esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 3 Methodology and Data
### Methodology of the Study
#### 3.2.1 Methodology of Data Preparation
Thus, creating a total of 72 individual regression models was necessary. Each model was initially created as a full model containing all explanatory variables. Then, the final model containing only statistically significant explanatory variables and achieving the best quality was sought using the stepwise method. The quality of the regression models was evaluated using the coefficient of determination \(R^{2}\), with values ranging from 0 to 100%. The closer the value is to 100%, the better the model captures the variability of the explained variable, making it more reliable. Associated with this is the test of the significance of the overall regression model using Analysis of Variance (ANOVA), which tests whether the model is statistically significant and whether the regression function was correctly chosen. Rejecting the null hypothesis of the model's insignificance means that the created model is statistically significant and, thus, suitable for predictions. Figure 6: Example of failure in reporting vehicle numbers in one sensor. The created regression models were subsequently used to predict missing values during sensor failures. Based on the values of the input variables (day of the week, time, holiday, vacation, variables related to the pandemic, etc., listed in Table 3 above as explanatory variables) in a specific time interval, the models estimated the vehicle numbers for all the vehicle types. The estimated values were carefully reviewed, and any negative predictions were replaced with zero values. However, it was necessary to adjust the predicted values so that their sum corresponded to the cumulative number of vehicles of the respective type reported by the sensor after the failure and communication restoration. To preserve this cumulative number, each predicted value was adjusted by an individually determined correction coefficient, calculated as the ratio of the actual to the estimated cumulative vehicle numbers during the outage. In cases where the correction coefficient could not be calculated (specifically when the estimated values were summed to zero), the missing corrected values were replaced with values corresponding to the average obtained from the cumulative vehicle numbers after the communication failure ended. All corrected values were then rounded to integer values. To verify the accuracy of this procedure for correcting incorrect values during sensor communication failures, we calculated the accuracy of the estimates using statistical characteristics (mean, mode, median, standard deviation, maximum, minimum, and sum of values). Additionally, we quantified the deviations of the predicted corrected values from the actual values, both in absolute numbers of vehicles and as a percentage. The results for sensor 1, vehicle types, and directions are provided in Section 4.
## 4 Target Identification Method between Star Trackers
### Target Detection Based on Projection Time Information  
On the maximum projected frame after star removal, only candidate moving targets remain, which include stars that are not removed, and false target points composed of isolated noise, as shown below. In this paper, we propose a target detection method based on temporal information, which uses the trajectory of moving targets as well as the continuity of temporal information to detect moving targets while removing false targets. The principle of the algorithm is shown in Figures 8 and 9. Figure 9 shows the process of setting a tracking window based on speed to detect possible objects, and the specific steps are as follows.  
(1) The search starts with the candidate target \(P3\) with time number T = 3, and the search radius is set to R (R is the maximum motion speed threshold of the moving target). When the candidate target points of T = 2 and T = 4 are found in the search range, the distance \(D_{23}\) between T = 3 and T = 2 is calculated, and the distance \(D_{34}\) between T = 3 and T = 4 is calculated. If \(|D_{23}-D_{34}|\leq D_{th}\), (where \(D_{th}\) is the distance threshold, because the target is moving at a uniform speed, so \(D_{th}\) is set to a very small value), then P2, P3, and P4 are judged to be a candidate trajectory; if the distance condition is not satisfied, the trajectory is judged as false trajectory.  
(2) Determine the angle of the candidate trajectories \(P2\), \(P3\), and \(P4\) that meet the distance judgment conditions, and calculate the angle between the straight line composed of \(P2\) and \(P3\) and the straight line composed of \(P3\) and \(P4\) and denote it as \(\ldots\). If \(Angle_{234}\leq Angle_{th}\) (where \(Angle_{th}\) is the angle threshold. Because the target is moving in a straight line, it is set to a small value), then confirm that \(P2\), \(P3\), and \(P4\) are a true track, if not, it is judged as a false trajectory.  
(3) The estimated velocity of the moving target is obtained by calculating the mean value of \(D_{23}\), \(D_{34}\): \(V\ =\ (D_{23}+D_{34})/2\). The forward and backward searches are performed on the trajectories of \(P2\), \(P3\), and \(P4\) to obtain the estimated positions \(P1_{e}\) and \(P5_{e}\) of \(P1\) and \(P5\). A tracking window of a certain size is established with the estimated position as  
Figure 8: Time information frame after stellar removal.  
the center, and the detection of targets within the window is performed by binarization and center-of-mass localization to obtain all candidate targets within the window. If a candidate target, \(P\), satisfies \(|P-P_{e}|\leq P_{th}\) (where \(P_{e}\) is the estimated position \(P1_{e}\), and \(P_{th}\) is the position error threshold), then \(P\) is determined to be the true position of \(P1\) or \(P5\). In this way, the position of \(P1\), \(P5\) can be detected.  
(4) Use the time information to judge the time markers of \(P1\)-\(P5\) for the trajectory again and declare it as the real trajectory if it is the relation of contact increment.
## Data and Methodology
### Data Preparation
In this study, the SW solar radiation (\(<\)5 \(\upmu\)m) that comprises more than 99.5\(\%\) of total solar energy is assumed as the total incoming solar radiation at the TOA and surface. Although the BUGSrad defines the SW from 0-4 \(\upmu\)m, the solar energy at 4-5 \(\upmu\)m is excessively low (approximately 0.39% of total solar energy) [18]. Therefore, the effect of this difference is ignorable in the definitions of the SW between CERES (0-5 \(\upmu\)m) and BUGSrad (0-4 \(\upmu\)m). BUGSrad is the official radiative transfer model used by the product of CloudSat 2B-FLXHR [18; 26]. This model is based on the two-stream doubling-adding solution to the radiative transfer equation with the assumption of a plane-parallel atmosphere [27]. BUGSrad computes molecular absorption and scattering properties based on correlated-\(k\) formulation [26]. The model provides the two broadband-flux estimates. One broadband flux covers the SW at 0-4 \(\upmu\)m, and the other one covers the LW above 4 \(\upmu\)m. Based on the comparison experiment with observations of the Atmospheric Radiation Measurement program, the mean biases of SW and LW in clear-sky conditions are 1.2 and 2.2 W m\({}^{-2}\) at the TOA, respectively [27].
## 1 Introduction
[PERSON] _et al._[26] also presented an interesting interpolation method for modeling the 3D flow distribution based on ADCP-measured data, dividing the flow data into depth layers, thereby creating an areal 3D flow field. However, a method for rapidly measuring and visualizing areal 3D flow field data and visualizing it in a virtual 3D environment for analysis has thus far not been presented. The aim of this paper is to measure, model and analyze multitemporal 3D flow characteristics of a natural river over a continuous areal extent, quantifying flow velocity, 3D flow directions, volumes, water depth and their changes over time:  
First, we present a measurement and empirical modeling method that combines areal ADCP flow data with highly accurate positional data to yield a 3D point cloud of measured flow properties. The new processing method presented makes it possible to represent the raw measured three-dimensional flow field at the reach scale in a virtual 3D environment. Second, we demonstrate how this methodology can be used to capture river reaches at different scales and resolutions, from a several km-long reach at a coarse resolution, down to focusing on one meander bend at high resolution. Third, we model and analyze the 3D flow field in a meander bend over a two-week time period of a snow-melt-induced flood event. We analyze the spatial distribution of the high-velocity core and its movement, as well as changes in vertical flow patterns and river bed topography over the time period of the flood event. Finally, we use a 3D interpolation to transform the raw data onto a regular grid, which enables us to conduct a quantitative analysis of flow volumes and the volumetric distribution of different flow velocities in a given reach of a river. The focus of this paper is on presenting the survey and empirical modeling methodology representing raw measured flow data as a 3D point cloud and to show how it can be applied to analyze 3D flow fields. A detailed geomorphological analysis will be the subject of a future publication and is therefore beyond the scope of this work.
## 4 Correctness Argument  
### Performance Analysis  
There are \(\frac{n}{k}\) nodes in the shard, the time for a node to process a message is fixed at \(t_{1}\), and the message delivery time is fixed at \(t_{2}\). \(T\) is the time required to complete the intra-shard consensus process in this protocol. According to the dynamic transaction confirmation consensus process, there are four stages of message transmission. Nodes must process a \(Tct\) message, a transaction message, and \(Tct\) confirmation messages. The time complexity for achieving consensus is calculated in Equation (4).  
\[T=4t_{2}+(Tct+2)t_{1} \tag{4}\]  
Set \(T^{\prime}\) as the time required to complete consensus in the PBFT. The PBFT consensus process has a total of 5 stages of message transmission. Nodes in a shard need to process a request message, a pre-prepare message, \(\frac{2n}{3k}\) prepare messages, and \(\frac{2n}{3k}\)+1 commit messages. The time complexity of achieving consensus is calculated as shown in Equation (5).  
\[T^{\prime}=5t_{2}+(\frac{4n}{3k}+3)t_{1} \tag{5}\]  
Equation (6) is obtained from (4) and (5).  
\[T^{\prime}-T=t_{2}+(\frac{4n}{3k}+1-Tct)t_{1} \tag{6}\]  
In the intra-shard transaction consensus phase, the dynamic transaction confirmation consensus outperforms PBFT in terms of efficiency. The specific value of the improvement is jointly determined by \(t_{1}\), \(t_{2}\), \(\frac{n}{k}\), and \(Tct\).  
The values of \(Tct\) are set to \(Tct_{1}\) and \(Tct_{2}\), and the difference in message confirmation completion time is equal to the reduced transaction confirmation latency \(\triangle T\) as shown in Equation (7).  
\[\triangle T=|Tct_{1}-Tct_{2}|t_{1} \tag{7}\]  
If the \(Tct\) in the dynamic transaction confirmation consensus changes, the transaction latency will also change. \(t_{1}\) is usually measured in milliseconds, so \(Tct\) has little impact on system throughput. \(T_{0}\) is the time for the consensus committee to reach a PBFT consensus after a Leader node submits the microblock. The number of shards affects both the microblock verification and bigblock packaging time, as shown in Equation (8).  
\[T_{0}=5t_{2}+(\frac{4k}{3}+3)t_{1} \tag{8}\]
## 4 Conclusions  
To summarize, a compact VCSEL laser diode-based OFI microfluidic flowmetry sensor system was fabricated using innovative additive manufacturing techniques based on commercial photoresists. To achieve maximal sensitivity, an integrated collimating microlens on the VCSEL chips, as well as a focusing lens on the top surface of the channel chip, were designed and fabricated using dry thick resist film soft-transfer, direct laser writing, and inkjet printing. We obtained a significant reduction of the laser beam divergence (division by 5), as well as an efficient beam focusing at the center of the channel (beam size <20 \(\upmu\)m).  
To validate the sensing capability of this new system, we performed flow rate measurements in a microchannel with polystyrene particles as the tracing targets. The proposed miniaturized lensed system exhibited an outstanding signal level (higher by 16 dB compared to the standard bulk glass doublet lenses configuration tested under similar experimental conditions). These results demonstrated that our sensor could satisfy the flowing sensing requirements even in extremely tiny channels. To the best of our knowledge, this is the highest signal level in the existing relevant literature. We believe this miniaturized system can be a potential tool in the design of future lab-on-a-chip devices requiring precise and sensitive flowing velocity monitoring.  
Figure 8: Frequency spectra at different currents. (**a**) The miniaturized lensed system, and (**b**) bulk glass lens system.  
Figure 9: The signal level measured as a function of the current with error bars: miniaturized lensed system (in blue), bulk glass lens system (in red).  
**Author Contributions:** Conceptualization, T.C., J.P., and V.B.; Formal analysis, Y.Z., and Q.L.; Funding acquisition, J.P. and V.B.; Investigation, Y.Z., Q.L., J.-B.D., P.-F.C., F.M., B.R., C.T., and T.C.; Project administration, J.P. and V.B.; Resources, J.-B.D., P.-F.C., F.M., B.R., and C.T.; Software, Y.Z., and Q.L.; Supervision, T.C., J.P., and V.B.; Validation, Y.Z., and Q.L.; Visualization, Y.Z., Q.L. and B.R.; Writing--original draft, Y.Z.; Writing--review & editing, J.P. and V.B.  
**Funding:** This research was funded by the Agence Nationale de la Recherche (ANR) (grant number: ANR-15-CE19-0012 DOCT-VCSEL) and the China Scholarship Council (CSC).  
**Acknowledgments:** The authors acknowledge RENATECH (French Network of Major Technology Centers) within LAAS-CNRS, as well as [PERSON] and [PERSON] for their contribution to the DF microfluidic channel fabrication processing, and [PERSON] for the assembling and bonding steps. The authors also sincerely appreciate [PERSON] for his help in the electronic designing work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location
## 2 Graph Theory and Complex Network of Public Transportation  
The graph is composed of set V and set E, denoted as \(\mathrm{G}=(V,E)\). Set V is a set of vertices of a graph, which are generally used to represent individuals in a real system; set E is a set of edges of a graph, which is a set of relations between vertices, most of which are used to represent the relationships or interactions among individuals in a real system.  
In \(\mathrm{G}=(V,E)\), if any node for \(\mathrm{a},\mathrm{b}\in\mathrm{V}\), when \((\mathrm{a},\mathrm{b})\in\mathrm{E}\), \((\mathrm{b},\mathrm{a})\in\mathrm{E}\) may not be true, then we call this graph a directed graph. In the directed graph, two related nodes are usually connected by the edges with an arrow; when \((\mathrm{a},\mathrm{b})\in\mathrm{V}\), \((\mathrm{b},\mathrm{a})\in\mathrm{E}\), then we call this this graph an undirected graph. The edge of an undirected graph has no direction, and the relation between vertices is represented by an edge without an arrow. The shortest path in an undirected and unweighted graph is the path with the smallest total distance from the source node to the destination node among all possible paths, while the shortest path of an undirected and weighted graph is that with the smallest sum of weights.  
This paper uses graph theory and the basic theory of complex networks to model and analyze a city bus station network [6], taking the station as the node, and with edges existing between two adjacent stations belonging to the same bus line. The bus network studied in this paper is an undirected graph, which does not distinguish between uplink and downlink, and uplink and downlink symmetric stations are considered to be the same station [7].  
The adjacency matrix A (n * n) of the bus network is used to express the connection relationship between bus stations. n represents the number of stations in the network, \(a_{ij}=1\) indicates that the station \(i\) and station \(j\) belong to the same bus route and are adjacent; \(a_{ij}=0\) represents that station \(i\) and station \(j\) are not adjacent or do not belong to the same line. Figure 1 is an adjacency matrix of a line in a bus station network. This matrix uses 0,1 to represent the connection relationship of each station. Based on this matrix, the complex bus network diagram of Jinan can be modeled as shown in Figure 2.  
Figure 1: Adjacency matrix of a bus station network.
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 4 Experimental Results and Analysis
### Aging Effects on Retention Error
#### 4.3.2 Error Distribution with Aging  
In order to illustrate the aging effects on the retention behavior of DRAM modules, we explore the distribution of error across the banks and group of rows. Figure 6 illustrates the retention errors for pattern 0x00 in each bank of a Hynix 2 GB DRAM module and their corresponding distribution curve. In Figure 5(a)-c, the number of total retention errors across the banks is plotted at three aging points. We observe that the total retention errors in each bank increases consistently with aging as the wear-out effects occur. This phenomena is consistent for different retention times, as shown in Figure 5(a)-c.  
Retention errors for consecutive 512 rows are aggregated in a group in order to observe the changes across the rows. Figure 6(a)-c show the histogram and the approximate normal distribution curve of errors in a group of rows. Additionally, Table 2 presents the corresponding mean and standard deviation of the retention error distribution curves. We observe that as the device ages, the mean of the distribution curve shifts rightward. Moreover, the standard deviation of the distribution increases as the variation in total errors in a row across the bank is higher as the device ages. In these figures, we notice that the error distribution of higher retention times is more useful as they induce a high number of errors, and the difference between the new and aged/recycled modules is very evident. These retention-based signatures can be utilized to observe the evidence of device aging and classify the recycled devices by comparing them with the signatures of a new device.  
Figure 6: Retention error for 0x00 pattern in each bank with aging in a Hynix 2 GB DRAM module.
## 6 Transceiver Architectures' Classification and Basic Concepts  
This subsection focuses on the transceiver architectures and their related features such as the achievable optical spectrum efficiency (OSE), support for the digital compensation of chromatic dispersion at the receiver, and the minimum required electrical bandwidth of the receiver [14]. The transceiver architecture can be broadly classified based on the offered DoF or the number of dimensions, as illustrated in Figure 9.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Unsupervised Learning
#### 4.2.6 UAV Networks  
Future 6G networks will support high transmission data rates and wireless broadcast. Unmanned Aerial Vehicle (UAV)-assisted communication networks will be widely used towards achieving these challenges [90]. In UAV-NOMA systems, an UAV often acts as a flying BS to boost the capacity of an existing terrestrial network. In [91], a K-means clustering algorithm is used to spatially cluster correlated users and then a reinforcement Q-learning algorithm is used to place the UAV as BS in a 3-D manner. The authors in [92] proposed MLP and LSTM algorithms techniques to predict the optimal UAV location and optimize user throughout and system performance. The proposed model accurately predicts UAV position and enhances user throughput and system performance.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 5 Results and Analysis
### Discussion  
We perform five experiments in this section, including (1) IDR\({}^{2}\)ENet's performance on the real blur datasets RealBlur (Table 1 and Figure 8) and RWBI (Figures 9 and 10), (2) IDR\({}^{2}\)ENet's performance on real captured blur images (Figure 11), (3) IDR\({}^{2}\)ENet's performance on low-contrast blur images (Figure 12), (4) a comparison of IDR\({}^{2}\)ENet's complexity and running time with other networks (Table 2), and (5) ablation experiments to verify the role of the degradation estimation subnetwork and reblurring subnetwork of IDR\({}^{2}\)ENet (Figures 13 and 14, and Table 3). The overall results show that our IDR\({}^{2}\)ENet not only achieves good performance on various kinds of real blurred images, but it also has a smaller network complexity as well as better quantitative metrics, known as PSNR and SSIM. The results of the ablation study also demonstrate the effectiveness of our methods--reblur estimation and degradation estimation.  
However, there are still areas where our results can be improved. For example, the deblurring effect of IDR\({}^{2}\)ENet on real captured blurred images (Figure 11) can still be enhanced, which proves that the network's understanding of degradation representations in blurred images is perhaps not sufficient. Therefore, it might be useful to consider introducing GAN-based structures in the design of the degradation estimation subnetwork and reblurring network to enhance the understanding, constraint and utilization of degradation representations.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Stochastic Models
#### 3.2.7 PREMChlor  
PREMChlor is a probabilistic model that accounts for uncertainties in all key parameters required to simulate the remediation of chlorinated solvents. This tool can support the optimal remediation strategy based on the results of source and plume treatment scenarios. PREMChlor is the result of coupling the analytical model REMChlor with the Monte Carlo modelling package GoldSim. All input parameters are considered stochastic and are represented by probability density functions (PDFs). With PREMChlor, source remediation is simulated as partial removal of a mass by dissolution, advection, and decay, while plume remediation is modelled by time- and distance-dependent decay using the first-order sequential decay chain [404]. The results of the model include concentrations, mass release, cancer risk, and remediation costs. Although PREMChlor is a relatively new tool, it has been successfully tested, with the results published in the papers of Liang et al. [479; 480]. The PREMChlor model was evaluated for its ability to predict the impact of remediation on the TCE plume, taking into account uncertainties in seven key reactive transport parameters: initial source mass and concentration, correlation between source mass removal and concentration, source remediation effectiveness, groundwater velocity, background plume degradation rate, and plume treatment effectiveness.  
It requires the installation of the GoldSim player, which can also be downloaded free of charge.