esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Future Research Directions and Opportunities for SRQs  
This section outlines future improvements and planned expansions of the framework to support long-term evolution and broader application. As an initial, work-in-progress framework, the agenda presents several opportunities for further research aligned with the SRQs. Each development stage--AI agent development, cybersecurity roadmap creation, resource development, and industry application--will be evaluated in greater detail in future studies, allowing each SRQ to evolve into main RQs specific to each stage's focus area.  
Future research could investigate the scalability of AI-assisted consulting frameworks, examine the role of Human-AI collaboration in cybersecurity settings, and assess the long-term effectiveness of the resources developed for small businesses. These studies aim to deepen our understanding of how AI capabilities can be leveraged to enhance consulting frameworks and drive meaningful improvements in real-world cybersecurity outcomes.  
Designed for scalability and long-term relevance, the R&D agenda's modular structure is ideal for academic adoption and adaptation, fitting various curricula, resource availabilities, and student skill levels. The incorporation of open-source tools, customisable templates, and flexible methodologies facilitates adaptation across a wide range of educational and consulting scenarios. Additionally, the agenda's Human-AI collaborative model is designed to adapt with emerging technologies and industry practices, supporting ongoing refinement of the framework. This ensures the agenda's sustainability and adaptability over time, making it a robust foundation for continuous innovation in cybersecurity consulting and education.
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 4 Comparison
Time cost and real-time performance: Because the time an approach takes determines the real-time performance of the approach, the two indicators are discussed together in this part. Learning-based approaches can be divided into two steps: offline training and online measuring. The offline step is time-consuming because it needs to train a model using plenty of data, which means that repeated calculations are necessary, although a GPU could accelerate the training process. However, in the online step, the 6D pose can be measured directly by the trained model, which costs very little time. 2D-information-based approaches and 3D-information-based approaches an also be divided into an offline step and an online step. In the offline step, many templates are generated from different angles (however, there are also some methods that do not need a lot of templates). Meanwhile, in the online step, the proper template needs to be retrieved from template dataset, which is a little time-consuming. Therefore, in the online step, the real-time performance of learning-based approaches is the best. 2D-information-based approaches cost less time than 3D-information-based approaches because the retrieval of 3D information is more time-consuming. Range of application: Due to their principle, 3D-information-based approaches are not able to handle the problem of 6D pose estimation of reflective objects. However, such objects are common in industry, such as metal parts. Learning-based approaches need plenty of time to train a network, which may not satisfy the requirements of real-time performance. In this section, we compared the approaches with respect to six different aspects. In general, learning-based approaches have the best robustness, but these approaches need lots of storage to save training data and plenty of time to train the model. 3D-information-based approaches achieve the most accurate results, but they cannot be used on reflective objects. The range of application of 2D-based approaches is the widest; however, 2D information is easily affected by the environment. Therefore, the three approaches all have their advantages. In different situations, different approaches are needed.
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 5 Performance Evaluation
### Performance Metrics
#### 5.1.1 Mean Squared Error (MSE):  
Considering the measurement transmission of only a subset of nodes of each cluster represent all other nodes in the cluster, it is possible to calculate the mean squared error (\(MSE\)) of each cluster. That is, how much the other measurements (called \(X_{o}\)) differ from the measurements of the transmitting sensor nodes (\(X_{T}\)). In this way, it is possible to evaluate the accuracy of the measurements sent by each cluster. The overall accuracy of a specific set of clusters can be defined as the average of all clusters' \(MSE\) and can be expressed as:  
\[MSE=1/N_{C}\sum_{c=1}^{N_{C}}1/|C_{c,t}|\sum_{o=1}^{|C_{c,t}|}||X_{o}-X_{T}||^{2} \tag{16}\]
## 6 Conclusions  
This paper proposes a template-matching method combined with area-based matching and feature-point detection to measure the angular position of a welding wire spool, and presents a mechanical system that can rotate the spool to match the drive rod and push the spool onto the spindle. The robustness and accuracy of the template-matching-based measuring method were validated using a built prototype of the mechanical system. Based on the above, the following conclusions were made.  
1. A novel feature-point distribution density (FPDD) strategy was developed to accelerate the matching process and to improve matching reliability by pre-locating the searching area. The FPDD strategy was based on the AKAZE feature-detection algorithm and the pre-locating accuracy was tested in range of [-6\({}^{\circ}\), +4\({}^{\circ}\)] with good robustness.  
Figure 12: Drive bore templates intercepted for direct template matching.  
Figure 13: Measuring result for direct template matching.  
* AKAZE was found to be the most suitable algorithm for this application due to its high efficiency, high feature density, and edge-preserving properties compared to other algorithms such as SIFT, SURF, ORB, and MinEigen.
* The proposed method was superior in robustness, accuracy, and speed and it was efficient for automatic spool positioning in the welding wire winding process, with [\(-1^{\circ}\), \(0.5^{\circ}\)] accuracy and less than 1 s computation time.
* Multi-template matching was unable to measure the angular position of welding wire spool due to too much mismatching, even with eight templates.  
Conceptualization, J.X.; Project administration, J.X.; Resources, W.J.; Software, X.H.; Writing--original draft, J.X.; Writing--review & editing, J.X. All authors have read and agree to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [Grant No. 51805212] and the Natural Science Foundation of Jiangsu Province grant number BK20160182. And the APC was funded by the Natural Science Foundation of Jiangsu Province.  
This research is supported by the National Natural Science Foundation of China [Grant No. 51805212] and the Natural Science Foundation of Jiangsu Province [Grant No. BK20160182].  
The authors declare no conflicts of interest.
## 2 State of the Art
### Technologies
#### 2.3.1 Category 1 (Manufacturing Processes)  
Regarding the modules taken into account herein, the attribute \"Process\" regards all the production processes that can be used for manufacturing a product. Moreover, constraints from the client can be adopted on the novel shop floor, while the integration of additive manufacturing (AM) in small and medium enterprises (SME's) could provide the solution to this challenge with minimal changes in each request and in a cost-effective way [77; 78].  
Subtractive, joining, deforming, forming as well as property changing [44], additive manufacturing and custom processes have to be taken into consideration. At this point it would be useful to mention that one of the fastest growing and most promising manufacturing technologies is additive manufacturing, which provides major advantages over traditional production processes [79]. However additive manufacturing technologies have problems linked to low productivity, poor quality and uncertainty about the mechanical properties of the final part [80]. In Appendix A, this classification is further analyzed, and it has to be noted that the aforementioned families consider only the mechanisms. In order to enhance metamodelling, the involved variables have to be taken into consideration, namely the process parameters and the performance indicators. These entities are considered in a separate category, to indicate their independence. They can, however, be linked to processes. For instance, a laser welding machine is related to the laser welding process, and also has additional process parameters: either directly linked to the process, such as laser power, laser frequency and scanning speed, or linked to the machine, such as the shield gas flow rate [81].
###### Abstract  
Background: Determining the association of neutrophil to lymphocyte ratio (NLR) and clinical disease severity in patients with mild and advanced stage knee osteoarthritis (OA).Method: We analyzed clinical scores and routine hematology panels of 111 subjects with symptomatic knee degeneration: mild OA (66) who underwent knee arthroscopy and advanced OA (45) who had total replacement. Results: Compared to the advanced OA group, the mild OA group was younger (54/67), had fewer females (1.75/5.43), and better scores: Euroqol EQ5D Index (0.50/0.25), VAS (visual analog scale) (65/44); International knee documentation committee subjective knee evaluation form--IKDC (31.6/20.24); Knee disability and osteoarthritis outcome score for joint replacement--KOOSJR (15.5/18.3); and better Kellgren-Lawrence stage (1.3/3.8). Patients with advanced disease had higher NLRs compared to mild OA group and controls: 2.82 versus 1.99 (\(p\) = 0.004) and 1.98 (\(p\) = 0.002). In the multiple regression model, NLR was only influenced by age (\(p\) < 0.001). In the subgroup analysis, for the early OA cases, NLR was significantly dependent to VAS (\(p\) = 0.006), IKDC (\(p\) = 0.001) and KOOSJR (\(p\) < 0.001). Conclusion: NLR was not associated with symptomatic knee OA, as determined by commonly used patient reported outcomes. However, for patients with mild degenerative modifications, EQ5D, VAS, and IKDC were independent predictors of NLR.  
k knee joint; osteoarthritis; inflammation mediators; neutrophil to lymphocyte ratio; patient reported outcome measures; arthroscopy; arthroplasty
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 3 Improved Ant Colony Algorithm  
### Improved Heuristic Information Function  
The conventional ant colony optimization algorithm has several limitations. At the beginning of the search, the chemical trail levels are identical across all paths, making the heuristic information the primary differentiator. However, the original formula for path selection heuristics lacks robustness, leading to a blind search and suboptimal initial paths. Additionally, while the algorithm's positive feedback mechanism is effective in reinforcing favorable paths, it can result in slow search progress during the early stages and may cause the algorithm to become trapped in local optima.  
To address these challenges, this paper proposes modifying the heuristic information function by incorporating an artificial potential field force. This adjustment aims to enhance the targeting of the heuristic function's evaluation toward the goal and improve search efficiency. The improved heuristic information \(\gamma_{ij}(t)*\) is defined as follows:  
\[\gamma_{ij}(t)*=\frac{\psi}{d_{ij}} \tag{7}\]  
\[\psi=\mu^{fa} \tag{8}\]  
\[fa=\varphi\cdot d(j,E) \tag{9}\]  
Substitute (1) (2) (3) into the expected heuristic information to obtain \(\gamma_{ij}(t)^{*}\).  
\[\gamma_{ij}(t)*=\frac{\mu^{\varphi\cdot d(j,E)}}{d_{ij}} \tag{10}\]  
Among these parameters, \(\psi\) is the factor added to the artificial potential field gravity; \(fa\) represents the artificial field of potential gravity from the present node \(j\) to the destination point; \(\mu\) is a constant with a value range of (0,1); and \(\varphi\) is a proportional coefficient. \(d(j,E)\) denotes the gap from the present node \(j\) to the destination point \(E\). With increasing distance, the potential energy level for the inspection robot rises; conversely, the lesser the distance, the lesser the potential energy level.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Parameter**} \\ \hline Maximum iteration count \(K\) & Node \(ij\) \\ Number of ants \(M\) & Factor for artificial potential field attraction \(\gamma\) \\ Pheromone importance factor \(\alpha\) & Constant \(\mu\) \\ Importance factor of expected heuristic information \(\beta\) & Number of ants on the optimal path in this iteration \(\varepsilon\) \\ Pheromone evaporation coefficient \(\rho\) & Number of ants on the worst path in this iteration \(\varepsilon\) \\ Pheromone intensity \(Q\) & Number of elite ants \(b\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial parameter settings.
## 2 Numerical Simulation
### The Basic Theory of the Vehicle-Bridge Interaction System
The vehicle traveling effect transforms road unevenness into a time function, as shown in the following formula:  
\[r_{i}(t)=R(x_{i}(t)) \tag{15}\]  
The bridge vibration component \(\widetilde{\mathbf{y}}(t)\) is also derived by considering the traveling effect of the vehicle. The bridge deflection input to the vehicle is originally a time-space function \(y(x,t)\). However, as the location \(x\) becomes a vehicular wheel position \(x_{i}(t)\), which is a time function, the bridge deflection is transformed from a time-space function \(y(x,t)\) to a time function \(\widetilde{y}_{i}(t)\), as shown in the following formula:  
\[\widetilde{\boldsymbol{y}}(t)= \begin{cases}y(x_{1}(t),t)\\ y(x_{2}(t),t)\end{cases} = \begin{cases}\mathbf{N}(x_{1}(t))\cdot\boldsymbol{y}(t )\\ \mathbf{N}(x_{2}(t))\cdot\boldsymbol{y}(t)\end{cases} =\mathbf{L}(t)^{\text{T}} \boldsymbol{y}(t) \tag{16}\]  
By integrating all formulas above, the equation of motion of the VBI system can be expressed by the following formula:  
\[ \begin{bmatrix}\mathbf{M}_{v}&\mathbf{O}\\ \mathbf{L}^{\text{T}}(t)\mathbf{M}_{p}&\mathbf{M}_{b}\end{bmatrix}\begin{cases} \tilde{\underline{z}}(t)\\ \tilde{\boldsymbol{y}}(t)\end{cases} + \begin{bmatrix}\mathbf{C}_{v}&\mathbf{O} \\ \mathbf{O}&\mathbf{C}_{b}\end{bmatrix}\begin{cases}\dot{\underline{z}}(t)\\ \dot{\boldsymbol{y}}(t)\end{cases} +\begin{bmatrix}\mathbf{K}_{v}&-\mathbf{K}_{ p}\mathbf{L}^{\text{T}}(t)\\ \mathbf{O}&\mathbf{K}_{b}\end{cases} \begin{cases}\underline{z}(t)\\ \mathbf{y}(t)\end{cases} = \begin{cases}\mathbf{K}_{p}r(t)\\ \mathbf{L}(t)\mathbf{M}_{v}g+\mathbf{R}(t)\end{cases}  \tag{17}\]  
where \(\mathbf{O}\) represents a zero matrix, and  
\[\mathbf{K}_{p}= \begin{bmatrix}0&0\\ 0&0\\ k_{u1}&0\\ 0&k_{u2}\end{bmatrix}  \tag{18}\]  
\[\mathbf{M}_{p}= \begin{bmatrix}\frac{d_{2}m_{s}}{d_{1}+d_{2}}&0&m_{u1}&0\\ 0&\frac{d_{1}m_{s}}{d_{1}+d_{2}}&0&m_{u2}\end{bmatrix}  \tag{19}\]  
Since the mass and stiffness matrices of the VBI system include a time function, the VBI system is non-linear. This equation is solved by the Newton-Raphson scheme to simulate the dynamic responses of the vehicle and bridge models in this study.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Homogeneous Coordinates  
Calibration and triangulation use homogeneous coordinates [57], to represent coordinates in projective space, such that affine and projective transformations can be implemented through matrix multiplication. Appending a scaling variable of unity to a \(n\) dimensional vector in Euclidean space converts it to an \(n+1\) dimensional vector in projective space. Converting back to Euclidean space, represented as function \(\Phi\), entails dividing the vector's elements by the scaling variable before eliminating it to reduce the vector to \(n\) dimensions. Underlined variables denote homogenous coordinate vectors.
# MPC and PSO Based Control Methodology for Path Tracking of 4 WS4 WD Vehicles
## 2 Vehicle System Modelling
### Driving Unit Dynamic Model  
While the force sensors provide measurement of the current forces acting on each wheel, controller design requires prediction of future values based on control inputs. The drive unit dynamic model as shown in Figure 3 is described in this section.  
Figure 2: Vehicle body dynamic model.  
In each driving unit, the force sensor mounted on the wheel hub rotate with the wheel. Force measurement is in the direction of the steering angle. According to Figure 3, the dynamics of driving unit can be expressed by  
\[\mathbf{F}_{ASi}=\begin{bmatrix}F_{Sli}&F_{SLi}\end{bmatrix}^{T}=\mathbf{F}_{wi}-\begin{bmatrix} m_{d}a_{wli}&m_{d}a_{wLi}\end{bmatrix}^{T}, \tag{7}\]  
where \(a_{wli}\) and \(a_{wLi}\) denote the longitudinal and lateral accelerations of each driving unit \(i\) in the wheel coordinate system (\(X_{Wi}-O_{Wi}-Y_{Wi}\)), respectively. Using the transfer matrices \(R_{i}\) and the accelerations \(a_{l}\), \(a_{r}\) and \(\gamma\) given in vehicle body dynamic model, \(a_{wli}\) and \(a_{wLi}\) can be deduced as  
\[\begin{split}\begin{bmatrix}a_{w1I}&a_{wL1}\end{bmatrix}= \begin{bmatrix}a_{l}-L_{h}\gamma&a_{r}+L_{f}\gamma\end{bmatrix}R_{1},\\ \begin{bmatrix}a_{wL2}&a_{wL2}\end{bmatrix}=\begin{bmatrix}a_{l}+L_{h}\gamma&a _{r}+L_{f}\gamma\end{bmatrix}R_{2},\\ \begin{bmatrix}a_{w1J}&a_{wL3}\end{bmatrix}=\begin{bmatrix}a_{l}+L_{h}\gamma&a _{r}-L_{r}\gamma\end{bmatrix}R_{3},\\ \begin{bmatrix}a_{wL4}&a_{wL4}\end{bmatrix}=\begin{bmatrix}a_{l}-L_{h}\gamma&a _{r}-L_{r}\gamma\end{bmatrix}R_{4}.\end{split} \tag{8}\]  
In Equation (7), \(\mathbf{F}_{wi}\) represents the force vector acting on each wheel, which is written as  
\[\mathbf{F}_{wi}=\begin{bmatrix}F_{wli}&F_{wLi}\end{bmatrix}^{T}. \tag{9}\]
###### Abstract
However, it is necessary to indicate which types of foodstuffs are easily consumed by consumers in their daily lives and contribute to the control of blood glucose elevation. Although it might be assumed that low GI foods with alpha-glucosidase inhibitory properties would be most effective when taken at the onset of a meal, little is known about them. Apples are a familiar fruit, available at low prices year-round, and highly valued for their richness in dietary fiber, vitamin C, potassium, and other nutrients. An apple is rich in organic acids, such as citric acid, and dietary fiber, which slows down the rate of gastric emptying [15; 16; 17], and thus might suppress the postprandial blood glucose elevation. Apples can also be cut in half and eaten easily, making them ideal for those with busy mornings and otherwise inadequate nutritional intake. As the saying goes, \"an apple a day keeps the doctor away\". Apples are beneficial to the body and high in pectin, a soluble dietary fiber. Pectin is not digested or absorbed by human digestive enzymes, but it inhibits the absorption of cholesterol [18] and moderates the absorption of glucose. Thus, apples may help maintain good health [19]. We previously reported that changes in postprandial blood glucose levels depend on the order of consumption of vegetable juice and rice. The postprandial blood glucose elevation was significantly lower when vegetable juice was consumed before versus after rice [20]. We previously reported on the effect of gold kiwifruit intake timing on blood glucose levels [21]. This fruit contains glucose and fructose as carbohydrates. Unlike glucose, fructose is thought to be less likely to raise blood glucose. It would, therefore, be interesting to investigate the main carbohydrates in apples. Therefore, the consumption of apples at the beginning of a meal may effectively reduce the increase in postprandial glucose levels due to the fiber and polyphenols, which are abundant in apples, and the carbohydrate composition, fructose. It contains apple polyphenols, such as procyanidin and catechins, which have been reported to have antioxidant effects and regulate lipid metabolism. An apple's dietary fiber includes pectin and cellulose, which may be related to the prevention of obesity and diabetes [22]. In other words, if evidence can be presented to show the effect of apple intake in healthy people and people with impaired glucose tolerance, it may contribute to dietary and nutritional guidance for the treatment of lifestylerelated diseases. It has been observed that individuals who consume antioxidant-rich foods have a lower risk of T2D [23]. The present study investigated the effects of apple consumption before versus after white rice consumption on the postprandial blood glucose and insulin profiles of subjects with normal versus impaired glucose tolerance to test its ability to contribute to the prevention of lifestyle-related diseases such as diabetes [24].
## 3 Results
### Baseline and Parameter Settings  
We compare the DK-HTAN with one machine learning method, two non-attention based neural network methods, and two attention-based methods:  
SVR [32]: Support vector regression. A simple and robust machine learning network, the basic idea is to find a regression plane so that all points are closest to the plane.  
RNN: Convolution neural network. The RNN model is mainly composed of three type of layer: input layer, output layer, and hidden layer.  
LSTM: Long short-term memory network, a variant of RNN, adopts gating mechanism and optimizes the network of RNN.  
STALSTM [33]: The STALSTM model adds attention modular to the original LSTM model. Similarly to LSTM, the primary architecture and parameters are used.  
AGCLSTM [34]: A novel graph convolution-based spatiotemporal attention LSTM (AGCLSTM) network to tackle the time-series prediction problem of flood forecasting.  
DK-HTAN: The DK-HTAN model adds spatiotemporal attention module and an output interval prediction constructed by error factors on the basis of the original HCN model and TCN. The main architecture and parameters are the same as HCN and TCN.  
Parameter settings: Experiments using Linux service platform (CPU: Intel (R) Xeon (R) CPU E5-2630) v4 @ 2.20 GHz, graphics processor NVIDIA GeForce TITAN Xp, 12 GB, operating system Ubuntu 16.04, deep learning library Python, programming language Python 3.6) completed. For all datasets, 80% are used for training and 20% for testing. We trained the model to adjust the super parameters, such as the learning rate LR = 0.001, and the training batch size was set to 256.