esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of the Marginal Spectrum  
The MSP provides a clearer view of the energy distribution of each seismic signal frequency throughout the whole signal range than the Hilbert spectrum does. The monitoring point's MSP amplitude at the damage site will grow drastically if the slope structure has a damage crack that seriously compromises its integrity in the event of an earthquake. It is possible to determine the dynamic failure mechanism of the slope structure by evaluating the change in the internal damage fracture of the slope under the impact of an earthquake. The slope failure condition may be examined using the MSP, as seen in Figure 12.  
To better understand the damage process of the locked segment under the action of earthquakes, this part calculates the MSP of the lower monitoring point M4 and the higher monitoring point M9 of the locked segment under the excitation of 0.3 g and 0.4 g seismic waves. The results are shown in Figure 13. It is evident that under the two operating situations, the MSPs of the monitoring sites close to the locked segment show two noticeable peaks. The frequency of the first peak, which is primarily spread around 13 Hz, is comparable to the input seismic wave's main frequency, \(f_{0}\), as seen in Figure 5b. It can be considered that it is mainly controlled by the loading seismic wave. The second peak is mainly distributed near 18 Hz, and it is speculated that it may be affected  
Figure 11: Time history curves and 3D Hilbert spectra of homogeneous slope at different monitoring points under action of 0.1 g seismic wave.  
Figure 12: Steps of slope damage state study using marginal spectra.  
by the natural frequency of the slope. Furthermore, the Hilbert energy peak controlled by the main earthquake frequency in the monitoring point's MSP under the excitation of a 0.4 g seismic wave is significantly higher than that under the excitation of a 0.3 g seismic wave. This suggests that the Hilbert energy of the seismic wave-controlled MSP gradually dominates, indicating that the slope is deformed and damaged. In the meantime, the upper monitoring point M9 of the locked segment has a higher peak value in its MSP Hilbert energy than the lower monitoring point M4 of the locked segment, and the high-frequency band of its MSP has a more noticeable amplification phenomenon (i.e., a sudden increase). This suggests that the internal part of the locked segment has started to produce damage under the action of a 0.4 g seismic wave at this time. Though this is not through actual damage, there is already a precursor.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 2 Materials and Methods
### Thermal Imagery  
The TIR data were acquired using a high-sensitivity thermal sensor (TAU2, FLIR, Wilsonville, OR, USA) on a fixed-wing UAV between 13:00 and 13:30 on 17 September 2018. The TIR sensor was a focal plane array with an uncooled vanadium oxide microbolometer and heat sensitivity below 50 mK. The flight took place at a height of 280 m, and the flight path was designed with overlaps of 60% (side) and 80% (forward). The pixel value of the TIR images obtained from the thermal sensor was the digital number (DN) of the land surface thermal radiation, which required correction by temperature. The produced resolution of the TIR data was 0.23 m.
## 3 Results  
### Inhibition of KpSSB by the Extract of Plinia cauliflora  
Due to the importance in DNA replication, repair, and recombination, the inhibition of SSB protein by the fruit extract of _P. cauliflora_ as a potential antimicrobial therapy was investigated. KpSSB was used in this demonstration. This extract of _P. cauliflora_ was obtained by 50% ethanol (Figure 1). Recombinant KpSSB was purified by Ni\({}^{2+}\)-affinity chromatography. To quantify the binding affinity, we conducted EMSA to study the interaction between KpSSB and ssDNA at various protein concentrations. A biotinylated deoxythymidine (dT) homopolymer dT30 was used as a standard assay for the binding analysis of KpSSB. Through a streptavidin-horseradish peroxidase conjugate for detecting the biotin-labeled ssDNA and the complex, incubation of dT30 with KpSSB produced a band shift (Figure 2A). Input dT30 incubated with KpSSB of 625 nM was completely bound (Figure 2A). Through the titration curve, the binding constant ([Protein]\({}_{50}\)) was calculated to be 175 \(\pm\) 12 nM (Figure 2B). The extract of _P. cauliflora_ was used to inhibit KpSSB activity (Figure 2C). In this inhibition assay, the extract at concentrations of 7.8-1000 ug/mL was used. The EMSA result showed that the ssDNA binding activity of KpSSB (625 nM) was inhibited by this extract at concentrations of 62.5-1000 ug/mL (Figure 2D). At a concentration of 250 ug/mL, this extract could completely inhibit the binding of KpSSB to dT30. Through the titration curve, the extract of _P. cauliflora_ exhibited an inhibition capacity against KpSSB with an IC\({}_{50}\) value of 73 \(\pm\) 8 ug/mL (Figure 2D). The tuber extract from _Sinningia bullata_ obtained by 50% ethanol was also used for investigating the anti-KpSSB properties; however, the extract of _S. bullata_ (1000 ug/mL) did not influence the binding of KpSSB. Accordingly, a certain compound in the extract of _P. cauliflora_, alone or in combination, could be a potential anti-KpSSB agent.
## 2 Related Work
Anomaly detection requires a simplified and accurate amount of features and reasonable threshold settings to ensure the optimal performance [15]. Anomaly detection can quickly detect network intrusions, but it requires heavy computation, leading to relatively high resource requirements. The latter monitors data at the gateway, compares the data signature with those in the database to determine if an intrusion is present [16]. However, it is impossible to locate the intrusion. Additionally, digital signatures are system dependent, making it difficult to standardise the detection procedure. The KDD99 dataset was the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with The Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99). The KDD99 dataset is the most widely used dataset for intrusion detection. It consists of network data collected by Lincoln Laboratory over 69 days simulating the US Air Force LAN system with various types of network hosts and attacks [11]. The Network Security Laboratory - Knowledge Discovery in Databases (NSL-KDD) dataset is an improved version of the KDD99 dataset. It removes most of the duplicate data from the original KDD99 dataset. Each data entry in NSL-KDD contains 41-dimensional features and 1-dimensional label feature. Four types of feature data are available in the NSL-KDD, whose data label can indicate whether the data is normal data or not [12], with the data tag indicating the attack type. The NSL-KDD data set includes four main parts, KDDTrain+, KDDTest+, KDDTrain+_20 Percent and KDDTest-21 [17], where KDDTrain+ and KDDTest+ contain 125,973 and 22,543 data sets, respectively. The redundant part of the KDD99 data set is eliminated, KDDTrain+_20 Percent provides an additional subset for training. In this dataset, network data is divided into five types: Normal, DoS attacks, user-to-root (U2R) attacks, root-to-local (R2L) attacks and Probe attacks. The normal type represents normal data; DoS attacks prevent the destination host from responding to external requests and cause a waste of resources; U2R attacks are user-unauthorised attacks, which attempt to gain root access; R2L attacks are login and access attacks by unauthorised hosts on the system; Probe attacks are port monitoring or port scanning. These five types include a total of 39 subtypes of attack types [18]. The specific classifications are shown in the Table 2.  
In this paper, NSL-KDD dataset is used for the experiments. The dataset is first normalised to generate a standard dataset. With classical machine learning methods, a classifier is built for the standard dataset as a control group, and then the data features are extracted by the proposed ADL algorithm, and the generated data features are used to build a classifier to evaluate the effectiveness and usefulness of the proposed ADL algorithm.
## 3 The Proposed Method
### Learnable Polar Transformation Module
#### 3.1.2 Polar Converter  
In the original fundus datasets, the OC is contained in the OD, both are approximately elliptical, and there is variability of the object (size, position, etc.). However, the representation of this information and the constraints are difficult to implement in the network. From observations, under the polar coordinate system, this geometric constraint can be easily converted into spatial relationships and can present an ordered layer structure. Following [31], we employ log-polar coordinates for original datasets that effectively improve the cup-to-disk ratio, balance the dataset, prevent overfitting and improve the segmentation accuracy. Inspired by a Spatial Transformer Network (STN) [32], we formulate \(\gamma_{i}\) to denote the output coordinates, where \(X\) denotes the input:  
\[x_{i}^{s}=x_{0}+r^{x_{i}^{t}/W_{0}}\cos\frac{2\pi y_{i}^{t}}{H_{0}}, \tag{1}\]  
\[y_{i}^{s}=y_{0}+r^{x_{i}^{t}/W_{0}}\sin\frac{2\pi y_{i}^{t}}{H_{0}}, \tag{2}\]  
where \((x_{0},y_{0})\) is the origin input coordinates. \(W_{0}\) and \(H_{0}\) are the width and height of the output. \(\gamma\) is the maximum distance to the origin, which we set to 0.5\(\sqrt{H_{0}^{2}+W_{0}^{2}}\) in our experiments. \((x_{i}^{s},y_{i}^{s})\) denotes the source sample point coordinates, and \((x_{i}^{t},y_{i}^{t})\) denotes the transformed log-polar coordinates.  
Figure 3: An example of segmentation using polar coordinates. (**a**) Glaucoma fundus image. (**b**) Fundus image in polar coordinates. (**c**) The ground truth in polar coordinates.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 2 Relevant Work  
In this section, we will briefly introduce current research achievements and elaborate on our innovative contributions.  
The deployment problem of the emergency communication network is a type of Hub Location Problem (HLP), which is an important research area in operations research. It primarily focuses on selecting optimal locations for establishing hubs within a given network to achieve predefined optimization objectives. This problem widely arises in various fields such as transportation, spatial planning, and military deployment. Many studies have been conducted to address the hub location problem, and the existing methods can generally be categorized into three types: rule-based, heuristic, and machine learning-based methods.
## 2 Materials and Methods
### An Enhanced Approach to Wind Power Curve Optimization via the Improved PCF Algorithm
#### 2.1.2 Adjustment of Power Outputs  
The generation capacity of wind turbines exhibits a direct correlation with the ambient air density, necessitating the conversion of manufacturer-provided power curves to those applicable under standardized atmospheric conditions. Under these standard conditions, characterized by a defined air density \(\mathrm{\rho_{0}}=1.225\,\mathrm{kg/m^{3}}\) and atmospheric pressure \(\mathrm{P_{0}}=101.33\,\mathrm{kPa}\), the conversion is governed by the following relationship:  
\[\frac{\mathrm{P_{0}}}{\mathrm{P}}=\frac{\mathrm{\rho_{0}}}{\mathrm{\rho}} \tag{3}\]  
This leads to the derivation of wind speeds and power outputs under standard conditions, as expressed by  
\[\mathrm{v_{N}}=\mathrm{v_{10 min}}\cdot\left(\frac{\mathrm{\rho_{10 min}}}{ \mathrm{\rho}}\right)^{\frac{1}{3}} \tag{4}\]  
\[\mathrm{P_{N}}=\mathrm{P_{10 min}}\cdot\frac{\mathrm{\rho}}{\mathrm{\rho_{10 min}}} \tag{5}\]  
In the above equation, \(\mathrm{v_{N}}\) symbolizes the air density-normalized wind speed, with \(\mathrm{v_{10 min}}\) denoting the mean wind speed over a 10 min interval. Similarly, \(\mathrm{P_{N}}\) represents the air density-adjusted power output, with \(\mathrm{P_{10 min}}\) indicating the mean power output over the same duration. \(\mathrm{\rho}\) stands for the standard air density, while \(\mathrm{\rho_{10 min}}\) refers to the mean air density measured over a 10 min span.  
It is important to note that the power adjustments described herein pertain to the net power delivered to the grid by the generator, distinct from the raw values depicted in the initial power curve. The latter does not account for the energy consumption inherent to the turbine's operation. To bridge this gap and align closer with the theoretical power curve, one must deduct the turbine's internal energy expenditure from the measured output values. Estimating this consumption can be achieved by contrasting the power outputs recorded at wind speeds surpassing the rated threshold against the nominal power specified in the original curve. This comparison facilitates a preliminary assessment of the turbine's operational energy demand.
## 3 Results and Discussion
### Instrumental Color
The microorganisms are involved in color development in dry-cured products through different mechanisms: they are involved in changes in pH (which affect the color reactions), in nitrate and nitrite reduction (which favors color reaction development), in the peroxide generation (peroxide accumulation leads to iron oxidation and discoloration), and in the catalase activity (which degrades the peroxides), with microorganisms such as staphylococci increasing this catalase activity [39]. In this respect, our results showed that the instrumental color and the microbial counts were related. \(a^{*}\) and \(h^{\circ}\) were correlated with all the microbial groups included in Table 2: the mesophilic and psychrophilic bacteria, the LAB, and molds and yeast were correlated with \(a^{*}\) (\(-\)0.619, \(-\)0.635, \(-\)0.449, and 0.452, respectively, \(p\) = 0.003\(-\)0.045) and \(h^{\circ}\) (0.593, 0.650, 0.480, \(-\)0.474, respectively, \(p\) = 0.002\(-\)0.035); conversely, \(L^{*}\), \(b^{*}\), and \(C^{*}\) were not correlated with any microbial group. These correlations might reveal either a direct effect of those microorganisms on \(a^{*}\) or a similar trend resulting from a common source of variation, namely a common factor causing low \(a^{*}\) and also favoring high counts in most microbial groups. To sum up, the grape pomace was not effective at improving the instrumental color of dry-cured sausages and, therefore, it is not an alternative to nitrites in terms of color. This indicates that a strategy aimed at producing nitrite-free sausages with the HHP-stabilized pomace should also include additional actions to improve color. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Control** & **NITRASC** & **0.5\%Pomace** & **3\%Pomace** & **\(p\)** \\ \hline \(L^{*}\) & 47.3 \(\pm\) 1.7 & 48.0 \(\pm\) 1.8 & 47.3 \(\pm\) 2.1 & 46.6 \(\pm\) 2.0 & 0.738 \\ \(a^{*}\) & 4.8 \(\pm\) 1.3 b & 6.9 \(\pm\) 0.7 a & 4.9 \(\pm\) 0.5 b & 4.6 \(\pm\) 1.1 b & 0.005 \\ \(b^{*}\) & 8.8 \(\pm\) 1.3 a & 6.2 \(\pm\) 1.5 b & 9.3 \(\pm\) 1.6 a & 8.3 \(\pm\) 1.4 ab & 0.018 \\ _Chroma_ & 10.1 \(\pm\) 1.7 & 9.3 \(\pm\) 1.4 & 10.5 \(\pm\) 1.3 & 9.5 \(\pm\) 1.7 & 0.608 \\ _Hue_ & 62.1 \(\pm\) 4.9 a & 41.2 \(\pm\) 5.6 b & 61.9 \(\pm\) 5.4 a & 61.4 \(\pm\) 4.8 a & \textless{}0.001 \\ \hline \end{tabular} The results are expressed as mean \(\pm\) standard deviation and significance (\(p\)) from a one-way ANOVA. Different letters in the same row indicate significant differences in [PERSON]’s test.  
\end{table} 
Table 4: Instrumental color of Control dry-cured sausages and dry-cured sausages with synthetic additives (NITRASC) or with stabilized grape pomace at 0.5% (0.5%Pomace) or 3% (3%Pomace).
## 3 Results and Discussion
### Hardness  
Nanoindentation is a useful technique that can be used for a comprehensive mechanical analysis of thin films and coatings. Nanoindentation is also considered an alternative to macroscale mechanical characterizations [51]. Figure 8a shows the time vs. the penetration depth of the indenter, whereas Figure 8b shows the force vs. the penetration depth. It is revealed from the indentation curves that the lowest surface hardness was found in G/PP/SP-2, as the penetration was higher in the samples (~12,000 nm) when around 3 mN of force was applied (Figure 8a,b). The corresponding indentation image is shown in Figure 8e, whereas sample G-2 (simple gelatin film) exhibited the lowest penetration depth, i.e., 3000 nm, even when a force of 10 mN was applied (Figure 8a,b), and the corresponding indentation is shown in Figure 8c. The films of G/PP-2 showed the penetration depth which lies between the penetration depth of the G-2 and G/PP/SP-2. This result suggests that the films of G/PP-2 are neither as soft as G/PPSP-2 nor hard like G-2 (the corresponding indentation is shown in Figure 8d). This result is also in line with the result obtained via bulk tensile strength analysis, as mentioned previously in Table 4. The possible cause of this result could be higher content of papaya as well as starch in the gelatin film. This result is also in accordance with the study carried out by [PERSON] et al. [25]. In their study, they concluded that the linear papaya chains prefer to interact with hydrogen bonds, resulting in stronger films. Additionally, they also concluded that the starch chains maintain molecular mobility and hence are the main reason for maintaining the hardness. Another possible reason for lower hardness of G-2 and G/PP/SP-2 films could also be surface roughness. The surface roughness values of these films were found to be 0.14 um and 0.49 um, respectively. Furthermore, according to [PERSON] et al. [51], surface roughness and surface nano-hardness are negatively linked with each other; a smooth surface may yield a harder surface [51; 54].  
Figure 7: Normalized WVTR of edible films vs. the number of bending cycles with a bending radius of 6.35 cm. The black curve represents gelatin (G-2) film, the red curve represents G/PP-s film, and the blue curve represents G/PP/SP-2 films, and the thickness of each tested film is about 100 μm.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
###### Abstract  
In the nearest decades, the rapidly increasing demand of wireless connectivity has resulted in the ubiquitous deployment of wireless systems as well as heavily congested wireless spectrum. Owing to the various inherent advantages, such as spectral and bandwidth relief, no healthy concern, high security, low cost, and low interference with Radio Frequency (RF) waves, visible light communication (VLC) has been an emerging optical wireless data transmission approach that can act as a good complement to and substitute for Radio Frequency. How to achieve a high-speed data transmission is a key problem to be solved in the VLC system. This review mainly focuses on the enabling technologies for high-speed VLC systems, including novel transmitter architectures, blue filters and advanced modulation, and equalization technologies. And the inherent advantages, potential applications, and some issues of VLC that need further study are presented as well. Finally, a comprehensive survey on the recent developments and the key contributions by research groups involved in the field of high-speed VLC is provided.  
visible light communications; high-speed; LED; blue filter; modulation; equalization +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Cell Proliferation/Viability and Metabolic Activity Assays  
First, 200 \(\upmu\)L of DMEM with 2.5 \(\times\) 10\({}^{4}\) hG-MSCs/mL was seeded onto GPP, VSCM, or TCP, respectively. Consequently, the total cell number on each surface was approximately 5 \(\times\) 10\({}^{3}\). The proliferation/viability was determined by a cell counting kit (CCK-8, Dojindo Laboratories, Kumamoto, Japan) on 3, 7, and 14 days. CCK-8 reagent was added at 10% and incubated for 4 h. The amount of formazan dehydrogenized from tetrazolium salt was measured photometrically at 450 nm wavelength using a microplate reader (Synergy HTX; BioTek, Winooski, VT, USA). Metabolic activity was tested using a resazurin-based toxicity assay (Sigma-Aldrich, St. Louis, MO, USA). Here, 20 \(\upmu\)L of resazurin-based solution was added to culture wells containing 200 \(\upmu\)L of cell solution, followed by incubation for 6 h. The reduction of resazurin to resorufin was monitored using a fluorescence-based plate reader (Synergy HTX; BioTek) at 540/34 nm excitation and 600/40 nm emission wavelength. The assays were performed with hG-MSCs isolated from 5 individual donors and two technical replicates.
# Propagation Attenuation Maps Based on Parabolic Equation Method +
###### Abstract  
Modern wireless communication systems use various technological solutions to increase the efficiency of created radio networks. This efficiency also applies to radio resources. Currently, the utilization of a radio environment map (REM) is one of the directions allowing to improve radio resource management. The REM is increasingly used in emerging mobile ad-hoc networks (MANETs), in particular military tactical networks. In this case, the use of new technologies such as software-defined radio and network, cognitive radio, radio sensing, and building electromagnetic situational awareness made it possible to implement REM in tactical MANETs. Propagation attenuation maps (PAMs) are crucial REM elements that allow for determining the ranges of radio network nodes. In this paper, we present a novel algorithm for PAM based on a parabolic equation method (PEM). The PEM allows determining the signal attenuation along the assumed propagation direction. In this case, we consider terrain topography to obtain a more realistic analysis. Then, we average the adjacent attenuation profiles defined for the selected directions in places where attenuation has not been calculated. To this aim, linear regression is applied. Finally, we define several metrics that allow for the accuracy assessment of determining the PAM as a function of its dimensions.  
Keywords:wireless communications; mobile ad-hoc network (MANET); radio environment map (REM); radio wave propagation; propagation attenuation map (PAM); parabolic equation method (PEM); path loss; terrain topography; digital terrain elevation data (DTED) +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))).