esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber-Physical-Social Frameworks for Urban Big Data Systems: A Survey
## 6 Data Fusion
### Tensor Decomposition  
The tensor-based methods are employed in existing approaches for data fusion that can detect hidden information and community structure. Following representation of a video clip, an ontology document capturing cyber-space data and social relationships as low-order ranked tensors, Higher-Order Singular Value Decomposition (HOSVD) is then employed to extract the core data which contains the more valuable information [42]. A similarity computation approach is then applied, with a supervised learning method making use of a similarity tensor model, for relation establishment between the various CPSS data objects.  
A similar technique is employed in [67], which uses a distributed and incremental version of the tensor decomposition method HOSVD to capture object and social connections between three families and their smart objects. The tensor method first detects the underlying community structure, which is also updated from time to time using incremental HOSVD (IHOSVD).  
Tensor decomposition is used to analyze the behavior similarity of users in [66]. The authors reduce the dimensionality of user behavior data and address the data sparsity by mapping the initial data to a denser approximate tensor representation. Group-centric data fusion is performed based on the approximate tensor, with each element in the approximate tensor representing the prevalence of the corresponding behavior in the group. By calculating the weight to be assigned to each behavior, the authors perform group discovery based on the tensor approximation.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 2 Error Control Scheme (ECS)
### Forward Error Correction (FEC)
Therefore, the energy analysis comparison between coded and uncoded systems should be dealt with transmission power for redundant bits as well as the decoding cost per bit. In literature, there are various types of FEC with different decoding algorithms that will produce diverse performance. In general, ECCs is broadly divided into two classes: block codes and convolutional codes [2].
# Particle Swarm Optimization Based on a Novel Evaluation of Diversity
## 3 Pso-Ed
### Evaluation of the Exploration Degree
#### 3.2.2 Switch of Evolutionary States  
A conventional state transition of PSO simply set the initial state, the middle state and the last state of evolution as Exploration, Exploitation and Convergence, respectively. We are more flexible in switching the evolution states, and we allow multiple state switches during the whole evolution process. The swarm starts with the Exploration state, and its switching circle is Exploration\(\rightarrow\)Exploitation\(\rightarrow\)Convergence\(\rightarrow\)Exploration. The process terminates as the maximum number of FEs is invoked.  
As for the conditions of state switches, let \(N_{ud}\) be the number of consecutive generations when the result of the swarm evolution has not been improved, if \(N_{ud}>N_{umax}\), the state should be switched at this time to enhance the chance of achieving better results (exploration\(\rightarrow\)exploitation, exploitation\(\rightarrow\)convergence) or jumping out of the local trap (convergence\(\rightarrow\)exploration). However, if the state switches too frequently, it may not lead to any promising result while wasting time in state switch. To address this issue, let \(N_{state}\) be the number of generations the current state has been maintained, depending on whether when \(N_{ud}>N_{umax}\), we constraint \(N_{state}\) in a range of generations (i.e., [\(N_{smin}\), \(N_{smax}\)]) to control the switching frequency as follows:  
* \(N_{state}\leq N_{smin}\): No switch
* \(N_{smin}<N_{state}\leq N_{smax}\): Switch only when \(N_{ud}>N_{umax}\)
* \(N_{smax}<N_{sate}\): Switch  
Note that the state \(S\) shall be used in the Section 3.2.3 to determine the upper and lower bound of the exploration degree.  
Since the swarm may not evolve in every iteration during the evolution process, we use the notion of average evolution rate to judge whether the current iteration has evolved. As shown in Equation (6), the average evolution rate of consecutive \(K\) generations is obtained as the total evolution rate of these \(K\) generations divided by \(K\). When the evolution rate is less than a user-defined threshold \(\alpha\), we increase \(N_{ud}\) by 1, otherwise, we set \(N_{ud}\) as 0.  
\[Er(t)=\ \frac{F(t-K)-F(t)}{K*F(t-K)} \tag{6}\]  
where the \(F(t)\) is the best result obtained in \(t\)-generation.  
\[N_{ud}(t+1)=\ \begin{cases}N_{ud}(t)+1&Er(t)<\alpha\\ 0&otherwise\end{cases} \tag{7}\]
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 2 Families of Sequence-Specific Endoribonuclease TA Toxins
### ToxNp\({}_{a}\), ToxN\({}_{b}\), AbiQ  
ToxN and AbiQ are toxins of Type III TA systems, while the above-mentioned ribonucleases belong to Type II TA systems. In Type III TA systems, RNA functions as an antitoxin that blocks protein toxins by forming antitoxin RNA-toxin protein complexes rather than the protein-protein complexes formed in Type II TA systems [86; 87; 88; 89].  
ToxN from _Pectobacterium atroseticum_ (ToxN\({}_{\text{Pa}}\)) cleaves AA^AK (K represents U or G), and _Bacillus thuringiensis_ cleaves at A^AAA [47; 87]. Cleavage of the antitoxin RNA by AbiQ from _Lactococcus lactis_ demonstrated that this enzyme also has ribonuclease activity, although the sequence specificity has not yet been determined [90]. ToxN expression inhibits cell growth, and the co-expression of the antitoxin (_toxI_) neutralizes the toxicity. Inhibition of ToxN activity by antitoxin RNA has also been confirmed in vitro [47].  
The antitoxin, _toxI_ of _toxIN_, is a non-coding RNA that is an oligomer of 5.5 repeats of nearly identical 36-base sequences [47]. The antitoxin RNA is digested to the monomeric fragments by its cognate toxin. The co-crystal structure of the TA complex indicates that toxin-processed antitoxin RNA remains bound to the toxin at its active site and inhibits the toxin's activity. The toxin has a stronger affinity to unprocessed RNA than to processed RNA, suggesting the involvement of toxins in the antitoxin processing and their importance in the inhibition of toxin [47].
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 2 Problem Formulation  
### Navigational Challenges in Multilevel Environments  
In multilevel environments, 2D SLAM techniques face inherent limitations. For instance, as depicted in Figure 1, consider a robot labeled as A positioned at an elevated level, while another robot, designated as B, is located at a lower elevation. When a 2D LiDAR sensor mounted on robot A perceives the location of robot B, it may incorrectly interpret the path as traversable. However, physical constraints prevent wheeled robots from overcoming significant vertical obstructions, rendering the path infeasible. This discrepancy between perception and reality is exemplified in Figure 1b, where GMapping [6], a 2D LiDAR-based SLAM algorithm, erroneously calculates a direct route across a wheelchair ramp (dotted arrow). In contrast, the actual feasible path (solid arrow) requires the robot to navigate the ramp appropriately. This highlights the pressing need for navigation systems that can accurately account for multilevel environments and robot mobility constraints.
# Analytic Derivation of Ascent Trajectories and Performance of Launch Vehicles
## 1 Introduction
The method is based on simple mathematics and physics principles and only a few seconds are needed to determine the payload mass that can be set into orbits of different altitude and inclinations, starting from any location of the launch pad. The major simplification made in order to obtain these results is that the aerodynamic drag is neglected, and this produces an error in the computation of the launcher trajectories. It is rather difficult to treat the aerodynamic effect analytically due to its highly non-linear dependence on velocity and altitude. Some analytic formulas including aerodynamics are derived in [17]; these however are restricted to the vertical arc of the trajectory. To evaluate the impact of drag, the method presented was compared to numerical results obtained by a high-fidelity level of representation of the launcher dynamics. It is shown that the error in the estimate of the optimal payload mass to be inserted into orbit is relevant only for orbits \begin{table}
\begin{tabular}{l l l l l} \hline
**Launcher** & **Total Mass** & **Number of Stages** & **Max Payload to LEO** & **Maiden Flight** \\ \hline Simorgh, Iran & 78,926 kg & 3 & 350 kg & 2021 \\ \hline Unha, North Korea & 68,039 kg & 3 & 200 kg & 2012 \\ \hline LongMarc11, China & 58,000 kg & 4 & 700 kg & 2015 \\ \hline Spectrum, Germany & 50,000 kg & 2 & 950 kg & 2022 (planned) \\ \hline Minotaur1, USA & 36,200 kg & 4 & 580 kg & 2000 \\ \hline Zhu-Que1, China & 24,600 kg & 3 & 300 kg & 2018 \\ \hline LauncherOne, USA & 23,410 kg & 2 & 500 kg & 2000 \\ \hline Quased, Iran & 22,700 kg & 3 & 50 kg & 2020 \\ \hline Kuoizhou1A, China & 15,100 kg & 3 & 300 kg & 2017 \\ \hline Electron, USA & 11,400 kg & 2 & 225 kg & 2017 \\ \hline Astra R3, USA & 9980 kg & 2 & 150 kg & 2020 \\ \hline \end{tabular}
\end{table} 
Table 1: Listof Micro-Launchers. of altitude below 600 km, and in these cases the error does not exceed 10% Moreover, it is proved that the analytic approach proposed here can provide good guesses for numerical solvers of the optimal control problem. The paper is organized as follows: in Section 2, a first sizing of a space launcher is given based on the Tziolkowski formula. Assuming a payload mass to be delivered in LEO orbit and assuming a fixed percentage of losses, a method is derived to size the staging of a multistage rocket in an optimal way. In Section 3, analytical formulas for the launcher gravity-turn phase are developed in detail for the case of multiple stage rockets. These formulas provide the launcher trajectory of the first stages and allow for the evaluation of the so-called gravity loss. Sections 4 and 5 describe the method of computation of the full launcher trajectory till the injection into orbit. Section 6 is dedicated to testing the method with respect to numerical computations.
## 5 Parameter Optimization Method Based on DE Algorithm
### Solution Evaluation  
Each solution is evaluated by the fitness function designed by us. However, the ground truth needs to be known in advance and there is no appropriate training data with ground truth. In order to solve this problem, we explore a training strategy that a passive stereo vision dataset with ground truth is used as training data, then apply the learned parameter value to the speckle stereo image pairs. Here we select the Middlebury [30] dataset because it is a dataset of indoor scene that is similar to our image acquisition environment. Note that the stereo image pairs for training are grayscale images, which are the same as the speckle stereo image pairs.  
Based on above training strategy, the process of solution evaluation is as follows. Disparity maps corresponding to each solution are obtained by using the stereo matching method shown in Figure 2. It should be noted that the value of N in STMCF is set to 1 at this time. Afterwards, according to the ground truth and mask images provided by training sets of Middlebury 2014 datasets, the average percentage of those pixels whose error is greater than \(K\) (\(K=1\)) pixels in non-occluded area is computed by the fitness function as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Name** & **Data Type** & **Value Range** \\ \hline
1 & r & Integer & [1, 20] \\
2 & eps & Float point & [0.0001, 1] \\
3 & W\_AD & Float point & [0, 1] \\
4 & W\_Census & Float point & [0, 1] \\
5 & W\_grad\_x & Float point & [0, 1] \\
6 & W\_grad\_y & Float point & [0, 1] \\
7 & cen\_win\_h & Integer & [3, 21] \\
8 & cen\_win\_w & Integer & [3, 21] \\
9 & T\_ad & Float point & [0, 0.5] \\
10 & T\_census & Float point & [0, 0.5] \\
11 & T\_grad\_x & Float point & [0, 0.5] \\
12 & T\_grad\_y & Float point & [0, 0.5] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data type and value range of parameters in STMCF.  
Figure 3: The flowchart of the parameter optimization method based on DE algorithm.  
\[E(s)=\frac{1}{n}\sum_{i=1}^{n}\left(\frac{100}{N_{\text{val,nocc}}(i)}\sum_{k\in I _{i}^{\text{val,nocc}}}\left\{\begin{array}{ll}0,&\text{if }\left|d_{i}^{s}(k)-d_{i}^{\text{GT}}(k)\right|\leq K\\ 1,&\text{otherwise}\end{array}\right.\right) \tag{16}\]  
In Equation (16), \(N_{\text{val,nocc}}\) (\(i\)) is calculated by:  
\[N_{\text{val,nocc}}(i)=\sum_{q\in M_{i}}\left\{\begin{array}{ll}1,&\text{if }M_{i}(q)=255\\ 0,&\text{otherwise}\end{array}\right. \tag{17}\]  
where \(E(s)\) denotes the fitness value of solution \(s\), \(n\) is the number of disparity maps, \(N_{\text{val,nocc}}\) (\(i\)) represents the number of valid pixels within the non-occluded area in the \(i\)-th ground truth, \(I_{i}^{\text{val,nocc}}\) denotes the set of all valid pixels within the non-occluded area in the \(i\)-th ground truth, \(d_{i}^{s}\) is the \(i\)-th disparity map corresponding to solution \(s\), \(d_{i}^{\text{GT}}\) represents the \(i\)-th ground truth and \(M_{i}\) denotes the \(i\)-th mask image.
# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
## 4 Methodology
### Discrete Wavelet Transform  
DWT was first proposed by French scholars [PERSON] and [PERSON] in their geologic data analysis in the early 1980s [20]. They found that the conventional Fourier transform could not obtain ideal results; thus, the wavelet concept was imported into the signal analysis. In comparison to Fourier transform or short time span Fourier transform, wavelet transform has multi-resolution characteristics. Therefore, transient signals and non-stationary signals can be detected effectively by using the high flexibility of wavelet transform in local time-frequency analyses.  
The basis function of wavelet transformation is derived from \(a\) mother wavelet function \(\psi(t)\) (mother wavelet) with the compression or expansion of the scale parameter \(a\) (scale parameter), and the translation parameter \(b\) (shifted parameter) along time. The function of \(a\) is to compress or expand the mother wavelet function, and \(b\) represents the time when the mother wavelet function moves along the time axis. The basis function can be expressed as (1) [21]:  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{a}}\psi\Big{(}\frac{t-b}{a}\Big{)},\quad a,b\in R ;a\
eq 0 \tag{1}\]  
Using (1) continuous wavelet transform (CWT), the scale parameter \(a\), and the translation parameter \(b\) of the CWT, the discrete wavelet transform can be obtained. When (\(a\), \(b\)) are discrete values, \(a=a_{0}^{m}\), \(b=na\), where \(m\) and \(n\) are integers, \(a_{0}\
eq 1\), DWT can be expressed as (2):  
\[DWT_{f}(m,n)=\int f(t)\psi_{m,n}(t)\ dt \tag{2}\]  
where the discrete mother wavelet function \(\psi_{m,n}(t)\) is:  
\[\psi_{m,n}(t)=a_{0}^{-m/2}\psi\big{(}a_{0}^{-m}t-n\big{)} \tag{3}\]  
Figure 10: PD detection flow chart.  
If \(a_{0}=2\), it is called a dyadic wavelet, and (3) can be expressed as:  
\[\psi_{m,n}(t)=\frac{1}{\sqrt{2^{m}}}\psi\bigg{(}\frac{t-n}{2^{m}}\bigg{)} \tag{4}\]  
As long as \(m\) is adjusted appropriately, the detailed or approximate analysis of the signal will be available. The discrete wavelet can be expressed as:  
\[DWT_{f}(m,n)=\frac{1}{\sqrt{2^{m}}}\sum_{k}f(k)\psi^{*}\bigg{(}\frac{k-n}{2^{m}} \bigg{)} \tag{5}\]  
where \(DWT_{f}(m,n)\) is the wavelet transform coefficient value of signal \(f(t)\); and \(n\) is the operation index. The symbols and acronyms used in this section are presented in Table 1. Figures 11-14 show the surge arrester's PD signals after DWT.
## 4 Case Study
### Carbon Footprint Accounting  
Equations (1)-(6) are used to compute each stage's carbon footprint. The results are shown in Table 4.  
About \(4.68\times 10^{6}\) kg CO\({}_{2}\) was produced during the physical phase of this example, resulting in a 377.98 kg CO\({}_{2}\) carbon footprint on each square meter of floor surface. Construction material production and processing had the largest carbon footprint, followed by on-site construction, building material transportation, prefabricated component transportation, and prefabricated element processing. Upon examining solely the carbon footprint accounting of the materialization stage, we discovered that the manufacture and processing of building materials are the primary targets for reducing carbon emissions. The reinforcement of concrete and steel, two common building materials, currently accounts for each of the top two greenhouse gas emissions.
## 3 Results and Discussion
### The Impacts of Frequency on XPD at Various Time Percentages  
The dynamic relationship between XPD, frequency, and seasonal rain attenuation in Pretoria was also studied. Seasonal rain-induced attenuations at 0.01%, 0.001%, and 0.0001% of time were selected to investigate the effects of frequency on XPD. XPD generally decreases as the frequency increases from 10 GHz to about 20 GHz, showing that maximum  
Figure 3: Variation of XPD with CPA (CPA) at (**a**) 11.7 GHz, (**b**) 15 GHz, (**c**) 20 GHz, (**d**) 25 GHz, (**e**) 30 GHz, and (**f**) 35 GHz.  
XPD could be attained around 10 GHz as depicted in Figure 4a-d. However, XPD begins to increase with increasing frequency after the dip, resulting in higher values at frequencies above 30 GHz.  
This trend was observed throughout the seasons, as shown in Figure 4a-d; hence the studied frequencies were divided into three segments. The low-range frequency (10-15 GHz), where XPD is relatively high due to lower rain attenuation and depolarization effects, these frequencies are less affected by non-spherical raindrops that cause depolarization. The mid-range frequency (15-25 GHz) range, where XPD reaches its minimum, indicating significant depolarization caused by rain attenuation. This is consistent with raindrop sizes being comparable to the wavelengths at these frequencies, maximizing depolarization effects. The high-frequency region (>25 GHz), where rain attenuation is higher, and the cross-polarized signal leakage decreases due to reduced scattering effects.  
For all seasons, XPD values are higher for lower percentages of time exceedance (e.g., 0.01%) and progressively lower for higher time exceedance percentages (e.g., 0.0001%). This reflects the impact of rain attenuation, as lower percentages of exceedance correspond to severe rainy events, which cause higher depolarization and thus reduce XPD. The values of XPD during autumn are moderate compared to the other seasons. The XPD values for different time percentages are relatively higher compared to summer and spring. This is because autumn rainfall is not as intense as in summer, resulting in less pronounced  
Figure 4: Variation in seasonal XPD with frequency during the: (**a**) autumn (**b**) winter, (**c**) spring, and (**d**) summer.  
depolarization and better XPD. For instance, Table 3 shows that the XPD for 30 GHz at 0.01%, 0.001%, and 0.0001% during the autumn season are 14 dB, 9 dB, and 8 dB while the corresponding values in the summer are 13 dB, 8 dB, and 7 dB, respectively.  
The winter season has the highest XPD across all time percentages and frequencies as shown in Table 3. Although the minimum XPD occurs around 20 GHz, it is not as low as other seasons. XPD values in spring exhibits similar behaviour to autumn but with slightly lower overall values, especially at lower time percentages. Spring rain patterns likely involve lighter but more frequent rainfall, contributing to moderate depolarization effects. XPD remains lowest in summer with a steep reduction for higher time percentages, especially around 20 GHz.
# Blockchain-Assisted Verifiable and Multi-User Fuzzy Search Encryption Scheme
## 5 Proposed Scheme
### Concrete Construction
#### 5.2.2 Encryption Work
The index vector construction for \(F_{i}\) is demonstrated by an example, as shown in the left side of Figure 3. Suppose \(F_{i}\) has three keywords \"_happily_, _teachers_, _likely_\", where \(S=\left[1,0,1,0,0,1,1,0,0,1\right]\), \(l=10\), \(M_{1},\ M_{2}\) are invertible matrices and \(M_{1},\ M_{2}\in R^{10\times 10}\). \(DO\) constructs an \(l-\) bit bloom filter \(B_{i}\) for \(F_{i}\), then converts the set of keywords to the stem set \(ST_{kw}=\left\{happy, teach,like\right\}\). The stem keyword \"_happy_\" is first converted to \(Bv_{1}=\left\{ha_{1},ap_{1},pp_{1},py_{1}\right\}\) and then converted to vector \(bv_{1}\). For ease of representation, Figure 3 is represented by a string of 0 and 1 vectors. Then \(Bv_{2},\ Bv_{3}\) and \(bv_{2},\ bv_{3}\) are constructed for the rest stem keywords in the same way, finally, \(bv_{1}\), \(bv_{2}\), and \(bv_{3}\) are mapped as inputs to \(k\) LSHs on \(B_{i}\), respectively, and the corresponding position of \(B_{i}\) is set to \(TF_{i,j}/k\). Assuming three keywords have the same weight \(TF_{i,j}/k\) of 0.5, the final index vector \(B_{i}\) of the file \(F_{i}\) is obtained. C. Index Tree GenerationDenote \(S\) in \(KS\) and bloom filter \(B_{i}\) of file \(F_{i}\) as \(S=(s_{1},s_{2},\ldots s_{m})\) and \(B_{i}=(b_{1},b_{2},\ldots b_{m})\), respectively, where \(B_{i}\) and \(S\) have the same structure. For each \(b_{i}\) in \(B_{i}\), let \(B_{i^{\prime}}=(b_{1^{\prime}},b_{2^{\prime}},\ldots,b_{m^{\prime}})\), \(B_{i^{\prime}}^{\prime\prime}=(b_{1}^{\prime\prime},b_{2}^{\prime\prime},\ldots,b_{m}^{\prime\prime})\). If \(s_{i}=1\), then \(b_{i}=b_{i^{\prime}}=b_{i^{\prime}}^{\prime\prime}\): If \(s_{i}=0\), then \(b_{i^{\prime}}=b_{i}/2+r\), \(b_{i^{\prime}}^{\prime\prime}=b_{i}/2-r\), where \(r\) is a random parameter and \(r\in R\). Finally, the encrypted index \(l_{i}=(l_{i^{\prime}}^{\prime},l_{i^{\prime\prime}}^{\prime\prime})\) of the file \(F_{i}\) is obtained, where \(l_{i^{\prime}}=(M_{1}^{T}\cdot B_{i^{\prime}})\), \(l_{i^{\prime\prime}}^{\prime\prime}=(M_{2}^{T}\cdot B_{i^{\prime}}^{\prime \prime})\). The index tree construction of the scheme in [15] is used to construct the \(Index_{tree}\) and the storage structure of the leaf nodes is improved on this basis. Leaf nodes store file identifiers and vectors in addition to filing authentication codes and key ciphertexts with access control structures. The internal nodes store the optimal vectors of their leaf node index vectors and the internal node vectors are encrypted in accordance with the index encryption method of \(F_{i}\). Finally, the \(Index_{tree}\) is sent to \(SC\), the \(Index_{tree}\) is shown in Figure 4.