esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Sensory Evaluation  
Sensory evaluation of spinach leaves with and without impregnation of GABA, SA and sucrose was conducted in order to evaluate the chilling injury of the spinach and to acquire the consumers' preferences. The scores given by the 60 panelists were categorized by freshness, color, texture, odor and overall appearance. For freshness, there is no significant decrease in scores of GABA impregnated leaves stored from day 0 to day 7 at 4 \({}^{\circ}\)C. This indicates that spinach leaves impregnated with GABA can maintain their freshness even until 7 days of storage. On the other hand, non-impregnated leaves were given lower scores as compared to sucrose and SA impregnated leaves signifying that sucrose and SA impregnated leaves could improve the freshness of spinach leaves during storage in chilling temperature. For color and texture, spinach leaves impregnated with all three compounds showed higher scores as compared to non-impregnated leaves. These results are in line with both chlorophyll (Figure 1b) and proline contents (Figure 1a) as the green color of spinach leaves reflects the chlorophyll content and lower chlorophyll degradation with the increase of proline content. Thus, the green color is maintained. Meanwhile, for the odor, there were no significant differences in the scores for non-impregnated and impregnated spinach leaves throughout the storage time, as the original form of the impregnated substances did not exhibit any trace of odor upon treatment of the spinach leaves. From this result, it can be proven that leaves impregnated with these three compounds did not give any unpleasant smell of chemicals and was found not significantly different with the non-impregnated leaves. The score for the overall appearance revealed that at the end of storage, GABA impregnated leaves (68.7%) scored the highest followed by sucrose impregnated leaves (61%), SA impregnated leaves (56.7%) and non-impregnated leaves (51%). Through the organoleptic evaluation result, it can be concluded that all compounds used are able to reduce the chilling injury in spinach leaves with GABA being the most effective followed by sucrose and SA.
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
###### Abstract  
Constraints of the underwater environment pose certain challenges to the design of routing protocols for underwater sensor networks. One such constraint is free mobility of sensor nodes with water currents. Free mobility and asymmetric acoustic propagation characteristics may lead to network partitioning which results in one or more nodes being unable to connect to the rest of the network and thus unable to report their sensed data. In this work, we propose a two-stage routing protocol to enable not only the connected nodes but also the partitioned nodes to successfully report their data thus improving the overall packet delivery ratio. We also introduce a minimum energy threshold and a rerouting scheme to delay death of busier nodes, thereby ensuring that nodes stay alive longer for their sensing job, and to avoid connectivity holes, respectively. Moreover, we also resolve forwarding loops to avoid the unnecessary waste of resources. Our results show that the proposed scheme successfully resolves network partitions and achieves a higher packet delivery ratio while avoiding early death of sensor nodes.  
Keywords:underwater acoustic sensor networks; routing; partition handling; connectivity hole management; mobility +
Footnote †: journal:
# Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach
## 1 Introduction
Furthermore, since it is a modeling process, finding a solution that meets the desired characteristics involves calculating solutions iteratively, which can take several days or weeks to obtain a final result. In this work, we propose a robust numerical solution for 3D joint inversion for gravimetric and magnetic data using Gramian-based structural similarity and greater structural direction constraints, using parallelization as a high-performance computing technique, which allows us to significantly increase the processing speed and improve the efficiency of interpretation.
## 2 Materials and Methods
### Cardio Data Protection Procedure
#### 2.2.1 Discrete Wavelet Transform  
Discrete Wavelet Transform, based on the concept of Multi-Resolution Analysis, can be applied to each signal examined. The studied input (ECG, PPG, Holter) signal can be represented by a wavelet function \(\varphi_{i,\ j}\) and scaling function \(\psi_{i,n}\)[25]:  
\[\varphi_{i,j}(t)=2^{-i/2}\varphi_{0}\Big{(}2^{-i}t-j\Big{)},\ j\in Z; \tag{1}\]  
\[\psi_{i,j}(t)=2^{-i/2}\psi_{0}\Big{(}2^{-i}t-j\Big{)},\ j\in Z. \tag{2}\]  
In the implementation of the wavelet transform, the digital signal \(x(t)\in\ L^{2}(R)\) is represented as the sum of orthogonal scaling functions and wavelets collection of details and a low-resolution approximation [26]:  
\[x(t)=approx_{N}(t)+\sum_{i=1}^{N}detail_{i}(t)=\] \[\sum_{j}a_{x}(N,j)\varphi_{N,j}(t)+\sum_{i=1}^{N}\sum_{j}d_{x}(N,j )\varphi_{N,j}(t). \tag{3}\]  
In this paper, Daubechies wavelet transform is implemented. Wavelet transforms are suitable for studying the properties of cardio signals (which are non-stationary and dynamic in nature) because in their application they provide information about the signal in both the frequency and time domains. The classical Fourier transform provides good information about the frequency distribution of the signal but does not provide information about the exact moment at which a frequency component appears. The Daubechies discrete WT used in this study has a compactly maintained orthonormal basis. A Daubechies transform with four wavelet coefficients (Db4) and four levels of decomposition is chosen.
## 3 Results and Discussion
### Analysis of Bonding Properties  
The results of the evaluation of the bonding properties of the test specimens are presented in Table 7 in accordance with the theoretical calculation formula of the bonding properties evaluation index proposed in Section 2.3 and the results of the PE\(-\)ECC uniaxial tensile test.  
Table 7 shows that the bonding strength of NSPE\(-\)ECC was better than that of DSPE\(-\)ECC at 3 and 28 days, and this difference was most apparent at 3 days. At this time, the bond strength of NSPE\(-\)ECC was much higher than that of DSPE\(-\)ECC, and the data on DSPE\(-\)ECC's bond strength showed that the adhesive substances made by DSPE\(-\)ECC were not very stable. Moreover, the bond strength of DSPE\(-\)ECC was 36.11% lower than that of NSPE\(-\)ECC at 3 days. However, the bond strength of DSPE\(-\)ECC at 28 days increased by 56.51% compared with that at 3 days, which was slightly less than that of NSPE\(-\)ECC. The bond strengths of NSPE\(-\)ECC and DSPE\(-\)ECC were the same at 7 and 14 days. This finding suggests that the degree of cement hydration of NSPE\(-\)ECC and DSPE\(-\)ECC is similar at these times. In addition, the rate of bond strength growth slowed down after 7 days, which indicates that desert sand has a large impact on how cement starts to harden.  
The average number of cracks in NSPE\(-\)ECC at 3 and 28 days was higher than that in DSPE\(-\)ECC at the same age, and the number of cracks in NSPE\(-\)ECC and DSPE\(-\)ECC was similar at 7 and 14 days, which suggests the two PE\(-\)ECCs did not differ appreciably at this time. However, the data was discrete, which meant that the cement matrix hydration products were unstable. Moreover, the fiber/matrix bonding properties at this time did not show many differences. A positive correlation existed between the average number of cracks and PE\(-\)ECC displacement. The calculated results in Table 7 match the displacement of NSPE\(-\)ECC in the uniaxial tensile test results, which was greater than that of DSPE\(-\)ECC. The number of cracks in DSPE\(-\)ECC at 28 days was much higher than at 3 days. This result shows that as the curing ages increase, the number of cracks in DSPE\(-\)ECC also increases. The 28 th day bonding properties of DSPE\(-\)ECC were only slightly lower than those of NSPE\(-\)ECC in terms of bond strength, and the bonding properties became steadier over time.
## 2 Sensor Structure and Mathematical Model
### Mathematical Model of the Sensor  
The excitation coil is driven by a constant current to produce a high-gradient magnetic field in a local region of the oil path that is close to the air gap. As shown in Figure 2, when the wear debris flows through this region, the induced voltage will be detected. In previous work [25], the mathematical model of the sensor was established. Supposing the debris is a cube with \(\delta_{x}\times\delta_{y}\times\delta_{z}\). The induced voltage and the magnetic flux variation can be described as:  
\[u=N\lim_{\Delta t\to 0}\frac{\Delta\phi}{\Delta t} \tag{1}\]  
\[\Delta\phi=F\delta_{x}\delta_{y}\delta_{z}\mu\bigg{(}1-\frac{1}{\mu_{d}} \bigg{)}\frac{\sin\alpha}{l2(x)} \tag{2}\]  
where \(u\) is the output voltage of the inductive coil, \(N\) presents the turn number of the induction coil, \(\Delta\phi\) is the magnetic flux variation caused by the wear debris, and \(\Delta t\) is the time lag, \(\mu\) and \(\mu_{d}\) represent the vacuum permeability and the relative permeability of the debris, \(l(x)\) is the length of the debris path, and \(\alpha\) is the angle between the direction of debris movement and magnetic field direction. The magnetic potential can be calculated as \(F=N_{D}l\), where \(l\) is the exciting current and \(N_{D}\) is the turn number of the excitation coil. Combining Equations (1) and (2), the sensor output voltage is:  
\[u=-2 NN_{D}l\delta_{x}\delta_{y}\delta_{z}\frac{\mu(\mu_{d}-1)}{\mu_{d}}v\cdot \frac{d}{dx}\bigg{(}\frac{\sin\alpha}{l2(x)}\bigg{)} \tag{3}\]  
where \(v\) is the debris speed along the x-axis. It can be seen from Equation (3) that the output voltage is proportional to the velocity of the wear debris and the size of the current.  
Figure 1: The structure diagram of the induction sensor: (**a**) cutaway view; and (**b**) 3-D model.
## 5 ML Model with Time-Domain Input
[46] used joint-probability discrepancy transfer learning with features from 0-40 Hz wavelet packets, achieving 86.31% accuracy in cross-subject classification. [PERSON] et al. [47] applied neural mass models to analyze time-domain EEG dynamics and predict seizures up to 1 h in advance. [PERSON] et al. [48] used Particle Swarm Optimization (PSO) to enhance both model performance and feature selection strategies, achieving 99.3% accuracy. Recent innovations have also integrated spatial and temporal dependencies using graph-based learning. [PERSON] et al. [49] and [PERSON] et al. [50] implemented Graph Convolutional Networks (GCNs) and Spatial-Temporal Graph Attention Networks (STGATs), reaching sensitivity levels of 98.5%. [PERSON] et al. [51] and [PERSON] et al. [52] leveraged directed graph models to emphasize inter-channel temporal connectivity, reporting 98.15% classification accuracy. Finally, time-domain modeling beyond scalp EEG has been explored. [PERSON] et al. [53] decoded motor activity from electrocorticography (ECoG) using Partial Least Squares (PLS), achieving >99% sensitivity. [PERSON] et al. [54] identified high-frequency oscillations (HFOs) using Kruskal-Wallis tests, and [PERSON] et al. [55] utilized DL models to detect intericital epileptiform discharges (IEDs), achieving 95.1% accuracy. Table 1 provides a summary of the key approaches described above. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref** & **Temporal Feature Representation** & **Model Type** & **Dataset** & **Performance** \\ \hline [PERSON] et al. [26] & Approximate Entropy (ApEn) & Elman NN, Probabilistic NN & EEG & 100\% overall accuracy \\ \hline [PERSON] et al. [27] & Energy, SEN, IQR, MAD & Statistical Classifiers & CHB-MIT & 84.2\% overall accuracy \\ \hline [PERSON] et al. [28] & Energy, Fractal Dim., Sample Entropy & & & 95.67\% average \\  & (via ITD) & Decision Tree & EEG & classification accuracy \\ \hline [PERSON] et al. [29] & Weighted Permutation Entropy (WPE) & Not specified & EEG & Not specified \\ \hline [PERSON] et al. [30] & Min, Max, Mean, Variance, Skewness, & Supervised Model & EEG & Not specified \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of performance of ML models with time-domain input.
## 3 The Virtual-Force-Based Guidance Law
### Virtual Repulsive Force for Obstacle Avoidance  
This method is based on the concept of virtual forces. A virtual repulsive force can be designed to avoid the obstacle detected in the reference path. Supposing that the UAV is equipped with LIDAR. The LIDAR can detect a series of points (\(\textbf{P}_{o}\)) on the obstacle's surface within a certain range as shown in Figure 4.  
Figure 3: Diagram of the virtual centripetal force for curve tracking.  
\(\mathbf{P}_{o}\) is a subset of \(\{(d_{o},\theta_{o})|d_{o}\in(0,R_{d}],\theta_{o}\in[\theta_{r},\theta_{l}]\}\), where \(d_{o}\) is the distance between the point and the UAV. \(\theta_{o}\) is the angle of sight with counterclockwise as its positive direction. \(R_{d}\) is the range of the LIDAR. \([\theta_{r},\theta_{l}]\) is the angle range of the LIDAR.  
The leftmost point \(p_{1}=(d_{o1},\theta_{o1})\) and the rightmost point \(p_{2}=(d_{o2},\theta_{o2})\) can be found in \(\mathbf{P}_{o}\). Then, the virtual repulsive force \(F_{\varpi r}\) is designed perpendicular to \(\varpi\), the positive direction of \(F_{\varpi r}\) is to the left of \(v\). \(F_{\varpi r}\) is given by  
\[F_{\varpi r}=k_{rep}l_{o} \tag{12}\]  
\[l_{o}=\left\{\begin{array}{ll}-(d_{s}+d_{o1}\theta_{o1})&\theta_{o2}+\theta _{o1}<0,\;d_{s}+d_{o1}\theta_{o1}>0\\ (d_{s}-d_{o2}\theta_{o2})&\theta_{o2}+\theta_{o1}\geq 0,\;d_{s}-d_{o2}\theta_{o2}>0 \\ 0&else\end{array}\right. \tag{13}\]  
where \(k_{rep}\) is the coefficient of the virtual repulsive force. \(d_{s}\) is a parameter related to the safe clearance between the UAV and the obstacle.  
The virtual repulsive force is designed to repel the vehicle to its lateral; this means that the virtual repulsive force only affects the course of the UAV and does not change the speed of the UAV; when \(|\theta_{o1}|>|\theta_{o2}|\), \(F_{\varpi r}\) repels the UAV to turn to its right; when \(|\theta_{o1}|\leq|\theta_{o2}|\), \(F_{\varpi r}\) repels the UAV to turn to its left. The UAV is always repelled to turn to the side with a smaller angle of view to move around of the obstacle.
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
## 4 DSP Techniques on Compensation of MDL Effects in FMF Links
### Space-Time Coding Technique (STC)
Moreover, the DSP techniques at the receiver for MDM systems were also widely studied to better suite with STC schemes. In [71] and [72], a STBC-aided RLS algorithm was developed for MDL mitigation in MDM systems to achieve quasi-RLS convergence speed as well as quasi-LMS complexity, with which assistance a 7.8 dB OSNR tolerance was obtained for polarization-division multiplexed (PDM) 64 QAM system. In [73], a receiver-side linear decoding schemes was proposed for the orthogonal STBC-aided MDM system to achieve near-ML performance with reducing decoding complexity, which grows linearly with the order of constellations. In [74], a novel hybrid detection with combination of the IRS method and the decision feedback equalizer (DFE), was realized for the threaded algebraic space-time codes (TAST) assisted system, where the proposed detector achieved near-ML performance with a lower computational complexity. We have concluded the above three DSP solutions for suppressing or mitigating MDL effect in Table 2 to help readers understand their advantages and disadvantages. As we can, see ML detector and IC techniques are receiver-side equalizers while STC is a transmitter-side coder technique which means it is easy to cooperate with other receiver-side solutions.
## 3 Results and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Static Load** [**kPa**]} & \multirow{2}{*}{**Zone Interface**} & \multicolumn{5}{c}{**Temperature [\({}^{\circ}\)C]**} \\ \cline{3-6}  & & \(-\)**20** & **0** & **+20** & **+40** \\ \hline \multirow{4}{*}{4.651} & \multirow{4}{*}{Direct coupling/amplification} & Frequency [Hz] & 21.9055 & 17.7626 & 12.8658 & 12.1458 \\  & & Transmissibility & 1.1073 & 1.1010 & 1.1020 & 1.1013 \\  & & Damping ratio & 1.0517 & 1.0855 & 1.0797 & 1.0837 \\ \hline \multirow{4}{*}{4.651} & \multirow{4}{*}{Peak transmissibility} & Frequency [Hz] & 68.8338 & 53.0573 & 37.9029 & 35.8641 \\  & & Transmissibility & 8.0947 & 6.7340 & 6.1872 & 6.3288 \\  & & Damping ratio & 0.0618 & 0.0743 & 0.0808 & 0.0790 \\ \hline \multirow{4}{*}{6.976} & \multirow{4}{*}{Amplification/attenuation} & Frequency [Hz] & 106.389 & 81.4401 & 56.5925 & 55.9444 \\  & & Transmissibility & 0.9883 & 0.9977 & 0.9930 & 0.9937 \\ \hline \multirow{4}{*}{6.976} & \multirow{4}{*}{Peak transmissibility} & Frequency [Hz] & 17.2386 & 14.2383 & 11.9515 & 11.0511 \\  & & Transmissibility & 1.1006 & 1.1009 & 1.1050 & 1.1022 \\  & & Damping ratio & 1.0875 & 1.0860 & 1.0635 & 1.0789 \\ \hline \multirow{4}{*}{6.976} & \multirow{4}{*}{Peak transmissibility} & Frequency [Hz] & 54.6703 & 41.5616 & 33.3922 & 31.8152 \\  & & Transmissibility & 7.7215 & 7.4278 & 7.7390 & 8.2612 \\  & & Damping ratio & 0.0648 & 0.0673 & 0.0646 & 0.0605 \\ \hline \multirow{4}{*}{6.976} & \multirow{4}{*}{Amplification/attenuation} & Frequency [Hz] & 82.3836 & 61.7701 & 51.4919 & 49.1732 \\  & & Transmissibility & 0.9997 & 0.9937 & 0.9927 & 0.9938 \\ \cline{1-1}  & & Damping ratio & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 1 Introduction  
Object detection for remote sensing images is an important research field. With the development of remote sensing technology, information carried by remote sensing images is more abundant than before. The applications of object detection in remote sensing images are more and more popular, such as city planning and environmental exploration.  
However, object detection for remote sensing images is a more difficult job since remote sensing images are quite different from regular images. Objects in regular images have some properties: many objects rarely appear in one image; the main objects are regularly located in the image center, occupying the main parts and significantly different from the background.  
However, one high-resolution optical remote sensing image contains more objects with more shapes and texture information than a regular image, and the objects may be scattered in the whole image. Besides, the object to be detected is relatively small and close to the background. If we zoom out from a remote sensing image to a small size for a global view, we would lose many details, and the objects may almost be invisible. Therefore, object detection for remote sensing images is harder work than for regular images to some extent.  
At present, object detection methods in remote sensing images are mainly based on traditional image processing technology with machine learning, which requires rich experience and complete prior knowledge. Furthermore, most of them are only effective in a specific environment, so they have poor scalability. With the advent of deep learning technology, we introduce deep learning into thefield of object detection for remote sensing images. Nevertheless, deep learning is still in its infancy for remote sensing images. One of its biggest problems is the decency on labeled datasets.  
However, with the fast improvement of deep learning-based object detection on regular images, many labeled regular image datasets have appeared in recent years. Therefore, in this paper, we present a novel transfer deep learning approach to detect objects in high-resolution remote sensing images. It employs transfer learning to supply the gap that deep learning on remote sensing images lacks labeled training datasets. Besides, we improved the candidate window selection process and designed a deep feature extraction method with context scene feature fusion and detection. Finally, the proposed approach is validated on different scales of high-resolution optical remote sensing images.  
This paper is organized as follows: Section 2 introduces the related works. Section 3 describes the framework of our algorithm. Our size-scalable region proposal algorithm is given in Section 4. Our deep feature extraction method with context scene feature fusion and detection is proposed in Section 5. Section 6 concludes the paper.
# HP-LSTM: Hawkes Process-LSTM-Based Detection of DDoS Attack for In-Vehicle Network
## 6 Discussion  
### Application  
Through extensive experimentation comparing four variations of the HP-LSTM structure with the LSTM, we identified the optimal HP-LSTM structure. Our experimental evaluations demonstrate the superiority of the proposed HP-LSTM model in detecting DDoS attacks based on Ethernet SOME/IP, compared to other detection models. The HP-LSTM model exhibited greater precision in capturing attacker behavior characteristics and more accurately detecting DDoS attacks. Additionally, we further validated the feasibility of the Hawkes process in detecting attacks in vehicular networks, filling a research gap in DDoS attacks targeting the SOME/IP.  
We can integrate HP-LSTM as middleware into automotive communication networks to achieve the real-time monitoring of SOME/IP network activities. This middleware can operate in both local and cloud-connected modes. In a local environment, the middleware can be embedded directly into the vehicle system, ensuring the real-time monitoring and attack detection of the in-vehicle network while safeguarding the vehicle system's security. In contrast, the cloud-connected mode allows the middleware to connect to cloud services, enabling the remote monitoring and management of vehicle network data. Local deployment maximizes real-time functionality, while cloud connectivity allows the middleware to receive updated model parameters and security policies from the cloud to address evolving security threats. Although detection time may be affected by factors such as model learning and cloud transmission speed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Params** & **Params Size (MB)** & **Input Size (MB)** & **Time Cost (s)** \\ \hline HP-LSTM & 2441351 & 9.75 & 0.01 & 0.000022 \\ \hline LSTM & 2415239 & 9.66 & 0.01 & 0.000019 \\ \hline HP-LSTM hted-chm & 2441863 & 9.76 & 0.01 & 0.000023 \\ \hline HP-LSTM ht-chm & 2441351 & 9.75 & 0.01 & 0.000020 \\ \hline HP-LSTM hte-chp & 2441351 & 9.75 & 0.01 & 0.000021 \\ \hline \hline \end{tabular}
\end{table}
Table 8: HP-LSTM’s time cost and params.  
Figure 9: ROC curves and AUC values for LSTM and four variants of HP-LSTM.  
The primary focus of this study was on addressing DDoS attacks on the novel inv-vehicle Ethernet protocol, SOME/IP. Considering the complex vehicular communication environment and diverse attack methods, our future work will explore and experiment with various common and severe network attacks, including man-in-the-middle attacks, and assess the feasibility of applying the model to traditional in-vehicle communication protocols such as CAN and LIN.