esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Virtual Reality-Based System for Simulating Welding Processes
## 2 Materials and Methods
### Simulation of the Weld Bead Geometry  
When simulating the welding process, only the surface of the weld bead is visible, which shows the bead width and the bead reinforcement.  
Figure 12: Algorithm for generating the virtual reality environment.  
To describe the surface of the weld bead in the virtual environment, the cross-section of the weld bead is approximately parabolic as shown in Figure 13[11; 40].  
\[Z=-aY^{2}+h \tag{2}\]  
where \(Y\) is the position of the welding torch in \(Y\) direction and \(b\) represents the width. \(h\) is the maximum height of the weld bead, and \(a\) is a constant. The values of \(a\) and \(h\) were determined as follows [14]:  
\[a=\frac{4D^{2}f\Delta t}{b^{4}} \tag{3}\]  
\[h=\frac{D^{2}f\Delta t}{b^{2}} \tag{4}\]  
In these equations, \(D\) (mm) is the diameter of the electrode wire in the case of the MIG welding process or the diameter of the weld stick or filler rod in the case of the SMAW and TIG welding processes, respectively. The wire feed rate is \(f\) (mm/min). \(\Delta t\) is time step (min).  
By substituting Equations (2) and (3), the relationship between the height of the weld bead (\(Z\)) with the bead width (\(b\)) and the position of the welding torch (\(Y\)) in \(Y\) direction can be expressed in Equation (5).  
\[Z=-\left(\frac{4D^{2}f\Delta t}{b^{4}}\right)Y^{2}+\frac{D^{2}f\Delta t}{b^{2}} \tag{5}\]  
In consideration of the influence of welding process parameters, the weld bead width for each welding process was determined as follows [41]:  
For the SMAW welding process:  
\[b=9.41-0.0072\;A+0.064\;B-0.00697\;C+0.123\;D+0.293\;E \tag{6}\]  
where \(A\): welding current (\(A\)), \(B\): arc length (mm), \(C\): welding speed (mm/min), \(D\): electrode diameter (mm), and \(E\): joint gap (mm).  
For the MIG welding process:  
\[b=-6.502-0.1\;p+0.57969\;m+0.5813\;f+0.01562\;d-0.05375\;e+0.00271\;n \tag{7}\]  
where \(p\): welding speed (cm/min), \(m\): arc voltage (V), \(f\): wire feed rate (m/min), \(d\): gas flow rate (L/min), \(e\): nozzle to plate distance (mm), and \(n\): torch angle (degree).  
For the TIG welding process:  
\[b=-0.79-0.1468\;M+0.01713\;O+0.654\;P+0.1146\;Q \tag{8}\]  
where \(M\): welding speed (cm/min), \(N\): wire feed rate (cm/min), \(O\): % cleaning, \(P\): joint gap (mm), and \(Q\): welding current (A).  
Figure 13: Cross-section of the weld bead.
## 3 Surface-Based In-Situ Measurement Techniques
### Sonic anemometers
Sonic anemometers are also sensitive to temperature changes, which can affect the measurement of the  
Figure 3: Examples of (ultra)sonic anemometers, from the left: (**a**) CSAT3 3D sonic anemometer by Campbell Scientific, Inc.; (**b**) uSonic-3 Scientific (former: USA-1) 3D sonic anemometer by METEK GmbH; (**c**) Ultrasonic Anemometer 2D by Thies Clima; and (**d**) WINDCAP 2D Ultrasonic Wind Sensor WMT700 by Vaisala. travel times along the sonic paths [66]. In addition to these known error sources, sonic anemometers may have challenges in rain and snow; these conditions can affect the propagation of sound pulses. Sometimes, instruments internal errors result in unrealistically high (or low) values in the measured time series, called spikes. Even only one such value in the time series may lead to overestimation of the wind gust speed. Methods, called spike detection and removal, have been proposed to correct for these outliers [67; 68; 39; 69]. They are based on two-point statistics of the time series, using information about the previous values in the time series to provide a forecast for the next value. If the magnitude of the next observed value is several standard deviations away from the forecasted one, the observed value is considered as a spike and removed from the time series for example by linear interpolation using neighboring non-spike values. The method performs well when there are only a few outliers, but deteriorates when unrealistic values start to dominate the time series [39]. Consecutive anomalous values may sometimes be caused by real atmospheric phenomena. To account for that, a criterion is introduced that if there are at least four consecutive detected values in row, the values are not considered as spikes, but as a physical feature of the flow [68; 69].
## 1 Introduction
To the best of our knowledge, sparse adaptive CS-based channel estimation approach has not yet been studied for FBMC/OQAM systems. The advantage of the proposed ARCoSaMP method is that it does not need priori channel sparse information. Furthermore, the proposed algorithm is based on the idea of regularization and the backtracking mechanism that attaches to CoSaMP algorithm, which removes the unreliable support and refines the current approximation iteratively. Simulations verify the proposed channel estimation scheme performs better than the conventional SAMP algorithm and the proposed algorithm can obtain an approximate performance compared with the CoSaMP algorithm. The purpose of this paper is to propose an efficient sparse adaptive channel estimation method. We would like to convince the reader with the potential of the proposed method as a high performance channel estimator. The remainder of this paper is organized as follows. The FBMC/OQAM transmission system model is described in Section 2. Section 3 reviews some conventional channel estimation methods (including preamble-based methods and conventional CS recovery algorithms) and presents the proposed scheme. In Section 4, the performances of the proposed scheme associated with the conventional preamble-based and CS-based schemes are compared and simulation results are shown. Finally, Section 5 gives the concluding remarks.
## 6 AOG Downtime Reduction and Implementation Results
### Error Evaluation Metrics  
RMSE: Measures the average magnitude of prediction errors. For ARIMA (3.19) and Holt-Winters (2.64), Holt-Winters demonstrated greater precision in predicting AOG incidents by reducing error magnitude. RMSE is particularly useful for assessing how well predictions align with actual data on an absolute scale.  
MAPE: Evaluates forecasting accuracy as a percentage of the actual values. Holt-Winters achieved a lower MAPE (45.65%) compared to ARIMA (54.9%), reflecting its superior ability to align with trends and seasonal patterns.  
Table 9 provides a comparative analysis of the performance of three forecasting models, ARIMA, Holt-Winters exponential smoothing, and Logistic Regression, alongside  
Although advanced models such as Long Short-Term Memory (LSTM) networks and S-curve logistic growth functions could be considered for this study due to their theoretical strengths in capturing complex temporal patterns and long-term adoption behaviors, they were ultimately not implemented. The primary reason was the limited availability of high-frequency, granular datasets necessary to train deep learning models effectively, largely due to confidentiality restrictions and regulatory data governance. Moreover, the black-box nature of neural networks poses significant challenges in aviation, where explainability and traceability of decision-making are mandated by compliance standards. S-curve models, while useful for macro-level lifecycle modeling, are not well suited for short-term, event-based forecasting of discrete maintenance events such as AOG incidents. In contrast, the models selected, Logistic Regression, ARIMA, and Holt-Winters offered greater alignment with the available data, provided transparent and interpretable results, and could be feasibly integrated into existing airline operational systems such as RAIDO and Power BI, as seen in the table below.  
Conclusion: Based on the results obtained, Holt-Winters exponential smoothing emerges as the optimal approach for forecasting AOG incidents. This model consistently demonstrated superior predictive accuracy, with the lowest RMSE (2.64) and MAPE (45.6%) among the evaluated methods. Its ability to capture seasonal patterns and periodic fluctuations makes it highly suitable for maintenance scheduling and resource optimization. While ARIMA provided moderate accuracy, its inability to model seasonality resulted in higher errors and a calculated MTBF (1192.54 h) that deviated significantly from the airline's benchmark (1200 h). Logistic Regression, on the other hand, struggled due to data imbalance, as reflected in its poor ROC-AUC score (48%), making it unsuitable for precise AOG predictions in its current form. Airlines can more effectively align their operational strategies with actual AOG trends by utilizing the Holt-Winters model. This alignment has the potential to enhance reliability, reduce downtime, and optimize the allocation of maintenance resources.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
# Blockchain and Smart Contracts for Digital Copyright Protection
## 4 Basic Assumptions  
The implementation of the watermarking protocol presented in this paper needs some basic security facilities: PKI, TLS (Transport Layer Security) secure communication, homomorphic cryptosystem with respect to watermark insertion [18], and blind and readable watermarking scheme able to embed fingerprinting codes [2; 34].  
The protocol assumes that digital contents and watermarks can be represented block-wise in the forms \(X=\{x_{1},x_{2},\ldots x_{l}\}\) and \(W=\{w_{1},w_{2},\ldots w_{l}\}\), respectively. The elements of \(X\) can be either the original host signal samples or the features of the host signal computed by transforms such as, for example, the discrete Fourier transform or the discrete cosine transform [34]. The elements of \(W\) are usually binary values representing fingerprinting anti-collusion codes [35].  
The protocol also assumes that encryption and watermarking processes are represented by block-wise functions. As a consequence, the encryption \(E\) of a content \(X=\{x_{1},x_{2}\ldots x_{l}\}\) under the key \(k\) can be calculated as [34]:  
\[E_{k}(X)=E_{k}(x_{1},x_{2}\ldots x_{l})=(E_{k}(x_{1}),E_{k}(x_{2})\ldots E_{k }(x_{l}))\]  
Likewise, the insertion of the watermark \(W\) into the content \(X\) can be expressed in the form:  
\[X\oplus W=\{x_{1}\oplus w_{1},x_{2}\oplus w_{2},\ldots x_{l}\oplus w_{l}\}=\bar {X}\]  
in which \(\bar{X}\) is the watermarked content and the symbol \(\oplus\) represents, in principle, an arbitrary function [1; 36; 37].  
Finally, the protocol employs an encryption function \(\mathbb{E}\), which is \"homomorphic\" with respect to the watermark insertion [18]. Therefore, the following formula can be used to insert any linear watermark directly into the encrypted domain [37]:  
\[\mathbb{E}_{k}(X\oplus W)=\mathbb{E}_{k}(X)\oplus\mathbb{E}_{k}(W)=\mathbb{E}_{ k}(\bar{X})\]  
In particular, the function \(\mathbb{E}\) has to be \"probabilistic\" and \"semantically secure\". This means that the knowledge of a ciphertext does not provide any useful information on the plaintext to an adversary having only a reasonably restricted computational power represented by polynomial resources [1]. Furthermore, \(\mathbb{E}\) can be limited to homomorphic encryption schemes that support addition or multiplication since such operations are functionally complete sets over finite sets [1; 18]. More in detail, if \(x\) is an element of \(X\) and \(w\) is an element of \(W\), the multiplicatively homomorphic encryption schemes ensure that [38]  
\[\mathbb{E}_{k}(x\cdot w)=\mathbb{E}_{k}(x)\cdot\mathbb{E}_{k}(w)\]  
whereas the additively homomorphic encryption schemes ensure that [39; 40]  
\[\mathbb{E}_{k}(x+w)=\mathbb{E}_{k}(x)\cdot\mathbb{E}_{k}(w)\]
## 2 Numerical Modeling of the AGF Process including Phase Transition Conditions
### Coupling Calculation Model of Shield Machine Heating and Freezing Cooling  
#### 2.2.1 Layout Scheme of Freezing System  
The coupling model of the shield tunneling breakthrough working shaft was established to reference the layout of freezing pipe proposed by [PERSON] [20]. This model adopts a three-dimensional finite element simulation with a total of 435,242 solid elements. The construction is based on a Cartesian rectangular coordinate system. The tunnel excavation direction is along the \(y\)-axis direction, the soil depth direction is along the \(z\)-axis direction, and the horizontal direction perpendicular to the tunnel excavation direction is the \(x\)-axis direction.  
The size of the frozen soil curtain is designed according to the size of the shield machine and the tunnel, the buried depth of the tunnel, the layout of the freezing pipe, and other factors in the actual project. The general diameter of the shield is 6.85 m, and the distance from the ground to the freezing pipe is 15 m, close to the tunnel depth. A total of 53 freezing pipes are arranged in three circles inside and outside the opening center. Among them, the outer ring layout has 31 freezing pipes with a ring radius R3 of 3.75 m, a freezing pipe spacing arc of 0.76 m, a freezing pipe length of 10 m; 14 freezing pipes are arranged in the middle ring with the ring radius R2 of 2.55 m, a freezing pipe spacing arc of 1.14 m, and a freezing pipe length of 2.8 m; 7 freezing pipes are arranged in the inner ring with the ring radius R1 of 1.35 m, a freezing pipe spacing of 1.21 m, and a freezing pipe length of 2.8 m; a freezing pipe with a length of 2.8 m was laid in the center of the shield. The layout scheme of freezing pipes is shown in Figure 2.
## 4 Results and Discussion
### Parametric Study
The decrease in the number of bars in those beams led to an increase in the spacing between the rebars, which in turn caused an increase in the crack width. The larger spacing between the rebars resulted in a larger area of concrete being affected by shear and bending forces. This led to faster damage of the concrete and larger crack widths at the soft of the RC beam compared to beams with smaller diameter reinforcements but thicker spacing between them. The reference beam with 5d14 exhibited the maximum mean crack height of approximately 133.5 mm due to its high load-carrying capacity of 228.67 kN. As the load increased, the crack height also increased proportionally. The parametric study conducted on rebar diameter, number, and spacing in RC beams provides deeper insights into the critical factors affecting the load-carrying capacity and crack behavior. The structural performance of RC beams can be improved by optimizing the combination of rebar diameter, number, and spacing. The results hold substantial \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Beam ID**} & \multirow{2}{*}{**Number of Rebars**} & \multicolumn{2}{c}{**Rebar Cross-Section**} & \multirow{2}{*}{\(\boldsymbol{\Delta}\)} & \multirow{2}{*}{\(\boldsymbol{\Delta}\)} & **Center-Center** \\  & & & **Area (mm2)** & & **Space (mm)** \\ \hline REF beam & 5d14 & 769.69 & 0.00\% & 56.5 \\ B12 & 7d12 & 791.68 & 2.86\% & 37.66 \\ B16 & 4d16 & 804.24 & 4.49\% & 75.33 \\ B18 & 3d18 & 763.40 & 0.82\% & 113 \\ \hline \hline \end{tabular} Note: \(\boldsymbol{\Delta}\) represents the error in the cross-sectional area of the reinforcing bars for beams B12, B16, and B18 in comparison to the REF beam.  
\end{table} 
Table 8: Design of diameter and spacing of reinforcements for parametric beams. significance for the design and construction of RC structures, providing valuable assistance to engineers in improving the durability and performance of these structures.
## 5 Integration of EOLSR and SERENA
### Application to the OCARI Project
With SERENA, bandwidth is saved because of collision avoidance and spatial reuse. More generally, network resources are more efficiently used. A prototype integrating OCARI physical layer, MaCARI, SERENA and EOLSR is being implemented. It shows the feasibility of the OCARI stack and will be experimented in an industrial environment.
# A Multi-Carrier Waveform Design for 5G and beyond Communication Systems
## 3 Proposed Waveform  
The block diagram of proposed waveform architecture is depicted in Figure 2. According to Figure 2, a \(k\) number of mobile users are communicating with the base station (BS) simultaneously, over the same frequency band, but with different powers. Assuming there are \(k\) users, where each user is utilizing an entire band with total of \(N\) sub-carriers. The Quadrature Amplitude Modulation (QAM) signal \(X_{l}\) of any \(i\)th user, after serial-to-parallel (S/P) conversions can be written as below:  
\[X_{l}=[X_{0},X_{1},X_{2},\ldots X_{N-1}]^{T} \tag{1}\]  
where \(N\) is total number of sub-carriers. After S/P conversions, an entire band of having \(N\) sub-carriers for \(i\)th user is broken down into \(S\) sub-bands, where each sub-band in \(S\) is further sub-divided into a fixed number of C sub-carriers. Mathematically, for any \(i\)th user, its time domain signal with \(S\) sub-band that is the IFFT having \(C\) points can be expressed as:  
\[x_{s_{i}}[n]=\frac{1}{\sqrt{C}}\sum\
olimits_{l=0}^{C-1}X_{l}e^{2\pi\xi l}\, \ 0\leq n\leq C-1 \tag{2}\]  
where \(X_{l}\) represents the SLM-based GCL precoded constellation symbols, contained in \(S\) sub-bands. After that, the \(C\) point IFFT based time domain signal is convolved with a filter \(f\) of length \(L\), and the resultant signal can be expressed as:  
\[\hat{x}_{s_{i}j}=x_{s_{i}}*f_{L} \tag{3}\]  
Figure 2: Proposed uplink multi-carrier waveform design.  
After the filtering operation, all the sub-bands are added together to get the filtered time domain UFMC signal and can be given as:  
\[x_{i}=\sum\
olimits_{l=0}^{S-1}\hat{x}_{s_{i,l}} \tag{4}\]  
where \(x_{i}\) represents the UFMC signal for \(i\)th mobile user. Finally, the selected time domain UFMC signal with minimum PAPR is multiplexed in the power domain, where different power levels are assigned by the BS to each mobile user separately and each mobile user transmits the signal by using the same allocated frequency band, but with different power levels. At the receiver side, received signal denoted by \(y\) can be expressed mathematically as follows:  
\[y=\sum\
olimits_{i=1}^{k}h_{i}\otimes x_{i}+w \tag{5}\]  
where \(h_{i}\) is coefficients of the time domain channel for any \(i\)th mobile user and \(w\) represents additive white Gaussian noise (AWGN). The multi-user diversity and successive interference cancelation (MUD-SIC) operation is applied on the received signal to separate the different users after the UFMC demodulation. Then, to combat the effect of channel and band filtering, sub-carrier equalization is carried out and eventually, data are unmapped to retrieve the original data bits.
# Improvement of Fast Simplified Successive-Cancellation Decoder for Polar Codes
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**R**} & \multirow{2}{*}{**Node**} & \multicolumn{8}{c}{**Length**} \\ \cline{3-10}  & & **2** & **4** & **8** & **16** & **32** & **64** & **128** & **256** & **512** \\ \hline \multirow{5}{*}{\(\frac{1}{4}\)} & R-other & 0 & 4 & 15 & 15 & 12 & 9 & 6 & 3 & 2 \\  & R-1 & 4 & 3 & 2 & 1 & 0 & 0 & 0 & 0 & 0 \\  & R-0 & 4 & 3 & 3 & 3 & 3 & 2 & 0 & 0 & 0 \\  & Rep & 0 & 10 & 6 & 3 & 2 & 0 & 0 & 1 & 0 \\  & SPC & 0 & 10 & 4 & 2 & 1 & 1 & 0 & 0 & 0 \\ \hline \multirow{5}{*}{\(\frac{1}{2}\)} & R-other & 0 & 6 & 20 & 20 & 16 & 10 & 6 & 4 & 2 \\  & R-1 & 6 & 5 & 4 & 3 & 2 & 0 & 0 & 0 & 0 \\  & R-0 & 6 & 5 & 4 & 3 & 2 & 0 & 0 & 0 & 0 \\  & Rep & 0 & 12 & 6 & 3 & 0 & 1 & 1 & 0 & 0 \\  & SPC & 0 & 12 & 6 & 3 & 0 & 1 & 1 & 0 & 0 \\ \hline \multirow{5}{*}{\(\frac{1}{4}\)} & R-other & 0 & 5 & 15 & 15 & 12 & 9 & 6 & 3 & 2 \\  & R-1 & 5 & 4 & 3 & 2 & 0 & 2 & 0 & 0 & 0 \\  & R-0 & 5 & 4 & 4 & 3 & 3 & 0 & 0 & 0 & 0 \\  & Rep & 0 & 8 & 3 & 1 & 1 & 1 & 0 & 0 & 0 \\  & SPC & 0 & 9 & 5 & 3 & 2 & 0 & 0 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Various node types at (1024, R). Figure 4: Average percentages of R-other nodes under Fast-ISSC satisfying FC. The polar codes (256, 1/2),(1024, 3/4),(2048, 1/4), and (4096, 1/2) were used for comparison of the error-correction performance. Figures 7 and 8 show the BERs and frame error rates (FERs) of four systematic polar codes (SPC) under the Fast-ISSC decoder and the corresponding non-systematic polar codes (NSPC) under the SC decoder. Although we cannot offer a rigorous proof for the BER, we conjecture that the BER of the Fast-ISSC decoder is no worse than that of the original SC decoder. The simulation result in Figure 7 supports this conjecture. Since the SPC is used, the BERs of the Fast-ISSC decoder are improved, which is consistent with the result in [16]. The FERs of the Fast-ISSC decoder are the same as those of the SC decoder. These simulation results, in Figure 8, are consistent with Proposition 1. Figure 5: Relative latency reduction \(L(N,R)\) with \(P=256\). Figure 6: \(L(4096,1/2)\) for different process elements.
# Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon
## 4 Discussion
### The Correlation of Soil Sample Depth and SOC Storage  
The loss of SOC storage is relatively less in deeper soil. The conversion of primary forests to farmland not only directly affects the content and distribution of SOC at different soil depths but also indirectly affects them through factors that influence the formation and transformation of SOC. This conversion can also change the decomposition rate of SOC, with the decomposition of SOM accelerated in the surface soil due to the strong disturbance of mechanical tillage by humans, leading to a low SOC content in the surface soil [20]. Additionally, the application of fertilizers after conversion can also have a certain impact on the topsoil. According to relevant studies, fertilization has an effect on SOC with soil depth [21]. In contrast, microorganisms play a crucial role in determining SOC content in deeper soil layers. The amount of soil microorganisms reflects the soil's assimilation and mineralization ability and constitutes a vital source of SOC in deep soil [22]. Microbial activity is also responsible for the decomposition of plant residues, contributing to the replenishment of SOC stocks.
# A Real-Time Novelty Recognition Framework Based on Machine Learning for Fault Detection
## 3 Proposed Predictive Maintenance Architecture
### AI Unit
An example of the update/retrain function is shown in the following \"Digital Model\" paragraph. The model update function also collects consistent datasets of the component under study to help the beginning data collection, which is useful to create the machine's behavior history for future PdM implementation. Recapping the AI Unit's phases (Figure 3):  
Figure 8: (**a**) Error computation on the last two records; (**b**) Error computation on the last five records. * Data Fetching Phase: in a continuous loop, the Unit waits for new data uploaded in the local/remote database to take them for the following Training or Testing phase;
* Training Phase: when a predefined number of datasets are uploaded to the database (depending on how long the train dataset collection lasts), the framework understands that the training process can start. After the Training process, several models are created as a function of the features extracted (hence depending on the number of windows of the windowing function);
* Testing Phase: after the training phase, every dataset uploaded is processed by the framework with a number of predictors equal to the number of models previously created. Then, every predictor estimates its label with an error; the upcoming failures conduct the dataset to change its shape, increasing the error in the prediction. The number of predictors equal to the number of features can help in error recognition by covering all the data distribution. * Retraining/Updating Phase: in some cases, the model error deviates without a real problem on the machine; in this case, the maintainer can update the models trained, considering incoming new data as \"normal\", resetting the error value. The retraining process is necessary when a maintenance operation is performed; then, past data are neglected, and only new data are used for future predictions.