esa-sceva/satcom-chunk-collection huggingface dataset:
# Animal Movement Prediction Based on Predictive Recurrent Neural Network
## 5 Conclusions  
In this paper, we proposed a novel approach for predicting animal movements using a predictive RNN. For accurate movement prediction, we first showed how to compensate for any missing geo records using random forest regression. To interpolate the missing geo records, we used random forest based on animal movement features and environmental features such as terrain and weather. Compared with other popular interpolation methods, our proposed model achieved higher interpolation accuracy. After augmenting geo records, we grouped geo records by various units and created a sequence of movement density images, which represent movement trends of animals. Then, we built a movement prediction model by training PredRNN++ using these sequence data. We evaluated the model's performance through various experiments. The results showed that our model is quite effective for animal movement prediction.  
**Author Contributions:** Conceptualization, [PERSON]; investigation, [PERSON] and S.J.; methodology, [PERSON] and S.P.; software, [PERSON] and Y.C.; visualization, J.R.; writing--original draft preparation, J.R.; writing--review and editing, E.H.; supervision, E.H.  
**Funding:** This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Public Technology Program based on Environmental Policy, funded by Korea Ministry of Environment (MOE) (2017000210001).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Prediction day T**} \\ \cline{2-13}  & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** \\ \hline RMSE & **9.80** & 10.55 & 9.41 & 10.10 & 11.57 & 9.39 & 10.51 & 10.45 & 15.43 & 19.18 & 12.82 & 9.91 \\ SSIM & **0.95** & 0.95 & 0.94 & 0.94 & 0.94 & 0.94 & 0.94 & 0.93 & 0.90 & 0.90 & 0.93 & 0.95 \\ Cent-Dist in pixels & **1.4** & 1.6 & 1.9 & 3.6 & 2.9 & 2.7 & 3.4 & 3.8 & 8.3 & 15.5 & 12.2 & 3.2 \\ Cent-Dist in km & **34.1** & 40.5 & 46.7 & 88.2 & 70.3 & 65.0 & 82.7 & 93.4 & 201.7 & 373.8 & 294.7 & 78.21 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Daily comparison of RMSE, SSIM, and centroid distance of case 10.  
Figure 14: Movement prediction result-(**a**) input sequence; (**b**) ground truth; (**c**) predicted sequence.
# Vertical GaN MOSFET Power Devices
## 4 Techniques to Boost the Breakdown Voltage  
The most prevalent approaches to enhance the \(V_{BD}\) in GaN vertical devices, including GaN vertical MOSFETs, typically involve the following methods: (a) lowering n-GaN drift layer doping concentration, (b) optimizing n-GaN drift layer thickness, (c) incorporating field plates, and (d) implementing edge termination techniques. Furthermore, the \(V_{BD}\) of GaN vertical MOSFETs is also influenced by the characteristics of the metal-oxide-semiconductor capacitor (MOSCAP) within the device. The thickness of the gate oxide affects the distribution of the electric field, as the gate oxide has a higher ability to withstand a higher electric field, thus increasing the breakdown voltage, while a thinner gate oxide increases the leakage current through the gate, resulting in premature breakdown. However, increasing the gate oxide thickness increases MOSCAP, thus reducing the speed of the device [76].  
Moreover, the durability of the oxide dielectric, particularly a high-quality gate oxide, is pivotal as it can withstand a substantial electric field, ultimately contributing to an augmentation in the \(V_{BD}\). The dielectric constant (\(k\)) of the gate oxide plays a crucial role in determining the device's performance. A high \(k\) gate oxide can achieve a thicker gate dielectric with the same capacitance as that of a smaller \(k\), thus achieving a higher \(V_{BD}\) with a similar speed as that of a smaller \(k\). Additionally, a higher \(k\) gate oxide reduces the gate leakage current, thus improving the \(V_{BD}\)[77]. However, although high \(k\) gate oxide can improve \(V_{BD}\) and reduce gate leakage, high \(k\) gate dielectric materials are susceptible to thermal stress; hence, proper thermal management is required to avoid reliability issues. The gate dielectric material with positive valence band offset with GaN, namely, SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), etc., tends to exhibit catastrophic breakdown, while the dielectric with a negative valence band offset, namely, SiN, shows an increase in offset leakage current [78].
# Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
## 2 Non-Gaussian and Nonlinear Environment (NGNLE) with Finite Memory
### Long-Haul Fiber Optic OFDM Link an Example of NGNLE  
Typically, in the analysis of long-haul fiber optic systems, the fiber optic channel is assumed to be nonlinear and the noise is assumed to be Gaussian to simplify the problem [34]. Under the Gaussian noise assumption, it follows that the variance of nonlinear noise should be proportional to the total distance \(L\). However, in [34], it was shown that the variance of nonlinear noise scales as \(L^{x}\) where \(x\in(1,\ 2)\) depending on the dispersion map. Therefore, the Gaussian noise assumption is not accurate for OFDM systems in the presence of nonlinearity. In this paper, we investigate the performance of a long-haul fiber optic OFDM link that is an example of a NGNLE. Figure 4 shows an OFDM system with a CDS. In Figure 4, we assumed that the CDS is placed in the receiver, but the CDS can also send commands to the transmitter, such as changing the data-rate or the transmitted signal power. In addition, the intelligent subsystem of the CDS can be considered as a subsystem of the data symbol decision part of the optical transceiver.  
We should mention here the conventional long-haul fiber optic system uses soft decision (SD) and hard-decision (HD) forward error correction (FEC). This leads to additional overhead to the data rate that reduces the system's efficiency. Typically, the SD-FEC is used to guarantee the decision-making error before the HD-FEC is under the HD threshold. However, when the CDS is used for decision making as proposed in this paper, it can guarantee that the decision-making error is always under the HD-FEC threshold. Hence, the SD-FEC may no longer be needed when the CDS is used, and therefore, the additional overhead due to SD-FEC could be removed. Figure 3 shows the conventional OFDM-based fiber optic system [28] and Figure 4 shows the upgraded system with the proposed CDS.  
Figure 3: The conventional orthogonal frequency division multiplexing (OFDM)-based fiber optic system. (ADC: Analog to Digital Converter, DAC: Digital to Analog Converter, FEC: Forward Error Correction, FFT: Fast Fourier Transform, IFFT: Inverse Fast Fourier Transform, P/S: Parallel to Serial, and S/P: Serial to Parallel).
## 6 Lessons Learned
### Advanced Use of Cryptographic Techniques  
[PERSON] and [PERSON] taught us the lesson that the attackers are not shy to use advanced cryptographic methods to achieve their goals. The MD5 collision attack that resulted in the fake public key certificate that [PERSON] used to masquerade as a legitimate Windows Update has a very straightforward message: _everyone should stop using MD5 in signature applications, and in particular, for signing certificates_. Researchers have already tried to call attention to the weaknesses of MD5 earlier by demonstrating the feasibility of issuing fake certificates [18], but apparently that message was not strong enough to some parties. Now, the scenario envisioned by researchers did indeed realize in real life, and we hope that this will generate reactions, and ultimately lead to the discontinuation of using MD5 based signatures.  
As described earlier, the Godel module of Gauss is encrypted in a way that it can only be decrypted on its target systems. Again, the possibility of such an encrypted malware has already been envisioned by researchers earlier (see, e.g., [31,32]), but to the best of our knowledge, Gauss was the first targeted malware that used this technique in practice. This sort of encrypted warhead in a malware has clear advantages for the attackers, because it cannot be analyzed even if the malware itself is detected and identified. Consequently, the exploits that are valuable for the attackers and can otherwise be discovered and patched remain hidden. We conjecture that this technique will be used more frequently in the future in targeted attacks, where the objective of the attackers is not to create an epidemic infection but rather to compromise only a few specific systems. On the one hand, this is a great challenge for those who try to analyze targeted threats; on the other hand, such encrypted malware generates less collateral damage, which can be perceived as an advantageous feature.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 5 Conclusions  
The AODV routing protocol in the UAV communication network has good performance, but its security is poor and it is easy to be attacked. In this paper, the elliptic cryptosystem is introduced into AODV routing protocol to complete the authentication function, and an improved secure routing protocol is proposed based on the advantages of the existing SAODV routing protocol. Through the simulation of three routing protocols (AODV, SAODV, ISAODV), the performance indicators such as packet delivery rate, throughput, and end-to-end delay are compared and studied. The simulation results show that the ISAODV routing protocol not only inherits the efficient route discovery and maintenance capabilities of the AODV routing protocol, but also reduces the complexity of the  
Figure 11: The routing overhead after adding malicious nodes.  
Figure 10: The routing overhead under normal conditions.  
algorithm and has lower delay compared with the SAODV routing protocol. When there are malicious nodes in the UAV communication network, the ISAODV routing protocol can effectively improve the security of the network.  
Conceptualization, X.T. and S.S.; formal analysis, S.S.; investigation, Z.Z.; methodology, Z.Z. and X.G.; resources, X.G.; software, X.T. and X.S.; supervision, Z.Z. and S.S.; validation, X.T. and X.S.; writing--original draft, X.T., Z.Z., and X.G.; writing--review and editing, S.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Hunan Province, under grant number 2018 JJ3607.  
The authors declare no conflict of interest.
## 5 Experimental Validation
### Simulation vs. Real Results  
Simulation results are generated by setting the parameters of the inverter and IM as mentioned in Tables 1 and 2. In this section, a comparison takes place between the simulation results and the existing inverter data of the Simrod EV. Real high-frequency data are logged by considering three different cases for this comparison. In case-1, the reference speed was set at 2760 RPM, and the motor tracked the command with measured torque of 105 Nm. In case 2, the reference speed was set at 5382 RPM, and the torque became 24.6 Nm, and in case 3, the reference speed was set at 7700 RPM, and the torque was 14.5 Nm. Thus, the simulation results of the three-phase currents are perfectly matched with existing inverter data. Comparison plots are shown in Figures 20-22. Hence, the inverter's dynamic simulation model with control is as accurate as the inverter.  
Figure 19: Installed setup; inside current sensors (**a**) and Simrod EV on chassis dynamometer (**b**).
###### Abstract
In addition to that, this paper also explores the evolving role of machine learning (ML) in both facilitating and combating blockchain security attacks. In summary, we want to answer the following concerns in this survey:  
* RQ1: How can blockchain security threats be categorized and analyzed based on the specific layer of the blockchain system they target? * RQ2: What are the common attack vectors and their potential impact on different blockchain layers? What countermeasures can be implemented to mitigate these threats? * RQ3: What are the prominent categories of research on blockchain vulnerability detection, and how can they be systematically classified based on their methodologies and focal problems? * RQ4: What are the strengths and weaknesses of each vulnerability detection category, and how can this comparative analysis inform the selection of appropriate tools and techniques? The remainder of this article is structured as follows: Section 2 provides a comprehensive overview of blockchain architecture and the system's operational mechanisms. In Section 3, we introduce a unique layer-based framework to systematically categorize and analyze blockchain security attacks, presenting a detailed table that outlines common attack vectors, their target layers, potential impact, and countermeasures. This section aims to provide a clear and structured understanding of how vulnerabilities manifest and can be mitigated across different layers of a blockchain system. Section 4 presents a vulnerability detection taxonomy and provides a detailed analysis of various vulnerability detection approaches, along with a preliminary discussion on the evolving role of machine learning (ML) in both enhancing and threatening blockchain security. More details on specific aspects of ML are presented in Section 5. Finally, Section 6 concludesthe article by summarizing key findings, emphasizing the significance of the proposed taxonomy and layer-based framework, and highlighting crucial areas for future research in blockchain security.
## 3 Results and Discussion  
### Bacteriological Analysis  
Microbial activity is one of the main factors leading to the spoilage of preserved food with high moisture content. The total viable count (TVC) was used as an indicator ofthe quality of hygiene and the degree of spoilage; 7 log CFU g\({}^{-1}\) is often used as the maximum level for fish sold for human consumption [15]. Changes in TVC for crisp grass carp during 4 \({}^{\circ}\)C storage are presented in Figure 1. The initial TVC of the fresh fish fillets was 4.08 log CFU g\({}^{-1}\). Our results showed that the microbial growth started immediately in the control fish fillets but had a lag phase of about 2 days in samples treated with tea polyphenols. During the chilled storage, the TVC values of the tea polyphenol-treated fish were lower than the controls at all time points (1-2 log CFU g\({}^{-1}\)). After 6 days, the TVC in the control increased to 7.32 log CFU g\({}^{-1}\) and reached the maximum limit of acceptable microbial count after 6 days, as reported in previous reports [16]. In contrast, the TVC value for the fish fillets treated with the tea polyphenols was 7.09 log CFU g\({}^{-1}\) after 12 days (Figure 1). Our results showed that the tea polyphenols significantly inhibited the bacterial growth compared to the control groups (\(p<0.05\)) and extended crisp grass carp shelf life by 6 days. The bacteriostatic effects of the tea polyphenols have been reported in previous studies [6; 7; 8; 9; 10]. In comparison, the shelf life of crisp grass carp treated with or without tea polyphenols was shorter than that of crucial carp. The main reason is that the initial TVC of crisp grass carp which was 4.08 log CFU g\({}^{-1}\) was higher than that of crucial carp which was 2.84 log CFU g\({}^{-1}\)[8].
## 4 Results and Discussion
### Influence Factors in the Mixing Process
#### 4.1.3 The Effect of Incident Angle of T-Junction on Mixing Effect  
According to the analysis of the numerical simulation results, the incident angle is one parameter influencing the mixing characteristics in this model. As shown in Table 3, it is found that the change processes of \(L_{EML}\) which vary with the incident angles of T-junctions differ under different main pipe diameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Diameter** & **Incident** & \(L_{EML}\) **of** & \(L_{EML}\) **of** & \(L_{EML}\) **of** & \(L_{EML}\) **of** \\
**(mm)** & **Angle** & **Group A (m)** & **Group B(m)** & **Group C (m)** & **Group D (m)** \\ \hline \multirow{4}{*}{51.4} & 30\({}^{\circ}\) & 1.8124 & 1.8150 & 1.8161 & 1.8164 \\  & 60\({}^{\circ}\) & 1.9167 & 1.9168 & 1.9172 & 1.9220 \\  & 90\({}^{\circ}\) & 1.8496 & 1.8520 & 1.8599 & 1.8610 \\  & 120\({}^{\circ}\) & 1.7747 & 1.7753 & 1.7796 & 1.7800 \\  & 150\({}^{\circ}\) & 2.0830 & 2.0879 & 2.1070 & 2.1090 \\ \hline \multirow{4}{*}{61.4} & 30\({}^{\circ}\) & 2.6286 & 2.6345 & 2.6347 & 2.6353 \\  & 60\({}^{\circ}\) & 2.7339 & 2.7450 & 2.7520 & 2.7549 \\ \cline{1-1}  & 90\({}^{\circ}\) & 2.6790 & 2.6830 & 2.6850 & 2.6882 \\ \cline{1-1}  & 120\({}^{\circ}\) & 2.7638 & 2.7754 & 2.7769 & 2.7804 \\ \cline{1-1}  & 150\({}^{\circ}\) & 2.5650 & 2.5752 & 2.5970 & 2.5990 \\ \hline \multirow{4}{*}{73.6} & 30\({}^{\circ}\) & 3.8650 & 3.8710 & 3.8740 & 3.8790 \\  & 60\({}^{\circ}\) & 3.5270 & 3.5402 & 3.5600 & 3.5632 \\ \cline{1-1}  & 90\({}^{\circ}\) & 3.7007 & 3.7208 & 3.7240 & 3.7439 \\ \cline{1-1}  & 120\({}^{\circ}\) & 3.6640 & 3.6808 & 3.6868 & 3.7010 \\ \cline{1-1}  & 150\({}^{\circ}\) & 3.5600 & 3.5680 & 3.6092 & 3.6120 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of \(L_{EML}\) under different diameters and incident angles.  
Figure 10: The average cross section velocities of mixture, water, and salt solution in the selected region.  
As mentioned above, a longitudinal section was cut from the position where there was a distance of 8 mm behind the junction of a T-junction and the area with the VOF of the salt solution above 5% was selected. The contours of the distribution of the salt solution and water velocity in the region are shown in Figure 11. It can be clearly seen that the VOF of the salt solution near the wall is relatively large when it just enters the main pipe, and the distribution of water velocity in the near-wall flow field is extremely uneven with a long span. In addition, the water velocity distribution is different for different pipe diameters. On account of the enormous effect of water velocity to the horizontal velocity of salt solution, the change law of \(L_{EML}\) with the incident angles of T-junctions differ under different main pipe diameters. In the next section, the dimensional analysis (D-A) was adopted to further research the influences of the four variables on \(L_{EML}\).
## 3 Experimental Program
### Motivation for Selecting the Cylindrical Specimens  
The cylindrical specimens are obviously more suitable to achieve the research goals because there is no local stress concentration problem as is the case, for example, in the corners of the prismatic ones. Additionally, cylindrical specimens are characterized by uniform deformation development along the entire perimeter of the specimen section. Thus, the edge effect problem is avoided in the tests.  
The ratio between the diameter and the height of the concrete cylindrical specimen was 1:2 (15:30 cm). This ratio allows the modeling of real full-scale compressed structural element behavior. Additionally, the chosen ratio guarantees the presence of a relatively large unrestrained area in the middle of the specimen's height [20]. In this case, the element buckling problem is practically prevented. At the same time, due to some inaccuracy in the transmission of external compressive forces and concrete heterogeneity, additional stresses associated with small (technological) eccentricities may appear. This problem was solved by selecting a proper test setup.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Units** & **Values** \\ \hline Cement density, \(\rho_{ccm}\) & kg/m\({}^{3}\) & 2900 \\ Standard cement consistency, \(K_{ccc}\) & \% & 27.5 \\ Sand fineness modulus, \(FM\) & - & 1.85 \\ Sand particle density, \(\rho_{s}\) & kg/m\({}^{3}\) & 2644.5 \\ Crushed stone bulk density (oven-dry), \(\rho_{csb}\) & kg/m\({}^{3}\) & 1270.9 \\ Crushed stone particle density, \(\rho_{cs}\) & kg/m\({}^{3}\) & 2586.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete component properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Units** & **Values** \\ \hline Cement, C & kg/m\({}^{3}\) & 448.4 \\ Water, W & kg/m\({}^{3}\) & 224.2 \\ Water-cement ratio, W/C & - & 0.5 \\ Sand, S & kg/m\({}^{3}\) & 602.2 \\ Crushed stone, CS & kg/m\({}^{3}\) & 1017.8 \\ Fresh concrete density in compacted state, \(\rho_{c}\) & kg/m\({}^{3}\) & 2293 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Concrete mixture proportioning.
# Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems
## 3 SRbPLRC Framework for Large-Size Video Face Recognition
### AAPSRbPLRC Algorithm  
The APSRbPLRC algorithm uses the mean image of each subset as the anchor point; however, we believe that a good anchor point construction strategy should adaptively weigh and combine the images in each subset. Hence, we adopted an attention mechanism to obtain more discriminative anchor points.  
Suppose that we obtained subset \(\{x_{1},\cdots,x_{m}\}\) using the HDC algorithm, and then initialized the query vector \(q^{0}\in\mathcal{R}^{q\times 1}\). We can then obtain the score of each image \(x_{i}\) by computing the dot product as follows:  
\[e_{k}=(q^{0})^{T}x_{k}, \tag{7}\]  
Figure 3: An illustration of the proposed DSRbPLRC and APSRbPLRC algorithms. DSRbPLRC directly divides a large-size video into several small-size sub-videos, and each sub-video is used for classification. Different from DSRbPLRC, APSRbPLRC uses the HDC algorithm to extract anchor points, which will not increase the number of videos.  
where \(T\) is the transpose operation. The attention distribution \(\alpha_{k}\) can then also be computed, as follows:  
\[\alpha_{k}=\frac{\exp(\alpha_{k})}{\sum_{i}\exp(\epsilon_{i})}. \tag{8}\]  
Finally, the weighted average of subset can be obtained as follows:  
\[r^{0}=\sum\limits_{k=1}^{m}\alpha_{k}x_{k}. \tag{9}\]  
The obtained \(r^{0}\) can be input into the following equation to obtain the new query vector \(q^{1}\), \(q^{2}\):  
\[\begin{array}{l}q^{1}=Tanh(W_{1}r^{0}+b_{1}),\\ q^{2}=Tanh(W_{2}q^{1}+b_{2}).\end{array} \tag{10}\]  
Finally, the anchor point \(r^{2}\) of the subset generated by \(q^{2}\) can be obtained using Equations (7)-(9).  
In order to solve the parameters in Equation (10), the subspace-based contrastive loss is used in this paper. Suppose that \(\{X_{1}^{qp},\cdots,X_{C}^{qp}\}\) are anchor point gallery videos computed after the above attention mechanism, then the distance \(d^{ij}\) between \(X_{i}^{qp}\) and \(X_{j}^{qp}\) can be computed by Equations (5) and (6). Thus, the subspace based contrastive loss can be defined as:  
\[L=\sum\limits_{ij}y_{ij}d^{ij}+(1-y_{ij})\text{max}(0,m-d^{ij}) \tag{11}\]  
where \(y_{ij}\) equals \(1\) or \(0\) (\(y_{ij}=1\) means that \(X_{i}^{qp}\) and \(X_{j}^{qp}\) belong to the same class, whereas \(y_{ij}=0\) means that \(X_{i}^{qp}\) and \(X_{j}^{qp}\) belong to different classes).  
After obtaining more discriminative anchor points for each video, the AAPSRbPLRC method can effectively produce the labels for the probe video.  
In summary, when we suppose that each sub-video or subset has a similar number of images, then the PLRC algorithm needs to compute \(C\) large-size distances to classify one probe video, while the DSRbPLRC algorithm needs to compute \(C\times a\times b\) small-size distances, and the APSRbPLRC and AAPSRbPLRC algorithms both need to compute \(C\) small-size distances.