dataesr/scientific-paragraphs-categorization huggingface dataset:
14  (sends a positive or a negative value to motors depending on the value of a boolean variable, representing whether there is or not an obstacle ahead, pointed by ''EvaluateDistance''). This ROSMiLan instance could, in theory, be transformed into a ROSModL instance according to the principles shown in Section 4. Then, a ROSMiLan instance could be transformed in a ROSModL instance and, from it, have its code generated. Using a sample of a transformation model written in Acceleo for automatically generating code, it was possible to generate compilable (i. e. absent of compilation errors) functionality codes from the ROSProML instances, as presented in Listing 1 and Listing 2. It is clear from the generated codes that they would not work in a real environment, since publishing to the global variables was not addressed.
MAVROS communication As a second variant, the same mission is instantiated but with a ''DecideMotionDirection'' that relates to an existing MAVROS topic, as is shown in Fig. 15. For this, MAVROS had to be instantiated so that its elements could be accessible by the model, as seen in Fig. 16. Using the code generation algorithm developed for ROSModL, it was possible to generate a compilable (i. e. absent of compilation errors) 1 // AUTOMATICALLY GENERATED FILE FOR: commandThrottle.
Related work There have been some efforts in the direction of easing the creation and execution of personalised drone missions. The following researches have brought important contributions. Yet, they differ from RoBMEX in certain key points. RoboChart is a DSML for robotics that represents sets of state machines  [14]. This modelling tool is built on formal semantics that enable the verification of the models. It provides constructs to model realtime concurrent designs and includes the notions of robotic platforms with distributed controllers, which may contain parallel threads. On the other hand, automatic code generation is not fully supported by the software, nor is its integration in ROS. FlyAQ  [15]  is a tool able to automatically generate a complete Python code for executing a mission.
Using two different sets of objects on a map, a mission specification (trajectories, regions of interest and activities) is merged with a context definition (geofencing and obstacles) to automatically generate a sequence of functions that complete the designated mission taking the constraints into account. Then, this sequence is automatically transformed in a python script that commands a drone fleet over a GCS using DroneKit  [9] , a Python implementation of MAVLink commands. It is important to note that the generated application is meant to be only executed on a GCS. FlytOS is an operating system built on ROS (Robot Operating System) and Linux, made to facilitate the development of drones  [10]. It provides APIs and SDKs for building high-level applications, and interacts with the drone/autopilot through drone-adapters, exposing the high-level APIs in ROS, C++, Python, REST and Websocket.
It is very similar to the RoBMEX approach, using different editors to different needs, but when developing software components, the code generation requires the user to manually complete the code. SmartSoft  [19, 20]  is a framework for using different software components to compose a robotic system, in a similar approach to that of RoBMEX, but also tools for optimising the design. They do not describe, however, any kind of DSML for creating and generating components. RobotML  [21]  was part of the now finished PROTEUS project. It uses robot ontologies to provide ''a common ground for designing and implementing component-based robotic systems''. This approach focuses on deploying code to OROCOS-RTT5, RTMaps6, Urbi7 or Arrocam (which were defined by the Proteus project), while RoBMEX is focused on -but not limited to -ROS.
The connection between execution nodes can be instantiated using ROSMiLan, the DSML responsible for designing the mission itself by giving a non-expert end-user a simpler language that does not require any expertise in terms of the drone technologies. Then, an example of a ROSModL instance was made, with a variant that is based on the existing system MAVROS and another that is completely independent of other technologies. Some of MAVROS's elements were successfully represented using ROSModL, and could then be referenced by an instance of ROSMiLan (a model of a system that intends to communicate with an executing MAVROS node). Two examples of ROSProML instances were also made based on hypothetical tasks, and had most of their code automatically generated in C/C++. We plan to stress RoBMEX through different use cases in order to obtain a wide choices of reusable libraries and missions.
Uniting the code created by the transformation over ROSModL and the one over ROSProML, it will be possible to generate a complete compilable and executable file. This shall be the mission of the code generation for ROSMiLan models, which is still in development. It is important to note that the code generation can have other targets -not only C/C++ but also Python, and if no ROSModL instance is used, other generators can be implemented to work on ROSProML and ROSMiLan instances to create code in other languages used in embedded systems, such as Ada.
In the context of the French CAP 2025 I-Site project, an environmental data lake called CEBA is built at an Auvergne regional level. Its goal is to integrate data from heterogeneous sensors, provide end users tools to query and analyse georeferenced environmental data, and open data. The sensors collect different environmental measures according to their location (air and soil temperature, water quality, etc. ). The measures are used by different research laboratories to analyse the environment. The main component for data shipping and storing is the ELK stack. Data are collected from sensors through Beats and streamed by Logstash to Elasticsearch. Scientists can query the data through Kibana. In this paper, we propose a data warehouse frontend to CEBA based on the ELK stack. We as well propose an additional component to the ELK stack that operates streaming ETL which allows integrating and aggregating streaming data from different sensors and sources given the user configuration in order to provide end users more analytical capabilities on the data.
Its main objective is to An interesting functionality for CEBA users would be to provide them with analytical tools. Hence, in this paper, we aim to investigate using Elasticsearch as a data warehouse and Kibana as a Spatial OLAP visualisation tool. Data warehouses support managers for decision-making  (Jarke, 2002) ,  (Inmon, 2005) ,  (Pinet, 2010). Traditionally, data warehouses are based on relational data models, but this type of models is not the most efficient model for real-time sensor streams. ELK stack is more suitable for stream management, but this approach does not provide analytical features as proposed in data warehouses. The authors of  (Bicevska, 2017)  discussed the NoSQL-based data warehouse solutions and provided some positive points for this solution. They noted however the lack of reporting tools compatible with NoSQL systems.
In this paper, we propose a method to model a spatial data warehouse model with ELK stack. We present the main structure of a component called IAT (Integration and Aggregation Tool) that allows users defining mappings  (Lenzerini, 2002)  and aggregation options between sensors sources and a target index in Elasticsearch. IAT acts as a streaming ETL  (Sabtu, 2017). It continuously extracts records from Logstash aggregate records, transforms and maps them according to the output schema. The output data is in JSON format and is stored in an Elasticsearch index. Elasticsearch (ES) is powerful in search and aggregation queries but less for join queries  (Pilato, 2017). Hence, we store the data going out from IAT in one ES index. The paper is organised as follows. In the next section, we present some related work.
Sensor Data Sensors are popular technology solutions to collect environmental data. With the developing technologies, there are many kinds of environmental sensors, e. g. ,  (Werner-Allen, 2006) ,  (Yick, 2008) ,  (Richter, 2009) ,  (Noury, 2018). Usually, sensor data are georeferenced data. The records consist of measurements or observations got at a specific location (geo-point) or within a specific area (geo-shape). The geographical information in the measurement is usually the physical location of the sensor. In CEBA, data collected from sensors are georeferenced data.
Batch and Streaming ETL (Extract Transform Load) Traditionally, ETL is a process for (i) extracting data from multiple sources, (ii) transforming and (iii) loading them into a data warehouse  (Bansal, 2015). Batch ETL corresponds to the ETL process, it is triggered at a specific time and which processes a large volume of data in one time. The streaming ETL is an enhanced approach of the ETL process. It executes the ETL process in near real-time. This approach solves the limitations of the batch ETL for streaming data and allows analysing data in a short time after it is produced by the sources. Logstash is a data processing pipeline. Elasticsearch is a document-oriented database. The documents are JSON objects and Kibana is a user interface tool of Elasticsearch for administration and queries.
Elasticsearch Elasticsearch (ES) is the heart of the ELK stack. It is a search engine with real-time and full-text searching. It is also a document-oriented database. In ES, documents are JSON objects that are stored within an Elasticsearch index. An index in ES is comparable to a database in the relational database world. A document is like a row in a table and document fields are table attributes. A field can be of many different types such as a string, a number, etc. The mapping of an index in ES is like a database schema. It defines the types of the fields for the documents such as keyword, integer, date, etc. and how the fields should be indexed and stored in ES. ES supports two spatial data types: geo_point and geo_shape  (Elasticsearch, 2020). Geo_point type combines latitude and longitude information.
SPATIAL DATA WAREHOUSE WITH ELASTICSEARCH In this paper, we show how to design an analytical tool based on Elasticsearch to allow users get useful insights from the sensor data. Currently, we experiment our approach on CEBA which is composed of several components for e. g. data collecting, storing, archiving. ELK stack is one of the main CEBA components for collecting, storing and visualising data. As soon as a new record is generated by a sensor, the record is collected by Beats and sent to Logstash. The record is parsed and transformed to cope with the corresponding index in Elasticsearch. Finally, users can query and visualize the data through Kibana. However, it is not efficient to run complex analytical queries upon different sensor data in the current configuration for two main reasons: (i) The mapping (schema) of the indexes may not be homogeneous between sensors, (ii) Elasticsearch does not perform well for joining indexes  (Pilato, 2017).
Hence, our solution consists in building one index in Elasticsearch for analytical purposes that we call the target index. We also propose a streaming ETL application (IAT). Its role is to pipeline data from Logstash to the target index. This process is driven by a user configuration described in section 3. 4. In the following, we first present the global system architecture. Then we present a multidimensional model for our use case and its corresponding physical mapping in the ES target index. We describe the functionalities of the streaming ETL application. Finally, we show an example of dashboard with use case data queries and visualisations.
System Architecture Figure  2  describes the architecture of our system. Data generated by sensors are collected by Beats and sent to Logstash. Then, the output of Logstash is both stored in ES and extracted by IAT. IAT processes the data following the user configuration (see section 3. 4) and stores the results in ES as well. The output of IAT can be also sent to applications other than ES. The functionalities of IAT are (i) fields mapping, (ii) time window aggregation and (iii) integration with external sources. (i) Fields mapping includes mapping one or several fields in sources to one or several fields in the target index. (ii) Time window aggregation consists in aggregating sensor data during a certain interval of time and operating functions on measurement fields. (iii) IAT integrates the sensor data with additional sources in order to enrich the data with additional information (e.
Multidimensional Model and Its Corresponding Physical Elasticsearch Index In this section, we present one example of multidimensional schema related to CEBA use case. We also present the corresponding physical mapping of the index that will store the data in Elasticsearch. The multidimensional model in Figure  3  presents the data warehouse classes and aggregating associations noted by "←"  (Pinet, 2010). The fact is the Measurement class. The dimensions are hierarchies of classes that start from Measurement. The fact includes five sensor measurements: temperature, water level, rain amount, illuminance, and air humidity. We define four dimensions including: sensor location, time of measurement, sensor information and the scientific projects associated to the data. The time dimension is a hierarchy that allows aggregating measurements according to days, months, and years.
CONCLUSION AND FUTURE WORK We presented our work for creating an analytical tool on top of Elasticsearch for sensor data. We presented the general architecture of our system. We also presented a streaming ETL application that pipelines sensor data in near real time to Elasticsearch and showed a dashboard based on the use case data. For future work, we want to provide the users of CEBA with our analytical tool and evaluate its impact on their respective work. We also aim to improve IAT tool to be able for example to handle additional measurements for an already existing target index, or to propose spatial aggregation functions for georeferenced measures. We as well want to automate the creation of the target index mapping from the multidimensional conceptual model.
The growing aging of the world population is leading to an aggravation of diseases, which affect the autonomy of the elderly. Wireless body sensor networks (WBSN) are part of the solutions studied for several years to monitor and prevent loss of autonomy. The use of optical wireless communications (OWC) is seen as an alternative to radio frequencies, relevant when electromagnetic interference and data security considerations are important. One of the main challenges in this context is optical channel modeling for efficiently designing high-reliability systems. We propose here a suitable optical WBSN channel model for tracking the elderly during a walk. We discuss the specificities related to the model of the body, to movements, and to the walking speed by comparing elderly and young models, taking into account the walk temporal evolution using the sliding windowing technique.
In such a remote monitoring context, the wireless BSN (WBSN) system has specific requirements such as high reliability, low power consumption, and high data security. In addition, a WBSN can involve off-body, on-body, and inbody sensors; thus, we can differentiate extra-WBSN and intra-WBSN communication  [5]. Connectivity solutions mostly use radiofrequency (RF) wireless technologies through different standards such as Bluetooth Low Energy among others  [6, 7] , with air then being the communication medium. In addition, the human body is also used as a communication medium for intra-WBSN  [8]. We focus in this study on wearable on-body sensors with extra-WBSN transmissions. In the case of extra-WBSN, there are still challenges related to RF spectrum congestion, interference, data security, and privacy.
The behavior of the OWC channel is analyzed from Monte Carlo ray tracing simulations (MCRT) using dedicated software called RapSor  [37]. We discuss the interest of a specific model for the elderly, by comparing the characteristics of the channel with those obtained with a classic young person model. Optical WBSN performance for walk monitoring is then dynamically evaluated in terms of the outage probability with an approach taking into account the time correlation during the walk. The rest of the article is organized as follows: the description of the optical WBSN system, as well as two body models for young people and the elderly, is presented in Section 2. We also define the movements during walking linked to the limbs, the walking speed, and the RW trajectory. In Section 3, the channel statistical analysis is performed, showing the channel gain and delay spread distributions for the two models.
Local Motion In order to consider movements of arms and legs, we use the 3D modeling software Blender  [40]  that provides animated bodies of young and old people. These animations are from a database containing real motion captures of a person  [36]. They are recorded on different frames constituting a walking cycle, as partially illustrated in Figure  3a  for the elderly and in Figure  3b  for young people. We can see in Figure  2  that the transmitters located at the wrist are almost at the same height from the floor. The coordinates of the position and orientation vector → v of Tx from the origin of the coordinate system (0, 0, 0) located between the legs (see Figure  2 ) are shown in Table  2  for both body models. These body representations correspond to the starting images of a walking cycle, which can be then modeled using 3D animation software.
Local Motion In order to consider movements of arms and legs, we use the 3D modeling software Blender  [40]  that provides animated bodies of young and old people. These animations are from a database containing real motion captures of a person  [36]. They are recorded on different frames constituting a walking cycle, as partially illustrated in Figure  3a  for the elderly and in Figure  3b  for young people. There are differences between both movement patterns. The first concerns the orientation changes of the transmitter at the wrist during the gait cycle. Indeed, we can see in Figure  3a  that the orientation vector → v in the case of the elderly varies but is always in the same direction. On the contrary, for the young model in Figure  3b , the vector may vary in the opposite direction depending on the swing of the arms, for example, between frame 1 and frame 17.
Channel Characterization To analyze the optical WBSN channel behavior, we used MCRT-based simulations of channel impulse response (CIR) coupled with the body and mobility modeling that we previously defined. We used the RaPSor simulation software (Ray Propagation Simulator) developed in our laboratory. It is an extensible tool based on the Netbeans platform and coded in Java, for modeling wave propagation in realistic environments in different frequency domains, from the radio range to the optical one. This simulation software has already been validated for the propagation of IR waves in confined environments and the WBSN context  [37, 42]. It is based on a stochastic Monte Carlo method, associated with a ray-tracing algorithm, numerically determining both LOS and non-LOS paths contributions (time of arrival and attenuation) from analytical models of reflection and propagation and for one defined link configuration.
Motivation, Challenges and Needs We motivate our work through OpenCEMS 4  : a Web platform that provides solutions for energy data management in connected environments. The platform allows to connect (1) stationary and mobile objects that provide static/dynamic resources, and (2) Web applications exposed as static resources. The resources, described in Hydra, are used for: collecting on-site data, preprocessing collected data, and analyzing data. Many requests occur in OpenCEMS. We consider a building manager that wants to predict the temperature in the conference room A. To satisfy his request, specified by the "ATP" (Air Temperature Prediction) function, it is important to select the appropriate resources realizing his need 5. However, several challenges arise, as illustrated in Figure  1 :  When several resources are identified having the same function required to answer "ATP", as "Air Temperature Collection", selecting the appropriate ones among others is not obvious for end-users, as the building manager.
Evaluation and Discussion In this section, we compare our QoR model to existing works, and evaluate our resource selection solution in a simulated environment provided by OpenCEMS. OpenCEMS offers 2 types of operation: real and simulated. As the real environment is currently being developed with a limited number of resources, in this chapter, we evaluated our work in the simulated functioning of OpenCEMS. The evaluation in the real environment will be presented in a dedicated work. In the experiments, we considered the requested composition type is hybrid, to focus on resources dynamcity aspect while forming the compositions. During the tests, conducted on a Linux Debian (64 bits) virtual machine, with 1 dedicated Intel® Core™ i7-46000 CPU @ 2. 10GHz 2. 70GHz processor and 1 GB RAM, we show the response time (in ms) based on an average of 5 sequential executions.
Implementation results We implemented the lexicographic order algorithm (LT, Eq. 1) using the BGV scheme  [11]. The code is written in the HElib library  [27]. For a fair comparison with the prior work, we also implemented the algorithm of Tan et al. [37]. The code is publicly available via https: //github. com/iliailia/comparison-circuit-over-fq. In all the experiments, we used an average commodity laptop equipped with an Intel Dual-Core i5-7267U CPU (running at 3. 1 GHz) and 8 GB of RAM. Multi-threading was turned off. In the results presented below, the following notation is used: p : the plaintext modulus of the BGV scheme; q : the initial ciphertext modulus of the BGV scheme; m : the cyclotomic order of the ring R; n : the degree of the ring R (n = φ(m)); : the number of SIMD slots; d : the dimension of a slot subspace used for digit encoding (d in Eq.
Chilloti et al. [19, 20]  constructed a deterministic weighted automata that can compute the maximum function using the TFHE scheme. The same automata can compute the less-than function without any performance loss. In this case, the running time of the less-than function of two b-bit integers takes 170b microseconds on a hardware similar to ours and with the encryption parameters supporting at least 152 bits of security. This security level might be lowered by a re- cent attack of Espitau et al. [23]. Note that this running time is achieved in the leveled mode of TFHE, i. e. without bootstrapping. Unfortunately, we could not manage to run the code of this implementation on our machine. Cheon et al. [15]  designed a polynomial approximation of the less-than function over real numbers. Since the precision of this approximation depends on the ciphertext noise which cannot be reduced in CKKS, only a few consecutive comparisons are possible to perform correctly unlike in TFHE and BGV/BFV.
Acknowledgments This work was supported in part by the  Research Council KU Leuven  grant  C14/18/067 , and by  CyberSecurity Research Flanders  with reference number  VR20192203. The first author is supported by a  Junior Postdoctoral Fellowship  from the  Research Foundation -Flanders (FWO). The authors would like to thank the anonymous reviewers whose comments helped to improve this paper.