esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Flood Mapping  
The integration of Sentinel-1 GRD radar images, processed with ESA SNAP software, enabled the creation of a detailed flood map for the Jiului Gorge region (Figure 4). The images were processed to produce interferograms that distinguished between flooded areas and permanent water bodies ([PERSON] et al., 2010 [3]; [PERSON] et al., 2005 [12]; [PERSON] 2019 [13]). The processing workflow involved data calibration, terrain correction, and RGB composites, which enhanced image clarity and flood dynamics interpretation ([PERSON] et al., 2010 [14]; [PERSON] and [PERSON] 2021 [15]; [PERSON] et al., 2013 [16]). RGB composites were created by merging pre-flood and post-flood images, assigning the red channel to the pre-flood image and the blue and green channels to the post-flood image.
## 2 Materials and Methods
### Resolution  
Several methods were used to determine whether the inversion results are reliable for interpretation or not. Firstly, the Checkerboard Resolution Test (CRT) was used to test the resolution of the tomography model by making a synthetic velocity model. The CRT results showed a pattern of high and low-velocity anomalies relative to the 1D initial model, which is \(\pm\)10% relative to the 1D initial model. Secondly, the Derivative Weight Sum (DWS) calculated the density of ray paths that pass through the grid nodes by giving them a certain weight. A high DWS value represents a high-resolution zone (Figure S6). Thirdly, the Ray Hit Count (RHC) was used to locate the number of ray paths that pass through the grid nodes. An increased number of ray paths represents high resolution zones that can be well-interpreted (Figure S7). Fourthly, the Diagonal Resolution Element (DRE) was used, as shown in Figure S8, which relates to the resolution matrix R (damped least-squares problems); these have an R-value between 0 and 1 [39; 43; 44].  
The focus of this research on the vertical cross-section for the resolution test and tomography inversion consisted of four areas in the north-south cross-section: profile A-A\({}^{\prime}\) (longitude 123.80\({}^{\circ}\)) and profile B-B\({}^{\prime}\) (longitude 127.58\({}^{\circ}\)), and the west-east cross-section,profile C-C\({}^{\prime}\) (latitude 3.26\({}^{\circ}\)) and profile D-D\({}^{\prime}\) (latitude 1.10\({}^{\circ}\)). The horizontal grid size used in this study was 60 km \(\times\) 60 km; the vertical grid size ranged from 30 km to a depth of 330 km, as shown in Figure 4.
## 3 Results  
### Particle Number Concentration Results  
Figure 3 shows the UFP and CFP number concentration results versus time profile of each sanding paper grit performance. The measurements are shown from the time as the experiments, i.e., when the sanding operation were performed and subsequent sampling of residual concentration. Total concentration from each minute and replication of the experiment was averaged. Periods with no activity before the sanding process (until the fifth minute from sampling initialization) were excluded from the graph, because the background number concentrations of UFP and CFP were always <5000 \(\mathrm{\#/cm^{3}}\) and <150 \(\mathrm{\#/cm^{3}}\), respectively, in the test room, and the magnitude of the standard deviation (SD) from the average value indicated insignificant fluctuations.  
Figure 3: Comparison of mean (whisker: mean \(\pm\) SD) total number concentration of particles during the sanding process (0:05:00-0:10:00) and residual sampling (until 0:15:00) for sanding grits P60, P120, and P240: (**a**) Time profile of measured data with NanoScan SMPS; (**b**) Time profile of measured data with OPS. Red dashed line represents upper background level.  
Figure 3 shows long-lasting high concentrations of UFP and CFP in the breathing zone of the operator with the peak moment being after the sanding process was over.  
In Table 1, the descriptive statistics of the measured data from NanoScan SMPS and OPS are available. Data were averaged out of 15 recorded minutes of each treatment in 10 replications. A comparison of the time profile results showed that fine sanding gri P240 generated the highest mean number concentrations of UFP and CFP, followed by P120 and P60, respectively. On the other hand, the maximum observed number concentrations were slightly higher for P60, compared with P120.  
By comparing the average mean diameter (MD) and geometric mean diameter (GMD) of UFP generated by particular sanding paper grits, a decreasing trend from P60 to P240 was found. On the contrary, the size of CFP increased with the fineness of the sanding gri.  
The results of the ANOVA (Table 2) showed significant (\(p<0.001\)) difference among investigated sanding paper grit performance for both UFP and CFP in terms of number concentration. Therefore, we used [PERSON]'s honestly significant difference (HSD) procedure. Pairwise comparisons of means within data showed that the difference was remarkable among UFP generated by P240 sanding gri. At micro-scale, each of investigated sanding paper grits were significantly different compared with the other two. Moreover, similar results were obtained by the statistical analysis of MD and GMD of the particles.
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 3 Introduction to Signal Quality Index for Photoplethysmographic Signals
### Definition of Quality
#### 3.1.3 Annotation  
From the above sections, quality has been defined as how well the signal translates the variation in the blood flow of a patient with the metrological quality and how well the signal can be used for the derived application with physiological quality using continuous scales. In practice, quality is commonly addressed as discrete. Most of the reviewed papers presented in this survey associate the quality of the PPG signal binarily (\"good\" or \"bad\" quality) or with intermediate discrete levels. Passing from two continuous notions of quality with respect to metrological and physiological factors to a discrete representation makes the annotation of a set of signals difficult. However, it is a necessary step for the evaluation of the performance of an SQI algorithm.  
**Automatic annotation**  
Automatic annotation processes refer to annotation methods without the intervention of a human operator.  
A common method is to extract a physiological variable (e.g., PR, stroke volume) from the PPG signal and compare it to the value of the same physiological variable extracted from another device and/or another signal (e.g., electrocardiogram (ECG)) serving as a reference. If the difference between the values does not exceed a threshold, then the PPG signal is considered of good quality; otherwise, it is considered of low quality. In this case, the notion of quality is principally driven by the application.  
Another method consists in annotating the signals with the help of an already existing SQI. This method is used for the case of papers wanting to deploy an SQI algorithm replacing one or several older SQI algorithms.  
Figure 3: Non-exhaustive example of physiological quality illustrated with the waveform of a fingertip contact PPG. The physiological factors presented here are not measurement perturbations. Here, the case of optimal measurement conditions with a perfect sensor is represented. The signal reflects the variation in blood volume. However, the shape of the obtained signal cannot be used for its application.
## 4 The Application of the Ontology System
### Reasoning Engine, Logic  
The inference module in the ontology is a code, a piece of software. It infers logical consequences from facts and axioms. It supports decision-making and performs an integrity test.  
In the following, we will generally show how to generate answers to user questions using the reasoning engine of our ontology system.  
Key components of the example ontology: \(NODE=\{node_{1},\ldots,node_{n}\}\), transporting \(PRODUCT=\{product_{1},\ldots,product_{m}\}\) with \(VEHICLE=\{vehicle_{1},\ldots,vehicle_{k}\}\):  
Question 1.:  
What other products can be shipped from \(NODE=\{node_{1},\ldots,node_{n}\}\) with \(VEHICLE=\{vehicle_{1},\ldots,vehicle_{k}\}\) if \(PRODUCT=\{product_{1},\ldots,product_{m}\}\) are also transported?  
Answer 1:  
\[\begin{array}{l}\{canShippedFrom(node_{1},x)\vee\ldots\lor canShippedFrom( node_{n},x)\})\\ \bigwedge\{canShippedTogether(product_{1},x)\vee\ldots\lor canShippedTogether( product_{m},x)\}\\ \bigwedge\{canTransport(vehicle_{1},x)\vee\ldots\lor canTransport(vehicle_{k},x)\}\\ \end{array} \tag{1}\]  
Question 2.:  
What other products can be shipped to \(NODE=\{node_{1},\ldots,node_{n}\}\) with \(VEHICLE=\{vehicle_{1},\ldots,vehicle_{k}\}\) if \(CT=\{product_{1},\ldots,product_{m}\}\) are also transported?  
Answer 2.:  
\[\begin{array}{l}\{canDemand(node_{1},x)\vee\ldots\lor canDemand(node_{n},x)\} \\ \bigwedge\{canShippedTogether(product_{1},x)\vee\ldots\lor canShippedTogether( product_{m},x)\}\\ \bigwedge\{canTransport(vehicle_{1},x)\vee\ldots\lor canTransport(vehicle_{k},x)\}\\ \end{array} \tag{2}\]  
Question 3.:  
What vehicles can be used to transport \(PRODUCT=\{product_{1},\ldots,product_{m}\}\) to \(NODE=\{node_{1},\ldots,node_{n}\}\)?  
Answer 3.:  
\[canShippedWith(product_{1},x)\vee\ldots\lor canShippedWith(product_{m},x) \tag{3}\]  
Question 4.:  
What Vehicle Routing components can be used when visiting \(NODE=\{node_{1},\ldots,node_{n}\}\) with \(VEHICLE=\{vehicle_{1},\ldots,vehicle_{k}\}\) if \(PRODUCT=\{product_{1},\ldots,product_{m}\}\) are transported?  
Answer 4.:  
\[\begin{array}{l}\{canComponent(node_{1},\ x)\vee\ldots\lor canComponent( node_{n},\ x)\}\\ \vee\{canComponent(vehicle_{1},\ x)\vee\ldots\lor canComponent(vehicle_{k},\ x)\}\\ \end{array} \tag{4}\]  
Question 5.:  
What Vehicle Routing metrics can be used when visiting \(NODE=\{node_{1},\ldots,node_{n}\}\) with \(VEHICLE=\{vehicle_{1},\ldots,vehicle_{k}\}\) if \(PRODUCT=\{product_{1},\ldots,product_{m}\}\) are transported?  
Answer 5.:  
\[\begin{array}{l}\{canMetrics(node_{1},\ x)\vee\ldots\lor canMetrics( node_{n},\ x)\}\\ \vee\{canMetrics(vehicle_{1},\ x)\vee\ldots\lor canMetrics(vehicle_{k},\ x)\} \\ \end{array} \tag{5}\]  
With the help of the inference engine, we can also check consistency. In our system, consistency check contains the following: the vehicles do not exceed their capacity limit, which products can be transported together, which products can be transported by a specific vehicle type.
## 5 Conclusions and Future Work  
In this study, we developed a novel conversational model that leverages effective context to improve the coherence and consistency of conversations. Our model is based on a Transformer-based sequence-to-sequence model, which utilizes BERT to encode the current utterance's left-context. By employing a reinforcement learning strategy and building the corresponding reward function, we can incorporate the right-context of conversations during the training process to enhance the generation model. The proposed model can capture the flow of conversation and the relationships between utterances more effectively by utilizing both left-context and right-context. The left-context helps the system keep track of the conversation's history, including the topics discussed and any pertinent information mentioned earlier. This feature assists the system in generating more appropriate responses that consider the current state of the conversation. On the other hand, the right-context enables the system to anticipate the conversation's direction and generate more forward-looking responses.  
Experimental results have shown that our proposals effectively improve the quality of generated responses. In our comparison, we discovered that the left-context-based model increased the average BLEU score by 5% and the average ROUGE score by 7% compared to the baseline when utilizing a single context. Meanwhile, the right-context-based model achieved an 8% BLEU score and a 10% ROUGE score improvement. However, when we employed both left and right contexts, we observed that the RL-based models generated more coherent results than the baseline model. Specifically, our best model increased the average BLEU score by 24% and the average ROUGE score by 29%. We compared the outcomes of our proposed models with those of other studies in the literature. Our model consistently outperformed them by an average of 5% to 151% based on the BLEU score.
# Dynamic Investigation of a Solar-Driven Brayton Cycle with Supercritical CO2
## Material and Methods
### Modeling of the Brayton Cycle
Due to this, the isentropic enthalpy of the expansion (\(\rm{h^{\prime}_{out,turb}}\)) can be found, and then, using the following equation which represents the definition of the turbine's isentropic efficiency (\(\rm{\eta_{is,turb}}\)), the real enthalpy at the outlet (\(\rm{h_{out,turb}}\)) of the turbine can then be calculated [39]. \[\rm{\eta_{is,turb}}=\frac{h_{in,turb}-\ h_{out,turb}}{h_{in,turb}-h_{out,turb}} \tag{29}\]  
The total power consumed by the compressor (\(\rm{P_{comp}}\)) is calculated by the following equation, where \(\rm{\eta_{m,comp}}\) is the mechanical efficiency of the turbine, \(\rm{m_{CO_{2}}}\) is the mass flow rate of the carbon dioxide and \(\mathrm{h_{in,comp}}\) and \(\mathrm{h_{out,comp}}\) are the specific enthalpies of the carbon dioxide at the inlet and the outlet of the turbine, respectively [30]. \[\mathrm{P_{comp}}=\mathrm{\dot{m}_{CO_{2}}}\times\left(\mathrm{h_{out,comp}}- \mathrm{h_{in,comp}}\right)/\mathrm{\eta_{m,comp}} \tag{30}\]  
The enthalpy at the outlet of the compressor (\(\mathrm{h_{out,comp}}\)) is calculated using the equation below, which refers to the isentropic efficiency of the compressor [39]. \[\mathrm{\eta_{is,comp}}=\frac{\mathrm{h_{out,comp}}-\mathrm{h_{in,comp}}}{ \mathrm{h_{out,comp}}-\mathrm{h_{in,comp}}} \tag{31}\]  
This power is supplied to the compressor from the turbine through the common shaft that connects the turbomachinery of the cycle. Thus, the net power output (\(\mathrm{P_{net}}\)) is calculated by subtracting the consumed power of the compressor (\(\mathrm{P_{comp}}\)) from the total power output of the turbine (\(\mathrm{P_{turb}}\)), using the equation below [39]. \[\mathrm{P_{net}}=\mathrm{P_{turb}}-\mathrm{P_{comp}} \tag{32}\]  
Additionally, in this model a recuperator is used, aiming to increase the total cycle efficiency. This recuperator is modeled based on the NTU method for a counter-current heat exchanger [37]. Moreover, this part of the cycle is modeled so that, if the temperature of \(\mathrm{CO_{2}}\) at the outlet of the compressor is greater than the temperature at the outlet of the turbine, the recuperator is being bypassed [40]. The gas cooler in the cycle cools the working fluid before it enters the compressor. The temperature at the inlet of the compressor is chosen to be 5K greater than the ambient temperature [41]. The cooling fluid is chosen to be air since these kinds of power plants are installed in areas where water cannot be wasted [27]. The gas cooler is also modeled based on the NTU method. The \(\mathrm{C_{p}}\) used for the \(\mathrm{CO_{2}}\) is the one for the inlet of the working fluid in the gas cooler [39]. Finally, the net electrical power output of the system (\(\mathrm{P_{el}}\)) is calculated based on the mechanical (\(\mathrm{\eta_{m,g}}\)) and electrical losses (\(\mathrm{\eta_{el}}\)) of the generator, using the equation below [40]. \[\rm{Q_{cold,CO_{2}}}=\dot{m}_{CO_{2}}\times\ C_{P,CO_{2}}\times\left(T_{CO_{2}, out}-T_{CO_{2},in}\right) \tag{24}\]0
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Validation of the Optimized Control Elements  
To investigate whether the mathematically determined control element parameters led to high control accuracy, user experience, and presence, as well as low time consumption and mental workload, confirmation runs were conducted with the optimized control elements. Therefore, four additional test participants (m: 4; age: 26.8 \(\pm\) 4.5 years) performed the procedure described in Section 2.2 with the joystick and rotary control optimized for VR. Subsequently, the evaluation parameters were determined. These parameters were then compared with the calculated parameters. For example, the mathematical models calculate that a _time on task_ of 20 \(\pm\) 10 s should be possible with the optimized design of the rotary control. This mathematical model could be confirmed if the value determined in the confirmation runs is within the 95% prediction interval (95% PI).
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 5 Role of Pulsed Light Technology in Food Safety
### Food Package Decontamination  
There are several published results on the use of PL for the inactivation of microorganisms on food packaging materials. The characteristics of packaging material also determine PL treatment efficiency. The packaging materials should be transparent to UV light such as polyethylene, polypropylene, nylon, ethyl vinyl acetate (EVA), polybutylene, ethyl vinyl alcohol (EVOH) and Aclar for suitable PL treatment [4]. The application of PL has been shown to impart reductions of around 9 logs for vegetative microorganisms and above 7 logs for bacterial spores on the packaging materials surfaces [16]. In a study by [PERSON] et al. [122] of various packaging materials, surfaces were inoculated with 10-1000 CFU/cm\({}^{2}\) and subjected to pulses of light. Single pulse of 1.25 J/cm\({}^{2}\) could inactivate _S. aureus_, while intensities more than 2 J/cm\({}^{2}\) were needed for inactivation of _B. cereus_ and Aspergillus spp. spores. Molds species such as _A. niger_, _A. cinnamomeus_, _A. repens_, and _C. herbarum_, that are prevalent on the food packaging surface, after PL treatment, were reduced significantly by 2.7 logs on a paper-polyethylene packaging surface [128]. Currently, the form fill seal type or aseptic packaging films materials are sterilized in packaging machine by either H\({}_{2}\)O\({}_{2}\) and/or UV light. An innovation suggested by the application of PL lies in the disinfection of such packaging materials in place of H\({}_{2}\)O\({}_{2}\) or UV lamps [4].
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification Methods** & **Classifiers** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **TPR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **FPR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Tree} & Fine Tree & 97.7 & 98.84 & 48.53 & 98.84 & 98.81 & 98.82 \\ \cline{2-7}  & Medium Tree & 97.93 & 99.15 & 45.59 & 99.15 & 98.81 & 98.95 \\ \cline{2-7}  & Coarse Tree & 98.2 & 99.42 & 45.59 & 99.42 & 98.75 & 99.08 \\ \hline \multirow{4}{*}{SVM} & Linear SVM & 98.27 & 99.18 & 58.82 & 99.18 & 98.75 & 99.11 \\ \cline{2-7}  & Quadratic SVM & 78.61 & 79.51 & 39.71 & 79.51 & 98.27 & 87.9 \\ \cline{2-7}  & Cubic SVM & 76.83 & 77.86 & 33.82 & 77.86 & 98.27 & 86.78 \\ \cline{2-7}  & Fine Gaussian SVM & 98.33 & 99.52 & 47.06 & 99.52 & 98.78 & 99.15 \\ \cline{2-7}  & **Medium Gaussian SVM** & **98.37** & **99.5** & **50** & **99.49** & **98.78** & **99.17** \\ \cline{2-7}  & Coarse Gaussian SVM & 97.73 & 100 & 0 & 100 & 97.73 & 98.85 \\ \hline \multirow{4}{*}{KNN} & Fine KNN & 97.67 & 98.94 & 42.65 & 98.94 & 97.73 & 98.81 \\ \cline{2-7}  & Medium KNN & 98 & 99.28 & 42.65 & 99.28 & 98.68 & 98.98 \\ \cline{2-7}  & Coarse KNN & 98.03 & 99.83 & 20.59 & 99.83 & 98.68 & 99 \\ \cline{2-7}  & Cosine KNN & 97.73 & 100 & 0 & 100 & 97.73 & 98.85 \\ \cline{2-7}  & Cubic KNN & 98 & 99.28 & 42.65 & 99.28 & 97.73 & 98.98 \\ \cline{2-7}  & Weighted KNN & 97.8 & 99.05 & 44.12 & 99.05 & 98.71 & 98.88 \\ \hline \multirow{4}{*}{Ensemble} & Boosted Trees & 97.67 & 98.94 & 42.65 & 98.94 & 98.71 & 98.81 \\ \cline{2-7}  & Bagged Trees & 97.63 & 98.94 & 41.18 & 98.94 & 98.64 & 98.79 \\ \cline{1-1} \cline{2-7}  & Subspace Discriminant & 97.73 & 100 & 0 & 100 & 98.64 & 98.85 \\ \cline{1-1} \cline{2-7}  & Subspace KNN & 97.67 & 98.94 & 42.65 & 98.94 & 98.67 & 98.81 \\ \cline{1-1} \cline{2-7}  & RUSBoostedTrees & 97.17 & 97.14 & 98.53 & 97.14 & 98.67 & 98.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy for DC chopper and CBFCL protection system using various classifiers.
# 6G Enabled Industrial Internet of Everything: Towards a Theoretical Framework
## 6 GIIoE Architectural Framework
### Software-Defined Radio  
At present many industrial facilities have implemented wired networks (LAN) and wireless networks such as Wi-Fi. A legacy IIoT architecture incorporates multiple radio protocols and standards. Most industries are exploring the deployment of new types of connectivity such as 5G or the emerging 6G to exploit high-value use cases. Hence, it is important to create backward compatible and an efficient 6 GIIoE architecture that can accommodate software-defined radio (SDR)--a radio communication [44] (p. 134011) method where most signal processing is done using software. IIoE gateways leveraging SDR or 6G can implement and decode different protocols to save infrastructure cost and complexity. With 6G wireless connectivity, the above architecture can achieve simple software updates offering the possibility to enterprises to dynamically adapt to future operational and technological changes.
## 2 Material and Methods
### Classifiers
In particular, they highlighted this approach's merits in automating the data division process and ensuring the statistical significance of the result. In summary, YOLOv8 and YOLOv9 embody groundbreaking progress in object detection. These advancements offer distinct speed, accuracy, and adaptability advantages, rendering them particularly well-suited for solar panel defect classification. While this study commenced by evaluating pre-trained classical architectures such as VGG16 and ResNet, for the sake of comparison, YOLO-based models exhibited superior performance in detecting surface anomalies of a subtle nature, including micro-cracks in broken panels and uneven dirt accumulation. These anomalies are significant in real-time diagnostics in solar farm maintenance workflows. The capacity of YOLO-based models to process high-resolution imagery while preserving contextual details renders them especially well-suited to large-scale solar installations, where rapid and reliable defect identification is paramount. Convolutional Neural Networks (CNNs) are indispensable for robust image classification in complement to the existing object detection frameworks. In the present work, the aforementioned paradigm is advanced by proposing a novel CNN architecture explicitly designed for the ternary classification of solar panels (i.e., broken, clean, and dirty). The architecture incorporates domain-specific layers to enhance feature extraction for textural patterns, such as dirt granularity and crack morphology, which play a pivotal role in distinguishing between different degradation types. The proposed model was assessed using a rigorous k-fold cross-validation strategy to ensure the model's robustness and provide a fair comparison with the YOLO-based models. Combining YOLO's localization capabilities with CNN's classification precision highlights their complementary roles in solar panel monitoring systems. While YOLO variants have been shown to quickly localize defects across large solar arrays, our custom CNN architecture demonstrates that task-specific designs, validated through k-fold cross-validation, can achieve superior classification accuracy for nuanced defect categories. The dual approach underscores the importance of tailoring model selection to operational priorities. YOLO is particularly well-suited to large-scale, real-time inspection workflows, while CNN excels in fine-grained, pixel-level diagnostic tasks. Integrating these two approaches is pivotal in enhancing the effectiveness of automated solar panel maintenance systems, ensuring a balance between speed, accuracy, and adaptability in diverse environmental conditions.