esa-sceva/satcom-chunk-collection huggingface dataset:
# Educational Organization's Security Level Estimation Model
###### Abstract  
During the pandemic, distance learning gained its necessity. Most schools and universities were forced to use e-learning tools. The fast transition to distance learning increased the digitalization of the educational system and influenced the increase of security incident numbers as there was no time to estimate the security level change by incorporating new e-learning systems. Notably, preparation for distance learning was accompanied by several limitations: lack of time, lack of resources to manage the information technologies and systems, lack of knowledge on information security management, and security level modeling. In this paper, we propose a security level estimation model for educational organizations. This model takes into account distance learning specifics and allows quantitative estimation of an organization's security level. It is based on 49 criteria values, structured into an AHP (Analytic Hierarchy Process) tree, and arranged to final security level metric by incorporating experts' opinion-based criteria importance coefficients. The research proposed a criteria tree and obtained experts' opinions lead to educational organization security level evaluation model, resulting in one quantitative metric. It can be used to model different situations and find the better alternative in case of security level, without external security experts usage. Use case analysis results and their similarity to security experts' evaluation are presented in this paper as validation of the proposed model. It confirms the model meets experts-based information security level ranking, therefore, can be used for simpler security modeling in educational organizations.  
Keywords:information security; cybersecurity; e-learning; estimation; modeling +
Footnote †: journal: Applied Sciences
# Image Mosaicing Applied on UAVs Survey
## 4 Aerial Panorama Applications
### Feature-Based: Global Single Transformation
#### 4.1.6. BRISK  
Comparable with BRIEF applied in ORB, BRISK methodology outperforms SIFT and SURF in speed. With similar results and low calculation cost, this makes it ideal for UAV aerial panoramas, as shown with previous methods. An improved BRISK methodology developed to acquire reliable control points for image registration is presented in [103].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Advantage** \\ \hline [PERSON], [PERSON], [PERSON]. [100] & The algorithm is relatively fast compared to alignment algorithms based on SIFT feature matching with a high-quality alignment. \\ \hline [PERSON]. [PERSON], [PERSON] [102] & A real-time reconnaissance and monitoring application can achieve an accurate positioning without the need of increasing the camera accuracy. \\ \hline [PERSON], [PERSON] [101] & Implementing a semiautomatic object tracking method using SIFT or SURF with a high detection rate, the region of interest is specified by the user. \\ \hline [PERSON], [PERSON], [PERSON] et al. [99] & The workflow approach generates an automatic mosaic of UAV images with the flexibility to edit the workflow depending on the user needs. \\ \hline \hline \end{tabular}
\end{table}
Table 6: SURF applied on UAVs.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Advantage** \\ \hline [PERSON] et al. (2018) [95] & The LPM with Bayesian framework improves the computation time and the efficiency while ensuring accuracy compared with the state-of-the-art methods. \\ \hline [PERSON] et al. (2019) [42] & By using fisheye lenses, a good region of overlap is obtained between adjacent cameras. \\ \hline [PERSON]. [PERSON] et al. (2018) [96] & ORB / PCA splice detection is faster and more accurate than the classic SIFT and SURF approaches. In addition, the GPU performs the test 2.6 times faster than the CPU test. \\ \hline [PERSON] et al. (2019) [97] & The methodology reduces the calculation time of completing the reconstruction of the panorama compared to SIFT and classic ORB. \\ \hline [PERSON] et al. (2013) [53] & A fast visual odometry tracking system is developed. \\ \hline \hline \end{tabular}
\end{table}
Table 5: ORB applied on UAVs.  
The spatial relationship is analyzed, with the key points derived from the coincidence of descriptors to eliminate the corresponding false points. This methodology proves to be 4.7 times faster than classic BRISK. The use of ground control points allows a more accurate position, as shown in [15], where ground control points are used for thermal orthomosaics generated by BRISK and an RGB camera analyzes the blooming of flowers for their apple orchard management system. This information is summarized and presented in Table 7.
## 2 Materials and Methods  
### Study Site Characterisation  
A pilot-IMTA system was settled in a local aquaculture of sea bream and sea bass (40.12 North, \(-\)8.836 East, Lavos, Figueira da Foz, Portugal) and was composed by six semi-transparent plastic cubic tanks (High-Density Polyethylene (HDPE), 1000 L) with galvanised frame and UN 31 HA1/Y food certification (SIIE--Sociedade Internacional de Embalagens, S.A.).  
The pilot system was aligned into two parallel rows (3 tanks per row). An aeration system was installed in each row, presenting a membrane air compressor (AirPump DB-100, flow \(\approx\) 6 m\({}^{3}\)/h) connected to a perforated hose (Ethylene Propylene Diene Methylene rubber (EPDM)), spirally fixed on the bottom of each tank. The top of the tanks was protected by a white mesh.  
A submersible pump (HCP-IC215T, 1.5 Hp, 2\" (400 V), flow \(\approx\) 25 m\({}^{3}\)/h) was installed in one of the aquaculture tanks (where sea bass and sea bream are produced) and connected to the pilot system through a PVC pipe. At the inlet of each tank row, an in-line filtration system of two 50 \(\upmu\)m filters was installed to filter the effluent and reduce the presence of suspended solid material. No alterations to the normal functioning of the aquaculture were made throughout the experimental trials.  
For the trials, specimens of _Ulva_ spp. were generally collected in _Mondego_ river estuary and in _Buarcos_ coast. _Mondego_ river estuary is an 860 ha transitional area divided by an alluvium-formed island (Murraceira island) in two arms--North and South-with dissimilar hydomorphological characteristics. Approximately 2 km long, the north arm can reach 5 to 10 m of depth during high tide. The south arm is 7 km long and is shallower than the north arm, reaching 2 to 4 m of depth during high tide.  
Both estuary margins are subjected to continuous anthropogenic pressure given its location next to an urban center (_Figueira da Foz_ city (Portugal)), holding _Figueira da Foz_ harbour (at the north arm), small mercantile and fishing harbours, several small and medium-sized enterprises (salt-works, aquaculture farms, paper and canning industries) and receiving run-off from upstream agriculture activities [21]. The south arm presents longer water residence than north arm and the constant flow of nutrients from anthropogenic activities promotes the proliferation of macroalgae, marine and salt marsh plants all year [22].  
With a WNW-ESE orientation, _Buarcos_ is a rocky bay located north to the city of _Figueira da Foz_, which extends until _Cabo Mondego_, a natural sea cliff [23]. This area is a relevant touristic area, the shoreline being highly populated in summer period. This area has low influence from the river since the costal currents are generally north-south oriented [23]. This rocky area is characterised for having one of the most diverse macroalgae communities in Portugal, probably due to its location within a transitional area of cool and temperate waters which provides high variation of species round the year [24].
# On-the-Fly Machine Learning for Improving Image Resolution in Tomography
## 4 Results
All networks are trained from scratch in each experiment. The training procedure minimizes the mean square error between the output and target images, and the networks are trained using the ADAM algorithm [37] with a mini-batch size of one. The network output is evaluated on a test set containing the low- and high-resolution reconstructions of the upper region of interest. The training and test set are thus non-overlapping. **Metrics and evaluation** On both datasets, the on-the-fly machine learning approach is evaluated using the structural similarity index (SSIM) [38] and the mean square error (MSE) metrics. These metrics are used to compare the output slices of the networks to target slices from the upper ROI reconstruction. To compare methods A and B on the same grid, the output volume of method B is cubically up-sampled before computing the MSE and SSIM metrics. The output slices of method A are not processed, since they are the same size as the target slices of the test set. Both metrics are computed slice by slice and averaged. To prevent the influence of reconstruction artifacts at the boundaries of the high-resolution reconstructions, all metrics are calculated on pixels that are at least eight pixels from the boundary of the volume. Likewise, all displayed images are cropped to remove eight pixels from all sides. The error metrics of our approach are compared to a baseline: a full-volume three-dimensional cubic up-sampling of the low-resolution input volume. We visually compare all methods on the central slice of the upper ROI reconstruction on both the simulated and the experimental data. All computations were performed on a server with 192 GB of RAM and four Nvidia GeForce GTX 1080 Ti GPUs (Nvidia, Santa Clara, CA, USA) or on a workstation with 64 GB of RAM and one Nvidia GeForce GTX 1070. Figure 5: For each object, three projection datasets are acquired. The first dataset has the entire object in the field of view, and its reconstruction is used as input data for the learning step. The second dataset has a central region of interest in view (dark gray), the reconstruction of which is used as target data for the learning step. The third dataset has an upper region of interest in view (dark gray), the reconstruction of which is used as target data for evaluation.
## 2 Nanoparticle-Modified Electrodes
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Electrode** & **Antibiotic** &
\begin{tabular}{c} **Determination** \\ **Technique** \\ \end{tabular} & **Detection Limit** & **Sample** & **Reference** \\ \hline GCE modified with & & & & & \\ reduced graphene & & & & & \\ oxide (rGO) and silver & chloramphenicol & & & & \\ nanoparticles & & & & & \\ \hline paraffin composite & & & & 24 nmol L\({}^{-1}\) & & \\ electrode with & & & (6.1 \(\mu\)g L\({}^{-1}\)) for & & \\ multi-walled carbon & sulfamethoxazole & differential pulse & sulfamethoxazole and & natural water & [48] \\ nanotubes (MWCNT) & and trimethoprim & voltammetry & 31 nmol L\({}^{-1}\) & & \\ modified with antimony & & & (9.0 \(\mu\)g L\({}^{-1}\)) for & & \\ nanoparticles & & & trimethoprim & & \\ \hline GCE modified with & & & & \\ platinum nanoparticles & tetracycline & & voltammetry & 4.28 \(\mu\)mol L\({}^{-1}\) & urine & [62] \\ supported on carbon & & & & & \\ \hline CP modified with & & & & 0.0021 \(\mu\)M and & & \\ graphene and copper & & & stripping & 0.0025 \(\mu\)M gatifloxacin & shrimp and & \\ nanoparticles & perflocacin & voltammetry & and perflocacin, & chicken serum & \\ \hline Glassy carbon electrode & & & & \\ modified with graphene & & & & \\ oxide decorated with & & & & \\ Cu–Ag core–shell & & & & \\ nanoparticles & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summarization of selected published papers about different electrodes modified with nanoparticles for the determination of antibiotics in various samples.
# Cationic Lipid Derived from a Basic Amino Acid: Design and Synthesis
## 4 Discussion
This size is considered suitable for gene transfer, as studies have shown that small unilamellar liposomes (~70 nm) are more efficient for gene transfer compared to larger ones (~135 nm) [70; 71]. In the case of lipoplexes, there was no variation in size when genetic material was added. However, lipoplexes formed with the mixture of lipids containing DOPE exhibited associations between them with tubular connections, resembling a beaded necklace, which has been described with other CLs [72]. Agarose gel electrophoresis was performed to determine the interaction between liposomes and pDNA, using a lipid/DNA (\(w/w\)) ratio of 1:0.4 as a reference, as this ratio resulted in the best transfection efficiency with the tested CLs. Increasing the amount of pDNA relative to the amount of liposomes resulted in free or weakly associated pDNA on the liposome surface (retarded pDNA), saturating the positive charges on the liposome surface and hindering interaction with the cell membrane, consequently reducing transfection [73; 74]. Conversely, decreasing the amount of DNA relative to the amount of liposomes allowed better interaction and uptake by the cell, as surplus positive charges remained for the interaction during transfection. The lipid/DNA ratio of 1:0.4 showed the best transfection efficiencies, which implies that this ratio is ideal to allow the amount of pDNA used to associate with the positive charges on the liposome's surface without saturating it completely. This ensures there are remaining positive charges on the lipoplex surface for interaction with the cell membrane during transfection. Modifying this ratio altered transfection efficiency accordingly. This study successfully demonstrates the potential of CholCadLys as a cationic lipid for gene delivery, though several limitations need to be addressed. Conducted in the Hek-293 FT cell line, which is known for its high transfection susceptibility, transfection efficiency may vary significantly across other cell lines or primary cells, potentially limiting its broader applicability. Additionally, while CholCadLys showed low cytotoxicity in vitro, further in vivo studies are essential to confirm its safety profile, particularly regarding the metabolic pathways of its hydrolysis products (lysine, cadaverine, and cholesterol) and their interactions within complex physiological environments. The purity of the synthesized CholCadLys lipid was assessed by thin-layer chromatography and mass spectrometry; however, minor contaminants may still influence liposome stability and transfection efficiency. Finally, the stability and solubility of CholCadLys-based lipoplexes were evaluated in vitro, so their behavior in physiological environments remains to be fully validated. This limitation highlights the need to assess potential degradation, interactions with serum proteins, and efficacy under in vivo conditions. Addressing these limitations could enhance the utility of CholCadLys-based lipoplexes as robust gene delivery systems in various biomedical applications.
## 3 Results and Discussion
### Honey Matrix  
From the analysis of the concentrations of elements in honey (mg kg\({}^{-1}\), dry weight), it was observed that site 1 (Ponte Rio) showed the highest average values of Pb (0.024) and Zn (9.44) in June, while for Hg (0.009), V (0.02) and Fe (24.51), it showed the highest average values in October. On the other hand, site 2 (Ponte Valleceppi) showed the highest average values for the remaining elements in June: Cr (0.03), Co (0.07), Ni (0.60), As (0.004), Cd (0.007), Al (70.45), Mn (6.18), and Cu (12.57). Finally, site 3 (Pianello) did not show the maximum contamination values in either June or October (Table 1d).  
The Honeybee Contamination Index for honey allowed for the assessment of contamination by Cd, Pb, Cr, and Ni. The HCI showed a low level of contamination by Cd, except for site 2 in June (intermediate level of contamination), and by Pb, except for site 1 (intermediate level of contamination). Cr showed an intermediate level of contamination in all cases, except for site 2 in June (high level of contamination) and site 3 (low level of contamination). Finally, Ni showed an intermediate level of contamination, except for site 2 in June (high level of contamination) and at both site 2 and site 3 in October, with a low level of contamination (Figure 3).  
The comparison of the average concentrations of elements between the honeybee body and honey matrices shows the following sequence of honeybee/honey ratios: Al (0.33) and Hg (0.53), both \(<\) 1; V (1.50), Cr (2.15), Ni (2.60), Cu (2.84) Fe (6.89), Co (7.11), As (10.35), Pb (10.74), Zn (14.59), Mn (21.89), and Cd (30.66). It is evident that there is a biofiltration mechanism in the production of honey for certain elements, such as Fe, Co, As, Pb, Zn, Mn, and especially for Cd.  
The process of removing some elements from honey is probably due to the considerable chelating activity of certain enzymes [55]. In fact, the process of transformation of nectar into honey in the crop (the expansion of the terminal oesophageal tract) leads to the formation of temporary biocomplexes, in which some elements are chelated to organic molecules. These biocomplexes are then able to pass through the crop and reach the stomach, in particular the proventriculus, which can capture them [55].  
Figure 3: The Honeybee Contamination Index (HCI) for honey samples collected from the three sites of the study area during the two sampling occasions (June and October 2022). (**a**) Cd honey HCI; (**b**) Cr honey HCI; (**c**) Ni honey HCI; (**d**) Pb honey (HCI).
# Reliability of Perceptual-Cognitive Skills in a Complex, Laboratory-Based Team-Sport Setting
## 3 Results
### Initialization Time  
The results for ItMR (Figure 7) show a consistent decrease of movement time with increasing information. Repeated measures ANOVA revealed significant effects for the occlusion condition in all right-handed (breakthrough: \(F(4.42,176.64)=37.72\), \(p<0.001\), \(\eta^{2}_{\ p}=0.49\); jump throw: \(F(4.74,246.66)=43.62\), \(p<0.001\), \(\eta^{2}_{\ p}=0.46\); standing throw: \(F(4.11,192.51)=40.41\), \(p<0.001\), \(\eta^{2}_{\ p}=0.46\); pass: \(F(5.06,268.27)=13.24\), \(p<0.001\), \(\eta^{2}_{\ p}=0.20\)) and left-handed attacks (breakthrough: \(F(4.17,204.52)=78.78\), \(p<0.001\), \(\eta^{2}_{\ p}=0.62\); jump throw: \(F(4.03,185.15)=121.14\), \(p<0.001\), \(\eta^{2}_{\ p}=0.73\); standing throw: \(F(3.60,201.39)=65.60\), \(p<0.001\), \(\eta^{2}_{\ p}=0.54\); pass: \(F(4.21,206.15)=63.64\), \(p<0.001\), \(\eta^{2}_{\ p}=0.57\)). Globally, the fastest response times were found for t\({}_{0}\); the slowest response times were to the t\({}_{6}\) videos.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{6}{c}{**Right-Handed Attacks**} & \multicolumn{6}{c}{**Left-Handed Attacks**} \\ \hline
**Attack Action** & **Occlusion** & \(K\) & **95\% CI** & \(p\) & \(K\) & **95\% CI** & \(p\) \\ \hline \multicolumn{2}{c}{Standing throw} & & & & & & & & \\ \hline t6 (\(-1200\) ms) & 0.587 & 0.442 & - & 0.732 & 0.000 & 0.423 & 0.286 & - & 0.560 & 0.000 \\ t5 (\(-1000\) ms) & 0.543 & 0.395 & - & 0.691 & 0.000 & 0.467 & 0.318 & - & 0.616 & 0.000 \\ t4 (\(-800\) ms) & 0.424 & 0.261 & - & 0.586 & 0.000 & 0.497 & 0.318 & - & 0.676 & 0.000 \\ t3 (\(-600\) ms) & 0.132 & \(-0.038\) & - & 0.303 & 0.128 & 0.460 & 0.291 & - & 0.629 & 0.000 \\ t2 (\(-400\) ms) & 0.374 & 0.208 & - & 0.541 & 0.000 & 0.346 & 0.171 & - & 0.520 & 0.000 \\ t1 (\(-200\) ms) & 0.356 & 0.187 & - & 0.524 & 0.000 & 0.416 & 0.244 & - & 0.588 & 0.000 \\ t0 (0 ms) & 0.485 & 0.320 & - & 0.650 & 0.000 & 0.631 & 0.468 & - & 0.794 & 0.000 \\ Overall & 0.415 & & & & & 0.463 & & & & \\ \hline \multicolumn{2}{c}{Pass} & & & & & & & & & & \\ \hline  & t6 (\(-1200\) ms) & 0.382 & 0.240 & - & 0.523 & 0.000 & 0.431 & 0.273 & - & 0.590 & 0.000 \\ t5 (\(-1000\) ms) & 0.353 & 0.185 & - & 0.522 & 0.000 & 0.499 & 0.328 & - & 0.670 & 0.000 \\ t4 (\(-800\) ms) & 0.279 & 0.108 & - & 0.449 & 0.001 & 0.248 & 0.077 & - & 0.418 & 0.004 \\ t3 (\(-600\) ms) & 0.226 & 0.047 & - & 0.405 & 0.013 & 0.440 & 0.265 & - & 0.615 & 0.000 \\ t2 (\(-400\) ms) & 0.360 & 0.191 & - & 0.528 & 0.000 & 0.450 & 0.279 & - & 0.621 & 0.000 \\ t1 (\(-200\) ms) & 0.276 & 0.128 & - & 0.425 & 0.000 & 0.271 & 0.136 & - & 0.407 & 0.000 \\ t0 (0 ms) & 0.295 & 0.152 & - & 0.438 & 0.000 & 0.624 & 0.475 & - & 0.773 & 0.000 \\ Overall & 0.378 & & & & & 0.477 & & & & \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: Mean kappa agreement (for both hands) of CoMR for each occlusion and every attack action (intra- and inter-session).  
Figure 7: Intra-session initialization times (mean of video pairs in ms) of motor responses for all attacks (*** \(p<0.001\)).
###### Abstract  
The release of Android 7.0 has made raw GNSS positioning data available on smartphones and, as a result, this has allowed many experiments to be developed to evaluate the quality of GNSS positioning using mobile devices. This paper investigates the best positioning, using pseudorange measurement in the Differential Global Navigation Satellite System (DGNSS) and Single Point Positioning (SPP), obtained by smartphones. The experimental results show that SPP can be comparable to the DGNSS solution and can generally achieve an accuracy of one meter in planimetric positioning; in some conditions, an accuracy of less than one meter was achieved in the Easting coordinate. As far as altimetric positioning is concerned, it has been demonstrated that DGNSS is largely preferable to SPP. The aim of the research is to introduce a statistical method to evaluate the accuracy and precision of smartphone positioning that can be applied to any device since it is based only on the pseudoranges of the code. In order to improve the accuracy of positioning from mobile devices, two methods (Tukey and K-means) were used and applied, as they can detect and eliminate outliers in the data. Finally, the paper shows a case study on how the implementation of SPP on GIS applications for smartphones could improve citizen science experiments.  
smartphone; Xiaomi Mi 10; GNSS; SPP; DGNSS; statistical analysis Article
## 9 Open Challenges and Future Research Direction
* **Collusion resistance:** Colluding must be prevented due to illegal privilege accumulation among different colluded, revoked users who are willing to either access authentic data or try to falsely redact data. * **Consistency preservation:** Redacted chain stability remains a major obstacle in designing any redaction mechanism. Removal operation performed after storing transaction/block state prior redaction in the current proposal's methods is as yet vulnerable due to verifications and transaction chain failure.
## 1 Introduction
[23] showed that the two-layered sphere model is appropriate for modeling of remote-sensing reflectance and IOPs in high biomass Case 1 waters. The real refractive index of the chloroplast and the relative volume of the chloroplast are key parameters impacting the backscattering efficiency of phytoplankton cells. This was recently confirmed by two studies where measurements of light scattering by phytoplankton cultures were well reproduced by the two-sphere model [15; 16]. For these reasons, in this study, phytoplankton optical properties have been simulated considering a two-layered sphere model. The size range of the different considered particles (viruses, bacteria, phytoplankton, and organic detritus), as well as their real and imaginary refractive index values are defined from literature [21; 25]. To establish the foundations of the present study, the different theoretical considerations as well as the two different numerical codes used for the calculations are first presented. Then, we describe the different sub-populations of particles and their associated size distribution, refractive index, and internal structure used to simulate their optical properties. The impact of the modeled structural heterogeneity of phytoplankton cells is then discussed for the three realistic water bodies as mentioned previously.