esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this study, we investigated and analyzed the RSSI behavior on grass environments with low transmitter and receiver antenna heights from the ground. Accordingly, we introduced a fuzzy MBFLR model to predict the near-ground path loss in different environment scenarios. The obtained results showed that the proposed MBRLR model with multi-boundaries provides the best RMSE with measurement results in the short grass, tall grass and weed, and blockage environments. The fuzzy MBFLR model achieved the lowest RMSE score of 0.8 for the short grass environment, the lowest RMSE score of 1.2 for the tall grass with weed environment, and an RMSE score of 2.6 for one and two blockages between the nodes. The Optimized FITU-R Near-ground Model achieved the highest RMSE score of 25.2 for the short grass environment, the lowest RMSE score of 24.7 for the tall grass with weed environment, and an RMSE score of 27.7 for one and two blockages between the nodes. A large error was also found for the NLOS in the case of blockages, although this model was used for the vegetation loss. The ITU-R MA FSPL, One Slope PL open-area 433 MHz, and dual-slope models based on the measurements provided good predictions only for the open-area environment without any blockages. The RMSE values indicated that the Optimized FITU-R Near-ground Model was not suitable for predicting the RSSI value in all situations in the grass environment due to its large PLE. By contrast, the fuzzy MBFLR performed the most accurate prediction and cover of the RSSI values for the near-ground propagation for all environments. However, further studies should be carried out on a large scale in other environments or with different types of vegetation and performed during all seasons in order to obtain coverage path loss predictions as well.  
Conceptualization, S.P.; methodology, S.P.; software, P.P.; validation, P.P.; formal analysis, P.P.; investigation, S.P.; resources, S.P.; data curation, S.P.; writing--original draft preparation, S.P.; writing--review and editing, P.P.; visualization, P.P.; supervision, P.P.; project administration, S.P.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This material is based upon work supported by the Mini-Engineering Project, Mahidol University (MU) under award number MINI-ENG-03-2. The research work was conducted at the engineer garden, Nakornchaisri, Thailand.  
Not applicable.  
The authors would like to thank The Engineer Garden, Nakornchaisri for allowing us to conduct the experiments in the garden site.  
The authors declare no conflict of interest.
# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 3 Experimental Results
### Effect of Features from Textual and Pictorial Regions  
The quality-aware features used by the proposed BSRSF metric are derived from two kinds of regions in SCIs: textual and pictorial regions. To investigate the impact of the employed features from textual and pictorial regions in the BSRSF metric, we devised two metrics: Metric-T and Metric-P. Metric-T uses only the features of textual regions and does not use the features of pictorial regions. The predictive value \(Q_{T}\) for textual regions in (31) is used as the final assessment value of Metric-T. On the contrary, Metric-P adopts only the features of pictorial regions and discards the features of textual regions. The predictive value \(Q_{P}\) of pictorial regions in (34) is used as the final assessment value of Metric-P. Here, the BSRSF metric is compared with these two metrics and the experimental results on SIQAD are listed in Table 6.  
From Table 6, two conclusions can be drawn. Firstly, the BSRSF metric employing the features from two kinds of regions achieves better predictive ability than Metric-T and Metric-P, which employ features from only one kind of region. This indicates that, to improve the performance of the quality evaluation method of SCIs, it is necessary to simultaneously deal with the features from the two kinds of regions. Secondly, the performance of Metric-T is much better than that of Metric-P, which shows that the features of textual regions are more important than those of pictorial regions. Certainly, the features of pictorial regions have an indispensable effect for the overall quality evaluation of SCIs.
## 2 Background and Related Work
### Product Line Engineering  
Compared to single-system development, adopting a PLE approach requires an upfront investment in establishing reusable platforms, feature models, and supporting processes. However, this investment typically pays off after developing more than one product, as the cumulative benefits increase with each additional product. The point at which these investments are recovered is called the breakeven point. Beyond this point, PLE enables organizations to achieve a significant return on investment (ROI), particularly in terms of scalability and efficiency compared to developing each product independently. As shown in Figure 3, the PLE process is structured around two complementary lifecycle activities: domain engineering and application engineering. In domain engineering, the focus is on creating reusable platforms, product line architectures, and variability models that define the commonalities and variabilities across the product family. These assets are then used in application engineering, where individual products are developed by selecting and configuring features based on specific requirements. These lifecycles are supported by a management process that addresses technical and organizational aspects, ensuring alignment with business goals and the effective execution of PLE practices.  
PLE is used extensively in software engineering, where shared codebases, libraries, and architectures form the foundation for reusable platforms. However, its principles are equally applicable to systems engineering, where reusable hardware components, interfaces, and subsystems play a similar role. This dual applicability makes PLE a powerful approach for managing variability and achieving reuse across diverse domains, from software-intensive systems to integrated hardware-software solutions.  
Figure 2: Metamodel representing key concepts of Machine Learning.
## Methodology
The first section in the objective function (i.e., \(\overline{f}\)) is used to denote the system cost, while minimizing the weight sum among the expected objective values; the second section [i.e., \(\lambda_{1}\cdot\left(f_{\text{max}}-\overline{f}\right)\;+\;\lambda_{2}\cdot \left(\overline{f}-f_{\text{min}}\right)\)] represents the differences between two extreme possible values, which can enhance the robustness of optimization solutions, where \(\lambda_{1}\) and \(\lambda_{2}\) represent their respective weight coefficients; and the last section (i.e., \(\rho_{2}\cdot[B^{R}(\alpha)-B_{r}{}^{1}]\)) denotes the difference between the extreme values of fuzzy uncertainty information, which can control the feasibility robustness of the generated solution. \(\rho_{2}\) is a goal programming weight for fuzzy uncertainties [25]. The evaluation of the trade-offs between system cost and risk can be obtained under different coefficients \(\lambda_{1}\), \(\lambda_{2}\), \(\rho_{1}\), \(\rho_{2}\), and \(\alpha\). The constraint (16) can be tackled by a conventional FPP algorithm.
# Event-Based Path-Planning and Path-Following
## 3 Two-Level Control System
### Event-Based Path-Planning
#### 3.3.2 Robust Convex Outline Algorithm  
For efficient path-planning, we propose a simple algorithm that transforms a sequence of obstacle points \(\mathcal{Q}_{in}\) that are produced by the FLS into another sequence of points \(\mathcal{Q}_{out}\) that forms a convex outline of the obstacle. Usually, the sequence \(\mathcal{Q}_{out}\) contains a relatively small number of points. The algorithm implies the following steps.  
* If sequence \(\mathcal{Q}_{in}\) consists of less than 3 points, mark these points for further inclusion in the output sequence \(\mathcal{Q}_{out}\) and go to Step 5.
* Mark the start and end points of \(\mathcal{Q}_{in}\). Assign \(i_{0}=1\).
* By going through the points, starting from point \(Q_{i_{0}}\), find the first point \(Q_{i_{k}}\) that satisfies the following condition: there exists a point \(Q_{i}\) between \(Q_{i_{0}}\) and \(Q_{i_{k}}\) such that the shortest distance from it to the line passing through the points \(Q_{i_{0}}\) and \(Q_{i_{k}}\) is greater than a predetermined value \(D_{m}\) and it lies on the same side of the line as the AUV (see Figure 4). If such a point does not exist, go to Step 5.
* Mark the point \(Q_{i_{k}-1}\) immediately preceding point \(Q_{i_{k}}\). If \(Q_{i_{k}}\) is not the end point of \(\mathcal{Q}_{in}\), then \(i_{0}=i_{k}-1\) and go to Step 3.
* Form the sequence \(\mathcal{Q}_{out}\) from all marked points of \(\mathcal{Q}_{in}\) keeping the order.  
Note that the proposed algorithm is robust in the sense that small measurement errors and the irregularity of the obstacle do not significantly increase the number of generated points. When using the resulting convex outline in the path-planning algorithm presented in the next section, the safe distance parameter \(D_{s}\) should be increased by \(D_{m}\).
# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
## 4 Discussion
### SAR Imagery
While we acquired only a sample of TerraSAR-X imagery to assess the backscatter of walrus aggregations with a higher resolution X-band instrument, operationally, TerraSAR-X is capable of collecting stripmap imagery of any locale in the Arctic with revisit rates as often as 1-3 days. Furthermore, several other SAR missions have launched multi-satellite constellations capable of imaging high-latitude areas every day, or potentially multiple times a day. In 2019, Canada launched the RADARSAT Constellation [38] comprised of three C-band SAR instruments. RADARSAT data are freely available to approved Canadian users, and possibly to approved international users in the future. Italy's COSMO-SkyMed [39] is a 4-satellite X-band SAR constellation that is currently being upgraded with second generation instruments. Among several modes of operation, COSMO-SkyMed has a 3 m resolution stripmap product that may be well-suited for walrus haulout monitoring. Finland's ICEYE [40] is an X-band SAR constellation presently comprised of 10 satellites with plans to launch 8 more in 2022. ICEYE also has a 3-m resolution stripmap mode. In the United States, two commercial SAR companies have recently been established: Capella Space [41] and Umbra Labs Inc. [42]. Capella has launched three X-band SAR satellites with plans to launch four more by late 2021, and Umbra may begin launching its X-band constellation in late 2021. Both Capella and Umbra are designed to acquire high-resolution imagery (\(\sim\)1-m and finer), and similar to TerraSAR-X, both have developed automated interfaces to give customers the ability to schedule data acquisitions themselves on-demand. Collectively, the many existing and emerging sources of SAR imagery suggest that daily or near-daily all-weather monitoring of walrus haulouts may soon be attainable, given advanced scheduling arrangements.
## 1 Introduction
### Titanium Degradation
#### 1.2.2 Titanium Surface Energy Changes  
As mentioned in the previous paragraph, the osseointegration of bone to implant is influenced by the implant's surface characteristics such as surface chemistry, topography, and wettability, along with the presence of impurities. The passivation and thickening of TiO\({}_{2}\) layers occur when an electron from titanium adsorbs oxygen from the air and produces oxygen ions. The ions on the surface are relatively electronegative and continuously maintain electronegative charges in the presence of air when stored in a gas-permeable container over time. Compared with the newly produced highly electropositive implant with high surface free energy [64], the positively charged new titanium surface directly interacts with the negatively charged biological cells. This interaction between the new titanium surface and osteoblasts occurs through electrostatic forces without cell-protein interaction.  
Figure 1: Straumann SLActive® Implant system (Institute Straumann AG, Basel, Switzerland); (**a**) isotonic saline solution as novel storage to maintain surface bioactivity; (**b**) wettability of implant shown as blood drawn onto the implant surface.  
[PERSON] et al. [47] reported that the application of divalent cations such as calcium ions on a four-week-old titanium surface increased albumin adsorption, whereas application on a new TiO\({}_{2}\) surface exhibited otherwise. Corresponding with the above-mentioned reaction, researchers [47] suggested that the divalent calcium cations deposited onto the old titanium surface act as bridges between the negative TiO\({}_{2}\) surface and the protein molecules. This finding indicates that the electrostatic property of the titanium surface plays a role in protein adsorption and biological interaction and is a critical factor in determining titanium bioactivity.
## 2 Results and Discussion
### Results
#### 2.1.7 Identification of Candidate Genes Involved in Ginsenoside Biosynthesis  
MeJA functions as an effective elicitor to boost the production of many secondary metabolites. Recent research shows that MeJA markedly promotes saikosaponin production in adventitious roots of _Bupleurum falcatum_[30] and regulates the phenolics, terpen and alkaloids accumulation in plant cell suspension cultures [31]. Treatment of _P. ginseng_ hairy roots and cultured cells by MeJA has been shown to increase ginseng content [22,23]. Increasing reports reveal that MeJA can up-regulate metabolite-related enzyme genes as well [32]. Supporting these premises, MeJA elicited adventitious roots of _P. ginseng_ were used to screen genes related to ginseng biosynthesis. More recent evidence shows that both the MVA and the non-MVA (also known as MEP or DOXP) pathways are involved in ginseng biosynthesis. The synthesis is performed in the cytosol from acetyl-CoA via the MVA pathway and from pyruvate and glyceraldehyde-3-phosphate in the plastid via the non-MVA pathway [33]. It is noteworthy that a total of 749 unigenes (Table S5) in our Solexa/Illumina dataset encoding almost all the known enzymes were found to be involved in ginseng biosynthesis via the  
Figure 6: FPKM values of all-unigenes expressed in Pg-Con and Pg-MeJA. Each dot indicates one unigene. The dots above the yellow line indicate up-regulated genes and below this line are down-regulated genes. The horizontal coordinates represent the FPKM values of Pg-Con and the vertical coordinates the Pg-MeJA FPKM values. The X and Y-axis are in log-scale. MVA pathway. However, we did not find any enzyme gene participating in the non-MVA pathway. The identified enzymes from our dataset involved from acetyl-CoA to ginseng biosynthesis are shown in three steps (Table 2, Figure 7). **Step I**: Genes encoding all the ginseng biosynthetic enzymes involved in this step were found in our study including acetyl-CoA acetyltransferase (AACT), HMG-CoA synthase (HMGS), HMG-CoA reductase (HMGR), mevalonate kinase (MVK), phosphomevalonate kinase (PMK), mevalonate diphosphate decarboxylase (MDD) and isopentenyl diphosphate isomerase (IDI). Here, MVK catalyzing the production of mevalonate phosphate ranked the highest (20 unigenes) and most of them (14 unigenes) were found to be up-regulated. However, no _MVK_ gene was previously detected in untreated 11-year-old _P. ginseng_[34] and 4-year-old American ginseng root [35], with the exception of only 3 candidate genes in 4-year-old untreated root of _P. ginseng_[15]. HMGS involved in this step was not previously discovered from 4-year-old of _P. notoginseng_[16] and _P. quinquefolius_[35] untreated root, while we obtained 9 candidates from our work. The difference of the above results may be due to the MeJA induction or the type of organs, or species. IDI catalyzes the reversible conversion
# PhotoMatch: An Open-Source Tool for Multi-View and Multi-Modal Feature-Based Image Matching
## 3 Experimental Results and Discussion
### Results
#### 3.5.2 Multi-Modal  
The results of the multi-modal dataset are outlined in Table 2. The learning-based algorithms outperform the hand-crafted based algorithms for every dataset. D2-Net is the best-performing algorithm for every case, with the exception of the visible thermal aerial, where R2D2 obtains the highest accuracy. For the visible-range dataset, no acceptable results were obtained using any of the tested algorithms. For the visible-thermal dataset, the mean error was above the threshold, even for the D2-Net algorithm.  
The hand-crafted algorithms performed much worse than learning-based algorithms. They were capable of obtaining a mean error below the threshold of ten pixels in only one case (SURF + SIFT for the magnetic resonance dataset).  
Learning-based algorithms are shown to be a suitable approach for multimodal image matching for different datasets and applications. An algorithm such as D2-Net has been able to achieve good results for the majority of the presented datasets. Nevertheless, the difference in results for different types of images encourages the study and comparison of different approaches and parameters for any specific application requiring multimodal image matching.  
The final matchings obtained for the two best-performing algorithms for each multi-modal dataset can be analysed in Figure 7.  
The combination of different hand-crafted algorithms could be useful for some types of multi-modal data [21]. Nevertheless, some learning-based algorithms greatly outperform hand-crafted methods in multi-modal cases, being able to obtain acceptable results when hand-crafted algorithms fail.  
In general, the experimental results presented in this paper demonstrate the great variability of results for different approaches and with different case studies. This highlights the importance of offering an educational and open-source tool, PhotoMatch, to compare and assess different algorithms through an experimental evaluation of learning-based and hand-crafted algorithms to better understand their performance across a wide range of scenarios.
## 1 Introduction  
Thraustochytrids are unicellular eukaryotic protists belonging to the superphylum Heterokonta (also known as Stramenopiles) and more specifically to the heterotrophic phylum Bigyra and the class Labyrinthulae [1]. Thraustochytrids are associated with a wide range of biotechnological applications, which triggered their industrial production for different high-value markets [2; 3; 4]. From a biotechnological point of view, two main features are responsible for the increasing interest in this family of microorganisms. They display high growth performance, reaching very high cell concentrations in a few days, and exhibit the striking ability to produce and accumulate docosahexaenoic acid (DHA) [4; 5; 6]. In contrast with the majority of microorganisms that produce saturated fatty acids (FA) as energy storage lipids, thraustochytrids synthesize long chain-polyunsaturated fatty acids (LC-PUFAs), like DHA, as energy storage, by an alternative pathway catalyzed by the polykefide synthase (PKS)enzymatic complex [5, 7]. Usually, besides DHA, they also produce docosapentaenoic acid (DPA), another typical LC-PUFA in thraustochytrids [4, 5].  
DHA belongs to a class of LC-PUFA often called \"omega-3 fatty acids\", which are in high demand due to the many known benefits in nutrition. DHA is also widely known for its fundamental role in preventing cardiovascular diseases, being essential for brain and eye development (particularly in children), learning capacities, as well as for the prevention of neurodegenerative conditions, arthritis, asthma and skin diseases [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. In addition, DHA plays a key role in membrane fluidity, cell signaling, cell interactions and in the development of the immune system and inflammatory responses [21, 22, 23].  
Although DHA is well established in the food, feed, nutraceutical, and dietary supplement markets, it has been sold mainly in the form of fish oil, of which 70% is currently used for aquafeeds [3, 5]. Nevertheless, several sustainability problems are strongly related to fish oil, namely overfishing and chemical products used in the extraction of this oil [4, 7, 14, 17, 20, 24]. In addition, fish oil is associated with an unpleasant smell, taste, low DHA amount, and possible presence of harmful contaminants, due to ocean pollution and bioaccumulation of heavy metals, dioxins and polychlorinated biphenyls [7, 14, 16, 17, 20, 24, 25]. Moreover, the increasing need for food and the spread of aquaculture worldwide will also require higher amounts of these oils for aquafeed. In addition, the growing demand for DHA from a non-animal origin created a gap in the market, which could be fulfilled by DHA of microbial origin, with particular relevance in the vegan market.  
In this context, the present work reports the optimization of lab-scale culture media of _Aurantiochytrium_ sp. AF0043 for the purpose of maximizing biomass, lipid and DHA yields. In addition, the impact of the different conditions on the biochemical composition, in particular on the proximate composition and FAME profile, was evaluated.
# Serological Investigation and Epidemiological Analysis of Bovine Leptospirosis in Egypt
###### Abstract  
Bovine leptospirosis is a bacterial zoonotic disease of worldwide distribution. Little information is available regarding the occurrence of the disease in the Nile Delta provinces, Egypt. The present study investigated the seroprevalence of leptospirosis among cattle from Dakahlia province, Northern Egypt, and identified the individual variables factors associated with infection. To this end, a total of 600 serum samples from cattle of small stakeholders with various clinical manifestations possibly associated with leptospirosis were collected from different localities across Dakahlia province, Egypt. Sera were examined serologically via ELISA to investigate the occurrence of the disease among animals. Chi-square test and multivariable logistic regression analyses were applied to determine the association between hypothesized risk factors and the disease. Interestingly, our findings showed that 39.33% of the examined sera were positive for _Leptospira_ antibodies, with significant differences among different localities. In addition, statistical analysis showed significant differences among age groups. Notably, the highest prevalence rate (22%) was observed in those aged between 3 and 5 years (\(p<0.0001\)), whereas the lowest prevalence (2.66%) was reported in cattle <1 year old (\(p<0.0001\)). Moreover, females had a significantly higher prevalence rate (35.33%) than males (4%) (\(p<0.0001\)). Furthermore, our results showed significant differences in the occurrence of infection and reported clinical signs (\(p<0.0001\)). Multivariable logistic regression identified repeated and drop milk yield as the best predictors for prediction of ELISA results and linear discriminant analysis (LDA) model showed that overall classification accuracy of ELISA result using clinical signs and demographic data as predictors was 70.7%. The current study concluded a relative high prevalence of leptospirosis among cows bred in movable herds and households in the studied area and that age, repeated breeder and drop milk yield can be considered major risk factors associated with infection.  
leptospirosis; bovine; epidemiology; serology; Egypt +
Footnote †: journal: Medical Medicine and Infectious Disease