esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
In modern industrial environments, ensuring the continuous and reliable operation of machinery is crucial for maintaining productivity and minimizing downtime [21]. Fault detection and diagnosis play a pivotal role in this context, allowing for proactive maintenance and timely interventions to prevent costly failures. To address these challenges, researchers and practitioners have turned to machine learning algorithms, which offer powerful tools for analyzing complex data and identifying patterns indicative of machinery faults [21]. This section explores various machine learning techniques employed in fault detection and diagnosis for industrial systems. The discussion encompasses popular algorithms such as K-Means clustering, linear regression, Long Short-Term Memory (LSTM) networks, Levenberg-Marquardt Neural Networks (LMNN), Random Forest Classifiers, Gradient Boosting Machines (GBMs), Extreme Gradient Boosting (XGBoost), Logistic Regression Classifiers, Support Vector Machines (SVMs) with linear kernels, Decision Tree Classifiers, and AdaBoost with weak classifiers. Each algorithm is examined in terms of its underlying principles, applications, and effectiveness in fault detection and diagnosis tasks. This exploration aims to provide insights into the diverse range of machine learning approaches available for fault detection and diagnosis in industrial settings. By understanding the strengths and limitations of each algorithm, practitioners can make informed decisions when selecting the most appropriate techniques for their specific application scenarios. This discussion also shows how important it is to use advanced machine learning techniques to make fault detection and diagnosis more reliable and effective. This will lead to better operational performance and cost savings in industrial systems [21].
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Application Scenarios
#### 4.2.6 Smart Factory Plus  
The 6G system collects operating data in real time from machine tools, workshops, and accessory components by using ultra-high bandwidth, extremely low latency, and great dependability. Through the integration of edge computing and AI technology, the system enables the direct monitoring and transmission of data at the terminal level for real-time order execution [43; 44]. In 6G, blockchain technology facilitates the direct exchange of data among all terminals in a smart factory without the need for an intermediary transportation center. This decentralized approach enhances operational efficiency. The scope of 6G extends beyond the confines of the factory, ensuring seamless connectivity throughout the manufacturing cycle.  
By utilizing the 6G network, the system may dynamically link any smart device or terminal within the plant, enabling quick deployment of device combinations according to the demands of the production line. The expectations of C2B (customer-to-business) interactions for personalization and customization are met by this flexibility. With the use of 6G and related technologies, the Smart Factory Plus creates an end-to-end closed loop that links the factory's ability to meet specific client requests to its delivery capabilities.
## 5 Cavity Analysis in CW Operation
### Linear Astigmatism  
Linear astigmatism is an important consideration in folded cavities with Brewster-cut windows/crystals. Defining the tangential plane as the plane of refraction/reflection (parallel to the main plane of the cavity) and the sagittal plane perpendicular to it, we find that in the tangential plane, the mode size expands (divergence is reduced) as the beam refracts into the Brewster-cut crystal, while in the sagittal plane, the mode size remains the same. This introduces _astigmatism_, since the beam experiences a different geometric path in each plane, given by: \(L_{s}=L/n\) for the sagittal plane and \(L_{t}=L/n^{3}\) for the tangential plane (\(L\) is the thickness of the crystal, and \(n\) is the refractive index). This aberration differently shifts the stability limits of the two planes from the non-astigmatic stability limits in Equation (5), resulting in different stability ranges for each plane. Therefore, astigmatism compensation is necessary to ensure stable oscillation in both planes and to obtain a circular Gaussian mode at the end mirror.  
The material astigmatism from Brewster-cut windows/crystals can be compensated for using the curved mirrors that fold the beam in the main plane of the cavity. Since the focus of an off axis curved mirror (or lens) is different in the two planes, the folding angles of the curved mirrors introduce an additional astigmatism that (luckily) is of opposite sign to that of a Brewster-cut window, allowing for compensation. The curved mirror focal length for reflection at an angle, \(\theta\), is \(f_{s}(f,\theta)=f/\cos\theta\) for the sagittal plane and \(f_{t}(f,\theta)=f\cos\theta\) for the tangential plane [42], where \(f\) is the paraxial focus. Therefore, the mirrors astigmatism \(\Delta f(f,\theta)=f_{s}(f,\theta)-f_{t}(f,\theta)\) can compensate for the crystal astigmatism \(\Delta L=L_{s}-L_{t}\), by solving:  
\[\Delta f(f_{1},\theta_{1})+\Delta f(f_{2},\theta_{2})=\Delta L \tag{9}\]  
For \(\theta_{1}=\theta_{2}=\theta\) and equal paraxial foci \(f_{1}=f_{2}=f\), the solution of Equation (9) is [43]:  
\[\theta=\arccos(\sqrt{1+N^{2}}-N) \tag{10}\]  
Figure 3: Continuous-wave (CW) mode size on the end mirrors, \(EM1\) and \(EM2\), for the cavity illustrated in Figure 1 with \(f_{1}=f_{2}\) = 7.5 cm and arm lengths of \(L_{1}\) = 30 cm (short arm) and \(L_{2}\) = 40 cm (long arm). At \(\delta_{1}\), the beam is collimated in the short arm and focused on \(EM2\) in the long arm. This behavior is reversed at \(\delta_{2}\).  
where \(N=(L/4 fn)(1-1/n^{2})\).  
Note that the solution given in Equation (10) compensates for astigmatism only in the plane-plane configuration, at \(\delta=0\). In general, astigmatism can only be compensated for a specific distance, \(\delta\), in the cavity. In the Ti:sapphire oscillator, because of the very small pump (and laser) mode size inside the crystal, one usually operates near one of the stability limits, and astigmatism should compensate for that limit. Appendix discusses compensation for the other stability limits.
# A Study on Karst Cave Collapse Based on Improved Terzaghi Theory and Upper Limit Analysis
## 4 Discussion
### Parameter Analysis
#### 4.2.2 Effect of Shear Strength of the Soil  
The shear strength of the soil is directly related to its cohesion (\(c\)) and internal friction angle (\(\varphi\)). When the groundwater table is located below the karst cave, the soil unit weight \(\gamma=18.5\)\(\text{kN}/\text{m}^{3}\), \(d=6\) m, \(h=10\) m, static earth pressure coefficient \(K_{0}=0.58\), the angle between perpendicular lines of the surface and failure profile \(\alpha=0^{\circ}\), and vacuum absorption erosion pressure \(\sigma_{p}=50\) kPa. The relationship between the critical surface load \(\sigma_{1}\) and the shear strength of soil \(c\), \(\varphi\) is illustrated in Figure 11.  
Figure 11 illustrates that the improved Terzaghi theory and limit analysis method yielded results with a constant difference when \(d=6\) m was fixed. Concurrently, the critical surface load \(\sigma_{1}\) increased with increases in \(c\) and \(\varphi\), which was consistent with the shear strength of the soil and conclusions of previous studies [35; 36].
# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 6 Results and Discussion
### Scenario-2  
This scenario has device configurations as in Scenario 1, but we varied the number of IoT client devices (number of Z1 motes ranging 1-4) in a dumbbell-shaped network topology, having all the devices/senders in direct one-hop connectivity to the border router/cloud gateway. Here, offered/data load increases in the form of multiple active senders ranging from one to four devices. Comparisons was done among: (i) rest-2R (nodes registering data to two resources with separate endpoints); (ii) rest-3R (sender nodes submitting data to three separate resources at the cloud with CoAP server); and (iii) using proposed framework implementation for sending data to three cloud resources. Here, we also show results for PDR and system throughput (in terms of number of successful data transaction) in Figure 11, because all the sender devices are in the same contention area to the border node in the used topology. For both values, the proposed scheme shows better performance in comparison to REST architecture.  
With the increase in offered data load, increase in the delay is minimal and values are lowest for the proposed scheme in comparison in all the cases. Both the REST architecture schemes with two and three resources subscription have started to suffer when three active devices are communicating simultaneously. Similarly, the least used and the marginal increase in energy consumption is observed in the proposed scheme with increase in offered load, but the REST-based approach are behind and their energy consumption increase is linearly going up when multiple devices submit their data to the cloud data repositories. These schemes also suffer in terms of packet losses on high load and rest-3R scheme shows significant wastage of energy in terms of data loss on load of four active devices. The throughput result also shows some achievement in the case of the proposed framework.  
Figure 10: Scenario 1: Average delay and energy consumption comparison of proposed approach against REST based communication to two and three data resources.  
The proposed approach outperforms state of the art REST-based schemes in this scenario too and achieves energy-efficient, in-time delivery with a minimum loss of data packets. In the graphs of delay and energy, we can see the the proposed approach also shows better spread of the values with confidence, in comparison to other two schemes.
## 3 Results and Discussions
### Sensory Analysis
The participants were instructed to evaluate the crispiness as the force and noise created when the sample breaks during chewing the sample between the molar teeth. It was noted that the biscuits with higher amount of RFSF presented lower amount of moisture (Table 4). Negative correlation (\(p<0.05\)) was found between the consumer's perception of crispiness and the amount of moisture and total carbohydrates of the biscuits (\(-0.85\) and \(-0.82\), respectively). Similar findings were published by [PERSON] et al. [19] and [PERSON] et al. [13]. This result can be explained by flaxseed's capacity to bind water, which influences the textural properties of the final product [76]. Taste and aroma improved with the increasing amount of the flaxseed in the biscuits. The flaxseed has a unique nutty flavour (Tables 3 and 7) which was found pleasant by the consumers, therefore the taste and aroma characteristics of samples with RFSF scored higher than the control (B0 \(>\) B10 \(>\) B25 \(>\) B40). Moreover, the taste and aroma of the samples was positively correlated with the biscuits' content in protein (0.99, \(p<0.005\)), ash (0.99, \(p<0.005\)), fat (0.99, \(p<0.005\)) and crude fiber (0.99, \(p<0.001\)) and negatively correlated with moisture (\(-0.99\), \(p<0.005\)) and total carbohydrates (\(-0.99\), \(p<0.01\)). Some authors mention a bitter taste in samples prepared with high amounts of flaxseed flour [78]. However, in this study, the nutty flavour was appreciated by the consumers which are accustomed to the taste and flavor of different types of traditional biscuits and cookies prepared with various amounts of nuts. The aftertaste correlated negatively with the biscuit's content in protein (\(-0.97\), \(p<0.05\)), ash (\(-0.95\), \(p<0.05\)), fat (\(-0.96\), \(p<0.05\)) and crude fiber (\(-0.95\), \(p<0.05\)) and correlated positively with moisture (0.97, \(p<0.05\)) and total carbohydrates (\(0.96\), \(p<0.05\)). Although the sensory attributes of the samples with RFSF were decreased compared to the control, the aroma and crispiness increased and the significant differences between samples in appearance, hardness, chewiness and aftertaste did not reflect on the overall appreciation of the biscuits.
## 4 Discussion  
FRP materials are finding increasing numbers of applications in many areas of engineering in recent years. Particularly in structural retrofitting, the application of FRP jackets is a widely used alternative to steel confinement. As an alternative to carbon and glass fibers, aramid fibers are also finding applications in this area, due to their favorable ductility and strength properties. However, the lack of reliable equations can be a significant hurdle in the way of wide-scale applications of these materials in engineering design.  
The current study showed the application of state-of-the-art ensemble learning techniques in mechanical properties predictions of AFRP-confined concrete. A dataset gathered from the literature served as the basis for training the machine learning models. The reliability and accuracy of machine learning models can be increased in proportion to the size of their training dataset. Based on this assumption, the current study proposes a novel technique of generating additional data points using generative adversarial networks (GANs). These models consist of a generator and a discriminator component, where the performance of the generator component iteratively improves, such that the newly generated data points are indistinguishable from the original data points. It was shown that the predictive models trained on the dataset enhanced with the synthetic data points can significantly outperform the predictive equations currently available in the literature. One of the major reasons for the inaccuracy of the equations is that most of the equations in the literature are developed based on regression techniques fine-tuned towards a particular dataset and therefore cannot be generalized sufficiently. Therefore, it is crucial to train any statistical model on a large enough dataset.  
In the next stage of the study, the machine learning models have been further investigated using the SHapley Additive exPlanations (SHAP) approach. The SHAP results provide explanations for the predictive models by measuring the contribution of different input features to the model output. Using the SHAP technique, the input features have been ranked with respect to their impact on the model predictions for the confined compressive strength and ultimate strain. The unconfined compressive strength of concrete and the ultimate tensile strength of the AFRP wrap have been found to be the most impactful input features in the prediction of the confined compressive strength and ultimate tensile strain, respectively. Finally, the variations of the confined compressive strength and ultimate strain, with respect to different input features, have been visualized using individual conditional expectations plots.
# Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems
## 2 Materials and Methods  
### Experimental Site  
Long-term continuous in-situ spectral measurements based on tower-based observations were conducted in 2019 and 2020 at the Arou (AR) site, a grassland site (Figure 1) in the ChinaSpec network, a ground-based continuous SIF observation network of flux tower sites in China [44]. The AR site was also established for use in the Heile Watershed Allied Telemetry Experimental Research (HiWATER) experiment [45]. The AR site is located in Arou Township, Qilian County, Qinghai Province, Western China (100.4643E, 38.0473N, altitude 3033 m), and in the hinterland of the middle Qilian Mountains in the northeastern region of the Qinghai-Tibet Plateau, with a typical plateau continental climate [5]. The average multi-year temperature is around 0.7 \({}^{\circ}\)C, the average yearly precipitation is 400 mm, and the yearly snowfall period reaches about 240-270 days, with absence of absolute frost-free period all year round. The underlying surface of the AR site is mainly alpine meadow. The grass can reach heights of 30 cm during the peak growing period in summer. With high atmospheric transparency, thin air, strong solar radiation, long illuminating hours, and abundant incoming radiation throughout the year, SIF and GPP relationship for alpine meadow may, to some extent, differ from those of other ecosystems.
## 2 Data and Methods
### IMERG Data  
The IMERG is a unified US algorithm that benefits from CMORPH, PERSIANN, and TMPA algorithms (15; 16). IMERG suite provides global precipitation estimates at half-hourly interval and at 0.1\({}^{\circ}\) spatial resolution. There are three Runs of this product intended for different user requirements based on latency and accuracy. Early Run or IMERG-E is available after 4 h of acquisition time and relevant for flash flooding, whereas Late Run or IMERG-L product is available after 14 h of satellite acquisition time and is supposed to be good for crop forecasting. Final Run or IMERG-F is intended for research and is available after 3.5 months. IMERG-F utilizes monthly rain gauge information over land from the Global Precipitation Climatology Center (GPCC) for bias adjustment. However, the number of rain gauges utilized in IMERG-F through GPCC is less than 300. It indicates that IMERG-F uses about one-tenth of the total number of rain gauges used in the IMD rain-gauge-based gridded rainfall data. In addition, IMERG-F uses monthly rain gauge observations and further downsches the precipitation estimates to half-hourly scale. In V6, IMERG has been computed for the first time for both TRMM- and GPM-era. Thus, IMERG V6 is available since June 2000. A detailed description of the changes in V6 from V5 is provided by [PERSON] et al. (39). Version 06B (V6 hereafter) of half-hourly IMERG-E, IMERG-L, and IMERG-F, and version 05B (V5 hereafter) of IMERG-F for the southwest monsoon season of 2014-2017 has been used in this study. Additionally, IMERG-F V06B for June to September of 2010-2013 has been used to compare the changes in error characteristics during pre-GPM (e.g., 2010-2013) and GPM (2014-2017) periods. All IMERG products (e.g., near-real-time versus research product, V5 versus V6 research product, and pre-GPM versus GPM periods) have been considered for an equal 4-year period for consistency.
## 3 Modeling of SIC and Time Series Analysis
### Model Identification and Forecast  
For an ARIMA \((p,d,q)\times(P,D,Q,S)\) model, the parameter \(d\) can be chosen by testing the stationarity of the \(d\)-th differenced series; \(S\) can be obtained from the seasonal decomposition, e.g., the Census X-11 method [26]. The remaining orders \(p\), \(P\), \(q\), and \(Q\) can be determined from the properties of the AR\((p)\) and MA\((q)\) models, respectively.  
Given an arbitrary AR\((p)\) model, from its partial autocorrelation function (PACF), we say that the AR\((p)\) model is \"cutting off\" at lag \(p\). Consequently, \(p\) and \(P\) can be determined by this property from the PACF of series. Regarding an arbitrary MA\((q)\) model, its autocorrelation function (ACF) has the property of \"cutting off\" at \(q\) lags, which can help to find the order of \(q\) and \(Q\). Once the orders have been determined, the parameters \(\vartheta_{i}\), \(\theta_{i}\), \(\varphi_{i}\), and \(\phi_{i}\) in Equation (11) are then estimated by using the maximum likelihood estimation (MLE) [27]. Finally, the minimum mean square error (MMSE) forecast [28] can be performed to produce a prediction.
# Explainable Internet Traffic Classification
## 4 Experimental Results
### Comparison among the Different Classification Models  
To easily compare the achieved results, Table 19 reports the best results, in terms of accuracy, per each classifier on the two considered data sets, both for the training set and the test set, respectively. Starting by comparing the performance of our method with those of SVM on the test set, it is easy to see that our method achieves more or less the same accuracy than SVM, with a maximum accuracy of 0.875 (against 0.874) on the UniBS data set, and 0.886 (against 0.896) over the UPC data set. On the contrary, considering again the test set, our method is outperformed, in terms of accuracy, by C4.5 over both the data sets. Similar results are obtained on the training set. Nevertheless, in this case, note that the overfitting is very high for the SVM algorithm. Furthermore, the decision tree and the proposed PAES-RCS algorithms suffer from this problem, but in this case the phenomenon is less evident. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**First**} & \multicolumn{3}{c}{**Median**} & \multicolumn{3}{c}{**Last**} \\
**Feature** & **Class** & **TPR** & **FPR** & **TPR** & **FPR** & **TPR** & **FPR** \\ \hline \multirow{4}{*}{Statistical} & Mail & 0.814 & 0.006 & 0.801 & 0.006 & 0.152 & 0.027 \\  & Skype & 0.636 & 0.006 & 0.619 & 0.007 & 0.503 & 0.009 \\  & Firefox & 0.696 & 0.002 & 0.653 & 0.002 & 0.375 & 0.004 \\  & Safari & 0.955 & 0.005 & 0.941 & 0.006 & 0.810 & 0.018 \\  & BitTorrent & 0.955 & 0.001 & 0.949 & 0.001 & 0.653 & 0.006 \\  & Amule & 0.836 & 0.006 & 0.819 & 0.007 & 0.499 & 0.019 \\ \hline \multirow{4}{*}{Composite (\(H=3\))} & Mail & 0.000 & 0.032 & 0.000 & 0.032 & 0.000 & 0.032 \\  & Skype & 0.588 & 0.007 & 0.592 & 0.007 & 0.592 & 0.007 \\  & Firefox & 0.892 & 0.001 & 0.736 & 0.002 & 0.637 & 0.002 \\  & Safari & 0.876 & 0.013 & 0.865 & 0.014 & 0.944 & 0.006 \\  & BitTorrent & 0.968 & 0.001 & 0.968 & 0.001 & 0.807 & 0.003 \\  & Amule & 0.791 & 0.008 & 0.771 & 0.009 & 0.312 & 0.025 \\ \hline \multirow{4}{*}{Composite (\(H=5\))} & Mail & 0.088 & 0.029 & 0.029 & 0.031 & 0.000 & 0.032 \\  & Skype & 0.583 & 0.007 & 0.583 & 0.007 & 0.583 & 0.007 \\  & Firefox & 0.902 & 0.001 & 0.822 & 0.001 & 0.492 & 0.003 \\  & Safari & 0.896 & 0.011 & 0.877 & 0.013 & 0.901 & 0.010 \\  & BitTorrent & 0.969 & 0.001 & 0.969 & 0.001 & 0.839 & 0.003 \\  & Amule & 0.758 & 0.009 & 0.776 & 0.009 & 0.564 & 0.016 \\ \hline \multirow{4}{*}{Composite (\(H=10\))} & Mail & 0.547 & 0.015 & 0.502 & 0.017 & 0.048 & 0.031 \\  & Skype & 0.572 & 0.007 & 0.572 & 0.007 & 0.572 & 0.007 \\  & Firefox & 0.892 & 0.001 & 0.768 & 0.001 & 0.398 & 0.004 \\  & Safari & 0.917 & 0.009 & 0.892 & 0.011 & 0.891 & 0.012 \\  & BitTorrent & 0.957 & 0.001 & 0.956 & 0.001 & 0.750 & 0.003 \\  & Amule & 0.788 & 0.008 & 0.779 & 0.009 & 0.506 & 0.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Proposed method: Performance over UniBS data set.