esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Relevant Frameworks and the Challenged Status Quo
### Pre-Selected Frameworks for the Healthcare Domain
#### 2.1.1 VUCA Framework  
The VUCA framework refers to a new era of Volatility, Uncertainty, Complexity, and Ambiguity calling for corresponding VUCA capabilities, such as a clear vision, understanding, clarity, and agility. In brief, it is assumed that the sinister Ds (discontinuity, disorder, devaluation, dismantling, displacement, destabilization, or disruption) are part of a radical transformation leading to a paradigm shift. In the highly regulated healthcare industry, changes related to demographics, technology, and the economy occur as it becomes more digitalized and industrialized. Adopting a marketing stance, patients emerge as demanding customers and co-value creators, claiming convenience-centered services. Thus, medical experts, consultants, and paramedics are challenged to offer a new level of service following e.g., a 24/7 service-to-go logic. Here, coping with VUCA mandates dynamic capabilities resembling agile features of robustness, responsiveness, resilience, and anti-fragility [2].
## 4 Use Cases
### IEEE-33 System  
The IEEE-33 system consists of 33 buses, including a root bus and 32 PQ buses, operating at a base voltage of 12.66 kV and a base power of 100 MVA. Detailed information on the buses and branches of the IEEE-33 system, as well as the additional branches used for reconfiguration, can be found in [54]. The additional branches considered are 8-14, 7-20, 11-21, and 24-28.  
Flexible buses are also introduced, which allows adjustments in both active and reactive power. These flexible buses, represented as nodes 9, 5, 17, 21, 30, 26, and 25, follow  
Figure 10: Total costs of reconfigured systems, in blue without use and in orange with use of flexible power (optimized).  
Figure 9: Correlation between voltage peaks and resulting losses in optimized systems.  
the base power settings of the IEEE-33 system. The flexibility range for active and reactive power is determined based on a fixed percentage, similar to the approach used in the Kumamoto system. The interval of allowed variation for the active and reactive power values is shown in Table 3, reflecting the flexibility needed for optimization.  
The cost parameters are set at 0.4 EUR/kW for power losses and 0.04 EUR/kVA for flexible power usage.  
The execution resulted in the identification and optimization of 2241 feasible radial systems, with the optimal system identified as system number 1577, yielding significant cost reductions.  
The runtime for this case was 369.13 s. The initial IEEE-33 system exhibited 0.0021 p.u. (210 kW) of power loss with a total cost of EUR 84.4. After optimization, the optimal configuration, identified as system number 1577, showed a power loss of 0.0014 p.u. (140 kW) with a total cost of EUR 55.48. This reflects a power loss reduction of 66.6% and a significant cost saving. The optimal configuration for the IEEE-33 system did not utilize flexible power, as the cost-benefit analysis determined that its application would not result in a profitable outcome, similar to the findings observed in the Kumamoto case. However, there are configurations where the use of flexible power contributed to a reduction in the total costs, as illustrated in Figure 11.
# Understanding Consumer Acceptance for Blockchain-Based Digital Payment Systems in Bhutan
## 3 Theoretical Framework and Hypothesis Development
### Performance Expectancy  
Performance expectancy (PE) measures the amount of value consumers will receive from a technology [53; 92; 93]. It is a major consideration in the use of that technology, especially when it comes to digital payments [94; 95; 96]. It measures people's perception of how that technology will improve their productivity or give them an actual benefit over the current system. [PERSON] et al. [97] and [PERSON] et al. [10] emphasize that users are more willing to accept blockchain if it offers tangible advantages, including faster transaction speeds, increased security, and increased efficiency. This is especially true in financially disadvantaged countries such as Bhutan, where banking services are limited in scope and availability. [PERSON] and [PERSON] [98] further underscore the fact that blockchain is efficient, secure and inexpensive, making it an attractive candidate for use in these applications. To gain traction, customers in Bhutan's digital payment ecosystem will need to feel that blockchain delivers improved payments by reducing errors, improving processing times, and increasing security. Research consistently finds that perceived usefulness, which lies at the heart of performance expectancy, is a very good predictor of technology adoption [99; 100; 101]. User adoption is more inclined toward technologies that offer valuable benefits. In Bhutan, with limited bank systems and digital literacy still being hurdles, blockchain could reduce waste and benefit both urban and rural citizens [29]. Using these tangible benefits and creating higher levels of user engagement, blockchain can help to gain acceptance and spur its adoption in digital payment infrastructures. Therefore, we propose the following hypothesis:  
**H1**: _Performance expectancy positively affects consumer acceptance of blockchain adoption for digital payments in Bhutan._
## 3 Results  
### Evaluation of the Coagulation Bath  
#### 3.1.1 Influence of Different Lengths  
Three F\({}_{\mathrm{uni}}\) and three F\({}_{\mathrm{hol}}\) composed of CA were initially produced in coagulation baths of different lengths (0.50, 1.00, 1.25 m), using dH\({}_{2}\)O as the coagulation medium. This stage aimed to optimise the production of PCFs, focusing on the efficient removal of the DMF solvent and the uniform formation of the protective CA sheath. As DMF has toxic properties, its reduction is essential [23]. dH\({}_{2}\)O acts as an extraction agent, promoting solvent exchange due to the solubility of DMF in the aqueous medium. Longer coagulation baths theoretically allow more time for DMF extraction and better coagulation of the protective CA sheath, allowing the formation of uniform fibres all the way to the collector [28]. During the production of F\({}_{\mathrm{uni}}\) and F\({}_{\mathrm{hol}}\) in the three sizes of coagulation bath studied, F\({}_{\mathrm{uni}}\) showed no production difficulties. However, F\({}_{\mathrm{hol}}\) showed greater difficulty in maintaining its structural cohesion in the longest bath (1.25 m). This problem for F\({}_{\mathrm{hol}}\) may be directly related to the longer exposure time to the liquid medium (dH\({}_{2}\)O) in longer baths, which provides more time and opportunity for solvent exchange with the coagulant. In this case, the CA sheath begins to solidify in contact with the dH\({}_{2}\)O, while the core, also composed of dH\({}_{2}\)O, remains in a liquid state. In other words, this longer exchange time can lead to rapid removal of the liquid core, compromising the structural stability of the fibre and increasing the probability of collapse.  
[PERSON] et al. [18] studied the possibility of an excessive exchange of the DMF solvent for the coagulant (dH\({}_{2}\)O), which may cause an increase in tensions between the solidified sheath and the liquid core. Furthermore, in very long coagulation baths, the problem of the exchange of DMF for the coagulant dH\({}_{2}\)O may lead to an uneven uniformity gradient along the length of the F\({}_{\mathrm{hol}}\), resulting in a fragile, heterogeneous structure susceptible to rupture [28].
## 5 Results and Discussions  
This section explains the results of three different approaches: the Complete Region Depth Extraction, the Central Region Depth Extraction, and the Grid Central Region Depth Extraction. These approaches leverage object detection and distance measurement through camera-LiDAR fusion to enhance depth perception. LiDAR points were projected onto a transparent sparse depth map, which was fused with the output of the object detection method. The object detection framework provided the bounding box and the segmentation mask frame data, allowing precise depth extraction. Furthermore, the depth value extraction time and the number of extracted points for both the bounding box and the segmentation mask were recorded. These results help to evaluate the efficiency and accuracy of each method in extracting depth information.  
The tables present the mean depth extraction time (in milliseconds), accuracy, and root mean squared error (in meters) for different object classes using the Complete Region Depth Extraction, the Central Region Depth Extraction, and the grid Central Region Depth Extraction. The results are shown for the bounding box and the segmentation mask under different occlusion levels (Easy, Moderate, Hard, and Unknown). Performance times presented in Figures 20-33 were computed using the system configuration outlined in Section 4.
# Reversible Circuit Synthesis Time Reduction Based on Subtree-Circuit Mapping
## 5 Synthesis Time Reduction
### Algorithm Run Complexity  
The basic operation of the one-to-one based algorithm is to map each BDD node to its corresponding reversible gates (previously Toffoli gates). Conventionally, the algorithm relies on a table that maps each possible case (node type) to its corresponding reversible gate. The size of the table is seven entries, as mentioned earlier. The number of times that one-to-one mapping repeats the mapping process depends on the number of BDD nodes (\(n\)). The number of the nodes \(n\) depends on the number of variables of the reversible circuit. The worst-case scenario happens when we have an irreducible BDD; in this case, the number of BDD nodes is the same as the number of nodes in a binary tree \(n=2^{p+1}-1\) nodes, where \(v\) represents the number of variables. Thus, we can say that the worst case in the one-to-one algorithm is \(2^{v+1}-1\) operations. The paradigm we used to reduce the algorithm complexity can be categorized as prepossessing. We first prepare a hash table that maps all the possible--up to three nodes--subtrees to their corresponding reversible gates. The preparation of the table is performed only once. The same table can be used to realize any reversible circuit. Therefore, the table can be considered as a fixed part of the algorithm. It does not depend on the input size (in our case, the number of variables). In this case, the size of the look-up table is 35 entries instead of seven entries as in the one-to-one algorithm. This addition of space usage is constant and negligible.  
The BDD is divided into subtrees. As described above, each subtree is defined by its parent (or root) node. The parents are defined by starting from the bottom and skipping one level each time. This process can be viewed as creating a new tree of subtrees. The number of levels of the new tree is half of the number of levels of the original tree. The new tree is not binary. Each node on the new tree has four children. The number of resulting nodes--which is the number of subtrees--can be calculated in terms of the number of circuit variables by Equation (2), where \(s\) represents the number of subtrees and \(v\) again is the number of variables.  
\[s=\sum_{k=0}^{\frac{1}{2}v}4^{k}=\frac{4^{\frac{v+1}{2}}-1}{4-1}=\frac{2^{v+1 }-1}{3} \tag{2}\]  
This results in a reduction of the number of searches using the subtree-based algorithm to three.  
The look-up table is a hash table, and so the retrieval speed is constant; i.e., \(O(1)\). Therefore, the entry access time using the one-to-one algorithm with seven entries is the same as the access time from the look-up table with 35 entries.
# Heat Stress Affects H3K9 me3 Level at Human Alpha Satellite DNA Repeats
## Results  
### Dispersion Profile and Polymorphism of Dispersed Alpha Satellite Repeats  
Alpha satellite repeats (ARs) annotated in the human genome were downloaded using the UCSC table browser. The human reference genome GRCh38.hg38 was also searched with the alignment program BLASTN version 2.3.1+ using as the query a dimer of human alphoid 171 bp repeat consensus sequence derived from 293 cloned monomers of wide-ranging chromosomal origins [26]. The analyses revealed preferential organization of alpha satellite DNA within clusters composed of tandemly arranged repeats and the largest clusters are located within centromeric and pericentromeric regions of all chromosomes. A search for alpha repeats in chromosomal regions outside of (peri)centromeres revealed their presence not only in long clusters but also as short arrays of 1-4 monomers, located at intergenic regions or within introns. Only hits with at least 50% of 171 bp monomer sequence length were considered for further analysis. We detected in total 31 short dispersed alpha satellite DNA elements, mostly partial or full size monomers (Table S3). Ten of the elements are within introns while the remaining 21 are in intergenic regions and the genes closest to the intergenic alpha repeats from 5\({}^{\prime}\) and 3\({}^{\prime}\) site are listed in Table S3. Some of the alpha repeats are located between the same genes (No 11 and 12; 22 and 23; 26 and 27), and the total number of genes that are associated with intronic or intergenic alpha repeats is 46 (Table S3).  
We first decided to analyze the insertion polymorphism of short dispersed alpha satellite among 16 different human cell lines. Almost all dispersed alpha repeats have in the vicinity other repetitive elements such as _Alu_, L1, or LTR and for some alpha repeats flanking repetitive sequence prevented construction of single locus-specific primers. Analysis of insertion polymorphism was finally performed for all 10 alpha satellite elements located within introns as well as for 10 elements in intergenic regions (Table S3). Out of 20 tested dispersed alpha satellite elements only two revealed insertion polymorphisms: element No 29 located within an intron of _zinc finger protein 675_ gene (_ZNF675_) and element No 10 located within intron of _nuclear receptor subfamily 3 group C member 1_ gene (_NR3C1_) (Table S3). Element No 29 corresponds to 0.6 of alpha satellite monomer length and is homozygous in all tested cell lines except Cal 27 and OV-90 where it is absent on both alleles (Figure S1A,B). Alpha element No 10 corresponds to a 1.1 satellite monomer and is heterozygous in the HepG2 cell line being absent on one allele as revealed by sequencing (Figure S1C), while in all other tested cell lines it is homozygous.
# Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
## 4 Contribution: Hierarchical Graph2 Vec (HG2V)
### Hierarchy of Neighborhoods  
Taking advantage of the previous observation, we propose to build a hierarchy of coarsened graphs \(g_{i}^{l}\) using [PERSON]'s method. It induces a hierarchy of nested neighborhoods \(u,\mathcal{P}(u),\mathcal{P}(\mathcal{P}(u)),\ldots,\mathcal{P}^{L}(u)\) by pooling the nodes at each level.  
We learn the node embedding \(g^{l}(u)\) (of node \(u\)) at each level. This node embedding is used to produce a local neighborhood embedding \(x^{l}(u)\) using function \(H_{\theta}^{l}\), and to produce the node embedding of the next level \(g^{l+1}(\mathcal{P}(u))\) using function \(F_{\theta}^{l}\). Formally, the recurrent equations defining the successive embeddings are:  
\[g^{0}(u) =Z(u) \tag{8}\] \[x^{l}(u) =H_{\theta}^{l}(\{g^{l}(v)|v\in\mathcal{N}(u)\cup\{u\}\})\] (9) \[g^{l+1}(\mathcal{P}(u)) =F_{\theta}^{l}(\{g^{l}(v)|v\in\mathcal{N}(u)\cup\{u\}\}) \tag{10}\]  
The procedure is illustrated in Figure 5. In practice, functions \(H_{\theta}^{l}\) and \(F_{\theta}^{l}\) are graph neural networks parametrized by \(\theta\), and whose exact form will be specified in next Section.  
Figure 4: Wasserstein distance between spectra of graphs \(g^{0}\) and \(h^{0}\) sampled from different datasets (see Section 5 for their description) compared to the same distance between their coarsened graphs \(g^{1}\) and \(h^{1}\) and their two times coarsened graphs \(g^{2}\) and \(h^{2}\). In blue (resp. red) are given the correlation coefficients of the distance between \(g^{0}\) & \(h^{0}\) and \(g^{1}\) & \(h^{1}\) (resp. \(g^{2}\) & \(h^{2}\)). These coefficients have been computed after averaging 10 runs where we sampled 1000 graphs couples (\(g^{0}\), \(h^{0}\)) for each dataset. As expected the more we coarsened graph the more the correlation coefficient decreases because coarsening always lost some structural informations.
## 4 The Role of UAV-Base Station in Wireless Communications
### Data Harvesting from IoT Devices  
Due to the promising solutions that UAVs provide in different application areas, UAVs has attracted research attention in IoT applications in order to facilitate data harvesting and relaying [74]. This is very important for cases where the IoT devices are deployed in areas where there are no wireless network facilities, such as in rural farmlands or offshore locations, to help transmit the data to the decision-making centers. In such situations, UAVs can be deployed to that location to assist in data collection [75; 76]. Research works on UAV-enabled data harvesting mainly focus on UAV path planning and throughput maximization in order to ensure that the UAV collect sufficient data and returns to data center before its battery depletes [77; 78; 79].  
Following this research direction, a deep RL framework for path planning in a multi-UAV-based data harvesting system that can adapt to dynamically changing network parameters, such as varying number of UAVs and IoT devices, different flight schedules, and amount of data to be harvested, was proposed in [78]. The path planning problem was modeled as a cooperative team of UAVs saddled with the responsibility of maximizing data collection from several IoT devices distributed in an area. Then, a multi-agent \(Q\)-learning algorithm was developed to determine the optimal trajectories that would maximize data collection from the distributed IoT devices. In [77], the authors considered the joint optimization of both the UAV trajectory and power allocation to the ground nodes in order to maximize the throughput and coverage probability in a UAV-enabled data harvesting system with distributed beamforming. Heuristic algorithms based on convex optimization  
Figure 4: Different UAV use cases involving the UAV serving as a wireless backhaul to connect users located in remote areas to the core network, as a emergency network for restoring service to an area covered by a malfunctioning BS, as a aerial relay for providing coverage extension to users separated by obstacles such hills and mountains, data offloader for offloading user traffic from overloaded terrestrial BSs to ensure load balancing and throughput enhancement, as aerial cache for serving popular content to ground users to reduce backhaul congestion and achieve EE and as a data harvester for collecting data from ground sensor nodes.  
and approximations were developed to find the optimal trajectory and power allocation strategy that would maximize both performance metrics.
## 6 Numerical Simulation Results
### Parametric Analysis of Displacement Measurement Depth on Deformation Modulus  
With regard to the common practices of plate load testing, in which the load is measured on the surface of the plate and displacements are measured at varying depths within the rock mass, there is an apparent trend of increasing moduli with the depth of deformation measurement for a given test. In some cases, this will cause significant error such that the calculated rock mass moduli are much greater than that of the intact rock. This issue was investigated numerically. The constructed Flac-3D model represents the dam right abutment fairly well and the simulated test location was selected at a chainage of about 30 m from the test gallery entrance as in the actual test. Accordingly, it was assured that the test boundary conditions (from rock type variation, in-situ stress, excavation induced stress, abutment geometry, etc. points of view) were considered properly. The  
Figure 11: Stress-Strain curves and linear fits for five loading-unloading cycles; determined at a depth of 60 cm: (**a**) 1 st cycle, (**b**) 2 nd cycle, (**c**) 3 rd cycle, (**d**) 4 th cycle, (**e**) 5 th cycle [21].  
variation of deformation modulus as a function measurement depth was calculated and illustrated in Figure 12. It is observed that at a depth equal and beyond plate diameter, the calculated modulus is greater than that of the intact rock. With regard to the conducted numerical analyses, the plate diameter is the maximum depth at which displacement measurement should be conducted to determine the rock mass deformation modulus. Concerning the rock mass conditions, several parameters affect the maximum suitable measurement depth. To evaluate the effect of plate diameter on test results, a series of numerical analyses were carried out. The results are presented in the next section.
# BiModalClust: Fused Data and Neighborhood Variation for Advanced K-Means Big Data Clustering
## 3 Variable Neighborhood Search
### Neighborhood Change Step  
The neighborhood change procedure plays a pivotal role in guiding the Variable Neighborhood Search (VNS) heuristic through the solution space. It determines both the sequence of neighborhoods to explore and whether a candidate solution should replace the incumbent. Different strategies for neighborhood change have been explored in the literature [21], with the sequential and cyclic methods being the most commonly employed.  
1. In the sequential approach, the search systematically navigates through the neighborhoods, restarting at the first neighborhood in the structure whenever an improvement to the incumbent solution is found. If no improvement occurs, the search progresses to the next neighborhood in the sequence. This strategy ensures that the search refocuses on promising areas of the solution space after discovering a better solution.  
The sequential neighborhood change procedure is outlined in Algorithm 2:  
```
1 FunctionNeighboroodchange_sequential(\(x\), \(x^{\prime}\), \(k\)):
2 Input:Incumbent solution \(x\);
3 Candidate solution \(x^{\prime}\);
4 Neighborhood index \(k\).
5
6 if\(f(x^{\prime})<f(x)\)then
7\(x\gets x^{\prime}\);// move
8\(k\gets 1\);// return to first neighborhood
9 else
10\(k\gets k+1\);// proceed to next neighborhood
11
12 end if
13
14
```  
**Algorithm 2**Sequential Neighborhood Change Step  
In the cyclic approach, the search moves to the next neighborhood regardless of whether an improvement in the incumbent solution occurs. This method maintains a consistent exploration cycle through all neighborhoods, avoiding overcommitment to any single region of the solution space.  
The cyclic neighborhood change procedure is described in Algorithm 3:  
```
1 FunctionNeighboroodchange_cyclic(\(x\), \(x^{\prime}\), \(k\)):
2 Input:Incumbent solution \(x\);
3 Candidate solution \(x^{\prime}\);
4 Neighborhood index \(k\).
5 if\(f(x^{\prime})<f(x)\)then
6\(x\gets x^{\prime}\);// reassign incumbent solution
7 end if
8\(k\gets k+1\);// proceed to next neighborhood
```  
**Algorithm 3**Cyclic Neighborhood Change Step