esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Use of Personalized Ontologies for Query Expansion
### Approaches to Query Expansion Based on the Use of Personalized Ontologies
To address this problem, in [53] a search engine called _KeyConcept_ has been developed. This search engine takes into account, in addition to the query terms, the concepts related to the query and selected from the ODP. In particular, the user can directly select the ODP concepts most related to the keywords written in the query with the help of an ad-hoc interface. Furthermore, the documents returned after the evaluation of a query are indexed by their keywords and by their concepts again obtained from the ODP. Thus, documents are re-ranked according to a match between the user-supplied keywords/concepts and those associated with the documents. In detail, for each document two scores are calculated representing the number of keywords/concepts matching those specified by the user, called \(Score_{k}\) and \(Score_{c}\), respectively. A user, during the definition of the query, can specify an \(\alpha\) parameter, between \(0\) and \(1\), in order to establish if during the computation of the document score, the \(Score_{c}\) parameter can assume more importance than the \(Score_{k}\) parameter. The document score, \(Score_{d}\), is computed as: \(Score_{d}=(\alpha\cdot Score_{c})+((1-\alpha)\cdot Score_{k})\). Once a score has been calculated for each document, then they are sorted in decreasing order and presented to the user. To avoid having a great number of documents, the hierarchical structure of the ODP is explored in order to prune the result set. In particular, the document that does not share the nodes belonging to the same direct path of the ODP structure with respect to the user-supplied concepts is removed. This way the result set is made up of documents related to the user's request.
### Theoretical Framework
#### 3.3.5. User Propagation Influence-Based Linear Threshold Model (UPI-LT)
Activation thresholds are recalibrated continuously based on the evolving network topology and user similarity. This dynamic adjustment allows the model to more effectively capture the fluctuating influence of nodes, particularly in response to changes in network structure or user behavior. The use of dynamic thresholds ensures that the influence propagation process remains responsive to ongoing changes, resulting in more robust and realistic predictions.
# Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications
## 3 BEAST System Model
### System Process
The priority index of a user is calculated as follows. \[\Pr_{i}=\frac{1}{1+\ln(t_{di}+1)}\bullet\frac{\omega}{1+e^{-TV_{i}}} \tag{1}\]  
where \(\omega\) is the weight index to adjust the influence of the trust value on the priority index. \(\omega\) can be adjusted from 0 to 1. Step 4. Generating a transaction. Once completing spectrum distribution tasks, the smart contract returns the distribution results to MNOs. Then, a transaction, \(TX_{dis\_t}\), is generated within a certain time. Meanwhile, \(TX_{dis\_t}\) is signed with \(PK\). Step 5. Signing and encryption. MNO signs the authorization information for channel access with the symmetric encryption algorithm and asymmetric encryption algorithm. The signing process is performed locally with the MNO, and then it uploads the signature result to the blockchain. We define \(E\) and \(D\) as, respectively, the encryption and decryption process of the symmetric encryption algorithm, and \(K\) is the symmetric encryption key. We define \(Enc\) and \(Dec\) as, respectively, the encryption and decryption process of the asymmetric encryption algorithm, and \(K\) is the symmetric encryption key. (\(PK_{MNO}\), \(SK_{MNO}\)) and (\(PK_{PUli}\), \(SK_{PUli}\)) are, respectively, the public and secret key pairs. The authorization information is denoted as \(M_{A}\). MNO first uploads \((E_{K}(M_{A}),Enc_{PK_{PUli}}(K),sig)\) to the blockchain, where \(sig=Sig_{SK_{MNO}}(H(E_{K}(M_{A})||\left[Enc_{PK_{PUli}}(K)\right])\). After PU obtains the message on the blockchain, it first verifies the identity of MNO--\(verifySig_{PK_{MNO}}(sig)=H(E_{K}(M_{A})||\left[Enc_{PK_{PUli}}(K)\right]\). If the verification is passed, then it computes \(K=Dec_{SK_{PUli}}(Enc_{PUli}(K))\), and computes \(M_{A}=D_{K}(E_{K}(M_{A}))\). Step 6. PoT consensus process. In BEAST, we propose a lightweight consensus mechanism named proof of trust (PoT) based on a user's trust value. The trust value is accumulated through the collected transactions. When strong nodes collect transactions, they also broadcast the new generating block to the network for consensus. After the consensus procedure, the block is recorded on the global ledger. And the trust value of the spectrum users is updated according to their regulation compliance performance during the spectrum occupation period. The detail designs and performance evaluation of PoT will be discussed in Section 4.
## 1 Introduction  
Permanent magnet synchronous machines (PMSMs) are becoming essential for applications such as home appliances, industrial tools, and electrical vehicles. Accordingly, interest in motor faults is increasing, and remedying motor faults at industrial sites is crucial. The statistics provided in a recent study [1] show that 41% of motor faults are bearing faults, 37% are stator faults, 10% are rotor faults, and 12% are miscellaneous faults. Therefore, motor malfunction is being actively investigated. One of the factors contributing to motor malfunction is rotor eccentricity, wherein the center of the rotor axis deviates from the center of the stator, rendering the air gap non-uniform. Rotor eccentricity interrupts the uniform distribution of the magnetic flux to the stator, resulting in an increased cogging torque and an unbalanced magnetic force. This adversely affects the motor performance [2; 3; 4; 5]. In the case of severe eccentricity, the air-gap size is reduced significantly, and an accident occurs between the stator and rotor. Consequently, the insulation between the stator core and stator winding may be destroyed, which may cause a fire in the electric motor. It is important to prevent this in advance as eccentricity occurs in the rotor. Methods for diagnosing eccentricity include the TIR test using a dial indicator, impedance measurement using an LCR meter, and impedance measurement after inputting a diagnostic signal using an inverter. As described above, studies regarding eccentricity diagnosis have been actively conducted. In a previous study, the analysis of current harmonics generated when eccentricity occurred was performed via a dynamic analysis [6; 7; 8; 9]; however, the characteristics of the motor owing to the current harmonics were not analyzed. To analyze the dynamic characteristics of a motor, the whole drive system must be modeled to confirm the current, voltage, and torque characteristics via a coupled analysis; however, this is time consuming. Hence, a co-simulation was devised. In a co-simulation, the data are primarily stored based on the rotor position. The results are the same as those of coupled analysis, but require comparatively less process time.  
In this study, we performed an electromagnetic characteristic analysis of a PMSM considering the current waveform based on rotor eccentricity. First, the characteristics of the back electromotive force (EMF) affecting the control aspect were analyzed based on the rotor eccentricity. Next, we confirmed the harmonics of the input current based on the rotor eccentricity through a dynamic analysis. The tendency of the generated current harmonics was confirmed by comparing the experimental results. Finally, we compared the considered and non-considered current harmonics.
## 4 Property-Based Ontology Representation  
### Formal Model  
In this section, we introduce a property-based ontology description approach for OWL ontologies. The input ontology model is provided by  
\[\mathcal{O}:=\{\mathcal{C},\mathcal{I},\prec,\mathcal{P},\mathcal{T}\},\]  
where  
* \(\mathcal{C}\): the set of concepts;
* \(\mathcal{I}\): the set of individuals;
* \(\prec\subset C\times\mathcal{C}\): the concept taxonomy relationship (acyclic);
* \(\mathcal{P}=\{p:\mathcal{I}\to dom_{p}\}\): the set of properties, where \(dom_{p}\) denotes the value set of property \(p\); The properties can be used as follows:
* in individual assertion triplets \((i,p,v)\), in which case the value of \(p\) is equal to \(v\) and \(p(i)=v\);
* in class axioms of the following forms:
* \(p\) domain \(c\) (or \(\exists p.\top\subseteq c\)
* \(c\subseteq\exists p.v\)
* \(c\subseteq=p.v\) where the symbol \(v\) denotes the value of property \(p\);
* \(\mathcal{T}\subset\mathcal{I}\times\mathcal{C}\): type assignment to the individuals. Starting with the input ontology, the first step is to generate the related set of base properties, where every property-value pair found in the ontology corresponds to a base attribute:  
\[\mathcal{A}=\{a\}=\{(p,v)|p\in\mathcal{P}\wedge(p,v)\text{ occurs in an assertion triplet or in a class axiom})\}\]  
We can extend the attribute set with generalization of the property values:  
\[\{(p,\top)|p\in\mathcal{P}\}\subset\mathcal{A}\]  
For an arbitrary \(a\in\mathcal{A}\) and \(i\in\mathcal{I}\), the symbol \(a(i)\) is true if individual \(i\) meets property \(a\). The attribute notation \((p,\top)\) is true only if, for some value \(v\), that property \((p,v)\) is met. In this case, the value of the property is not important. In the next step, we introduce a new attribute property mapping:  
\[A:\mathcal{C}\to\wp(\mathcal{A}),A^{\prime}:\mathcal{I}\to\wp(\mathcal{A})\]  
where* \(\forall i\in\mathcal{I}:A^{\prime}(i)=\{a|a(i)\}\)
* \(\forall c\in\mathcal{C}:A(c)=\{a|\forall i\in c:a(i)\}\)  
The set \(A(c)\) corresponds to the intent set of the formal concepts in FCA. In order to determine the \(A(c)\) sets, we perform a sequence of specialization and generalization steps. In the case of specialization, the properties of parent concepts are inferred to the lower levels, that is, to the descendant concepts. The explicit specialization process is based on property-oriented class axioms in the ontology. Having an axiom \(c\subseteq\exists a\) or \(c\subseteq=a\), we can conclude that  
\(a\in A(c)\). If \(c_{1}\prec c_{2}\wedge a\in A(c_{2})\), then  
\(a\in A(c_{1})\)  
is met as well. The specialization process terminates when all of the individuals are processed. Thus, an updated \(A^{\prime}(i)\) set is obtained for every individual. The result is a context in which the objects correspond to the individuals. A row related to individual \(i\) contains the elements of \(A^{\prime\prime}(i)\). As an example, let us take the following ontology fragment:  
<DataPropertyAssertion>  <DataProperty IRI=''#color'/>  <NameIndividual IRI=''#chair!
## 6 Validations
### Non-Dimensional Natural Frequencies of an Exponentially Graded Beam  
Since the data on the EG rotor-bearing system is unavailable in the literature, non-dimensional natural frequencies (\(\overline{\omega}=\omega(L/h)\sqrt{\rho_{m}h^{2}/E_{m}}\)) of an exponentially graded simply supported beam are computed using the developed Python code to substantiate the exponential material gradation, and temperature distribution as well as the mass and stiffness matrices used in the present work. The top and bottom layers of the EG beam are made of Stainless Steel (SS), and the centre layer is wholly composed of Alumina (Al\({}_{2}\)O\({}_{3}\)), as shown in Figure 3. The width of the beam is taken as 0.0254 m. The temperature of the middle surface is assumed at 293 K, whereas the temperature at top and bottom surfaces is considered at 393 K. The non-dimensional fundamental frequencies of an EG beam are calculated for two different slenderness ratios and tabulated in Table 1. Since the percentage of the error is minute, the computed values are in good accordance with the literature [15]. Therefore, it is confirmed that the material modelling, temperature distribution, mass and resulting stiffness matrices used to determine natural frequencies are precise. \(\rho_{m}\) is the density of Stainless Steel, \(E_{m}\) is the Young's modulus of Stainless Steel and \(h\) is the thickness of the EG beam.
# General Position Subset Selection in Line Arrangements +
###### Abstract  
Given a set of \(n\) points in a plane, the General Position Subset Selection problem is that of finding a maximum-size subset of points in general position, i.e., with no three points collinear. The problem is known to be hard computationally, and the best approximation ratio known is \(\Omega(n^{-1/2})\). Here, we obtain better approximations in three special cases: (I) a constant-factor approximation for the case where the input set consists of lattice points and is _dense_, which means that the ratio between the maximum and the minimum distance in \(P\) is of the order of \(\Theta(\sqrt{n})\); (II) an \(\Omega\big{(}(\log n)^{-1/2}\big{)}\)-approximation for the case where the input set is the set of vertices of a _generic_\(n\)-line arrangement, i.e., one with \(\Omega(n^{2})\) vertices; and (III) an \(\Omega\big{(}(\log n)^{-1/2}\big{)}\)-approximation for the case where the input set has at most \(O(\sqrt{n})\) points collinear and can be covered by \(O(\sqrt{n})\) lines. The scenario in (I) is a special case of that in (II). Our approximations rely on probabilistic methods and results from incidence geometry.  
approximation algorithm; randomized algorithm; general position; collinear triples +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing
### Mobility Models
#### 2.2.4 Random Waypoint  
Random Waypoint is a randomized mobility model. At every time instance, each node has a distinct and random velocity, destination and a randomized stop time. The UAVs' destination and stop time both get re-updated upon each randomization iteration, depending on which event precedes the other: reaching of node destination or timeout of waypoint validity.  
When simulating a FANET deployment, it is of utmost importance to select a fitting mobility model. As previously stated, the overall mobility of a node and its network strongly affects networking metrics and route computation-related parameters. Consideration of mobility models as a routing metric is therefore a valid reason to avoid or resort to the usage of a mobility model. Location or prediction-dependent schemes especially, may present an altered/deteriorated or generally false performance, should the UAVs' locations be altered in an unexpected/rapid manner (e.g., random definition of velocity vectors instead of a collectively coordinated mobility plan and movement path).
# Image Interpolation with Regional Gradient Estimation
## 5 Conclusions  
The processing of edges is essential for high-resolution image interpolation. The standard nonlinear interpolation methods tend to blur edges. To address the shortcomings of traditional nonlinear interpolation methods for non-edge pixels, this paper proposes an image interpolation method based on a region gradient estimation strategy.  
To begin, the proposed method used the CGI method's edge diffusion idea to perform edge diffusion on low-resolution images. Then, this paper proposed a gradient estimation strategy to estimate the gradient of unknown pixels in high-resolution images to improve the shortcomings of the CGI in calculating the gradient, namely, using region-based bicubic interpolation to estimate the gradient of the pixel and judging its properties. This approach can determine the properties more effectively because this strategy takes into account the image's local characteristics. Following that, a 1D directional filter was used to process edge pixels, while a 2D directionless filter interpolated non-edge pixels. To validate the efficacy of the proposed method, experiments were conducted using a variety of representative interpolation methods for both grayscale and color images; we compared the results in terms of visual effect, objective criteria, and computational complexity. Extensive simulation results from both grayscale and color test images showed that our proposed image interpolation approach using regional gradient estimation outperformed various representative image interpolation methods with respect to both objective criteria and subjective visual quality assessments. Furthermore, when compared to other methods, the low computational complexity of the proposed method was a clear advantage.  
Figure 12: The comparison of color images interpolated by CGI and our method. (**a**) test image; (**b**) interpolation result by CGI; (**c**) interpolation result by GEI.  
**Author Contributions:** Conceptualization, Z.J.; methodology, Z.J.; software, Z.J.; validation, Z.J.; formal analysis, Z.J.; investigation, Z.J.; resources, Z.J.; data curation, Z.J.; writing--original draft preparation, Z.J.; writing--review and editing, Z.J. and Q.H.; visualization, Z.J. and Q.H.; supervision, Q.H.; project administration, Q.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** The study did not involve humans.  
**Data Availability Statement:** Data sharing is not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 5 Discussion and Analysis
### Compare with Other Frame Selection Methods  
#### 5.2.1 Maximum Temperature Difference Method  
In many cases of defect detection, the image with the largest temperature contrast is used, i.e., the image where the temperature difference between the defective area and the sound surface is the largest [4]. To have an accurate defect size, there is a need to analyze the specific IR image captured at \(t_{max}\) (time that records \(\Delta T_{max}\)) or immediately after it [5], and the method is called the maximum temperature difference method (MTDM), which give a method of how to determine a key frame from the image sequence as an original image to image processing.  
A number of sampling areas were taken at the defect area and the non-defect area to eliminate the influence of thermal excitation unevenness, as shown in Figure 25. The average temperature of different areas and the temperature difference between the defect area and non-defect area was calculated respectively, and the course curves of the average temperature difference over time were drawn, as shown in Figure 26. According to the peak values of curves, Frame 188 and Frame 196 were captured for the extraction of defects #1 and #2, respectively.  
Figure 24: The scatter diagram of the experiment conducted 100 times to be compared with Figure 19: (**a**) The scatter diagram of the experiment conducted 100 times by uniform selection; (**b**) The scatter diagram of the experiment conducted 100 times by random selection.  
Figure 25: Schematic diagram of defect area and sound area sampling.  
After image processing, the relative data were counted in the Table 4 and were compared with our proposed method (data in Section 4), as shown in Table 5. Both the acquired frames for each defect and their relative errors by MTDM are equivalent to the preliminary results of our proposed method (MSDSMM), and the accuracy is much lower than that of our method of secondary detection.
# A UWB-Driven Self-Actuated Projector Platform for Interactive Augmented Reality Applications
## 7 Discussion and Candidate Applications  
### Influence of the Interaction Distance on Interaction Accuracy, Interaction Time, and Workload  
Regarding the first stage, we noticed that increasing the interaction distance decreased the interaction error and time significantly (Figures 12 and 13). When participants were asked to interact closely, they were 0.3-0.7 m from the wall, mainly to avoid blocking the projector-beam with their body. Most participants stood behind the USAP platform for far interactions and were 0.2-0.5 m away from it. The majority of participants interacted faster and more accurately when interacting from a far distance because they could see the full projected screen without colliding the screen with their body or arms. As shown in Figure 14, participants' lower workload was achieved when interacting from a far distance. Even though a close interaction with the USAP platform is possible, participants did not prefer it because of the projector's beam blockage problem. Despite the freedom given to the participants to select the interaction distance inside the coverage envelope of the UWB wireless sensor network, the maximum distance they selected for interaction was 2.7 m from the projected screen. According to the room dimensions, participants can stand away up to 7 m from the projected screen. However, they selected a shorter interaction distance which provides the best visual feedback. To validate our design and test it under the maximum limit, we tried to interact with the projected screen over S\({}_{1}\) from the maximum possible distance, and we found the interaction accuracy under 0.1 m matches the results shown in Figure 12.  
Systems that convert surrounding spaces to support interactive applications like Wall++ [42] and IBM's Everywhere Display [6] face the problem where far distance interaction with a projected content is either less accurate or impossible. Our system allows us to interact with the projected content from both close and far distances with a fair interaction accuracy. Moreover, it affords the user the ability to customize the projected screen on a relatively large space indo  
Figure 15: Customized screen accuracy and USAP platform navigation performance: (**a**) Customized screen 1 and USAP’s first path, (**b**) customized screen 2 and USAP’s second path, (**c**) customized screen 3 and USAP’s third path, and (**d**) customized screen 4 and USAP’s fourth path.  
gives more freedom of movement for the user as it is not restricting the interaction place or define a specific range to remain able to interact.