esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Basic Notch Filters for Semiconductor Optical Amplifier Pattern Effect Mitigation
## 3 Optical Notch Filter Requirements for SOA Pattern Effect Suppression  
The profiles of the ODI and MMR spectral responses in Figure 5 allow both schemes to act as optical notch filters for mitigating the SOA pattern-dependent performance degradation. For this purpose, it is necessary to suitably tailor the spectral characteristics of these responses so that the spectral components of the amplified pulses are forced to lie close to the notches and, hence, are suppressed in direct analogy to the degree of their red shift. This involves choosing and controlling the wavelength spacing (FSR), the contrast and the position of the notches, according to the following requirements [33]: (1) The FSR must be such that the notches are not spaced too close or too far, but sufficiently apart. Thus, making the FSR either too small or large prevents the red-shifted components of the amplified signal from distinctly falling along the response's slope and accordingly from being filtered analogously to their SPM-induced spectral magnitude. Instead, the FSR must be chosen so as to ensure that: (a) there is a sufficiently wide margin available for the red-shifted spectral components to be contained within the spectral borders defined by the difference \(\lambda_{notch,ODI}-\lambda_{amp}\) and \(\lambda_{notch,MRR}-\lambda_{amp}\); (b) the passband width is neither too narrow nor too broad so that the red-shifted spectral components do not fall outside or are loosely acted upon, respectively; (c) the suppression exerted by the filter on these components scales with the uneven peak amplitude they originate from, in order to clamp the higher amplitude amplified pulses while comparatively enhancing the lower amplitude ones; (d) the notches antipodes, i.e., the transmission peaks, are close enough so that the reference data wavelength can fall in their vicinity and perceive a sufficient fraction of the transmission maximum. In this manner, the amplified signal can pass through the notch filter without suffering a significant reduction in its amplitude or equivalently without significant elimination of the useful information it carries [21], which is critical when employing spectral elimination techniques for combating signal impairments [24]. (2) The repetitive notches must be sharp and deep enough to maximize their magnitude difference from their adjacent transmission peaks, which defines the peak-to-notch contrast ratio (PNCR) and ensure that the asymmetrically-broadened spectral components after the SOA are suppressed not weakly, but strongly and not identically, but to an extent directly dependent on the position in the data stream of the pulse from which they originate. (3) The notches must occur at a higher wavelength than that of the amplified signal, so that the transmittance is decreased as the wavelength is increased. This is again indispensable in order for the spectrally-broadened components to be suppressed analogously to the degree of their red shift.
###### Abstract
system (i.e., the data is also represented virtually using the deployed DTs). The outputs will be indicating whether the inputs data are considered failures or erroneous. This output will be published as semantic information so that the decision-makers can take advantage of it by receiving an early notification about the production system's failure. In addition, this semantic information could be stored within the knowledgebase to be used for future auto-detection of the sensors in the cyber-physical production system. Furthermore, the proposed conceptual framework's workflow will be presented; and it consists mainly of four phases: information extraction, change detection, synchronization, and notification.
## 2 Materials and Methods
### Node Network  
Wired networking between the nodes is realized via the Controller-Area-Network (CAN) protocol and wireless networking is realized using a self-organizing adaptive Media Access Control (MAC) protocol supporting the dynamic addition and removal of nodes during run time while keeping collisions in the wireless channel to a minimum. The protocol is described in further detail in [37]. Although the ultra-wide band characteristics of the wireless communication hardware and the adaptive MAC protocol should reduce the risk of collisions on the wireless channel in the dynamically changing node network, packet/data loss due to collisions or interference are, apart from transmission delays, still one of the major disturbances in the network. In the case of identical independent distributed random loss events, the network can be modeled by a two-state Markov chain or Bernoulli process with the following state transition probabilities:  
\[\begin{split} P(\theta(t_{k})=1|\theta(t_{k-1})=1)& =p_{l}\\ P(\theta(t_{k})=1|\theta(t_{k-1})=0)&=p_{l}\\ P(\theta(t_{k})=0|\theta(t_{k-1})=1)&=1-p_{l}\\ P(\theta(t_{k})=0|\theta(t_{k-1})=0)&=1-p_{l}\\ \end{split} \tag{1}\]  
For \(\theta(t_{k})=1\) all packets are lost during \(t\in[t_{k},t_{k+1})\). The probability for a lost packet between two nodes is denoted with \(p_{l}\) and a successful transfer has a probability of \(1-p_{l}\). The loss probability is assumed to be independent of the current network state and the transition from one network state to another is equal to \(p_{l}\) and \(1-p_{l}\) respectively. This is visualized in Figure 3.  
Figure 2: (a) The sensor/actuator HW base board with a STM32F4 microcontroller and a DWM1000 ultra-wideband transceiver. (b) Base board with daughter board extensions to form a GPS sensor node unit.  
The limited range of the wireless transceiver of about 100 m is denoted as \(d_{cmax}\). Usually there exists a direct point-to-point link between two nodes in range and we model the distance between nodes as number of hops that a data packet must traverse until it reaches the endpoint as visualized in Figure 4. Each hop introduces processing-delays on the relay nodes and more hops could in general also be attributed to higher physical node distances. Therefore, the hop-distance is later taken into account as one objective that should be minimized in the control task distribution.  
Compensation for bounded delays in the network of the closed-loop control system could directly be incorporated in the controller design as demonstrated in [25] for attitude control and [27] for formation control. Strategies for dealing with short term packet loss in the closed-loop system are proposed as well in the aforementioned publications and as we are building upon these designs, bounded delays and short term packet loss are not further considered in the rest of this work.  
Figure 4: Multi hop data exchange between satellite nodes that are further apart.  
Figure 3: Node network modelled as two-state markov chain with equal loss probability \(p_{l}\) for both states \(\theta\).
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Scattering  
Hybrid beamforming is able to handle rich scattering as well as isotropic scattering.
* **Rich Scattering** In the analog beamformer, the precoder and combiner are acquired using singular-value decomposition (SVD). The objective is to reduce the overall strength of inter-stream interference (ISI), a phenomenon caused by the eigenmode, while maximizing the power of the desired data stream. The original analog beamformers in the channel's transmitter and receiver served as the basis for the digital beamformers. The findings demonstrated superior performance in difficult scattering settings [53].
* **Isotropic Scattering** A generic hybrid beamforming architecture with selection is proposed for low-complexity multi-user MIMO transceivers. One possible shape for the beamforming matrix is a rectangular matrix with columns that are not orthogonal. It is possible to solve a connected Grassmannian subspace packing issue for a channel with isotropic scattering inside the subspace covered by a Dirty-paper coding beamformer [54].
## 5 Prototype and Experimental Results
### Prototype Design
#### 5.1.2 The Elongation of the EHs  
According to the basic action in Section 3, during stair-climbing, the maximal elongation of each EH should be a little bigger than \(2h\). According to Reference [30], \(h<200\) mm should be satisfied; thus, the designed elongation \(\Delta l_{max}\) was chosen as 400 mm.
## 2 Proposed Multi-Stage Control Scheme and OPF Formulation  
This paper deals with coordinated multi-stage control for improving technical conditions in PV-rich DN. As presented in Figure 1, the observed control scheme is fully centralized, i.e., all variables are controlled by DSO. The aim is to use the existing network components such as OLTC with integrated PV reactive power capabilities to diminish voltage violations and losses. The main advantage of using the existing network components is cost savings. DNs primarily consist of transformers and power lines and represent the significant sunk cost for DSO and adding new components causes additional capital cost. In the first stage, OLTC is assigned to control voltages at the hourly scale. The first stage aims to reduce the main issue caused by high PV integration--the overvoltage problem. OLTC is known as a slow-response device because it relies on mechanical change in the tap position and is best suited for steady-state voltage control. The design of OLTC is not appropriate for rapid changes and dynamic control such as in ADNs. When the overvoltage issue is solved, DSO strives to minimize power losses in DN to improve technical conditions as well as economic aspects. PV inverter RPC is controlled in the second stage with the obtained tap position from the first stage. As a response to the dynamic nature of ADN, RPC is performed at an intra-hourly scale (the typical time step for DSO is 15 min).
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 3 Communication
### Coding Experiment  
The interference-resistant 2-Frequency shift keying (2 FSK) modulation technique was selected for the uplink communication across the water-air interface. In this modulation scheme, we used 146 Hz and 243 Hz as the underwater sources to represent binary \"0\" and \"1\", respectively. To determine when the code elements were sent, a data encoding unit was employed, consisting of two start bits, eight data bits, one parity bit, and one stop bit. The data bits were encoded using ASCII codes as a reference. For instance, the letter \"T\" was encoded as \"0001010011\".
## 4 Classification Method
### Network Architecture  
The network architecture plays a crucial role in the recognition and feature extraction of MFCC spectrograms. We used a network model based on the classical ResNet-18 architecture, which is known for its compact size and exceptional performance, making it a preferred choice in various research and practical applications [36]. The basic configuration of ResNet is depicted in Figure 9.  
The network comprises three primary sections. The initial section, referred to as low-level feature extraction, includes an input layer, a convolutional layer, and a pooling layer. Here, the MFCC spectrogram data from the dataset were inputted into the network. Through convolutional operations and pooling, low-level image features are extracted and forwarded to the subsequent high-level feature extraction section.  
The core of the ResNet model constitutes the high-level feature extraction section, which consists of three residual blocks. Each residual block contains multiple convolutional layers interconnected with skip connections spanning these layers, as depicted in Figure 10.  
Figure 9: The structure of the residual network.  
Each residual block is composed of several residual layers, each incorporating convolutional operations. These operations encompass two essential actions, namely local correlation and window sliding, collectively known as convolution operations. Training proceeds accordingly, with the training configurations detailed in Table 4.  
The mini-batch size, maximum number of iterations, and the initial learning rate were set, and the Adam optimizer was chosen to update model parameters. The validation frequency was determined during training. If the current validation accuracy surpassed all previous records, the model was saved as the optimal model, and we proceeded to the next iteration. If not, we continued to the next iteration until reaching the maximum number of iterations. Upon completing the training process, the optimal network model was established along with its validation accuracy. Subsequently, five different test sets were randomly selected, each comprising 20% of the original dataset, and the trained residual network model was evaluated on these test sets.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 3 Finite Element Modeling and Analysis
### Model Simplification  
The airborne equipment was designed to meet the requirements of machining and assembly. To obtain accurate analysis results, we ensured that the load transmission path did not change, the connections of the parts were consistent with the actual situation, and the integrity of the main structure was not compromised [15]. Accordingly, the following simplifications were performed before modeling.  
1. Treatment of transition fillets: Too many fillets lead to the generation of a complex mesh and increase the amount of meaningless computation. Therefore, the circular angle that exists in the non-stress concentration region and the radius \(r\leq 3\) mm was directly ignored, and the circular angle that affects the analysis results in the stress concentration region was retained. When the fillet radius was \(3\) mm \(<r\leq 8\) mm, the transition was divided by a row of grids. The transition was divided by two rows of grids when the fillet radius was \(8\) mm \(<r\leq 15\) mm.
2. Treatment of holes: Excessive perforation of the structure leads to uneven meshing and stress concentration which not only increases the amount of calculation but may also lead to divergence of simulation results. Thus, the non-installed hole with a radius of \(r\leq 2\) mm was ignored, and the hole was located in the stress concentration area left behind. When the radius of the hole was \(2\) mm \(<r\leq 3\) mm, the tetrahedral mesh with a central node was used.
3. Simplification of the bolt connection: The bolt connection is the main fixation way employed for the electrical control box and the aircraft body. The screw thread structure is complex, and the bolt model exhibits a cumbersome nonlinear contact problem, which does not reflect the load and load transmission form of the main structure and results in excessive calculation time. Therefore, the bolt connection was simulated using a common node with the connector.  
By using the aforementioned methods, under the premise of satisfying the calculation and analysis accuracy, the simplified electrical control box model was obtained to improve the modeling efficiency.
## 4 Results and Evaluation
### Objective Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Romanesque–Mudejar Dataset**} \\ \hline
**Method/Metric** & **SSIM**\(\uparrow\) & **PSNR**\(\uparrow\) & **LPIPS**\(\downarrow\) & **Train** & **FPS** & **Mem** \\ \hline GS Mudejar 30 K & 0.705 \(\uparrow\) & 22.61 \(\uparrow\) & 0.371 \(\uparrow\) & 0 h 21 min 31 s \(\uparrow\) & - & 51.8 MB \(\uparrow\) \\ \hline Mip-splatting & \multirow{2}{*}{0.665 \(\uparrow\)} & \multirow{2}{*}{20.66 \(\uparrow\)} & \multirow{2}{*}{0.335 \(\uparrow\)} & \multirow{2}{*}{0 h 22 min 00 s \(\uparrow\)} & \multirow{2}{*}{-} & \multirow{2}{*}{103.9 MB \(\uparrow\)} \\ Mudéjar/30 K & & & & & & & \\ \hline SuGaR & \multirow{2}{*}{0.482 \(\uparrow\)} & \multirow{2}{*}{9.88 \(\uparrow\)} & \multirow{2}{*}{0.630 \(\uparrow\)} & \multirow{2}{*}{2 h 01 min 15 s \(\uparrow\)} & \multirow{2}{*}{-} & \multirow{2}{*}{146.8 MB \(\uparrow\)} \\ Mudéjar/30 K & & & & & & & \\ \hline \multicolumn{6}{c}{Mip-NeRF360 dataset} \\ \hline Plenoxels & 0.626 & 23.08 & 0.463 & 0 h 25 min 49 s & 6.79 & 2.1 GB \\ \hline INGP-Base & 0.671 & 25.30 & 0.371 & 0 h 05 min 37 s & 11.7 & 13 MB \\ \hline INGP-Big & 0.699 & 25.59 & 0.331 & 0 h 07 min 30 s & 9.4 & 348 MB \\ \hline M-NeRF360 & 0.792 & 27.69 & 0.237 & 48 h 10 min 50 s & 0.06 & 8.6 MB \\ \hline GS-Kerbl/7 K & 0.770 & 25.60 & 0.279 & 0 h 06 min 25 s & 160 & 523 MB \\ \hline GS-Kerbl/30 K & 0.815 & 27.21 & 0.214 & 0 h 41 min 33 s & 134 & 734 MB \\ \hline \hline \end{tabular}  
* The arrows (\(\uparrow\), \(\downarrow\)) indicate the direction in which the proposed indicator shows a positive outcome. The SSIM range, which measures the similarity between two images, spans from 0 to 1, with 1 indicating maximum similarity. PSNR reflects the compression level of the reconstructed image compared to the original, where higher values denote better performance. LPIPS also measures image similarity on a scale from 0 to 1, using a different method than SSIM. Unlike SSIM, in LPIPS a value of 0 indicates the highest similarity. Data compiled by the authors, based on [23].  
\end{table} 
Table 1: Objective metrics for evaluating and comparing the GS, Mip-splatting, and SuGaR Romanesque–Mudejar performances with other Mip-NeRF360 dataset SOTA performances in [23]. Results marked with (\(\uparrow\)) were obtained in our experiments; all others have been directly transcribed from their original papers.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 2 Materials and Methods
### Hardware Architecture
Built upon this foundation, the RPSC's propulsion system consists of four GoBilda Yellow Jacket 5203-series motors, goBILDA, Greenville, TX, USA, selected for their balanced speed-to-torque ratio. These motors have Hall-effect encoders, making them compatible with the autopilot's odometry estimation requirements. They are paired with all-terrain wheels from the same manufacturer, featuring foam inserts and a high-profile design to enhance adaptability across diverse surfaces. To manage these capabilities, the RPSC's autopilot controller is based on the Pixhawk 2.4.8, Holybro, Shenzhen, China, an open-architecture controller widely recognised in robotics applications. Its selection is based on its support for multiple sensors and its ability to generate Pulse Width Modulation (PWM) signals for motor control. The Pixhawk contains internal sensors, including an accelerometer, gyroscope, and magnetometer. It also allows the integration of external modules via UART, Inter-Integrated Circuit (I2C), Controller Area Network (CAN), and Serial Peripheral Interface (SPI) interfaces. This versatility enables the connection of encoders and proximity sensors, fulfilling the system's autonomous navigation requirements. In turn, the RPSC's positioning is provided by the Here3+ GNSS module, CubePilot, Taipei, Taiwan, which incorporates RTK corrections to achieve high accuracy. This module offers redundancy through an independent IMU and magnetometer. Its capability to operate with multiple satellite constellations (Global Positioning System (GPS), Global'naya Navigationnaya Sputnikovaya Sistema (GLONASS), Galileo, and BeiDou) ensures constant signal availability, even in partially obstructed areas, making it ideal for road work environments. The OPU, implemented with a Raspberry Pi 5, Raspberry Pi Ltd., Cambridge, UK, serves as the RPSC's logical core. Its quad-core ARM Cortex-A76, ARM Ltd., Cambridge,  
Figure 3: RPSC assembly stages: (**a**) RPSC internal components, including CPU, controller, and driver; (**b**) RPSC assembled without cone; (**c**) RPSC with cone and emergency light, ready for use. UK, architecture provides the computational capacity to run ROS2, manage additional sensors, interface with the Pixhawk via MAVROS, and transmit data to remote stations over Wi-Fi. It is equipped with an Alfa AWUS036 ACH, Alfa Network, Taipei, Taiwan, dual-band (2.4/5 GHz) adapter to enhance this wireless connectivity, offering greater range and stability than the built-in Wi-Fi module.
## 8 Discussion  
One of the modern approaches to combating cyberattacks is the use of artificial intelligence (AI) to forecast and detect deviations from normal behavior [47; 63; 64; 65]. While our proposed mitigation mechanism for SIP-DRDoS attacks does not specifically utilize AI, it is possible to incorporate AI techniques to further enhance its performance. For example, machine learning algorithms could be trained on historical data to recognize patterns of attack traffic and improve the accuracy of detection. As a future research direction, we identify the use of AI to optimize the attack response strategy in our mechanism, e.g., AI can be used to dynamically adjust the threshold values and determine the most effective response based on the historical training data and the context of the attack.  
We would also like to note that it is important to consider the limitations and potential challenges of using AI in the context of cybersecurity. One issue is the need for a sufficiently large quantity of high-quality training data to accurately model the behavior of both normal and attack traffic. Another challenge is the possibility of adversarial attacks, where attackers attempt to manipulate or deceive the AI system. Hence, it is important to carefully design and test the robustness of any AI-based approach to ensure its effectiveness in real-world scenarios. While AI has the potential to significantly enhance the capabilities of cyberdefense systems, it is not a silver bullet and should be carefully considered in the context of the specific threat landscape and system requirements [63; 65; 66].  
Figure 10: Average SIP server CPU load after the attack and after the mitigation for all types of users.  
**Author Contributions:** Conceptualization, I.M.T. and S.B.; methodology, I.M.T.; validation, I.M.T. and S.B.; investigation, I.M.T.; writing--original draft, I.M.T.; writing--review and editing, I.M.T. and S.B.; visualization, I.M.T.; supervision, S.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.