esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 3 Methodology
### LoRa Measurement Descriptions
#### 3.2.1 Pycom Transmitter Module  
This module was programmed with the aim of presenting two functions:  
* Generate a known data string.
* Send this chain to another Pycom module wirelessly and at a certain speed.  
Through the implemented program, the user decides the data-sending speed and once that speed has been assigned, the module sends known characters every certain period of time. In this study, the sending speed was 2 data/s.
## 3 Microalgal/Cyanobacterial Metabolites and Phytohormones with Potential Interest for Agriculture
### Terpenoids  
Terpenoids are organic compounds that derive from the inclusion of methyl- or oxygen-based functional groups to terpen. Based on the number of five-carbon isoprene units that constitute them, terpenoids can be classified into: hemiterpenoids (one isoprene unit, C5), monoterpenoids (two isoprene units, C10), sesquiterpenoids (three isoprene units, C15), triterpenoids (four isoprene units, C20), triterpenoids (six isoprene units, C30), tetraterrepenoids (eight isoprene units, C40) and polyterpenoids (more than eight isoprene units, >C40) [2; 7]. Moreover, terpenoids can be classified according to the number of cyclic structures that constitute them, being the main categories monocyclic, bicyclic, rearranged and linear [7]. These compounds are secondary metabolites of several algal and cyanobacterial species. Among algae, red algae (Rhodophyta) are the richest source of secondary metabolites, with many genera known for their ability to produce terpenoids [28]. For example, 512 sesquiterpenes and 133 diterpenes with different structures have been extracted from the genus _Laurentia_[56]. Other red algae species that produce terpenoids include _Chondroococcus hornemanni, Hypnea pamuosa, Spharedococcus coronopifolnuis, Plocanumorum_, _Plocanum leptophyllum_ and _Porticia hornemannii_[28]. In the case of cyanabateria, genes encoding the enzymes involved in the terpenoid biosynthetic pathway were identified in _Synechocystis_ sp. PCC 6714 and _Thermosynechococcus elongatus_. Similarly, ma- rine cyanobacteria, such as those from the genera _Prochlorococcus_, _Synechococcus_ and _Trich-odesinum_ are known for their ability to produce isoprene. Finally, some filamentous cyanobacteria (e.g., _Pseudanabuena_ sp., _Pseudanabuena articulate_, _Planktonitroids raciborskii_ and _Oscillatoria perornata_) were also reported as a source for terpenoids [25]. Due to their bioactivity, especially in terms of antibacterial, antioxidant and anticancer properties, terpenoids are widely used in pharmaceutical and cosmetic formulations [22; 23; 27]. As precursors of linear terpenes, terpenoids are also being explored as potential candidates to replace fossil fuels [25; 57]. In agriculture, the antimicrobial, allelochemical and antioxidant properties of terpenoids make them valuable resources for use as biopesticides, with major impact on crops' protection against bacteria, insects and other organisms [26]. In addition, it has been reported that terpenoids can play an important role during the preliminary growth and development of plants, as well as on the attraction of pollinators [24].
# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Analysis of Canopy Interception Variation  
The spatial and temporal variations in canopy interception were investigated by the Mann-Kendal (MK) test, which is a non-parametric method recommended by the World Meteorological Organization. This test is frequently utilized to determine the long-term trend and variation of precipitation, temperature and runoff because it does not need to obey the same probability distribution [35].  
The MK test was calculated using Equations (7) and (8), assuming that the runoff sequence is \(x_{1},x_{2},x_{3},\cdots,x_{n}\). \(S_{k}\) represents the cumulative number of the first sample \(x_{i}>x_{j}(1\leq j\leq i)\) and it is defined as:  
\[S_{k}=\sum_{i=1}^{n}\sum_{j=1}^{i-1}sign(x_{i}-x_{j}) \tag{7}\]  
where \(sign(x_{i}-x_{j})\) is a step function. The statistical variable \(Z\) is defined as:  
\[Z=\left\{\begin{array}{cc}\frac{(S-1)}{\sqrt{\frac{n(n-1)(2n+5)}{18}}},&S_{k }>0\\ 0&,&S_{k}=0\\ \frac{(S+1)}{\sqrt{\frac{n(n-1)(2n+5)}{18}}},&S_{k}<0\end{array}\right. \tag{8}\]  
where \(Z\) obeys a normal distribution. \(Z>0\) indicates an upward trend, while \(Z<0\) means a downward trend. When \(|Z|\) is greater than 1.96, the serial trend passes the 95% significance test.
## 2 Background  
### The Internet of Medical Things  
A typical Internet of Things (IoT) infrastructure is made up of several devices connected to the Internet able to communicate with each other. More in general, any electronic device that has the capability of interfacing with and communicating with other nodes of the Internet can be considered part of the IoT network, i.e., smartphones.  
The Internet of Medical Things (IoMT) envisions a network of medical devices and people, which use wireless communication to enable the exchange of healthcare data [3]. Thus, this specific context put into place significant issues in terms of privacy and security that need to be considered: health data are sensitive data that must be appropriately protected across the network.  
If we consider Europe, all private companies and public bodies have been obliged to comply with the General Data Protection Regulation (GDPR). For what concerns health data handling, sources such as \"genetic data\", \"biometric data\", and \"health data\" must be managed carefully since these data are traced back to the sensitive category. Thus, they cannot be used without explicit consent unless for some cases (i.e., occupational medicine, health therapy, public interest). The same applies to data portability which places constraints on how data are shared [1].  
Thus, we just highlighted how much privacy and security risks are vital factors to consider. When devices are, in fact, connected to the network for exchanging information, they represent a perfect target to hit by malicious users. This kind of scenario should be prevented in the healthcare and so,regardless of the security problems related to the device itself (i.e., software and hardware weaknesses), the main threats are represented by the network used for sharing data. As a consequence, most of the implementations are usually forced to anonymize the information, definitely impacting on data exploitability, since anonymization forces the removal of personally identifiable information that has an impact on data integrity and thus quality [4].
## 5 Conclusions  
In this paper, the erosion (or cleaning) effect of cavitation nozzles was investigated. Five shear nozzles and two self-excited oscillating nozzles were researched through both simulation analysis and experimental study. The results showed that the shear nozzle had a better cleaning effect than the two self-excited oscillating nozzles. For shear nozzles, increasing the orifice diameter can enlarge the cleaning area. The expanded cylinder segment can mitigate the decrease in vapor volume fraction and extend the outlet pressure holding distance. A spherical outlet can facilitate the cavitation bubbles gathering together, thereby allowing more bubbles to reach and collapse at the wall surface. Therefore, the shear nozzle with no pre-shrinking section, a spherical outlet connecting to a diffusion cylindrical section outlet shape and orifice diameter of 1.5 mm had the best cleaning effect. This nozzle primarily capitalizes on denudation induced by bubble collapse during cleaning, thereby ensuring that the surface paint layer remains undamaged when cleaning ship hull. Furthermore, the nozzle maintained consistent erosion ability at a standoff distance of 30-50 mm, enabling it to adapt to changes in the cleaning standoff distance triggered by waves. A self-rotating cleaning device based on the optimum nozzle was designed and manufactured, and a test rig was established to test the cleaning effect. The cleaning area, and reaction force of one nozzle were obtained as well, which provides a design basis for the cleaning module of a cleaning robot.  
Conceptualization, methodology, K.H.; investigation, writing--original draft preparation, S.H.; project administration, writing--review and editing, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shenzhen Science and Technology Program (SGDX2021112-3150002002), Leading Talent of Guangdong Special Support Program (2021 TX05Z041).  
Not applicable.  
The data presented in this study are available in the article.  
The research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Pharmacological and Cosmeceutical Potential of Seaweed Beach-Casts of Macaronesia
###### Abstract  
Seaweed beach-casts are a seasonal phenomenon that regularly deposits tons of algae biomass on beaches, which are usually disposed of in landfills. The present work aimed to contribute to the valorization of this biomass by studying bioactivities that reveal its potential in the pharmaceutical and/or cosmeceutical industries. Methanol and ethanol extracts and fractions from 15 beach-casts biomass were tested for a range of bioactivities. Eight of the most active samples exhibit IC\({}_{50}\) values between 11.38 \(\upmu\)g/mL and 19.28 \(\upmu\)g/mL and selectivity indexes higher than 3.8, against NCI-H1299 (lung cancer) and T47D (breast cancer). Concerning antiaging potential, all the extracts tested presented UV protection, with SPF values above 30, like the standard sunscreen. Regarding aging-related enzymes, 24 samples were tyrosinase inhibitors exhibiting IC\({}_{50}\) values from 9.01 \(\upmu\)g/mL to 200.09 \(\upmu\)g/mL. Eight extracts inhibited collagenase (IC\({}_{50}<248.76\)\(\upmu\)g/mL), two of them more actively than positive control EDTA (IC\({}_{50}=59.26\)\(\upmu\)g/mL). In addition to UV protection, the cumulative effect of antityrosinase and anticollagenase activities shown by the samples suggest that they may play a significant role in preventing skin photoaging. The results obtained demonstrate the high potential of beach-cast seaweed biomass as sources of cosmetic bioproducts with antiaging effects, and of selective cytotoxic metabolites.  
beach-cast seaweeds; biomass valorization; anticancer; antiaging; antityrosinase; UV protection
# BIM-Based Model Checking: A Scientometric Analysis and Critical Review
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.4 Artificial Intelligence
This knowledge can classify and associate various rules, forming systematic rule sets to adapt to more complex BMC requirements. BMC is an interdisciplinary field that requires collaboration across different domains. This requires practitioners to possess diverse expertise, such as civil engineering and computer knowledge. However, this increases learning and communication costs, hindering the development and popularization of BMC. Visual programming language (VPL) alleviates this problem by allowing users to create programs through graphical operations, rather than complex computer codes [76; 80; 82; 112], thus enabling practitioners to invest less time in mastering this skill. VPL serves as an intermediary to translate professional requirements into computer programs. For example, Autodesk Revit provides a VPL component called Dynamo, which allows users to create, extract, and edit data for BIM models, providing great convenience for BMC [114; 138; 141]. Compared to traditional computer programming languages, such as Java or Python, the performance of VPL programs may not be optimal. However, considering the current level of BMC development, the operability and feasibility of the program is emphasized more strongly than its performance. Therefore, VPL has been applied in multiple studies in the field of AEC, including BMC. It has become a tool for researchers with domain knowledge, but who lack the programming knowledge needed to implement their ideas. AI Validation of Model Information  
Machine learning methods have also been introduced for validating model data, including model classification and anomaly detection. Automated classification of models or components enables the definition of the geometric and functional aspects of the building elements to support precise simulation and compliance checking. The preliminary detection of model anomalies is often considered the preprocessing step in the BMC process, such as anomalies in file formats, naming errors, data integrity, etc. Only after these basic data anomalies are addressed can compliance with standards be further verified. Artificial intelligence methods can describe features that are difficult for humans to describe, and they are more automatic, flexible, and efficient, which can reduce the manual workload of constructing rules. With further research and optimization, the performance of the models has improved significantly, with most studies achieving accuracy and precision of over 90%. However, compared to more precise logic-based methods, machine learning is probabilistic and therefore struggles to achieve 100% performance. Additionally, it requires effective data to drive. Many semantic features calculated through deep neural networks are inexplicable, posing certain data risks, so it needs to be comprehensively studied and regulated.
## 2 Materials and Methods
### Methodology
#### 2.2.4 Hazard Scenarios  
FlamMap 6.2 (United States Forest Service) is a fire behavior analysis software that computes the potential fire behavior characteristics such as spread rate, flame length, and fireline intensity under constant environmental conditions at a specific time over the entire landscape (LCP) using local weather and fuel moisture conditions and the local topographic layers. The creation of the LCP file requires topographic data, fire behavior fuel models, and forest canopy characteristics such as cover, height, base height, and bulk density to simulate the surface fire spread, while also including the possibility of a crown fire. In addition to these layers, information on dead and live fuel moisture, weather conditions, and wind speed and direction are necessary inputs for accurate fire behavior simulations [39].  
However, at least five geospatial layers, those of elevation, slope, aspect, fuel model, and canopy cover, could be inserted as inputs in order to simulate the surface fire spread, along with the location of ignition points, weather data, and wind direction and intensity. In this study, FlamMap was modified to function in a Linux environment and was translated into the Python programming language. The necessary input layers were used, meaning the Digital Elevation Model (DEM), aspect, slope, fuel map, and canopy bulk density (as depicted in Figure 4). This streamlined configuration improves the time efficiency of fire behavior simulations while retaining the crucial data needed for precise and reliable results.  
With regard to the data sources, the DEM was derived from orthophoto maps from the Hellenic Cadastre with a spatial resolution of 5 m and was utilized to generate the aspect and slope (in degrees) layers through GIS. In this study, the surface fuel, the fuel's moisture content, and the vegetation characteristics of the area were determined using an adjusted land use/cover map via GIS, high-resolution satellite imagery, and photo interpretation. Each fuel model was assigned to a vegetation type as a combination of custom and standard fuel models based on the research work of [40; 41].  
More specifically, code 18 refers to forests and shrublands, code 22 represents permanent crops, 24 is the non-fuels and 25 corresponds to the grasslands and annual crops (Figure 3). Considering the density layer, it represents the amount of available fuel per unit volume of the surface covered by the canopy space. It is a crucial factor that affects the probability of a fire outbreak and the rate of crown fire spread. This layer was categorized into 4 classes (0 m\({}^{3}\), up to 100 m\({}^{3}\), 100-300 m\({}^{3}\), and above 300 m\({}^{3}\)) for the area of interest, referring to the estimated timber volume of the available fuel per m\({}^{3}\).  
Figure 3: Wind rose of the dominant wind directions and intensities.
## 3 Results and Discussion
### Z-Score Normalization Impact on Inter-Parameter Correlations  
Figure 5 shows the dataset inter-parameter correlations before and after normalization of Z-score for different channel scenarios. The highly positive correlations tend to be bright orange. The highly negative correlations tend to be dark blue. All \(D_{\sigma}\), \(P_{L}\), \(K_{F}\), \(\sigma_{EoA}\), \(\sigma_{EoD}\), \(\sigma_{AoA}\), and \(\sigma_{AoD}\) are represented as DS, PL, KF, esD, esA, asD, and asA, respectively.  
The parts Figure 5a,c,e,g represent the correlation matrices for each scenario before normalizing the features, while the parts Figure 5b,d,f,h display the final result for the correlation after normalization. It is easily noted that the correlation coefficients differences between normalized and un-normalized parameters are negligible as the normalization process only standardizes the data without significantly affecting the inter-parameter correlation. The Z-score keeps the inter-parameter correlations in its region after normalization. The strong, medium, and weak correlations are retained. This determines the robustness of the Z-score and its ability to normalize the data while keeping the data within the correlation region.  
Figure 4: ROC curve illustration.  
Figure 5: Cont.
## 3 Results
### The Relationship between Top Genera and Environmental Factors  
The commonly accepted view is that variables such as the Aw, pH, salt content, POV, and nitrite can induce changes in microbial communities in microbial ecosystems [36]. Five environmental factors were screened using VIF and BioENV tools, and the results demonstrated that the environmental factors had a significant impact on the microbiota. Parametric correspondence analysis (CCA, Figure 5A) and Spearman analysis (Figure 5B) were used to further investigate the relationship between the top genera (top 15 genera of relative abundance) and environmental factors. Obviously, the salt content and peroxide value had no significant (\(p\) > 0.05) effect on the bacterial community, owing to no significant difference between the three groups in this test. Aw was found to be positively correlated with the relative abundances of _Lactobacillus_ and negatively correlated with the relative abundances of _Brochotrix_, _Vibrio_, _Delftia_, and _Psychroacter_. The pH was negatively correlated with the relative abundances of _Delftia_, _Phylobacterium_, and _Psychroacter_. Nitrite was positively correlated with the relative abundances of unidentified Clostridiales, _Brucella_, _Lactococcus_, _Pseudomonas_, and unidentified enterobacteriaceae and negatively correlated with the relative abundances of _Psychrobacter_, _Brochotrix_, _Vibrio_, _Phylobacterium_, and _Delftia_. The results showed that nitrite was the most important factor due to significant correlation  
Figure 3: The beta diversity of the 39 bacons at the genus level (PCA (**A**), FCoA (**B**), and NMDS (**C**) scores plotted based on Weighted Unifrac).  
with many critical genera, followed by the Aw and pH. Nitrite plays a decisive role in cured meat products, providing a high-quality red color and flavor for products, especially for being recognized as bacteriostatic and bactericidal agents against pathogenic bacteria [37]. Nitrite that considered harmful to human health is not added in traditional Sichuan bacon, however its integrated functions are still difficult to replace in the meat industrialization. A combined strategy may be a better solution than a single agent. Interestingly, all three environmental factors were mostly negatively correlated with the undesired microorganisms in Sichuan bacon, which revealed that an appropriate level of Aw, pH, and nitrite plays a vital role in the safety of Sichuan bacons.
# Neural-Network-Assisted Polar Code Decoding Schemes
###### Abstract
However, when the code length is longer, the number of neural network models that need to be called for decoding is higher, and the increase in the calculation amount is not negligible. None of the above studies have achieved a low-complexity low-bit-error decoding performance by combining the SC algorithm and machine learning. To achieve a low-complexity low-bit-error decoding performance by combining SC algorithm and machine learning, this paper proposes a decoding method combining neural network nodes (NNNs) and traditional nodes. This scheme avoids the extra complexity caused by frequent calls of the neural network model and proposes a decoding scheme that can not only reduce the delay, but also improve the decoding performance. The remainder of this paper is organized as follows: Section 2 introduces the knowledge required for polar code decoding, including the SC decoding scheme, traditional special nodes, and the fast SC decoding scheme. Section 3 introduces the system model and the decoding scheme based on the NNN and CNN structure and then analyzes the performance of the model in decoding. Two NNN recognition strategies are explored. Section 4 presents the simulation results over the additive white Gaussian noise (AWGN) and Rayleigh channel. Section 5 summarizes the paper.
## 3 Results
### Brianna White Wine Aroma Analysis by SPME-GC-MS-O  
White wine from Brianna grapes had 34 recorded aroma events across all samples. The \"rotten eggs\" aroma had no variation in OD and was not used in the multivariate analysis similarly to Frontenac gris. The most intense aromas in these Brianna wines were \"overripe fruit 2\", \"floral\", and \"unknown neutral 5\". The most representative aromas, as indicated by cluster analysis, in these Brianna wines were \"banana\", \"floral\", \"honey, caramel\", \"butterscotch 1\", \"tomato 1\", and \"overripe fruit 2\". Corresponding compounds from mass spectral searches are isoamyl acetate (banana, CAS 123-92-2), ethyl Isobutyrate(\"honey, caramel\", CAS 97-62-1), and isoamyl alcohol (\"overripe fruit 2\", CAS 123-51-3). A suitable mass spectral match was not found for the \"floral\" aroma compound. A chromatographic peak was not recorded corresponding to \"butterscotch 1\", although the database lists methyl vanillate [7] as a source of this aroma. Two mass spectral matches were identified for \"tomato 1\": diphenylmethane (\"green\", CAS 101-81-5) [24] and isobutyl decanoate (\"fermented\", CAS 30673-38-2) [24]. The \"floral\" aroma is associated with 48 different compounds [7]. As pH and sugar accumulation in these Brianna berries increased, key odor-active compounds for each harvest changed (Figure 4). When harvested at the lowest sugar content and pH, the wines had a \"cotton candy\" (ethyl decanoate, CAS 110-38-3) and \"floral\" aroma. From 17.6 to 18.6 \({}^{\circ}\)Brix, aromas changed from \"banana\" to \"butterscotch.\" At the highest sugar and pH, the key aromas in the Brianna wines were \"honey, caramel\" and \"unknown  
Figure 3: A principal components analysis (PCA) biplot of volatiles from the aroma extract dilution analysis of Frontenac gris wines made from berries harvested at three different ripening stages. Wines were made from Frontenac gris cold-hardy grapes harvested at 19.5, 23.1, and 23.6 \({}^{\circ}\)Brix. The juice was adjusted to 24 \({}^{\circ}\)Brix prior to fermentation. Wine headspace samples were collected by solid-phase microextraction (SPME) and analyzed with gas chromatography-mass spectrometry-olfactometry (GC-MS-O). Aroma descriptors were recorded by a trained human panelist. A shift of the aroma profile from “fruity 1”to “rotten eggs, suflurry” to “rose 1” was observed. Over 98% of the variation in harvest time is correlated with key odor-active compounds.  
neutral 1\" (isobutyl alcohol, CAS 78-83-1). This change in aromas over Brianna berry ripening is shown in Figure 4.