esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Challenges
### Security Issues of DL Applications in IoMT
Replay attack: Within this attack, the attacker uses redirection to steal or intercept transmitted information, which can have severe consequences for systems like medical networks. The attacker records the intercepted packets and then \"plays back\" to the recipient at a later time. This attack can result in two primary outcomes: theft of sensitive information and unauthorized access to specific medical systems. The core strategy involves deceiving the recipient by storing data stored by a malicious node without authorization and retransmitting them as if they originated from the legitimate sender. The malicious sensor can record network traffic to impersonate the original sender and connect with the recipient. This attack is particularly effective in bypassing authentication mechanisms, such as certificates. Even in cases where communications are encrypted, the attacker can gain network access by resending legitimate connection messages without needing to know the actual keys or passwords [33]. Similar to the mechanisms employed for mitigating hole attacks, DL can enhance security against replay attacks by leveraging rapid anomaly detection, pattern recognition, and adaptive security strategies to identify and prevent unauthorized retransmission of data packets. Sybil attack: A Sybil attack occurs when a node unlawfully assumes multiple identities, disrupting the network's routing protocol. These attacks compromise data security, resource utilization, and system integrity. Using either a newly created or a hijacked legitimate identity, the Sybil node can communicate with nearby nodes, leading to a disorganized network that may eventually collapse [219]. Additionally, in typical network scenarios, sensors can fail due to power depletion. Exploiting this, an attacker can replace the sensor with a malicious one to perform harmful activities. This can include modifying patient data or introducing false information into the system [220]. DL can significantly enhance security by recognizing and preventing unauthorized access through behavior analysis, pattern recognition, and adaptive security mechanisms. Table 11 summarizes various types of attacks on DL-based IoMT networks; their descriptions and corresponding solution methods are given.
## 4 Separation Mechanisms of Networks
### Methods and Stimuli  
According to cues to be explored, a series of artificial stimuli are designed as the network input. The network performances (SI-SDRi) are regarded as indicators to analyze the underlying separation mechanisms. There are two main reasons for choosing these simple artificial tones for testing rather than natural sound sources. On the one hand, these stimuli are more controllable, providing the possibility to explore individual cues learned by networks. On the other hand, networks have never been trained by these stimuli. Their separation only relies on general principles learned from unrelated natural sources rather than pattern matching specifically trained sources. All experiments are conducted on these networks trained in Section 3 and without any other fine-tuning and operations.  
As shown in Figure 4, two kinds of stimuli are used in simultaneous and sequential organization experiments, respectively. The stimuli for simultaneous organization are two pure tones with different frequency intervals (\(\Delta\)Fs), where tone A varies from 0.1 kHz to 2 kHz in steps of 10 Hz, and \(\Delta\)F varies from 0 to 2 kHz in steps of 10 Hz. The duration is set to 3 s, including 200 ms raised cosine onset and offset ramps to reduce transient effects. Each frequency component has equal amplitude.  
Figure 3: SI-SDR improvement (dB) of different types for mixtures in the test dataset for (**a**) ConvTasNet, (**b**) DPRNN, (**c**) DPTNet.  
The stimuli used in sequential organization experiments are two alternating tones, A and B, with different frequency intervals (\(\Delta\)Fs) and tone repetition times (TRTs, the onset to onset of two adjacent pure tones), as shown in Figure 4b. In order to compare with the temporal coherence boundary, the parameters in [PERSON]'s psychoacoustic experiments were replicated. The duration of pure tones A and B is 40 ms, including 5 ms raised cosine onset and offset ramp to reduce transient effects. Pure tone B is fixed at 1 kHz, and \(\Delta\)F varies from 0 to 15 semitones. The semitone scale is one of the most commonly used scales in psychoacoustic experiments. A semitone is one-twelfth of an octave, and an octave is an interval between one tone and another with double frequency. If \(f_{B}\) is one semitone higher than \(f_{A}\), \(f_{B}=f_{A}\times 2^{1/12}\). TRT varies from 50 to 200 ms. Each sequence contains a total of 10 A-B tones, and each tone has equal amplitude.
# ResADM: A Transfer-Learning-Based Attack Detection Method for Cyber-Physical Systems
## 4 Experiment and Analysis
### Dataset Selection  
The completeness of the training data directly affects the effectiveness of deep-learning models and their performance in real-world applications. Public datasets in the field of intrusion detection, such as KDD CUP 99 and NSLKDD, have had some limitations. These included outdated data; lack of integrity and diversity in attacks; and data sanitization issues. As a result, most attacks in these datasets have lacked meaningful payload information and thus have not effectively reflected attack trends. After conducting multiple experimental attempts, we found in this study that the UNSW-NB15 and CICIDS2017 datasets contained a significant amount of valid attack payloads. These datasets could accurately reflect the trends of network attacks over a period of time, meeting the requirements of this study. The UNSW-NB15 dataset consisted of 257,673 flow records, with 175,341 records used for the training set and 82,332 records used for the testing set. Each flow record contained 49 features, including 5 flow features, 13 basic features, 8 content features, 9 time features, 12 additional features, and 2 label tags. The CICIDS2017 dataset consisted of 8 CSV files, comprising a total of 2,273,097 network flow samples. Each flow sample included 79 features and 1 label.
## Appendix B
\begin{array}{ ll}(1-\varepsilon+\frac{\varepsilon}{|\mathcal{A}(s)|})\sum\limits_{a\in \mathcal{A}}\Delta_{a},\ if\ a=\underset{a}{\mathrm{argmax}}Q(s,a)\\ \frac{\varepsilon}{|\mathcal{A}(s)|}\sum\limits_{a\in\mathcal{A}}\Delta_{a}, \ if\ a\
eq\underset{a}{\mathrm{argmax}}Q(s,a)\end{array} \right. \tag{13}\]\[L_{t} \approx\int_{t}l_{t}\mathrm{d}t=\int\limits_{t}\frac{\varepsilon}{| \mathcal{A}|}\sum\limits_{a\in\mathcal{A}}\Delta_{a}\mathrm{d}t\] (A11) \[=\left\{ \begin{array}{l}\int\limits_{t}\frac{\sum\limits_{a \in\mathcal{A}}\Delta_{a}}{|\mathcal{A}|}(0.5+\sqrt{0.25-(t/T)^{2}})\mathrm{d}t,\ (t/T)\in[0,0.5]\\ \int\limits_{t}\frac{\varepsilon\Delta_{a}}{|\mathcal{A}|}(0.5-\sqrt{0.25-(1-t /T)^{2}})\mathrm{d}t,\ (t/T)\in(0.5,1]\end{array} \right.\] \[\rightarrow\left\{ \begin{array}{l}\frac{\Delta_{a}}{|\mathcal{A }|}(\frac{t}{2}+\frac{1}{8}T\arcsin(\frac{2t}{T})+\frac{t}{4}\sqrt{1-4(\frac{t }{T})^{2}}),\ (t/T)\in[0,0.5]\\ \frac{\Delta_{a}}{|\mathcal{A}|}(\frac{t}{2}+\frac{1}{8}T\arcsin(2(1-\frac{t}{ T}))+\frac{T}{4}(1-\frac{t}{T})\sqrt{1-4(1-\frac{t}{T})^{2}}),\ (t/T)\in(0.5,1]\end{array} \right.\]  
Plot the trends of \(L_{t}\) and its derivative \(L_{t}{}^{\prime}\) as the number of iterations increases. In Figure A1, it can be seen that \(L_{t}{}^{\prime}\) is 0 in the later stage of iteration, and the value of \(L_{t}\) no longer increases, indicating that the improved \(\varepsilon\)-greedy tends to converge. In order to verify the universal applicability of the creep control based unmanned speed control method for mining electric locomotives proposed in this paper on different vehicles and road sections, we conduct simulation experiments on the autonomous operation of an 8-ton mining electric locomotive in this section. The hourly speed of the electric locomotive is 7.8 km/h (2.167 m/s). From the Figures 19 and 20, it can be seen that the intelligent agent can control the creep rate within \(\pm\)10%\(\sim\)\(\pm\)30% during acceleration/deceleration sections. This indicates that our speed control method for autonomous operation of electric locomotives is effective in controlling creep rate. In addition, to verify the effectiveness of this method in ensuring driving safety, we set the safety distance to 5 m when encountering obstacles or maintaining a safe distance from the preceding vehicle. From the Figure 21, it can be seen that the autonomous driving of the electric locomotive using this method is safe and effective, ensuring that the vehicle always maintains a distance of 5 m from obstacles or the preceding vehicle. Therefore, this method can be applied to different vehicles traveling on different track conditions. This method has good universal applicability in the control of creep rate and obstacle avoidance of mining electric locomotives.
## 1 Introduction
### FRT Capability and DRES Reaction During Grid Faults
#### 3.5.1 Proposed Definition and Its Justification
an FSS system allows the DRES to inject controllable currents during the faults independently of the magnitude of the primary source. Based on the aforementioned analysis it can be said that, an FSS system enables the DRES to follow any FRT requested by the DSO and inject controllable currents. This ability allows for the proper configuration of the DRES reaction during faults as function of the short-circuit capacity of the upstream grid while avoiding upgrades in the legacy protection system. Thus, for example, in cases where the short-circuit capacity of the upstream is large enough, the DRES within the distribution system may inject zero currents while remaining connected-this case is similar to the early days of DRES penetration but with enhanced FRT capability. In cases the upstream grid is weak, the DRES may be forced to inject controllable currents as function of their capacity and location [104], so that the operation of the legacy protection system is preserved.
# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 3 Compression Experiment of Shipborne Vision Sensor Video
### Compression Performance Analysis
When the QP value is set above 27, the slope of the R-D curve is larger, indicating that the video quality is significantly improved, and the code rate changes more slowly. When the QP value is less than 27, the slope of the R-D curve decreases, indicating that the video bit rate grows faster while the growth of the peak signal-to-noise ratio becomes slower. It can also be seen from Figure 8e that when the QP value is 37, the edge contour of the target in the visible image becomes relatively blurred compared with the original image, but it does not affect the discrimination of the target on the sea surface. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Video Type**} & \multirow{2}{*}{**Resolving Power**} & \multirow{2}{*}{**Navigation Environment**} & \multicolumn{3}{c}{**QP = 32**} \\ \cline{5-6}  & & & **Bitrate** & **PSNR** & **SSIM** \\ \hline \multirow{6}{*}{Visible light video} & \multirow{3}{*}{1920 \(\times\) 1080} & Anchoring & 780 kbps & 40.614 & 0.946 \\  & & Underway & 631 kbps & 41.323 & 0.953 \\  & & Port & 869 kbps & 39.965 & 0.940 \\  & & Anchoring & 319 kbps & 42.464 & 0.953 \\  & & Underway & 226 kbps & 43.141 & 0.961 \\  & & Port & 388 kbps & 42.573 & 0.958 \\  & & Anchoring & 27 kbps & 43.430 & 0.989 \\  & & Underway & 18 kbps & 43.501 & 0.989 \\  & & Port & 47 kbps & 42.793 & 0.984 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of compressed shipborne video under the same QP. Figure 7: R-D curve of compressed shipborne video under different QPs: (**a**) R-D curve of compressed visible video; (**b**) R-D curve of compressed IR video. The experimental results of compressed SVSV show that intra-frame prediction coding of HEVC largely reduces the redundant data in the null domain of the SVSV. However, in the process of intra-frame prediction, HEVC recursively divides the CU downward and traverses the computational rate distortion cost to get the best division according to the texture complexity of the QP and CTU, and the decision of CU division brings high computational complexity to the intra-frame prediction coding. Therefore, optimizing the computational complexity of the compression process for the characteristics of SVSV is one of the very critical steps to further upgrade the intelligent ship tele pilot system. In this section, based on the characteristics and usage scenarios of SVSV images, an algorithm acting in the intra-frame PC session for fast compression of SVSV by predicting the CU division structure in advance is proposed in combination with deep learning methods without affecting the compression quality.
## 3 Results
### Transcriptional Regulation of Phenylpropanol Pathway and Tannin Metabolism Regulation Genes
The seed maturity stage had a substantial influence on the expression of the phenylpropanol pathway genes, as the majority of these genes were found to be highly expressed in S1 but not in S2 or S3. In contrast, P levels in the treatments appeared to affect only specific genes within the pathway (Figure 10). Figure 8: Schematic illustration of the phenylpropanol pathway leading to tannin formation in the lentil seed coat. The pathway demonstrates all the intermediate interventions of the enzymes contributing to proanthocyanidins biosynthesis. Abbreviations: PAL, phenylalanine ammonia lyase; CHS, chalcone synthase; F3H, flavanone-3-hydroxylase; F3\({}^{\prime}\)5\({}^{\prime}\)H, flavonoidoid-3\({}^{\prime}\)5\({}^{\prime}\)-hydroxyase; FLS, flavonoid synthase; DFR, dihydroflavonol 4- reductase; ANR, anthocyanin reductase; and LAR, leucoanthocyanidin reductase. Figure 10: Heatmap illustrating the DEGs of the phenylpropanol pathway that were found in key positions in the pathway, and the alterations in their expression determine the proanthocyanins accumulation in the seed. Hierarchical clustering with Pearson correlation and normalized counts indicates the impact of seed maturity stage and P treatment in these genes of the pathway. Figure 9: Volcano plots demonstrate the upregulated and downregulated DEGs related to the phenylpropanol pathway. (**A**) Total DEGs related to the phenylpropanol pathway, (**B**) DEGs among the P treatments considering the P0 as the control condition and (**C**) DEGs among the pod maturity stages in each P treatment considering the S1 as the control stage. \(X\)-axis: \(-\)log10(q-value) and \(Y\)-axis: \(-\)log10(0.05).
## 4 Discussion  
### Performance of the ResU-Net Model for Individual Tree Crown Segmentation  
Overall, the ResU-Net model together with UAV datasets was able to recognize the crowns of Japanese oak in an uneven-aged mixed forest with reliable accuracy in terms of OA values (0.87 to 0.90) and F1 scores (0.71 to 0.81), as summarized in Tables 1 and 2. Particularly, the model could be trained successfully with a small training dataset. This is probably due to the backbone ResNet101, the weight of which was initialized on the ImageNet dataset, because the previous researchers have already discussed the applicability of pre-trained models to deal with insufficient training datasets in the case of applying the deep learning models [27; 41; 42; 43].  
The F1 scores of the ResU-Net model using the non-augmented and augmented October UAV datasets (0.80 and 0.76) as well as the non-augmented September UAV dataset (0.81) were higher than the F1 score (0.75) obtained in the previous study [44] for the  
Figure 16: Visualization of the predicted Japanese oak crown distribution in the Sub-compartment 68E by the ResU-Net model using UAV dataset; (**a**) non-augmented September UAV dataset; (**b**) augmented September UAV dataset; (**c**) non-augmented October UAV dataset; and (**d**) augmented October UAV dataset.  
canopy detection of a deciduous tree species (_Fagus sylivatia_) in structurally heterogeneous forests using aerial RGB imagery and Faster R-CNN. Moreover, our study provided higher performance in terms of OA when compared to [PERSON] et al. (2020) [45] (OA = 0.63 and 0.73), which classified five categories of the tree canopy including Japanese oak in uneven-aged mixed forests using object-based random forest classification algorithm and digital aerial gradiometry derived from UAV imagery.  
Our ResU-Net model also performed better than the model described in the past study [22] that investigated different ResU-Net model structures. Their overall classification accuracy was 87% for tree species classified using airborne high-resolution images. However, the performance of our model using the UAV datasets did not match that of a Mask R-CNN model employed, in combination with aerial images [42], to map the distribution of dominant tree species, including Mongolian oak, in an uneven-aged mixed forest. In that study, F1 scores ranged from 0.20 to 0.94 for six tree species and the F1 score for Mongolian oak was 0.91. The difference compared to our study may be due to the larger number of reference trees in the training dataset, as their total number of reference trees was 958, while ours was only 319. In future research, the performance of the model should be improved by using larger sample datasets.
## 3 Techniques for Characterizing Fe\({}_{3}\)O\({}_{4}\) NPs  
A combination of different methods is usually required to identify the composition and purity and evaluate the properties of Fe\({}_{3}\)O\({}_{4}\) NPs. The most important aspect is to distinguish Fe\({}_{3}\)O\({}_{4}\) from the maghemite phase and identify the compositional uniformity. Because the oxidation of Fe\({}^{2+}\) to Fe\({}^{3+}\) is a thermodynamically favored process, the coexistence of magnetite and maghemite phases is usually observed in Fe\({}_{3}\)O\({}_{4}\) nanoparticles [56; 91]. The presence of the maghemite phase in the magnetite nanoparticles may lead to a decrease in saturation magnetization. X-ray diffraction (XRD) is generally unable to distinguish between magnetite and maghemite phases due to similar patterns originating from the same cubic spinel structures [91]. However, two features that can be deduced from XRD are matching lattice parameters, and d-spacings have been used to further confirm the existence of the dominant magnetite phase [50; 54; 57]. For example, a slight difference in the standard lattice parameters of magnetite (8.396 A) and maghemite (8.346 A) has been identified [57]. X-ray photoelectron spectroscopy (XPS) is an effective method to prove the existence of the magnetite phase because of the coexistence of Fe\({}^{2+}\) and Fe\({}^{3+}\) cations [92]. Infrared spectroscopy [66; 93] and Raman spectroscopy [94; 95] are also widely used to assign and identify magnetite materials. For the quantitative analysis of the coexistence of different iron oxide phases in NPs, X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism spectroscopy (XMCD) have been used to quantitatively estimate the compositions of the mixture of magnetite and magnetite (\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\))\({}_{1-x}\)(Fe\({}_{3}\)O\({}_{4}\))\({}_{x}\), with the value of x ranging from 0.20 to 1.00 [56]. In addition to spectroscopic techniques, magnetic properties are also measured to characterize magnetic NPs. For example, magnetization versus temperature measurements reveal a kink at low temperature related to the Verwey transition of Fe\({}_{3}\)O\({}_{4}\), while this feature does not occur in \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\). The Verwey transition in Fe\({}_{3}\)O\({}_{4}\), which is usually observed for bulk magnetite at approximately 115-124 K, is also a good parameter to evaluate the level of crystallinity and match the stoichiometry of Fe\({}_{3}\)O\({}_{4}\) NPs [41; 96]. Hence, a combination of different characterization techniques is important to identify important factors that affect the magnetic properties of Fe\({}_{3}\)O\({}_{4}\) NPs, such as the existence of the material phases, the uniformity of compositions, and the crystalline level.
## 6 EEG Signal Pre-Processing and Feature Extraction
### EEG Signals Classification
#### 6.3.3 Challenges in EEG Signal Classification  
A major challenge in EEG classification is the high variability of EEG signals among individuals, compounded by the presence of artifacts and noise. This variability can markedly affect the accuracy of classification algorithms. Overcoming this challenge requires sophisticated preprocessing and feature extraction techniques to ensure the use of the most relevant and noise-free features for classification [110].
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 5 Experiments  
### Qualitative Evaluation  
#### 5.1.1 Analysis of Initialization  
Figure 8 in Section 3.3 shows that the mask generation is capable of reducing untraceable flow errors (see rectangle). In this section, we analyze the capability of reducing flow traceable errors (including ghosting artefacts), which mainly degrades the visual image quality. Figure 10 shows the effect of the proposed initialization on the reduction of flow traceable errors. Starting from three adjacent original video frames, the mask generation outputs a mask with much fewer traceable flow errors (white regions in the right side) than previous single scale mask. This directly helps the composed initial image contain more consistent textures (see rectangles) than \(x^{t}_{init}\) in Figure 3 in Section 3.1. The small discontinuous textures on the left are then fixed by the gradient-based optimization network with Sharpness Losses.  
**Ablation study on proposed mask techniques.** As mentioned in Section 3.3, we propose a multi-scale scheme, incremental mask and multi-frame mask fusion for initialization generation. To analyze these techniques fairly, we divided the proposed mask techniques into four different groups: naive method (without any proposed techniques), w/ multi-scale scheme, w/ multi-scale + incremental and w/ multi-scale + incremental + multi-frame. The outputs of the four groups are shown in Figure 11. As can be seen, the zoomed-in rectangles on naive method indicate that the general flow mask proposed by [24] causes the ghosting artefacts. Adding a multi-scale scheme into the naive method is  
Figure 10: The decrease in traceable flow errors (white regions in the right side) by using the proposed initialization. The rectangles indicate the error difference between the initialization \(\widehat{x}^{t}_{init}\) and \(x^{t}_{init}\) without our mask generation. The fusion in a maximum/minimum value manner indicates that we remain the maximum/minimum values from those masks at each pixel location.  
able to reduce the untraceable flow errors (see Figure 8) while also causing worse ghosting artefacts. We gradually add incremental mask (bottom-left) and multi-frame mask fusion (bottom-right) techniques into the w / multi-scale scheme method, which finally produces results without ghosting artefacts.