esa-sceva/satcom-chunk-collection huggingface dataset:
# Glucose Data Classification for Diabetic Patient Monitoring
## 7 Result and Discussion
The performance of the proposed classification approach was evaluated using different standard measures based on true positive (TP), false positive (FP), precision, recall, and F-measure [20]. The accuracy of algorithms (shown in Table 3 and Figure 13) was measured with the help of parameters such as false positive rate, true positive rate, recall, precision, and F-measure. These parameters are defined as follows:  
* True positive (TP): represents the instances that have been correctly classified with respect to a given class. * False positive (FP): represents the instances that have been incorrectly classified with respect to a given class. * Precision: the proportion of the correctly classified instances to a particular class divided by overall instances classified with respect to that class. \[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{1}\]  
Figure 14: Graphical representation of false positive (FP), true positive (TP), precision, recall, and F-measure of different algorithms. Figure 13: Visualized tree with random tree. * Recall: the proportion of the incorrectly classified instances that have been classified by a class divided by the total instances present in the class. \[\text{Recall}=\text{TP/(TP+FN)}\] (2)
* F-Measure: it is calculated by combining the measure of recall and precision according to Equation (3): F-Measure = (2
* Recall)/(Precision + Recall), (3)  
Figure 13 represents TP rate, FP rate, precision, recall, and F-measure for the chosen classifiers. The TP rate, precision, recall, F-measure values for J48 were better than the other classifiers, but the FP Rate for ZeroR was better than the others. According to the precedent equations, we can obtain the sensitivity, specificity, and accuracy of different algorithms. Table 2.0  
Table 2.1  
Table 2.2  
Table 3 shows the value of TP Rate, FP Rate, Precision, Recall, F-Measure, and Table 4 shows Sensitivity, specificity, and Accuracy for the Naive Bayes algorithm, Random Tree, ZeroR and J48. Figure 15 represents the sensitivity, specificity, and accuracy of different algorithms. The sensitivity, specificity, and accuracy for J48 were better than for other classifiers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline Naive Bayes & 0.36 & 0.87 & 0.95 \\ RandomTree & 0.42 & 0.99 & 0.99 \\ ZeroR & 0 & 0 & 0.69 \\ J48 & 0.43 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of sensitivity, accuracy, and specificity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** \\ \hline Naive Bayes & 0.996 & 0.129 & 0.946 & 0.996 & 0.970 \\ RandomTree & 0.994 & 0.016 & 0.993 & 0.994 & 0.994 \\ ZeroR & 1 & 1 & 0.69 & 1 & 0.82 \\ J48 & 0.993 & 0.010 & 0.996 & 0.993 & 0.994 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Values of TP, FP, precision, recall, and F-measure for algorithms.
## 2 Materials and Methods
### Extracellular Biosynthesis and Characterization of AgNPs  
The cultural filtrates from _S. obliquaus_ cultivated with different nitrogen sources were collected during various growth phases for use in extracellular biosynthesis of AgNPs. After each treatment, the cultural filtrates were collected via centrifugation at 5000 rcf for 10 min (Herarus Megafuge; Thermo ScientificsTM, Langensel Bold, Germany) at 6-day intervals representing the log, early- and late-stationary growth phases. Equal volumes of cell-free cultural filtrates from each treatment were added to silver nitrate solution (0.01 M). The mixtures were incubated at room temperature (28 \(\pm\) 2 \({}^{\circ}\)C) for 24 h to allow biosynthesis of AgNPs. Visible observations (changes in the color from yellow to dark brown) and UV-Visible spectroscopy of the reaction mixture were performed to check the synthesis of the nanoparticles using a UV/VIS spectrophotometer (Jenway UV/Visible-2605 Spectrophotometer, UK). The biologically synthesized nanoparticles were centrifuged, washed twice with deionized water, and air dried for further examinations. The size and shape of the synthesized AgNPs in selected samples were studied under HRTEM (JEM-2100 TEM, JEOL Ltd., Tokyo, Japan).
## 1 Introduction
The damage mechanism and damage effect of polarized laser on thin-film are understood by conducting the experiments of thin-film laser damage to provide a basis for improving the damage threshold and laser protection of optical thin film. In addition, among many thin films, the Ta\({}_{2}\)O\({}_{5}\) thin film has excellent potential application due to its advantages and is one of the research hotspots in recent years. Firstly, it has a high dielectric constant (30-35), chemical stability, and thermal stability. Secondly, it is considered to be the most advanced technology in the field of microelectronics due to its easy compatibility with semiconductor integrated circuits/processes. Thirdly, it has been widely used in the optical field due to its high refractive index, low absorption rate, and wide spectral transmittance range (300 nm-10 \(\upmu\)m). Finally, its high hardness can be used as a protective film [14; 15; 16; 17]. So, the laser damage of a Ta\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) multilayer film prepared by electron beam evaporation was studied by 1-on-1 and 5-on-1 measurements under linearly polarized and circular-polarized femtosecond lasers. The damage threshold of the film for both polarized lasers was obtained, and a microscope observed the damage morphology of the film for both polarized lasers.
## 3 Materials and Methods
### Model Training  
In the images obtained, the number of pixels in the area occupied by the animal, that is, the segmentation data, does not make any sense on its own. Even if an animal is light in weight, it will take up a lot of space in the image if it is viewed close to the plane of the camera. The opposite is also possible. Pixel numbers are directly proportional to weight, and disparity value is inversely proportional to weight. An increase in the disparity value means that the animal is viewed from a point close to the camera plane. In the study, distance-related errors are eliminated, since the stereo camera setup is calibrated. In the images obtained, the values were made meaningful by considering the stereo distance variable. When the prepared dataset is examined, it is seen that there are data at very different scales from each other. While the pixel numbers in the image are expressed in thousands, the stereo distances are expressed in a few meters, and the disparity values are expressed in the range of 5 and 30 pixels. Training a neural network with such inputs may take a lot of time and the network may not be successful enough. Data at such different scales should be expressed as values close to each other by normalization techniques. The main reason for this is that these features are multiplied by the model weights. Data normalization also accelerates the training time by transforming the raw data into a specific range. Data normalization is extremely useful for modeling applications where the inputs are often at very different scales [33]. In this study, the Z-score normalization technique was used. Here, the Z-score value is calculated by Equation (3), where \(\mu\) represents the arithmetic mean of the data, \(\sigma\) standard deviation, and \(Z_{k}\) the data to be normalized.  
\[Z_{k}^{{}^{\prime}}=\frac{Z_{k}-\mu}{\sigma} \tag{3}\]  
In the study, three different artificial neural networks were trained after the data obtained from the images taken from different directions were normalized. The first network (ANN-1) is trained with image data taken from the side, the second network (ANN-2) from the back, and the third network (ANN-3) from both directions. A total of 90% of the dataset is reserved for training artificial neural networks and 10% for testing. The architecture of artificial neural networks used in the proposed system is shown in Table 4.  
ANN-1 and ANN-2 artificial neural networks used for training are fully connected networks with a three-element input layer, two hidden layers consisting of 64 nodes, and an output layer consisting of one element. Each network has 4488 parameters. The ReLU function is used as the activation function, the mean absolute error function is used as the loss function, the Adam optimizer is used as the optimizer, and a constant value of \(10^{-3}\) is used as the learning rate value. A total of 1000 training steps were seen as sufficient. The ANN-3 network has the same features as other networks. It covers the entire dataset. Therefore, the number of inputs is 8 and the total number of parameters is 4818.
# An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications
## 2 Preliminary Work
### Adaptive Predistortion  
The LED is a nonlinear device, and its behavior can be modeled using the measured input and output characteristics of the adopted LED. To compensate for the LED nonlinearity, a predistortion block is applied to \(\mathbf{x}^{\text{(DCO)}}\), as shown in Figure 2. In the transmitter, a PD is used to build a feedback signal \(y(t)\) to serve as a reference signal for the sake of adaptively adjusting the predistor to make it capable of tracking the LED characteristics due to temperature variation and aging.  
Generally, an error signal \(\mathbf{e}^{\text{(DCO)}}\), the difference between \(\mathbf{x}^{\text{(DCO)}}\) and \(\alpha\mathbf{y}^{\text{(DCO)}}\), where \(\alpha\) is a scaling factor, has been used to update parameters in adaptive predistorters, and an NLMS-based algorithm has been proposed to update LUTs [12]. Assuming that at time instant \(t\), the \(i\)-th signal \(x_{i}^{\text{(DCO)}}\) in \(\mathbf{x}^{\text{(DCO)}}\) would be in the range of the \(k\)-th partition cell in the LUT, and it would be predistorted by the representation value of that cell \(w_{k}^{(t)}\), then the \(k\)-th representation value could be updated as follows:  
\[w_{k}^{(t+1)}=w_{k}^{(t)}+\mu_{k}e_{i}^{\text{(DCO)}}x_{i}^{\text{(DCO)}}, \tag{2}\]  
where \(e_{i}^{\text{(DCO)}}=x_{i}^{\text{(DCO)}}-\alpha\mathbf{y}^{\text{(DCO)}}\) and \(\mu_{k}\) denotes the step size for the NLMS algorithm [12]. Meanwhile, Chebyshev polynomials have been used to approximate the inverse of the LED nonlinearity as compared to just using a predistortion factor in the NLMS-based predistorter [13]. In the Chebyshev polynomial-based predistorter, the \(k\)-th representation value was updated as follows:  
\[w_{k}^{(t+1)}=w_{k}^{(t)}+\mu_{k}e_{i}^{\text{(DCO)}}T_{j}\Big{(}x_{i}^{\text{ (DCO)}}\Big{)}, \tag{3}\]  
Figure 2: Illustration of DCO-OFDM VLC system with adaptive predistorter.  
where \(T_{j}\) denotes the \(j\)-th Chebyshev polynomial of the first kind.
## 4 Overview: Signal Processing Methods
### Dimensionality Reduction Methods  
Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are essential dimensionality reduction techniques in machine learning, serving multiple purposes such as feature dimension reduction, visualization, and noise reduction. They transform high-dimensional data into lower-dimensional representations, simplifying computational tasks and aiding in understanding complex data structures. By focusing on the most informative features or directions of variance, LDA and PCA enhance model interpretability, mitigate collinearity issues, and improve overall model performance, making them valuable tools in data preprocessing for machine learning applications.
## 4 Machine Learning Techniques for Forecasting
### BP Neural Networks  
Neural networks construct the functional form of \(\mathbf{y}=f(\mathbf{x})\) from the viewpoint of a network model [26; 51]. For an input vector \(\mathbf{x}=\left(x^{(1)},x^{(2)},\ldots,x^{(D_{1})}\right)^{\text{T}}\), a feedforward network with \(K\)-1 hidden layers can be expressed by:  
\[\mathbf{y}\approx f_{\text{BP}}(\mathbf{x})=g^{(K)}\Big{(}\mathbf{W}^{(K)}g^{(K-1)}\Big{(} \mathbf{W}^{(K-1)}g^{(K-2)}\Big{(}\ldots g^{(1)}(\mathbf{W}^{(1)}\mathbf{x}+\mathbf{b}^{(1)}) \ldots\Big{)}+\mathbf{b}^{(K-1)}\Big{)}+\mathbf{b}^{(K)}\Big{)} \tag{8}\]  
where \(\mathbf{W}^{(k)}\in\Re^{d_{k}\times d_{k-1}}\) is the weights matrix in the \(k\)-th hidden layer, \(\mathbf{b}^{(k)}\in\Re^{d_{k}\times 1}\) is the corresponding bias vector, \(g^{(k)}(\cdot)\) is the nonlinear activation function adopted in the \(k\)-th hidden layer, \(d_{0}=D_{1}\) and \(d_{K}=D_{2}\).  
Denote the model parameters set by \(\mathbf{\theta}=\left\{\mathbf{W}^{(k)},\mathbf{b}^{(k)}\right\}_{k=1}^{K}\). By training the network according to all training samples, the optimal network parameters \(\mathbf{\theta}\) can be obtained. For this purpose, we minimize the following error function:  
\[E(\mathbf{\theta})=\frac{1}{2}\sum_{i=1}^{N}\|f_{\text{BP}}(\mathbf{x}_{i})-\mathbf{y}_{i} \|_{2}^{2} \tag{9}\]  
The simplest and the most effective approach is the gradient descent, and the update formulation is  
\[\mathbf{\theta}\leftarrow\mathbf{\theta}-\eta\
abla E(\mathbf{\theta}) \tag{10}\]  
where \(\
abla E(\mathbf{\theta})\) is the gradient of \(E(\mathbf{\theta})\) with respect to \(\mathbf{\theta}\), and the step size \(\eta\) is called the learning rate.  
Each parameter updating step consists of two stages. The first stage evaluates the derivatives of the error function with respect to the weight matrices and the bias vectors. The backpropagation technique propagates errors backwards through the network and it has become a computationally efficient method for evaluating the derivatives. The derivatives are employed to adjust all parameters in the second stage. Hence, the multilayer perceptron is also called a back-propagation (BP) neural network. Figure 5 depicts the topological structure of a BP neural network with one hidden layer. In detailed implementation, mini-batch gradient descent is usually utilized to update parameters to reduce the computation burden.  
Figure 5: Diagram of a BP neural network with a single hidden layer.
## 4 Results and Discussion
### BERT and EMLO Embedding  
Our study provides novel experimentation and analysis to contrast standalone BERT and ELMo models for social media spam detection. We employ state-of-the-art transfer learning approaches by leveraging the innate contextual modeling capabilities of these pretrained language representations. Specifically, we instantiate BERTBASE cased and ELMo original 5.5B models for generating token embeddings on the Twitter and YouTube datasets. While both architectures use LSTM networks, a key distinction lies in ELMo's deeper bidirectional conditioning compared with BERT's single direction. We hypothesize this enables richer semantic encoding vital for handling informal textual styles. As shown in Table 5, ELISA consistently attains a higher accuracy of 90-94% against BERT's 84-91%. We posit the performance gap arises from ELISA's intrinsic focus on modeling linguistic contexts across two directions. This allows capturing nuanced syntactic and semantic patterns within noisy social posts to improve discrimination. Additionally, we employ an innovative pipeline (Algorithm 2) involving pairing pretrained embedders like BERT and ELISA with simple downstream classifiers (logistic regression). This end-to-end approach eliminates extensive task-specific fine-tuning and provides strong off-the-shelf performance, uniquely suited for real-world spam filtering systems that need to rapidly adapt to evolving data. Algorithm 2 leverages pretrained contextualized embedding models like BERT and ELMo to generate representations of text for training classifiers to categorize posts as spam or not spam. The input data from two separate datasets (e.g., Twitter and YouTube) are first preprocessed, converted to lowercase, and cleaned by removing URLs and special characters. Helper functions are set up to make embeddings from BERT and ELMo models. For BERT, the text is tokenized using a predefined tokenizer and then fed into the model to get token embeddings that match. For ELMo, the raw text strings are directly input into the model to produce embeddings. These pretrained models encode rich semantic representations of text by considering contextual information. The embeddings are then used as features for training machine learning classifiers on the downstream spam detection task. Then, the data are split into training and test sets. Classifiers like logistic regression are trained on the embedding inputs from the training set, so we can apply model performance that is evaluated on the test set using metrics (accuracy, precision, and recall.) This provides insights into how these deep contextual models perform for spam detection across different platforms, like Twitter and YouTube, and how the pretrained representations augment sequential networks to handle noisy, informal language better.
# Design and Application of Daylight-Based Lighting Controller on LED Luminaire
## 2 Simulation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Simulation Case** & **Average Light** & **Minimum Light** & **Maximum Light** & **Uniformity of** \\
**Intensity (lux)** & **Intensity (lux)** & **Intensity (lux)** & **Light** \\ \hline Daylighting & 253 & 31.6 & 1657 & 0.12 \\ Daylighting and artificial lighting & 1140 & 591 & 1507 & 0.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The lighting characteristic from simulation on experimental setup. Figure 2: Light distribution profile of the room modelled by DIALux software: (**a**) In case of only daylighting, (**b**) In case of both daylighting and artificial lighting. However, both types of luminaire could provide an illuminance that was within the international commission on illumination (CIE) standard at 300-350 lux. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & **North** & **South** & **East** & **West** \\ \hline
06:00 & 0 & 0 & 0 & 0 \\
07:00 & 44.1 & 95.7 & 304 & 31.8 \\
08:00 & 145 & 453 & 1051 & 105 \\
09:00 & 224 & 843 & 1377 & 178 \\
10:00 & 232 & 1104 & 1197 & 224 \\
11:00 & 198 & 1204 & 663 & 251 \\
12:00 & 178 & 1250 & 313 & 309 \\
13:00 & 198 & 1200 & 253 & 632 \\
14:00 & 237 & 1094 & 225 & 1173 \\
15:00 & 233 & 832 & 181 & 1379 \\
16:00 & 155 & 443 & 109 & 1080 \\
17:00 & 49.7 & 97.9 & 35 & 347 \\
18:00 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average illuminance (lux) on working plane in case of buildings facing different directions. Figure 3: Light distribution profile of the test setup modelled by DIALux software: (**a**) Two sets of \(3\times 3\) W light emitting diode (LED) luminaire in the test setup; (**b**) Fluorescent T5 luminaire in the test setup. Simulation results in terms of light intensity uniformity of LED luminaire and T5 luminaire are shown in Table 3. In the case of the LED luminaire, the light distribution was a point. It could be seen that the light intensity at the ceiling and walls were low at 42 and 85 lux, respectively. However, in the case of T5 fluorescent, the light distribution was scattered in all directions. The luminaire's light intensity at the ceiling and walls were 216 and 254 lux, respectively. For the light intensity on the working plane, the results were that the LED luminaire contributed higher illuminance at 294 lux compared to 202 lux contributed by the T5 fluorescent luminaire. Hence, it can be concluded that LED luminaire gave a better performance at the working plane and with less power consumption. Thus, it was the best choice to use with the daylighting control circuit to achieve even further efficiency and less power consumption.
## 4 Data Augmentation
### Definition Transformation  
We utilize Gaussian noise as a data augmentation technique to simulate sensor noise resulting from poor lighting and/or high temperature during data acquisition. The primary objective is to introduce noise to elements within the image of the source dataset, resulting in an image with added noise. To accomplish this, we utilize a Gaussian kernel function to perform a convolution on the image, transforming it into a blurred image. The integration of artificially generated images during the training phase can lead to a considerable enhancement in both accuracy and stability of detection models.
## 7 Discussion  
The primary strategic plan presented in this article can vary depending on several factors, such as the geopolitical landscape of the city, the Gross Domestic Production (GDP), and individual income levels of the modern city. Cities with higher GDP can adapt to a highly technical and sophisticated IoT-based composting platform with fewer issues in scalability and budgetary constraints. Nevertheless, cities with lower GDPs must adhere to a less sophisticated IoT platform, vital for creating employment opportunities. Therefore, the thorough examination of the economic markers of a city prior to the execution of the plan is vital to its success.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Proposed Composing Systems** & **Contribution to SDGs** & **Examples of Real-World** & **Reference** \\ \hline \multirow{6}{*}{Compososting Parameter Sensors} & \multirow{3}{*}{SDG 12 (Responsible Consumption and Production)} & Uses machine learning-based moisture sensors to monitor the moisture content of composting in real time and uses self-adjustment to control the moisture content to improve product quality & [28] \\ \cline{3-4}  & & Utilizes highly robust Wi-Fi connectivity to monitor the & \\ \cline{3-4}  & & SDG 9 (Industry, Innovation, and Infrastructure) & compososting process and real time waste segregation using convolutional neural networks to accelerate production & [23] \\ \hline \multirow{6}{*}{Connectivity} & \multirow{3}{*}{SDG 12 (Responsible Consumption and Production)} & Utilization of near infrared spectroscopy (NIR) to accurately predict moisture content, total carbon, carbon/nitrogen ration and organic matter content during composing and accurately predict compost maturity and save time & [120] \\ \cline{3-4}  & & Uses 3D object detection model based on convolutional neural networks to detect and classify compostable and non-compostable waste in real time to reduce contamination and product pollution & [121] \\ \hline \multirow{6}{*}{UI} & \multirow{3}{*}{SDG 12 (Responsible Consumption and Production)} & Utilization of node MCU (micro-controller unit) to automate amount of feedstock, compositing process, moisture content and reduce odor emissions to reduce air pollution & [73] \\ \cline{3-4}  & & Utilization of middleware and cloud and edge-abstraction layers for secure communication of compositing data to reduce data theft & [82] \\ \cline{3-4}  & & Conversion of organic waste into compost within 4 weeks of time and maintaining optimal 40\% moisture and 65\% operational efficiency in solar power via photovoltaic energy storage system to reduce the dependence on fossil fuel energy & [21] \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Contributions to SDGs related to the contributions of IoT technologies in composting processes.