esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 4 Data-Driven Network Latency Processing  
Based on the proposed network architecture, we deployed a real VPP system in Shanghai, China, as shown in Figure 3. The VPP system manages the air-conditioning load of ten office buildings distributed in the central city of Shanghai and aggregates them into a whole to participate in the power system interaction. Each building uses a gateway cascade to access the air-conditioning load inside the building, and the boundary gateway uses a public network broadband for backhaul. The local network in this project has been carefully designed, and the network traffic and network resources have been calculated in detail. Therefore, the local network has very high reliability and can meet the needs of various VPP services. The backhaul network is the bottleneck for DERs to participate in a VPP service in this project.  
Fortunately, we use the above network latency acquisition mechanism to obtain the latency data of the network. In order to facilitate the evaluation and prediction of network latency, we set the second cycle to obtain the network latency data denoted as  
\[X=\{x_{1},x_{2},\ldots,x_{t},\ldots,x_{T}\}. \tag{1}\]  
Figure 4 shows a typical backhaul network latency. It shows the backhaul network latency at a continuous 24 h with a sample interval of 1 s, and we will zoom in to show 3 min of it. By observing the data, it can be found that there will be a period of discontinuity after a period of time interval in the sampling time dimension, and the network latency measurement may also be negative in the latency dimension. This is because of the synchronization error and the implementation of the delay probe; the data obtained in the actual acquisition process are not ideal. At the same time, we also find that the network latency may have short-term fluctuations, and from a larger time scale, we can find that when the network environment becomes bad, the network latency also has a certain trend. Our goal is to anticipate this trend and make the DERs in the VPP system able to participate in the interaction with a power system at a high response rate.  
Figure 3: The location of buildings (map data come from OpenStreetMap).
###### Abstract  
Ecosystem productivity models rely on regional climatic information to estimate temperature and moisture constraints influencing plant growth. However, the productivity response to these environmental factors is uncertain at the global scale and has largely been defined using limited observations from sparse monitoring sites, including carbon flux towers. Recent studies have shown that satellite observations of Solar-Induced chlorophyll Fluorescence (SIF) are highly correlated with ecosystem Gross Primary Productivity (GPP). Here, we use a relatively long-term global SIF observational record from the Global Ozone Monitoring Experiment-2 (GOME-2) sensors to investigate the relationships between SIF, used as a proxy for GPP, and selected bio-climatic factors constraining plant growth at the global scale. We compared the satellite SIF retrievals with collocated GPP observations from a global network of tower carbon flux monitoring sites and surface meteorological data from model reanalysis, including soil moisture, Vapor Pressure Deficit (VPD), and minimum daily air temperature (Tmin). We found strong correspondence (\(R^{2}\) > 80%) between SIF and GPP monthly climatologies for tower sites characterized by mixed, deciduous broadleaf, evergreen needleleaf forests, and croplands. For other land cover types including savanna, shrubland, and evergreen broadleaf forest, SIF showed significant but higher variability in correlations between sites. In order to analyze temperature and moisture related effects on ecosystem productivity, we divided SIF by photosynthetically active radiation (SIF\({}_{\text{P}}\)) and examined partial correlations between SIF\({}_{\text{P}}\) and the climatic factors across a global range of flux tower sites, and over broader regional and global extents. We found that productivity in arid ecosystems is more strongly controlled by soil water content to an extent that soil moisture explains a higher proportion of the seasonal cycle in productivity than VPD. At the global scale, ecosystem productivity is affected by joint climatic constraint factors so that VPD, Tmin, and soil moisture were significant (\(p\) < 0.05) controls over 60, 59, and 35 percent of the global domain, respectively. Our study identifies and confirms dominant climate control factors influencing productivity at the global scale indicated from satellite SIF observations. The results are generally consistent with climate response characteristics indicated from sparse global tower observations, while providing more extensive coverage for verifying and refining global carbon and climate model assumptions and predictions.  
ecosystem productivity; FLUXNET; GPP; MODIS-MOD17; phenology; SIF; SMAP-L4C
## 3 Validations of the Two-Phase and Porous Media Model  
### Geometries of the Small- and Full-Scale FGD  
A small-scale pilot FGD tower was used for validation first. Figure 2(a) illustrates the details of the facility. All the spray nozzles and four sieve trays are set up in the right tower while the left tower is closed in this series of experiments. The height of the tower is 2.4 m, and the diameter is 0.6 m. The spacing between each tray is 0.4 m, and the spray system is placed above the highest tray by 0.2 m as shown in Figure 2(a),b. The liquid Mg(OH)\({}_{2}\) solution is injected downward without spray angle by 33 nozzles, as arranged in Figure 2(b) from the top view. The array of nozzles has a radial distance of 0.05 m, and the diameter of each nozzle is 0.01 m. The pH value of liquid Mg(OH)\({}_{2}\) solution is 7.7 at the exit of nozzle. The gas with SO\({}_{2}\) enters the domain from a pipeline of a diameter 0.4 m in the rightmost face, as shown in Figure 2(a). With a series of chemical reactions shown in Section 2.2, the desulfurization process reduces the concentration of SO\({}_{2}\), while the sulfurous process increases the acidity of the slurry. The liquid slurry then is discharged through the outlet under the bottom tank. Furthermore, Figure 2(b) highlights the sampling locations to measure the SO\({}_{2}\) removal efficiency. Assuming point P in Figure 2(b) is the origin in the \(x\)-\(y\) plane, the locations of the sampling points from GT1 to GT3 are (\(-\)0.2, 0.93), (\(-\)0.2, 1.345), and (\(-\)0.263, 2.16), respectively, with meter as the unit.  
Figure 4 illustrates the full-scale FGD tower. The height and diameter of the tower are 23 and 7 m. The tower has three sieve trays, and the gas is transported by a pipeline with diameter of 3.6 m and it leaves the domain from the outlet with a diameter of 3.6 m. There are two sets of nozzle arrays. One is above the highest tray by 1 m with 192 nozzles, and the other is below the lowest tray by 1.2 m with 29 nozzles. Both sets of nozzles have spray angle of 65\({}^{\circ}\)and diameter of 0.05 m. The experiments for both small- and large-scale towers have already been conducted by China Steel Cooperation [34] with measured data for desulfurization and pressure at designated sampling points.
## 5 Empirical Results
### Box-Behnken Design (BBD)
Figure 20: BBD—residual plots for TC and CSL. Figure 18: BBD—response surface regression of CSL (%) versus T, Q, and SS. when the CSL was at 95%, the possible minimum total cost was 85,920 of the optimal settings were T = 3, Q = 3690, and SS = 1250. Moreover, the surface and contour plots for CSL are shown in Figure 22. The results suggest that the possible minimum total cost was in an area of light green (<74,000), the area of SS less than 1000, and the area of Q was less than 3500 according to the contour plot. The trade-off existed in the objective function of the model, which means, the more the customer service level is satisfied, the more of the total cost will be paid by the distributor.
## 4 The Application of Tikhonov Regularization in THz SAR
### Wavenumber Spectrum Reconstruction Model of THz SAR Azimuth Non-Uniform Sampling Signals
The relationship between \(\theta\) and \(\theta_{bw}\) can be expressed as  
\[-\frac{\theta_{bw}}{2}<\theta<\frac{\theta_{bw}}{2} \tag{27}\]  
According to Equation (25), the range of \(K_{x}\) can be represented as  
Figure 6:0  
Due to the fact that \(f_{k}\) is within a certain frequency band, the wavenumber spectrum of the SAR azimuthal sampling signal is band-limited. Its maximum value is related to the center frequency, radar bandwidth, and antenna beamwidth. Even with the presence of azimuthal envelope functions and potential effects caused by signal truncation processing, the SAR azimuthal sampling signal can still be considered approximately band-limited. For a given \(f_{k}\), the azimuth non-uniformly sampled signal can be represented as an \(N_{m}\)-dimensional vector \(s_{f_{k}}\), where  
Figure 6:1Taking azimuth wavenumber spectrum interval \(\Delta k<1/(x_{N_{m}}-x_{1})\), with  
Figure 6:2  
where \(\alpha\) is the oversampling factor and \(M=\lfloor K_{\max}/\Delta k\rfloor\), the wavenumber spectrum of SAR echo signal at \(f_{k}\) is represented as a \((2M+1)\)-dimensional vector \(S_{f_{k}}\), where  
Figure 6:3  
The established SAR azimuthal non-uniform sampling wavenumber spectrum reconstruction model is  
Figure 6:4  
where \(A(i,m)=e^{2\pi m\Delta k(x_{i}-x_{1})}\) is a linear operator matrix of dimensions \(N_{m}\times 2M+1\), and \(\tilde{n}_{f_{k}}\) is the error term at range-domain frequency \(f_{k}\).
## 1 Introduction
For example, biofbers pretreated with 6% CaO could increase methane production by 66% compared to untreated biofbers within 10 days [28]. The ethanol yield was improved 74% within 79 h when leaf star rice straw was pretreated with CaO and fermented at 30 \({}^{\circ}\)C [20]. Regarding the Ca(OH)\({}_{2}\) dose in pretreatment, a ratio of Ca(OH)\({}_{2}\) to NaOH of 7%-10% was reported to be the optimum for the pretreatment of extruded rice straw [29]. Degradation of 44% of lignin was also observed when sugarcane bagasse was pretreated by 8.5% Ca(OH)\({}_{2}\)[27]. In another study, 10% Ca(OH)\({}_{2}\) and an isothermal treatment time of 106.3 min could extract 70% of lignin from the hydrothermally treated sweet sorghum bagasse [30]. A total of 87.5% of the initial lignin was removed with 0.073 g Ca(OH)\({}_{2}\) per g of raw corn stover (RCS)at a mild temperature (e.g., 25-55 \({}^{\circ}\)C), while cellulose being not affected by the CaO pretreatment [31]. The reason that CaO shows a lower pretreatment efficiency is its weak alkalinity and the calcium-lignin complex which delays the lignin removal [32]. CaO is less expensive than NaOH while NaOH provides high alkalinity in the pretreatment, therefore, a mixture of CaO and NaOH was used for cost-effectively pretreating CS. In a study, CS was pretreated with a mixture of CaO and NaOH, and more of it could be converted to sugar than that pretreated only with NaOH [33]. CaO was also added to reduce the dose of the costly NaOH in an alkaline pretreatment of switch grass without a negative effect on the pretreatment efficiency observed [34; 35; 36]. Until now, however, research on CS pretreatment by a mixture of CS and NaOH only focused on its effect on sugar and ethanol production [34; 37]. There is a lack of research and knowledge on the effect of the pretreatment of CS by the mixture of CaO and NaOH on biogas production. Research on the economics of CS pretreatment, especially the CS pretreatment using a mixture of CaO and NaOH, has seldom been done. Therefore, in this study, NaOH combined with CaO was applied for the pretreatment of CS which was used as a feedstock along with swine manure for the mesophilic anaerobic co-digestion. Methane yield, biomass recovery, lignin content, reduced sugar, DT\({}_{80}\), and residue composition were measured to evaluate the effects of the CS pretreatment by the NaOH and CaO mixture and the potential cost-effectiveness.
# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
## 3 Simulation of Bearing Response and Fault Detection Results
Overall for both cases, PSO-based weight optimization significantly enhanced discrimination of the different types of bearing defects. It also seems that the performance of Isomap representation with PSO optimization equaled or slightly exceeded that of PCA with PSO results. Comparing the Isomap results with those of PCA showed that the distance between points within the same defect group increased, but the distance between the other defect groups has increased even more, making it much easier to distinguish them from each other. This is because the Isomap algorithm possesses the property of keeping the phase of the characteristic vector in the multidimensional space as much as possible, even at low dimensions (two- or three-dimensions). Thus, this advantage is particularly useful for distinguishing defective groups, since they provide an enhanced visualization when projecting high dimensional data onto a low dimensional space. Figure 8: Isomap representation of training characteristic vectors from simulated bearing model: conditions of intact (), inner-race defect (), outer-race defect (), and rolling-element defect (), (a) before, and (b) after PSO optimization. Figure 7: PCA representation of testing characteristic vectors from simulated bearing model: conditions of intact (), inner-race defect (), outer-race defect (), and rolling-element defect (), (a) before, and (b) after PSO optimization.
## 3 Database Augmentation  
Although 4386 signals were obtained, considering the complexity of the system, the amount of data was not sufficient. Therefore, the number of training data was increased through augmentation. In terms of data augmentation, noise addition and time shifting were effective in various studies [3,10].  
Because ultrasonic testing is usually performed in harsh environments, the possibility of the testing results being corrupted by electric noise, presented as white Gaussian noise (WGN), exists. Additive WGN was used to initiate this. The mathematical details of WGN are described in [12,13]. The WGN is added with two different signal-to-noise ratios (SNR) levels i.e., SNR 20 and SNR 15 (Figure 2). The number of testing signals increased threefold from 4386 to 13,158, including the original signal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Title 1** & **Flaws** & **No. of Signals** \\ \hline
1 & Crack & 2899 \\
2 & Lack of Fusion & 1196 \\
3 & Slag Inclusion & 634 \\
4 & Porosity & 493 \\
5 & Incomplete Penetration & 617 \\ Total & 5839 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ultrasonic weldment flaw database.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Weldment Flaw Database** \\ \hline
**Training Database** & \multicolumn{2}{c}{**Testing Database**} \\ \hline Crack & 1740 & Crack & 1159 \\ Lack of Fusion & 1076 & Lack of Fusion & 120 \\ Slag Inclusion & 571 & Slag Inclusion & 63 \\ Porosity & 444 & Porosity & 49 \\ Incomplete Penetration & 555 & Incomplete Penetration & 62 \\ Total & 4386 & Total & 1453 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ultrasonic weldment flaw database.  
Figure 2: Additive WGN: **(a)** non-_; **(b)** SNR 15.  
Another augmentation technique was adjusted after developing the augmentation database with WGN. In terms of ultrasonic testing signals, time shifting is equivalent to changing the distance between the transducer and the flaw location [10]. Therefore, new signals were created that shifted 20 and 10 points backward, and 20 and 10 points forward (Figure 3). Because the system features a sampling frequency of 50 MHz, one point on the time axis is 0.02 us. Because of time shifting, including the original signal, the testing signal number increased fivefold, from 13,158 to 65,790. This augmented dataset is referred to as the original database and is described in Table 4.
## 2 Materials and Methods
### Evaluation Metrics  
This study used the _precision_, _recall_, and _mAP_ evaluation metrics to evaluate the proposed deep learning model. _Precision_ is the ratio of correctly detected objects to the total number of objects identified by a model. _Recall_ is the ratio of correctly detected objects by the model among the actual objects. Therefore, a high _precision_ value indicates that the model accurately identifies areas containing objects with minimal false detections, whereas a high _recall_ value indicates that the model rarely fails to detect the actual behavior of objects. _mAP_, which is commonly used as a performance evaluation metric for object detection models, is the mean of the average _precision (AP)_ for each class based on an intersection over a union threshold of 0.5. _AP_ refers to the area under the _precision-recall (P-R) curve_, which illustrates the change in _precision_ values under varying _recall_ levels.  
\[Precision=\frac{TP}{TP+FP}\]  
\[Recall=\frac{TP}{TP+FN}\]  
\[AP=Area\;of\;Precision-Recall\;Curve\]  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i}\]  
True positive (_TP_) refers to instances wherein the model correctly identifies an object in an image, whereas false positive (_FP_) indicates the incorrect identification of an object that is absent. Finally, false negative (_FN_) indicates that the model fails to detect an object.
## 3 Results and Discussion
### Water Recycling with Urea and Sulfate Supplementation  
A second series of eight batch processes with _M. salina_ in a TLC photobioreactor with repeated water recycling was performed at the same simulated Mediterranean summer climate. To circumvent algal growth inhibition due to the accumulation of potassium ions, KNO\({}_{3}\) as a nitrogen source was replaced with urea in ASW and in the concentrated feed medium. In this case, 0.3 g L\({}^{-1}\) of urea was used per 1 g L\({}^{-1}\) of KNO\({}_{3}\), such that the molar nitrogen concentration remained constant. Additionally, the sulfate concentration in the growth medium was regularly measured and manually supplemented with 4.5 g L\({}^{-1}\) MgSO\({}_{4}\cdot 7\) H\({}_{2}\)O when the sulfate concentration dropped below 0.8 g L\({}^{-1}\) in order to restore the original sulfate concentration in the ASW medium.  
Analogous to the initial batch process series, a CDW concentration of 31.3 g L\({}^{-1}\) was achieved after 14 days with fresh ASW. However, the changes in nitrogen supply and sulfate supplementation surprisingly instigated an overall productivity increase by 37% from 15.6 g m\({}^{-2}\) d\({}^{-1}\) (2.3 g L\({}^{-1}\) d\({}^{-1}\)) with fresh ASW to a maximum of 21.3 g m\({}^{-2}\) d\({}^{-1}\) (3.1 g L\({}^{-1}\) d\({}^{-1}\)) with twice recycled water. Thereafter, the productivity remained stable with three- and four-times recycled water at 19.4 and 19.3 g m\({}^{-2}\) d\({}^{-1}\), respectively. In contrast to the initial batch process series, the productivity with five- to seven-times reused water decreased only slightly, from 16.8 to 14.9 g m\({}^{-2}\) d\({}^{-1}\). With urea and sulfate supplementation, specific growth rates during the exponential phase in the first 5 days of each process remained stable at 0.58-0.74 d\({}^{-1}\) with no significant difference at the 95% confidence interval level (Figure 2b). However, growth rates with urea and sulfate supplementation were significantly higher (\(p\) = 0.0052) than growth rates in the first series of batch processes with water recycling (Figure 2a), in which KNO\({}_{3}\) was used and sulfate was not regularly supplemented.  
In summary, the mean CDW at the end of eight serial batch processes with water recycling was 34.7 \(\pm\) 3.5 g L\({}^{-1}\), thereby increasing the mean overall productivity by 86% to 16.8 \(\pm\) 1.7 g m\({}^{-2}\) d\({}^{-1}\) compared to water recycling without sulfur supplementation and KNO\({}_{3}\) replacement with urea (Figure 3). Thus, the decline in microalgal growth and productivity over eight serial batch processes with sequential water recycling was effectively mitigated by regular nutrient supplementation and replacement of KNO\({}_{3}\) with urea to prevent the accumulation of potassium ions.
## 3 Results and Discussion  
### Simulation Analysis of the Migration Tank  
Before measuring the ion migration number, the electric field distribution in the electro-migration cell must be simulated to evaluate cell reliability. Considering the experimental tank as a prototype, a two-way coupling model of the flow field and electric field was established using the COMSOL system. An internal electric field distribution was obtained, and the model reliability, boundary conditions, and related parameters were determined.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 3 System Model and Problem Formulation
### Multi-Antenna Receiver Design  
In general, the AmBC receiver owes larger physical size and more power and thus can support multiple antennas receiving together and more complex receiving algorithms than the AmBC tag [18]. In addition, due to the insurmountable BER floor problem in a single antenna, while the spatial diversity can be used with multiple antennas to effectively improve the system's transmission performance, the multiple antennas at the backscatter receiver are exploited for joint reception.  
Signals from the RF source and the tag are received at the reader. There are two components in the signal from the RF source, i.e., one is the direct RF source-receiver link, and the other one is the reflected RF source-RIS-receiver link. Furthermore, there are also two components in the signal from the tag; i.e., one is the direct tag-receiver link, and the other one is the reflected tag-RIS-receiver link. Thus, for the \(k\)-th receiver antenna, the received signal \(r_{k}(n)\) can be expressed as:  
\[r^{k}(n)=\mathbf{H}_{sd}^{k}s(n)+\mathbf{H}_{td}^{k}d(n)+u^{k}(n), \tag{4}\]  
where \(\mathbf{H}_{sd}^{k}=h^{k}+\mathbf{h}_{sr}^{H}\mathbf{\phi}\mathbf{h}_{rd}^{k}\), \(\mathbf{H}_{td}^{k}=h_{td}^{k}+\mathbf{h}_{rr}^{H}\mathbf{\phi}\mathbf{h}_{rd}^{k}\), and \(u^{k}(n)\sim CN(0,\sigma^{2})\) denotes the equivalent noise at the receiver. The received signal at all \(K\) receiver antennas is expressed as \(\mathbf{r}(n)=\left[r^{1}(n)\,r^{2}(n)\,\cdots\,r^{k}(n)\right]^{T}\). Separating the two cases of \(d=0\) and \(d=1\), the received signal \(\mathbf{r}(n)\) can be written as:  
\[\mathbf{r}(n)=\begin{cases}\mathbf{H}_{sd}s(n)+\mathbf{u}(n)&,d=0\\ \mathbf{H}_{sd}s(n)+\mathbf{H}_{td}t(n)+\mathbf{u}(n)&,d=1\end{cases}. \tag{5}\]  
Taking Equations (1) and (2) into Equation (7), the following equation can be achieved:  
\[\mathbf{r}(n)=\begin{cases}\mathbf{H}_{1}s(n)+\mathbf{u}(n)&,d=0\\ (\mathbf{H}_{1}+\mathbf{H}_{2})s(n)+\mathbf{u}(n)&,d=1\end{cases} \tag{6}\]  
where  
\[\mathbf{H}_{1}=\left(\mathbf{h}+\mathbf{h}_{sr}^{H}\mathbf{\phi}\mathbf{h}_{rd}\right) \tag{7}\]  
\[\mathbf{H}_{2}=\eta\left(\mathbf{h}_{td}+\mathbf{h}_{tr}^{H}\mathbf{\phi}\mathbf{h}_{rd}^{k}\right) \left(\mathbf{h}_{0}+\mathbf{h}_{sr}^{H}\mathbf{\phi}\mathbf{h}_{rt}\right) \tag{8}\]  
Finally, the received signals on the multiple antennas are combined by a linear combiner at the receiver. The combine coefficient is represented by the vector \(\mathbf{f}\in\mathbb{C}^{K\times 1}\), generally, \(\left\|\mathbf{f}\right\|^{2}=1\) without loss of generality. Therefore, the received signal after the combiner can be expressed as \(y(n)=\mathbf{f}^{H}\mathbf{r}(n)\).