dataesr/scientific-paragraphs-categorization huggingface dataset:
Eventually a quite high value was chosen (96) and the validation showed a good reproduction of the steady-state as well as the transient-state behaviors of a solar collector. Hamed et al. [16]  developed a non discretized 4nodes model to analyze the energy and the exergy of a flat-plate solar thermal collector in transientstate. They assume a uniform repartition of the fluid in the solar collector. Herrero Lopez et al. [17]  developed and compared two solar collector models: a simplified 1 node-lumped capacity model based on the ISO 9806:2014 norm and a more detailed 5 nodes-model. The validation with transientstate experimental data shows a better accuracy of the detailed model. Kamiński and Krzyżyński  [18]  proposed two ways to simulate a solar thermal collector: a discretized 3xN-nodes model and a CFD model.
In the discretized model, the absorber plate presents a 2D discretization to better take account the repartition of the temperature. The validation with short-term and long-term experimental data demonstrates that the CFD model is more appropriate for a geometrical optimization. In the other cases the discretized model shows a better compromise between computational time and accuracy. The presented studies do not use the models to reproduce the faulty operation of a solar collector. And to the best of our knowledge, such study with a detailed model does not exist. There are currently no existing studies about the impact of a fault on the efficiency curve of a thermal solar collector. The most similar studies concern the impact of improvements on a solar collector and they only cover one particular case with one set of efficiency coefficients.
IMPACT OF FAULTS ON THE SOLAR COLLECTOR EFFICIENCY CURVE In this section, the developed and validated numerical models were used to analyse the impact of the three faults on the solar collector efficiency curve. For each fault, efficiency curves are obtained with the same procedure as the one used for the validation. Several severities are tested to analyse their impact on the results. Finally, these results are compared to the limited data available in the literature. The configuration of the model corresponds to a typical flat plate solar collector designed for large solar thermal installations: the edge insulation is reinforced and there is a FEP foil in addition to the cover glass.
Thus, the main objective of the present paper is to address this gap by examining the effect of orthogonal fracture networks on the Elder problem. In this context, we address three particular research questions:  (1)  The effect of fracture networks on the mesh sensitivity and bifurcation states of the Elder problem, (2) the impact of fracture networks on the critical Rayleigh number for the onset of instability and (3) how do the structural properties of fracture networks (aperture and density of the fractures) control convective flow patterns? For this purpose, we considered a new configuration of the Elder problem (fractured Elder problem) by embedding orthogonal fracture networks and we performed numerical simulations using COMSOL Multiphysics (COMSOL, Inc. , Palo Alto, CA, USA), which is a commercial finite element simulation package widely used in various physical and engineering applications.
, Palo Alto, CA, USA), which is a commercial finite element simulation package widely used in various physical and engineering applications. This paper is structured as follows: First, in the Methods section, the conceptual model of the fractured Elder problem is presented, the governing equations and the different fractured scenarios are summarized, some measurable characteristics are defined to be used in our analysis and the COMSOL model is briefly presented. In the Results and Discussion section, first, the validity of the COMSOL model results is investigated, then we address the three research questions formulated in this paper using this new model.
The Average Sherwood Number To characterize free convection, we used the dimensionless Sherwood number (Sh). Sh is commonly used as an indicator for the strength of convection, and equal to the ratio of the actual mass transfer due to free convection to the rate of the mass transfer due to diffusion  [3, 26]. The average Sherwood number over the source zone boundary can be calculated using the concentration gradient, as follows: Sh = 1 2 3H H ∂c ∂y y=H dx. (8) 2. 6. The Simulation Tool: COMSOL Multiphysics ® COMSOL Multiphysics was used in this work to perform the simulations of the fractured Elder problem. This software package has an interactive environment for modeling scientific problems based on the finite element method. To simulate DDF, we coupled the "Darcy's flow" interface from the "Subsurface flow" module and the "transport of diluted species in porous media" interface from the "chemical species transport" model, as in Mozafari et al.
The beam propagation method (BPM) represents an extension to the propagator approach which uses approximations but works in optically inhomogeneous media  [12]. The BPM has been used in a software package termed BioBeam that simulates light propagation through optically inhomogeneous media both in the illumination and detection path for different light sheet types at greatly improved speed by relying on a GPU  [29]. In principle, our analysis can be readily applied to BioBeam results as well to assess performance in inhomogeneous media. We showed that two-photon excitation generates much better optical sectioning without compromising axial resolution for many light-sheets, especially the Bessel beam but not the Double beam. However, even though the side lobes do not excite fluorescence they still carry a substantial fraction of the total power of the light sheet.
Materials and Methods Two patients underwent laparoscopic surgery for adenomyomas. Patients provided signed consent, which included a clause for no modification to surgery, in accordance with local institutional review board approval. Before surgery, MRI was performed with classic MRI sequences (i. e. , T1and T2-weighted MRI along the 3 planes [axial, coronal, and sagittal]). We adjusted the MRI settings to have a 1-mm resolution with a slice thickness of 3 mm. Before surgery, the contours of the uterus, uterine cavity, and adenomyoma were established in conjunction with preoperative T2-weighted MRI (Fig. 1 ). This segmentation phase was made possible by the use of interactive segmentation software (Medical Imaging Interaction Toolkit; German Cancer Research Center, Heidelberg, Germany  [9] ).
A standard laparoscopic technique and laparoscopic set were used with a 0˚laparoscope (Spies; Karl Storz, Tuttlingen, Germany). The surgical procedure began by treatment of endometriotic lesions followed by activation of the realtime AR software (>10 frames per second) to visualize the uterine cavity and adenomyoma. The latter comprises 3 stages  [10, 11] , the first using a "dense structure-from-motion" process (i. e. , capturing a small number of images of the uterus [simple pictures taken with the laparoscopic camera] taken from various angles and automatically reconstructing a 3dimensional [3D] intraoperative mesh model of the shape of the uterus). In the second stage, the preoperative uterus model was aligned to the intraoperative 3D model by a semiautomatic registration process  [11] ; the 3D model is registered to the live laparoscopic video using a novel wide-baseline approach that uses many texture maps to capture the real changes in appearance of the uterus.
In contrast to AR systems previously reported in the literature, our AR system requires no additional laparoscopic hardware, requiring a classic laparoscope only. The software runs on a standard personal computer (i7 desktop personal computer; Intel, Santa Clara, CA)  [11]. It does not require artificial boundary marking and unlike other systems  [3, 16]  does not fail with motion blur or when the laparoscope is removed (e. g. , for cleaning) and then reinserted. The system provides a solution to the most challenging stage, the tracking and fusion phase, in real time  [7]. We previously reported greater accuracy in the localization of small myomas when using AR in a uterine model  [7]  and a laparoscopic tumor resection model  [8] , in addition to the feasibility of the system in the operating room  [4].
Conclusion This study showed that the use of our AR system is feasible in patients with adenomyomas. The software takes into account the mobility of the uterus and thus differs markedly from nongynecologic uses of AR, opening the way to making laparoscopic adenomyomectomy easier, safer, and faster. With just small modifications, this technique may be used for the majority of gynecologic surgeries and, therefore, facilitate the localization of anatomic markers  [19].
2, Section 4. 3, Section 4. 4 present for each sensor module, the prototype brought to the project, its optimized version and some characterization results after optimization. Note that the optimization phase has delivered a preliminary version and a final version of the optimized modules. Only results for the final version are provided here. INSPEX partners also chose to develop a stand-alone version of each module even if this increases the consumption, size and weight of the overall integrated device. In this way, each module can extend its potential exploitation routes. Lastly, the project delivers prototypes of the optimized sensor modules at TRL4  [21]. Therefore, their cost is not considered in the present work, even if it will have a strong impact on the market uptake of their more mature version.
Optimized Module As the range required is not as large as the original prototype, it was possible to reduce the power consumption. This involved a redesign of the motherboard pre-amplification circuit. The size of the optics was also substantially reduced compared with the original implementation. The optimized module comprises a motherboard and an optics board to allow for different configurations when integrated into the INSPEX device, see Figure  8. The motherboard contains a microcontroller and an FPGA to drive the optics devices and to process the data and provide power management. The optics board includes the laser diode and the silicon photomultiplier to detect the returned laser signal. The lenses for focusing and collimating the beam can be added to the board (Figure  8d ) or integrated into the package.
Results for the Optimized Long-Range LiDAR Module The optimized module characteristics (design and measured) are reported in Table  4  together with the prototype and requirement ones. We decreased the power consumption but not enough to meet the module requirements. One solution is to implement duty cycling in the power manager of the INSPEX device to decrease the mean power consumption. The other characteristics are consistent with the requirements. To evaluate the measurement, a single point measurement of a stationary object at a known.
Results for the Optimized Long-Range LiDAR Module The optimized module characteristics (design and measured) are reported in Table  4  together with the prototype and requirement ones. We decreased the power consumption but not enough to meet the module requirements. One solution is to implement duty cycling in the power manager of the INSPEX device to decrease the mean power consumption. The other characteristics are consistent with the requirements. To evaluate the measurement, a single point measurement of a stationary object at a known distance is performed. The laser diode (SPL PL90) is pulsed at 150 ps. A clock is started at the initiation of the pulse. The detector is a MicroFC-10020. When a returned signal is detected, the clock is stopped, the time-of-flight is stored and the distance is computed on the FPGA.
Optimized Module The ultra-wideband radar module is mainly an optimization in form factor and integration of the initial prototype. It is made of three stacked boards, namely, the antenna board, the RF board and the digital board, see Figure  12. The digital board has the FPGA and the SPI interface to the INSPEX main computing platform. The module main characteristics are:   The optimized module characteristics are reported in Table  6  together with the prototype and requirement ones. The power consumption exceeds the module requirements. However, this value corresponds to the peak (measured) one. The field-of-view is equal to 56 • in elevation and 56 • in azimuth (3-dB beam width), which is consistent with the requirements. The main underperformance is the measurement range that is clearly less than expected.
Data Fusion to Build a Model of the Environment Data acquired by the different range sensors are fused in real-time using the SigmaFusion™ technology  [15]  that builds an occupancy grid to model the device surroundings. An occupancy grid, initially introduced by  [16] , is composed of a finite number of disjoint cells. A probabilistic estimate of the possible state is assigned to each cell. A cell can have two possible states: "occupied" or "empty". It is considered occupied if an obstacle is present, partially or totally, in the cell. It is considered empty otherwise. An obstacle can be any possible body in the scene, for example cars, human beings, animals, plants, buildings, traffic signs, etc. The occupancy state of each cell is estimated through a perception model evaluated for each measurement returned by the range sensors.
Discussion, and Future Work Directions The primary goal of INSPEX was to optimize the range sensors embedded in a portable device to detect obstacles in the surrounding of the device. To provide realistic constraints, the smart white cane application was chosen. Table  7  summarizes the characteristics of the four modules optimized in the course of the project. The values that exceed the characteristics are highlighted in bold: the main drawback is the measurement range for the ultra-wideband radar that is halved when compared to its requirements. We explained and quantified this under-performance above. Improvement of this module will require the redesign of the ASIC, which is currently out of the project timeframe. Power consumption figures correspond to peak measured values. The mean power consumption value is decreased via an ad-hoc power management of the sensing modules designed using formal methods  [25].
Remind that we designed each sensor module as a stand-alone one for its partner's owner to explore other exploitation routes. Consequently, it embeds its own microcontroller with its dedicated firmware (for sensor control, calibration, and data pre-treatment and treatment purposes) and the power supply circuitry. An advanced version of the modules will be less consuming because the firmware of each module will be ported on the INSPEX main computing platform, leading to the remove of the dedicated microcontroller. Moreover, the INSPEX main computing platform delivers the power supply to all the modules, therefore the associated circuitry will be removed from the advanced modules. The optimized modules have been delivered to the consortium and the final prototype is under assembling. Its mockup is shown in Figure  17.
Information about obstacles entering the user's "safety cocoon" (see Figure  1 ) are extracted from the occupancy grid computed by SigmaFusion™  [15]. This information is sent to the user's smartphone via Bluetooth Low Energy (BLE). 3D spatial sound that provides a virtual image of the obstacle is output to the user with an extra-auricular headset  [26]. Tests with the INSPEX integrated portable device will be conducted both in laboratory conditions and with VIB people in realistic environments. The test results are not reported in the present paper whose main objective was to present the optimization work carried out in parallel for each range sensing technology integrated in the portable device. The optimized modules have been delivered to the consortium and the final prototype is under assembling.
Users and Internet service providers (ISPs) are constantly a ected by denial-of-service (DoS) attacks. is cyber threat continues to grow even with the development of new protection technologies. Developing mechanisms to detect this threat is a current challenge in network security. is article presents a machine learning-(ML-) based DoS detection system. e proposed approach makes inferences based on signatures previously extracted from samples of network tra c. e experiments were performed using four modern benchmark datasets. e results show an online detection rate (DR) of attacks above 96%, with high precision (PREC) and low false alarm rate (FAR) using a sampling rate (SR) of 20% of network tra c.
But the industry also has problems, such as fragility in routing traffic, usually via Domain Name System (DNS) or Border Gateway Protocol (BGP), difficulty detecting slow attacks, and privacy issues, which drive away some customer segments as governments. Finding the balance between academic propositions and the industrial practice of combating DDoS is a big challenge. e academy invests in techniques such as machine learning (ML) and proposes to apply them in areas such as DDoS detection in Internet of ings (IoT)  [20, 21]  sensors, wireless sensors  [22] , cloud computing  [23]  and softwaredefined networking (SDN)  [18]  and work on producing more realistic datasets  [24, 25]  and more effective means of result validation  [26, 27]. On the other hand, industry segments gradually invested in new paradigms in their solutions such as network function virtualization (NFV) and SDN  [28, 29]  to apply scientific discoveries and modernize network structures.
Even so, the incidents of DDoS still happen daily, reinforcing that the problem is not solved. 1. 2. Proposal. Realizing these issues, this article proposes Smart Detection, a novel defense mechanism against DDoS attacks. e system architecture was designed to detect both high-and low-volume DDoS attacks. e proposed system acts as a sensor that can be installed anywhere on the network and classifies online traffic using an MLA-based strategy that makes inferences utilizing random traffic samples collected on network devices via stream protocol. e proposed approach is compatible with the Internet infrastructure and does not require software or hardware upgrades. Besides, user data privacy is guaranteed at all stages of system operation.
Contributions. In summary, the significant contributions of Smart Detection are as follows: (i) e modeling, development, and validation of the detection system are done using a customized dataset and other three well-known ones called CIC-DoS, CICIDS2017, and CSE-CIC-IDS2018, where the system receives online random samples of network traffic and classifies them as DoS attacks or normal. (ii) e proposed detection system differs from other approaches by early identification of a variety of volumetric attacks, such as TCP flood, UDP flood, and HTTP flood, as well as stealth attacks such as HTTP slow headers, HTTP slow body, and HTTP slow read, even with a low traffic sampling rate. In addition, Smart Detection is compatible with the current Internet infrastructure and does not require any software or hardware upgrades on ISPs.
At the same time, the proposed system uses advanced technologies such as ML and NFV. (iii) Unlike existing security service providers, the proposed system does not require traffic redirection or connection intermediation. Data privacy is guaranteed at all stages. First, the system randomly processes only a small part of the network traffic. Second, it does not do packet deep inspection. Instead, Smart Detection parses only network layer header data. (iv) e pattern recognition of normal network traffic and several DoS attack types are addressed. As a result, a novel signature database is created, which is used by Smart Detection and can be applied to other systems. (v) An approach to automatic feature selection has been developed using the cross-validation technique for model searches that meet specific classification quality criteria.
In this context, an FE is similar to a volumetric DDoS wherein thousands of legitimate users try to access a particular computing resource such as a website simultaneously. e results show that D-FACE can detect DDoS attacks and FEs. Although the work presents relevant contributions, the validation used obsolete datasets. In addition, the proposed collaboration approach requires a high degree of ISP engagement, so it restricts the industrial use of the solution. Antidose system  [33]  presents a means of interaction between a vulnerable peripheral service and an indirectly related Autonomous System (AS), which allows the AS to confidently deploy local filtering rules under the control of the remote service. e system was evaluated using Mininet, but no benchmark dataset was used. e approach proposed by the authors faces strong resistance from ISPs for two reasons: the first is software and hardware update requirement and the second is having no control over local traffic control policies.
e SkyShield system  [35]  has been proposed to detect and mitigate DDoS attacks on the application layer. In the detection phase, SkyShield exploits the divergence between two hash tables (Sketches) to detect anomalies caused by attacker hosts. e mitigation phase uses filtering, whitelisting, blacklisting, and CAPTCHA as protection mechanisms. e system was evaluated using customized datasets. SkyShield has focused on the application layer, more specifically on the HTTP protocol, so the proposed system is vulnerable to flooding at the network layer and transport layer. Umbrella  [36]  develops a multilayered defense architecture to defend against a wide spectrum of DDoS attacks. e authors proposed an approach based on detection and protection exclusively on the victim's side. e system was evaluated using the customized testbed in terms of traffic control.
e authors claim that the system is capable of dealing with mass attacks. However, this approach is widely used in industry and proved to be inefficient against truly massive DDoS attacks. Recently, a semi-supervised machine learning system addressed the classification of DDoS attacks. In this approach, CICIDS2017 dataset was used to evaluate system performance metrics  [37]. Although the work addresses the recent DoS vectors, the online performance of the method has not been evaluated. Finally, Table  1  summarizes these recent works whose approach is related to the proposal of this article. In Table  1 , Online indicates that the proposed system has been tested in online experiments and dataset informs the dataset used for validation, while L/H DoS indicates whether it detects slow and high DDoS attacks.
Smart Detection Smart Detection is designed to combat DDoS attacks on the Internet in a modern collaborative way. In this approach, the system collects network traffic samples and classifies them. Attack notification messages are shared using a cloud platform for convenient use by traffic control protection systems. e whole process is illustrated in Figure  1. e core of the detection system consists of a Signature Dataset (SDS) and a machine learning algorithm (MLA). Figure  2  shows the crucial steps from model build to system operation. First, normal traffic and DDoS signatures were extracted, labeled, and stored in a database. SDS was then created using feature selection techniques. Finally, the most accurate MLA was selected, trained, and loaded into the traffic classification system. e architecture of the detection system was designed to work with samples of network traffic provided by industrial standard traffic sampling protocols, collected from network devices.