esa-sceva/satcom-chunk-collection huggingface dataset:
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Cooperative Games
#### 3.1.3 Reliable Coalition Formation Routing (RCFR) for WSNs
The main differences between RCFR and AODV are:  
* Information (\(PFR_{i}\), \(RCR_{i}\), \(RER_{i}\)) regarding the created coalitions is put into the table entry that is maintained by each node;
* In the route reply message of AODV, the route residual energy ratio and route cost fields are appended;
* Routes with the lowest costs are selected to find an optimal pathway using coalitional game theory;
* Rate of route residual energy parameter is presented for the optimization of the route maintenance mechanism. In the simulation scenario in [35], 100 packets were sent to the destination. These packets were sent taking into consideration that a certain percentage of nodes were selfish. According to the simulation results in [35], as the number of selfish nodes increased, the packet delivery ratio decreased. RCFR was compared with the AODV protocol [55] and the method designed by [PERSON] [57]. It performed better than the methods discussed in [55] and [57] in terms of packet delivery ratio. Furthermore, the simulation experiments were undertaken on route establishment time to evaluate the convergence time of RCFR. One hundred rounds of simulation were conducted and, in each round, the time for establishing a successful route between a source node and the destination node was recorded. After calculating the average route establishment time for the three protocols, it was concluded that the performance of RCFR was superior. The amount of consumed energy increased as the number of selfish nodes increased. The simulation results in [35] also demonstrated that RCFR outperformed all the compared protocols, even in terms of energy consumption. Technical strengths:  
* Reliable data delivery and route maintenance;
* The coalition formation game model can be integrated with the traditional protocols like AODV and dynamic source routing (DSR) [1] for better performance. Technical weaknesses:
* RCFR did not consider the scenario where joining a coalition would yield a negative pay-off. * No consideration for node failure scenario. Applications:
* Applicable for a scenario where reliable data delivery is needed. Probable future improvements:
* More routing metrics can be introduced for performance analysis;
* Implementation with DSR can be presented.
## 3 Theoretical Background  
The link budget is a theoretical calculation that incorporates factors of loss and gain, related to antennas and the propagation channel, in order to examine whether the connection is stable and operates without issues at the maximum distance between the transmitter and the receiver [6].
# Analysis of the Development Status of eLoran Time Service System in China
## 3 Current Development Status of eLoran Time Service in China  
The earliest broadcasting station that adopted Loran-C in China was the BPL long wave time service system built by the National Time Service Center of Chinese Academy of Sciences in the 1970s, which is still operated and maintained, mainly providing time and frequency services.  
The BPL Long Wave Time Service station was completed and put into use in 1983 and included in the operation and management of the National Science and Technology Project in 1989. The BPL long wave time service system is the only high-precision microsecond time service method for space launch missions in China in the 20 th century. The National Time Service Center of Chinese Academy of Sciences modernized the BPL long wave time service station in 2008. The data channel uses Eurofix technology to broadcast time code information, making it capable of eLoran business [29; 30].  
The BPL long wave time service station is located in the hinterland of China, Pucheng, Weinan, Shaanxi Province. According to the characteristics of signal (long wave) ground wave propagation, BPL mainly covers the central and eastern regions of China. Other Loran broadcaster stations are mainly distributed in the coastal and nearby areas of China. Their ground wave signal mainly covers the coastal and surrounding areas, filling the blind spots of BPL ground wave signal coverage in the southern, southeastern, and northeastern regions of China. The coverage of the time service signals in existing eLoran stations in China is shown in Figure 2. However, the southwestern, western, and northwestern regions of China are still the blind spots of ground wave signal coverage in the long wave time service system, which are unable to use eLoran signal to achieve high-precision timing and frequency calibration.
## 1 Introduction
Other widely used data-compression techniques are the principal component analysis (PCA) [44], the linear discriminant analysis (LDA) [45], and the partial least square (PLS) [46], which are based on an algebraic calculation and geometric projection to create the separability into the feature space. However, these approaches lack an appropriate explanation of the feature selection process and a proper justification about the influence of each feature on the final output of the IFD. Without a proper understanding of the feature selection process, all the ML-based classifiers only focus on the classification accuracy, such as the black box approach [9]. The SHAP (SHapley Additive exPlanations) value [47] is a novel feature importance calculation method, which is suitable for both regression and classification problems that can represent a valid alternative to the most used feature selection methods, because it can provide a local interpretation of the model on a single prediction, as well as the global average behaviour of the model in terms of the contribution of each feature to the outcome of the ML algorithm [48].According to the existing literature [9; 49; 50; 51], the terms \"interpretability\" and \"explainability\" are often used synonymously, referring to models where users can understand how the inputs are mapped into outputs. This is the case for the techniques aimed at quantifying the contribution given by each feature to determine the outcome of their importance and highlight those with the greatest impact on prediction. In recent years, the SHAP value has become a popular method for the interpretation of the feature contributions in fitted ML models, as it can be seen in the references [47; 52; 53; 54; 55; 56; 57], posing the bases to Explainable Artificial Intelligence (XAI) [58] and new feature selection methods.
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 7 Experimental Results  
### Setup  
For the experiments, we used a computer with a 3.7 GHz i7 CPU, 16 GB of memory, a Nvidia 1080 GPU, and a Windows 10 Pro operating system. The structure of the proposed system is shown in Figure 1. First, we extracted the packet information from the MQTT pcap files using Tshark. Second, we preprocessed the packet information to extract sequence data. Third, we classified MQTT attacks using the Seq2 Seq model.  
We used six pcap files, as listed in Table 3 [6]. These include normal MQTT traffic, DoS, flood, SlowTe, malformed, and brute-force attacks. The file names and numbers of packets in each file are listed in Table 3.  
First, we extracted packet information using Tshark as follows:  
tshark -r capture_1w_mqtt.pcap -T fields -E separator=, -e frame.time_relative -e ip.src-e tcp.srcport-e ip.dst-e tcp.dstport-e mqtt.msgtype-e mqtt.kalive-e mqtt.conack.val > capture_1w_mqtt.csv.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attack** & **File Name** & **Number of Packets** \\ \hline Normal & capture\_1w\_mqtt\_1800s.pcap & 22,360 \\ DoS & capture\_malariaDoS\_mqtt.pcap & 93,317 \\ Flood & capture\_flood\_mqtt.pcap & 303 \\ SlowTe & slowite\_mqtt.pcap & 3048 \\ Malformed & malformed\_mqtt.pcap & 3656 \\ Brute force & Brute-force\_mqtt.pcap & 2921 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MQTT pcap files.  
Figure 12: Seq2 Seq model to detect malicious MQTT traffic.  
Second, we extracted the sequence data from the packet information by considering \(k\) consecutive packets. We used the source port index, TCP length, MQTT message type, keep alive, and connect ACK.  
\[\mathbf{s}_{i,j}=\left\{\mathbf{p}_{i,j},\mathbf{p}_{i,j+1},\ldots,\mathbf{p}_{i,j+k-1}\right\}\]  
Third, Keras [25] was used to implement the Seq2 Seq model to detect malicious MQTT traffic. We measured the accuracy of our method using five-fold cross-validation. A confusion matrix was used because there were six types of MQTT traffic.
## Enhancement Effects of the Terahertz Near-Field Microscopy  
**[PERSON] 1,\({}^{\dagger}\), [PERSON] 1,\({}^{\dagger}\), [PERSON] 1,\({}^{\ast}\), [PERSON] 1 and [PERSON] 1,2,\({}^{\ast}\)**  
1 Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; E-Mails: [EMAIL_ADDRESS] (J.H.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (C.D.)
2 College of Instrumentation Science and Electrical Engineering, Jilin University, Changchun 130061, China  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\ast}\) Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (D.W.); [EMAIL_ADDRESS] (H.-L.C.); Tel.: +86-23-6593-5641 (D.W.); +86-23-6593-5275 (H.-L.C.).  
Academic Editor: [PERSON]  
_Received: 12 October 2015 / Accepted: 8 December 2015 / Published: 11 December 2015_  
**Abstract**: Terahertz near-field detection based and imaging on a nanotip has drawn wide attention following extensive applications of terahertz imaging technologies. Through the local enhanced electric field created by a terahertz nanotip in the near field, it is very likely to attain superior detection sensitivity and higher spatial resolution. This paper simulates the local enhancement effects of the terahertz near-field microscopy using a two-dimension finite difference time domain (2D-FDTD) method. Factors that influence the enhancement effects are investigated and analyzed in detail. Simulation results show that the size of the nanotip apex, the apex-substrate distance, dielectric properties of the substrate and the detected sample, _etc.,_ have significant impacts on the electric field enhancement and spatial resolution of the terahertz near-field nanotip, which can be explained from the effective polarizability of the nanotip-sample/substrate system.  
**Keywords**: terahertz; nanotip; finite difference time domain; enhancement effect; effective polarizability; dielectric constant
## 2 Related Works
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Method** & \multicolumn{1}{c}{**Classifier**} & \multicolumn{1}{c}{**F1-Score, (\%)**} & \multicolumn{1}{c}{**Classes**} \\ \hline \multirow{2}{*}{[PERSON], [PERSON] et al. [37]} & DT(CART) & 87.36 & \\  & RF & 88.13 & \\  & CatBoost & 88.84 & \\ \hline \multirow{3}{*}{[PERSON] et al. [38]} & DSSTE + miniVGGNet & 97.04 & \\  & DSSTE + AlexNet & 96.49 & \\ \cline{1-1}  & DSSTE + LSTM & 96.50 & \\ \hline \multirow{6}{*}{[PERSON] [39]} & CatBoost & 91.65 & \\  & LightGBM & 94.69 & \\ \cline{1-1}  & DT & 88.58 & \\ \cline{1-1}  & LR & 49.47 & 7 \\ \cline{1-1}  & NB & 24.31 & \\ \cline{1-1}  & RF & 92.94 & \\ \cline{1-1}  & XGBoost & 93.64 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multi-classification accuracy results for the CSECIC-IDS2018 dataset. It has been observed that hybrid deep learning architectures tend to outperform other machine learning algorithms in terms of accuracy results. All these studies have shown that the majority of _F1-scores_ are above 95% for classifications of up to 14 classes. In addition, such common metrics as recall, precision, ACC (Accuracy) and _F1-score_ are provided, but the weighted average _F1-score_ is a more useful metric in this case, as it takes into account the relative importance of each class in terms of its support, which is particularly important for unbalanced datasets. Macro averaging is another commonly used technique for evaluating performance metrics in highly imbalanced datasets; however, none of these metrics were provided in the studies. Many studies have reported promising results using deep learning architectures on this dataset, classifying network traffic as either benign or malicious (as a binary classification task) [9; 11; 43]. However, this does not mean that increasing the number of classes will always result in decreased accuracy, and such trends have not been observed. Observations have indicated that the impact of model architecture or hyperparameters on accuracy is minimal [9; 44]. Instead, factors such as training and testing data, data processing and preparation, the number of sampled features and other similar considerations have a more significant influence. Nevertheless, there remains uncertainty regarding whether more complex hybrid models actually yield more accurate results. Additionally, questions persist regarding the rationale and benefits of merging classes, conducting extensive data cleaning, or reducing features. Thus, in this study, experiments were carried out separately on the raw CIC-IDS2017 and CSE-CIC-IDS-2018 datasets, i.e., keeping the initial number of attack types (the number of classes) at 15 and 28, respectively. For further investigation, these datasets were merged into a single dataset, which resulted in 19,063,686 instances, 79 features (initially 85) and 28 classes.
## 2 Materials and Methods
### Measurement of Antimicrobial Activity  
All the glassware used was autoclaved before carrying out the photocatalytic experiments. _E. coli_ (Gram-negative bacterium) and _S. aureus_ (Gram-positive bacterium) were used to study the antibacterial activity of the Cu-TiO\({}_{2}\). Nutrient broth culture medium was prepared by dissolving 25 g of nutrient broth No.2 (10 g peptone, 5.0 g NaCl, 10 g beef extract) in 1 L of distilled water followed by sterilization at 121 \({}^{\circ}\)C using an autoclave. The 90 mm agar plates were prepared by dissolving 28 g nutrient agar (5 g peptone, 8.0 g NaCl, 3.0 g Beef extract, Agar No. 2) in 1 L distilled water, then autoclaved at 121 \({}^{\circ}\)C for 15 min and poured into petri dishes. The strains were inoculated in 20 mL of nutrient broth and incubated for 24 h at 37 \({}^{\circ}\)C. A definite amount of the grown culture was transferred to the cylindrical glass vessel to make the working solution of 10\({}^{4}\) CFU/mL (colony forming unit/mL). The reaction mixture was kept inside the photo test chamber for visible light irradiation. The photocatalyst concentration was fixed at 1 g/L and irradiated for 3 h. Aliquots of 1 mL were extracted out from the reaction mixture at a regular time interval of 10 min. In order to attain a countable CFU, the bacterial solution was diluted in a PBS solution once and later 100 \(\mu\)L of the diluted solution was plated. Finally, the plates were incubated at 37 \({}^{\circ}\)C for 24 h. The colonies formed on the agar plates were counted and recorded as CFU/mL. A control experiment was also conducted in the absence of the photocatalyst. The same set of samples was tested simultaneously under dark conditions. The antimicrobial efficiency was calculated using two methods; (i) log reduction and (ii) N/N\({}_{0}\)% versus time.  
\[\text{log reduction }=\text{log}_{10}\text{ }\frac{\text{(A)}}{\text{(B)}} \text{ }\Rightarrow\text{ log}_{10}\text{ (A) }-\text{ log}_{10}\text{ (B)} \tag{5}\]  
where, A = initial number of viable microorganisms, B = number of viable microorganisms at any time \"t\", log\({}_{10}\) (A) \(\rightarrow\) N\({}_{0}\), log\({}_{10}\) (B) \(\rightarrow\) N for any time \"t\".
## 1 Introduction
In order to estimate surface albedo from Landsat without concurrent coarse-resolution data, [PERSON] _et al._[23] proposed an algorithm for forest albedo estimation using a look-up table derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface BRDF product [25] relying onthe forest disturbance inventory information, which achieved a low root-mean-square-error (RMSE) of 0.016 (\(\sim\)15%) over six forest sites of AmeriFlux. Existing algorithms designed for medium-resolution data are mainly limited by the availability of concurrent coarse resolution data or ancillary information used to infer the shape of surface BRDF, which is not ideal for cases with rapidly changing surfaces (e.g., ephemeral snow and harvest) or for cases with lack of high-quality ancillary information. In addition, none of these previous efforts have attempted to estimate surface albedo under snow-covered conditions. A series of approaches that directly link surface albedo with top-of-atmosphere (TOA) observations based on statistical relationships built from radiative transfer simulations can therefore be utilized for broadband albedo estimation using one single multispectral observation under such conditions [24,28,29]. Unlike the methods that rely on the concurrent coarse resolution data, this type of approach treats the surface BRDF as a priori knowledge in the radiative transfer simulations; this surface BRDF information can be extracted and synthesized into a database from the MODIS data [25]. This type of approach has been successfully applied to coarse-resolution data; however, it has never been attempted or validated using medium-resolution data. The main objective of this study is to demonstrate the feasibility of the HJ satellite charge-coupled device (CCD) data to be used for albedo monitoring under various surface conditions based on the direct estimation approach adopted and refined from [PERSON] _et al._[29] and a surface BRDF database extracted from MODIS products. In this paper, brief introductions of HJ data as well as the methodology are given in Section 2, followed by an analysis of validation against ground measurements and MODIS albedo product in Section 3.
## 6 ICN and Edge Computing Convergence Suitability Analysis for IIoV
### Collaborative Perception and Driving: Use Case  
To demonstrate the potential of ICN and EC convergence in the realization of IIoV, we conducted a suitability analysis by exploring enabling technologies and considering cooperative perception and driving as a use case. In the cooperative driving use case, large vehicles, such as trucks and mixnus, share camera views of road conditions ahead of them with lagging vehicles. A driver in front might warn the lagging driver intending to pass about an oncoming vehicle in the passing lane with the goal of preventing catastrophic head-on collisions during overtaking. Consider the scenario shown in Figure 13, where two connected autonomous vehicles are moving one behind the other, in the same lane, while engaged in cooperative collaborative perception to improve their individual capabilities and exchange safety information through V2X communications. The driver of vehicle A in front spots a pedestrian ahead on the adjacent lane, which is not in the field of view of vehicle B in the rear, as vehicle A has obstructed the view. Vehicle B is planning to make a passing/overtaking maneuver, but it is unaware of the pedestrian. The knowledge about the pedestrian becomes crucial for vehicle B, as it would not have enough time to avoid colliding with the pedestrian after the lane change. The remedy would be to  
Figure 12: Protocol stack of converged architecture for ICN IIoV.  
avoid performing the lane change at this time. Accurate perception of the surrounding environment is a vital requirement for safety. In cooperative driving, vehicles exchange safety information in a variety of ways--vehicles could share raw sensor data; however it would require significant communication bandwidth which cannot always be guaranteed. Another option is to exchange information about the detected objects including their position, speed, etc., as cooperative awareness messages (CAMs). Thus, cooperative and autonomous driving is one application of IoV that should benefit from the synergy of ICN and edge computing. The content distribution and caching capability of ICN networks can be leveraged to share cooperative awareness messages (CAMs) among vehicles. On the other hand, edge computing enables services to be deployed and provides local computing and storage. For instance, by caching and reusing computation results, other consumers can avoid repeating computations. Interests aggregation can also be performed on edge nodes thus avoiding duplicates of interests for the same content from multiple consumers. Therefore, ICN and edge computing share complementary properties, local storage, and the ability to reduce latency, enhance mobility, and ensure scalability.
# Find the first common frame firstCommonFrame = first element in commonFrames  
Calculate transformation to align GraphB to GraphA's coordinate system RtBW2 AW = concatenateRts(inverseRt(GraphA.Mot for firstCommonFrame), GraphB.Mot for firstCommonFrame)  
Transform GraphB's 3D points using the calculated transformation GraphB.Str = transformPtsByRt(GraphB.Str, RtBW2 AW)  
Update GraphB's camera poses to reflect the new coordinate system for each frame in GraphB: GraphB.Mot for current frame = concatenateRts(GraphB.Mot for current frame, inverseRt(RtBW2 AW))  
Add new frames and camera poses from GraphB to GraphAB GraphAB.frames = combine GraphA.frames and newFramesFromB GraphAB.Mot = combine GraphA.Mot and GraphB.Mot for new frames  
Iterate through common frames to merge and update tracks (3D points and observations) for each commonFrame in commonFrames:  
Find corresponding camera IDs in both graphs cameraIDA = index of commonFrame in GraphA.frames cameraIDB = index of commonFrame in GraphB.framesGet tracks (3D point indices) and observations for the common frame in both graphs trA, xyA = get tracks and observations from GraphA for cameraIDA trB, xyB = get tracks and observations from GraphB for cameraIDB  
Find common observations (matching 2D points) between the two graphs xyCommon, iA, iB = intersect(xyA, xyB)  
Extend existing tracks in GraphAB with observations from GraphB for each common observation: idA = track index in GraphA corresponding to current common observation idB = track index in GraphB corresponding to current common observation  
Add observations from new frames in GraphB to the existing track in GraphAB for each new frame in GraphB: if observation exists for idB in current new frame: add observation to GraphAB for track idA and current new frame
### Motivation  
Current emphasis within the academic research community is on the conceptual design and sector-specific content of DPPs, with less attention being directed towards the foundational technical IT infrastructure that supports these DPP systems. In this context, the present paper proposes a technology framework based on a multi-blockchain approach for implementing a DPP concept focusing on the data model required to store all information related to such assets. In addition, decentralized identifier (DID) technology [6] is used for managing the DPP's unique IDs.  
A key aspect of implementing a DPP solution is the unique identification of products. This is crucial for ensuring the accurate tracking, verification, and management of product information across their entire lifecycle. DIDs offer a unique opportunity to enhance the identification process by providing a secure, decentralized method of identity verification and management. This integration could significantly improve the reliability and integrity of DPP systems, ensuring that product data remain consistent, tamper-proof, and accessible across various stages of the product's life [7]. In [8], the authors discuss the use of DIDs for creating product passports, focusing on their application in uniquely identifying products. As noted, DIDs offer several empowering properties compared to traditional identifiers like domain names or UUIDs. They are designed to be decentralized, cryptographically verifiable, persistent, and resolvable, making them ideal for identifying products and linking to their verifiable metadata.