esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 GNSS/IMU Sensors Data Characteristics Analysis
### IMU Data
The CDF of three-axis gyroscope and accelerometer differences are shown in Figure 9. The pitch, roll, and yaw differences between Mate 30 and ADIS16405 are lower than those between Mate 30 and Mate 10 within the same percentile in Figure 9a, and the differences are 3.39, 0.95, and 0.54 rad/s within one sigma, respectively. Meanwhile, the CDF of three-axis accelerometer differences between Mate 30 and ADIS16405 are also lower than those between Mate 30 and Mate 10 within the same percentile in Figure 9b. Compared with the gyroscope and accelerometer differences between Mate 30 and Mate 10, the IMU measurements from Mate 30 match slightly better with those from ADIS16405. Figure 8: Comparisons of the IMU data from Mate 30 and Mate 10. (**a**) Three-axis gyroscope; (**b**) three-axis accelerometer.
## 6 Discussion and Future Considerations
### Network Architecture and Size  
Minimizing AoI in large-sized networks with a large set of nodes to visit in each round can be pretty challenging. At some point, when the number of sensors increases significantly, it becomes difficult for some heuristic algorithms to deliver high-quality results. In this case, computation-intensive algorithms may also not be the best option as high computation time negatively impacts the quality of obtained solutions [87]. In such cases, the best option might be to resort to algorithms that could produce near-optimal solutions. Artificial-intelligence-enabled IoT can improve system performance and optimize energy consumption using different training models [108]; however, this architecture is yet to be studied for AoI minimization in UAV-assisted IoT.
# Laser R-Test for Angular Positioning Calibration and Compensation of the Five-Axis Machine Tools
## 1 Introduction
In 2016, [PERSON] [10] established a new PIGEs transformation matrix model, and through simulation and DBB measurement, PIGEs were able to predict the five-axis volumetric behavior at 5.39 \(\upmu\)m, which validated the motion accuracy of the model. Renishaw [11], Heidenhain [12], and Blum [13] are measurement devices for positioning error and five-axis geometric error. These measurement instruments are based on the assumption that three translational axes of a five-axis machine tool are measured without error, but in fact, there are still considerable errors in the three translational axes. IBS developed an R-test system [14, 15, 16] for the machine tools five-axis simultaneous static error and dynamic assembly error. In 2018, [PERSON] [17] mentioned an instrument that could measure all the motion errors of a rotary axis, but in machining, translational and rotary components are in synchrony motions. Influences under this point will affect the result in reality. In 2019, [PERSON] [18] introduced a multi-station measurement system using a laser tracer applied on large high-precision rotary tables; however, the system facesconcentricity problems between the laser tracer and the rotary table. In 2021, [PERSON] [19] executed a new measurement method as an independent system for all motion errors of a rotary axis based on a polyhedral prism. This experiment requires that the polyhedron and the rotary table are concentric after installation, otherwise, it causes unexpected errors. From the literature mentioned above, it can be concluded that there are many measurement instruments, and most of them need to be performed by professional technicians to analyze measurement data. However, limitations and disadvantages still exist in each measurement method itself, such as concentricity problems and feasibility in applying methods. Therefore, the system in this study is proposed to measure both the five location errors and six component errors simultaneously and improve the mentioned issues. The error of rotary axes and the error of translation along an axis merely cause the errors mentioned above. In order to reduce the errors as much as possible, we improve the accuracy of translational axes and rotary axes by measuring the values of error and compensating them to the corresponding axis. The process must be done in order. The translational axes should be done first, then the rotary axes. This study is going to focus on rotary axes and deploy analysis. The error compensation process on translational axes is not shown in detail but still needs to be done before taking any measurement for angular positioning error.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 7 Performance Evaluation
### Simulation Scenario  
To simulate the proposed model, two simulation scenarios have been proposed. The first one is the default scenario of the ONE simulator, and the second one is the DakNet scenario, which simulates three villages connected with each other. In all simulations, the focus is on three parameters: delivery probability, overhead ratio, and latency. The proposed algorithm is compared to the most important DTN routing algorithms implemented in the ONE simulator, which are: Spray and Wait, Prophet, Epidemic, and Fresh. The ping-pong application can be configured to send pings with a fixed interval or to only answer pings it receives. When the application receives a ping, it sends a pong message in response. In the simulation scenario, all the nodes are considered to be mobile. The nodes communicate with each other using the Bluetooth interface at a 250 KB/s data rate and a 10 m radio range. This scenario includes a part of the Helsinki downtown area (4500 \(\times\) 3400 m). The total number of nodes is divided into three main groups. Group 1 and group 3 nodes are pedestrians who move at random speeds in the range of 0.5-1.5 m/s, which is the typical walking speed, with pause times between 0 and 120 s. Group 2 nodes are cars moving at the speeds of 2.7-13.9 m/s (10-50 km/h), with pause times between 0 and 120 s. Group 4 nodes are terms that move at the speeds of 7-10 m/s with pause times between 10 and 30 s. Groups 1, 2, and 3 consist of 40 nodes each, while group 4 only consists of 6 nodes. Group 1, 2, and 3 nodes have up to 20 MB of free buffer space, while group 4 nodes have 50 MB of free space for relaying and forwarding messages. Note that the group 4 communication interface has a data rate of 10 MB/s and a transmission range of 1000 m. A new message is generated on average every 25 to 35 s. The message size varies between 500 KB and 1 MB, with the time to live set to 300 min. Each simulation runs for 43,200 s (12 h).
## 3 Current Differential Protection Based on Adaptive Angle Compensation Coefficient
### Principle of Current Differential Protection Based on Adaptive Phase Angle Compensation Coefficient
\tag{9}\]  
From Equation (9), the protection proposed in this paper adaptively corrects the measured current on the DG side of the feeder according to the amplitude ratio and phase difference of the PSCFCs on both sides of the feeder to meet the protection needs of active distribution networks containing various types of DGs. Its action characteristics are analyzed as follows:  
* For non-fault sections, considering the measurement error of the CT, the lock angle of the CDP, and a certain margin, it can be considered that the amplitude ratio of PSCFCs is \(\varepsilon\)[0.8,1.25], and the phase difference of PSCFCs is \(\delta\)\(\in\)[135\({}^{\circ}\),225\({}^{\circ}\)]. When taking the limit case \(\varepsilon=1.25\), \(\delta=135^{\circ}\), the proposed protection is not compensated, the restraint current is still greater than the differential current, and the protection does not trip reliably;
* For the fault section with SBDGs connected downstream of the fault point, it is known from Section 2 that the phase angle difference \(\delta\) of the PSCFCs on both sides is small. At this time, regardless of the value of \(\varepsilon\), the protection can trip sensitively;
* For the fault section with IBDGs connected downstream of the fault point, the amplitude ratio of the PSCFCs on both sides of the protected feeder is greater than 1.5. In this case, the measured current \(\Delta I_{\text{n}}\) is compensated so that the phase of \(\Delta I_{\text{n}}\) after compensation equals the phase of \(\Delta\dot{I}_{\text{m}}\). After compensation, the points falling into the light red area in Figure 5 are moved to the phase angle difference 0\({}^{\circ}\) axis of the tripping area. The protection can trip reliably and with high sensitivity. The action characteristics of the ACDP and TCDP in active distribution networks are shown in Figure 6. When a fault occurs outside the protected feeder, the pale purple area must be included in the non-tripping area in Figure 6. Compared with the tripping area of the TCDP, the ACDP adds light green and light red areas. Due to the fault characteristics of IBDG and considering the time synchronization error, the fault point may fall in the light red area, so adding this area to the tripping area is necessary. Figure 6: Action characteristics of ACDP and TCDP in active distribute networks.
## 3 Mathematical Models
### Genetic Algorithm-Emotional Neural Network (GA-ENN)  
The GA-ENN (Genetic Algorithm-Emotional Neural Network) method is a hybrid approach that combines Genetic Algorithms (GA) and Ensemble Neural Networks (ENN). It is primarily used for classification tasks in machine learning and data mining ([PERSON], 1998). In the GA-ENN method, a Genetic Algorithm is employed to optimize the architecture and parameters of an Ensemble Neural Network. The Genetic Algorithm applies principles inspired by natural selection and genetic mechanisms, such as mutation, crossover, and selection, to iteratively evolve a population of potential neural network architectures ([PERSON], 1998).  
Recently, [PERSON] and [PERSON] (2007) pioneered the development of a limbic-based Emotional Neural Network (ENN). This novel network architecture draws inspiration from the emotional processes in the brain and is characterized by a single-layer design. In contrast to artificial neural networks (ANNs) that imitate biological neurons, ENNs are crafted to replicate the intricate interactions among four specific neural regions within the emotional brain: the thalamus, sensory cortex, orbitofrontal cortex (OFC), and amygdala.  
During each iteration of the Genetic Algorithm, a set of candidate neural network architectures is evaluated based on their fitness, which is typically defined by the classification accuracy on a validation set ([PERSON], 1998). The fittest architectures are selected to undergo genetic operators like crossover and mutation, which create new offspring architectures with modified characteristics ([PERSON], 1998). Once the genetic evolution process is complete, an ensemble of neural networks is constructed by training each candidate architecture on the training dataset. The ensemble combines the predictions of multiple neural networks to make the final classification decision. This aggregation of predictions from diverse models often leads to improved generalization performance and robustness.  
The GA-ENN method offers several advantages. Firstly, it leverages the optimization power of Genetic Algorithms to search for optimal neural network architectures in a vast solution space. This helps to identify architectures that can capture complex patterns in the data and improve classification accuracy. Secondly, by creating an ensemble of neural networks, it reduces the risk of overfitting and enhances the model's ability to handle noise and variability in the data.
# Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach
###### Abstract
[26] presents a novel strategy for detecting and isolating cyber-attacks on quadrotor UAVs, using modified sliding innovation sequences (MSIS) combined with an extended Kalman filter (EKF) to enhance robustness against various cyber-threats, including false data injection and denial-of-service attacks, with demonstrated effectiveness in simulations. Additionally, Ref. [27] proposes a sliding mode control approach for multi-UAV air combat management, integrating a threat assessment model and matrix game theory for strategic target allocation, supported by a social behavioral-based control for managing UAV swarm motion, which has been validated through simulation tests. Meanwhile, Ref. [28] presents an advanced control methodology for fixed-wing UAVs, introducing an adaptive backstepping sliding mode control technique that effectively manages altitude, attitude, and velocity despite non-linearity and multi-variable coupling, with real-time adjustments for uncertainties validated through extensive numerical simulations. Similarly, Ref. [29] focuses on a robust control algorithm for quadrotor UAVs, utilizing fixed-time sliding mode control (FTSMC) based on Lyapunov theory to ensure stability and precise trajectory tracking despite disturbances. This study compares FTSMC with non-singular terminal sliding mode control (NTSMC), highlighting FTSMC's superior efficiency in reducing disturbances, making it well-suited for dynamic operational environments. Consequently, this paper focuses on developing and implementing a novel sliding mode control (SMC)-based framework specifically designed to enhance UAV security by dynamically responding to both cyber and physical threats. Unlike previous studies that have typically addressed either cyber or physical threats in isolation, this work integrates both into a unified control strategy, thereby improving UAV resilience and operational integrity in hostile environments. The proposed framework not only leverages the robustness of SMC against external disturbances but also introduces adaptive mechanisms that allow the UAV to respond in real-time to evolving threats, which represents a significant advancement over existing methods that often lack such adaptability and comprehensive threat-mitigation capabilities. This paper is organized into four sections: Section 2 discusses the proposed control methodology and denotes the simulation procedure, Section 3 shows the results, and Section 4 concludes the paper.
# A Next-Generation Core Network Architecture for Mobile Networks
## 3 A Novel Architecture  
Unlike current efforts that propose to virtualize single elements of the existing core network architecture (see Figure 2), we propose a completely different and novel virtualized architecture.  
The new core network is a P2P network of VMs, where each VM can have the role of either an SN or a VCN. An SN is responsible for managing a certain number of UEs. In particular, it performs basic tasks (i.e., paging, voicemail and SMS delivery) as well as complex orchestration tasks (i.e., creating and destroying VMs that run VCN functionality). A VCN, on the other hand, is assigned to a single UE and provides voice and data services to that UE. Once a UE is associated with a VCN, the VCN takes over all tasks, including those previously handled by the SN with the exception of orchestration.  
We envision two scenarios in the proposed architecture. In the _first scenario_, we keep the existing protocol stack based on tunneling, in order to maintain compatibility with the existing access networks. Due to historical reasons, in current cellular core networks, tunneling is heavily used at a very fine granularity. For example, in the downlink, UE's IP packets are fragmented and encapsulated in GPRS Tunneling Protocol (GTP) tunnels over UDP: in other words, the UE's IP layer is encapsulated over the underlying IP layer of the Multi-Protocol Label Switching (MPLS) core network. This encapsulation is shown in Figure 4. Furthermore, a single UE often uses multiple tunnels to different gateways. This is highly inefficient and leads to high latency and long delays regardless of the access network. In the _second scenario_, we remove the assumption of backwards-compatibility with existing access networks and introduce an enhanced eNodeB (eeNodeB). In doing so, we introduce a single MPLS network, with IP on top, for both core network nodes (i.e., SNs and VCNs) and UEs, thus avoiding the IP-in-IP tunneling typical of today's core networks.  
Figure 5a,b show the protocol stack of a new Core Network Node (CNN) for the user plane and the control plane, respectively. As mentioned above, when using the eNodeB, the new core network uses MPLS with IP on top for both CNNs and UEs, and no IP-in-IP tunneling. Above IP, the usual transport layer protocols are considered and, at the application layer, CNNs must support RELOAD [16], SIP [17] and P2 PSIP (peer-to-peer version of SIP which relies on the RELOAD overlay) [18] in the control plane and RTP and RTP-related protocols in the user plane. As will be described in more detail in Section 5, RELOAD is used in order for the UE to attach to the network, while SIP and P2 PSIP are used for Voice over LTE (VoLTE) call session setup. On the other hand, RTP is used for real-time media since a VCN always remains in the media path of a call.  
Let us now look at the main three elements of the new architecture: SN, VCN, and eeNodeB.  
Figure 4: Current network protocols with IP-in-IP tunneling.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 3 On-Board Processing Unit Design
### On-Board Processing Pipeline
#### 3.4.2 Reconfigurable Pipeline Framework  
A more general setup shown in Figure 6 reveals two main changes and additions: Another application for on-board software processing, and a framework for reconfiguring the FPGA with processing modules other than CCSDS123 compression. The MOBIP pipeline will be one specific configuration of the general pipeline framework.  
The architecture of the on-board processing pipeline application is implemented as shown in Figure 7. The pipeline is a single C program, consisting of the pipeline engine and the pipeline modules. Each module is configured with its own module configurationfile. A pipeline configuration file defines which modules to run as a pipeline stage and the order in which they are run. Stages need not depend on each other. A stage may or may not interface with the FPGA or reconfigure the FPGA. Different sets of configuration files are stored on-board for different pipelines. Different pipelines are run depending on the imaging target.  
The setup and capture stages of the MOBIP pipeline are (up to capture parameters) included in each pipeline configuration, as recorded hyperspectral data will always be required. The same holds for time stamping and binning.  
The workflow for additional pipeline stages consists of algorithm prototyping in Python, implementation and testing in C, potential parallel implementation in Hardware Description Language (HDL) such as VHSIC Hardware Description Language (VHDL) or in a High-Level Synthesis (HLS) language and testing on Programmable Logic (PL), and lastly integration and testing on target hardware before updating the software on HYPSO-1. Currently implemented stages and their motivation are shown in Table 4. These stages have been tested on the ground [71].
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 1 Introduction
[23] carried out a self-correlation operation and applied statistical operation to develop an automatic extraction algorithm for measuring the length of an in-service rock bolt. The peak position occurrence for multiple measurement signal waves after the self-correlation operation was estimated by statistical method. This method reduced the average error to 2.1% with multiple measurements compared to the PAM average error of 3.1%. Time-frequency analysis based on image processing for feature extraction has been used in equipment fault detection [24], medical diagnosis [25; 26], radar signal recognition [27; 28], and nondestructive testing [29; 30; 31], etc. [PERSON] et al. [28] proposed a self-correlation characteristic image construction technique (ACFICT) combined with a convolutional neural network (CNN) to realize the classification of radar signal modulation types. The authors used the self-correlation algorithm in the step of signal preprocessing and time-frequency image feature enhancement, which makes the algorithm adapt to the feature image during a low signal-noise ratio (SNR) signal. [PERSON] et al. [29] utilized the weight vector to enhance the information features of the time-frequency image, and then extracted and analyzed the local maxima in the frequency domain along the time direction for damage detection in rotating machines. The local increase in amplitude may relate to the pulse disturbance caused by damage. [PERSON] et al. [30] extracted invariant moment features from the adaptive time-frequency distribution map. [PERSON] et al. [31] generated a time-frequency spectrogram from the detection signals of a vehicle-bridge interaction and analyzed the change in instantaneous frequency to evaluate the state of the bridge. These methods mainly extract the feature points of the time-frequency spectrogram, but do not study the pixel distribution rule of the whole time-frequency spectrogram. However, the period of pixel distribution in the time-frequency spectrogram is found to be related to the reflection signal period in this research. In this paper, a new algorithm for the automatic extraction of the stress wave reflection period based on image processing of a time-frequency spectrogram is presented. The image is the short-time Fourier transform (STFT) spectrogram of the reflected wave signal after applying wavelet denoising and quadratic self-correlation operations. The reflection period is calculated by using the relationship between the period of pixel distribution in the time-frequency spectrogram. To the best of our knowledge, there is no study retrieving the stress wave reflection period based on image processing. The validity of the algorithm is verified by a length measurement experiment of metal piles buried under varying depths.