esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Trajectory Planning Problem
### System Model
Let \(\mu_{N}\) be the nominal friction coefficient value and \(T_{N}\) be the torque required to hold the forward speed \(V_{D}\). Using the classical linearization arguments, the behavior of the system (10) around the nominal condition \(\tilde{z}=[V_{D}\ 0\ 0\ 0\ 0\ 0]^{T}\), \(\tilde{u}=[T_{N}\ 0\ 0]^{T}\), \(\tilde{\mu}=\mu_{N}\) is described by the following linear time-invariant model, which can be written as follows:  
\[\dot{\delta}_{z}(t)=A_{C}\delta_{z}(t)+B_{C}\delta_{u}(t)+B_{dC}\delta_{\mu}(t) \tag{11}\]  
with \(A_{C}\), \(B_{C}\), and \(B_{dC}\) being matrices of proper dimensions. With \(T_{s}>0\) being the uniform sampling step, it is possible to obtain the following discrete-time mathematical model:  
\[\delta_{z}(k+1)=A_{D}\delta_{z}(k)+B_{D}\delta_{u}(k)+B_{dD}\delta_{\mu}(k) \tag{12}\]  
where \(A_{D}=e^{A_{C}T_{S}}\), \(B_{D}=\int_{0}^{T_{s}}e^{A_{C}\tau}B_{C}d\tau\), and \(B_{dD}=\int_{0}^{T_{s}}e^{A_{C}\tau}B_{dC}d\tau\). As previously introduced, this work assumes that some elements of the model (12) may be subject to uncertainties. In particular, the mass of the vehicle (which also affects the moment of inertia around the yaw axis) and cornering stiffness of the tires are assumed to fall withing a bounded interval. Such uncertainty can be conveniently embedded in a polytopic mathematical representation [59] through the following convex hull:  
\[Co\{\psi_{1},\psi_{2},\cdots\psi_{N_{v}}\}:=\bigg{\{}\sum_{n=1}^{N_{v}}\alpha_ {n}\psi_{n}\bigg{|}\sum_{n=1}^{N_{v}}\alpha_{n}=1\bigg{\}} \tag{13}\]  
with \(N_{v}\) vertices and \(\psi_{n}=(A_{Dn},B_{Dn},B_{dD})\) being its \(n\)-th  
Thus, taking into account (17), the closed-loop system can be rewritten in the following polytopic form:  
\[\delta_{z}(k+1)=\Phi_{n}\delta_{z}(k)+B_{dD}\delta_{n}(k),\ \ n=1\cdots N_{v} \tag{19}\]  
being \(\Phi_{n}=A_{Dn}+B_{Dn}\,K\) with \(n=1\cdots N_{v}\).
## 3 Results and Discussion
### Mineralogical Results of Laboratory and Pilot Scale Experiments
#### 3.2.3 Collective Mineralogical Results  
The EMP analyses performed on spinel grains indicate that in most samples derived from the laboratory and pilot scale experiments, an oxidation, especially of iron, was not completely suppressed, although a nitrogen atmosphere and/or iron filings were provided, because the total values were below 100% (Tables S2-S22).  
Neither in the laboratory nor in the pilot scale experiments were glassy SiO\({}_{2}\) phases found, although the relatively high SiO\({}_{2}\) input changed the basicity to very low values of 0.57-0.69 (Table 1, Section 3.3) and some samples were cooled rapidly. It was assumed that the high Al\({}_{2}\)O\({}_{3}\) and CaO concentrations (Table 1, Section 3.3) in the original samples A and B prohibit a glass phase formation as stated by [PERSON] et al. (2017) [32].  
Based on the performed experiments, the driving force for the enhanced spinel formation could not be distinctly identified. It could not be confirmed that the formation of magnesiowuestite indirectly prohibits the spinel formation [9] by withdrawing MgO, which fosters spinel formation [5; 29] from the melt. Spinel formation was increased in all samples: the remelted samples with high magnesiowuestite abundance and the conditioned samples with hardly any magnesiowuestite formation. Additionally, the influence of the crucible and refractory material could not be identified as the driving force for the increased spinel formation in all samples. Furthermore, after the quartz sand addition in the laboratory scale, silicate phases with up to 40 mass-% MgO were formed, suggesting that most of the available MgO was consumed by the silicate phases and not by the spinel phase. Although the performed FactSageTM calculations (Figure 1a-c, Section 3.1) suggested that spinels were formed before calcium silicate phases, especially olivine, the fractional crystallization theory and investigations on igneous rocks [18; 33; 34; 35] postulate that spinel and olivine crystallize together, which also explains the observed zoning in spinel grains (see above). No Mg-rich olivine phase was found in the pilot scale experiments. The MgO inclusions with a diameter of several 100 \(\upmu\)m, seen in the rapidly cooled conditioned sample BC1 (Figure S4), are most likely due to parts of the damaged refractory material. In general, it is assumed that the cooling rate, the oxygen fugacity, and the decrease in viscosity [36] has a higher impact on spinel formation than the MgO concentrations in the molten slag.
## 2 Limits, Regulations, and Methods for the EMF (electromagnetic field) Assessment Process
The probe must be oriented so that the vertical component of the electric field is recorded because this component is used to characterize the induction effects in objects close to the surface of the earth [13]. The distance between the electric field probe and the operator must be, according to the standards, at least 1.5 m. It is to be emphasized that this minimum distance is necessary to avoid disturbance of the electric field by the operator. The distance between the probe and the operator must be at least 1.5 m for the measurement uncertainties to be less than 5% or 3 m for them to fall below 1%. It is obvious that in order to ensure these distances and to eliminate the operator's influence on the measured values, it is necessary to use a fiber-optic connection between the electric field probe and the device on which the values are read (and if possible, stored). In general, as mentioned above, high voltage stations represent electric field places of greatest interest. The level of the electric field is much lower in the case of distribution lines and medium voltage equipment, as in the vicinity of the electric stations. Usually, cable connections are not sources of an additional electric field. Figure 2: Specific actions for different action levels in the case of low-frequency EMF (electromagnetic field). The electric field varies with the height of suspension of the conductors and, therefore, its maximum value can be found by scanning the conductor in a longitudinal profile on a path parallel to it and at intervals (with steps) small enough to capture the maximum value. At the point where the maximum value of the longitudinal profile was found (usually at the middle of the opening where the lengths of conductors are on maximum), the profile of the structure is explored in a direction perpendicular to its longitudinal axis. This last exploitation leads to the identification of the maximum field point [14]. Magnetic field sources near the measurement points, other than those of the systems to be evaluated, must be removed or disconnected in order to minimize the effect on the measured values. If it is impossible to perform such maneuvers, then the following data about them will be recorded; the type of source, their position in relation to the measurement points, etc. The magnetic field must be measured at a height of 1 m from the ground level around the considered equipment at a horizontal distance of 0.3 m from the surface of the equipment, at intervals considered appropriate to find the maximum level point of the magnetic field [15]. Concerning non-permanent (movable) objects such as ferromagnetic materials, the measuring points will be arranged at distances at least three times higher than the height of those objects, in order to carry out measurements in non-disturbed (uniform) field areas. Locally, higher levels of the magnetic field can be found at smaller distances from the surface of the considered equipment. These values will not be considered as being representative of situations of normal public exposure.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 2 Background Material
### Making Hubert Robust to Noise  
While larger versions of HuBERT have shown improved ASR accuracy in noisy conditions [17], for edge applications such as large footprint models, they can be problematic, and smaller models can be more sensitive to noise. To this end, the authors in [15] proposed the so-called _Robust HuBERT_ method, where domain adversarial training was used to make the system more robust to environmental factors. More specifically, a domain discriminator is responsible for classifying the source of the distortions applied to the utterance. The HuBERT Base model was continually trained on more diverse data, with a probability of receiving speech data corrupted by varying types and levels of noise, including Gaussian noise and recorded noises taken from the MUSAN database [25]. Moreover, another technique to increase robustness is that of noise disentangling, as showed in [16]. The authors propose _deHuBERT_, a novel self-supervised training framework that encourages noise invariance in HuBERT's embedded contextual representations by introducing a second embedding from different noise-augmented signals, using a shared CNN encoder and a new pair of auxiliary loss functions.
# Laboratory Evaluation and Finite Element Modeling of SBS and Basalt Fiber Modified Mixtures
## 1 Introduction
Dynamic stability and bending beam tests were carried out to evaluate the mixtures' resistance to rutting and low-temperature cracking. Results showed that the addition of basalt fiber to the recycled asphalt mixtures improved the stripping resistance, rutting resistance and cracking resistance by 46.9%, 105.2% and 102.3%, respectively. The influence of fibers on asphalt mixtures and their comparison against the different percentages of basalt fibers have not been addressed before with the aid of finite element modeling in previous research; therefore, in this current research, a 3% SBS mixture with basalt fiber was tested against the 7% SBS conventional mixture. Furthermore, the performance evaluation of lignin, basalt and polyester fibers has been compared against the SBS mixture previously; however, in this research, the 3% SBS mixture was used in order to evaluate the performance directly with the SBS-7% mixture in terms of dynamic stability.
## 3 Finite Element Simulation
### Element Type, Boundary Condition, and Solution Method  
The loading and foundation beams were simulated using the shell element SHELL181. The steel tube of the BLC-C composite wall used the three-dimensional solid element SOLID45. The contact of the finite element model was simulated using the contact element CONTAC173 and the target element TARGE170. The infill wall and the concrete of the BLC-C composite wall adopted the three-dimensional solid element SOLID65, which was defined by eight nodes and could simulate cracking, crushing, plastic deformation, and creep. The smeared crack model and the failure criterion of Willam-Warnke were utilized in the simulation cracking [26]. When cracking occurred at an integration point, the cracking was modeled through an adjustment to the material properties [27]. This method is widely used and was conductive to the realization of the finite element program.  
The finite element model was divided by mapping the mesh with a mesh size of 50 mm. The nodes on the contact surface of the BLC-C composite wall and the steel beam were coupled through the translational degrees of freedom in the \(x,y,\) and \(z\) directions to simulate the bolt connection. All the degrees of freedom of the nodes on the bottom of the foundation beam were restrained and the displacement load acted on the end of the loading beam. A full Newton-Raphson analysis with unsymmetric matrices of elements was used to solve the non-linear equations in the finite element analysis. For the convergence criteria, the tolerance was 5% for the force and displacement. Figure 6 shows the finite element model of specimen W2.  
Figure 6: Finite element model of specimen W2.
## 3 Results and Analysis  
### Experimental Setup  
All experiments in this paper are performed on a Linux operating system with GPU RTX 3090 (24 GB), 45 GB of RAM, and a CPU Intel(R) Xeon(R) Platinum 8362 CPU @ 2.80 GHz. Experimental models are built using the current popular Pytorch 2.0.0 deep learning framework to build network models. The experimental training phase is optimized using the Adam algorithm. \(\beta_{1}=0.99,\beta_{2}=0.999\). The learning rate is set to \(1\times 10^{-3}\) for 70 epochs of iterations, and the learning rate is adjusted after every 15 epochs. Each time the learning rate is adjusted, the new learning rate is 0.95 times the old learning rate.  
In the road extraction task, this paper adopts both qualitative and quantitative dimensions for the evaluation of classification results. For qualitative assessment, it is mainly based on subjective evaluation, including the judgment of the completeness of the results,  
Figure 6: SAFM structure diagram.  
the clarity of the edges, and the consistency of the edges. For quantitative evaluation, this paper adopts several commonly used performance metrics to measure the effectiveness of the model, including recall, precision, F1-score, overall accuracy (OA), and intersection over union (IoU). TP stands for the number of pixels correctly predicted as roads; TN is the number of pixels correctly predicted as non-roads; FP stands for the number of pixels incorrectly predicted as roads, and FN stands for the number of pixels incorrectly predicted as non-roads.  
Recall is used to evaluate the accuracy of the model in recognizing road pixels. It is the ratio of the number of pixels correctly predicted as roads to the total number of actual road pixels. It is calculated using the following formula:  
\[Recall=\frac{TP}{TP+FN} \tag{13}\]  
Precision, also known as accuracy, is one of the most intuitive metrics to evaluate, reflecting the number of pixels correctly predicted (road and non-road) as a percentage of the total number of pixels. The specific calculation formula is as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
F1-score  
The F1-score is a composite measure of model performance that provides a consistent perspective for evaluating the overall effectiveness of the model by calculating the reconciled mean of precision and recall. When the F1-score rises, it indicates an overall enhancement of the model's performance. The F1-score is a key metric for evaluating the model's balance between precision and recall. The specific calculation formula is as follows:  
\[F1 score=2*\frac{Recall*Precision}{Recall+Precision} \tag{15}\]  
OA is the ratio of the number of correctly categorized samples to the total number of samples in the sample. The specific calculation formula is as follows:  
\[OA=\frac{TP+TN}{TP+FN+FP+TN} \tag{16}\]  
IoU is an important metric for measuring the extent to which predicted roadway areas overlap with real roadway areas. Specifically, IoU calculates the ratio of the area of intersection of the predicted area with the real area to the area of their concurrency. The specific calculation formula is as follows:  
\[IoU=\frac{TP}{TP+FP+FN} \tag{17}\]
## 3 Results and Discussion
### Detecting and Tracking PLs  
Figure 2 shows the tracks of four PLs as detected from the cloud structures on satellite images and derived from the reanalysis data sets. In all three data sets, only the track of the most intense PL**3** is reproduced in both the vorticity (Figure 2a) and SLP fields (Figure 2b). The PL**1** track is also reproduced in the vorticity field from all reanalysis data sets, but in the SLP fields CFSv2 could not identify it. PL**2** were not reproduced by any reanalysis data set, despite its significant intensity (wind speed up to 25 m/s). We assume that PL**2** and PL**3** developed as a unified mesocyclonic system, in which the horizontal size of the PL**2** did not exceed 200 km. However, the CFSv2 dataset could detect the PL**4** near the coast of Chukotka despite its small scale (about 100 km).  
Separately, it should be noted that the CFSv2 in the vorticity fields falsely detected two PLs with a lifetime of over 12 h that were not identified either on satellite images or in the ASCAT vector winds. We suspect that, under certain conditions, the atmospheric model component in the CFSv2 analysis generates non-existent mesoscale low-level circulations.  
We calculated the PL track errors as the average distances between the centers identified from each reanalysis data set and close in time (less than 30 min) to satellite images. Table 1 shows the calculations performed for ERA5, MERRA-2 and CFSv2 datasets using two methods of tracking--based on SLP and 850-hPa relative vorticity (see Section 2.3). For both PLs tracking algorithms, ERA5 showed the best correspondence with satellite observations in detecting the PLs center with track errors of 50-60 km.  
The CFSv2 exhibits slightly worse results for the vorticity-based tracking algorithm (compared to ERA5), and, in the SLP fields, this operational analysis retrieved only the most intense PL**3**. Note that CFSv2, unlike the others, could detect a small-scale PL**4** with life-time less than 12 h. Finally, MERRA-2 showed the largest track errors (80-90 km) for both PL**1** and PL**3**, regardless of the tracking algorithm used.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**PLs** & **ERA5** & **CFSv2** & **MERRA-2** \\ \hline \(\zeta_{850}\)-based tracking algorithm & & & \\ PL**1** & 50 & 60 & 90 \\ PL**2** & - & - & - \\ PL**3** & 60 & 70 & 80 \\ PL**4** & - & + * & - \\ SLP-based tracking algorithm & & & \\ PL**1** & 60 & - & 90 \\ PL**2** & - & - & - \\ PL**3** & 60 & 90 & 90 \\ PL**4** & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: PL track errors (km) for ERA5, MERRA-2 and CFSv2 data sets compared to satellite observations. *—the “+” symbol indicates that the PL**4** has been identified, but the track error is omitted because of the limited number of points matched in time
## 3 Results and Discussion
### Analysis of Adulterated Milk and the Influence of Temperature
The mean values obtained from all impedance phase measurements were \(-8.39^{\circ}\), \(-7.55^{\circ}\), and \(-6.91^{\circ}\) for temperatures of 16, 28, and 37 \({}^{\circ}\)C, respectively. The highest phase impedance value was found at 37 \({}^{\circ}\)C and the lowest at 16 \({}^{\circ}\)C, both at \(2.3\times 10^{6}\) Hz. The temperature varied slightly with the value of the impedance phase, with the highest values at the highest temperatures. The response of the impedance phase is different depending on the frequency applied. At a temperature of 16 \({}^{\circ}\)C, the impedance phase gradually increases to the frequency of 10\({}^{5}\) Hz. At temperatures of 28 and 37 \({}^{\circ}\)C, the increase is more pronounced up to 10\({}^{3}\) Hz, and then, remains similar up to 10\({}^{5}\) Hz (Figures 8-10). At all temperatures, an abrupt reduction in the values of the impedance phase is observed after 10\({}^{5}\) Hz frequency; i.e., up to 10\({}^{5}\) Hz the value of the impedance phase is directly proportional to the increase in frequency, and after, it presents an inversely proportional characteristic. In the milk samples with a percentage above 67% water, a greater difference between the phase values is observed. The phase angle allows the measurement of the delay between the external excitation electrical signal and the electrical response. Given this, if adulterants are added to the milk, the ionic content present in the sample changes, i.e., the electrical behaviour of the milk increases or decreases depending on the formation or dissolution of protein particles [16]. The impedance phase behaviour at frequencies higher than 10\({}^{6}\) Hz assumed a behaviour contrary to that of the other analysed frequencies (Figures 8-10). At the lower frequencies, gradual growth is observed with the increase in the percentage of water, already in the frequencies above 10\({}^{6}\) Hz, a phase decay occurs, with a sudden reduction above 83% water. This action is even more noticeable at the frequency of \(2.3\times 10^{6}\) Hz. Temperature had the greatest influence on the phase behaviour at frequencies above 10\({}^{6}\) Hz. At 16 \({}^{\circ}\)C, the drop in values is sharper than at other temperatures. Moreover, it is more abrupt from 62.5% water and at temperatures of 28 and 37 \({}^{\circ}\)C from 75% water. The increase in temperature provided higher phase values for frequencies above 10\({}^{6}\) Hz. Tables 1-3 summarise the measured impedance and phase values in both unadulterated and adulterated milk samples with distilled water, at some frequency points and temperatures of 16 \({}^{\circ}\)C, 28 \({}^{\circ}\)C, and 37 \({}^{\circ}\)C.
## 4 Results and Discussion
### Analysis of Multiple Stations
In the case of \(N_{station}\) = 2, only 3-4 kSFU, 7-8 kSFU, 8-9 kSFU, and 10 kSFU have detection rates \(\geq\) 80%. However, for GPS L2, in cases of \(N_{station}\) = 2, 3, and 4, the detection thresholds that meet the detection rate of 80% are in the ranges of 1-2 kSFU, 1-2 kSFU, and 2-3 kSFU, respectively. There is a sudden and obvious drop in the range of 9-10 kSFU at both the L1 frequency and L2 frequency. A possible reason for this is that merely 7 samples are in the range of 9-10 kSFU. Due to the detection rate at the L2 frequency being higher than that at the L1 frequency, we further divide the range of 0.1-1 kSFU at the L2 frequency for more in-depth analysis. As shown in Figure 7, there is a trend of fewer samples and a higher detection rate with  
Figure 6: Detection rate for 4 intense L-band SRB events. The histogram shows the number of samples in each flux range. increasing solar radio emission power. In the case of \(N_{station}\) = 3, 4 or 5, there are no flux ranges with detection rates reaching 80%. However, in the case of \(N_{station}\) = 2, the flux density threshold for a detection rate of 80% is in the range of 800-900 SFU. Specifically, the detection rates of 500-600 SFU, 600-700 SFU, and 700-800 SFU reach 80%, 75.68%, 79.17%, and 76.19%, respectively. As mentioned above, we can conclude that the optimal value of \(N_{station}\) is 2.