esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Failure Mode Analysis
### Vertical Failure 1 Set  
When any one of the vertical sensors Z1, Z2, Z3, or Z4 fails, it means the measurement signal d\({}_{x1}\) is invalid. Assuming that eddy current sensor Z1 fails, Equation (10) is rewritten as:  
\[\left\{\begin{array}{l}-x+y\theta_{z}-z\theta_{y}-\theta_{z}R_{x1}=d_{x1}\\ x-y\theta_{z}+z\theta_{y}+\theta_{z}R_{x2}=d_{x2}\\ -y-x\theta_{z}+z\theta_{x}+\theta_{z}R_{y1}=d_{y1}\\ y+x\theta_{z}-z\theta_{x}+\theta_{z}R_{y2}=d_{y2}\\ z-x\theta_{y}+y\theta_{x}-R_{z2x}\theta_{y}-R_{z2y}\theta_{x}=d_{z2}\\ z-x\theta_{y}+y\theta_{x}+R_{z3x}\theta_{y}-R_{z3y}\theta_{x}=d_{z3}\\ z-x\theta_{y}+y\theta_{x}-R_{z4x}\theta_{y}+R_{z4y}\theta_{x}=d_{z4}\\ \end{array}\right. \tag{16}\]Similarly, the relative position between the base and the floating platform can be expressed as:  
\[\begin{bmatrix}x\\ y\\ z\end{bmatrix}=\begin{bmatrix}1&-\theta_{z}&\theta_{y}\\ \theta_{z}&1&-\theta_{x}\\ -\theta_{y}&\theta_{x}&1\end{bmatrix}^{-1}\begin{bmatrix}m_{1}\\ m_{2}\\ m_{3}\end{bmatrix} \tag{17}\]  
where:  
\[\left\{\begin{array}{c}m_{1}=\frac{1}{2}[(d_{x2}-d_{x1})-\theta_{z}(R_{x1}+R_ {x2})]\\ m_{2}=\frac{1}{2}[(d_{y2}-d_{y1})+\theta_{z}(R_{y1}-R_{y2})]\\ m_{3}=\frac{1}{3}[(d_{x2}+d_{z3}+d_{z4})+\theta_{x}(R_{x2y}+R_{z3y}-R_{z4y})+ \theta_{y}(R_{z2x}+R_{z4x}-R_{z3x})]\\ \end{array}\right. \tag{18}\]  
\[\begin{vmatrix}1&-\theta_{z}&\theta_{y}\\ \theta_{z}&1&-\theta_{x}\\ -\theta_{y}&\theta_{x}&1\end{vmatrix}=1+\theta_{x}^{2}+\theta_{y}^{2}+\theta _{z}^{2}\
eq 0 \tag{19}\]  
The formula for solving the relative position between the base and the floating platform in three degrees of freedom is as follows:  
\[\left\{\begin{array}{c}x=m_{1}+m_{2}\theta_{z}-m_{3}\theta_{y}\\ y=m_{2}-m_{1}\theta_{z}+m_{3}\theta_{x}\\ z=m_{3}+m_{1}\theta_{y}-m_{2}\theta_{x}\end{array}\right. \tag{20}\]  
The formula for solving the relative angle between the base and the floating platform in three degrees of freedom is as follows:  
\[\left\{\begin{array}{c}\theta_{x}=\frac{(R_{x3x}+R_{x4y})(d_{x3}-d_{x2})-(R_ {x2x}+R_{x3x})(d_{x3}-d_{x4})}{(R_{z2x}+R_{x3x})(R_{x3y}+R_{x4y})+(R_{x3x}+R_{ x4x})(R_{x2y}-R_{x3y})}\\ \theta_{y}=\frac{(R_{x3y}+R_{x4y})(d_{x3}-d_{x2})+(R_{x2y}-R_{x3y})(d_{x3}-d_{ x4})}{(R_{z2x}+R_{x3x})(R_{x3y}+R_{x4y})+(R_{x3x}+R_{x4x})(R_{x2y}-R_{x3y})}\\ \theta_{z}=\frac{1}{2}\left(\frac{d_{x1}+d_{x2}}{R_{x2}-R_{x1}}+\frac{d_{y1}+ d_{x2}}{R_{y1}+R_{y2}}\right)\end{array}\right. \tag{21}\]  
From the above derivation, it can be seen that the position and attitude measurement system has high redundancy and can still complete relative position and attitude measurement and resolution, even in the event of the failure of one vertical sensor.
## 3 Evaluation of Melt Test Parameters and Analysis of DSL Service Provision
### Process of Evaluation of MELT Test Parameters and Analysis of Quantities  
The process begins with the MELT test procedure realized by means of Nokia test software 5520 AMS GUI, version 9.7.05-426853. The evaluation of MELT test parameters consists of comparing the quantity value with the values given in the previous Table 1. In total, three quantities (DC, AC and C) are evaluated between three sections (RG, TR and TG), such that the total number of executed quantities is nine. The analysis of quantities consists of determining whether a given value of the quantity meets a specified allowed range, is within the permitted tolerance or is outside an expected range. If the value of the quantity is within the permissible tolerance or outside the specified range, the possible cause of the failure can be determined.  
For the purposes of evaluation of the MELT test parameters and analysis of quantities, we realized a mobile application in the environment of the Android operating system [36] which can evaluate MELT test parameters and analyze appropriate homogeneous symmetric lines for DSL service provision.  
Mobile Application Functionality for Evaluation of Melt Test Parameters and Analysis of Homogeneous Symmetric Lines  
For technicians of network operators and service providers, a mobile application is an auxiliary tool for diagnosing and solving faults in metallic access networks. This mobile application, as mentioned, has been designed and optimized for use in mobile devices working with the Android operating system [36]. The main and most important functionality of this mobile application is to evaluate MELT test parameters and analyze any real deployed homogeneous symmetric line for DSL service provision. Since no identical or similar mechanism and/or application has been found in any specialized literature or resources, it can be considered as a new possibility for evaluating measured parameters of a metallic transmission medium and for analyzing DSL service provision on metallic access networks.
## 3 Results
### Tempered EM: Application to Mixtures of Gaussian
#### 3.2.2 Experimental Results Analysis
Its standard deviation is also lower by a factor of about 2. Overall, _oscillating_ tmp-EM dominates the simulation. Its error rates on the recovery of \(\mu_{1}\) and \(\mu_{2}\) are always the best, and they remain at low levels even with the most adversarial initialisations. To bolster this last point, we underline that the highest relative error reached by _oscillating_ tmp-EM over all the various scenarios (0.39 on parameter family 3 with _2v1_ initialisation) is still lower than the lowest relative error of both classical EM (0.52 on parameter family 1 with _barycenter_ initialisation) and _decreasing_ tmp-EM (0.60 on parameter family 1 with _barycenter_ initialisation). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**EM**} & \multicolumn{2}{c}{**tmp-EM (_Decreasing_**T**)} & \multicolumn{2}{c}{**tmp-EM (_Decreasing_**Oscillating_**T**)**} \\ \hline Parameter & & & & & & & \\ Family & cl. & barycenter & 2v1 & barycenter & 2v1 & barycenter & 2v1 \\ \hline \multirow{3}{*}{1} & 1 & 0.52 (1.01) & 1.52 (1.24) & 0.60 (1.08) & 0.87 (1.20) & **0.04 (0.26)** & **0.29 (0.64)** \\  & 2 & 0.55 (1.05) & 1.53 (1.25) & 0.64 (1.10) & 0.96 (1.25) & **0.05 (0.31)** & **0.30 (0.64)** \\  & 3 & **0.01 (0.06)** & 0.01 (0.03) & 0.01 (0.10) & **0.01 (0.02)** & 0.03 (0.17) & 0.03 (0.19) \\ \hline \multirow{3}{*}{2} & 1 & 1.00 (1.42) & 1.69 (1.51) & 0.96 (1.41) & 1.10 (1.46) & **0.09 (0.47)** & **0.37 (0.86)** \\  & 2 & 1.03 (1.44) & 1.71 (1.52) & 1.08 (1.46) & 1.11 (1.46) & **0.12 (0.57)** & **0.32 (0.79)** \\  & 3 & 0.01 (0.05) & **0.02 (0.03)** & 0.01 (**0.03)** & 0.01 (0.04) & **0.01** (0.05) & 0.04 (0.22) \\ \hline \multirow{3}{*}{3} & 1 & 1.56 (1.75) & 1.79 (1.77) & 1.63 (1.76) & 1.38 (1.71) & **0.31 (0.97)** & **0.39 (0.98)** \\  & 2 & 1.51 (1.74) & 1.88 (1.76) & 1.52 (1.74) & 1.30 (1.68) & **0.30 (0.93)** & **0.39 (0.97)** \\  & 3 & 0.02 (0.04) & **0.02 (0.04)** & 0.01 (**0.03)** & 0.02 (0.06) & **0.01** (0.04) & 0.07 (0.30) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average and standard deviation of the relative error on \(\mu_{k}\), \(\frac{\|\hat{\beta}_{k}-\mu_{k}\|^{2}}{\|\mu_{k}\|^{2}}\), made by EM, tmp-EM with _decreasing_ temperature and tmp-EM with _oscillating_ temperature over 1000 simulated dataset with two different initialisations. The three different parameter families, described in Figure 4, correspond to increasingly ambiguous positions of classes 1 and 2. For both initialisations type, the identification of these two clusters is drastically improved by the tempering. Best results highlighted in **bold**.
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 3 Salt-Stress Treatment for Caroenoid Production from Microalgae
### Microalgal Responses to Salt Stress  
Salt stress is one of the major abiotic stresses decreasing productivity of agriculture all over the earth [68; 69]. Salinity inhibits plant growth, development, seed germination and yields. In contrast, plants have developed strategies to adapt to high salt concentrations, such as regulating stress hormones and growth hormones to balance growth and stress responses [69]. Currently, research on salt stress is more in-depth and comprehensive in higher plants, but there are a few studies on microalgae.  
Figure 2: The chemical structure of (_all-E_)-fucoxanthin.  
Due to the fast growth rate of microalgae and their richness of high-value products, large-scale microalgae cultivation is imperative at present [6]. Microalgae are widely distributed in the ocean and freshwater, while salt stress is still a threat to the large-scale production of freshwater microalgae. As freshwater resources are becoming scarce today, the cultivation of freshwater microalgae with seawater is of great significance. On the other hand, appropriate salt stress shows promoting effects on secondary metabolite accumulation (such as secondary carotenoids and lipids) [70]. Therefore, it is of great significance to study the response of microalgae to salt stress. The possible responses in microalgae cells caused by salt stress are summarized in Figure 3.  
Salt stress can affect microalgae on multiple levels, as physiological characteristics, gene expression and metabolic pathways. Salt stress can reduce cell growth, decrease chlorophyll content, inhibit photosynthesis and cause morphological changes in microalgae [9]. Under salt stress, the cell wall of _H. pluvialis_ thickens, the cell volume becomes larger and gradually becomes immobile cysts [10]. These morphological changes are related to a series of in-depth signaling and downstream changes in both genetic and metabolic aspects.
# Computational Vibro-Acoustic Time Reversal for Source and Novelty Localization
## 6 Conclusions  
In this work, we have numerically studied the applicability of a time reversal for source and novelty detection and localization in a vibro-acoustic environment. The necessary computational setup regarding the coupling between the acoustic fluid and the structural components, which are here represented by beams, along with all the adopted simplifications and approximations, has been presented. The implemented numerical framework was used for two problems. The first one deals with the localization of some source that originally excites the vibro-acoustic system and the second with the problem of localizing some novelties presented at some components of the system. It has been demonstrated that both problems can be successfully addressed. It was also demonstrated that accounting for the presence of structural components and their coupling within the acoustic domain is important and improves the results, leading to higher SNR values. The advantage of the presented methodology is that it is rather simple and therefore easy to use in practical applications. A possible disadvantage is the accuracy with which the structure should be modeled. Further investigations in this direction, considering the complex environments of fully three dimensional elastodynamic domains coupled with acoustic fluids, would be a natural extension of this work.  
Conceptualization, C.G.P.; methodology, C.G.P. and C.T.; software, C.G.P. and S.K.; validation, S.K. and C.G.P.; formal analysis, C.G.P. and C.T.; investigation, S.K. and C.G.P.; data curation, S.K.; writing--original draft preparation, C.G.P.; writing--review and editing, S.K. and C.T.; visualization, S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experiment
### Authentic Transmission
#### 4.1.2. Decryption Mechanism  
_Step-1:_ To decrypt the data, the same ciphertext has been used that was obtained in encryption. The ciphertext is shown in Figure 18.  
_Step-2:_ First, cipher text has been converted to its equivalent ASCII, as shown in Figure 19.  
_Step-3:_ After obtaining different ASCII values, \"\(P=\sqrt{(X-K)}\)\" formula has been implemented on decimal values, and different results have been obtained, as shown in Figure 20.  
_Step-4:_ After getting different decimal results, each decimal has been converted to binary, as shown in Figure 21.  
_Step-5:  
When clicking on the _\"Replace,\"_ the previous file will be deleted, and the new file will be saved in place of the previous file. When clicking _\"Keep both,\"_ both files will be saved, as shown in Figure 27.  
The authenticity of the _\"demo (2).txt\"_ file has been checked with the help of the decryption tool, as shown in Figure 28. First, ciphertext has been inserted into the text box, and the text has been decrypted.  
When the text was successfully decrypted, the authenticity of the cipher text was verified using the _Key_ and _Username_, as shown in Figure 29.  
The verification time values could not be successfully decrypted due to changes in ciphertext, so the receiver could not receive the data. Whenever there is an inside attack on a cloud server, it is difficult to stop the attack, but with the help of a cryptosystem, the data can be secured. The most significant advantage of cryptosystems is that if someone tries to carry out a replay attack, such data can be detected during verification time, as is achieved in this paper.  
Different time testing has been undertaken to identify the performance and time complexity of the tool, in which the length of each plain text and Key has been kept different and different results have been obtained by implementing it on the tool. Different tool  
Figure 28: Ciphertext Decryption.  
Figure 27: Replay Attack.  
Figure 29: Inauthentic Transmission result.  
testing results are shown in Table 1, and the time complexity of encryption and decryption is shown in Table 2.
## 4 IDPSO Algorithm Analysis and Design  
Particle swarm optimization (PSO) is an evolutionary computing technique derived from the study of bird predation behavior. The basic idea of the particle swarm optimization algorithm is to find the optimal solution through cooperation and information sharing among individuals in the group. As shown in Table 2, the parameters of six commonly used algorithms are counted. Compared with the other five algorithms, PSO has the fewest number of parameters, which is only four. In addition, the literature [20] points out that the advantage of PSOs is that they are simple and easy to implement and do not have many parameters to adjust.  
(1) Basic idea  
PSO simulates birds in a flock by designing a massless particle with only two properties: speed, which represents how fast it moves, and position, which represents the direction of movement. Each particle separately searches for the optimal solution in the search space, and records it as the current individual extreme value, shares the individual extreme value with other particles in the whole particle swarm, and finds the optimal individual extreme value as the current global optimal solution of the whole particle swarm. All the particles in the swarm adjust their speed and position according to the current individual extreme value they find and the current global optimal solution shared by the whole swarm.  
(2) Update rule  
PSO is initialized to a group of random particles (random solutions). Then, the optimal solution is found through iteration. In each iteration, the particle updates itself by tracking two extreme values (_pbest_; _gbest_). After finding these two optimal values, the particle updates its velocity and position by using the following formula.  
\[v_{i+1}=wv_{i}+c_{1}r(pbest_{i}-x_{i})+c_{2}r(gbest-x_{i}) \tag{18}\]  
\[x_{i+1}=x_{i}+v_{i+1} \tag{19}\]  
where \(i=1\), \(2\), \(3\ldots\), \(N\). \(N\) is t he total number of particles in this group. \(W\) is the inertia factor, and \(c_{1}\) and \(c_{2}\) are learning factors. \(W\) keeps the particles moving inerthly, makes them have the trend of expanding the search space, and gives them the ability to explore new areas. \(c_{1}\) and \(c_{2}\) represent the weights of the statistical accelerators that push each particle into _pbest\({}_{i}\)_ and _gbest_ positions; _pbest\({}_{i}\)_ is the individual optimal location searched by the current \(i\)-th particle, and _gbest_ is the global optimal location searched by the group particle.
# Reducing Railway Track Vibrations by Applying Particle-Damping Systems
## 3 Results
### FIMO and Natural Rubber Particles  
Results regarding FIMO and natural rubber particles will be discussed in the present section. The summarised conditions regarding mass and filling ratio were presented in Table 4. Taking natural rubber as the reference case for mass (1.1 kg), it was only possible to test this material with the maximum mass (entire reservoir volume) of 1.1 kg and 100% filling and with a smaller filling ratio, 0.9 kg at an 80% filling ratio. For FIMO, three cases were studied: 1.3 kg, filling 100% (entire volume of the reservoir fulfilled); 1.1 kg, 100%; and 1.1 kg, 80% fill. Figure 6a presents the results for the entire frequency range. Figure 6b presents the results for the lower frequencies. It can be seen that FIMO (polymeric clay) presented better results than the rubber band (natural rubber) for almost the entire frequency range (except for very low frequencies, below 100 Hz). Peak vibrations (around 450 Hz) were attenuated by a factor of 3. It is important to emphasise that the particle's total mass is very small when compared with the total mass of the rail track, being only 1.3 kg (for FIMO, 100% filling ratio), while the rail mass is approximately 60 kg. In some cases, almost no vibration reductions were obtained from rubber band particles, and similar acceleration amplitudes were obtained when comparing the results with the empty reservoir.  
Figure 6c presents the results for the higher frequency band. As it was observed in the lower frequency domain, the overall performance of FIMO was superior to that obtained from the rubber band. In this frequency range, the best case for vibration attenuation was a factor of two (for FIMO 1.3 kg, 100% fill, reducing peak vibration at around 3200 Hz from 85 m/s\({}^{2}\) to 40 m/s\({}^{2}\)).
###### Abstract
The main contributions of this paper include the following:  
* An in-depth analysis of the impact of different loss function, with different encoder architectures over different types of aerial images is performed. Firefront_Gestosa and FLAME are two dataset of aerial images covering different scenarios. The first set contains very limited fire pixels, less than 1% over the final dataset. The second set contains a higher ratio of fire pixels in comparison to the first one, but it includes some different images of the same view. Usually, the aerial datasets draw segmentation results very low in comparison with the attended performance presented in this paper. * Deeplabv3+ parameter fine tuning to train a model in order to efficiently segment aerial images with limited flame area. Moreover, choosing the adequate encoder architecture combined with a proper loss function will reduce the false negatives (FN) and boost the intersection over union (IoU) and BF score. * A private labeled set of aerial fire pictures named Firefront_Gestosa dataset has been used in the experiments. The labeling task of such aerial profiles is challenging since the part of smoke sometimes fully cover the flame part. A wrong labeled data while induce a misleading trained classifier. The firefighters are more interested in localizing the exact GPS positions of flames to promptly start intervention to limit the propagation. With huge smoke clouds, it is unbearable to visibly localize the flame positions from soil or air. The remainder of the paper is organized as follows: Section 2 discusses the related work and the state-of-the-art techniques and its performance in fire pixel detection. Section 3 explains the methods of the proposed framework, detailing the segmentation process of the Deeplabv3+ model with different backbones and loss functions. Moreover, the datasets adopted in the present study are explained with data preparation and processing techniques. Section 4 discusses the experimental setup, and the results of the flame detection are visualized and evaluated in detail. Section 5 summarizes and concludes the present work.
# Microseismic Electronic Fencing for Monitoring of Transboundary Mining in Mines
## 2 Factors Affecting Localization Accuracy
### Analysis of Localization Methods  
Microseismic localization methods have now been further developed and can generally be divided into two categories of methods based on arrival time and waveforms according to the localization principle. The arrival time-based localization method generally uses the first arrival time of the P-wave, which has a faster propagation and is easy to identify, to locate the source of the earthquake. Conventional microseismic source localization is usually based on establishing an objective function and then selecting an appropriate optimization algorithm to solve the function.  
The objective function used in this paper is:  
\[f=\sum_{i=1}^{n}\lvert t_{i}-t_{i}^{c}-t_{m}\rvert \tag{1}\]  
\[t_{i}^{c}=\frac{d_{i}}{v} \tag{2}\]  
where, \(t_{i}\) is the observed travel time, \(t_{i}^{c}\) is the propagation time from the source to the sensor; \(t_{m}\) is the best estimate of the moment of onset of an earthquake, which is here estimated using the median of \(\left(t_{i}-t_{i}^{c}\right)\)[22]; \(d\) is the distance between the sensor and the source, \(v\) is the stratigraphic wave velocity, and \(n\) is the number of sensors.  
Figure 1: Schematic model and vibration signal recording: (**a**) example of 3D model (**b**) vibration signal recording.  
We evaluate localization methods using the Geiger method, simplex algorithm, and grid search algorithm (the initial source coordinates for the simplex and Geiger methods are the sensor coordinates at the middle position of the observing system). Six events were selected for localization, with a model speed of 3500 m/s, ensuring that the localization error is solely related to the method employed. From the localization results in Table 1, it can be observed that the localization accuracies of the grid and simplex methods for events located at different locations within the observing system are basically equal and both achieve an error of 1-2 m (the localization errors calculated in the paper include the first-to-pickup errors). In contrast, the Geiger method has a larger localization error and even fails to converge for Event 1, which is attributed to its dependence on the selection of the initial seismic source. Therefore, in practice, the Geiger method is not recommended for the localization of such a wide range of undefined events. Since the model used in the simulation is known and considering the grid search algorithm is time-consuming to compute when the accuracy requirement is high, the localization methods employed in subsequent studies will exclusively use the simplex method. The implementation steps of the simplex mainly include: (1) initializing the simplex, (2) calculating the function values of each vertex, (3) updating the simplex, and (4) checking the convergence state.
## 3 Computation of the principal components
### Distributed estimation of the covariance matrix
#### 3.3.2 Scalability analysis  
Highest network load:Each update requires a node \(i\) to send one packet (its measurement) and to receive \(|\mathcal{N}_{i}|\) packets (other sensors' measurements). Let \(i^{*}_{\mathcal{N}}\) be the node that has the largest number of neighbors, i.e., \(i_{\mathcal{N}}^{*}=\arg\max_{i}|\mathcal{N}_{i}|\). The highest network load is therefore \(O(t|\mathcal{N}_{i_{\mathcal{N}}^{*}}|)\).  
Computational and memory costs:From Equation (10), the updates of \(S_{j}\) and \(S_{ij}\) demand \(3|\mathcal{N}_{i}|\) floating points additions/subtractions, \(2|\mathcal{N}_{i}|\) floating points multiplications and \(2|\mathcal{N}_{i}|\) floating points divisions. The highest computational cost therefore scales in \(O(|\mathcal{N}_{i_{\mathcal{N}}^{*}}|)\). The number of variables to maintain at a sensor node is one integer and \(2|\mathcal{N}_{i}|\) floating points numbers, so the highest memory cost amounts to \(O(|\mathcal{N}_{i_{\mathcal{N}}^{*}}|)\).