dataesr/scientific-paragraphs-categorization huggingface dataset:
Network simulators with node models emphasize more on discrete event scheduling, the radio medium, network modeling, and perhaps the sleep duty cycles of the sensor node. Network modeling is the predominate object. Many node models implemented by this kind of simulator are simple power and estimated timing profiles. Network simulators with node emulators integrate the advantages of both the network simulators and node emulators. The network simulator provides the detailed network model. The node emulator gives accurate timing information of the software execution because they simulate the system performance with instruction cycle granularity. However, the interconnection between the network simulator and node emulator may take much time. In node system simulators with network models, the node system is often modeled by system-level description languages (SLDL), such as SystemC  [35].
They have a simulation kernel which supports modeling the concurrency and synchronization among different hardware components. SLDL can also model the software, which allows the HW/SW co-design and co-simulation. It models the node hardware in different abstraction levels with different degrees of detail (e. g. , system level, transaction level, and register transfer level). Compared with network simulators with node models, node system simulators with network models can provide more accurate power and timing modeling. The node emulators with network models can be divided into two different sets: ISSs for special microcontrollers or processors, and emulators designed to emulate the execution of the application code of an operating system (e. g. , TinyOS  [36] , SOS  [37] , and Contiki  [38] ).
The performance of wireless LAN is studied in  [41, 42]. According to  [43] , it is the most used simulator in mobile ad hoc network (MANET) research. Regarding WSN, it includes many ad hoc and WSN-specific protocols  [8]. An IEEE 802. 15. 4 model is developed in  [44]. An energy-efficient routing in WSNs  [45]  and directional routing in vehicular networks  [46]  are evaluated using NS-2 simulations based on a simple energy model. However, NS-2 does not scale well in terms of memory usage and simulation time  [47]. It also lacks detailed support to measure the energy utilization of different hardware, software, and firmware components of a WSN node  [48]. SensorSim  [12]  is built on top of an NS-2 802. 11 network model. It models the sensor node in two parts: software model (function model) and hardware model.
The power models of different hardware components have been implemented. The state of the hardware model is changed based on the function that is carried out by the software model. Therefore, the power consumption of the whole network can be simulated. In addition, SensorSim can be interacted with real nodes. However, the CPU and sensor models have not been implemented. Furthermore, IEEE 802. 11 is designed for high-speed connectivity and not optimized for WSNs. OMNeT++  [32]  is a component-based network simulator, with an Eclipse-based integrated development environment (IDE) and a graphical runtime environment. The IDE supports all stages of a simulation project: developing, building, configuring, and running simulation models and analyzing results. OMNeT++ consists of modules that communicate with message passing.
Simple modules implement the atomic behavior of a model, e. g. , a particular protocol. Multiple simple modules can be linked together and form a compound module. OMNeT++ provides the infrastructure to assemble simulations from these modules and configure them (NED language). OMNeT++ can be extended easily by interfaces for realtime simulation, emulation, parallel distributed simulation, SystemC integration, and so on. As OMNeT++ is becoming more popular, many contributions have been added to it. The Mobility Framework (MF)  [49]   found in  [50]. PAWiS  [48, 51]  is an OMNeT++ based WSN simulator. Its architecture is similar to SensorSim. It can evaluate the power consumption of WSN systems with many levels of accuracy which can still be balanced with complexity. The model programmer has to insert special framework requests to the CPU module to simulate the execution time and power consumption.
Once a parallel algorithm is selected, the analyst can additionally indicate the mapping strategy and number of processors. Taking advantage of parallel simulation, GloMoSim has been shown to scale to 10,000 nodes  [54]. QualNet [55] is a commercial derivative of GloMoSim. It has extended GloMoSim to other networks, such as satellite, cellular, and sensor networks. ZigBee protocol model is also provided. Prowler  [56]  is an event-driven network simulator running in Matlab environment. Benefits gained from Matlab environment are easy prototyping of applications and GUI interface. Prowler is capable of simulating the radio transmission, propagation, and the MAC-layer operation in ad hoc networks. The radio models are based on specific signal strength calculations combined with random errors.
Prowler is well suited for protocol and algorithm development. However, it does not have sensor node energy modeling. NetTopo  [57]  is an integrated WSN-specific network simulator that provides the simulation of virtual WSN and the visualization of real testbeds. It also supports the interaction between the simulated WSN and real testbeds. Shawn  [17]  is another open-source discrete event simulator written in C++. It is suitable for large-scale network simulations benefitting from the high-level abstraction. Some customized simulators are developed to study some particular problems in WSNs. For example, the sensor placement problem  [58]  and routing protocol design  [59]  are studied by Matlab. Sometimes, the collected traces from the real deployments  [60, 61]  are imported into the simulation models to reproduce the transmission behaviors of links and paths in WSNs.
A discrete event simulator is developed in Java to study the MAC performance of WSNs  [62]. The data aggregation and coverage problems for WSNs are studied by C++ based simulations in  [63, 64]. The channel assignment problem is studied in  [65]. We classify these simulators as network simulators. The main advantages of network simulator are that they usually have a rich library of the radio modules and protocol implementations. Many contributions to these tools are carried out ceaselessly. For example, the performances of NS-2 in the aspects of scalability and extensibility are improved by its successor, NS-3  [66]. It also simplified the model implementation by choosing C++ as the sole development language, and the usage of Python scripting language can be optionally enable  [67]. However, the network simulators are dedicated to model the network.
Node emulators with network models Two kinds of node emulator, operating system emulator and instruction set simulator, are studied separately in this section. One special simulator providing both features will also be presented. TOSSIM  [16]  and PowerTOSSIM  [25]  are two emulators designed to emulate the execution of TinyOS  [36]. Software development for WSN can be simplified by using these emulators. They permit developing algorithms, studying system behaviors, and observing interactions among the nodes in a controlled environment. The application code of TinyOS can be compiled to the simulation framework by only replacing a few low-level TinyOS components that deal with hardware. TOSSIM can capture the behavior of the network of thousands of TinyOS nodes at bit granularity. TOSSIM allows the developer to easily transition between running an application on motes and in the simulation environment.
PowerTOSSIM is an extension to TOSSIM in evaluation of the power consumption. The main problem of such frameworks is that the user is constrained to a specific platform (typically MICA motes) and a single programming language (typically TinyOS/NesC)  [51]. In addition, TOSSIM loses the fine-grained timing and interrupts properties of the code that can be important when the application runs on the hardware and interacts with other nodes  [34]. ATEMU  [68]  is an instruction-level, cycle-accurate emulator for WSN written in C. It simulates programs of each individual node with accuracy down to the clock cycle. Its core is an ISS. Along with support for the AVR processor, it also includes support for other peripheral devices on the MICA2 sensor node platform, such as the transceiver. ATEMU provides a GUI, called Xatdb, which provides users a complete system for debugging and monitoring the execution of their code.
Avrora  [34] , written in Java, improves the performance of ATEMU in the scalability aspect. Avrora can scale to networks of up to 10,000 nodes. Both ATEMU and Avrora provide a high behavioral and timing accuracy of the WSN programs. Moreover, they are both language and operating system independent. The main disadvantage of such frameworks is that they only support systems based on components that have already existed, e. g. , memories and processors, like MICA motes. Unfortunately, they do not cover systems containing new hardware blocks. WSim is the low-level simulator in Worldsens  [52]. It is based on cycle-accurate full platform simulation using microprocessor instruction driven timings. It provides many hardware block descriptions of components on the chip level. A sensor node platform can be built by describing the physical interconnection among these components.
WSim can also handle real target binary code simulation and debugging. The time resolution can be at nanosecond level. By combining WSim and WSNet, Worldsens can provide a complete design flow of WSN application, from the high-level design choices down to the target code implementation, debug, and performance analysis. Fummi et al. [69]  have developed an energy-aware simulator by integrating an ISS of the node's microcontroller and a functional SystemC model of the network module on SCNSL  [33]. SCNSL is a networked embedded system simulator, which will be introduced in the 'Node system simulator with network models' section. μCsim is used as the ISS for the Intel 8051 microcontroller of the Texas Instruments CC2430F128 chip. Using ISS makes it possible to run the exact binary embedded software on the simulated hardware platform.
The SystemC kernel is modified to communicate with the ISS through inter-process communication primitives (e. g. , a socket or shared memory). COOJA  [70]  is a Java-based simulator that provides both the operating system emulation and the instruction set emulation in a single framework. The Contiki operating system  [38]  can be compiled to the simulation framework. It executes native code by making Java Native Interface (JNI) calls from the Java environment to a compiled Contiki system. MSPSim  [71]  is used as the instruction set simulator in the COOJA. MSPSim is also written in Java. It supports the Texas Instruments MSP430 microcontroller and includes some hardware peripherals such as sensor, communication ports, LEDs, and sound devices. Recently, the COOJAMSPSim platform  [72]  has been extended to support the TinyOS.
Node system simulator with network models Wireless Sensor Network Simulator (WISENES)  [73]  is developed in specification and description language (SDL)  [74] , which is a high-level abstraction language widely http://jwcn. eurasipjournals. com/content/2014/1/106 used in communication protocol design and can be converted to C code automatically. The key feature of WISENES is that its simulation models are reusable in the embedded software design for the final system. However, WISENES only contributes to the software implementation. SDL is unsuitable to model synchronous digital circuits because the SDL system behavior is defined as a network of extended finite state machines that communicate with each other using asynchronous signals  [75]. On the other hand, SystemC provides native supports of HW/SW co-simulation.
ATLeS-SN demonstrated the feasibility of using TLM for sensor network application, but no standard networking protocol has been implemented. The SNOPS framework  [78]  is another TLM-based WSN simulator. A sensor node transmits or receives a data packet to or from an environment model by transaction exchanges. The SNOPS framework  [78]  requires 49. 7% less simulation time than PAWiS  [48]. SystemC Network Simulation Library (SCNSL)  [33]  is a networked embedded system simulator, written in SystemC and C++. It includes three modules: node (SystemC), node-proxy (SystemC), and network (C++). During the initialization of simulation, each node registers its information (e. g. , location, TX power, and RX sensitivity) at a network class which maintains the network topology and transmits packets to other nodes.
The nodeproxy is an interface between the network and nodes. By using node-proxy, nodes can be designed as pure SystemC modules so as to exploit all advantages of SystemC in HW/SW co-design and verification. SCNSL demonstrates a great perspective for system-level simulation of WSN system, but it still has some limitations such as nodelevel simulation without any specific hardware platform or energy model. IDEA1  [1, 79, 80]  is based on the SCNSL library of alpha version. The network model of IDEA1 is inherited from SCNSL; however, many contributions have been developed. Emphasizing the modular design, IDEA1 models a sensor node exactly according to its hardware architecture. Each hardware component is modeled as an individual module. By doing this, the behaviors of hardware components can be accurately captured, which is the basis of energy consumption estimation.
An energy model  [81]  has been developed to enable the accurate energy consumption prediction. It has been calibrated by some experimental measurements. Both free space and International Telecommunication Union (ITU) indoor propagation models are implemented. The simulation results of IDEA1 have been validated by a testbed consisting of nine nodes and compared with NS-2. Based on the efficient simulation kernel of SystemC and optimized model implementation, the simulation speed of IDEA1 is twice faster than NS-2. Using IDEA1, the performance of IEEE 802. 15. 4 MAC protocols is studied in  [82]  and a real application of WSNs on vibration measurements is studied in  [83]. These simulators scale better than node emulators since they usually model sensor node at system level. Additionally, new hardware and software modules can be easily added to the existing library.
Network simulators with node emulators Two main simulators have been developed in this category. Park et al. [84]  have developed a unified network and node level simulation framework. They developed the Embedded Systems Power Simulator (ESyPS) by integrating sensor and radio modules into EMSIM  [85]. EMSIM is an energy simulation framework for embedded systems featured in StrongARM microprocessor and Linux OS. Then, they integrated the ESyPS with SensorSim  [12]. The framework can explore the interactions between network level and node level. Another example is sQualNet  [86] , which is a scalable and extensible sensor network simulation framework built on top of QualNet  [55]. It uses QualNet as the network simulator and provides the emulation of the SOS operating system  [37]. sQualNet allows using the QualNet's detailed models of channel, propagation, mobility, etc.
Methods: The study was based on data collected during 1990-2011 by 34 registries. The registries are populationbased and use multiple sources of information to collect data on all types of birth using standardized definitions, methodology and coding. Diagnostic criteria for inclusion in the study were the presence of radial ray abnormalities and congenital heart disease (CHD), or the presence of either radial ray anomaly or CHD, with family history of HOS. Results: A total of 73 cases of HOS were identified, including 11 (15. 1%) TOPFA and 62 (84. 9%) LB. Out of 73 HOS cases, 30. 8% (20/65) were suspected prenatally, 55. 4% (36/65) at birth, 10. 7% (7/65) in the first week of life, and 3. 1% (2/65) in the first year of life. The prenatal detection rate was 39. 2% (20/51), with no significant change over the study period.
The spectrum of skeletal upper limb defects ranges from an abnormal carpal bone or triphalangeal/fingerlike thumb to bilateral phocomelia. CHD is present in 75% of individuals with HOS. Ostium secundum atrial septal defect (ASD) and ventricular septal defect (VSD) are the most common, but other more severe heart anomalies have been reported as well  [2, 17]. Conduction heart disease may occur in the absence of structural anomaly. No correlation between the severity of heart and limb defects has been established. Several large clinical series of HOS patients have been published examining the clinical presentation, differential diagnosis and diagnostic criteria  [16, 18, 19] , but population-based epidemiological studies are rare because of the need for large populations and standardised data collection.
The prevalence of HOS is estimated to be 0. 95/100,000 births, based on a single epidemiological study from Hungary  [20]. Most clinical reports refer to live born (LB), children or adults, while there is little information on fetal deaths (FD), terminations of pregnancy after prenatal ultrasound detection of severe anomaly/ anomalies (TOPFA) and on patients diagnosed in the neonatal period. The aim of this study was to investigate the epidemiological and clinical aspects of HOS patients diagnosed prenatally or in the early years of life, using data notified to the European Surveillance of Congenital Anomalies (EUROCAT) database.
Methods The study was based on data routinely collected in the period from 1990 to 2011 by 34 EUROCAT registries in 16 countries covering approximately 30% of Europe's annual birth population and extracted from the central database in June 2013. The EUROCAT registries are population-based and use multiple sources of information to collect data on all major structural congenital anomalies, chromosomal abnormalities, and other genetic and environmental conditions presenting with structural defects among LB, FD with gestational age (GA) ≥20 weeks, and TOPFA using standardized definitions and coding. Six registries include cases diagnosed up to one week after birth, three up to one month, and 25 registries include cases diagnosed up to at least one year of age. A detailed description of registries, methods of case ascertainment, data collection and processing is available elsewhere  [21, 22].
The central EUROCAT database was searched for the International Classification of Diseases (ICD)/British Paediatric Association (BPA) version 9 (759. 842, 759. 84), ICD10/BPA (Q87. 20) and OMIM (142900) codes assigned to cases of HOS. Diagnostic criteria for inclusion in the study were the presence of radial ray abnormalities and CHD, or the presence of either radial ray anomaly or CHD, with family history of HOS  [2, 4]. Minor anomalies, including those detectable exclusively by x-ray (e. g. , carpal bone defects, hypoplasia of hand/arm/shoulder muscles, sloping shoulders), functional limb defects (e. g. , minor limitation of movements of thumbs, elbows, or shoulders) or heart defects (e. g. , conduction abnormalities) were not systematically recorded, as the EUROCAT focus is on collecting data on major structural anomalies  [21].
Statistics Descriptive data are presented as numbers and percentages for categorical data. Means and 95% confidence intervals, based on Poisson distribution, were used to calculate prevalence and a Generalized Linear/Nonlinear model based on Poisson distribution was used for statistical testing of time trends. A difference between two proportions test was used to test prevalence differences between registries and the total prevalence rate. A chisquare test was performed to determine differences in maternal age distribution between HOS and other EUROCAT cases. Maternal age comparisons were restricted to the registries/time period with available maternal age denominator data (63% of EUROCAT total birth population). Statistical analysis was performed using STATISTICA 6. 1 StatSoft Inc. 1983-2003 (serial number AGA304B211928E61).
The mean GA at termination was 21. 1 ± 1. 8 (range 14-35) gestational weeks. The diagnosis was verified by post mortem examination in 10 of 13 cases (eight TOPFA and two neonatal deaths). In 31 out of 51 cases with available data (60. 8%), prenatal ultrasound was performed but did not detect any anomaly, although major anomalies (e. g. , absence of forearm, bilateral radial aplasia, tetralogy of Fallot, pentalogy of Fallot, etc. ) were present in 10/31 patients detected postnatally. Three undetected cases were familial. Characteristics of the HOS patients are shown in Table  2. The male-to-female ratio was 1. 1:1. The mean GA in LB was 39. 3 ± 3. 1 (range 33-42) in males and 38. 4 ± 2. 9 (range 27-42) in females. The mean BW was 2998 ± 370 g (range 2240-3800 g) for males and 3241 ± 458 g (range 2400-4200 g) for females.
Of 62 live born cases, 96. 8% survived the first week of life. Eleven families had more than one affected member. Maternal age distribution did not differ significantly from that of the total EUROCAT population (P = 0. 06). Multiple pregnancies were not noted. Three of 34 patients with known information on ART were conceived by induced ovulation. There were no cases of in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). Data on karyotyping were available for 46. 1% of cases and all results were normal. Description of the type and frequency of major congenital anomalies was available for 61 HOS patients. The results are presented in Table  3 , together with those on the three large series of HOS patients published so far. Thumb anomalies were present in all patients. Agenesis/ hypoplasia of radius was present in 49.
Distribution of CHD types among familial and sporadic cases is shown in Table  4. The prevalence of HOS was restricted to data from 16 registries with an above average prevalence of genetic syndromes and microdeletions (EUROCAT average is 4. 8 per 10,000 births) according to the Data Quality Indicators developed by EUROCAT (http://www. eurocatnetwork. eu/content/DQI-2013. pdf ). This was done in order to ensure a more homogeneous ascertainment of cases. During the 1990-2011 period, a total population of 5,017,781 births were monitored in these registries and 37 cases of HOS identified. Therefore, the mean prevalence of HOS diagnosed in the early years of life was 0. 7 per 100,000 births or 1: 135,615 births. The prevalence rates for 1990-2000 and 2001-2011 were 1. 1 and 0. 4 per 100,000 births, respectively (P = 0.
Clinical manifestations The distribution of gender was equal. The intrauterine growth and development was not affected. Over twothirds of HOS patients were born at term and of 50 patients born after 37 weeks of gestation, only four (8%) weighed less than 2,500 g. Likewise, the first week survival was excellent, taking into account a rate of severe congenital heart anomalies. By definition, all HOS patients have upper extremity anomalies. Bone abnormalities are usually bilateral and asymmetric, with left side often more affected than the right side. Most skeletal manifestations are visible on inspection but some patients have only functional abnormalities or carpal/metacarpal bone anomalies visible on x-ray. These functional and subclinical skeletal manifestations of HOS were not systematically reported in our dataset, as EUROCAT records major anomalies, while recording of minor anomalies and functional symptoms is optional and would be included in the database only if associated with a major defect.