esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Immersive Cyber Situational Awareness Systems
## 5 Evaluation Mechanisms for ICSA Systems  
This section answers _RQ3: How are ICSA systems evaluated?_ From the reviewed studies, we identified that researchers have employed various user-experience research methods to assess ICSA systems. These methods include questionnaires, surveys, situational awareness evaluation techniques, and usability evaluation mechanisms. User-oriented studies have provided comprehensive insights into several key aspects. They report on users' demographics, including factors such as gender and ethnicity, which help in understanding the diversity of participants involved in the studies. Performance metrics such as threat response time and task completion time are also analyzed, offering quantitative measures of user efficiency and effectiveness when interacting with ICSA systems. Additionally, cognition parameters such as distraction levels and memory retention are evaluated, providing valuable data on the cognitive impact of using immersive technologies for cyber situational awareness. The findings from these studies consistently indicate that ICSA systems significantly enhance users' performance and cognitive capabilities. By improving threat detection and response times and reducing cognitive load, ICSA systems enable users to manage cybersecurity tasks more effectively. Table 7 provides a detailed summary of the evaluation mechanisms employed in the existing literature. While we reference all the evaluation mechanisms used, due to space constraints, we have not included every individual study. However, the evaluation methods mentioned are representative of those commonly cited across multiple studies. The table includes the demographics of the users involved, the specific performance and cognition metrics assessed, and the overall results. Consistent findings across studies confirm that immersive technologies improve user performance and cognition.
# Enhanced Phosphorus Removal from Wastewater Using RSPRC and a Novel Reactor
## 3 Results and Discussion
### RSPRC Adsorption Kinetics
The reason for the difference is that when the adsorption process continues, the P concentration of the wastewater continues to decrease, the conditions required for each adsorption mechanism change, the molecular force between the surface of the adsorbent and phosphorus is reduced, and the concentration difference required for the chemical adsorption decreases. These all lead to a decrease in the adsorption capacity. In the second-order kinetic model, \(\mathrm{k_{2}}\) represents the adsorption rate constant of the pseudo-second order kinetic equation, which describes how fast the adsorption is carried out under the corresponding conditions. Table 5 suggests that when the initial phosphorus concentration was 1.0 mg/L, the \(\mathrm{k_{2}}\) was 0.742, which was the maximum value. The \(\mathrm{k_{2}}\) decreased with the increase in the initial P concentration. The \(\mathrm{k_{2}}\) was only 0.301 under the condition that the initial P concentration was 0.3 mg/L. These results further demonstrate that under reaction conditions with a high P concentration, the adsorption capacity of the adsorbent increases, and the efficiency of phosphorus removal also increases remarkably. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Initial** \\ **Concentration** \\ **(mg.L\({}^{-1}\))** \\ \end{tabular} } & \multicolumn{3}{c}{**Pseudo-First Order Kinetic Model**} & \multicolumn{3}{c}{**Pseudo-Second Order Kinetic Model**} \\ \cline{2-9}  & \multicolumn{1}{c}{**(mg.g\({}^{-1}\))**} & \multicolumn{1}{c}{**q\({}_{e}\)**} & \multicolumn{1}{c}{**K\({}_{1}\) (h\({}^{-1}\))**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**q\({}_{e}\)**} & \multicolumn{1}{c}{**K\({}_{2}\) (h\({}^{-1}\))**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \hline
0.3 & 0.038 & 0.036 & 0.016 & 0.9246 & 0.039 & 0.301 & 0.9813 \\
0.5 & 0.068 & 0.064 & 0.012 & 0.9638 & 0.072 & 0.416 & 0.9842 \\
1.0 & 0.116 & 0.110 & 0.72 & 0.9644 & 0.118 & 0.742 & 0.9954 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Kinetics of phosphorus removal by RSPRC at different initial concentrations.
## 3 Experimental Results
### Experimental Data Processing
#### 3.1.2 Image Mosaicing and Fusion  
Faced with the shortage of defect samples in a single production line, this paper randomly selects four 64 \(\times\) 64 defect samples of the same type for splicing, and the image resolution after splicing is 128 \(\times\) 128. To solve the problem that the gaps in the image edges interfere with the stitching effect, the median fuzzy method is used to process the gaps in the image stitching edges. Some original images of the samples are shown in Figure 8a. The result of image mosaicing is shown in Figure 8b, and the result after median blurring is shown in Figure 8c. Then, the resolution of the processed image is readjusted to 64 \(\times\) 64 to form a new defect sample.  
According to the analysis of Figure 8b, we can see that the stitching generated the same type of defect samples. After median fuzzy processing, the stitching gap in Figure 8c has been completely eliminated, and the image can be used as a new sample after resizing.  
After image fusion processing in Figure 9, fine-grained texture information of two defect sample images can be collected, and it can be seen that the number of defects in the generated images has increased, but the types remain unchanged.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **In** & **Cr** & **Pa** & **Ps** & **Rs** & **Sc** \\ \hline Original & & & & & & \\ drawing & & & & & & \\ Image & & & & & & \\ enhancement & & & & & & \\ Threshold & & & & & & \\ segmentation & & & & & & \\ Defect & & & & & & \\ calibration & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image preprocessing.  
Figure 8: Image mosaicing. (**a**) The image to be spliced. (**b**) Image mosaicing. (**c**) Median fuzzy image.
# Drug-Target Affinity Prediction Based on Cross-Modal Fusion of Text and Graph
## 3 Experiments
### Ablation Study  
To evaluate the impact of each module on model performance, this study conducted a systematic ablation experiment on the Davis and KIBA datasets, with the results presented in Table 7. In the experimental setup, MA refers to the GIN model enhanced by the multi-perceptive neighborhood self-attention aggregation strategy, MB represents the cross-modal bidirectional adaptive guided fusion module, and SMB denotes the cross-modal single-guided adaptive fusion module (including SMB1, where text guides the graph, and SMB2, where the graph guides the text). MC stands for the Multi-Head Collaborative Attention  
Figure 8: The real affinity against the predicted value on Davis (**a**) and KIBA (**b**) datasets.  
Explicit Prediction Strategy, while RE represents the introduction of residual connections, applied to the GIN, MA, and MB modules. The key findings are summarized as follows:  
Single-Modality Baselines (Models 1 and 2): Using GRU for text modality (Model 1) and GIN for graph modality (Model 2) independently achieves limited performance, with higher MSE and lower and CI values. This highlights that single-modality features cannot fully capture the complex interactions between drugs and targets.  
Effect of MA (Model 5): Incorporating the MA in GIN significantly improves performance over the basic GIN (Model 2), reducing MSE and enhancing CI values. This demonstrates the importance of capturing both first-order and second-order neighborhood information for better structural representation.  
Basic Cross-Modal Fusion (Model 6): Combining text and graph features using GRU and GIN improves performance compared to single modalities (Models 1 and 2). However, this basic concatenation approach lacks deep interaction modeling, leading to limited gains compared to advanced fusion strategies.  
Advanced Cross-Modal Fusion (Models 7-9): Introducing MB (Model 8) outperforms SMA (Model 9) and basic fusion (Model 6). The bidirectional mechanism enables more effective interaction between modalities, enhancing complementary information exchange. Effect of Explicit Prediction (Model 10): Adding the MC further improves performance, as it deepens the interaction between drug and protein features while increasing biological interpretability.  
Final Model (Model 13): The integration of all modules (GRU+MA+MB+RE+MC) achieves the best results on both datasets, with the lowest MSE and highest CI values. The RE effectively stabilizes training and enhances generalization, while the combination of MA, MB, and MC captures fine-grained interactions and improves predictive performance. In summary, each module contributes to model performance, with the full integration of MA, MB, MC, and RE achieving the strongest results by capturing both intra- and inter-modality relationships and effectively leveraging complementary information.
# The Origin and Biomedical Relevance of Cannabigerol
## 7 Summary  
At the moment, cannabigerol is one of the least-known phytocannabinoids found in _Cannabis sativa_ L., which, however, shows promising potential in therapeutic actions. Considering that both CBG and its precursors and metabolites are lipophilic, it favors the penetration the penetration through biological membranes and indicates the possibility of biological activity in the lipid sphere mainly through interactions with the endocannabinoid system, including G-protein-coupled receptors. As a result of these interactions as well as direct actions, CBD exhibits antioxidant and anti-inflammatory properties, while both CBG and CBGA as well as its synthetic derivatives exhibit neuromodulatory effects. Moreover, CBG has been shown to reduce the survival of glioblastoma cells, similar to temozolomide used both in monotherapy and with CBG [80]. So far, however, the data in this regard are inconclusive and, moreover, come from in vitro and animal studies that require validation on human tissues and cells used ex vivo, prior to possible clinical trials. No harmful effect of CBG on the human body has been found so far, and the proven biological activity indicates CBG and its derivatives as very promising natural compounds that should be thoroughly tested both in vitro and in vivo in order to unequivocally determine the therapeutic usefulness, especially with regard to inflammatory diseases. Therefore, it seems obvious that new therapeutic approaches using the non-psychoactive ingredients of _Cannabis sativa_ L, including CBG, can be expected in the nearest future.  
**Author Contributions:** Conceptualization, E.S.; writing--original draft preparation, A.J. and I.J.-K.; writing--review and editing, E.S.; visualization, A.J. and I.J.-K.; supervision, E.S.; funding acquisition, E.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 4 Performance Evaluation
### Evaluate System with Network Simulator  
To evaluate the overall design of our proposed system, we built our own simulation for evaluating network performance of AAN. The simulation was built on network simulator 3 (NS3)-based [40] LEO satellite network simulation framework [41], which does not include UAVs in the simulation environment. To build our own simulation, we implemented functionalities related to UAVs and connections between satellites and UAVs with inter satellite links (ISLs). Furthermore, our simulator receives traffic map which represents traffic demand and the position of the UAVs as input.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 3 GOCDM Underwater Acoustic Communication System
### Underwater Acoustic Communication GOCDM System
 \tag{24}\]  
To equalize the signal, the phase shift caused by the diagonal matrix \(\mathbf{T}_{K}\) must first be removed. Then, the channel equalization matrix \(\mathbf{R}_{K}\) is applied to compensate for the channel effects. Finally, the signal undergoes an IFFT, as follows:  
\[ \begin{array}{ll}\mathbf{\hat{d}}&=\mathbf{F}_{K}^{\rm H}\mathbf{R}_{K} \mathbf{T}_{K}\mathbf{x}\\ &=\mathbf{F}_{K}^{\rm H}\mathbf{R}_{K}\mathbf{T}_{K}\mathbf{\Lambda}_{K} \mathbf{T}_{K}^{\rm H}\mathbf{F}_{K}\mathbf{d}+\mathbf{F}_{K}^{\rm H}\mathbf{R }_{K}\mathbf{T}_{K}\mathbf{v}\\ &=\mathbf{F}_{K}^{\rm H}\mathbf{R}_{K}\mathbf{\Lambda}_{K}\mathbf{F}_{K} \mathbf{d}+\mathbf{F}_{K}^{\rm H}\mathbf{R}_{K}\mathbf{T}_{K}\mathbf{v},\end{array}  \tag{25}\]  
The equalization matrix \(\mathbf{R}_{K}\) is a diagonal matrix. For zero-forcing (ZF) equalization,  
\[\mathbf{R}_{K}(k)=\mathbf{\Lambda}_{K}^{-1}(k), \tag{26}\]  
and, for minimum mean square error (MMSE) equalization,  
\[\mathbf{R}_{K}(k)=\frac{\mathbf{\Lambda}_{K}^{*}(k)}{\left|\mathbf{\Lambda}_{ K}(k)\right|^{2}+\rho^{-1}}, \tag{27}\]  
where \(\rho\) is the signal-to-noise ratio (SNR).
## 2 Materials and Methods
### Chemical Analysis  
#### Ergosterol  
ERG is a typical component of fungal cell membranes that is not present in higher plants; therefore, it can be used as a natural, selective indicator for the presence of fungi in different matrices. Dried and milled samples of maize or wheat grain in the amount of 100 mg were suspended in 2 mL of methanol and 0.5 mL of a 2 M aqueous solution of sodium hydroxide. Next, samples in tubes were tightly screwed and microwaved three times (370 W power) for 10 s, and stored at room temperature for cooling down. Subsequently, samples were neutralized with 1 mL of 1 M hydrochloric acid solution, and 2 mL of methanol. After mixing, triple extraction by 4 mL of n-pentane was performed. Each time, the n-pentane layer was transferred to the vials and dried under a stream of nitrogen. The dry residue was dissolved in 1 mL of methanol and filtered through a 0.20 \(\upmu\)m syringe filter (Chromafil, Macherey-Nagel, Duren, Germany) before HPLC/PDA analysis. ERG was detected on a HPLC Waters Alliance system with a Waters 2996 Photodiode Array Detector (Waters Division of Millipore, Milford, MA, USA) set at 282 nm and a 3.9 \(\times\) 150 mm Nova Pak C-18 chromatographic column. The mobile phase was methanol:acetonitrile (90:10, \(v/v\)) at a flow rate of 1.0 mL/min. The concentration of ERG was determined as a result of the comparison of retention times with the external standard. The detection limit was 10 ng/g.
## 2 Materials and Methods  
The manufacturer and physical properties of commercial PAN- and pitch-based CFs in this study are shown in Table 1. They were embedded in epoxy resins and sliced into thin films using focused ion-beam processing. The sample thicknesses were adjusted to 100 nm via Ar milling. We fabricated two thin films from the cross and longitudinal sections of the PAN- and pitch-based CFs. STXM measurements were performed using a compact STXM [13] at Beamline BL-13A of the Photon Factory of the High Energy Accelerator Research Organization (KEK) in Japan. STXM enabled us to obtain XANES mapping at a spatial resolution of approximately 50 nm. The APPLE-II type undulator of BL-13 can generate synchrotron X-rays with both linear horizontal (LH) and linear vertical (LV) polarization modes [14]. An image stack measurement, where a XANES spectrum around the C \(K\)-edge was obtained for each sample position, was performed using both polarized X-rays [7]. It is generally known that the intensity of the peak in XANES, corresponding to the excitation of the 1\(s\)-\(\pi\)\(\pi\)\(\pi\) transitions (due to C = C), is proportional to the square of the inner product between the directions of the orbital and the polarization vector of the X-ray [15; 16; 17; 18; 19]. To analyze the distribution of the oriented \(\pi\)-orbitals, we performed XANES mapping across the entire cross and longitudinal sections using two types of polarized X-rays: LH and LV, respectively. The aXis2000 software was used for the analysis of the image stacks (a set of X-ray absorption images for a sequence of synchrotron radiation X-ray energies near the C \(K\)-edge) [20].  
X-ray diffraction (XRD) measurements were also performed to evaluate the crystallinity of graphite in each CF using pulverized specimens. Powders of the model samples were packed into an aluminum  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PAN-Based CF** & **Pitch-Based CF** \\ \hline
**Manufacturer** & Toray Industries, Inc. & Nippon Graphite Fiber Corporation \\
**Product name** & M46] & YSH50A \\
**Density (g/cm\({}^{3}\))** & 1.84 & 2.10 \\
**Tensile strength (MPa)** & 4210 & 3830 \\
**Tensile modulus (GPa)** & 436 & 520 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The manufacturer and physical properties of commercial polyacrylonitrile (PAN-) and pitch-based carbon fibers (CFs) in this study.  
sample holder (sample part: \(\varphi\) 20 mm \(\times\) 0.5 mm depth), and XRD measurements were performed with a diffractometer (Smartlab(r), Rigaku Corporation). The tube current and tube voltage were set at 36 mA and 40 kV, respectively. The goniometer radius was 285 mm. A high-speed, one-dimensional detector was used as the X-ray detector, and measurements were performed using the Bragg-Brentano focusing geometry (i.e., the \(K_{\rm\beta}\) filter method with Co \(K_{\rm\alpha}\) using Fe filters). The measurement conditions were as follows: measurement angle range 2\(\theta\) = 15-40\({}^{\circ}\), step angle width \(\Delta 2\theta\) = 0.02\({}^{\circ}\). The Halder-Wagner method was used to calculate the crystallite size of graphite [21].
# Adaptive Feature Fusion for Small Object Detection
## 2 Materials and Methods
### Materials
#### 2.1.1 Small Object Detection  
The research on object detection in the field of deep learning has been extensively studied, and has good effects and influence. With the development of deep learning, the detection of small objects has gradually begun. The authors of [25] proposed a scale matching method to align object scales between two datasets to obtain favorable representations of tiny objects. Although it effectively improved the detection performance of small objects, it required a lot of prior knowledge. SNIP [26] and SNIPER [27] use a scale regularization strategy to guarantee that the object size is within a fixed range of images of different resolutions. SNIPER uses super-resolution to recover the information of low-resolution objects, and adopts the strategy of regional sampling to further improve the training efficiency.  
The addition of a super-resolution network will increase the network overhead and bring a burden to the network. Inspired by reference image super-resolution, EFPN [28] proposes an extended pyramid network from the perspective of enhancing feature map resolution to build a feature layer with more geometric details. It was designed for small objects by [PERSON] et al. [29], who proposed a feature-level super-resolution method that uses high-resolution object features as supervision signals and matches the relevant receptive fields of input and object features; however, due to the imperfect construction of its FIT module, the restoration of the feature resolution is not effective. [PERSON] et al. [30] proposed a feedback-driven data provider to balance the loss of small object detection. TridentNet [31] builds parallel multi-branch networks with different receptive fields by deepening the width of the network, and generates different small object features to improve performance, but increases the amount of network parameters. The above methods improve the performance of small object detection to varying degrees, but they are either for a certain type of object or not universal.
# Identification of Wiener Box-Jenkins Model for Anesthesia Using Particle Swarm Optimization
## 2 MPP Anesthesia Model Description  
The modeling of drug dynamics during anesthesia concentrates on describing the gap between the plasma concentration and consequence of the drug delivery. One common model for this purpose is the PK/PD model which can be simplified to the minimally parametrized parsimonious (MPP) model presented in [PERSON] [4]. The model describes the relationship between the infusion rate of muscle relaxant, e.g., atracurium, \(u(t)\) in [ugkg\({}^{-1}\)min\({}^{-1}\)] and the resulting level of neuromuscular blockade \(r(t)\) in [%].  
The MPP model consists of a linear subsystem cascaded by a nonlinear function. The linear subsystem is approximated by the following third-order state space model:  
\[\frac{dx_{1}(t)}{dt} =-k_{3}\alpha x_{1}(t)+k_{3}\alpha u(t) \tag{1}\] \[\frac{dx_{2}(t)}{dt} =k_{2}\alpha x_{1}(t)-k_{2}\alpha x_{2}(t)\] \[\frac{dx_{3}(t)}{dt} =k_{1}\alpha x_{2}(t)-k_{1}\alpha x_{3}(t)\]where \(x_{1}\), \(x_{2}\) and \(x_{3}\) denote system states in [\(\mathrm{\SIUnitSymbolMicro mL^{-1}}\)], \(k_{1}\), \(k_{2}\) and \(k_{3}\) are parameters that have been suitably determined by [PERSON] [4] for atracurium, and \(\alpha>0\) is a patient-dependent parameter.  
The nonlinear subsystem can be described using the following so-called hill function:  
\[r(t)=\frac{100\cdot\bar{C}_{50}^{7}}{\bar{C}_{50}^{7}+x_{3}^{7}(t)} \tag{2}\]  
where the parameter \(\bar{C}_{50}\), in [\(\mathrm{\SIUnitSymbolMicro mL^{-1}}\)] denotes the half of maximum effect concentration and has a fixed value of 3.2425 [29], and \(\gamma\) is another patient-dependent parameter.  
Although, the MPP model given by Equations (1) and (2) has a clear interpretation, the estimation of its parameters is not straightforward. This was the motivation of the current paper to employ system identification techniques which, although being black box, can be applied directly to measured input-output data and provide quick and accurate description of the dynamics of the anesthesia process.
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
###### Abstract  
The application of the high gain boost DC-DC converter is gaining more attention due to an increasingly wide range of applications for sustainable green energy solutions, as well as other high voltage applications. In this study, a modified high gain quadratic boost converter is proposed using a single switch. The proposed topology is a member of the family of the non-isolated category with a common ground feature and can operate in a wide range of duty ratios, and is able to provide the required voltage gain. In this proposed circuit configuration, a dual voltage boost cell was formed by incorporating two capacitors in series with two inductors of a conventional quadratic boost converter. Additionally, a capacitor was integrated with a second voltage boost cell. This special configuration increases the voltage gain as well as reduces the voltage stress across the switch. To show its feasibility, a 200-W prototype setup with 48 V input and 400 V output was designed, and the required PWM signal was fed from the microcontroller unit. A detailed analysis of the design parameters and losses are formulated and are shown in this paper. The simulation was performed in SIMPLIS software, and the experimental results agreed with the obtained output voltage gain. The proposed topology showed a peak efficiency of 94.5% at 150-W output power after considering the power losses in all the components of the PCB.  
DC-DC converter; quadratic boost converter; high voltage gain; non-isolated topology; ultra-fast recovery diode +
Footnote †: journal: Applied  
Article  
A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage Stress Half of Output Voltage  
[PERSON], [PERSON], [PERSON] and [PERSON]