esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The increasing number of Artificial Intelligence of Things (AIoT) devices at the edge layer brings serious challenges to the traditional access network architecture, which results in a decrease in data transmission due to different QoS requirements. To improve the QoS of the URLLC service and mMTC service in the AIoT, a Hybrid Services Collaborative Resource Scheduling Strategy (HSCRS) is proposed. First, a multi-layer collaborative resource scheduling framework for the AIoT hybrid services is designed based on the F-RAN. Then, a throughput weighting model for hybrid services is constructed to analyze the throughput characteristics of mMTC service and URLLC service. Furthermore, a sub-channel allocation and power control method is designed to solve the resource scheduling strategy of hybrid services. Experimental results show that the proposed method can largely improve the network throughput performance.  
Artificial Intelligence of Things; resource scheduling; hybrid services; URLLC; mMTC 2023 13 7956  
delay and reliability. When an access node needs to meet the co-transmission of the URLLC and mMTC services, the URLLC service will increase the transmission delay due to the reduction in the bandwidth resources obtained. The Quality of Service (QoS) requirements cannot be satisfied. Therefore, in the coexistence scenario of URLLC and mMTC services, it is important to schedule resources according to the QoS of different services for improving the network throughput and realizing the coordinated transmission of mixed services.  
Therefore, to improve the URLLC service and mMTC service in the network, we propose a Hybrid Services Collaborative Resource Scheduling Strategy (HSCRS). Firstly, the throughput-weighted modeling is carried out based on the data characteristics of the URLLC service. Secondly, to achieve the goal of improving the network throughput, a multi-agent model for the coexistence scenario of URLLC and mMTC mixed services is constructed. The contributions are as follows:  
* A multi-layer collaborative resource scheduling framework for the AIoT hybrid services is designed based on the F-RAN, and resource scheduling is performed based on the QoS requirements of different IoT service types.
* A throughput weighting model for hybrid services is constructed to analyze the throughput characteristics of the mMTC service and URLLC service in the AIoT.
* A sub-channel allocation and power control method is designed to solve the better resource scheduling strategy of AIoT hybrid services in complex environments. At the same time, a multi-agent model is constructed to improve the network throughput in a mixed service scenario.  
The remainder of this paper is organized as follows. Section 2 introduces the related work. Section 3 designs the hybrid business system model. Section 4 proposes the throughput optimization strategy of the hybrid service. Section 5 is the experiment analysis. Section 6 concludes the paper.
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 2 The Dataset and the Studied Buildings
### Multi-Channel SAR Estimation  
InSAR and DInSAR images can be considered as multi-channel images after their coregistration. Four channels are acquired for PolSAR images, by the successive emission of wave in the \(H\) and \(V\) polarizations and the simultaneous reception of the two polarizations.  
Thus, for these acquisition modes, each pixel of these images can be described by the scattering vector \(\mathbf{p}\) whose dimension \(N\) depends on the number of images constituting the dataset.  
In the case of speckle, \(\mathbf{p}\) follows a circular-centered-normal distribution of covariance matrix \(\mathbf{C}\)[19]. The diagonal elements of the matrix is the power of the channel \(k\in[1,N]\), \(\mathbf{C}_{k,k}=\sigma_{k}\) while the off-diagonal element \(\mathbf{C}_{k,j}=\sqrt{\sigma_{k}\sigma_{j}}\gamma_{k,j}e^{i\phi_{k,j}}\) also depends on the degree of coherence between the \(k\) and \(j\) channel \(\gamma_{k,j}\) and the phase difference \(\phi_{k,j}\).  
To estimate the covariance matrix, we use the maximum-likelihood estimator \(\hat{\mathbf{C}}=\frac{1}{L}\sum_{l=1}^{L}\mathbf{p}\mathbf{p}^{T}\) where the number of views \(L\) is the size of the sample used for the estimation. In the case of speckle, the rank of the covariance matrix is full and \(L\geq N\). The estimation sample can be chosen from neighborhood pixels, using non-local methods [20] or a temporal estimation [21] depending on the spatial and temporal variability of the studied targets.  
If targets are deterministic as some very bright point-target present in urban areas, the rank of the covariance is 1. No averaging are needed to estimate the power or the phase difference, except to reduce the impact of residual thermal noise.
## 7 Experimental Results and Discussion
### Adjacent Channel Leakage Ratio  
ACLR also called as Adjacent Channel Power Ratio (ACPR) is a quantity that determines the distortion components outside the useful signal bandwidth. It is expressed as [5]:  
\[ACLR_{dBc}=10\log_{10}\left[\frac{J_{ab_{i}}^{ab_{i}}T(f)\;df}{J_{ab_{i}}^{ab_{ i}}T(f)df}\right] \tag{22}\]  
where \(T(f)\) denotes Power Spectral Density (PSD) of the output signal while \(ab_{u}\) and \(ab_{l}\) are the upper and lower frequency limits of the adjacent channel; \(ub_{l}\) and \(ub_{u}\) are the frequency bounds of useful bands.  
In addition to EVM, we look at the ACLR also called as MIA. In Figure 12(a), ACLR for given input power variations and for different methodologies is represented. We can see that, at an RF 1/P power of 0 dBm, the ACPR value for no DPD utilization is around \(-28\) dBc, for DPD-GMP the value is around \(-41\) dBc, for DPD-CPWL, it is around \(-44\) dBc for MSA-DPD, it is around \(-45\) dBc and finally for DPD-NN, the value is around \(-48\) dBc keeping the ACPR below \(-45\) dBc set by 3 GPP [74]. With this it is obvious that DPD-NN has achieved significantly 3 dBc lower ACPR value as compared to MSA-DPD which has the best performance as compared to other Volterra series methods.  
Similarly, in Figure 12(b), the spectral density is shown. The power spectral density (PSD) with and without DPD at 3 GHz is shown. It is observed that MSA technique is achieving good performance as compared to CPWL while the NN DPD has a little advantage over the MSA with spectral regrowth suppression in the 10 to 15 MHz zones.  
Similarly, in Figure 13c, the electrical spectra are shown, there are two main components that show the carrier signals at 3 and 20 GHz, respectively. It represents that the aim of the proposed MSA-DPD technique further reduces the complexity and implementation of the DPD methodology and enhance the performance of the link as compared to CPWL, GMP and when no DPD is applied. Therefore, it is observable that ACPR is reduced with the proposed MSA-DPD methods as compared to CPWL and GMP method by a good proportion keeping the ACPR below \(-45\) dBc set by 3 GPP [74]. It is important to observe that MSA-DPD performs a little better than CPWL, however, the performance gain is not the only benefit, but the complexity reduction is the most important benefit of the proposed MSA-DPD technique (discussed in Section 7.3).  
Figure 12: EVM results (**a**) for DPD methods utilized in this study vs. No DPD case. DPD with CPWL (blue), GMP (black), MSA (green), NN (light blue) and No DPD (red). (**b**) 5G NR OFH performance DPD efficacy with MSA, CPWL, GMP and without DPD for flexible 5G transceiver waveforms.  
Figure 13: 5G NR OFH performance (**a**) ACPR vs. varying RF input power. (**b**) DPD efficacy with MSA, CPWL, GMP and without DPD in terms of Power Spectral Density/Spectral Regrowth. (**c**) Electrical Spectra showing the two carrier frequencies.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 4 State-of-the-Art Real-Time Metaverse
### Data Fusion and Real-Time Interaction  
Figure 5 elaborates on the technical process of data fusion within the real-time metaverse. A variety of sensors, such as radar [112], LiDAR [113], RGB cameras [114], multispectral sensors [115], and hyperspectral sensors [25; 116], collect data. With the high-speed connectivity of advanced communication systems, these data are transmitted to the cloud, where they are fed into the data fusion process [117; 118]. For instance, LiDAR provides depth and spatial information, while RGB cameras contribute visual details like color and texture [119]. Thermal cameras capture temperature differences, and multispectral or hyperspectral sensors gather data across different wavelengths, offering unique insights into material properties [120]. This fusion of different sensor streams enables the creation of an integrated and high-fidelity virtual model that accurately reflects the physical world [121]. The fused data are then transmitted to robots and haptic control systems [122], enabling both physical and virtual objects to interact in real-time. For instance, an avatar in the metaverse could control a robot in the real world, with haptic feedback systems giving the user real-world sensations based on the robot's actions. Haptic controls play a vital role in ensuring that users can feel and interact with virtual objects as if they were physical [76]. This real-time feedback system enables greater immersion and interaction, bridging the gap between the virtual and real worlds. In combination with AI, these systems allow for complex interactions between physical and virtual entities, whether it is for remote collaboration, education, or e-commerce applications.  
Cloud-edge computing, as depicted, ensures that data processing occurs as close as possible to the source, reducing the latency involved in transferring data to central servers [123]. Data and commands are processed in centralized servers or cloud-based processing centers. This setup supports complex computations and large-scale data storage, enabling the system to handle vast amounts of information and deliver real-time responses. The system benefits from scalable processing power and robust data management capabilities by leveraging cloud integration.
# MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
## 2 Methodology
### Improved YOLOv12 Algorithm
#### 2.2.2 Adaptive Hierarchical Feature Integration Network (AHFIN)  
The core design of the Adaptive Hierarchical Feature Integration Network focuses on adaptively weighting and integrating multi-scale features to precisely target critical regions of PCB defects, particularly those that are small in size and exhibit diverse morphologies. Given that PCB defects often manifest as localized details against complex backgrounds, traditional methods struggle to simultaneously preserve fine-grained information and global semantic context. AHFIN effectively addresses this challenge through its adaptive mechanisms.  
The network first outputs feature maps at different levels through the Backbone network, ranging from shallow to deep layers [38]. Shallow features primarily capture detailed information, while deep features provide global semantic information. For each feature map, a convolutional layer and a Sigmoid activation function are used to generate a weight map that reflects the importance of features at each scale. Specifically, for each feature map, the weight is obtained through the Sigmoid function as follows:  
\[W_{\text{scale}}\ =\sigma(Conv(X)). \tag{7}\]  
where \(\sigma\) denotes the Sigmoid activation function, while \(Conv(X)\) refers to the convolution applied to the input feature map. This weighting diagram allows the network to adaptively adjust the importance of each feature based on the different scales of the feature map.  
To ensure spatial alignment across features of different scales, AHFIN incorporates lightweight Deformable Convolutions to spatially calibrate low-resolution features. After alignment, the multi-scale features are adaptively fused according to their respective weights, resulting in an integrated feature map, which can be expressed as follows:  
\[X_{\text{fused}}=\sum_{i=1}^{N}W_{\text{scale}}\odot X_{i}. \tag{8}\]  
where \(W_{\text{scale}}\) represents the weight of the \(i\)-th scale, \(X_{i}\) denotes the feature map of the \(i\)-th scale, and the symbol \(\odot\) indicates element-wise multiplication. The integrated feature map retains both shallow-level details and deep-level semantics, which helps enhance the detection capability for complex shapes and minor defects.  
Finally, the fused features are further processed through a gradient compression module to reduce dimensionality, followed by the introduction of residual connections to ensure efficient information flow throughout the network. This process enhances gradient propagation and improves the model's robustness, thereby strengthening its ability to detect defects in complex scenarios. By integrating Deformable Convolution for spatial alignment and gradient compression for dimensionality reduction, AHFIN significantly improves sensitivity and processing efficiency for small and complex-shaped defects in PCB defect detection tasks. In particular, it substantially enhances the model's capability to identify subtle defects within cluttered backgrounds.
# A Brazilian Space Launch System for the Small Satellite Market
## Abbreviations
### Nomenclature  
\(g_{0}\)Gravitational acceleration  
\(h\)Altitude  
\(M\)Mach number  
\(m\)Mass  
\(M_{0}\)Launch vehicle total dry mass  
\(m\)Propellant mass flow  
\(m_{0i}\)Initial mass of vehicle stage  
\(R\)Radius of the Earth  
\(x\)Coordinate  
\(y\)Coordinate  
\(c_{x}\)Aerodynamic coefficient  
\(c_{y}\)Aerodynamic coefficient  
\(r\)Radial distance  
\(t\)Time  
\(I_{sp}\)Specific impulse  
\(V\)Velocity  
\(\pi\)Ratio of atmospheric pressure to sea-level pressure  
\(\theta\)Pitch angle  
\(\alpha\)Angle of attack  
\(\mu\)Independent variable (dimensionless)  
\(f_{0}\)System engineering integration factor  
\(H\)Development cost  
\(K_{p}\)Parameter  
\(fab\)Index for fabrication  
\(N_{s}\)Number of vehicle stages  
\(N_{e}\)Number of engines in vehicle stage  
\(f_{6}\)Programmatic cost impact factor for deviation of optimum time schedule  
\(f_{7}\)Programmatic cost impact factor for development by parallel contractors  
\(f_{8}\)Programmatic cost impact factor for productivity  
\(\lambda_{01}\)Thrust to weight ratio  
\(f_{1}\)Technical development status factor  
\(f_{2}\)Technical quality factor  
\(f_{3}\)Team experience factor  
\begin{tabular}{l l} \(f_{4}\) & Cost reduction factor for series production \\ \(f_{v}\) & Impact factor for vehicle type \\ \(f_{c}\) & Impact factor for assembling integration mode \\ \(L\) & Launch rate (number of launches per year) \\ \(dev\) & Index for development \\ \(S\) & Index for stage \\ \(E\) & Index for engine \\ \(ES\) & Index for solid engine \\ \(EL\) & Index for liquid engine \\ \(SS\) & Index for solid stage \\ \(SL\) & Index for liquid stage \\ \(atz\) & Index for amortization \\ \(pft\) & Index for profit \\ \(pp\) & Index for propellant \\ \(pld\) & Index for payload \\ \(add\) & Index for additional \\ \(F\) & Fabrication cost \\ \(GOC\) & Index for ground operation costs \\ \(FOC\) & Index for flight operation costs \\ \(C\) & Cost \\ \end{tabular}
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 5 Management and Countermeasures
### Safety Devices within Battery
#### 5.1.3 Positive Temperature Coefficient  
Besides, the PTC based on materials are also widely used among LIBs, whose resistance increases dramatically in response to the rapid rise of temperature [194; 195; 196; 197; 198]. For example, if a large current flows across the PTC element, its temperature increases abruptly, while the PTC works. A concomitant and abnormally high resistance of the PTC element prevents current flow which limits the further deterioration of heat generation. Once the large current is removed, the battery and PTC element cool and the resistance of the latter drops, allowing the resumption of charge/discharge. Overall, the primary purpose of PTC devices is to protect batteries against abusive electrical conditions such as external short circuit and overcharge etc.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 4 Comparative Analysis and Technical Difficulties
### Technical Difficulties and Solutions for Linewidth Characterization  
The improvements in characterization methods such as the delayed self-homodyne difference and delayed self-heterodyne have improved the resolution of linewidth measurements. However, the delayed self-homodyne difference beats at zero frequency as the center frequency, which is greatly affected by external factors such as temperature and mechanical oscillation. The delayed self-heterodyne shifts the beat signal frequency by modulating the laser and still relies more commonly on complex measurement systems with long delay fibers. The long length of the fiber introduces \(1/f\) noise, leading to broadening of the beat frequency spectrum. Therefore, both methods are susceptible to \(1/f\) noise, which can cause the laser phase noise to shake.  
Short delay fibers can lead to coherent envelope distinct beat spectra, and the laser linewidth can be obtained by fitting the beat spectra [133; 134; 135]. We have successively proposed coherent envelope demodulation methods based on iterative algorithms [136; 137] and self-coherent detection methods based on strong coherent envelopes [138].
## 2 Previous Models
### Fire-Spotting  
The spot fire ignition of a wildland fire by hot sparks and firebrands is an important fire ignition pathway by which wildfires are started and may propagate. Once the wildfire has been ignited and grows, it can spread rapidly through ember spotting, where pieces of burning material are lofted by the plume of the fire and then transported forward by the wind, landing where they can start spot fires downwind beyond the main fire's direct ignition zone. These secondary fires frequently cause hazardous situations for firefighters and contribute to the increase in the rate of fire spread. There are decisive factors affecting the generation of fire-spotting and the firebrands' landing patterns and flight paths. Some are macro-scale factors such as the atmospheric stability [37] and others are meso-scale factors such as the flame geometry [38]. Nevertheless, fire-spotting has a strong random component, so available fire-spotting models are inherently probabilistic.  
In [18], we proposed a preliminary fire-spotting sub-model that follows the characteristics of our model based on the ideas of the RandomFront 2.3 in [39], as a random heat contribution added to the right-hand side of the energy conservation equation. The heat contribution due to fire-spotting is written in terms of the distance of firebrand distribution \(\phi(\ell)\):  
\[q=F_{q}\times N_{q}\times\Delta_{t}\times\phi(\ell) \tag{29}\]  
where \(F_{q}\) is a factor transforming the probability density function \(\phi(\ell)\) into energy, \(\Delta t\) is the time step of the time discretization used to numerically solve the PhyFire model equations, and \(N_{q}\) is a fire-spotting index introduced to significantly reduce the computational effort of the fire-spotting module. In practice, this computation is performed every \(N_{q}\times\Delta t\), instead of every \(\Delta t\).  
The firebrand landing distance \(\ell\) can be assumed to follow a log-normal distribution [40]:  
\[\phi(\ell)=\frac{1}{\ell\sigma\sqrt{2\pi}}\exp(-\frac{(\ln(\ell)-\mu)^{2}}{2 \sigma^{2}}) \tag{30}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of the logarithm of the variable \(\ell\), that is \(\ln\ell\sim N(\mu,\sigma)\).  
The set of possible firebrand emitter nodes is computed as a subset of the fire front. Next, the set of possible receiving nodes of the firebrand from each emitting node is computed as a random subset such that the main direction of firebrand propagation is the wind direction, and the firebrands' landing distance depends on the mean distance of firebrand landing. The details of these calculations can be found in [18].
## 2 Materials and Methods  
### Geographic Background  
Aral Sea is a closed inland lake, formerly the largest Lake in Central Asia, with the runoff of the largest rivers, Amu Darya and the Syr Darya in Central Asia (Figure 1). Total surface area of Aral Sea Basin is about \(154.9\times 10^{4}\) km\({}^{2}\)[35]. Before 1960, the entire Aral Sea remained at \(6.8\times 10^{4}\) km\({}^{2}\) with a water salinity of about 10 g/L [35]. The records show that although the irrigated area of the basin increased from 1911 to 1960, human activities did not significantly affect the lake area [7]. Before 1960,the inflow into the entire Aral Sea was about 63 km\({}^{3}\). After the 1960s, the cultivation of crops resulted in a large increase in agricultural irrigation and domestic water consumption. During the period from 2006 to 2010, the inflow to the lake was reduced to only 4.1 km\({}^{3}\)[7; 36]. In 1988, the North Aral Sea (small Aral) became separated from the South Aral Sea; however, a channel continued to connect them. In 1988, the water level of the lake was the lowest, with an elevation of 39.7 m (above the Baltic Sea level) [37; 38; 39]. From the sampling position (Figure 1), it ensured that our sampling point was always within the range of the lake. Kokaral Dam was built in 1992, which prevent the outflow of water from the North Aral to the South Aral [40]. The flow of the Syr Darya into the North Aral Sea induced the stability and increasement of water level of North Aral Sea [37; 38; 39]. Different from the North Aral Sea, the water quality of the South Aral continues to deteriorate, not only in terms of the continuous decline in lake water level [37; 39]. The current maximum salinity of the South Aral Sea is estimated to be 200 g/L [41]. Field surveys in South Aral Sea conducted between 1990 and 2002 found that many species of phytoplankton (216 species), zooplankton (38 species) and benthic fauna (57 species) had disappeared, and only two of the original 20 fish species were observed [6].  
Figure 1: (**a**) Location of the Aral Sea in Central Asia; and (**b**) topographic map of North Aral Sea and the sampling location of the sediment core (NAS-01) denoted by the white cycle.
## 2 Experimental Procedures
### Photoluminescence  
PL studies have offered a simple, non-destructive method for assessing the electronic bandgaps (E\({}_{\mathrm{g}}^{\mathrm{PL}}\)) of binary and ternary alloy semiconductors. On GS-MBE-grown InP\({}_{1-x}\)Sb\({}_{x}\)/n-InAs samples, we perform PL measurements in a wide range of temperatures (10 K-300 K) by calibrating the radiation lines of a Xe lamp and exploiting the SPEX 500M monochromator (Section 4.2). A 532 nm DP-SSL excitation source is used by adjusting its maximum output power at 110 mW. The luminescence dispersed from each sample by a monochromator is collected by a liquid-nitrogen-cooled InSb detector. A correct beam path is adapted by using two pin holes and placing a lens to focus the incoming laser beam at a spot of nearly \(\sim\)0.1 mm diameter. For each epifilm, the results of PL spectra at 13 K are recorded by exploiting the standard lock-in techniques. CaF\({}_{2}\) lenses and optical windows are used for preventing absorption in the spectral range of water vapor (\(\sim\)2.7-\(\upmu\)m) and carbon monoxide (\(\sim\)4.1-\(\upmu\)m). Like HR-XRD, our PL measurements on InP\({}_{1-x}\)Sb\({}_{x}\)/n-InAs samples have confirmed two energy phases with smaller values of x (\(\equiv\)0.10, 0.16, 0.17), while for x \(\geq\) 0.30, the study provided a single energy phase. Our PL measurements for a sample with x = 0.33 offered the lowest bandgap energy E\({}_{0}\) (near \(\sim\)0.49 eV), in very good agreement with the MDF calculations (Section 4.2).