esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 4 Discussion  
### Classification of Wetland Vegetation Habitat Types  
Classification of wetland vegetation habitat types using remote sensing data has been undertaken many times. The methods, most often used, concerned the application of optical data [44; 45; 46; 47; 48]. [PERSON] et al. [44] derived six wetland habitats from the classification of MERIS (Medium Spectral Resolution Imaging Spectrometer) images acquired on 21 April 2003: anthropogenic, pastures, sedge-murnsh, rush-sedge, reeds, and scrubs. [PERSON] and [PERSON] [48] distinguished five classes: aquatic bed (vegetation on or below the water surface), emergent (herbaceous hydrophytes), forested, scrub-shrub (height \(<\) 6 m), and unconsolidated shore, applying Landsat TM imagery. Recently, the application of microwave data for the classification of vegetation habitats has been expanded by [PERSON] et al. [49; 50]. [PERSON] et al. [51] who studied ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar) data found that better classification results were obtained from HV than from HH polarization. [PERSON] et al. [43] and [PERSON] et al. [52] also presented wetland classification using ALOS PALSAR data acquired in HV polarization. [PERSON] et al. [53] found that a combination of HH and HV polarization modes from ALOS PALSAR data was better at separating forested wetlands using an object-based classification approach. [PERSON] et al. [54] applied optical (Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) and JERS-1 (Japanese Earth Resources Satellite-1) data to classify the wetland and non-wetland classes of the Congo Basin. [PERSON] and [PERSON] [55] reported that the combination of radar and optical data will improve wetland classification.  
There is a lack of a detailed classification of European wetland vegetation. In this study we decided to apply microwave and optical data for the classification of wetland vegetation habitats. Compared to the classification results undertaken using ALOS PALSAR HV [43], in the present study it was possible to distinguish wetland habitat types more precisely. The obtained classification results of wetland habitat types (two forest and five non-forest habitats) presented in Figure 2 were not found in the literature before and are promising in wetland changes monitoring.
# Green Production and Biotechnological Applications of Cell Wall Lytic Enzymes
## 3 Degradation of Lignocellulose by CWLEs
### Hemicellulases  
Hemicellulose is abundant in lignocellulosic, accounting for 20-30% of total dry weight depending on the plant species. Both the heterogeneous composition and the branched structure of hemicellulose limit the access of cellulolytic enzymes to cellulose; therefore hemicellulose depolymerization is needed to efficiently degrade cellulose [29] (Figure 2b). Moreover, inefficient enzymatic degradation of hemicelluloses generates inhibitory by-products towards both CWLEs and fermentative processes, further reducing the conversion of lignocellulosic into valuable products [30]. For example, oligomers derived from the partial breakdown of xylan, a hemicellulose abundant in secondary cell walls, inhibit the activity of cellobiohydrolase, a key enzyme for the degradation of cellulose [31; 32]. Importantly, several commercial cellulolytic powders, commonly employed in the pretreatment of raw lignocellulosic material for use in biofuel paths, are enriched in hemicellulases with different substrate specificities [33]. Amongst the many hemicellulose enzymes employed in the degradation of lignocellulosic substrates at the industrial scale, it is worth mentioning  
* xylanolytic enzymes: endo-1,4-\(\beta\)-xylanase degrades xylan and arabinoxal in oligomers with different degrees of polymerization and xylobiose that, in turn, is degraded by \(\beta\)-xylosidase in two d-xylose units;
* manmanolytic enzymes: endo-1,4-\(\beta\)-mannanase degrades mannan and galactomannan in oligomers with different degrees of polymerization and mannobiose that, in turn, is degraded by \(\beta\)-mannosidase in two d-mannnose units;
* galactanolytic enzymes: endo-1,4-\(\beta\)-galactanase degrades galactan and type I arabinoxalatan in oligomers with different degrees of polymerization and galactobiose that, in turn, is degraded by \(\beta\)-galactosidase in two d-galactose units;
* \(\alpha\)-l-arabinofuranosidase hydrolyses terminal, nonreducing \(\alpha\)-l-arabinofuranoside residues in \(\alpha\)-l-arabinosides, which can be found in arabinoxalan and arabinoxalatan;
* xyloglucanolytic enzymes: xyloglucanases are divided into two subgroups depending on their ability to cleave xyloglucan through an endo- or an exo-mode of action. Xyloglucan is mainly distributed in the primary cell walls of dicotyledonous plants [34].
## 3 Results
### Analysis of the Second Point at 100 \(\mu\)m Subsurface from the Border of the Root Canal Lumen
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Section** & **Group** & **N** & **Mean** & **Std. Deviation** & **Minimum** & **Maximum** & **\(p\)-Value** \\ \hline \multirow{6}{*}{Coronal} & Control & 10 & 29.07 & 8.89 & 14.67 & 40.18 & \\ \cline{2-7}  & UN & 10 & 28.84 & 6.41 & 14.82 & 37.92 & \\ \cline{2-7}  & UI & 10 & 30.87 & 8.01 & 23.15 & 48.24 & \\ \cline{2-7}  & MSI & 10 & 28.96 & 6.29 & 21.24 & 40.63 & \\ \cline{2-7}  & MUI & 10 & 29.30 & 5.83 & 23.71 & 38.60 & \\ \cline{2-7}  & EDTA & 10 & 23.79 & 8.66 & 12.32 & 35.71 & \\ \hline \multirow{6}{*}{Middle} & Control & 10 & 33.16 \({}^{\text{a}}\) & 6.72 & 22.44 & 44.15 & \\ \cline{2-7}  & UN & 10 & 32.82 \({}^{\text{b}}\) & 5.84 & 19.71 & 39.67 & \\ \cline{2-7}  & UI & 10 & 31.75 & 6.18 & 21.79 & 39.65 & \\ \cline{2-7}  & MSI & 10 & 31.62 & 7.87 & 22.37 & 49.31 & \\ \cline{2-7}  & MUI & 10 & 33.51 \({}^{\text{c}}\) & 4.22 & 29.72 & 42.86 & \\ \cline{2-7}  & EDTA & 10 & 24.38 \({}^{\text{a,b,c}}\) & 5.93 & 15.68 & 32.80 & \\ \hline \multirow{6}{*}{Apical} & Control & 10 & 30.41 & 6.18 & 22.43 & 39.75 & \\ \cline{2-7}  & UN & 10 & 31.59 & 5.46 & 24.47 & 39.09 & \\ \cline{2-7}  & UI & 10 & 31.82 & 5.77 & 21.88 & 37.92 & \\ \cline{2-7}  & MSI & 10 & 32.68 & 4.39 & 24.05 & 39.24 & \\ \cline{2-7}  & MUI & 10 & 35.02 & 6.43 & 24.29 & 46.54 & \\ \cline{2-7}  & EDTA & 10 & 27.73 & 4.33 & 21.05 & 34.27 & \\ \hline \hline \end{tabular}  
* UN—ultrasound device and normal saline; UI—ultrasound device and IONPs; MSI—magnetic field, endodontic needle and IONPs; MUI—magnetic field, ultrasound device, and IONPs; EDTA—ethylenediaminetetraacetic acid. One-way ANOVA, Identical superscript small letters represent significant differences between relevant groups in each section (Tukey post hoc test, \(p<0.05\)).  
\end{table} 
Table 4: Descriptive data of groups’ dentine microhardness in each section at 1 mm below the midpoint of each third at the lumen surface of the root canal after using different techniques with different materials.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 3 Data Caching and Data Sharing Security in 5G
### Capabilities and Attributes  
The access control technique is based on attributes and capabilities with security context from the federated identity-based protocol [4], which are used to enable the UE to request caching and sharing permission of the data. With capabilities, the mechanism leverages CBAC and ABAC to enable the UE to obtain the data cache and share authorization from the SP. The capabilities techniques used are based on the abilities defined in [42] and labels defined in [44] as token claims. After being granted access to the service, the UE retrieves the data and then sends another request to SP for caching and sharing authorization. The SP generates a security token that defines the object and subject abilities, the data is the object and UE is the subject. The generated tokens are cache and share tokens with capabilities and attributes parameters, the tokens are similar in structure, but their roles differ. They are linked with the data object name and the UE, which is used to define objects and subjects' abilities. As in [4,44], dot-separated sequence of numbers are used for an ability in form of a label whereby an ability is a string \(i_{1}\)-\(i_{2}\)-\(i_{3}\)-\(i_{n}\) of value \(n\) where \(i_{1}\), \(i_{2}\), \(i_{3}\)..., \(i_{n}\) are integers. An example of abilities for a UE or Network Data Object (NDO) will be 1.2.3.4, or 01.02.03.04.  
During the authorization, procedure tokens are created and sent to the relevant entities for verification. Both the data and UE will be given labels as part of security tokens validation and verification. Access to a data object is granted if the data object's label is a prefix of the UE's label. Whereby, a data object with a label \"2.0.0\" with abilities \"0.1.0\" for caching and \"0.0.1\" for sharing could only be accessed by UE with abilities such as \"2.0.0\", \"2.1.0\", \"2.1.1\"...etc, i.e., whenever an authenticated UE requests data, it needs to provide the right label to SS that confirms its rights to access, cache, or share the data. The SP creates the labels and links to the subject and object, the UE cannot promote themselves to access other data objects but the permission can be delegated to other UE if delegation was part of the initial request of the access token [4]. The labels are integrated into the security tokens as access rights. Any other subject to access the data will require the nonce key to decrypt that data object as the data can only be accessed with the access token or nonce key both generated by SP. The label consists of user capabilities (ucap) and data capabilities(dcap), label = \((ucap,dcap)\). A generic security structure and attributes are described in Table 1.
###### Abstract  
A PM orbital friction vibration actuator (OFVA) which composes four linear oscillating actuators (LOA) is proposed in this paper. This paper presents the design, analysis, and experimental validation of stator configuration of a permanent magnet LOA to improve its thrust force characteristics. First, the magnetized topology and the coil configuration are interpreted. The optimization design goal of the LOA was established and the end effects of the actuator are illustrated. The influences of stator design parameters on the performance of LOA were investigated and the optimal parameters have been identified with reference to the thrust force density and thrust force ripple. Results showed that a quasi-Halbach magnetized E-cored LOA with obtrapezoid teeth has the best electromagnetic performances of all the LOAs examined here. Finally, the predicted thrust force characteristics were validated by measurements on a prototype actuator.  
Keywords:linear oscillating actuator; permanent magnet actuator; stator configuration; thrust force; orbital friction vibration actuator +
Footnote †: journal: Applied
## 3 Specifying Behavioral Profiles in IoT
### MUD Architecture  
The MUD architecture consists of four main components. Figure 1 shows the interactions among them for obtaining MUD files:  
* Thing or Device, which is responsible for sending an MUD URL,
* Router or Switch to which the device is connected,
* MUD Manager, which is in charge (among other tasks) of requesting an MUD file based on a certain MUD URL,
* MUD File Server, a web server hosting MUD files.  
As defined in the MUD standard, the process for obtaining an MUD file requires the thing to communicate the location of the MUD file by using an MUD URL. Toward this end, the standard defines three alternatives: DHCP, LLDP or X.509 certificates. However, the standard also recognizes the possibility to consider other options for devices that are not able to communicate the MUD URL, or in scenarios with limited Internet connection. Then, the router or switch receives the MUD URL, which is forwarded to the MUD manager, which represents the core component of the architecture. Then, this entity requests the MUD file (and a signature file associated) to a certain MUD file server. After validating the corresponding signature, the MUD manager is also intended to configure the corresponding network components (e.g., a switch) based on the information contained in the MUD file. Indeed, it is also in charge of translating (and maintaining updated) MUD rules to specific network configurations [5].  
As described in Section 2, DHCP and LLDP options set out security issues. Moreover, the use of X.509 certificates may represent an obstacle to consider MUD in scenarios with resource-constrained devices. Furthermore, the standard does not define the process required for the device to communicate the MUD URL once it has been successfully identified and authenticated in the domain where it is deployed. Moreover, according to the standard, the enforcement of the MUD rules remains a local deployment decision. In our solution, we address both aspects by defining a standard-based architecture to enable the obtaining and enforcement of MUD rules through SDN. Our architecture integrates and instantiates the MUD architecture's components, as will be described in Section 4.  
Figure 1: Manufacturer Usage Description architecture.
## 3 Results and Discussion
### AFM Analysis
#### 3.2.2 Advanced Analysis of ISO and Morphological Parameters  
The remarkable differences in spatial patterns as a function of scale size can also be observed by the evolution of some other non-functional ISO and morphological statistical parameters. The graphical representations of these parameters are shown in Figure S1, where their detailed physical definitions can be found in refs. [71; 72; 73]. The ISO Sk parameter (Figure S1d-f) reveals a decrease of the core roughness from S50 to S10 due to the decrease in global height roughness (Ra and RRMS). The same phenomenon was seen for reduced peak height (Spk), but for reduced valley depth (Svk), an increase is observed, attributed to a more platykurtic surface for smaller areas. Moreover, due to the decrease in global roughness, the reduced peaks (Smr1) were more abundant for S10, although the lowest value was found for S20. In contrast, the greater abundance of reduced valleys (Smr2) occurred for S50, decreasing to S10. Additionally, the behavior of the volume parameters (Figure S1g-i) reveals the different settings for the distribution of material portions in the core. As shown in Table 2, the highest volume of material at the peaks (Vmp) was observed for S50 and the lowest for S10. This was expected because of the highly rough profile of S50 compared to S10. However, the largest void volume in the valleys (Vvv) was recorded for S20 and the lowest for S10. Probably, the deep valleys between the guardian cells explain  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Units** & **S50** & **S20** & **S10** \\ \hline R\({}_{\text{a}}\) & \(\upmu\)m & 0.571 \(\pm\) 0.065 & 0.34 \(\pm\) 0.027 & 0.237 \(\pm\) 0.015 \\ \hline R\({}_{\text{RMS}}\) & \(\upmu\)m & 0.700 \(\pm\) 0.081 & 0.413 \(\pm\) 0.033 & 0.296 \(\pm\) 0.018 \\ \hline S\({}_{\text{S}}\) & - & 0.136 \(\pm\) 0.073 & \(-\)0.424 \(\pm\) 0.005 & 0.851 \(\pm\) 0.014 \\ \hline K\({}_{\text{s}}\) & - & 2.582 \(\pm\) 0.045 & 2.853 \(\pm\) 0.018 & 3.061 \(\pm\) 0.067 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface height parameters R\({}_{\text{a}}\), R\({}_{\text{RMS}}\), S\({}_{\text{S}}\), and Ks. The average results were expressed as mean values and standard deviation.  
this phenomenon. Moreover, it is observed that both the volume of material (Vmc) and the empty volume (Vvc) of the central part of the images decrease fromS50 to S10, as an effect of the change in roughness.  
Regarding the surface coverage (Figure S1j-m), larger particle distributions are computed in large areas, as the MountainsMap segmentation algorithm found only 50 particles for S10, a much lower value than for the others, which we attribute to the smaller area size. The surface coverage was higher in S50 and intermediate in S20, confirming the effect of the area size on the particle distribution. Moreover, the highest density was also observed for S50, and the lowest occurred for S10. These particles indicate cells or clusters of cells on the surface, e.g., guardian cells with their particular function in the plant's working mechanism.
## 3 Experimental and Calculation Procedures
### Investigation Methods
#### 3.3.2 Nitrogen Adsorption-Desorption Measurements  
The porosity of the materials was examined applying low-temperature nitrogen adsorption-desorption isotherms at 77 K. The values of the parameters characterizing the properties of analyzed samples were obtained: the BET specific surface area (S\({}_{\text{BET}}\)) (assessed from the linear BET plot of adsorption data), the total pore volume (V\({}_{\text{J}}\)) (from the adsorption value at the relative pressure \(p/p_{o}\)-0.99), the micropore volume (V\({}_{\text{mic}}\)) (from the t-plot), and the pore size distributions (PSD) followed by the Barrett, Joyner, and Halenda (BJH) procedure and the Non-Local Density Functional Theory (NLDFT) approach (ASAP 2020 analyzer, Micromerities, USA Before the measurement, volatile materials adsorbed on the surface were driven by thermal degassing. In this procedure, the precisely weighed amount of the samples (\(\sim\)0.15 g) were outgassed at 120 \({}^{\circ}\)C and pressure of 1 mmHg for 24 h in a degas port of analyser.
## 2 Dynamic Nuclear Polarization in Semiconductor Nano-and Microstructures
### Optically Induced DNP in Semiconductors  
#### 2.1.1 First Demonstration of DNP  
Vividly developing applications of spintronics (see for example Reference [13]) Ref, in particular, in biomedicine, require a secure control over the spin polarization in semiconductors. The first demonstration of an enhanced DNP was obtained by optical pumping in very pure \(n\)-type Si (with the concentration \(5\times 10^{16}\) m\({}^{-3}\) of phosphorus atoms, which was much lower than the concentration \(2\times 10^{17}\) m\({}^{-3}\) of conduction electrons) [14,15] (see Figure 2). Optically induced DNP in semiconductors can be achieved in two ways--(i) saturation of the electronic spin magnetization with an unpolarized light, which generates an equal number of spins up and spins down and (ii) production of highly polarized conduction electrons by irradiation with circularly polarized light, which generates spin-polarized electrons and holes [14].
## 2 Materials and Methods
### Analysis  
The audio-recorded interviews were transcribed verbatim and analysed using NVivo 11. All transcripts were anonymised to protect participant identity. The analysis followed a three-stage coding process consisting of open, axial and selective coding [43]. In the open coding stage, a detailed line-by-line examination of the transcripts was conducted, breaking the data into units of meaning and assigning initial codes [26]. For example, one participant's statement \"I use AI technology for emotion regulation when I feel stressed and overwhelmed\" was coded as \"usefulness\" and \"regulating emotional states\" [44]. Another participant's remark \"AI tools don't really understand my feelings; they just give generic suggestions\" was coded as \"emotions hard to understand\" and \"lack of trust\". This process enabled a detailed understanding of participants' experiences with emotion regulation and their perceptions on using AI technology in this context. Through constant comparisons across interviews, similar codes were grouped in the axial coding phase into higher-level categories [45]. For instance, \"emotions hard to understand\" and \"lack of trust\" were integrated into the category \"functional barriers\", while \"usefulness\" and \"regulating emotional states\" were grouped under \"functional outcomes\". Axial coding thus facilitated the development of a conceptual framework by linking causal conditions, contextual factors, intervening variables and consequences [46]. In the selective coding phase, these axial categories contributed to the core constructs that formed the theoretical model: personal factors, technological factors and environmental factors influencing the use or non-use of AI technology for emotion regulation amongst young people [26]. This iterative process allowed the model to be built directly from participants' narratives, grounded in the data. The coding was initially performed in Mandarin to retain the cultural and emotional nuances expressed by participants. Two researchers with training in qualitative methods independently conducted the initial open coding. They then met regularly to compare codes, resolve discrepancies through discussion and iteratively refine the coding framework. Axial and selective coding were jointly performed using the agreed-upon framework. Although intercoder reliability coefficients such as Cohen's kappa were not calculated, coding consistency was ensured through intercoder dialogue and consensus-building, which are widely accepted practices in grounded theory research. The coding scheme and representative quotations were subsequently translated into English by bilingual researchers with experience in qualitative research and cross-cultural studies. To further enhance the accuracy of the data, back-translation and team discussions were conducted to resolve any ambiguities and ensure faithful representation of their meaning.
## 3 Results and Discussions
### Pullulan Oxidation  
Figure 3 presents the reaction mechanism of pullulan oxidation in the presence of the NHPI-supported solid catalysts, NaClO/NaBr. As shown in Figure 3, the hydroxyl group of pullulan at the C6 is selectively oxidized to the carboxyl group via intermediate aldehyde. Under reaction conditions, the catalyst is in situ converted into nitroxyl radical in the presence of the NaOCI/NaBr (**I**). In addition, the nitroxyl radical is converted into more powerful nitrosonium salt, the actual oxidant from system (**II**). Furthermore, the nitrosonium salt reacts with the primary hydroxyl group of pullulan and forms an aldehyde intermediate in a first stage, which is then oxidized further to the carboxyl group. After each reaction, the catalysts were recovered and reused, see Table 2. The catalytic activity of the NHPI was observed and evaluated using FTIR, NMR, and particle charge detector techniques.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pullulan (g)**} & \multicolumn{2}{c}{**Catalyst**} & \multicolumn{2}{c}{**NaBr**} & \multicolumn{2}{c}{**NaOCI**} & \multirow{2}{*}{**EC (\%)**} & \multirow{2}{*}{**\(\zeta\) (mV)**} \\ \cline{2-3} \cline{5-7}  & **Type** & **Recycle** & **Amount (g)** & & & & \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{APS-NHCO-NHPI-1 (APS)} & 0 & 0.8 & 1 & & 21 \(\pm\) 0.4 & \(-\)19.7 \(\pm\) 0.6 \\  & & 1 & 0.75 & 0.94 & & 26 \(\pm\) 0.2 & \(-\)19.5 \(\pm\) 0.4 \\  & & 2 & 0.6 & 0.76 & & 27 \(\pm\) 0.3 & \(-\)18.8 \(\pm\) 0.2 \\  & & 3 & 0.5 & 0.63 & & 27 \(\pm\) 0.6 & \(-\)22.1 \(\pm\) 0.8 \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{AMPS-OCO-NHPI-1 (AMPS)} & 0 & 0.8 & 1 & & 17 \(\pm\) 0.1 & \(-\)18.6 \(\pm\) 0.2 \\  & & 1 & 0.75 & 0.94 & & 21 \(\pm\) 0.3 & \(-\)17.6 \(\pm\) 0.6 \\  & & 2 & 0.7 & 0.88 & & 13 \(\pm\) 0.9 & \(-\)16.2 \(\pm\) 0.4 \\  & & 3 & 0.6 & 0.86 & & 16 \(\pm\) 0.6 & \(-\)16.6 \(\pm\) 0.2 \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{BMPS-OCO-NHPI-2 (BMPS)} & 0 & 0.8 & 1 & & 17 \(\pm\) 0.5 & \(-\)24.7 \(\pm\) 0.2 \\  & & 1 & 0.75 & 0.96 & & 18 \(\pm\) 0.3 & \(-\)21.8 \(\pm\) 0.2 \\ \cline{1-1}  & & 2 & 0.65 & 0.85 & & 27 \(\pm\) 0.4 & \(-\)24.9 \(\pm\) 0.5 \\ \cline{1-1}  & & 3 & 0.5 & 0.65 & & 35 \(\pm\) 1.5 & \(-\)27.3 \(\pm\) 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electrokinetic charge (EC) and zeta potential (\(\zeta\)) for pullulan samples oxidized in the presence of the NHPI-supported catalysts.  
Figure 2: \(N\)-hydroxyphthalimide (NHPI) immobilization on various polymer supports via amide or ester bonds.