esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results & Discussion
### MAE of Bioactive Compounds
#### 3.1.4 Effect of Microwave Power  
The effect of increasing microwave power, from 300 to 550 and eventually 800 watts, on the MAE at 15 min, 55 \(\mathrm{mL}_{\mathrm{solv}}/\mathrm{g}_{\mathrm{biom}}\) and 50 \({}^{\circ}\)C is illustrated in Figure 4. The extraction yield was overall not favored by the microwave power elevation. A decrease at 550 watts was noted, followed by a slight increase at 800 watts, which, however, did not exceed the extraction yield at 300 watts (Figure 4a). Microwave power increase from 300 to 550 watts significantly favored total phenolic (Figure 4b), chlorophyll (Figure 4c), and carotenoid content (Figure 4e), while the extract's antioxidant activity (Figure 4f) was affected accordingly. Nevertheless, MAE under the high microwave power value of 800 watts worsened the aforementioned responses.  
During the microwave power increase from 300 to 550 watts, the augmented microwave radiation improved the content of the examined bioactive compounds as a result of the boosted molecular interaction between the biomass and the electromagnetic field. However, further increase of the microwave power above 550 watts could possibly be responsible for the deterioration and thermal degradation of the extract's bioactive components [63; 64]. Similar findings were also observed regarding the extracted phenolic compounds and pigments, as well as the extract's bioactivity from other natural raw materials [46; 53; 63]. Finally, an MAE study of proteins from _C. vulgaris_ showed that the microwave power increase significantly reduced the protein recovery yield [22]. Considering the high protein content of _C. vulgaris_, as emerged from a previous study (\(\sim\)45% _dw_) [25], a decreasing extraction yield could be considered a possible outcome during extraction under excessive microwave power.
## 2 Design Parameters of the Metamaterial and Simulation Setup
### Parametric Studies
#### 2.2.3 Unit Cell Dimension Optimization  
Various sizes of unit cell for the same dielectric (RT6002) and for the same metal (copper annealed) are inspected to select the appropriate size of the proposed metamaterial. First, the unit cell is simulated with substrate dimensions of 13 \(\times\) 13 \(\times\) 1.524 mm\({}^{3}\), exhibiting quad-band resonance at 2.58 GHz, 7.60 GHz, 9.47 GHz and 10.36 GHz. Secondly, it is simulated with a unit cell with dimensions of 12 \(\times\) 12 \(\times\) 1.524 mm\({}^{3}\), showing quad-band resonance peaks with a small decrement of resonance frequencies. Lastly, it is simulated for dimensions of 11 \(\times\) 11 \(\times\) 1.524 mm\({}^{3}\), showing quad-band resonance at 2.35 GHz, 7.72 GHz, 9.23 GHz and 10.68 GHz, with a better progression of bandwidths. Figure 5 demonstrates the simulated results for the selected sizes of the unit cell.
## 1 Introduction
It is therefore important to design IoT access network deployments in an efficient way that will consider both the individual access network capabilities and the performance requirements of the applications that will use them. Towards that objective, it will be important to develop accurate yet comprehensive network planning and capacity dimensioning methodologies in order to assess the actual coverage and capacity that can be offered by an IoT access network deployment and to match the relevant present and future use case requirements. The contributions of this paper are the following: (a) it will provide a concise overview of key characteristics of representative IoT access network standards, namely LoRaWAN and 3 GPP NB-IoT, that are considered for being deployed in unlicensed and licensed bands, respectively; (b) it will present a methodology for modeling the characteristics of both access network technologies and for assessing their coverage and capacity considering different parameters. This paper is structured in the following way: Section 2 provides a concise survey of related works in the area of LoRaWAN and NB-IoT access network characteristics, as well as in terms of assessing their respective coverage and capacity. Then, Section 3 synthesizes the related state-of-the-art principles and presents a complete methodology for assessing analytically the coverage and capacity capabilities of LoRaWAN and NB-IoT. Section 4 then presents results for key application types for both IoT access networks and highlights key tradeoffs in each of them. Finally, Section 5 provides the key paper conclusions and open issues for further study.
# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 4 Experimental Verification
### Case Study 1: Application in CWRU Datasets
#### 4.1.3 Feature Learning Capability  
To visually analyze the feature learning effect of the proposed method on the unbalanced datasets, the test samples in Dataset C were input to the model and the middle layer features learned by the model were extracted. Then, the features were reduced from high-dimension to two-dimension and visualized using t-SNE. The outcomes are represented in Figure 7, in which the middle part is the model's network composition. Different colors in Figure 7a-f represent different samples, and each point represents one sample. It can be seen from Figure 7a that due to the influence of noise, redundancy, and other factors, all samples are mixed together. After the input signals are extracted by the first convolutional pooling layer, the sample distribution of each category is improved, but it is still difficult to distinguish, as shown in Figure 7b. After the second convolutional pooling layer feature extraction, the samples of 5 states, such as OF021, NORMAL, IF014, IF007, and IF021, are more obviously aggregated, but the other five categories are still difficult to distinguish, as shown in Figure 7c. After further feature extraction by the third convolutional pooling layer, the four classes of samples, such as BF021, IF007, IF021, and OF021, are basically separated, and NORMAL, BF014 and IF014 gather obviously; however, the other three categories are still mixed together, as shown in Figure 7d. After the fourth convolutional pooling layer feature extraction, most samples have been completely separated, and only two classes  
Figure 6: Confusion matrix of different datasets: (**a**) the result of Dataset A; (**b**) the result of Dataset B; (**c**) the result of Dataset C.  
of samples, BF007 and OF007, are still not completely aggregated, as shown in Figure 7e. At the end of the model, two full connection layers are designed, of which the second one is used for classification, so only the feature extraction of the first one is visualized, as in Figure 7f. It indicates that all the samples of 10 different categories have been separated, and the samples of the same category are completely clustered in the same area.  
From the above analysis, the original vibration signal can clearly output the signal features hidden in the data to the new space after convolution and pooling. In the process of feature extraction layer by layer, the information learned in the shallow layer is usually simple signal features. Until now, those samples are still difficult to distinguish, and with the increase in the number of network layers, the model can abstract higher dimensional fault features, and various input samples can be well identified according to these features.
# Time-Domain Near-Infrared Spectroscopy in Subjects with Asymptomatic Cerebral Small Vessel Disease
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **noWMHs** & **pvWMHs** & **d+pvWMHs** & \\  & \(n\) = 6 & \(n\) = 5 & \(n\) = 9 & \(p\) \\ \hline HBR (\(\mu\)M) & 23.7 (22.1–25.3) & 19.0 (18.6–19.1) & 23.0 (17.9–25.8) & 0.32 \\ HDO (\(\mu\)M) & 34.1 (31.9–35.6) & 29.8 (26.5–35.2) & 26.9 (25.7–29.3) & 0.26 \\ HBT (\(\mu\)M) & 57.5 (54.8–61.0) & 48.8 (45.2–54.3) & 51.1 (44.2–55.1) & 0.28 \\ StO\({}_{2}\) (\%) & 58.8 (57.5–59.5) & 61.1 (58.6–61.6) & 54.8 (53.2–57.3) & 0.007 \\ \hline \hline \end{tabular}  
* The values are reported as median (IQR). \(p\): \(p\)-value of statistical significance of differences among groups according to independent sample Kruskal-Wallis test.  
\end{table} 
Table 2: Comparison of concentrations of hemoglobin species and StO\({}_{2}\) values in Fazekas groups. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Independent** & **t** & **Beta** & \(p\) \\
**Variables** & \(\mathbf{t}\) & **Beta** & \(p\) \\ \hline StO\({}_{2}\) & \(-2.5\) & \(-0.96\) (\(-0.18\)—\(-0.015\)) & 0.023 \\ Age & 3.7 & 0.06 (0.025–0.093) & 0.002 \\ Arterial hypertension & 2.3 & 0.55 (0.040–1.01) & 0.037 \\ Smoking & \(-1.5\) & \(-0.63\) (\(-1.5\)–0.26) & 0.152 \\ SpO\({}_{2}\) & 2.0 & 0.21 (\(-0.01\)–0.43) & 0.066 \\ Constant & \(-1.6\) & \(-18.1\) (\(-42.9\)–6.7) & \\ \hline \hline \end{tabular}  
* In an explorative ROC analysis, grouping together noWMHs and pvWMHs subjects, a cut-off of cerebral StO\({}_{2}\) < 56.7% indicated the presence of d+pvWMHs with 91% sensitivity and 67% specificity [AUC 91% (CI 95% 78–100%)] (Figure 2).  
\end{table} 
Table 3: Binomial linear regression (dependent variable: Fazekas groups).
# Numerical Study on the Bending Resistance of Lightweight Built-Up Steel-Concrete Composite Beams
## 4 Comparison of Analytical and Numerical Results
### Steel Beam Height of 400 mm  
Figure 19 compares the analytically calculated results of different approaches with numerically obtained bending capacities for a steel beam height of 400 mm. In the analyzed models, steel elements were tied, and different degrees of shear connections were observed.  
Figure 19a shows that the numerical model resulted in a bending capacity that exceeded the analytically calculated characteristic bending resistance, with a full shear connection, as shown in Table 1. Figure 19b,c show results that are in accordance with the analytical approaches, where \(\text{M}_{\text{RK,kt}}\) and \(\text{M}_{\text{RK,without kt}}\) are the values shown in Table 1. The bending resistance was calculated in accordance with Equations (16) and (17); the non-linear elastic resistance for the propped and unpropped constructions were \(\text{M}_{\text{RK,propped}}\) and \(\text{M}_{\text{RK,unpropped}}\). For partial shear connection values calculated without considering the reduction factor, \(\text{k}_{\text{t}}\), give characteristic bending resistances close to bending capacities computed by numerical models Characteristic bending resistances that were calculated in accordance with the non-linear elastic resistance for both propped and unpropped constructions gave the lowest values; they were approximately 100 kNm lower than the numerically obtained capacities.
## 3 Results and Discussion
### Changes in VOCs
Proteins, especially structural proteins, can be hydrolyzed into peptides and amino acids by microbial proteases; the peptides can be transferred into bacterial cells and subsequently degraded into amino acids. These amino acids undergo transamination and decarboxylation to produce alpha-keto acids. The alpha-keto acids are enzymatically reduced to their respective alcohols via aldehydes [61]. 3-Methyl-1-butanol is produced by leucine catabolism [17]. Spoilage-causing bacteria such as _Pseudomonas_, _Shewanella_, and _Brochottrix_ can produce 2-methyl-1-butanol and 3-methyl-1-butanol [17]. 2,3-Butanedione and 3-hydroxy-2-butanone in DM and OM, with the exception of some samples, significantly increased during the late storage period at all storage temperatures. Thus, it is suggested that the above-mentioned esters and alcohols, 2,3-butanedione and 3-hydroxy-2-butanone, are possible signs of spoilage during the storage of yellowtail muscle (DM and OM). 3-Hydroxy-2-butanone and its oxidized form, 2, 3-butanedione, are produced by pyruvate fermentation under anaerobic conditions [62]. 3-Hydroxy-2-butanone can originate from microbial degradation of aspartate [17]. _Pseudomonas_ and _Brochottrix_ are spoil-causing bacteria capable of producing these VOCs [17]. On the other hand, because the change in the behavior of volatile components during storage differs depending on the muscle type and storage conditions, there are no common markers of quality deterioration of the yellowtail muscles. (_E,E_)-2,4-ocatdienal and (_E,E_)-3,5-ocatdien-2-one were identified as potential quality deterioration markers for OM, whereas 1-hexanol was a candidate for DM. Aldehydes are generated during the hydrolysis of triglycerides and fatty acid metabolism such as the beta-oxidation of unsaturated fatty acids or the autoxidation of lipids [17]. Aldehydes can also be generated from amino acids via transamination [17]. Some methyl ketones are produced from lipolytic activity but also from several other pathways, including alkane degradation by unique alpha-oxidation by bacteria and the dehydrogenation of secondary alcohols by bacteria. (_E,E_)-2,4-ocatdienal and (_E,E_)-3,5-ocatdien-2-one are produced in fish oil by the action of macroalgal lipoxygenase with high 13-lipoxygenase activity [63]. (_E,E_)-3,5-ocatdien-2-one formed by the n-3 fatty acid oxidation is a VOC of rotten yellowtail [64]. In our previous study, we reported that (_E,E_)-3,5-ocatdien-2-one is a characteristic component of red sea bream spoilage [23]. However, it is unclear whether these compounds are produced by spoilage-causing bacteria, and further investigation is required. 1-Hexanol is produced by the reduction of hexanal, which is produced by lipid oxidation [17]. _Pseudomonas_ and _Shewanella_ generate this VOC. These underscore the importance of understanding the dynamics of spoilage-causing bacteria and changes in VOCs in raw fish meat (OM and DM) to maintain meat quality.
## 3 Results
### Evaluation of MIP Images After Segmentation  
To evaluate the impact of the segmentation model on MIP image quality, the degree of removal of non-intracranial arteries was assessed using a scoring system. The evaluation focused on three external carotid artery branches: the superficial temporal artery, middle meningeal artery, and occipital artery. The mean scores and their respective 95% confidence intervals (95% CIs) were calculated for each artery. The superficial temporal artery had a mean removal score of 3.75, with a 95% CI of 3.54 to 3.96. The middle meningeal artery had a mean removal score of 3.80, with a 95% CI of 3.61 to 3.99. The occipital artery had a mean removal score of 3.60, with a 95% CI of 3.36 to 3.84. These scores suggest that the segmentation model was highly effective in suppressing undesired vascular structures, minimizing interference from non-intracranial arteries in MIP images.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** & **Mean \(\pm\) SD** \\ \hline DSC & 0.9428 & 0.9594 & 0.9674 & 0.9679 & 0.9702 & 0.9615 \(\pm\) 0.0112 \\ IoU & 0.8918 & 0.9219 & 0.9369 & 0.9378 & 0.9420 & 0.9261 \(\pm\) 0.0206 \\ fps & 17.58 & 16.22 & 16.07 & 17.60 & 15.59 & 16.61 \(\pm\) 0.92 \\ \hline \end{tabular} DSC: Dice Similarity Coefficient, IoU: Intersection over Union, fps: frames per second, and SD: standard deviation.  
\end{table}
Table 2: Segmentation evaluation metrics.  
Figure 7 illustrates a qualitative comparison between the MIP images generated from the original TOF-MRA dataset and those produced after applying the segmentation model. The images processed through the developed software show a significant reduction in non-essential vascular structures, particularly in the external carotid branches. The results indicate that the segmentation model successfully isolates the intracranial arteries while preserving their anatomical integrity, thereby enhancing the clarity of MIP images for diagnostic purposes.
# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 2 Materials and Methods
### Cnn
#### 2.1.2 Pooling Layer  
The use of pooling in fault diagnosis can filter out irrelevant information [30]. The operation of the pooling layer changes the dimensionality of the features, reducing the complexity of the model, while speeding up the operation and alleviating overfitting, the principle of pooling calculation is shown in Equation (2):  
\[a_{j}^{l}=P_{n\in R}\left(z_{j}^{l}(n)\right) \tag{2}\]  
where \(a_{j}^{l}\) is the output after pooling, \(R\) is the pooling range, and \(n\) is the coordinate of the pooling location.
## 1 Introduction
The research proposes a new blockchain-enabled degree attestation and verification system for the Higher Education Commission of Pakistan, a federal government body responsible for attesting and verifying the degrees issued by HEC-recognized universities and institutes. In our proposed solution, we used Hyperledger Fabric technology to create a private permissioned blockchain network. Different stakeholders are identified and added to the private permissioned network to achieve transparency, privacy, and secrecy. The proposed solution provides a robust, decentralized, and distributed hash-enabled chain-like structure called a ledger to store and record various transactions performed on the network. The research achieves the following objectives:  
* We studied, examined, and analyzed several online degree credential verification methods, including traditional centralized database storage systems. * In this paper, we evaluated the data authentication and privacy protection mechanisms used by the HEC to verify and attest to the degrees/certificates. * A new and novel hyperledger fabric blockchain architecture is proposed for HEC to replace the existing e-portal centralized system that delivers improved performance, transparency, security, and complete provenance to authenticate and verify the originality of academic credentials. * The transaction activities and operations of events delivery are illustrated using sequence diagrams to show how various steps are executed on the blockchain network while performing a degree verification traceability process. * Finally, we designed the consensus policy for nodes transactions execution and explained their uses along with some open challenges, and we also discussed implementation-related issues that emerged while simulating this proposed solution in the future by the use of an educational benchmark dataset. The rest of this paper is organized as follows. The related work section discusses different degree verification and attestation processes and solutions using blockchain technology. Section 3 focuses on blockchain-enabled digital ledger technology and its impact on higher education degree attestation security and privacy process. Section 4 explains the proposed novel architecture using hyperledger fabric technology for degree attestation and verification using the distributed application (DAPP). Section 5 presents the sequence diagrams to show the implementation of the proposed solution. Finally, various implementation challenges are discussed, and conclude this paper in Section 6.
## 2 Materials and Methods
### Instruments and Conditions  
To study the sub-micron surface features of the SPME coatings, the coatings were examined by a scanning electron microscope (FE-SEM, Hitachi, Tokyo, Japan, S-4800) under an operating voltage of 2.0 kV. FT-IR spectra of the samples were obtained from a Smart iTR diamond ATR in a Nicolet Nexus 470 FT-IR spectrometer (Thermo Electron, Waltham, MA, USA), equipped with a DTGS detector. The spectra were recorded at the absorbance mode from 4000 to 650 cm\({}^{-1}\) at a resolution of 4 cm\({}^{-1}\) with 32 scans. For thermo-gravimetric analysis (TGA), an amount of 5.0 mg film sample was weighed into a crucible and was scanned using a thermo-gravimetric analyzer (TGA2, Meteler, Zurich, Switzerland) from 25 to 800 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min in nitrogen atmosphere at a flow rate of 50 mL/min. Heating and stirring was carried out by a magnetic stirrer (C-MAG HS 7 IKAMAG, IKA, Konigswinter, Germany). pH measurements were conducted on a Mettler-Toledo pH meter (FiveEasy 20, Suzhou, China) with a combined glass electrode. A gas chromatography time-of-flight mass spectrometer (LECO, St. Joseph, MI, America), PAL RTC automatic sampling system, incubator and heating agitation module (CTC analytics AG, Zwingen, Switzerland) were used. The solid phase microextraction head comprised 120 \(\upmu\)m divinylbenzene/carbon/polydimethyl siloxane (DVB/CAR/PDMS). The gas chromatography conditions were as follows: Agilent 7890 B, column DB-5 MS, 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m. The temperature programming was as follows: the initial temperature of 40 \({}^{\circ}\)C was held for 2 min; then it was increased to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, which was held for 6 min. The following conditions were applied: transmission line temperature: 280 \({}^{\circ}\)C; ion source temperature: 210 \({}^{\circ}\)C; mass scanning range: 33-400 amu; carrier gas He (99.999%), constant current mode, flow rate: 1.0 mL/min; no split injection was used.
# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 3 Results  
### Experimental Setups  
Our experiment is carried out on a personal computer-based python 3.7 environment, which uses Intel (R) Core (TM) i5-9400F 2.90 GH CPU(Shanghai, China) and Nvidia GeForce RTX 2070 GPU (8 GB memory), using Darknet-53 as the deep learning framework. The parameters of the algorithm are set as follows: the initial learning rate is set to 0.001, and the learning strategy of steps is adopted. The SGD (Stochastic Gradient Descent) optimizer with a momentum of 0.9 is utilized to adjust the parameters of the network. Moreover, a weight decay of 0.0005 is used to prevent model overfitting. In all experiments, we use the same superior parameters to train 51,000 batches. The 35,000 th and 46,000 th batches of the method reduced the learning rate to 1/10 of the original value.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 6 Conclusions  
A thorough comparative review of the state-of-the-art OBS techniques for junction temperature sensing in power switching semiconductors has been performed. It was established that IRC rendered a better 2D temperature mapping but could not be embedded in power electronic circuits. The TSOP technique, on the other hand, is simple to implement but only practically applicable for SiC MOSFET. The FBG technique exhibits high spatial resolution and compact size, which makes it attractive to be embedded in power electronic circuits. However, logical positioning and a suitable packaging method with a uniform shear strength are required to obtain accurate temperature measurements. With the rapid growth and deployment of optical fiber sensors for various applications, multiparameter and distributed temperature sensing should be considered to gain widespread use for commercial applications. So far, we have showcased the implementation of distributed temperature sensing and intercommunication for data acquisition to enable sensor integration to a central monitoring platform through a communication link. These areas needed more attention to facilitate the development of other necessary features, such as automatic calibration, to make FBG adaptable for other power electronic applications. Lastly, we highly suggest that researchers explore this domain in order to achieve industrial breakthroughs.  
Writing--Original Draft: [PERSON], [PERSON] and [PERSON]; Writing--Reviewing and Editing: [PERSON] and [PERSON]; Supervision: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
[PERSON] and [PERSON] would like to acknowledge the support provided by the IRC for Communication Systems and Sensing, KFUPM, through project no. INCS2303.  
Not applicable, as no datasets were generated or analyzed for this research work.  
The authors declare no conflict of interest.