esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parameter Sensitivity Analysis Based on Orthogonal Design  
### Orthogonal Design of Calculation Scheme  
The orthogonal experiment is a methodology based on orthogonality, which involves selecting several representative experiments from a comprehensive set of trials. This approach is used to determine the sensitivity of various factors by analyzing the experimental results. The notation \(L_{M}\big{(}Q^{F}\big{)}\) represents an orthogonal array with \(F\) factors and \(Q\) levels. Here, \(M\) stands for the number of designed experiments, which is typically much less than the total number of comprehensive experiments denoted by \(Q^{F}\). In the safety and stability analysis of large underground chambers, in addition to the rock's inherent strength parameters, the presence of groundwater imposes external hydrostatic pressure on the tunnel overburden. Furthermore, the surrounding rock, due to prolonged immersion in groundwater, remains in a fully saturated state. Thus, both environmental factors and rock parameters are considered. The depth of the tunnel and water pressure are chosen as environmental factors. The elastic modulus, Poisson ratio, cohesion, internal friction angle, and saturated density are chosen as rock parameters. Benchmark values for the physical properties of the rock mass are determined based on engineering geological data; the reference mean for the sensitivity analysis of influencing factors is presented in Table 2.  
The variation of influencing factors within a \(\pm\)20% range is studied. The mean value listed in Table 2 is set as a reference value, as shown in Table 3. It is assumed that these parameters follow a uniform distribution, and the construction process remains constant. This approach significantly enhances model accuracy while reducing model complexity and minimizing the workload associated with data analysis. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Depth (m)** & \begin{tabular}{c} **Water Pressure** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Elasticity** \\ **Modulus (GPa)** \\ \end{tabular} & \begin{tabular}{c} **Poisson** \\ **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Cohesion** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Internal Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **Saturated Density** \\ **(kN/m\({}^{3}\))** \\ \end{tabular} \\ \hline
300 & 0.35 & 2.84 & 0.31 & 0.42 & 32.78 & 25.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Reference mean for sensitivity analysis of influencing factors.
## 2 Materials and Methods
### Fluorescence Microscopy  
For fluorescence microscopy, transparent 96-well plates (Eppendorf, Hamburg, Germany) were used to grow biofilms. The LIVE/DEAD BacLight Bacterial Viability Kit (containing SYTO9 to stain all microorganisms, and propidium iodide (PI) to stain dead cells) was used according to the manufacturer's protocol. Widefield fluorescence images were acquired using an _Operetta CLS_ high-content imaging device (PerkinElmer, Hamburg, Germany). For whole-well imaging, four fields of view were stitched digitally. A 5x objective (air, NA = 0.16, Zeiss, Oberkochen, Germany) was used. The dye SYTO9 was excited by a 475 nm (110 mW) LED, and the fluorescence was collected through a \(525\pm 25\) nm bandpass filter. The PI was excited by a 550 nm (170 mW) LED, and the emission light was collected through a \(610\pm 40\) nm bandpass filter. The laser autofocus (785 nm) provided exact focusing across all fields of view. For display, three stacks were merged into a maximum intensity projection to account for topographical particularities in the z-plane (focus \(\pm\) 25 \(\upmu\)m). For 3D images of biofilms, 30 z-planes (stacks) with 1.5 \(\upmu\)m between each plane were measured using a 40x air objective (NA = 0.6). Three-dimensional reconstruction, image stitching, and quantification were done using _Harmony_ 4.8 software (PerkinElmer, Hamburg, Germany).
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Tiling
#### 3.2.3 MiniView Layout  
To reduce the bandwidth requirement of 360-degree video streaming, [PERSON], [PERSON] [36] proposed the MiniView Layout which has saved up to 16% of the encoded video without downgrading the visual qualities. In this method, the video was projected into equalized tiles with each MiniView independently encoded into segments. It increases the number of segments and higher in the number of requests parallelly to the streaming client. Plus, [PERSON]. [36] showed improvements in projection efficiency as it created a set of views with the rectilinear projection referred to as \"miniviview\", which has smaller FOVs than cube faces, hence able to save encoded 360-degree videos' storage size without quality loss. Each miniview has its parameters which include FOV, orientation and pixel density [36].
## 2 Chemical Composition of _Chlorella vulgaris_
### Lipids  
The lipid composition of _C. vulgaris_ is of particular interest, as it can be used in various applications, such as the production of biofuels, nutritional supplements, and cosmetic ingredients [36]. Lipids in _C. vulgaris_ represent around 5 to 58% [9] and are primarily composed of polyunsaturated fatty acids (PUFAs). The most prominent fatty acids include linoleic acid (18:2n-6), oleic acid (18:1c9), palmitic acid (16:0), and alpha-linolenic acid (18:3n-3), with linoleic and alpha-linolenic acids being the primary omega-6 and omega-3 fatty acids, respectively [37]. According to Zhang et al. [38], the fatty acid composition of _C. vulgaris_ exhibits significant variability depending on the cultivation conditions, specifically between phototrophic and heterotrophic methods. Under phototrophic conditions, _C. vulgaris_ displays a diverse fatty acid profile, with palmitic acid (C16:0) being the most abundant at 44.99 \(\pm\) 0.21 mg/g, followed by linoleic acid (C18:2) at 25.4 \(\pm\) 0.29 mg/g and alpha-linolenic acid (C18:3) at 12.49 \(\pm\) 0.37 mg/g. Additionally, oleic acid (C18:1) constitutes 1.67 \(\pm\) 0.01 mg/g, while stearic acid (C18:0) accounts for 1.09 \(\pm\) 0.01 mg/g. Conversely, under heterotrophic conditions, the fatty acid profile shifts; palmitic acid remains significant at 28.01 \(\pm\) 0.77 mg/g, while alpha-linolenic acid was not determined. Oleic acid experiences a notable increase, reaching 32.89 \(\pm\) 0.10 mg/g. This variability highlights the influence of cultivation strategies on the lipid composition of _C. vulgaris_, which is essential for its applications in food, feed, health-promoting products, and even biofuels. The lipid profile of _C. vulgaris_ has garnered attention due to its associated health benefits, particularly its anti-inflammatory effects, which may help reduce chronic inflammation, and cardiovascular protective properties that can lower the risk of heart disease and improve overall heart health [31].
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 4 Evaluation
### Granularity Comparisons  
Granularity improvement was evaluated using a methodology similar to that of [PERSON] et al. [7], based on comparative analysis of fingerprint cardinality and feature dimensionality. The baseline ActiveTLS fingerprints were compared to the new enriched TLS-HTTP fingerprints, to quantify improvements across granularity and classification ability. The three datasets used in the active scan were comprised of the domains selected from those in Table 2. The percentage increases of distinct fingerprints across the datasets can be seen in Table 4, and they were calculated by (ActiveTLS fingerprint--enriched fingerprint/ActiveTLS fingerprint) \(\times\) 100. The calculation provided a measure of how much additional differentiation the HTTP header enrichment added to the ActiveTLS fingerprint. By comparing the baseline and enriched fingerprints, we could assess how much more uniquely each domain was represented, particularly in environments with TLS feature similarity, such as CDNs. The percentage range increase represented the data subsets within each of the three scans. For example, in the mixed host dataset, domains from the Tranco list saw the largest increase in distinct domains, increasing from 2638 TLS fingerprints to 7910 TLS-HTTP fingerprints.  
The largest increase in granularity was observed in the Cloudflare CDN dataset, where the improvements ranged from 66.7% to 4523.7%. This increase is noteworthy, given the similarity of TLS features in CDN environments. The results indicate that HTTP header enrichment is an effective addition that improves the ability to differentiate between domains with similar TLS configurations.  
The Mixed Host dataset showed improvements ranging from 4% to 199%, while maintaining the highest set of features at 2124. This broader range indicates that domains hosted outside CDN environments tend to exhibit greater TLS feature diversity, resulting in more varied impact from HTTP header enrichment.  
Finally, the Malicious dataset showed improvements ranging from 4% to 118%, with a feature dimensionality of 306. This aligned with the tendency of malicious applications to share similar deployment patterns and configurations, limiting the effect of enrichment.
### Background  
Parameters such as timestamp, gender, and other demographic features may also impact the SE in the real classroom environment. It has been observed that learning during different sessions such as morning or evening may also impact SE [22]. More precisely, it is found that studying in the morning positively impacts and studying in the evening negatively impacts the student's academic performance; however, these results were reported while considering only questionnaires or surveys but not the visual features and actions of students [24; 25; 26]. Therefore, the real-time objective assessment for such findings in a classroom environment still requires the attention of the researchers. Toidentify the impact of these parameters, firstly we need to detect SE in the classroom during lecture process; then, we can perform such analysis based on engagement [23; 27; 28]. There are several methods for measuring active state, but all these methods have constraints as mentioned above. Detecting timestamp/gender influence on students' affective state may help improve the learning experience for students in the future. So, it is important to analyze the affective states in the real classroom environment. Furthermore, objective assessment and analysis (using visual features and action space) may also provide strong evidence as compared to subjective tools (e.g., surveys or questionnaires). Therefore, in this paper, at first we collected the data of the students during lecture process in a classroom environment using high-definition cameras during different timestamps (morning and evening). Later, we extracted the relevant student data from the recordings into frames and further annotated it into two categories: (1) engaged and (2) non-engaged based on their emotional and behavioral patterns to train the model. Using the transfer-learning-assisted trained model, the affective states of students were computed, and subsequently, we computed the overall student affective score for engagement and non-engagement during the different sessions (morning and evening). Moreover, we also computed the student affective state score gender-wise and timestamp-wise. In the end, we compared their affective score in different timestamps and evaluated the results using inferential statistics. Thus, the primary objective is to compute the students' affective states in the real classroom environment and subsequently to analyze students' engagement gender-wise as well as in distinct timestamps. This work aims to respond to research questions including: How to compute the affective state of many students in the offline classroom environment? How can transfer learning assist to compute a model for the extraction of students' affective state? Whether male or female students remain more engaged during lecture process? Which time of day is better for scheduling classes having more male and female students? What are the takeaways of the underlying research in terms of policy recommendations?
## 6 Challenges and Future Research Directions
### Real-Time Realization under Resource Constraints for IoT Devices  
Most of the DDoS detection presented ignores real-time scenarios in an IoT environment to achieve the desired results. To solve real-time problems, modern smart devices have data processing abilities that may permit the use of a deep learning model to learn the processed data. Deep learning models demand that the machines be provided with adequate processing capacity, such as graphic processing units (GPUs), which consume a lot of power. IoT devices are well known for having limited power and size. In addition, the UAVs used in the IoT and IoD networks have very limited computational resources. As a result, the potential of deep learning cannot be fully leveraged, due to the long processing time and computational overhead that could deplete the power of IoT devices and other UAVs used in these networks. Even reducing the training time for GPU acceleration is difficult. Thus, maintaining security under these conditions is a major concern.
## 5 Prediction Performance of Tunneling Excavation Speed under Various Machine-Learning Algorithms
### Calculation Results  
Leveraging Python software for computation, a 70% portion is designated as the training set, and the remaining 30% serves as the test set. The error values for the six machine-learning algorithms are illustrated in Figure 15.  
The mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R\({}^{2}\)) are chosen as performance evaluation metrics for the prediction models across the different algorithms. The outcomes are presented in Table 12.  
Table 12 reveals that the RF model outperforms the others, displaying the lowest mean square error, root mean square error, average determination error, and the highest determination coefficient. This suggests that the RF model excels in predicting the tunneling excavation speed. Conversely, the SVM and MNR models exhibit poor performance, suggesting potential unsuitability for this prediction task. The performance of the BPNN, DNN, and ANN models is relatively close in each index but falls short of the RF model.
###### Abstract  
Full-duplex (FD) communication and spatial modulation (SM) are two promising techniques to achieve high spectral efficiency. Recent works in the literature have investigated the possibility of combining the FD mode with SM in the relay system to benefit their advantages. In this paper, we analyze the performance of the FD-SM decode-and-forward (DF) relay system and derive the closed-form expression for the symbol error probability (SEP). To tackle the residual self-interference (RSI) due to the FD mode at the relay, we propose a simple yet effective power allocation algorithm to compensate for the RSI impact and improve the system SEP performance. Both numerical and simulation results confirm the accuracy of the derived SEP expression and the efficacy of the proposed optimal power allocation.  
Keywords:spatial modulation; multiple-input multiple-output; full-duplex; self-interference cancellation; symbol error probability +
Footnote †: journal:
# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 5 Experimental Results
### Experiment 2: Specimen 1, Cavity Configuration 3
Since the highest temperature increase occurs in zone 4, a large amount of heat laterally diffuses to its surroundings. The impact of the  
Figure 9: PCT analysis; experiment 2. (**a**) EOF2. (**b**) EOF3. (**c**) EOF3. (**d**) EOF3. (**e**) EOF4. (**f**) EOF7. (**g**) EOF8. (**h**) EOF9. Heating up period for (**a**–**h**): 10, 10, 25, 40, 10, 25, and 40 s, respectively. lateral heat conduction can be clearly seen in Figure 11e. As a result, the temperature of zone 5 rises to a higher level than expected. Another interesting behavior of this zone is that its temperature gradually rises above zone 4 at around 70 s. This phenomenon can be described as follows. The high thermal effusivity of water causes quick cooling of zone 5 because of the absorption of a larger amount of energy. Over the course of heating and cooling, the internal energy of cavity 4 increases owing to the heat transfer from zone 4 to cavity 4. Part of this energy is transferred to cavity 5 due to the adjacency of zones 4 and 5 and the higher temperature of zone 4. As the heat capacity of water is a few times larger than that of air and soil, it can store a higher amount of heat energy. When the temperature of the surroundings drops below the water cavity, the water role becomes reverse and starts acting as a heat source until the system reaches the thermal equilibrium. The small ball (cavity 3) could not be detected owing to its small size. Although we could detect a deep cavity (cavity 1) using methods such as PCT and gradients, the temperature curves revealed that there is a very small temperature difference between zone 1 and the normal areas. Therefore, the reliability of this approach for detection of in-depth cavities is of concern. Figure 10: LPT analysis; experiment 2. (**a**) An example of the raw image. (**b–e**) After pre- and post-processing. (**f**) A sample thermogram for experiment 2 without any specimen for cavity configuration 3. (**g**) Thresholding. (**h**,**i**) Gradient of the image along the \(x\) and \(y\) direction: \(Gx=\frac{\partial I(x,y)}{\partial x}\) and \(Gy=\frac{\partial I(x,y)}{\partial y}\).
## 3 Material and Methods
### Antioxidant Potential of the Tested Herbal Mixtures
#### 3.6.2 ABTS Assay  
The ABTS assay, also known as the Trolox equivalent antioxidant capacity (TEAC) assay, compares the ability of antioxidants to scavenge ABTS in the aqueous phase to a Trolox standard [62]. The extracts were prepared at the concentration of 0.5 mg/mL in methanol, except for sample EDN, which was prepared at the concentration of 1 mg/mL. A Trolox stock solution of 1 mM in methanol was prepared, and 5 serial dilutions were prepared in the concentrations of 400, 350, 300, 250, 200, 150, 100, and 50 uM (Figure 3). The assay was carried out according to the method of [PERSON] [63], with minor modifications carried out in the microplates. Briefly, 192 mg of ABTS was dissolved in distilled water and transferred to a 50 mL volumetric flask, and then the volume was completed with distilled water. Then 1 mL of the previous solution was added to 17 uL of 140 mM potassium persulphate, and the mixture was left in the dark for 24 h. After that, 1 mL of the reaction mixture was completed to 50 mL with methanol to obtain the final ABTS dilution used in the assay. Then 190 uL of the freshly prepared ABTS reagent was mixed with 10 uL of the  
Figure 2: Vapor-phase minimum inhibitory concentration assay (VP-MIC). (**A**) Photo of the control plate showing the bacterial growth of the nine selected microorganisms. (**B**) Photo of the sample plate showing inhibition of bacterial growth. The numbers 1–9 on photos (**A**) and (**B**) correspond to the tested microorganisms as follows: 1. Methicillin-resistant _Staphylococcus aureus_ (MRSA USA300), 2. _Staphylococcus aureus_ Newman, 3. _Enterococcus faecalis_ ATCC19433, 4. _Klebsiella pneumoniae_ ATCC13883, 5. _Acinetobacter baumannii_ AB5075, 6. _Enterobacter cloaeae_, 7. _Escherichia coli_ ATCC87, 8. _Salmonella typli_ ATCC35664, and 9. _Pseudomonas aeruginosa_ PAO1. The arrow in photo B2 shows growth suppression of the tested microorganism. The VP-MIC is identified as the least concentration of the tested mixture that causes growth suppression of the tested microorganism when compared to the control.  
sample/compound in 96-well plates (\(n\) = 6), and the reaction was incubated at room temp. for 30 min in dark. At the end of the incubation time, the decrease in ABTS color intensity was measured at 734 nm. Data were represented as means \(\pm\) SD according to the following equation: percentage inhibition = ((average absorbance of blank \(-\) average absorbance of the test)/(average absorbance of blank)) \(\times\) 100.
## 3 Modeling
### Electro-Magnetic Subsystem  
The magnetic force is the driving force of the direct operating solenoid valve which is generated by the magnetic circuit when electric current is input. The magnetic circuit is formed of a fixed core surrounded by the coil turns and an armature connected to the spool which moves with the spool under the effect of the exerted magnetic force.  
According to [PERSON]'s Voltage Law, the following equation must be held for each loop in the magnetic circuit:  
\[\sum V_{m,n}=\sum\Phi_{n}\cdot R_{m,n}=0 \tag{1}\]  
where \(V_{m,n}\) is the magnetic voltage, \(\Phi_{n}\) is the magnetic flux, \(R_{m,n}\) is the magnetic resistance.  
With the definition of the magnetic flux, the following statement is applied to any connection between these components of the magnetic circuit:  
\[\sum\Phi_{i}=0 \tag{2}\]  
From the first equation of [PERSON], the electric field could be expressed as follows:  
\[\oint H\cdot ds=\theta \tag{3}\]  
Figure 3: (**a**) Structure of the direct operating solenoid valve (**b**) Working principle of the valve.  
where \(H\) is the magnetic field strength.  
The relationship between magnetic field and electric field is  
\[V_{m}=\theta=w\cdot i \tag{4}\]  
where \(w\) is the winding number, \(i\) is the electric current.  
In the electromagnetic field, the electric voltage difference \(V_{Ei}\) consists of the voltage drop and the induced voltage.  
\[V_{Ei}=R_{Ei}\cdot i+w\cdot\frac{d\phi}{dt} \tag{5}\]  
where \(R_{Ei}\) is the electric resistance.  
According to the following fundamental relations:  
The magnetic field intensity \(B\) is  
\[B=\frac{\Phi}{A_{a}} \tag{6}\]  
where \(A_{a}\) is the area of air gap.  
The magnetic field strength is  
\[H=\frac{V_{m}}{l} \tag{7}\]  
where \(l\) is the air gap length.  
The magnetic resistance \(R_{m}\) can be stated as  
\[R_{m}=\frac{l}{\mu_{0}\cdot A_{c}} \tag{8}\]  
where \(\mu_{0}\) is the permeability of vacuum.  
The cross-sectional area \(A_{c}\) is  
\[A_{c}=\frac{\pi}{4}\cdot d^{2} \tag{9}\]  
where \(d\) is the diameter of air gap.  
The magnetic voltage is  
\[V_{m}=R_{m}\cdot\Phi \tag{10}\]  
\[H=\frac{B}{\mu_{0}} \tag{11}\]  
Hence, the static magnetic force is  
\[F_{ms}=\frac{1}{2}\cdot\Phi_{Air}^{2}\cdot R_{m}=\frac{1}{2}\cdot\Phi_{Air}^{ 2}\cdot\frac{l}{\mu_{o}\cdot A} \tag{12}\]  
where \(\Phi_{Air}\) is magnetic flux of airgap.  
However, the air gap length will keep changing when the spool starts to move with the magnetic force. Thus, the dynamic magnetic force \(F_{md}\) is calculated as  
\[F_{md}=\frac{1}{2}\cdot\Phi_{Air}^{2}\cdot\frac{dR_{m}}{dl} \tag{13}\]