esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
###### Abstract  
Wave energy converters (WECs) harness the untapped power of ocean waves to generate renewable energy, offering a promising solution to sustainable energy. An optimal WEC control strategy is essential to maximize power capture that dynamically adjusts system parameters in response to rapidly changing sea states. This study presents a novel control approach that leverages neural networks to estimate sea states from onboard WEC measurements such as position, velocity, and force. Using a point absorber WEC device as a test platform, our proposed approach estimates sea states in real-time and subsequently adjusts PID controller gains to maximize energy extraction. Simulation results across diverse sea conditions demonstrate that our strategy eliminates the need for external wave monitoring equipment while maintaining power capture efficiency. The results show that our neural network-based control technique can improve power capture by 25.6% while significantly reducing system complexity. This approach offers a practical alternative for WEC deployments where direct wave measurements are either infeasible or cost prohibitive.  
wave energy converter (WEC); sea state estimation; machine learning; PIDcontrol
## 3 PNCC Method Based on Wavelet Packet Transform
### Raw Data Analysis and Preprocessing  
In order to improve the PNCC method, we first analyze the audio signals collected in the acoustic environment of the laboratory (as shown in Figure 2).  
According to the communication standard of the international digital telephone, we thought that the frequency range of human voice signal is mainly concentrated between 300 and 3400 Hz. Many parameters of the original PNCC algorithm are determined according to this frequency range. In this study, through Fourier analysis, it is found that the audio range of trunk borer mainly consists of two bands: 0-400 Hz and 1400-2300 Hz. The peak appears at about 1500 Hz, 1850 Hz and 70 Hz, as shown in Figure 2. Therefore, in the following chapters, we will improve the PNCC according to the main frequency range of stem borer.  
It can be observed that there are some differences in frequency distribution among different data in Figure 2. Some audio data include the frequency components of the 1000 and 1300 Hz bands, while others do not. The main reasons for this difference are the species, age and behavior of trunk borers. In addition, we can also observe, from Figure 2, that the vibration duration of the trunk borer signal is about 15 ms.  
Generally speaking, when using sound detection technology to monitor trunk borers, the recorded audio data are carried out in a harsh acoustic environment. Figure 3 shows an audio signal in a harsh acoustic environment, which comes from the side of the road. The audio mainly includes the sound of conversation, the roar of cars and the faint sound of trunk beetles eating tree trunks. By observing the frequency spectrum of this signal, we can observe that the audio information of trunk borers is completely obscured by noise and reverberation. Thus, it can be seen that the audio of trunk borer is not easy to observe over a long period of time. In order to obtain better classification results, we pre-filter all the audio data collected in a bad acoustic environment to remove the frequency components which are larger than 2400 Hz or between 400 and 800 Hz as much as possible.  
Figure 2: This picture shows the audio clip and its spectrum, recorded in the laboratory acoustic environment with a sampling frequency of 8000 Hz. In this study, this is considered to be the ideal audio data of trunk borers, without any interference.
## 1 Introduction
Both the range of carrier Doppler frequency and code phase are reduced by using INS velocity- and position-assisted acquisition. Figure 1: Schematic diagram of the search scope under three conditions. The Doppler frequency shift is related to the relative position and velocity of the receiver with respect to the transmitter. The former research mainly concentrated in low dynamic environments, such as vehicles and handheld devices. The velocity and position changes are small under such conditions, so the Doppler frequency shift is relatively small. However, in a high dynamics environment, such as missiles and aircrafts, the Doppler frequency shift is large. Therefore, this paper defines a high dynamic scene which includes acceleration, uniform motion, turning and climbing. In this high dynamic scene, the GNSS acquisition performance aided by different grade INS can be fully verified. In the INS-aided acquisition, the INS accuracy has crucial effects on the Doppler and the code phase estimation errors. Different grade INS devices afford different velocity errors and position errors. In order to analyze the influence of different INS grades on acquisition performance, the mathematical model between them is deduced in detail. Furthermore, the relationships between INS accuracy, Doppler estimation error, code phase error and acquisition performance are verified by signal acquisition experiments which use the trajectory of the above high dynamic scene. Finally, the influences of different grades of INS on the acquisition performance and the tracking performance are analyzed. BDS is the independently developed Chinese global navigation satellite system, which has many similarities with GPS. Based on the analysis of the characteristics of GPS L1 frequency and BDS B1I signals modulation, the INS-aided acquisition performance is proved by using GPS/BDS dual-mode software receiver. This paper is structured as follows: Section 2 shows the characteristics of the GPS L1 and BDS B1 signals, and then presents the INS-aided acquisition methodology. In Section 3, the frequency shift and code phase estimation theory are described. In Section 4, the Doppler shift estimation error and the code phase estimation error caused by INS velocity error and position error are derived in detail. Furthermore, the acquisition experiments assisted with different grade INS under high dynamics are performed in Section 5. In Section 6, the final conclusions are given.
## 3 Design of SROA
### Packet Structure of SROA Protocol  
Figure 3 shows the data packet structure of SROA. The packet format is used to convey information across nodes and to coordinate the clustering and routing processes. There are two parts in SROA: The header and the data. The header contains packet-related fields and routing-related fields. The first two fields denote the source and destination address. Other header fields are the routing decision-related fields, including source node ID, residual energy, V value, cluster ID, and relay set.  
Once an ordinary node receives the data packet, it updates the local neighbor table through the received packet header and then forwards the data packet to its CH or PCH. If the data packet is received by a CH or PCH, it retrieves the packet header and neighbor table for the information updates. If it is in the relay set, the candidate set will be constructed by the calculated the Q-values of the Q-Learning, based on related factors and the packet header is wrapped with the relevant fields. A waiting timer then starts. Otherwise, the current node is not in the relay set and the reception will be dropped.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(R_{0}\) & the constant cost \\ \(T_{delay}\) & The predefined maximum delay \\ \(C\) & The set of clusters \\ \(n_{i}\) & the \(i\)th node \\ \(Neighbor_{i}\) & neighbors of \(n_{i}\) \\ \(neighbor_{i}\) & The \(j\)th neighbor of \(n_{i}\) \\ \(E_{n_{i}}^{res}\) & \(n_{i}\)’s remaining energy \\ \(E_{n_{i}}^{init}\) & \(n_{i}\)’s initial energy \\ \(T_{i}\) & The waiting time of \(n_{i}\) before forwarding \\ \(P_{i}^{i}\) & Buffered packets of \(n_{j}\) \\ \(E_{r}\),\(E_{s}\) & Energy for packet reception and transmission \\ \(Cl_{j}\) & The \(j\)th cluster \\ \(CH\), \(PCH\) & Cluster head, potential cluster head \\ \(CR\) & Communication Range \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of symbols.  
The payload data field is not required. When no payload data is present, data from the upper level will be relayed to the sink node. Otherwise, the data packet serves only to exchange information.
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 2 Distributed Localization Method
### Consistent Extened Kalman Filter Algorithm  
In centralized filtering algorithms, observation data from all satellites must be fused in a fusion center; then, the upscaled system performs state estimation with centralized algorithms. This method makes system dimensionality rise, leads to a sharp increase in computational costs, and has weak robustness. In contrast, distributed filtering methods allow each satellite to independently perform local filtering, avoiding the need to upscale the observation matrix. Each filter operates with a lower computational burden and retains the capability to accurately perform filtering and positioning even if one satellite signal is lost, thus enhancing interference resistance. Therefore, this paper designed a distributed extended Kalman filter (EKF) algorithm based on consensus on estimates (CE) to collaboratively solve the localization of non-cooperative targets.  
The prediction equation for the Consensus Extended Kalman Filter is given by the following:  
\[\left\{\begin{array}{l}\mathbf{X}_{k,k-1}^{i}=f^{i}\big{(}\mathbf{\hat{X}}_{ k-1}\big{)}\\ \mathbf{P}_{k,k-1}^{i}=\mathbf{\Phi}_{k,k-1}\mathbf{P}_{k-1}^{i}\mathbf{\Phi}_{k,k-1}^ {T}+\mathbf{\Gamma}_{k-1}\mathbf{Q}_{k-1}\mathbf{\Gamma}_{k-1}^{T}\end{array}\right. \tag{12}\]  
and the update equation for the Consensus Extended Kalman Filter is  
\[\left\{\begin{array}{l}\mathbf{K}_{k}^{i}=\mathbf{P}_{k,k-1}^{i}\Big{(} \mathbf{H}_{k}^{i}\Big{)}^{T}\Big{(}\mathbf{H}_{k}^{i}\mathbf{p}_{k,k-1}^{i} \Big{(}\mathbf{H}_{k}^{i}\Big{)}^{T}+\mathbf{R}_{k}\Big{)}^{-1}\\ \mathbf{\hat{X}}_{k}^{i}=\mathbf{\hat{X}}_{k,k-1}^{i}+\mathbf{K}_{k}^{i}\Big{[} \mathbf{Z}_{k}^{i}-h_{i}\Big{(}\mathbf{\hat{X}}_{k,k-1}^{i}\Big{)}\Big{]}+ \mathbf{C}_{i}\underset{j\in N_{i}}{\sum}(\mathbf{\hat{X}}_{k,k-1}^{j}- \mathbf{\hat{X}}_{k,k-1}^{i})\\ \mathbf{C}_{i}=\gamma\dfrac{\mathbf{P}_{k,k-1}^{i}}{1+\left\|\mathbf{P}_{k,k- 1}^{i}\right\|_{F}}\\ \mathbf{P}_{k}^{i}=\Big{(}\mathbf{I}-\mathbf{K}_{k}^{i}\mathbf{H}_{k}^{i} \Big{)}\mathbf{P}_{k,k-1}^{i}\Big{(}\mathbf{I}-\mathbf{K}_{k}^{i}\mathbf{H}_ {k}^{i}\Big{)}^{T}+\mathbf{K}_{k}^{i}\mathbf{R}_{k}\Big{(}\mathbf{K}_{k}^{i} \Big{)}^{T}\end{array}\right. \tag{13}\]  
where \(i=1,2,3\) denotes different tracking satellites; \(\mathbf{Q}_{k-1}\) is the system noise covariance matrix; \(\mathbf{R}_{k}\) is the measurement noise covariance matrix; \(\mathbf{P}_{k}\) is the estimation error covariance matrix; \(\gamma>0\) denotes the consensus gain coefficient, which influences the convergence and convergence speed of the consensus filtering algorithm and must be set sufficiently small to ensure stability [20]; and \(N_{i}\) denotes the node that communicates with node \(i\). In this paper, all three tracking satellites, 1, 2, and 3, can communicate with each other.
## 2 Recent Application of Hyperspectral in Food Authentication
### Animal Proteins (Meat and Meat Products)
The results showed that information derived from the NIR HSI system could be used for rapid and non-intrusive analysis of sausage samples from China [13].
## 3 System Model  
We consider an indoor warehouse, where the source node (\(S\)) moves along a preassigned pathway, and the relay switch (\(R\)) is located at the highest reachable point inside the warehouse, where a direct line-of-sight (LoS) path can be made with \(S\). The destination node (\(D\)) is an IoT receiver that processes the data for the required information. We consider that this system is implemented in a standard warehouse with boxes stored and we assume that this consideration allows for a better visualization of the real-world warehouse environment within the simulation.  
It is assumed that the data transmission occurs from \(S\) to \(R\), and \(R\) switches to either PS-SWIPT or ABCS based on received signal parameters. There is no direct link from \(S\) to \(D\) due to severe path loss and obstacles (which would affect the assumption of the simulation modeling the real-world scenario). For the scope of this system, we consider that there is no data processing occurring at \(R\), and all processing functions are carried out once the signal is received at \(D\)[26]. The warehouse environment is shown in Figure 1.
## 3 Experiments and Comparisons
### Feature Sets  
To create a feature set using the proposed selective multi-stage feature method, we compared the feature set dimensions. As discussed above, two criteria were considered for the feature extraction from each CNN layer: The characteristic vehicle image information was included in the trained feature map, and the feature map with the largest activation in each layer was selected. The Maxpool_1, fourth convolution, and fifth convolution layers were selected for the feature extraction, and we input the validation data to the trained CNN model to generate the feature maps for each layer. Three feature maps were selected in the increasing order of the activation value for each layer, as shown in Figure 10 and Table 2.
## 3 Threat Detection System for the Security of Critical Infrastructures
### Software Architecture of the Threat Detection Framework
#### 3.3.2 3D Video Tracking  
The purpose of the 3D Video tracking is to support fluent and accurate hand-over from the 2D Video to Sensor 2 system, which is guided by an internal three-dimensional video camera (EXTRAS system in DEXTER deployment). The 3D Video handover procedure, together with location prediction computations, compensate the internal INSTEAD system communications and processing latency and enable the Sensor 2 system to take proactive adjustment actions on the slow hardware components.  
The 3D Video tracks all persons accurately in the field of view of the three-dimensional sensor independent of all other systems. Figure 3 depicts the 3D Video system software architecture. The system receives track data from the three-dimensional sensor over an UDP socket and stores track data object-specifically (i.e., per a person) for two seconds starting from the current timepoint backwards. The management procedure keeps computational memory constraints in check by removing obsolete track samples and expired tracks periodically.  
When the 2D Video has re-identified a person in vicinity of the Sensor 2 system, it generates a hand-over trigger to the 3D Video via C&C. The trigger embeds both timestamp and location in X-Y real-world coordinates, which are the key input to the 3D Video. The 3D Video completes a hand-over procedure finding a matching person in available up-to-date three-dimensional sensor data in both spatial and temporal spaces and computes location predictions in the real-world coordinate system for a second in future for the triggered person(s) only.  
The 2D Video to 3D Video tracking handover is described in Section 4.3.3.
## 2 Main Result
### Robustness Lyapunov Analysis for Error System
#### 2.5.4. Upper Bounding \(|\operatorname{tr}(L_{*}\gamma_{y}^{-1}\Delta L^{\dagger})|\)  
Transitioning, lets consider bounding the \(|\operatorname{tr}(L_{*}\gamma_{y}^{-1}\Delta L^{\dagger})|\) term from Equation (32). Using the Cauchy-Schwarz inequality, alternatively written as \(|\operatorname{tr}(AB^{\dagger})|\leq\operatorname{tr}(AA^{\dagger})^{\frac{ 1}{2}}\operatorname{tr}(BB^{\dagger})^{\frac{1}{2}}\), on \(|\operatorname{tr}(L_{*}\gamma_{y}^{-1}\Delta L^{\dagger})|\):  
\[\begin{split}|\operatorname{tr}(L_{*}\gamma_{y}^{-1}\Delta L^{ \dagger})|&\leq\operatorname{tr}(L_{*}\gamma_{y}^{-1}L_{*}^{ \dagger})^{\frac{1}{2}}\underbrace{\operatorname{tr}(\Delta L\gamma_{y}^{-1} \Delta L^{\dagger})^{\frac{1}{2}}}_{=(2V_{L})^{\frac{1}{2}}\leq(2V_{el})^{ \frac{1}{2}}}\\ &\leq\operatorname{tr}(L_{*}\gamma_{y}^{-1}L_{*}^{\dagger})^{ \frac{1}{2}}(2V_{el})^{\frac{1}{2}}.\end{split} \tag{39}\]  
Using the trace operator \((\operatorname{tr})\) property of invariant under circular shifts (i.e., \(\operatorname{tr}(ABC)=\operatorname{tr}(CAB)=\operatorname{tr}( BCA)\)), the \(\operatorname{tr}(L_{*}\gamma_{y}^{-1}L_{*}^{\dagger})^{\frac{1}{2}}\) term from Equation (39) can be rewritten as follows:  
\[\operatorname{tr}(L_{*}\gamma_{y}^{-1}L_{*}^{\dagger})^{\frac{1}{2}}= \operatorname{tr}(L_{*}^{\dagger}L_{*}\gamma_{y}^{-1})^{\frac{1}{2}}. \tag{40}\]  
Equation (40) can be upper bounded by applying the Cauchy-Schwarz inequality:  
\[\begin{split}\operatorname{tr}(L_{*}^{\dagger}L_{*}\gamma_{y}^{-1 })^{\frac{1}{2}}&\leq|\operatorname{tr}(L_{*}^{\dagger}L_{*} \gamma_{y}^{-1})^{\frac{1}{2}}|&\leq\operatorname{tr}(L_{*}^{ \dagger}L_{*}L_{*}^{\dagger}L_{*})^{\frac{1}{2}}\operatorname{tr}(\gamma_{y}^{ -1}\gamma_{y}^{-1})^{\frac{1}{2}}\\ &\leq\operatorname{tr}(L_{*}^{\dagger}L_{*})^{\frac{1}{2}} \operatorname{tr}(\gamma_{y}^{-1})^{\frac{1}{2}}.\end{split} \tag{41}\]  
For notation purposes, allow the following assumptions:  
\[M_{k}>0\
i\operatorname{tr}(L_{*}^{\dagger}L_{*})^{\frac{1}{2}}\leq M_{k} \tag{42}\]  
and  
\[\operatorname{tr}(\gamma_{y}^{-1})\leq\bigg{(}\frac{M_{v}}{aM_{k}}\bigg{)}^{2}. \tag{43}\]  
Substituting Equations (42) and (43) in Equation (41),  
\[\begin{split}\operatorname{tr}(L_{*}^{\dagger}L_{*}\gamma_{y}^{-1 })^{\frac{1}{2}}&\leq\operatorname{tr}(L_{*}^{\dagger}L_{*})^{ \frac{1}{2}}\operatorname{tr}(\gamma_{y}^{-1})^{\frac{1}{2}}\\ &\leq M_{k}\bigg{(}\frac{M_{v}}{aM_{k}}\bigg{)}\\ &\leq\bigg{(}\frac{M_{v}}{a}\bigg{)}.\end{split} \tag{44}\]  
Following, Equation (44) can be substituted in Equation (39):  
\[|\operatorname{tr}(L_{*}\gamma_{y}^{-1}\Delta L^{\dagger})|\leq \operatorname{tr}(L_{*}\gamma_{y}^{-1}L_{*}^{\dagger})^{\frac{1}{2}}(2V_{el})^ {\frac{1}{2}}\leq\bigg{(}\frac{M_{v}}{a}\bigg{)}(2V_{el})^{\frac{1}{2}}. \tag{45}\]
## 1 Introduction
The self-sensing approach has been widely applied for carbon fibre reinforced polymers (CFRPs), where carbon fibres also functioned as sensing elements for the structure, due to their electrical conductivity [22; 23]. This approach was also reported to provide in-plane damage mapping [24; 25]. Damage monitoring has also been demonstrated for composites with non-conductive reinforcement, such as GFRPs, where the polymeric matrix is \"doped\" with conductive elements [18; 26; 27]. A major challenge in imparting multifunctionality to composite materials via the exploitation of electrical properties is tailoring electrical conductivity so that the response of the material to various external fields is noise-free and within the sensitivity and time frame of the monitoring process. The addition of a conductive nano- or micro-scaled filler inside the dielectric epoxy matrix has been reported to improve the mechanical, electrical and thermal properties of the composites [28; 29]. According to various studies, the most promising nano-fillers are carbon nanotubes (CNTs) due to their exceptional properties [30]. As an alternative filler, carbon black (CB) is a widely used carbonaceous filler, capable of enhancing these properties [31; 32]. Therefore, the homogenous dispersion of these fillers overcomes the obstacles of imparting functional properties to the epoxy resin and more specifically, the SHM functionality [18; 33; 34]. Within the scope of this research was to impart self-sensing capabilities to the composite to map high-velocity impact damage. Self-sensing was achieved by using the carbon fabric strips both as reinforcing elements and embedded electrodes, in a specific layout between the glass fabric plies, thus eliminating the need for \"external\" sensors. This targeted layout enabled the structural integrity monitoring through the thickness of the composite, in the junction points of the carbon strips. The strips were selected instead of a sheet of carbon fabric that could not provide damage localization (mapping). The simultaneous incorporation of multi-wall carbon nanotubes (MWCNTs) and CB allowed tailoring the electrical conductivity of the matrix that led to faster transitions and reduced monitoring time and noise. The formed sensing elements enabled the delamination damage mapping topography of the composite in two and three dimensions (2D and 3D damage topography). Multiplexing with an IS spectrometer provided all the in situ records for the structural integrity of the studied material.
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 4 Simulation Studies: Effects of V2V Communication
### Drive Mode Decision  
The human centered risk assessment algorithm works with a probabilistic threat assessment incorporating human factor, and decision of two control functions' intervention moment. The algorithm computes _maxCp_ (peak collision risk) and _DpmaxCp_ (most probable predicted collision distance) from the preceding threat vehicles.  
Figure 9 demonstrates simulation results when the preceding remote vehicle applies the brake on the highway as described in Figure 6b. The host vehicle speed is cruising with the longitudinal velocity of 100 km/h for whole period of simulation. The remote vehicle applies the brake at 4 s for two cases: V2V/Radar fusion and radar only cases. As seen in Figure 9c, the radar only case shows  
Figure 8: Simulation results for the braking scenario in Figure 6a: (**a**) Radar only; (**b**) V2V/Radar fusion.  
Figure 7: Remote vehicle state estimation results: (**a**) Longitudinal Velocity; (**b**) Longitudinal Acceleration.  
the decision moment of drive mode at 9.2 s. The V2V/Radar fusion case, however, shows earlier timing for drive mode decision of \(1.1\) s due to the fact that V2V/Radar fusion algorithm has lower prediction uncertainty level than radar only case. This means that the V2V communication has much faster drive mode decision timing by reducing the chances of underestimate of radar only case as seen in Figure 9a,b.  
Figure 9 also describe the simulation results for the intervention moment of the active safety function. In the case of V2V/Radar fusion, the collision risk of sudden deceleration of the remote vehicle can be more precisely and reliably computed on the host vehicle as seen in Figure 9c. The active safety intervention moment of V2V/Radar fusion described in Figure 9a is much earlier than that of radar only case in Figure 9b by 0.6 s. For an emergency situation, even 0.1 s is crucial for vehicle crash. Thus, 0.6 s is quite a long amount of time to prevent the collision and give driver more reaction time to stop the vehicle.  
Figure 9a,b show the area of safe triangle of V2V/Radar fusion is more enlarged by 13.65% than that of radar only case. This is due to the fact that the distance between two crossing points between the active safety control and the drive mode decision is elongated in the case of V2V/Radar fusion. In other words, V2V communication has much enough time to react hazardous situation by reducing the chances of underestimate risky situation. The safe triangle analysis will be used in the vehicle and it is expected to compute the size of the triangle real time.  
Figure 9: Parameters schematic view of collision probability-based risk assessment: (**a**) safe triangle analysis: V2V/Radar fusion; (**b**) safe triangle analysis: Radar only; (**c**) Peak collision risk of the vehicles: V2V/Radar fusion and Radar only.