esa-sceva/satcom-chunk-collection huggingface dataset:
# Interferometry of Gas-Phase Flows during Selective Laser Melting
## Appendix A  
The experimental data show that the gas-phase jet is always directed normal to the evaporating surface. Therefore, the general picture of the gas-phase jet flow and the surrounding gas during SLM looks like shown in Figure A1.  
Let the coordinate axis (OZ) be directed along the vapor jet, and the plane \(z=0\) coincide with the surface to be treated. Then, in the half-space \(z>0\), the problem of viscous gas flow, defined by the continuity and Navier-Stokes equations, is solved Equation (A1):  
\[\langle\Delta n\rangle\rho+\
abla\cdot(\rho\mathbf{u})=0,\ (\rho\mathbf{u})_{t}+ \
abla\cdot\mathbf{\Pi}=0,\] (A1)  
where index \(t\) denotes the partial derivative with respect to time, \(\rho\)--the density, \(\mathbf{u}\)--the flow velocity, and \(\mathbf{\Pi}\)--the momentum flux density:  
\[\mathbf{\Pi}=\rho\mathbf{I}+\rho\mathbf{u}\otimes\mathbf{u}-\boldsymbol{ \sigma},\] (A2)  
where \(p\) is the pressure, \(\mathbf{I}\) the unit tensor, and viscous stress tensor  
\[\boldsymbol{\sigma}=2\eta\Big{[}\mathbf{e}-\frac{1}{3}\mathbf{I}\mathbf{r}( \mathbf{e})\Big{]},\] (A3)  
where \(\eta\) is the dynamic viscosity and \(\mathbf{e}\) the strain rate tensor defined as the symmetric part of tensor \(\mathbf{V}\mathbf{u}\). It is assumed that the flow velocity is small compared to the speed of sound, therefore, the gas is considered incompressible and its density practically does not differ from the density under normal conditions \(\rho_{0}\). The mass of the surrounding gas involved in the movement by the steam jet is much larger than the mass of the steam itself, therefore, the thermal energy of the steam is neglected and the energy equation is not considered.  
On the surface \(z=0\), the mass flux density in the axial direction is set within the limits of a round evaporation spot with a diameter \(D\) and the boundary conditions of adhesion on the rest of this surface. Evaporation rate increases with surface temperature. At the boundary of the evaporation spot, the temperature is equal to the boiling point by default, and the mass flux density is zero. The maximum temperature and maximum mass flux density are achieved near the center of the spot. In the calculations, the parabolic distribution of the mass flow over the evaporation spot is accepted Equation (A4):  
\[\rho u_{z}=2\rho_{0}U\left(1-\frac{4r^{2}}{D^{2}}\right),\ r\leq\frac{D}{2},\] (A4)  
where \(U\) is the average velocity of the jet outflow, and \(r\) the distance from the center of the laser spot (radial coordinate).
# Argininemia: Pathophysiology and Novel Methods for Evaluation of the Disease
## 5 Conclusions and Future Perspectives  
Argininemia is a rare metabolic disorder resulting from mutations in the ARG-1 gene, leading to deficient or absent ARG1 activity, and elevated concentration of arginine and other nitrogen metabolites.  
The precise pathophysiology associated with ARG1-D remains yet to be unveiled, but the neurotoxicity of increased arginine levels, episodes of hyperammonemia, and the produced guanidino compounds are considered crucial for the development of the disease. Clinically, it manifests through progressive development of spasticity, seizures, developmental delay, cognitive impairment, and extra-neurological symptoms such as hepatic dysfunction. Current diagnostic means include plasma arginine, other amino acid and ammonia levels measurement, red blood cell arginase activity evaluation, genetic testing (also used in NBS), and a couple of clinical assessment methods (mainly physical examination, EEG, brain imaging, and family history). The methods used for treatment include severe dietary protein restriction, nitrogen scavengers, dialysis and blood transfusion, ornithine and lysine supplements, liver transplantation, enzyme replacement therapy, and symptomatic treatment.  
There is a plethora of novel methods for diagnosis and treatment, which include the use of plasma arginine/ornithine ratio, metabolomics and machine learning, genetic databases, and mobile apps for diet management. In addition, advanced neurophysiological methods, multi-modal imaging, the detection of new genes, and the utilization of new modelling methods, like knockout mice and iPSCs, are bringing optimism for the future pathophysiological study of the disease.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study (First Author, Year of Publication)** & **Design/Method of Evaluation** & & **Key Findings** \\ \hline \multirow{6}{*}{[PERSON] et al., 2021 [PERSON] et al., 2  
Thus, a better understanding of the exact nature of the disease should allow the invention of new more accurate diagnostic methods, and the discovery of innovative and more effective treatments.  
Conceptualization, C.B., E.K. and C.S.; investigation, D.N., K.K., A.F., M.B., M.T.-C. and F.C.; writing-original draft preparation, D.N., K.K., I.M., S.S.D.L. and M.N.N.; writing-review and editing, D.N., M.N., C.B., E.K., S.D.L. and E.S.; visualization, D.N. and M.N.; supervision, C.B., E.K. and C.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.  
ARG1-D = arginase-1 deficiency, IEMs: inborn errors of metabolism, Arg/Orm ratio: arginine/ornithine ratio, NBS: newborn screening, UCDs: urea cycle disorders, MRS: magnetic resonance spectroscopy, MEPs: motor evoked potentials, SEPs: somatosensory evoked potentials, NCV: nerve conduction velocity.
# Data-Driven Convolutional Model for Digital Color Image Demosaicing
## 4 Results
When the algorithm implementation was available we computed the relative error function, but when no implementation was available we marked the entry as \"not available\". We collected the results in Table 1. Visual comparison is provided in Figure 11.  
We analyzed the effect of both signal noise and quantization noise and compared the performance of our algorithm in both scenarios. The proposed model has a minimal performance drop due to signal noise and it is robust to quantization noise. Further improvement in robustness to signal noise can be achieved if the training set contains input samples corrupted by noise. To estimate image robustness to signal noise, we generated a number of samples corrupted by Gaussian random noise with varying intensity in order to simulate electrical noise captured by the camera sensor. We trained two models on the original training dataset and on a training dataset that included corrupted samples and compared the output produced by the two models after training. We reported results in Figure 12.  
We measured filter coefficient quantization by applying post-train quantization on the resulting model. We compared the original floating point implementation to the model after coefficient quantization on 8-bit and measured the reconstruction error relative to the original input image. From the measurements we collected, the effect of quantization in this model is negligible and this enables the implementation of low-end devices that do not support floating-point units (FPGAs, low-end processors). To test the performance on a real-world application, we implemented our model in NVIDIA CUDA on Jetson Nano on-board GPU and measured its performance over 1024 runs. We measured an end-to-end running for full frame high-resolution 12 MPixel image (4000 \(\times\) 3000) of 996.22 \(\pm\) 24.978 ms. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MSE** & **PSNR** & **SSIM** \\ \hline bilinear & 0.14 & 29.2 & 0.93 \\ [PERSON] [24] & 0.07 & 35.4 & 0.98 \\ [PERSON] [25] & 0.05 & 39.2 & 0.99 \\ [PERSON] [14] * & - & 36.5 & - \\ [PERSON] [17] * & - & 37.3 & - \\ [PERSON] [27] * & - & 40.0 & - \\ [PERSON] [28] * & - & 36.4 & - \\ [PERSON] [29] * & - & 35.5 & - \\ [PERSON] [30] * & - & 36.1 & - \\ ours & 0.04 & 43.1 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean measured error performance in reconstruction for the entire Kodak dataset. For all three error functions we achieve the best performance when compared to other methods. (*) No implementation was available and only published results are reported.
## 3 Relationship between UPV and Strength Evolution Index of Concrete
### A Unified Equation for the Relationship between UPV and the Strength Evolution Index  
The aforementioned results indicated that the adoption of various mixture proportions in this study, namely with respect to the water-to-cementitious material ratio, paste volume ratio, sand-aggregate ratio, type of aggregate source, and amount of pozzolanic material, exhibited limited influence on the concrete UPV and strength evolution index. Therefore, the test data of all mixture proportions in Tables 1-4 were compiled to conduct a regression analysis on the UPV and strength evolution index (Figure 8). As indicated in Figure 8, the data points for the UPV and strength evolution index of concrete specimens with various mixture proportions exhibited a concentrated distribution. Specifically, these data points were distributed evenly along both sides of the regression curve of the relationship, suggesting favorable correlation between the UPV and the strength evolution index of concrete specimens with different mixture proportions at different ages. For practical application, the relationship between the UPV and the strength evolution index should be as simplified as much as possible. The regression function as given in Equation (3), derived from Figure 8, was used as a unified equation for subsequent estimations of the concrete strength evolution index using the UPV evolution index.  
\[S_{g}=0.0660e^{(0.0732V_{g})}\text{ with R}^{2}=0.947 \tag{3}\]  
Figure 8: Compilation of the test data of all mixture proportions to establish a general regression relationship curve of the UPV and the strength evolution index.  
Figure 7: Relationship between the UPV and strength evolution index of normal and pozzolanic concrete.
# Passenger Flow-Oriented Metro Operation without Timetables
## 3 Mathematical Description  
Two figures are applied to describe the problem. In Figure 1, there is a metro system; in general, trains will depart and stop at all the stations. We assume the passenger flow monitoring system, which can record passengers' origins and destinations (ODs) based on their tickets, has been installed in all the stations. Thus, the average speed of entering passengers in a fixed period with different ODs could be counted. Passengers can only travel from a station labelled with a lower number to one marked with a higher number.  
The process flow chart of RPOM is shown in Figure 2. The passenger flow entering speed will be kept up-to-date in every new detecting period, and the operation strategy will be optimised based on the objective function after the updating. As this process needs to be completed rapidly, the optimising method is essential.  
Based on the above description, the considered problem should be formulated to a RPOM that constantly optimises the operation strategy based on the updated data. The following part focuses on the model's details, including parameters, decision variables, constraint conditions, and objective functions. Several assumptions and explanations should be given first to make the scheduling process straightforward.  
(A1) Discrete time, i.e., 1, 2,..., T, will be used in this research;  
(A2) Between stations, trains will run based on the predetermined running time, unless it is against safety constraints;  
(A3) With the implementation of platform screen doors (PSD) and the deployment of station staffs, the impacts of passenger flow on the trains' dwelling times have been significantly decreased in recent years. Thus, we assume the generated dwelling strategies can be followed strictly.  
Figure 1: System description.  
Based on the Field Study in the London Underground Bakerloo Line Operation Department, Modifying the Number of Trains in a Specific Operation Time Horizon Will Severely Affect the Economic Operation of the Metro System and Lead to More Operation Problems, such as the Staffs' Redeployment. Thus, the Number of Trains Active in the Research Time Horizon Is Fixed for in this Research.
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 1 Introduction
A strategy must be designed and developed to leverage cloud- and edge-resources to improve network performance. * Currently, cloud edge collaboration technology is an important component of next generation networks. Since the application of edge computing is not yet fully developed, the research of using cloud-edge collaboration to accomplish data offloading is not yet deep. Most of the current studies consider data offloading on the cloud-side or end-side. This paper focuses on data offloading as a critical technology for cloud-edge collaboration. In order to ensure a user's QoS in the network architecture of cloud-edge collaboration, a performant cloud and edge computing are necessary. However, the existing data offloading model needs to be revised to meet these requirements, and a new data offloading strategy is needed to adapt to cloud-edge collaboration. Due to the large amount of video data, network users tend to utilize the data center's bandwidth for extended periods, which can result in some users not receiving data and questioning the reliability of the cloud. Therefore, minimizing the delay gap between users is crucial to our work. **In summary, we make the following contributions:**  
* A cloud-edge collaborative data offloading strategy is proposed. WFO can serve more users with the limited outgoing bandwidth of the source node and ensure the delay requirements of users. * We analyze the limitations of the traditional data offloading model in the cloud-edge collaboration architecture. The application scenario of multiple users accessing the same data in the same node is modeled. * We evaluate WFO's sensitivity to cloud-edge collaboration data offloading challenges such as the number of user services, the average delay of data, and the discrete distribution of delay. This paper is organized as follows. Section 3 introduces the system model and the problem, and composes the framework of the paper. Section 2 introduces the related works of cloud-edge collaborative data offloading strategies and two typical data offloading models, FQ and FCFS, followed by some discussions. Section 4 introduces the data offloading strategy WFO which can greatly increase the number of users while ensuring the quality of service. Experimental results are presented in Section 5 and concluding remarks in Section 6.
# Validation of AISI Design of Cold-Formed Steel Beams Using Non-Linear Finite Element Analysis
Flexural strength was also affected by screw spacing [12]. The flexural behavior of CFS four-limb built-up beams with both closed and open sections was investigated through experiments and FE modeling. Four-point bending tests reveal that the open-section beams have higher ultimate load capacity than closed-section beams, with local buckling as the failure mode. Validated FE models show that flexural moment capacity increases with thickness and web depth [13]. The buckling mode is a key factor that influences the bending strength of the specimens. Also, the finite element method (FEM) is a reliable tool to obtain quite accurate results in a reasonable amount of time [14] as an optimization framework for CFS members to maximize their flexure capacity through EC3. It was found that for the same amount of material, folded-flange and super-sigma sections have a higher flexural capacity enhancement of approx. 60% and 65%, respectively [15]. A simplified finite element model was defined to analyze stresses in the components. The Bending capacity and flexural behavior were investigated numerically by using FEM and through experimental evaluations. The moment capacity of the beams decreased as the compression flange's plate slenderness increased. The results were used to evaluate,and propose modifications to, the Direct Strength Method (DSM) for cold-formed steel specialized beams prone to local buckling [16; 17; 18]. Cold-formed steel (CFS) upright sections were evaluated using finite element analysis and AI/ML techniques to predict normalized ultimate load and deflection. The finite element method (FEM) provided data for analysis, and the \"feature selection\" method identified key factors affecting flexural strength [19]. Cold-formed thin-walled C steel channel is not only being used as an independent member but also in composite form as a composite floor system [20]. Non-linear FEM results are effectively used to describe all the buckling modes. Key parameters like length, thickness, and geometric imperfections significantly impact the beams' behavior [21; 22]. This study aims to validate the analytical approach of CUFSM and AISI DSM using the non-linear finite element method (FEM), employing ABAQUS for channel section flexural members. By conducting comparative analyses on eight different sections, this research evaluates the accuracy of DSM and CUFSM predictions for CFS beam sections. The findings will contribute to a better understanding of the strengths and limitations of these methods, ultimately leading to their effective application in the design of cold-formed steel structures.
## 3 Results and Discussion
### Characterisation of Tyrosine Biosensor  
Electrochemical behaviour of Trolox was investigated at bare CPE, CPE/MWCNTs, and CPE/MWCNTs/TYR/Nafion\({}^{\textregistered}\) (TYR biosensor), to find out an effect of each component. Trolox provided a distinct oxidation signal with cathodic counterpart (\(\Delta E_{\mathrm{P}}\) = 65 mV) in 0.1 mol\(\cdot\)L\({}^{-1}\) phosphate buffer pH 7.0 at bare CPE, attributed to one-electron oxidation to radical cation with subsequent proton transfer to form phenoxy radical and then immediate oxidation by one electron to phenoxonium cation (ECE mechanism), similarly to \(\alpha\)-tocopherol [16]. After repetitive cycles, another redox couple was observed (\(\Delta E\) = 280 mV). It is assumed that after the formation of phenoxonium cation, a nucleophilic addition of water starts in aqueous solution with concomitant formation of Trolox quinone via hemiketal intermediate. Trolox quinone is then reduced to corresponding hydroquinone, which can be oxidized to quinone in subsequent scan to positive potentials [10; 16]. Increasing current responses of the second redox couple with each cycle is caused by gradual buildup of Trolox quinone by oxidation of the original compound (Figure 2A).  
The ratio of the peak currents (\(I_{\mathrm{p}}\)\({}^{\mathrm{a}}\)/\(I_{\mathrm{p}}\)\({}^{\mathrm{c}}\)) was equal to 1.544 at bare CPE, unlike ratio values of 0.615 and 0.320 obtained for CPE/MWCNTs and CPE/MWCNTs/TYR/Nafion\({}^{\textregistered}\), respectively. Several times, a higher reduction peak of Trolox was observed at the TYR biosensor at peak potential (\(E_{\mathrm{p}}\)\({}^{\mathrm{c}}\)) of +0.049 V (compare with values of +0.079 and +0.099 V for CPE/MWCNTs and bare CPE, respectively), whereas its oxidation signal markedly decreased owing to catalytical activity of the TYR (Figure 2B) towards Trolox. According to these findings, working potential with values lower than +0.050 V should be investigated for amperometric detection in batch configuration. Based on these experimental data, a principle of amperometric tyrosinase biosensor for evaluation of TEAC (Scheme 1) was established, which will enable electrochemical detection of Trolox at low potentials with markedly improved sensitivity.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
### Example Application and Outline  
The test case for the new approach includes sensor measurements with timing deviations and the need for single-delay resampling. This can be illustrated by an example application from our previous research on remote container monitoring. Wireless sensors were mounted inside a reef container with bananas [4]. Data were transmitted over local and global networks to a server. Subsequent signal processing on the server requires uniform sampling intervals for estimation of cooling efficiency and of the amount of heat that is produced by ripening processes. Some measurements are lost due to occasional communication failures. Slack battery contacts and mechanical shocks during container handling can lead to power failure and restart of the wireless sensor with a time-shifted communication interval. Thus far, a short transmission interval of 10 min was necessary to make sure that at least one measurement with approximately correct timing arrived for each step of the subsequent processing. If adequate resampling is available, the battery lifetime of the wireless sensors can be extended by increasing the transmission interval, e.g., to once per hour. After arrival of a measurement with deviating timing, the resampling algorithm must process it on the instance. Waiting several hours to collect sufficient samples for a symmetrical filter is not acceptable.  
The state-of-the-art and recent literature on resampling applications are reviewed in Section 2, followed by mathematical description of the selected reference algorithms and of Kriging. The new approach for setting the Kriging variogram parameters and methods to evaluate the accuracy of the suggested resampling methods are presented in Section 3. Section 4 presents the results of a detailed comparison of the group- and single-delay scenarios for Kriging and three other methods described in literature. The discussion and conclusions follow in Section 5. Findings are summarized in Section 6.
# An Improved Fuzzy Vector Signature with Reusability
## Preliminaries
### Bilinear Maps  
Let \(\mathbb{G}_{1},\mathbb{G}_{2},\mathbb{G}_{T}\) be groups of some prime order \(q\) and a bilinear map (or pairing) \(e:\mathbb{G}_{1}\times\mathbb{G}_{2}\rightarrow\mathbb{G}_{T}\) over \((\mathbb{G}_{1},\mathbb{G}_{2})\) be admissible if it satisfies the following properties:  
1. [noitemsep]
2. Bilinear: The map is bilinear if \(e(g^{a},h^{b})=e(g,h)^{ab}\) for all \(g\in\mathbb{G}_{1},h\in\mathbb{G}_{2}\).
3. Non-degenerate: \(e(g,h)\
eq 1\).
4. Computable: There is an efficient algorithm to compute \(e(g,h)\) for all \(g\in\mathbb{G}_{1},h\in\mathbb{G}_{2}\).  
Our fuzzy vector signature is constructed using a Type-3 pairing where \(\mathbb{G}_{1}\
eq\mathbb{G}_{2}\) and there is no known efficiently computable isomorphism between \(\mathbb{G}_{1}\) and \(\mathbb{G}_{2}\).