esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of the Analytical Segment Length on the Tram Track Quality Assessment
## 2 Materials and Methods
### Track Geometry Data Collection  
Before track geometry data acquisition, the tram tracks in the City of Osijek were divided into sections. This was performed in two steps. In the first step, the direction of tram traffic was considered. Then, an additional division was made due to the lines' specific track layout and organization. As a result, the network was divided into three sections: Lines L1 and L2A are part of the double-track tram network, while L2B is part of the single-track tram network with passing loops at tram stops.  
Track geometry irregularities were measured in November 2016 by manually operated trolley TET-1000 (Figure 3) that meets the requirements of the European Standard EN 13848-4, addressing manual and lightweight devices for track geometry measurements [27]. This is a device for the manual continuous measurement of track geometry whose structure consists of transverse and longitudinal beams, supported by three rollers, and the control unit with memory (data logger). Measurement elements include a distance counter, an inductive linear displacement sensor for measuring track gauge as well as vertical and horizontal track irregularities, and an electronic clinometer (inclination needle) for measuring the cant. Track twist is calculated during measurements as the algebraic difference between two consecutive cant values 10 m apart (5 m in front and behind the measurement cross section) [28].  
The measurements were recorded with a 1.0 m resolution, as a function of track chainage. During measurements, each of the track measuring profiles was assigned with the value (signal) of the five track geometry parameters and track chainage. For the measured data processing and analysis, after data transfer from the TET-1000 logger to the personal computer, conversion to Excel format was performed by specialized Track Gauge software. The data were georeferenced along track sections and structured into the created  
Figure 3: TET-1000 trolley.  
GIS database. The distribution of the geometry data collected along 27,520 measurement points is shown in Figure 4. An example of a graphical representation of the track gauge values along the tracks expressed as relative values to the regular track width, i.e., gauge deviations, is shown in Figure 5.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 6 Solving a Dynamic Scenario
### Convergence Speed  
In this section, we compare the convergence speed of our two heuristics (GA and PSO) to validate PSO's faster convergence speed and justify its use for periodical relocation of UAVs.  
We have performed the following experiment. We have placed 100 users on different sized areas (from 1000 to 10,000 m\({}^{2}\)). Ground users move according to a correlated random walk model (the rest of the settings are shown in Table 3). Each user moves at a walking speed of 5.3 km/h with a probability of 0.8, and in such case, the direction remains unchanged with probability 0.8. Users rotate 180 degrees when they reach the area bounds. Algorithm 1 is used for the initial deployment, and the number of drones deployed \(D\) remains unchanged for the remainder of the simulation (15 min). To achieve this, we limit subsequent solutions to the best solution found with \(D\) drones (even though problem constraints were not satisfied). Every 30 s (\(T=30\)), we update drones' location by executing one of the resolution methods: (a) GA, or (b) the PSO-based search we introduced in this section. Figure 6 illustrates the obtained results (x-axis represents the sparsity of users in m\({}^{2}\)/U). With every new solution, drones are relocated (considering a speed of 60 Km/h) so that the sum of the distances traveled by all drones is the minimum possible.  
Results prove that using the PSO-based algorithm significantly improves the convergence speed, reporting up to an 80% improvement in the resolution time. With the PSO-based search, the resolution time (\(T_{\text{solve}}\)) never takes longer than 25 s, which validates its use with our preferential sampling time (\(T=30\) s). Results also show that scenarios with high user sparsity are easier to solve, and the algorithm takes less time to converge to solutions.
## 2 Background
### Security Challenges in O-RAN  
The open and decentralized nature of O-RAN, while essential for vendor-neutral deployment and rapid innovation, significantly expands the attack surface. Unlike traditional RAN systems that rely on proprietary hardware and tightly coupled software, O-RAN allows the integration of third-party xApps and rApps--components that may not undergo uniform vetting or security certification [21].  
These third-party modules may contain exploitable vulnerabilities or be weaponized to introduce backdoors or logic bombs or manipulate radio resource control to achieve denial of service. Moreover, the use of ML models to optimize RAN behavior exposes the system to adversarial ML threats, such as [5]  
* **Data poisoning:** corrupting training data to mislead model behavior [9];
* **Model inversion:** inferring private training data from outputs [9];
* **Evasion attacks:** manipulating features to bypass detection [27].  
These attacks could undermine core functions such as user admission control, handover decisions, or anomaly detection [28].  
Compounding this risk is the fact that O-RAN components often lack centralized security governance. Traditional perimeter-based defenses are inadequate in this context, prompting a need for Zero Trust Architectures (ZTA) that assume no inherent trust and instead verify every component and interaction continuously [15]. Additionally, privacypreserving learning techniques like FL have emerged, allowing distributed training without central data aggregation, but these too suffer from vulnerabilities to model poisoning and a lack of accountability among participants. Table 1 summarizes the quantitative impact of key security challenges in O-RAN, including data poisoning attacks, unauthorized access, and multi-vendor risks.
# Human Activity Classification Using the 3 DCNN Architecture
## 3 Experimental Results
### UCF YouTube Action Dataset  
The UCF YouTube Action dataset by the Center for Research in Computer Vision at the University of Central Florida was created. This dataset is composed of videos categorized into 11 classes of human activities: Basketball, Biking, Diving, GolfSwing, HorseRiding, SoccerJuggling, Swing, TennisSwing, TrampolineJumping, VolleyballSpiking, and WalkingDog. The example of this dataset is shown in Figure 7.  
The dataset contains 1160 video sequences downloaded from YouTube. Each class consists of 25 different groups of videos containing concrete human activity. Each of these groups contains at least four videos. The grouped video clips contain some similar characteristics, such as the same actors, similar backgrounds, similar variations in camera motion, etc.  
All videos are in mpeg4 format. The video size is 320 \(\times\) 240 and the frame rate is 29.97 fps. The complete dataset is demanding due to large differences in camera movement,appearance and pose of the object, angle of view, crowded backgrounds and lighting conditions. The dataset is one of the largest datasets in the vision community [5].  
In [5], the authors used different architectures for motion, static and hybrid features and achieved accuracies of 65.4%, 63.1% and 71.2%, respectively. In [6], another approach for computer vision on this dataset was proposed and achieved an average accuracy of 76.1%.
# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 3 Proposed Scheme
### Workflow of the Proposed Scheme
#### 3.2.4 Homomorphic Secret Sharing  
This paper proposes that multi-party computation is performed at separate VENs to hide operations from the untrusted cloud network. We implement a homomorphic secret sharing (HSS) scheme to divide computation functions across different VENs. Each share's data is encrypted using the Enc () function described in the homomorphic encryption process. The process flow of secret sharing is described in the following steps:  
1. The base station encrypts the data using the ENC () function as part of the homomorphic encryption process.
2. Different data bits collected from varying IoMT devices (DO) have separate computation processes required by the DU. The encrypted ciphertext, using Enc (), is forwarded to each cluster head VEN and other nodes in different clusters.
3. The selection of clusters is based on the distance measured from the physical base station. As the number of computation tasks grows, other cluster heads are selected based on their distance proximity with the previous cluster head. If the resources required for computation is high, then the computation is shared with other virtual nodes present within the same cluster.
4. The threshold value for share (_sh_n) generation is based on the computation requests generated by the DU where _DUQ_n = (_sh_1, _sh_2, _sh_3, \ldots\ldots,\ s_{ln}\)).
5. The base station stores the assigned cluster heads assigned as shared to perform the computation tasks.
6. Data computation results are transmitted back to the base station, which is aware of each assigned node required to return computed data. Data received from other VENs are rejected as unauthorized packets.  
Finally, DU performs the DEC () function on the results to obtain plaintext. DU forwards the final data to the DO IoMT devices.
## 2 Theoretical Basis
### Principle of Convergence-Constraint Method  
The main content of the convergence-constraint method includes three parts: the characteristic rock curve (GRC), the characteristic support curve (SCC), and the deformation curve of the longitudinal section (LDP) [27].  
The key steps to rationally applying the convergence-constraint method and evaluating the stability of the rock and support structure are to (1) draw characteristic curves of the rock and support structure based on the mechanical parameters of the pluton and the support structure, (2) understand the stress and displacement change law between the rock and support structure, and (3) determine the optimal support timing of the rock to establish an optimal support system [28; 29].
###### Abstract  
In this paper, we study joint relay selection and the power control optimization problem in an anti-jamming relay communication system. Considering the hierarchical competitive relationship between a user and jammer, we formulate the anti-jamming problem as a Stackelberg game. From the perspective of game, the user selects relay and power strategy firstly which acts as the leader, while the jammer chooses power strategy then that acts as follower. Moreover, we prove the existence of Stackelberg equilibrium. Based on the Q-learning algorithm and multi-armed bandit method, a hierarchical joint optimization algorithm is proposed. Simulation results show the user's strategy selection probability and the jammer's regret. We compare the user's and jammer's utility under the proposed algorithm with a random selection algorithm to verify the algorithm's superiority. Moreover, the influence of feedback error and eavesdropping error on utility is analyzed.  
Keywords:anti-jamming; relay selection; power control; Stackelberg game; Q-learning; multi-armed bandit +
Footnote †: journal: Applied
## 3 Results and Discussion
### Application of Artificial Intelligence, Predictive Analytics, and Digital Twins  
The increasing complexity of smart microgrid operations and the integration of high shares of RES have accelerated advancements in the application of AI, predictive analytics, and digital twins. These technologies support improved decision-making, enhance system reliability, and enable predictive maintenance and operational optimization.
# Efficient Runtime Firmware Update Mechanism for LoRaWAN Class A Devices
###### Abstract
To demonstrate the effectiveness of the proposed update mechanism, we developed an IoT application based on a proprietary end device equipped with sensors, actuators, a LoRaWAN transceiver, and an 8-bit microcontroller. The LoRaWAN gateways and The Things Network servers are utilized to provide communication capabilities and interface the end devices with the application server. To fix a bug in the developed firmware and validate the proposed method, instead of sending the full-image update that comprises 108 kBytes, only 643 bytes were transferred via LoRaWAN. This corresponds to a data reduction of 99.42 per cent compared to the traditional approach. The proposed method significantly reduces the data required for firmware updates, preserving network capacity and reducing transmission time. Furthermore, the developed procedure maintains the IoT device's class throughout the update process, ensuring data integrity and security without switching to other classes. This is crucial for maintaining energy efficiency and operational simplicity. Moreover, experimental results show a substantial decrease in device downtime, reduced to just 16.88 ms, compared to several minutes required by other methods. This minimal downtime is particularly beneficial for applications requiring high availability and continuous operation. Additionally, the proposed method optimizes energy consumption, crucial for Class A IoT devices, by minimizing active transmission time and reducing the computational load associated with firmware updates. Unlike conventional methods that require a complete reboot or use bootloaders, our approach enables updates during runtime without rebooting, enhancing system efficiency and robustness. This study presents an innovative contribution to the field of firmware updates in IoT devices, offering a scalable and robust solution that can be broadly applied across different IoT applications. This document is organized as follows: Section 2 presents related work, Section 3 contains the proposed methodology, Section 4 discusses the obtained results and compares them with related work, and Section 5 presents conclusions and suggestions for future work.
# Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
## 6 Discussion of Results
### Application of UFSSOD, xStream, and Loda Two Hist. in a Streaming Setting
implementation, with approximately 3 s average runtime per dataset and 1.4 ms/sample. Compared to the other classifiers in the measurements, xStream was the only one with this significant runtime, also explaining why the authors of xStream provided a C++ streaming version rather than a Python pendant to their static one. Considering the fast runtime of LodaTwo Hist., significant improvements of _avg_t_ could only be achieved for datasets whose number of data instances and dimension ratio is higher (ID 6, 8, and 11) than those of the others and, on average, reduces _avg_t_ by 7%. As an example, for _optdigits_ with 2216 data instances and 64 dimensions, UFSSOD could reduce _avg_t_ by approximately 22%. As for the percentage improvement of \(F1\), excluding the statistical strays of dataset 6 and 15 for xStream, an improvement of approximately 22% on average could be obtained. For LodaTwo Hist, the improvement is even more notable with approximately 45% (ID 6 and 15 excluded). Figure 12: \(F1/avg\_t\) results for well-performing datasets _mnist_ (**a**) and _satellite_ (**b**) referring to Table 7.  
It should be noted that xStream worked in the truly online mode. Therefore similar performance boosts, as with \(\text{Loda}_{\text{Two Hist}}\), might also be achieved for xStream if using the proposed windowed approach mentioned in Section 4. In summary, applying UFSSOD in a streaming setting notably increases both the classification and computational performance if the dataset is of high-dimensional nature and has a high number of data instances. In a real-world application, the former depends on the applied domain but fits perfectly with current observable trends. The latter is only part of this evaluation setup since in the real-world the data stream has an infinite amount of samples. Although xStream and Loda are representatives that work better on high-dimensional data [14] and one might assume that a reduction of features by UFSSOD might degrade their performance, the experiments show that the opposite is true, with an overall improved result. Thus, we assume that using UFSSOD with other online classifiers, such as ones based on iForest that handle changing feature sets during runtime, may boost the performance even more.
# Efficient Continuous Subgraph Matching Scheme Based on Trie Indexing for Graph Stream Processing
## 3 Proposed Continuous Subgraph Matching Scheme
### Index Manager  
A uniform response to a common pattern of queries is an efficient method for multiple subgraph matching processing tasks. The Index Manager is used to analyze patterns of duplicated queries and index a duplicated query area. When common patterns are indexed at once, the cost of indexing new similar queries can be reduced. As the Index Manager calculates query processing results in a duplicated area, it also reduces the query processing cost. The query decomposition and indexing technique proposed by [19] is used in this study. The Index Manager divides a query into max covering paths, which are a set of the shortest paths including all the vertices and edges of a query graph. A query \(\mathsf{Q}_{\mathsf{i}}\) is divided into k paths (MCP(\(\mathsf{Q}_{\mathsf{i}}\)) = {P\({}_{1}\), P\({}_{2}\),..., P\({}_{\mathsf{k}}\)}). The edge indexing process records information on a query (\(\mathsf{Q}_{\mathsf{i}}\)) including the corresponding edges of each path to determine a query affected by an input graph stream during query processing. The trie indexing process stores the edges of each path in trie nodes. Each node maintains its unique node number and information on parent nodes and child nodes. This process conducts join operations based on the parent and child nodes of the corresponding node during query processing to generate query results. The reference indexing process records reference information on the last terminal node in a path stored in the trie indexing process according to queries. The Index Manager detects a query affected by the edge indexing process and identifies the location of the node that contains the corresponding edges based on reference information. Based on the method described above, the Index Manager records queries with the same path in the same trie node and uses shared information on similar queries to respond to multiple queries efficiently.  
Figure 2 shows the process of indexing a query graph \(\mathsf{Q}_{\mathsf{1}}\). \(\mathsf{Q}_{\mathsf{1}}\) is divided into three max covering paths (MCP(\(\mathsf{Q}_{\mathsf{1}}\)) = {P\({}_{1}\), P\({}_{2}\), P\({}_{3}\)}). For example, \(\mathsf{P}_{\mathsf{1}}\) indicates the following three edges: e\({}_{1}\) = <v\({}_{1}\), var>, e\({}_{3}\) = <var, v\({}_{2}\)>, and var = <v\({}_{2}\), v\({}_{4}\)>. Here, var refers to a wildcard operation. This operation indicates that information on vertices and edges has not  
Figure 1: Overall system structure.
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 5 Performance Evaluation
### Performance Evaluation of SDNPS
#### 5.1.3 Delay and Loss Rate for M:1 Transmission  
In this section, we conduct a set of experiments to evaluate the performance of the SDNPS when facing multi-publishers and one subscribers. Table 3 shows the results. For every test, there are two publishers sending 100,000 packets simultaneously to one subscriber. There is no doubt that the bottleneck is still on the side of end hosts.
## 3 Methodology
### Second Phase  
#### 3.2.1 Geological Characterisation  
The fieldwork at \"Las Cabras\" hill focused on studying the terrain's morphology and obtaining measurements of heading and dip of strata and other geological structures. Three geological sections were made in the study area, and a representative stratigraphic column.  
Based on the identification of areas of geological interest, 18 vertical electrical soundings (VESs) were planned using the Schlumberger method [46; 47]. The VESs carried out allowed estimating the thickness of the layers at depth. To verify the information obtained from the VESs, strategic points were established for drilling and pits.  
Three rotational type boreholes were drilled with sample recovery. Six VESs close to the perforations were chosen to correlate them. Additionally, eight pits were made (excavations of at least one meter deep) to analyse in detail the materials (rocks and soils) present (see Figure 3). These actions allowed an adequate geological interpretation of the study area through the correlations of geoelectric profiles, the geological field survey and the geological drilling record.
# Seismic Isolation Effect of Non-Water Reacted Two-Component Polymeric Material Coating on Tunnels
## 4 Results and Discussion of Numerical Analysis
### Effect of Layer Thickness(Case 9 to 16)  
As shown in Figure 13, the thicker the NRTCPM layer is, the lower the tensile stresses and absolute values of the compressive stress on the NRTCPM layer and lining are. Taking the eight points on the lining as an example, the tensile stresses and absolute values of the compressive stress on the lining decrease sharply when the isolation layer increases from a thickness of 5 cm to 20 cm; however, they change slightly when the thickness of the isolation layer increases from 20 cm to 40 cm (Figures 13 and 14). We can see from Figure 13 that the drop values of the tensile stress are 2304 KPa, 1024 KPa, and 588 KPa on average for all points when the thickness of the NRTCPM layer increases from 5 mm to 10 mm, from 10 mm to 15 mm, and from 15 mm to 20 mm, respectively. In Figure 14, the compressive stresses have a similar trend. The increased values of the compressive stress are 2313 KPa and 1204 KPa on average for all points when the thickness of the NRTCPM layer increases from 5 mm to 10 mm and from 10 mm to 15 mm.  
The absolute values of the compressive stressesand tensile stresses from number 9 to number 16 on the NRTCPM layer tend to decline in a similar trend as the points on the lining(Figures 15 and 16). According to Figure 15, the average increases in the values of the compressive stresses on eight points of the NRTCPM layer are 224 KPa, 134 KPa, 77 KPa, and 47 KPa, when the thickness of the NRTCPM layer increases from 5 mm to 10 mm, from 10 mm to 15 mm, from 15 mm to 20 mm, and from 25 mm to 30 mm, respectively. On the contrary, the average decrease in the values of the tensile stress on eight points on the NRTCPMlayer are 204 KPa, 124 KPa, 77 KPa, and 42 KPa in Figure 16. We can conclude that a further increase of theNRTCPM layer's thickness would not have a significant effect  
Figure 12: Tensile stress of eight points on the lining.  
When the thickness of the isolation layer is 20 cm, the tensile stresses and absolute values of the compressive stress can reduce70% of stress on average compared with the condition without the NRTCPM isolation layer. Therefore, we recommend an optimum NRTCPM layer thickness of 20 cm considering the stress reduction rate and the construction costs of the polymeric material.