dataesr/scientific-paragraphs-categorization huggingface dataset:
Chem. Acta 2022, 94 (2)  DOI: 10. 5562/cca3863 lipophilic compounds. The results of the solubility predictions for ChCl/glycerol (1 : 3) calculated by pseudocomponent approach were discarded due to the poor quality and performance of the model (Table  3  and 4 ). COSMO-RS calculations were performed by two different approaches. The first, individual constituent approach, is performed by modeling the NADES as a real mixture of defined molar ratios of individual components, which are input separately in the COSMO-RS software, i. e. , if a mixture of choline chloride and urea in a ratio 1 : 2 behaves as a deep eutectic at the simulation temperature, then the system is regarded as a mixture of choline cation, chloride anion and two molecules of urea as separate compounds (Figure  S2 ). The second, pseudocomponent approach, observes NADES as a single molecular entity (Figure  S3 ) with HBD and HBA species joined by the hydrogen bond.
It comprises an additional step, computed by TURBOMOLE 17 software and performed before the solubility screening. In this step, energy minimization and a selection of the most stable conformation of NADES candidate is performed. The individual constituent approach was applied as it presents a more physical representation of NADES with their reduced eutectic point, while the pseudocomponent approach was tested as it allows easier handling and parametrization due to a reduced number of species. This preliminary study was focused on the choice of the NADES systems composed exclusively of choline chloride as a HBA, while the HBD and thus the final composition of the NADES was chosen by applying additional three criteria: i) the NADES system is known and described in the literature ii) the eutectic point of the NADES is below or near 25 °C and iii) the pH of the HBD and the resulting NADES should not cause the degradation of screened solutes.
Scenarios of production and model parameter values Once the model had been designed, the next stage to conduct the simulation analysis is to set up the different scenarios of production and the model parameter values that we employed to obtain the baseline values of each scenario. To estimate suitable parameter values, we have selected data of a sample of ten grow-out facilities from a representative group of European firms producing sea bass in the Mediterranean Sea (five from Croatia, three from Spain, one from Italy, and one from Cyprus), that were gathered through an extensive regional survey.
Other operating costs include a set of miscellaneous expenses from different external services (rents, fees, repairs, transports, etc. ). conducted by MedAID H2020 project  (Cidad et al. , 2018). The data employed to estimate the parameter values were collected between 2015 and 2017. For our analysis, we have considered three different farm sizes, or typologies, depending on the annual biomass produced (Table  2 ) and three production strategies depending on the size of the fish produced (Table  3 ), so that we have contemplated nine different scenarios of production. Looking at Table 2, we have assumed an annual production for a micro-sized farm of around 180 tons obtained with the use of 12 cages of 1000 m 3  (each cage would have approximately 15 m of diameter and 4 m deep) and a biomass density of 15 kg/m 3 in each one.
9  To date, learners and teachers have free access to most Thematic Digital Universities and corresponding educational resources. Registration is not required; however, without registration neither the learner nor the teacher can analyze her/his search for OER based on informational behaviors, needs, and preferences. 10  We will focus on the analysis of OER metadata records in the context of Thematic Digital Universities. Each OER in the repository holds a metadata record to precisely describe its specifications to the learner or teacher (e. g. , the learning level, language, and topics). Specifications are written according to the Institute of Electrical and Electronics Engineers (IEEE) standards for Learning Object Metadata (LOM),  11  LOMFR, and SupLOMFR. LOM provides an accurate descriptive schema of a learning object suitable for educational resources  12  (e.
g. , the classification and identification of an educational resource). LOMFR and SupLOMFR are currently applications of LOM in the French educational community. 13  The Digital University Engineering and Technology attracted our attention because of the following characteristics: clear presentation of its objectives, regular information updates, priority for free access to OER and open data, 3,000 published educational resources, extensive documentation of OER indexing, interoperability of OER and metadata records, and an advanced search engine for OER. Each metadata record describes precise information on the OER, including the main title, keywords, descriptive text, educational types (or resources), learning level, copyrights, knowledge domains, topics, authors, and publishers. It is processed and structured with XML language which is human-readable and machine-readable.
Digital user profiles relate to the digital representation of a person's identity and characteristics. 14  Digital identity is the sum of digital traces (or "footprints") relating to an individual or a community found on the Web or in digital systems. Digital traces correspond to the user's profile, browsing history, and contribution actions. 15  Our focus is the learner who wishes to use the Thematic Digital Universities for tailor-made analysis of retrieved information based on her/his needs and preferences. We offer the learner an option to register on these platforms to track behavior over time while searching for OER. Analyses are based on criteria the learner has previously chosen to personalize this search. Subsequently, we suggest using the term "digital user-learner profile. " We will do our best to respect the General Data Protection Regulations when collecting information on the digital userlearner profile.
16  The General Data Protection Regulations are privacy laws drafted and passed by ONTOLOGY FOR THE USER-LEARNER PROFILE | KORDAHI 3 the European Union that prohibit the processing, storage, or sharing of certain types of information about individuals without their knowledge and consent. The research questions are as follows: 1. In the context of Thematic Digital Universities, how can a user-learner personalize the search for open educational resources according to her/his digital profile? 2. In this same context, what kinds of information can a user-learner analyze in a search for open educational resources according to her/his digital profile? The objectives of this article are to present the preliminary results of work in progress on the design of the ontology for the digital User-Learner Profile (OntoULP) and its application program, the Personalized modeling System for the User-Learner profile (PSUL).
PSUL will include a secure database and web pages, namely those for registering and editing the user-learner profile and its dashboard. 24  OntoULP and its application program will offer each registered user-learner an opportunity to analyze the search for OER according to informational behaviors and needs. OntoULP and PSUL could be implemented in the structure of information systems for educational and research institutions, documentation and information centers, and many others. We will finetune our analysis by relying on a case example-the Thematic Digital Universities. This article comprises six sections. First, we will explain the exploratory research carried out in the context of Thematic Digital Universities. Second, we will present the main published works related to the subject of the article.
Methods used During the first 18 months of the COVID-19 pandemic (November 2020-May 2021), we conducted qualitative research to deepen our comprehension of the practices of Thematic Digital Universities. We collected and interpreted primary and secondary information. Primary information: We contacted the Digital University Association and their six Thematic Digital Universities. 25  Because of their extensive expertise and robust knowledge in leading or managing Thematic Digital Universities, Directors and General Secretaries were chosen to selfadminister an electronic semistructured questionnaire. We contacted seven individuals and received six responses. In this questionnaire, we asked about the following topics: the recent knowledge of Thematic Digital Universities, conditions of access to OER, metadata records indexing as well as user-learner's expectations.
"  43  This model has been successfully used by many renowned researchers (e. g. , Paquette 2010  44  ) to design a learner model and then adapt it to appropriate contexts. IMS LIP's reliability, accuracy, and flexibility match well with the OntoULP motives. We will use it to begin designing the structure of the OntoULP and adapt it to the Thematic Digital Universities context. We will also consider the IEEE LOM, LOMFR, and SupLOMFR classification fields. This measure will be used to improve semantic matching between the OntoULP and OER metadata records. ONTOLOGY FOR THE USER-LEARNER PROFILE | KORDAHI 6.
Knowledge domains Knowledge organization systems  48  are seen as a valuable component for searching for OER. 49  Our research includes analyses of OER metadata records to establish relationships between their knowledge topics and the user-learner's topics of interest. In the Thematic Digital Universities' metadata records, a precise classification is reported respecting both knowledge topics and Dewey Decimal Classification (e. g. , geographic information systems (526. 028 5)). 50  The Dewey Decimal Classification and Relative Index 22 nd edition,  51  published in 2003 by the Online Computer Library Center,  52  is being used worldwide in digital libraries and by the Thematic Digital Universities. 53  In their works published in 2009, Green and Panzer have developed an ontology to structure knowledge domains.
In the context of Thematic Digital Universities, the resulting subsystem is composed of eight classes instead of eleven. The userlearner model subsystem conveys the structured information to the user-learner subsystem. Figure  1  shows the structure of both subsystems, the user-learner and user-learner model. The learning design subsystem is an adaptation of the IMS Learning Design model, in the context of Thematic Digital Universities. 56  The learning design subsystem has two main classes: the userlearner's environment and learning activities. The environment class has six Thematic Digital Universities as subclasses. In a general manner, information about the environment class comes from Thematic Digital Universities platforms (e. g. , the viewed metadata records). The learning activities class has resources as a subclass.
PERSONALIZED MODELING SYSTEM FOR THE USER-LEARNER PROFILE The PSUL is based on the works discussed in the previous sections. It is written with PHP, JavaScript, and XML, computing languages for the Web. This new modeling system comprises three classes of models: the general behavioral, personal behavioral, and topical (see fig. 4 ). The general behavioral model has two roles. It registers a user-learner's digital profile in order to determine informational needs and preferences for OER. It also collects informational behaviors of a user-learner while viewing OER metadata records for tailor-made analyses. The general ONTOLOGY FOR THE USER-LEARNER PROFILE | KORDAHI 11 behavioral model includes the ontology OntoULP as well as user-learner registration and editing pages. The registration page contains relevant information about a user-learner, an option to accept or reject data collection, and a list of choices for behavioral analyses.
Once registered, the user-learner can modify her/his profile from the editing page. Both pages are mapped to the OntoULP to populate criteria fields. The user-learner profile information is stored in a secure database (as described in the introduction). The personal behavioral model is used to analyze information according to the registered digital user-learner profile and informational behaviors. It contains a set of queries to collect and tailor information for each user-learner. The sources of information are the general behavioral model and OER metadata records. This model is designed based on analyses of the general behavioral model. When a user-learner begins searching for OER, the general behavioral model provides the personal behavioral model with all profile information as well as the history of OER search.
Topical model 1 is used to synthesize each user-learner's search history and to suggest a profile adjustment. The suggested adjustment is based on analyses of user-learner behavioral trends. 59  Topical model 2 allows each user-learner to examine the list of knowledge topics which have caught her/his attention. It contains two separate lists describing viewed OER metadata records and matching them to the chosen topics of interest. Topical model 3 shows comparative analyses between the user-learner's preference criteria and viewed metadata records. The user-learner can interact with this model by comparing the chosen topics of interest to the viewed knowledge topics. The user-learner can also compare the chosen learning activities to the viewed teaching pedagogies. The teaching pedagogies as well as knowledge topics are extracted from OER metadata records (see fig.
We have made every effort to define the different terms correctly and to cite the corresponding researchers. The second difficulty relates to the design of OntoULP. Published works dealing with this topic are rare. We used an exploratory research approach and the published works of renowned international researchers to fine-tune our study (see the Exploratory research approach and Literature review sections). We then determined the classes and objects as well as relationships between them. The third constraint concerns the design of the PSUL by following the Thematic Digital Universities policies and respecting the General Data Protection Regulations. According to the regulations, we have opted for an optional registration to Thematic Digital Universities and to collecting information on the digital user-learner profile.
IDEASSat was successfully launched into orbit on 24 January 2021. After 21 days, the satellite stopped transmitting beacon signals and failed to respond to uplinked commands. After 1. 5 months, the satellite resumed communication briefly for three days, which was sufficient time for commands to be uplinked ordering the spacecraft to replay saved flight data, allowing for the cause of the blackout to be pinpointed. In the second section, this paper will introduce the implementation of the spacecraft sub-systems used in the final flight model (FM). Since the IDEASSat mission concept and critical design was presented by Duann et al. , this article will describe the flight model design that was actually implemented, as well as the flight software, and related environmental testing in detail. Due to the limitations in funding and time, a single protoflight model of the full spacecraft was built, as opposed to a separate engineering qualification model of the entire spacecraft system.
IDEASSat Satellite Subsystems As a spacecraft, IDEASSat was an integrated system composed of multiple subsystems, including the electrical power subsystem (EPS), telecommunication system (COMM), attitude determination and control subsystem (ADCS), command and data handling subsystem (CDH), spacecraft flight software (FSW), structure and mechanisms (STR), and thermal control subsystem (TCS). The entire spacecraft system was designed and integrated in-house, using a combination of commercial off the shelf (COTS) and self-developed subsystems. The solutions used to implement the spacecraft subsystems are shown in Table  1 , as well as their technological readiness level (TRL) at the time of spacecraft decommissioning. Subsystems developed in-house are further denoted with an asterisk. During the preliminary design phase of the mission, it was decided to use self-developed subsystems for those where there was a reasonable level of confidence that the flight model requirements and specifications could be met within a two-year development period.
Thermal Control Subsystem (TCS) IDEASSat's thermal control design was based on passive temperature control. According to the thermal analysis and the recommendations of the sub-system datasheets, some of the sub-systems were to be augmented with heat conducting metal for use as heat From the -Z to +Z direction, we use different colors to mark the different components inside the spacecraft, which are respectively the Compact Ionospheric Probe (CIP) payload, S-band transmitter module, CDH, EPS power control board, battery module, ADCS, UHF transceiver, UHF antenna deployment module. On the +Y side, there is an S-band patch antenna, a body-mounted solar panel on the -Y side, and a GPS receiving antenna and coarse sun sensor are also arranged near the +Z end of the +Y face. IDEASSat's main task was to perform in situ measurements of the ionosphere.
Thermal Control Subsystem (TCS) IDEASSat's thermal control design was based on passive temperature control. According to the thermal analysis and the recommendations of the sub-system datasheets, some of the sub-systems were to be augmented with heat conducting metal for use as heat distributors or polyimide film heaters to maintain temperatures within the operating temperature range. Take the battery module as an example, since the battery operating temperature range was 0-40 • C, a thin film heater was added as a heat source in the design of the battery module. Considering that the battery itself was a stable heat source while operating, a pressurized heat dissipation pad was also added between the battery and the securing fixture as a passive heat dissipation element. In the case of the S-band transmitter, an aluminum block was added to serve as a heat conductor between the transmitter and the spacecraft chassis.
Spacecraft Flight Software (FSW) The spacecraft autonomous flight software (FSW) was developed in-house using C, Verilog, and assembly language. FSW could continuously check the status of all subsystems running in the background of the program, and transition between the corresponding operation mode according to the state of the subsystem telemetry. When the spacecraft passed within the line of sight of ground stations used for command and control, it could perform related actions in response to the commands from the ground station and also adjust the spacecraft attitude to orient the S-band patch antenna at the ground station when needed. IDEASSat's flight software had two major modes: emergency and nominal. There were 2 sub-modes in emergency and 3 sub-modes in nominal, as shown in Table  6.
S-Band Ground Station The S-band ground station at NCU uses a COTS solution provided by Comtech Telecommunications Corp and the QubeFlex satellite modem from Teledyne Paradise Datacom for signal reception and signal processing  [21, 22]. The use of the COTS modem was motivated as a result of the self-developed GNU Radio signal processing software exhibiting an unacceptably high bit error rate when operated at S-band. The demodulated Sband data packets are transmitted to a PC using the user datagram protocol (UDP), and depacketized using self-developed Matlab processing software. The IDEASSat S-Band ground system only had a receiving function. Figure  6  is the ground receiving station architecture of IDEASSat S-band. A 3. 4 m diameter parabolic dish antenna with an L-band/S-band feed is installed on a rotator inside a radome.
S-Band Ground Station The S-band ground station at NCU uses a COTS solution provided by Comtech Telecommunications Corp and the QubeFlex satellite modem from Teledyne Paradise Datacom for signal reception and signal processing  [21, 22]. The use of the COTS modem was motivated as a result of the self-developed GNU Radio signal processing software exhibiting an unacceptably high bit error rate when operated at S-band. The demodulated S-band data packets are transmitted to a PC using the user datagram protocol (UDP), and depacketized using self-developed Matlab processing software. The IDEASSat S-Band ground system only had a receiving function. Figure  6  is the ground receiving station architecture of IDEASSat S-band. A 3. 4 m diameter parabolic dish antenna with an L-band/S-band feed is installed on a rotator inside a radome.
The open-source GNU Radio software package is used for signal processing for the SDR, including modulation and demodulation  [23]. Matlab was used to implement the front end of the mission operations software, which was used for depacketization and visualization of the received bitstream from the SDR, as well as encoding of commands to be transmitted. Received beacon packets were stored in text and binary format. The block diagram of Figure  8  is the architecture of the UHF ground station. The NI USRP-2900 SDR is used for both downlink Rx (NI USRP-2900 Rx) and as the signal source for command Tx (NI USRP-2900 Tx). A low noise amplifier (LNA) is installed on the Rx line to mitigate line loss, while two preamplifiers are implemented on the Tx line, the first of which (preamp 1) is required to boost the low output power of the USRP-2900 to a level high enough to drive the larger 80 W amplifier (preamp 2).
Flatsat A flatsat refers to a complete tabletop integration test of spacecraft subsystems before the final flight model is assembled  [25]. The purpose is to confirm that there will be no interference or abnormality between sub-systems in all operating modes. Since the correct ADCS information cannot be obtained, virtual ADCS information was provided in FSW test code when performing the ground test for operating mode transitions. The flatsat layout of IDEASSat as partly assembled is shown in Figure  9. During the flatsat stage, the main task was to confirm whether the subsystem functions are normal in various operations, and whether the corresponding subsystems are normally turned on. Additional functional testing also took place at this stage to ensure that the spacecraft EPS was capable of charging the batteries under sunlight, as well as regulating power distribution to the spacecraft subsystems.
For future OBCs designed using a similar hardware architecture with periodic logging of FSW variables to the SD-cards, we will prefer to use a higher-level SmartFusion2 model, such as the M2S090, with built-in error correction code (ECC) memory or implement error correction algorithms such as BCH code or Reed-Solomon code. Considering the use of SD cards for non-volatile data logging, the SEU rate in SD cards is very low, according to NASA/TP-2020-5008734, State-of-the-Art, Small Spacecraft Technology  [12]. If the SD card is to be replaced by an even more robust option with respect to SEUs, magnetoresistive random-access memory (MRAM) or ferroelectric random-access memory (FRAM) can be considered [12].
Thermal Performance Figure  21  shows the temperature ranges of the various IDEASSat subsystems during on-orbit operations (light blue bars) compared to their respective operational temperature ranges (orange bars), as well as those measured during thermal vacuum testing (dark blue bars), and values predicted using the Thermal Desktop analysis software (green bars). From top to bottom, the subsystems shown include the battery module, the body mounted (PV0) and deployable solar panels (PV1 and PV2), the UHF transceiver, the EPS controller board, the CDH OBC, as well as the ADCS inertial measurement unit (IMU), star tracker (Track), and exterior. It can be seen that the on-orbit temperature ranges all fell within the operational limits, indicating that good thermal margins were achieved. It can also be seen that the actual on-orbit temperatures generally trended higher than those predicted by the thermal analysis and encountered during thermal vacuum testing.