esa-sceva/satcom-chunk-collection huggingface dataset:
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 4 Simulations and Results
### Scenario 2: Hybrid Topology with Multiple Producers  
In this scenario, we aim to present the network performance of cache schemes on a complex hybrid network topology. To evaluate the performance in a realistic scenario, video servers are connected to the edges of the access networks. We place three streaming sources that are connected to the black routers, and 100 video viewers are connected to other leaf nodes (white routers) in each access network, as shown in Figure 5b. We randomly place 25 video streaming files in these streaming sources so that the viewers may request these videos from other access networks. With the exception of the topology difference, all of the configurations are the same as in scenario 1.  
In the complex network topology and request model, the performance improvement of the RXI-based algorithm is still good, as shown in Figure 8. Since the average cache hit ratio and the average hop count are significantly improved by the RXI-based algorithm compared with the other three policies, unnecessary network traffic and the content retrieval time are reduced.  
Figure 8: Simulation results of hybrid topology. (**a**) average cache hit ratio; (**b**) average hop count; (**c**) network traffic; (**d**) total retrieval time.  
Figure 7: Average cache hit ratio in different levels of hierarchical topology.  
Because the \(RXI(i,j)\) is calculated independently at each router based on real-time observations and current streaming request status, the complexity of the network topology and the request model does not affect the performance of the RXI-based algorithm. In addition, the RXI-based algorithm can also avoid additional management overhead, such as messages or control packets transmission, which may obviously increase the network burden in the complex network topology. On the basis of these results, we can conclude that the RXI-based algorithm can be deployed in complex network scenarios.
###### Abstract  
Lithium-sulfur (Li-S) batteries are regarded as one of the promising advanced energy storage systems due to their ultrahigh capacity and energy density. However, their practical applications are still hindered by the serious shuttle effect and sluggish reaction kinetics of soluble lithium polysulfides. Herein, g-C\({}_{3}\)N\({}_{4}\) nanosheets and graphene decorated with an ultrafine Co-species nanodot heterostructure (Co@g-C\({}_{3}\)N\({}_{4}\)/G) as separator coatings were designed following a facile approach. Such an interlayer can not only enable effective polysulfide affinity through the physical barrier and chemical binding but also simultaneously have a catalytic effect on polysulfide conversion. Because of these superior merits, the Li-S cells assembled with Co@g-C\({}_{3}\)N\({}_{4}\)/G-PP separators matched with the S/KB composites (up to \(\sim\)70 wt% sulfur in the final cathode) exhibit excellent rate capability and good cyclic stability. A high specific capacity of \(\sim\)660 mAh g\({}^{-1}\) at 2.0 C as well as a capacity-fading rate of only \(\sim\)0.035% per cycle over 350 cycles at 0.5 C can be achieved. This bifunctional separator can even endow a Li-S cell at a low current density to exhibit excellent cycling capability, with a capacity retention rate of \(\sim\)88.4% at 0.2 C over 250 cycles. Furthermore, a Li-S cell with a Co@g-C\({}_{3}\)N\({}_{4}\)/G-PP separator possesses a stable specific capacity of 785 mAh g\({}^{-1}\) at 0.2 C after 150 cycles and a superior capacity retention rate of \(\sim\)84.6% with a high sulfur loading of \(\sim\)3.0 mg cm\({}^{-2}\). This effective polysulfide-confined separator holds good promise for promoting the further development of high-energy-density Li-S batteries.  
Keywords:co-species nanodots; g-C\({}_{3}\)N\({}_{4}\) and graphene; polysulfide barrier; chemical adsorption; catalytic conversion +
Footnote †: journal: _molecules_
###### Abstract  
The development of a facile method for the synthesis of GaN:ZnO solid solution, an attractive material with a wurtzite-type structure, is vital to enhance its photocatalytic activity toward H\({}_{2}\) evolution. Herein, GaN:ZnO solid solution nanorods with diameters of around 180 nm were fabricated by combining the electro-spun method with a sequentially calculating process. Photocatalytic water-splitting activities of the as-obtained samples loaded with Rh2-YCrYO\({}_{3}\) co-catalyst were estimated by H\({}_{2}\) evolution under visible-light irradiation. The as-prepared GaN:ZnO nanorods at a nitridation temperature of 850 \({}^{\circ}\)C showed the optimal performance. Careful characterization of the GaN:ZnO solid solution nanorods indicated that the nitridation temperature is an important parameter affecting the photocatalytic performance, which is related to the specific surface area and the absorbable visible-light wavelength range. Finally, the mechanism of the GaN:ZnO solid solution nanorods was also investigated. The proposed synthesis strategy paves a new way to realize excellent activity and recyclability of GaN:ZnO solid solution nanorod photocatalysts for hydrogen generation.  
Keywords:GaN:ZnO solid solution nanorods; electrospinning homology; hydrogen evolution; visible-light photocatalysis +
Footnote †: journal: Applied Sciences
# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction
## 4 Results  
To demonstrate the advantages achieved by the proposed underwater image enhancement method with Retinex transmission map and adaptive color correction, we compare it against several state-of-the-art underwater image enhancement methods. The experiments are conducted on the UIEB dataset [10], which is the first widely used real-world underwater image dataset with reference images. UIEB includes 890 raw underwater images with the corresponding high-quality reference images, within which 800 pairs are for training in data-driven learning and 90 pairs are for testing. Since the proposed method is not learning-based, we conducted experiments only on the testing set.
## 4 Discussion
We applied the NDMI based model to data from Sapelo Island, GA and demonstrated that the GPP modeled from tide and cloud-free NDMI composites results in a more reliable product than the coarser scaled existing MODIS MOD17A2. We can derive two important conclusions from this observation. First, the MOD17A2 GPP product available at the time of this study is not suitable for wetlands. This may be in part because of its coarser scale though we think this contributed only to minor differences because the entire marsh area was an _S. alterniflora_ monoculture. However, more importantly, the MOD17A2 GPP product uses an LUE-based approach by incorporating ground-based meteorological measurements, a grassland biome-properties look-up table (BPLUT), and fPAR and LAI (from MOD 15) [45, 46, 47, 48, 49, 50]. These variables need to be parametrized and recalibrated for coastal marshes. The main improvement our modeling approach provided was to show that a tidally filtered vegetation index can be more accurate than a complex modeling approach that is not parametrized for local conditions or species. Second, although significant, NDMI only explained 46% of the variability in observed GPP which is much lower than the LUE-based PEM models developed for mangroves wetlands (e.g., [PERSON] et al., 2013)[1]. These mangrove canopies stick above high tide and therefore do not require spectral tidal filtering that salt marshes require. However, unlike LUE, vegetation indices are often used as proxies for greenness and not carbon uptake. In this study, we have shown that a simple vegetation index based GPP model can be more accurate than a poorly calibrated complex LUE based model (MOD17A2). [PERSON] et al. [25] also observed that LUE-based models without tidal filtering can give a poor performance in tidal salt marshes. Therefore, we conclude that existing MODIS-based PEM or VPM models for wetlands can be improved significantly by filtering and re-compositing the vegetation index used in model parametrization. While a well-parameterized LUE-based model may ultimately give better results, this study is a step forward because this simple VI MODIS models of GPP explains a large fraction of GPP variation within the pixel best representing the flux tower footprint and is easy to calculate. The high temporal resolution of MODIS coupled with the moderate spatial resolution of a 500 m algorithm can result in an immense potential for studying and monitoring both long and short-term tidal wetland health and physiological statuses, and for allowing GPP comparisons with the upland areas that MOD17A2 was designed to estimate.
## 4 Conclusions  
Based on the classical explicit central difference method and implicit Newmark method, this paper derives the iterative expressions of the field variables of different methods in the form of local and global solutions of the shallow water seismic wavefield equation. The numerical simulation results are verified by comparing them with the COMSOL data, and the conclusions are as follows.  
1. Stability: The explicit method is conditionally stable, which is more limited by the time step, and the implicit Newmark method is unconditionally stable. Under the guarantee of accuracy, the implicit method of the global solution is the optimal choice both in terms of accuracy and efficiency.
2. Computational efficiency: In the global solving method, the explicit and implicit methods are comparable in terms of time consumption. In the local solving method, the explicit-explicit method and the explicit-implicit hybrid method are comparable in terms of time consumption, and the implicit-implicit method consumes three times as much time as the former two. Under the same time step, the local solving implicit-implicit method takes the most time, and the global solving method takes more time than the other local solving methods.
3. Computational accuracy: For the same time step, the accuracy of the implicit method is better than the explicit method in both local and global solving methods. The accuracy of the explicit method in global solving is better than that of the explicit method in local solving, and the accuracy of the implicit method in global solving with a large step size is comparable to that of the explicit-explicit method in local solving with a small step size for different time steps.  
In this paper, the optimal solution of the shallow water seismic wavefield equation is given based on the classical explicit center difference and implicit Newmark methods, which also apply to solving similar problems with fluid-elastomer coupling. The next step will be to investigate the solution method with higher accuracy and efficiency in the case of unconditional stability.  
Conceptualization, W.C.; methodology, W.C.; writing--original draft preparation, W.C.; writing--review and editing, G.C. and Y.C.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China: 12104510; General Program of China Postdoctoral Science Foundation: 2022M723882.  
Not applicable.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Plant-Bacteria Interaction Regulates Plant Protein and Metabolites Production
### Bacterial Proteins and Metabolites in Plant Stress Mitigation (Direct Mechanism)
#### Extracellular Polymeric Substance (EPS)  
EPS are polymers that microorganisms biosynthesize. It is composed of polysaccharides, proteins, and DNA, and its synthesis is predominantly prompted by environmental signals [34]. Since the carboxyl and hydroxyl functional groups predominate in varying amounts depending on EPS composition, the majority of the examined EPS are negatively charged [35]. The key elements influencing biosorption of other ions are connected to the binding sites or their chemical nature, such as pH, metal content, ionic strength, surface characteristics, EPS molecular weight, and branching degree [36]. EPS binds with sodium ions, minimizing the consequences of high soil salinity, while EPS produced by bacteria reduces salt stress by maintaining a Na\({}^{+}\)/K\({}^{+}\) equilibrium, allowing plants to thrive under unfavorable soil conditions (Figure 2B) [37].  
A rise in salt concentration also drives an increase in bacterial EPS synthesis, which leads to the formation of biofilms and the strengthening of Na\({}^{+}\) chelation [38]. In line with this, [PERSON] et al. [39] revealed that in salt-tolerant _Pseudomonas_ spp., EPS increased with increasing NaCl concentration (up to 500 mM). In comparison to sodium ion absorption by root, potassium ion uptake was increased (up to 65%) in soybean plants grown under salt stress conditions. Inoculation with EPS producing _Bacillus aryabhattai_ ALT29 and _Arthobacter voluwensis_ ALT43, on the other hand, increased K\({}^{+}\) ion absorption up to 67% as compared to the control plant [40]. _Planococcus rifletoensis_ RT4 produced EPS and biofilm which alleviated salinity stress in _Cicer arietinum_ plants cultivated in 200 mM salt stress [41].
## 3 Energy Efficient Routing
### Principles
#### 3.1.1 Energy Consumption Model  
Energy efficient routing is aware of energy dissipated during a transmission. An energy consumption model is used by the routing protocol itself to take its decisions. It can also be used by the protocol designers to evaluate the performances of their protocol. The energy consumed by a node depends on its state. In our work, we suppose that all nodes transmit with the same transmission power allowing to reach nodes at the maximum transmission range. As we are interested in the additional energy spent during the transmission of a flow packet from its source to its destination with reference to the idle state, the values used for transmission power and reception power are calculated as follows: \(P_{trans}=P_{transmit}-P_{idle}\), \(P_{recv}=P_{receive}-P_{idle}\). Now, we can determine the energy dissipated in transmitting (\(E_{trans}\)) or receiving (\(E_{recv}\)) a packet. Let \(Duration\) denote the transmission duration of a packet. We have: \(E_{trans}=P_{trans}*Duration\), \(E_{recv}=P_{recv}*Duration\).  
Interference and overhearing generate loss of energy. Hence, energy dissipated by a transmission by sender \(i\) is calculated as follows:  
\[cost_{transmission}(i)=E_{trans}+n*E_{recv} \tag{1}\]  
where \(n\) represents the number of non-sleeping nodes belonging to the overhearing and interference zones of the transmitter \(i\). This cost indicates the quantity of energy consumed by a packet of the flow to reach the next hop. The energy dissipated by an end-to-end transmission over a path \(P\) can be computed as follows:  
\[cost(flow)=\sum_{i\in sender(flow)}cost_{transmission}(i) \tag{2}\]  
where \(i\) is a sender of \(flow\) on its path \(P\).
## 3 Results
### Bulk Properties of SRO
\[T_{\rm C}^{\rm SCGA}\simeq\theta_{\rm C}T_{\rm C}^{\rm MF}. \tag{4}\]  
Here, \(\theta_{\rm C}\) is a structure dependent parameter. Because the Ru ions are arranged in the simple cubic structure, the parameter \(\theta_{\rm C}\simeq 0.719\) is used accordingly [30]. The Curie Temperature calculated by the SCGA method is \(T_{\rm C}^{\rm SCGA}\simeq 124.6\,\rm K\), which is \(~{}16\%\) underestimated in comparison with the experiment result \(T_{\rm C,SRO}\simeq 150\,\rm K\)[13]. To see the influence of on-site Coulomb repulsion \(U\) and exchange \(J\) on \(T_{\rm C}\), we have done DFT total energy calculations with \(U\) ranging from 2.0 eV to 3.5 eV and \(J\) ranging from 0.2 eV to 0.6 eV. The estimated \(T_{\rm C}\) values are presented in Figure 1d. As can be seen in the figure, the highly overestimated \(T_{\rm C}\) with \(U=2.0\) eV decreases along with increasing \(U\). With the \(U\) value of 3.5 eV, the estimated \(T_{\rm C}\) approaches the experimental \(T_{\rm C}\). This behavior that the on-site \(U\) can lower the \(T_{\rm C}\) of SRO is compatible with the Goodenough-Kanamori superexchange rule that the Hubbard \(U\) can stabilize the AFM phase. On the other hand, the on-site exchange \(J\) plays a different role to enhance \(T_{C}\), as shown in Figure 1e. It should be noted that, in addition to Hubbard \(U\), \(T_{\rm C}\) is also sensitive to the on-site exchange \(J\) in the opposite direction. These contrary trends imply that the Liechtenstein's rotationally invariant LDA + \(U\) scheme [40] and the [PERSON]'s simplified LDA + \(U\) scheme [46], which takes \(U_{eff}\equiv U-J\) as a single parameter, could lead to different behavior of the Curie temperature. Furthermore, we also perform the Monte Carlo simulations for the magnetizations of SRO as functions of temperature based on the Heisenberg model with \(U,J=3.5,0.6\) eV and \(U,J=3.0,0.6\) eV, as presented in Figure 1f. We then fit the Monte Carlo magnetizations by the formula \(M(T)=M(0\,\mathrm{K})\left(1-T/T_{\mathrm{C}}^{\mathrm{MC}}\right)^{\beta}\) to estimate \(T_{\mathrm{C}}^{\mathrm{MC}}\) with the fitted curves also depicted in Figure 1f. Here, \(\beta=0.3456(0.3430)\) is the critical exponent for the \(U=3.5(3.0)\) eV case. Surprisingly, for both cases with different \(U\) values of 3.0 and 3.5 eV, our Monte Carlo \(T_{\mathrm{C}}\) values agree well with our SCGA \(T_{\mathrm{C}}\) within 2%. Moreover, for \(U,J=3.5,0.6\) eV, the estimated \(T_{\mathrm{C}}^{\mathrm{MC}}\simeq 126\) K is close to the experimental \(T_{\mathrm{C}}\simeq 150\) K. Consequently our LDA + \(U\) calculations and Monte Carlo \(T_{\mathrm{C}}\) simulations for SRO are reliable.
## 2 Design Method, Study Case, Objectives and Assumptions
### Network Model for HEBP Design  
Once the ability to predict the amount of energy harvestable by each WS deployed on the system was effective, a minimum required QoS was defined. The network was partitioned into three WS groups, as shown in Figure 2 below. First, the highest harvest rate WS was designated as the CH node. In addition to collect data at its location, acts as a gateway between the other WSs and the BS. In a second step, energy assistance was envisaged between the other WSs of the network. As shown in Figure 2, there is energy assistance between the different WSs of the network. Based on the energy predicted by the PHPV, it was evaluated as to whether the WS has an energy greater than the required QoS; in this case, the WS is said to be self-sufficient. For a WS with less energy than the  
Figure 1: Different steps for the implementation of the PHPV and the HEPB.  
required QoS, the WS was classified as needy and received energy from the self-sufficient WSs. Once the WS energy level balance is achieved, each WS transfers the collected data directly to the CH node. The CH node is the node whose residual energy added to the predicted energy harvest rate is the highest energy level in the network. The energy cost of the CH is then associated with the data reception, data aggregation, and data transmission to the Base Station (BS). The combined solutions (PHPV and HEPB) thus maximize the size of the data transmitted by each needy WS while improving the network's QoS through the number of WSs that can send information about the system's state.
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 4 Observation and Results
### Confusion Matrix  
The confusion matrix (shown in Figure 6) is a tool for evaluating the performance of classification models that provides a summary of their binary outputs. It is calculated based on four parameters: true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). TP refers to correctly predicted positive values, TN refers to correctly predicted negative values, FP refers to incorrectly predicted positive values, and FN refers to incorrectly predicted negative values. The confusion matrix and its four parameters are illustrated in Figure 7 below.  
There are several metrics that can be used to evaluate the performance of classification models and summarize their results. These include:  
_Accuracy_: This metric measures the proportion of correctly classified instances out of the total number of records. It is calculated as:  
\[Accuracy=\frac{TN+TP}{TN+FP+TP+FN}\]  
Figure 6: The proposed method of GRU ([PERSON], [PERSON], [PERSON], 2021).  
Figure 7: Confusion matrix visualization.  
Precision: Precision is a measure of the model's performance, with a high precision value indicating better performance. It is calculated as:  
\[Precision=\frac{TP}{TP+FP}\]  
Recall: Recall, also known as sensitivity or true positive rate, is a measure of the proportion of actual positive instances that are correctly identified by the model. A high recall value, ideally close to 1, is desired for a successful proposed method. It is calculated as:  
\[Recall=\frac{TP}{TP+FN}\]  
F1 Score: This is a metric that combines precision and recall, with a higher value indicating better performance. It is calculated as:  
\[F1\ Score=2\times\frac{Precision\times Recall}{Precision+Recall}\]  
In the context of detecting DDoS attacks in SDN environments, high values for accuracy, precision, recall, and F1 Score are desirable, as they indicate that the model is able to accurately identify and mitigate attack traffic while minimizing false alarms and correctly identifying normal traffic.
## 2 Background and Related Technologies
### NTNs  
Advances in communication technologies and growing demand for next-generation services have accelerated the development of non-terrestrial networks (NTNs). The exponential growth of smart devices further drives this expansion.  
NTNs provide stable and extensive connectivity in areas with limited terrestrial coverage, such as rural regions, maritime zones, and airborne platforms. According to the 3 rd Generation Partnership Project (3 GPP), NTNs can use relay nodes and base stations positioned at various altitudes. These include space-based systems (GEO, MEO, and LEO) and airborne systems like UAS and HAPS. For example, UASs (Unmanned Aerial Systems) offer flexible deployment for temporary or emergency coverage, while HAPSs (High Altitude Platform Stations) provide quasi-stationary platforms that bridge the gap between terrestrial and satellite networks [11].  
A key benefit of NTNs is their ability to offer reliable connectivity in regions where building terrestrial networks is economically unfeasible. In addition to supplementing terrestrial networks, NTNs support continuous service for Machine-to-Machine (M2M) communications and Internet of Things (IoT) devices, thereby enabling seamless data exchange and real-time operations.  
Given these advantages, NTNs are becoming vital for modern satellite constellations in commercial, scientific, and military applications. Their broad coverage, scalability, and reliability are crucial for next-generation space-based communication infrastructure. However, NTNs also face challenges such as Doppler shift compensation and complex satellite-to-ground handovers, which require effective solutions. In this study, we investigate the use of NTN technologies to facilitate communication between LEO and VLEO satellite constellations and ground-based IoT devices.
## 5 Simulation Results and Analysis  
In this section, detailed simulations are carried out to validate the performance of the proposed super-cumulant-feature-based AMR recognizer. The simulations were performed in MATLAB R2020, on a Windows 10 professional platform with an i5 core processor (sixth generation) and 16 GB of RAM. The merit of the problem is the percentage recognition accuracy (PRA). A comprehensive analysis of our proposed AMR approach and its comparison with existing state-of-the-art techniques are also presented in this section. The modulation of interest for recognition is BPSK, QPSK, QAM, 16-QAM, and 64-QAM. The performance is also compared on a fading channel, i.e., a Rayleigh fading channel with different numbers of samples and different SNRs. The simulation parameters are shown in Table 3.