esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Autonomous vehicles (AVs) rely on a heterogeneous sensor suite of RGB cameras, LiDAR, GPS/IMU, and emerging event-based dynamic vision sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically implement seven attack vectors in the CARLA simulator--salt and pepper noise, event flooding, depth map tampering, LiDAR phantom injection, GPS spoofing, denial of service, and steering bias control--and measure their impact on a state-of-the-art end-to-end driving agent. We then equip each sensor with tailored defenses (e.g., adaptive median filtering for RGB and spatial clustering for DVS) and integrate a unsupervised anomaly detector (EfficientAD from anomalib) trained exclusively on benign data. Our detector achieves clear separation between normal and attacked conditions (mean RGB anomaly scores of 0.00 vs. 0.38; DVS: 0.61 vs. 0.76), yielding over 95% detection accuracy with fewer than 5% false positives. Defense evaluations reveal that GPS spoofing is fully mitigated, whereas RGB- and depth-based attacks still induce 30-45% trajectory drift despite filtering. Notably, our research-focused evaluation of DVS sensors suggests potential intrinsic resilience advantages in high-dynamic-range scenarios, though their asynchronous output necessitates carefully tuned thresholds. These findings underscore the critical role of multi-modal anomaly detection and demonstrate that DVS sensors exhibit greater intrinsic resilience in high-dynamic-range scenarios, suggesting their potential to enhance AV cybersecurity when integrated with conventional sensors.  
autonomous vehicles; cybersecurity attacks; dynamic vision sensor Article
## 3 Proposed Control Method  
A new control system was designed in which the ASG and battery supply compensatory power in coordination with the power system to enhance the stability of the power system. In the control system, the battery first responds quickly to the system frequency deviation due to a grid fault and improves the frequency nadir, and then the ASG starts to supply the compensatory power to recover the system frequency to the rated frequency.  
Figure 11 shows diagrams of the proposed ASG control systems for the generating operation mode and the pumping operation mode, respectively. In the control systems, the frequency deviation of the system was passed through the \(P\)Gain, divided by \(P_{\text{ASG}}\), and then input to the rate limiter of 100 (%MW/min). The output from the rate limiter was added by \(P_{\text{ASG}0}\) (the steady-state reference output), then passed through the upper/lower limiter, and finally the reference signal, ASG_\(P_{\text{ref}}\), was obtained. It was clear that the ASG had a weak point and that its output could not be increased/decreased rapidly due to the 100 (%MW/min) rate limiter (this value is a standard value for pumping generators in Japan). This weak point was compensated for by the battery as follows.  
Figure 12 depicts a diagram of the battery controller. In the battery controller, \(P_{\text{Ideal}}\) is the ASG signal without the rate limiter, and the difference between \(P_{\text{Ideal}}\) and \(P_{\text{ASG}\,\text{Signal}}\) was utilized to determine the battery output reference signal. Consequently, the fast component in the deviation of the grid frequency that the ASG could not compensate for was supplied by the battery. As a result, if the speed of the grid frequency deviation was slow, the battery supplied no power.  
Figure 11: Frequency control systems of the ASG.  
Figure 10: Simple battery model.
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
## 2 Materials and Methods
### Mathematical Model  
As shown in Figure 5, the vertex distance between elliptical paraboloid \(i\) and \(j\) is \(d_{x}\) and \(d_{y}\) in the X and Y direction, respectively. First, point A on the elliptical paraboloid \(i\) is measured by the optical slope sensor to obtain the light spot central position \((x_{CA},y_{CA})\). Then, an optical slope sensor moves about 50 mm along the X direction to point B on the elliptical paraboloid \(j\) and the central position \((x_{CB},y_{CB})\) is acquired. Point A' is the mapping point of point A, which means that point A' and point A will be in the same position if they are on an identical elliptical paraboloid. Therefore, the distance between point A' and point A is the same as the vertex distance of two elliptical paraboloids.  
According to the geometric relationship in Figure 5, the following equation can be determined:  
\[\left\{\begin{array}{c}d_{x}=L_{x}-l_{x}\\ d_{y}=l_{y}\end{array}\right. \tag{1}\]  
where \(l_{x}\) and \(l_{y}\) are the distance between point A' and B, and \(L_{x}\) is the length between point A and B along the X direction monitored by a laser interferometer. \(l_{x}\) and \(l_{y}\) are calculated by displacement of the spot, as follows:  
\[\left\{\begin{array}{c}l_{x}=(x_{CB}-x_{CA})c_{1}\\ l_{y}=(y_{CB}-y_{CA})c_{1}\end{array}\right. \tag{2}\]  
where \(c_{1}\) represents the displacement coefficient that needs to be calibrated.  
The above model used the calibration formula in the case of only translation occurring between the elliptical paraboloids. However, the relative angular (pitch and yaw) elliptical Y and X axis between them is neglected. The pitch between two elliptical paraboloids is shown in Figure 6, which will result in the influence on spot displacement. The relative pitch (\(\Delta\theta\)) and yaw (\(\Delta\varphi\)) between each elliptical paraboloid should be calibrated, and their influence on displacement can be calculated by the following formula:  
\[\left\{\begin{array}{c}x_{CA^{\prime}}-x_{CA}=\Delta\theta/c_{2}\\ y_{CA^{\prime}}-y_{CA}=\Delta\varphi/c_{2}\end{array}\right. \tag{3}\]  
where \(c_{2}\) represents the angle coefficient that needs to be calibrated.  
Figure 5: Calibration principle according to the geometric relationship.  
Finally, the calibration formula for the vertex distance between paraboloids can be expressed as  
\[\left\{\begin{array}{c}d_{x}=L_{x}-(x_{cB}-x_{cA}-\Delta\theta/c_{2})c_{1}\\ d_{y}=(y_{cB}-y_{cA}-\Delta\varphi/c_{2})c_{1}\end{array}\right. \tag{4}\]
## 3 Spontaneous Brillouin Scattering in PFGI-POFs  
A standard POF composed of PMMA [1,2] is optimally designed for light transmission at 650 nm, with propagation loss of \(\sim\)200 dB/km. Its loss at telecommunication wavelength is, however, so high (\(>>1\,\times 10^{5}\) dB/km) that the Brillouin signal cannot be detected. In the meantime, in order to observe Brillouin scattering in a PMMA-based POF at 650 nm, we are required to prepare all the necessary optical devices at this wavelength, which are not easy from the viewpoint of availability and cost. Therefore, instead of PMMA-based POFs, we used PFGI-POFs with lower loss (\(\sim\)250 dB/km) even at 1.55 \(\upmu\)m, which is the optimal wavelength for long distance data transmission in silica optical fibers.  
Up to now, the spontaneous Brillouin scattering properties in a PFGI-POF with 120-\(\upmu\)m core diameter have been investigated at 1.55 \(\upmu\)m [27]. The BFS and the Brillouin linewidth were 2.83 GHz and 105 MHz, respectively. Using these values, the Brillouin gain coefficient was calculated to be 3.09 \(\times 10^{-11}\) m/W, which is almost the same as that of standard silica SMFs and higher than that of silica GI-MMFs. Here, we should bear in mind that the actual Brillouin gain coefficient may be higher than this value due to the multimode nature of the PFGI-POF. The Brillouin threshold power of a 100-m PFGI-POF was estimated to be as high as 24 W, and the Stokes signal was not sufficiently large to be applied to practical devices and systems.  
The BFS dependences on strain and temperature in a 5-m PFGI-POF have also been investigated at 1.55 \(\upmu\)m [30]. They showed negative dependences with coefficients of \(-\)121.8 MHz/% and \(-\)4.09 MHz/K, respectively, which are \(-\)0.2 and \(-\)3.5 times as large as those in silica fibers. These BFS dependences were found to originate from the dependences of the Young's modulus on strain and temperature. Thus, Brillouin scattering in PFGI-POFs has a big potential for high-accuracy temperature sensing with reduced strain sensitivity.
## 4 Numerical Examples
### Analysis and Discussion of Model Performance Comparison Using LSE Estimation Parameter Values  
In terms of fitting, model parameter values are fitted and estimated using 100% of the fault data, and the model fitting performances are compared. In terms of prediction, the model parameter values are fitted and estimated using 85% of the fault data, and we use the residual fault data (15% of the fault data) to compare the models' predictive performance. From Tables 3-5, we can see that using 100% of the data (DS1-1, DS1-2, and DS1-3), the _MSE_, \(R^{2}\), _RMSE_, _KD_, _TS_, and _Bias_ values of the PM are 35.85, 0.9456, 5.99, 0.1438, 12.68, and 4.82; 39.4, 0.9809, 6.28, 0.1762, 9.22, and 4.99; and 16.02, 0.9554, 4.0, 0.1732, 13.25, and 3.67, respectively. In Table 3, the _MSE_, _RMSE_, _TS_, and _Bias_ values of the ISS model which ranked second are approximately 1.45, 1.21, 1.21, and 1.28 times as large as those of the PM, respectively. In Table 4, we can see that the _MSE_, _RMSE_, _TS_, and _Bias_ values of the DSS model which ranked second are nearly 2.85, 1.78, 1.28, and 1.73 times as large as those of the PM, respectively. Table 5 shows that the _MSE_, _RMSE_, and _TS_ values of the DSS model which ranked second are about 1.64, 1.28, and 1.28 times as large as those of the PM, respectively. The PM outperforms the G-O model, DSS model, ISS model, Yamada Imperfect-2 model, P-N-Z model, GGO model, Wang model, and Li model in terms of fitting performance. Using 100% of the data (DS1-1, DS1-2, and DS1-3), the second best is the ISS model run once and the DSS model run twice. The worst is the Li model, which is three times worse in its ranking. These results can be seen in Figure 1a,c,e. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Parameter Estimation Values** & _MSE_ & \(R^{2}\) & _RMSE_ & _KD_ & _TS_ & _Bias_ \\ \hline G-O model & \(a\) = 14,739; \(b\) = 0.000104 & 373.29 & 0.819 & 19.32 & 0.2872 & 28.37 & 17 \\ \hline Delayed S-shaped model & \(a\) = 140.2; \(b\) = 0.007262 & 124.31 & 0.9397 & 11.15 & 0.3572 & 16.37 & 8.64 \\ \hline Inflection S-shaped model & \(a\) = 1219.5; \(b\) = 0.01225; \(b\) = 12.621 & 265.25 & 0.8714 & 16.29 & 0.288 & 23.92 & 13.51 \\ \hline Yamada Imperfect-2 model & \(a\) = 1069; \(b\) = 0.001073; \(c\) = 0.01393 & 257.66 & 0.8751 & 16.05 & 0.2793 & 23.57 & 13.57 \\ \hline P-N-Z model & \(a\) = 414.45; \(b\) = 0.001462; \(b\) = 0.095315; \(a\) = 0.05415 & 247.04 & 0.8802 & 15.72 & 0.3398 & 23.08 & 11.36 \\ \hline Weibull distribution model & \(a\) = 988.87; \(b\) = 0.000455; \(c\) = 1.323 & 224.85 & 0.891 & 15 & 0.2736 & 22.02 & 12.59 \\ \hline Wang model & \(a\) = 120; \(b\) = 0.016791; _β_ = 1.0771; \(d\) = 1.2024 & 987.14 & 0.5215 & 31.42 & 0.5562 & 46.14 & 26.89 \\ \hline Li model & \(a\) = 120; \(b\) = 0.026661; _β_ = 0.38421; \(N\) = 3.7994 & 994.26 & 0.518 & 31.53 & 0.5497 & 46.3 & 26.66 \\ \hline PM & \(a\) = 102.05; \(b\) = 0.12683; _β_ = 0.15916; \(d\) = 0.10146; & **39.4** & **0.9809** & **6.28** & **0.1762** & **9.22** & **4.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance comparison using 100% of the data (DS1-2).
## 5 Numerical Simulations and Analysis of Results
### Computational Complexity Analysis of the Proposed PCSDF Cooperation  
The computational complexity of the proposed PCSDF cooperation mainly includes two parts, i.e., the encoding and decoding of the polar code and the cooperative signal processing. With a code length \(N\) and information bit length \(M\) in a codeword, the complexity of the polar successive cancellation (SC) decoding is provided by \(O[N\cdot\log(\mathrm{M})]\). In the cooperative signal processing, with an average of \(\eta\) successful forwarding and 1-\(\eta\) retransmission along with an average of \(t\) relay participating the compound relay node, there are \(4\eta t\) multiplications and \(2t\) additions for each signal sample of successful forwarding, by (19) and (21), respectively. Otherwise, retransmission is performed and an alternative \(4\eta t\) multiplications and \(2t\) additions for each signal sample retransmission are determined by (20). The computational complexity of CSI estimation is fixed as \(\beta_{r_{grp}}\) and \(\beta_{sd}\) in the cooperative  
Figure 11: The FER performance of the proposed schemes with different numbers of relays.  
transmissions, with one division and one comparison in (21) and (22), respectively. The complexity of the cooperative threshold \(\theta\) is one exponential calculation, one subtraction, and one division, respectively, according to (18) and the corresponding explanations. In addition, the threshold is directly calculated in the polar SC decoding after the combination of MRC in the forwarding procedure. The core procedure of multi-path combination to generate a compound link is performed and organized to exceed the threshold using the powerful destination. The destination notifies the proper relay to participate in cooperation for the compound relay with low-cost limited feedback.
## 1 Introduction
Using a thick wedge-shaped LC through a space along the passive path, hyperspectral images were obtained [21]. The large amount of processing resources required means that it is time consuming to acquire hyperspectral images. Based on previous studies, this paper describes a compact, non-mechanical, high-resolution fast Fourier transform spectroscopy system based on LC delayers. The relaxation characteristics of LC are utilized to quickly obtain the interference intensity corresponding to different optical paths. By measuring the continuous change in light intensity during the LC relaxation time, optical paths with different times can be obtained quickly. The hyperspectral imaging speed can be improved using LC delayers. The application of this system in optical component defect detection is also demonstrated.
## 2 Orientation and Localization Equations
### System Overview
Then, their respective positions \(\vec{p}_{i}=(x_{i},y_{i},z_{i})^{T}\) in \(\{G\}\) can be expressed using the rotation matrix \(R_{G}^{L_{u}}(\alpha,\beta,\gamma)=R_{x}(\gamma)\ R_{y}(\beta)\ R_{z}(\alpha)\):  
\[\vec{p}_{i}=R_{G}^{L_{u}}(\alpha,\beta,\gamma)\ \vec{p}_{i0}+\vec{p}_{D}, \tag{4}\]  
\[R_{z}(\alpha)= \begin{pmatrix}c_{\alpha}&s_{\alpha}&0\\ -s_{\alpha}&c_{\alpha}&0\\ 0&0&1\end{pmatrix} ,\ R_{y}(\beta)= \begin{pmatrix}c_{\beta}&0&-s_{\beta}\\ 0&1&0\\ s_{\beta}&0&c_{\beta}\end{pmatrix} ,\ R_{x}(\gamma)= \begin{pmatrix}1&0&0\\ 0&c_{\gamma}&s_{\gamma}\\ 0&-s_{\gamma}&c_{\gamma}\end{pmatrix} .\]  
where \(s_{x}\) and \(c_{x}\) are abbreviations for \(sin\,x\) and \(cos\,x\) needed in the following. From Equation (3), it can be seen that the position of the UAV along the cables (\(x\)-direction) cannot be determined because the magnetic field does not provide sufficient information. Thus, only the projections of the magnetometers and the UAV frame origin in the \(yz\)-plane can be computed. The representation of the magnetic field \(\vec{v}_{i}\) measured by the \(i\)-th sensor at position \(\vec{p}_{i}\) expressed in \(\{G\}\) then becomes:  
\[\vec{B}(\vec{p}_{i})=R_{G}^{L_{u}}(\alpha,\beta,\gamma)\ R_{L_{u}}^{L_{u}}\ \vec{v}_{i}, \tag{5}\]  
where \(R_{L_{u}}^{L_{u}}\) is the user-defined orientation of \(L_{i}\) in the UAV body frame \(L_{u}\) and \(R_{G}^{L_{u}}\) is the orientation of \(L_{u}\) in the global frame \(\{G\}\). If \(L_{i}\) is aligned with \(\{G\}\), then it is \(R_{G}^{L_{u}}R_{L_{u}}^{L_{i}}=I\), where \(I\) is the identity matrix. In this case, the sensor measures exactly the magnetic field given by Equation (3), i.e., \(\vec{B}(\vec{p}_{i})=\vec{v}_{i}\). The system of three equations in (5) establishes a relationship between the sensor measurement, sensor position, and UAV orientation. It is nonlinear and has five unknowns \((y_{i},z_{i},\alpha,\beta,\gamma)^{T}\), which are determined in the following sections.
## 3 Results
### Meteorological and Carbon Flux Data  
Seasonal time series of net ecosystem exchange (NEE), gross primary productivity (GPP), and respiration (\(\mathrm{R_{eco}}\)) were compared to meteorological variables and allowed the identification of climatic drivers directing intra-seasonal variability in carbon fluxes. Key connections between carbon fluxes and meteorological variables provided indications of the underlying mechanisms affecting vegetation efficiency over the season and during individual phenolic stages (maturity and leaf senescence). A more detailed analysis of carbon fluxes and meteorological data can be found in Appendix A. PPFD appeared to be a strong driver of productivity during maturity. Comparison of diurnal patterns of GPP and PPFD (Figure 5) showed significant correlations during the months of June (\(R^{2}\) = 0.91, \(p<0.05\)), July (\(R^{2}\) = 0.90, \(p<0.05\)), August (\(R^{2}\) = 0.85, \(p<0.05\)), and September (\(R^{2}\) = 0.80, \(p<0.05\)). Productivity during the leaf senescence months, April and October, showed no association to PPFD conditions.  
Figure 5: Mean diurnal monthly pattern of gross primary productivity (GPP) (black dotted line) and photosynthetic photon flux density (red line) for the growing season (April–October).  
A combination of meteorological factors affecting apparent quantum yield (\(\alpha\)) made it hard to determine the proportion of effect of each variable and how these may change over the growing season. However, some key components were identified and further discussed in the Appendix A. Seasonal comparison of GPP and \(\alpha\) (Figure 6a) were poorly correlated (\(R^{2}\) = 0.33) over the whole season. However, during leaf senescence, (Figure 6b) as GPP starts to decline, GPP and \(\alpha\) showed similar temporal changes and were significantly correlated (\(R^{2}\) = 0.73, \(p\) < 0.05).
## 2 System Model and Evaluated Precoding Schemes  
This paper studies an MU mmW downlink system with \(N_{BS}\) Base Stations (BSs) and \(K\) User Equipment devices (UEs). Each BS is equipped with \(N_{t}\) antennas and \(P\leq N_{t}\) RF chains. For the sake of simplicity in the simulations, a single narrow receive beam is assumed at each UE (which would require an antenna array in practice), in order to simulate a beamforming gain at the receiver as in [21]. Furthermore, the pointing direction of the directive beam is selected by the user to maximize the signal to noise ratio (SNR) at the receiver.  
The downlink transmission resources are partitioned in the time-frequency plane. In the time domain, the resource space is divided into subframes of a certain time duration. In the frequency domain, resources are grouped in frequency blocks (FB). The minimum time-frequency resource is known as resource block (RB), and it is composed of one frequency block during one subframe period. This terminology is the same as the one used by the the 3 rd Generation Partnership Project (3 GPP) and the METIS project [1] to carry out performance evaluations.  
In order to exploit the BS multi-antenna architecture to either multiplex different data streams or to provide beamforming gains to the different users, some form of precoding or beamforming must be applied at the transmitter side. The schemes considered in this work are next detailed.
# Improved Duplication-Transfer-Loss Reconciliation with
## 2 Definitions and Preliminaries
### Transfers from Unsampled Lineages  
In most cases, it is difficult or impossible to achieve complete taxon sampling for the group of species under consideration. Unsampled taxa/lineages in the species tree can affect the accuracy of DTL reconciliation since transfers may have occurred from unsampled to sampled lineages. Even with complete taxon sampling, the species represented on a species tree are only those that have surviving descendant species. It is well-understood that the vast majority of species that have ever existed have gone extinct without leaving surviving descendants. This is depicted in Figure 2(a). It is reasonable to expect that such extinct species lineages would have engaged in gene transfer with the species that are represented in the \"visible\" species tree [10]. However, conventional DTL reconciliation algorithms do not explicitly model transfers from such unsampled lineages (with the exception of [10; 16], which include a simple mechanism to allow for such transfers).  
Figure 2: An example gene tree (**a**) and a possible augmented version with hidden nodes in blue (**b**).  
We will show how transfers from unsampled species can be detected and correctly handled by appropriately augmenting the input species tree with additional edges that represent unsampled lineages and then leveraging \(\mathbb{TL}\) events to allow for transfers from these additional edges to the rest of the species tree. Formally, we define the augmented species tree, denoted \(S^{\prime}\), as follows:  
**Definition 4** (Augmented species tree and extra nodes, leaves, and edges).: _Given the species tree \(S\), the augmented species tree \(S^{\prime}\) is defined to be the tree obtained from \(S\) by (i) subdividing each edge in \(E(S)\) by a new extra node such that each edge \((s,s^{\prime})\in E(S)\) is replaced by the two edges \((s,e)\) and \((e,s^{\prime})\), where \(e\) is the new extra node, and connecting \(e\) by an edge to a new extra leaf, and (ii) creating a new root node \(r\) and connecting \(r\) by an edge to \(r\!t(S)\) and by another edge to a new extra leaf. Each edge of \(S^{\prime}\) connecting an extra node with an extra leaf is called an extra edge._  
Figure 3c show an example of an augmented species tree. We use \(\mathcal{X}(S^{\prime})\) to denote the set of all extra leaves in \(S^{\prime}\). The extra edges in \(S^{\prime}\) represent unsampled lineages and the new DTLx model allows for transfers to originate from these extra edges of \(S^{\prime}\), and any transfer that originates at an extinct edge is labeled as a \(\mathbb{TX}\) event.