esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Metagenomic Analysis
#### 3.1.4 Eukaryotic Communities  
Eukaryotic communities were mainly present in samples AV3 and EZ1 and only represented a low proportion of the microbiome of samples AV1 and EZ4. The eukaryotic sequences were affiliated with the fungi kingdom, in which the phylum _Ascomycata_ represented more than 98% of the total fungi reads, with the phylum _Basidiomycata_ around 1%. The phylum _Mucoromycata_ was present only in two samples with very low relative abundance, less than to 0.1%.  
The relative abundance of fungal communities at the class level is reported in Supplementary Figure S3. Within the phylum _Ascomycata_, the _Dothideomycetes_ class was dominant in samples AV1, EZ1 and EZ4 with a very high relative abundance (42-58% of fungi) and accounted for 19% in sample AV3. The classes _Sordariomycetes_, _Leotionomycetes_ and _Eurotimycetes_ were represented in all the samples with similar proportions, ranging from 7 to 17% of the total fungi, followed by the class _Pezizomycetes_, which contributed between 1 and 6% of the total fungal reads. The class _Lecanoromycetes_ was well-represented in sample AV3 (21% of fungi) and in lower proportion in AV1 and EZ1 (1 and 7%, respectively). Finally, the class _Orbiliomycetes_ was only significant in sample AV3 (0.6%).  
The phylum _Basidiomycata_ was mainly represented by the _Agaricomycetes_ class, present with low relative abundance (~0.6% of fungi) in samples AV1, AV3 and EZ4.  
Differently from the bacterial communities, which clearly showed the dominance of some genera, fungal communities revealed very high biodiversity but no dominant taxa, with most of the genera accounting for less than 1% of the total microbiome of each sample. Nevertheless, in samples AV3 and EZ1, a few genera contributing with higher proportion were detected (Figure 4). In sample AV3 the genus _Letharia_ (class _Lecanoromycetes_) represented 6% of the total reads, while the genus _Tuber_ (class _Pezizomycetes_) and the genus _Pseudogymnoascus_ (class _Leotiomycetes_) accounted for 1.3% of the total microbiome. In sample EZ1, the genera _Alternaria_ (class _Dothidomyces_), _Lachnella_ (class _Leotiomycetes_), _Letharia_ (class _Lecanoromycetes_) and _Metarhizium_ (class _Sordariomycetes_) were present with a relative abundance of around 2.5%, while the genera _Diplodia_ (class _Dothidomyces_), _Talaromyces_ (class _Eurotionycetes_) and _Verruconis_ (class _Dothidomyces_) made up between 1 and 1.9% of the total reads.
## 3 Engineering Applications
### Inversion Model  
Based on the layout of the underground roadways in the field, a three-dimensional numerical simulation model with dimensions of 17.2 m \(\times\) 90 m \(\times\) 25 m was established (Figure 12). The rectangular numerical model consists of 86,750 elements, with element sizes ranging from 0.06 m to 0.096 m. The loading process of the numerical model involves three main aspects: balancing the initial stress, applying stress boundary conditions, and excavating the roadways. According to the mining process of the Macheng Iron Mine, drifts were designed with different dimensions. The strike drift has a span of 3.7 m, a vertical wall height of 2.9 m, an arch height of 1.27 m, and a length of 25 m. The across-vein roadway has the same dimensions, and a length of 80 m. To improve the accuracy of the inversion process, the surrounding rock masses of the roadways were finely divided into a mesh. The \(x\)-axis is parallel to the direction of the cross-vein roadways, the \(y\)-axis is the vertical axis, and the \(z\)-axis is parallel to the direction of the along-vein roadways.
# Feasibility of Wave Simulation in Typhoon Using WaveWATCH-III Forced by Remote-Sensed Wind
## 3 Results
### Discussion  
The ERA-5 winds and assembling winds were used as the forcing field for the WW3 model, and we compared the wave parameters from July to October 2021. Figure 8 shows the comparison between the SWH simulated via the WW3 model with the ERA-5 wind forcing, and the assembling wind forcing and the measurements from SWIM. Statistical analysis was conducted, yielding a Cor of 0.91, with an RMSE of SWH of 0.41m simulated via the WW3 model with the assembling wind forcing and an SI of 0.20 (Figure 8a), which is better than the SWH simulated via the WW3 model with the ERA-5 wind forcing shown in Figure 8b with a Cor of 0.86, an RMSE of 0.49 m, and an SI of 0.27. In addition, the Cor of the PWP simulated via the WW3 model with the assembling wind forcing was 0.69, with an RMSE of 1.23 m and an SI of 0.13 (Figure 8c). The result is better than the PWP simulated via the WW3 model with the ERA-5 winds forcing, as exhibited in Figure 8d.  
Figure 6: (**a**) Comparison of SWHs between WW3 and SWIM. (**b**) Comparison of peak wave period (PWP) between WW3 and SWIM.  
Figure 7: Bias (WW3-simulated SWH minus ERA-5) versus assembling wind speed.  
Furthermore, Figure 9 shows a variation in the SWH relative to the measurements from SWIM, in which the red error bar represents the bias of SWHs (simulations from the WW3 model with the assembling wind forcing minus the measurements from SWIM), and the black error bar is the bias of SWHs (simulations from the WW3 model with the ERA-5 wind forcing minus the measurements from SWIM). It was found that the bias of SWHs simulated via assembling forcing winds was less than that via ERA-5 forcing winds. With the increase in the SWIM-measured SWHs, the bias increased. However, the bias of SWHs simulated via assembling forcing winds was significantly smaller than that via ERA-5 forcing winds at SWH > 3 m, and this behavior was even more obvious at the extreme sea state. Under this circumstance, it is believed that assembling winds derived from multiple remote-sensed products is better for typhoon-induced wave simulation using the WW3 model than using ERA-5 winds.  
Figure 8: (**a**) SWH comparison between WW3 model with the assembling wind forcing and SWIM. (**b**) SWH comparison between WW3 model with the ERA-5 wind forcing and SWIM. (**c**) Comparison of peak wave period (PWP) between WW3 model with the assembling wind forcing and SWIM. (**d**) Comparison of peak wave period (PWP) between WW3 model with the ERA-5 wind forcing and SWIM.
# An Improved V-Net Model for Thyroid Nodule Segmentation
## 2 Literature Review
TNSeg includes a segmentation block and a discriminative block. The UperNet framework and Swin-Unet were combined in the segmentation block. In addition, the study proposed a new multiscale loss function that outperforms traditional loss functions in performance optimization. The authors achieved a Dice score of 85.71%, an Hd95 of 12.93, and a Jaccard score of 73.18% on the TN3K dataset; a Dice score of 74.93%, an Hd95 of 24.51, and a Jaccard score of 61.11% on the DDTI dataset; and a Dice score of 92.06%, a Jaccard score of 90.02%, and an Hd95 of 13.35 on the TNUD dataset. [PERSON] et al. [26] proposed a multi-attention-guided U-Net (MAUNeT) approach, which includes a multi-scale cross-attention (MSCA) module and a dual-attention (DA) module that enhances encoder features, to address the issue of significant data discrepancies among data from different centers. They evaluated the effectiveness of the developed model on multi-center ultrasound images of thyroid nodules obtained from three different sources involving 17 hospitals. They utilized a federated learning approach to protect privacy during training. Using the proposed model, they achieved Dice scores of 90.8%, 91.2%, and 88.7% for datasets from three different centers, D1, D2 and D3, respectively. [PERSON] et al. [27] developed YOLO-Thyroid, based on the YOLOv8 architecture, to tackle issues stemming from ultrasound image limitations in thyroid nodule detection. Their methodology incorporates SIoU for accurate boundary regression and employs a class-weighted binary cross-entropy loss function to alleviate class imbalance effects. Additionally, they presented a C2 fA module featuring coordinate attention (CA) to improve feature extraction capabilities. In assessments using the DDTI dataset, YOLO-Thyroid achieved an impressive 43.6% mAP0.5 (mean average precision at an IoU threshold of 0.5),outperforming leading YOLO models. They recorded a weight average recall rate of 58.2% when identifying nodules with at least one characteristic suggestive of a malignancy. Table 1 provides an overview of the literature review summarizing DL-based studies on thyroid nodule segmentation. The presentation of these studies in the table is organized based on the content methodology, data sources, and experimental results. Although the studies presented here yielded successful results in performance, there is a pressing need for further investigation because of the complexities in the thyroid area,
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 4 Results
### Discussion  
This paper's analysis indicates that the abnormal phenomenon of the ETC gantry data is not only accidental but also indicates systematic problems from technical failures to deliberate avoidance strategies. We identified and corrected data anomalies based on the completion model. The initial repair strategy, including deletion, expansion, completion, and transformation, greatly improved the availability of data, ensuring that the subsequent analysis was based on as accurate data as possible.  
The impact of weather and other environmental conditions on data quality cannot be ignored. The research results of G50 indicate that adverse weather conditions, especially rainfall, can have a negative impact on the quality of ETC gantry data. This is a key area of future research, as developing more powerful systems that can maintain high data quality regardless of environmental conditions is crucial for the reliability of traffic data analysis.  
The IDTD model was used for tensor decomposition and compared with the MATRIX and LSTM models, demonstrating the potential of advanced computing techniques to address the challenges of data sparsity and complexity. The excellent performance of the IDTD model in accuracy and reducing computational load indicates its potential applicability in a range of scenarios where large-scale sparse datasets are prevalent. This study lays the foundation for future research. It is necessary to further improve data preprocessing techniques, develop more flexible data collection systems for environmental factors, and explore the scalability of IDTD models.
## 3 Results
### Remotely Sensed Environmental Parameters Applied to Pasture Monitoring
A significant portion of studies used weather and climate data from meteorological stations (Figure 4b); very few were derived (e.g., groundwater content) from sensors as a proxy and compared with ground measurements (i.e., wet and dry pasture biomass) [76].
## 3 Results and Discussions
### Impact Dynamics
In order to quantitatively capture the load-carrying capacity and energy absorption characteristics of the composite plate specimen, the specific values of peak force and absorbed energy for different laminates for the case of 32-J impact energy are listed in Table 5.  
Figures 8 and 9 show the typical force-time history curves and absorbed energy-time history curves for different laminates from experimental and numerical results for the case of 64-J impact energy, and (a)-(d) indicate the laminate types of V, VI, VII and VIII, respectively. As seen from Figures 8 and 9, the _f-t_ curve obtained by numerical simulations match well with the _f-t_ curve obtained by the experiments in all of the specimens, and the _e-t_ curve obtained by the numerical simulations matches well with the _e-t_ curve obtained by experiments, except for the laminate type of [0/SMA/90/(0/90)\({}_{3}\)/SMA/(0/90)\({}_{4}\)]. Moreover, it is found that the FE model has better conformity  
Figure 6: The comparison plots of force–time from experimental and numerical results for the case of 32 J of impact energy. Figure 7: The comparison plots of energy–time from experimental and numerical results for the case of 32 J of impact energy. with the experimental results in the perforation condition of the 64-J case than in the non-perforation condition of the 32-J case. In order to quantitatively represent the carrying capacity and energy absorption characteristics, the specific values of peak force and absorbed energy for different laminates for the case of 64-J impact energy are listed in Table 6.
## 1 Introduction
He has inferred that the soil internal friction angle is the most sensitive parameter, while cohesion and unit weight are less sensitive with respect to the global safety factor. [PERSON] et al. [14] present the re-assessment stability analysis of soil nailing design and construction in a heavily weathered granite (residual soil) using a limit equilibrium sliding block method (bi-linear failure surface). They found that even after a maximum acceleration of 0.63 g of a stronger earthquake, the nailed wall did not show any damage, probably due to the use of un-drained shear strength parameters. [PERSON] [15] has studied the hybrid mechanically stabilized earth and soil nail structure and has proposed the optimum length of nails required to reduce the structure displacement. A slope stability analysis of a combined system of soil nails and stabilization piles on loess soil is presented by [PERSON] et al. [16]. [PERSON] et al. [17] have proposed the genetic algorithm-based optimization of the soil-nailed structure design. [PERSON] et al. [18] have developed the novel element nail pull-out test to describe the shearing mechanisms at the soil-nail interface in nailed cohesive soils. The stability and displacement characteristics of a composite silt-soil-nailed symmetrical foundation pit are numerically investigated by [PERSON] et al. [19]. They have found the critical values of inclination and spacing of the soil nails, the diameter and embedded depth of the mixing pile for the stability of the foundation pit. The soil slope deformation pattern is investigated by [PERSON] and [PERSON] [20] using the numerical and experimental modeling of layered and homogenous piled stabilized earth slopes. They study the impact of pile position and surcharge distance from the slope crest on soil deformation patterns, the bearing capacity improvement ratio and slope stability improvement ratio. Various parameters affect the behavior of the earth nailing systems, such as nail parameters, method of installation of nails, soil density, soil slope height and angle, foundation parameters and loadings, among others. It is clear from the literature survey that the effect of the building foundation parameter on nailed soil slope has not been given due attention. The present paper has investigated the performance of soil-nailed slopes laboratory models under varying soil and building foundation parameters. The effect of the type of soil, soil slope angle, footing width and position of footing on the force mobilized in the nail, lateral displacement of the slope, settlement of the footing and the earth pressure at the slope face, under and behind the soil mass at various foundation pressure have been observed. The results for lateral displacement and footing settlement are presented in terms of the percentage of slope height.
## 1 Introduction  
Cataract is the most common cause of blindness worldwide [1]; its current treatment is the surgical removal and the replacement with an intraocular lens (IOL) [2]. Phacoemulsification is considered the preferred surgical technique [3].  
Implanted IOLs are available in four types of optic material: polymethylmethacrylate (PMMA), hydrophilic acrylic, hydrophobic acrylic and hydrophobic silicone. Hydrophobic acrylic IOLs are considered the best recommendation [3].  
During the surgical procedure, however, all epithelial cells (LECs) from the lens capsular bag cannot be mechanically removed; as a consequence, the residual equatorial LECs proliferate, migrate, transdifferentiate into myofibals and form posterior capsule opacification (PCO) [4], a fibrotic condition whose incidence is reported between 22.8% and 38.5% in cataract patients [5]. PCO causes light scatter within the visual axis, thus interfering with various aspects of visual function, including glare disability, contrast sensitivity, color and stereoscopic vision [6].  
PCO is usually treated with Neodymium:yttrium-aluminum-garnet (Nd:YAG) laser to create an opening in the central posterior lens capsule [7]. This procedure, although considered safe, may lead to some complications, among which intraocular pressure rise, cystoid macular edema, retinal detachment and IOL damage have been described [8].  
The effects of Nd:YAG laser treatment on IOLs surface were examined with the scanning electron microscope (SEM) [9; 10; 11; 12]. It was shown that each lens type had different patterns of lesions on their surface: in fact, stellate craters were present in PMMA IOLs, while smooth splash-like craters were demonstrated in silicone IOLs [8]. Furthermore, the role of IOLs material was considered, demonstrating that PMMA IOLs were more resilient to laser damage [13].  
However, as far as we know, no data are currently available on the response of the same type of IOLs to the same physical stress after a different number of laser treatments. For this purpose, the effects of critical point drying (CPD), a common and reliable procedure of dehydration of biological samples [14], were studied on PMMA IOLs. Furthermore, IOLs surface around and into the laser-damaged zones was examined with Energy Dispersive X-ray spectrometry (EDX) to evaluate the possible changes in their organic composition.
# Computing the Atom Graph of a Graph and the Union Join Graph of a Hypergraph
## 4 Computing the Atom Graph
### Algorithm Forest Join
Considering the same relation between the 2-pairs and the edges of an atom tree \(T\) of \(G\), a pair \(\{x,y\}\) associated with an edge \(KL\) of \(T\) is a 2-pair, since \(KL\) is an edge of the atom graph, but the converse does not hold. Contrary to the atom graph, \(\{x,y\}\) can be associated with at most one edge of \(T\), namely the unique edge connecting the subtrees of \(T\) induced by the sets of nodes containing \(x\) and \(y\), respectively, which are necessarily disjoint and at distance 1 from each other in \(T\). Thus, the atom graph of \(G\) can be computed from an atom tree of \(G\) in \(O(min(n^{\omega},nm,\)\(n(n+\overline{m})))\). This time complexity considers the worst case, where the algorithm Components searches the whole tree \(T\), whereas it only searches the set \(\mathcal{A}_{S}\) and its neighborhood, which may be very small w.r.t. the set of nodes of \(T\). It may be more efficient in practice to execute the algorithm Forest Join without pre-computing the subset relation \(sub\) and directly evaluate \(sub(AB,XY)\) and \(sub(XY,AB)\) when needed.
## 4 Discussion
### Future Approaches  
The intra-oral scanning technologies are becoming more accurate and more capable of digitizing soft tissues. Their introduction in the production protocol of RPD metal frameworks explores the advantages of the total digital workflow (defined as protocol 11 in this review), but also the potential to overcome the limitation of the soft tissues' information acquisition by conventional impressions, namely the floppy tissue depression and material distortions.  
The finishing procedures of RPD metal frameworks use burs, rubbers, brushes, pastes, and electrolytic baths, with an average surface metal loss of 127 \(\upmu\)m [104]. This value corresponds to almost 75% of the gap value identified in the fit accuracy assessment of the conventional protocol (protocol 1) [18,23]. To overcome this problem and complete the entire production cycle, some studies in the literature have already explored digital finishing in clasps through milling after direct additive manufacturing [105; 106], producing smoother surfaces and higher accuracy [14; 27; 106].  
Co-Cr is still the preferred alloy for RPD metal frameworks due to the advantages of its mechanical properties and costs over Ti [10; 27]. However, the disadvantage of esthetic concerns and corrosion led to increasing research in non-metallic materials, namely poly aryl-ether-ketone polymers (PAKP) [5] such as polyether-ether-ketone (PEEK) [48; 91] and yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) [14; 107].  
Digital technology is a viable means to reduce cost, time, and production errors. Nevertheless, it is unanimous among the various researchers that future investigations will be essential to increase the scientific evidence and the quantitative fit accuracy assessment through clinical studies with larger sample sizes and lower risk of bias. Studies with controlled confounding variables are needed to assess the clinical fit accuracy of digital RPD frameworks in the long term. Other research will be fundamental to improve prosthetic design software (such as prediction of retention force according to tooth mobility) and to explore equipment, production parameters, and mechanical properties of the digital RPD metal frameworks. This way, in the future, it will become possible to produce RPD frameworks more quickly, with fewer human errors, less waste, higher reproducibility, and higher accuracy, improving the success of patients' oral rehabilitation.
# The Odyssey of Entropy: Cryptography
## 3 Entropy and Encryption
### Analysis and Evaluation
#### 3.2.2 Applications in Security Proof  
Entropy has been used in several types of security proofs including zero-knowledge proof and random oracles. Some related works are briefly reviewed in the following.  
* **Zero-Knowledge Proof:** Zero-knowledge proof is about proving the possession of some information by one party (the prover) to the other party (the verifier) without revealing the information itself. Zero-knowledge proofs are widely studied in cryptography. [PERSON] et al. [159] further developed the notion of non-interactive statistical zero-knowledge proof introduces by [PERSON] et al. [160]. They used entropy measures to highlight some conditions under which every statistical zero-knowledge proof can be made non-interactive. [PERSON] and [PERSON] [161] studied some black box algorithms in order to be used in zero-knowledge proofs. These algorithms can reverse the entropy of a function. It was shown in this report that a black box function of this type incurs an exponential loss of parameters, which makes it impossible for such an algorithm to be implemented in an efficient way. A new hard problem related to lattices, named ILP (isometric lattice problem) was introduced by [PERSON] and [PERSON] [162], who used entropy to show that there is an efficient zero-knowledge proof for this problem.
* **Random Oracle:** Random oracles are widely used in security proofs in order to model perfect hash algorithms. A random oracle is a hypothetical black box that responds to each query by producing a truly random number uniformly chosen from a predefined domain. There are a few research works that use entropy-related concepts in the analysis of random oracles. For example, it was demonstrated by [PERSON] and [PERSON] [163] that random oracles cannot help the extraction of mutual information. Moreover, [PERSON] et al. [164] worked on information-theoretic proofs, and the relations among different information-theoretic security proofs were studied by [PERSON] et al. [165].