esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
## 3 Failure Type Prediction Method Using Physical Indices and Data Features
### Feature Extraction  
Wavelet packet decomposition can decompose nonstationary signals into independent, arbitrary, and fine frequency bands without redundancy, omission, and orthogonality. It can solve the problem that the frequency resolution of wavelet decomposition is poor in the high-frequency band but that the time resolution is poor in the low-frequency band.  
In this section, three-layer wavelet packet decomposition is used to process the current change rate curve and the energy of each frequency band is calculated. The result of three-layer wavelet packet decomposition is shown in Figure 6. As the energy value of each frequency band may vary too much, it is normalized as the data feature.  
The calculation and normalization of frequency band energy are shown as Equations (7)-(12) [30].  
\[E_{j}=\sum_{k=1}^{n}\left|x_{jk}\right|^{2} \tag{7}\]  
where \(x_{jk}(j=1.7k=1.n)\) is the amplitude of discrete point of reconstructed signal \(S_{j}\).  
The energy feature vector is as follows:  
\[E=\left[E_{1},E_{2},E_{3},E_{4},E_{5},E_{6},E_{7},E_{8}\right] \tag{8}\]  
Figure 6: Results of feature extraction by wavelet packet decomposition on the current change rate curve of the high-speed solenoid valve: (**a**) Driven current curve of the solenoid valve on the 3000 th cycle; (**b**) current change rate curve of the solenoid valve on the 3000 th cycle; and (**c**) the result of three-layer wavelet packet decomposition on the current change rate curve of the solenoid valve on the 3000 th cycle.  
\[E_{s}=\left(\sum_{j=1}^{8}\left|E_{j}\right|^{2}\right)^{\frac{1}{2}} \tag{9}\]  
\[E_{i}^{\;*}=\frac{E_{i}}{E_{s}}(i=1,2,...,3) \tag{10}\]  
The standard data feature vector is as follows:  
\[E^{\;*}=\left[E_{1}^{\;*},E_{2}^{\;*},...,E_{8}^{\;*}\right] \tag{11}\]  
The comprehensive feature vector (CFV) is composed of physical indices and data feature vector:  
\[CFV=\left[i\,_{1},i\,_{2},t\,_{1},t\,_{2},\Delta i,E^{\;*}\right] \tag{12}\]  
The comprehensive feature vectors of the new valve, the valve with aged coil, and the valve with dried lubricant are shown in Table 3.
## 3 DenseNet-Hybrid Control Plane Architecture
### Hybrid Node Architecture
#### 3.2.1 Data Plane Forwarding Pipe  
We adopted the Open Source Hybrid IP/SDN networking (OSHI) framework that was designed in refs. [19; 20; 21]. This framework allows nodes to concurrently run a distributed control plane and a centralized control plane. To attain this goal, the hybrid node embeds an SDN Capable Switch (SCS)--in our case, Open vSwitch, an IP-based forwarding engine (i.e., the one provided by the Linux kernel), and an IP routing daemon based on Quagga to calculate distributed routes (see Figure 2).  
The SCS is connected to the physical network via the physical interfaces, while the IP forwarding engine is connected to the SCS via a set of internal virtual ports endowed in the SCS. We used Multiple Flow Tables (MFTs) [22] to separate: (i) flow rules that forward packets between SCS physical ports and internal virtual ports for having distributed operation and (ii) flow rules that are installed by the intervention of the SDN controller to handle centralized operation.  
The main flow table embedded in the SCS allows for distinguishing between regular IP packets that are needed to be processed by the distributed control plane (i.e., Quagga routing daemon) and those needed to be processed by the SDN controller. VLAN (Virtual  
Figure 1: The proposed hybrid control plane architecture for DenseNet.  
LAN) IDs have been used to distinguish between packets that need to be processed by a distributed control plane and packets that have to be processed by SDN controller. In particular, the SDN controller is responsible for policy making, while packets come with VLAN IDs. On the other hand, packets without a VLAN ID are processed by IP routing daemon.  
Figure 3 describes the flow chart that shows the processing of packets in the SCS flow table depending on the operational mode (i.e., distributed or centralized). Packet processing starts at the main flow table, which is _Table-0_. In the case of centralized operation, the VLAN ID is not removed from the incoming packets, and the packets are directed to _Table-1_. If there is a matching rule for the packets, the corresponding action is taken against the packets. Otherwise, packets are forwarded to the SDN controller for policy making.  
The policies that are set by the SDN controller are then installed into _Table-1_ as flow entry rules. The subsequent packets are then forwarded according to the instructions set by the SDN controller. On the other hand, while distributed operation prevails in the network and is responsible for distributed policy making, the VLAN ID is removed from the packets, and the packets are directed to _Table-2_. The actions set that are integrated into _Table-2_ are then executed to perform IP routing.
## 3 Results and Discussion
This led to photogenerated charges which efficiently transferred to the external circuit and produced a large photocurrent. This photocurrent was enhanced by further increasing the negative V\({}_{\text{g}}\) due to a thin hole Schottky barrier at the Au/MoS\({}_{2}\) interface. The photocurrent was also enhanced by the application of a higher positive bias V\({}_{\text{ds}}\) due to the bending of the MoS\({}_{2}\) channel. Figure 5c shows the transfer curves of the MoS\({}_{2}\) device with V\({}_{\text{ds}}\) equal to 4 V under UV illumination and dark conditions, respectively. Compared with dark conditions, the V\({}_{\text{g(th)}}\) obtained from the UV illumination had a greater absolute value. This indicates that the photoexcited holes are transferred to the electrodes due to the _p_-type behavior of the MoS\({}_{2}\), while the photoexcited electrons are trapped within the MoS\({}_{2}\). This is consistent with the results shown in previous literature, indicating that MoS\({}_{2}\) is an effective charge trapping layer [25]. The time-resolved characteristics revealed a reliable photoresponse with a stabilized photocurrent, as shown in Figure 5d. Under 365 nm illumination, the photocurrent of the MoS\({}_{2}\) PFET increased rapidly after exposure to UV radiation, with a rise time of 37 ms, significantly faster than those of other reported MoS\({}_{2}\) photodetectors [3, 4, 15, 26], as shown in Figure 5e. This photocurrent remained nearly constant during the UV exposure (10 s) and decayed quickly during dark conditions with a decay time of approximately 39 ms. The current fully recovered after repeated cycles, which shows excellent repeatability and optical response for this MoS\({}_{2}\) PFET photodetector. Figure 5: (**a**) I\({}_{\text{ds}}\)–V\({}_{\text{ds}}\) output curves of the MoS\({}_{2}\) PFET under ultraviolet (\(\lambda\) = 365 nm) illumination (symbols lines) and in dark conditions (solid lines) with varying gate voltages. (**b**) Photoresponsivity as a function of V\({}_{\text{g}}\) at V\({}_{\text{ds}}\) value of 4 V. (**c**) I\({}_{\text{ds}}\)–V\({}_{\text{g}}\) graph displaying transfer behavior of the MoS\({}_{2}\) PFET for V\({}_{\text{ds}}\) = 4 V under dark and UV illumination. (**d**) Photoresponse of the _p_-type MoS\({}_{2}\) device revealing the stable and repeated changes in response to the light at on and off conditions at V\({}_{\text{ds}}\) = 6 V. (**e**) Photoresponse rate of the _p_-type MoS\({}_{2}\) device.
## 5 Discussion
### Limitations  
While the findings of this study offer promising insights into the potential benefits of dynamic lane assignment (DLA) in urban traffic corridors, several limitations should be acknowledged. First, the analysis was conducted using a single case study--Vukovar Street in Zagreb--which, although representative of directional congestion in urban settings, may limit the generalizability of the results to cities with different geometric, regulatory, or traffic flow characteristics.  
Second, the model assumes the reliable and timely availability of traffic data to trigger lane direction changes. In practice, the effectiveness of a DLA system would depend heavily on the accuracy and responsiveness of real-time data collection infrastructure, such as traffic detectors, sensors, and communication systems. Third, driver behavior in the model does not fully account for behavioral adaptation to dynamic lane changes, especially in the early phases of implementation when unfamiliarity with the system may affect compliance and performance.  
Finally, the implementation of DLA systems requires significant technological, institutional, and operational readiness, including dynamic signage, signal control integration, and traffic management protocols. These factors were not explicitly modeled but would play a crucial role in real-world deployment. Despite these limitations, the study provides a robust framework for evaluating core operational benefits under controlled conditions and can serve as a foundation for future, more complex investigations.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### Proposed Model Training Setup  
The proposed model training setup follows a well-structured approach to TL in UWA communication. Training includes pre-training and fine-tuning of the model; during pre-training, the training features from each channel are concatenated. Afterwards, 80% is for training and 20% for validation in order for the model to learn the feature extraction effectively. Then, 50% of the Qingdao Lake dataset was used for fine-tuning, further divided into 80% for training and 20% for validation. This can be visualized in Table 8. In this process, convolutional layers are frozen; only fully connected layers are updated to allow the model to adapt exclusively to the new dataset while retaining general features learned during pre-training. At last, a testing phase using the remaining 50% of the Qingdao Lake dataset, kept exclusively for model performance evaluation, is performed. The validation metrics used are accuracy, precision, recall, F1-score, and a confusion matrix.
# A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection
## 3 Methods
### Architecture
#### 3.1.2 Local Context Gathering by Depthwise Separable Convolution  
In addition to feature fusion, we also consider adding multi-context to improve the capability of detecting faces. In our method, several local context paths are attached on top of P9, P13 and P17 to add local contextual information and a global context path starting from conv17 is used to add global contextual information, which is inspired by [13; 22; 24; 27].  
In CNN, convolutional filters extract features from input feature maps by way of a sliding window. Different sizes of filters will extract different sizes of pixel areas, such as 3 \(\times\) 3, 5 \(\times\) 5 and etc., so these filters can naturally be used as context extractors. With these context extractors, we propose a depthwise separable convolution based approach to add local context. Depthwise separable convolution [16] is a factorization method that can greatly reduce computation while maintaining the capability of conventional convolutions. Unlike conventional convolution, in depthwise separable convolution, the 3 \(\times\) 3 filters first convolve each channel of the input feature maps independently, and then 1 \(\times\) 1 filters are used to compute the linear combination of the previous results. With depthwise separable convolution, calculation will be reduced by 8-9 times and our detection speed can then effectively be promoted.  
We use three paths to add local context. Different sizes of neighboring pixels are incorporated together to add different scales of context. As illustrated in Figure 4, the first local context path uses two stacked 3 \(\times\) 3 depthwise separable convolution to incorporate 5 \(\times\) 5 size of pixels of the input feature maps, which adds 2 pixels worth of context to the original feature maps. The second local context path uses three stacked 3 \(\times\) 3 depthwise separable convolution to incorporate 7 \(\times\) 7 size of pixels of the input feature maps, which adds 4 pixels worth of context. And the third local context path incorporates 9 \(\times\) 9 size of pixels, which adds 6 pixels worth of context. Therefore, our method will incorporate three scales of local context. A batch normalization layer and a ReLU nonlinearity layer are followed each convolutional layer. The number of input and output channels of each path are uniformly set to 128, 64 and 32 with respect to each scale of feature maps.  
Figure 4: Context incorporation. Depthwise separable convolution (dw for short) is used to reduce computation, each convolutional layer is followed by a batchnorm layer and a ReLU layer.
# Sources of Extraterrestrial Rare Earth Elements: To the Moon and Beyond
## 2 The Criticality of REEs in Our Society
Identifying new REE resources, whilst continuing to improve and expand recycling efforts, is an absolute requirement for the continued utilization of REEs in the modern consumer economy and the emerging global green economy. The Earth's population is projected to reach 8.5 billion by 2030 [20] yet the mining industry is currently downsizing [21]. This temporary decline is predominantly associated with quotas that are being placed on REE exportation, and a reduction of illegal mining operations by the Chinese government [4]. However, material and energy resources on Earth are ultimately limited hence sustaining our reliance on raw materials will likely require investment in, and utilization of, extraterrestrial resources. A first natural (and logical) step is the investigation and exploitation of resources on our Moon [22].
## 3 Results
### Raman and XRD Characterization of the Synthesized TiO\({}_{2}\)  
Raman investigations were carried out to discriminate the crystalline nature, phase purity and size of synthesized TiO\({}_{2}\) particles. In Figure 3a, the Raman spectrum of N-TiA powder, obtained by drying the sol, is reported: the characteristic Raman peaks of crystalline TiO\({}_{2}\) with an anatase structure and a small contribution of brookite (marked B in the figure) are observed. Raman spectroscopy is very sensitive to the local order of TiO\({}_{2}\): the Raman peaks, as the particle size decreases, show asymmetric broadening and a large blue shift with respect to crystalline (bulk) anatase [90]. The main peak of anatase at 144 cm\({}^{-1}\) is shifted to 151-155 cm\({}^{-1}\) in N-TiA, and its width (FWHM) increases from 12 to 22 cm\({}^{-1}\). This is compatible with anatase nanocrystals in N-TiA of 5-10 nm size [91].  
The XRD pattern of the TiO\({}_{2}\) powder, obtained by drying the synthesized nanosol at 60 \({}^{\circ}\)C, is shown in Figure 3b. The anatase and brookite phases in the diffractogram are clearly identified, in agreement with Raman results. The main diffraction peak of anatase (101) overlaps with the (120) and (111) reflections of brookite. The crystallite size of the N-TiA nanoparticles was determined by the line broadening of the main diffraction peak (101) of anatase by the Scherrer equation [63]: the result gives 4.5 \(\pm\) 1.0 nm.  
Figure 3: _Cont._  
Figure 2: Polarized transmitted light optical microscope photomicrographs of _Lecce stone_. Several microfossils are recognizable: planktonic Foraminifera, Globigerina-like (e.g., red arrow and red ellipse), fragments of bivalves (e.g., yellow arrow) and Ostracols (e.g., blue ellipse) in (**a**); bivalves (e.g., green ellipse), Foraminifera (e.g., red ellipse) and Echinorms (e.g., blue ellipse) in (**b**).
## 4 Discussion
The Receiver Operating Curve was used to evaluate the diagnostic value for each of the proposed diagnostic methods. Furthermore, the area under the ROC curves was calculated for use as a one-dimensional measure of diagnostic value. The ROC curves of the fat-to-lesion ratios and Ueno elastography scores showed much better AUC values of 0.90 and 0.88, respectively (a theoretically perfect diagnostic having AUC = 1), than the curve for classical ultrasound BIRADS (0.77). Other studies also showed that the values are slightly better for FLR in comparison with Ueno scores (0.85 and 0.79, respectively) [15]. For the different risk-reassessment algorithms, the AUC values were 0.80 for EFSUMB BIRADS and 0.83 for worst-case scenario BIRADS, and the proposed BIRADS TM showed the best result of 0.86 (as can be seen in Table 8). In our study, elastography improved the AUC value of breast cancer ultrasound screening, starting from 0.77 for classical ultrasound and improving to 0.86 when adjusting the classical ultrasonography BIRADS score by upgrading or downgrading based on both qualitative and semiquantitative elastographic results (\"BIRADS TM\").
# A Redactable Blockchain Scheme Supporting Quantum-Resistance and Trapdoor Updates
## 1 Introduction
### Contributions  
For this paper, we separately worked on security, decentralized features, and compatibility to achieve a redactable blockchain with quantum-resistant and revocable subkey capabilities. The specific contributions of the scheme are delineated as follows:  
1. Hierarchical identity-based chameleon hash: To regulate edit permissions and facilitate trapdoor management, this paper employed hierarchical identity for the decentralized delegation of trapdoors. Simultaneously, we introduced the concept of a slave key algorithm within hierarchical identity to avoid parent nodes that can deduce child nodes' keys and the key dimensions increasing during the key delegation process. This integration imparted the revocable subkey mechanism's delegation attributes.
2. Redactable scheme with quantum resistance and revocability: We used a lattice-based cryptographic scheme to support more secure editing operations. In the redactable scheme, we also implemented the method of withdrawing the edit operation by saving the edit history.
3. Private data were edited and managed by the individual user: We cryptographically verified private data to protect the security and privacy of the redactable blockchain.
4. Addressing consistency, traceability, and accountability problems: In Section 4, we present three algorithms to illustrate how to achieve consistency, traceability, and accountability. For this, we advocate caching modify transactions with consensus authority nodes before their final commitment. The block number recording the edit history is stored in the redactable transaction to prove whether the transaction was modified. Demonstrating the viability of our redactable scheme, we opted for the Hyperledger Fabric platform, a widely embraced federated blockchain solution, to implement the scheme.  
Therefore, It is a novel work to use a hierarchical identity structure to manage the trapdoor of chameleon hashing. However, in this paper, through analysis and design, we have circumvented its drawbacks and realized the possibility of combining hierarchical identity structure with chameleon hashing.  
Secondly, chameleon hash functions based on post-quantum cryptography for resisting quantum attacks are a novel work in recent years, and few schemes can demonstrate their performance through experiments. However, this paper not only reports a real-world deployment with Hyperledger Fabric but also provides concrete performance data.
# In Situ Rubber-Wheel Contact on Road Surface Using Ultraviolet-Induced Fluorescence Method
## 2 Methodology
### Apparent Contact Area Determination  
As the current study used irregularly patterned road surface replica, the biggest problem was light reflection and scattering, as well as the impurity inside urethane material. Consequently, it was difficult to directly identify the contact region based on fluorescence intensity. There is currently no method to determine the correct boundary of the contact area other than using the thresholding number. According to studies, the Otsu thresholding method is the best in deciding the most appropriate number in image analysis. It was reported that the accuracy of the Otsu thresholding method through an experiment using the Weizmann Segmentation Database was more than 93% [25]. In line with this, the current study used the Otsu thresholding method to determine the boundary for the apparent contact area of rubber-wheel and road surface replica.  
Despite its excellency in determining thresholding value, the Otsu method cannot directly be used on rubber-wheel contact on road surface replica. This is because the irregular pattern of the road surface disturbs the calculation of determining thresholding value. In the Otsu thresholding method, all image information is recognized as essential data to be calculated. It cannot be explicitly classified into intended information with many variables on the image. As shown in Figure 5, the binary image based on the Otsu thresholding method for road surface indicates the contact and other areas with no rubber-wheel contact. Therefore, the thresholding value was decided based on rubber-wheel contact on the flat surface of urethane material. The urethane material had the same properties as road surface replica. Figure 6 shows the image of rubber-wheel contact on a flat surface of urethane material and its binary image. The value produced using the Otsu thresholding method was 36. Later, this thresholding value was used to measure the apparent contact area of rubber-wheel and road surface replica. Figure 7  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Contact Condition** & **Applied Load (N)** & **Moving Speed (mm/s)** \\ \hline Dynamic contact & 4 \\ Dynamic contact & 60.8 & 20 \\ Dynamic contact & 80.4 & 40 \\ Dynamic contact & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental conditions for dynamic contact.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Camera** & **High-Speed Camera** \\ \hline Field of view, mm & \(40\times 30\) \\ Number of pixels, pixels & \(1600\times 1200\) \\ Frame rate, fps & 100 \\ Shutter speed, & 1/200 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specification of the captured image.  
shows the image of rubber-wheel contact on a road surface replica. Also, it shows the binary images of the apparent contact area based on the Otsu thresholding method. The binary image in Figure 7b,c display only the contact area of rubber-wheel and road surface replica. The following experimental data of the contact area between rubber-wheel and road surface replica were measured based on a thresholding value of 36.