esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Adsorption Data
#### 4.3.4 Temperature Effect  
The effect of temperature on the removal of Cr(VI) and MB by CPW, APWN, APWH, and APWP at an initial concentration of 100 mg/L is shown in Figure 7a,b. Increasing the temperature of the MB and Cr(VI) solutions increases the adsorption capacities of the pollutants, suggesting that adsorption is endothermic. These increased adsorption capacities could be due to the adequate kinetic energy, the decrease in the viscosity of the solution, and the change in the textural properties of the adsorbents [36].
# Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer
## 5 Experimental Results
### Load-Variant Datasets  
In this subsection, based on Table 1 we re-configured the available CWRU dataset and created three custom load-variant datasets to validate the robustness of the proposed fault identification technique under changing load conditions. In more detail, each of the three new datasets corresponded to three different sizes of the crack (i.e., 0.007, 0.014, and 0.021 inches in diameter), which contains \(N_{f}\times N_{load}\times N_{w}\) data instances, where \(N_{f}\) is the number of fault conditions presented in the available CWRU dataset, \(N_{load}\) is the number of load levels, and \(N_{w}\) is the number of windows cut from the residual signals delivered by the AFSMO. The results obtained during this experiment are presented in Table 7.  
The results presented in this table allow us to conclude that the proposed method is highly robust to the changing experimental conditions, such as torque loads and rotating speeds. The proposed approach outperformed its counterparts with the lowest average classification accuracy of 93.8%, achieved for the crack with a diameter of 0.007 inches under different load level conditions in this case study.
## 3 Results
### Suitable SNR Range for Soil Moisture  
The influence of different SNR on the estimation accuracy of soil moisture content was explored when the spectral resolution is 34 nm in the sensitive wavelengths of soil moisture content (1010 nm, 1068 nm, 1915 nm, 1946 nm and 1988 nm). In Figure 7a, the black spots show the change of the RMSEP with the SNR. It can be seen that the RMSEP decreases with the increase of the SNR. The relationship between the RMSEP and the SNR is fitted by using the MATLAB curve fitting toolbox.  
In order to further determine the change of the decline rate of the RMSEP with the SNR, the derivative analysis of the fitted relationship was carried out. It can be seen from Figure 7a,b that when the SNR is less than 150, the RMSEP decreases rapidly with the SNR; when the SNR ranges from 150 to 350, the RMSEP decreases slowly with the SNR; when the SNR is greater than 350, the decline rate of the RMSEP with the SNR tends to 0, indicating that the SNR's influence on the RMSEP is no longer significant. Therefore, in the sensitive wavelengths of soil moisture content (1010 nm, 1068 nm, 1915 nm, 1946 nm and 1988 nm), when the spectral resolution is 34 nm, the applicable SNR range is 150-350.  
Figure 6: The influence of the spectral resolution on the estimation accuracy in the sensitive wavelengths of soil moisture.  
Figure 7: The influence of the SNR on the estimation accuracy in the sensitive wavelengths of soil moisture (**a**) Original. (**b**) Derivative.
# Effect of Solid Particles on Droplet Size Applying the Time-Shift Method for Spray Investigation
## 4 Results and Discussion
### Spray Characterization
For the suspension, no ligaments were identified in contrast to the pure liquid. Nevertheless, in both cases large fragments exit the field of view. For both media, a pulsating Kelvin-Helmholtz wave can be identified as the predominant instability according to [14]. The significant difference in droplet size can be attributed to a difference in secondary atomization. As described by the tensile force approach mentioned above. Here, for the previously described force balance, applying Equation (5), a glycerol/water (\(\eta_{liq}=70\) mPa\(\cdot\)s) droplet with a diameter of \(d_{32}=300\) um requires a gas velocity of \(v_{gas}>14.6\) m\(\cdot\)s\({}^{-1}\) for secondary breakup due to the surface tension force. Contrastingly and according to [PERSON] [27], a relative gas velocity of \(v_{gas}>21.9\) m\(\cdot\)s\({}^{-1}\) is needed for the further breakup of the suspension droplet. Figure 8: Radial distribution of the Sauter mean diameter at \(z=150\) mm below the nozzle orifice as a function of the glass mass fraction for \(v_{gas}=80\) m\(\cdot\)s\({}^{-1}\) (dots: SpraySpy®, values: LDS) (left); High-speed camera images of the primary breakup for \(v_{gas}=80\) m\(\cdot\)s\({}^{-1}\) at (**a**) \(\phi_{P}=0\) wt-% and (**b**) \(\phi_{P}=30\) wt-% (right). Finally, the tendencies observed with the SpraySpy(r) system are compared with the laser diffraction measurements. The results for both solid mass fractions at increased viscosity (\(\eta_{liq}=68\)-\(70\) mPa\(\cdot\)s) are illustrated as values in Figure 9 (left). For both measurement techniques, with increasing solid mass fractions and constant viscosity, the droplet size increases. The LDS showed an increment of \(\Delta d_{32,LDS}=73.1\) um, whereas the SpraySpy(r) system detected an increment of \(\Delta d_{32}=60\)-\(80\) um depending on radial position. Thus, the tendency detected by the SpraySpy(r) were validated by the commonly used laser diffraction system, which confirms the reliability of the local measurements. Concluding the comparison, the maximum deviation between the measurement systems in the measured tendencies of droplet size was \(\Delta d_{32}=30\) um, revealed for the variation of solid mass fraction from \(\phi_{P}=0\) wt-% to \(\phi_{P}=30\) wt-% at low viscosity.
# A Balanced Symmetrical Branch-Line Microstrip Coupler for 5G Applications
## 3 Results and Comparison
The comparison results are summarized in Table 2. The operating frequency, filtering response, the last frequency with suppressed harmonics and the phase unbalance are depicted by f\({}_{o}\), FR, LSFH and PU, respectively. As can be seen, the majority of previously reported couplers did not have a filtering frequency response and subsequently could not suppress the harmonics. Only the phase balances in [5,19,21] are a little better than ours. However, we were able to better suppress the harmonics. Moreover, the proposed designs in [5,19,21] do not have filtering frequency responses. There are four smaller couplers in Table 2 compared to ours. However, they could not suppress the harmonics well. At the operating frequency, the values of S\({}_{21}\) and S\({}_{31}\) are \(-3.28\) dB and \(-3.56\) dB. Therefore, there is a \(\pm 0.28\) dB amplitude unbalance, which is considered acceptable, since it does not have a significant negative impact on the performance. As shown in Table 2, the isolation factor (S\({}_{41}\)) near the operating frequency of this work is acceptable. The presented couplers in [5,11,19,21] were not able to suppress the harmonics, as they do not have a filtering frequency response, while our coupler suppresses harmonics of up to 15.5 GHz. The size of the proposed coupler in [16] is larger than ours. Also, our design has a better balanced amplitude and phase than the designed coupler in [16].
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Tool Comparison
#### 5.2.4 TFX  
TensorFlow Extended (TFX) [62] is an end-to-end platform for deploying production-ready ML pipelines. The pipeline is formed as a sequence of components that implement an ML system. To benefit from TFX, one has to use the TF framework libraries for building individual components of the pipeline. The pipeline can be orchestrated using common orchestrators like Apache Beam, Apache Airflow, and Kubeflow pipelines. It has higher integration costs and might not be ideal for small projects where new models are required less frequently.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 2 Proposed Model
### Mobility-Based Model Function  
Mobile node movement is often referred to as the mobility function. It contains the speed and direction of the node. The distance between the nodes can be calculated by utilizing a constant \(k\) and the required transmission/receiving power:  
\[e=\sqrt[4]{j\cdot\frac{tr}{ts}} \tag{1}\]  
Here, \(e\) defines the mobility function, \(j\) shows the mobility constant, \(tr\) and \(ts\) show the time for the first node's traveling distance and traveling speed, respectively, and \(r\) represents the traveling distance.  
The neighbor speed (\(w\)) can be computed as:  
\[w=\frac{\Delta e}{\Delta r} \tag{2}\]  
The level of the neighboring node in relation to the selected node primarily depends on the speed constraint. The following condition is used:  
\[\text{Direction }=\left\{\begin{array}{l}\bar{w}>0.\text{\ onside}\\ \bar{w}=0.\text{\ fixed}\\ \bar{w}<0.\text{\ inside}\end{array}\right. \tag{3}\]  
Here, \(\bar{w}\) shows the direction of the nodes.  
The mobility function can be defined as:  
\[M_{i}=VTR_{i}+e \tag{4}\]  
where \(M_{i}\) defines the mobility of \(i\)th node, \(VTR_{i}\) represents the velocity transmission rate of \(i\)th node, and \(e\) shows the obtained mobility value using Equation (1).
## 2 Study Area  
In this study, a Choongmo Chemical Co., Ltd. underground limestone mine (37\({}^{\circ}\)31\({}^{\prime}\)92.31\({}^{\prime\prime}\) N, 128\({}^{\circ}\)72\({}^{\circ}\)52.44\({}^{\prime\prime}\) E) located in Jeongseon-gun, Gangwon-do, Korea was selected as the research area (Figure 1) for the development of a system that could collect and analyze log data related to truck transport times and diagnose and analyze the truck transport routes through a field application experiment.  
The limestone was mined using the room and pillar method, the main adit (entrance) was in the direction of the N20E strike, and the crosscut was horizontal. The ore bodies located at the bottom were connected using lamps, leading to each level. The ratio of the room and the pillar was maintained at approximately 1:1, and the size of the room was 12 m wide and 8 m high. The mining process proceeded in the following order: drilling, blasting, loading, haulage, and roof control (rock bolt and wire mesh). Blasting was performed an average of eight times a day using ammonium nitrate fuel oil and electric detonators, producing approximately 5000 tons of limestone per day. Moreover, the limestone produced was loaded on a dump truck (24 tons) and transported to a crusher located outside the mine at a distance of approximately 1-1.5 km.  
To analyze the loading-haulage system in the study area and introduce a system that could collect data related to truck transport times, environmental surveys inside and outside the mine and 3D drawing analyses were conducted. High humidity was observed in all routes in the mine constituting the loading-haulage system, and groundwater leakage was observed at some points. The concentration of fine dust in the mine was also high because of the scattering dust during the loading and transportation of the limestone.  
Figure 1: Map of the study area (Choongmoo Chemical Co., Ltd. underground limestone mine, Jeongsun-gun, Gangwon-do, Korea) showing the loading areas and dumping areas.  
Moreover, irregularities were observed on the walls of the mine, although the bottom surface of the transport route remained relatively flat.  
An analysis of the mined limestone transportation route was conducted through direct investigation--performed by boarding trucks dispatched to the production operation--and the analysis of images using drones and 3D drawings. As a result of the analysis, eight loading areas and four dumping areas in the study area were operational, and three loaders and 10 trucks were dispatched to the loading-haulage operations to produce limestone. Limestone transported out of the mine was sorted visually by utilizing rock color differences, and the final dumping point was determined.
## 1 Introduction
BELM estimated the output probability distribution to optimize the output weights of the network and is used to solve linear regression problems [21; 22; 23]. Sparse Bayesian ELM (SBELM) [24] combined the sparse Bayesian theory with ELM to achieve a sparse representation of output weight by setting a hyperparameter for each hidden layer output weight. SBELM not only possess excellent learning ability but is also able to output probabilistic results [25]. In addition, the best solution to improve the limitation of single-label classification in simultaneous fault recognition is to use multi-label classification, which defines an m-dimensional vector for the category label of each sample and trains the intelligent diagnostic model using samples of single fault types [26; 27; 28]. In order to identify simultaneous fault types in the fault diagnosis process, the diagnostic model must be able to provide the probability of all single fault types. The probability output is used to judge and identify the simultaneous fault types according to the ranking of the probability. Considering the superiority of SBELM, this paper proposes a simultaneous fault diagnosis framework based on hierarchical multi-label classification and SBELM. The function of the diagnostic mechanism of the proposed framework is to compare the similarity between the unknown sample to be diagnosed and each single fault mode, which is measured by the Bayesian-based probabilistic output. The principal contributions of this paper are as follows:  
1. A simultaneous fault diagnosis framework based on a hierarchical multi-label classification strategy and a feed-forward neural network based on sparse Bayesian is proposed to effectively recognize single failures and simultaneous failures. Only single failure samples are participating in the training procedure of the model. 2. There is a certain correlation between each pair of single faults, to improve the classification accuracy, this paper introduces paired strategy into multi-label classification to create a hierarchical multi-label classification model. 3. The proposed diagnostic framework is utilized for the intelligent fault diagnosis of the main reducer. When simultaneous fault exists, it's achievable to accurately identify multiple single faults that occur at the same time. The experimental results indicate that the framework performance is superior in recognition accuracy.
## 3 Numerical Experiments and Analysis
### Algorithm Performance Calculation  
The meta-heuristic algorithm has the feature of random search. It usually obtains the approximate optimal solution, which may be different from the global optimal solution. The calculation results may be different each time [10]. In order to reduce the randomness bias, we perform twenty independent operations on each test function. The mean and standard deviation of fitness values serve as the metrics for evaluating algorithm performance. The mean value is utilized to verify the convergence rate and solution accuracy of the algorithm, while the standard deviation is employed to reveal the stability. Table 3 shows the test results of four different algorithms after 20 independent repeated experiments.  
As shown in Table 3, the WaOA manages to find the optimal results in the majority of test functions. Particularly, on the functions F5, F6, F9, and F11, the theoretical optimal value 0 can be realized. It shows that the WaOA is very competitive among the eight algorithms. However, the optimization accuracy of the WaOA on function F10 is slightly worse than that of the SCSO. This phenomenon can be reasonably explained by the famous No Free Lunch (NFL) theorem; it means there is no universal algorithm that can show optimal performance on all test functions [19]. It emphasizes the close relationship between algorithm performance and problem characteristics.  
However, relying on the data in Table 3 solely is not sufficient to fully evaluate the optimization effect of the WaOA on the benchmark function. In order to further understand its performance, the convergence curves on different functions for the WaOA and other algorithms are plotted, as shown in Figure 3.  
As can be seen more intuitively from the Figure 3, the WaOA demonstrates excellent convergence speed and accuracy in the majority of test functions, and it can discover the optimal value more rapidly. Via the thorough analysis of the table data and the fitness curve, we can conclude that, among the four algorithms, the WaOA performs best and is capable of achieving better results.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Earthquakes
#### 5.2.2 Earthquake M7.3 Rio Caribe, Venezuela  
The investigation is performed using CSES HPM (FGM1 and CDSM) and the data availability is from July 2018 to July 2019. In this period, USGS reported 21 earthquakes with magnitude M \(\geq\) 7 and a wide spread in focal depth, ranging from shallow, e.g., 8 km in the case of the Ridgecrest earthquake sequence, up to deep seated earthquakes with a focal depth of 670.8 km in the case of Suva, Fiji. The Rio Caribe, Venezuela, EQ (USGS ID us1000 gez7) had magnitude M7.3 with a focal depth of 146.8 km, the distribution of the magnetic field fluctuations are shown in Figure 4. CDSM shows wavelike features in the frequency bands, i.e., enhancement and attenuation in varying order. For FGM1 the fluctuations in the Y-component are larger than in the X- and Z-components, but for all three components the fluctuations over the whole frequency range are higher after the earthquake.  
Figure 3: Combined CSES and _Swarm_ investigation of the earthquake M6.5 (focal depth 20 km), Severo-Kurilsk, Russia, the box size is in both cases \(\pm\)10 degrees. **Top**: Distribution of CSES FGM1 magnetic field fluctuations for the X-, Y-, and Z-components grouped in individual frequency bands. The time span is \(\pm\)2 segments, i.e., in this case \(\Delta\)t = \(\{-1.36\), \(-0.37\), \(-0.31\), \(0.67\), \(2.65\}\) days shift to the EQ at 2018-10-10 TZ3:16:02. **Bottom**: Distribution of _Swarm_ A/B/C VFM magnetic field fluctuations after wavelet analysis. The relative timing for the segments are t\({}_{A}\)=\(\{-1.96\), \(-1.46\), \(0.54\), \(1.01\}\) days, t\({}_{B}\) = \(\{0.85\}\) days (only one post crossing), and t\({}_{C}\)=\(\{-1.97\), \(-1.46\), \(0.54\), \(1.01\}\) days. The colour coding is the same as in Figure 2, i.e., \(\{\)pre/post\(\}\) is \(\{\)blue/red\(\}\) with gradually desaturation (fading) for larger At (interval between crossing and event). Geomagnetic activity, derived from the Disturbance Storm Time (Dst) index, is \(|\)Dst\(|\)\(\leq\) 41 nT for the whole period.  
Figure 4: CSES investigation of the earthquake USGS event us1000 ge27 with magnitude M7.3, Rio Caribe, Venezuela, focal depth 146.8 km, with a spatial dimension (box size) of \(\pm\) 10 degrees. Top: Distribution of CSES CDSM magnetic field fluctuations grouped in individual frequency bands. The step size is in log-scale according to \(\ln(f_{n})=-0.7651-0.069315\times\) n, shown is the range n = \(\{1-64\}\), i.e., the left box plot (n = 1) is f\({}_{1}\) = 0.4341 Hz (Nyquist frequency \(f_{s}/2=1/2\) Hz) and the right box plot (n = 64) is f\({}_{64}\) = 0.0055 Hz. Only satellite orbits during night times are considered. The time span around the EQ is \(\pm\) 2 segments, i.e., in this case \(\Delta\)t = \(\{-2.61,\,-1.62,\,-0.64,\,0.34,\,1.33\}\) days shift to the EQ at 2018-08-21T21:31:47. Bottom: Distribution of the CSES FGM1 magnetic field fluctuations after scalogram processing. The colour coding is the same as in Figure 2, the geomagnetic activity is \(|\)Dst\(|\)\(\leq\) 20 nT for the selected time span.
## 5 Discussion
### Research Innovativeness  
Traditional studies on the application of digital twin technology in education primarily employ case study investigations (Sen (2018); Sen (2018); Sen (2018)), but they lack empirical validation. These studies often showcase the potential and application scenarios of the technology through individual cases, without systematic empirical data support and a lack of research on the use of digital twins in landscape architecture courses. This study, by utilizing empirical research methods, allows for a direct observation and measurement of students' performance and feedback while using the digital twin learning system in real-world environments. This approach provides more practical and valuable conclusions, aiding in the validation of the actual effectiveness and applicability of digital twin technology in education. This systematic and comprehensive approach more accurately reflects the true context of use and provides a more comprehensive analytical framework.  
Currently, there are few studies that explore the acceptance and influencing factors of digital twin learning systems ([PERSON] (2018)). Most existing research focuses on the application effects of the technology itself and lacks in-depth investigation into user acceptance and its influencing factors. This study constructs a second-generation unified theory of acceptance and use of technology (UTAUT2) model with six dimensions to explore the intention to use digital twin learning systems in engineering education courses. The model considers factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit, which are crucial for understanding students' usage intentions. The research findings indicate that teachers can effectively guide students to enhance their utilization of digital twin learning systems. This has significant implications for improving teaching effectiveness and student learning experiences. Based on the findings of this study, teachers can adjust their instructional strategies to fully leverage the advantages of digital twin technology, thereby promoting students' learning and development.  
In the future, digital twin technology will create an intelligent learning environment where the virtual and real worlds are seamlessly integrated. Learners will continuously learn, optimize, and progress alongside their digital twin avatars in a virtual world, ultimately achieving personalized development. Digital twin technology not only provides a dynamic learning platform but also offers real-time data feedback and interaction, helping learners to continuously improve and enhance their skills. Through systematic empirical research and innovative model construction, this study not only fills the gaps in existing research but also provides a solid theoretical and practical foundation for the future application of digital twin technology in education.
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 4 Methodology  
Our framework consists of two modules, user allocation and traffic prediction. Each of our models can be either deployed separately or as an end-to-end framework, where user traffic prediction can facilitate resource allocation in 5G environments.