esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Contours in the Complex Domain for Salient Object Detection
## 2 Related Work
### Active Contours with Selective Local or Global Segmentation (ACSLGS)  
The level set formulation of [PERSON]. [PERSON] et al. [41] used the signed pressure force in [29], which assumes that object intensities are homogenous. ACSLGS [41] proposed a signed pressure function that is different from that in [29] and it plays an important role in segmentation. It acts as a global force that drags the corresponding pixels to the foreground or background. The force function makes the region shrink when it is outside the object and expand when it is inside the object. Their pressure function is in the range of \([-1,1]\) and it has the form  
\[\mathrm{spf}(I)=\frac{I-\frac{c_{1}+c_{2}}{2}}{\max\Bigl{(}\big{|}I-\frac{c_{1} +c_{2}}{2}\Bigr{|}\Bigr{)}} \tag{11}\]  
where \(c_{1}\) and \(c_{2}\) are defined in Equations (7) and (8). By plugging them into Equation (5), the solution for the ACSLGS model is  
\[\frac{\partial\phi}{\partial t}=\mathrm{spf}(I).\Biggl{(}div\Biggl{(}\frac{ \
abla\phi}{\big{|}\
abla\phi\big{|}}\Biggr{)}+\alpha\Biggr{)}\Biggl{|}\
abla \phi\Bigr{|}+\
abla\mathrm{spf}(I)\
abla\phi \tag{12}\]  
Assume that both of the intensities are homogenous. Since \(\min(I)\leq c_{1},c_{2}\leq\max(I)\) the inside and outside intensities cannot be obtained concurrently, where the contour is. Therefore,  
\[\min(I)\leq\frac{c_{1}+c_{2}}{2}\leq\max(I) \tag{13}\]  
The dynamic contour has distinct intensity signs inside and outside of the object and the level-set function is initialized to a constant. \(div\Biggl{(}\frac{\
abla\phi}{\big{|}\
abla\phi\big{|}}\Biggr{)}\Biggl{|} \
abla\phi\Bigr{|}\) is used for regularization of the level-set function \(\phi\)[43]. By removing both functions \(div\Biggl{(}\frac{\
abla\phi}{\big{|}\
abla\phi\big{|}}\Bigr{)}\Biggl{|} \
abla\phi\Bigr{|},spf(I(x))\
abla\phi\) in Equation (14), which is unnecessary, the level set formulation can finally be written as  
\[\frac{\partial\phi}{\partial t}=\mathrm{spf}(I).\alpha\Bigl{|}\
abla\phi \Bigr{|}.\ x\in\Omega \tag{14}\]  
The initialization of the active contour with selective local or global segmentation is  
\[\phi(x,t=0)=\left\{\begin{array}{cc}p,&x\in\Omega_{0}-\partial\Omega_{0},\\ 0,&x\in\partial\Omega_{0},\\ -p,&x\in\Omega-\Omega_{0},\end{array}\right. \tag{15}\]  
where \(\Omega\) is the image domain, \(\Omega_{0}\) is a subset of \(\Omega\) for the initialization, \(\partial\Omega_{0}\) defines the boundary of the initialized region, and \(p\) is a constant, where \(p>0\). The active contour with selective local and global segmentation model has the same disadvantages as the CV model.
## 3 Optical Indices
### Field of View  
Different from an imaging spectrometer, an optical system based on the transit-spectroscopy method is a spectrophotometer without the angular-resolution requirement that is utilized to image a single point object at infinity. Under the condition of an ideal image, only the central FOV is needed. However, due to the existence of an Airy spot,  
Figure 3: Number of transits to achieve \(SNR_{goal}\) under different apertures and throughput for GJ 1214 b. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Exoplanets** & \(A_{pt}\) & \(N_{transit}\) \\ \hline hHD209458b & \(8.31227\times 10^{-4}\) & 1 \\ HD189733b & \(5.16466\times 10^{-4}\) & 1 \\ WASP-13b & \(6.21407\times 10^{-4}\) & 5 \\ WASP-34b & \(9.91337\times 10^{-4}\) & 8 \\ HAT-P1b & \(6.92584\times 10^{-4}\) & 10 \\ HAT-P26b & \(1.00658\times 10^{-3}\) & 10 \\ WASP-12b & \(7.61745\times 10^{-4}\) & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Atmospheric contribution and number of transits for representative exoplanets under condition of \(D_{tel}=2\) m, \(\tau=0.5\). an extra FOV around the central FOV should also be accepted to image the target star. Thus, when the FOV of the optical system is calculated, the Airy spot and pointing accuracy of payloads need to be considered [36]. The pointing system, referring to the structure and performance of ARIEL-FGS [37], ensures that the target falls into the FOV by regulating the absolute pointing error (APE). The FOV of the pointing system is expressed in _arcsec_ as follows. \[FOV_{FGS}\geq\frac{2\times 1.22\lambda_{max}}{D_{tel}}\frac{180\times 3600}{ \pi}+2 APE_{c}. \tag{21}\]  
Similarly, the FOV requirement of spectrometer in the spectral direction is  
\[FOV_{IRS}\geq\frac{2\times 1.22\lambda_{max}}{D_{tel}}\frac{180\times 3600}{ \pi}+2 APE_{f}. \tag{22}\]  
The maximal wavelength for the pointing system is \(\lambda_{max}\) = 2 \(\upmu\)m, and for the two channels of the spectrometer is \(\lambda_{max}(CH1)\) = 4 \(\upmu\)m and \(\lambda_{max}(CH2)\) = 8 \(\upmu\)m. The APE in coarse- and fine-pointing mode adopt \(APE_{c}=8^{\prime\prime}\) and \(APE_{f}=1^{\prime\prime}\), respectively. An extended FOV of \(\pm 10^{\prime\prime}\) in the spatial direction of the spectrometer monitors the background using off-source pixels. Assuming that the telescope aperture is fixed as \(D_{tel}\) = 2.1 m, the FOV requirements of each optical channel are summarized in Table 4.  
The overall instrument FOV should both include the requirements of payloads, and consider the additional FOV generated from the assembly process, such as the misalignment between telescope and payload modules. Therefore, a margin of 12\" must be attached to the half FOV mentioned above. The final field of view of the telescope is shown in Table 4. Figure 4 shows the layouts of the telescope FOV with the ideal alignment and final dimensions with an additional margin.
## 3 Problem Formulation  
Assuming that there are \(n\) jobs and \(m\) machines, and that all jobs require to be processed on machines 1, 2, 3,..., and \(m\), one after another, the PFSP is aimed at finding a permutation \(\pi\) of jobs, identical for all machines, that minimizes the completion time of the last job on the last machine, referred to as _makespan_.  
Given a permutation \(\pi=(\pi_{1},\pi_{2},\ldots,\pi_{n})\), in which \(\pi_{k}\) shows the number of job in the sequence of jobs, and denoting the processing time of job \(j\) on machine \(i\), as \(t_{ji}\), the makespan of the problem can be calculated by rearranging the jobs based on \(\pi\). In effect, by denoting the earliest completion time of job \(\pi_{k}\) on the machine \(i\) as \(c_{ki}\), the value of \(c_{nm}\) shows the exact amount of makespan. Hence, the objective can be stated as finding a permutation \(\pi^{*}\) over the set of all permutations, \(\Pi\), so that \(c_{nm}\) is minimized. A simple formulation of the PFSP, based on the formulation presented in [41] is as follows.  
\[\underset{\pi\in\Pi}{\text{min}}c_{nm} \tag{1}\]  
where,  
\[c_{11} =t_{\pi_{1},1} \tag{2}\] \[c_{1i} =c_{1,i-1}+t_{\pi_{1},i} i=2,3,\ldots,m\] (3) \[c_{k1} =c_{k-1,1}+t_{\pi_{k},1} k=2,3,\ldots,n\] (4) \[c_{ki} =\max\left(c_{k,i-1},c_{k-1,i}\right)+t_{\pi_{k},i} i=2,3,\ldots,m\] \[k=2,3,\ldots,n \tag{5}\]  
It is worth noting that Formulas (2)-(5) also shows how, for a given permutation, the makespan can be calculated in \(O(nm)\).  
Figure 1 shows a sample 4-job 4-Machine PFSP problem, with the permutation \(\pi_{1}=(1,2,3,4)\) of jobs leading to the solution value of 30. The arrows originating from each circle in Figure 1, show the corresponding processing time of the corresponding job on the corresponding machine and the sum of processing times on the bold arrows indicates the makespan. Figure 2 shows how by changing the order of \(\pi_{1}=(1,2,3,4)\) to \(\pi_{2}=(1,4,3,2)\), the makespan has changed to 29, which is the optimal solution.  
Figure 1: A sample 4-job 4-machine Permutation Flow-shop Scheduling Problem (PFSP) problem, with the permutation of (1,2,3,4) of jobs leading to the solution value of 30.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
###### Abstract  
Bananas are the world's most popular fruit and an important staple food source. Recent outbreaks of Panama TR4 disease are threatening the global banana industry, which is worth an estimated $8 billion. Current methods to map land uses are time- and resource-intensive and result in delays in the timely release of data. We have used existing land use mapping to train a U-Net neural network to detect banana plantations in the Wet Tropics of Queensland, Australia, using high-resolution aerial photography. Accuracy assessments, based on a stratified random sample of points, revealed the classification achieves a user's accuracy of 98% and a producer's accuracy of 96%. This is more accurate compared to existing (manual) methods, which achieved a user's and producer's accuracy of 86% and 92% respectively. Using a neural network is substantially more efficient than manual methods and can inform a more rapid respond to existing and new biosecurity threats. The method is robust and repeatable and has potential for mapping other commodities and land uses which is the focus of future work.  
convolutional neural network; U-Net; segmentation; deep learning; land use; banana plantation; Panama TR4; aerial photography +
Footnote †: journal: Applied sciences
## 4 Performance Metric
### Meteor  
The METeor metric differs from BLEU in that it considers not only simple \(n\)-gram matches but also synonymy, stemming, and word order alignment. METeor utilizes the harmonic mean of unigram precision and recall, making it more closely correlated with human evaluation compared to BLEU [48]. The corresponding formula is as follows:  
\[F_{\text{mean}}=\frac{P\cdot R}{\alpha P+(1-\alpha)R} \tag{4}\]  
where \(P\) is precision, \(R\) is recall, and \(\alpha\) is weight factor, normally set to 0.9.
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
## 2 Problem Formulation
### Target Dynamics and Radar Measurement Model
\begin{array}{l}\widetilde{x}_{i}(k)=\widetilde{r}_{i}(k)\cos\widetilde{ \theta}_{i}(k)\cos\widetilde{\varphi}_{i}(k)\\ \widetilde{y}_{i}(k)=\widetilde{r}_{i}(k)\cos\widetilde{\theta}_{i}(k)\sin \widetilde{\varphi}_{i}(k)\\ \widetilde{z}_{i}(k)=\widetilde{r}_{i}(k)\sin\widetilde{\theta}_{i}(k)\\ \end{array}  \tag{7}\]  
Substitute Equation (3) into Equation (7), and by using a first order approximation, we can obtain  
\[\mathbf{y}_{i}(k)=\mathbf{H}(k)\mathbf{x}_{i}(k)+\mathbf{A}_{i}(k)\mathbf{a}_{i}(k)+\mathbf{v}_{i}(k) \tag{8}\]  
where \(\mathbf{H}(k)=\left[ \begin{array}{cccccc}1&1&1&0&0&0\end{array} \right]\) is the measurement matrix and \(\mathbf{a}_{i}(k)=\left[r_{i}^{a}(k)&\theta_{i}^{a}k\right.\\ \varphi_{i}^{a}(k)\right]^{T}\) is the attack parameter. In the following, the subscript \(i\) and symbol \(k\) of the true measurements in the spherical coordinates are omitted for simplicity, and \(A_{i}(k)\) is defined as  
\[\mathbf{A}_{i}(k)=\left[ \begin{array}{ccc}\cos\theta\cos\varphi&-r\sin\theta \cos\varphi&-r\cos\theta\sin\varphi\\ \cos\theta\sin\varphi&-r\sin\theta\sin\varphi&r\cos\theta\cos\varphi\\ \sin\theta&r\cos\theta&0\end{array} \right] \tag{9}\]where \(\mathbf{v}_{i}(k)\) is the measurement noise in the Cartesian coordinates with covariance \(\mathbf{R}_{i}(k)\), such that  
\[ \begin{array}{ll}\mathbf{R}_{i}^{11}(k)=&\sigma_{ir}^{2}\cos^{2}\theta\cos^{2} \varphi+\sigma_{i\theta^{\prime}}^{2}r^{2}\sin^{2}\theta\cos^{2}\varphi+\sigma_ {i\theta^{\prime}}^{2}r^{2}\cos^{2}\theta\sin^{2}\varphi\\ \mathbf{R}_{i}^{22}(k)=&\sigma_{ir}^{2}\cos^{2}\theta\sin^{2}\varphi+\sigma_{i \theta^{\prime}}^{2}r^{2}\sin^{2}\theta\sin^{2}\varphi+\sigma_{i\theta^{ \prime}}^{2}r^{2}\cos^{2}\theta\cos^{2}\varphi\\ \mathbf{R}_{i}^{33}(k)=&\sigma_{ir}^{2}\sin^{2}\theta+\sigma_{i\theta^{\prime}}^{2 }r^{2}\cos^{2}\theta\\ \mathbf{R}_{i}^{12}(k)=&\mathbf{R}_{i}^{21}(k)=\sigma_{ir}^{2}\cos^{2}\theta\cos \varphi\sin\varphi+\sigma_{i\theta^{\prime}}^{2}r^{2}\sin^{2}\theta\sin\varphi \cos\varphi\\ &-\sigma_{ir}^{2}r^{2}\cos^{2}\theta\sin\varphi\cos\varphi\\ \mathbf{R}_{i}^{13}(k)=&\mathbf{R}_{i}^{31}(k)=\sigma_{ir}^{2}\sin\theta\cos\theta \cos\varphi-\sigma_{i\theta^{\prime}}^{2}r^{2}\sin\theta\cos\theta\cos\varphi \\ \mathbf{R}_{i}^{23}(k)=&\mathbf{R}_{i}^{32}(k)=\sigma_{ir}^{2}\sin\theta\cos\theta\sin \varphi-\sigma_{i\theta^{\prime}}^{2}r^{2}\sin\theta\cos\theta\sin\varphi\\ \end{array}  \tag{10}\]  
where \(\mathbf{R}_{i}^{bc}(k)\) denotes the \(b\)th row and the \(c\)th column of \(\mathbf{R}_{i}(k)\).
## 3 Required Data Rate in V2V for Safe Overtaking
### Cooperative Perception and Scenario Description
The driving scenario was that the ego vehicle tries to overtake the blue leading vehicle. Since frequent acceleration and deceleration do not occur on a straight road, the ego and the oncoming vehicles run with the same velocity \(V\) for simplicity. When the ego vehicle tries to overtake, the blue vehicle drives slow enough for simplicity. Moreover, considering that many Level 4 automated vehicles are equipped with the feature of lane centering, we assumed that all vehicles ran on the center of the road [21]. Using this lane centering function, we also assumed that beam alignment for the V2V communication was ideally performed. When the ego vehicle recognizes the oncoming vehicle by a 3D LiDAR sensor on a roof and safety is not ensured, the ego vehicle does not execute overtaking. The problem is that many beams from the LiDAR sensor are blocked by the leading vehicle. From this point, we call the leading vehicle the blocking vehicle. In this analysis, the blocking vehicle was assumed as an automated vehicle, and the oncoming vehicle was assumed as a human-driven vehicle. Therefore, the ego vehicle can communicate with the blocking vehicle and compensate for the blocked area by cooperative perception. The following sections explain the details of the scenario factors.
## 3 Results
### Modeling  
#### 3.3.1 Feature Engineering  
The transformations applied to the variables are described below:  
* Variables were grouped because the components they represent are similar and only differ in brand or reference. The quantities of each grouped variable were summed to create a new single variable. For example, instead of having three references for cement, a single variable called \"Cement 1\" was kept. Similarly, instead of having five references for a retarding admixture, a single variable called \"Admixture_1 Retarding\" was kept.
* Variables with zero values were removed.
* In order to observe the influence of different independent variables on the output variable, linear and non-linear relationship analyses were conducted using the mutual_info_regression method, this method quantifies the interdependence of two variables, a higher mutual information suggests a stronger correlation between the variables [49]. Unlike the linear correlation coefficient, mutual information is sensitive to dependencies that may not be apparent in the covariance [50]. For 28-day compressive strength, it was found that in addition to cement, retarding and superplasticizer admixtures, supplementary cementitious materials, and free water, there is also a relationship with the moisture content of the aggregates and their sources. In the case of 28-day flexural strength, the results are similar, but there is a stronger relationship with the superplasticizing admixtures than with the retarding agents, and there is a stronger relationship with the plant where the concrete is produced. Finally, in the prediction of slump, it was found that water has a stronger relationship than in the strength predictions.
* Pearson correlation was analyzed to detect dangerously high relationships between independent variables, verifying that there are no correlations greater than 0.8 or less than \(-0.8\) between independent variables, unless they correspond to the quantity of aggregate and its moisture content from the same source. This way, the existence of redundant variables is ruled out.
* All independent variables were scaled to the production of one cubic meter of concrete.  
At the end of the data transformation process, a dataset with 20,108 rows and 47 columns was obtained for the compressive strength model, a dataset with 551 rows and 41 columns for the flexural strength model, and a dataset with 20,662 rows and 47 columns for the slump model.
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
## 4 Analysis, Comparisons, and Future Plans
### Simulation Results
Figure 11 depicts the relationship between upload bandwidth occupancy and download bandwidth occupancy. The satellite network simply forwards data packets, so the upload bandwidth occupancy is the same as the download bandwidth occupancy. However, the packets will be processed before they are forwarded in 5 GsatEC. Different data types have different degrees of compressibility. Figure 11 shows the downstream bandwidth occupancy of the satellite network and 5 GsatEC when they deal with different data packets. The 5 GsatEC saves a significant amount of download bandwidth compared to the satellite network. 5 GsatEC has the characteristic of broader coverage, like the satellite network; meanwhile, on-board data processing and storage make up the long end-to-end delay. Service performance in 5 GsatEC is related to storage and computing resources. More storage and computing resources can be placed on board as weight and power consumption permit. Figure 11: Occupancy differences between the satellite network and 5 GsatEC. Figure 10: Packet loss rate between the satellite network and 5 GsatEC.
## 5 Experiment Results
### Effectiveness of the Local Search Strategy  
To evaluate the performance of the local search strategy, an IIG algorithm without the local search strategy (IIG_NL) was developed and compared to the IIG algorithm. The two compared algorithms were run independently 30 times on 56 instances, and the average fitness values for each instance are collected. The relative percentage increase (RPI) is used to measure the algorithm's performance, which is calculated by Equation (14) as follows:  
\[RPI=\frac{(f_{c}-f_{b})}{f_{b}}\times 100 \tag{14}\]  
where \(f_{b}\) is the best value obtained by all compared algorithms, and \(f_{c}\) denotes the best value achieved by a specified algorithm. Table 4 presents the comparison results, with the first two columns indicating the instances and the better values achieved by two compared algorithms. The subsequent four columns display the average fitness values and RPI values obtained by the two algorithms. It can be seen from Table 4 that: (1) The IIG achieved 50 optimal average fitness values out of the 56 instances, while the IIG_NL obtained only 3 optimal values and (2) the average RPI achieved by IIG is 0.11, which is lower than that of IIG_NL, demonstrating that the proposed IIG outperforms IIG_NL and the local search strategy enhances the algorithm's exploitation capability. A multifactor analysis of variance (ANOVA) was conducted to evaluate the significance of the differences between the two algorithms. Figure 4 presents the ANOVA results, which indicate that the stability of the IIG is significantly better than that of IIG_NL, with a \(p\)-value of 1.4181 \(\times\) 10\({}^{-17}\), far less than 0.05, demonstrating a significant difference between the IIG and IIG_NL. Figure 4: ANOVA of the IIG and IIG_NL. \begin{table}
\begin{tabular}{c c c} \hline \hline \(d\) & \(T\) & **Average Values** \\ \hline
1 & 1 & 1862.75 \\
1 & 2 & 1886.63 \\
1 & 3 & 1896.69 \\
1 & 4 & 1892.37 \\
2 & 1 & 1704.23 \\
2 & 2 & 1754.04 \\
2 & 3 & 1712.81 \\
2 & 4 & 1716.56 \\
3 & 1 & 1799.72 \\
3 & 2 & 1803.39 \\
3 & 3 & 1776.82 \\
3 & 4 & 1798.90 \\
4 & 1 & 1867.13 \\
4 & 2 & 1841.73 \\
4 & 3 & 1819.77 \\
4 & 4 & 1866.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The response variable of the four parameters.
## 4 Discussion
### The Role of the Fragmentation Index  
To assess the reliability of the GFI, the index was applied to multiple regions with varying environmental conditions to determine its consistency. Results showed that the GFI remained stable across different datasets, indicating its robustness. Additionally, previous studies [46] have successfully utilized similar indices, further supporting its reliability. However, it should be noted that the GFI may be influenced by factors such as data resolution and image quality. In areas with lower resolution or significant cloud cover, the accuracy of the index may be reduced.  
Combining Tables 5 and 6, it is evident that the fragmentation index holds predictive value for extraction outcomes, with less accurate results observed in areas exhibiting greater fragmentation. Thus, analyzing ground fragmentation allows for advance prediction of extraction accuracy. Additionally, examining garlic extraction results and analyzing actual planting fragmentation can inform garlic policy formulation, thereby reducing planting costs.
## 4 Starlink as Case Study
### Stage 3: Parametric Estimation of Propellant Mass
The high specific impulse for Hall-effect thrusters is the key reason why the propellent mass varies drastically from traditional chemical propellant, and it proves the importance of this detailed propellant mass calculation for the estimation of the final mass budget. By equating specific impulse and g value into Equation (18), we can obtain the mass of propellant m\({}_{\text{p}}\):  
\[a_{\rm{tx}}=(r_{\rm{A}}+r_{\rm{B}})/2 \tag{3}\]7  
Hence, the mass of the propellant is 4.2 kg. Therefore, the dry mass can be represented as Total mass (m\({}_{\text{f}}\)) \(-\) Mass of propellant (m\({}_{\text{p}}\)) = 260 \(-\) 4.2 = 255.8 kg.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 1 Introduction
Therefore, the main contributions of this work were:  
* Presenting and describing an architecture that uses OF networks and is compatible with current inter-domain routing protocol (BGP);
* Exploring OF features for network traffic engineering tasks in inter-domain routing; and
* Analyzing how the incoming traffic can be managed using the proposed architecture and BGP. The remainder of the text is organized as follows. Section 2 discusses the related works regarding SDN solutions for inter-domain routing and Section 3 details the proposed architecture. Then, Section 4 describes how the ASes can apply inbound control to manage network traffic towards its networks using SDN applications executed inside the architecture and Section 5 performs an evaluation of the proposal with a proof of concept. Finally, Section 6 provides more discussion on the architecture and Section 7 presents the final thoughts of this work.