esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
These techniques enabled the development of compact array structures with improved bandwidth and greater control over element placement. In [19], a fractal-based MIMO radar array was implemented using a combination of Fudgeflake and Gosper Island geometries to form transmit and receive sub-arrays. This configuration created virtual elements that enhanced angular resolution and suppressed sidelobes. Previous works in antenna array synthesis have shown that effective sidelobe-level (SLL) suppression plays a critical role in reducing interference, improving signal-to-noise ratio (SNR), and enhancing realized gain in targeted directions. Techniques such as weighted multi-beam synthesis [20] and multi-beam transmit array design for 5G platforms [21] have demonstrated that improving pattern control not only suppresses SLL but also contributes to better spectral efficiency and spatial reuse. The application of Eisenstein geometry and Eisenstein-based fractals in wideband array design was first introduced in [22]. This approach builds upon the Eisenstein integer packing described in [23], which uses a set of complex numbers to form a hexagonal lattice that uniformly fills the complex plane, ensuring equal spacing between adjacent elements, facilitating array miniaturization and reducing mutual interference. The research in [22] involved analyzing multiple fractal stages with arrays of 16, 64, 256, and 1024 elements. In that prior study, array thinning was performed using a genetic algorithm combined with least mean squares (GA-LMS) optimization, resulting in sub-aperture designs at each fractalstage. The thinned arrays with 10, 40, 160, and 640 elements prioritized sidelobe-level (SLL) minimization at a single operating frequency, at the cost of reduced performance across wideband frequencies. This work aims to advance antenna array design for wideband applications by leveraging the geometric characteristics of Eisenstein fractals. The methodology focuses on optimizing wideband performance while minimizing the number of active elements through efficient spatial packing and array thinning. By capitalizing on the self-similarity and hexagonal symmetry inherent to Eisenstein geometry, a scalable and performance-optimized approach to array configuration is introduced. The remainder of this paper is structured as follows. Section 2 presents the proposed methodology for antenna array design, including a detailed description of the Eisenstein fractal generator (Section 2.1), the array factor formulation and parameter calculations (Section 2.2), and the proposed multi-objective wideband optimization framework (Section 2.3). Section 3 reports the simulation results for two design scenarios, evaluating their performance across different fractal growth stages. Section 4 discusses the comparative advantages of the proposed designs relative to existing wideband antenna array methodologies. Finally, Section 5 concludes the paper by summarizing the main findings and outlining potential directions for future research.
## 3 Results and Discussions
### Prediction of Traffic Volume at These Four Signalized Road Intersections
* **Signalized Road Intersection 4**  
From Figure 12, another significant result when observing road intersection 4 is that the periods 05:00:00-09:59:59, 10:00:00-14:59:59, and 15:00:00-19:59:59 are all regarded as on-peak periods, which is when there is an increase in traffic volume.
## 6 Data Retrieval  
### Retrieval Pipeline  
To overcome the issue of limited content availability, we have implemented server-based Python scripts to retrieve 3D models from Sketchfab, Europeana, the HS Mainz 3D Repository, and the UrbanHistory4D repository through API calls by name or location. To ensure legal compliance, we selected Creative Commons CC0 or CC-BY [137] licensed content only.  
The retrieval service operates as a series of server-side scripts written in Python and PHP, feeding into a SQL database and a Unix file storage system. The scripts gain input from (1) the GeoNames \"Major cities of the world\" [138] dataset, (2) a keyword-based retrieval from Europeana, (3) JSON-based database retrieval for Mainz and UrbanHistory4D, and (4) locations called by users in our mobile 4D city app, which are resolved into place names using GeoNames [139]. Scripts 1 and 2 are operated on a one-time basis, and 3 and 4 work as cron jobs. As of early 2023, a total of 3922 3D models have been retrieved from these sources (see Table 4).
# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
## 4 Simulation and Comparative Analysis
### Turn-to-Turn Fault Scheme  
In this scheme, the fault happened at t = 3 s. The number of short turns on the primary winding varied between 0.8% and 16% from the total winding turn. The turn-to-turn fault did not make a difference in the terminal current of PT or voltage while in primary or secondary current (see Figure 8). Therefore, the conventional differential protection algorithm failed to detect this kind of fault. In the early stage of the short turn level, the conventional algorithm was incapable of detecting the fault because the  
Figure 6: The TTF in 3 phase power transformer model.  
Figure 7: The equivalent circuit for the short turn in primary winding phase A.  
differential current was too small and did not exceed the predefined threshold value. The conventional algorithm was also unable to distinguish whether the fault caused by an internal or external fault.  
The negative-sequence directional algorithm can give a correct decision when the TTF occurs. When the turn-to-turn fault occurred, the negative -sequence current in the primary side (Ip2) increased significantly and increased slightly on the secondary side (Is2) (see Figure 9). The phase difference between the negative sequence current in the primary and secondary side decreased to between +60\({}^{\circ}\) and +85\({}^{\circ}\) like in Figure 10. So that the negative sequence directional algorithm declares internal fault. EPVA current (\(i_{epva}\)) rose to 104.8 A (see Figure 11), and the differential negative sequence current increased to 85.55 A (see Figure 12). The result for the EPVA and differential negative sequence algorithm showed the correct result to decide the fault. However, these algorithms cannot distinguish between normal and external fault conditions discussed in the external scheme.  
Figure 8: Fundamental component under 2.4% TTF condition.  
Figure 9: The negative sequence current from the primary and secondary side under 2.4% TTF condition.  
For comparison, the result table shows that in the early stage of the TTF, with a 0.8 and 1.6% short turns level, the EPVA and differential negative sequence current were more sensitive to detecting the fault. The fuzzy algorithm showed that the internal fault crisp value was more significant than the external fault (see Figure 13). The internal fault crisp value was 1559, and the external fault crisp value was 53.42. Based on the result, the fuzzy algorithm declared an internal fault state at the 1.6% turns-to-turns short level.  
Figure 11: EPVA current (\(i_{\text{cpu}}\)) under 2.4% TTF condition.  
Figure 12: The differential negative sequence current under 2.4% TTF condition.  
Figure 10: The phase difference between Ip2 and Is2 under 2.4% TTF condition.  
Figure 13: The internal and external crisp value under 1.6% TTF condition.
## 1 Introduction
### Using a Grounding Point as the Voltage Reference Point  
This is the favored solution adopted and recommended by many manufacturers of power analyzers for situations in which either the load neutral point (n) or the source neutral point (N) is not accessible.  
The instantaneous power registered by a power analyzer placed at the PCC of the power system in Figure 3 when the grounding point (G) is used as the voltage reference point can be expressed as follows:  
\[p_{pcc}(t)=v_{AG}i_{A}+v_{BG}i_{B}+v_{CG}i_{C}. \tag{12}\]  
The result of Equation (12) is different from the load and source instantaneous powers, in general. Hence, the absolute error of the measured load instantaneous power when the grounding point (G) is used as the voltage reference point instead of the load neutral point (n) is given by  
\[p_{nl}^{G}(t)=p_{l}(t)-p_{pcc}(t)=(v_{An0}-v_{AC0})\cdot(i_{A}+i_{B}+i_{C}), \tag{13}\]  
where \(v_{Gn}=v_{An0}-v_{AG0}\), according to [PERSON]'s theorem; the load zero-sequence voltages (\(v_{An0}\)) are determined by Equation (5), and \(v_{AC0}\) is the zero-sequence component of the set (\(v_{AG},v_{BG},v_{CG}\)). If the grounding point is ideal, \(v_{AG0}=0\), and Equation (13) can be simplified as  
\[p_{nl}^{G}(t)=v_{An0}\cdot(i_{A}+i_{B}+i_{C})=p_{nl}(t). \tag{14}\]Hence, the absolute error in measuring the load instantaneous power using an ideal grounding point as the voltage reference point is equal to the load component of the neutral-displacement instantaneous power.  
Similarly, because \(v_{GN}=v_{AN0}-v_{AG0}\), the absolute error of the measured source instantaneous power when the grounding point (\(G\)) is used as the voltage reference point instead of the source neutral point (\(N\)) is given by  
\[p_{ns}^{G}(t)=p_{s}(t)-p_{pcc}(t)=(v_{AN0}-v_{AG0})\cdot(i_{A}+i_{B}+i_{C}), \tag{15}\]  
where the source zero-sequence voltage (\(v_{AN0}\)) is determined by Equation (5). Likewise, if the grounding point (\(G\)) is ideal, Equation (15) can be reduced to  
\[p_{ns}^{G}(t)=v_{AN0}\cdot(i_{A}+i_{B}+i_{C})=p_{ns}(t). \tag{16}\]  
Hence, the absolute error in measuring the source instantaneous power when the grounding point (\(G\)) is used as the voltage reference point instead of the source neutral point (\(N\)) is the source component of the neutral-displacement power.  
From Equations (13) and (15), it can be noted that  
\[p_{n}(t)=p_{nl}^{G}(t)-p_{ns}^{G}(t)=p_{l}(t)-p_{s}(t). \tag{17}\]  
The neutral-displacement instantaneous power is the difference between the measurement errors of the load and source instantaneous powers when the voltage reference point is another point other than the load and source neutrals points, respectively.
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning
### Privacy Attacks in FL  
Federated learning [3] introduces the assumption that it is safer for user data privacy thanks to its topology. Each client shares its model updates instead of its dataset, preventing user data interception while communicating with the server or peer [38]. However, several studies have shone light on many attacks that still compromise data privacy. This section provides the answer to the RQ4 question by presenting several popular attacks, allowing user data to be inferred or the local model to be reconstructed.
## 1 Introduction
More recently, applications of various implementations of visual servoing in the field of marine robotics can be seen in [24], where a visual servo control approach was successfully transferred from an industrial manufacturing context to underwater robotics tasks autonomously performed by a subsea hydraulic manipulator mounted on a work-class ROV. In [25], visual servoing is used as a method of achieving station-keeping in an underwater environment, with unmarked natural features acting as targets for the image tracking algorithm and enabling an unmanned underwater vehicle (UUV) to hover above planar targets on the sea bed. In [26], a more complex hierarchical control structure featuring visual servoing is introduced to achieve dynamic positioning of a fully actuated underwater vehicle. Recent related work [27] is focused on the analysis and improvement of long-term autonomy of the subCULTron system, in particular by using the potential of the energy exchange capabilities between aPad and aMussel robots. The paper focuses on efforts to create a means of studying the effects of decision-making and scheduling algorithms in a logistically viable way, and also describes data acquisition experiments performed during efforts to model subCULTron swarm agent batteries as well as validate the long-term potential of the agents thanks to low-power and sleep modes of operation. Due to the limited processing power of swarm agents, and taking into account minimizing energy consumption as an important goal during long-term swarm operations, approaches to both hardware and software design in the subCULTron swarm needed to be simple yet effective. This paper presents an overview of in-house developed hardware and software with the purpose of enabling autonomous docking between different agents in the robotic swarm using a visual servoing approach and fusing the results of classical image processing methods and a neural network trained for object detection. It also describes several successfully performed real-world marine environment trials with completely autonomous agent cooperation resulting in successful wireless energy transfer in different conditions. The paper is organized as follows: Section 2 describes the development of the system, with Section 2.1 focusing on the evolution of the mechanical segments of the aMussel and aPad robots relevant to the docking algorithm, and Section 2.2 describing the software segment and overall control structure of the developed system and giving insight into its lower-level controllers for surge and yaw speeds. Section 2.3 gives an overview of image processing algorithms and models used to achieve object recognition and visual servoing. Section 2.4 describes the docking system implementation of a filter node featuring an extended Kalman filter for sensor fusion and object tracking. Section 3 contains a detailed writeup of a structured docking experiment in controlled conditions and an experiment undertaken in challenging real-world conditions, including results and commentary. Finally, some conclusions on the work are drawn in Section 4.
## 3 Numerical Simulation Calculation Method and Experimental Validation  
### Computational Model and Meshing  
The physical 3D model was simplified and imported into ICEM CFD and divided into an inlet fluid domain, a moving vane fluid domain, a rear guide vane fluid domain and an outlet fluid domain according to the actual airflow area, as shown in Figure 4 below. To accurately predict the actual operating conditions of the flow fan, the length of the inlet and outlet basins was extended to five times the existing channel and outlet diameter size.  
In the numerical simulation calculation, to save calculation time and resources and to choose a suitable grid model to ensure accurate results, this paper adopts a hexahedral structured grid for the diagonal flow fan motor vane and rear guide vane. An unstructured  
Figure 4: The calculation domain division.  
Figure 3: A structure diagram of each component.  
Figure 2: A structure diagram of a small diagonal flow fan.  
grid is used for irregular structures such as inlet and outlet, flow collectors and diffusers. Figure 5 shows the grid division of each part.  
The internal circulation fluid domain of the flow fan has meshed, and the main components are the moving vane and the rear guide vane. TurboGrid is a professional turbine lobe grid channel meshing software that allows high-quality structured meshing of complex-shaped blades to improve the accuracy of the calculation. The drawn mesh file is imported into the ICEM CFD for analysis.  
The moving vane area grid is shown in Figure 4(a), and the rear guide vane area grid is shown in Figure 4(b); the inlet and outlet fluid domains, which are more regular in shape, are divided into O-Block grids. Due to their complex and irregular structure, non-structural meshing is used for the rest of the overflow components. When dividing the mesh, the first node is required to be arranged in the region of the viscous boundary layer so that it is \(y^{+}\leq 5\), where the empirical formula for \(y^{+}\) is [11]:  
\[Y_{wall}=6\left(\frac{V_{ref}}{v}\right)^{-\frac{7}{8}}\left(\frac{L_{ref}}{2} \right)^{\frac{1}{8}}y^{+} \tag{1}\]  
\(V_{ref}\) is the reference velocity, m/s; \(L_{ref}\) is the reference length, m; \(v\) is the kinematic viscosity of the fluid, m\({}^{2}\)/s; \(y^{+}\) is a dimensionless parameter. According to the empirical formula, the height of the first layer of this boundary layer grid should be less than 0.65 mm.  
Figure 5: A computational grid model.
# Cost-Aware IoT Extension of DISSECT-CF
## 2 Related Work  
There are many simulators available to examine distributed and specifically cloud systems. Nevertheless, there are some more specific IoT simulators closer to our approach. [PERSON] et al. [2] have designed the Devices Profile for Web Services Simulation Toolkit (DPWSim), which is a simulation toolkit to support the development of service-oriented and event-driven IoT applications with secure web service capabilities. Its aim is to support the OASIS (Organization for the Advancement of Structured Information Standards) standard Devices Profile for Web Services. SimIoT [1] is derived from the SimIC simulation framework [7]. It introduces several techniques to simulate the communication between an IoT sensor and the cloud, but it is limited to compute activity modelling.  
[PERSON] and [PERSON] [8] introduce several simulation concepts for IoT systems. First, they show how the interfacing between the various cloud providers and IoT systems could be modelled (even including workload models) in a simulation. Unfortunately, they mainly discuss the behaviour of cloud systems that support the processing of data originated from the IoT system. [PERSON] et al. [9] deal with the dynamic nature of IoT systems, they investigate fault behaviours and introduce a fault model for such systems. Although faults are important, the scalability of the introduced fault behaviours and concepts are insufficient for large scale systems.  
[PERSON] et al. [10] introduce a novel infrastructure coordination technique that supports the use of larger scale IoT systems. They build on CloudSim [11] and provide customizations that are tailored for their specific home automation scenarios and therefore limit the applicability of their extensions. [PERSON] et al. [3] proposed IOTSim that supports and enables simulation of big data processing in IoT systems limiting themselves to the MapReduce model. They also presented a real case study that validates the effectiveness of their simulator.  
In the field of resource abstraction for IoT, efforts aimed at the description and implementation of languages and frameworks for efficient representation, annotation and processing of sensed data. The integration of IoT and clouds has been envisioned by [PERSON] et al. [12] and by [PERSON] et al. [13]. They argue that system designers and operations managers face numerous challenges to realize IoT cloud systems in practice, due to the complexity and diversity of their requirements in terms of IoT resources consumption, customization and runtime governance as well as context awareness [14]. We build on these results and target our contribution in the field of cost modelling in IoT Cloud simulations.
## 3 Implementation  
### Multiple Frequencies Lock-In Amplifier  
Supposing that there is a low SNR signal sequence \(s[n]\) containing \(K\) frequency components and noise \(w[n]\):  
\[s[n]=\sum_{k=1}^{K}x_{k}[n]+w[n]=\sum_{k=1}^{K}a_{k}\text{sin}(2\pi\frac{f_{k}} {f_{s}}n+\phi_{k})+w[n] \tag{18}\]  
where \(a_{k}\), \(f_{k  
At this time, through the orthogonal DLIA structure shown in Figure 1, the results of multiplying the measured signal and two quadrature reference signals (19a,b) are as follows:  
\[R_{c}[n] =\frac{a_{m1}B}{2}\{\sin(\phi_{m1})+\sin(4\pi\hat{f}_{m1}\frac{n}{f_{s}}+ \phi_{m1})\} \tag{20}\] \[+\sum_{k=0,k\
eq m1}^{K}\frac{a_{k}B}{2}\text{sin}(2\pi(f_{k}- \hat{f}_{m1})\frac{n}{f_{s}}+\phi_{k})\] \[+\sum_{k=0,k\
eq m1}^{K}\frac{a_{k}B}{2}\text{sin}(2\pi(f_{k}+ \hat{f}_{m1})\frac{n}{f_{s}}+\phi_{k})+w[n]r_{c}[n]\]  
\[R_{s}[n] =\frac{a_{m1}B}{2}\{\cos(\phi_{m1})-\cos(4\pi\hat{f}_{m1}\frac{n}{ f_{s}}+\phi_{m1})\} \tag{21}\] \[+\sum_{k=0,k\
eq m1}^{K}\frac{a_{k}B}{2}\text{cos}(2\pi(f_{k}- \hat{f}_{m1})\frac{n}{f_{s}}+\phi_{k})\] \[-\sum_{k=0,k\
eq m1}^{K}\frac{a_{k}B}{2}\text{cos}(2\pi(f_{k}+ \hat{f}_{m1})\frac{n}{f_{s}}+\phi_{k})+w[n]r_{s}[n]\]  
The IAF filters out the noise and high-frequency interference terms. Then, the amplitude estimation \(\hat{a}_{m1}\) and initial-phase estimation \(\hat{\phi}_{m1}\) can be obtained by Equation (6). After completing the parameters estimation of frequency component \(f_{m1}\), it can be removed from the original signal:  
\[s[n]=s[n]-\hat{a}_{m1}\text{sin}(2\pi\hat{f}_{m1}\frac{n}{f_{s}}+\hat{\phi}_{ m1}) \tag{22}\]  
After that, DFT is processed on the new signal sequence \(s[n]\), and the next spectral line \(p_{m2}\) with largest amplitude can be found out. The estimation values of frequency \(\hat{f}_{m2}\), amplitude \(\hat{a}_{m2}\), and initial phase \(\hat{\phi}_{m2}\) are calculated by using the same frequency estimation method and DLIA structure. The flow of iterative low SNR signal parameters estimation method is shown in Algorithm 2.  
Figure 4: Spectrum of a multi-frequency signal.  
```
1:The signal \(s(t)\), sequence length \(N\), and sampling rate \(f_{s}\);
2:Number of frequency components K;
3:Integral average filter length L;
4:Frequency estimation value \(\hat{f}_{k}\), \((k=1,2,\cdots,K)\);
5:Amplitude estimation value \(\hat{a}_{k}\), \((k=1,2,\cdots,K)\);
6:Phase estimation value \(\hat{\phi}_{k}\), \((k=1,2,\cdots,K)\);
7:for\(k<K\)do
8:calculate frequency value \(\hat{f}_{k}\) using PSO-based frequency estimation method in Algorithm 1;
9:generate reference signals by Equation (19);
10: using orthogonal LIA and L-length IAF estimate amplitude \(\hat{a}_{k}\) and \(\hat{\phi}_{k}\);
11: remove k-th signal by Equation (22);
12:endfor
```  
**Algorithm 2** Pseudo-code of the proposed low SNR multi-frequency signal parameters estimation method.
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 3 Quantum Immune Security Solutions  
Quantum immune, or so called post-quantum, security solutions address both the passive and/or active threat models that we described in the previous section. Quantum immune security solutions apply algorithms that are unaffected by (known) cryptanalysis techniques based on quantum computers. Essentially, the hardness of these solutions is based on something else than integer factorization or discrete logarithm problems. In this section, we will present three alternative solutions--the lattice-based cryptographic algorithms NewHope and Frodo as well as the polar-coding-based physical layer security--and explore their feasibility to provide quantum immunity for energy and resource restricted IoT devices.  
Figure 1: A passive threat model: a physical layer eavesdropper with quantum capabilities in the future.  
Figure 2: An active threat model: a physical layer man-in-the-middle attacker with existing quantum capabilities.