esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stepwise LDA Based on ATR-FTIR Spectra  
The development of the calibration model was based on spectral differences in the 1500-750 cm\({}^{-1}\) spectral region. From the application of the stepwise algorithm, the statistically significant spectral regions (Figure 7), for the discrimination, were 1390-945 and 847-803 cm\({}^{-1}\). The first spectral region was assigned to the stretching vibration of C-O and bending of C-C and -CH\({}_{2}\)- of glucose and fructose. The second was correlated with C-C-H of fructose. Subsequently, the calibration model was performed based on the LDA algorithm. Wilks' Lambda test showed that a significant difference exists between the mean vectors of the four monofloral honey groups (Wilks' Lambda = 0.063 with \(p<0.05\) for the first canonical discriminant function; 0.251 with \(p<0.05\) for the second and 0.651 with \(p<0.05\) for the third). The eigenvalues of the three discriminant functions (3.021; 1.589 and 0.538) confirmed the calibration model and revealed that the first two discriminant functions were significant for the differentiation of the honey samples, while the values of the corresponding canonical correlations were 86.7%, 78.3%, and 59.1%, respectively. As per the previous results, the discriminant scatter plot (Figure 8) confirmed our current understanding of honey. Thus, 93% of samples were classified correctly, with  
Figure 7: The statistically significant spectral region on which the chemometric model was based for the differentiation of four honey varieties. the cross-validation being 82.3% (Table 3). Of the 26 \"unknown\" samples, 84.6% were identified correctly. From the standardized canonical discriminant function coefficients for ATR-FTIR spectroscopy, several spectral regions responsible for the discrimination of specific honey groups were revealed. While the first function separates thyme honey from fir and pine honey, the second function separates a large percentage of citrus honey from fir honey. Specifically, the functional groups corresponding to the characteristic groups C-H, C-O, C-O-C, and C-OH permit more refined discrimination of certain botanical origins with particularly high absolute coefficients, which correspond mainly to the sugars (fructose and glucose). This result could be explained by the fact that blossom honey has a different proportion of sugars from honeydew honey [43]. \begin{table}
## 3 Data sets and Processing Strategies
### Processing Strategies  
In our study, SF PPP was performed in simulated kinematic and static modes. The satellite cut-off elevation angle was set to \(7^{\circ}\). The initial standard deviation (STD) values were 0.003 and 0.3 m for the raw phase and code [18]. The slant ionospheric delays were treated as white noise [29], and the GIMs product was used as a virtual value for ionospheric constraints, and the ionospheric delay noise was estimated by Random walk [15]. The hydrostatic tropospheric delay was corrected by the Saastamoinen model [30], and the tropospheric constraints were products released by IGS. Earth, pole, and ocean tides were corrected according to [31], and model corrections for the Sagnac [32] and the relativistic effect [33] have been used for model corrections. The Kalman filtering method was adopted to estimate the parameters, and finally the float solution was obtained. A flowchart of our study procedure is shown in Figure 2.  
To evaluate the ionospheric disturbances, the 3-h Kp-index published from the official GFZ Potsdam products was used as it is shown in Figure 3 for the time period selected above. Kp-index is a global indicator of the earth's geomagnetic field disturbances and can be used to characterize the ionospheric activity levels. Table 1 shows the relationship between the Kp-index, the G-scale, and the auroral activity. The Kp-index utilizes 28 values instead of the 10 values that the preliminary Kp-index utilizes ([[https://www.spaceweatherlive.com/en/help/the-kp-index](https://www.spaceweatherlive.com/en/help/the-kp-index)]([https://www.spaceweatherlive.com/en/help/the-kp-index](https://www.spaceweatherlive.com/en/help/the-kp-index))). Based on the values above the threshold in Figure 3, 11,625 of the 90,000 test periods were deemed to be magnetic storm periods.  
Figure 1: Geographical distribution of the selected 375 global IGS tracking stations in September 2017.  
Figure 3: Geomagnetic Kp-index in Sep 2017. According to Table 1, when the Kp-index is greater than 4.67, the conditions can be considered a magnetic storm. Therefore, the periods above the red dotted line mark magnetic storm occurrences.  
Figure 2: The flowchart of our study procedure.
## 3 Artificial Intelligence Approach
### Supervised ML
#### 3.1.5 Random Forest (RF)  
RF is an ensemble machine learning algorithm that trains many decision trees and combines the predictions of individual trees for improving classification or regression predictive performance. It has good robustness and is capable of handling high-dimensionality data [31]. The potential of RF in rapidly and confidently food monitoring and assessing the durability and variety of food products has been demonstrated, making this method an applicable model in these areas. Also, the combination of RF with CARS to select the wavelength has shown promising results in the classification tasks. In this context, RF has classified Coix seed samples regarding storage years based on HSI data. Although the developed RF classifier had a good performance, other deep learning models, including ResNet and SVM, achieved better accuracy [31]. As a result, this model is useful for non-destructive food analysis.  
In another application, RF was applied to classify 27 different varieties of sorghum using VNIR-HSI. The model returned a calibration precision of 94.58% and a prediction precision of 64.44%. With dimensional reduction using the CARS algorithm, the proposed CARS-RF model improved the prediction accuracy to 84.07%, reflecting the capability of RF to deal with high-dimensional spectral data from imaging techniques [120]. It was further shared how RF can predict wheat characteristics and compared similar models such as the SVM and XGBoost gradient boosting trees. While RF was outperformed by more advanced ensemble models, RF performed well, thus indicating its reliability for spectral data in food quality prediction [121].
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
###### Abstract
In the negotiation phase, a hybrid candidate set selection algorithm based on fuzzy logic is presented, which integrates topologically information, link quality, and congestion. In addition, a back-off strategy in CLOR is introduced to alleviate the collision problem. In the transmission phase, an adaptive transmission strategy based on inter-session network coding is utilized to improve transmission performance. Then, a burst transmission strategy is also adopted to accommodate the networks with various traffic rates. Further, we execute a random linear packet coding by using systematic network coding to improve the transmission reliability. Lastly, this paper analyzes the effectiveness of negotiation, the effectiveness of burst transmission, and the reliability of adaptive coding strategy. Moreover, the Bellhop ray tracer is used to model the underwater acoustic channel of a specific sea region, and then, the performance of CLOR and typical routing protocols is comprehensively evaluated and compared. The rest of this paper is organized as follows: In Section 2, the related works with respect to OR protocols are summarized. Section 3 defines network architecture and channel model exploited in this paper. Then, Section 4 introduces the major strategy used in CLOR and describes the process of CLOR. In Section 5, we analyze the major strategies used in CLOR. The performance evaluation and discussion are presented in Section 6. Finally, the paper is concluded in Section 7.
# Novel Algorithm for Multi-Time Data Implantation in a Special Cyber-Manufacturing Architecture
## 6 Result Analysis and Discussions
### Expansion  
The methods that we studied, which are state-of-the-art methods, indicate that due to data embedment, the size of stego nucleotides increases. The method of computing the expansion rate is a process of measuring the ratio between the differences in length of stego  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Genome Bank**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**[PERSON]**} & & **Proposed** \\ \cline{3-6}  & & & & **1 Cycle** & **2 Cycle** & **3 Cycle** \\ \hline MN908947.3 & 9676 & 9965 & 29,000 & 58,000 & 116,000 & 17,4000 \\ \hline NC045512.2 & 9786 & 9965 & 29,000 & 58,000 & 116,000 & 174,000 \\ \hline MW738481 & 9569 & 9943 & 29,000 & 58,000 & 116,000 & 174,000 \\ \hline OU055787.1 & 9235 & 9936 & 29,000 & 58,000 & 116,000 & 174,000 \\ \hline AC153526 & 69,674 & 66,695 & 200,000 & 400,000 & 800,000 & 1,200,000 \\ \hline OurDNA.1 & 34,367 & 33,329 & 100,000 & 200,000 & 400,000 & 600,000 \\ \hline OurDNA.2 & 3 & 3 & 10 & 20 & 40 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Payload of the proposed method for different sequences.  
Figure 7: Payload analysis of different schemes. Here, we presented payloads of three competing methods and the results of our method for three different cycles. In the whole dataset, the proposed scheme gives higher payloads. The payload increased along with the higher embedding cycle.  
and cover nucleotides and the cover nucleotides. For the proposed scheme, it is \(c\times 100\%\), as shown in Equation (10).  
\[ER=\frac{(|\tilde{N}_{x}|-|N_{r}|)}{|N_{r}|}\times 100\%=\frac{(c\times|N_{r}|)}{|N _{r}|}\times 100\%=c\times 100 \tag{10}\]  
We observed that the stego DNAs other than the last cycle are in \(\tilde{N}_{x}\) and the final stego DNAs are in \(\tilde{N}_{r}\). If we keep both the stego DNAs in the cloud, an intruder might feel interested to look for the secret message in the stego DNA due to the large size of stego DNA. Therefore, we propose to upload only \(\tilde{N}_{r}\) at the cloud and to make \(\tilde{N}_{x}\) as a part of \(SK\). Thus, we minimize the expansion rate of stego DNA to 0%. Even then, the size of \(SK\) is \(L\times n+20\) bits which is smaller than the competing method [30] because the length of \(SK\) in [30] is \(2\times|M|+136\). In fact, this \(2\times|M|+136\) is about double of \(L\times n+136\).  
Table 14 shows the results of the expansion rate. Our method presents dominating results. Although we executed our method for 16 cycles, for the purpose of the paper organization, only the results of the first three cycles are provided in Tables 13 and 14.
## 3 Methods for the Characterisation and Evaluation of the Skid Resistance of Asphalt Pavements
### Effect of Microtexture on Skid Resistance of Asphalt Pavements
#### 4.2.2 Wet Pavement  
For wet road surfaces, using the same principle as that utilised in Section 4.2.1, the microtexture also plays a key role in the skid resistance of low-speed vehicles. The moderate height and sharpness of the microconvex body can enhance the safety of driving in wet conditions. When there is water on the pavement of a road, the microconvex bodies directly pierce the water film and are embedded in the rubber tyre, which is conducive to creating direct contact between the road surface and the rubber tyre, increasing the contact area between the tyre and the road, and preventing the vehicle from skidding [113]. The density of the microtexture determines the effective contact surface between the tyre and the road pavement; the higher the density of the microtexture, the more points of attachment of the microconvex bodies to the tyre per unit area, and a large number of concentrated stresses are generated at the locations where the road surface is in contact with the tyre [114]. Therefore, the height characteristics, density, and sharpness of the microtexture can help to improve the anti-skid performance of wet road pavement surfaces [91; 115].
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 1 Introduction
In particular, we calculate the probability that an aircraft arriving during a particular brief period of time will need to revert to SDA under the initial modeling assumption that the controller will permit CDA for all aircraft arrivals during that period. If this calculated probability is low enough, the controller may be comfortable in planning to permit CDA for all arriving aircrafts during that time period, and otherwise, they will not permit any of them to use CDA. Our model utilizes an analytical approach and queueing theory framework that considers factors such traffic and weather conditions to estimate the probability. The non-queueing portion of our modelling provides the number of aircrafts that can be accommodated within the airport's stacking space when CDA is used for all aircrafts arriving during the time period. This number provides insight to the controller on whether to permit a particular aircraft to use CDA during the period when all (or nearly all) arriving aircrafts will be permitted to descend with CDA (due to the low probability an aircraft will need to use SDA instead). Through the use of this modeling, it is our hope that CDA will be used more often and, thus, reduce noise, fuel consumption, harmful emissions and, thus, provide greener and more sustainable air transportation operations. This paper should draw attention to the opportunity to systematically increase the use of CDA for aviation green operations and air transportation sustainability. The paper is organized as follows. Section 2 describes the airspace around airports with respect to the terminal maneuvering area and describes descent and approach operations. In particular, two approach operations (CDA and SDA) are discussed and compared. In Section 3, we present the adoptability of CDA and the factors that impact aircraft landing time; the Base of Aircraft Data (BADA) Aircraft Performance Calculation (APC) is estimated and validated against actual landing times of flights operated at Nashville International Airport (BNA). Section 4 presents a background on the queuing theory and the main assumptions and fundamental components used to develop our model, while Section 5 presents the concept of the probability of an aircraft being denied CDA entry as a key output of the queuing model. In Section 6, we illustrate the calculation of the model probabilitiesusing standard industry data (e.g., separation distance requirements) and actual flight data from the BNA airport. Finally, Section 7 presents our main findings and conclusions.
# Low-Light Photodetectors for Fluorescence Microscopy
## 3 Single Point Detectors
### High Resolution of Photon Counting
#### 3.4.2 HPD and Fluorescence Microscopy  
The features of HPDs mentioned in Section 3.4.1 show the preeminence of HPDs in fluorescence microscopy applications, such as LSM, FLIM, and FCS. In practice, HPDs are incorporated into commercial microscopes and products of Becker & Hickl GmbH and PicoQuant.  
FCS  
In FCS, the presence of afterpulses deforms the correlation spectra. To avoid this issue, the fluorescence signal is commonly divided into two and detected by two detectors, and the cross-correlation between the two signals is calculated. This procedure is complicated and decreases the signal-to-noise ratio of each acquired datapoint. In contrast, the afterpulse-free feature of the HPD makes FCS measurement simpler, and a single HPD provides better data than PMT [13; 26]. As shown in Figure 10, the autocorrelation spectrum acquired by an HPD is of good quality, whereas the spectrum acquired by PMT contains overlapped afterpulse noise below the 2 \(\mu\)s region.  
FLIM  
FLIM utilizes time-correlated single photon counting (TCSPC) and acquires higher temporal resolution data (\(<\)ns) than FCS. Figure 11 shows a comparison of fluorescence life  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Photomultiplier Tube (PMT)** & \
\begin{tabular}{c} **Single Photon** \\ **Avalanche Diode** \\ **(SPAD)** \\ \end{tabular} &
\begin{tabular}{c} **Hybrid** \\ **Photodetector (HPD)** \\ \end{tabular} \\ \hline Afterpulse & High & High & Low \\ Transit time spread & \(\sim\)300 ps & \(\sim\)300 ps & \(\sim\)100 ps \\ (TTS) & & & \\ Diameter of active & \(\sim\)5 mm & \(\sim\)several 100 \(\mu\)m & \(\sim\)5 mm \\ (Operating & \(\sim\)270 K & 210–250 K & Room temperature \\ temperature & & & \\ \hline \end{tabular}
\end{table}
Table 2: Characteristics of point photodetectors [23; 27; 32].  
Figure 10: Autocorrelation data obtained by an HPD and a PMT [32]. The data were acquired using 100 nM Alexa Fluor 532 dye solution.  
time measurement data obtained by an HPD and a PMT. Afterpulses are also problematic for FLIM. The afterpulse raises the baseline of the PMT data. Consequently, the dynamic range of PMT is lower by an order of magnitude than that of HPD and seriously interrupts determination of the decay time constant.  
Compared with SPADs, HPDs can obtain brighter FLIM images due to the larger active area of the HPD. Figure 12 shows FLIM images acquired by an HPD and a SPAD. Although the quantum yields of the photodetectors were similar, the image acquired by the HPD collected twice as many photons as acquired by the PMT.
## 2 Materials and Methods
### Data Collection
#### 2.3.2 Core Flexion to Extension Strength Ratio  
The core flexion and extension strength test is part of the test battery for junior ski athletes as described by [PERSON] et al. [21], which was established in consultation with sports scientists, ski racing experts, and coaches. The maximal isometric core flexion and extension strength were measured using the slightly modified Back-Check (Dr. Wolff Sports & Prevention GmbH, Arnsberg, Germany). The athlete was in an upright standing position, with knees slightly flexed and pelvis stabilized; pads, which were connected to a force transducer, were set at sternum level (anteriorly and posteriorly). Against these cushioned pads, the athlete contracted maximally. The athletes conducted three trials each (flexion and extension), and the highest force of the three attempts each were recorded [21]. Then, the ratio of flexion to extension strength was calculated by dividing the flexion strength by the extension strength. The test-retest reliability analysis among high-level athletes identified intra class correlation coefficients (ICCs) of 0.77 (flexion) and 0.90 (extension) [22]. Subsequently, the ratio of core flexion to extension strength was categorized to 0 for a ratio \(\leq\)1 indicating higher strength in extensors or equal strength; and to 1 for a ratio \(>\)1 indicating higher strength in flexors.
## 4 Bending Effect on Wearable Antennas
### Distinct Effects on the E- and H-Planes  
This section explores how E- and H-planes' form and bending affect the reflection coefficient in these orientations, as shown in Figure 8. This matters because antenna orientation affects performance and behavior in each plane. E- and H-plane bending influences S\({}_{11}\) differentially depending on antenna geometry. If the reflection coefficients alter dramatically, bending may impair the efficiency, range, signal loss, and frequency. Engineers can solve problems and improve antenna design for realistic applications by altering the antenna's shape during bending with S11 in the E- and H-planes. Figure 8A,B illustrate the virtual return loss and the ability of antennas to bend with a changing main plane radio. It show how the E- and H-planes bend material at 0 (flat), 15, 25, and 35 mm. Bendable antennas operate in the target band without frequency detuning [77].  
Figure 7: Effects of bending at different angles in E- and H-planes.  
Table 7 summarizes the results of our analysis of bending at different angles, and it can show the changes in the resonant frequency and radiation efficiency. Various bending angles are used for the application of wearable antennas and these bending angles are based on different criteria such as the radius, radiation, and some other factors that play an important role in design. However, the effect of S\({}_{11}\) variations in impedance caused by the antenna's changing form may result in different degrees of signal reflection. This impacts S\({}_{11}\), which gauges the amount of signal bounced back from the antenna. An antenna's impedance characteristics might alter when it is physically bent or deformed from its initial shape, changing its reflection coefficient (S\({}_{11}\)).  
Figure 8: Reform of shape during bending with S\({}_{11}\) in the (**A**) E-plane and (**B**) H-plane.  
There is also a wider radiation pattern when the antenna is placed on the body because human cells are lossy. This means that a wider bandwidth is achieved. As a result, the power is spread out in all directions, creating a diffuse radiation pattern across both bands.
# Assessing Interactive Web-Based Systems Using Behavioral Measurement Techniques
## 4 Data Analysis
### Heuristic Evaluation Analysis
#### 4.1.2 Heuristic Evaluation Analysis for Jarir Website  
Table 4 shows the average and frequency of the severity of the problems based on the principles of heuristic evaluation for the Jarir website.  
For the Jarir website, the initial evaluation showed that nine problems (34.62% of total identified problems) were minor, seven problems (26.92%) were major, five problems (19.23%) were catastrophic, and five problems (19.23%) were merely cosmetic. Regarding individual principles, \"help users recognize, diagnose, and recover from errors\" and \"help and documentation\" resulted in average severity scores of 4 (catastrophic problem). Following that, \"match between the system and real world\", \"user control and freedom\", \"recognition rather than recall\", and \"flexibility and efficiency of use\" resulted in average severity scores of 3 (major problem). \"Visibility of the system status\", \"consistency and standards\", \"error prevention\", and \"aesthetic and minimal design\" resulted in average severity scores of 2 (minor problem) average severity score.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Heuristic Evaluation Principle**} & \multicolumn{4}{c}{**Severity**} & \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**Average Severity**} \\ \cline{2-2} \cline{4-6}  & **1** & & & **2** & & **3** & **4** \\ \hline Visibility of system status & 0 & 2 & 1 & 0 & 3 & 2: Minor problem \\ \hline Match between the system and real world & 0 & 1 & 1 & 0 & 2 & 3: Major problem \\ \hline User control and freedom & 0 & 1 & 2 & 1 & 4 & 3: Major problem \\ \hline Consistency and standards & 2 & 0 & 1 & 2 & 5 & 3: Major problem \\ \hline Error prevention & 0 & 2 & 0 & 0 & 2 & 2: Minor problem \\ \hline Recognition rather than recall & 1 & 1 & 1 & 0 & 3 & 2: Minor problem \\ \hline Flexibility and efficiency of use & 0 & 0 & 1 & 1 & 2 & 4: Catastrophic problem \\ \hline Aesthetic and minimal design & 0 & 0 & 2 & 0 & 2 & 3: Major problem \\ \hline Help users recognize, diagnose, and recover from errors. & 0 & 0 & 0 & 0 & 0 & N/A \\ \hline Help and documentation & 0 & 0 & 0 & 1 & 1 & 4: Catastrophic problem \\ \hline \multirow{2}{*}{Total} & Frequency & 3 & 7 & 9 & 5 & 24 & \\ \cline{2-6}  & \% & 13.64 & 31.82 & 40.91 & 22.73 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Heuristic evaluation for Extra website.  
Among the notable usability issues for the Jarir website was the limitation in user controls, such as the number of items in an order. The user could only order up to 20 instances of certain items using a drop-down menu to select a quantity. The user had to scroll down to reach the required number and was unable to simply type it. On other items, the user could only order one instance. Jarir also does not include basic error prevention functions, such as input validation. For instance, an expert provided an invalid phone number. However, the website did not show an error message or a notification.