esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Verification
### Awgn  
In this experiment, the BPL decoding algorithm based on information geometry (BPLIG) proposed in this paper is compared with the traditional BPL decoding algorithm based on path selection criteria and the original BP decoding. Simulations were conducted with code lengths of (1024, 512), and the maximum number of iterations was set to both 30 and 60, respectively. The simulation results are shown in Figures 6 and 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameter/Condition** & **Value/Description** \\ \hline \multirow{3}{*}{Polar Code Parameters} & Code Length & 1024 \\  & Information Bit Length & 512/307 \\  & Construction Method & Gaussian Approximation \\ \hline \multirow{3}{*}{Channel Parameters} & Modulation Scheme & BPSK \\  & Channel Type & AWGN/Fading channel \\  & \(E_{b}/N_{0}\) & 1.4–3 \\ \hline \multirow{3}{*}{Decoder Parameters} & BPL Decoder & BP, BPL and BPLIG \\  & Number of DecoderS & 5 and 10 \\  & Maximum Number of & 30 and 60 \\  & Iterations & \\ \hline Hardware Environment & CPU Model & AMD Ryzen 5 5600H \\ \hline \hline \end{tabular}
\end{table}
Table 2: The simulation parameters and environment.  
The simulation results demonstrate that both the BPL decoding algorithm based on traditional path selection criteria and the BPL decoding algorithm based on information  
Figure 5: Bit error rate for different factor graphs at \(E_{b}/N_{0}=2\) dB.  
Figure 6: The bit error rate performance of BPLIG compared to BPL and BP (\(I_{max}=30\), \(L=5\)).  
Figure 7: The bit error rate performance of BPLIG compared to BPL and BP (\(I_{max}=60\), \(L=5\)).  
geometry can effectively enhance the error correction performance of BP decoding. Under equivalent conditions, compared to the BPL decoding algorithm based on traditional path selection criteria, the BPL decoding algorithm based on information geometry proposed in this paper reduces the bit error rate (BER) by 15.9-59.8% when the maximum number of iterations is 30 and by 16.2-74.9% when the maximum number of iterations is 60.  
To provide a more intuitive comparison of the decoding performance between the two path selection criteria, this paper simulates the traditional BPL decoding algorithm with 10 decoders. The simulation results are shown in Figure 8.  
The simulation results indicate that the decoding accuracy of the BPL decoding algorithm based on information geometry proposed in this paper, with a maximum of 30 iterations and 5 decoders, is comparable to that of the BPL decoding algorithm based on traditional path selection criteria, which uses a maximum of 60 iterations and 10 decoders.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency, GHz**} & \multicolumn{6}{c}{**PyC Thickness, 9 nm**} \\ \cline{2-7}  & \(R_{\text{exp}}\) & \(R_{\text{th}}\) & \(T_{\text{exp}}\) & \(T_{\text{th}}\) & \(A_{\text{exp}}\) & \(A_{\text{th}}\) \\ \hline
26 & 0.19 & 0.19 & 0.65 & 0.63 & 0.16 & 0.18 \\
30 & 0.18 & 0.17 & 0.67 & 0.67 & 0.16 & 0.16 \\
32 & 0.17 & 0.17 & 0.68 & 0.68 & 0.16 & 0.15 \\
34 & 0.17 & 0.17 & 0.69 & 0.68 & 0.14 & 0.15 \\
36 & 0.16 & 0.18 & 0.69 & 0.68 & 0.15 & 0.14 \\ \hline \multirow{2}{*}{**Frequency, GHz**} & \multicolumn{6}{c}{**PyC Thickness, 20 nm**} \\ \cline{2-7}  & \(R_{\text{exp}}\) & \(R_{\text{th}}\) & \(T_{\text{exp}}\) & \(T_{\text{th}}\) & \(A_{\text{exp}}\) & \(A_{\text{th}}\) \\ \hline
26 & 0.18 & 0.17 & 0.49 & 0.51 & 0.33 & 0.32 \\
30 & 0.15 & 0.15 & 0.51 & 0.56 & 0.34 & 0.29 \\
32 & 0.14 & 0.15 & 0.54 & 0.57 & 0.32 & 0.28 \\
34 & 0.13 & 0.15 & 0.57 & 0.58 & 0.29 & 0.28 \\
36 & 0.13 & 0.15 & 0.57 & 0.58 & 0.31 & 0.27 \\ \hline \multirow{2}{*}{**Frequency, GHz**} & \multicolumn{6}{c}{**PyC Thickness, 30 nm**} \\ \cline{2-7}  & \(R_{\text{exp}}\) & \(R_{\text{th}}\) & \(T_{\text{exp}}\) & \(T_{\text{th}}\) & \(A_{\text{exp}}\) & \(A_{\text{th}}\) \\ \hline
26 & 0.20 & 0.17 & 0.40 & 0.43 & 0.40 & 0.40 \\
30 & 0.16 & 0.14 & 0.41 & 0.48 & 0.43 & 0.38 \\
32 & 0.14 & 0. Thus conductivity is the major parameter that influences the absorption and transmission of the studied PyC. This fact is confirmed by excellent coincidence of experimental and numerical results shown in Figure 3 and Table 2. Specifically, all experimental data in whole K\({}_{\text{a}}\)-band is described by the only parameter, conductivity \(\sigma=4.5\times 10^{14}\) s\({}^{-1}\) (\(5.0\times 10^{4}\) S/m), taken from the experimental data [6].
###### Abstract  
Since its advent in the 1970s, digital image correlation (DIC) applications have been rapidly growing in different engineering fields including composite material testing and analysis. DIC combined with a stereo camera system offers full-field measurements of three-dimensional shapes, deformations (i.e., in-plane and out-of-plane deformations), and surface strains, which are of most interest in many structural testing applications. DIC systems have been used in many conventional structural testing applications in composite structures. However, DIC applications in automated composite manufacturing and inspection are scarce. There are challenges in inspection of a composite ply during automated manufacturing of composites and in measuring transient strain during in-situ manufacturing of thermoplastic composites. This article presents methodologies using DIC techniques to address these challenges. First, a few case studies where DIC was used in composite structural testing are presented, followed by development of new applications for DIC in composite manufacturing and inspection.  
Digital image correlation (DIC); composite structures; structural testing; experimental mechanics; composite materials; automated composite manufacturing; composite inspection; automated fiber placement (AFP)
# A Review: Factors Affecting Internal Two-Phase Flow-Induced Vibrations
## 2 Factors Affecting FIV in Internal Two-Phase Flow
### Effect of Flow Velocity
[67] to investigate the dynamic response of a horizontal pipe under slug flow. It was reported that these dynamic responses are created as a combined effect of the interaction between fluid and structure, and flow characteristics. The velocity of slug body has a considerable effect on the response of vibrations since this velocity affects centrifugal and Coriolis forces. The effect of slug transitional velocity was found to be intense, considering that it affects the rate of change of system properties, such as damping, stiffness, and loading. [PERSON] et al. [59] applied the one-way fluid structure interaction model to study the interaction of multiphase flow in a 90\({}^{\circ}\) pipe bend using numerical simulation. The geometry and flow parameters used were similar to [PERSON] et al. [66]. The two-phase flow was simulated using the volume of fluid (VOF) model and realizable k-\(e\) turbulence model. It was reported that at fixed liquid superficial velocity, the increasing gas velocity increases the maximum total deformation and equivalent stress, while the decreasing trend was observed with the increasing superficial liquid velocity, while maintaining a constant gas superficial velocity. The evolution of slug flow affected the position of distribution of maximum stress and deformation. The maximum value of total deformation was found at the 90\({}^{\circ}\) pipe bend for low liquid superficial velocities, but as the superficial velocity of liquid phase increases, the location of maximum value of total deformation was in the horizontal section of pipe. As a result, serious cyclic impact on the 90\({}^{\circ}\) pipe bend will be produced for higher superficial gas velocity. In summary, fluctuation forces increase with the increase in gas superficial velocity while maintaining a constant liquid superficial velocity. Moreover, this condition is true if we reverse the conditions, where liquid superficial velocity increases while maintaining a constant gas superficial velocity. A few exceptions exist as reported, which are primarily due to flow regime transition. The peak/dominant frequency of fluctuations increases with liquid superficial velocity, but shows inconsistent behavior with the increasing gas superficial velocity, depending on flow regime change.
# Transient Analysis of a Selective Partial-Update LMS Algorithm
## 5 Results
### Extensions of the Proposed Framework Simulations
In the settings depicted in Figure 10a,b, the computational cost reduction provided by the SPU-FX-LMS algorithm amounts to approximately 8%. Greater computational cost reductions can be achieved if the constraints of the application permit, given that such reductions entail a larger decrease in the convergence rate. While both figures emphasize the superior convergence rate of FX-LMS during the transient regime, and in the steady-state regime where both FX-LMS and SPU-FX-LMS exhibit similarity, it is crucial to underscore that the implementation of SPU-FX-LMS concurrently reduces computational complexity while maintaining an equivalent performance during steady-state operation. Therefore, in the context of a Mean Squared Error (MSE) comparison, there is an important choice between FX-LMS and SPU-FX-LMS that becomes pivotal for designers with specific objectives in mind. Figure 10b illustrates similar comparisons of empirical MSE over iterations for FX-LMS and SPU-FX-LMS, leveraging the same parameters as in Figure 10a. The theoretical framework expounded in the antecedent Figure 10a suggests a diminished computational demand. Nevertheless, a meticulous scrutiny of Figure 10b elucidates that, particularly in the presence of intensified noise, the augmentation of \(\sigma_{\
u}^{2}\) values in the MSE calculations over a span of up to \(10^{4}\) iterations proves to be notably efficacious when employing SPU-FX-LMS. This effectiveness arises from the algorithm's capacity to optimize performance under conditions of high noise while concurrently exhibiting reduced computational complexity. The observed parity in steady-state results between SPU-FX-LMS and FX-LMS further underscores the former's viability. Consequently, the strategic application of SPU-FX-LMS is advocated in scenarios characterized by constrained computational resources, even in the presence of substantial ambient noise. Notably, in applications such as Acoustic Echo Cancellation (AEC), the SPU strategy demonstrates potential benefits, including potential reductions in battery usage for hands-free devices, while maintaining robust echo cancellation capabilities in comparison to conventional LMS techniques. Figure 9: Comparison between empirical SPU-Variable Tap Length (red) and Variable Tap Length (blue). (a) MSE (dB); (b) Tap Length.
## 5 Simulation and Analysis
### Performance of the Routing Protocols with the Mobility Models
#### 5.2.2 Grp  
Figure 11 shows that GRP performs better with RWP in terms of PDR. The performance with the other mobility models in terms of PDR is almost similar, as the slight differences that are observed are not statistically significant. The PDR here ranges from about 40% to about 55%, thus a range length of about 14 percentage points. This range length (of 15%) is double the one for AODV, and it is at a lower level (from 45%). This PDR being best under RWP can be explained by the factors discussed above. RWP results in the smallest network diameter of all mobility models. The network is on average more connected under RWP (see the lowest average number of components). Moreover, the network presents more redundancy under RWP (highest average clustering coefficient) than under the other mobility models. The conjunction of all of these factors explains why GRP achieves a better performance in terms of PDR under the RWP model.  
In terms of routing overhead (Figure 12), the best performance (least overhead) is observed with ST. We saw earlier that the network topology changes the least or the slowest under ST, and that certainly has an impact on the routing overhead of GRP. More specifically, in GRP, nodes flood control messages in order to communicate their new positions regularly. When a node crosses a quadrant boundary, it floods the control message, and the extent of this flooding is dependent on the distance moved by the node with respect to the quadrant boundary. If a node only moves within its quadrant (1000 m \(\times\) 1000 m), flooding packets are sent out to nodes only within the quadrant. Therefore, since the nodes are moving smoothly with ST, yielding very slow network topology changes, much of the time, the flooding of control packets is quadrant limited compared to when other mobility models are used; hence, the lowest overhead is observed under ST.  
Figure 11: PDR of the geographic routing protocol (GRP) under various mobility models.
## 3 Results and Discussion
### Nutritional Compositions
#### 3.1.7 Amylose Content  
Starch is recognised as the main composition of rice flour. Generally, rice starch comprises amylose and amylopectin [49]. The quality of gluten-free rice bread generally relies on the amylose content of rice flour [50; 51]. The amylose content of control and Bario rice flour varieties are shown in Table 1. In this research, the highest amylose content was presented by Bario _Tuan_ (36.52%), followed by the control sample (32.97%), Bario _Merah Sederhana_ (32.05%), and Bario _Celum_ (26.68%), and the lowest amylose content was from Bario _Adam Halus_ (26.67%). No significant differences (\(p>0.05\)) were exhibited between the three highest amylose contents: Bario _Tuan_, IQR, and Bario _Merah Sederhana_. There are five classifications of amylose content: waxy, very low, low, intermediate, and high. Waxy rice possesses amylose contents in the ranges from 0% to 2%, very low ranges from 2% to 10%, low ranges from 10% to 20%, intermediate ranges from 20% to 25% and high has contents above 25% [52]. Table 1 shows that all samples of Bario rice flour had a high amylose content, with values ranging from 26.67% to 36.53%.  
Several studies have examined the role of amylose content in developing gluten-free rice bread. Most researchers reported that amylose positively impacts the development of rice-based products. It has been revealed that amylose content positively impacted specific volumes and the leavening process of the dough [14; 53]. High amylose content rice flour possesses good gas-holding properties, resulting in a greater load volume in 100% prepared rice bread [51]. The reason for this is that amylose can influence the hydrophobicity of starch granules toward stable Pickering emulsion [54]. On the other hand, a few studies found that softer bread textures were developed from rice flour containing low to medium amylose content; on the contrary, high amylose content rice flour produces rice bread with a sandy texture [55; 56]. Even though low-amylose rice flour could develop rice bread with a soft texture, [PERSON] et al. [55] revealed that the final bread quality is not favourable due to its inferior shape. Hence, Bario rice varieties are predicted to have potential as an ingredient in making rice bread with acceptable quality in terms of loaf volume.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 5 Development of 1-D CNN Models
### 1-D CNN Model Using SVD Responses  
#### 5.2.1 Databank of SVD Responses  
Figure 17 shows examples of contaminated SVD responses compared to their original ones. The training, validation, and evaluation datasets for the CNN model were generated from 120 SVD responses obtained from eight damage levels (D0-D7), for which 80% of the data (96 responses) were used for training and validation, and 20% of the data (24 responses) were used for evaluation. Noise was introduced at 1-10% standard deviation levels to SVD responses across ten generating iterations. After the noise contamination process, the training and validation datasets consisted of 4896 data, and the evaluation dataset consisted of 1224 data.  
Figure 16: Noise effect on the 1-D CNN model trained by time-history data: (**a**) accuracy; (**b**) false positive rate; (**c**) false negative rate; (**d**) F1-score.  
Figure 18 visualizes 4896 data points in training and validation datasets for eight-foundation damage levels (D0-D7). Each response comprises 5000 data points ranging in magnitude from 0 to 1. These SVD responses were labeled accordingly before input into the 1-D CNN model.
## 2 Materials and Methods
### Urban Morphology Parameters
#### 2.2.2 Buildings on Both Sides (BBS)  
This categorical parameter assesses the presence of buildings on both sides (value 2), one side (value 1), or neither side (value 0) of each 50 m road/street segment. To obtain this information, data on streets and roads from OSM, the building footprints from cadastral data, and the DSM are required.  
Specifically, a 20 m buffer from the axis of each 50 m road/street segment is automatically created. This buffer is analyzed to determine whether it intersects with building footprints. The center of each intersecting building and the angle formed between the building and the street/road segment are calculated.  
If all the angles have the same sign, there are buildings on one side of the road (value 1 for the parameter). If the angles have different signs, there are buildings on both sides(value 2 for the parameter). Finally, if no buildings intersect with the buffer, there are no buildings on either side (value 0 for the parameter).
## 8 Current Challenges and Future Prospects
### Drones and Other Robots  
Farmers and growers use UAVs to evaluate crop growth and monitor biodiversity and ecological landscape features [189]. Additionally, considering the rugged farmland, particularly when the farmers have to grow crops on the rugged landscape, UAVs can effectively be used for water spraying and other pesticides. Compared with traditional machinery, with the same propose, robots and drones have confirmed they are worth spraying with haste and accuracy. With the latest improvements in swarm technology and task-based control, multiple sets of robots and drones armed with diverse devices such as 3D cameras can work together to give growers inclusive land management capabilities. In addition to drones, robotics in the agricultural sector have increased productivity and brought higher crop production. Robots, for weeding and spraying, are decreasing the use of pesticides.
## 4 Experimental Results and Dataset
### Performance Metric
#### 4.2.1 Spearman Rank-Order Correlation Coefficient  
SROCC is a commonly used metric that has the ability to measure the correlation between predicted scores and annotated scores. A value close to 1 indicates high performance of the IQA method. It can be computed as follows:  
\[SROCC=\frac{1-6\sum_{i=1}^{n}d_{i}^{2}}{n(n^{2}-1)} \tag{13}\]  
where \(d_{i}\) is the difference between the i-th image ranks in annotated scores and predicted scores; n is the number of images in the evaluation dataset. As the annotated score was a relative value in each group of images, we calculated the SROCC value of each evaluation image group and finally calculated the average value of them.
## 4 Conclusions  
The phase-sensitive techniques described above differ in their technical complexity, the use of X-ray optical elements, the readout process and the method of extracting the signals to detect and to measure the phase contrast and dark field. Thus, all these modalities face various limitations for the transfer to conventional systems. PBI and ABI both require  
Figure 11: MoBI principle outline. From several acquisitions of the membrane alone and membrane plus sample in the path of the beam, numerical processing allows one to retrieve attenuation dark-field and phase contrast images.  
highly coherent sources (mainly spatial coherence for PBI and temporal coherence for ABI), and very few works mention their use for extracting the dark-field signal in the literature. In addition, ABI requires a complex set-up with a high flux, and is therefore difficult to implement with conventional X-ray tubes. PBI has a very simplistic set-up, but the source coherence requirement makes its transfer to low-cost X-ray source devices hard.  
GI, EI, MBI and MoBI have proven to be transferable to conventional sources; however, GI and EI have quite complex set-ups and are only sensitive to one direction of refraction that causes bad performance in terms of noise power spectrum for tomographic reconstruction [46]. Moreover, the presence of numerous optical elements, in GI and EI setups, yields as long as 7 s acquisition time for chest radiography [43]. On the contrary, MBI and MoBI have a simple set-up sensitive to two aspects. MBI and MoBI, despite their limited sensitivity, have the advantage of a high simplicity of implementation that might facilitate a transfer of phase contrast and dark field on clinical devices with gantries that rotate around a sample or a patient.  
Finally, to enhance the quality of phase and dark-field retrieval maps, artificial intelligence algorithms are starting to appear [67, 68] for different set-ups that enhance the quality of the images and/or reduce virtually the radiation dose deposited to the sample. This could tackle the last challenges of phase contrast and dark-field transfer in conventional systems for clinics.  
All authors conceived and designed the analysis, collected the litterature data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was performed within the framework of LABEX PRIMES (ANR-11-LABX-0063) of Universite de Lyon, within the \"Investissements d'Avenir\" program (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Methodology  
### Post-Processing Methods  
In this study, two state of art post-processing methods, a distribution-based method, BMA, and a generator-based method, GPP, were used to correct ensemble precipitation forecasts. They have been widely used in the domain of meteorology and hydrology [19; 21; 22; 27].  
First and foremost, the parametric PDF for the precipitation is defined, represented as \(g\):  
\[y|x_{1},\ldots,x_{M}\sim g(y|x_{1},\ldots,x_{k}) \tag{1}\]  
where \(y\) is the precipitation variable and \(x_{1},\ldots,x_{k}\) corresponds to the ensemble precipitation forecasts comprising K members. Notably, the distribution of \(g\) for precipitation can be described as a mixed discrete/continuous model. This includes a nonzero probability of being zero and a continuous, skewed distribution for positive precipitation amounts. [PERSON], [PERSON] [15] proposed the mixed distribution model for precipitation as:  
\[g(y|f_{k})=P(y=0|f_{k})\cdot I(y=0)+P(y>0|f_{k})\cdot g_{k}(y|f_{k})\cdot I(y>0) \tag{2}\]  
where \(g(y|f_{k})\) represents the probability distribution given the member forecast; \(f_{k}\). \(I[\ldots]\) equals one if its argument is true and zero otherwise; \(P(y=0|f_{k})\) and \(g(y>0|f_{k})\) represent the probabilities of non-precipitation and precipitation for a given forecast, \(f_{k}\), respectively; and \(g_{k}(y|f_{k})\) represents a two-parameter gamma distribution.  
The difference between the chosen post-processing methods lies in their approach to calibrating the precipitation PDF and their consequent generation of post-processed ensemble precipitation forecasts.