esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Challenges and Opportunities
### Ethical and Privacy Considerations
Al-powered human activity recognition and predictive analytics can unintentionally systematize bias and exclusion if training data or algorithmic designs do not represent diverse populations and contexts [119; 120]. This can result in unwanted the uneven distribution of safety resources, the misidentification of innocuous behaviors as suspicious, or systematically excluding specific demographics from early warning systems. To combat these risks, system developers need to use inclusive datasets, regularly audit their algorithms for disparate impacts, and include a diverse group of stakeholders in the model development and vetting process. As part of the responsible aspect of AI, best practices, such as explainable models and continuous monitoring of potential bias, can be incorporated into IoPVT platforms to ensure that the safety benefits are delivered fairly and equitably. There are also significant environmental justice considerations that must be addressed. Communities with historical exposure to environmental hazards may be justifiably skeptical of new monitoring systems. Implementation teams should acknowledge this history and work with community leaders to establish trust. Furthermore, the environmental footprint of the technology itself--including energy consumption, electronic waste, and resource extraction for manufacturing components--must be minimized and transparently reported as part of a comprehensive ethical framework. Ultimately, ethical and privacy considerations are not simply additional constraints but rather foundational pillars of a sustainable IoPVT ecosystem. When handled transparently and collaboratively, these measures protect citizens' rights, increase public trust, and facilitate the long-term viability of outdoor safety monitoring as a force for communal well-being. By centering equity, privacy, and ethical governance in system design from the outset, we can ensure that technological benefits are distributed justly and do not come at the expense of civil liberties or social cohesion.
## 3 Test Results and Analysis
### Changes in Mechanical Properties  
#### 3.2.1 Stress-Strain Curve  
In order to analyze the stress-strain characteristics of granite under different cooling methods, the stress-strain curves related are obtained, as shown in Figure 7. The stress-strain curve is divided into the following four stages: (1) In the crack compaction stage, there are irregular cracks inside the natural rock and the cracks in the samples are compacted at the initial loading stage. The stress-strain curve of the samples at this stage shows a nonlinear change; (2) In the elastic deformation stage, the deformation of the samples can be restored with the load occurring. The stress-strain curve shows a linear change; (3) In the nonlinear deformation stage, the internal damage of the samples begins to increase gradually following the loading. The strength of the sample increases gradually until the peak, which is the maximum limit that the samples can bear; (4) In the failure stage, when the sample reaches the peak bearing capacity, damage starts to show. The internal cracks of the samples happen rapidly and penetrate. Macroracks begin to appear on the surface of samples and the samples are on the verge of being damaged.  
Figure 6: Density change under different cooling methods.  
Figure 7: Stress–strain curves of samples under different cooling methods. (**a**) The stress–strain curve of the sample in natural cooling mode; (**b**) The stress–strain curve of the sample cooled in water.
## 4 AI-Driven Concepts in MCNs
### Decision-Making AI/ML Methods  
Unlike conventional predictive and clustering methods, AI/ML can also be used to provide intelligent decision-making policies. Reinforcement Learning (RL) enables agents  
Figure 4: Unsupervised Learning-based clustering of vessel types.  
Figure 3: Generalized Supervised Learning workflow.  
(e.g., ships, controllers, UAVs, BSs) to learn through trial and error, optimizing policies based on long-term rewards [65]. It is a paradigm where one or multiple agent(s) learn(s) to make decisions by interacting with an environment and receiving feedback in the form of rewards. An indicative RL interaction cycle is shown in Figure 5, where the RL optimizes the UAV 3D trajectory to maximize sea users' QoS based on a multivariate state vector. Deep RL (DRL) combines RL with deep learning, allowing it to handle large and continuous state/action spaces. In MCNs, RL/DRL can be applied indicatively to:  
* **Dynamic adaptive routing:** Ships or UAVs can learn optimal routing strategies that minimize fuel use, avoid congestion, or maintain strong communication links in changing sea/weather conditions [65].
* **Power control and spectrum management:** In networks where resources are shared (e.g., RIS-assisted UAV relays), agents can learn to allocate power or frequency dynamically for maximum efficiency (e.g., maximize throughput and minimize interference) [66].
* **UAV/AUV path planning:** UAVs can learn energy-efficient mission paths while maintaining data links. Underwater vehicles operating in hostile, GPS-denied environments can learn robust paths that conserve energy while maintaining intermittent connectivity with sea BSs or buoys [67].
* **Fleet coordination:** Learn optimal positioning and relaying strategies among collaborative vessels and UAVs to improve network throughput and reliability [68]. Popular RL techniques include Q-Learning, Deep Q-Networks (DQN), Policy Gradient, Proximal Policy Optimization (PPO), and Actor-Critic methods, which are particularly effective in these sequential decision-making tasks.
## 4 Results and Discussion
### Motion Stability  
The motion stability analyzed in this study refers to the method ever adopted by [PERSON] and [PERSON] [12]. When the fixed point \(\hat{\mathcal{Q}}_{con}^{*}\) (the generated continuous states at the Poincare section) is subjected to a small disturbance at the Poincare section, the increments of the continuous states \({}^{k}\mathcal{Q}_{con}^{*}\) and \({}^{k+1}\mathcal{Q}_{con}^{*}\) (the Left superscripts '\(K\)' and '\(k+1\)' correspond to the \(k\)th and (\(k+1\))th period, respectively) to the fixed point \(\hat{\mathcal{Q}}_{con}^{*}\) can be related by  
\[{}^{k+1}\mathcal{Q}_{con}^{*}-\hat{\mathcal{Q}}_{con}^{*}=A({}^{k}\mathcal{Q }_{con}^{*}-\hat{\mathcal{Q}}_{con}^{*}) \tag{26}\]  
at the Poincare section, where \(A\) is the Poincare matrix. The maximum eigenvalue \(\rho(A)\) of the matrix \(A\) is calculated to reflect the motion stability. The smaller \(\rho(A)\) is, the more stable the quadruped bounding is. It is worth noting that this motion stability reflects self-stability in fact because of no external inputs and controls in all joints of this model. As mentioned earlier, this simplified model is used to capture the motion stability of the natural behavior of quadruped bounding.  
The effect of MCPSS on the motion stability is presented at the low, medium and high horizontal speeds, respectively, as shown in Figure 5. It can be seen that the quadruped bounding shows better stability with faster horizontal speed. The stability gets worsen as MCPSS moves from the geometric center of spinal segment (\(\lambda=0.5\)) to hip joint (\(\lambda=1\)) at all speeds, and the worsen trend increases gradually obviously. Moreover, when MCPSS moves from the spinal joint (\(\lambda=0\)) to the geometric center of spinal segment, the motion stability remains a small change at the medium or high speeds, but the stability gets better evidently at the low speed. That is, the motion stability corresponding to MCPSS being at the geometric center of spinal segment can be used as the optimal or suboptimal solution at all speeds.  
Figure 5: Relation between motion stability with different mass-center position of spinal segment (MCPSS) at the low (red circle), medium (green triangle), and high (blue quadrilateral) horizontal speeds, respectively.
## 2 Experimental Investigation  
A schematic view of the experimental system used in the present study is shown in Figure 1. The system consists of two parts. One is the oxygen generator system, which consisted of a pressure swing adsorption (PSA) unit, a zirconia sensor oxygen measurement system, a buffer tank, a control panel, a rotometer, and control valves. The other is the oxygen delivery system, consisting of a oxygen delivery port, 22 oxygen sensors, a data collector, and a computer.  
All experiments were carried out in an artificial climate chamber; the dimensions were 6.8 m in length, 4 m in width, and 2.9 m in height. The PSA oxygen generator is a two-stage PSA oxygen system developed by our research group [39; 40], and the obtained oxygen volume fraction is 99%. The zirconia sensor oxygen measurement system (ZO-101T) was used to measure the oxygen concentration produced by the PSA oxygen generator, with an accuracy of \(\pm\)2.0% of full scale and a measuring range of 0.1-100%. The volumetric flow rate of the oxygen was controlled by the rotameter (DK800-6F), with a measuring range of 0-2 m\({}^{3}\cdot\)h\({}^{-1}\). The data collector (USB5936) was used to collect the oxygen concentration signal and record it in real time. The oxygen sensors (Figaro, KE-25) were used to measure oxygen concentration at different locations, with an accuracy of \(\pm\)1% and a measuring range of 0-100%. The oxygen sensor was placed at a height of 1.5 m from the ground.
# Development of Multi-Channel Seismic-Electrical Combined Rolling Coverage Measurement System
## 3 Instrument Testing and Result Analysis  
### Field Test Environment and Setup Description  
To verify the feasibility of the integrated measurement system, a field survey was carried out in Changping District, Beijing, China, with field photographs shown in Figure 20. The survey used a high-density electrical method with a Wenner array, which involves setting up all electrodes in advance and sequentially energizing different pairs. This automated technique is widely used in mineral exploration, geological hazard assessment, and related fields [35; 36; 37]. The Wenner array arranges the current electrodes \(A\) and \(B\) and the potential electrodes \(M\) and \(N\) at equal spacing in the sequence \(A^{*}M^{*}N^{*}B\) during power injection and measurement. By using coverage cables instead of centralized ones, the time required for instrument relocation is significantly reduced. As shown in Figure 21, the \(L1\) survey line was investigated, starting at point \(P1\) (40.242054\({}^{\circ}\) N, 116.159484\({}^{\circ}\) E) and ending at point \(P2\) (40.237318\({}^{\circ}\) N, 116.16251\({}^{\circ}\) E), with a total length of 580 m.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
###### Abstract  
HY-2A, as the first Chinese ocean dynamic environment satellite, provides an effective and efficient way of observing ocean properties. However, in the operational stage, some inconveniences of the existing ground application system have appeared. Based on the review of users' requirements for data services, the Customized Automatic Processing Framework (CAF) for HY-2A advanced products is proposed and has been developed. As an extension of the existing ground application system, the framework provides interfaces for adding customized algorithms, designing on-demand processing workflows, and scheduling the processing procedures. With the customized processing templates, the framework allows users to easily process the products according to their own expectations, which facilitates the usage of HY-2A satellite advanced products.  
ocean remote sensing; HY-2A satellite; radar data processing; radar altimeter; microwave scatterometer; on-demand processing; web services 2016
###### Abstract  
This paper proposes a new low complexity angle of arrival (AOA) method for signal direction estimation in multi-element smart wireless communication systems. The new method estimates the AOAs of the received signals directly from the received signals with significantly reduced complexity since it does not need to construct the correlation matrix, invert the matrix or apply eigen-decomposition, which are computationally expensive. A mathematical model of the proposed method is illustrated and then verified using extensive computer simulations. Both linear and circular sensors arrays are studied using various numerical examples. The method is systematically compared with other common and recently introduced AOA methods over a wide range of scenarios. The simulated results show that the new method has several advantages in terms of reduced complexity and improved accuracy under the assumptions of correlated signals and limited numbers of snapshots.  
direction estimation; smart antennas; wireless communication systems; direct data acquisition (DDA); multiple input multiple output (MIMO); angles of arrival (AOAs); covariance matrix; computational complexity +
Footnote †: journal: Sensors
## 2 Current Research of Cyber-Physical Power System (CPPS) and Co-Simulation Platforms
### Application Scenarios of Co-Simulation Platforms
#### 2.2.3 Demand Response  
Under the complex operation and full state awareness of CPPS, an assorted demand response theory is required to ensure the stability of the power grid. The research team at Ghent University developed an integration platform based on MATLAB and OMNET++ where an intelligent charging algorithm of electric vehicles was put forward to reduce the peak load, minimize the energy costs, and promote renewable energy [23]. A research team at Ohio State University developed a real-time co-simulation platform based on RT-LAB and OPNET where a micro-grid system with distributed generation, load, energy storage, and control devices was built [24]. In the islanded mode and grid-connected mode, corresponding demand response algorithms considering the communication influence were utilized to achieve the balance of energy production, supply, and sale. Overall, this kind of application is used to analyze the optimal load response algorithm from the perspective of equipment response, network condition, and multi-agent interaction to ensure high-quality and uninterrupted power for users.  
By taking a large number of computing devices, sensing devices and information devices into consideration, current research is aimed at revealing the external characteristics and internal relationships of a large complex system.
## 4 Discussion
### The Essential Elements and IoT Architecture Framework to Implement an IAQ Monitoring Study
#### 4.1.1 Framework  
The hardware and software architecture framework (see Section 2 and Figure 1) with the unified data management system that we presented could be adopted by any users. Using this framework, the essential elements of an IoT architecture could be identified, which included the sensing devices, the gateways for the devices to the wider network, their respective cloud platforms, the data management system, and end-user applications. Another key element was the communication protocol between each of these elements.
## 3 Results and Discussion
### Elemental Analysis  
Elemental analysis confirmed the chemical modification of CH by expressing a change in the percentage of nitrogen and carbon along with their correspondent C/N molar ratio. The results obtained are summarized in Table 1. The change in the amount of these elements (\(L_{0}\)) was determined using Equation (2) [22,34]. When chitosan was treated with glycidyl methacrylate in the first step, the amount of carbon increased, and the nitrogen decreased from 7.39 to 5.89 mmol g\({}^{-1}\), whereas the C/N ratio increased from 6.42 to 8.73 mmol g\({}^{-1}\) due to the absence of nitrogen inside the glycidyl methacrylate molecule. On the other hand, the quantity of nitrogen increased, and the C/N ratio dropped to 7.31 mmol g\({}^{-1}\) when this material was reacted with 1.4-bis(3-aminopropyl) piperazine. It is speculated that the increase in the amount of nitrogen will provide excessive basic centers for cations. These results show the incorporation of 1.4-bis(3-aminopropyl) piperazine molecule in the chitosan backbone. Hence, it will result in a higher sorption of cations from aqueous solution.  
\[L_{0}=\frac{\text{\text@underline{\%Element}}\times 10}{\text{\text@underline{Atomic \,\,mass\,\,of\,\,element}}} \tag{2}\]
# Crack Location and Degree Detection Method Based on YOLOX Model
## 4 Experimental Results and Analysis
### Crack Degree Transformation Test and Results  
The output of the crack location and degree detector proposed in this study is the pixel level, and the actual crack degree needs to be obtained according to the resolution.  
The application images are collected under different working distances by the constructed image acquisition system. The application image I3 is still obtained at a working distance of 0.35 m, the pixel resolution K1 is 0.048 mm/pixel; the application image I4 is obtained when the working distance is 0.70 m, and the FOV is 291 mm \(\times\) 397 mm, the corresponding pixel resolution K2 is 0.097 mm/pixel. The application images are shown in Figure 12(a),b, and the actual width of the crack measured by the crack width measuring instrument is 0.45 mm.  
Figure 12(c),d show the detection results of application images using the proposed crack location and degree detector. There are two super wide cracks (the width greater than 0.3 mm) in I3, which is consistent with the actual situation and is correctly identified. Nevertheless, the detection results are two wide cracks for I4, because of the difference in resolution. The pixel resolution of the data set in this paper is 0.048 mm/pixel, and the defined wide crack is a crack with a width between 0.2 mm and 0.3 mm. Therefore, the corresponding crack pixel width in the image is 4-6 pixels. According to the pixel resolution transformation relationship, K2 is 0.097 mm/pixel. The wide crack is between 0.388 mm and 0.582 mm. The actual crack width is 0.45 mm; it is also a correct detection. Therefore, the actual crack degree can be obtained by the proposed detector.  
Figure 12: Detection results of application images.
## 4 Discussion
### Discussion of Major Quantitative Findings and Qualitative Feedback from the Users
#### 4.1.4 Perceived Enjoyment  
This study does not find a substantial effect on different interactive interfaces for PE. From the qualitative inquiries, we discovered that hand gesture recognition is enjoyable to use, provides an innovative experience, and is appealing to its users, whereas users believe that controlling the joystick is simple but rather tedious. This echoes the finding by [PERSON] et al. [6] and suggests that the joystick controller produces a lower degree of pleasure for its users while the gesture recognition provides a more enjoyable experience while operating (see Table 9).
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 2 Literature Review
### Small Object Detection  
Objects in an image are typically categorised as large, medium, or small based on their physical size or spatial extent within the image. For instance, COCO defines these three categories in terms of object area [21]. Unlike large and medium-sized objects, small objects face challenges such as fewer pixels and semantic information, as well as noise from compression, movement, and occlusions that can have a more pronounced impact than on larger objects. These challenges result in unwanted detection effects in small object detection within existing models. Small object detection is currently applied in specific scenarios, such as traffic sign detection [22; 23], person detection through aerial images [24], or weapons detection.  
Many domain-specific proposals employ various object detection algorithms, such as Faster R-CNN, with different backbones or modifications. For example, in the traffic sign detection domain, [PERSON] et al. [25] applied a generative adversarial network, and in the aerial detection domain, [PERSON] et al. [26] implemented various modules in Faster R-CNN for detecting people in aerial images. [PERSON] et al. [27] presented a method called Scale Match to reduce the scale mismatch between the pre-training dataset (COCO) and the fine-tuning dataset (TinyPerson dataset), improving the detection of people in aerial images. [PERSON] et al. [28] suggested adding a deconvolution layer after the last convolution layer in Faster R-CNN to enhance the detection of small objects in remote sensing images.  
[PERSON] et al. [29] introduced a new feature extractor called Feature Pyramid Network (FPN), which has been adopted in various studies to extend other architectures. [PERSON] and [PERSON] [30] modified YOLOv2 using FPN, and [PERSON] et al. [31] proposed a similar modification for Faster R-CNN. [PERSON] et al. [14] implemented a classifier using diverse binarisation techniques, such as One-Versus-All and One-Versus-One, to enhance the detection of small objects.  
For weapons detection, [PERSON] et al. [1] applied FPN with Faster R-CNN to detect handguns in CCTV surveillance, while [PERSON] et al. [11] implemented a receptive field enhancement module in YOLOv4 to improve the model's perceptiveness in the region of interest.  
In this paper, [PERSON] et al.'s technique, along with other methods, is employed to address the scale mismatch between different datasets for weapons detection. Additionally, two datasets are generated based on two versions of this technique with the goal of improving the precision of weapons detection in CCTV systems.