esa-sceva/satcom-chunk-collection huggingface dataset:
# Microalgae: Potential for Bioeconomy in Food Systems
## 1 Introduction  
Colombia has five large continental hydrological zones and numerous rivers, including the Magdalena, Putumayo, and Cauca [1]. Within these zones, six units comprise approximately 1900 swamps and wetlands, of which 80% are located in the Caribbean region [2]. It also has marine and oceanic waters, with two coastlines spanning 1642 km in the Caribbean and 1300 km in the Pacific [3]. The climatic, orographic, and hydrological characteristics allow for the existence of numerous ecosystems and appreciable biodiversity, which makes Colombia a megadiverse country [4]. Together, these natural resources offer the potential for biotechnological development, sustainable use, and growth of the bioeconomy in the country.  
Paradoxically, 27% of Colombian ecosystems are at critical risk and 17% are at risk in terms of biodiversity, ecosystem services, and social welfare [5]. In addition, poverty has historically been a social problem, and in the last 10 years, 34.7-42.5% of Colombians have been classified as poor [6]. In this regard, conservation of biodiversity is essential for facing economic, social, and climatic problems.  
Bieconomy integrates science, society, economy, industry, and environment for sustainable development [7]. It aims at satisfying the needs of individuals in the agri-food and energy sectors mainly, in balance with the environment [8]. Since the implementation of the United Nations 2030 Agenda, bioeconomy has been fostered in Latin America as a commitment to sustainable development [9]. In Colombia, the green growth policy focuses on technological development and boosting economic productivity on the basis of environmental sustainability and social inclusion [10].  
Microalgae are aquatic microorganisms [11] that are an important source of biomass in food chains [11; 12; 13]. These microorganisms contain chloroplasts or photosynthetic cellstructures, and phycobiliproteins such as chlorophylls and carotenoids [11; 14; 15]. The estimated number of microalgae species ranges from 200,000 to several million [11; 16]. Many species are used in agriculture, food, bioremediation, biological control, CO\({}_{2}\) fixation, and alternative energy production [17; 18; 19; 20].  
Microalgae are widely distributed in a variety of aquatic and terrestrial environments; they are part of the plankton and play a major role in ecosystems. Approximately half of the planet's photosynthesis and oxygen production can be attributed to microalgae, which makes them essential for carbon fixation [12; 13; 21].  
Considering the abundance of hydric resources and ecosystems, a high diversity of microalgae is expected in Colombia. Nevertheless, microalgae and their applications have been poorly studied in the country. Therefore, there is considerable potential in Colombia for the development of biotechnology and bioeconomy using microalgae, which are necessary for agri-food security. This review discusses some applications of microalgae biomass that could be developed in Colombia.
## 2 Results
### PCA Analysis at Different Ripening Stage of Two Banana Varieties  
Principal component analysis (PCA) was performed to provide a general overview of the samples and an easy visualization of the complete data set in a reduced dimension plot. It was also used to get a primary evaluation of maturity stage of samples, to explore interdependencies among the tested volatiles and to identify variant volatiles groups with similar characters per cultivar. For this analysis, all volatile production at three ripening stage were studied in this work. As shown in Figure 2, volatiles production showed quite different characters in the Fenjiao and Brazilian banana fruit. For the cv. Brazilian (Figure 2A), the first two components could explain 98% of the variability among the samples, i.e., 65.3% (principal component 1, PC1) and 32.7% (principal component 1, PC1). Generally, volatiles components were distributed from the positive to the negative side on the \(x\)-axis (PC1) according to different ripening stages (Figure 2A). Different distribution indicates different maturity stages of the samples. As we can see that green stage situated in the upper and full-ripening stage in the lower quadrant. And turning stage located in the middle, which on the \(x\)-axis. Aldehydes are the most important volatile component for the green stage of Brazilian banana fruit, as the total aldehydes contribute most to the green stage. ALD1 (_trans_-2-hexenal) and ALD4 (hexenal) are the principal, main contributors to the total aldehyde content. For the fruit at turning stage, esters, aldehydes and others non-name volatiles were important. E17 (butanoic acid, 3-methyl-, 3-methylbutyl ester), E26 (butanoic acid, 3-methylbutyl ester), E34 (1-butanol, 3-methyl-, acetate) and O1 (others volatiles) are the principal, main contributors to the volatiles at this stage. Esters were the predominance of volatiles at the full ripening stage, which the total esters contribute most to the volatiles at full ripening stage. E17 (butanoic acid, 3-methyl-, 3-methylbutyl ester), E26 (butanoic acid, 3-methyl-, acetate) are the most produced ester at this stage.
## 5 Experimental Setup and Verification Analysis
### Natural Frequency Measurement  
To validate the accuracy of the natural frequency analysis, it is essential to measure the natural frequency of the elastic steel sheet with an attached magnet within the experimental system. We employed an impact hammer (model 2302, Meggitt, UK) and an accelerometer, which are connected to the imc() (CS-5008-1, TUV Rheinland, Kolle, Germany) data processor for analysis. Our findings indicate that the natural vibration frequencies of the first three modes of the elastic steel sheet with the attached magnet are 4.56 Hz, 21.48 Hz, and 60.06 Hz, respectively (see Figure 17). After the numerical calculation of Equation (29), the first three eigenvalues can be obtained: 2.1264, 4.7142, and 7.8590. The theoretical dimensional frequencies for the first three modes are 4.5216 Hz, 22.2237 Hz, and 61.7639 Hz, respectively.
## 3 Materials and Methods
### Evaluation and Statistical Testing  
The classification results of the CNN were evaluated in terms of accuracy (ratio between the numbers of correctly classified instances and all the instances) after each 20 iterations. We use [PERSON]'s threshold based on the predictions of the training data of each specified set of images to convert the numerical predictions into binary labels. We then computed the mean and the standard deviation of the classification accuracy of the test set over these 20 iterations. To compare the values of accuracy obtained with training datasets with and without GAN-generated images, we use the Mann-Whitney U test [22]. The Mann-Whitney U test is a statistical nonparametric text for the null hypothesis [23]. This test was conducted between the accuracy values of the test set predictions by the CNN, whose training data contain both original and GAN-generated images, and the corresponding accuracy obtained without using any GAN-generated images. Our null hypothesis was that the use of GAN-generated images in the training did not cause significant differences in the accuracy. We use the typical level of significance of 5% but take the multiple comparisons problem into account before drawing final conclusions.  
Figure 1: A graph explaining the dataset division.  
Figure 2: GAN output using 1000 training datasets at the following epochs: (**a**) 1 epoch; (**b**) 100 epochs.
## 3 Methods  
### RTLSR BRDF Model  
The BRDF is a pivotal function that quantitatively delineates the relationship between ground cover reflectance and the geometry of incidence and observation [39]. Nearly all models for angle effect correction are predicated on the BRDF model framework [40]. The RossThick-LiSparse R (RTLSR) BRDF model [12] stands as one of the most esteemed and widely utilized semi-empirical models, and it is integral to the operational production algorithms for MODIS BRDF and albedo products [41].  
Within the RTLSR BRDF model, a linear composite of three distinct scattering components is formulated, encompassing isotropic scattering, volumetric scattering, and geometric-optical scattering. The model's expression is articulated as follows [12; 42]:  
\[R_{\lambda}(\theta_{s},\theta_{v},\phi)=f_{iso}(\lambda)+f_{vol}(\lambda)K_{ vol}(\theta_{s},\theta_{v},\phi)+f_{geo}(\lambda)K_{geo}(\theta_{s},\theta_{v},\phi) \tag{1}\]  
where \(R_{\lambda}(\theta_{s},\theta_{v},\phi)\) is the Bidirectional Reflectance Distribution Function of \(\lambda\) band; \(f_{iso}(\lambda)\), \(f_{vol}(\lambda)\), and \(f_{geo}(\lambda)\) are the weights of isotropic scattering, volume scattering, and geometrically optical scattering, respectively; and \(K_{vol}(\theta_{s},\theta_{v},\phi)\) and \(K_{geo}(\theta_{s},\theta_{v},\phi)\) are the volume scattering kernel and the geometric optical kernel, respectively, which are both functions of the solar zenith (\(\theta_{s}\)), view zenith (\(\theta_{v}\)) and relative azimuth (\(\phi\)).
## 4 Discussion  
We determined that the frequency of cases in which a seeding effect causing rainfall or snowfall can be generated on the ground is generally low and depends on the wind system of the vertical atmosphere. Because the Korean Peninsula is located in the westerly wind zone, a seeding effect on land can be expected when clouds located in the northern and southern areas of the West Sea are seeded. In the typical west-high, east-low pressure pattern of the Korean Peninsula during winter, a rainfall (snowfall) effect on land can be expected due to northeast winds flowing to the East Coast [52; 55; 56; 57]. Seeding effects can be expected in the upwind or downwind sides around mountainous terrain with high land on the Korean Peninsula for water phase seedable clouds [9; 23; 26]. These results suggest that it may be useful to seed seeding material such as AgI in ice phase clouds occurring in the Northeast and West Sea to form ice nuclei and induce both snowfall and rainfall. Also, it could be possible to seed seeding material such as CaCl\({}_{2}\) in water phase clouds occurring in the Southwest Sea and West Coast to form condensation nuclei and to induce precipitation. In particular, the Taebaek Mountains is located between the inland and the sea, and the clouds are generated from November to March and remain below a temperature of zero degrees. Therefore, these clouds are well suited for precipitation experiments using AgI.  
The COMS cloud products used to detect seedable clouds in this study include uncertainty for each product, as shown in Section 2. This uncertainty leads to false detection of seedable clouds. However, geostationary satellites cannot detect lower-level clouds as they are underneath the upper clouds. In other words, lower-level clouds underneath the clouds exceeding the cloud top height, set in this study as seedable clouds, cannot be detected. [PERSON] et al. [51] reported that, on average, approximately 11% of these multi-layered clouds occur in the middle latitudes. In other words, the frequency of the undetected seedable clouds in the lower level, underneath the upper cloud, may be higher than the seedable clouds not detected due to the uncertainty of COMS cloud products. Therefore, the occurrence frequency of the seedable clouds analyzed may be higher or similar. However, land- and sea-based, as well as regional differences, may occur [51; 58].
# Effects of Cement Treatment on Mechanical Properties and Microstructure of a Granite Residual Soil
## 2 Experimental Procedures
### Microscopic Tests  
XRD, SEM, and MIP tests are conducted to investigate the mineral compositions, soil fabric, and pore size distribution of the untreated and cement-treated GRSs following water saturation, respectively. The specimens for preparing the samples used in the microscopic tests are 61.8 mm in diameter and 20 mm in height. A series of specimens with volumetric cement contents of 0% (untreated), 1%, 2%, 3%, 4%, and 5% are cured to 28 d and subsequently vacuum-saturated under confined conditions. For each cement content, cube samples with a side length of approximately 2 cm are taken from the saturated specimen and immediately freeze-dried using liquid nitrogen and a vacuum-freezing dryer. The freeze-dried bulk samples are directly used for the SEM and MIP tests, and one of the freeze-dried bulk samples is ground into powder for use in the XRD tests. The equipment used for the XRD tests is the Bruker D8 Advance. XRD patterns in a range of 20 = 5-80\({}^{\circ}\) are obtained using a Cu-\(Ka\) (\(\lambda\) = 1.515 A) X-ray tube and a continuous scan rate of 2\({}^{\circ}\)/min. SEM tests are conducted using a FEI Quanta 250; SEM images are captured from fresh fractures sprayed with gold. MIP tests are carried out using the Micromeritics AutoPore IV 9510 and each sample has accomplished an intrusion-extrusion cycle. The arrangements for the mechanical and microscopic tests are summarized in Table 3.
## 4 Experimental Results  
### Experimental Setups  
In this paper, the Dueling-DQN-based routing algorithm was evaluated in the following on-board environment. First, the training to obtain the.pt file containing the weight information was performed on a Jupyter notebook v7.0.8 in the Anaconda v24.3.0 virtual environment, and the training was performed on a desktop PC with i7-10700 and Nvidia Geforce RTX 3070. The model for training was a 15 \(\times\) 15 grid map consisting of four channels: agent position, goal position, obstacles, and boundaries. The total number of training runs was 100, with a maximum number of steps per run of 300 and a batch size of 1024. In the convolution process, each kernel was set to 6 \(\times\) 6, 4 \(\times\) 4, 3 \(\times\) 3, and 2 \(\times\) 2, resulting in 15 \(\times\) 15 \(\times\) 4 inputs and 4 \(\times\) 4 \(\times\) 32 data outputs.  
In addition, for the heterogeneous device co-design of CPU and FPGA, the Board of PYNQ-Z2, which has both PS and PL, was selected. The PYNQ-Z2 is based on the ZYNQ XCZ020-1 CLG400C chipset, which includes a 650 MHz ARM Cortex-A9 dual-core processor and an Artix-7 level FPGA. Figure 4 shows the real experimental environment as configured. The FPGA development platform was Xilinx Vivado HLS 2018.3, Vivado 2018.3. First, the code for parallelization was configured in Vivado HLS 2018.3, and the.v file containing the IP information was extracted by synthesizing the configured code. The previously developed IP was then imported into Vivado 2018.3 to design the entire hardware platform. Finally, the.hwh,.tcl, and.bit files containing information about the hardware platform were extracted. PYNQ also provides various tools and libraries, of which we used the overlay library to develop through python. Figure 5 shows the development flow described above schematically.  
Figure 4: Configured real experimental environment.  
Figure 5: The overall flow of development.
## 4 Methodology and Testing Process
### Testing Process
#### 4.3.1 Image-to-Image  
Sampling steps and denoising strength: When other hyperparameters remain unchanged, changing the sampling steps and denoising strength produces the results shown in Figure 6. The following phenomena can be observed: when both the denoising strength and sampling steps are relatively low, the generated results show minimal changes compared to the input image. However, with higher denoising strength, the generated image boundaries slightly deviate from the input boundaries. Nevertheless, when the denoising strength exceeds 0.94, the image quality does not significantly improve. With sampling steps between 15 and 30, the generated images maintain a high quality, but further increasing the sampling steps may result in unclear boundaries of color blocks. Additionally, the higher the sampling steps, the greater the computational resource consumption. Therefore, to achieve clear functional zoning and accurate color values in the generated floor plan images, it is recommended to set the denoising strength between 0.94 and 0.95 and the sampling steps between 20 and 25. This ensures the balance between image quality, computational resource consumption, and processing efficiency.  
Figure 6: Sampling steps and denoising strength.  
Input image and seed: The testing mentioned above represents the better outcomes achieved under the same random seed input. When other hyperparameters remain unchanged but different random seeds are used, the generated functional color blocks exhibit weaker regularity, and the area of each color block does not match the corresponding functional area of the nursing unit (Figure 7). By introducing a significant spatial organizational condition (in this case, a main corridor) to the input image, the generation results can be significantly improved (Figure 8). Under unchanged hyperparameters, this approach enables the generation of relatively ideal architectural floor plan color block images with clear functional block boundaries, reasonable layouts, and relatively reasonable shapes and area ratios of functional zones, regardless of the random seed used. However, the outer boundary of the nursing unit may change.  
Summarize: Under the Image-to-Image control, the denoising strength and sampling steps affect the boundaries and color values of the generated image blocks. When the color values are accurately represented, the boundaries of the output result may exhibit slight deformation, making them inconsistent with the input image. Adding control structures to the input image can effectively optimize the layout of various functions in the generated image. Under different random seed inputs, good results can be obtained.
## 3 Results and Discussion  
### Spectral Analysis  
Figure 1 presents the spectra of an EVOO and lampante olive oil sample belonging to the \"other\" class in the region 4000-900 cm\({}^{-1}\). They are comparable, as may be observed, and their spectral characteristics can be connected to those of triacylglycerlycerols. Three different spectral regions were observed at 3100-2800 cm\({}^{-1}\), 1850-1650 cm\({}^{-1}\), and 1500-900 cm\({}^{-1}\), with the latter being the \"fingerprint region\" because the band pattern is particularly characteristic of molecular composition and can be used to identify minor substances [26]. In the high-wavenumber region, the band at 3005 cm\({}^{-1}\) is due to the CH stretching of _cis_ double bonds, whereas the bands centered at 2922 and 2853 cm\({}^{-1}\) are associated with the hydrogen bonds and C-H bonds of the aliphatic chains of fatty acids and, in particular, vibrations of the terminal methyl groups (-CH\({}_{3}\)) [27; 28]. The very strong absorbance at 1744 cm\({}^{-1}\) was assigned to the carbonyl group (C=O) of triacylglycerlycerols and specifically to the ketone group [29]. The triacylglycerglycerol ester linkage is one of the most stable bonds in olive oil, and harsh conditions may induce changes in that band [30]. It is referred that during oxidation, the spectral region between 3050 and 2740 cm\({}^{-1}\) and the band at 1744 cm\({}^{-1}\) undergo several changes due to the production of saturated aldehyde functional groups or other secondary oxidation products [26]. The peak at \(\sim\)1460 cm\({}^{-1}\) is associated with the bending vibrations of CH\({}_{2}\) and CH\({}_{3}\) in the aliphatic chains of fatty acids. Bending vibrations of the CH\({}_{2}\) groups correspond to the peak at 1377 cm\({}^{-1}\)[18]. Shoulder peaks at 1096 and 1237 cm\({}^{-1}\) are assigned to triacylglycerlycerols, with a strong peak at 1160 cm\({}^{-1}\) corresponding to stretching vibrations of C-O ester groups [31]. Finally, bands in the 1125-1095 cm\({}^{-1}\) region correspond to the stretching vibration of C-O ester groups and CH2 wag [27].
_Applied_  
_sciences_  
_Authentication in the Internet of Medical Things: Taxonomy, Review, and Open Issues_  
**[PERSON] 12,*\(\copyright\) and [PERSON] 1**  
1 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jedda 21589, Saudi Arabia; [EMAIL_ADDRESS]  
2 Department of Computer Science, The Applied College, King Khalid University, Abha 61421, Saudi Arabia  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract:** The Internet of Medical Things (IoMT) has revolutionized the world of healthcare by remotely connecting patients to healthcare providers through medical devices connected over the Internet. IoMT devices collect patients' medical data and share them with healthcare providers, who analyze it for early control of diseases. The security of patients' data is of prime importance in IoMT. Authentication of users and devices is the first layer of security in IoMT. However, because of diverse and resource-constrained devices, authentication in IoMT is a challenging task. Several authentication schemes for IoMT have been proposed in the literature. However, each of them has its own pros and cons. To identify, evaluate and summarize the current literature on authentication in IoMT, we conducted a systematic review of 118 articles published between 2016 and 2021. We also established a taxonomy of authentication schemes in IoMT from seven different perspectives. We observed that most of the authentication schemes use a distributed architecture and public key infrastructure. It was also observed that hybrid cryptography approaches have become popular to overcome the shortcomings of single cryptographic approaches. Authentication schemes in IoMT need to support end-to-end, cross-layer, and cross-domain authentication. Finally, we discuss some open issues and future directions.  
**Keywords:** Internet of Medical Thing; security requirements; IoMT authentication scheme; IoMT authentication attacks