esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Security Considerations for Marine Network Protocols
### Integrity  
Ensuring integrity involves protecting information from being modified by unauthorized parties. Similar to confidentiality, access control and validation are common methods that manufacturers utilize to ensure data and communication integrity. Administrative solutions also include policies, separation of duties and training.  
In addition, many vessels contain multiple devices that generate communication traffic as mentioned in Section 2. Some of them generate big burst of network traffic which exceeds the bandwidth that a ship can handle. This can lead to disruption and latency as services try to compete for bandwidth. One solution to this challenge was to have a master device and multiple remote devices. When a need for high level of bandwidth is detected, a remote device can be temporarily be assigned to an auxiliary (\"spillover\") channel to handle the traffic [11].
## Nomenclature  
\begin{tabular}{l l l l} \(A\) & Area (m\({}^{2}\)) & Symbols & \\ \(c_{p}\) & Specific heat capacity (J/kg\(\cdot\)K) & \(\pi\) & Pi \\ \(D\) & Diameter of tip (m) & \(\rho\) & Density (kg/m\({}^{3}\)) \\ \(h\) & Heat transfer coefficient (W/m\({}^{2}\cdot\)K) & \(\mu\) & Viscosity (Pa\(\cdot\)s) \\ \(I\) & Solar intensity (W/m\({}^{2}\)) & \(\eta\) & Efficiency \\ \(k\) & Thermal conductivity (W/m\(\cdot\)K) & \(\Delta\) & Difference \\ \(L\) & Length of tube (m) & \(\Phi\) & Ratio of fugacity coefficient \\ \(m\) & Mass flow rate (kg/s) & \(\phi\) & Fugacity coefficients \\ \(N\) & Number of tubes & \(\gamma\) & Activity coefficients \\ \(Nu\) & Nusselt number & Subscripts & \\ \(P\) & Pressure (atm) & \(a\) & Ambient \\ \(Pr\) & Prandtl number & ETC & Evacuated tuber solar collector \\ \(\hat{Q}\) & Heat transfer rate (W) & _loss_ & Heat loss \\ \(R\) & Gas constant (J/mol\(\cdot\)K) & _man_ & Outlet from manifold \\ \(Re\) & Reynolds number & _rad_ & Radiation \\ \(T\) & Temperature (\({}^{\circ}\)C or K) & \(s\) & Surface \\ \(T_{int}\) & Log mean temperature (\({}^{\circ}\)C or K) & _St_ & Storage \\ \(U\) & Overall heat transfer coefficient (W/m\({}^{2}\cdot\)K) & _tip_ & Tip \\ \(v\) & Velocity (m/s) & \(i\) & Composition \\ \(V\) & Volume of heating medium (m\({}^{3}\)) & Superscripts & \\ \(x\),\(y\) & Mole fraction & \(l\) & Liquid \\  & _sat_ & Saturated \\  & \(v\) & Vapor \\ \end{tabular}
## 4 Evaluation
### Performance  
The SymCHM with the basic pattern selection algorithm was submitted to the MIREX 2015 discovery of repeated themes and sections task. The results are shown in Table 2. The submitted model learned a six layer hierarchy, where activations of parts on Layers 4-6 were output as the found pattern occurrences.  
In the MIREX 2015 evaluation [20], the two state-of-the art approaches by Velarde and Meredith (VM2) [32] and Lartillot (OL1 ) [34] achieved better overall results. However, the SymCHM outperformed other algorithms on the first piece in the MIREX evaluation dataset and achieved better results than VM2 in pattern occurrence measures, which indicated the model's ability to robustly identify the occurrences of the identified patterns. Compared to other approaches proposed in previous MIREX evaluations, such as NF1'14 [37] and DM1'13 [45], SymCHM found more pattern occurrences, as well a higher number of exact matches. The SymCHM also achieved a higher \(TLF_{1}\) score when compared to NF1'14 submission.  
To increase diversity and decrease redundancy, we introduced the SymCHMerge with an improved pattern selection algorithm. Activations of parts on Layers 2-6 were considered for finding pattern occurrences, where each layer included 16% more parts on average due to the more relaxed learning conditions.  
A comparison between both models on the JKU PDD dataset showed that the SymCHMerge achieved significantly better results ([PERSON]'s test: \(\chi^{2}=7.2,p<0.01\)). It mostly improved in establishment measures, which indicated an improvement of the algorithm's ability to discover at least one occurrence of a pattern, tolerating for time shift and transposition [20]. On the other hand, occurrence measures \(F_{1 occ(c=0.75)}\) and \(F_{1 occ(c=0.5)}\) which evaluated the algorithm's ability to find all occurrences of the established patterns, have dropped by 5%. We attribute this drop to a higher number of established patterns, for which the occurrence measure is calculated. Finally, the absolute precision, recall and F scores significantly increased due to the SymCHMerge's pattern merging procedure and increased pattern diversity.
## 2 Related Work
### Related Work of iBeacon Positioning Methods
#### 2.1.1 Positioning Using Trilateration Method  
Trilateration locates the device by intersecting the distances between the transmitter and the device estimated from RSSI. Experimental results from [PERSON], [PERSON] and [PERSON] [22] showed that while iBeacon has promising accuracy in distance estimation and proximity, signal strength readings vary greatly, depending on the manufacturer, mobile platform (Android/iOS), environmental and deployment factors, and use cases. Using maximum TX power of 4 dBm at 1.2 m height, they found that RSSI readings for Android and iOS platforms decreased at different rates with increasing distances and temporary variation as well. Accuracy analysis of BLE for indoor positioning applications [23] clarifies that low bandwidth of BLE signals is the cause of significant measurement error when using the three designated BLE advertising channels. To eliminate the measurement error in received signals, some researchers enhanced the positioning accuracy using particle filtering [24], Kalman, Gaussian, and hybrid filters [25]. [PERSON], [PERSON] and [PERSON] [26] demonstrated that errors could increase significantly with increased distance when the RSSI signals varied over time. To improve positioning accuracy, previous studies conducted experimental tests in different indoor situations, with various signal database matching and signal-to-distance estimation methods. [PERSON] [27] revealed an average error of 0.43 m when measuring the distance from 0 m to 3 m with a constant speed of 0.01 m/s, and the average percentage error of 41.47%. This indicated that using iBeacon for precise positioning with consideration of height, localisation, geometry and TX power, could lead to fast fading and RSSI fluctuations, because of the logarithmic nature of power-based distance prediction and the low bandwidth of BLE. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [28] achieved positioning accuracy of 0.53 m in a 9 m \(\times\) 10 m area by modelling the distance with exponential function of RSSI and TX power. [PERSON] et al. [29] used two classification algorithms to improve the TX power settings for each iBeacon, and discovered a custom TX power level for each BLE Beacon could greatly improve positioning accuracy.
# Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch
## 2 System Architecture
### Connection-State Processing Function  
In the agriculture field, the vehicle sensor network is not stable. To achieve smooth handoff according to different scenario states when a packet matches rules, the connection-state processing function performs a stateful match by detecting the connection state between a switch and a controller and extending a new match field named connection-state.  
At present, packets match non-stateful rules in an OpenFlow switch, because the match fields of rules only include L2-L4 fields extracted by packet headers and metadata fields like ImPort [21], which aren't related to the state of the network. It is important to add a stateful matching field in the rules. Connection-state processing function realizes that a packet matches a specific rule in different connection states of a controller. To detect connection states and use them properly, there are two main steps: (i) connection state detection; (ii) connection state synchronization and encapsulation.
# Enhanced FANO Structure Based on Tip-Field-Enhancement Theory
## 1 Introduction
High-Q resonance is introduced by the coupling of asymmetric structures [22], resulting in electric charges accumulate at the border. If there are more charges accumulated at the border, the resonance would be enhanced, which means that it can achieve a higher-Q factor or higher resonance intensity. In static electric-field theory, charges gather at the sharpest points. It is possible that if we sharpen the coupling zones of the structure, FANO resonance could be enhanced. In this paper, an enhanced-FANO structure with sharpened tips is discussed, and a positive correlation between sharp structures and a high-Q factor can be observed. The Q factor of the structure could be considerably increased by simply sharpening the coupling tips in an asymmetric split-ring FANO structure while keeping resonance intensity above a reasonable level. The 2D metallic patterns are also easy to fabricate. Therefore, this can be applied to microfluidic chips to increase its sensitivity on particle detection.
# Real-Time Tomography of Gas-Jets with a Wollaston Interferometer
## 2 The Single Beam Wollaston Interferometer Setup
### Experimental Setup for Gas Density Measurements  
The Wollaston interferometer to measure the gas density is set up on a single breadboard (75 cm \(\times\) 125 cm), such that the optical setup and vacuum chamber with the gas jet form a compact and transportable unit.
## 3 Definition of Optimization Problem
### Conic Quasi-Uniform B-Spline  
In previous studies on cable pre-tension force optimization, cable pre-tension force was usually defined as a design variable. The application of the dense cable system greatly increases the number of variables, leading to an increase in the computational cost of optimization algorithms [22; 23]. In order to avoid these shortcomings, the quasi-uniform B-spline curve is used in this study to represent the distribution of cable pre-tension forces [24; 25], as shown in Figure 3. When control points are set, B-spline curves can generate smooth curves between these points, and its shape is mainly affected by neighboring control points.  
Given \(n+1\) control points \(P_{i}\) (\(i=0\), \(1\), \(\ldots\), \(n\)) and a node vector \(U=\{u_{0}\), \(u_{1}\), \(\ldots\), \(u_{m}\}\), conic quasi-uniform B-spline curve (\(k=2\)) is defined as follows:  
\[P(u)=\sum_{i=0}^{n}P_{i}G_{i2}(u)\ 0\leq u\leq 1 \tag{1}\]  
where G\({}_{i,2}(u)\) are the conic B-spline basis functions. The basis functions are given as:  
\[G_{i,k}(u)=\left\{\begin{array}{ll}\begin{cases}1\ if\ \ u_{i}\leq u\leq u_{i+1} \\ 0&\text{otherwise}\end{cases}&k=0\\ \frac{u-u_{i}}{u_{i+k}-u_{i}}G_{i,k-1}(u)+\frac{u_{i+k+1}-u}{u_{i+k+1}-u_{i+1} }G_{i+1,k-1}(u)&k\geq 1\end{array}\right. \tag{2}\]  
And the nodes at both ends of the node vector in the conic quasi-uniform B-spline curve have a repetition degree of \(k+1\). The node vector of the conic quasi-uniform B-spline is as follows:  
\[U=\{0,0,0,u_{k+1},\ldots,u_{m-k-1},1,1,1\} \tag{3}\]  
The number of node vectors (\(m\)), the number of control points (\(n+1\)), and the degree of the basis function (\(k\)) satisfy the relation \(m=n+k+1\). The calculation process of the conic quasi-uniform B-spline curve is summarized as follows:  
1. Determine the number of control points (\(n+1\)) and nodes (\(m+1\)).
2. Define control point location parameters.
3. The basis function corresponding to the \(i\) th control point is calculated from Equation (2).
4. Multiply the control point by the basis function.  
Figure 3: Quasi-uniform B-spline curve.  
Due to structural symmetry, three conic quasi-uniform B-spline curves were used to represent the distribution of cable pre-tension forces on the side span and main span, as shown in Figure 4. By adjusting the coordinates of the control points, the distribution of cable pre-tension force can be changed.
# Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection
## 3 Materials and Methods  
### Theory  
#### 3.1.1 Wavelet Transform Modulus Maxima  
The wavelet transform inherits and develops the idea of localization of the Fourier transform, while overcoming the disadvantages of the window size not varying with  
Figure 1: Partial image of the bridge.  
Figure 2: Overhead image of the bridge.  
frequency. Its main feature is to highlight the characteristics of certain aspects of the signal after the transform, and to gradually refine the signal on multiple scales through the telescopic translation operation, eventually achieving time subdivision at high frequencies and frequency subdivision at low frequencies, which can automatically adapt to the requirements of time-frequency signal analysis and thus focus on arbitrary details of the signal [23; 24; 25]. Therefore, wavelet transform has been successfully applied to many fields, especially the discrete numerical algorithm of wavelet transform, which is widely used in the study of many theoretical problems.  
The modulus maximum Is defined as follow: \(W_{2^{l}}[f(t)]\) is the coefficient of the scale I of the wavelet transform of the signal \(f(t)\), and its modulus maximum is written as \(Max[W_{2^{l}}[f(t_{k_{l}})]],l=1,2,\cdots,L,\)\(l\) is the scale, \(k_{l}=1_{l},2_{l},\cdots,M_{l}\), is the serial number of the modulus maximum on the scale I. The modulus maximums are located at the sudden changed points [26]. The modulus maxima satisfies the 2 conditions in Equation (1).  
\[\left\{\begin{array}{l}|W_{2^{l}}[f(t)]|>|W_{2^{l}}[f(t-1)]|\\ |W_{2^{l}}[f(t)]|>|W_{2^{l}}[f(t+1)]|\end{array}\right. \tag{1}\]  
If there is \(W_{2^{l}}[f(t)]|=|W_{2^{l}}[f(t+1)]|\) the equation \(W_{2^{l}}[f(t+1)]|=|W_{2^{l}}[f(t+2)]|\) must be satisfied as well; and if there is \(W_{2^{l}}[f(t+1)]|=|W_{2^{l}}[f(t+2)]|\), the equation \(|W_{2^{l}}[f(t-1)]|=|W_{2^{l}}[f(t-2)]|\) must be satisfied as well.  
The general steps of the wavelet transform mode maxima method applied in edge detection [27] are as follow: (1) use a fast algorithm, find the multi-scale wavelet coefficients of the image; (2) calculate the modulus and magnitude angle of each pixel, respectively; (3) calculate the mode maxima point after wavelet transform; (4) set the threshold, and where the modulus value is greater than the threshold is the edge point; and (5) output the multi-scale edge of the image. The specific workflow is shown in Figure 3, where the modulus and magnitude angle in step 2 are calculated, as in Equations (2) and (3).  
\[M=\sqrt{w_{1}^{2}+w_{2}^{2}} \tag{2}\]  
\[A=\arctan(w_{2}/w_{1}) \tag{3}\]  
Figure 3: Wavelet transform modulus maxima flow.
## 3 Numerical Example
### Data Organization
As the number of iterations increases, the Pearson correlation coefficient gradually increases. After 600 iterations, the Pearson correlation coefficient value reached 99.5%, and various indicators reached stability. It indicates that the network has converged, has strong positive correlations between features and variables, and can capture the similarity between the predicted output and the expected data.
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 4 Method
### System Model  
When it comes to retaining facial semantics, concealing the identity of individuals is complicated work. Previous approaches do not take the possible loss of the utility of images into consideration in the process of removing identical information. As a result, for specific tasks, the quality of service will be far from expected. To tackle the trade-off between privacy and semantics, we propose the Facial Privacy and Semantic Preservation (FPSP) model with a delicate architecture design and loss evaluating facial semantics. Figure 1 showcases the general architecture of the model.  
Firstly, to all images in the dataset, we cropped faces from images and generated a 128-dimensional face feature embedding for each face through the network. Then, we classified images into 15 clusters according to the face embedding. In the next stage, in each cluster, we generated a surrogate face for every image and applied four protection methods to every face image to construct de-identified datasets. Following that, we input these datasets into a generative model guided by a semantic-related quality evaluation. In the training process, the semantics of generated images were evaluated to calculate the loss, which is a part of the loss function. In maintaining service quality, backpropagation is a significant part that helps update the output continuously.
## Security Analysis
### Integrity  
To preserve the integrity of the data in this paper, we used a public key, a private key, and digital signatures. When a malicious person modifies the data, the receiver can verify whether the data has been modified or not by verifying the signatures. The user can use the receiver's public key to encrypt data for transmission. Malicious people are not able to obtain the encrypted data because they do not have a valid private key, so he/she is unable to access and tamper with the data.  
In the information transmission process, preventing the modification of transmitted data is as follows: Equations (21), (22), (30), (31), (35), (36), (44), (45), (53), (54), (58), (59), (67), (68), (76), (77), (85) and (86).  
Scenario: The attacker intercepts the message from the SCC authority to the accessing party and tampers with the message before sending it to the accessing party.  
Analysis: The attacker will fail because our proposed method uses the receiver's public key to encrypt the data, and only the receiver's private key can decrypt the data. Therefore, when the attacker intercepts the message, the attacker cannot decrypt the message and maliciously modify the data.
# NB-IoT for D2D-Enhanced Content Uploading with Social Trustworthiness in 5G Systems
## 1 Introduction
In this paper we take forward our research, investigating among other issues the use of the recent narrowband IoT (NB-IoT) standard [11], which is currently considered the reference cellular technology for IoT communications for the next 5G systems. The _trust_ constraints for successful D2D-based content uploading are modeled by including sociality among devices, as a measure of _reliability_, and historical _reputation_. The objective is to define multihop D2D topologies that meet the constraints of reciprocal user equipment (UE) proximity for the direct links activation and, at the same time, of an adequate _trust_ level among the cooperating devices. Through simulation-based performance evaluations, we show that it is possible to significantly reduce the impact of malicious behaviors on the performance of involved devices, with gains in terms of data loss, energy consumption, and data uploading time. The remainder of the paper is organized as follows: Section 2 reviews the related work; Section 3 introduces the research background and motivation; the algorithmic solution for the definition of trusted D2D cooperative topologies is given in Section 4; a detailed description of the proposed trust model and the sociality concepts is given in Section 5; and numerical results and conclusions are provided in Section 6 and Section 7, respectively.
# Global IoT Mobility: A Path Based Forwarding Approach
## 6 Conclusions  
This paper has presented a global mobility management solution for IoT networks that exploits a path-based forwarding fabric together with mechanisms from Information-Centric Networking. The solution offers micro (intra-domain) and macro (inter-domain) mobility services with a path-based forwarding infrastructure at its heart. The solution offers the flexibility required in many IoT scenarios to support a range of different protocols at the edge such as HTTP, CoAP, TCP or IPv4/v6, while the NAP serves as an entry gateway point to the ICN network and maps the chosen protocol abstraction to ICN. The solution also addresses the fact that most IoT applications and use cases today are no longer centred around end host connectivity but rather around the information content available. Therefore, emerging mobile Information-Centric IoT use cases (specifically IoT sensory applications) such as healthcare, marine cartography, habitat conservation, climate change, etc, can be supported more efficiently.  
Conceptualization, M.A.-K.; methodology, M.A.-K. and M.I.B.; software, M.A.-K.; validation, M.A.-K. and H.M.P.; formal analysis, M.A.-K. and R.A.-Z.; investigation, M.A.-K. and R.A.-Z.; resources, M.A.-K. and A.M.A.; data curation, R.A.-Z.; writing--original draft preparation, M.A.-K.; writing--review and editing, M.A.-K., A.M.A., H.M.P. and M.I.B.; visualization, R.A.-Z.; supervision, M.H.; project administration, M.H.; funding acquisition, M.A.-K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: Total signalling cost (SC) and packet delivery cost (PDC) for IP-over-ICN Micro and global mobility support.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 1 Introduction
To summarize the contributions of this paper,  
* we proposed a novel AAN system design with the FRL-enabled UAV and the LEO satellites,
* we presented FRL-enabled UAVs which find areas with high traffic demand based on traffic map, and
* we validated that the UAV-aided AAN provides more network resources and has less latency than the satellite-only AAN. This paper is organized as follows. In Section 2, we describe preliminary knowledge and studies related to our research. We explain our proposed system and give detailed explanations about the system design and learning algorithm in Section 3. After that, we explain the implementation, experiments, and performance evaluation results in Section 4. Finally, Section 5 concludes this paper with explaining remarks and future directions.