esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusion and Perspectives
The commercial exploitation of the natural microalgal diversity for the production of carotenoids and PUFA has already started up with few strains such as _Chlorella vulgaris_ (Trebouxiophyceaeae), _Dunaliella salina_ (Chlorophyceaeae), _Haematococcus pluvialis_ (Chlorophyceaeae) [249,296] and _Odontella aurita_ (Bacillariophyceaeae) [7,177]. Microalgae are specifically cultivated at an industrial scale for the production of high amounts in EPA and DHA. For example, _Isochrysis_ sp. is produced by Innovative Aquaculture Products Ltd. (Lasqueti Island, Canada) and the diatom _Odontella aurita_ is produced by BlueBiotech InT (Kollmar, Germany) and Innovalg (Bouin, France). The marine dinoflagellate, _Crypthecodimium cohnii_ can produce DHA heterophically. In this way, a DHA-rich oil is produced by the American company Martek Biosciences Corporation [297] and commonly used in supplementation particularly in infant products [186]. Transgenic microalgae can be used as bioreactors for the production of bioactive molecules. This technology is very promising as it would simplify the production process and would lead to a significant decrease of the production costs. To date, a variety of recombinant proteins have been produced experimentally from the nuclear or chloroplast genome of transgenic microalgae, e.g., [298]. For the production of phycoerythrin, [PERSON] [299] claims that further improvement could be pursued, e.g., _P. cruentum_ biomass production could be optimized in tubular photobioreactors or open-ponds together with molecular biology approaches. For carotenoid production, the use of transgenic microorganisms is still difficult. The knowledge on carotenogenesis pathways in algae is only partial; several genes have been putatively identified in sequenced genomes [222,300]. However, engineering of high-yield algae still exits [301]. Efforts are being developed to engineer fatty acid biosynthesis in microalgae. A recent study shows the importance of a thorough characterization of the algal fatty acid synthase, including protein-protein interactions and regulation, for successful engineering [302]. It is clear from the genome information that most microorganisms have the potential to produce a far greater number of natural products than have been isolated previously [116]. In the future, a better knowledge on pigment and lipid pathways will be determinant to develop new technologies using transgenic marine microorganisms as \"high-producers\" of bioactive molecules.
## 4 Results
### Summary of Findings  
This research explored the influence of neuroimaging, cognitive, and combined features on predicting dementia through the AdaBoost Ensemble classifier. Based on the analysis of the ADNIMERGE dataset, the best number of features was selected for each type: 5 neuroimaging features, 18 cognitive features, and 19 combined features. The best weighted combined features were identified using Neighborhood Component Analysis and Correlation-Based Filtration (NCA-F), and four different machine learning models (AdaBoost (AdB), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Naive Bayes (NB)) were employed to predict dementia. The performance of these models was cross-validated through a 10-fold process.  
In terms of feature types, SVM outperformed the other models on neuroimaging features, achieving approximately 74% classification accuracy. On cognitive features, the AdaBoost model had the highest performance, with approximately 83% accuracy. When it comes to combined features, which contain 7 neuroimaging and 12 cognitive features, AdaBoost again had the best performance with approximately 83% accuracy.  
Interestingly, neuroimaging features alone did not yield high performance for dementia prediction. This study found that cognitive features are more effective than neuroimaging features for prediction, and combined features also performed well. However, the challenge with combined features is that neuroimaging features are required. Among the four models, AdaBoost showed good performance results for all three feature types.  
Comparative analysis was also performed between the proposed NCA-F method and previous work by [PERSON] et al., which also focused on the early detection of dementia using neuroimaging and cognitive features. The proposed NCA-F method achieved better performance on both the updated ADNI-1 and ADNIMERGE datasets when compared to the existing approach by [PERSON] et al.  
To this end, in this study, distinct advantages were observed depending on the types of features used for Alzheimer's Disease (AD) diagnosis. Models built on biomarkers such as CDRSB, AV45-PET, and FDG-PET offered robustness and were particularly effective at capturing advanced stages of the disease, corroborating previous research [31; 32]. However, models relying solely on cognitive features, as assessed through tools such as the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), exhibited higher sensitivity in the early detection of AD, a crucial requirement for timely intervention. Interestingly, our combined feature models, incorporating both biomarkers and cognitive features, showed the most balanced performance across the early and late stages. This balance is evidenced by an 83% accuracy rate and consistently high AUC values, suggesting that a multi-dimensional approach is often more comprehensive for diagnosing a complex disorder like AD.
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 3 Results  
### DEMs of Difference  
The GDEM2 had the highest standard deviations and uncertainty in each AOI (Table 3). For the Pisang Landslide DEMs of difference, the GDEM2 had a standard deviation of 12.26 m compared to 7.88 m, 7.74 m, and 2.61 m for the AW3D30, SRTM, and HMA DEMs, respectively.  
The GDEM2 was not used in Tal village analysis due to the prevalence of artifacts (Figure 2), which were likely caused by persistent cloud cover. The artifacts caused a deviation of the GDEM2 delineated river channels from the AW3D30 channels by hundreds of metres, in some cases exceeding a kilometre (Figure 2b). By comparison, the AW3D30 was closely geolocated to the UAV DEM, with shifts of \(-2\), \(-1\), and \(-6\) m in x, y, z applied during the coregistration (Table S1). The low gradient floodplain of Tal village was within \(\pm 2\) m vertically between the UAV and AW3D30 DEMs (Figure 2d).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **DEM Difference** & **Mean Elevation Difference (m)** & **Standard Deviation (m)** & **Uncertainty (m)** \\ \hline Tal village & UAV – AW3D30 & \(-1.26\) & \(13.43\) & \(2.38\) \\ \hline \multirow{3}{*}{Pisang landslides} & UAV – HMA & \(-0.11\) & \(2.61\) & \(0.22\) \\  & UAV – GDEM2 & \(1.00\) & \(12.26\) & \(2.09\) \\ \cline{1-1}  & UAV – AW3D30 & \(-0.13\) & \(7.88\) & \(1.19\) \\ \cline{1-1}  & UAV – SRTM & \(0.06\) & \(7.74\) & \(1.16\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of DEMs of difference after coregistration.  
Figure 2: (**a**) AW3D30 hillshade and river network; (**b**) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM2) hillshade and river networks; (**c**) UAV hillshade and the Marsyangdi River channel; (**d**) DEM of difference (UAV DEM minus the AW3D30 DEM). The mean and one standard deviation are shown in the lower right corner.
## 3 Proposed Schemes
### Alpha-Beta Filter  
Filters belonging to the alpha-beta family of filters are the simplest filters used for smoothness, control, and estimation. Their architecture is similar to linear filters. The main benefit of the alpha-beta filter is that it is very easy to use because a complex benchmark is not required in order to train it for another algorithm. The model is obtained from the KF [2]. The alpha-beta filter needs very little space and computation power compared to the Kalman filter. The equations shown below are mathematical calculations which define each algorithm step. In the first step, initiation occurs, as represented in Equations (28) and (29):  
\[x_{k-1}=c_{1} \tag{28}\]  
\[v_{k-1}=c_{2} \tag{29}\]  
Equation (30) was applied to update the position, and to read sensor data, Equation (31) was used:  
\[\hat{x}_{k}=x_{k-1}+v_{k-1}.\;\Delta t \tag{30}\]  
\[x_{j}=Sensor() \tag{31}\]  
To compute the difference, Equation (32) was used, and for the calculation and prediction of positions, Equation (33) was applied:  
\[\Delta\widetilde{x}_{k}=x_{j}-\hat{x}_{k} \tag{32}\]  
Figure 5: The structure of the proposed SVM-based alpha-beta filter.  
\[\tilde{x_{k}}=\tilde{x_{k}}+\alpha\cdot\Delta\tilde{x_{k}} \tag{33}\]  
To calculate predicted velocity, Equation (34) was applied, and to update the position and velocity for the next iteration, we used Equations (35) and (36):  
\[\hat{v}_{k}=\hat{v}_{k-1}+\beta\cdot\Delta\tilde{x_{k}}/\Delta t \tag{34}\]  
\[i_{k-1}=i_{k} \tag{35}\]  
\[v_{k-1}=v_{k} \tag{36}\]
# BIM and GIS Applications in Bridge Projects: A Critical Review
## 4 Discussion and Future Work
### Information Exchange  
As a research prototype, bridge information models, in their current state, consider only a few standards and applications. Many data entities, which are necessary to fully support other bridge monitoring and management applications, are lacking [36]. Although BIM based on the IFC data model is the core of information interoperability, due to the lack of IFC support for bridge structure [19; 36; 44; 59] and the target classification depending on the quality of the geometric model [55], it is still not enough to realize a real BIM model [19]. Some existing bridges lack digital drawings and even paper drawings [57; 58]; hence, most of the data transmission needs to be manual [18; 33; 34; 35] which would affect the efficiency of BIM/GIS integration [20; 90]. BIM/GIS models have inconsistent data exchange format [41; 63] and a slow information feedback speed [77], which affect interoperability between models. The poor interoperability of BIM/GIS software may hinder effective development of related software [22; 41; 60; 61; 62; 63; 64; 65].  
Regarding future work, it is important to clarify and pinpoint the requirements that each domain sets, especially if BIM-GIS integration (e.g., geometry conversion [20] and semantic mapping [20; 55]) are to be addressed [20]. It is suggested that researchers could develop a more accurate bridge information model with detailed parameter information and determine the model development specifications of existing bridges to meet the needs of stakeholders [34]. It is very important to compare BIM standards (such as IFC) with the infrastructure module of GIS standards (such as CityGML) to determine the most favorable integration degree of these two fields in the field of asset management [15; 20; 90]. When using BIM/GIS technology, managers need to pay more attention to the use of interoperable and open data exchange formats [41] to enhance the operational efficiency of the platform.
# Roles for Exosomes in the Pathogenesis, Drug Delivery and Therapy of Psoriasis
## 3 The Immunological Function of Exosomes That Are or Are Not Associated with Soriasis
### Immune Cell-Derived Exosomes
#### 3.1.5 T Cell-Derived Exosomes  
T cells are critical for the pathogenesis of psoriasis [2; 56]. Over twenty years ago, psoriasis was originally considered as a Th1-type disease, in which IFN-\(\gamma\) and TNF\(\alpha\) were the predominant pathogenic cytokines [58]. But later on with the discovery of the Th17 subset, the IL-23/Th17 axis was thought to be predominant in the pathogenesis of psoriasis [59]. Moreover, impaired function and proliferation of Tregs were found in patients with psoriasis [60; 61; 62].  
It has been shown that T cell-derived exosomes contain TCR, CD3, APO2 ligand, chemokine receptors, CD2, LFA-1 and FasL [63]. T cell-derived exosomes also express CD3, CD2, CD4, CD8, CD11c, CD25, CD69, LFA-1, CXCR4 and GITR [64]. Upon being taken up by different types of cells, T cell-derived exosomes induced a variety of immunoregulatory effects, such as improving APC antigen presentation [65], inhibiting NK cell cytotoxicity [66], regulating DC maturation [67] and enhancing B cell responses and antibody production [68; 69]. Moreover, [PERSON] et al. revealed that T cell-derived exosomes stimulated mast cells to degranulate and release IL-24, which in turn activated keratinocytes in vitro [37]. On the other hand, exosomes released from different T cell subsets may have distinct functions. For example, Treg cell-derived exosomes expressed CD73 that contributed to their suppressive function by converting extracellular adenosine-5-monophosphate to adenosine [38]. Treg-derived exosomes also inhibited proliferation of Th1 cells and their IFN-\(\gamma\) secretion by transferring Let-7d to them [39]. These findings indicate that T cell-derived exosomes have dual immunomodulatory effects, depending on the function of their original cells.
## 3 Discrete Element Model Building and Verification
### Experimental Validation of Numerical Model  
The change in specimen height was an intuitive reflection of the forming effect. Throughout the indoor compaction test, the specimen heights were measured after each compaction. After the test, the specimen's **height change** and the number of compactions were obtained. In the discrete element simulation process, the loading plate's height change during the loading cycle was observed after each compaction, and the change in specimen height was reflected by the average value of the center height of the loading plate. The specimen's height change was used as a comparison object; indoor test results and discrete element simulation results were compared to verify the correctness of the numerical simulation in this study. The Comparison of tamping settlement between the indoor compaction test and the discrete element simulation test in this paper is shown in Figure 4.  
The discrete element simulation results were close to the indoor test results. Specim height decreased faster at the beginning of compaction, and the speed gradually decreased as the number of compactions increased. Although height variation in the first ten simulations was slightly smaller than that of the indoor experiments, the decreasing trend was the same. Specimens' heights after compaction were very close to that of the experimental measurements.
## 2 Experimental Study
### Experimental Results
#### 2.3.2 Mechanical Results  
In all the tests carried out, deflection was measured over the fire test to ascertain how the temperature affects its value. When the load is applied, the central area withstands the greatest bending moment and consequently the maximum deflection. This means that cracks begin to appear in this area, becoming shorter in the supports. The cracks are distanced from each other, in a range of 100 to 180 mm, as shown in the Figure 6c.  
The average vertical deflection for the four slabs is shown in Figure 7. The deflection measured for each type of composite slab is very similar. In all four cases, the deflection rises steeply for the first 50 s. Then, the increase drops off until 300 s, when it picks up again as shown in Figure 7. These marginal changes in the slope are related to the debonding process and crack growth. After 300 s, there is a continuous increase of deflection until the end of the tests. It is observed that the behavior of the lighter slabs (LWC-1 and LWC-2) is almost identical until 900 s. LWC-3 and NC also have similar structural behavior until 1300 s. The tests for LWC-1, LWC-2 and NC ended because the deflection exceeded the load-bearing capacity criterion (61 mm). After 1800 s, only LWC-3 and NC had a deflection lower than 61 mm.  
Figure 6: Fire test phenomena of composite slabs: (**a**) Debonding of the steel decking. (**b**) Water at the top of the slab. (**c**) Crack pattern (unit: mm).
# Mass Processing of Sentinel-1 Images for
## 3 Methods
### Recurrent Targets Analysis
In the study, the definition of recurrence is purely based on location: detections in two images are said to represent the same target if they are separated by less than 50 m. This value has been chosen empirically: it is short enough to minimise the risk that two different ships in two different images will be classified as recurrent, and at the same time it still allows for some perturbation in the detected position of large fixed structures. Furthermore, detection in at least three different images is needed to classify those detections as recurrent. A source of recurrence is defined as a geographic location in which recurrent detections appear. As an example, if there are 10 detections in almost exactly the same geographic location (to within 50 m) and each detection is in a different image, the 10 detections will be classified as recurrent, and the location marked by the centroid of the 10 detections will become one source of recurrence. A source of recurrence (and the recurrent detections associated to it) will be categorised as a fixed structure if it has been detected in at least two different observation geometries. If geometric diversity in the acquisitions is missing (i.e., if that position has only been imaged in one geometry), it has been decided to categorise that source of recurrence as fixed structure as well. In any other case (i.e., imaged in multiple geometries, but detected only in one of them), a source of recurrence (and the associated detections) will be categorised as an ambiguity.
## 2 Materials and Methods  
Aluminum (99.9% purity, Aldrich) and antimony shots (99.999% purity, Kojundo) were weighed in a glove box with inert (Ar gas) atmosphere. A graphite crucible with stopper was used for the controlled melting. The inner walls were coated with a thick layer of boron nitride, and an argon gas environment was maintained inside. The prepared crucible with sample was heated to 1273 K in a vacuum furnace. The cooled ingot was then pulverized either by mortar-pestle (MP) or by a high energy vibratory mill (HEVM; KMTech TMM-70, Korea) using zirconia vial and balls (5 mm). The ball to sample ratio was maintained at 10:1. A vibratory mill was used for an hour at 1080 rpm to ensure better pulverization. Then the samples were sieved using a 325-mesh sieve. The samples were consolidated by Vacuum hot pressing (VHP) at 80 MPa pressure and 1173 K temperature for 6 h. BN coated graphite die of 10 mm diameter was used for this process.  
Post-synthesis, phase transformation was analyzed using Cu-K\(\alpha\) radiation of an X-ray diffractometer (XRD; BRUKER AXS Advance D-8, Germany) and surface morphology was investigatedusing a scanning electron microscopy (SEM; Quanta-400, Netherlands). A cylindrical sample of \(3\times 3\times 10\) cm\({}^{3}\) was used to measure Seebeck coefficient and electrical conductivity over the range of 300-873 K by ZEM-3 (ULVAC-RIKO, Japan), which runs by the four-probe method. Thermal diffusivity was measured by laser flash method using TC-9000H (ULVAC-RIKO, Japan). The equation \(\kappa=\rho\times\mathrm{C_{P}}\times\mathrm{d}\) was used to calculate thermal conductivity, where \(\rho\) is the density calculated using the Archimedes principle, \(\mathrm{C_{P}}\) is the specific heat capacity, and \(\mathrm{d}\) is thermal diffusivity. The measured values were then put into the Equation (1) to give the dimensionless figure of merit. VHPed samples were also mounted into a polymer matrix and polished to test for mechanical properties. Vickers micro-hardness was measured using PMT-X7B (MATSUZAWA, Japan). A load of 50 g-force (490.33 mN) was applied.
# High-Brightness Image Enhancement Algorithm
## 2 Related Work  
Generally, we divide tone mapping into two categories: global tone mapping and local tone mapping. Histogram-based algorithms and S-shaped transfer function-based algorithms (such as gamma correction) are the two main categories of global operators. The key for global tone mapping methods is to establish one-to-one mapping or low dynamic radiance mapping. In reference [19], the statuses and categories of tone mappings were summarized. Reference [20] proposed a multiscale image decomposition method that can preserve image edges. The results show that this type of image decomposition method is superior to bilateral filtering in producing range-compressed images.  
Unlike the methods above, our algorithm does not need to decompose the input image into binary or fractional images. Therefore, it avoids the problems associated with layer decomposition. We propose a global optimization framework to constrain each pixel value naturally. In addition, our method does not need to impose any smoothness constraint on the final output.  
Comparing our work with existing tone mapping algorithms, our method can significantly improve the pixel intensity in the overexposed area and balance the brightness of other areas to ensure that the overall contrast of the image will not change too much.
## 6 Evaluation of the USN-Platform Semantic Extension  
Tests were conducted to evaluate the performance of the semantic extension of the USN-Platform. We analyzed the overhead of smart device observations when including semantic information, and evaluated the performance of the Observation Storage extended with the Sesame SPARQL Endpoint.
###### Abstract  
As the communication distance changes, the received signal strength of an underwater optical communication system will change, and the range of its variation may not only exceed the dynamic range of the photoelectric detection device but also cause the reliability of communication to change due to the change in the received signal-to-noise ratio. In order to maintain better communication over a longer distance, this paper proposes a rate-adaptive method for underwater optical communication with joint control in the photoelectric domain. In the optical domain, the incident light's power is adaptively adjusted by controlling the transmittance of the liquid crystal light valve to reduce saturation distortion. In the electrical domain, the constellation distribution is optimized according to the desired probability mass function, and the modulation order is adjusted in real time by estimating the received signal-to-noise ratio of the link. The simulation results show that under the forward error correction (FEC) threshold, the proposed method increases the dynamic range of the photomultiplier tube (PMT) by about 10 dB and expands the dynamic range of the system's communication distance.  
rate-adaptive control; signal-to-noise ratio; constellation probability shaping; incident light power; liquid crystal light valve +
Footnote †: journal: _Photonics_  
0
Footnote 0: _Princetonics_: 02042, 11, 1004. [[https://doi.org/10.3390/photonics1111004](https://doi.org/10.3390/photonics1111004)]([https://doi.org/10.3390/photonics1111004](https://doi.org/10.3390/photonics1111004))