esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Telemedicine and AI Support Systems
### Astronaut Training Programs  
Comprehensive astronaut training programs are essential for equipping crews to manage ocular health autonomously during space missions. Virtual reality (VR) simulations provide an immersive platform for practicing complex procedures, such as bioprinting and emergency ocular surgery, under microgravity conditions [10; 32]. These simulations replicate real-time scenarios, including the assembly and operation of bioprinting systems to address corneal emergencies [53]. Training programs should also emphasize diagnostic techniques, enabling astronauts to identify retinal or corneal abnormalities and make informed decisions in resource-limited environments. Incorporating AI-driven decision support ensures astronauts are familiar with the tools they will rely on during missions [34, 77].  
To bridge the knowledge gap in applying terrestrial surgical techniques in space, astronauts may undergo VR-based procedural training before launch, simulating microgravity conditions for corneal imaging, bioprinting, and emergency interventions [99, 100]. Inflight, AI-driven decision support systems provide real-time procedural guidance, enabling astronauts to autonomously manage ocular injuries with stepwise instructions [101]. Additionally, telemedicine-assisted procedures allow specialists on Earth to guide astronauts remotely using compact ophthalmic toolkits and imaging systems [22, 83, 84]. These advancements ensure that even non-medical crew members can perform critical interventions with minimal terrestrial dependence.  
Virtual Reality (VR) simulations are valuable in ophthalmology for surgical training and can help astronauts practice managing ocular injuries in space. These simulations provide hands-on training for conditions like corneal abrasions and foreign body removal, ensuring astronauts can respond effectively to vision-threatening emergencies during long-duration missions [100]. Gamification and real-time biometric feedback enhance cognitive adaptability and motivation, crucial for coping with the isolation and stress of space missions [99]. Additionally, integrating diagnostic tools like SANS-CNN during training prepares astronauts to independently manage neuro-ocular conditions, addressing communication delays with Earth-based experts [101].  
Strategic planning for planetary missions must integrate advanced medical technologies and astronaut training to address ocular health challenges in space. VR and AI-driven tools enhance procedural preparedness, ensuring astronauts can manage ocular conditions autonomously.
# Processing of AZ91D Magnesium Alloy by Laser Powder Bed Fusion
## 2 Materials and Methods
### Fabrication of Samples  
All samples were produced by an SLM 280\({}^{\mathrm{HL}}\) machine (SLM Solutions Group AG, Lubeck, Germany) equipped with a 400 W ytterbium continuous wave fiber laser. The laser source has Gaussian power distribution focused on the spot size diameter of 82 \(\upmu\)m. Argon inert gas was used to prevent the magnesium alloy AZ91D from oxidation. The fumes created by the vaporization of alloying elements of magnesium alloy AZ91D powder blur the laser and reduce the energy of the laser beam on the melting of the powder particles. Therefore, the more powerful gas pump ([PERSON], SV300/2, Cuyahoga Falls, OH, USA) was used to increase the suction of fumes. A layer thickness of 50 \(\upmu\)m and a platform preheated to 135 \({}^{\circ}\)C were used in all experiments.  
Magnesium powder AZ91D (Dome Metals Co., Ltd., Zhengzhou, China) was used for the experiments. The powder particles had a median diameter distribution of 38.6 \(\upmu\)m, D\({}_{10}\) 21.8 \(\upmu\)m and D\({}_{90}\) 56.9 \(\upmu\)m (Figure 1). The chemical composition of powder particles determined by atomic absorption spectroscopy is described in Table 1 (values were supplied by powder producer).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Element** & **Mg** & **Al** & **Zn** & **Mn** & **Si** & **Cu** & **Fe** \\ \hline Wt.\% & Balance & 9.08 & 0.65 & 0.23 & 0.04 & \textless{}0.01 & \textless{}0.01 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of magnesium alloy AZ91D powder.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 5 Discussion
### Comparative Analysis with Other Models  
Aside from the focused examination of TSA-GR, we also conducted a performance comparison with a number of commonly used knowledge tracing models. Whereas tradi  
Figure 19: Accuracy trend per fold.  
tional models like BKT and PFA provide interpretability, they do not allow the modeling of long-term learning and contextual influences. Therefore, each of these models had lower than 60% accuracy and high loss values in a way that did not capture more nuanced student behavior. Similarly, attention-based models (the MSA-GRU and TSA models) performed better than the traditional models but worse than TSA-GRU. For example, MSA-GRU achieved training and testing accuracies of 81.36% and 80.00%, respectively, while TSA alone performed at around 75%. TSA-GRU, by contrast, reached training and testing accuracies close to 95%, with significantly reduced loss values (\(\approx\)0.496 for raining and 0.209 for testing).  
These enhancements arise from combining existing methodologies as well as a novel way of incorporating TSA into the GRU model architecture. While MSA-GRU attends to each time step uniformly through its attentional mechanism, TSA-GRU uses a learnable sparsity threshold to eliminate irrelevant or noisy temporal information. This attention mechanism allows the model to attend more closely to its most informative sequence segments, improves learning efficiency, improves interpretability, and aloows better generalization. All in all, the results illustrate that TSA-GRU is not just a combination of two existing models; it is a hybrid explicitly created to improve on previous models by shifting and relaxing attention to allow a more focused and adaptive attention mechanism. It creates a new and substantial advancement in knowledge tracing models.
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 4 Discussion  
From the above experiments, it is considered that the overall fitting performance of the \(\alpha\)-\(\mu\) distribution is the best among the three. The \(\alpha\)-\(\mu\) distribution adopts two parameters in fitting the signal SNR, increasing the degree of freedom. The standard Nakagami-\(m\) distribution only uses one parameter, but it achieves good fitting performance when \(S4\geq 0.8\). Although the \(\kappa\)-\(\mu\) distribution also uses two parameters, its performance is limited, and it only achieves better performance when \(S4\leq 0.5\). The \(\alpha\)-\(\mu\) distribution fits well in the whole range of the normalized S4 with a 100% confidence level. The Nakagami-\(m\) distribution continues to improve with increasing normalized S4, indicating that the Nakagami-\(m\) distribution is more suitable under strong scintillation intensities. The fitting performance was also evaluated in high and low solar activity years, which shows that the results were influenced by solar activity, but not by much. A probable reason is that more moderate and strong scintillation occurred in the high solar activity year (2014 for example), as indicated by Figure 3.  
When using the standard deviation, information entropy and kurtosis for fitting evaluation, there are consistent conclusions. The \(\alpha\)-\(\mu\) distribution has the best performance for all scintillation intensity ranges, the Nakagami-\(m\) distribution gradually achieves better performance with increasing scintillation intensity, and the \(\kappa\)-\(\mu\) distribution outperforms the Nakagami-\(m\) distribution under weak scintillations.  
The amplitude scintillation distribution for the GPS-LEO propagation channel shows different features from that for the GPS-ground propagation channel, as revealed by Figures 5 and 6. For the Nakagami-\(m\) distribution, the fitting performance improves under strong and severe scintillation for the GPS-LEO propagation channel, while for the GPS-ground propagation channel, the Nakagami-\(m\) distribution achieves good performance for moderate scintillation. The possible reason is that for the GPS-LEO propagation channel, the receiving signal travels a bending path, with more interaction with the ionosphere than the GPS-ground propagation condition.
## 3 Results and Discussion
### Phytoremediation of Aquaculture Wastewater  
The removal rates of TSS, NH\({}_{3}\)-N, and phosphate were determined by using five selected aquatic plants as phytoremediation agents. These plants were chosen due to their great ability in removing pollutants from various wastewater. As stated by [PERSON] et al., _C. asiatica_ leaves, roots, and stems are good pollutant accumulation agents. Since it needs extra nutrients, such as zinc and lead, for biochemical processes, especially photosynthesis, the roots actively translocate nutrients to the leaves, making _C. asiatica_ an efficient phytoremediation agent [19]. _I. aquatic_ stems and leaves are also very efficient in absorbing nutrients and heavy metals, and use the pollutants as food in order to grow, which is why this species is known as a fast-growing plant [20]. Meanwhile, the roots of _S. molesta_ are very fibrous, and the same goes for _P. striatotes_, allowing it to easily and efficiently trap suspended solids in the water body. It is also a fast-growing plant; therefore, it needs a high amount of nutrients to stay healthy, alive, and grow. According to [PERSON] et al. [21], _E. crassipes_ is one of the best macrophytes to be used as a phytoremediation agent because it removes pollutants from wastewater efficiently while having a high tolerance to the polluted environment [21].  
According to [PERSON] et al. [22], _I. aquatic_ can reduce 92.6% of total suspended solids (TSS) and 82.7% of ammoniacal nitrogen (NH\({}_{3}\)-N) from palm oil mill wastewater. _C. asiatica_ is able to trap 100% of TSS from water body and _S. molesta_'s leaves and roots are highly efficient in accumulating and removing pollutants, especially heavy metals [22; 23; 24]. [PERSON] et al. [25] stated that _E. crassipes_ and _P. striatotes_ efficiently removed heavy metals without showing any kind of damage or bad reactions to the metals.
## 4 Critical Comparison  
In an attempt to remove As(V) from potable water, mine effluent, industrial effluent, or other fluids and, generally, in water treatment (in both industrial and residential wastewater treatment) [74], several studies have been conducted, taking advantage of the properties of MIPs and preparing different materials, such as polymer beads [90], resins [63], molecularly imprinted polymer cryogels (MIP-cryo) [32], monoliths [32,78], magnetic arsenate-imprinted polymer (mMIIP) [79], and membranes. Similar to the removal of As(V), there have been fewer studies so far on the removal of As(III), in which the researchers have prepared molecularly imprinted polymer beads and membranes [77]. Table 1 summarizes the main characteristics of molecularly imprinted polymers (MIPs) in their applications regarding the removal of As(V) and As(III) from water. As it turns out, it is only in recent years that their application has been developed in terms of the removal of As(III), showing the emerging nature of these materials for use as an arsenic removal technology. From the above studies, it seems that the technology of MIPs has been used to remove arsenic from environmental water samples and water solutions in the form of both As(V) and As(III), by the preparation of several materials. These materials have different advantages and disadvantages, and their use depends on their application. The properties of these materials appear to be influenced by various factors, such as the template, the functional and cross-linking monomers, the initiator, the porogen, their relative proportions, the polymerization technique, and the polymerization conditions. The main features used for the creation of MIPs, as described in this review, are classified in Figure 3. As depicted, there are several functional monomers used, but the most commonly used cross-linking monomers are ethylene glycol di-methacrylate (EGDMA) and N,N\({}^{\prime}\)-methylene bisacrylamide (MBA), while for the initiator, 2,2\({}^{\prime}\)-azobisisobutyronitrile (AIBN) and ammonium persulfate (APS) are most frequently utilized [91]. From the studies that have been carried out so far, it seems that the greater adsorption capacity (130 and 151 mg As/g for As(V) and As(III), respectively) was exhibited by the As(III)- and (V)-imprinted membranes, designed by \"grafting from\" and imprinting technology [80]. However, optimizations are required in ready-prepared materials for enhancing the removal of As(III), since MIPs seem to be very promising materials with a wide range of applications. In particular, molecularly imprinted polymer beads for As(V) removal from potable water, mine effluent, industrial effluent, or other fluids, have been reported in previous research [90] with quite promising results. Regarding this study [90], W/O/W polymer
## 3 Analysis Method and Assumptions  
In engineering design, for issues such as gravity dams and slope stability, the limit equilibrium method (LEM) is widely adopted due to its clear and straightforward mechanical concept. This article also employs the limit equilibrium method (LEM), which is valued for its utility in preliminary design and rapid evaluation. LEMs are usually established based on hypothetical potential rupture surfaces. According to the previous discussion, the existing limit equilibrium methods have their own limitations. Therefore, in this paper, we first analyze the morphology pattern of the potential rupture surface, and based on this, the calculation of the safety factor is carried out. This analysis is carried out only under the isotropic condition of the rock and focuses mainly on the uplift failure due to the macroscopic fracture behavior of the rock while ignoring the effect of the microscopic cracking behavior. From the LRC design concept, it can be assumed that the internal pressure loads sustained by the lining are negligible, and therefore, the effect of the lining is not considered in the computational model. Additionally, considering that the operational process of the gas storage involves multiple inflation and deflation cycles, meaning the cavern is subjected to a cyclic load, this study sets the gas pressure parameter to the maximum operational pressure and does not account for the mechanical responses to cyclic loading and temperature effects. For the tunnel-type LRC, a two-dimensional model consistent with plane strain conditions is developed. Meanwhile, a three-dimensional model is formulated for the silo-type LRC, without taking the second principal stress into account.
## 2 Problem Formulation
### Objective Functions  
The principal optimization objective \(F\) has contemplated in the objective functions is the fuel cost of the energy units (_Fcost_). The cost of any generator is shown as a second-class optimization problem of the generation power of any unit (\(P_{G}\)):  
\[\min\limits{Fcost(x,y)}=\min\limits_{i=1}^{NG}{(\alpha_{i}+b_{i}P_{Gi}+c_{i}P_{Gi }^{2})} \tag{16}\]  
where \(a_{i}\), \(b_{i}\) and \(c_{i}\) show the cost factors of the \(i\)th generator.  
To decrease active loss (_Ploss_) in the energy network OPF function for optimization has showed:  
\[\min\limits{Ploss(x,y)}=\min\limits_{i=1}^{NTL}{\sum\limits_{\begin{array}{c} j=1\\ j\
eq i\end{array}}^{NTL}{\left({G_{ij}\,V_{i}^{2}\,+\,\,B_{ij}V_{j}^{2}\,-2V_{i} \,V_{j}\cos\delta_{ij}}\right)}} \tag{17}\]  
The OPF function to optimize the voltage deviations (\(VD\)) is as follows:  
\[\min\limits{VD(x,y)}=\min\limits_{i=1}^{NCL}{\left|{V_{i}-V_{i}^{ref}}\right|} \tag{18}\]  
where \(V_{i}\) indicates the voltage value of the \(i\)th bus, and \(V_{i}^{ref}\) has been contemplated as 1 p.u.  
In this study, the emission level of the two significant pollutants, sulfur oxides (SOx) and nitrogen oxides (NOx), are considered to be minimized [80]:  
\[\min\limits{Emission(x,y)}=\min\limits_{i=1}^{NG}{\left({\alpha_{i}+\xi_{i}\exp( \theta_{i}P_{Gi})+\gamma_{i}P_{Gi}^{2}+\beta_{i}P_{Gi}}\right)} \tag{19}\]  
where \(\xi_{i}\) (ton/h), \(\gamma_{i}\) (ton/h MW2), \(\beta_{i}\) (ton/h MW), \(\alpha_{i}\) (ton/h), and \(\theta_{i}\) (1/MW) are pollution factors of \(i\)th unit.  
So, the main function is considered as follows:  
\[\begin{array}{l}J\,=\sum\limits_{i=1}^{NG}{F_{i}\,(\,P_{Gi})\,+\,\lambda_{P} (\,P_{G1}-P_{Gi}^{\text{lim}}\,)^{2}\,+\,\lambda_{Q}\sum\limits_{i=1}^{NG}{ \left({\,Q_{Gi}-Q_{Gi}^{\text{lim}}\,}\right)^{2}}}\\ +\,\lambda_{V}\sum\limits_{i=1}^{NCL}{\left({\,VL_{i}-VL_{i}^{\text{lim}}\,} \right)^{2}\,+\,\lambda_{S}\sum\limits_{i=1}^{NTL}{\left({\,S_{i}-S_{i}^{ \text{lim}}\,}\right)^{2}}}\end{array} \tag{20}\]  
where \(\lambda_{Q}\), \(\lambda_{V}\), \(\lambda_{p}\), and \(\lambda_{S}\) show the penalty coefficients; and \(x^{lim}\) shows a variable as an auxiliary variable:  
\[x^{\text{lim}}\,=\left\{{\begin{array}{l}x^{\text{min}};\,x<x^{\text{min}}\\ x^{\text{max}};\,x>x^{\text{max}}\\ x\,\,\,\,\,\,\,x^{\text{min}}\,\,\leq\,\,x\,\,\leq\,\,x^{\text{max}}\end{array}}\right. \tag{21}\]
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 3 The Proposed Method
### Tools and Environment
The dataset's features, samples, and variety of attacks make it suitable for addressing the challenges of detecting DDoS attacks against IoT devices in real-world environments. The dataset was pre-processed by removing null and infinite values, unchanged features, and features that were deemed useless or highly correlated with other features. After pre-processing, the remaining features included Source Port, Destination Port, Protocol, Flow Duration, Total Fwd Packets, Total Backward Packets, Total Length of Fwd Packets, Fwd Packet Length Max, Fwd Packet Length Min, Fwd Packet Length Std, and so on. The final set of features also included Down/Up Ratio, Init_Win_bytes_forward, Init_Win_bytes_backward, Active Mean, Active Std, Active Max, Active Min, Idle Std, Inbound, and Label. These features were used for the classification of network traffic in the CICDDoS2019 dataset. Overall, the use of this dataset allowed us to specifically focus on the challenges of detecting volumetric DDoS attacks in SDN environments, providing valuable insights for future research in this area. 8--Generated dataset: In this work, we used a simulation approach to build a training dataset specifically designed to capture low-rate DDoS attacks in software-defined networking (SDN) environments. Many publicly available datasets are unrealistic and lack diversity in attack types, making it difficult to effectively train deep learning models on real-world scenarios. To address this issue, we generated traffic over a two-day period of normal/benign traffic and 25 min of DDoS traffic, divided into three attack periods of 8 min each. We labeled benign and malicious packets during the benign and malicious  
Figure 1: The percentage of traffic types of the CICDDoS2019 dataset. traffic generation to simplify the deep learning process. To mimic an SDN environment, we collected the dataset by adding simulated traffic flow entries from the switch flow table. The data were then pre-processed and converted into a.csv file format that could be read by the Python-based deep learning module. Figure 2 shows the percentage breakdown of benign and attack traffic in the dataset. This approach allowed us to create a realistic and diverse dataset for training a deep learning model to detect low-rate DDoS attacks in SDN environments. Additionally, Figure 3 demonstrates the stages of the generation of the dataset. Figure 3: Stages of generating the dataset. Figure 2: The percentage of traffic types of generated dataset. Overall, this approach allowed us to create a realistic and diverse dataset that accurately reflects the challenges of detecting low-rate DDoS attacks in SDN environments, providing a strong foundation for the development of effective deep learning models for this task.
# Spaceborne GNSS-R Observation of Global Lake Level: First Results from the TechDemoSat-1 Mission
## 4 Conclusions  
In summary, the capability of using spaceborne TDS-1 GNSS reflections to estimate global lake level has been demonstrated for the first time, and the results have been compared with the results from traditional satellite altimeters. Strong correlations (overall R\({}^{2}\) greater than 0.95) are observed among lake levels derived from TDS-1, CryoSat-2 and Hydroweb data, with large RMSE (tens of meters) mainly due to the fact that TDS-1 is not dedicated for altimetry measuring purpose. With the launch of NASA's CYGNSS in 2016, Japan's WNISAT-1R in 2017, and the plan for several GNSS-R missions dedicated to altimetry applications (e.g., G-TERN [32], Cookie [33], and Pretty [34]), spaceborne GNSS-R data with higher spatial/temporal resolution and higher quality has been (or will be) acquired. This could bring more potential applications in altimetry retrievals. The results in this paper provide supportive information for further designation of GNSS-R constellations to measure altimetry of inland water bodies.  
The following are available online at [[http://www.mdpi.com/2072-4292/11/12/1438/s1](http://www.mdpi.com/2072-4292/11/12/1438/s1)]([http://www.mdpi.com/2072-4292/11/12/1438/s1](http://www.mdpi.com/2072-4292/11/12/1438/s1)), Table S1: Hydroweb data source of the water levels for lakes used in this study.  
Conceptualization, W.W., X.C., and Y.H.; methodology, L.X., W.W., B.L., and S.Z.; validation, L.X. and S.Z.; formal analysis, L.X., W.W., and B.L.; resources, X.C., W.W., and Y.H.; writing--original draft preparation, L.X. and W.W.; writing--review and editing, L.X., W.W., and X.C.; supervision, X.C., W.W., and Y.H.; funding acquisition, X.C., W.W., and Y.H.  
This work is jointly supported by the National Key Research and Development Program of China (Grant No. 2017 YFC1500900 and No. 2018 YFC1506504), and the National Natural Science Foundation of China (Grant No. 41501360).  
The authors are grateful to the TDS-1 Team at SSTL (Surrey Satellite Technology Ltd.) for making the data publicly available. The authors appreciate ESA for providing CryoSat-2 data and the Hydroweb website ([[http://hydroweb.theia-land.fr/](http://hydroweb.theia-land.fr/)]([http://hydroweb.theia-land.fr/](http://hydroweb.theia-land.fr/))) for providing water level data. The authors also appreciate the providers of the open source MATLAB codes used to calculate the global height anomalies between the WGS84 and GGM02C. These are found at [[http://gps.alsaka.edu/jeft/Classes/GEOS602/downloads.html](http://gps.alsaka.edu/jeft/Classes/GEOS602/downloads.html)]([http://gps.alsaka.edu/jeft/Classes/GEOS602/downloads.html](http://gps.alsaka.edu/jeft/Classes/GEOS602/downloads.html)).  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, and in the decision to publish the results.
# Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks
## 4 Discussion
### Relation to Quantum Teleconing
For example, in the case of the multicast butterfly network, the rate is 2, which is double the conventional scheme that is explained above. Hence, our protocol can be used as an efficient preparation for asymmetric telecloning over quantum networks.