esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 3 Materials and Methods
### Proposed Methodology  
In this section, we present the architecture and the methodology of our proposed maize leaf disease classification and quantification framework. Unlike other works, the framework presented here accepts raw RGB maize leaf images and outputs the predicted class as well as a severity estimate. Below are the steps taken to achieve the objective. A diagramatic view of this framework is shown in Figure 5.  
1. We train a wide variety of well-known deep learning models for the maize leaf disease classification problem. Then, we compare the performance of the deep learning models and extract the class activation heatmaps from the prediction layers of the CNN models.
2. We extract the class activation heatmap as a 2D matrix from the covnet layers and determine the area affected by the disease (region of interest).
3. We use these regions of interest to estimate image leaf disease severity.
# Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
## 3 Results and Discussions  
### Groundwater Contamination Vulnerability Mapping  
The purpose of using the DRASTIC model in this study is to compare the DRASTIC model and the advanced ML approaches in order to establish a superior method for groundwater contamination assessment. The DRASTIC model has been used largely for groundwater contamination assessment in the past. This approach has been previously utilized in different parts of the world, including Iran, Pakistan, and India [2; 3; 10; 69]. [PERSON] et al. [3] applied the DRASTIC model in the same study area, and produced the DRASTIC risk map of the area. The DRASTIC model was utilized to produce the groundwater vulnerability map, as shown in Figure 3.  
Equation (1) was utilized to obtain the DRASTIC Index (DI). In the groundwater vulnerability map, the DRASTIC index (DI) was categorized into five classes: very low, low, moderate, high, and very high, based on the level of vulnerability. Figure 3 shows that the northeast side of the area is exposed to high to very high vulnerability, whereas the southwest area has a considerably low vulnerability. However, the middle portion of the area has mostly moderate to high groundwater contamination vulnerability.
# Influence of the Water-Cooled Heat Exchanger on the Performance of a Pulse Tube Refrigerator
## 3 Experimental Results
### Influence of Length  
Generally, a longer WCHX means a greater heat transfer area, which is helpful for the overall heat transfer, but also results in a greater flow loss. So, an ideal WCHX is a compromise between heat transfer and flow loss. Figure 7 presents the relative Carnot efficiency of a refrigerator with two different length WCHXs. The length of WCHX 6 was half the length of WCHX 4. The system performance of the two WCHX designs was almost the same, because the warm-end temperatures of the regenerator were almost the same, as shown in Figure 8. Thus, the half length WCHX met the heat transfer requirements of the system. The gas temperature at the inlet of the WCHXs was also compared, as shown in Figure 9. The figure shows that the inlet gas temperature of WCHX 4 was smaller than the shorter WCHX 6. This was conducive for reducing the outlet gas temperature of the compressor, which has a positive impact on the stability of the system. From the aspects of improved performance and economy, there is no need to make the WCHX too long.  
Figure 6: Temperature distribution in the regenerator at input acoustic power of (**a**) 1270 W and (**b**) 5000 W for WCHXs 1, 2, and 4.  
Figure 7: Dependence of relative Carnot efficiency on the input acoustic power for WCHXs 4 and 6.
## 1 Introduction  
_Staphylococcus aureus_ is a common inhabitant of the human body that also causes numerous infections, including skin and soft tissue infections as well as more serious infections, such as pneumonia and bacteremia [1]. Presently, around 60% of _S. aureus_ clinical isolates are methicillin-resistant _S. aureus_ (MRSA) [2], and this bacterium is a leading cause of nosocomial infections in the United States [3,4]. In 1997, community-associated methicillin-resistant _S. aureus_ (CA-MRSA) strains emerged in the United States, causing infections in younger people, including necrotizing pneumonia [5, 6, 7]. Although skin infections caused by CA-MRSA are still prevalent, invasive MRSA infections have decreased [3,8]. In addition to methicillin resistance, CA-MRSA strains are becoming multidrug resistant at an alarming rate [9, 10, 11]. Heterogeneous vancomycin-intermediate _S. aureus_ and vancomycin-resistant strains of _S. aureus_ have led to vancomycin being less effective against some _S. aureus_ infections [12, 13, 14, 15]. Tolerance to vancomycin now has been reported to be as low as 3% and as high as 47% [16, 17]. New drugs are needed to treat MRSA infections; however, most drugs currently in development are derivatives of drugs already being marketed [18, 19]. _S. aureus_ is one of the ESKAPE pathogens (_Enterococcusecium_, _Staphylococcus aureus_, _Klebsiella pneumoniae_, _Acinetobacter baumanniiii_, _Pseudomonas aeruginosa_ and _Enterobacter_ species) targeted by the 10 \(\times\) 20 initiative to develop 10 new, safe and effective antibiotics approved by 2020 [20].  
In support of the 10 \(\times\) 20 initiative, a new antibiotic identified as (E)-3-hydroxy-5-methoxystilbene with promising activity against _S. aureus_ was identified from _Comptonia peregrina_ ([PERSON] (\"sweet fern\") [21]. A structure-activity relationship analysis identified our lead compound, (E)-3-(2-(benzo[b]thiophen-2-yl)vinyl)-5-methoxyphenyl; for simplicity, SK-03-92. SK-03-92 was rapidly bactericidal (killing 90% of the population within an hour) against every Gram-positive species that was tested, including MRSA strains [22]. Importantly, a combined safety and pharmacokinetic study demonstrated that the SK-03-92 lead drug was safe in mice [23]. As with all antimicrobials, therapeutic treatment can result in residual bacteria not being killed by that antimicrobial, a phenomenon known as persistence [24, 25, 26]. Drug persisters are phenotypically different than the parent strain, but are not true drug resistant variants because the MICs of the drug persisters are the same as their parent strains [27, 28]. Persisters are thought to be a major component of bacterial biofilms, allowing significant drug tolerance [29, 30]. Many drugs used to treat _S. aureus_ infections have drug persister population emerge that are recalcitrant to treatment. To gain insight into the mechanism of action of SK-03-92 and the mechanism of _S. aureus_ persistence to SK-03-92 treatment, the effect of SK-03-92 on _S. aureus_ cells was assessed by transcriptional profiling in the _S. aureus_ strain MW2.
## 3 Pavement Self-Sensing Technology Based on Conductive Polymer Composite Materials
### Factors Affecting the Electrical Conductivity of Polymer Composites
#### 3.1.2 Conductive Fillers
In addition, compound conductors composed of conductive fillers of different shapes or sizes often exhibit synergistic or antagonistic effects due to the interaction of their structures and shapes. [PERSON] et al. [111] determined that the addition of carbon CNTs to graphene had a synergistic enhancement effect on a polymer matrix, as CNTs inhibited stacking of the graphene sheets, thus providing a nanophase with a larger surface area in to bind to the polymer matrix and provide mechanical support by sharing loads and inhibiting crack extensions. [PERSON] et al. [112] comparatively investigated the effects of graphene and MWCNT on the mechanical, thermal, and dielectric properties of epoxy composites at different filler amounts, and the results show that graphene-filled epoxy nanocomposites had higher thermal and dielectric properties but slightly lower mechanical properties compared to MWCNT-filled epoxy composites. The tensile strength, flexural strength, thermal conductivity, and dielectric constant of the graphene-filled epoxy composites increased by 11%, 17%, 126%, and 171%, respectively, and those of the MWCNT-filled epoxy nanocomposites increased by 26%, 29%, 60% and 73%, respectively. Asphalt pavement strain monitoring focuses on the internal structural layer of micro-strain-level changes, especially within the core of 100 \(\upmu\)e, where the conductive material is not only a sensitive composite material with electrical conductivity, sensitivity, and self-sensing abilities but also the core of the development of monitoring elements. Therefore, in order to obtain materials with higher response accuracy, it is recommended to use nano-conductive fillers, such as CNTs, nano-carbon black, graphene, etc.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 3 Related Work
### Detection and Defense Mechanisms  
Various mitigation strategies have been explored to detect and prevent DoS attacks in HLF, ranging from machine learning-based traffic analysis to system-level monitoring. [PERSON] et al. [48] proposed a Cross-Multilayer Convolutional Neural Network (CMCNN)-based detection method, enhancing traffic classification in HLF and Ethereum environments. Their model showed better generalizability and higher accuracy rates compared with traditional detection methods. Similarly, a two-layer GRU deep learning model [49] demonstrated high accuracy in detecting DoS attacks within the HLF environment, significantly outperforming conventional machine learning techniques. While these data-driven approaches focus on runtime detection, other studies target the development phase. To mitigate Chaincode-related DoS attacks, a static analysis technique based on the Static Single Assignment (SSA) form of Go's intermediate representation has been developed to detect vulnerabilities before deployment [50]. However, this method is limited to pre-deployment detection and lacks runtime mitigation strategies. In terms of system-level monitoring, [PERSON] and [PERSON] [52] developed a set of threat indicators and introduced a threat-monitoring framework. The framework aggregates log data from HLF components and external sources to detect security anomalies. However, their framework was not specifically designed for DoS attack detection and lacks empirical validation within HLF deployments. Building on the need for more targeted and dynamic defenses, [PERSON] et al. [51] explored the use of Software-Defined Networking (SDN) to enhance the security of containerized blockchain environments, specifically testing on HLF. Their study introduced the Advanced Monitoring and Response Application (AMRA) framework. Experimental results demonstrated that the proposed method effectively detects potential threats and provides visualization for security engineers. These studies illustrate that current solutions are often fragmented and narrowly focused, lacking comprehensive integration across system layers. This underscores the need for holistic, adaptive defense frameworks that align with the modular and layered architecture of HLF.
###### Abstract  
The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent, resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on the terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory-2 (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly in the spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe's land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean conditions (i.e., those in 2015-2017), we found a change in the intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring, followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about a 31% decrease in SIF values during July and August as compared to the mean over the previous three years. Furthermore, our MODIS (Moderate Resolution Imaging Spectroradiometer) and OCO-2 comparative results indicate that especially for coniferous and mixed forests, OCO-2 SIF has a quicker response and a possible higher sensitivity to drought in comparison to MODIS's fPAR (fraction of absorbed photosynthetically active radiation) and the Normalized Difference Vegetation Index (NDVI) when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.  
chlorophyll fluorescence; remote sensing; ecosystems; spring-summer; forest 2020 IEEE/RSJ Web Conference 2020 12 (2020) 11 August 2020; Accepted: 30 September 2020; Published: 6 October 2020
# Low-Profile Dual-Polarized Antenna Integrated with Horn and Vivaldi Antenna in Millimeter-Wave Band
## 2 Design of Dual-Polarized Antenna
The thickness \(t_{1}\) also affects the impedance matching of the [PERSON] antenna and almost has no effect on the horn antenna. The reflection coefficient of the Vivaldi antenna as \(t_{1}\) varies is shown in Figure 9. As the \(t_{1}\) increases from 0.2 mm to 0.4 mm, the impedance matching of the antenna is improved at 22.5 GHz but becomes worse at 21 GHz and 23.5 GHz. When \(t_{1}\) is 0.3 mm, the [PERSON] antenna has the best impedance matching at 26 GHz, and both polarizations obtain a wide bandwidth at both polarizations. The final thickness of the Substrate 1 is determined as 0.3 mm. In order to obtain the widest operating band in the two polarizations, the width of the horn antenna \(W_{6}\) is analyzed. The reflection coefficient \(\mid\!S_{22}\!\mid\) of the horn antenna varies with \(W_{6}\), while the reflection coefficient \(\mid\!S_{11}\!\mid\) hardly changes. As shown in the Figure 10 plot, the resonant frequency of the horn antenna shifts towards lower frequencies as \(W_{6}\) increases from 7.7 mm to 8.1 mm. The resonant mode of the horn antenna is the \(TE_{10}\) mode. By exciting the \(TE_{10}\) mode, HP is obtained. With \(W_{6}\) increases, the frequency corresponding to the \(TE_{10}\) mode in the horn antenna decreases. Therefore, by changing \(W_{6}\), the impedance matching of the horn antenna can be optimized independently. Finally, the width \(W_{6}\) is  
Figure 2.0 Reflection coefficient of the Vivaldi antenna with respect to different \(R\). Figure 2.1 Reflection coefficient of the Vivaldi antenna with respect to different \(t_{1}\). determined to be 7.9 mm for the best impedance bandwidth. By analyzing the parameters of the proposed antenna, the best impedance matching can be obtained quickly. It is helpful to get the best performance of the antenna.
# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
## 4 Experimental Results
For the FRVO, the runtime of 0.03 s does not include the time for the dense disparity map computation, which is usually a time-consuming process and requires at least an extra 0.03 s. The proposed approach jointly determines the suitable KLT window size using the average disparity, the translation velocity, and the yaw angle; thus, no time-consuming computation of a dense disparity map is needed. Although the accuracy of the proposed method is slightly inferior in terms of relative translation errors, with an error of 1.14% against the 0.98% of the FRVO, its main advantage is the real-time performance even on a lower-speed processor. Moreover, Table 4 compares the performance of our method and two state-of-the-art SVO methods that are implemented in parallel with the FPGA and the GPU, namely the SVO-FPGA and the VOLDOR. It can be observed that the proposed method can strike a good trade-off between efficiency and accuracy and can greatly improve \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Data** & **Runtime** & \(\epsilon_{t}\) & \(\epsilon_{r}\) \\ \hline [PERSON]–Whitney U & 46.0000 & 25.0000 & 43.5000 \\ [PERSON] W & 101.0000 & 80.0000 & 99.5000 \\ Z & \(-\)0.3024 & \(-\)1.8898 & \(-\)0.4925 \\ Asymp. Sig. (2-tailed) & 0.7624 & 0.0059 & 0.6224 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hypothesis Testing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Our Method**} & \multicolumn{2}{c}{**SVO**} \\
**Stage** & **Seq. 05** & **Seq. 07** & **Seq. 05** & **Seq. 07** \\ \hline Feature detection & 20.4340 & 19.8128 & 61.0502 & 62.4008 \\ Stereo matching & 1.8556 & 1.8549 & 5.6062 & 6.3031 \\ AKT & 4.1532 & 3.6080 & 2.6890 & 2.8666 \\ SKM & 2.5029 & 2.5025 & 2.5195 & 2.5339 \\ VCM & 18.1453 & 17.9953 & \(\backslash\) & \(\backslash\) \\ Motion estimation & 4.1176 & 5.0820 & 22.3288 & 16.9736 \\ Total & 59.7044 & 59.4217 & 103.0680 & 100.0306 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Processing time in milliseconds of each stage for sequences 05 and 07 in the KITTI dataset. the computational efficiency, while not needing to sacrifice accuracy. Furthermore, the results confirm that the proposed algorithm can run much faster in C/C++. This distinctly demonstrates that the three strategies, i.e., the FI, the AKT, and the SKM, in the proposed method contribute to a significant improvement in the SVO real-time performance.
# Continuous-Time Perfect Control Algorithm--A State Feedback Approach
## 6 Simulation Examples
### Two-By-One System  
In the first simulation scenario, we consider the continuous-time LTI system with two inputs, one output, and two state variables described in the state-space framework that is under the following matrices \(\mathbf{A}=\left[\begin{array}{cc}-0.21&-0.40\\ 0.20&-0.37\end{array}\right]\), \(\mathbf{B}=\left[\begin{array}{cc}1.10&-0.70\\ 0.50&-1.00\end{array}\right]\), \(\mathbf{C}=\left[\begin{array}{cc}-0.90&-0.40\end{array}\right]\) and initial condition \(\mathbf{x}_{0}^{\mathsf{T}}=\left[3\;-5\right]\). Additionally, we declare that the simulation step time is equal to \(\Delta t=0.1\) s. As we can see, this plant is stable, since the open-loop eigenvalues are equal to \(s_{1,2}=-0.2900\pm 0.2713i\). For such a system, let us design the perfect control scheme guaranteeing the law from (12). In this scenario, we use the minimum-norm right \(T\)-inverse of (3).  
As we can observe, a steady-state zero-error output is obtained here just after a single calculation step (see Figure 1). Once more, the calculation step \(\Delta t\) shall be recalled again,as it affects the possible solution of stability criteria mentioned in the previous section. Thus, we obtain the closed-loop perfect control system matrix  
\[\mathbf{A}-\mathbf{BK}=\left[\begin{array}{cc}-7.4965&-3.2618\\ -5.6328&-2.6609\end{array}\right], \tag{24}\]  
and, according to Equation (20), our poles are equal to \(s_{1}=-10.0000\) and \(s_{2}=-0.1574\). Of course, with such a solution, this system is stable.  
The resulted stable state and control signals can be observed in Figure 1.  
**Remark 8**.: _Alternatively, with the use of the classical closed-loop poles definition from (19), the eigenvalues \(s_{1}=0\) and \(s_{2}=-0.1574\) are obtained. Moreover, for the simulation example repeated with calculation step time \(\Delta t=0.01s\), we receive \(s_{1}=-100.0000\) and \(s_{2}=-0.1574\). The connection between \(\frac{1}{\Delta t}\) parameter and closed-loop poles is obvious here. It is clear that more aggressive calculations entail faster dynamic if the goal is to obtain the possibly smallest control time. This faster dynamics is mapped by the faster closed-loop perfect control poles._  
In this scenario, a relatively simple second-order plant was considered in order to clearly introduce and show all peculiarities associated with the innovative continuous-time perfect control systems. Let us now continue this study with some more complex instances involving the nonunique generalized right matrix inverse.
# Security Challenges in Industry 4.0 PLC Systems
## 3 Risk Sources in OT Systems
### Software in Network Systems
#### 3.3.2 Cloud SCADA Systems  
SCADA systems in the cloud are an increasingly popular option when implementing new solutions in the industry. They can offer a reliable and secure approach to control, and resources and employees can be supplemented by remote support, continuous monitoring and automatic updates that are provided by the service. Design of communication between the systems is similar to the solutions in earlier SCADA systems; however, the matter is different if the cybersecurity of such systems is concerned. The problem of cybersecurity is crucial when approaching any of the implementations as the number of threats to industrial control systems is constantly increasing. In SCADA systems based on cloud operations, cybersecurity measures are very often ignored or applied inappropriately to the problem. Unsecured connections via satellite, radio communication or even network infrastructure give hackers the opportunity to target even distant objects.
## 2 Materials and Methods
### Determination of Antimicrobial Activity  
#### 2.5.1 Test Microorganisms and Culture Media  
Antimicrobial activity of the SEWS was evaluated against 17 test microorganisms including eight Gram-positive bacteria (_Bacillus subtilis_ ATCC 6633, _Bacillus cereus_, _Bacillus amyloliquefaciens_ 4 BCL-YT, _Staphylococcus aureus_ ATCC 25923, _Listeria monocytogenes_ ATCC 8632, _Enterococcus faecalis_, _Microoccus luteus_ 2 YC-YT, _Curbobacterium floccumficients_ PM-YT), four Gram-negative bacteria (_Salmonella enteritidis_, _Escherichia coli_ ATCC 8739, _Proteus vulgaris_ ATCC 6380, _Pseudomonas aeruginosa_ ATCC 9027), and five fungi (_Candidia albicans_ NBIMCC 74, _Saccharomyces cerevisiae_, _Apergillus niger_ ATCC 1015, _Apergillus flavus_, _Fusarium moniliforme_ ATCC 38932). Luria-Bertani agar supplemented with glucose (LBG agar) and malt extract agar (MEA) media were used for cultivation of test microorganisms and implementation of agar-well diffusion assay. Both culture media were prepared as per the manufacturer's instructions.