esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Study Area and Data
### Data  
The 36 ascending SAR images used in this paper are from Sentinel-1A launched by the European Space Agency (ESA) ([[https://scichub.copernicus.eu/dhus/#/home](https://scichub.copernicus.eu/dhus/#/home)]([https://scichub.copernicus.eu/dhus/#/home](https://scichub.copernicus.eu/dhus/#/home)), 20 May 2020), the satellite carries C-band synthetic aperture radar antenna with a 12-day revisit cycle. It can monitor the ground micro-deformation effectively. The time span of the 36 SAR images are from January 2018 to March 2020. The incidence angle of the image center is 36.6\({}^{\circ}\). All of the SAR images are in VH polarization with a resolution of 1.2 m in range and 13.9 m in azimuth. Specific image parameters are illustrated in Table 2. The three arc-second Shuttle Radar Topography Mission (SRTM) DEM provided by the National Aeronautics and Space Administration (NASA) ([[http://srtm.csi.cgiar.org/srtmdata](http://srtm.csi.cgiar.org/srtmdata)]([http://srtm.csi.cgiar.org/srtmdata](http://srtm.csi.cgiar.org/srtmdata)), 2 June 2020) was used to remove the topographic phase. We adopted the ESA-released precise orbit data ([[https://qc.sentinel.e.e.esa.int/aux_poeorb](https://qc.sentinel.e.e.esa.int/aux_poeorb)]([https://qc.sentinel.e.e.esa.int/aux_poeorb](https://qc.sentinel.e.e.esa.int/aux_poeorb)), 22 June 2020) to execute orbital refinement and phase re-flattening for all interferometric pairs. The rainfall data were provided by China National Meteorological Science Data Center, the groundwater level and the position of water level monitoring wells were provided by Shanghai Geological Data Information Sharing Platform, and the night-light remote sensing data were the monthly composite product of NPP-VIIRS. The above data were used to analyze the mechanisms of surface subsidence in the study area. The spatial distribution of data is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline Imaging mode & IW \\ Data type & SLC \\ Band & C (5.63 cm) \\ Revisit cycle & 12 days \\ Polarization mode & VH \\ Time span & January 2018–March 2020 \\ Orbit direction & Ascending \\ Central incidence angle on the test site & 36.6\({}^{\circ}\) \\ Range resolution & 1.2 m \\ Azimuth resolution & 13.9 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific parameters of Sentinel-1A SAR images.  
Figure 3: Distribution of data points. The green triangles depict the water level of wells, green pentagrams denote the geological boreholes, red circles represent the PS feature points, and the red squares denote the weather stations.
## 2 Materials and Methods
### Analytical Methods  
The scope of the experimental process was to evaluate the efficiency of the PABR under different conditions in terms of organic load reduction and biogas and biomethane productivity. Initially, the COD was high (10,000 mg/L) in order to achieve good anaerobic digestion operation and then, as the municipal wastewater typically has COD concentrations under 1000 mg/L, the organic load of the feed mixture was progressively reduced while keeping the organic loading rate constant. Therefore, the bioreactor operated under various HRTs and T while the organic loading rate was kept at values near 1 gsCOD/L/d (as outlined in Tables 1 and 2).  
The reactor was fed with the synthetic wastewater and operated under mesophilic conditions (35 \({}^{\circ}\)C), for 242 consecutive days. Two distinct experimental cycles were conducted, as shown in Tables 1 and 2. During the first cycle, six consecutive experimental phases were conducted, with the reactor operating in a switching mode with constant HRT/T and OLR/HRT ratios. During the second experimental cycle, four experimental phases were conducted with similar OLR/HRT ratios, but the reactor operated in the ABR mode with no switching imposed. Throughout the experimental process, pH, total alkalinity, total suspended solids (TSS), volatile suspended solids (VSS), soluble chemical oxygen demand (sCOD), biogas production and methane content were monitored at regular intervals. TSS, VSS, sCOD and alkalinity were measured according to standard methods (APHA, 1995),  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**First Experimental Cycle** & \multicolumn{6}{c}{**Experimental Phases**} \\ \hline
**Operational Parameters** & **First** & **Second** & **3 rd** & **4 th** & **5 th** & **6 th** \\ \hline Duration (d) & 21 & 33 & 20 & 10 & 69 & 9 \\ HRT (d) & 10 & 6 & 4 & 3 & 2 & 1 \\ T (d) & 10 & 6 & 4 & 3 & 2 & 1 \\ OLR (g sCOD/L/d) & 0.91 & 0.96 & 0.95 & 0.90 & 0.91 & 1.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operational parameters imposed during the first experimental cycle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Second Experimental Cycle** & \multicolumn{4}{c}{**Experimental Phases**} \\ \hline
**Operational Parameters** & **First** & **Second** & **3 rd** & **4 th** \\ \hline Duration (d) & 28 & 15 & 15 & 22 \\ HRT (d) & 4 & 3 & 2 & 1 \\ OLR (gCOD/L/d) & 0.97 & 0.97 & 1.02 & 1.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operational parameters imposed during the second experimental cycle.  
while a GC-TCD (Shimadzu GC-2014, Duisburg, Germany) was used for the measurement of the methane content of the generated biogas.
# Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT +
## 2 System Model
Consequently, the received sequence on the a-th array element and the k-th sub-carrier can be written as  
\[\mathcal{R}_{\text{a}}(\text{k})=\mathcal{H}_{\text{a}}(\text{k})\mathcal{U} _{\text{k}}+\mathcal{W}_{\text{a}}(\text{k}),\text{k}=0,1,\ldots,N_{\text{K} }-1, \tag{2}\]  
where \(\mathcal{H}_{\text{a}}(\text{k})\) and \(\mathcal{W}_{\text{a}}(\text{k})\) are the corresponding channel frequency response and additive white Gaussian noise (AWGN), respectively. Considering the features of the AoAZ, AoAe, and ToA of the UCA, the channel response yields  
\[\mathcal{H}_{\text{i,a,k}}=\sum_{\text{i}=0}^{N_{\text{K}}-1}h_{\text{i}}e^{ |2\pi\text{k}\Delta/\tau_{\text{i}}+2\pi\tau_{\text{i}}^{T}\sin\phi_{\text{i }}\cos(\theta_{\text{i}}-\text{a}\Delta_{\text{A}})} \tag{3}\]  
where \(\Delta f\) is the sub-carrier frequency interval, and \(h_{\text{i}}\) the channel gain. Figure 2: The 3D coordinate system, in which the propagation model is demonstrated with one MS (the orange ball) and three propagation paths to the UCA mounted on a BS at the origin. Without loss of generality, we assume that the array elements of the UCA are identical and there is no mutual coupling or non-uniformity among them. Thus, for simplicity, we can normalize the gain in (3) [15]. Taking into account all the array elements of the Uniform Circular Array (UCA), sub-carriers, and multipath components, we can express (2) in matrix form as  
\[\mathbf{R}(\theta,\phi,\tau)=\mathbf{S}(\theta,\phi,\tau)\mathbf{H}+\mathbf{W} \tag{4}\]  
with \(\mathbf{H}\in\mathbb{C}^{\mathbb{N}_{\mathbb{R}}\times 1}\), \(\mathbf{W}\in\mathbb{C}^{\mathbb{N}_{\mathbb{R}}\times 1}\). The steering matrix \(\mathbf{S}(\theta,\phi,\tau)\in\mathbb{C}^{\mathbb{N}_{\mathbb{R}}\times N_{ \mathbb{R}}}\) is formed from the steering element function:  
\[s(\theta,\phi,\tau|\mathbb{k},\mathbb{a},\mathbb{i})=e^{j2\pi\mathbb{k}\Delta f _{\tau_{\mathbb{I}}}+j2\pi\frac{\tau}{\chi}\sin\phi_{\mathbb{i}}\cos(\theta_{ \mathbb{i}}-\mathbb{a}\mathbb{A}_{\mathbb{k}})}. \tag{5}\]  
The formation rule, as depicted in (6), shows only the indexes of \(\mathbb{k},\mathbb{a},\mathbb{i}\) within brackets, while omitting \(\theta,\phi,\tau\) to simplify the notations. Now, we will introduce a three-step MUSIC-Taylor-based method to jointly estimate the AoAZ, AoAE, and ToA using the steering matrix \(\mathbf{S}(\theta,\phi,\tau)\). \[\mathbf{S}(\theta,\phi,\tau)= \begin{bmatrix}s(0,0,0),&s(0,0,1),&\ldots&s(0,0,N_{ \mathbb{L}}-1)\\ s(0,1,0),&s(0,1,1),&\ldots&s(0,1,N_{\mathbb{L}}-1)\\ \vdots&\vdots&\ddots&\vdots\\ s(0,N_{\mathbb{A}}-1,0),&s(0,N_{\mathbb{A}}-1,1),&\ldots&s(0,N_{\mathbb{A}}-1, N_{\mathbb{L}}-1)\\ s(1,0,0),&s(1,0,1),&\ldots&s(1,0,N_{\mathbb{L}}-1)\\ \vdots&\vdots&\ddots&\vdots\\ s(N_{\mathbb{K}}-1,N_{\mathbb{A}}-1,0),&s(N_{\mathbb{K}}-1,N_{\mathbb{A}}-1,1),& \ldots&s(N_{\mathbb{K}}-1,N_{\mathbb{A}}-1,N_{\mathbb{L}}-1)\end{bmatrix}  \tag{6}\]
## 4 Discussion  
In the present study, we performed an exploratory whole-brain VBM analysis to investigate structural brain differences in CSVD patients with and without sleep disturbance. Results showed that both CSVD groups exhibited structural brain alterations compared to the HC group. And compared to the CSVD-GS group, the CSVD-PS group showed significantly reduced GMV in the bilateral calcarine cortex and increased GMV in the right caudate. In addition, the mean GMV of the right caudate and bilateral calcarine cortex were both associated with the total PSQI score in CSVD patients. The current study demonstrated that CSVD patients exhibited gray matter alterations in cortical (frontal lobe, inferior temporal lobe, occipital lobe, and cingulate gyrus) and subcortical (thalamus and caudate) regions compared with the HC. Similar to our results, the decreased GMV in the frontal lobe and thalamus has been observed in several other neuroimaging studies among CSVD patients [22; 23; 24]. The frontal lobe and thalamus are a part of the salience network and play an important role in cognitive processes. Therefore, the decreased GMV of the frontal lobe and thalamus may be associated with cognitive impairment in CSVD patients [22]. Additionally, previous neuroimaging studies examining CSVD patients with cognitive impairment or gait disorders also found increased GMV and functional connectivity within the caudate, suggesting that the caudate may be involved in motor disturbance in CSVD patients [22; 25]. Although our study showed that brain structure differed between the CSVD groups and the HC group, when comparing brain structure between the CSVD-GS group and CSVD-PS group, differences were only found in the right caudate and bilateral calcarine gyrus, indicating that these two regions may underlie sleep disturbance in CSVD patients. We identified significantly increased GMV of the right caudate in CSVD patients with poor sleep quality compared to CSVD patients with good sleep quality. GMV of the right caudate was positively associated with sleep quality severity. The caudate nucleus is a main component of the corpus striatum and, together with the putamen, forms the dorsal striatum [26]. The dorsal striatum is a major input pathway of the basal ganglia and is involved in sleep-wake regulation [27]. Experimental studies using animal models have shown that caudate lesions in cats induce restlessness and hyperreactivity [28]. Further, among inpatients with intractable epilepsy, electrical stimulation of the caudate affects cortical excitability [29]. Similar to our results, two previous studies found that the increased
# Protograph LDPC Code Design for Asynchronous Random Access +
## 2 System Model  
In this Section, we describe the system model, starting with the medium access policy in Section 2.1. Section 2.2 follows with the physical layer model.
## 4 Conclusions  
We present an assessment of the data from the IMERG algorithm for the Sao Francisco River basin in Brazil. For that, we used data from 18 surface weather stations, from which statistics were determined for each location. The results obtained depend on the basin region. The bias ranged from \(-1.67\) to \(0.34\) mm/day, the RMSE ranged from \(5.36\) to \(10.36\) mm/day and the values of the correlation coefficients between the daily data from the IMERG and rain gauge ranged from \(0.28\) to \(0.61\). The results obtained by Student \(t\)-test, density curves and regression analysis, in general, show that the IMERG is able to satisfactorily represent rain gauge data. We observed that the product is suitable for represent the precipitation variability, except in the region closest to the east coast, where the product presents the worst statistical metrics. A possible explanation for underestimation of precipitation in the NEB east coast is the type of weather systems which usually affect this region. Previous studies have suggested the formation of warm clouds over this region. Another hypothesis is that the product is not suitable to represent the variability of precipitation over the region closest to the east coast, due to the product's deficiency in efficiently representing points that are at the ocean-continent interface. This question is still open among researchers regarding precipitation estimation algorithms globally.  
Conceptualization, D.T.R. and C.M.S.e.S.; methodology, D.T.R. and B.G.B.; software, D.T.R. and J.S.d.R.; validation, R.S.A.P., W.A.G. and P.R.M.; visualizacao, R.S.A.P.; data curation, J.S.d.R. and H.J.F.d.S.; writing--original draft preparation, D.T.R.; writing--review and editing, D.T.R., C.M.S.e.S. and J.B.C.J.; All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data used in this manuscript are available by writing to the corresponding authors.  
We are thankful to the National Council for Scientific and Technological Development (CNPq) for the research productivity grant of the second author (Process n\({}^{\circ}\) 310781/2020-5).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Characterization of Asphalt Concrete Mixtures' Empirical Properties
#### 3.1.3 Water Sensitivity and Index of Retained Stability (ITSR and IRS)  
The water sensitivity of the specimens was evaluated through the ITSR. The result for the AC14-NA was equal to 87%, similar to the result of 86% for AC14-RCA. These values are not high but align with results obtained in identical asphalt concrete mixtures made with other comparable granitoid aggregates. However, the index of retained stability (IRS) was 93% for AC14-NA, while, for AC14-RCA, it was 99% (which can be considered an excellent result).  
The result observed in ITS\({}_{\text{wet}}\) (group of wet specimens), 2452 kPa, although affected by the presence of mortar in some of the RCA particles (which led to more significant water absorption), was not low. However, in the case of NA, the test result for the same group of wet specimens was only 1513 kPa, which is much lower. This value could be related to the low affinity between the aggregate and the bitumen--a known limitation of the granitoid used.  
Regarding the determination of the tensile strength (TS), it should be noted that the results obtained according to the European standards (EN) cannot be directly compared, for example, with US standards (ASTM). Among other essential differences, the EN 12697-12:2018 stipulates that the specimens must be compacted to obtain samples with porosities greater than or equal to the upper limit required in the field or using one of the following energy levels (in impact compaction, respecting EN 12697-30): 2 \(\times\) 25; 2 \(\times\) 35; or 2 \(\times\) 50 blows (in addition, some Technical Specifications also define a number of blows equal to 2 \(\times\) 75 [53], which was the same number used in the \"_CirMat_\" project). However, the ASTM D4867/D4867M\(-\)22 requires specimens to be compacted to (7 \(\pm\) 1)% air voids or with a level of voids equal to that expected in the field. As for the test temperature, EN 12697-12:2018 defines (15 \(\pm\) 1) \({}^{\circ}\)C (for asphalt mixtures which include binders with penetration \(\leq\)70 \(\times\) 0.1 mm, at 25 \({}^{\circ}\)C), while ASTM D4867/D4867M-22 specifies a higher temperature, equal to (25 \(\pm\) 1.0) \({}^{\circ}\)C.
# Study on Coupled Vertical Vehicle-Bridge Dynamic Performance of Medium and Low-Speed Maglev Train
## 4 Test Results and Analysis
### Parametric Analysis
#### 4.2.1 Vehicle Load
According to the code for design of medium and low-speed maglev traffic (CJ)/T 262-2017), the vertical first natural frequency of bridge design should be greater than \(64/L\), where \(L\) is the span of the bridge 6. The measured results show that the bridge had enough vertical stiffness. Figure 11 shows the effect of load on the vertical deflection of bridge. In Figure 10(a), the wire-type displacement sensor is arranged in the middle of the single-span steel beam, and the initial value was 42 mm. In each static levitation test, the train ran to the middle of the bridge. Under overload conditions, the displacement became 38.41 mm, and the corresponding maximum static deflection of the bridge was about 3.59 mm. Under the no-load condition, the displacement became 39.59 mm, and the maximum static deflection was about 2.41 mm, indicating that the maximum static deflection of the bridge increased by 1.18 mm when the load was increased by 6 tons. According to the static analysis, when the uniformly distributed load was located in the middle of the simple elastic supported beam, the maximum static deflection of the bridge can be expressed as follows:  
\[W_{\text{max}}=\frac{qbL^{3}}{384 EI}\bigg{(}8-\frac{4b^{2}}{L^{2}}+\frac{b^{3} }{L^{3}}\bigg{)} \tag{19}\]  
where \(W\) is static deflection, \(q\) is the mass of distributed load, and \(b\) is the length of distributed load. Assuming that the load distribution of a maglev train is uniform, the greater the mass, the greater the maximum static deflection of the bridge. Figure 10(b) shows  
Figure 10: Effect of vehicle load on the vertical acceleration of the bridge. the relative change of the vertical deflection of the bridge. Due to the existence of bridge vibration, the mid-span deflection changed dynamically with time, and the deflection under overload was greater than that under no-load, which was consistent with the law that vertical acceleration of bridge under overloaded conditions is greater, as shown in Figure 10, the law is shown in Figure 3b.
## 5 Discussion  
The analysis of different cases shows significant differences between charging schemes with single-criterion and multicriteria optimization. Differences are also noticeable in the case of different preferences of decision-makers. In the case where battery management is optimized only by maximizing the EV charging energy originating from renewable sources, 103.91 kWh (86.7% of required energy) comes from the solar power plant. Most of this energy comes directly from the power plant, at the time when the greatest sunshine and the arrival of the vehicle coincide (Figure 5c). The battery is charging and discharging more intensively but does not exceed the battery capacity.  
In the case of multicriteria optimization, the battery degradation criterion is influencing the charging/discharging process, and the amount of renewable energy in the charging process is reduced to 67.2 kWh (56%). The charging of the vehicle itself is now more balanced and does not reach the maximum allowable value.  
Figure 8: Utility function dependence on battery capacity for the additive case.  
Figure 9: Utility function dependence on battery capacity for the complementary case.  
Finally, in the complementary attribute case, the amount of renewable energy in the charging process is increased in comparison with the previous case to 87.7 kW (73%). The energy supply costs are higher, but the final solution demonstrates the balanced performance of all criteria.  
The methodology proved that the energy stored in the battery greatly depends on the load of other consumers in the station because the amount of solar energy is proportional to the ratio of individual load (battery, EV, consumers) and the total load of the network. In the complementary case, the balanced performance across renewable energy amount, total costs and battery degradation are prevalent. In the additive case, the performance of one criterion does not interact with the value of the other ones, but two criteria are worse than in the other two scenarios. The integral optimization of the battery size and schedule, as well as the EV charging optimization, will be the focus of our future research.  
In the case of an additive model, it can be seen that the maximum utility function is achieved at a battery capacity of 30 kWh. In case there is no battery installed, there is no battery wear (\(U_{bd}=1\)), the total costs are also close to the minimum values (\(U_{sc}=0.9\)), but the share of energy from renewable sources is the lowest (\(U_{res}=0.62\)). As the capacity increases, the value of this function increases to a maximum of (\(U_{res}=0.85\)). This value also determines the total maximum of the system. As battery capacity continues to grow, the share of renewables is not increased due to constrained battery charging and discharging power.  
In the complementary model, the emphasis is on the balanced values of all individual criteria. It can be seen that preference is given to lower values of battery capacity since then system costs and battery degradation are as important as the share of renewable sources. The optimal value of battery capacity is achieved with \(C=10\) kWh.
## 2 Study Area and Datasets  
### Study Area  
The study area is located in Zhangye, Gansu, China, and it is from 38.83\({}^{\circ}\)N to 38.93\({}^{\circ}\)N in latitude and from 100.32\({}^{\circ}\)E to 100.42\({}^{\circ}\)E in longitude, as shown in Figure 1. It belongs to the middle reaches of Heihe River that is the second longest inland river in China. The annual mean air temperature is 7.0 \({}^{\circ}\)C. The climate is dry and rainless, with an annual mean precipitation of 124.9 mm and a potential evaporation of 2000 mm. The ground is flat and cultivated with maize, vegetable crops, orchards and woodlands. Some built-up areas (villages) are spread throughout the study area. Every year from May to September, staple crops are grown, including seed corns, vegetables and fruit trees.  
To estimate the ET, remote sensing data that include ASTER images and HJ-1A/B images and ground-based observations that include meteorological variables and surface fluxes are required.
## 4 Optimal Operation Model of the RIES System
### Objective Function
#### 4.1.1 Energy Interaction Cost  
The \(C_{\text{buy}}\) includes the power transaction cost between the system and the bulk grid and the gas transaction cost between the system and the gas network. \(C_{\text{sell}}\) stands for profit from the sale of excess electricity. If the electric energy provided by each power equipment in RIES is not enough to fulfill its own power requirements, the system needs to acquire it from the bulk grid; if not, excess energy is sold. Similarly, the gas turbine and gas boiler in the system need to consume gas and produce electricity, so the RIES has to acquire gas from the gas network to maintain the system's operation. Therefore, the cost of purchasing and selling energy can be expressed as:  
\[C_{\text{buy}}=\sum_{t=1}^{24}{(P_{\text{b},t}^{\text{e}}k_{t}^{\text{e}}+Q_{ \text{b},t}^{\text{g}}k_{t}^{\text{g}})}, \tag{22}\]  
\[C_{\text{sell}}=[\sum_{i=1}^{24}{\sum_{n=1}^{N}{(P_{n,i}^{\text{e},s})}}-\sum _{i=1}^{24}{\sum_{m=1}^{M}{(P_{n,i}^{\text{e},c})}}-\sum_{i=1}^{24}{P_{\text{ load}}^{\text{e}}}]*k_{s}^{\text{e}}, \tag{23}\]  
where \(k_{t}^{\text{e}}\) and \(k_{s}^{\text{e}}\) are purchase and sale prices of electricity at time \(t\), respectively; and \(k_{t}^{\text{g}}\) is the gas price.