esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study of Concrete: A Mesoscopic Scale Simulation Methodology
## 3 Numerical and Experimental Results
### Experimental Results  
In Figure 5, the setup for one of the experimental tests conducted is depicted. Two strain gauges affixed to opposite faces of the specimen are utilized to determine the stress-strain curve of the material. Figure 6 illustrates the stress-strain curves from four experimental tests. The results indicate a consistent pattern of behavior, with slight variations attributed to experimental conditions. The tests were performed using a Sistemas de Ensayo S.L. multi-test machine (Fuentabrada, Spain) with a capacity of 20 Tm and a test speed of 1500 N/s. This machine is connected to a Quantum MX1615 data analyzer (HBM, Darmstadt, Germany) and a PC for data processing. The 50 \(\times\) 50 \(\times\) 50 mm specimens were instrumented with HBM-LY11-1-6/350 strain gauges (Hottinger Bruel & Kjaer, Copenhagen, Denmark), featuring a measurement grid of 1 \(\times\) 2.5 mm and connected to the Quantum MX1615 device prior to testing.  
The initial test exhibits an almost linear phase that gradually deviates as deformations increase. It reaches an ultimate tensile strength of 45.9 MPa with a strain close to 1.8 per thousand. In the second test, a slight deviation in the curve occurs when deformations exceed eight per thousand, attributed to a temporary malfunction in one of the strain gauges. Nevertheless, the test progresses normally until failure, achieving deformation levels like the previous test and a tensile strength slightly higher (49.9 MPa). The third experimental test yields an ultimate tensile strength of 57.1 MPa at a strain close to 1.6 per thousand, with a less pronounced curvature compared to the preceding tests. Finally, in the fourth test, a stress-strain curve characteristic of the material is observed, culminating in failure at a tension level comparable to the first two tests (48.7 MPa) and a strain close to 1.7 per thousand.  
Figure 5: Experimental test employing strain gauges.
# A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography
proposed the CS theory in 2006, for the first time, which is based on a prior knowledge of unknowns [30]. This method can be used in data acquisition procedure of PACT [31], which leads to information reconstruction based on the convex optimization from some observations that seem highly incomplete [28]. This concept has been used in the reconstruction of magnetic resonance imaging (MRI) and Computed Tomography (CT)-MRI in order to reduce the scan time and have an inherent registration in space and time [32]. In addition, CS has been used for thermoacoustic imaging (TAI) [33]. [PERSON] and [PERSON] [28] have used CS for PACT image reconstruction, where due to the advantages of sparse characteristic of a sample, fewer projections were used to reconstruct the optical absorption distribution map of the tissue [34]. Signal sparsification can be done in two different ways: (i) applying a dictionary learning and (ii) using standard transform functions. Even though dictionary learning-based methods outperform the standard transform functions, in order to make the signal sparser, they impose a higher complexity for reconstruction [35; 36]. On the other hand, standard transform functions can simply translate the signal to a sparse shape. The Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV) are some of the popular sparsifying transforms. Such transformations result different sparse representations of the original signal. Similar to compression algorithms, the combination of the original signal in different transformation domains, can provide an image with an improved quality. In this paper, we introduce a novel dictionary for sparse representation of PA signals in order to improve the quality of the reconstructed PA image. The proposed method consists of three sparsifying transformation functions: WT, DCT, and TV. Using the proposed method, users are able to highlight information provided by each of the functions. We compared the performance of the proposed algorithm with the performance of BP and sparse reconstruction based on different methods when the same number of view angles is used. We use two established quality assessment metrics--peak signal-to-noise ratio (PSNR) and edge preservation index (EPI)--for quantitative evaluation of the results. Quantitative and qualitative results indicate that the proposed method can be a proper option when we face a limited number of angle views.
## 4 Control Strategies  
The servomechanism is a closed automatic control system that compensates for external forces and external or internal disturbances. The output signal can be position, speed or acceleration, i.e., angular or linear. The setpoint of one of these variables is compared by the transducer with the actual output signal and the resulting control deviation signal is fed to the correction element, after which it is amplified and then fed to the servo actuator input. The servo control system fulfils the task of minimizing the control error that occurs during step or pulse changes in the set value.  
The servomechanism that is numerically studied in this research has the structure of a typical control system. It does not control the technological process but, as a rule, a single executive element in order to improve the performance of the executive line.  
Nonlinear dynamics of servos containing integral components forces the necessity to use a proportional corrector with some high gain, and even three-state amplifiers.  
The integrative nature of the actuator should provide theoretically zero static error. The high gain in the main circuit of the control system improves the follower properties of the system but reduces the stability margin. This can be improved by introducing, e.g., a proportional-differential controller or a proportional-integral-differential controller on the mainline. The PID controller will be implemented in this work.  
In this work, we distinguish two realizations of closed-loop control strategy applied to assess the influence of various disturbances appearing in the reality, i.e., a sudden step increase of the loading torque, represented by \(T_{la}\), and also the uncertainty of the rotor's damping coefficient \(b_{la}\). The two realizations presented below are based on two on-line adaptation techniques of parameters of the PID regulator. This continues the research [19] of low-speed voltage-input tracking control of a DC motor numerically modeled by a dynamical system with stick-slip friction.  
One may note that by applying small values of stiffness and damping coefficients, the investigated dynamical system has been situated in a soft elastic and weak damping environment. It does control of the system more difficult due to the high tendency to oscillations that might be decisive for the performance of the control process.  
Moreover, the system is parametric (parametrically uncertain) and externally forced, where the parameter of damping and external loading torque varies in time according to almost-discontinuous functions of frequency, respectively. The control strategies 2 and 3 described below are based on the nonlinear controller, making the whole closed-loop control dynamical system nonlinear.
## 3 Pre-Trained Models and Unsupervised NMTs
### Pre-Trained Models and Unsupervised NMTs  
We build a pre-trained model for each language pair using the same hyper-parameters and model configuration, as suggested in ([PERSON] et al., 2019). For the sake of clarity, the hyper-parameters used in this study are presented in Table 2. We shared the same hyper-parameters to build a pre-trained model, an unsupervised NMT, and a supervised NMT system except for the mini-batch size.  
An unsupervised NMT system is built based on a pre-trained model using the same monolingual corpora and a back-translation loss while a supervised NMT system is built based on a pre-trained model using parallel corpora and a cross-entropy loss.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Language Pair for** & **The Number of Voc. in** & **The Number of Shared Voc./The Number** \\
**Pre-Training** & **Monolingual Lexicon** & **of Voc. in Joint Lexicon (the Ration)** \\ \hline \multirow{2}{*}{Korean–Japanese} & Korean: 37,670 & 33/75,147 \\  & Japanese: 37,510 & (0.089\%) \\ \hline \multirow{2}{*}{Korean–English} & Korean: 47,805 & 20,174/65,176 \\  & English: 38,085 & (30.9\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The numbers of vocabularies in the lexicons built from each corpus.  
Table 3 lists the preliminary results of the NMT systems, measured by BLEU scores [16].  
We can observe the following three facts from the experimental results. First, pre-trained models are beneficial to improving the performance of a supervised NMT system. Second, when trained with the same number of parallel sentences and evaluated on the same sentence triples, the NMT system of Korean-Japanese performs better than that of Korean-English in every case. Third, the BLEU scores of the tasks of Korean-Japanese unsupervised NMT are quite high--32.76 for Japanese to Korean, and 29.07 for Korean to Japanese--even when there is no lexical overlap in the training dataset between the two languages. However, the Korean-English unsupervised NMT shows very disappointing results despite sharing the greatest number of vocabularies between the two languages. We can anticipate that the NMT system for the pair of Korean-English cannot be trained well without a parallel corpus.  
In the following section, we list several experiments we performed to determine factors behind the effectiveness of an unsupervised NMT system between Korean and Japanese.
###### Abstract  
An efficient iterative timing recovery via steepest descent of low-density parity-check (LDPC) decoding metrics is presented. In the proposed algorithm, a more accurate symbol timing synchronization is achieved at a low signal-to-noise (SNR) without any pilot symbol by maximizing the sum of the square of all soft metrics in LDPC decoding. The principle of the above-proposed algorithm is analyzed theoretically with the evolution trend of the probability mean of the soft LDPC decoding metrics by the Gaussian approximation. In addition, an efficiently approximate gradient descent algorithm is adopted to obtain excellent timing recovery with rather low complexity and global convergence. Finally, a complete timing recovery is accomplished where the proposed scheme performs fine timing capture, followed by a traditional Mueller-Muller (M&M) timing recovery, which acquires timing track. Using the proposed iterative timing recovery method, the simulation results indicate that the performance of the LDPC coded binary phase shift keying (BPSK) scheme with rather large timing errors is just within 0.1 dB of the ideal code performance at the cost of some rational computation and storage. Therefore, the proposed iterative timing recovery can be efficiently applied on occasions of the weak signal timing synchronization in satellite communications and so on.  
iterative timing recovery; timing synchronization; LDPC decoding; soft metrics; steepest descent algorithm +
Footnote †: journal: Electronics  
0021  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Y. Efficient Iterative Timing Recovery of Low-Density Parity-Check Decoding Metrics Using the Steepest Descent Algorithm for Satellite Communications at Low SNRs
## 1 Introduction
In recent years, research on the working mechanism of eddy current dampers has gained attention. [PERSON] et al. [15] proposed a novel eddy current damper for spacecraft vibration suppression based on eddy current principles. Through numerical simulations and damping characteristic tests, they thoroughly studied its damping properties. Eddy current damping technology, which avoids primary-secondary contact during damping generation, offers advantages such as minimal wear, simple structure, and ease of control. It has found widespread applications in vibration control, machining, and train braking [16; 17; 18; 19]. In another study, [PERSON] et al. [19] optimized the magnetic circuit and parameters of a plate-type tuned mass damper (TMD) using Ansys Maxwell software v2023 R1. They analyzed the energy dissipation characteristics of permanent magnet damping forces over time, providing insights into the damper's operational behavior. [PERSON] and [PERSON] [20] validated the application of eddy current dampers in vibration suppression during CNC machining of low-rigidity components through cutting experiments and dynamic characteristic tests. Additionally, [PERSON] [21] studied the theoretical model, 3D magnetic field finite element analysis, and kinematic performance simulation of an eddy current velocity damper, supporting its theoretical design. These studies collectively highlight the promising prospects and effectiveness of eddy current damping technology across various domains, providing a theoretical foundation for designing an eddy current damper. Under actual seat vibration suppression conditions, using an eddy current damping structure with an electromagnet can better meet the real-time changing working environment [22]. This paper focuses on analyzing the vibration damping characteristics of a designed seat based on eddy current damping technology. Combined with the air suspension vibration damping seat structure, in the face of the requirement of improving the semi-active vibration resistance, a semi-active suspension vibration damping structure based on an eddy current parameter control is designed, and its working mode is analyzed. The designed automobile seat is simplified as a single-degree-of-freedom vibration system, whose theoretical model is established and vibration damping performance is analyzed. The electromagnetic finite element simulation model of the eddy current semi-active suspension damped automotive seat is built, and the damping characteristics of the seat are analyzed by using different weights and different excitation conditions, which provides the basis for the data of the semi-active control current. The research results can support and inform the design and application of the semi-active control eddy current damping seats.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Proposed Mechanism
#### 3.2.2 Selection of Relay Nodes  
While selecting a route, the source node sends RREQs to an upper-hop node that has the highest \(\lambda\) value. Upon receiving the RREQ, the relay node then further requests its ascendant node, which has a higher \(\lambda\) value. This process is repeated by all forwarders until the intermediate nodes with a hop level of one receive the RREQ. The last-mile node in the route, as the nearest node to the sink, then replies the RREPs to the requested nodes. Subsequently, the RREP is acknowledged to the source node by the forwarders along the reverse route. In Figure 8, the possible connections are shown, among which a route has been selected based on the calculated \(\lambda\) scores.
## 5 Calculation Results
### Calculation and Results of Quantitative Risk Assessment  
To determine the final priority of the identified CRFs, first the decision matrix should be formed based on the evaluation criteria and using the TOPSIS technique. Then, the normal matrix is extracted and the relative proximity of each option to the solution is determined. The first step in the TOPSIS technique is the formation of a decision matrix, which is prepared by gathering the opinions of experts through a decision matrix questionnaire. This matrix is needed for evaluating the importance of risks based on criteria. Table 11 (Appendix A) shows the results of the questionnaires collected from 30 experts. Normalization is the second step in solving all MCDM techniques based on the decision matrix. In the present study, normalization is performed by the vector method, which results in normalization according to Table 12. In the TOPSIS method, to create a normal matrix, the weight of each criterion is multiplied by all the numbers below each of the same criteria. Accordingly, the weight of the proposed criteria is according to Table 13. After applying in the normal matrix, the normal matrix will be in accordance with Table 14.  
In order to determine the risk rating, the relative proximity of each option to the ideal solution must be extracted. The Euclidean distance of each option from the positive and negative ideals was calculated, and the positive and negative ideals of each criterion were calculated according to Table 15. Formula (6) is also used to calculate the relative proximity of each option to the ideal solution. Finally, the rating of each risk is determined based on Confidence Interval (CL), which is a number between one and zero. The closer this value is to one, the higher the risk priority; conversely, the closer the value is to zero, the lower the risk significance. Table 7 shows the final results of the CRFs ranking of CRCBPs using the TOPSIS method. \[\mathrm{d}_{\mathrm{i}}^{+}=\sqrt{\sum_{\mathrm{j=1}}^{\mathrm{n}}\left( \mathrm{v}_{\mathrm{ij}}-\mathrm{v}_{\mathrm{j}}^{+}\right)^{2}} \tag{4}\]  
\[\mathrm{d}_{\mathrm{i}}^{-}=\sqrt{\sum_{\mathrm{j=1}}^{\mathrm{n}}\left( \mathrm{v}_{\mathrm{ij}}-\mathrm{v}_{\mathrm{j}}^{-}\right)^{2}} \tag{5}\]  
\[\mathrm{CL}_{\mathrm{i}}^{+}=\frac{\mathrm{d}_{\mathrm{i}}^{-}}{\mathrm{d}_{ \mathrm{i}}^{-}+\mathrm{d}_{\mathrm{i}}^{+}} \tag{6}\]  
Results shown in Table 7 highlight that the five CRFs with the highest importance for CRCBPs are (1) the risk of external threats due to international factors (with a relative distance of 0.895), (2) exchange rate fluctuations and changes (with a relative distance of 0.852), (3) bank interest rate fluctuations (with a relative distance of 0.795), (4) traffic licenses (with a relative distance of 0.681), and (5) access to skilled labor (with a relative distance of 0.678).
# Probabilistic Models for Competence Assessment in Education
### Flow of Probabilistic Influence  
The structure of a Bayesian network contains information regarding how variables in the model interact with each other. In other words, it is possible to determine whether the injection of information about a variable \(X\) updates our knowledge about another variable \(Y\) in the graph.  
Considering the case of only two variables \(X\) and \(Y\), if \(X\) is a parent of \(Y\), then any update in the probability distribution associated with \(X\) will be reflected in changes in the probability distribution associated with \(Y\). Likewise, knowing \(Y\) will update our information about \(X\).  
If we now think about three variables \(X,Y,W\), three main situations can be distinguished:  
1. If \(W\) is an intermediate node and all the edges go in the same direction (Figure 3), then an update in \(X\) will be reflected in \(Y\) if and only if \(W\) is not an observed variable, and vice versa: an update in \(Y\) will be reflected in \(X\) if and only if \(W\) is not an observed variable.
2. The same applies if \(W\) is a parent of two children \(X\) and \(Y\) (Figure 4). Again, there will be a flow of probabilistic influence from \(X\) to \(Y\) if and only if \(W\) is not observed.  
3. Finally, if \(X\) and \(Y\) are parents of \(W\) (v-structure, Figure 5), then the situation reverses, and there is a flow of probabilistic influence from \(X\) to \(Y\) if and only if \(W\) is observed.  
In short, it can be stated that in Bayesian networks, influence flow is stopped by observed nodes and nonobserved v-structures. A v-structure is observed if \(W\) or any of its descendants is observed.  
Formally, we say that there is a flow of probabilistic influence from \(X\) to \(Y\) if there is an active trail from \(X\) to \(Y\), where an active trail is defined as follows:  
* Let \(\mathcal{G}\) be a DAG.
* Let \(X_{1}\rightleftharpoons...\rightleftharpoons X_{2}\) be a trail in \(\mathcal{G}\).
* A trail is active given a set of observed variables \(W\) if 1. Whenever there is a v-structure \(X_{i-1}\to X_{i}\gets X_{i+1}\), \(X_{i}\) or one of its descendants is in \(W\). 2. no other node along the trail is in \(W\).  
Let \(X\), \(Y\), and \(W\) be three disjoint sets of variables in \(\mathcal{G}\). \(W\) d-separates \(X\) from \(Y\) in \(\mathcal{G}\) if \(XY|W\) holds in \(\mathcal{G}\). \(XY|W\) holds in \(\mathcal{G}\) if there is no active trail between any variable in \(X\) and any variable in \(Y\) given \(W\).  
Figure 4: Case 2.  
Figure 5: Case 3.  
Given a variable \(X_{j}\) and its Markov blanket, \(\mathbf{Mb}_{j}\), for any set of variables \(\mathbf{X}_{c}\subseteq(\mathbf{V}\backslash\mathbf{Mb}_{j})\):  
\[\mathbf{X}_{c}X_{j}|\mathbf{Mb}_{j} \tag{2}\]  
Hence, the Markov blanket of a variable in a BN d-separates that variable from the rest of the network.
## 2 Materials and Methods
### Criteria for the Development of Empirical Laws
#### 2.3.2 Process for Obtaining Formulas  
The first step to obtaining a certain empirical law is to analyse the geometry of the trends shown by the empirical data, for example, by paying attention to the number of curvatures, whether they show an increasing or decreasing trend, and whether they show asymptotic trends when the variable approaches lower or upper limit values or both. The logic of behaviour must be included in this process. For example, it is reasonable to consider that the porosity of a granular medium involving equidimensional grains is higher than that of a medium involving heterogeneous grain sizes, which can fill the remaining pores between those of a larger size, independently of whether this is confirmed by the empirical results.  
After analysing the empirical data characteristics, a function that shows a similar aspect must be chosen. For non-harmonic behaviour, this step requires some knowledge on simple algebraic expressions. In this sense, the use of the trial-and-error method to select an initial ratio function should not be ruled out. Once a function is chosen, which should preferably contain few coefficients, the optimal value of these coefficients will be chosen to achieve the best fit to the empirical data, in which minimum deviation (i.e., adopting coefficients that minimise the difference between the fit function and empirical data) is an important criterion. This process can be performed using known codes, such as the MS Excel@ solver function.  
Although the individual steps to reach each formula are not shown in the different examples of empirical laws presented here, a common subsequent process has been searching secondary relationships for the coefficients of that first formula. For example, after analysing the trend of the empirical viscosity data of some silty-clay muds as a function of temperature (T), an ordinate determined at T \(\geq\) 0 \({}^{\circ}\)C and an asymptotic decrease when T\(\rightarrow\)100 \({}^{\circ}\)C was observed; thus, the most relevant function appears to be a decreasing exponential \(\eta\)(T) = a\(\cdot\)exp(\(-\)b\(\cdot\)T), where a and b are fitting coefficients. However, an adequate fit to the data cannot be achieved using any set of coefficients despite obtaining an acceptable regression coefficient (R\({}^{2}\) = 0.93). Applying logarithms, it can be seen that the dependence of the data on temperature, with the exception of the constant value ln(a), shows that a power function explains the observed behaviour better. The chosen relationship for which the minimum deviation coefficients are calculated is \(\eta\)(T) = n1\(\cdot\)exp(\(-\)n2\(\cdot\)T\({}^{3}\)). An empirical relationship that more adequately describes that dependence (R\({}^{2}\) = 0.99) is thus given as \(\eta\)(T) = 1.8\(\cdot\)exp(\(-\)0.044\(\cdot\)T\({}^{0.85}\)) by [PERSON] [25]. However, this relationship cannot be considered a law, despite its usefulness in determining the changes in mud viscosity with temperature in water and oil wells, because the analysed fluid has somewhat specific characteristics.