esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Subgrade Coefficient on Static Work of a Pontoon Made as a Monolithic Closed Tank
## 5 Conclusions  
The calculation results presented in the article were obtained using the finite difference method in variational approach, assuming [PERSON]'s ratio \(\
u=0\) and subgrade coefficient \(\mathrm{K}_{\mathrm{z}}=5000\)\(\mathrm{kN}/\mathrm{m}^{3}\). The following real dimensions of the pontoon were adopted: length \(\mathrm{l}_{\mathrm{x}}=10\) m, width \(\mathrm{l}_{\mathrm{y}}=2.5\) m, height \(\mathrm{l}_{\mathrm{z}}=1.25\) m and thickness of all walls \(\mathrm{h}=0.08\) m. Based on the analyses carried out, the following can be concluded:  
* taking into account in calculations the cooperation of the tank walls with its elastic substrate filling reduces bending moments and, in some cases, changes the sign of bending moments (Table 1). For example, the bending moment \(\mathrm{M}_{\mathrm{x}}\)[813] (according to designations in Figure 4) occurring in the middle of the bottom length, above the support, which due to the hydrostatic load acting on the walls and the bottom, without taking into account its elastic substrate, was \(\mathrm{M}_{\mathrm{x}}\)[813]= 10.24996 \(\mathrm{qs}^{2}\) while, taking into account its elastic substrate, was reduced to \(\mathrm{M}_{\mathrm{x}}\)[813] = 8.16864 \(\mathrm{qs}^{2}\), so it decreased by 20%;
* taking into account in calculations an internal Styrofoam filling treated as an elastic substrate reduces the values of bending moments and consequently the area that requires necessary reinforcement and thus the solution becomes more financially beneficial;
* taking into account in calculations the cooperation of the pontoon's structure with an internal filling does not affect its buoyancy and stability;
* the pontoon presented in the article can be a ready-made, prefabricated reinforced concrete shipping element, prepared to be built in at its final location, which can be used as a float in amphibious construction.
###### Abstract
In this paper, a new mechanically stable design of an electrostatically actuated \(z\)-axis resonant MEMS gyroscope is presented that utilizes two separate masses for the drive and sense axis. The two separate-masses-based configuration allows minimizing the cross-axis coupling and achieves high thermal stability. The proposed MEMS gyroscope sensor is designed to be used in industrial automation based IoTs and has the main advantage of improving the reliability of the measurement with respect to environmental and constructive imperfections. Moreover, the design is developed and optimized considering the micro-fabrication process limitations of the commercially available low-cost silicon-on-insulator multi-user MEMS processes (SOIMUMPs) microfabrication process. The working of the proposed design is verified through coupled field electro-mechanical-thermal finite element method (FEM) simulations with the demonstration of the electrostatic tuning effectiveness for the mode-matching between the drive and sense modes of the MEMS gyroscope.
## 3 Results and Discussion
### Box Enclosure  
The effect of a box enclosure is presented in Table 1. The box enclosure experiment explored the effect of light and shadows on the captured images and, consequently, on the measured color intensity. From the 10 measurements done, the coefficients of determination (R\({}^{2}\)) were calculated and there was a notable difference between images captured by photography in an enclosed box and images scanned without the enclosure. From Table 1, it was observed that images from the enclosed box resulted in poor R\({}^{2}\) values (0.709-0.859) compared to those captured from tabletop scanning (0.976-0.995). The use of an enclosure to capture images in colorimetric detection of toxic metals has been reported with positive outcomes [29, 41, 42]; however, some studies yielded have successfully used smartphone in colorimetric detection without an enclosure [44, 45, 46]. Other studies on smartphone-based colorimetric detection methods have also reported the effect of ambient light and presented their suggested solutions. For instance, one study attached a phone cover-like encloser on the smartphone lens to house the colorimetric device in order to minimize ambient light  
Figure 3: Calibration curves of Fe using the silica-based device showing: (**a**) color intensity of un-inverted images of detection points measured in mean gray; (**b**) color intensity of un-inverted images of detection points measured in integrated density; (**c**) color intensity of inverted images of detection points measured in mean gray; and (**d**) color intensity of inverted images of detection points measured in integrated density.  
Figure 2: Images of the silica device for color detection of Fe: (**a**) photographed image before processing; (**b**) image after inverting and color thresholding; (**c**) image after inverting and manual contrast adjustment instead of color thresholding; and (**d**) image after automatic contrast adjustment.  
during image capturing [47]. In another study, instead of an encloser, an application called Colourine was used, which turns the background into a constant and thereby accounts for light interference in the measured color intensity [48]. Practically, in the case of field testing, the less bulky the device, the more portable it will be and, ultimately, the better.  
It is clear that scanning produced better precision for analysis based on the improved R\({}^{2}\) values. This was attributed to how the scanning application automatically adjusts lighting to eliminate shadows on the silica plate. A box enclosure was therefore not needed for this device.
## 3 Fast Batches Processing of Sparse Representation and Recovery in Delay-Doppler Domain
### Fast Batches Processing of Sparse Representation
To establish the sparse representation model, let \(\mathbf{\sigma}_{S,r,c}\in\mathbb{C}^{L^{\prime}M^{\prime}\times 1}\) be the sparse vector corresponding to the \((r,c)\)-th sub-block, i.e.,  
\[\mathbf{\sigma}_{S,r,c}= \begin{bmatrix}\sigma_{1,1}^{r,c}&\sigma_{1,2}^{r,c}&\cdots &\sigma_{1,L^{\prime}}^{r,c}&\cdots&\sigma_{m^{\prime},l^{\prime}}^{r,c}&\cdots &\sigma_{M^{\prime},L^{\prime}}^{r,c}\end{bmatrix} ^{T} \tag{24}\]  
Therefore, the sparse representation model for the \((r,c)\)-th sub-block is given by  
\[\mathbf{a}_{S,r,c}=\mathbf{B}_{S,1,1}\mathbf{\sigma}_{S,r,c}+\mathbf{n}_{r,c} \tag{25}\]  
where \(\mathbf{n}_{r,c}\in\mathbb{C}^{L^{\prime}M^{\prime}\times 1}\) represents the impact of noise on the  
Therefore, the sparse matrix \(\mathbf{\Sigma}_{S}\in\mathbb{C}^{L\times M}\) corresponding to the CAF matrix \(A_{S}\) is given by  
\[\mathbf{\Sigma}_{S}= \begin{bmatrix}\mathbf{\Sigma}_{S,1,1}&\mathbf{\Sigma}_{S,1,2}&\cdots& \mathbf{\Sigma}_{S,1,C}\\ \mathbf{\Sigma}_{S,2,1}&\mathbf{\Sigma}_{S,2,2}&\cdots&\mathbf{\Sigma}_{S,2,C}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{\Sigma}_{S,R,1}&\mathbf{\Sigma}_{S,R,2}&\cdots&\mathbf{\Sigma}_{S,R,C}\end{bmatrix}  \tag{27}\]  
Here, we have completed the rapid construction of the sparse representation model for CAF. The main steps are as follows:  
1. Compute CAF for reference signal \(u_{ref}(t)\) and surveillance signal \(u_{surv}(t)\) to obtain \(\mathbf{A}_{S}\). Divide \(\mathbf{A}_{S}\) into equally sized blocks: \(A_{S,1,1}\ \cdots\ A_{S,R,C}\). 2. Transform the CAF blocks \(A_{S,1,1}\ \cdots\ A_{S,R,C}\) into vector form, denoted as \(\mathbf{a}_{S,1,1}\ \cdots\ \mathbf{a}_{S,R,C}\). 3. Compute the self ambiguity function for the reference signal \(u_{ref}(t)\) to obtain \(\mathbf{A}_{ref}\). Crop \(\mathbf{A}_{ref}\) to obtain CAF copies \(\mathbf{X}_{1,1}^{1,1}\ \cdots\ \mathbf{X}_{M^{\prime},L^{\prime}}^{1,1}\). 4. Convert the CAF copies \(\mathbf{X}_{1,1}^{1,1}\ \cdots\ \mathbf{X}_{M^{\prime},L^{\prime}}^{1,1}\) into vector form, i.e., \(\mathbf{\chi}_{1,1}^{1,1}\ \cdots\ \mathbf{\chi}_{M^{\prime},L^{\prime}}^{1,1}\). 5. Join the vectorized CAF copies \(\mathbf{\chi}_{1,1}^{1,1}\ \cdots\ \mathbf{\chi}_{M^{\prime},L^{\prime}}^{1,1}\) to obtain the measurement matrix \(\mathbf{B}_{S,1,1}\). 6. Use the measurement matrix \(\mathbf{B}_{S,1,1}\) to perform sparse recovery on the vectorized CAF blocks \(\mathbf{a}_{S,1,1}\ \cdots\ \mathbf{a}_{S,R,C}\), to obtain sparse vectors \(\mathbf{\sigma}_{S,1,1}\ \cdots\ \mathbf{\sigma}_{S,R,C}\). 7. Convert the sparse vectors \(\mathbf{\sigma}_{S,1,1}\ \cdots\ \mathbf{\sigma}_{S,R,C}\) into matrix form, i.e., \(\mathbf{\Sigma}_{S,1,1}\ \cdots\ \mathbf{\Sigma}_{S,R,C}\). 8. Join the sparse matrices \(\mathbf{\Sigma}_{S,1,1}\ \cdots\ \mathbf{\Sigma}_{S,R,C}\) to obtain the sparse matrix \(\mathbf{\Sigma}_{S}\) corresponding to \(\mathbf{A}_{S}\).
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 4 Numerical Results  
In this section, we will divide the discussion into five parts. First, ACF and PSD are analyzed as they are the most essential characteristics for a navigation signal. The second part will focus on the tracking precision, which is the most concerned in most civil applications. The multipath resistance will be discussed in the third part, as it is the main error source in a GNSS-like positioning system and can hardly be eliminated. Then, the tracking ambiguity is analyzed as the ACF contain multiple peaks. Finally, the cross-correlation comparison among several signal signals. Since ChPN(5,1) keeps the spectral characteristics of chirp with a relatively reasonable bandwidth, it is selected as the representative for the comparison between different signal waveforms. The analysis will be carried out in three situations as Table 1 shows.  
Figure 2: ChPN generating schematic diagram (**a**); Regular acquisition scheme of ChPN signal (**b**).
# Quantum Private Comparison Based on Four-Particle Cluster State
## 4 Security Analysis
### External Attacks  
An external attacker, referred to as [PERSON], may employ various quantum attacks to glean information about the participants' inputs. These potential quantum attacks include intercept-measure-resend, entangle-measure, and Trojan horse attacks [50]. However, such attacks are insufficient to compromise private data due to the utilization of the decoy state method, which effectively prevents eavesdropping.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 2 Related Works
### Optimized DTW for the Alignment Step  
When considering the alignment step \(\mathcal{A}\), we note that a majority of the approaches discussed so far rely on Dynamic Time Warping or one of its variants. We briefly introduce the research dedicated to its optimization.  
The textbook DTW algorithm to align two sequences \(\mathbf{a}\) and \(\mathbf{b}\) implies a pairwise sequence element comparison (forward pass) and traces back the optimal alignment path minimizing the total alignment cost (backward pass). The forward pass computing the pairwise distance matrix has a quadratic complexity in \(\mathcal{O}(|\mathbf{a}||\mathbf{b}|)\) in time and space, which quickly becomes problematic for many use cases, such as a similar sequence search in large corpora or long sequence alignment.  
In the context of sequence indexing, a common solution to speed up the computation of the DTW alignment cost was to seek for a cheap-to-compute lower bounding function to prune unpromising candidates [50; 51; 52]. This approach proved its efficiency to mine trillions of sequences [53], but discards the concept of the alignment path that is required to address the alignment problem, per se.  
A classical approach to speed up the computation of the alignment path--and not only the alignment cost--is to avoid an exhaustive search among all possible paths, by restricting the exploration within some boundaries, e.g., a band or a parallelogram around the diagonal of the pairwise distance matrix [5; 54]. This is generally an effective solution, as extreme warping between sequences is often unlikely, but yields an incorrect result if the actual alignment lies outside the explored area.  
Several global adaptive constraints have thus been proposed to restrict the exploration area, such as multi-scale alignments where an optimal path is found at a coarse level and is gradually refined at increasingly accurate resolutions [55; 56; 57], or segment-level alignments that serve to further refine frame-level alignments [58]. Further improvements have been proposed with local adaptive constraints to restrict the exploration more precisely to the vicinity of the optimal path, for instance by gradually expanding the current optimal path via a local greedy search [59; 60], or by searching and concatenating overlapping block sub-alignments [61]. Early abandoning is another adaptive strategy used to prune unpromising alignment paths when the alignment cost exceeds some threshold [62; 63]. These constrained algorithms generally achieve a linear space complexity at the cost of only providing an approximate solution.  
On the contrary, [PERSON] and [PERSON] [40] recently proposed a clever reformulation of the textbook DTW that is both exact and has linear space complexity. In the next section, we will detail this algorithm and explain how our own algorithm adapts these ideas to the CTC context.
## 3 S-Box Construction  
### Preliminaries  
Table 2 is an example of a non-permutation power function generated over finite field in \(\mathbb{F}_{2^{4}}\). In this case, we use \(x^{3}\). We can extract the following information: (1) \(\mathrm{D_{EL}}\), (2) \(\mathrm{R_{EL}}\) and (3) \(\mathrm{N_{EL}}\) from Table 2.  
\(\mathrm{D_{EL}}\) is a notation for _Desired Element_. A desired element only occurs once in a permutation function, such that a bijective S-box is achieved. From Table 2, only one element is found in the \(\mathrm{D_{EL}}\) category, which is the element 0.  
\(\mathrm{R_{EL}}\) is a notation for _Repeated Elements_. The repeated elements were obtained from generated functions that occur more than once. From the analysis of Table 2, there are five elements that fall into this category, which are the elements 1, 8, A, C and F. All five repeated elements are found to be repeated three times.  
\(\mathrm{N_{EL}}\) denotes the _Non-existent Elements_ which means that the elements are'missing' from the generated function. This happens because the element that should exist has been replaced by a repeated element. From Table 2, we can determine that there are a total of 10 elements that do not exist in \(x^{3}\) over \(\mathbb{F}_{2^{4}}\), which are 2, 3, 4, 5, 6, 7, 9, B, D and E.  
After these three group of elements are obtained, a _Redundancy Analysis Table_ will be constructed. The _Redundancy Analysis Table_ is a representation of bits error between all elements in \(\mathrm{R_{EL}}\) compared to the elements in \(\mathrm{N_{EL}}\). Table 3 shows the redundancy analysis table of \(x^{3}\) over the finite field \(\mathbb{F}_{2^{4}}\). For example, there are 3 bits that needs to be changed to replace element 1 from \(\mathrm{R_{EL}}\) with element 6 from \(\mathrm{N_{EL}}\), as illustrated in Figure 1. Likewise, the element F from \(\mathrm{R_{EL}}\) has three options which only involve one bit change. The elements listed from \(\mathrm{N_{EL}}\) are 7, B and E.
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Microstructure Properties of Hardened Mortar
#### 3.4.2 TGA and DTA  
TGA and DTA analyses were carried out on mortar specimens containing 0%, 10%, and 30% MGWP at both 7 and 28 days of curing. The sample materials were heated up to 900 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min to analyze mass loss and thermal transitions. Figure 13 presents the TGA-DTA curves for 7-day cured mortar, while Figure 14 shows the results for 28-day cured mortar. These analyses help to quantify the loss of mass due to the evaporation of free water in C-S-H, the decomposition of CH, and the decarbonation of calcium carbonates.  
The TGA-DTA curves reveal three major endothermic peaks. The first peak, observed at approximately 125 \({}^{\circ}\)C, 141 \({}^{\circ}\)C, and 156 \({}^{\circ}\)C for 0%, 10%, and 30% MGWP, respectively, corresponds to the evaporation of physically and chemically bound water in C-S-H, as well as Ettringite. Mass losses associated with this peak were recorded on TGA curves. This process is indicative of the ongoing hydration and pozzolanic reactions occurring within the samples [66].  
The second peak, occurring between 450 \({}^{\circ}\)C and 550 \({}^{\circ}\)C, corresponds to the dehydroxylation of Portlandite or calcium hydroxide Ca(OH)\({}_{2}\). The temperatures for this event were recorded at 500 \({}^{\circ}\)C, 499 \({}^{\circ}\)C, and 497 \({}^{\circ}\)C for the 0%, 10%, and 30% MGWP mixes, respectively. A reduction in the mass loss of CH at 10% MGWP suggests that glass waste powder reacts with Ca(OH)\({}_{2}\), thereby consuming Portlandite and forming additional C-S-H gel. This further reduces the chemically bound water content at 10% MGWP, contributing to a denser microstructure.  
Figure 13: TGA and DTA analyses of mortar specimens containing MGWP at curing age of 7 days.  
The third peak, between 500 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, is associated with the decarbonation of calcite (CaCO\({}_{3}\)). The temperatures for this reaction were found to be 737 \({}^{\circ}\)C, 748 \({}^{\circ}\)C, and 755 \({}^{\circ}\)C for the 0%, 10%, and 30% MGWP mixes, respectively. This event signifies the breakdown of calcium carbonate in the samples, and the higher temperature decomposition of calcite can be more easily distinguished from other carbonates, such as vaterite and aragonite, which decompose at lower temperatures [67].  
The TGA data summarized in Table 5 further support these observations. The lowest total mass loss was recorded for the 10% MGWP mix at 7 days of curing, which aligns with the highest compressive strength and lowest porosity observed for this mix. The results suggest that the presence of MGWP significantly impacts the cement hydration process, promoting the formation of additional C-S-H gel and reducing the porosity of the mortar.
## 2 Multi-Hop Bent-Pipe LEO Satellite Constellation
### Multi-Hop Bent-Pipe Beam-Hopping Payload Architecture  
Satellite networks can use WDM ISLs to achieve high resource utilization based on the aforementioned strategy. Therefore, a brand-new design for a satellite payload is shown in Figure 3. Once the optical carrier forwarded from another satellite \(\lambda_{SL}^{in}\) (or sent from the ground \(\lambda_{AC}^{in}\) into the payload with wavelengths which carry different RF signals) enters the satellite payload, the wavelength selective switch (WSS) sends the optical carrier signals of the corresponding wavelengths to optically controlled millimeter beamformers \(\lambda_{AC}^{out}\) or forwards them to adjacent satellites \(\lambda_{ISL}^{out}\) according to the routing table uploaded by the NOCC on the ground. Different beamformers respond to varied wavelengths with different beam-pointing responses, for example, wavelength 1 corresponds to a beam pointing of 10\({}^{\circ}\) in beamformer 1 and 20\({}^{\circ}\) in beamformer 2. This design expands the single bent-pipe satellite beam hopping technique for DVB-S2X to multiple hops while maximizing the use of LEO satellite resources [14]. The introduction of beam-hopping technology into non-regenerative satellite networks and resolving the issue of high-precision beam pointing of broadband RF signals could add significant benefits to the architecture of LEO satellite networks:  
* Saving regenerative processing loads between satellites, making satellites lighter and lower-power;
* Makes it simple to upgrade and alter satellite payload;
* Integrate beam-hopping into satellite network routing algorithms to achieve more flexible inter-satellite routing reconfiguration.
## 2 Basic Principles of RNS Arithmetic
### Mixed-Radix Representation  
In RNS arithmetic, the rank of a number is a positional characteristic of primary importance since it enables one to perform all the non-modular operations. In a Mixed Radix System (MRS) defined by a set \(\{m_{1},m_{2},\ldots,m_{k}\}\) of pairwise relatively prime moduli, an integer \(X\in\mathbb{Z}_{M_{k}}\) is represented by the \(k\)-tuple \(\langle x_{k},x_{k-1},\ldots,x_{1}\rangle\) of mixed-radix digits, resulting in  
\[X=\sum_{i=1}^{k}x_{i}M_{i-1}, \tag{2}\]  
where \(x_{i}\in\mathbb{Z}_{m_{i}}\)\((i=1,2,\ldots,k)\), \(M_{0}=1\), \(M_{i-1}=\prod_{j=1}^{i-1}m_{j}\)\((i=2,3\ldots,k)\)[8; 9].  
An MRS is advantageous over an RNS for non-modular operations such as magnitude comparison, sign determination, and overflow detection.
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
## 2 System Model
### Backscatter Channel  
The forward and backward channels between the reader and \(i\)-th tag are modeled as free-space channels and reciprocal. The channel gains of the forward and backward channels--denoted by \(h_{i}\) and \(g_{i}\)--remain constant over the frame slot. Moreover, the channel gain information is assumed to be perfectly known by the reader and tags. Therefore, the obtained total goodput of both cases of the passive and semi-passive tags using our proposed resource allocation policies are an upper bound of the total goodput with channel estimation errors in realistic scenarios.
# On-the-Fly Repairing of Atomicity Violations in ARINC 653 Software
## 2 Background
### Atomicity Violations
There are two types of repairing approaches, backward recovery and forward recovery [15]. Backward recovery makes use of checkpoints to recover to the last known correct state. Although it can fix all unexpected errors, it suffers from large time and space overheads. There are many different ways to achieve forward recovery of failures. In terms of time and space overheads, forward recovery is efficient. Since backward recovery may not meet the real-time constraints because of its protocol, it is necessary to apply forward recovery to the system. Figure 1: An example of atomicity violations (Issue #7129) in Ardupilot. recovery in mission-critical real-time applications such as airborne software. However, the forward recovery approach depends on the execution scenario. Thus, it has to be tailored to each program. Even in such scenarios, it can handle only the predictable errors in the system. Making use of software version redundancy and voting can also be a solution to the problem; however, it also increases the space complexity of the software and it is hard to identify the root cause of the failure. Moreover, if the system fails due to the violation, these approaches fail together. There are methods that exploit the synchronization mechanism or logging to find the root cause of the failure. Note that adopting them may increase the time complexity of the repairing tool. Table 1 summarizes the research studies on atomicity violations in airborne software which have been initiated since 2010. Research on types of analyses has mainly focused on design considerations and identification of the errors in software [32; 33]. There are no static analyses specific to airborne software because it inspects the source code. There are a few research studies on dynamic and mixed analyses targeted to airborne software [31; 34; 35]. In theory, there are backward and forward recovery schemes. However, research studies have been focused on forward recovery only [16; 17] and the most recent work dates back to 2011. We can see that the research community has focused on the detection of the errors but not on the repairing of software. It is a fact that not all errors can be fixed in software. Thus, the repairing of software is as important as the detection of errors to withstand the failures that manifest during operation.