esa-sceva/satcom-chunk-collection huggingface dataset:
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 3 Results and Discussion
### Channel Loss
It indicates that power loss is significant in clear water and at short attenuation lengths in the two other waters due to the attenuation effect of the water body and less at long attenuation lengths in more turbid waters because of scattering contribution if there is a linear misalignment between the transmitter and the receiver [32]. Additionally, in coastal and turbid harbor waters, the power losses at offsets away from the center reduce within an attenuation length range and then increase when exceeding a certain attenuation length, noticeably in turbid harbor water, following similar changing laws in Figure 4.  
The above analysis focuses on the characteristics of channel loss in underwater laser transmission. Channel loss has a significant impact on the strength of the received channel signal. The research indicates that channel loss is affected by water type, transmission distance, and the offset away from the center on the receiving plane. When the attenuation length increases, water bodies with strong scattering show a slower change in power loss compared to those with weak scattering. Additionally, the difference in channel energy loss at different offsets varies significantly across different water bodies and attenuation lengths.
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 3 SVM-Based Antenna Allocation
### Task 1: Designing a Training Sample Set
#### 3.1.2 Subtask 1-2. Training Set Design
\[T_{i,j}=\frac{Z_{i,j}-\mathbb{E}_{i}(Z_{i,j})}{\max_{i}(Z_{i,j})-\min_{i}(Z_{ i,j})}, \tag{7}\]  
where the term \(\max_{i}(Z_{i,j})-\min_{i}(Z_{i,j})\) in (7) indicates the normalization used for improving the learning speed/convergence or avoiding a precision issue with very large- or small-value data.
## 4 Discussion
### Privacy  
Big data are essential for developing universal and robust AI systems that can be later applied to healthcare purposes [68; 69]. Efforts have been undertaken to expand the availability and accessibility of healthcare data. Simultaneously, multiple entities overseeing privacy have been created in an attempt to strike a balance between patient privacy and the ability to exchange patient data for healthcare and research purposes [43; 44; 45; 46; 69]. According to our international survey, data unavailability, insufficient details on training and validation, and potential risks in external cloud processing or storage platforms are of concern. We propose the following recommendations for improving privacy:  
* _Data privacy impact assessments_. The implementation of rigorous data privacy impact assessments within healthcare centres permits finding practices that violate privacy and determining whether additional procedures need to be implemented to protect patient data further [70]. Likewise, procurement law must ensure all AI systems provided by third parties comply with strict privacy policies [70].
* _Audit trails_. Records of who is doing what, what data are being utilised/transmitted, and what system modifications are being performed must be retained, for example, by means of audit trails [70].
* _Cloud platforms and privacy_. Cloud platforms are excellent solutions for reducing operational costs and increasing productivity (processing speed, efficiency, and performance). At the same time, they require transmitting data from healthcare centres to an external location, thereby increasing the likelihood of tracking sensitive and private information [42; 71]. Appropriate strategies to regulate data access, anonymisation prior to transmission and storage are critical to ensure the privacy of patients is not put at risk.
* _Information availability_. Information regarding training and testing population characteristics, data acquisition protocols and equipment, and team expertise must be made clear and available to stakeholders since these permit determining potential limitations of the AI system. For example, evidence AI systems that have been tested on multiple and heterogeneous datasets need to be handed over by providers as proof it generalises well and can be safely deployed into healthcare practice.
## 3 Results
### Stepwise Multiple Linear Regression  
Table 2 and Figure 4 show the results of the stepwise multiple linear regression for various parameters and FMS scores (model 1). Table 3 and Figure 5 show the results of the stepwise multiple linear regression for various parameters and injury times (model 2). The variables considered in model 1 were KFA, PKFM, VAFM, and MPA, while those considered in model 2 were KFA, PKFM, AP GRF, and MPA. The parameters for the double-leg jump were prefixed with DLJ, while those for the single-leg jump were prefixed with SLJ.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Participants** & **Score** \\ \hline
1 & 17.0 \\
2 & 17.0 \\
3 & 18.0 \\
4 & 18.5 \\
5 & 17.5 \\
6 & 11.0 \\
7 & 18.5 \\
8 & 13.0 \\
9 & 15.5 \\
10 & 15.0 \\ Mean \(\pm\) SD & 16.1 \(\pm\) 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: FMS scores for different participants.  
Figure 2: The kinematic and dynamic parameters during the two jumps: (**a**) knee ROM, (**b**) ankle ROM, (**c**) KFM, (**d**) AFM, (**e**) AP GRF, (**f**) ML GRF, (**g**) CoM-CoP angle.  
Figure 4: The fitting result of the stepwise multiple linear regression model 1.  
Figure 3: The results of the statistical analysis: (**a**) knee and ankle ROM, (**b**) the PKFM and the VAFM, (**c**) peak AP and ML GRF, (**d**) CoM-CoP angle. *: \(p\) < 0.05, **: \(p\) < 0.005.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variables** & **Coeff. (SE)** & **95\% CI** & \(p\) \\ \hline DLJ-PKFM & \(-241.5880\) (\(41.8157\)) & \(-323.5487\)\(--159.6272\) & 0.0103 \\ DLJ-VAFM & \(-146.1730\) (\(20.9038\)) & \(-187.1436\)\(--105.2022\) & 0.0060 \\ DLJ-MPA & \(-0.5595\) (\(0.2149\)) & \(-0.9808\)\(--0.1383\) & 0.0802 \\ SLJ-KFA & \(0.2070\) (\(0.0434\)) & \(0.1219\)\(-0.2921\) & 0.0175 \\ SLJ-PKFM & \(117.3770\) (\(21.6048\)) & \(75.0324\)\(-159.7218\) & 0.0122 \\ SLJ-MPA & \(-0.7269\) (\(0.3005\)) & \(-1.3159\)\(--0.1379\) & 0.0943 \\ \hline \end{tabular}
\end{table}
Table 2: Stepwise multiple linear regression results of various parameters and FMS scores (model 1).  
The obtained regression model \(1\) was as follows:  
Predicted FMS \(=-241.5880\) DLJ-PKFM \(-146.1730\) DLJ-VAFM \(-0.5595\) DLJ-MPA \(+0.2070\) SLJ-KFA \(+117.3770\) SLJ-PKFM \(-0.7269\) SLJ-MPA \(+18.5806\), (\(p\) = 0.0392, R-square = 0.9552, Adj R-sq = 0.8657).  
The obtained regression model \(2\) was as follows:  
Predicted injury times \(=0.2618\) DLJ-KFA \(-188.5167\) DLJ-PKFM \(-1.4335\) DLJ-MPA \(+116.7351\) SLJ-PKFM \(-10.4907\) SLJ-AP GRF \(-0.5482\) SLJ-MPA \(+10.5691\), (\(p\) = 0.0145, R-square = 0.9774, Adj R-sq = 0.9322).
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 4 Implementation and Results
### Experimental Procedures and Results
#### Step 3 Vulnerability Analysis  
After obtaining port scan results, we sent the results of the open ports to the LLMs for vulnerability analysis. The prompt words and answer examples of GPT-4 and DeepSeek-R1 are listed in Table 5, respectively. As outlined in Table 5, both LLMs generated structured JSON outputs containing exploit paths, parameters, and success probabilities, showcasing their ability to translate port-scan results into actionable penetration-testing commands. Figure 3 illustrates that the LLMs' outputs are all in JSON format. They were intended to supply the complete path of the MSF module and the MSF console penetration-testing commands that can be directly run in the command line.  
Since LLMs may give different responses under the same prompt, we conducted three repeated experiments, sending the port-scan results to the LLM each time, and calculated the average number of response entries. Table 6 shows the port numbers analyzed using GPT-4 and DeepSeek-R1 in their three generations of results and the number of average analysis counts.  
The comparative analysis reveals methodological differences between GPT-4 and DeepSeek-R1 in vulnerability enumeration. Quantitative evaluation shows GPT-4 produced an average of 5 vulnerability analyses per assessment, while DeepSeek-R1 generated 5.7 instances. More critically, qualitative divergence emerges in two dimensions: scope completeness and vulnerability multiplicity handling.  
Regarding scope coverage, GPT-4 exhibited truncation at port 3306 during service enumeration, potentially constrained by inherent output length limitations. In contrast, DeepSeek-R1 demonstrated comprehensive port analysis, as evidenced by its complete reasoning trace documentation. Moreover, the LLMs diverged significantly in handling multi-vulnerability scenarios. For port 22 analysis, DeepSeek-R1 identified a single attack vector (\"exploit/linux/ssh/ssh_auth_bypass\"), whereas GPT-4 enumerated three distinct exploitation pathways: \"exploit/linux/ssh/ssh_auth_bypass\", \"exploit/multi/ssh/sshexec\", and \"auxiliary/scanner/ssh/ssh_version\". The observed variations underscore the need for model-specific output normalization when conducting automated vulnerability assessments across multiple LLM architectures.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Prompt**} \\ \hline \multicolumn{2}{c}{“Analyze the following Nmap scan results and provide:} \\
1. Possible vulnerabilities & \\
2. The Metasploit module (including the full path) is recommended. \\
3. Set required parameters. \\
## 5 Conclusions  
In this work, we demonstrated the physical model of the PolyX beamline created by combining two ray-tracing tools: XRT [5] and polycap libraries [7], which allowed us to simulate all crucial components of the beamline, including the polycapillary optics. The estimated flux was presented for different beamline configurations: (i) unfocused and focused white beam with various Al filters, (ii) DCM monochromator for unfocused and focused beam, and (iii) two types of DMM monochromators for unfocused and focused beam. Additionally, properties like transmission efficiencies and spot sizes were calculated for capillary lenses and AEXML. The results give better insight into the expected performance of the PolyX beamline and will be confronted with experimental data in the near future. The applied ray-tracing model can be also used for the preparation of different experiments in multiple measurement modes accessible at the end station. For example, an extension of the presented approach to simulate multi-beam X-ray imaging [16] or von Hamos spectrometers [17] seems to be feasible. The simulations of the X-ray spectra recorded for thin tissue slice samples with AEXML are very promising in terms of high-resolution, fast \(\mu\)-XRF imaging of elements up to zinc.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14177486/s1](https://www.mdpi.com/article/10.3390/app14177486/s1)]([https://www.mdpi.com/article/10.3390/app14177486/s1](https://www.mdpi.com/article/10.3390/app14177486/s1)). Data for four additional polycapillary lenses are presented in Supplementary Materials. It includes parameters of the lenses (Table S1), transmission efficiency (Figure S1), spot sizes (Figure S2), flux distribution in white beam mode (Figure S3), and flux with DMM and DCM (Figure S4).  
**Author Contributions:** Conceptualisation: P.K.; Methodology: F.K., P.W. and T.K.; Software: F.K.; Validation; F.K. and T.K.; Investigation: F.K. and P.W.; Writing--original draft: F.K.; Writing-review and editing: P.W., T.K., K.M.S. and P.K.; Visualisation: F.K. and P.W.; Supervision: P.W., M.S.-B. and P.K.; Project administration: K.M.S. and P.K.; Funding acquisition: P.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This publication was partially developed under the provision of the Polish Ministry of Science and Higher Education project \"Support for research and development with the use of research infrastructure of the National Synchrotron Radiation Centre SOLARIS\" under contract no 1/SOL/2021/2.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data presented in this study are available on request from the corresponding author.  
Figure 14: Simulated spectra of glass sample excited with polychromatic beam focused by AEXML.  
**Acknowledgments:** We are very grateful to [PERSON] for his help in proofreading the manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Electron Transfer-Based Assays
### ABTS\({}^{+}\) Assay
#### 4.4.2 Comparison of Methodological Approaches in Previous Research on Plant Materials  
The first protocol for the ABTS assay was developed by [PERSON] et al. [88]. In their method, ABTS was dissolved in water to a concentration of 7 mM, and 2.45 mM potassium persulfate was added, resulting in the formation of the ABTS\({}^{\bullet+}\) cation radical and a green-blue color change. The reaction mixture was incubated in the dark at room temperature for 12 to 16 h. After incubation, the ABTS\({}^{\bullet+}\) solution was diluted with ethanol to achieve an absorbance of 0.7 \(\pm\) 0.02 at 734 nm. Subsequently, 1 mL of ABTS\({}^{\bullet+}\) solution was mixed with 10 \(\upmu\)L of the ethanolic plant extract, and absorbance was measured at 734 nm after 1 to 6 min, with the entire procedure conducted at 30 \({}^{\circ}\)C [89]. This protocol has been widely adopted in subsequent studies [92; 93; 94; 95; 96; 97]. However, in the following years, researchers attempted to optimize this assay, which led to numerous methodological discrepancies (Table 4). Variations have been reported in the concentrations and absorbance values of the stock solution [98; 99; 100], as well as in the measurement wavelength [98; 99; 100; 101]. Additionally, discrepancies were observed in the incubation time of the ABTS\({}^{\bullet+}\) solution, which in some cases was shorter than the recommended minimum of 6 h [100; 103].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tested Material** & **Standard** & **Substrates** & **Type and Time of** & **Wavelength** & **Source** \\ \hline \multirow{4}{*}{Apricot} & \multirow{2}{*}{Trolox} & 1 mL of 10\% ABTS radical diluted in & \multirow{2}{*}{6 min, room temperature} & \multirow{2}{*}{734 nm} & \multirow{2}{*}{[65]} \\  & & ethanol, 4 mL of ethanol, 0.1–0.5 mL of & & & \\  & & sample extract, & & & \\ \hline \multirow{4}{*}{Various plant beverages} & \multirow{4}{*}{Gallic acid} & 1.425 mL of ABTS working solution & \multirow{4}{*}{10 min, room temperature} & \multirow{4}{*}{734 nm} & \multirow{4}{*}{[67]} \\  & & [equal volumes of ABTS solution (mM/L) & & & \\ \cline{1-1}  & & and potassium persulfate solution & & & \\ \cline{1-1}  & & (2.45 mM) were mixed, and after 16–20 h & & & \\ \cline{1-1}  & & in the dark, it was diluted with water at & & & \\ \cline{1-1}  & & the ratio 1:30], 0.075 mL of sample & & & \\ \hline \multirow{4}{*}{Meals in kindergarten} & \multirow{4}{*}{Ascorbic acid} & 3 mL of ABTS working solution [equal volumes of ABTS solution (7 mM) and potassium persulfate solution (2.45 mM) were mixed and after 16 h in the dark at 5\({}^{\circ}\)C, it was diluted with water at the ratio 1:50], 0.1 mL of sample & & \\ \cline{1-1}  & & 0.98 mL of ABTS working solution (equal volumes of: 2.5 mM of ABTS solution, 1 mM of 2,2-azio
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry
### Acting as Flavor and Fragrance
#### 3.1.1 Linalool  
Linalool is an acyclic monoterpene and is widely used in beverages and processed foods due to its flavoring and fragrant properties.  
It has two enantiomers, (S)-linalool and (R)-linalool. The two forms of linalool have different fragrance profiles and biological properties, while separation and purification are difficult when extracting from plants. [PERSON] et al. identified (S)-specific linalool synthase (LINS) and (R)-specific LINS and co-expressed them with mutated farnesyl diphosphate synthase (IspA*) in _Pantoea ananatis_ host. The titer of (S)-linalool and (R)-linalool reached 5.6 g/L and 3.71 g/L, respectively [91]. In order to improve the catalytic activity for linalool synthase, [PERSON] et al. semi-rationally designed its substrate binding pocket entrance and obtained a mutant t67 OMcLIS\({}_{\text{M}}\)F\({}^{447\text{E}}\) with higher affinity for GPP and higher catalytic activity to synthesize linalool. Furthermore, they targeted the linalool biosynthetic pathway to _S. cerevisiae_ peroxisomes and obtained a more stable diploid engineering strain through mating. The resultant strain achieved a titer of 2.6 g/L linalool in a 5 L fed-batch fermentation [89]. In another research, [PERSON] and co-workers introduced linalool dehydatase, which can isomerize geraniol to linalool and subsequently dehydrate linalool to myrcene, into _E. coli_. The engineered strain produced myrcene at a titer of 1.25 g/L in aerobic and anaerobic two-stage fermentation [27].
## 4 Experimental Results and Discussion
### Complexity Analysis  
We further analyze the complexity of the proposed J-DBN nonlinear equalizer and make a comparison with blind CMA and DD-LMS equalizers. We consider the complexity in two aspects, convergence steps and computation time, as is shown in Figure 8. Figure 8a shows that the convergence speeds of the DBN-1 and DBN-2 equalizers are faster than the conventional CMA and DD-LMS equalizers, which verifies the well-trained neural  
Figure 7: BER versus input power into each AIPM over one span of 20-km SSMF and 3-m wireless transmission for different proposed equalizers. (**a**) X-polarization. (**b**) Y-polarization.  
networks. Moreover, the computation time of DBN-based equalizers can be approximately 36.3% and 46% lower than that of CMA and DD-LMS-based methods with similar BER performance, as is shown in Figure 8b. Therefore, the conclusion can be reached that the J-DBN equalizer has a significant advantage in dealing with nonlinear distortion, making it quite suitable in high-speed THz-band wireless communication systems.
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 4 Simulation Results  
The proposed classifier structure's performance is evaluated in this section. For classification purposes, only the M-PSK and M-QAM signals are considered. The training and testing of the classifier show good classification accuracy. The proposed classifier structure is also compared with existing state-of-the-art techniques, which shows the supremacy of the classifier. The performance metric is the average classification accuracy (ACA). The simulation parameters are presented in the Table 1.  
The k-fold cross-validation technique was used for the data split, which chose the optimal set of hyperparameters for the GFN. Before training the model, hyperparameters such as the number of layers, filters per layer, and the learning rate were defined. The hyperparameters that give the best results can be chosen by adjusting these hyperparameters and assessing the model's performance on the validation set.  
After optimizing the GFN, the final model was trained on the whole training set, i.e., 80%, with the specified hyperparameters. The trained model's performance was then assessed on the test dataset, i.e., 10%, to approximate the model's performance on unseen data. The test set must be distinct from the training and validation dataset, i.e., 10%.
# Improvement Technique for Group Search Optimization Using Experimental Design Method
## 3 Proposed Method
### Robust-GSO Algorithm  
The GSO algorithm uses the rangers to exploit the search results. The PS mechanism of the GSO algorithm then compares the fitness values of the producers and selects the producer that has the best fitness for exploring the solution.  
Other individuals in the group are then moved close to the position of the individual with the best fitness value. If only a producer is selected for use in leading scroungers for positioning scroungers close to the best position, the local minimum would be easy to receive. Therefore, this paper proposes a multi-producer mechanism that improves the algorithmic capability of the GSO by integrating Taguchi method in the search for the best individual. Given the robust results obtained by integrating the Taguchi method, the proposed method is referred to as the robust-GSO algorithm (R-GSO).  
The R-GSO first provides a flexible number of producers \(X_{p}\). In each iteration the best and the second-best members (\(X_{p1}\) and \(X_{p2}\), respectively) are selected as the producers. To ensure diversity, one of the remaining producers is randomly selected to be the third producer, \(X_{p3}\). The three producers, \(X_{p1}\), \(X_{p2}\), and \(X_{p3}\), are then used to set the levels of variables when implementing Taguchi method. Thus, three crossover types are selected: \(X_{p1}\) and \(X_{p2}\) as the first and second levels, \(X_{p1}\) and \(X_{p3}\) as the first and second levels, and \(X_{p2}\) and \(X_{p3}\) as the first and second levels. Additionally, an \(L_{m}(2^{m-1})\) orthogonal table is selected. After the Taguchi method is performed, three new producers, \(X_{mp1}\), \(X_{mp2}\), and \(X_{mp3}\), can be obtained, and then the one with the best performance among them is the optimal producer, \(X_{qp}\).  
For example, each producer has five dimensions, i.e., \(X_{p1}=(X_{p1}^{1},X_{p1}^{2},X_{p1}^{3},X_{p1}^{4},X_{p1}^{5})\), \(X_{p2}=(X_{p2}^{1},X_{p2}^{2},X_{p2}^{3},X_{p2}^{4},X_{p2}^{5})\), and \(X_{p3}=(X_{p3}^{1},X_{p3}^{2},X_{p3}^{3},X_{p3}^{4},X_{p3}^{5})\). Table 8 shows an orthogonal table \(L_{8}(2^{7})\), and this table can satisfy the dimension, so it is used to determine the new producers. For illustration, \(X_{p1}\) and \(X_{p2}\) are adopted as the first and second levels, respectively. Because the dimension is five, the first five columns were selected for the crossover operation, and then each variable was put into the orthogonal table. Thus, Table 9 can be obtained. Table 9 shows the layout from \(X_{p1}\) and \(X_{p2}\) as the first and second levels, respectively. Based on the response table, a new producer, \(X_{mp1}\), is obtainable. The \(X_{mp2}\) and \(X_{mp3}\) can also be obtained by applying this rule. The tunable parameters should be one more for matching this architecture. Thus, Table 10 shows that a \(U_{10}(10^{6})\) uniform layout must be selected to fit six parameters. Figure 4 is a flowchart of the R-GSO.