esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 6 Implementation and Experiments
### Reception Time of an AFR Message
#### 6.2.1 Reception Time of an AFR Message under Message Flooding Attacks  
As shown in Table 4, the sum of \(T_{Auth}^{M}\) and \(T_{Gen}^{R}\) is approximately 56.4 us and less than the transmission time of an AFR message, i.e., 444 \(\upmu\)s = \(\frac{Packet\ Size}{Bus\_Sped}\) = \(2\times\frac{111\ bits}{500,000\ bits/s}\) (111 bits is size of a CAN data frame with an 8 byte data field if the bit-stuffing rule of the CAN standard is ignored). Since the time to authenticate a CAN message and to generate a report message is less than the transmission time of the report message, the authenticator can authenticate all CAN messages without increasing its own message queue. Thus, every report message for an invalid CAN message can be transmitted successfully within a bounded time, which is the length of 4 \(\times\) the transmission time as described in Theorem 1. According to our implementation result, the worst time of report reception under the flooding attacks is approximately 1012 \(\upmu\)s, as shown in Figure 15a.  
The reception time shown in Figure 15a is slightly larger than 888 \(\upmu\)s = \(\frac{Packet\ Size}{Bus\_Sped}\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Authenticator**} & \multicolumn{2}{c}{**ECU**} \\ \hline
**Message** & **Report** & **Message** & **Report** \\ \hline
**Authentication (\(T_{Auth}^{M}\))** & **Generation (\(T_{Gen}^{R}\))** & **Generation (\(T_{Gen}^{M}\))** & **Verification (\(T_{Ver}^{R}\))** \\ \hline Mean & 28.26 & 28.14 & 258.8 & 516.4 \\ \hline Std. Dev. & 0.46 & 0.34 & 0.43 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Individual operations of MAuth-CAN (\(\upmu\)s).  
Figure 15: The reception time of an AFR message under attacks. (**a**) This figure shows the AFR reception time under message flooding. Note that the maximum time of AFRs is 1012 \(\upmu\)s. (**b**) This figure shows the ARF reception time under BoA where the malicious ECU creates the bit-error at the FIRST bit position in the AFR message. (**c**) This figure shows the ARF reception time under BoA where the malicious ECU creates the bit-error at the LAST bit position in the AFR message.  
= 4 \(\times\)\(\frac{111\ bits/s}{500,000\ bits/s}\), which is the theoretical transmission time of four CAN packets. The reason is that this experimental time is affected by the bit stuffing rule for synchronization of CAN bus and the time measurement error originating from Arduino UNO. In order to maintain synchronization of CAN bus, a bit stuffing rule is defined in the CAN standard. In this rule, a bit of opposite value is inserted after every five consecutive bits of the same value. For example, if six consecutive dominant bits, 000000, are transmitted by the host controller of an ECU, the CAN controller of the ECU adds one recessive bit after every five consecutive dominant bits 0000010. This additional bit is automatically removed by the CAN controllers of receiver ECUs.
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
## 4 Verification of the Prediction Accuracy of the Proposed Method

Table 2: Example of past vessel data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Column Name** & **Meaning** \\ \hline MMSI & Maritime Mobile Service Identity \\ \hline SPEED & Voyage speed of transit site \\ \hline LON & Longitude of transit site \\ \hline LAT & Latitude of transit site \\ \hline COURSE & Voyage direction at transit site \\ \hline HEADING & Vessel direction at transit site \\ \hline TIMESTAMP & Passing time as transit site \\ \hline HTSGW & Significant wave height \\ \hline PERPW & Primary wave mean period \\ \hline DIRPW & Primary wave direction \\ \hline UGRD & North–south wind component \\ \hline VGRD & East–west wind component \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Meaning of Table 2 columns. We evaluate prediction accuracy with Equation (6) which is the MAPE subtracted from 100% so that the prediction accuracy is higher as the predicted arrival time is closer to the actual arrival time. \[Prediction\ accuracy=100\ (\%)-MAPE \tag{6}\]  
Table 4 shows the results for the ten routes listed in Table 1. The proposed method has a higher prediction accuracy than the method of [PERSON] et al. for all ten routes. The mean prediction accuracy is 90% for the proposed method and 62% for the method of [PERSON] et al. Thus, the proposed method is more accurate than the method of [PERSON] et al. To verify the proposed method's advantages, its prediction accuracy was verified on 30 other routes between Japan and Taiwan. The mean prediction accuracies of the proposed method and the method of [PERSON] et al. were calculated with Equation (6). As a result, the mean prediction accuracy of 30 routes is 85% for the proposed method compared with 69% for the method of [PERSON] et al. More detailed results are shown in Appendix A. Moreover, we evaluate whether there is statistical significance of the difference between their mean prediction accuracies with a t-test whose significance level is 0.05. As a result, the 95% confidence interval of the difference between their mean prediction accuracies is from 0.081 to 0.23, the degree of freedom is 29, and the t-value is 4.03. The p-value is 0.00036 which is smaller than the significance level of 0.05. Therefore, there is statistical significance between the mean prediction accuracies. Thus, it is confirmed that the proposed method can predict the arrival time more accurately. Figure 9 shows the actual route taken by TS OSAKA, which departed from KEELUNG on 29 April 2020 at 11:30 a.m. and compares the arrival times and routes predicted using the method of [PERSON] et al. and the proposed method. The route obtained using the predicted method is closer to the actual route than the one obtained by [PERSON] et al.
## 5 Experimental Analysis
### Experimental Results and Analysis
#### 5.4.3 Measurement of Average Velocity of Suspended Solid Feed  
In order to calculate the speed of suspended particles, a high-speed camera is used to shoot the motion state of the solid feed in the sampling pipe section, so as to calculate the  
\begin{table}
\begin{tabular}{l c c c c c} \hline Conveying wind speed (m/s) & 15.5 & 16.9 & 17.5 & 20.6 & 25.8 \\ \hline Mass flow rate (kg/s) & 0.128 & 0.239 & 0.288 & 0.288 & 0.288 \\ \hline \end{tabular}
\end{table}
Table 4: Maximum feeding amount corresponding to different conveying wind speeds.  
Figure 9: Motion process of particle slug under different wind speeds.  
speed here. As shown in Figure 10, the length of the sampling pipeline captured by the high-speed camera is 0.3 m, and its FPS parameter is set to 12 frames per second. In any time period, five suspended particles are randomly selected to measure their movement time and distance, and their numbers are 1-5. The speed of the five particles is calculated and the average value is taken, as shown in Table 5. The average speed is 8.34 m/s. According to the above method, ten different time periods were selected in the transport process, and the average value was calculated. The solid feed speed corresponding to the five transport wind speeds was divided into 8.57 m/s, 10.29 m/s, 12.28 m/s, 14.66 m/s and 17.36 m/s. According to the data, the particle velocities were 55.3%, 57.1%, 59.6%, 63.2% and 67.3% of the conveying wind speed, respectively. With the increase in conveying wind speed, the solid feed velocity in a suspension state increased, and its increment gradually increased.
## 4 Experimental Validation
### Case 1
#### 4.1.4 Comparison of Domain Adaptation Algorithms  
The choice of domain adaptation method significantly impacts the model's fault diagnosis results. This paper validates the reliability of the proposed integrated domain adaptation method by comparing different domain adaptation strategies. The method using only the MK-MMD domain adaptation algorithm is referred to as Method A, and the method using only the CDAN+E algorithm is referred to as Method B. These two methods are compared with the proposed method, which integrates both the MK-MMD and CDAN+E domain adaptation algorithms.  
To ensure the rigor of the experiments, all three methods use images processed using the proposed MCR method as input. The feature extraction model for all methods is the proposed KAResNet model. The results of the domain adaptation algorithm experiments are shown in Table 7 and Figure 9.  
The experimental results show that theMCR-KAResNet-TLDAF method achieved the highest average accuracy among the three methods. MCR-KAResNet-TLDAF attained the highest accuracy in most transfer tasks, particularly in transfer task 3-0, where Method A and Method B struggled to achieve a high accuracy. In the 3-0 transfer task, the MCR-KAResNet-TLDAF method improved the accuracy by 8.13% and 14.8% compared to Method A and Method B, respectively. This indicates that the integrated MK-MMD and CDAN+E domain adaptation method performs better in complex transfer tasks.
# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
## 2 The Fuse-ResNet Network Model  
The Fuse-ResNet network model consists of two parts: an attention-enhanced encoder and a multi-scale fusion decoder. First, to effectively extract multi-scale features from watershed images, the Fuse-ResNet encoder adopts ResNet-50 as the backbone network. The CBAM attention mechanism is added to the output features of each residual block. The CBAM consists of two steps: the channel attention module and the spatial attention module. The channel attention module calculates the relationships between channels and assigns a weight to each channel, emphasizing important channels and suppressing irrelevant ones. The spatial attention module weights the features at each spatial location, thereby enhancing the model's focus on the target region while suppressing background noise.  
Next, in the decoder stage, the model uses the ASPP module to process low-level detail features and high-level semantic features. The ASPP module includes dilated convolutions with different expansion rates, each capturing features at different scales: dilated convolutions with small expansion rates capture local detail information, while dilated convolutions with large expansion rates help to capture broader contextual information.  
These feature maps with different expansion rates are concatenated and then fused through a 1 \(\times\) 1 convolution layer to generate a feature map rich in both contextual and detailed information. Subsequently, the model uses the FPN feature pyramid structure for multi-scale feature fusion, and finally recovers the number of channels through a 3 \(\times\) 3 convolution layer, outputting the final segmentation map. Figure 1 shows the model structure of Fuse-ResNet.
## 4 Conclusions  
In conclusion, we have present a home-built bistatic pulsed-LiDAR system equipped with a gated-PMT detector, providing readers with an in-depth understanding of its operational principles for construction and utilization. To obtain high SNR data, a comprehensive suite of denoising techniques was employed, including temporal, spatial, spectral, and polarization filtering methods. The proposed system demonstrated the capability to operate effectively under various challenging conditions, including daytime and other complex scenarios, leveraging advanced denoising technologies. Especially, it possesses the capability to mitigate the impact of scattering and solar background radiation, which is the guarantee that the equipment can work properly outdoors. Regarding operational range, the system exhibited impressive capabilities, achieving distances exceeding 4000 m in clear daylight, 19,200 m under nocturnal conditions, 6700 m during hazy daytime conditions, and 2000 m amidst daylight rain. Furthermore, it boasts an angular resolution of 0.01 mrad. By applying compressive sensing algorithms, it is observed that a sampling rate of 20% was adequate for discerning basic signal outlines, thereby meeting the requirements of imaging detection, significantly reducing processing time. The findings presented herein offer valuable insights into the pragmatic realization of imaging systems with promising potential for compact construction, facilitating the acquisition of data for subsequent processing and application.  
Figure 7: Time consumed by imaging reconstruction under different imaging parameters.  
**Author Contributions:** Conceptualization, H.Z., H.C. and G.W.; methodology, H.Z.; software, Y.Z. ([PERSON]); validation, Y.H. ([PERSON]), L.Q. and Y.Z. ([PERSON]); formal analysis, J.L. ([PERSON]); resources, Y.H. ([PERSON]); data curation, J.L. ([PERSON]); writing--original draft preparation, Y.H. ([PERSON]); writing--review and editing, H.Z.; visualization, H.Z.; supervision, Z.X.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (62375215), Shaanxi Key Research and Development Project (Grant No. 2019 ZDLGY09-10), and Shaanxi Key Research and Development Project (2019 ZDLGY09-09).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Results
### Dynamic Performance  
Two tests were executed to attest that the proposed sensorless scheme exhibits the accepted dynamic performance. In Figure 12, the acceleration operation is performed by abruptly transforming the speed command from 200 to 500 rpm, with the torque command being 6 N\(\cdot\)m. The result is smooth speed profiles with negligible overshoot, with the whole response being accomplished within roughly 200 ms. Additionally, the electromagnetic torque is capable of tracking the load command immediately, and the NPV remains constant around 0 V in the whole dynamic process of speed change. Additionally, Figure 13 exhibits the experimental waveforms of the speed-tracking response and the position tracking response of the corresponding speed step-change test. As illustrated in the figure, the estimated speed follows the actual speed accurately during the speed step-change, and position observations can also quickly respond to changes in the speed and accurately track the actual position of the motor.  
In addition, the torque step-change test is executed at 300 rpm, as shown in Figure 14. It can be observed that the response torque step-change is completed within 200 ms, where the torque is abruptly decreased from 6 N\(\cdot\)m to 0 N\(\cdot\)m. The neutral-point voltage can be kept as a constant value of 0 V during the entire transient process, while the phase-A current and speed are all regulated to follow the load change in view of a negligible overshoot of speed occurring in the process of the load step-change. Meanwhile, a dynamic tracking waveform of speed and rotor position at the torque step-change is exhibited in Figure 15. It is worthwhile noting that both speed-tracking performance and position-tracking performance are acceptable and remain unchanged in the process of the torque step-change. Although the high-frequency oscillations (and even some spikes) in the feedback signal may directly result in the output of controller performance at the same oscillations, on the whole, the proposed sensorless scheme can obtain an acceptable quick dynamic response performance.  
Figure 11: Tracking performances of speed and position under a speed command of 500 rpm (the red line represents the actual value, and the blue line represents the estimated value).  
Figure 10: Tracking performances of speed and position under a speed command of 50 rpm (the red line represents the actual value, and the blue line represents the estimated value).  
Figure 14: Dynamic experimental results during the torque step-change.  
Figure 12: Dynamic experimental results during the speed step-change.  
Figure 13: Tracking performances of speed and position during the speed step-change (the red line represents the actual value, and the blue line represents the estimated value).
## 2 Materials and Methods
### Real-Time qPCR  
IL-10 mRNA expression was quantified using real-time qPCR. Total RNA was extracted, reverse-transcribed into cDNA, and amplified using SYBR Green Supermix (Bio-Rad, Hercules, CA, USA). GAPDH served as the reference gene, and relative expression levels were calculated using the \(\Delta\Delta\)Ct method, with results expressed as fold changes relative to the control or Con A group. The primer sequences used for IL-10 amplification were 5\({}^{\prime}\)-TAACTGCACCCACTTCCCAG-3\({}^{\prime}\) for the forward primer and 5\({}^{\prime}\)-AGGCTTGGCAACCC AAGTAA-3\({}^{\prime}\) for the reverse primer (NCBI accession number: NM_010548.2).
## 4 Discussion
### Boxplot Analysis  
In this section, the error distributions for the best performing models of each machine learning strategy studied are presented and compared.  
Figure 26 presents the boxplot chart containing the error distributions for models ANN_C2, SVM_C5 and SVM+SA_C5 in forecasting cooling energy demand, while Table 3 highlights some statistical parameters for particular hours of the day.  
Once again, and confirming the information given by Figures 23 and 25, the error distribution shows that the most effective model in forecasting cooling energy demand is model C5, using SVM+SA.  
Figure 25: Dispersion chart of forecasted values vs. reference for cooling period (**top**) and heating period (**bottom**).  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Statistical**} & \multicolumn{3}{c}{**ANN_C2**} & \multicolumn{3}{c}{**SVM_C5**} & \multicolumn{3}{c}{**SVM+SA_C5**} \\
**Parameter [kW]** & **8 a.m.** & **12 p.m.** & **8 p.m.** & **8 a.m.** & **12 p.m.** & **8 p.m.** & **8 a.m.** & **12 p.m.** & **8 p.m.** \\ \hline Mean & 6.  
Figure 27 presents the error distribution for models ANN_H4, SVM_H4 and SVM+SA_H4 in forecasting heating energy demand, while Table 4 highlights some statistical parameters for particular hours of the day.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Statistical**} & \multicolumn{3}{c}{**ANN_H4**} & \multicolumn{3}{c}{**SVM_H4**} & \multicolumn{3}{c}{**SVM+SA_H4**} \\
**Parameter [kW]** & **8 a.m.** & **12 p.m.** & **8 p.m.** & **8 a.m.** & **12 p.m.** & **8 p.m.** & **8 a.m.** & **12 p.m.** & **8 p.m.** \\ \hline Mean & 2.03 & 5.41 & \(-\)9.30 & 13.44 & 24.72 & \(-\)1.49 & 0.24 & 13.99 & \(-\)1.22 \\ Std. deviation & 29.96 & 18.57 & 15.21 & 36.52 & 31.78 & 18.48 & 29.57 & 23.24 & 17.93 \\ \hline Minimum & \(-\)162.04 & \(-\)71.67 & \(-\)102.12 & \(-\)160.18 & \(-\)46.90 & \(-\)112.25 & \(-\)159.73 & \(-\)60.42 & \(-\)108.59 \\
1 st Quartile & \(-\)5.42 & \(-\)3.26 & \(-\)11.38 & \(-\)0.51 & \(-\)0.50 & \(-\)9.68 & \(-\)4.71 & \(-\)3.47 & \(-\)5.81 \\ Median & \(-\)0.35 & 0.00 & \(-\)2.71 & 0.99 & 12.48 & \(-\)0.49 & \(-\)1.00 & 7.04 & \(-\)2.97 \\
3 rd Quartile & 29.80 & 22.58 & 0.00 & 46.25 & 42.99 & 12.84 & 17.69 & 23.71 & 16.87 \\ Maximum & 73.07 & 69.95 & 25.50 & 92.05 & 153.69 & 47.58 & 84.50 & 104.52 & 46.77 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical parameters for heating energy demand error.  
Figure 26: Error distribution for the best performing models in the cooling period.  
Figure 27: Error distribution for the best performing models in the heating period.  
Figure 27 and Table 4 show that model ANN_H4 has an overall more satisfactory error distribution than models SVM_H4 and SVM+SA_H4, confirming the information given by Figures 24 and 25.  
It must also be pointed out that a generalized improvement in prediction accuracy occurs when SA is adopted in SVM models.
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 2 Materials and Methods
### Acoustic Feature Extraction
#### 2.2.4 Discrete Wavelet Transform  
Like the spectrogram, the DWT is a time-frequency representation that is widely used in time series analysis and has shown promising results for beehive acoustics monitoring applications [29; 31]. DWTs rely on so-called wavelets to represent signals as a superposition of short finite-length waves that can be scaled and shifted to capture changes in the time-frequency space. The DWT is the discrete form of continuous wavelet transform (CWT) described by Equation (2):  
\[C(a,b)=\int_{-\infty}^{\infty}x(t)\psi_{a,b}^{*}(t)dt, \tag{2}\]  
where \(x(t)\) is the audio signal, \(\psi^{*}\) is the complex conjugate of the mother wavelet \(\psi\) and \(a,b\) are the translation and scale, respectively. The mother wavelet is given by  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{b}}\psi(\frac{t-a}{b}). \tag{3}\]  
In the DWT, the translation and scale values have the following conditions:  
\[b=2^{-s}\qquad a=2^{-s}l, \tag{4}\]  
where \(l\) and \(s\) are discrete values.  
Combining these conditions with the assumption of discretization of the signal results in the DWT shown in Equation (5), we obtain the following:  
\[W(l,s)=2^{\frac{s}{2}}\sum_{n}x(n)\psi(2^{s}n-1);\qquad n=0,1,\ldots,L. \tag{5}\]  
In this study, the audio is decomposed into an \"approximation\" and a \"detail\" component using a filtering implementation of the DWT [61]. In this case, a low-pass (G)  
Figure 5: RMS curve and corresponding MFCCs for a hive with with 3 full boxes of bees (12 August 2021).  
and a high-pass (H) filter are used to separate the approximate (\(A(k)\)) and detail (\(D(k)\)) components of the signal, respectively:  
\[A(k)=\sum_{n}x(n)h(2k-n),and \tag{6}\]  
\[D(k)=\sum_{n}x(n)g(2k-n). \tag{7}\]  
Here, we used the second order Daubechies wavelet [62] (\"db2\") and 10 levels of decomposition based on combinations reported in the literature.  
With these common features extracted for all 11 hives over three months, we used the empty hive and rainfall information to detect environmental sounds of interest and explore the effects they have on these features.