esa-sceva/satcom-chunk-collection huggingface dataset:
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 4 Conclusions  
In this paper, we introduce an end- to-end video steganography architecture named as PyraGAN, which supports different-sized cover video frames and arbitrary binary data. Furthermore, we improve the performance of the model based on the CU mask made from VVC and attention mechanism. Experimental results of PyraGAN, HiDDeN [23], and SteganoGAN [23] demonstrate that proposed PyraGAN achieves a better performance in invisibility, capacity of hidden message, and accuracy of decoded message. This paper provides probability for end-to-end video steganography combined with coding unit of VVC. In future work, the robustness of the proposed steganography network will be the first important issue, such as adding a differentiable noise layer after encoder to resist some popular attacks. Moreover, the research will be conducted combined with other characteristics of video compression such as inter prediction. Furthermore, using a suitable error correction encoding method for perfect extraction of hidden messages which will be another research direction of our future work.  
**Author Contributions:** Conceptualization, H.C. and Z.L.; methodology, H.C., Z.L. and Z.Z.; software, H.C.; validation, H.C.; formal analysis, H.C. and Z.L.; investigation, H.C. and F.L.; resources, H.C.; data curation, H.C.; writing--original draft preparation, H.C., Z.L. and Z.Z.; writing--review and editing, H.C. and Z.Z.; visualization, H.C.; supervision, Z.L., F.L. and Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was founded by The Scientific Research Common Program of the Beijing Municipal Commission of Education (KM202110015004).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 System Modelling and Analysis
### The Receiver for the Slotted-ALOHA System
If two users are involved in conflict, the receiver randomly selects either of them to feed back a NACK and requests that the sender retransmits a replica. The whole process (including feedback and retransmission) needs two slots. If three users are involved in a conflict, according to the feedback mechanism and Figure 4, the receiver selects two of them to feed back a NACK and requests the senders to retransmit their replicas. The whole process needs four slots. The system can be represented by a bipartite graph in Figure 4(a), where the set of user node \(\{u_{1},u_{2},\ldots,u_{M}\}\) represents the packet sent by active users. The set of slot node \(\{y_{1},y_{2},\ldots,y_{j},\ldots\}\) indicates all packets received by a common receiver in slot \(j\). The line between user node \(u_{m}\) and slot node \(y_{j}\) indicates that \(u_{m}\) is one of the packets received in \(y_{j}\). In order to display the user status intuitively, the system is represented by a unipartite graph, as shown in Figure 4(b). Although a black node indicates that the user is free, a white node represents that the user is backlogged. The connection between two nodes indicates that the receiver has received the packets involving the two users. A group of connected packets is an element, such as \(\{u_{1},u_{2},u_{3}\}\) and \(\{u_{4},u_{5}\}\). There can be only one white node in each element. Once the receiver obtains a replica, the black node in this element turns white immediately.
## 2 Preliminaries
### Altitude and Attitude Subsystems  
The lateral component \(f_{3}sin\alpha\) of (17) is the main contributor of the yaw control input; therefore, the 6 DOF system can be decoupled to an altitude subsystem and an attitude subsystem. \(f_{z}\) is the altitude control input and \(\tau_{m}\) is the attitude control input.  
The altitude equation is  
\[(m+Z_{\hat{\imath}\hat{\imath}})\dot{z}-Z_{w}\dot{z}-(W-B)=f_{z}cos\phi cos\theta \tag{28}\]  
where \(W=mg\) is the rigid body weight in the air, \(B=\rho g\
abla\) is the buoyancy in the water, \(g\) is the gravity constant, and \(\
abla\) is the displacement.  
The attitude equation is  
\[(I_{0}+I_{A})\dot{\imath}\dot{\imath}_{2}+(-S((I_{0}+I_{A})\
u_{2}))\imath_{2 }+D\
u_{2}=\tau_{m} \tag{29}\]  
where  
\[I_{A}=-diag\{K_{p},M_{d},N_{\tau}\} \tag{30}\]\[D=-diag\{K_{p},M_{q},N_{r}\} \tag{31}\]  
As \(S((I_{0}+I_{A})\
u_{2})\
u_{2}=S(\
u_{2})(I_{0}+I_{A})\
u_{2}\), we rewrite the attitude equation  
\[(I_{0}+I_{A})\dot{\
u_{2}}-S(\
u_{2})(I_{0}+I_{A})\
u_{2}+D\
u_{2}=\tau_{m} \tag{32}\]  
The whole underactuated system is decoupled into two fully actuated subsystems: altitude subsystem and attitude subsystem. We could design the controller separately to control the whole system. The attitude control is the core function of NTAUV; we design the attitude controller in Section 3.
## 4 Case Study
### Modeling Prediction Analysis
#### 4.2.1 Learning Results Analysis  
In terms of the variable's selection process in Section 4.1, we use the most positively related nine parameters as input to predict the target output concentration \(C_{m}\) (\(\%\)). Considering the data amount effects on the learning ability in data-driven models, we divide the input with a proportion of 6:4 and 7:3 to test the model twice. The learning results are shown in Figures 8 and 9 respectively.  
Figure 7: The correlation matrix of the variables.  
Concentration changes with the working conditions. As shown in the learning results, the normal range of the concentration is from 0 to 45%, which is a comprehensive result of the multiple factors interaction. High concentration is not necessarily good for the production since it may cause sedimentation or clogging in the pipeline. The results in this case are all normal and satisfactory. However, when it comes to the detailed comparison, the learning process with 60% of the dataset performs better than 70% dataset. For the 60% data training, its maximum and minimum error are 0.3091 and 0.0149 respectively. However, the maximum error is 0.526 in the 70% data training process. Also, as shown in Figure 10, for 60% dataset, the loss value decreases and then keeps steady in the training process. The error in testing falls and then keeps steady. However, for the 70% dataset, both training and testing error are less stable and consistent.  
Figure 8: The learning results of 6:4 proportion.  
Figure 10: Loss curve of the different datasets.  
Figure 9: The learning results of 7:3 proportion.
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 3 Results  
### Spatial Pattern of EEQ in China  
In order to gain insight into the degree of superiority and inferiority of China's EEQ from 2000-2017, this paper classified China's M-RSEQI from 2000-2017 based on Table 3, and the results are shown in Figures 3 and 4.  
Based on the analysis of Figures 2 and 3, several conclusions can be drawn regarding China's EEQ from 2000 to 2017. Firstly, it is evident that a significant portion of China's EEQ fell into the \"bad\" grade, remaining stable at approximately 9 million km\({}^{2}\). Secondly, the regions categorized as \"poor\" grade spanned between 3 million km\({}^{2}\) and 4 million km\({}^{2}\). The subsequent grade is \"average,\" while the areas designated as \"good\" or \"excellent\" were relatively small. Among the five grades, the \"average\" and \"good\" grades exhibited notable changes, while the remaining three grades showed insignificant variations. Regions with poor EEQ in China during the aforementioned period were primarily concentrated in the Beijing-Tianjin-Hebei region, Tacheng region, Turpan city, Bortala Mongol Autonomous Prefecture, Karamay city, Altai region, and Changji Hui Autonomous Prefecture. Conversely, areas with average or better EEQ were predominantly located in the Qinghai-Tibet Plateau, certain parts of Yunnan Province, and specific regions of Taiwan Province. Notably, the EEQ of the Beijing-Tianjin-Hebei region displayed significant improvement from 2000  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Criteria** & \(\geq\)**0.60** & **045\textless{}0.60** & **0.30\textless{}0.45** & **0.15\textless{}0.30** & \(\leq\)**0.15** \\ \hline Level & Excellent & Good & Average & Poor & Bad \\ \hline \hline \end{tabular}
\end{table}
Table 3: M-RSEQI classification table based on natural breakpoint method.  
to 2017, with a slight fluctuation in the area characterized by poor EEG throughout this period. Overall, there was a considerable reduction in poor EEG, closely associated with the industrial restructuring efforts undertaken in the Beijing-Tianjin-Hebei region [42; 43].
## 6 Experimental Results and Analysis
### Experimental Environment and Model Hyperparameters  
The operating system used for the experiments is Windows 11, the environment is Python 3.9.7, the processor is Intel(R) Core(TM) m3-6Y30 CPU @ 0.90 GHz1.51 GHz, and the RAM is 4.00 GB. The experiments are conducted using the WDBC dataset, and the data are divided into a test set and training set according to a ratio of 8:2. Both the grid search method and the cross-validation method are used to improve the accuracy of the model. The grid search method is a parameter-tuning method to find the best combination of hyperparameters by trying all possible combinations of hyperparameters to improve the accuracy of the model. Also, to avoid model overfitting, the cross-validation method is used to assess the generalization ability of the model. This impact of the differences between the training set and the test set is reduced. The commonly used cross-validation methods are K-fold cross-validation and leave-one-out cross-validation. In this paper, we use 5-fold cross-validation to divide the dataset into five copies. Each time, four copies are used as the training set and the remaining one as the validation set. This is repeated five times, and the average value is taken as the final result. The specific steps are as follows:  
1. Firstly, select a set of parameter value ranges for each hyperparameter.
2. Then, evaluate the performance of the adjusted model by the cross-validation method.
3. Finally, select the parameter with the best performance as the best combination.  
The optimal parameter combinations of the model determined according to the above method are shown in Table 3.
# Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
## 12 Security Issues concerning the Data Link Layer

Table 6: Contributions and research gaps of related recent work undertaken. transfer data or information at a rapid rate so that the recipient can comprehend and process it. Flow control in the data link layer effectively limits and controls the number of frames or data that a sender may transmit before waiting for a response from the recipient [81]. In such scenarios, DoS assaults that consume resources and potentially squander network bandwidth are widespread and well known. Furthermore, a node capture attack can capture a network node to get its cryptographic keys and protocol status and then redeploy malicious nodes across the network [15]. Similarly, the classical MAC protocol, which is based on a competitive process, is incapable of meeting the requirements of WSNs. Because content-based MAC protocols have several handshakes, there is a significant possibility of data collisions that cause energy waste in WSNs. As a result, to protect node energy, MAC protocols for WSNs often use pre-planned techniques such as TDMA [113]. Identifying critical QoS criteria to evaluate network performance is part of success assessment. The QoS parameters in network performance indicate that these factors substantially affect network metrics in communications, with the impact differing based on the network criteria and network communication aspects [114]. The QoS parameters include network performance analysis, availability, bandwidth, throughput, transit delay, jitter, resilience, protection, and packet loss rate [81]. QoS can assign distinct goals to different programs, clients, or data flows or promise a set production level for a data flow; thus, it is critical when conveying traffic with unique demands [82]. Implementing QoS can help organizations generate significant profits. Performance improvements in applying QoS can help reduce packet loss, error, and latency by defining and prioritizing sensitive applications based on their network traffic. Because QoS network traffic recognition prioritizes traffic and creates application-specific policies, costly and high-performance network bandwidth can be used mainly or exclusively by the applications that require it, resulting in improved network utilization. An entity can more effectively direct network traffic to its destination by defining the application connected to a given network link and by adding application-specific policies, which optimizes traffic routing. QoS can help minimize network congestion by dropping or throttling low-priority traffic during high-use times and via application-specific traffic filtering to relieve congestion in important network sectors. Table 7 describes the QoS parameters, their necessity for enhancing network performance, and details how their absence affects network performance [115; 116; 117].
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### Internet Engineering Task Force (IETF)  
The IETF strives to standardize areas related to IP-based connectivity of LPWAN systems [25; 51; 52]. It is an open standard organization with no formal membership or membership requirements. All participants and managers are volunteers with funding provided by employers or sponsors. The IETF presently considers IPv6 particularly for low power wireless personal area networks (6 LoWPAN) [53; 54]. One requirement is to ensure that the IP stack is lightweight enough to guarantee strict compliance with the limitations of LPWAN technologies, such as their operation under strict regulations in ISM bands, the asymmetry of their uplink and downlink channels, and the existence of different packet formats from different LPWAN developers in the IoT market [25; 51; 52]. Following the completion of the task of the 6 LowPAN IETF IoT WG in 2014, another WG called the 6 Lo WG was initiated in order to cover a wider range of radio technologies including Bluetooth, cordless phones, and Digital Enhanced Cordless Telecommunication (DECT) devices. The IETF group aims to solve issues of header compression, fragmentation and reassembly, management, and security, integrity and privacy issues [4].
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 3 Analysis of the Delay Performance
### SNC in the SNR Domain  
A notable aspect of SNC is that it can obtain the delay distribution and the UB-QDVP based on statistical characterizations of the arrival and service processes in terms of their Mellin transforms. However, it is still difficult to capture the statistics of the random arrival and service processes in practical scenarios. To facilitate the analysis, we convert these processes from the bit domain through the exponential domain to the SNR domain. These cumulative processes in the SNR domain are denoted by calligraphic letters [54] as follows:  
\[\left\{\begin{array}{l}\mathcal{A}_{k}(\tau,t)=e^{\mathcal{A}_{k}(\tau,t)}, \\ \mathcal{R}_{k}(\tau,t)=e^{\mathcal{R}_{k}(\tau,t)},\\ \mathcal{U}_{k}(\tau,t)=e^{U_{k}(\tau,t)}.\end{array}\right. \tag{22}\]  
\(\mathcal{M}_{\mathcal{X}}(s)=\mathbb{E}\big{[}\mathcal{X}^{s-1}\big{]}\) is the Mellin transform of any nonnegative random variable \(\mathcal{X}\) for a parameter \(s\in\mathbb{R}\)[37]. Both \(a_{k}(i)\) and \(r_{k}(i)\) are independent of each other at different time slots, so we use two time-independent random variables \(a_{k}\) and \(r_{k}\) to denote the number of arrival and service bits at either time slot, respectively. Then, the Mellin transform over the accumulated arrival process is denoted by \(\mathcal{M}_{\mathcal{A}_{k}}(s,\tau,t)=\mathcal{M}_{\mathcal{A}_{k}(\tau,t) }(s)=\mathbb{E}\bigg{(}\Big{(}e^{\mathcal{A}_{k}(\tau,t)}\Big{)}^{s-1}\bigg{)} =\big{(}\mathcal{M}_{\varphi_{k}}(s)\big{)}^{t-\tau}\), where \(\varphi_{k}=e^{\mathcal{A}_{k}}\). The Mellin transform over the accumulated achievable service process is \(\mathcal{M}_{\mathcal{R}_{k}}(s,\tau,t)=\mathcal{M}_{\mathcal{R}_{k}(\tau,t) }(s)=\mathbb{E}\bigg{(}\Big{(}e^{\mathcal{R}_{k}(\tau,t)}\Big{)}^{s-1}\bigg{)} =\big{(}\mathcal{M}_{\varphi_{k}}(s)\big{)}^{t-\tau}\), where \(\varphi_{k}=e^{\mathcal{A}_{k}}\).  
If the stability condition \(\mathcal{M}_{\varphi_{k}}(1+s)\mathcal{M}_{\varphi_{k}}(1-s)<1\) holds, denoting \(t_{\text{T}}\) as a target delay (target number of time slots), the kernel function \(\mathcal{K}(s,t_{\text{T}})\) is defined as [37]  
\[\mathcal{K}(s,t_{\text{T}})=\frac{\mathcal{M}_{\varphi_{k}}(1-s)^{t_{\text{T} }}}{1-\mathcal{M}_{\varphi_{k}}(1+s)\mathcal{M}_{\varphi_{k}}(1-s)}. \tag{23}\]  
Thus, the UB-QDVP denoted by \(\varepsilon_{k}^{\text{,UB}}\) is [22; 37]  
\[\varepsilon_{k}^{\text{,UB}}=\Pr\{w_{k}(t)>t_{\text{T}}\}\leq\inf_{0<s<s_{0}} \mathcal{K}(s,t_{\text{T}}), \tag{24}\]  
where \(s_{0}=\sup\big{\{}s:\mathcal{M}_{\varphi_{k}}(1+s)\mathcal{M}_{\varphi_{k}}(1 -s)<1\big{\}}\).
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Durability Properties  
3.3.1. Sulfate Attack  
The combined effects of MGWP on sulfate resistance are illustrated in Figure 9. The mortar cubes were immersed in a sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)) solution according to ASTM C1012-09 and tested at 3, 7, 28, 56, and 91 days of curing. The average value of three mortar cubes was recorded to assess the compressive strength loss due to the sulfate attack. The incorporation of MGWP as a partial replacement at 10% and 15% significantly improved the sulfate resistance of mortar compared to the control mix. Using MGWP as an SCM improves sulfate resistance and prevents thaumasite formation by decreasing the Ca(OH)\({}_{2}\) content, reducing the calcium to silica (Ca/Si) ratio in C-S-H, and refining the pore structure to limit the penetration of external aggressive ions [59]. This enhancement is attributed to the densification effect (micro-filling) and additional C-S-H gel formation, which restricts sulfate ingress into the mortar matrix [60]. As shown in Figure 9, compressive strength loss decreases with MGWP replacement up to 15%, beyond which it increases at 30% replacement.
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 3 A Branch-and-Price Algorithm
### Valid Inequalities
#### 3.4.4 Lifted Subtour Elimination Inequalities  
Subtour elimination constraints can be used to strengthen formulation (TDTSP-A). They can be separated in polynomial time by solving a series of flow problems on the underlying graph \(D\). To further strengthen SECs for the SDTSP, it was observed in [15] that any tour has to leave \(S\) sufficiently early in order to be able to reach the vertices in \(V\setminus S\) and return to \(s\) within the time horizon. We proceed to adapt the approach to the TDTSP. Let \(\hat{\theta}\) be such that  
\[\hat{\theta}\geq\max\{\theta\mid\text{there exists a tour }T\text{ leaving }S\text{ for the first time at }\theta\}.\]  
Then, the following lifted subtour elimination constraint (LSEC) is valid for (TDTSP-A):  
\[\sum_{\begin{subarray}{c}(u,v,\theta)\in\delta^{+}(A^{\mathcal{T}}(S))\\ \theta\leq\hat{\theta}\end{subarray}}x_{uv,\theta}\geq 1.\]  
Figure 2: A path-free set of arcs on three vertices.  
The value of \(\hat{\theta}\) is maximized for a tour which first serves \(S\), then \(V\setminus S\) and returns to \(s\) immediately afterwards. Computing the optimum value of \(\hat{\theta}\) is intractable in practice, as it would involve the solution of a TDTSP on \(S\) itself.  
Instead of maximizing the time spent in \(S\), we therefore minimize the time spent in \(V\setminus S\). Since we do not know the optimum value of \(\hat{\theta}\), we do not know the values of the cost function \(c\). Hence, we relax the problem by replacing the time-dependent cost functions \(c_{uv}(\theta)\) by its static underestimator \(c_{uv}\). Minimizing the length of a static tour leaving \(s\), traversing \(V\setminus S\), and finally returning to \(s\) is easily formulated as an ATSP, yielding an optimal value of \(\hat{\theta}\). The latest departure time \(\hat{\theta}\) can then be determined as \(\theta^{\max}-\hat{\theta}\).  
This estimation of the optimal \(\hat{\theta}\) can be rather rough if the cost functions \(c\) differ significantly from their underestimators \(\check{c}\). As a result, the computational effort required to solve the ATSP does not seem merited. We choose to instead bound the static tour cost from below by computing an arborescence of minimum weight.
# Video Super Resolution Using a Selective Edge Aggregation Network
## 5 Conclusions  
We add structural information by using an edge map in this study and use detect blur to minimize noise before adding information. In addition, the difference between output structural information and GT structural information generated by using magnitude loss was minimized, and a refinement module was used to improve the problem caused by the artifact of the test set. We found the optimal refinement module through experiments and showed consistent performance improvement. Among any other models, when combined with the Stage 1 model we propose, the performance improvement was the greatest. Therefore, we finally propose this model.  
Figure 8: Visual results on Vid4 for 4\(\times\) scaling factor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SEAN** & **RBPN** & **MuCAN** & **EDVR** \\ \hline Vid4 & 27.79/0.8439 & 27.12/0.8180 & 27.26/0.8215 & 27.35/0.8264 \\ REDS4 & 32.32/0.9069 & 30.09/0.8590 & 30.88/0.8750 & 31.09/0.8800 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Final result.  
Figure 9: Visual results on REDS4 for 4\(\times\) scaling factor.  
**Author Contributions:** Conceptualization and methodology, S.K. and Y.-M.S.; investigation, S.K.; data curation and conceiving experiments, Y.-M.S.; performing experiments and designing results, S.K. and Y.-M.S.; writing--original draft preparation, S.K. and Y.-M.S.; writing--review and editing, S.K., Y.-M.S. and Y.-S.C.; supervision, Y.-S.C.; funding acquisition, Y.-S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2017-0-01642) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation and supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(\"MSIT) (No.2018R1A5A7059549) and supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(\"MSIT) (No. 2020R1A2C1014037) and supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)) \"Ministry of Science and ICT.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.