dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, the decentralized platform is requested to protect such data from leakage and provide certain opacity using access control. At this level, questions are raised about the effectiveness of the aforementioned techniques on transparency control. How can we mitigate the gap behind Blockchain data transparency and obtain access control? In this context, the fully decentralized system (public Blockchain) prevents partners from controlling their data as if it were in the centralized systems. This inability to control opacity leads the partners to prefer the private Blockchain to the public one, knowing that the latter is much more recommended for the global supply chain. Hence, there is a need to accomplish the access control feature within the global Blockchain. Table  5  depicts the techniques' impacts on the transparency access control, their advantages, and limitations.
Starting with the public Blockchain, the ZKP promises to preserve their privacy, encouraging them to go public. Other cryptographic proposals would also have a large impact on data privacy, such as homomorphic encryption integrated with Blockchain  [77]. These algorithms protect privacy and advance public Blockchain usage. However, they have a medium impact on data transparency and opacity control. The Merkle tree technique facilitates traceability without exerting a significant impact on the control side. Regarding transparency, smart contracts and IoT techniques can significantly provide access control if employed precisely. The recent projects listed above, which investigated smart contract development, ignore the transparency access control and concentrate on their functionality part only.
To satisfy both transparency and opacity requirements, a hybrid Blockchain is an appropriate solution to regain partners' confidence in the supply chain decentralization. On the one hand, partners are able to run and store their sensitive data off-chain, thus achieving opacity. On the other hand, partners could publish different data to the Blockchain to ensure transparency. In addition, a hybrid smart contract  [78]  was recently proposed, which permits the control of off-chain data and lets partners build smart contracts that cover both on-chain and off-chain data.
This technique may drive the adoption of open supply chain platforms in the future. Any supply chain planning to move into Blockchain could pass through the above policy enforcement steps for the required validation to recognize the best-fit Blockchain type and techniques. After this, it can explore the techniques mentioned above that fit its requirements. Nevertheless, the data transparency topic still takes considerable effort. The aforementioned projects lack transparency standards to manage and organize the data transparency requirements within the new DLT technology. This paper aims not to prompt actors to choose between different projects (like Vechain, Ambrosus, OriginTrail, etc). Instead, it sheds light on the available techniques and drives stakeholders to integrate more techniques in different ways to mitigate the gap behind the transparency concerns of the new introducing DLT-based supply chain.
Conclusions This work highlights essential questions related to the Blockchain-based supply chains' data transparency. It considers the transparency challenges, the DLT transparency techniques, and additional measures that can be employed. Accordingly, the existing projects are presented with their adopted techniques. It is noted that some of them implement standard Blockchain features, including transparency and traceability. However, some other projects exploit additional techniques to enhance transparency and satisfy their requirements. We highlight these techniques and analyze them to investigate their impacts on a Blockchain-based supply chain. IoT technology and an advanced smart contract are the most-used techniques to achieve more transparency, as well as what Blockchain provides. Few projects use alternative methods, such as involving cryptographic tools like Merkle tree and zero-knowledge.
It is based on Ethereum Blockchain with a POA consensus algorithm to enhance the scalability. The FarmaTrust platform named "Zoi" is shared among the global community, including suppliers, logistics and shipping companies, wholesalers, distributors, pharmacies, and hospitals. This global network uses the FamraTrust platform to ensure data transparency to ensure that medicines and related medical products are genuine. As a final step toward complete transparency, the consumer is allowed to use the FarmaTrust mobile app to verify the product's authenticity via a QR code scanner; • BlockGrain [68] is an agriculture supply chain that allows farmers, brokers, and companies to track the path of grains from the place of harvest to consumer destination. It is a decentralized platform using Ethereum Blockchain for the agriculture supply chain.
It is a data acyclic graph (DAG)-based platform managed by the IOTA Foundation with the collaboration of Pickert (an ISO-certified company). DAG is an alternative DLT technology of Blockchain. It has good adaptation with high incoming data loads, and has nominal transaction fees and computation. • Bext360 [66] is a supply chain platform used to enhance the global food commodities and provide full transparency from farmer to table. It is a software as a service (SaaS), which integrates Blockchain and sustainability measurements to provide a traceable fingerprint from manufacturers to consumers. It runs a RESTful API that allows retailers and wholesalers to insert the technology into their websites, point- of-sale systems, or supply chain management tools. The SaaS platform allows each stakeholder to track food products independently throughout each phase of their supply chain and enhances its overall transparency; • FarmaTrust [67] provides a robust cloud-based platform to track pharmaceuticals through a supply chain that links digital systems to pharmaceuticals moving in the physical world.
The CIGALE code We use the SED modelling and fitting code CIGALE 2   (Boquien et al. 2019). CIGALE models and fits the UV to sub-millimetre (submm) emission of galaxies assuming an energy balance between the emission absorbed by dust in UV-optical and reemitted in IR. It is a versatile code composed of modules modelling the star formation history of galaxies, the stellar emission, the dust emission, the Active Galactic Nucleus (AGN) contribution, as well as the radio emission of galaxies. In CIGALE, the SFH can be handled through analytical functions or using simulated SFHs  (Boquien et al. 2014; Ciesla et al. 2015 Ciesla et al. , 2017)). In a previous study, we investigated the use of simple analytical SFH forms to recover galaxy parameters  (Ciesla et al. 2015). A set of SFH from semi-analytical models with known associated properties (SFR, M * , etc.
To do so, we compute the Bayesian Information Criterion (BIC) for each SFH assumption and calculate the difference between them, that is ∆BIC (see  Ciesla et al. 2018; Buat et al. 2019; Aufort et al. 2020 , for more details). For the 7 galaxies of our final sample, ∆BIC is larger than 10 which is the threshold to claim that the evidence against a normal delayed-τ SFH is decisive (see, e. g. ,  Robert 2007). This strengthens the results of the  Aufort et al. (2020)  method in selecting galaxies with a recent and strong variation of SFH and ensure that our sample is not contaminated by passive and smoothly quenched galaxies. To confirm if our selection yields a sample of quenched galaxies, we search for optical spectra for the 7 galaxies. We find three galaxies with a zCOSMOS  (Lilly et al. 2009 ) optical spectra, that we retrieved from the ASPIC 3 database, shown in Appendix A.
We perform UV-to-IR SED modelling of the HRS+COSMOS sources to estimate the age of the quenching and the L IR of the galaxies. For the HRS galaxies, an IR only SED modelling is performed to estimate the present L IR of the galaxies. We validate our estimate of the age of quenching with the results of  Boselli et al. (2016)  who used spectroscopy in addition of photometry and performed a detailed and more specific analysis of these sources. For the COSMOS galaxies, we use the measurement of the present L IR provided by the UV-to-IR SED modelling by CIGALE and checked its validity in case of absence of IR data point. For both the HRS and COSMOS quenched galaxies, we estimate the L IR just before the quenching happened using the SED of each quenched source at the moment right before quenching.
The IoT system must thus be able to identify missing data and perform the appropriate corrections. Usually, the gateway groups the data received from the end devices into records. Each record contains measurements from different sensors. For instance, a record may have a timestamp and values of atmospheric pressure, temperature, and relative humidity in weather-monitoring systems. If the record for a given timestamp lacks at least one sensor measurement, this record is considered incomplete. However, most data analysis applications only work with complete records  [8, 9]. In this context, the application server typically discards incomplete records received from the IoT gateway. However, depending on the amount of missing data, the database might suffer a significant reduction. As a result, discarding an entire record may reduce the reliability of data analysis applications.
We thus evaluated our proposal using real weather data collected during six years in Rio de Janeiro. More specifically, we used measurements of atmospheric pressure, temperature, and relative humidity. We artificially removed a controlled amount of data and evaluated the estimation's RMSE and coefficient of determination (R 2 ). We compared our approach with a simple method of repeating the previous value to estimate the missing one. The results show that the proposed neural network models perform better than the other methods for different percentages of missing data. The estimated values are close to that of the actual sensor measurement, presenting a R 2 higher than 0. 92. Furthermore, we employed a Raspberry Pi Model B to show that the models use less than 140 KiB of memory, regardless of the percentage of missing data, and have a short execution time.
Related Work In data analysis, several algorithms do not correctly perform when there are missing data in the dataset  [8, 9, 16, 24]. Therefore, the missing data problem is one of the biggest challenges for IoT-based systems  [9]. A typical approach to solve this problem consists of eliminating incomplete records, that is, records with one or more attributes with missing values. However, this approach reduces the amount of available data, and as a consequence, the application may misinterpret the data  [17]. This problem thus justifies the need for imputing missing data with appropriated values. A widely used method replaces the missing value with the average of all non-missing values received for such an attribute in the dataset. However, this method distorts the attribute's probability distribution  [24]  and does not perform well in most cases  [14].
As IoT missing data mainly occur due to sensor failures or network problems-which fits the MCAR category  [19] -many works in the literature start from this premise to impute missing data  [18] [19] [20]. The authors in  [27]  proposed a decision-making approach to deal with the missing data in a real-time healthcare application, using a multiple imputation approach  [28]. For each missing value detected in the heart rate sensor, the application uses medical history and additional state information (i. e. , sleep, light activity, vigorous activity) collected by the other sensors to estimate multiple values. Then, they aggregate these values using a weighted arithmetic mean, in which the weights are determined according to the additional state information associated with the missing heart rate.
This method performs better than other methods in the literature that deal with databases with large missing data gaps. The authors in  [12]  presented a multi-agent system (MAS) technique to impute missing values in an edge environment. More specifically, they consider IoT devices such as ad hoc sensors and mobile devices. In this approach, each device is called an agent, and the MAS allows distributing the computation among them. The agents can have fixed sensing devices or not. The former ones are divided into regions according to their dynamics, and the latter, which is usually mobile, cooperates with the agents in the same region they are. To impute a missing value, the authors computed the inverse distance weighting (IDW) interpolation and compared the result with the closest device's value and the values of the same region.
[29]  claimed that the PMF execution time increases as the number of missing data increases. Their work proposes a framework that imputes missing data using the Bayesian maximum entropy (BME) method. BMF is a mapping method to estimate spatial-temporal values that allow the use of multiple knowledge databases. This method uses two databases: one with statistical data and one more specific, which contains data collected by sensors with different precision. The first database is used in the maximum entropy function, and then it is combined with the specific database. This combination minimizes the quadratic squared error, resulting in the spatial-temporal mapping. The results show that this method performs better than the PMF approach, with a shorter execution time. The authors also stated, with no experimental results, that they can extend their framework to impute data in an online scenario.
Li et al. [15]  proposed imputing missing data using a fuzzy K-means algorithm, in which each record has a membership function. This function describes the degree to which the record belongs to a specific cluster. Therefore, this method uses the degree information and the cluster's centroid values to impute missing data. This approach performs better than using averages and the K-means algorithm. In Mary et al. [16] , the authors first identified the sensors correlated to the sensor that was responsible for the missing data by using the Pearson correlation coefficient. Then, they replaced the missing data using the value collected at the same hour by the sensor with the highest correlation. Although these clustering algorithms performed better than some well-known methods, such as K-NN and SVM, they imputed all missing data from the database at once.
Hence, they did not consider the order of the records' timestamps. In this way, old records with missing data may remain unvalued until the algorithm runs. In addition, records received after the missing data may be used to predict a value for the respective record, which makes it unusable in online applications that consider timestamp ordering. Furthermore, these clustering approaches do not mention whether the algorithm should run whenever an incomplete record arrives at the dataset, which would increase their computational cost. Izonin et al. [30]  described a regression method based on general regression neural network (GRNN), which has three layers: input, radial, and output. First, they compute the Euclidian distance from the input array to all other training datasets samples. This result was transformed into a Gaussian distance and compared to the predicted values for evaluation.
They tested this method for only one attribute of the dataset, and its results are better than for any other known methods, including AdaBoost  [31]  and support vector regression (SVR)  [32]. However, the GRNN training may become slower depending on the amount and structure of the training samples. Turabieh et al. [21]  used the deep learning model layered recurrent neural network (L-RNN) to dynamically predict the missing values. As soon as their method receives a record, it is inserted in the training dataset if such a record is complete. If the record has missing values, they used L-RNN to impute the value and then insert it in the training dataset. Therefore, records with imputed values are used for training and for producing new predictions. These approach results are better than K-NN and a decision tree.
Zhang et al. [20]  recovered missing data time series in wireless sensors networks using a long short-term memory (LSTM) neural network. In addition, they proposed a sliding window algorithm to produce more training samples from small datasets. This approach performs better than expected maximization (EM) and autoregressive integrated moving average (ARIMA)  [33] , for example. However, it uses both old and future data to predict the current missing value. Guzel et al. [17]  proposed models using LSTM and fuzzy logic. They assumed that a record has three attributes and predicts the missing value using the other two attributes received in the same timestamp. In  [19] , Kök et al. , used the same models as those proposed by  [17] , but they implemented them in edge and Cloud devices. Their goal was to have a low delay and efficient use of the network's bandwidth.
However, both  [17]  and  [19]  only work with one missing attribute at a time. Nikfalazar et al. [18]  used decision trees and fuzzy K-means algorithms. The decision trees compute the first predictions for a missing value, and then these values are updated using the fuzzy K-means algorithm. The fuzzy K-means always uses the last predictions as input values until a stop criterion is reached. This method generates good predictions in different databases. However, it takes more execution time than others in the literature. Al-Milli et al. [10]  proposed a hybrid model with a neural network and a genetic algorithm (GA) to impute missing data. The former predicts the missing values, while the GA is used to optimize the neural network's weight. The authors compared the application performance with missing data and after using their hybrid imputation method.
Therefore, they showed that missing data imputation helps improve the application's result. Wang et al. [34]  proposed a missing data imputation method based on functional dependencies and association rules. The authors first modeled a big data schema in which the record's attributes describe a specific object. They focused on finding the relationship between these attributes and the sequence of events that frequently occurred since knowing the patterns in the sequences helps predict their occurrence in the future. Thus, they used a probabilistic production dependency method which produces classification rules valid for a significant number of entities from a selection. Then, they used the sequence of rules to build an operator to recover the missing data. To evaluate the proposed method, the authors used a classifier built on the original dataset.
Their methods performed better than the expectation-maximization (EM) and random forest (RF) methods when up to 30% of data were missing. For higher percentages, these lose to EM. Regarding the time analysis, their approach has better results than SVM, EM, and association rules. The main difference between  [34]  and our work is that they did not address an online scenario, thus using all the data available in the dataset. Most related work has exclusively focused on the prediction quality and did not consider computational resources requirements such as memory and processing. This concern is crucial in an IoT environment since the hardware usually has limited resources. Only a few of them  [14, 17, 18, 29, 30, 34]  considered the execution time of the proposed methods. The works in  [12, 19, 27, 29]  claimed that their methods might execute in an edge computing device, such as IoT gateways or IoT devices, but they did not provide experimental results.
However, none of the works analyzed the amount of memory that the methods consumed. In addition, most works considered that all the dataset was available, so in order to impute a missing value, these methods can use values received after the missing value timestamp. Table  1  compares the main characteristics of the mentioned works. Unlike literature approaches, this work imputes missing data on the fly, namely, as soon as they arrive at the IoT gateway and before forwarding the record to the application server. In this way, only data received before the missing data are available to predict such a value. Another critical difference is that we measure the execution time and the amount of memory used for the methods since our approach runs at IoT gateways.
For implementing the MLP models, we use Scikit-learn 0. 24. 1  [36] , a machine learning library. When using the MLP as a regression model, the activation function in the output layer is the identity function, and we minimize the squared error. As MLP is sensitive to the data scale, we have to apply a normalization function to obtain good results. To create and train an MLP model, we need to define a set of hyperparameters. We empirically chose the hyperparameters as follows. We used different hyperparameters' combinations to train the models and then evaluate the model's performance using the test dataset. Then, we computed the R 2 score and RMSE metrics using the original values of the test dataset and the predicted values. Finally, we chose the combination with the highest R 2 score and lower RMSE in the following experiments.
Both neural network models use the same set of hyperparameters. Table  2  shows the hyperparameters and their respective values. The hidden layer has 100 neurons and the activation function in this layer is ReLU (Agarap-"Deep learning using rectified linear units (ReLU)", available at https://arxiv. org/abs/1803. 08375-accessed on 12 October 2021), where f (x) = max(0, x). ReLU returns 0 when x is negative. Otherwise, it returns the value itself. The optimization algorithm is Adam (Kingma and Ba-"Adam: A method for stochastic optimization", available at https://arxiv. org/abs/1412. 6980-accessed on 12 October 2021), since it performs well in large databases, with thousands of training samples and quickly converges. The learning rate to update the weights is constant. The batch_size hyperparameter defines the number of training samples to be used in an iteration.
MonitorAr Dataset In this work, we used the MonitorAr (Cityhall of Rio de Janeiro-"MonitorAr dataset", available at https://www. data. rio/datasets/dados-horários-do-monitoramento-da-qual idade-do-ar-monitorar/explore-accessed on 16 October 2021) dataset, which contains meteorological data, measured every hour, from a station in Rio de Janeiro, located in the neighborhood of São Cristóvão. We used the atmospheric pressure (Pres) (mbar), temperature (Temp) ( • C), and relative humidity (UR) (%) attributes from 2011 to 2019. The station has complete records, records with missing values in some attributes, and gaps between timestamps, i. e. , hours when no data were received. To overcome this issue, we preprocessed the dataset by inserting empty records whenever a timestamp was missing. As a result, each day has 24 records (i.
e. , one per hour), and each year has 8760 or 8784 records, depending on whether the year is a leap year. Figure  2  shows the missing data percentage for each attribute over the years. More precisely, it shows the number of records without a specific attribute over the total number of records expected for the year. It is possible to note that the missing data problem is serious in this station, especially from 2017 to 2019. Considering the interval from 2011 to 2019, the number of records with at least one missing attribute-not shown in this figure-represents 16. 8% of the total number of records. If these incomplete records are deleted, the other sensors' measures are lost, and the application will have fewer data to work with. This result highlights the need to impute missing data. Our experiments consisted of varying the missing data percentage of the dataset by removing valid measures to verify the effectiveness of our models.