esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome
## 4 Results
### Model Evaluation using Deep Learning  
This study evaluates two Deep Learning models, Deep Neural Network (DNN) and Convolutional Neural Network (CNN). ML uses simpler principles for creating predictive  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model MI** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **AUC** & **Precision-Recall Score** \\ \hline
**Logistic Regression** & 0.92 & 0.92 & 0.92 & 0.93 & 0.99 & 0.98 \\ \hline
**Decision Trees** & 0.91 & 0.83 & 0.87 & 0.89 & 0.96 & 0.94 \\ \hline
**Random Forest** & 0.97 & 0.97 & 0.97 & 0.97 & 1.00 & 1.00 \\ \hline
**Support Vector Machine Linear** & 0.92 & 0.94 & 0.93 & 0.94 & 0.98 & 0.98 \\ \hline
**Support Vector Machine Polynomial** & 0.94 & 0.95 & 0.95 & 0.95 & 0.98 & 0.93 \\ \hline
**Support Vector Machine Gaussian** & 0.93 & 1.00 & 0.96 & 0.97 & 1.00 & 0.99 \\ \hline
**Support Vector Machine Sigmoid** & 0.92 & 0.94 & 0.93 & 0.94 & 0.98 & 0.98 \\ \hline
**Naïve Bayes** & 0.91 & 0.98 & 0.91 & 0.91 & 0.98 & 0.98 \\ \hline
**KNN** & 0.91 & 0.98 & 0.95 & 0.95 & 0.98 & 0.97 \\ \hline
**AdaBoost** & 0.91 & 0.98 & 0.95 & 0.95 & 0.99 & 0.99 \\ \hline
**XGBoost** & 0.93 & 0.98 & 0.95 & 0.96 & 1.00 & 1.00 \\ \hline
**ExtraTrees** & 0.91 & 0.95 & 0.95 & 0.94 & 0.99 & 0.99 \\ \hline
**STACK-1** & 0.96 & 1.00 & 0.98 & 0.98 & 0.99 & 0.99 \\ \hline
**STACK-2** & 0.95 & 0.98 & 0.97 & 0.97 & 1.00 & 1.00 \\ \hline
**STACK-3** & 0.97 & 0.98 & 0.98 & 0.98 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of the models trained on 80:20 split Mutual information engineered data.  
Figure 4: Performance evaluation of STACK-3 by AUC Curves and Precision Recall curves of STACK-3 (multilevel-stacking) for feature selection. The first, second and third rows indicate the performance of STACK-3 with HHO, SSA and MI respectively.  
models and can perform a task without explicit programming. In contrast, DL uses complex algorithms that mimic the human brain's capability to learn and train. DL is based on neural networks that detect patterns in structured and unstructured datasets. Deep learning models help avoid an extra step of feature selection [42]. We aim to compare the performance of these state-of-the-art models against the results obtained with ML models trained on three different engineered datasets.
## 1 Introduction  
Since its inception, SMS messaging has stimulated the need for additional visual information to help define the context of short messages. While users convey their messages using different facial expressions, in textual communication people relish adding emotional clues through emoj to compensate for limited or unavailable facial expression. Moreover, individuals appreciate adding reactions as feedback to live video communication (e.g., live streaming, video calls). Pictures are among the most straightforward clues for eliciting emotional and empathetic communication through communication media. Thus, when images are not made readable for all users, the resulting accessibility failure introduces a critical bias against individuals with a visual impairment. Originally, the issue was simply resolved using _emoticons_, i.e., pictograms mimicking facial expressions or objects encoded by standard sequences of characters. However, as smartphone technology evolved, software text systems began directly replacing emoticons with related pictures. Such pictograms were visible only to the user but still encoded with characters for the software, which could employ its own set of pictograms to visualize the emoticon. Thus, the same text could be augmented with different pictograms based on the underlying software used to read it. This encoding strategy allowed algorithms to understand the picture and provide its meaning to automated systems (e.g., for sentiment analysis) or aiding software (e.g., vocal readers for people with vision impairment).  
Recently, the demand for more complex and descriptive images has introduced in every messaging system and social network the possibility to include images instead of emoticons, usually coded as Graphics Interchange Format (GIF), called _emojis_. These pictograms express emotions, represent objects, or refer to standardized meanings with memes. In most cases, emojis show the same appearance for graphical emoticons, but differences in encoding do not allow for an automated recognition of context analysis, emotion recognition, and readers for people with vision impairment. In fact, besides being encoded as images, they also do not include any alternative text which could enable the image content to be read aloud. This situation sets assistive and accessibility systems back a few decades.  
On the other hand, such a situation presents a novel research problem, unprecedented in the existing literature, where sentiment analysis is often performed to identify positive or negative polarity of emojis [1; 2; 3], while emotion recognition is neglected.  
Therefore, identifying an automated process that can recognize the content of emojis is paramount in restoring accessibility for ethical applications.
# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 3 Methods
### Optimization Algorithm
#### 3.4.1 Learning Rate Adjustment  
The learning rate is a crucial parameter that determines the step size for updating the neural network's weights and biases. Selecting the appropriate learning rate is vital to ensuring efficient network convergence during training, thereby avoiding problems like slow convergence or divergence. In general, a larger learning rate speeds up convergence but can make the training process unstable. Conversely, a smaller learning rate improves training stability but may require more training iterations to achieve optimal performance. Commonly used methods for adjusting the learning rate include learning rate decay, learning rate warm-up, periodic adjustments, and various adaptive learning rate techniques. Adaptive learning rate methods can assign different learning rates to each parameter, including algorithms such as AdaGrad [42], AdaDelta [43], and RMSprop [44].  
In this paper, the RMSprop algorithm is used to adapt the learning rate. RMSprop is an adaptive learning rate method proposed by [PERSON] and is employed to mitigate certain limitations associated with other adaptive algorithms, where the learning rate continuously decreases to the extent of premature decay in specific situations.  
The RMSprop algorithm computes an exponentially decaying moving average of the squared gradient (\(\mathbf{g_{t}}\)) for each iteration using Equation (7) and subsequently calculates the parameter update differences based on Equation (8).  
\[\mathrm{G_{t}}=\beta\mathrm{G_{t-1}}+(1-\beta)\mathbf{g_{t}}\odot\mathbf{g_{t} }=(1-\beta)\sum_{\tau=1}^{t}\beta^{t-\tau}\mathbf{g_{\tau}}\odot\mathbf{g_{\tau}} \tag{7}\]  
\[\Delta\theta_{t}=-\frac{\alpha}{\sqrt{\mathrm{G_{t}}+\epsilon}}\odot\mathbf{g_ {t}} \tag{8}\]  
In these equations, \(\beta\) represents the decay rate, typically set to 0.9; \(\alpha\) is the initial learning rate, usually set to 0.001; and \(\epsilon\) is a small constant used to ensure numerical stability, typically falling within the range of \(\mathrm{e}\times 10^{-7}\) to \(\mathrm{e}\times 10^{-10}\).
## 3 Results
### Setting up a Reference State  
To establish a benchmark for reference states, from the trajectory analysis (described earlier), we used the areas that maintained pixel observations above 80% of forest canopy cover as a base for the determination of \"stable\" forest cover. It has been assumed that the areas identified as \"stable\" are forest ecosystems that have reached maturity approaching an \"equilibrium state\". For these areas, therefore, the carbon stocks are regarded as a reference of the maximum capacity of the forest to accumulate Carbon under similar conditions ([PERSON], 1998). The forest that remains \"stable\" (i.e., with no significant change in canopy cover during the period evaluated) represents 43% (7444 ha) of the total surface of the watershed (Figure 5).  
The \"stable\" forest cover layer identified was used to determine and delineate areas where the carbon capacity of the forest was set according to estimations of above-ground carbon density. Since the carbon capacity in this study was taken as equivalent to the total carbon in the ecosystem, the value that was reached as aboveground carbon was 51.84 Mg/ha, and the maximum rate of fixation of Carbon was estimated as 844 gC/m\({}^{2}\)/year (Table 8).  
Figure 5: Observations where forest remains “stable”.  
The integration of the different elements for the assessment and monitoring of forest degradation enabled the identification of areas that maintained a \"stable\" condition and areas that changed over the period evaluated. The latter are labelled as \"forest degradation\" areas (Figure 6).  
The integration of the methods showed that the sub-watersheds in the study area with higher population density (\"E. Zapata\", \"31-132\", and \"Xul\") have the upper rates of forest degradation (2865, 1611, and 1596 ha/year, respectively). Aboveground biomass estimations in forest areas identified as \"not degraded\" were in the range of 20 to 108 Mg\(\cdot\)ha\({}^{-1}\); meanwhile, in \"degraded\" areas, the range classified as \"degraded\" ranged from 1 to 24 Mg ha\({}^{-1}\). The mean value of Net Primary Productivity for each one of the years evaluated was used as the threshold value for nondegraded areas. The areas classified with low NPP (<205 gC/m\({}^{2}\)/year) were located near urban settlements, which matches the results found earlier with Canopy cover (Figure 6).
## 3 Data Acquisition
### Landslide Conditioning Factors
#### 3.2.2 Slope Aspect  
The slope aspect is defined as the cardinal direction of the maximum slope [81]. We extracted nine slope aspect classes from the DEM with a resolution of 12.5 m, obtained from Advanced Land Observing Satellite (ALOS) Phased Array L-type Synthetic Aperture Radar (PALSAR): North, northeast, northwest, east, southeast, southwest, south, west, and flat. Most landslides are located on the southwest- and east-facing slopes (Figure 3b).  
Figure 3: Spatial database for landslide susceptibility analysis: (**a**) slope, (**b**) aspect, (**c**) elevation, (**d**) distance to road, (**e**) TWI, (**f**) NDVI, (**g**) lithology, (**h**) land cover, (**i**) rainfall, (**j**) distance to fault, (**k**) plan curvature, (**l**) profile curvature, (**m**) LS, (**n**) solar radiation, (**o**) SPI, (**p**) distance to river, (**q**) river density, (**r**) fault density.
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 2 AIRs, CM, and Proposed Design
### Advanced CM Schemes
#### 2.2.3 Gs-Sa-Scc  
In the paper, we propose a CM scheme via combining geometric shaping and soft-aided SCC (GS-SA-SCC). As shown in Figure 2, information bits are encoded by a staircase encoder and then mapped to symbol \(\alpha_{k}\) from an obtained GS constellation with labeling via (3) as matrices pair \(\{\mathbb{S}^{*},\mathbb{B}^{*}\}\), where \(k\) is the discrete time index. The receiver for the GS-SA-SCC uses an HD-based demapper to estimate the bits. The HD-estimated bits \(\hat{c}_{1,k},\ldots,\hat{c}_{m,k}\) are then decoded by a staircase decoder implemented with SABM decoding. The LLR values \(\lambda_{1,k},\ldots,\lambda_{m,k}\) for the \(m\) bits per symbol are calculated as in [50] (Equation (25)), according to the channel information \(\mathbf{y}_{k}\) and also the transmitted modulation format. The marked information is denoted by \(\hat{q}_{i,k},i=1,\ldots,m\), which can be HRB, HUB, or unmarked.  
Typically, GMI-optimized constellations translate into improved performance of the binary SD-FEC. However, it also has been shown that the GMI-optimized constellations could provide a considerable gain for a HD decoder [31; 63]. In addition, the FEC we considered in this paper is a hybrid HD/SD FEC. Due to the use of a soft-aided decoding algorithm, it should in principle also benefit from the GMI-optimized constellations. The intuition behind the chosen performance metric is that for a hybrid HD/SD decoding, the more soft information is harvested in the FEC decoder, more potential gains can be achieved by using GMI-based optimization. For these reasons, we optimize the 2D constellation by maximizing \(I_{\text{SD}}\) instead of \(I_{\text{HD}}\) in (3). In the next sections, we will investigate how much improvement will be achieved by using the proposed GS-SA-SCC CM scheme.
# Effective Receiver Design for MIMO Visible Light Communication with Quadrichromatic LEDs
## 2 System Model
,\]  
where \(h_{ij}^{\mathrm{c}}\) represents the gain factor between the \(j\)-th QLED's chip of the \(c\) color and the \(i\)-th receiver's PD of \(c\) color. For simplicity, since both QLEDs and multi-color receivers are assumed to be assembled in a compact package, and the distances between the transmitters and the multi-color receivers are much larger than either the physical size of the QLED clusters or the receiver clusters; hence, both could be viewed as points [5] and [7], respectively. Consequently, we can consider the same channel matrices \(\mathbf{H}^{\mathrm{c}}\) for \(c\in\{\)R,A,G,B\(\}\) and omit the color notation \(c\) for a simple representation. Furthermore, the LOS link mainly contributes to the received power at the receiver side; therefore, as an initial exploration, we only consider the room with very low reflection coefficients. In other words, the received signal can be considered to contain only the LOS link for simplicity [8]. Consequently, we have the following channel coefficient between each LED from the \(j\)-th QLED and each PD from the \(i\)-th receiver [1]  
\[h_{ij}= \begin{cases}\frac{(\gamma+1)A_{r}\delta}{2\pi d_{ij}^{2}}\cos^{7} \left(\phi_{ij}\right)\cos\left(\theta_{ij}\right),&0<\theta_{ij}\leq\theta_{ \mathrm{f}ov},\\ 0,&\theta_{ij}>\theta_{\mathrm{f}ov},\end{cases}  \tag{3}\]  
where \(\phi_{ij}\) is the angle of emergence with respect to the transmitter axis and \(\theta_{ij}\) is the angle of incidence with respect to their normal axes, respectively. \(A_{r}\), \(\theta_{\mathrm{f}ov}\), and \(\delta\) denote the effective area of the detector, the field-of-view (FOV) semiangle of the detector, and the responsivity of the receiver, respectively. \(d_{ji}\) is the distance between the \(j\)-th QLED and the \(i\)-th receiver. The Lambertian mode order \(\gamma\) is \(\gamma=\frac{-\ln 2}{\ln(\cos\phi_{1/2})}\), where \(\Phi_{1/2}\) is the half power semiangle of the QLED. Moreover, at the receiver side, assuming perfect knowledge of the channel and ideal time synchronization, the maximum likelihood (ML) detector [7] can be employed. More specifically, by minimizing the Euclidean distance between the received signal vector \(\mathbf{r}\) and all possible received signals \(\mathbf{H}\mathbf{x}\) as \(\mathbf{\tilde{x}}=\arg\underset{\mathbf{r}}{\min}\|\mathbf{r}-\mathbf{H} \mathbf{x}\|^{2}\), the receiver tries to decide which symbol \(\mathbf{\tilde{x}}\) was transmitted.
###### Abstract  
This study proposes a modified convolutional neural network (CNN) algorithm that is based on dropout and the stochastic gradient descent (SGD) optimizer (MCNN-DS), after analyzing the problems of CNNs in extracting the convolution features, to improve the feature recognition rate and reduce the time-cost of CNNs. The MCNN-DS has a quadratic CNN structure and adopts the rectified linear unit as the activation function to avoid the gradient problem and accelerate convergence. To address the overfitting problem, the algorithm uses an SGD optimizer, which is implemented by inserting a dropout layer into the all-connected and output layers, to minimize cross entropy. This study used the datasets MNIST, HCL2000, and EnglishHand as the benchmark data, analyzed the performance of the SGD optimizer under different learning parameters, and found that the proposed algorithm exhibited good recognition performance when the learning rate was set to [0.05, 0.07]. The performances of WCNN, MLP-CNN, SVM-ELM, and MCNN-DS were compared. Statistical results showed the following: (1) For the benchmark MNIST, the MCNN-DS exhibited a high recognition rate of 99.97%, and the time-cost of the proposed algorithm was merely 21.95% of MLP-CNN, and 10.02% of SVM-ELM; (2) Compared with SVM-ELM, the average improvement in the recognition rate of MCNN-DS was 2.35% for the benchmark HCL2000, and the time-cost of MCNN-DS was only 15.41%; (3) For the EnglishHand test set, the lowest recognition rate of the algorithm was 84.93%, the highest recognition rate was 95.29%, and the average recognition rate was 89.77%.  
convolutional neural network; activate function; dropout; SGD optimizer +
Footnote †: journal:  
1
## 2 Materials and Methods  
An SC with an active area of \(\sim\)2.5 \(\times\) 2.8 cm\({}^{2}\) was used as a reference SC. The BSG and PMMA sheets were cut according to the active area of the SC. First, the substrates were cleaned sequentially with acetone, isopropyl alcohol (IPA) and deionized water (DIW) for 10 min each in an ultra-sonic bath. The BSG substrates were dried by blowing N\({}_{2}\) gas and then heated on a hot plate at \(\sim\)100 \({}^{\circ}\)C for 20 min to remove moisture. The cleaned BSG substrates were loaded immediately into the vacuum thermal evaporation chamber to deposit the thin CaF\({}_{2}\) as the ARC. The deposition was performed at \(\sim\)110 A current and under a pressure of \(\sim\)10\({}^{-6}\) torr. The thickness of the CaF\({}_{2}\) coating on the glass was measured by nano-view Spectroscopic Ellipsometer MF-1000 which was 120 \(\pm\) 4 nm. The PMMA was cleaned using soapy water and dried by blowing N\({}_{2}\) gas. Finally, the electrical parameters such as the short circuit current (\(J_{sc}\)) and efficiency (\(\eta\)) were characterized by a light-current-voltage (LIV) measurement system under Air Mass 1.5 G 1 sun illumination conditions (100 mW/cm\({}^{2}\)). The potential was swept from short circuit condition towards open circuit. The step voltage and delay time were 10 mV and 1 ms, respectively.
## 3 Results and Discussion  
### Physical Properties  
Figure 3 shows the variations in the mass, P-wave velocity, and water absorption of the selected samples with increasing drying\(-\)wetting cycles in different pH solutions. As the number of drying\(-\)wetting cycles increased, the mass and P-wave velocity decreased monotonically, while the water absorption exhibited the opposite trend. For instance, for the acidic solution with pH = 3, the mass loss was 1.72%, 2.50%, 3.08%, and 3.52% as the drying\(-\)wetting cycles progressed; the loss of P-wave velocity reached 11.12%, 16.48%, 20.19%, and 21.31%, respectively; and the increment in water absorption was 12.75%, 20.83%, 25.78%, and 29.45%, respectively. These physical parameters changed quickly in the initial stage and slowly afterward, showing obvious non-linear characteristics. This indicates that the damage of drying\(-\)wetting cycles on the red sandstone is a step-by-step accumulating process. Furthermore, the changing ranges of physical parameters were highly dependent on the solution pH. As the pH value decreased, the mass loss of the samples subjected to the maximal cycles (\(\eta\) = 40) increased from 2.62% to 3.52%; the loss of P-wave velocity increased from 16.88% to 21.31%; and the increment in water absorption increased from 16.40% to 29.45%. These differences demonstrate that the variation in the physical parameters of red sandstone becomes more severe with increasing acid concentration.  
The obtained results were consistent with those of previous studies [35; 36], and the maximum loss of these physical parameters was approximate. This may be due to the similar mineral compositions and microstructures of these sandstones. The variation in the physical parameters actually represents the response of the microstructure and components of red sandstone to acidic drying\(-\)wetting cycles, especially the P-wave velocity and water absorption. The P-wave velocity reflects the developing degree of pores inside the rock. It decreased gradually with increasing drying\(-\)wetting cycles and acid concentration, indicating that the rock structure became loose, and the number and size of pores increased. Compared with the P-wave velocity, the water absorption reflects rock porosity in different states more directly. An increase in water absorption indicates the growth in the proportion of pores inside the rock. Hence, the two physical parameters show opposite variation trends with acidic drying\(-\)wetting cycles. In addition, the variation in water absorption was more obvious than that of the other parameter. For this reason, water absorption is considered an efficient index to evaluate rock damage under drying\(-\)wetting cycles [16].
# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
###### Abstract
In addition, we combined the recent state of the art techniques in weather forecasting such as multimodal learning and multitask learning and examined their effects on temperature forecasting performance. For multimodal learning, we performed temperature forecasting by employing additional satellite images as well as the existing numerical meteorological data. For multitask learning, we predicted several temperatures simultaneously to take advantage of shared information among different types of temperatures in different regions. To analyze the effectiveness of multimodal learning and multitask learning, we compared their results with those of the ordinary models. The rest of the paper is organized as follows. In Section 2, we present the literature review. In Section 3, we introduce the neural network architectures we proposed for forecasting the temperature. In Section 4, we describe the data we used and how to preprocess the data. Section 5 presents the experimental design and our interpretation of the results. In Section 6, we conclude the paper with a summary.