esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Literature Review
#### 1.1.2 Deep Learning-Based Methods
proposed a short-term load-forecasting framework based on GNN and temporal convolution module, and the experiments showed that the GNN-based model outperforms three conventional methods and six deep learning methods [31]. [PERSON] et al. proposed a spatial-temporal graph neural network with a spatial-temporal attention layer for medium-term wind power forecasting [32]. [PERSON] et al. proposed a self-learning dynamic graph neural network, and an evolving adjacency matrix was constructed to capture the dynamic relationships between changing feature quantities [33]. Meanwhile, GNNs are increasingly used to process multi-sensor signals in prognostics and health management (PHM), which can quantify the interdependencies between signals and embed them into extracted features [34]. [PERSON] et al. proposed a GNN model integrating a GCN and GRU for bearing remaining useful life (RUL) prediction [35]. [PERSON] et al. proposed a GNN with spatial-temporal feature fusion for turbofan engine RUL prediction, which can simultaneously match information propagation at spatial-temporal dimensions [36]. [PERSON] and [PERSON] proposed a GNN-based bearing fault diagnosis method with a causality graph between fault signals and health signals [37]. [PERSON] et al. proposed a pre-trained model based on a GNN for tool wear prediction, which was outstanding on small-scale datasets under variable working conditions [38].
## 4 Simulation of DRSCEM  
To simulate a DRSCEM, a fixed phase spectrum \(\phi\left(\omega\right)\) and initial Fourier amplitude spectrum \(A_{int}\left(\omega\right)\) are needed. Although the Fourier phase uncertainty has a strong influence on the fluctuation of YSC demand spectrum, the initial Fourier amplitude also has a minor effect on it because the frequency content of the initial Fourier amplitude differs from one observed earthquake Fourier amplitude to another. The difference in the initial Fourier amplitude level can be adjusted in the process of attaining the compatibility between the response spectrum calculated by DRSCEM and the design-response spectrum. Therefore, the initial Fourier amplitude spectrum should contain enough frequency components. To certain this, we assigned the initial Fourier amplitude spectrum to the direct averaging of several Fourier amplitude spectra of the recorded time histories in the frequency domain. To guarantee its efficiency we used the average of normalized Fourier amplitude spectra.  
Figure 9a shows the amplitudes of ten recorded earthquake ground motions (Northwest China-01, Northwest China-03, Northridge-01, Kobe Japan, Off-Kushiro Japan, Imperial Valley-02, Tabas Iran-Tabas, New Zealand-02, Landers-Fort Irwin, and Cape Mendocino), and the black line is the average Fourier amplitude. Additionally, in Figure 9b shows the YSC demand spectra calculated from the DRSCEMs, which are simulated using the average initial Fourier amplitude of the ten recorded ground motions mentioned above. From Figure 9b, it is apparent that it is more appropriate to initially use the average initial Fourier amplitude than individual Fourier amplitudes because the YSC demand spectra obtained from the average Fourier amplitude are located within the fluctuation range and are smoother. It is commonly recognized that the response spectrum recalculated using DRSCEMs coincides with the target response spectrum regulated in the design code. Figure 10 is an example that demonstrates matching a simulated DRSCEM with the target design-response spectrum. The red line is the target response spectrum. The black line is the recalculated response spectrum using a DRSCEM simulated by taking the initial Fourier amplitude from the El Centro earthquake record and the phase spectrum simulated by fBm. The blue line is directly obtained from the El Centro record. By comparing the red and black lines, we confirm the commonly recognized fact except that rather lower fluctuations are seen at a lower natural period.  
Figure 8: A Sample of the fluctuation part of the phase difference simulated by modified fBm process.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 5 Results
### Agreement Indicators  
In cluster routing, the standard deviation of the votes within a cluster serves as a metric for assessing the consensus among votes surrounding the cluster centroids. Specifically, \(\frac{1}{\sigma}\) is utilized to calculate the agreement weighting for the cluster centroids in cluster routing.  
We performed experiments to investigate alternative agreement indicators, including \(\frac{1}{\sqrt{\sigma}}\), \(\frac{1}{\sigma^{2}}\), \(\frac{1}{\sigma^{3}}\), and the mean of absolute difference. However, no significant variations in network performance were observed. Additionally, we explored alternative indicators, such as the L1 norm (Manhattan norm) and the L2 norm (Euclidean norm), utilizing a moving 3D kernel for norm calculations. Despite these efforts, such alternatives led to a decrease in performance.  
The summarized performance results for these investigations are provided in Table 3. Based on these findings, we concluded that \(\frac{1}{\sigma}\) stands out as the optimal choice for the agreement indicator, due to its simplicity and efficiency.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Azimuth (\%)**} & \multicolumn{3}{c}{**Elevation (\%)**} \\
**Model** & **CS** & **CM** & **V2S** & **V1M** & **CS** & **CM** & **V2S** & **V1M** \\
**\#Params** & **2.55M** & **2.89M** & **2.55M** & **2.89M** & **2.55M** & **2.89M** & **2.55M** & **2.89M** \\ \hline Novel View & 83.31 & 84.89 & 83.84 & 85.27 & 83.79 & 84.25 & 84.44 & 83.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of generalization capabilities on the _smallNORB_ data set across various viewpoints.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \(\sqrt{\sigma}\) & \(\sigma\) & \(\sigma^{2}\) & \(\sigma^{3}\) & **MAD** & **L1 Norm** & **L2 Norm** \\ \hline
10.92 \(\pm\) 0.33 & 10.78 \(\pm\) 0.34 & 10.75 \(\pm\) 0.37 & 11.05 \(\pm\) 0.54 & 11.26 \(\pm\) 0.39 & 34.41 \(\pm\) 0.16 & 29.44 \(\pm\) 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of _second variation_ on CIFAR-10 with diverse agreement indicators.
## 4 Results Analysis
### Land-Use Rising/Falling Tupu Analysis
#### 4.3.2 Land-Use Falling Tupu  
The temporal and spatial evolution of land-use changes (LUCs) in Putian City was explored by creating the descending Tupu for the city for the periods from 2000 to 2010 and from 2010 to 2020, as shown in Figure 5. Concurrently, a statistical analysis was performed on the data regarding these transitions, which is detailed in Table 8.  
Based on the information provided in Table 8 and illustrated in Figure 5, it is clear that the area of CL that decreased in Putian City from 2000 to 2010 was 150.94 km\({}^{2}\), which constituted 61.24% of the total reduction in land area. This was followed by decreases in GL and FL, which accounted for 24.38% and 13.64% of the total decrease, respectively. The areas of OL, WB, and UL that experienced a decline were significantly smaller, each representing less than 1% of the total decrease. In terms of the geographical distribution of these decreases, the loss of CL was mainly concentrated in Xianyou County and Xiuyu District, which together made up 55.55% of the overall reduction. Within the central region of Putian City, which includes Chengxiang District and Licheng District, the predominant types of land that decreased were CL and GL, with a combined area of 62.76 km\({}^{2}\). This  
Figure 5: Land-use falling Tupu in Putian City from 2000 to 2020. Sources: Drawings from this study.  
is markedly larger than the newly added area for these two land types, which was only 5.30 km\({}^{2}\) during the same timeframe.  
From 2010 to 2020, the pattern of land type reduction in terms of area and structure in Putian City closely resembled that of the previous period. It is significant to note that while the area of CL that decreased was not considerably different from the prior period, it continued to be the land type with the largest reduction, suggesting that the trend of CL being taken over had persisted. In comparison to the period from 2000 to 2010, there was a substantial decrease in the area of GL that diminished in Xiuyu District, dropping from 32.66 km\({}^{2}\) in the earlier period to 2.31 km\({}^{2}\), which is a reduction of 92.93%. This significant reduction is linked to the establishment of the district, where land that was previously abandoned and had turned into GL was subsequently developed into BL. The reduction in other land types was relatively minor, with no major changes observed. Among all the districts and counties, Xiuyu District had the largest decrease in the area of CL, amounting to 71.56 km\({}^{2}\), followed by Xianyou County with a decrease of 27.65 km\({}^{2}\). Other districts and counties also reported reductions. Specifically, the decreased area for FL and GL in Xianyou County was 12.12 km\({}^{2}\) and 4.46 km\({}^{2}\), respectively, while Hanjiang District saw a decrease in FL of 4.76 km\({}^{2}\).
## 5 Transmission Power Management
### Power Management for SU-Network MIMO  
For a specific CCS, define \(K_{\text{max}}\) to be the power ratio consumed by inter-cells' CEUs, i.e., we partition the total transmitter power \(P_{\text{max}}\) into two parts: power for intra-cell users \(((1-K_{\text{max}})P_{\text{max}})\) and power for inter-cells' CEUs \((K_{\text{max}}P_{\text{max}})\). Accordingly, we can use the power ratio \(K_{\text{max}}\) to limit the maximum power dispensed to inter-cells CEUs. Therefore, the power consumption \(zP_{E}^{\prime}\) is limited within \(K_{\text{max}}P_{\text{max}}\). In a per-sector antenna basis, the procedure of power management is presented as follows.  
1. Initialize the power allocation to each of CCUs and CEUs by \(P_{\rm C}=P_{b}\) and \(P_{E}=P_{E}^{\prime}=\alpha P_{b}\), respectively. 2. Calculate the amount of power consumed by the other cells' CEUs (i.e., \(zP_{E}^{\prime}\)). If \(zP_{E}^{\prime}>K_{\rm max}P_{\rm max}\), then reduce the power allocation for each inter-cell CEU by a factor of \(\rho_{1}\) such that the amount of power consumption by the other cells' CEUs equals the power constraint \(K_{\rm max}P_{\rm max}\), where \(\rho_{1}=K_{\rm max}P_{\rm max}/zP_{E}^{\prime}\);ewise \(\rho_{1}=1\). 3. Next calculate the total transmission power for intra-cell users (i.e., \(xP_{C}+yP_{E}\)). If \(xP_{C}+yP_{E}>(1-K_{\rm max})P_{\rm max}\), then reduce the initial power by a factor of \(\rho_{2}\) such that \(xP_{C}+yP_{E}\) equals the upper bound power \((1-K_{\rm max})P_{\rm max}\), where \(\rho_{2}=(1-K_{\rm max})P_{\rm max}/(xP_{C}+yP_{E})\);ewise \(\rho_{2}=1\). 4. Finally, we achieve \(P_{\rm C}=\rho_{2}P_{b}\), \(P_{E}=\alpha(\rho_{2}P_{b})\) and \(P_{E}^{\prime}=\rho_{1}(\alpha P_{b})\).  
In summary, the method consists of a CEU power coefficient \(\alpha\) and two power reduction factors (\(\rho_{1}\) and \(\rho_{2}\)). The coefficient \(\alpha\) is used to determine the power proportion between a CCU and a CEU. The power allotted to other cells' CEUs can be restricted by power reduction factor \(\rho_{1}\) together with the power ratio \(K_{\rm max}\). As for the power reduction factor \(\rho_{2}\), it prohibits a power requirement from exceeding the corresponding power constraint \((1-K_{\rm max})P_{\rm max}\). To best use the transmitter power, we initially assume that the baseline power \(P_{b}\) is equal to \(P_{\rm max}\). Thus, the two power portions \((1-K_{\rm max})P_{\rm max}\) and \(K_{\rm max}P_{\rm max}\) can be fully used.
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 5 Results and Discussion
### Phase Index  
The phase of the image was obtained by calculating the real part and imaginary part of the 2D image at the ROI. The imaginary part is obtained by transforming the real part using the Hilbert transform. The phase index is calculated as the total value of absolute phase intensity in the ROI divided by the number of pixels in the ROI. Implementation of the phase attribute on the data sets is shown in Figure 6. The figure indicates that the phase index increased consistently from normal to osteoporosis in all modalities of CT scans and DEXA images. Additionally, the visual of these attributes will bring a new appearance of the ROI that is different from the original images, where the osteoporotic bone is visualized with more white spots than the normal one. The image of the selected ROI has a significantly different pattern between normal and osteoporotic bone.
## 3 Localization Algorithm
### The Dynamic Reference Selection-Based Self-Localization Algorithm  
In this section, we will propose a dynamic reference selection-based self-localization algorithm for DUANs. The algorithm combines the optimal reference selection scheme and the prediction-based self-localization algorithm together to select the optimal reference node dynamically. The dynamic reference selection-based self-localization algorithm can solve both the insufficient anchors' problem and the topology changing problem which is more appropriate for DUANs with mobile sensor nodes and can increase the localization accuracy dramatically.
## 5 Experiment
### Datasets and Settings  
**Datasets:** The MN-CH parallel corpus used in this paper contained a wide range of proper nouns or phrases, literary works, computer-related terms, news reports, hiatus and slang, company or organization names, daily chats, etc. We randomly shuffled the corpus after word segmentation and saved sentences with a length of 8-100. The total number of sentences in the filtered MN-CH parallel corpus was 498,921, and the training set, validation set, and test set were divided by a ratio of approximately 0.96:0.02:0.02. The number of sentences in each dataset is shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Corpus** & **MN-CH** \\ \hline Training Set & 479,163 \\ Validation Set & 9979 \\ Test Set & 9779 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of sentences in Traditional Mongolian-Chinese dataset.  
Figure 6: Dependency syntax matrix. The figure shows the rules for the construction of the \(DP\) matrix: the coordinates corresponding to the two words for which there is a dependency are set to one.  
**Settings:** The model training was run under the following experimental conditions. The CPU was an Intel(R) Xeon(R) Gold 6130 with two Nvidia Tesla P100 graphics cards and each graphics card with 12,198 MB of memory, and the server operating system was Ubuntu 16.04.6.The main packages in the Python virtual environment for the experiments were Python 3.6.13, Pytorch 1.7 (CUDA 10.1),torchtext 0.6.0, scikit-learn 0.24.2, sacrebleu 2.0.0, pandas 1.1.5, numpy 1.19.2, matplotlib 3.2.2, and stanfordcornlp 3.9.1.1.  
The hyperparameters of the model were set as follows: word embedding dimension \(d\_model=256\), feedforward fully connected layer of 2048, \(head\_size=8\), \(dropout\_rate=0.2\), \(batch\_size=120\); \(max\_length=100\), AdamW was set with an initial learning rate of 0.0001, \(\beta_{1}\)\(=0.9\), \(\beta_{2}\)\(=0.999\), \(\epsilon=1\times 10^{-8}\), and \(\lambda=0.02\).  
To exclude randomness of the experimental results, all models were trained on the network parameters corresponding to five random seeds, and the average BLEU value was taken as the evaluation metric.  
The Chinese \(DP\) matrix was only used during training. The validation set and test set did not provide any syntactic information, and the \(DP\) matrix extracted from the training set was about 18.3 GB.
## 2 Methodology and Principles
### The Principle of Singular Value Decomposition
#### 2.2.2 SVD Noise Reduction Order Determination
To further determine the change characteristics of neighboring singular values through higher-order changes and avoid threshold screening errors caused by abrupt changes in the singular values of a certain order due to strong noise, the difference between the energy difference spectrum of singular values and the singular value difference spectrum is set as the discriminative threshold. By comparing the difference between the energy difference spectrum and the singular value difference spectrum of the singular values, a discriminative threshold can be established. Larger differences in the energy difference spectrum and the singular value difference spectrum are attributed to signal components, while smaller differences are attributed to noise. This allows for noise reduction and removal, while retaining important signal components, ultimately improving data quality and analysis accuracy. The discriminative threshold can be defined as follows:  
\[\zeta_{threshold}=\frac{\sum_{m=i-j}^{m=i+j}\lvert Q_{1}(m)-Q_{0}(m)\rvert}{2j} \leq\delta\qquad\qquad(m,i\in 1,2,\ldots,k,j=3) \tag{25}\]  
When the difference is less than the preset limit \(\delta\), it means that the current singular value change trend is small; then, the m singular values corresponding to the spectral peaks will be filtered out as excluded singular values, all the m-order singular values will be set to 0, and the rest of them will be retained as valid singular values. According to Equation(24), the SVD inverse transform operation can be performed to obtain the noise-canceled Hankel matrix. Then, the reconstructed noise reduced signal is extracted according to Equation (19).
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 3 Cyberattack Scenarios on Autonomous Ships
### Sensor Data Attack  
Evasion attacks not only include a method of falsifying the objects captured by a camera but also the possibility of attacks such as spoofing and jamming by exploiting vulnerabilities in the ship system. It is possible to attack the ship system and cause malfunction of AI through infringement such as data tampering and disturbance. As shown in Figure 7, if the sensor data used as AI input data are breached by a cyberattack, safe autonomous navigation cannot be guaranteed. For example, the AIS is mandatory on many large ships, but the AIS standard does not take security into account [29].  
AIS messages are not encrypted and are thus at risk of exploitation and manipulation by attackers [30; 31; 32]. Attacks using AIS vulnerabilities have been shown as possible in a number of scenarios, including weather information manipulation, false collision warnings, and denial-of-service attacks [30]. Data tampering and disturbance are expected to have a significant impact on unmanned autonomous navigation. As described above, spoofing, jamming, and DoS are known methods that allow the attacker-attack sensor data. If an evasion attack is used to disturb a camera or lead the sensor data in an automated berthing system to miscalculate the port distance, there is a possibility that the ship will crash into the port. Autonomous ships use many internal or external sensors, including AIS, radar, camera, SONAR, and LiDAR. Through these multiple ocean sensor data, the ship recognizes the situation and detects objects to make appropriate judgments such as course correction and collision avoidance [12; 33]. Attacks that deceive or degrade sensor data can occur on the sensors used to identify ship positions and obstacles, such as the AIS,  
Figure 6: Ship dataset for a poisoning attack scenario.  
Figure 7: Cyberattack scenario of identification data.  
GNSS, camera, RADAR, etc. For example, GNSS spoofing sends false signals to the receiver antenna. Sensor spoofing attacks can cause errors in situational awareness and detection, leading autonomous ships to deviate from their normal course or to behave abnormally. As shown in Figure 8, errors caused by damaged sensor data lead to incorrect ship control via AI and control systems.  
Autonomous driving of cars on land and autonomous aviation technology of airplanes also operates based on sensor data. Although there is some overlap in the study of autonomous navigation in cars and airplanes, the risk must be assessed considering the characteristics of autonomous ships, just as cars and airplanes have different risk profiles [34]. Software security framework, encryption, obfuscate signals, and (remote) attestation have been proposed as countermeasures in opposition to cyberthreats to sensor data, such as AIS, GNSS, and LiDAR, which are essential for ships [29; 35; 36].
## 2 Materials and Methods
### Amplification and Sequencing of PZA Resistance-Associated Genes  
Genomic DNA was extracted from freshly grown Mtb colonies using the MagMAX Total Nucleic Acid Isolation Kit (Ambion, Life Technologies, Carlsbad, NY, USA) as directed by the manufacturer. PZA resistance-associated customary genes for molecular diagnosis (_pnCA_, _panD_, _rpsA_, and _Rv2783c_), including around \(\pm\) 200 bp from their 5' upstream and 3' downstream flanking regions, were amplified in all Mtb isolates using the primers listed in Table 1[12]. PCR amplicons were examined on agarose gels, purified using a PCR purification kit (Qiagen, Hilden, Germany), and sequenced at the BGI Genomics (Beijing Genomics Institute, Guangzhou, China). The sequences were analyzed using BioEdit v7.2.6.1 and compared to the Mtb H37 Rv reference genome (NC_000962.3).