dataesr/scientific-paragraphs-categorization huggingface dataset:
Radiosity light transport algorithm The objective is to calculate the hourly illuminances on each sensor during a whole year, and to deduce the performance metrics. Instead of using the widely used methods of ray tracing, implemented in the Radiance calculation engine  (Ward, 1994)  and the majority of commercial software  (Ayoub, 2020) , we chose to develop a program based on the radiosity method. Ray tracing and radiosity are the two main families of radiation simulation methods and each one offers different possibilities. In the ray tracing approach, a high number of rays are traced from each sensor to find the path leading to the light sources directly or after reflections. From these paths, we obtain the impact of the light sources on the illuminance of the sensors or, more specifically, the daylight coefficients in the case of an annual calculation of daylighting  (Tregenza and Waters, 1983; Mardaljevic, 1999; Reinhart and Walkenhorst, 2001).
With this number, the error of the DA100 and Eavg falls below 1% in the ground floor room (Figure  5 ). The total computing time ranges from 1 minute, in a light well without mirrors, to 3 minutes in the extreme case with all mirror walls. The hardware is a laptop equipped with an Intel i7 CPU. Since the solution time of the radiosity system under MATLAB © takes only 5 seconds, the calculation of the extended view factors is the most time-consuming part. Percent Error [%] = (X, n refl. -X, ∞ refl. ) / X, ∞ refl. × 100 (5)   In the light well with dark walls, there is a significant disparity between the different floors.
More specifically, in this local setting (namely Local Differential Privacy -LDP), each user perturbs its data before sending it to the untrusted server. LDP has been widely applied and accepted in the process of data collection. Google applies the RAPPOR mechanism  [9]  to collect web browsing behavior and user's settings in Chrome. Apple  [10]  applies LDP to collect population statistics aiming to find commonly used emojis and new words. To other application domains,  [11]  applied LDP for collecting indoor position data; authors in  [12]  proposed a variant of LDP suitable for metric spaces (e. g. , location data); and  [13]  proposed a protocol for finding frequent items in the set-valued LDP setting. After receiving the noisy data from LDP, the server can compute population statistics on the sanitized dataset.
These processed data are then used for learning and prediction purposes: a multi-prediction task of the number of interventions per region, with both raw and anonymized data, is then proposed. Considered approaches encompass the use of long short-term memory for the total number of interventions  [2] ; a multilayer perceptron for the total number of interventions again  [3] ; and finally the use of XGBoost over 3h time slot, one model per two important regions, and models per motive  [8]. In Figure  1 , a flowchart summarizes the approach proposed and implemented in this paper. First, the algorithm takes as input the raw database (presented in Section 2) and the parameter of DP (where its theoretical background is explained in Section 3). Secondly, the LDP-based mechanism is applied to anonymize each data point (location of an intervention), which is introduced as a methodology for a privacy-preserving collection of data in Section 4.
Thirdly, an intuitive Statistical-Based approach is used to estimate statistics and build a synthetic dataset (non-interactive approach in DP), which is detailed in Section 6. Both the second and third steps are based on the RAPPOR mechanism presented in  [9]. Finally, using an anonymized version of the dataset (synthetic), the XGBoost technique is trained and tested for the specific task of predicting the number of interventions per region (presented in Section 7). This article ends with a conclusion section, in which the contribution is summarized and intended future work is outlined.
Data Presentation The database at our disposal was provided by the fire and rescue department, SDIS 25, in the region of Doubs-France. This file has information about 382,046 interventions attended by the fire brigade from 2006 to 2018 inside their department. Each intervention is recorded in a file as a line and the main attributes of this file are shown in the Table  1  with artificial information and described as follows: • ID is the intervention identifier, which is used in supplementary files;   • Station is the fire station name who attended the intervention; • Town is the name of the municipality where the operation took place; • Location gives the precise location (latitude, longitude) of the intervention. Moreover, Table  2.
Theoretical Background on (Local) Differential Privacy Let A be an algorithm used to publish aggregate information of a private database. Differential privacy (DP)  [14]  is as a constraint (property) on A which limits the disclosure of private information of records whose information is in the database. Roughly, A is differentially private if an observer seeing its output cannot tell if a particular individual's information was used in the computation. Let be a positive real number which intuitively corresponds to the leakage level. The higher the value of this variable, the more important is the information leakage. Let im (A) denotes the image of A, i. e. , the set of all possible outcomes by A. The algorithm A is said to provide -differential privacy if, for all datasets D 1 and D 2 that differ on the data of one person, and for all subsets R of im (A), we have Pr[A(D 1 ) ∈ R] ≤ e × Pr[A(D 2 ) ∈ R].
(1) Intuitively, given Pr[A(D 2 ) ∈ R] (the probability that a D 2 data set can be anonymized into an element of R) and given the amount of leakage. This equation gives an upper bound of the probability that a data set D 1 can be anonymized into an element of R, which is thus an information leakage. Differential privacy allows composability (of independent mechanisms who are 1 ,. , n DP. ), robustness to post-processing (F (A) is n DP for any function F ). However, this approach requires the whole dataset to be complete, stored in a safe manner and further anonymized. Anonymisation is not done before. It is the objective of the local differential privacy introduced in  [15]. In this approach, data are sanitized by the user in a probabilistic manner before sending them to the collector. A simple example is to ask a person to answer the question "Do you live in Belfort?", according to the following procedure: Throw a coin.
Privacy-Preserving Firemen Interventions' Location Data Collection (User side) The first question one can ask is if an intervention is a sensitive attribute. The answer is certainly yes because the fire brigade would not have been called if the situation had not been severe enough. For example, consider the scenario 230 where a person who habits in a small town has acquired a very particular disease. If it is known that for this period one intervention happened in this town where normally it seldom does, there is a high probability that the fire brigade intervened for this person. Therefore, the purpose of this task is to implement a privacy-preserving mechanism to firefighters' intervention's location using the concept of local differential privacy previously described. Next, given a specific period, the challenge is to estimate the number of firemen interventions within the considered locations using the anonymized data to build a synthetic dataset.
Generating a synthetic dataset (Server side) Considering a specific study period, the objective is to estimate the number of interventions per location associated with the i-th region, r i. In this context, a synthetic dataset can be built with this estimation, which is considered as a non-interactive case of DP. More specifically, this dataset is generated by statistics using only anonymous location data and it is released just one time for all other intended tasks. Hence, within a specific time, let set(U ) be a set of permanent randomized responses and set(B) be the corresponding set of original location bit arrays. Therefore, the estimated number of interventions N Bint est per region location r i for i ∈ [1, n] is acquired by a Statistical-Based (SB) approach as follows  [9] : N Bint est (r i ) = 1 1 -f • N i - f • N total 2 (8) where N total is the number of permanent randomized responses |set(U )| and N i is the total number of permanent randomized responses whose i-th bit is set to 1.
Anonymization Experiments To evaluate the proposed approach of anonymizing firemen intervention's location, several simulations are performed with different values of f , which determines the level of ∞ -differential privacy. In the experiments, f will vary in [0. 1, 0. 2,. , 0. 8, 0. 9], which guarantees ∞ -differential privacy between These experiments will allow evaluating the relationship between ER versus data size (period of analysis) according to ∞ in order to find the best privacyutility trade-off for different applications. Each scenario allows the fire brigade to have an anonymous database of intervention's locations where third party companies or the human resources department itself could acquire high-utility statistics. More specifically, synthetic datasets will be build based on the SB approach, which will contain the number of interventions per region for each scenario of time.
Data preparation Three initial sources were considered: • A list of geometric locations with map projection epsg:2154 for each town belonging to the Doubs department, obtained from the SDIS 25. • A list of towns grouped in 17 regions for the Doubs department. The file was extracted from the public dataset available in  [16]. From the first source, it was extracted the polygons that describe each town. Then, they were grouped by region considering the second source. Thus, it is obtained a final list with the new polygons for each region as illustrated in.
For instance, will be added to the dataset more explanatory variables such as meteorological and traffic data, where feature selection techniques will be used to improve the performance of models. Moreover, techniques for tuning the hyperparameters of the models will be implemented. • for k, 2h ≤ k ≤ n, |U k -B 1 k | -|U k -B 2 k | = 1 -1; Therefore max U,B 1 ,B 2 ∈B n n k=1 2 f -1 |U k -B 1 k |-|U k -B 2 k | ≤ 2 f -1 2h and the proof is established.
interventions' location is grouping the towns which happened each intervention at the level of a bigger city (region) to obtain events that are sufficiently representative in number. For example, one can notice in Figure3that a set of C = {c 1 , c 2 ,. , c 12 ,. , c m } small towns are grouped to n = 6 regions. In this context, using the data at our disposal, 608 towns where interventions happened in the Doubs department were generalized to n = 17 regions using the public dataset available in [16]. The 17 regions are: (1) CA du Grand Besançon, (2) CA Pays de Montbéliard Agglomeration, (3) CC Altitude 800, (4) CC de Montbenoit, (5) CC des Deux Vallées Vertes, (6) CC des Lacs et Montagnes du Haut-Doubs, (7) CC des Portes du Haut-Doubs, (8) CC du Doubs Baumois, (9) CC du Grand Pontarlier, (10) CC du Pays d'Héricourt, (11) CC du Pays de Maîche, (12) CC du Pays de Sancey-Belleherbe, (13) CC du Plateau de Frasne et du Val Rasne et du Val de Drugeon (CFD), (14) CC du Plateau de Russey, (15) CC du Val de Morteau, (16) CC du Val Marnaysien, (17) CC Loue-Lison.
The ratio is the result of dividing the means of the number of incidents happened in the previous year (2017) from the real dataset and the anonymized one, according to the town. Thus, the new number of anonymized interventions in each data point of a town is the result of dividing again the number of anonymized interventions by their respective calculated ratio. The data are considered sequential in each dataset. The target is a vector, where each position and value represent the region and the number of its interventions respectively, for the next hour (t + 1) of a present sample (t). A present.
The discussions above indicate that a practical and generalised modelling approach regarding various hydraulic turbines is still required for achieving efficient PHIL real-time simulations. In this paper, a generalized hydraulic modelling approach is presented for achieving efficient PHIL real-time simulations. The static models are based on the commonly used 'Hill Charts' measurements provided by industrial manufacturers. The regression models of water flow rate and hydraulic efficiency are computed by using optimization routines. The optimization routine is defined with the objective of minimizing the difference between the measured data points and the determined regression models. In addition, we can update the models with recent 'Hill Charts' measurements to avoid the model deviations due to the ageing or localisations.
γ m Q ∈ R 1×(m Q +1) , A T (γ) = 1 γ. γ m T ∈ R m T +1 C Q (N 11 ) = 1 N 11. N n Q 11 ∈ R 1×(n Q +1) , C T (N 11 ) = 1 N 11. N n T 11 ∈ R n T +1 where the parameters m Q , n Q , m T , and n T need to be chosen. The optimization routine lsqlin defined in Matlab is chosen to solve the related optimization problems (  4 ) and (  5 ) to get the regression parameters ). By solving the least square optimization problems (  4 ) and (  5 ), we minimize the mismatch between the chosen regression model and the provided data points, while making sure that, whenever γ = 0, we get Q = 0 regardless of the rotational speed, in addition to ensuring that T = 0 when γ = 0 and N = 0: B Q ∈ R (m Q +1)×(n Q +1) and B T ∈ R (m T +1)×(n T +1 min B Q 1 2 Q 11 -Q11 (B Q , γ, N 11 ) 2 s. t. Q11 (γ = 0) = 0 (4) min B T 1 2 T 11 -T11 (B T , γ, N 11 ) 2 s.
dSPACE Modular, dSPACE Controller, and dSPACE Environment The dSPACE as a rapid-prototyping system allows the implementation of numerical models and any control structures for energetic conversion systems  [39, 40]. In this work, the model of hydraulic turbine and the control diagram are both achieved with the dSPACE real-time digital simulator based on the MATLAB/Simulink environment. The processor of the dSPACE card is dedicated to compute the model and synchronised on a timer; this allows for running the model in real time  [41]. Moreover, some extra hardware interfaces are provided to facilitate the design of hardware prototype, such as Analog to Digital (ADC), Digital to Analog (DAC), Pulse Width Modulation (PWM), Encoder, Resolver, etc. In addition, dSPACE provides add-in toolboxes to Matlab/Simulink software which allows for easy setting of interface cards and easy development of control system in a Simulink environment  [39, 40].
Real-Time Physical Emulator Real-Time Physical Emulator (RTPE) is the core module of the PHIL benchmark, which includes two key elements: the RTSS and the actuator (see Figure  10 ). The RTSS can digitally emulate the hydraulic behaviours by using the obtained mathematical model. In addition, the computed torque and the measured rotational speed information exchange at the interface. The RTSS computes the torque reference and sends it to the DCM control system, and the coupling turbine speed value is then sent back to the RTSS. The hydraulic model is generated in the Matlab/Simulink environment with the dSPACE modular (DS1005). The torque control is implemented in the DSP TMS320F240. The DC/DC power electronics converter is composed of a four-quadrant Pulse Width Module (PWM) IGBT chopper connected with a diode rectifier  [19].
As the current control relates to the electrical control category, which is sufficiently faster than mechanical dynamics. (3) The overall execution time of the complete simulation system (the computational time of hydraulic model, the execution time of control laws, the time on ADC and DAC process, etc. ) must be shorter than the sampling period of the simulation diagram under the dSPACE environment. Otherwise, if the computational demands of the overall program make the processor take more time than the sampling period, an overrun condition would happen. Then, we could not ensure real-time execution of the control program  [40].
Control Laws under Test The controllers under test are running with the Matlab/Simulink based on the dSPACE DS1005. The control input is then converted to switch commands for the power electronics converters in real time  [19]. The digital variables and the analog measurements are exchanged via the I/O interface. The control diagram of the grid-connected VS-HEP is presented in Figure  11. In the figure, i g (a, b, c) represents the grid currents, i m (a, b, c) are the machine currents, v g (a, b, c) are the grid voltages, i * gd and i * gq are the d-axis and the q-axis grid currents references, respectively, i * md and i * mq are the d-axis and the q-axis machine currents references, respectively, P g is the grid-injected active power, ω is the rotational speed, ω * is the rotational speed reference, L f and R f are the filter inductance and the filter resistance, respectively, V dc is the DC-link voltage, and C is the DC-link capacitance.
The operation time of the guide vane actuator in this work is around 2∼4 s. The torque control of DCM with a settling time of around 4ms ensures that the emulated mechanical torque can efficiently replicate the hydraulic dynamics. The hydraulic dynamic features in the offline simulation in Matlab/Simulink and the PHIL real-time simulation are presented in Figures  13  and 14 , respectively. A soft starting is employed in the beginning; the variable speed control starts at t = 40 s, with the guide vane opening of γ = [0. 5, 0. 6, 0. 5]. The two figures show that the emulated hydraulic torque T hdy , the water flow rate Q, and the output hydraulic power P hdy in real time could achieve similar dynamic features as that of offline simulations. However, we notice some differences on the dynamic behaviour; the most relevant difference is dealing with the overshoot that affects the torque.
Several searchable encryption (SE) techniques have been proposed for keyword searching over encrypted data  [5, 3, 4, 2, 21]. The main idea is to associate a trapdoor with each keyword to allow searching for these keywords within a given encrypted data. Ideally, an entity which does not have access to the plaintext and encryption key should learn nothing about the plaintext except the presence or the absence of the keyword. For most existing SE techniques, searches are performed on keywords that have been pre-chosen by the entity encrypting the data. Such approaches are more suitable for specific types of searches, such as database searches in which records are already indexed by keywords, or in the case of emails filtering in which flags such as "urgent" are used. Unfortunately, SE techniques become useless when the set of keywords cannot be known before encryption.
The two constructions we propose in this paper address the main drawbacks of these tokenization-based techniques since they allow for arbitrary trapdoors to be matched against the encrypted data, without false negatives or false positives. Several approaches  [11] [12] [13]  proposed solutions for substring search over encrypted data based on secure multi-party computation. Unfortunately, to offer pattern matching operation, these solutions require often several interactions between the searcher and the data encrypter. As pointed out in  [1] , anonymous predicate encryption (e. g. ,  [14] ) or hidden vector encryption  [15]  may provide a convenient solution for pattern matching over encrypted data. However, in order to search a pattern p of length l on a data of length n, the searcher should obtain n -l keys to be able to check the presence of p on every possible offset of the data, which is clearly a problem when dealing with large datasets.
Definition 5 (Encrypted Data Forgery). Let λ be a security parameter, Σ be the alphabet to be used, A be the adversary, C be the challenger, O s be an oracle that issues a trapdoor for any adaptively chosen pattern, and O r be an oracle that encrypts any adaptively chosen data. We consider the following Exp S 4 E EDF A game: (1) Setup: C executes Setup(1 λ , f s, p max ) to generate params and the algorithm Keygen(params, Σ) to generate the keys K s and K t. Then it sends params to the adversary. (2) Query: -A can ask O s for issuing the trapdoor td wi for any adaptively chosen pattern w i = σ i,1 • • • σ i,li where σ i,j ∈ Σ. We denote W the set of patterns submitted by A to O s in this phase. -A can adaptively ask O r to create C T = Encrypt(params, K s , T ). We denote T the set of datasets encrypted by the O r.
Empirical Evaluation In this section, we experimentally evaluate the performance of S 4  E and AS 3 E 4. We implement the two constructions using the RELIC cryptographic library  [23]  over the 254-bits Barreto-Naehrig curve. For all conducted experiments, we used real network traces as the data to be encrypted and analyzed, and we (pseudo) randomly generated the analysis patterns to be searched. In addition, since in both S 4 E and AS 3 E, the encryption and the trapdoor generation algorithms are to be performed by entities (data owners in case of S 4 E or data sender in case of AS 3 E) which may not have a large computation power, we run both the trapdoor generation and the encryption algorithms tests on an Amazon EC2 instance (a1. 2xlarge) running Linux with an Intel Xeon E5-2680 v4 Processor with 8 vCPU and 16 GB of RAM.
In contrast, as the search operations are performed by SP which is supposed to have a large computation power, we run search experiments on an Amazon EC2 instance (m5. 24xlarge) running Linux with an Intel Xeon E5-2680 v4 Processor with 96 vCPU and 64 GB of RAM. In our empirical evaluation, we aim to quantify the following characteristics of the proposed constructions: -The time required to generate a trapdoor and its corresponding size as a function of the size of the largest analysis pattern p max that can be searched. -The time taken to encrypt a data stream as a function of its size (i. e. the size of the sequence of symbols that composed the data to be encrypted), the fragmentation size f s and the size of the considered alphabet. -The time needed to perform a pattern matching query as a function of the size of the data to be queried and the size of the patterns to be searched.
For f s, we consider 3 different fragment sizes: 10 3 , 10 4 , and 10 5 symbols. As illustrated in Fig. 3 , the time required for encrypting a dataset composed of 10 5 symbols increases only by a factor of 0. 02 (from 3,04 to 3,2 seconds) when increasing the size of the fragments by a factor of 100 (from 10 3 to 10 5 ) and increasing the size of the considered alphabet by a factor of 128 (from a base 2 alphabet where Σ = {0, 1} to a base 256 alphabet where Σ = {0, 1, • • • , 255}). The previous results show that the increase of the size of supported patterns and the size of the considered alphabet affects very little the encryption time required by the proposed constructions. Search time. As shown in Section 7, the complexity of the search operation depends mainly on the number of encrypted symbols n that compose the data to be analyzed.
Fig. 4  describes the time required for searching a pattern as a function of the number of encrypted symbols in the data to be analyzed. The conducted evaluations show that the average search throughput of our construction is 139078 symbols per second with a multi-threaded implementation  6. Thus, if an ASCII (resp. binary) alphabet is considered, the search throughput is 139 KB (resp. Kb) per second. ) compares the time needed for both our and the SEST (both its asymmetric  [1]  and symmetric (Section 5. 6) variants) constructions to test the presence of a pattern of bytes (resp. of bits) in a 10 MB (resp. Mb) dataset as a function of the length of the pattern to be searched. In both bit and byte searches, our construction drastically reduces the search time compared to SEST. This is because that our Test algorithm is constant on the size and on the content of the searched pattern which is not the case for SEST.