dataesr/scientific-paragraphs-categorization huggingface dataset:
Listing 2 shows our implementation of this areal distance test, performed using the PostGIS DBMS of our framework. As previously mentioned in Section 2. 3, the identity question is central in our approach and for the fine-tuning of the change detection algorithm. This is why, within the Theseus framework, we have made possible the parameterization of what constitutes-and for what part-the identity of TUs within the studied TSN. This definition results in a weighted function of several distance tests, that accounts for the part of each of the TU's attributes in the TU's identity. For instance, an expert might consider that in a TSN called N, for each TU of N, the code attribute accounts for 40%, the geometry accounts for 40%, its name accounts for 10%, and its super unit attribute accounts for 10%.
g. , http://purl. org/steamer/); {TSNacronym} is the acronym of the studied TSN (e. g. , SAU for the Swiss Administrative Units TSN); V{TSN version Identifier} is the TSNVersion identifier (generally, a date is chosen as the identifier of the TU version, e. g. , V2018); L{Level Identifier} is an identifier of the level in this TSN version (generally, a number is chosen e. g. , L2); {TU Code} is the code of the TU in the TSN version (e. g. , in the SAU TSN the Canton of Neuchatel has for code 2400 and the URI of this TU according to our pattern is http://purl. org/steamer/sau/V2018_L1_2400). Four existing software have been used to implement the publication step: the purl. org service (which provides persistent URI for data), the GraphDB triplestore (used to publish the created RDF data), the Apache Server and configuration files (htaccess file) for dereference URI, and the GeoServer software (used to publish as OGC Web services the TU geometries linked to their RDF representation, using TUs' URIs).
| Explore To provide users with tools to explore and exploit the published KGs, we have implemented various search engines, using standard Web mechanisms: a SPARQL access point to perform SPARQL and GeoSPARQL queries on RDF data (available from the URL http://steamerlod. imag. fr/sparql); OGC Web services (WMS and WFS) generated by instantiating the GeoServer software connecting to the Theseus PostGIS database; a Web GUI prototype, called Theseus Visualization Module, for geovisualizing the geospatial KGs and the territorial changes descriptions they contain. This prototype is a Web application developed in JavaEE, using several libraries such as Leaflet for Web cartography. Figure  8  shows a screenshot of its GUI. The following Section 4 describes how to use the Theseus framework through a case study based on a real dataset called the Swiss Administrative Units (SAU) TSN.
| The SAU dataset We present in this subsection the specificity of the SAU dataset and the KGs that have been generated using the Theseus framework on this dataset. Each year, Swisstopo, the Federal Office of Topography for Switzerland, makes available a new version of the SAU, under the name swissBOUNDARIES3D, at three territorial levels: cantons, districts, and municipalities. The SAU versions that we have processed are versions 2017 and 2018, composed of these three territorial levels, respectively. These two versions contain the entire Swiss administrative divisions as they were in 2017 and in 2018. Since our algorithm computes intersections between version-to-version polygons (to detect and characterize changes in the geometry of each TU), it is worth considering characteristics such as the number of TU's vertices, or the number of TUs in the datasets.
Indeed, these key figures will be decisive for estimating the computation time and the complexity of the algorithm. Table  1  shows the main figures of the SAU dataset, as well as the key figures of the other two nomenclatures processed, for comparison purposes. Because SAU includes a significant number of TUs with high accuracy considering the number of vertices per TU (see the line 1. Average Number of vertices per TU, in Table  1 ), this dataset represents an interesting use case, halfway between the NUTS and ASGS datasets, which contains respectively 28 vertices per TU on average and 1900 TUs, 4900 vertices per TU on average, and 2700 TUs (see Table  1 ). It is also a good example of the benefits of using our algorithm for change detection. Until now, statistical institutions still build manually their yearly catalogs of changes, etc.
| Data pre-processing Using the Theseus Prep module (see Figure  4 ), we transform the original SAU shapefiles into TSN-shapefiles ready to be processed by Theseus. After this pre-processing step, from the Theseus Knowledge Graph Creator GUI, the  TSN-shapefiles are integrated into the PostGIS database of the system. Then, this relational database is exploited for the detection of the changes between two versions of a dataset, as explained in the following subsection.
| Populating the TSN and TSN-Change ontologies To populate the TSN ontological model, from the Theseus Knowledge Graph Creator GUI, a user (e. g. , an expert of the processed nomenclature) activates three mains modules: the TSN Semantic Annotator, the TSN-Change Detector, and the TSN-Change Semantic Annotator modules. First, to detect and semantically annotate the territorial changes between two TSN versions, the user has to set up through a GUI, the parameters of the algorithm for change detection. Some parameters allow the user to draw up a list of TU attributes to be taken into account in the similarity tests between two versions, as well as the weights associated with each of these attributes. The other parameters correspond to thresholds that set a tolerance limit for the shift between TU geometries when detecting changes.
Moreover, our KGs are visualizable by standard tools adapted to RDF data visualization and through the geographical GUI we have designed for the exploration of territorial changes. Many perspectives are offered to improve the first version of the Theseus framework. First, regarding the change detection algorithm, we plan to improve it using machine learning techniques, to automatically find the best hyper-parameters (thresholds and weights) for the algorithm according to the submitted TSN dataset. Another perspective is the enrichment of our KGs to automatically contextualize territorial changes for instance, by connecting to historical event databases or law texts, such as the European Legislation Identifier 18 in the LOD Cloud. We intend to enrich our graphs according to research axes such as law texts, socioeconomic data, environmental data related to the observed TU.
3. 2. 2 | Step 2: Generating TSN and TSN-Change KGsAt this step, we populate our ontological model. It consists of two sub-steps: (1) populating the TSN Ontology by transforming the preformatted TSN-shapefiles into KG using the RDF framework and the TSN Ontology concepts; and (2) populating the TSN-Change Ontology by automatically detecting and describing changes between the TSN-shapefile versions using the TSN-Change Ontology for the description of changes. We have developed a specific software application, called Theseus Knowledge Graph Creator, to perform these two sub-steps. This application is composed of different software modules (see Figure4): the Theseus PostGIS database; the TSN semantic annotator; and the TSN-Change detector and semantic annotator.
Contributions of this paper: To sum up, the contributions of this paper are: (1) the measurement system, including X-Fi and X-Perf , built for studying the current landscape of WiFi offloading based on provider-managed APs; (2) the one-of-its-kind dataset, which demonstrates the feasibility and capability of WiFi offloading via providermanaged networks; (3) the analysis and implications made from the dataset, which shed light on the use of provider-managed WiFi for connected vehicles. We have released the dataset 4 and the code 5 of X-Fi and X-Perf for the community to further study the vehicular WiFi offloading landscape. The remainder of this paper is organized as follows. We describe the related work in Section 2. Next, we present the measurement system in Section 3. Then, the methodology that we used for the study is presented in Section 4.
Related work Using WiFi to provide moving vehicles with Internet access has been studied by many researchers in the past. Table  2  summarizes a comparison between our work and previous art. Notably, our results, 4 https://github. com/yangfurong/X-Fi_dataset. 5 https://github. com/yangfurong/X-Fi-code. especially in goodput, are very different from the previous ones, which shows how the V2I WiFi-offloading landscape has changed. Accessing the network: Ott et al. [39]  confirmed a vehicle can connect with individual WiFi APs placed along the roadside. Gass et al. and Hadaller et al. [40, 41]  studied the impact of the back-haul on WiFibased V2I connections. Besides, Hadaller et al. [41]  identified limits of stock WiFi stack implementations and provided guidelines to improve them. While they focus on characterizing the V2I connectivity from individual WiFi APs, our work focuses on deployment-ready WiFi-based in-the-wild offloading using provider-managed WiFi networks.
X-Fi : connecting a moving vehicle to provider-managed WiFi hotspots Motivation: Given that the encounters between vehicles and APs are commonly short, a WiFi system that can quickly set up connectivity with encountered APs is of great importance. Although Cabernet  [3]  proposed a vehicular WiFi client tuned for open WiFi networks via a set of heuristics, it does not support EAP authentication  [49] , which is prevalent in current provider-managed WiFi networks and is more suitable for vehicular uses than WEB-based authentication. On the other side, the mainstream WiFi systems for connectivity setup, such as wpa_supplicant and dhcpcd on Linux, support EAP authentication, but, they are not well-tuned for vehicular uses. Therefore, we propose X-Fi that consolidates the advantages from the two sides above.
IP address acquisition: Once the supplicant has established L2 connectivity to the access point, it is necessary for the client to obtain an IP address in order to communicate with other hosts on the Internet. This is done through the DHCP. The off-the-shelf DHCP client, i. e. dhcpcd on Linux, is not tuned for vehicular environments. Specifically, the inefficiency of dhcpcd mainly comes from two perspectives: (1) the timeouts for DHCP messages are too long for vehicular scenarios as demonstrated by Cabernet  [3] ; (2) the ARP probing mechanism, sending ARP probe packets to check whether the assigned address is in-use by other clients in the same LAN  [50] , is enabled by default, which may lengthen the IP acquisition process by several seconds. Therefore, we implemented a custom DHCP client based on dhcpcd that has shortened timeouts and disables the APR probing mechanism.
Thereafter, it measures the TCP throughput by performing data bulk transfers between a remote server and the local onboard computer. During the TCP transmission, all exchanged TCP packets are recorded by tcpdump at the client-side. As soon as X-Perf finds that the WiFi interface has lost its IP address, which means that the WiFi connectivity is interrupted, X-Perf stops the TCP throughput measurement and archives all data collected in this measurement run. The collected data contain a wealth of information from L2 to the application layer including packet-level traces, and all information is also geo-referenced and synced with GPS. X-Perf repeats the above process each time X-Fi connects to an AP until the end of the experiment or until the vehicle is turned off.
Software: The onboard computer runs Ubuntu 16. 04. 1 with 4. 10. 0-33generic Linux kernel. At the computer boot, systemd launches X-Perf (Section 3. 2). Once up, it executes gpxlogger and X-Fi (Section 3. 1), respectively to track the GPS coordinates and to start the association and connection attempts with nearby WiFi hotspots. Thereafter, X-Perf starts measuring the performance of WiFi offloading and collecting measurement results as explained in Section 3. 2. We configured X-Perf to use the following configuration pool (explained in Section 3. 2): CCA ∈ {BBR, CUBIC}, direction ∈ {upload, download}, flow number ∈  [1, 16]. BBR  [37]  and CUBIC  [38]  are chosen because the two are now the predominant CCAs on the Internet  [54]. BBR is a recent work adopted by many Google services and outperforms classic window-based CCAs across various scenarios, especially over lossy links.
Dataset summary The salient features of our dataset are summarized in Table  3. The WiFi networks we connected to in Bologna and Paris (Fastweb, Emilia  We confirmed that by connecting to some of these APs which failed to respond to the DHCP requests at locations where their signal strength was decent, between -40 dBm and -50 dBm. As we have no control nor inside knowledge of the network infrastructure used in Paris, we could not go further explaining the root cause of that. Although the ratios of IP acquisitions over associations are much different across the four cities, the ratios of TCP sessions over IP acquisitions are similar. Therefore, in each of the analyzed cities, once a moving vehicle obtains an IP address it is very likely able to establish TCP connections. The complementary CDF (CCDF) of the average driving speeds per association in every city is shown in Fig.
Acknowledgments We are grateful to the review panel for their insightful comments and feedback, which have greatly improved the quality of this paper. We appreciate  Luca De Mori  and  Émilie Mespoulhes  for their contribution to the experiments in Paris. We also thank  Zhenyu Li  for his constructive suggestions during the revision stage of this paper. This work is supported in part by the  National Key R&D Program of China  (Grant No. 2019YFB1802800 ), the  National Natural Science Foundation of China  (Grant No. 61725206 ), and the  CAS-Austria Joint Project  ( 171111KYSB20200001 ), the  Macao Polytechnic Institute  project ( RP/ESCA-01/2020 ), the  US National Science Foundation  grants  CNS-1719336 ,  CNS-1647152 ,  CNS-1629833 , and  CNS-2003129  and an award from the  US Department of Commerce  with award number  70NANB21H043.
, 2005 (Wu et al. , , 2002)). There exist well defined pipelines including a kinematic model, a markerset and the associated processing tools for the lower limb segments (e. g. Conventional Gait Model). Models embedded in freely available tools such as OpenSim have become standards in the biomechanics community  (Delp et al. , 2007). Still, these standards cover only a part of the process (e. g. ISB recommendations), and are specific to some anatomical region (e. g. lower limbs for the Conventional Gait Model) or are software specific (e. g. OpenSim). As such, many studies rely on alternative "home-made" models. When considering such models, all the necessary characteristics (model parameters and personalization techniques) are often barely described. Indeed, it represents multitude of technical information while it is rarely the main focus of the studies.
, 2019)  nor the model used for the inverse kinematic (IK) approaches  (Kanko et al. , 2021). Here again, there is a need for a well described model, and its association with the underlying anatomy and with the measured quantities. Moreover, it would be particularly interesting if both "marker-based" and "markerless" models could provide comparable outputs. Indeed, markerless approaches are still in a development phase that requires evaluation by comparing their outputs versus classical marker-based results. It would also be essential that results from studies obtained via markerless approaches could be compared to previous results and databases constructed using markerbased approaches. Therefore, this study aims at providing two whole body models (with male and female versions for each), and their full descriptions, ready to be used for whole-body human movement analysis based either on marker-based or markerless approaches.
We also provide insights into the comparison of results obtained from both models. The markerless approach used is Theia3D (Theia Markerless Inc. , Kingston, Ontario, Canada, v2021. 2), a commercial human pose estimation software that has recently attained substantial popularity in the field of biomechanics. Models are proposed for use in Visual3D  (C-motion, Germantown, USA, v2021. 11. 3). The following section details the kinematic analysis pipeline of Visual3D and its use of models for both marker-based and markerless approaches.
Overview of Visual3D kinematic pipeline Visual3D is a commercial software for calculating kinematic and kinetic variables for biomechanical analysis of 3D motion capture data. Figure  1  displays the classical pipeline used for kinematic analysis in Visual3D. A subject-specific model is generated from data of a calibration trial (e. g. a static trial) and a model template. Based on the movement trial data (stored in a c3d file) and using this subject-specific model, kinematic data (such as joint coordinates or joint positions) are computed by using IK step. Depending on the motion capture approach (marker-based or markerless), the movement data may differ and the model generation and IK steps also differ.
BSIP The BSIP for all the segments: segment mass, position of CoM and moment of inertia are detailed in Table  5  and Table 6. These values are adopted from  (Dumas and Wojtusch, 2018). We did our best to identify how SCS were constructed based on Theia3D documentation and to adapt the BSIP definition accordingly. Positions of CoM and components of the inertia matrix are calculated by using the segment lengths. which are defined by means of segment endpoints. For thighs, shanks, upper arms, and forearms, the segment length is computed as the distance between the proximal and distal endpoints, based on the data from the pose matrices. For other segments (hands, feet, pelvis, thorax, and head), mostly because of the lack of appropriate distal endpoint, segment length is calculated as the product of a precomputed ratio (eq.
During the new century, the deep learning technology grew rapidly after 2006  [48, 49] , and a series of algorithms were derived from the deepening of the structure of artificial neural network model, also called deep neural networks (DNN). This was supported by the development of big data science and the improvement of computer performance that provided hardware support. Meanwhile, the development of open source and flexible software platforms, such as TensorFlow  [50] , PyTorch  [51] , and Keras  [52] , made it easy for beginners to build generic deep architectures. The third-wave of AI started to bring profound industrial changes in various aspects of modern society, such as computer vision  [53] , natural language processing  [54] , speech recognition  [55] , AlphaGo  [56] , robotic controls  [57] , etc.
Auto-encoders (AE) is a special DNN structure, which can extract features and reduce data dimension with the help of the powerful nonlinear processing ability of neural network. The first part of AE is the encoder, which is used to extract features from the original input data, and the second part is the decoder, which reconstructs the original data according to the extracted features, as shown in Figure  3(a). The input data successively passes through the encoder and decoder to obtain the output vector, and then calculate the reconstruction error from the comparison of the output vector and the original input vector. The reconstruction error is defined as the cost function, and employ back propagation algorithm to minimize it. Tracing to the source, the AE uses the original input data as labels instead of the sample labels in the dataset, so it belongs to unsupervised learning.
Both supervised learning and unsupervised learning belong to data-driven methods, and their computational accuracy largely depends on the size and distribution of data. Meanwhile, the reasonable selection of hyperparameters will also have a certain impact. Therefore, when aiming at a specific problem, it is necessary to obtain the dataset according to the complexity of the problem, and check the distribution of the data before it is used for model training. In the training process, the crossvalidation method is usually used to select the optimal hyperparameters. As for the calculation cost, it depends on the amount of data, the structural complexity of neural network model, the selection of activation function and so on. Reinforcement learning does not need data training, but gradually approaches the preset goal through iteration.
Multi-order Helmholtz resonators can provide multi-order resonances and realize richer airborne applications. In practical applications, it is a great challenge to inverse design the structures according to the desired STL. The study of  Sun et al. shows that this difficulty comes from the process of inferring equivalent electrical parameters (EEPs) from the desired STL spectrum which requires solving a six-degree equation for the resonant frequencies  [100]. Indeed, the knowledge of EEPs allows easily accessing the geometrical parameters (GPs) by using lumped-parameter techniques (LPT). To solve this issue, they trained the DNN to realize the mapping between STL and EEPs, to avoid solving higher-order equations. It is worth noting that the forward process from GPs to EEPs and STL is easy to implement, so the dataset can be obtained by analysis.
In general, this ingenious inverse design process is divided into two steps (see Figure  4  Different from inverse design, forward properties prediction can usually be realized by analytical or numerical methods, the latter being often necessary for some complex structures. In this case, the role of ML is not to solve the impossible or difficult tasks, but to surpass the efficiency of numerical methods by reducing the time-consuming calculations. Donda et al. applied CNN to accurately predict the absorption spectrum of a given metasurface  [101]. In this work, the metasurface consists of randomly curled patterns inside the cavity, as shown in Figure  4(c). The total lattice is encoded into a matrix where 0 and 1 represent air and polylactic acid (PLA) respectively. To obtain dataset for neural networks training, they calculated the absorption spectra of some randomly generated coding modes by finite element method (FEM).
Achieving local enhancement or attenuation through sound field regulation is one of the important topics in acoustics research. The degree of enhancement or attenuation should generally be determined on the basis of actual engineering or environmental needs, which allows all candidates designed by ML to have the actual working conditions. Zhao et al. proposed an ML optimization method to design the phase gradient of the acoustic metasurface for control of the local sound field  [104]. The phase gradient of metasurface is digitized into 0/1 bit sequence, and the sound field is calculated by FEM to generate dataset. CNN1 is trained to realize the mapping between the absolute sound field and the metasurface sequence, while CNN2 is trained to realize the mapping between the metasurface sequence and the average sound field.
The mechanical properties of curved beams such as stiffness (E), forward (S) and backward (B) snapping force can be adjusted by changing the thickness distribution of the beam. They randomly generated the thickness distribution of the curved beam and obtained the corresponding mechanical properties through FEM, Figure  7 (a). Through the above steps, they generate a dataset for DNN training to capture the complex nonlinear relationship between the variable thickness curved beam and its mechanical properties. According to the requirements of different mechanical properties, an optimization step is implemented to obtain a high-property curved beam. Their work applied ML algorithms to efficiently and accurately design curved beams, which shows the suitability of ML for mechanical metamaterial structure design.