esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Uncertainty Quantification System Performance
#### 3.3.1 Confidence Interval Construction  
The two-stage architecture enables the construction of calibrated confidence intervals through a systematic mathematical framework. For any new input combination, the system first generates a central UCS prediction (\(\hat{y}\)) using the primary Random Forest model. Simultaneously, the uncertainty model predicts the expected absolute error magnitude (\(\hat{\sigma}\)) for that specific prediction, utilizing both the original input features and the central prediction itself as inputs.  
Confidence intervals are then constructed assuming a Gaussian distribution around the central prediction, with interval bounds calculated as  
\[\text{CI}_{\alpha}=\hat{y}\pm z_{\alpha/2}\times\hat{\sigma} \tag{3}\]  
where \(z_{\alpha/2}\) represents the appropriate standard normal quantile for the desired confidence level \(\alpha\) (e.g., 1.96 for 95% confidence). This approach enables the system to provide prediction-specific interval widths that reflect the varying uncertainty across different input conditions, rather than applying a constant uncertainty estimate across all predictions.  
For example, when predicting UCS for a soil treated with 2.5% cement, compacted at 1.0 mm/min and cured for 28 days, the system generates \(\hat{y}=1066\) kPa and \(\hat{\sigma}=140\) kPa, resulting in a 95% confidence interval of \([1066\pm 1.96\times 140]=[792,1340]\) kPa. This heteroscedastic approach correctly reflects that prediction uncertainty varies systematically with input conditions, a critical capability that distinguishes sophisticated uncertainty quantification from simplified constant-error approaches.
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 2 Network Traffic Modelling
### Network Traffic Measurement  
The essential step in network management for performance and configuration is understanding the dynamics of the existing traffic in the network, i.e., modeling the network traffic. The modelling usually builds on the characterization of network flows and profiling traffic types for analyzing the requirements as well as guaranteeing intended performance criteria in the provisioning and dimensioning of the network.  
Flow-based measurement for network configuration management lacks the required scalability due to the complexity of communication patterns between devices. Moreover, the common assumption on single-type traffic for modeling traffic types does not hold for traffic collected from brownfield since various types of traffic can be generated from the same sources [10; 17]. To overcome the challenges of flow-based characterization and remove the assumption of single-type traffic, an approach is proposed in [10] that sets the measurement scope on the network level, i.e., accumulated traffic in a specific time interval. We adopt the same measurement for outlining the network traffic modeling. While the network level measurement can reduce the analysis complexity and reveal the repetition of communication patterns correspondence to the system work-cycle, it also affirms enough variances to exclude the deterministic traffic modeling of the communication system.  
In general, the generated traffic in the network consists of periodic, sporadic, and burst traffic types [18], which means in any time interval we have a mixture of the traffic of some or all of these types. Since not all the involved parameters are known, e.g., sporadic, irregular IT traffics, various size payloads, and a different number of transmissions, the accumulated traffic in any specific time intervals cannot be considered deterministic.  
To characterize the traffic dynamics using throughput and bandwidth consumption, the uncertainties need to be parametrized. In flow-based characterization, part of the uncertainties can be parameterized through packet generation intensity parameter. Each data stream and generated packets from one source at different points in time is studied. In the network-level measurement, the parameterization of the intensity parameter needs to consider the accumulated transmissions in the network.
# Unbiased Fuzzy Estimators in Fuzzy Hypothesis Testing
## 2 Classical Hypothesis Testing  
In classical crisp statistics, the problem of testing a hypothesis for a parameter \(\theta\) of the distribution of a random variable \(X\) is deciding whether to reject or accept the null hypothesis \(H_{0}:\,\theta=\theta_{0}\) at a significance level \(\gamma\) against the alternative hypothesis \(H_{1}\) from a random sample of observations of \(X\), using a test statistic \(U\), which is evaluated for the sample observations, resulting in a value \(u\). The space of possible values of \(U\) is decomposed into a rejection region and its complement, the acceptance region [1]. Depending on the alternative hypothesis \(H_{1}\), the rejection region has one of the following forms:1. \(U\geq u_{c1}\), if the alternative hypothesis is \(H_{1}:\ \theta>\theta_{0}\) (one sided test from the right), where the following is true: \[P(U\geq u_{c1})=\gamma\] (1) 2. \(U\leq u_{c2}\), if the alternative hypothesis is \(H_{1}:\ \theta<\theta_{0}\) (one sided test from the left), where the following is true: \[P(U\leq u_{c2})=\gamma\] (2) 3. \(U\geq u_{c3}\) or \(U\leq u_{c4}\), if the alternative hypothesis is \(H_{1}:\ \theta\
eq\theta_{0}\) (two sided test), where the following is true: \[P(U\geq u_{c3})=\frac{\gamma}{2}\ \ \ \text{and}\ \ \ P(U\leq u_{c4})=\frac{\gamma}{2}\] (3) and \(u_{ci}\) are the critical values of the test, which are determined by the distribution of \(U\). So, \(H_{0}\) is rejected if the value \(u\) of the test statistic \(U\) is in the rejection region and not rejected if \(u\) is in the acceptance region. If the value \(u\) of the test statistic of a hypothesis test is close to its critical value, then the crisp test is unstable since a very small change in the sample may lead from rejection to no rejection of \(H_{0}\) or vice-versa, as shown in Example 1. Using a degree of rejection or acceptance of \(H_{0}\) (see [30]) the above problem is eliminated.
## 2 Materials and Methods  
### Plant Materials and Growth Conditions  
In December 2011, 3-year-old seedlings of _F. crenata_ and 2-year-old seedlings of _Q. serrata_, _Q. monoglica_ var. _crispula_ and _B. platyphyllla_ var. _japonica_ were individually planted in 1/2000 [PERSON]'s pots (volume: 12 L, diameter: 240 mm, depth: 258.5 mm) filled with commercial horticultural soil (Takii & Co., Ltd., Kyoto, Japan). The N, P and K contents in the potted soil were 320, 210 and 300 mg L\({}^{-1}\), respectively. On 25 April 2012, the seedlings were transferred into 12 rectangular open-top chambers (OTCs, 13.0 m\({}^{2}\) of growth space and 2.4 m in height) located at the Akagi Testing Center of the Central Research Institute of Electric Power Industry (Maebashi, Guma, Japan) and eight seedlings per species were grown in each chamber. To measure the initial whole-plant dry mass (WDM), including belowground parts, the 10 seedlings of each species, which were not assigned to the chamber and whose mean height and stem base diameter were similar with those of the seedlings assigned to the chambers, were harvested, dried at 80\({}^{\circ}\)C in an oven for more than 5 days and weighed. The initial WDMs of _F. crenata_, _Q. serrata_, _Q. mongolica_ var. _crispula_ and _B. platyphyllla_ var. _japonica_ seedlings were 11.1 \(\pm\) 5.2 g, 36.9 \(\pm\) 8.1 g, 23.7 \(\pm\) 5.51 g and 11.1 \(\pm\) 1.8 g, respectively (\(\pm\)SD, \(n\) = 10). The latitude, longitude and elevation above sea level at the experimental site are 36\({}^{\circ}\)28\({}^{\prime}\) N, 139\({}^{\circ}\)11\({}^{\prime}\) E and 540 m, respectively. In late August 2012 and in late April and late July 2013, the seedlings were rotated among and inside the OTCs to minimize variation among the OTCs for each treatment and among the seedlings within the OTC owing to chamber and positional effects, respectively. All the seedlings were regularly irrigated to keep the potted soil moist. On 12 April and 5 July 2013, all the seedlings were fertilized with 8 g m\({}^{-2}\) of a slow-release solid fertilizer (N:P:K = 8:8:8, Ohmiya Green Service Co., Ltd., Saitama, Japan) to avoid the nutrient deficiency, which could limit physiological processes. The air temperature, relative air humidity and photosynthetic photon flux density (PPFD) inside the OTCs were monitored during the experimental period from 26 April 2012 to 14 November 2013 (Table S1). The vapor pressure deficit (VPD) was calculated from the saturated vapor pressure, which was calculated from air temperature and the relative air humidity.
# Unsupervised Classification of Human Activity with Hidden Semi-Markov Models
## 5 Conclusions  
In this work, we presented an unsupervised method for activity inference from accelerometer data using a hidden semi-Markov model. This work presented an unsupervised method that successfully tackles the issues associated with conventional methods for activity classification from accelerometers, namely the need for calibration studies or for large labelled datasets. We implemented the proposed algorithm on a sample from the UK Biobank, and selected the best model based on features and prior informativeness. Additionally, we verified the usefulness of the model for epidemiological research by testing some well-known association between physical behaviours and anthropometric measures, namely BMI and WC. The results showed that the activities inferred with the proposed method had good correlation and agreement with true activities, and the comparability of the regression coefficients proved that the method was a viable alternative for research exploring the effects of physical behaviours on health.  
**Author Contributions:** Conceptualisation, F.R.C.; methodology, F.R.C.; software, F.R.C.; validation, F.R.C.; formal analysis, F.R.C.; investigation, F.R.C.; resources, F.R.C. and C.T.; data curation, F.R.C.; writing--original draft preparation, F.R.C.; writing--review and editing, F.R.C. and K.N.; visualisation, F.R.C.; supervision, K.N. and C.T.; funding acquisition, C.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the EPSRC Doctoral Training Partnership.  
**Institutional Review Board Statement:** The ethics approval for the UK Biobank study was obtained from the North West Centre for Research Ethics Committee (REC reference: 21/NW/0157).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the UK Biobank study.  
**Data Availability Statement:** The data used in this study can be obtained from the UK Biobank. From more information please visit [[https://www.ukbiobank.ac.uk](https://www.ukbiobank.ac.uk)]([https://www.ukbiobank.ac.uk](https://www.ukbiobank.ac.uk)) (accessed on 16 August 2022).  
**Conflicts of Interest:** [PERSON] is the co-founder of DnaNudge Ltd. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Analysis
### Urban Canyon Signal Behavior Study  
In order to study GNSS signal behavior under harsh urban conditions, a 20-min test was conducted inside the 12-m high metallic mesh sculpture located in front of the 235-m high The Bow building in Calgary. The sculpture is shown on the left side of Figure 27 while the south and west skyline view from the sculpture is shown in the panoramic picture on the right. The skyline to the east is somewhat more open. As a consequence, line-of-sight signals are limited and all signals are subject to extreme multipath and attenuation. A GPSMap 66 was held 30-cm above the head of the observer standing inside the sculpture. Sufficient GPS measurements were made to evaluate their behavior. Their C/N\({}_{0}\) values and coordinate variations are shown in Figures 28 and 29. The C/N\({}_{0}\) spread ranges from 18 to 51 dB-Hz. Since this is an open area, multipath delays are longer, resulting in RMS coordinate component variations of the order of 150 m.  
Figure 26: Combined GPSMap 66 GPS and Galileo residuals from least-squares solution of indoor test.  
Figure 25: GPSMap 66 epoch-by-epoch GPS single point positions during indoor test.
# A Multi-Level Fuzzy Evaluation Method for the Reliability of Integrated Energy Systems
## 4 Results and Discussion
### Discussion  
The multi-level fuzzy evaluation model based on combined empowerment was used to evaluate the reliability of an IES in this paper, and the evaluation score was 85.15, which means the system was in good condition. According to the membership vector, combination weight and fuzzy evaluation matrix during the process of evaluation, the total scores, scores and scoring rates of all indicators can be calculated, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline
**1 st Level Indicators** & **2 nd Level Indicators** & **Membership** \\ \hline \multirow{2}{*}{Reliability} & Average time of disability & [0.3, 0.2, 0.3, 0.1, 0.1] \\  & Reliability of power supply & [0.3, 0.1, 0.4, 0.1, 0.1] \\  & Energy inefficiencies & [0.2, 0.3, 0.2, 0.2, 0.1] \\ \hline \multirow{2}{*}{Network loss} & Electricity network loss rate & [0.2, 0.1, 0.4, 0.2, 0.1] \\  & Heat network loss rate & [0.2, 0.5, 0, 0.0, 0.3] \\  & Cold network loss rate & [0.6, 0.3, 0.1, 0, 0] \\ \hline \multirow{2}{*}{Response efficiency} & Adjustment amount & [0.4, 0.3, 0.3, 0, 0] \\  & Info collection coverage & [0.5, 0.4, 0, 0.1] \\ \hline \multirow{2}{*}{Equipment economy} & Equipment utilization & [0.1, 0.4, 0.3, 0.2, 0] \\  & Cost savings & [0.2, 0.5, 0.3, 0, 0] \\ \hline \multirow{2}{*}{Environmental friendliness} & Pollutant gas reduction & [0.5, 0, 0.3, 0.2, 0] \\  & Carbon dioxide reduction & [0.3, 0.1, 0.4, 0.1, 0.1] \\ \hline \end{tabular}
\end{table}
Table 4: Results of index membership.  
The high score (91.34%) of the response efficiency is due to the complete hardware facilities of information collection in an integrated energy system, which enables the control platform to monitor the energy flow in real time and optimize the allocation of energy accordingly. Reliability has the greatest impact on the evaluation results, but its score is not satisfactory (84.25%). According to the analysis of the secondary indicator, reliability of the power supply is the biggest hidden danger, so we need to pay attention to it in our subsequent optimization work. The environmental friendliness score is excellent (87%) because the inclusion of renewable energy sources reduces the park's electricity consumption, thereby reducing the amount of carbon dioxide and gas pollutants. Network loss has the lowest score rate (82.43%), maybe because the obsolescence of the power grid or heat network equipment leads to a low transmission completion rate.
## 4 Experimental Results and Discussion
### Confusion Matrix and Analysis  
The confusion matrix was used to analyze the classification model's performance, including True Positives, True Negatives, False Positives, and False Negatives. Figure 9 shows the confusion matrix for the YOLOv5s6-Yarn Hairiness model, which achieved an average accuracy of 65% for loop fibers and 75% for protruding fibers. This evaluation provided a thorough understanding of the model's predictive capabilities for different fiber classifications.
# A Dual Stage Low Power Converter Driving for Piezoelectric Actuator Applied in Micro Mobile Robot
## 4 Simulation Verification  
To verify the feasibility of the circuit, its topology was simulated in the MATLAB Simulink environment. The specifications of the components used in the simulation are provided in Table 2. Among these, magnetizing inductance, output filter capacitor, resonant inductor, and transformer turn ratio are the same as those of the transformer and other circuit parameters used in subsequent experiments.  
Figure 8: Inductor current, output voltage, and switching pulse waveforms during each half cycle.  
For the DC/DC stage, Figure 9 shows the simulated waveforms of the control signal of the power MOSFET Q, the input voltage \(V_{bat}\), the currents \(I_{p}\) and \(I_{d}\) through the primary and secondary sides of the transformer, and the output voltage \(V_{c}\). Before the switch is turned on, the current through the rectifier diode drops to zero. Therefore, the circuit operates in DCM, which is consistent with the theoretical analysis.  
The switching frequency of the MOSFET Q is 55.5 kHz. When Q is turned on, the primary inductance is linearly charged by the battery's DC voltage of 3.7 V. During this phase, the current \(I_{d}\) and the drain-source voltage \(v_{ds}\) of Q are both zero. When Q is turned off, the current \(I_{p}\) becomes zero, and the current of the rectifier diode \(I_{d}\) starts to decrease from the maximum value \(I_{d}/n\). When the current \(I_{d}\) drops to zero, the load is supplied by the output filter capacitor, and the primary and secondary sides of the transformer have zero current. When Q is turned on again, the new switching cycle of the operation is implemented.  
The voltage signals of \(V_{c1}\) and \(V_{c2}\) generated by the DC/AC half-bridge drive circuit should meet the requirements of drive signals for piezoelectric bimorph actuators. The simulation results in the DC/AC stage are presented in Figure 10. The voltages of \(V_{c1}\) and \(V_{c2}\) are 100 V sinusoidal signals with 100 Hz resonant frequency and 180\({}^{\circ}\) phase delay. In addition, various types of signals can be generated based on the design of the DC/AC circuit, including square, triangle, and sinusoidal signals.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Specifications** \\ \hline \(V_{bat}\) & 3.7 V \\ \(f_{s}\) (switching frequency) & 55.5 kHz \\ D (duty cycle) & 50\% \\ \(n\) (turn ratio) & 1:10 \\ \(L_{m}\) & 2 uH \\ \(C\) & 1 uF \\ \(L\) & 15 uH \\ \(C_{1}\),\(C_{2}\) & 48 nF \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the utilized components.  
Figure 9: Simulation waveforms of each node in flyback DC/DC stage.
## 3 Problem Statement and Approach Overview
### Approach Overview  
As shown in Figure 3, we first use raw data from two different sources, namely physiological data obtained by special medical equipment and sleep data gathered from portable sensing devices (e.g., a standard EEG according to the international 10 to 20 system). Then, we extract stroke predictors, including clinical statistical features, sleep structure related features, and sleep EEG related features. Afterwards, we propose a two-step stroke prediction method, in which we combined the regular machine learning algorithm with the pre-selection model. More specifically, during the stroke pattern analysis process, we obtain primary results using the machine learning algorithm at the beginning, and then apply the pre-selection model to optimize them, aiming at minimizing the false negative rate without severely affecting the false positive rate.  
In the following sections, the extracted features will be first introduced and then the prediction model will be described in more detail.  
Figure 2: A representative of sleep cycles and concomitant EEG signal.
# Special Issue: Novel Approaches for Nondestructive Testing and Evaluation
## 6 An Attention-Based Network for Textured Surface Anomaly Detection by [PERSON] et al.  
Textured surface anomaly detection is a significant task in industrial scenarios. In order to further improve the detection performance, the authors of this study proposed a novel two-stage approach with an attention mechanism [5]. Firstly, in the segmentation network, the feature extraction and anomaly attention modules were designed to capture detailed information as much as possible and focused on the anomalies, respectively. To strike dynamic balances between these two parts, an adaptive scheme in which learnable parameters are gradually optimized was introduced. Subsequently, the weights of the segmentation network were frozen, and the outputs were fed into the classification network, which was trained independently in this stage. Finally, the proposed approach was evaluated on the DAGM 2007 dataset, which consists of diverse textured surfaces with weakly labeled anomalies; the experiments revealed that this method can achieve 100% detection rates in terms of true-positive rate (TPR) and true-negative rate (TNR).  
A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM, and CNN-LSTM by [PERSON] et al.  
The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise, and automated processes. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To this end, several machine learning algorithms are currently being exploited. Among them, for the present work [6], the long short-term memory (LSTM), convolutional neural networks (CNNs), convolutional neural network-long short-term memory (CNN-LSTM), and the CNN-LSTM with adjusted hyperparameters were compared. As a preliminary stage of the research, the voltage and current time signals were simulated using MATLAB Simulink. From the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated, and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch, and Interruption. Data augmentation techniques were used in order to increase the variability of the training and validation dataset, to obtain a generalized result. Afterward, the networks were fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks were compared, resulting in a 79.14% correct classification rate with the LSTM network versus 84.58% for the CNN, 84.76% for the CNN-LSTM. and 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks were tested using real measurements.