esa-sceva/satcom-chunk-collection huggingface dataset:
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 5 Results
The IF bandwidth of the SDR is set to 56 MHz, 77.5 dB above 1 Hz, the maximum allowed value; this also corresponds to the highest noise-floor level (worst case). The constellation is recorded for input signal power levels in the range from \(-50\) to \(-80\) dBm. Since the breadboard noise figure is 3.9 dB, the  
Figure 8: Gain and noise. Measured receiver breadboard performance in different conditions. **(a)** IF signal when the receiver input is terminated on a 50 \(\Omega\) resistor at 295 K; **(b)** IF signal with a 28.65 GHz input signal having a \(-60\) dBm available power; **(c)** gain versus frequency; **(d)** noise figure versus frequency. The receiver front-end overall gain is of about 46 dB and the corresponding noise figure is 3.4 dB. These values include the coaxial-to-CPW end launcher and the feeding line (insertion loss of about 1.5 dB). The gain and noise figure measurements have been carried out with the Y-factor method, exploiting the 346C-K01 noise source from Keysight (\(ENR=11.73\) dB at 28 GHz). The receiver breadboard uses a rectangular waveguide image reject filter with 300 MHz bandwidth. \(-80\) dBm case corresponds to a SNR of about \(12\) dB. This justifies the spreading observed in the I/Q plane. When the input power is increased such a spreading is reduced but not completely eliminated. Such a behavior is due to the phase noise of the local oscillators used in the down-converters, mainly that in the Ka-band front-end. In all cases of Figure 10 the receiver was in lock condition. Figure 10: Quadrature phase shift keying (QPSK) test. Measured constellation in the I/Q plane for the whole receiver (Ka-band front-end, SDR and demodulator) at different input power levels. (**a**) \(P_{RX}=-60\) dBm; (**b**) \(P_{RX}=-70\) dBm; (**c**) \(P_{RX}=-80\) dBm. A QPSK signal at \(40\) Msym/s, roll-off factor 0.25, is provided at the input. The receiver bandwidth is set to \(56\) MHz. Considering the breadboard noise figure, a \(12\) dB SNR is achieved in the \(-80\) dBm case. Figure 9: Linearity. Measured large signal receiver gain versus the available input power at \(28.65\) GHz. The input-referred \(1\) dB compression point is \(P_{1 dB}=-37\) dBm. This value includes the \(1.5\) dB insertion loss due to the coaxial-to-CPW end launcher and input feeding line. To test the receiver robustness, an interferer at 28.6505 GHz (i.e., at 1 MHz offset from the main signal center frequency) is injected at the receiver input. The interferer is a pure carrier and has the same \(-60\) dBm power as the signal (the signal to interferer power ratio is \(S/I=0\) dB). The resulting power spectrum and I/Q constellation are represented in Figure 11. As a consequence of the interference, a rotation of the received data around the expected \((\pm\sqrt{2},\pm\sqrt{2})\) points in the I/Q plane is observed. The receiver was in lock condition even in this case.
## 2 Structural Design
### Main Structural Design
#### 2.2.2 H-Shaped Frame Design for Quadrotor  
Unlike the conventional X-frame architecture, which is typically used in quadrotor UAVs, the present design innovatively employs an H-shaped quadcopter frame skeleton structure, as shown in Figure 6. Compared to traditional X-frame structures, the H-frame features an elongated geometry that increases the lateral separation between adjacent rotors. This configuration not only enables the integration of the separation cage structure but also simultaneously improves flight stability through enhanced aerodynamic decoupling.
###### Abstract  
In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform-Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which the encryption process is applied in the frequency domain. The proposed DCBS secret key generator generates a very large key space of \(2^{2200}\). The proposed DCBS secret keys generator can achieve the confidentiality and authentication properties. Statistical analysis, differential analysis and a key sensitivity test are performed to estimate the security strengths of the proposed DCBS-MP-FrFT-OFDM cryptosystem over the IoT network. The experimental results show that the proposed DCBS-MP-FrFT-OFDM cryptosystem is robust against common signal processing attacks and provides a high security level for image encryption application.  
IoT; physical layer security; dynamic chaotic biometric signature; encryption 20232337843330/12 September 2023  
[PERSON]. [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON] Physical Layer Authenticated Image Encryption for IoT Network Based on Biometric Chaotic Signature for MPFrFT OFDM System. _Sensors_**2023**, 23, 7843. [[https://doi.org/10.3390/23187843](https://doi.org/10.3390/23187843)]([https://doi.org/10.3390/23187843](https://doi.org/10.3390/23187843))
# RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8
## 5 Discussion  
To address the high computational costs of detecting small underwater objects in complex environments while maintaining accuracy, this paper proposes RHS-YOLOv8, a lightweight detection algorithm based on YOLOv8, validated on the DUO and RUOD datasets. Key improvements include the following: (1) replacing the standard bottleneck block with a Ref-Dilated block to expand the convolutional receptive field and reduce computational costs; (2) integrating a lightweight Involution block to reduce inter-channel redundancy; (3) adding a small object detection branch at low feature layers to enhance generalization; (4) introducing a novel fusion module to optimize feature combination; and (5) employing the ELA attention mechanism to improve spatial feature extraction. On the DUO dataset, these modifications reduced computational costs by 9.95% (from 164.8 to 146.8 GFLOPS) while improving [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 by 2.54% (0.888) and 4.31% (0.726), respectively.  
However, this study has limitations. First, the dataset suffers from insufficient sample size and limited detection scenarios, potentially affecting model generalizability. Second, the native underwater images in the dataset exhibit quality issues (e.g., low contrast and light distortion), which may degrade detection performance. Third, while the model achieves the lightweight design goals, its compatibility and adaptability for real-world underwater embedded systems (e.g., cross-platform deployment and real-time inference under resource constraints) remain unverified through sufficient experiments.
## 2 Results and Discussion
### Matrix Effect  
For food samples, matrix interference is a common phenomenon that may lead to either ion enhancement or the suppression of the target compound. For the purpose of decreasing the matrix effect, MSPE based cleanup was processed in a sample preparation combination of DETA-MPs-GO. The comparison between standards in neat solvent and spiked standards in three types of fruits (apple, orange, and grape) with a MSPE cleanup step showed a low matrix effect ranging from 86.4% to 99.0%, which is listed in Table 2. Therefore, it was necessary to process a MSPE cleanup with DETA-MPs-GO before UPLC-HRMS analysis, as described above. Nevertheless, little difference between the response standards in spiked fruits and in neat acetonitrile was still existent, \({}^{13}\)C or D-labelled fungicides were applied as internal standards to reduce the matrix interference.
## 3 Experiment
### Parameter Settings  
All 50 kHz files were downsampled to 25 kHz to ensure that all voice files had the same sampling frequency. All 3-second files were edited to include only the first second of phonation. The 36-dimensional MFCC parameters and their first and second derivatives were extracted from each file in the database. The parameters for extracting the MFCC features were set as follows: frame size = 32 ms, frame overlapping rate = 50%, Mel filter order = 24. For FC-SMOTE, the 3 nearest neighbours SMOTE algorithm was used (k = 3), four clusters needed to be found by c-means for an imbalanced class of four pathology types (\(j=4\)), and two clusters needed to be found by c-means for an imbalanced class of normal and pathological voices (\(j=2\)). This work focused on the robustness of the method to generate minority class samples rather than adjusting the classifier hyperparameters to obtain the classifier's optimal performance. Therefore, Bayesian Optimization [42] was used to find the relatively optimal hyperparameters so that the machine learning classifier's performance was in a good performance range. Table 3 lists the main hyperparameter search space of each model. For the experimental verification of the deep learning model CNN, we refer to the method proposed in [2] to construct the network. Experiment results were obtained by 10-fold cross-validation to sure that each fold of the training or testing contained at least one sample of the minority class. To illustrate the generated data's reliability, we randomly isolated 10% of the dataset not used for oversampling from the initial dataset as external validation. Table 4 presents the training and test data distribution information of the experimental database. In the experiment based on the MEEI database, we set up an external dataset to prove the effectiveness of the FC-SMOTE algorithm in generating samples. Therefore, in the SVD database experiment no external dataset was set to repeat this verification experiment. The calculations and models were implemented by Python 3.7 with the Imbalanced-learn, Scikit-learn, and TensorFlow 2.0 libraries.  
Figure 4: Sample point distribution (**a**) before and (**b**) after using FC-SMOTE in the database; sample point distribution (**c**) before and (**d**) after using FC-SMOTE in the Saarbruecken Voice Database (SVD) database.
## 2 Theoretical Analysis
### Governing Differential Equations and Transfer-Matrix Coefficient for Pile in the Active Segment  
Analogously to the foregoing approaches, for the lateral response in the active segment of the pile shaft, an independent local coordinate system on the \(i\)Similarly, the average vertical friction stiffness coefficient \(\overline{R}_{nai}\) and the average horizontal foundation resistance coefficient \(\overline{k}_{ai}\) surrounding it for \(i\)th active segment of pile shaft are  
\[\overline{R}_{nai}=[R_{nai}(ih_{a}-h_{a})+R_{nai}(ih_{a})]/2 \tag{26}\]  
\[\overline{k}_{ai}=\frac{m\big{[}\big{(}z_{0}+L_{p}+ih_{a}\big{)}^{n+1}-\big{(}z _{0}+L_{p}+(i-1)h_{a}\big{)}^{n+1}\big{]}}{(n+1)h_{a}} \tag{27}\]  
For the \(i\)th pile segment in the passive segment shown in Figure 6b, the differential equation is expressed as  
\[ \begin{array}{c}\frac{d^{4}y_{ai}}{dz_{ai}^{4}}+\frac{1}{E_{1}}\frac{d}{dz_{ ai}}\big{[}\overline{N}_{ai}\frac{dy_{ai}}{dz_{ai}}\big{]}-\frac{q_{IN}}{E_{1}} \frac{d}{dz_{ai}}\Big{[}\overline{N}_{ai}\frac{dy_{ai}}{dz_{ai}}\Big{]}\\ +\overline{R}_{nai}^{4}\frac{dy_{ai}}{dz_{ai}^{2}}+\frac{\big{(}2\overline{R}_ {nai}E_{1}+\kappa A_{c}G_{c}E_{1}}\big{)}b_{1}\overline{R}_{pi}=0\end{array}  \tag{28}\]  
where the notations of \(a\) indicate the active segment for the pile shaft. Similarly, the transfer-matrix equation for the \(i\)th discrete active segment of the pile shaft can be formulated as (Appendix C). \[S_{ai}=U_{ai}S_{a(i-1)} \tag{29}\]  
where \(S_{ai}=\big{[}y_{ai}\ \ \ \varphi_{ai}\ \ \ M_{ai}\ \ \ Q_{ai}\ \ \ 1\big{]}^{T}\) and \(S_{a(i-1)}=\big{[}y_{a(i-1)}\ \ \ \varphi_{a(i-1)}\ \ \ M_{a(i-1)}\ \ \ Q_{a(i-1)}\ \ \ 1\big{]}^{T}\) are all 5-by-1 transposed matrices and represent the pile horizontal deformation, axis rotation angle, bending moment and shear force for the \(i\)th upper and \(i-1\)th lower discrete active segment for the pile shaft, respectively. In which \(U_{ai}\) is the transfer matrix coefficient of the \(i\)th pile slice of the pile in the active segment, by setting \(T_{ai}=\overline{N}_{ai}+\overline{R}_{nai}\), \(D_{ai}=T_{ai}+\Big{(}b_{1}\overline{k}_{ai}E_{1}/\kappa A_{c}G_{c}\Big{)}\), \(\lambda_{ai}=D_{ai}/4E_{1}\), \(J_{ai}=1+\big{(}\overline{R}_{nai}/\kappa A_{c}G_{c}\big{)}\eta_{ai}=b_{1} \overline{k}_{ai}/4E_{1}\), \(\beta_{ai}^{2}=\Big{(}b_{1}\overline{k}_{ai}/4E_{1}\Big{)}^{1/2}\), \(\kappa_{ai}=\big{(}\beta_{ai}^{2}-\lambda_{ai}\big{)}^{1/2}\), \(\overline{\chi}_{ai}=\big{(}\beta_{ai}^{2}+\lambda_{ai}\big{)}^{1/2}\), and \(\omega_{x,ai}=\pm(\kappa_{ai}\pm\xi_{ai}\mathrm{i})\) (where \(x\) equals 1, 2, 3, 4, respectively, and the symbol \"i\" refers to imaginary,\(\kappa_{ai}\) and \(\zeta_{ai}\) are real part and imaginary part), the transfer-matrix equation \(U_{ai}\) for \(i\)th active segment is capable of evaluating in following form:  
Figure 6: Discretization for active segment and element analysis for a segment. (**a**) Discretization for active segment. (**b**) Element analysis for a segment. \[U_{ai}=\left[
## 2 Materials and Experimental Work
### Warm Forming: Tool Design and Technical Issues
#### 2.2.3 Lubrication  
As is well known, deformation by stretch forming can only be achieved or maximized by using suitable lubrication conditions that provide the least-possible friction between the punch and the specimen. Thus, different lubrication conditions are experimented with for the cold and warm stretch forming tests. After the first trials, a lubrication setup composed of PP/PE copolymer film and grease could be recommended for forming at RT. However, at elevated temperatures, a Teflon-film was selected as a lubricant.
# Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network
## 5 Conclusions  
Image compression leads to undesired compression artifacts due to the lossy coding that occurs through quantization. These artifacts generally degrade the performance of image restoration techniques, such as super-resolution and object detection. In this study, we propose a DCRN, which consists of the input layer, a densely cascading feature extractor, a channel attention block, and the output layer. The DCRN aims to recover compression artifacts. To optimize the proposed network architecture, we extracted 800 training images from the DIV2K dataset and investigated the trade-off between the network complexity and quality enhancement achieved. Experimental results showed that the proposed DCRN can lead to the best SSIM for compressed JPEG images compared to that of other existing methods, except for IDCN. In terms of network complexity, the proposed DCRN reduced the number of parameters by as low as 72%, 5% and 2% compared to DnCNN, IDCN and RDN, respectively. In addition, the total memory size was as low as 91%, 41%, 17% and 5% of that required for DnCNN, DCSC, IDCN and RDN, respectively. Even though the proposed method was slower than ARCNN, it's PSNR, SSIM, and PSNR-B are clearly better than those of ARCNN.  
Conceptualization, Y.L., B.-G.K. and D.J.; methodology, Y.L., B.-G.K. and D.J.; software, Y.L.; validation, S.-h.P., E.R., B.-G.K. and D.J.; formal analysis, Y.L., B.-G.K. and D.J.; investigation, Y.L., B.-G.K. and D.J.; resources, B.-G.K. and D.J.; data curation, Y.L., S.-h.P. and E.R.; writing--original draft preparation, Y.L.; writing--review and editing, B.-G.K. and D.J.; visualization, Y.L.; supervision, B.-G.K. and D.J.; project administration, B.-G.K. and D.J.; funding acquisition, E.R. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Science and ICT (Grant 21 PQWO-B153349-03).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: Visual comparisons on a JPEG compressed images where the figures of the second row represent the zoom-in for the area represented by the red box.
## 2 Materials and Methods
### Experimental Procedure  
Electrodes were arranged on the surface of the coal samples in a regular array. Considering the influence of the conductive properties and the contact resistance of the coal sample, a copper electrode (8 mm long and 4 mm wide) was selected as the potential electrode. A conductive adhesive was used for the coupling. The surface electrode observation system is illustrated in Figure 3a. Four electrode lines were arranged on each coal sample with eight electrodes in three electrode lines and seven electrodes in one electrode line. The distance between the adjacent electrodes was 10 mm. The placement of the inner electrode was determined in consideration of the feasibility of the experiment and the reliability of the collected data. An electric drill was used to drill a hole (diameter of 3.8 mm and depth of 4 cm) in the middle of each coal sample (Figure 3b). The inner electrode and electrode line were buried in the hole and the hole was sealed with pulverized coal and conductive glue (the glue thickness was approximately 10 mm).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Name** & **Diameter (mm)** & **Height (mm)** & **Quality (g)** & **Loading Rate (mm/s)** \\ \hline A1 & 49.79 & 99.31 & 246.6 & 0.001 \\ A2 & 49.79 & 99.69 & 244.5 & 0.001 \\ A3 & 49.80 & 99.56 & 238.3 & 0.002 \\ A4 & 49.80 & 99.62 & 250.7 & 0.002 \\ A5 & 49.79 & 99.49 & 247.8 & 0.005 \\ A6 & 49.74 & 99.67 & 244.1 & 0.002 \\ B1 & 49.84 & 99.45 & 275.3 & 0.001 \\ B2 & 49.43 & 99.81 & 242.0 & 0.005 \\ B3 & 49.35 & 99.78 & 239.5 & 0.005 \\ B4 & 49.58 & 99.70 & 250.9 & 0.005 \\ B5 & 49.83 & 99.97 & 254.3 & 0.002 \\ B6 & 49.46 & 99.96 & 241.5 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information on the bituminous coal samples.  
Figure 2: Photographs of the rock samples used in the study. Bituminous coal samples A1 to A6 were obtained along the bedding direction and bituminous coal samples B1 to B6 were obtained perpendicular to bedding.
# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 3 Experimental Process and Result Analysis  
### Study Area and Data Collection  
This research focuses on the Yangtze River Delta Region, which is located in the lower-middle reaches of the Yangtze River near the East China Sea as shown in Figure 3. The  
Figure 2: The detailed structure of LSTM.  
Yangtze River Delta Region covers an area of 3.58 \(\times\) 105 km\({}^{2}\) including Shanghai, Jiangsu, Zhejiang and Anhui, where the total population reaches up to 227 million. The Yangtze River Delta is one of the regions in China with the most active economic development, so it has created nearly a quarter of China's total economy with less than four percent of the country's land area. Due to the coastal location, the Yangtze River Delta has been suffering from multiple disasters, especially typhoons and the resulting storm surge. Hence it is necessary to provide a fast prediction for the water level, which usually exceeds the safety threshold during the typhoon period. In this research, the LSTM model was trained for typhoon water level prediction by the observation data which is derived from the oceanic and meteorological observation station at Lusi Port of Jiangsu province near the north branch of the Yangtze River. The data were collected during the 12 typhoon-induced storm surges occurred from 1986 to 2016 and their tracks are shown in Figure 3. In particular, the oceanic and meteorological sample data of Typhoon No. 1410 in 2014 are shown in Table 1. ML algorithms require large amounts of data as training sets for higher reliability. Air pressure, wind speed, wind direction and water level are set as the main training input factors. Typhoon central pressure, central wind speed, moving speed, and moving direction are set as related auxiliary reference factors.
## 5 Measurement of Shape Deviation  
The next stage of the experimental research involves information on the dimensional deviation and shape of the holes for the three manufactured test samples that were examined and that were not post-processed. Figure 8 shows the rating of all samples. The results are distinguished by colored arrows.  
Figure 8: Three test samples with different holes: rhombus shape (**A**), square shape (**B**), round shape (**C**).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Chemical Element** & & **Al** & **Si** & **Mg** & **Fe** \\ \hline Material datasheet [\%] & & Balance & 9–10 & 0.25–0.45 & \textless{}0.25 \\ \hline \multirow{4}{*}{Chemical analysis [\%]} & Measurement 1 & 90.00 & 9.60 & 0.70 & 0.23 \\  & Measurement 2 & 89.60 & 9.90 & 1.00 & 0.40 \\ \cline{1-1}  & Measurement 3 & 89.30 & 10.00 & 0.93 & 0.43 \\ \cline{1-1}  & Arithmetic average & 89.63 & 9.83 & 0.88 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The chemical composition of samples with AlSi10 Mg material.  
Figure 7: Structure of the surface structure of AlSi10 Mg (**a**) and view of the coarse-grained surface structure with selected grains L1, L2, and L3 (**b**).  
Experimental measurement of the deviation of the shape and position of all holes was carried out using a FARO Quantum V2 FaroArm 3D scanner (Faro, Lake Mary, FL, USA). The three investigated printed-out test samples were not post-processed. The samples with rhombus-shaped holes (Figure 8A) had a shape deviation of only 0.4 mm. Similarly, the sample with circular holes (Figure 8C) achieved encouraging results, representing deviation values up to 0.4 mm. A more pronounced shape deviation was in the case of the second sample with a square-shaped bore (Figure 8B), wherein the square-shaped hole of 7 to 10 mm achieved the largest discrepancies in shapes from 0.81 to 1.2 mm. In the following lines of the manuscript, more detailed results of the shape deviations for the individual test samples are provided. Faro Quantum is a portable coordinate measuring device. The device allows easy verification of the product and its quality during inspection using 3D measurements. This device uses blue laser technology, which has a shorter wavelength than a red laser, providing better scanning results with higher resolution. The blue laser offers 50% less scanning noise than the red laser. Fine details of objects can also be recorded. Technical data on the FARO laser settings used during the measurements are displayed in Table 4.