esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Experimental Results  
Let us apply the developed approaches to the analysis of self-collisions of robots with two multilink manipulators SAR-401 and \"Ichtiandr\". Video of the experiments can be viewed at the link [[https://youtu.be/Qw7](https://youtu.be/Qw7) ksK1N1 Hw]([https://youtu.be/Qw7](https://youtu.be/Qw7) ksK1N1 Hw) accessed on 10 December 2023, [[https://youtu.be/6v5](https://youtu.be/6v5) OiB0T4B8]([https://youtu.be/6v5](https://youtu.be/6v5) OiB0T4B8) accessed on 10 December 2023.  
Figure 23: The confusion matrix.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Sample** & **Test Sample** & **Random Sample** \\ \hline Precision & 99.30\% & 99.15\% & 99.17\% \\ \hline Recall & 99.19\% & 99.01\% & 98.81\% \\ \hline Error & 1.50\% & 1.84\% & 2.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Accuracy of collision detection in Network 7.  
Figure 22: The result of the training Network 7 (**a**) model accuracy; (**b**) model loss.  
We will conduct the experiment in three stages. Stage 1--testing the methods using a kinematic model built in Matlab. Stage 2--testing methods using simulators. Stage 3--testing the methods on real robots. Simulation of a self-collision at stage 3 was carried out using the actions of an operator in a copying suit to prevent the situation of the algorithm not working and the occurrence of a self-collision and damage to the robot.  
The main problem of the experiment on a real robot is the risk of damage to the robot when the manipulators collide. Therefore, the main tests were carried out on a simulator and only when the ANN was developed was an experiment carried out on a real robot.  
The appearance of the copying suits is shown in Figure 1. A view of the operator in a suit is shown in Figure 3.  
The underwater robot was tested in a pool at a depth of 4 m. The control algorithm when working with real robots includes a self-collision control algorithm and a duplicate, previously developed algorithm based on solving the regression problem. This is due to the fact that the probability of detecting a self-collision using the developed algorithm is 2% and an unauthorized self-collision is possible, due to which they may break.  
Having trained Network 7, we will perform all three stages of testing for each of the robots. In Figures 24 and 25, the examples of experimental results are given. In Figures 24 and 25, the right and left robot manipulators are indicated in red and blue, respectively.  
Based on the results of a quantitative analysis of 2000 positions leading to self-collisions for the \"Ichtiandr\" robot, 1963 identified self-collisions were obtained, which gives 1.85% of unpredicted collisions, which is consistent with the results obtained.  
Based on the results of a quantitative analysis of 2000 positions leading to self-collisions for the SAR-401 robot, 1968 identified self-collisions were obtained, which gives 1.6% of unpredicted collisions, which is consistent with the results obtained.
# Nanofiltration of Simulated Acid Mine Drainage: Effect of pH and Membrane Charge
## 4 Results and Discussion  
### Streaming Potential Data  
In this study, the streaming potential measurements were conducted between pH values of 3 to 10. As can be seen in Figure 2, the isoelectric point (IEP) of the membrane in a KCl solution was found to be at a pH of 4.7 below which the surface becomes positively charged.  
Specific adsorption SO42- anions could be expected to shift the IEP to a lower pH value. In any case, at pH 7, for which all standard rejection experiments using magnesium sulfate were performed, Membrane A is negatively charged. At this pH value, rejection of sulfate is greater than the chloride due to stronger electrostatic repulsion (higher valency of SO42- vs. Cl-), thus resulting in the Donnan effect, namely preferential rejection of divalent to monovalent ions of the same charge [23]. Passage of Cl- ions is more prominent at higher salt concentrations due to the screening effect brought about by increasing Na+ concentration. The presence of Na+ shields the negative charge on the membrane surface causing a decreased Cl- rejection. Due to the higher valency and larger size of sulfate, sulfate rejection remained high for all tested concentrations (see Section 4.2).
# Development of Ultrasonic Pulsed Plasma Jet Source for Remote Surface Treatment
## 2 Materials and Methods
### Dependence of Hydrophilic Effect on Plasma Gas Species  
The hydrophilic effect of plasma has been reported to depend on the gas species [17]. In addition, gas species may be limited by gas price and safety considerations. The hydrophilic effect of plasma on each gas species was evaluated using supersonic pulsed plasma jets.  
The contact angle was determined by measuring the contact angle of a water droplet resting on a copper plate (C1100P) surface and the evaluation was based on the magnitude of the angle. The smaller the contact angle of a solid surface, the more hydrophilic the surface, and the easier it is for water and other solvents to wet it, whereas the larger the contact angle, the less hydrophilic the surface, and the easier it repels water.  
The contact angle measurement method is defined by JIS as the JIS R 3257 Wettability test method for glass substrate surfaces [18]. The principle of the contact angle measurement method is illustrated in the figure. A drop of water was then placed on the horizontal specimen. If the volume of the droplet is 4 \(\mu\)L or less, the shape of the droplet can be  
Figure 2: Structure of the supersonic pulsed plasma jet.  
regarded as a part of a sphere, and the following relationship is established between the contact angle \(\theta\) and droplet shape.  
\[\theta=2\tan^{-1}\frac{h}{r} \tag{1}\]  
where \(r\) is the radius of the water droplet on the surface in contact with the specimen (mm) and \(h\) is the height from the specimen surface to the apex of the droplet (mm). The values of r and h were measured, and the contact angles were obtained using the relational equation.  
In the experiment, a contact angle meter (PG-X, Matsubo Co., Ltd., Minato-ku, Tokyo, Japan) was used to measure the contact angle between the specimen surface and water droplets to evaluate the hydrophilic effect. The volume of each water droplet was 2 \(\mu\)L.  
The plasma treatment time was 1 s, the plasma irradiation distance was 5-40 mm, and the supersonic plasma was irradiated at 13 Hz and 10 ms. The gas species used for the supersonic plasma were nitrogen, carbon dioxide, air, argon, and helium.
## 3 Results
### Sea Ice  
Figure 4 shows GPS and Galileo DDMs collected on 18 October 2018 over sea-ice (no BeiDou-3 reflected data are currently available over sea-ice). DDMs over sea-ice show similar \"k-shapes\" as described by [6], i.e., showing both a horseshoe and Doppler side-lobes. The Doppler side-lobes, although faint, are clearly much sharper and better defined in the case of the Galileo DDM. Of the two DDMs, a higher SNR is seen in the GPS collection; the peak in the Galileo DDM is noticeably sharper than the GPS DDM meaning that the currently selected size of the signal box is likely too large, thus picking up information from pixels too far away from the peak, and leading to non-optimal estimation of the reflected SNR.  
Figure 4: DDMs retrieved on 18 October 2018 over sea-ice from GPS (**a**) and Galileo (**b**), and a close up of the peak area of the Galileo DDM (**c**). The GPS and Galileo DDMs were collected from parallel tracks having an average spatial separation of \(\sim\)214 km. Both DDMs show a similar, albeit faint, “k-shape” form over sea-ice—showing some promise for the development of sea-ice detection algorithms for either navigation system, although these will require adaptation for a number of factors including different average SNR and distinctive shape of the correlated Galileo waveform (triple peak).  
Figure 3: Relationship between Galileo signal-to-noise ration (SNR) and ERA-5 wind speed, coloured by transmitting Galileo satellite and associated Pseudorandom Noise code (PRN) (Exv). The red line shows the line of best fit using an exponential curve, similar in shape to the geophysical model function (GMF) that was found for GPS reflected signals. These preliminary results show some promise for the future development of an ocean wind speed retrieval algorithm based on reflected Galileo signals. See Supporting Figure S3 for a similar plot coloured by antenna gain towards the specular point (AGSP).  
In Figure 4c a close-up is shown of the Galileo DDM signal; this shows a distinctive triple peak in delay space. This triple peak derives from the modulation strategy (composite binary offset carrier (CBOC)) used by Galileo E1 OS signals, whereas binary phase shift keying (BPSK) is adopted in GPS L1 C/A. Note that because of the input filtering associated with the TDS-1 RF chain only the BOC(1, 1) component is collected by the instrument. The SNRs of each specular point of the two simultaneous GPS and Galileo tracks collected in October 2018 are shown in Figure 5. There is a marked increase in SNR (\(\sim\)2 dB for Galileo, \(\sim\)4 dB for GPS) as the specular point transitions from the open ocean on to the sea-ice edge. This shows that Galileo signals are responding to changes in the nature of the geophysical surface in a similar way to GPS.
## 2 VDES Data Link Credibility Monitoring Approach
### Radio Interference Detection Based on Message Characteristics  
The presence of radio interference within VDES data links impedes the accurate demodulation of VDES messages by equipment, resulting in transmission errors that pose risks to maritime navigation safety. To facilitate the prompt identification of these errors by traffic managers or crew members, detecting radio interference in VDES data links is imperative. This study adopts a radio interference detection methodology based on message characteristics. It entails a meticulous examination of message format adherence, message parameter legitimacy, message content integrity, and message transmission and reception consistency to assess the potential impact of radio interference on the faithful transmission of VDES messages. In scrutinizing message content integrity, the MD5 algorithm is employed to pad and segment parsed VDES messages. Each segment undergoes cyclic processing to yield a 128-bit hash value for the VDES message. Simultaneously, the MD5 algorithm is used to compute the hash value of authenticated messages received from ship stations by the base station in the original VDES message repository. Subsequently, the hash values of these two messages are compared for congruence. A congruent hash value suggests the integrity of the received VDES message content, thereby confirming the integrity assessment. Conversely, incongruent hash values indicate the potential presence of radio interference. VDES base stations disseminate link management messages through TDB statements, which VDES ship stations automatically forward via VEM statements upon receipt. Verifying message transmission and reception consistency involves utilizing the initial seven characters of link management messages broadcast by VDES base stations in TDB statements as a shared key. This key is extended to 64 bytes using the HMAC algorithm, the required fixed length for the hash function, to generate the HMAC value for the entire message. Concurrently, the HMAC value of the message received by VDES base stations through VEM statements relayed by VDES ship stations is computed, and the HMAC values of both messages are compared. A match in HMAC values indicates identical transmitted and received message contents, affirming adherence to the consistency check. Conversely, disparate HMAC values suggest inconsistencies in transmitted and received message contents, signaling potential deviations caused by radio interference. Thus, the precise detection of radio interference can be achieved through the methodologies outlined above.
###### Abstract  
Under a dense and large IoT network, a star topology where each device is directly connected to the Internet gateway may cause serious waste of energy and congestion issues. Grouping network devices into clusters provides a suitable architecture to reduce the energy consumption and allows an effective management of communication channels. Although several clustering approaches were proposed in the literature, most of them use the single-hop intra-clustering model. In a large network, the number of clusters increases and the energy draining remains almost the same as in un-clustered architecture. To solve the problem, several approaches use the k-hop intra-clustering to generate a reduced number of large clusters. However, k-hop proposed schemes are, generally, centralized and only assume the node direct neighbors information which lack of robustness. In this regard, the present work proposes a distributed approach for the k-hop intra-clustering called Distributed Clustering based 2-Hop Connectivity (DC2 HC). The algorithm uses the two-hop neighbors connectivity to elect the appropriate set of cluster heads and strengthen the clusters connectivity. The objective is to optimize the set of representative cluster heads to minimize the number of long range communication channels and expand the network lifetime. The paper provides the convergence proof of the proposed solution. Simulation results show that our proposed protocol outperforms similar approaches available in the literature by reducing the number of generated cluster heads and achieving longer network lifetime.  
IoT; WSN; multi-hop clustering; distributed clustering; dynamic intra-clustering 202121873390/s21030873  
11 December 2020222 January 20212 January 202128 January 2021  
**Subisew's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 2 Results
### Functional Validation of DnSQS-2  
Because the terpene synthase gene (LOC110109817, here named SQS-2) was highly responsive to MeJA (Figure 7), we speculate that this gene may play a vital role in dendrobine biosynthesis upon exposure to MeJA. To validate our hypothesis, this gene was cloned into yeast engineering bacteria (WAT11/pESC-TRP::DnSQS-2). The results of gene cloning and the double enzyme digestion of the recombinant plasmid are shown in Figure S9. After galactose-induced fermentation, the extracted product was measured using GC-MS (Figure 11). The results show that DnSQS-2 can catalyze the production of the monnier-penoids (C10) geraniol, cis-2,6-dimethyl-2, and 6-octalidene; the sesquiterpenes (C15) farnesol and (35,6E)-neroidolol; the diterpeneoid (C20) geranylgeraniol; the triterpenoids (C30) ambrein and squalene; and the tetrterpenoid (C40) caroten. In contrast, no product peak  
Figure 10: Putative biosynthetic pathways of dendrobine in _D. nobile._ A: the MVA (**A**) pathway. (**B**) the biosynthetic pathways of dendrobine and heat map of different genes. Acetoacetyl-CoA thiolase (AACT); 3-hydroxy-3-methylglutaryl-CoA synthase (HMGS); 3-hydroxy- 3-methylglutaryl-CoA reductase (HMGR); mexulonic acid (MVA); mexulonate kinase (MK); mexulonate 5-phosphate (MVAP); phosphomevalonate kinase (PMK); mexulonate diphosphate (MVAPP); mexulonate diphosphate decarboxylase (MVD); isopentenyl diphosphate (IPP); dimethylallyl diphosphate (DMAPP); isopentenyl diphosphate isomerase (IDI); famesyl diphosphate synthase (FPFS); endoplasmic reticulum (ER); terpene synthase (TPS); cytochrome P450 oxidase (P450); NH\({}_{2}\), amination reaction; and Me, methylation reaction. The expression levels of genes at the six time points are represented by heatmaps, in which the blocks shown from left to right represent CK, TC7, TC14, TF, TF7, and TF14.  
was detected in the yeast engineering bacteria WAT11 and WAT11 (pESC-TRP). These results indicate that DnSQS-2 may be a multifunctional enzyme.
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 6 Conclusions and Future Work  
This research employed the ResUNet model to perform automated liver and liver tumor segmentation in public CT images. In this paper, the proposed technique has shown high effectiveness in liver and tumor segmentation and holds promise for similar research on lung and brain tumors. In this study, a 3D model was trained using Monai, a deep-learning framework for medical imaging. Ongoing performance-based studies explore networks tailored for liver and tumor segmentation. The proposed approach is being tested and implemented across diverse datasets, with the potential for continuous improvement in results. Despite advancements in medical image segmentation, there remains substantial room for developing novel techniques that balance high accuracy and sensitivity while minimizing computational complexity. This study involves training a 3D model using Monai, a deep-learning framework specializing in medical imaging. Although the results are promising, it is important to note that medical imaging tasks are inherently challenging, and perfect segmentation is often unfeasible due to the difficulty of accessing medical images. For better performance and more accurate results, larger datasets are needed. The potential for enhancing liver segmentation performance using deep learning techniques, particularly with the extensive use of larger liver datasets, appears promising in future endeavors. Data augmentation techniques, such as the utilization of generative adversarial networks (GANs), can be employed to increase the number of liver images accessible for analysis.  
[PERSON] is the first author, and he carried out liver tumor segmentation experiments from a public CT image dataset and tested and trained the ResUNet Model loaded from Monai. [PERSON] drafted the manuscript, and [PERSON] participated in its design as a coauthor and helped draft the manuscript. All authors have read and agreed to the published version of the manuscript.  
2022-2025 National Natural Science Foundation of China under Grand, No. 52171310. 2021-2023 National Natural Science Foundation of China under Grand (Youth) No. 52001039.  
Not applicable.  
The dataset associated with this work is available at http://medicalde cathlon.com/ (accessed on 2 February 2023).  
The authors of this article would like to express gratitude to those who have provided support for the completion of the paper, especially to [PERSON], the Dean of the School of Data Intelligence at Yantai Institute of Science and Technology, for providing technical advice and guidance in this article.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Plant Growth  
A hydroponic growing tray consisting of about 16 _Ocinum basilicum_ plants approximately one month old was placed in the chamber unit. Open loop controllers introduced in Section 2.2.5 were used for light gas and humidity regulation. The circadian generator was set to generate day-night (square wave) light cycles corresponding to an 18 h ON and 6 h OFF cycle. Ventilator was operated at a cyclic ON and OFF time of 120 s and 3420 s, respectively. The TEC was operated at a constant power of 40% in cooling mode to condense the transpired water. The data collected from this experiment are presented in Figure 12 for a period of 150 h.  
The LED lighting status, indicated by \(u_{I}\) in Figure 12a, shows the ON and OFF cycles. The temperature inside the chamber follows the trend of the outside temperature when the LED is ON and falls by 5 \({}^{\circ}\)C when the LED is OFF. This can be explained by the heat generated from the LED panels and the TEC operating at constant power. Variation of carbon dioxide inside and outside the chamber can be seen in Figure 12b. The CO\({}_{2}\) variation during the LED ON cycle, LED OFF cycle and over the entire time frame have different trend. During the LED ON cycle, the concentration drops when the ventilator is OFF and it increases when ventilator is ON. During the LED OFF cycle, the concentration increases when the ventilator is OFF and it decreases, reaching the same concentrations as outside, when ventilator is ON. The rate change during LED ON cycle has a correlation to the temperature. These observations can be used to draw conclusions about photosynthesis and respiration taking place in these plants. It can be stated that light and temperature influence plant photosynthesis resulting in production of CO\({}_{2}\). Net CO\({}_{2}\) produced in the absence of light is negative, which indicates the respiration process. The plant species under study shows increase in photosynthesis rate with decrease in temperatures. Humidity, as shown in Figure 12c, stays constant during the LED ON cycle and is slightly elevated during the OFF cycle, indicating higher transpiration.  
Figure 12: Plant growth experiment: (**a**) temperature and Light intensity variation; (**b**) CO\({}_{2}\) concentration variation due to ventilation, photosynthesis, and respiration; and (**c**) humidity variation inside and outside the chamber unit. E1 corresponds to measurements made outside the chamber using a reference sensor and data represented by \(u\) correspond to actuator state.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 2 Related Works
### Student Performance Prediction in Online Learning  
In [31], the authors examine deep learning techniques to forecast student performance in MOOCs and LMS, focusing on backpropagation-based models like LSTM. This research provides a systematic filtering of a broad literature pool, resulting in 23 key papers in the area of deep learning and MOOCs/LMS, pinpointing patterns in model architecture, prediction accuracy, and possibilities for early intervention, which demonstrates the growing efficacy of deep neural networks handling complex educational datasets. The work in [32] pertains to an integration module that combines behavioral analysis and machine learning algorithms for an augmented performance prediction in online continuing education, specifically in MOOCs. To develop this integration approach, a scientometric review and clustering analysis of the literature was applied, revealing a range of learning patterns (e.g., low autonomy and motivation) and showing how adding behavioral data may improve prediction accuracy that exceeds the conventional machine learning algorithms utilized in the field, such as XGBoost. The authors of [33] make use of advanced analytical methods involving spiking neural networks (SNNs) and more than 300,000 records of past behavior data from the edX platform to predict whether students completed an MOOC. The analysis achieved an impressive 99.8% prediction accuracy. The study shows that student activity, such as posting in discussion forums and watching videos, are linked to the ability to complete a course. All of this highlights the importance of understanding behavioral characteristics to help better design timely interventions to reduce dropout rates. The research presented in [34] investigates predicting student performance in MOOCs using machine learning models to analyze demographics, assessment test scores, and student engagement metrics from the Open University Learning Analytics Dataset (OULAD). Techniques such as logistic regression and random forest regression showed that simple student behaviors, such as levels of daily activities, predicted success, with random forest demonstrating elevated prediction accuracy. The research pointed to the value of integrating a variety of data to identify at-risk students earlier and inform retention strategies.