esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Slip Detection  
#### 2.3.1 Optical Flow  
The slip detection process relied on optical flow estimation between two frames, which were masked by the ellipse to approximate the general outline of the object. The magnitude and direction of the flow were then calculated to approximate the motion field of the object. For the optical flow calculations, the open-source computer vision library, OpenCV [17], was utilized. To accommodate less textured objects, the Farneback optical flow algorithm [18] was chosen over alternative methods like Lucas-Kanade [19], which would have been computationally less intensive. The Farneback algorithm is described in more detail in the next paragraph. Feature-based methods work effectively on printed or textured surfaces; however, many everyday objects lack stable features, especially since the exact borders of the object are often not included after the masking. In contrast, for textured objects, feature-based flow estimation is a viable alternative that is computationally less intensive. Nonetheless, the Farneback algorithm for dense optical flow estimation proved to be fast enough to run in near real time. The chosen parameters for the Farneback optical flow are as follows :  
* Pyramid Scale: 0.5;
* Level: 3;
* Window Size: 15;
* Iterations: 3;
* Poly n: 5;
* Poly \(\sigma\): 1.2.
## 5 Empirical Case Study: Courier Company in Bangkok
### Didactic Example  
Let us start with an example consisting of 10 zones. Figure 1 contains three subfigures. Figure 1a illustrates the original distribution of delivery locations and driver delivery zones across the service region. Figure 1b displays the adjusted delivery zones to eliminate vehicle time violations. Figure 1c presents a graph with lines tracking changes in violation amount when applying the route adjustment algorithm by keeping track of the adjusted working time violation of each vehicle while going through Phase II as a function of the swap number.  
Initially, after Phase I, the assignment of customers using a vehicle-zone map leads to three vehicles with working time violation (those initially having positive time violation), namely, vehicle numbers 1, 3 and 9. Applying the route adjustment algorithm in Phase II (Algorithm 2), the first swap moved a customer from vehicle 1 to vehicle 5. Starting from swap 2, customers were moved from vehicle 1 to vehicle 8 until the target vehicle 8 has no available working time left. From swap 39 onward, customers from vehicle 3 were assigned to vehicle 2 until swap 71 when the target vehicle 2 was full. The algorithm continued until no vehicle has time violation using a total of 128 swaps to solve the problem. Post-adjusted assignment is given in Figure 0(b). Two untouched vehicles were vehicles 4 and 7 as the zones corresponding to these two vehicles are relatively far away from those vehicles with time violation. Another important observation is that it is possible that the working time of a target vehicle reduces after having a customer inserted. This is due to the fact that the shortest travel time according to the traffic condition between any two locations is used as a distance function as opposed to a road distance. For example, this happened at swaps 40 and 42.
# GC-MS Analysis of the Volatile Constituents in the Leaves of 14 Composite Plants
## 2 Results
### Volatile Composition Patterns in 14 Composite Plants  
Although the n-hexane extracts revealed different chemical compositions, the terpenoid components (40.45-90.38%) were generally the predominant compounds in most samples. Figure 3 reveals that except for _A. vulgaris_ \"Variegate' (**S3**), _A. sericea_ (**S7**) and _C. chinense_ (**S12**), with lower relative terpenoid contents, the other 11 species contained terpenoid contents that accounted for more than 60% of the total compound contents. Furthermore, oxygenated terpenoids, accounting for 2.98-72.54% of the total compounds, were the main type of terpenoids in most samples. As exceptions, _A. sacrorum_ (**S1**), _A. vulgaris_ \"Variegate' (**S3**) and _A. japonica_ (**S5**) contained sesquiterpenes as the predominant terpenoid (55.45%, 30.60%, and 46.68% of the total compounds, respectively), and _A. absinthium_ (**S2**) had the highest relative content of monoterpenes (42.29%). Considering the detected oxygenated terpenoids, consisting of oxygenated monoterpenes (0.00% to 70.27% of the total compounds), oxygenated sesquiterpenes (0.00% to 53.53% of the total compounds), oxygenatediterpenes (0.00% to 2.69% of the total compounds) and minor oxygenated triterpenes (0.00% to 3.87% of the total compounds), together with the terpenes, _C. indicium_ var. _aromatic_ (**S10**) and _A. yunnanensis_ (**S4**) possessed the highest relative contents of monoterpene (70.27%) and sesquiterpene (68.71%) derivatives among the total compounds in their leaves. Diterpene derivatives were only found in the leaves of _A. absinthium_ (**S2**), _A. yunnanensis_ (**S4**) and _A. abrotanum_ (**S6**), accounting for 10.53%, 0.78% and 0.84%, respectively. Negligible amounts of oxygenated triterpenes (0.37%, 3.87%, and 0.48%) were detected in leaves of _A. yunnanensis_ (**S4**), _C. chinense_ (**S12**), and _O. taihangensis_ (**S14**), respectively. The remaining constituents, such as aromatic compounds, also contributing to scent only accounted for 0.00% to 14.53% of the total compounds, with the highest relative content present in the leaves of _C. chinense_ (**S12**).
## 4 Results
### Spatial Autocorrelation Analysis of Vegetation Dynamics in the ARB
The most significant changes in the clustering of GPP were found in Mohe County, the far most northern part of the Inner Mongolia Autonomous Region of China and the nearby border region with Russia; these areas exhibited a significant increase in high-high clustering. The Relationships between Vegetation Dynamics and Changes in Climatic, Anthropogenic, and Hydrological Factors  
As previously mentioned in Section 3.1, in order to analyze the relationship between vegetation status and climate changes, human activities and hydrological elements in  
Figure 5: Clustering maps of local Moran’s I of LAI, FVC and GPP in the ARB for four studied periods: (**a–d**) show clustering maps of LAI for Periods 1–4, respectively. (**e–h**) and (**i–l**) are the same as (**a–d**) of four studied periods, but for clustering maps of FVC and GPP, respectively. the ARB, area-average climatic factors, land use area ratios, and simulated hydrological factors for 134 sub-basins--averaged annually over four phases--were used as explanatory variables to represent the environmental status of the ARB at different geographic locations and at different times. The statistical results of the explanatory variables in each sub-basin at different time periods are shown in Table 3.  
However, analysis of the variance inflation factor (VIF) suggested a strong multicollinearity between these independent variables (i.e., VIF > 10). In order to obtain more stable and accurate model results, a global analysis of relationships between vegetation indexes and environmental factors in the ARB were conducted using the PLSR model [93; 94]; an appropriate model suitable for cases where multicollinearity between the independent or dependent variables exists. The independent variables were filtered using variable importance in the projection (VIP) values. The PLSR model results are summarized in Table 4, where strong (Adjusted R\({}^{2}\) >0.8) relationships between vegetation indexes and environmental factors over the ARB can be observed. The VIP values of the independent variables are shown in Table 5. Independent variables with VIP values greater than one are usually considered as relatively important in a PLSR model. For further analysis, the independent variables with VIP values greater than 0.85 (Pcp, Tmax, Forest, Pasture, Surface runoff (SURQ), Amount of water stored in the soil profile (SW), and Snowmelt (SM)) were used as inputs for the GTWR model. Regionally averaged sub-basin LAI, FVC, and GPP values were used as dependent variable inputs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Latent Factors (LF)** & **Y Variance** & \(\mathbf{R^{2}}\) & **Adjusted R\({}^{2}\)** \\ \hline
1 & 0.665 & 0.665 & 0.665 \\
2 & 0.097 & 0.762 & 0.761 \\
3 & 0.062 & 0.825 & 0.824 \\
4 & 0.008 & 0.833 & 0.832 \\
5 & 0.004 & 0.837 & 0.835 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the partial least squares regression (PLSR) modeled results over the ARB.
## 7 Testbeds and Experimental Resources
**TWIST**[160] is an open access test bed having 204 nodes, middleware support heterogeneous devices and allows integration with Cooja Simulator. TWIST testbed provides indoor experimentation facility and consist of TelosB and eyesIFX nodes. **IoT Lab**[161] provides a very large scale open source facility for testing and evaluating wireless sensor heterogeneous devices and software architectures. It consists of 2700 wireless sensor nodes spread across 6 different cities. IoT Lab's middleware supports virtualization and provides high level APIs for drivers, OS and libraries for programming and testing algorithms and protocols. Furthermore, IoT Lab testbed allows development of the applications and middlewares on top the infrastructure provided. Moreover, it can be testbed with or without the OS support. Table 3 shows the testbeds that support virtualization; it also shows the devices that are used by the testbed. Furthermore, the number of devices that are part of the testbed and the services offered by the testbed. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Testbed**} & \multirow{2}{*}{**Device Used**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Services Offered**} & \multicolumn{2}{c}{**Virtualization**} \\  & & & **Sensor Nodes** & & **Support** \\ \hline \multirow{3}{*}{University of Lübeck Smart Stender [141]} & \(\bullet\) & iSense & & \(\bullet\) & Environment & \\  & \(\bullet\) & TelosB & 200 & \(\bullet\) & Light & \\ \cline{1-1}  & \(\bullet\) & Pacemate & & \(\bullet\) & Security & \\ \cline{1-1}  & \(\bullet\) & & & \(\bullet\) & Accelerometer & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Testbeds for evaluation of middleware designs.
# Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field
## 4 A Prototype Network-Based Risk Level Identification Model for Lane Change
### Theory  
The proposed model applies all instances to a network that is similar to a typical prototype network, and it uses a neural network to generate feature vectors (class prototypes). The embeddings of the query instances are categorized in accordance with their distance from the relationship prototype.  
By preprocessing the data, a new dataset is generated. According to the prototype network, the data set is separated. The support sets, \(S_{B}\), refer to distinct classes, each of which has labeled data. The query sets, \(Q_{B}\), have unlabeled data. It is important to note that each dataset contains support sets and query sets with the same classes. This issue is known as the \"n-way k-shot\".  
During the meta-testing process, new categories, \(N\), with minimal diversification are added. The model test results are taken as input. With the set of support sampling \(S_{N}\), the model aims to classify each unmarked research data, \(Q_{N}\). It can be described such that \(\mathbb{D}=\{S_{B},Q_{B}\}=(x_{1},\mathbb{E}_{1}),(x_{2},\mathbb{E}_{2}), \ldots,(x_{n},\mathbb{E}_{n})\), where \(x\) is the input risk characteristic parameter, which constitutes the category of data set required for the test to add physiological data and scattered classification data as additional cases. For each category, the generated class prototype is shown in Equation (12).  
\[c_{m}=\frac{1}{|S_{m}|}\sum_{(x_{i},y_{i};S_{m})}\mathcal{F}_{\phi}(x_{i}) \tag{12}\]  
Figure 5: Vehicle lane change risk level assessment model.  
Figure 6: Prototype network for few-shot classification.  
Meanwhile, the distance formula between samples is the Euclidean distance formula, as shown in Equation (13). The probabilities of the relations for the query instance can be computed as \(x\), as shown in Equation (14).  
\[d=\sqrt{\sum_{k=1}^{n}(x_{1k}-x_{2k})^{2}} \tag{13}\]  
\[\mathbb{P}_{w}(y=m_{i}\mid x)=\frac{exp(-\mathcal{D}(\mathcal{F}_{\phi}),c_{m_{ i}})}{\sum_{j}exp(-\mathcal{D}(\mathcal{F}_{\phi}),c_{j})} \tag{14}\]  
where \(\mathcal{D}\) is the Euclidean distance function used to calculate the distance between two given vectors. Finally, the prediction label y of the sample x to be measured is obtained, as shown in Equation (15).  
\[y=\sum_{m=1}^{h}P_{w}(\mathcal{F}_{\phi}(x),c_{m_{i}}),y_{m} \tag{15}\]  
The loss function \(\mathbb{L}(\diagup)\) is shown in Equation (16).  
\[\mathbb{L}(\diagup)=-log\mathbb{P}_{w}(y=m_{i}\mid x) \tag{16}\]  
The original feature map insertion network is optimized by maximizing the probability of the correct classification of cases and minimizing the loss function.
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 4 SNR Estimators
### Performance Index  
Two estimators are simulated in the MATLAB simulation platform and the SNR fitting curve is drawn. The abscissa is the SNR value of the theory. The ordinate is the SNR value of the estimate, which can determine whether the SNR estimators are unbiased. The main index to measure the performance of SNR estimators in the simulation is NMSE, which can be expressed as  
\[NMSE(\hat{\rho})=\frac{E[(\hat{\rho}-\rho)^{2}]}{\rho^{2}}, \tag{38}\]where \(\rho\) is the SNR value of the theory and \(\hat{\rho}\) is the SNR value of the estimate. The estimator performs better when the NMSE is smaller.  
The CRLB provides absolute lower limit of the estimation. The NMSE can be compared with the CRLB to illustrate the absolute performance of the estimator. A parameter vector \(\boldsymbol{\beta}=\begin{bmatrix}S&N\end{bmatrix}^{T}\) is defined as the relationship between SNR, and the parameter vector is  
\[g(\boldsymbol{\beta})=\rho=\frac{S}{N}, \tag{39}\]  
where \(S=\left(\frac{A}{m_{1}}\right)^{2}\) is the signal power and \(N=\delta^{2}\) is the noise power. The formula given by [PERSON] in Reference [26] shows that  
\[\mathrm{var}(\rho)\geq\frac{\partial g(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}}I^{-1}(\boldsymbol{\beta})\frac{\partial g(\boldsymbol{ \beta})^{T}}{\partial\boldsymbol{\beta}}, \tag{40}\]  
where \(\frac{\partial g(\boldsymbol{\beta})}{\partial\boldsymbol{\beta}}I^{-1}( \boldsymbol{\beta})\frac{\partial g(\boldsymbol{\beta})^{T}}{\partial \boldsymbol{\beta}}\) is the CRLB. \(I(\boldsymbol{\beta})\) is the Fisher Information Matrix, which can be expressed as  
\[I(\boldsymbol{\beta})=\left[\begin{array}{cc}-E(\frac{\partial^{2}\ln p( \boldsymbol{t})}{\partial S^{2}})&-E(\frac{\partial^{2}\ln p(\boldsymbol{t} )}{\partial S\partial N})\\ -E(\frac{\partial^{2}\ln p(\boldsymbol{t})}{\partial N\partial S})&-E(\frac{ \partial^{2}\ln p(\boldsymbol{t})}{\partial N^{2}})\end{array}\right]. \tag{41}\]  
When the SNR is low (0-6 dB), the NDA CRLB is higher than DA CRLB; however, the two lower bounds tend to coincide with the increase of SNR (greater than 6 dB) [27]. The CRLB of the DA SNR estimation is simple and unaffected by the shaping filter and sampling. Thus, this study uses DA CRLB as the reference of absolute performance. The CRLB of the complex signal can be expressed as [28]  
\[NMSE(\hat{\rho})\geq\frac{1}{K}(\frac{2}{\rho}+1). \tag{42}\]  
MATLAB tools are used to simulate and analyze the above theoretical models. The data source produces a binary sequence and sequenced through the CPM modulator. Transmitted the modulation signal to the Ka-band fading channel in a number of frames in the range of SNR. Seven weather conditions are generated according to the numerical information in Table 1. After the fading factor is obtained, the fading channel simulation model is established by using the theoretical channel model in Section 2. The SNR is estimated according to Formulas (28) and (35) in Section 4. Numerical results and simulation performance are shown in Section 5.
## 3 Methodology
### Data Enhancement Based on Diffusion Model  
This study mainly aims to build a data-driven SHM system, which, most importantly, needs data support. Considering the limitations of the experimental conditions and the specific conditions of the experimental object, some experimental signals have poor detection effects, and some experiments have insufficient signal detection to build a sufficient number of data sets for training. It may have certain impact on the training of the experimental model, and will also have a certain interference with the analysis of the experiment. At the same time, the experimental data has uneven samples, so we consider expanding and enhancing some data to increase the number of samples. Data enhancement is also called data amplification, which means that limited data can generate the value equivalent to more data without substantially increasing data. These can expand the dataset, improve the robustness of the model, improve the generalization ability of the model and avoid sample imbalance. Now, we mainly consider testing several generation models in this study, including Generic Adversary Network (GAN), Variable Automatic Encoder (VAE) and diffusion models (DM).
# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 2 Methods  
### POCS Method  
POCS is an ensemble-based theoretical method that can flexibly utilize all kinds of prior knowledge, and the relevant prior knowledge can be used as constraints for image reconstruction [23]. The method is an iterative process; the projection operator of the corresponding constraint set projects a point in the solution space to the point closest to the surface of the convex set and repeatedly performs the iterative operation to obtain a solution that converges to the intersection of the convex constraint set [24].  
When applying the POCS method for the super-resolution reconstruction of range profiles, it is necessary to establish a link between the original low-resolution range profile sequence and the high-resolution range profile, as shown in Equation (1):  
\[d_{k}(x,y)=\sum_{(i,j)}D(i,j)\cdot h(x,y;i,j)+\mu(x,y) \tag{1}\]  
where \(d_{k}(x,y)\) is the \(k_{th}\) frame of the low-resolution (LR) observation range profile, the \(D(i,j)\) is the original high-resolution (HR) range profile, \(h(x,y;i,j)\) is the point spread function (PSF), and \(\mu(x,y)\) is additive noise.  
\(C_{k}(i,j)\) is the data consistency constraint, which is the basic constraint for recovery reconstruction, defined as shown in Equation (2):  
\[C_{k}(i,j)=\{D(i,j):|r_{d}(x,y)|\leq\delta\} \tag{2}\]  
\(\delta\) is the correction threshold and \(r_{d}\) indicates the residual difference between the low-resolution distance value and the reference high-resolution estimated distance value, calculated as shown in Equation (3):  
\[r_{d}(x,y)=d(x,y)-\sum_{(i,j)}D(i,j)\cdot h(x,y;i,j) \tag{3}\]  
For any point \(D(i,j)\) in the reference high-resolution range profile, the projection under constraint \(C_{k}(i,j)\) is satisfied:  
\[D^{\prime}(i,j)=D(i,j)+\left\{\begin{array}{lcl}\frac{r(x,y)-\delta}{\sum \limits_{i}\sum\limits_{j}h^{2}(x,y;i,j)}\cdot h(x,y;i,j)&,&r(x,y)>\delta\\ \\ 0&,&|r(x,y)|\leq\delta\\ \\ \frac{r(x,y)+\delta}{\sum\limits_{i}\sum\limits_{j}h^{2}(x,y;i,j)}\cdot h(x,y; i,j)&,&r(x,y)<-\delta\end{array}\right. \tag{4}\]
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 3 Health Monitoring in Corrosion
### Conventional Corrosion Sensors
#### 3.1.1 Corrosion  
A corrosion coupon involves placing a certain patch of a material for a certain corrosive environment. The coupon is designed to be able to satisfy our needs to realize the rates of corrosion. It has a known weight and shape. The weight it loses is measured to find out the actual damage done by corrosion [68]. This type of technique is unable to give an analysis of corrosion when it actually happens and just gives a mean rate of the corrosion that would take place. The coupon is normally placed within the working material, and hence, another one of its drawbacks includes the fact that it is difficult to remove and then to be placed again (Figure 2). Its versatility lies in the fact that any shape or weight can be used to find out the corrosion rate.
## 9 Conclusions  
This study investigated the technical problems that occurred in the field application of AROS and implemented techniques to resolve these issues. Focus group interviews revealed that the functions provided in AROS had high utilization rates for safety and quality inspection-related tasks. However, the model projections were unstable, revealing cases of miscalibration and projection losses, i.e., showing limitations in their projection stability and placement accuracy.  
Projection stability was improved by reducing the number of triangle meshes of the model to relieve the need for processing power. Specifically, simplification rates were investigated to formalize the optimal percentages depending on the shape compositions of the model. The results revealed that simplification rates of 20 and 40% were optimal for models with and without any curvilinear components, respectively. Post-evaluations revealed that these rates allowed stable model projections for prolonged inspection times in AROS.  
Placement accuracy was improved by implementing a hybrid of target anchoring methods. Specifically, ARWorldMap was used to add additional anchor points, which were identified from the topologies of the structure and surrounding planes. Post-evaluations revealed that mean displacement errors along the longitudinal (x-y plane) and transverse (x-z plane) were improved by +0.069 and +0.097 m, respectively, satisfying the target accuracy threshold. The highest accuracy was observed at a projection distance of 9 m.  
However, these solutions need to be tested on bridges of a larger scale and under construction, as well as incorporating LOD and model segmentation approaches to relieve the burden of model rendering in handheld devices. These issues are planned for future work.  
This study focused on verifying the practical applicability of using AR in the field, which has not been rigorously documented or previously demonstrated. Expert evaluations revealed the importance of projection stability and accuracy in making AR a feasible inspection tool, specifically for infrastructure projects. In this regard, this study contributed to accelerating the use of AR technology in the construction industry by formalizing ways to use optimal simplification rates and a hybrid target method to improve model projections in the field.  
**Author Contributions:** Supervision, B.K.; Writing--original draft, Y.Y. and H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Korea Agency for Infrastructure Technology Advancement (KAIA) (grant no. 22 RBIM-C158183-03).  
**Institutional Review Board Statement:** Not applicable.