esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Dynamics of Persistent Organic Pollutants in Water Bodies of the Aral Sea-Syrdarya Basin
### Partly Successful Problem Management in the Kazakh Part of the Aral Sea Basin  
The first step to save the Northern (Small) Aral Sea located in Kazakhstan was taken in 1992 when a temporary earnthen dam was constructed in the Berg Strait. In August 2005, with the completion of the capital Kokaral dam, the Berg Strait was blocked at the level of the Small Aral Sea of about 39.0 m BS. The level of the Small Aral Sea was rising intensively as a result of large winter releases along the Syrdarya River, and it reached 42 m BS in April 2006. At this level, the Small Aral Sea had the following parameters: volume 27.07 km\({}^{3}\), lake area 3288.0 km\({}^{2}\), maximum depth 12.5 m, and average depth 8.2 m [35]. As a result of a significant increase in the desalinized zone in the Small Aral Sea, the area of native commercial fish species expanded. The creation of this water body has played a positive role in the socio-economic development of this Kazakh part of the Aral Sea region.  
As a consequence of the intensive development of irrigated agriculture and the construction of drainage systems in Central Asia, a rapid increase in the collector-drainage water (CDW) volume has taken place. According to the available data from [PERSON] and [PERSON] et al. [35; 36], the volume of return water in the Syrdarya River basin increased from 6.72 to 12.70 km\({}^{3}\) per year from 1950 to 1970. The annual volume of return water in Central Asia between 1975 and 1990 was 33.1 km\({}^{3}\), of which 13.1 km\({}^{3}\) was in the Syrdarya River basin [37; 38]. About two-thirds (or about 8.5 km\({}^{3}\)/year) of the drainage water is discharged into the Syrdarya River system annually. The Syrdarya River system carries the main mass of various pollutants and salts. Between 1960 and 1990, according to [PERSON]. [39], the volume of CDW reached 13.5-15.5 km\({}^{3}\)/year in the Syrdarya River basin and 16.19 km\({}^{3}\)/year in the Amudarya River basin. As a rule, CDW contains increased concentrations of mineral salts, residues of nitrogen-phosphorus fertilizers, pesticides, and other toxic substances that significantly pollute the river water.  
Thus, the formation of the hydrochemical regime and water quality indicators in the lower reaches of the Syrdarya River occurs under the influence of CDW and industrial, with domestic effluents entering the river network in the middle and lower reaches of the river. The most dangerous and widespread toxic compounds in the Aral Sea basin are pesticides, especially when widely used on irrigated lands in the years of maximum chemicalization of agriculture (1950-1990).
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 3 Results and Discussion
### Mechanical and Electrical Properties of Graphene-Enhanced CuW Electrical Contacts  
The density, bending strength, electrical conductivity, hardness, and relative density of CuW and graphene-enhanced CuW electrical contacts materials were measured and calculated. The resulting data and the corresponding standard deviation are summarized in Table 1.  
As shown in Table 1, the bending strength and hardness of CuW electrical contacts materials without graphene are 1058 MPa and 234 HB. With the doping reinforcement of graphene, the mechanical properties increased significantly, and the bending strength and hardness increased to 1160 MPa and 248 HB, respectively. The occurrence of this phenomenon can be attributed to the improvement of the size of tungsten particles in the composites by graphene. The higher the graphene content, the smaller the size of tungsten particles in the system; therefore, the hardness increases. From the micro-morphology, the fine tungsten particles are dispersed in copper, which strengthens the weaker copper phase  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CuW80** & **SD** & **Gr-CuW80** & **SD** \\ \hline Density (g/cm\({}^{3}\)) & 15.47 & 0.06 & 15.49 & 0.03 \\ Bending strength (MPa) & 1058 & 5.66 & 1160 & 6.48 \\ Electrical conductivity (\%IACS) & 38 & 1.41 & 46.1 & 0.51 \\ Hardness (HB) & 234 & 4.32 & 248 & 3.74 \\ Relative density (\%) & 98 & / & 99.38 & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical and electrical properties of graphene-enhanced CuW composites and ordinary CuW composites.  
Figure 7: XRD patterns of CuW composites and graphene-enhanced CuW composites.  
indirectly and is resisted by fine tungsten particles in the process of deformation; therefore, the hardness is improved.  
From the data of electrical conductivity, it can be seen that the addition of graphene also have an effect on the electrical conductivity of CuW electrical contacts materials. Different from traditional reinforcing additive, the addition of graphene significantly improved the electrical conductivity of CuW electrical contacts materials, which increased from 38%IACS to 46.1%IACS. This is because graphene itself has higher electrical conductivity than copper, and also the graphene phase mainly exists at the microstructure defect and the grain boundary of copper and tungsten to construct a new continuous high conductivity network, which improves the electrical conductivity of materials.  
In summary, graphene-reinforcement plays a key role in the synergistic improvement of mechanical and electrical properties of CuW composites. As the density is close, after adding graphene, the bending strength, hardness, and electrical conductivity of CuW composites increase by about 10%, 6%, and 20%, respectively.
## 5 Simulation Results
### Simulation Results
#### 5.2.2 Packet Reception Ratio  
Packet reception ratio is calculated as the percentage of total packets received to total packets transmitted. Figure 11 compares packet reception ratio (PRR) with a different number of asymmetric links. The figure shows that, if the number of asymmetric links is below 30%, the PRRs of both protocols remain the same. However, if there is an increase in the number of asymmetric links, the PRR curve of ASYM-MAC declines much faster than COASYM-MAC.  
\[PRR=\frac{\text{Number of received packets}}{\text{Number of transmitted packets}} \tag{7}\]  
Figure 11: Packet reception ratio: (**a**) for 27 nodes; (**b**) for 54 nodes; and (**c**) for 90 nodes.  
It was also observed that, if the link asymmetry occurs from sender to receiver, then the PRRs of both protocols remain approximately the same; however, if the link asymmetry occurs from either receiver to sender or on both ends, then COASYM-MAC's PRR is far better than ASYM-MAC. The PRR was also compared with the existing ASYM-MAC protocol with varying asymmetric nodes. The PRR of COASYM-MAC is better than ASYM-MAC because, in ASYM-MAC, the receiver cannot receive data in the case of link asymmetry. On the other hand, COASYM-MAC overcomes this problem by using a cooperative communication technique. The sender waits \(t\) time and then sends a long preamble. After getting the receiver's attention, it sends the data to the receiver. In this long time period, the receiver receives few packets compared to packets transmitted. The data reception rate decreases with the increase of asymmetric percentages. This is true for ASYM-MAC as well as COASYM-MAC, but, in the case of link asymmetry for COASYM-MAC, where the sender cannot transmit data to the receiver, it waits \(t\) time and sends data using cooperative nodes. In the case of 90% of link asymmetry, COASYM-MAC performs better than the ASYM-MAC in terms of PRR as it achieves 17% improvement for 27 nodes (Figure 11a), 19% for 54 nodes (Figure 11b), and 20% for 90 nodes (Figure 11c).
## 4 Experimental Results
### Earthquake Excitation Case  
To investigate the seismic control performance of the SPPTMD for structures in site classes I-IV, three seismic records per site class were selected. For each class, one record was utilized for both shaking-table tests and numerical simulations, while two additional records were used exclusively for simulations, and the seismic record parameters are  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Vibration-Reduction**} & \multicolumn{8}{c}{**Excitation Frequency**} \\ \cline{2-9}  & **1.5 Hz** & **1.7 Hz** & **1.9 Hz** & **2.1 Hz** & **2.3 Hz** & **2.5 Hz** & **2.7 Hz** & **2.9 Hz** \\ \hline Peak (\%) & 86.13 & 62.73 & 52.68 & 42.94 & 44.38 & 38.64 & 37.59 & 33.87 \\ RMS (\%) & 87.88 & 63.12 & 55.29 & 47.92 & 42.85 & 35.69 & 33.71 & 31.89 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vibration-reduction ratio of the model under resonance vibration condition.  
Figure 8: Frequency–domain displacement response.  
Figure 7: Displacement response of the model under resonance vibration condition.  
detailed in Table 2. During the loading process, the peak ground acceleration (PGA) was amplitude-adjusted to 50 gal, corresponding to the peak acceleration of a moderate-intensity earthquake with seismic intensity VI.
## 3 Scenario Description and Connectivity Network Configuration
### WiBACK Network Devices and Measurements  
WiBACK network monitoring software shows the implemented network topology and presents real-time data and characteristics of each link. In Figure 3 are shown established links between network nodes (Chalan-Almagra and Almagra-La Pena) with their respective signal levels measured in decibels (dB).  
According to WiBACK established parameters to measure devices' performance, signal levels between 30 and 70 dB are referred to suitable performance, and optimal signal levels are those in the range of 51 to 70 dB [16]. Obtained network evaluation values are at optimal signal levels: Chalan-Almagra link presents values of 55 and 57 dB, and Almagra-La Pena link shows values of 51 and 52 dB.
## 4 Discussion  
The valuable information obtained from optical and SAR remotely sensed data allows us to evaluate the combined ability of the two sources of data by assimilating them into the crop growth model. We considered that there are two strategies to assimilate optical and SAR remotely-sensed data into the crop model. One strategy is to retrieve state variables from the optical data and SAR data individually and then assimilate these parameters into the crop model on the date of observation [46; 47; 48]. The advantage of this method is that the two data sources can complement each other at a temporal scale. Another strategy, which was adopted in this study, is to combine the two data sources to retrieve  
Figure 8: Winter wheat yield estimation (**a**) without assimilation and (**b**) with EnKF assimilation.  
Figure 7: Regional winter wheat yield estimation without and with the EnKF assimilation method (**(a)** without assimilation; (**b**) with EnKF assimilation).  
state variables (e.g., soil moisture) through a mechanism model and then assimilate the retrieved parameters into the crop model. An important issue for this strategy is the time consistency of the two data sources. For this study, we assumed that the NDVI will not change dramatically in a short time. In this paper, the SM was assimilated into the WOFOST model as a state variable using the EnKF assimilation algorithm to estimate the winter wheat yield at a regional scale. The SM images were retrieved by the water cloud model using Sentinel-1 C-band SAR data and Sentinel-2 MSI optical data.
# Resistance of Concretes to External Chlorides in the Presence and Absence of Sulfphates: A Review
## 2 Resistance of Concrete to External Chlorides
### The Role of Chloride Binding  
When external chlorides permeate into concrete, they can exist as free ions dissolved in the pore water, can be bound chemically in the form of Friedel's or Kuzel's salt or bound physically to the surface of the hydration products (e.g., C-S-H). Since it is free chlorides that can induce reinforcement corrosion, chloride binding is beneficial to the durability of a concrete structure. Furthermore, the formation of Friedel's salt can also lead to pore blocking, slowing down the rate of chloride ingress [1, 43, 44]. Hence, the higher the chloride binding ability of cement, the less likely it is for chlorides to migrate through the concrete cover to the embedded steel reinforcement.
# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 2 Materials and Methods
### Deep Learning Merging Model Training  
Statistical analysis (Figure S1) shows that GPP data belong to heavy-tail distribution. The characteristic of the skewed distribution data is that there are more samples with little influence and fewer samples with substantial impact. The concrete manifestation in GPP data is that the number of pixels near the value of 0 is far more than that of other values. In addition, the larger the value, the smaller the number of pixels. Such datasets will make the deep learning network perform well in the low-value range, while the efficiency is relatively low in the high-value range, resulting in overall reduced accuracy. Therefore, the log1p function converts GPP data to make it more obedient to Gaussian distribution during data preprocessing.  
The period from 1982 to 2014, when CMIP6 GPP data and GLASS GPP data coincide, was selected to establish the relationship between input data and reference data using the deep learning network. The GPP data simulated by 23 CMIP6 models and the reference data in the same month were matched as a group, totaling 396 samples. All samples were divided into three datasets according to time: training (1982-2010), validation (2011-2012), and test dataset (2013-2014). The number of iterations and the initial learning rate were 150 times and \(1\times 10^{-4}\) during the training stage, respectively.  
Figure 2 shows loss function curves for training and validation datasets. As shown in the figure, the loss reduced sharply in the first ten iterations of the training process. Subsequently, it shows a fluctuant reduction. The model is stable after 77 iterations, with a decreased loss. Loss no longer changes after 112 iterations, indicating that the model has been trained.
## 3 Results
### Single Discriminator GAN Model with Only DSC Loss Functions (Exp_model_2)  
Another experiment compares the model's performance with only the DSC loss function. We kept all parameters the same for this experiment, along with the same training dataset. The loss function for the update of the generator is:  
\[l=l_{DSC}+10^{-3}l_{Gen} \tag{13}\]  
For the testing, we took slices from 15 to 145, 30 to 180, and 25 to 145 in axial, coronal, and sagittal planes, respectively, similar to the training data. Thus, we have 130, 150, and 120 slices per subject in the axial, coronal, and sagittal planes, respectively. We chose twenty-five subjects from the dataset which were not included in the training data. We extracted slices from each subject, calculated the average DSC score for that plane, and calculated the standard deviation for each calculated average. As discussed, the test data were predicted using three models (the proposed model and two models addressed in the experiment to check the improvement). As we aim to improve the DSC score by providing additional dissimilarity loss to the model, we compared and focused the result mainly on the DSC scores obtained by the models.  
We evaluated the models using another evaluation metric, VOE, as in Table 1. From the table, it can be seen that the VOE score is decreased by up to 2% in some cases of the axial and coronal planes. The lower the value of VOE, the better the result will be. Hence, we can say that our model has less volumetric error in case of the axial and coronal planes. However, the score for the sagittal plane is similar to the Exp_model_1. As the values for sagittal planes are not higher than the Exp_model_1, and the values in two other planes are less than the Exp_model_1, we can claim that our proposed model outperforms the other models.  
In Table 2, we compared the DSC scores of the models. We extracted 2D images from a plane and segmented it to create the segmented 3D image from those 2D slices. In the axial plane we can see that the DSC score for the WM, GM, and CSF, are higher in the proposed model by 1% than the exp_model_1. Also, if we compare it with the exp_model_2, the scores are improved by 2% for all classes. Hence, in the axial plane we can claim that our proposed model is better than other models. For the coronal plane, the scores are slightly higher in the proposed model than in the exp_model_1, but not by a significant margin.  
On the other hand, the scores are greater than scores of exp_model_2 by up to 1%. The same condition occurred in the sagittal plane, where the score of the proposed model is higher than exp_model_2 by 1% but had just a slight improvement in comparison to the exp_model_1. However, we have already seen that the VOE for the axial and coronal planes is less in the proposed model. Also, in the sagittal plane, our proposed model's scores are not less than the other models. Hence, we can claim that our model outperforms other models in the segmentation of the brain tissues.
# A Multi-Period Optimal Reactive Power Dispatch Approach Considering Multiple Operative Goals
## 2 Proposed Mathematical Modeling
### Objective Function  
The proposed objective function (OF) given by Equation (1) is made up of three elements described in Equations (2)-(4); each term minimizes the deviations of the control variables from period to period. The first term, labeled the TVD (total voltage deviation), seeks to minimize the voltage difference of the set of pilot nodes with their reference values. These voltage references are changed to typical values along the optimization horizon. The second term, labeled the TQS (total reactive shunt deviation), seeks to reduce the movement of shunt elements between continuous periods. Finally, the third term, labeled the TQG (total reactive generator deviation), aims to maintain reactive power reserves; that is, the generation units are at a floating point of reactive power. In this case, \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\) are penalty factors associated with each objective; \(v_{it}\) is the voltage magnitude at pilot bus \(i\) at time \(t\); \(V_{ref,it}\) is the reference magnitude of pilot bus \(i\) at time \(t\); \(n^{S}_{kt}\) and \(n^{S}_{k(t-1)}\) are the step numbers of shunt element \(k\in\mathcal{S}\) in period \(t\) and period \(t-1\); \(q^{S}_{gt}\) is the reactive power generated by generator \(g\) in period \(t\); finally, \(\mathcal{T},\ \mathcal{S}\), and \(\mathcal{N}p\) are the sets of periods, shunt elements, and pilot nodes, respectively.  
\[\text{Min }(\beta_{1}\text{TVD }+\beta_{2}\text{TQD }+\beta_{3}\text{TQG}) \tag{1}\]  
\[\text{TVD}=\sum_{t\in\mathcal{T}}\sum_{i\in\mathcal{N}_{p}}\Bigl{(}v_{it}-V_{ ref,it}\Bigr{)}^{2} \tag{2}\]  
\[\text{TQD}=\sum_{i\in\mathcal{T}}\sum_{k\in\mathcal{S}}\Bigl{(}n^{\mathcal{S}} _{kt}-n^{\mathcal{S}}_{k(t-1)}\Bigr{)}^{2} \tag{3}\]  
\[\text{TQG}=\sum_{t\in\mathcal{T}}\sum_{g\in\mathcal{G}}\left(q^{\mathcal{G}}_{ gt}\right)^{2} \tag{4}\]
# DataMatrix Code Recognition Method Based on Coarse Positioning of Images
## 3 Method of Adaptive Sampling Grid Partitioning
### Waveform Extraction  
The preprocessed image is converted into a gray value matrix, denoted as GreyMatrix, as shown in Equation (1):  
\[GreyMatrix=\begin{bmatrix}a_{11}&&a_{12}&\cdots&a_{1n}\\ a_{21}&&a_{22}&&a_{2n}\\ &\vdots&&\ddots&\vdots\\ a_{n1}&&a_{n2}&\cdots&a_{nn}\end{bmatrix}_{256\times 256}, \tag{1}\]  
where \(a_{ij}\) represents the gray value of pixel \(p(i,j)\), where \(i\) represents the row and \(j\) represents the column. Then, the cumulative gray deviation value of \(p(i,j)\) in the horizontal direction is shown in Equation (2):  
\[f(j)=\begin{cases}\sum\limits_{n=1}^{256}\left|a_{n(j+1)}-a_{nj}\right|&,\ \ 1 \leq j\leq 255\\ \sum\limits_{n=1}^{256}a_{nj}&,\ j=256\end{cases}. \tag{2}\]  
The cumulative gray deviation value of \(p(i,j)\) in the vertical direction is shown in Equation (3):  
\[f(i)=\begin{cases}\sum\limits_{n=1}^{256}\left|a_{(i+1)n}-a_{in}\right|&,\ \ 1 \leq i\leq 255\\ \sum\limits_{n=1}^{256}a_{in}&,\ i=256\end{cases}. \tag{3}\]  
Figure 4b,c display the distribution of gray deviation values in the horizontal and vertical directions of Figure 4a, respectively. The two-dimensional barcode is transformed into a one-dimensional waveform for analysis.
## 1 Introduction
[PERSON] _et al._ developed a long-term Vth calculation formula based on the R-D model and noted that degradation caused by NBTI effect could cause a delay fault [14]. Moreover, NBTI may cause significant noise margin changes for digital circuits during the entire lifetime of circuits [15], and thus, transient fault rates are difficult to estimate. In [16], the authors proposed a methodology for modeling the transient fault propagation characteristics in the presence of process variation effect and obtained accurate estimation of transient fault rates for logic circuits. However, transient fault rate analysis under the influence of both environmental noise and aging effects has not yet been investigated in detail. In this paper, we propose an NBTI-aware statistical analysis method to address transient fault rate estimation for combination logic circuit that considers both environmental noise and the NBTI effect. Our work is based on probability voltage transfer characteristics (PVTC) analysis and statistical modeling, which can provide accurate and efficient estimation of NBTI-aware transient fault rates, along with available statistical static timing analysis tools (SSTA) [17]. It should be noted that by introducing high-k/metal gate transistors in sub-45-nm technology, the PBTI effect becomes comparable to the NBTI effect [18]. Since the degradation effect of PBTI is similar to that of NBTI, the proposed approach can be easily extended to consider the PBTI effect as well. The remainder of the paper is organized as follows. In Section 2, the principle of PVTC method is described. The procedure for performing PVTC analysis for single- and multi-gate circuits is presented. The NBTI-aware transient fault rate estimation is also introduced. Verification of the effectiveness of the proposed method by comparing the transient fault rate estimation results between the PVTC method and MC simulation is presented in Section 3. Moreover, the NBTI effect on changes in circuit characteristics and transient fault rates are also discussed. Conclusions are presented in Section 4.
## 4 Experiments and Analysis
### Data Collection  
From Shanxi Meteorological Administration, China, we got the ground surface meteorological observation data in Taiyuan, a city of Shanxi Province, China from May 2007 to October 2007, May 2008 to October 2008, and May 2009 to October 2009, which are the rainy seasons in that city. The specific meteorological elements included the day's atmospheric pressure, relative humidity, dry and wet bulb temperature, wind speed, and accumulated precipitation at 20 o'clock (Beijing Time, equivalent to 1200 UTC), which we took into our experiments.