esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dataset Preparation for Both Use-Cases  
Histopathology slides from all patients of interest for the study design were retrieved from the archives of the Dept. of Neuropathology (see below) and subsequently digitized using a Hamamatsu S60 scanner with a 40\(\times\) magnification. We included only H&E stainings, thus, eliminating the need for more complex and expensive immunostainings. The WSI of our dataset were reviewed by two expert neuropathologists of our institute.  
Use case 1: For the DNET and ganglioglioma, classifier slides from 219 patients were used. In total, 52 of them were DNETs and 167 were ganglioglioma. QuPAth was used by two of our expert neuropathologists in epilepsy pathology to define polygonal ROIs containing tumor tissue in the WSI, and we exported their coordinates to JSON files. These JSON files were then used by the library to extract tiles from the relevant regions of the WSI. In total 171,514 tiles from GG and 34,520 tiles from DNETs with a size of 1024 \(\times\) 1024 pixels were defined for further processing and training.  
Use case 2: To train and evaluate the pituitary adenoma classifier, H&E and immunohistochemically stained (ACTH, LH, FSH) tissue slides of 410 patients were collected. In total, 181 of these were diagnosed with corticotroph and 229 with gonadotroph adenoma of the pituitary gland (Supplement S1 and S2). Overall, the dataset consisted of 431 H&E (202 corticotroph and 229 gonadotroph) slides with the corresponding ACTH LH/ FSH whole-slide images for comparing and identifying the correct ROI (Figure 5). The ROIs on an individual H&E slide were defined as regions, where the immunostainings showed tumor expressions of the specific hormone. Care was taken that no normal pituitary gland tissue was included (Figure 5). This time-consuming ROI selection process was necessary to ensure the correct labeling of each tile and, therefore, the validity of the resulting models. Otherwise, biases through wrong labeled areas could have worsened the performance. For example, areas with only connective tissue were excluded. Moreover, the hormone expression of the adenoma is not homogeneously spread over the sample. This was particularly important to consider for gonadotropin adenomas. When an adenoma expresses LH and FSH that does not mean that all subregions express both hormones. So, there can be tiles that are only labeled with LH or FSH, although the whole tumor expresses both. ROIs were defined at 40\(\times\) magnification level and cropped into smaller tiles of 1024 \(\times\) 1024 pixels to further preprocess and feed into our model (Figure 5). The tile extraction resulted in 206,517 gonadotropic and 63,893 corticotropic tiles.
# A Finite Element Method for Nonlinear and Fully Coupled Transient Hydrothermal Transfer
## 5 Illustrative Examples
### Transient 1D Moisture and Heat Transfer in a Solid Disk with Constant Material Properties
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{\(tt/t_{0}\),\(t_{0}\)=\(\frac{b^{2}}{D_{b}}\)} & \multirow{2}{*}{\(rlb\)} & \(\mathbf{T/(\Lambda H_{0})}\) & \multirow{2}{*}{\(\mathbf{T/(\Lambda H_{0})}\)} & \multirow{2}{*}{\(\mathbf{Relative}\)} & \(\mathbf{H/H_{0}}\) & \multirow{2}{*}{\(\mathbf{H/H_{0}}\)} & \multirow{2}{*}{\(\mathbf{Relative}\)} \\  & & **Finite** & **(Analytic)** & **Difference (\%)** & **Finite** & **(Analytic)** & **Difference (\%)** \\ \hline \multirow{8}{*}{\(0.1\)} & 0.0 & 0.08991 & 0.08987 & 0 & 0.1663 & 0.1659 & 0.24 \\ \cline{2-8}  & 0.2 & 0.08597 & 0.08601 & 0 & 0.2032 & 0.2021 & 0.10 \\ \cline{2-8}  & 0.4 & 0.07388 & 0.07407 & 0.25 & 0.3146 & 0.3129 & 0.54 \\ \cline{2-8}  & 0.6 & 0.05373 & 0.05392 & 0.4 & 0.5017 & 0.4999 & 0.36 \\ \cline{2-8}  & 0.8 & 0.02744 & 0.02757 & 0.5 & 0.7455 & 0.7443 & 0.16 \\ \cline{2-8}  & 1.0 & 0.0000 & 0.0000 & 0 & 1.0000 & 1.0000 & 0 \\ \hline \multirow{8}{*}{\(0.2\)} & 0.0 & 0.05394 & 0.05415 & 0.39 & 0.4999 & 0.4980 & 0.34 \\ \cline{2-8}  & 0.2 & 0.05083 & 0.05111 & 0.55 & 0.5288 & 0.5260 & 0.53 \\ \cline{2-8}  & 0.4 & 0.04226 & 0.04253 & 0.64 & 0.6083 & 0.6057 & 0.43 \\ \cline{2-8}  & 0.6 & 0.02949 & 0.02969 & 0.68 & 0.7266 & 0.7247 & 0.26 \\ \cline{2-8}  & 0.8 & 0.01459 & 0.01469 & 0.69 & 0.8648 & 0.8638 & 0.12 \\ \cline{2-8}  & 1.0 & 0.000 & 0.0000 & 0 & 1.0000 & 1.0000 & 0 \\ \hline \multirow{8}{*}{\(0.3\)} & 0.0 & 0.03075 & 0.03099 & 0.78 & 0.7149 & 0.7127 & 0.31 \\ \cline{2-8}  & 0.2 & 0.02894 & 0.02923 & 1.0 & 0.7317 & 0.7291 & 0.36 \\ \cline{1-1} \cline{2-8}  & 0.4 & 0.02399 & 0.02424 & 1.04 & 0.7776 & 0.7753 & 0.30 \\ \cline{1-1} \cline{2-8}  & 0.6 & 0.01668 & 0.01685 & 1.02 & 0.8454 & 0.8438 & 0.19 \\ \cline{1-1} \cline{2-8}  & 0.8 & 0.008227 & 0.008313 & 1.05 & 0.9237 & 0.9229 & 0.09 \\ \cline{1-1} \cline{2-8}  & 1.00 & 0.0000 & 0.0000 & 0 & 1.0000 & 1.0000 & 0 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of finite element results and the analytical results (Equation (27)) for sudden change of surface moisture content from 0 to \(H_{0}\). Figure 9: Two-dimensional finite element mesh for one-quarter of the disk.
# OTFS: A Potential Waveform for Space-Air-Ground Integrated Networks in 6G and Beyond
## 4 Results and Discussion
### Channels with Doppler Effects
In contrast, OTFS consistently outperforms OFDM across all modulation schemes. It effectively mitigates Doppler and multipath effects, resulting in significantly lower BER. Unlike OFDM, the BER of OTFS continues to decrease with increasing SNR, demonstrating its robustness in extreme mobility scenarios. This is because OTFS operates in the delay-Doppler domain, spreading symbols across both time and frequency. This efficiently manages the time and frequency domain distortions, offering resilience against mobility-induced ICI. OTFS also benefits from more accurate channel estimation in high-mobility environments. By leveraging the delay-Doppler representation, OTFS enables effective pilot symbol utilization and a structured channel matrix, simplifying equalization and improving the BER performance. Figure 10 shows the BER vs. SNR performance of OTFS and OFDM in multipath channels under varying mobility conditions (20 km/h, 400 km/h, and 2000 km/h). This provides a clear comparison of how increasing mobility affects both modulation schemes. As mobility increases from 20 km/h to 2000 km/h, the BER of OFDM degrades significantly. While OFDM performs well at low mobility (20 km/h), where the BER decreases with increasing SNR, its performance deteriorates at higher mobility (2000 km/h). At SNR values beyond 15 dB (at 2000 km/h), the BER curve flattens out, indicating that OFDM struggles with ISI and ICI caused by high Doppler effects. In contrast, OTFS consistently outperforms OFDM across all mobility conditions. The BER curve for OTFS continues to decrease as SNR increases, achieving a BER of \(10^{-3}\) at approximately 20 dB SNR. It is also observed that OTFS shows improved performance as mobility increases (from 20 to 2000 km/h), demonstrating that it thrives in high-mobility environments. This behaviour is attributed to OTFS's delay-Doppler domain processing, which effectively mitigates Doppler spread and multipath fading. Figure 10: BER performance comparison of OTFS and OFDM in a multipath channel under different mobility scenarios (20 km/h, 400 km/h, and 2000 km/h).
# Impact Load Sparse Recognition Method Based on Mc Penalty Function
###### Abstract  
The rotor system is an important part of large-scale rotating machinery. Bearings, as a key component of the rotor system, play a vital role in the healthy operation of the rotor system. The bearings operate under harsh conditions such as high temperature, high pressure, and high speed. They are complex and extremely prone to failure, especially when the bearing is affected by impact load, which seriously affects the remaining service life of the bearing. Uneven bearing friction, caused by the impact, is one of the main factors that cause premature failure of the bearing. The early identification of shock loads and reasonable measures are extremely important for the safe operation of equipment. This paper proposes an impact load identification method based on the sparse decomposition of the Mini-max concave penalty function (Mini-max concave penalty function, MC). The method uses the MC penalty function to reconstruct the regularized sparse recognition model, and then uses the improved original dual interior point method to solve the problem. This model realizes the identification of vibration and shock loads. Relevant experimental verification was carried out, and the results show that the sparse decomposition result based on the MC penalty function is better than the L1-regularized sparse decomposition result, and the noise is well suppressed in the non-loaded area of the impact load. This method can be applied to the early fault diagnosis of the vibration signal of the gas turbine rotor.  
rotating machinery; impact load recognition model; penalty function; sparse decomposition
## 3 Results and Discussion
### Analysis Outputs
#### 3.2.1 Plant Performance Indicators  
Plant Gross Power  
The gross power is of course the main parameter to study. As expected and in line with the choice to optimize the global CHP efficiency after the retrofit (see Section 2.1 and [30]), the mean decreases from 703 MW to 382 MW (see Figure 5). The standard deviation is rather low and has a negligible decrease from 17.17 MW for coal to 15.95 MW for biomass (see Figure 5); thus, the expected power is not more uncertain after the retrofit. Nevertheless, the relative standard deviation (mean versus standard deviation) of biomass is higher than the relative standard deviation of coal, which is in line with what was observed in [30].  
\begin{table}
\begin{tabular}{c} \hline
**Fuel** \\ \hline Solid-fuel-weight percent of moisture \\ Solid-fuel-weight percent of ash \\ Solid-fuel-weight percent of C \\ Solid-fuel-weight percent of H \\ Solid-fuel-weight percent of S (only for coal) \\ Solid-fuel-specified LHV @ 25C \\ \hline
**Furnace** \\ \hline Evaporator steam outlet temperature \\ Flue gas O\({}_{2}\) content \\ Waterwall radiant flux correction factor \\ Carbon-to-soot conversion rate \\ Ash emissivity exponent correction factor \\ Ash particle mean diameter \\ Non-uniform radiant flux factor \\ Radiant cooler: minor heat loss \\ Gas/air side convective heat transfer coefficient adjustment factor—SH1 \\ Gas/air side convective heat transfer coefficient adjustment factor—SH2 \\ Gas/air side convective heat transfer coefficient adjustment factor—SH3 \\ \hline
**Combustion air** \\ \hline Rotary air heater: cleanliness factor \\ \hline
**Water/steam** \\ \hline High-pressure preheater 3: condensing zone heat transfer coefficient correction factor (only for biomass) \\ \hline \end{tabular}
\end{table}
Table 4: Input parameters with Sobol’ index above 1% for at least one output.  
Plant Gross Electric Efficiency  
The CHP efficiency increases from 41.5% to 65.3% and the standard deviation from 1.4% to 1.5% (see Figure 7). The plant CHP efficiency (\(\eta_{CHP}\)) is calculated by summing the produced net electrical power (\(P_{el,net}\)) and the delivered thermal power as two equivalent forms of energy:  
Figure 5: Gross power. Coal: mean = 703.2 MW, standard deviation = 17.1 MW. Biomass: mean = 382.3 MW, standard deviation = 15.8 MW.  
Figure 6: Gross efficiency. Coal: mean = 45.3%, standard deviation = 1.1%. Biomass: mean = 30.8%, standard deviation = 1.3%.  
\[\eta_{CHP}\ =\ \frac{P_{el,net}+P_{th}}{P_{fuel}}\ \text{[-]} \tag{12}\]  
where \(P_{th}\) is the thermal power delivered to the heat client and \(P_{fuel}\) is the thermal power input in the boiler, calculated as the product of the fuel flow rate and its LHV. Because the coal-fired power plant only delivers electricity to the grid, while the biomass CHP also delivers heat to the DH, the CHP efficiency will obviously increase after conversion. Again, the expected performances are not much more uncertain after the retrofit.
## 3 Results and Discussion
### Impact Resistance Analysis of Double Raschel GCCM  
To confirm the reinforcing effect on brittle fracture occurrence in general concrete, both general concrete and the double raschel GCCM were cured for 30 days. An impact resistance test was conducted through a free fall test on the general concrete and double raschel GCCM samples. The photographs resulting from the free fall test are shown in Figure 8. After conducting the free fall test, it was observed that when dropped from a height of 0.25 m, the normal concrete exhibited an immediate tendency to fail. However, in the case of the double raschel GCCM, deformation occurred on the rear side without resulting in destruction. The double raschel GCCM displayed backside deformations measuring 0.2 mm, 0.5 mm, and 1.2 mm at drop heights of 0.25 m, 0.5 m, and 1.0 m, respectively. Figure 9 shows the double raschel GCCM sample after the free fall test. General concrete showed a brittle fracture owing to the rapid propagation of cracks, contrasting with the double raschel GCCM which remained free from fractures at drop heights of 0.25 m and 0.5 m; however, a partial plastic fracture was observed at a drop height of 1.0 m. This indicates the reinforcing effect of the double raschel GCCM on the brittle fracture likelihood in concrete.  
Furthermore, in order to perform a quantitative analysis of the impact energy applied to each sample, the impact performance of the concrete was evaluated both before and after the introduction of the double raschel GCCM. This evaluation was carried out using an impact strength tester, and the corresponding test results are shown in Table 4. The average bending strength value was obtained from five measurements for each individual experimental condition.  
Figure 7: Fracture surface of double raschel GCCM with blowing agent addition. (**a**) For using reinforcing fiber; (**b**) For not using reinforcing fiber.  
Figure 8: Free fall test result of double raschel GCCM; (**a**) before test, (**b**) after test.  
Figure 9: Free fall test result of double raschel GCCM; (**a**) 0.25 m, (**b**) 0.5 m, (**c**) 1.0 m.  
The impact test results showed that the shock absorption rate of the double raschel GCCM reached 127.78 KJ/m\({}^{2}\), representing a significant increase compared to the pre-reinforcement value of 83.22 KJ/m\({}^{2}\). From this, it is seen that the double raschel GCCM is indeed suitable as a reinforcing material.
# Analysis of a Landscape Intensely Modified by Agriculture in the Tiete-Jacare Watershed, Brazil
## 2 Materials and Methods
### Methodology
#### 2.2.1 Random Forest Approach Applied in Sentinel-2 Images
A total of 1600 training areas containing an average size of 1.36 ha were randomly selected in ArcGIS. The areas were labelled as exposed soil, citriculture, pasture, silviculture, sugar cane, urban area, vegetation, and water. A total of 200 samples were adopted per class. Afterwards, the RF algorithm was applied using the Sentinel-2 processed images as input.
## 4 Economical Miniaturized Arduino-Based Metamaterial Setup  
### Factors Effecting Economics in SHM  
The first important aspect of economics in SHM is to reduce the cost of VNA, which are quite expensive of over US$40 K. However, it should be noted that in the initial years of research the cost of any equipment is usually higher than in the later years. There are several examples to prove that the cost gradually reduces over time. If we consider Hewlett Packard's impedance analyzers [127] of the 1990s, they were very expensive compared to analyzers or LCR meters of the later decades [128].  
The cost of VNA depends on (a) range of wavelength or frequency, and (b) measuring parameters. Thus, the cost hugely depends on the end user requirements, metamaterial designs, and models of analysers [129]. Further research is required to make it more affordable to developing countries such as India and China, where there is huge potential for metamaterials [130].  
The next important aspect is the portability of devices, which plays a prominent role in SHM than in science or medicine. For example, a patient can walk in to get himself/herself admitted into the clinic for health issues but for SHM, the technology should walk-out from laboratories to the engineering structure under investigation. In the recent past, wireless devices are playing a major role by reducing all the complications involved in the laboratories [131].  
The next important aspect is the simplification of the input source and the output probe to suit the end user requirement. Researchers have always explored metamaterials by changing input, elements and patterns to get optimum output signals to suit specific functions. Thus, it is quite common to use only limited features of expensive VNA for NDT/SHM. This paves a way to use an alternative and a limited device system such as Arduino kit, which is economical, portable, and lightweight compared to VNA. Thus, we adopted a highly economical and cost-effective Arduino Uno [132].  
Another important aspect is the minuscule details of elements, which are generally important for physics or medical groups but are not that important for SHM. This is because CMA engineers prefer procurement of a commercially available cost-effective metamaterial sensor rather than designing and fabricating them. As seen earlier in Figure 3b, the longitudinal-LSP design is our newest entry, and it is highly economical (US$ 9) and has a larger sensing area than both the radial-LSP and spiral-LSP designs [77; 102; 113]. This was earlier validated as a force sensing resistor, but its resemblance to established LSP designs prompted us to adopt these sensors in our studies.
## 3 DRL-Based Joint User Selection and Blocklength Allocation Design
### EMPPO Algorithm
```
0: Initialize policy network \(\pi_{\theta}(a|s)\) with parameters \(\theta\)
0: Initialize value function \(V_{\phi}(s)\) with parameters \(\phi\)
0: Initial policy parameters \(\theta_{old}\leftarrow\theta\)
0: Hyperparameters: clipping parameter \(\epsilon\), discount factor \(\gamma\), GAE parameter \(\lambda\)
1: Initialize experience replay buffer \(\mathcal{B}\)
2:for\(iteration\gets 1\) to \(N\)do
3: Collect a set of partial trajectories \(\mathcal{D}_{iteration}\) by running policy \(\pi_{\theta_{old}}\)
4:for each timestep \(t\) within these trajectories do
5: Compute advantage estimates \(\hat{A}_{t}\) using GAE
6: Store transition \((s_{t},a_{t},r_{t},s^{\prime})\) in buffer \(\mathcal{B}\)
7:endfor
8:for each epoch do
9: Sample mini-batch from buffer \(\mathcal{B}\)
10:for each sampled transition \((s,a,r,s^{\prime})\)do
11: Estimate probability ratio \(r_{t}(\theta)\) using the current and old policies
12: Mask actions using function \(M(s)\)
13:endfor
14: Optimize policy using the clipped objective function \(L^{CLIP}(\theta)\)
15: Update \(\theta\)
16:endfor
17: Update old policy parameters \(\theta_{old}\leftarrow\theta\)
18:endfor
```  
**Algorithm 1** EMPPO algorithm for UE selection in low-latency MEC network.
# A Computational Study on Magnetic Nanoparticles Hyperthermia of Ellipsoidal Tumors
###### Abstract  
The modelling of magnetic hyperthermia using nanoparticles of ellipsoid tumor shapes has not been studied adequately. To fill this gap, a computational study has been carried out to determine two key treatment parameters: the therapeutic temperature distribution and the extent of thermal damage. Prolate and oblate spheroidal tumors, of various aspect ratios, surrounded by a large healthy tissue region are assumed. Tissue temperatures are determined from the solution of Pennes' bio-heat transfer equation. The mortality of the tissues is determined by the Arrhenius kinetic model. The computational model is successfully verified against a closed-form solution for a perfectly spherical tumor. The therapeutic temperature and the thermal damage in the tumor center decrease as the aspect ratio increases and it is insensitive to whether tumors of the same aspect ratio are oblate or prolate spheroids. The necrotic tumor area is affected by the tumor prolateness and oblateness. Good comparison is obtained of the present model with three sets of experimental measurements taken from the literature, for animal tumors exhibiting ellipsoid-like geometry. The computational model enables the determination of the therapeutic temperature and tissue thermal damage for magnetic hyperthermia of ellipsoidal tumors. It can be easily reproduced for various treatment scenarios and may be useful for an effective treatment planning of ellipsoidal tumor geometries.  
nanoparticles; hyperthermia; ellipsoidal tumor; aspect ratio; bioheat; Arrhenius damage +
Footnote †: journal: Applied Physics Letters
# Multifractal Characterization and Modeling of Blood Pressure Signals
## 3 Results and Discussion
### Multifractal Characterization of the BPd Signal
Panels (**c**–**e**) report the mass exponent \(\tau(q)\) vs. \(q\), the generalized dimension \(D(q)\) vs. \(q\) and the Hölder exponent \(\widetilde{\alpha}(q)\) vs. \(q\) diagrams derived from the multifractal formalism. Panel (**f**) shows the multifractal spectrum for the BPd signal. The high amplitude \(\Delta_{\widetilde{\alpha}}\) of the multifractal spectrum together with the non-linear behavior of \(h(q)\) and \(\tau(q)\) demonstrate a strong multifractal signature of the analyzed signal. Shuffled and surrogate series indicate that the multifractality is due to long-range correlations. Finally, the near-optimal fitting of the modified multiplicative cascade model (A3), represented as circles in panels (**b**), shows that the BPd signal can be described concisely by only two parameters \(a\) and \(b\). In order to give a complete picture of the multifractal characteristics of the analyzed BPd time series, in Figure 5 and its respective panels, the main multifractal diagrams obtained from the MFDFA and the application of the whole multifractal analysis procedure are reported. Panel (a) reports the scaling function \(F_{q}(s)\) vs. \(s\) for the MFDFA3 parametrized with the moment values \(q\in\{-15,-10,-5,-2,2,5,10,15\}\). The BPd signal scales differently depending on \(q\) for a wide range of timescales \(s\) after \(s_{\times}=150\), indicating the presence of multifractality. Panel (b) shows how the generalized Hurst exponent \(h(q)\) is a non-linear decreasing function of \(q\) that, together with the non-linear behavior of the mass exponent \(\tau(q)\) vs. \(q\)--panel (c)--the Holder exponent \(\widetilde{\alpha}(q)\) vs. \(q\)--panel (e)--and the amplitude of the singularity spectrum \(f(\widetilde{\alpha})\)--panel (f)--demonstrate the multifractality of the BPd time series. In particular, from panel (b), by considering the diagram for the shuffled series, we can see that the shuffling procedure actually destroys all long-range correlations in the original signal (indicated as \"normal\" in the plot) with \(h_{\text{shuffle}}(2)\) being nearly linear, and \(h(2)\) close to the value for an uncorrelated signal. Therefore, it is possible to conclude that the long-range correlations do not depend on the wideness of the PDF of the signal: rather, they are an intrinsic feature of the complexity of the signal under analysis. Finally, the surrogate series, with \(h(2)\approx h_{\text{surf}}(2)\), shows that the long-range correlations in the \"monofractal zone\", described by the exponent \(h(2)\), are not destroyed, while the intrinsic non-linearity are eliminated.