esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications to a Waste Separation System
### Evaluation of the Expert Judgments  
This section aims to give an evaluation method for the provided expert judgments by comparing the original expert-based distributions before aggregation to the estimated ones.  
An example of a graphical comparison of two distributions can be seen in the Q-Q plot of \(r_{2}^{LDPE}\) in the case of Expert 2. in Figure 15. Here, the estimated distribution regarding \(r_{2}^{LDPE}\) is compared to the expert judgment given by Expert 2. represented in the form of uniform distribution, as discussed in Section 2.1. A Q-Q plot depicts the quantiles of two distributions on the axes. If the two distributions to compare are the same, the plot covers the 45-degree line (in red).  
As seen in Figure 15, the plot tends to be linear, so the two distributions are supposed to be close to each other in the case of \(r_{2}^{LDPE}\). To give a more precise measure, the Jaccard index is calculated, which serves as the goodness values of the expert judgments.  
Using the resulting empirical distributions in Section 3.4, the goodness values (\(g_{i,e}\)) of the expert judgments can be calculated according to the method introduced in Section 2.3. This measure represents how similar the distribution given by an expert is to the estimated one.  
The outputs were assessed by three experts and the parameters by two who were independent of the former. The uniform distributions created from the intervals were compared to the estimated distributions. The calculated goodness values for the outputs and the parameters can be seen in Figures 16 and 17, respectively.  
Figure 15: Q-Q plot for comparing the uniform distribution provided by Expert 2. and the estimated empirical distributions of \(r_{2}^{LDPE}\). If the two distributions were the same, the plot would appear linear. The 45\({}^{\circ}\) line as a reference is shown in red, and the quantiles of the two distributions in the function of each other in blue crosses.  
The goodness values fall into the \([0,1]\) interval, showing the similarity of the two distributions. If they are the same, the goodness value belonging to them is 1, and if they are completely different, they obtain 0. As seen in Figures 16 and 17, there are some values around 0, suggesting that incorrect expert judgments would be preferable to be eliminated from the estimate.
## 3 Results and Discussion  
### Discharge Characteristics of Plasma  
The voltage and current waveforms of the plasma discharge of the WF-DBD reactor, as measured experimentally, are illustrated in Figure 2a. Whereas the current waveform contains two distinct components, the first constituting a sinusoidal current that runs orthogonal to the voltage and the second consisting of a pulsed current that arises as a result of the WF-DBD [23].  
Figure 1: Experimental setup of the WF-DBD reactor for ammonia production.  
From Figure 2a, it is evident that the WF-DBD discharge is influenced by the wall charge and corona effect in the positive polarity. Furthermore, the current pulse count and amplitude in the positive half cycle of the current curve exceed those in the negative half cycle. Many burrs are visible on the current curve shown in Figure 2a. These burrs are discharge filaments that are formed during the discharge process. Their number and intensity indicate the current strength during the discharge process and the number of discharge channels [24]. Observation of the voltage and current waveforms reveals that current peaks occur during both the rising and falling phases of the discharge voltage. Wherein the positive portion of the current peak corresponds to the rising phase of the discharge voltage, when a portion of the charge accumulates on the wall of the quartz tube, and the negative portion of the current peak corresponds to the falling phase of the discharge voltage, when the accumulated charge produces a similar change in the negative direction of the current peak [17; 25].  
The power consumption of a WF-DBD is commonly determined by the area encompassed by the Lissajous figure [26]. From Figure 2b, it is evident that the Lissajous figure closely resembles a parallelogram, and there is no net charge transfer during one complete cycle [27]. The formula to calculate the power of WF-DBD discharge is as follows [28]:  
\[P=\frac{1}{T}\int_{0}^{T}UIdt=\frac{C_{0}}{T}\int_{0}^{T}U\frac{dU_{M}}{dt}dt =fC_{0}\int_{0}^{T}UdU_{M}=fC_{0}S \tag{1}\]  
where \(f\) is the discharge frequency, \(C_{0}\) is the measured capacitance value (0.47 pF), and \(S\) is the area of the Lissajous figure.
## 6 Numerical Experiments
### Comprehensive Evaluation of the Algorithms
#### 6.4.2 Convergence Patterns
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multirow{2}{*}{**Number of Vessels**} & \multirow{2}{*}{**Number of Berths**} & \multicolumn{2}{c}{**EA**} & \multicolumn{2}{c}{**DPCEA**} & \multicolumn{2}{c}{**AEA**} & \multicolumn{2}{c}{**SAEA**} \\ \cline{3-11}  & & & \(avg(CR)\) & \(std(CR)\) & \(avg(CR)\) & \(std(CR)\) & \(avg(CR)\) & \(std(CR)\) & \(avg(CR)\) & \(std(CR)\) \\ \hline  
\hline \multicolumn{1}{c}{**Average:**} & \multicolumn{2}{c}{**0.860**} & \multicolumn{2}{c}{**0.123**} & \multicolumn{2}{c}{**0.889**} & \multicolumn{2}{c}{**0.095**} & \multicolumn{2}{c}{**0.954**} & \multicolumn{2}{c}{**0.040**} & \multicolumn{2}{c}{**0.916**} & \multicolumn{2}{c}{**0.084**} \\ \hline \end{tabular}
\end{table} 
Table 7: The convergence rate values for the developed solution algorithms and large size problem instances. Based on the conducted numerical experiments, the average convergence rate values comprised 0.916, 0.954, 0.889, and 0.860 for the SAEA, AEA, DPCEA, and EA algorithms, respectively, over the performed replications and generated large size problem instances. Fairly large convergence rate values indicate that all the developed algorithms kept discovering superior solutions throughout the search process. However, lower convergence rate values (i.e., greater convergence speed values) were typically recorded for the EA algorithm. Although relatively large convergence rate values were observed for all the developed solution algorithms, the SAEA algorithm was able to discover more promising solutions for all the generated large size problem instances (as discussed in Section 6.4.1 of the manuscript).
## 2 Related Work
Besides various NLP-related issues listed in Section 5.2, we identify several other fields that are relevant to our future research and may provide additional capabilities for model processing in the context of M2M transformation:  
* Error correction is performed to identify and correct typographical, grammatical, and semantic errors (e.g., accidental use of homophones). Multiple approaches are used to solve these issues, including string distances, similarity techniques, n-grams, and rule-based, statistical, or probabilistic techniques [44]. Recent approaches apply statistical machine translation [45] or neural machine translation principles, mostly transformer architecture [46; 47]; however, application of the latter techniques might be limited due to the lack of context required for detection. * Parse tree generation is focused on performing grammatical analysis for the given text using identified parse trees. Dependency parsing tries to extract intra-sentence relationships between words, while constituency tree generation is focused on grammatical processing using context-free grammars [48] or recurrent neural networks [49; 50]. * Dependency parsing is used to identify dependencies between entities within the given text. As with most natural language-related tasks, currently, the best performance is obtained by applying deep learning-based techniques, e.g., Stanford's deep bi-affine parser based on bidirectional long-short term memory (Bi-LSTM) network to produce vector representations for part-of-speech (POS) tagging and dependency relations tasks [51]. * Relation extraction is focused on detecting semantic relationship mentions within the given text. Such relationships can be defined between two or more entities and represent particular connections between them. A wide range of techniques are applied for solving this task, including similarity-based [52], supervised [53; 54], and multiple deep learning techniques; for extensive surveys of these approaches, one may refer to [55; 56]. * Role labeling seeks to determine the roles of entities by performing classification. Its goal essentially is to determine who did what to whom, when, and where [57]. Current advances to solve this task are related to deep learning techniques, e.g., bidirectional LSTM [57]. * Relation classification is focused on selecting proper relations from a predefined set of classes for two given entities. Consider a tuple (Manager, Invoice, write (Manager, Invoice)) consisting of two subjects and the associated verb \"write.\" The presence of the context word \"write\" justifies that this is related to a person and enforces assigning the class PERSON-ACTION. This is particularly important in the context of our research, as identifying particular types of entities will lead to the generation of different target elements. The state of the art in this research is focused on identifying such relation classes in unstructured text, with bidirectional LSTM [58] or convolutional neural networks (CNNs) [59]. For more information on this subject, we refer the reader to the most recent survey papers [5; 6; 60].
## 4 Experimental Results
### Experimental Series 1: Comparison with Single Classifiers
The same low performance in terms of macro-f1 can be noticed for DT when classifying DatasetB. Thus, it can be concluded that LR and DT suffer from low performance when dealing with imbalanced datasets in this case. Overall, OCATC shows the best results in micro-f1 and macro-f1 compared with all single classifiers over all datasets. Figure 3 shows the average of precision, recall, and f1-score overall ten folds for each dataset obtained by OCATC. Concerning DatasetA and DatasetD, which are balanced datasets, the Politics category in both datasets had the lowest classification results. In the case of DatasetA, politics documents have been misclassified into Law and Economic categories. Whereas, in DatasetD, Politics documents have been misclassified into Economy and Art categories. Moreover, in the balanced dataset, DatasetE, the lowest classification results were in the Finance category. Entertainments, Highlights, and Medicine categories possess the lowest classification results in terms of recall and f1-score in DatasetB, DatasetC, and DatasetF, receptively. For the Entertainments category in DatasetB, their documents have been misclassified over the rest of the categories. In DatasetC, documents in the Highlights category have been misclassified into Middle East News and World News categories with a percentage of almost 24% and 16%, respectively. In DatasetF, almost 52% of Medicine documents have been misclassified into the Science category.
## 4 Discussion  
An automatic screening system of DR is essential and a difficult task for computer-aided diagnosis systems due to the uneven illumination inside the image caused by the process of image acquisition and the main focus of retinal analysis (nasal view or macula view). In this study, we proposed a framework (see Figure 3) that differentiates DR and Non-DR automatically using fundus images. The proposed method comprised five processing steps.  
Image preprocessing was applied to standardize the image size and improve the retinal image quality. Then DR lesions were segmented using an improved approach over our previous work in [15]. Post-processing was further applied to eliminate any remaining noise causing poor image quality.  
After the DR lesions were segmented, the healthy retinal and DR images were differentiated based on the pixel density of the binary image (see Figure 4e) and the information of texture, color, and intensity of the detected regions (see Figure 4f), in which eight feature sets with a total of 208 features were extracted. Directly using all features from feature extraction may include the irrelevant features, which cause heavy computation time, redundancy, and even reduce predictive performance.  
To deal with this problem, we studied and compared the performance of four famous feature selection methods namely GA, PSO, HSA and HACO to select the optimal feature set as the input to the classifiers. Since the input data set is unbalanced (960 DR images and only 240 non-DR images), an EB based classifier was selected. To evaluate the performance of EB classifier, GA, PSO, HAS and HACO coupling with SVM, DT, LR, LDA, KNN and EB based classifiers were employed as reported in Tables 3-6 and the AUROC as depicted in Table 7.  
To evaluate the performance of the proposed method, six performance measures were compared using: sensitivity, specificity, accuracy, F-measure, precision and AUROC. As presented in Table 8, our methods give better results compared to the conventional methods because we carefully investigate and experiment throughout each step of the process.
## 5 Open Questions for Future Research  
At the time of the experiment, the pandemic of COVID-19 was a serious issue to deal with. To obey the epidemiological measures that were in force at the time, thus meeting the hygienic standards and reducing the health risk for the subjects, the authors chose to use a dummy head representing a person the subject listened to and were reading to as the visual stimulus, rather than involving a real person. As the proposed non-laboratory level calibration method involves human-to-human interaction between the reader of a text and the subject involved in a listening test that requires calibration, the behavior of the readers when reading to a real person needs to be examined. Therefore, in the next stages of the research, the reading part of the presented experiment shall be conducted once more with a real person serving as the visual stimulus for the readers. The difference between having a real person vs. a dummy head as a visual stimulus for the reader shall be assessed.  
The proposed level calibration method assumes that the subject will need to perform a purely subjective loudness matching between the speech uttered by a live person in the role of the reader and the pre-recorded speech sample as an integral part of the listening test for which the calibration is required. The ability of the listeners to perform such loudness matching of speech needs to be examined as well, with the emphasis on the possible influence of the (mis)match of the gender of the pre-recorded speaker and the live reader and the (mis)match of the read and pre-recorded speech content.  
The degree of the level mismatch between headphone and loudspeaker reproduction needs to be examined for conditions specific to this research, i.e., for speech sounds reproduced in acoustically ordinary rooms. To do so, another listening experiment needs to be conducted, in which the listeners are to be asked specifically to match the loudness of speech reproduced over loudspeakers and headphones, instead of simply setting the normal speech level on both reproduction systems, as was conducted here.  
Finally, the proposed level calibration method has the potential to provide a reasonably stable calibration level that is unlikely to be achieved simply by listening to a speech sample and adjusting the volume to \"normal\" speech level. However, the trade-off is the increased complexity of the calibration procedure that requires additional effort from the test subject before the listening test itself has even begun. Therefore, the ability and the willingness of potential test subjects in non-laboratory tests to perform such a calibration procedure needs to be investigated as well.
# Electrochemically Activated Screen-Printed Carbon Electrode for Determination of Ibuprofen
## 2 Materials and Methods
### Ibuprofen (IBP) Analysis  
Under optimized conditions IBP determinations were done in 0.25 M acetate buffer solution of pH 4.5 \(\pm\) 0.1 using differential-pulse voltammetry (DPV). After 45 s of stirring solution under open circuit, DPVs were recorded from 0.2 to 1.8 V with an amplitude (\(\Delta\)E\({}_{\text{A}}\)) of 150 mV, a scan rate (v) of 200 mV s\({}^{-1}\), and a modulation time (t\({}_{\text{m}}\)) of 25 ms. The background curve was subtracted from each voltammogram. The average values of Ip are shown with the standard deviation of \(n\) = 3.
# A Scalable Spatial-Temporal Correlated Non-Stationary Channel Fading Generation Method
## 4 Measurement Results and Analysis
### Statistical Properties of Generated Fading
Figure 9a shows the comparison between the theoretical modulus and the simulated modulus of the cross-correlation coefficients of sub-channel \(\tilde{\mathbf{R}}_{1,2}(t)\) and \(\tilde{\mathbf{R}}_{2,1}(t)\), \(\tilde{\mathbf{R}}_{2,2}(t)\), \(\tilde{\mathbf{R}}_{2,3}(t)\), and \(\tilde{\mathbf{R}}_{2,4}(t)\). In this simulation scenario, where the solid line represents the theoretical value and the mark represents the simulated value, it can be seen from the figure that the sub-channel correlation coefficient is time-varying. Figure 9b presents the relative error of the corresponding sub-channel, the maximum error does not exceed \(1.98\%\), and the average error is approximately \(1.47\%\), which meets the accuracy requirements in most non-stationary communication scenarios. Figure 10 shows the instantaneous value of the sub-channel spatial correlation coefficient (SCC) when \(t=24\) s. It can be seen from the figure that the theoretical value is consistent with the simulated value, and the average error is approximately \(1.86\%\), which verifies the reliability of the spatial domain cross-correlation coefficient matrix of each sub-channel derived in this paper. Figure 8: (**a**) theoretical, generated, and simulated absolute value of TCFs at different time points, (**b**) theoretical, generated, and simulated DPSDs at different time points, and (**c**) the simulated time–variant DPSDs measured by spectrum analyzer. Figure 9: (**a**)The modulus of sub–channel SCF and (**b**) The relative error of sub–channel SCF.
# Initialize GroupKFold for cross-validation group_kfold = GroupKFold(n_splits = 5)
### Technical Implementation Details  
Several important technical choices were made during implementation:  
* Feature selection thresholds: The correlation threshold of 0.8 was selected based on the preliminary testing of thresholds between 0.75 and 0.85, with 0.8 providing the optimal balance between feature reduction (43% fewer features) and predictive power retention (95% of baseline R2).
* Overfitting prevention: We employed GroupKFold cross-validation to prevent data leakage between networks, along with standard regularization parameters in Tree-based models (max_depth = 10, min_samples_split = 5). The gap between training and validation performance was monitored and maintained below 0.05 for all reported models.
* Synthetic network parameters: Networks were generated with parameters reflecting real social media structures: Random networks (edge probability: 0.05-0.20), Scale-Free networks (preferential attachment: 2-5), Small-World networks (rewiring probability: 0.1-0.6, clustering coefficients: 0.02-0.42), Tree networks (branching factor: 2-5), and Community networks (communities: 3-8, inter-community connectivity: 0.01-0.05).
* The negative correlation between network size (edges: r = \(-\)0.605, nodes: r = \(-\)0.598) and coverage suggests that larger networks may experience information dilution, requiring targeted seeding strategies rather than relying solely on organic spread.
# Transbulbar B-Mode Sonography for Clinical Phenotyping Multiple Sclerosis
###### Abstract  
The aim of this study was to assess putative differences in optic nerve sheath diameter (ONSD) and associated clinical/paraclinical variables between relapsing remitting (RR) and secondary progressive (SP) multiple sclerosis (MS) patients. We examined 60 relapse-free MS patients and 35 healthy controls by means of transbulbar B-mode sonography (TBS). Expanded disability status scale (EDSS) values were from 3 to 4 indicated patients with a transitional RR to SP phenotype. Mean ONSD was significantly lower in MS patients. Mean ONSD measured at 5 mm from the eyeball (ONSD5) was significantly lower in SP than in RR patients, while ONSD measured at 3 mm from the eyeball (ONSD3) was statistically higher in RR than in the transitional group. The myelination index (MI), i.e., the ratio of ONSD3 to ONSD5, was used to assess the relative myelination of the optic nerve (ON). Higher ONSD5 and MI (0.90) corresponded to patients with the RR phenotype having a mean EDSS of 2.0; lower MI (0.84) clustered the transitional patients having a mean EDSS of 3.7. Finally, lower MI with low ONSD3 identified the SP phenotype having a mean EDSS \(\geq\) 4.0. The TBS in MS highlights chronic optic neuropathy, caused by early subclinical axonal loss and demyelination.  
high-resolution ultrasound; multiple sclerosis; optic nerve; transbulbar B-mode sonography
## 4 Materials and Methods
### RNA Extraction and cDNA Synthesis  
Both before (BT) and after (AT) profoxydim spraying, from the susceptible (SUS) and resistant (RES1 and RES2) biotypes, total RNA was extracted from a pool of frozen leaf tissues obtained from 3 different plants (100 mg each) per pot. The RNeasy Plant Kit (QIAGEN SpA) was used, following the manufacturer's instructions. RNA concentration and quality were measured using the Qubit RNA Assay Kit on a Qubit 3.0 Fluorometer (Life Technologies) according to the manufacturer's protocol. In samples where RNA concentration was below the instrument detection threshold, extraction was repeated. On average, the extracted RNA concentration was around 100 ng/\(\mu\)L. RNA was conserved at \(-80\)\({}^{\circ}\)C.  
cDNA was obtained following a reverse transcription reaction with the miRCURY LNA RT Kit (QIAGEN SpA). The reaction mixture contained 10 \(\upmu\)L of RNA template (5 ng/\(\upmu\)L), 4 \(\upmu\)L of 5\(\times\) miRCURY RT reaction buffer, 2 \(\upmu\)L of 10\(\times\) miRCURY RT enzyme mix, and 10 \(\upmu\)L of nuclease-free H\({}_{2}\)O. The reverse transcription reaction was performed at 37 \({}^{\circ}\)C for 60 min, followed by 95 \({}^{\circ}\)C for 10 min. cDNA was stored at \(-\)20 \({}^{\circ}\)C.