dataesr/scientific-paragraphs-categorization huggingface dataset:
The maximum location estimation error is approximately 6 m. Whereas in  [23] , the authors proposed a new positioning algorithm for underground coal mines based on Zigbee technology, in which the nearest neighbor in signal space (NNSS) algorithm was adopted for the preliminary location and the dynamic triangular measurement (DTM) localization method was used for precise positioning. The algorithm provides personnel tracking, monitoring, in addition to other related functions. No results are displayed related to the location estimation error. In  [24] , software and hardware for the location system of underground mines personnel were designed. The system consists of a gateway node, reference node, location node, and the connection of the three devices to a monitor. The system is capable of transmitting the location of the personnel in real-time to the software graphical monitor, and achieve a real-time tracking function for the location node.
Description of the Proposed System The proposed system is based on a telecommunications network that has the three fundamental layers within the network design: access, distribution, and core. Each layer can be considered as a well-defined structured module, with specific functions. The three-layer model ensures that the network maintains sufficient strength and flexibility to provide critical services as well as allowing for growth and changes that occur over time  [42]. In the monitoring center which is within the core layer, a router is able to provide connectivity to the high-speed backbone (Internet) so that the data collected by the location system and stored in the server database is available at any time and in any place. In addition, the core layer through the router is interconnected with the distribution routers through an optical fiber link.
Conclusions Localization of miners, objects, and trucks, among others, is one of the most important applications in underground mining, especially for security and safety. The traditional GPS is not useful in underground mining for indoor positioning; therefore, other short range wireless technologies such as RFID, Wi-Fi, Zigbee, and VLC have been preferred to meet this purpose. Hence, in this manuscript a novel visible light communication system (VLC) for localization in under ground mining was proposed. A novel 3D VLC-based localization system was introduced by placing at least three LEDs in each sector of the mine, and using photodetectors in the objects to localize. The LEDs transmit information in different carrier tones and the photodetector receives the superposition of these signals.
e. biomassbased); in contrast, the indicators IVI, MTI, TLc, and TLcVar were calculated from catch data (i. e. catch-based). Within each of the 10 ecosystem models, these 14 ecological indicators were averaged over the last 10 years of simulation period under each scenario of varying fishing mortality rate and primary productivity, which implies that these indicator data points corresponding to different fishing mortality rates and primary productivity levels are independent of each other. Generalized additive models: linearity/non-linearity and directions Nonparametric regression methods such as generalized additive models (GAMs;  Hastie and Tibshirani, 1990)  are powerful tools for exploring linear or non-linear responses of a variable to predictors without being constrained to an underlying parametric model of a specific form, which is particularly useful when ecological thresholds of non-linear responses are of interest  (Lintz et al.
The smoothing parameter was estimated using the generalized cross-validation criterion (GCV criterion;  Wood et al. , 2016)  implemented in R package "mgcv" (Version 1. 8-24;  Wood, 2017). Thin plate regression spline smoothing terms with a ridge penalty were employed, such that the smoothing term could be eliminated and the predicted curve could be reduced to a linear function when the smoothing term did not improve fit. Confidence intervals were calculated for each GAM with a naive bootstrap using random sampling and replacement [see  Large et al. (2013)  for more details on the naive bootstrap approach]. For the purpose of exploring linear or non-linear responses of ecological indicators to fishing mortality, the present study focuses on the development and exploration of 10 Â 14 Â 3 Â 4 ¼ 1680 GAMs, covering 10 ecosystems, 14 ecological indicators, 3 fishing strategies  (F_ltl, F_htl, and F_all) , and 4 scenarios of primary productivity change (directional change, and random change while assuming r ¼ 0.
In contrast to empirical timeseries data explored in  Large et al. (2013)  and  Burthe et al. (2016) , the ecological indicator data in the present study were obtained through individual simulations with different fishing mortality and primary productivity and are thus independent data points and not time-series, as stated above. Thus, the linearity/non-linearity was explored in a more literal sense using specifically designed methods for these datasets, as described below. For each of the 1680 GAMs with respect to fishing mortality, we first determine whether the indicator's response to fishing mortality is linear by fitting a linear regression to the GAM predicted values. If the correlation coefficient between the GAM predicted values and the linear regression fitted values is >0. 95, the indicator's response to fishing mortality (derived from the GAM) is considered linear.
The analytical root(s) of the second derivatives, where ecological thresholds reside, are derived from: f 00 ðxÞ ¼ 2a 2 þ 6a 3 x þ 12a 4 x 2 þ20a 5 x 3 ¼ 0: (3) The analytical root(s) (x ¼ x i ) are solved using the "rootSolve" package in R (R Core Team, 2019). Only root(s) within the range of F MSY multiplier (0. 25$1. 25) are retained for consideration. In addition, root(s) with their predicted values f(x i ) being outside the range defined by the indicator value on either side of the x i value are not considered as thresholds, as they are inflection points resulting from local maxima or minima artificially introduced by high-order polynomial fits.
Discussion The multi-ecosystem, multi-model simulation experiment conducted within the IndiSeas programme  (Shin et al. , 2018; Fu et al. , 2019)  has generated a comprehensive simulation database of ecological indicators under two ecosystem stressors, fishing pressure, and primary productivity change. The GAMs employed in the present study have allowed us to produce response curves for 14 major ecological indicators evaluated for 10 ecosystems in response to fishing mortality, revealing linearity or non-linearity in the responses, with decreasing or increasing trends and thresholds associated with non-linear responses. This comprehensive and comparative research thus provides a rich knowledge base of commonalities and differences among ecosystems in indicators' dynamics and performance at different levels of fishing mortality under different primary productivity scenarios, as well as thresholds associated with non-linear responses of indicators, thereby providing essential knowledge to facilitate the move towards ecosystem-based fisheries management.
Acknowledgements This is a contribution to the IndiSeas Working Group, co-funded by  IOC-UNESCO  (www. ioc-unesco. org)and EuroMarine (http://www. euromarinenetwork. eu), to the project EMIBIOS (FRB, contract no. APP-SCEN-2010-II) and to the  IOC-UNESCO GOOS Programme and GOOS Biology and Ecosystems Panel. The work on Canada West Coast ecosystem was sponsored by  Fisheries & Oceans Canada  under the  Aquatic Climate Change and Adaptation Services Programme. YJS and LV were supported by the project EMIBIOS (FRB, contract no. APP-SCEN-2010-II). AG was supported by  NOAA 's  Integrated Ecosystem Assessment (IEA) programme  (http://www. noaa. gov/iea/). MC was supported by the  Marie Curie Career Integration  Grant Fellowships-PCIG10-GA-2011-303534-to the  BIOWEB  project and the  Spanish National Project PELWEB  ( CTM2017-88939-R ).
NDN edge computing  [34]  Distributed The less congested node is selected as the executor. CS-Man  [42, 43]  Centralized A service manager is in charge of identifying the executors of IoT services according to their capabilities. ICN-isapiens  [44, 45]  Centralized A software component called deployer is in charge of loading the IoT services in specific fog nodes depending on the physical IoT devices they control.
First, the network tries to find a cached copy of the result, i. e. , the transcoded video in the above example. Therefore, the request is forwarded towards the data producer, on path, according to the component /nameo fmedia. If this search fails, FoX tries to compute the result in the NFN node n closer to the data producer. If not locally available, this node has to retrieve the content and the function code by sending separate interests for /nameo fmedia and /nameo ftransconder. If n is not able to perform the service, it can push the computation towards another on-path node or even towards an off-path node, e. g. , a cloud facility. In Reference  [10] , FoX is extended for orchestrating IoT services at the edge. The resulting EdgeFoX strategy assumes that nodes at the network edge are NFN capable and that they may advertise the name of the functions they are able to execute.
Centralized Mechanisms In Reference  [42] , a centralized solution for service orchestration in NFN is provided that is called Computation Service Management (CS-Man). The authors consider a static IoT network where a service manager assigns tasks to other nodes according to their capabilities, as recorded in a database called Service Repository. Despite the advantage of leveraging a centralized knowledge of the nodes' capabilities, in such a design, the service manager represents a single point of failure and a potential bottleneck for the provisioning of services, as it is queried at every service request. In References  [44, 45] , a 3-level framework called ICN-isapiens is presented, where a fog layer, consisting of smart home servers (HSs), is introduced between the physical world and the remote cloud to support real-time services and to hide the heterogeneity of IoT devices.
In ICN-isapiens, HSs are installed in houses and provided with a multi-agent software application to manage predefined low-latency monitoring and actuation services. Long-term storage and complex analysis are, instead, performed in the cloud. Each HS leverages NDN primitives to control a predefined set of IoT devices, while communication with the cloud is performed via IP. The application developers define the association between HS, IoT devices, and agents. Then, a system component called deployer is in charge to load the agents in the designed HSs and to start the system. As a result, ICN-isapiens depend on a centralized component that assigns the agents and, therefore, the services to specific nodes and in a static fashion. Hence, it may lack the flexibility required to properly manage a dynamic fog environment.
It can be composed of a single data packet, e. g. , a float value indicating the average temperature in a room or a boolean value indicating if there are movements in an area, or multiple data packets, e. g. , a compressed video or a dataset indicating the average, maximum, and minimum humidity values in a room. Generally, there are no restrictions in the way a function must be implemented. According to the literature, functions are generic software programs that can be in the form of unikernel, as in Reference [8], or containers, as in Reference  [42] , or even λ-expressions. In the following, we assume that a function is a self-consistent code, identified by a unique name and with a well-defined list of optional input parameters, which can be specified by the consumer; otherwise, a default value will be used.
• Function storing module decides which functions should be installed in the node and which functions should be evicted. The selection is performed in a proactive way by following a popularity-based approach, similar to Reference [8]. During the network bootstrap, a first manual configuration is performed by the network administrator, who may also decide that specific functions must be installed only in specific locations, e. g. , for privacy purposes. When a function is available in the node, a FIB entry is configured that binds the function name with the AppFace of the node.
• Service allocation module decides, after the service interest reception, if the node can execute the service and manages the signalling related to this operation. NDN-fog nodes can dynamically download the function code from the so-called Function Data Base, a storage unit located in the campus domain that contains the function catalog. A default route is installed in the FIBs of the NDN-fog nodes towards the Function Data Base; thus, functions can be retrieved by name with the standard NDN interest/data exchange. Of course, if the function code is available in the CS of an on-path node, the latter can directly satisfy the request without further forwarding it.
Service Provisioning Service provisioning is performed on demand when service interest packets are sent by consumer applications. The latter are hosted in end-devices (e. g. , users' smartphones or laptops, monitoring utilities, etc. ) connected to the leaf nodes of the campus network. When receiving the request, each NDN-fog node applies a processing routine to discover if the result is already cached locally or if itself can be a candidate service executor. The service interest processing includes a bidding procedure that aims at executing the service at the edge in the best available NDN-fog node, which is the one offering the lowest service provisioning time. The latter is computed as the sum of the estimated processing time and the IoT data collection time. The processing time is estimated according to the currently available node's resources, as provided by a standard CPU monitoring utility available in the node, once the CPU requirements of the service are known.
The IoT data collection is estimated by reading the routing cost from the FIB entry per the requested content name. The estimated service processing time is included in a new header field of the service interest that we call SPT. The consumer sets the SPT field in the interest to an upper bound value, which represents the time of executing the service in the remote cloud. The parameter is overwritten by an NDN-fog node offering a shorter delay. If no cached result is located at the edge and no NDN-fog node is available to satisfy the request, e. g. , because there are not enough computing resources, then the interest is forwarded to the cloud. As a result, two different forwarding steps are foreseen. First, the service interest is forwarded in the campus network towards the IoT producer and the discovery is performed by on-path NDN-fog nodes.
On-Path Forwarding In this first stage, the service interest is forwarded from the leaf node connected to the consumer, which in the following is referred to as Consumer Access Node (CAN), and to the leaf node connected to the IoT data producer, which in the following is referred to as Producer Access Node (PAN). In case the consumer and producer have the same access node (CAN = PAN), then the candidate on-path executor is just this single node. Forwarding is guided by the first component of the hierarchical service name, that is, the content name. Specifically, when the CAN or a subsequent NDN-fog node (let us call it N i ) receives the service interest, the following processing is performed.
Thus, it transmits an ACK back. Once the ACK packet reaches the node that advertised the lowest SPT, it becomes the executor. Therefore, it starts the data retrieval from the IoT data producer through legacy NDN interest/data packets. Once data are collected, the executor performs the requested computation and sends the result to the consumer using the same name as the original service interest. Service executed off-path: In such a case (Figure  4b ), the NDN-fog nodes of the domain cannot ensure a service provisioning time shorter than the cloud; hence, a NACK is sent back by the on-path nodes. The CAN, then, issues a service interest towards the cloud. All nodes forward the packet by overwriting the SPT field whenever possible. The root node detects the availability of an executor (N 2 ) and sends an ACK back.
GT>GC 5' splice site (or +2T>C) variants have been frequently reported to cause human genetic disease. However, although we have demonstrated that GT>GC variants in human disease genes may not invariably be pathogenic, none of the currently available splicing prediction tools appear to be capable of reliably distinguishing those GT>GC variants that generate wild-type transcripts from those that do not. Recently, SpliceAI, a novel deep residual neural network tool, has been developed for splicing prediction. Methodologically distinct from previous approaches that either rely on human-engineered features and/or which focus on short nucleotide windows adjoining exon-intron boundaries, SpliceAI assesses splicing determinants by evaluating 10,000 nucleotides of flanking contextual sequence to predict the functional role in splicing of each position in the pre-mRNA transcript.
Herein, we evaluated the performance of SpliceAI in the context of three datasets of GT>GC variants, all of which had been characterized functionally in terms of their impact on mRNA splicing. The first two datasets refer to our recently described "in vivo" dataset of 45 disease-causing GT>GC variants and the "in vitro" dataset of 103 GT>GC substitutions. The third dataset comprised 12 BRCA1 GT>GC variants that were recently analyzed by saturation genome editing. We processed all GT>GC variants using the default settings of SpliceAI. Comparison of the SpliceAI-predicted and experimentally obtained functional impact assessments of the analyzed GT>GC variants revealed that although SpliceAI performed rather better than other prediction tools, it was still far from perfect. A key issue is that the impact of GT>GC (as well as GT>GA or +2T>A) variants that generated wild-type transcripts represents a quantitative change that can vary from barely detectable to almost full expression of wild-type transcripts, with wild-type transcripts often co-existing with aberrantly spliced transcripts.
2. 1. Source of GT>GC variants Three datasets of GT>GC variants, all of which have been characterized functionally in terms of their impact on splicing, were employed in this study. The first two datasets correspond to our previously described "in vivo" dataset of 45 disease-causing GT>GC variants and the "in vitro" dataset of 103 GT>GC substitutions  (Lin et al. , 2019b). The third dataset comprised 12 GT>GC variants from the BRCA1 gene, which were extracted from a recent study that prospectively analyzed the functional impact of over 4000 BRCA1 variants by means of saturation genome editing  (Findlay et al. , 2018). In the context of the first dataset (Supplementary Table  S1 ), the precise level of the variant allele-derived wild-type transcripts was available for four of the seven diseasecausing GT>GC variants that generated wild-type transcripts in the corresponding original publications (Table  1 ).
For the three remaining variants (i. e. , CAV3 c. 114+2T>C in  (Muller et al. , 2006) ; PLP1 c. 696+2T>C in  (Aoyagi et al. , 1999)  and SPINK1 c. 194+2T>C in  (Kume et al. , 2006) ), it is apparent from RT-PCR gel photographs in the original publications that all three were associated with the generation of both wild-type and aberrantly spliced transcripts. We employed ImageJ (https://imagej. net) to provide approximate estimates of the levels of the variant allele-derived wild-transcripts for each of the three variants (Table  1 ).
2. 3. SpliceAI prediction GT>GC variants or substitutions as well as their corresponding GT>GA and GT>GG counterparts were processed (during October 2019) using the default settings of SpliceAI version 1. 2. 1, with a custom gene annotation file containing NCBI reference sequence transcript start and end coordinates. Default settings, and instruction for use of custom annotation files, were taken from https://pypi. org/project/spliceai/.
2. 4. Performance testing Two statistical tests, a Matthews correlation coefficient (MCC) and a Receiver operating characteristic (ROC) curve, were carried out on the dataset 2 substitutions assessed by SpliceAI. MCC test is a correlation coefficient between the observed and predicted binary classifications. For a perfect prediction, the coefficient is +1; a coefficient of 0 is no better than random, and no correlation between observed and predicted yields -1  (Matthews, 1975). A ROC curve illustrates the diagnostic specificity and sensitivity of a binary classifier system as its discrimination threshold is varied; this enables the selection of an optimum threshold value. To assess the difference between the diagonal and the ROC curve obtained, the area under the ROC curve is measured (AUC).
An AUC of 0. 5 would be a random prediction whilst a perfect predictor would score 1. ROC analysis was carried out using the R-based web tool easyROC  (Goksuluk et al. , 2016). For the MCC test, a contingency table was derived from dataset 2 (Supplementary Table  S2 ) where a true positive is defined as a predicted splice altering substitution for which FLGSA produced no transcript and a true negative is a substitution not predicted to alter splicing and for which FLGSA produces transcript.
Accuracy and reliability of the experimentally obtained functional assessment of the GC>GT variants analyzed We employed SpliceAI to make predictions as to the splicing consequences of GT>GC variants from three distinct datasets. Since the experimentally ascertained functional impact of the GT>GC variants analyzed was used as the starting point for our analysis, their accuracy and reliability were of critical importance. Regarding the first dataset of known pathogenic variants (Supplementary Table  S1 ), several points are worth highlighting. First, all 45 disease-causing variants were either homozygotes, hemizygotes or compound heterozygotes, a prerequisite for determining the presence or absence of the variant allelederived normal transcripts. Second, for each variant, patient-derived tissue or cells (pathologically relevant in about half of the cases) had been used to perform the RT-PCR analysis that had unequivocally demonstrated the presence or absence of variant allelederived wild-type transcripts in the corresponding original publication.
Third, the levels of the variant allele-derived wild-type transcripts in the seven disease-causing GT>GC variants that generated wild-type transcripts were very low (≤15% of normal; Table  1 ), potentially explicable by the ascertainment bias inherent to all disease-causing variants. Nonetheless, all seven of these variants were noted to be associated with a milder clinical phenotype than would have been expected from a functionally null variant  (Lin et al. , 2019b) , consistent with other findings that even the retention of a small fraction of normal gene function can significantly impact the clinical phenotype (Den  Uijl et al. , 2011; Ramalho et al. , 2002; Raraigh et al. , 2018; Scalet et al. , 2019). In the case of the second dataset (Supplementary Table  S2 ), the functional effects of all 103 engineered GT>GC substitutions (from 30 different genes) were analyzed by Full-Length Gene Splicing Assay (FLGSA) in transfected HEK293T cells  (Lin et al.