esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence
## 1 Introduction
(1) The process of using Nupack to calculate the unpaired probability of each nucleotide is time-consuming. Therefore, it is possible to use only a few local features of the sequence, which are derived directly from the sequence itself, avoiding the need for a complex and uncertain thermodynamic calculation process. (2) NCP-based sequence encoding leads to less information input because it only describes the characteristics of the sequence without considering the dependence of nucleotides in the sequence. Thus, the representation of sequences can be achieved with higher-dimensional matrices. dna2 vec enables the application of NLP techniques to DNA sequences by using sequence information to acquire the _k_-mer distributed representation [19]. (3) Although the attention mechanism has been widely applied in bioinformatics and possesses a better prediction ability, it has not yet been commonly applied in the field of sequencing depth prediction, so it can be introduced to improve the accuracy of the sequencing depth prediction. To overcome these limitations, we proposed a low-complexity deep learning model to predict the sequencing depth of probe sequences in targeted sequencing. Specifically, in this model, the target and probe sequences were first split into _k_-mer word vector representations using _k_-mer processing. Subsequently, the association information between different _k_-mer word vectors was extracted from the probe sequences using the dna2 vec model to obtain the high-dimensional vector representation of the _k_-mer word vectors. The sequences were transformed into distributed representations based on the different high-dimensional vector representation combinations. Then, the Bi-LSTM was utilized to process the sequences sequentially according to base positions, and interactions between adjacent bases (short-range interactions), as well as interactions between bases that were far apart (long-range interactions), were captured as output for subsequent processing. Later, the attention mechanism was employed to process the output of the Bi-LSTM. Its ability to represent the different effects of nucleotides at different positions on sequencing depth by setting different weights allowed the model to selectively focus on positions that were more important for sequencing depth prediction. Finally, the predicted log sequencing depth was derived through a deep neural network. Figure 1B provides an overview of the DLM and the proposed sequencing depth prediction model.
###### Abstract  
For optical sensing or biomedical sensing where the light source usually has a stable and narrow linewidth, the design rule of the tunable optical delay line (ODL) can be different from the ODLs for optical communications and buffering. We present here a novel way to tune a racetrack resonator-based ODL by push-pull operation to stabilize the resonant wavelength. Full device simulation that accounts for the thermal tuning effect and the photonic characteristics of the whole integrated device is conducted to verify the characteristics of the tunable ODLs. With the simple racetrack resonator, the group delay can simply be tuned by changing the coupling coefficient of the resonator while the wavelength is stabilized by tuning the racetrack loop. A tuning of hundreds of picoseconds is achievable with a very compact device and small power consumption.  
balanced Mach-Zehnder interferometer (BMZI); group delay; racetrack resonator; tunable optical delay line; thermal tuning
# The Influence of Filtration on the Results of Measurements Made with Optical Coordinate Systems
## 4 Discussion  
The initial statistical analysis included the identification of outliers. Basic descriptive parameters were determined, as shown in Table 1. The statistical analysis is presented in Figure 7. Using the multiple comparison test and [PERSON]'s test [17,18] for parameters subject to a normal distribution, a statistically significant difference was found at the significance level of \(\alpha\) = 0.05 between the variances at the analyzed levels of the studied factor: multiple comparison test--\(p\)-posttest = 0.004, [PERSON]'s test--\(p\)-posttest = 0.004 (Figure 8).  
The classic tool for assessing the influence of a selected factor on the behavior of the result parameter is the analysis of variance (ANOVA). The use of this analysis requires meeting certain assumptions, i.e., randomization, equality of variances, and normality of residuals. In a situation where these assumptions are not met, an alternative to one-factor ANOVA is the Welch test or the nonparametric Kruskal-Wallis test (the so-called distribution-free ANOVA) [18,19].  
Due to the lack of equality of variances found at the levels of the analyzed factor (which were the filters used: \"0\", \"5\", \"10\", and \"20\"), the Welch test [20] was used to assess the impact of filtering on the expected assessment result. If the Welch test indicates that there is a significant difference between the levels of the analyzed factor (filters), the Games-Howell multiple comparison test can be used to determine between which levels a statistically significant difference is observed [17]. Through this test, we rejected the hypothesis of equality of mean values at the analyzed levels of the tested factor in favor of the alternative hypothesis that there is a statistically significant difference between at least two levels (\(p\)-posttest = 0.000). The very high coefficient of determination, R2 = 98.31%, is also noteworthy and is presented below (Figure 9).  
Figure 8: Graphical interpretation of the multiple comparison test.  
In view of the rejection of the hypothesis of equality of mean values at the analyzed filtering levels, the Games-Howell multiple comparison test (post hoc test) was performed to identify the significance of differences between the means for each pair of filtering levels. At the significance level of \(\alpha=0.05\), there was a statistically significant difference between each pair of filtering levels (Figure 10).  
The analyses and results obtained clearly show that the use of filters with higher values cuts off the noise to a much greater extent than the assumed need. The use of filters with values of \"5\" or \"10\" gives a real answer as to the value of the measured characteristic and does not distort the actual values. This is a compromise between the benefits and losses obtained from the use of the filtration value.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 7 Evaluation
### Experimental Setup
#### 7.1.4 SL Settings  
In our experiment, the total number \(K\) of **MCs** is set to 10, and all **MCs** are selected in each iteration during training. And we selected the models derived from convolutional neural networks (CNNs) with structure: \(Input\to Conv\to FC\to Output\), to train three different datasets using Python, Numpy, and Tensorflow. Then, the training data are evenly distributed to each **MC**, i.e., for both MNIST and FashionMNIST datasets, the size of each local dataset is 6000. We considered the proportion of different malicious **MCs** from 10% to 50%. Our PBSL scheme is compared with SL and Krum. And the batch size b is set as 128.
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
## 7 Evaluation of the Solutions
### Runs and Results
#### 7.2.2 Effect of the Leaves in \(V_{1}\)  
In the second case, we examined the effect of the nodes with a degree bound of one. For this, \(D_{min}=1\) was set. In the generated graphs, the number of nodes and edges were 70 and 136, respectively. The average number of arcs in the directed graphs after the reduction is indicated in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(D_{max}\) & **c(StT)** & **arc(StT)** & **c(DCMStT)** & **arc(DCMStT)** & **c(DCMStH)** & **arc(DCMStH)** \\ \hline
3 & 75.56 & 37.04 & 92.898 & 38.9388 & 84.28 & 42.32 \\
4 & 73.1 & 35.78 & 83.9 & 37.42 & 79.2 & 39.28 \\
5 & 72.82 & 36.1 & 82.58 & 37.72 & 77.4 & 39.38 \\
6 & 74.2 & 36.12 & 80.68 & 37 & 77.72 & 38.6 \\
7 & 74.62 & 36.3 & 80.42 & 37.2 & 78.08 & 38.76 \\
8 & 71.94 & 36.08 & 77.32 & 36.82 & 74.86 & 38 \\
9 & 73.8 & 35.72 & 77.68 & 36.2 & 76.12 & 37.22 \\
10 & 71.46 & 35.78 & 74.96 & 36.36 & 73.34 & 37 \\
11 & 73.18 & 36.28 & 77.14 & 36.78 & 75.62 & 37.82 \\
12 & 70.58 & 35.88 & 73.54 & 36.04 & 72.42 & 37.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results by varying \(D_{max}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(D_{max}\) & \(|V_{1}|\) & \(|A|\) & **c(StT)** & **arc(StT)** & **c(DCMStT)** & **arc(DCMStT)** & **c(DCMStH)** & **arc(DCMStH)** \\ \hline
3 & 22.5 & 184 & 74 & 37 & 89 & 39.2 & 81.6 & 41.2 \\
4 & 17.3 & 198.4 & 71 & 35.8 & 77.6 & 36.2 & 75.6 & 37.8 \\
5 & 13.1 & 214.4 & 69.4 & 35.2 & 75.2 & 36.2 & 73 & 37.2 \\
6 & 11.4 & 227.9 & 69.6 & 36.2 & 78.2 & 37.6 & 74.2 & 39.6 \\
7 & 10.3 & 232.9 & 73.4 & 37.2 & 78.6 & 38 & 76.4 & 39.2 \\
8 & 7.2 & 245.2 & 77 & 35.8 & 80.8 & 36.6 & 79.6 & 37.2 \\
9 & 9.2 & 233.4 & 71.6 & 34.8 & 74.4 & 35.8 & 74 & 37.2 \\
10 & 7.5 & 242.3 & 78 & 36.8 & 84 & 37 & 82 & 38 \\
11 & 6.7 & 246.9 & 74.4 & 36.6 & 81 & 38.6 & 78.6 & 40.4 \\
12 & 5.1 & 251.5 & 76.6 & 35.8 & 79.4 & 36.6 & 78.6 & 36.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of nodes in \(V_{1}\) by varying \(D_{max}\).  
The average number of nodes in \(V_{1}\) and the number of cases without solution are also indicated. When \(D_{max}\) is increased, the proportion of nodes in \(V_{1}\) decreases (the degree bounds are uniformly distributed in \([D_{min},D_{max}]\)). The number of arcs \(|A|\) after the reduction increases. Note that the number of failed computations can be high when the number of nodes in \(V_{1}\) is high due to the separators. In some cases, the degree-constrained Steiner tree cannot be computed even though the hierarchy exists. The values correspond to the average values of the cases with successful computation.
## 3 Synthesis of Crown Ethers and Their Derivatives
### Synthesis of Aza-Crown Ether-Squaramide Conjugates  
This crowded Scheme 5 presents the formation of compounds (**8-11**) [55]. As reported earlier, an important structure 1,10-diaza-18-crown-6 was used as a starting material for the synthesis of compounds 2,2\({}^{\prime}\)-(1,4,10,13-tetraoxa-7,16-diazacyclooctadecane-7,16-diyl)diethanamine (**2**) and 2-(16-ethyl-1,4,10,13-tetraoxa-7,16-diazacyclo-octadecan-7-yl)ethaneamine (**5**) [56]. The product formation involved the reaction between 1,10-diaza-18-crown-6 and \(N\)-(2-bromoethyl)phthalimide giving compounds 2,2\({}^{\prime}\)-((1,4,10,13-tetraoxa-7,16-diazacyclooctadecane-7,16-diyl)bis(ethane-2,1-diyl))-bis(isoindoline-1,3-dione) (**1**) and 2-(2-(1,4,10,13-tetraoxa-7,16-diazacyclooctadecan-7-yl)ethyl)isoindoline-1,3-dione (**3**). Compound 2-(2-(16-ethyl-1,4,10,13-tetraoxa-7,16-diazacyclooctadecan-7-yl)ethyl)isoindoline-1,3-dione (**4**) was formed by process of alkylation of compound 2-(2-(1,4,10,13-tetraoxa-7,16-diazacyclooctadecan-7-yl)ethyl)isoindoline-1,3-dione (**3**) with ethyliodide. Similarly compounds 2,2\({}^{\prime}\)-((1,4,10,13-tetraoxa-7,16-diazacyclooctadecane-7,16-diyl)bis(ethane-2,1-diyl))bis-(isoindoline-1,3-dione) (**1**) and 2-(2-(16-ethyl-1,4,10,13-tetraoxa-7,16-diazacyclooctadecan-7,16-diyl)diethanolamine (**2**) and 2-(16-ethyl-1,4,10,13-tetraoxa-7,16-diazacyclooctadecan-7-yl)ethanamine (**5**), respectively. Then reaction of a specific compound 3,4-diethoxycyclobut-3-ene-1,2-dione at different conditions like anhydrous EtOH at room temperature with Zn(Cr\({}_{3}\)SO\({}_{3}\))\({}_{2}\) and 4-trifluoromethylaniline resulted in compounds 3-((3,5-bis(trifluoromethylophenyl)phenyl)amino)-4-ethoxycyclobut-3-ene-1,2-dione (**6**) and 3-ethoxy-4-((4-trifluoromethylomethyl)phenyl)amino)cyclo but-3-ene-1,2-dione (7) [53; 57]. Finally, according to literature, reactions between compounds 2,2\({}^{\prime}\)-(1,4,10,13-tetraoxa-7,16-diazacyclooctadecane-7,16-diyl)diethane-amine (**2**), 2-(16-ethyl-1,4,10,13-tetraoxa-7,16-diazacyclooctadecan-7-yl)ethaneamine (**5**) and compounds 3-((3,5-bis(trifluoromethylophenyl)phenyl)amino)-4-ethoxycyclobut-3-ene-1,2-dione (**6**), 3-ethoxy4 ((4(trifluoromethylphenyl)phenyl)amino)cyclobut-3-ene-1,2-dione (**7**) in the presence of triethylamine (Et\({}_{3}\)N) and ethanol (EtOH) at room temperature yielded in the ultimateaza-crown ether-squaramide conjugate compounds (**8-11**) (Scheme 5) [57; 58].
# Machine Learning-Assisted Secure Random Communication System
## 2 Machine Learning for Physical Layer Security
The transmitter 'T' generates SkaS noise as a random carrier on the basis of the incoming binary bits. The inherent symmetry of the skewness parameter of the SkaS noise is exploited to transmit binary bits '0' and '1'. The legitimate receiver 'LR' uses the least complex ML algorithm, that is, a DT, which is pretrained on a developed \(\alpha\)-stable noise signal model (\(\alpha\)-SNSM). The \(\alpha\)-SNSM comprisesall the combinations of parameters required to generate the Sk\(\alpha\)S noise as a random carrier. Moreover, we include a unique private key, that is, the pulse length, in the \(\alpha\)-SNSM while training the DT-based receiver, and we assume that it is known only to the T and LR. Only the LR can decrypt the transmitted Sk\(\alpha\)S noise and retrieve the binary information via the pretrained DT and a unique private key. However, no eavesdropper can decrypt the transmitted Sk\(\alpha\)S noise and retrieve the binary information without knowledge of the pretrained DT and the unique private key. Integrating ML into an RCS in this unique way produces an extremely efficient BER performance and enhanced covertness in comparison with previous RCSs, which were based on static estimators. The proposed ML-RCS can retrieve a unit binary bit from transmitted random carrier signals by utilizing an extremely small number of Sk\(\alpha\)S noise samples. Hence, it provides a significantly increased data rate in comparison with previously proposed RCSs. The reason for choosing DTs is their probable suitability as receivers operating with alpha-stable noise since they have a non-parametric nature and provide robustness against outliers. Opposite to the prominent ML algorithms (KNNs, SVMs, and neural networks), DTs do not rely on distance metrics or statistical assumptions due to their absence under heavy-tailed alpha-stable noise. They have been shown to operate with nonlinear decision boundaries in a good manner. Most importantly, they require minimum computational complexity for real-time applications, making them a feasible choice for signal classification in non-Gaussian noise conditions. Therefore, we expect DTs to be a more practical choice with which to establish a baseline ML-RCS model which can be used as a benchmark in the future. Their use makes the ML-RCS a potential candidate for providing security for future communication systems in which security through unconventional mechanisms is explicitly needed.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation
### Research on Vulnerability Discovery  
With the increase in the number of vulnerabilities in IoT devices and the rise of attack trends, security researchers pay more and more attention to the vulnerability mining of devices. This section describes the technology of vulnerability discovery, including dynamic analysis and static analysis. By studying traditional program security analysis, we find the dynamic analysis that involves fuzzing [49] and taint checking [50], while the static analysis involves symbolic execution [51], taint analysis, and data-flow analysis [50].
###### Abstract  
Two types of grounding systems are recommended for use in the international standard IEC 62305-3, Part 3: Physical damage to structures and life hazard. One of these is a radial-based grounding system (type-A), which is used in soil resistivities of up to 3000 \(\Omega\)m and is considered in this paper. It is a well-known fact that during lightning strikes, only a part of the grounding wire contributes to dissipating the lightning current into the surrounding soil. This effective part of the grounding system depends on several features, such as soil resistivity, burial depth, and rise time of the dissipated lightning current. The effect of all of these features on the effective length of the type-A grounding system is explored in this paper. A suitable supervised machine learning regression model is developed, which will enable readers to accurately approximate the effective length of the type-A grounding system for realistic values of input features. The trained model in the paper yielded an \(R^{2}\) value of 0.99998 on the test set. In addition, two simple mathematical formulas are also provided, which produce similar but less accurate results (\(R^{2}\) values of 0.989883 and 0.998557, respectively).  
lighning; grounding; effective length; impulse impedance; nonlinear regression; Gaussian process regression; machine learning Article
## 1 Introduction
### Related Work  
In [7], the authors only considered the transmitter optimization problem for a multiuser downlink channel with multiple transmit antennas at the BS, however, they did not leverage the DNN. Additionally, in [8; 9], the optimal beamforming design was derived based on optimization theory. However, they did not consider the LoRa modulation and the DL. Additionally, the optimization theory based power allocation scheme was developed in [10; 11; 12], however, they also did not consider the LoRa modulation and the DL. In [13; 14], the authors considered the optimal beamforming problem via deep learning, but they did not deal with the LoRa modulation. In [15], the authors used the beamforming technique in the LoRa-based sensing system. However, this work did not mention the method for designing the optimal beamforming vector. To the best of our knowledge, for the LoRa modulation signal, the deep learning based beamforming design or power allocation scheme has not been investigated in the literature.
## 3 Results and Investigation
### Temperature Characteristics of a Cross-Bar during Induction Heating
#### 3.2.2 Experimental Results and Investigation
Therefore, if the coupling efficiency between the reinforcing bar and coil is poor, a large current flow. Furthermore, there are capacitors and transformers in the matching device (circuit), which act as resistors. \[F=G\frac{m_{1}m_{2}}{r^{2}} \tag{8}\]  
Here, \(G=6.672\times 10^{-11}m^{3}/kg\cdot s^{2}\)  
\(m=\) mass  
\(r=\) distance  
\(F=\) magnetic force  
\[\text{magnetic force}\;(\text{intensity of magnetic field})=\text{A}(\text{current})\times\text{T}(\text{coil differential}) \tag{9}\]  
As shown in Figure 14, the distance between the coils is the same, and the distance between the heating coil and the surface of the reinforcing bar is the same. (3) Frequency--10 KW output  
Using a maximum output of 10 kW, the crossed reinforcing bars were heated, and the results according to the reinforcing bar type and temperature characteristics are shown in Figure 16.  
In the case of 10 kW, when the distance from the heating coil was close to 5 kW, the target temperature was reached in a shorter time than 5 kW, while showing a sudden temperature rise gradient. At a short distance of less than 10 mm, the temperature rose sharply and exceeded 800 \({}^{\circ}\)C within 20 s, making the temperature impossible to measure. In addition, as the output increased, it was confirmed that the heating range expanded, and a magnetic field was formed at 5 kW until the temperature exceeded the range of temperature rise, affecting the reinforcement surface up to 90 mm from the center. Figure 15: Temperature characteristics of crossed rebar (5 kW). Figure 13:0 _Cont_. Figure 13:1 _Cont._  
In the case of the lower reinforcing bars, a heating distance of 30 mm or less was within the magnetic field formation range, and the heating distance of the reinforcing bar was 40 mm or more, which was higher than the heat conduction effect of the upper reinforcing bar. This was determined to be the result of the increase in penetration depth due to the output structure, indicating the possibility of adjusting the range of magnetic field formation by structuring the output volume without consideration of the diameter of the coil.
# Non-Traditional Layout Design for Robotic Mobile Fulfillment System with Multiple Workstations
## 3 Problem Description and Distance Expression
### Travel Distance Expression in a Traditional Layout  
Following the same assumption as [PERSON] et al. (2018), we specify that each pod must take the shortest path if it will be delivered to a candidate workstation. In addition, the distance equations under traditional layout are similar to those in [PERSON] et al. (2018). Figure 3 illustrates a sketch map of a traditional warehouse layout. The number of rows and columns are \(L\) and \(N\) respectively. Without loss of generality, \(N\) is supposed to be an even number and \(N\geqslant 4\). We define a coordinate system where the origin locates in the center of the rectangle bottom edge. As shown in Figure 3, a pod is denoted by \((i,j)\), which represents that the pod is in row \(j,j\in[1,L]\), column \(i,i\in\left[-\frac{N}{2},\frac{N}{2}\right]\). The width of each picking aisle is \(w_{a}\), and the width of each pod is \(w_{p}\). We only consider two cross aisles at the top and bottom edges with width \(w_{e}\). We assume that there are \(|\mathcal{R}|\) candidate workstations (stars in Figure 3), and each is denoted by \((x_{r},y_{r})\), \(r\in\mathcal{R}\) (where \(\mathcal{R}\) is the set of candidate workstations). The center of a workstation is \(\frac{1}{2}w_{p}\) away from the edge. Each pod is associated with a point by its geometric center, denoted by \((x_{ij},y_{ij})\) in the coordinate system that:  
\[x_{ij} =\left(\frac{i}{|i|}\right)\cdot\left[\frac{1}{2}w_{a}+\left(\lceil \frac{|i|}{2}\rceil-1\right)w_{a}+\left(|i|-\frac{1}{2}\right)w_{p}\right]\] \[y_{ij} =\left(j-\frac{1}{2}\right)w_{p}+w_{e} \tag{1}\]  
where \(\lceil\rceil\) refers to the ceiling function. We define the travel distance error caused by whether the column index is an odd number or an even number as  
\[e_{i}=\left[(|i|+1)mod\ 2\right]\left(w_{p}+w_{a}\right) \tag{2}\]  
If pod \((i,j)\), located at \((x_{ij},y_{ij})\), is retrieved and delivered to the workstation \(r\), then the distance can be measured by the following expression:  
\[d_{ijr}=|x_{ij}-x_{r}|+e_{i}+|y_{ij}-y_{r}| \tag{3}\]  
We notice that the error term, \(e_{i}\), exists in the travel distance estimation for 50% of the pods in the warehouse, but its impact on the calculation results is marginal and hence can be neglected. Therefore, we rewrite Equation (3) by  
\[d_{ijr}=|x_{ij}-x_{r}|+|y_{ij}-y_{r}| \tag{4}\]