esa-sceva/satcom-chunk-collection huggingface dataset:
# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
## 5 Conclusions and Future Work  
In this paper, a rapid mini-crack detection method of conveyor belt with laser excited thermography is proposed. An experimental platform equipped with a mini-crack vision detection system based on laser excited thermography was designed to verify the method. The active infrared imaging method is used to highlight the infrared features of the mini-crack, and the frame difference method is used to extract the image to quickly record the temperature change of the mini-crack under laser beam heating. At the same time, the power function image enhancement algorithm based on thermal signal further highlights the features of mini-crack and improves the precision of feature segmentation and recognition. Active thermography method based on laser beam heating can quickly change the temperature of the mini-crack feature and background. Real-time heating and  
Figure 12: Comparison of accuracy for result of mini-crack measurement. (**a**) Width; (**b**) Length.  
rapid thermal image acquisition can effectively avoid the problem of large measurement results caused by temperature diffusion in low temperature environments. The experimental results show that, for a mini-crack with width than 1 mm, the measurement error of the laser active thermography method is less than 4%. In addition, the identification accuracy is higher than 96%, the damage sensitivity is higher (mini-crack detection of 1 mm diameter), and the external noise interference is reduced. The method not only realizes accurate mini-crack detection and identifies mini-cracks early on, but it also meets the requirements of different working environments. The proposed method has good application prospects. In future work, adverse factors such as changing conveyor belt rotating speed and limited thermograph resolution will be considered to further improve the mini-crack detection method. In addition, the follow-up studies will include applying the developed method in detecting other types of damage, such as surface wear and breakdown.  
Conceptualization, F.Z. and Q.W.; Methodology, S.Z.; Formal analysis, T.W.; Validation, S.Z. Investigation, S.Z. and F.Z.; Writing--original draft preparation, F.Z.; Writing--review and editing, T.W. and F.Z.; Funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant number 61703215, the Ministry of Transport and applied basic research project of China under grant number 2013329811340, and the National Defense Pre-Research Project of Wuhan University of Science and Technology under grant number GF201809.  
Not applicable.  
Due to the nature of this research, participants of this study did not agree for their data to be shared publicly and only available upon reasonable request.  
The authors declare no conflict of interest.
## 1 Introduction
Although WRIMs require more maintenance due to slip rings, advancements in materials have improved their durability, making them viable for long-term ship propulsion applications [36]. Their capacity to adjust torque and efficiency dynamically makes them well-suited for marine environments where load demands fluctuate [37]. Despite the growing interest in electric propulsion, there is a lack of comprehensive studies comparing PMSMs and IMs in terms of efficiency under variable load profiles, thermal limitations, control strategies, lifetime costs, and overall optimization strategies. This study aims to bridge this gap by providing a comprehensive design and optimization analysis of both PMSMs and IMs under marine operating conditions, offering insights into their respective strengths and limitations in ship propulsion applications, thereby informing more effective motor selection and design processes. The main objective of this paper is the design and analysis of a PMSM with a surface magnet topology in order to characterize its integration in propulsion systems. Section 2 will describe the geometric characteristics of the SCIM design in order to apply finite element analysis to simulate the operating characteristics of the machine. In addition, a wound rotor is included instead of a SC to show the differences and improvements which seek to meet the specific operational needs of electric propulsion of ships with an induction motor. In Section 3, the design methodology of the PMSM will be analyzed in order to minimize losses and reduce torque ripple during nominal operation by sensitivity analysis. In Section 4, the appropriate final PMSM manufacturing configuration topology will be derived, while Section 5 gives the comparative selection criteria derived from the design and includes the initial and operational costs, maintenance costs, cooling and control systems, and the life cycle for the aforementioned application. Finally, Section 6 discusses the challenges and possible future implications that could be presented in other work, while Section 7 summarizes the key findings of the present research.
# Leveraging Blockchain Technology for Secure 5G Offloading Processes +
## 3 Blockchain-Based Secure 5G Offloading Mechanism
This process is managed by SSI logic with interactions with the Hyperledger Indy Blockchain network where information about user equipment DIDs and some additional details are available (see Section 3.2 for internal details). As a result, the credential is created, and issued to the user equipment, where it is securely stored. In addition, it also releases the user resources and updated the associated information about available resources and service in the marketplace. In parallel, when the event is received in the selected secondary operator, it starts making a proof request of suitable agreement credential to the particular user equipment. This proof request will be automatically answered by the user equipment if it is already available (because it has received it from the primary operator). The received proof is then validated against the information in the Hyperledger Indy Blockchain network in terms of content as well as signature (see Section 3.2 for internal details). If the validation is correct, the agreement is considered suitable and the secondary operator allocates the required resources to the particular user equipment, making the user to be connected to the secondary operator. In addition, it updates the associated information about his available resources and the provided service in the marketplace. Finally, the appropriate payment needs to be made from the primary operator (the one requesting the offloading process for his user) to the selected secondary operator. This process has been left open to different alternatives to allow compatibility with existing financial systems from operators. However, a Blockchain-based payment system could also be applicable if all operators registered in the marketplace agree on its use [96; 97].
## 2 Materials and Methods
### Determinations of saccharides and Organic Acids  
The saccharide and organic acid contents were analyzed by GC-FID and quantified with an internal standard method [16]. Samples weighing 5 g were extracted by 40 mL distilled water. After centrifugation, supernatant was collected and filtered by 0.45 \(\upmu\)m filter. D-xylose and succinic acid were separately used as internal standards for the qualifications of saccharides and organic acids, which were mixed with extracts and dried by nitrogen and phosphorus pentoxide. Hexamethyldisilazane/trimethylchlorosilane/pyridine (2:1:10, V/V/V) was then added for dissolution and detection. Agilent 6890 N network GC system with DB-1 chromatographic column and Agilent 7683 autosampler were used for determination. Values were presented as mean \(\pm\) SD mg\(\cdot\)g\({}^{-1}\) fresh weight (FW) (\(n\) = 3).
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 3 Background
### Preliminaries
#### 3.1.3 Paillier Cryptosystem  
The Paillier cryptosystem is a classic homomorphic encryption and is usually used to implement addition operations over the ciphertext domain [25]. In general, it consists of three polynomial-time algorithms (i.e., \(Gen\), \(Enc\), and \(Dec\)).  
\(Gen(1^{n})\): Firstly, two independent large prime numbers \(p\) and \(q\) are randomly selected. Then we compute \(N=p\cdot q\) and \(\lambda=lcm(p-1,q-1)\), where \(lcm(\ )\) is the least common multiple function. Finally, the public key \(pk=N\) and private key \(sk=(\lambda,\psi(N))\) can be obtained, where \(\psi(N)=\lambda^{-1}mod\ N\).  
\(Enc(pk,m)\): Assume \(m\) is a plaintext to be encrypted. Firstly, a random number \(r\in\mathbb{Z}_{N}^{*}\) is selected. Then the encrypted result \(c\) can be computed by Equation (1):  
\[c=[(1+N)^{m}\cdot r^{N}mod\ N^{2}] \tag{1}\]  
\(Dec(sk,c)\): To obtain the plaintext \(m\), the encrypted result \(c\) can be recovered with the private key \(sk\) by Equation (2):  
\[m=\frac{(c^{\lambda}modN^{2})-1}{N}\cdot\psi(N)\ mod\ N \tag{2}\]
## 4 Simulations
### Protocols for Comparison  
The performance of our proposed protocol is compared with the simple-link protocol and a direct reconfiguration scheme (DRS). The simple-link sets up the communication between BS and GSs by allocating each GS an uplink node that is one hop closer to the BS. The details of this protocol are introduced in [8], and it works without any reconfiguration scheme. Eventually, the computed set of paths from GSs to the BS forms an oriented forest with the roots in the CHs. A DRS is also designed to evaluate the advantages of RRP, especially the reconfiguration routing in RRP. The DRS is a simple routing strategy, which has two phases: routing establishment and data transmission.  
* Routing Establishment
* The BS broadcasts a packet with its location information to CHs.
* Upon receipt, each CH checks if its flag _rv_bs_info_ initially set as _false_ has been changed to _true_. If yes, then it discards the received packet. Otherwise, it stores the information of BS and then marks the flag _rv_bs_info_ as _true,_ indicating that it has learnt the BS information. Afterwards, the CH broadcasts the packet to its successors.  
\begin{table}
\begin{tabular}{l l} \hline
**Operation** & **Energy Dissipation** \\ \hline Active Transmitter (\(\varepsilon\)) & 4 pJ/m\({}^{2}\) \\ Passive Transmitter (\(E_{CCR}\)) & 16 pJ/bit \\ Receiver (\(E_{R}\)) & 60 pJ/bit \\ \hline \end{tabular}
\end{table}
Table 3: Energy Model.  
When a GS gets a packet, it also checks the flag _rv_bs_info_. The packet is discarded if the flag is _true_. Otherwise, the GS stores the information of BS and then marks the flag _rv_bs_info_ as _true_ before broadcasting it to the successors. After the broadcasting period, each GS has learnt the BS information and then adjusts its orientation and communication radius to reach the BS for data communication.
* Data Transmission
* The CHs transmit the sensed data to the BS by their passive transmitter. The GSs, which have set up direct communication with the BS in the former phase, transmit their sensed data by their active transmitter.
## 5 Trade-Off between Communication Rate and Convergence Performance
### Trade-Off between Convergence Performance and Communication Rate
Figure 11 describes, under the current simulation conditions, how the index \(\Omega_{N}\) approximating the convergence rate varies with the proportion \(\alpha\) of data used for estimating the probability of packet loss. In this case, the optimal value of \(\alpha_{N}^{*}\) is 0.16. Take \(\alpha=\alpha_{N}^{*}\) and any \(\alpha\) that is different from \(\alpha_{N}^{*}\), such as \(\alpha=0.1,0.3,0.5\), respectively. Figure 12 further verifies the optimality of \(\alpha_{N}^{*}\) by using the average of \(|\widehat{\theta}_{N}-\theta||\) calculated from 200 trajectories as a representation of the convergence performance. The convergence performance is best when \(\alpha=\alpha_{N}^{*}\). Figure 10: The relationship between AIC and fitting order \(h\). Figure 9: The relationship between communication rate and \(\alpha\).
# Simplification of Complex Structural Dynamic Models:
## 2 Model Descriptions
### Experimental Modal Analysis  
For the experimental modal analysis (EMA), a contact-free measurement setup using a laser Doppler vibrometer (PSV-400, Polytec, GmbH, Waldbronn, Germany) was used, c.f. Figure 2. The six specimens, each one a distinct body, appearing as a beam with a tip mass attached to the free end, were suspended on two elastic strings and were acoustically activated by a loudspeaker using a ramped sinusoidal sweep in the frequency range to 1 kHz with a sampling frequency of \(f_{s}\,=\,4000\) cycles/s.  
Measurements were performed in an anechoic chamber to avoid the influence of acoustic reflections from the walls. A microphone measured the effective sound pressure generated by the loudspeaker to find the input value for the frequency response function (FRF). This procedure allows for extracting the eigenfrequencies of the structural modes and is, thus, sufficient for the presented study. However, when seeking to determine the modal damping parameters, a different technique for measuring the excitation has to be used. The deflection shape of each of the six specimens was measured by a laser Doppler vibrometer in front of the investigated structure.  
By using a contact-free measurement setup, any artificial effect of the measurement equipment, e.g., the mass added by applying an accelerometer for measuring the deflection, is eliminated. Further, the suspension of the specimens was chosen such that the frequencies of the rigid body modes were at least one order of magnitude below the first eigenfrequency of interest. Therefore, the suspension can be adequately modelled by ideal free-free boundary conditions [29]. A review of different measurement techniques is provided, for example, by [PERSON] [29] and the International Organization for Standardization [30].  
Figure 2: Image and sketch of the experimental setup showing: elastic strings (1), specimen mass (2a), specimen beam (2b), acoustic excitation (3), and laser Doppler vibrometer (4).
## 2 Materials and Methods
### Input Data
#### 2.2.3 Downscaling and Bias-Correction of Global Climate Model (GCM) Data  
GCMs are important tools, which are preferable to understand climate change scenarios. These scenarios simulate the response of the global climate system to changes occurring in the atmospheric conditions [36]. Future monthly streamflow was simulated with five global climate models (GCMs) participating in the Coupled Model Inter-comparison Project Phase 5 (CMIP5) under four representative concentration pathways (RCPs) for the period of 2020-2050, considering the historical period of 1971-2001 for the baseline flow. Streamflow projections were carried out by assuming the projected precipitation and temperature inputs to SWAT, assuming non-evolution of the LULC ESA 2005 and soil properties in the future period. In this study, we analyze Geophysical Fluid Dynamics Laboratory, USA (GFDL-ESM2M), Meteorological Office Hadley Centre, United Kingdom (HadGEM2-ES), Institute Pieree Simon Laplace, France (IPSL-CM5A-LR), Atmospheric and Ocean Research Institute, The University of Tokyo, Japan (MIROC-ESM-CHEM), and Bjerknes Centre for Climate Research, Norwegian Meteorological Institute, Norway (NorESM1-M), of the Inter-Sectoral Impact Model Inter-comparison Project (ISI-MIP) [37], under the emission scenarios of all four representative concentration pathways (RCPs) (RCP-2.6, RCP-4.5, RCP-6.0, and RCP-8.5). The outputs of the GCMs were statistically downscaled using the bias-correction special disaggregation (BCSD) tool in the climate change toolkit (CCT) [38; 39; 40]. In detail, the climate change toolkit (CCT) extracted, downscaled, bias corrected, and interpolated the GCM outputs. All climate data sets (i.e., precipitation, temperature, relative humidity, solar radiation, and wind) were downscaled using a 0.5-degree spatial resolution and are available in simple text format. Projected changes in mean monthly streamflow were analyzed by the percent change method, yielding a percent increase or decrease. The historical GCM (1971-2001) was used as a baseline for future projection changes.
# Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations
## 4 Evaluation of the Proposed Protection Levels for PPP-RTK
### Comparison of the VRS-RTK Position Errors and Computed Protection Levels  
Figure 7 shows the resultant ENU position errors of zero-baseline VRS-RTK using dual-frequency GPS and Galileo satellites with the test dataset. To determine the fixed integers, a conventional test value ratio, above 3, was used. The RMS of the positioning errors with the fixed integers is 4, 2, and 14 cm in the East, North, and up directions, respectively. At certain epochs, our VRS-RTK software failed to resolve fixed integers due to cycle slips. For this particular test, 2.5% of the data contained a float solution.  
Figure 3: Number of visible GPS and Galileo satellites during a day in the 7-day period.  
Figure 2: GNSS data obtained from a GEONET base station located at Chome-1 Nagatacho, Chiyoda City, Tokyo 100-0014, Japan (35.677\({}^{\circ}\) latitude and 139.748\({}^{\circ}\) longitude) [27].  
Figures 8 and 9 show the computed protection levels and the position errors for the fixed integers cases with a fault-free risk probability (\(I_{H\text{pro}}\)) of \(10^{-7}\). If the test value ratio was less than 3, that is, float solutions, no protection levels were computed. As shown in Figures 8 and 9, all of the computed HPL and VPL were larger than the horizontal positioning errors (HPE) and vertical positioning errors (VPE), respectively. The RMS of the HPE was 4.6 cm whereas that of the HPL ranged from 25 cm to 2.3 m. The RMS of the VPE was 14 cm, whereas the VPL ranged from 50 cm to 5.2 m. At certain epochs, there are large peaks in the HPL and VPL, and these peaks occur when there is a jump in quality indices. These large protection levels occurred at very small percentages such that HPL was less than 1 m in 99.4% and VPL was less than 3 m in 99.6% of the dataset, respectively.  
Figure 4: Time series of the Ionospheric correction quality, \(\sigma_{\text{iono}}\), in meters.  
Figure 5: Time series of the Proposphere correction quality, \(\sigma_{\text{propo}}\), in meters.  
Figure 6: Time series of the Signal in Space correction quality, \(\sigma_{\rm{sis}}\), for GPS and Galileo satellites.  
Figure 7: VRS\(-\)RTK position errors for float and fixed integer cases with the test dataset.
## 2 Related Work
### Attention Applied in SAR Images  
Attention mechanisms have gained significant interest for improving image processing tasks. However, applying them to SAR images, which are often noisy and complex, remains challenging. Following the introduction of attention mechanisms [20], many works have attempted to integrate this mechanism into image processing. [PERSON] et al. [21] designed the self-attention module, which has become widely popular in the field of neural networks. [PERSON] et al. [22] developed an efficient multi-scale attention module (EMA) for cross-spatial learning, achieving better performance than traditional CNNs. [PERSON] et al. [23] sought to combine semantic information with image processing. However, simply applying attention mechanisms to image understanding, especially for SAR images, is cumbersome and challenging for real-time processing [24]. [PERSON] et al. [25] improved YOLOv5 to detect small ships in SAR images with a dynamic sparse attention mechanism, which achieved a good performance in small object detection. But, it has weaknesses in multi-scale and multi-class datasets. As a result, incorporating the attention mechanism into the YOLO framework has been proposed as a feasible approach to leverage the strengths of both [26].
# Study on the Particle Surface Fractal Characteristics of
## 3 Calculation Models  
The perimeter-area approach is derived in the manner described below. The perimeter \(L\) of a geometric object with a regular shape, such a circle, is proportional to the area \(A\), so  
\[(L)^{1/1}=2\pi^{1/2}(A)^{1/2} \tag{1}\]  
In addition, a square has an area of \(A\) and a perimeter of \(L\), so  
\[(L)^{1/1}=4(A)^{1/2} \tag{2}\]  
The regular geometric figure's perimeter \(L\) is proportionate to its area \(A\), as can be shown from (1) and (2), which means  
\[(L)^{1/1}\propto(A)^{1/2} \tag{3}\]  
According to our study, a geometric figure with an irregular shape would be  
\[(L)^{D_{L}/1}\propto(A)^{1/2} \tag{4}\]  
where \(D_{L}\) is the particle's fractal dimension.  
The tiny structure of an irregular particle will continue to be discovered with the improvement in the resolution of the measuring microscope or scanning electron microscope since its center of gravity is in the most stable condition. Under various magnification circumstances, the perimeter and area that the microparticle's projection showed were measured; the relationship between the two may be found in the following formula:  
\[(1/D_{L})\text{lg}L=k_{0}+(1/2)\text{lg}A\text{ ($k_{0}$ is a plus constant)} \tag{5}\]  
\[\text{lg}L=(D_{L}/2)\text{lg}A+k_{0\text{ ($k_{0}$ is a plus constant)}} \tag{6}\]  
Making a straight line based on the relationship of _LgL-lgA_, then \(D_{L}\) is 2 times the line slope, that is  
\[\frac{D_{L}}{2}=k \tag{7}\]  
The method described above can be used to determine the projection profile fractal dimension of particulate materials, and the formula [13,14] that follows can be used to determine the single particle surface fractal dimension:  
\[Ds=D_{L}+1\text{ (2}\leq Ds\leq\] 3)  
Through the use of microscopic analysis apparatus, the projected area \(A\) and projected perimeter \(L\) of a single particle can be determined in this study. Using the formula above, the fractal dimension of the projection of a single particle and the surface fractal dimension can then be estimated.
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 3 Clustering in Stable Instances
### Warm-Up: Phase 2 of Single-Linkage Algorithm  
In order to describe some difficulties and to convey some basic ideas, we shall first consider implementing Phase 2 of Single-Linkage algorithm only. We thus assume that the (exact) Minimum Spanning Tree (MST) has been already computed (or it is given). Observe that Phase 2 of Single-Linkage algorithm boils down to the problem of selecting the top \(k-1\) elements from a set of \(n-1\) edges of the MST, thus a simple matroid. We first observe the following three facts on this task (details below):  
1. There is a naive approach using \(O(k+(n-k)\log k)=O(n\log k)\) high-cost operations in total (and no low-cost operation).
2. Approximate sorting (Lemma 2) can reduce the total number of high-cost operations to \(O(k+d\log k)\), where \(d=O(\log n)\).
3. Approximate the matroid (Lemma 3) would directly further improve the above bound for some values of \(k\). Unfortunately, this leads to a solution which is far more costly than the one returned by the algorithm with exact comparisons (or with the previous methods).  
As we discuss below, though Single-Linkage has no approximation guarantee, we show that, in some stable instances where it would find an optimal solution, the matroids' approximation makes it compute a solution of much larger cost. This suggests that Phase 2 of this algorithm (removing the \(k-1\) edges) is a \"fragile\" part of the algorithm (also for the more complex Single-Linkage++). Instead, we show in the next section that the first step (computing the MST) can be done approximately without \"destroying\" the clustering optimality.  
In the remainder of this section, we discuss briefly some details on the three items above.