esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Intersection Delay Fairness Analysis
### Delay Model Fairness Analysis
#### 3.1.3 Sample Analysis  
To further analyze, this study takes a four-phase single-point intersection as a sample and randomly generates 362 sets of effective basic data for different intersections. Using the Webster model, the cycle length, green time ratio, and phase-average delay of each intersection in each group are calculated. Considering that the coefficient of variation can eliminate the influence of measurement scales and dimensions, it is chosen as a parameter to measure the degree of difference in data, such as phase-average delay.  
1. Prevalence of unfairness  
To measure the volatility of the dataset, the ratio of the standard deviation to the mean of the data is defined as the coefficient of variation, which is expressed as Equations (10)-(12). Here, the delay coefficient of variation and phase saturation coefficient of variation for each group of phase vehicles were calculated separately.  
\[CV=\frac{\sigma}{\mu} \tag{10}\]  
\[\sigma=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(x_{i}-\mu\right)^{2}} \tag{11}\]  
\[\mu=\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{12}\]  
The results indicate that unfairness is indeed widespread. The coefficient of variation for phase delay among the 362 intersection groups is greater than zero, with a mean of 0.37. Moreover, there is a roughly proportional relationship between the coefficient of variation for phase saturation and the coefficient of variation for phase-average delay. In other words, the greater the difference in phase saturation, the worse the fairness of phase-average delay. The scatter plot below illustrates the relationship between the coefficient of variation for phase saturation and the coefficient of variation for delay, as shown in Figure 3:  
2. The less saturated, the less fair  
By dividing the 362 sets of data into three saturation intervals, it is observed that the lower the intersection saturation, the greater the coefficient of variation of phase-average delay. The average coefficient of variation for saturation in the [0.0-0.2] range is 0.56, which is 1.87 times that of the other three saturation groups. This indicates a more severe unfairness phenomenon. Figure 4 below presents the curves of different saturation coefficients of variation.  
Figure 3: Phase delay coefficient of variation scatter.  
Combining the above analysis, it can be inferred that under low saturation conditions, with lower traffic volume and poorer fairness, there is a larger space for adjusting traffic efficiency and a greater necessity for improving the fairness of phase-average delay.
# Effects of Atmospheric Turbulence on Lensless Ghost Imaging with Partially Coherent Light
## 5 Conclusions  
As a summary, we have studied the quality and visibility of the GI through monostatic and bistatic turbulences, both theoretically and experimentally. Analytical expressions for the FOCFs, which contain the background noise and the image information, are derived for the cases of monostatic and bistatic turbulences with the help of a tensor method. It was found that the visibility and the quality of the image in two types of atmospheric turbulences were different. In bistatic turbulence, the visibility and the quality of the image all degraded by the turbulence due to the statistical independence of the two paths in the GI system. In monostatic turbulence, the quality of the image can keep invariant as that in free space in some range of turbulence strength, whereas the visibility decreases with the increase of the turbulence. When the distance from the light source to the detection plane is an order of a hundred meters, the GI information can remain a longer distance in monostatic turbulence than that in the bistatic case under the same condition. The experiments for GI through monostatic and bistatic turbulences were demonstrated in the laboratory. Our results agree well with the theoretical predictions and are consistent with the experiment results in [39].  
X.L. (data curation, writing, original draft, methodology); M.Z. (formal analysis, writing, review and editing); F.W. (supervision, writing, review and editing); Y.C. (supervision, project administration, writing, review and editing).  
This research was funded by the National Natural Science Fund for Distinguished Young Scholar (Grant Number 11525418), the National Natural Science Foundation of China (Grant Numbers 91750201 and 11474213) and the Qing Lan Project of Jiangsu Province.  
The authors declare no conflict of interest.
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 5 Performance Analysis
### Throughput  
The simulation results of the average throughput vs. the UE moving speed, the cache size, and its CDF are illustrated in Figure 13a-c, respectively. On average, as \(UE\) moving speed increases, the average throughput decrease. As shown in Figure 13a, the usage probability of packets in cache will be decreased if the UE moving speed increases, it implies that the average throughput will be decreases. Figure 13a shows that the average throughput is 8.6 (M/s) using the PMIPv6 with FMC under the UE moving speed is 5 (m/s). However, the average throughput of our proposed scheme with FMCL are 8.8 (M/s), 9 (M/s), 10 (M/s), and 11 (M/s) if the cache hit rate is 0, 10%, 40%, and 70%, under the UE moving speed is 5 m/s, respectively. Figure 13a shows that the average throughput of PMIPv6 with FMC was \(<\) that of our proposed scheme with FMCL (the cache hit rate = 0%) was \(<\) that of our proposed scheme with FMCL (the cache hit rate = 10%) was \(<\) our proposed scheme with FMCL (the cache hit rate = 40%) was \(<\) our proposed scheme with FMCL (the cache hit rate = 70%) was, under the different UE moving speed. As shown in Figure 13b, the simulation result of the average throughput vs. the different cache size (is ranging from 100 M to 200 M). As the higher the size of the cache is, the higher the average throughput will be. Figure 13b shows that the average throughput is 8.9 (M/s) using the PMIPv6 with FMC under the UE moving speed is 5 (m/s). However, the average throughput of our proposed scheme with FMCL are 9.1 (M/s), 9.5 (M/s), 10.9 (M/s), and 12.8 (M/s) if the cache hit rate is 0, 10%, 40%, and 70%, under the cache size is 150 (M), respectively. Figure 13b also shows that the average throughput of PMIPv6 with FMC was \(<\) that of our proposed scheme with FMCL (the cache hit rate = 0%) was \(<\) that of our proposed scheme with FMCL (the cache hit rate = 0%) was \(<\) our proposed scheme with FMCL (the cache hit rate = 70%) was, under the different cache size. In addition, the CDF vs. the average throughput is provided in Figure 13c.  
Figure 12: Total transmission time vs. (**a**) UE moving speed and (**b**) cache size, and (**c**) CDF vs. the total transmission time.  
Figure 13: Throughput vs. (**a**) UE moving speed. and (**b**) cache size, and (**c**) CDF vs. throughput.
## 4 Example of Application: Performance Simulations using the EIPS framework
### Microwave System Configurations
#### 4.2.2 Configuration C: TES-Based MW Instrument  
Superconducting transition edge sensor (TES)-based filterbank spectrometer technology has recently been developed for enabling a future ultra-low-noise _hyperspectral_ MW instrument for atmospheric remote sensing applications [16]. For the purposes of this illustration only, we simulate a proposed _multi-spectral_ instrument based on this superconducting filterbank technology, but with a channel configuration that exactly replicates those of ATMS. In this way, we can directly compare the impact of instrument noise on the information content for each instrument. The channel characteristics of configuration C (a TES technology-based MW Sounder) have been kept same as those in configuration A. The sensor parameter model for this new technology-based instrument has been used to estimate the radiometric sensitivities, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Centre Frequency (GHz)**} & \multirow{2}{*}{**Bandwidth (MHz)**} & \multirow{2}{*}{**Application**} & \multicolumn{3}{c}{**NETD of MW System Configurations (K)**} \\ \cline{4-5}  & & & **(A)** & **(B)** & **(C)** \\  & & & **ATMS-Type** & **AMSU-A-Type** & **TES-Based** \\  & & & **ATMS-Type** & **AMSU-A-Type** & **MW** \\ \hline
23.8 \({}^{*}\) & 270 & Window-Water vapour & 0.9 & 0.3 & 0.025 \\ \hline
31.4 \({}^{*}\) & 180 & Window-water vapour & 0.9 & 0.3 & 0.038 \\ \hline
50.3 \({}^{*}\) & 180 & Window-surface emissivity & 1.2 & 0.4 & 0.038 \\ \hline
51.7 & 400 & Window-surface emissivity & 0.75 & 0.25 & 0.017 \\ \hline
52.8 \({}^{*}\) & 400 & & 0.75 & 0.25 & 0.017 \\ \hline
53.596 \(\pm\) 0.115 \({}^{*}\) & 170 & & 0.75 & 0.25 & 0.041 \\ \hline
54.40 \({}^{*}\) & 400 & & 0.75 & 0.25 & 0.017 \\ \hline
54.94 \({}^{*}\) & 400 & & 0.75 & 0.25 & 0.017 \\ \hline
55.50 \({}^{*}\) & 330 & & 0.75 & 0.25 & 0.021 \\ \hline
57.290344 \(\pm\) 0.217 \({}^{*}\) & 78 & & 1.20 & 0.40 & 0.089 \\ \hline
57.290344 \(\pm\) 0.3222 & 36 & & 1.20 & 0.40 & 0.192 \\ \hline
57.290344 \(\pm\) 0.3222 & 16 & & 1.50 & 0.60 & 0.433 \\ \hline
57.290344 \(\pm\) 0.022 \({}^{*}\) & 8 & & 2.40 & 0.80 & 0.865 \\ \hline
57.290344 \(\pm\) 0.3222 & 3 & & 3.60 & 1.20 & 2.308 \\ \hline
89.0 \({}^{*}\) & 6000 & & 0.50 & 0.50 & \\ \hline
89.5 & 5000 & & 0.50 & 0.001 \\ \hline
165.5 & 3000 & & 0.60 & 0.002 \\ \hline
183.31 \(\pm\) 7.0 & 2000 & & 0.80 & 0.004 \\ \hline
183.31 \(\pm\) 4.5 & 2000 & & 0.80 & 0.007 \\ \hline
183.31 \(\pm\) 3.0 & 1000 & & 0.80 & 0.007 \\ \hline
183.31 \(\pm\) 1.8 & 1000 & & 0.90 & 0.014 \\ \hline \hline \end{tabular} \({}^{*}\) AMSU-A-type (Configuration B) instrument channels and corresponding bandwidths.  
\end{table}
Table 2: Spectral and radiometric specifications of set up MW system configurations A, B and C.
## 4 Case Study of Data Collaboration
### Visual Traffic Flow Detection Based on DFA
#### 4.3.1 Differential Fusion Algorithm  
The main acquisition area of UAV video is the intersection, and the vehicles at the intersection move alternately. In order to avoid repeated detection, multi-frame difference processing is performed first. The difference operation is performed between the (\(n+1\))th frame and the \(n\)th frame image, and between the \(n\)th frame and the (\(n-1\))th frame image and the moving target is selected by the difference operation between the next frame and the selected frame, as shown in Figure 8a. In the figure, the white area is the moving target, and the frame difference is defined as:  
\[\left\{\begin{array}{l}D_{(n+1,n)}(x,y)=\left|f_{n+1}(x,y)-f_{n}(x,y)\right| \\ D_{(n,n-1)}(x,y)=\left|f_{n}(x,y)-f_{n-1}(x,y)\right|\end{array}\right. \tag{6}\]  
\[\text{DM}_{n}(x,y)=\left\{\begin{array}{l}255\quad\text{if}\left|D_{n}(x,y) +D_{n+1}(x,y)\right|\geq 255\\ \left|D_{n}(x,y)+D_{n+1}(x,y)\right|\quad\text{else}\end{array}\right. \tag{7}\]  
In the equation, \(f_{n}(x,y)\) is the image of this frame, \(f_{n-1}(x,y)\) is the image of the previous frame, \(f_{n-1}(x,y)\) is the image of the next frame, and \(\text{DM}_{n}(x,y)\) is the image after multi-frame differential processing. Then, the image \(\text{DM}_{n}(x,y)\) after the multi-frame difference processing is processed by thresholding, as shown in Figure 8b. Thresholding is also a process of noise reduction. The pixels are binarized using T as a threshold, filtering out the non-connected white noise in the background. Then, the current background model is extracted, and the foreground binarized image is shown as Equation (8). Since the image has been processed previously, the extracted foreground is the foreground object of the moving target output.  
\[B_{n}(x,y)=\left\{\begin{array}{l}255\quad|f_{n}(x,y)-f_{bn}(x,y)|>T\\ 0\quad\quad\text{else}\end{array}\right. \tag{8}\]  
Due to the high shooting Angle of UAVs, some distant vehicles are blurred, so the images after thresholding are expanded to obtain a clearer binarized target image as shown in Figure 8c. Image expansion is defined as:  
\[\text{A}\oplus\text{B}=\left\{\text{x}\mid(\text{B})_{\text{x}}\cap\text{A} \
eq\Theta\right\} \tag{9}\]  
Figure 7: Heat map of spatial distribution of travel hotspots. (**a**) Hotspots of Monday. (**b**) Hotspots of Thursday. (**c**) Hotspots of Saturday. (**d**) Hotspots of Sunday.  
where B is a convolution kernel with a circular shape. We used template B to convolve with image A, scan every pixel in the image, and do \"and\" operation with the kernel element and the binary image element. If both are 0, then the target pixel is 0, otherwise, it is 1. Thus, the maximum value of the pixel in the coverage area of B is calculated, and the pixel value of the reference point is replaced by this value to realize image dilation.
# Soil and Rockfill Dams Safety Assessment for Henan Province: Monitoring, Analysis and Prediction
## 5 Discussion
### Stability Analysis of Soil and Rockfill Dam
#### 5.4.2 Stress Stability Analysis  
Because the soil and rockfill dam is a water-holding structure that is submerged for an extended period, the dam body can experience deformation and damage due to the pressure of water and gravity, leading to dam instability. Therefore, the stability of the soil and rockfill dam during the impoundment period is analyzed. During the impounding period, when the water level in the reservoir reaches 260 m, the displacement maps of both horizontal and vertical components of the dam body are computed, as shown in Figure 20.  
Figure 19: (**a**) Map of total head distribution in high water level dam body; (**b**) map of the total head distribution of the dam body at low water level (down 15 m compared with high water level).  
During the impounding period, the maximum horizontal displacement of the soil and rockfill dam is \(-7.426\) cm, the maximum vertical displacement is \(-12.125\) cm, the maximum effective stress in the horizontal direction is \(89.879\)\(\text{KN}/\text{m}^{2}\), and the maximum effective stress in the vertical direction is \(35.272\)\(\text{KN}/\text{m}^{2}\). According to Figure 20a, in the horizontal direction, when the dam body is under the upstream water pressure, the displacement of the core wall is the largest, and it is found that the core wall plays an important role as a water-holding structure. According to Figure 20b, the displacement of the dam body from the dam crest to the dam bottom presents a \"stepped\" deformation state in the vertical direction. The model result is the same as the deformation state monitored by the InSAR technique. In addition, the horizontal stress generated during impoundment is greater than the vertical stress, which is because the water pressure generated within the upstream will be greater in the lateral pressure during impoundment. Under the influence of the gravity of the dam body, the compressive stress of the dam body from top to bottom decreases in turn. From the above conclusion, the horizontal displacement and vertical displacement are in line with the general deformation law of the dam and are consistent with the deformation trend of the dam body monitored by InSAR.
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 4 Pre-Processing of Data and Selection of Neural Model
### Spatio-Temporal Prediction  
#### 4.4.1 Test and Training Sets  
In order to carry out the space-time prediction of PM\({}_{2.5}\), the following steps were considered: (i) First, the training and testing set were modified (see, Table 4) by inserting 2 new columns, (E' and N'), representing the spatial coordinates of the location where we need to predict the PM\({}_{2.5}\). The inclusion of these two new input variables is necessary for associating the geographic coordinate to the station to be predicted, which we call the target station. Then, (ii) the variables of the target station were filled with \(-1\). The previous step (ii) was replicated for all stations (iii) which generated the database that was the input of the neural network. For example, if we have \(11\) stations, during the training there will be \(10\) stations that generate the input variables with values different from \(-1\), and the target station to predict, that is, the \(11\)th, will be the only station that will provide the output. In addition, in the input variables, the coordinates of the target station will appear at the beginning, and in contrast, the historical variables of contamination or others of this station will not appear. By not including the historical information of the target station to be predicted as input variables, we prevented this prediction using the information which corresponds to a typical temporal model. An example of the resulting input can be seen in Table 6. Since the coordinates vary in the input variables, and this can change arbitrarily even when the other input variables remain the same, this process allows generating a space-time prediction. Note that this process is repeated for both the training and testing processes; the only difference is that in testing the station to consider in the output variables corresponds to the target station. To strengthen the prediction, so that most of the stations were considered, high dropout values (greater than 0.4) were used in the neural networks tested. At the same time, this complete procedure was repeated for all the stations in order to evaluate the model in different target stations. Finally, it should be noted that in this process the weights of the neural networks were initialized randomly--that is, only the structure of the previous step given in Section 4.3 was considered.  
Initially, we eliminated the input data of the station to be predicted during the training to prevent the network from looking only at the historical data of the station; however, the results improved when considering it. We believe this happened because valuable information was not lost during training, and on the other hand, the dropout operator allowed the network to consider all available stations.
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 3 Proposed Method  
This work proposes the combination of PCA and DWT as a compression algorithm for high-frequency sampled data. This section briefly describes the fundamentals of both algorithms and how their combination leads to a better compression performance.  
The combination of the PCA and DWT algorithms, with the support of quantization, entropy encoding, and thresholding, is illustrated in Figure 1 and in pseudo-code in Algorithm 1. The algorithm takes as parameters the principal components count (pc), the energy threshold (et), the quantization level (quant), and the time series to be compressed (data). The first three steps of compression act as a noise filter through PCA and quantization. PCA is a dimensionality reduction operation where the data is represented in principal components. We utilize a truncated form of the PCA, explained in more detail below, that can approximate the original signal even with a reduced number of principal components. In line 1, the number of principal components (pc) determines how many are kept for later reconstruction. The thresholding and quantization remove less representative data from the sparse representation. In line 2, the quantization and thresholding operation verifies each component of the PCA output and changes them to zero according to the specified threshold. In line 3, the PCA decompression returns the signal to the time domain. The DWT, in line 4, performs the wavelet decomposition of the time series and discards wavelets with the least representative wavelet coefficients. The last step, in line 5, is entropy encoding that optimizes the binary representation of the compressed data. The more straightforward decompression steps are shown in Algorithm 2. In line 1, entropy decoding decompresses the wavelet coefficients and, in line 2, the DWT decompression (also named inverse DWT) reconstructs the time series from the wavelet coefficients. The following sections describe the components of the algorithm in more detail.
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 5 Discussion
In summary, the high boundary effects of square washers, vortex formation at their edges in real-world applications, and the sensitivity of inductance to changes in the inner diameter highlight that traditional square washers no longer meet the requirements for high sensitivity and low noise compared to the newer octagonal washers. While this research has made significant strides in optimizing the SQUID washer design and providing new perspectives for enhancing device performance through improved washer geometries, it primarily focuses on the core component of the DC-SQUID--the washer. The intricate coupling relationships among various coils have historically been overlooked in computational and simulation studies, indicating the need for further in-depth exploration in this area. This limitation could potentially constrain our conclusions, as the ultimate performance of SQUID devices is not solely dependent on washer design but also on the cumulative effects of other components, such as Josephson junctions and superconducting circuit layouts. Therefore, while our research underscores the effectiveness of washer geometry optimization for enhancing device performance, a holistic optimization of the entire system is essential for practical applications. Moving forward, our research will focus on two key areas to achieve groundbreaking advancements in SQUID design and application:  
1. System-level simulation and optimization: We plan to develop more comprehensive simulation models that consider the interactions among all critical componentswithin the SQUID system. By integrating various elements such as Josephson junctions, superconducting loops, and coupling circuits, we aim to optimize the overall performance of SQUIDs for diverse applications. 2. In-depth study of coupling effects: Through experimental and theoretical analysis, we intend to thoroughly investigate the coupling mechanisms between different components in SQUID systems. By understanding how these interactions collectively impact the sensitivity and noise characteristics of SQUIDs, we can develop strategies to minimize unwanted coupling and enhance device performance. Through these forthcoming research endeavors, we anticipate overcoming the limitations identified in this study and making significant contributions to the advancement of superconducting quantum sensing technology.