dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors are grateful to  Nathalie Rochet  who provided BCP particles, numerus and useful advices. We thank  P. Battiston-Montagne  and  C. Vanbelle , CRCL-PIC cytometry and imaging platform;  J. Valantin  and  C. Le Nevé ,  Research Pathology Platform, Centre Léon Bérard  and  B. Manship  for the English edition. This work was funded by the  association Ligue contre le Cancer (Comités du Puy de Dôme, Drôme, Loire, Saone et Loire and Rhone)  to T. V. and V. M. -S. ALTE-SMP;  FI-LMC , " Fondation de France "  2014-0047501  and  2017-00076282 /  Fondation Ramona Ehrman Amador  and  Agence Nationale de la Recherche :  ANR : Institut Convergence PLAsCAN ,  ANR-17-CONV-0002  grants to V. M. S. and S. L. K. G. was funded on a  INCA-ARC-Ligue-11905  grant to V. M. -S. on BM-SC characterization.
The distortion is measured in terms of Signal-to-Noise Ratio SNR. It is expressed as: SNR dB ¼ 10log 10 P N i¼1 x 2 n ð Þ P N i¼1 x n ð Þ À b x n ð Þ ð Þ 2 ð18Þ where, x n ð Þ and b x n ð Þ are respectively the original and the reconstructed signals. N is the length of the signal. Here, three speech signals obtained from the TIMIT database are used for the evaluation of the filter banks coding performance. In experimentation, the wavelet based system discussed in Sect. 2 has been used for speech compression. In order to verify the effectiveness of our design method, we compare the performance of the optimized filter bank with the performance of Daubechies orthogonal filter bank labelled ''Db4"  [27]  in Table  1. We present the SNR results of all test signals at 0. 5, 0. 85, 1, 1. 3bps (bit per sample), highlighting the best results in each case.
Microstructural investigations The microstructures were observed using a binocular magnifier, an optical microscope and a Zeiss Sigma 300 field emission gun Scanning Electron Microscope (SEM) operated in secondary electron imaging mode. The specimens were cut perpendicular to the welding direction, and close to the welded thermocouple in the case of laboratory heat-treated specimens. They were ground with fine grit silicon carbide papers and polished with diamond pastes. Two chemical etchants were used to reveal the macrostructure and the microstructure, namely, 2% nital and Béchet-Beaujard reagents. suspension polishing under the following conditions: acceleration voltage, 20 kV; tilt angle, 70°; step size, 0. 4 µm; magnification, 250X; size, 340x340 µm. The crystallographic orientation information data were then analyzed with TSL OIM software.
Comparison to other neuromorphic self-organizing networks Unsupervised self-organizing networks have been previously studied on different hardware substrates, such as Self-organizing Maps (SOM) on Field Programmable Gate Array (FPGA)  39  , and reservoir computing using nano-wire networks  40, 41. The FPGA substrate was used to implement SOMs for multi-modal sensory processing  39. Hebbian learning was employed to create the SOM in each map, by calculating the weight update based on the input signal. The difference between this implementation and MEMSORN is two-fold; (i) the learning in the SOM network is offline and is learned based on batches of data that are given to the network during learning. In contrast, MEMSORN learns in a fully online fashion, as the input is being streamed, making it suitable for edge computing applications, embedded next to the sensors.
The excitatory population connects to itself with a probability of p EE , and connects to the inhibitory population with the probability p EI. The inhibitory population connects to the excitatory population with probability p EI. The SRNN represents a complex dynamical system governed by many parameters. In detail, neurons have three parameters (gain, time-constant and threshold), and synapses have 2 parameters (time constant and weight). Table  1  shows the parameter values for the initialization of the SRNN. The output from each of the neurons in the excitatory pool is projected to the output layer, constituted of neurons that encode the output of the network. MEMSORN. MEMSORN is equipped with the two technologically plausible learning rules of SDSP and IP. These local learning rules are only applied to the excitatory neurons and EE connections.
Clustering Analysis We show 50 sequences of length 12 (n = 10, 600 letters) to the static and MEMSORN networks and record the spike times of all the excitatory populations for each shown symbol. This spiking data is then low pass filtered with a time constant of τ = 5 ms. We take the data point at the last time step of the symbol presentation for all the symbols in the two sequences during 50 data presentations. PCA. We reduce the dimension of the SRNN activity from 160 excitatory neurons to the first two Principle Components (PCs) and plot the first and second PCs in time. This is plotted in Fig. 6a. Hierarchical clustering. We also analyze the obtained SRNN activity using hierarchical clustering analysis. This is a type of unsupervised learning algorithm used to cluster the data points with similar characteristics.
We use the agglomerative hierarchical clustering which is a "bottom-up" approach where each data point starts in its own cluster. Moving up the hierarchy, clusters are formed by joining the two closest data points resulting in increasingly fewer clusters with higher distance or dissimilarity. This hierarchy of clusters is represented as a tree (or dendrogram). The root of the tree is the unique cluster that gathers all the samples, the leaves being the clusters with only one sample. For performing this analysis, we have used the "cluster. hierarchy. linkage" function from the python scipy library. We have calculated the distance between the clusters with the "ward" method which minimizes the total within-cluster variance. At each step, this method merges pairs of clusters which lead to a minimum increase in the total withincluster variance after merging.
Acknowledgements This work is partly supported by Toshiba corporation,  EU Horizon 2020   Memscales  project ( 871371 ), and the  SNSF  grant SMALL ( 20CH21_18699 ). We are grateful to  Stefano Brivio  from the  National Research Counsil (CNR), Italy , and  Christian Tetzlaff  from the  University of Gottingen, Germany  for their critical and valuable comments on the manuscript.
An unusually strong and early regional dust storm and its associated thermal response were also observed. Plain Language Summary Data returned from the Emirates Mars Infrared Spectrometer (EMIRS) have been used to characterize the atmosphere of Mars. These data allow estimates of atmospheric temperature as a function of height, the amount of dust and water ice clouds in the atmosphere, and the abundance of water vapor. In this work we describe the process for estimating these quantities and we give a brief overview of our first results. The results show that Mars was relatively cool, relatively cloudy, but with little dust. This was expected given the season on Mars that was observed. More surprising was the observation of a dust storm that occurred earlier in the Martian year than usual. 2022 ) and for thermal tides by  Fan et al.
Only observations with emergence angle less than 70° are used, so the effects of spherical geometry are neglected. The model accounts for the absorptions from CO 2 and water vapor gases using the HITRAN2020 database  (Gordon et al. , 2022)  and the correlated-k approximation  (Lacis & Oinas, 1991). Coefficients for CO 2 broadening of water vapor are taken from  Brown et al. (2007) , which includes the most important lines in the EMIRS spectral range. Other weak lines not included in Brown's work have their air-broadened value multiplied by a constant factor of 1. 5. Following  Conrath et al. (2000) , a smoothness constraint is applied for the temperature profile retrieval using a two-point correlation matrix with a correlation length of 0. 75 scale heights, and the first-guess profile is obtained from the observed radiances.
The bottom of the temperature profile (lowest ∼0. 75 scale heights) is constrained to have a lapse rate consistent with the Mars Climate Database (MCD;  Forget et al. , 1999; Millour et al. , 2018) , while the top of the profile above an altitude of ∼4 scale heights is constrained by temperatures from the MCD. Given that the spectral signatures of gases and aerosols are relatively well separated in the spectral range observed by EMIRS, the retrieval is performed sequentially. First, surface temperature and the atmospheric temperature profile is retrieved using the strong CO 2 absorption centered at 667 cm -1 (15 μm). Next, the column optical depths of dust and water ice aerosol are retrieved along with a refinement of surface temperature using a large portion of the EMIRS spectrum between 250 and 1,315 cm -1 (excluding the CO 2 band).
Finally, the column abundance of water vapor is retrieved using the rotation bands between 200 and 350 cm -1. This sequence can be iterated to obtain a self-consistent solution, with the entire process completing in less than a second on a desktop computer. There are a number of assumptions that must be made to perform the retrieval. The surface pressure cannot be reliably retrieved with the EMIRS data alone, and so it is instead taken from the MCD for the given L s , latitude, longitude, local time, and size of the EMIRS field of view on the surface for each observation. Aerosol optical properties are taken from  Wolff et al. (2006) , with aerosol effective radii of 1. 5 μm for dust and 2. 0 μm for water ice to be consistent with previous retrievals (e. g. ,  Smith, 2004 Smith, , 2019)). The vertical distribution of dust is assumed to follow a Conrath profile  (Conrath, 1975).
Data Availability Statement Data from the Emirates Mars Mission (EMM) are freely and publicly available on the EMM Science Data Center (SDC, http://sdc. emiratesmarsmission. ae). This location is designated as the primary repository for all data products produced by the EMM team and is designated as long-term repository as required by the UAE Space Agency. The data available (http://sdc. emiratesmarsmission. ae/data) include ancillary spacecraft data, instrument telemetry, Level 1 (raw instrument data), Level 2 (calibrated radiance spectra), Level 3 (derived science products), quicklook products, and data users guides (https://sdc. emiratesmarsmission. ae/documentation) to assist in the analysis of the data. Following the creation of a free login, all EMM data are searchable via parameters such as product file name, solar longitude, acquisition time, sub-spacecraft latitude & longitude, instrument, data product level, etc.
The IoT systems allow data gathering from the environment through different sensor types (according to the application) and data analysis to facilitate decision making by the end-user. Although the control-command concept used by IoT system is not revolutionary, IoT has allowed the gathering of a huge amount of data (big data), through wireless and Internet networks, and their storage in databases/data lakes/data spaces in the cloud for further analysis and the creation of valuable information. At the end of 2019, there were 7. 6 billion active IoT devices, a figure which is expected to grow to 24. 1 billion in 2030, a compound annual growth rate (CAGR) of 11%, according to the research published by Transforma Insights  [4]. Short range connectivity solutions, (e. g. Wi-Fi, Bluetooth and ZigBee), will dominate connections, accounting for 72% in 2030, largely unchanged compared to the 74% it accounts for today.
Processing Layer This layer addresses edge computing, information element analysis and transformation, analytics, mining, machine learning, in a pervasive manner considering that autonomic services must be provided through ubiquitous machines in both "autonomic" and "smart" way. The processing layer provides the ability to process and act upon events created by the edge devices and store the data into a database in the storage layer. The processing layer can be closely interlinked with data analytics platform(s) based on edge-/cloudscalable platform that supports information processing technologies. The green IoT and energy efficiency optimisation depend on the type of information procession, location, real-time requirements, AI components, platforms, and system architecture (e. g. centralised, decentralised, distributed).
Developments of a new range of IoT applications is enhancing the role of this layer that is being used to analyse, process and in many cases store the information at the edge level. This layer provides support to the lower layers, allocates resources for efficient storage in virtual and physical machines and converts the data into the required form. The components of this layer include HW/SW processing elements/units (e. g. , operating systems, embedded software, MCUs, CPUs, GPUs, FPGAs, other AI-based accelerators, etc. ), semantic-based and service-based middleware, databases virtual machines, resource allocators, information convertors and translators. Processing the information at the edge where data is being generated, and providing insights from the information kept at the network and processing level has the benefits of reducing both latency and the demand on network bandwidth, preventing downtime, reducing CO 2 emissions, and improving the overall system efficiency.
Storage Layer The storage layer addresses the efficient storage and organisation of data and the continuous update with new information, as it is made available through the capturing and processing of IoT channels. The functions implemented include archiving the raw and processed data for offline long-term storage and the storage of information that is not needed for the IoT system's real-time operations. Centralised storage considers the deployment of storage structures that adapt to the various data types and the frequency of data capture. Decentralised and distributed storage is implemented with the functions in the processing layer. Relational database management systems that involves the organisation of data into table schemas with predefined interrelationships and metadata for efficient retrieval for later use and processing are used for different IoT systems.
An optimisation problem was formulated that aimed at reducing the communication cost of federated learning and provided the solution by decomposing it and using DNN for communication resource allocation. Numerical results on the benchmark real-world dataset corroborated that our proposed mechanism could improve the communication efficiency and reduce the overall energy cost  [38]. It is evident that standardisation efforts, research, and development funding programmes, as well as reference implementations on local, national, and international level with public-private funding schemes, will be the main drivers towards acceleration and market up-take of DT technology.
g. Open, Closed)  [28]. IoT can be one major application for Dew where heterogeneous devices act together to perform a set of tasks and do not need to be connected to the Cloud services all the time creating the so call Dew of Things (i. e. DoT). The Dew computing technical challenges include power management, processor utility, data storage, viability of existing operating system, network model, communication protocols, programming principles, database security, and data efficiency exchange. The Dew computing paradigm is different from the Fog/Cloud paradigms, as the edge and low-level devices cannot be used in a "conventional" programmable way, but they have to cooperate on the lowest level to solve processing needs, and be able to pass (and consume) information from all hierarchical levels.
DL techniques can work with very large data sets by analysing data, recognizing patterns and making prediction on next data. With DL, a computer can train itself with a large set of data collected for this purpose. If the Training stage, in which the neural network learns to classify different patterns, use datasets labelled in advance the process is referred to as Supervised Learning. In the case of unlabelled datasets, the learning process is called Unsupervised Learning and the neural network tries to cluster the dataset into groups with similar patterns. In both cases the result is an Artificial Neural Network (ANN) that contains all the information necessary to carry out the task. The ANN uses the knowledge acquired in the training to infer data features from new incoming data. This is called Inference stage and can be deployed in embedded devices with memory and processing capabilities orders of magnitude smaller than the servers used to train the ANN itself.
Training is a key part of AI technology stack. AI training is defined as the process of creating ML algorithms and involves the use of a learning framework (e. g. , TensorFlow) and training datasets provided by static or realtime sources (e. g. databases, IoT/IIoT edge devices, etc. ). The data collected is the source of the training data that can be used to train AI-based models for (e. g. ML, DL, etc. ) a different use cases, from pattern recognition, object detection, failure detection to consumer intelligence. AI training systems need to store large volumes of data as the systems refine the algorithms. AI inference systems store only input data that could be useful in future training. The AI training requires hardware platforms optimised for the type of AI neural networks and algorithms for efficient training used to address how the processing of neural networks is being performed on the platforms, and how application-specific accelerators are designed for specific neural networks for optimising the throughput and energy efficiency.
As a result, sensitive data are not exposed to the entity that maintains the global ML model. In certain cases, this approach further yields network resource savings, where training data transfer volume exceeds that of the ML model e. g. , video stream data. ML frameworks such as TensorFlow and PyTorch have already taken steps recently towards privacy with solutions that incorporate federated learning. Instead of gathering data in the cloud from users to train data sets, federated learning trains AI models on mobile devices in large batches, then transfers those learnings back to a global model without the need for data to leave the device. With open source initiatives such as OpenMined [152], AI models can be governed by multiple owners and trained securely on an unseen, distributed dataset.
ZigBee WPAN -IEEE 802. 15. 4 ZigBee is a short-range, low-power, wireless standard (IEEE 802. 15. 4), deployed in mesh topology to extend coverage by relaying IoT sensor data over multiple sensor nodes. ZigBee/ZigBee Pro is based on a specification that adds application profile, security, and network layers to IEEE 802. 15. 4 standard for wireless low-rate personal area networks. It operates in the UHF/microwave bandwidth with battery-powered tags that communicate with each other. Zigbee protocol features include support for multiple network topologies such as point-to-point, point-to-multipoint and mesh networks, low duty cycle that provides long battery life, low latency, direct ZigBee protocols are designed to be used in embedded applications requiring low data rates and low power consumption, enabling devices to form a mesh network of up to 65 000 nodes, covering a very large area target general-purpose, inexpensive, self-organising, mesh networks which are deployed for building/home automation, industrial control, embedded sensing, medical data collection, smoke and intruder warning, domotics, etc.
ZigBee networks use low power for communication, and individual devices can run for a few years on the installed battery. ZigBee is a perfect complement to Wi-Fi in many of these applications. ZigBee provides data rates at medium power-efficiency due to mesh configuration. The protocol is designed for physical short-range (< 100m) mesh configurations best-suited for medium-range IoT applications with an even distribution of nodes in proximity. The Zigbee PRO Specification adds child device management, improved security features, and new network topology options to Zigbee networks. Commissioning devices into networks has also been improved and made more consistent through Base Device Behaviour (BDB). The specification furthermore requires Green Power Basic Proxy functionality in all devices to further support Green Power capabilities and compiles all profile clusters into a single specification.
The Zigbee standard operates on the IEEE 802. 15. 4 physical radio specification and operates in unlicensed bands including 2. 4 GHz, 900 MHz and 868 MHz Zigbee 3. 0 supports the increasing scale and complexity of wireless networks, and copes with large local networks of greater than 250 nodes. The data rates provided are 250 kbps (2. 4 GHz) 40kbps (915 MHz) 20kbps (868 MHz). Zigbee also handles the dynamic behaviour of these networks (with nodes appearing, disappearing, and re-appearing in the network) and allows orphaned nodes, which result from the loss of a parent, to re-join the network via a different parent. The self-healing nature of Zigbee Mesh networks also allows nodes to drop out of the network without any disruption to internal routing. Zigbee's supports over-the-air (OTA) upgrade for software updates during device operation and provides enhanced network security using methods such as centralised security by employing a coordinator/trust centre that forms the network and manages the allocation of network and link security keys to joining nodes or distributed security where there is no coordinator/trust centre.
A 128-bits symmetric-key cryptography is implemented in ZigBee's security architecture. JupiterMesh is a robust, low-power industrial IoT wireless mesh network for Neighbourhood Area Network (NAN) with flexible data rates that enables neighbourhood and field area communications for utilities and municipalities deploying intelligent grid and smart city solutions. JupiterMesh is supported by ZigBee Alliance and is built on open IETF and IEEE standards uses parts of the IEEE 802. 15. 4g standard used by the Wi-SUN Alliance, as well as IEEE 802. 15. 4e and the IETF's Ipv6, 6LoWPAN, UDP, TCP, RPL, and CoAP protocols. The protocol includes advanced technologies such as IPv6, frequency hopping, multi-band operation, authentication, encryption, and key management to drive industry realisation of interoperable multi-vendor implementations that scale and that are secure and easy to manage.
Intelligent Connectivity 119 Wi-Fi provides high-throughput data transfer for both industrial and home environments. Wi-Fi has high energy requirements and is used for IoT applications and services that do not require large networks of battery-operated IoT sensors. For such applications Wi-Fi major limitations are in coverage, scalability, and power consumption. Many IoT devices that are connected to power outlet (e. g. smart home IoT monitoring devices and appliances, digital signages or security cameras, etc. ) are using Wi-Fi as connectivity protocol. Wi-Fi -specification (ISO/IEC 8802-11) is an international standard describing the characteristics of a wireless local area network (WLAN). The name Wi-Fi (short for "Wireless Fidelity") corresponds to the name of the certification given by the Wi-Fi Alliance, formerly WECA (Wireless Ethernet Compatibility Alliance), the group which ensures compatibility between hardware devices that use the 802.