esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Transfection Efficiency and Cell Viability Dependence on Plasmid Concentration
To address this, we have calculated the distance between the cell membrane and the nearest plasmid and plotted it against the plasmid concentration (Figure 4). We can see that the distance between the electroporated cell membrane and the nearest plasmid is directly dependent on plasmid  
Figure 3: The dependence of treated cell electrotransfection efficiency (**A**) and total fluorescence (**B**) on plasmid concentration after treatment with 1, 2 and 3 HV (1400 V/cm, 100 \(\upmu\)s) pulses at 1 Hz repetition frequency. The results represent all treated cells and account for cell viability. The concentration of GFP coding plasmid was 10–600 \(\upmu\)g/mL. Error bars represent standard error of mean (SEM). The significance of differences was calculated using one-way ANOVA. 3 symbols *** represents differences between neighboring points after treatment with 1 HV, **—**after treatment with 2 HV, and **—**after the treatment with 3 HV. concentration. However, the relation is non-linear and accurately (R\({}^{2}>0.95\)) satisfies the second order exponential fitting. If we plot the distance factor in the x-axis and the number of transfected cells (Figure 5A) or cell viability (Figure 5B), we obtain the curves that adhere to a sigmoidal fit with good accuracy (R\({}^{2}>0.95\)). The linear part of the sigmoidal curve in Figure 5A shows that transfection efficiency is highly dependent on the distance value in the range of \(\sim\)0.2-0.35 \(\upmu\)m as minor changes in the distance induce high changes in transfection efficiency. Therefore, small optimizations of other electroporation parameters while the plasmid concentration is in the aforementioned range will have the highest impact on the transfection efficiency. Conversely, when the distance value is higher (\(>\)\(\sim\)0.35 \(\upmu\)m) or lower (\(<\)\(\sim\)0.2 \(\upmu\)m) than the high sensitivity range, the transfection efficiency is not very sensitive to  
Figure 4: The dependence between the plasmid concentration and the distance between the electroporated cell membrane and the nearest plasmid. Figure 5: The dependence of the number of surviving cells with successful electrotransfection (**A**) and the cell viability (**B**) on the distance between electroporated cell membrane and the nearest plasmid. the changes in the distance between the plasmid and the membrane, with a small exception for 1 HV electroporation conditions and low distance between the cell and the plasmid. The stabilization of transfection efficiency at distances lower than 0.2 \(\upmu\)m after the treatment with two or three HV pulses is due to the increasing cell death in these conditions. Indeed, the cell viability curves for all the pulse combinations are stationary for distances higher than \(\sim\)0.3 \(\upmu\)m and then start to decrease linearly with decreasing distance between the membrane and the plasmid (see Figure 5B).
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 1 Introduction
However, as we all know that the energy of underwater nodes is extremely limited because the nodes are powered by batteries. Furthermore, nodes are anchored to the sea bottom in most cases so that it is difficult to recharge the batteries. Therefore, to some extent, these methods will shorten the lifetime of nodes and make networks disconnected and unreliable faster. Moreover, the above algorithms do not pay attention to using advanced nodes that can be free to move to maintain the connectivity of networks. Considering that many data acquisition tasks in UASNs are completed by both static nodes and autonomous mobile nodes [8; 9], we apply autonomous mobile nodes that perform data acquisition tasks to assist in repairing the link-disruption to maintain network topology more flexibly and efficiently. As a new type of monitoring device with unique driving mode, gliders have advantages of lower energy consumption, longer run-time, lower noise and lower energy consumption compared with AUVs [9]. Therefore, gliders are usually used in collecting environmental parameters missions in many long-time and large-scale three-dimensional ocean [10; 11]. Therefore, for less real-time networks, underwater gliders can assist the network in restoring a link disruption while carrying out data acquisition tasks. However, due to their single sawtooth trajectory on the vertical plane, it is necessary to fully consider the movement characteristics of gliders when we design an underwater acoustic channel link restoration mechanism. It is also of great significance for maintaining network topology and restoring the connectivity and reliability in UASNs. In view of above problems, we propose a link disruption recognition and glider-scheduling algorithm (LDR-GS) and a glider-assisted relay location optimization algorithm (GALO) to assist in restoring link disruptions using existing nearby gliders. In LDR-GS, the cluster head nodes collect the link state and schedule gliders to prepare restoring the link-disruption. Since the location of restoring the link-disruption is limited due to the unique trajectory of gliders, GALO is proposed to select the optimal position for a glider as the relay node of the disrupted link, to accurately restore the connectivity and reliability of network. In GALO, the optimal location is selected by combining the unique characteristics of underwater gliders and the channel attenuation model. Combining LDR-GS and GALO, connectivity of the networks can be restored. The rest of this paper is organized as follows: in Section 2, related works on mechanisms for restoring underwater network connectivity are discussed briefly. In Section 3, the proposed network model and LDR-GS mechanism are introduced. In Section 4, GALO algorithm is described in detail. The simulation results are shown and discussed in Section 5. At last, we conclude this paper in Section 6.
# A Soft, Biocompatible Magnetic Field Enabled Wireless Surgical Lighting Patty for Neurosurgery
## 4 Discussion
Alternatively, the power supply can be oriented external to the light-emitting element or be positioned outside of the surgical area during deployment of the light-emitting element. The light source also can be powered by inductive coupling from a base station. The surgical lighting patty can incorporate an over-sized retrieval string that serves the purpose of providing the operating surgeon a mechanism to pull a surgical lighting patty from the surgical field. Additionally, the retrieval string could be modified in manners that serve as an insulated electrical conduit to supply the power to the lighted elements in the surgical patty upon contact or at all times. The lights arranged in specific formations or arrays could be another implementation. Each array may have a specific function or advantage. Some embodiments include lights or LEDs arranged in a columnar, staggered, multi-row, triangular, circular fashion, or alternating wavelengths. They can provide diffuse ambient light to a surgical field or emit light from only one of two faces of the lighted surgical patty, depending on their configuration. It is advantageous for various types of surgical settings. In another implementation, various types of LEDs with different wavelengths can be used to output a wide range of wavelengths. Such configuration allows for illuminating dyes that are often inserted into the body, including vasculature, neurological tissue, and ducts to better visualize cancerous tissue and arterial vasculature. When a light at a certain wavelength from the patty activates molecules contained in the dyes, the molecules emit fluorescence light that is detectable either by sight or other detection equipment. This permits surgeons to detect movement of fluids, uptake of molecules, blockage, breakages, etc. Similarly, blue spectrum lighting can be utilized to identify tumor cells when patients are administered a photosensitizer, 5-aminolevulinic acid (5-ALA), to differentiate cancerous tissue from normal tissue [31; 32]. Light at the wavelength of 494 nm can excite fluorescein fluorophore for intra-operative separation of tumor tissue from normal stromal tissue [33; 34], allowing the surgeon to identify cancerous tissue. Light in the ultraviolet-C (UV-C) range has been considered carcinogenic [35; 36]. However, recent studies suggest that UV-C light is highly bactericidal without having a significant effect on human cells [37; 38; 39; 40]. UV-C light could be deployed from the surgical lighting patty unit to decontaminate or sterilize the surgical field from antibiotic-resistant bacteria.
###### Abstract  
This study evaluates the anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW) and digested sewage sludge (DSS) at lowered temperatures. AD batch tests for CH\({}_{4}\) yield determination were carried out with DSS as inoculum between 23 and 40 \({}^{\circ}\)C. All results were related to organic dry matter and calculated for standard conditions (1013 hPa, 0 \({}^{\circ}\)C). The AD experiments at 40 \({}^{\circ}\)C and at 35 \({}^{\circ}\)C delivered specific CH\({}_{4}\) yields of 325 \(\pm\) 6 mL/g and 268 \(\pm\) 27 mL/g for OFMSW alone. At lower temperatures, specific CH\({}_{4}\) yields of 364 \(\pm\) 25 mL/g (25 \({}^{\circ}\)C) and 172 \(\pm\) 21 mL/g (23 \({}^{\circ}\)C) were reached. AD at 25 \({}^{\circ}\)C could be beneficial regarding energy input (heating costs) and energy output (CH\({}_{4}\) yield). Plant operators could increase AD efficiencies by avoiding heating costs. The co-digestion of OFMSW together with DSS could lead to further synergies such as better exploitation of the energy potentials of DSS, but the digestate utilization could become problematic due to hygienic requirements. Efficiency potentials through lowered operating temperatures are limited. In further research, lowered process temperatures could be applied in the AD of energy crops due to large numbers of existing plants.  
OFMSW; digested sewage sludge; waste characterization; anaerobic digestion; operating temperature; mesophilic; psychrophilic +
Footnote †: journal: Applied Sci. 2021, 11, 2907. [[https://doi.org/10.3390/app1107290](https://doi.org/10.3390/app1107290)]([https://doi.org/10.3390/app1107290](https://doi.org/10.3390/app1107290))
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 3 Result and Discussion
### Comparison with Other Models  
To show the effectiveness of the knowledge distillation algorithm, we compared it with other YOLO lightweight networks, and the experimental results are shown in Table 6.  
From the table, it can be seen that, compared to the lightweight network YOLOv3-tiny, YOLOv5n(KD), after using the distillation algorithm in this paper, was improved by 0.59% in [EMAIL_ADDRESS], reduced 79.7% in parameters, and reduced by 19.05% in inference time. The pruned model YOLOv5n-slim(KD) was 1.91% lower in mAP and had 94.35% less parameters and 42.86% less inference time.  
Compared with YOLOv4-tiny, the model after using knowledge distillation in this paper had a 13.03% and 10.53% improvement in [EMAIL_ADDRESS], a 70.46% and 91.91% reduction in the number of parameters, and a 65.31% and 75.51% reduction in inference time, respectively.  
Compared with the lightweight model YOLOv7-tiny in the latest YOLO version YOLOv7, the distilled model [EMAIL_ADDRESS] was improved by 11. 84% and 9.34%, the number of parameters was reduced by 70.76% and 91.86%, and the inference time was reduced by 66% and 76%, respectively. The knowledge distillation algorithm used in this paper can assist the student network to better learn the features of the target to be detected and effectively improve the detection accuracy of the model by distilling the features of the target region without increasing the complexity of the model.  
As shown in Table 7, we also compared our research methods with those of others in the same area. Although the experimental environment and equipment were different, our method achieved a detection accuracy similar to other algorithms on devices with small differences in performance, and it achieved a significant improvement in detection speed.  
In conclusion, we compared all the algorithms mentioned in this paper, and the results are shown in Figure 12:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]\%** & **Params/M** & **Inference Time/ms** \\ \hline YOLOv3-tiny & 92.46 & 93.47 & 96.74 & 8.67 & 2.1 \\ YOLOv4-tiny & 67.39 & 86.68 & 84.30 & 6.06 & 4.9 \\ YOLOv7-tiny & 72.46 & 90.92 & 85.49 & 6.02 & 5.0 \\ YOLOv5n(KD)(ours) & 90.75 & 93.47 & 97.33 & 1.76 & 1.7 \\ YOLOv5n-slim(KD)(ours) & 84.60 & 88.60 & 94.83 & 0.49 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of YOLO Lightweight Network Performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **[EMAIL_ADDRESS]/\%** & **Inference Time/ms** \\ \hline EfficientNet-YOLOv3 & 97.26 & 23.8 \\ Yolov-BTFPN & 98.45 & 25.6 \\ MobilenetV3-YOLOv4 & 93.08 & 22.7 \\ MCC-CycleGAN & 96.88 & 22.6 \\ YOLOv5n(KD)(ours) & 97.33 & 1.7 \\ YOLOv5n-slim(KD)(ours) & 94.83 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with other model detection results.
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 3 Description of the Uvsq-Sat NG Space-Based Mission
### Concept of Operations--ConOps
The payloads operate according to several ADCS modes (Ground Point Tracking Mode, Sun Tracking Mode, Earth's limb, deep space). Sequences of rapid satellite rotation are planned to study the significance of atmospheric effects on ECVs measurements and to correctthe data. Other observations such as Boresight Solar Zenith Angle or Pitch Angle Adjustment are planned. Lastly, the optimization campaigns of the Uvsq-Sat NG Spectrometer are scheduled, focusing on SNR versus exposure time measurements. * Anomaly Detection and Recovery: The satellite's systems continuously monitor for any anomalies or deviations from expected behavior, and any anomalies are quickly identified (Anomaly Detection). Procedures are in place to diagnose and recover from anomalies, including contingency plans for different scenarios (Anomaly Recovery). * End of Mission: A plan for end-of-life Uvsq-Sat NG disposal is executed to prevent orbital debris. Using the Damocles unit, the satellite must be passivated, ensuring that no energy sources (batteries) can disrupt or contaminate outer space. * Ground Segment: A network of ground stations is established to communicate with the satellite and collect data (ground stations from LATMOS and ACRI-ST). A dedicated facility manages all aspects of satellite operations, including commanding, monitoring, and data processing (Mission Operations Center from LATMOS and ACRI-ST). A LATMOS scientific facility is responsible for managing and coordinating the scientific operations and activities of the Uvsq-Sat NG educational and scientific mission (Scientific Operations Center). * Regulatory Compliance: The ConOps ensures that the Uvsq-Sat NG mission adheres to all relevant international space treaties, national regulations (Loi des Operations Spatiales (LOS)--French Space Operations Act), and space debris mitigation guidelines.
## 3 Optimization Design
### Single Objective Optimization of HE Based on Genetic Algorithm
It can be seen from Figure 4a,b that the effectiveness (\(\epsilon\)) and NTU of HE decreases with EGN, where the enhancement of \(\epsilon\) indicates an improvement on the heat transfer performance of HE. The result in Figure 4b means that the heat transfer capacity of HE is improved while more heat transfer area is needed for HE, probably for the better heat transfer based on Equation (20). When comparing the tube numbers in preliminary designed results with those in optimization designed results, as listed in Tables 5 and 6, although the number of tubes of the optimized HE is more than twice that of the preliminary HE, the diameter of the tubes is reduced by nearly half. The heat transfer area of the optimized HE is increased by 5.2% compared with the preliminary HE through calculation. Although the heat exchange area is increased, the pressure bearing capacity of the optimized tubes is improved. In addition, the calculated effectiveness of the preliminary HE is 0.567 and NTU is 3.17. Compared with the preliminary design, the effectiveness and NTU of the optimized HE is increased by 5.2% and 75.9%, respectively. The improvement of the heat performance of HE is considerable so that the increase in heat transfer area is acceptable. Based on the calculation results illustrated in Figure 4c, the influence of MEGN on the total pumping power is not monotonous. In general, the curve of the total pumping power shows a down-up-down tendency with the increase in MEGN. In this case, it is necessary to evaluate whether the overall performance of the heat exchanger is improved. The overall performance evaluation factor \(\zeta\) is used to estimate the enhanced heat transfer performance of optimized HE. The expression of \(\zeta\) is:  
\[\zeta=\frac{(Nu/Nu_{0})}{\left(f/f_{0}\right)^{1/3}} \tag{23}\]  
where \(Nu\) and \(f\) are the Nusselt number and friction factor of HE, respectively. \(Nu_{0}\) and \(f_{0}\) represent the preliminary HE. The factor \(\zeta\) is used to evaluate the performance of the shell-side and tube-side, respectively, and the results are shown in Table 7.  
It can be seen from Table 7 that the performance of the shell-side of the heat exchanger is basically unchanged, but the performance of the tube-side is enhanced, which proves the improvement of the comprehensive performance of the optimized HE. In addition, MEGN and the related thermal-hydraulic parameters are considerably optimized after only four generations compared with the first generation, though the total number of generations is 300. Therefore, the genetic algorithm can provide a better model HE and improve the efficient design of HE.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 4 Security Scheme
### Key Distribution Phase  
In the first phase, the key distribution phase, the TTP selects three master secret values \(x,y,z\in F_{2}^{d}\) and generates two different types of security related information. The first type is for the nodes \(N_{i}\) with \(i=1,\ldots,m\) in the network and the second type is for the NMM. Denote the identity of \(N_{i}\) by \(ID_{i}\) and NMM by \(ID_{NMM}\).  
For each \(N_{i}\), the TTP executes the following computations:  
\[K_{i} = H(ID_{i}\|x\|y\|z)\] \[A_{i} = H(ID_{i}\|x)\] \[B_{i} = H(y)\oplus A_{i}\] \[H(A_{i}) = H(H(ID_{i}\|x))\]  
The values \(K_{i}\), \(B_{i}\), \(H(A_{i})\), \(H(x)\), \(ID_{NMM}\) are sent over a secure channel (for instance by pre-storage) to each node \(N_{i}\). Here, \(B_{i}\) replaces the identity of the node for the outside world. The parameter \(H(A_{i})\) is used to authenticate its identity with the NMM, and \(H(x)\) for the authentication of the NMM with the node. The value \(K_{i}\) represents a common shared secret key with the TTP and is used to remotely update the security material by the TTP at later stages.  
The security related information for the NMM is limited to the two parameters \(H(ID_{NMM}\|H(x))\) and \(H(y)\), which are also securely sent by the TTP to the NMM. Please note that the scheme is constructed in such a way that multiple NMMs can be considered. Only the identities of the different NMMs should then be communicated to the nodes. For ease in notation, we restrict the explanation to a situation involving only one NMM.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 2 Contributions  
This paper makes several key contributions to understanding RPL protocol vulnerabilities and the implications of the Hello Flood attack in mobile IoT environments. Firstly, we contribute by conducting a comprehensive simulation-based analysis, specifically focusing on the vulnerability of the RPL protocol to the Hello Flood attack in mobile scenarios. While previous studies have primarily concentrated on static environments, our investigation sheds light on the unique challenges posed by mobile settings. Secondly, we employ four different group mobility models--the Column Mobility Model (CMM), Reference Point Group Mobility Model (RPGM), Nomadic Community Mobility Model (NCM), and Pursue Mobility Model (PMM)--to explore the impact of mobility on the effectiveness of the Hello Flood attack. By utilizing these diverse mobility models, we provide a nuanced understanding of how different movement patterns influence the attack's efficacy and the network's resilience. Additionally, our systematic evaluation encompasses critical performance metrics, including the Packet Delivery Ratio (PDR), End-to-End Delay (E2 ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption (APC). Through rigorous analysis, we uncover the disruptive effects of the Hello Flood attack on these metrics across various mobility models, highlighting the need for robust security measures to safeguard RPL-based IoT networks in mobile environments. Furthermore, our proposed system serves as a timely reminder of the evolving security landscape in IoT networks. As mobile IoT applications become increasingly prevalent, understanding the exacerbated impact of attacks like the Hello Flood attack in such scenarios becomes imperative. By shedding light on these challenges, we contribute to the ongoing discourse on IoT network security and advocate for proactive measures to mitigate emerging threats.  
This paper is structured into several key sections, each contributing to a comprehensive exploration of the subject matter. Section 3 delves into the core RPL specifications, providing insights into the attack employed in this study and examining mobility within the RPL framework. Building upon this foundation, Section 4 offers a thorough review of relevant research concerning RPL attacks in static environments. Section 5 details the specific setups and methodology used in our simulations and subsequent performance evaluations. The experimental outcomes and a comparative analysis are unveiled in Section 6, providing deeper insights into the results. Section 7 establishes a discussion presenting the strong and weak points of each utilized model. Finally, Section 8 concludes the paper by summarizing our findings and charting a course for future research endeavors in this domain.