esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Numerical Studies on the Beam Profile  
As element interconnection only happens in the elevation direction, our numerical evaluation was only conducted in the elevation direction. Simulations were conducted in Field II [38; 39] to investigate the beam profile of the different transmit/receive sequences. The image reconstruction in azimuthal direction is unaffected and therefore neglected. We varied the settings provided in Table 1.  
The impulse response consisted of a Gaussian-modulated sinusoidal waveform (central frequency of 7.5  MHz, bandwidth of 80%). Normalization and log-compression were used to form final images. The response of receive aperture size (Nr), the shift of the receiving aperture between sequential shots  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Shots** & **20** \\ \hline Receive aperture (Nr) & 1–20 elements \\ Element size & 130 \(\upmu\)m \\ Pitch & 150 \(\upmu\)m \\ Transmit aperture & 1–80 elements \\ Shift between sequential shots of the receive & 3–4 (unit is Pitch) \\ aperture (Ns) & 1.5 cycles \\ Transmit pulse length & 1.5 cycles \\ Central frequency & 7.5 MHz, \(\lambda\) = 0.2 mm \\ Scatterer position & 10, 20, and 30 mm \\ Speed of sound & 1500 m/s \\ Sampling frequency & 100 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: The variables used in the numerical study.  
Figure 1: **(a)** Schematic of a single tile consisting of \(12\times 40\) acoustic elements on top of an Application Specific Integrated Circuit (ASIC) with element-level switches and row-level circuits. **(b)** The proposed transducer consisting of \(4\times 2\) tiles with respect to the carotid artery (carotid has a diameter of 6–9 mm; the array is \(12\times 14\) mm).  
Figure 2: **(a)** The generated pressure field with a full transmission approach (all the elements are excited together); the dashed lines show the direction of grating lobes with different Ns. **(b)** A schematic of the aperture definition procedure; Ns = 3, Nr = 10, and Nt = 50. Receive and transmit apertures have the same center.  
(Ns) and the transmit aperture size (Nt) were evaluated using point scatterers positioned at depths of 10, 15, and 20 mm, as we expect the carotid artery in this range [8]. Figure 2b illustrates the way that the Nr, Nt, and Ns parameters were defined and implemented in 20 transmit-receive shots. The receive and transmit apertures were concentric and shifted over the same distance.  
The level of grating lobes, lateral width of point spread function (PSF) at \(-6\) dB called full-width-half-maximum (FWHM) and level of sidelobes were used to evaluate the image quality as result of the different settings [24]. The finalized imaging sequence was evaluated with a numerical tissue mimicking phantom in which an anechoic cyst having a radius of 3 mm was positioned at the depth of 15 mm. The phantom contained scatterers from \(-15\) to 15 mm in the lateral direction to visualize the off beam energy including the grating lobes. Multi-scatterer and multi-cyst phantoms were also imaged. We use column-by-column imaging scheme as the ground truth (GT) in our study.
## Abbreviations  
HIPVs Herbivore-induced plant volatiles  
JA Jasmonic acid  
SA Salicylic acid  
ABA Abscisic acid  
ET Ethylene  
TPS Terpene synthase genes  
_AFS_ & _a-Farnesene synthase_  
_BGL_ _& _b-1, 3-Glucanase_  
_EF1 Encoding elongation factor 1_  
_ACS_ & _1-Aminocyclopropane-1-carboxylate synthase_  
_EIN3_ & _Ethylene insensitive protein 3_  
FPP Farnesyl diphosphate  
GPP Geranyl diphosphate  
GC-MS Gas chromatography-mass spectrometry  
SPME Solid phase microextraction  
UPLC-QTOF-MS Ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry  
Figure 7: Summary on herbivore-induced formation of _a-_farnesene and its effect on defense ability of neighboring tea plants as a volatile signal. JA, jasmonic acid; _AFS_, _a-farnesene synthase_; SA, salicylic acid; _BGL_, _b-1, 3-glucanase_.
# Soil Depth Prediction Model Using Terrain Attributes in Gangwon-do, South Korea
## 3 Predicted Soil Depth Model
Figure 7: The proposed statistical prediction models result in the study area, (**a**) The soil depth map, Scatterplots showing the relationship between predicted and measured soil depth for (**b**) Igneous rock (\(R^{2}\)= 0.702), (**c**) Metamorphic rock (\(R^{2}\) = 0.686), (**d**) Sedimentary rock (\(R^{2}\) = 0.693).
## 2 Notes on the Theory, Method and Data
### Data: Remote Sensing and Terrestrial
#### 2.4.2 Surface Topography Data  
ETOPO 1 is a part of the global 1' relief model of the Earth's surface that integrates land topography and ocean bathymetry from a large number of satellite measurements [19]. It is one of the global topography models; it is used here (we chose this one among other potential models due to its simplicity). Its precision should be \(\sigma=\pm 10\) m in height (but not everywhere). Our experience (over Sahara) is that the stated precision of such models is in fact only their internal precision and not the actual external accuracy. We conservatively assume \(\sigma=\pm 20\) m for our study areas.
# Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models
## 4 Result and Discussion
### Historical Event  
To show the applicability of the model, the historical influenza event of the season 2016/2017 is recreated. The 2016/2017 season influenza epidemic in Switzerland lasted from week 50/2016 to week 8/2017. Extrapolated during the wave of influenza (i.e., during the period when the threshold was exceeded), around 2.75% of the Swiss population had visited a doctor for flu-like symptoms. In Switzerland, influenza A viruses of subtype H3N2 circulated almost exclusively throughout the season. It peaked in week 2/2017 with 372 consultations per 100,000 inhabitants. Within the metropolitan area of Zurich, the peak was at 360 consultations per 100,000 inhabitants [35].  
Figure 7: Some of the simulation results from the SIR model with varying input parameters \(\mathbb{P}_{I}\), \(\mathbb{P}_{\beta}\) and \(\mathbb{P}_{\gamma}\). With: (**a**) reference scenario, (**b**) increased initially infected population, (**c**) increased infection probability, (**d**) decreased recovery probability, (**e**) increased recovery probability, and (**f**) decreased infection and recovery probabilities.  
Based on the observed data the model parameters are estimated to \(\mu_{\mathbb{P}_{I}}=0.000632\) (\(\sigma_{\mathbb{P}_{I}}=0.000217\)), \(\mu_{\mathbb{P}_{\hat{f}}}=0.585\) (\(\sigma_{\mathbb{P}_{\hat{f}}}=0.104\)) and \(\mu_{\mathbb{P}_{\gamma}}=0.345\) (\(\sigma_{\mathbb{P}_{\gamma}}=0.213\)). Figure 8 shows the agent-based epidemic spread model and the classical compartment SIR model fitted to real data of seasonal influenza in the season 2016/2017 observed in the Zurich area. The fitted agent-based model has an average least square error of \(1.62\times 10^{-6}\) while the SIR model has \(7.96\times 10^{-6}\). The SIR model approximates the data with a smooth function, caused by the underlying analytical form (see Section 2.1), while the agent-based model allows reproduction of the non-smooth behavior.
## 4 Analysis and Discussion  
Figure 7 shows that the accident rate of the scenarios generated by the value of \(\mathcal{ESC}\) was much higher than that selected by the possibility. (a) and (b) depict the accident rate of simulation experiments in the selected scenarios library obtained for the car-following case using the two datasets. The green curve shows the accident rate in the chosen scenarios using the \(\mathcal{ESC}\) algorithm, and the blue curve shows the accident rate in the chosen scenarios just by the possibility of occurrence. The accident rate of (a) showed the most significant increase, with a 22.6% increase over 0.07%, which is more than 300. (b) shows that the accident rate of the test scenarios generated by this method is about 35%. (c) and (d) show the accident rate in the cut-in case. The accident rates selected by \(\mathcal{ESC}\) were about 40% and 34%, much higher than those selected by the blue. Therefore, this method can generate challenging scenarios in both the car-following and cut-in cases.  
When comparing these four pictures horizontally, it is clear that the larger the space, the lower the accident rate in the same case using different datasets, which aligns with cognition. However, by comparing the two pictures using the same dataset, the cut-in case with a larger modeling space is shown to have a similar or higher accuracy. This problem is related to our simulation model IDM. For example, in HighD, IDM only focuses on horizontal interactions, although the space of the cut-in case is:  
\[\begin{split} size(state)&=20\times 36\times 346 \times 18\times 6\\ &=2.69\times 10^{7},\\ size(action)&=31*23=713,\\ size(\mathcal{ESC}\ matrix)&=1.92\times 10^{10}, \end{split} \tag{26}\]  
This is much larger than the space of the car-following case. The horizontal space of the cut-in case is:  
\[size(horizon)=20\times 36\times 346\times 31=7.72\times 10^{6}, \tag{27}\]  
This is smaller than the space of the car-following case.  
In addition to accuracy, the method's coverage should be considered. Coverage, like recall in classification problems, ensures that high-risk scenarios are selected, whereas accuracy, like precision, focuses on selecting high-risk scenarios. First, we computed the \(esc\) of the accident scenarios chosen from the control experiments, and their \(esc\) values were all greater than the threshold of 0. This means that these scenarios are part of the scenario library chosen by \(esc\). Second, theoretical analysis shows that each state and action was assigned the \(esc\) valve and occurred in a specific space. The BV decision was based on the current state. The \(esc\) value of the final accident state must exceed the threshold, and the influence can be passed to the previous state. As a result, this is reasonable to ensure coverage. The quantification method based on functional safety is practical and feasible in the testing scenario generation problem due to its high accuracy and coverage.
# Multi-LEO Satellite Stereo Winds
## 3 Results  
There are 14 to 15 overpasses of each pole by Terra and both Sentinel-3 satellites each day. High-quality stereo winds have been produced over both poles and the descending (daylight) passage across the midlatitudes and equator. The ascending (nighttime) passage has been found to be problematic, possibly due to larger geo-registration errors because of the lack of reflective channel ground control points during the nighttime ascending segment of the Sentinel-3 orbit; however, the focus of this paper is the polar regions. There have been no problems encountered either during polar summer (day) or polar winter (night) using the LWIR and Mid-Wave IR (MWIR). In the LWIR case, MODIS Band 31 is used with SLSTR Band S8. In the MWIR case, MODIS Band 22 is used with SLSTR S7. Visible (VIS) stereo winds have been successfully performed with MODIS Band 1 and SLSTR S2 during polar summers. In the next section, we discuss validation of the polar stereo-wind retrievals using clear-sky terrain and LiDAR and applications for polar stereo winds.  
Figure 6 shows an example of an LWIR case over Antarctica (Appendix B shows a full daily cycle for both poles). The orbital periods for the Sentinel-3 satellites (101 min) differ slightly from that of Terra (99 min), so the phasing of a Sentinel-3 overpass with respect to Terra advances each orbit by about 2 min, while Sentinel-3A and -3B remain phase-locked with respect to each other with Sentinel-3B trailing -3A by 40 min. The time difference between the SLSTR oblique and the nadir views is approximately constant at less than 3 min. In general, when the time between the Sentinel-3 and Terra (\(\Delta t\)) is small, then the patterns being tracked have less time to change shape. The method assumes that the tracked patterns are undergoing pure translation, which is an assumption best satisfied in the limit, as \(\Delta t\) tends to zero; the most high-quality matches (as indicated by the peak normalized cross correlation) are found in this limit. However, the wind retrieval precision must depend inversely on \(1\,\Delta t\,\), meaning it would be better to have a larger \(1\,\Delta t\,\)!. There are two Sentinel-3 satellites from which to choose and, when one is either too far or too close to Terra, the other one can be used instead.  
Figure 6: A swath of stereo winds over the South Pole for South Polar summer (21 December 2021, LWIR) with an intersecting line of LiDAR top-layer cloud-top heights. Wind vectors indicate speed and direction. A heatmap codes the retrieved wind height. The time difference of Sentinel-3 relative to Terra, \(\Delta t\), is noted in minutes in parentheses in the title. The horizontal and vertical axes are km in a polar stereographic projection.  
LWIR, MWIR, and VIS cases for polar summer and winter are included in the Supplementary Materials as network Common Data Form (netCDF) files.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 3 Methods  
The process chain (Figure 4) of the time series approach for the derivation of the flood related classes consists of pixel- (deep orange arrow) and segment-based (bright orange arrows) parts. The pixel-based steps include the generation of time series features and their normalisation based on VV- and VH-polarisation layer stacks (see Section 3.2). Simultaneously, the object generation takes place using a clustering approach based on the combination of the VV and VH time series layer stacks (see Section 3.3). In the next step, normalised time series features are combined with the generated cluster image to produce cluster-based time series features. The features are the basis for a thresholding approach (see Section 3.4) that enables the derivation of TOW and TFV. It can be performed using pixel-based or object-based time series features, while the latter also requires the generation of pixel-based normalized time series features. The exclusion layers, including the GUF and the HAND index, allow one to mask out urban areas and to consider topographical information before the extraction of times series features and the generation of objects is performed. The preprocessing is not shown in Figure 4; however, it is essential for the creation of time series stacks. The implemented preprocessing steps are described in Section 3.1.  
Figure 3: Additional information in the study area Namibia. Global Urban Footprint (GUF) (**a**), exclusion mask (threshold 20 m) of the height above nearest drainage (HAND) index (**b**), SRTM Water Body Data (SWBD) (**c**).
## 2 Principle Analysis of the Proposed JSG-DPD Technique
### Noise Reduction Method  
The transmission frame structure of the proposed superimposing training sequences method is illustrated in Figure 4, which mainly consists of three parts: frame header, data  
Figure 3: Schematic diagram of the distance variation of LEO satellite–ground data transmission link.  
block, and pilot signal. The pilot signal is composed of \(H\) identical training sequences. Assume that the discrete OFDM signal subject to AWGN interference is  
\[x(n)=s(n)+u(n),n=0,1,\cdots,N, \tag{9}\]  
where \(s(n)\) and \(u(n)\) denote the discrete OFDM signal and the noise signal, respectively. After superimposing the above equation for \(H\) times, we obtain  
\[x_{h}(n)=\sum\limits_{h=1}^{H}s_{h}(n)+\sum\limits_{h=1}^{H}u_{h}(n),n=0,1, \cdots,N. \tag{10}\]  
Since AWGN is not only irrelevant between random variables at any two distinct moments, it is also statistically independent. Hence, the noise part \(\sum\limits_{h=1}^{H}u_{h}(n)\) is almost zero, while the signal part is amplified \(H\) times, and the above equation is then averaged and divided by \(H\) on both sides to yield  
\[x^{\prime}(n)=s(n)+\mathbf{U}^{\prime},n=0,1,\cdots,N. \tag{11}\]  
When the number \(H\) of superpositions is large enough, theoretically \(\mathbf{U}^{\prime}\approx 0\), \(x^{\prime}(n)\approx x(n)\). With this method, the valuable signal is coherent and can be accumulated, while the noise signal is irrelevant and cannot be gathered. Therefore, the SNR of the received signal is improved by increasing the signal power, which can be equivalent to noise reduction. Moreover, the frame length and the propagation time occupied by the pilot signal consisting of the superimposed training sequence, etc., can be acceptable during several minutes of LEO satellite data transmission.  
Furthermore, for non-Gaussian noise such as heavy-tailed noise [23; 24], other methods [25; 26; 27] are needed for noise reduction.
## 3 Results and Discussion  
### Morphological Characterization and Surface Area Analysis of SiO\({}_{2}\) NPs  
The SEM and TEM morphological characterizations for the OTS-treated and untreated SiO\({}_{2}\) NPs are shown in Figure 2. The corresponding SEM micrographs in Figure 2b,c mainly show the presence of SiO\({}_{2}\) NP cluster agglomerations. It is possible to note that, in Figure 2c, for the OTS-treated SiO\({}_{2}\) NPs, there is an increment in size of the observed NP cluster agglomerations, which may be the result of their entanglement caused by the OTS long-chain alkyl groups. A close up of the SiO\({}_{2}\) NPs is given by the TEM micrograph in Figure 2a, revealing dimensions of about 15-20 nm.  
Figure 2: (a) TEM micrograph of an isolated SiO\({}_{2}\) nanoparticle with a size of about 15-20 nm. SEM micrographs for (b) untreated and (c) OTS-treated SiO\({}_{2}\) NPs showing the presence of cluster agglomerations.  
The BET area values calculated for both the OTS-treated and untreated SiO\({}_{2}\) NPs are listed in Table 2. The results for the untreated SiO\({}_{2}\) NPs exhibit a surface area average of 483.5 m\({}^{2}\)/g, while the OTS-treated SiO\({}_{2}\) NPs exhibit a value of 81.5 m\({}^{2}\)/g. However, the pore volume value obtained for the untreated SiO\({}_{2}\) NPs is 0.74 cm\({}^{3}\)/g, with an average pore diameter of 6.7 nm, while for the OTS-treated SiO\({}_{2}\) NPs, a pore volume value of 0.27 cm\({}^{3}\)/g is obtained, suggesting that the OTS long-chain alkyl groups can occupy the surface of the SiO\({}_{2}\) NPs and, hence, decrease such a value. The corresponding isotherms can be found in Supplementary Materials (Figure S1).
## 5 Discussion
### Comparison with Other Studies
However, due to the lack of access to the original stakes, we could only compare general trends observed by geodetic and glaciological mass balance. We used the glaciological mass balance data reported by DGA during four-time periods for which DEMs were available (Figure 4a). For the earliest observation periods, data from each method was not directly comparable, as glaciological monitoring started in 1975 only. For the period of 1975-2000, the glaciological mass balance showed a mean rate of \(-0.40\) m w.e. a\({}^{-1}\), whereas for 1955-2000, the observed mean geodetic mass balance rate was \(-0.85\) m w.e. a\({}^{-1}\) (Table 3). This difference of \(-0.45\) m w.e. a\({}^{-1}\) could be explained by the difference in time span between these two estimates, alongside density factors and the uncertainty estimation. Between 2000 and 2009, the average glacier mass balance observed by the glaciological method showed the same positive trends with the geodetic mass balance. The DGA glaciological mass balance data available from WGMS identified four positive mass balance seasons, 2000-2001, 2002-2003, 2005-2006 and 2008-2009, that also coincident with positive and neutral phases of ENSO (Figure 5). This indicates that the positive mass balance years led to an overall positive mass balance for the entire period. The difference in magnitude between both methods could be caused by the assumption that the snow pit is representative for the whole glacier. Since the 1990s, the elevation changes started to modify the slope of the location (field observations). The current location of the snow pit is in an area with a higher slope and elevation than the average glacier. After 2008, field campaigns soundings with a steam drill (DGA personal communication) indicated that \(\sim\)40% or more snow could be present in areas with gentler slopes than where the snow pit is regularly dug. This difference possibly causes an underestimation of snow accumulation. This may explain the difference in estimations between geodetic and glaciological mass balance methods. Figure 5: Comparison of the annual mass balance of the Echaurren Norte Glacier between 1975–2015 and annual precipitation between 1975 and 2015 at El Yeso station (black bars). El Niño-Southern Oscillation (ENSO) 3.4 region data were retrieved from the National Ocean and Atmospheric Administration (NOAA). Drought period [D] in orange box [12]. Other potential factors of difference between the geodetic and glaciological approaches is that the glaciological mass balance data were collected at different dates due to field work logistics, particularly during the early years of the measurement program [24; 39]. Hence, accomplishing the fixed-day system [37] was especially challenging. There was also a lag between the collection of geodetic data and the hydrological year. Finally, we have to consign that our co-registration area for the 2000-2009 was small, where the uncertainty for determination of glacier mass balance represent \(\sim\)75% (Table 3).
## 1 Introduction
However, as technology nodes undergo rapid dimensional scaling, the characteristic lengths of semiconductor devices have reached a point where they are comparable to or even smaller than the mean free paths (MFPs) of heat carriers [17]. For example, atomic layer deposition (ALD) has been employed to fabricate high-k dielectric layers, such as Al\({}_{2}\)O\({}_{3}\) and HfO\({}_{2}\), with thicknesses below 10 nm [18]. To reduce the overall electrical resistance of Cu/liner/barrierinterconnects, the thickness of liner and barrier layers should be only \(\sim\)2 nm [19]. These characteristic lengths are considerably smaller than the MFPs of phonons in silicon [20] and even smaller than the MFPs of electrons in silicon and Cu [21; 22]. Furthermore, the interface density increases significantly because of the multilevel structure of the interconnect system, becoming even more pronounced in 3D IC architectures [23]. Thermal transport in these nanostructures exhibits a ballistic or quasi-ballistic nature rather than diffusive behavior [24]. Accordingly, the overall thermal resistance of these deeply scaled semiconductor devices is determined primarily by the TBR at the interfaces, rather than by the intrinsic thermal conductivity of the constituent materials. Therefore, the TBR between the different constituent materials should be decreased optimally for adequate thermal management of semiconductor devices. Here, we provide a comprehensive overview of the effects of the TBR on the thermal management of GaN-based semiconductor devices. This review is organized as follows. Section 2 presents a review of the theoretical and computational methods for TBR prediction and the experimental methods for TBR measurement. In Section 3, recent experimental and simulated TBR data for GaN/SiC and GaN/diamond interfaces are reviewed. The effects of the growth methods, growth conditions, integration methods, and interlayer structures on the TBR of GaN/substrate interfaces are summarized and discussed. In Section 4, the conclusion and an outlook for reducing the TBR for thermal management are provided as a guideline for the design and implementation of GaN-based semiconductor devices.