esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effect of the Generated (Reactive) CAP Species on the Biofilms
#### 4.2.3 DNA Damage  
For both model biofilms, DNA damage (Figure 8) was observed for most of the treatment conditions. Therefore, it can be concluded that the intracellular DNA is one of the main targets of the generated CAP species. Among others, DNA damage has been reported to be the result of the generation of UV photons. Especially UV-C light, ranging between 200 and 280 nm, can result in cross-linking of neighbouring pyrimidine nucleoside bases, which eventually affects DNA transcription and replication [43; 54; 63]. Although previous studies reported that the power density of UV light during CAP treatment is generally too low (i.e., \(<\)50 \(\mu\)W/cm\({}^{2}\)) to contribute to CAP's bactericidal effect, the results of this study (Figure 6) indicated that the UV photons did in fact contribute to the obtained log-reductions values for some treatment conditions. Nevertheless, as previously mentioned, more research will be required to examine whether UV photons were indeed generated during CAP treatment. The measured ROS (Figures 3 and 5) and RNS (Figure 3), can also have contributed to the DNA damage observed following CAP treatment since these species can diffuse into the cell and cause damage to the DNA by oxidizing the nucleic acids [8]. Regarding the influence of the plasma characteristics on the ability of CAP to cause DNA damage, it was concluded that the addition of oxygen resulted in less DNA damage for some treatment conditions. This cannot be directly linked to the lower generation of certain CAP species and/or the measured pH values. The SBD electrode was favored over the DBD electrode and a lower discharge power was more effective. Nevertheless, it was again not possible to link these observations to the observed concentrations of ROS/RNS and the measured pH values. Further research is therefore still required.  
Generally, more DNA damage was observed for the _Listeria monocytogenes_ biofilm-associated cells than for the corresponding _Salmonella_ Typhimurium cells. This can partially be explained based on the limited thickness of the former model biofilm, allowing more UV photons, if any generated at all, to interact with the cells. Nevertheless, other factors can be involved as well, e.g., the ability of the ROS and RNS to penetrate into the cells and/or the ability of the biofilm-associated cells to repair damage to their DNA.  
As mentioned in Section 2.5.3, the currently applied detection method can only be used to examine whether CAP treatment can damage dsDNA when this damage results in the generation of ssDNA within the biofilm-associated cells. For future research, fragmentation of dsDNA following CAP treatment can be examined as well, e.g., by means of gel electrophoreses and subsequent staining with SYBR Green I.
## 3 Problem Definition and Scenario of Application
### Problem Definition  
Let \(\langle t_{i}\rangle=\langle x_{i},y_{i}\rangle\) be the spatial coordinates of the table \(i\) considered during the optimization process, \(n_{s}\) the number of students in the classroom and consequently the number of tables to be located, \(T\) the set including every possible combination of tables in the classroom, \(T_{i}\) a subset containing a possible disposition of the tables in the classroom, \(T_{j}\) the subset containing every possible combination of \(T\) except \(T_{i}\), \(f_{T_{i}}(t_{i})\) the evaluation of the quality of the location of the table \(i\) belonging to the set \(T_{i}\), the TLP is defined as finding the optimal \(T_{i}\) fulfilling the following relation:\[(T_{i}=(\langle t_{1}\rangle,\ldots,\langle t_{n_{s}}\rangle))\subset T:\left\{ \frac{\sum_{i=1}^{n_{s}}f_{T_{i}}(t_{i})}{n_{s}}\geq\max\left(\frac{\sum_{i=1}^{ n_{s}}f_{T_{i}}(t_{i})}{n_{s}}\right)\right\} \tag{2}\]  
Consequently, the TLP is defined as the finding of the optimal table disposition in the classroom for maximizing a fitness function of which the main purpose is the maximization of the distances among tables but preserving the legislation of each country, which restricts the table disposition to having a minimum required distance among every table.
## 4 Point Cloud Uncertainty Evaluation: Explicit Models
### Probe Qualification Effects (Pq)  
For error models with an explicit dependence on the probe offset \(\mathbf{p}_{k}\), the fact that the probe configuration geometry is usually determined in probe qualification experiments [30] means that there will be uncertainties associated with estimates of the offsets. If \(\mathbf{x}_{i}\) is a measurement using the \(k\)th probe, then the uncertainty contribution arising from the probe qualification can be modelled as  
\[\mathbf{x}_{i}=\mathbf{x}_{i}^{*}+\mathbf{p}_{k}+\mathbf{e}_{PQ,k}+\mathbf{e}_{i},\quad\mathbf{e}_{PQ, k}\in\mathcal{N}(\mathbf{0},\sigma_{PQ,k}^{2}I), \tag{9}\]  
where \(\mathbf{p}_{k}\) is the calibrated probe offset vector for the \(k\)th probe, and \(\mathbf{e}_{PQ,k}\) models the difference between the actual probe offset and its calibrated value, \(k=1,\ldots,n_{P}\). (Model Equation (8) can be regarded as a special case in which the probe offset is \(\mathbf{p}=\mathbf{0}\) and is known exactly.) An important feature of the model is that all measurements with the \(k\)th probe are associated with the same systematic effect \(\mathbf{e}_{PQ,k}\). The variance contribution associated with probe qualification is given by  
\[V_{X|PQ}=G_{X|PQ}V_{PQ}G_{X|PQ}^{\top}\]  
where \(V_{X|PQ}\) is the \(3n_{P}\times 3n_{P}\) variance matrix associated with the systematic effects \(\mathbf{e}_{PQ,k}\), and \(G_{X|PQ}\) is the \(3m\times 3n_{P}\) sensitivity matrix. The variance matrix \(V_{PQ}\) is a diagonal matrix with the \(3\times 3\) matrix \(\sigma_{PQ,k}^{2}I\) in the \(k\)th diagonal block. If the \(i\)th measurement is associated with the \(k\)th probe, then  
\[G_{X|PQ}(3i-2:3i,3k-2:3k)=I\]  
the \(3\times 3\) identity matrix, and all other elements in these three rows are zero.
## 3 Singular Spectrum Analysis
### Reconstruction  
The grouping step of the reconstruction stage is to decompose the \(L\times K\) matrix \(T_{i}\) into subgroups according to the trend, the seasonal and semi-seasonal components, and residuals. The grouping step of the reconstruction stage is a partition of the set of indices \(1,\ldots,d\) into the collection of \(m\) disjoined subsets of \(I=I_{1},\ldots,I_{m}\). Thus, \(T_{i}\) corresponds to the group \(I=\{I_{1},\ldots,I_{m}\}\). \(T_{I_{i}}\) is a sum of \(T_{j}\), where \(j\in I_{i}\). So, \(T_{X}\) can be expanded as  
\[T_{X}=T_{1}+\ldots+T_{L}=T_{I_{1}}+\ldots+T_{I_{m}}. \tag{2}\]  
Assume that there are two groups of eigentriples of the trajectory matrix \(T_{X}\): \(T_{L}\) and \(T_{R}\). The whole set will be \(I=\{1,\ldots,d\}\), \(R\cup L=I\). However, \(R\) is not a subset of \(L\). \(T_{I}\) is  
\[T_{I}=\sum_{i\in I}\sqrt{\lambda_{i}}U_{i}V_{i}^{T} \tag{3}\]  
and we can calculate \(T_{L}=T_{I}-T_{R}\) under the assumption of weak separability. Thus, \(T_{L}\) can be written as  
\[T_{L}=\sum_{i\in L}\sqrt{\lambda_{i}}U_{i}V_{i}^{T}. \tag{4}\]  
The final step is diagonal averaging. In the basic SSA algorithm, the diagonal averaging step is to transform the grouped matrices \(T_{I_{i}}\) into a new time series of length \(N\). We obtain the time series \(\overset{\rightarrow}{T}^{(i)}\) from an averaging of the corresponding diagonals of the matrix \(T_{I_{i}}\).  
\[\overset{\rightarrow}{T}_{i,j}=\left\{\begin{array}{cc}\frac{1}{s-1}\sum \limits_{j=1}^{s-1}x_{j,s-j}&2\leq s\leq L\\ \\ \frac{1}{L}\sum\limits_{j=1}^{L}x_{j,s-j}&L+1\leq s\leq K+1\\ \\ \frac{1}{N-s+2}\sum\limits_{j=s-K}^{N-s+2}x_{j,s-j}&K+2\leq s\leq N+1\end{array}\right.. \tag{5}\]
density along the line of sight; this measure thus acts as a proxy for the distance between the source and the observer. Astronomers search for pulses over a range of dispersion values, up to the largest distance they want to cover. Until 2007, no searches were carried out for bright pulses beyond the local Galactic neighbourhood, while searches in nearby galaxies were sporadic [5]. All that was about to change when an extremely bright burst was discovered in 2007, with a dispersion much larger than what was expected from the Milky Way, in archival data from the Murriyang (Parkes) telescope. This seminal event in time-domain astronomy opened up a new parameter space of cosmological radio bursts with tremendous scientific potential [6].  
The discovery made the community realise that there are regions on the transient phase space that remain unexplored because (1) they are selected against in our surveys and (2) we did not expect any sources in those regions. Recent discoveries of FRBs and other radio transients have proven point 1 to be true, but point 2 much less so--highlighting the importance of revising our data processing techniques and conducting sensitive searches for fast radio transients with current and future telescopes. In this review, we provide a summary of the past and present techniques used by radio astronomers to find FRBs in time-domain data. We go through the search methodologies and discuss the advantages and the underlying biases. We also discuss some newer techniques that are currently being deployed to expand our horizons within the radio transient phase space and we provide an outlook towards the future of FRB searches with next-generation radio telescopes.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology
### Identification of Full-Field Mode Shapes  
The operational modal analysis (OMA) method [32] is applied on the displacement time history extracted from the above procedure to identify the full-field mode shapes of the structure. Firstly, the SSI-COV method is applied to the full-field displacement time history to determine the modal parameters that are represented in the stability diagram. Then, the hierarchical cluster analysis is used, which leads to the automatic identification of mode shapes from the stability diagram while eliminating the false modes. The details are as follows.  
Figure 6: Calibration of edge displacement.
## 3 Model Establishment and Parameter Determination
### Parameter Selection  
Based on the geological data provided by the mine, the hanging wall surrounding rock of the ore body is granite porphyry, and the football is crystal tuff. The mechanical parameters of the rock in the numerical simulation come from the measurement of the mine drilling core. The core obtained is shown in Figure 5. All types of rock exhibit high strength. Therefore, the mechanical parameters of three types of rock were determined through laboratory tests: granite porphyry, crystal tuff, and porphyry ore body. For each type of rock, uniaxial compression, shear, and tensile tests were conducted. To avoid randomness, each test was repeated four times. The mine uses cemented backfill, and the backfill parameters were provided by the mine. The measured parameters were then reduced using the GSI method to better align with actual mining conditions. The reduced physical and mechanical parameters of the rock and backfill are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ground Settlement Value** **(mm)** & **Terrain Curvature** **Deformation (mm/m2)** & **Surface Tilt Rate** **(mm/m)** & ** Lateral Deformation** **(mm/m)** \\ \hline \(<\)30 & \(<\)0.2 & \(<\)3 & \(<\)2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Allowable surface deformation value of buildings.  
Figure 4: Ore body model.
# Topology Optimization of Anisotropic Materials with Smooth Fiber Orientation
## 4 Conclusions  
This paper provides a topology optimization method for anisotropic materials with smooth fiber orientation, which combines a fiber angle constraint strategy and adaptive filtering technology to complete concurrent optimization of topology and the fiber angle. On the one hand, the fiber angle constraint function is created and integrated into the moving asymptote algorithm. This method quantifies the continuity of the fiber angle and greatly enhances the continuity of the fiber angle but sacrifices the stiffness of the overall structure. On the other hand, the adaptive filtering technology is introduced to smooth the fiber angle through the Gaussian function, and the standard deviation of the Gaussian function is adjusted according to the maximum change value of the fiber angle so as to guide the subsequent iterative optimization and realize the rationalization of the fiber distribution and the improvement of the structural stiffness.  
Figure 18: Optimization results with adaptive Gaussian filtering under different volume constraints. The fcr was set to 0.98.  
Numerical examples show that the fiber angle constraint strategy significantly improves the continuity of the fiber but also sacrifices the overall structural stiffness. However, by introducing adaptive filtering technology, not only is the continuity of the fiber significantly improved but also the structural stiffness is guaranteed, and the effective balance between the two is achieved.  
However, the concurrent optimization of topology and the fiber angle also faces serious initial dependence problems. The appropriate initial value of the fiber angle is very important so as to get rid of the risk of falling into the local optimum. In addition, the MATLAB program provided in the Appendix is written based on the logic of 99 lines of code, which has high readability, but the running speed is slow. Therefore, vectorization operation or different optimization algorithms can be used in subsequent research to improve the running speed. These studies are necessary to further improve the optimization in future work and provide a more efficient and reliable solution for the topology optimization design and manufacturing of composite materials.  
Conceptualization, X.P. and C.S.; methodology, S.J.; software, J.L.; validation, B.Y., C.S. and X.P.; formal analysis, J.L.; investigation, S.J.; resources, C.S.; data curation, X.P.; writing--original draft preparation, C.S.; writing--review and editing, C.S.; visualization, S.J.; supervision, S.J.; project administration, S.J.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Ningbo Key Research and Development Program [grant number 2023Z134] and the Taizhou Science and Technology Plan Program [23 GYB37].  
Not applicable.  
The data used to support the findings of this study are available within the article.  
The authors declare no conflicts of interest.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 5 Spacecraft-Relative Navigation Model
### Measurement Model  
Several optical beacons of known number and position are mounted on the chief spacecraft. The deputy spacecraft is equipped with PSD, and this paper does not assume that chief body (\(O_{c}-X_{c}Y_{c}Z_{c}\)) and its LVLH frame (\(O_{L}-X_{L}Y_{L}Z_{L}\)) are consistent. When the chief spacecraft and deputy spacecraft are close to each other, e.g., distances ranging from several meters to several hundred meters [39], the details of each part of chief spacecraft could be displayed on the pixel plane [40]. Based on the measurements from the cameras, it is possible to estimate the relative position and velocity between them and also to estimate their relative attitude. The VISNAV system is illustrated in Figure 6, where PSD is denoted with a cube, and this paper assumes that the coordinate system of PSD overlaps with deputy body \(O_{d}-X_{d}Y_{d}Z_{d}\). The \(i\)-th measurement line of sight (LOS) vector is defined by the following:  
\[\tilde{\mathbf{b}}_{i}=\mathbf{A}\left(\mathbf{q}_{d/L}\right)\mathbf{r}_{i}+\mathbf{w}_{i} \tag{61}\]  
with  
\[\mathbf{r}_{i}=\frac{\mathbf{X^{\prime}}_{i}-\mathbf{\rho}}{\left\|\mathbf{X^{\prime}}_{i}-\bm {\rho}\right\|}=\frac{\left[\mathbf{X^{\prime}}_{i}-x,\mathbf{Y^{\prime}}_{i}-y,\mathbf{Z^{ \prime}}_{i}-z\right]^{T}}{\sqrt{\left(\mathbf{X^{\prime}}_{i}-x\right)^{2}+\left( \mathbf{Y^{\prime}}_{i}-y\right)^{2}+\left(\mathbf{Z^{\prime}}_{i}-z\right)^{2}}} \tag{62}\]  
where \(\tilde{\mathbf{b}}_{i}\) is the measurement of the \(i\)-th beacon in \(O_{d}-X_{d}Y_{d}Z_{d}\). \((x,y,z)\) indicates the position of deputy spacecraft in \(O_{L}-X_{L}Y_{L}Z_{L}\). \(\mathbf{A}\left(\mathbf{q}_{d/L}\right)\) denotes the rotation matrix from \(O_{L}-X_{L}Y_{L}Z_{L}\) to \(O_{d}-X_{d}Y_{d}Z_{d}\). In addition, \(\mathbf{X^{\prime}}_{i}=\left[\mathbf{A}\left(\mathbf{q}_{c/L}\right)\right]^{T}\!\mathbf{X}_ {i}\) is the coordinate of beacon in \(O_{L}-X_{L}Y_{L}Z_{L}\), and \(\mathbf{X}_{i}\equiv\left(X_{i},Y_{i},Z_{i}\right)\) is the \(i\)-th beacon's known position in \(O_{c}-X_{c}Y_{c}Z_{c}\).  
Therefore, with N optical beacons providing N lines of sight, the optical camera measurement equation is written as follows:  
\[\mathbf{z}_{k}=\left[\begin{array}{c}\tilde{\mathbf{b}}_{1}\\ \tilde{\mathbf{b}}_{2}\\ \vdots\\ \tilde{\mathbf{b}}_{N}\end{array}\right]_{k}=\left[\begin{array}{c}\mathbf{A}\left( \mathbf{q}_{d/L}\right)\mathbf{r}_{1}\\ \mathbf{A}\left(\mathbf{q}_{d/L}\right)\mathbf{r}_{2}\\ \vdots\\ \mathbf{A}\left(\mathbf{q}_{d/L}\right)\mathbf{r}_{N}\end{array}\right]_{k}+\left[\begin{array} []{c}\mathbf{w}_{1}\\ \mathbf{w}_{2}\\ \vdots\\ \mathbf{w}_{N}\end{array}\right]_{k} \tag{63}\]
# High-Pressure Technologies for the Recovery of Bioactive Molecules from Agro-Industrial Waste
## 2 Presurized Liquid Extraction Processes
### PLE of Bioactive Compounds from Waste
#### 2.2.1 Proteins  
The growing world population requires a more sustainable source of protein for food and technological aims. Thus, new sources include insects, fungi, and algae, as well as waste streams from food processing [86]. Conventional methods for the recovery of proteins involve the use of alkali, since the basic environment allows the breakage of disulfide bonds in protein and higher solubility providing high extraction yields, increases their bioavailability and digestibility, but degrades protein quality [87]. In recent years, pressure-assisted extraction has been successfully applied to extract proteins from a variety of food processing residues. [PERSON] et al. [56] optimized pressurized hot water extraction conditions (50 \({}^{\circ}\)C, 15 MPa, and 5 min as extraction time) for the recovery of proteins from _Sambucus nigra_ L. branches, demonstrating the highest yields and better reproducibility of the pressure-assisted technique compared to traditional solid-liquid extraction. [PERSON] et al. [58] used PLE to extract proteins and bioactive compounds from spirulina. Response surface methodology (RSM)-centered composite design (CCD) was used to evaluate and optimize the extraction time (5-15 min), temperature (20-60 \({}^{\circ}\)C), and pH (4-10) of the PLE process for extraction (103.4 bar). The results showed that under optimal extraction conditions (10 min, 40 \({}^{\circ}\)C, and pH 4), PLE significantly increased the protein yield of the extracts (46.8 \(\pm\) 3.1%) compared to non-pressurized extraction.  
PLE was also applied to produce extracts with antioxidant activity from _Sparus aurata_ side streams. Protein recoveries of 22% (muscle), 33% (head), 78% (viscera), 24% (skin), and 26% (tailfin) were obtained, with values 1.2-4.5-fold higher compared to samples obtained from conventional stirred extraction [59]. Similarly, [PERSON] et, al [60,61] used the PLE technique to obtain protein extracts with antioxidant capacity from muscle remains, head, viscera, skin, and caudal fins of salmonids. Protein recovery was \(\approx\)92% for all samples except viscera, implying a 1.5-4.8-fold increase compared to stirred extraction (\(\approx\)28%). The effect of pressure in enhancing extraction yields of proteins can be ascribed to the alteration of covalent bonds and to the breakdown of proteins into soluble peptides, as well as to the induced variation of dielectric and physical properties of solvent (such as subcritical water) through the operating conditions enabled by high-pressure-assisted processes [88].
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 7 Contributions to the Use of USVs for Disaster Applications
### Simulation of Scenarios Involving USVs and Floods  
During a flood, the USV might encounter strong water current, winds, water vortexes, and floating debris that might jeopardize the mission. Before testing with real boats, the software designers must have tools to test the USVs safely, in simulation scenarios. Simulators intend to prepare the USV and the rescue team for the actual mission. Presently, there are few open source robotics simulators and fewer which simulate aquatic environments with enough accuracy to simulate a flooding disaster. One of our research goals is to design a robotic simulator to support accurate wind, wave, and hydrologic models to mimic a flooding scenario [251].  
Fluid simulation models such as HEC-RAS ([[http://www.hec.usace.army.mil/software/hec-ras/](http://www.hec.usace.army.mil/software/hec-ras/)]([http://www.hec.usace.army.mil/software/hec-ras/](http://www.hec.usace.army.mil/software/hec-ras/))) and Openfoam ([[https://openfoam.org/](https://openfoam.org/)]([https://openfoam.org/](https://openfoam.org/))) were incorporated into the Gazebo ([[http://gazebosim.org/](http://gazebosim.org/)]([http://gazebosim.org/](http://gazebosim.org/))) robotic simulator. Also, several different USV models (e.g., airboat, differential boat, rudder boat, and sailboat) were designed to test the performance of those models under the same simulated disaster scenario. Some of these USV models are being calibrated with the actual boats described in Section 7.1, through several field trials to generate more accurate USV models. Therefore, our simulation environment allows not only for testing different boats but also to test a series of algorithms such as control strategies, path planning, and obstacle avoidance under controlled and repeatable disaster environments. Soon, we intend to benchmark different control and obstacle avoidance strategies for boats in flooding environments using this simulator.
###### Abstract  
This article presents the results of a systematic review investigating the potential of agricultural wastes as sustainable and low-carbon alternatives in reinforced concrete (RC) production. Background: The depletion of natural resources and the environmental burden of conventional construction materials necessitate innovative solutions to reduce the carbon footprint of construction. Agricultural wastes, including coconut shells (CSs), rice husk Ash (RHA), and palm oil (PO) fuel Ash, emerge as promising materials due to their abundance and mechanical benefits. Objective: This review evaluates the potential of agricultural wastes to improve sustainability and enhance the mechanical properties of RC structural elements while reducing carbon emissions. Design: Studies were systematically analyzed to explore the sources, classification, and material properties of agro-wastes (AWs), with a particular focus on their environmental benefits and performance in concrete. Results: Key findings demonstrate that AWs enhance compressive strength, tensile strength, and modulus of elasticity while reducing the carbon footprint of construction. However, challenges such as variability in material properties, limited long-term durability data, and lack of standardized guidelines hinder their broader adoption. Conclusions: AWs hold significant potential as sustainable additives for RC elements, aligning with global sustainability goals. Future research should address material optimization, lifecycle assessments, and regulatory integration to facilitate their mainstream adoption in construction.  
agro-wastes; structural elements; sustainability; mechanical properties; reinforced concrete (RC); construction standards 2020