esa-sceva/satcom-chunk-collection huggingface dataset:
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 3 Experiments and Results
### Performance Comparison
#### 3.4.1 Overall Comparison  
Table 2 shows the results of the overall performance comparison. The smaller the values of MAE, RMSE, and MAPE, the better the prediction effect. Although the HA method based on statistics showed higher efficiency than the other methods and the evaluation indicators were not all larger than the others, this simple algorithm has an inability to process complex, changeable, real-time traffic flow data. The ARIMA method focuses on the regularity and stability of traffic flow data in the time dimension, and therefore it performed better than the traditional machine learning method SVM, but the accuracy was still low. The performance of GRU was superior to ARIMA, but both of them lack spatial dependency, which is exactly what T-GCN has to help it perform better. Although ST-AFN and ASTGCN both take the spatio-temporal characteristics of the data into account, the latter method is more accurate for constructing a multi-branched structure. The results show that the proposed heterogeneous graph convolution network performs best overall and that that MAE, RMSE, and MAPE reached 16.1654, 24.656, and 12.413% in PeMSD4 and 15.486, 22.513, and 13.641% in PeMSD8, respectively.  
In order to visualize the difference between the test performances of different methods, we constructed a bar chart and box chart according to Table 2, as shown in Figure 8. Through detailed analysis and graphical presentation of the experimental results, it can be  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**PeMSD4**} & \multicolumn{2}{c}{**PeMSD8**} \\ \cline{2-7}  & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline HA & 35.428 & 51.144 & 25.156\% & 28.438 & 48.367 & 30.579\% \\ SVM & 42.924 & 48.843 & 22.511\% & 30.546 & 48.645 & 28.647\% \\ ARMA & 32.540 & 42.596 & 20.168\% & 33.726 & 45.286 & 25.213\% \\ GRU & 29.120 & 40.603 & 18.551\% & 28.863 & 41.565 & 27.226\% \\ T-GCN & 25.842 & 31.457 & 14.657\% & 24.643 & 30.665 & 15.691\% \\ ST-AFN & 22.293 & 28.032 & 16.195\% & 21.156 & 25.562 & 18.532\% \\ ASTGCN & 19.531 & 26.924 & 14.522\% & 18.685 & 27.651 & 16.650\% \\
**Ours** & **16.165** & **24.656** & **12.413\%** & **15.486** & **22.513** & **13.641\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall performance comparison.  
determined that the the new proposed algorithm outperformed the other seven methods in the overall performance of the prediction task.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 2 Proposed End-to-End Video Steganography System
### Architecture
#### 2.1.2 Encoder Network Design
(8) In order to avoid vanishing and exploding of gradients, the idea of the ResNet [34] network is adopted to make identity mapping for the input cover video frame, that is, the cover frame is added to the features of a size of \(C\times H\times W\) pixel by pixel. In fact, the encoder processes the residual between the stego video frames and the cover video frames, that is, the secret message is hidden in the high-frequency part of the cover video frames. Because human eyes are not sensitive to the high-frequency part of the video frames, hiding messages in the high-frequency part helps to facilitate the visual quality of the stego video frames.
## 4 Discussion  
The radiometric calibration results for OHSs on board the Zhuhai-1 micro-nano satellite constellation presented here show that the proposed method can be efficiently used for the fast and low-cost on-orbit radiometric calibration of similar satellite constellations containing a large number of micro-nano satellites. The comprehensive gain defined in this work can effectively establish the relationship between the integration stages and the physical gains. Taking the OHS-F sensor on board the Zhuhai-1 satellite constellation as an example, the calibration of three CMOS sensors under all of the integration stages was conducted based on RadCalNet multi-site automatic continuous monitoring data. The calibration results between different sites and different integration stages were consistent, and the comprehensive gain could also effectively characterize the radiometric response of the sensors. Taking the OHS-F sensor on board the Zhuhai-1 satellite constellation as an example, the calibration of three CMOS sensors under all of the integration stages was conducted based on the RadCalNet multi-site automatic continuous monitoring data. For  
Figure 16: The satellite reflectance retrieved from OHS-F CMOS2 image and corresponding ground synchronous surface reflectance.  
three CMOS sensors on board the OHS-F satellite, the spectral channels at the wavelength range of 550 nm to 880 nm were relatively stable. For other bands, the stability was relatively poor, mainly because the noise effect on these bands was obvious. Therefore, this also shows that it is necessary to carry out high-frequency radiometric calibration for these bands. In addition, the radiometric calibration results for the three CMOS sensors on board the OHS-F satellite in 2020 and 2021 were consistent, and the radiometric stability in 2021 was higher.  
The fitted comprehensive gains of three CMOS sensors applicable to all of the integration stages have been proven to have good accuracy. Except for individual atmospheric absorption bands and low signal-to-noise ratio bands, the fitting error was basically within 10%. Compared to the ground measured reflectance of corn, the retrieved surface reflectance from OHS images was shown to be quite accurate and reliable. The absolute radiometric correction of OHS data was based on the fitted comprehensive gains, which further proved that the fitted comprehensive gains were reliable. In addition to using vegetation reflectance in validation, using other targets such as sandy targets can better validate the accuracy and reliability of the comprehensive gains. Unfortunately, we have not obtained the synchronous reflectance data for these targets, and we will continue to carry out validation work in the future.
## 1 Introduction
Some V2X non-geometric stochastic models can be found in [28; 29] for the 5-6 GHz band. If the spatial distribution of the environment is available, geometric stochastic models (GSCM) can be implemented using a simplified distribution of scatterers around the transceivers to identify and emulate the statistics of the physical environment. Some recent propagation models under this category can be found in [30] for mmWave bands and for sub 6 GHz in [31; 32]. Another geometric approach is the use of deterministic propagation models, this kind of modeling technique characterizes the communication channel using detailed scenario information. They are site-specific methods and are generally very exhaustive at a computational level. Ray Tracing (RT)/Launching (RL) is a deterministic approach that, at the expense of its high level of computational load, achieves a good tradeoff between accuracy and computational cost. In this work, we present the analysis of an urban V2X mmWave link scenario using a deterministic in-house Matlab-based 3D ray-launching (3D-RL) algorithm and empirical data obtained throughout a measurements campaign [33]. The proposed methodology enables the obtaining, for the complete volume of the scenario under analysis, of estimation of frequency/power as well as time domain parameters. The main contributions of this work are the following:  
* Full volumetric deterministic scenario simulation capabilities, which enable the analysis and evaluation of wireless system radio planning tasks, considering multi-link, multi-node configurations of any type. * Estimation of frequency/power as well as time domain statistics for any given distribution of scatterers, considering frequency dispersive properties of all the elements within the scenario. This enables the analysis of multiple vehicular communication link types as a function of scenario as well as traffic conditions. * Optimized simulation technique based on the use of hybrid simulation approach, based on the combination of 3D-RL with multiple acceleration techniques, in order to effectively consider large scenarios with topological detail levels. A schematic overview of the main contributions of this work is given in Figure 1. The 3D-RL analysis can be extended to any requirement in terms of network/node configuration, node density or user location. The manuscript is organized as follows: an overview of some relevant related investigations is presented in Section 2. Then, the proposed simulation technique and the scenario description is presented in Section 3, as well as the simulation parameters. Section 4 shows the outcomes of the simulation in terms of the received power, Power Delay Profile (PDP) and time dispersion parameters like the Root-mean-squared Delay Spread (RMS DS). Section 5 presents the measurements results, where all data collected in the real scenario is analyzed, and comparisons are drawn against the results obtained in the simulation stages. Finally, conclusions and the main takeaways are highlighted and summarized in Section 6.
## 3 How to Calculate Accurate Airtime at the AP Side
### Analysis of UDP Flows
Let \(i\) represent the number of retransmissions and \(W_{i}\) represent the range of the CW window for the \(i\)-th transmission attempt. The backoff time will be a random integer chosen from the interval \([0,W_{i}-1]\). Here, \(W_{0}=CW_{\text{min}}\), and \(W_{i}=\min(2\cdot W_{i-1},CW_{\text{max}})\). Let \(p\) represent the average packet loss probability, which can be obtained through AP-side statistics. The backoff process follows a general Markov process, and its expected value is:  
\[Backoff=\sum_{i=0}^{R}\frac{W_{i}-1}{2}\times(1-p)\times p^{i} \tag{4}\]  
Therefore, the indirect transmission time of a UDP stream can be expressed as follows:  
\[t_{ind}=Backoff+2\times SIFS+2\times Slot+\frac{s}{r} \tag{5}\]  
where \(s\) is the frame size of the wireless ACK, and \(r\) is the minimum data frame rate supported by the wireless network. In summary, the AP side can calculate the exact airtime required for a single transmission of a UDP stream based on the state of each stream.
# Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach
## 4 System Model
### Utility of Prosumer  
Generally, a PVT-HP prosumer can get revenue through trading energy with utility grid, CHP operator and other prosumers, which means that the utility function of a prosumer should consider the profit of selling energy and the cost of purchasing energy firstly. For prosumer \(i,i\in V_{P}\), its profit or cost at each time slot is defined as:  
\[P_{P}(t)=p_{e}(t)(PV_{i}(t)-P_{i}(t)-P_{i}^{hp}(t))+p_{h}(t)(H_{i}^{hp}(t)-H_{ i}(t)) \tag{6}\]  
where \(P_{P}(t)\) is the profit (positive) or cost (negative) of a prosumer at time slot \(t\), \(p_{h}(t)\) is the thermal price at time slot \(t\), \(P_{i}(t)\) is the load demand of electric power, \(P_{i}^{hp}(t)\) is the electric power consumed by the heat pump, \(PV_{i}\) is the predicted electric power of PVT system, \(H_{i}\) is the load demand of thermal power, \(H_{i}^{hp}\) is the thermal power produced by the heat pump.  
For the heat pump, it uses the water from the tank of PVT system as the low temperature heat resource to produce high temperature hot water. The thermal power produced by the heat pump is denoted by:  
\[H_{i}^{hp}(t)=P_{i}^{hp}(t)\mathit{COP} \tag{7}\]  
where \(\mathit{COP}\) is the coefficient of performance of the heat pump.  
Moreover, from the perspective of DR, the prosumers adjust their usage of energy motivated by the prices, and then get financially benefit. Simultaneously, adjusting the prosumer's load profiles would cause uncomfortable [33] or inconvenience [10] impact. Thus, the equivalent negative cost on the utility of prosumers can be defined as follows.  
\[l_{i}(t)=v_{i}(P_{i}(t)-P_{i}^{0}(t))^{2}+\omega_{i}(H_{i}(t)-H_{i}^{0}(t))^{2} \tag{8}\]  
where \(v_{i}\), \(\omega_{i}\) are the coefficients of inconvenience and uncomfortable, respectively; \(P_{i}^{0}(t)\) and \(H_{i}^{0}(t)\) are the initial electric power and thermal power consumption, respectively.  
From Equation (8), the negative impact of DR increases with the deviation of power consumption, both on electricity and heat. Hence, for prosumer \(i,i\in V_{P}\), its profit function at each time slot can be updated as follows:  
\[P_{P}(t)=p_{e}(t)(PV_{i}(t)-P_{i}(t)-P_{i}^{hp}(t))-p_{h}(t)(P_{i}^{hp}(t) \mathit{COP}-H_{i}(t))-l_{i}(t) \tag{9}\]
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 6 Particulate Matter Emission Mitigating Strategies
### Bedding Materials  
Poultry litter/bedding material is well-known as the mixture of initial bedding material and the manure deposited by the birds on the floor [55]. Bedding material is the main contributor to PM emission from CF poultry facilities than caged. According to [PERSON] et al. (2012), using different kinds of bedding material significantly reduces PM emissions; maize silage has shown a 19% PM\({}_{2.5}\) reduction compared to wood shaving [55]. However, it does not show any significant differences in PM\({}_{10}\) reduction. Similarly, when different kinds of bedding materials (chopped straw, gravel, peat, wood shaving, chopped paper, or clay pellets) were used in layer housing, peat or clay pellets resulted in lower PM production [54].  
Several studies have shown that bedding increases dust concentrations, so changing bedding materials might be an alternative to reduce PM emissions [54,55] (Table 11). However, changing bedding materials in every flock (broilers) is impossible from an economic point of view, so there is a general trend in the US of reusing litter for several flocks [128]. Reusing bedding material might be a good idea to increase profitability, but it also inherits the challenges of higher PM emissions, so deep littering or topping of bedding  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Filter/Biofilter** & **PM Size** & **PM Reduction (\%)** & **References** \\ \hline Wood-chip Bio-filter & PM\({}_{10}\) & 62 and 89.7 & \\
127 mm & TSP & 62.9 and 96.3 & [9] \\
254 mm & & & \\ \hline Stuffrink dry filter & PM\({}_{2.5}\) and PM\({}_{10}\) & 41 and 64 & [44] \\ U-bend baffle filter & \
\begin{tabular}{c} PM\({}_{2.5}\) and PM\({}_{10}\) \\ \end{tabular} &
\begin{tabular}{c} 41 and 64 \\ 19 and 22 \\ \end{tabular} & [44] \\ \hline Dry filter & PM concentrations & 55 & [62] \\ \hline Dry filter & PM\({}_{2.5}\) & 7.1 & [66] \\  & PM\({}_{10}\) & 40.7 & [66] \\ \hline Biotrickling filter and & & & \\ denitrification & & 38 & \\ EBRT * = 3 s & PM\({}_{10}\) & 60 & [124] \\ EBRT = 0.71 s & & 69 & \\ EBRT = 3.6 s & & & \\ \hline Stuffrink dry filter & Fine dust & 20–60 & [125] \\ \hline Trickling biofilter using & & & \\ acidified water & & & \\ \hline Bio-filter & TSP & 79–96 & [127] \\ \hline \hline \end{tabular}  
* EBRT = Empty bed air residence time.  
\end{table}
Table 10: Air filtration systems used to mitigate PM emissions.  
materials can be an alternate way to reduce PM emissions from the farm. According to [PERSON] et al. (unpublished), top application of different bedding materials (fine wood shaving, large wood shaving, and aspen wood chips) over reused litter significantly reduced PM concentration by up to 40%. Moreover, using deep bedding material has reported many beneficial aspects like increased growth rate, weight gain, and feed conversion ratio in poultry [129,130].
# Degradation Characteristics and Reliability Assessment of 1310 nm VCSEL for Microwave Photonic Link
## 4 Results  
The semiconductor laser-accelerated aging test with the electric stress can be described by the inverse power law, as shown in Equation (4).  
\[\mathrm{T}=\mathrm{A}v^{-c} \tag{4}\]  
where T is the lifetime, A is the constant, \(v\) is the electric stress, \(c\) is a constant related to activation energy. Variations of the output power degradation rates with the aging  
Figure 4: The current noise power spectral density curve of VCSELs versus frequency for different bias currents, (**a**) after 4000 h aging at 6 mA, (**b**) after 4000 h aging at 8 mA and (**c**) after 2500 h aging at 10 mA.  
Figure 5: The current noise power spectral density curve of the VCSEL before and after aging.  
time at different electric stresses are shown in Figure 6. As can be seen from the figure, when the aging current at 6 mA, 8 mA and 10 mA, the rate of change in the output power increased linearly with the aging time. In addition, as the electric stress increased, the power degradation rate became faster.  
The mathematical relationships between output power degradation rates and aging time at 6 mA, 8 mA and 10 mA are fitted to Equations (5) to (7), respectively. According to Equations (5) to (7), the lifetime of devices at different currents can be calculated. Using the lifetime at different currents to make linear regression of the inverse power law, the linear change of lifetime with current is obtained, as shown in Figure 7. Then, Formula (8) of the device lifetime and the current is obtained, where \(\tau\) is the lifetime, I is the magnitude of the electric stress and the unit is mA. By processing the test data, the lifetime of the device at 5.5 mA operating current is predicted to be 67 years.  
Accelerated life testing can be used to quickly obtain the reliability and lifetime distribution of the device. This method is faster and cheaper than aging under normal operating conditions, and aging under normal operating conditions is not feasible because  
Figure 6: The rate of change in output power varies with the aging time at different electric stress.  
Figure 7: Variation of device life with electrical stress.  
of the long lifetime. However, the reliability assessment technique is a statistical method that has certain differences for a single device. Besides, the technique is not suitable for the devices with changing operating conditions, such as intermittent life evaluation of power devices.  
\[\frac{\Delta P}{P_{0}}=1.36\times 10^{-4}.t^{0.6} \tag{5}\]  
\[\frac{\Delta P}{P_{0}}=6.80\times 10^{-4}.t^{0.6} \tag{6}\]  
\[\frac{\Delta P}{P_{0}}=4.08\times 10^{-3}.t^{0.6} \tag{7}\]  
\[\tau=8.2\times 10^{13}.I^{-11} \tag{8}\]
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 4 Methods for Order Quantification  
The nanofabrication of ordered patterns by bottom-up approach besides needing a suitable assembly technique requires tools for evaluating and quantifying the order. As already mentioned, in fact, defect-free periodic structures are practically impossible to obtain by self-assembly, therefore the effectivity and reproducibility of the chosen processing method, as well as the comparison between different samples can only be achieved by defect analysis and order quantification. Moreover, the sensing performances will likely depend on the uniformity and degree of order of the nanostructure, especially when the objective of the nanofabrication is an OF probe. In this prospective, order quantification can correlate the structural quality with the optical quality and therefore con provides a prediction of the functionality of the sensor. On the other hand, order evaluation data allow us to determine how much disorder can be tolerated in a sample before significant departures from the ideal sensing properties.  
Different order evaluation methods are available. For most of them, the starting point of the analysis is a 2D image of the periodic structure obtained by optical microscopy, atomic force microscopy (AFM), scanning electron microscopy (SEM), or transmission electron microscopy (TEM). The main characteristics of the different order evaluation methods are summarized at the end of the chapter, in Table 2.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results
### Benchmarking and Comparison against State-of-the-Art  
In order to study the generalizability of the proposed rPPG method, we benchmark its performance on a range of datasets. To assess accuracy, we measure the deviation of the predicted HR/HRV measures from the ground truth in terms of mean absolute error (MAE), which is the average of the absolute differences between predicted and true values (obtained within a set time window for HR). The results for heart rate and heart rate variability analysis are listed in Tables 2 and 3 respectively. These results are also compared against prior work to provide context w.r.t. the state of the art. In addition, results of a blind baseline estimator that always predicts a heart rate of 75 bpm (mean HR of all datasets) are also included in the table for additional context.  
\begin{table}
\begin{tabular}{r r r} \hline \hline Dataset & HR (bpm) & HRV [RMSSD] (ms) \\ \hline PURE & 0.34 \(\pm\)1.8 & 7.9 \(\pm\)13.1 \\ UBFC-RPPG & 0.24 \(\pm\)0.6 & 11.2 \(\pm\)8.4 \\ MMSE-HR & 3.57 \(\pm\)3.5 & — \\ VIPL-HR & 0.57 \(\pm\)3 & 17.33 \(\pm\)57.9 \\ COHFACE & 0.46 \(\pm\)2.2 & 14.36 \(\pm\)49.6 \\ ECG-Fitness & 3.47 \(\pm\)15.2 & 19.24 \(\pm\)76.1 \\ \hline \hline \end{tabular}
\begin{tabular}{r r r} \hline \hline Dataset & HR (bpm) & HRV [RMSSD] (ms) \\ \hline MoLi-PPG & 1.6 \(\pm\)9.3 & 4.4 \(\pm\)16.1 \\ DEAP & 0.41 \(\pm\)1.7 & 11.85 \(\pm\)37.4 \\ VicarPPG & 0.01 \(\pm\)0.02 & 0.11 \(\pm\)0.3 \\ VicarPPG 2 & 0.08 \(\pm\)0.5 & 4.52 \(\pm\)19.4 \\ EatingSet & 4.33 \(\pm\)1.6 & 6.54 \(\pm\)10.1 \\ StableSet & 1.11 \(\pm\)6.1 & 10.15 \(\pm\)43.6 \\ MAHNOB & 0.27 \(\pm\)1.8 & 3.31 \(\pm\)23.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Deviations (in terms of mean absolute error) in HR (bpm) and HRV [RMSSD] (ms) calculations between the raw ground truth and the hand-cleaned peaks from the CleanerPPG set. Many datasets exhibit notable deviations due to artefacts in the raw signal. This leads to miscalculation of error metrics used for evaluation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline Method & **PMR** & **UUF-APG** & **MR-UAPG** & **UAP-UAPG** & **CO-FUNDS** & **MUAP-ACO** & **MC-FUNDS** & **MUAP-ACO** & **MUAP-ACO** & **DAP-PPG** & **DualP** & **VoxelPPG** & **VoxelPPG** & **VoxelPPG** & **VoxelPPG** & **VoxelPPG** \\ \hline Baseline(TS) & 17.6\(\pm\)15.1 & 13.7\(\pm\)1.3 & 12.6\(\pm\)13.4 & 9.16\(\pm\)8.5 & 10.7\(\pm\)5.8 & 35.3\(\pm\)01.6 & 9.7\(\pm\)6.6 & 8.9\(\pm\)8.3 & 8.03\(\pm\)3.7 & 16.2\(\pm\)10.8 & 8.3\(\pm\)8.1 & 10.1\(\pm\)7.2 & 7.9\(\pm\)6.3 \\  & 0.36\(\pm\)0.4 & 2.6\(\pm\)3.0 & 3.82\(\pm\)10.5 & 3.82\(\pm\)10.5 & 10.6\(\pm\)11.0 & 12.9\(\pm\)30.0 & 24.9\(\pm\)22.0 & 16.5\(\pm\)9.1 & 4.85\(\pm\)2.0 & 27.1\(\pm\)5.0 & 18.3\(\pm\)3.0 & 3.4\(\pm\)3.0 & 3.18\(\pm\)2.0 & 0.9\(\pm\)4.0 \\ \multirow{3}{*}{FaceRPGG [Ours]} & 0.26\(\pm\)0.3  & 3.7\(\pm\)1.3  & 3.7\(\pm\)1.3  & 3.7\(\pm\)1.0  & 3.