esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Building
### Design Variables  
The corresponding parameters in the aerodynamic force as the main source of the blade load will be selected as the design variables. The aerodynamic force acting on a blade section under the wind speed \(V_{1}\) is shown as Figure 1. The normal force \(F_{n}\) and the tangential force \(F_{t}\) could be expressed as follows:  
\[\left\{\begin{array}{l}F_{n}=\frac{1}{2}\rho V_{0}{}^{2}cC_{n}\\ F_{t}=\frac{1}{2}\rho V_{0}{}^{2}cC_{t}\end{array}\right. \tag{1}\]  
where \(\rho\) is the air density, \(c\) is chord length, \(C_{n}\), \(C_{t}\) are respectively the normal coefficient and tangential coefficient as:  
\[\left\{\begin{array}{l}C_{n}=C_{l}\cos\phi+C_{d}\sin\phi\\ C_{t}=C_{l}\sin\phi-C_{d}\cos\phi\end{array}\right. \tag{2}\]  
where \(C_{l}\) is the lift coefficient, \(C_{d}\) is the drag coefficient, \(\phi\) is the inflow angle, from which the attack angle \(\alpha\) will be get by subtracting the twist angle \(\beta_{1}\) and pitch angle \(\beta_{2}\), i.e.,  
\[\alpha=\phi-\theta=\phi-(\beta_{1}+\beta_{2}) \tag{3}\]  
\[\phi=\arctan\frac{(1-a)V_{1}}{(1+b)\Omega r} \tag{4}\]  
The inflow velocity \(V_{0}\) in Equation (1) is  
\[V_{0}=\sqrt{{V_{x}}_{0}{}^{2}+{V_{y}}_{0}{}^{2}}=\sqrt{(1-a)^{2}{V_{1}}^{2}+ (1+b)^{2}(\Omega r)^{2}} \tag{5}\]  
where \(a\) is the axial induction factor, \(b\) is the tangential induction factor, \(\Omega\) is angular speed of the rotor shaft, that is rotor speed in this article.  
Figure 1: Aerodynamic forces acting on a blade section.  
It can be seen that the load is influenced by lots of variables from Equation (1), such as the wind speed, the chord length, and the pitch angle. Moreover, the wind turbine parameters such as yaw angle \(\gamma\), azimuth angle \(\psi\) also affect the angle between the wind direction and chord line, so they are all taken into account to obtain the extreme loads. However, some of them are prefixed. Therefore, the rotor speed, wind speed, pitch angle, yaw angle, and azimuth angle will be selected as design variables.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Enhanced ResNet Architecture
#### 3.2.3 Regression Head  
The final stage of the Enhanced ResNet is the regression head, which transforms the high-dimensional attention-enhanced feature maps into a scalar estimate of forest carbon stock for each input patch. This head is designed to be both efficient and expressive, ensuring that spatially and spectrally processed features are effectively aggregated for prediction. Specifically, the feature map \(\mathbf{F}_{\text{att}}\in\mathbb{R}^{C\times H\times W}\) is first passed through a global average pooling (GAP) layer to generate a compact vector representation. This operation collapses the spatial dimensions by computing the average of each channel, resulting in a feature vector \(\mathbf{f}\in\mathbb{R}^{C}\):  
\[\mathbf{f}_{c}=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}\mathbf{F}_{ \text{att}}^{(c)}(i,j),\quad\text{for }c=1,\dots,C \tag{6}\]  
This step ensures that the model aggregates spatial context from the entire input region while preserving the learned importance of each channel from the attention mechanism. The resulting vector \(\mathbf{f}\) serves as a compact global descriptor of the input patch, summarizing both structural and spectral information. Next, the descriptor is passed to a fully connected (FC) layer for final prediction. This linear transformation maps the feature vector to a scalar output \(\hat{y}\), representing the estimated carbon stock (in Mg/ha):  
\[\hat{y}=\mathbf{w}^{T}\cdot\mathbf{f}+b \tag{7}\]  
where \(\mathbf{w}\in\mathbb{R}^{C}\) and \(b\in\mathbb{R}\) are the learnable weights and bias of the regression layer. This minimalist yet effective regression design allows the model to remain lightweight while capturing rich information for precise estimation. The use of GAP also helps mitigate overfitting by reducing the number of parameters and enforcing global spatial aggregation, which is particularly useful when dealing with limited field inventory data.  
Overall, the regression head completes the end-to-end mapping from raw multispectral image patches to carbon stock values, enabling pixel- or patch-level prediction suitable for large-scale forest monitoring and carbon accounting applications.
## 2 Materials and Methods
### Mathematical Modeling
Concerning the anodic exchange current density, it is described as follows:  
\[\text{i}_{0,\text{Fe}}=\text{z}_{\text{Fe}^{2+}}\cdot\text{F}\cdot\left[\text{ H}^{+}\right]^{\text{m}_{\text{Fe}}}\cdot\text{k}_{\text{Fe}}^{\text{c}} \cdot\text{e}^{-\frac{\Delta\text{H}_{\text{Fe}}}{\text{K}}(\frac{1}{1}-\frac{ 1}{\tau_{0}})}, \tag{6}\]  
where \(\text{z}_{\text{Fe}}\) is the valence of the iron dissolution reaction, \(\text{m}_{\text{Fe}}\) is the anodic reaction order with respect to pH, \(\text{k}_{\text{Fe}}^{\text{c}}\) is the anodic reaction rate constant at standard temperature, and \(\Delta\text{H}_{\text{Fe}}\) is the enthalpy of the iron dissolution reaction. Finally, the anodic Tafel slope is described as follows:  
\[\text{b}_{\text{Fe}}=\frac{\text{RT}\cdot\text{ln}\,10}{(2-\alpha_{\text{Fe}} )\text{F}}, \tag{7}\]  
where \(\alpha_{\text{Fe}}\) is the anodic charge transfer coefficient. All the coefficients employed for the description of the kinetic parameters in the model were obtained from the results presented in [59; 60] and are reported in Table 2.  
Figure 2: Evans diagram of anodic and cathodic reactions considered in Tafel-Piontelli model. Considering the theoretical formulation of the different parameters affecting the corrosion rate, the final equation of the Tafel-Piontelli model is reported as follows:  
\[ \begin{split}\text{CR}(\text{T},\text{pH})=\frac{\text{M}_{\text{Fe} }}{\text{z}_{\text{Fe}^{2+}}\cdot\text{F}\text{P}\text{P}\text{P}\text{e}_{ \text{Fe}}}\cdot\left[\text{z}_{\text{H}^{+}}\cdot\text{F}\cdot\left[\text{H}^ {+}\right]^{\text{m}\text{H}_{2}}\cdot\text{k}_{\text{H}_{2}}^{\circ}\cdot \text{e}^{-\frac{\text{A}\text{H}_{\text{H}_{2}}}{\text{R}}(\frac{1}{1}-\frac{1 }{Q_{0}})}\right]^{\frac{2-\alpha_{\text{Fe}}}{2-\alpha_{\text{Fe}}+\alpha_{ \text{H}_{2}}}}\\ \cdot\text{F}_{\text{Fe}^{2+}}\cdot\text{F}\cdot\left[\text{H}^{+} \right]^{\text{m}\text{Fe}}\cdot\text{k}_{\text{Fe}}^{\circ}\cdot\text{e}^{- \frac{\text{A}\text{H}_{\text{Fe}}}{\text{R}}(\frac{1}{1}-\frac{1}{Q_{0}})^{ \frac{\text{A}\text{H}_{2}}{2-\alpha_{\text{Fe}}+\alpha_{\text{H}_{2}}}}}\\ \cdot\text{10}^{\frac{\text{a}\text{H}_{2}}{2-\alpha_{\text{Fe}}+ \alpha_{\text{H}_{2}}}}(\frac{\text{F}_{\text{B}}\text{F}\text{F}\text{F}\text{ F}\text{G}\text{e}}_{\text{R}}+\frac{\text{T}\text{log}\left[\text{P}^{2+}\left[ \text{pH}\text{T}\right]+\frac{1}{\text{T}}\text{P}\text{H}\text{T}\text{P} \text{e}^{2+}\right]}{\text{F}_{\text{H}^{+}}+\text{pH}}),\end{split}  \tag{8}\]  
where the activity of the iron ions \(\left[\text{Fe}^{2+}\right]\) is defined with a mechanistic framework as reported in Section 2.3. The Tafel-Piontelli model has been described in this work for the most commonly used metal in industrial scenarios: iron. Nonetheless, its theoretical nature allows us to extend its principles to any metal with active behavior by adapting its material-dependent parameters.
## 2 Materials and Methods
### Mechanical Characterization  
Plasma treatment is the most common technique to modify the surface chemistry of different biomaterials, since it does not alter their bulk properties [14]. However, this golden standard depends on the operating conditions that are used. Two mechanisms have been reported about the chemical reactions during the plasma treatment of polylactic acid: an oxidative one and a destructive one [32]. The interaction of oxygen and free radicals could lead to the formation of hydroxyl and peroxide groups,while the mechanism of destruction of plasma-treated PLA generates free radicals, accompanied with the formation of volatile gases. If the treatment is too aggressive, the chemical reaction may take place preferentially through the destruction mechanism rather than the oxidation one and, as a consequence, the mechanical properties of the structure could be affected. Therefore, it is recommended to confirm the lack of modification on the mechanical properties of the structure after plasma treatment. For this reason, non-treated and plasma-treated samples were subjected to mechanical characterization in the compression mode. In addition, aloe vera coated scaffolds after plasma treatment were tested to assess the effect of the different coatings (pH 3, 4 and 5) on the mechanical properties of the structures. Four replicas per group were used. Scaffolds with a diameter of 9.8 mm and a height of 5 mm were tested in a LIYI (LI-1065, Dongguan Liyi Environmental Technology Co., Ltd., China) testing machine in displacement control mode. Crosshead speed was set at 1 mm\(\cdot\)min\({}^{-1}\). The compressive modulus and the offset compressive yield strength (2% deviation from linearity) were calculated according to ASTM D695-15.
###### Abstract  
Biogas production through anaerobic digestion has proven to be one of the most important pillars of the transition into the circular economy concept, a sustainable approach for biorefinery. This work aims to extend and improve knowledge in the anaerobic co-digestion of complementary substrates, given insights into wastes biodegradability and the influence of manure composition on the anaerobic process stability. Anaerobic co-digestion of sugar beet by-products with two kinds of animal manure (pig and cow) was investigated in semi-continuous assays, analyzing both common and non-classical parameters. Co-digestion with manure clearly mitigated the inhibitory effect of volatile fatty acids at high organic loading rates, leading to increases in methane production by 70% and 31% in comparison with individual digestion of sugar beet by-products, for co-digestion with pig and cow manure, respectively. Non-classical parameters could give more insight into the coupling/uncoupling of the anaerobic digestion phases and the involved microorganisms. Indirect parameters indicated that the process failure at the critical organic loading rates was mainly due to methanogenesis inhibition in the co-digestion with pig manure, while in co-digestion with cow manure or in individual digestion of sugar beet by-products, both hydrolysis-acidogenesis and methanogenesis phases were affected. Biomethanation degree refers to the maximum methane potential of organic wastes. Sugar beet by-products required a long digestion-time to reach high biodegradability. However, short digestion-times for co-digestion assays led to a high biomethanation degree.  
an aerobic co-digestion; sugar beet by-products; manure; semi-continuous feeding mode; methane improvement; non-classical parameters +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Fecal SCFAs Measurement  
Concentrations of SCFAs, including the acetic, propionic, isobutyric, butyric, isovaleric and valeric in fecal samples were measured as described previously [30]. Fecal samples (50 mg) were diluted 1:9 (\(v/v\)) with methanol (\(\geq\)99.0% purity, Thermo Fisher Scientific) and homogenized on a vortex mixer. After centrifugation at 20,000 rpm at 4 \({}^{\circ}\)C for 15 min, the supernatant was mixed with 20 \(\mu\)L internal standard, including acetic acid-d4 (\(\geq\)99.0% purity, CATO, Guangzhou, China), propionic acid-d5, butyric acid-d7, isovaleric acid-d9 and valeric acid-d9 (\(\geq\)99.0% purity, MACKLIN, Shanghai, China), and the final volume equaled 100 \(\mu\)L with the methanol. Supernatant (50 \(\mu\)L) from the final volume was mixed with 3% (\(v/v\)) pyridine solution (50 \(\mu\)L, diluted with pure methanol, MACKLIN), 2-NPH:HCl (50 \(\mu\)L, 20 mmol/L, diluted with prenat, Aladdin, Shanghai, China) [31]. After being mixed by a vortex mixer and water bath at 60 \({}^{\circ}\)C for 20 min, the mixtures were mixed with 15% (\(w/v\)) KOH (Aladdin) solution, and the water bath was continued for 20 min. When restored to room temperature, the mixtures were mixed with 1 mL of 0.5 mol/L phosphoric acid aqueous solution (Aladdin) and 1 mL of ether (CATO) and fully shaken for 3 min. After centrifugation at 3000 r/min for 10 min, the organic layer was taken and nitrogen-blown for initial mobility re-solubilization. The mixtures were transferred into autosampler vials and then analyzed on a liquid chromatography-triple quadrupole (TSQ AltisTM) tandem mass spectrometric (LC-TQ-MS) (Thermo Scientific) with an ACQUITY UPLC(tm) BEH C18 column (2.1 \(\times\) 100 mm, 1.7 \(\mu\)m, Waters, Milford, MA, USA). Concentrations of SCFAs were quantified using the Thermo XcaliburTM 4.0 software.
## 4 Case Analysis of Concepcion Earthquake  
### Space Environment Analysis  
The active solar and geomagnetic state will cause a great disturbance to the ionosphere [36; 37]. The variations of these two factors should be analyzed before analyzing ionosphere disturbance. Figure 4 shows the changes in the Dst, Auroral electrojet (AE), and Kp indices 15 days before the Concepcion earthquake. It can be seen that the geomagnetic activity is relatively active 12 days before the earthquake (February 15) and 11 days before the earthquake (February 16), and the Dst index drops below \(-30\) nT. Simultaneously, the Kp index was 4, with the AE index (expansion phase) dramatically expanding to 720 nT. In these two periods, 12 h on February 15 and 6 h on February 16 reach the level of medium and small geomagnetic storms.  
Figure 4: Geomagnetic environment. Daily variations of the geomagnetic Dst index (above), the auroral electrojet (AE), and the Kp indices (below).  
Figure 3: Comparison of Nmf2 extracted from global Nmf2 maps with the TECs extracted from global ionosphere maps for 5183 (71 \(\times\) 73) cells, with the limitation of middle-low latitudes (top sub-panel) and high-latitudes (bottom sub-panel). The corresponding correlation coefficients are indicated.  
According to the solar flare data, the M flares occurred 21-19 and 15 days before the Concepcion earthquake (February 6-8 and February 12) but without the proton event. Besides these periods, the solar activity before the shocks were relatively quiet (F10.7 < 100 SFU), with weak force fluctuations on the ionosphere.
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 5 Conclusions  
In this study, we focused on downscaling TMPA and IMERG data to generate precipitation estimates with a finer spatial resolution (\(\sim\)1 km) at annual scale, and a disaggregation algorithm was applied to obtain the monthly downscaled results based on those at annual scale. Both of the original satellite-based precipitation products (TMPA and IMERG) and the downscaled results were evaluated against ground observations. The main conclusions from this study were as follows:  
Figure 14: The spatial distribution of radar stations over the TP.  
1 The IMERG data provided more accurate precipitation estimates than TMPA data at both the annual and monthly scales. What's more, compared with TMPA data, IMERG data did not have systematic anomalies any more.
2 The RF algorithm demonstrated potentials in downscaling TMPA and IMERG to obtain precipitation estimates with improved spatial resolution and reasonable accuracy over TP.
3 The downscaled results based on IMERG exhibited better performance than those based on TMPA data at both the annual and monthly scales.  
Precipitation data with both fine spatial resolution and accuracy is highly needed in related fields. While precipitation data only with finer resolutions but low accuracy is, to some extent, useless, and vice versa. IMERG data indicated great potentials for researchers to obtain precipitation estimates with finer spatial resolutions and better accuracy worldwide. Additionally, after this study, we also found that the RF algorithm could serve as an alternative approach to improve both the spatial resolutions and accuracy of the coarse satellite-based products.  
Conceptualization, Z.M., K.H. and Y.H.; methodology, Z.M., K.H. and J.X.; software, J.X., X.T. and W.F.; validation, J.X., X.T., W.F., Y.H.; formal analysis, Z.M., K.H, J.X., X.T. and W.F.; investigation, Z.M., K.H, J.X., [PERSON], [PERSON] and [PERSON]; resources, [PERSON], [PERSON], [PERSON] and [PERSON]; data curation, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and Y.H.; visualization, [PERSON], [PERSON], [PERSON], [PERSON] and Y.H.; supervision, [PERSON], [PERSON]; project administration, [PERSON], [PERSON]; funding acquisition, [PERSON], [PERSON].  
This study was financially supported by the NSFC (Grant Nos. 91437214, 71461010701 and 41471430) and MOST Key-Project (Grant No. 2013 CB036406) and China Postdoctoral Science Foundation (No. 2018M630037).  
The authors would like to appreciate the NASA Precipitation Measurement Missions (PMM), the Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC), the Land Process (LP) Distributed Active Archive Center (DAAC) and the CGIAR Consortium for Spatial Information (CSI) for making the datasets available and the Third Pole Environment Database (TPE) for providing us with the gauge data for our areas of interests.  
The authors declare no conflict of interest.
## 3 Interference Suppression through Beamforming Techniques  
Beamforming techniques can be useful to spatially filter interference sources, improving the performance of a PR system. In bistatic scenarios such as the one described in Section 2, strong interference sources can be distributed out of the area of interest, and can generate radar returns stronger than those produced by desired targets. Their characteristics can vary along time, but with effects that persist during system operation. This situation is the result of using non-cooperative LoOs, which are not designed for the surveillance application of interest. Because of this, the generation of deterministic nulls along bearings at the back of the surveillance antenna array and the possibility of adapting these nulls to variations in interference properties are studied [27,28].
## 4 Engineering Case Application  
### Project Overview  
The tunnel is located in Yunnan, China, as shown in Figure 9. The left side starts and ends at pile numbers K281 + 506 and K289 + 090, respectively, and is 7560 m long; the right side starts and ends at pile numbers K281 + 530 and K289 + 103, respectively, is 7597 m long, and passes through 11 fault fracture zones. The geological setting of the tunnel-site area is shown in Figure 10, with various adverse geological bodies being widely distributed along the tunnel route.  
Due to the inaccurate classification of surrounding rock in the early stage of construction, the construction excavation disturbed the weak surrounding rock, causing large deformation of the vault, followed by landslide and water and mud inflow, which seriously affected the construction (Figure 11).  
Figure 11: The tunnel collapsed.  
Figure 9: Location of the Tunnel.  
To reduce the occurrence of geological hazards, detect the distributions of adverse geological bodies, predict the classes of surrounding rocks in front of the tunnel face, and ensure the safety of tunnel construction, the KOKEN RPD-180 CBR multifunctional drilling rig was used to conduct advance drilling and record MWD data in depth-sequence for the section from YK282 + 084.5 to YK282 + 501.0 on the right.  
The advance drilling started on 16 June 2018 and ended on 11 December 2018, with a total of 10 advanced boreholes being used for forecasting the 416.5-m rock mass, while recording a total of 27,607 sets of data as well as the corresponding surrounding rock classes. According to the 'Specifications for Design of Highway Tunnels Section 1 Civil Engineering, China' [34], China, the surrounding rock classes were III, IV, and V. The drilling dataset was constructed according to the method described in this study, and the BILTSM deep neural network model was used to train the drilling dataset and predict the surrounding rock classes.
# Predicting Location of Tweets Using Machine Learning Approaches
## 5 Experiments and Results  
### Relevance of Geo-Tagging Features  
The use of geo-tagging features for predicting the location of tweets is a challenging task that requires careful consideration of the features and models used. In our study, we explored the effectiveness of using both the home location found in users' profiles and named entities (Table 6 shows some of the names extracted from the tweets) extracted from the tweets as features for location prediction.  
Our results showed that the home location was a much more informative feature than the named entities, as its inclusion led to an increase in the prediction accuracy of around 25%. However, we also found that textual features, such as TF-IDF, played an important role in predicting the location, which suggests that a combination of textual and geo-tagging features may be the most effective approach.
# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 1 Introduction
### Related Works
This method transforms collision avoidance constraints--whose computational scale vary with environmental complexity--into predefined rectangular DC constraints that restrict the vehicle's motion to specific regions during designated time intervals. By decoupling obstacle avoidance constraints from environmental complexity, the DC method reduces nonlinearity in the problem formulation, thereby facilitating subsequent numerical solver implementations. However, the DC construction process itself presents computational efficiency limitations. The DC construction strategy adopted in [27] requires iterative region expansion to progressively determine feasible areas, where each expansion necessitates obstacle collision detection to ensure corridor safety--a process that incurs significant computational overhead.
# A Virtual Reality Soldier Simulator with Body Area Networks for Team Training
## 2 System Description  
The proposed system, which is deployed in five-square-meter area (Figure 2), is a robust training solution that can support up to six soldiers. The virtual reality environment is implemented in C++  
Figure 1: System training effectiveness process.  
Figure 2: System architecture: single-soldier layout (**a**); multi-soldier network (**b**).  
and DirectX 11. In addition to the virtual system, each individual soldier stands in the center of an ODT. The ODTs are customized hardware designed for the soldier training simulator, which is equipped with a high-performance workstation that generates the visual graphics for the HMD and provides voice communication functions based on VoIP technology. The dimensions of the ODT are 47\" x47\" x78\", 187 lbs. The computing workstation on the ODT is equipped with a 3.1 GHz Intel Core i7 processor, 8 GB RAM, and an NVidia GTX 980M graphics card. The HMD is an Oculus Rift DK2, which has two displays with 960 \(\times\) 1080 resolution per eye. The VoIP software is implemented based on Asterisk open-source PBX. Soldiers are not only outfitted with HMDs, but also equipped with multiple inertial sensors, which consist of wearable sensor nodes (Figure 3) deployed over the full body (Figure 4). As depicted in Figure 3, a sensor node consists of an ARM-based microcontroller and nine axial inertial measurement units (IMUs), which are equipped with ARM Cotex-M4 microprocessors and ST-MEMS chips. Note that these tiny wireless sensor nodes can work as a group to form a wireless BAN, which uses an ultra-low power radio frequency band of 2.4G.