esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture  
Figure 1 illustrates the block diagram of a multi-loop PLL configuration, consisting of ultra coarse, coarse, and fine loops. The ultra coarse loop quickly performs large frequency adjustments to roughly match the PLL output frequency, while the coarse loop refines the frequency with medium-sized adjustments. The fine loop makes small adjustments to precisely achieve the desired frequency, ensuring high-frequency stability and low phase noise. Each loop can be configured using various PLL structures, such as divider-based PLL, counter-assisted phase domain PLL, sub-sampling PLL, and injection-locked PLL, chosen based on the application requirements [15; 16; 17; 18]. The primary requirements for the proposed PLL application (e.g., 5G, IoT, etc.) are to provide a wide frequency tuning range with low area and low power characteristics. To achieve this, we propose the architecture of a triple-loop wideband fractional-N all-digital phase-locked loop (WBPLL) supporting frequency scaling from 1.9 to 6.1 GHz (Figure 2). The system employs three types of  
loops for frequency acquisition, consisting of an automatic frequency calibration (AFC) for frequency band selection, a counter-assisted phase-locked loop (CAPLL) for coarse frequency tuning, and a sub-sampling phase-locked loop (SSPLL) for fine tracking.
## 1 Introduction  
Traumatic loss [1], or congenital absence of one anterior maxillary incisor [2] in adolescents, requires immediate treatment with temporary or definitive solutions for aesthetic and functional reasons. Resin-bonded fixed dental prosthesis (RBFDP) represents a highly aesthetic and conservative treatment option to replace a single tooth in a younger patient, before implant treatment becomes available [3] or after orthodontic treatment [4]. In literature, survival rates of RBFDPs were 87.7% in medium-term observation [5]. The main factors of failure include debonding [6], secondary caries on the abutment tooth [5], and fracture of the retainers [7]. These causes are determined by two prosthetic characteristics of RBFDPs: retainer designs [3; 8] and properties of the materials [9]. The framework designed with two-retainer has been the most used by clinicians and dental technicians because it was considered with higher fracture resistance than the frameworks with one retainer (cantilever). However, several studies demonstrated that two-retainer RBFDPs have a higher fracture rate and lower survival rate than cantilever RBFDPs [10; 11; 12; 13; 14; 15]. Traditionally, the framework of RBFDPs is made of metal alloy, but different metal-free materials with more aesthetics and bond strengths are available. Several authors showed excellent longevity of zirconia [16; 17] and lithium ceramic [18] cantilever RBFDPs over 10-20 years with few mechanical complications. However, the mechanical complications were different, according to the material. Debonding rate of 8% and one loss of restoration was revealed by [PERSON] et al. [17] for zirconia cantilever RBFDPs; no debonding was recorded by [PERSON] et al. [18] for lithium disilicate cantilever RBFDPs. However, the assessment of the mechanical performance of prosthetic materials through clinical trials is difficult because of several conditions and characteristics of the patients.  
In the last few years, digital technologies have been introduced in dentistry to improve patient comfort, decrease operative time, and reduce clinical treatment [19; 20; 21; 22]. The use of scanners, computer-aided design (CAD) software, and computer-aided manufacturing (CAM) machines have opened many possibilities to replicate and fabricate dental prosthesis in different materials.  
Therefore, uniform fabrications of cantilever RBFDPs with different materials by the same standard tessellation language (STL) file allow knowing the real performance of these materials, and the possible adverse compartments on the abutment tooth in extreme conditions.  
The purpose of this in vitro study was to compare, using the universal testing machine, the fracture strength and the different types of failure on anterior cantilever RBFDPs fabricated using zirconia, lithium disilicate, and PMMA-based material with ceramic fillers by the same STL file.
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 4 Blockchain Copyright Protection Model Based on Vector Map Unique Identification  
The BCPM-UI model is a blockchain-based framework designed to ensure secure registration and the reliable verification of vector map copyrights. At its core, the model integrates digital watermarking and unique identifier construction with blockchain technology to provide a robust solution for copyright protection. Watermark information is embedded into the vector map in a manner that ensures imperceptibility and robustness. Simultaneously, unique feature identifiers are derived from the geometric and topological characteristics of the watermarked map. These identifiers, along with associated metadata, are securely recorded on the blockchain. By combining the immutability of blockchain with the resilience of watermarks and identifiers, the model achieves traceability, confidentiality, and accountability, offering a comprehensive solution for vector map copyright management.  
The implementation of the model is divided into two primary processes: the copyright registration process and the copyright verification process. During registration, watermark information is embedded into the vector map and unique identifiers are constructed. Both of these are securely registered on the blockchain, along with relevant metadata. The verification process focuses on identifying potential copyright infringements by constructing unique identifiers and watermark data from suspected infringing datasets and comparing them with blockchain records. This ensures the precise and reliable validation of ownership and infringement detection, providing robust evidence for arbitration and accountability in vector map transactions.
###### Abstract  
The task of detecting and classifying highly maneuverable and unidentified underwater targets in complex environments is significant in active sonar systems. Previous studies have applied many detection schemes to this task using signals above a preset threshold to separate targets from clutter; this is because a high signal-to-noise ratio (SNR) target has sufficient feature vector components to be separated out. However, in real environments, the received target return's SNR is not always above the threshold. Therefore, a target detection algorithm is needed for varied target SNR conditions. When the clutter energy is too strong, false detection can occur, and the probability of detection is reduced due to the weak target signature. Furthermore, since a long pulse repetition interval is used for long-range detection and ambient noise tends to be high, classification processing for each ping is needed. This paper proposes a multilayer classification algorithm applicable to all signals in real underwater environments above the noise level without thresholding and verifies the algorithm's classification performance. We obtained a variety of experimental data by using a real underwater target and a hull-mounted active sonar system operated on Korean naval ships in the East Sea, Korea. The detection performance of the proposed algorithm was evaluated in terms of the classification rate and false alarm rate as a function of the SNR. Since experimental environment data, including the sea state, target maneuvering patterns, and sound speed, were available, we selected 1123 instances of ping data from the target over all experiments and randomly selected 1000 clutters based on the distribution of clutters for each ping. A support vector machine was employed as the classifier, and 80% of the data were selected for training, leaving the remaining data for testing. This process was carried out 1000 times. For the performance analysis and discussions, samples of scatter diagrams and feature characteristics are shown and classification tables and receiver operation characteristic (ROC) curves are presented. The results show that the proposed algorithm is effective under a variety of target strengths and ambient noise levels.  
active sonar; target classification; support vector machine; underwater target detection 2019 9 4617 (); doi:10.3390/app9214617
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 3 Proposed Watermarking Scheme
### Local Square Feature Region Construction
#### 3.1.4 LSFRs for Watermarking  
Considering the severe distortion during the screen-cam process, the constructed LSFRs should have a sufficient range to ensure that information can survive. Thus, the feature points with appropriate scale and location are selected, and the side length \(L_{0}\) of LSFR is designed as:  
\[L_{0}=2\cdot\text{floor}(k_{1}\cdot s)+1 \tag{11}\]  
where \(k_{1}\) is a constant coefficient, and \(s\) is the feature scale value.  
In Figure 5, are shown the LSFRs for the 8-image test set. Because the watermark information will be embedded in the DFT coefficients, according to its characteristics, the following two situations are also feasible. When a small part of the candidate LSFR exists outside the image, shown in Figure 5f, or when a small part of the two LSFRs overlapped, shown in Figure 5g, these LSFRs can also be utilized as embedding areas.  
Figure 5: The selected local square feature regions (LSFRs) for watermark embedding of eight host images.
###### Abstract  
The microbial communities associated with alkaline-surfactant-polymer (ASP)-flooded reservoirs have rarely been investigated. In this study, high-throughput sequencing was used to analyse the indigenous microbial communities in two different blocks, the water flooding after the alkaline-surfactant-polymer flooding block and the alkaline-surfactant-polymer flooding block, and to ascertain the optimal stage for the implementation of indigenous microbial oil recovery technology. The different displacement blocks had significant effects on the indigenous microbial community at the genus level according to an alpha diversity analysis and community composition. In water flooding after alkaline-surfactant-polymer flooding, the dominant genus of _Pseudomonas_ exceeded 30%, increasing to 52.1% in alkaline-surfactant-polymer flooding, but alpha diversity decreased. Through a co-occurrence network analysis, it was found that the complexity of the water flooding after alkaline-surfactant-polymer flooding was higher than that of alkaline-surfactant-polymer flooding. This means that the water flooding ecosystem after alkaline-surfactant-polymer flooding was more stable and less susceptible to external environmental influences. In addition, there were significant differences in the functional redundancy of microbial communities in different blocks. In summary, the optimal stage for implementing local microbial oil recovery technology may be water flooding after alkaline-surfactant-polymer flooding.  
Keywords:indigenous microbial community structure; high-throughput sequencing; MEOR; alkaline-surfactant-polymer flooding +
Footnote †: journal: Applied Sciences
## 6 Discussion
### Benefits
#### 6.1.2 Better Data Storage and Data Interoperability  
In blockchains, data are added to a block by the miner, and that block is distributed among blockchain nodes in the network. As a result, data are stored as clones across the blockchain system, increasing data storage trustworthiness. This avoids the problem of centrally managed storage's sole source of malfunction, where the data are lost in the event of damage or loss of centralized data storage. In addition, the blockchain's self-executing contracts have the capacity to reduce the amount of time and expense associated with labeling and pre-processing data. Smart contracts can be configured to define rules such that data are automatically categorized and processed beforehand on the blockchain without the need for human participation.  
When it comes to data interoperability, a KDN network that employs numerous virtual network slices may be used to transmit data using cross-blockchain technology, which has the capacity to exchange information among various blockchain networks.
# Space Charge Effect and Resistance Switching in Doped Monocrystalline Silicones
## 1 Introduction  
In semiconductors, dopants profoundly influence the space charge effect (SCE). It is assumed that dopants provide carriers and weaken SCE, while ionized dopants capture carriers and strengthen SCE. Thus, the shape of a current-voltage (I-V) curve is modulated by the fast dynamics of ionization or filling process [1; 2; 3; 4; 5; 6]. The dynamics of carriers may have a characteristic of historical memory due to the localized charge accumulation or dissipation. In this work, we reported that the hysteresis of I-V curve initiates from an effective trap-filling (ETF) regime and extend to the Mott-Gurney (MG) regime, forming the resistance-switching (RS) loop. As the observed RS loop in monocrystalline silicon is induced by SCE, it is hereby called the SCE-RS loop.
## 3 Algorithm Flow
### Infrared Polarization Image Fusion
#### 3.2.1 DoP and AoP Image Solution and Polarization Feature Image Fusion  
In order to reduce the impact of pixel position difference and noise pollution, it is necessary to denoise and register the image in advance. In this paper, a grayscale transformation is adopted to enhance the image contrast, and the polarization images of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) after pretreatment are calculated to obtain S\({}_{0}\), S\({}_{1}\), and S\({}_{2}\) images. Then, the polarization degree and polarization angle images are calculated according to Equations (5) and (6). DoP and AoP images can provide a lot of target details in the fusion process, which plays an important role in improving image quality. DoP images can highlight the polarization characteristic information of the target; AoP images have a low signal-to-noise ratio but contain many detailed texture information. The two images can be fused to remove redundancy and increase useful information of the image, and play a role in the subsequent fusion with the intensity image. In order to make better use of the  
Figure 5: Flowchart of polarization image fusion.  
complementarity between images, this paper chooses the minimum value fusion method, which can be described as follows:  
\[C_{F}(\eta,\xi)=\begin{cases}C_{A}(\eta,\xi),\ |C_{A}(\eta,\xi)|\leq|C_{B}(\eta,\xi)|\\ C_{B}(\eta,\xi),\ |C_{A}(\eta,\xi)|>|C_{B}(\eta,\xi)|\end{cases} \tag{8}\]  
Here \(C_{A}(\eta,\xi)\) and \(C_{B}(\eta,\xi)\) are a set of decomposition coefficients of source image \(A\) and source image \(B\) respectively, and \(C_{F}(\eta,\xi)\) is the coefficient after fusion.
# Viability of Cupola Slag as an Alternative Eco-Binder and Filler in Concrete and Mortars
## 3 Results  
### Consistence of Fresh Mortars  
Table 5 shows the results of the consistency on the mortars that incorporate various admixtures. It can be seen that cupola slag requires a lower effective water/cement ratio to obtain the same slump as the LF. This is in part contradictory to the fineness obtained for both compounds, since having a higher specific surface should require more water than LF. This effect is probably offset by the shape of the cupola slag, which would not offer great resistance to paste runoff, and as can be seen after applying 15 strokes on the shaking table (the runoff increase is 30% higher).  
Figure 7 shows the results of the consistency test on mortars with different replacement levels in the fresh state. It can be observed that the consistency increases as the replacement does, in a linear and proportional manner, as shown by a correlation coefficient over 0.99, and increasing by 12.5% with replacements of 30%, with respect to the control mortar (0%). This increase in the workability of the mortar is because the effective water/cement ratio has remained constant, while the fineness of the cupola slag is 13% lower, which would generate a greater volume of free water.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Admixture** & **W/c Ratio** & **Average Diameter (mm)** & **Diameter after 15 Strokes (mm)** \\ \hline Limestone filler & 0.43 & 155 & 190 \\ Fly ash & 0.47 & 160 & 215 \\ Cupola slag & 0.40 & 150 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mortar consistency with different admixtures.
# Activation-Based Pruning of Neural Networks
## 2 Related Work
### Iterative/One-Shot Pruning  
Pruning can also be categorized as either iterative [2; 6; 7; 21] or one-shot [20]. Iterative pruning is a multi-step process of assigning a score, pruning the network, and retraining. [PERSON] et al. [2] proposed a three-step pipeline to prune redundant connections in neural networks without affecting accuracy. They first trained the dense network, then pruned low-weight connections below a threshold to obtain a sparse network, and finally, retrained the sparse network to learn the weight parameters. [PERSON] et al. [6] introduced a two-step process called pruning and splicing, where weight connections can be removed and added back, based on solving a constrained optimization problem for each layer. [PERSON] et al. [7] used a three-pronged approach of pruning, quantization, and Huffman coding, to achieve substantial compression of the network. [PERSON] et al. [21] demonstrated how networks can be grown and pruned dynamically during the training phase using structured continuous sparsification. Growing and pruning of the network are often performed by introducing a regularization parameter in the cost function and relaxing the initial optimization problem. [PERSON] et al. [20] implemented one-shot pruning to prune weights in a single step. Activation-based pruning is an iterative method. Initially, a score is assigned to each neuron, and subsequently, the neurons with the lowest score are pruned. Afterward, the network is retrained, and this cycle is repeated multiple times.
# UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios
## 3 System Model
### Optimization Problem Definition  
To extend the lifetime of the UAV-assisted task-offloading system while ensuring user service requirements, it is required to complete the computational tasks generated by ground users within a specified time frame and to enable each device to operate continuously with limited resources. In this paper, we propose to jointly optimize the UAV flight trajectory and task-offloading policy to maximize the energy efficiency of the system under the condition of limited resources of ground user devices and UAV clusters. Therefore, the optimization problem and constraints in this paper are formulated as:  
\[\max\sum_{t=1}^{T}\sum_{g=1}^{G}M_{g}(t)/\sum_{u=1}^{U}\sum_{u^{ \prime}=1}^{U}E^{total}_{g,u,u^{\prime}}(t) \tag{17}\] \[\text{s.t.}\quad\mathcal{C}1:\sum_{u=1}^{U}\alpha_{g,u}(t)\leq 1, \forall t,g,\] (17a) \[\quad\mathcal{C}2:\sum_{u^{\prime}=1}^{U}\beta_{g,u,u^{\prime}}( t)\leq 1,\forall t,g,u,\] (17b) \[\quad\mathcal{C}3:0\leq\lambda_{g2}(t)\leq\lambda_{g1}(t)\leq 1, \forall t,g,\] (17c) \[\quad\mathcal{C}4:T^{total}_{g,u,u^{\prime}}(t)\leq T_{g}(t), \forall t,g,\] (17d) \[\quad\mathcal{C}5:\|L_{u}(t)-L_{u^{\prime}}(t)\|\geq d_{\min}, \forall u,u^{\prime}\in U,u\
eq u^{\prime},\] (17e) \[\quad\mathcal{C}6:\|L_{u}(t+1)-L_{u}(t)\|/\xi\leq V_{\max},t=1, \ldots,T-1. \tag{17f}\]  
Constraint \(\mathcal{C}1\) ensures that each ground user can select at most one UAV to upload task data. Constraint \(\mathcal{C}2\) ensures that the task data uploaded by the ground user can be forwarded at most once. \(\mathcal{C}3\) indicates the proportion of task offload and the proportion of UAV task forwarding for each ground user device. Constraint \(\mathcal{C}4\) ensures that user generated computational tasks are completed within a tolerable time frame. \(C5\) guarantees a minimum safe distance \(d_{\text{min}}\) between deployed UAVs to avoid collisions. \(C6\) limits the maximum flight speed of UAVs.  
It can be seen that (17) is a mixed integer nonlinear programming problem (MINLP), which is usually difficult to solve by conventional methods due to the presence of multivariate variables \(\alpha_{g,u}(t)\) and \(\beta_{g,u,u^{\prime}}(t)\), and nonconvex objective functions. In addition, the trajectory of the UAV is usually optimized by offline processing before scheduling the UAV, but the system does not have access to the latest changes in the terminal at the next moment before scheduling. Although theoretically, some heuristics may be able to alleviate this problem, it is impractical to try to explore all possible changes in the system environment. Given the high complexity and uncertainty of the problem, efficient and low-complexity alternatives are needed to solve the situation. Therefore, this paper introduces deep reinforcement learning algorithms to observe and learn the state changes in the environment and to solve the proposed problem. The next section specifies the proposed algorithm.