esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Analysis
### Reflection Power Spectrum
#### 3.2.1 Frequency Response of Reflection Power  
Figures 5 and 6 show the frequency response of time-averaged \(P_{T}(t,k|\mathbf{y}_{o})\) for different source positions in Room-1 and Room-2, respectively. These figures provide a clear view of the low-frequency peaks and the decay of power towards the higher frequencies. In Room-1, we can observe high powers around 164 Hz, 211 Hz, and 281 Hz before the onset of the power decay. Compared to Figure 2a, 164 Hz and 211 Hz are closer to the theoretical room modes, whereas many other predicted modes do not appear in the observed response in Figure 5. Similarly, some of the observed peaks in Room-2 around 164 Hz, 304 Hz, 328 Hz, and 492 Hz vary from the theoretical room mode estimates shown in Figure 2b. Additionally, the identification of \(\
u_{S}\) is difficult from these responses, but is clearly greater than the predicted \(\
u_{S}\) values mentioned in Section 3.1.2. This error is caused by the approximation in (16) by use of frequency-averaged \(T_{60}\) and from the influence of source directivity.  
Figure 4: Reflection power response of Room-2 for different source positions.  
Figure 3: Reflection power response of Room-1 for different source positions.  
It should also be noted that there are no substantial variations in the frequency response of Room-2 for different source positions. Additionally, in Room-1, the differences are not drastic as should be expected in a smaller room with significant reverberation. This is the result of the formulation of reflection gains with respect to a common listening position (\(O\)) and the separation of the direct path component from the reflections. A direct analysis of the frequency response of RIR will show significant differences with the change in source positions. Therefore, the proposed technique can be used to predict the room response behavior independent of the source positions.
## Materials and Methods
### Analysis  
Analytical thin-layer chromatography (TLC) was performed on silica gel-coated aluminum plates (DC-Alufolien Kieselgel 60 F254, Merck, Darmstadt, Germany) with a mixture of hexane and acetone in various ratios. Compounds were detected by spraying the plates with 20% ethanolic H\({}_{2}\)SO\({}_{4}\) containing 0.1% of anisaldehyde, followed by heating to 120 \({}^{\circ}\)C. Preparative column chromatographywas performed on silica gel (Kieselgel 60, 230-400 mesh ASTM, Merck) with a mixture of hexane and acetone (20:1) as the eluent. Gas chromatography (GC) analysis was carried out on an Agilent Technologies 6890N instrument (H\({}_{2}\) as the gas carrier) using a DB-17 column (cross-linked methyl silicone gum, 30 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m) and the following temperature program: 110 \({}^{\circ}\)C for 7 min, 30 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C (hold for 1 min); injector temperature 200 \({}^{\circ}\)C; detector temperature 280 \({}^{\circ}\)C, or on an Varian CP3380 instrument (H\({}_{2}\) as gas carrier) using the capillary columns of Thermo TR-5 (30 m \(\times\) 0.32 mm \(\times\) 1.0 \(\upmu\)m) and the following temperature program: 115 \({}^{\circ}\)C for 7 min, 3 \({}^{\circ}\)C/min to 120 \({}^{\circ}\)C, 30 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C (hold for 1 min); injector temperature 200 \({}^{\circ}\)C; and detector temperature 300 \({}^{\circ}\)C. The structures of the obtained compounds were also confirmed by gas chromatography-mass spectrometry (GC-MS) analysis using a Varian SATURN 2000 instrument (electron ionization (EI), He as the carrier gas) with an HP-1 column (cross-linked methyl silicone gum, 25 m \(\times\) 0.32 mm \(\times\) 0.52 \(\upmu\)m) under the following conditions: injector 200 \({}^{\circ}\)C, detector 300 \({}^{\circ}\)C, column temperature: 120 \({}^{\circ}\)C (hold for 2 min), 120-300 \({}^{\circ}\)C (rate 20 \({}^{\circ}\)C/min), 300 \({}^{\circ}\)C (hold for 3 min).  
Optical rotations were determined on a P-2000 polarimeter (Jasco) in chloroform solutions, and concentrations are denoted in g/100 mL.  
NMR spectra were recorded in a CDCl\({}_{3}\) solution on a Bruker AdvanceTM 600 MHz spectrometer. IR spectra were recorded on a Thermo-Nicolet IR300 FT-IR spectrometer.
# Design of Conical Foundations with Increased Bearing Capacity in Areas of Undermined Soils
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Foundation Model Type**} & \multirow{2}{*}{**Horizontal Tensile Strains of**} & \multicolumn{6}{c}{**Settlement under Load, mm**} \\  & & **Soil, mm/m** & **0.1 kN** & **0.12 kN** & **0.14 kN** & **0.16 N** & **0.18 kN** & **0.2 kN** \\ \hline \multirow{3}{*}{Conical foundation with an aperture angle of 90\({}^{\circ}\)} & 0 & 0.77 & 1.21 & 2.03 & 3.60 & 5.82 & 8.96 \\  & 3 & 1.05 & 1.72 & 2.62 & 4.43 & 7.05 & 10.38 \\  & 6 & 1.35 & 2.10 & 3.23 & 5.12 & 8.02 & 11.51 \\  & 9 & 1.58 & 2.40 & 3.71 & 5.85 & 9.00 & 12.62 \\ \hline \multirow{3}{*}{Conical foundation with an aperture angle of 80\({}^{\circ}\)} & 0 & 0.82 & 1.48 & 2.71 & 4.64 & 7.40 & 11.50 \\  & 3 & 1.05 & 1.82 & 3.28 & 5.62 & 8.72 & 12.91 \\ \cline{1-1}  & 6 & 1.35 & 2.23 & 3.75 & 6.32 & 9.84 & 14.08 \\ \cline{1-1}  & 9 & 1.61 & 2.58 & 4.42 & 7.21 & 10.53 & 15.15 \\ \hline \multirow{3}{*}{Isolated shallow foundation} & 0 & 1.30 & 2.32 & 4.44 & - & - & - \\  & 3 & 1.53 & 2.71 & 5.25 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the laboratory modeling for loading–settlement interaction. Figure 14: Results of full-scale loading tests: 1, 3—curves obtained for horizontal tensile strains \(\epsilon=0\), conical foundations with an aperture angle of 90\({}^{\circ}\) and 80\({}^{\circ}\); 2, 4—curves obtained for horizontal tensile strains \(\epsilon=2.6\) mm/m, conical foundations with an aperture angle of 90\({}^{\circ}\) and 80\({}^{\circ}\); 5, 6—curves obtained for horizontal tensile strains 0 and 2.6 mm/m for isolated shallow foundations. At the same time, a failure was observed for columnar foundations with a load of more than 80 kN. With horizontal tensile deformations \(\epsilon=2.6\times 10^{-3}\) m, the bearing capacity is reduced by 13-14% for conical foundations and by 8% for columnar-type foundation, although superiority in the load-bearing capacity of conical foundations is obvious already at a load of 50 kN. As the load rose, the increase in the cross-section of foundations was proportionally enhancing its load-bearing capacity. The results of the numerical modeling are presented in Table 6. The total settlement for conical foundation with an aperture angle of 90\({}^{\circ}\) was 9.54 mm under a load of 0.2 kN at the first stage (without horizontal displacement of soils). Horizontal displacement of the soil by 3 mm led to a decrease in bearing capacity by approximately 0.008-0.010 kN for conical models of foundation. Each further displacement of the soil led to a decrease in bearing capacity by approximately 0.006-0.008 kN both for conical models with an aperture angle of 90\({}^{\circ}\) and 80\({}^{\circ}\). Numerical methods confirmed the stability of a conical foundation under horizontal soil displacements compared to isolated shallow foundations with the same cross-sectional area in contact with the ground surface. The discrepancies between settlements during laboratory tests and numerical modeling were no more than 9%.
## 3 The Proposed Method
### Edge Detection  
To extract sufficient important information from an image, the edge and texture information needs to be detected. SVD is good at classifying texture information. Edge detection involves extracting object edges, a task at which the Canny operator [21] achieves the best effect. Therefore, we apply these two methods to generate watermarks. The calculation process can be summarized into four steps.  
* Step 1. Convolve a Gaussian function \(G(x,y)\) and the original image \(f(x,y)\) to obtain a smoothed image \(f_{\text{s}}(x,y)\), as shown in Equations (3) and (4), respectively: \[G(x,y)=\text{e}^{-\frac{x^{2}+y^{2}}{2\omega^{2}}},\] (3) \[f_{\text{s}}(x,y)=G(x,y)*f(x,y),\] (4) where the default value of \(\sigma^{2}\) is 2, \((x,y)\) represents the pixel position and the \(*\) operator represents a convolution operation.
* Step 2. Compute the gradient magnitude image \(M(x,y)\) and angle images \(\theta(x,y)\), shown in Equations (5) and (6), respectively: \[M(x,y)=\sqrt{g_{x}^{2}+g_{y}^{2}},\] (5) \[\theta(x,y)=\arctan[g_{y}/g_{x}],\] (6) where \(g_{x}\) and \(g_{y}\) represent the partial derivations of image \(f_{\text{s}}(x,y)\) to \(x\) and \(y\), respectively.
* Step 3. Apply non-maximum suppression to the gradient magnitude image.
* Step 4. Use double threshold and connectivity analysis to detect and link the edges.
# A Modified Bio-Ecological Process for Rural Wastewater Treatment
## 3 Results and Discussion
### Optimization of the Anoxic Tank Configuration  
The flow ratio from the oxic unit to that from the anaerobic reactor in the anoxic unit is called the reflux ratio. This ratio determines the carbon source and nitrate quantity in the anoxic tank, and both these parameters can affect the denitrification process [40]. Therefore, the reflux ratio is one of the most significant parameters affecting the treatment efficiency. The reflux ratio was adjusted to 25%, 50%,  
Figure 4: Effects of organic loading rate (OLR) on the removal rates of COD under HRT on 3 h in ABR.  
Figure 3: COD removal efficiencies in different compartments of ABR under different HRTs. Comparment I; Comparment II; Comparment III; Comparment IV; Comparment V.  
100%, and 150%, which resulted in HRTs in the aerobic unit of 8 h, 6.5 h, 5 h, and 3.5 h, respectively. The removal efficiencies of COD, NH4-N, and TN were also investigated (Figure 5).  
The average ABR influuent concentrations of COD and NO3-N were 85.2-92.3 mg/L\({}^{-1}\) and 39.2-40.5 mg/L\({}^{-1}\), respectively, while the influuents to the oxic unit were 25.5-54.7 mg/L\({}^{-1}\) and 2.1-3.25 mg/L\({}^{-1}\), respectively. Increasing the reflux ratio increased the removal efficiency. When the reflux ratio was greater than 100%, the influence on TN removal was more significant than that for the removal of COD and NH4-N. TN removal in the anoxic tank was largely dependent on the concentration of NO3-N, which was mainly drained from the oxic unit [41].  
Figure 5: (a) Reflux ratio effect on COD removal in anoxic tank. (a) anaerobic inflow; (b) Reflux ratio effect on NH4-N removal in anoxic tank. (b) anaerobic ammonia; (c) anoxic ammonia; (d) anaerobic nitrate; (b) anoxic nitrate; (c) Reflux ratio effect on TN removal in anoxic tank. (a) anaerobic flow; (a) anoxic effluent; (b)oxic flow; (b) TN removal.  
At the reflux ratios of 25%, 50%, 100%, and 150%, TN removal rates were 22.5%, 27.5%, 40.1%, and 42.5%, respectively. For 25% to 100% reflux ratios, the TN removal rate increased significantly, but when the reflux ratio increased to 150%, the increase in TN removal efficiency was not remarkable. This result may be caused by nitrogen limitation in the anoxic zone [35]. Therefore, a reflux ratio of 100% was optimal.
## 2 Materials and Methods
### Chemicals  
The 39 external standards and 1 internal standard were purchased from 4 different companies. For example, 2-methyl-1-butanol (\(\geq\)98.0%), 1-hexanol (\(\geq\)98.0%), butyl propionate (\(\geq\)98.5%), pentyl acetate (\(\geq\)98.5%) and naphthalene (\(\geq\)99.0%) were provided by J&K Chemical Ltd. (Beijing, China). butyl octanoate (>99.0%), hexanal (>98.0%), ethyl butyrate (>98.0%), butyl acetate (>99.0%), _E_-2-hexanal (>97.0%), ethyl 2-methylbutyrate (>98.0%), D-limonene (>99.0%), 1-heptanol (>98.0%), butyl butyrate (>99.0%), ethyl hexanoate (>99.0%), hexyl acetate (>99.0%), 2-ethylhexanol (\(\geq\)99.0%), 1-octanol (>99.5%), propyl hexanoate (>98.0%), linalool (>96.0%), nonanal (>95.0%), hexyl propionate (>98.0%), hexyl hexaoate (>98.0%), hexyl butyrate (>98.0%) and 2-octanol (>98.0%) were obtained from TCI Development Co., Ltd. (Shanghai, China). _Z_-3-hexenyl acetate (\(\geq\)97.0%), pentyl butyrate (\(\geq\)98.0%), hexyl-butenoate (\(\geq\)97.0%), hexyl octanoate (\(\geq\)97.0%), _E_-2-hexen1-ol (\(\geq\)95.5%), butyl hexanoate(\(\geq\)98.0%), ethyl octanoate (>99.0%), anethole (>98.0%), dodecanol (>99.0%), pentyl acetate (>98.0%) and _E_-2-nonneal (\(\geq\)95.0%) were gained from H&K Flavor (Shanghai, China). 6-methyl-5-hepten-2-one (>98.5%), butyl 3-methylbutanoate (\(\geq\)98.0%), hexyl 2-methylbutanoate (\(\geq\)98.5%) and estragcide (\(\geq\)98.0%) were obtained from Yuanpeng Flavor & Fragrance Group Co., Ltd. (Tianjin, China). _n_-alkane series (C\({}_{7}\)-C\({}_{30}\)) were purchased from Sigma-Aldrich, St. Louis, MO, USA. All the chemicals mentioned above were GC grade. In addition, naphthalene was in crystal flake form, and was dissolved in methanol before use, others were in solution form. Other chemicals, such as sodium chloride and methanol were purchased from Sinopharm Group Chemical Reagent Co., Ltd. (Shanghai, China).
# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 2 Materials and Methods
### UCE Preprocessing Techniques
#### 2.3.4 FFT Window Length  
A thorough description of the FFT and the concept of windowing time-domain signals can be found in a book by [PERSON] and [PERSON] [17]. An example equation of window application is shown in Equation (4).  
\[y(n)=x(n)w(n),\ \ 0\leq n\leq(N-1), \tag{4}\]  
where \(x(n)\) represents the time-domain samples being windowed and \(w(n)\) represents the windowing coefficients being applied. This window application is performed over a finite interval length \(N\). Since increasing \(N\) results in higher resolution in the frequency domain, one set of experiments varying \(N\) was performed to determine the effects of the additional spectral resolution. Increasing spectral resolution should improve classification results until the resolution exceeds the spectral width of the features determined by the classifier. The noise floor also drops about 3 dB for each doubling of the value of \(N\). On the down side, larger values of \(N\) results in lower resolution in the time-domain. Therefore, an important assumption in this trade-space evaluation and feature hypothesis generation (limited explainability attempt for the ResNet classifier) is the assumption that \(N\) will not be chosen to exceed the short-term stationarity of the features and noise being used for classification of the devices. A rectangular uniform window was used for the window length experiment. The amount of data used for training, validation and testing was the same regardless of the window length used (smaller window lengths produced more machine learning samples).
## 4 Industry Perception Study
### Feedback about Ability of DePaaS to Address SDP Challenges
#### 4.3.4 Solving SDP Problems Related to Building SDP Models  
The sample pool was briefed about problems related to building SDP models and was asked whether DePaaS could help solve those problems.  
Respondent data indicates that DePaaS could help enforcing a robust SDP model building methodology (56% positive, \(\chi^{2}\) = 6.55, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0105) and comparing performance of multiple SDP models (60% positive, \(\chi^{2}\) = 6.76, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0093). Respondents believed that the model improvement program of DePaaS could collect and analyse model performance which might help consistent performance of SDP models across multiple runs (58% positive) and across models (72% positive). There was no statistical evidence to support either of these beliefs (\(\chi^{2}\) = 3.00, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0833 and \(\chi^{2}\) = 9.00, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0027, respectively).  
Respondent data indicates that DePaaS may not help improving prediction accuracy (58% negative, \(\chi^{2}\) = 4.84, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0278) or incorporating security specific models (63% negative, \(\chi^{2}\) = 11.64, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0006). Respondent data indicates that DePaaS could promote use of SBT and HBT models (\(\chi^{2}\) = 3.85, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0499).
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 4 Analysis and Discussion
### Analysis B
#### 4.2.2 Indirect Influence Factor Group
According to the basic concept of the applied interaction matrix, the row of the subject factor represents the causes it has on other factors, and the sum of the rows of the matrix indicates its causes on the entire system. Similarly, the column of the factor represents the influence of other factors, corresponding to the effects. The sum of the columns, therefore, represents the effects on the entire system [49; 52]. Based on these concepts, a representative value can be assessed to express the degree of influence of each selected factor on the location. The representative value is calculated as follows. First, the rows and columns of the interaction matrix are added so that each factor represents the cause and effect of the influence on the entire system. The degree of influence of each factor (i) on the entire system as a cause is denoted by \(\mathrm{C_{pi}}\), and as an effect by \(\mathrm{E_{pi}}\). In Figure 13, \(\mathrm{C_{pi}}\) is indicated on the right of each row heading and \(\mathrm{E_{pi}}\) is indicated below each column heading. The two categories can be expressed as Equation (2)  
\[ \begin{array}{l}C_{pi}=\sum\limits_{n=1}^{n_{max}}(I_{nn}),\ m=i\\ E_{pi}=\sum\limits_{n=1}^{n_{max}}(I_{nn}),\ n=i\end{array}  \tag{2}\]  
Figure 12: ANN structure for the analysis of indirect influence factors. Figure 13: Interaction matrix for (**a**) coal mines and (**b**) other types of mines. where \(\rm I_{mn}\) represents each component of the m \(\times\) n interaction matrix, \(\rm n_{max}\) represents the total number of columns, and \(\rm m_{max}\) represents the total number of rows. Consequently, the interaction matrix must be able to comprehensively express the links among the influence factors of the given location related to subsidence. The influence weights assigned to each factor are expressed as a percentage of the sum of the system's cause (\(\rm C_{pi}\)) and effect (\(\rm E_{pi}\)) according to the following equation. The total sums of causes and effects are expressed as Equation (3)  
\[W_{i}=\frac{\left(C_{pi}+E_{pi}\right)}{\left(\sum\ C_{pi}+\sum\ P_{pi}\right)} \times\frac{100}{N_{grade}} \tag{3}\]  
where \(N_{grade}\) is the number of grades assigned to the factor i. Figure 14 provides the influence weights for the five influence factors calculated from Equation 3. The results show that the most sensitive factor affecting the subsidence for both types of mines was RMR, while the load type and groundwater level were least sensitive for subsidence for coal mines and other types of mines, respectively. Accordingly, the subsidence risk for indirect influence factors can be defined as  
\[\rm Subsidence\;risk\;for\;indirect\;factors\;(F_{I})=\ \sum W_{i}\cdot V_{i} \tag{4}\]  
where \(\rm V_{i}\) denotes the normalized value for each factor i at the location in question, as indicated in Section 4.1.1. The final possible \(\rm F_{I}\) ranges from 0 to 100.
## 4 Proposed Secure Search Algorithm
### Security Threat Model  
In this section, we perform threat modeling to identify the different threats and attack techniques that can be used against this PoC environment. We used one of the most known threat models, called STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service (DoS), and elevation of privilege). STRIDE focuses on identifying the following threats and their affected security property, as shown in Table 3[45]. STRIDE is generally used to categorize and identify threat vectors in threat modeling. It has been examined in the past to be used along with the industry-known MITRE ATT&CK framework to identify threats presented as tactics, techniques and procedures (TTPs). There are two types of threats which target different industries and sectors; internal threats and external threats. Internal threats exist within organizations' trusted boundaries. Internal threat sources can be categorized as follows:  
1. Intended malicious insider: intent to affect the confidentiality, integrity, and/or availability of systems and data.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Parameters** & **Details** \\ \hline Dataset size & 100 number of blocks + PHR \\ Hardware & GPU-enabled system \\ Software & Ethereum, Hyperledger Fabric \\ Parameters & Block height, number of blocks, no. transac, no. PHR, delay, signature creation \\ Performance metric & Efficiency (average percentage of gas, no. packets, no. dead nodes, no. alive nodes), \\  & security (execution time of policies) and cost (execution time of blocks) \\ Number of simulations & Number of test performed on single dataset \\ Number of rounds or transactions & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation setup, configurations, and specifications.  
Unintended innocent insider: person working for the organization making a human error in their day-to-day duties.
* Compromised insider: involves compromising an employee's user account due to the lack of security awareness from sources such as phishing and trojans.
* External threats are generated from the exploitation of internal vulnerabilities to assist attackers to gain access to environments. The attack sources can be categorized as follows:
* Malicious actor.
* Compromised supply chain.
* External insider threats.  
Using the STRIDE model, there are a few external and internal threats that pose risks to this solution. This consists of the following:  
1. Internal threats
* External threat attacks  
T1--A malicious insider can gain access to all the interactions occurring in a network through nodes and store them for malicious purposes [46].  
T2--A node can be compromised by a malicious actor. This can expose all the blockchain and allow the actor to get the full transaction and blockchain history continuously.  
T3--A malicious attacker can try to find some vulnerabilities in existing deployed smart contract.  
T4--A malicious attacker may find some sensitive/secret information exposed in the transactions and/or the smart contracts such as private credentials [45; 47; 48].