esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Interactions and Dispersion Stability of Anion-\(\pi\) Type PNPs with R-254 and Y-180
These interactions worked synergistically to facilitate the dissociation of the intermolecular \(\pi\)-\(\pi\) stacking backbone of CuPc. Additionally, the -PhSO3\({}^{-}\) participated in classical and salt-bridge H-bonds with the N-H groups in pigments R-254 or Y-180. These interactions weakened the intermolecular H-bonds of the pigments and enhanced their dispersion stability in water (Figure 5a). Consequently, PNPs-5 was adopted in subsequent experiments to investigate the effect of varying mixing ratios on pigments. Specifically, the stability in water was investigated by testing \(\zeta\) values at different mixing ratios (PNPs-5: pigments = 1:1, 2:1, 3:1, 4:1, 5:1, 6:1). The results showed that the highest absolute value of \(\zeta\), which reached 61.5 \(\pm\) 0.3 mV, could be obtained when the mixture ratio of PNPs-5 to CuPc was 3:1 (Figure 5b). In addition, the preferred mixing ratios of PNPs with CB, R-254, and Y-180 were 4:1, 2:1, and 2:1, respectively (Figure 5c-e). The effect on the color strength of the pigments at the respective preferred mixing ratios was further investigated. The results of ultraviolet-visible diffuse reflection (UV-Vis DR) spectra showed that the reflectance values of the mixtures of PNPs-5 with CuPc, R-254, or Y-180 varied very little (Figure 5f-h). Similarly, the results of UV-Vis absorption spectra showed almost no change in absorbance values in the mixtures of PNPs-5 with R-254 and Y-180. The absorbance value of PNPs-5 when mixed with CuPc exhibited a small decrease (Figure S17). Furthermore, the water-based dispersion of PNPs-5 at 4% content was transparent after drying, with a transmittance value of 88% in the range of 400-800 nm (Figures 5i and S18). These results indicated that the mixing of PNPs-5 with the four pigments at preferred ratios exhibited little effect on the color strength of these pigments.
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 9 Discussion  
The soil parameters in Table 2 indicate a transition in the soil from a cohesive soil to a coarser soil. Due to soft ground feature transition, the EPB TBM machine parameters are exposed to fluctuation, and they are shown in Figures 11-13. Utilizing these operational parameters with the EPB TBM operator allows for an increase in machine advancement parameters, such as thrust and torque. Therefore, the EPB TBM cutter tools experience damage and wear. Figure 12a-d show the pressure in the cutter chamber along with the tunnel. Moreover, Figure 5 indicates the difference in grain size characteristics. Due to the grain size characteristics, the pressures in the cutter chamber demonstrate a sharp fluctuation during excavation. To control the pressure in the cutter chamber, the TBM operator tends to increase the thrust, RPM, and torque. It is obvious that the operational parameters influence cutter wear during tunnel excavation. Furthermore, by using the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R}^{2}\) & **MSE** & **MAE** & **Time** \\ \hline Proposed 1D CNN & 89.6 & 57.5 & 1.6 & 3 min 22 s \\ MLPRegressor & 77.8 & 107 & 5.93 & 3 min 26 s \\ LSTMRegressor & 39 & 326.3 & 11.12 & 12 min 38 s \\ Extratree regression & 77.90 & 111.16 & 7.31 & 0.497 s \\ Light gradient boosting machine & 73.98 & 130.19 & 8.04 & 0.101 s \\ Gradient boosting machine & 71.81 & 141.01 & 8.71 & 0.182 s \\ Random forest regression & 70.72 & 147.54 & 8.35 & 0.671 s \\ AdaBoost regressor & 59.12 & 202.48 & 11.84 & 0.127 s \\ k-NN regression & 19.04 & 452.03 & 15.47 & 0.062 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model comparison.  
Figure 15: (a) Actual and predicted data distribution with fit line; (b) error distribution between actual and predicted data; (c) 1D CNN learning curve.  
proposed 1D CNN model on the operational data collected from the pipe jacking water transport tunnel project, an attempt was made to provide a fast and practical estimation for the cutter wear of the EPB TBM by employing high-dimensional machine parameters. The proposed 1D CNN indicated a strong correlation between the machine parameters and cutter wear index (m\({}^{3}\)/mm). The 1D CNN model can be implemented in soft ground mechanized tunnelling projects to predict cutter wear and excavation efficiency. However, it should be noted that current studies are far from providing a universal model for cutter wear in soft ground TBMs.
# Contribution of Gray and White Matter Abnormalities to Cognitive Impairment in Multiple Sclerosis
## 2 Results  
### Multiple Sclerosis (MS) Patients vs. Healthy Controls (HCs)  
#### 2.1.1 Comparison of Cognitive Test Scores  
Correcting for Hamilton Anxiety (HAMA) and Hamilton Depression (HAMD) scores, MS patients exhibited worse performance in the Symbol digit modalities test (SDMT) and PASAT 3 seconds (PASAT-3\") compared with HCs. These two tests primarily reflect information processing speed and working memory (Table 1).
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 2 Background  
With sensors invading human's daily lives, data streams are growing exponentially. Driven by the expansion of the Internet of Things (IoT) and the connected real-time data sources, many applications generate vast amounts of critical data streams that evolve. Data streams are a continuous, infinite series of data records followed and arranged by embedded or precise timestamps [14]. Analyzing such data streams efficiently offers useful insights for many application domains. Yet, it is a significant challenge. One of the techniques that deal with analyzing the collected data stream effectively is called anomaly detection [5]. Anomaly detection refers to the identification of events or patterns in a dataset that differ significantly from the majority of items or the expected pattern, and those unexpected patterns are referred to as anomalies, novelties, exceptions, noise, surprises, or deviations [7; 8; 20]. Anomaly detection plays a considerable role in the analysis of anomalies in many applications. In almost every use case, early detection is useful. Looking at a device that monitors a cardiac patient's heart rate continuously, an anomaly may result in a heart attack. Detecting such anomalies minutes in advance is far better than detecting it a few seconds ahead, or detecting it after the event occurs [21]. Detecting anomalies in the data stream has practical and important applications across a wide range of fields [15].  
One of the main approaches used for anomaly detection is machine learning techniques. As stated in [22], machine learning enables computers to learn without explicit programming. It is intended to allow a system to learn from the past or the present and to use the knowledge to make future predictions or decisions [18]. Even though \"learning\" is extremely vital in machine learning, it is not the objective. The primary objective of machine learning is to create a system that can identify relevant patterns in data automatically and correctly [23].  
Recent advancement in deep learning techniques has made it also possible to largely improve anomaly detection performance compared to the classical approaches. Deeplearning is defined as a subset of machine learning in artificial intelligence that has networks capable of unsupervised learning from data that are unstructured [5].
# Finite Element Analysis of the Epiretinal Membrane Contraction
## 3 Results  
Simulations using the constitutive models with the optimized material properties, obtained in the calibration process were run and for each patient, the maximum displacements suffered by the retina and the membrane final thickness were measured. The numerical results were compared with the displacements measured for both patients and the already known thickness, allowing to obtain the relative errors.  
For the selected material model, simulation results concerning the displacements are shown in Figures 7-9 for both patients. By the analysis of these Figures and Table 4, it is possible to conclude that the retina of the Recovered Patient suffered a higher contraction than the retina of the non-recovered Patient.  
The simulation results for both cases are presented in Table 4. For the patient that showed signs of recovery the minimum errors for both the horizontal and vertical displacements were 2.668% and 8.383% respectively and the membrane final thickness error was 2.798%.  
On other hand, for the patient that did not show condition improvement, the minimum errors for horizontal and vertical displacements were 0.271% and 4.527%, respectively and the membrane final thickness error was 3.347%.  
This allows to conclude that for both patients the models used, namely the Martins constitutive model and the Mooney-Rivlin constitutive model, were able to correctly simulate the contraction of the ERM and the deformations that this contraction causes on the retina.  
The difference between the retina contraction of the patients may indicate a connection between the recovery of one of the patients after the surgery and the non-recovery of the other patient. In fact, for the non-recovered Patient, the low contraction of the membrane and retina, resulted in smaller displacements and in a lower difference between the initial and the final membrane thickness. For this patient, this means that if the membrane is removed, the displacements suffered by the retina will also be small, which results in minor changes to the retina configuration and reduced recovery of visual acuity.  
On the other side, for the recovered Patient, since the displacements induced in the retina, by the ERM contraction, are high, the retina will show large displacements if the ERM is removed. When the membrane is removed, the displacements suffered by the retina, to return to its initial configuration are much higher, which will lead to an improvement of the visual acuity.
## 3 Results and Discussion
### Mineral's Analysis  
Figure 3 presents the results of the mineral analysis of WFSP and WFP. The results show that WFSP obtained the highest Fe content of 0.32 \(\pm\) 0.02, Ca content of 0.74 \(\pm\) 0.04, P content of 0.57 \(\pm\) 0.04, K content of 8.11 \(\pm\) 0.09, Mg content of 1.12 \(\pm\) 0.03, Na content of 0.46 \(\pm\) 0.03, Cu content of 0.01 \(\pm\) 0.00 and Zn content of 0.03 \(\pm\) 0.00 mg/g DW. Similarly, WFP exhibited the highest Fe, Ca, P, K, Mg, Na, Cu and Zn value of 0.34 \(\pm\) 0.03, 0.81 \(\pm\) 0.09, 0.42 \(\pm\) 0.06, 7.99 \(\pm\) 0.05, 1.22 \(\pm\) 0.05, 0.51 \(\pm\) 0.04, 0.01 \(\pm\) 0.00 and 0.03 \(\pm\) 0.00 mg/g DW, respectively. WFSP showed better K and P content than WFP, while Fe, Ca, Mg and Na were higher in WFP than WFSP. It was also noted that K was the predominant mineral in both varieties, while Cu and Zn were trace elements. [PERSON] et al. (2018) showed comparative results, experimenting on nine different varieties of orange-fleshed sweet potato (OFSP), reporting that BARI SP4 possessed the highest iron, copper, zinc, calcium, potassium, and magnesium amounts of 1.40 \(\pm\) 0.10, 0.67 \(\pm\) 0.07, 4.17 \(\pm\) 0.21, 22.04 \(\pm\) 1.08, 336.10 \(\pm\) 3.64 and 23.37 \(\pm\) 0.78 mg/100 g DW, respectively. [PERSON] and co-workers (2018) demonstrated that Brazilian-cultivated purple-fleshed sweet potato (PFSP) and WFSP showed the highest Ca content of 0.35 \(\pm\) 0.0058% and 0.62 \(\pm\) 0.0058%, K content of 3.47 \(\pm\) 0.06% and 3.84 \(\pm\) 0.11%, Mg content of 0.37 \(\pm\) 0.0058% and 0.41 \(\pm\) 0.0058%, Na  
Figure 2: Colour analysis of the powder of WFSP and WFP. L* (lightness), b* (yellowness), and a* (redness).  
content of \(0.0168\pm 0.0058\%\) and 0.0167 \(\pm\) 0.002% and P content of 0.31 \(\pm\) 0.01% and 0.23 \(\pm\) 0.0057% of total content, respectively.
## 2 Materials and Methods
### Statistical Analysis  
The data were stored in Microsoft Office Excel 2019 version. Statistical analyses were performed using Epi Info version 7.2.4.2020 and IBM-SPSS software version 20.0.0. The distribution of variables was evaluated using the Kolmogorov-Smirnov test. Continuous variables with a normal distribution are presented as means \(\pm\) standard deviations (SDs). Data without a normal distribution are presented as medians and interquartile ranges, and categorical variables are presented as percentages. To detect the presence of a statistically significant difference between groups, we performed Student's \(t\) test and the Mann-Whitney U test, and \(p\) < 0.05 was considered significant. A receiver operating characteristic (ROC) curve analysis was used to identify the cut-off values for the prediction of severe fibrosis. The sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) were also calculated. We considered a confidence level of 95% for the estimation of intervals. Multivariate logistic regression analyses were used to determine the parameters related to severe fibrosis. We established a new scoring system--a composite score--for the prediction of severe fibrosis based on these parameters. The area under the ROC (AUROC) was used to assess the diagnostic performances of the noninvasive tests.
# Characterization of Surface Ozone Behavior at Different Regimes
## 4 Conclusions  
Linear correlation analysis showed a positive relationship between O\({}_{3}\) concentrations with \(T\) and _WS_, while NO, NO\({}_{2}\), and _RH_ showed a negative effect. In the studied period, the highest O\({}_{3}\) concentrations were observed for low NO\({}_{\rm x}\) concentrations and high wind speed. Threshold models with ANNs and those defined by genetic algorithms define three important variables that could define different O\({}_{3}\) regimes: (i) a wind speed of 4.9 m\(\cdot\)s\({}^{-1}\); (ii) a temperature of 17.5 \({}^{\circ}\)C; and (iii) an NO\({}_{2}\) concentration of 26.6 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\). The achieved models enabled the evaluation of the combined effect of two input variables in different O\({}_{3}\) regimes. This information may be useful for defining policy strategies for human health protection concerning surface ozone.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/7/9/944/s1](http://www.mdpi.com/2076-3417/7/9/944/s1)]([http://www.mdpi.com/2076-3417/7/9/944/s1](http://www.mdpi.com/2076-3417/7/9/944/s1)). Figure S1: The combined effect of NO\({}_{2}\) concentrations, temperature, and wind speed on O\({}_{3}\) concentrations based on the model determined in Simulation II; Figure S2: The combined effect of NO\({}_{2}\) concentrations, temperature, and wind speed on O\({}_{3}\) concentrations based on the model determined in Simulation III.  
**Acknowledgments:** This work was financially supported by Project POCI-01-0145-FEDER-006939 (LEPABE) funded by FEDER funds through COMPETE2020--Programa Operacional Competitividade e Internacionalizacao (POCI)--and by national funds through FCT--Fundacao para a Ciencia e a Tecnologia. J.C.M.P. acknowledges the FCT Investigator 2015 Programme (IF/01341/2015).  
**Author Contributions:** [PERSON] collected the data, ran the simulations, analyzed the results, and wrote the paper; [PERSON] conceived the study, developed the model code, and revised the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: The combined effect of NO\({}_{2}\) concentrations, temperature, and wind speed on O\({}_{3}\) concentrations based on model determined in Simulation I where _WS_ (wind speed) \(\leq\) 4.9 m\(\cdot\)s\({}^{-1}\) (**a**,**c**,**e**) and where _WS_ > 4.9 m\(\cdot\)s\({}^{-1}\) (**b**,**d**,**f**).
# An FDM-Based Dynamic Zoning Method for Disturbed Rock Masses above a Longwall Mining Panel
## 4 Discussion
Compared with the settlement coefficient obtained from several neighboring mines in Shandong Province (Table 2), the settlement coefficient calculated by Taixi Coal Mine is reasonable:  
\[q=\frac{W_{cm}}{M\cdot cos\ \alpha} \tag{4}\]  
where \(W_{cm}\) is the maximum value of subsidence, \(M\) is the mining thickness, \(q\) is the surface subsidence coefficient, and \(\alpha\) is the dip angle of the coal seam. The above comparison shows that the numerical simulation results obtained by this work, such as the periodic caving of the coal-seam roof and the dynamic development characteristics of the four zones, are consistent with the observed distributions and other research results. The heights of the caved and fractured zones are basically consistent with the empirical formula. The calculated subsidence coefficient is similar to the measured subsidence coefficients of neighboring coal mines. Thus, the dynamic zoning method is capable of analyzing and predicting the dynamic development characteristics of the four zones. Compared with other zoning methods, the dynamic zoning method proposed in this paper specifically considers the dynamic zoning characteristics of disturbed rock masses in the longwall mining process to achieve more precise numerical results. More specifically, in the proposed method, the complete stress-strain curves of the disturbed rock mass are first simplified into four stages. Then, according to the simplified complete stress-strain curves, the zoning criteria are established by considering the deformation and failure characteristics of the disturbed rock masses, and the mechanical parameters of the disturbed rock masses are adaptively adjusted. Finally, the dynamic zoning of the disturbed rock masses in the mining process is effectively simulated on those bases, and the dynamic development characteristics of the four zones are obtained. The influence of deformation and failure of the overlying disturbed rock masses on the mechanical parameters are considered, which improves the numerical modeling precision. However, it should be noted that in the proposed method, only the cohesion and internal friction angle of the disturbed rock masses are adjusted during the longwall mining process. Therefore, future work has been planned to adjust the elastic modulus and Poisson's ratio by considering the effect of deformation and failure of disturbed rock masses.
# Usability Testing of Mobile Applications:
## 4 Methodological Framework for Mobile Usability Testing
### GAQM Framework  
By definition, a research framework refers to the overall approach that combines conceptualizations and principles that serve as the basis for a phenomenon to be investigated [131]. More specifically, it is a systematic way of organizing and conceptualizing the research process, including the goal of the study, research questions and data collection methods that guide a research study. To design and develop our framework for mobile usability testing, the Goal-Question-Metric (GQM) approach was adopted and adapted [132] since it has been widely recognized due to its capacity for facilitating software quality [133].  
By definition, GQM is based on goal orientation theory [134] and is designed in a top-down fashion. First, one must specify the rationale behind the measurement plan, which in turn must inform one of the goals. Second, questions are then derived to articulate the goal defined for an object. A goal should be expressed in terms of a measurable outcome. Next, each goal is broken down into at least one question, which should provide a definition for the measurement object with respect to a particular quality issue. Finally, one or more metrics are assigned to each question.  
Based on this notion and the theoretical underpinnings discussed earlier, we introduce the Goal-Attribute-Question-Metric (GAQM) framework to structure, conceptualize, and operationalize the study of mobile usability testing. The GAQM defines a research process on four levels (see Figure 2):1. Conceptual level (goal). The research goal is defined, including the usability dimension (observed or perceived) and the name of the mobile application (subject of the study); a goal could also refer to usability in general.
2. Contextual level (attribute). The mobile usability attributes are specified.
3. Operational level (question). At least one research question is formulated to operationalize each specific attribute.
4. Quantitative level (metric). At least one directly observable metric is assigned for an observed attribute, while two or more are assigned for a perceived attribute.  
We argue that the GCAM framework can be applied to any study of mobile usability testing. It is designed to clarify and emphasize both research assumptions, intentions, and measurements by providing a generic and structured approach. However, to support our view, the three use cases of the GAQM framework are discussed below.  
Note that, the chosen usability testing environment (i.e., lab or field) should be able to address the mobile context (e.g., network connectivity) or the performance of specific application features (e.g., route updating). The context may be deliberately formulated, or it may be extracted from either the research objective or the specified tasks. In addition, any planned data collection techniques should also be explicitly stated.
## 2 Materials and Methods
### Analysis
#### 2.2.5 E-Nose Analysis  
Researchers from the School of Industrial Engineering at the University of Extremadura were the designers of the handmade electronic device (E-nose) used in this experiment. This device contains four electronic chips, in which there is a total of eleven signals from metal oxide (MOX) sensors. These belong to the latest generation of digital gas sensors. One or more gas sensors, other sensors (temperature, humidity and atmospheric pressure) alongside the signal processor, the controller of the whole system and the I2C communication module can be found in the same housing. Each of them emits a different signal regarding the aroma of the sample headspace, and thus a different fingerprint signal is obtained for different samples. The composition of the array of the sensors was optimized by performing previous measurements for typical compounds of food aromas. The designed E-nose also incorporates temperature and humidity sensors for ambient temperature and humidity monitoring and correction with compensation algorithms.  
Samples (~3 g) were placed in the tasting glasses in the same way as the tasting panel and the E-nose was put on the upper part of the cup. The standard glasses with the samples were put on a heating block at 25 \({}^{\circ}\)C. Each cycle of acquisition of the data of the sample consisted of two different parts after an initial period of sensor stabilization. In a first phase, known as the adsorption phase, the E-nose was left to adsorb the headspace of the sample for 60 s. Once this time elapsed, the electronic device was moved to another empty cup to carry out the desorption phase for 30 s to obtain the baseline. A relative resistance method was used for sensor calibration with respect to the baseline and for the feature extraction. Eight measurements (repetitions) were made for each rice sample. The analytical data were transferred from an electronic nose to an intelligent device (smartphone) via Bluetooth, and the multivariate analysis was completed [19].