esa-sceva/satcom-chunk-collection huggingface dataset:
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
###### Abstract  
Wireless sensor networks (WSNs) have become an important and promising technology owing to their wide range of applications in disaster response, battle field surveillance, wildfire monitoring, radioactivity monitoring, etc. In WSNs, routing plays a significant role in delivery latency, energy consumption, and packet delivery ratio. Furthermore, as these applications are used in critical operations with limited irreplaceable batteries, routing protocols are required to be flawless as well as energy efficient. The dynamic environment also requires intelligent and adaptive routing. Game theory is widely used for designing routing protocols in WSNs to achieve not only reduced energy consumption but also increased packet delivery ratio. The core features of efficiently designed game theory-based routing protocols include optimal cluster head selection in hierarchical routing, energy-efficient and delay-aware route discovery, fault-tolerant data delivery, and coalition forming and grouping among nodes for stringent data transfer. In this paper, different routing protocols based on various types of games are extensively reviewed, which have been reported so far for improving energy consumption, delay, route establishment time, packet delivery ratio, and network lifetime. The different game theory-based routing protocols are qualitatively compared with each other in terms of major features, advantages, limitations, and key characteristics. For each protocol, possible applications and future improvements are summarized. Certain important open concerns and challenges are also discussed, along with future research directions.  
energy consumption; game theory; network lifetime; packet delivery ratio; routing protocol; wireless sensor networks +
Footnote †: journal: Applied Sciences
## 3 Regulatory Mechanisms Controlling AHL Synthesis in _Sinorhizobium meliloti_
### Transcriptional Mechanisms
#### 3.1.2 Nutrient Availability  
Environmental factors including stress are known to regulate QS in rhizobia. As reported in different bacteria, nutrient availability also influences QS regulation in _S. meliloti_. Specifically, expression of _sint_ and _sint_ are positively regulated under low phosphate concentrations [34]. This regulation is mediated by PhoB, the response regulator of the two-component regulatory system PhoR/PhoB, which is responsible for the phosphate stress response in the alfalfa symbiont [52]. Therefore, this system links nutritional stress response to social behavior, probably increasing adaptability of bacteria. It is not known whether _sint_ is directly regulated by PhoB because a Pho box identified in the _sint_ promoter region is not required in the effect of PhoB on _sint_ expression.  
Under sudden nutritional downshifts, the stringent response alarmone guanosine tetraphosphate (ppGpp) accumulates in _S. meliloti_ cells, which leads to transcriptome remodeling. The protein RelA is responsible for the synthesis of ppGpp. Additional evidence for nutrient availability influencing AHL production was obtained after expression profiling performed on _S. meliloti_ cells under nitrogen or carbon starvation. These studies revealed that _sint_ was induced in a RelA-dependent manner [53]. Likewise, in the bean symbiont _Rhizobium elti_, AHL accumulation is dependent on the _relA_ homologue [54].
## 4 Substation  
Process Level is where there is direct communication with physical substation devices, such as circuit breakers, voltage, current sensors, and measuring units.  
The Bay Level is where the information from devices is relayed, and from the data, local control decisions are executed using IEDs. This level conveys the data to the station level.  
Station Level is where the devices for supervision and operation are located. Upon receiving the data from the bay level, human operators can execute various tasks and configurations of SCADAs and Human Machine Interfaces (HMIs). It also connects the substation to a remote-controlled room from kilometers away, allowing a central operator or utility to manually modify the state of equipment and monitor the substation's activities [67].  
The connection of these different levels is through two busses as per IEC61850, which are the process bus and the station bus, as depicted in Figure 4 below. This is where the interconnection of the process level interconnects with the bay level and the bay level connects with the station level, where different network protocols are supported with varying communication needs [67]. Internet protocols are slow during transient in real-time systems, so the IEC 61850 employs several protocols to attain quicker responses.  
Manufacturing Messaging Specification (MMS): Used to deal with applications, data exchange, and configuration with information between IED and SCADA. Sampled Mea  
Figure 4: System architecture of IEC61850-based substation [24].  
sured Values (SMV): Delivers quick and dependable communication of power system measurement from both current and voltage transformers. Generic Object-Oriented Substation Events (GOOSE): It is utilized to convert status information between IEDs to isolate faults and prevent damage to critical parts of the electrical network.  
With the introduction of IEC61850, the increasing energy challenge has been mitigated by the smart grid owing to its high scalability and interoperability on various IEDs. However, with the vast data exchange, there are several security threats that inhibit its smooth operation, such as compromising data integrity, confidentiality, and availability [72; 73]. The next section introduces the threats and security concerns associated with IEC 61850.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 5 Sillcom\({}_{3R}\): Basic Design with Three Online Rounds
### Round-Efficient Secure Top-\(k\) Ranking via FSS \(\Pi_{Rank}\)
Offline preprocessing:
1: P\({}_{2}\) generates \(m\) random masks \((r_{0},\ldots,r_{m-1})\). 2:for\(i\gets 0\) to \(m-1\)do
3:for\(j\gets i\) to \(m-1\)do
4: P\({}_{2}\) generates DDCF keys: \((k_{0}^{i,j},k_{1}^{i,j})\leftarrow\text{Gen}^{DDCF}(1^{\lambda},f_{(r_{i}-r_{ j})})\). 5: P\({}_{2}\) sends \(k_{b}^{i,j}\) to \(P_{b}\), \(b\in\{0,1\}\). 6:endfor
7:endfor Online computing:
1: \(P_{0}\) and \(P_{1}\) exchange \(m\) values to get \(m\) masked distances \(d_{iq}+r_{i}\), \(i\in[m]\). 2:for\(i\gets 0\) to \(m-1\)do
3:for\(j\gets i\) to \(m-1\)do
4:\(P_{b}\) computes 2P-ASS shared \(1\{d_{iq}>d_{iq}\}\): \(\langle gt_{ij}\rangle_{b}=\mathbf{Eval}^{\text{DDCF}}(b,d_{iq}+r_{i},d_{iq}+r _{j})\). 5:endfor
6:\(P_{b}\) gets 2P-ASS shared rank of \(d_{iq}\): \(\langle rank_{iq}\rangle_{b}=\sum_{j=1}^{m}\langle gt_{ij}\rangle_{b}\). 7:endfor
```  
**Algorithm 2** Secure ranking via FSS \(\Pi_{rank}\)
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
## 3 System Model
### Block-Type Pilot-Based LS Channel Estimation Method  
After multipath fading and AWGN, the time-domain received OFDM symbols \(\hat{\mathbf{y}}_{i}(n)\) can be represented as:  
\[\hat{\mathbf{y}}_{i}(n)=\hat{\mathbf{x}}_{i}(n)\otimes\hat{\mathbf{h}}_{i}(n)+\hat{\mathbf{w}}_ {i}(n),\ \ 0\leq n<N, \tag{1}\]  
where \(\otimes\) represents the cyclic convolution operation and \(i\) is the frame number. The frequency-domain received OFDM symbols \(\hat{\mathbf{Y}}_{i}(n)\) can be represented as:  
\[\hat{\mathbf{Y}}_{i}(n)=\hat{\mathbf{X}}_{i}(n)\cdot\hat{\mathbf{H}}_{i}(n)+\hat{\mathbf{W}}_ {i}(n),\ 0\leq n<N. \tag{2}\]  
Figure 1: System model of cyclic prefix (CP)-OFDM. OFDM: orthogonal frequency division multiplexing; QAM: quadrature amplitude modulation; IFFT: inverse fast Fourier transform; FFT: fast Fourier transform.  
In the LS channel estimation method, block-type pilots are used to estimate the channel state information. It can be shown that the estimated channel frequency response (CFR) at pilots by the LS method is represented as:  
\[\hat{\mathbf{H}}_{i}(n_{p})=\frac{\hat{\mathbf{Y}}_{i}(n_{p})}{\hat{\mathbf{X}}_{i}(n_{p})}+ \hat{\mathbf{W}}_{i}(n_{p}), \tag{3}\]  
where \(\hat{\mathbf{X}}_{i}(n_{p})\) and \(\hat{\mathbf{Y}}_{i}(n_{p})\) are the transmitted and received pilot signals on the \(n_{p}\)-th pilot symbol, \(\hat{\mathbf{W}}_{i}(n_{p})\) is the frequency-domain AWGN on the \(n_{p}\)-th pilot symbol.  
After one-dimensional linear interpolation, the estimated CFR is obtained in the entire frequency domain, which can be represented by:  
\[\hat{\mathbf{H}}_{i}(n+l)=(1-\frac{l}{Q})\hat{\mathbf{H}}_{i}(n_{p})+\frac{1}{Q}\hat{ \mathbf{H}}_{i}(n_{p}+Q),\ 0\leq n<N, \tag{4}\]  
where \(\hat{\mathbf{H}}_{i}(n_{p})\) denotes the channel estimation results for the \(n_{p}\)-th pilot symbol. \(\hat{\mathbf{H}}_{i}(n_{p}+Q)\) represents the channel estimation value of the \((n_{p}+Q)\)-th pilot symbol. \(Q\) represents the interval between two pilot symbols. The time-domain estimated CIR \(\hat{\mathbf{h}}_{i}(n)\) could be obtained by the IFFT calculation of \(\hat{\mathbf{H}}_{i}(n)\). Usually, \(\hat{\mathbf{h}}_{\text{LS},i}(n)\) contains time-domain noise \(\hat{\mathbf{w}}_{i}(n)\), which can be denoted by:  
\[\hat{\mathbf{h}}_{\text{LS},i}(n)=\hat{\mathbf{h}}_{i}(n)+\hat{\mathbf{w}}_{i}(n),\ 0\leq n<N. \tag{5}\]  
The advantage of block-type pilot-based LS channel estimation is that the channel correlation time is very small, and it is not sensitive to the Doppler effect. The block-type pilot-based LS channel estimation method can be used for mobile reception, and it is simple to design the pattern, easy to implement and saves hardware resources. The disadvantage of the method is that because the interval between pilot points occupies a long time, interpolation filtering cannot accurately reflect the variation of the CIR.
## 3 Results and Discussion
### Performance Testing  
#### 3.2.1 Effect of Pyrolysis Time on Catalyst Performance  
The catalytic performance of Fenton sludge biochar catalysts prepared under different pyrolysis times is shown in Figure 6. As observed in Figure 6a, the system reached adsorption equilibrium in 60 min. As shown in Figure 6b, in the Fenton sludge biochar catalyst/H\({}_{2}\)O\({}_{2}\) system, the concentration of MB gradually decreased within 90 min of reaction. When H\({}_{2}\)O\({}_{2}\) was activated using Cat\(-\)1, Cat\(-\)2, Cat\(-\)3, Cat\(-\)4, and Cat\(-\)5, the MB removal efficiencies after 90 min were 86.6%, 96.1%, 76.0%, 80.3%, and 88.7%, respectively. Therefore, considering both the MB degradation efficiency and the preparation cost of the Fenton sludge biochar catalyst, Cat\(-\)2 was selected for subsequent experiments.  
Figure 5: The contents of the corresponding active sites of (**a**) C1s, (**b**) N1s, (**c**) O1s, and (**d**) Fe2p in Cat\(-\)1, Cat\(-\)2, Cat\(-\)3, Cat\(-\)4, and Cat\(-\)5.
## 2 Literature Review: Offshore Maintenance and Asset Management  
A systematic search strategy was employed to structure the literature review, covering relevant studies published in the last five years. The academic databases selected were Web of Science (WoS) and Scopus due to their broad engineering and applied sciences coverage. The keywords used in the search included \"offshore maintenance\", \"corrosion\", \"resource optimisation\", \"structural integrity\", and \"asset management\", ensuring an accurate selection of works directly related to the topic of study. The selection process for the studies involved a thorough analysis, including a review of the titles, abstracts, and, where necessary, the full text of each identified article. Only studies addressing relevant aspects of maintenance planning in offshore environments, corrosion management, and resource optimisation methodologies were considered. This strategy allowed for a solid foundation of references supporting this research's arguments and methodological approach, ensuring that the theoretical framework is aligned with the most recent and pertinent developments in the field.
# Development of Modular Bio-Inspired Autonomous Underwater Vehicle for Close Subsea Asset Inspection
## 3 Robofish Design
### Electronic Design  
A simplified design schematic of the RoboFish electronic systems is shown in Figure 7. RoboFish uses a modular software and hardware architecture. Each segment is self-contained and includes self-managed battery power, internal and external sensor data, and actuator control using a low-cost microcontroller. Communications and power transfer between segments are performed through a customised 100 Mbit Ethernet bus, and it can charge autonomously underwater by docking with a source such as EC-OG's Subsea Power Hub. The head segment contains a powerful Xilinx Zynq SoC that serves as a master control node, communications router, and FPGA-accelerated vision platform with an acoustic rangefinder for position detection. While Wi-Fi communication is only available on the surface, RoboFish can also communicate at low rates underwater by an acoustic modem. It currently uses vision for close-range navigation and inspection of structures, with the ability to build complete visual models of the structure by using 3D reconstruction methods.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods
### Preprocessing  
The atmosphere correction was applied to satellite images (Sentinel-2, ASTER, and Landsat 9) using Quick Atmospheric Correction (QUAC). QUAC can derive atmospheric correction parameters directly from the image itself without requiring any external data [36].  
With the atmospheric correction completed, all images were resampled to a spatial resolution of 3 m using the \"Resize Data\" tool in ENVI 6.0. This resampling was performed to standardize the spatial resolution across all sensors, enabling consistent inter-sensor comparison.
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 6 Conclusions  
In this article, the fault diagnosis system is divided into a real-time processing system, quasi-real-time processing system and batch processing system from the perspective of data processing real-time capability.  
The real-time processing system in the dispatching center establishes a fault diagnosis model based on the object-oriented information representation of power grid topology and electrical elements. According to accident-level information, this subsystem can find faulty elements by Algorithms 2 and 3, provide the judgment of incorrect actions of protections and circuit breakers by Algorithm 3 and analyze the problem of partial accident-level information loss by Algorithm 4.  
According to warning-level information, quasi-real-time processing systems in substations output the reasons for incorrect actions of circuit breakers and relay protection devices by fault reason tracking process in Section 4.1.2.  
The batch processing system in the dispatching center verifies the diagnosis results of the real-time processing system and provides a detailed fault diagnosis report according to fault recording documents and related technologies in Section 4.2.  
Tests in different power systems show that the fault diagnosis and tracking system can accurately and rapidly complete the fault diagnosis and tracking work in the case of partial information missing and output a complete fault report including faulty elements, fault phases, fault types, fault times, fault locations, protections and circuit breakers that operate incorrectly and reasons for incorrect actions.  
Compared with the existing related fault diagnosis and tracking methods and systems, this system fully considers the current status of data sources available in the power system and the processing needs of dispatching centers and substations after fault, which improves the efficiency and reliability of fault diagnosis. It helps to rapidly complete the maintenance and self-healing work of the power grid after a fault and has a preferable application value.  
**Author Contributions:** Conceptualization, J.J. and Q.C.; data curation, J.J. and L.J.; formal analysis, J.J., Q.C. and X.Z.; funding acquisition, Q.C.; methodology, J.J., Q.C. and L.J.; project administration, Q.C.; software, J.J.; validation, J.J. and Q.C.; visualization, J.J.; writing original draft, J.J. and W.D.; writing review and editing, J.J., Q.C. and X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. 5187070349).  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript or in the decision to publish the results.
# A Perturbation and Symmetry-Based Analysis of Mobile Malware Dynamics in Smartphone Networks
## 7 Model Limitations and Future Directions  
The mobile malware propagation model faces one significant limitation that requires attention in future research. Although the model parameters are derived from real-world sources such as documented malware campaigns, antivirus reports, and network statistics, the validation relies entirely on synthetic simulations. The model has not been tested against actual malware propagation datasets from real outbreaks.  
This limitation exists because obtaining comprehensive real-world malware propagation data is difficult due to privacy concerns and the distributed nature of mobile networks. The authors acknowledge that their modeling assumptions may overestimate transmission rates by 15-25% and ignore real-world device differences.  
Future work should incorporate real-world validation by accessing anonymized malware datasets from security vendors, benchmarking against documented outbreak case studies, and comparing model predictions with observed behaviors in historical malware campaigns. This would strengthen the model's practical applicability and empirical credibility.
# Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty
## Appendix A  
For brevity, Equation (2), \(E_{p}^{u wafer}\) can be simplified as:  
\[E_{p}^{u wafer}=\gamma_{P}^{x_{s}y_{s}}\otimes\Theta_{defocus}, \tag{10}\]  
where  
\[\gamma_{P}^{x_{s}y_{s}}=\frac{2\pi}{n_{w}R}\mathcal{F}^{-1}\{C\times V(x_{s},y _{s})\odot U_{ideal}\odot\mathcal{F}\left[M_{near}(x_{s},y_{s})\right]\odot E _{i}(x_{s},y_{s})\}, \tag{11}\]  
\[\Theta_{defocus}=\mathcal{F}^{-1}[Def], \tag{12}\]  
Similarly, according to our previous work [16], \(E_{p}^{u wafer}\) can also be simplified as:  
\[E_{p}^{u wafer}=H_{p}^{x_{s}y_{s}}\otimes(B^{x_{s}y_{s}}\odot M), \tag{13}\]  
where \(M\) is the mask layout, \(B^{x_{s}y_{s}}\) is the mask diffraction matrix, and \(H_{p}^{x_{s}y_{s}}\) is the transfer function of the project lens.  
Then, discretize the sensitivity penalty \(Y\) in each defocus disturbance \(Y_{i}\). Based on the chain rule, according to Equations (6) and (10), the analytical sensitivity penalty \(Y_{i}\) can be expressed as:  
\[\begin{array}{ll}Y_{i}&=\left(\|\frac{\partial\mu^{defocus}(\beta_{i})}{ \partial\beta_{i}}\|_{2}^{2}\right)^{2}\\ &=\sum\limits_{m,n}\left(\frac{\partial\mu^{defocus}_{m}(\beta_{i})}{\partial \Theta^{defocus}_{defocus}}\frac{\partial\Theta^{xs}_{defocus}}{\partial \beta_{i}}\right)^{2}\\ &=\sum\limits_{m,n}\left(\frac{2}{\gamma_{sum}}\sum\limits_{x_{s}}\sum \limits_{y_{s}}P^{x_{s}y_{s}}\sum\limits_{r_{s}}\sum\limits_{\rho=x,y,z}D_{1} \text{Re}\{\Phi\}+D_{2}\text{Im}\{\Phi\}\right)^{2},\end{array} \tag{14}\]  
where \(I_{mn}^{defocus}(\beta_{i})\) represents the \(m\) and \(n\)th sampling point in the aerial image matrix \(I^{defocus}(\beta_{i})\), and \(\Theta^{rs}_{defocus}\) represents the \(r\) and \(sth\) sampling point in matrix \(\Theta_{defocus}\) as well. Details of \(D_{1}\), \(D_{2}\) and \(\Phi\) can be formulated as:  
\[\begin{array}{ll}D_{1}&=\frac{\partial\text{Re}\left(\Theta^{rs}_{defocus} \right)}{\partial\beta}\\ &=-\text{Re}\left\{\mathcal{F}^{-1}[\omega^{rs}\sin(\omega^{rs}\beta)]\right\} -\text{Im}\left\{\mathcal{F}^{-1}[\omega^{rs}\cos(\omega^{rs}\beta)]\right\}, \\ D_{2}&=\frac{\partial\text{Im}\left(\Theta^{rs}_{defocus}\right)}{\partial \beta}\\ &=-\text{Im}\left\{\mathcal{F}^{-1}[\omega^{rs}\sin(\omega^{rs}\beta)]\right\} +\text{Re}\left\{\mathcal{F}^{-1}[\omega^{rs}\cos(\omega^{rs}\beta)]\right\}, \end{array} \tag{15}\]  
and  
\[\Phi=\sum\limits_{r=1}^{M}\sum\limits_{s=1}^{N}\left(\gamma_{P,m-r,n-s}^{x_{s} y_{s}}\Theta^{rs}_{defocus}\right)\left(\gamma_{P,m-r,n-s}^{x_{s}y_{s}} \right)^{*}, \tag{16}\]
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
###### Abstract  
Predictive Maintenance (PdM) is the newest strategy for maintenance management in industrial contexts. It aims to predict the occurrence of a failure to minimize unexpected downtimes and maximize the useful life of components. In data-driven approaches, PdM makes use of Machine Learning (ML) algorithms to extract relevant features from signals, identify and classify possible faults (diagnostics), and predict the components' remaining useful life (prognostics). The major challenge lies in the high complexity of industrial plants, where both operational conditions change over time and a large number of unknown modes occur. A solution to this problem is offered by novelty detection, where a representation of the machinery normal operating state is learned and compared with online measurements to identify new operating conditions. In this paper, a systematic study of autoencoder-based methods for novelty detection is conducted. We introduce an architecture template, which includes a classification layer to detect and separate the operative conditions, and a localizer for identifying the most influencing signals. Four implementations, with different deep learning models, are described and used to evaluate the approach on data collected from a test rig. The evaluation shows the effectiveness of the architecture and that the autoencoders outperform the current baselines.  
n _Applied_  
11 April 2022  
11 May 2022  
13 May 2022  
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