esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 4 Implementation and Experimental Analysis
### Experiment Scenarios
#### 4.2.3 ZKP-Based Verification Performance  
The objective is to evaluate the performance impact and accuracy of using zero-knowledge proofs for data integrity verification, comparing it to traditional hash-based verification. We measure how quickly and reliably the system can perform a ZKP verification of a private data record. For this test, we focused on a representative private dataset. We ran our ZKP generation and verification process on the data. Specifically, we generated a ZK proof \(\pi\)(D_priv) and posted it to Ethereum, then later regenerated a proof \(\pi\)(D_priv') for the same data and ran the verification check. We recorded the time taken for each step--especially the proof generation time and the proof verification time--as these are critical to performance. We also conducted a baseline measurement, i.e., computing just a hash of the data and comparing hashes to see the difference in overhead. Each test was repeated multiple times to obtain an average timing and to ensure consistency.  
The ZKP-based verification succeeded in every trial, correctly validating the data integrity each time. Figure 2 provides a comparison of transaction throughput between centralized and hybrid blockchain systems. On average, generating and verifying the ZK proof for a single data item took about 0.73 s. In comparison, a simple hash computation and comparison for the same data took only around 0.1-0.2 s on average. Thus, the ZKP approach was somewhat slower--roughly on the order of a second--whereas direct hash comparison was near-instantaneous. The roughly 0.73 s delay stems from the computational complexity of ZK proof algorithms and the on-chain verification cost. However, this level of performance remains feasible for many use cases. For example, verifying a piece of data in under a second is acceptable for periodic integrity checks or for data that isn't requested constantly. Furthermore, during this experiment, we found that the verification process was accurate. Whenever we intentionally altered the private data and generated a new proof, the verification failed, indicating detection of tampering. There were no instances where tampered data passed the ZKP check or where untampered data failed it, confirming the reliability of the cryptographic method. In summary, ZKP-based verification adds a slight overhead compared to basic hashing, but provides the invaluable benefit of privacy-preserving integrity checks. We consider this overhead to be a reasonable trade-off in the context of space missions, where data integrity and confidentiality are paramount. Potential optimizations could further reduce this delay in the future.
# An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars
## 4 Results
### Optically Very Thin Ice Clouds Horizontal Extent  
In addition to understanding how the optically very thin ice clouds reveal themselves temporally in the observations (see the Case Study), the horizontal extent of the optically very thin ice clouds is also of interest. The horizontal extent of these clouds and ice clouds more broadly are necessary to improve projections of climatic effects [48]. For the RL, the temporal extent of the optically very thin ice clouds can be translated into the horizontal extent by incorporating the winds from nearby radiosondes. For each transparent profile with an ice cloud column optical depth below 0.01, the closest radiosonde to the profile's time is found and the wind speed is matched by height for all ice clouds observed in the profile. The wind speed is multiplied by the time step (i.e., 8 min for SGP and 24 min for TWP) to yield a horizontal distance. The column horizontal extent is then determined as the mean horizontal distance of ice clouds observed within the profile. The group horizontal extent is therefore the sum of the column horizontal extent in a group of optically very thin ice clouds (i.e., continuous transparent ice cloud profiles of optically very thin ice clouds, identical to that defined in Figure 4). For CALIPSO, the horizontal extent can be calculated by summing the number of 5 km profiles for each group of optically very thin ice clouds.  
Figure 6 shows the probability distribution functions (PDF) of horizontal extent for optically very thin ice cloud groups observed by the RL and CALIPSO. CALIPSO observes a smaller mean and median horizontal extent compared to the RL over both SGP and TWP. This aligns with the observation that the higher sensitivity RL observes more optically very thin ice clouds. Therefore, the RL likely observes more of the tail end of a group of optically very thin ice clouds compared to CALIPSO, which would increase the horizontal extent observed overall and result in a higher mean and median horizontal extent for the RL. The distinct peaks in the CALIPSO PDFs are the result of CALIPSO's retrieval scheme. CALIPSO retrieves optical depths for clouds detected at horizontal averaging distances of 5 km, 20 km and 80 km [44]. Therefore, the peaks correspond to these horizontal averaging distances and any combination of the horizontal averaging distances.  
Figure 6: Probability distribution functions (PDFs) of the horizontal extent for groups of continuous transparent ice cloud profiles of ice cloud column optical depths below 0.01 for the RL (solid black) and CALIPSO (dashed red) over the (**a**) SGP and (**b**) TWP sites. Mean and median values are given in the parentheses. Data for the RL at the SGP site are from August 2008 to August 2016 and at the TWP site from December 2010 to January 2015. Data for CALIPSO for both the SGP and TWP site are from June 2006 to August 2016.
# On the Use of Deep Learning for Video Classification
## 3 Video Classification
### Breakthroughs  
The breakthroughs in recognition of still-images originated with the introduction of a deep learning model called AlexNet [13]. The same concept of still-image recognition using deep learning is also extended for videos, where individual video frames are collectively processed as images by a deep learning model to predict the contents of a video. The features from individual video frames are extracted, and then, temporal integration of such features into a fixed-size descriptor using pooling is performed. The task is either done using high-dimensional feature encoding [45; 46] or through the RNN architectures [47; 48; 49; 50]. For un-supervised spatiotemporal feature learning in 3D convolutions, restricted Boltzmann machines [51] and stacked ISA [52] are also studied in parallel. The 3D-CNNs using temporal convolutions to extract temporal features automatically were first proposed by [PERSON] et al. [53] and by [PERSON] et al. [54].
## 3 Results and Discussion
### Sequential Batch Photobioreactor Experimentation with Duckweed and microalgae
#### 3.3.3 Microalgae bioremediation in Duckweed-Treated RW Media
(**F**) Photograph showing part of the effluents finally treated or clarified by microalgal cultivation on the last day of treatment. Bars represent standard error. Interestingly, while NO3 -RE% appears unaffected by variations in MEDIUM (i.e., TRW%) and ALGA (Figure S4), other pollutants exhibit differential responses, suggesting complex interactions between culture medium composition and microalgal species. For instance, the decreasing trend of COD-RE% with increasing RW% concentration highlights a potential inhibitory effect of higher RW% levels on COD removal, being more pronounced for _Scenedesmus_. The trend of PO4 -RE% for both microalgae was opposite up to TRW-20%; above this medium level, it remained statistically similar in both microalgae. Differences in SO4 -RE% between both microalgae were observed for medium levels below TRW-30%, with SO4 -RE% being higher for _Scenedesmus_. The above suggests that pollutant removal efficiency is influenced by nuanced interactions between TRW% concentration and microalgal species, as evidenced by MANOVA (Wilk's Lambda = 0.049, F (8, 20) = 7.9, \(p<0.001\)) (Figure S4C,F,I,L). This emphasizes the significance of simultaneously considering both factors in the design of bioremediation systems for raw whey. By comprehending the interaction between culture medium composition and microalgal species selection, researchers can devise more precise and efficient bioremediation strategies tailored to the specific environmental remediation requirements of raw whey.
# Advances in the Synthesis and Physiological Metabolic Regulation of Nicotinamide Mononucleotide
## 3 Biosynthesis of NMN
### Metabolic Engineering for NMN Synthesis  
In addition to enhancing the enzyme activity of NMN synthase, optimizing the metabolic network of host strains is also a crucial approach for achieving efficient production of NMN. In the NMN synthesis pathway, one of the substrates of NAMPT, phosphoribosyl pyrophosphate (PRPP), is a key limiting factor affecting NMN production and is too expensive to add externally [52]. Therefore, how to accumulate a large amount of PRPP and thereby increase NMN production is a major issue that needs to be explored. On the other hand, the substrate of the NRK pathway, nicotinamide riboside, is also expensive and difficult to obtain, so its direct use as raw material is not feasible.
## 4 Numerical Results  
This section presents the numerical results of the proposed authentication/spoofing detection method derived by means of simulations using the Matlab environment. An area \(\mathcal{A}=30\times 30\,\mathrm{m}\) representing a large indoor hall with the sink node positioned in the center is considered. The number of connected nodes is \(N=15\) if not differently indicated. The channel attributes are characterized stochastically as described in Section 2, taking into account also their time variability due to the scatterers' movement. This allows to analyze different scenarios, as detailed later, and the capability of the proposed scheme to follow the attributes' variations. As specified in each scenario, nodes are randomly placed in the considered area with a uniform distribution or following a cluster distribution. Moreover, for what concerns the spoofing detection capability of the system, this is evaluated by averaging the value \(P_{msd}\) over different positions of the spoofing node in the area as specified later.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 4 Experiments
### Comparison with State-of-the-Art Methods
#### 4.3.2 Evaluations on the GoPro Dataset  
We conducted comparative evaluations on the GoPro dataset. As shown in Table 2, our proposed method achieved excellent objective metrics on this dataset, achieving leading performance in both PSNR and SSIM. Figure 5 presents visual comparison results of different methods on the GoPro dataset. Unlike the DVD dataset, which primarily contains static objects, the GoPro dataset includes more moving objects and complex textures, presenting new challenges for deblurring tasks. In the \"GOPR0384_11_00\" sequence, despite severe motion blur in the pedestrian's foot area due to rapid movement, our method successfully reconstructed clear boundary contours. For the \"GOPR0385_11_01\" sequence, in complex regions where pedestrian hands overlap with building edges, our method not only recovered hand details but also avoided edge confusion, demonstrating strong robustness in handling complex scenes. In the \"GOPR0410_11_00\" sequence, while other methods could partially restore the basic outlines of numbers, our method reconstructed sharper and clearer digits that more closely match the visual quality of the original image.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **DVD**[36] & **SRN**[29] & **DVDSFE**[45] & **ESTRNN**[39] & **CDVD-TSP**[7] & **MPRNet**[46] \\ \hline PSNR & 30.01 & 29.98 & 31.71 & 32.01 & 32.13 & 32.24 \\ SSIM & 0.8877 & 0.8842 & 0.9159 & 0.9162 & 0.9268 & 0.9253 \\ \hline
**Method** & **STDAN**[9] & **RNN-MBP**[40] & **FGST**[12] & **LightViD**[47] & **STCT**[48] & **Ours** \\ \hline PSNR & 33.05 & 32.49 & 33.36 & 32.51 & 33.45 & **34.28** \\ SSIM & 0.9374 & 0.9568 & 0.9500 & 0.9460 & 0.9421 & **0.9655** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparisons on the DVD dataset. Bold indicates the best performance and underline indicates the second best performance.  
Figure 5: Comparison of visual results on the GoPro dataset. Zoom in for better visualization.
## 5 The Potential of Computer Vision Research in UAV Datasets
### Development of Novel Methods Using UAV Datasets  
UAV datasets have spurred the development of innovative methods in computer vision. As an example, the Guided Transformer I3D framework, which addresses distortions through unbounded transformations guided by flat RGB videos, was developed using the UAV-Human dataset. This framework enhances action recognition performance in fisheye videos. This approach is a prime example of how UAV datasets drive the creation of specialized algorithms to address particular difficulties brought about by aerial viewpoints.  
The DarkTrack2021 benchmark introduces a Spatial-Channel Transformer (SCT) for enhancing low-light images in nighttime UAV tracking. Meanwhile, [PERSON] et al. present the UAVDAR135 dataset and the ADTrack algorithm for all-day aerial object tracking. ADTrack, equipped with adaptive illumination and anti-dark capabilities, outperforms other trackers in both well-lit and dark conditions. It processes over 30 frames per second on a single CPU, ensuring efficient tracking under various lighting conditions. The study emphasizes how crucial image illuminance data are and suggests a useful image enhancer to improve tracking performance in all-day situations.  
For emergency response applications, the AIDER dataset has facilitated the development of ERNet, a lightweight CNN architecture optimized for embedded platforms. ERNet's architecture, which incorporates downsampling at an early stage and efficient convolutional layers, allows for real-time classification of aerial images on low-power devices. This showcases the practical use of UAV datasets in disaster management.  
The VERI-Wild dataset introduces a novel approach called FDA-Net for vehicle reidentification. This method utilizes a unique type of network to generate difficult negative examples in the feature space. On the other hand, the VRAI dataset developed a specialized vehicle ReID algorithm that leverages detailed annotation information to explicitly identify unique parts for each vehicle instance in object detection.  
Ultimately, UAV datasets are essential in the field of computer vision research, providing distinct data that are invaluable for a diverse array of applications. They allow for the development of novel methods tailored to the specific challenges and opportunities presented by UAV technology, accelerating progress in areas such as human behavior analysis, emergency response, and nighttime tracking.
## 5 Conclusions  
The power turbine rotor from the dual rotor system of the aircraft engine is taken as the research object. Considering the misalignment, the rub-impact, the imbalance and the disk offset of rotor, the equation of motion is established to study the influence of the rotating speed, the misalignment angles and the rub-impact clearance on the dynamic characteristics of the system; the finite element analysis was carried out to validate the correctness of the theoretical modeling method. The main conclusions are as follows:  
1. The existence of the rub-impact increases the stiffness of the system, thereby increasing the critical speed. Compared with the system of the misalignment fault, the critical speed of the coupling fault system increases by 54%, and Hopf bifurcation occurs in the system.
2. The vibration stability near half of the switching speed slumps with the increase of the misalignment angle, the maximum amplitude of the vibration response decreases, the range of the quasi-periodic motion is widening; the frequency 2X is the characteristic frequency of the misalignment. Besides, when \(\alpha=11^{\circ}\), the system is in the chaotic state, which is so unpredictable that this misalignment angle should be avoided in design.
3. Increasing the rub-impact stiffness reduces the stability of the system. The rotor system with rub-impact is actually a nonlinear system with a non-smooth clearance. Rub-impact stiffness has important effects on the nonlinear characteristics of the system. With the increase of stiffness, the number of the chaotic zone increases; and the range of the chaos is widening; the maximum amplitude of the frequency at the critical speed decreases.
4. Increasing the rubbing clearance is beneficial to reduce the degree of the rub-impact and enhance the stability of the system. With an increase in clearance, the interval of the quasi-periodic motion decreases, the maximum amplitude of the vibration decreases; the fluctuation range of the vibration gradually narrows. With an increase in clearance up to 0.15 mm, the rub-impact disappears and there is a reverse jumping phenomenon.  
Software (MATLAB 2021b, ANSYS 2020R2, CREO 9.0), D.Y.; Validation, S.Y.; Writing--original draft, G.N.; Writing--review & editing, S.Y. All authors have read and agreed to the published version of the manuscript.  
This project is supported by the National Natural Science Foundation of China (Grant No. 52275118).  
Not applicable.  
Figure 36: Comparison between FEM result and TMM result.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author (privacy).  
Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 3 Proposed Method
### MIC Calculation  
MIC is used to define the relevance between features and features, and the relevance between features and categories. A matrix called FF-MIC is formed by the MIC between features and features, and a matrix called FC-MIC is formed by the MIC between features and fault categories. The construction details of FF-MIC and FC-MIC are described in Algorithm 2.  
```
Input:\(FS=\{f_{1},f_{2},\ldots f_{m}\}\), where \(C=\{c_{1},c_{2},\ldots c_{p}\}\) are fault categories, and \(p\) is the number of fault categories. Output: FF-MIC and FC-MIC.
1. for each \(f_{i},f_{j}(1\leq i,j\leq m)\) in \(FS\)
2. calculate MIC between \(f_{i}\) and \(f_{j}\), obtaining the FF-MIC matrix with a size of \(m\times m\); the row and column denote the serial number of features.
3. for each \(f_{i}\) in \(FS\)
4. for each \(c_{j}\) in \(C\)
5. calculate MIC between \(f_{i}\) and \(c_{j}\), obtaining the FC-MIC matrix with a size of \(m\times p\); the row denotes the serial number of features and the column denotes the serial number of fault categories.
6. endfor
```  
**Algorithm 2**MIC calculation.
## 4 Evaluation of One-Hop Neighbor Wireless Link Status Based on Fuzzy Logic Model
### Q-Learning Routing Decisions  
#### 4.4.1 Q-Learning Model  
Q-learning [7] is one of the early breakthroughs of a reinforcement learning algorithm that does not rely on a model of its environment and learns by directly interacting with its dynamic environment (Figure 2). Q-learning works by trial-and-error for estimating the values of state-action pairs. The Q-value Q(s, \(C_{i}\)) (s \(\in\) S, a \(\in\) A) in Q-learning is utilized to directly approximates the optimal action-value function (Q*) for the value of future rewards if the agent performs a particular action (a) when it is in a particular state (s) [42; 43]. By exploring the environment, the agents build a table of Q-values for each environment state and each possible action. The Q-learning control algorithm in FQ-AGO is defined as follows. The opportunistic network is the entire environment. The mobile nodes are the learning agents, where they observe and learn the dynamic environment by exchanging Hello messages with neighbor agents. The set of all nodes in the network is the state space. The action that a forwarder node can perform is to select the best next-hop candidate to relay the data packet. Therefore, the set of one-hop neighbors is the possible actions that are allowed at each forwarder [26; 42]. A state transition is the process of delivering a packet from the forwarder node to the selected candidate node. There is a set of state transition P(s! s, a) that model the probabilities of transiting from state (s) to state (s') by performing the action (a). Every node maintains a Q-table, which consists of Q-value, which range 0-1.
## 4 Results and Discussion
### Indication of Received Signal Strength (RSSI) vs. Signal-to-Noise Ratio (SNR)  
About the presented graphs (boxplot) Q1 is the first and Q2 is the second quartile, which represents the upper and lower range of values. ICmin and ICmax are the confidence intervals, in python, they are calculated automatically. Points below ICmin or above ICmax are the outliers, i.e., error values that are far outside the confidence interval of the data. The bar that is approximately in the middle of the mean area is the median of the mean values obtained. This analysis was performed as shown in Figure 26.
## 3 Emergency-Aware Fuzzy Logic-Based Dynamic Weighting for MPC
### Emergency-Driven Nonlinear Weight Adaptation and Mode-Specific Control Strategy  
To enhance responsiveness under critical conditions, refined nonlinear mapping \(C_{1}(E)\) is introduced to amplify the velocity tracking weight adaptively. While \(\alpha_{1}(E)\) already reflects emergency-aware adjustment, \(C_{1}(E)\) enables sharper and faster prioritization for high-risk scenarios.  
The mapping is defined as follows:  
\[C_{1}(E)=100\cdot 10^{\left(\frac{E-0.1}{0.9}:\frac{1}{1+\exp(-10(E-0.25))} \right)}, \tag{22}\]  
Figure 2: Fuzzy inference structure for dynamic weight adaptation. (**Top**): Input membership functions for spacing and velocity errors. (**Bottom-left**): Fuzzy rule table. (**Center-right**): Defuzzified emergency surface. (**Bottom**): output membership functions for the emergency coefficient.  
which ensures smooth scaling in nominal conditions and rapid escalation when emergency levels rise.  
For real-time implementation, the control space is divided into four discrete driving modes according to the emergency coefficient \(E\). Each mode is associated with fixed values of acceleration (\(C_{2}\)) and control effort (\(C_{3}\)) weights, as summarized in Table 1. The specific threshold values (0.25, 0.5, and 0.75) were selected through empirical tuning to reflect gradual shifts in urgency level observed under typical car-following scenarios and to ensure stable transitions between adjacent driving modes.  
The weight parameters in Table 1 are designed according to the control priorities of each driving mode. In low-risk conditions (\(E<0.25\)), the controller emphasizes comfort, assigning a high jerk penalty (\(C_{3}=2000\)) and low acceleration responsiveness (\(C_{2}=1\)). As the emergency coefficient increases, \(C_{2}\) is elevated to enable faster acceleration, while \(C_{3}\) is reduced to allow more aggressive but less smooth actions. This mode-specific philosophy enables smooth transitions between energy-saving and safety-critical behavior and has been validated through simulation results presented in Section 5.  
The overall structure of the proposed emergency-aware dynamic weighting mechanism is illustrated in Figure 3.