esa-sceva/satcom-chunk-collection huggingface dataset:
# Status, Features, and Future Development of the LIFUS5/Mod4 Experimental Facility Design
## 3 Materials and Methods
### Instrumentation
#### 3.4.1 Instrumentation List and Description
This will allow the monitoring of the quantities of hydrogen produced during the in-box LOCA event and the evaluation of the amount of lithium reacted with water. All these instruments will acquire data at high speed to continuously characterize the dynamic interaction taking place inside the system. The hydrogen analysis procedure will be carried out after the facility has stabilized to its final state. This procedure is challenging, due to the conditions in which the measurement takes place (high temperature and pressure) and due to the presence of molten PbLi in the zones where gases are extracted. However, thanks to the previous experience accumulated from the operation of other facilities, several mitigation solutions have been put in place to avoid disruptions, such as:  
* Pre-pressurization of the hydrogen analysis system to avoid sudden suction of material from the facility piping into the hydrogen analysis piping;
* Heat tracing of the whole hydrogen piping to avoid cold spots that might freeze during the procedure;
* Redundancy of the pathways from the various zones of the facility to the hydrogen analyzer to reroute gasses in case of plugging;
* Presence of a separation tank to effectively separate possible residual of PbLi or other materials from the gas before the analysis. Moreover, the water utilized for the in-box LOCA tests will be supplied through the water loop facility, which will install both mass flow meters and differential pressure meters to have a direct and indirect measurement of the injected water mass and a comparison with the prediction obtained via stoichiometric calculations. Details about these sensors will be included in future publications.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 4 Simulation
### Possible Extension to Multi-Source 2D DOA Estimation  
All preceding simulations were implemented based on single-source 1D DOA estimation, which exclusively considers azimuth angle estimation without accounting for pitch angle. However, in practical scenarios where UAVs operate in three-dimensional space, DOA estimation of UAVs predominantly relies on multi-source 2D DOA estimations. This subsection proposes potential methods to extend the current single-source 1D DOA estimation model to a multi-source 2D DOA estimation framework. For simulation settings, multi-source model training requires incorporating multiple sound sources into the established 3D anechoic chamber while other configurations remain essentially unchanged. Regarding dataset generation, the method of rotating the microphone array employed in the 1D model is superseded by varying the 3D coordinates of sound sources within the chamber, which means that modifying source positions inherently alters their azimuth angles, pitch angles, horizontal distances, and vertical heights, thereby generating distinct datasets with corresponding target vectors. Crucially, the multi-source 2D DOA estimation model may demand substantially more training data than its 1D counterpart. Furthermore, the target variable transitions from a single azimuth angle to a combined azimuth-pitch vector, with the model's final output being the predicted azimuth and pitch angles. This extension achieves multi-source 2D DOA estimation, thereby enabling a more authentic simulation of the DOA estimation of UAVs.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 3 Results
### Preliminary Analysis  
To locate the most probable cracking zone, a case of reservoir level equal to the Maximum Normal Level and zero thermal stress was calculated, proving that the zone with tension greater than 0.2 MPa was located mainly at the base of the upstream face of the central blocks (Figure 8).  
Once the models for the nine cracks were created, the dam behavior for five reservoir levels was simulated, showing that the crack opened in all cases for levels greater than 30 m above the foundation (Figure 9).  
To verify the accuracy of estimating displacement results, the simulations for all cracks with reservoir levels of 35 and 45 m were compared against interpolation equations based on the other simulations (Figure 10 shows one example). These interpolations were performed using parabolic equations based on simulations for 30, 40, and 49.24 reservoir levels. It was concluded that the error in the displacements was found to be acceptable (Table 3 shows mean errors for all monitoring points).  
Figure 8: Vertical stress in the dam. Blue—compression; yellow—tension less than 0.2 MPa; red—tension greater than 0.2 MPa.  
Figure 9: Crack opening depending on reservoir level.  
In order to assess the magnitude of the influence of the friction coefficient and the coefficient of thermal expansion, tests were conducted. This was achieved by simulations with maximum normal reservoir level, maximum temperature, horizontal cracking, maximum crack length, and maximum crack depth. The results in Table 4 show that the coefficient of friction plays a negligible significance in the displacements recorded at the monitoring points. On the other hand, having an adequate coefficient of thermal expansion is very relevant for the results.  
Simulations were performed to test the impact of the temperature drop on the cracks (Figure 11). These tests were performed for a crack 3 m deep and 36 m long under the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Friction Coefficient** & **Coefficient of Thermal Expansion (\({}^{\circ}\)C)** & **Radial Displacement at the Crest of Central** \\  & & **Cantilever (mm)** \\ \hline
0.8 & \(10^{-5}\) & \(-10\),512 \\
0.9 & \(10^{-5}\) & \(-10\),512 \\
0.7 & \(10^{-5}\) & \(-10\),512 \\
0.8 & \(2\times 0^{-5}\) & \(-15\),478 \\
0.8 & \(0.5\times 10^{-5}\) & \(-80\),289 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Influence of concrete properties.  
Figure 10: Interpolation equation at the crest of block 0 for the largest crack.  
pressure of 82 m of reservoir level. The results clearly show good agreement with a linear law.
# A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
## 6 Simulation Results and Discussion
### Performance with Cross-Wind Disturbances  
The cross-wind disturbance has an important effect on the tracking performance of autonomous vehicles. The cross-wind disturbance was considered in the tube MPC system, and the effectiveness of the tube MPC system under uncertain cross-wind disturbance is demonstrated. A strong wind disturbance was always present, and this resulted in sharp changes in the direction at the two boundaries. According to 'the wind speed impact on land and sea' detailed in the references, the max cross-wind speed reached 19 km/h, which meant that the leaves and smaller twigs were in constant motion. [PERSON] et al. [31] concluded that cross-wind disturbances are a random disturbance and that the state may be perturbed by the bound [0.05; 0.2; 0.005; 0.02] under cross-wind disturbance conditions. In this paper, the state disturbances resulting from the cross-wind were added to the states from the reference path module at each sample time. Figure 10 shows the state disturbance that resulted from the cross-wind disturbance. The results were compared to a traditional MPC without robustness guarantees. A total of 20 trials were run for each controller when subjected to random disturbances. Figure 11 shows the trajectory performances of the traditional MPC under cross-wind disturbances. In Figures 11 and 12, the blue and red lines represented the trajectories that successfully avoided the obstacle and failed to avoid the obstacle, respectively. The trajectories of the traditional MPC were scattered over a large area, and there was a failure in avoiding the obstacle in five of the trials. The tube MPC system was carried out under the same cross-wind disturbances. The trajectory performances of the tube MPC are shown in Figure 12. Compared with Figures 11 and 12, it can be observed that the range of the trajectories of the tube MPC system was much tighter and that all of trajectories successfully avoided the obstacle on the road. The tube MPC system was robust under cross-wind disturbance conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Proposed Tube MPC Method** & **Traditional MPC Method** \\ \hline Number of trajectories & 20 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of trajectories where the obstacle was avoided successfully when using the proposed and traditional methods.  
Figure 8: Trajectories of the traditional MPC system under uncertain tire road friction coefficient conditions.  
Figure 9: Trajectories of tube MPC system under uncertain tire road friction coefficient conditions.
## 5 Experimental Results and Discussion
### Metrics
#### 5.1.2 Intersection over Union  
We further computed the Intersection over Union (IoU) metric. The actual area of the segmented objects can be called as the ground truth whereas the connected areas are formed by connecting the adjacent pixel classifications belong to the same class can be called as the prediction area. The IoU can be computed by dividing the intersection of these two areas into the union of these areas.
## 4 Simulation Experiment
### Simulation Results
To illustrate the effectiveness of the proposed algorithm, the contrast imaging results of the several traditional algorithms are given in Figure 13. Figure 13a is the Radon-Wigner transform (RWT) imaging result, which shows that the RWT algorithm has a certain improvement effect on the azimuth focusing, but there is still defocusing and smearing. Figure 13b-d are the instantaneous imaging results using different time-frequency algorithms at t = 0.0851 s. Figure 13b is the STFT-based range instantaneous Doppler imaging result. The result shows that the imaging focusing effect is limited because the time-frequency resolution is affected by the window function. Figure 13d is the WVD-based range instantaneous Doppler imaging result, in which the WVD time-frequency resolution is better than STFT, but the azimuth echo is a multi-component signal, resulting in many cross-terms. The imaging results are so poor that the target cannot be identified. In order to suppress cross terms, windowing and smoothing operations are usually used for WVD (that is, SPWVD); the imaging result is shown in Figure 13d. The result shows that although the cross terms are effectively suppressed compared with WVD, the frequency resolution is reduced. Notably, the time-frequency method used in Figure 13b-d shows the azimuth Doppler variation with time and depicts the imaging results at different times. By comparing Figures 10 and 13, the proposed algorithm achieves a demonstrably better azimuth focusing effect. In the traditional algorithm, RWT can improve the azimuth defocus to a certain extent, but the effect is general; the instantaneous imaging result depends on the time-frequency distribution algorithm used. It is usually difficult to balance both time-frequency resolution and cross-term terms. Figure 11: The SPWVD of 113 th range cell (**a**) before and (**b**) after the MCFT focus process. Figure 12: The Doppler spectral at the 113 th range cell (**a**) before and (**b**) after the focus process. In order to quantitatively evaluate the performance of the MCFT algorithm against other algorithms in terms of imaging quality, we used three common indicators: image entropy, contrast [20], and running time. We compared the algorithms proposed in Figure 10. The comparison results of the traditional algorithms mentioned in Figure 13 and the algorithm proposed in this paper are shown in Table 4. From the results, the MCFT algorithm has lower image entropy and larger image contrast values than the other algorithms, which shows that its image quality is better. In terms of running time, the MCFT algorithm requires more time than the RD and STFT algorithms, but less time than other algorithms, considering both imaging quality and imaging efficiency. The running time of MCFT consists of three parts: the coarse rotation ratio estimation by the DCFT method required 0.8558 s, the accurate rotation ratio estimation by the entropy method required 1.8248 s, and the MCFT imaging required 0.5541 s.
# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 3 Method
### ConvNeXt Branch  
There are two components to the ConvNeXt branch encoder. In Figure 1, it is displayed in the yellow dashed box. The initial component serves as a stem that pre-processes the original image for feature extraction using a convolutional layer with a 4 \(\times\) 4 kernel and a stride of 4. This is followed by a layer of standard normalization (LN) to hasten the training process of the neural network. At this point, the image dimension changes from [H, W, 3] to [H/4, W/4, C]. The second part contains 4 stages, the block count of each stage is changed from (3, 3, 9, 3) to (3, 3, 3, 3), and downsampling operation is performed between every two stages, which expands the receptive fields of different layers of ConvNeXt and enables each neuron to perceive a wider area of the pathology image. This helps ConvNeXt learn a larger range of contextual information and improves understanding of the overall structure. Every downsampling operation employs a convolutional layer with a 2 \(\times\) 2 kernel size and a stride of 2. The downsampling is preceded by a standard normalization (LN) layer to prevent gradient explosion. The AtFF module receives the feature maps that were extracted at each step. The output feature map dimensions are [H/4, W/4, C], [H/8, W/8, 2C], [H/16, W/16, 4C], and [H/32, W/32, 8C]. After extensive experimentation, the hyperparameter C has a value of 96.  
Figure 2: General framework of the GLFUnet model, consisting of the CNN branch, the Transformer branch, AtFF module and skip connections.  
The ConvNeXt architecture has three layers. The first layer is a 7 \(\times\) 7 depth-separable convolution and a standard normalization (LN) layer with 96 output channels. The subsequent layer consists of a 1 \(\times\) 1 convolution followed by a GELU (Gaussian Error Linear Unit) activation function. This layer does only channel fusion, mapping channels C = 96 to 384. The third layer is a 1 \(\times\) 1 convolution that maps channels C = 384 from the second layer to 96. Finally, there is a residual join that sums the input and the outputs are summed.
# On the Security of Offloading Post-Processing
## 2 Quantum Key Distribution
### Motivation to Offload Post-Processing  
From a computational perspective, information reconciliation is by far the most expensive task computationally in the stack and can limit the throughput in high-speed systems [14; 15]. The second-most computationally demanding task is privacy amplification [6]. The rest of the protocol steps are rather simple tasks and can be executed in real time even on embedded platforms.  
Therefore, we introduce and study the idea of offloading these tasks from devices by outsourcing computation to untrusted or less trusted hardware in an ITS way. The ability to outsource information reconciliation (IR) and potentially privacy amplification (PA) would enable new application scenarios for both access networks and transmission systems. On top of this, satellite-based QKD could become more versatile if processing resources can be shifted around more easily.  
The two main advantages gained by offloading processing to external hardware are increased efficiency and flexibility in the use of compute resources, also resulting in a higher energy efficiency, and a reduced attack surface by limiting the number of components dealing with secure key material.  
QKD systems are deployed for long-term security and produce large capital expenditure (CAPEX) spending (i.e., they are used over a long period of time). Putting all the processing power into the devices at build time hinders later updates and prevents the operator benefiting from Moore's law. If the hardware is outsourced, then it could be updated during the lifetime of the system with new technologies, resulting in further optimized efficiency. Furthermore, if the hardware need not be trustworthy and certified,then cheaper commercial off-the-shelf (COTS) hardware can be used. It would even be possible to completely outsource this to public cloud infrastructures in an extreme case.  
Additionally, time sharing allows for further improvements in certain use cases. If QKD is used in hybrid encryption protocols to establish session keys [16], then high key rates are not needed, and sharing the computational resources between links can further reduce the CAPEX and also the operational cost (OPEX). Hence, putting the computationally expensive tasks into efficient data centers which do not even need to be trusted is very desirable. This allows for hardware updates and joint management of all QKD workloads in the field with its continuous upgrading probabilities, which is especially favorable for operators of QKD networks. Because the communication overhead is minimal compared with the computational one, a clear advantage in terms of energy efficiency arises, and the gained flexibility in managing tasks is very advantageous.  
Furthermore, the proposed approach could also be used within a system. Treating parts of the system as untrusted could eventually provide the possibility for system updates without compromising system certification and help to reduce the OPEX cost during the system's lifetime.
## 2 Computational Approach
### Non-Equilibrium Green's Function Method  
The Nonequilibrium Green's Function (NEGF) formalism is a widely utilized method for determining electron or phonon transport properties of extended systems. This approach involves the construction of a simulated system with two semi-infinite leads serving as electron or heat baths, connected by a central conductor region [98]. The transmission of an electron or phonon is calculated based on the Green's function for the center region and the self-energy of the leads, which describes the lead-center interaction [99]. The NEGF method offers a distinct advantage. It preserves quantum mechanical effects such as tunneling and diffraction, providing a highly accurate description of nanoscale devices. However, it is worth noting that the NEGF method can be computationally expensive for larger devices.
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 3 An Architecture Template for Novelty Detection
This value is increased by an \(\alpha\) score (we experimented with two values of \(\alpha\) score in our implementation, 0.02 in operating conditions with anomalies, 0.10 in the other scenarios) to reduce the number of false positives. We provide two implementations for the Classifier, based on a supervised and an unsupervised approach, respectively. The supervised approach is based on a feed-forward neural network that we train in our implementation on the series generated by the encoder and on labels provided by experts to identify the operating conditions. The unsupervised approach is always built upon the series generated by the encoder and relies on a clustering algorithm. The optimal number of clusters (i.e., operating conditions) is automatically selected through the elbow method. In Section 4, we experiment with three metrics for the creation of the clusters. The Localizer relies on the reconstruction error to compute the signals that mainly contributed to the inference. This component computes the error on the raw input signals, and not on the transformed version as for the Scorer. In this way, the impact of the normalization processes introduced by the data transformer component and the AE network is reduced and the error computation is more reliable. Figure 2a,b show the reconstruction of the input signals performed by the localizer. The plots refer to a CNN-based AE, trained on the signals from the operating conditions C1, C2, and C4 and aimed at identifying the  
Figure 1: Autoencoder Architecture Template. condition C3 as a new condition. Figure 2a refers to the reconstruction of the input signals of a time series of status C2 (composed of 9 input signals), which are part of the series used for the training. Figure 2b shows the original and reconstructed signals on C3. The number on the top of each plot is the mean reconstruction error. Figure 2a shows that the model can learn the input signals, and the error is generally low. Figure 2b shows that the reconstructed signals are not able to infer the actual signal. In particular, the higher error is measured on Y3, which represents one of the main reasons for the prediction of C3 as a novel state.
## 1 Introduction
After the first and last received beacons, which are called beacon points, from the AUV are projected onto the horizontal plane on which a sensor node resides, the 2D location of a sensor node can be determined based on the distance between the two projected beacon points. At this point, the location of the sensor node remains ambiguous, because the two projected beacon points provide two possible node locations. If the sensor node obtains two more beacon points, the ambiguity can be resolved. The first two beacon points are used for location computation and the other two beacon points are used for resolving the ambiguity. However, if the two beacon points for location computation have a large error, the error in the estimated sensor location also increases. Even though the location of a sensor node can be simply computed from the first two beacon points, the use of just two points out of all the beacon points can make localization very error-prone. To improve the localization accuracy in challenging underwater environments, we propose a new localization scheme LoMoB that obtains a set of potential locations as candidates for a node's location using the bilateration method and then localizes the sensor node through the weighted mean of all the potential locations with the weights computed based on residuals.