dataesr/scientific-paragraphs-categorization huggingface dataset:
Key to this success is an organizational setup whereby linguists receive not only a set of stylesheets and instructions, but also hands-on support from a L A T E X expert all along the typesetting process. Undeniably complex software is only accessible to people with no prior knowledge of it if support is available. Automatic speech recognition software should be equally accessible for language workers, given the right organization and setup. Accordingly, special emphasis is placed on user design in the Elpis project. This aspect of the work falls outside of the scope of the present paper, but we wanted to reassure potential users that it is clear to Elpis developers that the goal is to make the technology available to people who do not use the command line. If users can operate software such as ELAN then they will be more than equipped for the skills of uploading ELAN files to Elpis and clicking the Train button.
Will the technology deliver on its promise? A second concern among language workers is whether the technology can deliver on its promise, or whether transcription acceleration projects are a case of "digital innovation fetishism"  (Ampuja, 2020). Some language workers have reported a feeling that integration of automatic transcription into the language documentation workflow (as described in  Michaud et al. , 2018)  feels out of reach for them. There is no denying that natural language processing tools such as ESPnet and Kaldi are very complex, and that currently, the help of specialists is still needed to make use of this technology in language documentation. However, progress is clearly being made, and a motivated interdisciplinary community is growing at the intersection of language documentation and computer science, comprising linguists who are interested in investing time to learn about natural language processing and computer scientists who want to achieve "great things with small languages"  (Thieberger and Nordlinger, 2006).
, 2019). They have shown an ability to integrate new models quickly after their initial publication. Usability and stability of the interface is dictated by the quality of the code that is written by the authors of the backend library. If this is done well then the state of the art can be integrated with minimal coding effort by users of the library. For this reason, we are not so concerned about the shifting sands of the underlying building blocks, but the choice of quality backend library does count here. While it is true that there will have to be some modest effort to keep up to date with ESPnet (as would be the case using any other tool), in using ESPnet we are optimistic that the models supported by Elpis can remain up to date with the state of the art without too much hassle.
Further improvements The broader context to the work reported here is a rapidly evolving field in which various initiatives aim to package natural language processing toolkits in intuitive interfaces so as to allow a wider audience to leverage the power of these toolkits. Directions for new developments in Elpis include (i) refining the ESPnet recipe, (ii) refining the user interface through user design processes, (iii) preparing pre-trained models that can be adapted to a small amount of data in a target language, and (iv) providing Elpis as a web service.
Refining the ESPnet recipe Refinement of the ESPnet recipe that is used in the Elpis pipeline, such that it works as well as possible given the type of data found in language documentation contexts, is a top priority. This work focuses on achieving lower error rates across data sets, starting with refining hyperparameters for model training and extends to other project objectives including providing pre-trained models (see §6. 3). This work is of a more experimental nature and can be done largely independently of the Elpis front-end.
Refining the interface In parallel with the technical integration of ESPnet with Elpis, a user-design process has been investigating how users expect to use these new features. In a series of sessions, linguists and language workers discussed their diverse needs with a designer. The feedback from this process informed the building of a prototype interface based on the latest version of Elpis at the time. The test interface was then used in individual testing sessions to discover points of confusion and uncertainty in the interface. Results of the design process will guide an update to the interface and further work on writing supporting documentation and user guides. The details of this process are beyond the scope of this paper and will be reported separately in future.
One of the features Elpis could provide is to include pre-trained models in its distribution or via an online service. Such models may be pre-trained in a self-supervised manner on lots of untranscribed speech, trained in a supervised manner on transcribed speech in other languages, or use a combination of both pre-training tasks. In cases where the pre-trained model was trained in a supervised manner, there is scope to deploy techniques to reconcile the differences in acoustic realization between phonemes of different languages via methods such as that of Allosaurus  (Li et al. , 2020)  which uses a joint model of language-independent phones and language-dependent phonemes. Providing a variety of pretrained models would be valuable, since the best seed model for adaptation may vary on the basis of the data in the target language  (Adams et al.
Providing Elpis as a web service Training models requires a lot of computing power. Elpis now supports high-speed parallel processing in situations where the user's operating system has compatible GPUs (see Section 3. 2). However, many users don't have this technology in the computers they have ready access to, so we also plan to investigate possibilities for host-ing Elpis on a high-capacity server for end-user access. Providing language technologies via web services appears to be a successful method of making tools widely available, with examples including the WebMAUS forced-alignment tool. 12 The suite of tools provided by the Bavarian Speech Archive  (Kisler et al. , 2017)  have successfully processed more than ten million media files since their introduction in 2012. For users who want to avoid sending data to a server, there are other possibilities: Kaldi can be compiled to Web Assembly so it can do decoding in a browser  (Hu et al.
vulgaris 'Conditiva' Group) are grown both for their root (beetroot) and their leaves (beet greens). Some familiar foods, such as carrots (Daucus carota), are grown for their roots and are considered vegetables. However, potatoes (Solanum tuberosum), whose starchy tubers are grown as a staple crop, are considered 'root crops', along with cassava (Manihot esculenta), sweet potato (Ipomoea batatas), taro (Colocasia esculenta), yam (Dioscorea spp. ) and corms of the Araceae family (Xanthosoma spp. ). These crops are categorized separately from vegetables in the FAOSTAT primary production database and are beyond the scope of this document. However, the leaves of sweet potato, taro and cassava, which are a critically important part of many diets, are considered vegetables.
Baby corn or sweet corn, which is harvested very early from the field and eaten fresh, is also considered a vegetable. The organization of fruit and vegetable crops and the botanical families they belong to, as applied in the FAOSTAT production and trade databases  (FAO, 1994a) , are shown in Tables  1  and 2 , respectively. In the FAOSTAT databases, several vegetable and fruit crops are not named individually because they are of relatively minor international importance in terms of trade.
Fruit and vegetables statistics In the FAOSTAT primary production databases, the aggregated list of vegetables, which is used to derive total production figures (area cultivated, yield, production quantity), includes mushrooms and truffles, which are not considered in this publication. In the FAOSTAT trade databases, which provide data on the quantity and value of imports and exports, fruit and vegetables are aggregated together, and the list includes many commodities that fall outside the scope of this publication (nuts, root crops, pulses) as well as derived products (flours, juices and homogenized preparations). As with vegetables, FAOSTAT production and trade databases group fruit that have relatively minor international importance under a single category: "fruit, fresh, not elsewhere specified (NES)".
It is also important to note that much of the fruit and vegetables grown are often not sold at all or are sold at informal markets, and in many countries, the production statistics reported will not capture the complete picture. Nevertheless, it is worthwhile reviewing the available data in FAOSTAT to appreciate the most commonly reported fruit and vegetable crops and the total production of fruit and vegetables per region and per country, shown in Table  3. FAOSTAT shows clear trends in increases in the global total production of fruit (Figure  2 ) and vegetables (Figure  3 ) between 1968 and 2018. While there are differences between different regions, the rapid increases in Asia are remarkable. The types of fruit and vegetables categorized in FAOSTAT represent only a small fraction of the total fruit and vegetable diversity.
(2020)  that lists 100 NUS, many of which are fruit and vegetables. The publication cites the scientific studies or projects, networks or international agencies that recommend them and defines which parts are edible (leaves, inflorescences/flowers, fruit, seeds, roots/tubers and/or stems/shoots based on  Diazgranados et al. (2020)  and whether they are wild, cultivated or both. Many fruit and vegetable crops are ignored by FAO and national statistics. Despite a lack of data on NUS, they are nutritionally important and many such as tropical fruit from the Amazonian basin (passiflora, roselle, jackfruit, dragon fruit) and from Asia (durian) are traded at local, informal markets. Similarly, numerous tropical vegetables are not included in national databases, especially leafy vegetables such as amaranths or spider plants (Cleome spp.
Resistant varieties and healthy seeds Deployment of resistant varieties forms the foundation of any IPM programme. Whenever they are available, fruit and vegetable varieties with known resistance against common pests or diseases in a given locale should be selected and used. Using healthy seeds will reduce the incidence of seed-borne diseases. Seed treatment using biological control agents such as Trichoderma spp. or Pseudomonas spp. has been shown to reduce the incidence of some seed-and soil-borne pathogens in vegetables  (Mancini and Romanazzi, 2013). Managing the plant diversity (within and among varieties and species) is also a strategy to sustain the efficiency of resistance genes. The Hortivar database (http://www. fao. org/hortivar/) is based on a network of scientists who are engaged in collating and disseminating information on the field performance of fruit and vegetable cultivars including their resistance to pests and diseases, to help farmers identify the best-adapted varieties.
BOX 11. Tree tenure. Source:  FAO and ICRAF (2019)  small-scale farmers is complex and challenging for many governments across the world. Solutions will depend heavily on the local context. The Voluntary Guidelines on the Responsible Governance of Tenure of Land, Fisheries and Forests in the Context of National Food Security provides support to countries in promoting secure tenure rights and equitable access to land, fisheries and forests as a means of eradicating hunger and poverty, supporting sustainable development and enhancing the environment  (FAO, 2012f). FAO also published a technical guide to achieving gender-equitable governance of land tenure  (FAO, 2013b). See the FAO Gender and Landrights Database (http://www. fao. org/ gender-landrights-database/en/).
Distributed ledger technology and blockchain Distributed ledger technologies (DLT), which are often referred to as blockchain (although technically blockchain is one particular type of DLT) are an area where policy actions along with research and innovation can support small-scale farmers in general and fruit and vegetable farmers in particular. DLTs are used to create a digital database that records, tracks and monitors transactions of physical and digital assets in real time. For example, DLTs can improve food safety by significantly increasing the traceability of products in agricultural supply chains, allowing for the rapid identification of the source of contaminated products and the mobilization of quick responses to minimize health impacts and financial losses  (Mattern and Ramirez, 2017).
It is relevant that the fruit and vegetables currently categorised in FAOSTAT only represent a fraction of the global diversity of fruit and vegetables and that many are labelled as neglected and as underutilized species. Yet fruit and vegetables have enormous potential to achieve multiple SDGs due to their high nutritive contents, adaption to cultivation under different environmental conditions, and their resistance to prevalent pests and diseases. Data collection and reporting mechanisms need to be strengthened to capture this potential and to inform agricultural policies and not overcomplicate this into agricultural policies. Fruit and vegetables are high value. Cultivating them can be profitable because they can grow on relatively small amounts of land and with less water and nutrients than are required by other crops in rural, urban and peri-urban environments.
The former enables recording network information of air interface and mobile application quality-of-service and quality-of-experience, such as the Nemo Handy from KeySight Technologies  (16). While drive test solutions are very expensive and not accessible for the public to perform daily personal measurements, android-based applications (e. g. , XMobiSense) allow measuring several parameters but not the TX power nor throughput. Nonetheless, a specific equipment (i. e. , OPTis-P8E, Innowireless Co. , Ltd. ) with a control software is also used for the recording of both DL and UL EMF exposure, even in fifth generation (5G) new radio environment  (17, 18). Recently, machine learning (ML) and artificial neural networks (ANNs) are intensively applied in the 5G cellular networks and beyond. The potential of ML and ANN are also being investigated in the field of RF propagation and human exposure to RF-EMF.
From the DL point of view, the exposure map of the 14th district of Paris was built using a hybrid connected ANN, which is trained with both simulated drive test and sensor network measurements  (19). In  (20) , a simple feed forward ANN was proposed for multi-source indoor WiFi scenarios, where three access points and several WiFi clients were distributed in the floor layout of the building. Both DL and UL exposure were evaluated by feeding the ANN model with position and type of WiFi sources, position and material characteristics of walls. The collection of data about the electric field strength (i. e. , the ground truth) was performed through simulations according to a deterministic method. In  (21) , the UL exposure due to 4G connections is assessed by predicting the UE TX power using three different ML algorithms and by changing the set of input parameters.
The ground truth was obtained through conducting measurement campaigns while driving a car in Germany. Different from an outdoor scenario, the indoor environment is much more complicated due to the existence of multi walls, multi floors, furniture and their different penetration losses. The challenges are how to incorporate the uncertainty caused by real-life indoor measurements and how to extract key features that affect UE TX power from measured network parameters as well as the indoor environments. In this paper, we propose a feed-forward ANN model that allows predicting the UE TX power, while training the model with measurement data in an indoor environment. The Nemo Handy is used to collect data on each floor of a residential building, while it is connected to a 4G outdoor base station.
Measurement Description and Protocol We carried out measurement campaigns in a 6-floor residential building in May and August 2021. At each floor level (from ground floor to floor 6), one measurement location is considered, which corresponds to almost the same relative position in corridor of the stairs. The measurements were repeated 3 times over the journey: in the morning (10-11 a. m. ), at noon (12-2 p. m. ), and in the afternoon (4-5 p. m. ), in order to take into consideration the time-varying traffic of the connected base station. We note that the measurements at night, which have the lowest traffic load  (23) , were not performed due to the requirement of human intervention. We focus in this work on predicting the UE UL TX power while connected to 4G networks. To this end, we need to gather data about the UE UL power as well as other parameters influencing it (as explained in section 3.
1). The UE UL power is recorded using dedicated drive test mobile phone solutions. Accordingly, the Nemo Handy from KeySight Technologies (  16 ) is used in our measurement campaigns in order to log data about the UE TX power and other network parameters. The Nemo Handy is equipped on a Samsung Galaxy S20+ 5G. It supports frequency bands from 2G to 5G NR networks (up to 40 GHz). Moreover, Nemo Handy allows scheduling certain usage services by creating scripts. Indeed, it records network parameters and saves log files while running these scripts. This is crucial because the UE UL power depends on the usage service. In our measurement campaigns, we lock the Nemo Handy to 4G networks, without locking to a given frequency band. The Nemo Handy is scheduled to run automatically the following usage services: We note that we mimic a realistic usage scenario during the measurements.
The mobile phone was held by the experimenter at a height of about 1 m. After carrying out the measurements, we use the Nemo outdoor software (installed on a laptop) in order to extract the log files several parameters, mainly the following data: the UE TX power, the RSRP, and RSSI. All these powers were recorded in dBm. Moreover, we extract the physical cell identity (PCI) of the connected base station in order to check if the active base station remains the same in all the measurements. We are not able to locate the connected base station since its PCI is not accessible to public. It is just known by the network operator. Furthermore, we note that Nemo sampling method of the recorded data is unknown. The number of samples per second is not constant, it changes between 0 and 5 samples per second.
This implies that the corresponding prediction results in terms of MAE, RMSE, and R 2 reveal the same improvement, as shown in Table  5. Consistently, the scattering plots in Figure  5  show that a tighter prediction is obtained with the increase of averaging duration. More clearly, the problem of vertical lines with dots from the left figure, which is caused by noise in the input data, is solved. However, we only did the averaging up to 5 s due to the limited number of measurement data (number of datasets after removal of duplicates are shown in Table  5 ).
Measurement campaigns have been carried out on a single location at each floor of a 6-floor residential building. While using several usage services (i. e. , WhatsApp voice and video calls, file uploading via DropBox), data on UE TX power and input features, such as RSRP, are collected. The results are very promising with a MAE of 2. 334 dB. Indeed, we believe that the proposed model has high potential in applying to more general scenarios. As shown in Table  2 , UE TX power is closely dependent on the DL received power, which is RSSI or RSRP in our study. This strong correlation guarantees an acceptable prediction. So, the difficulty is to further improve the prediction performance by taking into account the varying environments. Even though measurement campaigns are very time-consuming and complicated to perform in indoor environments, more measurements are required to cover several location points on the same floor.
Hess et al. (2020)  [43]  created an empirical model of materials to study a first-order MCM La(Fe,Mn,Si)13 alloy-based. They used a modified Cauchy-Lorentz function as a base function of the specific heat capacity. The model of the relevant equations is determined from the specific heat capacity also function of the temperature and the magnetic field. The performances of the model are compared to measured data (Figure  7 ). According to the authors, a better agreement with measured data can be achieved through more degrees of liberty of the model. Nevertheless, it also increases the complexity of the model. The model can be implemented in a more global model to simulate cooling devices using a first-order MCM. Then the chosen compatibility seems to be a best way compared to the obtained accuracy of the model integrated into a complete modeling system.
Their model gives an analytical description of the thermal behavior of the caloric material. Previously, another model has been developed by  Hess et al. (2018)   [44]. [43]  created an empirical model of materials to study a first-order MCM La(Fe,Mn,Si) 13 alloy-based. They used a modified Cauchy-Lorentz function as a base function of the specific heat capacity. The model of the relevant equations is determined from the specific heat capacity also function of the temperature and the magnetic field. The performances of the model are compared to measured data (Figure  7 ). According to the authors, a better agreement with measured data can be achieved through more degrees of liberty of the model. Nevertheless, it also increases the complexity of the model. The model can be implemented in a more global model to simulate cooling devices using a first-order MCM.
The first one-called 'hot layer'-is composed of Gd; and the second one-called 'cold layer'-is an alloy of Gd and GdY. To validate their model, they used two-layer performance data measured by an AMRR test device (Figure  25 ). They finally found that the semi-analytical AMRR model gave good predictive capabilities. A temperature range of 40 K was experimentally measured. One year later, Teyber et al. (2019)  [90]  performed another semi-analytical model validated by experimental measurements. They extended the previous model by discretizing the MCM in four elements being described by equations. They also validated the model by comparing simulation with experimental data, for a 100 K measured temperature range. With the model, they were able to calculate the cooling performance and the efficiency according to the thermodynamic second law by observing the influence of the temperature range and the heat transfer fluid density variations.
The studied system consists of a MCM (pure Gd) positioned between two PMs that have a linear alternative movement. To explore a maximum of configurations, they developed a software called HEAt TRAnsfer in PYthon (HEATRAPY). This mathematical model has been developed by  Silva et al. (2018)   [112]. The working cycles are simulated in order to find the best configuration and to determine the temperature range reached by the studied system. They considered the model as validated by comparing the results with COMSOL Multiphysics ® results. With this 1-D model, they have tested 66 combinations of operating modes and found a maximum temperature range of 0. 87 K by optimizing the applied magnetic field (Figure  26 ). According to the authors, this temperature span can be easily increased by optimizing the MCM or the fluid flow rate and the frequency.