esa-sceva/satcom-chunk-collection huggingface dataset:
# Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches
## 5 Conclusions  
In this paper, a novel PBCRF model is proposed for ISS with image-level labels, which provides an effective solution to the weakly supervised ISS problems. It has three advantages over existing approaches. First, based on the improved SLIC algorithm, optimal numbers of superpixels are automatically estimated for different images for improving the accuracy of image segmentation rather than using a fixed parameter. Second, by taking an image patch as the basic processing unit of ISS, this has significantly improved the performance and reduced the computational costs. Last but not the least, by combining category correlation and similarity information of each semantic category in training the PBCRF model, the inference of semantic label is transformed into the problem of minimizing a potential energy function. Extensive experimental results conducted on the MSRC and PASCAL VOC 2012 datasets segmentation benchmark have demonstrated that the proposed IPCRFWSS algorithm can produce improved or comparable results in comparison to a few state-of-the-art, even some deep learning methods. An improved or much higher mIoU along with a lower variance has also indicated the proposed approach is more robust to different semantic categories. To further improve the results in semantic image segmentation, we will focus on three topics in the future. The first is fusion of color, edge and other information for refined segmentation [47; 48], and the second is saliency based extraction of objects from images [49; 50]. The third direction is deep learning based image segmentation and object detection, where convolutional neural networks and other models will be explored [51; 52], even in combination with the first two topics such as multiscale segmentation and extreme learning machines [53; 54].  
Conceptualization, X.X. and Y.X.; methodology, X.X.; software, Y.X.; validation, X.X. and Y.X.; formal analysis, X.H.; investigation, Z.Z.; resources, X.X. and J.R.; data curation, Y.X.; writing--original draft preparation, X.X.; writing--Review and editing X.X., Y.X. and J.R.; visualization, X.X. and Y.X.; supervision, J.X.; project administration, X.X.; funding acquisition, X.X., X.H., J.X. and J.R. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (21606159), the Natural Science Foundation of Shanxi Province (201801D121144, 201801D221190) the Key Research and Development Program of Shanxi Province (201803D121039), Hundred Talents Program of the Shanxi Province, China.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Mobility Indicator Design
#### 2.2.3 Link Duration  
The link duration (\(LD\)) is the link's average duration between two nodes \(x\) and \(y\). It is a measure of link stability between nodes, which can be calculated as follows:  
\[LD(x,y)=\left\{\begin{array}{ll}\frac{\sum_{t=1}^{T}C(x,y,t)}{linkchange(x,y)}&linkchange(x,y)\
eq 0\\ \sum_{t=1}^{T}C(x,y,t)&otherwise\end{array}\right. \tag{9}\]  
In which \(C(x,y,t)\) is the indicator random variable which has a value of 1 if there is a link between nodes \(x\) and \(y\) at time \(t\), otherwise it is 0, and \(linkchange(x,y)\) is the number of link changes for a pair of nodes \(x\) and \(y\), which is the number of times the link between them transitions from \"down\" to \"up\".
# Deep Morphological Anomaly Detection Based on Angular Margin Loss
## 5 Experimental Results and Discussion
### Experimental Results on the MVTec Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Class**} & **[PERSON] et al. [7]** & **[PERSON] et al. [9]** & **[PERSON] et al. [32]** & **[PERSON] et al. [33]** & **Proposed** \\ \hline \multirow{7}{*}{Texture} & Carpet & 38.1 (3.7) & 57.9 (2.4) & 90.6 (0.4) & 48.2 (2.3) & **95.3 (0.2)** \\  & Grid & 31.4 (2.4) & 29.9 (4.4) & 52.4 (1.9) & 32.9 (1.7) & **86.1 (0.8)** \\  & Leather & 64.1 (0.8) & 82.9 (0.3) & 78.3 (0.7) & 65.9 (0.8) & **93.4 (0.4)** \\  & Tile & 52.2 (1.8) & 93.6 (0.2) & 96.5 (0.3) & 73.4 (0.2) & **98.4 (0.1)** \\  & Wood & 84.3 (0.6) & 87.4 (0.5) & 91.6 (0.1) & 83.5 (0.2) & **95.8 (0.7)** \\  & Mean & 54.0 & 70.3 & 81.6 & 60.9 & **93.8** \\ \hline \multirow{7}{*}{Object} & Bottle & 83.1 (0.5) & 95.2 (0.3) & **99.6 (0.2)** & 90.4 (0.5) & 98.2 (0.6) \\  & Cable & 77.8 (0.2) & 80.3 (0.7) & 90.9 (0.1) & 83.8 (0.3) & **94.0 (0.2)** \\  & Capsule & 75.3 (0.6) & 73.1 (0.2) & **91.0 (0.4)** & 75.4 (0.7) & 87.7 (0.1) \\  & Hazelnut & 67.1 (1.3) & 68.0 (1.2) & 95.0 (0.2) & 73.1 (0.4) & **96.8 (0.3)** \\  & Metal nut & 69.3 (0.8) & 72.7 (0.6) & 85.2 (0.3) & 60.4 (0.9) & **86.9 (0.8)** \\  & Pill & 62.2 (1.1) & 57.2 (2.7) & 80.4 (0.7) & 81.2 (1.1) & **83.7 (0.6)** \\  & Screw & 27.7 (3.7) & 61.9 (2.6) & 86.9 (1.7) & 66.4 (0.5) & **88.3 (0.2)** \\  & Toothbrush & 82.7 (0.6) & 91.7 (0.2) & 96.4 (0.5) & 83.4 (0.2) & **99.2 (0.0)** \\  & Transistor & 88.2 (0.3) & 83.3 (1.2) & **90.8 (1.7)** & 82.5 (0.7) & 89.4 (0.6) \\  & Zipper & 72.3 (0.5) & 75.7 (0.3) & 92.4 (0.7) & 72.7 (1.7) & **95.8 (0.7)** \\  & Mean & 70.6 & 75.9 & 90.9 & 76.9 & **92.0** \\ \hline \hline \multicolumn{2}{c}{Mean} & 65.1 & 74.0 & 87.9 & 71.53 & **92.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of mean and variance AUROC performances for various algorithms on the MVTec dataset.
## 2 Development of Full-Process Numerical Analyzing Method
### Analysis Procedure of Impact Damage and Residual Strength  
Based on stress analysis of composite laminate, failure criteria, and the stiffness degradation model, combined with the finite element method, an integrated finite element model is developed to simulate low-velocity impact damage and to predict the residual strength of stitched composite laminates. Figure 3 shows the simulation procedure in detail. In order to conduct failure analysis based on an element-by-element scheme, the constitutive equations formulated and the failure criteria and the degradation model were implemented with APDL language.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Damage Modes** & **Expression** \\ \hline Fiber tension failure (FTF) & \(\left(\frac{\sigma_{1}}{X_{T}}\right)^{2}+\alpha\left(\frac{\sigma_{12}}{S_{12 }}\right)^{2}+\alpha\left(\frac{\sigma_{12}}{S_{12}}\right)^{2}\geq 1\) (\(\sigma_{1}>0\)) \\ Fiber compression failure (FCF) & \(\left(\frac{\sigma_{1}}{X_{C}}\right)^{2}\geq 1\) (\(\sigma_{1}<0\)) \\ Matrix tension failure (MTF) & \(\left(\frac{\sigma_{2}}{Y_{T}}\right)^{2}+\alpha\left(\frac{\sigma_{12}}{S_{12 }}\right)^{2}+\left(\frac{\sigma_{23}}{S_{23}}\right)^{2}\geq 1\) (\(\sigma_{2}>0\)) \\ Matrix compression failure (MCF) & \(\left(\frac{\sigma_{2}}{Y_{C}}\right)^{2}+\alpha\left(\frac{\sigma_{12}}{S_{12 }}\right)^{2}+\left(\frac{\sigma_{23}}{S_{23}}\right)^{2}\geq 1\) (\(\sigma_{2}<0\)) \\ Delamination failure (DF) & \(\left\{\begin{array}{c}\left(\frac{\sigma_{1}+\sigma_{1}+\sigma_{1}}{X_{TM}} \right)^{2}+\frac{\tau_{12}^{2}}{S_{12n}}+\frac{\tau_{13}^{2}}{S_{13n}^{2}}+ \frac{\tau_{13}^{2}}{S_{23n}^{2}}\geq 1\) (\(\sigma_{11}+\sigma_{22}+\sigma_{33}>0\)) \\ \left(\frac{\sigma_{1}+\sigma_{1}+\sigma_{1}}{X_{CM}}\right)^{2}+\frac{\tau_{12 }^{2}}{S_{12n}^{2}}+\frac{\tau_{13}^{2}}{S_{13n}^{2}}+\frac{\tau_{13}^{2}}{S_{ 23n}^{2}}\geq 1\) (\(\sigma_{11}+\sigma_{22}+\sigma_{33}<0\)) \\ \end{tabular} \\
\begin{tabular}{c c} \hline \hline
**SIF** & \(\left(\frac{\sigma_{1}}{X_{C}}\right)^{2}+\alpha\left(\frac{\sigma_{12}}{S_{12 }}\right)^{2}+\alpha\left(\frac{\sigma_{13}}{S_{13}}\right)^{2}\geq 1\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Expression of six damage modes for residual strength.
## 5 State-of-the-Art
### OS Development  
The operating system is a key component for managing the activities of Wasm. Wasm-machine, an operating system designed to securely and efficiently run Wasm applications in IoT and Fog devices with constrained resources, is presented in [57]. Instead of the conventional approach, Wasmachine compiles Wasm ahead-of-time (AOT) into native binary, executing it in kernel mode to enable zero-cost system calls. To ensure memory safety, Wasmachine's OS kernel is implemented in Rust. By utilizing Wasm's sandboxing features, Wasmachine achieves high security. Benchmark tests have demonstrated Wasmachine's performance, revealing up to an 11% speed advantage over Linux. Adoption of Wasm for non-web environments is tricky due to its high portability and security. Another similar study [58] acknowledges the performance gap of Wasm with native code, primarily due to the conventional Wasm runtimes' lack of complex code optimization and the high overhead of system calls. It uses Wasmachine with an improved performance evaluations, with results indicating that Wasm applications running on Wasmachine are up to 21% faster than native Linux applications. Another work [59] introduces ThingSpire OS, an IoT operating system that integrates cloud-edge computing through Wasm. Wasm is central to ThingSpire OS's design, which uses it to ensure consistent execution across IoT devices, edges, and the cloud. ThingSpire OS employs a Wasm runtime based on Ahead-of-Time (AoT) compilation to facilitate efficient execution on resource-constrained devices. Other notable features include seamless inter-module communication regardless of the modules' locations and optimizations such as lightweight pre-emptible invocation for memory isolation and control-flow integrity. The abstract concludes with a brief mention of preliminary evaluations on the prototype of ThingSpire OS's intermodule communication performance.
# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 3 Experimental Set-Up
### Motion Detection by Background Subtraction (MDBS)
Calculate the principal components considering \(\mathbf{x}\) (of dimension \(n\)), which represents the \(M\) points with coordinates \((x,y)\) corresponding to the pixels in \(B\), whose value is 1. The mean value \(\mathbf{m_{x}}\) is given by Equation (6). \[\mathbf{m_{x}}=\frac{1}{M}\sum_{i=1}^{M}\mathbf{x}_{i}. \tag{6}\]  
Similarly, the covariance matrix \(\mathbf{C_{x}}\) of size \(n\times n\), can be approximated by Equation (7). \[\mathbf{C_{x}}=(\mathbf{x}-\mathbf{m_{x}})(\mathbf{x}-\mathbf{m_{x}})^{T}. \tag{7}\]  
The principal component transform (also called Hotelling transform [34]), is obtained by Equation (8). \[\mathbf{y}=\mathbf{A}(\mathbf{x}-\mathbf{m_{x}}), \tag{8}\]  
where \(A\) is the transformation matrix that applies \(\mathbf{x}\) on \(\mathbf{y}\), and the mean of \(\mathbf{y}\) is \(\mathbf{m_{y}}=0\). Rows in the matrix \(\mathbf{A}\) are eigenvectors of \(\mathbf{C_{x}}\), sorted from largest to smallest eigenvalues. The covariance matrix of \(\mathbf{y}\) is obtained by \(\mathbf{A}\) and \(\mathbf{C_{x}}\) as follows:  
Figure 2:0  
Therefore, the values of \(\mathbf{x}\) can be obtained through the inverse transformation in Equation (10). Figure 2:1  
10. Finally, to delimit the region of movement (see Figure 3), estimation of the extreme points is performed: \(P1(x_{1},y_{1})\), \(P2(x_{2},y_{2})\), \(P3(x_{3},y_{3})\), and \(P4(x_{4},y_{4})\), of the lines \(L_{1}\) and \(L_{2}\), whose direction vectors are the eigenvectors \([\vec{v}_{1},\vec{v}_{2}]\). The orthogonal lines intersect at the midpoint \(O(\vec{x}_{i},\vec{y}_{i})\) of the pixels in \(B\) whose value is 1. Therefore, the region of movement is bounded by the points \(E1(x_{4},y_{1})\), \(E2(x_{2},y_{1})\), \(E3(x_{2},y_{3})\), and \(E4(x_{4},y_{3})\), as shown in Figure 2:2 The points that form the line \(L_{1}\) can also be calculated using the regression line of Equation (3).
## 5 Results
### Visualization Experiments  
To visually demonstrate the advantage of our approach, here we give three examples of the model's output in Figure 15. To each sample, the top 10 returned results of two methods are displayed in two line according to the similarities to the sample. The first line shows the results of the model trained by baseline (original softmax loss + triplet loss) and the second line shows the results of our model (normalized virtual softmax loss + triplet loss). Green boxes represent the the true positive results while the red boxes represent the false positive results.  
In the first two examples, it is easy to see that in our method the position of true positive results in green boxes are more forward, because we increase similarities of intra-class samples and decreasing them of inter-class samples by normalized virtual softmax loss. That leads to improvements in all the metrics of Rank-1, Rank-5, Rank-10 and mAP. In some extreme cases, the third query for example, our model can at least promote the position of true positive results into top 10 list. Considering that in the practical application people often check the top 10 list manually, our method can provide a better reference value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline baseline & 76.07 & 87.86 & 91.40 & 81.25 \\ A-Softmax [25] & **76.87** & 89.84 & 95.03 & 82.63 \\ V-Softmax [26] & 76.71 & 89.87 & 95.07 & 82.61 \\ Ours & 76.85 & **89.92** & **95.16** & **82.72** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison experiments on VehicleID.  
Figure 15: Visualization results on VRAI dataset. For each query, the first line shows the results of baseline and the second line of our method. True positive results are in green boxes while false positive results in red.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline baseline & 92.23 & 95.72 & 97.08 & 67.14 \\ A-Softmax [25] & 92.32 & 96.01 & 97.14 & 67.29 \\ V-Softmax [26] & 92.38 & 96.12 & 97.16 & 67.32 \\ Ours & **92.79** & **96.13** & **97.56** & **68.19** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison experiments on VeRi.
## 3 Results
### Effect of Inclination Ratio  
In a dynamic water environment, the upper part of the TCC will have a certain offset from the lower stable pier. In general, as the inflow velocity increases, the inclination distance increases, and the inclination ratio increases. The range of inclination ratio is 0-1.0, and the step size is 0.1. The assumptions are similar to the area ratio. As the inclination ratio increases, the drag coefficient decreases linearly (Figure 11). When the inclination ratio is 0, the calculated maximum drag coefficient is 1. A fitting formula is investigated to quantify the effect of the inclination ratio on the drag coefficient. The results of the fitting formula show that the change in the drag coefficient obeys a linear distribution and linearly decreases at different inclination ratios. When the inflow velocity remains constant, the relationship between the TCC load and the inclination ratio also follows a linear function.  
Figure 10: (**a**) Drag coefficient and (**b**) force at different area ratios (data sources: D1–D18).
## 4 Delay and Energy Consumption Evaluation
### Results Summary  
Based on the results presented in the previous sub-sections, we now provide two insights regarding the optimal configuration of each FSA protocol variant.  
First, the optimum frame length (\(m\)) in FSA-ACK and FSA-FBP to minimize the delay and coordinator energy consumption depends on the number of end-devices (\(n\)) and is reached when \(m=n/2\). Results also show that the end-device energy consumption is minimal when \(m\geq n\). Hence, the number of slots in FSA-ACK and FSA-FBP has to be pre-adjusted according to the number of end-devices expected in the network. Also, there is a trade-off between the delay and network coordinator energy consumption, and the end-device energy consumption. If the number of slots per frame \(m\) increases above the number of end-devices \(n\), then each end-device consumes less energy, but the delay and network coordinator energy consumption increases slightly.  
Figure 8: Network coordinator average delay (seconds) depending on the number of end-devices when using the optimal FSA-ACK, FSA-FBP and DFSA configuration.  
Second, DFSA results show that, to minimize the delay and the energy consumption of the coordinator, the number of slots per frame (\(m\)) has to be adjusted to be equal to the number of end-devices (\(n\)) that contend in every frame, i.e., \(\rho=1\). In addition, the end-device energy consumption tends to a minimum value as \(\rho\) increases. However, there is a trade-off between the delay and coordinator energy consumption, and the end-device energy consumption. When the value of \(\rho\) increases, end-devices consume less energy, but the delay and coordinator energy consumption increase slightly.  
Hence, considering that in most real-world deployments, the end-devices are battery-powered but the network coordinator is mains-powered, the optimal configuration for FSA-ACK and FSA-FBP is \(m>n/2\), and for DFSA is \(m=n\).
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 3 Moonlight and LCNS
### Rover LCNS Receiver
Within this study, a tactical grade IMU similar to LN200S [37] is considered, which has been used by the most recent NASA Mars rovers (i.e., Spirit, Opportunity, Curiosity and Perseverance). Within this study, the impact of the IMU is not directly modelled; however, its contribution is accounted for in the definition of the process noise of the EKF (for more details refer to Section 4 or previous publications [29]). Table 4 provides examples of the characteristics of different grades of IMUs. The process noise indicated in Table 4 will be used to define the process noise in the simulation, as further detailed in Section 4.4. Figure 5: Antenna pattern of the receiver. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline RF Front-end Noise Figure & 1 dB \\ Noise Temperature & 113 K \\ LNA Gain & 30 dB \\ Coherent Integration Time (\(T_{l}\)) & 20 ms \\ \(C/N_{0}\) cutoff & 30 dB-Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of the rover LCNS S-band receiver.
# Dynamic Analysis of Fuzzy Systems
## 6 Conclusions  
In this article, a dynamic analysis of first-order and second-order systems was performed. First, a non-linear first-order dynamic system is modelled, and the settling time is determined using the Mamdani fuzzy model. It is deduced that the higher the slope, as the derivative approaches zero, the shorter the settling time, and the lower the slope, the longer the settling time is. Furthermore, it has been shown that the settling time is accurately obtained by switching from a triangular membership function formulation to linguistic variables for non-linear models.  
Figure 27: Simple pendulum response.  
Secondly, the specifications of the time-domain features, such as overshoot percentage and peak time, were graphed and compared with the second-order model features. The percentage of Mp was determined to reach zero when the damping ratio \(\zeta\) is equal to or greater than one. The percentage of Mp reaches zero when the damping ratio \(\zeta\) is similar to or greater than one. In both cases, this occurs due to the linguistic hedge of concentration in response to a step input and dilation in response to a step-down input. In these cases, one is shorter than the peak time in second-order linear systems.  
Furthermore, when the percentage of Mp reaches zero and the damping ratio \(\zeta\) is less than one, the linguistic hedge of concentration is in response to a step-down and dilation in response to a step. In the applications section, examples of tank and pendulum models were tested in which the settling time was determined. The performance of different models was compared, and it was demonstrated that the fuzzy model is the best.  
Conceptualisation, F.M. and B.M.A.-H.; methodology, F.M and B.M.A.-H.; software, M.B.; validation, M.B., F.M. and B.M.A.-H.; formal analysis, M.B., F.M. and B.M.A.-H.; investigation, M.B., F.M. and B.M.A.-H.; writing--review and editing, M.B., F.M. and B.M.A.-H. All authors have read and agreed to the published version of the manuscript.  
This publication is part of the R&D project \"Cognitive Personal Assistance for Social Environments (ACOGES)\", reference PID2020-113096 RB-I00, funded by MCIN/AEI/10.13039/501100011033: \"and ESF Investing in your future\".  
Not applicable.  
In this section, you can acknowledge any support given which is not covered by the author contribution or funding sections.  
The authors declare no conflict of interest.  
Samples of the compounds are available from the authors.  
The following abbreviations are used in this manuscript:  
T-S Takagi-Sugeno  
\(t_{s}\) settling time  
Mp per cent overshoot  
tp peak time  
\(t_{d}\) delay time  
NLARX non-linear ARX
# Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey +
## 3 Literature Review
### Sources of Energy Wastage
\(E_{elec}\) is illustrated the energy dissipated to perform the transmitter/receiver board, and \(\epsilon_{amp}\)  
Figure 9: A source node transmits to its destination and neighboring nodes overhearing the communication. is the energy spent in transmission process to amplify the signal enough to reach the next target. Thus, most of the literature shows that multihop communication is the best way to minimize the transmission distance between nodes. Figure 10 shows single and multihop scenarios between nodes. A lower transmission distance between a node and next-hop target/CH/BS reduces energy depletion of a node and prolongs the network lifetime [68]. * Non-Clustering: direct transmission distance from a source to the next-hop node can reduce the sensor network lifetime significantly due to the additional energy consumption. As a solution, hierarchical routing protocols are adopted, see Figure 11 which shows chain-based, tree-based and cluster-based protocols, which are the most commonly used protocols [82]. In a chain-based method, sensor nodes are organized chain-like where one of these nodes is elected to serve as the CH node to transmit the information coming from all sensors to the BS [83]. With cluster-based, the sensing field is partitioned into subgroups and each sub-group has some sensor nodes connected to a CH node to forward their information to the BS [84]. In tree-based clusters, the collected data are forwarded from node to their associated CH node based on multihop concept [85]. For sensor networks, clustering is the best solution for reducing communication costs and maximizing network lifetime.