seems that the average temperature value did not provide any added information. We tried to limit any extreme temperatures and to concentrate only on temperatures around zero or moving averages. None of this brought any prediction improvement. On the contrary, there was significant deterioration in most of the situations.  
This is similar even with other meteorological data available. We tried aggregate values, e.g., visibility as a combination of the sun-above-the-horizon coefficient, cloud amount and fog, or black ice as precipitation for temperature around 0 \({}^{\circ}\)C. However, nothing improved the prediction.  
The only exception was the total amount precipitation in the past 6 h (or the moving average for the same period), where the prediction improved slightly yet visibly. Nevertheless, the improvement was not as significant for the model to be extended by this input. Every new input constitutes a risk that the robustness of the model is decreased and there is also the issue concerning the weather forecast error. Due to this, the weather was not included in the model at this point.  
We can explain the meteorological data issue with the following causes:  
* First, only a very short history (6 months and approximately 20 cases per each day of the week and the given hour) was available, so excessive expansion of vectors of explanatory quantities either led to the overfitting of linear regression, or the number of historical cases even dropped below the number of explanatory quantities (in such a case, there is not a single solution, thus the linear regression cannot be calculated).  
* Second, winter 2019/20 was extremely mild in the Czech Republic, thus low temperatures were not sufficiently shown among the data. At the same time, the precipitation amount was relatively low or, more precisely, the precipitation amount in spring reached nearly zero. Fog occurrence was also only minimal.  
The situation could be improved once there are data available over a longer period of time and provided that the weather is more variable. At the same time, we need consider the fact that there is only a weather prediction available rather than an actual measurement. Albeit the short-term (in units of hours) weather forecast is very accurate, it may not detect all actual phenomena or their intensity (e.g., precipitation).
## 3 Imaging Results from Hot Commissioning Stage
### X-ray Phase Contrast CT Result  
For the tomographic scanning reported in this section, the sample is rotated over a 180\({}^{\circ}\) range. The data consisted of 1201 projections with a step-angle of 0.15\({}^{\circ}\). Before and after the scan, 100 dark-current images and 100 flat-field images were collected.  
Pre-processing each of the projection images is required prior to 3D reconstructions. This includes the correction for the dark-current and flat-field images. The pre-processing and the 3D reconstruction step were applied using the MCT processing pipeline and data processing user interface, developed by the Australian Synchrotron's in-house Scientific Computing group. The workflow and pipeline are configured and run from Jupyter notebooks. The pipeline is based on the open source Insight Toolkit (ITK) [27; 28] and ASTRA toolbox [29] projects. The MCT processing pipeline was used for 3D reconstruction including the TIE-Hom phase-retrieval step. The phase-retrieval algorithm was applied to the projection data set, to exploit the information from the phase, using a \(\gamma\) (or the ratio \(\delta/\beta\)) value applicable to the relevant materials at the experiment's X-ray energy. The Filtered Back Projection (FBP) algorithm [30] was used for the CT reconstruction. The MCT processing pipeline is available in the interactive computing environment ASCI (Australian Synchrotron Computer Infrastructure) desktop that provides high-performance computing resources [31]. The ASCI desktop provides a convenient web-based interface to launch Linux desktops on high-performance computer hardware. The experiment data are also stored in the ASCI desktop. The ASCI desktop can be used to analyze users' data immediately following acquisition at the beamline and/or after their beam time by logging in remotely. Analysis software such as ORS-Dragonfly and Avizo are also available in the ASCI desktop for MCT users. These software packages are useful for 3D viewing, image rendering, image segmentation to calculate volume, pore network analysis, bone analysis, etc.  
Figure 7: X-ray phase-contrast images (at 11 keV monochromatic beam collected using 5\(\times\) objective lens): (**a**) an insect, (**b**) fibrous paper, and (**c**) a eucalyptus leaf.  
Figure 8a,b show the reconstructed slices without and with phase-retrieval, respectively. We can see that the contrast is improved by TIE-Hom phase retrieval and shows an increased signal-to-noise ratio (SNR) [32; 33] in Figure 8b compared to the one without phase retrieval (Figure 8a). The data were extracted using a white beam with in vacuo filtration of 0.2 mm diamond, 5 mm Al, and 0.1 mm Cu. The white beam detector was used with a 20\(\times\) objective lens with an exposure time of 0.7 s. The distance between the sample and detector was 160 mm. ORS-Dragonfly was then used for 3D representation.
# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 4 Experiment  
In this section, to verify the performance of the proposed TP-DSDPC algorithm, we conduct experiments on both synthetic and real-world datasets. All of the experiments were organized in Matlab2016a on a laptop computer running Windows 10 with a 2.50 GHz CPU and 8G RAM. At the same time, we compared it with other clustering methods, including K-means [47], DBSCAN [48], DPC [19], KNN-DPC [21] and DGDPC [28]. Every method was repeated ten times with either the default parameter values given by the respective authors or tuned to achieve the best performance. After each run, the ACC, NMI and ARI indices of each algorithm were obtained, and the average result from several runs was used to determine final performance. The standard deviation (std) values are reported in brackets.
## 4 Experimental Results
### Comparison with Similar Study  
A comparison table was designed in Table 5 to show number of samples, accuracy and image preprocessing between the proposed model and some other models. As is shown, the study carried by [PERSON] [20] produced remarkable early results with their proposed Xception CNN reaching an accuracy of 91% using image preprocessing. The study led by [PERSON] [21] outperformed DL-CRC by reaching an accuracy of 93.94% by using GAN for image preprocessing. A state of the art research by [PERSON] [30] delivered early and substantial results with their proposed ChexNet reaching an accuracy of 99.7%. Image augmentation was carried out for the training of ChexNet which resulted in exceptional accuracy surpassing the techniques used in other studies. As reported in Table 5, we were able to achieve a 98.87% accuracy rate with the customized CNN model with fewer layers compared to ChexNet. The customized CNN model also outperformed other models in term of accuracy. Two datasets containing COVID-19 and normal chest X-rays were used to prevent the class imbalance problem.  
Figure 11: COVID-19 X-rays were tested output which produced by model are shown.  
Figure 12: Normal X-rays and outputs produced by the model.  
It was further shown in the table that five out of seven models including the proposed models used image preprocessing so the model could produce efficient results.
## 4 Mixed Reality for Human-Robot Interaction
### Mixed Reality-Enhanced Robotic Tele-Welding
#### 4.4.1 Robotic Tele-Welding
Teleoperation allows remote control and monitoring of welding robots, while AI algorithms enhance precision and efficiency [140]. Machine learning adapts welding parameters based on historical data, ensuring tailored and optimal welding conditions. AI-powered computer vision systems enable real-time quality control by detecting defects, leading to immediate adjustments. Predictive maintenance algorithms anticipate equipment issues, reducing downtime [154]. Additionally, AI optimizes path planning, minimizing unnecessary movements, and collaborative robots, guided by AI, work alongside human welders, ensuring precise and efficient welding outcomes [155]. These integrations enhance adaptability, productivity, quality, and cost-effectiveness across various industries.