esa-sceva/satcom-chunk-collection huggingface dataset:
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 4 Experiment
### Methods for Comparison  
This paper compares the proposed drift detection method, which combines deep neural networks and autoencoders, with existing classical detection methods, all of which are available from the skmultiflow ([[https://scikit-multiflow.readthedocs.io/en/stable/](https://scikit-multiflow.readthedocs.io/en/stable/)]([https://scikit-multiflow.readthedocs.io/en/stable/](https://scikit-multiflow.readthedocs.io/en/stable/))  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Number of Features** & **Number of Samples** & **Drift Position** \\ \hline Weather & 8 & 18,159 & 3631, 7262, 10,893 \\ Electricity & 8 & 45,312 & 9062, 18,124, 27,186 \\ Usenet1 & 99 & 1500 & 300, 600, 900 \\ Usenet2 & 99 & 1500 & 300, 600, 900 \\ Email data & 913 & 1500 & 300, 600, 900 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Real-world data with multiple drifts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Number of Features** & **Number of Samples** & **Drift Type** & **Drift Position** \\ \hline Weather & 8 & 18,159 & 68.62 & Abrupt & 6054 \\ Electricity & 8 & 45,312 & 57.55 & Abrupt & 15,105 \\ Usenet1 & 99 & 1500 & 53.33 & Abrupt & 501 \\ Usenet2 & 99 & 1500 & 66.67 & Abrupt & 501 \\ Email data & 913 & 1500 & 53.33 & Abrupt & 501 \\ NSL-KDD & 41 & 35,141 & 53.14 & Abrupt & 12,597 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Real-world data with single drift.  
api/api.html(accessed on 28 July 2023)) library. scikit-multiflow [38] is a Python package designed for handling stream data problems. The following is a brief introduction to these methods:  
* DDM [11]: This algorithm primarily monitors the model's error rate to detect drift. During the detection process, it continuously updates the minimum error rate and its corresponding standard deviation. If the current error rate exceeds the sum of the minimum error rate and three times the minimum standard deviation, concept drift is detected, and the minimum error rate and standard deviation are updated accordingly.
* EDDM [20]: An improved version of DDM that refines the drift detection criteria to enhance performance.
* HDDM_W [21]: Utilizes [PERSON]'s bound and EWMA statistics to assess the presence of drift.
* ADWIN [12]: This method dynamically adjusts the window size, calculates statistical differences between two sub-windows, and triggers drift detection when the difference exceeds a predefined threshold.
## 2 Design Proposal of the Three-Phase Modular Rectifier
### Mitigation of Current Interaction
Here, \(F_{a}\) and \(F_{b}\) represent the phase a and b terminals, \(Z_{FaN}\) is the phase a impedance with neutral, \(Z_{Fab}\) is the impedance in the combination of the phase a with phase b, \(L_{b}\) is the boost inductor, \(R\) is the resistance, and \(L\) is the inductor. Mathematical Analysis of Current Interaction Mitigation  
Analyzing Figure 6, it is noted that the secret to a better elimination of the interaction is to make the impedance value \(Z_{FaN}\) where the normal current flows \(i_{a}\) are as low as possible and the value of impedance \(Z_{Fab}\) where the current interaction \(i_{t}\) travels is as high as possible. For this analysis, the impedances of the diodes, the output capacitor, and the load can be disregarded, since these impedances will be the same for the two analyzed paths. To do this, first the analysis of the \(Z_{FaN}\) impedance is done. In the boost 1 converter, the currents \(I_{11}\) e \(I_{12}\) are the currents that flow through each coil of the DMCF, \(R_{11}\) and \(R_{12}\) are the two DC resistances respectively, so their impedances (\(Z\)) are given similarly in [22]:  
\[Z_{1}=R_{11}+jwL_{11}+jwM\frac{I_{12}}{I_{11}} \tag{5}\]  
\[Z_{2}=R_{12}+jwL_{12}+jwM\frac{I_{11}}{I_{12}} \tag{6}\]  
If \(L_{11}=L_{12}=L\) and \(K=1\), then M will be given by:  
\[M=K\sqrt{L_{11}L_{12}} \tag{7}\]  
Figure 6: Representation of the analysis of current interaction mitigation in the proposed rectifier. Here \(M\) is the mutual inductance; \(K\) is the magnetic coupling coefficient. Knowing that a DMCF is applied, its current travels in opposite directions in the coupled inductors (\(I_{11}=-I_{12}\)), and if assuming that \(M=L\), then it is obtained:  
\[Z_{1}=R_{11}+jw(L_{11}-M) \tag{8}\]  
\[Z_{2}=R_{12}+jw(L_{12}-M) \tag{9}\]  
With this, it can be calculated the impedance \(Z_{FaN}\), taking into account the impedances of the coupled inductor (\(Z_{12}\)) and the boost inductors (\(Z_{b1}\)), given by:  
Figure 4:0  
Figure 4:1  
Thus, the \(Z_{FaN}\) impedance will be given by:  
Figure 4:2  
To calculate the impedance \(Z_{Fab}\), the path of the current \(I_{t}\) must first be analyzed. It is noted that it travels in three inductors connected in series (L\({}_{b1}\), L\({}_{11}\), L\({}_{22}\)), with L\({}_{b1}\) and L\({}_{11}\) being inductors in the first boost converter and L\({}_{22}\) being an inductor in the second boost converter. For better understanding, the impedances will be separated in two impedances, corresponding to \(Z_{ab1}\) for the first boost converter and to \(Z_{ab2}\) for the second, as given below:  
Figure 4:3  
Figure 4:4  
The \(Z_{Fab}\) impedance is given by:  
Figure 4:5  
The mitigation of the current interaction occurs when the impedance \(Z_{FaN}\) is much lower than the impedance \(Z_{Fab}\), according to (16), which proves the appearance of current \(I_{a}\) and not current \(I_{t}\). Figure 4:6
## 2 Methodology
### Application Layer  
The environment is developed by components to implement the application of intelligence on the streaming service. The following elements are necessary to meet the service requirements, that is, the satellite content receiver, content transmitter, video quality analyzer, and intelligence optimizer for application control. The fundamental components are the following:  
* Satellite. The antenna receives the content via satellite. This content is processed (received in raw format, converted to H.264) and relayed within the laboratory.
* Optimizer. In charge of optimizing the service. Provides intelligence to the entire use case through DRL algorithms. It receives data from the communication bus to obtain probe data and, in addition, transcoder status values. These values are used to establish the set of states so that the current situation of the environment is monitored and the optimizer has sufficient data to predict actions accordingly.
* Transcoder. Performs bitrate control tasks, adjusting the streaming according to the optimizer's needs. It uses the H.264 encoder [14] for image processing, since the content received in the media production is based on the same codec. Apart from being one of the most used codecs in the industry, we maintain the same encoding type, which makes it easy to avoid changes in the content. This component is based on open source services, including multimedia libraries such as FFmpeg [15].
* Receiver. Multi-service component that allows both content visualization and analysis thanks to an integrated quality probe. This component simulates an end user on stage who views and evaluates the content in real time. The visualization part is based on a multimedia playback framework such as VLC [16], which is capable of receiving streaming content. The content analysis is performed by the quality probe through a non-reference analysis, determining the QoE of the received content through internal AI capabilities. The QoE metric is based on the Mean Opinion Score (MOS), used in many standardized subjective video quality assessment methodologies and ITU-T recommendations such as P.910 [17], among others. Given the subjectivity of the metric, the value scale was trained in an environment controlled by a complete evaluation procedure by different users with different content.
# Failure Mode Analysis and Dynamic Response of a Coal Mine Refuge Chamber with a Gas Explosion
## The Finite Element Model
### Material Models and Equation of State  
One of Chinese coal mine safety code manuscripts, \"Specifications for mine mobile refuge chambers\", refers that the explosive source is a high-pressure gas, such as methane/air mixture, with the initial volume of 200 m\({}^{3}\) at the end of the tunnel. To simulate the propagation of the shockwave generated by instantaneous detonation of the source and its impact on the refuge chamber, the specifications state that the blast wave pressure on the front surface of the refuge chamber which is 100 m away from the explosive source is not less than 0.6 MPa.  
A null material model and linear polynomial equation of state are used to describe the characteristics of air and gas/air mixture. The following equation is the linear polynomial equation of state:  
\[P=C_{0}+C_{1}\mu+C_{2}\mu^{2}+C_{3}\mu^{3}+\left(C_{4}+C_{5}\mu+C_{6}\mu^{2} \right)E \tag{1}\]  
where, \(\rho_{0}\) is the initial density, \(\rho\) is current density, \(E\) is the internal energy. \(C_{0}\)-\(C_{6}\) are state parameters of the Equation (1), \(\mu=\frac{\rho}{\rho_{0}}-1\). The main parameter values of the null material model and linear polynomial equation of state are shown in Table 2[12; 21; 22].  
The mechanical properties of steel are affected noticeably at a high strain rate. The strain rate effect of steel must be taken into account under the explosion. Previous studies have shown that the yielding strength of the most strain rate-sensitive materials will increase substantially under a high strain rate [23]. In this paper, the plastic-kinematic mode (MAT_03 in LS-DYNA) and the Von-Miss yielding criterion are used to simulate the steel material, and the rate-dependent plasticity model of yielding stress, put forward by [PERSON] and [PERSON] [24]:  
\[\sigma_{dy}=[1+(\frac{\varepsilon^{pl}}{\gamma})^{m}]\sigma_{y} \tag{2}\]  
where, \(\sigma_{dy}\) is the dynamic yielding strength considering strain rate effect, \(\sigma_{y}\) is the static yielding strength, \(\varepsilon^{pl}\) is the equivalent plastic strain rate, \(\gamma\) is the viscosity parameter, \(m\) is the strain-rate hardening parameter. The suggested values of \(\gamma\) and \(m\) for steel are 40.4 s\({}^{-1}\) and 0.5 [23]. The main parameter values of the steel materials in this paper are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Material** & **Density (kg/m\({}^{3}\))** & \(C_{0}\) **(Pa)** & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) & \(C_{4}\) & \(C_{5}\) & \(C_{6}\) & \(E\) **(J/m\({}^{3}\))** & \(V_{0}\)** \\ \hline Air & 1.29 & \(-1.0\times 10^{5}\) & 0 & 0 & 0 & 0.4 & 0.4 & 0 & \(2.5\times 10^{5}\) & 1.0 \\ Methane/air mixture & 1.235 & 0 & 0 & 0 & 0 & 0.274 & 0.274 & 0 & \(3.408\times 10^{5}\) & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of linear polynomial equation of state.
## 5 Concluding Discussion
### A Multilevel Learning Process and Implementation  
In the scientific literature, a lack of clarity regarding regulations [49; 50], and a lack of coordination between policy instruments [51] have been identified as major challenges in relation to SPP. This is also visible in different ways in the case studies, such as in Australia, where overlapping sustainability guidelines resulted in burdensome administration, and in the Swedish case, where industry-wide standards were called for to provide more efficient implementation of carbon reduction measures in the supply chain. Previous studies argue that training could be a remedy for this, e.g., [21; 26; 29; 49; 50], since training enhances the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **Project** & **Carbon Emission Reduction Visions** & **Project Role in Relation to Carbon Emission Reduction Development** \\ \hline USA, California, regional level more ambitious than the national level. & CHSR: higher ambitions than regional/state level & The project aimed to elevate the level of environmental concern in the industry, raising the bar for prospective contracts, framed as an obligation to do good, due to the size of the project. & Driver \\ \hline USA, California, regional level more ambitious than the national level. & SFO: higher ambitions than regional/state level & The project aimed to be the first climate-neutral airport. Ambition to be ahead, and strive for developing a strong sustainability stewardship. & Driver \\ \hline Australia, New South Wales, regional level more ambitious than the national level. & Sydney Metro: aligns with regional/state-level policy & The client aimed to be world-class in sustainability and leave transforme legacies. & Frontrunner \\ \hline Netherlands & Almere: aligns with national and regional level & The main pressure came from the municipality of Almere, which adopted high sustainability goals. Directly related to the government goals. & Translator \\ \hline United Kingdom & HS2: aligns with national-level policy & The project had ambitions to be a frontrunner and clearly expressed the ambition to contribute to the fulfillment of the national Climate Act through promoting innovation in the sector. & Frontrunner \\ \hline Sweden & Control Station 2018: aligns with national policy & The Swedish Transport Administration (STA) had developed their carbon emission reduction requirement model in response to national and international reduction goals. & Translator \\ \hline \hline \end{tabular}
\end{table}
Table 5: Projects’ carbon emission reduction ambitions and role in relation to national or regional policy.  
client competence and thus the ability to set appropriate guidelines and standards. In the studied projects, the need and support for a learning process are emphasized at three levels: project, industry, and policy level, and with slightly different approaches depending on the type of project.
# Investigation of Gentamicin Release from
## 3 Results and Discussion
### Kinetic Study of Gentamicin Release
The zwitterionic behavior is controlled by the protonation and deprotonation of the amine and hydroxyl groups, respectively. Therefore, at alkaline pH during in situ loading, its hydroxyl groups form positive-negative chemical interaction and/or hydrogen bonding with amine groups of gentamicin. Thereby, during in vitro release, the PDA structural change takes place at an acidic pH, shifting towards a positive charge, hence allowing the drug to release. This phenomenon supports the kinetic release mechanism of the G-PDA NPs which follows the non-Fickian release mechanism. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**G-PDA 1:1**} & \multicolumn{3}{c}{**Ritger–Peppas Model**} & \multicolumn{2}{c}{**Higuchi Model**} \\ \cline{2-6}  & \(k\) & **N** & **R\({}^{2}\)** & **\(k_{H}\)** & **R\({}^{2}\)** \\ \hline pH 7.4 & 0.1669 & 0.5105 & 0.9862 & 1.6571 & 0.9728 \\ \hline pH 5 & 0.1393 & 0.5586 & 0.9897 & 4.3954 & 0.9779 \\ \hline pH 3 & 0.1484 & 0.5422 & 0.9887 & 6.5609 & 0.9769 \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters for release kinetics obtained after fitting of Plots in Ritger–Peppas and Higuchi Models. Figure 6: Fitting of cumulative % drug release data to (**A**) Ritger–Peppas and (**B**) Higuchi model for pH 3. The diffusion-controlled drug release mechanism was also justified by the Higuchi model, which describes the drug release as a diffusion process based on square root time-dependent ([PERSON]'s law) [33; 35]. This model is represented by the following equation:  
\[M_{t}=k_{H}t^{1/2} \tag{2}\]  
where \(M_{t}\) is the cumulative release (%) of the drug at time t and \(k_{H}\) is the kinetic release constant. The experimental data were plotted and fitted in this model, as shown in Figures 4B, 5B and 6B. It was observed that the values of kinetic release constant (\(k_{H}\)) are changing with the change of pH, hence supporting the pH-controlled diffusion of gentamicin from the host molecules.
## 5 Discussion
By correlating NPD optimization areas with PLM features based on QFD analysis, the global impact of each PLM functional aspect on the company's NPD capabilities is determined, indicating the implementation efforts priorities. According to the QFD ranks, PLM functional aspects are sorted based on their level of importance as follows: Pr.Str.Mng > Pr.Conf.Mng > Cross-dom.Pr.Desgn.Col > Desig.Rel.Chan.Mng > 3D Vis.Dig.Mock > Sys.Arch.Mng > Sim.Proc.Dat.Mng > Suppl.Ch.Sour.Mng > Req.Mng > Dig.Manuf > Progr.Proj.Mng > Qual.Pl.Mng > Class.Mng > Pr.Doc.Mng > Manuf.Proc.Plan > Envir.Comp.Mng > Serv.Eng > Prod.Cost.Mng > Anal.Rep  
Analyzing the rankings from the QFD process reveals that the PLM functional aspects most influential on the company's NPD process performance are Cross-domain Product Design Collaboration, Product Structure Management, and Design Release and Change Management. These insights can be considered significant for identifying and evaluating the PLM functional domains that would most effectively support an organization's needs related to NPD process optimization, offering objective guidelines for setting PLM strategy from the perspective of enhancing innovation potential. However, this study has several limitations, including (i) the subjective assessments of experts that may affect the accuracy of the input data; (ii) the AHP method does not account for potential interdependencies among NPD objectives; (iii) the empirical analysis is based on a single company case; and (iv) the complexity of the approach may hinder its practical applicability. Future research could focus on (i) employing methods that examine the relationships between NPD objectives; (ii) using alternative approaches for mathematically modeling vague linguistic variables in QFD analysis; (iii) conducting empirical research to evaluate how widely the identified NPD best practices are adopted in the industry and to assess the level of NPD capabilities achieved by companies; (iv) validating the model through various cases in other industries; and (v) developing adequate software solution to facilitate the application of the developed approach.
## 3 Results  
### Physicochemical and Barrier Properties of Used Packaging Materials  
The measurement data of film thickness, transparency (\(T_{600}\)), transmittance, and water vapor barrier parameters are given in Table 2. Among the used films, PA/PE was the thickest, followed by LDPE, PLA, and BOPP. All results are close to the technical data sheet provided by the producer, except for PA/PE ribbed, where the differences were attributed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phenolic Standard** & **Equation** & **R2** \\ \hline Caffeoylmalic acid & y = 20,636.8x + 16,807 & 0.9997280 \\ Chlorogenic acid & y = 14511.7x \(-\) 66,325.3 & 0.9997641 \\ Coumaric acid & y = 2551.11x + 2349.01 & 0.9998387 \\ Ellagic acid & y = 33,829.1x \(-\) 6862.97 & 0.999998 \\ Ferulic acid & y = 27,461.5x \(-\) 90,059.3 & 0.9870226 \\ Gallic acid & y = 60,235.9x \(-\) 117,854 & 0.9998908 \\ \(p\)-hydroxybenzoic acid & y = 87,853.4x + 2,145,860 & 0.9871377 \\ Protocatechuic acid & y = 90,775.3x \(-\) 595,177 & 0.9997176 \\ Vanillic acid & y = 65,980.1x + 143,637 & 0.9999301 \\ Kaempfer & y = 88,470.8x \(-\) 1,047,410 & 0.9998243 \\ Naringin & y = 3941.28x \(-\) 30,329.7 & 0.9829743 \\ Quercetin & y = 111,886x \(-\) 377,503 & 0.9999638 \\ Quercetin-3-glucose & y = 50,449x \(-\) 342,648 & 0.9997252 \\ Rutin trihydrate & y = 34,107.6x \(-\) 238,617 & 0.9989752 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calibration curve equations of the individual phenolic standards.  
to the bulges due to the ribbed surface. The most transparent material (\(T_{600}\)) was LDPE, followed by BOPP, while the least transparent was PA/PEs. The highest transmittance was observed for PLA, with an even 80% higher value compared to PA/PEr, for which the lowest transmittance was measured.  
The highest _WVP_ was measured for PLA film, while the lowest was for BOPP and PA/PE film. There was no statistically significant difference between the smooth and ribbed PA/PE film, which was to be expected due to the same composition. Obtained results indicate that PLA was even 87 times more permeable to water vapor than BOPP, while LDPE was 10 times. Additionally, PLA had approximately 64-fold and 46-fold higher _WVTR_ than BOPP and PA/PEs, respectively.  
The permeability coefficients (_P_) of tested materials to O\({}_{2}\) (_P_O\({}_{2}\)) and CO\({}_{2}\) (_P_CO\({}_{2}\)) gases are given in Figure 1. Like the water vapor barrier performance, as expected, and due to its biobased character, the PLA had the highest permeability values for both gases, while the PA/PE had the lowest. LDPE was more permeable to O\({}_{2}\) than BOPP.  
Figure 1: Gas permeability coefficients (_P_) for oxygen (PO\({}_{2}\)) and carbon dioxide (PCO\({}_{2}\)) for analyzed packaging materials. Values are represented as means \(\pm\) standard deviation, and different superscript letters (\({}^{\text{a-c}}\)) indicate statistically significant differences between samples at \(p\leq 0.05\). PA/PEr—ribbed, PA/PEs—smooth side of film.
# Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function
## 3 Reliability Measurement of the Air Traffic Network
### Modeling of the Air Traffic Network  
An air traffic network consists of airports as nodes and air routes as edges. The network structure is shown in Figure 1. Airports A and B are directly connected by route AB, indicating that there are direct flights between airports A and B. Airports B and C can be directly connected by route BC or indirectly connected by BE and EC. There are no direct routes between A and D.  
Figure 1: Sketch of a map of an air traffic network.  
The topological structure of the air traffic network can be expressed as \(G=(V_{G},E_{G},\phi_{G})\), where the airport set is \(V_{G}=\{v_{1},v_{2},\cdots,v_{i},\cdots,v_{n}\}\), the air route set is \(E_{G}=\{e_{1},e_{2},\cdots,e_{i},\cdots,e_{n}\}\), and \(\phi_{G}\) represents the association functions of node sets and edge sets.