esa-sceva/satcom-chunk-collection huggingface dataset:
# Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On
###### Abstract  
Thanks to the unprecedented success of Gravity Recovery and Climate Experiment (GRACE), its successive mission GRACE Follow-On (GFO) has been in orbit since May 2018 to continue measuring the Earth's mass transport. In order to possibly enhance GFO in terms of mass transport estimates, four orbit configurations of future polar-type gravity mission (FPG) (with the same payload accuracy and orbit parameters as GRACE, but differing in orbit inclination) are investigated by full-scale simulations in both standalone and jointly with GFO. The results demonstrate that the retrograde orbit modes used in FPG are generally superior to prograde in terms of gravity field estimation in the case of a joint GFO configuration. Considering the FPG's independent capability, the orbit configurations with 89- and 91-degree inclinations (namely FPG-89 and FPG-91) are further analyzed by joint GFO monthly gravity field models over the period of one-year. Our analyses show that the FPG-91 basically outperforms the FPG-89 in mass change estimates, especially at the medium- and low-latitude regions. Compared to GFO & FPG-89, about 22% noise reduction over the ocean area and 17% over land areas are achieved by the GFO & FPG-91 combined model. Therefore, the FPG-91 is worthy to be recommended for the further orbit design of FPGAs.  
gravity field recovery; GRACE Follow-On; orbit configuration; synergistic observation 2019
# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
## 2 Background
### IDS Design  
Drone-based attacker tactics against heterogeneous smart city ICT platforms are reliant upon the ability of the threat actor to intrude the drone firmware and/or the ground controller with the intent to either divert the same to an unwarranted locale, to cause it to crash or to modify its trajectory and make it observe and report phenomena back to a rogue command and control center (C2). Traditionally, intrusion detection for cyber physical systems can be categorized into signature-based and anomaly-based. For the former, the detection system has to be pre-configured with signatures of known attacks, which can subsequently be matched with live/observed drone data, possibly at the ground controller unit. Anomaly-based systems, on the other hand, are trained to identify normal/legitimate data flow, including network traffic which refers to routine drone behavior. Deviation from the norm is subsequently tagged as an attack. Contemporary intrusion detection system design comprises robust machine learning techniques that allow for correlation of multiple data streams that can emanate from the drone, ground controllers and edge nodes of a drone control system. Popular techniques that can be adopted for detecting adversarial attempts to penetrate the drone system include support vector machines (SVMs), deep learning, and extreme learning machines (ELMs) [15]. To place an intrusion detection system as an overlay on a ground controller would entail significant computing and storage usage.  
The concept of training an intrusion detection system with known or malicious signatures, for subsequent detection, can be adopted without modification albeit the data itself will require massaging and preparation for presentation to the intrusion detection system during the training as well as the testing phases.  
Whilst an intrusion detection system can be placed in a high performance device, including a ground controller to detect adversarial attempts to compromise a drone, the ability of such a system to function on resource-constrained devices, such as drones, is a challenge. We present an approach based on random forest machine learning, to enable a drone to re-train on classification data, with minimal overhead (see Section 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Resources Affected** & **Motive** & **Impact** \\ \hline Hardware & Drone, ground controller & Sabotage, data exfiltration & Flight path, crash \\ Firmware & Drone, ground controller & Process and state tampering, & Crash, data loss \\  & Drone, ground controller, edge & Napotage, compromise & Process disruption, Flight path/services \\ Network & Communication channel & Data exfiltration, modification & Flight disruption, access denial \\ Process & Drone, ground controller, edge, cloud & Software malfunction & Flight disruption, data loss \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attack impact analysis.
# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Acoustic  
Acoustic sensors, such as microphone arrays, capture the generated audio from the rotors and propellers of the drone and then compare the extracted features, including mel- frequency cepstral coefficients (MFCC) and short-time Fourier transform (STFT), with acoustic signature databases for the detection and classification of drones and UAVs using conventional and AI-based architectures. MFCC is a set of reflected human perception features of sounds, which is used in audio classification when paired with machine learning approaches. STFT is considered an intermediate feature compared to MFCC. MFCC compresses signals while representing them with coefficients set. On the other hand, STFT features contain more information and noise than MFCC, giving STFT an advantage. Deep learning models can easily adopt STFT and manage it given more complex data [37]. Authors proposed a machine learning framework in [38], shown in Figure 4, to detect and classify ADr sounds in a noisy environment, among other sounds. The required features are extracted from ADr sound using the feature extraction techniques of MFCC and linear predictive cepstral coefficients (LPCC). Following the feature extraction process, these sounds are then identified using SVMs. The results show that the SVM cubic kernel with MFCC outperforms the LPCC technique by detecting ADr sounds with 96.7% accuracy. Acoustic-based technologies are effective for detecting UAVs since they are not affected by the UAV's frequency range, weather fluctuations, e.g., fog, environmental disturbance, and noise. Hence, such technologies do not block the acoustic sensors' earshot to detect the UAV's acoustic signals. Acoustic signals produced by the engine and propeller blades of the UAV are collected and processed to classify the UAV and calculate its distance, direction, and location [33]. Authors in [39] proposed a CNN-based system to detect drones using acoustic signals received by a microphone. STFT magnitude is used as the two-dimensional feature in the study since drones' harmonic properties differ from those of other devices that make a similar noise. The dataset comprised 68,931 and 41,958 frames of drone and non-drone sounds collected using DJI Phantom 3 and 4 drones flying outdoors. The proposed approach has a detection rate of 98.97% for the 100-epoch model and a false alarm rate of 1.28. Figure 5 illustrates the system overview of the proposed approach.
## 1 Introduction  
Obviously, the operational loading for which a real structure is designed can differ from the real loading in many cases. Based on statistical records, the majority of damages and collapses of structures in operation are caused merely by a change in operating conditions, except for those caused by a human factor [1, 2]. In the case of pressurized pipelines (e.g., gas transit systems, oil pipelines, or other liquid pipeline systems under pressure), the piping systems may be subjected to a quasi-static loading or of a predominantly variable loading due to a varying pressure of the operating medium. An expected design loading may change only in some locations, which are really difficult to predict in the design stage. In the case of compressor stations, for instance, or the geometrical-fluid ratios in the closed side branches, the setting of the compressor's operating point can cause dynamic phenomena, i.e., pipeline vibrations, as pointed out in [3, 4, 5, 6, 7, 8, 9].  
Other changes occurring in the operation of piping systems may occur in the subsoil of the pipeline. For example, the subsoil slide on a slope or subsoil slip after a prolonged rainfall can change the design of the pipeline stress. Most accidents of the pipeline systems may arise as a result of the following simultaneous interactions (\"system synergy\") of unexpected changes during the operations, e.g.,  
* internal pressure with induced vibrations,
* additional bending stress from the subsoil drop loaded with an internal pressure, and
* the pipeline wall thickness decreases caused by corrosion at the point of additional bending stress with an internal pressure, etc.  
The occurrence of such nonstandard and unplanned situations in a real operation is generally difficult to predict. The only possible solution for how to record these states is to develop and implement monitoring systems that can monitor not only the operating parameters, such as the pressure and temperature of transported media, but, also, the construction parameters, enabling to determine the reliability and operational safety [10]. Based on ten years of our experience with the development of several safety monitoring systems of pipeline operations in the field of gas transportation, two systems have turned out to be beneficial: the monitoring of dangerous vibrations and the monitoring of pipes with corrosion defects and additional bending.  
Hence, the objective of this paper is to further analyze three important problems that arise out of the previously stated; that is, to determine:  
* which quantities should be measured continuously by sensors,
* what are their permissible limit values for a safe operation of the pipeline systems, and
* how to predict the corrosion process and its synergy with additional bending loading.
## 2 Materials and Methods
### Construction of the PSAD:GPD2:PSAD Transgene and Generation of Transgenic Strains  
The _GPD2_ (Cre01.g053000) coding sequence was amplified by PCR from plasmid pIVEX-GPD2 [15] by using primers GPD2_F (5\({}^{\prime}\)-GGTACCATGATGCTGTCAGGCGCACCGTG) and GPD2-AcV5_R (5\({}^{\prime}\)-GAATTCtcagcGCGCgtgggcttccagctCACGCCTGTTGTGGCAGC). The reverse primer inserted the sequence of the AcV5 tag [33] at the 3\({}^{\prime}\) end of the _GPD2_ CDS. This PCR product was cloned into pSTBlue1 (Novagen, Madison, WI, USA), verified by sequencing, and then excised as a GPD2-AcV5 fragment by successive digestion with _KpnI_, blunting by T4 polymerase treatment, and digestion with _EcoRI_. Simultaneously, plasmid NE589, derived from pGenD containing _PSAD_ regulatory sequences [34], was digested with _NcoI_, blunted by T4 polymerase treatment, and digested with _EcoRI_. The linearized NE589 plasmid and the GPD2-AcV5 fragment were then ligated, and this new construct was digested with _Nsi_, blunted by T4 polymerase treatment, and self-ligated to generate the _PSAD:GPD2:PSAD_ transgene in the proper reading frame within plasmid NE589. This transgene was excised by digestion with _KpnI_ and _Xbal_ and inserted by blunt-end cloning, after treatment with T4 DNA polymerase, into the _EcoRI_ site of plasmid pSP124S, which contains the Zeocin-resistance Ble\({}^{\prime}\) selectable marker [35]. The final plasmid was transformed into CC-124 by electroporation, as previously described [36], and colonies were selected on TAP-agar plates containing 16 \(\mu\)g mL\({}^{-1}\) zeocin (Invitrogen, Carlsbad, CA, USA, R25001). Expression of the GPD2 recombinant protein was corroborated in several independent transgenic strains by immunoblotting with an anti-AcV5 monoclonal antibody (please see below).
## 4 Results
### Prediction Performance in Different Depths Beneath the Subgrade  
To further assess the suitability of the NARX network model for predicting temperatures at varying subgrade depths, prediction results across these depths were compared. This comparison not only gauged the model's predictive accuracy and generalization but also confirmed its applicability in real-world engineering scenarios.  
Upon examining Figure 10 and Table 4, it is evident that the NARX network consistently outperforms the BP network in predictive accuracy across various subgrade depths and all evaluated metrics. Specifically, at a depth of 0.5 m below the subgrade, the NARX model's performance improved by 76%, 74%, and 53% in terms of RMSE, MAE, and R\({}^{2}\) metrics, respectively. At depths of 1.5 m, 2.9 m, and 4.7 m, the respective improvements were 57%, 54%, and 9%; 58%, 64%, and 3%; and 56%, 54%, and 1%. These findings highlight  
Figure 10: Model prediction comparison at different subgrade depths: (**a**) 0.5 m beneath the subgrade surface; (**b**) 1.5 m beneath the subgrade surface; (**c**) 2.9 m beneath the subgrade surface; (**d**) 4.7 m beneath the subgrade surface.  
the superior capability of the NARX model to predict temperatures beneath seasonally frozen subgrades compared to the BP model.
## 2 Background and Related Work
### Background
#### 2.1.6 The Common Vulnerabilities and Exposures (CVE)  
To ensure a standardized and widely understood approach to identifying and discussing cybersecurity flaws, our work references Common Vulnerabilities and Exposures (CVE). CVE is not a database in itself but rather a dictionary or catalog of publicly known information security vulnerabilities and exposures. Established and maintained by the MITRE Corporation with U.S. government funding, the primary purpose of the CVE program is to provide unique, standardized identifiers (known as CVE IDs, e.g., CVE-2024-12345) for specific security vulnerabilities found in software, firmware, or hardware [36].  
Each CVE ID comes with a brief, standardized description of the vulnerability. This common naming convention is crucial because it allows security professionals, researchers, vendors, and organizations worldwide to refer to the exact same issue using a universal language. Without CVE, different entities might use their own names for the same vulnerability, leading to confusion, missed patches, and inefficient communication.  
While the CVE list itself provides only a basic description and identifier, these CVE IDs often serve as a key reference point for more detailed information found in other public resources, such as the National Vulnerability Database (NVD) [37]. The NVD enriches CVE entries with additional details, including severity scores (often using the Common Vulnerability Scoring System, CVSS), potential impacts, and suggested remediation steps. In essence, CVE acts as the common 'name tag' for a vulnerability, facilitating efficient information sharing, coordinated response efforts, and improved overall cybersecurity posture across the global community. Organizations use CVEs to track, prioritize, and address security threats effectively.
## 3 Semantic Service Description and Context-Based Orchestration
### Semantic Annotation of Web Services  
The modeled ontologies described in Section 3.2 can be used for the semantic description of the web services provided by field devices based on OWL-S. Because OWL-S itself is based on OWL, it allows an easy integration with our domain and application ontologies. For each operation of a web service, an OWL-S AtomicProcess is created using Protege 3.2.1 and the OWL-S Editor plugin. Thereby, the WSDL2 OWL-S Tool is used initially to create the basic structure and the grounding of the OWL-S AtomicProcess. In the next step, the OWL-S description of a service operation is connected to the device ontology by referring to the respective device instance using the OWL-S  
Figure 4: Part of the parameter ontology.  
serviceCategory attribute. The semantic description of the input and output parameters is integrated in the OWL-S Grounding, which basically describes a mapping between syntactical descriptions of a web service (e.g., WSDL files) and the parameter concepts of our parameter ontology. The semantic annotation of further functional and non-functional service properties are modeled using the OWL-S serviceParameter reference. By modeling each aspect as a subclass of ServiceParameter, we are able to utilize arbitrary semantic mappings to our sub ontologies. In this way, instances such as the consumed resource or provided function can be referenced, but also numeric values can be assigned (e.g., operating hours of a device).  
In addition to the direct annotation of our services in OWL-S, further implicitly assigned knowledge about the service can be used in the later matching, selection and orchestration processes using a reasoning system. For instance, as the OWL-S description of a service is assigned to its corresponding device ontology, which is again connected to the plant ontology, structural knowledge about the plant can be used to determine if the present service is dependent on other services in the plant. The same holds true for domain-specific knowledge modeled in the equipment ontology, for example.
## Nomenclature  
\begin{tabular}{l l} HWB & Hybrid Wing Body \\ AOA & Angle of Attack \\ CFD & Computational Fluid Dynamics \\ CSD & Computational Structure Dynamics \\ RBD & Rigid-Body Dynamics \\ RANS & Reynolds-averaged Navier-Stokes equations \\ MN & Mach Number \\ AVL & Athena Vortex Lattice \\ FSI & Flutter Speed Index \\ FEM & Finite Element \\ UDF & User Defined Function \\ DLM & Double-let Lattice Method \\ \(\Phi\) & Matrix of Mode Shape \\ \(M\) & Matrix of Mass \\ \(C\) & Matrix of Damping \\ \(K\) & Matrix of Stiffness \\ \(u,v,w\) & Component of Velocity relative to body frame \\ \(p,q,r\) & Component of Angular Velocity relative to body frame \\ \(\phi\) & Thin Plate Spline Basic Function \\ \(H\) & Solid/Fluid Interface Interpolation Matrix \\ \(x_{cm,l},y_{cm,l},z_{cm,l}\) & Center of Mass of Aircraft in Inertial Frame \\ \(\varphi_{l},\theta_{l},\psi_{l}\) & Attitude angle of Aircraft in Inertial Frame \\ \(x_{cm,c},y_{cm,c},z_{cm,c}\) & Center of Mass of Aircraft in CFD Frame \\ \(\varphi_{l},\theta_{l},\psi_{l}\) & Attitude angle of Aircraft in CFD Frame \\ \(x_{f,C},y_{f,C},z_{f,C}\) & Coordinates of Fuselage Mesh in CFD Frame \\ \(x_{e,C},y_{e,C},z_{e,C}\) & Coordinates of Elevator Mesh in CFD Frame \\ \(x_{f,B},y_{f,B},z_{f,B}\) & Coordinates of Fuselage Mesh in body Frame \\ \(x_{e,B},y_{e,B},z_{e,B}\) & Coordinates of Elevator Mesh in body Frame \\ \(x_{f,B},y_{f,B},z_{f,B}\) & Coordinates of Deformed Fuselage Mesh in body Frame \\ \(x_{e,B},y_{e,B},z_{e,B}\) & Coordinates of deflected Elevator Mesh in body Frame \\ \(dx_{f,B},dy_{f,B},dz_{f,B}\) & Displacement on Coordinates of Fuselage Mesh in body Frame \\ \(\Delta\hat{e}_{e}\) & Deflected Angle of Elevator \\ \end{tabular}
###### Abstract  
As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system.  
spatio-temporal big data; subvector commitment; blockchain; smart construct +
Footnote †: journal: Geo-Informat  
Mdpj
## 2 Theoretical Basis
### Dessensitization Design Method  
In this section, a design method for a catadioptric optical system with low tilt sensitivity of the secondary mirror is proposed. The design process is shown in Figure 3, which is mainly divided into four steps:  
1. Construct the initial system according to the design index of the optical system;
2. Imaging performance optimization. In the optimization process, parameters such as thickness, radius, conic coefficient are used as optimization variables, and the structure size of the optical system is controlled at the same time. For example, if the designed system is a catoptric system, considering the requirements of processing, it is necessary to limit the interval between the primary and secondary mirrors, and at the same time, the distance from the last surface to the image surface can not be too small.  
After optimization, the system that meets the imaging performance requirements enters the desensitization design step;
3. Tilt sensitivity optimization. The core of the desensitization design process is to control the desensitization function \(C\). Within the value range of \(C\), continuously reduce the value of \(C\) until the optical system meets the requirements of imaging quality and low tilt sensitivity of the secondary mirror at the same time, and then output the system;
4. Verify tilt sensitivity. To avoid the special case where the optimized system is low sensitivity, a sensitivity simulation of a large sample of the optical system can be performed. The core of this step is to establish a Monte-Carlo simulation of a large sample of the system, and to evaluate the results of the Monte-Carlo analysis. If the imaging quality requirements are met and the performance distribution is concentrated, the design can be output as the final result. If the requirements are not met, the system needs to be optimized again.