esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 4 Experimental Results and Analyses
### The Evaluation of Traffic Information Estimation Methods
#### 4.2.2 The Evaluation of Traffic Density Estimation  
For the evaluation of traffic density estimation, the simulated amounts of CAs and PLUs from Cell\({}_{i}\) are collected as CFVD to generate \(k_{i,a}\) and \(k_{i,p}\). For instance, the amount of CAs in Cell\({}_{1}\) (\(a_{1}\)) was 97 at 8 a.m., so the traffic density of the first road segment (\(k_{1,a}\)) could be estimated as 97 car/km by Equation (6) and MS communication behaviors (e.g., the expected value (1/\(\lambda\)) of the call inter-arrival time is 1 h/call). Furthermore, because 126 PLUs were performed in Cell\({}_{1}\) at 8 a.m., the traffic density of the first road segment (\(k_{1,p}\)) could be estimated as 128 car/h in accordance with Equation (8) and MS communication behaviors (e.g., call inter-arrival time). This study calculated the accuracies of traffic density estimation as \(1-\dfrac{\left|K_{i}-k_{i,a}\right|}{K_{i}}\) and \(1-\dfrac{\left|K_{i}-k_{i,p}\right|}{K_{i}}\). Table 4 shows that the average accuracies of traffic density estimation of the first road segment between 8 a.m. and 22 p.m. are 91% for the amount of CAs and 81% for the amount of PLUs. As shown in Table 5, the results of the traffic density estimation between \(k_{i,a}\) and \(k_{i,p}\) indicate that the amount of CAs is more suitable for traffic density estimation than the amount of PLUs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{\(\mathbf{Q_{1}}\)} & **The Amount** & **The Amount** & \multirow{2}{*}{\(\mathbf{q_{1,h}}\)} & \multirow{2}{*}{\(\mathbf{q_{1,n}}\)} & \multirow{2}{*}{\(1-\dfrac{\left|Q_{1}-q_{1,h}\right|}{Q_{1}}\)} & \multirow{2}{*}{\(1-\dfrac{\left|Q_{1}-q_{1,n}\right|}{Q_{1}}\)} \\ \cline{1-1} \cline{6-6} \cline{8-6} \cline{
# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
## 4 Results  
### Typhoon Characteristics and Wind Field Simulation  
The daily wind speed data of Ningbo ground meteorological stations (58,562 Yinzhou and 58,467 Cixi) were extracted from the China Meteorological Data System (CMDS) ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 1 May 2021). According to the meteorological data specification, the collected wind speed data were carefully calibrated by adjusting the observation height, observation time interval, and so on, to the standard condition. However, it was found that the daily maximum 10 min average wind speeds observed at the meteo  
Figure 4: The flow chart for construction of typhoon loss assessment model for rural housing in Ningbo.  
rological stations during the typhoon strike were small, so additional numerical simulation of the typhoon wind field was needed. The reason for the low observed typhoon wind speeds may be due to the change of the terrain near the meteorological station. Attributable to the rapid urbanization in China since the 1980s, the terrain near the meteorological station might have changed dramatically so that the exposure category in the vicinity of the original anemometer site might become very different from the initial open rural exposure [32]. Therefore, it is questionable to directly utilize the observed wind speed series to represent true typhoon wind speeds near the ground. In this paper, the [PERSON] wind field model [22] was used to simulate the near-surface typhoon wind speeds in the Ningbo area. The schematic diagram of the activating wind field model is shown in Figure 5. The Vickery empirical model [33] was used to determine the values of key wind field parameters such as the maximum wind speed radius \(R_{max}\) and Holland pressure profile parameter \(B\). The roughness length was taken as 0.05 m. The 10 min average maximum wind speeds at a height of 10 m near the ground in the Ningbo area under the influence of 25 typhoons were obtained by wind field simulation. The specific characteristics of 25 typhoons are shown in Table 1. From Table 1, it can be seen that the simulated typhoon wind speeds were larger than the observed wind speeds. The average number of typhoons affecting the Ningbo area is four to five per year from 2014 to 2019. Among these, type I typhoons that landed on the coast of Zhejiang and caused serious impacts on Ningbo occurred on average one time per year. The 25 selected typhoons affected Ningbo for three to four days on average, with the shortest lasting effect of only one day (Typhoon Mitag 1918) and the longest lasting effect of eight days (Typhoon Jongdari 1812).
# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 2 Data Pre-Processing Methodology  
### Data Cleaning and Feature Construction  
SCADA systems can collect multi-source heterogeneous data for the operational management of wind turbines, including but not limited to operating state data, environmental data, grid side data, and fault records. These data are transmitted to the intermediate central server via a local area network [29]. Researchers have proposed various SCADA data pre-processing methods, such as data filtering, averaging, and kernel density-based clustering algorithms [30; 31]. Following extensive data cleaning, the SCADA data are utilized to perform operational state assessments and the power curve modeling of wind turbines. Before delving into factors, such as sensor accuracy, storage exceptions, communication issues, and system failures, it is crucial to emphasize the significance of data preparation and filtering prior to data mining [32]. This study employs a three-step process to clean the raw data, which includes the following:  
Step 1: Eliminate null values, data beyond reasonable ranges, and other invalid data.  
Step 2: Remove data from periods of shutdown, power limitation, and fault occurrences according to the operation status and maintenance records.  
Step 3: Exclude slipped outliers based on a density-based clustering algorithm DBSCAN.  
A total of 26,280 records are collected from a 2.0 MW wind turbine over a six-month period. Following data cleaning, 17,890 valid records remain, as illustrated in Figure 1. It can be observed from the figure that the data in shutdown status, fault, and power limitation status, especially the outlier data, have been accurately identified. The residual data, assumed to be within normal operational status, will be further analyzed to evaluate the power performance of wind turbines in detail.  
Gathering the relevant parameters is essential to characterizing the degradation in performance. An initial set of variables, including wind speed, ambient temperature, rotor speed, pitch angle, yaw error, and generator temperature, along with an additional 22 variables, was identified. The raw data were processed to create new derived variables using methods, such as moving average, moving standard deviation, and temporal differencing. This processing employs sliding window widths of 10 minutes, 30 minutes, and 1 hour. Following feature construction, variables that are presumed to affect output power, including both basic and derived variables, are incorporated into the pool of input variables. Subsequently, any invalid data introduced during the feature construction are eliminated repeatedly.  
The feature construction process yields a total of 28 basic variables and 28 \(\times\) 3 derived variables, which serve as input variables, thereby effectively preserving the potential correlations between environmental factors, unit-specific characteristics, and the power performance. This holds substantial importance in enhancing the accuracy of wind turbine power performance assessments.
## 4 Discussion
### Effectiveness of the FMME  
Unlike the MFACNet, CenterTrack uses the fused information from two consecutive frames and heatmaps of the previous detection as inputs, directly predicting object displacements. After incorporating the FMME module, the model achieved a slight improvement in both the MOTA and IDF1, while also observing a significant decrease of 2859 in the IDSWs. This demonstrates that the FMME module provides better displacement predictions through inter-feature mapping construction and ReID sub-network optimization. This helps to reduce identity changes caused by erroneous associations between the trajectories and detection, enhancing the ability to manage target identities. It is worth noting that the appearance features generated using the ReID sub-network are not utilized in the association process between the detection and trajectories.
## 4 Near-Field and Far-Field Reconstruction  
Once \(\textbf{\emph{I}}_{i}^{(n)}(w)\) is reconstructed, starting from Equations (9) and (13), \(\textbf{\emph{I}}_{e}^{(n)}(w)\) can be readily obtained, i.e.,  
\[\textbf{\emph{I}}_{e}^{(n)}(w)=-\bigg{(}\widetilde{\underline{\textbf{G}}}(w)- R_{e}\textbf{\emph{I}}\bigg{)}^{-1}\bigg{[}\bigg{(}\widetilde{\underline{\textbf{G}}}(w)- R_{i}\textbf{\emph{I}}\bigg{)}\textbf{\emph{I}}_{i}^{(n)}(w)+\widetilde{ \textbf{E}}_{t}^{\sim\text{inc}(n)}(w,0)\bigg{]}. \tag{20}\]  
On the other hand, it is possible to show that the \(n\)-th harmonic of the field scattered by the homogeneous resistive plane, of resistivity \(R_{e}\), can be reconstructed by replacing \(\textbf{\emph{I}}_{i}^{(n)}(w)+\textbf{\emph{I}}_{e}^{(n)}(w)\) in (3) with  
\[\textbf{\emph{I}}_{0}^{(n)}(w)=-\bigg{(}\widetilde{\underline{\textbf{G}}}(k _{0}s_{\theta_{0}})-R_{e}\textbf{\emph{I}}_{e}^{\sim}\bigg{)}^{-1}\widetilde{ \textbf{E}}_{t}^{\text{inc}(n)}(w,0). \tag{21}\]  
As a result, the field scattered by the source \(\textbf{\emph{I}}_{i}^{(n)}(w)+\textbf{\emph{I}}_{e}^{(n)}(w)-\textbf{\emph{I }}_{0}^{(n)}(w)\) can be reinterpreted as the field diffracted due to the discontinuity between the involved resistive media. Hence, from (3), (10), (20), and (21), it is simple to conclude that  
\[\textbf{\emph{E}}_{t}^{\text{diffr}(n)}(\rho,z)=(R_{i}-R_{e})\int \limits_{0}^{+\infty}\textbf{\emph{H}}^{(n)}(w\rho)\widetilde{\underline{ \textbf{G}}}(w)\bigg{(}\widetilde{\underline{\textbf{G}}}(w)-R_{e}\textbf{ \emph{I}}\bigg{)}^{-1}\textbf{\emph{I}}_{i}^{(n)}(w)e^{-\text{j}\sqrt{k_{0}^{2 }-w^{2}}|z|}wdw, \tag{22a}\] \[E_{z}^{\text{diffr}(n)}(\rho,z)=-\text{j}\frac{\text{sgn}(z)}{2\omega\varepsilon _{0}}(R_{i}-R_{e})\int\limits_{0}^{+\infty}J_{n}(w\rho)\bigg{(}\widetilde{ \text{G}}_{\text{C}}(w)-R_{e}\bigg{)}^{-1}\textbf{\emph{I}}_{i,\mathcal{C}}^{( n)}(w)e^{-\text{j}\sqrt{k_{0}^{2}-w^{2}}|z|}w^{2}dw. \tag{22b}\]  
As expected, the diffracted field vanishes for \(R_{i}=R_{e}\), i.e., when the inhomogeneity in the plane disappears.  
Moreover, by means of the stationary phase method [53], the far diffracted electric field can be expressed in closed form as:  
\[E_{s}^{\text{diffr}}(\underline{r})\overset{r\rightarrow+\infty}{\sim}\frac{ e^{-\text{j}k_{0}r}}{r}F_{s}(\theta,\phi) \tag{23}\]  
for \(s=\theta,\phi\), where  
\[F_{\theta}(\theta,\phi)=-\frac{\omega\mu_{0}}{2}c_{\theta}(R_{i}-R_{e})\bigg{(} \widetilde{\text{G}}_{\text{C}}(k_{0}s_{\theta})-R_{e}\bigg{)}^{-1}\sum_{n=- \infty}^{+\infty}e^{\text{j}n(\phi+\frac{\pi}{2})}\textbf{\emph{I}}_{i, \mathcal{C}}^{(n)}(k_{0}s_{\theta}), \tag{24a}\] \[F_{\phi}(\theta,\phi)=-\frac{\omega\mu_{0}}{2}(R_{i}-R_{e})\bigg{(}\widetilde{ \text{G}}_{\text{D}}(k_{0}s_{\theta})-R_{e}\bigg{)}^{-1}\sum_{n=-\infty}^{+ \infty}e^{\text{j}n(\phi+\frac{\pi}{2})}\textbf{\emph{I}}_{i,\mathcal{D}}^{( n)}(k_{0}s_{\theta}), \tag{24b}\]  
and the bistatic radar cross-section (BRCS), defined by the diffracted field, can be readily obtained.
# Improved Least Squares Phase Unwrapping Method Based on Chebyshev Filter
## 3 Results  
The simulated data and the real InSAR phase data are used in this section to verify the effectiveness of the proposed method of unwrapping. The simulated wrapped phase data were generated using the PEAKS function in MATLAB (Version 9.8.0.1323502, R2020a), and the real InSAR phase data were obtained by InSAR processing of the Sentinel-1 satellite [29].  
To verify the superiority of the improved LS phase unwrapping method in this paper, we compared the proposed method with traditional LS, as well as the recently improved LS phase unwrapping methods, PCUA and RPUA. The proposed method is an improvement on the LS method, so the LS method was used in the comparison. PCUA and RPUA are also improved methods of the LS method in recent years and have good phase unwrapping effects. Therefore, comparison with LS, PCUA, and RPUA can better demonstrate the effectiveness of our proposed method.  
The iterative method is also used in PCUA and RPUA, and \(e=10^{-3}\) and \(Nit=300\) are set in the experiment. Root mean square error (RMSE) between the unwrapped phase and the true phase is used to evaluate the quality of phase unwrapping [30]:  
\[RMSE=\sqrt{\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}\bigl{(}\phi_{i,j}- \varphi_{i,j}\bigr{)}^{2}}. \tag{17}\]
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 6 Results and Discussions  
To verify the effectiveness of our ICIC scheme, a Matlab(r) based simulation tool was implemented.  
The simulation tool enables us to change system model and genetic algorithm parameters. System model parameters that were changed are the number of cells, the bandwidth, the number of active users, and the simulation time in terms of number of TTIs. Genetic algorithm parameters that were changed are population size, selection rate, crossover rate, mutation rate and number of generations.  
We conducted simulations for an LTE network with 19 cells [16] with one enodeB located on each cell; a bandwidth of 5 MHz [16], which corresponds to 25 RBs to share among users; and a simulation time of 3000 TTIs. Different number of active users were taken (50, 150, 200, 2500, 300, 350, 400, 450, 500, 550 and 600) to observe the performance of the proposed method and its overhead. Each enodeB used omnidirectional antenna. UEs were randomly distributed in each cell. Voice traffic was considered with a Poisson distribution. Each user's transmission queue was updated with a new packet every 20 ms corresponding to inter arrival time [38]. Head of line delay and buffer size were set according to VoIP QoS requirements [39].  
For the EARL parameters, we tried many combinations but, for results shown here, we used a population size of 500, a selection rate of 0.5, a crossover rate of 0.4, a mutation rate of 0.1 and a number of generations of 50. We choose maximum transmit power to 43 dBm [27] and we set minimum transmit power to 35 dBm. The range of transmit power was [35 dBm, 43 dBm] for the proposed power allocation algorithm, and 43 dBm for the uniform transmit power scenario. Note that, for uniform transmit power, Equation (1) (Section 3) was used but all transmit power values were equal to 43 dBm.  
System parameters are summarized in Table 4 while EARL parameters are summarized in Table 5.  
For each active users number, we executed 100 simulation runs. At the beginning of each simulation, each user's position was randomly generated within a cell. Performance of proposed ICIC scheme was evaluated in terms of average packet loss ratio, delay and SINR.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 6 Performance Evaluation Results
### Results of Congestion Use Case Evaluation  
This subsection presents an analysis of the congestion scenarios with a focus on variable speed limits and re-routing strategies. The evaluation of the variable speed limits is conducted under two conditions: without incidents (1a), and with an incident (1b). Simulations were conducted using the KPIs defined in Section 4.2.
## 2 Related Work
Meek is another popular PT on which much work has been done. Starting from the connection characteristics of Meek, [PERSON] et al. [9] combined static and dynamic packet features, and performed analysis on the Meek flow fragmentation mechanism. They extracted relevant features and achieved over 97% accuracy and 99% recall using the SVM machine learning algorithm. [PERSON] et al. [10] proposed a method using deep learning methods to automatically learn and extract communication fingerprints using key packet sequences for the efficient identification of Meek-based Tor hidden services access activities. The method can significantly reduce the identification time and storage consumption and achieve a better identification accuracy. Snowflake, an emerging Tor PT based on WebRTC connections, has few research works on it. [PERSON] et al. [25] analyzed differences in WebRTC instances by manual fingerprinting and experimentally discovered the possibility of classifying WebRTC applications using fingerprinting methods. They pointed out the limitations of Snowflake in bypassing censorship and indicated future directions for using WebRTC to evade censorship. The threat modeling and circumvention of censorship are discussed by the same author in [26]. They also discussed the design of Snowflake and WebRTC fingerprint information. [PERSON] et al. [27] collected a wide range of TLS traffic in real-world data, and used these data to analyze TLS implementations of several popular censorship circumvention tools, including Snowflake. From the collected fingerprint information, Snowflake has some fingerprint features that significantly distinguish it from other applications. These works are fragmented presentations for Snowflake or analyses of the possibility of Snowflake identification by fingerprinting. In summary, current research has focused more on the identification and classification of Tor traffic than Tor PT traffic. With the improvement of the censors' abilities, more and more users prefer using Tor PT for anonymous communication. Research on Tor PT will be a trend in the future. Previous work has provided direction and reference for our research, but some existing methods are no longer applicable due to the development and changes of Tor-Snowflake. Some of the works [10; 28] identify hidden service access behavior under Tor PT through key message sequences, but the start and end locations of key messages are difficult to determine and can significantly affect the effectiveness of the identification. In this paper, we study the emerging Tor PT Snowflake, for which there is little research available on traffic identification and the identification of hidden service traffic.
## 3 CV QKD over Satellite Channels
### Satellite-to-Ground Channel Model
The analytical form of the probability distribution of atmospheric transmittance is the log-negative Weibull distribution [64]:  
\[L(\zeta)=\sqrt{H^{2}+2 HR_{\oplus}+R_{\oplus}^{2}\cos^{2}\zeta}-R_{\oplus}\cos\zeta, \tag{5}\]1  
We simulate the values of \(\eta\) for each zenith angle \(\zeta\in[-\zeta_{max},\,\zeta_{max}]\) within the communication window with respect to the clock rate of the CV QKD protocol and combine it with systematic receiver loss \(\eta_{det}\) and respective extinction ratio \(\eta_{ext}\left(\zeta\right)\) to obtain an overall mean transmittance \(\left\langle\eta_{tot}\right\rangle\) (as well as \(\left\langle\sqrt{\eta_{tot}}\right\rangle\)) of the satellite pass. Hence the transmittance statistics during an overall communication window consists of contributions from individual simulated free-space channels at every permitted zenith angle \(\zeta\). Both \(\left\langle\eta_{tot}\right\rangle\)and \(\langle\sqrt{\eta_{tot}}\rangle\) govern the evolution of a covariance matrix of the state shared between [PERSON] and [PERSON] over the fluctuating channel [65]. Note that we evaluate the mutual information between the trusted parties, \(I_{AB}\), from the overall covariance matrix, averaged over the whole transmittance distribution (similarly to evaluating the Holevo bound), which we observe to be lower than the average mutual information, hence being the pessimistic estimate. Following is the list of parameters used in the simulation: the receiver aperture radius \(a=0.5\) (m) (respective plots shown in green color) 0.75 (m) (shown in blue), and 1 (m) (red), wavelength \(\lambda=1550\) (nm), detection efficiency \(\eta_{det}=-3\) dB [56], tracking and pointing accuracy \(\theta_{p}=1.2\) (\(\upmu\)rad) [22], and beam divergence \(\theta_{d}=10\) (\(\upmu\)rad) [7]. The resulting mean transmittance for a single pass at altitude \(H\) is shown in \[L(\zeta)=\sqrt{H^{2}+2 HR_{\oplus}+R_{\oplus}^{2}\cos^{2}\zeta}-R_{\oplus}\cos\zeta, \tag{5}\]2
## 2 Materials and Methods
### Characteristics of the Brewery Wastewater  
BWW was sampled from a brewery in southern Europe before discharge into the sewer system. The samples have been stored in plastic accumulation tanks and homogenized using submersible mixers.  
For the lab-scale phase, a total of 25 L of BWW has been collected in each plastic tank. Samples were then stored in the incubation unit at a temperature of 5 \({}^{\circ}\)C to prevent organic degradation. For the industrial-scale phase, 350 m\({}^{3}\) were sampled and used in the tests. Table 1 reports the main characteristics of the untreated BWW. Compared to urban wastewater, BWW was characterized by a higher concentration of organic matter (with a lower biodegradability index) and nutrients. The low amount of total suspended solids (TSS) suggests that the organic pollutants are primarily present in a dissolved form rather than in particulate. Furthermore, a high variability in pH in the case of BWW was detected. The characteristics of raw BWW agree with those measured in previous studies [2; 3; 4; 9; 13; 16; 17; 18; 19].  
\begin{table}
\begin{tabular}{l l c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**u.m.**} & **BWW** & **Urban WW a** \\  & & **(Tested in This Study)** & **(as Comparison)** \\ \hline TSS & mg\(\cdot\)L\({}^{-1}\) & 60–65 & 250–400 \\ BOD\({}_{5}\) & mg\(\cdot\)L\({}^{-1}\) & 2300–2400 & 230–350 b \\ COD & mg\(\cdot\)L\({}^{-1}\) & 5000–5200 & 500–750 \\ N\({}_{\text{tot}}\) & mg\(\cdot\)L\({}^{-1}\) & 120–130 & 30–60 \\ P\({}_{\text{tot}}\) & mg\(\cdot\)L\({}^{-1}\) & 19–20 & 6–15 \\ P-PO\({}_{4}\)3\({}^{-}\) & mg\(\cdot\)L\({}^{-1}\) & 14–16 & 4–10 \\ pH & - & 5.6–10.2 & n.a. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical and biochemical properties of the BWW compared to a typical urban WW. u.m.: unit of measure. I\({}_{\text{biod}}\): biodegradability index. a: data was taken from Henze and Comeau [20]. b: expressed as total BOD.
# Head-Related Transfer Functions for Dynamic Listeners in Virtual Reality
## 6 Discussion  
### Objective Deviations  
From the analysis in Section 2, the generic HRTFs were found statistically different from either of the individual sets based on the auditory model predictions, which supports our first hypothesis. However, the measured and modeled HRTF sets were found to be objectively significantly different also from each other. If the spectral cues in measured and modeled HRTF sets were similar, we would assume the elevation error predictions, in Figure 2, for the measured template and modeled target HRTF case to be close to the matching template and target errors, as shown in [34] for the own versus other HRTF cases. This, however, is not the case; the local elevation error and quadrant errors for this mixed prediction are closer to the cases with generic HRTFs. Similarly, the ITD and ILD analysis in Figure 1 showed significant differences between all three sets. Therefore, we reject our second hypothesis about the similarity of these sets. Recent work has found corresponding results regarding deviations between simulated and measured HRTFs, where significant differences were found in perceived coloration and localization between the sets [39]. There, [PERSON] et al. [39] suspected the absorbing effect of hair and clothing, missing from the modeled HRTFs, to at least partially explain the differences. However, the absorbing effect does not explain the relatively large deviation found in ITD values at lateral positions in our analysis. Here, the anthropometric measurement process may be a source of error.