esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 6 Discussion  
Compared to traditional 3D model watermarking algorithms, this article improves the current 3 DMOP security protection method through the collaborative application of digital watermarking and data encryption, and further expands its application scope.  
Compared with traditional 3D model watermarking algorithms, this paper uses clustering algorithms to group vertex coordinate data and perform multiple embeddings of vertex coordinate watermarks, improving the robustness of the vertex coordinate watermarking algorithm. We chose texture coordinates, which are rarely used in traditional watermarking algorithms, as the target for watermark embedding. Texture coordinates are used to determine how texture maps on the model surface are mapped to geometric coordinates and are less affected by watermarking attacks such as cropping, rotation, and point deletion. Experimental results show that texture coordinate watermarking can effectively resist various common attacks. In the past, research on oblique photography 3D models focused more on embedding copyright information, ignoring texture image data. Texture image data contains rich privacy information, including license plate numbers, advertising logos, face recognition data, and detailed information about surrounding buildings. Therefore, algorithm encryption is used to improve the security of texture data and ensure that sensitive information is not leaked by unauthorized access.  
However, some aspects of the algorithm in this article still need further research and resolution. For example, for vertex coordinates, when performing common model watermark attacks, the sensitivity of different regions of data varies depending on the same proportion of point deletion and cropping. The different regions of action also have different results in extracting the vertex coordinate watermark. The 3 DMOP data may undergo a data format conversion during use, but the watermark embedded in the texture coordinates will change after the 3D model format conversion, making it impossible to extract the copyright information normally.  
In subsequent research, we will conduct in-depth research on the above issues, such as whether it is possible to modify the clustering algorithm to make the clustering clusters evenly distributed and not affected by common watermark attacks, further improving the robustness of watermark cropping/deletion; we will further study how to fully extract the texture coordinate watermark after the 3D model format conversion, thereby effectively improving the robustness of the texture coordinate watermark algorithm; when conducting security analysis on encrypted images, the structural similarity index can be evaluated in combination with the Diophantine equation fuzzy set.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 5 Challenges in Smart Grids
### Challenges Regarding Contingency Responses  
The challenges regarding the contingency responses within smart grids are multi-faceted, primarily due to the absence of a real-time, convincing qualitative resilience index or a reliable metric that can accurately reflect the grid's resilience or reliability in the face of disruptions. The traditional approaches to measuring grid resilience have relied heavily on historical high-impact, low-frequency (HILF) events, utilizing metrics such as the SAIFI (System Average Interruption Frequency Index), SAIDI (System Average Interruption Duration Index), and LOLP (Loss of Load Probability) [120].  
Furthermore, the integration of renewable energy sources, the proliferation of electric vehicles, and the advent of microgrids have significantly increased the complexity and volatility of power systems [121]. These developments require a shift towards more adaptive, real-time resilience assessment tools that can accommodate the rapidly changing energy landscape and ensure robust contingency response mechanisms. Future research could benefit from incorporating Explainable Artificial Intelligence (XAI) into performance-based resilience and reliability assessments. This integration aims to enhance user comprehension by offering clear insights into the system states before, during, and after events and ensuring that the users are well-informed in terms of system management and contingency responses.
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 5 Performance Evaluation
### Comparison with Classic DTN Protocols  
In this simulation, we compare our proposed protocols with the classic DTN protocols available in The One simulation environment. For this comparison, GeoSaW is configured with TTA Evaluation Variant 2 and Message Deletion Variant 1, having just one packet copy in the network. From Figure 4, we can see that the average packet delivery ratio for GeoSaW is almost 100%, comparable to Epidemic and MaxProp, under varying network density. On the contrary, the values for the First Contact protocol increase monotonically with the network density until a plateau is reached (90%), while Spray And Wait starts at a lower delivery rate, and grows as expected, although remaining under a 90% delivery rate.  
Figure 5 shows the average overhead as the network density increases. This value generally increases as the number of nodes increases, with the exception of Spray and Wait and GeoSaW. In fact, Spray and Wait limits the number of copies in the forwarding process, whereas in GeoSaW the number of injected packets is always one. Furthermore, although First Contact does not use a multi-copy approach, it results with more traffic overhead than GeoSaW. The other protocols do not have a packet number limitation, contributing to increasing traffic. Concluding, GeoSaW presents the best performance in terms of overhead.  
Figure 6 shows the average latency as the number of nodes increases. GeoSaW presents acceptable results, in particular better than Spray And Wait. The average time difference between Epidemic values and GeoSaW values is about 120 s. This difference is due to the limitation of GeoSaW path prediction: when a node meets another node, it knows the entire future path of that node, but it is not able to predict future connections with other nodes that could deliver the message faster; such ideal case could present the same performance as Epidemic in terms of latency.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline MAC type & IEEE 802.11g, FreeSpace model \\ Simulation area & 4000 m \(\times\) 4000 m \\ Simulation time & 60 min \\ Transmission range & 500 m \\ Transmission speed & 1 Mbps \\ Buffer size & 50 MB \\ Message size & 250 KB \\ Message TTL & 30 min \\ Message creation interval & 30 s \\ Number of scanning UAVs & 5 \\ Number of delivery UAVs & 5, 10, 15, 20 \\ Scanning UAVs mobility model & Scan \\ Delivery UAVs mobility model & RWP \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Average delivery ratio of GeoSaW compared with other DTN routing protocols when varying the number of nodes.  
Figure 5: Average overhead ratio of GeoSaW compared with other DTN routing protocols when varying the number of nodes.  
Figure 6: Average latency of GeoSaW compared with other DTN routing protocols when varying the number of nodes.
# Sending-or-Not-Sending Twin-Field Quantum Key Distribution with Light Source Monitoring
## 3 Performance with Numerical Simulation
\[P(\mu)=\frac{1}{\sqrt{2\pi}\sigma_{\mu}}\exp[-\frac{(\mu-\mu_{0})^{2}}{2\sigma_{ \mu}^{2}}], \tag{24}\]  
where \(\mu_{0},\sigma_{\mu}\) are the mean value and the standard deviation of \(\mu\). Without the LSM module, the probabilities \(P_{n}(\mu)\) can only be estimated by assuming that \(\mu\) belongs to a confidence interval, that is, \(\mu\in[\mu_{L},\mu_{U}]\), with a confidence level \(\varepsilon=\int_{\mu_{L}}^{\mu_{U}}P(\mu)d\mu\), where  
\[\mu_{L}=(1-\delta)\mu_{0},\mu_{U}=(1+\delta)\mu_{0}, \tag{25}\]  
hence the results are  
\[P_{0}^{L(U)}(\mu) =e^{-\mu_{U(L)}},P_{1}^{L(U)}(\mu)=\mu_{L(U)}e^{-\mu_{L(U)}},\] \[P_{2}^{L(U)}(\mu) =\frac{\mu_{L(U)}^{2}}{2}e^{-\mu_{L(U)}}. \tag{26}\]  
For the LSM scheme, the probabilities \(P^{\mu}(\eta)\) can be simulated as  
\[P^{\mu}(\eta_{i})=(1-Y_{0})\exp{[-\eta_{i}\mu_{0}-\frac{(\eta_{i}\sigma_{\mu} )^{2}}{2}]} \tag{27}\]  
after considering the source fluctuation [36; 37], and \(P_{n}(\mu)\) can be estimated by \(P^{\mu}(\eta_{i})\) with Equations (17)-(21). The performance of both the original SNS protocol and the original SNS protocol with LSM are simulated under different fluctuation coefficient \(\sigma=\sigma_{\mu}/\mu_{0}\). The parameters are changed to be consistent with those in Reference [21] in Table 2 to simulate a more realistic condition, which is close to the practical experimental conditions [28], and \(\epsilon,\mu_{s},\mu_{d_{s}},\mu_{d_{2}}\) are optimized as well. For the original SNS protocol, the confidence levels are set as \(\varepsilon=1-10^{-10}\), and in the LSM scheme, the attenuation coefficients are set as \(\eta_{0}=1,\eta_{1}=0.95,\eta_{2}=0.9\). The simulation results under the practical simulation condition shown in Figure 3 indicate that the LSM scheme still performs well in practice, as its performance remains almost the same under the fluctuated light source. As a comparison, the performance of the original SNS protocol decreases obviously with a light source fluctuation \(\sigma=1\%\), and its transmission distance even reduces to less than 500 km when \(\sigma=2\%\). Since the condition \(\sigma>1\%\) is common in real-life QKD systems [40; 41], the LSM scheme will have a better performance compared to the original SNS protocol in practice, although only the fluctuation problem is considered. Figure 3: The performance of the LSM scheme with an untrusted and fluctuated light source compared to original SNS protocol. \(\sigma\): the fluctuation coefficient. For the LSM scheme, we consider a small fluctuation condition \(\sigma=1\%\) (black dash curve) and a large fluctuation condition \(\sigma=5\%\) (red dash curve), and the performance between them is still close. For the original protocol, we consider a small fluctuation condition \(\sigma=1\%\) (blue curve) and a relatively large fluctuation condition \(\sigma=2\%\) (yellow curve), since the condition \(\sigma=2\%\) already has an obviously worse performance than \(\sigma=1\%\).
## 3 SDN, NFV, and SFC: An Overview
### NFV/SFC and 5G Networks  
The chaining of virtual network functions for effective end-to-end service delivery is a key enabler of Beyond 5G networks [60; 61]. Since 5G-enabled networks are characterized by low latency, programmability, and the support for diverse use-cases of the future, technologies such as NFV can allow providers to deploy services that are suitable for radio access networks (RANs) and mobile core networks [60].  
By implication, using NFV, SDN, and SFC, service providers can easily provide tailored solutions that meet customer demands, by carefully orchestrating user-generated traffic between an ordered list of network functions. As described in Section 2.2, the chaining of virtual network functions in SFC enables use cases such as the Gi-LAN mobile core network, residential and customer services, and inter and intra-datacentre networks. Other important use cases such as self-driving cars, e-healthcare [62], and mixed reality (MR) and 5G-enabled IoT [62; 63] are also possible due to the flexibility offered by 5G network slicing.  
Efforts such as the work by [PERSON] et al. [64] focus on showcasing how machine learning (ML) can be used to leverage the benefits provided by Beyond 5G networks. ML can be used with enhanced mobile broadband (eMBB) and support future Beyond 5G applications, that are envisaged to have high data rate requirements. Massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) are also required to provide support for future use cases for Beyond 5G networks [65; 66].  
[PERSON] et al. [67] explored how the 5G RAN can be enhanced using NFV, which could also lead to a reduction in the overall capital expenditure for telecommunications service providers (TSPs). As detailed in [67], some challenges that are related to 5G networks such as efficient scalability of vNFs between physical networks, vNF performance guarantees, and simultaneously supporting the deployment of hardware and virtualized network functions, can be overcome with the flexibility offered by NFV implementations.
## 3 Application to Secure Communications  
### Symmetric Chaos Shift Keying Modulation  
In this section, the proposed chaotic system is applied to the problem of secure communications. The methodology considered is the symmetric chaos shift keying (SCSK) modulation, which was applied in [7, 28]; see also [27, 33, 34, 35, 36, 37, 38, 39] for similar approaches. The system design is shown in Figure 9. The system is comprised of two units, a transmitter and a receiver, connected through a noisy communication channel.  
In the transmitter unit, the information signal is multiplied (modulated) with a chaotic signal generated by the chaotic system, resulting in a masked signal. The generated masked signal is then transmitted to the receiver. In the simulations, the information signal is taken as a binary signal with a period of 1 s. For the chaotic signal used, different options were considered, as will be seen in the following subsection, generated from (1), with \(a=0.65\), \(b=0.1\). During transmission, it is also assumed that the channel is disturbed by an additive white Gaussian noise (AWGN). In the receiver end, the received masked signal is multiplied by the chaotic signal generated by the same chaotic system as in the receiver, and the resulting signal is integrated in the correlator. This way, the bit energy of the signal is calculated and the binary information signal is reconstructed by passing the resulting signal through a threshold detector, with threshold set to 0. So, this threshold detector generates a \"1\" if its input is positive, and a \"\(-1\)\" if its input is negative.  
Note that in this design, the same chaotic signal is used in the transmitter and receiver ends; hence, the term symmetric is used in its description. So in the receiver end, in order to replicate the chaotic signal, knowledge of the systems initial conditions \(x_{0},y_{0},z_{0}\) and parameter values \(a,b\) is required. These five parameters are called the key space of the design. So assuming a 16-digit accuracy, an upper bound of the key space is \(10^{5.16}=10^{80}=(10^{3})^{26.6}\approx(2^{10})^{26.6}=2^{266}\). This is higher than the upper bound of \(2^{100}\) that is generally required to resist brute force attacks [40].  
The SCSK communication system was studied under the presence of AWGN in the communications channel. The retrieved signal is delayed by 0.5 s, due to the sample/hold block on the receiving unit. Therefore, there is a 0.5 s delay between the transmitted information signal and the retrieved information signal on the receiving unit.  
Figure 9: SCSK secure communications design.
# LoRa Communication Using TVWS Frequencies: Range and Data Rate
## 4 Communication Range and Data Rate for TVWS-Based LoRa  
Firstly, to investigate TVWS-based LoRa, the data rate and range estimation for different scenarios, _SFs_ and _BWs_, and path-loss models is analyzed. Additionally, the comparative analysis of uplink delivery rate is carried out.
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 5 Performance Analysis of RICC
### Network Reliability of RICC  
#### 5.2.1 End-to-end MFR  
**Theorem 4.** End-to-end MFR is refer to the final MFR of a packet from the source cluster to the destination cluster. In RICC, for a node that is \(x\)\(m\)_away from the sink, the end-to-end MFR of a packet produced by this node is as follows_.  
\[MFR^{x}_{c2e}=1-(1-MFR_{s})\ \ \times\ \prod_{i=1}^{h_{p}-1}\left(1-MFR^{i}_{o}\right) \tag{33}\]  
_where \(MFR^{i}_{o}\) is the MFR of a packet at the \(i\)-th hop of a routing path, \(MFR_{s}\) is the last hop of the transmission, and \(h_{p}\) represents the total number of hops from this node to the sink, that is, \(h_{p}=\lfloor(R-x)/r\rfloor\). As shown in Equation (12), when \(E_{Amp}\) is large enough, \(p_{l}\to 0\) and \(MFR^{x}_{c2e}\) can be rewritten as:_  
\[MFR^{x}_{c2e}\to 1-\ \ (1-p_{N,M})\ \ \times\ \ \left\{1\ -\ \left[p_{N,M}+\ \ (p_{r})^{N}\right]\right\}^{h_{p}-1} \tag{34}\]  
**Proof.** Considering the probability that the packet is obtained by sink node is the product of the probabilities that the packet can be successfully transmitted at each hop. In particular, the MFR of the last hop is different from others; see the reliability model in Section 4.  
Figure 11 shows a comparison of the end-to-end MFR versus hops counts between RICC and NCCC. The results indicate that RICC can always achieve reliable cooperative communication. Having more nodes in a cluster weakens the improvement in reliability since increasing the size of the cluster can also enhance the reliability [9].
# Application of the Segmented Correlation Technology in Seismic Communication with Morse Code
## 6 Conclusions  
In this paper, we first discuss the influence of other vibration signals on the target signal using different transmitting devices, such as hammers and vibroseis sources. By contrast, vibroseis sources accepted in this study have great advantages in signal modulation compared with hammer sources. However, it is difficult to extract the target signal because there are a large number of strong vibration signals in a complex environment. In order to solve the problem, the SCTMC is proposed to extract the target signal in the  
Figure 6: The collected signal at 60 m; **(a)** The original data at 60 m; **(b)**The spectral analysis of the original data; **(c)** The target signal extraction with a band-pass filter; **(d)** The target signal extraction with the segmented correlation technology.  
collected data. The experimental results show that the SCTMC is well combined with the advantages of vibroseis sources to extract the target signal. At its core, it effectively extracts the similar signal, suppresses other signals, improves the SNR of seismic signal and extends the relay distance of the communication system, which greatly reduces the cost of the seismic communication system. Implementing the transmission of information recognition with SCTMC is a breakthrough which offers a better application of the vibroseis sources and geophones, constitutes an innovative form of wireless communication and lays the foundation for the long-distance communication for the seismic communication system. Maybe in the near future, this technology might be widely employed in our production and life.  
Y.J.: writing--original draft preparation; Y.C., R.T., L.W., S.L., J.L. and X.X.: offering some suggestions and guidance for the original draft preparation; Y.J., and Y.C.: conducting the experiments or partial experiments. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 41827803.  
Not applicable.  
The data generated or analyzed in this study are included in the submitted article.  
Authors acknowledge Jilin University who provides instruments, funding and experimental sites. Authors would also like to thank anonymous reviewers who have provided many helpful comments on the manuscript.  
The authors declare no conflict of interest.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
###### Abstract  
Infrared and visible image fusion can obtain combined images with salient hidden objectives and abundant visible details simultaneously. In this paper, we propose a novel method for infrared and visible image fusion with a deep learning framework based on a generative adversarial network (GAN) and a residual network (ResNet). The fusion is accomplished with an adversarial game and directed by the unique loss functions. The generator with residual blocks and skip connections can extract deep features of source image pairs and generate an elementary fused image with infrared thermal radiation information and visible texture information, and more details in visible images are added to the final images through the discriminator. It is unnecessary to design the activity level measurements and fusion rules manually, which are now implemented automatically. Also, there are no complicated multi-scale transforms in this method, so the computational cost and complexity can be reduced. Experiment results demonstrate that the proposed method eventually gets desirable images, achieving better performance in objective assessment and visual quality compared with nine representative infrared and visible image fusion methods.  
infrared and visible image fusion; deep learning; generative adversarial network; residual network; structural similarity loss Article
## 5 Three-Dimensional Bioprinting of the Cornea
### Addressing Space-Specific Challenges  
As bioprinting technologies advance, their application in space exploration offers promising opportunities and significant challenges. Three-dimensional bioprinting offers a sustainable solution for corneal tissue generation during space missions, overcoming logistical limitations associated with transporting donor tissues. Unlike vascularized tissues, the cornea's avascular nature simplifies bioprinting and makes it a prime candidate for space-based applications [15; 65].  
However, the microgravity environment introduces challenges. Bioink cohesion, layer stability, and structural integrity are all affected by the absence of gravitational forces, requiring adaptations to existing techniques and formulations. Feedback-controlled bioreactors have emerged as a key innovation, stabilizing layers and ensuring consistent cell viability during the bioprinting process [34; 64]. These systems replicate physiological cues, such as oxygen gradients and mechanical forces, to enhance tissue maturation.  
Sterility and precision are critical in space-based bioprinting. Enclosed sterile systems mitigate contamination risks, while modular bioprinters designed for space missions maximize resource efficiency. These compact units support multi-material capabilities, enabling astronauts to address a range of medical needs with limited onboard resources [52; 69].  
Innovative bioinks, such as those designed to remain stable under microgravity, address challenges related to layer cohesion and material flow. UNION bioinks, for example, provide the mechanical strength and biocompatibility necessary for creating corneal tissues in space environments [15; 51]. Furthermore, incorporating autonomous diagnostic and therapeutic tools into bioprinting frameworks will expand the capabilities of astronauts to manage ocular emergencies, such as corneal ulcers or abrasions, without reliance on terrestrial support [63; 64].  
Future advancements in space bioprinting will focus on achieving long-term durability and scalability of constructs while integrating them with other space-based healthcare technologies. Addressing these challenges will be crucial for enabling sustainable medical support during deep-space missions and beyond.