esa-sceva/satcom-chunk-collection huggingface dataset:
# A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform
## 2 Related Work
### Multi-Radius Local Binary Pattern Descriptors
In this case, we consider the four discrete pixel points Q\({}_{\text{11}}\), Q\({}_{\text{12}}\), Q\({}_{\text{21}}\), and Q\({}_{\text{22}}\), with coordinates (x1, y1), (x\({}_{\text{1}}\), y2), (x\({}_{\text{2}}\), y1), and (x\({}_{\text{2}}\), y2), respectively. The pixel value corresponding to this sampling point on the image can be approximated as f(x, y). Note that, here, f(, ) represents only the expression for calculating the pixel value at this virtual coordinate point. For point Qij that  
Figure 2: Schematic diagram of bilinear interpolation. can be determined on the discrete image, \(f(\text{Qij})\) equals the pixel value of that point without needing to use the function expression calculation, as shown in Formula (3). \[ \begin{array}{c}f(x,y)\approx\frac{f(Q_{11})}{(x_{2}-x_{1})(y_{2}-y_{1})}(x_{2 }-x)(y_{2}-y)+\\ \frac{f(Q_{21})}{(x_{2}-x_{1})(y_{2}-y_{1})}(x-x_{1})(y_{2}-y)+\\ \frac{f(Q_{22})}{(x_{2}-x_{1})(y_{2}-y_{1})}(x_{2}-x)(y-y_{1})+\\ \frac{f(Q_{22})}{(x_{2}-x_{1})(y_{2}-y_{1})}(x-x_{1})(y-y_{1})\end{array}  \tag{3}\]  
Unlike traditional zero-watermarking algorithms, the construction of the feature matrix based on SIFT does not aim to compute the overall feature information of the image, but instead focuses on computing various local feature descriptors centered around keypoints. As the feature matrix generated by the MrLBP algorithm consists of binary elements (0 s and 1 s), while the keypoint feature matrix after feature compression has not undergone binarization, it requires the introduction of a threshold for binarization to ultimately generate a binary matrix. To enhance the security of the algorithm, the zero-watermark feature matrix needs to adhere to statistical principles in cryptography, specifically maintaining a balance between the quantities of 0 s and 1 s in the matrix. To achieve this, when invoking the MrLBP algorithm to generate texture features, the algorithm in this paper also counts the occurrences of 0 s and 1 s in the statistical MrLBP descriptors. This step statistically constrains the binary matrix of keypoint descriptors in terms of quantity relationships. Multiple vectors correspond to multiple thresholds, and the use of different thresholds with overall constraints strengthens the coordination of various local regions in the image, thus improving the overall security and robustness of the algorithm. In conclusion, the algorithm combines the feature matrix of compressed keypoint descriptors with the local texture feature matrix to generate the SIFT feature matrix, which exhibits excellent performance. The matrix dimension of the SIFT feature matrix can be flexibly adjusted according to the size of the target copyright image, aiming to retain the stable feature information of digital images as much as possible. Moreover, it innovatively integrates texture feature information, demonstrating good distinguishability and robustness in subsequent experiments.
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 3 System Architecture
### Cluster Node Stack  
Figure 2 presents a comprehensive architecture of the main interconnected components that comprise a single node in our cluster design. The architecture is organized into three main sections: the _Local User's GUI_, the _Virtualization Stack_, and the _Operating System_, all unified under the _Cluster Node_.  
Figure 1: High-level architecture schema.  
On the left, the _Local User's GUI_ serves as the interface through which users seamlessly interact with the virtual environment. Key components, including the _Controller_ and _VNC Client_ within the _VM Viewer_, provide direct control and monitoring capabilities for virtual machines, enabling local users to manage the VMs intuitively and effectively. A _Desktop Environment_ is required for these components to function properly.  
At the heart of the diagram, the _Virtualization Stack_ drives the entire virtualization process. A _Web Application_ layer at the top enables remote access and management, offering a centralized web-based interface for streamlined control. Directly beneath, the _Virtualization Management_ layer orchestrates the entire virtual infrastructure, overseeing the deployment, monitoring, and administration of both virtual machines and containers. This centralized management ensures cohesive and efficient control over all virtualized resources and provides the functionality for connecting each node to the Virtualization Cluster.  
Within the _Virtualization Stack_, the _Hypervisor_ and _OS Virtualization_ components represent the two main virtualization technologies powering the system. The _Hypervisor_ facilitates the creation of _Virtual Machines_ (VMs), each operating as an independent, fully isolated environment with its own dedicated resources and operating system. This design is ideal for use cases requiring high levels of isolation and security. In contrast, _OS Virtualization_ supports _Containers_, which are lightweight and efficient and share the host OS kernel while remaining securely isolated from one another. Containers are well-suited for rapid scaling and microservices architectures, allowing for agile and resource-efficient deployments.  
The _Operating System_ layer at the base underpins the entire setup, providing essential resources such as CPU, memory, and storage for both VMs and containers. As the foundation for the Virtualization Stack, it establishes a stable and reliable environment that enables seamless operation and scalability across all virtualized workloads.  
Overall, this cluster node architecture provides a robust, flexible framework that integrates user-friendly interfaces, powerful virtualization management, and efficient resource allocation. Its dual support for VMs and containers ensures that diverse workload requirements are met with precision and agility, making it an ideal solution for modern cloud, data center, and enterprise environments.  
Figure 2: Architecture of cluster’s single node.
## 6 Simulation Results
### Energy Consumption
#### 6.2.1 Total Energy Consumption  
At first, the energy consumption is studied with different threshold value of trail age \(\varepsilon\). The \(\varepsilon\) varies from 0 to 100, and the \(\tau_{A}\) is fixed at 5. As shown in Figure 7, the energy consumption in TS and TS-R decreases with the growth of \(\varepsilon\). A larger \(\varepsilon\) leads to a shorter search phase and a longer chase phase. However, the reduced energy consumption in the search phase always dominates the total energy consumption. In addition, comparing with TS, the energy consumption in TS-R drops more obviously with the growth of \(\varepsilon\). The reason for this is that a larger \(\varepsilon\) leads to more redundancy, and the effect of redundancy reduction scheme grows in this scenario. Since there is no trail information in ERS and XYLS, their energy consumption has no relation with \(\varepsilon\).  
Figure 6: Delivery rate of TS with different footprint update interval.  
Then we study the energy consumption with different waiting time of actor \(\tau_{A}\). The \(\tau_{A}\) grows from 3 to 8, while the \(\varepsilon\) is set to be \(20\times\tau_{A}\) to ensure the actor trails have the same length. Figure 8 shows that the energy consumption in TS and TS-R decreases with the growth of \(\tau_{A}\). Compared with ERS, the advantage of TS increases with the growth of \(\tau_{A}\). These results support the analysis given in Section 5.3. A larger \(\tau_{A}\) reduces the process of the chase phase and hence reduce the energy consumption in TS. Meanwhile, the energy consumption in TS drops more than that in TS-R. The reason for this is that the transmission redundancy decreases with the growth of \(\tau_{A}\). On the other hand, the energy consumption in XYLS grows dramatically when the \(\tau_{A}\) decreases. The reason for this is that a smaller \(\tau_{A}\) means the actor moves faster. In this case, the frequency of location update should increase to guarantee reliable transmission which leads to the growth of energy consumption. Therefore, we can conclude that the energy efficiency of TS is better than ERS and XYLS, and the advantages is larger with larger \(\varepsilon\) and smaller \(\tau_{A}\).
## 4 Simulation Results
### Modal Analysis to Determine Critical Frequencies  
The CAD model of the engine rotor before the bird strike event and the deformed CAD model of the engine rotor after the bird strike are shown in Figures 15 and 16, respectively.  
In this case, the eigenvalue frequencies were obtained for the jet engine rotor at a rotational speed from 0 to 21,000 RPM at different load steps, using increments of 3000 RPM in each increasing load step. A comparison of the critical frequencies before and after the bird strike is shown in Table 4.  
Figure 16: Deformed model after the bird strike.  
Figure 15: Initial model.  
From Table 4, we can observe that all of the extracted modes were stable. It can be observed that before the bird strike event, the first forward whirl critical mode was excited at 3074 RPM. The second forward whirl critical mode was excited at 6405 RPM. It is noted that as the load step RPMs increased, the forward whirl frequencies increased and the backward whirl frequencies decreased. The deformation plots of the first conical and first bending mode before the bird strike event are shown in Figures 17 and 18, respectively.  
In the modal analysis of the engine rotor after the bird strike, the first forward whirl critical mode was excited at 3149 RPM. A difference of 91 RPM was noted in the excitation of mode. Similarly, the second forward whirl critical mode was excited at 7286 RPM. There was difference of 881 RPMs in the mode excitation after the bird strike. This is a significant increase in the required RPM for the mode excitation.  
Table 4 shows that the frequencies of the critical modes of the engine rotor were increased after the bird strike. Also, the critical modes were excited at higher RPMs after the bird strike compared with the values of the excitation of the critical modes before the bird strike. Table 4 also shows that the whirl direction of some of the modes was changed after the bird strike event. It is noted that there was no change in the whirl direction of the first four modes. However, the whirl direction of modes five, seven, eight, nine, eleven, twelve, fourteen, and fifteen were reversed.  
The deformation plots of the first conical and first bending mode after the bird strike event are shown in Figures 19 and 20, respectively.  
The Campbell diagrams of the analyses before and after the bird strike are shown in Figures 21 and 22, respectively.  
Figure 21: Campbell diagram before the bird strike.  
Figure 20: First bending mode after the bird strike.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 3 Background
### BGP Control Messages  
The BGP exchanges messages between ASs using the Transmission Control Protocol (TCP). The TCP port number 179 is allocated exclusively for the BGP protocol. The advantage of using TCP is avoiding the BGP control plane having to manage message delivery and flow control between BGP speakers (or peers), which simplified the BGP design. Thus, a BGP peer has a reliable way to exchange Network Layer Reachability Information (NLRI) and compose the Routing Information Base (RIB).  
Each BGP peer needs to be manually configured with a set of parameters to enable a BGP connection [6; 19]. There are four types of BGP control messages to be exchanged after a TCP connection is established between two BGP peers [6]:  
* OPEN message: sent to open a BGP session and to verify the connection's parameters;
* UPDATE message: to transfer network reachability information by advertising and withdrawing routes;
* KEEPALIVE message: periodically sent to ensure that the connection between the peers is still reachable;
* NOTIFICATION message: used in response to special or error conditions.  
Additionally, the BGP is a path vector protocol, and it uses a sequence of Autonomous System Numbers (ASNs) for characterizing the path. Thereby, when the BGP updates travel through different ASs, the BGP routers prepend their ASN to the AS-Path attribute. The AS-Path attribute of BGP carries the ASNs that a given network prefix traversed. If a router receives a BGP update message and detects its own ASN in the AS-Path attribute, then the router will ignore the update because it is a routing loop once the message has already passed through the AS.  
Each BGP update message contains NLRI and BGP information to apply the routing process. For the BGP, the RIB is constructed through the BGP decision process executed in the BGP control plane.
###### Abstract  
Bearings are one of the most critical components in many industrial machines. Predicting remaining useful life (RUL) of bearings has been an important task for condition-based maintenance of industrial machines. One critical challenge for performing such tasks in the era of the Internet of Things and Industrial 4.0, is to automatically process massive amounts of data and accurately predict the RUL of bearings. This paper addresses the limitations of traditional data-driven prognostics, and presents a new method that integrates a deep belief network and a particle filter for RUL prediction of hybrid ceramic bearings. Real data collected from hybrid ceramic bearing run-to-failure tests were used to test and validate the integrated method. The performance of the integrated method was also compared with deep belief network and particle filter-based approaches. The validation and comparison results showed that RUL prediction performance using the integrated method was promising.  
progostics; deep learning; particle filter; bearing; RUL prediction +
Footnote †: journal: Applied sciences
# Aspects of Cyber Security in Autonomous and Connected Vehicles
## 6 Defenses or Countermeasures for Autonomous and Connected Vehicles
### Defenses for V2X
#### 6.1.2 Intrusion Detection Systems (IDS)  
Networks play an important role in a vehicle to keep connectivity and communication. The automobile includes wired and wireless networks which are targets of the attacker. IDS is a reliable method to secure networks. IDS is carried out by signature-based detection and anomaly-based detection [86; 87].  
In this approach signatures of the various attacks are stored in the database and retrieved to compare. If the comparison matches it detects the related attack shown in Figure 7a. In [88] this method is shown for vehicle movement data and it can detect a single fake vehicle and detect sybil attacks. In [89] a novel IDS was developed for detecting false messages in VANETs. It uses a dynamic engine to analyze and monitor the data. The detection rate of fake messages is very high with this method.  
Figure 6: (a) Symmetric encryption (b) Asymmetric encryption.  
The signature-based approach has the drawback of detecting new attacks. The anomaly-based approach is designed to overcome it by predefining the baseline for normal cases. If a new type of attack appears it will detect the abnormal conditions with baseline as shown in Figure 7b. In [90] a presence evidence system (PES) was developed to detect the sybil attacks. This method considers the analysis of signal strength distribution to detect the vehicle location. If the vehicle's position is known, then it is easy to detect sybil attacks. In [91] an AECFV method was developed to detect blackhole, wormholes, and sybil attacks. This kind of IDS is carried out in three levels of cluster members and heads and roadside units with the help of a supervised machine learning algorithm. In [92] a domain-aware IDS method was developed to detect falsification information. This method used the ternary content addressable memories (TCAMs) approach for achieving anomaly IDS for the CAN bus to detect traffic on the CAN bus to find fake information.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 4 Propulsion Systems in Nanosatellites
### Propulsion Fundamentals in Nanosatellites
\[P=\frac{Fl_{sp}g_{0}}{2\eta} \tag{5}\]  
For example, ENPULSION's NANO FEEP thrusters operate with a power input of 5-10 W, generating thrust levels of 0.1 mN at exhaust velocities exceeding 10,000 m/s. This high efficiency allows nanosatellites to achieve long-duration operations while staying within power constraints. The power-to-thrust ratio is one of the most critical parameters in an electric propulsion system. It measures the power required to produce a certain amount of thrust, given the propellant exhaust velocity (\(v_{e}\)) and thruster efficiency (\(\eta\)), as shown in Equation (6). \[\frac{P}{F}=\frac{v_{e}}{2\eta} \tag{6}\]  
For electric systems such as Hall-effect thrusters, achieving a balance between power input and thrust efficiency is crucial. For instance, a Hall thruster with an exhaust velocity of 15,000 m/s and efficiency of 60% has a power-to-thrust ratio of approximately 12.5 kW per Newton. This highlights the trade-offs involved in power availability for nanosatellites. Nanosatellites primarily operate in LEO, with altitudes typically ranging between 500 and 800 km. While LEO offers reduced launch costs and shorter communication delays, it imposes significant constraints due to atmospheric drag, which accelerates orbital decay. To counter this, nanosatellites often rely on low-thrust propulsion systems for station-keeping and orbit adjustments. Power availability is another critical factor, as nanosatellites are limited to solar arrays capable of generating 20-50 W of power. This constraint significantly influences system operations, including data transmission, sensor operations, and propulsion performance. For example, electric propulsion systems, while efficient, require substantial onboard power, making them challenging to use in nanosatellites with tight energy budgets. Typical mission durations for nanosatellites range from 6 months to 3 years, depending on altitude, power efficiency, and the presence of propulsion systems. Missions like the PlanetScope constellation mitigate orbital decay by deploying new satellites periodically, while other systems, such as SNAP-1, utilize cold gas thrusters for extended lifetimes.
# Quantitative Analysis of Caffe in Roasted Coffee: A Comparison of Brewing Methods
## 3 Results and Discussion
### Caffee Determination Methods
All three methods were based on extracting caffeine from coffee samples with chloroform, spectrophotometric measurement of absorbance, and calculating caffeine content from a calibration curve. The main differences were the wavelength used for the measurement and the method of sample preparation and calibration. In contrast, in a study by [PERSON] et al. [35], coffee extracts were cold-dried and then dissolved in D\({}_{2}\)O to a concentration of 5-20 mg/mL, followed by sonication and centrifugation before NMR analysis. An internal standard was added to the supernatant, the pH of the samples was adjusted, and \({}^{1}\)H NMR spectroscopy was performed using appropriate pulse sequences. In turn, comparing the HPLC method to other methods of quantitative determination of caffeine, in the study by [PERSON] et al. [46], an HPLC method was used to determine caffeine content in addition to the spectrophotometric method. The caffeine content of coffee determined by the HPLC method was higher than the caffeine content determined spectrophotometrically. The authors indicated that the HPLC method is more advantageous as it allows faster analysis, minimizes analyte loss during sample preparation, and results are more accurate and reproducible. In conclusion, the HPLC method is the most common method for determining caffeine in roasted coffee. Caffeine analysis in different studies using the HPLC method shows significant differences in the parameters and procedures used. Each parameter applied during the analysis may have significantly impacted the quantitative determination of caffeine, potentially influencing the accuracy and precision of the results. Future research should prioritize the standardization of methods to ensure greater comparability of results across studies. Consistent parameters such as mobile phase composition, detector types, and column configurations would help minimize inter-study variability. This approach would enhance understanding of how caffeine content correlates with the brewing method, coffee's geographic origin, and other key characteristics.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 3 Interest Flooding Attack: Concept and Taxonomy
### Types of IFAs
#### 3.1.1 Fake Interest Attacks  
In a Fake Interest Attack (FIA), attackers generate interest packets requesting non-existent or intentionally invalid content. These interests are designed to have no corresponding data packets, causing them to remain unresolved in the PIT of NDN routers until they time out. The accumulation of such unsatisfiable interests can exhaust PIT resources, leading to network congestion and denial of service for legitimate users [24]. The FIA attack exploits the stateful nature of NDN's forwarding plane, where each interest packet consumes memory resources until satisfied or expired. Mitigation strategies involve monitoring interest patterns to detect anomalies and implementing rate-limiting mechanisms to prevent PIT overflow. However, distinguishing between legitimate and malicious interests remains a significant challenge [11; 25].
## 3 Methodology
### eXtreme Gradient Boosting  
eXtreme Gradient Boosting [30] (XGBoost) is a scalable machine learning technique for tree gradient boosting. XGBoost employs a methodology similar to other gradient boosting techniques, constructing a mathematical framework to predict \(y_{i}\) from input \(\mathbf{x}_{i}\)  
Specifically, XGBoost forms an ensemble \(\mathcal{F}=\{f(\mathbf{x})=w_{q(\mathbf{x})}\}(q:\mathbb{R}^{m}\to T,w\in \mathbb{R}^{T})\) comprising K Classification and Regression Trees (CART). Each \(f_{k}\) corresponds to a distinct tree \(q\) with its own structure characterized by the number of leaves \(T\) and the set of leaf weights \(w\). Given this established framework, the predicted output is determined by a \(K\) additive function as follows:  
\[\hat{y}_{i}=\phi(\mathbf{x})=\sum_{k=1}^{K}f_{k}(\mathbf{x}_{i}),\;f_{k}\in\mathcal{F} \tag{2}\]  
In this specific instance, the data values \(\mathbf{x}_{i}\) include factors such as the geographical location of the evaluated section, the time of data collection, and other data engineering variables discussed later. Rather than purely categorizing like conventional decision trees, this model incorporates continuous scores obtained from the weights \(w_{i}\) associated with each leaf across all defined trees \(q\). To enable the learning process of the \(K\) specified trees, a convex loss function acts as a regularized objective:  
\[\mathcal{L}(\phi)=\sum_{i=1}^{n}l(\hat{y}_{i},y_{i})+\sum_{k=1}^{K}\Omega(f_{k}) \tag{3}\]  
Here, \(l\) represents a differentiable loss function measuring the disparity between the target \(\hat{y}_{i}\) and the predicted value \(y_{i}\), while \(\Omega\) is defined as a regularization term penalizing potential overfitting of the model expressed as follows:  
\[\Omega(f)=\lambda T+\frac{1}{2}\lambda||w||^{2} \tag{4}\]  
Instead of adhering to optimization methods based in the Euclidean space, XGBoost undergoes training via an additive approach. In this method, for a given \(i\)-th instance at the \(t\)-th iteration, a distinct \(f_{t}\) is introduced to minimize the objective function, represented as follows:  
\[\mathcal{L}^{(t)}=\sum_{i=1}^{n}l(y_{i},\hat{y}_{i}^{(t-1)}+f_{t}(\mathbf{x}_{i})) +\Omega(f_{t}) \tag{5}\]  
Here, \(\hat{y}^{(t)}\) denotes the prediction at that specific instance and iteration. This process means that \(f_{t}\) is incrementally included based on its performance across optimization instances and iterations.