esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 2 Related Work
### Ransomware Attacks and Trends  
On 21 February 2024, Change Healthcare, a key healthcare technology unit of United-Healthcare Group, was severely disrupted by a ransomware attack attributed to the ALPHV (also known as BlackCat) ransomware group. This breach, as reported by The Washington Post and later confirmed by UnitedHealth Group, exposed the data of an estimated 190 million Americans and crippled critical operations across the U.S. healthcare system. The immediate consequences were widespread; pharmacies experienced significant disruptions in processing insurance claims and paying providers, often forcing them to resort to manual workarounds and cash transactions. This incident serves as a stark reminder of the acute vulnerability of essential healthcare infrastructure to the devastating effects of  
Figure 1: Generalized view: classic internet-bridged Supervisory Control and Data Acquisition (SCADA) architecture.  
ransomware attacks, highlighting the critical need for robust cybersecurity measures within the sector. Consequently, pharmacies experienced widespread disruptions in processing insurance claims and paying providers, forcing them to rely on manual workarounds and cash transactions. This incident serves as a stark reminder of the acute vulnerability of essential healthcare infrastructure to the devastating effects of ransomware.  
Beyond healthcare, Phobos ransomware has remained a persistent threat since May 2019, utilizing a Ransomware-as-a-Service model to target government, education, and other critical infrastructures. These groups employ techniques such as network intrusion, data exfiltration, and encryption, prompting U.S. government agencies to issue urgent mitigation guidelines.  
The year 2021 witnessed two devastating ransomware attacks that laid bare the vulnerabilities of U.S. critical cyber-physical infrastructure. In May, the Colonial Pipeline attack crippled fuel supplies across the East Coast, sparking widespread panic buying, severe shortages, and soaring prices. This disruption forced the company to capitulate, paying a USD 4.4 million ransom. Just weeks later, JBS, a global meat processing giant, suffered a similar fate, halting operations in the U.S., Canada, and Australia and ultimately succumbing to a USD 11 million ransom demand. Both attacks were executed using sophisticated ransomware variants, notably Ryuk, highlighting the escalating threat to vital sectors.  
These incidents highlight the growing sophistication and persistence of ransomware campaigns and their capacity to inflict widespread economic and social disruptions. They also emphasize the urgent need for organizations managing critical infrastructure to implement robust cybersecurity measures and ensure regular system updates and patching to mitigate these escalating risks.
###### Abstract  
Connected and automated vehicles (CAVs) as a part of Intelligent Transportation Systems (ITS) are projected to revolutionise the transportation industry, primarily by allowing real-time and seamless information exchange of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). However, these connectivity and automation are expected to offer vast numbers of benefits, new challenges in terms of safety, security and privacy also emerge. CAVs continue to rely heavily on their sensor readings, the input obtained from other vehicles and the road side units to inspect roadways. Consequently, anomalous reading of sensors triggered by malicious cyber attacks may lead to fatal consequences. Hence, like all other safety-critical applications, in CAVs also, reliable and secure information dissemination is of utmost importance. As a result, real time detection of anomaly along with identifying the source is a pre-requisite for mass deployment of CAVs. Motivated by this safety concerns in CAVs, we develop an efficient anomaly detection method through the combination of Bayesian deep learning (BDL) with discrete wavelet transform (DWT) to improve the safety and security in CAVs. In particular, DWT is used to smooth sensor reading of a CAV and then feed the data to a BDL module for analysis of the detection and identification of anomalous sensor behavior/data points caused by either malicious cyber attacks or faulty vehicle sensors. Our numerical experiments show that the proposed method demonstrates significant improvement in detection anomalies in terms of accuracy, sensitivity, precision, and F1-score evaluation metrics. For these metrics, the proposed method shows an average performance gain of 7.95%, 9% 8.77% and 7.33%, respectively when compared with Convolutional Neural Network (CNN-1D), and when compared with BDL, the corresponding numbers are 5%, 7.9% 7.54% and 4.1% respectively.  
connected and automated vehicles; discrete wavelet transform; intelligent transportation system; Bayesian deep learning; convolution neural network 2020 10 7833; doi:10.3390/appl0217833
## 2 Previous Work
### Motivation and Contributions  
Most of the vector based forwarding schemes are merely focused on the packet forwarding by estimating the holding time using the node's directional and position information. Energy efficiency is achieved through transmission power adaptation but not the available energy of the nodes. The proximity closeness of the node towards the sink minimizes the end-to-end delay. Inspired from the above discussion, we proposed a novel energy scaled and expanded vector based (ESEVBF) for UASNs. ESEVBG estimates holding time of the potential forwarders by keeping the following points under consideration.  
1. The holding time of all the potential forwarders is scaled using the neighboring nodes' energy information. It increases the holding time _difference_ between them even for a small variation in the energy level of neighbors.
2. The expanded proximity closeness ratio of the forwarding candidate nodes towards the virtual pipeline between sender and sink is added in holding time computation to signify the node preference. Both (1) and (2) scale and signify the holding time difference between the candidate forwarders for small parameter variance. This ensures that all nodes in the transmission range of the suitable forwarder (with minimum holding time) must receive copy of the packet before their holding time expiration.
3. Each candidate forwarder uses its neighboring node information to find suitability to abbreviate its holding time duration to curtail the end-to-end delay.  
Energy efficiency and energy balancing are achieved by employing the normalized residual energy information of the neighboring nodes in the holding time and suppressing more number of packets.
5. No constant parameters in the holding time estimation are used.
6. The proposed scheme is analyzed in the network scenarios with and without sink mobility.  
The simulation results show that ESEVBF improves energy efficiency and reduces end-to-end delay without compromising the reliability compared to its counterpart, AHH-VBF.
# A Novel Robust Control System Design and Its Application to Servo Motor Drive
## 6 Simulation Results and Discussion
### Simulations for Systems under the Conditions of Nominal, Load, and Uncertainty
The position commands are the same as the conditions shown in Figure 5. When the load is applied to the system or released from the system, it shows an undershoot or overshoot for the system controlled by integral state feedback only (without SMC), as the zoomed-in figure in Figure 6a, b shows. Since the system structure with SMC has an extra switching control force, \(-q\text{sgn}(\sigma)=-3\frac{\sigma}{|\sigma|+0.001}\), which leads to a system with robust performance by demonstrating a response with a small undershoot or overshoot compared to the system in which only the state feedback control is applied. They can be checked using the zoomed-in pictures in Figure 6a,b in which the traces red from the system with SMC almost overlap the nominal system, i.e., the total invariant properties of SMC are preserved. The performance under both the parameter's variations and load is shown in Figure 7, where the inertia uncertainty and load conditions are the same settings as in Figures 5 and 6. As stated in the previous section, the system parameters determine the pole locations of the closed-loop control system. A changed system model parameter causes the system performance to differ from the ideal one. All of the changes, including the command change or load added or removed, cause the responses to be far from the ideal conditions. On the contrary, the control system with SMC compensates for those frustrations and results in good responses for both the position and speed. For the system with SMC and integral state feedback control, once the system can be kept on the sliding surface \(\sigma(t,\mathbf{z})=0\), this will show that the system response is very close to the desired response, i.e., its equivalent system state matrix is \(\mathbf{A}_{\text{c1}}\).
# Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
## 3 Three-dimensional Imaging Analysis of an SMS-TomoSAR System  
As shown in Figure 2, an SMS-TomoSAR system observes the same target from different spatial positions, and obtains _MN_ SAR Single Light Complex (SLC) images after azimuth-range two-dimensional compression. The appropriate main image is selected based on factors such as image correlation. After pairing and de-ramping, the complex value of the \(p\)th image at the same position [24] is expressed as:  
\[g(p)=\int_{s_{\min}}^{s_{\max}}\gamma(s)\exp\Bigl{(}j2\pi\xi_{p}s\Bigr{)}ds \quad\xi_{p}=2b_{\perp p}/\lambda R, \tag{4}\]  
where \(\lambda\) is the radar wavelength; \(\gamma(s)\) is the electromagnetic scattering characteristic function of the target in height domain; \(R\) is the distance from the phase center of the radar antenna to the reference point where the height is 0 at the time of capturing the main image; \([s_{\min},s_{\max}]\) is the height span of the target; \(\xi_{p}\) is the spatial frequency corresponding to the height \(s\), and its relationship between the spatial angular frequency is \(\xi=\omega_{s}/2\pi\), \(b_{\perp p}\) for the vertical baseline. The specific derivation process is given in [24,25].  
Overall, it can be seen from the above derivation process that the complex value of the same-name resolution unit \(g(p),\ p=1,\ 2,\ \ldots,\ MN-1\) is the discrete sampling in \(\xi_{P}\) of the spectrum of the electromagnetic scattering characteristic function \(\gamma(s)\) of the target in the height direction after de-ramping. Therefore, imaging along the height direction is essentially a problem of reconstructing the original signal using spectral discrete sampling, while the vertical baseline \(b_{\perp p}\) directly affects system measurement results. Thus, this paper explores the baseline design issues by directly analyzing the vertical baseline, and all baselines discussed below refer to the vertical baseline.
## 4 Discussion
Improved fault diagnostics and data augmentation for robust models enables faster and more reliable detection of anomalies in real time based on live sensor data, identifying patterns that indicate, for example, early signs of equipment degradation. This allows maintenance schedules to be dynamically adjusted based on real-time information, on the one hand avoiding downtime and on the other reducing unnecessary maintenance. Clear, explainable GenAI models with easily interpretable results/hints are key to earning operator trust and ensuring that generative AI-based fault predictions are consistent with expert knowledge. Combining AI-generated insights with expert judgment improves decision-making, ensuring that maintenance personnel can verify and refine GenAI-based fault diagnosis recommendations. For these reasons, protecting sensor communications, databases, and GenAI algorithms from attacks and data manipulation is essential to maintaining reliable predictions [75].
# Harnessing Artificial Intelligence for Wildlife Conservation
###### Abstract  
The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes these data with convolutional neural networks (CNNs) and transformer architectures to monitor species, including those that are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g., poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform's success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.  
artificial intelligence (AI); wildlife conservation; machine learning; species identification; poaching prevention; biodiversity monitoring
## 2 Materials and Methods
### Coarse Detection of Moving Objects  
A background model will be established in this part. We assume that in a time series of images, each background pixel x can be modeled by a collection of \(N\) background sample values taken from previous frames of the same position. This can be mathematically expressed as  
\[M(x)=\{v_{1},v_{2},...,v_{N}\}, \tag{13}\]  
where \(M(x)\) is the background collection of pixel \(x\).\(v_{i}\) is the background sample value with index \(i\), which is obtained from previous frames.  
Once the model is established, we can use the model \(M(x)\) to determine a pixel background or foreground. A circle of radius \(R\) centered at pixel \(v(x)\) is defined. We compared the pixel value \(v(x)\) to its corresponding model \(M(x)\). For each background sample \(v_{i}\), if its distance to \(v(x)\) is less than \(R\), a flag \(f_{i}\) will be labeled as 0, and it will be 1 otherwise. We will then sum up \(f_{i}\) as \(S\). The pixel value \(v(x)\) is classified as background if its \(S\) is smaller than or equal to a given threshold \(T\). Otherwise, it will be regarded as foreground. This can be mathematically expressed as follows:  
\[f_{i}=\begin{cases}0\;\;if\;|v(x)-v_{i}|\leq R\;\;\forall v_{i}\in M(x)\\ 1\;\;if\;|v(x)-v_{i}|>R\end{cases} \tag{14}\]  
\[S=\sum\limits_{i=1}^{N}f_{i} \tag{15}\]  
\[g(x)=\begin{cases}0\;\;if\;S\leq\;T\\ 1\;\;if\;S>T\end{cases} \tag{16}\]  
For the very first frame, background model \(M(x)\) cannot be obtained, as there is no previous frame. It is then filled with values taken randomly from its eight connected neighborhoods. Therefore, in the first frame, the background model at location \(x\) can be expressed mathematically as  
\[M^{0}(x)=\left\{v^{0}(y|y\in N_{G}(x))\right\} \tag{17}\]  
where \(N_{G}(x)\) is the spatial neighborhood of a pixel at location \(x\). \(y\) is the location chosen at random uniformly. \(v^{0}(y)\) is the pixel value obtained by the pixel located at location \(y\).  
The last question is how to continuously update the background model so that it can be adapted to light changes and new objects appearing in a scene. It consists of three important components: (1) a memory-less update policy, ensuring a smooth decay lifespan for the samples stored in the background pixel models, (2) random time subsampling, extending the time windows covered by the background pixel models, and (3) a mechanism that propagates background pixel samples spatially to ensure spatial consistency and to allow the adaptation of the background pixel models that are masked by the foreground.  
In traditional methods, some hyperparameter must be tuned to fit the data, leading to either high moving object detection precision but low recall rate, or low precision but high recall rate. This means one has to balance accuracy and recall. Even so, the problem of pseudo moving objects still exists due to parallax. The proposed method deals with this contradiction by taking full advantage of the spatial information derived from the road.
## 2 Design and Performance of Unit Cells and AMs
Based on the generalized [PERSON]'s law [44], the relationship between the incident angle \(\theta_{\rm i}\) and the reflected angle \(\theta_{\rm r}\) created by the AM is expressed as  
\[\sin\theta_{\rm r}=\sin\theta_{\rm i}+\frac{1}{k}\frac{\mathrm{d}\varphi}{ \mathrm{d}x}, \tag{1}\]  
in which \(\theta_{\rm i}\) and \(\theta_{\rm r}\) are defined as those between the incident and reflected directions and the normal line (black dashed line), and the signs \(+\) and \(-\) of both angles represent the right and left propagation directions, respectively. Here, by substituting the phase gradient \(\mathrm{d}\varphi/\mathrm{d}x=-k\) into Equation (1), we can obtain  
\[\sin\theta_{\rm r}=\sin\theta_{\rm i}-1. \tag{2}\]  
Figure 2c-h show the theoretical propagation paths of reflected waves through the AM with different incident angles. Here, based on Equation (2), we can theoretically calculate the reflected angles \(\theta_{\rm r}=0^{\circ}\), \(-30^{\circ}\) and \(-90^{\circ}\) for the incident angles \(\theta_{\rm i}=90^{\circ}\), \(30^{\circ}\) and \(0^{\circ}\), which are shown in Figure 2c,e,g, respectively. Beyond that, when the incident angle decreases from \(90^{\circ}\) to \(30^{\circ}\) (Figure 2d) and from \(30^{\circ}\) to \(0^{\circ}\) (Figure 2f), the corresponding reflected angles change from \(0^{\circ}\) to \(-30^{\circ}\) and from \(-30^{\circ}\) to \(-90^{\circ}\), respectively. Here, note that the incident waves with the angle \(0^{\circ}\leq\theta_{\rm i}\leq 90^{\circ}\) cannot reach the right side of the AM (yellow region), which is defined as an acoustic blind area (ABA). As shown in Figure 2h, when the incident angle is inside the ABA (\(-90^{\circ}\leq\theta_{\rm i}<0^{\circ}\)), we obtain \(\sin\theta_{\rm r}<-1\) based on Equation (2); therefore, the incident sound energy cannot be reflected by the AM but is transformed as the form of surface evanescent wave [44].
## 2 Materials and Methods
### FireBot System Overview
#### 2.1.1 FireBot Architecture  
FireBot presents a new and innovative concept for FPDE. It utilizes state-of-the-art technologies that enable autonomous navigation, including avoiding all obstacles, video surveillance, and FPDE. It has a LiDAR and a depth camera, as well as the IR and ultrasonic sensors used in RTAB, a cutting-edge SLAM (simultaneous localization and mapping) algorithm for autonomous mapping and navigation [22], and a modern convolutional neural network (CNN) paired with infrared thermal (IRT) and RGB cameras for fire and temperature-anomaly detection. The final version of FireBot is currently in development, and the final model is presented in Figure 1.  
FireBot has various other sensors for monitoring the surroundings and detection of potential anomalies (various gas sensors, microphone for detecting water and gas leaks,  
Figure 1: Model of a final version of FireBot.  
intrusion detection, etc.). There are three types of fire-extinguishing devices attached to FireBot (foam, powder, and CO2) for extinguishing different types of fires. Paired with a rotating mechanical hand, on top of which is an electronic nozzle, FireBot can precisely direct its nozzle to the source of fire for fast and efficient extinguishing.  
The FireBot system architecture consists of the three main logical components for indoor FPDE: an embedded system module for sensor and actuator management (ESMSAM), a system module for SLAM and navigation (SMSN), and a system module for fire prevention and detection (SMFPD). Another important module is a system module for fire extinguishing (SMFE), which consists of three different fire-extinguishing devices (powder, foam, and CO2) along with the movable arm and electronic nozzle. Furthermore, a charging station is provided separately from the robot. The complete proposed system-architecture diagram is provided in Figure 2, and a detailed description of FireBot's architecture and all its subsystems can be found in [23].
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
###### Abstract
The radio-and-fiber (R&F) network, in contrast, is an approach based on connecting distributed radio transmitters (such as wireless local area network (WLAN) access points (APs)) and centralized WLAN controllers with fiber-optic networks. R&F networks have the advantage of building wireless local area network (WLAN)-based FiWi networks. These advantages are reflected in the possible coverage of larger areas without the limitations related to the optical back-end size, while the RoF network has a limited reach of deployed fiber imposed by the propagation delay in fiber. Using the same infrastructure for both wired and wireless services, FiWi networks can merge the traditionally separate optical and wireless access networks, thus leading to potential cost savings [8]. The high performance requirements of future 5G networks, including extremely low latency, uninterrupted user experience and high throughput and reliability, will necessitate the use of localized services that are within RANs located closer to mobile subscribers. The multi-access edge computing (MEC) paradigm is exploited as a solution for offering services closer to mobile subscribers. Through telecom operators, information technology (IT) and cloud computing, the MEC aims to bring cloud services to the usersdirectly from the network edge [9]. However, the concept of MEC over FiWi networks must address not only the 5G network realization challenges, but it must also address unique challenges related to the integration of the MEC concept with existing wired and wireless infrastructures, and managing resources effectively in the context of backhaul and RAN coordination [9]. Furthermore, a software-defined network (SDN) architecture which uses a centralized controller to manage networks is a revolutionary approach that has the potential to greatly improve the control and management of FiWi access networks, and thus increase the EE of the entire FiWi network [10]. Therefore, in this paper, EE aspects of the FiWi networks with particular emphases on the RoF, R&F, MEC and SDN technologies were overviewed. The paper is further structured as follows: the overview of the FiWi access networks is presented in Section 2. In Section 3, the EE challenges of R&F networks are discussed. The energy-efficient RoF solutions are elaborated in Section 4, with an emphasis on the impact of the C-RAN network architecture on the EE. The EE solutions for MEC-based FiWi access networks are reviewed in Section 5. In Section 6, the SDN-based energy-saving schemes in FiWi networks are elaborated. Section 7 discusses the results of performed analyses dedicated to improving FiWi networks EE. Finally, conclusion remarks related to the review of EE methods for FiWi networks are given in Section 8.