esa-sceva/satcom-chunk-collection huggingface dataset:
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 5 Discussion  
In this work, the LEO satellite network using ISLL with stochastic link failure is modeled, in which the constraints and objective functions are given. Then, we propose an improved GA called GA-AS to optimize the model with the indicators of task revenue, switching times and routing cost in the case of the stochastic link failure. For reducing the cost of computation, a new initial solution generator is presented by combining the A* with the roulette wheel selection strategy. Finally, experimental results show that the GA-AS can significantly reduce the cost of computation and improve the optimization efficiency compared to GA.  
Data curation, G.Z.; Formal analysis, G.Z.; Funding acquisition, S.W.; Investigation, G.Z.; Methodology, G.Z. and Z.K.; Software, G.Z.; Supervision, Z.K. and S.W.; Writing--original draft, G.Z.; Writing--review and editing, G.Z., Z.K. and Y.H. 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, grant number [U20B2056].  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study can be made available on request from the corresponding author.  
The authors are grateful to the Shanghai Jiao Tong University, in particular to The school of Aeronautics and Astronautics, for their support and assistance.  
The authors declare no conflicts of interest.
## 2 Background Projects and Database Construction
### Categories of Factors Related to Primary Support Deformation
#### 2.2.4 Construction Parameters  
The deformation of tunnel structures is a complex process, typically divided into two crucial stages: the predeformation stage and the initial support deformation stage. Both of these stages play a vital role in tunnel engineering. In the predeformation stage, factors such as the geological conditions, groundwater levels, and the impact of existing underground structures before construction have a significant influence on the tunnel's initial state.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Parameter** & **Unit** & **Notation** \\ \hline
1 & Tunnel area & m\({}^{2}\) & \(A\) \\
2 & Depth of tunnel & m & \(H\) \\
3 & Width of tunnel & m & W \\
4 & High of tunnel & m & \(Q\) \\
5 & Stiffness of primary support & MPa & \(E_{p}\) \\
6 & Stiffness of secondary support & MPa & \(E_{s}\) \\ \hline \end{tabular}
\end{table}
Table 2: Tunnel geometry and support parameters.  
\begin{table}
\begin{tabular}{c c  
While some deformation during this stage may be inevitable, precise geological surveys and well-considered designs can be employed to minimize it. During the initial support deformation stage, tunnel structures are subjected to a more extensive array of influencing factors, with tunnel construction parameters being one of the primary determinants. These parameters encompass the excavation method, excavation depth, and excavation area. The selection of the excavation method can yield different responses in the tunnel structure, as distinct methods involve varying degrees of ground loosening and stress distribution. Moreover, the size of the excavation depth and the range of the excavation area directly affect the tunnel's support requirements and the stress distribution within the support structures. In the realm of engineering construction, these parameters are of paramount importance to ensure the safety and stability of the tunnel. In tunnel construction, the method of excavation, excavation footage (\(E_{f}\)), and excavation area (\(E_{a}\)) are the parameters in engineering construction.
# Research Progress on Router Devices for the OAM Optical Communication
## 4 OAM Channel Filtering
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Equipment**} & \multicolumn{2}{c}{**Strengths/**} & \multicolumn{2}{c}{**Transmission**} & \multirow{2}{*}{**Ref.**} \\  & & & **Weaknesses** & & **Result** & \\ \hline \multirow{2}{*}{2014} & Optical geometric transformation & \multirow{2}{*}{SLM} & Mode adjustable/ Difficult to filter out OAM with small mode intervals & The output power rejection ratio and propagation mode exceeds 14.5 dB. & \multirow{2}{*}{[134]} \\  & Labeling & & & & \\ \multirow{2}{*}{2016} & overlapped optical flows & Dammann optical vortex grating & Diversified function/- & The power of the blocked output port is suppressed by more than 23 dB by the filter & \multirow{2}{*}{[135]} \\  & Controlling the phase difference & \multirow{2}{*}{Interferometer} & Reduce the overlapping effect of adjacent OAM & Specific OAM schema values can & \multirow{2}{*}{[136]} \\  & & & & & achieve 100\% passing & \\ \hline \multirow{2}{*}{2022} & Chiral long-period fiber grating & \multirow{2}{*}{SLM, grating} & Without exerting extra loss & \multirow{2}{*}{-} & \multirow{2}{*}{[137]} \\  & & & for other OAM/- & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of channel filtering routers used for the OAM-based optical communications. Figure 5: Principle or experimental setup of OAM-RDs based on OAM channel filtering: (**a**) the tunable mode-blocking filter [134]. (**b**) Schematic diagram of OAM filter based on multiple-beam interference [136]. (**c**) Schematic diagram of the transmission loss in the righthanded CLPG [137]. the cladding mode and suffer loss. Therefore, VVB \(1\,R\), \(-1\)) will suffer more loss than VVB \(|\,L,1\rangle\) and their difference is the loss caused by chirality selected VVB \(1\,R\), \(-1\)) coupling. Thus, the filtering function is realized. In contrast to an OAM filter based on twisted photonic crystal fiber, the CLPG-based scheme has the advantages of narrow bandwidth and wavelength selectivity. In contrast to the OAM filters based on spatial optical devices, the CLPG-based scheme has the advantages of small size and good compatibility with the existing fiber-based OAM applications, which is promising for all-fiber OAM multiplexing communications, as illustrated in Table 4.  
The OAM filter routers can filter out unnecessary OAM beams via the selective filtering of multiplexed OAM beams, which meets the requirement of channel filtering of specific nodes in optical networks [138; 139; 140]. Initially, the OAM filter components only filtered OAM beams with large mode spacing. However, it is now possible to carry out high-fidelity filtering for any OAM beam, which has a broad application prospect in future optical communication networks. Table 4 summarizes the works reported to date for the channel filtering OAM-based optical communication routers.
## 5 Security Analysis
### BAN Logic Analysis
#### 5.2.2 Avispa  
We employed a widely utilized, open-source, and reputable tool known as AVISPA to validate security objectives. In AVISPA, programmers utilize the \"High-Level Protocol Specification Language\" (HLPSL) to write code, which is then converted into the \"Intermediate Format\" (IF) using HLPSL2 IF. Following the execution, AVISPA provides one of three specific outcomes: \"safe\", \"unsafe\", or \"inconclusive\". In the context of AVISPA, the channel is susceptible to all the attacks outlined in Section 3.2. Our proposed scheme consists of two basic roles of entities: The user device as a client and the server as a gateway. These basic roles comprise agent details as C and S. The description of the basic roles of the agents also includes crypto-operations, local declarations, channel (dy), initial state, and transitions to limit the boundaries of evaluation. Other roles are environment role and session role. The environment role specifies the global constants, knowledge of the intruder primarily based on the adversary model (dy), and the information of all the communication sessions between authentic and unauthentic communicating entities. The AVISPA code is publicly accessible on GitHub [48]. The simulation results reveal that, in the backend, the OFMC (Observational Finite Model Checker) algorithm explored 466 nodes up to a depth of four layers in just 0:53 s to confirm the protocol's safety against security threats. Similarly, the CL-AtSe (Compositional Logic for Attack and Security) tool in the backend analyzed 48 states in just 0:04 s to assert the protocol's safety, as shown in Figure 4.  
Figure 4: Simulation results from OFMC and CL-AtSe on AVISPA.
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 5 Results  
### Performance of the Traffic Congestion Detection System  
The results were obtained by simulating 64 traffic congesestions in different avenues and streets and at different times. Table 9 shows the results of two tests where the top row contains the first test's results and the lower row contains the second test's results. The first test results indicate that 63 alerts were generated by the big data cluster, with one false alerts and two alerts which were not generated. The first test presented a traffic detection accuracy of 93.7% in a scenario with a total of 236 vehicles, of which 24 (10.1%) had OBUs.  
The second test results indicated that the big data cluster generated 63 alerts, with zero false alert and one traffic congestion alert which was not generated. The second test presented a 98.4% accuracy in detecting traffic congestion in a scenario in which 118 vehicles, or 50% of all vehicles, had an OBU.  
The results of both tests show that the accuracy increases when more vehicles use OBU, although the accuracy percentage remains acceptable when a smaller number of vehicles use OBU.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
###### Abstract  
Space-borne synthetic aperture radar (SAR), as an all-weather observation sensor, is an important means in modern information electronic warfare. Since SAR is a broadband active radar system, radio frequency interference (RFI) in the same frequency band will affect the normal observation of the SAR system. Untangling the above problem, this research explores a quick and accurate method for detecting and segmenting RFI-contaminated images. The purpose of the current method is to quickly detect the existence of RFI and to locate it in massive SAR data. Based on deep learning, the method shown in this paper realizes the existence of RFI by determining the presence or absence of interference in the image domain and then performs pixel-level image segmentation on Sentinel-1 RFI-affected quick-look images to locate RFI. Considering the need to quickly detect RFI in massive SAR data, an improved network based on MobileNet is proposed, which replaces some inverted residual blocks in the network with ghost blocks, reducing the number of network parameters and the inference time to 6.1 ms per image. Further, this paper also proposes an improved network called the Smart Interference Segmentation Network (SISNet), which is based on U2 Net and replaces the convolution of the VGG blocks in U2 Net with a residual convolution and introduces attention mechanisms and a modified RFB module to improve the segmentation mIoU to 87.46% on average. Experiment results and statistical analysis based on the MID dataset and PAIS dataset show that the proposed methods can achieve quicker detection than other CNNs while ensuring a certain accuracy and can significantly improve segmentation performance under the same conditions compared to the original U2 Net and other semantic segmentation networks.  
radio frequency interference (RFI); interference detection; synthetic aperture radar (SAR); semantic segmentation 202315462[[https://doi.org/10.3390/rs15235462](https://doi.org/10.3390/rs15235462)]([https://doi.org/10.3390/rs15235462](https://doi.org/10.3390/rs15235462))
# Secrecy Rate of Resource-Constrained Mobile Relay Model under Two-Way Wiretap Channel
## 2 Materials and Methods
### System Model
#### 2.1.3 Downlink Signal Model  
For downlink transmission, the relay uses \(\mathbf{w}_{m}^{H}\mathbf{Y}_{m}\) to generate a symbol \(\mathbf{b}_{m}\in\mathbb{C}^{L_{m}\times 1}\) with power \(p_{m}=\frac{1}{l_{m}}||\mathbf{b}_{m}||^{2}\). Then, the relay transmits \(\mathbf{b}_{m}\) to Bob with the transmit beamforming vector \(\mathbf{v}_{m}\in\mathbb{C}^{N\times 1}\), which is given by \(\mathbf{v}_{m}=\frac{\mathbf{g}_{m}}{||\mathbf{g}_{m}||}\), \(||\mathbf{v}_{m}||=1\). Therefore, the received signal \(\mathbf{r}_{m}^{T}\in\mathbb{C}^{1\times L_{m}}\) at Bob is \(\mathbf{r}_{m}^{T}=\mathbf{g}_{m}^{H}\mathbf{v}_{m}\mathbf{b}_{m}^{T}+\mathbf{ n}_{m}^{T}\), where \(\mathbf{g}_{m}^{H}\in\mathbb{C}^{1\times N}\) is the downlink channel vector of relay-to-Bob, and \(\mathbf{n}_{m}^{T}\in\mathbb{C}^{1\times L_{m}}\) is the Gaussian noise at Bob with \(\mathbb{E}[\mathbf{n}_{m}\mathbf{n}_{m}^{H}]=\sigma_{u}^{2}\mathbf{I}_{m}\). The received signal \(\mathbf{Y}_{e}^{DL}\in\mathbb{C}^{N\times L_{m}}\) at Eve is given by \(\mathbf{Y}_{e}^{DL}=\mathbf{g}_{e,m}\mathbf{x}_{m}^{T}+\mathbf{N}_{e}\), where \(\mathbf{g}_{e,m}\in\mathbb{C}^{N\times 1}\) denotes the downlink wiretap channel vector of relay-to-Eve, and \(\mathbf{N}_{e}\in\mathbb{C}^{N\times L_{m}}\) is the Gaussian noise with \(\mathbb{E}[vec(\mathbf{n}_{e})vec(\mathbf{N}_{e})^{H}]=\sigma_{e}^{2} \mathbf{I}_{N\times L_{m}}\). Applying a receive beamformer \(\mathbf{w}_{e}^{\prime}\) with \(||\mathbf{w}_{e}^{\prime}||=1,\mathbf{w}_{e}^{\prime}=\frac{\mathbf{g}_{e,m} }{||\mathbf{g}_{e,m}||}\) to \(\mathbf{Y}_{e}^{DL}\), the downlink wiretap SNR of relay-to-Eve at Eve is \(q_{m}||\mathbf{w}_{e}^{\prime H}\mathbf{g}_{e,m}||^{2}/\sigma_{e}^{2}=q_{m}|| \mathbf{g}_{e,m}||^{2}/\sigma_{e}^{2}\). Therefore, the downlink achievable secrecy rate \(R_{m}^{DL-Sec}\) at the \(m^{th}\) stopping point from relay to Bob is given by  
\[R_{m}^{DL-Sec}=\Big{[}\log_{2}(1+\frac{p_{m}||\mathbf{g}_{m}||^{2}}{\sigma_{u} ^{2}})-\log_{2}(1+\frac{p_{m}||\mathbf{g}_{e,m}||^{2}}{\sigma_{e}^{2}})\Big{]} ^{+} \tag{6}\]
# A Review of Multifunctional Antenna Designs for Internet of Things
## 2 Materials and Methods
### Biomedical Devices
#### 2.1.6. Discussions and Perspectives through Interdisciplinary Collaborations
By fostering a more organized and collaborative approach, the development of biomedical devices can better align with the realities of clinical practice, ultimately improving patient outcomes and advancing the field of medical technology. In addition, implantable or wearable antennas and devices require complicated and miniaturized mechanical designs, and any materials used must be biocompatible. This issue can greatly benefit from recent advancements in 3D printing if they are appropriately directed through feedback from scientists developing antennas or biomedical devices. This perspective was emphasized a decade ago by organizations and universities, such as in the MIT white paper [53]. To our knowledge, this enlightening idea is not yet widely established. Organizing interdisciplinary committees at each university, possibly managed by national forums, could be a good approach. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Antenna** & **Gain (dBi)** & **Size (mm3)** & **Q** & **Frequency** & **Reference** \\ \hline \hline Meandered Patch & \(-32.1/-31.5\) & \(5\times 5\times 0.635\) & 4.05/6.45 & 1.4/2.45 GHz & [37] \\ Dif. Meandered Patch & \(-27.9\) & \(25\times 11\times 11\) & 10 & 2.45 GHz & [18] \\ Impl. Vivaldi & \(-15.7\) & 44 & 2 & 4 GHz & [42] \\ Rectangular Loop & \(-32\) & \(5\times 7\times 0.3\) & 5.9 & 5.9 GHz & [34] \\ Meandered Patch & \(-28.65\) & \(6\times 9.55\times 0.2\) & 5.21 & 2.4 GHz & [21] \\ Dif. Meandered Loop & \(-28.6\) & \(25\times 11\times 11\) & 19.6 & 2.45 GHz & [36] \\ Diel. Resonator & \(-25.7\) & \(3\times 4.7\times 1\) & - & 2.45 GHz & [19] \\ Metamaterial & \(-15.2\) & \(8\times 8\times 0.15\) & - & 2.45 GHz & [41] \\ Conformal Loop & \(-28.9/-18.6\) & \(28\times 9\) & - & 0.434/2.45 GHz & [29] \\ PIFA & \(-33.6/-21/-15.49/-10.25\) & 154 & \(3.19/2.25/8.33/14\) & 0.403/0.915/1.4/2.45 GHz & [39] \\ PIFA & \(-45.6/-27.6/-25.4\) & \(11\times 20.5\times 18\) & - & 0.403/0.902/2.45 GHz & [38] \\ ME & \(-18.1\) & \(0.2\times 0.05\times 0.001\) & 632 & 2.53 GHz & [50] \\ ME Disc & \(-15.59\) & \(0.2\times 0.2\times 0.001\) & 16 & 2.49 GHz & [51] \\ Self-biased ME & \(-61\) & \(3.5\times 5\times 0.025\) & 1970 & 49.9 kHz & [46] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison table of different antenna designs for implantable and digestible devices.
## 4 Experimental Results
### Comparison with Mask R-CNN-Based Method
Figure 11: Instance segmentation results: (**a1-a4**) input, (**b1–b4**) ground truth, (**c1–c4**) proposed, and (**d1–d4**) Mask R-CNN. To evaluate the execution speed, 300 psoriasis images were used for testing. The performance index, frame (i.e., image) per second (FPS), is adopted here. The FPSs were 0.44 and 15.1 for the Mask-RCNN-based method and the proposed scheme, respectively. The result shows that the proposed scheme is much faster than the Mask-RCNN-based method. This means that the proposed scheme is suitable for real applications. According to the abovementioned experimental results, the proposed scheme not only had a higher F1_Score but also a larger FPS compared with the Mask-RCNN-based method for psoriasis image segmentation. Therefore, these results demonstrate that the proposed instance segmentation scheme based on YOLACT++ is superior to the Mask-RCNN-based method in terms of the F1_score and the FPS for dealing with unconstrained psoriasis images.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 1 Introduction
### Related Work
#### 1.1.4 Sigfox  
Sigfox ([[http://www.sigfox.com](http://www.sigfox.com)]([http://www.sigfox.com](http://www.sigfox.com))) is a variation of the cellular system that enables remote devices to connect to ab access point with Ultra Narrow Band (UNB). A proprietary technology, developed and delivered by the French company Sigfox, no detailed public specification is available. Sigfox operates on the 868-MHz frequency band, with the spectrum divided into 400 channels of 100 Hz [9].  
Each end-device can send up to 140 messages per day, with a payload size of 12 octets, at a data rate up to 100 bps. Sigfox claims that each access point can handle up to a million end-devices, with a coverage area of 30-50 km in rural areas and 3-10 km in urban areas. Sigfox's claim to being a low power technology stems, in no small part, from end-devices being heavily duty-cycled due to an assumption of the nature of the data traffic patterns in the IoT: when an end-device has a message to send, the Sigfox interface circuitry wakes up, and the message is transmitted \"uplink\", from the end-device; then, the end-device listens for a short duration in case there are data being sent \"downlink\", to the end-device. In other words, downlink traffic is supported by the end-device actively polling, which makes Sigfox an interesting choice for data acquisition, but perhaps less so for command-and-control scenarios.
# Ellipsoidal K-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions
## 3 Integrating Adaptive Ellipsoidal Clustering with Simulated Annealing Heuristic  
As discussed in previous sections, one of the significant challenges in center-based clustering is the possibility of convergence to local optima. To address this issue, various heuristic methods have been developed to solve combinatorial optimization problems. Examples of such methods include Tabu search [35; 36], simulated annealing [37], evolutionary techniques [38], and swarm optimization methods [39].  
Simulated annealing has been extensively studied and applied to clustering problems due to its effectiveness in reducing the likelihood of becoming trapped in local optima [40; 41; 42]. In light of its proven benefits, this research adopted simulated annealing as the primary heuristic method to tackle the challenges associated with clustering.  
The core concept of the proposed algorithm is to use simulated annealing heuristics to enable non-local movements of cluster centers, thereby improving the search for optimal solutions. Numerous heuristic algorithms can be combined with traditional clustering techniques, such as \(K\)-means or fuzzy \(K\)-means. In our approach, we integrate the ellipsoidal clustering technique with simulated annealing to enhance the clustering performance.  
In the following subsection, we explain the basic principles of the simulated annealing process and demonstrate how it can be applied to cluster ellipsoidal data.  
Figure 5: Results after applying \(ESF\) on Dataset_3_2, (**a**) shows Dataset_3_2 clustered for two clusters without using the nearest-neighbor algorithm. (**b**) shows Dataset_3_2 clustered using the nearest-neighbor distance \(ESF-NN\).
# A Reference Model for Monitoring IoT WSN-Based Applications
## 5 Analysis and Design Examples
### Design
#### 5.2.3 Interchange Layer  
As previously described in the model, the main function of Interchange Layer consists in moving the information from capture elements to analysis element, and then from analysis element to visualization element. As previously described, it is necessary the definition of a Hardware Abstraction sub-layer which permits the use a standard communication and storage systems. An implementation of the Hardware Abstraction Sub-Layer may offer, as mentioned, the discovering of existing monitoring devices (attached motes and sniffers), which inform the system about their capabilities and transparent data storage.  
In our example, communications could be implemented by means of wired networks. The sniffers are static and are powered with a wired network, so an Ethernet wired network may be used to connect these motes with the analysis system. Monitor motes attached to the motes of the WSN may also be provided with an Ethernet adapter. Communication between Analysis and visualization subsystem may also be implemented by the Ethernet network.  
The transparent storage could be implemented by means of a SQL database located in the same Analysis Subsystem. Motes may introduce its information by means of SQL commands over a TCP/IP connection. The implementation of this Interchange layer would be easy by combining PAD and LIVENET Interchange layers. Off-the-shelf components that implement these features would communicate through standard interfaces to provide the required Information Layer implementation. The characteristics of this WSN-MP are summarized in Table 3.  
This work will open a discussion about the appropriateness of each of the proposed layers, and reveals the need for the entire model standardization to guarantee its correct operation. However, it is clear the advantages of a reference model as proposed in this paper. It can also be considered a starting point for establishing a universally accepted standard.  
Standardization organisms should be aware of the need of this environment where WSN-MP can evolve. For instance, ISO is doing a huge standardizing effort in WSN standards through its ISO/IEC JTC 1/WG 7 Working Group on Sensor Networks committee [9]. Maybe this committee should also take this task into account.