esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The complex dynamic flow behaviour on arterial roads can be challenging to predict because some vehicles may stop to pick up or drop off passengers, or vehicles might turn into adjacent streets or parks. This is further complicated by the presence of traffic junctions, which interrupt traffic flow and make it more challenging to provide accurate predictions [1, 2]. Such predictions, however, are important for Intelligent Transport Systems (ITS) applications and have been the focus of many research efforts in the past, but accuracy has remained a challenge. This study aims to address this challenge by focusing on the development and evaluation of short-term traffic flow and speed prediction models that can be used for arterial roads. The study considers this in terms of a case study on Hoddle Street, Melbourne, Australia, which is one of the busiest arterial roads in Melbourne.  
Multiple machine learning models were developed and tested in this research for short-term traffic prediction up to 60 min into the future using traffic speed and traffic volume data. These included BiLSTM, unidirectional LSTM, Recurrent Neural Networks (RNN), Elman, and Deep Learning Backpropagation (DLBP) models. The performance of multiple AI models was assessed in this study for different time horizons using data obtained from a commercial company, Intelematics, who collect real-time traffic data across many cities in Australia. The traffic volumes and speed field data used in this research were collected from Hoddle Street in Melbourne. A total of 70,072 data observations were extracted during a 12-month period from the southbound and northbound directions of Hoddle Street. The analysis also considered other factors influencing the performance of the prediction model, such as weather conditions.  
The main research objective tackled in this research was to determine the prediction models' capabilities, particularly the well-known BiLSTM, in capturing the dynamic traffic behaviour of vehicles on one of the busiest arterial roads in Melbourne. Although there were a few research studies that explored the capability of BiLSTM models for short-term traffic prediction, the majority of those existing studies focused on freeways [3; 4; 5]. As outlined above, arterial roads present more challenging conditions, mainly due to traffic interruptions. Hence, this study evaluates model performance using more complex traffic patterns from arterials and provides short-term forecasting up to 60 min into the future. In addition, this study incorporates BiLSTM and LSTM models for traffic and weather prediction and evaluates the model prediction accuracy under multiple weather conditions such as rainfall, wind, humidity, and air temperature for Hoddle Street, Melbourne, Australia.
## 4 Results and Discussion  
In this section, we present and analyze the results obtained from our proposed methodology, structured into two distinct subsections. First, we evaluate the classification performance, demonstrating the effectiveness of our approach in distinguishing between static and dynamic tracks based on LiDAR data. This classification is fundamental for accurate localization, as static tracks serve as reliable landmarks while dynamic tracks provide crucial contextual information about the surrounding environment. In the second part we present the results of a more generalized approach by incorporating a combined dictionary that integrates multiple static track interactions. This approach enables a more robust localization framework by leveraging multiple reference tracks simultaneously, addressing  
Figure 8: DBN structure during the testing phase. Only LiDAR observations are used to match tracks and estimate ego vehicle positions.  
challenges such as no observation gaps and multi-track interaction scenarios. Through this structured evaluation, we aim to demonstrate the advantages and limitations of each approach while providing insights into how track classification and probabilistic modeling contribute to improving ego vehicle localization in LiDAR-based environments.
## 5 Discussion
### Ethical Considerations
#### 5.4.4 Transparency and Accountability  
To promote transparency, we have made our dataset generation methodology, evaluation criteria, and analysis techniques fully documented. The research follows established ethical AI frameworks, and the results are reported with thorough explanations of their implications. We also encourage independent validation of our findings by making key resources and evaluation scripts available for replication. Moreover, our study adheres to ethical guidelines set forth by relevant research institutions and follows best practices in NLP research to uphold accountability and responsible AI development.
## 1 Introduction
Previous research indicates that the number of decomposition modes (K-value) is a significant parameter in the performance of VMD. Current research in the selection of the optimal K-value for VMD generally focuses on two aspects. Firstly, the relationship between the original signal and sub-signals, which is known as Instinct Mode Function (IMF). [PERSON] [21] applied kurtosis to develop an indicator for the effect of the relationship on the performance of the VMD algorithm. The improved VMD was then used to detect the early failure of bearings through chatter signals. [PERSON] [22] proposed \"envelope kurtosis maximum\" as an indicator to select the optimal K-value, again by evaluating the relationship between the original signal and sub-signals. The second popular approach for K-value selection considers the Initial Centre Frequency (ICF) parameter. This approach is suitable for periodic signals, for which it is easy to calculate a centre frequency [16; 23]. [PERSON] [24] proposed an ICF-guide VMD algorithm for extracting minor damage information from bearings. However, impact response signals are different from the rotational signals considered in the above references. Impact response signals are impulse signals with gradually decreasing energy, differing from rotational signals, which contain a periodic peak in the time domain. Hence, kurtosis-based methods are generally not considered suitable, because of their dependence on the distribution of peaks within the signal. Additionally, it is generally difficult to calculate the centre frequency of non-periodic signals, such as impact response signals. Hence, current techniques for determining K-value are not suitable for selecting the optimal K-value for impact response applications. Therefore, it is necessary to seek an algorithm to select the most suitable decomposed modes for VMD when applied to impact response signals. In this paper, based on the impact response signal, an improved algorithm for detecting corrosion in the underside of rails is proposed. The algorithm combines VMD, Singular Value Decomposition (SVD), and Shannon entropy techniques. The proposed IVMD-SVD method, through its SVD component, automatically selects the initial parameters for the VMD element. SVD is then further applied to the VMD result to obtain Shannon Entropy, which is used in the detection and classification of underside corrosion. The classification result from a Radial Basis Function (RBF) neural network indicated that the proposedmethod could be used in diagnosing underside corrosion. A flow chart describing the structure of the proposed algorithm is provided in Figure 1.
## 3 Semi-Analytical Echo Model of Airborne SAR Altimeters  
In this section, we provide a semi-analytical echo model of airborne SAR altimeters through an approximation that reformulates \(P_{I}(t_{r},k)\) into the following convolution:  
\[P_{I}(t_{r},k)=\frac{c}{2}p^{\prime}(t_{r})*PTR(t_{r},k)*FSR(t_{r},k). \tag{13}\]  
Here, \(p^{\prime}(t_{r})=p(-ct_{r}/2)\) and \(FSR(t_{r},k)\) is the impulse response from a flat surface, which is defined in (17). The derivation of the convolution model is first provided in Section 3.1. Then, an approximate semi-analytical expression of \(FSR(t_{r},k)\) is provided in Section 3.2. The echo power after three-term convolution requires delay compensation to obtain multilook waveforms; thus, a novel delay compensation method is provided in Section 3.3. The multilook waveform model used for airborne SAR altimeters with antenna mis-pointing and vertical movement is characterized by six altimetric parameters: the significant wave height (SWH), epoch (\(\tau\)), amplitude (\(P_{u}\)), flight path angle (\(\mu\)), and on-track and cross-track mis-pointing angles (\(\psi_{ac}\) and \(\psi_{at}\), respectively). Finally, the effects of the mis-pointing angles on the multilook echoes are analyzed in Section 3.4.
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 2 Overview and Related Works  
In this section, we start by briefly describing the M2M communication and the packet scheduling in the LTE network, in order to provide the requisite background of this article's proposal. Then, we will discuss some solutions proposed in the literature which study uplink scheduling for M2M in LTE/LTE-A networks.
## 4 Materials and Methods
### Methods  
#### 4.3.1 Estimation of \(F_{\
u}\)  
\(F_{\
u}\) is an index directly used to determine grassland health condition. We estimated the grassland coverage by using the \(NDVI\) data due to the significant linear correlation relationship between grassland coverage and \(NDVI\). The calculated model is pixel dichotomy model. The specific calculation formula is as follows:  
\[F_{\
u}=\frac{NDVI-NDVI_{\min}}{NDVI_{\max}-NDVI_{\min}}\times 100\% \tag{1}\]  
where \(F_{\
u}\) is the grassland coverage (%), \(NDVI\) is the \(NDVI\) value of a single pixel, \(NDVI_{\min}\) is the NDVI value of bare soil or areas without vegetation coverage, and \(NDVI_{\max}\) is the NDVI value of pixels completely covered by vegetation. Theoretically, the \(NDVI_{\min}\) value should be close to 0, and the \(NDVI_{\max}\) value represents the maximum value of \(NDVI\) per  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Original Serial** & **Original Land Use Type** & **Serial Number after** & **Land Use Type after** \\
**Number** & & **Reclassification** & **Reclassification** \\ \hline
1 & Evergreen Needleleaf Forest & & \\
2 & Evergreen Broadleaf Forest & & \\
3 & Deciduous Needleleaf Forest & 1 & Forest \\
4 & Deciduous Broadleaf Forest & & \\
5 & Mixed Forest & & \\
6 & Closed Shrublands & & \\
7 & Open Shrublands & 2 & Shrubland \\
8 & Woody Savannas & & \\
9 & Savannas & & \\
10 & Grasslands & 3 & Grassland \\
11 & Permanent Wetlands & 4 & Wetland \\
12 & Croplands & 5 & Farmland \\
13 & Cropland/Natural & & \\  & Vegetation Mosaic & & \\
14 & Barren or Sparsely Vegetated & 6 & Desert \\
15 & Snow and Ice & 7 & Water \\
16 & Water Bodies & & \\
17 & City and Built-up & 8 & City \\ \hline \hline \end{tabular}
\end{table}
Table 1: The reclassification of land use type according to the IGBP classification system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**scPDSI** & **Classification of Dry and Wet** & **scPDSI** & **Classification of Dry and Wet** \\ \hline \(\geq\)4 & Extreme moist & \(-1\)\(\sim\)\(-2\) & Slightly drought \\
3\(-\)4 & Heavy moist & \(-2\)\(\sim\)\(-3\) & Moderate drought \\
2\(\sim\)3 & Moderate moist & \(-3\)\(\sim\)\(-4\) & Heavy drought \\
1\(\sim\)2 & Slightly moist & \(\leq\)\(-4\) & Extreme drought \\ \(-1\)\(-\)1 & Normally & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification relevant of self-calibrating Palmer drought severity index (scPDSI).  
unit pixel of total vegetation coverage. However, considering the influence of vegetation type, noise, terrain, image quality, and other factors, the \(NDVI_{\text{min}}\) and \(NDVI_{\text{max}}\) values will deviate from the actual values, which are generally represented by the maximum and minimum values within a certain confidence range. In this paper, \(NDVI\) values near 2% and 98% of the cumulative percentage of \(NDVI\) values in remote sensing images in the study area are selected as \(NDVI_{\text{min}}\) and \(NDVI_{\text{max}}\) values.
## 2 Materials and Methods
### Measurements of Plasma Emissions  
#### 2.2.1 Optical Emission Spectroscopy (OES)  
The SteriPlas was ignited at 150 W forward power, at a frequency of 2.45 GHz, with an argon gas flow rate of 2.2 L/min, where the argon gas purity was 99.998%. Then the power was reduced to 80 W forward power, which resulted in circa 2 W reflected power. Cooling air flow to the plasma torch was delivered by either a 55 L/min or 80 L/min air pump, respectively. A fibre optic spectrometer (Ocean Optics USB2000+, Rochester, NY, USA) was connected to a UV-VIS high OH multi-mode optical fibre, which was in turn connected to a UV/Visible Cosine Corrector (Avantes, Apeldoom, The Netherlands) with a 3.9 mm active area. Data were extracted from the spectrometer by a USB cable to a computer running Ocean View software.  
The Ocean Optics USB2000+ fibre optic spectrometer used a 2048-pixel linear charge-coupled device (CCD) array (Toshiba Electronics Asia Pte Ltd., Singapore), with a 5 \(\upmu\)m wide'slit', which was the smallest slit size; hence giving the maximum spectral resolution. The spectral resolution was calculated by taking the spectral range and dividing it by the number of pixels in the CCD array, which provided a relatively high spectral resolution of 161 pm.  
Calibration of the spectrometer was performed using a DH-2000 Deuterium Halogen Light Source from Ocean Optics. The deuterium light source was used to calibrate the spectrometer for absolute spectral irradiance measurements. This type of calibration compares the measured spectrum from a calibration light source against the known spectrum from a National Metrology Institute. Such a calibration removes errors in the measurement system due to attenuation of the optical fibre, the potential for solarisation of the optical fibre, noise due to dark current, and noise due to thermal electron effects in the CCD. The calibration process achieves this by comparing the signal being measured to that of a defined standard for a deuterium light source. The calibration process ensures that the absolute irradiance measurements are traceable to National Metrology Institute (NMI) standards.  
Next, the absolute spectral irradiance measurements were recorded at varying distances from the treatment area. The treatment area is defined as the cage under the SteriPlas plasma torch. All measurements were made down the centre of the axis of the torch. The 0 mm distance position was at the cage. The experiments were conducted on the SteriPlas with both the 55 L/min and 80 L/min extraction systems sequentially. Figure 2 shows the experimental method.  
Figure 2: Experimental setup of the spectral irradiance measurement (**left**) and the schematic of the plasma torch (**right**) [15].
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 1 Introduction  
### Research Background and Necessity  
Blockchain technology, first employed in Bitcoin, has been embraced by various other firms because of its decentralization and security benefits. However, blockchain fragmentation is a problem caused by the proliferation of isolated blockchains. Each blockchain operates independently and cannot do cross-platform transactions or instant communication. This issue highlights the need for heterogeneous blockchain interoperability (HBI) to develop a shared ecosystem. Cosmos [1] overcomes this challenge by employing the Inter-Blockchain Communication (IBC) protocol to connect separate blockchains.  
Unlike other heterogeneous blockchains [2; 3; 4; 5; 6], Cosmos uses parallel blockchains and Inter-Blockchain Communication (IBC) to address scalability, governance, and energy efficiency concerns. The IBC protocol allows the Cosmos Network of 35 blockchains to connect, facilitating token transfers, data exchange, and blockchain interoperability [7; 8; 9]. However, as cross-chain connections increase, the network's overall transaction per second (TPS) may decrease. Inter-blockchain communication protocols must be modified to improve performance, scalability, and compatibility. This study aims to address these challenges by measuring and increasing the efficiency of message transportation across blockchains in the Cosmos.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 3 Research and Applications of RO to TC Forecast
### Effect of RO Data on TC Forecasts
However, for the TC prediction, RO data below 6 km are crucial as they provide more accurate initial conditions of the water vapor and help adjust wind analysis above oceans. Figure 11: Left column shows the image of Ernesto’s genesis taken by a satellite after 54, 78, and 102 h; the middle column shows the model result with RO data; the right column shows the model results without the RO data. Ernesto’s genesis is predicted successfully after assimilating the RO observation [60]. Similarly, the authors of [62] investigated the impact of COSMIC RO data on the track of the super cyclone Gonu. Considering the error of the lower troposphere, they assimilated only the RO data in the UTLS region. The results show that the 72-96 h forecast of Gonu's track was improved because the RO data provided more accurate temperature information in the UTLS region. The energy for the development of a mature TC comes from the cold air above the TC. Therefore, assimilating RO data in UTLS region can improve the TC forecast in the later stage. The above two studies emphasized that RO data both in the UTLS region and below 6 km are essential to TC prediction. To further study how RO measurements help improve the accuracy of TC forecast, the authors of [63] used RO data to simulate the vertical structure of forty-two cases of TCs formed over the North Atlantic basin during 2002-2010. The TC parameters were statistically analyzed as a function of the distance from the eye of the cyclones. In comparison with the ECMWF reanalysis, the result confirmed that the RO observation could resolve the major characteristics of the TC structure--the eye and eyewall; the inflow, which is the mark of a mature TC; the outflow layers; and even the rain bands at a confidence level of 95%. The relative humidity of the simulated rain bands 200 km from the center, shown in Figure 13, is approximately 25% higher than ECMWF reanalysis. However, the location of the rain bands may not be that distinct because of the low horizontal coverage of the RO water vapor data. Therefore, the improvement in the TC forecast can be attributed to assimilating RO data. Figure 12: Forty-eight-hour prediction results of Storm Ernesto: (**a**) central sea level pressure; (**b**) track error; and (**c**) total column cloud liquid water obtained from three experiments and satellite infrared cloud image of the actual storm. The central sea level pressure (SLP) in the RO group is approximately 15 hPa lower than those in CTRL and RO6 km, which makes the TC in RO stronger [61]. Figure 13: TC relative humidity fractional difference between GPS simulation and ECMWF reanalysis. As the vertical profile of the TC, the humidity is statistically analyzed as a function of the altitude and distance from the eye of the cyclones. The relative humidity of the simulated rain bands 200 km from the center is approximately 25% higher than ECMWF reanalysis [63].
# Communication-Efficient Federated Learning with Adaptive Consensus ADMM
## 5 Experiments
### Global Collecting Step Size  
As we note in Section 5.2, although \(\rho^{0}=10^{-3}\) is desirable in most cases, it is not satisfactory for FMNIST in the 1000-client and non-IID data distribution settings. In subsequent experiments, we study the convergence of the model for tuning the server cloth length under a data distribution of 1000 clients and non-IID data distributions. The reduction in data heterogeneity and random algorithm turbulence that can affect experimental results can be achieved by the global collecting step size \(\eta\), as demonstrated in (11).  
So we fix \(\rho^{0}=10^{-3}\) and alter \(\eta\in\{0.1,0.2,0.4,0.5,0.6,0.8,1\}\). As shown in Figure 8a, \(\eta\in(0.4,0.6)\) gives better test accuracy and robust convergence with \(\rho^{0}=10^{-3}\) for FMNIST in the 1000-client and non-IID data distributions setting. When the value of initial variable parameter \(\rho^{0}\) is small, reducing \(\eta\) synchronously to a rational range promotes better convergence. To further validate our assumption, we conduct experiments with the condition \(\rho^{0}=10^{-2}\), which was excellent in the previous experiment of changing \(\rho^{0}\). It can be concluded from Figure 8b that \(\eta=1\) is the best choice at \(\rho^{0}=10^{-2}\), and the test accuracy quickly reaches the optimal value and the convergence is robust and stable.  
Optimizing the global collecting step size \(\eta\) is critical for mitigating data heterogeneity and random algorithmic turbulence that could impede experimental outcomes. An appropriately selected global collecting step size \(\eta\) ensures efficient information exchange between clients and the server, leading to improved aggregation at the server. An effective combination of \(\eta\) and \(\rho^{0}\) can make the algorithm experiment more effective.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 4 Case Study: Platform Screen Door  
### Definition of Accident and Hazard  
The Platform Screen Door (PSD) system is a critical safety feature implemented in modern railway transportation systems. Its primary function is to block the space between the area where trains pass and the interior of the station [28]. When the train stops, it opens and closes the door again after boarding passengers. PSD provides benefits for passenger safety and efficient train operation. However, if errors occur in the PSD system, it can disrupt other train operations and lead to passenger safety incidents and boarding/unboarding failures. Potential losses in the system are defined as human and economic losses [29]. To prevent safety-related accidents, the STPA is utilized to conduct risk analysis on PSD system.  
Define the following accidents. A01: Death or injury due to exposure to a train in operation because the door is not closed. A02: A person or object is caught in the door when it is closed with a person or object present. A03: Passengers fail to get on and off because the door does not open when the train stops. Next, based on the addressed accidents, define the hazards.  
* H01: The door opens while the train is in operation (A01).
* H02: The door closes when a person or object is present (A02).
* H03: The door does not open after the train stops (A03).
* H04: The train does not close the door as it prepares to depart (A01, A03).  
Then, derive safety constraints. Safety constraints are specific limitations placed on a system to ensure that it operates within acceptable safety limits [5]. SC01: The door must be closed during train moving(H01). SC02: The door should not close if an obstacle is detected(H02). SC03: The door must open after the train stops(H03). SC04: The door must be closed when the train is ready to depart(H04).
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 2 Highly Adaptive Reconfigurable Receiver Front-End (HARRF) for 5G and Satellite Applications
### Design Parameter Definitions  
To achieve these objectives, the following parameters are defined:  
Noise Figure (NF):  
The NF of the receiver front-end is given by:  
\[NF=10\log_{10}\biggl{(}1+\frac{F-1}{G}\biggr{)}\quad\text{(dB)} \tag{1}\]  
where \(F\) is the noise factor and \(G\) is the system gain.  
Gain (\(G_{T}\)):  
The total gain of the receiver front-end is calculated using:  
\[G_{T}=G_{1}G_{2}G_{3}\cdots G_{N} \tag{2}\]  
where \(G_{n}\) represents the individual stage gain.  
Linearity and Third-Order Intercept Point (IP3):  
The overall IP3 of cascaded stages is approximated as:  
\[\frac{1}{IP3_{total}}=\frac{1}{IP3_{1}}+\frac{G_{1}}{IP3_{2}}+\frac{G_{1}G_{2}}{ IP3_{3}}+\cdots+\frac{G_{1}G_{2}\cdots G_{N-1}}{IP3_{N}} \tag{3}\]