esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Coatings  
Coatings of the copolymers were manufactured by pressing polymer powders onto a silane-pretreated [1] AA2024 specimen, using polyetheretherketone (PEEK) foil to separate the PPM-based polymers from the pressing instrument. Pressing was performed for both copolymers (6.1% mol/mol and 13.4% mol/mol octyloxy units) at a temperature of 120 \({}^{\circ}\)C for 30 s. The thickness  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Tg (\({}^{\circ}\)C)** & **(T-Tg) (\({}^{\circ}\)C)** \\ \hline PPM & 55 & 65 \\
6.1\% octyloxy units & 48 & 71 \\
13.4\% octyloxy units & 31 & 89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Glass transition temperatures (Tg) and the difference between the temperature T applied in rheology experiments (120 \({}^{\circ}\)C and Tg), of PPM and copoylmers with (6.1% and 13.4% octyloxy groups, respectively.  
Figure 4: Effect of \(n\)-octyloxy side chains on the complex viscosity. From top to bottom: PPM (pink line), 6.1% mol/mol octyloxy units (blue line) and 13.4% mol/mol octyloxy units (black line).  
of the resulting films was about 30 \(\upmu\)m (Figures S1 and S2), and the coatings appeared very uniform and homogeneous although no rheological additive was added (Figure 5). Notably, at the processing temperature, the low viscosity of the molten polymer with 13.4% mol/mol octyloxy units enhances self-diffusion within the polymeric matrix, and therefore, during film formation, the cohesion between the polymer chains, and thus, coalescence are promoted [20]. Moreover, polymers which contain side chains that do not strikingly hinder self-diffusion may have a greater cohesive strength than non-branched polymers, based on a firmer anchoring of such macromolecules in the polymeric matrix [20].  
Remarkably, it is common knowledge that corrosion inhibition may also be favored by the hydrophobic behavior of polymer coatings and the homogeneity of their surface. Advancing and receding contact angles of water on copolymer coatings confirmed the low wettability of the protective films (Table 3). Moreover, the remarkably low contact angle hysteresis (4\({}^{\circ}\) and 1\({}^{\circ}\), for 6.1% mol/mol and 13.4% mol/mol octyloxy units) indicates a very smooth and uniform surface (notably, the contact angle hysteresis was even lower than that on AA2024 itself) [1].
## 3 Results and Discussion
### Industrial Application Prospects  
Lignite is an important fuel for many national economies around the world and has higher proximity to biomass materials than high-rank coals. On the other hand, agricultural wastes are generated in large quantities in most countries at low or no cost and possess high energy availability. Therefore, the co-processing of these fuels will increase the operating flexibility of power plants, and thereby, their economic sustainability, reducing at the same time the carbon footprint on the environment. The development of transportation and storage methods of biomass materials is necessary for their use together with lignite in power plants.  
The results of this study are promising, suggesting that agricultural wastes such as barley straw could be used as feedstocks in co-gasification processes with lignite for syngas production. However, further tests on a larger scale and techno-economic analyses are required prior to industrial applications. The expansion and optimization of operating parameters, the use of catalysts to increase the rate of gasification reactions and yield of syngas, the use of additives to capture carbon dioxide during the steam gasification process, as well as computational modeling studies are among the important issues to be investigated. The energy source for heating up the gasifier could be provided by coupling the process with a power plant, or could have a renewable origin, such as the by-products of the biomass pyrolysis step.  
Unlike steam gasification, which is quite an established process, for the deployment of carbon dioxide gasification technology on an industrial scale, additional aspects must be investigated and implemented. A principal challenge is to examine the optimum  
Figure 10: DTG gasification profiles of Kardia lignite/barley straw char mixtures under carbon dioxide atmosphere (RSD= 0.5–1%).  
concentration of carbon dioxide in the feed gas, in terms of gasification efficiency and syngas quality, by mixing it with other gasifying agents, as the use of pure carbon dioxide in commercial gasifiers is not feasible.
## 2 Overall Structure of Fertilization and Spraying System  
### Design Basis  
The agronomic aspects related to corn fertilization and spraying in the Huanghuai region of China are of significant importance and warrant a comprehensive understanding. Firstly, research findings suggest that a row spacing of 600 mm for corn cultivation is optimal for attaining a high yield [18]. This spacing allows for sufficient space between rows, thereby enabling each corn plant to thrive by receiving an adequate amount of sunlight, water, and essential nutrients. Secondly, in the context of fertilization, the hole application method has been proven to be more advantageous. Compared with the traditional fertilization method, the hole application method can accurately place the fertilizer near the roots of the corn, reducing the loss of fertilizer nutrients. Through field measurements, the loss rate of the nitrogen element can reach 30% under the traditional fertilization method, while the hole application method can control the loss rate of the nitrogen element within 15%. The recommended spacing for fertilization is 400-600 mm, and the depth for fertilizer application is around 100-150 mm [19]. This depth is in close proximity to the root zone where the plants can efficiently absorb the nutrients. Regarding the spraying of pesticides and herbicides, specific procedures are involved. Prior to the emergence of corn seedlings, it is essential to conduct comprehensive ground spraying to establish a protective layer.  
Through field experiments, it has been found that this spraying can reduce the number of germinating weed seeds by approximately 80-90%, effectively inhibiting the growth of weeds and preventing them from competing with the corn plants for available resources. After the seedlings have emerged, the spraying should reach a depth of around 80 mm to 100 mm. Additionally, it is crucial to spray both the leaves of the corn plants and the surrounding ground. The leaves require protection against pests and diseases, and spraying the leaves can reduce the incidence of pest damage by about 60-70%. Spraying the ground helps to safeguard the plants from harmful organisms present in the soil, and the control effect on soil-borne diseases can reach about 70-80% [20].  
Collectively, these considerations emphasize the necessity of designing the fertilizer and spraying mechanism for corn fields in the Huanghuai region in a manner that takes into account the growth characteristics of corn plants. By doing so, the fertilizer and spraying process can be optimized to promote the healthy growth of corn and enhance the overall productivity.
## 4 Results and Discussion
### Characterization Results of Diplexers
#### 4.1.2 Interdigital Diplexer  
A comparison of the simulated and measured results of the interdigital diplexer for bands n258 and n260 is shown in Figure 14a. Its insertion loss, return loss and VSWR are similar to those of hairpin diplexers. The isolation between the two filters is better than 45 dB, and the cross-over point is at 31.43 GHz, with 34 dB insertion loss.
# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
## 3 Multidimensional Adaptive Factor-Based Strong Tracking UKF Algorithm  
In this section, the principle of traditional strong tracking UKF is first analyzed, followed by a detailed analysis of the multidimensional adaptive attenuation factor and MAST-UKF algorithm. Then, simulation experiments are designed to verify the effectiveness of the algorithm by combining the traffic flow characteristics of urban roads during peak hours.
## 4 Results and Discussion  
### Kernel Density Estimation Analysis  
To analyze the distribution of environmental sensor data, we utilized the non-parametric density estimation method known as Kernel Density Estimation (KDE). Among the various kernel functions available, we specifically employed the commonly used Gaussian kernel density estimation [50; 51] in this study. The Gaussian function and the Gaussian kernel density estimation are shown in Equations (11) and (12), respectively:  
\[K(x)=\frac{1}{\sqrt{2\pi}}exp(\frac{-x^{2}}{2}). \tag{11}\]  
\[\hat{f}_{h}(x)=\frac{1}{nh}{\sum_{i=1}^{n}}K(\frac{x-x_{i}}{h}), \tag{12}\]  
where \(x\) represents a data and \(x_{i}\) represents the \(i^{th}\) data point in the dataset of size \(n\). The parameter \(h\) denotes the bandwidth and \(K\) represents the kernel function. The Gaussian kernel function is as follows [52; 53]:  
Figure 11 shows a Gaussian KDE of the environmental sensor data, comprising air temperature, humidity, CO\({}_{2}\), soil temperature, soil moisture, insolation, soil EC, drainage EC, and drainage pH arranged from the top left. During the data distribution analysis, specific patterns were observed in the environmental variables. Air temperature data displayed a notable concentration of around 25 \({}^{\circ}\)C, while humidity exhibited a high distribution within the range of 90% and 100%. CO\({}_{2}\) concentrations demonstrated a significant presence between 200 and 300 ppm, and soil temperature displayed a high distribution between 25 \({}^{\circ}\)C and 27.5 \({}^{\circ}\)C. Soil moisture levels ranged from 26% to 28%, while insolation varied from 0 to 100 w/m\({}^{2}\). Additionally, both soil EC and drainage EC showed prominent distributions centered around 0.5 dS/m and 3 dS/m, respectively. Lastly, drainage pH exhibited a high distribution between 6.5 and 7.0 pH.  
Figure 11: Gaussian kernel density estimation.
# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 2 Methods  
This study proposes LCAS-DetNet for the detection of surface ships based on SAR images. LCAS-DetNet provides a small target detection solution with advantages in denoising and real-time performance for ship detection tasks. As shown in Figure 1, it follows the YOLO structure, consisting of multiple location feature extractor, a feature fusion neck layer, and a detection head. To more accurately pinpoint the locations of small target ships, this study introduces an attention scheme called Multi-Location Cross-Attention (MLCA) into the feature extractor. MLCA implicitly re-encodes the spatial locations of targets in the feature extractor, enhancing the network's spatial perception and object location capabilities. At the same time, it acts as a filter by calculating correlations to filter out salt-and-pepper noise in SAR data.  
As a Multi-Location Cross Attention mechanism, LCAS-DetNet demonstrates significant potential in enhancing the precision of ship localization. This improvement is primarily facilitated by the integration of implicit global position encoding, achieved through the attention mechanism, alongside local position encoding, enabled by convolutional operations. This dual encoding framework effectively delineates the spatial relationships between the ship and its surrounding environment, as well as the interactions among ships at varying distances. Consequently, this design offers more possibilities for multi-target monitoring.
## 4 Strain Measurement System Development and Results
### Strain on the Lattice-Structure Gear  
The results of the radial strains over time for the lattice-structure gear are shown in Figure 10. Strains on three parts--namely, the tooth root, ring and hub of the lattice-structure gear--are plotted in three different curves. The measurements are shown from  
Figure 8: The radial strain signals of the solid-body gear over time.  
Figure 9: The strain signals of the solid gear over time when pressure is applied in the tangential direction.  
0.74 s to 0.95 s. The results show that the strain on the tooth root is significantly higher compared to the ring and hub portions. On the other hand, the strain on the ring is comparatively higher than on the hub. In addition, the strain on the hub is stable and lower in the radial direction of the lattice structure. This phenomenon occurs because the structure is flexible, resulting in a lower variable load transmitted from the gear ring to the hub.  
The strains on the lattice-structure gear in the tangential direction over time are shown in Figure 11. When the pressure was applied in the tangential direction, the strain on the ring was not stable over time. A comparison of the strain on the lattice-structure gear, as illustrated in Figures 10 and 11, reveals that the radial strain is less than in the tangential direction. Therefore, for both solid and lattice-structure gears, the tangential load is higher than the radial load, which fulfils the phenomenon of stress distribution.  
In order to assess the effects of the lattice structure on the overall mechanical behavior, the strain recorded in the hub area must be compared for both tested gears. If the radial strain of the two tested gears is compared (Figures 8 and 10), it becomes clear that the radial strain of the printed gear is lower and stable, with no fluctuations over time. If one also compares the strain of the two tested gears in the tangential direction (Figures 9 and 11), it becomes clear that the strain of the printed gear at the hub is lower and more stable over time. It is clear that the lattice structure has a significant influence on strain reduction, especially on the gear hub.  
Figure 11: Strain signals over time with pressure is applied in the tangential direction of the lattice-structure gear.  
Figure 10: The radial strain signals of the lattice-structure gear over time.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 2 Fundamentals of Optical Wireless Communications
### Channel Model in VLC
#### 2.1.1 Directed LOS Channel Gain  
Frequency response of optical channel is relatively flat near Direct Current (DC), so the most important quantity for characterizing this channel is the DC gain \(H(0)\)[14], which relates the transmitted and received average power, see Equation (2):  
\[P_{r}=H(0)\cdot P_{t} \tag{2}\]  
In VLC, the received power can be expressed as the sum of LOS and NLOS components [21]. In directed LOS links, the \(h(t)\), hence the DC gain, can be computed fairly accurately by considering only the direct LOS propagation path. Figure 3 shows an example of a directed LOS link. An optical source can be modeled by its position vector, a unit-length orientation vector \(\vec{o}_{t}\), transmission power \(P_{t}\) and a radiation intensity pattern \(I(\theta,m)\) emitted in direction \(\theta\). Where \(m\) is the mode number of the radiation lobe, which specifies the directionality of the source, and is related to the transmitter half power angle \(\theta_{1/2}\). Similarly, a receiver is defined by its position, orientation \(\vec{o}_{r}\), photo detector area \(A\), and field of view (FOV). The angle formed between the optical incident signal and the orientation vector \(\vec{o}_{r}\) is called the incident angle \(\psi\). The maximum incident angle defines the receiver FOV.  
According to [14], when considering only the direct LOS propagation path, the channel DC gain H(0) is given by Equation (3):  
\[H(0)=\frac{P_{r}}{P_{t}}=\left\{\begin{array}{ll}\frac{m+1}{2\pi+d^{2}}\cdot \cos^{m}(\theta)\cdot A\cdot G(\psi)\cdot T(\psi)\cdot cos(\psi)&0<\psi\leq FOV \\ 0&\psi>FOV\end{array}\right\} \tag{3}\]  
Figure 3: Transmitter and receiver in directed LOS link configuration.  
In Equation (3), \(T(\psi)\) is the signal transmission coefficient of the optical filter in receiver, \(G(\psi)\) is the receiver optical concentrator gain, and \(d\) is the distance between transmitter and receiver. The Equation (3) for channel DC gain in the directed LOS link configuration is based on considering the optical transmitter as a single point source, though VLC transmitters tend to be composed by a large LED array in order to improve illumination capacity. Authors in [22] compare the channel characteristics of both the single point-source model and the array of LEDs. The results obtained show that the deviations are acceptable in terms of the channel optical path loss, as well as bandwidth. There are differences in terms of Root Mean Square (RMS) delay-spread results, though they remain acceptable as long as the LED array is of moderate size.
## 3 Circuit Implementation
### ID Cell Reliability  
Knowing the relationship between \(p\) and \(\Delta V_{T}\) allows for the reliability of each ID cell to be determined quickly. The magnitude of \(\Delta V_{T}\) necessary for a given reliability threshold \(\epsilon\), \(V_{R}\), can be calculated by inverting Equation (9) with \(p=1-\epsilon\).  
\[V_{R}=\sqrt{2}\sigma_{V_{in}}\mathrm{erf}^{-1}(1-2\epsilon) \tag{10}\]  
As described in [17], two tests can then be performed on each ID cell, first by adding a difference voltage \(V_{R}\) between the gates of M1 and M2 during read, such that \(V_{G1}-V_{G2}=V_{R}\). If the result is a \"0\", then \(\Delta V_{T}<-V_{R}\). Next, a difference voltage \(-V_{R}\) is applied. If the result is a \"1\", then \(\Delta V_{T}>V_{R}\)  
Figure 3: Simulated operation of SA. Stage I: pre-charge, Stage II: integration, Stage III: positive feedback.  
The ID cell can then be classified into one of three categories: reliable \"1\", reliable \"0\", or unreliable, based on the two tests. The classification system is shown in Table 1.
## 3 Background
### HSR Concepts
#### 3.1.2 Connected-Ring Networks  
To allow more complex network topologies, HSR rings can be connected through the use of QuadBoxes. A QuadBox forwards frames over each ring, passing the frames to another ring without changes. Although one QuadBox is sufficient to conduct the traffic in a fault-free network, two QuadBox nodes are used to prevent a single point of failure [1]. Figure 2 shows an example of a connected-ring network that consists of four QuadBoxes used to connect four DANH rings.
## 4 Performance Analysis and Simulation Results
### Improvement of the Energy Efficiency  
In this subsection, we analyze the EE performance based on the PAPR reduction. When PAPR reduction techniques are employed [13], we can secure a room to increase the energy efficiency. As mentioned in the previous sections, two strategies to improve the EE, which are the ITXP and RPC schemes, can be applied.
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 3 Existing Security Incidents and Limitations of Security Products
### Limitations of Traditional Integrated Security Products and Common Security Vulnerabilities
By leveraging advancements in artificial intelligence, machine learning, and frameworks such as MITRE ATT&CK, the proposed scenario-based security incident prediction system addresses these challenges directly. This system inte \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **SIEM** & **ESM** \\ \hline Main Function & Centralized management of information and security events occurring within the organization. & Centralized management of security information collected to monitor and operate security systems. \\ \hline \multirow{4}{*}{Collection Save} & Collects information and logs on resources and security threats occurring within the organization (both structured and unstructured data). & Collects security threat information required for security monitoring (structured data). \\ \hline \multirow{4}{*}{Analysis Method} & Collects logs and events from security systems, server systems, networks, and applications, and analyzes them through security personnel. & Consolidates security threat information from security systems and server systems, providing it to the security monitoring team. \\ \hline \multirow{4}{*}{Analysis Results and Characteristics} & Correlation analysis of applications, users, and protocols. Applies security rules defined by security personnel. & Detects network threats using rules such as IP/Port. Detects known attacks through short-term, large-scale analysis. \\  & Detects new security threats such as APTs and ransomware. & \\ \hline \multirow{4}{*}{Usability} & Integrates external security threat information and internal vulnerabilities for analysis. Used by security personnel to investigate security incidents & Used for real-time security monitoring. \\  & (post-event security audit). & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Functions and usage according to the distinction between SIEM and ESM. grates structured and unstructured data, automates the generation of threat scenarios, and provides context-aware analysis to enhance predictive capabilities and mitigate vulnerabilities effectively [17]. By addressing the limitations of SIEM and ESM, this research lays the groundwork for a more robust and adaptive cybersecurity framework that aligns with the demands of increasingly interconnected and complex digital ecosystems.