esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Three-Dimensional World Point Reconstruction and Structural Displacement Extraction
### Three-Dimentional World Point Reconstruction and Structural Displacement Extraction  
Given the reference target image coordinates \([u,v]^{T}\), camera intrinsic parameters \(K\), and camera extrinsic parameters \([R\,|\,t]\), the 3-D world point of the reference target can be reconstructed based on the camera projection equation in Equation (7). The test video is captured by a monocular camera mounted on the UAV. Therefore, scale ambiguity leads to four unknown variables (world coordinates \(XYZ\) and the scale factor \(s\)) in Equation (7). However, one may assume that the \(Z\)-coordinates of all moving targets remained constant as \(Z_{0}\) during the test (i.e., zero vertical displacements for the targets). This assumption is considered reasonable in the scope of this study because the displacements of the building specimen in the vertical direction are much smaller than the displacements in the lateral direction due to the extremely large self-weight of the structure. In addition, the change in image point coordinates caused by the out-of-plane motion relative to the camera view plane is small because of the large camera-to-scene distance (around 35 m between camera and roof level) [53]. Based on this assumption, Equation (7) can be expanded into the following form:  
\[s\begin{bmatrix}u\\ v\\ 1\end{bmatrix}=K\left(R\begin{bmatrix}X\\ Y\\ Z_{0}\end{bmatrix}+t\right) \tag{8}\]  
To solve the world coordinates \(X\) and \(Y\), Equation (8) is re-arranged as follows:  
\[\begin{bmatrix}X\\ Y\\ Z_{0}\end{bmatrix}=sR^{-1}K^{-1}\begin{bmatrix}u\\ v\\ 1\end{bmatrix}-R^{-1}t \tag{9}\]  
The scale factor \(s\) can be calculated by solving the equation defined by the 3 rd row of Equation (9), where the scale factor is the only unknown variable. Then, world coordinates \(X\) and \(Y\) can be calculated by back-substituting the scale factor into the first two rows of Equation (9).  
Based on the 3-D world point reconstruction procedures above, the world coordinates \(X\) and \(Y\) of the reference target in each video frame can be solved. The displacement of the reference target at a specific video frame is calculated as the change in the world coordinates of the target relative to the reference video frame (the first frame in the test video). It is noted that the calculated displacement is the absolute (total) displacement of the roof. The motion of the shake table platen is also included in the displacement.
## 3 Comparison between Circular and Slab Diffusion Models
### Durability Design of the RC Circular Column against Chloride Degradation
#### 3.3.2 The Effect of RC Circular Section Shape on the Estimation of Reliability Index  
Taking reference on the field research results of [PERSON] et al. [28], for splashing, tidal and submerged zones the mean chloride diffusion coefficients of concrete was \(2.32\times 10^{-12}\) m\({}^{2}\)/s and the mean concrete cover thickness was 36 mm whereas the mean chloride diffusion coefficients and concrete cover thickness were \(4.38\times 10^{-12}\) m\({}^{2}\)/s and 52 mm for atmospheric zone. And water/cement ratio used in marine environment is 0.33. The statistical properties of model parameters in Tables 2 and 4 used in the HZM project are suitable for structural concrete having \(w\)/\(c\) = 0.35. Thus, these model parameters are available for service life prediction of RC circular column. The design reliability index \(\beta_{\text{d}}\) of the RC facilities in the HZM Project was set to 1.3 [29]. The same target reliability index is adopted in this paper. Sampling numbers of 100,000 is generated for Monte Carlo simulation. Under a series of radius of circular section, the reliability index for different service time is calculated, as shown in Figure 12. It can be seen from Figure 12 that the reliability index is overestimated when circular column is regarded as slab element. The data collected in Table 5 indicates that the service life of circular column is also overestimated by slab diffusion model. For the radius of column less 50 cm, the effect of RC circular column shape should be considered.
## 4 Discussion
### Limitations and Future Studies  
There are some limitations associated with this study that need to be mentioned. We believe that the main limitation of this study is our attempt to be as close as possible to real maintenance conditions by not imposing any constraints on the operator's activities. This has implications for methodological choices. The mental workload measurement might have been addressed using physiological indicators [89], such as heart rate or pupil diameter. However, the lighting conditions of our hangar or the movement inherent to maintenance activity made these indicators difficult to set up.  
Regarding the depth of the analysis of eye-tracking data, our study considers only basic indicators and procedural documentation in a single object of interest. It would be beneficial to include an analysis of the information extracted from the documentation to further develop the initial contributions presented in this study. For example, it might be interesting to analyze the effect of expertise on document navigation.  
Finally, while the primary focus of our study is on procedural aspects, we recognize the importance of including the physical dimension, which may provide a perspective for a more comprehensive understanding of expertise in maintenance tasks.
###### Abstract  
Using a single-point sensor, non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components. To classify devices, potential features need to be extracted from the electrical signatures. In this article, a novel features extraction method based on current shapelets is proposed. Time-series current shapelets are determined from the normalized current data recorded from different devices. In general, shapelets can be defined as the subsequences constituting the most distinguished shapes of a time-series sequence from a particular class and can be used to discern the class among many subsequences from different classes. In this work, current envelopes are determined from the original current data by locating and connecting the peak points for each sample. Then, a unique approach is proposed to extract shapelets from the starting phase (device is turned on) of the time-series current envelopes. Subsequences windowed from the starting moment to a few seconds of stable device operation are taken into account. Based on these shapelets, a multi-class classification model consisting of five different supervised algorithms is developed. The performance evaluations corroborate the efficacy of the proposed framework.  
classifiers; current shapelets; electrical appliances; envelopes; load disaggregation; non-intrusive load monitoring; single-point sensor; smart grid; subsequences +
Footnote †: journal: Applied Mechanics and Physics Letters
## 6 Future Research Areas  
In this paper, many key issues related to the URLLC implementation are discussed. Once the standards for 5G NR are matured, the researchers can work towards improving the core network, backhaul, and transport delays [39]. Issues related to UE energy efficiency (discussed in Sections 3.5 and 4.1) can be further discussed in the light of the new NR defined energy-efficient state called INACTIVE [55]. This state is defined as a state existing between the CONNECTED and IDLE states of a UE. The performance evaluation of the INACTIVE state remains open for further examination. One of the concerns related to the coexistence of URLLC with eMBB (discussed in Section 3.2) is not only limited to the existence of the mentioned categories of 5G. With the backward compatibility of 5G NR, the coexistence of URLLC with distributed system architecture and diverse application requirements will need further study to avoid latency concerns [55]. Likewise, handover related \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & \multicolumn{2}{c}{**Radio Access Network**} & \multicolumn{2}{c}{**Core Network**} \\ \cline{2-5}  & \multicolumn{1}{c}{**SA**} & \multicolumn{1}{c}{**NSA**} & \multicolumn{1}{c}{**EPC**} & \multicolumn{1}{c}{**5 GC**} \\ \hline \multirow{3}{*}{**Advantages**} & Simple management & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } & Supports existing LTE & Supports current & Cloud-native \\  & Support handover & deployment & & & multiple access is \\  & between 4G and 5G & & & & easy to support \\  & Will not be able to & & & & \\  & support existing LTE & & & & \\  & deployment if NR is & & & & \\  & used in SA & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Contrast of core network and 5G RAN [51]. SA, standalone; NSA, non-standalone; EPC, evolved packet core; 5 GC, 5G core; LTE, long-term evolution; NR, new radio. issues (discussed in Sections 3.6 and 4.3) can be further discussed in terms of the reestablishment of connection in the case of the radio link failure, keeping in view that LTE and NR traffic will coexist in the same carrier. Many areas need further investigation to operate IoT devices over URLLC. Although URLLC can provide low latency with high reliability, the characteristics of massive connectivity can degrade such requirements, as discussed in Section 4.1. As discussed in Section 4.2, on-device AI and ML will play a major role in an upcoming era, and hence researchers can further study the designing of AI/ML algorithms that do not require high resources and are better suited for devices with limited resources. A comprehensive summary of the issues discussed in the paper is presented in Table 6.
## 5 Conclusions  
The present work showed that the estimation of rice LAI using CIs derived from UAV RGB images was strongly influenced by variations in illumination and the image background. The variation in illumination resulted in changes in image colors, making it challenging to compare crop phenotypic traits over time and space. However, employingmulti-scale Retinex correction could mitigate the impact of variable illumination on CIs and improve the accuracy of LAI estimation. Because CIs were calculated based on the average values of the entire image, rice plants combined with the background could lead to underestimation or overestimation of rice LAI without proper segmentation. Overall, our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis using UAV RGB images. These findings underscored the importance of image processing techniques in enhancing the utility of UAV RGB images in agricultural phenotyping. Furthermore, this study provided a method to improve the accuracy of LAI estimation based on UAV RGB images.  
Investigation, writing--original draft, and funding acquisition, B.S.; writing--review and editing, Y.L.; data curation, J.H.; data curation, Z.C.; visualization, X.P. 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 (41961048), by Key Research and Development Plan Project of Jiangxi Province (20212 BBF63040, 20212 BBF61013), by National Key Research and Development Program of China (2022 YFD2001005), and by Jiangxi Modern Agricultural Research Collaborative Innovation Project (JXCTCXQN201904, JXCTCXQN202110).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors thank LetPub (www.letpub.com (accessed on 1 April 2024)) for its linguistic assistance during the preparation of this manuscript. The authors would like to thank all the reviewers.  
The authors declare no conflicts of interest.
# Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System
## 4 Performance TEST
### Comparison of 2D Exploration with ERMS and Commercial Equipment  
The 2D exploration performance test was conducted using the ERMS verified by 1D exploration. The objective of the 2D exploration performance test was to identify abnormal resistance zones characterized by rapid changes in resistance. The local area was measured using the developed equipment and compared to the larger area measured by the commercial equipment rather than matching the measurement ranges exactly. The commercial equipment used was an ABEM Terrometer LS2. The test location consisted of a uniform distribution of shale and rock (Figure 11). For the commercial equipment, electrodes were placed at 2 m intervals with a total of twenty-four electrodes, while for the ERMS, electrodes were placed at 1 m intervals with a total of twenty-four electrodes. The ERMS measurement location was centered within the electrode interval of the commercial equipment. For visual comparison of the measurement results, the commercial inversion software DIPRO (VER. 4.1) [63] was used to display the 2D data. Figure 12 shows the inversion results of the measured data. Figure 12a shows the results obtained using the commercial equipment, Figure 12b the results obtained using the ERMS, and Figure 12c the partially overlapping measurement results obtained using both the commercial equipment and ERMS. In the measurement results from the commercial equipment, a high-resistivity zone can be observed at a depth of 14-20 m along the x-direction and 6 m along the y-direction. By comparing Figure 12a,c, it can be seen that the ERMS measurement results also indicate the formation of a high-resistivity zone in a similar location to that observed with the commercial equipment.  
Figure 11: 2D exploration location of ERMS and commercial equipment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Pseudo-Depth** \\ **Level** \\ \end{tabular} } & \begin{tabular}{c} **ERMS** \\ **Apparent** \\ **Resistivity** \\ **[Ohm\(\cdot\)m]** \\ \end{tabular} & \begin{tabular}{c} **MINISTING R1** \\ **Apparent** \\ **Resistivity** \\ **[Ohm\(\cdot\)m]** \\ \end{tabular} & \
\begin{tabular}{c} **Error Rate** \\ **[\%]** \\ \end{tabular} &
\begin{tabular}{c} **Note** \\ **[\%]** \\ \end{tabular} \\ \hline
1 & 90.35 & 91.63 & 1.42 & \\
2 & 111.51 & 109.59 & 1.72 & First point of \\
3 & 136.1 & 133.96 & 1.57 & each \\
4 & 143.84 & 145.23 & 0.97 & pseudo-depth \\
5 & 175.35 & 175.66 & 0.17 & level \\
6 & 180.86 & 178.36 & 1.38 & \\
7 & 188.79 & 185.96 & 1.5 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: 1D exploration results of EMRS and MINISTING R1.
# OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm
## 5 Experimental Analysis  
### Experimental Environment  
Experiments demonstrate the effectiveness of our improved multi-threshold image segmentation algorithm. The test image is taken from the PASCAL 2012 data set [25]. The experimental environment consists of MATLAB 2018a, Windows 10, and an Intel(R) Core(TM) i5-5600H @ 2.4 GHz CPU with 8 GB of RAM. We set population size \(m=50\), maximum number of iterations \(k_{max}=100\), particle position range \(x_{i}\in[0,255]\), velocity range \(v_{i}\in[-10,10]\), individual learning factor \(c_{1}\in[0.5,2.5]\), and social learning factor \(c_{2}\in[0.5,2.5]\).
# Evolutionary Approach to the Euclidean Steiner Tree Problem in n-Space
## 1 Introduction  
The Euclidean Steiner Tree Problem (ESTP) in \(\mathbb{R}^{n}\) concerns finding the minimal network of connections for a set of points \(P\). This network forms a tree structure, where the length of the network is the sum of the lengths of edges connecting the vertices. If no additional points can be used to connect the points in \(P\), the problem reduces to finding a minimum spanning tree (MST) for \(P\), which can be efficiently solved using algorithms such as [PERSON]'s algorithm. However, if it is possible to add additional intermediate points, known as Steiner points, networks of shorter length (Steiner Minimal Tree, SMT) can be obtained. This, however, is a significantly more complex problem as both the number and the location of the additional Steiner points are unknown. It has been proven to be an NP-hard problem in both 2D Euclidean space and in higher-dimensional spaces.  
Figure 1 shows an example of a minimal spanning tree and a minimal Steiner tree for ten points in 2D space (\(\mathbb{R}^{2}\)). A similar example is presented in Figure 2 for ten points in 3D space (\(\mathbb{R}^{3}\)). In both cases, the total length of edges is smaller for SMT compared to MST.  
ESTP in 2D Euclidean space \(\mathbb{R}^{2}\) is particularly noteworthy because there exist efficient algorithms that can solve problems for \(p=|P|\) on the order of thousands. An example of such an algorithm is the GeoSteiner algorithm. However, efficient algorithms in 2D are based on characteristics of the Steiner problem specific to the plane and cannot be utilized to develop equally efficient algorithms in 3D space or in higher-dimensional spaces.  
The aim of this study is to exploit the potential of the genetic algorithm to build a search algorithm for exploring the space of possible full Steiner topologies (FST) and then to evaluate the so-called relative minimal trees (RMT), which can be obtained from them through the optimization of the Steiner points' positions. Among the evaluated RMTs,the one with the smallest sum of edges is selected as the SMT. All necessary definitions and characteristics of the used structures will be described in the following sections. The proposed algorithm will be tested on a set of problems in 3D space with sizes \(p=17\) and \(p=100\).
## 3 Finite Element Models of Anti-Loosening Bolted Connections
The thread meshes of an eccentric double nut with high density were built based on [PERSON] and [PERSON]'s method. The meshes in the interference area between convex and concave nuts required careful generation. All the meshes were hexahedral in shape, and the density was set to a value as small as possible in order to improve calculation efficiency. The inclined surfaces in the FEMs of convex and concave nuts had similar meshes in order to attain a solid match when in contact. It should be noted that there was an eccentricity in the FEM of the convex nut. The entire bolted joint with an eccentric double nut was established as shown in Figure 8f, where the bolt and moving plate were unchanged. The external thread shape of the double-thread bolt was complex, combining both left-hand and right-hand threads. The eccentricity of the eccentric double nut was set to be 0.1 mm. The FEMs of normal bolts with left-hand and right-hand threads were first established. Next, a Boolean operation was used to retain the common parts of the two models to generate the FEM of a double-thread bolt, as shown in Figure 8g. The FEMs of nuts with opposite thread  
Figure 7: Three-dimensional FEM of a regular bolted joint: (**a**) entire regular bolted joint; (**b**) sectional view of the threaded part. directions were generated based on [PERSON] and [PERSON]'s method, and the moving plate remained unchanged. The material of all the parts was 1045 steel, and the parameters of FEMs are shown in Table 1. Considering that a wedge-locking nuts avoid loosening via plastic deformation of threads, elastic-plastic parameters were added in the finite element model. The bilinear yielding model was applied to model material plastic deformation, and the yield strength of a wedge-locking nut was set to 355 MPa. Each pair of contact interfaces consisted of a contact component and a target component. The element types of the contact component and the target component were set as CONTA173 and TARGET170, respectively. A friction coefficient value of 0.15 was assigned to the contact interfaces, which is a typical value adopted by other researchers [13]. In the modeling progress, the boundary conditions were as follows: (1) movements in all directions for the bottom of the bolt head were fully  
Figure 8: Three-dimensional FEMs of different anti-loosening bolted connections: (**a**) plain washer; (**b**) wedge washer; (**c**) wedge-locking nut; (**d**) variable-diameter nut; (**e**) regular double nuts; (**f**) eccentric double nut; (**g**) double-thread bolt connection. constrained, and (2) movement in the direction parallel to the bolt axis for the bottom of the movable plate was constrained.
## 4 Practical Implementation
### Special Risk Groups
#### 4.1.1 Obese andariatric Patients  
One steadily growing sub-population consists of patients who are already metabolically challenged (i.e., diabetic, severely obese, and post-bariatric/obese surgery patients) [91].  
As the number of bariatric operations increases, one must keep in mind that approximately 30% of bariatric patients develop nutritional complications [91]. Furthermore, the proportion of obese critically ill patients is growing parallel to the increasing prevalence in the general population [92]. The ASPEN guidelines suggest to additionally focus the nutritional assessment of these patients on biomarkers of metabolic syndrome, evaluation of comorbidities, and the level of inflammation [27].  
In order to estimate patient nutritional needs, resting EE is best assessed by IC [20]. If not available, energy intake can be guided by adjusted body weight (BW) and protein delivery by urinary nitrogen losses or lean body mass determination (using CT or other tools), according to the ESPEN guidelines [17].  
Several existing predictive scores, such as the Mifflin-St Jeor (MSJ) and the Harris-Benedict equations [93; 94], can be applied if IC is not available in the case of complicated bariatric patients [20; 94]. However, it is important to consider that these scores are not specifically validated for post-bariatric patients and IC is recommended in the first place [17].  
The ESPEN guidelines recommend an isocaloric high protein diet including a protein intake of 1.3 g/kg adjusted BW/day (GPP) [17]. On the other hand, the ASPEN guidelines [27] suggest a hypocaloric high-protein feeding protocol, with 65%-70% of measured energy requirements using IC or weight-based equations (calories: BMI 30-50 kg/m\({}^{2}\) = 11-14 kcal/kg actual BW/day, BMI \(>\) 50 kg/m\({}^{2}\) = 22-25 kcal/kg ideal BW/day; protein: BMI 30-40 kg/m\({}^{2}\) = 2 g/kg ideal BW/day, BMI \(\geq\) 40 kg/m\({}^{2}\) = 2.5/kg ideal BW/day), for all classes of obesity.  
In order to avoid micronutrient (iron, calcium, vitamin D, vitamin B\({}_{12}\), folate) deficiency after bariatric surgery, life-long multivitamin supplementation is necessary for post-bariatric patients. Since many patients show malabsorption for iron, a documented deficiency is best supplemented intravenously [91].