esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Research Progress on Membranes for Redox Flow Batteries
### Anion Exchange Membranes  
While it is difficult to prevent the cross-over of vanadium ions in the case of cation exchange membranes, it guarantees high proton conductivity and high energy efficiency. Meanwhile, using anion exchange membrane may significantly reduce the cross-over of vanadium ions through the membrane owing to Donnan exclusion effect, but it may not assist in the conduction of protons which decreases the voltage efficiency of the battery. Early studies of anion exchange membranes for use in the VRB were reported by [PERSON] and [PERSON] [62]. Of the anion exchange membranes evaluated, Selemion AMV (Asahi Glass, Japan) showed relatively poor stability in the VRB during long-term charge-discharge cycling. In the case of the New Selemion Type 2 membrane however, the area resistance and diffusivity values remained almost constant after six months of testing, while weight loss was negligible after 2 months of soaking in V (V) solution. Further evaluation of modified Selemion AMV and New Selemion Type 2 anion exchange membranes was carried out by [PERSON] [59].  
In more recent work by [PERSON] _et al._[77], commercially available anion exchange membranes were modified by cross-linking through accelerated electron radiation. The cross-linked membranes exhibited better performance, indicating that cross-linking may improve the mechanical strength of the polymers. An anionic membrane, JAM-1-10 (JAM) was also tested and evaluated in the VRB by [PERSON] and co-workers [109]. In comparison to other commercial ion exchange membranes, it showed the lowest permeability of vanadium ions and the best chemical stability in V (V) solution. When this anion exchange membrane was modified using poly (sodium 4-styrenesulfonate) by _in situ_ polymerization, it resulted in an improved selectivity to cations [109].  
To produce ETFE-g-PDMAEMA membrane, Dimethylaminoethyl methacrylate (DMAEMA) was grafted onto Ethylene tetrafluoroethylene (ETFE) membrane by radiation grafting. This grafted membrane was then immersed in acid to improve the anion exchange capability in the form of -R\({}_{3}\)NH\({}^{+}\). The optimization of dose used, or grafting yield was found to be important in producing the membrane with good area resistance [110]. As expected, the anion exchange membrane showed much lower permeability of vanadium ions when compared to Nafion 117. This is because, the positively charged quaternary ammonium salts repel the diffusion of positively charged vanadium ions.  
Poly (phthalazinone ether ketone/sulfone) also known as PPEK membranes, were prepared by chloromethylation, followed by quaternization to produce membranes with anion exchange properties. The membranes showed much lower vanadium ions permeability and a higher Coulombic efficiency than Nafion 117 [111]. The effect of amination agent on the properties of quaternized poly(phthalazinone ether sulfone) anion exchange membrane has been investigated by [PERSON] _et al._[112]. It was found that the addition of ethylenediamine could have improved the stability of the membrane.
# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
###### Abstract  
In order to enhance the efficiency and precision of graph-type planting operations for disordered stacked sugarcane, and to achieve rapid deployment of the visual detection model on automatic sugarcane seed-cane planters, this study proposes a sugarcane detection algorithm based on an improved YOLOv8n model. Firstly, the backbone network of YOLOv8n is replaced with VanillaNet to optimize feature extraction capability and computational efficiency; the InceptionNeXt deep convolutional structure is integrated, utilizing its multi-scale processing feature to enhance the model's ability to recognize sugarcane of different shapes and sizes. Secondly, the ECA attention mechanism is incorporated in the feature fusion module C2F to further enhance the recognition model's capability to capture key features of sugarcane. The MPDIOU loss function is employed to improve the resolution of recognizing overlapping sugarcane, reducing misidentification and missed detection. Experimental results show that the improved YOLOv8n model achieves 96% and 71.5% in [EMAIL_ADDRESS] and [EMAIL_ADDRESS] respectively, which are increases of 5.1 and 6.4 percentage points compared to the original YOLOv8n model; moreover, compared to the currently popular Faster-RCNN, SSD, and other YOLO series object detection models, it not only improves detection accuracy but also significantly reduces the number of model parameters. The research results provide technical support for subsequent sugarcane grab-type planting recognition and mobile deployment.  
object detection; disordered stacked sugarcane; YOLOv8n; experimental validation +
Footnote †: journal: Applied sciences
## 4 Conclusions  
This study successfully synthesized AuNPs of 18.5 nm using a sonochemical approach and sodium citrate as a stabilizing and reducing agent. After five min of ultrasonic irradiation treatment, the citrate-coated AuNPs, dissolved in water and PBS, have the hydrodynamic size in aqueous suspension of about 22.17 nm and 23.49 nm with a PDI of 0.368 and 0.406, respectively. Sonochemical treatment also ensured that the colloidal suspension of nanoparticles was highly stable in different media (AuNPs in water and PBS was \(-\)42.1 mV and \(-\)39.5 mV, respectively). Due to their exceptional stability observed by zeta potential, AuNPs could also be used for various medical applications. The results confirm the use of ultrasonic treatment for the production of metal nanostructures, in particular non-toxic AuNPs, with a highly stable colloidal suspension and good morphology. In addition, a significantly enhanced CT value (HU = 395) show that AuNPs have the potential to be employed as a contrast CT agent in which their X-ray attenuation was linearly correlated with AuNP concentration.  
[PERSON], [PERSON] and [PERSON] conceived the idea and designed the experiments. [PERSON] supervised all the experiments and analyses. [PERSON], [PERSON] and [PERSON] prepared materials, performed characterizations, measurements and analyzed the results. [PERSON], [PERSON] and [PERSON] commented on manuscript writing. [PERSON] wrote the manuscript and all authors discussed the results and commented on the manuscript of the work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Malaysia Ministry of Higher Education FRGS grant, grant number 203/PFIZIK/6711678. The APC was funded by Malaysia Ministry of Higher Education FRGS grant.  
The authors would also like to thank School of Physics, Universiti Sains Malaysia for supporting this research work.  
There are no conflicts of interest.  
Figure 7: Computed tomography (CT) image of the AuNPs with different concentrations (1, 2, 3, 4 and 5 mg/mL) and X-ray attenuation intensity, the dotted line shows that 2.3 mg Au/mL gave an equivalent X-ray absorption as 7.2 mg/mL Iodine in Omnipaque (viz., 178 HU).
## 2 Theory
### Problem Statement
 \tag{5}\]  
External Applied Magnetic Field  
When an external magnetic field is applied, the general treatment of the fluid flow requires the consideration of charge carriers in the medium. Ionic species will be transported by the fluid flow, which are subjected to an SMF. The induced current due to the external \(\mathbf{B}\) leads to the extra force of the form expressed in Equation (12), whose components need to be included in Equation (1). To determine the regime under which the interaction between the magnetic flux density \(\mathbf{B}\) and the medium occurs, the magnetic Reynolds number (\(Re_{m}\)) is introduced. It is defined in Equation (6), quantifying the ratio of advection to diffusion of \(\mathbf{B}\), where \(\lambda=(\mu_{0}\sigma)^{-1}\) is the magnetic diffusivity, \(\mu_{0}\) the magnetic permeability of vacuum, and \(\sigma\) is the fluid conductivity. For \(Re_{m}\ll 1\), a diffusion regime dominates, meaning that \(\mathbf{B}\)-field lines are determined only by BC and not the fluid flow. Conversely, for \(Re_{m}\gg 1\), an advection regime governs, meaning that \(\mathbf{B}\)-field lines follow the fluid. \[Re_{m}=\frac{UL}{\lambda} \tag{6}\]  
Considering the \(Re_{m}\ll 1\) case, let \(\mathbf{E_{0}}\), \(\mathbf{J_{0}}\), and \(\mathbf{B_{0}}\) be the fields when \(\mathbf{u_{l}}=0\), and let \(\mathbf{e}\), \(\mathbf{j}\), and \(\mathbf{b}\) be the infinitesimal perturbation in \(\mathbf{E}\), \(\mathbf{J}\), and \(\mathbf{B}\), which are produced due to the velocity \(\mathbf{u_{l}}\) transporting ionic species. These considerations lead to the following coupled equations, noting that this work is constrained to SMFs, i.e., \(\
abla\times\mathbf{E_{0}}=\partial\mathbf{B_{0}}/\partial t=0.\) Since \(\mathbf{E_{0}}\) is irrotational, it can be expressed as \(\mathbf{E_{0}}=-\
abla\ V\). The extra contribution \(\mathbf{u_{l}}\times\mathbf{b}\) in Equation (8) was not considered. From Equation (7), it can be shown that \(|\mathbf{e}|/L\ \sim\ |\mathbf{b}|/t\). In addition, \(|\mathbf{b}|\sim|\mathbf{B_{0}}|Re_{m}\)[32], so \(\mathbf{e}\ \sim\ U|\mathbf{B_{0}}|Re_{m}\). Therefore, \(\mathbf{e}\) can be neglected in Equation (8), which, in combination with Equation (7), results in the current \(\mathbf{J_{1}}\) in Equation (10). If there are no external electric fields applied, \(\mathbf{J_{0}}=0\), leading to Equation (11). The form of this current reveals that, under the condition of \(Re_{m}\ll 1\), \(\mathbf{B}\) influences the velocity field \(\mathbf{u_{l}}\); however, there is little influence of \(\mathbf{u_{l}}\) on \(\mathbf{B}\). The final form of the Lorentz force is expressed in Equation (12) [33]. \[\
abla\times\mathbf{e}=\partial\mathbf{b}/\partial t \tag{7}\]  
\[\mathbf{j}=\sigma(\mathbf{e}+\mathbf{u_{l}}\times\mathbf{B_{0}}) \tag{8}\]  
\[\mathbf{J_{0}}=\sigma\ \mathbf{E_{0}} \tag{9}\]  
\[\mathbf{J_{1}}=\mathbf{j}+\mathbf{J_{0}}=\ \sigma(-\
abla\ V+\ \mathbf{u_{l}}\times\mathbf{B_{0}}) \tag{10}\]  
\[\mathbf{J}=\sigma(\mathbf{u_{l}}\times\mathbf{B_{0}}) \tag{11}\]  
\[\mathbf{f_{m
## 1 Introduction
### Color Evaluation Based on a Digital Camera and a CDS  
The on-site colorimetric measurement of the paintings of ancient architectures is a difficult work. This is because in many cases, the shape, the size, and the uniformity of the color samples on the architectures are changeable. Therefore, it is difficult for us to measure the samples with the standard colorimetric instruments. In this paper, we propose using a standard color camera system and the CDS (Color Design System) to measure and evaluate the colors of such paintings. Note that the CDS is a new color order system recommended by the Chinese national technical committee for color standardization (SAC/TC 120) in 2017 (GB/Z 35473-2017) [8]. The current version of the CDS atlas contains 2740 samples arranged on 40 pages of constant hues. Specifically, the CDS has been expanded to a digital color system with over 400 thousand color samples arranged on 80 pages of constant hues, and each CDS sample was related, respectively, with a set of chromaticity values of CIE (Commission Internationale de L'Eclairage) standards.  
On the other hand, the so-called standard color camera system is a digital RGB camera with a colorimetric characterization model. Based on the colorimetric characterization model, the RGB space of the camera can be transformed to the standard CIEXYZ space. By using such a standard color camera system, the on-site colorimetric measurement for the paintings of ancient architectures can be realized. Then, the standard colorimetric values of paintings can be transformed into CDS notations. Furthermore, based on the CDS notations, the color degradation with the different years of the paintings can be predicted.
# Robust Structural Damage Detection Using Analysis of the CMSE Residual's Sensitivity to Damage
## 3 Robust Damage Identification Scheme
Good robustness of the CMSE system against noise necessitates finding \(N_{m}\left(N_{e}\leq N_{m}\leq N_{q}\right)\) CMSE equations corresponding to the first \(N_{m}\) lowest sensitivity indices. Since the sensitivity index as given by Equation (37) is computable, these \(N_{m}\) CMSE equations can be selected to reassemble the CMSE system and identify damage. An interesting question then arises as to what guidelines one can follow to determine \(N_{m}\). Minimizing the level of correlation between the measured and predicted natural frequencies and mode shapes provides a simple but effective way to indicate whether the selected \(N_{m}\) is optimal for this case. In this regard, the optimal \(N_{m}\) is the one within the range \(\left[N_{e},N_{q}\right]\) that maximizes the following fitness function:  
\[f(N_{m})=-\lg\left[\sum\
olimits_{j=1}^{N_{j}}\frac{\left|\omega_{j}^{*}- \widetilde{\omega_{j}^{*}}(N_{m})\right|}{\omega_{j}^{*}}+\sum\
olimits_{j=1 }^{N_{j}}\frac{\left\|\phi_{F,j}^{*}-\widetilde{\phi}_{F,j}^{*}(N_{m})\right\| _{2}}{\left\|\phi_{F,j}^{*}\right\|_{2}}\right] \tag{37}\]  
where \(\omega_{j}^{*}\) and \(\phi_{F,j}^{*}\) are the \(j\)-th natural frequency and mode shape partitioned to \(F\) measured degrees of freedom (DoFs) of the damaged structure, respectively; likewise, \(\widetilde{\omega_{j}^{*}}(N_{m})\) and \(\widetilde{\phi}_{F,j}^{*}(N_{m})\) are the correspondingly predicted values produced by the selected \(N_{m}\) CMSE equations. Note that compared with directly using all \(N_{q}\) equations, a larger amount of computation is required in order to ascertain the optimal subset of equations. However, there is only a small increase in computational costs but a clear improvement in damage detection performance. It is also noted from Equation (37) that the sensitivity index \(S_{m}^{*}\) is determined by the eigen-parameters associated with a unique MC, i.e., the \(i\)-th mode of the healthy structure and the \(j\)-th mode of the damaged structure. Therefore, it can also serve as a pre-test tool to select suitable modes for the CMSE-based methods and, in turn, lead to sensor placement requirements in a built-in health monitoring system.
# Analyzing Port State Control Data to Explore Future Improvements to GMDSS Training
## 3 Results  
### Checking the Hypothesis-1: HRSC1 vs. HRSC4  
The first research question is whether the total number of deficiencies in the period of time considered is a better indicator of risk of safety in maritime communications than the number of times in which a vessel has been involved in inspections with findings related to the GDMSS (no matter how many deficiencies) in the same period. To carry out this diagnostics, both outcomes of binary logistic models are assessed. On this occasion, the classification criteria to define the risks (high: 1 and moderate: 0) will be carried out taking into account the decile of vessels (or the value closest to 10%) that reflects the worst performance either by part of the number of inspections (criterion 1); or the number of failures (criterion 4) that the ship has suffered in 18 years of records. To avoid creating inconsistencies, a cut-off criterion is added (Table 6). This does not allow one ship to be in the high-risk subset and another with the same number of inspections (or defects) to be in the complementary (moderate-risk) subset.  
The results of the Chi-square show a large statistical significance with a \(p\)-value less than 0.001. Several outcomes from the dichotomous model predictor have been taken into consideration (Table 7). The comparison between \(-2\) log-likelihood, the Nagelkerke pseudo-R Square, and the Classification Table gives the best alternative (cut value 0.5). Independent variables Flag List, Type of ship, and Ship age.  
It is more robust to use for the classification tool the number of times a ship has had a radio inspection with findings related to the GDMSS in a given period than the number of findings in that period. That is why it will be used for the number of inspections from now on and the classification tools.
## 4 Discussion
Thus, the use of the cryptanalyzed protocol would require a joint deployment of one of these emerging technologies (or others) in order to obtain authentication. Another alternative is the use of other group Key Management Protocols that have been shown to be secure in this cryptographic model, cf. [8]. From the cryptographic point of view, it has been noted previously in this paper that the emergence of quantum computers represents a major challenge for most cryptographic algorithms that nowadays are mainly based on mathematical problems that are difficult to solve from a computational point of view. However, this new generation of computers increases the computational power exponentially and represents one of the major challenges in cybersecurity, given that they overcome all these mathematical problems that underlie the security of the most-used cryptographic algorithms. To address this challenge, in 2017, the NIST called for a contest to select new cryptographic primitives that allowed to obtain secure algorithms against the attack of a quantum computer. In 2023, four algorithms were selected, one for key encapsulation and three others for digital signature. Very recently, two digital signatures and a key encapsulation method were officially standardized [14]. These algorithms address, on the one hand, the problem of authentication and, on the other hand, the problem of sending a secret through an insecure channel, but as of yet, there is no algorithm showing an alternative to quantum computers that allows establishing a secret collaboratively among two or more parties through such insecure channels. This does not mean, in principle, that Diffie-Hellman [13] or Group Key Management protocols, such as [5] or [9], will be obsolete in the next few years. The problem is finding algebraic structuresthat can offer a mathematical problem that is difficult to solve even for quantum computers. In [15; 16], it is shown that we can extend these protocols to more general mathematical settings, so one current challenge is finding a suitable mathematical environment to apply these Group Key Exchange protocols. At the same time, it is also crucial to continue studying methods that test the security of these protocols in determined cryptographic models in order to know the requirements that have to be fulfilled and to keep using them in a secure manner. The discussion in this paper is proof of that.
## 3. Results
### Analysis of Expansion Test Results  
Due to space limitations, the relationship of absolute expansion rate of the mixture with time was analyzed for samples with 8% lime content, and the results are shown in Figure 10. The changes in the contents of other groups were basically the same.  
Figure 10 shows that when the lime content was 8%, the absolute expansion rate of the mixture increased with time, and the expansion had not yet reached stability at 223 h. At this time, the absolute expansion rates of the mixtures with different phosphogypsum contents ranged from 38% to 55%.  
Figure 8: 8%L + 24%P + 68%T, K = 90%. (**a**) Soaked in water for 10 s; (**b**) Soaked in water for 120 s; (**c**) Soaked in water for 24 h.  
Figure 9: 8%L + 31%P + 61%T, K = 90%. (**a**) Soaked in water for 10 s; (**b**) Soaked in water for 120 s; (**c**) Soaked in water for 24 h.  
It can be clearly seen from Figure 11 that with the increase of phosphogypsum content, the absolute expansion rate of lime-phosphogypsum-stabilized red clay presents a trend of first increasing and then decreasing, and the greater the compaction degree is when the content of phosphogypsum is the same, the greater the absolute expansion rate of the mixture. For example, when the lime content is 8%, when K = 90%, the absolute expansion rate of lime-phosphogypsum = 1:2.8 reaches the maximum; when K = 95%, the absolute expansion ratio of lime-phosphogypsum = 1:3.0, the absolute expansion rate reaches a maximum. The samples with different degrees of compaction reach their maximum value between lime-phosphogypsum = 1:2.8 and 1:3. Phosphogypsum-time-stabilized red clay's absolute expansion rate is affected by the cementitious material and ettringite content, with the phosphogypsum content increases to generate ettringite, which destroys the mix structure, causing the mix to produce greater expansion; phosphogypsum content is too much, the mix PH value decreases, generating less ettringite, while the mix has a low content of viscous particles, the mix expansion is reduced.  
Figure 11: Relationship between absolute expansion rate of the mixture and phosphogypsum content (L = 8%).
## 4 Results
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**First Author and Date** & **Approach** & **Population** & **Algorithm Used** & **Key Findings** \\ \hline [PERSON] (2018) & Wearable Sensors, Deep Learning & Children & Convolutional Neural Network (CNN) & Deep learning detected stereotypical motor movements with wearable sensors; high classification accuracy. \\ \hline [PERSON] et al. (2023) [34] & EEG-based Detection, Wearable Sensors & Children with ASD & Support Vector Machine (SVM) & Energy-efficient EEG scheme for ASD detection; demonstrated strong sensitivity and specificity. \\ \hline [PERSON] et al. (2020) [6] & Eye-Tracking & Children with ASD & Random Forest & Scanpath trend analysis (the sequence and movement of gaze points) differentiated ASD from non-ASD groups; identified distinct gaze patterns in ASD individuals. \\ \hline [PERSON] et al. (2022) [35] & AI Diagnosis Aid, Primary Care (device using mobile caregiver questionnaire, analysis of two home videos by experts, and a healthcare provider questionnaire). & Children in Primary Care & Naive Bayes Classifier & Integrated AI aid in primary care reduced time for ASD diagnosis and improved diagnostic accuracy. \\ \hline [PERSON] et al. (2024) [8] & Neuroimaging (fMRI) and AI & Children with ASD & XGBoost & Quantitative assessment of brain abnormalities in ASD; identified structural differences with high classification accuracy. \\ \hline [PERSON] et al. (2012) [36] & Behavioral Diagnosis, AI-based Question & Children with ASD & Decision Tree & Reduced ASD diagnosis time by using AI to condense diagnostic questionnaire to essential items; high sensitivity observed. \\ \hline [PERSON] (2021) [37] & Medical Device with AI for ASD Diagnosis & Children in Primary Care & Random Forest & AI-based medical device streamlined ASD diagnosis in primary care; strong diagnostic accuracy and specificity reported. \\ \hline [PERSON] et al. (2022) [3] & AI and IoT in ASD Detection & Children with ASD & K-Nearest Neighbors (KNNs) & IoT-based continuous monitoring system for ASD; effective in real-time ASD behavior detection and classification. \\ \hline [PERSON] and [PERSON] (2020) [38] & AI-based Mobile Screening System & Children with ASD & Convolutional Neural Network (CNN) & AI-based mobile app achieved high accuracy for ASD screening, providing user-friendly solution for caregivers. \\ \hline [PERSON] et al. (2009) [7] & Vocal Analysis, Phone-like Units & Children with ASD & Support Vector Machine (SVM) & Localization decomposition method achieved high accuracy in detecting ASD-specific vocal characteristics. \\ \hline [PERSON] et al. (2024) [39] & Explainable AI, Microbiome Analysis & Children with ASD & Explainable AI (XAI) - SHAP & Identified ASD-related bacteria in the gut microbiome; potential biomarker for ASD diagnosis with high AU-ROC. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._