esa-sceva/satcom-chunk-collection huggingface dataset:
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 1 Introduction
### Problematic Communication Channels  
In certain application cases, the target network may be unable to use a reliable and fast communication channel. The main example of such situations involve underwater networks of unmanned underwater vehicles (UUVs) coordinating missions at sea without cable connection (see Figures 1 and 2). As the radio channel is unavailable in this case, the standard option is to use an acoustic channel. From now on, we focus on networks of UUVs as a reference model for our considerations.  
Unfortunately, acoustic underwater communication is substantially slower and less reliable compared to radio communication over the air. Table 1 provides example acoustic channel characteristics. Note that the most important application case is communication in shallow water, where the data rate is the lowest and where additional factors degrading the channel performance are more likely. As can be seen, in this case the physical channel capacity may become a major bottleneck for the network even if there are only a few communicating devices. Thus, the communication framework must be designed very carefully in order to avoid any unnecessary communication overhead on top of the transmitted payload bits.  
In many cases, not only is the communication bandwidth low, the error rate is also much higher than typical for radio communication. For example, the Bit Error Rate (BER) for underwater acoustic channels can be greater than \(10^{-1}\) (cf. [2] and [3], among others). In addition to the need to occasionally retransmit the payload data, this may influence the choice of security mechanisms used to protect the communication channel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Rate (bps)** & **Bandwidth/ Carrier (kHz)** & **Bandwidth Efficiency (bps/Hz)** & **Range (km)** & **Shallow/Deep** \\ \hline
1600 & 10/50 & 0.16 & 0.1 & shallow \\
16,000 & 8/20 & 2.0 & 6.5 & deep \\
20,000 & 10/50 & 2.0 & 1.0 & deep \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected communication systems based on DPSK (Differential Phase Shift Keying) modulation in shallow and deep water (cf. [1]).  
Figure 1: An example of underwater network of UUVs with one-to-one communication to the central unit.  
Figure 2: An example of multicast groups; the sender is the central unit.
## 3 Results and Discussion
### Strategies for Upgrading the Dielectric Strength Prediction Protocol
#### 3.2.2 Strategy II: Incorporation of a Novel Factor into the Previous Protocol  
To overcome the limitation of the aforementioned equational framework, which is structurally fixed by the polarizability, IE, and MW, a novel factor was incorporated and parameterized (Figure 5; Table 1). The prediction accuracy of the newly designed protocol, PIMN, was expected to strongly rely on the identity of the incorporated novel factor. Three distinct types of properties, namely electronic, physical, and chemical properties, were introduced as novel factors. The number of electrons, HOMO, HOMO-LUMO gap, and electron spatial extent gap were categorized as electronic properties, whereas the formation energy and molar volume were used as chemical and physical properties, respectively. Depending on the incorporated new factor, the PIMN protocol gave relative errors of 16-18% with RMSD values of 0.289-0.346 for predicting the dielectric strengths of the 168 PFC and non-PFC compounds (Figure 5a,b). For most of the novel factors,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Prediction Protocol and Core Factor** & **Average Relative Error** & **RMSD** & **x** & **y** & **z** & **m** & **n** \\ \hline \multirow{2}{*}{**Equation (1). PIM**} & 19.83\% & 0.329 & 0.0012 & 1 & 0.288 & 0.401 & - \\ \hline \multirow{2}{*}{**Equation (2). re-PIM**} & 18.94\% & 0.328 & 1 & 1 & 0.327 & 0.457 & - \\ \hline \multirow{4}{*}{**Equation (3).**} & Number of electrons (\(N_{\mathrm{e}}\)) & 18.20\% & 0.329 & 1 & 0.94 & 0.32 & 0.1 & 0.522 \\ \cline{2-8}  & HOMO [eV] & 17.59\% & 0.313 & 0.98 & 1.275 & 0.005 & 0.408 & 1 \\ \cline{2-8}  & HOMO–LUMO gap [eV] & 16.72\% & 0.289 & 1.21 & 0.916 & 0.217 & 0.732 & 0.65 \\ \cline{2-8}  & Electronic spatial extent gap [a.u.] & 18.96\% & 0.328 & 0.65 & 1.012 & 0.37 & 0.459 & 0.001 \\ \cline{2-8}  & Formation energy [a.u.] & 18.45\% & 0.320 & 0.38 & 0.83 & 0.48 & 0.461 & 0.106 \\ \cline{2-8}  & Molar volume [cm\({}^{3}\) mol\({}^{-1}\)] & 17.89\% & 0.346 & 1 & 0.638 & 0.281 & 0.757 & 0.253 \\ \hline \multirow{2}{*}{**Prediction protocol and core factor**} & **Average relative error** & **RMSD** & \(x\) & \(y\) & \(z\) & \(m\) & \(n\) & \(l\) \\ \hline \multirow{2}{*}{**Equation (4). OPT**} & 17.70\% & 0.340 & 0.365 & 0.549 & 0.9 & 0.001 & 0.079 & 0.008 \\ \cline{2-8}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Developed equation-based protocols with parameterized coefficients, average relative error, and root mean squared deviation (RMSD).  
the accuracy level was slightly better than that of re-PIM. The highest accuracy level (relative error = 16.72%; RMSD = 0.289) was observed for the incorporation of the HOMO-LUMO gap, indicating that this novel factor is optimal for predicting the dielectric strengths of the 168 PFC and non-PFC compounds.
## 4 Optimal Frame Generation Time Threshold Calculation
### Overall Structure
#### 4.1.1 State Space  
In this paper, the training cycle is divided into several continuous time slots. At time slot \(t\), the agent observes and collects the environment state \(s_{t}\), and the state space for all time slots is represented as \(S=[s_{1},s_{2},\cdots,s_{t}],s_{t}\), defined as Equation (12):  
\[s_{t}=\{R_{s},L_{s},T_{f},L_{f}\} \tag{12}\]  
where \(R_{s}\) represents the average arrival rate of packets in time slot \(t\), and \(L_{s}\) represents the average queue length in time slot \(t\), which is used to determine the current load status. Specifically, if the queue length is large, it indicates that the current queue is in a state of congestion. Thus, the next round should be framed as soon as possible, and the frame threshold should be reduced. \(T_{f}\) represents the average frame generation time in time slot \(t\), and \(L_{f}\) represents the average frame length in time slot \(t\).  
This state space representation is independently constructed by us, taking into account the queuing dynamics and framing requirements specific to the AOS environment.
# Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
## 2 Preliminaries
### Cbf  
Consider the following control affine system:  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x})+\mathbf{g}(\mathbf{x})\mathbf{u}, \tag{1}\]  
where \(\mathbf{x}\in\mathbb{R}^{n}\) is the state, \(\mathbf{u}\in\mathrm{U}\subset\mathbb{R}^{m}\) is the control input, \(\mathbf{f}:\mathbb{R}^{n}\to\mathbb{R}^{n}\) and \(\mathbf{g}:\mathbb{R}^{n}\to\mathbb{R}^{n\times m}\) are locally Lipchitz continuous functions. A set \(\mathcal{S}\) is called forward controlled invariant with respect to system (1) if for every \(\mathbf{x}_{0}\in\mathcal{S}\), there exists a control signal \(\mathbf{u}(t)\) such that \(\mathbf{x}(t;t_{0},\mathbf{x}_{0})\in\mathcal{S}\) for all \(t\geq t_{0}\), where \(\mathbf{x}(t;t_{0},\mathbf{x}_{0})\) denotes the solution of (1) at time \(t\) with initial condition \(\mathbf{x}_{0}\in\mathbb{R}^{n}\) at time \(t_{0}\).  
Consider control system (1) and a set \(\mathcal{C}\subset\mathbb{R}^{n}\) defined by  
\[\mathcal{C}=\{\mathbf{x}\in\mathbb{R}^{n}:h(\mathbf{x})\geq 0\}, \tag{2}\]  
for a continuously differentiable function \(h(\mathbf{x}):\mathbb{R}^{n}\to\mathbb{R}\) that has a relative degree one. The function \(h\) is called a (zeroing) CBF if there exists a constant \(\gamma>0\) such that  
\[\sup_{\mathbf{u}\in\mathrm{U}}\Bigl{[}\mathcal{L}_{f}h(\mathbf{x})+\mathcal{L}_{g}h( \mathbf{x})\mathbf{u}+\gamma h(\mathbf{x})\Bigr{]}\geq 0, \tag{3}\]  
where \(\mathcal{L}_{f}h(\mathbf{x})=\frac{\partial h}{\partial\mathbf{x}}\mathbf{f}(\mathbf{x})\), \(\mathcal{L}_{g}h(\mathbf{x})=\frac{\partial h}{\partial\mathbf{x}}\mathbf{g}(\mathbf{x})\) are the Lie derivatives [53]. Given a CBF \(h(\mathbf{x})\), the set of all control values that satisfy (3) for all \(\mathbf{x}\in\mathbb{R}^{n}\) is defined as \(K_{bf}(\mathbf{x})=\{\mathbf{u}\in\mathrm{U}:\mathcal{L}_{f}h(\mathbf{x})+\mathcal{L}_{g}h (\mathbf{x})\mathbf{u}+\gamma h(\mathbf{x})\geq 0\}\). It was proven in [53] that any Lipschitz continuous controller \(\mathbf{u}(\mathbf{x})\in K_{bf}(\mathbf{x})\) for every \(\mathbf{x}\in\mathbb{R}^{n}\) will guarantee the forward invariance of \(\mathcal{C}\). The provably safe control law is obtained by solving an online quadratic program (QP) problem that includes the control barrier condition as its constraint.
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 3 Cyberattack Scenarios on Autonomous Ships
### Ship Dataset Attack  
If an attacker attacks the vulnerable points found at the AI learning stage, it can lead to wrong judgments owing to erroneous learning. In such cases, an attack scenario can be anticipated in which a threat is mistaken for a safe element during the ship's voyage, and the ship is led to allow access. [PERSON] et al. [26] used the poisoning attack with particle swarm optimization technique to inject some malicious samples into the training dataset, resulting in a performance reduction of up to 95-33% in the worst case. In this way, poisoning attacks can be attempted on the ship identification learning as shown in Figure 5; (a) is an image of a normal ship, and (b) is an image with noise added to the existing image. To the naked eye, the images can be recognized as the same ship, but when they are injected into AI training data, the accuracy of classification can be significantly reduced [27].  
Figure 6 illustrates a possible scenario when an attacker attempts a poisoning attack on the AI model training data. It is crucial for autonomous ships to undergo learning on the coast, where there are many obstacles. Through coastal navigation, the ship system learns about the environment, such as bridges, surrounding islands, and weather. In this process, a poisoning attack can occur if incorrect learning is induced in the system regarding bridges, islands, or land for malicious purposes. An AI model that learns from the erroneous data of an attacker receives real-time sensor data and makes a judgment with lower accuracy. Such misjudgments can lead to collisions as the ship deviates from its optimal or normal path and fails to identify threats. As a result, the control system may issue wrong commands, making normal operation impossible and even requiring reinstallation of the autonomous navigation system. [PERSON] et al. [28] propose a novel approach to poisoning attack defense that, unlike traditional techniques, can be implemented using DNNs to outperform state-of-the-art attack techniques and develop an authenticated defense against common poisoning attacks.  
Figure 5: (a) Dataset sample (**left**) and (b) modified dataset sample (**right**) for a poisoning attack.
###### Abstract
The main topic of the present paper is represented by the combined experimental and 1D numerical analyses of a small turbocharged PFI spark ignition engine in order to provide fast and accurate predictions for individual cylinder-out emissions at different operations and with an apparent cylinder-by-cylinder variation. A previous dedicated experimental study on the examined engine has shown a certain difference in the injected fuel quantity by the port-injectors, mainly ascribed to the fuel rail geometry [15]. In this work, in a first stage, the original engine test bench was modified with the aim to measure both the cylinder-out exhaust emissions and the overall emissions at the engine exhaust, just upstream of the TWC. An extensive experimental campaign was carried out: at each operating condition, the individual cylinder behavior was characterized both in terms of performance, combustion evolution and stability, knock occurrence and exhaust emissions. Tests were performed at various speed/load points of the engine domain, including operations under different external EGR rates to accurately explore the emission variations for the engine and cylinders. In particular, part-load points typical of the engine Worldwide harmonized Light-duty vehicles Test Cycle (WLTC) were investigated under stoichiometric A/F ratio and increased residual contents. The experimental outcomes were employed to validate a 1D model of the entire engine, developed within the GT-PowerTM code. Refined sub-models of turbulence, combustion, heat transfer and pollutant emissions were utilized and integrated within the commercial code. The propagation of the cylinder-out noxious species within the exhaust system up to the TWC was also considered. The main innovative aspect of present work is represented by the adopted modeling approach which allows to easily forecast the effects of cylinder-by-cylinder variation on both combustion and exhaust emissions of a SI engine and in a wide range of operating conditions. An additional novelty of work, compared to the ones reported in the current literature, is represented by the first-attempt prediction of the single cylinder emission characteristics. Once validated, the model is applied to reproduce the improvements in terms of Indicated Specific Fuel Consumption (ISFC) and pollutants resulting from the suppression of A/F ratio imbalance between engine cylinders. Summarizing, the proposed numerical procedure can be considered a valuable tool to control the cylinder-by-cylinder variation with the aim to optimize the individual cylinder behavior for improved engine stability, fuel economy and pollutant emissions.
# Adaptive Beamformer Combined with Phase Coherence Weighting Applied to Ultrafast Ultrasound
## 3 Results
### Basic Experimental Results Using Phantom
As shown in Figure 6a, echoes from diffuse scattering medium are suppressed by the PCF when sub-aperture beamforming was not used, and such a negative effect of the PCF estimated from element echo signals can be reduced by sub-aperture beamforming as shown in Figure 3. Figure 7a,b show the lateral resolution and the peak-to-speckle ratio at each number of sub-apertures obtained from point targets at axial depths of 12 mm and 22 mm, respectively. In the present study, mean gray levels in a rectangular region (from 10.8 to 13.2 mm in the range direction, from 3 to 5 mm in the lateral direction) and another rectangular region (from 20.8 to 23.2 mm in the range direction, from 3 to 5 mm in the lateral direction) were used for evaluation of the peak-to-speckle ratios in Figure 7a,b, respectively. The gPCF obtained without sub-aperture beamforming corresponds to the number of sub-apertures of 72. In Figure 7a,b, the lateral full widths at half maxima do not vary with increasing the number of sub-apertures, whereas the peak-to-speckle ratios increase. Experimental results show that the gray level in diffuse scattering medium becomes relatively low under a large number of sub-apertures and, hence, the speckles are not visualized well when the proposed method is used without sub-aperture beamforming. Figure 6: B-mode images of (**a**) wire phantom and (**b**) cyst phantom obtained by mAPES beamformer with gPCF estimated in a conventional way, i.e., gPCF was estimated without sub-aperture beamforming. Element echo signals before application of mAPES beamformer weights were used for estimation of gPCF.
###### Abstract  
The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT-based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, a digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software-defined radio (SDR) are presented.  
Keywords:approximate discrete Fourier transform; wideband spectrum perception; wideband multibeam RF digital beamforming; low-complexity algorithms +
Footnote †: journal: _Algorithms_
# How Close Is Existing C/C++ Code to a Safe Subset?
## 3 Safe C/C++ Standards
### Misra C++  
In safety critical industries such as the aerospace and automotive industries, MISRA (Motor Industry Software Reliability Association) C++ is a widely used safe subset of the language [24]. MISRA C++ is derived from the MISRA C guidelines and extends the same principles to C++. It focuses on avoiding undefined behavior, reducing the use of dangerous language features, and promoting a consistent coding style to enhance code readability and maintainability. The MISRA C++ rules cover the categories of language-independent issues, general, lexical conventions, basic concepts, standard conversions, expressions, statements, declarations, declarators, classes, derived classes, member access control, special member functions, templates, exception handling, preprocessing directives, libraries, language support libraries, diagnostics libraries, and input/output libraries.
# A Mesh-Free Particle Method for Simulation of Mobile-Bed Behavior Induced by Dam Break
## Moving Particle Semi-Implicit Method  
### Governing Equations  
The continuity and Navier-Stokes equations are governing equations of the MPS method for incompressible viscous flows.  
\[\frac{D\rho}{Dt}=0 \tag{1}\]  
\[\frac{D\overset{\rightarrow}{u}}{Dt}=-\frac{1}{\rho}\
abla P+\
u\
abla^{2} \overset{\rightarrow}{u}+\overset{\rightarrow}{g}+\frac{1}{\rho}\sigma \kappa\overset{\rightarrow}{n}+\overset{\rightarrow}{F} \tag{2}\]  
where \(\rho\) is the density of a fluid, \(t\) is time, \(\overset{\rightarrow}{u}\) is a velocity of a particle, \(P\) is pressure, \(\
u\) is kinematic viscosity, \(\overset{\rightarrow}{g}\) is gravitational acceleration, \(\sigma\) is surface tension coefficient, \(\kappa\) is the curvature of an interface, \(\overset{\rightarrow}{n}\) is a unit normal vector for an interface, \(\
abla\) is a gradient, \(\
abla^{2}\) is a Laplacian, and \(\overset{\rightarrow}{F}\) is an acceleration of external force. For the term of the surface tension, it automatically vanishes when the particle is not on the interface or free surface.  
Since the MPS method follows the Lagrangian approach, the differential operator needs to be replaced by the particle interaction method. In the particle interaction method, the kernel function was introduced, which represents the effects of neighboring particles with respect to the distance between the center and neighboring particle. It was originally suggested by [PERSON] and [PERSON] [18] and modified with a third-order function.  
\[w\big{(}|r_{ij}|\big{)}=\left\{\begin{array}{cc}\left(1-\frac{|r_{ij}|}{r_{e }}\right)^{3}\!\!\left(1+\frac{|r_{ij}|}{r_{e}}\right)^{3}&\left(0\leq\!|r_{ij} |\!<r_{e}\right)\\ 0&\left(r_{e}<\!|r_{ij}|\right)\end{array}\right. \tag{3}\]  
where subscript \(ij\) denotes the substitution of arbitrary physical quantities between the center particle and neighboring particle (\(r_{ij}=r_{j}-r_{i}\)), and \(r_{e}\) is an effective range of the particle interaction. According to Equation (3), the value of the kernel function will be zero when the particle distance exceeds an effective range. In this study, the default value of the effective range is set to \(2.1\times l_{0}\), where \(l_{0}\) is the particle distance at an initial arrangement.
## 5 Discussion
### Qualitative Results Analysis
The edges of the lesions are more accurately captured, which is crucial for clinical applications where precise boundary detection is necessary for diagnosis and treatment planning. The method showed consistent and reliable segmentation across different regions of the dataset, highlighting its effectiveness in leveraging both labeled and unlabeled data. In summary, the qualitative experimental results in our study indicate that under conditions with a small proportion of labeled samples, semi-supervised segmentation methods outperform fully supervised methods. This is because semi-supervised methods not only use labeled data as a supervisory signal but also exploit the internal information of unlabeled data, enhancing the model's learning performance and generalization ability. Compared to other semi-supervised approaches, SemiPolypSeg (Ours) showed better segmentation performance, with a higher overlap with the ground truth and smoother segmentation results at the lesion edges. We attribute this to two main factors:  
Firstly, the HTCSNet architecture combines the advantages of CNNs for capturing local details and transformers for global modeling, significantly enhancing effective image semantic representation. Secondly, compared to other methods, SemiPolypSeg effectively leverages CPS loss and patch-wise contrastive loss to learn semantic features from unlabeled images, improving the model's learning ability and generalization performance. CPS loss ensures prediction consistency, while contrastive loss further enhances the utilization of unlabeled images by imposing feature similarity constraints between positive and negative sample pairs. The combination of these two losses enhances model performance while diminishing the dependence on extensively labeled datasets. Ablation studies in this research also demonstrate the effectiveness of each module of our method.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Known Key Security  
Our proposed scheme can provide known key security, which means that when the authentication and key agreement protocol is executed, both the client and server generate a unique session key. In other words, the disclosure of some session keys has no effect on the security of the others. In our proposed scheme, \(SK=h(J_{i}||T_{1}||T_{2})\) = \(h(r_{c}r_{s}A_{i}P||T_{1}||T_{2})\), where timestamps and random integers are exploited in the computation. Therefore, even if \(A\) knows some session keys, without knowing the timestamps and random integers generated in a certain communication, \(A\) cannot obtain the needed session key. Hence, our proposed scheme provides known key security.