esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Objective Functions
#### 2.2.2 Dynamic Information Redundancy  
To solve the problem of data redundancy, [PERSON] et al. [14] proposed a redundancy function by considering the similarity of the two-node modal vectors collected by sensors and defined the two-node similarity function according to Equation (4) by using the L2-norm operator on the matrix.  
\[SIM_{i,j}=1-\frac{\parallel\Phi_{i}-\Phi_{j}\parallel_{2}}{\parallel\Phi_{i} \parallel_{2}+\parallel\Phi_{j}\parallel_{2}}, \tag{4}\]  
where \(SIM_{i,j}\) is the similarity degree between the \(i\)th and \(j\)th sensors. If the mode shape matrices collected by two acceleration sensors have the same direction, the value of \(SIM_{i,j}\) is 1. If the mode shape vectors collected by two sensors have opposite directions, the value of \(SIM_{i,j}\) is 0. Thus, the redundancy factor can be calculated as follows:  
\[f_{2}=\parallel SIM-I\parallel_{2}, \tag{5}\]where \(I\) is an identity matrix. The redundancy of the data collected under a certain sensor layout can be determined from the \(f_{2}\) value. If the redundancy of the modal data collected under a certain sensor layout is high, the value of \(f_{2}\) is large.
## 2 Materials and Methods  
The following chemicals were used for the syntheses: MgCl\({}_{2}\)-6H\({}_{2}\)O (>99%, Penta, Czech Republic), MgO (>8%, Penta, Czech Republic), and deionized water (16.8 M\(\Omega\)).  
The ratio of 5 MgO to MgCl\({}_{2}\)6H\({}_{2}\)O was used for the synthesis of a stoichiometric phase of MOC with a composition 5 Mg(OH)\({}_{2}\)-MgCl\({}_{2}\)-8H\({}_{2}\)O (termed as Phase 5, also known as Mg\({}_{3}\)(OH)\({}_{5}\)Cl-4H\({}_{2}\)O). To prepare the first sample, 30.6 g of MgCl\({}_{2}\)-6H\({}_{2}\)O was dissolved in 19.0 g of deionized water. In the next step, 30.4 g of magnesium oxide was added, and the suspension was intensively stirred for 10 min. Equation (1) summarizes the formation of Phase 5:  
\[5\,MgO+MgCl_{2}\text{-}6H_{2}O+3\,H_{2}O\text{-}5 Mg(OH)_{2}\text{-}MgCl_{2} \text{-}8H_{2}O \tag{1}\]  
Similarly, the stoichiometric Phase 3 (3 Mg(OH)\({}_{2}\)-MgCl\({}_{2}\)-8H\({}_{2}\)O or Mg\({}_{2}\)(OH)\({}_{3}\)Cl-4H\({}_{2}\)O) was prepared by mixing MgO, MgCl\({}_{2}\)6H\({}_{2}\)O and H\({}_{2}\)O in the molar ratio 3:1:5. To prepare the second sample, 39.1 g of MgCl\({}_{2}\)-6H\({}_{2}\)O was dissolved in 17.3 g of deionized water. In the next step, 23.2 g of magnesium oxide was added, and the suspension was intensively stirred for 5 min. The formation of magnesium oxychloride cement MOC 3-1-8 is summarized in the following Equation (2):  
\[3\,MgO+MgCl_{2}\text{-}6H_{2}O+5\,H_{2}O\to 3 Mg(OH)_{2}\text{-}MgCl_{2} \text{-}8H_{2}O. \tag{2}\]  
Suspensions were used to prepare small samples in XRD holders (Figure 2). Samples were stored indoor at ambient temperature, with an excess of humidity and carbon dioxide. Figure 2 shows the photography of prepared samples in XRD holders.  
For the study of the surface morphology, scanning electron microscopy (SEM) was used (Tescan MAIA 3). Elemental composition and mapping were characterized by energy dispersive spectroscopy (EDS) using X-Max150 analyzer equipped with a 20-mm\({}^{2}\) SDD detector (Oxford instruments) and AZtecEnergy software. The sample was put on a carbon conductive tape in order to ensure the conductivity of the experiments. For both SEM and SEM-EDS analyses, the electron beam was set to 10 kV with a 10-mm work distance.  
X-ray powder diffraction (XRD) was carried out by a Bruker D2 Phaser, a powder diffractometer with Bragg-Brentano geometry, applying CuK\({}_{\alpha}\) radiation (\(\lambda\) = 0.15418 nm, U = 30 kV, I = 10 mA) with a rotation of 5 rpm. The step size was set to of 0.02025\({}^{\circ}\) (20), and the overall data were acquired in the range of 5-80\({}^{\circ}\). Measurements were performed on samples matured for 7, 28, 50, 100, 150, 200, and 250 days. After 250 days, the particular samples were manually homogenized in an agate mortar and measured in a powder form to obtain information on the bulk phase composition. Rietveld analysis was performed for the quantification of the identified phases.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Algorithms for Transmission Scheduling of Physiological Signals
#### 3.4.4 Method 4: WFQ with Timestamp and Empty Buffer (WFQ_Timestamp_Empty)  
Referring to Methods 2 and 3, the higher-priority packets can be served first, but since the sampling frequencies of various physiological signals are different, polling according  
Figure 12: The procedure of the WFQ with Round Robin.  
Figure 13: The procedure of the WFQ with Round Robin and empty buffer.  
to the priority order may result in a larger delay for lower priority signals. Accordingly, Method 4 considers the arrival timestamp of the first packet in each queue, then arranges the transmission order according to the timestamp (i.e., the first arriving queue will be transmitted first). As shown in Figure 14, once the signals of different sampling frequencies enter the corresponding queue, the first packet in the current queue generates a timestamp, (e.g., the system timestamp of ECG = 1,662,219,425.096878 (s), EEG = 1,662,219,425.099889 (s) and EMG = 1,662,219,425.072183 (s), respectively). Then, the queue transmission order is determined by comparing the respective timestamp. In the above example, the EMG timestamp is the smallest, which will be transmitted first, followed by ECG, EEG. Therefore, each queue is assigned a transmission bandwidth according to its Equation (2), where each queue still follows the FIFO principle that the packets in each queue are sent in sequence, and then the queue is empty before the next queue is allowed to transmit.
## 4 Results  
### Initial Test Results  
Figure 7 presents the depth map quality MOS results for both the 2D and 3D video subjective assessments together with their 95% confidence intervals. The series (as seen in the legend) in both charts represent the depth map utilised for rendering the virtual view at camera location CAM 3 for each sequence. It is worth noting that the MOS values for the hybrid recursive matching (HRM) based depth map are not present for the _Act_ and _Parishand_ sequences, as the corresponding depth maps were not available for these sequences.  
Figure 7: Subjective depth map quality assessment results across a range of depth maps used in DIBR based view rendering: (**a**) 2D video; (**b**) 3D video (HRM; DERS_OFF/ON: MPEG depth estimation reference software (DERS) temporal enhancement feature of/on [41; 42]; NL: Non-Local method [8]; ST; GF: Guided Filter method [43]; RSGM; RSGM_FIL; RSGM_GAUS: Gaussian blur introduced to RSGM_FIL; RSGM_SCAN: Scan-line error introduced to RSGM_FIL).  
From the results presented in Figure 7, the following observations can be made. Firstly, neither graph shows any significant changes in the MOS patterns between the 2D and 3D results. This observation indicates that the depth maps utilised in the rendering process have not resulted in rendered views that contain parallax problems when utilised in a 3D viewing environment. Secondly, the results indicate that different depth maps utilised in rendering the views have a significant effect on the opinion scores as they vary greatly. Thirdly, it can be observed that the same depth map (i.e., same depth/disparity estimation algorithm) has a variable level of performance when utilised for rendering virtual views of different sequences. In other words, the content (i.e., spatiotemporal information) of the sequence can influence the performance of a specific depth map, and as such depth map rendering performance is dependent on the sequence utilised. These results and observations are used to develop our proposed depth map quality evaluation model next.
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 3 The Proposed VQoSRR Framework
### The VQoSRR QoS Routing Mechanism
#### 3.2.1 The Dynamic Traffic Rerouting Algorithm (DR-RA)  
Most QoS routing methods use on-demand path computation, but this has two disadvantages. First, it delays the process of forwarding traffic. Second, it involves the execution of a path computation algorithm for each flow request, adding further overhead to the routers (controllers in case of SDN), notably when the frequency of path calculation is high. In addition, if the QoS metrics change frequently, this will lead to frequent routing updates, which means more computation overhead. Thus, the collecting link metrics should not be reliant on too much dynamism. Furthermore, the complexity of the routing algorithm increased processing overhead [3,4]. Thus, this study proposes a dynamic traffic rerouting algorithm to address these mentioned issues.  
The dynamic traffic re-routing algorithm (DR-RA) is responsible for updating the route's cache and rerouting traffic by using the alternative path or generating a new one. Briefly, it performs in this manner: it runs periodically, starting with reading the network statistics based on a predefined interval time. Next, it checks if the current path violates the flow QoS requirements, then deletes its flow entries from the switch. Afterward, it examines the alternative route to see if it satisfies the flow quality requirement. If not, it calculates another path. Using the alternative path presents the following advantages: (1) decreasing the time spent recalculating the routing path by utilizing the alternative route rather than using the routing algorithm again; (2) increasing response times for installing flow; and (3) providing two paths could increase resilience when a path fails. Figure 2 illustrates the flowchart for DR-RA algorithm.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 3 Materials and Methods: A Novel Proposed EEG Analog Front-End Circuit System
### EEG Sub-Circuit Modeling the First Band-Pass Filter  
This sub-circuit is precisely designed to achieve two specific goals: (i) safeguarding the user of the novel EEG circuit, and (ii) simultaneously constructing a traditional RC bandpass filter. The positioning of the high-pass filter at the beginning of the band-pass filter sub-circuit accounts for the fluctuating resistance characteristic of the electrode contact. The high-pass filter has an input resistance of 330 K\(\Omega\). The indicated quantity is the minimum requirement to adhere to safety regulations, which restrict the amount of input to the body to a maximum of 10 \(\mu\)A, as specified by IEC-601-1 safety rules.  
A 2.2 pF capacitor was used to ensure that, despite the dry electrode's greatest input resistance of 1.4 M\(\Omega\) in combination with the 330 K\(\Omega\) resistor, the filter effectively includes the highest frequency of interest, 20 kHz, inside the pass band's 0 dB gain range. The exact configuration of electrode resistances yields cut-off frequencies ranging from 41.81 kHz (with an input resistance of 1.73 M\(\Omega\)) to 218.56 kHz (with an input resistance of 331 K\(\Omega\)).  
Figure 10: EEG sub-circuit modeling the DRL circuit.  
Furthermore, a first high-pass filter is used at around 0.4822 Hz to eliminate any variations in DC offset and somewhat reduce muscle interference, while having negligible impact on the signal prior to amplification. While a cut-off frequency of 0.05 Hz or lower would be ideal for preventing signal distortion, implementing such a low frequency would necessitate the use of excessively large capacitors. This is especially true in scenarios where there is substantial muscle noise, such as during physical exertion or when experiencing the revival of embodied psychological trauma. Therefore, making slight adjustments to the EEG signals in the delta wave region is an intentional choice that aims to find a compromise between the accuracy of signal capture and the practical limitations imposed by significant muscle interference.  
The first band-pass filter exhibits a Q factor of 0.0014854, a damping ratio of \(\zeta=336.598\), and a center frequency of 324.6667 Hz under the condition that the input resistance is 331 K\(\Omega\). The filter exhibits a Q factor of 0.0033969, a damping ratio of \(\zeta=147.191\), and a center frequency of 141.972 Hz under the condition that the input resistance is 1731 K\(\Omega\). The considerations described above result in minimal phase distortion within the acceptable frequency range for our amplification needs. The system takes around 30 msec to reach 99.99% of its final value. The equation describing the circuit above is  
\[\mathrm{f_{3 db}=s*}\frac{1}{s^{2}+s*}\frac{1}{\left(\frac{1}{RT12*CIT2}+\frac{ 1}{RT11*CIT2}+\frac{1}{RT11*CIT1}\right)+\frac{1}{RT12*CIT2*RT2*CIT2}} \tag{1}\]  
In Figure 11 is depicted the EEG sub-circuit modeling the first band-pass filter.
# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 5 Results and Discussion
### Experimental Results
#### 5.2.3 Pixel Dimension Analysis  
The image resolution of the generated images plays a pivotal role in the performance of these models. To assess the impact of different resolution settings on the final classification performance of Wide-TSNet, we examined how the image size affected the Accuracy. We compared Wide-TSNet with the various classifiers and image generation methods, as depicted in Figure 6. We chose different pixel dimensions (224 \(\times\) 224, 112 \(\times\) 112, and 56 \(\times\) 56), and used the resulting images in the selected classification models.  
It appears that the Wide-TSNet model performed best with an image size of 56 \(\times\) 56. It also provided better performance than the other models at most resolutions, which could be attributed to the model's ability to extract more features from higher-resolution images. This provides a richer data set for the model to analyze and interpret, improving its performance. The GASP image generation method demonstrated consistent performance across varying resolutions. This robustness is a significant advantage in applications where the image resolution varies. Additionally, the MTF model showed an improvement with increasing pixel dimensions, suggesting that this model performs better at higher  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **F1 Score** & **Precision** & **Recall** \\ \hline Wide-TSNet & 0.939614 & 0.900686 & 0.901232 & 0.900054 \\ \hline Wide ResNet + MTF & 0.874510 & 0.710430 & 0.711149 & 0.709712 \\ Wide ResNet + MTF (RGB) & 0.862267 & 0.707301 & 0.708016 & 0.706587 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization test of the Wide-TSNet model.  
Figure 6: Effect of image size on the classification accuracy of the various classifiers, as compared to Wide-TSNet: (**a**) Wide ResNet; (**b**) EfficientNet; and (**c**) SqueezeNet.  
resolutions. These insights could be instrumental in tailoring pre-processing strategies to enhance classification efficacy while mitigating computational overheads.  
On the other hand, the EfficientNet and SqueezeNet models exhibited an inverse relationship between image size and classification accuracy, suggesting that these models may experience overfitting at higher resolutions. Overfitting occurs when a model captures noise or irrelevant features in the data, which can compromise its performance. This highlights the need for a balanced approach that considers the model's specific architecture and application domain when choosing the image resolution.
# A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra
## 2 Materials and Methods
### Data Analysis  
In order to evaluate the classification models, each of the three different groups of spectra was separated into a calibration set and a test set, comprising, respectively, about 70% and 30% of the total number of spectra of each group. The spectra assigned to the test group were randomly chosen using a random number generator.  
Exploratory data analysis was performed for each calibration set by means of PCA, in order to visualize in the score plots the separation of the two different classes of samples and the spectral variables to which this separation is related. Full cross-validation was used to validate the PCA results.  
Classification models for discriminating samples from the two classes of each group were built by using the PCA-LDA and PLS-DA techniques for the calibration set, whereas the test set is used to evaluate the model classification performance, that is the obtained values of accuracy, sensitivity and specificity. Accuracy corresponds to the total number of samples correctly classified considering true and false negatives, sensitivity assesses the ability of the test to classify positive cases (e.g., cases with disease) while specificity is a measure of the ability of the test to identify negative cases (e.g., cases without disease).  
All chemometric analyses were performed with the Unscrambler X CAMO software (version 10.4), whereas \(t\)-test analysis was performed by SigmaPlot software (version 12.5, Systat Software, San Jose, CA, USA).
## 4 Research Challenges and Opportunities  
So far, wireless sensor networks have been widely applied to terrestrial areas, and some of these deployments have achieved satisfactory performance. However, the application of WSNs in marine environment monitoring is still in its infancy, and most WSN-based systems are purely experimental [22]. This section discusses a few challenges of wireless sensor networks for marine environment monitoring including oceanographic sensors protection, advanced buoy design, energy harvesting system design, and system stability and reliability.
# Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data
## Materials and Methods
### Platform and Data  
Google Earth Engine (GEE, [[https://eartheengine.google.com](https://eartheengine.google.com)]([https://eartheengine.google.com](https://eartheengine.google.com))) consists of a multipetabyte satellite imagery data catalogocated with a high-performance, intrinsically parallel cloud computation service.  
Figure 1: Location of Poyang lake within the Yangtze River Basin (**left**) and at an average level (**right**).  
Users can access GEE through an Internet-accessible application programming interface (API) and an associated web-based interactive development environment (IDE) that enables rapid prototyping and visualization of results. This cloud computing platform not only makes it easy to access most of the geospatial datasets but also enables high throughput analysis. There are many examples where environmental scientists empowered their research with help of GEE, such as population mapping [36], cropland mapping [37], extraction of glacial lakes [38] and probabilistic wetland mapping [39].  
Sentinel-1 is the first Copernicus Program satellite constellation deployed by the European Space Agency. This space mission is composed of two satellites, Sentinel-1A and Sentinel-1B, carrying a C-band synthetic-aperture radar instrument which collects data in all weather, day or night [40]. Since radar sensors have the advantage in detecting moisture and water because of their ability to penetrate clouds, Sentinel-1 is one of the most common datasets for surface water detection [41] and flood mapping [42].  
The winter low-water season of Poyang lake provides important foraging habitat and wintering area for many waterbirds of special concern, including the critically endangered Siberian crane. Because of the importance of water level during this time, we looked at images taken in January over subsequent years. We loaded Sentinel-1 Level-1 IW GRD images from the data catalog of GEE from January 2017-2020 (Table 1). The imagery acquired on January of 2020 was used to evaluate our Otsu Method implementation on GEE. The others were used to analyze the water area change in January across 4 years from 2017 to 2020.
# Effect of Moderate-Intensity Endurance Exercise on Inflammatory Cytokines in Leukocytes of Dogs
## 2 Materials and Methods
### Quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR)  
Regarding total RNA extraction, EDTA-blood samples were collected from dogs before exercise, two weeks after exercise, and post-exercise. Total RNAs were extracted from the leukocytes of all samples following the manufacturer's instructions using the QIAamp RNA Blood Mini Kit (Qiagen, Hilden, Germany). Total RNA extracted was quantified using a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) and stored at \(-80\)\({}^{\circ}\)C until used for complementary DNA (cDNA) synthesis. cDNA was synthesized using all collected RNA with a Maxime RT premix kit (iiNtRON, Gyeonggi, Korea). Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed using the StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The final reaction mixture (20 \(\upmu\)L) included 0.4 \(\upmu\)L forward primer, 0.4 \(\upmu\)L reverse primer, 10 \(\upmu\)L SYBR Green PCR master mix (Applied Biosystems), 8.2 \(\upmu\)L diethylprocarbonetreated water, and 1 \(\upmu\)L cDNA under the following conditions: 40 cycles of 95 \({}^{\circ}\)C for 10 s, 60 \({}^{\circ}\)C for 20 s, and 72 \({}^{\circ}\)C for 40 s. The beta-actin gene was used as an internal control to normalize quantification for comparative analyses. Relative gene expressions were calculated using the \(2^{-\Delta\Delta\text{Ct}}\) method [26]. The relative formula (R) was assessed using the formula: R = 2 - [\(\Delta\)Ct sample \(-\)\(\Delta\)Ct control group]; each obtained value was normalized to the beta-actin value, and the average value for each gene expression of both groups at the time point before exercise was set to one [27]. All primer sequences are listed in Table 3.