esa-sceva/satcom-chunk-collection huggingface dataset:
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 3 Locking Detection, Assessment, and Resolution
### Calculation of the Optimized Value of \(p\)  
The idea presented in this section lies in making an attempt to assess the locking phenomena strength. This strength is measured with the \(p\) value necessary to remove the locking phenomenon. The criterion (32) assumes that the value of \(p=8\) is necessary for the removal.  
This value corresponds to the coarsest mesh possible, composed of one rectangle (hexahedra) or two triangles (prisms). However, depending on the structure thickness \(t\) and the initial global discretization characterized by the value of \(h\), the removal may require much lower (more optimal) value of the longitudinal order \(p\). Moreover, in such circumstances, \(p=p_{max}=8\) leads to too rich discretizations. Note also that setting \(p=8\) in the modified mesh reduces further \(hp\)-adaptation to \(h\)-adaptation only as the maximum value of the longitudinal order of approximation \(p=p_{max}=8\) is applied in the modified mesh. We address both situations in numerical tests presented in the next sections of the paper.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 2 Physical Components and Methods
### Components of the Satellite  
A nano-satellite with FSO capability requires the following three logical units: (i) standard nano-satellite platform; (ii) attitude determination and control system (ADCS); and (iii) FSO modem.  
This framework is designed to support a sizeable nano-satellite constellation composed of dynamic FSO links between multiple satellites and ground stations. In order to optimize the performance and the cost-effectiveness of such FSO constellation, each unit should be designed for mass production. In particular, such a satellite should be as small as possible since the cost of its launching is determined by the satellite's weight and volume.  
Each nano-satellite is equipped with standard RF communication, commonly ultrahigh frequency (UHF) with limited bandwidth. The RF modem transmits to the ground beacons containing information about the satellite's state, such as location, battery status, speed, angle, and short information regarding the data required to be downloaded. The FSO link functions for wide-band communication, particularly for transmitting extensive data, such as images and videos.  
The satellite in this model, see Figure 4, has three main parts:  
* **Standard satellite unit.** This unit contains (i) a lithium battery and a solar panel that provides and preserves the satellite's energy; (ii) a micro-controller board that controls the different components of the satellite. This controller is in charge of commands to start UHF or laser communication, open the camera, and track the ground station. This board is also programmed to schedule the satellite's full-power and power-save modes according to the communication demand and distance from the ground station.
* **FSO communication unit.** This unit contains two communication components: (i) UHF radio; (ii) FSO laser beam and LED light that signals to the ground station the location and orientation of the satellite.
* **Tracking unit.** This unit contains all the components that allow the satellite attitude control. It includes reaction wheels that perform the satellite's aiming towards a target, two sensors that provide the data for the tracking, a camera, and an inertial measurement unit (IMU). Accurate high-frequency tracking is accomplished via an algorithm that fuses the data from the camera's image and the self-orientation provided by the IMU.
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 3 Methods
### Mixer of FSR  
In far-field speech recognition (FSR) scenarios, speech signals attenuate as they propagate over distance, resulting in a decreased signal-to-noise ratio (SNR). Additionally, these signals undergo reflections from surfaces and diffraction around obstacles, leading to reverberation. Moreover, microphones frequently capture unwanted environmental noise. These effects distort the speech signal in different manners, and thereby require much more training data to achieve high accuracy and generalization.  
We propose a data augmentation method for far-field ASR, called Mixer, which mixes any representations of an E2E ASR model. Consider the input log-Mel spectrogram feature vectors denoted as \(X=\{x_{1},x_{2},\dots,x_{T}\}\), and that a target transcription is represented by a sequence of \(N\) tokens denoted as \(Y=\{y_{1},y_{2},\dots,y_{N}\}\). For an arbitrary K-layers model like Whisper, we denote \(g_{k}(\cdot)\) as the function that processes data from the input to the \(k\)-th layer, and \(f_{k}(\cdot)\) as the function that maps data from the \(k\)-th layer to the output. As is seen in Figure 2, Mixer includes two stages to implement. First, Mixer mixes any two feature representations \(g_{k}(X_{i})\) and \(g_{k}(X_{j})\) extracted from audio samples:  
\[g_{k}(\hat{X})=\lambda g_{k}(X_{i})+(1-\lambda)g_{k}(X_{j}) \tag{6}\]  
where \(g_{k}(X_{i})\) denotes the output of \(k\)-th layer and \(k\) is the layer of the model that requires mixing. Second, Mixer trains the model to optimize the mixed loss functions by aligning the mixed weight to the corresponding loss. The process can be denoted as follows:  
\[L_{\text{mix}}=\lambda L(f_{k}(g_{k}(\hat{X})),Y_{i})+(1-\lambda)L(f_{k}(g_{k} (\hat{X})),Y_{j}) \tag{7}\]where \(\lambda\sim eBeta(\alpha,\alpha)\) with \(\alpha>0\). And \(e\) is the maximum value of the mixing weight. By aligning the interpolation weight in the mixing of features with that of the loss, the method establishes a linear relationship between the input and output space of the model. When \(k=0\), since the hidden representations are log-Mel spectrograms of the input, Mixer naturally extends the MixSpeech [9] method.
## 3 Structuring of the Proposed Forecast Framework
### Data Standardization and Differential Processing  
Too large or too small an input can easily lead to unstable gradient values. Data normalization can effectively avoid such problems. This paper uses the min-max scaling method to normalize the data linearly. The min-max scaling formula is as follows: mapping the data to the range of 0 to 1 by min-max scaling is more beneficial to the training of network model parameters.  
\[x_{i}^{\prime}=\frac{x_{i}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{23}\]  
where \(x_{\text{min}}\) and \(x_{\text{max}}\) represent the minimum and maximum values in the data; \(x_{i}^{\prime}\) represents the data after standardization; \(x_{i}\) represents the data before standardization.  
The prediction method in this paper uses a differential prediction method, which is stated in Section 2.1. The differential processing is divided into differential processing for the target value and differential processing for the input. The differential processing for the target value is shown below.  
\[\Delta y=y_{t+k}-y_{t} \tag{24}\]  
where \(y_{t+k}\) is the value of the predicted target at time \(t+k\); \(y_{t}\) is the value of the predicted target at time \(t\); \(\Delta y\) is the differential value between the two moments. The prediction model obtains the value of \(y_{t+k}\) by predicting \(\Delta y\). The \(k\) is set to 6 in this paper.  
In addition, the prediction method in this paper also performs differential processing on the input of the model. The differential processing for the input is shown in Equation (2).
## 2 Background
### Power Consumption  
The miniatured batteries can be used for WBASN nodes. For efficient energy utilisation, new energy-efficient communication protocols are used at the MAC and network layers. These protocols reduce power consumption by introducing duty-cycle mechanisms. Table 6 describes the power consumption, data rate and battery lifetime comparison of WBASNs with other existing wireless technologies. The data rate and communication protocol's carrier frequency have a huge impact on power requirements/consumption, and generally, higher frequency + higher data rate will mean higher power consumption.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 4 Optimal Path Discovery Phase
### Optimal Path Discovery
#### 4.2.1 Scenario 1
stays behind D. Reducing the number of hops and delays during the transmission of WMs accomplishes our direction-based priority assignment process in the definition of the \(O_{f}\) node. Reducing the number of hops and delays during the transmission of WMs completes our direction-based priority assignment process for the \(O_{f}\) node. Reducing packet dropouts also increases the transmission dependability of these warning messages. Our suggested protocol is reliable because of this minimal delay and packet loss feature.
## 3 Results and Discussion
### Shear Strength Analysis of Test Blocks  
Figure 10 illustrates the shear strength, internal friction angle, and cohesion of test blocks poured under different environments and curing times, as well as their ratios.  
From Figure 10a, it can be observed that the shear strength, cohesion, and internal friction angle of the test blocks exhibit similar trends with increasing curing time. Specifically, there is a rapid increase in the early stages, with the 7 th-day strength of most test blocks reaching about 85% of the 28 th-day strength, followed by slower growth later. Additionally, as the complexity of the pouring environment increases, the shear strength of the test blocks gradually decreases. This trend is consistent with the variations in compressive and tensile strengths, which may be due to the medium from the pouring environment infiltrating the test blocks as the mortar is continuously poured, thereby affecting their performance. In a dry environment, the pouring of test blocks has almost no effect on their shear strength. In a pure water environment, the impact of water on the shear strength is relatively weak, with a 28 th-day strength loss of only about 5%. However, in a slurry environment, the impact on shear strength is significant, with a 28 th-day strength loss exceeding 16%, leaving only about 84% of the strength compared to blocks poured in a dry environment. The shear strength of test blocks also varies with different shear angles. Under the same curing time, an increase in shear angle results in a decrease in shear strength, indicating that the test blocks become more prone to failure as the shear angle increases.  
Figure 9: Change in tensile strength/strength ratio of test blocks with curing time under different pouring environments: (**a**) strength; (**b**) strength ratio.  
To study the strength loss under different pouring environments--dry, pure water, and slurry--the shear strength ratio (strength loss) of the test blocks indicates that, regardless of whether they are poured in a slurry or pure water environment, the ratio of shear strength to that of blocks poured in a dry environment shows the same trend with increasing curing time. Specifically, the shear strength ratio increases rapidly in the early stages and grows slowly later on, which is consistent with the trends observed in compressive and tensile strength ratios over time. Additionally, no distinct pattern has been observed for the changes in cohesion and internal friction angle with curing time, but their strength ratios reach their maximum at the longest curing time. The cohesion ratio in pure water versus dry environments is about 99%, while in slurry versus dry environments, it is about 92%. Simultaneously, the internal friction angle ratio in pure water versus dry environments is about 95%, and in slurry versus dry environments, it is about 84%. Compared to compressive and tensile strengths, the shear strength loss of test blocks poured in a slurry environment is relatively small.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 5 Method for Scenario Optimisation and Data Acquisition through Simulation
### Scenario Scoring  
In order to be able to model the concrete scenario data using a Gaussian Process, a label or score needs to be assigned to each concrete scenario. Therefore, we assess the input parameters on their impact on the outcome of the scenario, by evaluating the data provided by the simulator. The score of the scenario needs to reflect the criticality, in order to focus the testing effort towards more critical scenarios, close to the performance boundary. The intention behind this is to omit concrete scenarios that certainly do or do not end in a collision. These are concrete scenarios far from the performance boundary and therefore less interesting for the validation of AVs. As scoring metric, the minimal time-to-collision (TTC) was therefore chosen as criticality measure. The TTC has previously been described as an effective measure for rating the severity of scenarios involving longitudinal conflicts in collision avoidance systems [43] and can thus be used to differentiate between critical and non-critical scenarios for AVs in the considered scenarios. The TTC can be calculated by  
\[TTC=\frac{d_{rel}}{v_{rel}}\,, \tag{21}\]  
wherein \(d_{rel}\) and \(v_{rel}\) are the relative distance and relative velocity between the ego vehicle and in the exemplary scenario, the pedestrian that is stepping onto the road. In order to obtain a single score for the concrete scenario, the TTC is calculated at every time step of the simulation. The minimum TTC value over the entire duration of the concrete scenario is then taken as its criticality measure. It is to be noted that \(d_{rel}\) and \(v_{rel}\) are not equivalent to the scenario parameters \(d\) and \(S_{1}/S_{2}\), as described in Section 6.1. The quantities, \(d_{rel}\) and \(v_{rel}\) change over the course of the scenario, depending on the actions of the ego vehicle and the pedestrian. The scenario parameters influence \(d_{rel}\) and \(v_{rel}\) and thus the TTC, but the exact relationship is to be analysed hereby.  
[PERSON] and [PERSON] [43] specified the threshold for a critical scenario to be \(1.5\) s, which we will use as an optimisation criterion in the following section. A TTC threshold of \(1.5\) s will not target scenarios which result in a collision. While collision scenarios are certainly of value in an AV validation, the goal in this work is to find critical corner case scenarios, which are scenarios that almost resulted in a collision.
## 4 Understanding the Physical Principles of a Meteorological System
The power scattered by the particle at a distance \(R\) is calculated as the surface integral over a sphere with radius, R  
\[P_{s}=\iint\mathcal{S}_{s}(\theta,\phi)R^{2}d\Omega \tag{4}\]  
where \(\mathcal{S}_{s}(\theta,\phi)\) is the power density of radiation is scattered in directions \((\theta,\phi)\) at a distance R from the particle. The scattering cross section is therefore defined as  
\[Q_{s}=\frac{P_{s}}{\mathcal{S}_{i}}\left[\mathrm{m}^{2}\right] \tag{5}\]  
The total power extracted from the EM wave is \(P_{a}+P_{s}\) and the extinction cross section is defined as  
\[Q_{e}=Q_{a}+Q_{s} \tag{6}\]  
Since we are interested in meteorological applications, our focus is on the backscattering power towards the radar--\(\mathcal{S}_{b}=\left.\mathcal{S}_{s}\right|_{\theta=\pi}\), acting as an active MW sensor. Therefore, the backscattering cross section can be defined as  
Figure 3: Self. Particles hit by EM waves. Source: Self. the radar. The received power is also influenced by the radar's antenna gain (\(G_{0}\)) and the transmitted wavelength (\(\lambda_{0}\)):  
\[P_{received\ power}=\frac{P_{t}{G_{0}}^{2}{\lambda_{0}}^{2}}{\left(4\pi\right)^{3} R^{4}}\sigma_{V}e^{-2\tau} \tag{13}\]  
where \(\tau\) is the total one-way path attenuation between the radar and the volume at a distance (R):  
\[\tau=\int_{0}^{R}\big{(}K_{g}+K_{C_{Clouds}}+K_{F_{Rain}}\big{)}dr \tag{14}\]  
Neglecting factors such as antenna beamwidths at half-power, pulse duration, and other secondary parameters, the amount of received power corresponding to the transmitted frequencies toward a given volume provides an indication of the composition of that volume. This information contributes to constructing a precipitation profile (as in the rainfall example discussed) for a specific region of the atmosphere at a given moment in time. Thus, the theoretical description of the fundamental principles governing the operation of meteorological radars is concluded.
## 2 Related Work  
### Reinforcement Learning Types  
#### 2.1.1 Model-Based vs. Model-Free  
Reinforcement learning algorithms can be classified into Model-Free and Model-Based types. These types can be classified by whether the environment model can be defined or not. If the environmental model can be defined in terms of external changes and states, a plan can be established accordingly.  
The Model-Based type means planning is possible. In other words, if agents can predict how their actions will change the environment, they can anticipate changes before taking action and plan and execute optimal actions, allowing agents to act much more efficiently. Under this assumption, when an action is taken in a specific state, the algorithm form that defines the probability of the next state and probabilistically predicts the change of the environment according to the action is called the Model-Based type [4; 5]. However, it is difficult to predict the exact model of the environment, and if the model does not properly reflect the environment, the agent behaves incorrectly. Model-Based reinforcement learning algorithms are Alpha Zero, Imagination-Augmented Agents (I2A), Model-Based Model-Free (MBMF), Model-Based Value Expansion (MBVE), Dyna, etc. [4; 6].  
In the Model-Free type, the agent seeks to find the future value of the reward as a policy function by means of its actions. Since this algorithm does not know about the environment, it passively obtains the next state and reward informed by the environment. The algorithm has to explore because it no longer knows how the environment will behave. It is a method that gradually learns policy functions by means of trial and error during such exploration [4; 5]. Model-Free Reinforcement Learning can be divided into the Value-Based and Policy-Based types. We will briefly review these types.  
Figures 1 and 2 illustrate the Model-Free and Model-Based types.
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
## 4 Results and Discussion  
All discussed predictive control algorithms were implemented using the dSpace with DS 1007 power dual-core 2 GHz processor board. A flow chart showing the implementation steps of all algorithms can be found in Figure 6. The maximum achieved turn around time was around 22 \(\upmu\)s. It should be noted that 9 \(\upmu\)s was introduced by the auxiliary tasks (A/D conversion). In order to reduce the time taken by the A/D, an observer could be used to estimate the load current instead of measuring it. Since the dSpace A/D samples at a rate of 1 MS/s, that will save 3 \(\upmu\)s from the total needed time. Moreover, the computational delay of exact _Ts_ is compensated as discussed in [33]. The overall parameter of the test setup and simulation model can be seen in Table 1, and the setup is shown in Figure 7. Due to the setup limitation, both the simulation model and the setup have been operating at a 40 kHz sampling frequency. In order to illustrate the effect of different samplings, simulation results, in which the sampling is 80 kHz, are provided. FCS-MPCs based on the periodic, notch, penalization control algorithms have been verified experimentally, where a 5.5 kW rated power system has been built for that purpose. The power stage comprises a delta-star transformer to provide the DC-link voltage through an input filter to a rectifier. A two-level three-phase VSC (Danfoss type), the LC filter on the converter side, is loaded with a linear load. It should be noted that the DanfossVSC contains electrolytic DC-link capacitor. In this work, the LC filter design was based on choosing low resonance frequency, as shown in Figure 8, to verify and make the FCS-MPC algorithms feasible for the study. As future work, each algorithm should have its optimized filter design, which will be briefly summarized later in the end of this paper.  
Figure 6: Flowchart of the implemented algorithms using dSpace.
## 2 Design and Implementation of On-Line PD Monitoring System
### Partial Discharge Detectors
#### 2.2.2 Ultra-High Frequency Antenna
The research results confirmed the high sensitivity of the prototype antenna. For the initial form of the surface discharges of the average apparent charge equal to \(q_{avg}=895\) pC, the growth of the amplitude of the registered UHF pulses compared to the disk antenna by \(7.80\pm 0.9\) times on average was found. In turn, in case of high energy creeping sparks with an average apparent charge equal to \(q_{avg}=5990\) pC, lower growth of the amplitude, equal to \(1.75\pm 0.7\) times on average was observed. The comparison of the exemplary time waveforms of the PD pulses registered by the prototype antenna and commercial disk antenna is demonstrated in Figure 11.
## Appendix B NRMSE
### Bland-Altman Plot  
Figure 14 shows the Bland-Altman plot for the ensemble model with multiple annotations compared to other models using the NRMSE metric; each row in the graph represents the implementation of a separate dataset. Previously, the NMI metric suggested that ensemble models outperform other models; conversely, the GDC metric showed the superiority of MC dropout over ensemble models, especially with 40% and 60% dropout rates. Similar to the GDC, the NRMSE metric shows dataset dependence results. For example, for the Lung dataset, the MA model scored a positive bias (For NRMSE metric, positive MD means the MA model performs worse) compared to other models, suggesting the MA model performs poorly. For the SIJ dataset, the MA model outperforms the SA model by scoring \(-21\%\) MD; it also outperforms the MC model with an 80% dropout rate by \(-8\%\) MD and scored almost similar MD with the regression and remaining MC models. For the HHS dataset, the regression model scored the best NRMSE, yet the MA model scored higher MD compared to all MC versions but still less than the SA model. For the HLS dataset, similar to the GDC metric, MC models with 40% and 60% outperform the MA model by +5% and +8%, respectively. Yet the MA model scored higher bias \(-2\%\) and \(-10\%\) MD compared to 20% and 80% MC versions. However, both the regression and SA models outperform the MA model by +1% and +6% MD. Finally, for the Knee dataset, the MA model only outperforms the regression and MC with a 60% dropout, but the other models outperform the MA model by less than 10% or less MD. Considering LoA, the plot shows wide LoA in many cases, suggesting strong disagreement between the models, which is also indicated by the box and correlation plots.