esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The same authors in another work explored the combined use of three instrumental techniques, namely, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR), and UV-visible spectroscopy (UV-vis), to detect the presence of the same defects in virgin olive oil that was previously organoleptically characterized by an accredited taste panel [20]. The authors employed different data fusion approaches (low- and mid-level fusion) combined with a PLS-DA model development and reported that low-level data fusion from the three instruments was most suitable for detecting musty, winey, and fusty defects, whereas the rancid defect was identified with a combination of headspace-mass spectrometry (HS-MS) and MIR spectroscopy techniques. However, FTIR spectroscopy has also been used for the quality assessment of other foodstuffs, in addition to olive oil. One of these is wine, where FTIR spectra have been used to determine phenols and quantify ellagitannin, which affects the organoleptic characteristics of wine during aging [21]. In addition, FTIR spectroscopy was also applied to the classification of milk and specifically to the differentiation of goat and sheep milk, where the fingerprint region 1.840-950 cm\({}^{-1}\) was examined, as this is the region where absorption by carbonyl groups is located [22]. The aim of the present study was to investigate the potential of FTIR as a rapid and non-invasive technique to discriminate extra virgin olive oil (EVOO) from other olive oil categories (virgin olive oil, ordinary, and lampante) based on the acquired spectral profile of olive oil. For this purpose, spectral data were collected, pre-processed, and correlated by Random Forest (RF) analysis with the sensory category (EVOO vs. other) of olive oil samples, as defined by sensory analysis undertaken previously by trained panelists.
## 3 Model Development
### Derivation of Induction Time with AS (\(t_{A}\))  
The presence of AS in the solution hinders nucleation because it competes with scalars for bonding sites on the clusters [36; 37]. Molecules of the antiscalant attached to the cluster's surface create a surface coverage fraction denoted as \(\theta\), leaving a surface fraction of \(1-\theta\) for further cluster growth. The growth of \(\theta\) is governed by the reaction equation [38]:  
\[\mathrm{d}\theta/\mathrm{dt}=\mathrm{k_{a}C_{a}}(1-\theta)-\mathrm{k_{d}}\theta \tag{16}\]  
where \(\mathrm{k_{a}}\) is the adsorption rate constant of AS molecules on the cluster surface, \(\mathrm{C_{a}}\) is the AS concentration, and \(\mathrm{k_{d}}\) is the desorption rate constant of the AS from the cluster surface. The solution of Equation (16) with \(\theta(0)=0\) yields  
\[\theta(\mathrm{t})=(\mathrm{a}/\mathrm{b})(1-\mathrm{e^{-bt}})\mathrm{;\,a= k_{a}C_{a},\,b=a+k_{d}} \tag{17}\]  
At time \(\mathrm{t}\), the surface coverage fraction \(\theta\), representing the cluster's occupancy by the AS, leaves a free surface area of \((1-\theta)\)A available for further cluster growth. In this case, the surface area A in Equation (10) is substituted with \((1-\theta)\)A to obtain the following:  
\[\mathrm{dm}/\mathrm{dt}=(4\mathrm{k_{c}A}(1-\theta)(\mathrm{C_{f}-C_{e}})^{2} )/(4+\pi\mathrm{u_{f}}\mathrm{k_{s}}) \tag{18}\]  
Integration of Equation (18) yields the required induction time \(\mathrm{t_{A}}\), accounting for the impact of AS on increasing \(\mathrm{t_{A}}\) over \(\mathrm{t_{0A}}\).  
\[\mathrm{t_{A}}=\frac{1}{\mathrm{b-a}}(\mathrm{bt_{0A}}-\frac{\mathrm{a}}{ \mathrm{b}}\Big{(}1-\mathrm{e^{-bt_{A}}}\Big{)}) \tag{19}\]  
According to Equation (19), the induction time \(\mathrm{t_{A}}\) is approximately proportional to the concentration of the AS, \(\mathrm{C_{a}}\). When \(\mathrm{C_{a}}=0\), \(\mathrm{t_{A}}=\mathrm{t_{0A}}\), as derived in Equations (17) and (19). Equations (15) and (19) predict the induction times \(\mathrm{t_{0A}}\) and \(\mathrm{t_{A}}\) respectively, based on the determination of the parameters \(\mathrm{k_{s}}\) in Equation (15) and \(\mathrm{k_{a}}\) and \(\mathrm{k_{d}}\) in Equation (19) for specific datasets.  
It is worth noting that the model is applicable when the feed velocity (\(\mathrm{u_{f}}\)) is constant, >> than the permeate velocity, as in the case of RO desalination. Otherwise, where both permeate velocity and Stokes gravitational velocity become significant, these factors were not taken into account in the present model and are irrelevant to RO systems. Consequently, the analytical model is not valid for such low bulk velocities.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 3 System Description
### System Description
#### 3.2.1 Solar PV System  
The solar photovoltaic module harnesses the energy from the sun and converts it into electric energy. Based on the specifications and capital cost, a 'Sharp ND-250 QC' model is considered for this work. The generated energy from the sun can be expressed as follows [51]:  
\[E_{PV}=R_{PV}\times PSH\times\eta_{PV}\times 365\;\mathrm{day}/\mathrm{year} \tag{1}\]  
Figure 1: Architecture of the proposed system.  
where \(R_{PV}\) is the rated size, and \(PSH\) is the peak solar hour to average direct sunlight per day in kWh/m\({}^{2}\)/day.
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 4 Experiments
### Datasets
#### 4.3.2 Sarcasm Detection (SaD)  
SaD task typically involves identifying whether a given sentence is sarcastic or not. The task is challenging, because it requires an understanding of subtle hints and context to identify sarcasm. For the SaD task, we employed four datasets, including Mawqif that was briefly described in the NLI and SD task group. All datasets in this task group primarily consist of DA text, along with some MSA text.  
* Saudi Dialect Irony Dataset (Saudilrony): SaudiIrony is a collection of tweets specifically designed to help with detecting irony in the Saudi dialect [56]. The dataset contains about 19,800 tweets.
* iSarcasmEval: iSarcasmEval is a collection of Arabic sarcastic texts specifically designed to help detect intended sarcasm in text [57]. It contains 4887 texts from different dialects such as the Nile Basin, the Gulf area, and North Africa.
* ArSarcasmV2: ArSarcasm is an Arabic SaD dataset [58]. It is an extension of the original ArSarcasm dataset [59]. The expanded corpus contains 15,548 tweets, with each labeled for sarcasm, and which are manually annotated with one of three sentiments: positive, negative, or neutral.
## 5 Conclusions  
Numerical and experimental studies were conducted to investigate the DC positive corona discharge characteristics at a tip defect of a conductor. On the basis of analyzing the chemical reaction and dynamic motion of microscopic particles in the corona layer, the influence of the geometrical structure of wire tip defects and gas pressure on corona discharge characteristics was further calculated and studied numerically. Different aspects in the structure of conductor tip defects were controllable, namely the radius \(R\) of the conductor (5 mm, 7.5 mm, 10 mm), the height \(h\) of the tip (2 mm-20 mm), and the curvature radius \(r\) of the tip (0.1 mm-0.5 mm). The inception voltage and pulse current of corona discharge were extensively studied. From the results obtained, it is concluded that:  
1. Through experimental research, the rationality of the model is verified. Compared with the experimental results, the maximum error of the corona inception voltage under different pressures is less than 0.2%. For comparison of discharge current, it is shown that the discharge current exhibits pulse characteristics: its rise time is about 10 ns and its decay time is about 300-500 ns and the current amplitude is between 2.0 uA and 2.5 uA. In general, the results in the test and simulation are consistent;
2. Based on the hybrid numerical model of fluid-chemical reactions, in the process of positive corona discharge developing from the wire's tip defect to the cathode, the evolution of streamer morphology can be observed intuitively, and the density variation of space particles and the distribution of the electric field can be obtained. There are some representative results: the field strength reaches a peak value (92.1 kV/cm) within 1.2798 ns at the beginning of discharge and the maximum electron density of the streamer head in one discharge is about 1 \(\times\) 10\({}^{20}\) m\({}^{-3}\);
3. The simulation results show that, considering the geometry of conductor tip defects, the corona inception voltage decreases with an increase of tip height and decreases in tip curvature radius and conductor radius and the amplitude of the current pulse increases with increases in wire radius and tip curvature radius and a decrease in tip height. As for air pressure ascending from 600 hPa to 1013 hPa, the corona inception voltage obtained by simulation has an increase of 81.5% and the amplitude of current pulse has a drop of 9.1%.  
Conceptualization, S.W. and J.M.; methodology, S.W. and J.M.; software, J.M. and L.W.; validation, S.W., J.M. and L.W.; formal analysis, J.M.; investigation, J.M.; resources, J.M. and L.W.; data curation, J.M. and L.W.; writing--original draft preparation, J.M. and L.W.; writing--review and editing, S.W., J.M. and L.W.; visualization, J.M.; supervision, J.M.; project administration, S.W. and J.M.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Multi-Hop State-Aware Routing Strategy Based on Traffic Flow Forecasting
### Setup and Update of Node State  
In this section, we proposed a node state awareness mechanism, processed at the enhanced nodes. Each enhanced node takes the ordinary nodes closest to them as a node-set and collects node states. In the proposed MHSA-TFF, each enhanced node keeps a Node State Table (NST). The node states are stored as the style in Table 2.  
It can be seen from Table 2 that the energy and traffic load of ordinary nodes are collected. There are three levels of energy and traffic load, low/medium/high, free/busy/ congested, respectively. The online/offline represents whether the ordinary node returns the ACK packet. The commonly used hello packet strategy is adopted to achieve the link state table [24]. The enhanced node sends hello packets to nodes belonging to its ordinary node-set and receives the reported packet. When the enhanced node receives the message from its ordinary node-set, it updates NST. Algorithm 2 shows the process of the multi-hop state-aware scheme.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Node** & **State** & **Energy** & **Traffic Load** & **Timestamp** \\ \hline \(v_{i}\) & Online & Medium & Free & \(t\) \\ \(v_{j}\) & Online & High & Congested & \(t\) \\ \(v_{k}\) & Offline & Low & Busy & \(t\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Link state table.  
Figure 3: The structure of RNN.  
```
State Updating: while true do  broadcasts hello packets to its ordinary node set  wait \(t_{hello}\) endwhile if receive ACK within \(t_{ack}\)then if(\(t\) is up to date) then  NST(\(v_{l}\)).state \(\leftarrow\) Online else  drop the ACK message endif else  NST(\(v_{l}\)).state \(\leftarrow\) Offline endif
```  
Energy and Traffic Load Updating: Receive reported packet if(\(t\) is up to date) then  continue else  drop the reported packet endif if(NST(\(v_{l}\)).energy is not equal to _energy_) then  NST(\(v_{l}\)).energy \(\leftarrow\) energy else if(NST(\(v_{l}\)).traffic load is not equal to _traffic load_) then  NST(\(v_{l}\)).traffic load \(\leftarrow\) traffic load else  drop the packet endif ```
**Algorithm 2** Multi-hop State-Aware Scheme  
Based on the above information, the enhanced node has the traffic flow and the state of the ordinary node within the multi-hop. This information will be used to make the routing decision.
## 3 Data and Method
### Random Forest Regression Algorithm  
The random forest (RF) algorithm is one of the most widely used machine learning methods, which has the advantages of less input parameters, simple operation, and strong stability. It is an ensemble learning method for classification, regression and other tasks. To achieve these tasks, a multitude of decision trees are randomly generated at the training stage, and each decision tree inside would make a decision about the problem independently by selecting a random set of input data [61]. In the case of classification problems, the result depends on the results of most decision trees. While in the case of regression problems, the result depends on the average value of the result of each decision tree [61; 62]. In this study, we used RF regression function implemented in the \"Random Forest\" package within Matlab R2017b software developed by MathWorks (Natick, MA, USA) to estimate the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Model 1** & **Model 2** & **Model 3** & **Model 4** & **Model 5** & **Model 6** & **Model 7** & **Model 8** \\ \hline  
& & & & \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & & & & & & \\
29 & N\_260 & & & & & & & \\ \hline \hline \end{tabular} Note: N stands for NDVI, and inputs for models 1 to 6 for sunflower start from N_160.  
\end{table}
Table 3: Inputs for the crop estimation models.  
crop yield. Using remote sensing data as inputs, RF regression algorithm has been successfully applied to crop yield estimation in recent years [7; 41; 42].  
The yield estimation in this study is based on pixel-level, and the annual crop yield is estimated from [7]:  
\[Y_{i,j,k}=F(x_{i,j,k}) \tag{4}\]  
where \(Y_{i,j,k}\) is crop yield at pixel \((i,j)\) in the \(k\)th year, \(x\) is the vector of predictors and \(F\) is the predictive function of RF regression algorithm.  
In the RF regression algorithm, the performance of the model could be improved by adjusting three major parameters. The first one was ntree that represents the number of decision trees with the default value of five hundred, the second one was mtry that represents the number of features used at each node with the default value to be \(1/3\) of the total number of the features, and the third one is nodesize that represents the minimum size of the terminal nodes of decision trees with the default value of one [63]. Moreover, some studies have shown that the prediction accuracy of the RF model is acceptable when nodesize takes the default value [44; 62].
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
###### Abstract  
Accurate prediction of the height of ocean waves is critical to ensuring maritime safety, optimizing offshore operations, and mitigating coastal hazards. To improve the accuracy of ocean wave height prediction, we developed a hybrid model that integrates decomposition and deep learning. The approach combines Fourier transform, seasonal and trend decomposition using Loess, and various deep learning models, which can more accurately capture the periodicity, trends, and random fluctuations. The trend, seasonality, and residual components are predicted using the LSTM model, SARIMAX, and 1D-CNN, respectively. The mean square error of the model prediction was calculated to be 0.0087 and the root mean square error was 0.0935. The results show that the hybrid model outperforms the other methods compared in our experiments. This model can accurately predict ocean wave heights and provides a reference for predicting time-series data with seasonal fluctuations.  
deep learning; ocean wave height; data decomposition Article
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 3 Materials and Methods
### Hardware
#### 3.2.2 Sensors  
(1) ZH03A Laser Dust Sensor  
The ZH03A laser dust sensor (Zhengzhou Winsen Electronics Technology Co., Ltd., Zhengzhou, China), with a minimum resolution particle diameter of 1.0 micron, is a versatile, miniaturized module that uses the principle of laser light scattering to detect dust particles present in the air. By designing different channels to distinguish the size of the particles, the PM2.5 and PM10 concentration values can be obtained separately. Besides, ZH03A has good consistency, stability, real-time response, and also provides a rich interface with digital output, PWM (Pulse Width Modulation) output and analog output. The 24 bit data packet is sent to the RPi by UART (Universal Asynchronous Receiver/Transmitter) transmission, and the PM2.5 and PM10 concentrations are obtained by reading the value of the specific bit. The price of ZH03A is around $11.  
(2) SGA-700 Intelligent Gas Sensor  
The SGA-700 series of intelligent gas sensor (Shenzhen Singoan Electronic Technology Co., Ltd., Shenzhen, China) modules carry out signal amplification, data processing, temperature and humidity compensation, and it has the benefits of a smaller size, lower price and more stable performance. SGA-700 can directly output voltage signals such as 0.4-2 V, 0-1.6 V, 0-4 V, 0-5 V, and serial port signals are reserved. The standard signal after processing can be directly collected and uploaded to the control host RPi. The price of the SGA-700 series gas sensor is less than $3. SGA-700-CO, SGA-700-NO2, SGA-700-SO2 and SGA-700-O3 are used to measure CO, NO\({}_{2}\), SO\({}_{2}\) and O\({}_{3}\) concentrations, respectively.  
(3) ESP8266 Wireless Sensor  
The ESP8266 (ESPRESSIF SYSTEMS (SHANGHAI) Co., Ltd., Shanghai, China) is a low-power, low-cost, highly integrated Wi-Fi microchip with a full TCP/IP stack, which adds Wi-Fi capabilities to the RPi via a UART serial connection. When the RPi is networking, it can send the predicted results of each pollutant and the data monitored by the sensor to the cloud.  
At present, the price of the existing AQ monitoring system ranges from $750 to $3000, while the total cost of our proposed solution is only around $75, which has been reduced by nearly one-tenth, and each module is easily accessible.
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 4 Tensor Operations and Decompositions
### PARAFAC and Tucker Decompositions  
The PARAFAC decomposition [13] is also called CANDECOMP (canonical decomposition) by [75] and CP for CANDECOMP/PARAFAC by [76] when applied to decompose a data tensor. In the context of system modeling, it is called a PARAFAC model. It amounts to decomposing a tensor into a sum of \(R\) polyads, i.e., \(R\) rank-one tensors [10]. For an \(N\)th-order tensor \(\mathcal{X}\), each polyad corresponds to the outer product of the \(r\)th columns of \(N\) factor matrices \(\mathbf{A}^{(n)}\in\mathbb{K}^{I_{n}\times R}\), i.e., \(\overset{N}{\alpha}\mathbf{A}_{r}^{(n)}\). When \(R\) is minimal, it is called the tensor rank or canonical rank of \(\mathcal{X}\). PARAFAC is also called a canonical polyadic decomposition (CPD), and concisely written as \(\{\mathbf{A}^{(1)},\cdots,\mathbf{A}^{(N)};R\}\). When \(R=1\), \(\mathcal{X}\in\mathbb{K}^{I_{N}}\) is a rank-one tensor, also called a separable tensor. Then, it can be written as the outer product of \(N\) non-zero vectors \(\mathbf{a}^{(n)}\in\mathbb{K}^{I_{n}}\)  
\[\mathcal{X}=\overset{N}{\underset{n=1}{\circ}}\mathbf{a}^{(n)}\iff x_{ \mathbf{I_{N}}}=\prod_{n=1}^{N}a_{i_{n}}^{(n)}. \tag{8}\]  
In the case of a symmetric rank-one tensor \(\mathcal{X}\in\mathbb{K}^{[N,I]}\), all the vectors \(\mathbf{a}^{(n)}\in\mathbb{K}^{I}\) are identical [77]. In Table 10, we present different ways of writing a PARAFAC decomposition for a third-order and \(N\)th-order tensor: scalar writing, with mode-\(p\) and outer products, and matrix unfoldings as defined in (3). PARAFAC models have the following two main features:  
1. Essential uniqueness, i.e., uniqueness up to trivial indeterminacies corresponding to permutation and scalar ambiguities of the columns of the factor matrices (see [78; 79]);
2. Existence of a simple algorithm, the so-called alternating least-squares (ALS), for estimating the PARAFAC parameters for a tensor of an arbitrary order \(N\). The Tucker decomposition [12] of a tensor \(\mathcal{X}\in\mathbb{K}^{I_{N}}\) can be viewed as a generalization of the PARAFAC decomposition in the sense that such a decomposition allows taking into account all interactions between distinct columns of the factor matrices \(\mathbf{A}^{(n)}\in\mathbb{K}^{I_{n}\times R_{n}}\), when a PARAFAC model only involves interactions between the same columns \(r\in\langle R\rangle\) of the factor matrices \(\mathbf{A}^{(n)}\in\mathbb{K}^{I_{n}\times R}\). In Table 11, we present different ways of writing a Tucker decomposition for a third-order and \(N\)th-order tensor. From the writing with outer products, we can conclude that the Tucker model of a \(N\)th-order tensor consists in a weighted sum of \(\prod_{n=1}^{N}R_{n}\) rank-one tensors, where the coefficients \(g_{r_{1},\cdots,r_{N}}\) of the core tensor \(\mathcal{G}\in\mathbb{K}^{R_{N}}\), define the weights of the interactions between the columns \(\mathbf{A}_{r_{n}}^{(n)}\) of the factor matrices.
## 4 Tailoring Pre-Trained Transformer Models for Detection in Spanish
### Selecting Pre-Trained Models for Detecting Fake News in Spanish
In our approach to text classification in Spanish, our experiments involved both the base and cased versions of BERT and RoBERTa models (identified hereafter as _BERT-base_ and _RoBERTa-base_, respectively). In addition to these, we also included their corresponding multilingual variants (denoted as _BERT-multilingual_ and _RoBERTa-multilingual_, respectively), along with the models specifically pre-trained on Spanish corpora (_BETO_, _RoBERTa-BERTIN_ and _RoBERTa-BNE_). This selection is guided by several critical factors:  
* The inclusion of models trained in English serves as a benchmark, allowing us to quantify their inferior performance compared to the variants adapted specifically for Spanish. This comparative analysis provides valuable insights into the extent of the performance gap between the English-trained models and their Spanish-tailored counterparts. * Base models strike an effective balance between computational resources and their capacity to capture information and represent language, aligning well with the resources available in our Google Colab environment. * In light of the unavailability of _uncased_ RoBERTa models, we have opted to use _cased_ BERT models to ensure a fair comparison. The dataset format has been meticulously inspected to guarantee the correct usage of lowercase and uppercase letters. * The dedicated support for the Spanish language ensures the versatility and efficiency required to attain effective text classification within this specific linguistic context. * Last but not least, all the above models are primarily aimed at being fine-tuned on tasks such as sequence classification, making them useful for our fake news detection purposes.