esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Tooth Gear Impact-Echo System for Repeated and Rapid Data Acquisition
## 4 Experiment and Results
### Data Visualization  
The speed of data acquisition by the tooth gear IE system was much faster than the conventional IE test. Based on numerous data, the visualized images help the operator make decisions [22,23]. The signal-processing scheme using a spectrogram was applied to visualize the obtained data from the tooth gear IE system as shown in Figure 9. After each 10 ms long signal was obtained from the tooth gear IE system, the amplitude of the signal was normalized. Next, the signals were added in the time domain--50 signals were used in this study. Spectrogram analysis was performed on the total signal set, based on a short-time Fourier transform, representing the time-frequency information with a color scheme. The dominant frequencies from the total signal set are clearly shown as a band. In addition to the data visualization, the analysis of the total signal set demonstrated the repeatability of the vibration modes during the IE testing. For a signal set of 500 ms length, the windowing samples and numbers of overlap were set to 2048 and 1900 respectively.  
Figures 10 and 11 show the spectrograms from the shallow and deep delaminations between the impactors. From the shallow delamination, a band of 5.4 kHz is clearly shown from all impactors. In the case of the 11-tooth gear, a little noise content was found from the signal set. However, the band of flexural mode frequencies were clearly shown. The thickness mode frequencies from the steel ball were clearly shown in the spectrogram. In the case of the 30-tooth gear, the frequency band was detected while a portion of noise content was also captured. However, the 11-tooth gear exhibited limited results of thickness mode detection due to its long contact duration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Testing Locations**} & \multirow{2}{*}{**Depth (mm)**} & \multicolumn{3}{c}{**Measured Frequency (kHz)**} \\ \cline{3-5}  & & **12 mm Ball Hammer** & **11-Tooth Gear** & **30-Tooth Gear** \\ \hline Shallow & \multirow{2}{*}{60} & \multirow{2}{*}{5.4} & \multirow{2}{*}{10.2} \\ delamination & & & & \\ Deep delamination & 200 & 10.2 & 1.5 & 10.2 \\ \hline \end{tabular}
\end{table}
Table 2: Results of obtained frequency.  
Figure 9: Applied signal processing scheme to visualize frequency information.  
In terms of repeatability, the tooth gear showed great potential for the air-coupled IE test. Air-coupled sensing technology inherently contains noise contents from the field (traffic noise). The impacts of tooth gears are easily repeatable while the system moves and generates the source simultaneously. The large quantity of data obtained improves the decision-making process in the field. In fact, delamination in the field has arbitrary boundary conditions so that the vibration modes and frequency components can be many. Under those conditions, repeatable data with rapid data acquisition helps the analysis of the IE test.
## 3 Results and Discussion
### Unit Weight  
The unit weight results are shown in Figure 8. It can be seen that the unit weights of the specimens follow the order of SSF0 > SF25 > S0F5. When only FA was used to replace 50% of cement (S0F5), the unit weight was 1869 kg/m\({}^{3}\); when both FA and GGBFS were each used to replace 25% of cement (SF25), the unit weight increased to 1885 kg/m\({}^{3}\); finally, when FA was not used and only GGBFS was used to replace 50% of cement (S5F0), the unit weight reached the highest value, 1932 kg/m\({}^{3}\). The higher the content of GGBFS, the more the unit weight of the specimen increases. This is because the specific gravity of GGBFS is greater than that of FA, so a higher content of GGBFS increases the overall density and unit weight of the specimen.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mix No.** & **\(\Delta\)H (cm)** & **Tilt Angle (Degree)** \\ \hline SSF0 & 0.2 & 1.51 \\ SF25 & 0.1 & 1.33 \\ S0F5 & 0.3 & 1.36 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Buildability of OPC/GGBFS/FA mortars.  
Figure 8: Unit weight of 3 DPC.  
Figure 7: Buildability of 3 DPC.
# Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention
## 3 Materials and Methods  
### Dataset Collection  
In this study, multiple data sources were selected to construct a comprehensive and representative dataset for financial attack detection. The dataset comprises publicly available financial transaction records, synthetic data, and anonymized data from financial institutions.  
1. Public financial transaction records: Data from publicly available sources, such as stock and credit card transactions, typically include information on transaction time, amount, and parties involved. The advantages of public data lie in their transparency and accessibility, contributing to the study's general applicability and reproducibility.
2. Synthetic data: Considering the sensitivity and difficulty in obtaining real financial data, synthetic data serve as an essential supplement. Algorithms are utilized to generate synthetic data with realistic characteristics, such as using Monte Carlo simulations for transaction patterns. This approach aids in simulating complex attack scenarios while preserving privacy.
3. Anonymized data from financial institutions: In collaboration with financial institutions, a portion of real financial transaction data were obtained. These data were anonymized before sharing to protect customer privacy. The authenticity and complexity of these data are crucial in enhancing the practicality and accuracy of the model.  
The rationale for selecting these datasets for experimentation is based on several considerations: diversity and representativeness, combining public, synthetic, and real anonymized data to ensure the dataset covers a wide range of scenarios and patterns, thus enhancing the model's generalization ability. Authenticity and reliability, as real data provide a credible benchmark for assessing the model's performance in practical applications. Privacy protection, as the use of anonymized and synthetic data allows for research without disclosing sensitive information.
## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols  
The QoS-aware routing protocols are modular-based protocols and use different modules for different types of QoS metrics. The design of these protocols is a challenging job, due to the complexity of considering different modules for different QoS metrics and coordination between these modules. In the following sections different QoS-aware routing protocols for WBSNs are discussed.
# Extraction of Humic Acid from Lignite by KOH-Hydrothermal Method
## 3 Results
### Effects of the Water-to-Coal Mass Ratio and Coal Size  
The effect of the water-to-coal mass ratio (ranging from 5:1 to 25:1) was then investigated. As shown in Figure 5, an increase in HA yield was observed as the water-to-coal mass ratio increased. When the water-to-coal mass ratio increased from 20:1 to 25:1, the HA yield increased from 90.2% to 91.2%. The increase in HA yield was not obvious. Therefore, 20:1 was considered the optimal water-to-coal mass ratio.  
The effect of different coal sizes on the HA yield is shown in Figure 6. When the coal sample size decreased from 2-0.850 mm to 0.850-0.425 mm, the HA yield slowly increased from 78.53% to 79.13%. The HA yield increased rapidly from 79.13% to 90.2% in the size range from 0.425-0.250 mm to 0.250-0.180 mm but did not increase thereafter. After the raw coal was ground, its specific surface area and pore structure increased [51], and the organic matter in the coal was mildly oxidized, leading to the breakage and deformation of weak chemical bonds and alkyl structure, the reduction of relative molecular mass, and an increase in the content of oxygen-containing functional groups [52]. Therefore, decreasing the coal sample size contributed not only to the contact between the KOH solution and the coal sample but also to the improvement in the HA yield.  
Figure 4: Effect of the reaction temperature on the HA yield.  
Figure 5: Effect of the water-to-coal mass ratio on the HA yield.  
The observations above indicated that a satisfactory yield of HA (90.02%) was obtained from the 0.250-0.180 mm size fraction of the coal sample at a reaction temperature of 190 \({}^{\circ}\)C, reaction time of 7 h, alkali-to-carbon mass ratio of 1:1, and water-to-coal mass ratio of 20:1.
## 3 Efficient Superframe Structures with Adaptive Duty Cycle \((Ess_{ADC})\)
### GTS Request Frame Structure  
In \(Ess_{ADC}\), each CFP slot is halved, which increases the GTS capacity from 7 to 14 without including any new parameter values in the standard. This doubles the GTS capacity in each SD at the cost of marginally increased computing, as each node need has to determine the data slot capacity of each CFP slot \(DS_{Cap}^{Ess_{ADC}}\) with the help of following equation.  
\[CFP^{SLOT}=960\times 2^{SO-3}(bits) \tag{10}\]  
All the \(Node_{GTS}\) calculate their \(GTS_{req}^{Ess_{ADC}}\) in \(Ess_{ADC}\) as:  
\[GTS_{req}^{Ess_{ADC}}=\frac{DR}{DS_{Cap}^{Ess_{ADC}}} \tag{11}\]  
Equation (11) generates a fractional number, that is transmitted by each requesting node to PAN coordinator. In this work, two unused bits \(b6\) and \(b7\) of each GTS requesting frame are used as shown in Figure 3. The value of these bits is determined by computing the value of \(X\) as:  
\[X=\left|\left\lceil log_{2}(\frac{DR}{DS_{Cap}})\right\rceil\right| \tag{12}\]  
Each requesting node fills these two bits as shown in Table 1.  
These bits helps PAN coordinator to know the fractional value of data slots requested by a node. The information of these two bits are only meaningful when node either requests for one GTS or by requesting 14 GTS by sending value from b0 to b3 as 0001 and 1110 respectively. There are chances that the requested data of the node might be smaller in size as compared to the \(DS_{Cap}\). Decrease of 1 in \(SO\), halves the GTS capacity to its previous capacity. That's why, the PAN coordinator needs to know about GTS utilized before decreasing its size. However, b0 to b3 value 1110 means, that a node has requested maximum available GTS. In this case, b6 and b7 will mention, how many more CFP slots, a node requires to send its data completely.  
PAN coordinator at the end of CAP, accumulates all GTS requests and apply the \(ESS_{ADC}\) algorithm as described in Section 3.2.
# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 3 Materials and Methods
### Loop Closure  
The ORB-SLAM3, on which our system is based, utilizes the bag-of-words (BOW) method for loop closure detection. However, BOW-based approaches have limited descriptive capabilities for scenes and tend to lose spatial information about depicted objects. Hence, we replace the original BOW method with global descriptors generated by HFNet. When the local mapping thread receives a new keyframe, it computes its global descriptor vector, denoted as \(gobaldes1\), and saves the current keyframe's global descriptor to the keyframe library. It then calculates the Euclidean norm (\(\ell_{2}\)-norm) between the global descriptor vectors of the current keyframe and other keyframes in the library:  
\[Gdist=\|gdes1-gdes2\|_{2} \tag{18}\]  
Figure 4: In a rotating scene, detect the matching situation before and after frame rotation. The first row uses HFNet descriptors. The second row is the frame identified as rotating, with red points indicating the optimized rotation center. The third row uses ORB descriptors instead.  
Here, \(gdes1\) and \(gdes2\) represent the global descriptors extracted by HFNet, each consisting of 4096 floating-point numbers. After obtaining global descriptors for keyframes, the system calculates the distance with all stored global descriptors in the keyframe library. A smaller \(Gdist\) indicates higher similarity between the two frames, increasing the likelihood of a loop closure. The system selects frames with the highest similarity as loop closure candidates based on their similarity with all descriptors in the keyframe library. Following this, in a manner akin to ORB-SLAM3, a geometric verification is conducted on co-visible keyframes to ascertain the occurrence of a loop closure. Our approach effectively leverages the global descriptor capabilities of HFNet, providing more accurate descriptions of keyframes compared to ORB-SLAM3 in experiments. This leads to more accurate loop closure detection, thereby mitigating substantial trajectory deviations.
## 2 Materials and Methods
### Time-Series MODIS Data Processing
#### 2.3.2 Crop Masks  
Crop masks could be generated using crop classification maps; however, annual crop inventory maps in Canada became operational only after 2011. In this study, cropland masks were generated from the MODIS land cover product, and classification of the time-series reflectance data was conducted using a fuzzy decision tree algorithm [31]. Firstly, a general cropland mask was generated by merging classification codes 12 (cropland) and 14 (cropland/natural vegetation mosaic) in the land cover type-1 classifications in the MCD12Q1 product. A pixel was included in the cropland mask if it was classified as class 12 or class 14 in any of the years between 2000 and 2014. The data were then resampled from 500 m into 250 m resolution using the nearest neighbor method to match that of the reflectance data. Secondly, the TIMESAT software package [37; 38] was used to model the time-series EVI2 using logistic functions, from which several phenological indicators characterizing the shape of the seasonal growth curves were defined, such as the maximum EVI2, the time when peak EVI2 (85% of the maximum) started and ended, and the time span when EVI2 was larger than 60% peak level. Thirdly, a fuzzy decision tree classifier based on the phenological indicators was developed to identify the major crop types in this region--com, soybean, winter wheat, and forage. The classifier was trained using 30 m high resolution annual crop inventory map of 2013 [39], resampled to the pixel size of the MODIS products, and tested using crop inventory maps for 2011 and 2012. The accuracies for the two tested years were comparable with the accuracy of the training year, with an overall accuracy about 75%. Detailed information on the classification algorithm and results are reported in [PERSON] et al. [31].  
The classifier was applied to identify pure pixels of the major crop types annually between 2003 and 2016, and annual crop-specific masks were derived for MODIS data extraction.
## 4 Application of the Design Method
### Validation and Modification of the Isolation System  
It can be found from Table 5 that the efficiency of the isolation system (i.e., \(\beta\)) and the stresses of the isolators under the gravity and seismic loads all satisfy the requirements specified in the GB50011-2010 [17], which is consistent with the expected results of the proposed design method. However, the MBD under the MCE with a value of 788 mm exceeds the limit value of 500 mm. As expected and recommended by the proposed method, the NFVDs are introduced for further control.  
From the design experience of the 29 seismically isolated tall buildings conducted by the authors, 16 NFVDs are adopted in the isolation system as shown in Figure 16. The parameters of the NFVDs are determined by a few number of trials. When the damping exponent and the damping coefficient reach 0.3 and 220 kN\(\cdot\)s/mm, respectively, the MBD under the MCE decreases to 493 mm, which is lower than 500 mm. The maximum damping forces of these NFVDs are approximately 2000 kN. The corresponding critical design parameters and indexes are presented in Table 6. It is obvious that the seismically isolated structure achieves the expected seismic performance.  
The above discussion demonstrates that the numbers, sizes and types of isolators can be directly determined with the proposed method. Only limited numbers of iteration are conducted to determine  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Value** \\ \hline \(T_{\text{f}}\) (s) & 1.798 \\ \(T_{\text{is}}\) (s) & 4.294 \\ \(Q_{\text{y}}/W\) (\%) & 2.3 \\ \(\beta\) & 0.37 \(<\) 0.38 \\ _MBD_ (mm) & 492 \(<\) 500 \\ \(\sigma_{\text{par}}^{B}\) (MPa) & 11.1 \(<\) 15 \\ \(\sigma_{\text{max}}^{\text{t}}\) (MPa) & 27.5 \(<\) 30 \\ \(\sigma_{\text{max}}^{\text{t}}\) (MPa) & 0.00 \(<\) 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Critical design parameters and indexes of the isolated tall building with NFVDs.  
Figure 16: Isolation system with nonlinear fluid viscous dampers (NFVDs) of the RC frame-core tube building.  
the quantity and the properties of the NFVDs. The efficiency can be substantially increased with the proposed design method.
# Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
## 2 Luojia-1A Satellite Platform  
Luojia-1A is a small scientific experimental satellite with night light remote sensing and LEO signal navigation augmentation functions. The weight of the satellite is 19.8 kg, and its size is \(520\times 870\times 390\) mm. Luojia-1A satellite is operated in a sun-synchronous orbit at 645 km. The outlook of Luojia-1A satellite is illustrated in Figure 1, three GNSS antennas were installed: two of them are used to receive GPS/BDS dual-frequency signals in the opposite direction, and one antenna is used to transmit the navigation augmentation signals to users. Besides, a night light camera and a satellite communication antenna are installed on the +Z side of the satellite. Luojia-1A satellite can receive the GPS/BDS dual-frequency signals and provide autonomous orbit and timing information onboard and transmit dual-frequency ranging signals to the users.  
A satellite-body-fixed (SBF) coordinate system of Luojia-1A is established for LEO POD, which is the right-handed coordinate system. The origin of the SBF system is at the center of mass (COM) of the satellite. The +X axis is aligned to the satellite velocity direction. The +Z axis points to the optical camera, and the +Y axis is perpendicular to the XOZ plane. Moreover, two receive antennas of Luojia-1A are in the +Z and \(-\)Z direction, respectively. The +Z axis point to the earth during the data transferring, and the solar panel is kept facing the sun in standby mode. The small size of the satellite body makes the offset between the GNSS antennas and the satellite COM at the decimeter level, which is smaller than most satellites. The satellite attitude mode of the Luojia-1A satellite is also special since the satellite keeps its solar panel facing the sun to receive more solar power, and it turns its +Z side facing to the earth during the mission period. Most navigation satellites always keep their +Z side toward the earth since they continuously transmit navigation signals. Hence, the Luojia-1A satellite is more complicated, which also affects the BeiDou signal tracking.
# Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity
## 3 On the Performance Improvement of Hybrid NoCs
### Adaptive Routing
#### 3.2.3 Deadlock Avoidance  
Routing in this router is minimal and fully adaptive and is hence prone to be deadlocked. To break the cycles in the resource dependency graph [52], we require at least two VCs (VC0 and VC1) in each VN. A packet is assigned to a VC during injection according to the position of its destination. Packets with a destination located at the left and right side of its source node are assigned to VC0 and VC1, respectively. If a packet's destination is in the same column with the source node, the packet can be assigned to either VC randomly or according to congestion status. As the routing is minimal, turns in neither VC form a circle. Therefore, both VC0 and VC1 are deadlock-free.  
Packets from all VCs have the chance to use the SVC buffer, so SVC can potentially be shared media that chain the turns of VC0 and VC1 to form a circle. To prevent this deadlock configuration, we only allow one packet to stay in the SVC buffer. This is achieved by controlling SVC tagging: a head filt will be tagged SVC only when the downstream SVC buffer is empty as imposed by the second rule in Section 3.1.3. Because SVC contains at most one packet, it will not chain up the turns of different VCs. The rules above all together guarantee a deadlock-free network. Sharing the SVC is also protocol-level deadlock-free. Suppose all SVCs are occupied by a certain class of message; messages of other classes can still reach their destination through the normal VCs, which are guaranteed to drain. Therefore, there will not be dependency between different classes of messages, making the network protocol-level deadlock-free.  
Figure 8: Router micro-architecture.
# Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China
###### Abstract  
The collapsibility of aeolian sand has hindered the development of oil and gas resources and the construction of oil and gas stations in the Mu Us Desert. This study considered aeolian sand on the southern edge of the Mu Us Desert as the research object. Based on a water immersion load test, standard penetration test, and indoor geotechnical test, four evaluation indicators were selected, the water content, dry density, void ratio, and saturation. Combined with the support vector machine method, we established a method for evaluating the collapsibility of aeolian sand based on basic physical indicators. The results showed the following: (1) The degree of collapsibility was slight, with a small portion showing no collapsibility. And the load-settlement curve (P-s) was divided into three stages: the linear elastic deformation stage, the elastic-plastic deformation stage, and the collapsible deformation stage. (2) There was a strong relationship between the collapsibility coefficient and the four evaluation indicators for aeolian sand. Based on these indicators, we could accurately predict and evaluate the collapsibility coefficient. (3) Machine learning methods, such as the support vector machine, can effectively solve prediction and evaluation problems between variables when there is no clear mathematical relationship between multiple independent variables and a single dependent variable.  
Mu Us Desert; aeolian sand; collapsibility evaluation; basic physical indicators; support vector machine +
Footnote †: journal: applied sciences  
0000-0002-414-3099][PERSON]  
1 School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China; [EMAIL_ADDRESS]
## 4 Experimental Results
### Horizontal Supplementary Recognition  
The correlation segmentation method can recognize and extract point targets and streaks with 1D Gaussian morphology. We choose two simple scenes for analysis, and the difference between the two scenes is whether a point object in the image is close to a streak, as shown in Figure 6. It is found that the vertical Gaussian features of objects at the upper and lower edges are not significant, so the identified point object is a strip and the longitudinal edges are lost. Besides, when an object and a star are approaching vertically, the column recognition method is influenced, making the point object unrecognizable. Therefore, the horizontal supplementary recognition should be carried out at the upper and lower lines (about 2 lines) of the identified objects and stars by using the same modeling method. This has a good supplementary effect on point object recognition but only has low impact on the recognition of extending object streaks.