esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Parameters for PLA Through Additive Manufacturing: Taking Mixed Shapes as an Example
## 3 Results and Discussion
### Validation of Optimal Parameter Combination
#### 3.4.2 Tensile Strength  
Three experiments were conducted with the Taguchi experimental method, response surface method, and single factor experiment to verify the optimal parameter combination. The set parameters are shown in Table 25. The experimental results are analyzed in Table 26. The experimental error is the ratio of the maximum tensile strength to the theoretical value of 60 MPa for PLA material [14]. The stress-strain diagram of the tensile test is shown in Figure 11. It can be seen that the parameter combinations obtained from the Taguchi experiment for printing models can withstand larger loads. The tensile test model is shown in Figure 12. After conducting the tensile test, data on its tensile strength was collected. Additionally, it is evident that the fracture points differ with various parameter combinations. Perhaps further research can be conducted on this to analyze whether the printing parameters are related to the fracture locations.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Company** & **Taguchi** & **RSM** & **ANOVA** \\ \hline
**Normalize gap height (mm)** & 0.2 & 0.1 & 0.05 & 0.1 \\ \hline
**Extruder temperature (\({}^{\circ}\)C)** & 210 & 185 & 230 & 220 \\ \hline
**Bed temperature (\({}^{\circ}\)C)** & 60 & 75 & 75 & 70 \\ \hline
**Infill print head velocity (mm/s)** & 40 & 35 & 35 & 30 \\ \hline
**Retraction velocity (mm/s)** & 45 & 65 & 65 & 65 \\ \hline
**Wall thickness (mm)** & & 0.8 & \\ \hline
**Infill pattern** & & straight line & \\ \hline
**Infill spacing (mm)** & & 0.8 & \\ \hline
**Fan speed** & & 100\% & \\ \hline \end{tabular}
\end{table}
Table 23: Taguchi method, response surface, and single factor experiment settings (Dimensional accuracy).  
Figure 10: End product of tensile test model (Dimensional accuracy).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Company** & **Taguchi** & **RSM** & **ANOVA** \\ \hline
**Tensile strength (MPa)** & 46.559 & 52.29 & 49.628 & 48.537 \\ \hline
**Experimental error** & 22.40\% & 12.85\% & 17.29\% & 19.11\% \\ \hline
3 & & & & \\  
As shown in Table 26, the experimental error is the ratio of the maximum tensile strength to the theoretical value, and the theoretical value is the maximum tensile strength of PLA material 60 MPa. The maximum tensile strength is the Tensile strength of Taguchi Method is 52.29 MPa, Experimental error is 12.85%
## 3 Method
### Wind-Field Propagation
\begin{array}{cc}1,&t\leq m_{c}-\frac{m_{c}}{2}\\ \frac{1+\cos\left(\frac{\pi}{2}+\frac{m_{c}}{m}\left(\frac{t}{t_{n+1}}-m_{c} \right)\right)}{2},&m_{c}-\frac{m_{c}}{2}<t<m_{c}+\frac{m_{c}}{2}\\ 0,&m_{c}+\frac{m_{c}}{2}\leq t\end{array} \right. \tag{20}\]  
Thus, the weighting function, \(w_{n}(t)\), has total width of \(t_{n+1}-t_{n}\) and gives variable results in the range from \(t>m_{c}-\frac{m_{c}}{2}\) to \(t<m_{c}+\frac{m_{c}}{2}\). The variable, \(m_{c}\), serves as the point in time between \(t_{n}\) and \(t_{n+1}\) at which the weighting functions, \(w_{n}(t)\), for forward propagation and \(1-w_{n}(t)\) for backwards propagation are in equilibrium. The variable, \(m_{r}=\frac{4}{5}(t_{n+1}-t_{n})\), represents the centred window around \(m_{c}=\frac{2}{5}t_{n}+\frac{3}{5}t_{n+1}\), within which \(w_{n}(t)\) can take values between \(1\) and \(0\). Outside of \(m_{r}\), the weighting function is either \(1\) or \(0\). The shift in the centre of the weighting function (\(m_{c}\)) towards \(t_{n+1}\) suggests the greater effect of forward propagation compared with the backwards propagation, which accords with the irreversibility of the mixing processes. The behaviour of the weighting functions is illustrated in Figure 4. This treatment of \(w_{n}(t)\) and the configuration of \(m_{r}\) and \(m_{c}\) were developed in an unpublished internal study that we will only outline briefly here. Different approaches for mixing functions were tested in parametric studies to find the functions that give the smallest error of mixed propagation in comparison to the mean and standard deviation of the LES reference wind-speed values. We tested a linear approach, an exponential approach, and the trigonometric approach that we eventually used. The exponential increase in the error with each interpolation step was damped the most robustly when using the trigonometric function. The definition of wind-field propagation implies that \(\Delta t\) is the variable that determines the error. The number of interpolation steps (\(\Pi_{\phi}\), \(\Pi_{\theta}\)) between two subsequent scans depends on this interval. We define the number of propagation steps between two scans as:  
\[\Pi_{\phi}=\bigg{|}\frac{T_{\phi}+t_{r}}{\Delta t}\bigg{|}, \tag{21}\]  
with \(\Pi_{\phi}\in\mathbb{N}\).
## 7 Evaluation Results
### Number of Bundles Sent  
In our simulation, one sending node in each of the three points sent bundles to four destination areas. Thus, 12 bundles were sent. The number of bundles that were actually sent and the number of bundles that were not sent are shown in Figure 21. With the proposed routing, whether the circular covered area model or the vectorial covered area model was used, four bundles were not sent because the duration of contacts between sending nodes and relay nodes was too short. In the case of the vectorial covered area model, two more bundles were not sent because the relay nodes concerned decided that these bundles could not reach their destinations. This means that the shape of the covered area affects the number of the bundles that are found unable to reach their destinations. The following evaluation considers only those bundles that were actually sent.  
Figure 21: Number of bundles that were sent from sending nodes.  
Figure 20: Application conditions for existing routing methods.
## 2 Methods  
The extended model is based on previous theories and empirical formulations [18; 19; 20; 21; 22; 23; 24; 25; 26]. The thermomechanical properties and their relevant equations are given in reference [15].  
The mechanism of FGR includes two components: athermal and thermal FGR [27]. Athermal release happens at a high burnup when gas atoms escape from the free surfaces of the fuel through recoil and knockout. When the temperature exceeds the incubation threshold, thermal release drops with increasing temperature [28]. The exact value of the threshold can be derived from the occurrence of intergranular gas. Gas atoms diffuse from the inner side of the fuel grains to the grain boundaries. The diffusion velocity rises with temperature.  
In addition, a part of intergranular atoms is brought back into the solution through fission spikes. This is known as resolution. The resolution flux will prevent the flux diffusion to some extent. Resolution occurs within the grain, where atoms enter and form small bubbles. These bubbles eventually reintegrate into the solution through recombination and resolution, restoring equilibrium.  
The mathematical framework of [PERSON] [29] and [PERSON] [24] provided a good solution method to the diffusion problem. [PERSON] offered analytical expressions for constant irradiation under the LWR operating conditions, and two cases were used to present steady and transient state conditions in LWR that regarded the constant gas production case as the steady state condition and the zero-gas production case as the transient condition. Diffusion is neglected during transient operation since the power level is high enough for the gas production. The limitations of this study include the assumption of a perfect sink boundary condition, where there is a release as soon as gas atoms reach the grain boundary. In this case, the concentration at the grain boundary is zero in the mathematical expression. Resolution was later introduced by [PERSON] to improve the [PERSON] model [24]. Furthermore, the concentration at the grain boundary under imperfect sink conditions remains finite. [PERSON] derived a closed form analytical solution for the model [25].  
The extended model we present here is based on real physical process. The magnitude order of the physical quantities is important for matching the numerical algorithm to best suit the physical problem. In order to ensure the accuracy of a wide range of algorithm conditions, all the situations encountered in the normal and abnormal operations of the LWR were restricted to Class-II incidents.  
The FGR model adopts an idealized spherical UO\({}_{2}\) grain. The parameters and expressions are given, such as the grain size and the diffusion coefficient of Xenon inside UO\({}_{2}\). These parameters are essential to solve the ensuing mathematical problem efficiently. The details of the parameters are presented below.
# Boosted Prediction of Antihypertensive Peptides Using Deep Learning
## 2 Materials and Methods
### Feature Extraction
#### 2.2.2 G-Gap Dipeptide Composition (DPC)  
A dipeptide is a composite of two amino acid residues, with or without a gap between them. DPC is computed as the ratio of the number of occurrences of a dipeptide to the entire length of the sequence. It can also be computed with a gap of amino acids, where the value of g ranges from 0 to 3. Such a DPC feature is called g-gap dipeptide composition. DPC generates a 20-by-20 matrix that contains 400 features for each value of the g-gap, as shown in Figure 3 (for g-gap = 0).  
Figure 1: Steps of the proposed methodology.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c} \hline
**Dataset** & **A** & **R** & **N** & **D** & **C** & **Q** & **E** & **G** & **H** & **I** & **L** & **K** & **M** & **F** & **P** & **S** & **T** & **W** & **Y** & **V** \\ \hline Independent Positive & 164 & 99 & 46 & 62 & 20 & 120 & 103 & 157 & 82 & 133 & 225 & 164 & 99 & 46 & 62 & 20 & 120 & 103 & 157 & 82 \\ Independent Negative & 319 & 270 & 337 & 306 & 280 & 276 & 297 & 304 & 247 & 301 & 341 & 319 & 270 & 337 & 306 & 280 & 276 & 297 & 304 & 247 \\ All Independent & 483 & 369 & 383 & 368 & 300 & 396 & 400 & 461 & 329 & 434 & 566 & 483 & 369 & 383 & 368 & 300 & 396 & 400 & 461 & 329 \\ Benchmarking & 374 & 263 & 236 & 174 & 48 & 410 & 354 & 442 & 202 & 368 & 625 & 374 & 263 & 236 & 174 & 48 & 410 & 354 & 442 & 202 \\ Benchmarking Negative & 607 & 420 & 331 & 404 & 77 & 305 & 454 & 547 & 149 & 414 & 687 & 607 & 420 & 331 & 404 & 77 & 305 & 454 & 547 & 149 \\ All Benchmarking & 981 & 683 & 567 & 578 & 125 & 715 & 808 & 989 & 351 & 782 & 1312 & 981 & 683 & 567 & 578 & 125 & 715 & 808 & 989 & 351 \\ \hline \end{tabular}
\end{table}
Table 1: Occurrence of each amino acid residue in both datasets.  
Figure 3: Dipeptide composition feature of a sample peptide with a gap of 0.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Min Length of Sequence** & **Max Length of Sequence** & **Avg. Length of Sequence** \\ \hline Benchmarking | (Negative Class) & 5 & 45 & 8.048 \\ Benchmarking & 5 & 81 & 7.746 \\ (Positive Class) & 5 & 81 & 7.897 \\ ALL Benchmarking & 5 & 81 & 7.897 \\ Independent & 5.0 & 29.0 & 6.48 \\ (Negative Class) & 5.0 & 24.0 & 15.42 \\ All Independent & 5.0 & 29.0 & 10.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Minimum, maximum, and average length of peptide sequences in both datasets.  
Figure 2: Example of amino acid composition (AAC) features for sample peptide sequence.
# Analytic Form Fitting in Poor Triangular Meshes
## 3 Methodology
### Extraction of Subset \(S\) Based on Fitting Quality
#### 3.4.1 Fitting-Based Extraction of Cylinders and Cones  
Cones and cylinders are ruled surfaces. As a consequence (Figure 16), at each point of them, there is a direction \(u_{min}\) of \(K_{min}=0\) minimal curvature (i.e., generatrix direction). As always, the direction of \(K_{max}\) maximal curvature \(u_{max}\) is normal to the \(u_{min}\) and both span the plane tangent to the surface at such a point.  
Our algorithm aims to fit a reliable \(g()\) analytic form to the support triangle subset \(S\) as early as possible. Triangles in the direction of maximal curvature \(u_{max}\) (obviously) provide information on the cone or cylinder radii in addition to generatrices. Due to this reason, \(S\) primarily grows in the direction \(u_{max}\) (box \"select candidate from \(B(S)\)\" in Figures 13 and 14). However, it is important to notice that \(S\) must eventually encompass the whole set of triangles supporting \(g()\).  
The process to calculate the local curvature at a given seed triangle \(t_{s}\) (with normal vector \(n\) and baricenter \(b\)) is described below.  
1 Build a homogeneous coordinate frame \(S_{i}\): \[S_{i}=\left[\begin{array}{cccc}e_{1}&e_{2}&\hat{n}&b\\ 0&0&0&1\end{array}\right],\] with \(e_{1}\), \(e_{2}\) spanning the plane tangent to surface \(M\) at \(b\) and making \([e_{1},e_{2},n]\) a Special Orthogonal SO(3) (\(3\times 3\)) submatrix.
2 Apply a coordinate transformation \(T\) to rigidly move \(t_{s}\) and support subset \(S\) to the World Coordinate System \(S_{w}\) with \(n\) and \(b\) mapped to direction \(z\) and point \([0,0,0]^{\prime}\), respectively (Figure 16). \[\begin{split}& T\,S_{i}=S_{w},\\ & T=S_{i}\,S_{w}^{-1}.\end{split}\] (5)
3 Fit a quadratic surface (Equation (6)) to \(T*S\) by multi-variable linear regression. \[f(x,y)\ =a+bx+cy+dxy+ex^{2}+fy^{2}.\] (6)
4 Find the minimum and maximum curvature directions \(v_{max},v_{min}\) with the Hessian eigenvalues and eigenvectors. \[\begin{split}& H(f)\ =\ \begin{bmatrix}2e&d\\ d&2f\end{bmatrix},\\ & H[v_{max}\,v_{min}]=[v_{max}\,v_{min}]\begin{bmatrix}K_{max}&0\\ 0&K_{min}\end{bmatrix}.\end{split}\] (7)
5 Convert direction \(v_{max}\)\(v_{min}\) back to global coordinates, with: \[[u_{max}\,u_{min}]=T^{-1}[v_{max}\,v_{min}],\] (8)  
where, for the sake of simplified notation, we use the same symbols for 2D, 3D and 3D homogeneous vectors. Notice that \(T^{-}1\) does not involve inverting a matrix. Instead, as \(T\) is an SO(3) rotation plus translation, the _transpose_ of the rotation is its inverse.
## 5 FPGA Implementation  
The PCB design and manufacturing of the hardware platform of the proposed DSSS software transceiver are described in this section. The specific FPGA implementation of the carrier synchronization module, the code acquisition module, the code tracking module are also presented here, along with the implementation of the transmitter and receiver. The full software-based DSSS transceiver design was thus hardware tested on this platform, and its correctness and feasibility were verified through the demonstration of a communications' channel between two PCs.
# Enhanced Phosphorus Removal from Wastewater Using RSPRC and a Novel Reactor
## 2 Materials and Methods
### Study on P Removal Characteristics of RSPRC  
At first, for the purpose of exploring the effect of dosage on the phosphorus removal of RSPRC, 2, 3, 4, 5, and 6 g RSPRC samples with a particle size of 5 mm were placed in five conical flasks, which contained 100 mL of KH\({}_{2}\)PO\({}_{4}\) standard solution with a P concentration of 0.5 mg/L and pH = 7. The five conical flasks were shaken well in a 150 r/min shaker. After 24 h, the supernatant was taken and the P concentration was measured. Secondly, in order to study the effect of contact time, when the static adsorption times were 4, 8, 24, 72 and 144 h, the supernatant was taken and the P concentration was determined. Thirdly, 5 g of RSPRC was separately put into 1 L of simulated wastewater with pH = 7 and P concentrations of 0.3, 0.5 and 1.0 mg/L. The P concentration was detected at different times to investigate the effect of initial P concentration on the phosphorus removal of RSPRC.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 3 Main Experimental Results and Discussion
### Noise Power Spectral Density Evolution at Different Times of the Day  
Figures 10 and 11 show the evolution of the noise PSD depending on the frequency in the transformer substation and the customer's electrical installation respectively. The aim is to compare the experimental measurements performed at 10 a.m. and 2 p.m. To simplify the analysis, the measurements from the sub rural and urban dense 2 sites are discussed.  
The background noise is mostly constant over the period of a few hours. Even if a few variations can be occasionally measured, the trend line of the background noise is low. It may be explained by the increasing usage of the electrical appliances at the customer side. The narrowband noise and the impulsive noise are still present at the same frequencies. They remain in the range from 40 to 60 dB \(\upmu\)V, except at the lower part of the spectrum, where it can rise to 80 dB \(\upmu\)V.  
Figure 8: Noise power spectral density (PSD) in the transformer substation.  
Figure 9: Noise PSD at the customer side at the smart electricity meter.
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 2 Basic Measurement Principles  
### Measurement Principle of LDV  
LDV measures velocity and displacement using the Doppler effect and heterodyne interference principle [35]. It emits a laser beam to the test object and is scattered back by it. Based on the Doppler effect, a slight frequency shift occurs in the signal returned to the LDV, known as the Doppler frequency shift \(f_{D}\). The functional relationship between the frequency \(f_{D}\) and velocity \(V\) of the object to be measured along the beam direction can be calculated by Equation (1).  
\[f_{D}=2f_{C}\frac{V}{c}=2\frac{V}{\lambda} \tag{1}\]  
where, \(f_{C}\), \(c\), and \(\lambda\) represent the laser's frequency, wave velocity, and wavelength, respectively.
## 3 Results
### Hexagonal Pattern of Sampling  
The hexagonal pattern of spatial sampling resulted in a total of 195 hexagons of 17 km\({}^{2}\) each. Thus, a total of 195 observation locations were selected from 3415 (base case scenario) through the hexagonal pattern of sampling. The potentiometric head surfaces were generated using the groundwater head values in these 195 wells for pre-monsoon (October) and post-monsoon (April) seasons using an IDW interpolation. These potentiometric head surfaces were compared with the base case. The standard error raster was generated by subtracting the potentiometric head surface obtained by the 195 observation wells from the potentiometric head surfaces obtained by the base case (i.e., 3415 observations). The mean error was found to be 0.098 m with a standard deviation of 0.58 m (Table 1). These results can be considered satisfactory when the reduction in observation wells is more than 94% (195 out of 3415 wells).  
Figure 7: Heads (in meters) obtained at the end of the model calibration period (April 2014) compared with observed heads measured at over 35 wells across the model domain.
## 3 Results and Discussion
### Fluctuation of Environment Factors  
#### 3.2.1 Time-Temperature Fluctuation  
The experiment lasted 18 days for eggs stored in scenario 1 under room temperature and 30 days for those in scenario 2 in refrigeration when the HU decreased to 55 or YI down to 0.15.  
The ambient temperature was not stable during the experiment period under both room temperature and refrigeration (Figure 4). The room temperature was around 25 \({}^{\circ}\)C with the lowest temperature down to 22 \({}^{\circ}\)C and the highest up to 31 \({}^{\circ}\)C. The room temperature fluctuated mainly because of the changes of weather in June 2017, and other factors, such as the refrigeration equipment and people's activities in the room.  
The temperature of the refrigerator was set to 4 \({}^{\circ}\)C in this experiment. Although the lowest temperature was kept at 4 \({}^{\circ}\)C, the temperature in the refrigerator was sometimes higher than 7 \({}^{\circ}\)C during the experiment, as lots of refrigerators are in reality running at higher temperature than set [35]. In our experiment, the temperature fluctuation did happen (Figure 4) when the refrigerator was opened for taking out sample eggs and fluctuated with the cooling system running.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Temperature Sensor** & **Humidity Sensor** \\ \hline Sensor range & \(-\)55–125 \({}^{\circ}\)C & 0–99\% \\ Data accuracy & 0.1 \({}^{\circ}\)C & 1\% \\ Response time & 5 s & 5 s \\ Sampling interval & 1.5 s & 1.5 s \\ Voltage range & 3.0–5.5 V & 3.0–6.0 V \\ Power consumption & \textless{}0.1 mW & \textless{}0.1 mW \\ Battery charge & & 90–99\% (1 day) \\ Packet losses rate & & \textless{}0.5\% (3000 *) \\ \hline \hline \end{tabular}  
* Number of measurements.  
\end{table}
Table 1: Performance parameters of the wireless sensor network (WSN).  
Figure 3: Physical implementation of the sensor node hardware.
## 3 Results
### Evaluation on Multiple Scenarios  
To validate the proposed algorithm, simulations were conducted in three production scenarios, varying the number of products and operators. The configurations considered were as follows: (i) two products with two operators (2P \(\times\) 2W), (ii) three products with three operators (3P \(\times\) 3W--baseline case), and (iii) four products with four operators (4P \(\times\) 4W). In all cases, the layout, task sequence, and variability remained unchanged to isolate the effect of workload on the performance of the compared methods.  
The results, presented in Table 6, show that the EA achieved the best performance across all scenarios. In terms of total travel distance, the EA achieved reductions of 63.45% in 2P \(\times\) 2W, 53.91% in 3P \(\times\) 3W, and 57.18% in 4P \(\times\) 4W, compared to the initial layout. In contrast, the traditional methods SLP and CRAFT obtained smaller reductions in all configurations.  
These results indicate that the EA remains effective in both low- and high-workload conditions. Its consistent behavior supports its application in smaller manufacturing systems with variable workflows. Furthermore, as the number of products and operators increases, the EA maintains its performance, confirming its applicability to larger and more complex systems.
## 3 Framework
### Path Distance-Weighted Attention (PDWA) Model
#### 3.5.2 PDWA Construction Steps  
The process of PDWA is shown in Algorithm 1; it consists of four main steps.  
**Step 1 (lines 2-5):** For each document path, we identify its most relation-relevant sentence by applying the attention mechanism with the target relation representation as the query vector. The GumbelSoftmax function enables differentiable discrete selection of the most informative sentence while maintaining gradient flow during training.  
**Step 2 (lines 7-10):** We compute semantic distances between all path pairs based on their relation-relevant sentence representations. The distance is inversely proportional to the dot-product similarity, meaning semantically similar paths have smaller distances. Normalization ensures that distance weights sum to unity across all path pairs.  
**Step 3 (lines 12-16):** The original relation matrix is partitioned into sub-matrices corresponding to entity relations within and across document paths. Each sub-matrix's attention weights are adjusted by the corresponding distance weights, ensuring that semantically similar paths receive higher attention during relation reasoning.  
**Step 4 (lines 18-20):** After \(l\) layers of distance-weighted attention processing, we extract the final target relation representations for each path from the enhanced relation matrix.