esa-sceva/satcom-chunk-collection huggingface dataset:
# Pulse Peak Delay-Total Focusing Method for Ultrasonic Tomography on Concrete Structure
## 3 Simulation
### Simulation Results  
A sectional image generated by an ultrasonic array system is called B-scan. B-scans by both the TFM and the PPD-TFM are plotted in Figure 3. Each B-scan is normalized by its maximum reflection value. In the B-scan by the TFM, an intense reflection is formed below the exact reflector location. The distance between the intense reflection and the reflector location is about half the measured PPD length, 103.5 mm, as the ultrasonic array system is a pulse-echo system where a pulse usually travels more than twice of the pixel depth. However, the PPD-TFM considering the PPD focuses a strong reflection at the exact reflector location.  
Although the input electronic signal is a one-cycle wave pulse, the generated ultrasonic pulse has multiple cycles of waves. In addition, an inadequate focusing technique of the TFM made subsidiary reflections next to the strong reflection. However, the PPD-TFM weakened the subsidiary reflections by the exact focusing.
# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
## 2 Materials and Methods
### Neural Network  
In this section, we introduce our AER2U-Net model, whose architecture is shown in Figure 2. AER2U-Net is designed to address the challenge of accurately segmenting complex flap shapes in thermographic images, which requires both fine-grained feature extraction and global context understanding. We strategically combined elements from the recurrent residual U-Net [36] (R2U-Net) and the attention U-Net [33] (AttU-Net), both variants of the popular U-Net [21]. The architecture incorporates Recurrent Residual Convolutional Layers (R2 CLs) for enhanced feature representation, attention gates (AGs) to highlight salient features, an Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contextual information, and a Dual Attention Mechanism (DAM) to refine feature maps. Each component was selected based on its specific contribution to the segmentation task, with R2 CLs enabling complex pattern learning, AG focusing on relevant image regions, ASPP capturing varying flap sizes, and DAM improving accuracy for complex flap images. From an explainable AI perspective, the attention mechanisms provide visual insights into the focal areas of the model that are critical for clinical interpretation. ASPP's multi-scale approach aligns with clinical assessment processes and increases the model's potential for integration into medical workflows. By combining these elements, AER2U-Net aims to provide a robust and accurate solution for flap segmentation in thermographic images, addressing the complexity of this important medical imaging task.
## 3 The Optimal Selection Model of the Backhaul Links
### Definition of the Optimization Model
According to the different requirements, a ratio \(\alpha,0\leq\alpha\leq 1\) is set to adjust the two parameters. Therefore, the comprehensive influence factor \(Q(t)\) is:  
\[Q(t)=\alpha R^{\prime}_{sys}(t)+(1-\alpha)C^{\prime}_{sys}(t) \tag{6}\]  
Since the selected satellite connected to the UAV has a great influence on the optimization objective, the associated satellite set is the decision variable in the optimization model, as in Equation (7). \[F(t)=\{f(i)\},i=1,2,\ldots,N \tag{7}\]  
Typically, one UAV can only choose one backhaul link associated with a satellite in our system. Therefore, only one node can be selected from every array \(H_{i}\). The UAV node \(U\) associated with satellite set \(F(t)\) reflects the selection method of the backhaullinks. Therefore, the first constraint is set as Equation (9). Moreover, since the storage and forwarding capabilities of the satellites are limited, the number of UAVs connected to each satellite should also be capped. Assume that the number of UAVs connected to each satellite is \(P(t)=\{p_{1},p_{2},\ldots,p_{M}\}\), which we can get from \(F\). The maximum number of UAVs connected to a satellite can be set as \(P_{max}\). The second constraint is set as in Equation (10). Above all, the objective function is to maximize the comprehensive influence factor \(Q(t)\) to optimize the performance of the total of the backhaul links from the UAVs to the satellites as follows:  
\[\max_{F(t)}\ Q(t) \tag{8}\] \[s.t.\quad f(i)(t)\in H_{i}(t),i=1,2,\ldots,N\] (9) \[C_{ij}(t) = W\log_{2}\left(1+\frac{P_{r}(d_{ij}(t))}{N_{0}}\right) \tag{2}\]0  
The objective function consists of two parts: capacity and service time. The capacity part contains a nonlinear function as formulated by Equation (2). Thus, the optimization model is nonlinear. Because the objective function varies over time, the problem is a dynamic nonlinear optimization problem. In other words, we can break down the optimization problem into simpler sub-problems and solve the sub-problems using a heuristic algorithm. Due to the characteristics of satellite and UAV motion, the snapshot method can be used to make the topology unchanged within a period of time. A heuristic algorithm is used to solve it in the next subsection.
# Development of a Nonlinear Piezoelectric Energy Harvester for Alternating Air Load
## 2 The Structure and Working Principle  
To gain a better understanding of the motivation for developing a nonlinear piezoelectric energy harvester, let us first consider the excitation that can be provided in the pneumatic system. The cylinder is the most important component in the pneumatic system, and the piston can be moved by to the driving force of the compressed air. In this study, the cylinder is used to cause alternating air load through the moving piston. The stable compressed air, which is adjusted by pneumatic system, can provide the driving force that moves the piston. The structure and working principle of the presented harvester is shown in Figure 1.  
The presented harvester consists of a power chamber, a compressed chamber, a piston, and a PZT patch that can transform the compressed energy under the direct piezoelectric effect. An airtight space is formed by the power chamber and compressed chamber. The power chamber is designed to have an orifice, which can quarantine the complex changing air, and the piezoceramic patch only can be excited by the alternating air load. When the stable compressed air load acts on the piston, the volume of compressed chamber is condensed. The alternating air loads act on the PZT patch and electricity can be produced through the deformation of the PZT patch. The pressure difference between the power chamber and the micro-closed chamber causes the maximum deformation of the PZT patch.
## 5 Simulation and Results
### Energy Consumption  
Figure 7 illustrates the energy consumption of the distributed systems with the conventional hard decision, SH-DFS, the conventional soft decision, and the centralized system. In this paper, the conventional hard decision was that each local sensor made a one-bit hard decision according to its observation and transmitted it to the FC under the non-ideal channel; then, the FC combined all decisions from local sensors into a global decision. In addition, the conventional soft decision in this paper was that each local sensor made a multi-bit decision according to its observation and transmitted it to the FC under the non-ideal channel; then, the FC combined all decisions from local sensors into a global decision. Figure 7 shows the comparisons of four different systems by using the number of alive nodes with the round increasing, in which Con/Hard represents the distributed system with the conventional hard decision, SH-DFS represents the distributed system with the proposed method in this paper, Con/Soft represents the distributed system with conventional soft decision, and the Centralized represents the centralized system.  
We can find that the centralized system consumes the most energy because raw information was transmitted by all local sensors to the FC, although it has the optimal detection performance because there was little loss of information. Conversely, the system with a conventional hard decision consumed the least energy because the least information (a one-bit decision) was transmitted by all local sensors to the FC. The energy consumption in the proposed method mainly included the energy consumption for clustering in every round and the energy consumption for bits transmission. However, the conventional soft decision fusion mainly included energy consumption for bits transmission. On the surface, the proposed method consumed extra energy for clustering. However, Figure 7 shows that the system with conventional soft decision fusion (a three-bit decision) consumed more energy than the system with the proposed method (a three-bit decision in the soft decision, M = 4). This is reasonable for two reasons. The first reason is that, in the system with the conventional soft decision fusion, every local sensor needed to transfer a soft decision to the FC. However, in the clustering network system with the proposed method, every local sensor only needed to transfer a soft decision to its corresponding CH, which had a shorter distance. In addition, those CHs only needed to transfer one bit to the FC in the proposed method. These help the system with the proposed method consume less energy for bits transmission. The second reason is that the cluster was reconfigured using FLS and the fuzzy c-means clustering algorithm in every round, which helped the system share the overload in all local sensors. Furthermore, it made the number of alive nodes in the proposed method be more than that in the conventional soft decision fusion method in every round.  
Figure 7: The number of alive nodes versus the round.
# Named Entity Recognition Based on Multi-Class Label Prompt Selection and Core Entity Replacement
## 4 Experimental Results and Analysis  
### Datasets and Experimental Settings  
In this study, BERT-base-cased is used as the encoder, AdamW is used as an optimizer, the PyTorch 1.7 library is used for implementing the code, and an Nvidia GeForce RTX2080 Ti GPU is used for training.  
The CoNLL-2003 [23] dataset is a news text containing English and German texts; it is used to train and evaluate NER systems. Moreover, it is widely used to evaluate the ability of natural language processing models to recognize named entities, such as the names of people, places, and organizations. The Ontonotes 5.0 [24] dataset contains textual data from a wide range of sources, such as news, conversations, web, and radio; it covers a large number of linguistic tasks such as named entities, semantic role annotations, and denotational disambiguation. OntoNotes 4.0 [25] is a dataset covering Chinese-English named entity annotation. BC5 CDR [26] is a dataset for Named Entity Recognition tasks in the biomedical field. These datasets are often used to validate the performances of various NER models. The dataset statistics are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Dataset** & **Named Entity Type** & **Dataset Size** \\ \hline
1 & CoNLL-2003 & 4 & 22k \\
2 & Ontonotes 5.0 & 18 & 625k \\
3 & Ontonotes 4.0 & 4 & 100k \\
4 & BC5 CDR & 2 & 70k \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset statistics.  
Note the following hyper-parameters: 'Learning rate' denotes the learning rate, which is set to \(2\times 10^{-5}\); 'Batch size' denotes the batch, the number of which is set to 64; 'Epoch' denotes the number of training rounds, which is set to 50; and 'Dropout' denotes the dropout rate, which is set to 0.5. Training will stop if the \(F1\) does not improve after Maxnoincrere epochs. The experimental hyper-parameter settings are shown in Table 2.
## 3 Context-Aware Positioning Model
### Management of Contextual Information
#### 3.2.2 Process Context Sensing Feature Vectors and Assemble Recommendations
A categorical feature (such as positioning capabilities) is considered \"remaining\" if the same categorical feature has not been chosen previously in a particular decision path starting from the root of tree to the current node. However, continuous features (such as fitness or current energy level) can be chosen more than once in the same decision path, and each time that such a feature is chosen, the threshold is determined randomly. Depending on which decision path is chosen in each decision node, the scores of the recommendation vector's positioning strategies are modified. Once all the attributes from the device's feature vector for such a scenario have been chosen (_i.e._, there are no remaining features), the recommendation vector is considered to be assembled. Table 4 shows an example of the training data used to tailor the thresholds of the decision tree's nodes. Each training scenario feature vector (representing the context of a device) carries an associated recommendation vector, with empirically determined scores obtained from controlled real world \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Strategies** & **S1** & **S2** & **S3** & **S4** & **S5** & **C1** & **C2** & **C3** \\ \hline
**Score** & 0.35 & 0.15 & 0.05 & 0.10 & 0.30 & 0.0 & 0.00 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recommendation vector example. experiments, and from literature. This data was used to determine the values of the thresholds for each of the feature vector's attributes, and their effect over the recommendation vector's strategy scores. Figure 3 shows an example of two randomized trees. \(X\) represents the input of the tree, the device's associated scenario feature vector. \(\varphi_{1}\) and \(\varphi_{n}\) represent two different random decision trees, and \(\varphi_{i}=(Y=c|X=x)\) represents the probability of obtaining the recommendation set \(Y\) given feature vector \(x\). Once the summary and recommendation vectors have been assembled, they are stored locally for future reference. The next time that a positioning request is sent to the model, the context information manager will assemble a new summary and compare it with the latest it has stored, in order to check for changes in the navigation context of the device. If the model determines that the navigation context has not changed (_i.e._, the user has not moved), the manager skips the context-aware positioning stage and just sends back the last estimated position (if available) to the consumer application. Otherwise, the positioning process is performed as usual. Both the contextual information summary and the set of recommendations are the inputs used during the context-aware positioning.
# Recent Advances in Nanoparticle-Mediated Treatment of Inflammatory Bowel Diseases
## 15 Other Types of Nanoparticle
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**N. Crt.** & **Author** & **Type of Nanoparticle** & **Size** & **Pathology** & & **Method** \\ \hline
1. [PERSON], B 11 & \
\begin{tabular}{c} scCD98- \\ functionalized isCD98- \\ nanoparticles \\ \end{tabular} & \(\sim\)200 nm & IBD & - &
\begin{tabular}{c} coacervation technique \\ fluorescence microscopy \\ \end{tabular} \\
2. [PERSON], A 17 & \
\begin{tabular}{c} PLGA/Eudragit S100 pH-sensitive \\ polymeric CC-loaded NPs \\ glycyrrhizic acid-loaded Eudragit R \\ S100/poly-(lactic-co-glycolic acid) \\ nanoparticles \\ \end{tabular} & \(\sim\)100 nm & IBD &
\begin{tabular}{c} emulsion-solvent evaporation \\ method \\ modified double-emulsion \\ evaporation coupled with solvent \\ evaporation coating techniques \\ self-assembling amphiphilic block \\ copolymer (MoO-PEG-b-PMOT) \\ composed of a hydrophilic PEG \\ segment and a hydrophobic \\ poly(4-methylstyrene) segment \\ petarnethylpiperidine-1-oxyl \\ moieties via ether linkage \\ \end{tabular} \\
4. & Vong, LB 8 & \
\begin{tabular}{c} a novel redo \\ Nanoparticle (RNP) with ROS \\ scavenging potential of \\ stable nitroxide radicals. \\ \end{tabular} & \(\sim\)40 nm & UC &
\begin{tabular}{c} only(4-methylstyrene) segment \\ possessing 2,2,6,6- \\ tetarnethylpiperidine-1-oxyl \\ (TEMPO) \\ moieties via ether linkage \\ \end{tabular} \\
5. & Date, A.A. 3 & \begin{tabular}{c} budesonide nanosuspensions \\ (NS) containing drug particles \\ of the appropriate size and formulated with muco-inert coatings \\ \end{tabular} & \
\begin{tabular}{c} - using stabilizer in Entocort®®, polyvinylpyrrolidone PVP. \\ -2 \(\mu\)m \\ PVP-coated 2 \(\mu\)m PN particles \\ \end{tabular} & IBD &
\begin{tabular}{c} oil/water emulsification-solvent evaporation method. Using a biodegradable polyester \\ (polylatide-coglycolide 75/25; \\ molecular mass, 20 kDa) providing a solidified polymer network \\ (entrapment of the drug) \\ \end{tabular} \\
6. & Lamprecht, A 23 & - & containing polymeric NP & \(\sim\)100 nm & IBD &
\begin{tabular}{c} oil/water emulsification-solvent evaporation method. Using a biodegradable polyester \\ (polylatide-coglycolide 75/25; \\ molecular mass, 20 kDa) providing a solidified polymer network \\ (entrapment of the drug) \\ \end{tabular} \\ \end{tabular}
\end{table} 
Table 1: Synthesis of most common therapeutic approaches using nanoparticles in inflammatory bowel disease (IBD) treatment.
## 4 Results and Discussion
### Workpiece Temperature  
Workpiece temperature recorded for both systems at various cutting conditions are shown in Figure 6. Workpiece temperatures ranged between 20.8 and 25.7, and 20.6 and 24.2 \({}^{\circ}\)C, for the Cut-list and conventional systems, respectively. Seemingly, both systems showed reduced workpiece temperature with a marginal variation (maximum of 1.5 \({}^{\circ}\)C). This is likely because the small proportion (\(\sim\)20%) of the heat generated when cutting titanium is conducted into workpiece and chip while 80% of the heat is expected to transfer to cutting tool due to low thermal conductivity of titanium alloy [1]. In addition, cutting fluid can dissipate more than 30% of generated heat if it penetrates into a machining zone effectively (e.g., using high jet fluid stream at targeted heat-affected zones) [2]. Possibly, the Cut-list helped to form efficient fluid trapping between the tool and workpiece surfaces due to the nozzle positioned in the feed direction, which led to improving fluid accessibility into the machining zone, resulting in more heat transferred from the workpiece to the cutting fluid. Additionally, the impingement effect of the high-velocity fluid's droplets allowed the fluid to exert its coolant function more efficiently and thus assisted in reducing workpiece temperature. All of these helped the new system in reducing workpiece temperature and compensating the reduction (about 42%, see Table 3) in the amount of cutting fluid supplied. In the same vein, both systems exhibited marginal reductions in average workpiece temperature with increased cutting speed and feed rate. This could be due to the increased fluid flow rate associated with increasing cutting energy, which improved the fluid's ability to dissipate heat [62; 63].
# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 3 Methods
### Dual-Aware Enhancement Module (DAEM)  
In the realm of underwater image processing, the correction of color distortions and the recovery of texture details in degraded images necessitate meticulous decomposition. Many existing methods tend to overlook the integration of optical and visual perception, relying solely on simple convolution for high-frequency feature extraction. However, the unique characteristics of underwater images demand separate operations for distinct colors due to uneven color recession. Additionally, the entire image is affected by uniform blurring caused by light scattering and absorption issues, which extend beyond localized regions. Consequently, incorporating long-range dependencies becomes crucial to further enhance reconstruction results.  
To address the challenges posed by the diverse complexities of the underwater environment and to elevate image reconstruction performance, we propose the dual-aware enhancement module (DAEM). This module captures dual-aware information, integrating both local and global features to progressively generate natural and realistic textures.  
As shown in Figure 1, the DAEM consists of two MCCBs, a 3 \(\times\) 3 convolutional layer, and an STL. The MCCB mitigates color shifts and distortions in underwater images caused by uneven light attenuation. More details will be shown in Section 3.3. Given the input features \(F_{\text{d}-1}\), it first undergoes processing in MCCBs to achieve color bias correction and high-frequency detail recovery, and then fuses the different features using a 3 \(\times\) 3 convolution. Finally, the STL models long-range dependencies to generate powerful feature representation.
###### Abstract  
This paper presents a novel control strategy for transferring large inertia loads using flexible space manipulators in orbit. The proposed strategy employs a Luenberger state observer and damping-stiffness controller to address issues of large tracking error and vibration. A comprehensive joint dynamics model is developed to identify the main sources of disturbance, and a Luenberger state observer is designed to estimate unmeasurable transmission deformation. Transmission stiffness and load inertia perturbations are identified based on the estimated results. By adjusting velocity damping and the gain of the forward channel, perturbations are suppressed to maintain optimal system damping and stiffness. Simulation and physical experiments demonstrate the effectiveness of the algorithm, with simulation experiments showing smoother joint output characteristics and minimal vibration under large load inertia changes, and a 97% reduction in internal deformation. Physical experiments demonstrate improved joint dynamic command tracking performance, with an 88% reduction in position tracking error. The algorithm provides a practical and efficient approach for transferring large inertia scientific payloads in space.  
space manipulator; flexible joint; dynamic; Luenberger observer; automatic control Article  
Method for Flexible Space Manipulators Using Luenberger State Observer.  
[PERSON]; [PERSON]; [PERSON];  
[PERSON]; [PERSON]; [PERSON] A Novel Constant Damping and High  
Stiffness Control Method for Flexible Space Manipulators Using  
L Luenberger State Observer.  
139541033990/app13137954