esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Few-Layer Transition Metal Dichalcogenides
### Nonlinear Optical Properties
#### 2.3.2 Origin of Sub-Bandgap Saturable Absorption  
Surprisingly, many of the reports to date observing saturable absorption in few-layer TMDs have been demonstrated at near-infrared wavelengths, which corresponds to photon energies less than the expected material bandgap for most TMDs. In a perfect crystalline semiconductor, incident photons with energy lower than the bandgap would not be absorbed [27]. However, crystallographic defects, including edges and vacancies, have been proposed to explain this phenomena [32].  
For the wide body of work reporting SAs based on LPE-processed TMD flakes, the flake size is often on a sub-wavelength scale, and as such, they cannot be considered as infinite crystal structures. Broken symmetry at the edges of atomic planes and unsatisfied bonds of M and X atoms at edges can modify the electronic structure, leading to the creation of edge-states in the bandgap. Given the large edge to surface area ratio of nano flakes, the contribution of edges is significant, as verified by photothermal deflection spectroscopy measurements of sub-bandgap absorption [57]. More recently, the role of defects in TMDs has been verified by theoretical studies of the band structure, showing that edges result in a finite local density of states within the bandgap of MoS\({}_{2}\) crystals [58]. Absorption of photons with sub-bandgap energies is thus permitted by electron transitions from the valence band to these mid-gap states, which can be saturated at high intensity by Pauli blocking [32].  
A distribution of edge states within the bandgap could explain the recent experimental reports of wideband saturable absorption, although the density of states is not expected to be constant throughout the bandgap, as shown in [58], and will depend on the geometry and edge termination (whether M or X sites). Defects may also explain observations of SA at sub-bandgap photon energies in TMD flakes grown using CVD [30; 34]; however, the role of edges may be less significant due to typically larger flake sizes. Other crystallographic defects, such as vacancies and grain boundaries, could contribute to sub-bandgap absorption, as verified by numerical simulations in both MoS\({}_{2}\)[58; 59] and WS\({}_{2}\)[53].
# Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
## 2 System Model
### Assumptions of the Data Sharing Model  
The operation of system model is based on certain assumptions, which are explained below:  
1. Figure 1 shows the details of the data exchange between different entities. The data demander and the RSU use cables to realize B2R communication, while the RSUs communicate with each other through R2R, which also requires cables. Since the RSU and the moving vehicles communicate with each other through V2R, we assume that there are a set of equally spaced RSUs deployed on the road, and the communicable range radius of the RSUs is described as \(R^{RSU}\). \(P_{j}(t)\) represents the position of the RSU \(j\), and \(P_{l}(t)\) is the position of the moving vehicle \(i\), when \[\Big{|}\Big{|}P_{i}(t_{1}^{-})-P_{j}(t_{1}^{-}) > R^{RSU}\Big{|}\Big{|},\] \[\Big{|}\Big{|}P_{i}(t_{1})-P_{j}(t_{1}) \leq R^{RSU}\Big{|}\Big{|}\Big{|}\] \[\Big{|}\Big{|}P_{i}(t_{2})-P_{j}(t_{2}) > R^{RSU}\Big{|}\Big{|},\] we set that the moving vehicle \(i\) and \(RSU_{j}\) in \(t\in[t_{1},t_{2})\) produce a contact (here, \(||.||\) is Euclidean distance, \(t_{1}\) and \(t_{2}\) denote the time points before and after \(t\)), the vehicle and the RSU establish a data link;
2. Divide the whole time duration into a series of time slots; the data sharing market for vehicles is explored within a specific time period;
3. Regulate data sharing through smart contracts on RSU; the RSU is completely neutral between buyers and sellers, but it will also give appropriate assistance when the requests come from legal entities;
4. There are several data buyers and sellers active in each time slot. For the convenience of research, only the data sharing activities of buyer and the seller managed by RSU are discussed. Of course, other situations can also be represented by the proposed system model;
5. We consider the shared price standard for all data to be unified. In addition, for the buyer, the purpose of the data is to conduct traffic flow analysis, automatic driving  
Figure 1: Diagram of system model.  
analysis training, etc. The larger the amount of data, the more accurate the analysis results and the better the training effect that can be obtained.
# A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
## 2 Related Work
### Smart Defense Technology for Jamming Attacks  
Jamming attacks should be given higher priority than other types of attacks in UAV networks, since available radio channels are the physical basis of any type of UAV communications. Using game theory and reinforcement learning techniques to tackle the smart jamming problem is an emerging academic research hotspot. [PERSON] et al. [18] proposed a Colonel Blotto anti-jamming game-based power allocation scheme which can enhance the anti-jamming performance in cognitive radio networks. [PERSON] et al. [19] studied the interactions among a source node, a relay node, and a jamming mode using a Stackelberg game, and these nodes chose their transmit power in turn with the premise that they did not interfere with primary users. [PERSON] et al. [20] proposed a Stackelberg game-based packets transmission scheme to establish a power allocation strategy in order to improve the SINR (Signal to Interference plus Noise Ratio) of radio channels. [PERSON] et al. [21] investigated the subjective decision process of smart jammers in a time-varying environment based on prospect theory. [PERSON] et al. [22] introduced a stochastic differential game model to solve the power allocation problems under conditions of uncertain channel gains. [PERSON] et al. [23] intended to provide a systematic review of the UAV networks from a cyber-physical system perspective. They summarized the security requirements of UAV networks as sensing security, storage security, communication security, actuation control security, and feedback security. They divided the UAV network into three hierarchies, i.e., the cell level, the system level, and the system of system level, for detailed investigation, and the coupling effects were discussed as well. They outlined several uses of intelligent algorithms for UAV networks, including flight control, path planning, machine vision, and pattern recognition, among others.  
In conclusion, most studies related to the application of SDNs in UAV networks focused on the deployment of the SDN controller, routing control, and load balance problems. To the best of our knowledge, no existing works focus on the joint optimization between network performance and UAV track planning. Although some researchers have obtained research progress in anti-jamming by formulating a game between UAVs and jammers, they do not consider the multi-UAV scenario. Those studies on multi-UAV scenarios and smart attacks assume that UAVs are stationary, which is not consistent with reality. In this paper, we focus on the smart defense communication strategy of UAV networks by optimizing the power allocation of each UAV under the action space of radio channel selecting and trace planning.
## 5 Discussion
### Theoretical and Practical Implications  
The results of this study offer several theoretical contributions to the literature on omnichannel service design and Quality of Experience (QoE). First, by confirming the moderate to strong associations between omnichannel performance and satisfaction in a digitally emergent environment, the study extends existing models--often validated in developed markets--into new geographies and socio-digital contexts. This supports the argument that omnichannel theory is adaptable across digital maturity levels but that the relative influence of each channel dimension may vary depending on cultural, infrastructural, and economic factors. Second, this research reinforces the value of combining the customer journey perspective with the expectancy-disconfirmation paradigm to capture both the structural and perceptual aspects of service evaluation. While the journey model highlights process flows, touchpoints, and cross-platform transitions [7], the expectancy-disconfirmation paradigm introduces the cognitive mechanisms behind user satisfaction, such as expectation calibration and perceived effort [27]. Together, these frameworks offer a multi-layered understanding of how users interact with and interpret omnichannel systems. From a practical standpoint, the study provides actionable insights for service providers, particularly in regions undergoing digital transformation without full infrastructure readiness. The evidence suggests that businesses should prioritize investment in the following areas:  
* Data centralization and synchronization, to ensure that users encounter coherent, accurate, and personalized information across all platforms.
* Logistics transparency and flexibility, as users value the ability to select pickup or delivery options and trust services that fulfill promises reliably.
* The incremental introduction of digital payment options, accompanied by user education, interface simplicity, and the retention of cash alternatives to avoid alienating less digitally fluent customers.  
These actions align with the principle of progressive digital inclusion, where service systems are designed not only for performance optimization but also for accessibility and usability across diverse user profiles [4]. The findings highlight the importance of designing with the user, not just for the user, particularly in emerging economies where infrastructural asymmetries may shape unique behavioral patterns.
## 5 Results
### Evaluating the Reflective Measurement Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Constructs**} & \multirow{2}{*}{**Indictors**} & \multirow{2}{*}{**Factor Loadings**} & \multicolumn{2}{c}{**Cronbach’s**} & \multicolumn{2}{c}{**Composite**} & \multirow{2}{*}{**AVE**} \\  & & & **Alpha** & & **Reliability (rho\_A)** & & \\ \hline \multirow{4}{*}{Vak-LS} & Vak-LS1 & 0.882 & & & & \\  & Vak-LS2 & 0.920 & 0.870 & 0.874 & 0.794 \\  & Vak-LS3 & 0.870 & & & & \\  & PT1 & 0.858 & & & & \\ PT2 & 0.930 & & & & \\ PT3 & 0.912 & & & & \\  & PT4 & 0.912 & & & & \\  & PU1 & 0.915 & & & & \\ PU2 & 0.928 & & 0.936 & 0.937 & 0.840 \\ PU & PU3 & 0.940 & & & & \\  & PU4 & 0.881 & & & & \\  & PE1 & 0.931 & & & & \\ PE & PE2 & 0.854 & & 0.930 & 0.933 & 0.828 \\ PE & PE3 & 0.926 & & & & \\  & PE4 & 0.927 & & & & \\  & TTT1 & 0.934 & & & & \\ TTT & TTT2 & 0.935 & 0.920 & 0.921 & 0.863 \\  & TTT3 & 0.917 & & & & \\  & INT1 & 0.937 & & & & \\ INT2 & 0.939 & 0.938 & 0.939 & 0.890 \\  & INT3 & 0.954 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Assessment of reflective measurement models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **INT** & **PE** & **PLT** & **PU** & **Vak-LS** \\ \hline INT & 0.943 & & & & \\ PE & 0.871 & 0.91 & & & \\ PLT & 0.807 & 0.795 & 0.903 & & \\ PU & 0.848 & 0.846 & 0.878 & 0.916 & \\ Vak-LS & 0.631 & 0.623 & 0.761 & 0.684 & 0.891 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fornell-Larcker criterion. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **INT** & **PE** & **PLT** & **PU** & **Vak-LS** & **TTT** \\ \hline INT1 & **0.937** & 0.828 & 0.776 & 0.812 & 0.641 & 0.851 \\ \hline INT2 & **0.939** & 0.811 & 0.728 & 0.775 & 0.551 & 0.779 \\ INT3 & **0.954** & 0.826 & 0.779 & 0.811 & 0.591 & 0.824 \\ \hline PE1 & 0.817 & **0.931** & 0.739 & 0.798 & 0.595 & 0.786 \\ \hline PE2 & 0.721 & **0.854** & 0.691 & 0.728 & 0.511 & 0.737 \\ \hline PE3 & 0.803 & **0.926** & 0.711 & 0.765 & 0.569 & 0.770 \\ \hline PE4 & 0.824 & **0.927** & 0.748 & 0.787 & 0.590 & 0.791 \\ \hline PLT1 & 0.650 & 0.633 & **0.858** & 0.713 & 0.696 & 0.585 \\ \hline PLT2 & 0.772 & 0.766 & **0.930** & 0.823 & 0.693 & 0.733 \\ \hline PLT3 & 0.720 & 0.706 & **0.912** & 0.783 & 0.687 & 0.723 \\ \hline PLT4 & 0.769 & 0.757 & **0.912** & 0.846 & 0.676 & 0.747 \\ \hline PU1 & 0.764 & 0.769 & 0.825 & **0.915** & 0.639 & 0.768 \\ \hline PU2 & 0.766 & 0.761 & 0.806 & **0.928** & 0.647 & 0.755 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross loadings.
# Multifunction RF Systems for Naval Platforms
## 4 MFRF System Prototype Development Programs
### InTop
#### 4.2.5 LowRID ADM  
The frequency range of operation for the lowrider ADM is VHF to C-band. Communications, EA and ES functions are supported throughout this frequency range. In the case of the communications function, the focus is on line-of-sight terrestrial communications, specifically Link 16, Identification Friend or Foe (IFF) and Tactical Air Navigation (TACAN) [51].
###### Abstract  
This study addresses the potential contribution of the first pair of Galileo FOC satellites sent into incorrect highly eccentric orbits for geodetic and surveying applications. We began with an analysis of the carrier to noise density ratio and the stochastic properties of GNSS measurements. The investigations revealed that the signal power of E14 & E18 satellites is higher than for regular Galileo satellites, what is related to their lower altitude over the experiment area. With regard to the noise of the observables, there are no significant differences between all Galileo satellites. Furthermore, the study confirmed that the precision of Galileo data is higher than that of GPS, especially in the case of code measurements. Next analysis considered selected domains of precise instantaneous medium-range positioning: ambiguity resolution and coordinate accuracy as well as observable residuals. On the basis of test solutions, with and without E14 & E18 data, we found that these satellites did not noticeably influence the ambiguity resolution process. The discrepancy in ambiguity success rate between test solutions did not exceed 2%. The differences between standard deviations of the fixed coordinates did not exceed 1 mm for horizontal components. The standard deviation of the L1/E1 phase residuals, corresponding to regular GPS and Galileo, and E14 & E18 satellite signals, was at a comparable level, in the range of 6.5-8.7 mm. The study revealed that the Galileo satellites with incorrect orbits were fully usable in most geodetic, surveying and many other post-processed applications and may be beneficial especially for positioning during obstructed visibility of satellites. This claim holds true when providing precise ephemeris of satellites.  
Galileo; GNSS; RTK; satellite positioning Article On the Applicability of Galileo FOC Satellites with Incorrect Highly Eccentric Orbits: An Evaluation of Instantaneous Medium-Range Positioning
## 2 Materials and Methods  
### General Concept of Neutrosophic Number  
Some scientific problems with uncertainty cannot be directly estimated in a quantitative manner in the real world. The uncertain parts need to be translated into mathematical forms for utilization. Hence, [PERSON] [33] originally proposed a concept of NN to express real information. In reality, different kinds of data are used to denote different attributes, which are distinct and can be expressed by crisp numbers, such as 0 and 1. Under neutrosophic environment, the crisp number can be denoted as \(N=Q+EI\), and this expression contains two parts, i.e., the determinate part \(Q\) and the indeterminate part \(EI\), where the values \(Q\) and \(E\) represent the real number. The indeterminacy \(I\) is a dynamic interval number owing to the domain, which is variable according to the actual situation. The indeterminacy \(I\) has the following properties: \(I^{2}=I,\ 0\times I=0\), and \(I/I=\) undefined.  
Taking a neutrosophic number \(N=6+4I\), as an example, if \(I\in[0,\ 0.5]\), it is equivalent to \(N\in[6,8]\) and therefore \(N\geq 6\). This number consists of the determinate part 6 and the indeterminate part \(4I\). The domain \(I\in\left[I^{L},\ I^{L}\right]\) can be selected according to the practical situation. Here, \(L\) and \(U\) stand for the lower and upper bounds for the domain, respectively. When an event is considered to be true, the value of indeterminacy becomes 0. For example, if the value of a true event equals to 8, the neutrosophic number will be \(N\in[8,\ 8]\), and it is defined as \(N=8+0\times I\).  
Considering two neutrosophic numbers \(N_{1}=E_{1}I+Q_{1}\), and \(N_{2}=E_{2}I+Q_{2}\), where \(I\in\left[I^{L},\ I^{L}\right]\), the algorithm of neutrosophic number is as follows [27; 28; 29].  
\[N_{1}+N_{2}=Q_{1}+Q_{2}+\left(E_{1}+E_{2}\right)I \tag{1}\]  
\[N_{1}-N_{2}=Q_{1}-Q_{2}+\left(E_{1}-E_{2}\right)I \tag{2}\]  
\[N_{1}\times N_{2}=Q_{1}Q_{2}+\left(Q_{1}E_{2}+E_{1}Q_{2}+E_{1}E_{2}\right)I \tag{3}\]  
\[N_{1}^{2}=\left(Q_{1}+E_{1}I\right)^{2}={Q_{1}}^{2}+\left(2Q_{1}E_{1}+E_{1}{}^ {2}\right)I \tag{4}\]  
\[\frac{N_{1}}{N_{2}}=\frac{Q_{1}+E_{1}I}{Q_{2}+E_{2}I}=\frac{Q_{1}}{Q_{2}}+ \frac{Q_{2}E_{1}+Q_{1}E_{2}}{Q_{2}\left(Q_{2}+E_{2}\right)}I\text{ for }Q_{2}\
eq 0\text{ and }Q_{2}\
eq E_{2} \tag{5}\]  
\[\sqrt{N_{1}}=\sqrt{Q_{1}+E_{1}I}=\left\{\begin{array}{c}\sqrt{Q_{1}}-\left( \sqrt{Q_{1}}+\sqrt{Q_{1}+E_{1}}\right)I\\ \sqrt{Q_{1}}-\left(\sqrt{Q_{1}}-\sqrt{Q_{1}+E_{1}}\right)I\\ -\sqrt{Q_{1}}+\left(\sqrt{Q_{1}}+\sqrt{Q_{1}+E_{1}}\right)I\\ -\sqrt{Q_{1}}+\left(\sqrt{Q_{1}}-\sqrt{Q_{1}+E_{1}}\right)I\end{array}\right. \tag{6}\]
## 3 Simulation Results
### Solution Quality and Stability of Algorithm in Terms of Best Fitness  
Table 7 shows the best solution quality and stability of the algorithm for four different PV array configurations, two with three rows and two with four rows. The best fitness values of Scenario 1 and Scenario 2 configurations with three PV arrays are 0.17 and 0.6, respectively. Similarly, the best fitness values (CVI) of the four-row configurations are calculated as 0.35 and 0.56, respectively. The average worst fitness value in Scenario 1 is 7.44, and the average best fitness value is 2.54. Considering that the best fitness value is 0.17, it is seen that there is superior optimization performance and high best solution quality. Similar optimization performance and solution quality are clearly seen in other scenarios. On the other hand, Scenario 1 and Scenario 2 exhibit excellent stability with a standard deviation of 0.0. Scenario 3 and Scenario 4 show that the algorithm of stability is at a very acceptable level with standard deviation values of 0.129 and 0.101, respectively. All these results highlight the balance between solution quality and algorithm stability among different configurations.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 4 Experimental Results and Analysis
### Implementation Details  
All experiments are conducted on a workstation running CentOS 7, equipped with NVIDIA Tesla V100 GPUs. The model is implemented in PyTorch 1.13 with Python 3.8. For preprocessing, images from the ISIC 2018 and ACDC datasets are resized to 256 \(\times\) 256, while images from the Synapse dataset are resized to 224 \(\times\) 224. To enhance generalization and mitigate overfitting, standard data augmentation techniques, such as random horizontal and vertical flipping, are applied. Training is performed using the AdamW optimizer, which combines the adaptive learning rate of Adam with decoupled weight decay. The learning rate is initialized to 0.001 and gradually decayed using a cosine annealing schedule over 300 epochs, with a batch size of 32.
## 4 Results and Discussion
### Comparison of Regression and ANNs Models  
The comparison between the regression model and ANNs is an important process to validate the accuracy of benchmark equation to evaluate energy efficiency of accommodation buildings. Figure 5 illustrates the comparison between both models in two clusters with different numbers of samples.  
As shown in Figure 5a, cluster 1 has good consistency between predicted values and real data in both method, the regression method has less variance compared to the ANNs model. In Figure 5b, bother methods have less variance toward real values since cluster 2 has a sufficient size of samples to improve the accuracy of ANNs method.  
The mean absolute percentage error (MAPE) was calculated to evaluate the outputs of both methods for each cluster of DECC. Table 4 presents the value of MAPE for each cluster.  
Figure 4: Prediction results of ANN models using DECC. (**a**) cluster 1; (**b**) cluster 2; (**c**) cluster 3; (**d**) cluster 4.  
MAPE values shows close value of both methods in case of cluster \"2\" and cluster \"3\". In the other hand, in case of cluster \"1\", regression model shows best performance comparing to ANNs model. Cluster \"4\", shows big difference of performance between ANNs and regression model. The mean reason of MAPE for cluster \"1\" and \"4\" is the size of sample in these clusters. Since the regression model has less sensitivity toward data size, the outputs will not be affected as much as in case of ANNs model.
# Junction Selection Based on Optimal-Weighted Multiple Attributes for VANETs
## 4 Conclusions  
A novel routing protocol for junction selection based on optimal-weighted multiple attributes for VANETs has been proposed. The proposed scheme considers multiple matrices such as distance, traffic density, and angle between source junctions to destination junctions to improve the junction-selection accuracy. Furthermore, weight values are also optimized. These optimal values are then assigned to the matrices to select the most suitable junction to minimize the communication delay and enable the successful communication of the increasing number of vehicles in the network. The simulation results of the proposed scheme have been compared with the previously proposed routing protocols E- GyTAR, GSR, and DMJSR. The results show promising improvements by increasing the communication efficiency within the minimum delay. This achieves a 0.55 packet-delivery ratio in less than 4 secs and a minimal network load compared to GSR. In the future, we intend to include more parameters for junction selection and apply machine-learning techniques to make the system intelligent.  
Conceptualization, Y.R. and A.S.; Investigation, D.K.; Methodology, Y.R. and D.K.; Supervision, D.K.; Visualization, B.J.; Writing an original draft, Y.R. and A.S.; Writing, review and editing, A.S. and M.A. All authors have read and agreed to the published version of the manuscript.  
This research was collectively been supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government(MSIT), (NRF-2022R1A2C1003620) and by the MSIT (Ministry of Science and ICT), Korea, under the Innovative Human Resource Development for Local Intellectualization support program (IITP-2023-RS-2022-00156389) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
Not Applicable.  
The Authors have no conflict of interest.  
Figure 7: Routing overhead in an increasing number of vehicles.