dataesr/scientific-paragraphs-categorization huggingface dataset:
A. LoRa and LoRaWAN overview LoRa (Long Range) is a physical layer technology working on the ISM bands 433, 868 and 915 MHz  [6]. We assume the use of the band 868 MHz for our analysis in this work. LoRaWAN is an LPWAN technology that provides wireless connection for IoT applications. The offered data rate may vary from 0. 3 to 50 kbps, which is an acceptable rate for the collected data in our healthcare system based on the LoRaWAN protocol  [6]. Also, LoRa is convenient for our system due to its low cost, low power communication (where the duty cycle is limited to 1%), and the preservation of the data privacy. The range that reaches up to 20 km in rural regions and about 2 km in urban region is another reason for choosing LoRa. Note that an increase in the payload also increases the probability of loosing messages due to collisions and interferences  [23]   [24].
B. Physical layer LoRa is the physical layer for LoRaWAN. It is a spread spectrum modulation technique using the Chirp Spread Spectrum (CSS) technology. Each LoRa symbol is composed of 2 SF chirps (2 SF bits per symbol). Where SF is the Spreading Factor which varies between 7 and 12 providing different data rates. Beside the SF, LoRa has two others major configurable physical layer parameters, which are the BandWidth (BW) and the Coding Rate (CR). The SX1276 LoRa module used in this work has three optional bandwidths: 125, 250, and 500 kHz, where the smaller one is mostly required for coverage of longer distance and for the best receiver sensitivity while the higher one is usually used when faster transmission is required  [20]. The CR is used for error correction as it is used in any wireless communication and signal-processing module  [20].
The expression of the coding rate is given in the following equation: CR = 4 4 + n (1) n varies from 1 to 4. The smaller the code, the longer the transmission time. LoRa has a variable data rate depending on the parameters mentioned above. The expression of the LoRa's data rate is given by: R b = SF × BW 2 SF × CR (2) In figure  2 , the physical layer message format is detailed. It begins with a preamble of symbols (typically, eight symbols), used to synchronize the receiver with the transmitter. The header field includes information about CR, SF, etc. Header field also contains a Cyclic Redundancy Check (CRC) field that allows receiver to ignore packets with an invalid header. After the header, LoRaWAN defines a MAC message carried in the physical payload field. The MAC message contains a MAC header which defines if the frame is a data or an acknowledgment, the protocol version and if the direction is an uplink or a downlink.
III. EARLY WARNING SCORE (EWS) EWS is a medical system using scores to determine the medical situation of patients in order to identify patients in need of urgent intervention  [27]  [28]  [29]. Using EWS, we can determine the degree of criticality of a patient's medical situation, called score. After the determination of the score of each parameter, we can calculate the RL using the fuzzy toolbox of Matlab. The score varies between 0, indicating a normal situation, and 3 which indicates a very emergent situation. A high score corresponds to a high risk or critical medical conditions  [30]. In this work, the EWS parameters used are the body temperature, the blood pressure, the heart rate and the oxygen saturation. As shown in detail in table IV, referring to EWS, the normal value of the body temperature is between 36.
In case of any abnormality, the transceiver wakes up to send data to the medical server. This randomly selected sleep duration is flexible and could be adapted according to the doctor's need. The sleep mode duration is inversely proportional to the energy consumption. more this duration increases, more the system economizes energy. However, the medical staff will not be updated by the patient situation. The impact of this duration is subjected to be evaluated. Figure  4  shows the effect of the variation of this value on the lifetime of the system. After taking the decision about sending data or not, basing on the variation of the measured parameters scores, the system goes into sleep mode in both cases. When the sleeping duration ends, the system wakes up and repeats the same process. The time interval between two data collection is based on the RL explained later in this section.
D. Fuzzy Inference System (FIS) In this work, the RL is calculated using the fuzzy toolbox of Matlab  [32]  with four inputs representing the four measured medical parameters and one output representing the RL of the patient. Fuzzy inference is the formulation of mapping from specific inputs to outputs using the artificial intelligence fuzzy logic method for dealing with uncertainty using the approximate reasoning of the human mind. In order to design the fuzzy expert system using the Matlab fuzzy toolbox, the steps to follow are: (1) the fuzzification of the input variables, (2) the rule evaluation, (3) the aggregation of the rule outputs, and finally (4) the defuzzification  [32]   [30]. In the follow, we explain in details each of these four steps: Input Variables: four inputs presenting the values of the patient's body temperature, heart rate, blood pressure and oxygen saturation are used for the designing of the expert system.
In this work, the shape of the membership functions of the fuzzy sets are taken as trapezoidal. Figure  6  shows the implementation of the four inputs based on Mamdani fuzzy inference system using the fuzzy toolbox package of Matlab. We have specified the fuzzy sets for each input variable and Fuzzy Rules: for the fuzzy rule base, 256 rules are used by Matlab to cover all possible cases in our work. This number is obtained according to equation 3 by the multiplication of the number of scores with the number of inputs. K = S 0 × S 1 ×. × S j ( 3 ) Where K is the total number of possible rules for the fuzzy system, S j is the number of scores for each variable and j + 1 is the number of inputs. In our work j is equal to 3. Defuzzification: defuzzification is the inverse process of the fuzzification.
It combines the fuzzy output of all the rules to give one crisp value. We have used the centroid technique, which is the most popular method to accomplish the defuzzification of the data into a crisp output which is the RL. Output Variables: as mentioned before, there is one output variable which is the fuzzy representation of the RL. The sleep time is determined using the RL where the maximum duration between two measurements is 60 min for a normal situation and 1 min for a maximum RL where the patient is in an urgent situation which requires a continuous monitoring. More the RL increases, more the situation becomes more urgent and subsequently the time interval between two measurements must decrease to provide an efficient monitoring. The duration of the sleep time is determined referring to the following equation where t is measured in min: t = 60 - RL 12 × 59 (4) When the sleep duration is over, the system wakes up again, collects data, calculates the score of each parameter to take the decision about sending or not, and finally calculates the RL to take the decision of the sleeping duration.
The data is sent once each six hours even in case where the score is not varied. The number of measurements and transmissions increases with the increasing of the RL. We annulate the energy consumed by the LED of the MAX30102 in our study. Figure  9  shows the impact of the adopted SF on the lifetime of the system. The BW and the CR are fixed at BW=250 kHz and CR=1. And for collisions, we suppose that pcoll=0 for the three figures 9, 10 and 11. SF varies from 7 to 12 Lifetime in months SF Urgent case using HeaLoRa Reference system Normal case using HeaLoRa Fig. 9. Impact of the SF on the system's lifetime with BW =250 kHz and CR=1 according to the specifications of LoRaWAN. The lifetime decreases with the increase of the SF for the studied cases. This refers to the increasing of the time taken to send a packet with the increasing of the SF, which leads to an increase in the consumed energy needed to transmit data.
These constraints were implemented as input data regarding the number of vertebrae of biological snakes (N vb ) and the number of vertebrae of robotic snakes (N vr ), combined with snake swimming kinematics monitored with specifically developed software  [29]  (Software for the Analysis of Anguilliform Swimming (SAAS)). A ratio R v between N vr and N vb was defined. The diameter of the snake robot was scaled to the diameter of the biological snake D s. It enabled the sizing of the diameters of consecutive robot disks. The number of vertebrae, the length of a biological snake vertebra L 1vb , and the angles between the vertebrae α b were combined to adapt the length of the snake robot vertebrae and the number of vertebrae, and to size the mechanical stop of the geometric shape α r , respectively.
Step 2: Sizing the Geometric Shape As each vertebra of a backbone is unique, the angles (lateral side, dorsal, and lateral) of the mechanical stop vertebrae decreased from the neck to the body and increased from the body to the tail (see Figures  4 5 6 ). The neck and tail are not only used for swimming, but also for various other movements (e. g. , coiling) and maneuverability (orientation changes) during day-to-day displacements (e. g. , foraging). We measured the maximum angles of the neck, middle body, and tail of three snakes: two Python regius (terrestrial) and one Helicops angulatus (semi-aquatic snake) using 2D X-rays (see Figure  4 ). The angles and lengths of the vertebrae (see Figure  7 ) were extracted using ImageJ software. In order to obtain the largest swept area, the latter was computed with the measured angles and using a constant curvature assumption  [32].
In this paper, we propose an extension of our previous credibility model by integrating the detection of the topic in the tweet and calculating the topic credibility measure by considering hashtags. To do so, we evaluate and compare different topic detection algorithms, to finally integrate in our framework T-CREo, the one with better results. To evaluate the performance improvement of our extended credibility model and show the impact of hashtags, we performed experiments in the context of fake news detection using the PHEME dataset. Results demonstrate an improvement in our extended credibility model with respect to the original one, with up to 3. 04% F1 score when applying our approach to the whole PHEME dataset and up to 9. 60% F1 score when only considering tweets that contain hashtags from PHEME dataset, demonstrating the impact of hashtags in the topic detection process.
By default, all Twitter accounts are public; thus, anyone can read the tweets published in any account. The connected network of users is built in terms of followed (i. e. , accounts that a user chooses to follow) and followers (i. e. , users that follow an account); a user's "timeline" includes chronological updates of tweets from the users they follow  [7]. Each user has a social influence on his/her network and can make mentions of other tweets or replies of tweets (retweets). The popularity of Twitter is evident  [9] , and its success is partly due to the facility of information access for the masses and cheap cost  [10, 11]. Twitter also uses a very effective and scalable infrastructure to implement a straightforward data delivery paradigm  [12]. However, similar to other social networks, Twitter has been the victim of several hostile attempts.
To obtain the topic credibility measure, we evaluate and compare several topic detection algorithms to integrate the best one into the T-CREo framework  [41]. To evaluate the performance improvement of our extended credibility model and its implementation in T-CREo, we performed experiments in the context of fake news detection using the PHEME dataset. Results show an improvement in the extended credibility model with respect to the original, i. e. , up to 3. 04% F1 score when applying our approach to the whole PHEME dataset and up to 9. 60% F1 score when only considering tweets that contain hashtags. In summary, the main contributions of this work are as follows: (i) The integration, into a previous credibility model  [22] , of the topic detection algorithm that evaluates hashtags; hence, obtaining an extended model of credibility that considers four dimensionstext, user, social, and topic credibility measures.
Related Work The need for a topic detection system, particularly associated with Twitter, is motivated by the amount of information in microblogs and its use to spread information and express opinions. With this massive amount of Twitter data, users can get saturated and miss important topics  [33]. Then, topic detection, as a technique for discovering the main topics automatically, can help in many applications that analyze Twitter. Therefore, in the literature, there exists a huge number of scientific papers focused on topic detection on Twitter. For example, by a simple search of the Scopus database with the keywords topic + detection + Twitter between 2009 to 2022, 1692 related articles are obtained. In this section, we describe only a few of the most relevant studies related to our work.
Yang et al. [3]  designed a crowdsourcing-based credibility framework for Twitter in the context of disasterawareness situations. This framework is able to calculate in real-time the topic-level credibility (i. e. , emergency situations), by analyzing the text, linked URLs, number of retweets, and geographic information extracted from both a tweet's text and external URLs, which are kept in a database. Thus, the credibility of a detected event is increased when multiple sources (the three factors) refer to the same event: tweets, the linked URLs, and retweets referring to the same event. Thus, the tweet credibility score is calculated based on the information contained in its text and URL, and the accumulated credibility score for each event is calculated based on the number of tweets and retweets associated with the same event.
The classifier engine component distinguishes between credible and noncredible content from a user tagged dataset considering extracted features at the tweet-, user-, and hybridlevel. These features include structural aspects of the tweet, such as length, number of tags, mentions, positive and negative words, and URLs and hashtags. The classifier used for this component is the naïve Bayes classifier with a feature rank process. Shao at al. [18]  introduced Hoaxy, a platform for the collection, detection, and analysis of online misinformation and its related fact checking efforts. The platform collects and tracks misinformation and fact checking. The components of this platform consist of a monitor, a database, and different data sources (social networks and news sites). The monitor has a URL tracker for the Twitter API and a set of crawlers for both fake news and fact checking websites.
The extraction of social networks is performed via a stream API and, for the news sites, use an RSS (Rich Site Summary) Parser and Scrapy Spider technologies. Then, the collected data are stored in a database for future analysis. The aim of this work was to characterize the relation between the overall social sharing activity of misinformation and fact checking. Similar to these works, we consider hashtags as an extra factor in the topic analysis. In contrast with all the works described in this section, we propose the use of Hellinger distance for comparing the semantic proximity between the topic associated with a tweet and the topic associated with its hashtags. The topics are obtained using the NMF model, which produced better results than other topic detection algorithms such as K-means, latent semantic indexing (LSI), and latent Dirichlet allocation (LDA).
Topic Detection Methods Ibrahim et al. [33]  presented a survey about tools and approaches for topic detection from Twitter streams. They categorized the topic detection techniques into five categoriesclustering, frequent pattern mining, exemplar-based, matrix factorization, and probabilistic-and evaluated their performance using three Twitter datasets. In terms of precision, the best results were obtained with Soft Frequent Pattern Mining and Bngram, a cluster technique; while considering recall, the best results were obtained with Column Subset Selection, a matrix factorization technique. A good balance between recall and precision was obtained with an exemplar-based topic detection model. Considering this survey, we implement and test several of the categorized algorithms to select one to be integrated into the credibility model.
The Dataset Description The dataset used for this analysis corresponds to data collected by Quezada et al. [57] , a CC BY 4. 0 licensed public access database (https://figshare. com/articles/dataset/tweets_ csv_gz/3465974, accessed on 1 June 2022). The data contain tweets gathered from news headlines from a manually curated list of well-known news media accounts (e. g. , @CNN, @BreakingNews, @BBCNews) on Twitter. The dataset is composed of a total of 43,256,261 tweets distributed across 5234 different events. According to the privacy and tweet availability terms of Twitter (https://developer. twitter. com/en/developer-terms/agreement-and-policy), accessed on 1 June 2022, most available datasets only provide the id of tweets, as it is necessary to use the Twitter API to extract their actual texts.
This API has a limit (https://developer. twitter. com/en/ docs/twitter-api/rate-limits, accessed on 1 June 2022) of 900 tweet requests every 15 min; therefore, we only collected 2,000,000 tweets to create our dataset-this took us almost 1 month, managing to obtain only 86,400 tweets per day, which compose our dataset. The obtained dataset resulted as imbalanced: in some cases, an event has less than 1000 tweets and in other cases an event has more than 100,000 tweets. In order to obtain a balanced dataset, we selected 250 events (which will be used later as topics) with 8000 tweets each. This balanced dataset is a subset from the original dataset and contains 2,000,000 tweets for training and testing purposes. For the event (topic) selection, we have taken into account that each topic to be included in our balanced dataset has to satisfy that at least 8000 tweets belong to the topic and these 8000 tweets also contain 400 tweets with at least 1 hashtag.
• Removing frequent words and stopwords that would not provide specific semantics. These are commonly words that do not carry distinct semantic meaning, e. g. , the, an, and, what. Table  2  shows the result of applying our preprocessing step to five random tweets. After the cleaning task, we have split our dataset into training and testing sets. The proportion was 95% for training, i. e. , 7600 (tweets) × 250 (topics)= 1,900,000 tweets and 5% for tests, i. e. , 400 × 250 = 100,000 tweets.
Processing For the processing step, we train the algorithms for topic detection explained in Section 3: sequential k-means (KMEANS), latent semantic indexing (LSI), non-negative matrix factorization (NMF), and latent Dirichlet allocation (LDA). As the dataset was previously categorized by topic, we have proceeded with a supervised learning approach. The algorithms were executed iterating several times over different configurable variables to obtain the best results considering the evaluation metrics. The hyperparameters used are summarized in Table  3. The rest of the parameters corresponds to default values for each algorithm (https://scikit-learn. org/stable/modules/generated/sklearn. decomposition/, https://scikit-learn. org/stable/modules/generated/sklearn. cluster. KMeans/, accessed on 1 June 2022).
In the case of LDA, if the data size is large, the online update parameter will be much faster than the batch update parameter. The experiments were performed using Python 3. 8. 10 and libraries sklearn 1. 1. 1, on a computer with 16 GB memory, 8 AMD vCPUs, an 80 GB disk, and SFO3-Ubuntu 20. 04 (LTS) ×64.
Quantitative Analysis In the domain of fake news, most of the studies apply machine learning techniques for a binary classification  [66] [67] [68]  (whether it is fake or not). Its evaluations are made by the use of benchmarks that consist of labeled tweets from different topics. One well-known and available dataset is PHEME, proposed by Zubia  [69] , which contains 6424 labeled tweets, grouped by 9 events. Since credibility is a percentage between 0 and 100, we establish a threshold ([0, 100]). When the credibility value is less than the threshold, the tweet is considered as fake. To evaluate our proposal, we calculated the F1 score, based on the precision and recall, defined in Section 4. 4. A variation of step = 5 for the threshold is used in order to evaluate several cases. In the following sections, the results are described by event.
All Tweets from PHEME Dataset Figure  20  shows the precision, recall, and F1 score by thresholds of all tweets from the PHEME dataset. We can observe that our EM has better results than the OM for thresholds 45% until 75% (up to 3. 04% difference for the threshold of 60%). For other thresholds, the F1 score values are similar for models. The best F1 score was obtained with the threshold set at 95% (47. 43%). In Figure  21 , we show the precision, recall, and F1 score by thresholds of all tweets that have at least one hashtag. We can observe that our EM has better results for thresholds from 40% until 75% (up to 9. 60% of difference for threshold 60%). For other thresholds, the F1 score values are similar for both models. The best F1 score was obtained with the threshold set at 95% (56. 41%).
Conclusions and Future Work In this work, we extended a credibility model by adding topic analysis for tweets that have hashtags, since currently on Twitter it is very common to use hashtags to somehow label the tweet with words that are trending or relevant. To do so, we first compared different topic detection algorithms; we evaluated them using precision, recall, and F1 score, and we stayed with the one that gave the best results, which was NMF. We can notice that from the 6424 tweets of PHEME dataset that only 1987 have hashtags. That means the other remaining tweets will keep their credibility probability percentage the same as the original model. It can be seen in the 'Ebola' event that since it did not contain any hashtags, the extended model obtained the same results as the original one.
Moreover, the dataset with the greatest difference between the models is "Gurlitt", due to this being about stolen works from the Gurlitt Collection (arts collected by Cornelius Gurlitt); therefore, most of the tweets talk about art and museums, and the hashtags used for this event are also related to these words; for example: "#Entertainment", "#museum", and "#art". The extended model increases, in this event, up to 4. 80% the average of F1 score values. In general, the improvement of the extended model in the PHEME dataset can be shown. Although it is not much (up to 3. 04% F1 score for the 60% threshold), it is because of the fact that only 30% of the tweets had hashtags; for the case where all tweets have hashtags, the improvement is more significant (up to an F1 score of 9. 60%). Even though the NMF was not trained for PHEME dataset, we obtained high F1 score values for events such as Prince Toronto (97.