esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
###### Abstract  
Rock fragments obtained by excavation can provide information for evaluating the excavation efficiency, for which the coarseness index (\(CI\)) and particle size parameters (\(d_{50}\), \(d_{\text{MPS}}\), and \(d^{\prime}\)) are used. However, \(CI\) depends on the number and size of the sieves used, and the particle size parameters require mathematical calculations. In this study, the maximum diameter (\(d_{\text{max}}\)) of rock fragments was used as an indicator of the excavation efficiency. Linear cutting tests were performed and the rock fragments were sieved to obtain the \(CI\) and \(d_{\text{max}}\). The relationship between \(d_{\text{max}}\) and \(CI\) was similar to that between other particle parameters and \(CI\). \(d_{\text{max}}\) and \(CI\) increased with increasing penetration depth and spacing, but \(d_{\text{max}}\) followed a linear relationship, and \(CI\) demonstrated a power relationship. Both \(d_{\text{max}}\) and \(CI\) reached their maximum values at a specific ratio of spacing to penetration depth (\(s/p\) ratio) and were not affected by subsequent increases in \(s/p\). The cutting force and volume had positive relationships with \(d_{\text{max}}\) and \(CI\), linear with \(d_{\text{max}}\) and exponential with \(CI\), whereas the specific energy (\(SE\)) had an inverse relationship, showing exponential and linear relationships with \(d_{\text{max}}\) and \(CI\), respectively. When \(d_{\text{max}}\) was larger than a certain value, \(SE\) converged to a constant value. This study confirmed that \(d_{\text{max}}\) has an advantage over \(CI\) in determining excavation efficiency.  
Keywords:linear cutting test; rock fragments; rock chips; coarseness index; maximum diameter; specific energy; excavation efficiency +
Footnote †: journal:
## 5 PSI by Means of TSC: Results and Discussion
### Interferometric Processing Chain Considerations and CPT Overview  
#### 5.1.1 GB-SAR Interferometry  
Prior to applying any PSI technique, a pre-processing of GB-SAR data must be carried out in order to improve the quality of the SAR images corresponding to each measurement day and to compensate for the APS present in the long-time interferograms corresponding to the different measurement campaigns.  
Considering a total of \(N\) daily datasets, each one composed of \(M_{i}\) zero-baseline GB-SAR raw images, with \(1\leq i\leq N\), the process may be summarized in the following two blocks [33]:  
* The first block, referred to as short-term processing (STP) [33], basically consists of two steps. The first one is based on carrying out the focus of the raw data. On the one hand, since the RiskSAR-X sensor is based on a LFM-CW radar, the range compression can be carried out with a simple FFT of the time-domain deramped received signal [34, 35]. On the other hand, since the cross-range resolution is not constant due to the limited length of the synthetic aperture of GB-SAR sensors, the back-projection technique proved to be the most suited for the azimuth focusing [34].  
Figure 12: Number of pixel candidates selected using the TSC, the coherence stability and the \(D_{A}\) approaches for **(a)** the GB-SAR and **(b)** the TerraSAR-X datasets.  
Once the images has been focused, a temporal averaging of each daily dataset, composed of \(M_{i}\) zero-baseline acquisitions corresponding to the same measurement day \(i\), is carried out in order to improve the SNR of time-stationary targets, leading to a higher quality time-averaged SLC image from each daily dataset corresponding to each measurement campaign. For the dataset used in this paper, \(10\) time-averaged SLC images will be finally available after this step.
* The following step, referred to as long-term processing (LTP), consists of compensating for the APS present between the different time-averaged SLC images obtained in the previous STP block. From all of the methods available in the literature [33, 36, 37, 38], the RiskSAR-X makes use of model-based solutions [33]. This kind of solution proved to be very effective, reaching very good performances with no use of extra meteorological data or stable ground control point (GCP) information. The APS estimation and compensation process is a key issue in GB-SAR processing in order to obtain a reliable set of APS-free interferograms suitable for the PSI processing. For the dataset used in this paper, \(45\) APS-free interferograms are finally available. Finally, to face temporal decorrelation phenomena and enhance the phase quality of interferograms, the processing can be benefited by the exploitation of polarimetric information, such as the one provided by the RiskSAR-X sensor. In classical PSI, only a single-polarimetric channel is considered for the processing. This means that all pixels involved in PSI algorithms belong to the same polarimetric channel. In this context, polarimetric optimization techniques may be employed in order to improve the phase quality of interferograms [39].
###### Abstract  
We evaluated the incidence of intraforaminal and extraforaminal variations of the vertebral artery (VA) in the Oman population using computed tomography angiography (CTA). CTA results of 579 consecutive Omani patients (1158 VAs) were reviewed retrospectively for the analysis of unusual entrance transverse foramen (UE-V2), midline migration (MM), persistent first intesegmental artery (PFIA), and paracondyl process (PP) variations of VA. The anomalous origin and VA dominance were also determined. The sex and side differences in the incidence of these variations were analyzed using the Chi-square test. The incidence of UE-V2 was observed in 10.44% (121 out of 1158) of cases. The incidence of UE-V2 at different vertebral levels was observed in the sequence of C5 (71%) > C4 (20.7%) > C7 (7.43%) > C3 (0.8%). The incidence of MM variation was 1.29%. PFIA and PP variations were found in 0.17% and 0.60% of cases, respectively. Left-dominant VA was identified in 44.7% (259 out of 579) of subjects. The incidence of VA variations was not significantly associated with either sex or side. Anomalous VA origin from arch aorta (3.5%) and right common carotid artery (CCA) (0.08%) was identified. The incidence of UE-V2 in Oman subjects is comparatively higher than that reported in other Asian populations. A rare case of VA originating from the right CCA was also identified.  
cervical vertebra; computed tomography angiography; incidence; variation; vertebral artery 2023135822. [[https://doi.org/10.3390/app13105822](https://doi.org/10.3390/app13105822)]([https://doi.org/10.3390/app13105822](https://doi.org/10.3390/app13105822))
## 4 Approaches to Source Code Transformation into Vector-Based Representations
### Histograms of Abstract Syntax Tree Node Types  
Similar to histograms of assembly language instruction opcodes described in Section 4.4, we include histograms of AST node types in the comparison. The process of program embedding into vector space based on histograms of AST node types is described by Algorithm 4. In contrast to Algorithm 3, Algorithm 4 does not depend on the CPython interpreter implementation details. This makes the AST node type histogram-based approach similar to AST-based Markov chains, but without taking into account transitions among AST vertices.  
```
Input:\(S\triangleright\) a set of source codes to be converted into vector representations.
1. \(N=\varnothing\triangleright\) the ordered set of node type multisets associated with source codes.
2. \(T=\varnothing\triangleright\) the ordered set of known node types.
3. For each source code \(s\in S\)do:
4. Construct an AST \(a\) for the program \(s\) using the **ast.parse** function [41].
5. Extract a multiset of node types \(n\) that are present in \(a\) from \(a\).
6. \(N\gets N\cup\{n\}\triangleright\) add the multiset of node types \(n\) to \(N\).
7. \(T\gets T\cup n\triangleright\) add discovered node types to the \(T\) set.
8. End loop.
9. \(V=\varnothing\triangleright\) the ordered set of vectors.
10. For each multiset of node types \(n\in N\)do:
11. \(\overrightarrow{v}=\varnothing\).
12. For each known node type \(t\in T\)do:
13. \(\omega=\frac{1}{|\omega|}|\{n_{k}:n_{k}\in n\wedge n_{k}=t\}|\triangleright\) node type occurrence frequency.
14. \(\overrightarrow{v}\leftarrow\overrightarrow{v}\cup\{\omega\}\triangleright\) add a new component to the program vector.
15. End loop.
16. End loop.
17. Return the \(V\) set of source code vector-based representations.
```  
**Algorithm 4**Source code embedding based on AST node type histograms  
The histograms of relative occurrence frequencies of AST nodes of different types obtained for sample programs implementing recurrent formulas (see Table 1) and listed in the first row of Table 2 are shown in Figure 11. The histograms shown in Figure 11 were obtained using Algorithm 4. The first approach to recurrent formula implementation highlighted in magenta in Figure 9 uses a loop (see the first row of Table 2), and the second approach highlighted in cyan in Figure 9 uses a function that calls itself.  
Figure 10: Program embedding into vector space based on opcode histograms.  
Similar to Figure 9, we observe that the histograms shown in Figure 11 differ significantly depending on the algorithm and task implemented in source code, and histograms for programs solving different tasks differ even more. Figure 12 summarizes the process of AST node type histogram-based embedding of programs into vector space.
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
###### Abstract  
The increasing interest in renewable energy sources has brought attention to large voltage-gain dc-dc converters; among the different available solutions to perform a large voltage-gain conversion, this article presents an overview of non-isolated dc-dc converter topologies that utilize switched-capacitor circuits, i.e., diode-capacitors voltage multipliers. The review includes combinations of a traditional power stage with a diode-capacitor-based voltage multiplier, such as the multilevel boost converter. This article starts by reviewing switched-capacitor (SC) circuits, different topologies, and different types of charge exchange; it provides a straightforward analysis to understand the discharging losses. It then covers the multilevel boost converter and other topologies recently introduced to the state-of-the-art. Special attention is put on SC circuits with resonant charge interchange that have recently been probed to achieve very good efficiency. An additional contribution of the article is new proof of the discharging losses in resonant switched-capacitor circuits focused on the initial and final stored energy in capacitors, and this proof explains the relatively large efficiency obtained with SC resonant converters.  
multilevel boost converter; voltage multiplier; switched-capacitor converter +
Footnote †: journal: Applied  
**Keywords:** multilevel boost converter; voltage multiplier; switched-capacitor converter
## Collaboration between Professionals:  
The Use of Videoconferencing for Delivering E-Health  
**[PERSON]**  
Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway,  
Box 35, N-9038 Troms @ Norway; E-Mail: [EMAIL_ADDRESS];  
Tel.: +47-95-28-07-49; Fax: +47-77-75-40-98  
_Received: 15 March 2012; in revised form: 29 March 2012 / Accepted: 30 March 2012 / Published: 2 April 2012_  
This article explores the ways in which collaboration between professionals using videoconferencing affects the e-health delivered to patients. In Norway, general practitioners (GPs) and specialists routinely hold videoconferences. Observations of 42 VC meetings, each lasting from 5 to 40 min, were analysed in terms of the interactions. In addition, five semi-structured, face-to-face interviews were conducted, each lasting from 20 to 70 minutes. Statements were selected to illustrate the content of the interactions and how collaborative work affects the delivery of healthcare. Successful collaborative work provides practitioners with a new way of thinking: exchanging information and knowledge between levels of care in order to provide the best treatment for patients locally. The regularity makes the collaborative work a two-way achievement. GPs receive decision support and second opinions, and specialists receive information and opportunities to follow up. How the professionals manage their work (_i.e._, collaborating) may benefit their patients. The regular use of videoconferencing will furnish professionals with enhanced resources for the meeting of patients' demands in the future. Regularly informing one another and exchanging knowledge, benefits the professionals by providing increased certainty with regard to their medical decisions, and it benefits the patients because they will feel satisfied with the competence of the specialists where they live.  
collaborative work; e-health; videoconference; professionals; knowledge
## 2 Consensus Mechanisms in Multi-Agent Systems  
Multi-agent systems can be modeled through a network in which agents interacting with each other are directly connected by an edge. Connectivity is a crucial property for reaching a consensus in a network [6,17,18,19]. These foundational works lay on the idea that graph connectivity (often quantified by properties of the Laplacian matrix \(L\)) is a crucial condition for consensus. For instance, if the agents' communication graph has a spanning tree across time, the agents' states will converge to a common value. Connectivity can also be linked to spectral theory. The second-smallest eigenvalue of \(L\), known as the Fiedler value or algebraic connectivity, relates to how well the graph is connected [20]. In addition, continuous or, at least, frequent enough connectivity is also required to reach the consensus state. For static (fixed-topology) networks, consensus algorithms can be analyzed directly using Laplacian matrices. The standard first-order consensus update is given by  
\[\mathbf{x}(t+1) =\mathbf{x}(t)-\epsilon L\,\mathbf{x}(t);\quad\text{(discrete time), or} \tag{1}\] \[\hat{\mathbf{x}}(t) =-\epsilon L\,\mathbf{x}(t);\quad\text{(continuous time),} \tag{2}\]  
where \(L\) is the Laplacian matrix of the network, which drives all agent states \(\mathbf{x}\) to the agreement (consensus) on an average initial value when the graph is connected.  
The Fiedler value also provides the convergence rate to consensus; that is, a larger Fiedler eigenvalue yields faster consensus [21].  
Beyond one-hop (nearest-neighbor) interactions, recent studies consider whether leveraging multi-hop information can improve consensus speed or resilience. Traditional consensus uses only direct neighbor states; however, nodes in many real networks can also be indirectly influenced by more distant nodes (e.g., a friend-of-a-friend in a social network). Path-based Laplacians have been applied to consensus dynamics to capture such influences. [PERSON] [9] showed that allowing agents to utilize not only their immediate neighbors' states but also the states of nodes at a distance \(k\) (via the \(k\)-path Laplacian) can accelerate agreement; see also [12, 13, 22].  
Long-range interactions provide additional communication \"shortcuts\" in the network, speeding up information spread and consensus formation. This concept has been extended in control protocols [23]. Such approaches are particularly relevant in systems where influence extends beyond immediate contacts. In opinion dynamics, models incorporating second- or third-neighborhood influences (sometimes called \"social power\" or indirect influence) help explain how global consensus or polarization can emerge [24].Therefore, as network connectivity drives consensus, it is natural to consider how modern data-driven methods can further capture and predict such complex dynamics.
###### Abstract  
In situ combustion (ISC), an efficient and economical method for enhancing heavy oil recovery in high-pressure, high-viscosity, and thermally challenged reservoirs, relies on the kinetics of crude oil oxidation. Despite an increased focus on kinetic models, there is a gap in understanding how oxidation kinetic parameters impact ISC effectiveness in heavy oil reservoirs. This study addresses this by selecting heavy oil samples from the G Block in the Liaohe oilfield and the M Block in the Huabei oilfield and conducting ramped temperature oxidation (RTO), pressure differential scanning calorimetry (PDSC), and thermogravimetric analysis (TGA) experiments. RTO detailed the thermal conversion process, categorizing oxidation into low-temperature oxidation (LTO), fuel deposition (FD), and high-temperature oxidation (HTO) stages. PDSC and TGA provided thermal characteristics and kinetic parameters. The feasibility of fire flooding was evaluated. Using CMG-STARS, an ISC model was established to analyze the impact of kinetic parameter changes. Activation energy significantly affected coke combustion, while the pre-exponential factor had a notable impact on cracking reactions. The recommended values for activation energy and the pre-exponential factor are provided. This study not only guides fire flooding experiments but also supports field engineering practices, particularly for in situ combustion in heavy oil reservoirs.  
heavy oil; in situ combustion; kinetic parameter; thermal analysis experiment; numerical simulation +
Footnote †: journal: Applied Sciences  
Article  
An Experimental Study and Numerical Simulation Analysis of Thermal Oxidation Characteristics Based on Kinetic Parameters in Heavy Oil Reservoirs  
[PERSON], [PERSON], and [PERSON]  
1 School of Energy Resources, China University of Geosciences, Beijing 100083, China;  
1 [EMAIL_ADDRESS] (C.F.); [EMAIL_ADDRESS] (H.H.)  
2 Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China;  
[EMAIL_ADDRESS] (C.W.); [EMAIL_ADDRESS] (H.Z.);  
[EMAIL_ADDRESS] (P.L.); [EMAIL_ADDRESS] (W.G.);  
[EMAIL_ADDRESS] (Y.C.)  
3 State Key Laboratory of Enhanced Oil Recovery, PetroChina, Beijing 100083, China  
* Correspondence: [EMAIL_ADDRESS]
## 3 Results and Discussion  
### Comparison with the OTL Models  
To analyze the KPPP-estimated tidal constituent stability, according to Equations (6) and (13), we calculated the residual and systematic phasor differences between the GPS estimates (using the observed data for years 4 to 10 since 2008) and the GOT4.10c + STW OTL model, for each of the eight tidal constituents at 12 stations. The corresponding RMSs of the residual and systematic phasor difference of the eight tidal constituents were obtained by formula (5), as shown in Figure 3.  
Figure 3a,b indicate that, for the M2, N2, O1 and Q1 tidal constituents, after the fourth year, the RMS of the residual and systematic phasors for the four tidal constituents is stabilized. For the S2 tidal constituent, its residual RMS is stabilized after the fourth year; however, it is not until the ninth year that the systematic RMS of the S2 constituent begins to stabilize. Although the residual and systematic RMS of the P1 constituent is stabilized after the fourth year, the magnitude of the residual and systematic RMS for the P1 tidal constituent is significant. The residual RMS of the K1 and K2 constituents is stabilized after the fifth year, but their systematic RMS does not convergence until the tenth year. Figure 4a shows the spatial distributions of the phasor vector differences, between the GPS estimates (using the observed data for 2008-2017) and the GOT4.10c + STW OTL model, of the eight tidal constituents at 12 stations. Figure 4b shows the systematic phasor vector differences estimated from Figure 4a by formula (13) for the eight tidal constituents. The residual vector phasor differences were obtained by differencing Figure 4a,b, as shown in Figure 4c. The expression of the phasor vector difference for a given tidal constituent is given in Figure 2.  
Figure 3: GPS-estimated tidal stability for the eight tidal constituents: (**a**) the RMS of the systematic phasor difference; (**b**) the RMS of the residual phasor difference corresponding to (**a**).  
there were no distinguishing systematic differences for these three constituents, as shown in Figure 4b. Figure 4c indicates that the residual phasor differences of the eight tidal constituents had no spatial correlation, i.e., they are related to the location.
## 2 Theoretical Analyses  
### State of Stress in Natural Soils and behind Retaining Walls  
In soil mechanics, the state of stress in the soil mass (or fill material) is usually associated with the lateral translations of retaining walls and related to the vertical stress. Three distinct states and corresponding earth pressure coefficients are typically applied in geotechnical engineering for cohesionless soils (fills) to calculate the horizontal stresses [31; 32; 33].  
The situation is typically described using the conceptual model of a stiff and very thin wall (to minimize disturbance) which is theoretically introduced into an initially at-rest (natural) soil, and then removing the soil on one side of the wall and applying a horizontal force (pressure) to keep the vertical smooth wall in place. When the retaining wall is pushed (sufficiently) against the compressed soil, it may yield and fully mobilize its frictional strength to resist the horizontal movement. This corresponds to a passive state; such condition is not frequently encountered in backfilled openings. When the wall is allowed to move outward, the horizontal stress on this wall is reduced by shear yielding of the soil; the corresponding plastic equilibrium leads to an active state. For cohesionless fills having flat horizontal top surface, the coefficient \(K\) can then be represented by [PERSON]'s active earth pressure coefficient \(K_{\text{a}}\)[7; 31; 32; 33]:  
\[K_{a}=\frac{1-\sin\varphi t}{1+\sin\varphi t} \tag{2}\]  
Another (theoretical) situation arises when the retaining wall is fixed (immobile), without horizontal strain in the soil (fill). The stress state then remains unchanged from that of the natural soil deposit. This corresponds to the at-rest state, with \(K=K_{0}\). For loose granular soils, the following semi-empirical relationship is widely used to express the corresponding earth pressure coefficient [34; 35]:  
\[\left(K_{0}\right)_{\varphi t}=1-\sin\varphi t \tag{3}\]  
Alternatively, the value of \(K_{0}\) can be estimated, in a more fundamental manner, from [PERSON]'s ratio \(\
u\) using [PERSON]'s law for a linear-elastic, isotropic, and homogeneous material [36; 37]:  
\[\left(K_{0}\right)_{\
u}=\frac{\
u}{1-\
u} \tag{4}\]
## 10 Simulation Results
We also observed that both of them reach the optimum BER performance around \(B=7\) in the fourth iteration, which implies that both generation mechanisms can be considered as equivalent, if more iterations are performed by the receivers. Unlike the BER performance of OTMBMF-SIC, the BER performance of OGMBMF-SIC is poorer than that of OTMBMF-SIC in the first several iterations. This is because the feedback of OGMBMF-SIC is randomly generated given the _a priori_ probability from the output of the channel decoder, and we assume each of them has equal probability, which may introduce instability into the LLR calculation with the increasing number of feedback elements. For OTMBMF-SIC, each feedback candidate has been weighted given the probability, so the feedback elements with low probability will not have a significant influence on the BER performance. In other words, it only takes the most significant feedback candidates into account. Furthermore, MBMF-SIC is not equivalent to the MAP algorithm and employs the reduced channel  
Figure 8: BER performance against \(f_{d}T_{\text{OFDM}}\) of OGMBMF-SIC and MF-PIC using MSCE and single-burst channel estimation (SBCE) in the fourth iteration at SNR =16 dB. matrix in the multi-feedback cancellation. Thus, a large \(B\) does not significantly improve the BER performance as compared to a small \(B\).