esa-sceva/satcom-chunk-collection huggingface dataset:
# A Maximum-Entropy Fuzzy Clustering Approach for Cancer Detection When Data Are Uncertain
## 2 Principal Ingredients
### Euclidean Distance  
The generic interval-valued data pertaining to the \(i\)-th observation with respect to the \(j\)-th interval-valued feature can be shown as the pair \((c_{ij},r_{ij})\), \(i=1,\ldots,n\) and \(j=1,\ldots,J\), where \(c_{ij}\) denotes the center and \(r_{ij}\) the radius of the interval.  
In the literature, several metrics have been suggested for interval-valued. In this paper, we adopt a weighted distance measure, proposed by [35]. In this case, the distance between each pair of observations is measured by separately considering the distances for the centers and the radii of the interval-valued data and using a suitable weighting system for such distance components. Formally, by considering the \(i\)-th and \(i^{\prime}\)-th observations, we have  
\[d(\bar{\mathbf{x}}_{i},\bar{\mathbf{x}}_{i^{\prime}})=\Big{[}w_{c}^{2}d^{2}( \mathbf{c}_{i},\mathbf{c}_{i^{\prime}})+w_{r}^{2}d^{2}(\mathbf{r}_{i},\mathbf{ r}_{i^{\prime}})\Big{]}^{\frac{1}{2}}, \tag{2}\]  
where \(d^{2}(\mathbf{c}_{i},\mathbf{c}_{i^{\prime}})=\left\|\mathbf{c}_{i}-\mathbf{ c}_{i^{\prime}}\right\|^{2}\) is the squared Euclidean distance between the centers and \(d^{2}(\mathbf{r}_{i},\mathbf{r}_{i^{\prime}})=\left\|\mathbf{r}_{i}-\mathbf{ r}_{i^{\prime}}\right\|^{2}\) is the squared Euclidean distance between the radii, while \(w_{c}\) and \(w_{r}\) are suitable weight for the center component and the radius component, respectively.  
Moreover, we assume the following conditions: (_i_) \(w_{c}+w_{r}=1\) (_normalization condition_) and (_ii_) \(w_{c}\geq w_{r}\geq 0\) (_coherence condition_). In particular, by means of the _coherence condition_ we manage to exclude the anomalous case where the radius component, which represents the uncertainty around the centers of the interval-valued data, has more importance than the center component, which represents the core information of each interval-valued datum. Furthermore, through the _normalization condition_ we can easily assess, in a comparative fashion, the contributions of the center and radius components in the distance computation.  
The distance measure shown in Equation (2) has the following properties:  
1. \(d(\bar{\mathbf{x}}_{i},\bar{\mathbf{x}}_{i^{\prime}})\) is a metric, i.e., the properties of identity, non-negativity, symmetry, and the triangular inequality are satisfied (for details see [8]).
2. \(d(\bar{\mathbf{x}}_{i},\bar{\mathbf{x}}_{i^{\prime}})\) is computationally easy and theoretically intuitive.
3. \(d(\bar{\mathbf{x}}_{i},\bar{\mathbf{x}}_{i^{\prime}})\) tunes suitable the contribution of the (squared) distance measures of the center and radius components of the interval-valued data by means of a weighting system capable to assign objectively (by means of an optimization process) or subjectively (by means of the expertise and experience of the researcher) weights to the two distance components.
## 2 Satellite Sensor Data
### NOAA-AVHRR/3 and SeaStar-SeaWiFS  
The HRPT Data Acquisition System (Remote Sensing Center, University of Las Palmas of Gran Canaria) automatically tracks the NOAA and SeaStar satellites and archives and pre-processes the raw data to Level-1B [15].  
For AVHRR, the subsequent processing steps (radiometric, atmospheric and geometric corrections) are carried out automatically, obtaining sea surface temperature maps that are optimized for the science applications following the requirements recognized in the World Climate Research Program.  
On the other hand, the reception and processing of SeaWiFS images is carried out within the framework of the _SeaWiFS Project_, managed at a world wide scale by NASA for the scientific utilization of the data. Unlike the NOAA-AVHRR image processing, for which the corresponding _ad-hoc_ algorithms have been developed, the different processing levels applied to the SeaWiFS data are included in the _SeaDAS_ (SeaWiFS Data Analysis System) software package, developed under the NASA ocean biochemistry program [16]. The processing up to higher levels (1A and 2) is done automatically and the resulting level 2 files are processed to eliminate unnecessary data. Atmospheric correction algorithms use external data such as ozone concentrations and surface pressure fields.  
\begin{table}
\begin{tabular}{|l|c|c|c|c|} \hline
**Parameter** & \begin{tabular}{c} **NOAA** \\ **AVHRR/3** \\ \end{tabular} & \begin{tabular}{c} **SEASTAR** \\ **SEAWIFS** \\ \end{tabular} & \
\begin{tabular}{c} **AQUA** \\ **MODIS** \\ \end{tabular} &
\begin{tabular}{c} **ERS** \\ **ATSR** \\ \end{tabular} \\ \hline Altitude (km) & 833 & 705 & 705 & 785 \\ \hline Orbit Inclination (°) & 98.7 & 99.2 & 98.2 & 98.5 \\ \hline Nadir Resolution (km) & 1.1 & 1.1 & 1.0 & 1.0 \\ \hline Viewing Swath Width (km) & 2399 & 2800 & 2330 & 512 \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of the satellites/sensors used with the registration algorithm.  
In summary, for AVHRR and SeaWiFS data, all the daily acquisition and processing activities are performed in an unmanned way. Figures 2 (a) and (b) show the flowchart of the processing hierarchy levels applied to NOAA/AVHRR-3 and SeaStar-SeaWiFS data.
# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 1 Introduction
### Motivation  
The motivation behind this research is multifaceted. First, depression is a widespread mental health disorder that can significantly impact an individual's quality of life. Unfortunately, many individuals do not receive timely and effective treatment due to a shortage of mental health professionals or barriers to accessing care. Therefore, there is a pressing need for accurate and efficient automated tools that can detect depression and other psychological illnesses. Second, the current COVID-19 pandemic has resulted in increased mental health challenges globally, with depression rates rising due to isolation, fear, and uncertainty. Thus, there is an urgent need for reliable and accessible mental health support.  
Third, existing software applications that claim to monitor mental health often lack the necessary security protections, potentially resulting in sensitive data being compromised or used for targeted advertising. Therefore, our proposed optimized deep learning technique for detecting and assessing depression prioritizes security and accuracy to ensure that patients' sensitive information is protected. Finally, the development of a reliable and secure automated system for monitoring and detecting depression can provide critical data to healthcare professionals and researchers, enabling them to make informed decisions regarding treatment options and improving our understanding of the disease's underlying mechanisms.
###### Abstract  
An improved modulation strategy based on minimum energy storage for DC-link capacitance reduction in a six-switch AC-AC converter is proposed. The proposed modulation strategy enables the energy on the capacitor to accumulate and release twice each in a complete switching cycle, achieving the effect of \"fast charging and discharging\". Meanwhile, the inversion and rectification are modulated synchronously. Hence, there is minimum energy stored in the DC-link capacitor. Then, the time average modeling analysis is presented to take insight analysis. When there is the same voltage ripples constraint on the DC side for the conventional and improved modulation strategies, the six-switch converter under the improved modulation strategy has the much less capacitance value of the storage capacitor and even realizes non-electrolyte capacitance. Therefore, improving the system efficiency, power density, and output waveform quality and extending the system life can be achieved. The operation principle and modulation strategy are discussed in detail. Finally, the simulation model and experimental prototype are built to verify effectiveness of the topology and correctness of the proposed six-switch AC-AC converter modulation strategy.  
Keywords:six-switch converter; electrolytic capacitor-less; ripple reduction +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Data Sources  
The MODIS (NASA, Washington, United States) installed on Terra and Aqua satellites has a scan width of 2330 km and acquires global observation data every 1 to 2 days. Due to its wide coverage and short return visit period, it is widely used in large-scale ecological research. All indicators are derived from EOS/MODIS remote sensing products ([[https://lpdaacsvc.cr.usgs.gov/appeears/](https://lpdaacsvc.cr.usgs.gov/appeears/)]([https://lpdaacsvc.cr.usgs.gov/appeears/](https://lpdaacsvc.cr.usgs.gov/appeears/)) (accessed on 5 March 2021)), and the data of MODIS products are shown in Table 1.  
Figure 1: The geographical map of the location of the study area.  
The above MODIS data products have undergone radiometric calibration and atmospheric correction, and the data products were acquired from July to September in 2008 and 2018. This period is the vegetation growing season, and it is easy to distinguish between vegetation areas and bare snow areas. In this paper, MODIS Reprojection Tool software was used for data mosaic and projection conversion of each indicator raster datapoint. The unified resolution was 500 m \(\times\) 500 m, and the projection was GCS_WGS_1984. In the process of data screening, the data with less cloud cover and of high quality were selected to calculate the average value of the five indicators from July to September. Water area, bare land, and snow area cannot participate in ecological evaluation effectively because some indicators have no value, so the water area, bare land, and snow area were excluded by mask. The meteorological data used in this study were obtained from the China Meteorological Science Data Sharing Service Network ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 5 March 2021)), and the temperature and precipitation data from 54 national meteorological stations in Xinjiang Uygur Autonomous Region were collected. The statistical data were obtained from the Statistical Yearbook of Fuyun County.
## Appendix D Sentiment Polarity Association Mining via Co-Occurrence Network Analysis (CONA)  
This appendix provides detailed tables for the CONA (co-occurrence network analysis) results, which include the comprehensive weight calculations and association factors derived for specific keywords under different sentiment polarities.  
Table 10 outlines the comprehensive weights computed for the keyword \"interior\" in TC1, characterized by very negative sentiment extremity, identifying it as a \"pain point\" for the target competitor.  
Table 11 presents the comprehensive weights for the keyword \"interior\" in TC2, which indicated strong positive sentiment extremity and served as a \"best practice\" reference for the UX.  
Table 11 provides the factor calculation results for the keyword \"interior\" within the theme of SP, specifically focusing on the very negative sentiment extremities observed across all competitors.  
Table 12 illustrates the factor calculation results for the keyword \"interior\" with strong positive sentiment extremity for all competitors, highlighting positive aspects in sentiment analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Word** & **EC** & **JC** & **Normalized TF** &
\begin{tabular}{c} **Comprehensive Weights** \\ \end{tabular} \\ \hline Interior & 0.663071849 & 0.034785239 & 1 & 0.606797891 \\ Gap & 0.1045055 & 2.046825397 & 0.033333333 & 0.462147242 \\ Material & 0.108194489 & 1.622161172 & 0.02222222 & 0.379210531 \\ Owner & 0.104695556 & 1.346825397 & 0.01111111 & 0.32102281 \\ Rim & 0.088080172 & 1.336446886 & 0.022222222 & 0.31096858 \\ Feeling & 0.104851921 & 0.957936508 & 0.01111111 & 0.24512081 \\ Slot & 0.135149266 & 0.802020202 & 0.05555556 & 0.24222699 \\ Audio & 0.104998341 & 0.784437784 & 0.01111111 & 0.211305206 \\ Space & 0.166491493 & 0.465151515 & 0.11111111 & 0.206837322 \\ Domestic & 0.109944761 & 0.63525641 & 0.011111111 & 0.185202411 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comprehensive weight calculation for keywords with very negative sentiment in TC1’s “interior” tag.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Word** & **EC** & **JC** & **Normalized TF** &
\begin{tabular}{c} **Comprehensive Weights** \\ \end{tabular} \\ \hline Interior & 0.592284163 & 0.039215686 & 1 & 0.607136115 \\ Carbon Fiber & 0.072272343 & 1.813095238 & 0.012195122 & 0.354302794 \\ Embrace & 0.088655321 & 1.44675643 & 0.024390244 & 0.301989804 \\ Seat & 0.154745784 & 0.917814918 & 0.06097561 & 0.261104985 \\ Tradition & 0.198734645 & 0.552991453 & 0.06097561 & 0.226699165 \\ Function & 0.121187093 & 0.816977467 & 0.024390244 & 0.211040079 \\ Technology & 0.14788913 & 0.661669703 & 0.06097561 & 0.209508815 \\ Color & 0.111026294 & 0.267857143 & 0.012195122 & 0.101289857 \\ Texture & 0.125514678 & 0.273387723 & 0.036585366 & 0.060642182 \\ Leather & 0.042104428 & 0.252891156 & 0.024390244 & 0.050896889 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comprehensive weight calculation for keywords with strong positive sentiment in TC2’s “interior” tag.
## 6 Strategies to Improve the Efficiency of the AD Process
### Chemical Pre-Treatments  
A high VS/TS ratio and hemicelluloses content have a positive impact on the biogas yield while lignine has a negative effect [145]. [PERSON] et al. [146] found that the addition of rice straw to the vegetable and food wastes may promote the growth of microorganisms responsible for ligno-hemicellulose degradation with positive effects on the biogas production trough AD. However, substrate pretreatments are often necessary to degrade lignin polymers to further increase biogas productivity. For example, although straw has a positive effect on the methane yield by increasing the C/N ratio, it belongs to the class of difficult-to-degrade lignocellulosic materials; thus, a pretreatment stage is needed to improve the rate and degree of enzymatic hydrolysis during the degradation process. [PERSON] et al. [147] assessed the co-digestion of urea-ammoniated rice straw and FW with an inoculum of an anaerobic digester effluent. Urea pretreatment enhanced the hydrolysis of raw rice straw and increased biogas production. Moreover, the addition of cobalt and nickel to the raw material showed a positive effect on the reaction rate of co-digestion. In particular, the presence of nickel significantly enhanced the stability of co-digestion and increased the methane content. Alkali pretreatment of cattle manure with Ca(OH)\({}_{2}\) was an effective method for improving the biogas production from dry manure and the best condition was the treatment at 60 \({}^{\circ}\)C and pH 12 for 12 h [148]. This treatment probably increased the accessibility of microorganisms to the energy producing components (i.e., hemicellulose and cellulose). When different pre-treatments were compared for AD of Chlorella prototheoides algal biomass, methane yield was higher with sonication than with thermal and alkaline treatments [149].  
Most chemical pretreatments have negative environmental effects, whereas N-Methylmorpholine-N-oxide (NMMO), a non-derivativizing solvent breaking the intermolecular interactions in cellulose, does not generate toxic pollutants and it is recyclable. A study investigating the effects of NMMO pretreatment on biogas production from horse and cattle manures, both with a high content of straw, showed that this step improved the methane potential of pretreated materials [150].  
In spite of their efficiency, these pre-treatments are too expensive and they are not always necessary, since some organic substrates, although particulate, once immersed in water, tend to solubilize immediately. [PERSON] et al. (2014) [151] proved that in an AD process fed with organic solid particles with a porous texture and a chemical composition mainly made of simple carbohydrates, the hydrolysis can be faster than expected. This kind of organic solid can be easily disintegrated in water, resulting in immediate availability to microorganisms for their metabolism.
# Investigation on Cutting Blasting Efficiency of Hard Rock Tunnels under Different Charge Diameters
## 3 Numerical Simulation
### Material Models and Parameters  
In this simulation, a total of four materials were employed, namely, the explosives, the rock, standard air, and a tamping plug. However, the above four materials could be divided into two categories: fluid media and solid media, and the explosive and standard air used as fluid media usually needed to be described by using both a constitutive model and a state equation. Therefore, the *MAT_HIGH_EXPLOSIVE_BURN model and the JWL state equation were together used for the definition of the explosives. The JWL state equation possesses high accuracy in handling the relationship between the detonation pressure, relative volume, and internal energy of explosion products [50; 51], as shown below:  
\[P_{\text{e}}=A\bigg{(}1-\frac{\omega}{R_{1}V_{\text{e}}}\bigg{)}e^{-R_{1}V_{ \text{e}}}+B\bigg{(}1-\frac{\omega}{R_{2}V_{\text{e}}}\bigg{)}e^{-R_{2}V_{ \text{e}}}+\frac{\omega E_{0}}{V_{\text{e}}} \tag{2}\]  
where \(P_{\text{e}}\) represents the detonation pressure; \(A\), \(B\), \(R_{1}\), \(R_{2}\), and \(\omega\) represent the constants; \(E_{0}\) represents the specific internal energy; and \(V_{\text{e}}\) represents the relative volume.  
Similarly, the *MAT_NULL model and linear polynomial state equation were adopted to simulate the standard air. The linear polynomial state equation has superior advantages in simulating various ideal gases, such as standard air, and can be expressed as:  
\[P_{0}=C_{0}+C_{1}\eta+C_{2}\eta^{2}+C_{3}\eta^{3}+\Big{(}C_{4}+C_{5}\eta+C_{6} \eta^{2}\Big{)}E_{1} \tag{3}\]  
where \(P_{0}\) represents the pressure, \(C_{0}\)-\(C_{6}\) represent the material constants; \(\eta\) represents the viscosity coefficient; \(E_{1}\) represents the specific internal energy.  
For the other two materials viewed as solid media, the dynamic description of the rock used the *MAT_PLASTIC_KINEMATIC model, which has integrated the special mechanical effects of hard brittle materials caused by the increase in the strain rate under strong dynamic loads [52; 53]. The *MAT_DRUCKER_PRAGER model was adopted to simulate the soil used as a tamping plug, which is good at dealing with the mechanical deformation problems of various soft engineering materials. The main property parameters of the explosives and the rock have been given in Tables 1 and 2, and the parameters of the JWL equation, standard air, and tamping plug were taken from [54; 55; 56] and are shown in Tables 4-6.
## 2 Materials and Methods
### Learning Algorithms
#### 2.3.2 Gated Recurrent Unit (GRU)  
GRU is a simple version of LSTM that integrates the forget and the input gates [39]. The RNN also inspires the architecture of GRU, and the info within the GRU is regulated by a two-gate system, which has a quicker training time due to less computational load [5; 40].  
Figure 2: Study workflow for air pollution prediction.  
Also, GRU is appropriate for processing text, speech, and time series data, which utilize a gate structure to facilitate information flow with the reset and the update gate [37; 41]. GRU's high efficiency and excellent performance make it a good choice for many sequential data tasks [42; 43]. The advantages of GRU include fewer parameters, making it computationally less expensive and faster to train than LSTM. In contrast, the disadvantages of GRU included being more prone to overfitting on smaller datasets and requiring careful tuning of hyperparameters.
## 3 Evaluation
### Metrics  
Two types of metrics were used to assess the accuracy of the disparity estimation methodology: non-ground truth metrics and ground truth metrics. Ground-truth-based metrics provide dependable evaluation outcomes, but real images from the [PERSON] camera lack ground truth disparity, necessitating alternative measurements.  
**Non-ground truth metrics:** The consistency check metric, or left-right disparity consistency, evaluates disparity uniformity between left and right images, ensuring pixel correspondence. It is used for refining disparities by scanning both disparities to identify errors at the pixel level, with the error value indicating precision in the disparity map:  
\[E=|\ d_{\rm{l}}(x,y)-d_{\rm{r}}(x-d_{\rm{l}}(x,y),y)\ |\leq\theta \tag{12}\]  
The average error, \(E_{\rm{avg}}\), calculates the mean disparity error for each pixel:  
\[E_{\rm{avg}}=\frac{1}{N}\sum_{x,y}\lvert d_{\rm{l}}(x,y)-d_{\rm{r}}(x-d_{\rm{l }}(x,y),y)\rvert \tag{13}\]  
Edge alignment evaluates disparity near the edges using the Sobel operator for edge detection. The Mean Absolute Error (MAE) and its normalised version assess the disparity accuracy:  
\[\text{MAE}=\frac{1}{N}\sum_{(x,y)}\lvert I_{\rm{c}}(x,y)-d_{\rm{e}}(x,y)\rvert \tag{14}\]  
\[\text{MAE}_{\rm{norm}}=1-\frac{\text{MAE}}{\text{MAE}_{\rm{max}}} \tag{15}\]  
Non-ground truth metrics, though less reliable, provide insight into disparity errors.  
**Ground truth metrics:** For synthetic datasets, ground truth metrics include the Mean Absolute Error (MAE) for the average absolute difference between predicted and actual disparities, and the Percentage of Bad Pixels (PBP) for recognising significantly incorrect disparity pixels:  
\[\text{MAE}=\frac{1}{N}\sum_{x=1}^{W}\sum_{y=1}^{H}\left\lvert d_{\rm{e}}(x,y) -d_{\rm{gr}}(x,y)\right\rvert \tag{16}\]  
\[PBP=\frac{1}{N_{\rm{P}}}\sum_{(x,y)}\left(\lvert d(x,y)-d_{\rm{T}}(x,y)\rvert >\delta\right)\cdot 100 \tag{17}\]  
Figure 12: Brunel Holoscopic camera that includes a prime lens, a microlens array, a relay lens to focus light beams onto the sensor, and a CMOS imaging sensor.  
Both MAE and PBP metrics are utilised for evaluation, with values normalised for simplicity.