esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 2 Definition and Characteristics
### Characteristics
(5) Prediction analysis part  
The prediction analytics component in smart healthcare performs advanced data analysis on health data to identify useful patterns and trends. These insights help medical professionals accurately predict future health events and outcomes. The prediction analytics component frequently aids in managing chronic diseases, early detection of high-risk patients, and predicting disease outbreaks [30]. (6) Decision support part  
The decision support component in smart healthcare AI applications serves an advisory or watchdog role to reduce medical errors, including patient errors. This component falls into two main classes: the ML-based class, which relies on statistical inferences; and the ES-based class, which utilizes sophisticated rule-management subsystems within EHR systems [31]. (7) Feedback and optimization part  
The feedback and optimization component is crucial for enabling AI applications in smart healthcare to learn, improve efficiency, and enhance accuracy continuously. This involves monitoring real-time performance, incorporating user feedback after each operation, and utilizing this feedback for optimization algorithms to refine the application's parameters [32]. Figure 1 depicts a common architecture of AI applications in the smart healthcare field. In this architecture, the data collection and integration component gathers data from various sources and integrates them into a unified dataset. If the dataset consists of unstructured data, it is sent to the unstructured data processing component, where it is transformed into structured data before moving to the data preprocessing component. If the dataset is already structured data, it goes directly to the data preprocessing component. In the data preprocessing component, the dataset undergoes cleaning, transformation, and processing to enhance its quality, accuracy, and availability. Subsequently, it serves as the input for the algorithm and model component. The algorithm and model components employ specific AI algorithms and models on various input datasets for practical applications. The output either proceeds directly to the decision support component to provide evidence and support users in making informed decisions, or it first moves to the prediction analysis component to generate predictions of future events or outcomes. These predictions are then forwarded to the decision support component to aid users in making decisions. After processing through the decision support and prediction analysis components, the feedback and optimization component receives their results. It conducts a comprehensive evaluation of previous processes, assessing efficiency and accuracy, calculating parameters to enhance algorithms and models, and subsequently, updating these parameters.
## 4 Performance Evaluation of Post-Installed Anchors
### Static Loading Test  
#### 4.1.1 Experimental Method  
The static performance of the post-installed anchors was evaluated by conducting shear and pull-out tests using a dynamic 1000 kN universal testing machine (UTM) in a domestic certification test research center in accordance with American Society for Testing and Materials (ASTM) E 488 [13]. For the applied loading condition, a continuous loading method (T-01), in which the load is slowly applied until failure, was used to obtain the load-displacement data for the full behavior until the failure of the anchor bolt. The loading rate was set to 0.4 mm/min, allowing the tests to be slowly conducted while observing the concrete-anchor bond behavior.  
In the case of the shear tests, each concrete specimen with anchor bolts was erected vertically and fixed at the UTM, as shown in Figure 8a, and the equipment set for the shear tests was then fastened with the anchor. In the case of the pull-out tests, by contrast, each of the concrete specimens with the anchor bolts was fixed to the reaction floor of the UTM, as shown in Figure 8b, and the hydraulic tension grip of the UTM load cell was then fastened with the anchor. The tests were stopped when damage occurred or when the load resistance was lost from such damage. From the five tests conducted, three test results with an error rate of less than 20% were selected.  
Figure 7: Process of making concrete specimens.  
Based on the selected test results, the average maximum loads were calculated, and the pull-out performance was evaluated. The result was used to propose a load protocol for use in dynamic performance evaluation. Table 3 provides a list of the specimen names.
## 2 Methodology
### Method Overview
#### 2.4.3 Region Feature Extraction and Visibility Evaluation  
After segmenting the sub-regions, the features of the sub-regions need to be extracted to provide input variables for subsequent visibility estimation. The image of the sub-region contains lots of information and features, including features related to visibility estimation and other redundant information due to interference. If all the information of the sub-region image was directly mapped with the visibility value, the amount of data calculation for training was too large and the redundant information may cause errors in the final results. Therefore, feature extraction was an indispensable and important step for the image of the sub-region. Feature extraction was one of the most basic operations in image  
Figure 8: Threshold segmentation image after gray weighted average.  
processing. It considered the image as a data set and pixels' features as elements. The process aims at finding the elements in the data set that could give the best representation of the data characteristics. According to different needs of image classification and parameter evaluation, the method and dimension for feature extraction could be greatly different. In this paper, four common deep learning feature extraction methods based on the Keras-platform were used. These methods were (VGG)-16 network, (VGG)-19 network, DenseNet network and ResNet_50 network. The extracted features were used as the input variables for the visibility evaluation model, and the variables were used as input for the Support Vector Regression model, and then visibility evaluation was performed on the effective subregions. However, as the number of feature values at the output layer of the above ANN is still large, the computation load for the visibility estimation algorithm is quite high. Therefore, we proposed to use PSO method to find the optimal subset of the feature values at output convolution layer of ANN for visibility estimation.
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 5 Potential lncRNA-Based Biomarkers
### Malat1
MALAT1 inhibited the TEA domain family's transcriptional activity by sequestering the transcription factor. Their rigorous study addressed previous discrepancies by using precise methods, such as a transcriptional terminator insertion to inactivate MALAT1 without affecting neighboring genes [42]. In another study, the researchers investigated the impact of Phosphatase and Tensin Homology (PTEN) dysregulation on the noncoding transcriptome, identifying MALAT1 as a downstream target of PTEN across both human and mouse models. They characterized a PTEN-miRNA-MALAT1 axis that regulates tumorigenesis, demonstrating that MALAT1 possesses novel tumor-suppressive properties in colon and BC. PTEN-mediated regulation of MALAT1 relies on miRNAs, particularly miR-17 and 20a, which influence the expression of pro-migratory genes like Epithelial Cell Adhesion Molecule (EpCAM) and Integrin Subunit Beta 4 (ITGB4) [43]. [PERSON] et al. revealed that MALAT was downregulated in both tumor-ous tissue samples and BC cell lines (BT549, MCF7, MDA-MB-453, and MDA-MB-231). They observed that MALAT1 knockdown induced the EMT in BC cells via the PI3K-AKT pathway, leading to enhanced migration and invasion. Furthermore, a lower expression of MALAT1 was strongly associated with axillary LNM and shorter metastatic relapse-free survival [44]. The discrepancies in reports regarding MALAT1 in BC may stem from variations in experimental conditions, sample sizes, patient populations, and the complex expression pattern of various transcript variants of MALAT1. There is a need for large, multicenter studies to validate its role comprehensively. Nonetheless, MALAT1 remains a promising biomarker for BC progression [45].
## 2 Materials and Methods
### Neural Network-Based Methods
#### 2.3.2 Recurrent Neural Networks (RNNs)  
RNNs are a type of neural network designed to handle sequential data, such as time series. Unlike traditional neural networks, RNNs have feedback connections that allow them to retain previous states. This makes them ideal for predicting patterns in temporal data like daily GHI. RNNs utilize Long Short-Term Memory (LSTM) units to capture long-term dependencies and nonlinear trends in the data [37, 38].  
RNNs propagate information through memory units across a sequence of data, such as daily solar radiation records. Each unit in the network, equipped with LSTM, remembers and updates its internal state based on the current input and previously stored information. This enables the RNN to capture complex temporal dependencies, such as daily and seasonal variations in GHI, dynamically adapting to different patterns as it processes each sequential data point [37]. Figure 2 illustrates the comparison between the compressed (left) and expanded (right) configuration of a basic RNN.
## 2 Materials and Methods  
### Engine Specifications and Operating Conditions  
This relationship of the spark, in-cylinder flow-field, and flame kernel is observed inside an operating engine condition of a single-cylinder optical spark ignition engine. Table 1 summarizes the specification of this engine and the selected operating conditions. The engine capacity is 500 cm\({}^{3}\) with a bore of 86 mm and stroke of 86 mm and has a compression ratio of 10.4. The top of the piston (54 mm diameter) and the pent-roof liner is replaced by a quartz window to provide an optical access into the combustion chamber of the engine, as shown in Figure 1a. Reflection from a 45\({}^{\circ}\) mirror placed in the hollow extended-piston provides an optical-access path for the laser sheet for the observation of the pent-roof area in the vertical plane, which is the desired configuration for this study. This optical view of the vertical plane in the pent-roof via the pent-roof liner windows is also used for simultaneous high-speed spark imaging, which will be described in detail in the following section. The experiments were carried out with the engine operating at two different speeds, 1200 rpm and 1700 rpm in order to generate different magnitudes of flow-field near the spark plug. The engine operated with a port fuel injection and an absolute intake pressure of 60 kPa; the air fuel ratio (A/F) was kept at slightly lean A/F of 18. The spark timing was set at -40\({}^{\circ}\) Crank-Angle after Top Dead Center (CA aTDC). In-cylinder pressures were monitored and recorded using a Kistler pressure transducer and the engine was fired continuously during the PIV/spark capturing process. Nineteen cycles were captured for each condition.  
\begin{table}
\begin{tabular}{c c} Engine Type & 4 Stroke, Single Cylinder, port fuel injection (PFI) \\ Displacement & 500 cc \\ Bore x stroke & \(86\times 86\) mm \\ Compression ratio & 10.4 \\ Number of valves & Intake: 2, Exhaust: 2 \\ Engine speed & 1200 and 1700 rpm \\ Intake pressure & 60 kPa \\ Air fuel ratio (A/F) & 18 \\ Spark timing & 40 deg. before top dead center (BTDC) \\ \end{tabular}
\end{table}
Table 1: Engine Specifications and operating conditions.
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Quantum MIMO Transmission and Quantum Channel
#### 3.4.4 Amplitude Damping Noise  
Amplitude damping reflects energy-loss mechanisms common in physical quantum systems, such as spontaneous emission. It is defined using Kraus operators and is governed by amplitude damping probability \(p_{A}\), as in Equation (17).  
\[\mathcal{N}_{A}(\rho)=K_{0}\rho K_{0}^{\dagger}+K_{1}\rho K_{1}^{\dagger} \tag{17}\]  
The corresponding Kraus operators are represented in Equation (18).  
\[K_{0}=\begin{pmatrix}1&0\\ 0&\sqrt{1-p_{A}}\end{pmatrix},\quad K_{1}=\begin{pmatrix}0&\sqrt{p_{A}}\\ 0&0\end{pmatrix} \tag{18}\]  
These Kraus operators provide a mathematical framework for describing the effect of noise and decoherence on quantum states in an open quantum system. Any quantum noise channel \(\mathcal{N}\) acting on a density matrix \(\rho\) can be represented as a set of Kraus operators \(\{K_{i}\}\), which satisfy the completeness relation as in Equation (19).  
\[\sum_{i}K_{i}^{\dagger}K_{i}=I \tag{19}\]  
where \(I\) is the identity operator. The action of the noise channel on \(\rho\) is given by Equation (20).  
\[\mathcal{N}(\rho)=\sum_{i}K_{i}\rho K_{i}^{\dagger} \tag{20}\]  
Each Kraus operator \(K_{i}\) represents one possible way the environment can affect the quantum state. This approach is commonly used to describe different types of quantum noise, such as amplitude damping and phase damping.
# Revisiting the High-Performance Reconfigurable Computing for Future Datacenters
## 5 Open Problems and Discussion  
There is plenty to do in this area, but we would like to mention a few open problems. The foremost goal is to enable multi-tenant multi-FPGAs for medium to large-scale datacenters, only then we can unleash the real potential of FPGAs as a heterogeneous computing resource. This can be achieved either by developing FPGA operating system or improving existing virtualization methods. An intense investigation is required on how to compute over multiple FPGAs in a scalable manner. A design is required that can exploit multiple FPGAs via streaming between Catapult style or batching MapReduce style, other than OpenStack.  
A serious effort is required to make the shell and development stack modular. Currently, everything must compile against a shell and any change in shell requires recompiling accelerators. Likewise, a change in the Linux kernel means the recompilation of all user software, so one can imagine how bad is the FPGA ecosystem yet today. However, FPGAs provide a lot of customization, without which it would be meaningless to use FPGAs in the first place. Overlays solve this issue to an extent for a small class of application, but the solution is not scalable for general computation with FPGAs. Therefore, we need a set of APIs and standards in software stack to manage this heterogeneity in a sensible manner. Dynamic resource allocation somehow addresses this issue, but largely it remains an ignored area by the community.  
With multi-tenant support, efficient management and scheduling is required for the resources, an advanced resource manager that can fit the same workload on fewer FPGA resources should be the key point of future development.  
Security is another aspect that needs intense attention of the community with a lot of potential for development. The complex case of FPGAs in datacenter is vulnerable to all sorts of attacks, as the reported attacks include malicious bitstream and side channel that severely damage the availability. It also assists in segregation of many accelerators on same FPGA or network.
# Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
###### Abstract  
Detecting small dark targets underwater, such as fishing nets, is critical to the operation of underwater robots. Existing techniques often require more computational resources and operate under harsh underwater imaging conditions when handling such tasks. This study aims to develop a model with low computational resource consumption and high efficiency to improve the detection accuracy of fishing nets for safe and efficient underwater operations. The Light-YOLO model proposed in this paper introduces an attention mechanism based on sparse connectivity and deformable convolution optimized for complex underwater lighting and visual conditions. This novel attention mechanism enhances the detection performance by focusing on the key visual features of fishing nets, while the introduced CoTAttention and SEAM modules further improve the model's recognition accuracy of fishing nets through deeper feature interactions. The results demonstrate that the proposed Light-YOLO model achieves a precision of 89.3%, a recall of 80.7%, and an [EMAIL_ADDRESS] of 86.7%. Compared to other models, our model has the highest precision for its computational size and is the lightest while maintaining similar accuracy, providing an effective solution for fishing net detection and identification.  
YOLOv8; variational convolution; sparse connection; GAN; underwater fishing net vulnerability detection; lightweight +
Footnote †: journal: Applied
###### Abstract  
Background: Satisfaction with prosthesis plays a key role in regaining mobility and is important for optimizing prosthetic usage, mobility, and increasing compliance with medical regimen. Despite unchangeable factors like age and comorbidities, other factors, like pain, received rehabilitation, satisfaction with assistive devices, service, and information, can be changed and might contribute to a better usage and acceptance of the prosthesis and amputees' mobility. Objectives: The aim of the study was to analyze mobility, pain, supply of assistive devices, and additional therapies received after major limb amputations. Furthermore, a correlation of those parameters was evaluated. Methods: Retrospective identification of patients with major limb amputation (operation and procedures classification system (OPS)) and relevant related demographics within the clinical documentation system during a four-year observation time. In addition, we undertook prospective assessment of mobility (K-level), pain qualities, additional therapies, self-rated overall quality of life (QoL) and degree of adaptation to the life after amputation, dependency from caregivers, and satisfaction with the provided assistive devices (QUEST). Results: A total of 164 patients (mean age 68, age range: 19 to 97 years) underwent major limb amputation. A total of 27 questionnaires were returned and analyzed. All those traumatic and nontraumatic amputees received assistive devices. Although mobility and QoL decreased significantly after amputation, a high satisfaction with provided prosthetic and assistive devices and care was found. Conclusions: Amputation registries are becoming elementary to allow for nationwide comparisons of clinics, to identify the requirements of amputees, and to design an interdisciplinary care model for a successful comprehensive approach.  
apputation; rehabilitation; assistive technology; prosthesis; prosthetic supply Article
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 4 Simulation Results and Discussion
### Simulating the Data Transmission Rate in Diverse Environments
Figure 10: The data transmission rate of UAVs base station in urban environment: (**a**) 100 m hover height deployment and 40 dBm launch power; (**b**) 200 m hover height deployment and 60 dBm launch power. to 200 m and the launch power of the UAV base station to 20 dBm. At this time, in rural environments, the deployment of the central data transmission rate also reaches 500 Mbps, with the deployment of the edge data transmission rate also being 50 Mbps (Figure 12a); thus, according to Formula (21), we calculated the UAV base station usage time as 704.12 s. In rural environments, our approach increases the usage time of UAV base stations while maintaining the data transmission rate.
## 2 System Modeling and Problem Placement
### Problem Statement  
The primary control objective is to design a control law that can achieve a prescribed tracking performances including overshoot, convergence rate, and steady-state tracking error under the effects of disturbances on the mechanical and hydraulic systems with both smooth and non-smooth desired reference trajectories and the nominal system parameters are assumed to be known.  
To facilitate the control and observer design, the following reasonable assumptions are given as  
**Assumption 1** ([6; 7]).: _The pressures \(P_{1}\), \(P_{2}\) and the load pressure \(P_{L}\) are bounded by the supply pressure \(P_{S}\) to guarantee that the function \(g_{3}(\mathbf{x},u)\) is strictly positive._  
**Assumption 2** ([52]).: _The mismatched and matched lumped disturbances \(d_{1}(t)\) and \(d_{2}(t)\) are bounded, and their first-order derivatives are bounded by constants; i.e., \(\left|\dot{d}_{1}(t)\right|\leq\delta_{1}\) and \(\left|\dot{d}_{2}(t)\right|\leq\delta_{2}\)._  
**Assumption 3** ([44]).: _The function \(f_{2}(\mathbf{x})\) and \(f_{3}(\mathbf{x})\) are globally Lipschitz with respect to \(x_{2}\); and \(g_{3}(\mathbf{x})\) is also Lipschitz in the working condition. Their Lipschitz constants are \(k_{f_{2}}\), \(k_{f_{3}}\), and \(k_{g_{3}}\), respectively._  
**Lemma 1**.: _Consider the following Lyapunov function \(V\) satisfying_  
\[\dot{V}\leq-aV^{\gamma} \tag{5}\]  
_where \(a>0\) and \(0<\gamma<1\). It can be recognized that, \(V\) converges to origin in finite time \(t_{f}\) determined by_  
\[t_{f}\leq\frac{V^{1-\gamma}(0)}{a(1-\gamma)} \tag{6}\]  
Proof.: The inequality (5) can be rewritten as  
\[V^{-\gamma}\dot{V}\leq-a\Leftrightarrow\frac{1}{1-\gamma}\frac{ dV^{1-\gamma}}{dt}\leq-a \tag{7}\] \[\Rightarrow dV^{1-\gamma}\leq-a(1-\gamma)dt\]Integrating both side of (7), the converging time is bounded by  
\[t_{f}=\frac{V^{1-\gamma}(0)}{a(1-\gamma)} \tag{8}\]  
This completes the proof of Lemma 1.  
**Lemma 2** ([6]).: _Considering a Lyapunov function \(V\) that satisfy the following inequality_  
\[\dot{V}\leq-aV+b \tag{9}\]  
_where \(a\) and \(b\) are positive constants._  
_The function \(V\) reaches a region whose bound is determined by \(b/a\) as \(t\to\infty\), i.e., globally ultimately uniformly bounded stability (GUIUB) is achieved._