esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Key Concepts and Technologies
### Decentralised Identity Management Terminologies  
* **Self-Sovereign Identity (SSI):** SSI refers to a model where individuals have full control over their identity without relying on central authorities. Users gain full rights to manage and share their identity data directly with the SP, enhancing privacy and autonomy in identity management [16].
* **Blockchain:** It is a decentralised, distributed digital ledger where all records, transactions, or data are stored across multiple computers, ensuring security and maintaining integrity without needing a single central authority [17].
* **Distributed Ledger Technology (DLT):** DLT allows recording data and transactions in multiple places (nodes) rather than storing it centrally, reducing the risk of a single point of failure [17].
* **Decentralised Identifiers (DIDs):** DIDs are globally unique identifiers used in decentralised blockchain systems, allowing users to be validated without needing a central authority, enhancing system privacy [17].
* **Verifiable Credentials (VCs):** VCs are digital versions of physical identity documents like passports or driver's licenses, stored in digital wallets and issued by trusted authorities. They are used to validate and share a user's identity, disclosing only necessary information to third parties [18].
* **Consensus Mechanism:** A process that ensures all participants agree on the validity of a transaction, maintaining data integrity and preventing tampering. Common mechanisms include Proof of Work (PoW) and Byzantine Fault Tolerance (BFT) [17].
## 2 Experiments and Methods
### Penman-Montieth FAO-56 Model  
Actual ETo data were not available for the selected study sites; therefore, the FAO-56 method was used to estimate ETo for these sites. The estimated ETo from the FAO-56 method was used as targets for LSTMs and bidirectional LSTM neural networks. The FAO-56 model is accepted and has been widely used [3; 29; 30] in these situations, and is derived from:  
\[\text{ETo}\left(\frac{mm}{day}\right)=\frac{\Delta(R_{n}-G)+\rho_{a}c_{p}\left( \frac{s-ea}{ra}\right)}{\Delta+\gamma\left(1+\frac{\tau_{s}}{\tau_{a}}\right)}. \tag{1}\]  
The Penman-Monteith equation (Equation (1)) was derived from the most acceptable form by [PERSON] et al. [3] also known as the FAO-56 model expressed as Equation (2) below:  
\[\text{EETo}\left(\frac{mm}{day}\right)=\frac{0.408\Delta(R_{n}-G)+\gamma\left( \frac{900}{Tmann+271}\right)U(es-ea)}{\Delta+\gamma(1+0.34\text{U})}, \tag{2}\]  
where \(\Delta\) is the slope of the vapor saturation pressure, \(R_{n}\) is the net radiation, \(G\) is the soil heat flux, \(\rho_{a}\) is the mean air density at constant air pressure, \(C_{p}\) is the specific heat of the air, \(es-ea\) is the vapor pressure deficit, \(\gamma\) is the psychometric constant, \(U\) is wind speed at 2 m (m/s), \(Tmean\) is daily mean temperature, \(\gamma_{s}\) is the surface resistance, and \(\gamma_{a}\) is the aerodynamic resistance \(m\).
# Relation-Aware Graph Transformer for SQL-to-Text Generation
## 3 Model  
### SQL Tree Construction  
The entire node set of the constructed SQL tree \(V\) is split into two categories: intermediate nodes \(V^{I}=\{v_{i}^{1}\}_{i=1}^{|V^{i}|}\) and leaf nodes \(V^{L}=\{v_{i}^{L}\}_{i=1}^{|V^{L}|}\). _Intermediate nodes_ include three abstract concepts (SQL, TABLE and COLUMN), seven SQL-clause keywords (SELECT, WHERE, etc.) and binary operators (\(>\), \(<\), =, etc.), while _leaf nodes_ contain unary operators, raw table names and column words and placeholders for entity value (entity mentions such as \"new york\" are replaced with one special token _val0_ during preprocessing, called _dexicalization_). With this partition, the node embeddings of these two types can be updated using different relational information.  
Starting from the root node SQL, we firstly append the clause-level nodes as its children (see Figure 2). Then concept abstraction nodes, TABLE and COLUMN, and relevant operator nodes are accordingly attached to their parents. Next, for node COLUMN and TABLE, we append all the raw words, aggregators, and distinct flags as leaf nodes. Our SQL Tree consists of three levels (see Figure 3): clause level, schema level, and token level. Table 1 shows all types of nodes.  
* First, SQL is divided into some clauses such as SELECT clause, WHERE clause, nested SQL clause and so on (see Figure 3a).
* Then, each clause is composed of several tables, columns, and some other binary operators. Considering that some table and column names have multiple tokens, we design two abstract nodes (TABLE and COLUMN) to address this problem (see Figure 3c). With these two abstract nodes, the clause nodes can be represented as shown in Figure 3b. Noticing that binary operators can be regarded as a relation between several nodes, we set them as intermediate nodes (parents of some children nodes).
* For other unary operators and tokens (table and column), we put them on leaves.
## 2 Related Work
### Reference Architecture of a Vehicular OCC System  
Figure 2 illustrates the architecture of region-of-interest (RoI) signaling. The transmission of RoI is beneficial when the camera Rx has the ability to capture the area in which the communication link should be set up. The light source Tx continuously notifies the camera Rx via a known signal for differentiating it from other unwanted light sources and other bright objects. The transmission of the known signal is a type of light source identification, where the high-speed data stream is embedded into the low-speed RoI stream [8]. Moreover, dual streaming of light source identification signaling and high-speed data link is performed as a hybrid modulation.  
For the Rx, Tx light source identification and high-speed data demodulation can be time-slotted using a single camera. Therefore, the camera Rx first detects the RoI from the identification signal and then selects the RoI to accelerate the frame rate and achieve a high-speed data link. However, the movement between Tx and Rx is considered, and the RoI may also change. An alternative solution is the use of a dual-camera system in which one camera simultaneously detects the RoI, while another camera demodulates data at a high speed based on the constantly updated RoI from a low-speed (e.g., 30 fps) camera.  
A vehicular OWC/OCC system using a hybrid waveform that consists of low-rate and high-rate data streams can mitigate the computational load on the receiver and thus reduce the processing time. The hybrid spatial phase-shift keying (HS-PSK) single carrier is such a hybrid waveform (see [4] and [30]). HS-PSK is a dedicated combination of spatial 2-phase shift keying (S2-PSK) and dimmable spatial multiple-PSK (DSM-PSK). The S2-PSK data stream is generated based on the changes between the low and high dimming levels of the DS8-PSK waveform, as illustrated in Appendix 2 of [30]. S2-PSK had been fully demonstrated with implementation results in [8]. In Section 4, we will illustrate DSM-PSK with M = 8 for eight LEDs in each LED array before proceeding further on our proposed decoding method based on an NN for decoding the high-rate data stream.
## 3 Results and Discussions
### Data Processing and Validation
#### 3.2.1 PIV Processing
Due to the existence of the flame during combustion PIV application inside operating engine, the propagation of the flame could affect the seed particles by agglomeration or melting/burning of the particles and hence the velocity measured from PIV processing. To assess this potential issue, we took a PIV image during the flame developing period and analyze the velocity across the flame front. Figure 7 displays the PIV image as well as the processed information for the velocity. The difference in intensity between the burned and unburned region is quite obvious: the top right of the figure shows the intensity along a line drawn horizontally across the burned and unburned region (the line is shown in dashed white line on the image top left of the figure) and the intensity is shown to be distinctively lower in the burned region compared to the unburned region. The intensity transition between burned and unburned region is also a sharp and steep rise which indicates the error in the detected flame front will be quite low (\(\pm\) 10 pixels, which is \(\pm\) 0.5 mm). To estimate the error in velocity due to the flame front movement, an assessment of the velocity across the flame front at various interrogation area is carried out. As the interrogation area gets smaller, the less seed particles will be tracked and if the flame front affects the seeds movement, discrepancy with larger interrogation area that cover more than the flame front. The results were plotted as shown in bottom left; all the trends were similar with different interrogation area which see a smooth transition of velocity field across the flame front. This also suggests the unburned region contains enough valid particles for PIV analysis.
## 1 Introduction  
The notion of classification is very general. It can be used in many applications, for example, text mining, multimedia processing, medical or biological sciences, _etc_. The goal of text classification is to assign an electronic document to one or more categories based on its contents.  
The traditional single-label classification is concerned with a set of documents associated with a single label (class) from a set of disjoint labels. For multi-label classification, the problem arises as each document can have more than one label [1, 2].  
In some classification problems, labels are associated with a hierarchical structure, in which case the task belongs to hierarchical classification. If each document may correspond to more than one node of the hierarchy, then we deal with multi-label hierarchical classification. In this case, we can use both hierarchical and flat classification algorithms. In hierarchical classification, a hierarchy of classifiers can be built using a hierarchy of labels. If labels do not have a hierarchical structure, we can extract a hierarchy of labels from a dataset. It can help us to enhance classification performance.  
However, there are some general issues in the area of hierarchical classification:  
* In some cases classification performance cannot be enhanced using a hierarchy of labels. Some authors [3] showed that flat classification outperforms a hierarchical one in case of a large number of labels. So one should always compare a hierarchy with the flat case as a base line at each step of the hierarchy extraction.
* A hierarchy is built using some assumptions and estimations. One needs to make more accurate estimations in order not to let performance decrease due to rough computations. In hierarchical classification, a small mistake made by the top level of classifiers greatly affects the performance of the whole classification.
* Algorithms of extracting hierarchies require some external parameters. One needs to reach balance between the number of parameters and the effectiveness of an algorithm. Eventually, a smaller number of parameters leads to more simple usage.  
In the previous work [4] we proposed and benchmarked the PHOCS algorithm that extracts a hierarchy of labels, and we showed that hierarchical classification could be better than a flat one. The main contribution of this work is enhancing the PHOCS algorithm.  
We pursue the goal to increase performance of hierarchical classification using hierarchies built by the PHOCS algorithm. We want to reach this goal by solving the problems listed above. We make the estimation function more precise. We make estimation of false positives, true positives and false negatives and then calculate relative measures. We change stopping criteria as well. In the first version of the PHOCS, we used an external parameter that made the depth of our predicted hierarchy no more than five. We removed this parameter, and as stopping criteria, we now use a comparison of our estimated performance with the flat classification results.
# In-Silico Characterization of von Willebrand Factor Bound to FVIII
## 4 Discussion
Moreover, the PCN clustering and participation coefficient \(P\) calculations revealed that VWF binding to the B domain of FL-FVIII was characterized by lower number of residues with participation coefficient \(P>0\) (allosteric residues), compared to VWF/BDD-FVIII and \(\mathrm{\acute{e}anesoctocog\ alfa}\). The participation coefficient is an output parameter of PCN analysis, and has been found useful for identification of allosteric residues in different protein system and protein domains, according to the methodological approach hereby used and previously applied [31, 40]. The topological parameters, coming from PCN analysis, also provided information on the local contribution to interface energy, which is useful for identification of key residues (e.g., allosteric amino acids) in protein-protein complex formation. Therefore, we can hypothesize that one of the driving forces in VWF binding to FVIII is attributed to allosteric modulation of protein structure. Further investigations can shed light on putative allosteric and cooperative protein-protein interaction, e.g., by simulating FVIII structures with several replicas of VWF. These studies may provide new hints about the structural role of different domains of FVIII, along with interaction with other plasma proteins, such as the serum albumin. One of the main limitations of our study is attributed to the intrinsic approximation structural modeling of B domain, whose structure has never been solved, with the exclusion of Cryo-EM density images [25]. Another limitation is related to approximation of intrinsic and essential FVIII post-translational modifications, such as protein glycosylation (Figure 1), and the most glycosylated domain of FVIII is the B domain.
# Pilot Study of Microplastics in Snow from the Zhetysu Region (Kazakhstan)
###### Abstract  
The pilot study is devoted to the assessment of both the accumulation and spatial distribution of microplastics in the snow cover of the Zhetysu region. The height of snow cover in the study area varied from 4.0 to 80.5 cm, with a volume of melt water ranging from 1.5 to 143 L. The analysis of 53 snow samples taken at different altitudes (from 350 to 1500 m above sea level) showed the presence of microplastics in 92.6% of samples in concentrations from 1 to 12 particles per square meter. In total, 170 microplastic particles were identified. The main polymers identified by Raman spectroscopy were polyethylene (PE), polypropylene (PP), and polystyrene (PS). These are typical components of plastic waste. The spatial distribution of microplastics showed elevated concentrations near settlements and roads. Notable contaminations were also recorded in remote mountainous areas, confirming the significant role of long-range atmospheric transport. Particles smaller than 0.5 mm dominated, having high aerodynamic mobility and capable of long-range atmospheric transport. Quantitative and qualitative characteristics of microplastics in snow cover have been realized for the first time both in Kazakhstan and in the Central Asian region, which contributes to the formation of primary ideas and future approaches about microplastic pollution in continental inland regions. The obtained results demonstrate the importance of atmospheric transport in the distribution of microplastics. They indicate the need for further monitoring and microplastic pollution analyses in Central Asia, taking into account its detection even in hard-to-reach and remote areas.  
microplastics; pollution; snow cover; atmospheric transport; migration 2025
## 2 Polarization Control Structure Furthermore, Principle  
Due to the influence of the optical fiber link, the delivered LO undergoes random polarization state rotation in SHC systems, which results in LO power fluctuation on two polarizations. To ensure that both received polarization signals can be obtained with stable and equal amounts of LO light, an APC is required in the front end of the receiver for the LO branch [15], as shown in Figure 1. Different from traditional APC that needs to lock the input polarization to a certain point from the perspective of the Poincare sphere, the APC in SHC system only needs to lock to a ring, which can be realized by using a half-wave plate (HWP) or MZI, respectively. In the following section, their structures and operating principles will be studied and analyzed in detail.
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
## 2 Materials and Methods
### Adsorption Experiments  
In order to study the effects of pH, adsorbent usage, contact time, and initial concentration on the adsorption of Cs, the following adsorption experiments were conducted. After the geomaterials were passed through a 720 mesh sieve, they were transferred to a 200 mL Erlenmeyer flask with 100 mL of Cs solution with a known concentration, and were then placed in a constant temperature shaker (TAITEC Plus Shaker EP\({}^{-1}\) with Thermo Minder SX-10R, Saitama, Japan) set at a prescribed temperature. The temperature of the adsorption experiment was 25 \({}^{\circ}\)C, the pH values were 3, 4, 5, 6, 7, 8, and 9; the amounts of adsorbent used were 0.05 g, 0.1 g, 0.2 g, 0.4 g, 0.6 g, and 0.8 g; the contact times were 1 h, 3 h, 8 h, 24 h, and 72 h; and the initial concentrations were 5 mg\(\cdot\)L\({}^{-1}\), 10 mg\(\cdot\)L\({}^{-1}\), 20 mg\(\cdot\)L\({}^{-1}\), 30 mg\(\cdot\)L\({}^{-1}\), and 40 mg\(\cdot\)L\({}^{-1}\). The pH value was adjusted by using aqueous HCl and aqueous NaOH.  
Following each adsorption experiment, the suspension containing geomaterials and the above standard solution were filtered through a 0.45 \(\upmu\)m membrane filter (Mixed Cellulose Ester, 47 mm, Advantec, Tokyo, Japan) to remove the Cs\({}^{+}\) that had been adsorbed into the geomaterials, and the concentration of the Cs in the filtrate was determined with the ICP-MS (Thermo Scientific Center: X-series II, Waltham, MA, USA). The adsorption capacity of the geomaterials for the Cs ions was calculated by Equation (1).  
\[q_{e}=\frac{(C_{i}-C_{e})}{m}\cdot V\,, \tag{1}\]  
where \(q_{e}\) represents the adsorption capacity at equilibrium (mg\(\cdot\)g\({}^{-1}\)); \(C_{i}\) and \(C_{e}\) are the initial and equilibrium Cs concentrations (mg\(\cdot\)L\({}^{-1}\)) in the solution, respectively; \(V\) is the volume of the solution (L); and \(m\) is the mass of adsorbent (g) [29; 30; 31].
# Unexpected Ferromagnetism--A Review
## 2 Examples of \(d^{0}\) Ferromagnetism
### Non-Magnetic Oxides
#### 2.2.4 ZnO  
Another interesting oxide is ZnO, and its attractiveness from an experimental point of view is the fact that it is low-cost and is widely used in a various range of applications. ZnO gained considerable attention due to its possible use in spintronic applications [44]. As early as the year 2000, studies had been published on the generation of ferromagnetism in non-magnetic oxides by doping them with magnetic ions [8]. There were various studies on the origin of ferromagnetism in ZnO, both theoretically and experimentally. However, even so, the reason for ferromagnetism in ZnO is still debated. Some have suggested that the carrier-induced ferromagnetic behavior of ZnO can be controlled by modifying the carrier's density (electron or hole concentrations depending on the amount of dopant--cations or/and anions--introduced) [45; 46]; other investigations on the room temperature ferromagnetic behavior of un-doped ZnO single crystal have shown that it was induced due to oxygen vacancies generated by thermal annealing in Ar flow [47]. Moreover, the ferromagnetic behavior can be induced in ZnO (or other non-magnetic oxides) by doping [48].  
An idea that caught the attention of several researchers is that the exhibited ferromagnetism is related to grain boundaries. In 2009, an explanation was proposed in the hope of elucidating contradictory findings. They observed that bulk doped or un-doped ZnO does not exhibit ferromagnetic behavior. Further investigations showed that only polycrystalline ZnO with a high specific area of the grain boundaries showed ferromagnetism, irrelative to doping [49]. Studies on un-doped ZnO nanoparticles attributed the exhibited room temperature ferromagnetic behavior to lattice defects. Moreover, doping the above-mentioned nanoparticles with first group elements led to an enhancement in the ferromagnetic behavior [50].  
Room temperature ferromagnetic behavior was observed in pure ZnO nanoparticles. By varying experimental parameters, it was concluded that an important part in the generation of ferromagnetism in ZnO nanoparticles is played by oxygen vacancies [51]. Following this idea, the correlation between particle size and oxygen vacancies was investigated. The synthesized nanoparticles had a particle size of 6, 7, and 11 nm. It was observed that decreasing the particle size leads to an increase in oxygen vacancies, leading to a long-range ferromagnetic ordering in these nanoparticles. It was concluded that a crucial role in room temperature ferromagnetic behavior exhibited in un-doped ZnO nanoparticles is attributed to oxygen vacancies which can be controlled by particle size [52].
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### Sectorization of the Scenario  
A regular hexagon is a polygon that has desirable properties for the proposed communication protocol. Hexagons have six sides and corners. In a regular hexagon, the size from the corner to the center is the same as the hexagon's side. Moreover, hexagons have six adjacent neighbors, with equal distances between the center and the centers of all neighbors (see Figure 2).  
Thanks to these characteristics, hexagons can uniformly cover the plane efficiently without overlapping; moreover, it is possible to circumscribe the hexagon and its adjacent neighbors regularly [31; 32]. The center of the hexagon is shared with the center of the resulting circle.  
As a definition, the radius of the circle that circumscribes the hexagon and its six adjacent neighbors is calculated such that the vehicle located in the central sector (Figure 2) can communicate with any vehicle located in the neighboring sectors.  
An equation can be deduced to calculate a hexagon size that permits these communication characteristics. To do this, the hexagon is represented in a Cartesian plane (see  
Figure 2: Hexagon properties.  
Figure 2). The _size_ units are the distance between any corner and the hexagon's center. The angle of two adjacent sides is 120 grades, the hexagon's width (\(w\)) is  
\[w=2*size, \tag{1}\]  
and its height (\(h\)) is  
\[h=\sqrt{3}*size, \tag{2}\]  
where \(\sqrt{3}\) is derived from sin(60).  
The relations between \(h\) and \(w\) are defined by Equations (3) and (4):  
\[w=(2*h)/\sqrt{3}. \tag{3}\]  
\[h=(w*\sqrt{3})/2. \tag{4}\]  
A triangle is drawn on the plane that connects the most distant corners of two neighboring hexagons as the hypotenuse. The hypotenuse, represented by the \(radius\), is the node's communication coverage. The largest cathetus is represented by \(a\) and the smallest by \(b\). Equations (5) and (6) present the cathetus size inferred concerning the Cartesian plane:  
\[a=\frac{4}{7}w. \tag{5}\]  
\[b=\frac{\sqrt{3}}{4}w. \tag{6}\]  
Finally, the Pythagorean theorem is applied as follows (Equation (7)):  
\[radius^{2}=\left(\frac{4}{7}w\right)^{2}+\left(\frac{\sqrt{3}}{4}w\right)^{2}. \tag{7}\]  
Thus, the formula for calculating the size of the hexagon is obtained (Equation (8)):  
\[w\approx 0.5547*radius. \tag{8}\]  
For sectorization of the map, the Hexagonal Binning algorithm [33] is used, having as input the dimensions of the map and the hexagon height (\(h\)).