esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and Results
### Effect of Lateral Pressure on the Thickness of a Layer with Radial Cracks  
The plots in Figure 9 illustrate the variations in the thickness of the cracked layer, determined by Equation (10) as a function of axial and circumferential stresses for three different values of Poisson's ratio (0.1, 0.3, and 0.5) and three scenarios of proportional lateral pressure (\(\sigma_{cnf}=s\sigma_{z};s=0;\,0.05;\,0.1\)).  
An analysis of the results shown in Figure 9 reveals several important observations.  
An increase in lateral pressure corresponds to a reduction in the thickness of the layer with radial cracks.  
The extent of change in the thickness of the cracked layer is influenced by the Poisson ratio. Specifically, lower values of Poisson's ratio result in a more pronounced sensitivity of the protective layer to variations in proportional lateral pressure.  
Figure 9: Changes in the relative thickness of the cracked layer (determined by Equation (10)) as a function of axial and circumferential stresses for three values of Poisson’s ratio (0.1, 0.3, and 0.5).  
Within the range of axial compressive stress values from zero to approximately 100 MPa, the influence of lateral pressure on the thickness of the fractured layer increases more rapidly compared to higher axial stresses. This behavior is clearly depicted in Figure 8, where the surface becomes increasingly flattened along the \(\sigma_{z}\) axis. Such a pattern aligns with previously mentioned experimental findings, indicating that external (bandage) reinforcement is more effective for weaker rocks as opposed to stronger ones [30, 31].  
When the lateral pressure (Equation (7)) and radial stress (Equation (6)) are equal in absolute magnitude, the thickness of the cracked layer (determined by Equation (9)) becomes zero. Under these conditions, the circumferential stresses, given by \(\sigma_{t}=-n\sigma_{z}\) (Equation (5)), induce compression, leading to the closure of cracks.  
Figure 10 illustrates the influence of Poisson's ratio on the thickness of the layer with radial cracks as a function of ultimate strength relative to axial compressive stress. The plot considers three scenarios of proportional lateral pressure (\(\sigma_{cnf}=s\sigma_{z};s=0;\,0.05;\,0.1\)) (calculated using Equation (10)).  
The plots in Figure 10 reveal that the thickness of the cracked layer decreases as Poisson's ratio decreases and tensile strength increases.
# Imaging Flow Velocimetry with Laser Mie Scattering
## 4 Fundamental Measurement Limits
### Seeding  
#### 4.1.1 Influence on the Flow  
In the case that no scattering particles are naturally contained in the flow, seeding particles need to be added. For instance, this always applies for dry air flows. The insertion of seeding particles influences the flow properties. Hence, the seeding as a part of the measurement system causes a retroaction to the flow, i.e., the measurement object.  
Two typical seeding materials are listed in Table 1. DEHS particles are a typical choice for air flows at room temperature. The material is liquid at room temperature, so that the shape of small particles can be considered as approximately spherical. A common particle diameter is 1 \(\upmu\)m. In hot flows such as in flames, particle materials with a higher boiling point are applied. As an example, solid particles of titanium dioxide (TiO\({}_{2}\)) with a diameter of 0.4 \(\upmu\)m can be applied.  
The retroaction of the seeding is difficult to quantify regarding the measurement uncertainty of the flow velocity. However, the influence on the Reynolds number \(Re\), which is an important similarity figure for flow experiments, can be derived. The Reynolds number is obtained for the flow around or through a body with the characteristic length \(L\), the fluid density \(\rho\), the dynamic fluid viscosity \(\eta\) and the characteristic flow velocity \(v\) (relative to the body) [137]:  
\[Re=\frac{\rho vL}{\eta}. \tag{4}\]  
As a result, the seeding influences the mean fluid density and the fluid viscosity, which is the effect of the retroaction of the seeding.  
An example is given in Figure 6a, where the change of the mean air density (at room temperature) is shown as a function of the seeding particle concentration considering for DEHS and TiO\({}_{2}\) (cf. Table 1) as the seeding material, respectively. Even at particle concentrations of \(10^{13}\) /m\({}^{3}\), which are hardly achievable [138; 139], the density change is below 1%. Since the particle concentration is typically several orders of magnitude lower, the influence of the seeding on the air flow density is usually negligibly small. The change in viscosity and the general change of the flow due to the presence of two phases is not known here. However, the low volume percentage of the seeding leads to the assumption that it is negligible as well. The volume percentage is shown in Figure 6b over the particle concentration. It is always below 0.001%. As a result, the retroaction of the seeding is usually negligible compared with other error sources.  
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Material** & \(\rho_{\text{P}}\) **(kg/m\({}^{3}\))** & \(d_{\text{P}}\) **(\(\upmu\)m)** \\ \hline diethylhexyl sebacate (DEHS) & 912 & 1 \\ titanium dioxide (TiO\({}_{2}\)) & 3900 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material, density \(\rho_{\text{P}}\) and diameter \(d_{\text{P}}\) of two typical seeding particles.  
Figure 6: (**a**) Change of the mean density of the fluid due to the seeding and (**b**) volume percentage of the seeding over the particle concentration.
## 8 Challenges and Future Directions
### Challenges
#### 8.1.2 Data Collection and Labelling  
HAR systems require a large amount of labelled data, and this can be time-consuming. HAR models can only recognise activities contained in the dataset which they have been trained on. Usually such datasets are labelled manually in real-time, or once the activity is done [123]. This means that for a robust HAR model, a varied number of activities would be needed in the dataset, and the labelling of such a large dataset would be time-consuming.  
There is also the complexity of real-world human activities where activities sometimes overlap or can happen simultaneously. Also, due to the diversity of human behaviour, a single activity can be performed different ways by different people. This is not often captured in datasets collected in a laboratory setting. A model trained on general data may perform poorly for certain individuals due to this [124]. This is a challenge that needs to be tackled in order to create robust HAR systems that can handle real-world complexities.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios  
In this section, we present a comprehensive case study where multiple BSs cooperate to detect and locate vehicles and VRUs such as pedestrians and cyclists in an urban scenario. Through some numerical results, we aim to highlight key aspects of cooperative sensing in future mobile radio networks, including target and channel modeling, various fusion techniques, and resource allocation. Cooperative and distributed sensing technology aligns well with the anticipated network densification and massive deployments envisioned for 6G systems, making it a promising feature. The proposed cooperative data fusion strategy is summarized in the block diagram depicted in Figure 3.
# Security and Ownership in User-Defined Data Meshes
## 5 Framework Demonstration through a Real-World Case Study
### Use Case Scenarios  
As previously mentioned, a request to access a DL is supported by the utilization of the SDB semantic enrichment mechanism, which is the cornerstone for creating a data product as part of the DM according to user preferences and ownership granting. Access and ownership for a specific period of time is recorded on Blockchain using a dedicated NFT. The use case of the PARG factory focuses on the department of poultry feeding where sources produce data during the feed cycle of chickens within a specific farm. An excerpt of the structure of the corresponding SDB is depicted in Figure 8. We selected the following metadata characteristics to describe a source which produces data for monitoring chicken flock farming in different locations: (i) Source Name; (ii) Location; (iii) Feed cycle start; (iv) Feed cycle end; (v) Keywords; (vi) Variety; (vii) Velocity; (viii) Volume; and (ix) Source Path.  
The use case scenarios tested are based on user requests to access a specific portion of data. For example, PARG stakeholders (shift managers, farm carers, production workers) often need to consult data related to the number of chicks in a farm, the environmental conditions within a farm, electricity consumption, emissions in the atmosphere, biomassproduction, etc. Therefore, data products in the DM were constructed to reflect these pieces of available information. Furthermore, in the scenarios below, we also assume that the shift manager wishes to acquire access to all information related to the Limassol farm and that, at some point, (s)he wishes to transfer this access to the head of production. Normally, access permissions are requested by sending a message to the owner of the data through a dedicated SPARQL. This query is essentially executed in all scenarios that follow. Essentially, a user requests access to a specific portion (data sources) of a DL through the DM data products that are constructed to provide information for the location \"Limassol\", as presented in Figure 8.  
To evaluate the efficiency of the proposed framework, we first developed a dedicated smart contract that was deployed in the Sepolia Test Network, and then we executed a series of transactions. The smart contract's address is _0x88790 ed3407e3b395 ab0276d530 5a273a497612b_, and the contract owner is _0x9b43d1384 FC250B59996933 CA2D8C766722 7 BE52_. The reader can refer to the smart contract's URL on etherscan.io for complete access to the source code of the contract. By using the smart contract, we have explored various scenarios to showcase its fundamental features that include NFT minting with additional on-chain information, data retrieval, and transfer restrictions.
## 7 Conclusions  
This study uses a high-order algorithm to jointly estimate the vehicle state and road adhesion coefficient. Simulation analysis and experimental methods were used to verify the joint estimator of the vehicle state and road adhesion coefficient and, through further comparison among the GHCKF, AGHCKF, and CKF, the following conclusions can be obtained.  
The experimental results are consistent with the simulation analysis; both the experimental and the simulation results indicate that the AGHCKF, GHCKF, and CKF algorithms can estimate state parameters such as the vehicle longitudinal speed, lateral speed and yaw rate, and can effectively estimate the road adhesion coefficient. However, after in-depth comparison, we conclude that the AGHCKF algorithm is superior to the GHCKF and CKF algorithms in terms of its response speed, estimation accuracy, and filtering effect; this reflects the effectiveness and robustness of the AGHCKF algorithm.  
The joint estimator realizes the estimation of the vehicle state and road adhesion coefficient. The joint estimation method for the vehicle state and road adhesion coefficient proposed in this paper is feasible. According to the simulations and experiments, the vehicle state estimator can generally achieve an accurate estimation of the vehicle state, the road adhesion coefficient estimator can achieve an the effective estimation of the road adhesion coefficient, and the estimated value has always been within the effective range of the road adhesion coefficient; the estimated results can meet the requirements of vehicle dynamic simulation, control, and test.  
This study innovatively proposes a method for the joint estimation of the vehicle state and road adhesion coefficient, introduced high-order Kalman and adaptive algorithms, and developed an advanced AGHCKF algorithm. The comparison of the results with those of the GHCKF and CKF shows that the AGHCKF can better adapt to the errors and noise of nonlinear systems, has a strong anti-interference ability, and has a good robustness and stability. The experimental data to some extent reflect the true engineering values. Comparing the estimated values with the measured values of sensors, the RMSE values are all less than 0.04. Among them, the AGHCKF has the smallest RMSE value, the most accurate estimation of vehicle status and road adhesion coefficient, the shortest response time, and the most stable data. Therefore, the AGHCKF algorithm has great advantages in the field of vehicle control.  
Conceptualization, L.Q.; methodology, L.Q. and R.C.; software, R.C. and C.G; validation, R.C. and C.G.; formal analysis, L.Q.; investigation, C.G.; data curation, R.C.; writing--original draft preparation, L.Q. and R.C.; writing--review and editing, C.G.; visualization, R.C.; project administration, R.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
### Related Work  
The ADM minimization problem is defined in [6] for ring topology and \(g=1\). It is proven to be NP-complete in [7], and an \(3/2\)-approximation algorithm was presented in the study [8]. This result is improved in [9; 10] to \(10/7+\epsilon\) and \(10/7\), respectively. The current best result is a \(\frac{47}{34}+\epsilon\)-approximation [11] which states that for every \(\epsilon>0\) there exists a \(\frac{47}{34}+\epsilon\)-approximation algorithm whose running time is a polynomial the degree of which depends on \(\epsilon\).  
The studies [7; 12] consider the ADM minimization problem for general topology, and present the approximation algorithms with an approximation ratio of \(8/5\) and \(3/2+\epsilon\), respectively. The study [13] considers non-cooperative games in which users are selfish agents sharing the cost of the ADMs they use. This study presents games that converge in a polynomial number of steps to solutions with an approximation ratio between \(\frac{3}{2}\) and \(\frac{3}{2}+\frac{1}{k}\) where \(k\) is the maximum size of a coalition formed by the agents.  
The traffic grooming problem is introduced in [14] and studied in the context of ring topologies. The study in [15] shows that the ADM minimization problem is NP-completefor this topology when \(g\) is part of the input instance. In [16], it is shown that the same hardness result also holds for path and star networks, thus generalizing the result in [15]. In [17], the authors prove the NP-completeness of the problem in the strong sense, which implies the NP-completeness for bounded degree trees and directed trees.  
For ring topology, a \(2\ln g\)-approximation algorithm for the ADM minimization problem is presented in [18]. The running time of this algorithm, being exponential in \(g\), yields a polynomial-time algorithm when \(g\) is fixed, i.e., not part of the input. In [19], this algorithm is extended to directed trees, and undirected trees with the bounded degree. The problem has also been studied in [20], where it is shown to be APX-complete for fixed values of \(g\). Namely, for each of these values of \(g\), there exists a constant \(c_{g}>1\) such that there is no \(c_{g}\)-approximation algorithm for the problem unless \(\mathrm{P}=\mathrm{NP}\). The same study also presents an \(\mathcal{O}(n^{1/3}\log^{2}n)\)-approximation algorithm, where \(n\) is the number of nodes of the network. The paper [15] studies the special case of all-to-all traffic, in which there is a demand between every pair of nodes.  
The minimization of hardware components in optical networks using grooming has been studied in [21] for ring networks and in [22] for star networks, in a different scenario.
# An Overview of CRISPR-Based Technologies in Wine Yeasts to Improve Wine Flavor and Safety
## 5 Final Remarks  
Yeasts possess a major role in wine chemical composition due to their capacity of converting neutral grape metabolites into flavor active compounds and producing secondary metabolites during fermentation. Therefore, winemakers use yeasts as a tool to produce wines with specific flavor profiles. The best wine yeast will be the one that efficiently completes the fermentation process, and sensorially produces key metabolites without the development of off-flavors. This yeast must also be able to enhance the wines' varietal character and produce a \"safe wine\" free from health-hazard metabolites such as ethyl carbamate.  
In recent decades, due to molecular techniques and genetic manipulation, new possibilities have arisen for creating wine yeasts, resulting in the development of strains with better fermentation abilities able to improve the sensory quality of wines, increasing their pleasantness, and produce wines targeted to specific consumers, considering their health and nutrition requirements. However, only two genetically modified wine yeast strains have, until now, been officially registered and approved for commercial use.  
Compared with traditional genetic engineering methods and other gene-editing alternatives, CRISPR/Cas9 is described as efficient, easy-to-use, and able to target multiple sites, besides being \"nature-made,\" as bacteria have been using it for millions of years. CRISPR is being tested in vineards and wineries to target some of wine's biggest challenges. Therefore, CRISPR opens brand new doors for winemakers using commercial yeasts when it comes to efficiently produce target flavors and reducing harmful compounds and off-odors.  
However, in Europe and other countries, the adaptation for industrial biotechnology of this technique is still waiting for approval, as employing CRISPR/Cas9 for gene therapy still causes some disagreements. Thus, if the laws in the application of the CRISPR/Cas system were completed, it would promote rapid development in the fields of food production, agriculture, and Enology.  
This study was funded by the CQ-VR (grant number UIDB/00616/2020 and UIDP/00616/2020), FCT-Portugal, and COMPETE.  
Not applicable.  
The author would like to thank the Chemistry Center (CQ-VR) for the financial support.  
The authors declare no conflict of interest.
# Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration
## 3 Lightweight Unsteady State Constrained Iris Certification
### Feature Recognition Layer
#### 3.2.1 Gabor + Hamming
The Hamming distance between the test iris and the template iris is calculated as a feature similarity. The feature similarity calculation formula of Gabor + Hamming is shown in Equation (4). \[HD=\frac{1}{512}\times\overset{512}{\underset{i=1}{\sum}}\frac{1+\text{sgn}( AT_{n-i}-M_{1})}{2}\oplus\frac{1+\text{sgn}(BT_{n-i}-M_{1})}{2}\quad n=1,2, \ldots,16\quad i=1,2,\ldots,32\enspace, \tag{4}\]  
where \(AT_{n-i}\) denotes the average amplitude value of the \(i\)-th sub-graph of the \(n\)-th Gabor processed image in the test iris. \(BT_{n-i}\) denotes the average amplitude value of the \(i\)-th sub-graph of the \(n\)-th Gabor processed image in the template iris; and \(HD\) is the feature value of Gabor + Hamming distance recognition. The Gabor + Hamming distance feature value is compared with the set classification threshold. If it is less than the classification threshold, the test iris and the template iris are considered as the same category. The function of this recognition algorithm is to binary code the vibration amplitude value of feature texture in the form of sub-graphs by different filters and authenticate the same category through a certain range of iris texture change rules, thereby avoiding the influence of corresponding point feature difference caused by phase difference.
## 4 Results and Discussion
### Development of Predictive Models for Estimating Bolme Abrasion Value (BAV)
#### 4.2.1 Multivariate Adaptive Regression Spline (MARS)  
The MARS method is a hybrid linear approach designed for nonparametric regression, widely applied in prediction and optimization tasks [32; 33; 34; 35; 36; 37]. The method was first proposed by [PERSON] [38] and consists of two main components: the forward pass and the backward pass. Constant terms called basis functions (BFs) are generated randomly in the forward pass. During the backward pass, these BFs are optimized and combined with linear regression models. In this study, MARS analyses were conducted using the R software  
Figure 5: Rock material classification based on sampling locations.  
(v4-4-2), resulting in an effective predictive model for estimating BAV. As a result of MARS analyses, the BAV can be estimated using Equations (1)-(6).  
\[BAV=26.80+2.094 BF1-0.841 BF2+18.492 BF4+3.076 BF9 \tag{1}\]  
\[BF1=\max(0;w_{a}-18.25) \tag{2}\]  
\[BF2=\max(0;18.25-w_{a}) \tag{3}\]  
\[BF4=\max\bigl{(}0;2.45-V_{p}\bigr{)} \tag{4}\]  
\[BF8=\max(0;2.63-\rho_{d})\times BF2 \tag{5}\]  
\[BF9=\max\bigl{(}0;V_{p}-4.2\bigr{)}\times BF8 \tag{6}\]
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 3 Materials and Methods  
Figure 1 shows the pipeline of our Fusion Feature Matching with Transformer (**FFMT**). The pipeline takes a pair of RGB-D images as input and predicts the relative pose between them. Initially, RGB-D images are processed through a feature extraction and fusion module. The RGB branch and the point cloud branch employ U-Net [43]-like architectures using CNN and KPConv [44], respectively, for feature extraction. A bidirectional multiscale feature fusion strategy integrates information between the two branches. Once features are extracted, they are flattened into 1D vectors and combined with positional encodings. These enhanced features are then subjected to alternating layers of self-attention and cross-attention. Coarse correspondences are identified using [PERSON]'s ratio criterion based on the extracted features. These correspondences are subsequently refined through multiple RANSAC [28] iterations to achieve accurate matches. Finally, the estimated transformation is derived via a least-squares optimization. The entire framework is trained end-to-end with differentiable rendering. The details of our methods are explained in the following sections.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Methods Based on Transform Domain  
Most traditional fusion methods in multi-focus image fusion are based on the transform domain [34]. As shown in Figure 4, the transformation domain method mainly operates the decomposition coefficient after image transformation, which mainly includes three fusion stages: image transformation, coefficient decomposition, and inverse transformation reconstruction. Firstly, the source image is transformed into the transform domain by an image decomposition algorithm; then various fusion strategies are used to fuse different coefficients; finally, the corresponding inverse transformation of the fusion coefficient is processed to obtain the final fusion image. The more layers of decomposition are used, the more detailed the information will be however, the efficiency will decrease. Therefore, the fusion effect will be greatly improved by properly handling the relationship between decomposition layers and execution efficiency. In terms of the different transformations, the transform domain method is further divided into the method based on multi-scale decomposition (MSD), the method based on sparse representation (SR), and the method based on gradient domain (GD) in this paper.