esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Local Gas Flow in Full Penetration Laser Beam Welding with High Welding Speeds
###### Abstract  
Spatter formation is a major issue in deep penetration welding with solid-state lasers at high welding speeds above 8 m/min. In order to limit spatter formation, the use of local gas flows represents a technically feasible solution. By using the gas flow, the pressure balance inside the keyhole, and therefore the keyhole stability, is affected. Existing investigations demonstrate a reduction in spatter and pore formation for partial penetration welding up to a welding speed of 5 m/min. However, the effect of the gas flow is not yet clarified for full penetration welding at welding speeds above 8 m/min. By using a precisely adjustable shielding gas supply, the effect of a local gas flow of argon was characterized by welding stainless steel AISI304 (1.4301/X5 CrNi18-10). The influence of the gas flow on the melt pool dynamics and spatter formation was recorded by means of high-speed videography and subsequently analyzed by image processing. Schlieren videography was used to visualize the forming flow flied. By the use of the gas, a change in melt pool dynamics and gas flow conditions was observed, correlating to a reduction in loss of mass up to 70%. Based on the investigations, a model of the acting effect mechanism was given.  
deep penetration laser welding; spatter formation; local gas flow; melt pool dynamics +
Footnote †: journal: Applied Sciences
# Robust Template-Based Watermarking for DIBR 3D Images
## 4 Experimental Results
### Experiment Setting  
The test image sets were obtained from Heinrich Hertz Institute [2], Middlebury [47; 48; 49; 50] and Microsoft Research 3D Video Datasets [51]. Figure 10 shows pairs of center and depth images of the test image sets. They have various resolutions from 900 \(\times\) 720-1800 \(\times\) 1500. The total number of images used in the experiment was about 1800.  
The DIBR parameters are set to focal length \(f=1\) and baseline distance \(t_{x}=5\%\) of the image width, which are the recommended values for comfortable viewing conditions. Linear interpolation is used as hole-filling for simplicity and without loss of generality.  
For [PERSON]'s method, block size was set from 100 \(\times\) 100-200 \(\times\) 200 to match embedding capacity with other methods. Watermark strength was set as \(\alpha\) = 1; watermark pattern length = 5120; and the beginning of the embedding position is the 250 th coefficient of the zigzag scan order in the DCT domain.  
For [PERSON]'s method, block size was set to \((w/8)\)\(\times\)\((h/8)\). The weighting factor for coefficient magnitude was set as \(\mathbb{W}\) = 450, maximum quantization level \(maxBit\) = 2 and minimum difference between paired coefficients \(errMin\) = 8. These values are demonstrated in [20].  
In the proposed method, the size of each block is set to \((w/8)\)\(\times\)\((h/8)\). In the template embedding process, the template strengths \(\alpha_{t}\) and \(\beta_{t}\) are set to five and 50, experimentally. In the message watermark embedding process, \(\alpha_{m}\) is set to 0.5; the length of the watermark pattern is 40; and the start of the embedding position is the 45 th coefficient of 1D-DCT.  
Figure 9: Template extraction results: (**a**) for one template block; (**b**) for the whole rotated image. Red dots are points detected by corner detection.
# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 5 Discussion
Therefore, the advantage of the CDMV method has important practical significance for secondary disaster monitoring and early warning. In further research, the multi-model parallel decision making can be integrated into a more complex model, and the nodes of the neural network can be divided into regions, so that the memory units in a certain area can focus on recognizing a certain type of feature, and a multi-classification recognition model with higher accuracy can be formed under multi-region cluster decision making.
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 6 Moment-Rotation Model  
### Building Model  
The slope of the moment-rotation curves is increasing at the later stage of loading, as described accurately in the model proposed by the literature [29] and in the moment-rotation models here proposed. The moment-rotation model is consistent with the changing trend of the moment-rotation curves of DC joints, as in Section 4. The model begins with Equation (2). In this model, \(M_{\mathrm{i}}\) represents the idealized elastic-plastic mechanism moment, which refers to the limit bending moment when the material of the DC joints shows plastic strain; \(\theta_{0}\) means reference rotation; \(n\) represents the shape parameter of structure, is only related to \(\theta_{0}\) and needs to be obtained by fitting; the calculation of \(M_{\mathrm{i}}\) is irrelevant to some parameters such as \(h\) and \(h_{\mathrm{m}}\), while \(M_{\mathrm{i}}\) is relevant to \(h\) and \(h_{\mathrm{m}}\) in the numerical results. Therefore, the model needs to be modified, and the correction coefficient \(\lambda\) is introduced. The moment-rotation model of DC joints is listed in Equation (3).  
\[\frac{M}{M_{\mathrm{i}}}=n\left[\ln\left(1+\frac{\theta}{n\theta_{0}}\right)\right] \tag{2}\]  
\[M=\lambda\cdot M_{\mathrm{i}}\cdot n\left[\ln\left(1+\frac{\theta}{n\theta_{0 }}\right)\right] \tag{3}\]  
According to a series of numerical analysis results of DC joints under eccentric compression, the deformation of a DC joint is mainly caused by the steel wedges. The deformation of the middle-rib plate is only 1% of that of the steel wedges, due to the restriction of channel steel. The deformation of the middle plate is 7% of that of the steel wedges, due to the action of the stiffening ribs. Therefore, the deformation of the middle plate and middle-rib plate can be ignored, and it can be assumed that the bending of DC joints is caused entirely by the deformation of the steel wedges.
# Implementation of Atomically Thick Graphene and Its Derivatives in Electromagnetic Absorbers
## 2 Graphene-Based Quarter-Wavelength Absorbers  
Quarter-wavelength absorber is a type of EM absorbing material/device with a thickness of \(\lambda/4\) or \(N\cdot\lambda/4\), where \(\lambda\) is the wavelength in the material and \(N\) is a positive integer. The most famous quarter-wavelength absorbers are Salisbury screen [32], Jaumann [33] and Dallenbach absorbers [34]. Although their physical geometries are different from each other, they share an identical wave absorbing mechanism during the operation.
## 4 Results
### Completeness of the Capability Model  
To measure evaluation criterium 3, we researched the capability model's completeness, i.e., whether all capabilities important to LA adoption are present. Data collected during the group discussions as well as via the open-ended survey question were used to this end. We consider the model to be complete when the experts and participants do not know from their own experience capabilities that are not present in the model.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Question** & **Mean** & **Std. Dev.** & **Min.** & **Max.** \\ \hline Q1: Useful & 4.2 & 0.4 & 4 & 5 \\ Q2: Use in work & 4.0 & 0.4 & 3 & 5 \\ Q3: Comparison other models & 3.2 & 0.4 & 3 & 4 \\ \hline Q4: Insights & 4.3 & 0.5 & 3 & 5 \\ Q5: Identify & 4.3 & 0.5 & 4 & 5 \\ Q6: Operationalize & 3.7 & 0.8 & 2 & 5 \\ \hline Q7: Easy to understand & 3.9 & 0.8 & 2 & 5 \\ Q8: Intuitive & 3.6 & 0.7 & 2 & 5 \\ Q9: Overburden with information & 2.9 & 0.9 & 1 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics per survey item.  
Asked to comment on the model's completeness, the experts think that the pedagogical interventions that follow the analysis and visualization of learner data are yet underexposed. The model comprises a capability to monitor the performance of the analytical processes and applications (Performance monitoring), but this does not include the measurement of improvements made to education. The experts suggest adding this to the model. The participants in the pluralistic walk-throughs also mention some potential shortcomings. Most prominent, the position of pedagogical use of LA in the capability model is not apparent enough. That is, the role of remediation is underexposed (Alpha, P1), and it is unclear how to consider the contextual setting of education (Charlie, P1). Although these aspects are part of the capabilities Implementation, Deployment, and Application, and Evidence-based and Theory-driven, respectively, this might not be clear enough.  
Other improvements to the model are suggested in the answers to the open question in the survey. Two respondents think the capability model alone is not enough to put LA into practice. As one respondent puts it: \"additional materials can be developed that focus on the 'how'. The current model mainly visualizes the 'what'\({}^{\prime\prime}\) (R8). This calls for the development of additional materials like templates with concrete advice on how to plan the capabilities' development. Another respondent suggests adding elements specific to education to the model, as the current model is \"quite generic and could also be applied to other analytical domains\" (R18). This is in line with comments made in the group discussions with both practitioners and experts. That the model is generic is--at least to a certain degree--to be expected as the first version of the model is exapted from literature from the big data analytics and business analytics domains [13]. Nonetheless, it highlights the need to better position the role of educational context and theory in the model.
## 3 The Proposed Optimal Power Allocation Strategy
### (3) _Amplitude Constraint_  
For the VLC system, considering the nonlinearity for the LED, the modulated PAM signal should be nonnegative. Thus, the nonnegative constraint should satisfy \(\gamma_{i}\leq I_{DC}^{i}=c_{i}\Phi_{i}\) where \(c_{i}\) is the conversion coefficient for luminous flux to the forward current. Meanwhile, the transmitted signal exceeding the maximum value would suffer clipping distortion, so the maximum amplitude constraint should be satisfied as well and we have \(\gamma_{i}\leq I_{max}^{i}-I_{DC}^{i}=I_{max}^{i}-c_{i}\Phi_{i}\) where \(I_{max}^{i}\) is the maximum permissible value. As a result, the amplitude constraint can be expressed as  
\[0\leq\gamma\leq\min(\mathbf{I}_{max}-\mathbf{c}\circ\mathbf{\Phi},\mathbf{c} \circ\mathbf{\Phi}), \tag{13}\]  
where \(\mathbf{c}=[c_{1},c_{2},\cdots,c_{N}]^{T}\) denotes the luminous flux to the forward current conversion coefficient vector, \(\mathbf{I}_{max}=[I_{max}^{1},I_{max}^{2},\cdots,I_{max}^{N}]^{T}\) denotes the maximum permissible current level vector.
# Analysis of the Landfill Leachate Treatment System Using Arima Models: A Case Study in a Megacity
## 2 Materials and Methods  
### Study Site  
The 'Dona Juana' landfill is in Bogota city, Colombia (4\({}^{\circ}\)31\({}^{\prime}\)18\({}^{\prime\prime}\) N; 74\({}^{\circ}\)07\({}^{\prime}\)44\({}^{\prime\prime}\)). The climate in the landfill area is cold and dry (tropical mountain climate), with an average temperature of 13.6 \({}^{\circ}\)C. The annual distribution of rainfall has bimodal behaviour, with maximum values in April and October, and minimum values in January and July. The average annual rainfall in the study area is 799 mm. The landfill is located at an average elevation of 2758 m.a.s.l., and receives the MSW of 10.7 million people. The total area of the facility is 456 ha, of which about 40% is used as an MSW disposal area. The facility was divided into waste disposal zones, and during the study period Zone 8 was in operation. This disposal area had an area of 41 ha, an average depth of 40 m, and a storage capacity of 9.30 million tons of MSW (Figure 1). Between 700-800 compact collecting trucks entered the landfill daily, which deposited an average of 6400 tons of MSW.  
Figure 1: Location of the study area: (**a**) MSW disposal area and (**b**) leachate treatment plant (Google Earth Pro, 2021).  
The storm water control in the closed areas of the landfill is carried out through a network of open stone-clad canals. There are also storm water channels on access roads, temporary discharge areas, and around the deposited MSW. The waste disposal area under study has a leachate collection and conduction system. This secondary system connects to a 304.8-mm diameter main conduction network, which then connects to four leachate storage and homogenization ponds. The landfill has a leachate treatment plant with a design flow rate of 26 L/s (Figure 1). In this regard, average operating flows are observed during the periods of increased and decreased rainfall of 20.6 L/s and 7.11 L/s, respectively. The leachate treatment is carried out by physic-chemical (coagulation and chemical precipitation) and biological (extended aeration with denitrification) processes. The maximum design conditions of the leachate treatment plant consider a load of 10,500 kg/day of BOD\({}_{5}\), and concentrations of BOD\({}_{5}\), TSS, and nitrogen of 15,000 mg/L, 2500 mg/L, and 2000 mg/L, respectively.
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADR & Angle Diversity Receiver \\ AR & Anti-Reflection \\ AWGN & Additive White Gaussian Noise \\ AWGR & Arrayed Waveguide Grating Router \\ BER & Bit Error Rate \\ BERT & Bit Error Rate Tester \\ DCA & Digital Communication Analyzer \\ DUT & Device Under Test \\ EDFA & Erbium Doped Fiber Amplifier \\ FEC & Forward Error Correction \\ FoV & Field of View \\ IMOS & Indium-Phosphide Membrane on Silicon \\ IR & Infrared Light \\ ITU & International Telecommunication Union \\ MEMs & Micro Electrical-Mechanical systems \\ MMI & Multi-Mode Interferometer \\ MMF & Multi-Mode Fiber \\ MZM & Mach-Zehnder Modulator \\ NRZ & Non-return-to-zero \\ OOK & On Off Keying \\ OSA & Optical Spectrum Analyzer \\ OSNR & Optical Signal to Noise Ration \\ OWC & Optical Wireless Communication \\ PIC & Photonic Integrated Circuit \\ PRBS & Pseudo-Random Binary Sequence \\ PZT & Piezo-Electric Transducers \\ SDM & Space Division Multiplexing \\ SLM & Spatial Light Modulators \\ SNR & Signal to Noise Ratio \\ TIA & Trans-Impedance Amplifier \\ \end{tabular}
# Risk Propagation Mechanism and Prediction Model for the Highway Merging Area
## 5 Traffic Accident Risk Prediction Model
### Model Results  
The predicted values and actual values of the regional risk index for the first, second, and third impact areas are shown in Figure 12. It can be observed that in each experiment, there is a certain amount of fluctuation in the prediction results, which is the beginning and end of each experiment. In order to validate the performance of the proposed model, this study selected the RNN model and the LSTM model for training and testing the data samples and compared the risk prediction accuracy in the merging area.  
Table 5 presents a comparison of the prediction results between the RNN model and the LSTM model. It shows that the LSTM model outperforms the RNN model in all three evaluation indicators, with an improvement of over 20% in each indicator. It also suggests that the LSTM model is more suitable for traffic risk prediction tasks due to its ability to capture long-term dependencies in the data. Therefore, the LSTM model results in more accurate and reliable predictions.  
Specifically, Table 5 presents the performance of two different models (LSTM and RNN) across three different impact areas. It can be observed that the LSTM model consistently outperforms the RNN model in all three impact areas, with significantly lower RMSE, MSE, and MAE values. Moreover, the performance of the two models varies across the different impact areas. For instance, in impact area 1, the RMSE, MSE, and MAE of LSTM are 27.8%, 50.0%, and 35.4% lower than those of the RNN model, respectively. Similarly, in impact area 2, the LSTM model performs better than the RNN model, with 35.0%, 54.5%, and 36.7%, respectively. In impact area 3, the LSTM model performs better with 34.4%, 60.0%, and 26.36%, respectively. In summary, these findings indicate that the LSTM model performs much better than the RNN model in predicting the regional risk index in all impact areas.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Impact Area** & **Model** & **RMSE** & **MSE** & **MAE** \\ \hline \multirow{2}{*}{1} & LSTM & 0.0153 & 0.0002 & 0.0122 \\  & RNN & 0.0212 & 0.0004 & 0.0189 \\ \hline \multirow{2}{*}{2} & LSTM & 0.0219 & 0.0005 & 0.0136 \\  & RNN & 0.0337 & 0.0011 & 0.0215 \\ \hline \multirow{2}{*}{3} & LSTM & 0.0158 & 0.0002 & 0.0146 \\  & RNN & 0.0241 & 0.0005 & 0.0198 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction performance of the LSTM and RNN models for different impact areas.  
Figure 12: _Cont_.
## 3 Results
### Workspace Characterization  
The resulting robot rigid body tree, based on the mDH, has been modelled exploiting the Matlab(r) Robotics Toolbox library and is presented in Figure 10.  
The obtained robot rigid body tree, combined with the available ROM of each joint, was used to evaluate the cloud point of reachable Cartesian configuration.  
The cloud point (Figure 10) includes an area close to the patient's body in which collisions might occur. The extension of this region depends on the patient's anthropometric measurements. This area was defined as a cone region through mechanical and software constraints and is universal to all patient profiles. The robot never performs inside it in the Kinematic mode.  
Figure 10: Float exoskeleton cloud point of the end-effector (elbow) from different angles. (**A–D**) offer different view angles for the same cloud point.  
As a consequence of the robot's layout, there is a kinematic singularity in the z plane, when joints J3 and J5 are vertically aligned. This singularity is inside the non-accessible cone region and it is never reached in the Kinematic mode.  
The resting position of the robot (Figure 11) is the configuration the robot assumes when it is in the Kinematic mode but is not performing exercises. It was defined to avoid patient discomfort, fit to most anthropometrical measures and avoid the collision zone.  
The same approach has been carried out to generate the polyarticulated arm cloud point, depicted in red in Figure 12.  
The maximum volume of operation offered by the passive polyarticulated arm has been estimated computing the convex hull of the passive arm cloud point (see red cloud point of Figure 12). Its value is equal to 1.53 m\({}^{3}\). It is important to notice that the cloud point of the passive arm has a non-perfect convex shape, as observable from Figure 12; thus, the convex hull volume computation is necessarily an overestimation. Nonetheless, this overestimation is negligible for the purposes of the computation, since the interest of the calculation is to offer a qualitative upper bound to the maximum operational volume of the passive arm itself.  
The same volume computation operation has been performed also for the actuated exoskeleton offering as a result a volume of 0.12 m\({}^{3}\). In this case, the actuated exoskeleton cloud point is convex, and the convex hull volume estimation can be considered in a more quantitative way.  
Figure 11: Float exoskeleton resting position.
## 3 System Development: Methodologies and Analysis
### Flood Frequency Analysis
#### 3.2.2 Flood Stage and Flow Thresholds
\begin{table}
\begin{tabular}{c c} \hline \hline
**Stage (m)** & **Discharge Equation (m\({}^{3}\)/s)** \\ \hline
0 to 0.125 & Discharge = 0.470 \(\times\) Stage\({}^{1.232}\) \\ \textgreater{}0.125 to \textless{}0.375 & Discharge = 0.470 \(\times\) Stage\({}^{1.233}\) \\ \textgreater{}0.375 to \textless{}0.6 & Discharge = 0.470 \(\times\) Stage\({}^{1.234}\) \\ \textgreater{}0.6 to \textless{}0.825 & Discharge = 2.799 \(\times\) Stage\({}^{3.807}\) \\ \textgreater{}0.825 to \textless{}1.0625 & Discharge = 2.374 \(\times\) Stage\({}^{3.345}\) \\ \textgreater{}1.0625 to \textless{}1.3125 & Discharge = 2.364 \(\times\) Stage\({}^{3.261}\) \\ \textgreater{}1.3125 to \textless{}1.5625 & Discharge = 2.421 \(\times\) Stage\({}^{3.114}\) \\ \textgreater{}1.5625 to \textless{}1.825 & Discharge = 2.455 \(\times\) Stage\({}^{3.076}\) \\ \textgreater{}1.855 to \textless{}2.1125 & Discharge = 2.424 \(\times\) Stage\({}^{3.101}\) \\ \textgreater{}2.1125 to \textless{}2.375 & Discharge = 2.409 \(\times\) Stage\({}^{3.110}\) \\ \textgreater{}2.375 to \textless{}2.85 & Discharge = 2.710 \(\times\) Stage\({}^{2.965}\) \\ \textgreater{}2.85 & Discharge = 4.360 \(\times\) Stage\({}^{2.446}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rating curve equations for different stage-flow ranges at Alaoa East river gauge.
# A Practical Resource Management Scheme for Cellular Underlaid D2D Networks
## 3 System and Channel Models
Then, the sum-rate of a cellular underlaid D2D network can be calculated by \(\sum_{i=1}^{N}\log_{2}\left(1+\gamma_{i}(\mathcal{I})\right)\). Finally, we need to choose an optimal \(\mathcal{I}\) for maximizing the sum-rate while satisfying the interference constraint as follows:  
\[\mathcal{I}^{*} = \arg\max_{\mathcal{I}}\sum_{i=1}^{N}\log_{2}\left(1+\gamma_{i}( \mathcal{I})\right)\] s.t. \[\sum_{i=1}^{N}I_{i}\rho|g_{\mathcal{R}\mathcal{R}}|^{2}\leq I_{ \text{th}}^{\prime}. \tag{5}\]
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 3 SCAR-Phase of Spectrum Sensing
### Second Sample Processing Period: Enhanced ST-Based Detection
Firstly, the estimate of \(\tau\) modulo-\(P\) (i.e., \(\hat{\tau}\)) is obtained using (7). Secondly, it can be shown by using a Taylor series expansion of the likelihood function around \(\theta=0\), that  
\[\hat{\phi}=\angle\sum_{n=0}^{N-1}t^{*}[n-\hat{\tau}]x[n] \tag{19}\]  
can be used as a nearly maximum likelihood estimate for \(\phi\). It can be noticed that \(\Phi_{\mathrm{ST}_{1}}(\mathbf{x},\phi,\tau)\) also depends on the knowledge of the noise power \(\sigma^{2}\), which in the ideal case is assumed to be perfectly known. However, in realistic scenarios, random noise distribution is never known entirely. Hence, in the strict sense, noise is neither completely stationary, white nor Gaussian [24]. Therefore, the noise power must be estimated by using a proper calibration method, whose output could be stored in the SU's devices. For example, it can occur during the manufacturing process by replacing the antenna at the input of the SUs' receivers with a matched load and collecting a set of noise samples every certain given time during a specified period [25]. However, this might not be precise since this value is different for a myriad of application environments. In consequence, accurate estimation of the noise power is not achieved in practice, thus leading to the noise power uncertainty problem that limits the performance of noise power-based sensing techniques [5]. Alternatively, in a practical implementation, \(\sigma^{2}\) could be estimated by taking advantage of the silent periods in which the PU's signal is not present and then, performing measurements periodically to update the noise statistics. The effect of noise power uncertainty in the proposed scheme is a matter of further investigation. Since further simulations have shown a poor performance of \(\Phi_{\text{ST}_{1}}(\mathbf{x},\phi,\tau)\), the use of the second derivative of \(p(\mathbf{x},\theta,\phi)\) with respect to \(\theta\) in (14) is analyzed next to obtain the ST-Det for the second sample processing period. The second derivative of \(p(\mathbf{x};\theta)\) is computed by deriving (16) with respect to \(\theta\), which after some algebra can be written as:  
\[p^{(2)}(\mathbf{x};\theta,\phi)=p(\mathbf{x};\theta,\phi)\left\{\Lambda^{(1)} (\theta,\phi)+\Omega^{(1)}(\theta)+\left[\Lambda(\theta,\phi)+\Omega(\theta) \right]^{2}\right\}, \tag{20}\]  
where \(\Lambda^{(1)}(\theta,\phi)\) and \(\Omega^{(1)}(\theta)\) represents the first derivatives of \(\Lambda(\theta,\phi)\) and \(\Omega(\theta)\), respectively.