esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Electrical Resistivity Tomography  
The ERT profile was acquired at the northernmost end (Figure 2), just where the dolomite aquifer is too shallow to be discerned in the seismic profile (Figure 3A). Figure 3B shows the ERT profile, which reaches a depth of 220 m in the maximum penetration zone. From geoelectrical records, three resistivity ranges are differentiated. The first is characterized by high resistivity values in the 100-500 \(\Omega\)m range and corresponds to the Jurassic dolomite aquifer. These geoelectrical facies outcrops at the northern profile end and dip gently towards the south-southeast. The ERT results trace this facies up to 500 m where it suddenly ends, and this could be explained by the presence of a normal fault. In the local outcrops, normal faults affecting the Jurassic dolomites have been detected. In the same ERT profile, normal faults affecting this unit can be deduced. Field observations and geological maps (Figure 2) corroborate these interpretations. This Jurassic aquifer crops out in the northern sector and it is also found under the Miocene strata in the southern sector.  
Over the dolomite aquifer, the ERT profile also shows a discontinuous unit of variable thickness characterized by resistivity values in the 40-80 \(\Omega\)m range. This electrical behavior of this second unit may correspond to Early Tortonian sandy and gravel-rich sediments with dolostone fragments.  
Figure 3: Seismic reflection profile and interpretation (see location in Figure 2). The positions of the pumping boreholes and the TDEM (S1 to S10) measuring stations are indicated. M.a.s.l. (metres above sea level) (**A**). Electrical resistivity tomography (ERT) profile (**B**).  
The presence of pre-Late Tortonian faults determines the accommodation space for these syntectonic facies (Figure 2), as interpreted in previous studies [14]. In the northernmost sector, some of these facies having similar resistivity values could also correspond to weathered dolomites, conferring some uncertainty in the interpretation.  
A third unit characterized by resistivity values in the 5-20 \(\Omega\)m range can be identified at shallow depth from 400 m onwards. Based on its electrical behavior, this unit is interpreted to be Tortonian marks and marly limestones, which have great thickness in the southern sector. As can be deduced from the geological map (Figure 2), the Messinian facies are absent in the ERT profile.  
The resistivity of the different lithologies is related to different physical parameters of the geological materials, such as texture, pore fluids, density, etc. [21]. As deduced, if knowledge of the subsoil is scarce, results are more uncertainty. In contrast, if the information available is abundant, these techniques can have multiple applications, for instance to identify lateral changes of facies, thickness variations of stratigraphic units, water-table positions, or degree of saline contamination of an aquifer.
###### Abstract  
Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications, such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques that can convert words into precise vectors that represent the input text. There are two categories of text representation techniques: lexicon-based techniques and machine learning-based techniques. From research, both techniques have limitations. For instance, pre-trained word embeddings, such as Word2 Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N-grams, BERT, and CNN. In the model, sentiment lexicon, N-grams, and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on three public datasets, namely, Amazon products' reviews, Imbd movies' reviews, and Yelp restaurants' reviews datasets. Accuracy, precision, and F-measure are used as the model performance metrics. The experimental results indicate that the proposed LeBERT model outperforms the existing state-of-the-art models, with a F-measure score of 88.73% in binary sentiment classification.  
natural language processing; word embeddings; BERT; sentiment analysis; convolutional neural network; sentiment lexicon +
Footnote †: journal: Applied  
02023131445
# Monitoring Perishable Commodities Using Cellular IoT:
###### Abstract  
Perishable product losses can occur throughout postharvest handling. Proper monitoring of key environmental conditions during this period is essential for predicting quality losses throughout their shelf life. This paper presents the design and testing of a portable and compact datalogger for the real-time monitoring of environmental conditions throughout the food supply chain. The device developed incorporates high-precision sensors to measure temperature, relative humidity, CO\({}_{2}\) concentration, luminosity and vibrations, as well as wireless communication capabilities for data transmission, simplifying real-time monitoring over existing multi-component systems while keeping costs affordable. Strategies to optimize power consumption allow a month of battery life, being able to cover entire periods of transport and storage, according to the results of the autonomy test performed on the device. The datalogger uses NB-IoT and relies on other wireless communication protocols if not available to send sensor data to a cloud platform. Comparative testing with commercial dataloggers has been carried out to verify correct device measurements, and field testing has validated successful real-time data transmission along an entire refrigerated transport route. The functionality and autonomy of the proposed device meet the needs of live remote monitoring to help reduce food losses.  
IoT tracker; real-time monitoring; shelf-life prediction; food waste reduction; cellular LPWAN +
Footnote †: journal: Applied sciences  
Article Monitoring Perishable Commodities Using Cellular IoT:  
An Intelligent Real-Time Conditions Tracker Design
## 1 Introduction
### Methodology of UAV-Based Laser Scanning
Both position and orientation errors systematically propagate to errors in the point cloud [12]. Further errors are related to the system calibration with lever arms and the bore-sight angles already described above. These errors influence the point cloud uncertainty also in a systematic manner. In addition, the measurements of the laser scanner may contain errors due to atmospheric refraction, object characteristics, and the range measurement error itself. The error due to object characteristics is given due to different materials of the objects, the reflectivity or even the scan geometry. The remaining errors are caused by the time synchronization and the \begin{table}
\begin{tabular}{c c} \hline \hline  & **Errors** \\ \hline Trajectory estimation & Errors in position and orientation-sensor platform \\  & error in lever arm (GNSS antenna and IMU) \\ System calibration & error in lever arm (laser scanner and IMU) \\  & bore-sight angle error between IMU body and laser scanner \\  & range measurement error \\ Laser scanner & object characteristics \\  & atmospheric refraction \\  & time synchronisation \\ Miscellaneous errors & sensor mounting rigidity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Major error sources for UAV-based laser scanning systems (refer to [7]). rigidity of the sensor mounting. To make a qualitative specification of the UAV-based laser scanning error budget and quantify it more concretely, the different sensors of the system used must be considered, which will be done in Section 2.
###### Abstract  
The aim of this study was to assess the effects of three warm-up durations on post-exercise oxidative stress biomarkers, as well as recovery from the Wingate test in 14 amateur handball players. These players completed the Wingate test three times: after 5 min, after 10 min, and then after 15 min of warm-up, spread over 2 weeks with an interval of 1 week between each session. At the end of each session of the Wingate test, blood samples were taken: at rest (PR), after warming up (PWU) and immediately at the end of the test (P0), then after 10-min (P10) and after 20-min (P20) of recovery. The measured parameters are the total antioxidant status (TAS), superoxide dismutase (SOD), glutathione reductase (GR), glutathione peroxidase (GPx), malondialdehydedehyde (MDA), protein-bound-carbonyls (PCs) and thiobarbituric acid reactive substances (TBARSs). The main findings revealed that anaerobic exercise induces oxidative stress, as evidenced by changes in antioxidant status and significant increases in concentrations of the majority of oxidative stress indicators (\(p<0.05\)). At P20, plasma GPx, SOD, GR, TBARSs, and PC contents, are lower and significantly different after a 15-min warm-up than after a 10-min or 5-min warm-up. ANOVA showed a significant \"warm-up \(\times\) recovery\" effect on plasma GPx, SOD, GR, TBARSs, and PC contents [F(8.104) = 3.61; \(p<0.001;\)\(\eta^{2}\)p = 0.22, F(8.104): from 1.88 to \(19.97;\)\(\eta^{2}\)p: from 0.19 to 0.61: \(0.05<p<0.001\)], respectively. We concluded that a 15-min warm-up was more efficient than the other duration in reducing a free radical rise, emphasizing the importance of the warm-up length on plasma oxidative stress biomarkers.  
warm-up; oxidative stress; anaerobic exercise; handball players Article
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 4 Experiments
### Datasets
#### 4.3.1 Natural Language Inference (NLI) and Stance Detection (SD)  
NLI and SD are both subtasks of NLP that deal with understanding the relationships between two sentences associated with a label. However, the NLI task involves  
Figure 1: Overview of text preprocessing, tokenization stages, and BERT model pretraining steps.  
determining the relationship between two given sentences. The first sentence is typically called the \"premise\", and the second sentence is known as the \"hypothesis\". The model's objective is to classify whether the hypothesis is true (entailment), false (contradiction), or undetermined (neutral) based on the information in the premise. On the other hand, SD involves identifying the author's stance in a text concerning a specific target. The typical stances identified are 'favor', 'against', and 'neutral'. For this task group, we employed five datasets. Four consist of Modern Standard Arabic (MSA) text and one dialectal Arabic (DA) dataset, called Mawqif. Descriptions of employed five datasets are provided below:  
* Cross-lingual Natural Language Inference (XNLI): XNLI is a crowdsourced dataset designed to evaluate crosslingual representations [51]. It consists of 393 k sentence pairs, with 2490 in the validation set and 5010 in the test set. Each pair was annotated with one of three labels: entailment, contradiction, or neutral. The dataset has been machine-translated into 14 languages, including Arabic.
* The Multi-Genre Natural Language Inference (MultiNLI): Similarly, MultiNLI is also a crowdsourced dataset designed for crosslingual representations tasks [52]. It consists of 84 k sentence pairs with 21 k in the validation set. The dataset has been machine-translated into 15 languages, including Arabic.
* Arabic Stance Detection dataset (AraStance):AraStance is a collection of Arabic news websites annotated with three fact-checking stances [53]. The dataset consists of 4063 articles covering claims from different domains such as health, politics, and sports.
* Arabic News Stance Corpus (ANS-Stance): ANS-Stance is a collection that includes Arabic news titles, paraphrased titles, and corrupted titles designed for news claim verification and stance prediction [54]. It consists of 3786 titles, with each associated with pairs of claims and evidence.
* A Multi-label Arabic Dataset for Target-specific Stance Detection (Mawqif): Mawqif is a multilabel Arabic dataset that was crawled from Twitter and manually annotated for four different NLP tasks: stance detection, sarcasm detection, sentiment analysis, and text classification [55]. It consists of 4121 tweets in multidialectal Arabic, thus covering three topics: digital transformation, the COVID-19 vaccine, and women's empowerment.
# Improved ECG Watermarking Technique Using Curvelet Transform
###### Abstract  
Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient's data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient's data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance--even when the hidden messages were large size.  
ECG; steganography; curvelet transform; clustering; performance metric +
Footnote †: journal:  
Article
# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 3 Background Information
### EXplainable Artificial Intelligence (XAI)
#### 3.3.2 LIME Explanation Method  
LIME, an acronym for \"Local Interpretable Model-agnostic Explanations\", is a technique intended to clarify the connection between the outputs of a pre-trained model and its input parameters, whatever the model's underlying structure may be. LIME uses a subset of the original data centered on the instance of interest to train a more simple, interpretable model, usually a linear model, which approximates the behavior of complicated models close to specific data points [33]. This subset is generated by adjusting the instance's attributes while keeping the label unchanged. Examining the simpler model's performance with these altered samples might give insights into the behavior of the original model.  
The mathematical representation of LIME's explanations for a certain observation \(x\) is shown in Equation (5):  
\[\mathcal{O}(\mathrm{x})=\mathrm{argmin}_{\mathrm{g}\in\mathrm{G}}\mathrm{L}( \mathrm{f},\mathrm{g},\pi_{\mathrm{x}})+\Omega(\mathrm{g}) \tag{5}\]  
In this Equation, G signifies the collection of interpretable models, whereas \(\mathrm{g}\in\mathrm{G}\) specifies the explanatory model. The function f translates from R\({}^{\mathrm{d}}\) to R, and the closeness estimate between an instance z and x is denoted by \(\pi_{\mathrm{x}}(\mathrm{z})\). The complexity of g is represented by \(\Omega(\mathrm{g})\). LIME emphasizes its model-agnostic feature by minimizing the loss function L without taking on any particular form for f. The magnitude of L signifies the accuracy of the estimation, indicating the extent to which g represents f inside the domain specified by \(\mathcal{O}(\mathrm{x})\).  
The LIME procedure usually involves the following steps: (1) identify the precise data points requiring understanding for the algorithm's predictions; (2) generate novel data points by independently affecting the attributes of the chosen instance while preserving the label constant; (3) employ the complex black-box model to predict outcomes for each altered data point and document the appropriate input features; (4) train an interpretable model, such as linear regression or decision tree, utilizing the input variables as inputs and target values; and (5) analyze the coefficients from the interpretable model to ascertain which features substantially impact the prediction for the specified instance.  
The main advantage of LIME is its model-agnostic characteristic, which enables it to interpret many classifiers, such as decision trees and neural networks. However, it is essential to recognize that LIME provides localized explanations for specific predictions and may only partly reflect the overall behavior of the model.
## 6 Discussion  
In this study, we presented a DPSO algorithm and a hybrid DPSO with GA operators' algorithm for solving the MCMRS problem in a DSN and compared their performance in terms of solution optimality and system efficiency. Directional sensors are increasingly used in traditional WSNs for target monitoring and tracking. Therefore, determining an effective schedule of active sensors to maximize target coverage rates is a crucial research topic. This  
Figure 7: Effect of the sensing radius on the target coverage rate, active sensor rate, and fitness values under greedy, GA, DPSO, and DPSO_GA algorithms. (**a**) Target coverage rate, (**b**) active sensor rate, (**c**) fitness values, and (**d**) fitness values versus the number of generations for the GA, DPSO, and DPSO_GA algorithms.  
paper provides effective algorithms to obtain high coverage and quality of solutions and contributes to the work of research topics related to the target coverage problem.  
Although the proposed metaheuristic algorithms can effectively resolve the target coverage problem, this work has some limitations. (1) All targets were assumed to be static to reduce the complexity of the problem. (2) The coverage requirements of all targets were assumed to be identical; priority of target coverage was not considered. (3) All directional sensors were assumed to have identical parameters. (4) The proposed metaheuristic schemes have disadvantages, such as the premature convergence of the DPSO algorithm, and no guarantee of achieving the optimal solution.  
Despite these limitations, the simulation results reveal that these algorithms outperform a conventional heuristic algorithm for solving complex optimization problems, such as the MCMRS problem in a DSN.  
Our simulation results also revealed that the hybrid DPSO_GA algorithm outperformed both GA and DPSO algorithms in all of the evaluation metrics, especially for solving the largest and most complex problems. Thus, the hybrid algorithm effectively achieved the advantages of both the GA and DPSO algorithms; combining these algorithms resulted in a new metaheuristic algorithm with practical utility and high performance. Inspired by the satisfactory achievements obtained by the hybrid algorithm, it is worthy of discussing the applicability of the proposed mechanism to related nature-inspired and evolutionary algorithms to solve MCMRS problem. Finally, additional factors affecting the energy consumption of active sensors must be considered in practical applications, such as the energy required to rotate a sensor.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 4 Analysis Model  
Intuitively, the Msg3 PR will have a positive effect on the probability of RA success because it helps the eNB capture the Msg3, even in a collision situation. However, it is necessary to analyze how the proposed scheme is affected by the network parameters and to analyze negative aspects of the scheme, such as the increased energy consumption of the devices. In this section, we propose an RA procedure analysis model that considers the capture effect in the Msg3 reception. For mathematical tractability, we assume that network is in a steady-state (in [4,10,14], a heavily congested network reaches a steady-state. In other words, the number of arrivals of retransmitted devices is much greater than the number of new arrivals. When the network is in a steady state, the network states (ex. the number of RA arrivals, the distribution of the trial numbers in the network, the number of RA successes at each RA procedure, etc.) are merely constant). We provide an analytic model for three performance metrics: RA throughput, connection failure probability, and average energy consumption. The RA throughput and connection failure probability demonstrate the positive effect of the proposed Msg3 PR scheme, while the average energy consumption is a potential negative effect of the proposed scheme. Table 1 summarizes the notations used in this analysis.
# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 3 Results and Discussion
### Experiments  
In order to find the features that allow one to obtain better performance, the different classification algorithms were tested and evaluated in different subsets of features. The subset that obtains the best results contains the features referring to the average, maximum, and minimum of the general thermogram of the foot and the respective five clusters. The CTI index is also a relevant resource, so 19 features will be used as inputs to train the different classification algorithms.  
To assess the generalizability and validation of the both models, the \(k\)-fold cross-validation technique was adopted. In this technique, the dataset \(D\) is randomly divided into \(k\) subsets, \(D_{1}\); \(D_{2}\); \(\ldots\) ; \(D_{k}\), of approximately equal size. The model is trained and tested \(k\) times and, for each time \(I\) = 1,2,\(\cdots\), \(k\), it is trained in all subsets, except \(Di\), which is used for testing. In this work, the 10-fold cross-validation technique is used to divide the 80% of the whole sample into 10 equal-sized subsamples. While preparing the folds, the percentage of samples for each class was maintained in every fold, named the _stratified-fold_ technique. This way, the model gets equally distributed data for training and test folds.  
After finding the best classifier algorithm for each one of the two models presented in Section 2.4 and in order to be able to compare them, the remaining 20% of the dataset not used to train/validate the models was used for testing.