dataesr/scientific-paragraphs-categorization huggingface dataset:
Adapter sequences were removed from the demultiplexed reads, and quality trimming was performed using bbduk version 38. 87 in the BBTools package (https://sourceforge. net/projects/bbmap). Genomic mutations were identified by mapping the resulting reads onto the reference E. coli BW25113 genome sequence using breseq version 0. 35. 4 in polymorphism mode, using a frequency threshold of 5% to identify potential polymorphisms  (39, 40). The sequencing results showed that one of the four Y86* populations was contaminated with Micrococcus luteus; this population was excluded from the analysis. Mean coverage depths ranged from 92. 6 to 178. 6 (mean 142. 1) for each population in the first run and 126. 0 to 134. 3 (mean 129. 0) for each population in the second run. The percentage of mapped reads was > 98.
551, 16. 14, 0. 998; proS, -3. 427, 15. 05, 1. 000; gyrB, -3. 424, 15. 25, 0. 999; tus, -3. 434, 15. 33, 0. 999. Primer specificity was validated by agarose gel electrophoresis and melt curve analysis. Data analysis was performed based on the method of Ganger et al. (41)  in Microsoft Excel version 16. 5 and GraphPad Prism version 8. 3. 0. The efficiency-weighted C q values ( C q (w) ) for each genomic DNA sample was calculated using the following equation: C q (w) = C q (gene of interest) × log 10 E(gene of interest) -0. 5 × (C q (gyrB) × log 10 E(gyrB) + C q (tus) × log 10 E(tus)), where C q is the threshold cycle value and E is the primer efficiency. One-way ANOVA with Dunnett's test for multiple comparisons was used to assess the significance of differences in C q (w) values between the test samples and the wild-type control sample, using P < 0.
Isolation of mutant strains from evolved populations To obtain double and triple mutant strains, glycerol stocks of trmD mutant populations from the evolutionary experiment were struck onto LB agar and incubated overnight at 37 • C to obtain single colonies. Populations were selected in order to minimize the number of loci that needed to be sequenced to confirm the genotype of the isolated strains, based on the NGS data (Supplementary Tables  S1  and S3 ). The genomic region of interest was amplified by colony PCR and sequenced by Sanger sequencing. Strains containing the desired mutation were stored as glycerol stocks, and if necessary, sequencing of additional loci was performed to verify the genotype of the strain. For the majority of mutant strains, two strains derived from different colonies were isolated, in case additional mutations were acquired during processing and in case additional mutations were present in the population at a frequency too low to be detected by NGS.
The volume of DEPC water used to redissolve the tRNA pellet before biotinylation was adjusted to give a maximum of 2. 5 pmol of tRNA in the biotinylation reaction. For data analysis, the product concentration in each sample was taken as the fraction of aminoacylated tRNA determined by band integration multiplied by the total tRNA concentration. The substrate concentration was taken as the concentration of aminoacylation-competent tRNA, which was determined by multiplying the total tRNA concentration by the fraction of aminoacylated tRNA in the positive control. For determination of Michaelis-Menten parameters, the initial rate of product formation was plotted against the substrate concentration and fitted to the substrate inhibition model (for m 1 G37-modified tRNA) or a linear model (for G37unmodified tRNA, to estimate k cat /K M ) in GraphPad Prism version 8.
The travel distance levels selected represent a range of different types of navigation, from coastal navigation to longdistance travel. The survey protocol was presented to a panel of maritime professionals who helped select and validate the attributes and their levels. The resulting experimental design -matrix of all the attributes' levels (Table. 1) -in our study is composed of 4 × 4 × 4 × 2 × 4 = 512 alternatives. We then followed the different steps that are recommended and applied in the majority of the literature that uses the multi-attribute choice method to build an optimal design (e. g. ,  [34] ). A Doptimal fractional factorial design of 16 alternatives was generated 1 by the SAS® software using the OPTEX procedure (Edition 9. 3). This optimal design is composed of 8 sets of 2 alternatives.
Econometric Analysis of Choice Data We used a questionnaire based on the discrete choice method to collect empirical data on maritime professionals decisions to reduce the risk of whale-ship collision. This CE protocol aims to identify the preferences of these maritime professionals between the proposed solutions. We proposed eight choices sets, t={1,. ,8}, to each respondent, i={1,. ,n}, which were asked to choose among three alternatives, l={1,2,3} in each set. To study these choices, we assume as in McFadden's pioneering paper  [53]  that the underlying preferences are characterized by a separable additive utility function U = V + ε with ε corresponding to its random component. The probability of choosing the j th alternative is then characterized by the logit specification Pr y = j| = e ∑ e ! ⁄ if the random component have cumulative density function of Gompertz #$% &'( ) = *+, -*+,$% &'( ).
This exercise clearly indicates the covariance of dust and climatic forcing factors (especially precipitation) when considering the whole record. However, for the periods in which we infer local erosion and dust supply stimulating peat growth, there are either very weak or not statistically significant correlations between climate and dust (Table  S3 ). Such a finding indicates the disconnect between climate and dust flux over the last 2000 years and supports our assertion of local dust drivers in this period. This data comparison exercise clearly indicates the complexity of factors controlling mountain peat CARs as well as the fact that prediction of the behaviour of the peatland carbon sink is a challenge. Considering climate indices, it appears that there is a relationship between mean temperature and CAR and, potentially, between mean temperature and the availability of moisture during the growing season (Table  4 ).
The authors are indebted to Slobodan B. Marković, Nemanja Tomić, Igor Obreht, and Szabolcs Kelemen for fieldwork support. The authors acknowledge the financial support from the Romanian Ministry of Education and Research. We are grateful for the insightful comments from Joshua Ratcliffe and Nicole Sanderson, who helped improve the paper. Financial support. This research has been supported by the Romanian Ministry of Education and Research, CNCS -UEFISCDI (grant no. 85 PN-III-P4-ID-PCE-2020-0914).
Acknowledgements. The authors are indebted to Slobodan B. Marković, Nemanja Tomić, Igor Obreht, and Szabolcs Kelemen for fieldwork support. The authors acknowledge the financial support from the Romanian Ministry of Education and Research. We are grateful for the insightful comments from Joshua Ratcliffe and Nicole Sanderson, who helped improve the paper. Financial support. This research has been supported by the Romanian Ministry of Education and Research, CNCS -UEFISCDI (grant no. 85 PN-III-P4-ID-PCE-2020-0914).
IoV based ICN In view of the fast development of IoV and the IP network problems (security and quality of services for real-time traffic), recent contributions as for example in [  [12] -  [20] ] propose the deployment of IoV solutions over the new generation of the Internet Protocol (IP) network called ICN. In  [12] , the authors proposed new architecture named CCN-IoV that deploys content chunks of Content-Centric Network (CCN) in the network layer. The authors evaluated the performance of CCN-IoV using Named data networking (NDN) simulator. Their primary results confirm the advantage of CCN in the automotive context. In  [13]  authors designed CRoWN, a content-centric framework for VANET networks. CRoWN provides content-centric Layer-3 and Layer-4 operations implemented over the IEEE 802.
Accordingly, in this section, we specify an in-car gateway as a bridge that enables the three aforementioned zones to 'talk' with each other. Our proposed in-car gateway architecture would be adapted to the autonomous driving specific needs. It discovers and connects devices, as well as aggregates, identifies, converts, process, filters, analyzes, and shares collected data from those devices. It also provides devices connectivity by involving wireless backhaul modules and V2X network interfaces, and ensures that data can travel securely and safely from the edges to the cloud. Fig. 8  depicts a high-level overview of the in-car gateway architecture. In order to fulfill the required specifications for the in-car gateway, we need to specify the hardware and the software structures. In fact, the hardware structure consists of three main parts, which are: (i) The Central Processing Unit (CPU), (ii) The storage module, and (iii) The Input/Output interfaces; whereas the software structure is geared to the AUTOSAR's related basic software architecture that distinguishes between three main layers which run on a microcontroller: (i) Application Software layer, (ii) Runtime Environment (RTE) layer, (iii) and basic software layer  [93].
System Level Fig. 10  In-car gateway software architecture predictions and actions. Perception mechanisms enable many services for autonomous cars, however, we emphasize in this subsection on object detection service. -Object detection The object detection is a key task for bounding, describing, and recognizing the location of every class object (e. g. , trucks, bicycles, road signs, pedestrians, etc. ). It is mostly the domain of sensors. Many contributions have been introduced in this context like  [94] ,  [95] ,  [96] ,  [97]  and  [98] , as well as several algorithm models were adopted such as YOLO, Fast R-CNN, OverFeat, and SimpleNet  [99]. In order to reliably identify objects, the object detection subsystem has an interface towards various sensor input devices that includes a wide set of cameras (e.
), which implies the use of advanced perceptive sensors. Accordingly, the general process of lane detection involves data collection, pre-processing, feature extraction, and lane modeling. -Phase 2: Surrounding traffic assessment The surrounding traffic assessment phase is based on all the safety-perceived information such as relative position inputs, acceleration inputs, steering inputs, etc. , which are shared through the in-car gateway via the vehicular communication networks, and taken as indicators for the safe lane changing decision. Vehicles positions are derived from the GPS receivers and vehicles speeds are obtained from the speed sensors. Other several sensors like LIDAR, RADAR, video-based sensors are accurately applied to receive imaging data and detect closest vehicles within the driving zone.
All these information are available via the inter-vehicle data buses, processed through the in-car gateway CPU (e. g. , steering angle encoder), and distributed across the actuators in order to execute the desired task. V 3 ), the subject vehicle V 1 will make the decision for moving to the adjacent lane. After guaranteeing the availability of required safety spacing between the subject vehicle V 1 and its surrounding vehicles (i. e. , V 2 , V 3 , A 1 , L 2 , and F 3 ), and checking that there is no overtaking vehicle (i. e. , V 3 ), or realizing that the contiguous lane is free (i. e. , A 1 , L 2 , and F 3 GPS information's are not detected), the appropriate algorithms for speed and steering control that are based on considered vehicles datasets, will be generated to formalize safety distances, readjust adapted velocity, and decide desired steering, for enabling the subject vehicle V 1 to navigate safely the neighboring lane.
com/ebooks/book/chapter-pdf/919177/9781789060119_0415. pdf by TECHNISCHE UNIVERSITEIT DELFT user • KM 11. 1: Challenge -Keeping urban drainage and stormwater management metrology projects going long term is very challenging. • KM 11. 2: Harmonization -Existing urban drainage metrology projects tend to have bespoke data quality management and data/meta-data storage systems. The quest to achieve a more harmonized approach to data quality management and data storage remains open, with questions as to what level of harmonization would be desirable, and what such an approach should look like. • KM 11. 3: Maintenance efforts -The person-costs for management and maintenance of both sensors and databases should not be underestimated. These usually exceed the costs of the actual sensors by far and are ongoing throughout the project.
However, the application of this RTC scheme did not lead to full compliance with WFD requirements. van  Daal-Rombouts et al. (2017b)  described the problems that arise when trying to demonstrate how well an RTC system performs, based only on relatively short duration datasets. Hence, this project is still ongoing, with further improvements to the RTC strategy, as well as its robustness, currently being tested. Moreno-Rodenas et al. (2017)  and  Camacho-Suarez et al. (2019)  describe research on checking uncertainty in the models used and the impact of spatial and temporal variability of input data and models used to describe the system.
Sensor operation and maintenance The sensors are validated (see Chapters 7 and 9) within the Delft-FEWS (Flood Early Warning System) WIS (Water Information System) platform (Deltares, https://oss. deltares. nl/web/delft-fews, accessed 17 June 2021). This platform is mainly used as a central database, in order to assess if a particular sensor needs recalibration or other calamities are occurring with the sensor. If this is the case, the maintenance team can assess the situation on site. Cleaning maintenance is done regularly; the frequency of cleaning depends on the type of sensor, location and importance. Rain gauges are cleaned and recalibrated yearly, whereas oxygen sensors in the surface water are checked twice a month. Service levels are given to sensors that are maintained by third-party service providers.
Data management and data accessibility All the sewer-related data from WDD are currently logged within a GE Historian database (https://www. ge. com/digital/applications/proficy-historian, accessed 17 June 2021). This is connected to the central SCADA (supervisory control and data acquisition) at the WWTP, where all real-time information is used for the operation of the system. On a daily basis (at 6 a. m. ), the data are read into the FEWS Water Information System (WIS) system. WIS is similar to GIS (Geographic Information System) for water related data. It follows the same principles for more instinctive representation of data. GE Historian does not have an easy GIS interface and is used as a background storage place for the data, where the FEWS allows interaction. Front-ends: There are various platforms, through which the data can be retrieved.
The main forms of data collection (when larger datasets are required) are through Microsoft Excel ® (through the Proficy Historian Add-on) or Matlab. These forms require knowledge of the 'tagnames', which are scattered throughout the WDD systems. The data can also be approached through Z-Info and a FEWS WIS system. The FEWS WIS system is a GIS-based system which calls the data from GE Historian, allowing the user to find the relevant tags (and data, if desired) for known locations. It also instantly visualizes the requested data. Two others external 'data-tools' are in use by WDD: TMX and HydroNET. TMX (http://www. tmx. nl, accessed 28 April 2021) is a larger, system-wide framework for both soft-and hardware. The TMX system is used for surface water quality readings, but the sewer related data points are also read into the TMX environment.
There are several performance indicators (PIs) in use to improve the monitoring system. These are based on interviews with different departments in the WDD, to ensure PIs are aligned with what is required. The PIs used are data availability, fraction usable data, and accessibility. Data availability refers to the fraction of data points that arrive in the database (dependent on quality of data transmission). The fraction usable data is the part of the arrived data that passes the validation stages (quality of the sensors), and accessibility is the ability for the data to be used effectively and rapidly (therefore dependent on the database and data communication structure). There is a high variability in the quality of the data stream, for both data availability and fraction usable data. Over the 2007-2008 period, the average data availability per sensor cluster ranged from 70-99%, with the fraction usable data ranging from 0-99%.
Data transfer and communication system Depending on the purpose of the measurement, the dataflow goes through different communication platforms. If there is a need for a bidirectional communication because of a control measure, the communication needs are different. The communication from the SCADA to the actuators in the system is through TCP/IP (transmission control protocol/internet protocol). Given that these are large buildings, TCP/IP or Ethernet is viable, as opposed to more remote monitoring locations. For monitoring purposes only, GPRS is sufficient for data transmission. Vodafone and/or T-mobile SIM cards are in place to allow for the GPRS connection. When two are in place, the logging can continue during calamities with the parallel SIM card. 11. 2. 7 Reporting, management and availability of data files for research Within WDD, five-year cycles are defined, where monitoring targets are defined, including parameters, monitoring frequency, confidence intervals (% or absolute value) and availability (operational % of the time).
3). This way, the FEWS database takes in all the necessary data, with reporting on data and ease of analysis expected to improve significantly. The use of several platforms has led to a lacuna of knowledge, causing a lack of ability to use the entire range of options within each different platform. With the external tools (HydroNET and TMX mainly), the knowledge tends to reside outside WDD, which can lead to trouble for the maintenance or changes in the system. Although the current logging of the data is adequate for all purposes (both retrieval of past datasets and operation), data from before the change to the current database architecture seems to have been lost. Before the use of the GE Historian database, a database from another company (GBS) was used. Theoretically, the data within this database should still be there.
However, despite numerous efforts from various operators and other relevant WDD employees, these data points have not yet been recovered. Transitioning to new databases should therefore at all times consider future needs for data and ensure the availability of past data is preserved. A collaboration between several organizations (a waterboard, several municipalities, at least four consultants, several manufacturers and construction firms, and a university) whereby especially the waterboard, the municipalities and the university have different requirements regarding data quality and different aims in mind, needs careful project management that is considerate of these different viewpoints. Achieving high data quality is often not seen as financially viable or desirable by practitioners, hence collaboration and input of PhD students proved essential to achieve good data validation in this case study.
Location of monitors and measured variables As detecting flood (through rainfall prediction) and monitoring flow (to avoid flood) are the main focuses of this facility, SMART employs a rigorous monitoring system of rainfall gauges, and water level and river velocity measurement. There are 22 rainfall stations dedicated to the SMART facility distributed within the Klang River catchment, the distance between each station is less than 3 km. The rainfall data are measured using tipping bucket rain gauges at an interval time of 5 minutes. The control centre monitors real-time data of rainfall and water level, assisting in making the decision of when to activate the SMART facility. All measured information is transferred every 5 minutes to the centralized supervisory control and data acquisition (SCADA) by transmitting the telemetry data.
To ensure safety in the data transfer and storage, the communication of data to the SCADA is backed up by the GSM (global system for mobile communication) system when the radio system fails. Due to excessive data size, the data are not stored in the SIM card. A total of 16 compact bubbler sensors are installed at strategic locations within the catchment to measure water level. A bubbler system is employed as it is more robust, especially for rivers carrying large amounts of debris such as the Klang and Ampang Rivers, in particular during high flows. The velocity of 16 strategic locations within the Klang River stretch and its tributaries is measured using Doppler current meters (DCMs). The river discharge is calculated based on the developed rating curve and validated with the record (of autocorrected discharge) from the DCM.
Strategic and crucial measuring locations include the confluence of the upper Klang River/Ampang River, the SMART Control Centre (at Berembang Pond), and downstream of SMART at Kerayong River. The measurements for both river flow velocity and the water level are taken at 5-minute intervals, and data are transmitted via the same system as rainfall, through SCADA and GSM. All instruments are powered using a solar panel with a battery pack as a supporting energy plan. In addition to advocating green technology, the solar panel also minimizes dependence on the battery simultaneously reducing operation and maintenance costs. The calculation of power consumption for a solar system for rainfall and water level stations is described in the Hydrological Procedure 32 and 33, respectively  (DID, 2018a (DID, , 2018b)).
Data management and data accessibility All transferred raw data are stored in the SCADA database according to each measuring station, listed based on parameters and the recorded date. All recorded data are stored at the SMART data centre, internally managed by the DID. Retrieval of data is obtained using the ClearSCADA SCX MySQL relational database management system (RDBMS), allowing quick response and specific data requirements. Where the data need to be processed (usually for operational purposes), specific data will be retrieved and analysed elsewhere. In-house data quality analysis is done at the SMART Control Centre. Data availability is checked by a network connectivity test between the SCADA database and remote telemetry unit (RTU) at sites. The quality of the data is tested using hydrological modelling, by comparing the observed record with modelling results.
Although at the moment (i. e. 2020), the data are only accessible to DID, upon receiving permission from DID, the data are allowed to be distributed to the interested parties. DID acknowledges that with updated technology, highly accurate algorithms and mathematical modelling, data sharing is the way forward to ensure sustainable operation of SMART. Climate-change induced rainfall patterns obviously may alter the local rainfall intensity, and subsequently the runoff volume within the Klang River watershed. By sharing the data with others, more rigorous data analysis is feasible, which provides benefits to SMART operations and the DID. Realizing the importance of data availability, all recorded data are kept in the storage server (with no data discarded after a number of years), allowing a comprehensive data review when needed.