esa-sceva/satcom-chunk-collection huggingface dataset:
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Routing Protocols
#### 3.2.1 Ad hoc On-Demand Distance Vector (AODV)  
The name depicts it as an on-demand routing protocol with a conventional routing table, with a timer set for each node. If routing is not performed within a specific time frame, the node is removed from the table. The source, as well as intermediate nodes, carries all neighbours' information and messages. A hello message is sent for communication, and a route is discovered by sending Route Request Message (RREQ).
## 3 Results
### Determination of the Computing Speed  
To compare the speed and efficiency of the spec-radiation method compared to the re-radiation method in the numerical calculation obtained using the process shown in Figure 3, an idealized circular piston transducer was investigated. The amplitude distribution in the plane is depicted in Figure 18. In the red area, the amplitude is 1, and in the blue area the amplitude is 0. The signal is a pulse of two 50 kHz sinusoidal waves and the grid point distance is 2 mm. The surrounding medium was air. The propagation was calculated up to a distance of 1 m in increments of 1 mm, which requires the calculation of 1000 planes. In Figure 19, the amplitude distributions of the two calculations are given. These look almost identical. Differences only become prominent when the scaling is extended to \(-60\) dB (see Figure 20). The larger differences become visible especially at the borders. During the re-radiation method, it appears as if sound enters the volume from the sides and the sound from the piston transducer cannot spread uninhibitedly to the sides. This is not the case with spec-radiation. Corresponding to the pole point of the re-radiation method described in Section 3.1 (see Figure 7), it does not give the default values in layer 0, whereas the spec-radiation method reproduces this exactly.  
Figure 17: Amplitude at the top of the wooden particleboard with a e20 mm piece of paper. Evaluated with the spec-radiation method, for the frequency 48–52 kHz, with an original grid point distance of \(\mathrm{d}x=\mathrm{d}y=18\) mm enhanced to \(\mathrm{d}x_{Virt}=\mathrm{d}y_{Virt}=2\) mm and a grid of the size 512 mm \(\times\) 512 mm.  
Figure 18: Amplitude distribution of the idealized circular piston transducer.  
method, we produced the Video Supplementary Materials S2, where the amplitude distribution of each plane can be seen in 3D space. Therefore, the spec-radiation method is faster and more accurate than the re-radiation method in calculation.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 3 Performance Metrics of V2X Technologies
### Transmission Latency (L)
#### 3.2.1 L for 802.11p Protocol (L\({}^{11p}\))  
The L\({}^{11p}\) is the time required to transmit a packet. For a given \(P_{b}\), the L\({}^{11p}\) is calculated using an Equation (2).  
\[L^{11p}=t_{pre}^{11p}+t_{AIFS}+t_{sym}.n_{sym}^{11p}+t_{BO}, \tag{2}\]  
where \(t_{pre}^{11p}\) is the preamble duration (i.e., 40 \(\upmu\)s) that includes PLCP Header, \(t_{AIFS}\) is the arbitrary inter frame space (i.e., 32 \(\upmu\)s) for priority data, and \(t_{sym}\) is the OFDM symbol duration (i.e., 8 \(\upmu\)s). \(t_{BO}\) is the random (backoff) period before a transmission begins. For calculation purposes, we consider the worst-case scenario corresponding to 32 slots \(\times\) 20 \(\upmu\)s = 640 \(\upmu\)s. This time is crucial for avoiding collisions between vehicles, solving synchronization problems, ensuring fairness in the transmission opportunity between them, and allowing adaptation to change in the communication channel [34; 35].  
Equation (3) is used to compute the number of OFDM symbols (\(n_{sym}^{11p}\)).  
\[n_{sym}^{11p}=\frac{P_{b}.8}{n_{dc}^{11p}.CR^{11p}.n_{bps}} \tag{3}\]  
where \(n_{dc}^{11p}\) is the number of data carriers in an OFDM symbol (see [20], Table 17-4), and \(n_{bps}\) is the number of bits per OFDM symbol (\(n_{bps}\)= \(P_{b}.8/n_{sym}^{11p}\)).
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 3 Results
### Spatial Variability of NDVI
#### 3.2.2 NDVI Stability Analysis  
In terms of the area occupied by each type of variation level, low fluctuation areas > medium-low fluctuation areas > high fluctuation areas > medium fluctuation areas >  
Figure 4: Spatial trends distributions of NDVI in Inner Mongolia from 2000 to 2019. (**a**) levels of annual NDVI maximum, (**b**) Multi-year average NDVI trend levels.  
medium-high fluctuation areas. As shown in Figure 5, areas with large fluctuations in NDVI accounted for 27.2% or the entire area, and were mainly distributed in Xilingol league, parts of Ulanqab and Baotou, and Alxa league. This is due to the sparse precipitation and poor soils in these areas. The corresponding vegetation types are mainly grasslands, deserts, or transition areas between grasslands and deserts. These have fragile ecological environments and vegetation cover that is vulnerable to external influences. Areas with low NDVI fluctuations accounted for 61.7% of the total area, mainly distributed in Hulunbuir, the Xing'an League, Tongliao, and Chifeng, with the vegetation types of forest, typical steppe, and meadow steppe. Fluctuation of NDVI in the Xilingol League, where typical steppes are distributed, was also low, which may be related to the good ecological environmental conditions of its forest and grassland areas; these are not easily influenced by external forces [1]. Additionally, the amplitude of fluctuations in grassland areas was greater than that in forest areas, which may be attributed to the grassland areas being under high pressure from population and livestock, and more strongly disturbed by human activities than forest areas.  
Figure 5: Spatial distribution of NDVI stability levels in Inner Mongolia from 2000 to 2019.
# Analysis of Multilayered Ground Amplification Characteristics by Scaled-Down Model Tests
## 3 Multilayered Flat Model Ground Test Results and Analysis
### Response Spectral Acceleration  
Figure 11 represents the depth-specific response spectral and response spectral amplification coefficients of the multilayered, flat ground model. The amplification factor showed the degree of amplification within 10 cm thickness by dividing the soil layer into 10 cm intervals for detailed identification. Figure 11a-1 present the experimental results for the sine 10 Hz wave, the artificial seismic wave, and the sine sweep wave, respectively. Figure 11a,e,i show response spectral acceleration, and it can be seen that the acceleration is amplified according to the vertical height. Figure 11c,g,k are the response spectral amplification coefficients of the upper ground, and it was confirmed that in the loose ground, the amplification was about 1.5 times for each layer thickness. In contrast, as shown in Figure 11b,f,j it was confirmed that amplification hardly occurred in the dense ground. In Figure 11d,h,l the amplification of the inter-layer boundary of the multi-layered ground was compared with the amplification factor of the upper and lower ground. At the inter-layer boundary, it was amplified halfway between the bottom and the top.  
Figure 11: _Cont._Comparison and Analysis of Response Acceleration Results between the Experiment and Numerical Simulation Model
# Designing a Conceptual Digital Twin Architecture for High-Temperature Heat Upgrade Systems
## 5 Discussion
### Digital Twin Architectural Modularity  
System modularity is a design principle that involves decomposing a complex system into smaller, functionally independent modules [50]. This results in two main benefits [51]: the creation of flexible options for designers and users, and the capacity for evolutionary development, allowing for the iterative testing, selection, and integration of improved modules over time.  
The proposed digital twin architecture demonstrates modularity by explicitly dividing the system into distinct, loosely coupled layers, each addressing a specific set of functionalities, aligning with the core principles of modular design. This separation of concerns facilitates flexibility, maintainability, and scalability, as changes to one module can be made with minimal impact on the rest of the system.  
At the base layer, the DT architecture isolates physical equipment and control units from higher-level processing and service functions. The Physical Equipment layer contains all tangible assets, while the Hardware Control Units layer manages device-specific control with varying levels of digital readiness. This clear separation ensures that any evolution or replacement in physical devices or their immediate control mechanisms does not necessitate extensive changes in the upper layers. The Gateway layer main role is standardizing the communication across different protocols, ensuring interoperability between legacy systems and modern IoT devices, decoupling the heterogenous physical system from the digital process from the above layers.  
The upper digital layers, Processed Data, Services, and Visual Tools are subdivided into specialized modules that allows to independently process and manage data or to easily adopt different services and user interfaces according to the specific application needs. The Extensions for Industrial Integration layer further underlines the modular approach by offering components that facilitates the adoption of the proposed solution across diverse scenarios.
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 2 Related Work
### Overview of the SRDT-FRR Scheme  
This scheme establishes a comprehensive and well-coordinated architectural framework, with a focus on optimizing and managing backup paths in satellite networks. To mitigate transmission interruptions caused by link instability, the SRDT-FRR scheme is proposed. By leveraging the flexibility of segment routing (SR) technology, the speed of hardware-based detection, and the precision of link monitoring, the proposed approach enables the joint sensing of transient and stable failures, facilitating rapid fault recovery.
## 5 Conclusions  
The innovative LT-1 mission employs a non-interrupted phase synchronization scheme, which exchanges the synchronization pulse by the time-division mode. Therefore, the advanced synchronization scheme avoids periodic data loss caused by interrupting the normal BiSAR acquisition. More importantly, the maximum synchronization frequency can reach half of PRF for high synchronization accuracy. An advanced denoising approach based on compressive sensing has been presented to improve the accuracy of the synchronization for the LT-1 mission. The phase model of the synchronization scheme and the system design of the LT-1 synchronization module are described in detail. Multiple sets of data acquired from the ground validation system are used to demonstrate the noise reduce performance. The effectiveness of the proposed method is verified by the processing results, which indicates that it has the potential to improve synchronization phase accuracy in future on-orbit applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SNR (dB)** & **Before Denoising** & **Denoising by KF** & **Denoising by CS** \\ \hline
38 & 0.6163\({}^{\circ}\) & 0.3015\({}^{\circ}\) & 0.2273\({}^{\circ}\) \\
46 & 0.2172\({}^{\circ}\) & 0.1569\({}^{\circ}\) & 0.1287\({}^{\circ}\) \\
55 & 0.0984\({}^{\circ}\) & 0.0903\({}^{\circ}\) & 0.0851\({}^{\circ}\) \\
58 & 0.0875\({}^{\circ}\) & 0.0862\({}^{\circ}\) & 0.0861\({}^{\circ}\) \\
60 & 0.0783\({}^{\circ}\) & 0.0774\({}^{\circ}\) & 0.0774\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Synchronization Accuracy.  
Figure 11: Residual frequency and Allan deviations. (**a**) Frequency deviation and the residual frequency are derived by differentiating the imaging phase difference in Figure 9 and the residual phases in Figure 10, respectively. (**b**) Allan deviations of (**a**).  
**Author Contributions:** Conceptualization, Y.C. (Yonghua Cai), R.W. and D.L.; Data curation, K.L. and Y.C. ([PERSON]); Formal analysis, K.L. and Y.C. ([PERSON]); Funding acquisition, R.W.; Investigation, H.Z.; Methodology, Y.C. (Yonghua Cai), R.W. and W.Y.; Project administration, R.W. and W.Y.; Resources, Y.C. ([PERSON]); Software, Y.C. ([PERSON]) and D.L.; Validation, Y.C. ([PERSON]) and H.Z.; Visualization, Y.C. ([PERSON]); Writing--original draft, Y.C. ([PERSON]); Writing--review & editing, Y.C. ([PERSON]), R.W. and D.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the National Science Fund for Distinguished Young Scholars under grant number 61825106 and in part by the Medium- and Long-Term Development Plan for China's Civil Space Infrastructure, LuTan-1.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 3 Method
### Output Calculation  
Once a clean rPPG signal is obtained, we can perform peak detection on it to locate the individual beats in time in the signal. From the located beats, heart rate and heart rate variability can be calculated. To do this, we first extract the inter-beat-intervals (IBIs) from the signal, which are the time intervals between consecutive beats.
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 5 Experiments
### Ablation Study
#### 5.2.6 Choices of Flow Models  
We could use optical flow methods, e.g., FlowNet2 [56] and PWC-Net [57], as the flow model of our framework. However, the running speeds of these methods are even slower than our per-frame segmentation network (even slower than 20 fps), which means the warping operation will not speed up the inference phase. Additionally, when we apply these optical flow methods to warping, the segmentation accuracy is similar to our motion-vector version. Thus, we decide to use motion vectors. Table 5 shows the accuracy using different types of flows for warping.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **Object** & **Human** & **Vehicle** & **Nature** & **Construction** & **Sky** & **Flat** \\ \hline Warping & 43.8 & 56.7 & 82.2 & 86.6 & 87.1 & 91.6 & 96.6 \\ NKFC & 51.2 (+7.4) & 65.5 (+8.8) & 84.6 (+2.4) & 89.6 (+3.0) & 89.1 (+2.0) & 94.0 (+2.4) & 97.3 (+0.7) \\ NKFC + CFR & 62.2 (+18.4) & 75.2 (+18.5) & 89.7 (+7.5) & 91.3 (+4.7) & 90.8 (+3.7) & 94.2 (+2.6) & 97.9 (+1.3) \\ NKFC + CFR + RGA & 62.2 (+18.4) & 76.1 (+19.4) & 90.1 (+7.9) & 91.1 (4.5) & 91.0 (+3.9) & 94.2 (+2.6) & 98.0 (+1.4) \\ \hline \hline \end{tabular}
\end{table}
Table 4: IoU improvements of different categories. We choose Layer 1 here. The accuracy of non-rigid objects improves significantly. The improvements over five percentage points are shown in bold.  
Figure 7: Performance degradation of warp and warp-correct. (**a**): Layer 1 used for feature warping. (**b**): Layer 2 used for warping. \(T\): frame interval between the key frame and the frame to be evaluated. The correction module effectively alleviates the long-term error accumulation problem.
## 3 Design of Neural Network and Production of Training Data
### Underwater Channel Model and SPAD Receiver  
The relationship between the LED emission power and the average number of emitted photons can be expressed as:  
\[P_{t}=\frac{N_{t}hv}{T}, \tag{5}\]  
where \(P_{t}\) is the LED emission power, \(N_{t}\) is the average number of photons emitted by LED, \(h\) is the Planck constant, \(v\) is the frequency of light and \(T\) is the time slot interval.  
Optical signal transmission in an underwater channel will be affected by absorption and scattering. Since the absorption factor \(a(\lambda)\) and the scattering factor \(b(\lambda)\) are the main causes of channel attenuation, the accumulated total attenuation coefficient \(c(\lambda)=a(\lambda)+b(\lambda)\) in the underwater channel. According to the accumulated total attenuation coefficient, we use the Beer model to model the attenuation of underwater optical signal transmission; then, the number of photons \(Nr\) at the receiving end can be expressed as:  
\[N_{r}=N_{t}e^{-c(\lambda)L}, \tag{6}\]  
where \(L\) represents the underwater communication distance.  
The number of photons after SPAD optical detection can be expressed as:  
\[\lambda_{s}=\mu_{p}(N_{r}+N_{a}T)+N_{b}T, \tag{7}\]  
where \(\mu_{p}\) is the photoelectric conversion efficiency of SPAD, \(N_{a}\) is the number of background light noise photons per unit time and \(N_{b}\) is the dark count of SPAD per unit time.  
Generally speaking, the number of photons output by SPAD can be modeled as a Poisson statistical distribution under ideal circumstances, and the probability of detecting \(y\) photons can be expressed as:  
\[Pideal(y)=\frac{\lambda_{s}y^{s}e^{-\lambda_{s}}}{y!}, \tag{8}\]  
When considering the dead time of SPAD, the number of photons detected by SPAD no longer obey the standard Poisson statistical distribution. At this time, the probability of detecting \(y\) photons can be given by [19]:  
\[P(y)=\left\{\begin{array}{ll}\sum\limits_{i=0}^{y}\frac{\lambda_{s}^{i}(1-(i+ 1)\delta)^{i}}{i!}e^{-\lambda_{s}(1-(i+1)\delta)}\\ -\sum\limits_{i=0}^{y-1}\frac{\lambda_{s}^{i}(1-i\delta)^{i}}{i!}e^{-\
## 4 Evaluation
### Objective Evaluation  
For objective evaluation, three measurements, i.e., perceptual evaluation of speech quality (PESQ) [30], signal-to-distortion ratio (SDR) and signal-to-interference ratio (SIR) were adopted to evaluate the perceptual quality of extracted speech signals. Specifically, the PESQ generated by the evaluation software [30] was used to evaluate the perceptual similarity between the separated signal and the original signal. The score interval of PESQ is \([0,5]\), where smaller values imply a degradation of the quality of separated speech signal. The SDR and SIR were obtained by using the BSS EVAL Toolbox [31]; the SDR measures the overall performance (quality) of the algorithm, and the SIR focuses on the interference rejection. The tests were conducted in both simulated scenarios and real environments.  
For comparison, one of the most efficient sparse component separation (SCS) methods was selected as the reference method [28] to indicate the effect of the non-sparse component recovery by using the proposed method. Then, five outstanding existing methods were chosen for further evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulated Room** & **Absorption of the Wall** & \(RT_{60}\) **(ms)** & **Reflection Order** \\ \hline Anechoic Room & 1 & 0 & 0 \\ Room1 & 0.75 & 250 & 18 \\ Room2 & 0.75 & 500 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the testing room.  
Specifically, for the determined case (\(K=3\) in this paper), we compared the PESQ score of our proposed method with BSS methods, which belongs to other categories under simulated condition.
## 4 Method
### Water Balance Model  
The water balance model quantifies the change in water storage within a hydrological system over a specified time period. This model plays a crucial role in assessing water availability and distribution by accounting for inputs and outputs of water in the system. The components of the equation can be defined as follows:  
\[S_{(t)}=P_{(t)}-E_{(t)}-R_{(t)} \tag{5}\]  
where \(S\) represents the storage anomaly from GRACE, \(P\) refers to precipitation, \(E\) and \(R\) denote runoff and evapotranspiration. This model provides insights into the hydrological cycle, enabling researchers and water resource managers to make informed decisions regarding water management, agricultural practices, and environmental conservation. By analyzing the balance between precipitation, evapotranspiration, and runoff, this model facilitates a deeper understanding of water dynamics in various ecosystems, contributing to sustainable water resource planning and climate impact assessments.
# Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network
## 6 Conclusions  
This paper presents a magnetic moment fitting model based on a CNN and scalar magnetic anomaly signals, aimed at uncovering the complex relationship between magnetic anomaly signals, geomagnetic field signals, and the target magnetic moment. The dataset used for training the model includes 2D magnetic anomaly signals and three-component geomagnetic field signals, generated under various heights and magnetic moment conditions, with the three components of the magnetic moment serving as network labels.  
Additionally, Gaussian white noise with varying SNRs was added to the dataset. After training the model, the error demonstrated an overall decrease and stabilized as the SNR improved. When the SNR = \(-5\) dB, the model achieved the relative error in the magnetic moment magnitude is 4.92%, while the absolute errors in the magnetic moment declination and inclination are 4.73\({}^{\circ}\) and 1.54\({}^{\circ}\), respectively. The vector angle difference is 2.7\({}^{\circ}\). These results highlight the model stability, even in challenging environments.  
The primary goal of this model is to minimize the error in magnetic moment fitting under conditions with a scalar magnetic sensor and low SNR, effectively addressing the instability of vector magnetic sensors caused by steering discrepancies. By doing so, it enhances the efficiency of magnetic moment fitting across diverse environmental conditions. The model offers valuable insights into the precise fitting of magnetic moments under low SNR conditions, providing practical strategies for researchers in applications such as unexploded ordnance detection [33], urban pipeline installation [34], and medical robotics [35].  
Conceptualization, X.Y.; methodology, X.Y. and B.C.; software, X.Y. and J.Z.; validation, X.Y., J.Z. and K.Z.; investigation, B.C., K.Z. and X.L.; resources, B.Y. and W.Z.; data curation, J.Z., B.C. and X.L.; Writing original draft preparation, X.Y.; Writing review and editing, J.Z. and K.Z.; supervision, W.Z.; project administration, W.Z.; funding acquisition, B.Y. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was founded by the National Key Research and Development Program of China, grant number No. 2023 YFB3905002 and Youth Innovation Promotion Association CAS No. 2023138. Data Availability Statement: Data are contained within the article.  
The authors would like to thank the editors and reviewers for their efforts in helping with the publication of this work.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} STAR & Scalar Triangulation and Ranging \\ CNN & Convolutional neural network \\ SNR & Signal-to-noise ratio \\ MAPE & Mean absolute percentage error \\ MAE & Mean absolute error \\ \end{tabular}
## 2 Materials and Methods
### Sensitivity Analysis of Potential Improvements and Evaluation of Results  
Finally, findings from the entire analysis process were synthesized, involving the application of emission reduction percentages to calculate emissions for Valencia and its municipalities (see Figure 1). The outcome was a new, high-resolution representation of emissions, visually representing the estimated emissions with the OD matrix alongside the projected effects of implemented measures over time.  
While the high spatial resolution of our data offers detailed insights, it is important to acknowledge some limitations. Data accuracy can be affected by the resolution of input datasets, such as traffic volume estimates and transit schedules. Furthermore, the Origin-Destination matrices used may not fully capture real-time fluctuations in travel patterns, which could introduce some level of bias.