esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 4 Validation of Parabolic Mesh Reflector
### Verification of Surface Error
#### 4.1.1 Laser Measurements Along Ribs  
A reference plane was placed at the bottom of the reflector, and fixed points along the 24 ribs were measured with a laser meter that can be displaced along the reference place. Eleven points distanced 10 cm from each other were marked with reflective tapes. These points were marked on each rib's lateral, i.e., along its right and left sides. Misalignment errors were included in overall surface error. RMS surface error was computed as the RMS difference between theoretical value of parabolic rib shape and measured values. Results show average RMS error of 1.92 mm \(\pm\) 1.125 mm on the right side of the rib and 1.93 mm \(\pm\) 0.53 mm on the left side of the rib, both below the required 4.22 mm RMS surface error.
# Error-Correction Coding Using Polynomial Residue Number System
## 2 Modular Codes
### Codes of Polynomial Residue Number System  
The bases are irreducible polynomials, \(p_{i}(z)\), \(i=1,\ \ldots,\ n\), in a code of polynomial residue number system. It is necessary to convert an integer, \(D\), presented in a binary number system into a polynomial, \(D(z)\), prior to its conversion into the code combination of PRNS. After that, residues obtained by dividing the polynomial \(D(z)\) by bases of the code of PRNS are found in [2; 3; 4]:  
\[D(z)=(d_{1}(z),\ d_{2}(z),\ldots,\ d_{n}(z)) \tag{5}\]  
where \(d_{i}(z)\equiv D(z)\text{mod}p_{i}(z)\) and \(i=1,\ \ldots,\ n\).  
The operating range of the code of polynomial residue number system is defined as  
\[W_{n}(z)=\prod_{i=1}^{n}p_{i}(z) \tag{6}\]  
Then, using the isomorphism generated by the Chinese Remainder Theorem (CRT) in polynomials, we obtain  
\[D(z)\bullet U(z)=((d_{1}(z)\bullet u_{1}(z))\text{mod}p_{1}(z),\ \ldots,\ (d_{n}(z) \bullet u_{n}(z))\text{mod}p_{n}(z)) \tag{7}\]  
where \(\bullet\) are addition, subtraction, and multiplication operations, \(U(z)\equiv u_{i}(z)\text{mod}p_{i}(z)\), and \(i=1,\ 2,\ldots,\ n\).  
Despite the similar principles of implementing codes of RNS and codes of PRNS, the latter have not found as wide application due to the insufficient level of development of the theoretical foundations for constructing redundant codes of polynomial residue number system that are capable of detecting and correcting errors that occur during calculations in the polynomial ring.
## 3 Inverse Solving Process
### Sound Source Identification Method Based on Grid Splitting and Refinement
2: Establish the transfer matrix \(\mathbf{G}\) between the sound pressure and the source surface. 3: The initial value of the iteration number \"_iter_\" is set to 0. The decay factor \(\delta\) and the iteration stopping threshold \(\sigma\) are set. 4: while \(d_{\text{s}}^{(iter+1)}>\sigma\). 5: The initial source strength \(\mathbf{Q}^{(iter)}\) is obtained by sparse Bayesian learning. 6: Split the meshes to update near the local maximum of \(\mathbf{Q}^{(iter)}\), with \(A_{\text{s}}^{(iter+1)}=A_{\text{s}}^{(iter)}\times\delta^{iter}\) and \(d_{\text{s}}^{(iter+1)}=d_{\text{s}}^{(iter)}\times\delta^{iter}\). 7: Remove duplicate grid points and construct a new transfer model \(\mathbf{G}^{(iter+1)}\). 8: Form a new sound pressure \(\mathbf{P}^{(iter+1)}=\mathbf{G}^{(iter)}\mathbf{Q}^{(iter)}\), and enter it into the MoG-RPCA model. 9: Replace the new sound pressure with the separated low-rank matrix:  
\(\mathbf{P}^{(iter+1)}=\mathbf{U}^{(iter+1)}\mathbf{V}^{(iter+1)}\). 10: \(iter=iter+1\). 11: end while  
Output: The estimation of source strength \(\mathbf{Q}\). **Algorithm 1 VG-SBL Algorithm. **  
In general, this paper proposes a new strategy for the problem of high-resolution sound source identification with a wide range of source distribution under far-field measurement, which is different from the traditional fixed on-grid, off-grid, or grid-free methods. By  
Figure 2: Schematic diagram of the method. In the figure, the red stars represent the locations of the real sound sources, and the arrow represents the process guidance of the method. adaptively optimizing and adjusting the grid scale and center location while dynamically removing the influence of noise, this provides an effective way to solve the problem of sound source identification under far-field large-aperture array measurement.
## 2 Materials and Methods
### Statistical Analysis  
All statistical analysis, except meat sensory data, was performed using the GLM procedure of SAS (SAS Version 9.1, SAS Institute, Cary, NC, USA). Experimental diet was used as a fixed effect. Initial weight was covariate for blood parameters, performance, meat quality, and fatty acid profile. Meat sensory analysis was undertaken in a randomized block design (each panel member as one block and block effect considered as random). To verify the effect of the experimental diets (fixed effect) on acceptability, the MIXED procedure of SAS was used, considering the observation of each panel member as a repeated measure. \(T\)-test was applied at a significance level of \(p<0.05\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Symbol** & **Forward (F) and Reverse (R)** & **Access Number** & **Amplificon (bp)** & \(\mathbf{R^{2}}\) & **Efficiency** \\ \hline SREBP1 & F CTGCTATGCAGGCCAC & & KF360085.2 & 99 & 0.99 & 1.94 \\  & R GGTTGAGGCCAGCTTGT & & & & \\ PPAR\(\alpha\) & FAGACCAAAACACAAACACAGCC & & XM\_012175774 & 213 & 0.99 & 1.94 \\  & R AAGTCCAGGTTGCAAAACC & & XM\_01009254.1 & 124 & 0
###### Abstract  
(1) Background: Stererophotogrammetry has recently been investigated showing high accuracy in complete implant supported cases but has scarcely been investigated in cases of tilted implants. The aim of this in vitro study was to compare the accuracy of digital impression techniques (intraoral scanning and photogrammetry) at the level of intraoral scan bodies in terms of angular deviations and 3D discrepancies. (2) Methods: A stone master cast representing an edentulous maxilla using four implant analogs was fabricated. The two anterior implants were parallel to each other, and the two posterior implants were at an regulation of 17 degrees. Digital intraoral scanning (DIOS) impressions were taken after connecting implant level scan bodies to the master cast and STL files were exported (\(n=15\)). Digital photogrammetry (DPG) impressions were captured using a PIC Camera after tightening implant level PiC optical markers and STL files were exported (\(n=15\)). Superposition was carried out by a software for determining the accuracy of both. (3) Results: Significant angular discrepancies (\(\Delta\)A) and 3D deviations of scan bodies were found among the groups in trueness with lower deviations for the DPG (\(p\) value \(<\) 0.001). However, trueness within ISBs varied between angular and 3D deviations and outcomes were not specific to determine the effect of implant insulation. In precision, no significant differences were detected within ISBs and among both groups in terms of angular deviation. However, DPG had less deviations than DIOS group in terms of 3D deviations (\(p\) value \(<\) 0.001). (4) Conclusion: Digital photogrammetry technique conveyed the utmost accuracy in both trueness and precision for the intraoral scan bodies among both impression methods assessed. In addition, implant insulation did not influence the precision of the impression techniques but affected their trueness without explicit conclusions.  
angulated implants; implant supported prosthesis; intraoral scanning; photogrammetry +
Footnote †: journal: Applied Sciences
## 1 Introduction
Known passive schemes rely typically on the use of a parametricdown-conversion (PDC) source, where [PERSON] and [PERSON] passively and randomly choose which bases to measure each incoming pulse by means of a beamsplitter (BS) [40, 41]. Also, [PERSON] can exploit the photon number correlations that exist between the two output modes of a PDC source to passively generate decoy states [34]. More recently, it has been shown that phase-randomized coherent pulses are also suitable for passive preparation of decoy states [37, 38] and BB84 polarization signals [42], though the combination of both setups in a single passive source is cumbersome. Intuitively speaking, Refs. [37, 38, 42] take advantage of the random phase of the different generated pulses to passively prepare states with either distinct photon number statistics but with the same polarization [37, 38], or with different polarizations but equal intensities [42]. The preparation of phase-randomized coherent pulses could be achieved, for instance, by strongly modulating the laser diode, taking it below and above threshold [21, 22]. In this article, we present a complete passive decoy-state QKD transmitter with coherent light. It shows that it is indeed possible to do state preparation for QKD in an entirely passive way using coherent states, even for BB84 signals in combination with decoy levels. Our method employs sum-frequency generation (SFG) [43, 44, 45, 46] together with linear optical components and classical photodetectors. SFG has already exhibited its usefulness in quantum information [47, 48, 49, 50, 51, 52] and device-independent QKD [53] at the single-photon level. Here we use it in the conventional non-linear optics paradigm with strong coherent light. This fact might render our proposal particularly valuable from an experimental point of view. In the asymptotic limit of an infinite long experiment, it turns out that the secret key rate (per pulse) provided by such passive scheme is similar to the one delivered by an active decoy-state BB84 setup with infinite decoy settings. Let us emphasize, however, that it is uncertain whether in practice such passive scheme could beat active transmitters or passive solutions based on a PDC source in high-speed QKD in general. Importantly, the answer will depend on various technologies. The paper is organized as follows. In Section 2 we introduce a passive transmitter that generates decoy-state BB84 polarization signals using coherent light. Then, in Section 3 we evaluate its performance and we obtain a lower bound on the resulting secret key rate in the asymptotic regime. In Section 4 we consider the case where [PERSON] and [PERSON] use phase-encoding, which is more suitable to employ in combination with optical fibers than polarization encoding. Finally, Section 5 concludes the article with a summary. The paper includes as well some Appendixes with additional calculations.
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 2 Literature Review
This mutual reinforcement catalyzes iterative improvements to both the regional economy and transportation networks, underscoring the inherent interdependence between economic and mobility flows [26]. Current evaluations of synergistic development dynamics among cities in the Greater Bay Area, particularly the interplay between transportation demand and economic flows, remain inadequate. This gap stems from two key limitations: (1) the absence of geospatial big data, with reliance on statistical yearbooks and bulletins introducing inconsistencies; (2) insufficient attention to intra-cluster synergy heterogeneity, where variation in intercity coordination levels is often overlooked. The interaction between transport infrastructure and economic mobility plays a pivotal role in resource reallocation, regional economic agglomeration, and enhanced competitiveness via structural optimization of transportation systems. Empirical studies affirm a strong coupling effect between transportation and capital flows [27], underscoring the necessity of integrated planning to ensure the healthy and sustainable evolution of urban agglomerations [28]. Accordingly, this study adopts a flow space framework and a transportation-economic linkage perspective to construct an intercity economic network using multidimensional indicators. It further develops logistics and capital flow networks based on liner schedules and enterprise investment data, revealing the structural characteristics and synergistic mechanisms among cities in the Greater Bay Area. Employing complex network analysis, the study identifies the spatial positioning, dynamic interactions, and economic flow patterns within the region. This approach facilitates regional economic integration and enhances the synergistic development model among cities. By examining the transportation-economy relationship, the study highlights the pivotal role of the transportation network in fostering collaboration and resource convergence. It also provides theoretical guidance and actionable insights for governance, policy formulation, and regional planning.
## 2 Theory  
### Smooth Inversion of Gravity Data Based on Conjugate Gradient Algorithm  
In 3D inversion, the subsurface is typically divided into a finite number of prism units with equal sizes, uniform density distribution and close arrangements. The formula for gravity anomaly response generated by a single prism is as follows:  
\[\Delta g=-G\rho\iiint\int\frac{(z-\zeta)d\xi d\eta d\zeta}{\left[\left(x-\xi \right)^{2}+\left(y-\eta\right)^{2}+\left(z-\zeta\right)^{2}\right]^{3/2}} \tag{1}\]  
Among them, \(G=6.67\times 10^{-11}\)m\({}^{3}\).kg\({}^{-1}\).s\({}^{-2}\) is the gravitational constant, \(\rho\) is the density contrast, \((\xi_{1},\eta_{1},\xi_{1})\), \((\xi_{2},\eta_{2},\zeta_{2})\) are the diagonal coordinate of the prism and \((x,y,z)\) is the coordinate of the observation point. Equation (1) has various analytical solutions. The solution used in this paper is [25]:\[\Delta g=G\rho(\xi-x)log(r+y-\eta)+(\eta-y)log(r+x-\xi)+(\zeta-z)arctan\left[- \frac{(x-\xi)(y-\eta)}{r(z-\zeta)}\right]|_{\xi_{1}}^{2}|_{\eta_{1}}^{\eta_{2}}| _{\xi_{1}}^{\zeta_{2}} \tag{2}\]  
Among them, \(r=\sqrt{(x-\xi)^{2}+(y-\eta)^{2}+(z-\zeta)^{2}}\). Extracting the density \(\rho\) in Equation (2), that is, define the sensitivity matrix:  
\[S_{g}=G\bigg{\{}(\xi-x)log(r+y-\eta)+(\eta-y)log(r+x-\xi)+(\zeta-z)arctan\left[ -\frac{(x-\xi)(y-\eta)}{r(z-\zeta)}\right]\bigg{\}}|_{\xi_{1}}^{2}|_{\eta_{1}}^ {\eta_{2}}|_{\xi_{1}}^{\zeta_{2}} \tag{3}\]  
Then, the relation between vector \(\overset{\rightarrow}{\rho}\) composed of all the density value of all prisms in the whole underground space and all observed data \(\Delta\overset{\rightarrow}{g}\) is:  
\[ \begin{bmatrix}\Delta g_{1}\\ \Delta g_{2}\\ \vdots\\ \Delta g_{M}\end{bmatrix} = \begin{bmatrix}S_{11}&S_{12}&S_{13}&\ldots&S_{1N} \\ S_{21}&S_{22}&S_{23}&\ldots&S_{2N}\\ &&\vdots\\ S_{M1}&S_{M2}&S_{M3}&\ldots&S_{MN}\end{bmatrix}\begin{bmatrix}\rho_{1}\\ \rho_{2}\\ \rho_{3}\\ \vdots\\ \rho_{N}\end{bmatrix}  \tag{4}\]  
where, \(S_{i,j}\) represents the sensitivity matrix. Equation (4) can be expressed in matrix form as [6]:  
\[d=Sm \tag{5}\]  
where, \(d\) represents the column vector of observation data, \(m\) is the model parameter column vector, \(S\) is the sensitivity matrix of type \(M\times N\) and \(M<<N\). Since \(M<<N\), meaning that Equation (5) is an underdetermined system of equations, and given that there is always inevitable noise interference in the observed data, the inversion problem is often ill-conditioned, and the solution obtained through inversion has multiple solutions. The Tikhonov regularization method, as a conventional processing method, can effectively solve this problem. In other words, the aforementioned problems can be transformed into finding the optimal solution of the objective function. The objective function is:  
\[\phi=\phi_{d}+\alpha\phi_{m} \tag{6}\]  
In the formula, \(\phi_{d}\) represents data fitting term, \(\phi_{m}\) represents the model constraint term, \(\alpha\) represents regularization parameter.
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 4 Performance Evaluation  
The accelerated programmable deterministic multi-queue accelerator can theoretically support thousands of transmission queues for data transferring across multiple endpoints. The main differences between our accelerator and traditional DPDK-enabled NICs are:  
1. Our accelerator can forward the packet directly without the host involved and added the multi-layer forwarding such as VxLAN [30] encapsulation.
2. With the control of the hardware clock and transmit queue Tx scheduler, our accelerator can implement deterministic sending. DMA controllers were sending the higher priority for data flow with real-time requirements.  
This section evaluates our Accelerator prototype to show that it meets the design requirements above. In Section 4.1, We introduce the Accelerator prototype platform and each module's specific implementation. Section 4.2 presents the experimental simulation topology to verify the above two design requirements of the Accelerator. Section 4.3 is the research method. Finally, Section 4.4 first analyzes the experimental results, then shows the use of hardware resources of the accelerator prototype and compares it with related experiments of the same type. We implement different functional engines and test our Accelerator end-to-end; these results show that the Accelerator can support zero-copy DMA drive and send and receive packets according to user requirements.
# Antimicrobial and Odour Qualities of Alkylpyrazines Occurring in Chocolate and Cocoa Products
## 2 Pyrazines in Chocolate and Cocoa
### The Way of Formation of Pyrazine in Cocoa and Cocoa Products
#### 2.1.1 Fermentation Stage of Cocoa Manufacturing  
Raw cocoa beans have an unappealing acidic (vinegar-like) and astringent flavour and must ferment before being dried and roasted for the cocoa odour to fully develop. The enzymatic hydrolysis of polysaccharides and protein fractions results in the release of Maillard precursors, such as free amino acids and reducing sugars, which are then transformed into the essential volatile components of cocoa--many of which are pyrazines--during the roasting of cocoa beans via the Maillard and Strecker reactions [12,14]. Fresh cocoa beans undergo the rapid metabolization of sugars from the muclaginous pulp of the seeds to create volatile and nonvolatile organic acids; the degradation of proteins to produce peptides and free amino acids; the oxidation of polyphenols to create insoluble compounds (o-quinones); and the hydrolysis of glycosides (mainly anthocyanins). Firstly, a variety of yeasts and bacteria operate synergistically to ferment the pulp sugar, converting it to alcohol and CO2. Polysaccharides are degraded by enzymes and other glycosases. This is demonstrated by the fruit pulp turning into a liquid and draining away, which enhances aeration and causes acetic acid bacteria to convert alcohol to acetic acid. The pH of cocoa beans is lowered by acetic acid to between 4.5 and 5.5; the temperature rises to 45-50 \({}^{\circ}\)C and the cell walls of the cacao seed become permeable; and an oxidative process takes control of the entire mass, triggering the growth of lactic bacteria, allowing for enzymatic reactions and subsequent fermentation [15,16]. Then, phenolic compound condensation and oxidation processes become the focus. The initially bitter and astringent cocoa flavour is softened by a decrease in the number of soluble phenols. Drying the seeds to a water content of less than 8% finally puts an end to the oxidation reactions. The precursors of aroma compounds (reducing sugars and nitrogenous intermediates) are produced by the breakdown of proteins and peptides during fermentation along with the free amino acids. Additionally, it has been discovered that the amount and timing of the cocoa beans' acidification affect how some fragrance precursors are produced. Tetramethylpyrazine, 2-ethyl-3,5-dimethyl- and 2,3-diethyl-5-methylpyrazine are significant flavour-active substances created during fermentation. Additionally, roasting yields of Maillard intermediates were found to have more pyrazines in well-fermented beans than in little or unfermented beans [3,8,10,13,17].
## 2 Geometric Structure and Graphene Model  
The schematic and unit cell of the metal-graphene hybrid structure are depicted in Figure 1. The metal resonator arranged in periodic array contains a pair of gold cut-wires and a U-shaped ring located between the cut-wires. The geometrical parameters of the EIT metamaterial have been optimized and defined as shown in Figure 1b. The periodic length of the unit cell is \(P_{x}=P_{y}=100~{}\upmu\)m. The displacement between the unit center and the U-shaped ring is defined as \(s=2~{}\upmu\)m. The graphene strip with a width of \(w_{1}=22~{}\upmu\)m is placed between the metal patterns and the SiO\({}_{2}\)/Si substrate. The thickness of the silicon dioxide is \(100~{}\upmu\)m and the thickness of the silicon layer is \(1~{}\upmu\)m. Other structural parameters are as follows: \(l_{1}=50~{}\upmu\)m, \(l_{2}=36~{}\upmu\)m, \(l_{3}=15~{}\upmu\)m, \(w_{2}=6~{}\upmu\)m, \(w_{3}=3~{}\upmu\)m, and g = \(15~{}\upmu\)m. The graphene strips are connected to the gold film as the top electrode and the other electrode is on the side of the silicon substrate. Therefore, the bias voltages can be applied between the electrodes to tune the Fermi energy in graphene as shown in Figure 1a.  
The graphene is modeled as an infinitesimally thin film and its surface conductivity in the lower-THz region is dominated by the intraband transitions, which can be expressed using the well-known Kubo formula [25]:  
\[\sigma_{\text{intra}}(\omega,E_{F},\tau,\text{ T})=j\frac{e^{2}k_{B}T}{\pi\hbar^{2}(\omega+j\tau^{-1})}\bigg{(}\frac{E_{F}}{k_{B}T}+2 \ln(e^{\frac{-E_{F}}{k_{B}T}}+1)\bigg{)} \tag{1}\]where \(k_{B}\) and \(\hbar\) are the Boltzmann constant and the reduced Planck constant, respectively, \(T\) is the temperature, \(E_{F}\) is the Fermi energy in graphene, \(\omega\) is the operation circular frequency, and \(e\) is the electron charge. \(\tau\) is the carrier scattering time in graphene, which is related to carrier mobility \(\mu\), and Fermi energy according to \(\mu=E_{F}\upsilon_{F}{}^{2}\tau/e\), \(\upsilon_{F}=10^{6}\) m/s is the Fermi velocity. Here, we assumed \(\mu=3000\) cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\), which is a compromise value lower than the experimental results in refs. [26; 27].  
By applying an external voltage \(V_{g}\), the Fermi energy of the graphene layer can be actively tuned. For our structure, an approximate relation between \(E_{F}\) and \(V_{g}\) can be derived using a simple theoretical capacitor model which is presented by [28; 29]:  
\[E_{F}=\hbar v_{F}\sqrt{\pi\varepsilon_{0}\varepsilon_{r}\big{|}V_{g}-V_{d} \big{|}/ed_{s}} \tag{2}\]  
where \(\varepsilon_{0}\) is the permittivity of the vacuum, \(\varepsilon_{r}\) is the permittivity of the silicon dioxide, \(V_{d}\) is the Dirac point voltage, and \(d_{\rm s}\) is the thickness of the silicon dioxide layer.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
### Failure Modes of Railway S&C Systems
#### 2.4.2 Actuation Subsystem  
The different elements responsible for failure of the actuation subsystem can be broadly grouped into electrical and mechanical components. The different electrical components such as electrical motors, signaling logics, and relays often fail. Transformer failures within the POE, or any power source failure, also result in an actuation system fault. Some of these faults are also interdependent; for example, any damage to the connectors or relays can result in failure of the power source or a voltage drop to the actuator for electromechanical actuators. [PERSON] et al. [51] showed that for HW or clamplock switch systems, failure in actuation is the major source of system failure.  
Mechanical components of the actuation system often fail due to wear as these are exposed to the railway environment. In traditional machines, drive rods fail for various reasons like wear of the connecting elements, and loose fasteners after the maintenance process. The backdrives for longer switches are another source of faults seen in track switches. In recent research and developments in switch actuators, consideration has been given to reducing the number of mechanical elements to reduce failures in the actuation process [34].