esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Molecular Dynamics Simulations  
The stability and dependable binding affinities of the docked receptor-ligand complexes of the top four promising inhibitors, namely, compounds **1** and **20-22**, were analyzed further using the MD simulations. Figure 2 shows the stability of the four receptor-ligand complexes with time. The four complexes showed the same trends and were stable in the simulation process during the same period of time and fluctuated between 0 and 1500 s before they stabilized.  
Binding Free Energy Calculations  
In the molecular mechanics generalized born surface area (MM/GBSA) calculations, the affinity of one inhibitor binding to the receptor could be estimated using the snapshots from a trajectory of the complex. The absolute binding free energies of **1** and **20-22**, using the MM/GBSA technique, are shown in Table 6.  
Compound **20** showed a relatively weak binding energy compared to the others. The reported anti-TMV activities of compounds **1** and **20-22** were 69.3, 57.6, 55.3, and 50.0%, respectively (Table 1). Clearly, the predicted binding free energy of these four compounds was in good agreement with the inhibition results. Compound **22** showed a great binding energy, while **1** and **21** showed moderate binding energy.  
Generally, the molecular recognition of the ligand is important if the interaction energy between a residue and a ligand is lower than \(-0.8\) kcal/mol [46]. Compound **1** has a favorable energy contribution (\(-1.5\) to \(-3.1\) kcal/mol) originating predominately from a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compd** & **MM-GBSA** \\ \hline
**1** & \(-42.14\) \\
**20** & \(-19.95\) \\
**21** & \(-33.71\) \\
**22** & \(-49.95\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Calculated MM-GBSA binding energies (kcal/mol) for compounds **1** and **20-22** against 2 OM3 over MD simulations.  
Figure 2: The evaluation of potential energy of complexes of **1** and **20-22** with 2 OM3 receptor as a function of time.  
Glu95 H-donor (\(-\)3.1 and \(-\)1.5 kcal/mol), whereas the other energy contributions originate from an H\({}_{2}\)O H-donor (\(-\)0.3 to \(-\)2.3 kcal/mol) and H-acceptor (\(-\)1.1 to \(-\)2.2 kcal/mol).  
Figure 3 shows that compound **22** has a favorable energy contribution of \(-\)1.6 to \(-\)1.9 kcal/mol, which originates predominately from an Arg90 and Thr42 by an H\({}_{2}\)O through H-acceptor (\(-\)1.9 and \(-\)1.6 kcal/mol). Compound **21** has interactions with an Asp88 and Thr89 by an H\({}_{2}\)O through H-acceptor (\(-\)0.9 and \(-\)1.8 kcal/mol). Compound **20** has interactions with an Asn91 through H-acceptor (\(-\)1.4 kcal/mol) and an H\({}_{2}\)O through H-acceptor (\(-\)0.9 kcal/mol) and pi-H (\(-\)1.4 kcal/mol).  
Figure 3: Docked pose and binding affinity of compounds **1** and **20\(-\)22**.  
On the basis of the analysis performed, it appears that compound **21** interacted strongly with the 2 OM3 mainly through an Asp88 and Thr89, whereas compound **22** interacted with 2 OM3 through an Arg90 and Thr42. However, compound **20** has a single interaction with the 2 OM3 through an Asn91.
## 2 Related Work
In [34], mixed-integer programming formulations for max-min fair flow optimization in WMNs including both static and dynamic MCSs assignments, while [35] provides a complete optimization model for joint routing, power control, scheduling, channel assignment as well as rate control optimization in WMNs. Therefore, we believe that the work presented in this paper, which is an extension of the model that was introduced in [14] (extended in [17]), is the first complete optimization model for traffic throughput maximization admitting dynamic MCSs assignment and TPC in WSNs for multicast traffic.
# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 5 Empirical Research and Result Analysis
### Evaluation Indicators for Predictive Effect  
Prediction accuracy indicates how well a prediction model predicts the selected variables. Different accuracy measures are used to validate the fitness of a model for a particular data set. Typically, there are several accuracy measures such as mean error (ME), mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE) and mean square root error (RMSE). In order to investigate whether the prediction effect of the bat algorithm after improving the parameters of the SVR model is better than that of the same model without optimization, this paper integrates the existing regression prediction evaluation indexes. Three indicators of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are selected as the prediction accuracy indicators to compare and analyze the prediction accuracy of the BA-SVR hybrid prediction model with other control models. This criterion [17; 30] has been used in previous studies.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\Bigl{(}y_{i,\text{predict}}-y_{i, \text{real}}\,\Bigr{)}^{2}} \tag{10}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\Bigl{|}y_{i,\text{predict}}-y_{i,\text{ real}}\,\Bigr{|} \tag{11}\]  
\[\text{MAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\Bigl{|}\frac{y_{i,\text{predict}}-y _{i,\text{real}}}{y_{i,\text{real}}}\,\Bigr{|} \tag{12}\]  
where \(y_{i,\text{predict}}\,,\,y_{i,\text{real}}\) and \(\tilde{y}_{\text{real}}\) denote the predicted value, actual value, and the mean value of the actual value, respectively. According to the above definition, the three-regression goodness-of-fit evaluation indicators are all measuring the deviation between the predicted value of the model and the true value. The smaller these indicators, the better the degree of fitting. Since the algorithm normalizes and denormalizes the data in the model, the main purpose of this paper is to verify that the prediction of the SVR model after the bat algorithm optimization is relatively better than that of the default parameter SVR without optimization. Therefore, the predicted values after normalization are used in the calculation of the prediction values.
## 4 Experiments
### Experimental Results
#### 4.2.1 RQ1: Impacts on Convergence Speed and Recommendation Performance
Considering PFedMF, for datasets with sparsity levels of 93.70% (MovieLens-100K), 95.53% (MovieLens-1M), 99.07% (Lastfm-2K), and 99.86% (Foursquare), the improvement rates of I2 CL on HR are 16.10%, 8.15%, 1.66%, and 0.21%, respectively. To explain the above observations, we consider pure local training, which does not leverage the FL paradigm and instead involves each client independently training using only its local data. In this scenario, client communication is absent, and the model is susceptible to overfitting to the local data. In FRS, an appropriate degree of pure local training can enhance the client model's understanding of local data [34], ultimately the improving model performance. In FRS, an appropriate degree of pure local training can enhance the client model's understanding of local data [34], ultimately improving the model performance. The introduction of I2 CL essentially combines federated training with pure local training, where clients alternately engage in both types of training. For denser datasets, the local data contains more information, and the features extracted by the pure local training introduced by I2 CL are more accurate, thereby enhancing the model performance. In contrast, I2 CL in sparse datasets may have a minimal effect on improving the model performance, but it still advances the training process for communication-constrained clients, thereby accelerating the convergence of the entire system. It should be noted that I2 CL will damage the convergence speed and model performance on FRSs without DPM, as Figure 4e-h shows. As FRSs without DPM rely on global item embeddings for prediction, I2 CL results in a mismatch between purely locally trained client models and global item embeddings, thereby compromising the model performance. Consequently, I2 CL not only fails to expedite the FRSs' convergence but also diminishes their performance. Figure 4: Learning curves of PFedMF/FedMF with or without I2 CL. (**a–d**) PFedMF-I2 CL: (**a**) MovieLens-100K. (**b**) MovieLens-1M. (**c**) Lastfm-2K. (**d**) Foursquare. (**e–h**) FedMF-I2 CL: (**e**) MovieLens-100K. (**f**) MovieLens-1M. (**g**) Lastfm-2K. (**h**) Foursquare.
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 4 Results and Discussion
### Level of Security  
A brutal force attack is one of the common image technique attacks. It means thoroughly monitoring all possible stego keys until a correct one is recognized. The first element of the Stego key, a predefined pattern, can be selected from an unlimited number of images over the web, mobile telephones, computers, etc., in the steganography technique suggested. Hence, the number of distinct hidden patterns is very high. In addition to the secret pattern, the attacker should be aware of the secret message size to insert the secret text, given that the secret pattern size is connected with the secret message size. The text is also inserted in a random order based on a colour range distribution of the hidden design and the number of pixels matched. However, using a stego key, it is hard to retrieve the hidden text by searching for a particular example in a picture in stego in which the embedded order is executed, because the text is concealed in different addresses on each block according to the cover attributes and the secret pattern used. This research presents a new approach to steganography based on the LSB technique, which uses AES and BlowFish encryption algorithms to conceal text inside an image. The proposed method takes into account the limitations of the LSB process. It improves upon them through randomization in pixel selection and a mask as a password encryption key. This method is expected to be further improved with advancements in encryption and compression algorithms, as well as the implementation of machine learning and AI to enhance its resistance against cryptanalysis and steganalysis attacks. The user interface should also be improved to make it more user-friendly and customizable for different users. The proposed method effectively addresses the two limitations of traditional LSB (Least Significant Bit) processes by incorporating a secret key based on the image and dividing the image into smaller blocks. This not only adds an extra layer of security to the hidden text through the use of a mask as both a password encryption key and as a minimum bit shift during data hiding, but also reduces the visual impact of the embedded text. Furthermore, by splitting the image into N frames and disconnecting it from its sequential order, the proposed method increases its resistance to attacks and enhances its overall security. This approach can be improved in future work by adjusting the dimensions of the masks used. The current limitation of the proposed method is that it needs to be enhanced by a more complex encoding method. The improvement should focus on the encryption and decryption algorithms, making them faster using a lightweight algorithm. Additionally, a compression algorithm should be applied to more significant messages to reduce the message size. Machine learning and AI  
Figure 11: Cover Image 3 histogram.  
should test the method against cryptanalysis and steganalysis attacks. The model's GUI should also be enhanced to be more user friendly and usable, with added options to control desired processes.
## 4 Discussion
### Limitations  
This study used the lung simulator model due to medical ethical reasons. A dual adult lung simulator by TTL instrument simulated accurate, normal, and a variety of pathological pulmonary conditions. It is widely used to replace humans to research many invasive treatments. Our study used the TTL to mimic various pulmonary conditions to investigate the effect on ventilator-related parameters during mechanical ventilation with closed suction. It is plausible that some limitations could have influenced the results obtained. CSS uses endotracheal suctioning to remove secretions to prevent obstruction of the tube and lower airways in mechanical ventilation patients. Accumulation of secretion decreases the diameter of endotracheal tubes as well as being a factor in rising airway resistance. The ventilator-related parameters may be underestimated during closed suctioning, where there is a secretion in the airway. In addition, in this experiment, results are only relevant to patients with obstructive or restrictive conditions using closed suction and are unable to be applied to a discussion on the patient coexistence with obstructive and restrictive respiratory systems.
## 4 Recent Advances and Future Directions
### Ethical and Societal Implications  
The ethical and societal implications of ML and big data are becoming increasingly prominent as these technologies become more pervasive. Concerns about bias, fairness, accountability, and the potential for misuse are driving the development of ethical frameworks and guidelines for the responsible use of ML [343; 344].  
Recent advancements in fairness-aware ML aim to address biases that may arise from training data or algorithmic design. Techniques such as adversarial debiasing, reweighting, and fairness constraints are being incorporated into ML pipelines to ensure that models make equitable decisions across different demographic groups. Additionally, there is growing interest in XAI, which seeks to make ML models more transparent and understandable, thereby enhancing accountability [345; 346; 347].  
Another critical area of focus is the impact of ML on employment and society. As automation and AI technologies become more capable, there is a growing concern about job displacement and the widening of economic inequalities. Policy-makers, industry leaders, and researchers are increasingly collaborating to develop strategies to mitigate these impacts, such as workforce reskilling programs and policies that promote inclusive growth [348; 349].  
The future of ethical and societal considerations in ML will likely involve the development of more comprehensive frameworks that address not only technical issues but also the broader social, economic, and legal implications of these technologies. There is also a need for greater public engagement and dialogue about the role of ML in society, as well as for the establishment of regulatory bodies that can oversee the development and deployment of these technologies to ensure they are used responsibly [350; 351; 352].
# Experimental and Theoretical Study on Operation Characteristics of an Oscillating Heat Pipe
## 2 Experimental Setup  
### OHP Test System  
Figure 1 shows a schematic geometry of an OHP system and measurement points in the test. The condensation section is 40 mm, and the evaporation section is 60 mm.  
The width (L) and height (H) of the OHP is 450 mm and 180 mm, respectively. The pipe diameter can be determined using the Bond number:  
\[\text{Bo}=\frac{r^{2}g(\rho_{l}-\rho_{v})}{\sigma} \tag{1}\]  
where Bo is the Bond number, \(r\) is the hydraulic radius of an OHP, \(\rho_{l}\) is the liquid density, \(\rho_{v}\) is the vapor density, \(\sigma\) is the surface tension, and \(g\) is the gravitational acceleration. As reported by [PERSON] et al. (2011), a value of 0.85 could be used to determine the maximum hydraulic radius for the OHP. Then, the maximum radius in an OHP system is expressed as follows:  
\[r_{h,\text{max}}\leq 0.92\sqrt{\frac{\sigma}{g(\rho_{l}-\rho_{v})}} \tag{2}\]  
Based on Equation (2), the inner diameter of 3 mm is chosen for the studied OHP system. Copper tubes with inner diameter of 3 mm and outer diameter of 5 mm are bent in a U-shape, as shown in Figure 1. Deionized water is used as the working fluid, and the filling ratio is 62%.  
\[FR=\frac{V_{l}}{V_{HP}} \tag{3}\]  
where \(FR\) is the filling ratio of the OHP, %; \(V_{l}\) is the liquid volume, m\({}^{3}\); \(V_{HP}\) is the liquid volume, m\({}^{3}\).  
The OHP tester includes a thermal isolation box, heating elements, and a cooler. The box has an insulation layer filled with glass wool. Three resistance heating wires, each having resistance of 840 \(\Omega\), are wrapped in the evaporation section. The heating power is controlled between 25 and 100 W by adjusting the voltage transformer. The condensation section of the OHP is cooled by the water cooler. The water cooler is connected to the thermostatic bath, which can provide the chilled water with a specified temperature of 15 \(\pm\) 0.05 \({}^{\circ}\)C. The water temperatures at the inlet and outlet are measured using thermometers and recorded via the data acquisition system. The test unit is shown in Figure 2.  
Figure 1: Schematic geometry of the OHP and measurement points in the test.
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 2 Background
### IPv6 Protocol Overview  
The Internet Society statistics indicate that IPv6 deployment has increased globally in the seven years since its introduction. IPv6 deployment has increased dramatically at the network and service provider levels, and many new businesses rely on it. More than a quarter of all internet-connected users use IPv6 connectivity. According to Google statistics regarding IPv6 adoption, over 36.27% of users connected to Google services over the IPv6 protocol on 1 January 2022 [13]. Additionally, according to the Google report, 24 countries have more than 15% IPv6 traffic, with an increasing number of IPv6 attacks [14].  
IPv6 provides many advantages in terms of connectivity, expandability, and security. IPv6 is also a perfect solution for the massive number of IP addresses needed by IoT devices. Additionally, IPv6 introduces new protocols. For example, the Neighbor Discovery Protocol (NDP) is insecure and allows intruders to attack IPv6 networks. In addition, IPv6 is an OSI-model-compliant network protocol layer. For this reason, the IPv6 architecture is different from the IPv4 architecture in many ways, including how packet headers are formatted, the address range (32 bits for IPv4 and 128 bits for IPv6), and other characteristics, such as autoconfiguration and mobility [5; 15].  
Furthermore, IPv6 requires the mandatory Internet Protocol Security (IPSec) protocol, whereas IPv4 does not. However, even those additional features make the IPv6 protocol vulnerable to attacks since some unique IPv6 characteristics are exploitable by attackers. For example, an adversary could exploit the IPv6 multicast address, such as FF02::2, to discover routers on the network by sending a fake packet, forcing all routers in the network to respond and expose their presence, providing the adversary with enough information to attack the exposed routers [5].
# Isolation and Characterization of a Novel Cyanophage Encoding Multiple Auxiliary Metabolic Genes
## 3 Results and Discussion
### Genome Features and Taxonomy of S-SZBM1  
The genome sequencing data indicated that cyanophage S-SZBM1 genome is a 177,834 bp double-stranded, circularly permuted DNA molecule, with a G + C content of 43.31%. A total of 218 predicted ORFs, including 83 conserved domains and accounting for over 95% of the genome size, were detected as probable genes in cyanophage S-SZBM1 (Figure 2, Table S1). The majority of the genes (195 of 218) have plausible homologs in other genomes, with identities of 26.51-95.08%. Among all of the 90 functionally annotated genes, 32 genes were virion structural proteins, 23 genes were involved in DNA replication, recombination, and repair, one gene was viral lysis-related gene, four genes were related to transcription and translation, and 30 genes were annotated as other function. According to the core genes established in previous studies, a pool of 67 T4-like core genes involved in appropriating host metabolic machinery, DNA replication, virion construction, and hypothesis functions, were identified in cyanophage S-SZBM1 genome (Table S2) [14; 42; 44]. Among these core genes, 51 are in conserved domain. We also identified six tRNA genes in cyanophage S-SZBM1, including two clusters of four genes (Asn, Ala, Pro, Thr) and two genes (Arg, Val) (Figure 2, Table S3).  
An initial NCBI BLASTN analysis of the complete nucleotide sequence revealed that cyanophage S-SZBM1 shows limited similarity to 14 cyanophages with low coverages of 2-38% and identities of 74.58-85.49% (Table S4). Meanwhile, a total of 73 phages were predicted to be related with cyanophage S-SZBM1 by vConTACT2, and a GBDP phylogenetic tree was constructed based on amino acid sequences of cyanophage S-SZBM1 and these 73 related phages. In the resulting tree, cyanophage S-SZBM1 is most closely related to three cyanophages, namely S-WAM1, P-RSM6, and P-TIM40 (Figure 3A). In addition, nucleotide-based intergenic similarities were calculated by VIRIDIC and showed the highest value of only 28.1% between cyanophage S-SZBM1 and S-WAM1 (Figure 3B). According to the guidelines for the demarcation of genus-level rank of phage taxonomy, cyanophage S-SZBM1 should be classified as a representative of an unassigned genus under the family _Kyanovididae_, while 70% nucleotide identity of the full genome length was established as the cut-off for genera [37; 47].  
Figure 1: (**A**) Plaques of cyanophage S-SZBM1 formed on the lawn of _Synechococcus_ CB0101. (**B**) Transmission electron micrograph of cyanophage S-SZBM1 with non-contracted tail and contracted tail.  
Figure 3: _Cont._  
Figure 2: The genome map of cyanophage S-SZBM1. Genes within different functional categories are indicated by colors noted below.
## 5 Simulation Results  
The findings of our studies are presented through Cumulative Distribution Functions (CDF) illustrating the reduction in carrier-to-noise levels and the resulting throughput loss in the NTN satellite uplink and BER rise of the MSS composite link. These metrics allow us to effectively estimate the real degradation of the MSS and NTN systems' operations when they are interfered with by the Starlink systems in the 1990-1995 MHz band.  
It is worth noting that, in Recommendation ITU-R S.2131 satellite communications systems typically consider a threshold C/N reduction of 1 dB, corresponding to a 10% reduction in spectral efficiency [23]; this protection criterion can be applied to MSS systems.  
In accordance with 3 GPP specifications for terrestrial LTE/NR segments, the threshold for acceptable throughput loss stands at 5% [22]. This protection criterion can be applied be applied to the satellite NTN system offering D2D services.