esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 3 Experiments and Results
### Impact of Varying the Number of NFMs
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Metric** & **K and L** & **Metric** & **LIVE** & **TID2013** & **CSIQ** & **MD1** & **MD2** & **WIQCD** \\ \hline M1 & 2 & SROCC & 0.949 & 0.880 & 0.927 & 0.945 & 0.950 & 0.644 \\  & & PLCC & 0.951 & 0.889 & 0.919 & 0.959 & 0.955 & 0.655 \\  & & RMSE & 5.008 & 0.576 & 0.106 & 5.500 & 5.607 & 15.436 \\ \hline M2 & 2,4 & SROCC & 0.953 & 0.900 & 0.932 & 0.952 & 0.958 & 0.660 \\  & & PLCC & 0.956 & 0.903 & 0.923 & 0.965 & 0.963 & 0.676 \\  & & RMSE & 4.772 & 0.533 & 0.103 & 5.093 & 5.084 & 15.209 \\ \hline M3 & 2,4,6 & SROCC & 0.956 & 0.906 & 0.928 & 0.957 & 0.964 & 0.682 \\  & & PLCC & 0.958 & 0.909 & 0.917 & 0.969 & 0.968 & 0.696 \\  & & RMSE & 4.623 & 0.522 & 0.106 & 4.775 & 4.765 & 14.63 \\ \hline M4 & 2,4,6,8 & SROCC & 0.956 & 0.900 & 0.927 & 0.956 & 0.966 & 0.672 \\  & & PLCC & 0.958 & 0.904 & 0.918 & 0.968 & 0.970 & 0.689 \\  & & RMSE & 4.638 & 0.532 & 0.107 & 4.813 & 4.616 & 14.989 \\ \hline M5 & 2,4,6,8,10 & SROCC & 0.952 & 0.906 & 0.930 & 0.958 & 0.963 & 0.669 \\  & & PLCC & 0.955 & 0.908 & 0.921 & 0.969 & 0.968 & 0.685 \\  & & RMSE & 4.812 & 0.522 & 0.105 & 4.751 & 4.719 & 14.911 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Median SROCC of BIQA-SC on six databases when using different numbers of NFMs, where BIQA-SC using \(n(n\in 1,2,\ldots,5)\) NFMs is denoted as M\(n\).
## 3 Development of the Framework  
### Framework Proposal  
The successful implementation of Robotic Process Automation (RPA) and Artificial Intelligence (AI) within Industry 5.0 requires a structured, phased approach that harmonizes automation with human collaboration. This section introduces a novel framework designed to guide organizations in adopting these technologies systematically, addressing both technical and strategic dimensions.  
The proposed framework is built upon three foundational pillars:  
1. Task Automation (RPA-Driven Efficiency)--Streamlining repetitive, rule-based processes.
2. Cognitive Enhancement (AI-Driven Adaptability)--Enabling predictive and autonomous decision-making.
3. Human-Machine Symbiosis (Industry 5.0 Alignment)--Ensuring collaboration, sustainability, and resilience.  
The framework follows a five-stage implementation logic, ensuring seamless integration while mitigating risks (Table 3):  
Each stage is supported by key enablers, including workforce upskilling, IT infrastructure readiness, and ethical AI considerations (Figure 2).  
The convergence of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in the context of Industry 5.0 requires a systematic methodology that balances automation efficiency, cognitive adaptability, and human-centered collaboration. To meet this need, we propose a structured framework that guides organizations through a continuous, scalable, and risk-mitigated adoption process. This model rests on three interdependent pillars--task automation (RPA) to achieve quick wins, reduce operational costs, and free up people for higher-value activities; cognitive enhancement (AI) to enable predictive analytics, autonomous decision-making, and optimization of complex processes by dealing with unstructured data; and human-machine symbiosis, aligned with Industry 5.0, that promotes employee well-being, sustainability, and ethical governance--and unfolds in five logical steps: process assessment and selection, using mining and interviews to define a priority pipeline; technology stack configuration, integrating RPA tools such as UiPath or Blue Prism with AI models--ensuring interoperability and scalable infrastructure; pilot deployment in a controlled environment to validate performance, ROI, and user insights; enterprise-wide integration, with training on human-robot collaboration (cobots) and assistive AI, supported by change management; and, finally, continuous optimization, with model refinement, bias auditing, and ethical compliance. This framework also connects directly to Industry 5.0 principles by prioritizing human-centricity, sustainability--with optimized processes to reduce waste and energy consumption--resilience through adaptive systems in the face of disruptions, and differentiating itself by offering phased risk mitigation, ethical governance by design, and modular scalability. In this way, we provide a practical and holistic roadmap for organizations to leverage RPA and AI within the human-machine paradigm of Industry 5.0, ensuring that automation brings not only efficiency, but long-term operational and societal value.
## 4 Antennas and Antenna Related Technologies for Future Internet
This technology, which allows for a multibeamforming response, is considered a key ingredient for future wireless internet communications. Let us now steer our attention to the case when the angular representation of the channel is altered by employing enhanced network operation structures. Here, we will focus on antenna systems evolved in cell-free (or distributed MIMO) communications, side-link communications, and edge computing solutions. In the next subsections, we list some notable examples relative to the classification stated in Figure 12. The application-focused antenna designs identified here include simple (e.g., UE, IoT) and complex devices, both centralized (gMIMO) and distributed. In the latter case, both physics-based (e.g., NFC, RIS) and network/system-based (e.g., cell-free, side-link, edge computing) solutions are discussed.
## 2 State of the Art of DNA Microarray Image Compression
### Postprocessing  
After compression, general images are sometimes processed to enhance their visual quality or to provide new features. DNA microarray images are not usually postprocessed with these goals. Rather, they are analyzed in order to extract genetic information and estimate their certainty. Because of this, traditional quality measures such as MSE or PSNR may not be completely suitable when performing lossy compression on DNA microarray images. Inspired by the manner in which genetic information is extracted, some researchers have proposed quality metrics specific for microarray images, which are applied after segmenting the image into spots and background.  
[PERSON] _et al_. proposed in 2001 a combined quality index (\(q_{com}\)), which considered spot sizes, signal to noise ratios, background variability and excessively high local background [39]. In 2004, [PERSON] _et al_. analyzed this metric and proposed and extended it to two new quality measures, \(q_{com1}\) and \(q_{com2}\)[40]. In 2005, [PERSON] _et al_. defined an image segmentation quality metric [21] based on the \(q_{com2}\) measure. [PERSON] _et al_. defined another quality metric that considered signal and background noise, scale invariance, spot regularity and spot alignment [41]. Another factor to study is the information uncertainty of microarray experiments. It is common for DNA microarrays nowadays to replicate the same spot at least three times and to analyze the differences between the replicas to detect or alleviate the information variability of an experiment. Repeating an experiment using a number of different microarrays and comparing the information obtained for the same gene across them is another option to study the information uncertainty [23]. Different authors have used some of these concepts to define distortion measures for lossy microarray image compression, and have measured the performance of some of the discussed algorithms. The ideas on which these measures are sustained are described next.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 6 Benefits of Using a Mobile Manipulator for Railway Track Maintenance
### Safety of workforce  
Although many modern inspection technologies perform track inspection tasks in railways, confirmation, sizing, and criticality classification are often performed by human operators. Weather conditions, tiredness, monotony, etc., can negatively affect inspection results and the quality of repairs completed by a human operator. According to rail safety statistics, there were 3978 workforce physical injuries and 806 workforce shock and trauma incidents on the main line from March 2021 to April 2022 [155], accounting for 20 million pound cost for the companies [160]. A reduction in the number of injuries could be expected if robots could undertake those high-risk maintenance activities.
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 2 Power Load Demand Analysis
### Multi-Energy Coupling Analysis  
#### 2.2.1 Subject Analysis  
Load supplier generally refers to the corresponding power producer. At present, the main body of multi-energy coupling supply refers to the ways of wind, light, water, fire, storage, and other energy complementary power generation and the combined supply of cold, hot, and electricity. This is a typical way to realize the full utilization of energy resources through the collaborative supply of various energies under the background of energy Internet. It breaks the existing mode in which traditional energy supply system is independently planed and independently operated.  
The main sources of load demand include commercial users, industrial users, residential users, agricultural users, government agencies, public utilities, and traffic rails. They inevitably need to use a variety of energy sources in production and life, including electricity, heat, cold, gas, coal, oil, and so on. In the context of the continuous development of integrated energy sources, multi-energy coupling sources can usually be converted to each other. Based on the different demand characteristics of different users in terms of energy type, structure proportion, time distribution, spatial distribution  
Figure 1: Relative changes of load demand under the scenario of multi-energy coupling. (**a**) Scenarios in which multiple coupling technologies are evolving slowly; (**b**) Scenarios in which multiple coupling technologies are developing at a moderate rate; (**c**) Scenarios in which multiple coupling technologies are evolving rapidly.  
and other aspects, the multi-energy coupling mode on the demand side can be designed, which is the complementary of multi-energy on the demand side.  
Under the interactive coupling mode of supply and demand, the main body is still the corresponding body of supply and demand, but the boundary between supply and demand is not obvious in this process [9; 10]. Participants in this process include pumped storage power station, hydrogen energy storage, virtual power plant, electric vehicle, adjustable load, etc. They reduce load and increase output during peak load period, and increase electricity consumption and reduce output during trough load period, so as to improve resource utilization efficiency and ensure energy supply.  
There must be one or more of the above multi-energy coupling modes and two or more participants in a comprehensive energy system. Generally, there are multiple comprehensive energy systems in a region. Therefore, the power demand distribution structure based on the multi-energy coupling scenario is shown in Figure 2.
## 3 Results
### TF Shear Force  
A posterior (PCL-loading) TF shear force (\(\varphi_{\text{TF, shear}}>0\)) is developed for \(x_{\text{GR}}=x_{\text{ankle}}+13\) cm, in the whole range of knee angles, and for \(x_{\text{GR}}=x_{\text{ankle}}\), when \(\theta_{\text{knee}}\geq 45^{\circ}\) (Figure 6). An anterior (ACL-loading) TF shear force (\(\varphi_{\text{TF, shear}}<0\)) may only occur for \(x_{\text{GR}}=x_{\text{ankle}}\) and \(\theta_{\text{knee}}<45^{\circ}\). However, the peak value of this force (occurring at \(25^{\circ}\) of knee flexion for \(y_{\text{wall}}=y_{\text{hip}}\)) is always smaller than 60 N.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Conditions for Maximum Torque**} & \multicolumn{1}{c}{**Peak Isokinetic**} \\ \cline{3-7}
**Torque Type** & **Maximum** & **Knee** & **Distance** & **Type of** & **Body Weight at** & **Torque of Males** \\  & **Torque** & **Angle** & \(d\)** & **Support** & **Forefoot/Reafroot** & **(Females)** \\ \hline Hip extension & & & & & & \\ torque & 130 Nm & 80\({}^{\circ}\) & 50 cm & scapular & forefoot & 177 Nm (110 Nm) \\ Knee extension & & & & & & \\ torque & 250 Nm & 80\({}^{\circ}\) & 50 cm & pelvic & forefoot & 268 Nm (176 Nm) \\ Knee flexion torque & 65 Nm & 0\({}^{\circ}\) & 50 cm & scapular & forefoot & 171 Nm (110 Nm) \\ Ankle & & & & & & \\ plantar-flexion & 40 Nm & 0\({}^{\circ}\) & 50 cm & scapular/pelvic & forefoot & 171 Nm (108 Nm) \\ torque & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Maximum lower-limb joint torques developed during the static wall squat and corresponding exercise conditions. The maximum isokinetic concentric torques developed by males and females are also reported for comparison [15].  
Figure 5: Dependence of the ankle muscle torque \(\tau_{\text{ankle}}\) on the knee angle \(\theta_{\text{knee}}\) for different values of the horizontal ankle-to-hip distance \(d\) (\(d=0.2\), \(0.3\), \(0.4\), \(0.5\) m), two positions of the centre of pressure \(y_{\text{wall}}\) of the force exerted by the wall on the back (scapular support, \(y_{\text{wall}}=y_{\text{shoulder}}\), and pelvic support, \(y_{\text{wall}}=y_{\text{hip}}\)), and two positions of the centre of pressure \(x_{\text{GR}}\) of the ground reaction force (\(x_{\text{GR}}\) at rearfoot, \(x_{\text{GR}}=x_{\text{ankle}}\), and \(x_{\text{GR}}\) at forefoot, \(x_{\text{GR}}=x_{\text{ankle}}+13\) cm). Positive values of \(\tau_{\text{ankle}}\) reflect a plantar flexion muscle torque, whereas negative values reflect a dorsiflexion muscle torque. Data refer to a subject 175 cm tall with a body mass of 75 kg.
# Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House
## 2 Materials and Methods
### Mathematical Model
#### 2.1.5 Electric Vehicle (EV) Battery Subsystem  
The EV battery subsystem in this model is considered only as an appliance. The state of charge (SOC) of the battery in charging mode is given by:  
\[\text{SOC(k)}=\text{SOC(k}-1)+\text{P}_{\text{charging}}\cdot\epsilon_{\text {ch}}/\text{C}_{\text{max}}\cdot 100, \tag{10}\]  
where  
SOC(k) is the state of charge in the current step, %;  
SOC(k - 1) is the state of charge in the previous step, %;  
Pcharging is the charging power, kW;  
\(\epsilon_{\text{ch}}\) is the charging efficiency;  
Cmax is the maximum possible battery capacity, kWh.  
Figure 5: Forecast of the energy costs for the system with 10 m\({}^{2}\) of photovoltaic panels.  
Figure 6: Forecast of the energy costs for the system with 30 m\({}^{2}\) of photovoltaic panels.  
The discharging mode is described by:  
\[\text{SOC(k)}=\text{SOC(k}-1)-\text{P}_{\text{discharged}}/\varepsilon_{\text{d} }/\text{C}_{\text{max}}\cdot 100, \tag{11}\]  
where  
\(\text{P}_{\text{discharged}}\) is the discharged power on the output of the battery, kW;  
\(\varepsilon_{\text{d}}\) is the discharging efficiency.  
The energy from the battery in the model is only used for driving the electric vehicle and not as a supporting energy system for the home, which could be the next feature implemented in the model.  
The first part of Figure 7 shows the charging cycles that were fixed in the week. The second part shows the discharging, which reflects the car being driven. These timings were fixed for testing purposes. The third part shows the State of charge of the battery on the forecast horizon.  
These results were obtained for the battery defined with a maximum capacity of 24 kWh, maximal charging power of 3 kW, charging efficiency of 80%, maximal discharging power of 6 kW and discharging efficiency of 60%.
# Machine Learning: Models, Challenges, and Research Directions
## 4 Machine Learning Processes
### Evaluation Metrics
#### 4.3.4 Evaluation Metrics for Reinforcement Learning Models  
Cumulative Reward (Return): Cumulative reward, also called return, is the sum of rewards received by the agent over a sequence of actions in an episode. It measures the agent's ability to maximize its long-term objectives. Average Reward: Average reward calculates the expected value of the rewards received per time step or per episode. It provides a measure of the agent's efficiency and effectiveness in achieving its goals. Discounted Sum of Rewards: In some cases, it is important to consider future rewards with less weights. The discounted sum of rewards applies a discount factor,(gamma, to give more importance to immediate rewards while still considering long-term consequences. Episodic vs. Continuing Tasks: Depending on the task, you may need to use different evaluation methods. Episodic tasks have a clear beginning and end for each episode, while continuing tasks continue indefinitely. The choice of evaluation metric can vary accordingly. Exploration Rate: Exploration is crucial in reinforcement learning. Evaluating the agent's exploration rate assists in determining if it is effectively exploring the environment to uncover optimal policies or if it is becoming trapped in suboptimal ones. Learning Curve: A learning curve plots the agent's performance (e.g., cumulative reward) over time, showing how quickly it converges to an optimal or near-optimal policy. It helps to monitor the learning progress. Q-value Convergence: In Q-learning and other value-based methods, the convergence of Q-values can be evaluated to assess whether the agent has learned an accurate value function. Policy Convergence: For policy-based methods, such as policy gradients or REINFORCE, the convergence of the agent's policy can be evaluated to determine if it has found an optimal or near-optimal policy. Exploration-Exploitation Trade-off Metrics: Metrics like epsilon (\(\epsilon\))-greedy exploration rate, Boltzmann exploration, or UCB (Upper Confidence Bound) help assess how well the agent balances exploration (attempting new actions) and exploitation (selecting the best-known actions). Success Rate: In tasks where there is a specific goal or objective (e.g., reaching a target in a maze), the success rate measures the percentage of episodes in which the agent successfully achieves the goal. Entropy of Policy: Policy entropy quantifies the level of uncertainty in the agent's action selection. Higher entropy indicates more exploration, while lower entropy suggests a more deterministic policy. Time to Solve: This metric measures the time or number of steps it takes for the agent to achieve a predefined level of performance or solve a task. It helps assess efficiency. Sample Efficiency: Sample efficiency evaluates how quickly the agent learns from its interactions with the environment, considering the number of episodes or steps required to reach a certain level of performance. Table 8 summarizes the possible evaluation metrics for each ML category, as discussed above.
# Disguised Full-Duplex Covert Communications
## System Model  
### Received Signals  
Figure 1 illustrates the considered system model where the source node \(S\) sends a public message to the destination node \(D\). In the meantime, the seemingly receive-only destination node executes a covert transmission via an invisible extra antenna to the hidden receiver \(R\) in an FD manner when the warden node \(W\) keeps monitoring the existence of any such unexpected communications.  
First, the received signal at the disguised FD destination node is expressed by  
\[y_{D}=h_{SD}\sqrt{P_{S}}x_{P}+\tilde{h}_{DD}\sqrt{P_{D}}x_{C}+z_{D}, \tag{1}\]  
Figure 1: System model.  
where \(h_{\text{XY}}\) stands for the channel coefficient between node \(\mathsf{X}\) and \(\mathsf{Y}\) for \(\mathsf{X},\mathsf{Y}\in\{S,D,R,W\}\), \(\tilde{h}_{DD}\sim CN(0,\sigma_{\text{SI}}^{2})\) specifies the residual self-interference channel after self-interference cancellation, \(x_{p}\sim CN(0,1)\) and \(x_{C}\sim CN(0,1)\) denote the public and covert messages, respectively, \(P_{S}\) and \(P_{D}\) indicate the transmit power at the source node and destination node, respectively, and \(z_{\mathsf{X}}\sim CN(0,\sigma_{\text{X}}^{2})\) represents the additive noise at node \(\mathsf{X}\). Note that the destination node can keep the CSI of the source node \(h_{SD}\) since the covert transmission occurs internally under the normal \(S\)-\(D\) communications. The hidden receiver can also easily estimate the CSI of the source and destination nodes, \(h_{SR}\) and \(h_{DR}\), during channel estimation duration if pilot sequences are informed from the destination node in advance. As for the availability of CSI on the warden node, we assume the worst-case covert communications scenario in this work, meaning that the warden has the perfect knowledge of the CSI from the destination and hidden receiver to identify the worst-case achievable covert rate as a performance guideline in practice.  
The source node follows an adaptive transmission policy by which the public data rate \(r_{p,D}\) is adjusted based on the feedback from the destination, and the achievable public data rate \(\bar{r}_{p,D}\) at the destination can be written as [37]  
\[r_{p,D}=\log_{2}\!\left(1+\frac{|h_{SD}|^{2}P_{S}}{\left|\tilde{h}_{DD}\right| ^{2}P_{D}+\sigma_{D}^{2}}\right). \tag{2}\]  
The hidden receiver then receives both a public message through the direct link from the source node and a covert message from the destination node as  
\[y_{k}=h_{SR}\sqrt{P_{S}}x_{p}+h_{DR}\sqrt{P_{D}}x_{C}+z_{R}. \tag{3}\]  
Therefore, the hidden receiver needs to successfully decode and eliminate public messages before retrieving covert messages. This implies that the public data rate is also limited by the achievable public data rate \(r_{p,R}\) at the hidden receiver as  
\[\bar{r}_{p,R}=\log_{2}\!\left(1+\frac{|h_{SR}|^{2}P_{S}}{|h_{DR}|^{2}P_{D}+ \sigma_{R}^{2}}\right). \tag{4}\]  
The resulting achievable covert rate after removing \(x_{p}\) from \(y_{R}\) can also be calculated as  
\[r_{C,R}=\log_{2}\!\left(1+\frac{|h_{DR}|^{2}P_{D}}{\sigma_{R}^{2}}\right). \tag{5}\]
# A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI
## 2 Proposed Approach
### Time-Series Extraction from ROIs Using Brain Atlas  
Functional MRI scans produce a set of three-dimensional images recorded over time and measure a signal (most commonly, the blood-oxygen-level-dependent signal or BOLD signal) that is related to neural activity. In this case, a subject lies in the MRI scanner without thinking or doing anything in particular, while a series of brain images are generated over time that depicts the change in BOLD signal intensity [40]. Thus, a single preprocessed fMRI scan is a 4D time-series data including three spatial dimensions and time.  
Instead of working with the entire time series obtained from every brain voxel directly, certain brain regions of interest (as defined by the brain atlases) were considered here. The mean time-series signals or BOLD signal intensities from voxels enclosed within those regions were extracted using brain atlases. Four standard predefined brain atlases were used to extract ROIs. Among these, the Bootstrap Analysis of Stable Clusters (BASC) atlas, the Power atlas and the Craddock 200 (CC200) atlas are functional and Automated Anatomical Labeling (AAL) is a structural atlas. The number of ROIs defined by the BASC, Power, CC200, and AAL atlas were 122, 264, 200, and 116, respectively. Information regarding these atlases is provided in Section 2.2.1.