wanyu/IteraTeR_v2 huggingface dataset:
Original:  The collection of channels infered by oCSE describes a network of information flow within the swarm.
Revised:  The collection of channels inferred by oCSE describes a network of information flow within the swarm.
Edit intent: fluency
Original:  One novel feature coming with 5G is the ability to serve a dense crowd of devices, calling for radically new ways of accessing the network.
Revised:  One novel feature is the ability to serve a dense crowd of devices, calling for radically new ways of accessing the network.
Edit intent: clarity
Original:  This is the case in machine-type communications, but also urban environments or hotspots.
Revised:  This is the case in machine-type communications, but also in urban environments and hotspots.
Edit intent: fluency
Original:  In those use cases, pilot sequences are in shortage as the number of devices is much larger than the sample duration of the channel coherence interval  .
Revised:  In those use cases, the high number of devices is much larger than the sample duration of the channel coherence interval .
Edit intent: clarity
Original:  In those use cases, pilot sequences are in shortage as the number of devices is much larger than the sample duration of the channel coherence interval  .
Revised:  In those use cases, pilot sequences are in shortage as the number of devices and the relatively short channel coherence interval .
Edit intent: clarity
Original:  This article motivates the need for random access to pilot sequences by the devices  and shows that massive MIMO is a main enabler to achieve fast access with high data rates, and delay-tolerant access with different data rate levels.
Revised:  This article motivates the need for random access by the devices and shows that massive MIMO is a main enabler to achieve fast access with high data rates, and delay-tolerant access with different data rate levels.
Edit intent: clarity
Original:  This article motivates the need for random access to pilot sequences by the devices  and shows that massive MIMO is a main enabler to achieve fast access with high data rates, and delay-tolerant access with different data rate levels.
Revised:  This article motivates the need for random access to pilot sequences by the devices and shows that Massive MIMO is a main enabler to achieve fast access with high data rates, and delay-tolerant access with different data rate levels.
Edit intent: fluency
Original: Monitoring Wireless Sensor Networks (WSNs) are composed by sensor nodes that report temperature, relative humidity  and other environmental parameters.
Revised: Monitoring Wireless Sensor Networks (WSNs) are composed of sensor nodes that report temperature, relative humidity and other environmental parameters.
Edit intent: fluency
Original: Monitoring Wireless Sensor Networks (WSNs) are composed by sensor nodes that report temperature, relative humidity  and other environmental parameters.
Revised: Monitoring Wireless Sensor Networks (WSNs) are composed by sensor nodes that report temperature, relative humidity , and other environmental parameters.
Edit intent: fluency
Original:  The time between two successive measurements is a critical parameter to set during the WSN configuration , because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data.
Revised:  The time between two successive measurements is a critical parameter to set during the WSN configuration because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data.
Edit intent: fluency
Original:  The main goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application.
Revised:  The primary goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application.
Edit intent: clarity
Original:  In the proposed algorithm, the AP selection process is distributed at the user stations (STAs) and is based on the estimated signal-interference-plus noise ratio (SINR ) in the downlink.
Revised:  In the proposed algorithm, the AP selection process is distributed at the user stations (STAs) and is based on the estimated SINR in the downlink.
Edit intent: clarity
Original:  Based on simulated dense 802.11 networks , the proposed algorithms outperform the strongest signal first (SSF) AP selection scheme  in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [ 4 ]  . While increasing STA densification, the proposed scheme is shown to increase DL throughput across the network .
Revised:  Based on simulated dense 802.11 network , the proposed algorithms outperform the strongest signal first (SSF) AP selection scheme in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [ 4 ] . While increasing STA densification, the proposed scheme is shown to increase DL throughput across the network .
Edit intent: fluency
Original:  Based on simulated dense 802.11 networks , the proposed algorithms outperform the strongest signal first (SSF) AP selection scheme  in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [ 4 ]  . While increasing STA densification, the proposed scheme is shown to increase DL throughput across the network .
Revised:  Based on simulated dense 802.11 networks , the proposed scheme outperforms the strongest signal first (SSF) AP selection scheme in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [ 4 ] . While increasing STA densification, the proposed scheme is shown to increase DL throughput across the network .
Edit intent: clarity
Original:  Based on simulated dense 802.11 networks , the proposed algorithms outperform the strongest signal first (SSF) AP selection scheme  in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [ 4 ]  . While increasing STA densification, the proposed scheme is shown to increase DL throughput across the network .
Revised:  Based on simulated dense 802.11 networks , the proposed algorithms outperform the strongest signal first (SSF) AP selection scheme used in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [ 4 ] . While increasing STA densification, the proposed scheme is shown to increase DL throughput across the network .
Edit intent: clarity
Original:  Based on simulated dense 802.11 networks , the proposed algorithms outperform the strongest signal first (SSF) AP selection scheme  in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [ 4 ]  . While increasing STA densification, the proposed scheme is shown to increase DL throughput across the network .
Revised:  Based on simulated dense 802.11 networks , the proposed algorithms outperform the strongest signal first (SSF) AP selection scheme in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [ 4 ] . While increasing STA densification, the proposed scheme is shown to increase aggregate network performance .
Edit intent: clarity
Original: Cloud-Radio Access Network (C-RAN) , one of the prominent architectures for 5G cellular systems, is characterized by a hierarchical structure in which the baseband processing functionalities of remote radio heads (RRHs) are implemented by means of cloud computing at a Central Unit (CU).
Revised: Cloud-Radio Access Network (C-RAN) is characterized by a hierarchical structure in which the baseband processing functionalities of remote radio heads (RRHs) are implemented by means of cloud computing at a Central Unit (CU).
Edit intent: clarity
Original:  In particular, two baseline algorithmic solutions are studied, namely a standard C-RAN approach based on quantize-and-forward and an alternative scheme based on detect-and-forward . Both techniques leverage Bayesian compressive sensing (CS) algorithms and are elucidated with reference to the state-of-the-art on CS . Numerical results illustrate the advantages of centralized processing and the relative merits of the two schemes as a function of the system parameters.
Revised:  In particular, the standard C-RAN approach based on quantize-and-forward and an alternative scheme based on detect-and-forward . Both techniques leverage Bayesian compressive sensing (CS) algorithms and are elucidated with reference to the state-of-the-art on CS . Numerical results illustrate the advantages of centralized processing and the relative merits of the two schemes as a function of the system parameters.
Edit intent: clarity
Original:  In particular, two baseline algorithmic solutions are studied, namely a standard C-RAN approach based on quantize-and-forward and an alternative scheme based on detect-and-forward . Both techniques leverage Bayesian compressive sensing (CS) algorithms and are elucidated with reference to the state-of-the-art on CS . Numerical results illustrate the advantages of centralized processing and the relative merits of the two schemes as a function of the system parameters.
Revised:  In particular, two baseline algorithmic solutions are studied, namely a standard C-RAN approach based on quantize-and-forward and centralized detection is compared to a scheme based on detect-and-forward . Both techniques leverage Bayesian compressive sensing (CS) algorithms and are elucidated with reference to the state-of-the-art on CS . Numerical results illustrate the advantages of centralized processing and the relative merits of the two schemes as a function of the system parameters.
Edit intent: clarity
Original:  In particular, two baseline algorithmic solutions are studied, namely a standard C-RAN approach based on quantize-and-forward and an alternative scheme based on detect-and-forward . Both techniques leverage Bayesian compressive sensing (CS) algorithms and are elucidated with reference to the state-of-the-art on CS . Numerical results illustrate the advantages of centralized processing and the relative merits of the two schemes as a function of the system parameters.
Revised:  In particular, two baseline algorithmic solutions are studied, namely a standard C-RAN approach based on quantize-and-forward and an alternative scheme based on detect-and-forward . Both techniques leverage Bayesian compressive sensing (CS) algorithms and are elucidated with reference to the state-of-the-art on CS . Numerical results illustrate the relative merits of the two schemes as a function of the system parameters.
Edit intent: clarity
Original:  We show that the optimal performance can be quantified by the max relative entropy between a suitable operator, associated to the test, and the set of operators associated to non-informative tests . Building on the connection with the max relative entropy , we extend the notion of conditional min- entropy from quantum states to quantum causal networks , providing a measure of the total correlations generated by ordered sequences of quantum channels with memory .
Revised:  We show that the optimal performance is equal to a max relative entropy between a suitable operator, associated to the test, and the set of operators associated to non-informative tests . Building on the connection with the max relative entropy , we extend the notion of conditional min- entropy from quantum states to quantum causal networks , providing a measure of the total correlations generated by ordered sequences of quantum channels with memory .
Edit intent: clarity
Original:  We show that the optimal performance can be quantified by the max relative entropy between a suitable operator, associated to the test, and the set of operators associated to non-informative tests . Building on the connection with the max relative entropy , we extend the notion of conditional min- entropy from quantum states to quantum causal networks , providing a measure of the total correlations generated by ordered sequences of quantum channels with memory .
Revised:  We show that the optimal performance can be quantified by the max relative entropy , which quantifies the informativeness of the test . Building on the connection with the max relative entropy , we extend the notion of conditional min- entropy from quantum states to quantum causal networks , providing a measure of the total correlations generated by ordered sequences of quantum channels with memory .
Edit intent: clarity
Original:  We show that the optimal performance can be quantified by the max relative entropy between a suitable operator, associated to the test, and the set of operators associated to non-informative tests . Building on the connection with the max relative entropy , we extend the notion of conditional min- entropy from quantum states to quantum causal networks , providing a measure of the total correlations generated by ordered sequences of quantum channels with memory .
Revised:  We show that the optimal performance can be quantified by the max relative entropy between a suitable operator, associated to the test, and the set of operators associated to non-informative tests . Building on this result , we extend the notion of conditional min- entropy from quantum states to quantum causal networks , providing a measure of the total correlations generated by ordered sequences of quantum channels with memory .
Edit intent: clarity
Original:  We show that the optimal performance can be quantified by the max relative entropy between a suitable operator, associated to the test, and the set of operators associated to non-informative tests . Building on the connection with the max relative entropy , we extend the notion of conditional min- entropy from quantum states to quantum causal networks , providing a measure of the total correlations generated by ordered sequences of quantum channels with memory .
Revised:  We show that the optimal performance can be quantified by the max relative entropy between a suitable operator, associated to the test, and the set of operators associated to non-informative tests . Building on the connection with the max relative entropy , we extend the notion of conditional min-entropy from quantum states to quantum causal networks , providing a measure of the total correlations generated by ordered sequences of quantum channels with memory .
Edit intent: fluency
Original:  We show that the optimal performance can be quantified by the max relative entropy between a suitable operator, associated to the test, and the set of operators associated to non-informative tests . Building on the connection with the max relative entropy , we extend the notion of conditional min- entropy from quantum states to quantum causal networks , providing a measure of the total correlations generated by ordered sequences of quantum channels with memory .
Revised:  We show that the optimal performance can be quantified by the max relative entropy between a suitable operator, associated to the test, and the set of operators associated to non-informative tests . Building on the connection with the max relative entropy , we extend the notion of conditional min- entropy from quantum states to quantum causal networks .
Edit intent: clarity
Original:  For inverse models for instance, BI may be considered convenient as it introduces a statistically regularisation, which is not present in alternative approaches . Understanding the concepts and the application of BI is however not trivial if one is only familiar with identification approaches based on error minimisation.
Revised:  For inverse models for instance, BI may be considered convenient as it introduces a statistical regularisation . Understanding the concepts and the application of BI is however not trivial if one is only familiar with identification approaches based on error minimisation.
Edit intent: coherence
Original:  To this purpose a single spring is regarded , for which the stress-strain curves are artificially created.
Revised:  For this purpose a single spring is regarded , for which the stress-strain curves are artificially created.
Edit intent: fluency
Original:  To this purpose a single spring is regarded , for which the stress-strain curves are artificially created.
Revised:  To this purpose a single spring is considered , for which the stress-strain curves are artificially created.
Edit intent: clarity
Original:  Besides aiming to offer an introduction in BI, the main novelty of this contribution is the formulation to incorporate not only the statistical errors in the measured stresses, but also the statistical errors in the measured strains.
Revised:  Besides offering an introduction to BI, the main novelty of this contribution is the formulation to incorporate not only the statistical errors in the measured stresses, but also the statistical errors in the measured strains.
Edit intent: coherence
Original:  Besides aiming to offer an introduction in BI, the main novelty of this contribution is the formulation to incorporate not only the statistical errors in the measured stresses, but also the statistical errors in the measured strains.
Revised:  Besides aiming to offer an introduction in BI, the main novelty of this contribution is the formulation to not only incorporate statistical errors in the measured stresses, but also the statistical errors in the measured strains.
Edit intent: fluency
Original:  Besides aiming to offer an introduction in BI, the main novelty of this contribution is the formulation to incorporate not only the statistical errors in the measured stresses, but also the statistical errors in the measured strains.
Revised:  Besides aiming to offer an introduction in BI, the main novelty of this contribution is the formulation to incorporate not only the statistical errors in the measured stresses, but also statistical errors in the measured strains.
Edit intent: fluency
Original:  Furthermore, a number of possible misconceptions of BI are highlighted based on the purely elastic case.
Revised:  Furthermore, a number of possible misconceptions on BI are highlighted based on the purely elastic case.
Edit intent: fluency
Original:  Bayesian inference (BI) can be used for the probabilistic identification of material parameters.
Revised: We discuss Bayesian inference (BI) can be used for the probabilistic identification of material parameters.
Edit intent: clarity
Original:  Bayesian inference (BI) can be used for the probabilistic identification of material parameters.
Revised:  Bayesian inference (BI) for the probabilistic identification of material parameters.
Edit intent: clarity
Original:  Besides offering an introduction to BI, the main novelty of this contribution is the formulation to not only incorporate statistical errors  in the measured stresses, but also statistical errors in the measured strains  .
Revised:  Besides offering a didactic introduction to BI, the main novelty of this contribution is the formulation to not only incorporate statistical errors in the measured stresses, but also statistical errors in the measured strains .
Edit intent: clarity
Original:  Besides offering an introduction to BI, the main novelty of this contribution is the formulation to not only incorporate statistical errors  in the measured stresses, but also statistical errors in the measured strains  .
Revised:  Besides offering an introduction to BI, this paper proposes an approach to incorporate statistical errors in the measured stresses, but also statistical errors in the measured strains .
Edit intent: clarity
Original:  Besides offering an introduction to BI, the main novelty of this contribution is the formulation to not only incorporate statistical errors  in the measured stresses, but also statistical errors in the measured strains  .
Revised:  Besides offering an introduction to BI, the main novelty of this contribution is the formulation to not only incorporate statistical errors both in the measured stresses, but also statistical errors in the measured strains .
Edit intent: clarity
Original:  Besides offering an introduction to BI, the main novelty of this contribution is the formulation to not only incorporate statistical errors  in the measured stresses, but also statistical errors in the measured strains  .
Revised:  Besides offering an introduction to BI, the main novelty of this contribution is the formulation to not only incorporate statistical errors in the measured stresses, and in the measured strains .
Edit intent: coherence
Original:  Particularly, we introduce and implement a novel technique named Modular Dirichlet Energy (MDE) which allows robust and flexible analysis of the connectome with unprecedentedly high temporal precision.
Revised:  Particularly, we introduce and implement a novel technique named Modular Dirichlet Energy (MDE) which allows robust and flexible analysis of the functional network with unprecedented temporal precision.
Edit intent: clarity
Original:  We find that connectivity in the Bind condition is stronger than in the Shape condition in both occipital and frontal network modules during the encoding period of the right screen condition but not the left screen condition . Using MDE we are able to discern driving effects in the occipital module between 100-140ms,
Revised:  We find that connectivity in the Bind condition is stronger than in the Shape condition in both occipital and frontal network modules during the encoding period of the right screen condition . Using MDE we are able to discern driving effects in the occipital module between 100-140ms,
Edit intent: coherence
Original:  which noticeably coincides with the P100 visually evoked potential, and a driving effect in the interaction of occipital and frontal modules between 120-140ms, suggesting a delayed information processing difference between these modules.
Revised:  which coincides with the P100 visually evoked potential, and a driving effect in the interaction of occipital and frontal modules between 120-140ms, suggesting a delayed information processing difference between these modules.
Edit intent: clarity
Original:  This provides temporally precise information over a heterogenous population for tasks related to the sensitive and specific detection of AD.
Revised:  This provides temporally precise information over a heterogeneous population for tasks related to the sensitive and specific detection of AD.
Edit intent: fluency
Original: Visual short-term memory binding tasks are a promising early biomarker for Alzheimer's disease (AD).
Revised: Visual short-term memory binding tasks are a promising early marker for Alzheimer's disease (AD).
Edit intent: fluency
Original:  To uncover the functional deficits of AD in these tasks it is necessary to first study unimpaired brain function.
Revised:  To uncover functional deficits of AD in these tasks it is necessary to first study unimpaired brain function.
Edit intent: fluency
Original:  To uncover the functional deficits of AD in these tasks it is necessary to first study unimpaired brain function.
Revised:  To uncover the functional deficits of AD in these tasks it is meaningful to first study unimpaired brain function.
Edit intent: clarity
Original:  We find that connectivity in the Bind condition is stronger than in the Shape condition in both occipital and frontal network modules during the encoding period of the right screen condition.
Revised:  We find that connectivity in the Bind condition is stronger than in the Shape condition in occipital and frontal network modules during the encoding period of the right screen condition.
Edit intent: clarity
Original:  We find that connectivity in the Bind condition is stronger than in the Shape condition in both occipital and frontal network modules during the encoding period of the right screen condition.
Revised:  We find that connectivity in the Bind condition is stronger than in the Shape condition in both occipital and frontal modules during the encoding period of the right screen condition.
Edit intent: clarity