esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LCI System Design Framework
### Motion Artefacts and Fringe Washout
#### 3.3.2 Lateral Motion Effects  
Transverse motion will degrade the lateral resolution leading to a blurring of the image in the direction of travel [55] and a reduction in SNR [54]. For a continuous wave (CW) source, the SNR degradation (\(SNR_{decrease}\)) due to lateral motion is given by Equation (21) [54]:  
\[SNR_{decrease}\cong-5 log_{10}\left(1+0.5\frac{\Delta x^{2}}{w_{0}^{2}}\right), \tag{21}\]  
where \(x\) is the scanning distance during the camera integration time and \(w_{0}\) denotes FWHM of the beam profile. The normalised displacement is defined as \(\Delta x^{2}/w_{0}^{2}\). For pulsed illumination, \(\Delta x\) is replaced by \(\left(T_{pulse}/T_{camera}\right)\). \(\Delta x\), where \(T_{pulse}\) is the pulse width in time and \(T_{camera}\) is the integration time of the camera for a single data point capture. To combat the lateral motion artefacts, the spectra capture speed can be increased, the motion speed decreased, or a pulsed or stroboscopic instead of CW light source can be used. [54; 56; 57]. Additionally, pulsed illumination can also offer SNR improvements for measurement on the fly over CW sources [58].  
Figure 6: Effect of offset distance on artefact measurement.  
The irradiance distribution profile for a Gaussian beam can be found using Equation (22) whilst stationary. However, once there is a transverse movement between the probe and surface, then the irradiance distribution profile for a fixed exposure period (\(T\)) can be calculated by Equation (23) [54]:  
\[g(x,y,z)\approx\frac{4 ln2}{\pi w_{0}^{2}}exp\left[\frac{-4 ln2(x^{2}+y^{2})}{w_{0 }^{2}}\right], \tag{22}\]  
\[G(x,y)=\frac{1}{T}\int_{-T/2}^{T/2}g(x+v_{x}t,y+vyt)dt, \tag{23}\]  
where \(x\) and \(y\) are locations along the beam profile, \(w_{0}\) is the FWHM of the beam profile and \(v_{x}\) and \(v_{y}\) represent constant velocities along the transverse x and \(y\)-axis respectively. The impact on transverse resolution in the \(x\)-axis for instance can be found by solving for \(G(x,y=0)\) and then locating the FWHM of the calculated irradiance profile.
## 4 Discussion
### Strengths and Limitations of the Study  
This study has the following limitations: (i) despite conducting familiarization sessions, the boxers had not been previously exposed to AEL training through jumps, which could have generated greater gains in the variables analyzed given the new stimulus for the boxers; (ii) the potential influence of uncontrolled physical conditioning activities performed outside the supervised intervention sessions. Although participants were instructed to refrain from additional strength training, it is possible that variations in general conditioning routines may have influenced the observed adaptations; (iii) the sample size per group was relatively small, which may limit the statistical power to detect between-group differences and generalize the findings; (iv) no electromyographic or neuromuscular function measures were included, precluding direct conclusions about underlying activation patterns or neural adaptations; (v) it did not include physiological variables related to muscle damage that could provide a better understanding of the effects of AEL through jumps and; (vi) no quantitative method using ground reaction force (GRF) was employed to precisely identify the timing of dumbbell release during AEL jumps or to complement the kinetic analysis of the lower limbs during punching actions. On the other hand, this study has the following strengths: (i) being the first study to investigate the effects of AEL through jumps with the inclusion of two experimental groups and a control group, which increases the quality in the comparison of the effects of the variables analyzed; (ii) the inclusion of right cross and left cross punches as outcome measures, which are rarely analyzed in the literature, where research has predominantly focused on straight punches; (iii) the inclusion of Rs and NRs analysis, which allowed the analysis of the individual responses of boxers in each group; (iv) the reduction of the research gap on AEL through jumping, presenting evidence on the effects of AEL with 10-20% of BM; (v) the comprehensive assessment of both kinetic variables from the countermovement jump (e.g., peak power, relative peak power, propulsive impulse) and punching performance, providing a broader understanding of lower-limb power adaptations and their potential transfer to sport-specific performance in boxing.
# Elastic Modulus Prediction Model of Foamed Concrete Based on the Walsh Formula
## 2 Micromechanical Analysis of Foamed Concrete Elements
### Prediction Model for the Elastic Modulus of Famed Concrete  
[PERSON] et al. [39] proposed an analytical solution for elastic theory for the two-layer embedded model. Accordingly, this study performs the theoretical analysis of the mesomechanics of foamed concrete by introducing the model. The prediction model for the elastic modulus of foamed concrete is shown in Figure 5, and the derivation process is shown in Equations (14)-(18) [39].  
\[u_{0c}=\frac{1}{E_{0}}\bigg{[}(1+v_{0})\frac{b^{2}c^{2}(P-P_{0})}{(c^{2}-b^{2  
\[x_{1}=f(1+v_{1})+(1-v_{1}) \tag{20}\] \[x_{2}=(1+v_{1})+f(1-v_{1}) \tag{21}\]  
where \(f\) is the volume percentage of inclusions. The volume ratio of the spherical shell inclusion can be regarded as the bubble porosity for foamed concrete, as shown in Equation (22). \(E_{1}\) and \(v_{1}\) can be obtained through the performance test of foamed concrete matrix material. For the convenience of analysis, it is assumed that \(v_{2}=v_{1}\), but \(v_{0}\) is still unknown. Therefore, the 3D problem should be simplified to a 2D problem. The relationship between the size of the spherical shell and the volume ratio is shown in Equation (23).  
\[f=n_{0} \tag{22}\] \[b=a/\sqrt{f} \tag{23}\]  
With reference to the formula for calculating the shear modulus of single-inclusion composites proposed by [PERSON] et al. [40], the effective elastic modulus \(E_{0}\) of foamed concrete material can be completely determined, which is also based on the two-layer embedded model. The calculation formula for the effective shear modulus \(\mu_{0}\) can be derived, as shown in Equations (24)-(29) [40].  
\[[\mu_{0}/\mu_{1}]^{2}A+[\mu_{0}/\mu_{1}]B+D=0 \tag{24}\]  
\[\begin{array}{l}A\ \  
\[\eta_{1} =3-4v_{1} \tag{28}\] \[\eta_{2} =3-4v_{2} \tag{29}\]  
where \(\mu_{0}\), \(\mu_{1}\), and \(\mu_{2}\) represent the shear elastic moduli of the equivalent medium, matrix, and spherical shell, respectively. In accordance with the principle of elastic mechanics, the relationship between elastic modulus, shear modulus, and Poisson's ratio is shown in Equation (30).  
\[v_{0}=\frac{E_{0}}{2\mu_{0}}-1 \tag{30}\]  
The elastic modulus prediction model of foamed concrete can be obtained by solving Equations (19), (24) and (30) simultaneously.
## 3 Results
### Validation and Evaluation of Reconstruction Results
This strategy enhanced the ability to capture detailed local features while maintaining spatial coherence during the reconstruction, thus making the proposed CS-DINEOF method particularly suitable for handling chl-a data with complex spatial distributions. As for quantitative evaluations, Figure 11 provides density scatterplots of the original and reconstructed values by the CS-DINEOF method at cross-validation points across four example data. Relevant statistical parameters, including the number of validation points (N) and the RMSE for each single image, are also annotated in the plots. Most data points are close to the 1:1 line, indicating the high consistency between the reconstructed values by CS-DINEOF and the original values at these artificially set missing points. Additionally, the low RMSEs further confirm the minimal deviations between the reconstructed values and the original data. Therefore, these results illustrate the high reliability and accuracy of the CS-DINEOF method in reconstructing chl-a data in the Bohai Sea region. Figure 10: The reconstruction results using (**a**) ordinary DINEOF and (**b**) CS-DINEOF on 28 November 2019. Specific local area is marked with red box in (**a,b**), and the corresponding zoomed-in views are shown in panels (**c,d**).
## 3 Results and Discussion
### Microbial Diversity Analysis
#### 3.8.2 Fungal Diversity Analysis  
Figure 4A shows the top 20 dominant fungal genus in normal and spoiled hams: _Apergillus_, _Wallemia_, _Meyerozyma_, _Yamadazyma_, _Fungi\(\_\)gen\(\_\)Incertae\(\_\)sedis_, _Alternaria_, _Malassezia_, _Pleurotus_, _Penicillium_, _Blumeria_, _Kurtzmaniella_, _Debaryomyces_, _Starmerella_, _Scheffersomyces_, _Wickerhamiella_, _Schizophyllum_, _Eupentidiella_, _Cystofilobasidium_, _Diatrypaceae\(\_\)gen\(\_\)Incertae\(\_\)sedis_ and _Aureobasidium_. _Wallemia_ was the most dominant fungal genus in normal hams, accounting for 60-73%, which is consistent with normal Nuodeng hams [99]. Meanwhile, _Aspergillus_ was the most dominant fungal genus in spoiled hams, comprising 41-88%. It has been discovered that _Aspergillus_ is the sole representative of the _Aspergillus_ genus identified in spoiled ham; however, this species is typically not present in dry-cured ham. As a result, the presence of _Aspergillus_ in spoiled ham may be attributed to contamination  
Figure 3: Bacterial genus level histograms (**A**), Wayne diagrams (**B**) and bacterial species level heat maps (**C**) of different muscles in normal and spoiled hams. N–SM: normal ham–semimembranosus; N\(-\)BF: normal ham\(-\)biceps femoris; S–SM: spoiled ham\(-\)semimembranosus; S–BF: spoiled ham–biceps femoris.  
of the ham surface following contact with the surrounding environment [100]. From the Wayne diagram (Figure 4B), a total of 34 fungal species were identified, with eight species found in both the SM and BF in normal and spoiled hams. The clustered heat map at the species level (Figure 4C) revealed a significant difference distinguishing normal and spoiled hams. The most dominant fungal species in normal hams was _Wallemia ichthyophaga_, which was significantly and positively correlated with arginine, phenylalanine, leucine, isoleucine, and methionine in a study on Jinhua ham [25]. In contrast, _Aspergillus ruler_ was the most dominant fungal species in spoiled hams. Interestingly, it is popular because it contains a substantial amount of secondary metabolites belonging to various classes, some of which show promising biological activities [101].
## 4 Poster Presentations
### P-02. Dried Blood Spot Contamination--An Underestimate Risk in Newborn Screening
Assessing Performances and Usability of the NS2400 Automated Platform from Labsystems Diagnostics [PERSON] \({}^{1,2,\star}\), [PERSON] \({}^{1,2}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{3}\)  
\({}^{1}\) Swiss Newborn Screening Laboratory, University Children's Hospital, Zurich, Switzerland  
\({}^{2}\) Children's Research Center, Zurich, Switzerland  
\({}^{3}\) Labsystems Diagnostics Oy, Vantaa, Finland  
\({}^{\star}\) Correspondence: [PERSON]; Tel. : +41-44-266-7732; Fax: +41-44-266-8110  
Newborn screening, in most countries, is highly centralized, with daily sample loads of 300 to over 2000 in a few cases. Therefore, totally automated systems are highly desirable. The main advantage of these systems is less hands-on-time for laboratory personnel, better traceability of the working steps, and less danger of mistakes and false screening results. The NS2400 from Labsystems Diagnostics combines total automation by replacing exactly the technician processed without altering the product design. The same kit reagents can be used manually and/or be integrated in the NS2400 platform, ensuring continuity of measurement in the case of downtime. The automate's modules can also be used as standalone, providing further flexibility of use. We tested the system thoroughly with all, thus far, available tests (TSH, 17-OHP, IRT, Biotinidase, total Galactose, G-6-PDH, and PKU) and comparisons with routine tests are reported. P-08. MCAD Deficiency with Severe Neonatal Onset, Fatal Outcome, and Normal Acylcarnitine Profile [PERSON] \({}^{1,2,\star}\), [PERSON] \({}^{3}\), [PERSON] \({}^{1,2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{3}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{2,4}\)  
\({}^{1}\) Swiss Newborn Screening Laboratory, University Children's Hospital, Zurich, Switzerland  
\({}^{2}\) Children's Research Center, Zurich, Switzerland  
\({}^{3}\) University of Zurich, Institute for Medical Genetics, Zurich, Switzerland  
\({}^{4}\) Division of Metabolism, University Children's Hospital, Zurich, Switzerland  
\({}^{\star}\) Correspondence: [PERSON]; Tel. : +41-44-266-7732; Fax: +41-44-266-8110  
Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is an autosomal recessively-inherited disorder of fatty acid oxidation with potentially fatal outcomes in undiagnosed patients. Introduction of tandem mass spectrometry into newborn screening (NBS) has led to the inclusion of MCADD into NBS in many countries, which has resulted in a significant reduction of morbidity and mortality. We report a child with MCADD presenting neonatally with apnoea and heart arrest. Despite intensive efforts to rescue the child, including reanimation for 90 minutes, the child died at the 2 day of life. Autopsy revealed fatty liver and also fat storage in heart muscle, which was suggestive for a fatty acid oxidation defect. However, acylcarnitines determined from stored EDTA blood were not suggestive for MCAD deficiency. Nevertheless, a subsequent whole exome sequencing analysis revealed homozygosity for the ACAD gene c.1084A>G/p.Lys362 Glu mutation.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 5 Application Case: Method Refinement (Myeongnyang Site)  
To effectively combine a series of SS images, the presence of an object that is clearly distinguishable on the seafloor surface and can be used as a reference point is crucial. However, such an object is not always available, or its position may change mid-survey. Therefore, placing an artificial object in the survey site that effectively reflects sound waves without disturbing the survey is essential. Therefore, during the Myeongnyang site UE mission in 2019, we operated the SS after installing a reflector on the seafloor to test the practical efficacy of fixing an artificial object as a reference point.
# Evaluation of Different Simulation Methods for Analyzing Flood Scenarios in the Danube Delta
###### Abstract  
The present work is focused on the analysis of flood scenarios for the settlements near the Danube discharge area into the Black Sea. From this perspective, the aim of the research is the development of flood extension maps for localities in the Danube Delta. The emphasis is on collecting the data and information needed for the entire analysis process, such as hydrological data on Danube flows and water levels (which were analyzed for 51 years), topo-bathymetric data (where 1685 cross sections were processed, measured on an 87-km section of the Danube), a digital terrain model (DTM), and others. Two methods of flood scenario analysis for the localities targeted were used in this paper. The first method was an analysis of the flood scenarios by modeling a real scenario, where it was supposed that a 20 m breach appeared in the dam which protects the localities and remained present for 24 h. The second method consisted of a Geographic Information System (GIS) analysis (static from a hydraulic point of view), where the maximum water level was superimposed over the DTM. This corresponded to a scenario in which the breach in the flood-control levee remains present for a longer period. The validated results show that the dynamic method is more efficient than the static method, both in terms of estimated flooded surfaces and in terms of simulation accuracy (taking into account more input parameters than the static method). Thus, from the obtained simulations it was observed that applying the dynamic method resulted in smaller flooded surfaces in the settlements analyzed than when considering the static method. In some cases, the differences between the flooded surfaces reached up to about 22%. This information is important and of general interest since it can be used in various fields of work, such as flood defense strategies, and investment promotion activities in the Danube discharge area or similar locations.  
hydraulic modeling; GIS processing; flooding maps; Danube Delta +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Site Description
#### 2.1.2 Sampling and Indoor Experiment  
The centre point of each grid was the coordinate of the sampling point. After the grid of the study area is surveyed and determined, a real-time kinematic (RTK) instrument is used to determine the coordinates and elevation of each grid centre. The RTK precision of this instrument is \(\pm(8+1\times 10^{-6}\times\mathrm{D})\) mm (plane precision) and \(\pm(15+1\times 10^{-6}\times\mathrm{D})\) mm (elevation precision). To ensure that the sampling point was in the grid and avoided houses and streets, the actual sampling point deviated from the centre point (Figure 1). Soil samples were taken from 20-25 cm below the surface to avoid wrapping plant roots.  
Figure 1: Distribution of the study area and sampling points.  
Several surface soil samples were taken by ring knives, and the samples were transported to the laboratory for air drying. The division of soil particle size, the symbols and the test methods of soil properties are shown in Table 1. The test methods in Table 1 are strictly in accordance with the Standards for Geotechnical Testing Method (CN), GB/T50123-2019 [31]. Based on the test results, the soil texture distribution of 64 samples is shown in Figure 1. The classification of soil texture refers to the Code for Investigation of Geotechnical Engineering (CN), GB50021-2001 [32]. The coordinates of sampling points and test results can be seen in Table S1.
# Continuous Fiber-Reinforced Material Extrusion with Hybrid Composites of Carbon and Aramid Fibers
## 2 Materials and Methods
### Specimen Design and Fabrication  
For mechanical characterization, tensile, flexural and impact tests were carried out. The dimensions of the specimens can be taken from Figure 4. The machine code for fabricating the specimens was generated with an inhouse written slicing program in Rhinoceros 3D using the Grasshopper visual programming environment.  
All specimens were manufactured with unidirectional fiber orientation in the longitudinal direction. The fiber volume fraction describes the percentage of fibers in the total volume of the part. For the calculation of the fiber volume content, an ideal specimen without voids was assumed. The fiber volume fractions can be calculated using the following equation:  
\[V_{f}=\frac{v_{f}}{V_{t}}\times 100\% \tag{8}\]  
where \(V_{f}\) is the fiber volume fraction of the part, \(v_{f}\) is the fiber volume in the entire part and \(V_{t}\) is the total volume of the specimen. For the calculation, a fiber volume content of the fiber filament of 34% was assumed. The fiber volume contents of the specimens are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Parameter** & **Value** & **Unit** \\ \hline \multirow{4}{*}{C-FF} & Extruder temperature & 270 & \({}^{\circ}\)C \\  & Print bed temperature & 95 & \({}^{\circ}\)C \\  & Infill density & 100 & \% \\ \hline \multirow{4}{*}{C-FF} & Nylon filament diameter & 1.75 & mm \\  & Strand width & 0.4 & mm \\  & Nylon printing speed & 45 & mm/s \\  & Infill angle & +45/\(-\)45 & \({}^{\circ}\) \\  & Bottom and top layers height & 0.25 & mm \\ \hline \multirow{4}{*}{C-FF} & C-CFF/A-CFF diameter & 0.4 & mm \\  & Fiber strand width & 0.9 & mm \\ \cline{1-1}  & Fiber printing speed & 20 & mm/s \\ \cline{1-1}  & Layer height fiber/nylon & 0.1 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Process parameters for the fabrication of the specimens from nylon and continuous carbon/aramid fiber.  
Figure 3: (**a**) Renkforce RF2000 with Markforged Dual Extruder and with Polymaker PolyboxTM; (**b**) printing process during the deposition of carbon fiber strands on an aramid fiber layer.  
In order to determine and quantify the effect of different mixture ratios on the mechanical properties, the specimens were fabricated with different ratios of carbon fibers to aramid fibers. The ratios of all specimens of carbon fibers to aramid fibers were in percent 100/0, 70/30, 50/50, 30/70 and 0/100, resulting in 10 fiber layers for the tensile and flexural specimens and 20 fiber layers for the impact specimens (see Figure 5). The fiber filament was changed between layers when necessary. In addition, the specimens were fabricated with two bottom and top layers of nylon.
## 3 Overview of the Study Area and Data Sources
### Data Source  
Based on the geological disaster survey data along the pipeline and remote sensing data, a regional landslide disaster database was established to analyse and map the landslide disaster along the pipeline. The main data included: (1) DEM data along the pipeline to analyse basic information, such as elevation, slope, aspect, and curvature of the study area; (2) a 1:50,000 geological map along the pipeline to advance the formation information of the study area; (3) satellite remote sensing data along the pipeline to extract vegetation coverage in the study area; (4) landslide survey and survey data along the pipeline along with 91 satellite maps to determine the area and distribution of landslides.
## 2 Related Work
### Routing in Earth Observation Satellite Constellations  
A novel routing method was proposed to improve the transfer ratio of the whole observed data in a contact time by dividing the data and using them to modulate multiple carriers to send EOS data into two or more ground stations by multi-hop relaying [37]. A joint space-temporal routing algorithmic framework was devised in which disruption-tolerant-networking-based Earth-observing satellite networks with a frequently changing topology and sparse and intermittent connectivity were modeled as a space-time graph [38]. A minimum-cost constrained multipath (MCMP) algorithm was used to find a feasible set of available paths to send a certain amount of mission data to ground stations within a tolerable delay. In the same work, the earliest arrival multipath routing policy based on the contact graph routing was proposed. A cognitive engine architecture that used neural-network-based reinforcement learning (RLNN) was proposed to configure space links from NASA's International Space Station testbed to the ground station to achieve multiple optimization objectives such as delay, throughput, bandwidth, and power [9].