esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Reality for PCB Component Identification and Localization
## 5 Results
### Experiment 2
#### 5.2.1 Positional Accuracy Analysis  
Position accuracy measures the alignment of augmented (system-provided) and indicated (user-identified) positions with the reference (actual component) position, calculated using the Euclidean distance.  
Figure 12 illustrates the average error vectors for augmented and indicated positions at 90\({}^{\circ}\), 60\({}^{\circ}\), and 45\({}^{\circ}\) viewing angles. Average error vectors represent the mean displacement in X and Y coordinates between the reference position and the augmented or indicated positions for each component. These vectors indicate the magnitude and direction of error, with shorter vectors representing higher accuracy and longer vectors indicating larger deviations. At 90\({}^{\circ}\), both augmented and indicated positions are closely clustered around the reference position, with minimal error vectors, demonstrating the highest accuracy. At 60\({}^{\circ}\), augmented positions remain close to the reference, but indicated positions show greater variability, reflecting a slight decline in user interaction accuracy. At 45\({}^{\circ}\), both augmented and indicated positions exhibit significant deviations for the augmented positions.  
Figure 13 summarizes distance errors across all angles in more detail. The 90\({}^{\circ}\) angle yields the highest accuracy (lowest distance errors) for both augmented and indicated positions. There is a notable difference between augmentation accuracy and indicated position accuracy for decreasing viewing angles. While indication accuracy remains almost constant at moderate levels for all viewing angles, augmentation accuracy is very high at perpendicular viewing angles; however, it drops dramatically at a 45\({}^{\circ}\) angle. This finding is not unexpected, as the marker visibility (the actual PCB) decreases at low viewing angles. Also, the assumption that the texture image of the PCB is a good representation of the object to be tracked holds only for flat structures (like the PCB) under almost perpendicular viewing directions. Indicated position accuracy is, on average, about 2 mm, regardless of the viewing angle. Whereas marker position accuracy is far better than 2 mm at 90\({}^{\circ}\), it seems human perceptual and neuro-motoric factors set limits for indication accuracy. On the other hand, while marker augmentation accuracy decreased at low viewing angles, users still maintained better accuracy when indicating component positions. In other words, users were able to compensate for errors in the augmentation using contextual cues other than only the markers.  
Figure 12: Average error vectors for (**a**) 90-degree viewing angle, (**b**) 60-degree viewing angle, and (**c**) 45-degree viewing angle.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 2 Literature Review
### Developments in Fuzzy Control and Hybrid Systems  
Research by [16] demonstrates the industrial applications of robot arms using a dual-control approach: a fuzzy controller for effective object detection and a Proportional-Integral-Derivative (PID) controller for motion stability. Their experimental results confirm that proper \(K_{p}\) and \(K_{d}\) parameter configuration ensures stable transient and steady-state behavior in \"Pick and Place\" operations.  
In [17], researchers address the complexities of nonlinear system control, particularly in robot arm modeling. They categorize control strategies into classical methods, based on conventional control theory, and non-classical methods, utilizing intelligent system theory (fuzzy, neural networks, and neuro-fuzzy). The study highlights the challenges in modeling dynamic parameters such as friction and inertia, proposing a neuro-fuzzy hybrid control evaluated through Root Mean Square error measurements.  
The study presented in [18] details the modeling, simulation, and control of a 2-DoF robot arm through direct and inverse kinematics. The researchers derive the robot's dynamic equation using the Euler-Lagrange method and implement their simulation using MATLAB/Simulink's Simscape Multibody and Robotics System Toolbox with a fuzzy logic controller.  
These research findings demonstrate that fuzzy intelligent control effectively addresses system complexity in robotic control through two key mechanisms: sensor input processing and management of challenging dynamic parameters such as friction and inertia, complemented by PID control integration. Building upon these foundations, this study proposes a hybrid PD-PI fuzzy control architecture designed to enhance both stability and motion smoothness. In this context, hybrid systems represent control architectures that synergistically combine multiple control strategies--such as PID and fuzzy control--to capitalize on their respective advantages.
## 4 Results and Discussion  
### Forecasting Performance Analysis of Deep Learning Models Based on 10 Classifications  
In forecasting the Chaohu WQI, we set 10 different combinations of expected relevant feature variables based on the corresponding weights. In addition, we used six different deep learning models for covariate prediction based on different groupings, and MAE and MSE were used as two different metrics to evaluate the performance of the prediction models. The experimental results for all models and variable groupings are recorded in Table 7. In total, 10 \(\times\) 6 = 60 different cases of predictive ability of the corresponding models. Among the many prediction options, combining the two different performance parameters, Informer-S8 shows the best prediction performance in solving LSTF problems like water quality prediction: MSE = 0.2455 and MAE = 0.2  
MSE and MAE among many model groups, the best fitting the MSE and MAE of MLP-S10 have the lowest value, i.e., the best fit, among many model-groups. However, it is not difficult to see that the classical Deep Learning model often used in short-term prediction in solving the LSTF class water quality prediction problem,the accuracy of prediction, which shows a positive ratio with the number of input feature variables, in general, increases as the number of input variables increases. Although, the grouping with the highest prediction accuracy is not necessarily the one with the highest number of variables, in general, the group with the best prediction must have an accumulation of the number of variables. It is easy to see from Figure 6, such models in the face of water quality prediction problem, as the type of input feature variables continue to reduce, the actual--prediction of the goodness-of-fit also decreases continuously. In addition, in the face of long series of inputs, the prediction model's ability to grasp the features also decreases as the length increases. Overall, the prediction effect of such models are highly volatile, and the performance is not as good as that of LSTF-type models, so it is not suitable for solving this type of problem for long sequence input plus water quality prediction for the case.  
Figure 5: The best performance model for the corresponding algorithm during the test period observes and predicts the temporal variation of the WQI values: (**a**) MLP-S10 (**b**) LSTM-S10 (**c**) RNN-S9 (**d**) Transformer-S7.  
In addition, it is clear from what Figure 5 shows us that the model, which is used for short-term forecasting, is better in the case of smooth fluctuations when facing the LSTF problem. However, in the case of large fluctuations, the fit is poor, and the degree of control over the hidden laws of the predictor variables was lacking, which, in reality, means relatively weak control over seasonal patterns and cyclical changes. The characteristics of LSTF can make up for this deficiency, which shows us that LSTF has excellent adaptability and trial value in this field. This also directs our attention to the use of the LSTF, which is closer to real life, to solve related problems.
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## 5 Conclusions  
In order to reduce the transmission energy consumption, and balance the node load to achieve the purpose of extending the lifetime of wireless sensor networks, this paper proposes an equilibrium strategy based routing optimization algorithm for wireless sensor networks (ESRA). Considering the residual energy, single-hop energy consumption and path hops, the algorithm firstly establishes a minimum routing graph. Then, an edge-cutting strategy is adopted to balance the node load on the topology of the minimum routing graph. The simulation results show that our proposed algorithm can prolong the network lifetime, balance the node traffic and residual energy, and also reduce the transmission delay and packet loss rate. In the future work, we will focus on how to jointly utilize the energy-harvesting technology and routing optimization algorithm to enhance the network performance.  
Conceptualization, L.T. and Z.L.; Methodology, L.T., Z.L. and J.C.; Software, Z.L.; Validation, L.T. and Z.L.; Formal Analysis, L.T., Z.L. and J.C.; Investigation, Z.L.; Resources, L.T. and J.Y.; Data Curation, Z.L.; Writing-Original Draft Preparation, L.T. and Z.L.; Writing-Review & Editing, L.T., Z.L. and J.Y.; Visualization, L.T. and Z.L.; Supervision, L.T., Z.L. and J.C.; Project Administration, L.T. and Z.L.; Funding Acquisition, L.T. and J.Y.  
This research was funded by the National Natural Science Foundation of China (No. 51677065).  
The authors declare no conflict of interest.  
Figure 11: The NSR comparisons with varying rounds.  
Figure 10: The EV comparisons with varying rounds.
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 1 Introduction
### Approach  
While we considered different candidate options, we advocate for the use of Web Ontology Language (OWL) [2] in this paper. OWL is a formal language (with formal syntax and formal semantics) designed to represent rich and complex knowledge about things, groups of things, and relations between things.  
Though there are many advantages of using the semantics of the OWL language for representing and querying radio capabilities, it also comes with a price. For instance, there are time and space costs for deriving inferred facts with inference rules processed by an inference engine. When data need to be manipulated often, the inference engine should run every time a change is made to the data. We comprehensively explored the pros and cons of OWL versus eXtensible Markup Language (XML) [3] based schemas that match device descriptions against application requests in a quantitative way. While it is clear that OWL captures the semantics, the real question is how much the use of OWL semantics is advntageous for a specific application, versus an application that uses just XML. For this, we propose a metrics-based evaluation method to compare the two approaches. We performed experiments with the proposed method to prove the feasibility and correctness of the approach. To ensure that the evaluation results reflect practical concerns, our investigation was guided by the following principles:  
Figure 1: Problem scenarios—UML use case diagram.  
1. The comparison must be reasonably fair. Knowledge, datasets, and queries used to evaluate each of the two approaches must be comparable (equivalent).
2. The samples of knowledge, data, and queries used in the experiment should provide good coverage of the space of knowledge, device descriptions, and query properties. Test samples should be highly diversified and representative of the whole tested space of devices and queries.
3. The method must have access to the ground truth of each application request for the purpose of computing evaluation metrics.
4. The same metrics should be used to evaluate the results of each of the two approaches.
5. The evaluation must be scalable and extensible. The evaluation should not only be capable of dealing with different sizes of datasets and queries but should also be extensible for any updates with respect to the knowledge base, data, queries, and metrics.
## 4 Discussion
### Selection of Threshold  
According to the authors in [44], the selection of an appropriate threshold \(\epsilon\) for obtaining the recurrence plot and RQA measures is crucial, and the authors in [45] discussed the importance of keeping the recurrence matrix sparse by selecting a small threshold. The authors in [44] suggested that the threshold should not exceed 10% of the mean or maximum distance. Another approach for choosing a proper threshold was to seek a scaling region related to the correlation sum [46]. A threshold such that the recurrence point density is approximately 1% is also available. For the RQA of the gait, a threshold of 40% and 10% of the maximum Euclidean distance was chosen in studies [30; 47; 48] and [49], respectively. In addition, the threshold \(\epsilon\) was chosen as the one which corresponds to a fixed sparse recurrence point density in [50].  
We calculated the thresholds related to 10% and 40% of the maximum distance for all the subjects, with \(m=5\) and \(\tau=10\). It was found that the values with a 10% threshold laid in the noise regime, while those with a 40% threshold were much closer to the macroscopic regime when drawing the LSE curves. When using the 10% threshold, the recurrence point densities around 1% (0.9\(\sim\)3.5%) indicated that the corresponding recurrence matrix is sparse. On the other hand, the recurrence point densities (26\(\sim\)73%) for a 40% threshold are considered much denser. Similar to the results reported in Section 3, we obtained %DETs for all the subjects by setting the threshold to 40% of the maximum distance. No statistical differences among the three conditions were found. Therefore, RQA measures for the 40% threshold of the maximum distance would not be used to detect the changes in determinism for young healthy subjects with robotic walker, even though evident differences between the adults and toddlers groups were observed in [48]. Instead, a threshold of 10% of the maximum distance is considered suitable.
# Application of Elliptic Jerk Motion Profile to Cartesian Space Position Control of a Serial Robot
## 7 Conclusions  
In this paper, the application of the elliptic jerk profile (EJ) to position control of an RRFb robot has been studied, comparing it to other motion profiles: sinusoidal jerk (SJ), modified sinusoidal jerk (MSJ), cycloidal (CY), trapezoidal acceleration (TA), and trapezoidal velocity (TV). Regarding the controller, both a classical IO algorithm (KD) and a FO alternative (KDHD) have been considered in order to evaluate how the type of controller influences the motion profile comparison.  
Simulation results have shown that the influence of the controller type is lower than the influence of the motion profile, even if the KDHD controller is more efficient for fast motions, allowing to obtain lower maximum and integral errors even if the control effort is lower, especially in combination with the TV and CY profiles. On the contrary, for slower motions, the KD controller is slightly more accurate, with similar control effort, independently of the motion profile.  
With the considered rigid body model, the TV, TA and CY profiles, with acceleration discontinuities (TV) and jerk discontinuities (TA and CY) offer better performance (lower maximum and integral errors in correspondence with lower control effort). Nevertheless, it is well known that proper limitations on jerk discontinuities should be respected for limiting vibrational phenomena in real systems with distributed elasticity, adopting smoother motion profiles [5].  
The proposed EJ profile has continuous jerk, as the SJ and MSJ laws. The behavior of the considered robotic system with these three profiles is quite similar, as it occurs for the second-order SISO linear system discussed in [10]. As a matter of fact, this is due to the similar concept of these profiles, which starts from an alternation of phases with null jerk and phases with symmetrical jerk shape. For the SJ profile, the jerk shape is sinusoidal, for the MSJ the shape is sinusoidal modified with a constant section (constant maximum jerk for a finite time), and for the EJ profile the shape is elliptic. Even if the conception of these motion laws is similar, it is interesting to notice that the EJ profile shows a moderate advantage over the SJ and MSJ laws both in terms of accuracy (lower maximum absolute values of the end-effector errors, Figure 15, and of the _ISE_, Figure 18, left) and in terms of control effort (lower _ICE_, Figure 18, right).
# Permanent Deformation Mechanism of Steel Bridge Deck
## 1 Introduction
### Review of Relevant Research Literature
#### 1.2.2. Rutting Behavior of SBDP Investigated Through the Finite Element Method  
The FEM and the discrete element method (DEM) are two widely utilized numerical approaches for analyzing pavement systems. By employing FEM software, detailed local or global models of steel bridge structures can be developed to simulate the overall mechanical response of the pavement system under various loading conditions [11]. In recent years, in response to the recurrent rutting problems observed in SBDPs under high temperatures and heavy traffic loads, considerable research attention has been devoted to the hierarchical design of steel-asphal composite structural specimens (SACSS). FEM has been frequently employed to simulate various material layering configurations with the aim of optimizing rutting resistance. For instance, [PERSON] et al. [12] and [PERSON] et al. [13] constructed FEM models of commonly adopted composite systems incorporating gussasphalt (GA) and epoxy asphalt (EA). [PERSON] et al. [14] demonstrated that a combination of EA and stone mastic asphalt (SMA) resulted in the lowest permanent deformation and superior rutting resistance under coupled temperature-load conditions. [PERSON] et al. [15] developed a two-layer finite element model, consisting of SMA as the upper layer and mastic asphalt (MA) as the lower layer, to explore the effects of pavement thickness, loading conditions, and the geometric configuration of steel box girders on rutting development. [PERSON] et al. [16] evaluated five different material combinations involving GA, EA, and SMA, and found that the application of EA as the surface layer markedly improved rutting resistance. In a practical engineering context, [PERSON] et al. [17] proposed an FEM-based rutting prediction approach for GA pavements implemented in the Nanjing Fourth Yangtze River Bridge. Despite these advances, most FEM-based studies on SACSS have overlooked the steel-asphal interfacial bonding (SAIB) layer or have simplified its behavior by assuming either perfect bonding or complete debonding conditions [18; 19; 20]. In practice, however, SAIB degradation is prone to initiating various forms of distress in SBDPs [21; 22; 23; 24]. While FEM analyses, grounded in the framework of continuum mechanics, are capable of discretizing materials into continuous elements to resolve global stress-strain fields, they inherently lack the capacity to capture the inter-particle contact forces and dynamic motion at the mesoscopic scale.
# Sustainability Challenges in Maritime Transport and Logistics Industry and Its Way Ahead
## 2 Sustainability Issues in Shipping, Port and Maritime Logistics
(Contribution 7) performed the efficiency analysis for two airports and six seaport logistics companies in Vietnam from 2011 to 2022. The super- Slacks-Based Measures (SBM) model was used to analyze the efficiency ranking of eight logistics companies, as a way to clearly differentiate the efficiency scores among decision making units (DMUs). Several additive trend methods were examined and the additive Holt-Winters method was chosen to obtain the forecasts for input and output variables for the efficiency analysis of ports. The analysis showed that Da Nang Port always maintains the most efficient position and Noi Bai Cargo Terminal ranks in second position. The paper adopted an integrated approach to analyze the efficiency of port logistics companies by combining the forecasting and Data Envelopment Analysis (DEA) models in the research framework. It also gives valuable insights into the performance of Vietnamese port logistics industry in recent years. The Chinese government recently introduced several policy initiatives that will influence port and maritime industries. In July 2017, Ministry of Transport and National Development and Reform Commission deregulated the port pricing mechanism. Port tariff and charging method with regard to pricing will be transformed from government pricing to government guidance. Another important policy initiative is to promote the regional port integration, which is motivated by the success case of the Ningbo-Zhoushan Port in August 2017. Under such port reform policies, deregulation and regional port integration will be accelerated to increase port competition and efficiency and to optimize resource allocation between regional ports. [PERSON] and [PERSON] (Contribution 8) developed a game model to explore the possibility and degree of tacit collusion in the pricing strategy between regional ports under new environments with the deregulation of the port tariff and the development of regional port integration, particularly in the YREB. \"Will the regional ports fiercely compete or tacitly collude in order to get more benefits for itself or themselves?\" is one of the interesting questions explored using the proposed model. The authors suggested that the regional port's pricing strategy can be seen as a game under the Bertrand model. They formulated the game models--simultaneous game, sequential game, repeated game--to analyze three different scenarios of pricing decisions. The model is applied to regional ports in the YREB. The results revealed that tacit collusive pricing behavior exists under the deregulation of port tariffs. They concluded that the government should pay attention to monitoring and supervising to prevent tacit collusion that may lead to substantial welfare loss in the process of regional port integration. The paper contributes to increasing logical understanding of the pricing strategies under different regulative environments.