esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Antibacterial Polymer Coatings
## 2 Antibacterial Coatings
### Coatings That Kill Bacteria upon Contact
#### 2.2.1 Contact-Active Surfaces Containing Biomimetic Polymers  
An effective contact-active antibacterial coating is one based on chitosan, which is a natural cationic and biocompatible polysaccharide made up of N-acetylglucosamine and D-glucosamine. Chitosan is an environmentally friendly biocide because its antibacterial activity can be modified by modifying its positively charged amino groups and integrating them into suitable polymers. However, the chitosan-coated surfaces often lose their antibacterial activity as a result of covering the contact surface with dead microbial cells during their biocidal action. Their activity can be restored by washing these surfaces with cationic detergents [48].  
To enhance the antibacterial properties of chitosan, nanotechnology can be employed. For example, to modify the membrane surface of poly(ether sulfone) (PES), chitosan-based nanogels loaded with silver nanoparticles have been synthesized. The layer-by-layer deposition of these nanogels and PSS onto the membrane surface results in a coating with long-term antibacterial activity against _E. coli_ and _S. aureus_ due to the release of silver ions from the LbL coating. This approach combines the properties of chitosan as a polycationic biocide with the long-lasting antibacterial effect of silver nanoparticles, providing an effective solution for antibacterial coatings [49].  
Another example of combining chitosan with silver nanoparticles is the development of a coating based on dopamine-modified alginate and chitosan, which is deposited layer by layer onto a titanium alloy surface. This method allows the loading of silver ions and the production of silver nanoparticles. The obtained coatings demonstrated pronounced antibacterial activity against _E. coli_ and _S. aureus_ bacterial strains [50].  
An alternative category of antibacterial agents that can be applied to surfaces using polymer brushes is antimicrobial peptides (AMPs). Examples of AMPs include maganin and defensin, along with their derivatives, which can be incorporated into contact-active surfaces. The mechanism of action for AMPs is primarily based on two properties: first, their rigid backbone and, second, the arrangement of hydrophobic and cationic side groups. These characteristics enable the AMP to penetrate and disrupt the cell membrane effectively.  
One example of the successful application of antimicrobial peptides is the use of nisin, a low-molecular-weight antibacterial peptide (3500 g/mol). It is covalently bonded in a layer-by-layer (LbL) system, achieved by the covalent attachment of synthetic oxidized poly(3,4-dihydroxyphenylalanine) (Pox(mDOPA)) and poly(allylamine hydrochloride) (PAH) on stainless steel, as illustrated in Figure 3. This LbL coating, which stably immobilizes nisin, demonstrated a durable and effective antibacterial effect against Bacillus subtilis through contact killing [51].
# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
## 2 Related Work
The ongoing challenge of dysphonia voice research is the small size of the database produced by [PERSON] and [PERSON] [40]. It is very complicated to use more advanced deep learning methods without underfitting or overfitting. They proposed an adaptive method utilized to break down a signal into its components employing a Fourier-based sychrosqueezing change (FSST) for information enlargement and change. The 2D TF representation output becomes the input to CNN. It is clear that each voice disorder produces distinctive frequencies depending on the sort of voice disorder and its area on the vocal folds, as we observed. Thus, monitoring the frequency groups is exceptionally vital to evaluate which one contributes more to the discovery and classification of voice afflictions. For example, [PERSON] et al. [41] stated that lower frequencies (3000 Hz) are more reasonable for recognizing dysphonic voices than higher frequencies. Furthermore, [PERSON] et al. [42] demonstrated that the control of dysphonic voice flags is altogether less steady within the recurrence area between 2000 and 6400 Hz than the other recurrence regions. To discuss the outcomes of a comparative literature review, they analysed voice records of maintained phonation of the vowel/a/ as well. However, in contrast to past studies, we analyze a bigger database collected from SVD [1]. Moreover, to propose systems capable of powerful voice pathology detection and classification, we do not confine the database as if it were a subset of popular voice pathologies. In this study, the database includes an expansive number of pathologies with small recordings. As we observed in the related works, in spite of past work [1], no other studies have utilized deep learning methods for voice pathology identification. In the following sections we utilize a robust voice pathology identification model based on the acoustic feature extraction strategy. We use voice pathology detection and identification utilizing a CNN approach. We utilize the transfer learning method for using the current powerful CNN models. Particularly, the ResNet34 models were used. To handle the issue of inadequate distribution of an assortment of voice pathologies with few recordings in the datasets, we also explore the utilization of abnormality detection methods.
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Security Challenges in UIoT  
#### 4.2.1 Methods to Prevent DoS Attacks  
The existing techniques to prevent DoS attacks in UIoT are discussed herewith. In [107], [PERSON] et al. proposed a cautious calculation that checks the potential DoS attack. This approach breaks down centered and broadcasted DoS attacks to initially distinguish the attack and create pushback alerts or choke the malicious nodes as they enter the UIoT networks. Data entropy is a proportion of the vulnerability related to an irregular variable. It tends to be deciphered as the normal most limited message length in bits that can send an irregular variable to a recipient [108]. Entropy can be determined by figuring a progression of constant bundles. The entropy esteem gives a depiction of the comparing arbitrary appropriation of these sources IP addresses. The bigger the entropy, the more irregular the source IP. The more modest the entropy, the smaller the dispersion scope of the source IP locations of the parcels, and a few locations have a genuinely high likelihood of an event. The expression for calculating the entropy is shown below:  
\[E=-\sum_{k=1}^{Tn}=p_{k}\ log_{2}\ p_{k}\]  
Here \(p_{k}\) is the possible outcome probability, \(Tn\) is the number of packets analyzed, and \(E\) is the entropy.
## 3 Results  
### Analysis of Clusters  
#### 3.1.1 Clusters by Year and Size Distribution  
The outages listed in Table 1 are grouped together into clusters as summarized in Table 2. The inclusion of automatic outages for all TADS elements allows the capture of more nearby overlapping outages and a better evaluation of their risks to dynamic stability and resilience of the transmission system. As mentioned in Section 2.2.2, the clusters contain 21.6% of all automatic outages, indicating how common this type of outage and their clusters are for the North American BPS.  
Table 2 indicates that with an exception of the year 2014, the number of clusters in North America stayed consistent during the study period. In 2014 the number was significantly lower, and the largest cluster contained only seven outages. Overall, most clusters (76%) consist of two outages, with several outliers (clusters with sizes between 11 and 18). The average size of a cluster equals 2.4 outages. An empirical distribution of the cluster size is illustrated in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Voltage Class** & **AC Circuit** & **AC/DC BTB Converter** & **DC Circuit** & **Transformer** \\ \hline
200–299 kV & 3034 & 12 & 17 & 604 \\
300–399 kV & 1621 & 26 & & 403 \\
400–499 kV * & & & 37 & \\
400–599 kV & 594 & & & 389 \\
500–599 kV * & & & 39 & \\
600–799 kV & 123 & & & 43 \\
**Grand Total** & 5372 & 38 & 93 & 1439 \\ \hline \hline \end{tabular}  
* Voltage class for DC circuits only.  
\end{table}
Table 1: Transmission Availability Data System (TADS) automatic outages in clusters by element type and voltage class.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cluster Size** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** & **2013–2019** \\ \hline
2 & 290 & 249 & 340 & 320 & 336 & 334 & 349 &
## 3 Analysis of Strain Transfer Influential Factors
### The Influence of Temperature
Besides, since the thermal expansion coefficients of silica core, coating, adhesive layer, and CFRP laminates are different, the temperature changes can generate residual stress in the measurement structure, which will cause the distortion of FBG sensor spectra [18]. To avoid this, the installation of FBG sensors and their strain measurement experiments should be kept in the same temperature environment.
## 3 Results and Discussion
### Test on the Durability of Stabilized Soft Soil  
#### 3.3.1 Water Resistance Test  
The water stability coefficient of stabilized soft soil measures the strength loss rate after soaking in water following curing to a certain age. A higher water stability coefficient indicates a better water resistance of the stabilized soil. In the construction of soft foundations, the water table is typically high year-round. Additionally, the permeability coefficient of soft soil is low, meaning that water takes a long time to discharge from the soil. As a result, pile foundations may be submerged by groundwater for an extended period after construction. If the water stability of the stabilized soft soil is poor upon contact with water, it will not only affect its compressive strength but also compromise the quality and lifespan of the foundation. The water stability properties of cement soil GC and stabilized soils G4 and G7 after curing for 28 days were tested using an immersion test. The ratio of the compressive strength of the sample after soaking to the compressive strength of the 28-day reference sample was used to determine the water resistance coefficient. Figure 6 shows the curve of the compressive strength of stabilized soft soil over time during immersion, and Table 6 presents the water resistance coefficient of the stabilized soft soil. The test used stabilized soft soil mixed with seawater, with a water content of 65% and a content ratio of 20%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Coefficient of Water Stability**} \\ \cline{2-4}  & **Soaking 14 d** & **Submerged for 28 d** & **Submerged for 60 d** \\ \hline GC & 105.02\% & 103.88\% & 114.61\% \\ G4 & 113.64\% & 107.32\% & 119.80\% \\ G7 & 108.91\% & 101.72\% & 112.79\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Water stability coefficient of stabilized soft soil.  
Figure 6: Curve of compressive strength of stabilized soft soil with soaking time.  
As shown in Figure 6, with the increase in soaking days, the compressive strength of cement soil GC and stabilized soils G4 and G7 slightly decreased during the early soaking stage but then steadily increased. After 14 days of soaking, the compressive strengths of GC, G4, and G7 were 4.05 MPa, 7.99 MPa, and 8.19 MPa, respectively, with water stability coefficients of 105.02%, 113.64%, and 108.91%. After 28 days of soaking, the compressive strengths of GC, G4, and G7 decreased to 4.01 MPa, 7.54 MPa, and 7.65 MPa, respectively. However, after 60 days of soaking, the compressive strengths of GC, G4, and G7 increased to 4.42 MPa, 8.42 MPa, and 8.48 MPa, respectively, with water stability coefficients of 114.61%, 119.80%, and 112.79%.  
The samples demonstrated good water resistance, with both cement soil and stabilized soil (as shown in Figure 7) remaining intact and not disintegrating after long-term immersion.
# A Dynamic Risk Assessment and Mitigation Model
## 5 Case Study
### Risk Scenarios Creation  
In all scenarios, it is assumed that the attackers' ultimate goal is to reach and exploit the vulnerability located in the database. Consequently, all possible attacker movements are directed toward achieving this objective. Given that the scenario involves an asset (Workstation-WS) associated with two CVEs, two distinct risk scenarios are generated for these CVEs. Additionally, both the VPN and the Web Server act as potential entry points from the attacker's perspective, leading to the generation of two distinct risk scenarios. Therefore, a total of four distinct risk scenarios are identified for this case study. For simplicity, CVE-2017-0143 is mapped to WS1, and CVE-2017-8692 is mapped to WS2. Every risk scenario represents a possible pathway for an attacker to traverse the target environment to reach the database. The resulting risk scenarios (RS) are described below, with each scenario representing a unique potential attack:  
1. RS1: VPN--WS1--DB
2. RS2: VPN--WS2--DB
3. RS3: Web5--WS1--DB
4. RS4: Web5--WS2--DB
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Damson
#### 3.3.1 Scope and Objectives  
The main objective of the DAMSON project was to collect a large database of measurements over many months at high and mid-latitudes to quantify Doppler spreads, Doppler shifts, multipath delay and signal strengths. These database allowed to fulfill other major objectives: (a) Assessment of HF data modem waveforms: this information will be used for the specification of robust modulation scheme on high-latitudes paths, (b) Updating Propagation Prediction codes: the conducted measurements will derive rules that will be incorporated in propagation predictions, especially on polar-cap and auroral links, and (c) Application to HF Simulators: the measurements will be used to specify HF simulator characteristics to test the communication systems and modems, until the beginning of this project simulators were based on the Watterson model [34].
# Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment
## 3 Communication Model for Microservices-Based IoT Applications
### Processes for Edge Broker Clustering and Topic Channel Routing
As reported previously [28], in order to minimize the broker factors the event channel factors are set as constants and the derivative of (2) is taken to calculate \(\mathbf{x}_{b}\):  
\[\mathbf{x}_{b}=\Big{(}\mathbf{Y}^{T}\mathbf{Y}+\mathbf{Y}^{T}(\mathbf{C}^{b}-\mathbf{I})\mathbf{Y}+\lambda \mathbf{I}\Big{)}^{-1}\mathbf{Y}^{T}\mathbf{C}^{b}\mathbf{p}(b). \tag{3}\]  
Alternately, to minimize the event channel factors, the broker factor vectors are made constant and the derivative of (2) is taken to calculate \(\mathbf{y}_{e}\):  
\[\mathbf{y}_{e}=\Big{(}\mathbf{X}^{T}\mathbf{X}+\mathbf{X}^{T}(\mathbf{C}^{e}-\mathbf{I})\mathbf{X}+\lambda \mathbf{I}\Big{)}^{-1}\mathbf{X}^{T}\mathbf{C}^{e}\mathbf{p}(e). \tag{4}\]  
The loss function (2) can be minimized by computing (3) and (4) repeatedly in alternating fashion until reaching convergence. This yields two matrices, \(\mathbf{B}\) and \(\mathbf{E}\), which are referred to as the \"broker factors matrix\" and \"event channel factors matrix\", respectively. With these matrices, similar top brokers for each pub/sub broker \(b\) can be recommended based on their similarity scores. The similarity between brokers is calculated by computing the dot-product between the broker-factors vectors and their transpose:  
\[\textit{broker\_similarity\_scores}=\Big{(}\mathbf{B}\cdot\mathbf{B}_{b}^{T}\Big{)}. \tag{5}\]  
Third, with the recommended broker information provided, a broker utility value is calculated for each pair of brokers to build a broker-based utility matrix for further clustering. In this step, further constraints can be applied while considering potential neighbors for each broker, such as nearby geolocations (i.e., \(MAX\_DISTANCE=200\) km) and upper limit node degrees (i.e., \(\textit{UPPER\_LIMIT}=30\)). A proposed utility function from [29] was adapted to calculate the broker utility value for brokers \(b_{i},b_{j}\):  
\[\textit{broker\_utility}(b_{i},b_{j})=\frac{1}{distance(b_{i},b_{j})}*\frac{ \sum\limits_{e\in e\in e(b_{i})/even(b_{j})}\textit{erate}(e)}{\sum\limits_{e \in e(e(b_{i}))/even(b_{j})}\textit{erate}(e)} \tag{6}\]  
In (6), \(distance(b_{i},b_{j})\) is the geographical distance between the two brokers (coordinates) in the sphere, \(even(b_{i})\) denotes the list of event channels of broker \(b_{i}\), and \(\textit{erate}(e)\) indicates the transmission rate of event channel \(e\). Finally, the edge brokers are arranged into proper clusters based on the constructed utility matrix by applying the normalized spectral clustering algorithm using a normalized Laplacian (a symmetric matrix), as presentedelsewhere [30; 31]. Subsequently, a previous work [32] was used to perform intra-cluster and inter-cluster routing for the broker clusters to link all event channels in the system.
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 3 Proposed Method
### Encoder-Decoder  
We follow the Conv-TasNet [18] to build the encoder and decoder model. For the encoder, a 1-D convolution operation with kernel size \(K\) is used to extract the features. Moreover, the convolutional stride is \(K/2\) to reduce the length of ADS-B signal, which can reduce the computational complexity. The output of 1-D convolution is fed into the rectified linear unit (ReLU). The ADS-B signal \(\mathbf{x}\in\mathbb{R}^{1\times L}\) is encoded into feature \(\mathbf{x_{k}}\in\mathbb{R}^{C\times 2L/K-1}\) by the encoder model. \(C\) is the number of convolutional kernel.  
The decoder model is a reverse process of the encoder model and utilizes the 1-D transposed convolution operation to generate the separated ADS-B signals. The feature \(\mathbf{x_{k}}\) is fed into MCS network to extract the separation masks \(\mathbf{M}\in\mathbb{R}^{d\times C\times 2L/K-1}\). \(d\) is the number of separation signals. The element-wise multiplication between feature \(\mathbf{x_{k}}\) and mask \(\mathbf{M}\) is used to get the input of decoder. The whole process can be expressed:  
\[\mathbf{x_{i}^{\prime}}=Dconv(\delta(Conv(\mathbf{x}))\odot\mathbf{M_{i}}) \tag{5}\]  
where \(\mathbf{x_{i}^{\prime}}\) is \(i\)th separated signal and \(\delta\) is activate function.
## 3 Geometry of Test Transmission Poles and Determination of Soil Parameters
### Transmission Pole TP2  
The geometry of TP2 is presented in Figure 5.  
Figure 5 lists the necessary data, such as depths, position, etc., to define the geometry of the FEM model.  
Wenner's method measurement was used to determine the soil data [53; 54; 55; 56; 57]. The measured data are presented in Table 5.  
The same procedure for soil parameter determination was used as described for TP1 (Section 3.1). The difference is that, in this case, the apparent resistances are higher, and the limits of the \"Resistance of the first soil layer \(\rho_{1}\) (\(\Omega\)m)\" from Table 3 are set between 1000 and 1040 \(\Omega\)m.  
In Table 6, the calculation results are presented for both three- and four-layer soil models using 30 independent runs of ABC. The table contains the OF best value (B), worst value (W), mean value (M), and standard deviation (SD). The calculated parameters of the best solution are also presented.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**OF and Parameters**} & \multicolumn{3}{c}{**ABC**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Three-Layer**} & **Four-Layer** \\ \hline \multirow{4}{*}{OF (\%)} & B & 6.2156 & 6.1561 \\  & W & 18.0985 & 11.5610 \\ \cline{1-1}  & M & 9.5019 & 7.7921 \\ \cline{1-1}  & SD & 2.6419 & 1.3171 \\ \hline \(\rho_{1}\) (\(\Omega\)m) & B & 1010.2 & 1005.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: OF and best value of the calculated parameters for 30 independent runs for both the three- and four-layer models of TP2.  
Figure 5: Geometry of TP2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(d\) **(m)** & \(\rho\) **(\(\Omega\)m)** & \(d\) **(m)** & \(\rho\) **(\(\Omega\)m)** & \(d\) **(m)** & \(\rho\) **(\(\Omega\)m)** \\ \hline
0.5 & 1020 & 5 & 2360 & 10 & 920 \\ \hline
1 & 1830 & 6 & 2140 & 12 & 648 \\ \hline
2 & 2730 & 7 & 1740 & 15 & 418 \\ \hline
3 & 2690 & 8 & 1420 & & \\ \hline
4 & 2290 & 9 & 1400 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measured soil data for TP2.  
Based on Table 6, it can be seen that B for the four-layer model (6.1561) is better than B for the three-layer model (6.2156), and M for the four-layer model is lower (7.7921) than M for the three-layer model (9.5019). We decided to use the parameters of the best solution for the four-layer model, denoted in Table 6. The measured and calculated apparent resistivities used for the FEM model are presented in Figure 6.  
In Figure 6, good agreement can be seen between the measured and calculated values. The estimated fault current obtained by ELES, d.o.o., is 20 kA.
## 2 Proposed Method
### Demonstration of Execution Steps of the Proposed Method
The Kalman filter is used during the image optimization process. It is very effective in performing computational operations with linear filter modeling. It can produce an estimation of the current states of the system to remove the noise in the image. The main goal of utilizing the Kalman filter is to predict the position of a particular area of an image to be evaluated during the image optimization process. The expected position measures the prediction for the identifying area and it is identified using the variance and the confidence level. Building robust dictionaries is required to recover the high-resolution images and remove the dark regions of the images.
## 2 Materials and Methods
### F760 tot Calculation  
SIFtoc may be downscaled to SIFtot of all leaves or all photosystems by considering the absorption and scattering effects of SIF emissions within the canopy or the leaf [15], respectively. Based on the concept of recollision probability (or 'p-theory') [40], several studies aimed at describing the radiative transfer of SIF emissions within the canopy and the SIF escape probability (f\({}_{\text{esc}}\)) from the leaf to canopy level. For example, [PERSON] [41] and [PERSON] et al. [20] showed that f\({}_{\text{esc}}\) could be modeled as follows:  
\[\delta_{\text{FC}}(\lambda)=\frac{\text{BRF}(\lambda,\Omega)}{\text{i}_{0}\cdot \text{w}} \tag{6}\]  
where \(\delta_{\text{FC}}(\lambda)\) is the canopy scattering of SIF, \(\text{BRF}(\lambda,\Omega)\) is the directional canopy reflectance for the direction \(\Omega\), i\({}_{0}\) is the proportion of incident photons intercepted by the canopy, and w is the leaf-scattering coefficient or leaf single-scattering albedo.  
However, these parameters cannot, in general, be accurately estimated, so different available variables were chosen to represent the components of \(\delta_{\text{FC}}(\lambda)\), i\({}_{0}\), and w. For \(\text{BRF}(\lambda,\,\Omega)\), it was usually assumed to be consistent with \(\text{NIR}_{\text{v}}\)\(=\text{NDVI}*\text{NIR}\)[21], \(\text{R}_{\text{Veg}}\)\(=\text{R}_{\text{Obs}}-\text{R}_{\text{Soil}}\)[22], or \(\text{Red}_{\text{v}}\)\(=\text{Red}_{\text{tot}}\)\(\times\)NDVI\({}^{2}\)[42]. For i\({}_{0}\), it appeared to play a similar role to fPAR [42] or be calculated as \(1-\text{EXP}(-0.5\times\text{L}_{\text{e}}/\cos\theta_{\text{S}})\)[22]. For w, it was expected to be relatively stable (e.g., 0.853-0.888) at the far red band and 0.287-0.444 at the red band, as suggested by [PERSON] et al. [20]. In this study, \(\text{NIR}_{\text{v}}\) for BRF, fPAR for i\({}_{0}\), and 0.87 for w were combined to calculate F760 tot:  
\[\text{f}_{\text{esc}}=\frac{\text{NIR}_{\text{v}}}{0.87\cdot\text{fPAR}} \tag{7}\]  
Based on f\({}_{\text{esc}}\), F760\({}_{\text{tot}}\) can be calculated as:  
\[\text{F760}_{\text{tot}}\ =\frac{\text{F760}_{\text{loc}}}{\text{f}_{\text{esc}}} \tag{8}\]