esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 5 Experiments
### Datasets  
We utilize three datasets--two real-world datasets and one synthetically generated dataset--to evaluate the robustness, adaptability, and scalability of Adaptive-DecayRank under diverse graph structures and anomaly densities. These datasets were chosen to represent different types of dynamic graphs, including network intrusion detection, botnet traffic, and synthetic evolving networks with controlled anomaly injection. A summary of dataset attributes is provided in Table 3.  
**DARPA:** DARPA [41] contains 4.5_M IP-IP_ communications between 9.4\(K\) source IPs and 23.3\(K\) destination IPs over 87.7\(K\) timestamps, with each node representing an IP address, each edge representing network traffic, and each _IP-IP_ communication represented as a directed edge, (timestamp, srcIP, destIP, attack). Anomalies in this dataset primarily consist of network attacks, including Denial-of-Service (DoS), remote-to-local (R2L), user-to-root (U2R), and probing (scanning) activities [41]. These anomalies were labeled using ground-truth attack logs provided in the dataset, where each attack instance is _timestamp_, allowing for an evaluation of how well the algorithms detect real-time intrusion attempts.  
**CTU-13:** The CTU-13 dataset [42] consists of 13 scenarios capturing botnet traffic mixed with normal background traffic. It includes approximately **100** million _IP-IP_ communications, where nodes represent IP addresses, and directed edges signify network flows. Anomalies in CTU-13 correspond to botnet activity, which involves infected machines communicating with command-and-control (C&C) servers, scanning targets, and launching attacks [43]. The dataset's PCAP (packet capture) files were processed to extract bidirectional NetFlows (_timestamp, srcIP, destIP, and label_). We classify botnet-related connections as anomalies, leveraging the dataset's ground-truth labels for evaluation.  
**RTM Synthetic Graph:** The RTM dataset was synthetically generated using the Recursive Tensor Model (RTM) [44], where a Kronecker graph generation process is used to simulate realistic time-evolving graph structures. The generated dense graph contains 512 nodes and approximately 19.6 million edges across 1000 timestamps. To assess the robustness of our approach, we inject anomalies at two levels: a **10**% **injection** of structural node anomalies, disrupting the normal connectivity pattern, and a **30**% **injection** of a higher concentration of anomalies to test the model's robustness against varying anomaly densities and structural changes in dynamic graphs. The dataset statistics, including the number of nodes, edges, timespans, snapshots generated, and anomaly types, are summarized in Table 3.
## 4 Case Study: Validation of the Evaluation Approach in a Controlled Environment
### Description of the Case Study  
The validation has been performed based on the results obtained in a simple case which has been created for this aim as shown in the table below (Table 7).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Value** & **Picture** \\ \hline \multirow{2}{*}{Surface} & Wall surface: 75.60 m\({}^{2}\) & \\ \cline{2-3}  & Roof surface: 48.00 m\({}^{2}\) \\ \hline Spaces & 1 \\ \hline \multirow{2}{*}{Elements} & 4 walls \\ \cline{2-3}  & 1 roof \\ \cline{2-3}  & 0 windows \\ \hline \hline \end{tabular}
\end{table}
Table 7: Description of the case study.  
A total of 14 ECMs have been combined to create 178 scenarios that combine the measures. The measures selected are the following:  
* 4 types of external wall insulation with different thicknesses of insulation (50-100-150-200 mm)
* 4 types of internal wall insulation with different thicknesses of insulation (40-60-80-100 mm)
* 4 types of roof insulation with different thicknesses of insulation (40-60-80-100 mm)
* Replacement of the existing energy system with the installation of a natural gas boiler
* Installation of PV panels on roof  
The logic followed to generate the 178 candidate retrofitting scenarios considered different combinations of the measures listed above. First each passive measure has been considered separately, and then to each separate passive measure an active or renewable measure has been added. Next, the combination of passive measures has been explored (external/internal insulation + roof), without considering the implementation at the same time of internal and external insulation to facades. To it also active and renewable measures have been added. The list of scenarios is depicted within Table 8.
## 2 Materials and Methods
### Altimeter Error Clearance  
The ICAO PANS-OPS Volume III [19] states that _\"The combination of strong winds and mountainous terrain can cause local changes in atmospheric pressure due to the Bernoulli effect. This occurs particularly when the wind is across mountain crests or ridges. It is not possible to make an exact calculation, but theoretical studies (CFD Norway, Report 109.1989) have indicated altimeter errors (...)\"_. These altimeter errors are shown in the table below.  
From the values in Table 1, a function can be defined to obtain the altimeter error for any wind speed in order to estimate an altimeter error clearance for each phase of flight, according to its wind speed, as proposed by the ICAO.  
In Figure 1, the relation between the altimeter error in feet and the wind speed in knots is graphically represented, as is the trend line that best fits the function, obtaining an equation that relates these values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Wind Speed [kt]** & **Altimeter Error [ft]** \\ \hline
20 & 53 \\
40 & 201 \\
60 & 455 \\
80 & 812 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Altimeter error as a function of wind speed  
Figure 1: Altimeter error as a function of wind speed.  
Several functions were tested in MATLAB R2023b to find the one that best fit the values in Table 1, finding that the best one was a degree 3 polynomial function, resulting in the expression defined as follows:  
\[\text{F}(\text{x})=-6.25\cdot 10^{-5}\;\text{x}^{3}+0.14\;\text{x}^{2}-0.825\; \text{x}+14 \tag{1}\]  
where the value \"x\" is the known wind speed in knots, and \"F(x)\" would be its corresponding altimeter error in feet. The goodness of fit obtained from this function is as follows:  
* SSE: 8.1284\(\cdot 10^{-27}\);
* R-square: 1;
* Adj R-square: 1.  
In this way, the altimeter error can be determined for any wind speed value. This altimeter error is proposed to be added to the turbulence clearance, and it can be obtained by using the graph or the table below or by applying the formula provided, using in both cases the wind speed specified by the ICAO for each phase of flight.  
Finally, some values for the altimeter error [ft] as a function of wind speed [kt], from 10 kt to 80 kt, are shown in Table 2.
## 3 Methodology
### Preliminaries
#### 3.1.3. Predictive Coding  
Predictive coding, utilized in data compression, minimizes the data needed to represent information by transmitting or storing only the information that cannot be accurately predicted. Initially, it identifies repetition and similarity within the data, generates predicted values using a model, and compares them with actual values to derive residuals. These residuals, ideally smaller in size than the original data, facilitate more efficient transmission or storage [25]. The accuracy of the chosen predictive model significantly influences the effectiveness of predictive coding. It particularly excels in scenarios with high correlation or predictability between successive data points.
## 3 Composite Metric Routing Approach
### The Proposed Greedy Implementation
For the current permanent node, all neighboring nodes are considered and their tentative composite metric values (through the current permanent node) are calculated. The tentative composite metric value for each neighbor is compared against the corresponding current value in Step 6, and the smaller one is chosen. If the tentative composite metric value of a neighboring node (through the current permanent node) is found to be smaller than its corresponding current composite metric value, the composite metric value of this neighboring node is updated in Steps 7-9, and its predecessor node along the current path is updated to be the current permanent node. The latter is performed in Step 10. After exploring all the nodes, the permanent set of nodes will represent the set of all paths from the source \(s\) to _every_ other node in the graph. However, the path from node \(s\) to the particular destination node \(f\) can be constructed by tracing the node labels backwards, starting from node \(f\) until node \(s\) is reached. As discussed earlier, the proposed composite routing metric is _monotone_, but _not_ isotonic. The former guarantees the convergence of Algorithm 1. In spite of the latter, however, we have observed that Algorithm 1 is indeed able to find the optimal path in the vast majority of the numerical experiments. Moreover, our numerical results indicate that Algorithm 1 significantly outperforms the ACO-based benchmark. Finally, it is also worth noting that the link energy cost (8) depends on factors that are related to:  
1. the distance/length of the link;
2. the angle of inclination of the link;
3. the UGV itself; and
4. the soil trafficability component (RCI), which depends on the weather humidity conditions. The first three factors above are clearly fixed and are assumed to be known in advance. Factor (4) depends on the weather humidity conditions, and can be calculated using Figure 2 and following the techniques in [21, 26]. This justifies the assumption that the energy costs (8) are available prior to navigation. If the weather humidity conditions change, it is possible to dynamically update the RCI values depending on the current weather humidity. Consequently, the energy-cost (8) and composite metric values (11) for the terrain links can be updated as well. As will be seen in the next section, Algorithm 1 terminates in a few seconds for soil graphs with thousands of nodes and tens of thousands of links. Thus, the algorithm can be re-invoked dynamically whenever the weather humidity conditions change.
## 7 Security Analysis and Cryptographic Guarantees
### Cryptographic Assumptions  
The security of the protocol is predicated on the following well-established assumptions:  
* Elliptic Curve Discrete Logarithm Problem (ECDLP): Given a point \(Q=[\kappa]P\) on an elliptic curve \(E/\mathbb{F}_{2^{m}}\), it is computationally infeasible to recover the scalar \(\kappa\) for appropriately chosen \(P\) and curve parameters.
* Collision Resistance of the Hash Function: The function \(H(\cdot)\) behaves as a random oracle and is resistant to collision and preimage attacks.  
* IND-CPA Security of Asymmetric Encryption: The encryption function \(\mathsf{Enc}_{\mathsf{sk}}(\cdot)\) is semantically secure against chosen plaintext attacks.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 3 Problem Formulation and Existing Challenges
### Overview of WSN and IoT Communication Systems
#### 3.1.3 Clustering in IoT Communication System  
The use of clustering approaches for IoT communications under the 5G environment is determined by analyzing the main differences between WSNs and IoT based on different features. First, while WSNs were originally established for monitoring and collecting data for an exclusive environment such as military surveillance [21], health monitoring [22], IoT communication systems are applied in heterogeneous environments including homes, buildings, smart cities [23], and smart grids [24; 25; 26; 27]. Following their applications, the amount of data transmitted in the WSNs is relatively smaller than in the IoT communication systems, which require a very high bandwidth to accomplish their goal. Furthermore, while the wireless sensor nodes under the WSNs are generally assumed to be homogeneous in terms of their battery, memory, sensing, and processing capability, senor nodes in IoT applications are heterogeneous and are no longer restricted to sensor basic features, rather they have diverse characteristics and functionalities. Another attribute that makes WSNs different from IoT devices is the communication characteristics: communication range, communication band, and the consumption of transmission power.
# Contribution of Etalon Observation to Earth Rotation Parameters under a New Observation Scenario
## 3 Results
### Station Coordinates
However, the ratios of the observation numbers of Etalon satellites to LAGEOS satellites of station Komsomolsk (1868) and station Altay (1879) are 48.3% and 54.4%, respectively. For station Komsomolsk (1868), most of the residuals for the LAGEOS+Etalon solutions are smaller than those of the LAGEOS-only solutions for all three components. The RMS values of the residuals of the N, E, and U components for the LAGEOS+Etalon solutions are 3.1 cm, 2.0 cm, and 2.3 cm smaller than those of the LAGEOS-only solutions. Different from station Komsomolsk (1868), the RMS values the of residuals for station Altay (1879) in horizontal components of LAGEOS+Etalon solutions are 1.02 cm and 0.16 cm larger than those of the LAGEOS-only solutions, while the RMS in the U direction is 1.32 cm lower than that of the LAGEOS-only solutions. The different results for the horizontal and vertical components for station Altay (1879) should be studied. This part of the results shows that the increase in Etalon observations from the new tracking scenario can improve the station coordinates only for some specific stations that are dedicated for high-orbiter satellite tracking. Figure 5: Differences in the repeatability of station coordinates between LAGEOS-only solutions and LAGEOS+Etalon solutions in 2019. Figure 6: Residual series of the N, E, and U components of the station coordinates for station 7090 after the Helmert transformation. Figure 7: Residual series of the N, E, and U components of the station coordinates for station 8834 after the Helmert transformation.
# Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene
## 3 Methods  
### Traffic Police Gesture Recognition Network  
The traffic police gesture recognition method proposed in this paper was based on the ST-GCN. Figure 3 shows the complete process of traffic police gesture recognition:  
1. Use openpose algorithm to process collected videos containing gestures used by traffic police; extract skeleton data.
2. Construct the AGS on the collected skeleton data, extract the spatial-temporal features of gestures used by traffic police with the improved ST-GCN, and recognize gestures used by traffic police.  
The traditional ST-GCN model designed a universal skeleton representation for action recognition: a spatial-temporal skeleton graph. The nodes of the skeleton graph are human joints, and edges in the graph have spatial-temporal features. Edges can be divided into two categories: the connection between the human joints using bones is the first kind of edge, and the connection of the same node in different frames is the second kind of edge, as shown in Figure 4. In the spatial-temporal skeleton graph of the ST-GCN, the second kind of edge connects the same joints, which have a linear relationship, meaning it overcomes the requirement for the manually designed extraction of joint features or the design of traversal rules of joints in traditional methods.  
Figure 3: The complete process of traffic police gesture recognition.  
However, there are some drawbacks to the ST-GCN method, which are as follows:  
1. The skeleton graph, which is the physical structure of the human body, is predefined. It cannot ensure that it is the best skeleton graph for traffic police gesture recognition; for example, the dependency between the two hands of a traffic police member cannot be captured.
2. There are layers in the structure of GCN; different semantics are contained in different layers. However, the graph topology structure of the ST-GCN model is fixed in all the network layers and extracts the feature of the same topology, which causes them to lose the flexibility of information modeling.
3. A fixed graph structure cannot be the optimal representative method for different gesture actions; joints have different levels of importance in different gestures.  
Based on the analysis above, this paper extended the topology structure of the traffic police skeleton graph and learned features of gestures used by traffic police from the spatial dimension and temporal dimension. The attention mechanism was also introduced to learn the importance of each joint in different traffic gestures. The network model of traffic police gesture recognition proposed in this paper is shown in Figure 5.  
Figure 4: The spatial–temporal graph of skeleton sequence.  
Figure 5: The architecture of traffic police gesture recognition network.
## 3 Results and Discussion
### Results of Scenario 2: Parking with Rotation Optimization up to 45 Degrees on Parking Area 1  
In this scenario, vehicle rotations were optimized, allowing a maximum rotation of up to 45 degrees. According to Tables 1 and 2, MDE and the CSA achieved the best results, successfully placing a maximum of 13 vehicles. ABC and PPSO placed up to 10 vehicles, while PSO and GWO failed to produce valid solutions. HHO was able to place a maximum of 11 vehicles.  
As shown in Table 3, the MDE algorithm provided the fastest solution, placing 13 vehicles in an average of 13.05 s. Meanwhile, the CSA took 42.21, and HHO required 260.72 s to place 11 vehicles, indicating a significant performance gap. The cumulative maximization time for MDE was 29.82 s, whereas the CSA required 85.63 s. PPSO's total time for placing 10 vehicles was 46.18 s.  
Compared to Scenario 1, this scenario introduced additional complexity due to the inclusion of angle optimization, increasing the number of parameters to be optimized. This increased complexity resulted in longer computation times and reduced success rates for most algorithms except MDE. Surprisingly, MDE performed even better in this scenario, demonstrating how different optimization methods can yield varying results under different conditions.  
According to Table 5, MDE achieved an 87% success rate in placing 12 or 13 vehicles, making it the most reliable algorithm. The CSA followed with 67%. MDE successfully placed 13 vehicles in 43% of trials, compared to 10% for the CSA. Other methods, including PPSO and HHO, performed better than GWO and PSO but remained notably less competitive than MDE and the CSA.  
These results indicate that MDE is the most effective algorithm in terms of both speed and success rate. While the CSA demonstrated competitive performance under certain conditions, MDE was selected as the best optimization method for Scenario 2 due to its superior placement efficiency and stability.  
Overall, allowing rotation up to 45 degrees enabled a higher number of vehicles to be placed in the parking area. However, this optimization also posed challenges for some algorithms, with ABC, GWO, HHO, PSO, and PPSO performing worse compared to MDE and CSA.
## 3 Materials and Experimental Parameters
### Experimental Parameters  
The experiments were carried out in a homemade airtight climate chamber at FORTH and the aim was to evaluate the deformation of the surface under museum-like RH fluctuations. The DHSPI system has been developed by FORTH [1; 6; 7; 25]. The optical path of the system is based on off-axis transmission holography in the double exposure phase-shifted holographic interferometry mode of application. The laser is a doubled frequency Nd:YAG, it has a wavelength of 532 nm and it is a continuous laser with a maximum power of 300 mW [7]. To improve the signal-to-noise ratio, a piezoelectric (PZT) mirror is used in the DHSPI with a \(\frac{\pi}{2}\) phase shift. The detection system, CCD camera, allows getting a high resolution (2 Megapixels, 4.4 \(\mu\)m/pixel, 1600 \(\times\) 1200 pixels) suitable for the intended application. Images were acquired automatically every five minutes for more than 2 days, this acquisition time is chosen by the slow variations induced. These data are available as \".bmp\" images with pixel values in the interval of 0 and 255 thanks to the camera dynamics of 8 bits.  
The airtight chamber is made of Plexiglas and equipped with environmental reading sensors and a scale for sample weight measurement in line with the recording DHSPI system. The sample, the environmental reading sensors and the scale are inside the custom-made climate chamber but the DHSPI system is outside the chamber. The chamber has a salt solution part where trays of saturated salt solutions are placed. The vapour of the saturated salt solutions enters the main chamber through holes distributed all over to allow for a simultaneous homogenisation of RH in the main chamber. To achieve low relative humidity variations in slow rates of change, lithium chloride saturated solutions to decrease the RH of the chamber and water vapour to increase the chamber's RH were used. The experiments were planned as follows: (a) shrinkage procedure, decrease from 75% to 25% RH, (b) swelling procedure, an increase of RH from 25% to 75% RH.  
The images of the camera are cropped to obtain the entire surface of the sample.
# Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era
## 4 Experimental Results  
Assessing the performance of the proposed work involves evaluating the accuracy of the virtual representation in relation to the physical network. Additionally, considering the connection-aware capability of the proposal, evaluation scenarios vary 5G availability to test the impact of disconnections and the effectiveness of the routing strategy. For this purpose, a proof-of-concept of the proposal has been implemented, utilizing the core of The ONE Simulator [32] to perform visual representations of the network and execute customized scenarios. This section describes the implementation, details the evaluation setup and analyzes the obtained results.