esa-sceva/satcom-chunk-collection huggingface dataset:
# Classic Discrete Control Technique and 3D-SVPWM Applied to a Dual Unified Power Quality Conditioner
## 2 UPQC in Dual Topology
### Series Active Power Filter (Series APF)
Equation (1) obtains the equivalent resistance in the secondary (\(R_{\alpha\_s}\)) using the term \(R_{\alpha\_t}\) as the resistance of the power grid and \(R_{\mathrm{s}}\) as the secondary scattering resistors of the transformer. Equation (2) obtains the equivalent inductance in the secondary of the transformer (\(L_{\mathrm{eq\_s}}\)), by using the term \(L_{\alpha\_t}\) as the inductance of the power grid and \(L_{\mathrm{s}}\) as the secondary scattering inductance of the transformer. Equation (3) represents the output difference voltage in the transformer (\(\Delta v_{\mathrm{s}}\)), calculated as the difference between the power grid network (\(V_{\alpha\_t}\)) and the output voltage (\(V_{0}\)). Equation (4) represents the transformer relation \(n\), calculated using \(n_{p}\) as the number of turns in the primary coil of the transformer and \(n_{\mathrm{s}}\) as the number of turns in the secondary coil of the transformer. Finally, Equation (5) is the equivalent inductance in the primary of the transformer (\(L_{\mathrm{eq\_p}}\)), calculated using the terms \(L_{\mathrm{s}}\) and \(L_{p}\) defined as the secondary and primary scattering inductances of the transformer, respectively. \[R_{\alpha\_s}\ =\ R_{\alpha\_t}+R_{\mathrm{s}} \tag{1}\]  
\[L_{\alpha\_s}\ =\ L_{\alpha\_t}+L_{\mathrm{s}} \tag{2}\]  
\[\Delta v_{\mathrm{s}}\ =\ V_{\alpha\_t}(t)-V_{0}(t) \tag{3}\]  
\[n=\frac{n_{p}}{n_{\mathrm{s}}}=1 \tag{4}\]  
\[L_{\alpha\_p}=L_{\mathrm{sc}}+L_{\mathrm{p}}, \tag{5}\]  
Additionally, in previous figures the term \(R_{p}\) is the primary scattering resistors of the transformer, \(C_{b1}\) and \(C_{b1}\) are the corresponding capacitors in the DC bus, \(V_{A}\) is the PWM voltage in the output of the inverters. Furthermore, \(i_{\mathrm{cs\_p}}\) and \(i_{\mathrm{cs\_s}}\) are the currents of the shunt APF in the primary and secondary of the transformer, respectively. Finally, \(Z_{M}\) is the magnetizing impedance of the transformer model that considers \(R_{m}\) as the magnetizing resistance and \(L_{m}\) as the magnetizing inductance. The DC-AC inverter can be modeled as a half-bridge controlled by the two switches \(S_{1}\) and \(S_{2}\), obtaining an output voltage \(V_{A}\)[33; 34], as shown in Figure 7; where \(d\) is the duty cycle and \(T_{\mathrm{s}}\) is the commutation period. For the final equivalent circuit presented in Figure 6c, the state-space model is obtained as expressed in Equation (6):  
\[\left[ \begin{array}{c}V_{B}D(s)\\ 0\end{array} \right]=\left[ \begin{array}{cc}R_{p}+SL_{\alpha\_p}+Z_{M}&Z_{M} \\ Z_{M}&SL_{\alpha\_s}+R_{\alpha\_s}+Z_{M}\end{array} \right]\left[ \begin{array}[] {c}I_{\mathrm{cs\_p}}(s)\\ I_{\mathrm{cs\_s}}(s)\end{array} \right] \tag{6}\]  
Figure 6: Circuit to model the series APF in the UPQC, (**a**) single-phase circuit, (**b**) circuit referred to the primary of the transformer, and (**c**) equivalent circuit.
# Load Forecasting with Machine Learning and Deep Learning Methods
###### Abstract
On the other hand, multilayer perceptron (MLP), which is representative of feedforward artificial neural networks (ANN), long short-term memory (LSTM), a type of recurrent neural network (RNN), and a temporal convolutional network (Conv-1D), a representative model of convolutional neural networks (CNN), are employed to characterize the deep learning (DL) techniques. The transition to electrification requires significant changes in the building sector to improve its performance. The AI techniques offer many possibilities due to their accuracy and short computation times. In addition, the aforementioned models are flexible, adjusting in real time to the needs that are constantly presented in ZEBs. In recent years, multiple studies have been carried out applying these models to problems in the field of energy. However, it remains unclear which is the most reliable model in determining the forecast of electrical loads in the building sector. Thus, the aim of this paper is to analyze the most typical ML and DL techniques employed for regression, determining their quality and possible use in forecasting electric energy by analyzing their bias, variance, and performance to forecast future timesteps. The models are evaluated in an existing building allowing the presence of missing values and using k-fold cross-validation to guarantee the reproducibility of the results obtained. In addition, a seed is used to effectively compare the models. Thus, the main contribution of this paper is to compare different ML and DL techniques to determine which are most suitable for forecasting electricity consumption in buildings. The methodology conducted considers:  
* The dispersion of the different AI models with respect to the real data and how each model varies in the different folds to effectively determine the variance of the results with respect to the real data. * The tendency of the AI models to overpredict or underpredict the data in the different folds to determine the bias of these with respect to the real data. * The projection to the future by application to a dataset used to reproduce the behavior of the AI models in a composition different from the one used in the training. The content of this paper is organized as follows: background about the AI models used is presented in Section 2; the utilized methodology to compare the different AI techniques is explained in Section 3; the studied building, and the specific characteristics of the DL and ML tried are presented in Section 4; the evaluation of the model forecasts of electricity consumption is reported in Section 5; the reliability of the developed techniques is discussed in Section 6; and the conclusions are outlined in Section 7.
## 4 Modeling Errors of KANs and Discussion  
### Error Metrics  
Multiple error metrics were selected, including the Mean Squared Error (MSE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (sMAPE), MaxAE (Maximum Absolute Error), Coefficient of Determination (R\({}^{2}\)), and Adjusted R\({}^{2}\). Their mathematical formulas are defined as:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}} \tag{12}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}{|y_{i}-\hat{y}_{i}|} \tag{13}\]  
\[sMAPE=\frac{100\%}{n}\sum_{i=1}^{n}{\frac{|y_{i}-\hat{y}_{i}|}{(|y_{i}|+|\hat {y}_{i}|)/2}} \tag{14}\]  
\[MaxAE=\max(|y_{i}-\hat{y}_{i}|)\ (i=1,\ldots,n) \tag{15}\]\[R^{2}=1-\frac{SS_{res}}{SS_{tot}}=1-\frac{\sum{(y_{i}-\hat{y}_{i})^{2}}}{\sum{(y_{i }-\overline{y})^{2}}} \tag{16}\]  
\[R^{2}_{adju}=1-\frac{(1-R^{2})(n-1)}{n-p-1} \tag{17}\]  
where \(y_{i}\) is the actual observed value, \(\hat{y}_{i}\) is the predicted value, \(n\) is the total number of samples, \(SS_{res}\) represents the sum of the Squared Residuals, \(SS_{tot}\) represents the total Sum of Squares, and \(p\) is the number of independent variables.  
The MSE is sensitive to outliers due to squared errors, penalizing large deviations strongly. The MAE offers robustness against outliers and reflects overall performance on training and test sets. Unlike the MAPE, the sMAPE stabilizes near-zero values with a fixed range (0-200%), enabling better model comparability. The MaxAE captures the worst-case performance, while R\({}^{2}\) and Adjusted R\({}^{2}\) jointly prevent overfitting misjudgment by accounting for variable quantity.
## 3 Methods
### Deep Learning Model
#### 3.2.3 Attention Mechanism  
The Attention Mechanism (ATT) is a technique that simulates human visual and auditory attention allocation. Essentially, it involves using a neural network to autonomously learn a set of weight coefficients and apply dynamic weighting to enhance important information while suppressing less-important information [41; 42]. In magnitude prediction, by incorporating the ATT, the model can capture key features in the magnitude data and dynamically adjust the weights of different features, further improving forecast accuracy. This paper introduces the ATT into LSTM and BiLSTM models to learn the importance of each feature, enhancing the models' ability to focus on critical information. The ATT calculates feature weights \(\alpha_{t}\) using the output vector \(h_{t}\) as input, with the following calculation formula:  
\[u_{t} =\tanh\left(W_{a}h_{t}+b\right) \tag{24}\] \[\alpha_{t} =\frac{\exp\left(u_{t}^{T}u_{\omega}\right)}{\sum_{t}\exp\left(u_{ t}^{T}u_{\omega}\right)}\] (25) \[C_{t} =\sum_{t}\alpha_{t}h_{t} \tag{26}\]  
First, the intermediate vector \(u_{t}\) is calculated, followed by the computation of the attention weight \(\alpha_{t}\). After weighting, \(C_{t}\) is calculated and passed to the next layer of the neural network. Here, \(W_{a}\) is the weight coefficient matrix, \(b\) is the bias vector, and \(\tanh\) is the hyperbolic tangent activation function used to introduce nonlinearity. \(u_{\omega}\) is the initialized weight matrix. The numerator \(\exp\left(u_{t}^{T}u_{\omega}\right)\) in the formula calculates the importance score for the current time step \(t\), while the denominator \(\sum_{t}\exp\left(u_{t}^{T}u_{\omega}\right)\) is the normalization factor for the importance scores of all time steps, ensuring that the sum of the attention weights is 1.  
Figure 6: Basic structure of a BiLSTM.
## 2 Related Work
### Emojis and Emoticons Lexicons  
In text-based opinion mining, emojis and emoticons can be valuable features. The emoji sentiment lexicon is a crucial element of the use of emojis in opinion mining [38]. In [39], the authors proposed an Arabic emoji sentiment lexicon (ArabESL) to conduct a comparative analysis for consistency of context-based emoji usage across Arabic and European cultures and languages. Despite their assumed consistent usage worldwide, there are indications that the meaning of an emoji may change across different cultures and languages. The Arab-ESL lexicon contains 1034 emojis extracted from public Arabic textual data from the Twitter platform, where these data consist of different dialects, in addition to Modern Standard Arabic. Each emoji has a name, label, and sentiment score based on sentiment labels that take one of three values: negative, neutral, or positive. The results indicate that some cultural-specific aspects, such as nature, affect the sentiment indications of some emojis. Notably, their study focused on emojis without emoticons. Likewise, in [18], the authors considered only emojis while proposing the first and most prominent European emoji sentiment lexicon, the emoji sentiment ranking. The lexicon consists of 751 emojis with their names and their sentiment scores with values between \(-1\) and \(+1\), which have been calculated based on the sentiment of the data in which they occur. The data are tweets from thirteen European languages, each classified into three positive, neutral, or negative sentiments. A unique visualization in the lexicon shows all emoji sentiments as a sentiment bar. The authors found that most emojis are positive, particularly the most frequently used ones. Besides, in comparison based on emoji rankings, there were no significant differences in emoji rankings between the thirteen languages. Therefore, the proposed emoji sentiment ranking is assumed to be an independent resource for European languages that supports automated opinion mining.  
From another perspective, in [38], the authors created an Arabic emoji sentiment lexicon that is context-free, called CF-Arab-ESL. It is challenging to create an emoji sentiment lexicon that is context-free, as individuals interpret emojis according to their perspective, which is greatly affected by cultural background. Thus, the sentiment conveyed by each one is very subjective. In the presented lexicon, a total of 1069 emojis were labeled as positive, neutral, and negative by thirty-five native Arabians of different regions to discover how the sentiment of these emojis can be annotated without depending on a text-based context. The annotators agreed that only a subset of emojis represented particular sentiment. For example, there was an agreement on the positivity of all Arabian countries' flags as a sense of attachment to a nation, as well as an agreement that some animals' emojis, such as a horse, lion, and eagle, are positive, as they indicate positivity in Arabian culture, unlike lizards, pigs, and snakes.
## 5 Experiments
### Ablation Study  
In Equation (5), we model user preferences from both geographical and temporal perspectives. To validate the effectiveness of these two modules, we propose two variant models of MCGRec: MCGRec-G and MCGRec-T. In MCGRec-G, we consider only geographical factors for modeling user preferences, while in MCGRec-T, we focus solely on temporal factors. We then evaluate the performance of these two variants on the NYC dataset. The results are presented in Tables 7 and 8.  
The experimental results indicate that MCGRec outperforms both variant models, MCGRec-G and MCGRec-T, demonstrating that considering the combined effects of geographical and temporal factors on user preferences can significantly enhance model performance. Additionally, we observe that MCGRec-T performs better than MCGRec-G, suggesting that temporal factors are more influential than geographical factors when modeling user preferences.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NYC** & **Precision@5** & **Precision@10** & **Precision@15** \\ \hline MCGRec-G & 0.134 & 0.098 & 0.065 \\ \hline MCGRec-T & 0.152 & 0.114 & 0.069 \\ \hline MCGRec & 0.165 & 0.128 & 0.081 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performances of MCGRec and its variants on NYC in terms of Precision.  
Figure 3: Performance of MCGRec with different \(\beta\) on NYC in terms of Recall.
## 2 Analytical Strategies for Authentication and Quality Control
### Other Techniques
#### 2.5.2 Isotopic Analysis  
The elements carbon, hydrogen, oxygen and nitrogen are composed of several stable isotopes, and it has been observed that their ratios within a given molecule are linked to its origin [83]. Isotopic analysis is widely applied for the determination of origin of agri-food products. There are different hyphenated isotopic techniques, including elemental analyzer-isotope ratio mass spectrometry (EA-IRMS), GC-combustion-IRMS (GC-C-IRMS) and specific natural isotope fractionation-NMR of deuterium (SNIF-2H-NMR). These techniques have found various applications in verifying the authenticity of spirits, especially whisky [79; 84; 85]. In aged distillates, including Gr and WB, GC-C-IRMS has been successfully applied to differentiate wood-derived vanillin from synthetic and natural vanillin [86]. Vanillin is one of the degradation products of lignin that is released in distillates due to the toasting of wood [87]. The addition of synthetic or natural vanillin to distillates is prohibited by the European Regulation 2019/787 [5].  
The recent research works on the use of isotopic analysis, sensor arrays and other techniques for the authentication of Gr and WB, described above, are summarized in Table 3.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Samples** & **Disciminating Parameters** & **Method of Analysis** & **Data Analysis** & **Results** & **References** \\ \hline
14 \%print drinks (5 \% whiskkeys, 7 \% whiskes, 1 brands, 1 vodka) & Spirit drink category and \% adultertion for quality control & Colorimetric sensor array & HCA, PCA, support vector machine & Correct categorization with accuracy rate \% 9\%. & [67] \\ \hline
37 \%print drinks (2 \% Armstrong, 9 \% brandnes, 4 German \% Brannnnnn, 3 \% gramnnnn, 5 \% girltrype, brand, batch, aging time and adulteration for quality control & Hypothesis-free sensor array \% PCA, LDA & Classification accuracy: 99\% (rose-validation). & [68] \\ \hline
5 \%printwise, 5 German \% brainnnn, 8-fake \% brandnes, 1 aged Grappa. & & & & \\ \hline
57 Italian distillutes (58 \% Grappa, 17 \% sprints from fruits or cereals) & Grappa from other spirits & GC-MS, MIR and NIR spectroscopies & SO-PLS-LDA, VAidation. & SO-PCS-UL-LDA \% \% correct classifications for the category (“German” & [70] \\ \hline
15 Grappa (12 samples aged with oak and 2 with popular word shops of different sizes and toasting levels) & Aging technologies & SPEC-GC-MS, NIR (11,500-4000 cm\({}^{-1}\)) 1D & ANOVA and PCA (for GC-MS data), PCA (for NIR, NMR and E-nose data) & PCA (NIR and E-nose data) & [75] \\ \hline
32 distillutes (20 Scotch mult \% whiskes, 4 \% bouwdens, 3 \% cognps, 1 \% Grappa, 1 \% brand) & Wood-derived vanillin from coded vanillin & & ANOVA analysis the \%
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 3 Experiment 1: Preliminary Word Error Rate and Listening Effort Measurements  
### Materials and Methods  
#### 3.1.1 Experimental Design  
The main task for this web-based experiment [32] was the sentence recall and transcription task. Participants listened to a sentence and then typed what they had understood. To collect LE rating measures, we asked the participants to rate the sentences for LE on a 5-point Likert scale. The options were'very little', 'a little','some', 'quite', and 'a lot'. The sentences were played only once (to avoid any possible memory effect) and in a random order (to avoid sentence order bias).
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 4 Performance Analysis and Simulation Results  
In this section, the proposed DRL algorithm is simulated and compared to some classic algorithms to evaluate its performance.  
MSL, MDS-MAP, DV-hop are selected as the reference methods since all of them can be used in multi-hop networks and have low computation complexity which are more suitable for an underwater environment. Besides, the MSL algorithm only needs a few anchors to accomplish the localization procedure for multi-hop networks which is often used in UANs.  
Twenty nodes are deployed in a 1000 \(\times\) 1000 2-dimensional area and one of them is the anchor.
## 3 The Multiple Workflows Allocation Model
### Cloud Workflow Allocation Framework
#### 3.1.2 Middleware  
Middleware is a software platform that acts as an intermediary between the application and cloud resource layer. It makes the connection between any two clients, servers, or even applications possible. Middleware is not directly available to end users, while it is accessible in the form of a remote software platform. This layer can be further sub-divided into two:  
* **Workflow Allocator:** This module allocates workflows submitted by users to fulfill QoS requirements. This module is responsible for managing all aspects of allocation such as prioritizing workflow tasks, gathering status information on cloud resources, managing various constraints, and so on. Moreover, the workflow allocator aims to distribute the system load, reduce the cost and execution time, improve system performance, properly utilize the available resources, etc. In cloud computing, techniques such as Levelized Task Partitioning, Levelized Task Allocation, and Level-Wise Task Prioritization using upward rank are frequently used to efficiently manage multiple workflow allocation. Below is an explanation of these ideas: 1. **Task Partitioning:** A set of workflows can be divided into depth levels using the Levelized Task Partitioning technique, where each level corresponds to a distinct processing stage. Tasks can be arranged using this method according to their dependencies and execution order. 2. **Task Prioritization:** It assigns priorities based on task importance and dependencies, ensuring that critical tasks are handled appropriately. The technique of allocating these priorities such that crucial tasks are completed first is known as \"upward rank\". 3. **Levelized Task Allocation:** This involves distributing tasks across different levels to balance the workload and efficiently use computational resources.
* **VM Manager:** Hypervisor is a virtual machine (VM) monitor that abstracts and isolates the operating systems and workflow applications from the hardware. With the help of this abstraction, the physical host can run one or more VMs separately and efficiently share resources such as processing time, memory, network bandwidth, etc. Without hypervisors, physical (non-virtualized) servers might only host one operating system and workflow application.
# Ultrawideband Cross-Polarization Converter Using Anisotropic Reflective Metasurface
## 7 Parametric Variation  
The operational frequency band of the unit cell can be switched to any desired frequency range just by altering its physical dimensions, and, for that, the parameters are readjusted in the CST studio as p = 10 mm, 1 = 5.2 mm, a = 0.6 mm, b = 4.4 mm, and h = 2.4 mm. The boundary conditions are the same in both cases. As discussed in Section 3, the main criterion for an above 90% cross-polarization conversion is that the co-polarized coefficients should be less than \(-10\) dB and the cross-polarization coefficient value should be greater than \(-1\) dB, respectively: (\(r_{xx}<-10\) dB and \(r_{yy}<-10\) dB) and (\(r_{xy}>-1\) dB and \(r_{yx}>-1\) dB). It can be depicted from Figure 7a that the co-polarized coefficient \(r_{yy}\) is below \(-10\) dB for the wide frequency band of (9-18 GHz), and the cross-polarized coefficient \(r_{xy}\) is above \(-1\) dB. This means that there is a strong polarization conversion. After the simulation, it is evident from Figures 3a and 7a that the operating frequency band of the proposed polarization converter is shifted from (13-26.2 GHz) to (9-18 GHz). It can also be tuned to higher frequencies by lowering the size of the participating unit cell. From Equation (5), the _PCR_ can be demonstrated, and the efficiency at the frequency range of (9-18 GHz) can be predicted. As discussed above, if the _PCR_ approaches unity, the metasurface is 100% efficient. Figure 7b shows the results of Equation (11) for the frequency range of (9-18 GHz). The proposed structure is also efficient at low frequencies. It can be analyzed from the polarization conversion ratio shown in Figure 7b that the \(PCR>0.95\) for the frequency range (9-18 GHz), which means that the efficiency is greater than 95% and, for some frequencies, approaches near 100%. We used the criteria mentioned above in the Section 4 \"decomposed \(u-v\) incident analysis.\" For polarization conversion, considering the linear\(-\)to\(-\)cross\(-\)polarization case, the magnitude of \(r_{u}\) and \(r_{v}\) is approximately \(r_{u}\approx r_{v}\approx 1\), where the slight variation is due to metallic and dielectric losses caused by metal and the FR-4 substrate. Figure 7d shows the phases of \(r_{u}\) and \(r_{v}\), which are represented by \(\varphi_{u}\) and \(\Delta\varphi_{v}\), and \(\Delta\varphi_{uv}\) represents the phases difference between \(r_{u}\) and \(r_{v}\). Through the analysis of Figure 7d, we can conclude that the phase difference in \(\Delta\varphi_{u}\) and \(\Delta\varphi_{v}\) is approaching \(\Delta\varphi_{uv}\approx 180^{\circ}\). As a result, the suggested structure can effectively transform a \(y-\)polarized wave to an \(x-\)polarized wave at low frequencies and vice versa.  
Figure 6: (**a**) Schematic representation of the proposed anisotropic reflective metasurface under oblique incident wave; (**b**) angular dependent _PCR_ of the proposed anisotropic reflective metasurface.