esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Data Description
#### 4.2.3 Train Prediction Model  
After obtaining the set \(Opt_{DI}\), the prediction model is trained. In the prediction stage, the feature vectors of the test data set are input into the trained TCNQR network, and the predicted RUL values will be output. The prediction results will be evaluated based on the prediction accuracy measures in Section 3.6.  
In the TCNQR network, the number of residual blocks and the size of the convolution kernel will affect the computation amount and computation speed of the neural network, and the appropriate dropout rate can effectively solve the problem of neural network  
Figure 11: Features in the final optimal feature set \(Opt_{DI}\). (**a**) Features in the final optimal feature set \(Opt_{DI}\) under working condition B, (**b**) Features in the final optimal feature set \(Opt_{DI}\) under working condition C.  
over fitting, the network is built by using Keras deep learning framework, the activation function is the default Relu function. Using Adam optimization algorithm, we design independent adaptive learning rates for different parameters. The number of iterations is 100, the quantile interval is 0.01, then a TCNQR network was constructed. The optimal structure and parameters of the TCNQR model under each quantile are determined by training samples, based on the trained model, the quantiles of the predicted bearing at all quantiles are obtained, which are substituted into the Gaussian kernel density estimation function to estimate the probability density curve of the predicted bearing RUL.
# Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
## 6 Conclusions
###### Acknowledgements.  
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00217, Development of Immersive Signage Based on Variable Transparency and Multiple Layers) and was supported by R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2016M 1B3A1A02937594). Author ContributionsJ.[PERSON] proposed an algorithm for low complexity linear detection scheme and performed computational simulations; [PERSON] and [PERSON] supplemented an algorithm for higher error performance. They tried to minimize the loss of error performance; [PERSON] gave feedbacks about an modified algorithm  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Detection Scheme**} & \multicolumn{3}{c}{\(\boldsymbol{N_{t}}=\boldsymbol{N_{r}}=\boldsymbol{4}\)} & \multicolumn{3}{c}{\(\boldsymbol{N_{t}}=\boldsymbol{N_{r}}=\boldsymbol{8}\), **16-QAM**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Modulation: 16-QAM**} & \multicolumn{2}{c}{**Modulation: 16-QAM**} \\ \hline Low complexity ML & \(V=1\) & \(V=8\) & \(V=16\) & \(V=1\) & \(V=8\) & \(V=16\) \\ \cline{2-7}  & 29 dB & 25.5 dB & 25 dB & 27 dB & 25 dB & 24.5 dB \\ \hline Proposed scheme & \multicolumn{2}{c}{33 dB} & \multicolumn{2}{c}{36 dB} \\ \hline \end{tabular}
\end{table}
Table 2: The required signal-to-noise ratio (SNR) to obtain the BER performance for \(10^{-3}\) (BER: bit error rate; QAM: quadrature amplitude modulation; ML: maximum likelihood).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Detection Scheme**} & \multicolumn{3}{c}{\(\boldsymbol{N_{t}}=\boldsymbol{N_{r}}=\boldsymbol{4}\)} & \multicolumn{3}{c}{\(\boldsymbol{N_{t}}=\boldsymbol{N_{r}}=\boldsymbol{8}\)} \\ \cline{2-7}  & \multicolumn{2}{c}{**Modulation: 16-QAM**} & \multicolumn{3}{c}{**Modulation: 16-QAM**} \\ \hline \multirow{2}{*}{SMLD} & \(V=1\) & \(V=8\) & \(V=16\) & \(V=1\) & \(V=8\) & \(V=16\) \\ \cline{2-7}  & 892 & 1284 & 1732 & 6708 & 8836 & 11268 \\ \hline Proposed scheme & \multicolumn{2}{c}{1089} & \multicolumn{2}{c}{3842} \\ \hline \end{tabular}
\end{table}
Table 3: The required number of complex multiplications (SMLD: simplified ML decoding).  
and all simulation results. Also, [PERSON] provided the experimental materials for better computational simulations and revised critical errors of the manuscript.  
The authors declare no conflict of interest.
## 2 Study Area and Observations
The first survey was conducted in 16 permanent forest plots (25 m \(\times\) 25 m) and in 69 rectangular forest plots that had two sizes (20 m \(\times\) 20 m and 25 m \(\times\) 25 m). The second survey was conducted in 58 circular forest plots (with diameters ranging from 10 to 28 m). Obtained measurements included LAI, tree height (m), first branch height (FBH), the diameter at breast height (DBH) (cm), _etc._ A total of 133 forest plots measurements were used for this study. The 133 plots included a variety of different terrains (slope, aspect, altitude) and stand (age, crown coverage, AGB level) situations that represented local ecological gradients. According to inventory records, _Picea crassifolia_ comprised 99.39% of the total measured trees (8667 trees). Therefore, the _Picea crassifolia_ allometric equations of [PERSON] _et al._[59] were used for our analyses. The major input variables for SEBS include meteorological data (air temperature, wind speed, air and vapor pressures, and downwelling shortwave and long-wave radiation) and land surface parameters (LAI, LST, emissivity, Albedo, and NDVI). Meteorological forcing data for the HRB downscaled from estimates of the WRF model, based on the meteorological model (MicroMet) [60], were used in this study. WRF estimates were validated at 15 surface meteorological stations maintained by the China Meteorological Administration (CMA) and seven intensive observation stations setup by the WATER project. Based on past experiments, the data have been proven to be reliable [61, 62]. Land surface parameters were derived from MODIS data. Innovative MODIS products including LAI and Albedo were obtained from GLASS [63, 64]. Other parameters including NDVI and LST were downloaded from the National Aeronautics and Space Administration (NASA) [65]. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)Global Digital Elevation Map (GDEM) version-2 was downloaded from the Japan Aerospace Exploration Agency (JAXA) [66]. To perform multiple comparisons amongst the SEBS, EC, and MODIS ET estimates, 1 km, eight-day MODIS MOD16 ET (MOD16-ET) products obtained from 2000 to 2012 were downloaded from the Numerical Terradynamic Simulation Group (NTSG) [67] of the University of Montana.
###### Abstract  
Incorporation of TiO\({}_{2}\) into cementitious materials is an important technology in the field of photocatalytic pollution mitigation; however, the photocatalytic activity of TiO\({}_{2}\) is limited by specific surface area, poor gas diffusion and light transmission performance of cementitious materials. In this study, a novel photocatalytic lightweight aggregate--photocatalytic ceramsite sand (PCS) was synthesized by loading TiO\({}_{2}\) on activated porous ceramsite sand (CS) with negative pressure method to solve problems in application of photocatalysts in cementitious materials. Photocatalytic cement material (PCM) was prepared by loading PCS on the surface of cementitious materials, which improved the photocatalytic activity and efficiency of TiO\({}_{2}\) in cementitious materials. It was found that the pore structure (pore volume, size distribution and interconnectivity) of ceramsite sand (CS) varies with particle size. The photocatalytic removal rate of benzene on PCS increased significantly through adjusting ceramsite sands in appropriate pore structure and TiO\({}_{2}\) at best coating ratio. The photocatalytic activity of PCS slightly decreased but still remained active after incorporated into concrete. 2 \(\mu\)L benzene was degraded completely in 200 min by 5 g 4 PCS-1.25-2.35 and 300 min by PCM-5, and was still degraded over 80% in 400 min by PCM-5 after exposure to natural environment for 6 months. The results suggested that the photocatalytic activity of TiO\({}_{2}\) in cementitious materials was enhanced by the preparation of PCS and PCM, which could provide more gas diffusion, higher specific surface area, more TiO\({}_{2}\) active sites, and prevent TiO\({}_{2}\) particles from being influenced by the envelope of cement hydration products and the carbonation of cement.  
Keywords:photocatalytic ceramsite sand; photocatalytic cementitious material; pore structure; TiO\({}_{2}\) photocatalytic activity +
Footnote †: journal: Applied Sci. 2017
Accumulation of Methylmercury in the High-Altitude Lake Uru Uru (3686 m a.s.l, Bolivia) Controlled by Sediment Efflux and Photodegradation  
[PERSON]  
1 Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, IFSTTAR, ISTerre, 38000 Grenoble, France; [EMAIL_ADDRESS]  
[PERSON]  
3 Unidad de Calidad Ambiental (UCA)--Instituto de Ecologia--Universidad Mayor de San Andres, Campus Universitario de Cotta-Cota, casilla, 3161 La Paz, Bolivia; [EMAIL_ADDRESS] (M.F.); sriad@univ-pau.  
or biofilms developed at the sediment-water interface (SWI), implying various microorganisms such as methanogens, sulfate-reducing (SRB), and iron-reducing (IRB) bacteria [7; 8; 9; 10]. In shallow environments (e.g., shallow lakes, lagoons, ponds), both the shear stress and the bio-irrigation driven by tides, wind, and benthic organisms enhance the upward release of elements contained in the sediment porewater towards the water column [2; 3; 4; 5; 6; 11; 12; 13]. Such MMHg exchange is mainly regulated by the position of the redox front around the SWI where it is mainly produced, the sediment structure (e.g., porosity, tortuosity), and the abundance and type of (in)organic ligands [14; 15] and colloids [16].  
In high-altitude shallow lakes, extreme hydro-climatic constraints (e.g., high UV radiation and low oxygen) result in large diel variations of the water column's biogeochemical and physical parameters that can overwhelm seasonal amplitude [17]. This is the case in Lake Uru Uru (3686 m above sea level (a.s.l.)), one of the most Hg-contaminated lakes of the Bolivian Altiplano [18; 19]. Previous studies [18; 19] have shown that urban and mining discharges to the lake, combined with the strong diurnal biogeochemical gradients, have resulted in large diel variability in MMHg levels (0.2 to 4.5 ng L\({}^{-1}\)) in the water column. The quantification of methylation potential during incubation experiments with enriched isotopes has suggested that sediments could be the major source of MMHg for the water column [18]. Nevertheless, the sediment-water exchange has not yet been assessed.  
In this work, we studied Hg and MMHg sediment-water column exchanges in Lake Uru Uru during two 36-h cycles performed at the end of the wet (May) and at the end of the dry (November) seasons of 2014. Hg speciation was measured in the water column (i.e., filtered, particulate, and dissolved gaseous Hg) and in the surficial sediment porewater. We also calculated Hg and MMHg diffusive fluxes at the SWI to assess the sediment contribution in MMHg effluxes toward the water column. The final objective was to identify the biogeochemical and physicochemical factors influencing these sediment-water exchanges and to evaluate those regulating the accumulation of MMHg in the water column.
## 1 Introduction
Comprehensive Evaluation and Comparative Analysis of two complementary parameter estimation methods under identical experimental conditions: a modified Sliding Integral Algorithm (SIA) for continuous-time system modeling, which limits the data window to reduce the influence of transients and improves robustness of parameter estimation; and a Sliding Window Algorithm (SWA) for discrete-time modeling, which enhances responsiveness to abrupt parameter changes. This enables a comparative analysis of their performance and limitations, which is rarely addressed in the existing literature [19; 20; 21; 33]. 3. A modification of the SIA by limiting the data window, which reduces the influence of transients and improves the robustness of parameter estimation. 4. Demonstration of two complementary fault identification approaches that enable quantification of the change in affected physical parameters--a capability rarely addressed in the existing literature [13; 14; 16; 18; 19]. In addition to detecting and isolating faults, the proposed methods also estimate fault magnitude, which significantly enhances diagnostic resolution. This is achieved through:  
* A novel decision algorithm, based on the model obtained using SIA, which maps deviations in estimated model parameters to specific physical components;
* An indirect estimation method, applied in the SWA framework, supported by a selective Jacobian matrix approach that improves the accuracy of physical parameter identification and strengthens the overall diagnostic capability of the system, especially when the relation between model and physical parameters is nonlinear or non-negligible. This overcomes limitations of prior indirect approaches that typically rely on the full Jacobian matrix [3; 7]. In summary, this work presents a practical and robust methodology for detecting and identifying multiplicative faults using simple yet effective parameter estimation techniques. Its real-time implementation, experimental validation, and educational applicability make it a valuable contribution to both academic research and industrial practice.
## 3 Results and Discussion
### Oxidative Stability and Microbiological Safety During Storage
Microbiological quality remained within safe limits across all samples and storage periods (Table 10). Total mesophilic aerobic microorganisms (MAMs) remained below 1000 cfu/g in ESC at all timepoints. In FFS, MAM levels slightly increased after 6 months (1624 cfu/g) but stayed within acceptable food safety standards. Coliforms, Enterobacteriaceae-aceae, Staphylococcus aureus (CPS), yeasts and molds (Y&M), and Salmonella spp. were not detected in any sample throughout storage, confirming effective microbiological safety of both products. The combination of high oxidative stability, controlled microbial quality, and stable lipid profile confirms the suitability of ESC for prolonged storage and extended distribution chains. These properties are particularly relevant for both feed and functional food sectors, where product stability under varying storage conditions is a critical requirement. Additionally, the mild thermal conditions used during processing may contribute not only to oxidative stability but also to the reduction of allergenic protein isoforms, as previously reported for thermally processed soy products [50].
## 3 Results and Discussion
### Establishment of Collision Impact Force Model and Regression Analysis
#### 3.3.1 Collision Contact Stress Distribution Characteristics of Contact Material to Plug Seedlings  
According to the analysis of the orthogonal test results, the contact material and the drop height had a significant effect on the collision impact force. A drop impact test with oil sunflower plug seedlings and three contact materials was carried out, and the contact stress and distribution of the seedling were obtained through the flexible film network tactile pressure sensor and the I-Scan System data processing system. The results are shown in Figure 8. When the oil sunflower seedlings were dropped and collided with different contact materials, a contact stress of 10-30 kPa accounted for the main area, which played a significant role in the pot damage. A contact stress of 30-35 kPa occurred at higher drop heights. Combined with the overall structure of the transplanter and the assembly relationship between various components, the drop height was less than 350 mm during seedling picking, feeding, carrying, and planting. Therefore, contact stress was not the main stress that caused pot damage. When the contact stress was 10-15 kPa, the contact area showed little difference at different drop heights. When the contact stress was \(>\)15 kPa, the contact area with a drop height of 350 mm was much larger than that of other drop heights. This shows that with the increase in the drop height, the high stress area accounts for the main contact area, which is the main reason for the collision damage of these pots.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Freedom** & **Mean Square** & \(F\)**Value** & \(p\)**Value** \\ \hline Model & 21.80 & 9 & 2.42 & 62.21 & \textless{}0.0001 ** \\ \(X_{1}\) & 11.62 & 1 & 11.62 & 298.34 & \textless{}0.0001 ** \\ \(X_{2}\) & 0.42 & 1 & 0.42 & 10.87  
The stress distribution of different contact materials after collision is shown in Figure 9. The contours of the seedlings colliding with different contact materials were in a relatively standard shape, and the contact area between the seedlings and the soil was significantly larger than that of other two materials. The dark blue area is low stress, the red area is high stress, and the colorless area has a contact stress of 0 kPa. When the drop height was 50 mm, the low-stress area occupied the main area. When the drop height was greater than or equal to 150 mm, the high-stress area was generally located in the middle of the contact area. With the increase in the drop height, the low-stress-distribution area decreased significantly, the high-stress area showed a gradually increasing trend, and the pot damage was larger.
# Stability Evaluation of TBM Pilot Tunnels to Rear Blasting Using the Protection Shield
## 3 Numerical Analysis
### Target Ground and Protection Shield Design  
It is extremely important to understand the conditions of the target ground before evaluating the stability of a tunnel. The material parameters of the ground at the construction site of the high-speed railroad in Seoul were applied in this study (Table 1). The ground parameters found in the geotechnical survey report of the target site were referenced for use as the material parameters of the ground in the analysis, and the site boring logs were referenced to determine the boundary between soil and rocks [15]. A numerical analysis was conducted using a Mohr-Coulomb model for soil constitutive modeling and an elastic model for protection shield constitutive modeling. Figure 2 shows the design plan for the protection shield; the analysis was performed by modeling a steel frame with a diameter of 2.3 m and a length of 4.0 m and an inflater (polyester mat) with a diameter of 3 m according to the design plan. The material parameters of iron were used for the protection shield frame. The weight values were obtained through an experiment conducted by the Korea Conformity Laboratories. The material parameters of polyester were obtained through the literature and applied to the inflater, as shown in Table 2. The material parameters of the TBM were the same as those of the protection shield frame. In the future, more accurate material parameters will be input to analyze the effect on TBM itself.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Soil** & **Model** & \(\mathbf{\gamma}\) **(kN/m\({}^{3}\))** & **E (kN/m\({}^{2}\))** & \(\mathbf{\
u}\) & **c (kN/m\({}^{2}\))** & \(\mathbf{\Phi^{\prime}}\) (\({}^{\circ}\)) \\ \hline Reclamation soil & M-C & 18 & 13,000 & 0.3 & 0 & 31 \\ Hard rock (Gneiss) & M-C & 27 & 12,900,000 & 0.2 & 1890 & 40 \\ \hline \hline \end{tabular} \(\gamma\): Unit weight of soil, E: Young’s modulus, \(\
u\): Poisson’s ratio, c: Cohesion, \(\Phi^{\prime}\): Angle of shearing resistance.  
\end{table}
Table 1: Material parameters of ground.
## 2 Case Introduction
### Spatial Relationship between the Shield Tunnel and Line 5  
In some sections of Heping Avenue South Extension Tunnel in Wuhan, the shield tunneling will be nearly parallel with the existing subway Line 5, so the shield construction faces multiple challenges. Line 5, as the first fully automatic driverless subway line in Wuhan, is an important passenger transportation corridor along the Yangtze River. Metro operation has many requirements for tunnel displacement. Unmanned driving has more stringent requirements for the tunnel environment, resulting in higher requirements for the shield construction of the Heping Avenue South Extension Tunnel. In particular, at the early stage of the forward driving section of the north shield tunneling well, the distance between the tunnel to be constructed and Line 5 is very small, whether in the horizontal direction or the vertical direction. The distance between the tunnels in some sections is less  
Figure 1: Spatial relationship between the South Extension Tunnel Project of Heping Avenue and Line 5. (**a**) Profile view of the spatial relationship between the two tunnels; (**b**) plane view of the spatial relationship between two tunnels.  
Figure 2: Cross-sectional spatial relationship between the shield section of the South Extension Tunnel and Line 5 (unit: mm).  
than 10 m, which is less than twice the diameter of the shield tunneling tunnel. Line 5 is parallel to the planned southern extension of Heping Avenue on the east side.  
As shown in Figure 1, according to the different geotechnical properties in the section, the strata through which the shield passes are divided into intensely weathered, moderately weathered and slightly weathered rock sections. Considering that the engineering geological conditions in the intensely weathered area are the worst, the shield along this section will be closest to Line 5. Compared with other sections, the excavation construction in this section will have a greater impact on the stratum and the existing Line 5. Therefore, in this study, the intensely weathered section is selected to carry out research on the impact of further construction and accident prevention measures.  
The intensely weathered section is the shield starting shaft area, which is close to Tanhualin Station, with a horizontal spacing of approximately 9.4\(\sim\)12.9 m. The shield tunnel will be nearly parallel to Line 5 in the horizontal direction. The vertical spacing will be approximately \(-\)5.92-4.5 m (positive indicates that the shield tunnel top is below Line 5, and negative indicates that it is above Line 5). With shield tunneling, the tunnel will gradually extend downward, away from Line 5. The relationship between the cross sections of the two projects is shown in Figure 2.