esa-sceva/satcom-chunk-collection huggingface dataset:
# Interaction in eXtended Reality Applications for Cultural Heritage
## 3 Related Work
### AR and MR in Cultural Heritage  
Augmented reality offers a reality enriched with additional computer-generated information. Mixed reality gives the control of real and virtual objects to the user. Most applications that use AR in presenting cultural heritage are mobile-based [30; 31; 32]. Mobile-based applications are usually widely available, easy to deploy, and effective to adopt. AR applications are adopted in many fields such as tourism [31; 33; 34], education [35], medicine [36], engineering and construction [37], and many others.  
As stated in [27], the applications surveyed can be divided into two groups--those for indoor and those for outdoor use. The use of AR applications in the cultural heritage domain can have multiple purposes:  
* [noitemsep,topsep=0 pt]
* Exhibition enhancement--improving visitor experience, usually through guided tours;
* Reconstruction--an interactive presentation of reconstructed sites;
* Exploration--supports learning and motivates the user to explore and gain new insights.  
Consequently, AR provides us with a whole new level of exploration while the user is still present in the real world. \"Inaccessibility therefore becomes an opportunity to experiment with new forms of communication, from storytelling to gaming, through the use of VR and AR, optimized in relation to the characteristics of the user groups\" [38]. In the same paper, we could again see the inclusion of narration as well as the use of actors and 3D models.  
User interaction that plays a huge part in the AR and MR experience, in the same way as in VR, still needs to be developed and standardized. In [39], the authors presented \"the web-based Narrative Storyboard Editor (NSE) and the Narrative Mobile Player (NMP) app, developed with the objective to assist the creative process and promote research on different aspects of the application of mobile digital storytelling in cultural heritage settings\". They used it for the gathering of data from users regarding interaction, content, and ease of use. Many new approaches have been developed in user interaction, although some results [16; 40] still show a lack of user interaction that would be satisfactory for the users.
## 3 Experimental Setup
### Data Measurement and Class Configuration  
This experiment was conducted to classify three conditions of a three-phase induction motor: Normal, inter-turn short circuit (ITSC), and a 4 Turn short condition in the stator winding.  
As shown in Figure 4, the experimental setup includes a 1 HP three-phase induction motor coupled with a dynamometer. The three-phase current measured from the inverter was acquired using Fluke i5s AC current clamps connected to each phase. For data acquisition, an NI USB 9215 with a BNC DAQ device was used.  
Measurements were taken across a range of speeds, starting from the rated load speed of 1690 rpm up to a partial load speed of 1740 rpm, increasing in 10 rpm increments. At each speed, current data was collected for 30 s.  
Figure 3: Time-domain waveform of the D-axis current (Id) under three stator conditions: Normal, ITSC, and 4 Turn short-circuit faults.  
The sampling frequency was set at 10 kHz, yielding 10,000 samples per second, and the sampling interval was 1 s, in accordance with the DAQ device's capabilities.  
To simulate fault conditions, stator winding faults were intentionally created. As shown in Figure 5, the stator windings of the induction motor were configured to induce an ITSC with 4 turns shorted. Accordingly, the dataset was categorized into three classes: Normal, ITSC, and 4 Turn short.
## 5 Conclusions  
This paper addresses the issue of selection bias in recommendation systems by introducing the concept of inverse propensity score weighting and proposing a propensity score estimation method called SPE. The SPE method takes into account both user and item information and combines it with traditional matrix factorization-based algorithms, such as LFM and BiasSVD. Experimental evaluations were conducted on the publicly available MovieLens dataset, demonstrating that the SPE method effectively eliminates selection bias and improves recommendation performance. Furthermore, the proposed SPE method outperformed existing propensity score estimation methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Set** & **BiasSVD** & **LFM** & **LFM-IPS** & **LFM-DR** & **LFM-MRDR** & **BiasSVD-SPE** & **LFM-SPE** \\ \hline ML 100K & 0.702 & 0.683 & 0.675 & 0.671 & 0.665 & 0.679 & 0.661 \\ ML 1M & 0.678 & 0.659 & 0.652 & 0.641 & 0.635 & 0.654 & 0.638 \\ ML latest & 0.901 & 0.881 & 0.873 & 0.865 & 0.860 & 0.877 & 0.857 \\ \hline Average & 0.760 & 0.741 & 0.733 & 0.726 & 0.720 & 0.737 & 0.719 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of MAE experimental results of each model.  
While the propensity score estimation method presented in this paper has shown improvements in recommendation performance, there are still areas for further enhancement in future research. Firstly, the estimation of propensity scores may introduce high variance issues, and addressing this variance can lead to further performance improvements. Secondly, the methods employed in this paper primarily focus on explicit feedback data, but incorporating implicit feedback data generated by users and items could be explored for bias elimination research.  
Overall, the proposed SPE method offers a promising approach to mitigating selection bias in recommendation systems, and future investigations can explore strategies to enhance its performance.  
Conceptualization and methodology, T.M.; data curation and formal analysis, T.M.; experiments and analysis, T.M.; investigation, T.M.; validation and visualization, T.M.; writing--original draft preparation, T.M.; writing--review and editing, T.M. and S.Y.; resources and supervision, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the National Science and Technology Program during the Twelfth Five-year Plan Period (2015 BAF10B00) and the Science and Technology Innovation Plan Of Shanghai Science and Technology Commission (17511110204).  
Not applicable.  
The authors declare no conflict of interest.
# Intelligent Interference Management in UAV-Based HetNets +
## Machine Learning Approach for UAV ICIC and Placement
### Q Learning
As conventional Q learning algorithms maintain a Q table, with states as rows and actions as columns and with each cell representing the Q value of a specific action in a specific state, they cannot handle infinite state spaces, as the size of the Q table would become infinite. The Q table would take an extremely long duration to converge. Conventional Q learning suffers from issues of memory complexity, computational complexity, and sample complexity [24]. As illustrated in Figure 4, the parameter space for our scenario can become extremely large, and therefore a conventional Q learning approach is not feasible. Deep learning, relying on the powerful functional approximation and representational learning properties of deep neural networks, provides us with the tools for overcoming these problems [25].
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 3 Implementing Collaborative UVs for the OWT IMR Operations
### Collaborative Localization and Navigation Strategies
Using hierarchical landing guide points generated using four ultrasonic sensors on a USV, a UAV can be navigated safely towards the last guiding point on the USV's platform where a multi-ultrasonic joint dynamic positioning algorithm is activated to ensure accurate landing. For secure transportation, the landing platform can automatically fold up to lock and carry a UAV. While a UAV landing directly onto a USV is seen as the most practical solution for recovering a UAV, a different approach using a robotic recovery system has been proposed [71]. Instead of guiding a UAV precisely to land on a USV platform, this method allows a UAV to land smoothly on a nearby water surface without further computational burden to position itself precisely on a USV platform. The USV will then locate the position of the UAV using RTK GPS and computer vision, followed by recovering the UAV by deploying a submersible perforated net. The UAV will then be lifted and locked onto the USV platform. Apart from planned recovery, this method is extremely useful in the case of emergency recovery of a fallen UAV due to unexpected malfunctions during operations. However, it should be noted that this approach requires a UAV to keep affoat during recovery and it should basically be waterproof and saltwater-resistant.
# Electromagnetic Interference Shielding Anisotropy of Unidirectional CFRP Composites
## 2 Materials and Methods
### Electrical Conductivity of CFRP Composites  
The electrical conductivity of the unidirectional CFRP composites was tested by the four-probe method [28]. All specimens had a uniform dimension of 80 mm \(\times\) 10 mm \(\times\) 0.55 mm, and a silver paint was used to ensure good contact between the electrodes and specimens during testing. Four electrical contacts were symmetrically positioned relative to the center of the specimen, and the distance between the adjacent electrical contact was about 20 mm. Each electrical contact with silver paint was 2 mm wide. A current of 0.1 mA was provided by the DC power supply device (Takasago EX-375L2, Takasago Ltd., Kanagawa, Japan) at the outer electrical contacts, and the digital electrometer (Advantest R8240, Advantest Corporation, Tokyo, Japan) was used to measure the voltage at the inner contacts. Each test was carried out 5 times. Since the carbon fibers were arranged in the same direction in the unidirectional CFRPs, their ability to transfer electrons was different at various fiber orientations. The electrical conductivity of the specimens was measured at 0\({}^{\circ}\), 15\({}^{\circ}\) 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\). Where the 0\({}^{\circ}\) direction refers to the fiber direction, and 90\({}^{\circ}\) refers to the direction perpendicular to the carbon fiber.
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 2 Data Collection  
In order to review the relevant literatures in weld detection, we searched accessible databases. The publications collected were papers published between 1982 and 2019 from peer-reviewed journals and conference proceedings. This review was carried out only on automatic technologies for weld defects based on digital radiographic images.  
The peer-reviewed journals from a variety of fields including Expert Systems with Applications, NDT&E International, Fuzzy Sets and Systems, Information Sciences, Journal of Manufacturing Systems and so on. Weld defect detection automation has always been a topic of interest for NDT&E International. After conducting a comprehensive literature analysis through the literatures, it can be summarized that the automatic detection systems for weld defect mainly involve several technologies: image pre-processing, defect segmentation, feature extraction and selection, classification. Figure 1 shows a classical procedure welding defect detection system involving these aspects [6]. In our paper, the literatures are summarized through a detailed analysis of each section. The block diagram indicating the structure of this paper is shown in Figure 2. All the abbreviation in Figure 2 have full names in the text.  
Figure 1: Procedure of automatic welding defect detection system [6].
## 2 Related Work
### Question Reasoning Approaches  
Multi-hop questions are those that contain a topic entity and multiple relations. IRN [8] uses a hop-by-hop inference to approach the response entity and selects the next hop path by evaluating the similarity between the input question and the current path. UHop [28] turns multi-hop relational extraction for complicated issues into multiple single-hop relational extraction plus termination decision. Other models, such as PullNet [29; 30], use an iterative information retrieval technique to generate subgraphs from the knowledge graph and then use the generated subgraphs for answer prediction.  
Another class of models based on semantic extraction, such as the joint relation representation method [31] and SGREADER [32], intercepts the entire knowledge graph or all entities and relations within the K-hop range centered on the topic entity into subgraphs, and maps both the question and the subgraphs in the knowledge graph.  
The reinforcement-learning-based approach [33] provides a new perspective on knowledge reasoning that considerably improves its effectiveness and diversity in terms of interactive modeling ideas. Deeppath [34], as the archetypal approach, sees knowledge entities as state spaces that roam between entities via selection relations. Deeppath is awarded if it reaches the proper response entity. SRN [35] employs reinforcement learning for inference and handles multi-hop questions as sequential decision problems. Furthermore, a deep reinforcement-learning-based dynamic knowledge graph reasoning framework is proposed, which learns to browse the network under the constraint of input questions to locate likely target responses [36]. To obtain a dynamic reasoning model, the model is developed with a new reward function and dynamic rewards based on the proposed dynamic reasoning assumptions.  
However, when the questions are directly supplied into the neural network for feature representation, the constraint information will certainly produce interference. Furthermore, because the knowledge graph is so large, existing models usually search via random walk or enumeration, which is inefficient.
_applied_  
_sciences_  
_Article_  
Variable Stiffness Mechanism for the Reduction of Cutting  
Forces in Robotic Deburring  
[PERSON]  
Department of Industrial Engineering, University of Padova, Via Venezia 1, 35131 Padova, Italy; [EMAIL_ADDRESS] (M.B.); [EMAIL_ADDRESS] (M.M.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +39-049-8276793  
**Featured Application: The proposed variable stiffness mechanism will be used in robotic deburring applications, in particular in applications with high and irregular burr profiles, ensuring a precision cut without damaging the cutting tool or the robot.**  
**Abstract:** One of the main issues related to robotic deburring is that the tool can get damaged or stopped when the burr thickness exceeds a certain threshold. The aim of this work is to devise a mechanism that can reduce cutting forces automatically, in the event that the burr is too high, and is able to return to the baseline configuration when the burr thickness is acceptable again. On the one hand, in normal cutting conditions, the mechanism should have high stiffness to ensure high cutting precision. On the other hand, when the burr is too high the mechanism should exploit its compliance to reduce the cutting forces and, as a consequence, a second cutting cycle will be necessary to completely remove the burr. After the conceptual design of the mechanism and the specification of the desired stiffness curve, the main design parameters of the system are derived thanks to an optimization method. The effectiveness of the proposed mechanism is verified by means of dynamic simulations using selected test cases. A reduction up to 60% of the cutting forces is obtained, considering a steel burr up to 6 mm high.  
**Keywords:** deburring; robot; cutting forces; compliant mechanism
## 2 System Configuration and Modeling
### System Modeling
\begin{array}{c}d_{1}=-\frac{1}{a_{1}}m_{wx}\dot{r}_{wx}+\dot{y}_{b}\omega_{ zb}-\dot{z}_{b}\omega_{yb}+\frac{1}{a_{1}}\Big{[}-\dot{\omega}_{yb}\big{(}m_{ wy}h_{y}+m_{wx}h_{x}\big{)}+m_{wy}\omega_{zb}\dot{r}_{wx}+\dot{\omega}_{zb}m_{wy}r_{ wy}-F_{hx}-F_{hx}+F_{dx}\Big{]},\\ d_{2}=-\frac{1}{a_{1}}m_{wy}\ddot{r}_{wy}+\omega_{xb}\dot{z}_{b}-\omega_{xb} \dot{x}_{b}+\frac{1}{a_{1}}\Big{[}+\dot{\omega}_{xb}\big{(}m_{wx}h_{x}+m_{wyy }h_{y}\big{)}-m_{wx}\omega_{zb}\dot{r}_{wy}-m_{wx}\dot{\omega}_{zb}r_{wy}-F_{ hy}-F_{yy}+F_{dy}\Big{]},\\ d_{3}=\omega_{yb}\dot{x}_{b}-\omega_{xb}\dot{y}_{b}+\frac{1}{a_{1}}\Big{[}m_{ wx}\big{(}\omega_{yb}\dot{r}_{wx}+\dot{\omega}_{yb}r_{wx}\big{)}-m_{wy}\big{(} \omega_{b}\dot{r}_{wy}+\dot{\omega}_{xb}r_{wy}\big{)}+p_{y}A-F_{hx}-F_{hx}+F_{ dz}\Big{]},\\ w_{1}=\frac{1}{a_{1}}\left[\left(m_{wx}h_{x}+m_{wy}h_{y}\right)\ddot{y}_{b}-m_{ wy}r_{y}\ddot{z}_{b}+\dot{\omega}_{zb}m_{wx}r_{x}h_{x}+\omega_{zb}\omega_{yb}m_{wx}r_{ x}h_{x}-h\frac{4}{\sum\limits_{i=1}^{4}}\dot{m}_{i}\dot{y}_{i}\dot{y}_{i}+m_{wy}h_{y}r_{y} \Big{(}\omega_{yb}^{2}-\omega_{zb}^{2}\Big{)}\right.\\ \left.+\omega_{yb}\omega_{xb}\Big{(}m_{wx}\big{(}h_{x}^{2}+r_{x}^{2}\big{)}-m_{ wx}r_{x}^{2}+m_{wy}h_{y}^{2}-m_{wy}r_{y}^{2}-J_{zz}+J_{yy}\Big{)}+q_{xx}\\ w_{2}=\frac{1}{a_{2}}\left[-\left(m_{wx}h+m_{wy}h_{y}\right)\ddot{x}_{b}+\omega_{ zb}\omega_{xb}\Big{(}-m_{wx}h_{x}^{2}+m_{wx}r_{x}^{2}+m_{wy}r_{y}^{2}-m_{wy}\left(h_{y}^{ 2}+r_{y}^{2}\right)-J_{xx}+J_{zz}\right)\right.\\ \left.+m_{wx}r_{x}h_{x}\big{(}\omega_{zb}^{2}-\omega_{xb}^{2}\big{)}+m_{wx}r_{x} \ddot{z}_{b}+a_{6}+\dot{\omega}_{zb}m_{wy}h_{y}r_{y}+\dot{x}_{b}\frac{4}{\sum \limits_{i=1}^{4}}\dot{m}_{i}-\omega_{zb}\omega_{yb}m_{wy}h_{y}r_{y}+\gamma_{ dy}\right],\\ w_{3}=\frac{1}{a_{3}}\left[\begin{array}{c}\omega_{xb}\omega_{yb}\Big{(}-m_{ wx}\big{(}h_{x}^{2}+r_{x}^{2}\big{)}+m_{wx}h_{x}^{2}+m_{wy}\Big{(}h_{y}^{2}+r_{y}^{2} \Big{)}-m_{wy}h_{y}^{2}-J_{yy}+J_{xx}\Big{)}+\omega_{xb}\omega_{zb}\big{(}m_{ wy}h_{y}r_{y}-m_{wx}r_{x}h_{x}\Big{)}\\ -m_{wx}r_{x}\ddot{y}_{b}+m_{wy}r_{y}\ddot{x}_{b}+m_{wx}\dot{\omega}_{zb}r_{x}h_{x}+ m_{wy}\omega_{yb}r_{y}h_{y}-m_{wy}r_{y}\ddot{\cos}\sin\theta-m_{wx}r_{x}\delta\cos\theta \sin\phi+\tau_{dz}\end{array}\right]\end{array}  \tag{11}\]  
Equation (11) lists the remaining terms of the system model in addition to Equation (9), including the accelerations of the actuation system, the higher-order terms, the input-output coupling terms, the effects of the water sprinkler and the water hose, and the external disturbance \(\big{[}\mathbf{F_{t}}^{T}\mathbf{\tau_{d}}^{T}\mathbf{\tau_{d}}^{T}\big{]}^{T}\). It is worth noting that if the control inputs are the water flowrate and the position of the two weights, the yaw rotation is uncontrollable. Fortunately, it is stable, and the system can still operate with uncontrolled yaw rotation--for example, as in [28]. Furthermore, the effect of the acceleration of two weight blocks leads the system to a non-minimum phase phenomenon. That is, when a weight block moves in one direction, the robot initially moves backward before tilting and moving in that same direction.
## 7 Discussion  
The individual results of the evaluation measures performed have allowed us to draw some preliminary conclusions on the impact of some data-related factors on the effectiveness of the resampling strategies. An important observation is the bad behavior of RUS that led to a worsening of all metrics except precision compared to those from original datasets, being more pronounced for high imbalance ratios. This is consistent with findings of other studies [24] that relate class balancing with RUS to the small sample size problem in presence of high imbalance ratio, which has a negative impact on the model performance. In contrast to RUS, the rest of the resampling strategies did manage to improve the results of all metrics obtained from the original datasets in most cases, regardless of the factors studied, as stated in the literature [14; 79]. Furthermore, it has been found that the factors that most influence the loss of reliability of the classifiers according to the G-Mean, OP and IBA metrics, both with the original data and with the resampled data, are the high degree of overlap between classes and the number of instances. This behavior has also shown that these metrics are suitable for assessing the quality of the classification of unbalanced data, since class overlapping is one of the most influential factors in the precision of the minority class [14; 73; 74]. Other more specific findings related to the behavior of resampling strategies as a function of data characteristics, detailed in the results section, could not be compared with work in the literature because we have not found studies covering this range of techniques, metrics, datasets and related factors. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Characteristics** & **Best IBA** & **Conf.** & **Lift** & **Lev.** & **Conv.** \\ \hline IR = (\(-\infty\)–48] \& BL\% = (6.75–12.56] \& OVL\% = (78.8–\(\infty\)) & ROS & 1.00 & 9.00 & 0.03 & 1.78 \\ IR = (\(-\infty\)–48] \& BL\% = (12.56–18.38] \& OVL\% = (44.4–61.6] & Original & 1.00 & 6.75 & 0.03 & 1.70 \\ IR = (\(-\infty\)–48] \& BL\% = (\(-\infty\)–6.75] \& OVL\% = (44.4–61.6] & SMOTE + TL & 1.00 & 3.00 & 0.02 & 1.33 \\ BL\% = (\(-\infty\)–6.75] \& (61.6–78.8] & SMOTE + CNN & 1.00 & 1.64 & 0.01 & 0.78 \\ IR = (48–91] \& BL\% = (\(-\infty\)–6.75] \& OVL\% = (61.6–78.8] & SMOTE & 1.00 & 1.64 & 0.01 & 0.78 \\ IR = (48–91] \& BL\% = (\(-\infty\)–6.75] \& OVL\% = (61.6–78.8] & ROS & 1.00 & 1.64 & 0.01 & 0.78 \\ IR = (48–91] \& BL\% = (\(-\infty\)–6.75] \& OVL\% = (78.8–\(\infty\)) & ROS & 1.00 & 1.64 & 0.01 & 0.78 \\ IR = (\(-\infty\)–48] \& #instances = (\(-\infty\)–3051] \& SMOTE & 1.00 & 1.54 & 0.05 & 2.46 \\ \#instances = (\(-\infty\)–3007] \& \# Attributes = (\(-\infty\)–22] \& OVL\% = (61.6–78.8] & SMOTE & 1.00 & 1.29 & 0.02 & 1.11 \\ \#Attributes = (\(-\infty\)–3007] \& OVL\% = (44.4–61.6] & SMOTE & 1.00 & 1.54 & 0.03 & 1.41 \\ \#Attributes = (\(-\infty\)–22] \& BL\% = (\(-\infty\)–6.75] & SMOTE + OSS & 1.00 & 1.29 & 0.02 & 1.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of the best method regarding IBA values by means of association rules.
# Modulus Stretch-Based Circular SAR Imaging with Contour Thinning
## 3 Experimental Results
### Vehicle Imaging in Gotcha Dataset
Data for 150 vehicles were randomly selected from the Gotcha dataset, and they were individually imaged by the traditional backprojection algorithm and by the modulus stretch with piecewise linear transformation. Then the degree of contour thinning was calculated and is shown in Figure 11.  
It can be seen from the figure that the degree of contour thinning after the modulus is stretched by piecewise linear transformation is generally higher than the traditional backprojection algorithm. After modulus stretch, the degree of contour thinning is increased by 134% on average, which greatly enhances the recognizability of the target. Figure 9: Imaging results of different models of vehicle targets in the Gotcha dataset by piecewise linear transformation: (**a**,**e**) Chevrolet Impala LT; (**b**,**f**) Mitsubishi Galant ES; (**c**,**g**) Toyota Highlander; (**d**,**h**) Chevrolet HHR LT.  
We have made a preliminary study of target classification based on contour thinning imaging with piecewise linear transformation. Preliminary experimental results show that high classification accuracy can be achieved by using modulus stretch imaging combined with an appropriate classification method.