esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Surface Morphology
#### 3.2.1 Surface Roughness  
The penetration of AlSb and Mg-AlSb compounds on the glass substrate was studied by investigating the optical micrographs using the IMAGEJ software IJ 1.46r across a range of time, which revealed the surface roughness and uniformity of the deposited pure AlSb and Mg-AlSb films. Figure 3 shows the surface roughness of the pure AlSb at deposition times ranging from 1 to 3 h, with the needle shape of peaks defining substrate penetration and the number of needle-shaped peaks displaying the amount of penetration, with more needle-shaped peaks indicating more penetration and thus more surface roughness and nonuniformity. Figure 3a shows no prominent needle peaks at 1-h deposition time, indicating uniform material deposition; whereas Figure 3b shows some needle peaks at 1.5 h, indicating the beginning of penetration; however, Figure 3c,d show a greater number of needle peaks at 2-2.5 h of deposition time, indicating more nonuniformity. After 3 h of deposition (Figure 3e), the penetration achieved its maximum level, as evidenced by maximum needle peaks, demonstrating the nonuniformity of the pure AlSb layer formed on the glass substrate.  
Figure 4 shows the surface roughness of 10% Mg-doped AlSb deposited thin films, with Figure 4a showing surface roughness and nonuniformity, which was confirmed by its graph having a number of needle-shaped peaks, and Figure 4b,d showing the same phenomenon with a high number of peaks and thus high surface roughness at 1.5 and 2.5 h, respectively. Figure 4c, on the other hand, shows a smaller number of peaks at a 2-h time interval, but Figure 4e exhibits needle peaks after 3 h of deposition, showing that as the deposition time increased, so did the material penetration, resulting in surface nonuniformity. However, as compared to pure AlSb, the 10% Mg-doped AlSb exhibited fewer needle-shaped peaks, suggesting that the Mg-AlSb had more uniformity and less penetration than pure AlSb, and future device applications will have a better chance as a consequence because of their high uniformity. The tabulated surface roughness data for pure AlSb and 10% Mg-doped AlSb films are shown in Table 3.
## 3 Proposed Fault Diagnosis Scheme  
### Basic Structure of DCNN  
Three main types of layers are used to build DCNN architectures, namely, Convolutional Layer (CONV), Pooling Layer, and Fully-Connected Layer (FC), which are stacked to form a full DCNN architecture.  
1. INPUT holds the raw pixel values of the image with width W and height H in each of the three color channels R, G, and B.
2. CONV layer computes the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume.
3. RELU layer applies an element-wise activation function, such as max (0, x), thresholding at zero.
4. POOL layer performs a downsampling operation along the spatial dimensions (width, height), which ensures that contribution from the pixels that carry salient information in the feature maps are carried forward and leads to dimensional reduction by making maximum or average operation on the pooling region.
5. FC layer computes the class scores, resulting in a volume of size [1\(\times\)1\(\times\)m], where each of the m numbers corresponds to a class score. As with ordinary Neural Networks, and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
## 4 Results and Discussion
### Sample Code for Accuracy Assessment
#### 4.3.2 Class Level
Figure 17: Graphical and pictorial representation of the highest and lowest values of a CPLAND, class-level landscape metric. * **NLSI:** NLSI is a metric for aggregation. It has a lot in common with the landscape shape index. NLSI values lie between 0 and 1; 0 indicates only one squared patch, and 1 indicates the maximum disaggregated patch. When the value increases from 0 to 1, disaggregation increases. Patches that are closer to zero are compact, while those that are further away from zero are disaggregated (Figure 18). The NLSI quantified the district's compactness with lower values in 2021, but the value was higher in 2011, indicating landscape fragmentation and disaggregation in all classes. * **TE:** The TE metric is an area and edge metric. The value of TE starts at 0 and increases without limit; 0 indicates all cells are edge cells, and 1 indicates the landscape has become more fragmented. When the value increases, the landscape starts to become fragmented. Figure 19 shows that the study area is highly fragmented, and the landscape has many edges. All values that increased in 2021 show more fragmentation in the Sonipat district. Figure 12:0 Graphical and pictorial representation of the highest and lowest values of a TE, class-level landscape metric. * **PAFRAC:** PAFRAC describes the patch complexity of the class. Here, the values lie between 1 and 2, where 1 indicates patches with simple shapes, and 2 indicates patches with irregular shapes. Where values increase from 1 to 2, shapes begin to become irregular. PAFRAC results show that patches of other classes (1.41) are irregular in shape, and cropland (1.26) has simple-shaped patches in 2021, as shown in Figure 12:1
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
## 3 Proposed Matrix Completion Based Localization
### Matrix Completion: Optimization over GD and Its Variants  
In the following, we review the different optimization methods that are widely used by the deep learning community to update the localization matrix \(\hat{\mathbf{X}}\): Gradient descent (GD), Nesterov accelerated gradient (NAG), Adaptive Gradient (Adagrad), Root Mean Square Propagation (RMSProp), Adadelta and Adaptive Moment Estimation (Adam). We discard the class of algorithms that are computationally very expensive for high dimensional data sets, e.g., the second-order Newton's method [35].
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 3 Approach
### Energy Consumption Model  
The energy consumption model adopted in this paper is based on the proposed model in [44] which has been used by many researchers [45; 46; 47; 48; 49; 50]. In this model, it is assumed that the transmitter, power amplifier, and receiver all dissipate energy to run the radio electronics as shown in Figure 4.  
Figure 4: Radio Energy Dissipation Model.  
Figure 3: Cluster creation with cluster head elected.  
The power attenuation is dependent on the distance between the transmitter and the receiver. For relatively short distances, the propagation loss can be modeled as inversely proportional to \(d^{2}\), whereas for longer distances the propagation loss can be modeled as inversely proportional to \(d^{4}\). Power control can be used to invert this loss by setting the power amplifier to ensure a certain power at the receiver. Thus, to calculate the transmission cost (joule/bit) and the receiving cost (joule/bit) for a message of size \(k\) bits from a sensor node \(i\) (transmitter: \(T_{x}\)) to a sensor node \(j\) (receiver: \(R_{x}\)) over a distance \(d\)(\(i\),\(j\)) (Euclidian-Distance), where the energy consumption at node \(i\) comprises operating radio electronics and amplifiers while at node \(j\) only radio electronics, the following equations are used:  
Transmission cost:  
\[E_{TX}(k,d)=k\times E_{TX-elec}+E_{TX-amp}(k,d)=k\times E_{TX-elec}+k\times d ^{2}\times E_{amp}, \tag{1}\]  
Receiving cost:  
\[E_{RX}(k,d)=k\times E_{RX-elec}, \tag{2}\]  
where \(E_{elec}\) is the energy dissipation of the radio in order to run the transmitter and receiver circuitry, and \(E_{amp}\) is the transmitter amplifier.  
Typical values used in this paper during the simulation are:  
\(E_{elec}=50\) nJ/bit, \(E_{amp}=100\) pJ/bit/m\({}^{2}\), \(d^{2}=500\), and the packet size \(k=2000\) bits [49].  
If we consider that node \(i\) wants to transmit an amount of data \(f_{ij}\) to node \(j\), the energy consumption (Joules) at node \(i\) will be:  
\[E_{TX,I}=\sum\limits_{\begin{array}{c}j=1\\ i\
eq j\end{array}}^{n}\left(E_{TX,ij}*f_{ij}\right) \tag{3}\]  
On the other hand, if node \(j\) wants to send an amount of data \(f_{ji}\) to node \(i\), the energy consumption (Joules) at node \(i\) will be:  
\[E_{RX,I}=E_{RX}\sum\limits_{\begin{array}{c}j=1\\ i\
eq j\end{array}}^{n}f_{ji} \tag{4}\]  
Thus, the total energy consumed at node \(i\) will be:  
\[E_{consu,i}=E_{TX,i}+E_{RX,I} \tag{5}\]
# Prediction of Concrete Failure Time Based on Statistical Properties of Compressive Strength
## 8 Prediction Model of Equivalent Design Compressive Strength  
To test the accuracy of predicted results (concrete failure time) calculated by time-varying reliability, this paper applies the prediction model of equivalent design compressive strength (\(f^{\prime}_{cc}\)). By the definition of \(f^{\prime}_{cc}\), \(f^{\prime}_{cc}\leq f_{c}\) implies that \(p_{q}<p_{g}\), at which point the concrete is regarded as out of service. [PERSON] [41] calculated the equivalent design compressive strength from a set of core tested data by the following steps:  
\[V^{2}_{insitu}=V^{2}_{core}-0.002 \tag{28}\]  
Figure 19: Time-varying qualified rate and guaranteed rate of \(f_{c}=21.0\) N/mm\({}^{2}\).  
Figure 18: Time-varying qualified rate and guaranteed rate of \(f_{c}=18.0\) N/mm\({}^{2}\).  
\[V_{insitu_{u}30}^{2}=fV_{insitu} \tag{29}\] \[\overline{f}_{insitu,17.5}=[1.724-4.138(V_{insitu,30})]\overline{f}_{c,p} \times 1.08\] (30) \[\overline{f}_{c,p}=R\varsigma\times f_{c}\] (31) \[f_{c}^{\prime}=1.481\overline{f}_{insitu,17.5}-11.24 \tag{32}\]  
where \(V_{insitu}\) and \(V_{core}\) are the coefficients of variation of the situ compressive strength and the core test strength, respectively; \(V_{insitu,30}\) is the converting coefficient of variation when there are fewer than 30 tests; \(f\) is the multiplying factor (see [41]); \(\overline{f}_{c,p}\) is the mean value of core strength (with coefficient of variation = \(V_{core}\)); \(f_{insitu,17.5}\) is the mean value of situ compressive strength (with coefficient of variation = 17.5%); and \(f_{ce}^{\prime}\) is the equivalent design strength.  
The \(V_{core}\) in this paper is expressed as Equation (33) based on Figure 8.  
\[V_{core}(t)=0.07\times\exp(0.033t) \tag{33}\]  
The relative equivalent design compressive strength is expressed as  
\[R_{f}=f_{ce}^{\prime}/f_{c}. \tag{34}\]  
Failure mode III occurs when \(R_{f}<1\). Based on Equations (28)-(34), failure mode III was reached at 36.7 years when \(f_{c}=18.0\) N/mm\({}^{2}\), and at 38.3 years when \(f_{c}=21.0\) N/mm\({}^{2}\) (see Figure 20). These results were close to the calculated results of 35.8 years in Figure 18, and 38.8 years in Figure 19, respectively. The calculated results were in good agreement because both the mean and coefficient of variation were considered in the two prediction models. Moreover, the failure modes of the two models were based on the concrete guaranteed rate.
## 2 Materials and Methods
### Data
#### 2.2.2 Satellite and Reanalyzed Data  
* CHIRPS data In this study, we downloaded monthly precipitation data from the CHIRPS v2.0 product for the period 1981-2018 from the following link: [[https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_monthly/tifs/](https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_monthly/tifs/)]([https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_monthly/tifs/](https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_monthly/tifs/)) (accessed on 9 February 2022) [45]. We selected the CHIRPS product for its long-term records spanning over 30 years and its higher spatial resolution than those of other satellite precipitation products.  
The CHIRPS is a near-global dataset covering a geographic range from 50\({}^{\circ}\) S to 50\({}^{\circ}\) N and all longitudes. Developed by the University of California's Climate Hazard Group (CHG) in collaboration with the United States Geological Survey (USGS), the CHIRPS provides data from 1981 to the present, with a high spatial resolution of 0.05\({}^{\circ}\) (5 km) [46]. Its primary goal is to offer reliable, up-to-date datasets supporting early warning systems, such as drought monitoring and global environmental change assessments [34; 46]. The CHIRPS integrates various sources, including monthly precipitation data from five satellites at a 0.05\({}^{\circ}\) resolution and Climate Hazards Group Infrared Precipitation estimates (CHIRPs), which use thermal infrared bands to estimate precipitation through regressions with TRMM Multi-Satellite Precipitation Analysis. It also incorporates interpolated observed precipitation data from global and regional sources like the Global Telecommunication System to generate gridded precipitation time series for trend analysis and drought monitoring [46].  
* Era5_Land reanalysis Era5-Land is the fifth generation of atmospheric global climate reanalyzed data from the European Centre for Medium-Range Weather Forecasts (ECMWFs), replacing Era5_Land, which is a new climate reanalyzed data from the European Centre for Medium-Range Weather ForecastsInterim. Based on the ECMWF IFS (Cy41r2) from 2016 [47], it offers global coverage, with a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and temporal coverage from January 1950 to the present. We used the monthly temperature data from Era5-Land reanalysis for the period 1981-2018, downloaded from [[https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview)]([https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview)) (accessed on 19 December 2022) [48]. This dataset characterizes air temperature 2 m above land, sea, or inland waters, calculated through interpolation between the lowest model level and the Earth's surface, considering atmospheric conditions [48].
# Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks
## 3 Experimental Results  
This section provides evaluations of the proposed work. The setup of the experiments is the online surface inspection platform shown in Figure 7. The surface inspection platform contains a high resolution industrial camera FLIR Blackfly S USB 3 and a personal computer with NVIDIA RTX 2070X GPU. The development of neural network models is based on Keras [27] built on the top of Tensorflow 2.0. The training and testing images of the inspection targets for the neural network models are acquired from the industrial camera of the online surface inspection platform.  
Without loss of generality, examples of the inspection targets are the display cards. The inspection of the backplate of the cards and their gold finger connectors is considered in this study. The backplate of a display card usually contains multiple coherent regions. Each may have different characteristics such as patterns or colors. The backplate inspection would then be beneficial for demonstrating the effectiveness of the proposed algorithm. Furthermore, the defect detection for a gold finger connector is usually the major focus for the inspection of printed circuits. The images of gold finger connectors also contain multiple coherent regions. We therefore include the corresponding inspection in this study as well.
## 4 Results and Discussion
### Comparison Experiments and Results Analysis  
#### 4.3.1 Comparison of Different Word Embedding Models  
In order to prove that the proposed word embedding model has excellent ability regarding text sequence modeling, the proposed two-channel word embedding model is compared with the traditional word bag model. In addition, in order to prove the advance and advancements of the two-channel model proposed in this paper, the proposed model is compared with the classical Word2 Vec and GloVe models. Table 3 shows the recognition results of different models. Figure 3 shows the confusion matrixes of different models. Based on Table 3 and Figure 3, the following conclusions can be drawn:  
1. Compared with the word embedding modeling algorithm, the traditional word bag model achieves the worst classification result. This fully shows that the word embedding model can effectively extract the depth feature information from the original text data, which effectively optimizes the input of the subsequent classifier of the model. Therefore, the word embedding model has excellent application value in the field of emotion recognition.
2. Compared with the classical Word2 Vec and GloVe models, the two-channel word embedding model adopted in this paper has a more excellent classification effect. This fully proves that the proposed model has excellent application value in the field of emotion recognition modeling. Two-channel word embedding modeling extracts word vectors from two different angles and transmits the extracted feature information to the classifier. More feature data lay a foundation for further classification.
# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 2 Materials and Methods
### Multivariate Analysis for Classification Pipeline  
After the feature selection step, a classification pipeline has been implemented. Six well-known ML algorithms have been tested: Decision Tree (DT) [46], K-Nearest Neighbors (KNN) [47], Multi-Layer Perceptron (MLP) [48], Naive Bayes (NB) [49], Random Forest (RF) [50] and Support Vector Machine (SVM) [51]. In order to solve the data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) algorithm [52] has been used. This algorithm helps to overcome the over-fitting problem that shows up when a random oversampling is used. New instances in feature space are generated by using interpolation between the positive instances that lie together.  
The subsets \(S_{F1_{k}}\) and \(S_{F2_{k}}\) are used to feed the six ML models. As in the case of feature selection, LOO-CV on \(DS_{1}\) has been conducted to determine the predictive power of each model. F1 score, AUC and accuracy have been used to estimate the predictive performance of the models. In particular, AUC has been chosen as the main measure to quantify the predictive performance of the classification methods. In the case of multi-class classification (pT-Stage and Overall Stage), AUC has been computed with the One vs. Rest (OvR) strategy: one class was considered as \"positive\", while all the others were considered as the \"negative\" classes. Finally, the mean of the AUCs was computed.  
Thus, for each subset \(S_{F1_{k}}\) and \(S_{F2_{k}}\), the model with the best value of AUC has been selected. Next, the best models have been tested on the independent test set, \(DS_{2}\), in order to assess the validity and robustness of the approach. Furthermore, to better understand the classification results in terms of F1 score and AUC with respect to the selected features for each set of diagnostic images, we have used a horizontal bar-plot, where, on the x-axis, the value ranges for the F1 score and AUC are set, and the different colors of the bars refer to all possible combinations between the selected features with the proper legend. In this way, we have been easily able to assert which feature combinations had the highest values of F1 score and AUC, to determine the most accurate, reliable and suitable diagnostic tool for head and neck tumor discrimination. All the statistical analyses have been conducted by using Python v3.7.
# Numerical Simulation of Cavitation Erosion Aggressiveness Induced by Unsteady Cloud Cavitation
## 3 Results
### The Mechanism of Cavitation Erosion
These numerical predictions confirm the existence of a threshold level and the fact that it permits the correction of the estimation of the cavitation erosion risk based on the cavitation aggressiveness. Increasing \(P_{threshold}\), the contribution to erosion of the small intensity collapses taking place during a long time at the sheet cavity closure is dampened but it does not affect the results at the cloud collapse region where the \(P_{tot\_load}\) is very high and clearly above \(P_{threshold}\). Therefore, the setting of \(P_{threshold}\) is necessary to take into account the material resistance in the numerical prediction of cavitation erosion. Moreover, this is a concept that can also be used to explain the experimental observations found with different types of materials if it is assumed that it reproduces the effects of the material yield stress. For example, the numerical \(P_{tot\_load}\) and the experimental \(\tau_{n}\) and \(\tau_{v}\) have been normalized by their respective maxima along the chord and compared in Figure 14. It can be seen that the predicted results for \(P_{threshold}\) of 30 kW m\({}^{-2}\) compare well with the copper erosion measurements, and the ones for 90 kW m\({}^{-2}\) compare well with stainless steel especially for the pitting rate observations. In addition, the numerical results show a zone with a low erosion intensity like a groove between the two maximum erosion picks for \(P_{tot\_load}\), which is due to the fact that in the simulations the boundary between the vapor and water phases is very well delimited whereas in reality these boundaries are quite unstable and the cavity wake is much fuzzier due to the flow turbulence [21]. Figure 14: Comparison of dimensionless erosion intensity indicators between numerical and experimental results. Figure 13: Distribution of \(P_{tot\_load}\), \(N_{off}N_{nf}\) and \(P_{ave\_load}\) along the chord for \(P_{threshold}\) levels of 30 and 90 kW m\({}^{-2}\). Given the good results obtained with the present simulations, the Sauer model has been further used to investigate the effect of the free stream velocity on the cavitation erosion power.
## 2 Materials and Methods
### Microwave Plasma-Atomic Emission Spectrometry (MP-AES) Determination of Cu and Pd in the Complexes  
A total of 0.0200 g of sample was weighed on an analytical balance and dissolved with 65% nitric acid, p.a. (Chem-Lab NV, Zedelgem, Belgium) for Cu-complexes, nitric acid, p.a. (Chem-Lab NV, Zedelgem, Belgium) for Cu-complexes, nitric acid, p.  
acid, and 37% hydrochloric acid, and p.a. (Fluka AG, Buchs, Switzerland) for Pd-complexes. Blank solutions were prepared as well. After dilution, the concentration of Cu and Pd was determined via MP-AES 4200 (Agilent technologies, Santa Clara, CA, USA). Calibration standards were prepared from monoelemental standard solutions \(-\)1000 mg L\({}^{-1}\) Cu (Merck, Darmstadt, Germany) and 1000 mg L\({}^{-1}\) Pd (High-purity standards, Charlestone, UK). Conventional MP-AES operating conditions were used. Analytes were measured on three emission lines for estimation of potential spectral interferences, i.e., 324.754 nm, 327.395 nm, and 510.554 nm for Cu and 340.458 nm, 360.955 nm, and 363.470 nm for Pd. Five replicates and 5 s measurements were applied for all lines.  
General Procedure for the Synthesis of Cu(II) and Pd(II) Complexes of 6-Methyl-2-Thiouracil (L1) and 6-Propyl-2-Thiouracil (L2)  
All metal complexes were obtained after mixing water solutions of the corresponding metal salts and the ligands dissolved in DMSO and water solutions of NaOH, in a metal-to-ligand ratio of 1:4:2. Non-charged complexes were formed as precipitates, which were further filtrated, repeatedly washed with water, and dried over CaCl\({}_{2}\) for 2 weeks.