esa-sceva/satcom-chunk-collection huggingface dataset:
# Contribution of Atmospheric Fallout to the Soil-Root-Leaf Transfer of PAHs in Higher Plants
## 1 Introduction
demonstrated that the molecular weight-specific uptake of PAHs from rhizosphere soils was highly dependent on the growth stage of winter wheat [22]. [PERSON] et al., investigated the relationships between bioconcentration factors and the physicochemical properties of PAHs and found that solubility and the octanol-water partition coefficient were strongly correlated with the soil-to-root bioconcentration factors, while vapor pressure and the octanol-air partition coefficient proved to be good predictors for the air-to-leaf accumulation of PAHs [23]. In addition to direct deposition of particles on soil or plant surfaces, both gas- and particle-phase PAHs can be washed out by rain or snow, which is defined as wet deposition [24]. Wet deposition provides an important pathway for PAH removal from the atmosphere; however, it depends on the amount of precipitation, showing seasonal patterns [25]. The process has been proven efficient for all particle-associated PAHs [26]. While most studies assess dry deposition or bulk deposition [27], the direct effects of wet deposition have been rarely addressed. In our study, wet deposition was mimicked using the adopted version of the No. 227 OECD Guideline for the Testing of Chemicals:Terrestrial Plant Test: Vegetative Vigor Test (hereinafter referred to as No. 227 OECD Guideline) [28]. This standard was developed for herbicide regulation to evaluate the risk of general chemicals, biocides and crop protection products [29]. The original protocol has been slightly modified to test the potential phytotoxic effects of the water-soluble components of airborne particle-bound contaminants [30] and to assess their bioaccumulation [31]. The present study first intends to describe the treatment protocol used to simulate wet deposition. It is also aimed at quantifying the transfer of atmospheric PAHs via wet deposition both to soil and plant surfaces, under controlled conditions prescribed by the guideline. The major goal was to follow PAH accumulation in different compartments of the test plants, getting precise data on how PAHs move from the aerial parts to the roots following foliar uptake, and to what extent the uptake from the soil will contribute to the PAH content in the aerial parts. Rocket (_Eruca sativa_ Mill., family Brassicaceae) was selected as a model species. Different Brassicaceae species have been reported as sensitive and easy-to-use test species, showing good PAH accumulation capacity [32; 33; 34].
## 3 Results
### Computerized Tests
#### 3.1.1 Results of Test 1  
The number of correct responses and RT were classified into five different variables, one for each VA level described previously.  
Compared with the results obtained under white ambient lighting conditions using tests with black letters on a white background and white letters on a black background, no statistically significant differences were detected between the two groups in terms of either the number of correct responses or RT for any VA level. Therefore, when comparing different lighting conditions, these two groups were considered together within the white lighting condition.  
Thus, the best results in terms of correct responses (Figure 4A) were obtained with white ambient lighting, where statistically significant differences (\(p\leq 0.05\)) were found compared with the results obtained under red ambient lighting across all VA levels. Conversely, when comparing results between white and blue lighting conditions, statistically significant differences were found only at a VA of 0.05 logMAR (\(p=0.002\)).  
Figure 4: The comparison of the correct answers (**A**) and reaction times (**B**) for each VA in logMAR of Test 1 between each type of lighting. Each \(p\) value is represented on the bars, where * and bold letters indicate statistically significant differences (\(p\leq 0.05\)).  
Finally, among the results from the blue and red ambient lighting conditions, better results were achieved with blue lighting. However, statistically significant differences were found only at a VA of 0.0 (\(p=0.044\)).  
There was no clear lighting condition that globally reduced the RT (Figure 4B). Thus, red lighting resulted in a lower RT for a VA of 0.22 logMAR; blue lighting resulted in VAs of 0.15 logMAR and 0.10 logMAR; and white lighting resulted in VAs of 0.05 logMAR and 0.0 logMAR. However, statistically significant differences (\(p\leq 0.05\)) were found only when comparing TRs under white lighting with blue lighting at VAs of 0.15 logMAR and 0.10 logMAR and white lighting with red lighting at VAs of 0.22 logMAR and 0.15 logMAR. Additionally, when comparing blue lighting with red lighting, statistically significant differences in RT were found only at a VA of 0.15 logMAR, indicating faster reaction times with blue light.  
In all cases, as the level of VA required increased, the RT increased, and the number of correct answers decreased.
## 2 Materials and Methods
### Photoacoustic Imaging Experimentation and Machine Learning-Based Crack Detection  
#### 2.1.1 PAI Experimentation Setup  
An original photoacoustic microscopy (PAM) system is utilized for actual experimentation as shown in Figure 2. The target material is a steel sample with surface cracks; the crack dimensions utilized for experimentation are listed in Table 1. The target sample is placed in the container at the desired coordinates using an XY motorized stage (Thorlabs, Newton, NJ, USA, DDS220/M). The container is then filled with oil for better acoustic transmission and measurements. The acoustic excitation in the target sample is achieved using a pulsed laser module (CoLID-I, Connet, Shanghai, China) bearing an approximate pulse energy of 0.42 uJ, repetition rate of 1 kHz, pulse width of 20 ns, and wavelength of 1560 nm. Laser illumination and acoustic data acquisition processes are synchronized using an external trigger. A custom-made ultrasound transducer (Precision Acoustics: center frequency = 20 MHz and diameter = 20 mm) detects the generated PA waves. The acoustic signal is then amplified (Minicircuit, Brooklyn, NY, USA, Zx60-3018G-S+) and digitized (Signatec, Poway, CA, USA, PX14400) at a sampling rate of 400 MHz.  
Figure 2: Actual picture of the PAM setup utilized for experimentation.
## 4 Selection of Materials for Each Layer
### Selection of Adhesive Materials  
The ballistic resistance of armored composite materials correlates closely with the strength and thickness of the interlayer adhesive. Adhesive selection for bonding between different layers primarily considers the bonding between metals and non-metals, as well as ceramics and non-metals. The adhesive effect between adjacent layers can mitigate destructive forms such as delamination, layer cracking, and interlayer separation upon  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Armor Steel** & **Aluminum Alloy** & **Titanium Alloy** \\ \hline Density (g/cm\({}^{3}\)) & 7.86 & 2.7 & 4.5 \\ Tensile strength (MPa) & 1170 & 350 & 970 \\ Mass shielding factor (\(E_{m}\)) & 1 & 1.0–1.2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of the key parameter indicators for armored steel, aluminum alloy, and titanium alloy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Density (g/cm\({}^{3}\))** & **Tensile Strength (MPa)** & **Elongation (\%)** & **Specific Energy Absorption Rate (J-m\({}^{2}\)/kg)** \\ \hline Glass fiber & 2.5 & 2400 & 2.0–4.0 & 7.0 \\ UHMWPE & 0.98 & 3800 & 4.0–8.7 & 34.9 \\ Aramid fiber & 1.4 & 2200 & 1.4–3.2 & 9.7 \\ Carbon fiber T700 & 1.6 & 4900 & 2.4 & 13.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Major performance indicators of several fiber composite materials.  
impact, thus reducing the likelihood of interlayer collapse phenomena. In this study, epoxy resin AB adhesive was selected as a transparent adhesive consisting of an epoxy resin-based two-component fast-curing epoxy resin adhesive. It demonstrates excellent bonding strength after curing when used for bonding connections between metal materials, ceramic materials, wood, glass products, and hard plastics. However, its adhesive performance for soft and elastic materials, rubber, and leather items is unsatisfactory. The cured adhesive demonstrates good resistance to acid and alkaline substances as well as moisture, and it has waterproof properties. Table 5 presents the relevant parameter indicators for epoxy resin AB adhesive after curing.  
Based on the comparative analysis of the armor materials mentioned above, the panel material selected for this study's experiment is a ceramic with a 99.5% Al\({}_{2}\)O\({}_{3}\) content. The interlayer material chosen is carbon fiber T700, and the backing material selected is TC4 titanium alloy. The adhesive selected is epoxy resin AB adhesive.
## 5 IEEE 802.15.4 Technology  
IEEE 802.15.4 wireless technology is a short-range communication system intended to provide applications with relaxed throughput and latency requirements in WPAN. The key features of 802.15.4 wireless technology are low complexity, low cost, low power consumption, low data rate transmissions, to be supported by cheap either fixed or moving devices. The main field of application of this technology is the implementation of WSNs.  
The IEEE 802.15.4 Working Group (see also the website: [[http://www.ieee802.org/15/pub/TG4.html](http://www.ieee802.org/15/pub/TG4.html)]([http://www.ieee802.org/15/pub/TG4.html](http://www.ieee802.org/15/pub/TG4.html))) focuses on the standardization of the bottom two layers of ISO/OSI protocol stack. There are two options for the upper layers definition: Zigbee protocols, specified by the industrial consortia ZigBee Alliance (see also the website: [[http://www.zigbee.org/en/index.asp](http://www.zigbee.org/en/index.asp)]([http://www.zigbee.org/en/index.asp](http://www.zigbee.org/en/index.asp))) and 6 LowPAN.  
Figure 4: The behavior of the battery charge in Volt by passing time, expressed in hours, when photovoltaic panels are used and not.  
Figure 3: Screenshot of temperature behavior measured during June 2006 by 2 different nodes (red and blIn the following, some technical details related to the physical and MAC layers as defined in the standard are reported. Finally some characteristics related to higher layers will be presented, considering Zigbee and 6 LowPan, with particular attention to the former.
# Effect of Fireproof Coatings on the Post-Fire Behavior of CFRP Composite Sheets
## 1 Introduction
Additionally, the ACI 440.2R-17 [34] specification recommends using fireproof coatings to improve the performance of FRP composites under high temperature conditions. However, the available fireproof coating for CFRP composites and related studies are very limited. This study developed two novel types of fireproof coatings to minimize the performance degradation of CFRP composites under fire conditions. The self-developed fireproof coatings serve as a protective barrier, minimizing heat transfer to CFRP composites and preventing the resin from reaching critical temperatures during fire exposure. The objective of this study is to examine the effect of two self-developed fireproof coatings on the fire resistance of CFRP composite sheets. The assessment of fire resistance involves the retention of the mechanical and thermal properties and integrity of materials after fire exposure. Post-fire material properties are important indicators for evaluating the safety of the structure after fire exposure. Notably, it is important to mention that existing studies have mainly emphasized the tensile properties of CFRP composites, with relatively limited research on their flexural properties. However, in practical engineering applications, CFRP elements, especially the CFRP sheets for structural strengthening applications, may be subjected to both tensile and bending loads. Therefore, the post-fire flexural properties were chosen as an important indicator of fire resistance in this study. Three-point bending tests on CFRP sheets coated with fireproof coatings of three different thicknesses after being subjected to the standard fire and the large-space fire were conducted in this research to evaluate the post-fire flexural performance. The experimental results provide substantive references to the fire-resistance application and design of CFRP composites in infrastructure to ensure the fire safety of CFRP elements in newly built structures and CFRP sheets for structural reinforcement applications.
## 3 Materials and Methods
### Construction of Multiview Graphs
#### 3.2.1 Gene Co-Expression Graph  
Gene co-expression networks play a key role in revealing complex relationships between gene expressions, offering valuable insights into disease mechanisms [34]. In our study, we began with a carefully selected set of genes, treating each gene as a node in a network graph. We used the Pearson correlation coefficient, which ranges from \(-1\) to \(1\), to measure correlations between these genes [35]. A coefficient close to \(1\) implies a strong positive correlation, while a value close to \(-1\) indicates a strong negative correlation. Values around \(0\) suggest no linear correlation. In constructing the network graph, we connected gene pairs only if the absolute value of their Pearson correlation coefficient exceeded a soft threshold of \(\beta_{1}=0.4\), indicating a substantial correlation, regardless of whether it is positive or negative. This threshold was chosen to ensure that only gene pairs with a significant level of correlation, either positive or negative, are included, therefore enhancing the biological relevance of the network. The Pearson correlation coefficient itself, when its absolute value is greater than \(0.4\), determines the weight of each edge, representing the significant strength of gene expression correlation.
###### Abstract  
In this paper, we propose a new method for the binomial adaptive compression of binary sequences of finite length without loss of information. The advantage of the proposed binomial adaptive compression method compared with the binomial compression method previously developed by the authors is an increase in the compression rate. This speed is accompanied in the method by the appearance of a new quality--noise immunity of compression. The novelty of the proposed method, which makes it possible to achieve these positive results, is manifested in the adaptation of the compression ratio of compressible sequences to the required time, which is carried out by dividing the initial set of binary sequences into compressible and incompressible sequences. The method is based on the theorem proved by the authors on the decomposition of a stationary Bernoulli source of information into the combinatorial and probabilistic source. The last of them is the source of the number of units. It acquires an entropy close to zero and practically does not affect the compression ratio at considerable lengths of binary sequences. Therefore, for the proposed compression method, a combinatorial source generating equiprobable sequences is paramount since it does not require a set of statistical data and is implemented by numerical coding methods. As one of these methods, we choose a technique that uses binomial numbers based on the developed binomial number system. The corresponding compression procedure consists of three steps. The first is the transformation of the compressible sequence into an equilibrium combination, the second is its transformation into a binomial number, and the third is the transformation of a binomial number into a binary number. The restoration of the compressed sequence occurs in reverse order. In terms of the degree of compression and universalization, the method is similar to statistical methods of compression. The proposed method is convenient for hardware implementation using noise-immune binomial circuits. It also enables a potential opportunity to build effective systems for protecting information from unauthorized access.  
data compression; binomial code; lossless data compression; binomial numbers +
Footnote †: journal: Applied Physics Letters
## 4 Experiments
### CNN Model and Training Result that Generates Video Control Signal to Control 360-Degree Video  
The deep learning model used to verify the experiment used a CNN model. Table 1 shows a CNN model for inferring video control signals according to a 360-degree video. The video is converted to a **270 by 480 pixel gray image** and input to the CNN model. Convolutional layers consist of **one dropout, three convolution, and three max pooling layers**. The final result is deduced from the output layer using the results calculated in the convolutional layers.  
Figure 10 shows the change in learning rate during **40,000** epochs using the CNN model. The K-means algorithm-based learning rate converged higher (**to 0.0016**) than the user definition-based learning rate (which converged **to 0.0005**). In the case of training the CNN model based on user definition, the learning rate was lower than the K-means algorithm because the control label did not change much during the shooting of the athletes. The averages of the difference between the converged learning rate and the learning rate from the 10 th learning rate (the beginning of the interval where the learning rate does not decrease rapidly) until the end of the learning were compared. The averages of the differences were **0.0005 for the K-means algorithm** and **0.0004 for the user definition**. The averages of the differences were not much different. In addition, it can be confirmed that there were many sections wherein the user-defined learning rate increases rapidly compared to the learning rate of the K-means algorithm.
## 3 Results  
The results of the Shapiro-Wilk test of normality indicate that the distributions were non-normal for the variables \(ASR_{area}\), \(SODP_{area}\), \(PSD_{pkamp}\), and \(PSD_{ppow}\) while the variables \(SODP_{CTM}\) and \(PSD_{mfreq}\) were normally distributed. The same results were observed for the four IMF components of each feature. The results of [PERSON]'s homogeneity of variances test indicate equal variances for all features except for the \(SODP_{CTM}\) of the 1 st, 2 nd, and 4 th IMF components. A representative illustration of the resulting EMD applied to decompose the RR interval signal in a subject with no diagnosis of CAN is illustrated in Figure 2. The top row shows the original RR interval signal. The following rows demonstrate the first four IMFs and the residual component. As expected, the higher the IMF index, the lower its frequency content. The univariate ANOVA shows a significant difference between the CAN groups for the variables \(\log(ASR_{area})\), \(\log(SOD_{area})\), \(\mathrm{SODP_{CTM}}\), \(\log(PSD_{brow})\), and \(\log(PSD_{pkamp})\) and their components (i.e., IMF\({}_{1}\), IMF\({}_{2}\), IMF\({}_{3}\), and IMF\({}_{4}\)) (\(p<0.01\)). In contrast, there was no significant difference between the CAN groups for the \(PSD_{mfreq}\) variable and its EMD components. The feature values are presented in Table 2.  
The analytic signal and the second-order difference plots of the IMFs for the three groups (noCAN, subCAN, and estCAN) are demonstrated in Figures 3 and 4, respectively. From Figure 3, it can be observed that the spread of the analytic signal is lower for the estCAN group in all IMFs, resulting in a larger number of points inside the radius and, therefore, a larger \(ASR_{area}\). Similarly, Figure 4 reveals that the second-order difference plot exhibits a greater dispersion in the noCAN group but becomes progressively more clustered in the subCAN group and even more in the estCAN group. This result is reflected by the decrease in the \(SOD_{area}\) and an increase in the \(\mathrm{SODP_{CTM}}\), which calculate the ellipse area and the grouping of points around the origin. Figure 2: The first row represents the original RR interval signal, followed by its first four intrinsic mode functions and the residual obtained after empirical mode decomposition from a subject with no diagnosis of cardiovascular autonomic neuropathy.
## 1 Introduction  
As technology evolves and advances, the services provided by these improvements require new adaptive techniques optimized for frequent changes [1]. The Domain Name System (DNS) is a standard protocol that simplifies user interactions with network components and helps locate required resources [2]. It resolves domain names into Internet Protocols (IPs), enabling Unified Resource Locators (URLs) to function for websites and facilitating email communication. While the DNS can be a helpful tool for web browsers, it also exposes users to various types of DNS attacks, such as cache poisoning, where attackers insert corrupted data into the cache of a DNS resolver, causing it to return incorrect IP addresses. These redirected addresses can lead users to malicious websites [3]. Additionally, attackers can manipulate system settings to exploit vulnerabilities further. For instance, if a system is poorly configured with vulnerabilities, it becomes susceptible to Man-in-the-Middle (MitM) attacks [4].  
MitM is a cyberattack in which the hacker intercepts, steals, and eavesdrops on communication between parties without their knowledge [5]. In such attacks, attackers can manipulate DNSs to redirect users attempting to access a legitimate website to a malicious clone controlled by the attacker. This enables the attacker to steal login credentials or insert malware into the user's device without their knowledge [6]. There is, therefore, a need to develop robust anomaly detection schemes to enhance the security of the hosts and DNSto protect sensitive data, minimize false positives, monitor the system, and be adaptive to technological advancements.  
Each host on the network is embedded with a lightweight detection scheme to improve the detection of malicious activities in real-time while ensuring low latency in detecting and responding to security incidents. The study also employs the Cuckoo Search Algorithm (CSA), an optimization algorithm inspired by the brood parasitism of some cuckoo species, where they lay eggs in the nests of other host birds [7]. The use of CSA in our detection scheme improves the efficiency of the search space. It can adapt to various data types and achieves high accuracy, robustness, and scalability. Furthermore, a Decision Tree algorithm is utilized to classify the processed data, further enhancing normal and anomalous network traffic classification. The following section reviews the related work and highlights the research issues and gaps while discussing techniques and methods used to improve the accuracy and efficiency of detection schemes [8].  
Our study proposes a lightweight anomaly detection scheme for DNS MitM attacks by integrating the CSA for optimized feature selection and a Decision Tree for efficient classification. This integration enhances detection accuracy while maintaining low computational overhead, making it suitable for resource-constrained environments. The proposed approach demonstrates improved performance in detecting various DNS-based MitM attacks compared to existing methodologies.