esa-sceva/satcom-chunk-collection huggingface dataset:
# A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
## 3 Materials and Methods
### Histogram Analysis  
The histogram of an image graphically represents the intensity distribution of pixel values. A different histogram between an original image and a cover image can disclose the presence of embedded data in the image. To ensure high visual security, the histogram of the reference image and the visually meaningful encrypted image must be very similar. Figure 7b shows the histograms of the original reference image (Figure 7a). Figure 7d shows the comparison between the histograms of the encrypted images achieved after the proposed secure image encryption scheme (line red) and the histograms of the reference images (line blue).  
Figure 7: Histogram analysis of reference images and their corresponding visually meaningful encrypted images. (**a**) reference images; (**b**) histograms of reference images; (**c**) visually meaningful encrypted images; (**d**) comparison between the histograms of the encrypted images and the histograms of the reference images.  
Figure 7d shows that the histograms of the reference images and the histograms of the visually meaningful encrypted images are very similar because the two histograms are nearly perfectly overlapped at every point for each image. To test the difference between two histograms, the distance of histogram intersection has been computed following Equation (17). A value close to 1 indicates that the two histograms are very similar.  
\[d(H_{1},\;H_{2})=\frac{\sum_{l}\text{min}(H_{1}(I),\;H_{2}(I^{\prime}))}{N \times N} \tag{17}\]  
Table 2 shows the distances of histogram intersection between the reference images and the visually meaningful encrypted images.  
The distance values in Table 2 are above 0.98, and thus the visually meaningful encrypted images do not provide any information about the presence of embedded data.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 3 USVs for Natural Disasters
### Landslides & Erosion Risks  
Landslides are phenomena caused by the slipping of solid materials, such as soils, rocks, vegetation, and building material along sloping terrain. It occurs in areas of rugged relief, from which the original vegetation cover was removed, responsible for soil consistency and for preventing the flow of water through the roots. The landslide differs from the erosive processes by the amount of mass transported at a high velocity. These natural phenomena cause immediate problems for the population, regardless of their social condition, and for the environment. In this type of disaster, USVs can conduct underwater three dimensional (3D) surveys using sonars/side scanners to find underwater landslide signs. Another associated problem involves submarine landslides, which can affect man-made structures such as the massive gas and oil extraction platforms in the ocean [86]. Moreover, an underwater (or near the coast) landslide can even cause tsunamis [87].  
Accurate hydrographic surveying and coastal monitoring are essential for numerous reasons: coastal flood zone modeling; estimating storm and tidal surges; quantifying volumes of sand movement due to erosion and accretion [88]. In particular, coastal erosion and accretion (silting) is a major concern nearby populated zones [89; 90], as silting can increase the impact of floods and erosion might affect the structure of nearby buildings and lead to landslides. By accurately comparing the elevations in ocean tides, emergency agencies could plan evacuation routes for coastal communities in case of events such as imminent landslides caused by the elevation of the water surface.  
[PERSON] et al. [76] use the ROAZ USV [91] for risk assessment in shallow water and marine coastal regions. The USV was remote-controlled and used for wave modeling, and for searching for rocky outcrops and sandy patches. The accuracy of the bathymetric survey was not informed, even though [PERSON] et al. [77] point out that accuracy can vary about 30 cm, depending on observation conditions in the water body and sensor precision.  
[PERSON] et al. [77] conduct two surveys involving the risk of erosion and landslides next to the coast. They have used a remote-controlled USV, during high tides to get as close as possible to the shore, to perform sub-tidal bathymetry with sonar imagery. They classify the erosion risk in each monitored area in three classes: low, intermediate, and high risk.
## 3 Modeling and Analysis
### Simulation Results Analysis  
The simulation results of the impact dynamics of the TRUMs mainly include the stress of the stator, rotor and friction plate, and the force on the contact surface between the stator and rotor. For better analysis of the simulation results, the four motors are divided into two groups for comparison. The TRUM-**1** and TRUM-**2** are set as group 1, and the TRUM-3 and TRUM-4 are set as group 2.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 3 Data Collection  
The Interstate 75 (I-75) corridor is a limited-access facility and is one of the most critical transportation facilities in the state of Florida. It facilitates freight movement to, from, and within the state, starting from the south in Miami, running south-to-north over approximately 272 miles of I-75, crossing through the Florida Department of Transportation (FDOT) Districts 6, 1, 7, 5, and 2. Interstate 75 is mostly a four-lane highway, but there is a six-lane section with 12-foot lane widths and a minimum 40-foot median. I-75 is an integral part of the strategic intermodal system (SIS), a system of significant roadways intended to provide high-speed travel connections between major population centers throughout the states. Due to the growth in freight miles traveled, I-75 has experienced a significant increase in traffic volume, which has resulted in operational deficiencies and additional congestion. Given the importance of the I-75 corridor, it was selected as a study area for this research with the purpose of congestion mitigation. The study corridor evaluated includes 20 counties through north, central, and southeast Florida, with a total population of over 5 million residents, which represents approximately 25% of Florida's population. Figure 1 shows the spatial extent of the selected study area.  
Four sets of data were collected: crash data, incident data, a street network and historical traffic dataset, and alternative route selection criteria data.  
A list of the sources of these data is presented in Figure 2.  
Figure 1: Study area, counties along the I-75 corridor.
# Age-Dependent Pleomorphism in _Mycobacterium monacense_ Cultures
## 3 Results
### Identification of an MreB Homology Within DnaK_3  
A protein BLAST homology search (BLASTp) revealed that the _Streptomyces coelicolor_ MreB (SCO2611) is \(\sim\)30% identical with \(\sim\)56% query coverage to that of _Mmon_ DnaK3 (MMONDSM_00498; _dnaK_3 Mmon_). On the basis of protein domain similarity to the MreB-like domain MreB_Mbl, this raises the possibility that _dnaK_3_ might function as an _mreB_ homolog (Figure 5E; see Section 2). The actin homolog MreB is involved in attributing and controlling the rod shape of cells in both Gram-negative and Gram-positive bacteria, e.g., _Escherichia coli_, _Caulobacter crescentus_ and _B. subtilis_[85; 86; 87; 88; 89]. Specifically, MreB_Mbl proteins are constituents of the bacterial cytoskeleton and have mainly been reported for rod-shaped cells, the MreB_Mbl proteins/_mreB_ or _mreB_-like genes have not been identified in coccoid species or in mycobacteria [10; 60; 90]. In this context, it should be mentioned that the MreB inhibitor A22 [91] prevents growth of both _MmonT_ and _MboeT_, which makes it tempting to speculate that the MreB domain of DnaK_3 might influence cytoskeletal functions essential for viability of _MmonT_ and _MboeT_. However, the possibility that the MreB-like function is provided by some yet unknown protein cannot be ruled out.  
DnaK is a well-known heat shock chaperonin with a Hsp70 protein domain [92; 93], and its N-terminal domain shows MreB_Mbl domain architecture homology also in some other bacteria (Figure S6A). Moreover, _E. coli dnaK_ mutants were found to affect (directly or indirectly) cell shapes and division [94; 95; 96; 97; 98]. The mycobacterial _dnaK_3_ gene is localised together with the nucleotide exchange protein gene, _grPE_, the chaperone _dnaJ_1, and the transcriptional repressor _lspR_ (Figure 5F) [92; 99]. The mRNA levels for these transcripts also showed increments parallel to that of _dnaK_3_ with ageing (Figure 5G; Table S4) suggesting that _dnaK_3, grPE, dnaJ_1 and _lspR_ constitute a transcriptional unit in keeping with previous findings, see, e.g., ([100; 101] and the Refs therein).  
Three additional genes were annotated as _dnaK_ in the _MmonT_ genome, _dnaK_1, _dnaK_2 and _dnaK_4_. We therefore included these in the transcriptome analysis. However, in contrast to DnaK_3, these proteins either lack the MreB-Mbl domain (DnaK_2) or did not have a complete MreB-Mbl domain (DnaK_1 and DnaK_4), which encompasses 355 amino acids in DnaK_3 (Figure 5E; based on the Pfam database the average length for MreB_Mbl domains is \(\approx\)313 amino acids and for DnaK_1 and DnaK_4 \(\approx\) 200 amino acids are present that relate to the MreB_Mbl domain). Comparing the transcript levels for the _dnaK_ homologs in _MmonRFPHy8_ revealed that the mRNA level for _dnaK_3_ increased upon ageing, 2.5- and 4.4-fold dependent on 'age' (log2; see above), while _dnaK_1, _dnaK_2_ and _dnaK_4 mRNA levels were marginally higher in exponentially growing cells relative to older cells (Figure 5D; Table S4).
# Analytical Pyrolysis and Mass Spectrometry to Characterise Lignin in Archaeological Wood
###### Abstract  
This review describes the capability of analytical pyrolysis-based techniques to provide data on lignin composition and on the chemical alteration undergone by lignin in archaeological wooden objects. Applications of Direct Exposure Mass Spectrometry (DE-MS), Evolved Gas Analysis Mass Spectrometry (EGA-MS), and single and double-shot Pyrolysis-Gas Chromatography /Mass Spectrometry (Py-GC/MS) in archaeological lignin characterisation are described. With comparison to cellulose and hemicellulose, lignin is generally less prone to most degradation processes affecting archaeological artefacts in burial environments, especially waterlogged ones, which are the most favourable for wood preservation. Nevertheless, lignin also undergoes significant chemical changes. As wood from waterlogged environments is mainly composed of lignin, knowledge of its chemical structure and degradation pathways is fundamental for choosing preventive conservation conditions and for optimising consolidation methods and materials, which directly interact with the residual lignin. Analytical pyrolysis coupled with mass spectrometry, used in several complementary operational modes, can gather information regarding the chemical modifications and the state of preservation of lignin, especially concerning oxidation and depolymerisation phenomena. Several applications to the analysis of wood from archaeological artefacts affected by different conservation problems are presented to showcase the potential of analytical pyrolysis in various scenarios that can be encountered when investigating archaeological waterlogged wood.  
archaeological wood; lignin; analytical pyrolysis; mass spectrometry +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Principal Component Analysis  
The results of the principal component analysis (PCA) for the different batches of lamb burgers (Control, Pecan5, Pecan10 and Pecan15) are shown in Figure 3. As in most cases,  
Figure 1: Spider plot of sensory attributes of burgers from Merino lamb with different amounts of pecans in their formulation. (**\(p<0.01\)).  
Figure 2: Overall preference for cooked samples.  
two principal components were sufficient to explain a great proportion of the variation in the original parameters.  
The distribution of the scores for the first two principal components (Figure 3) shows four separate groups of points corresponding to the different treatments and mostly separated by PC1 (73.16%). The samples from Pecan10 and Pecan15 tended to have positive scores in Principal Component 1 (PC1), which could be mainly explained by the positive loadings of MUFAs, PUFAs and gamma tocopherol, whereas the Pecan5 and Control samples were located in the negative part of the PC1 area, since SFAs, AI and TI yielded negative loadings in PC1. Regarding PC2, the variables with high loadings in PC2 (which explained 6.61% of the variance) were cohesiveness, chewiness, redness, AAT and fat.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 2 Materials And Methods
### Tests Procedure and Evaluation Criteria
#### 2.6.1 ALOV Video Dataset  
The tests were performed using the ALOV video dataset (Figures 4 and 5) proposed by [3]. This dataset provides 314 videos and ground truth data for evaluation and has been used as a benchmark to test 19 different trackers.  
Figure 4: Example videos from the ALOV dataset (first 7 categories). Only first frames are shown. The target object is enclosed.  
The ALOV dataset has 14 categories and each one presents a different challenge for common trackers. The categories are:  
1. Light: 33 videos that have sudden and intense changes in the main light source or how it illuminates the tracked object.
2. Surface Cover: 15 videos where the tracked object changes its surface cover, but this cover adopts the form of the object it covers.
3. Specularity: 18 videos with shiny objects that reflect light and produce specularities.
4. Transparency: 20 videos where the tracked object is transparent and easily confused with the background.
5. Shape: 24 videos in which the tracked object drastically changes its shape.
6. Motion Smoothness: 22 videos that show an object that moves so slowly that no movement is detected whatsoever.
7. Motion Coherence: 12 videos where the tracked object does not follow a predictable route of movement.
8. Clutter: 15 videos with tracked objects that display similar patterns to the ones observed in the background.
9. Confusion: 37 videos that show objects that are very similar to the object of interest, thus confusing the tracker.
10. Low Contrast: 23 videos where the tracked object shows little contrast with the background or other objects.
11. Occlusion: 34 videos where the object of interest is occluded by other objects or is not in the field of vision at a certain point.
12. Moving Camera: 22 videos that are affected by the camera's sudden movements.
13. Zooming Camera: 29 videos where the zoom of the camera changes the displayed size of the object.
14. Long Duration: 10 videos that have greater duration, between one and two minutes.  
Figure 5: Example videos from the ALOV dataset (last seven categories). Only first frames are shown. The target object is enclosed.  
To perform a quantitative evaluation against the other 19 trackers, the dataset authors suggest using the F-score [3], which is a measurement tool based on the intersection and union area between the provided answer and the real answer. We have successfully applied this measure to other evolutionary algorithms dealing with video tracking [6; 7].  
The authors of the dataset suggest the use of a survival curve; a type of graph that shows the results sorted in descending order and is more commonly used in the field of medicine to compare how different treatments affect patients [41; 42]. The curves that are closer to the labeled horizontal line can be considered the most accurate.
###### Abstract
The authors conclude that, by generating new insights into mental health and well-being, the surveyed studies demonstrate AI/ML's great potential in improving the efficiency of clinical and research practices. In another literature review, ref. [13] examine the applications of more advanced ML models, namely deep learning, in psychiatry. The authors discuss the merits of using deep learning models in brain dynamics and behavioral studies and call for the integration of interpretable computational techniques in the commonly used traditional statistical models. Students' mental health (SMH) is a critical research area at the intersection of psychology and health, which has been grasping unprecedented attention from academics, practitioners, governments, and other higher education stakeholders, since the inception of the COVID-19 pandemic [14]. Nonetheless, there are no previous literature reviews about the applications of AI/ML in SMH research, despite the growing ethical concerns about the use of AI/ML models in social sciences in general and in mental health research in particular. To the best of our knowledge, only [15] discussed the ethical principles related to the applications of AI/ML in mental health research, yet the article does not explicitly address SMH work. We extend the work of this paper by developing unique, innovative AI/ML ethic scores and conducting an ethics-centered systematic literature review of prior SMH studies. Our aim is to investigate the following questions:  
1. What types of ML methods and techniques are employed in AI/ML-enabled SMH research? 2. What are the main mental health outcomes explored in this emerging research area? 3. What key themes are addressed within this research stream? 4. Are ethical considerations and principles adequately integrated into this research field? If so, how, and to what extent?
## 3 Results and Discussion
### Test Pulse Characterization
#### 3.2.2 Characterization of an Optical Beat
As expected from the above discussion, the two temporal intensity profiles of the optical beats are nearly identical to one another: only a slight difference in the relative intensities of the sharp peaks remains. The lack of information about the relative phase thus has given the ambiguity in the relative intensities of the optical beat and in the positions of the sinusoidal wave in the envelope. The other features of the temporal structure of the optical beat, which were determined by the four parameters (a-d), remained in these two measurements. As already discussed above, the two pulses at 800 nm and 1180 nm were not completely overlapping in time. This should result in a change in contrast of the optical beat; the highest contrast was observed in the middle of the temporal profile, while the lowest contrasts were at the leading and trailing edges where the two spectral components do not properly overlap. As mentioned earlier, the spectral component at 1180 nm wasslightly positively chirped, resulting in a decrease in the instantaneous frequency separation between pulses A and B. Thus, the temporal separation of the two adjacent peaks increased at the trailing edge. Accordingly, the temporal structure retrieved from the XFROG trace is determined by the timing and the chirp of the two pulses. It is difficult to characterize a train of pulses with a spectrum consisting of multiple lines by the XFROG because of a lack of information concerning relative phases. However, the temporal waveform of the optical beat consisting of two discrete spectral components is changed only slightly by the change of the relative phase, although the beat is slightly shifted in time. The temporal intensity profile is reported to drastically change with a change in the relative phases among the spectral components when the test pulse consists of more than two discrete spectral components [26]. The present XFROG system would be capable of characterizing such an ultrashort single optical pulse with smooth temporal and spectral profiles even using a long reference pulse [9; 27].
# Advancements in Semi-Active Automotive Suspension Systems with Magnetroheological Dampers: A Review
## 1 Introduction
MPC considers system dynamics constraints, making it suitable for practical applications. Robust control, including \(H_{\infty}\) control, addresses system uncertainties and non-linearities, ensuring precise control despite parameter perturbations. Adaptive control optimizes controller parameters automatically based on the current driving state. Intelligent control strategies include fuzzy control [18], neural network control [19], and bio-inspired optimization algorithms [20]. Intelligent control strategies leverage advanced algorithms and machine learning techniques to dynamically adapt to varying conditions, further enhancing suspension performance. In 1999, the collaboration between Lord Corporation and Delphi Corporation introduced the MagneRide system, marking a pioneering use of MRD in semi-active suspension technology. Initially implemented in the 2002 Cadillac Seville STS, MagneRide quickly garnered attention for enhancing vehicle stability and ride comfort and reducing vibrations. Its subsequent integration into numerous high-end vehicle models catalyzed significant advancements in automotive suspension systems. Empirical findings underscored its reliability and efficacy, leading to widespread adoption across various luxury cars and sports vehicles. This success has not only propelled continuous improvements in MRD technology for vehicle damping but also broadened its application to diverse vehicle types including SUVs, pickups, and even electric vehicles. Despite challenges such as cost and technical complexity, MagneRide's evolution reflects its enduring impact on enhancing driving dynamics and user experience across different automotive segments. In summary, the evolution of suspension systems from passive to semi-active and active configurations highlights the significant advancements in automotive technology. The advancements in semi-active suspension systems, particularly with the integration of MRDs, have advanced automotive suspension technologies, offering a balanced solution among performance, cost, and energy consumption. The integration of intelligent control mechanisms, particularly in semi-active suspensions, represents a promising direction for future research and development, aiming to enhance the overall driving experience and vehicle performance. This review aims to provide a comprehensive overview of the current state of semi-active suspension systems, focusing on the advancements in MRD technology and control strategies. By analyzing the evolution of suspension systems, the principles and performance of MRDs, and the development of control methods, this review highlights the significant progress made in this field. Additionally, the practical applications and prospects of semi-active suspensions were discussed, offering insights for researchers and industry professionals.