esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Reliability Analysis
### Sensitivity Analysis and Principal Stress for Determining Tensile and Compression Regions
#### 3.3.2 Derivative Sign Analysis: Sensitivity Analysis  
Taking the local calculation of the derivative of the von Mises stress of the element with respect to the components of the stress vector described respectively as  
\[\begin{split}\frac{\partial(\sigma_{x}^{\text{sm}})}{\partial x_{x}} =\frac{1}{2\sigma_{x}^{\text{sm}}}\big{(}2\sigma_{x}-\sigma_{y}-\sigma_{z} \big{)}\\ \frac{\partial(\sigma_{x}^{\text{sm}})}{\partial x_{z}}=\frac{1}{2 \sigma_{x}^{\text{sm}}}\big{(}2\sigma_{z}-\sigma_{x}-\sigma_{y}\big{)}\end{split} \tag{10}\]  
For \(\frac{\partial(\sigma_{x}^{\text{sm}})}{\partial x_{z}}\geq 0\) (blue color--ties) to be non-negative, it is necessary that \(2\sigma_{x}-\sigma_{y}-\sigma_{z}\geq 0\). This inequality will be satisfied when \(\sigma_{x}\) is sufficiently greater than the average stresses \(\sigma_{y}\) and \(\sigma_{z}\). This indicates that, in a tensile state where \(\sigma_{x}\) is the largest stress, as \(\sigma_{x}\) increases, the von Mises stress also increases, reflecting greater energy distortion in the material.  
For \(\frac{\partial(\sigma_{x}^{\text{sm}})}{\partial x_{z}}<0\) (red color--strut) to be negative, it is necessary that \(2\sigma_{z}-\sigma_{x}-\sigma_{y}\geq 0\). This inequality will be satisfied when \(\sigma_{z}\) is sufficiently smaller (more negative) than the average stresses \(\sigma_{x}\) and \(\sigma_{y}\). This indicates that, in a compressive state where \(\sigma_{z}\) is the largest stress in magnitude (most compression) becomes more negative, the von Mises stress decreases, reflecting less energy distortion in the material.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 8 Conclusions  
A two-stage framework for end-to-end fire detection and geo-localization using UAVs was described in this article. The initial phase was dedicated entirely to offline fire detection and utilized four YOLO models, including the two most recent models, YOLO-NAS and YOLO8, to determine which one was most suitable for fire detection. The models underwent training and evaluation using a compiled dataset comprising 12,530 images, in which regions delineating fire, non-fire, and smoke were manually annotated. The labeling required considerable time and effort. YOLO-NAS emerged as the best performing model among the four under consideration, exhibiting a modest superiority over YOLOv8 in each of the following metrics: precision, recall, F1_score, mAP50, and average class accuracy. YOLO-NAS was implemented in the second stage, which incorporated the analysis of real-life scenarios. In this stage, the images captured by a custom-built UAV were supplied to YOLO-NAS for the purposes of object detection and localization. Geo-localization was also considered by employing accurate camera calibration and depth estimation techniques. The test results were extremely encouraging and demonstrated the overall process's viability, although it could be enhanced in numerous ways. In the future, we intend to use optimization algorithms to fine-tune the hyper-parameters of the YOLO models, specifically YOLO8 and YOLO-NAS, in order to further enhance their performance. Furthermore, more advanced UAVs need to be used to evaluate the system in real-world forest fire settings.  
Conceptualization, K.C. and M.L.; data curation, F.B. and W.C.; methodology, S.M. and M.B.; software, K.C.; supervision, S.M., M.L. and M.B.; validation, M.B., K.C. and S.M.; writing--original draft, K.C.; writing--review and editing, M.B., M.L. and K.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R196), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
The datasets are available from the corresponding authors upon request.  
The authors would like to acknowledge the Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R196), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
_Algorithms_**2015**, \(8\), 466-483; doi:10.3390/a8030466  
_Keywords_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Review_  
**Conditional Random Fields for Pattern Recognition Applied to Structured Data**  
**[PERSON] 1,\({}^{\dagger,\dagger,\star}\) and [PERSON] 2,\({}^{\dagger}\)**  
\({}^{1}\) Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87544-87545, USA  
\({}^{2}\) Space Data Systems, Los Alamos National Laboratory, Los Alamos, NM 87544-87545, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-505-665-7865; Fax: +1-505-665-4078.  
Academic Editor: [PERSON]  
_Received: 16 April 2015 / Accepted: 25 June 2015 / Published: 14 July 2015_  
**Abstract:** Pattern recognition uses measurements from an input domain, \(X\), to predict their labels from an output domain, \(Y\). Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is \"manmade\" (such as a building) or \"natural\" (such as a tree). Suppose the label for a pixel patch is \"manmade\"; if the label for a nearby pixel patch is then more likely to be \"manmade\" there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling \(P(X)\) is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs) model structured data using the conditional distribution \(P(Y|X=x)\), without specifying a model for \(P(X)\), and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples.  
**Keywords:** conditional random fields; image analysis; pattern recognition
## 3 Seismic Control Effectiveness of the TID for Base-Isolated Structures
### Absolute Acceleration Response  
Controlling the absolute acceleration response of the structure is essential for protecting the contents within, making it a key factor in evaluating the effectiveness of the TID. Figure 14 illustrates the mean maximum absolute acceleration responses for the three base-isolated structures, both with and without the TID. Meanwhile, Figure 15 depicts the percentage reduction in the absolute acceleration response of the top storey. First of all, it is apparent that for the base-isolated structure, the acceleration response of the low structure with a shorter period is more serious, and the absolute acceleration about the top storey is generally the largest. In addition, the acceleration response of the base-isolated structure under the near-fault with pulse subset ground motion excitation is more severe, and the acceleration of some floors has exceeded the limit of 3 m/s\({}^{2}\). In terms of the control effectiveness of the TID on structural acceleration, it can be summarized as the following aspects. The higher the base-isolated structure or the longer the period of the base-isolated structure is, the worse control effectiveness of the TID is. The control effectiveness of the TID is best due to the far-fault ground motion excitation, and the worst under the near-fault with pulse subset ground motion excitation. Additionally, a general trend is that the mass ratio increases, the effectiveness the TID is in controlling the response also increases.  
Figure 13: Percentage reduction rate of the mean squared or peak displacement responses for the base storey of the three base-isolated structures with different mass ratio TIDs.  
Figure 14: Peak absolute acceleration response for each storey of the three base-isolated structures with and without different mass ratio TIDs under the three types of ground motion excitation.  
In general, for medium and low base-isolated structures that required to control acceleration response the acceleration response, it is a considerable solution to equip the TID in the isolation layer, and when the mass ratio of the TID is 0.2, the absolute acceleration of each storey can generally be controlled below the limit of 3 m/s\({}^{2}\).  
Based on the above analysis, it is evident that equipping TID in the isolation storey to control the dynamic response of the structure is an effective solution for low-rise base-isolated structures. However, while improving the mass ratio of TID can enhance the vibration reduction effect, its applicability to high-rise base-isolated structures remains limited. In light of this, it may be worth considering the arrangement of inerter-based vibration control devices between the storeys of high-rise structures or between adjacent structures to achieve improved vibration control [59; 60].
## 3 Results
### GMM Classifier  
Cross validation over the training data set was used for investigating and selecting the choices of parameters for the GMM models. These parameters include the number of Gaussian distributions needed to model each class, and the type of covariance matrix used (diagonal or full symmetrical). Our results show that 12 Gaussian components are needed to model the GMM of the apneic class and 11 Gaussian components are needed to model the GMM of the normal class along with diagonal covariance matrices for both GMMs.
# A Compact Printed Monopole Antenna for WiMAX/WLAN and UWB Applications
###### Abstract  
In this paper, a printed monopole antenna design for WiMAX/WLAN applications in cable-free self-positioning seismograph nodes is proposed. Great improvements were achieved in miniaturizing the antenna and in widening the narrow bandwidth of the high-frequency band. The antenna was fed by a microstrip gradient line and consisted of a triangle, an inverted-F shape, and an M-shaped structure, which was rotated 90\({}^{\circ}\) counterclockwise to form a surface-radiating patch. This structure effectively widened the operating bandwidth of the antenna. Excitation led to the generation of two impedance bands of 2.39-2.49 and 4.26-7.99 GHz for a voltage standing wave ratio of less than 2. The two impedance bandwidths were 100 MHz, i.e., 4.08% relative to the center frequency of 2.45 GHz, and 3730 MHz, i.e., 64.31% relative to the center frequency of 5.80 GHz, covering the WiMAX high-frequency band (5.25-5.85 GHz) and the WLAN band (2.4/5.2/5.8). This article describes the design details of the antenna and presents the results of both simulations and experiments that show good agreement. The proposed antenna meets the field-work requirements of cable-less seismograph nodes.  
antenna design; impedance bandwidth; WiMAX/WLAN band; compact printed monopole antenna +
Footnote †: journal: Future Internet
## 2 Theory and Methods
### Combination Ordered Weighted Averaging Operator  
The ordered weighted average (OWA) [19] is presented by Professor [PERSON]. The combination ordered weighted averaging (C-OWA) improves on OWA. The importance of each indicator in the C-OWA operator has an impact on the indicator weights; thus, it could cause weighting results to be more objective. The coding values of the expert in MFIM is calculated using the C-OWA operator in this paper, and the steps for calculating are as follows:  
1. Invite five experts to score the interrelationship of indicators from 0-4 as coding values shown in Table 1. The higher the score, the stronger the interaction relationship. Additionally, the results form an initial matrix as \(A=[a_{1},\,a_{2},\,\cdots,\,a_{m}]\).
2. The initial matrix \(A\) is arranged in descending order to obtain the new data descending order matrix \(B=[b_{0},\,b_{1},\,\cdots b_{n},\,\cdots,\,b_{m-1}]\). Additionally, \(b_{0}\geq b_{1}\geq\cdots\geq b_{n}\geq\cdots\geq b_{m-1}\). Suppose the effort vector of \(\mathsf{b}_{0}\) is \(w=[w_{1},w_{2},\,\cdots\,w_{h}\,\cdots,\,w_{m}]\). It is calculated by the following formula: \[w_{h+1}=\frac{C_{m-1}^{h}}{\sum\limits_{h=0}^{m-1}C_{m-1}^{h}}=\frac{C_{m-1}^{ h}}{2^{m-1}}\] (5) where \(m\) is the number of experts. \(w_{h+1}\) is the effort vector of \(b_{h}\). \(C_{m-1}^{h}\) is the number of combinations of rows \(h=0,\,1,\,\cdots,\,m-1\).
3. The effort value \(P_{i}\) is defined as: \[P_{i}=w_{1}b_{0}+w_{2}b_{1}+\cdots w_{m}b_{m-1}=\sum\limits_{h=0}^{m-1}\left[ (w_{h+1})^{T}\cdot b_{h}\right]\] (6) where \(P_{i}\) is the active or passive effort value of the \(i\)-th indicator. \(h=0,\,1,\,\cdots,\,m-1\).
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 3 Results
### NEE Modeling  
NEE values measured in-situ (Section 2.2) were related to various in-situ and satellite data to derive prediction models. The analyses were performed for particular vegetation habitat types (Figure 2) separately. The best models were found by applying Sentinel-1 to both VH and VV polarization (Table 7). These were multiple linear regression models. Since the \(p\)-value was less than 0.05, there is a statistically significant relationship between the variables at the 95.0% confidence level. The R\({}^{2}\) indicates that the model explains no more than 51%-58% of the variability in NEE. Thus, these preliminary results have to be validated and the model has to be corrected to improve statistical correlation. Equations from Table 7 have been applied for the NEE mapping over the Biebrza Wetland. Figures 12 and 13 present maps of NEE distribution over classified wetland habitats for two dates: 11 June 2015 and 10 July 2015. The NEE values varied from 4 to \(-\)10 \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\cdot\)s\({}^{-1}\). The prevailing NEE positive values (CO\({}_{2}\) emission to atmosphere, mean from 1 to 4 \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\cdot\)s\({}^{-1}\)) at the date 11 June 2015 belong to grass, grass-herbs, and sedges habitats, the biomass values of which were low at that time (harvested or grazed). For the date 10 July 2015 CO\({}_{2}\) absorption dominated (NEE negative values, mean from \(-\)1 to \(-\)10 \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\cdot\)s\({}^{-1}\)) in the most moist habitats, i.e., needs and sedge-moss, while emission was noted for the lower soil moisture values (NEE values were positive, mean: 0-2 \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\cdot\)s\({}^{-1}\)).  
Figure 11: 3D-area plot for Equation (3).  
Figure 12: Map of NEE distribution over Biebrza wetland on 11 June 2015.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Platform** & **Habitat** & **R2** & \begin{tabular}{c} **Standard Error** \\ **of Est.** \\ \end{tabular} & \(p\)**-Value** & \multicolumn{2}{c}{**Equation**} \\ \hline  & Reeds & 0.57 & 2.75 & 0.0043 & NEE = \(-42.85-4.25\times\sigma^{\circ}\)SiVH + 2.71 \(\times\sigma^{\circ}\)SiVV \\ Sentinel-1 & \
\begin{tabular}{c} Sedges-moss \\ \end{tabular} & 0.58 & 2.02 & 0.0057 & NEE = \(-7.22+1.42\times\sigma^{\circ}\)SiVH + 2.74 \(\times\sigma^{\circ}\)SiVV \\ IW VH VV &
\begin{tabular}{c} Sedges \\ Grass-herbs \\ \end{tabular} & 0.51 & 1.57 & 0.0024 & NEE = \(-2.28-0.86\times\sigma^{\circ}\)SiVH + 1.08 \(\times\sigma^{\circ}\)SiV \\  & Grass & 0.55 & 2.75 & 0.0072 & NEE = \(-2.75+1.87\times\sigma^{\circ}\)SiVH + 0.65 \(\times\sigma^{\circ}\)SiVV \\  & Grass & 0.55 & 3.07 & 0.0001 & NEE = \(11.22+2.34\times\sigma^{\circ}\)SiVH + 2.72 \(\times\sigma^{\circ}\)SiVV \\ \hline \hline \end{tabular}
\end{table}
Table 7: NEE (Net Ecosystem Exchange) models at 95% confidence level derived by applying in-situ and Sentinel-1 satellite data.  
Figure 13: Map of NEE distribution over Biebrza wetland on 10 July 2015.
# Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors
## 2 NCV-CPM Methodology  
### Construction of Strain-Arc Curvature Function for Sensing Points  
Since the variable wing leading edge only undergoes bending deformation [24], and the morphological changes along the \(Y\)-direction remain consistent throughout the deformation process, it is sufficient to select a section along the circumferential direction of the wing leading edge as the research object, as illustrated in Figure 1a. In the selected circumferential section of the leading edge, any arbitrary arc segment can be chosen for analysis, as depicted in Figure 1b.  
When the wing leading edge is undeformed, the curvature radius of the neutral layer within the selected arc segment is denoted as \(R_{1}\), and its corresponding arc angle is \(\varphi\), with a skin thickness of \(h\). With the internal actuation mechanism driving the bending deformation of the wing leading edge, the curvature radius of the neutral layer within the  
Figure 1: (a) Section selected in the leading edge of the variable wing; (b) comparison of the selected section before and after deformation.  
selected arc segment after deformation is defined as \(R_{2}\), with the corresponding arc angle being \(\varphi-\mathrm{d}\theta\), while the skin thickness remains unchanged at \(h\).  
\[\varepsilon=\frac{\left(R_{2}+\frac{h}{2}\right)(\varphi-\mathrm{d}\theta)- \left(R_{1}+\frac{h}{2}\right)\varphi}{\left(R_{1}+\frac{h}{2}\right)\varphi} \tag{1}\]  
Assuming that the arc length of the neutral surface within the selected arc segment remains constant during the deformation process [25]  
\[R_{2}(\varphi-\mathrm{d}\theta)=R_{1}\varphi \tag{2}\]  
Substituting Equation (2) into Equation (1), the curvature radius \(R_{2}\) of the neutral layer after deformation for the selected arc segment can be derived:  
\[R_{2}=\frac{hR_{1}}{2R_{1}\varepsilon+h\varepsilon+h} \tag{3}\]  
Therefore, the surface curvature \(q\) of the selected arc segment after deformation can be expressed in terms of the measured strain \(\varepsilon\) at the measurement point, the skin thickness \(h\), and the radius \(R_{1}\) of the neutral layer before deformation at the wing leading edge, as shown in Equation (4).  
\[q=\left(R_{2}+\frac{h}{2}\right)^{-1}=\frac{h+2\varepsilon R_{1}}{(1+ \varepsilon)hR_{1}} \tag{4}\]  
Defining Equation (4) as the \"Strain-Arc Curvature Function,\" in subsequent validations of leading-edge morphing, the strain data from the surface-sensing measurement points on the leading-edge skin, along with the corresponding curvilinear angles of each arc segment on the wing leading edge, are inputted into this function model. This enables the precise calculation of curvature information at the discrete locations of the sensing points.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 4 Analysis of Discharge Risk Coefficients of Overhead Conductors Under Different Icing Conditions
### Influence of Overhead Line Structure on Safety Distances  
The safety distance of the guide line is crucial for ensuring the safe operation of power lines. A smaller pitch results in a tauter guide wire with reduced sag, thereby maintaining a larger safety distance. However, this change also increases tower quantity, construction cost, and maintenance complexity. Conversely, a larger pitch causes the guide wire to sag more, potentially reducing the safety distance. By varying the pitch, we analyzed the maximum sag, conductor jump height and the risk interval for 30 mm of ice cover. The results are summarized in Table 7 and Figure 9. Each experiment required recalculating the static sag without ice cover due to changes in pitch.  
As the gear distance increased from 200 to 600 m, both the maximum sag of the ground and the conductor rose, peaking at 29.156 and 34.551 m, respectively. The conductor's de-icing bounce height grew from 6 to 33 m, an increase of 27 m. For gear distances of 200 and 300 m, the minimum distance between the conductor and the ground line exceeded the safe distance of 2 m, with a risk coefficient of less than 1, indicating no risk. However, at a gear distance from 400 to 600 m, the conductor's bounce height surpassed the lowest point of the ice-covered ground line, reducing the minimum distance between the conductor and the ground line to 2 m, with a risk coefficient of 1. Consequently, the risk zone expanded from the 197 m range to 442 m, with the 600 m gear distance resulting in a risk area of 73.667%. Therefore, during installation, selecting an appropriate gear distance is crucial to mitigate risk expansion.
## 3 Results
### Assessment by Vibrometry Approach
The values of the frequency \(f_{1,1}\) are consistent with expectations based on the bending velocity dispersion curve as determined in Figure 4 and Equation (10): setting the bending wavelength components \(\lambda_{x}/2=L_{x}\) = 3.49 m, \(\lambda_{y}/2=L_{y}\) = 3.09 m, using \(k\) = (\(k_{x}\)\({}^{2}\) + \(k_{y}\)\({}^{2}\))\({}^{1/2}\), with \(k_{x}\) = 2\(\pi\)\(\pi\)/\(\lambda_{x}\) and \(k_{y}\) = 2\(\pi\)/\(\lambda_{y}\), and inserting the best fitting dispersion curve \(c_{B}(f)\) = a\(\sqrt{f}\), with a = 38 m/s\({}^{3/2}\) in \(k\) = 2\(\pi\)\(\pi\)/\(c_{B}(f)\), the expected structural mode frequency is \(f_{1,1}\) = (a \(k\)/(2\(\pi\))) \({}^{2}\) (\(L_{x}\)\({}^{-2}\) + \(L_{y}\)\({}^{-2}\)) = 67 Hz. Below this frequency, the radiation efficiency increases 12 dB/octave, which is in accordance with theoretical behavior of a wall vibrating in its fundamental 1-1 mode (Fahy [50]). The width of the structural vibration induced maximum in the radiation efficiency is determined by the structural damping. The behavior below \(f_{1,1}\) and above \(f_{c}\) are known to be independent of the loss factor [45; 58]. Apart from this feature, the radiation efficiency rises to the coincidence (critical) frequency region, which, consistent with the previous analysis, sets in around 160 Hz. As explained in Section 2.2, the radiated sound power spectrum calculated from the ODS at different frequencies (Equation (5)) is independent of the room acoustic features of the receiving room and is thus an adequate measure for the pure acoustic insulation characteristics of the wall structures of interest. The availability of this result together with the insulation spectrum as obtained by classical microphone measurements have motivated us to compare both and thus to assess in detail  
Figure 4: Dispersion data obtained by ODS and 2D dispersion data fitting approaches: mean of the wave number (**a**); and wave propagation velocity (**b**) dispersion curve data and best fit for Wall 1 and Wall 2. The symbols were obtained by estimating the wavelength of the modes from the ODS. The dotted line in (**b**) depicts the speed of sound in air. The crossing point between the dispersion curve and the dotted line indicates where coincidence occurs. Figure 5: Spectrum of the radiation efficiency, extracted from the pattern of the measured normal velocity component and the calculated sound power. the effects of room acoustics on the sound characteristics in the receiving room and on the results of classical measurements.