esa-sceva/satcom-chunk-collection huggingface dataset:
# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 4 Results and Discussion
### Performance of DBT across the Classifier  
In this section, we assess whether performance of DBT is consistent across the classifiers or varies for different classifiers. To do this assessment, we used Kendall's 'w' statistics. When 'w' is 1, then there is complete agreement over the ranks; and when 'w' is 0, then there is complete disagreement over the ranks. The ranks of DBT and results of the Kendall's statistics are shown in Table 14. The results show that there is agreement over the ranking of data-balancing techniques. However, the concordance coefficient value (w) is 0.562, which indicates that there is partial agreement over the ranking. It is observed from Table 14 that there is consistency in the ranking only for None and ROSE techniques. However, there was no consistency in the performance of the US, OS, HS, SMOTE and CBUS techniques. The experimental results show that the performance of None and ROSE was poor and consistent. However, there was no consistency in the performance of US, OS, HS, SMOTE, and CBUS techniques, but its performance was better than ROSE and None.  
In this section of the paper, we have attempted to answer the following question: Is performance of DBT consistent across classifiers? The results show that the performance of DBT is not consistent across different classifiers.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 2 On-Board Ship Detection Based on Deep Learning
### Global Average Pooling  
The parameters of Full Connection (FC) layers account for about 80% of the parameters of the whole network, resulting in a large model, which is not suitable for on-board applications. Therefore, the method of GAP [41] is adopted after the ROI Pooling layer to reduce the parameters of the model. One advantage of global average pooling layer over the fully connected layers is that it is more native to the convolution structure and another advantage is that there is no parameter to optimize in the global average pooling, thus over-fitting is avoided at this layer [41].
## 3. Experimental Results and Discussion
### Simulation Validation Experiments
#### 3.2.1 Velocity Vector Distribution  
As shown in Figure 6, under different inlet airflow velocities, airflow is uniformly distributed within the pressurized tube, and the trend of velocity variations within the vane is generally consistent. In the straight section of the pressurized tube, there is some variation in airflow velocity, with the inner side of the bend a having higher airflow velocity and the outer side having a relatively lower airflow velocity. However, as the inlet airflow velocity increases, the differences in airflow velocity within the straight section decrease. As the airflow flows into the corrugated section, the change in pipe diameter results in an increase in airflow velocity. Aside from the region near the pipe wall, where the velocity is lower, the airflow velocity at other positions within the vane is essentially uniform. This indicates that this structural design can enhance the uniform distribution of airflow within the vane, ensuring the seeding system's seeding performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & **Inlet Air** & **Seeding** & **Seed Velocity at the** & **Air Velocity at the** & **Physesure** & **Distribution uniformity** & **Consistency Coefficient of** \\  & **Velocity** & **Rate** & **Pressurized** & **Pressurized** & **Loss/Pa** & **Coefficient**/**\%** & **Each Row**/**\%** \\ \hline
1 & 35 & 160 & 1.85 & 33.71 & 636 & 95.24 & 3.12 \\
2 & 35 & 240 & 4.63 & 33.04 & 700 & 96.85 & 2.79 \\
3 & 35 & 320 & 4.85 & 32.69 & 784 & 95.77 & 2.91 \\
4 & 45 & 160 & 6.29 & 40.67 & 511 & 97.18 & 2.72 \\
5 & 45 & 240 & 6.46 & 39.61 & 544 & 98.24 & 2.54 \\
6 & 45 & 320 & 6.11 & 38.98 & 784 & 97.63 & 2.63 \\
7 & 55 & 160 & 7.70 & 50.37 & 898 & 95.86 & 2.94 \\
8 & 55 & 240 & 8.16 & 49.57 & 900 & 96.72 & 2.77 \\
9 & 55 & 320 & 7.38 & 48.81 & 1015 & 96.83 & 2.83 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation experiment results.  
Figure 6: Airflow field distribution at different inlet air velocities.
## 5 Parameter Tuning
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy before Hyper-Tuning**} & \multicolumn{6}{c}{**Precision before Hyper-Tuning**} \\ \hline
**Datasets** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** \\ \hline IRIS & 95.71 & 98.34 & 95.71 & 93.08 & 95.08 & 87.81 & 94.36 & 96.99 & 94.36 & 91.73 & 93.73 & 86.46 \\ Glass & 95.56 & 96.29 & 92.32 & 87.29 & 89.89 & 85.34 & 94.21 & 94.94 & 90.97 & 85.94 & 88.54 & 83.99 \\ HAR & 89.42 & 88.27 & 87.17 & 82.57 & 83.97 & 81.31 & 88.07 & 86.92 & 85.82 & 81.22 & 82.62 & 79.96 \\ B\_Tissue & 96.68 & 97.54 & 93.83 & 87.83 & 89.54 & 81.54 & 95.33 & 96.19 & 92.48 & 86.48 & 88.19 & 80.19 \\ Car & 87.47 & 83.86 & 81.94 & 77.01 & 80.24 & 71.03 & 86.12 & 82.51 & 80.59 & 75.66 & 78.89 & 69.68 \\ CMC & 88.18 & 85.89 & 82.64 & 80.63 & 81.62 & 75.87 & 86.83 & 84.54 & 81.29 & 79.28 & 80.27 & 74.52 \\ Ecoli & 92.95 & 89.86 & 89.35 & 82.65 & 87.46 & 81.04 & 91.6 & 88.51 & 88.51 & 88.13 & 86.11 & 79.69 \\ Nursery & 83.91 & 84.64 & 82.96 & 79.79 & 81.91 & 71.68 & 82.56 & 83.29 & 81.61 & 78.44 & 80.56 & 70.33 \\ Opt\_Digits & 86.44 & 86.98 & 84.16 & 81.57 & 82.57 & 76.41 & 85.09 & 85.63 & 82.81 & 80.22 & 81.22 & 75.06 \\ Page\_Block & 86.82 & 84.34 & 85.38 & 73.82 & 83.12 & 74.21 & 85.47 & 82.99 & 84.03 & 72.47 & 81.77 & 72.86 \\ Satimage & 86.5 & 85.37 & 86.5 & 76.36 & 83.79 & 73.53 & 85.15 & 84.02 & 85.15 & 75.01 & 82.44 & 72.18 \\ Soyaben & 93.32 & 92.01 & 90.89 & 86.81 & 89.48 & 82.56 & 91.97 & 90.66 & 89.54 & 85.46 & 88.13 & 81.21 \\ Vehicle & 83.47 & 84.47 & 76.33 & 67.76 & 78.13 & 71.47 & 82.12 & 83.12 & 74.98 & 66.41 & 76.78 & 70.12 \\ Volcanoesa & 81.78 & 79.97 & 82.87 & 72.85 & 75.1 & 69.94 & 80.43 & 78.62 & 81.52 & 71.5 & 73.75 & 68.59 \\ Wine & 97.58 & 97.58 & 95.89 & 83.85 & 89.2 & 81.89 & 96.23 & 96.23 & 94.54 & 82.5 & 87.85 & 80.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy and Precision after Parameters Hyper-Tuning. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy before Hyper-Tuning**} & \multicolumn{6}{c}{**Precision before Hyper-Tuning**} \\ \hline
**Datasets** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** \\ \hline IRIS & 90
## 2 System Configuration of AC/DC Hybrid Microgrid  
### System Topology of AC/DC Hybrid Microgrid  
The AC/DC hybrid microgrid system studied in this paper belongs to a typical topology structure [15], including AC sub-microgrid, DC sub-microgrid, and interlinking converter, and the system is incorporated into the public grid by the AC bus. Its topology is shown in Figure 1.  
It can be seen from Figure 1 that in the DC sub-microgrid, a group of photovoltaic units is connected to the DC bus by Boost converter, and the other group of batteries is connected to the DC bus by Boost/Buck converter as DC energy storage. In the AC sub-microgrid, a group of photovoltaic units is connected to the AC bus through the Boost converter and DC/AC converter, and a group of batteries is connected to the AC bus as AC storage energy through Boost/Buck converter and bidirectional AC/DC converter. In order to increase the capacity of power flow between sub-microgrids, two parallel bidirectional AC/DC interlinking converters are placed in the middle of the system as a bridge to connect the two sub-microgrids.
## 4 Conclusions  
This study describes the metabolite profile and distribution of VOCs of ARR. An HS-SPME-GC-MS method was established, and a total of 153 VOCs were tentatively identified in rats in vivo, 101 of which were original constituents of ARR and 15 were metabolites, the metabolic reactions of which were mainly phase I (oxidation and reduction), with only two cases of phase II (methylation and esterification). Of the 153 VOCs identified, 131 are reported for the first time in rats after oral administration of ARR. The detected VOCs were wildly distributed to the feces, urine, blood, and organ tissues (brain, heart, lung, spleen, liver, kidney, stomach, and small intestine) in the rats after oral administration of ARR decoction or powder. The original constituents were found with the highest numbers in the stomach (65), followed by feces (52), urine (44), kidney (36), small intestine (33), liver (29), blood (24), spleen (17), brain (14), heart (14), and lung (14). Moreover, the original constituents were found with much higher numbers in the powder-treated group (98) than the decoction-treated group (43). Most metabolites were detected in the urine (12/15), whereas only 1 metabolite was found in the feces (1/15). In addition, the kidney was the main distribution organ of metabolites, with a total of four metabolites (4/15), compared to the liver, stomach, and small intestine, with one each. Methyleugenol, 3,5-DMT, 2,3,5-TMT, 3,4,5-TMT, and safrole were the main absorbed constituents in the powder-treated group, while 3,4,5-TMT, 2,3,5-TMT, and kakuol were the main absorbed constituents in the decoction-treated group. The different absorbed constituents between the two groups were mainly methyleugenol, safrole, and 3,5-DMT, which might play major roles in the acute toxicity of ARR powder. The results of this research provide a reference for evaluating the efficacy and safety of ARR.  
The following are available online: Figures S1-S2, Tables S1-S23.  
**Author Contributions:** Participated in research design: [PERSON]-[PERSON], [PERSON], [PERSON], and [PERSON]; Conducted experiments and performed data analysis: [PERSON]-[PERSON] and [PERSON]; Wrote or contributed to the writing of the manuscript: [PERSON]-X.L., M.-Y.S., [PERSON], and S.-Q.C.; Other: [PERSON] and S.-Q.C. acquired funding for the research. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financially supported by National Standardization Project of Traditional Chinese Medicine (No. ZYBZH-Y-J-43); General Program of National Natural Science Foundation of China (No. 81274073). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Underwater Routing Protocols
### Mmbr  
Markov model-based routing (MMBR) probabilistically selects routes from the bottom to the top that are more stable, adaptable with respect to the varying data traffic and have fewer hops [30]. However, the movement of nodes with water currents changes the positions of the nodes, and therefore, relay nodes may not be accessed at their calculated locations.
## 2 Method
### Positioning Methods  
The indoor visible light fingerprint localization method based on MPA-LSSVM proposed in this paper is divided into the following two main phases: In the offline phase, the fingerprint database is first constructed, where the classified labels are the positional coordinates of the reference points, and the features are the values of the light intensities received by a number of reference points selected in the localization area. Then, the MPA is introduced to iteratively find the optimal and establish the localization model for the two hyperparameters of the LSSVM; in the online stage, the light intensity signal at the preset test point is used as the fingerprint data, inputted into the localization model, and the coordinates of the to-be-measured point are computed and outputted to ultimately realize the localization.  
The MPA-LSSVM fingerprint localization modeling process is as follows:  
1. Perform (0, 1) normalization of the signal strength received by the illuminance meter. The specific formula is as follows: \[x_{i}^{\prime}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}},\] (10) where \(x_{i}\) is the collected fingerprint data; \(x_{max}\) and \(x_{min}\) are the minimum and maximum values in the sample, respectively; and \(x_{i}^{\prime}\) is the value after normalization.
2. Determine the parameters of the MPA algorithm, including the number of prey \(n\), the maximum number of iterations \(Max\_Iter\), the FADS, and the initial position of the prey \(X_{0}\). Let the optimal position be \(X_{best}\).
3. Define the fitness function. The fitness function chosen in this paper is as follows: \[f(x)=\frac{1}{2n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2},\] (11) where \(n\) is the number of samples, \(y_{i}\) is the true output of the training data \(x_{i}\), and \(\hat{y}_{i}\) is the predicted value of the model for \(x_{i}\). The closer the value converges to 0 the closer the predicted value is to the true value, indicating that the solution obtained is closer to the optimal solution. (4) Iterative optimization is performed in each of the three different phases to update the predator's position iteratively. Calculate the fitness value of the prey position and store the marine memory, integrate the effect of FADS on the fitness value, calculate the optimal predator position based on the prey position and behavior, and finally determine the current optimal position \(X_{best}\), and from this, obtain \([\gamma_{best},\sigma_{best}^{2}]\).
5. The iteration termination condition is determined. When the iteration proceeds to the maximum number of times or when the predator's fitness value reaches the preset accuracy, the iteration terminates and outputs the optimal parameter set \([\gamma_{best},\sigma_{best}^{2}]\), otherwise return to step 4.
6. The computed global optimal parameters of the MPA are substituted into the LSSVM to complete the model building for fingerprint localization.  
The flowchart for building the MPA-LSSVM fingerprint localization model is shown in Figure 2.
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 3 The Proposed Scheme
### Initial Phase  
In this phase, an IoT node \(DS_{l}\) acts as a data sender; it first selects two large, distinct primes, where one is \(p_{1}\) and the other is \(p_{2}\) such that \(n=p_{1}\cdot p_{2}\), where \(l=1\sim l\) and \(l\) are totally node numbers. \(DS_{i}\) also publishes this \(n\) and we could know that given a QR in \(Z_{n}^{*}\); there are four different square roots (or 2 roots) of the QR in \(Z_{n}^{*}\). From this property, we could derive the \(2^{i}\)th roots of the QR in \(Z_{n}^{*}\), where \(i\) must be larger than \(1\) in \(Z_{n}^{*}\). On one hand, we assume that there exists a powerful base station as a signer \(BS\), which also selects two large primes, where one is \(p_{3}\) and the other is \(p_{4}\) in the same IoT network environment. It also computes \(\hat{n}=p_{3}\cdot p_{4}\) and sets up to let \(n<\hat{n}\). Then, it publishes \(\hat{n}\) and its prefix string \(\Omega\). In the following, we take [PERSON] and [PERSON]'s Scheme [12] as our reference. Nevertheless, the data receiver (\(R\) for short) sets up its own private/public key pair as (\(sk_{R}\), \(pk_{R}\)). When the set-up is finished, it publishes its own public key to the IoT network.  
* First, a node \(DS_{i}\) randomly chooses its own QR numbers (\(z_{1}\), \(z_{2}\), \(z_{3}\)) from \(Z_{n}^{*}\) similar with \(y_{1}\), \(y_{2}\) and \(y_{3}\), where each of them is computed from \(y_{i}=(z_{1}^{2}\mod n)\) and \(i=1\sim 3\), respectively. Then, base station \(BS\) also selects two random QR numbers \(\alpha\) and \(\beta\) such that they allow \((\beta^{2}/\alpha^{2}\mod n)\) to belong to QR in \(Z_{n}^{*}\). \(DS_{i}\) also publishes (\(n\), \(y_{1}\), \(y_{2}\), \(y_{3}\)) to the signer \(BS\). Once the signer \(BS\) has received them from \(DS_{i}\), \(DS_{i}\) computes \(\gamma=(\kappa^{2}\mod\hat{n})\) with a random number \(\kappa\) and the identifier \(\hat{z}=H_{1}(z)\mod\hat{n}\) with an identifier number \(z\). After setting up these random numbers, \(BS\) forwards (\(\gamma\), \(\hat{n}\), \(z\)) to \(DS_{i}\) and enters the offline-signing phase.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 4 Performance Analysis
### SK/PK Lower Bound
#### 4.1.1 Derivation of \(R_{s}\)  
Assuming that [PERSON] and [PERSON] utilize the ZF method to obtain the observations of the reference channel \(G_{1}\), \(\hat{G}_{1,B}\) and \(\hat{G}_{1,C}\) are given by  
\[\hat{G}_{1,B} =\tilde{Y}_{R}^{(1)}-\tilde{Y}_{B}=G_{1}+N_{R}^{(1)}-N_{B}, \tag{16}\] \[\hat{G}_{1,C} =\tilde{Y}_{R}^{(2)}-\tilde{Y}_{C}=G_{1}+N_{R}^{(2)}-N_{C}, \tag{17}\]  
respectively. The calculation of the correlation coefficient between \(\tilde{Y}_{A}\) and \(\hat{G}_{1,B}\) is given by  
\[\rho_{\tilde{Y}_{A},\hat{G}_{1,B}}^{2} =\frac{\text{Cov}^{2}\bigl{(}\tilde{Y}_{A},\hat{G}_{1,B}\bigr{)} }{\text{Var}\bigl{(}\tilde{Y}_{A}\bigr{)}\text{Var}\bigl{(}\hat{G}_{1,B}\bigr{)}} \tag{18}\] \[=\frac{p_{A}^{2}}{\bigl{(}p_{A}+\sigma_{1}^{2}\bigr{)}\bigl{(}p_ {A}+\sigma_{1}^{2}+\sigma_{2}^{2}\bigr{)}}.\]  
Thus, \(I\bigl{(}\tilde{Y}_{A};\hat{G}_{1,B}\bigr{)}\) is given by  
\[I\bigl{(}\tilde{Y}_{A};\hat{G}_{1,B}\bigr{)} =-\text{log}_{2}\Bigl{(}1-\rho_{\tilde{Y}_{A},\hat{G}_{1,B}}^{2} \Bigr{)} \tag{19}\] \[=\text{log}_{2}\Biggl{(}1+\frac{p_{A}^{2}}{p_{A}\bigl{(}2\sigma_{ 1}^{2}+\sigma_{2}^{2}\bigr{)}+\sigma_{1}^{4}+\sigma_{1}^{2}\sigma_{2}^{2}} \Biggr{)}.\]Similarly, \(I\big{(}\tilde{Y}_{A};\hat{G}_{1,C}\big{)}\) is given by  
\[I\big{(}\tilde{Y}_{A};\hat{G}_{1,C}\big{)}=\log_{2}\Bigg{(}1+\frac{p_{A}^{2}}{p_ {A}\big{(}2\sigma_{1}^{2}+\sigma_{3}^{2}\big{)}+\sigma_{1}^{4}+\sigma_{1}^{2} \sigma_{3}^{2}}\Bigg{)} \tag{20}\]  
Hence, the expression of \(R_{s}\) can be obtained by substituting (19) and (20) into (14).
## 3 Results and Discussion
### COMSOL Simulation Settings  
In order to validate the results given by FLARE, two different COMSOL Multiphysics(r) simulations were conducted considering both the above-described three-phase balanced system and lightning scenarios. Both simulations were performed using the AC/DC module and, specifically, the magnetic field (mf) submodule. In order to perform a fair comparison between FLARE and COMSOL Multiphysics(r), we needed to employ the same triangular mesh for the two sets of calculations. Hence, we generated the triangular mesh using the Gmsh software, and then imported it into COMSOL Multiphysics(r) through an intermediate conversion to.nas format. In this regard, we report that the originally developed mesh led to some errors during the COMSOL Multiphysics(r) import process; the errors (misinterpreted triangles) were located in the fine-structured regions used for the pipeline. The errors disappeared once the local mesh size had been slightly relaxed. This fact did not seem to affect the results of the simulations, due to the relatively low frequency (and consequently the large penetration depth) used for both the time-harmonic analysis and the lightning simulation. This difference in local mesh size could, however, result in a meaningful alteration of the results for different physical situations involving high frequencies, e.g., calculations of impulse responses (not performed in this work with COMSOL Multiphysics(r)). Concerning the three-phase balanced system, a time-harmonic simulation was conducted, considering a frequency of \(60\,\mathrm{Hz}\) and imposing the complex values of the three currents corresponding to the phase conductors. Each current had a magnitude of \(250\,\mathrm{A}\) and a phase of \(0\), \(-120\), and \(-240\) degrees respectively. Note that, as stated previously and in order to avoid the influence of the self and mutually induced currents, the electrical conductivity of each of the three phase conductors was considered to be \(0\). Concerning the case of lightning-induced currents, a time-marching simulation was carried out, based on a backward difference formula scheme with variable accuracy order and adaptive \(\Delta t\). In this case, the time-dependent current given by (1) was imposed  
Figure 6: Comparison between the time-stepping solutions obtained with FLARE and COMSOL Multiphysics® for the same case of Figure 5 and the geometry of Figure 3.  
in phase conductor 1, which, for the reason stated above, has null conductivity. In order to calculate the values of the currents induced in the other phase conductors, the conductivity of the latter was considered to be \(3.5\times 10^{7}\,\mathrm{S/m}\).
# Green Small Cell Operation of Ultra-Dense Networks Using Device Assistance +
## 5 Numerical Results
### Expanding BP and DANCE Algorithms on UDN
#### 5.2.1 Dense Network  
Figure 7 shows the results in the case of 50 UEs where the number of APs varies from two to 100 in the hexagonal area with a radius of 158 m, so that the density of UEs, i.e., the number of UEs per m\({}^{2}\), remains the same as the small-sized network in Section 5.1. As can be seen in Figure 6(a), the energy efficiency of BP and DANCE is highly improved compared with the baseline. The energy efficiency of the baseline mainly decreases in the number of APs because power consumption is proportional to the number of APs. However, BP and DANCE successfully restrict the number of turned-on APs and consume lower power resulting in higher energy efficiency. For example, BP achieves five-times and AP/UE \(N\)-bit achieves nine-times higher energy efficiency than that of the baseline for the case of 100 APs. Note that, unlike the case of the small-sized network, AP/UE \(N\)-bit shows better energy efficiency than BP because BP uses the approximated factor graph. Consequently, the energy efficiency of BP increases in the practical number of APs, but has a peak around 10 APs and then decreases. However, note that the data rate of BP is the highest until 20 APs. As easily expected, the energy efficiency of proximity-ON is not very good, and that of the baseline is poor.  
Figure 6: The average energy efficiency, power consumption and throughput with different values of \(\eta\). (**a**) Average energy efficiency; (**b**) average power consumption; (**c**) average data rate.  
**Remark 1** (Comparison within DANCE).: _Recall that AP/UE N-bit and one-bit show almost the same energy efficiency and data rate in a small-sized network in Figures 4 and 5. Now, we compare them again in a dense network. As can be seen in Figure 7c, AP/UE N-bit, as well as AP/UE one-bit both exhibit low power consumption, which is because a small number of APs are on; see Figure 7d. However, data rates are very different; while AP/UE one-bit achieves the lowest data rate among DANCE, AP/UE N-bit shows a much higher data rate, which is even comparable to those of BP, proximity-ON and the baseline. For example, the data rate of UE N-bit is 64.8% higher than UE one-bit, and the data rate of AP N-bit is 103.2% higher than AP one-bit in the case of 100 APs. Consequently, this makes AP/UE N-bit achieve higher energy efficiency than all other algorithms. Furthermore, it is interesting to observe that AP/UE N-bit show steadily increasing energy efficiency in the number of APs unlike other algorithms._