dataesr/scientific-paragraphs-categorization huggingface dataset:
• Passives tags: passive tags, unlike active tags, work thanks to the energy provided by the reader. They integrate a dipole antenna that allows it to receive electromagnetic radiation from the reader. This radiation gives the passive tags enough power to authenticate themselves to the reader by transmitting their unique identification code. These passive tags are programmed with unmodifiable data for a capacity of 32 to 128 bits. They provide much lower unit costs than other technologies. In most cases, they are provided blank to the user, who will write the identification information and place them on the object that needs to be traced. This information can be read during the subsequent life of the tag but cannot be modified or completed. Passive tags are cheap and have an unlimited lifetime.
• Step3: the server calculates its temporary keys = R 1 and = R 1. It uses these two keys to extract the value of Z T by the following equation: Auth T -TK S1 -TK S2 = Z T + TK T1 + TK T2 -TK S1 -TK S2 = Z T + r 1 R 2 + r 1 Ps -r 1 R 1 -x S R 1 = Z T + r 1 r 1 P + r 1 x S P -r 1 r 1 P -x S r 1 P = Z T Then, the reader searches for the value of the tag identifier in its database. If found, the reader confirms the validity of the tag and obtains the corresponding private key x T. Then, the server calculates Auth S = x T R 1 + r 2 Z T and transmits (Auth S ) to be authenticated by the tag.
• Step3: after receiving Auth T and R 1 , the server calculates the two keys Tk S1 = (r 2 k x )R1 and Tk S2 = x S k y R 1 and checks if Z T = Auth T -Tk S1 -Tk S2. Then, the server checks if the calculated Z T is in its database. If it was the case, the server obtains the value of x T , calculates the quantity Auth S = x T R 1 + r 2 Z T and send the message {Auth S } to the tag. Else, the server stops the process.
Setup Phase The server generates all the system parameters. It first selects the identity of the tag. Then, it chooses the value Pr R as the secret key of the reader and calculates the point = P as its public key. At the end of this phase, the server stores in the reader database the values {X T , Pr R , Pu R } and in the tag database the values {X T , Pu R }. =. After that, the reader generates a number  and calculates  =   and it sends  and  to the tag.
• Step3: using the two quantities C 1 and C 2 , the reader calculates T 1 = (r 1 ) -1 C 1 and X T = C 2 -h(T 1 , R 1 , C 1 ) and it checks the value of X T in its database. If the value of X T calculated is equal to the one stored, the reader authenticates the tag and then calculates C 3 = Pr R T 1 and C 4 = h(C 3 , X T , T 1 , R 1 ). At the end of this step, the reader sends C 4 to the tag and calculates its key agreement RK ag = X T r 1 T 1.
Setup Phase During this phase, the server sets the public parameters of the curve and produces the public and private keys of the tag and its own. It chooses a random number x S as its private key and calculates = P as its public key. The server chose x T , a point on the curve as the unique tag identifier. Then, the server selects an ID S number, randomly, as a pseudonym of the tag and sets a number K as the secret key shared between the tag and the server. At the end of this phase, the server stores {ID S , x T , K} into its database, and {ID S , x T , P S , K} in the tag memory.
• Step3: as soon as it receives ID S , the server searches this value in its database. If it finds it, the server takes the corresponding secret key K and the point x T from its database and calculates: TK S1 = r 1 KR 2 , TK S2 = x S KR 2 , and Auth S = TK S1 TK S2 x T , and sends the message {Auth S } to the tag. Otherwise, if the value of ID S is not in the database of the server, the corresponding tag is assumed invalid.
• For the tag, the update of K and ID S is done as follows: ID S * = X(Tk T1 ) ⊕ ID S ⊕ K K * = X(TK T2 ) ⊕ 2K ID S = ID S * K = K * • And for the server, the update of K and ID S is as follows: If ID S old is received: ID S new = X(TK S2 )ID S old K K new = X(TK S2 )2K old • If ID S new is received: ID S old = ID S new K old = K new ID S new = X(TK S2 )ID S old K K new = X(TK S2 )2K old 6. 6. Benssalah et al. Protocol Benssalah proposed in his paper  [113]  published in 2020 a new RFID authentication protocol based on ECC. This protocol presents a modification of Dinarvand's protocol at the process level of the authentication phase. In fact, the protocol of Benssalah consists of two phases: the authentication phase and the updating phase. Initially, the server database and the tag database are stored successively by {x T , x S , ID S } and {x T , ID S , P S , P}.
• Step3: once it receives R 3 , R 4 , and ID S , the server uses its secret key x S to compute R * 2 = x S R 3 and x T = R 4 -h({R * 2 } x ||{R 3 } x || r 1 ). Then, based on the pseudonym ID S sent by the tag, the server looks for the value x T in its database to authenticate the tag. After that, the server computes R 5 = h(x T ||{R 2 } x ||r 1 ||R 4 ) and transmits it to the tag.
Setup Phase During this phase, the server and tag save their public and private keys and the public system parameters. First, the server chooses a number x S as its private key and calculates = P as its public key. Second, the tag selects a number x T as its private key and calculates = P as its public key. Finally, the server must store its public and private keys and the identity ID of each tag into its database, and each tag saves its identity and public and private keys.
• Step3: using its private key d r and the random generated number r r , the reader calculates the quantity (d r + r r ). R n = (h r1 , h r2 ) to find the tag secret key K n1 = A 1 ⊕ h r1 and searches it in its data base. If the reader cannot find any correspondence to this key in its database, the protocol stops. Otherwise, it recovers the identity id n of the tag related to this key and the second secret key K n2 and computes H(id n ||K n1 ||K n2 ||R r ||R n ||h r2 ) = (h 1 ||h 2 ). If the calculated h 2 value matches the received h 2 value, the reader authorizes the request and sends h 1 to the tag.
Initialization Phase This phase allows registering the secret data corresponding to the users, the tags, the readers and the medical server (MS). The tag pseudo identity, the server pseudo identity, the reader's public key and the reader's private key, the server's public key, and the server's private key, respectively PID T , PID R , Pu R , Pr R , Pu S , and Pr S are stored in the database of network manager (NM).
• Step3: after receiving the messages, the reader first checks the time spent. If the spent time is less than ∆T, the reader does not stop the session. Subsequently, using its private key, the reader extracts the tag's pseudo identifier PID T i new and search for it in its database. If the reader finds the identity of the tag in its database, the tag is successfully authenticated. Next, it calculates R * t1 = C 1. Pr R. Then, the reader communicates with the medical server (MS). it calculates the message N 1 = r 1. Pu S and initializes PID R new = h(PID R old init) and N 2 = PID R new + h((R r1 ) x ||ID R ||(N 1 ) x T 2 ) , where init is a random number selected by the MS and also inserted in the reader and tag memories during the initialization phase. The message {N 2 , R r1 , T 2 } is sent to the MS, where T 2 corresponds to the new timestamp.
This way, the attacker calculates X T = C 2 -h(T 1 , R 1 , C 1 ) = C 2 -h(C 1 , P, C 1 ) since both quantities C 1 and C 2 have been publicly sent. The attacker can therefore obtain the tag identity and present himself as a legitimate reader. Once the identity of the tag has been successfully extracted, this protocol will be vulnerable to tag impersonation attacks. In fact, during a new authentication session, when the legitimate reader sends R 1 = r 1 P to the tag, the attacker reacts and intercepts this message. Then, the attacker generates a random number t 1 and calculates T 1 = t 1 P, C 1 = t 1 R 1 , and C 2 = X T + h T 1 , R 1 , C 1 and reply to the reader by sending C 1 and C 1. The reader, this time, calculates T 1 and determines the value X T. Then, it compares the value of X T found to the one stored in its database and authenticates the attacker as the legitimate tag.
Consequently, the quantities shared between the tag and the server will be modified at each round and the attacker cannot find the link between the collected power traces and the processed data. This way, the implementation of the DPA and DEMA attacks cannot determine the values of the random numbers used in the encryption of the data shared between the tag and the server during the communication. However, the secret keys that are stored in the tag and server databases always remain sensitive to side-channel attacks, as long as the encryption primitive used is not protected against these attacks. Table  8  summarizes the vulnerability of the different protocols presented to SCA attacks, in both transmitted data and the secrets stored in their databases. For example, in the Liao protocol  [24] , the scalar multiplication algorithm used is the Montgomery ladder, which is assumed to be an effective countermeasure to the simple power analysis (SPA) attack  [122].
Security Requirements Table  9  examines the effectiveness of the proposed protocols in providing security requirements. As we can see from this table, in addition to mutual authentication, the protocol of Alamr ensures confidentiality, anonymity, forward security, and location privacy. However, it cannot ensure scalability or availability which is not desirable in IoT environments. This claim is explained in the paper of Naeem et al. [111]  where the author indicated that the reading scheme proposed by Alamr is dedicated to a single tag, whereas in general cases an RFID reader is supposed to work with hundreds or even thousands of tags. It can be noted that Zhao's protocol cannot ensure all security services. Indeed, it cannot assure data integrity  [125]. It means that the reader cannot detect if there is any modification or falsification of the data received by the tag.
Measurement of murine elastic fiber stiffness by atomic force microscopy. Cross-sections of aortas from WT or ApoE -/-mice fed or not with 1 mM NaCNO were analyzed by AFM (Bioscope Catalyst, Bruker, Billerica, USA, driven by the Nanoscope Analysis 1. 8 software) coupled to a Nikon Eclipse Ti inverted microscope (Nikon, Tokyo, Japan) in order to determine the stiffness of elastic fibers at the molecular level. Frozen 10 µm aorta cross-sections were thawed for 1 h at room temperature before applying a Krebs-Henseleit solution (118 mM NaCl, 4. 7 mM KCl, 1. 2 mM KH 2 PO 4 , 1. 2 mM MgSO 4 , 25 mM NaHCO 3 , 11 mM glucose, 2. 5 mM CaCl 2 , 3 mM ethylenediamine-tetracetic acid (EDTA) pH 7. 4) to the sample and waiting for equilibration at + 37 °C. The prepared samples were put onto the AFM stage and observed with Bright Field illumination in order to locate the spots of interest.
Mice were accustomed to the procedure for 5 consecutive days before measurement and were monitored weekly during the 7 weeks of the study. For blood pressure measurements, the mice were placed in a warm (37 °C) restraint device with an appropriately sized cuff around the tail. In order to minimize stress-induced blood pressure fluctuations in the animals, all measurements were taken after a 5-min adaptation period. All measurements of diastolic and systolic pressures were taken between 9:00 a. m. and 12:00 a. m. , and recordings were averaged from at least 5 consecutive readings using BP-2000 analysis software. Mean arterial pressure was calculated using following formula: [(2 × diastolic) + systolic]/3.
Statistical analysis. Nonlinear regression (second-order polynomial function with a 95% confidence interval) and linear regression were computed using GraphPad Prism 7. 0 software. Non-parametric Spearman's tests were used for the calculation of correlation coefficients. Values were compared using the non-parametric Mann-Whitney's U test. Differences were considered statistically significant for a p value below 0. 05. Values mentioned in the text are expressed as means ± standard deviations.
Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFIS-Dragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) were used to build the models.
, 2016; Premalatha and Naveen, 2018; Zou et al. , 2016). Most of the conventional methods showed poor performance in predicting SR  (Mohanty et al. , 2016). Besides, they are unstable and less reliable in case of missing values in the dataset. The performance of such methods is also found to deteriorate rapidly with time and therefore, unsuitable for long-term predictions. Artificial intelligence (AI) models have been used in recent years for better prediction of SR, considering their ability to simulate complex and nonlinear relationships and ability to handle missing data  (Benmouiza and Cheknane, 2016; Feng et al. , 2020; Kisi et al. , 2019; Ghimire et al. , 2019; Hai et al. , 2020; Quej et al. 2017; Üstün et al. 2020). Several AI models have been introduced for SR prediction including artificial neural network (ANN), regression tree, genetic programming, support vector regression, data mining, and fuzzy logic (FL)  (Voyant et al.
, 2019). The results illustrated that ANFIS provides more accurate result compared to other predictive models. A comparative analysis of different AI models in SR prediction revealed ANFIS is the most suitable for SR simulation due to its ability to capture the uncertainty associated with time series data  (Mohammadi et al. , 2016). However, the major problem of this model is the tuning of ANFIS hyperparameters such as, the optimization of membership function parameters  (Castillo and Melin, 2012). Therefore, the traditional ANFIS model was hybridized with different optimization algorithms in previous studies for improving its performance. Though the performance of the existing hybrid ANFIS model is encouraging, the prediction capability is still needed to be enhanced considering the importance of the accuracy needed in SR measurement.
Model development The main goal of this study is to evaluate the efficacy of the proposed ANFIS-muSG approach in the prediction of daily SR. The model was developed using MATLAB 2014b software in a computer with an Intel Core i5 and 4 GB of RAM. The training phase of ANFIS-muSG starts by producing a population x randomly; where each x i, contains one solution (i = 1, 2,. , N). The solutions are updated using both GOA and the improved SSA based on a probability (pl) which is defined as, pl i = f i ∑ n i=1 f i (18) where, f i is the current fitness value (which can be calculated using Eq. (  7 )). If pl i < rand(), the GOA algorithm is used, else the improved SSA is applied. The muSG helps GOA to overcome the drawbacks of the classic version of GOA, for instance, the premature convergence, getting trap in a local minimum, and the high computation time.
The current solution is tested using fitness function (Eq. (  17 )) to determine the quality of the current solution. These steps are iterated until the maximum iteration limit is reached. The best parameters are used to improve the original ANFIS model be to applied on test data. The performance of the model for the testing data is evaluated using a set of measures as shown in Section 3. 6. It is worth to mention, data were normalized into a scale between (0-1) and used for modeling SR in the present study to remove the influence of individual variables.
Even though, the median and interquartile range (IQR) of the ANFIS-muSG model were found nearest to the observed median and IQR. Similar results were observed at other locations. The median, IQR, and spread of observed SR data were found to simulate more accurately by ANFIS-muSG model compared to other models. The quantitative and visualized information of the performance of all predictive models establishes the superiority of the ANFIS-muSG model over other comparable models in reducing the prediction error. The incorporation of two novel algorithms plays an important role in efficiently optimizing the ANFIS parameters and thereby increasing the accuracy of SR prediction. The adopted predictive model (ANFIS-muSG) in this study exhibited high efficiency in handling intrigue systems such as SR which has several complex characteristics, including, uncertainty, noise, and limited and insufficient information of data.
The models developed in this study may be used for the prediction of SR from temperatures (maximum, mean, and minimum) only which are easily available in any region. Reliable prediction of SR only from temperature parameter indicates the efficacy of the ANFIS-muSG model. Such less resource-demanding models are highly important for developing countries where meteorological data except rainfall and temperature are not easily available. Therefore, the ANFIS-muSG model developed in this study can be employed for energy harvesting and monitoring in a wide range of geographical regions. However, the performance improvement of the model using other meteorological variables including wind-speed, cloud cover, sunshine, humidity, and rainfall can be evaluated in future studies. Besides, satellite remote sensing information can be incorporated as input to improve model performance in the prediction of SR  (Deo et al.
, 2019; Ghimire et al. , 2019). As process-based models are extensively resource-demanding and cost-prohibitive, the hybrid ANFIS-muSG model developed in this study may be a potential solution. The performance of the hybridized ANFIS-muSG model could be further improved through an ensemble approach. Besides, other advanced optimization techniques including Quantum-Behaved PSO and the Firefly Algorithm could be utilized to select input predictors that have been found effective in model input selection  (Salih et al. , 2019; Taormina and Chau, 2015). Besides, empirical wavelet transform  (Gilles, 2013)  and empirical mode composition  (Huang et al. , 1998)  might be investigated as additional approaches for data analysis.
This helped to improve the performance of ANFIS-muSG compared to other hybrid ANFIS models. • The ANFIS-muSG showed a prediction enhancement compared to the classical ANFIS model by 42. 2%, 32. 6, 54. 8%, 25. 7%, and 49. 0% in terms of RMSE at Baker, Beach, Cando, Crary and Fingal stations, respectively. • Although the proposed algorithm had successfully incorporated with ANFIS approach and reported desirable accuracy in several cases, it provided slightly higher error in some cases. Many reasons may explain this phenomenon, including high noise in the dataset as well as properties of uncertainty and stochastic. Furthermore, the exogenous parameters such as wind speed, cloud cover, sunshine, humidity, and rainfall affect the accuracy of the predictive model. However, the outcomes of the ANFIS-muSG model were very convincing compared to the results obtained in previous studies regarding the prediction of SR.
Open Access *Correspondence: laurent. servais@paediatrics. ox. ac. uk 1 Centre de Référence des Maladies Neuromusculaires, Centre Hospitalier Régional de la Citadelle, Boulevard du 12eme de Ligne 1, 4000 Liège, Belgium Full list of author information is available at the end of the article over a certain period of time, meant that pivotal trials required over 100 patients per group and trial durations of 18 months and 2 years (NCT02851797 and NCT02500381, respectively). To accelerate clinical development and investigate in parallel several approaches without being limited by the number of patients available, it is crucial to validate more powerful outcome measures. This is the case for DMD and for numerous other rare diseases, within or outside the neuromuscular field. The last decade has seen an increase in the availability of wearable technology for continuous monitoring of health and wellbeing  [11] ; for example, wearable devices that can be used to assess ambulation range from crude step counters to sophisticated multisensory systems  [12].
From the capture of any single movement, several outcomes may be extracted. In ambulant patients, the identification and quantification of every individual stride allows for the calculation of the distribution of stride length and stride speed as well as for the analyses of different centiles such as the 95th centile stride velocity (SV95C). These variables are measured in a homebased environment over a 180-h period and are reliable and highly sensitive to changes in ambulant patients with DMD  [16]. Recently, the EMA qualified SV95C as a valid secondary endpoint in clinical trials on ambulant patients with DMD  [17]. Additional data are needed to qualify the measure as a primary endpoint  [16]. Wearable technology is or has been used in clinical trials of therapies for spinal muscular atrophy  [18, 19] , facioscapulohumeral muscular dystrophy (NCT02579239, NCT04004000), limb girdle muscular dystrophy type 2E (NCT02579239), centronuclear myopathy (NCT02057705), and Angelman syndrome (NCT04259281).
Demographic data as well as medical and surgical histories were obtained during the baseline visit. A full physical examination, including weight, height, and vital signs, was performed at the first visit and at the 12-month visit. The subjects performed the 6MWT while wearing the sensors at baseline and at 12 months. They also performed the 4SC, rise from floor (RFF), and 10-m walk test (10MW). Assessment of dynamometric grip and pinch strength were evaluated with the Myogrio and Myopinch, as previously described  [22]. All tests were conducted by trained and certified physiotherapists from the neuromuscular centre.
Statistical analysis For analysis, data were excluded from subjects who recorded less than 50 h; 9 subjects at baseline and 18 subjects at follow-up, respectively. Analysis was first performed on the whole sample and then we analysed children (5 to 17 years) and adults (18 years and older) separately. Then within these groups, we analysed male and female subgroups. We used a series of Mann-Whitney U tests to compare subgroups. We then performed correlation analyses using Spearman's rank correlation coefficient based on accelerometer measures, time test performances, age, and height. Finally, we use the Wilcoxon test to assess the evolution of each measure after 12 months. For compliance analysis, we used the 50-h and 180-h thresholds as these two thresholds were defined in the EMA qualification document as acceptable and optimal, respectively (16).
Population We included 91 healthy individuals (41 males (45. 1%) and 50 females (54. 9%) aged between 6 and 85 years, with a mean age of 16 years 2 months. Seven participants withdrew between the baseline and the follow-up visit for personal reasons. A flow chart of subject participation is shown in Fig. 1. The demographic and clinical characteristics of participants whose baseline data were analysed, as well as ActiMyo ® variables, are provided in Table  1. Statistical analysis was performed on data from the 82 participants who achieved more than 50 h of recording at baseline. For further analysis, healthy subjects were distributed into two age subgroups: those 5 to 17 years of age and those aged 18 years and older. The first subgroup was composed of 65 participants, 34 of whom were females (52.