esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Correlation-Based Adaptive Power Transmission for Free-Space Optical Communications
## 4 Conclusions  
In conclusion, a channel correlation-based adaptive power transmission is proposed by deeply analyzing the correlation between uplink and downlink turbulence channels for FSO communications. The power of the reverse transmission signal is dynamically adjusted according to the fluctuation strength of the received uplink or downlink transmission signal and the channel correlation coefficient between the uplink and downlink. The uplink and downlink channels with different link distances and turbulence intensities were modeled. The BER performance of the proposed technique was compared to the FTD and ATD techniques to verify the reliability of the proposed technique. Simulation results showed that the proposed technique had a significant improvement compared to the FTD technique, and it was close to the ATD technique. Therefore, it is a promising technique for FSO communication systems.  
Conceptualization, B.-S.Z.; Formal analysis, P.-F.L. and B.-S.Z.; Data curation, P.-F.L. and Y.-Q.H.; Project administration, B.-S.Z.; Writing--original draft preparation, P.-F.L. and B.-S.Z.; Writing--review and editing, B.-S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Educational Department of Liaoning Province General. Program (JYTMS20231214).  
Not applicable.  
Data Availability Statement: Dataset available on request from the authors.  
The authors declare no conflicts of interest.  
Figure 5: Comparison of the BER performance of the proposed technique with FTD and ATD techniques under different turbulence intensities for a 40 km FSO link. (**a**) Uplink transmission, (**b**) downlink transmission.
## 4 Numerical Results
### Results and Discussion
#### 4.3.1 Single Secondary User Identification Performance in an Unencrypted Environment  
In this experiment, we focused on evaluating the effectiveness of the proposed hybrid fingerprint model for camera identification, considering only a single secondary user and without incorporating a privacy-preserving strategy. The performance of our model was compared to the statistical noise model proposed by [PERSON] et al. As illustrated in Figure 7, we randomly selected several camera models from the Dresden dataset to verify the identification performance. In this setup, Pentax was chosen as the target camera model (\(C_{0}\)), while Sony, Samsung, Nikon, and Fujifilm served as non-target models (\(C_{1}\)). The goal was to determine whether an image was captured using a Pentax or not. Figure 7 provides a detailed comparison of the experimental results between the existing method by [PERSON] et al. [12] and our proposed approach under the same conditions. Notably, the Area Under the Curve (AUC) values obtained by our method consistently outperformed those of [PERSON] et al. across four different scenarios. This demonstrates the superiority of our proposed scheme in achieving more accurate camera identification results. The results in Figure 7 further highlight that combining unique camera fingerprints with our carefully designed tag enhanced the performance of identifying the camera model that captured the image. These findings validate the advantages of our hybrid fingerprint model in distinguishing between camera models, showcasing its potential for more reliable source camera identification.
## 3 Experiment and Discussion
### Determination of Spectral Distribution in Laser Additive Manufacturing Process  
For the steel alloy manufactured by the laser additive manufacturing process, the spectral characteristics of the laser additive manufacturing process are obtained by a spectrometer and a two-color pyrometer, and its intensity and light source wavelength window of optical measurement are analyzed.  
The DED equipment (LSF1500) developed by the Automation Research Institute of China Ordnance Equipment Group Co., Ltd. located in the Mianyang city of China is used in this work and shown in Figure 8. It employs a semiconductor laser with the wavelength of 1064 \(\pm\) 1 nm and the maximum power of 4 kW. The forming size is 1500 \(\times\) 1000 \(\times\) 600 mm\({}^{3}\) with the accuracy of 0.2-0.5 mm. The used material for 3D printing is high strength alloy steel, 30 CrMnSiNi2A. The process parameters are: laser power 0.8-2 kW, spot size 4 mm, and scanning speed 10 mm/s. The measurement spectrometer for the test is the ANDOR-echelle 5000 model from the Oxford Instruments Company located in the Oxford city of England, with an accurate measurement range of 200-975 nm and a measurement accuracy of \(\pm\)0.05 nm. The measurement range of the used two-color pyrometer is 600-2300 \({}^{\circ}\)C.  
The laser power was gradually increased during the printing and measuring process. When the laser power is 1200 W, the intensity of the visible light region is high enough, and the change range of the wavelength intensity of other bands is very small, so the main distribution of the spectrum can be clearly identified. The maximum temperature of the 30 CrMnSiNi2A material during laser additive manufacturing is 1900-2050 \({}^{\circ}\)C, and the  
Figure 8: Printing environment and instrument composition.  
temperature of the substrate and the deposited part is less than 600 \({}^{\circ}\)C. The measurement results of the corresponding spectrometer are shown in Figure 9.  
For 30 CrMnSiNi2A steel, the measured spectral distribution in the visible and infrared bands is significantly stronger than that in other bands. However, the intensity fluctuation of the spectrum below the wavelength of 380 nm is not significantly related to the additive manufacturing process, and the intensity is much lower than that of other bands. This also means that we can choose a suitable spectral band to avoid the radiation interference of all temperature segments in the DED process. Therefore, a UV light source below 380 nm is selected as the measurement light source in this paper.
applied sciences Article  
The Effect of Freezing Raw Material on the Quality Changes and Safety of Salled Anch movies (_Engraulis encrasicolus_, [PERSON], 1758) at Cold Storage Conditions  
[PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 3,* (c)  
1 Department of Seafood Processing, Faculty of Fisheries, Izmir Katip Celebi University, Izmir 35620, Turkey; [EMAIL_ADDRESS]  
2 Department of Fisheries Technology Engineering, Faculty of Marine Sciences, Karadeniz Technical University, Trabzon 61530, Turkey; [EMAIL_ADDRESS]  
3 Department of Analytical Chemistry, Faculty of Chemistry and Chemical Technology, University of Ljubljana, Veena pr 113, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
This study demonstrates the effects of the freezing and frozen storage of anch movies prior to brining and dry-salting on quality changes and food safety during refrigerated storage (\(4\pm 1\)\({}^{\circ}\)C). Fresh anch movies were divided into two groups, one of which was used as a control representing fresh raw material; the other was the experimental group and consisted of frozen and thawed anch movies stored at \(-18\)\({}^{\circ}\)C for a year. Five different salt concentrations were used for brining (10, 15, 20, 25 and 30%) in addition to the dry-salting method. Microbiological, chemical, physical and sensory analyses were carried out during storage. Salt concentration and salting method had significant effects on the shelf-life of salted anchovy products, with the highest shelf-life corresponding to dry-salted anchovies (\(p<0.05\)). The effect of using frozen and thawed raw materials for salting on the shelf-life depended on the processing method since the experimental dry-salted group had a shelf-life one month longer than that of the control group, while the opposite situation occurred for the brined samples, with one exception. Strong correlations were usually found between sensory values and chemical quality parameters (R\({}^{2}\): 0.83-0.99 for the control group and 0.63-0.99 for the experimental group). The results demonstrated that the experimental group, with some exceptions, had better values for most quality and food safety parameters in comparison to the control group, indicating the advantage of using frozen and thawed raw materials before salting to prevent spoilage and enhance food safety. Considering that the experimental group was produced from one-year-stored raw material compared to the control group, the advantage of the freezing and frozen storage of anchovies can be accepted as much higher versus when freezing is not implemented. The positive effect is due to the fact that frozen anchovies absorb salt faster after thawing, especially in groups with high salt concentrations. Therefore, it can be concluded that frozen salted anchovies can be utilized for longer as they have a longer shelf-life, particularly when using either the 30% brining or dry-salting method.  
anchovy; freezing effect; salting; shelf-life; quality changes; biogenic amines; food safety
## 4 Antimicrobial Agents
### Surface Attachment Inhibitors  
The initial phases of attachment are very crucial in the biofilm development process. The control of surface attachment can inhibit the whole process of biofilm development. Biofilm formation can also be inhibited by the inhibition of adhesin and EPS molecules. When the bacteria have short-range interaction with the surface, the hydrophobic interactions, dipole, ionic, and hydrogen bonds begin to dominate over some other interactions, and then bacteria start to attach through the cellular or molecular phase [29] (Figure 1). Surfactants are the popular choice of antimicrobial agents for inhibiting bacterial adhesion to the surfaces as they decrease the interfacial tension between two substances. Surfactants are amphiphilic as they comprise of hydrophilic and hydrophobic moiety and at the same time, they can be categorized as non-ionic, anionic, cationic, and amphoteric surfactants [27]. Triton X-100 and Tween 80 (Polysorbate 80) are the two popular non-ionic, synthetically derived, and regularly used surfactants in laboratories (Table 2). Tween 80 decreased _S. aureus_ medical device associated biofilm development at concentrations safe in humans [30]. Triton X-100 could stimulate autolysis by enhancing bacterial vulnerability to antibiotics and altering the architecture and physiological features of biofilms by reducing the protein and carbohydrate constitution in the EPS [31; 32]. Biosurfactants are the surface-active compounds created by microorganisms that comprise structurally diverse biomolecules [33]. Cationic surfactants such as quaternary ammonium compounds (QACs) are used as disinfectants within the food industry and used in several medical conditions. QACs bind to negatively charged areas in microbes that cause stress to the cell wall, lysis, and cell death. QACs could also cause protein denaturation that affects cell wall permeability and reduces the uptake of nutrients. Non-ionic-based surfactants incorporating poloxamer 188, are regarded as non-cytotoxic and therefore represent a useful combination in wound care. Numerous studies using antimicrobials together with poloxamers showed enhanced antimicrobial efficacy [34]. Similar to regular surfactants, numerous biosurfactants have antimicrobial activities while some even seem to prevent surface colonization by pathogens [27]. One of them is rhamnoid, which is the chief glycolipid formed by several bacterial species, chiefly by _P. aeruginosa_[35], and stimulates biofilm dispersal in _P. aeruginosa_, _S. aureus_, _Salmonella enteritidis_, and _Listeria monocytogenes_[36]. Furthermore, rhamnoids from _P. aeruginosa_ W10 were also known to disperse biofilms of various industrial bacterial strains on the pipelines [37]. Since biosurfactants are usually associated together with isomers and cogeners and rarely in pure form, the purification process could be exhaustive and expensive [38]. At the same time, they could be very cytotoxic and hemolytic due to their activity with cellular membrane [39]. Therefore, their use for controlling biofilms could be limited to coating medical devices and anti-adhesive agents.
# LoS Theoretical and Experimental MIMO Study from 1-40 GHz in Indoor Environments
## Appendix A
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Position** & **Simulated (ns)** & **Measured (ns)** & **Diff. (dB)** \\ \hline
1 & 72.88 & 71.32 & 1.56 \\
2 & 72.33 & 71.11 & 1.22 \\
3 & 72.89 & 72.04 & 0.85 \\
4 & 72.08 & 69.35 & 2.73 \\
5 & 70.80 & 69.83 & 0.97 \\
6 & 70.34 & 68.61 & 1.73 \\
7 & 67.97 & 65.33 & 2.65 \\
8 & 68.68 & 66.56 & 2.12 \\
9 & 70.25 & 68.71 & 1.55 \\
10 & 67.94 & 68.45 & 0.51 \\
11 & 65.65 & 65.61 & 0.04 \\
12 & 62.08 & 62.97 & 0.89 \\
13 & 68.17 & 68.68 & 0.51 \\
14 & 65.15 & 65.78 & 0.63 \\ - & - & Mean & 1.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the measured and simulated PL between for the WRC band 24.25–27.5 GHz. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Position** & **Simulated (ns)** & **Measured (ns)** & **Acc. (\%)** \\ \hline
1 & 16.13 & 17.76 & 90.81 \\
2 & 16.77 & 17.34 & 96.73 \\
3 & 18.31 & 18.80 & 97.38 \\
4 & 14.48 & 16.35 & 88.58 \\
5 & 13.53 & 15.11 & 89.51 \\
6 & 12.38 & 13.38 & 92.49 \\
7 & 8.33 & 8.50 & 97.91 \\
8 & 9.77 & 10.08 & 96.94 \\
9 & 11.51 & 10.86 & 94.01 \\
10 & 8.31 & 9.67 & 85.95 \\
11 & 5.79 & 6.51 & 88.83 \\
12 & 3.79 & 4.10 & 92.49 \\
13 & 8.24 & 8.91 & 92.54 \\
14 & 5.44 & 5.68 & 95.72 \\ - & - & Mean & 92.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the measured and simulated mean delay for the WRC band 24.25–27.5 GHz. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Position** & **Simulated (ns)** & **Measured (ns)** & **Acc. (\%)** \\ \hline
1 & 16.13 & 17.76 & 90.81 \\
2 & 16.77 & 17.34 & 96.73 \\
3 & 18.31 & 18.80 & 97.38 \\
4 & 14.48 & 16.35 & 88.58 \\
5 & 13.53 & 15.11 & 89.51 \\
6 & 12.38 & 13.38 & 92.49 \\
7 & 8.33 & 8.50 & 97.91 \\
8 & 9.77 & 10.08 & 96.94 \\
9 & 11.51 & 10.86 & 94.01 \\
10 & 8.31 & 9.67 & 85.95 \\
11 & 5.79 & 6.51 & 88.83 \\
12 & 3.79 & 4.10 & 92.49 \\
13 & 8.24 & 8.91 & 92.54 \\
14 & 5.44 & 5.68 & 95.72 \\ - & - & Mean & 92.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the measured and simulated PL between for the WRC band 24.25–27.5 GHz.
## 4 Results  
### Descriptive Analysis  
Demographics of the respondents are shown in Table 1. Two hundred ninety-three (293) participants completed the questionnaire for choice A. Out of these 293 participants, 232 were men (79%) and 61 were women (21%). Most of the participants belonged to the youngest age group, aged < 30 (79.2%), followed by middle-aged group, aged 30-50 (19.5%). Only 1.4% belonged to the oldest age group, aged > 50. The major purpose of travelling was reported to be education and work, with 58% and 34%, respectively. On the other hand, two hundred (200) participants completed the questionnaire for choice B. Out of the 200 participants, 146 were men (73%) and 54 were women (27%). Most of the participants belonged to the youngest age group, aged < 30 (79%), followed by middle-aged group, aged 30-50 (20%). Only 1% belonged to the oldest age group, aged > 50. Again, the major purpose of travelling was reported to be education and work, with 54% and 40%, respectively. The distribution of responses to satisfaction levels (section D of the questionnaire) is shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Airlift**} & \multicolumn{3}{c}{**SWVL**} \\ \cline{3-6}  & & **N** & **\%** & **N** & **\%** \\ \hline \multirow{3}{*}{Gender} & Male & 146 & 73.0 & 232 & 79.2 \\  & Female & 54 & 27.0 & 61 & 20.8 \\  & \textless{}30 & 158 & 79.0 & 232 & 79.2 \\ \multirow{3}{*}{Age} & 30–50 & 40 & 20.0 & 57 & 19.5 \\  & \textgreater{}50 & 2 & 1.0 & 4 & 1.4 \\  & \textless{}30,000 & 33 & 16.5 & 41 & 14.0 \\ \multirow{3}{*}{Income} & 30,000–60,000 & 105 & 52.5 & 166 & 56.7 \\  & \textgreater{}60,000 & 62 & 31.0 & 86 & 29.4 \\ \multirow{3}{*}{Marital} & Single & 165 & 82.5 & 230 & 78.5 \\ \multirow{3}{*}{Status} & Married & 35 & 17.5 & 63 & 21.5 \\  & Student & 121 & 60.5 & 185 & 63.1 \\ \multirow{3}{*}{Profession} & Businessman & 7 & 3.5 & 22 & 7.5 \\  & Private Employee & 56 & 28.0 & 64 & 21.8 \\ \multirow{3}{*}{Education} & Govt. Employee & 14 & 7.0 & 18 & 6.1 \\  & Other & 2 & 1.0 & 4 & 1.4 \\ \multirow{3}{*}{Education} & Primary & 1 &.5 & 1 & 0.3 \\  & Middle & 1 &.5 & 5 & 1.7 \\ \multirow{3}{*}{Education} & High School & 37 & 18.5 & 43 & 14.7 \\  & Bachelors & 124 & 62.0 & 199 & 67.9 \\ \multirow{3}{*}{Driving} & Master’s and Higher & 37 & 18.5 & 45 & 15.4 \\  & Yes & 73 & 36.5 & 124 & 42.3 \\ \multirow{3}{*}{License} & No & 127 & 63.5 & 169 & 57.7 \\  & 1–2 & 3 & 1.5 & 9 & 3.1 \\ \multirow{3}{*}{Family} & 3–5 & 123 & 61.5 & 194 & 66.2 \\ \multirow{3}{*}{Members} & 5–7 & 69 & 34.5 & 85 & 29.0 \\  & \textgreater{}7 & 5 & 2.5 & 5 & 1.7 \\ \multirow{3}{*}{Car} & Yes & 81 & 40.5 & 140 & 47.8 \\ \multirow{3}{*}{Ownership} & No & 119 & 59.5 & 153 & 52.2 \\ \multirow{3}{*}{Motorcycle} & Yes & 133 & 66.5 & 228 & 77.8 \\ \multirow{3}{*}{Ownership} & No & 65 & 32.5 & 65 & 22.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of respondents’ socio-economic demographics.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
### Contributions  
The elevation angle characterization is relevant for LEO satellites because it is an always-changing variable for those communication systems. The effects of the elevation angle variations are observable in the received power, link quality, and channel behavior; then, an accurate characterization of this variable is a topic of interest for planning and implementation of LEO satellite systems.  
In this article, we have addressed the characterization of the elevation angle for LEO satellites, by obtaining its PDF and CDF, as well as the derivation of its first and second order statistics. First and second order statistics are relevant to evaluate the suitability of different LEO orbits and to determine which orbits are more convenient to provide coverage for a particular application or Earth station location.  
In addition, we have developed an extensive analysis to validate our results, and we include supplementary materials containing elevation angle times series. The supplementary materials will facilitate reproducibility of our work, and will allow future research based on our proposed methodology and results.
# Analysis of _In Vivo_ Plant Volatiles Using Active Sampling and TD-GC\(\times\)GC-TOFMS
## 2 Materials and Methods
### GC\(\times\)GC-FID/TOFMS Method  
An Agilent 7890A GC with an FID (Agilent Technologies, Santa Clara, CA, USA) equipped with an INSIGHT flow modulator (SepSolve Analytical, Waterloo, ON, Canada) and Markes BenchTOF Select (Markes International Ltd., Bridgend, UK) mass spectrometer was used for the analysis. The first-dimension column used was a 30.0 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m film thickness Rtx-5 column (Chromotagraphic Specialties, Brockville, ON, Canada). The second-dimension column was a 5.0 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m film thickness Rtx-17 (Chromographic Specialties, Brockville, ON, Canada). Helium (5.0 grade; Linde, Burr Ridge, IL, USA) was used as the carrier gas. The GC oven program was 50 \({}^{\circ}\)C (3 min), followed by an 8 \({}^{\circ}\)C/min ramp to 280 \({}^{\circ}\)C (4 min). Carrier gas flows were set to 0.73 mL/min in \({}^{1}\)D and 15.0 mL/min in \({}^{2}\)D with a modulation period of 2.2 s with a flush time of 100 ms, and the modulator operated in reverse fill-flush mode. After the \({}^{2}\)D separation, a purged microfluidic splitter (Markes International Ltd., Bridgend, UK) was used to split the flow between the FID and the MS. The MS and FID transfer lines were the deactivated fused silica of dimensions 2.5 m \(\times\) 0.18 mm and 5 m \(\times\) 0.32 mm, respectively. The hydrogen flow for the FID was 30 mL/min, the air flow was 400 mL/min, and the helium makeup flow was 20 mL/min. The MS used electron ionization at 16 eV and 70 eV in tandem with a \(m/z\) range of 40-600. The data acquisition rates of the Bench-TOF and the FID were set to 100 Hz.
# Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
## 4 Experiments
### Classification
#### 4.1.3 Results  
Table 1 compares the performance of ShufflePointNet for classification to recent state-of-the-art models in terms of accuracy and model complexity. Specifically, we decided to use as metric the mean per-class accuracy (mA%), the overall accuracy (OA%), the number of model parameters (Million), FLOPs (Million), and forward time (ms) on the ModelNet40 benchmark dataset [36]. It is worth mentioning that, compared to the backbone structure _PointNet++_, our model outperforms it by 1.4% in terms of accuracy, whilst also significantly reducing the amount of parameters, FLOPs and forward time by 41%, 75% and 47.8%, respectively. These results are strongly supporting the effectiveness of our deep-wide model as a reliable solution having high accuracy and lower computational complexity.  
Our model achieves comparable performance with PointCNN [14], which is also point-based-convolution methods over an unordered point cloud. PointCNN attempts to permute the neighbouring points into a canonical order by learning a feature transformer, after which standard CNNs are available to use. However, the performance is far from ideal due to the fact that the positions of neighbours are unordered and unpredictable, while ShufflePointNet uses MLP (Multi-Layer-Perceptron) convolutions over center points and corresponding neighbours, which is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **mA(\%)** & **OA(\%)** & **Params** & **FLOPs** & **Time** \\ \hline VoxNet [8] & 83.0 & 85.9 & - & - & - \\ PointNet [10] & 86.0 & 89.2 & 3.48 M & 957 M & 14.7 ms \\ PointNet++ [11] & - & 90.7 & 1.99 M & 3136 M & 32.0 ms \\ KC-Net [38] & - & 91.0 & - & - & - \\ SpecGCN [39] & - & 91.5 & 2.05 M & 1112 M & 11254 ms \\ KD-Net [23] & - & 91.8 & - & - & - \\ DGCNN [12] & **90.2** & 92.2 & 1.84 M & 2768 M & 52.0 ms \\ PCNN [40] & - & 92.3 & - & - & - \\ \hline Ours & 90.1 & 92.5 & 1.17 M & **770 M** & 16.7 ms \\ \hline PointCNN [14] & 88.8 & 92.5 & **0.6 M** & 1682 M & **13.0 ms** \\ RS-CNN [15] & - & **93.6** & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification results on the ModelNet40 dataset.  
naturally invariant to point cloud ordering. In addition, the deep and wide architecture allows for exploiting sufficient features with less computation. However, the receptive field of CNN is much larger than that of MLP, which leads to the fact that PointCNN still can achieve remarkable performance by leveraging powerful CNNs over approximate ordering of neighbours.  
It is also possible to observe that _PointCNN_[14] has larger FLOPs but minor forward time, while _SpecGCN_[39] has similar FLOPs to _PointNet_ but requires a much longer inference time. In addition, these results confirm our hypothesis that the use of FLOPs is an indirect method of estimating the model complexity, and it cannot be used as the only metric to estimate the computational efficiency of a deep learning model.