esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Prediction of Degradation Pathways  
AFB\({}_{1}\) has three reaction sites related to the 10 degradation products, which are the double bond on the 8, 9 carbons, the ketone group, and the methoxy group on the cyclopentanone. The degradation pathway of AFB\({}_{1}\) by _Kocuria rosen_ was proposed (Figure 4) according to the molecular structure and published reports. The 8, 9 double bonds on AFB\({}_{1}\) and the ketone group on the 14 th carbon can be converted into C\({}_{19}\)H\({}_{8}\)O\({}_{7}\) (Product 1) through addition and hydrogenation with formic acid. Product 1 can further form C\({}_{18}\)H\({}_{4}\)O\({}_{7}\) (Product 2) through demethylation and dehydrogenation. In addition, the ketone group on cyclopentanone on AFB\({}_{1}\) can be hydrogenated to form C\({}_{17}\)H\({}_{4}\)O\({}_{6}\) (Product 5), while the 8, 9 double bonds can be hydrogenated, and the methoxy group can form C\({}_{17}\)H\({}_{4}\)O\({}_{5}\) (Product 4) through methylation reaction. Studies have shown that AFL is converted from AFB\({}_{1}\) by cytoplasmic NADPH reductase [43; 44]. Therefore, the conversion of AFB\({}_{1}\) to AFL in the degradation pathway may be completed by cytoplasmic NADPH reductase. Product 3 and Product 4 and Product 4 and Product 5 can be converted to each other by an addition reaction, a substitution reaction, or an elimination reaction. Studies have shown that aflatoxin B\({}_{1}\) can be degraded into Product 3 via ultraviolet light irradiation [45; 46] or low-temperature radio-frequency plasma [47] and that the ketone group of cyclopentanone can be converted into the hydroxyl group. The reaction mechanism may be similar to the conversion of the ketone group in Product 4 to the hydroxyl group in Product 5. From Product 5 to Product 6, the CH\({}_{2}\)O group was removed, and a ring-opening reaction occurred. Product 6 can be converted to Product 8 by removing the group (\(-\)C\({}_{5}\)H\({}_{4}\)O) and then converted to Product 10 via an additional reaction of dehydration and hydroxylation. Product 6 can also be converted to Product 7 via hydrogenation and then converted to Product 9 via hydrogenation and hydroxylation again; then, it loses the group (\(-\)C\({}_{5}\)H\({}_{4}\)O) to form Product 10. It has been shown that the synthesized magnetic graphene oxide/titanium dioxide (MGO/TiO\({}_{2}\)) nanocomposites degrade AFB\({}_{1}\) in corn oil as C\({}_{16}\)H\({}_{12}\)O\({}_{5}\)[48], and its structure is consistent with the degradation molecule P7. The structures of the 10 degradation products (P1-P10) are similar to those of AFB\({}_{1}\), which were derived from the modification of the AFB\({}_{1}\)furan ring, cyclopentenone, and methoxy group.  
Figure 2: Structural formulae of AFB\({}_{1}\) degradation molecules (P1–P10) during HPLC–MS/MS.  
Figure 3: MS/MS spectra of 10 degradation products (P1–P10) and proposed fragmentation paths of the fragmented molecules.
## 3 Levelling Strategy and Analysis
### Levelling Accuracy Analysis  
The analysis of levelling accuracy is an important link in the design of levelling structures. The mobile platform is the carrier carried by the manipulator. When the posture of the mobile platform is tilted, the fixed platform of the manipulator is tilted, so the three-dimensional coordinates of the end effector of the manipulator are changed, resulting in a decline in picking accuracy. When the position error of the end actuator of the picking manipulator is <5 mm, the safflower can be accurately picked. Therefore, the levelling accuracy of the mobile platform determines the picking accuracy of the manipulator. The levelling error range of the mobile platform is determined by the reverse calculation of the picking accuracy error of the picking manipulator. \(\gamma\) is the rotation angle of the mobile platform about the \(Z\)-axis; then, the rotation matrix of the mobile platform about the \(Z\)-axis is:  
\[\text{R}_{Z}=\begin{pmatrix}\cos\gamma&-\sin\gamma&0\\ \sin\gamma&\cos\gamma&0\\ 0&0&1\end{pmatrix} \tag{7}\]  
Then, the rotation matrix of the mobile platform around the \(X\)-, \(Y\)-, and \(Z\)-axes  
\[\text{R}_{2}=\text{R}_{X}\text{R}_{Y}\text{R}_{Z}=\begin{pmatrix}\cos\beta \cos\gamma&-\cos\beta\sin\gamma&\sin\beta\\ \sin\alpha\sin\beta\cos\gamma+\cos\alpha\sin\gamma&\cos\alpha\cos\gamma-\sin \alpha\sin\beta\sin\gamma&-\sin\alpha\cos\beta\\ \sin\beta\sin\gamma-\cos\alpha\sin\beta\cos\gamma&\sin\beta\cos\gamma+\cos \alpha\sin\beta\sin\gamma&\cos\alpha\cos\beta\end{pmatrix} \tag{8}\]  
The end effector of the manipulator and the mobile platform use the same coordinate system, so the three-dimensional coordinates of the end effector of the manipulator can be obtained after the rotation matrix transformation. The levelling accuracy is calculated using MATLAB simulation software, and the levelling accuracy range of the platform is within 0.2\({}^{\circ}\).  
When the error of the moving platform is within 0.2\({}^{\circ}\), the error of the end actuator of the manipulator is within 5 mm. The error of the end effector of the manipulator is found to be within 5 mm through the forward solution of the determined levelling accuracy. It can be determined that the obtained accuracy meets the requirements. Since the levelling of the mobile platform is based on the rotation of the \(X\)- and \(Y\)-axes, the transformation of the mobile platform about the \(Z\)-axis is not considered. The levelling accuracy analysis of the mobile platform is an important parameter to evaluate whether the mobile platform has completed the levelling task.
## 3 Control System Design
### RST Controller Design  
The RST control algorithm was used with the MAGLEV system (Figure 9) [15; 19]:  
\[S\Big{(}q^{-1}\Big{)}u(k)+R\Big{(}q^{-1}\Big{)}\ y(k)=T\Big{(}q^{-1}\Big{)}y^{*}(k) \tag{12}\]  
where \(y(k)\) = process output, \(u(k)\) = algorithm output, and \(y^{*}(k)\) = trajectory or filtered set point. The corresponding parameters are:  
\[S\Big{(}q^{-1}\Big{)}=S_{0}+S_{1}q^{-1}+\cdots+S_{nS}q^{-nS} \tag{13}\]  
\[R\Big{(}q^{-1}\Big{)}=r_{0}+r_{1}q^{-1}+\cdots+r_{nR}q^{-nR} \tag{14}\]  
\[T\Big{(}q^{-1}\Big{)}=t_{0}+t_{1}q^{-1}+\cdots+t_{nT}q^{-nT} \tag{15}\]  
The RST controller with a closed-loop system is shown in Figure 9.  
Figure 8: Structure of the system with supervisory control architecture.  
Figure 9: RST algorithm.  
The control law in (12) can be redesigned as shown below:  
\[u(k)=\frac{1}{s_{0}}\left[-R\sum_{i=1}^{n_{S}}S_{i}u(k-i)-\sum_{i=0}^{n_{R}}r_{i} y(k-i)+\sum_{i=0}^{n_{T}}t_{i}y^{*}(k-i)\;\right] \tag{16}\]  
\(n_{S}\), \(n_{R}\), _and_\(n_{T}\) represent the respective polynomial degrees as well as the memory dimension for the algorithm's software implementation. For example, if \(n_{R}=2\), three memory places should be saved for the process's output: \(y(k),\ y(k-1),\ y(k-2)\). The same method applies to \(y^{*}(k)\) and \(u(k)\), respectively.  
One must be interested in the control method and, finally, the trajectory model generator for practical implementation. One cycle of the continuous monitoring procedure includes the following steps: acquisition of the process's inputs; computation of the trajectory (if necessary); computation of the control law; transmission of the controls to the actuators; graphical representation of the process's evolution; and memory update of the algorithms for the next iteration.  
For example, if we have \(n_{S}=n_{R}=n_{T}=2\), and without trajectory generator (\(y^{*}(k)=r(k)\)), the control law computation is as the following:  
\[u(k)=\frac{1}{s_{0}}\ (-S_{1}u(k-1)-r_{0}y(k)-r_{1}y(k-1)+t_{0}y^{*}(k)+t_{1}y^{* }(k-1)) \tag{17}\]  
and (11) gives the algorithm's memory actualization for the next iteration:  
\[u(k-1)=u(k);\ y(k-1)=y(k);\ y^{*}\ (k-1)=y^{*}\ (k\,); \tag{18}\]
# Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network
## 1 Introduction
The time segmentation-based hybrid caching strategy (TSBC), described in this research, divides a day into two types of periods based on the mobility access frequency of users. It is used in the 5G-ICN BN. In TSBC, various time periods utilize various improved caching strategies. This strategy somewhat enhances user experience and network performance. The main contributions of this paper are as follows:  
* This paper proposes a caching scheme of time segmentation in 5G-ICN BN. A time cycle is divided into two kinds of periods based on \"tidal phenomenon\". One is the high liquidity period and the other is the low liquidity period. * Different caching strategies are used during different periods. We employ the name resolution system (NRS) to determine whether the content has been cached during the high liquidity period. The least hot copy on the path will be replaced by un-cached content. The variety of cached copies is ensured by this strategy. We calculate the cache value based on the popularity, freshness, and hop during the low liquidity period to determine which nodes will cache independently. * We test the cache strategy's viability and performance by putting it to use in real network topologies. The results show that our caching strategy works better in various network topologies. The rest of the paper is organized as follows. Section 2 reviews the relevant research on 5G-based ICN cache schemes. Section 3 introduces our proposed scheme TSBC. In Section 4, we carry out simulation experiments and analyze the results and performance of our scheme. Finally, we summarized the scheme and simulation results.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Infrastructure-Based ITS Sensing  
A key objective of the ITS concept is to leverage the existing civil infrastructures to improve traffic performance. Transport infrastructure refers to roads, bridges, tunnels, terminals, railways, traffic controllers, traffic signs, other roadside units, and so on. Sensors installed with transport infrastructures monitor certain locations in a transportation system, such as intersections, roadway segments, freeway entrances/exists, and parking facilities.
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
###### Abstract  
Multi-instance learning (MIL) is a critical area in machine learning, particularly for applications where data points are grouped into bags. Traditional methods, however, often face challenges in accurately classifying these bags. This paper presents the multi-instance partial decision tree (MIPART), a method that incorporates the partial decision tree (PART) algorithm within a Bagging framework, utilizing the simple multi-instance classifier (SimpleMI) as its base. MIPART was evaluated on 12 real-world multi-instance datasets using various performance metrics. Experimental results show that MIPART achieved an average accuracy of 84.27%, outperforming benchmarks in the literature. Notably, MIPART outperformed established methods such as Citation-KNN, MIBost, MIEMDD, MILR, MISVM, and MITI, demonstrating a 15% improvement in average accuracy across the same datasets. The significance of these improvements was confirmed through rigorous nonparametric statistical tests, including Friedman aligned ranks and Wilcoxon signed-rank analyses. These findings suggest that the MIPART method is a significant advancement in multiple-instance classification, providing an effective tool for interpreting complex multi-instance datasets.  
machine learning; multi-instance learning; multiple-instance classification; partial decision tree algorithm; Bagging; simple multi-instance classifier; multi-instance dataset +
Footnote †: journal: Applied sciences
# Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
## 8 Results and Discussion
Table 4 shows the performance comparison of the individual deep learning models developed in [10; 11; 12; 47; 56; 57; 13; 19; 31; 32; 47] and the deep learning models developed in the proposed work for eight classes of skin cancer. It is observed from the table that the individual fine-tuned deep learning models perform better than the individual deep learning models developed in [13; 32; 47; 57]. Table 4 shows classification results with different numbers of classes. Usually, in machine learning models, as the number of classes increases the classification accuracy decreases due the increased model complexity. It is shown in the Table 4 that the individual models developed for the eight classes can perform in comparison to the models developed for the lesser number of classes. The comparison has been made with the classification model that uses the ISIC or HAM \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Classification Report of Majority Voting Ensemble**} \\ \hline
**Skin Cancer Type** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline AK & 0.96 & 0.99 & 0.97 & 261 \\ BCC & 0.98 & 0.99 & 0.98 & 292 \\ BKL & 0.99 & 0.99 & 0.99 & 306 \\ DF & 1.00 & 0.97 & 0.98 & 63 \\ MEL & 0.98 & 0.97 & 0.97 & 325 \\ NV & 0.97 & 0.99 & 0.96 & 305 \\ SCC & 1.00 & 0.93 & 0.96 & 173 \\ VASC & 1.00 & 0.99 & 0.99 & 72 \\ Accuracy & & & 0.98 & 1797 \\ Macro Avg. & 0.98 & 0.98 & 0.98 & 1797 \\ Weighted Avg. & 0.98 & 0.98 & 0.98 & 1797 \\ \hline \multicolumn{5}{c}{**Classification Report of Weighted Averaging Ensemble**} \\ \hline
**Skin Cancer Type** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline AK & 0.96 & 0.99 & 0.97 & 261 \\ BCC & 0.98 & 0.98 & 0.98 & 292 \\ BKL & 0.99 & 0.99 & 0.99 & 306 \\ DF & 1.00 & 0.97 & 0.98 & 63 \\ MEL & 0.97 & 0.97 & 0.97 & 325 \\ NV & 0.97 & 0.99 & 0.96 & 305 \\ SCC & 1.00 & 0.94 & 0.96 & 173 \\ VASC & 1.00 & 0.99 & 0.99 & 72 \\ Accuracy & & & 0.982 & 1797 \\ Macro Avg. & 0.98 & 0.98 & 0.98 & 1797 \\ Weighted Avg. & 0.98 & 0.98 & 0.98 & 1797 \\ \hline \hline \multicolumn{5}{c}{**Classification Report of Weighted Majority Ensemble**} \\ \hline
**Skin Cancer Type** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline AK & 0.96 & 0.99 & 0.98 & 261 \\ BCC & 0.98 & 0.99 & 0.99 & 292 \\ BKL & 0.99 & 0.99 & 0.99 & 306 \\ DF & 1.00 & 0.98 & 0.98 & 63 \\ MEL & 0.98 & 0.98 & 0.98 & 325 \\ NV & 0.97 & 0.99 & 0.98 & 305 \\ SCC & 1.00 & 0.95 & 0.97 & 173 \\ VASC & 1.00 & 0.99 & 0.99 & 72 \\ Accuracy & & & 0.986 & 1797 \\ Macro Avg. & 0.99 & 0.99 & 0.99 & 1797 \\ Weighted Avg. & 0.99 & 0.99 & 0.99 & 1797 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of proposed ensemble models. dataset that has been used in the ISIC 2018 challenge (Task 3) and is available on ([[https://challenge2018.isic-archive.com/](https://challenge2018.isic-archive.com/)]([https://challenge2018.isic-archive.com/](https://challenge2018.isic-archive.com/)), accessed on: 10 October 2021).
# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
## 4 Genetic Tabu Algorithm
### Fault Hypothesis  
The calculated fault sections, the T-connected sections with \(l_{i}=1\) and \(l_{i}=0\), and the sections inside the protection action region are used as the suspicious fault sections. The three factors, \"different combinations of suspicious fault sections fail\", \"protection of misoperation or rejection\", and \"error equipment status information\", are taken as a fault hypothesis. Suppose there are \(m_{l}\) suspicious fault sections. There are \(m_{e}\) protections and \(m_{s}\) nodes in the fault region. The basic form of the fault hypothesis is as follows:  
\[H=\left[L,\ O,\ R,\ A\right] \tag{24}\]  
(1) \(L=\left[l_{1},\ l_{2},\ \ldots,\ l_{m_{l}}\right]\), and the control variable is set as \(l_{i}\), depending on whether section \(i\) is faulty. \(l_{i}=0\) means section \(i\) is not faulty; \(l_{i}=1\) indicates section \(i\) is faulty. (2) \(O=\left[o_{1},\ o_{2},\ \ldots,o_{m_{e}}\right]\), and the control variable is set as \(o_{i}\), depending on whether protection \(i\) has a misoperation. \(o_{i}=0\) indicates that protection \(i\) does not have a misoperation; \(o_{i}=1\) shows protection \(i\) has a misoperation.  
Figure 4: Two kinds of \(l_{i}=1\) T-connected section fault schematic diagram. (**i**) Multiple faults with the T-connected section; (**ii**) Single fault.  
Figure 5: Two kinds of \(l_{i}=0\) T-connected section fault schematic diagram. (**i**) Multiple faults with the T-connected section; (**ii**) Single fault.  
3. \(R=[r_{1},\ r_{2},\ \ldots,\ r_{m_{e}}]\), and the control variable is set as \(r_{i}\), depending on whether protection \(i\) has a rejection. \(r_{i}=0\) indicates that protection \(i\) does not have a rejection; \(r_{i}=1\) indicates that protection \(i\) has a rejection. 4. \(A=[A_{1,1},\ A_{1,2},\ A_{2,1},\ A_{2,2},\ \ldots,\ A_{m_{s},1},\ A_{m_{s},2}]\), and the control variable is set as \(A_{i,1}\), \(A_{i,2}\), depending on whether there is error equipment status information for node \(i\). The error results are represented by \(A_{i,1}\), \(A_{i,2}\) shown in Table 3.
## 2 Results
### ETS-1 Is a CO2 Responsive Transcription Factor in Monocytes  
To better understand the transcriptional regulation of transcripts in response to elevated CO2 in monocytes, we performed MetaCore Transcriptional Analysis to determine the most interacted transcription factors with CO2-dependent transcripts in shNT-, shNR4A2, and shNR4A3 cells + LPS. The most interacted transcription factors are ranked by z-score in Supplementary Table S5. The most consistently CO2 dependent interacted transcription factors with the highest z-scores were RUNX Family Transcription Factor 1 (AML1), SRY (Sex Determining Region Y)-Box 17 (SOX17), T-Cell Acute Lymphocytic Leukaemia Protein 1 (TAL1), ETS-1, and LIM Domain Only 2 (LMO2).  
To narrow our search for associated transcription factors further, we next interrogated the EnCyclopedia of DNA Elements (ENCODE) project website (www.encodeproject.org) with nine fundamental gene signatures in response to CO2 (Figure 4D, Supplementary Figure S10), and investigated the evidence for regulation of these genes by our five highest scoring transcription factors. Interestingly, we identified ETS-1 as a putative transcriptional regulator of all nine CO2-sensitive genes in Figure 4D. For this reason, we next focused our attention on ETS-1. Given that ETS-1 is a nuclear transcription factor, we investigated the CO2-sensitivity of this protein in monocytes exposed to normocapnia or hypercapnia under pH-buffered conditions. Western blot analysis of nuclear lysates revealed a CO2-dependent increase in the nuclear expression of the ETS-1 protein, supporting the concept that this transcription factor is involved in CO2-dependent gene expression (Figure 5A,B). Lamin (acting as a control for nuclear protein expression) was not changed by the exposure to CO2 under the same conditions (Figure 5C,D). Taken together, these data further implicate ETS-1 as a novel CO2-responsive transcription factor in monocytes.
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 5 Conclusions  
To address the problem of efficiently and accurately identifying dynamic dim targets in complex environments, we propose the ILSN with an optimized feature extraction network design and similarity measurement. We establish a position-wise attention module to obtain target feature position information, which enhances the network's ability to extract weak targets while reducing model parameters, thus achieving a lightweight network and completing feature network optimization. The similarity measurement module with multi-layer feature fusion was used to improve the localization accuracy and tracking accuracy of the model. To evaluate the performance of the proposed method, we set up a simulation environment and a physical experimental platform to conduct comparative experiments on the attention module, tracking accuracy and precision. The simulation results show that ILSN has at least a 6.23% advantage over the KCF, MCNN, SiamRPN, DaSiamRPN, and SlamRPN++ algorithms in terms of detection accuracy for target tracking in complex environments, while maintaining a processing speed of 112 fps, which verifies the effectiveness of the proposed algorithm. The physical experiment verifies the feasibility of the proposed algorithm in eight scenarios, where the algorithm's performance is degraded due to interference, computational power and other constraints, but still meets the real-time requirements.  
However, the size of the targets tracked by ILSN is still significant, and there is insufficient consideration and analysis of tiny targets. Next, we will focus on the further optimization of the performance of the algorithm and transplanting the algorithm to the edge side to achieve autonomous and controllable target tracking for UAVs.  
Conceptualization, L.L. and X.Z.; methodology, L.L.; software, H.Z.; validation, L.L. and X.Z.; formal analysis, L.L.; investigation, L.L.; resources, L.L.; data curation, L.L.; writing--original draft preparation, L.L. and X.Z.; writing--review and editing, L.L. and X.Z.; visualization, H.Z.; supervision, H.Z.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Natural Science Foundation Grant No. 4214071 and Beijing Information Science and Technology University Foundation Grant No. 2022 XJJ10.  
Not applicable.  
Not application.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 10: The tracking accuracy and precision of f.
## 5 Discussion  
### Limitations  
The main goal of this research was to build a drop-in replacement for PHQ-9 and GAD-7 assessments, using asynchronous video responses. While the results indicate that the PHQ-V and GAD-V assessments are a reasonable replacement for the PHQ-9 and GAD-7 models, they are only as useful as the original assessments. One limitation of this approach is that any deficiencies in the PHQ-9 and GAD-7 assessments are likely also to be propagated into these models. Also, due to the structured nature of the dataset, these machine-learning models should only be used with the exact question set described above. The dataset did not include any minors, and it is recommended these assessments are used only for adults. While these assessments may still be valid for minors, and may generalize beyond the above question set, this remains to be demonstrated in future research.