esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Identification of Fungi and Bacteria by Molecular Techniques  
The phylogenetic identification was performed in the Department of Nucleic Acids of the instrumental techniques laboratory of the University of Leon (Spain). DNA isolation was performed using PrepMan(tm) Ultra (Applied Biosystems, Madrid, Spain) for each selected isolate. The amplification of genes of interest was carried out by PCR in 50 \(\upmu\)L of reaction with DreamTaq DNA polymerase Kit (Thermo Fisher Scientific, Madrid, Spain) from 1 \(\upmu\)L of DNA and 20 pmol of each primer. For fungi, the pairs of primers used were ITS1 (5\({}^{\prime}\)-TCCGATGTGAACCTGGG-3\({}^{\prime}\)) and ITS4 (5\({}^{\prime}\)-TCCTCCGCTTATTGATATGC-3\({}^{\prime}\)) [22], to amplify the internal transcriber spacer ITS1 and ITS2, that includes 5.88 rRNA genes. Also, the pair of primers NL1 (5\({}^{\prime}\)-GCATATCAATAAGCGGGAGAAGA-3\({}^{\prime}\)) and NL4 (5\({}^{\prime}\)-GGTCCGTTTTCAAGACG-3\({}^{\prime}\)) of the 28S rRNA gene region were used for yeasts. The pairs of primers used for bacteria were 27 F (5\({}^{\prime}\)-AGAGTTTGATCMTGGTCAG-3\({}^{\prime}\)) and 699R (5\({}^{\prime}\)-RGGGTTGCGCTCCTT-3\({}^{\prime}\)) and BA-RF (5\({}^{\prime}\)-GACGATATYWWGAAACCG-3\({}^{\prime}\)) and BA-RR (5\({}^{\prime}\)-GGNGYTCRATYGGACACAT-3\({}^{\prime}\)) [23] to amplify different regions of the bacterial 16S rRNA gene. A GeneAmp(r) PCR System 2700 (Applied Biosystems Thermocycler, Madrid, Spain) was used. The standard PCR program was performed with initial denaturing at 95 \({}^{\circ}\)C for 5 min for 35 cycles (95 \({}^{\circ}\)C for 30 s, 55 \({}^{\circ}\)C for 30 s and 72 \({}^{\circ}\)C for 1 min) with a final extension at 72 \({}^{\circ}\)C for 10 min. PCR products were visualised in 1% agarose gel and purified using NucleoSpin(r) Gel and PCR Clean-up Kit (Macherey-Nagel GmbH & Co., Duren, Germany) according to manufacturer's instructions and were sequenced by BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Madrid, Spain) in capillary automatic sequencer ABI 3130 xl (Applied Biosystems, Madrid, Spain). The consensus sequences obtained were edited using the program CHROMAS LITE v.2.0 (Chromas-Technelysium Pty Ltd., Brisbane, Australia) and compared with the sequences in GenBank database using the Basic Local Alignment Search Tool (National Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD, USA) software.
## 3 Preliminaries
### System Model for ASC-RL  
A cognitive sensor can be adjusted in various states according to its intrinsic components. A sensor consists mainly of three primary parts that participate in energy consumption. These are a microprocessor (\(MP_{n}\)), sensing module (\(SM_{n}\)), and radio module (\(RM_{n}\)) for communication. Consider a cognitive sensor already existing in any state (\(S_{n}\)) and an action \((AC_{n})\) needed to switch to another state. To represent this state and action, a real number vector is used as \(F(S_{n},Ac_{n})\). The agent, in the form of \(F(S_{n},Ac_{n})\), will provide a reward. The reward is responsible for changing \(S_{n}\) depending on the internal, environmental, and historical parameters. The Markov Decision Process is used for timely decision making in changing states from one state to another.
## 1 Introduction
As for other hydrophilic derivatives (cellulose, chitosan, schizophyllan, hyaluron, carboxymethyl cellulose) [17], the interactions between HPC and water are related to hydrogen bonding and the presence of \"nanocavities\" formed in the matrix [18]. Depending on water concentration and the grade of the polymer, new stable and reversible structures can form [19]. The existence of three distinct fractions of water is now widely assumed: free (or bulk) water (FW), freezing bound water (FBW), and non-freezing bound water (NFW) [20; 21]. Free water does not significantly differ in its melting and crystallization temperature and enthalpy from normal (bulk) water. The second type, freezing bound water, indicates the water fraction that is less  
Figure 1: An idealized structure of hydroxypropyl cellulose with a molar substitution of 3.0.  
closely associated with the polymer's chains. It reveals a crystallization phase transition and a melting point below 0 \({}^{\circ}\)C that distinguishes it from free water. The FBW fraction is also characterized by supercooling and significantly smaller enthalpies than the FW fraction. While first-order phase transitions of small fractions of water that are strongly associated with the polymer chains cannot be observed calorimetrically, we can describe the NFW fraction of water. There are reports showing that this type of water entrapped in a hydrophilic polymer does not crystallize even when cooled down to \(-100\)\({}^{\circ}\)C [18]. Moreover, for a low concentration of water, all water found in the matrix is considered non-freezing [18]. The sum of freezing bound and non-freezing bound water fractions is the bound water content. Two drugs, having distinct properties with respect to solubility, were used as model drugs. Due to structural similarities and their low molecular weight, salicylic acid (SA) and sodium salicate (NaSA) were selected. Both drugs are often used as model drugs [22; 23; 24; 25]. The former is poorly soluble in water, while the latter is freely soluble in water. Aromatic anions, such as salicate anion, are known to improve the solubility of various types of cellulose [26; 27]. Nevertheless, these interesting ionic liquids have not been analyzed. The aim of this work was to use AI/ML tools for the decomposition of DSC curves into elements representing different fractions of water bound to the HPC polymer. AI/ML tools were used for empirical data analysis, without any physical assumptions, and based on the behavior of the water fractions, some hypotheses elucidating possible mechanisms of interaction between water and cellulose polymers were postulated.
## 4 Properties
### Thermal Properties
#### 4.1.1 Dsc
In particular, if both parent homopolymers are able to crystallize, by plotting the degree of crystallinity and the \(\mathrm{T_{m}}\) as a function of the copolymer molar composition, a minimum in the two properties is usually observed when significant amounts of both co-units are present, regardless there is co-crystallization or complete rejection of the co-units from the crystalline domains [76; 77]. This is because the co-monomeric units hamper the efficient packing of the homopolymer sequences as the perfection of the crystalline domains is highly affected by the chain regularity [78]. The reactive blending also causes a progressive reduction of the \(\mathrm{T_{m}}\) of the prepared copolymerset with the increase of the mixing time, i.e., the decrease of the block length. On the other hand, through the chain-extension strategy, it is possible to limit this issue. Indeed, the coupling of OH-terminated prepolymers with an acceptable molecular weight (e.g., \(\mathrm{Mn}>5000\) g/mol) allows for the preservation of the crystallization ability of the homopolymeric sequences even in triphasic systems [53; 54]. Thermal transitions of APs are affected by the presence of nanofillers. Several studies have focused on PLA-based nanocomposites and have been recently reviewed [79; 80]. In general, independently of the employed nanofiller, e.g., CNTs, graphene, or graphene oxide (GO), an increase of the melt crystallization kinetics is observed, as the nanoparticles act as heterogeneous nucleating agents for the polyester matrix crystallization. Consequently, the degree of crystallinity and \(\mathrm{T_{m}}\) are enhanced, as due to the formation of a higher amount of more perfect crystals [12]. Usually, a threshold exists above which the particle loading is too high and a worsening of the crystallinity is detected, as due to agglomeration and poor nanometric dispersion [79]. A similar behavior has been reported for PBCE-based nanocomposites containing SWCNTs [13]. Indeed, an increment of the melting enthalpy and a disappearance of the melting and recrystallization peaks was detected, indicating an increase of the crystalline content and perfection. Increase of crystallization rate, due to particle nucleating effect, has also been observed in PCL-based nanocomposites containing nano-CaCO\({}_{3}\) and in PBS-based nanocomposites loaded with different clays [81]. On the contrary, the influence of nanoparticles on the polymer glass transition is less evident. In some cases, no variation has been recorded [13; 80; 82], in others a slight \(\mathrm{T_{g}}\) increase has been detected and ascribed to a good interaction between polymer matrix (PLA) and nanofillers, which restricted the macromolecular mobility by hydrogen bonding and electrostatic interactions [79].
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 3 CTI-Aware Rate Adaptation Framework
### Overview of the CARA Framework
This incorrect rate control unnecessarily reduces throughput. As the AP receives packets from STA-2 during the \(t_{4}\)-\(t_{5}\) slot, it recognizes the absence of interference during this period and sends feedback to STA-2, prompting it to increase the transmission rate by more than two levels (Rate \(\uparrow\uparrow\)). However, when STA-2 follows this instruction and transmits packets at an increased data rate during the third slot, it again encounters interference from CTI-A. With the increased rate, the signal becomes more vulnerable to interference, resulting in unsuccessful packet reception, packet loss, and delays caused by retransmissions. In contrast, as shown in Figure 3b, the proposed framework enables effective interference mitigation against CTI. Starting from \(t_{0}\), the AP identifies CTI interference in each slot. Using the identified information, the AP projects the expected occurrence of CTI based on the access patterns of CTI signals detected in previous slots (projected CTI information is represented by red crosshatched boxes). Specifically, the AP monitors interference from CTI-A during the \(t_{0}\)-\(t_{1}\) and \(t_{1}\)-\(t_{2}\) slots through CTI identification and uses this information to project future occurrences of CTI. Considering the interference pattern of CTI-A, the AP determines that interference is unlikely to occur when STA-2 transmits its packets during the \(t_{4}\)-\(t_{5}\) slot. Consequently, the AP sends a control message (Rate \(\uparrow\uparrow\)) to STA-2, instructing it to transmit at a higher data rate during \(t_{4}\)-\(t_{5}\). Similarly, for the \(t_{7}\)-\(t_{8}\), the AP anticipates interference from CTI-A and prompts STA-2 to use a simpler MCS to ensure transmission stability (Rate \(\downarrow\downarrow\)). Figure 2: CTI-aware rate adaptation framework. As demonstrated in this scenario, the success of the proposed framework in mitigating CTI interference in time-triggered networks relies on the performance of CTI identification and CTI-aware rate selection. In Sections 3.2 and 3.3, we will examine in detail the intelligent CTI identification model proposed within the framework and the CTI-aware rate selection algorithm, which integrates our framework's approach with measurement-based and packet error-based rate adaptation algorithms widely used in traditional WLANs.
###### Abstract  
The International Association of Geodesy (IAG) aims to establish the International Height Reference System (IHRS) as one of its primary scientific objectives. Central to the realization of the IHRS is global vertical datum unification, which requires the connection of existing local vertical height reference systems (VHS) robustly and consistently. However, conventional methods are not suitable for estimating the offsets between two distant local height systems. In this paper, we propose a framework for connecting two local VHSs using ultraprecise clock frequency signal links between satellites and ground stations, referred to as the satellite frequency signal transmission (SFST) approach. The SFST approach allows for the direct determination of the geopotential and height differences between two ground datum stations without any location restrictions between the two VHSs. The simulation results show that the VHSs of China and the US can be unified with an accuracy of several centimeters, provided that the stability of atomic clocks used on-board the satellite and at on-ground datum locations reaches \(4.8\times 10^{-17}\,\mathrm{\text{\textdegree}}^{-1/2}\) for an averaging time \(\tau\) (in seconds). We conclude that the SFST approach shows promise for achieving centimeter-level accuracy in unifying the global vertical height datum and represents a new paradigm for the realization of the IHRS.  
relativisited geodesy; satellite frequency signal transmission; vertical height reference system; global vertical height datum unification 2023
###### Abstract  
This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study's central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions' transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis.  
Keywords:ensemble learning; tree interpreter; random forest; explainable artificial intelligence (AI); algorithm CTCRA; GLOSA; connected vehicles; intelligent transportation systems +
Footnote †: journal: Applied Sociology
# An Evaluation of the Efficiency of Compartmented
## 1 Introduction
[15] studied the self healing performance of Porous Asphal (PA) mixes and used ITS and ITSM tests to evaluate the healing performance of asphalt mix. This study demonstrated that the test approach is effective, although ITS damages test samples to the extent that they are unsuitable for repeat testing. Furthermore, the ITS test system, the sample thickness, and the dark surface of the test specimens preclude observations of crack propagation and healing, unless damage to the test specimen is severe. This study evaluates the efficiency of asphalt mix healing employing both: (i) standard test methods for asphalt pavements such as the ITS and the 4 Point Bending (4 PB) Fatigue test; and (ii) alternative fracture tests such as the Semi Circular Bend test (SCB). The study employs fracture theory in order to evaluate the efficiency of the damage repair crack propagation. The study specifically examines the compartmented calcium-alginate fibres encapsulating asphalt binder healing agent (rejuvenator). The compartmented fibres are used to distribute the rejuvenator (healing agent) throughout the asphalt mix and to overcome the problems associated with spherical capsules and hollow fibres. The research findings presented demonstrate that the inclusion of compartmented calcium-alginate fibres encapsulating a rejuvenator into an asphalt pavement mix, does not significantly improve the healing properties of the asphalt pavement as previously thought [15]. Although the tests results did not demonstrate significant improvement in the system healing properties, they do indicate that compartmented calcium alginate fibres present a promising new approach for the development of self-healing asphalt pavement systems. In addition, the test results show that fracture tests, such as the 4 PB Fatigue test, are the most suitable for evaluating the performance of self healing asphalt. The form and testing process initiates minor damage (micro cracks) to the pavement mix, allowing the test specimen to keep its form and to be used repeatedly. However, the results also indicated that the ITS and the SCB tests are unsuitable testing methods, as the damage caused to the test specimens precluded repeat testing. Modification of the ITS and the SCB test procedures are required to improve their suitability for this purpose.
## 3 Ceria Shape Effects on the Structural Defects and Redox Properties
Figure 3: Indicative transmission electron microscopy (TEM) images of ceria (**a**,**d**) nanorods, (**b**,**e**) nanopolyhedra, and (**c**,**f**) nanocubes. More recently, the effect of ceria's exposed planes on the structural defects was more thoroughly investigated by in situ Raman spectroscopy, acquired under interchanging oxidized and reduced conditions [34]. Indicative results are depicted in Figure 5, where the in situ Raman spectra of bare ceria NR, NC and NP at 440 \({}^{\circ}\)C under oxidizing conditions are shown. The band at 457 cm\({}^{-1}\) is characteristic of F\({}_{\text{2g}}\) mode of Fm3m fluorite cubic structure, whereas the band D band at ca. 600 cm\({}^{-1}\) is ascribed to structural defects due to the perturbations in ceria lattice. Interestingly, the I\({}_{\text{D}}\)/I\({}_{\text{F2g}}\) ratio, which is a measure of lattice deformation and oxygen vacancies, follows the order NR \(>\) NP \(>\) NC in complete agreement with the reducibility order (_vide infra_). Hence, the relative population of structural defects and oxygen vacancies (Figure 5) coincides with ceria nanoparticle's reducibility (Figure 4 and Table 2), which is linked to Ce\({}^{4+}\)\(\rightarrow\) Ce\({}^{3+}\) partial reduction and oxygen atoms detachment. In addition, XPS analysis is performed to disclose the impact of ceria shape on elementary chemical states. Figure 6a demonstrates the Ce 3d XPS spectra of ceria nanoparticles, which can be deconvolved into eight components [117, 118, 119, 120, 131, 42, 130, 114, 121, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 14  
The O 1s XPS spectra of ceria nanoparticles are presented in Figure 6b, exhibiting two characteristic peaks. The low binding energy (BE) peak (O\({}_{\rm I}\)) at 529.4 eV corresponds to lattice oxygen, while the high BE peak (O\({}_{\rm II}\)), at 531.3 eV is attributed to chemisorbed oxygen species, involving also hydroxyl groups, carbonate species, and adsorbed water. Interestingly, the O\({}_{\rm I}\)/O\({}_{\rm II}\) ratio (Table 3) follows the order NC (1.99) \(<\) NP (2.04) \(<\) NR (2.13), perfectly matched with the order of reducibility (Figure 4 and Table 2) and structural defects (Figure 5). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**XPS Analysis**} \\ \cline{2-3}  & **O\({}_{\rm I}\)/O\({}_{\rm II}\)** & **Ce\({}^{3+}\)/Ce\({}^{4+}\)** \\ \hline CeO\({}_{2}\)-NC & 1.99 & 0.33 \\ CeO\({}_{2}\)-NP & 2.04 & 0.32 \\ CeO\({}_{2}\)-NR & 2.13 & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Surface properties of ceria nanoparticles. Figure 5: In situ Raman spectra for different ceria nanoshapes, i.e., NR, NP and NC. Inset depicts the I\({}_{\rm D}\)/I\({}_{\rm{FB2}}\) intensity ratio. Laser power, \(w=25\) mW; laser wavelength, \(\lambda_{0}=491.5\) nm; spectral slit width, 6 cm\({}^{-1}\). Adapted from Reference [34]. Copyright © 2018, Elsevier.
## 5 Experiments
### Discussion of the Results  
This subsection includes a brief discussion related to the results obtained throughout the present work. Regarding the use of methods to compress visual models, spectral clustering has proven to be, in general, more efficient than the SOM clustering. Furthermore, the global appearance descriptor, which presented better behaviour to carry out the clustering task, is _gist_. About the localization task,  
Figure 20: Results of the localization process in the Freiburg environment by using two types of models to retain visual representatives. Average localization error (cm) vs. number of clusters. Model 1 uses representatives obtained through spectral clustering, and Model 2 obtains the representatives through sampling the dataset. The localization task has been carried out with HOG and _gist_, and the distances are calculated through the cosine distance.  
HOG presented generally the best outcomes independently of the type of map. The best results are summarized in Figure 21. The best clustering results in Freiburg were obtained with _gist_ (\(k_{3}=[32,64]\) and \(n_{masks}=16\)) and using spectral clustering. Moreover, the best localization outcome in this environment was obtained through the use of HOG (\(k_{2}=[16,32]\)) with the cosine distance.  
Furthermore, comparing the localization results obtained after compaction and through using raw models, with no compaction (\(n_{c}=872\), \(n_{c}=519\), and \(n_{c}=566\) respectively for Quorum, Freiburg, and Saarbrucken), compact models have proven to be a successful tool to reduce computing time and keep the localization accuracy (see Section 4).  
Regarding the use of the global appearance descriptor to select the right map among several options (Section 5.3.3), _gist_ has proven to be the most efficient choice. Using this descriptor, 100% of success was reached independently of the number of clusters and the value \(k_{3}\).  
Finally, straightforward methods to compress the information can be discarded since they are not capable of keeping more information about the environment than the proposed spectral clustering method (Section 5.4). Despite that straightforward methods might be faster and easier, the localization outcomes obtained departing from spectral clustering proved to be, in general terms, more accurate.