esa-sceva/satcom-chunk-collection huggingface dataset:
# Denoising Directional Room Impulse Responses with Spatially Anisotropic Late Reverberation Tails
## 4 Denoising by Tail Resynthesis
### Summary of Proposed Denoising Process  
The full proposed process of denoising DRIRs measured with an SMA and presenting a spatially anisotropic late reverberation field can therefore be summarized in the following way:  
1. Exponential sweep method (ESM) [29] measurement by SMA, nonstationary artifact reduction [7], and DRIR deconvolution.
2. HOA encoding (SH transform and SMA mode strength correction).
3. PWD on a set of \(D\leq(L+1)^{2}\) look directions satisfying the maximum independent coverage criteria.
4. Incoherence analysis by covariance matrix eigendecomposition [23] in the PWD domain and subsequent mixing time estimation.
5. EDR analysis in the PWD domain (per look direction) and verification of incoherent field condition \(t_{\text{mix}}<\min\left(\hat{t}_{\text{lim}}^{(d)}\right)\).
6. Replacement of nondecaying noise floor by reverberation tail resynthesis in the PWD domain: zero-mean Gaussian noise signals synthesized per PWD look direction and subjected to frequency-dependent decay envelope from EDR analysis are used to replace and prolong (noisy) PWD domain DRIR signals from \(t_{\text{lim}}^{(d)}\) onwards.
7. Re-encode back to HOA with full-rank matrix \(\mathbf{Z}=\mathbf{Y}^{-1}\), which imposes \(D=(L+1)^{2}\).
## 5 Results
### Second Test Case
Furthermore, Table 4 demonstrates that all maximum accuracy values were achieved following FL updates, underscoring the value of FL in enhancing model performance  
Table 5 presents the average accuracy results for specific rows. Columns labeled B = 1 show the average accuracies for Batch 1 before and after the second iteration of the FL update. The results indicate that the second FL iteration does not significantly affect the overall average accuracy for Batch 1. In contrast, the columns B = 1, \(T=D2\) and B = 1,2, \(T=D2\) represent the average accuracy for models after the final training session using the second dataset for Batch 2 and for both batches combined, respectively. These columns reveal that Batch 2, initialized with the global model generated from training on Batch 1, slightly outperforms Batch 1. The final column contains the average accuracy for the last row in Table 5, where the value of 97.01 represents the overall maximum for both Table 4 and across both test \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**T**} & \multicolumn{6}{c}{**Batch 1**} & \multirow{2}{*}{**T**} & \multicolumn{6}{c}{**Batch 2**} \\ \cline{2-11}  & **0** & **1** & **2** & **3** & **4** & & **5** & **6** & **7** & **8** & **9** \\ \hline D1 & 90.37 & 88.70 & 87.07 & 78.90 & 92.53 & & & & & & \\ \hline F1 & 66.73 & 62.33 & 65.03 & 66.33 & 70.20 & & & & & & \\ \hline D2 & 95.50 & 93.80 & 90.93 & 95.53 & 95.27 & & & & & & \\ \hline F2 & 92.80 & 94.27 & 93.43 & 94.30 & 94.60 & & & & & & \\ \hline D2* & 95.50 & 93.80 & 90.93 & 95.53 & 95.27 & D1 & 96.73 & 96.90 & 97.73 & 96.07 & 95.97 \\ \hline F3 & **96.33** & **96.77** & 96.60 & 97.50 & 97.27 & F3 & 96.77 & 97.03 & 96.87 & 96.63 & 96.30 \\ \hline D2* & 95.50 & 93.80 & 90.93 & 95.53 & 95.27 & D2 & 97.30 & 96.67 & 96.83 & 96.50 & 96.47 \\ \hline F4 & **96.33** & 96.57 & **96.77** & **97.53** & **97.57** & F4 & **97.50** & **97.23** & **97.03** & **96.67** & **96.87** \\ \hline \hline \end{tabular}
*Results repeated without training for Batch 1. The bold values represent maximal values per column.  
\end{table} 
Table 4: Results of the evaluation for second test case. cases. This indicates that the FL update not only improves the performance of newly added devices but also enhances the accuracy of devices that were already deployed.
# Assessment of Catalyst Selectivity in Carbon-Nanotube Silylesterification
## 4 Conclusions  
In this report, we demonstrated the selectivity of two catalysts for several oxygenated functional groups in CNT silylesterification.  
To achieve this goal, we used a two-step approach that consisted of (i) the selective functionalization of CNT sidewalls with various oxygenated functional groups (carboxylic acid, alcohol, ketone, and ether) and (ii) a study of the selectivity of silylesterification catalysts using fluorinated hydrosilane as a probe molecule.  
Selective CNT sidewall functionalization with six different oxygenated functional groups was assessed using XPS survey spectra, high-resolution spectra of C1s peaks, and their characteristic HSPs. XPS C1s high-resolution spectra showed components that were characteristic of each of the grafted functional groups. HSP solubility characterization also demonstrated behaviors typical of the grafted functional groups.  
Subsequent derivatization of oxygenated side chains by silylesterification using either zinc chloride or [PERSON]'s catalyst showed differences in their selectivity. [PERSON]'s catalyst appeared to be highly efficient in the silylesterification of both alcohol and carboxylic acid, regardless of the presence of an aromatic ring. This catalyst was slightly less effective in the silylesterification of ethers and ketones. As previously reported, zinc chloride appears to be specific to carboxylic acid groups. Moreover, its selectivity is strictly limited to nonaromatic carboxylic acids.  
The difference in selectivity described in this report paves the way for the modification of CNTs using two different oxygenated groups.  
Conceptualization, S.D., J.D., and Z.M.; methodology, S.D. and P.N.; software, S.D. and A.K.B.; validation, S.D., A.K.B., P.N., Z.M., and J.D.; resources, Z.M.; writing--original-draft preparation, S.D. and A.K.B.; writing--review and editing, S.D., A.K.B., P.N., Z.M., and J.D.; supervision, Z.M. and J.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
[PERSON] thanks the University of Namur for the CERUNA doctoral fellowship.  
The authors declare no conflict of interest.
# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
Montana is also a major producer of wheat, barley and pulse crops and is representative of intermountain western agriculture. We used a satellite data-driven LUE modeling approach based on the MODIS MOD17 algorithm to derive relatively high spatial (30-m) resolution GPP and crop yield predictions across the region and spanning the recent (2008 to 2015) satellite record. A data fusion approach was used for blending 30-m Landsat 5 and Landsat 7 NDVI with 250-m eight-day MODIS NDVI observations to produce harmonized 30-m eight-day fused NDVI data record for Montana extending from 2008 to 2015. The 30-m NDVI record was used as a primary LUE model input to estimate _fPAR_ and GPP at a similar 30-m resolution and eight-day time step for the major Montana crop types. Both the LUE modeling framework and satellite NDVI data fusion were implemented within the GEE framework for this investigation. We derived 30-m annual crop yield maps encompassing Montana cropland areas using the resulting 30-m GPP record and crop-specific HI coefficients calibrated and validated using NASS county-level crop production data and field-level yield data. These results were used to assess regional patterns and anomalies in cropland productivity and yield over the multi-year satellite record. A detailed summary of the methods and results from this study is described below.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 4 Vision-Based State Estimator  
In this section, we explain the vision-based estimator of the pose of the UAV. This estimator outputs the relative position and orientation with respect to the target. By using the advantage of high-speed vision that objects appear to be stationary, simple high-speed image processing and state estimation is realized. The result is used in the controller explained in the next section.  
The estimator is composed of the following four parts: (1) feature detection, (2) inlier extraction, (3) relative pose estimation, and (4) noise reduction.
# Soil and Rockfill Dams Safety Assessment for Henan Province: Monitoring, Analysis and Prediction
## 5 Discussion
### Stability Analysis of Soil and Rockfill Dam
#### 5.4.1 Seepage Stability Analysis  
The nonlinear elastic incremental Duncan-Chang E-B model is used to determine the parameter values through a triaxial compression test. Different material mechanics values are determined according to different material zones. Different loads and boundary conditions are applied to calculate the seepage state of Xiaolangdi soil and rockfill dam and the deformation caused by the force on the dam body. The seepage steady-state analysis of soil and rockfill dam is carried out by using water levels at different heights, and the results are shown in Figure 19.  
Figure 18: (**a**) Soil and rockfill dam model and dam material zoning; (**b**) force distribution diagram; (**c**) seepage distribution map.  
It can be seen from the chart of total head distribution of soil and rockfill dam in Figure 19. Due to the impoundment of the upstream dam slope of the soil and rockfill dam, the water head distribution is the same, but it is uniformly distributed in the core wall area. In the downstream dam slope, due to the existence of downstream water storage, the water head at the slope toe is different from that in other areas. The core wall location is the main seepage area, and the low permeability of the core wall material makes it the main building to block the water infiltration. The pressure head formed at the upstream slope toe is the largest, and the pressure head formed at the downstream slope crest is the smallest. The total head is distributed from high to low, especially in the core wall area. This is due to the role of the core wall and impervious core, so the head pressure of the downstream dam slope is low. As can be seen from Figure 19b, the distribution of low water levels is roughly the same as that of high water levels. However, in the case of low water levels, the seepage of the dam body will be reduced compared with the high water levels. It can be seen that the core wall area of the soil and rockfill dam plays an important role in water infiltration and enhances the stability of the downstream dam slope. Under different water levels, the internal seepage trend of the dam body is consistent, and the core wall area plays a great role in the protection of the soil and rockfill dam. This is consistent with the conclusion drawn in Section 4, so the key monitoring of the core wall area should be strengthened.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### SSE and ASE Evaluation  
Source Spontaneous Emission (SSE) and Amplified Spontaneous Emission (ASE) are essential parameters for evaluating the noise figure, representing the noise floor in the input and output power spectra, respectively. Although named after spontaneous emission, these values also include other minor noise contributions, which are ignored for simplicity [28]. The process of determining the noise floor level is depicted in Figure 4. This approach involves identifying points between each pair of adjacent channels where the power value equals the noise floor. Then, the noise values at frequencies corresponding to the center frequencies of active channels are derived through linear interpolation. For the first and last channels, the first and last frequency points are used accordingly.  
Figure 4: Evaluation of SSE: The power density spectrum is convolved to obtain a smooth curve (green line). The middle points between two adjacent channels (green dots) are used for piecewise linear interpolation to evaluate the noise at the channel frequencies (yellow dots).  
In many datasets, the input power values show a noisy, wavy curve for the noise floor, which can be attributed to the sensitivity of the optical spectrum analyzer, corresponding to a noise floor of \(-65\) dBm in the optical spectrum analyzer setting for these measurements. Therefore, an additional convolution step is performed for data cleaning. This step follows the trend of the curve while avoiding points that correspond to the local maxima and minima of the noise. SSE and ASE are used to compute the \(P_{ASE_{AMP}}\) parameter shown in Equation (9). This value is the difference between the ASE noise and the amplified SSE noise and can be evaluated as:  
\[P_{ASE_{AMP}}|_{\text{dBm}}=10\log_{10}\left(10^{\frac{P_{ASE_{AMP}}}{10}}-10^{ \frac{P_{SSE_{AMP}}+C}{10}}\right) \tag{13}\]  
This process is the mathematical formulation of the quantity shown in Figure 2.
## 7 Analysis of Grouting Effect  
### Analysis of P-Q-t Curve  
The _P-Q-t_ curve analysis method is used to check the grouting effect by recording the grouting pressure \(P\) and grouting volume \(Q\) values with the grouting automatic recorder, drawing and analyzing the _P-t_ and _Q-t_ curves, so as to judge the grouting effect [25]. During the grouting construction, the starting grouting pressure is 0.1 MPa. When the grouting pressure reaches 0.38 MPa, the pressure rise will be stopped to keep the grouting machine under constant pressure. It can be seen from Figure 6 that the _P-t_ curve shows a slow upward trend during grouting. The grout is evenly infiltrated into the injected medium. With the increase in the grouting time, the grouting pressure needs to increase gradually. When the grouting time reaches about 40 min, the grouting pressure reaches the designed final pressure. Figure 7 shows that the _Q-t_ curve rises sharply at first and then drops slowly. This is because the permeability coefficient of the gravel layer is large, and the grout is easy to diffuse at the initial stage of grouting. As the pores of the injected medium are filled, the amount of grout absorption gradually decreases. The maximum amount of grout absorption occurs within 10 to 25 min after grouting, up to 80 L/min, and then starts to decline. When the grouting pressure reaches the design final pressure, the amount of grout absorption is about 2.5 L/min, and after 5 min, it can be considered that grouting has been completed. When the pipe is blocked, the _P-t_ curve will rise sharply and the _Q-t_ curve will remain unchanged. When the diffusion mode is changed to splitting or bleeding, the _P-t_ curve will remain unchanged and the _Q-t_ curve will jump up. It can be considered that the single-hole grouting has not achieved the expected effect.  
Figure 6: Relation between grouting pressure and time.
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
## 3 Modern Design
### Digital Domain Processing
#### 3.2.2 Analog and Digital Signal Convertor  
The analog to digital converter (ADC) and digital to analog convertor (DAC) for the modem is TLV320 AIC3106. It is a low-power stereo audio codec with a stereo headphone amplifier, as well as multiple inputs and outputs programmable in single-ended or fully differential configurations. The sampling rate of the stereo audio ADC and DAC can vary between 8 kHz and 96 kHz. Extensive register-based power control is included, enabling stereo 48 kHz DAC playback as low as 15 mW from a 3.3 V analog supply, making it ideal for the portable battery powered UAC modem. The TLV320 AIC3106 contains integrated microphone bias, a digitally controlled stereo microphone  
Figure 3: PBC micro-modem block diagram.  
Figure 2: Traditional modem block diagram.  
preamplifier, and automatic gain control (AGC), with mix/mux capability among the multiple analog inputs making the input SNR and the output SNR reach 102 dB and 92 dB, respectively.  
In this design, AIC3106 communicates with DSP through the McASP and IIC interface. DSP sends a control signal to AIC3106 through the IIC interface. The data received or sent by AIC3106 is transmitted through the McASP serial interface to DSP.
## 5 Discussion
### Impact Allocation and Product Displacement or Substitution  
The results of an LCA depend heavily on allocation and substitution choices. Feedback is often classified as waste with no environmental burden. Still, it can also be considered a resource for biogas production, in which upstream impacts like cultivation, processing, and transport are accounted for, increasing the environmental footprint [113; 121]. System product classification also affects results: if digestate and biogas are co-products, their impacts are allocated based on mass, energy, or economic criteria, while if only one is classified as a product, it bears the entire environmental burden.  
Product displacement and substitution play a crucial role in LCA outcomes. Anaerobic digestion (AD) generates co-products that replace market alternatives, assigning them environmental credits based on avoided impacts [104]. Biogas, a carbon-neutral energy source, can replace fossil fuels, significantly reducing GWP and resource depletion [62; 105; 109]. Studies highlight that increasing AD energy yield is the most effective way to lower overall emissions [101; 107; 110].  
Digestate can replace synthetic fertilizers, reducing the environmental burden of their production. The extent of substitution credits depends on the digestate form (SF, LF, compost, and pellets) and its efficiency in fertilizing land. A direct substitution approach considers digestate's nutrient content (N, P, and K) as a replacement for synthetic N, P\({}_{2}\)O\({}_{5}\), and K\({}_{2}\)O [59; 89; 107; 109]. Some studies adjust credits based on nutrient absorption rates [94], while others incorporate digestate into fertilizer production processes [97].
## 3 Experiments and Analysis
### Feature Interpretability of Representation Consistency  
As illustrated in Figure 9, we employ a feature visualization technique--Gradient weighted Class Activation Mapping (GradCAM++) [44]--to perform an interpretability analysis of the features during the change detection process in RCCD. We compare the feature visualizations under three conditions: with the representation consistency learning module (w/LS and GS), with only the GS module (w/GS), and without any representation consistency learning modules (w/o LS and GS). The model with the representation consistency deep supervision module demonstrates stable feature responses throughout both the encoding and decoding stages--the heatmaps exhibit more uniform intensities and do not fluctuate dramatically despite variations in the visual styles of the input images. A similar global stabilization effect can be observed in the second row: with the addition of the GS module, the perceived activation distribution in the heatmap becomes even more uniform. In the first row, we observe that, compared to the second row (which lacks the LS module), there is a more pronounced suppression of pseudo-changes in the target objects and adjacent background during the decoding process. This underscores the necessity of local spatial context measurement. In contrast, the third row--corresponding to the configuration without any representation consistency learning module--exhibits the highest number of false alarms and is more susceptible to interference from vegetation and shadows, resulting in aberrant heatmap activations. Overall, the feature visualizations obtained during the RCCD prediction process confirm that the LS module effectively measures the similarity between the target and its local spatial context to suppress false alarms, while the GS module plays a crucial role in globally stabilizing images with varied visual styles, thereby ensuring the construction of comparable features.
# The Deep Deterministic Policy Gradient Algorithm Based on RIS Technology in a Coal Mine Tunnel
## 2 System Model
### Systematic Performance Optimization Problem  
The power of the transmitted signal of a multi-antenna BS is constrained by the maximum transmit power:  
\[E\Big{\{}Tr(\mathbf{G}\mathbf{G}^{H})\Big{\}}\leq P_{\text{max}} \tag{18}\]  
where \(P_{\text{max}}\) is the maximum value of the transmitted power at the BS, \(E\) represents the statistical expectation value, and \(Tr(\cdot)\) represents the trace of the matrix.  
To maximize the total system link rate by optimizing \(\mathbf{G}\) and \(\mathbf{\Phi}\), the corresponding optimization problem is defined as follows:  
\[\begin{array}{l}\underset{G,\Phi}{\max}\sum\limits_{k=1}^{K}\log_{2}(1+\beta _{k})\\ s.t.\ Tr(\mathbf{G}\mathbf{G}^{H})\leq P_{\text{max}}\\ |\phi_{n}|^{2}=1\\ 0\leq\theta_{n}<2\pi,n=\{1,2,\dots,N\}\end{array} \tag{19}\]  
The optimization problem in Equation (19) is non-convex due to the non-convexity of the objective function and the complexity of the constraints. If the traditional method is used to solve this problem, a large number of iterative calculations are required, resulting in high computational complexity. Therefore, this paper adopts a DRL-based solution to solve the optimization problem in Equation (19) to obtain a feasible \(\mathbf{G}\) and \(\mathbf{\Phi}\).
## 2 Mathematical Description of the Proposed Hybrid System Components  
The schematic diagram of the suggested HRES is shown in Figure 1. Four scenarios are applied, which include the PV power plant, WT power plant, diesel generator, Biomass, BESS and inverter.  
Two strategies are adopted in this paper; the first is the biomass/PV as shown in Figure 2 and the second uses the PV or WT or both as in Figure 3. The main strategy steps for the operation of the proposed system can be explained as follows:  
* The PV and WT are used first as a principal power source and served the load needs.
* The battery is used when the PV and WT cannot serve it.
* The diesel system is working when the battery storage system is empty and starts when the need is higher than 30% of its nominal power.
# Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data
## Materials and Methods  
### Study Area  
Poyang lake, the largest freshwater lake in China, is located between Nanchang City and Jiujiang City, to the north of Jiangxi Province. The basin crosses from 28\({}^{\circ}\)22\({}^{\prime}\) to 29\({}^{\circ}\)45\({}^{\prime}\) N and 115\({}^{\circ}\)47\({}^{\prime}\) to 116\({}^{\circ}\)45\({}^{\prime}\) E,which belongs to a humid, subtropical monsoon climate zone, with an average annual temperature of 17.5 \({}^{\circ}\)C and average annual precipitation of 1665 mm [29]. Poyang lake basin is fed by the Xiu, Gan, Fu, Xin and Rao rivers, while the basin connects to the Yangtze river through an outflow channel at the north end of the lake (Figure 1). The lake has a surface area of approximately 4000 square kilometers at its summer high-water level [30; 31]. Beyond its size, Poyang lake is also significant for several economic and ecological reasons. For instance, Poyang lake's aquatic ecosystems are wintering home to thousands of migratory waterbirds, including the Siberian crane--a critically endangered species whose 4000 surviving individuals spend their winters almost solely in the wetlands around Poyang lake [32]. However, Poyang lake has undergone a series of significant transformations that threaten the variability and critical habitats in the region. While surface water areas have traditionally fluctuated on a seasonal scale--peaking in the summer and receding in the winter--large interannual declines in mean water level have been observed in recent years [33]. The substantial variations of the surface water area and dramatic seasonal water level fluctuations of 8 to 22 m each year are caused by the regional hydrological regime, which is controlled both by the five catchment rivers and the Yangtze River [34]. Additionally, groundwater dynamics are highly affected by the variations in the lake water level, rather than local precipitation, indicating a close hydraulic relationship between groundwater and the lake [35].