dataesr/scientific-paragraphs-categorization huggingface dataset:
In real life operation the ORC system will experience transient operation conditions. Waste heat recovery fuel economy has then to be assessed on a test cycle like the WLTC. In addition to that  (Horst et al, 2014)  showed the importance of assessing the negative impact of the interaction between the ORC system and the other vehicle systems. A global vehicle model is needed to estimate ReverCycle fuel economy on a WLTC cycle and to study its interaction with the engine cooling circuit. The vehicle model is developed within the Dymola environment. The model is composed of three different modules, which will be described in the following paragraphs.
MAC activation time The procedure used to estimate the MAC activation time is similar to the one developed by  (Johnson, 2002). The main difference is in the calculation of the soak temperature. In this paper the mono-zone cabin thermal model developed by  (Benouali, 2002)  is used instead of an empirical correlation. The first step of the procedure consists in selecting a climatic region and the corresponding weather data from a database  (Crawley et al, 2000). The weather data are selected exclusively during the 7am-8pm interval, the time step is one hour. For each time step the cabin soak temperature is calculated with the cabin thermal model. This information is the main input for the thermal comfort model developed by  (Fanger, 1982) , which is calculating the percentage of people that will turn on the MAC system due to thermal comfort dissatisfaction.
Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust training and optimization on large datasets at high computational cost. So far, limited research and optimization of DNNs in shock advisory systems is found on large ECG arrhythmia databases from out-of-hospital cardiac arrests (OHCA). The objective of this study is to optimize the hyperparameters (HPs) of deep convolutional neural networks (CNN) for detection of shockable (Sh) and nonshockable (NSh) rhythms, and to validate the best HP settings for short and long analysis durations (2-10 s). Large numbers of (Sh + NSh) ECG samples were used for training (720 + 3170) and validation (739 + 5921) from Holters and defibrillators in OHCA.
In the focus of AED rhythm analysis, a few recent studies have been found to apply DNNs for detection of life-threatening arrhythmias. Related to the problem for detection of pulseless and pulsatile rhythm,  Elola A et al. (2019)   [56]  show the superiority of two end-to-end DNN architectures (up to five CNN layers and one recurrent layer) vs. a classic machine learning approach (hand-crafted features and classifier) with analysis over 2-5 s on ECGs acquired by defibrillation pads from OHCA databases. The other problem for Sh/NSh rhythm detection has been addressed by four studies, proposing DNN architectures  [57] [58] [59] [60]. Nguyen and Kiseon (2018)   [57]  show the partial benefit of CNN, implementing three CNN layers instead of conventional feature extraction, but keeping the conservative approach with ECG preprocessing and support vector machines classifier.
Acharya et al. (2018)   [58]  demonstrate that end-to-end CNN architecture (four CNN layers and three fully connected layers) is able to detect very short Sh/NSh segments (of only 2 s in duration). A limitation of the above two studies is the DNN training and reporting results on public Holter databases, while there is evidence that ECGs gathered by Holters and defibrillators during treatment in OHCA may be very different both for Sh and NSh rhythms  [24, 60]. OHCA databases are, however, proprietary, and we find only two DNN models trained on OHCA ECGs, i. e. ,  Picon et al. (2019)   [60]  with two CNN layers and one long short-term memory (LSTM) layer;  Irusta et al. (2019)   [59]  with two or three CNN layers and two dense layers. Both studies are shown to be compliant with AHA performance goals using short ECG segment lengths (2 s to 8 s).
However, the object for DNN depth and hyperparameter optimization has not been in the scope of any study for shock advisory decision. The objective of this study is to optimize the hyperparameters of an end-to-end fully convolutional DNN architecture (one to seven CNN layers, 5 to 23 hidden layers) for Sh/NSh rhythm detection using single-lead raw ECG signals from public Holter and OHCA databases with life-threatening arrhythmias. The validation performance of our best model is reported for short and long analysis durations (from 2 s to 10 s). Comparison to other published studies aims to prove the superiority of our DNN design. While the network can be run alone, without the need for preprocessing, waveform measurements, transformations or other machine learning algorithms, it has perspectives for certain applications in unsupervised database annotation and diagnosis platforms, as well as in reliable real-time AED shock-advisory systems in OHCA.
• AHA fibrillation database (AHADB)  [61] , including 30 min ECG recordings from 10 patients (files A8001 to A8010); only the first out of the two available ECG channels is used; All ECG signals were stored at sampling frequency fs = 250 Hz. No additional filtering was applied, although ECGs might be prefiltered during their hardware acquisition in Holters. Each ECG recording was split in nonoverlapping 10 s strips, which are annotated following the rhythm annotation scheme (see Section 2. 3). Data augmentation by overlap of extracted strips in long-term Holter databases has not been applied because extra replication of identical rhythms from the same patient produces artificially big datasets without introducing statistically valuable information for extraction of new diagnostic features.
Random HP Search The first task of this study was to train different CNN models with settings of HPs = {N, F i , K i } N i=1 , which were selected by random search in a large grid of HP values: The random HPs search was performed under equal training conditions for all models (defined in Section 3. 3). The input ECG vector length was L 1 = 5 s (625 samples@125 Hz), taken from the initial part (0-5 s) of the annotated ECG strips (10 s) in databases after downsampling by 2. The concept for selection of the initial signal part (without shifting) simulates the real-case scenario when the AED analysis process was started without a delay to output the earliest Sh/NSh decision. • N = {1, All random search CNN models are evaluated according to their performance on the validation dataset. The performance was estimated at the point on the receiver operating characteristic curve (ROC) with balanced sensitivity (Se) and specificity (Sp), i.
Optimal HP Models For different CNN depths N = {1, 2, 3, 4, 5, 6, 7}, the HPrank distributions (median values) were used to design new statistically justified "median HP" models which were expected to perform equally well to the top-ranked models. We further denoted them as our optimal CNN configurations, valid for different N: HPopt = median(HPrank{F i , K i } N i=1 ). (6) All HPopt models were further trained under equal training conditions (defined in Section 3. 3) and optimized for different learning rates LR = {0. 01, 0. 005, 0. 001, 0. 0005, 0. 0001, 0. 00005, 0. 00001}, each LR used in 10 independent runs for model training. All trained HPopt models were further evaluated on the validation dataset.
Best Model The settings of the best-performing HPopt model are reported as our best CNN configuration: HPbest = HPopt{LR, N, F i , K i } N i=1 → max(BAC). (7) CNN models with HPbest settings were further trained for different ECG analysis durations L 1 = (2 s, 3 s, 4 s, 5 s, 7 s, 10 s), each one used in 10 independent runs for training. The evaluation of all trained models on the validation dataset justified our best CNN models for short and long analysis durations (2-10 s).
6%). The red model with maximal BAC (HPbest) is distinguished to reach the highest Se. 4  present the validation performance of our best model ( ) for different analysis durations (2-10 s) measured within the following ranges: Se (97. 6%-99. 6%), Sp (98. 7%-99. 7%), BAC (98. 2%-99. 5%). Maximal performance is measured at 5 s (maxSe = 99. 6%) and 10s (maxSp = 99. 7%). We note that BAC linearly increases from 25 s but further prolongation of the analysis duration (5-10 s) did not improve BAC owing to the effect of proportional Sp rise (+0. 3% points) and Se drop (-0. 3% points). Table  4  also presents the final report of the validation performance on the Public and OHCA databases. Overall, the performance is lower in OHCA vs. Public databases, estimated with an average drop of -2. 2% points for Se (95.
Best Model Figure  10  and Table  4  present the validation performance of our best model (HPbest) for different analysis durations (2-10 s) measured within the following ranges: Se (97. 6%-99. 6%), Sp (98. 7%-99. 7%), BAC (98. 2%-99. 5%). Maximal performance is measured at 5 s (maxSe = 99. 6%) and 10s (maxSp = 99. 7%). We note that BAC linearly increases from 25 s but further prolongation of the analysis duration (5-10 s) did not improve BAC owing to the effect of proportional Sp rise (+0. 3% points) and Se drop (-0. 3% points). Table  4  also presents the final report of the validation performance on the Public and OHCA databases. Overall, the performance is lower in OHCA vs. Public databases, estimated with an average drop of -2. 2% points for Se (95. 2%-98. 7% vs. 98. 6%-100%), -0. 3% points for Sp (98.
7%-99. 2% vs. 98. 6%-99. 8%), -1. 2% points for BAC (97%-99% vs. 99. 5%-99. 9%). 4  present the validation performance of our best model ( ) for different analysis durations (2-10 s) measured within the following ranges: Se (97. 6%-99. 6%), Sp (98. 7%-99. 7%), BAC (98. 2%-99. 5%). Maximal performance is measured at 5 s (maxSe = 99. 6%) and 10s (maxSp = 99. 7%). We note that BAC linearly increases from 25 s but further prolongation of the analysis duration (5-10 s) did not improve BAC owing to the effect of proportional Sp rise (+0. 3% points) and Se drop (-0. 3% points). Table  4  also presents the final report of the validation performance on the Public and OHCA databases. Overall, the performance is lower in OHCA vs. Public databases, estimated with an average drop of -2. 2% points for Se (95.
e. , BAC assumes imbalanced number of FP and FN, proportional to the class size (FP BAC 8FN BAC ). In contrast, other statistical metrics known to deal with imbalanced classes, such as F1-score = 2TP/(2TP + FP + FN) would assume balanced number of FP and FN (FP F1 FN F1 ) at the expense of increasing the number of FN F1 >>FN BAC. Thus, using FN F1 in the denominator of Se could lead to an intolerable Se drop below the AHA goals  [4]. Our results are distinctive to the general concept for optimal HPs design and the choice of the best performance model based on statistical analysis of a total of 4216 random search models. All models have been trained and evaluated under the same conditions with independent public and OHCA datasets for Sh/NSh classification over 5s ECG signals. Our initial observations reveal a large number of models, which have overlapping performances within the range (98%-99.
• The use of two ECG sources from the most famous public ventricular arrhythmia databases and private OHCA databases provides a robust setting for model optimization on a large scale of ECG rhythms that can be seen by Holters and defibrillators during treatment of cardiac arrest patients. This article uses the largest number of (Sh + NSh) samples for training (720 + 3170) and validation (739 + 5921), which is the important precondition for design of robust deep learning shock advisory systems;.
This comparative study demonstrates the possibility of applying transfer learning through relearning of models designed for different ECG diagnostic purposes. We, however, could not test the original concept for transfer learning through reusing pretrained CNNs on ECG data for different purposes, as pretrained versions of the models in Table  5  are not publicly accessible. A truthful comparative study was respected so that each CNN architecture in Table  5  was trained and evaluated under the same conditions, relying on the same data points, annotations, training and validation datasets from Holters and defibrillators, as used for training and validation of the best model in this study. All models were trained for input signals with durations 2-10 s and their BAC performance on the validation datasets is presented in Figure  11.
The following conclusions can be drawn: -Our best model outperforms all other models for both Public and OHCA databases. Its configuration can be distinguished as the deepest among others with five convolutional layers, while the number of filters and kernel sizes looks balanced within the middle range found in other studies. This result is a certain proof that HP optimization has an important role in accuracy and should always be carefully performed during DNN design for specific applications; -The models of Elola et al. [56]  and Kiranyaz et al. [44]  are the next best models with up to about -1% points and -1. 5% points' drop in BAC, respectively. These models are good examples for successful transfer learning of CNNs in ECG signal processing, where CNN designs optimized.
The general conclusion after evaluation in Holter and OHCA databases would be that our best CNN model provides very high Se (>95. 2%) and Sp (>98. 6%), even for very short analysis durations (2 s), and could be considered compliant with the AHA performance goals  [4]. While the optimized network, based on fully convolutional hidden layers with pretrained weights can be run alone in real time without the need of preprocessing, waveform measurements, transformations, or other machine learning algorithms, it has perspectives for certain applications in unsupervised database annotation and diagnosis platforms, as well as in reliable real-time AED shock-advisory systems in OHCA.
Limitations The size of the training/validation datasets is not divided according to the common scheme of 80%/20% as it isn't a manually controllable factor but depends only on the number of cases found in independent data sources, predefined before the study. Thus, the cases are distributed uncommonly with majority of data for validation. The scheme with independent data sources is, however, respected to avoid the common cross-validation scenario without a patientwise control, where rhythms from the same patient are randomly shuffled in the training and validation datasets, and thus an overtraining on the specific patient rhythm is highly probable in deep learners. BAC performance is reported on our validation dataset using analysis durations between 2 s and 10 s. The performance of the reference automatic external defibrillator (AED) algorithm is reported for the same databases, taken from Krasteva et al.
Limitations The size of the training/validation datasets is not divided according to the common scheme of 80%/20% as it isn't a manually controllable factor but depends only on the number of cases found in independent data sources, predefined before the study. Thus, the cases are distributed uncommonly with majority of data for validation. The scheme with independent data sources is, however, respected to avoid the common cross-validation scenario without a patientwise control, where rhythms from the same patient are randomly shuffled in the training and validation datasets, and thus an overtraining on the specific patient rhythm is highly probable in deep learners. From the methodological point of view, the HP optimization is done on the validation set, which is also used for evaluation. However, as soon as the study sets out to investigate the HP optimization itself, it is not an issue for the main conclusions, regarding the importance of HPs and comparison to other published DNN models, all trained and evaluated under the same conditions and datasets.
Although the presented study supports the derivation of valuable conclusions about the optimal depth and width of CNN models for Sh/NSh rhythm discrimination, we should note the inevitable influence of the size and content of the training and validation databases. Considering, however, the application of both public Holter and OHCA electrocardiograms, which cover the wide variety of Sh and NSh rhythms appearing during cardiac arrest and other critical situations and present as much as 3890 cases for training and 6660 cases for validation, we do not expect substantial differences in the context of Sh/NSh rhythm detection.
[37]' with learning rate LR = 0. 001, chosen as a good default setting[37], decay rate DR = LR/epochs, exponential decay rate for the first moment estimates β 1 = 0. 9 and exponential decay rate for the second moment estimates β 2 = 0. 999; • Training epochs: 400. Early stopping is applied if no improvement in performance is observed for more than 150 epochs; • Batch size: 256; • Kernel initializer: random uniform; • Optimizer: '• Loss function: binary cross-entropy for 2 target classes (Sh/NSh); • Metrics function: accuracy = (TP + TN)/(TP + TN + FP + FN). Owing to the concept for balanced training dataset during model fit, the metrics accuracy closely corresponds to BAC. • Saved model: the model with maximal accuracy after all training epochs.
Introduction Over the past two decades, the basis for the creation of archaeological knowledge about past societies archaeology has fundamentally changed. This shift has been driven by multiple factors, notably the acceleration of development-led archaeology  [1] [2] [3]  and the integration of large digital observational datasets, particularly those acquired through remote sensing methods for earth observation and monitoring  [4, 5] , the proliferation of scientific methods applied to gather diverse data  [6] , and the increasing digitization of archaeological data  [3, 7, 8]. Within these broader trends, emerging applications of machine learning methods for the identification of features of archaeological interest in remote sensing derived datasets (e. g. ,  [9, 10] ) are causing a particularly important change in knowledge production practices.
Biases Affecting the Corpus of Physical Features Used as Evidence of Movement As we noted above, there are two key sources which inform new identifications of features as evidence of past movement: domain literature and past expert identifications in observational datasets (remote sensing) or field observations. Each of these are affected by inter-related methodological, observational, contextual, and semantic biases. These biases are well known, as they impact on most observational datasets created and used by archaeologists, and we summarize the key impacts for studies of movement here. Methodological biases: The choice of remote sensing technique and the design of the survey affect the probability of detecting features, depending on their specific physical characteristics. Some techniques are better for observing features on the ground surface and others for detecting features below the surface.
This field-based description and its direct aerial-view correlate become the knowledge model used to recognize morphologically similar features in aerial imagery and terrain models as paths. Through this process, particularly when accelerated through machine learning applications, the generalization of field observations through extensive remote sensing datasets will reinforce existing ideas. This interpretive practice does not lend itself to insights into different systems of movement which do not align with our field experience, which is itself embedded in received ideas of what physical features related to movement should look like, ideas developed and reproduced through the literature and affected by semantic and contextual biases. In sum, through our practices of engaging with the literature, field observation, lidar-derived digital terrain models (LDTMs), and complementary remote sensing datasets, we have enhanced our ability to see more of what we already know.
The impact of these inter-related biases can be seen in projects that have generated spatially extensive but semantically narrow datasets. For example, projects working in Central America have identified extensive evidence for major Maya causeways, usually called sakbeh (white, constructed roads)  [22]. This type of road is well known from the fieldwork, and its connection to the major cities of the Mayan world is well established  [23, 24] , with key exemplars of the type well mapped and recognized as part of a network. Integration of this existing knowledge base with new observational data from lidar provided an extensive overview of this causeway system  [25]. The knowledge gained from the lidar data, however, is only relevant to the major transport network defined by these features. It does not extend our knowledge of different types of movement in this landscape and the traces these might leave.
In sum, through our practices of engaging with the literature, field observation, lidar-derived digital terrain models (LDTMs), and complementary remote sensing datasets, we have enhanced our ability to see more of what we already know. The impact of these inter-related biases can be seen in projects that have generated spatially extensive but semantically narrow datasets. For example, projects working in Central America have identified extensive evidence for major Maya causeways, usually called sakbeh (white, constructed roads)  [22]. This type of road is well known from the fieldwork, and its connection to the major cities of the Mayan world is well established  [23, 24] , with key exemplars of the type well mapped and recognized as part of a network. Integration of this existing knowledge base with new observational data from lidar provided an extensive overview of this causeway system  [25].
The way concepts are linked provides a common definition for assemblages of various morphological, temporal, spatial, and functional characteristics, which are not necessarily the same across different geographical areas or even within the whole of the geographic area where they appear. Formalizing these mappings into a UML schema to visually represent the structure of the knowledge domain precedes further formalization within an ontology editor (such as Protégé: https://protegewiki. stanford. edu/wiki/Main_Page). Using a logical language such as OWL (https://www. w3. org/2001/sw/wiki/OWL) allows for the visualization of concept hierarchies and the development of correspondences with other conceptual references, such as the CIDOC CRM. To illustrate our approach, we present three case studies from the literature for which we modeled the domain of movement concepts as a UML schema represented as a graph (produced with software such as yEd; https://www.
Caracol Region (Belize) In Caracol, numerous pathways were discovered through the analysis of the lidar-derived DTM. The resulting collection of new causeway segments adds to the dataset compiled from previous observations during fieldwork and from satellite imagery based surveys  [24]. The lidar based study revealed a large part of the main road system linking the major epicenter of Caracol to other places, called termini, understood as secondary centers which may be residential  [25]. The LDTM shows a largely continuous swath of settlement and terracing that exists between the epicenter and these termini. This extensive urbanized landscape area is characterized by small groups of houses organized around small squares, which are integrated into the agrarian landscape, a pattern recognized in other Mayan regions.