esa-sceva/satcom-chunk-collection huggingface dataset:
# Field Investigation and Rapid Deterioration Analysis of Heavy Haul Corrugation
## 3 Observation of Rapid Deterioration of Rail Corrugation under Poor Grinding Quality
### Reasons for the Rapid Development of Corrugation  
From the figures and data shown above, we can conclude that the grinding works will leave grinding marks because of the rolling of the grinding wheels. Roughness will be increased by the grinding marks, but it will also decrease in a short period of time because of the wear or plastic deformation between the heavy haul wheel and the rail. However, when it comes to the poor-quality grinding works, it seems there are no effects on the outer rail, while the roughness level of the inner rail increases sharply. Railhead photos taken after the two grinding processes are needed to attain reliable answers to the questions above.  
Figure 5a is a picture of the rail surface after the initial grinding on site. It can be seen from the picture that, although the rail was ground and repaired, the central crack in the rail's surface could not be eliminated entirely. This is due to the unregular profile of the inner rail that is caused by severe corrugation defects.  
Figure 5b is a picture of the rail surface after the second grinding car passed by on the 24 th day after grinding. Compared with the initial grinding, the rail's surface damage was not eliminated after the second grinding vehicle with poor grinding quality was used. Instead, there are more obvious grinding marks at the center of the rail's top where the fatigue cracks are concentrated, and they intersect with the original fatigue cracks.  
Regarding the effect of grinding marks on the outer rail, the grinding marks become worn in a short period of time. This is because there are no defects on the outer rail, as shown in Figure 1c. Combining all of the points all, the rapid deterioration of corrugation observed in the field under poor grinding quality could be summarized as follows:  
When the second grinding car passed by on the 24 th day of the test, poor grinding quality led to the interference between the grinding marks and the surface cracks. This made the rail's surface cracks change from a stable state to an unstable state, leading to further development. Crack propagation leads to a decrease in the overall anti-plastic ability of a rail's surface [30], in this case, the ratcheting and hardening mechanisms come into play. Under the multi-axis loading state from additional higher heavy haul vehicle loads, a rail's surface condition deteriorates rapidly, the plastic flow on the rail's surface becomes aggravated, and corrugation develops rapidly. Induced corrugation will further increase the wheel and rail forces and accelerate the evolution of plastic flow corrugation to a point beyond the allowable safety operating standards. In the whole process, residual marks accelerated the evolution process of corrugation and made the corrugation reach a rapid developmental stage.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 3 Networked Microgrids' Control
### Control
#### 3.2.2 Control Modes  
Control modes in the realm of networked microgrids encompass two fundamental approaches: master-slave and peer-to-peer control modes. In the master-slave control mode, a central controller, known as the master controller, takes charge of managing and making decisions for the entire networked microgrid. The other components or nodes, referred to as slaves, obediently execute the commands and instructions issued by the master controller. This mode is frequently utilized in situations involving NMGs that necessitate centralized control, encompassing both islanding scenarios [149; 112; 113; 109] and grid-connected setups [110; 111].  
Conversely, the peer-to-peer (P2P) control mode empowers diverse microgrid components or nodes to engage in direct communication and collaboration. In this setup, each node possesses the autonomy to make independent decisions and can seamlessly exchange information with other nodes as the situation demands. This mode is particularly well suited for dynamic and adaptable microgrid configurations, facilitating enhanced flexibility and responsiveness. A P2P control architecture is proposed for NMGs in studies [138; 140; 143; 148; 150; 151; 152; 153; 154; 155; 156], integrating multiple layers and facilitating interactions among different agents such as utility, MGs, and smart parking lots. The outcomes presented in paper [158] indicate that the P2P control architecture can reduce the annual energy cost of trading energy among microgrids by 0.75% while ensuring the maintenance of system reliability. P2P control relies on bidirectional network communication, rendering the system vulnerable to various attacks, including private data leakage, data breaches, collusion, distributed denial of service, and man-in-the-middle attacks [159; 160]. To tackle these challenges, several solutions, such as blockchain-based methods [143; 157], are proposed.
# Area-Time Efficient Hardware Architecture for CRYSTALS-Kyber
## 4 Implementation Results  
The proposed architectures for Kyber are modeled using Verilog HDL and synthesized using the Xilinx Vivado 2020.1. The implementation results are the placed-and-routed data on Xilinx Virtex-7. The results for key generation and encryption/decryption are presented in Tables 2 and 3, respectively.  
From Table 2, the proposed key generation architecture requires 22K LUTs, 12K FFs, 21 DSPs, and 4.5 BRAMs. As can be seen, the proposed key generation architecture can operate at a maximum frequency of 217 MHz. Public key _pk_ and secret key _sk_ are successfully generated after only 39 \(\upmu\)s. As a result, the proposed key generation architecture achieves the area-to-time value of 0.86.  
Table 3 describes a comparison between the proposed Kyber architecture with the most recent works [12, 13, 14, 22]. The reported values of the proposed work in Table 3 are for encryption and decryption operations. Generally, the proposed architecture offers the best value of area-time product. The proposed architecture requires 29,718 LUTs, 22,556 FFs, 71 DSPs, and 11 BRAMs to perform Kyber encryption and decryption operations. These hardware resource requirements are substantially lower than those of the architecture in [13]. The hardware cost of the proposed architecture is slightly higher than that of the architectures in [12,22]. However, the proposed architecture outperforms those architectures in terms of processing time and area-time product. Specifically, the total encryption and decryption time of the proposed architecture is approximately 57.5 \(\upmu\)s, which is approximately 3.6 times and 2.7 times faster than that of architectures in [12,14], respectively. Noticeably, the proposed architecture is much better than that of architectures in [13,22] in terms of latency. Furthermore, the area-time product value of the proposed architecture  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **This Work** \\ \hline Devices & Virtex-7 \\ \hline LUTs & 22K \\ \hline FFs & 12K \\ \hline DSPs & 21 \\ \hline BRAMs & 4.5 \\ \hline Frequency (MHz) & 217 \\ \hline Time (\(\upmu\)s) & **39** \\ \hline Area \(\times\) Time (LUTs \(\times\)_s_) & **0.86** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Implementation results of Kyber key generation architectures.  
Figure 7: Proposed decryption architecture.  
is 1.68, which is approximately half of that of the architectures in [12; 14]. Specifically, this area-time product value is approximately 189 times and 306 times smaller than that of the designs in [13; 22]. Furthermore, the proposed architecture can operate at the highest frequency of 226 MHz, which is higher than the frequency at which architectures in [12; 14; 14] operate.
## 2 Hybrid Framework Description
### ESKF Implementation
#### 2.4.1 Error-State Model  
Referring to [53], the continuous-time error-state model without a control input can be defined as:  
\[\delta\dot{\mathbf{x}}_{k}=\mathbf{F}_{c}\delta\mathbf{x}_{k}+\mathbf{G}_{c} \mathbf{w}_{k}, \tag{7}\]  
where \(\mathbf{F}_{c}\) is the system matrix, \(\mathbf{G}_{c}\) is the process noise gain matrix, and \(\mathbf{w}\) is the process noise of the inertial sensor (\(\mathbf{w}_{a}\) for the accelerometer and \(\mathbf{w}_{g}\) for the gyro), i.e.,  
\[\mathbf{w}=\left[\begin{array}{cc}\mathbf{w}_{a}&\mathbf{w}_{g}\end{array} \right]^{T}. \tag{8}\]  
The system matrix and process noise gain matrix can be written as  
\[\mathbf{F}_{c}=\left[\begin{array}{ccc}\mathbf{0}_{3\times 3}&\mathbf{0}_{3 \times 3}&\mathbf{0}_{3\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{0}_{3\times 3}&\mathbf{I}_{3\times 3}\\ -\mathbf{S}\left(\mathbf{f}_{k}^{n}\right)&\mathbf{0}_{3\times 3}&\mathbf{0}_{3 \times 3}\end{array}\right] \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State of Heading Error Correction Algorithms** & \multicolumn{2}{c}{**Weight**} \\ \hline
**EC** & **HDR** & \(\boldsymbol{a}_{m}\) & \(\boldsymbol{a}_{R}\) \\ \hline
1 & 0 & 0 & 0 \\
1 & 1 & \(\lambda_{1}\) & \(\lambda_{2}\) \\
2 & 0 & 1 & 0 \\
2 & 1 & \(\lambda_{3}\) & \(\lambda_{4}\) \\ \hline \hline \end{tabular}
\end{table}
and  
\[\mathbf{G}_{c}=\left[\begin{array}{cc}\mathbf{0}_{3\times 3}&\mathbf{I}_{3 \times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{0}_{3\times 3}\\ \mathbf{I}_{3\times 3}&\mathbf{0}_{3\times 3}\end{array}\right], \tag{10}\]  
where \(\mathbf{S}\big{(}\mathbf{f}_{k}^{n}\big{)}\) represents the skew-symmetric cross-product-operator matrix of the acceleration under the n-frame. This matrix relates the variation in the velocity error to the variation in the attitude error. The detailed derivations of the system matrix and process noise gain matrix are given in [53].  
Since a computer is a discrete-time system, we need to discretize the continuous-time model given above. According to [54], the discrete-time error state model can be written as:  
\[\delta\mathbf{x}_{k|k-1}=\mathbf{F}\delta\mathbf{x}_{k-1|k-1}+\mathbf{G} \mathbf{w}_{k-1}, \tag{11}\]  
where \(\delta\mathbf{x}_{k|k-1}\) is the predicted error-state vector at time \(k\) and \(\delta\mathbf{x}_{k-1|k-1}\) is the filtered error-state vector at time \(k-1\).  
Applying a first-order Taylor series expansion to Equation (7), the system matrix (\(\mathbf{F}\approx\mathbf{I}+\mathbf{F}_{c}\Delta t\)) and noise gain matrix (\(\mathbf{G}\approx\mathbf{G}_{c}\Delta t\)) of the discrete-time model can be obtained, where \(\Delta t\) is the sample interval.
# Effect of LNR-g-MMA on the Mechanical Properties and Lifetime Estimation of PLA/PP Blends
## 3 Results and Discussion
### FTIR Spectroscopy  
Using FT\(-\)IR analysis, as shown in Figure 4, the functionalities of PP, PLA, and PP/PLA blends with and without a compatibilizer were examined. The transmittance bands of PLA (Figure 4a) at 1746, 1178, and 1084 cm\({}^{-1}\) referred to C=O stretching, symmetric C-O-C stretching, and asymmetric CH\({}_{3}\), respectively. The transmittance bands of PP (Figure 4b) at 2918-2835, 1457, and 1375 cm\({}^{-1}\) were assigned to C-H stretching, -CH\({}_{3}\) bending, and C-H bending, respectively. For PLA/PP blends of ratios 70:30 wt%, the transmittance bands that represent PP and PLA were observed in the polymer blends around 2918-2835, 1746, 1457, 1375, 1178, and 1084 cm\({}^{-1}\) (Figure 4c,d). However, the characteristic spectrum of the ester linkage of (Figure 4e) LNR\(-\)g\(-\)MMA between 1729 and 837 cm\({}^{-1}\) was not observed.  
Figure 3: SEM micrograph of the tensile fractured specimen for PLA, PP, and PLA/PP blends with LNR\(-\)g\(-\)MMA as a compatibilizer. (**a**) PLA, (**b**) PP, (**c**) PLA/PP/0.0, (**d**) PLA/PP/2.5, (**e**) PLA/PP/5.0, and (**f**) PLA/PP/10.0. The yellow lines indicate the diameter.
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
## 4 Optimization Algorithm of Tension Distribution
### Factors Affecting Minimum Cable Tension
#### 4.5.4 Stiffness Coefficient  
The stiffness coefficient is an important parameter used to measure the stiffness of a cable system. Improving the rigidity of the system is helpful to improve its stability and control accuracy.  
The above parameters are the key factors that affect the tension distribution of the cable robot. By setting and optimizing these parameters reasonably, the minimum value of the tension equation can be reduced effectively. In the specific optimization process, the firefly algorithm and other optimization methods are used to find the optimal parameter combination through iterative calculation so that the tension distribution is more uniform and continuous.
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 4 Proposed System Setup  
A single bit in error or burst of errors in the received bitstream may influence the correct decoding of several succeeding frames in the predictive coding paradigm. Therefore, a clever system has to be designed to ensure an acceptable video quality in limiting the channel errors to a minimum acceptable level. This work proposes a complex video phone arrangement for the transmission of video bit-stream on a correlated Rayleigh fading channel, as shown in Figure 3. The different fragments of the proposed video phone arrangements are explained in the following subsections.
## 5 Discussion
### Difference in Input Wavelengths  
Based on detection performance comparisons in Section 4.3, a histogram-based analysis was included to identify why models trained with RGB and RE inputs showed better performance than other wavelength combinations under spatiotemporal conditions. To visualize distributions of each wavelength reflection in Figure 10, we randomly selected 100 multispectral images in the train, MBS, GNM, and ABD datasets, respectively. Two-sample t-tests were then performed between each wavelength distribution in the training dataset and three testing datasets. The null hypothesis states that means of each wavelength reflection for the training and testing dataset are equal. As shown in Table 3, \(p\)-values were zero, indicating that we could reject the null hypothesis. It demonstrated that a discrepancy exists between the wavelength distributions of the training and test sets. From these results, we claim that such differences caused the performance to degrade in models trained with RGB, RE, and NIR naively stacked along the channel dimension. [44]. Furthermore, we believe that these differences caused multispectral models to overfit to the training data as well. A recent study pointed out that naively stacking RGB with NIR/RE bandwidths can result in redundant information and mutual interference when training CNNs [45]. As shown in Table 2, we also observed a similar phenomenon. Because models with RGB and RE outperformed models with the other combinations of wavelengths, while models with RGB, RE, and NIR showed the worst detection performance. In addition, improvement for the performance of models with RGB and RE was larger than the improvement with RGB and NIR. Based on the results, we claim that RE wavelength could have a more positive effect on identifying cabbage fields under spatial and temporal differences than NIR wavelength. We believe that our proposed method can be improved by incorporating several encoders to learn separate low-level representations for each wavelength including RE and NIR.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**MBS**} & \multicolumn{4}{c}{**GNM**} & \multicolumn{4}{c}{**ABD**} \\  & **B** & **G** & **R** & **RE** & **NIR** & **B** & **G** & **R** & **RE** & **NIR** & **B** & **G** & **R** & **RE** & **NIR** \\ \hline T-statistic & 989.70 & 602.73 & 858.70 & 3540.27 & 6767.05 & \(-\)280.60 & \(-\)298.04 & 419.88 & 155.37 & 2394.65 & 1309.56 & 2190.63 & 1251.48 & 4035.60 & 7144.72 \\ \(p\)-value & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Two-sample t-test results between each wavelength reflection distribution in the training and testing sets, including MBS, GNM, and ABD.
## 3 Results and Discussion  
### Discontinuous LBI Measurement  
Figure 3 shows the backscattering images of oil samples from discontinuous LBI measurement. At Day 0, the crude oil displayed a typical backscattering pattern on the sample surface. Gradually, the laser images showed a linear shape with increasing length over time, indicating the path of the laser light (Tyndall effect). The intensity of the linear pattern decreased in the direction of light transmission. For the red laser, an additional scattering pattern was observed on the right side of the image due to excessive light reaching the bottom of the sample container. Notably, yellow hues were visible between the overexposed center and the surrounding scattering area for the red laser, and purple hues for the blue laser.  
At Day 0 of sedimentation, the round scattering spot can be attributed to the laser light interacting with particles that were still uniformly dispersed in the crude oil. Similar scattering spots were observed by other scientists when handling bio-materials [34; 35; 36] due to the porous nature of bio-materials. [PERSON] et al. suggested that this scattering spot should be related to the particle size of the material [37]. As the oil became clearer, laser light could travel deeper because there were fewer particles present to scatter and absorb the light, causing the scattering spot at the incident point to shrink. The Tyndall effect suggests that some particles remained within the oil even after sedimentation, which aligns with the research findings by [PERSON] and [PERSON] [15]. They found that the sedimentation process predominantly impacts particles of substantial size. [PERSON] et al. applied the Tyndall effect to detect Hg\({}^{2+}\) in a colloidal solution and found that this method increased accuracy by 5400 times compared to the traditional approach [38].
## 5 Experiment and Analysis
### Evaluation of SAPRM
#### 5.2.2 Ablation Study on SAPRM  
To demonstrate the positive impact of each part of the SAPRM on the overall performance, ablation experiments are conducted, and the results are shown in Table 5.  
We conduct the ablation study from four aspects. For the number of layers in the self-attention module, we attempt one layer and two layers. Due to the limitation of data scale, increasing the self-attention module to two layers results in the model failing to converge due to overly complex parameters. Therefore, setting the number of layers to 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models \(\dagger\)**} & \multicolumn{3}{c}{**NER**} & \multicolumn{3}{c}{**RE**} \\ \cline{2-7}  & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline TPLinker & - & - & - & 58.65 & 48.14 & 52.88 \\ PRGC & - & - & - & 65.43 & 54.12 & 59.24 \\ PFN & 84.98 & 79.95 & 82.39 & 76.79 & 64.84 & 70.31 \\ TablERT-CNN & 85.64 & 84.79 & 85.10 & 67.20 & 70.07 & 68.54 \\ SAPRM (Our Model) & 86.70 & 84.04 & 85.35 & 79.48 & 70.32 & 74.62 \\ \hline \hline \end{tabular} The symbol \(\dagger\) means the usage of BERT-BASE-CHINESE as encoder.  
\end{table}
Table 4: Experiment results of SAPRM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **SAPRM** & **Alignment** & **Explanation** \\ \hline epoch & 200 & 20 & number of train epoch \\ batch size & 20 & 8 & number of samples in one batch \\ lr & \(2\times 10^{-5}\) & \(2\times 10^{-5}\) & learning rate \\ weight decay & 0 & \(1\times 10^{-4}\) & weight decaying rate \\ dropout & 0.1 & 0.1 & dropout rate for input \\ clip & 0.25 & 0 & grad norm clip \\ \(\lambda\) & 1 & - & proportion of NER and RE loss \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters of SAPRM and knowledge alignment algorithm.  
Then, if the self-attention module is removed, the performance of SAPRM decreases significantly. For the NER task, the accuracy decreases by 6.38%, and for RE task, the accuracy decreases by 3.18%. This indicates that the self-attention module plays an important role in SAPRM.  
For the partition module, if partition is not performed and knowledge extraction is conducted directly, the performance of SAPRM is also significantly affected. For NER task, the accuracy decreases by 7.85%, and for RE task, the accuracy decreases by 4.74%. This illustrates the necessity and effectiveness of segmenting the training sequence into three partitions: entity-related, shared, and relation-related.  
Additionally, if the global mechanism is removed, it also affects the overall performance of SAPRM. Consequently, combining local features and global contexts is necessary in improving performance of SAPRM.  
The ablation study demonstrates that each module of our SAPRM model plays an indispensable positive role in the results, and proves the effectiveness of this model in handling knowledge extraction tasks in quality engineering of continuous casting.
## 7 Limitations
### Limitations of the Dataset and Annotations  
The dataset used in this study is primarily composed of images from drill-and-blast tunnels and shield tunnels, with sample distributions concentrated in common leakage patterns such as area-type and linear seepage. Rare forms of leakage, such as honeycomb-like seepage or intermittent dripping, are underrepresented, potentially limiting the model's generalization capability. Additionally, manual annotation introduces subjectivity, especially in cases involving mixed-form leakage where seepage and efflorescence coexist. In such cases, significant inter-annotator variation in IoU was observed, which can affect training accuracy. Future work should focus on constructing a multi-source heterogeneous dataset that incorporates tunnel images under various geological conditions and construction methods, alongside implementing expert consensus-based annotation to improve data quality.