esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The results showed that the ductility values of the walls that were flanged and end-confined were, respectively, 1.5 and 2 times those of their rectangular counterparts. To maintain the same lateral capacity, more than 40% in the amount of vertical reinforcement was saved by using the proposed methods. [PERSON] et al. [26] tested six RMSWs with different end configurations. The test results showed that the displacement ductility value of the rectangular, flanged wall and the wall with boundary elements with low reinforcement ratios were almost similar. However, with higher reinforcement ratios, the attained displacement ductility value for the walls that had flanges or boundary elements were 33% and 40% higher than that of their counterparts, respectively. However, few studies were conducted on the seismic behavior of prefabricated RMSWs with flanges and vertical joints connections in prefabricated RMSWs, which are considered to be one of the most crucial parts of the reinforced masonry shear wall structural system. To clarify and address this issue, an experimental study on the prefabricated RMSWs with flanges was conducted to evaluate the seismic performance. Four full-scale fully grouted RMSWs were designed under the Chinese code [27] and tested under reversed cyclic loading. The aim of the study was to evaluate the shear behavior of RMSWs with flanges at the wall ends as well as the effect of construction method on seismic performance. The experimental program and test results are summarized in this paper. The crack patterns, failure modes, and force-displacement responses were obtained. The influence of flanges and construction methods on the lateral capacity, displacement ductility, stiffness degradation, energy dissipation, and equivalent viscous damping was evaluated.
# Robust Full-Screen Wavelength Calibration Algorithm
## 4 Experimental Studies
### Simulated Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & \(\mathbf{a_{0}}\) & \(\mathbf{b_{0}}\) & \(\mathbf{c_{0}}\) & \(\mathbf{d_{0}}\) \\ \hline Initial value & \(max(\lambda_{c})-min(\lambda_{c})\) & \(2\pi freq\) & 0 & \(mean(\lambda_{c})\) \\ \hline \end{tabular}
\end{table} 
Table 1: The initial values of the parameters in Equation (5). Figure 3: Sinusoidal fitting results of the central pixels. With data pairs \(\Delta x\) and \(\Delta p\), we fitted the linear regression model using the LS method, Huber regression, and Turkey regression. The left subplot of Figure 4 shows the fitting results of Huber regression, LS regression, and Tukey regression. The horizontal and vertical axes represent \(\Delta p\) and \(\Delta x\), respectively. The blue points are the observed data and the lines are the linear models fitted by the three methods. To facilitate comparison, we enlarged the contents of the red box; it is shown in the right subplot. These results reveal that Huber regression performs better than the other methods in resisting outliers. The fitted parameters, obtained according to Huber regression, are shown in Table 4.  
To further demonstrate the advantages of Huber regression, we conducted a quantitative analysis of the fitting results. Since Figure 4 shows that Turkey regression works the worst, only LS and Huber regression were taken into account. First, we removed the nine farthest outliers from the linear function obtained using the LS method according to the absolute value of the residuals. Then, the LS RANSAC, Theil Sen, and Huber regression were compared on the remaining points. We computed the mean squared error (MSE), the maximum residual (RES) and the mean absolute error (MAE). Figure 5 demonstrates the obtained results, in which columns 1, 2, and 3 correspond to the MSE, RES, and MAE, respectively. Different colors represent different regression algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter (\(\beta\))** & \(\mathbf{e}\) & \(\mathbf{f}\) \\ \hline Fitting results & \(-0.519\) & \(-0.0981\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The fitting parameters of Algorithm 2. Figure 4: Comparison of the three fitting methods. Note: The blue points are the observed data. The blue, red, and green lines are the Huber, LS, and Tukey regression predictions, respectively. The image on the right is a partial enlargement in the red box on the left. Figure 5 shows that Huber regression performs better than LS, RANSAC, and Theil Sen regression in terms of the evaluation indices MSE, MAE and RES. In industrial practice, the maximum residual error is often chosen as the criterion by which to judge the quality of the algorithm. Therefore, the smaller the RES, the better the algorithm. Based on this fact, we chose the \(\hat{\beta}\) estimated by the Huber method.
## 3 Our Proposed Work
### Pre-Processing Module  
Suppose that the input load time series is shown by the following matrix:  
\[P=\begin{bmatrix}p_{h_{1}}^{d_{1}}&p_{h_{2}}^{d_{1}}&p_{h_{3}}^{d_{1}}&\ldots&p_{ h_{m}}^{d_{1}}\\ p_{h_{1}}^{d_{2}}&p_{h_{2}}^{d_{2}}&p_{h_{3}}^{d_{2}}&\ldots&p_{h_{m}}^{d_{2}}\\ p_{h_{1}}^{d_{3}}&p_{h_{2}}^{d_{3}}&p_{h_{3}}^{d_{3}}&\ldots&p_{h_{m}}^{d_{3}}\\ \vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ p_{h_{1}}^{d_{n}}&p_{h_{2}}^{d_{n}}&p_{h_{3}}^{d_{n}}&\ldots&p_{h_{m}}^{d_{n}} \end{bmatrix} \tag{1}\]  
where \(h_{m}\) is the \(m\)-th hour, \(d_{n}\) is the \(n\)-th day and \(p_{h_{m}}^{d_{n}}\) is the historical power consumption value at the \(m\)-th hour of the \(n\)-th day. As there are \(24\) h in a day, \(m=24\). The value of \(n\) depends on the designer's choice, _i.e._, a greater value of \(n\) leads to fine tuning during the training process of the forecast module, because more lagged samples of input data are available. However, this would lead to greater execution time.  
Prior to feeding the feature selection module with input matrix \(P\), the following step-wise operations are performed by the data preparation module (refer to Figure 3):  
1. Local maximum: Initially, a local maximum value is calculated for each column of the \(P\) matrix; \(p_{max}^{c_{i}}=max\{p_{h_{i}}^{d_{1}},p_{h_{i}}^{d_{2}},p_{h_{i}}^{d_{3}}, \ldots,p_{h_{i}}^{d_{n}}\}\), \(\forall\)\(i\in\{1,2,3,\ldots,n\}\).
2. Local normalization: In this step, each column of the matrix \(P\) is normalized by its respective local maxima, such that the resultant matrix is represented by \(P_{nrm}\). Now, each entry of \(P_{nrm}\) ranges between zero and one.
3. Local median: For each column of the \(P_{nrm}\) matrix, a local median value \(Med_{i}\) is calculated (\(\forall\)\(i\in\{1,2,3,\ldots,n\}\)).
4. Binary encoding: Each entry of the \(P_{nrm}\) matrix is compared to its respective \(Med_{i}\) value. If the entry is less than its respective local median value, then it is encoded with a binary zero; else, it is encoded with a binary one. In this way, a resultant matrix containing only binary values (zeroes and ones), \(P_{b}\), is obtained.  
Note: the load/consumption pattern is different for different days, _i.e._, the load pattern on holidays is different from that on working days. In order to enhance the accuracy of prediction strategy, the training samples must be relevant. Similarly, a lesser number of training samples will decrease the execution time of the prediction strategy. The above two reasons lead us to prefer local normalization over global normalization.  
At this stage, the \(P_{b}\) matrix is compatible with the feature selection module and is thus fed into it.  
Figure 3: Data preparation module.
###### Abstract  
There are only a few applications of infrared thermal imaging in aviation. In the area of turbojet engines, infrared imaging has been used to detect temperature field anomalies in order to identify structural defects in the materials of engine casings or other engine parts. In aviation applications, the evaluation of infrared images is usually performed manually by an expert. This paper deals with the design of an automatic intelligent system which evaluates the technical state and diagnoses a turbojet engine during its operation based on infrared thermal (IRT) images. A hybrid system interconnecting a self-organizing feature map and an expert system is designed for this purpose. A Kohonen neural network (the self-organizing feature map) is successfully applied to segment IRT images of a turbojet engine with high precision, and the expert system is then used to create diagnostic information from the segmented images. This paper represents a proof of concept of this hybrid system using data from a small iSTC-21v turbojet engine operating in laboratory conditions.  
Keywords:expert systems; infrared thermography; intelligent diagnostics; image segmentation; neural networks; thermal imaging camera; turbojet engines +
Footnote †: journal: Applied Physics Letters
# Protograph LDPC Code Design for Asynchronous Random Access +
To facilitate code design, we further approximate the aggregate interference contribution, possibly generated by a multitude of terminals, as Gaussian. In Section 3.4, we present a protograph LDPC code design for this channel model, and its iterative decoding threshold is compared with the one of a raptor-like LDPC code design proposed for the recently introduced fifth generation of mobile networks (5G) standard. Both code designs are compared with the Shannon limit. * The impact of the Gaussian interference assumption on the code performance is also considered in Section 3.5. The expression of the log-likelihood ratio (LLR) and the threshold performance, computed with quantized density evolution, are presented for a single interferer when the Gaussian assumption is dropped. * In order to get a first, yet not fully accurate, performance characteristic for the proposed LDPC codes in the random access (RA) channel, we elaborate on the decoding condition so as to abstract the physical layer in Section 4.1. A decoding region, as a function of the interference pattern for both a random code ensemble and for the LDPC code ensemble is derived. Although more accurate than the surrogate channel model, since the effective interference power and affected codeword position are considered, the abstraction is grounded on the iterative decoding threshold, and thus on large blocks assumption. * Since RA is particularly appealing for short packet transmission, we depart from the physical layer abstraction and present in Section 4.2 physical layer simulations considering a finite block length. Interestingly, the codes designed for the surrogate channel model still perform very well on the asynchronous multiple access channel. Moreover, the performance trends and relative performance identified via the simpler simulations with the abstracted physical layer are confirmed.
# Techno-Economic Potential of Plasma-Based CO\({}_{2}\) Splitting in Power-to-Liquid Plants
## 1 Introduction
The energy consumption of a big container ship with a typical 200.000 t payload over a 10.000 km transport distance (\(\approx 8\times 10^{6}\) kg CO\({}_{2}\)) can be estimated as bigger than \(10^{13}\) J (dimensions: 400 m, 60 m, 15 m; a cruising speed of 10 m/s, and friction value of 0.0025). Comparing the specific energy of chemical fuels (\(\sim 10,000\) Wh/kg) and electrochemical cells (\(\sim 300\) Wh/kg), much of the payload must be dispensed. With a usual 1 day of charging, the ship would need more than 300 MW of electrical power to fill the energy storage. On the other hand, conventional ship fuel releases sulfur emissions when burned; to reduce these emissions, the shipping industry is striving to further develop exhaust gas cleaning systems and to use alternative fuels. An alternative to synthetic fuel is the use of hydrogen and conversion to electricity in fuel cells. Since the storage of gaseous hydrogen under high pressure involves very large reservoirs  
Figure 1: PlasmaFuel Project scheme (credit to [PERSON]). due to the low density, and liquefaction requires very costly insulation and energy for cooling, integration in long-haul aircraft and container ships is still problematic. In summary, it can be said that the production of synthetic fuels for the ship and aviation sectors is an important part of the energy transition. The development and optimization of technologies such as CO\({}_{2}\) splitting are necessary to design the required power-to-liquid plants. In addition, considerations of the entire process chain and investigation and comparison of technical and economic performances are necessary. This can be carried out through techno-economic analyses, in which the costs of a fuel produced can be estimated. In addition to previous studies on the feasibility of synthetic fuels, this work provides insights into the integration of plasma-based CO\({}_{2}\) splitting into PtL plants. The work, thus, extends the previously considered paths to synthetic fuels by a plasma-based option. For this purpose, the reactors investigated in the PlasmaFuel joint project are integrated into a process chain to produce sulfur-free sustainable marine diesel. Since processes of plasma-based CO\({}_{2}\) splitting have several advantages but still require further development, this work examines its feasibility and economic potential. The modeling and techno-economic analyses address the research questions: How expensive is the production of plasma-induced, pollution-free sustainable fuels? What are the biggest levers regarding efficiency? Is it possible to produce plasma-based synthetic diesel for EUR 2.0/L or less? How big is the impact of the electricity costs via renewable energies?
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 2 State-of-the-Art in Non-Uniform and Low Latency Resampling
### Research Gap  
Although there is a large body of literature on resampling, the reduction of group delay is not addressed. Low-latency resampling is only understood in terms of processing delay. Several solutions are based on dedicated hardware such as FPGAs or GPUs. A group delay of half FIR order is stated in [2], which results in 20 to 60 samples depending on the selected example application. The resampling of data from rotating machines requires a filter order of 61 in [17]. The Farrow filter in [1] uses a filter of order 70.  
This focus on processing delays is justified for high-frequency applications. However, such high group delays are not acceptable for resampling data from wireless sensor networks with long sampling intervals.  
Only the method described in [21] focused both on processing and group delay. The Akima algorithm provides low-complexity and fast calculation. It requires a low number of future samples, equivalent to the group delay, namely three for standard implementation, and 2 for the improved version in [21]. These advantages must be paid for by a high OSR, which determines how many more samples are needed compared to the Nyquist rate. A study of the relation between OSR and resampling error was only found regarding Akima [21] and in [17]. Both articles recommend for their low complexity algorithm an OSR > 4.  
Lagrange polynomial interpolation, the improved method by [PERSON], and Akima were included as reference methods in this paper.
## 3 Results and Discussions
### Three-Dimensional Scanning of Sample 2
Hence, the role of watertightness is crucial when adopting modes A, B, and D of scanning except for mode C, which demonstrated a higher simplification PCR was than the PCR of watertightness. Though the error PCRs were found to be the highest in almost all the modes of 3D scanning, this directly reveals that the combined effects are likely affect the 3D scanning error positively. When considering all modes of scanning, this was not the case for S1 as error PCRs were also found to be lower for modes C and D, though the overall error PCRs in all modes of scanning for Sample 1 were found to be higher than the PCR of at least one scanning factor. Hence, a great extent of interaction between the 3D scanning factors (especially watertightness and simplification) possibly occurred to mitigate the scanning error regardless of the samples' geometry. Apart from the statistical errors, e.g., the error PCRs, the volumetric scanning errors were found to be lower, considering the lower values of the error range for S2 compared to S1. To be terse, scanning errors for the complex sample were reduced by up to 0.1%. Such a low value of error was never before achieved; however, a dimensional error of up to 0.33% was reported in a previous study [11]. This is still higher than the dimensional error of the current research study. Upon further comparison with the literature on non-contact 3D scanners, the dimensional errors were successfully reduced by up to 3% [33], 2.5% [2], 5% [34], 6% [35], and 16% [36]. Therefore, a reduction of these errors up to 0.1%, as obtained in the current study, has never been achieved before. Moreover, the error PCR was always found to be higher with insignificant scanning factors for mode B when considering both sample geometries as mode B involves using a texture scan with sharpness. Hence, sharpness may be treated as the secondary 3D scanning factor, even with texture option. When comparing the smoothness and sharpness, the effect of smoothness (applied together with other 3D scanning factors, as per Table 2) was found to be better than that of sharpness on mitigating the scanning errors. As far as the PCRs of the 3D scanning factors are concerned, the watertightness PCR was generally found to be higher than that of the simplification PCR for all modes of scanning except mode C for S2. Contrarily, the simplification PCR was found to be higher than the watertightness PCR for all modes of scanning for S1 except for mode D.  
Watertightness and simplification are both crucial for precise and accurate 3D scanning results for various geometries. ; however, watertightness takes primary importance in 3D scanning as it can readily deliver digital models for additive manufacturing with some secondary modifications in terms of adjusting the point cloud sizing with fitting polygons, which is usually accomplished via simplification. This again validates that intricate geometries are good candidates for 3D scanning instead of simple objects, which can easily be geometrically modeled using a CAD software.
# Mechanical Properties and Freeze-thaw Durability of Basalt Fiber Reactive Powder Concrete
## 3 Results and Discussion
### Effect of Fiber on Freeze-Thaw Durability of RPC  
#### 3.2.1 Mass Loss  
The mass loss of BFRPC with different basalt fiber contents was tested under the chloride-salt freeze-thaw cycle and compared with the PRPC and SFRPC to evaluate the effects of basalt fiber contents and fiber type on the freeze-thaw durability of RPC. Figure 6 shows that the effects of basalt fiber contents and fiber type on the mass loss of RPC. It can be known from Figure 6 that the mass loss rate of RPC increases with the increase in the number of freeze-thaw cycles. The mass loss of RPC is mainly caused by surface scaling and the edges of the specimen surface will fall off as the freeze-thaw cycle test continue, resulting in fiber exposure [2]. It is worth noting that the mass loss rate of BFRPC (8 and 12 kg/m\({}^{3}\) content) is negative in the initial part of the freeze-thaw test, which is related to the water absorption of basalt fiber. In the initial part of the freeze-thaw test, the mass of water absorbed by the basalt fiber is greater than that of the surface scaling of RPC caused by freeze-thaw damage. When freeze-thaw cycle levels are the same, the mass loss rate of RPC decreases as the basalt fiber content increase, which indicates that the basalt fiber could improve the freeze-thaw resistance. When the number of freeze-thaw cycles is less than 600, the mass loss rate of SFRPC is in a stable increase stage and the mass loss is mainly caused by surface scaling. When it exceeds 600, a significant increase in the mass loss rate occurs, and the reason is that the steel fiber exposed to the chloride ion environment corrodes rapidly, thus leading to more peeling off the edge of the specimen surface. The mass loss rate of SFRPC reaches 3.03% after 800 freeze-thaw cycles, which is higher than that of BFRPC. This indicates that steel fiber is not as effective as basalt fiber in improving the freeze-thaw durability of RPC.  
Figure 5: Effect of basalt fiber content and fiber type on the flexural strength of RPC.  
Figure 6: Mass loss rate versus freeze–thaw cycles.
## 5 Stability Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Factor of Safety**} \\ \cline{3-6}  & **Condition** & **Case** & \multicolumn{2}{c}{**The Ordinary Method**} & \multicolumn{2}{c}{**Bishop’s Simplified**} \\ \cline{3-6}  & & & **of Slices** & \multicolumn{2}{c}{**Method**} \\ \cline{3-6}  & & **Calculated** & **Standard** & **Calculated** & **Standard** \\ \hline Normal & No drainage & a & 1.235 & 1.25 & 1.268 & 1.35 \\ water level & Horizontal drainage & b & 1.321 & 1.25 & 1.362 & 1.35 \\  & New drainage & c & 1.542 & 1.25 & 1.677 & 1.35 \\  & No drainage & d & 1.063 & 1.15 & 1.126 & 1.25 \\  & Horizontal drainage & e & 1.142 & 1.15 & 1.236 & 1.25 \\ Extremely & New drainage & f & 1.521 & 1.15 & 1.580 & 1.25 \\ high water & \(k_{x}\) reduced 1.5 times & g & 1.437 & 1.15 & 1.478 & 1.25 \\  & \(k_{y}\) increased 1.5 times & h & 1.415 & 1.15 & 1.456 & 1.25 \\  & Earthquake & i & 1.106 & 1.05 & 1.165 & 1.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The calculated results of the FOSs (factor of safety) of the slope stability under different working conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Volume-Weight (kN/m3)**} & \multicolumn{2}{c}{**Soil Indicators in Nature**} \\ \cline{2-5}  & **Unity** & **Saturated Unity** & **Cohesion (\(\boldsymbol{c}\))** & **Friction Angle** \\  & **Weight (\(\boldsymbol{\gamma}\))** & **Weight(\(\boldsymbol{\gamma_{d}}\))** & **(kPa)** & **(\(\boldsymbol{\phi}^{\prime}\)) (\(\boldsymbol{\circ}\))** \\ \hline Moderately weathered crystal tuff & 26.9 & 27.9 & 2000.0 & 38.0 \\ Strongly weathered crystal tuff & 24.5 & 25.5 & 50.0 & 27.0 \\ Starter dam & 21.0 & 21.9 & 18.0 & 24.0 \\ Silty sand tailings & 19.2 & 20.1 & 4.0 & 30.0 \\ Silty soil tailings & 19.9 & 20.9 & 8.0 & 27.0 \\ Silty clay & 19.7 & 20.6 & 24.0 & 20.0 \\ Artificial clay I & 21.0 & 20.8 & 18.0 & 24.0 \\ Artificial clay II & 22.0 & 21.7 & 12.0 & 20.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The tests results of the physical and mechanical properties of the tailings samples.