esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Approach to Support Treatment Identification for Chari I Malformation
## 2 Methods
### Database
#### 2.1.2 Clinical and Radiological Data  
For each patient, electronic or paper medical records were consulted in order to establish the clinical-radiological course. Moreover, pre-surgery encephalic MRI was recorded: the sagittal section was used to measure morphological parameters, both manually and automatically. Specifically, manual segmentation was performed by two experienced neurosurgeons, who delineated precisely the contours of the cerebral and cerebellar regions on the patient's sagittal MRI and approximated the borders of the posterior cranial fossa with a pentagon (as in Figure 1). Each MRI was delineated twice and the average values of morphometrics were considered. The accuracy of the segmentation was measured in terms of percentage common pixels included in the segmented regions: it was \(96.3\pm 1.5\%\) (mean \(\pm\) std), \(90.3\pm 3.8\%\) and \(95.2\pm 1.1\%\), for brain, posterior fossa and cerebellum, respectively. The segmentation was also performed by the automated method detailed below.  
The following morphometric variables were chosen as potentially influential on the clinical-radiological outcome of the selected patients [19; 28; 30]. They were estimated either from the manual or the automated segmentation (except for the last three, measured only manually).  
* cerebellar tonsil descent (length of hernia--LenH);
* length of the tentorium (LenT);
* angle of the tentorium (AngT);
* ratio of cerebellum area to posterior cranial fossa area (C/PF);
* ratio of posterior cranial fossa area to brain area (PF/B);
* antero-posterior diameter of posterior fossa (DiaAntP);
* height of posterior cranial fossa (H_PF);
* length of the clivus (LenC);
* length of the foramen magnum (LenFM);
* distance between corpus callosum and foramen magnum (DCCFM--only measured manually);
* distance between pons and foramen magnum (DPoFM--only measured manually);
* distance between fastigium and foramen magnum (DFFM--only measured manually).
# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 3 Definitions and Improved Network Framework
### Basic Network Framework
#### 3.2.3 Feature Aggregation Module  
We obtain a series of preliminary candidate regions (Rols) through RPN, and for each RoI, RoI Align divides it into fixed-sized grid cells. We extract corresponding feature values from each grid cell on the feature map using bilinear interpolation to obtain feature representations that match the original RoI size. The best scenario is to divide the RoI region into \(7\times 7\) grids. For large-scale training samples, the introduction of interpolation operation enables RoI Align to perform more accurate feature cropping on each RoI on the feature map, greatly solving the problem of boundary pixel information loss [9]. However, in few-shot detection scenarios with only a small amount of training data, due to the extremely small number of feature points contained in the target area, using raw interpolation operations can result in fixed cell sizes that cannot accurately restore the features within the target area, leading to serious information loss issues.  
In addition, due to the nature of few-shot introducing scale variations, traditional models may lose their ability to generalize to new categories, making it difficult to fully adapt to targets of different scales. Therefore, we propose an attention mechanism-based feature aggregation module for aggregating feature information from different levels to obtain more comprehensive information and feature representations. We design three different parallel pooling operations using \(4\times 4\), \(7\times 7\), and \(10\times 10\) grids, respectively. Small grids help to obtain overall information and capture more high-level feature information. The large grid mainly focuses on low-level features to obtain detailed contextual information. Each generated feature contains different levels of semantic information [12]. By introducing attention mechanisms [11], the features generated by RoI pooling of different scales are effectively aggregated, improving the model's adaptability to features of different levels.  
As shown in Figure 5, the attention module consists of two branches: one branch is the average pooling layer, and the other branch is a fully connected layer and a Sigmoid activation function. Finally, the three features are aggregated through a multiplication operation. Assuming the input feature map is \(I\in R^{C\times H\times W}\). Here, \(C\) is the number of channels, \(H\) is the height, and \(W\) is the width. Firstly, the average pooling operation is used to take the average of the feature values of each channel, as shown in Formula (4).  
\[\
u=\  
where \(Y_{c}\) represents the feature values of the channel corresponding to the feature map after feature aggregation, \(\omega_{ci}\) is the output of the activation function, and \(l_{ci}\) represents the feature value of the \(c\)-th channel in the feature map \(I\) among the three grids.
## Materials
### Data Processing  
The remote sensed data we used in this study include the GLASS LAI product from 1997 to 2003, the \"MOD09A1\" 500-m MODIS atmospherically-corrected Level 3 8-day surface reflectance products, and the Level 3 MODIS \"MCD64A1\" monthly burned area products. The MOD09A1 and MCD64A1 data were reprojected from the MODIS sinusoidal projection into the Albers equal area projection at 1000 m resolution. In order to test the performance of the forest disturbance detection approach, Landsat TM images of 2003 were used to create high resolution burned area maps. The maps were taken as a reference to assess our disturbance detection results at 1 km spatial resolution.  
The GLASS LAI product was retrieved from time series MODIS and Advanced very-high-resolution radiometer (AVHRR) surface reflectance data using general regression neural networks (GRNNs), and the inter-comparison of GLASS LAI products and other existing operational global LAI products, including MODIS and CYCLOPES, indicate that the GLASS LAI product is the most spatially complete and temporally continuous [33].  
The mean phenological change of the LAI was computed for 5 years (1997-2001) on a pixel-by-pixel basis. The mean LAI reflects the undisturbed plant growth phenology and provides a background to assess a departure from the LAI variability. The vegetation in the northeast forest area was dominated by deciduous forests; therefore, LAI at the beginning and end of the year were at a very low level. In order to improve the simulation of the LAI model accuracy by excluding non-growth period data, the input parameters were taken from the 96 th day to the 306 th day of each year.  
The study focuses on forest land. We used land classification data to mask non-forest areas. Land use classification is the result of supervised classification using Landsat data to derive seven categories: forestland, artificial cover, water, shrubland, wetland, cropland, grassland, as shown in Figure 1A. We resampled the spatial resolution of the classifications from 30 m to 1000 m using a mode resampling method, and the land use was divided into two categories: forest land area and non-forest land. The classifications data was reprojected from the UTM projection into the Albers equal area projection. Land use classification results are shown in Figure 1B. The remote sensing data used in the present paper are in Table 1.
## 4 Discussion
### Phenological Development and Maturity Progress  
Projections of [PERSON] et al. [1] forecast an earlier bud break (\(-11\) days), begin of flowering (\(-11\) days) and veraison (\(-3\) days) for the 2050s at Geisenheim, Germany compared to the 1990s. Our long-term phenological data (not shown) for cv. Riesling trained in VSP indicates an earlier bud break (\(-6\) days; DOY 111), full bloom (\(-7\) days; DOY 163) and veraison (\(-8\) days; DOY 223) on average over the last 20 years (2001-2020) compared to the period 1991 to 2000. In the present study, full bloom occurred 4 days (DOY 159) earlier in VSP compared to the average between 2001 and 2020, which confirmed the projections in [PERSON] et al. [1]. In contrast, SMPH treatments delayed full bloom by 1 to 2 days, showing a slight trend for advanced phenological development with mechanical thinning treatments (i.e., SMPH ST and SMPH BT).  
The delay in maturity progress was more evident. For all SMPH treatments a delay in ripening was observed compared to VSP, which is in accordance with other cool-climate studies on SMPH [22]. On average, we observed a delay in attaining specific TSS levels (14.2 \({}^{\circ}\)Brix and 18.1 \({}^{\circ}\)Brix) between seven days to 16 days in SMPH, compared to VSP. Similarly, [PERSON] et al. [40] delayed a target TSS of 22 \({}^{\circ}\)Brix by 17 days with minimal pruning. The determined delay in ripening with SMPH might compensate (at least partly) the observed and projected earlier maturation period under warmer condition [4], which was caused by climate change. Negative temperature-based impacts on fruit composition, i.e., low acidity, high alcohol [2; 41] and detrimental TDN (1,1,6-trimethyl1,2-dihydronaphtalene) concentrations [32; 33; 42; 43], highlight the importance in shifting veraison and ripening to a period with moderate temperatures to preserve wine typicity. While TSS do not directly represent wine typicity, the present results demonstrate the suitability of SMPH as one potential part of a climate change adaptation strategy. Analyses on sensorial wine profiles of wines vinified, based on the present trials, are ongoing and will be published in an additional paper.  
The harvest was conducted 10 days (2017), 20 days (2018) and 22 days (2019) later in SMPH, compared to VSP with the goal to yield similar juice TSS. As discussed earlier,the delayed sugar accumulation seems to be related to the elevated yield level, increased biomass production and lower LFR in SMPH training system compared to VSP. The fact that all thinning treatments effectively accelerated ripening within SMPH strengthens this hypothesis. Although the response of SMPH sugar accumulation to yield level was nearly linear in 2017 and 2018 and based on the manipulation of LFR, the ratio of LFR to Brix was lower in VSP in the dry season of 2018 (Figure 5A), indicating a more efficient carbohydrate assimilation.
# An Effective Beam Width Model of Slab in Thick Wall-Thick Slab Joints Subjected to Lateral Forces
## 6 Suggestions for Practical Design and Limitations  
Based on the results obtained from this work, the following design recommendations on the wall-slab joints in TWTS structures can be drawn.  
Firstly, a design procedure for the application of the effective beam width prediction model has been suggested.  
Step 1: Determine the correction coefficient \(\rho\) using Equation (32).  
Step 2: Estimate the unknown value of lateral force \(Q\) carried by the target wall-slab joint at the top of the bearing wall using the correction coefficient \(\rho\) and Equation (33).  
Step 3: Apply \(Q\) into Equation (18) to obtain the prediction value of the effective beam width \(b_{e}\).  
Step 4: By using the effective beam width \(b_{e}\), the initial stiffness of the wall-slab joint can thus be predicted.  
Figure 12: Effective beam width prediction in the current RC Standard.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & **Code Formula Calculations (mm)** & **Proposed Model Calculations (mm)** \\ \hline \(1\) & 475.00 & 563.86 \\ \(2\) & 475.00 & 558.61 \\ \(3\) & 325.00 & 446.37 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the effective beam width.  
However, because of the assumptions and estimations for developing this model, the following limitations are needed to be noticed for practical design:  
* Only if this assumption that the bearing wall and the wall-slab connection portion being considered as rigid bodies is reasonably true, the proposed prediction model shown in Equation (18) is applicable.
* Using the proposed model, the effective beam width for the initial rotational stiffness can be accurately predicted if the applied lateral force at the 0.05% inter-story drift ratio is known. Nonetheless, it is difficult to obtain the accurate value of the lateral force at the elastic stage. To solve this problem, a correction coefficient employing the experimental results from the literature is used and gives a reasonable estimation for the practical design. However, as a new type of structural system, limited experimental tests and not sufficient studies have been conducted, which makes the lateral force estimation considered only applicable for wall-slab joints similar to the specimens from the experimental test referred to in this work. Thus, further research is needed to investigate the effective beam width for the initial stiffness in a larger scope.
# Influence of Cutter Head on Cavitation of Non-Jammed Submerged Grinder Pump
## 2 Numerical Calculation Method
### Meshing  
The model studied in this paper was meshed by ICEM-CFD. If an unstructured grid is used, the pseudo-diffusion of the calculation will be serious and the convergence will be difficult, due to the relatively long and narrow runner of the static cutter disk. Moreover, the pressure gradient at the cavitation site was large, the mesh needed to be locally refined, and a higher quality of mesh was required. The structural mesh can effectively control the quality and quantity of the mesh, which greatly reduces the level of calculation needed. Therefore, a hexahedral-structured mesh was chosen to mesh the model.  
Figure 5: Non-jammed submersible grinder pump.  
In order to determine whether the grid number and grid quality met the actual calculation requirements, the grid independence of the model was studied. As shown in Figure 7, when the number of grids was more than 700,000, the variation of the pump's rated operating point lift was less than 5%. In order to ensure both the accuracy and economy of the calculation, this is appropriate when the number of grids is close to 1,000,000.  
In order to reduce the pseudo-diffusion caused by the grid during the calculation process and to make the cavitation simulation more accurate, the single-phase flow simulation was firstly carried out, and the internal flow pattern was observed through post-processing--slightly fine-tuning for meshes whose trends do not match the direction of flow and recalculating the results until they were consistent in general. Therefore, at different operating points, there were always meshes that matched the flow conditions for each operating condition. The boundary layer Yplus of the hexahedral-structured meshes was always less than 100. Since cavitation is prone to occur near the cutter head, the meshes near the cutter head were refined, and the Yplus of the boundary layer of the cutter head was less than 30.  
Figure 8 shows an example of the grid of each component of the water body of a non-jammed submersible grinder pump with two cutter heads. There are 236,872 grid cells in the water body of the moving cutter head, 319,456 grid cells in the static cutter head and rear runner, 189,544 grid cells in the impeller water body, 166,126 grid cells in the volute and 166,126 grid cells in the inlet water body. A total of 80,856 grid cells were in the inlet water body. The total number of grid cells was 992,854.  
Figure 6: Water body assembly and test cutter. (**a**) Water body assembly, (**b**) Test cutter disk.  
Figure 7: Grid-independent test curve.
# Research on Real-Time Model of Turboshaft Engine with Surge Process
## 2 Turboshaft Engine Component Level Model
In transient calculation, the power of each rotating part has a difference, which makes the engine in a dynamic adjustment state where the rotor is in a power unbalanced state, but the flow of each section is considered continuous, and the pressure of the corresponding section is considered equal, that is, the dynamic point calculation should meet the quasi-balance condition of continuous flow and pressure balance. Therefore, in the dynamic calculation, the rotor dynamics equation shown in Equation (7) is used to replace the power balance equation. After the dynamic point is solved, the rotor dynamics is used to update the rotor speed in each simulation cycle. \[\left\{ \begin{array}{l}\frac{dN_{g}}{dt}=\left(P_{GT}-P_{C}/\eta_{mg}-P_{ext }\right)/\left[\left(\frac{\pi}{30}\right)^{2}N_{g}J_{GT}\right]\\ \frac{dN_{g}}{dt}=\left(P_{PT}-P_{L}/\eta_{mL}\right)/\left[\left(\frac{\pi}{30 }\right)^{2}N_{p}J_{PT}\right]\end{array} \right. \tag{7}\]  
where \(N_{g}\) is the gas turbine rotor speed, \(N_{p}\) is the power turbine rotor speed, \(J_{GT}\) and \(J_{PT}\) are moments of inertia of gas turbine shaft and power turbine shaft, \(t\) is time.
## 3 Result and Discussion
### Morphology of MRE  
Isotropic and anisotropic MRE cross-section morphologies from the FESEM are shown in Figure 4. The cobalt particle had been observed to be embedded well and dispersed in the silicone rubber matrix. In Figure 4a, the particles show random distribution. This is because, during the preparation of MRE, there is no magnet existence that can cause the interaction between particles. In Figure 4b, the particles form a column structure. The magnetic field has been used during preparation, so the particles are magnetized and attracted to one another. The particles start to vibrate and shape the column structures after the magnetic force defeats the matrix resistance. Whereas, in the isotropic orientation they are randomly distributed because the particles are not affected by the direction of the magnetic field. The discussion of the impacts of structuring during preparation on the morphology will be followed in connection with the MR effect measured under a magnetic field.  
Table 4 tabulated energy-dispersive X-ray spectroscopy (EDX) results obtained from the field emission scanning electron microscopy (FESEM) images. Based on the FESEM micrograph, several points were chosen to determine the composition of the elements in the MRE. Silicon exhibits the highest weight (41.66%) followed by cobalt and oxygen (31.37% and 26.97%). The element is present due to the use of silicone rubber as a matrix and cobalt as a magnetic particle in MRE fabrication.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Element** & **Weight (\%)** \\ \hline Si & 41.66 \\ Co & 31.37 \\ O & 26.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Elements percentage in the MRE.  
Figure 4: Field emission scanning electron microscopy (FESEM) images of (**a**) isotropic MRE and (**b**) anisotropic MRE with 53 wt % of cobalt.  
Figure 3: Magnetic hysteresis loop of the Co-MRE.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 4 Experimental Results and Analysis
### Speed Analysis  
In this section, we evaluate the inference speed of our model on an NVIDIA RTX 4090 GPU (24 GB GDDR6\(\lambda\)) and 16-core AMD EPYC 7502 CPU using three metrics: runtime per image in milliseconds, frames per second (FPS), and model complexity measured by million multiply-accumulate operations (MACC). The runtime denotes the average processing time over images from test set, and the FPS is calculated as the number of images inferred per second. The MACC is the total number of multiply-accumulate operations (in millions) required by the model, reflecting its computational cost. Table 6 compares these metrics for various combinations of the four modules: the baseline Swin Transformer alone and variants that incorporate the local adjustment module, the semantic attention module, and the hierarchical bilinear pooling (HBP) module, either individually or jointly.  
As shown in Table 6, the computational demands vary noticeably depending on which modules are included. The baseline Swin Transformer model achieves a runtime of 1.69 ms (\(\approx\)591 FPS) with 47.82 M MACC. Adding the local adjustment module increases the runtime to 1.97 ms (507 FPS) and MACC to 48.16 M, indicating a modest overhead. Similarly, incorporating the semantic attention module yields a runtime of 1.91 ms (523 FPS)  
Figure 4: MAE and CS(3) on Morph II using (**a**) different \(\lambda_{1}\) and fixed \(\lambda_{2}\) = 4, and (**b**) different \(\lambda_{2}\) and fixed \(\lambda_{1}\) = 4.  
and 47.99 M MACC. When both the local adjustment and semantic attention modules are combined, the runtime further rises to 2.14 ms (467 FPS) with 48.33 M MACC. In contrast, after incorporating the HBP module, the full model incurs the largest additional cost. Specially, the full model requires 3.52 ms per image (284 FPS) and 50.51 M MACC, roughly doubling the base inference time. Nonetheless, this full configuration achieves the best accuracy for the MAE on Morph II improving to 2.18 from 2.33 for the base model. These results demonstrate that while our full model incurs higher computational cost due to the integration of all modules, it achieves a significant breakthrough in accuracy. The performance gain justifies the added complexity, making the overall computation-effectiveness trade-off highly acceptable.
# Extraction and Synthesis of Silicon Nanoparticles (SiNPs) from Sugarcane Bagsase Ash: A Mini-Review
## 2 Extraction of Silica
### Ball Milling Process  
Ball milling is a popular method because of its simplicity and requiring relatively inexpensive equipment to operate and can be deployed on a wide range of other materials. Nano-crystalline materials can be successfully produced using the aforementioned process. [PERSON] et al., 2000, synthesized ultra-fine SiNPs with 5 nm using solid graphite (C) in concurrent with silicon dioxide \(SiO_{2}\) in a planetary ball miller shown in Figure 4 for about 7-10 days on a larger scale. The mixture was loaded on a stainless-steel vial together with stainless-steel balls (7.5 mm diameter), and the molar ratio employed was 1:1 graphite-\(SiO_{2}\) as shown in the reaction mechanism below [31].  
\[SiO_{2}+C\;(graphite)\to Si+\uparrow(CO_{2})_{x} \tag{14}\]  
The content was then sealed with a Viton O-ring. However, the vial was first purged with argon to remove other gases, with the remaining argon set to atmospheric pressure. After a period of 7-10 days, respectively, the powders (milled) were then annealed in a hydraulic press for 48 h at 150 \({}^{\circ}\)C under a loading of 30 kgf/mm\({}^{2}\). In addition, the annealed powders were then put back into a vial and further milled for another 2 more hours to break up the pressed tablet into powder form [31].  
The resulted NPs were reported to have been covered with an amorphous \(SiO_{2}\) layer of 1 nm in thickness and produced a multi-peak PL spectrum as a result of a wide range of particle sizes present.  
Triumphs and Challenges  
The physical methods usually are capable of achieving size controllability of the as-synthesized SiNPs. The major drawback is that the productivity is not scalable and the chemistry of Si QDs to be stabilized is not well established [27]. Furthermore, the presence of aggregates presents a complex and coarse shape apart from the size distribution. Thus, it limits the utilization of ball milling synthetic route in spite of its convenience and inexpensiveness [31].  
Figure 4: Planetary ball mill for the preparation of SiNPs.