esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Analysis of Experimental Results
#### 4.4.3 Ablation Experiments  
To further prove the effectiveness of the proposed improvement method in this paper and to explore the effectiveness and relevance of each improvement method, seven sets of ablation experiments were designed based on YOLOv5n, and each set of experiments uses the same initial conditions as well as data sets, the results of the ablation experiments are shown in Table 6.  
As can be seen from Table 6, the addition of the small target detection layer improves mAP by 3.6% and accuracy by 0.7%, proving a large improvement in the detection of small target bubbles, as well as dense edge bubbles. Modifying the bounding box loss function has almost no change in the size of the model and the amount of computation, but different bounding box loss functions for different datasets have different effects, as well as for the sample imbalance. The EIoU loss function for the dataset used in this paper improves the mAP by 1.8%; after introducing the C3 STR module, the mAP improves by 2.3%, which strongly demonstrates the feasibility of the Swin Transformer backbone network in the field of target detection; finally, by fusing the three improvements, the mAP is improved by  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Number of** & **Precision\%** & **mAP\%** & **GFLOPs/M** & **Weight/M** \\ \hline YOLOv5n\_C3 STR & 1 & 85.4 & 63.5 & 17.7 & 5.3 \\ YOLOv5n\_C3 STR & 2 & 74.1 & 61.1 & 18.1 & 8.0 \\ YOLOv5n\_C3 STR & 3 & 71.4 & 53.6 & 57.0 & 10.0 \\ YOLOv5n\_C3 STR & 4 & 78.2 & 60.1 & 71.3 & 20.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of test results with different numbers of C3 STR.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision\%** & **mAP\%** & **GFLOPs/M** & **Weight/M** \\ \hline YOLOv5n6\_CA & 80.1 & 56.1 & 5.6 & 6.8 \\ YOLOv5n6\_SE & 78.4 & 57.8 & 4.3 & 4.1 \\ YOLOv5n6\_CBAM & 68.8 & 57.3 & 4.23 & 4.0 \\ YOLOv5n6\_C3 CBAM & 71.2 & 53.6 & 57.0 & 8.0 \\ YOLOv5\_SEST & 85.4 & 63.5 & 17.7 & 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of C3 STR with common attention modules.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision\%** & **mAP\%** & **GFLOPs/M** & **Weight/M** \\ \hline YOLOv5n\_DIoU & 64.8 & 59.5 & 7.8 & 5.1 \\ YOLOv5n\_GIoU & 70.6 & 60.2 & 7.8 & 5.1 \\ YOLOv5n\_CIoU & 70.7 & 61.5 & 7.8 & 5.1 \\ YOLOv5n\_AlphaIoU & 66.1 & 59.7 & 7.8 & 5.1 \\ YOLOv5n\_EleU & 78.4 & 62.5 & 7.8 & 5.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different loss function detection results.  
5.6%, which is a great improvement for the detection of small target dense bubble defects in IGBT module solder layers.
## 4 Discussion
However, the relationship between the auxetic foam's area/volume and its energy attenuating ability is unclear. The exact mechanism for why a larger amount of auxetic foam enhances its performance warrants further investigation but could be related to either the density of the auxetic foam, the curvature of the impacting objects, or the ability of the auxetic foam to densify under impact more effectively in a larger volume. The thickness of the closed cell layer of native foam also varied within the helmet and was thicker in the front when compared to the side (\(\sim\)6 mm and \(\sim\)4 mm respectively, Table 1). This means the contribution of the conformable layer during an impact may have been reduced at the front location. In general, the auxetic foam appeared to outperform or be equivalent to the conventional foam for both impact orientations. Previous work [34; 38; 44] found much larger acceleration attenuation differences between the auxetic and conventional foams, on the scale of 300-800% compared to a maximum of 44% in this study. However, this reduction in the effectiveness of auxetic foams may be expected as they are no longer being tested in isolation, but as part of a multi-layered helmet. Findings agree with previous work [33; 36], and it is notable that the conformable layer can significantly affect the helmet's performance. As the comfort layer can influence impact properties, the use of tuned auxetic foams could be used to enhance a helmet's performance. However, the effect of modifying the open cell layer within a helmet on the additional functions (i.e., fit and comfort) are yet to be assessed. Anisotropic foams with low shear modulus could be used in helmets to specifically reduce rotational accelerations. Anisotropy and uniaxial Poisson's ratios can be increased in open cell foams by stretching parallel to cell rise during fabrication [34; 35; 49] rather than compressing (which typically reduces anisotropy and Poisson's ratio) [36]. Given that the open cell layer can reduce impact severity. Further work should expand upon reduced rotational accelerations exhibited by anisotropic foams with a low shear modulus and could be a way of reducing rotational acceleration in certain impacts, either as an alternative to or in parallel with slip plane technology [22; 24]. One emerging area requiring further investigation is the use of 3D printed foams/structures which could enhance and tailor the impact performance of helmets further still [50]. This study is limited to one helmet design at two impacting sites using one grade of conventional and auxetic foam. Further work is required to replicate this experiment for a range of different helmets, foam grades, and impact scenarios across a number of sports. It would also be necessary to investigate how the tailoring of helmet liners influences ergonomic considerations, including pressure distribution, comfort, and fit.
## 3 Comparison with the Literature
### Semi-Analytical Finite Element (SAFE) Method  
The SAFE method is similar to the WFE scheme, where the finite element method is used for discretising the cross-section of the waveguide [35]. The SAFE method uses a harmonic exponential term to describe the wave behaviour in the propagation direction. The FE discretisation occurs at the cross-section of the waveguide. For plane wave propagation in a plate, a 1D discretisation across the thickness of the plate is sufficient. Combining the harmonic assumption and FE discretisation, the particle displacements are expressed in terms of shape functions. Custom stiffness matrices are assembled using this representation, which are used to set up an eigenvalue problem for the frequency-dependent wavenumber calculation [36]. WFE has a major advantage as it is set up using standard stiffness and mass matrices and no custom formulation is required. Moreover, SAFE cannot be used to model the dispersion characteristics of periodic structures such as the one shown in Figure 0(b). The SAFE and WFE models are here compared for the 1 mm thick laminate \([+45/-45/0/90]_{s}\) presented in [35]. The composite laminate in this case is composed of unidirectional T800/924 graphite/epoxy laminae with the material properties described in [35]. The WFE model of the periodic section is shown in Figure 1(a), which is again modelled with standard eight-node hexahedral elements. The comparison results are shown in Figure 4, where a similar trend is observed in both approaches with a slight difference in the values.  
Figure 3: Wave velocity curves at 551 kHz for \([+45/-45/0/90]_{s}\) laminate obtained using the analytical (- -) and WFE (-) method.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 5 Experiment
### In-Orbit Test  
In the phase of in-orbit testing, the Digital Surface Model (DSM) results based on LT-1 bistatic SAR data reconstruction (LT-DSM) are shown in Figure 17a [33]. With a resolution of 10 m \(\times\) 10 m, the LT-DSM exhibits richer details and higher accuracy compared to the TanDEM-X data with a resolution of 90 m \(\times\) 90 m (see Figure 17b). To verify the accuracy of the LT-DSM, we first conducted an assessment using ICESat laser altimetry data. This assessment involved 6062 valid points, and the results indicated an average error of 1.3 m and a median error of 2.8 m, which meets the mapping accuracy standard of  
Figure 16: The results of synchronization accuracy of the on-ground temperature experiments.  
Figure 15: (a) Phase drift in internal calibration. (b) Residual phase before internal calibration compensation. (c) Residual phase after internal calibration compensation.  
China's 1:50,000 DEM. The histogram of error distribution (Figure 17c) exhibits Gaussian error distribution characteristics, with errors primarily concentrated within the range of \(\pm\)10 m. Subsequently, the LT-DSM data were resampled to match the resolution of the TanDEM-X data, and its accuracy was evaluated (Figure 17d). This assessment involved 29,850,659 valid points, with an average error of 0.168 m and a median error of 2.986 m. The acquisition of high-accuracy DSM products further validates the LT-1 BiSAR system.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 5 Conclusions  
The aim of the study was to develop a method for automatic classification of the three spatial audio scenes, differing in horizontal distribution of foreground and background audio content around a listener in binaurally rendered recordings of music. For the purpose of the study, audio recordings were synthesized using 13 sets of BRIRs, representing both semi-anechoic and reverberant conditions. The proposed method is assumption-free with regards to the number and characteristics of the audio sources (blind classification). Head movements were not considered in the study. A least absolute shrinkage and selection operator was employed as a classifier. According to the results, the method exhibits a satisfactory classification accuracy when it is trained and then tested on the stimuli synthesized using the same BRIRs (accuracy ranging from 74% to 98%), even in highly reverberant conditions. However, the generalizability of the method needs to be further improved.  
In addition to the binaural cues, the MFCCs emerged as the prominent metrics, influencing the accuracy of the classified spatial audio scenes. MFCCs are commonly used by researchers as the spectral descriptors of audio signals. This study demonstrates that the MFCCs also constitute a carrier of spatial information. A possible explanation of this observation could be linked to the mechanism of human hearing. Since the MFCCs are particularly effective in describing a spectral envelope of audio signals, it is hypothesized by the present authors that they capture the pinna-related frequency response, responsible for the front-back discrimination of spatial audio stimuli. Further research would be required to validate this hypothesis.  
[PERSON] developed the concepts and algorithms, carried out the experiments, and wrote the paper. [PERSON] provided input throughout the development process, undertook the informal listening tests, and provided help with paper writing.  
This work was partially supported by a grant S/WI/3/2018 from Bialystok University of Technology and funded from the resources for research by Ministry of Science and Higher Education in Poland.  
The authors declare no conflict of interest.
# High Optical Feedback Tolerance of a Detuned DBR Laser for 10-Gbps Isolator-Free Operation
## 3 Results
### Static Performance under Feedback  
#### 3.1.1 RIN Spectrum  
Three bias current points were chosen to analyze the typical noise characteristics of the DBR laser under feedback, which were 25 mA for low damping region, 50 mA for the red-detuned region before mode hopping, and 55 mA for the blue-detuned region after mode hopping, respectively. The detuning between the lasing wavelength and the center wavelength of the grating spectrum was controlled by adjusting the gain section current rather than the passive section current, since the shot noise and filter noise introduced by the current injected into the passive section are too significant to influence the accurate analysis of the RIN.  
In Figure 3a-c, each graph records two spectrums at different bias points, corresponding to the original noise under no feedback and the increased noise under critical feedback. When the laser was biased at 25 mA with no feedback, the overall RIN value was around \(-143\) dB/Hz, except for the peak of \(-133.1\) dB/Hz at 7.1 GHz, the relaxation oscillation frequency. As \(-20\) dB feedback was introduced, the overall RIN value was rapidly improved to over \(-130\) dB/Hz, at least 10 dB worse on average. Three peaks can be roughly identified, which were \(-97.43\) dB/Hz at 6.7 GHz, \(-107.01\) dB/Hz at 13.5 GHz, and \(-114.70\) dB/Hz at 19.9 GHz. When the bias current increased to 50 mA, in the absence of optical feedback, the RIN spectrum became flat due to the increased damping of the oscillation frequency. Note that, at this bias current, the laser should be working in the red-detuned region relative to the Bragg reflection peak. Under a maximum feedback level of \(-9\) dB, the RIN remained below \(-140\) dB/Hz in the whole measurement range of 20 GHz except for the very low-frequency range. Increasing the current to 55 mA, corresponding to a new blue-detuned region after the mode-hopping point, the RIN kept almost unchanged in the absence of optical feedback. However, when \(-14\) dB optical feedback was introduced, the RIN underwent an overall increase to above \(-135\) dB/Hz. The phenomena revealed that DBR lasers are more stable in the red-detuned region than in the blue-detuned region.  
Figure 3: The RIN spectrum measured under no feedback and feedback at (**a**) 25 mA; (**b**) 50 mA; (**c**) 55 mA; (**d**) the optical spectrum under feedback at 25 mA.  
Figure 3d demonstrated the optical spectrum under feedback at 25 mA. The three lines in the figure correspond to the cases of no feedback, \(-20\) dB, and \(-10\) dB feedback. It can be seen that the spectrum appeared small burrs under critical feedback. As the feedback level continued to increase, the spectrum showed a completely broadened state, indicating coherent collapse. This phenomenon suggested that feedback-induced noise will cause spectral deterioration, which is detrimental to the application.
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 3 Convolutional Neural Network Architecture
### CNN Model Salient Features
#### 3.1.4 Training Algorithm  
The CNN DPD model was trained using the following algorithm. Mean Square Error (MSE) is employed as the loss measuring function, ADAM is used for the optimization,  
Figure 3: \(N\) number of hidden layers has \(K\) neurons per hidden layer, as shown in the schematic picture of the feedforward fully connected NN structure utilised.  
ReLu is used as activation function and backpropagation is used to update the weights. To improve the performance, Z number of iterations are used.  
As previously stated, we use the input and the output of the original RoF connection to train our emulated RoF CNN model, and after this model is acquired, we connect the CNN DPD model to it. Then, once this training is complete, we attach the actual RoF link to this DPD CNN and begin the predistortion process (see pseudocode in Algorithm 1). It is shown in the algorithm that the CNN is made on \(I(\hat{x}(n))\) with updates on \(\hat{I}\) followed by the pre-distortion functionality usage in terms of \(\hat{I}^{-1}(x(n))\) as shown in Figure 2.  
```
\(\hat{x}(n)\gets x(n)\) for\(i\leq Z\)do \(y(n)\gets I(\hat{x}(n))\): //Radio over Fiber -Transmission \(\hat{I}\leftarrow\) Train on \(\hat{x}(n)\), \(\frac{y(n)}{G}\) //Update Radio over Fiber CNN //Fixed NN weights of \(\hat{I}\) \(\leftarrow\) Train on \(x(n)\). //Utilize \(\hat{I}^{-1}(I(x(n)))\) \(\hat{x}(n)\leftarrow\hat{I}^{-1}(x(n))\): //Predistort endfor
```  
**Algorithm 1**Training for linearization (DPD)
## 3 Results and Discussion
### Photocatalytic Activity Evaluation
#### 3.5.2 Composition of the Rice Husk Extract after the Photocatalytic Reaction
**Alcohols after the photocatalytic reaction:** Table 2 reports the presence of alcoholic compounds in the sample after the photocatalytic reaction, in a smaller amount, compared to alkanes. The peak at the retention time of 3.06 min represents 2-methyl 1-propanol, that at 5.08 min indicates 2-methyl 1-butanol, and that at 4.89 min peak represents of tri-methyl silyl methanol. **Other compounds in smaller quantity after the photocatalytic reaction:** Some other compounds were also detected by GC-MS after the photocatalytic reaction. Table 2 reports the detected compounds, i.e., amines, corresponding to the peak appearing at retention time of 3.73 min and assigned to 1,1-bis(2,2-dimethylpropoxy) N,N-dimethyl methanamine and the peak at 7.54 min indicating the presence of diethyl (3-decym-1-yl) amine, with % areas of 0.20 and 0.21, respectively. A smaller amount of acetic acid was also found as shown by the small peak at 6.54 min retention time, with area percentage of 0.44, representing butyl ester acetic acid. Based on GC-MS analysis, it is obvious that the rice husk extract obtained by pyrolysis from rice husk contained many products, some of which are desirable while others are undesirable. The desirable products are those allowing the use of rice husk extract as a biooil for heating purposes or for the production of many compounds, such as hydro-carbons  
Figure 10: Proposed photocatalytic reaction mechanisms for the conversion of the rice husk extract to value-added chemicals. and alcohols. The formation of undesirable products should be inhibited to make bio-oil the best alternative fuel [46]. These undesirable products are oxygenated compounds (such as carboxylic acids, phenols, aldehyde, ketones, esters, etc.) and nitrogenized compounds (nitromethane, etc.). These oxygenated and nitrogenized compounds are undesirable for the following main reasons [47]: (i) they promote corrosiveness when bio-oil is stored for some time, (ii) they promote the instability of components of bio-oil, (iii) they may reduce the heating value of bio-oil. Therefore, it is necessary to decrease or eliminate these undesirable oxygenated and nitrogenized compounds to make biomass such as bio-oil the best alternative fuel.
# High-Pressure Technologies for the Recovery of Bioactive Molecules from Agro-Industrial Waste
## 2 Presurized Liquid Extraction Processes
### PLE of Bioactive Compounds from Waste
#### 2.2.2 Polysaccharides  
Polysaccharides are polymers composed of more than 10 monosaccharides with straight or branched glycosidic bonds [89]. They are characterized by low toxicity, low cost,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Compounds** & **Source** & **Extraction Solvent** & **Operating Conditions** & \begin{tabular}{c} **Sample Weight** \\ **Treated** \\ \end{tabular} & \
\begin{tabular}{c} **Extraction Method** \\ \end{tabular} & **Ref** \\ \hline Caffe acid &
\begin{tabular}{c} Spent coffee (_Coffea_ \\ _anbica_ L.) \\ \end{tabular} & 50\% (\(\text{$\text{$\text{$\text{$\text{$\text{$\text{$\text{$\text{$\text{$ $\text{$\text{$\text{$\  
and high biocompatibility and biodegradability [90]. Natural polysaccharides have several potential applications in food industries due to their properties as stabilizers, emulsifying, thickeners, gelling agents, and film forming [64; 90; 91; 92; 93; 94]. In addition, they exert bioactivities that can be exploited in medicine, including antioxidant, antitumor, anti-hyperglycemic, and immune regulation activities [95].  
Plant polysaccharides' extraction efficiency is often reduced by the presence of cell wall polymers, so extraction aids able to disrupt the cell structure have to be applied to enhance extraction yields. The application of advanced extraction techniques such as PLE shows the great potential of polysaccharides recovery. The better efficiency towards polysaccharides was reported by [PERSON] et al. [38], who evaluated the effect of MAE and PLE parameters (solvent, temperature, time, and number of cycles) on the polysaccharide yield and chemical composition (total sugars) of _Fucus virsoides_ and _Cystoseira harbata_. The best PLE parameter was 0.1 M H\({}_{2}\)SO\({}_{4}\) as solvent at 140 \({}^{\circ}\)C for two cycles of 15 min. In addition, PLE obtained a significant increase in polysaccharide yield from _Fucus virsoides_ and the polysaccharides obtained using PLE had the highest polydispersity index, fucose, and sulfate content, and the lowest glyoxylate content; however, they had lower antioxidant activity. [PERSON] et al. [40] studied the yield of extracting valuable compounds from brewer's grains using PLE under constant pressure with varying temperature, solvent type, and flow rate, and the total phenolic compounds, total flavonoids compounds, antioxidant activity, reducing sugars, and total reducing sugars as indicators, and compared the results with the Soxhlet extract. The results show that PLE can maintain a high extraction rate of reducing sugars and total reducing sugars in the BSG matrix. The application of high pressure can modify the conformation and structure of polysaccharides, changing their biological activities [96; 97]. High-pressure extraction was also coupled with ultrasounds, creating hybrid techniques such as high-pressure ultrasonic-assisted extraction [98], which was employed for the recovery of polysaccharides from _Hovenia dulcis_.
## Security Analysis in Spatial and Frequency Domains
### NPCR (Number of Pixel Change Rate)  
Let \(I(i,j)\) and \(J(i,j)\) represent the actual intensity levels of the corresponding pixel \((i,j)\) on a host image and dual chaotic watermarked image. \(B(i,j)\) in Equation (4) was then defined as the binary array and NPCR was defined in Equation (5), where M and N stand for total numbers of pixels at each row and each column.  
\[B(i,j)=\{\begin{array}{ccc}0&if&I(i,j)=J(i,j)\\ 1&if&I(i,j)\
eq J(i,j)\end{array} \tag{4}\]  
\[\text{NPCR}=\frac{\sum_{i,j}B(i,j)}{M\times N}\times 100\% \tag{5}\]  
The scores of NPCR are listed in Table 1. The range of driving parameter \(\mu\) is from 3.6 to 4.0. The NPCR scores of the digital images after shuffling, after dual watermarking, as well as those of mismatches generated by shuffling and dual chaotic watermarking are listed in four separate rows. All the qualitative NPCR scores obtained were around 99%, which indicates that the dual chaotic watermarking scheme was very sensitive with respect to even low variations in the plaintext. NPCR was also applied to evaluate the impact of changing a single pixel in a host image on the dual chaotic watermarked image. The 99% NPCR score illustrates that the position of each pixel had been dramatically randomized which is very sensitive to the small variation. Thus it demonstrates suitable robustness of a dual chaotic watermarking scheme against possible differential attacks. After dual chaotic watermarking (2 nd row), it showed that, in general, a bigger driving parameter \(\mu\) corresponds to a larger NPCR score and better security. However the effect of driving parameter \(\mu\) on NPCR and security should be applied to a large quantity of digital images to make the existing conclusion more convincing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mu\)**Outcomes** & \(\mu\)**= 3.6** & \(\mu\)**= 3.7** & \(\mu\)**= 3.8** & \(\mu\)**= 3.9** & \(\mu\)**= 4.0** \\ \hline Chaotic Watermarking & 0.9883 & 0.9884 & 0.9882 & 0.9884 & 0.9886 \\ Dual Chaotic Watermarking & 0.9909 & 0.9909 & 0.9910 & 0.9911 & 0.9912 \\ Mismatch Chaotic Shuffling & 0.9895 & 0.9895 & 0.9895 & 0.9895 & 0.9895 \\ Mismatch Dual Watermark & 0.9894 & 0.9895 & 0.9894 & 0.9895 & 0.9895 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative Metric of Number of Pixel Change Rate (NPCR).
## 2 Materials and Methods
### Data Collection
#### 2.2.3 Estimation of Crop Water Requirements and Evapotranspiration Models
By linking vegetation indices to meteorological data, this methodology provides a scalable framework for estimating crop coefficients and reference evapotranspiration in regions with limited meteorological infrastructure, aligning with precision agriculture objectives.