esa-sceva/satcom-chunk-collection huggingface dataset:
# Location-Aware Point-to-Point RPL in Indoor IR-UWB Networks
## 5 Performance Evaluation
As shown in Figure 13, LA P2P-RPL achieves a very close performance results to ER-RPL and P2P-RPL for packet delivery ratio with symmetric physical links. Though partial nodes are involved in route discovery process, ER-RPL, and LA P2P-RPL can provide reliable routing path. In LA P2P-RPL, the temporary DODAG is rooted at the destination node, which can handle the route discovery under asymmetric links with low PDR performance degradation.
# Diversity and Seasonal Dynamics of Airborne Fungi in Nerja Cave, Spain
## 1 Introduction  
One of the most important topics in the conservation of caves with Paleolithic paintings is the assessment of the microbial communities inhabiting speleothems, rocks, water pools and air. A wide diversity of approaches on cave fungi can be found in the literature [1, 2, 3, 4, 5, 6] and aerobiological studies were introduced as a management tool to control airborne fungi in caves with paintings [7, 8, 9, 10].  
Microclimate control and monitoring of gases (CO\({}_{2}\), CH\({}_{4}\), \({}^{222}\)Rn, etc.) are used to investigate cave aerodynamic and ventilation or stagnation periods [11, 12, 13, 14, 15, 16, 17]. These studies, in combination with aerobiology, can provide clues for controlling airborne fungi and dispersion patterns in the caves [18, 19].  
The microbiology of Nerja Cave, Southern Spain, was studied regarding the impact of lighting on the development of phototrophic communities [20, 21, 22, 23, 24], but scarcely from an aerobiological point of view. A preliminary study carried out by [PERSON] et al. [25] was focused on the survey of pathogenic bacteria and the genus _Aspergillus_, very abundant in the cave air in summer. Further studies by [PERSON] et al. [26, 27] using nonviable first-type volumetric pollen traps located near the entrance and at the end of the visited area found that _Aspergillus/Penicillium_ were the most abundant spore types, representing 50% of the total, followed by _Cladosporium_. However, the morphology of the spores, studiedby microscopy, does not allow distinguishing between _Aspergillus_ and _Penicillium_ nor the identification of species.  
The main objective of this aerobiological study was to assess the presence of airborne fungi in the cave to elucidate its origin. The identification of fungi was carried out by molecular methods. The study quantified the fungi present in the air from 10 representative halls covering the three sectors composing the cave: Touristic Galleries, High Galleries, and New Galleries. A sampling point outside the cave was used as a control of environmental fungi. This monitoring allowed to know the dynamic of airborne fungi in two seasons of the year (winter and summer), corresponding to the strongest and the lowest cave ventilation, and to validate the influence that the transport of airborne fungi from outside may have on the cave itself.
## 5 Comparison with Related Works  
Table 6 compares our proposal and other related works focusing on applying RS techniques for environmental application. Please note that cells with (-) are missing the exact information. Most cited papers use scientific file formats, notably the NetCDF, HDF5, BUFR, data stream, or images regarding the input type. Instead, most of the presented studies collected data either from satellite sensors or the MGS. In contrast, in our study, we acquired data from both sources to have strong input data. Consequently, we obtained a combined output product. The streaming processing velocity sums up to millions of datasets per day, though, in batch processing, the speed is lower.  
Concerning data processing, some studies adopted batch and other stream processing. Thus, our collected data are stored and then pre-processed and integrated inside the Hadoop, so we used the batch processing paradigm. We found that Python and Java are most commonly used in all the studies regarding the development language. The majority of the approaches were executed in a distributed platform, especially Hadoop. Regarding the benchmarking, we also note that streaming processing solutions take a brief execution time: less than one minute with low RAM and CPU ingesting. However, batch processing necessitates a robust cluster of processing.  
Comparing this study to the SAT-ETL-Integrator software [21], we confirm that the two software have the same RS data input and output specifications. However, they differ in their processing architecture. We developed the SAT-ETL-Integrator software to pre-process RSBD in a single machine. However, with the SAT-Hadoop-Processor software, we optimized the code and integrated the cloud computing technology using parallel computing.  
Figure 13: The total execution time.  
ing. Finally, the final result will be integrated into the Hadoop environment. Therefore, the new solution is upgraded to support parallel processing and the Hadoop framework.  
The comparison of the SAT-Hadoop-Processor with [15,16] shows that they all implement distributed, cloud, Hadoop, and MR tools for scalable RSBD processing. Still, they differ in their input data format, architecture, and output. Our advanced solution has some advantages, notably the various inputs as scientific file formats provided from various satellite sensors, except optical ones providing images with pixels. The SAT-Hadoop-Processor can be customized to any EO application easily thanks to its ETL algorithm. This is not the case with other software.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 3 System Model
### Ground-Bound Vehicular Traffic Model
The probability density function (pdf) of \(v_{f}\) follows a truncated normal distribution [45]. \[f_{V}\Big{(}v_{f}\Big{)}=\frac{\xi}{\sqrt{2\pi}\sigma_{v}}e^{-(\frac{v_{f}-v_{ 2}}{\sqrt{2}\sigma_{v}})^{2}},\ v_{f}\in[v_{min},v_{max}], \tag{3}\]  
In addition, \(\xi=2\Big{[}erf\Big{(}v_{max}-\mu_{v}/\sqrt{2}\sigma_{v}\Big{)}-erf\Big{(}v_{ min}-\mu_{v}/\sqrt{2}\sigma_{v}\Big{)}\Big{]}\), and the time for a vehicle to pass through \([x_{0},x_{0}+D]\) is denoted as \(t_{r}\).
## 3 Results and Discussions
### Experimentation and Statistical Analysis
In other words, the model reached its maximum optimization point. The graphical comparison of the original configuration and the successive optimizations of the angles can be seen in Figure 7. In addition, Table 1 shows the numerical comparison between the worst screw angles configuration and the best one based on the last optimization in both bending and compression tests using interfragmentary strain (\(\epsilon\)) as numerical reference. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Ax** & **Ay** & **Cx** & **Cy** & **Ex** & **Ey** & **Flexion (\(\epsilon\) \%)** & **Compression** (\(\epsilon\) \%)** \\ \hline Max & \(5^{\circ}\) & \(-5^{\circ}\) & \(5^{\circ}\) & \(5^{\circ}\) & \(0^{\circ}\) & \(5^{\circ}\) & 6.76661 & 3.52573 \\ Min & \(-5^{\circ}\) & \(-10^{\circ}\) & \(-10^{\circ}\) & \(-5^{\circ}\) & \(0^{\circ}\) & \(-10^{\circ}\) & 6.25003 & 3.26282 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the worst and best screws angles configuration based on the optimization of the system using the percentage of interfragmentary strain as a reference, for the flexural and compression tests. Figure 6: Pareto diagrams to determine the magnitude and importance of the effects. In both tests, the effect of the Ey factor is statistically significant as the bar crosses the baseline.
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 5 Current Developments in TD-NIRS and Clinical Pertinence
### Toward TD-NIRS Standardisation  
The main reported drawback of NIRS is the lack of standardisation. Indeed, different systems can use different wavelengths, different source or detector types, or even different theoretical models. The use of TD-NIRS makes the standardisation of the NIRS measurement easier by extracting absolute information. Recently, new protocols have emerged in order to compare the results of different TD-NIRS systems. We can cite the MEDPHOT protocol [121], consisting of a set of calibrated phantoms that can be used to test the accuracy of the measured absorption and reduced scattering coefficient; the basic instrumental performance (BIP) protocol [122], consisting of a series of tests that enable us to compare the raw characteristics of different TD-NIRS instruments; and the nEUROPt protocol [123], that addresses the characteristics of optical brain imaging to detect, localize, and quantify absorption changes in the brain. The application of those protocols enables the community to qualitatively and quantitatively compare TD-NIRS instruments, independently of their raw characteristics or independently of the model used. Thus, large-scale clinical studies, even with slightly different TD-NIRS instruments, could be designed.
## 3 Data  
### Sentinel-1 C-Band SAR  
A total of 709 dual-polarized (VV + VH) Sentinel-1A and Sentinel-1B scenes for the Rur catchment (Figure 3) and 361 scenes for the Apulian Tavolieere site are used for the period between January 2018 to December 2020, covering all or parts of the study area. The polar-orbiting twin satellite constellation monitors the Earth's surface, using 5400 MHz C-band SAR instruments, while following the same orbital plane 180\({}^{\circ}\) apart. In this regard, a combined revisit time of 6 days can be achieved [44; 45]. Depending on the latitude, even denser time series are possible, as multiple orbits can cover a given area. In this study, four orbits (Orbit 139, Orbit 88, Orbit 15, Orbit 37) are covering the Rur catchment area, recording it in alternating incidence angles (Desc.: 43.8\({}^{\circ}\), Asc.: 30.2\({}^{\circ}\), Asc.: 35.4\({}^{\circ}\), Desc.: 39.5\({}^{\circ}\)). Hence, a temporal resolution of one to two days is achieved here. For the Apulian site, two orbits are used (Orbit 124, Orbit 146), recording in alternating incidence angles (Desc.: 38.9\({}^{\circ}\), Asc.: 32.3\({}^{\circ}\)), achieving a temporal resolution of five to two days. The scenes are in Interferometric Wide-Swath Mode (IW) and Ground Range Detected High Resolution (GRDH) format [46]. The IW mode captures a 250 km swath with a spatial resolution of 5 m by 20 m by combining three sub-swaths, using Terrain Observation with Progressive Scans SAR (TOPSAR). For GRDH product, the scenes are resampled into 10 m by 10 m pixel spacing. The combined time series from Sentinel-1A and Sentinel-1B are obtained and processed using the Google Earth Engine (GEE) web platform [47]. The platform provides Sentinel-1A and Sentinel-1B scenes, which are preprocessed by the following steps, using the Sentinel-1 Toolbox SNAP software: thermal noise removal, radiometric calibration, terrain correction using Shuttle Radar Topography Mission (SRTM) Version 3.0 Global 1 arc  
Figure 2: Overview of the Segezia experimental farm with the locations of capacitance stations (Cap.).  
second dataset (SRTMGL1) [48; 49]. The backscattering intensities are acquired as Digital Numbers (DN).  
Figure 3: Overview of the input datasets for the Rur catchment: Sentinel-1 dual-polarized vertical-vertical (VV) scene (**A**), CORINE land cover data (**B**), OpenLandMap Soil Texture data (**C**), and OpenLandMap Field Capacity (**D**).
# Parallel Computation of CRC-Code on an FPGA Platform for High Data Throughput
## 3 CRC Computational Process
### Matrix Transformation  
As mentioned before, the CRC linear shift register can be viewed as a linear system. Based on a superposition property, we can decompose the original input message M into multiple small blocks with m-bytes and compute their corresponding CRCs. The final CRC of the whole message is obtained by XORing all the partial CRCs. This method allows us to increase the throughput of the system by parallel computation of the partial CRC of each individual block as shown in Figure 4. One can pre-compute one lookup table for one block. However, it is unpractical to generate lookup tables for a very long input message. The matrix transformation method can be used where it requires only one look-up table for the first block. The CRC code of other blocks can be obtained by multiplying their CRC codes with their corresponding matrices based on Galois Filed Multiplication [14]. The linear shift register in Figure 3 can be described by the state vector state equation:  
\[\mathbf{x}(n+1)\ =\ \mathbf{Ax}(n)+b\mathbf{u}(n)\mathbf{x}(n+1)=\ \mathbf{Ax}(n)+b\mathbf{u}(n), \tag{4}\]where A is the g x g matrix derived from the input CRC polynomial and its degree:  
\[A\ =\ \ \left[\begin{array}{cccccc}0&0&0&\cdots&0&-g_{0}\\ 1&0&0&\cdots&0&-g_{1}\\ 0&1&0&\cdots&0&-g_{2}\\ \cdots&\cdots&\cdots&\ddots&0&\vdots\\ 0&0&0&\cdots&1&-g_{g-1}\\ \end{array}\right] \tag{5}\]  
and _bu(n)_ is the input term of the system at a given state.  
If we append the input with m bytes of zeros, the CRC code only changes based on its current state. It can be expressed as the following formula:  
\[x(n+m)\ =\ A^{m}x(n) \tag{6}\]
## Materials and Methods
### Evaluation of Models Performance  
The following statistical indicators were used to evaluate the performance of the models: (i) Regression coefficient (R\({}^{2}\)) and (ii) residual mean square error (RMSE) of each model. The higher the R\({}^{2}\) value and the lower the RMSE value, the better was the goodness-of-fit of the model.  
The agreement between predicted and observed populations of \(B\). _thermosphacta_ and LAB as well as \(a^{\star}\) value was assessed through the accuracy (\(\Delta f\)) (Equation (6)) and bias (\(Bf\)) factors (Equation (7)) [29]. Specifically, \(\Delta f\) and \(Bf\) were calculated by taking into account the fitted and observed data (log CFU/g of \(B\). _thermosphacta_ and LAB; \(a^{\star}\) value) per studied assay.  
\[A_{f} = 10^{-[\frac{\sum_{1}^{n}\left[log(\mu_{max})/\mu_{max})\right]}{ n}} \tag{6}\] \[B_{f} = 10^{-[\frac{\sum_{1}^{n}\left[log\left(\mu_{max})/\mu_{max}\right) \right]}{n}} \tag{7}\]  
where \(n\) equals to the number of observations. Perfect agreement between fitted and observed values is represented with accuracy and bias factors of 1 [29]. \(Bf\) values of 1.00-1.15 indicate that predictions exceed observations (over-prediction), while values between 0.70 and 1.00 are interpreted as under-prediction [30].  
Likewise, relative error (RE) of \(B\). _thermosphacta_ and LAB populations (log CFU/g) or \(a^{\star}\) value of individual predictions were calculated [31] by the following equation:  
\[\text{RE}=\text{(predicted}-\text{observed)/predicted} \tag{8}\]  
and RE \(<\) 0 represented under-prediction and RE \(>\) 0 represented over-prediction of the developed models. The proportion of RE (pRE) that fell in an acceptable prediction zone (namely the number of RE in the acceptable prediction zone/total number of prediction cases) from an RE of \(-0.3\) (under-predictions) to \(0.15\) (over-predictions) was calculated and used also as a measure of model performance. Models with pRE \(\geq 0.70\) were considered to provide predictions with acceptable bias and accuracy [32].
## 4 Performance Analysis
### Experimental Results and Discussion
#### 4.4.1 Comparison of Pre-Trained Model  
To confirm the better performance of the RoBERTa model, a comparison of the pre-trained models is conducted on the CCKS2019 and CCKS2020 entity-linking datasets. Specifically, the pre-trained model is employed to extract text semantic information, and then the semantic information is sent to the fully connected layer for prediction. The experimental comparison results in Table 4 illustrate that the RoBERTa model performs more effectively than the BERT model. For the CCKS2020 and CCKS2019 datasets, the F1 score for RoBERTa is 1.08% and 0.97% higher than that of BERT, respectively.
# Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants
## 2 Methodology
### Metropolis-Hastings Algorithm  
The Metropolis-Hastings (MH) algorithm is the cornerstone of Markov chain Monte Carlo sampling [32]. In order to make the Markov chain meet the detailed balance condition, the acceptance rate \(r(x^{(i-1)},x^{(i)})\) is introduced to make the original Markov chain jump from state \(i-1\) to \(i\), which is received with the probability of \(r(x^{(i-1)},x^{(i)})\)[33]. The original Markov chain is random. Assuming there is a stationary distribution of \(\pi(x)\), \(x\) is a sample obtained from the Markov chain, and the ratio of \(\pi(x^{(i)})\) and \(\pi(x^{(i-1)})\) can be calculated by Equation (2), while parameters \(x^{(i)}\) and \(x^{(i-1)}\) can be selected according to a certain probability. The parameters by multiple iteration sampling should be met \(\pi(x)\)[34; 35]. The iterative steps of the MH algorithm are as follows:  
* The MH algorithm takes random values in the parameter space as the starting parameter \(x^{(0)}\). The random parameter \(x^{(1)}\) is generated from the distribution function of parameters, and the probability density of the current parameter is calculated.
* First, a random number \(\mu_{t}\) is extracted from the uniform distribution of \([0,1]\), and then, we determine whether to keep the current parameter according to whether the probability density ratio between the current parameter \(x^{(i)}\) and the starting parameter \(x^{(i-1)}\) is greater than \(\mu_{t}\). If the reception probability of the current parameter is greater than \(\mu_{t}\), \(\mu_{t}\leq r(x^{(i-1)},x^{(i)})\), the state is accepted, and it needs to be updated \(x^{(i)}=x^{(i)}\), otherwise \(x^{(i)}=x^{(i-1)}\)[36]. The acceptance probability \(r(x^{(i-1)},x^{(i)})\) of MH can be described as:  
\[r\left(x^{(i-1)},x^{(i)}\right)=min\left\{1,\frac{\pi(x^{(i-1)})T\left(x^{(i) }\left|x^{(i-1)}\right.\right)}{\pi(x^{(i)})T\left(x^{(i-1)}\left|x^{(i)} \right.\right)}\right\} \tag{3}\]  
It is obvious that the acceptance probability \(r(x^{(i-1)},x^{(i)})\) value is between \([0,1]\), and its purpose is accelerating the convergence of the Markov chain to the detailed balance condition.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
###### Abstract  
Due to the various network requirements of applications, quality of service (QoS)-aware routing plays an important role in the networks. Recently proposed resource allocation algorithms focus on the current traffic matrix, which is not applicable for dynamic networks. In this paper, we exploit an estimation of flow matrix that gives our scheme the ability to sufficiently reduce the total packet loss and simultaneously raise the network throughput. In this way, we mathematically formulate the QoS-aware resource reallocation in software-defined networking (SDN) networks based on the traffic prediction. To solve this optimization problem, two schemes are proposed: (i) exact solution; and (ii) fast suboptimal one. The proposed schemes are compared with the accuracy perspective. Moreover, the impact of prediction on resource reallocation is discussed. In this regard, it is shown that, compared with the conventional scheme, the proposed scheme decreases the packet loss and increases the throughput significantly.  
Keywords:quality of service (QoS); resource reallocation; software-defined networking (SDN); network reconfiguration +
Footnote †: journal: Applied Sciences
## 2 Plant Secondary Metabolites and Their Biological Roles
### Nitrogen-Containing Compounds  
#### 2.3.1 Alkaloids  
Alkaloids are the major group of plant defense molecules that contain a nitrogen atom(s) derived from the decarboxylation of amino acids and are known to occur in 20% of plant species [32]. There are seven types of alkaloids based on their amino acid precursors (Figure 3). Tropane, pyrrolidine, and pyrrolidine alkaloids are derived from ornithine amino acid precursors; benzylisoquinoline from tyrosine amino acid precursors; indolequinoline from tryptophane amino acid precursors; and quinolizidine and piperidine alkaloids from lysine amino acid precursors [68]. Alkaloids are widely distributed among plant lineages and are particularly abundant in angiosperms. Individual plant species may contain fewer than five to more than 30 alkaloids (e.g., 74 alkaloids in _Catharantlus roseus_, 54 in _Strychulos toxifera_, and 39 in _Rauwolfia serpentina_) [68; 69]. Generally, a plant family produces only one type of alkaloid, although a few families such as Solanaceae and Rutaceae accumulate a broad spectrum of alkaloids [70]. For example, _Duboisia myoprooides_ R.Br. contains both a propane alkaloid (hyoscine) and a pyridine alkaloid (nicotine) [71]. More than 20,000 alkaloids have been isolated, of which about 600 are known to be bioactive [72], but the exact physiological or metabolic role of alkaloids in plants remains poorly understood [68]. Alkaloids are best known for their defensive role as insect-herbivore deterrents owing to their characteristic bitter taste [73]. Thus, according to [PERSON] [69], most alkaloid-bearing plants are found in the tropics, where intensive herbivore pressure is present. Defensive or toxic alkaloids in plants may be produced either by the plants themselves or by their symbiotic partners [74; 75]. For example, the symbiotic endophyte _Epichloe coenophilia_ in tall rescue grass [_Lolium arundinaccum_ ([PERSON].) [PERSON], syn. _Festuca arundinacea_ ([PERSON]), and _Shedonorus arundinacus_ ([PERSON].) [PERSON]) produces insecticidal alkaloids, lolines, and ergot, which cause 'fescue toxicosis' in grazing animals [76]. Alkaloid biosynthesis in plants is genetically controlled, but environmental factors such as light (UV), temperature, moisture, and soil nutrients also influence the type and rate of alkaloid production [76; 77].  
Figure 2: Representative examples of different subgroups of flavonoids: a major phenolic group of secondary metabolites.