esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Theoretical Uncertainty Characterization  
Potential LST retrieval errors were assessed through the use of the synthetic validation database described in Section 2.4, which contained over 15,500 independent simulations. For the uncertainty analysis presented in this study, we followed the approach of [PERSON] _et al._[12]. We provided TOA brightness temperatures, surface and atmospheric information from the database as input to the SMW and PMW model; the calculated LST output was then compared with the corresponding (\"true\") surface temperature from the database. In addition to the model error, we assessed the sensitivity of the SMW and PMW to radiometric noise, uncertainty in surface emissivity and NWP by superimposing artificial errors to PMW and SMW inputs.  
The value used for SEVIRI 10.8-\(\upmu\)m radiometric noise is based on radiometric performances for SEVIRI IR 10.8 \(\upmu\)m compared to the Infrared Atmospheric Sounding Interferometer (IASI) (bias \(<\) 0.2 K) [33]. Values for noise in brightness temperature were generated from a uniform random distribution within the conservative interval (\(-\)0.3 K, 0.3 K).  
For the estimation of the surface emissivity error, we took into account that bare ground and semi-arid surfaces (generally with lower emissivities) present higher variability and, therefore, higher uncertainty than areas covered by vegetation. As such, emissivity uncertainties used in this study were obtained from random uniform distributions within the intervals (\(-\)0.04, 0.04) for \(\varepsilon_{c}\)\(<\) 0.95, (\(-\)0.02, 0.04) for \(0.95\leq\varepsilon_{c}\)\(<\) 0.98 and (\(-\)0.01, 0.01) for \(\varepsilon_{c}\)\(\geq\) 0.98 The rationale for this choice is based on emissivity variability for bare ground surfaces (\(\varepsilon_{c}\)\(<\) 0.95), sparsely- (0.95 \(\leq\varepsilon_{c}\)\(<\) 0.98) and densely- (\(\varepsilon_{c}\)\(\geq\) 0.98) vegetated areas [12].  
The PMW model requires a characterization of the errors associated with the atmospheric profiles. Since these are obtained from ERA-Interim nearest in space and time to the satellite observation, we assume that the uncertainty in collocation may be used as a measure of the profile uncertainty. Thus, the impact of profile errors on retrieved LST values was estimated by replacing the profiles at hour h by the corresponding ones at hour h + 6. A similar procedure was used to determine the impact of TCWV errors on LST estimates from the SMW. It is worth recalling that TCWV is an implicit input to the SMW: This variable is used to determine the regression coefficients (Equation (4)). Therefore, and as explained in detail in [PERSON] _et al._[12], studies of the impact of TCWV uncertainties on LST need to combine: (i) The effect on the LST estimate due to the choice of the wrong set of coefficients; and (ii) the probability of that event.
# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
## 1 Introduction
Following our review of the literature, we concluded that, due to the lack of a real-world testbed, most of the methods do not leverage data acquired from IoT sensors, and instead focus on using imaging data as input. Consequently, this paper proposes a new LSTM-based approach to predict soil moisture and efficiently manage crop irrigation to provide intelligent irrigation while leveraging smart technologies such as the Internet of Things (IoT) to collect and manage data from various types of sensors. The paper is structured as follows. Section 2 discusses data collection and the methodology used to design our soil moisture forecasting model. Section 3 presents the results of our model, which was tested and validated using a real-world dataset that we collected. Section 4 discusses the performance and usability of our approach, as well as our approach's conclusions, and highlights potential areas for improvement to our proposed model.
## 2 Materials and Methods
### Preparation of Superhydrophobic CuBTC@PET Composite Membrane  
CuBTC@PET fiber was based on the method of synthesizing CuBTC powder in an aqueous phase under room-temperature conditions. PET electrospun fibers were used as the substrate so that Cu2+ ions and homobenzoic acid were used to directly grow CuBTC crystals in situ on PET fibers. DMF, deionized water, and ethanol were mixed in a 1:1:1 ratio to 48 mL, and 3.6 g of Cu(NO3)2-3H2O and 1.35 g of BTC were added. Then, the two solutions were dispersed by ultrasonication for 20 min; the specific parameters were as follows: the ultrasonic power was 230 W, the closed cycle time was 15 s, and the open cycle time was 45 s. The Cu2+-containing solution A and the BTC ligand solution B were obtained. The PET fiber membrane before and after recrystallization treatment was cut to 20 cm \(\times\) 20 cm, and the metal skeleton was grown layer-by-layer as follows: the fiber membrane was immersed into solution A for 15 min, washed with anhydrous ethanol for 15 s, immersed into solution B for 15 min, and then washed with anhydrous ethanol for 15 s. The above steps are one cycle. The sample was cycled for 3, 5, 7, or 9 cycles. Finally, the composite fiber membrane with ethanol was cleaned 4 times to remove the residual Cu2+  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Membrane**} & **Solvent Ratio** & **PET** & **Applied Voltage** & **Flow Rate** \\  & **TFA/DCM** & **wt\%** & **kV** & **mL/h** \\ \hline A & 1:1 & 12\% & 16 & 1 \\ B & 1:2 & 12\% & 16 & 1 \\ C & 2:1 & 12\% & 16 & 1 \\ D & 1:1 & 10\% & 16 & 1 \\ E & 1:1 & 14\% & 16 & 1 \\ F & 1:1 & 12\% & 14 & 1 \\ G & 1:1 & 12\% & 18 & 1 \\ H & 1:1 & 12\% & 16 & 0.8 \\ I & 1:1 & 12\% & 16 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the electrostatic spinning process.  
and ligand from the fiber surface. The prepared fiber membranes were dried in a fume hood for 12 h to obtain CuBTC@PET composite fiber membranes with different growth cycles. The detailed route of the experiment is shown in Figure 1.  
The CuBTC@PET composite fiber membrane prepared in the previous step was sheared to 4 cm \(\times\) 4 cm and washed twice using anhydrous ethanol to remove inter-fiber impurities. Subsequently, 1 mL of POTS solution was added to 20 mL of anhydrous ethanol, and the treated CuBTC@PET was immersed in the mixed solution at 30 \({}^{\circ}\)C for 1 h. Subsequently, the composite fiber membranes were taken out from the mixed solution and dried at 140 \({}^{\circ}\)C for 1 h in an electric blast drying oven, and the resulting superhydrophobic CuBTC@PET composite fiber membranes were prepared for further experiments.
## 6 Case Study and Discussion
### Simulation Results
#### 6.3.4 Case 4  
In Case 4, both the numbers of EVBCDIs of the three microgrids and installation capacities of RESs are optimized. The numbers of EVCDIs for RMG1, RMG2, and OBMG are 19, 8, and 27, respectively. WTs installation capacities for RMG1, RMG2, and OBMG are 80 kW, 80 kW, and 360 kW. PV installation capacities for RMG1, RMG2, and OBMG are 120 kW, 0 kW, and 400 kW. The two-day operation profiles of RMG1 and OBMG in Case 1 are shown in Figure 9.  
In this case, both EV charging devices and RESs are optimally allocated. Since the cost of PV is higher than wind generation and since the outputs of PV modules are unstable, the installation of PV modules is decreased. Meanwhile, the number of EVCDIs and the installation capacities of RESs are optimally matched. Neither redundant RES installation nor redundant installation of EVCDIs exists. Therefore, the total cost in this case is the lowest.  
In Case 4, the total cost of one year is $159,264.80. The costs for RMG1, RMG2, and OBMG are $56,012.30, $36,306.40, and $66,946.10, respectively.
# Target Selection Strategies for Demucs-Based Speech Enhancement
## 4 Training Methodology
Three datasets were created: 80% of the users for training, 10% of the users for validation (from which the best model is kept), and 10% of the users for evaluation (from which the evaluation metrics were calculated). In terms of training parameters, the Adam optimizer [41] was used, with a learning rate of \(3e^{-4}\), a \(\beta_{1}\) of 0.9, and a \(\beta_{2}\) of 0.999. The training was run for 75 epochs. If after 10 epochs, the validation loss did not improve, the learning rate was halved, to refine the optimization search space. The employed loss function is the same as the original implementation, presented in [12]. It is the weighted sum of the \(\ell_{1}\) metric between \(X\) and \(\hat{X}\), and a multiresolution short-time Fourier transform (STFT) loss function (\(MuSTFT\)), as presented in Equation (3). \[loss=\ell_{1}(X,\hat{X})+\gamma_{Mu}MuSTFT(X,\hat{X}) \tag{3}\]  
where  
\[\ell_{1}(X,\hat{X})=\frac{\sum_{f}^{\mathrm{F}}|X|f]-\hat{X}[f]|}{F} \tag{4}\]  
and  
\[MuSTFT(X,\hat{X})=\gamma_{Sp}\frac{\sum_{\theta}^{\Theta}SpecConv(X,\hat{X}, \theta)}{\Theta}+\gamma_{Lo}\frac{\sum_{\theta}^{\Theta}LogMag(X,\hat{X}, \theta)}{\Theta} \tag{5}\]which, in turn, gives  
\[SpecConv(X,\hat{X},\theta)=\frac{\sum_{f}^{F}\sum_{t}^{T}(X_{\theta}[t,f]-\hat{X} _{\theta}[t,f])^{2}}{\sum_{f}^{F}\sum_{t}^{T}X_{\theta}[t,f]^{2}} \tag{6}\]  
\[LogMag(X,\hat{X},\theta)=\frac{\sum_{f}^{F}\sum_{t}^{T}|\ln X_{\theta}[t,f]-\ln \hat{X}_{\theta}[t,f]|}{FT} \tag{7}\]  
where \(\gamma_{Mu}\), \(\gamma_{Sp}\), and \(\gamma_{Lo}\) are loss weights (with values of 0.3, 0.5, and 0.5, respectively, in this work); \(\theta\) is an STFT resolution (defined by a given Fourier size, window hop, and window size); \(X_{\theta}\) and \(\hat{X}_{\theta}\) are, respectively, the STFT'ed \(X\) and \(\hat{X}\) sampled at resolution \(\theta\); \(\Theta\) is the number of calculated resolutions; and \(t\) is the time index (of \(T\) total time bins). The following are the hyperparameters used as part of the architecture of the trained model: number of initial output channels (\(H\)) of 64, kernel size (\(K\)) of 8, stride (\(S\)) of 0.5 s, number of encoding/decoding layers (\(L\)) of 5, and number of LSTM layers (\(B\)) of 2. For replicability, the authors have made public the implementation of the training methodology and dataset creation, which can be accessed at [[https://github.com/balkce/demucstargestel](https://github.com/balkce/demucstargestel)]([https://github.com/balkce/demucstargestel](https://github.com/balkce/demucstargestel)) (accessed on 12 June 2023).
## 4 Example Analysis
### Influence of Changes of Power Structure on Benefits of the Robust Method  
In this case, based on the initial calculation example (penetrance 18.4%, water volume 2533 MW\(\cdot\)h), the water limit was expanded to 10,131 MW\(\cdot\)h, and the ratio of hydropower output was increased to observe the benefit change of the robust method.  
As can be seen from Table 6 and Figure 8, after the water limit is expanded, the robust cost decreases obviously, but the traditional cost first decreases and then increases. After the water volume is doubled, the robust benefit starts to increase obviously.  
In principle, after the water limit is expanded, the output ratio of hydropower units becomes larger and larger. Since hydropower has almost no power generation cost, the total cost should be reduced, which has been clearly shown in the robust cost. However, the cost of the traditional method increases with the output of hydropower units. The reasons are as follows:  
(a) After the water volume is expanded, the traditional method makes full use of the cost-free advantage of hydropower and increases the hydropower output. When the water volume is increased by four times, the hydropower has been in the rated output state of all units at full time, which makes the hydropower units lose the ability for peak regulation and standby capacity. All peak regulation and standby tasks of the system are undertaken by the primary energy unit with poor regulation ability. The consequence of the poor regulation ability of the system is that when the new energy fluctuates, more wind/light curtailment volume and cutting load will inevitably be generated, which leads to the sharp increase of risk cost and the final comprehensive cost will rise instead of falling.  
(b) After the increase of hydropower output in the traditional method, the load pressure assumed by other units will decrease, the number of thermal coal-fired units will be reduced, and all gas units will be shut down, which further reduces the peak load regulation and reserve capacity of the system. As a result, the risk cost caused by the fluctuation of new energy will be increased.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Water volume (MW-h)** & 2533 & 3722 & 5065 & 6285 & 7536 & 8793 & 10,131 \\ \hline
**Traditional method** & & & & & & & \\
**(kRMB)** & 11,600 & 11,324 & 11,213 & 11,316 & 11,486 & 11,641 & 11,814 \\ \hline
**Robust method (kRMB)** & 11,584 & 11,307 & 11,089 & 10,865 & 10,649 & 10,440 & 10,238 \\ \hline
**Robust benefit (RMB)** & 16,725 & 16,600 & 123,841 & 451,610 & 838,316 & 1,200,501 & 1,575,818 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Robustness benefits after water volume increases.  
Figure 8: Robustness benefit after increasing water quantity.  
After the above two reasons jointly lead to the increase of hydropower output, the cost of the traditional method increases instead of decreases, and the cost of the robust method gradually decreases, and the robust benefit becomes more and more obvious.
## 4 Discussion  
Research and development in the field of implantology are constantly focusing on combining the principles of cell biology, engineering, biochemistry, molecular biology, and biomaterials [28].  
In the 1970s, the accepted paradigm was that dental implants should have as polished a neck as possible to prevent plaque accumulation. For this reason, most implant manufacturers at this time adopted this design. However, finite element studies showed that stress peaks, especially with shear forces, and the stress is concentrated in the crestal bone surrounding the polished collars. Since cortical bone is 65% less resistant to shear forces than to compressive forces, we could say that the loss of bone level that occurred during the first year of function could be attributed to the lack of adequate distribution of mechanical stress between the coronal region of the implant and the surrounding bone [4].  
For this purpose, we have carried out the present five-year study comparing the result between three and nine microthreads with an extension between 1 and 3 mm on the surface of the crestal module.  
The microthreads are extremely effective in increasing the success rate of dental implants. Studies have shown that implants with microthreads have a greater success rate than traditional implants. This is due to the increased strength of the bond between the implant and the surrounding jawbone.  
Clinical findings suggest that the resulting changes, particularly in implant design, have contributed to the success of dental implantation [29]. These new designs help with load transfer to cancellous bone rather than cortical bone; thus, less cortical bone resorption may occur [10].  
Our study focused on the crest module and the influence of the number of microthreads on the B.L. The crest module is the neck portion of the implant and plays a critical role in minimizing stress on the neck portion of the dental implant [13].  
Microthreads are a feature of dental implant designs that consist small fine threads to help secure the implant after insertion into the implant site. Implant systems with microthreads are designed to provide greater implant stability and longer-term success than traditional implant systems. However, there are a wide variety of designs [30]. For this reason, we used the same implant with different numbers of microthreads to standardise the material, diameter, length, platform, and surface (RBM).  
Figure 5: Box plot of bone level for both implant designs at 5 years after loading (deadline), with no statistically significant difference between them. S = Standard implant, Q = Quattro implant. O = Outliers. * = Extreme outliers.  
Among the factors of the crest module that have attracted the attention of researchers, the following are worth noting:  
* Collar design;
* Presence of microthreads;
* Beginning of microthreads;
* Number and length of microthreads.
## 4 Distribution of the Subspace-Based Damage Detection Techniques
### Distribution of the Papers on Combined Subspace System Identification Approaches  
Table 15 in Appendix A shows the studies which used combined subspace system identification techniques. Based on results presented in the table, a total of 13 studies have used combined subspace system identification algorithms for analysis of various test structures. Though subspace system identification algorithm is originally a TD identification approach, some researchers have developed the FD version of the combined subspace system identification algorithm for identification of the vibration parameters [81; 171]. In order to meet interpretation challenges associated with system identification obtained from measured sensor data, [PERSON] [81] presented two FD system identification methods by adopting ERA and the McKelvey subspace system identification approaches. FE model of a plate structure was used for verification of the proposed algorithms. The methods were able to predict natural frequencies and damping ratio with a high accuracy. [PERSON] [171] proposed a two-step subspace algorithm by calculating minimal realization of the power spectrum samples and a canonical spectral factor. A numerical example is provided to illustrate the performance of the proposed algorithm. Serious drawbacks regarding reliable performance of the algorithm dealing with short data records and corrupted data were reported. Several studies are concerned with improving the performance of the combined subspace system identification algorithm [41; 42; 172; 173] to deal with these problem. [PERSON] and [PERSON] [41; 42] presented a theoretical framework to extract actual physical parameters of structures using a physics-based model and a data-driven mathematical model. Numerical model of a multi-DOF shear building structure and experimental verification test of a six-story steel frame structure under support excitation were tested. The proposed grey-box framework has shown a promising performance  
for SHM of civil engineering structures exposed to base motions. [PERSON] et al. [172] developed a novel multivariate input-output SSI-COV formulation for modal parameter identification. A 15-DOFs numerical example and an experimental application consisting of a thin-walled metallic structure were used for verification. The obtained results were similar to those reached by data-driven method. [PERSON] and [PERSON] [173] introduced data-driven input-output N2 SID using convex nuclear norm optimization. Mathematical formulations are furnished to derive the theory of the N2 SID algorithm. The sequence for derivation of the system parameters from N2 SID was clearly demonstrated. Table A3 provides the information of the selected papers which applied combined subspace system identification approaches.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 4 Proposed Dual Kalman Filter Structure for RTK and Its Rationales  
This section begins with a brief introduction to a conventional KF-based RTK approach with its outcomes. Subsequently, the dual KF structure proposed in this paper and the rationale behind it are investigated.
## 3 Results  
### Intraseasonal Variations of GPP  
MOD17A2H GPP has been extensively employed to evaluate the terrestrial carbon balance [1]. However, to have confidence in GPPMOD, it is critical to validate it against in situ measurements [49]. As shown in Figure 4a-c, the 8-day GPPMOD and GPPEC exhibit close agreement in their seasonal patterns, with peaks in July (May) for the rice (wheat) growing seasons across the three rice-wheat-rotation cropland sites. The GPP increases during the no-planting period in 2017 (Figure 4a), mainly due to weed photosynthesis. GPPMOD underestimated GPPEC during the rice (wheat) active growing periods from July to September (from March to May), with IA = 0.56 and RMSE = 47 g C m\({}^{-2}\) (IA = 0.61 and RMSE = 29 g C m\({}^{-2}\)) across the three sites. However, GPPMOD performed well during the intercropping periods from late May to early June (or late November), with IA = 0.77 and RMSE = 8 g C m\({}^{-2}\) across the three sites.  
The seasonal cumulative GPPEC at the three cropland sites was larger for the summer rice growing seasons (1170, 1066, and 889 g C m\({}^{-2}\) for Shouxian, Dongtai and Dafeng, respectively) than for wheat (609, 848 and 701 g C m\({}^{-2}\), respectively) (Figure 4d-f). The seasonal cumulative GPPMOD was significantly lower than GPPEC during the wheat growth seasons, with a 32-47% underestimation of the seasonal cumulative GPPEC at the three sites; the seasonal average GPPMOD was 27-47% lower than the seasonal cumulative GPP during the summer rice growth seasons (Figure 4d-f).  
Figure 3: Flowchart of the random forest model for estimating, validating, upscaling gross primary production (GPP), and calibrating the MOD17A2H GPP product.
# Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications
## 2 Related Work
### Auditing Mechanism Based on Blockchain  
Blockchain can be regarded as a time-stamped transaction recording system, which can record all transactions that have occurred on the blockchain. The transactions recorded on the blockchain are open, transparent, decentralized and hard to tamper with. To better evaluate the spectrum accessing service provided with the MNOs, it is important to supervise and audit the MNOs' behaviors. [PERSON] et al. propose a novel auditing mechanism supporting public auditing on shared data stored in the cloud. To improve the efficiency of auditing multiple tasks, the mechanism is further extended to support batch auditing [20]. [PERSON] et al. design an identity-based dynamic data auditing scheme that is capable of performing dynamic auditing for big data storage service. To guarantee the correctness of the data update each time, a data structure, namely a Merkle hash tree, is used. The scheme can authenticate block tags and support dynamic operation with integrity assurance [21]. For the illegal authorization and key disclosure risks, [PERSON] et al. design a blockchain-based auditing scheme; the auditor in the scheme can detect the malicious behaviors. Two smart contracts on Ethereum are respectively adopted to trace the two misbehaviors [22].  
Table 1 summarizes the general characteristics of the existing related work. In this paper, we present a new architecture for fair and secure licensed spectrum distribution towards a future 6G network. Spectrum distribution is a common spectrum allocation technology. We first introduce the latest development status. We adopt the spectrum using behavior detection technology to discover the dishonest behaviors, which lead to the unfair flaws in the existing spectrum management. By adopting blockchain technology, a two-level fair licensed spectrum distribution model is proposed; besides fairness, the model can also provide security assurance defending against common cyber attacks.