esa-sceva/satcom-chunk-collection huggingface dataset:
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 3 Verification with Cantilever Beam Vibration
### Results of Frequencies and Full-Field Mode Shapes  
The fast Fourier transform (FFT) was applied to the displacement time history and the frequency spectra were obtained, as illustrated in Figure 10 (according to the Nyquist-Shannon sampling theorem, the maximum horizontal coordinate value is set to half of the camera frame rate, i.e., 240). It is apparent in Figure 10 that only the first bending vibration frequency of the cantilever beam can be recognized, while the algorithm from the literature and the PAE-based algorithm can identify the first two bending vibration frequencies with some noise peaks in between. In contrast, the proposed method can clearly identify the first four bending vibration frequencies. Table 1 compares the frequencies identified by the proposed method with the theoretical values [46], which shows that the errors are within 3%.  
Figure 8: Comparison of edge-detection algorithms: (**a**) algorithm in reference [31]; (**b**) PAE-based algorithm; (**c**) proposed algorithm.  
Figure 10: Identified frequencies of the cantilever beam: (**a**) Canny operator; (**b**) algorithm in reference [31]; (**c**) PAE-based algorithm; (**d**) proposed algorithm.  
Figure 9: Displacement time histories of the cantilever beam: (**a**) Canny operator; (**b**) algorithm in reference [31]; (**c**) PAE-based algorithm; (**d**) proposed algorithm.  
represents the number of edges sampled and \(T\) is the data volume of each edge throughout the sampling period. Then, according to the assumption that the thickness of the cantilever beam is a constant, the matrix was calibrated using the adaptive scale coversion method. Furthermore, the SSI-COV method was applied to obtain the physical modes from the calibrated matrix. Different numbers of displacement time history were selected as input signals from the matrix \(D(X,T)\) to obtain sparse and full-field 2D mode shapes as shown in Figure 11.  
Figure 11: Bending vibration mode shapes of cantilever beam: (**a**) the first mode shape; (**b**) the second mode shape; (**c**) the third mode shape.  
The mode shapes of the cantilever beam can be identified based on the time histories of all the edge points. In this case, 316 subpixel points were identified for the edge of the cantilever beam. Thus, the full-field mode shapes containing any points (with a maximum of 316 points) as required can be identified. The fourth-order bending mode shape of the cantilever beam is weak and difficult to identify due to the effects of the camera recording quality and environmental noise. Herein, the first three bending vibration mode shapes of the cantilever beam identified using 10, 36, and 106 points are demonstrated in Figure 11, which shows that more edge points are selected, the higher the resolution of the 2D mode shapes that can be identified. The results show that the proposed method is capable of identifying the high-resolution full-field mode shapes without installing artificial targets.
## 4 Antenna Simulation and Measurement Result  
### Simulated Results  
The antenna was simulated with the help of the commercial Finite-Integration Technique (FIT) in the electromagnetic simulation software, CST-Microwave Studio-2015. The results were plotted with the help of originLab-2018, a graphing and analysis software. As explained in Section 2, the proposed antenna was designed in two steps. Its S\({}_{11}\) (dB) results are also explained in that section and are shown in Figure 3a. The parametric study of the defected ground structure (DGS) is explained in Section 3, and its simulated S\({}_{11}\) (dB) results are shown in Figure 3b. The proposed design results with a simulation setup of 2-43 GHz are shown in Figure 5b.  
The simulated S\({}_{11}\) (dB) shows that the antenna covers the S, C, X, Ku, k, and Ka-bands of the microwave range. The proposed antenna is horizontally polarized. The radiation patterns at four frequencies (8.5 GHz, 12 GHz, 18 GHz, and 35 GHz) are shown in Figure 4. The E-plane patterns are in a dumbbell shape, and at some frequencies, higher modes become excited, which may be seen from some of the unwanted ripples at the radiation patterns edges. At lower frequencies, the main lobe of the H-field is in the direction of the y axis, which shows the characteristics of end-fire. Such antennas are best suited to microwave imaging purposes. The patterns also show that the antenna is nearly omnidirectional and suitable for multiple communication applications. The efficiency and gain of the antenna are shown in Figure 5a. According to the simulation, the antenna showed a 95.5% maximum radiation efficiency, and an efficiency of more than 90% across the entire operating band. The simulated maximum gain of the antenna is 6.53 dBi, and the range of the gain at the operating bandwidth is 1.8-6.53 dB.  
Figure 4: Simulated and measured radiation patterns of the antenna at (**a**) 8.5 GHz (**b**) 12 GHz. (**c**) 18 GHz (**d**) 35 GHz.
# Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images
## 6 Conclusions  
In conclusion, our exploration of the ChangeFormer architecture has showcased its great potential in risk detection through automated pipeline monitoring. With an evaluation conducted on a dataset encompassing pipeline risks, we illustrate that ChangeFormer exhibits impressive performance even with low-resolution images.  
Among the many pipeline risks, construction sites and agricultural changes emerge as the most significant contributors. The cost-adjusted ChangeFormer accurately detected 88% of these risks, presenting a compelling alternative to previously utilized helicopter flights for monitoring purposes. The model's capabilities could be further enhanced by incorporating hyperspectral data. Moreover, expanding the dataset to include a broader range of samples would empower the model to generalize better and handle a wider set of scenarios in the real world. Furthermore, formulating the task of CD over not just one pair of images but rather a series of images could be of great potential for more accurate monitoring. In addition, providing the model with diverse change classes would enable it to understand the semantics of the underlying changes more effectively and is a fruitful research direction.  
All authors were involved in the conceptualization, methodology, and validation of the project. Data curation: [PERSON], K.W., and S.B.; formal analysis, investigation, software and visualization: D.O.; writing--original draft preparation: D.O.; writing--review and editing: D.S.D., K.W., and K.K.; supervision: D.S.D. and K.K.; project administration: K.W. and S.B. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the ESA InCubed program \"Automated satellite-based system for pipeline monitoring\" (Project Nr. 4000134395/21/I-NS-bgh), the ICT-48 Network of AI Research Excellence Center \"TAILOR\" (EU Horizon 2020, GA No 952215), and the Collaboration Lab with Nexplore \"AI in Construction\" (AICO). It also benefited from the BMBF Competence Center KompAKI and the HMWK cluster project \"The Third Wave of AI\".  
The dataset and helicopter flight reports are proprietary and, unfortunately, cannot be made public.  
Authors [PERSON] and [PERSON] were employed by the company SuperVision Earth. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CD & change detection \\ GT & ground truth \\ CNN & convolutional neural network \\ MLP & multilayer perceptron \\ IoU & Intersection over Union \\ ca & cost-adjusted \\ pt & pretrained \\ \end{tabular}
# Backlight Unit Excessive Dimming Based on Perceptual Image Analysis
## 2 Background
### Brightness Sensitivity  
In this paper, a method for maximizing the amount of excessive dimming is proposed in consideration of the perceived change in brightness by human eyes of the saturated pixels. For this, we consider the brightness sensitivity [17] of the corresponding pixel range. As the number of pixels falling into the saturated pixel area increases, the degree of deterioration of image quality increases. Therefore, it is necessary to consider how many pixels exist in the area. The degree of deterioration due to excessive dimming is determined by both the pixel population ratio and the brightness sensitivity slope from the max. pixel to the excessive dimming point (bed') among all pixels.  
Figure 2(a) shows the two cases (A, B) of pixel saturation by excessive dimming. Figure 2(b) indicates a nonlinear brightness sensitivity curve that corresponds to the relationship between luminance and brightness perceived by humans [18].  
The change in luminance by pixel saturation in Figure 2(a) can be multiplied by the brightness sensitivity of Figure 2(b) to obtain the brightness degradation perceived by humans. The A and B ranges shown in Figure 2(a),b are based on the same luminance difference at the input pixel level, but as shown in Figure 2(b), the perceived brightness difference of the range B' is larger than that of the range A'. This means that the perceived brightness difference caused by the same amount of luminance difference by excessive dimming can vary according to the luminance or pixel value range. Considering brightness sensitivity in the excessive dimming, more excessive dimming is possible if the brightness perception becomes less sensitive as pixel values saturate. This phenomenon will be mathematically formulated later in the paper.  
Figure 2: Comparisons of image quality with conventional dimming. (**a**) Max pixel (PSNR: 51); (**b**) 95% pixel count (PSNR: 42); (**c**) Histogram weighted average (PSNR: 24).  
In addition, according to light adaptation [19], as a person adapts to a specific brightness, the difference in brightness becomes more sensitive than in other brightness levels. Therefore, it is necessary to consider that the degree of deterioration of image quality due to excessive dimming is subject to change according to several factors.
## 1 Introduction
proposed a laser ultrasonic technique for in-line inspection of laser powder deposition (LPD) parts. Typical micro-defects such as near-surface and surface flaws can be detected [31]. [PERSON] et al. used LUT to identified the presence of four though holes (580-670 \(\upmu\)m at 800 \(\upmu\)m depth) in a Ti6 Al4V AM samples [32]. In order to obtain better results of ultrasonic testing, synthetic aperture focusing technique (SAFT) method are usually utilized. The SAFT has been used to restore ultrasonic images obtained either from B-Scans or C-Scans with focusing distortion. With the use of this technique, an improvement of the image resolution can be obtained. [PERSON] et al. used SAFT to reconstruct the volume inside cylindrical parts. The method shows a sensitivity improvement of magnitude compared to classical ultrasonic inspections [33]. [PERSON] et al. utilized scanning acoustic microscopy (SAM) to diagnostic micro cracks and pores. It was obtained that resolution of SAM operating with 230 MHz transducer is not worse than 40 \(\upmu\)m [34]. However, using the LUT method to identify sub-millimeter hole defects in AM part needs to be further studied. In this work, a non-contact laser ultrasonic inspection system was utilized. The inspection was conducted in pulse-echo mode, i.e., the generation laser and detection laser were located on the same side of the part. Laser-generated longitudinal ultrasonic waves were used to inspect sub-millimeter defects in the AM part. XCT was conducted to validate the results obtained from the LUT system. The relationship between the diameter of holes and the amplitude of laser-generated ultrasonic Rayleigh waves was also studied.
## 2 Methods
### Thermal Experiment  
In the thermal experiment, Samples D, E and F were used because the temperature increase in the samples with low concentrations was found to be insignificant. A Hydrosun(r) 750 lamp (Mullheim, Germany) was used with a Thorlabs FB780-10 bandpass filter (Newton, NJ, USA) to heat the samples. The bandpass filter was applied to further limit the radiation from the lamp to that of the optical window of biological tissues. The specific light intensity of the Hydrosun(r) 750 lamp (Mullheim, Germany) was previously analyzed by present authors [PERSON], et al. [15]. It should be noted that the Hydrosun(r) 750 lamp (Mullheim, Germany) was applied in this experiment because it has been applied in biomedical treatments for mild hyperthermia cancer therapy [41]. The transmission of the FB780-10 filter is shown in Figure 1. As can be seen from this figure, the transmission of NIR light at 780 nm wavelength is over 55%. The diameter of the filter was 1 inch (25.4 mm), which was larger than the width of the sample containers, 10 mm.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **Concentration (GNRs/mL)** & **Volume Fraction** \\ \hline A & 0 & 0 \\ B & \(1.04\times 10^{11}\) & \(0.31\times 10^{-6}\) \\ C & \(2.08\times 10^{11}\) & \(0.62\times 10^{-6}\) \\ D & \(3.12\times 10^{11}\) & \(0.93\times 10^{-6}\) \\ E & \(4.16\times 10^{11}\) & \(1.24\times 10^{-6}\) \\ F & \(5.20\times 10^{11}\) & \(1.55\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 1: Description of gold nanorods (GNRs) solution samples with various volume fractions.  
Figure 1: Transmission of the 780 nm bandpass filter.  
The intensity of the radiation was calculated using the following equations:  
\[\begin{split} q^{ext}=\int_{\lambda_{1}}^{\lambda_{2}}q^{ext}_{ \lambda}(\lambda)d\lambda\\ I=\int_{\lambda_{1}}^{\lambda_{2}}q^{ext}_{\lambda}(\lambda)\times T (\lambda)d\lambda\\ \lambda_{1}=770\;\text{nm},\;\lambda_{2}=790\;\text{nm}\end{split} \tag{2}\]  
where \(q^{ext}_{\lambda}\) is the light intensity of the Hydrosun(\({}^{r}\)) 750 lamp (Mullheim, Germany) at a wavelength \(\lambda\)[15], \(T(\lambda)\) is the transmittance of the FB780-10 filter (refer to Figure 1). The value of \(\lambda_{1}\) and \(\lambda_{2}\) was also determined by the transmittance spectrum while \(I\) represents the overall intensity transmitted through the filter from 770 nm to 790 nm wavelength.  
Figure 2 shows the set-up of the thermal experiment. As can be seen in Figure 2a, the sample was wrapped by Superwool(r) (Windsor, UK) insulation and reflective tape to limit heat loss and error of the experiment. As shown in Figure 2b, the surrounding area of the sample was not exposed to the light source to prevent heating from the sides. Thermocouples were applied to continuously measure local temperature at two locations inside the sample. The experimental data were captured by dataTaker(r) DT800 (Scoresby, VIC, Australia). The averaged value of temperature increase, at the measured locations after a 300 s of exposure to the filtered light, is presented in Table 2.
## 1 Introduction
### Organization of the Paper  
The rest of this paper is organized as follows. The system model, including the network model, attack model, and security model used in our proposed schemes, is introduced in Section 2. Section 3 provides a detailed description of our proposed SPS scheme, and Section 4 introduces our proposedDPS scheme. Section 5 presents the results and performance analysis. Finally, we conclude the paper in Section 6.
# On Sharing an FIB Table in Named Data Networking
## 4 Performance Evaluation
### Performance Metrics  
In evaluating the performance of routing schemes, we used a set of metrics: the average hop count, the total number of _Interest_ packets, and the memory consumption. The average hop count is the average number of nodes passed until the corresponding _Data_ packet arrives after an _Interest_ packet is sent out from a customer. Hence, the average hop count is defined as Equation (2), where \(C\) is the number of consumers, and \(R_{i}\) is the number of _Interests_ created by customer \(i\).  
\[AvgHop=\frac{\sum_{i=1}^{C}\sum_{j=1}^{R_{i}}\left(Hop\left(\text{Interest}_{i,j} \right)+Hop\left(\text{Data}_{i,j}\right)\right)}{\sum_{i=1}^{C}R_{i}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**AS** & **Name** & **Total Nodes** & **Backbone** & **Gateway** & **Leaf** & **Links** \\ \hline
1755 & Ebone & 163 & 23 & 68 & 72 & 366 \\
6461 & Abovenet & 176 & 13 & 33 & 130 & 289 \\
3936 & Exodus & 192 & 39 & 58 & 95 & 422 \\
1221 & Telstra & 279 & 65 & 45 & 169 & 731 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Topology information.  
The total number of _Interest_ packets is defined as Equation (3), where \(N_{r}\) is the number of routers in the network. If an _Interest_ is forwarded by three routers, the total number of _Interest_ packets is increased by three. This metric measures how many _Interest_ packets are forwarded towards multiple faces.  
\[TotalIntNum=\sum_{i=1}^{N_{r}}R_{i} \tag{3}\]  
Since the lengths of name prefixes are not fixed, the memory consumption is calculated in two ways, as shown in Equations (4) and (5). The \(Mem_{1}\) in Equation (4) assumes that each variable-length name prefix is stored in the FIB and the \(Mem_{2}\) in Equation (5) assumes that a name prefix is stored as a 128-bit signature, where \(|FIB_{i}|\) is the total number of FIB entries in node \(N_{i}\), \(Size(FIB_{i}[j])\) is the size of the \(j\)th entry to store a name prefix into \(FIB_{i}[j]\), \(Size(face)\) is the memory requirement for storing output faces, and \(Size(signature)\) is the memory requirement for storing the signature of a name prefix. Note that output faces are represented by a fixed-length vector, of which the number of bits is equal to the number of output faces. Although an FIB table also keeps the ranks of possible faces, the memory requirement for ranking is ignored for simplicity.  
\[Mem_{1}=\sum_{i=1}^{N_{r}}\left(\sum_{j=1}^{|FIB_{i}|}\left(Size(FIB_{i}[j])+ Size(face)\right)\right) \tag{4}\]  
\[Mem_{2}=\sum_{i=1}^{N_{r}}\left(|FIB_{i}|\cdot\left(Size(signature)+Size(face) \right)\right) \tag{5}\]
# Dynamic Proxemic Model for Human-Robot Interactions Using the Golden Ratio
## 5 Experimental Setup
### Subjective Feedback Instrument  
To quantify user perception, a modified version of the Godspeed Questionnaire Series (GQS) [35] was used. The original GQS contains five dimensions; in this study, four were retained: Animacy, Likeability, Perceived Intelligence, and Perceived Safety. The Anthropomorphism dimension was omitted, as neither robot has humanoid characteristics. The measurements of perceived comfort were assessed through participant questionnaires administered after each trial. Each item from the GQS was rated on a 5-point Likert scale. Additionally, two custom questions were added:  
\"Did the robot approach: too quickly/at a comfortable speed/too slowly?\"  
\"Did the robot stop: too close/at a comfortable distance/too far away?\"  
These were rated on a 3-point scale. The participants also reported age and gender to allow for subgroup analysis. The participants were not given detailed technical background information to ensure natural reactions.  
These answers were interpreted as numbers for each question from four dimensions of GQS, which were numbered for each dimension (see Figure 10). Also, the answers for two additional questions regarding the approaching speed and stopping distance of the robot were interpreted as numbers. The numerical designation of the responses, as shown in Figure 10, was removed from the actual questionnaire to avoid confusion when being filled in by the participants.  
In particular, the two additional questions regarding the perceived approach speed and stopping distance are of primary interest for the initial determination of the suitability of the proposed methodology. Based primarily on the answers to these questions, the evaluation will reveal how participants perceived the robot's movement and speed, and thus their comfort in terms of robot movement. The responses from the GQS are more for comparison of how the robot itself was perceived during the interaction.  
Figure 9: Dynamic zone progression during interaction: (**a**) the robot is outside of all comfort zones, moving at its full speed; (**b**) the robot starts to approach the person, continuously slowing down; (**c**) the robot stops in the physical interaction zone.
## 1 Introduction
The above studies achieved target point coordinate measurements in a range of about 200 m, and the results show that the larger the measurement range, the lower the coordinate measurement precision, and when the measurement range is further expanded, such as to 500 m or even longer distances, the methods mentioned in these references cannot be directly applied. Therefore, a new coordinate measurement method for even longer distances is studied in this article. In addition, the coordinate system conversion between multiple stations is involved in the measurement process of multiple device networking [12], so the layout of devices [13] and their common points among affect the measurement precision. [PERSON] et al. used an improved genetic algorithm (GA) to optimize the layout of multiple laser trackers [14]. [PERSON] et al. arranged laser tracker stations at different heights in the center of a circular plane, which improved the coordinate measurement accuracy [15]. [PERSON] et al., through simulation analysis, found that when four laser trackers form a right-angle regular triangular pyramid, the highest coordinate measurement accuracy can be obtained [16]. The above studies show that layout optimization provides theoretical guidance for practical networking, which can save measurement time, improve measurement efficiency, and even reduce costs. Therefore, based on the research of coordinate measurement method, the simulation analysis of the layout optimization of measurement devices is also conducted in this article. This article proposes a multi-level measurement method based on multi-type devices suitable for ultra-long distances, taking the target point coordinates on a rocket sled test track as the measurement object. A series of valuable conclusions are obtained through simulations and experiments. And the main contributions in this article are summarized as follows:  
* The measurement control network is graded, and a multi-level measurement and step-by-step control model was designed, providing a guarantee for overall measurement accuracy. * A length-angle mixed intersection adjustment model was established. Mathematical expressions for the projection of horizontal angles in different quadrants were derived to ensure the accuracy of model solutions. The model is applicable not only to the same devices but also to the different types of equipment. * Simulation analysis of the layout of measurement instruments was conducted, providing guidance for the setup of the measurement control network. * The method presented in this article has significant reference value for the construction of straight-track measurement networks and can be applied in large-scale projects such as power plant construction, tunnel construction, and oil and gas pipeline construction. In the rest of this article, Section 2 describes the establishment of the measurement control network. Section 3 describes the simulation analysis of the layout of measurement equipment and the adjustment model. Section 4 presents experimental results and analysis. Finally, Section 5 concludes the article.