esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this paper, C-HARQ has been designed keeping in mind the presence of multiple sensor nodes in the neighborhood, which could play a significant role in improving the throughput performance. Further, the underwater sensor nodes have limited energy sources (batteries) which cannot be easily replenished; that makes energy consumption a major issue when designing any sensor network protocol. Even high throughput protocols must be energy efficient in order to extend the network lifetime for long lasting sensor applications. Thus, energy efficiency has been used as one of the metrics for performance evaluation, along with throughput efficiency. Additionally, we compare the energy consumption burden that is carried by the neighbor nodes in providing cooperation for CARQ and C-HARQ protocols. It reveals that C-HARQ transfers less burden on the neighbor nodes compared to CARQ, while still fully utilizing them for cooperation. Some earlier work on combination of ARQ and FEC has been done for underwater scenarios [13; 14]. A hop-by-hop and block-by-block technique of transferring encoded packets has been adopted [13], but lacks the utilization of cooperative transmission. Relay-assisted HARQ has been studied [14], which primarily focused on the implementation of Luby Transform (LT) codes in IR-HARQ, not on the energy consumption analysis, or the effect of multiple relays on throughput and energy expenditure. In this paper, we propose and study the C-HARQ protocol of error control for underwater scenarios using RCPC codes to maximize throughput and energy efficiency. The main contributions of this paper can be summarized as follows:  
1. Achievement of better throughput performance by C-HARQ, compared to CARQ and S&W ARQ under similar network conditions. 2. Optimum data packet size for C-HARQ. 3. Optimum number of FEC packets for C-HARQ. 4. Maximum achievable throughput performance of C-HARQ. 5. Performance comparison in terms of energy consumption. 6. Performance analysis of C-HARQ against varying BER. The rest of the paper is organized as follows: Section 2 presents the preliminaries to the C-HARQ protocol. Section 3 presents C-HARQ protocol design in detail, including the network model and the packet format. The performance metrics used to compare the various protocols are defined and expressed in Section 4. Section 5 presents the simulation results and their discussions in detail. Finally, the concluding remarks are presented in Section 6.
## 3 Results and Discussion
### Analysis of the CATA Terms
#### 3.3.2 Frequency Results and Correspondence Analysis of the Reduced Terms  
The 108 CATA descriptors [19] were general terms for all coffees, not specific terms for cold brew coffee. Thus, some attributes were not checked or did not contribute towards discriminating among samples. Since the number of attributes for cold brew may be reduced, the frequency of attributes for each sample was investigated to identify unnecessary terms.  
Overall, at least 10% of consumers checked 17 attributes (acid, ashy, bitter, burnt, dark chocolate, grain, hazelnut, longevity, medicinal, mouth drying, musty/earthy, nutty, overall impact, roasted, sour, sour aromatics, and woody) when the sum of frequency of samples by attributes was reviewed. When the frequency of attributes for individual coffee was reviewed, 10% or more of consumers checked 48 attributes (acetic acid, acid, alcohol, ashy, beany, bitter, blended, brown sugar, burnt, caramelized, chocolate, citric acid, citrus fruit, dark chocolate, fermented, fresh, fruity, fullness, grain, green, hay-like, hazelnut, longevity, malic acid, medicinal, metallic, mouth drying, musty/dusty, musty/earthy, nutty, other fruit, overall impact, over-ripe, petroleum, pipe tobacco, pungent, roasted, salty, sour, sour aroma, stale, sweet, sweet aroma, thickness, tobacco, under-ripe, whisky, and woody) for at least one sample and the frequencies of each attribute were listed by coffee type. CB coffee samples (\(n=4\)) had more checks for citric acid, citrus fruit, fruit, and pungent attributes compared to other coffee groups. RTD coffee samples (\(n=4\)) were characterized by dark chocolate, fullness, hazelnut, and sweet aromatics and they had fewer checks for mouth drying attributes than other groups. Of all the samples evaluated, the CS coffee group (\(n=2\)) was the only group to have caramelized, petroleum, thickness, and whisky checked.  
Additional correspondence analyses were conducted using reduced attributes to investigate if the reduced number of attributes could be used to discriminate among the samples. The results of the correspondence analysis of the CATA terms using 17 and 48 attributes are shown in Figure 2. They explained 78.57% and 70.36%, respectively. All samples were positioned in the same quadrant compared to Figure 1, and the variability explained by the first two dimensions increased. [PERSON] [25] developed 88-term lexicon for 149 soy sauce products using a trained panel. Nineteen common attributes out of 88 descriptors could be used to differentiate intensities among soy sauce samples, saving time and costs. Similarly, 17 and 48 terms in this study could be utilized in future studies, lowering the time and cost required and still showing discrimination among cold coffee samples.  
Figure 2: Correspondence analysis bi-plot using 17 commonly selected terms for all cold brew coffee samples by at least 10% of consumers (**a**) and correspondence analysis bi-plot using 48 terms selected for one or more cold brew coffee samples by at least 10% of consumers (**b**).
# Complementing Solutions to Optimization Problems via Crowdsourcing on Video Game Plays
## 4 Results and Discussion
### Analysis of Results for Swain and London Datasets  
We ran the [PERSON] test to the results we have obtained after running different tests for genetic algorithms (GA), crowdsourcing (CS) and the complement approach (GA-CS). The [PERSON] test compares all methods (GA, CS and GA-CS) and tells if there exists statistical significant differences between the mean ranks of the methods compared.  
Figure 12: Computed hypervolume after solving the Facility Location Problem (FLP) by using the crowdsourcing approach and genetic algorithms (GA) for Swain (**a**) and London (**b**) datasets.  
[PERSON]'s test tells whether there are general differences, but it does not indicate which particular methods differ from each other. There was a statistical significant difference in the perceived distance depending on the type of method used whilst solving the FLP. As results we have for the Swain dataset \(X_{r}^{2}=25.75\), \(p=0.00001\) and for the London dataset \(X_{r}^{2}=24.01\), \(p=0.00001\). Hence, we can see that there is an overall statistically significant difference between the mean ranks while comparing the three methods.  
In order to be certain if our combined GA-CS method presents a statistical significant difference while computing the Pareto set and to complement findings from the hypervolume, we have applied the Wilcoxon signed-rank test for different combinations of the methods. Post hoc analysis with Wilcoxon signed rank tests was performed with a Bonferroni correction applied, which resulted in a significance level set at \(p<0.017\) and \(95\%\) confidence level. We note there was a statistically significant reduction in the perceived distance in the genetic algorithm versus the genetic algorithm supplemented with crowdsourcing solutions (GA-CS) for the Swain dataset \((Z=-4.8599,p=0.00001)\) and \((Z=-2.8405,p=0.00452)\) for the London dataset, respectively.
# Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks
## 4 Conclusions  
This study used an FFNN application for estimating PM\({}_{10}\) and PM\({}_{2.5}\) concentrations. ANN approaches, in general, have the advantage to be able to model effectively nonlinear relationships compared to other methodologies. An important aspect is the evaluation of the developed models during different scenarios of input parameters. In nearly all cases, the MAE and \(R^{2}\) values were lower and higher, respectively, when the meteorological values were added during the training process. The models that showcased a better performance were those who had as additional inputs both T and WS, although there were not crucial differences noticed among the schemes of the four different scenarios. Regarding the comparison between PM\({}_{10}\) and PM\({}_{2.5}\), the estimations for the latter had a higher degree of dispersion in the scatter diagrams of the observed versus the estimated values. This can be explained due to the more limited information provided during training (more input stations for PM\({}_{10}\)). The Garson methodology results reveal that all monitoring stations in the Attica region, which were involved in the FFNN development process, are important for the PM estimations. Future work can extend this methodology to include more target stations with different characteristics and/or add more climate parameters. These additions, considering their impact and usefulness for the models, can be further analyzed and supported by applying suitable feature selection and feature ranking techniques [65; 66; 67]. Finally, ANN ensemble approaches [68] can be examined, aiming to reduce the variance of predictions and the generalization error by combining the results of multiple models.  
[PERSON] and [PERSON] were involved in the investigation, conceptualization, writing--original draft preparation and writing--review and editing of this work, while, individually, [PERSON] was responsible for the data curation, validation of the results and supervised the whole procedure. Both [PERSON] and [PERSON] performed the various steps of the methodology, processed the data and developed the neural network models. Both authors were involved in the discussion of the results and commented on the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The air quality and meteorological datasets generated and/or analyzed during the current study are publicly available in the Ministry of Environment and Energy repository, (ypen.gov.gr) (accessed on 30 June 2020) and the National Observatory of Athens repository ([[https://meteosearch.meteo.gr/](https://meteosearch.meteo.gr/)]([https://meteosearch.meteo.gr/](https://meteosearch.meteo.gr/))) (accessed on 30 June 2020) respectively.  
The authors declare no conflict of interest.
## 2 Related Work
### Distributed Query Optimization  
The task of a distributed query optimizer is to split the query so that its sub-queries can be executed on many devices. In the case of many concurrent queries, it is also responsible for load balancing and sharing, thus reusing intermediate results within the database.  
Distributed query optimization strategies can be classified as shown in Figure 1.  
Modern database systems cannot access the routing protocol and cannot retrieve the topology information directly. Workarounds exist, such as explicitly providing a file with topology information or measuring the time it takes to send a message. However, these approaches require manual interaction to provide the network layout or ping messages to measure link speeds and derive the topology. If only small ping packets are used to determine the topology, the timing measurements may not be accurate because they take only fractions of a second. If large packets are used, much energy is wasted.  
The fact that network traffic slows down query evaluation is considered in many database systems [35; 36; 41; 42; 43; 9]. Therefore, there are already some approaches to reducing the network traffic.  
Figure 1: Classification of distributed query optimizers.  
One approach is to implement hashing to distribute the data and add some properties that allow joining operations to be performed locally on many different devices. After that, only the intermediate results are sent over the network. If there are many queries, this causes much less traffic [9; 35; 41; 42].  
Another widely used approach is duplicating data to add properties to the triple store. The k-hop property is used in many different database systems. In the k-hop strategy, triples are first assigned to database instances using a hash function. Then, each triple with a path length up to k is copied to all of the original triples in that instance. Each system uses a minor variation, mainly changing the value of k. The largest k used is three, resulting in many triples at almost every node. This property allows queries containing path expressions up to k in length to be evaluated locally on each node without communication. The main disadvantage of the k-hop property is that some results are calculated multiple times, which must be removed again to obtain a correct result [35; 36; 43].
## 4 Experimental Results and Analysis
### Model Evaluation and Predictive Assessment
It is mathematically defined as  
\[\text{IoU}=\frac{|P\cap T|}{|P\cup T|} \tag{24}\]  
Here, \(P\) denoted the predicted image output by the model, and \(T\) denoted the image data from the validation set. Compared to IoU, the Dice coefficient (also known as a variant of the F1-score) placed greater emphasis on the relative proportion of the intersection area. It was defined as  
\[\text{Dice}=\frac{2\times|P\cap T|}{|P|+|T|} \tag{25}\]  
In this study, the two metrics were used as complementary indicators to comprehensively characterize the model's performance in region localization and boundary reconstruction.
## 2 Methodology and Materials
### Data Preprocessing
#### 2.2.3 Feature Selection (FS)  
The third step is the FS technique. In the study, feature subsets are generated for two different FS techniques. These FS techniques are correlation coefficient and CfsSubsetEval algorithm approaches. The FS method based on correlation coefficient is used by measuring the linear relationships between independent variables (features) and dependent variables (target or output). The correlation coefficient of each feature is calculated and the importance of the features is evaluated according to this coefficient. This method is particularly effective in regression models and in cases where linear relationships are present. In this study, features with high positive or negative correlation values are taken into account in the applied model because they have a strong relationship with the target variable, while features with low correlation are excluded. The formula of this technique is shown in Equation (5) [61].  
\[r_{xy}=\frac{\sum{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{\sum{(x_{i}- \overline{x})^{2}\sum(y_{i}-\overline{y})^{2}}}} \tag{5}\]  
Here, \(r_{xy}\) is the correlation coefficient between the feature \(x\) and the target \(y\). The \(x_{i}\) denotes the feature value of \(x\) in the i-th observation. \(y_{i}\) is the value of the target variable \(y\) in the i-th observation. While \(\overline{x}\) is the mean of the feature \(x\), \(\overline{y}\) represents the mean of the target variable \(y\). The correlation coefficient \(r_{xy}\) takes a value between \(+1\) and \(-1\).  
Another FS technique is the CfsSubsetEval approach. CfsSubsetEval is an algorithm. It makes an evaluation based on the correlation of features with the target variable [62]. The basic logic of the algorithm is that the features should have a strong relationship with the target variable and at the same time, the features should have a low correlation with each other. This shows that this feature set is a good subset. In other words, the main objective is to select features that describe the target well but represent independent information. The mathematical model of the CfsSubsetEval algorithm is given in Equation (6).  
\[Merit(S)=\frac{k\cdot\overline{r_{cf}}}{\sqrt{k+k(k-1)\cdot\overline{r_{ff}}}} \tag{6}\]  
Merit(S) corresponds to the evaluation value of the selected feature subset, while \(k\) represents the number of features in the subset. In addition, \(\overline{r_{cf}}\) represents the average correlation of the features with the target variable, while \(\overline{r_{ff}}\) represents the average correlation value between the features. This algorithm aims to both increase the relationship of the features with the target and reduce the sharing of redundant information between the features. In this way, this method provides to create more compact and effective models by helping to remove unnecessary or repetitive features from the model.
# Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches
## 2 Materials and Methods
### CHO PET/CT Data Acquisition and Tumor Segmentation  
\({}^{18}\)F-fluoroethylcholine (CHO) was synthesized as described previously, and a dose of 5.55 MBq (0.15 mCi) CHO per kilogram of body weight was given intravenously. PET/CT was acquired 40-60 min after CHO injection on a Biograph 64 TruePoint TrueV PET/CT system (Siemens Medical Solutions, Erlangen, Germany). The original image was acquired with a slice thickness of 3 mm and interpolated into the DICOM data with a matrix of 336 \(\times\) 336 \(\times\) 148 as a standard protocol, with the physical size of each pixel being approximately 1 \(\times\) 1 \(\times\) 1.5 mm. The finally output DICOM data were processed by Gaussian post-filtering. Standard uptake value (SUV) of each pixel normalized by body weight and decay factor was subsequently calculated.  
Tumors were semiautomated segmented using 3D slicer 4.10.2 ([[https://www.slicer.org/](https://www.slicer.org/)]([https://www.slicer.org/](https://www.slicer.org/)), accessed on 13 November 2019) by a nuclear medicine physician as previously reported. Briefly, three spherical reference regions of interest (ROI) were first manually placed on the contralateral normal cortex to calculate the maximum and mean SUV (Nmax and Nmean) of the normal brain. The tumoral ROI was semiautomatically defined for regions with SUV/Nmean > 2.0 and SUV/Nmax > 1.0 if the lesion displayed significant CHO activity, or delineated on the CT image and co-registered to CHO PET image if the lesions did not reveal significant CHO uptake. Manual editing was performed to ensure the continuity of the ROI and to remove the structures with physiological CHO uptake.  
The brain was segmented based on CT image with the threshold to delineate the skull and floodfill function of OpenCV (version 4.5.3, [[https://opencv.org/](https://opencv.org/)]([https://opencv.org/](https://opencv.org/)), accessed on 6 October 2021) to fill the interior region (which represented the brain tissue). The CT-based brain segmentation was co-registered to the PET data using the resize function of OpenCV, and the normal brain was defined by subtracting the tumor segmentation from the brain segmentation.
## 6 Conclusions
For this reason, we proposed NB-IoT featuring coverage extension modes to boost the signal in such scenarios. Poor lighting conditions can also have a significant impact [48]. To address this, we designed a prototype with white LEDs. Another important aspect is the appropriate casing and placement ofthe prototype. It should be mounted in a secure plastic casing with suitable waterproof, dustproof, and disruption-proof fittings and mounting points. To address the imperfect digit recognition, we propose sending an occasional complete ROI image to the server to verify the digit recognition process periodically. Finally, appropriate adjustments (e.g., ROI) and model fine-tuning need to be performed during the installation and setup process for the particular scenario and TM. For the future work, the following should be considered to improve the efficiency of the proposed system:  
* test more sophisticated FL techniques to achieve even better performance on the edge;
* experiment with various CNNs and the other model architectures;
* subject the system to additional test cases;
* build an appropriate secure plastic 3D model with appropriate fittings, as a case for the fabricated prototype;
* test performance under different real-world metering scenarios. Conceptualization, M.L., D.V. and I.M. ; Methodology, M.L., D.V. and I.M. ; Software, V.N., D.B. and M.L. ; Investigation, V.N. and D.B. ; Resources, M.L. ; Data curation, V.N. and D.B. ; Writing - original draft, V.N., D.B., M.L. and I.M. ; Supervision, I.M. ; Project administration, I.M. ; Funding acquisition, D.V. All authors have read and agreed to the published version of the manuscript. This work is supported by the Ministry of Science, Republic of Serbia, through project No. 451-03-65/2024-03/200156 \"Scientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad\", by the Innovation Fund, Serbia, Proof of Concept project 5367, and was funded in part from the Horizon 2020 research and innovation staff exchange grant agreement No. 101086387. The data are available at [[https://github.com/dbortnik/digitsDataset](https://github.com/dbortnik/digitsDataset)]([https://github.com/dbortnik/digitsDataset](https://github.com/dbortnik/digitsDataset)), accessed on 30 October 2024. The authors appreciate the support from [PERSON] and A1 Serbia for providing access to the A1 NB-IoT service. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} SM & Smart meter \\ REST & Representational state transfer \\ MQTT & Message queuing telemetry transport \\ CoAP & Constrained application protocol \\ TM & Traditional meter \\ NB-IoT & Narrowband Internet of Things \\ PSM & Power saving mode \\ MNO & Mobile network operator \\ ML & Machine learning \\ MCU & Microcontroller unit \\ CNN & Convolutional neural network \\ FL & Federated learning \\ ROI & Region of interest \\ \end{tabular}
## 2 NOAA Ocean Satellite Course
### Data Access Tools
#### 2.3.1 ERDDAP  
The ERDDAP data server [19] is the primary source of satellite data products for the course. ERDDAP provides a simple, consistent way to download data, allowing the sub-setting of data both temporally and spatially, and providing over 30 common data, metadata, and image formats for downloading data. ERDDAP provides both a graphical interface to visualize and manually download selected data, and a RESTful URL (meaning it is based on representational state transfer (REST) technology) [20], where a data request is completely defined in a single URL. The RESTful URLallows users to bring selected satellite data directly into their analysis software. Behind the scenes, an additional benefit is that a single ERDDAP can serve as a conduit to access datasets housed on other servers (e.g., ERDDAP, THREDDS, and Sensor Observation Service (SOS)), thereby providing a single location and access protocol for datasets made available by multiple data providers (a \"one-stop shopping location\"). ERDDAP is used by over 80 institutions world-wide to serve satellite and other environmental data. For the course, we used the ERDDAP co-operated by CoastWatch WCN and the ERD of NOAA's SWFC, which serves over 1000 satellite datasets [19]. After completing the course students can continue to use the WCN/ERD ERDDAP as a rich source of satellite data for future projects. The demonstration on how to use ERDDAP that was developed for this course is publicly available on the WCN website [21] and on GitHub [22].