esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Rapid Evaluation System Based on CPM
#### 2.1.2 Risk Assessment Module for WMSDs  
Based on the accurate capture of skeletal points, further calculations of limb angle values based on the human skeletal joint coordinate data are required to analyze the special status presented by the occupational posture [38]. Additionally, differences in the spatial coordinates of images in image recognition can also affect limb angles. Therefore, in this study, the limb Li is defined as being composed of two adjacent joints \(Pi=(xi,yi,zi)\) and \(Pi\)\(+\)\(1=(xi+1,yi+1,zi+1)\), i.e., \(Li=PiPi+1\). Due to the complexity of three-dimensional spatial angles, in this research, the projection of limbs on the sagittal plane, coronal plane, and vertical plane and the projection angle between the sagittal axis, coronal axis, and vertical axis are used to analyze the working posture. Firstly, the length \(Di\) of the limb \(Li\) in three-dimensional space is defined as shown in Formula (1)  
\[D_{i}=\sqrt{\left(x_{i+1}-x_{i}\right)^{2}+\left(y_{i+1}-y_{i}\right)^{2}+ \left(z_{i+1}-z_{i}\right)^{2}} \tag{1}\]  
And the angles \(\theta_{x}\), \(\theta_{y}\), and \(\theta_{z}\) between the limb \(Li\) and the sagittal axis, coronal axis, and vertical axis base directions can be calculated using the spatial straight line projection, relationship, as seen in Formulas (2)-(4).  
\[\theta_{x}=cos^{-1}\frac{x_{i+1}-x_{i}}{D_{i}} \tag{2}\]  
\[\theta_{y}=cos^{-1}\frac{y_{i+1}-y_{i}}{D_{i}} \tag{3}\]  
\[\theta_{z}=cos^{-1}\frac{z_{i+1}-z_{i}}{D_{i}} \tag{4}\]  
Building upon the limb angle output and the REBA and RULA methods for human posture assessment, the foundational theory, formulas, and methods are written into modular code using computer programming methods. The work-related musculoskeletal disorder (WMSD) risk assessment module is based on the C language, with C++ as a supplementary language, and the entire coding work is completed in VS2015 software. The specific workflow is depicted in Figure 5.  
Figure 4: The calculation process of the loss value for the human skeleton recognition algorithm.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 2 State of the Art
### Attitude Determination and Control Systems  
An ADCS is a system that enables a CubeSat to orient itself in space and perform various maneuvers to achieve its mission objectives. There are different types of ADCS for CubeSats, depending on the level of accuracy, complexity, and cost required. The ADCS for CubeSats are passive or active.  
Passive ADCS relies on external forces, such as gravity gradient, magnetic field, or solar radiation pressure, to stabilize or align the CubeSat with a fixed reference frame. Passive ADCS does not require any active actuators or sensors, but it has limited pointing accuracy and stability. An example of passive ADCS is a permanent magnet that aligns the CubeSat with the Earth's magnetic field.  
Active ADCS uses active actuators, such as reaction wheels, magnetorquers, or thrusters, to generate torques that control the CubeSat's attitude. Active ADCS also requires sensors, such as magnetometers, sun sensors, star trackers, or gyroscopes, to measure the CubeSat's attitude and provide feedback for closed-loop control. Active ADCS can achieve higher pointing accuracy and stability than passive ADCS, but it also consumes more power and mass. An example of active ADCS is a three-axis reaction wheel system that can provide full attitude control.  
Both types of ADCS can be combined into a Hybrid system, that mixes passive and active elements to achieve a balance between performance and efficiency. Generally, hybrid ADCS use passive elements to provide coarse attitude control and active elements to provide fine attitude correction. Hybrid ACS can also switch between different modes of operation depending on the mission phase or environmental conditions.  
ADCS systems are composed of sensors, to measure the CubeSat's attitude and provide feedback for the control system, and actuators, to generate torques or forces to change the CubeSat's attitude. The most common sensors in CubeSats are magnetometers, that measure the magnetic field of the Earth and provide information about the CubeSat's orientation relative to the magnetic north; sun sensors, that detect the direction of the Sun and provides information about the CubeSat's orientation relative to the Sun; and star trackers, that capture images of the stars and identify them using a star catalog. It provides information about the CubeSat's orientation relative to an inertial frame. The most frequently employed satellite actuators are magnetorquers and reaction wheels, with occasional utilization of thrusters. However, for the purpose of attitude control, the utilization of thrusters is infrequent due to the associated penalties of increased fuel mass and volume, as well as the irreversible nature of its depletion [22].
## 5 Experimental Outcomes, Comparative Analysis, and Discussion
### Experimental Observations and Comparative Analysis  
The experiments tackled two problems. First, the classification of vessels was performed. Second, the trajectory of the vessel had to be determined. The contribution was composite, as both types of prediction affect the final decision that a human would make based on the results provided from the two different experimental groups. AIS data were used for both cases, and for both cases, first, error minimization was performed, followed by a second sub-group of experiments that evaluated the [PERSON]'s kappa coefficient maximization outcomes.
## 4 Results and Analysis
### Results of the Experiment  
The analyses of the experiment outcomes occurred after we split up the participants into two groups: 21 designers (with experience in public space or urban design) and 16 non-designers (residents and stakeholders) (Table 1).  
Figure 6 shows the main results of this experiment. Participants rated their experience with the different tools on a 10-point scale for each sub-test. The reported results distinguish between designers and non-designers. For each sub-test, a graph shows the rating for both designing on the one hand and communication on the other. The rating \"to design\" by non-designers indicates whether the tools can be helpful for co-creation and co-design in 3D. The results of this experiment are useful for answering the two research questions that probe the attraction (or resistance to) and usability of 3D and extended reality in 3D co-creation and co-design.  
The charts show a higher appreciation of digital tools compared with analog tools on paper for designing and communicating. The online 3D design \"Furnish-thing!\" tool was less valued than VR for designing purposes. Based on the personal communications used by the participants during the experiment, VR controllers in sub-test 4 were more user-friendly than a 2D computer mouse, as used in the \"Furnish-thing!\" tool in sub-test 3. The results showed a high appreciation for the VR tool for both design and communication. There was no significant difference in appreciation between designers and non-designers for digital tools. However, it is notable that 3D digital tools scored better than 2D analog tools for both groups; a non-parametric Wilcoxon rank sum test confirmed those indications (Figure 7). Even the most critical participants for 3D and VR said in verbal interviews after the experiment that they were amazed by the possibilities. The higher scores after experimenting, when compared with the online survey, indicated much ignorance about the possibilities of 3D and VR in the public space design process. Resistance is sometimes high because people have never tested these 3D and VR tools, but satisfaction increases once they have tried.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **21 Designers** &
\begin{tabular}{c} **16 Non-Designers** \\ **(Residents and Stakeholders)** \\ \end{tabular} \\ \hline Younger than 34 years & 47\% & 42\% \\ Between 35 and 54 years & 32\% & 53\% \\ +55 years & 21\% & 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic distribution.  
Figure 6: Results of the qualitative assessment of the experience using the different tools in the tests.
## 3 Results
### Temporal Relationship between \(\mathrm{GPP_{VI}}\) and \(\mathrm{GPP_{EC}}\)
There were fewer effective images obtained at the EBF sites than at the GRA sites; thus, the GPP\({}_{\text{VI}}\) did not track the GPP\({}_{\text{EC}}\) variance. From January 2017 to April 2017, there were little available data for VI retrieval, and all GPP\({}_{\text{VI}}\) had different trends with GPP\({}_{\text{EC}}\) at CUM. The GPP\({}_{\text{CIg}}\) showed limited variance at TUM; thus, it exhibited a high bias with GPP\({}_{\text{EC}}\) during the study period. At WOM, all of the GPP\({}_{\text{VI}}\) corresponded with GPP\({}_{\text{EC}}\) across the different seasons. Figure 3: The number of effective Sentinel-2 images obtained during the 16-day interval. An effective image here is defined as an image with less than 10% cloud cover. The legend on the top left represents the abbreviation for each site. The bottom \(x\)-axis is the date, and the top \(x\)-axis is the season in Australia. The pink dashed line is the launch date of Sentinel-2B. The Sentinel-2-based red-edge VIs such as Clr exhibited similar estimating accuracy with GPPE\({}_{\text{EC}}\) under gap-filled days and non-gap-filled days (Figure 5). At WOM, the rRMSE under a cloudy sky was 0.19, which was similar to the rRMSE under a clear sky (0.17). A similar result was also observed at the YNC site. However, the correlation coefficients a and b were different for cloudy and clear days at WOM and YNC. The seasonal change in Clr in WOM was not significant, and the GPP change was mostly accompanied by a change in PAR\({}_{\text{in}}\) (Figure 6). High cloud cover led to a low clear sky index (CSI), which influenced the image quality; thus, the gaps in the VIs in these periods needed to be filled (Figure 4). With the VIs rebuilt on a daily scale, the relationship between GPPE and GPPLr was different between clear and cloudy days; thus, more available data are required to investigate the relationship between the time series rebuilt in the red-edge index and GPPE  
Figure 4: Time series of gross primary productivity (GPP) estimated by vegetation indices (VIs) and GPPE\({}_{\text{EC}}\) for 16-day intervals at five sites (**a–e**). The solid circles are the VIs derived from satellite-based reflectance, whereas the hollow circles are the VIs derived from GPP\({}_{\text{VI}}\), which were gap-filled using the Savitzky–Golay filter. The unit of GPP is gC-m\({}^{-2}\)-day\({}^{-1}\).
## 2 Motion Capture System
### Markerless Systems
#### 2.1.1 Subject Volume Generation: The Visual Hull  
To derive the subject's volume, a technique called the \"visual hull\" is used. This involves backprojecting cones into space to obtain a three-dimensional volume, defined as the intersection of multiple cones, representing the volume occupied by the subject.  
The working volume is divided into voxels, which are the three-dimensional equivalents of pixels. Each voxel is compared with the silhouettes, and if its projection falls within a silhouette, the voxel is considered part of the visual hull. The process can be accelerated using an \"octree\" representation, where only voxels located in significant regions (e.g., edges) are compared. This approach is supported by filters that extract 3D contours. The generation of a visual hull, however, can result in ghost volumes, i.e., artifacts that appear when an area of the working volume is not visible to any camera (e.g., a hidden subject limb). Therefore, it is essential to choose the number and position of cameras carefully in order to optimize the process and achieve a more accurate result.
## 5 Water Treatment Employing Bioadsorbents
### Bioadsorbents Based on Herbage and Forage
#### 5.3.2 Industrial Contaminants  
Barley straw has been chemically and magnetically modified for the removal of four water-soluble dyes: bismarck brown Y, crystal violet, safranin O, and MB. The biomaterial modified with citric acid and NaOH presented the highest q\({}_{\text{m}}\) for bismarck brown Y (524.3 mg/g), followed by crystal violet (473.2 mg/g) and MB (498.1 mg/g), whereas its magnetic version attained the maximal adsorption for safranin O (460.7 mg/g). In contrast, the native and magnetic barley straw exhibited q\({}_{\text{m}}\) values below 124.3 and 137.6 mg/g, respectively [69]. [PERSON] et al. [70] reported the removal of MB using cauliflower leaves in a synthetic aqueous solution. The adsorption capacity of 149.22 mg/g fit the Freundlich model well and removed \(\sim\)88% of the dye, increasing the adsorbent concentration to 0.1 g. Furthermore, [PERSON] et al. [84] found an alternative way to capture MB and malachite green using potato waste powder; potato leaves showed higher efficiency (87 and 75%, respectively) than potato stems (82 and 67%, respectively) for both dyes. Both adsorbents presented a porous, uneven, and rough surface structure, although the potato leaves evidenced a higher fixed carbon percentage (2.15% vs. 1.725%), which may explain its higher removing capacity. In addition to the dye removal efficiencies and adsorption capacities (Table 5), the equilibrium data fit the Langmuir, Freundlich, and the pseudo-second order kinetics models for the different adsorbents described.
# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 4 Applications and Case Studies
Based on fuzzy logic, this study helps identify optimal planting seasons through IoT application development services, enabling farmers to proactively prevent the development of pests and achieve optimal yields. A study reported in [164] presents an IoT-based intelligent palm monitoring prototype designed to remotely monitor palms using smart agricultural sensors, with a particular focus on early detection of red palm weevil infestations. Leveraging an industrial-level IoT platform, the prototype enables users to interact with their palm plantations via web/mobile applications and receive early alerts of infestation. Selecting suitable sensors for weevil detection was a tedious process, involving the testing of seven types, including vibration, accelerometer, and sound sensors. Of these, accelerometer sensors, in particular the Grove 3-axis \(\pm\) 16 g ultra-low-power BMA400 digital accelerometer, proved the most effective in providing a distinctive signature of red palm weevil activity. The study high lights the challenges faced in sensor selection and the importance of early detection for effective pest control in intelligent agricultural systems.
## 3 Results and Discussion  
### Spectroscopic Characteristics  
Figure 6 shows the relative intensity as a function of the wavelength of radiation measured during mild-steel GMAW using the proposed measurement system. The relative intensity of the peak is approximately 495 nm, which indicates that the temperature distribution of the arc plasma is consistent with [PERSON]'s law for black-body radiation. Strong radiation was emitted in the wavelength band of approximately 250-600 nm, and the relative intensity was low in the wavelength band above 602 nm. To minimize the interference of the arc plasma, the wavelength band of 600 nm or less must be cut off, and the candidate wavelength band in Figure 6 must be selected. For image quality comparison, 420 nm and 508 nm were selected as wavelengths with high relative intensities, whereas 660, 752, 808, and 830 nm were selected as wavelengths with low relative intensities. Figure 7 illustrates the relative intensity of the radiation emitted in the selected wavelength bands for an FWHM of 10 nm. In Figure 7a,b, the relative intensity was high due to the intense radiation emitted from the arc plasma. Additionally, since the camera quantum efficiency in Figure 4 in the same wavelength range is significantly higher in blue and green, the arc plasma section of the image is saturated in Figure 8a,b. The relative intensity of the 660 nm wavelength in Figure 7c is low. Still, since the red in Figure 4 is very high, the image sensor captures radiation, and the image is also saturated. In Figure 7d,e, the relative intensity of radiation was above 0.1, and in Figure 7f, the relative intensity was the lowest. The discussion of images will continue in Section 3.2.  
Figure 6: Radiation from arc plasma and molten pool with arc during GMAW.  
Figure 8: Images of the molten pool obtained during mild-steel GMAW using band-pass filters: (**a**) 420 nm; (**b**) 508 nm; (**c**) 660 nm; (**d**) 752 nm; (**e**) 808 nm; (**f**) 830 nm.  
Figure 7: Relative intensity of radiation emitted from mild-steel GMAW for each wavelength: (**a**) 420 nm; (**b**) 508 nm; (**c**) 660 nm; (**d**) 752 nm; (**e**) 808 nm; (**f**) 830 nm.
## 4 Case Study: AQM Implementation
### Definition and Specification for the CNS  
Confidentiality is implemented as one of the agent quality goals in an open system to ensure agent data is protected [52]. Violations of data confidentiality can be in the form of data disclosures, where agent's data are unintentionally disclosed to other agents during social activities and information sharing [53]. Data confidentiality violations can put the safety of the agent's data at risk by disclosing both the agent identity and physical location at the same time. Therefore, the agent quality requirements are implemented to fulfill the need to determine the agent data confidentiality status during execution.  
Figure 7: The Crowdsourced Navigation System (CNS) architecture that incorporates the AQM framework.  
This can be achieved via verification and assessment by considering contextual information that provides the contributing factors or conditions for the data confidentiality violations to occur. Agent confidentiality criteria for this case study include the protection of an agent's personal identity and location, as well as social and shared content information, from potential disclosure during the execution of agent activities. The conditions include the disclosure of agent's personal identity, such as first names or company name as part of the username during account activation, the preference to allow the agent to become publicly visible, and the connection to online social network services containing personal identity together with location information.  
Every active agent discloses a certain level of data depending on agent conditions at (1) the individual level, depending on the account anonymity and presence visibility; (2) the social level, depending on the social networking connection; and (3) the content level, depending on the shared content types. The definition of agent confidentiality requirements and the possible violations for confidentiality criteria, the extracted metrics with attributes, and the constructed checking rules for each of the confidentiality metrics have been specified and reported [51]. Figure 8 summarizes the identified contributing factors or conditions of data confidentiality disclosures in the CNS. The contributing factors can be divided into three levels, which are the disclosures at the individual, social and content levels.
# Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues
## 1 Introduction  
Internet of Things (IoT) emerged initially in 1999 in supply chain industries in association with radio-frequency identification (RFID) [1]. The idea was to empower computers to observe, identify, and understand the world without the help of human beings. However, many IoT devices are designed to be battery operated and have a compact physical size; thus, they have very limited energy and computation resources. Such resource-constrained IoT devices are not well-equipped to perform complex processing, such as supporting artificial intelligence (AI) [2]. Although federated learning (FL) can be implemented by a group of IoT devices [3], such computational workload is still too heavy for IoT devices. To overcome this bottleneck, transmitting computational tasks to nearby servers is an attractive solution. Different from traditional cloud computing, such strategy as multi-access edge computing (MEC) delivers computation resources to the edge of the radio access network (RAN). Therefore, computational tasks have no need to travel through the core network, allowing IoT data to be processed and results consumed locally with minimal delay. This mode of computing, while minimizing latency and use of core network communication resources, has its own challenges. For example, security issues and incentives should be taken into considerations. To be specific, the transmitted data may contain private data about personal identity and financial account information. This raises the risk of privacy leakage and malicious attacks. Moreover, nearby servers or computing nodes may need incentives to process tasks for IoT devices. Furthermore, edge servers have limited computation power compared with the cloud. The computing operations also cost storage and energy resources. Therefore, a computing resource trading [4] and data sharing [5] framework or platform is needed to motivate edge servers. As a distributed ledger technology (DLT), blockchain has emerged as a potential solution for the above issues, due to its nature of data transparency, distributed operation, and reliability. It istimely to comprehensively survey the application of blockchain to enable edge intelligence in support of IoT applications.
# CANPerFL: Improve In-Vehicle Intrusion Detection Performance by Sharing Knowledge
## 3 Related Works
### Machine Learning-Based IDS
During the feature extraction phase, VoltageIDS extracts 60 features from normal CAN message signals, which are filtered in the feature selection phase to retain the most relevant features. In the intrusion detection phase, a multi-class classifier, such as Support Vector Machine, is constructed using attack-free CAN data to predict whether a message is normal or an intrusion. The authors of [28] proposed LSTM neural networks for anomaly detection in CAN bus messages in vehicles. Two pattern features, data and time intervals, were used for classification. The multi-dimensional LSTM framework combined these features and included a prediction and detection process. The proposed mobile edge-assisted multi-task LSTM reduced computation time and cost by enabling parallel computation on multiple servers. [PERSON] et al. [29] developed an in-vehicle network intrusion detection approach using a Binarized Neural Network (BNN) and Field-Programmable Grid Arrays (FPGAs). BNN uses binary values to accelerate intrusion detection, reducing memory usage and energy consumption. FPGAs improve performance by allowing for concurrent task processing. The IDS was three times faster than traditional IDSs, and 128 times faster after FPGA acceleration. A combination of CNN, LSTM, and attention mechanism was developed to build a robust model for CAN IDS [30]. In particular, the proposed approach uses a CNN to extract features from raw data, which are then fed into an LSTM to capture temporal dependencies. An attention mechanism is then applied to weight the contributions of different features to the anomaly detection process. The authors showed the effectiveness of their approach through a comprehensive evaluation using a publicly available dataset. The results demonstrate that the proposed approach outperforms several baseline methods in terms of accuracy, precision, recall, and F1 score. [PERSON] et al. [31] implemented a multi-model approach to classify attacks, utilizing both LSTM and ConvLSTM. While the former was utilized to process table data, the latter employed a recursion graph. The integration of the two models resulted in a 2% increase in accuracy, with an overall accuracy of 95.1%. It should be noted that this approach demands higher computational power and incorporates data appearing time, which may not significantly contribute to deep learning models.