esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Channel Model
### BER for the FSO Link  
Bit error rate (BER) is an important indicator to evaluate the performance of the FSO link. For an FSO link with IM/DD detection, the average BER is calculated by averaging the conditional BER over the PDF of \(f_{h_{FSO}}(h_{FSO})\) defined by  
\[P_{b}=\int_{0}^{\infty}P_{e}f_{h_{FSO}}(h_{FSO})dh_{FSO} \tag{10}\]  
where \(P_{e}\) is the conditional probability given by [37] as  
\[P_{e}=\frac{1}{2}erfc\left(\sqrt{\frac{h_{FSO}^{2}\overline{\gamma_{F}}}{\left( 2M_{o}I_{FSO}\right)^{2}}}\right) \tag{11}\]  
To simplify the calculation of the integral in (10), the erfc function can be expressed as a Meijer g function [14].  
\[erfc(\sqrt{y})=\frac{1}{\sqrt{\pi}}G_{1,2}^{2,0}\left[y\right]\begin{array}{c} 1\\ 0,1/2\end{array}\right] \tag{12}\]  
Thus, the BER of the FSO link can be expressed as  
\[\begin{array}{c}P_{b}(\overline{\gamma_{F}})=\frac{\varsigma^{2}}{2\sqrt{ \pi}h_{FSO}\Gamma(\alpha)\Gamma(\beta)}\times\int_{0}^{\infty}G_{1,2}^{2,0} \left[\frac{\overline{\gamma_{F}}}{\left(2M_{o}I_{FSO}\right)^{2}}h_{FSO}^{2 }\left|\begin{array}{c}1\\ 0,1/2\end{array}\right|\right]\\ \times G_{1,3}^{3,0}\left[\alpha\beta\frac{h_{FSO}}{I_{FSO}M_{o}}\left| \begin{array}{c}\varsigma^{2}\\ \varsigma^{2}-1,\alpha-1,\beta-1\end{array}\right|\right]dh_{FSO}\end{array} \tag{13}\]  
To simplify the BER of the FSO link, using the integral formula of the product of two Meijer g functions [34], the BER of the FSO link can be rewritten as  
\[P_{b}(\overline{\gamma_{F}})=\frac{2^{\alpha+\beta-4}\varsigma^{2}}{\pi^{3/2} \Gamma(\alpha)\Gamma(\beta)}\times G_{7,4}^{2,6}\left[\frac{4\overline{\gamma_ {F}}}{\left(\alpha\beta\varsigma\right)^{2}}\left|\begin{array}{c}\frac{1- \varsigma^{2}}{2},\frac{2-\varsigma^{2}}{2},\frac{1-\kappa}{2},\frac{2-\kappa }{2},\frac{1-\kappa}{2},\frac{2-\kappa}{2},\frac{1-\beta}{2},\frac{2-\beta}{2} \\ 0,\frac{1}{2},\frac{1-\varsigma^{2}}{2},\frac{1-\varsigma^{2}}{2}\end{array} \right|\right] \tag{14}\]  
At high SNR and similar to the CDF, the BER of the FSO link for high SNR can be given asymptotically as described in [13].  
\[\begin{array}{c}P_{b}(\overline{\gamma_{F}}\rightarrow\infty)=\frac{2^{ \alpha+\beta-4}\varsigma^{2}}{\pi^{3/2}\Gamma(\alpha)\Gamma(\beta)}\sum\limits _{k=1}^{6}z^{a_{k}-1}\\ \times\frac{\Pi_{-1/\mu\
eq\Gamma}^{\mu}(\alpha_{k}-a_{i})\Gamma(1+b_{1}-a_{k}) \Gamma(1+b_{2}-a_{k})}{\Pi_{-\alpha}^{\mu}\Gamma(a_{k}-b_{l})\Gamma(2-a_{k})} \end{array} \tag{15}\]  
Here, some of these parameters are expressed as follows:  
\[z=\frac{(\alpha\beta\varsigma)^{2}}{4\overline{\gamma_{F}}} \tag{16}\]  
\[a_{k}=\left\{\frac{1-\varsigma^{2}}{2},\frac{2-\varsigma^{2}}{2},\frac{1- \alpha}{2},\frac{2-\alpha}{2},\frac{1-\beta}{2},\frac{2-\beta}{2}\right\},for \;k\in\{1,\ldots,6\} \tag{17}\]  
\[b_{l}=\left\{0,\frac{1}{2},\frac{-\varsigma^{2}}{2},\frac{1-\varsigma^{2}}{2} \right\},for\;l\in\{1,\ldots,4\} \tag{18}\]
## 3 Results and Discussion
### Dielectric Properties Measurement Results at Different Flow Rates
The highest \(\epsilon^{\prime\prime}\) values at 0.5 L/min suggest that this flow rate is optimal for generating high concentrations of nitrate. Furthermore, the percentage error for most of the measured dielectric properties was below 1%, which highlights the reliability and precision of the data. The low percentage error indicates that the variations in \(\epsilon^{\prime}\) and \(\epsilon^{\prime\prime}\) are statistically significant and accurately reflect the effects of plasma treatment and flow rates on the dielectric properties of water. These findings highlight the importance of optimizing parameters such as flow rate and treatment duration to improve nitrate production efficiency using plasma technology. Understanding the interaction mechanisms between plasma and water is crucial for designing an effective plasma-based nitrate production system, and the observed trends in \(\epsilon^{\prime}\) and \(\epsilon^{\prime\prime}\) provide valuable insights for this optimization. After determining that the proposed PAW fertilizer at a flow rate of 0.5 L/min for 3 h yielded dielectric constant (\(\epsilon^{\prime}\)) and dielectric loss factor (\(\epsilon^{\prime\prime}\)) values close to those of aqueous nitrate fertilizer (NO\({}_{3}\) + RO), we measured the nitrate concentration using the cadmium reduction method and compared our results with those of other studies, as shown in Table 2. The results indicate that the PAW method used in this study provides a significantly higher nitrate concentration (235.2 ppm) while consuming less energy (100 W) compared to other PAW methods and the Continuous Flow Liquid-Phase Plasma Discharge (CFLPPD) technique. Although plasma electrolysis produced the highest nitrate concentration (2213.5 ppm), it required considerably more energy (700 W), demonstrating that the PAW process proposed in this study is a more energy-efficient alternative for nitrate production.
## 5 System-Level Performance Evaluation
### Performance Evaluation Setup
#### 5.1.2 Dataset Compression Characteristics  
The actual signals comprising the evaluation dataset comprise also a significant parameter influencing the performance of hardware accelerator. It is assumed that all datasets are parts of signals of equal time duration and specifically of 10 min interval. Specifically, the signals considered are derived from the extended dataset presented in Section 3.3 and the characteristics relevant to the specific evaluation are as follows:  
1. ECG_UoP  
An ECG signal with sampling rate 2.5 KHz, 16 bits per sample and 10 min duration leads to an amount of data equal to 3.000.000 Bytes; the implemented compression algorithm is able to compress them to 1.292.952 Bytes, thus yielding 56% compression rate. Shimmer Platform using BT technology  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Clock Frequency** & **Total Power (mW)** & \
\begin{tabular}{c} **Dynamic Power** \\ **(mW)** \\ \end{tabular} & **Static Power (mW)** &
\begin{tabular}{c} **Throughput** \\ **(Mbps)** \\ \end{tabular} \\ \hline
1 & 114.27 & 15.53 & 98.74 & 17.09 \\
20 & 142.38 & 43.20 & 99.18 & 34.19 \\
40 & 170.07 & 70.45 & 99.62 & 68.39 \\
62.5 & 198.52 & 98.45 & 100.07 & 106.86 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Power consumption and throughput performance of compression hardware component.  
is able to transfer compressed and uncompressed data in 60 and 25 s respectively. Shimmer Platform using IEEE 802.15.4 technology is able to transfer compressed and uncompressed data in 488 and 211 s respectively considering a configuration of 108 byte per packet and a packet transmitter every 2 ms.  
2. ECG_EkgMove  
An ECG signal acquired using the EkgMove sensor with sampling rate of 256 Hz, 16 bits per sample and 10 min duration leads to an amount of data equal to 307.200 Bytes; the implemented compression algorithm is able to compress to 86.536 Bytes yielding 72% compression rate. Shimmer Platform using BT technology is able to transfer compressed and uncompressed data in 6 and 2 s respectively. Shimmer Platform using IEEE 802.15.4 technology is able to transfer compressed and uncompressed data in 50 and 15 s respectively.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AWGN & Additive white Gaussian noise \\ BDGIM & BeiDou Global Broadcast Ionospheric Delay Correction Model \\ CDDIS & Crustal Dynamics Data Information System \\ CMEs & Coronal mass ejections \\ COSPAR & Committee on Space Research \\ DFRs & Dual-frequency receivers \\ ED & Electron density \\ FFT & Fast Fourier transform \\ GEO & Geo-stationary orbit \\ GIMs & Global ionospheric maps \\ GNSS & Global Navigation Satellite Systems \\ GPS & Global Positioning System \\ IAMM & Interpolated and Averaged Memory Model \\ ID & Ionopsheric delay \\ IGS & International GNSS Service \\ IONEX & IONosphere map EXchange format \\ IoT & Internet of Things \\ IRI & International Reference Ionosphere \\ ISR & Incoherent scattered radar \\ IWG & Ionosphere Working Group \\ LEO & Low Earth orbit \\ LOS & Line of sight \\ MEO & Medium Earth orbit \\ ML & Machine learning \\ NASA & National Aeronautics and Space Administration \\ NLOS & Non-line-of-sight \\ NLS & National Land Survey of Finland \\ NMEA & National Marine Electronics Association \\ NOAA & National Oceanic and Atmospheric Administration \\ PNT & Position, Navigation, and Timing \\ PPP & Precise point positioning \\ QZSS & Quasi-Zenith Satellite System \\ Q4 DIM & Quasi-4-Dimension Ionospheric Modeling \\ RF & Radio frequency \\ RIM & Regional ionospheric map \\ RINEX & Receiver-independent exchange format \\ RMS & Root mean square \\ RSS & Received signal strength \\ RT-GIMs & Real-time global ionospheric maps \\ SLM-MF & Single-layer model mapping function \\ SFRs & Single-frequency receivers \\ \end{tabular}
## 5 In Situ Coring Test
### Permeability Test  
Table 6 shows the permeability test results of HPCG. The permeability coefficients of HPCG-1 and HPCG-2 were about 7 \(\times\) 10\({}^{-8}\) cm/s at a seepage pressure of 300 kPa. The permeability coefficients of HPCG-3 to HPCG-6 ranged from 1.1 \(\times\) 10\({}^{-7}\) to 6.5 \(\times\) 10\({}^{-7}\) cm/s at a seepage pressure of 500 kPa. The average permeability coefficient of the seven samples was 2.61 \(\times\) 10\({}^{-7}\) cm/s, which was less than the 1.0 \(\times\) 10\({}^{-6}\) cm/s required by the specifications.  
Table 7 shows the permeability test results of RPCG. The permeability coefficient of RPCG-1 was 4.23 \(\times\) 10\({}^{-6}\) cm/s at a seepage pressure of 600 kPa. The permeability coefficients of RPCG-3 to RPCG C-5 were between 1.48 \(\times\) 10\({}^{-6}\) and 2.08 \(\times\) 10\({}^{-6}\) cm/s, respectively, at a seepage pressure of 500 kPa. The average permeability coefficient of the five samples was 2.7 \(\times\) 10\({}^{-6}\) cm/s, which was more than the standard requirement of 1 \(\times\) 10\({}^{-6}\) cm/s.  
Table 8 shows the permeability test results of MKCG. The permeability coefficients of MKCG-1 and MKCG-2 were about 3.08 \(\times\) 10\({}^{-6}\) cm/s at a seepage pressure of 200 kPa. The permeability coefficient of MKCG-3 was 2.42 \(\times\) 10\({}^{-6}\) cm/s at a seepage pressure of 1000 kPa. The permeability coefficients of MKCG-4 and MKCG-5 were 3.68 \(\times\) 10\({}^{-6}\) and 4.28 \(\times\) 10\({}^{-6}\) cm/s at a seepage pressure of 500 kPa. The average permeability coefficient of the five samples was 3.31 \(\times\) 10\({}^{-6}\) cm/s, and all five exceeded the standard requirement of 1 \(\times\) 10\({}^{-6}\) cm/s.  
Therefore, only the permeability coefficient of the HPCG core sample was less than the specification requirement of 1 \(\times\) 10\({}^{-6}\) cm/s, which reached an order of 10\({}^{-7}\); meanwhile, the permeability coefficients of RPCG and MKCG core samples were both on the order of 10\({}^{-6}\), and the permeability coefficient of the HPCG core sample was one order of magnitude higher, which met the water stopping requirement of the high-pressure jet column in the water-rich sand stratum.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case ID**} & **Seepage** & **Permeability** & **Average Permeability** \\  & **Pressure/(kPa)** & **Coefficient/(cm/s)** & **Coefficient/(cm/s)** \\ \hline HPCG-1 & 300 & 7.27 \(\times\) 10\({}^{-8}\) & \\ HPCG-2 & 300 & 6.50 \(\times\) 10\({}^{-8}\) & \\ HPCG-3 & 500 & 2.34 \(\times\) 10\({}^{-7}\) & \\ HPCG-4 & 500 & 1.87 \(\times\) 10\({}^{-7}\) & 2.61 \(\times\) 10\({}^{-7}\) \\ HPCG-5 & 500 & 6.45 \(\times\) 10\({}^{-7}\) & \\ HPCG-6 & 500 & 1.13 \(\times\) 10\({}^{-7}\) & \\ HPCG-7 & 300 & 5.14 \(\times\) 10\({}^{-7}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Permeability coefficients of HPCG.  
Figure 19: Round table samples of permeability coefficient tests. (**a**) HPCG. (**b**) RPCG. (**c**) MKCG.
# River Surface Space-Time Image Velocimetry Based on Dual-Channel Residual Network
## 5 Conclusions  
This paper proposes a Space-Time Image Velocimetry method based on a dual-channel residual network (DCResNet) through the joint learning of original and edge-enhanced STIs. The method has a dual-channel network structure, where one channel processes the original STI while the other analyzes the STI after adaptive threshold Sobel detection. This edge detection channel enhances texture features, and the weighted fusion of both channels' outputs feeds into the regression layer for final MOT detection. Experimental results show that DCResNet works best with a 7:3 fusion ratio of original and edge features on the Panzhihua station dataset. It achieves a 0.41\({}^{\circ}\) accuracy in normal scenarios and stays within 1.2\({}^{\circ}\) in complex noise scenarios. Compared with the single-channel models, DCResNet achieves a lower MAE and SD, while demonstrating enhanced robustness in complex scenarios. In three groups of surface velocity comparison experiments, DCResNet shows better performance than the FFT method. This method exhibited enhanced robustness, especially when processing STIs with weak texture features. Furthermore, it also has good applicability for the river not covered by the STI dataset. The vertical average velocity comparison experiment results show that DCResNet achieves an overall MOT detection AE below 1.65\({}^{\circ}\) and an MRE of 4.08%. The velocity distribution is consistent with the current meter results, confirming its practicality for flow velocity monitoring. However, compared to traditional methods, DCResNet requires greater computational resources and a longer training time. In future research, we will explore lightweight models to the reduce computational complexity, while expanding the scenario diversity of the STI dataset (e.g., including wave scenarios) to further improve its detection accuracy.  
Conceptualization, L.G. and Z.Z.; methodology, L.G., Z.Z. and H.L.; software, L.G., Z.Z., L.C. and H.L.; validation, L.G., L.C. and H.L.; formal analysis, L.G. and H.L.; investigation, L.G. and Z.Z.; resources, Z.Z.; data curation, L.G. and Z.Z.; writing--original draft preparation, L.G., L.C. and H.L.; writing--review and editing, L.G. and Z.Z.; visualization, L.G.; supervision, Z.Z.; project administration, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Postdoctoral Science Foundation, No. 2019M651673.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. (The data involve topographic data and hydrological information of key hydrological stations.)  
The authors declare no conflicts of interest.  
Figure 25: Vertical average velocity distribution.
# Profile of Accelerations and Decelerations in Young Basketball Players
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Guards** & **Forwards** & **Centers** \\ \hline
**Low ACC (0-1 m-s\({}^{-2}\))** & & & \\ \hline Max ACC (m-s\({}^{-2}\)) & 0.70 (0.52) & 0.68 (0.52) & 0.70 (0.51) \\ Distance (m) & 1.48 (2.02) & 1.50 (2.01) & 1.25 (1.80) \\ Start speed (km-h\({}^{-1}\)) & 2.03 (2.55) & 2.02 (2.42) & 1.69 (2.17) \\ Final speed (km-h\({}^{-1}\)) & 4.53 (3.39) & 4.53 (3.25) & 4.09 (2.95) \\ \hline
**Moderate ACC (2-4 m-s\({}^{-2}\))** & & & \\ \hline Max ACC (m-s\({}^{-2}\)) & 2.62 (0.48) & 2.61 (0.46) & 2.62 (0.48) \\ Distance (m) & 4.67 (3.30) & 4.87 (3.49) & 4.64 (3.51) \\ Start speed (km-h\({}^{-1}\)) & 2.37 (2.02) & 2.17 (1.84) & 2.02 (1.55) \\ Final speed (km-h\({}^{-1}\)) & 12.30 (4.28) & 12.28 (4.26) & 11.81 (4.36) \\ \hline
**High ACC (>4 m-s\({}^{-2}\))** & & & \\ \hline Max ACC (m-s\({}^{-2}\)) & 4.61 (0.73) & 4.51 (0.53) & 4.57 (0.72) \\ Distance (m) & 5.39 (3.27) c & 5.37 (3.08) c & 4.59 (3.06) \\ Start speed (km-h\({}^{-1}\)) & 3.39 (2.76) c & 3.53 (2.72) c & 2.71 (2.14) \\ Final speed (km-h\({}^{-1}\)) & 16.84 (5.18) c & 16.40 (5.26) c & 15.48 (4.31) \\ \hline
**Low DEC (0-1 m-s\({}^{-2}\))** & & & \\ \hline Max DEC (m-s\({}^{-2}\)) & 0.70 (0.51) & 0.70 (0.51) & 0.72 (0.50) \\ Distance (m) & 1.56 (2.26) & 1.69 (2.49) & 1.51 (2.46) \\ Start speed (km-h\({}^{-1}\)) & 4.59 (3.48) & 4.70 (3.48) & 4.32 (3.32) \\ Final speed (km-h\({}^{-1}\)) & 2.01 (2.54) & 2.00 (2.41) & 1.96 (2.15) \\ \hline
**Moderate DEC (2-4 m-s\({}^{-2}\))** & & & \\ \hline Max DEC (m-s\({}^{-2}\)) & 2.66 (0.50) & 2.60 (0.47) & 2.59 (0.48) \\ Distance (m) & 4.17 (3.08) & 4.34 (3.46) & 3.90 (3.42) \\ Start speed (km-h\({}^{-1}\)) & 12.08 (4.27) c & 11.89 (4.41) & 11.12 (4.42) \\ Final speed (km-h\({}^{-1}\)) & 2.34 (1.87) & 2.19 (1.71) & 1.96 (1.47) \\ \hline
**High DEC (>4 m-s\({}^{-2}\))** & & & \\ \hline Max DEC (m-s\({}^{-2}\)) & 4.65 (0.72) & 4.53 (0.58) & 4.57 (0.64) \\ Distance (m) & 4.92 (2.66) & 5.04 (2.87) & 4.64 (2.05) \\ Start speed (km-h\({}^{-1}\)) & 17.35 (4.82) & 16.69 (5.31) & 16.48 (4.88) \\ Final speed (km-h\({}^{-1}\)) & 2.73 (1.62) & 2.69 (1.41) & 2.38 (1.54) \\ \hline \hline \end{tabular}  
* ACC = acceleration; DEC = deceleration; c = statistical and practical different from Centers (\(p\leq 0.05\); \(ES\geq 0.2\)).  
\end{table} 
Table 2: Comparison of acceleration and deceleration profile during competitive basketball matches by playing position (mean and \(\pm\)sd).
# Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization
## 2 Data Description  
This study used the IMU data of gait motions from five healthy male subjects (28 \(\pm\) 3 years; 181.3 \(\pm\) 8.0 cm; 78.7 \(\pm\) 9.8 kg, mean \(\pm\) SD); these IMU data were obtained from a previously conducted experiment in a previous work [39]. In that experiment, two IMU sensors (MTi-3 AHRS, Xsens Technologies, the Netherlands) were attached to the anterior part of each thigh to collect the data, as shown in Figure 1. The subjects walked (1.5 m/s) and ran (2.5 m/s) on an instrumented treadmill with mounted force plates (Bertec, Columbus, OH, USA). The IMU sensors recorded the thigh angles and angular velocities at a 1 kHz sampling rate. Details regarding the size of the signal used in this study, including the number of strides and the total experimental time for each subject, are provided in Table 1. Seventy percent of the time-series data were used for training the MLP, the remaining 30% of the data were used for testing.  
The thigh motion IMU data of the five subjects are shown in Figure 2. The data show variations in gait patterns across subjects. Because of these variations, if the same feature set and LTW are used for every subject, the performance of the gait estimation algorithm may downgrade.  
Figure 1: Experimental setup for the acquisition of the gait motion data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Motion** & **Number of Strides** & **Time Lengths (s)** \\ \hline
1 & Walking & 12 & 12.210 \\  & Running & 29 & 22.885 \\  & Walking & 18 & 18.480 \\
2 & Running & 16 & 11.305 \\  & Walking & 15 & 13.905 \\
3 & Running & 20 & 13.815 \\  & Walking & 29 & 29.240 \\  & Running & 24 & 18.810 \\
5 & Walking & 22 & 21.975 \\  & Running & 12 & 9.240 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the IMU signal used in this study.
# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
## 6 Simulation Results and Discussion
In both cases (Figure 9a,b), when the energy level is above the threshold level (\(X_{level}\) = 70%), the sampling frequencies (rates) remain the same (\(f_{EASA}\) =\(f_{ASA}\)) and the energy levels are identical, which means that the energy consumed in both cases is same. When the energy level is below \(X_{level}\) the sampling frequencies are no longer the same, which is reflected in the energy consumed by the nodes. The node on which EASA is running saves energy by reducing its sampling rate according to the current energy level. If the sampling frequency is not adapted to the energy available in the battery, the energy levels can significantly drop (below 20% in (Figure 9a) and to 0% in (Figure 9b)). In both of the figures, the same trend is present where the node running the ASA and the node using the EASA have identical energy  
Figure 8: (a) Deployment (I): Sampling rates and maximum frequency of the average wind speed signal _vs._ samples; (b) Deployment (II): Sampling rates and maximum frequency of the average wind speed signal _vs._ samples. levels until the critical level \(X_{level}\) is reached. In this figure the difference between the energy levels of node sampling the sensors with fixed sampling rate (FSR) and harvesting energy from only one source (_i.e._, sun in this case) can be noticed. This confirms that a significant savings can be achieved using the EASA, in comparison with using a fixed sampling rate strategy.
# Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization
## 3 Experiments
### Case Study
#### 3.2.2 Dynamic Communities during Evening Rush Hours  
During the evening rush hours, people gradually left their workplaces after work, and went to the residential areas or entertainment areas. Therefore, the spatial interaction patterns changed due to mass movement of people. Some examples are given in Figure 16.  
We can see that community **C3** around Wangjing and Taiyanggong (two core central business districts (CBDs)) at 16:00-17:00 gradually splits into several smaller communities at 17:00-18:00 and 18:00-19:00. The reason for this may be that human activities around CBDs become strong when lots of people go to the CBDs for entertainment after work. Community **C6** (near an important CBD--Wanliu) at 16:00-17:00 gradually shrinks over time. During the evening rush hours, people try to avoid long-distance activities because of traffic congestion. Therefore, small communities appear around some local commercial or entertainment centers. We also found that the community around Ding'anli residential area and Aegean business district (C33 in Figure 16b and **C**32 in Figure 16c) reappeared at 17:00-18:00 and 18:00-19:00 (this community was also identified at 8:00-10:00). This community may be formed because of the large numbers of people returning to residential areas. We can infer that commuting activity is an important factor in changing the community structures.  
Figure 16: Some communities identified by EACO during evening rush hours: (a) 16:00–17:00; (b) 17:00–18:00; (c) 18:00–19:00.