esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 3 Methods
### Parallelized BPA on GPU
#### 3.4.2 Parallelized Back Projection on GPU  
In each imaged area, the corresponding time delay is calculated according to the slant range of each pixel, and a compensation factor is constructed to compensate for the phase of the pixel. Furthermore, both the sampling points processing at each portion and the pixel processing in the image are the same and independent. Therefore, all the pixels in the imaging area are implemented parallelly by an assigned thread, which was also used to calculate and process azimuth time images. In the viewpoint of thread mapping, there are two kinds of implementations for the parallel Back Projection step: pixel-oriented parallel and range code-oriented parallel. The two methods described above have good parallelism, the pixel-oriented parallelism is more suitable for the engineering realization of SAR image [30]. Thus, the pixel-oriented parallel BPA is adopted in this paper. The parallel implementation steps of pixel-oriented back-projection are as follows:  
1. Calculate the delay \(\tau_{mm}(\eta)\) by constructing a kernel function to allocate threads to each pixel at a range code.
2. According to the delay of each pixel \(\tau_{mm}(\eta)\), the information in the corresponding range gate in the range compression is selected, and the compensation factor \(h(\eta,m,n)\) is constructed according to the slant range information \(R(\eta,m,n)\) and the phase difference value.
3. Phase compensation is performed by complex multiplication of the range compression data \(F(\eta,\tau_{mm})\) for that pixel point with the compensation factor \(h(\eta,m,n)\) in the time domain. The kernel function will be invoked tens of thousands of times to complete the complex multiplication and complex addition processing.
4. The data \(S(\eta,x_{m},y_{n})\) from each azimuthal moment is coherently summed to generate the subimage \(S_{sub}(\eta,x_{m},y_{n})\).
5. Repeat steps (1)-(4) until all echo data are processed.
###### Abstract  
Recently, the concept of cell-free massive multiple-input multiple-output (CF-mMIMO) has been implemented in low-Earth-orbit (LEO) constellations to enhance energy efficiency. However, signal distortion caused by nonlinear power amplifiers (NPAs) significantly degrades the performance of LEO satellite communication (SATCOM) links. In this paper, we propose a serving cluster scheme and a hybrid precoding framework for CF-mMIMO-assisted LEO SATCOM systems, aiming to suppress the impact of NPAs while reducing onboard hardware complexity and power consumption. Specifically, we first develop a user-centric access point clustering strategy that incorporates elevation constraints and channel fading. Then, we formulate a precoding optimization problem to maximize energy efficiency by treating the product of tightly coupled analog and digital matrices as a single fully digital precoder. The proposed distortion-aware precoding design is achieved by integrating the weighted minimum mean square error (WMMSE) approach with a gradient descent method enhanced by [PERSON]'s accelerated momentum. Furthermore, an efficient hybrid precoding method based on alternating minimization is proposed to address the matrix decomposition challenge. Simulation results validate the effectiveness of our proposed user-centric serving clustering algorithm and the WMMSE-based precoding algorithm in CF-mMIMO-assisted LEO SATCOM systems.  
CF-mMIMO-assisted LEO SATCOM; hybrid precoding; serving cluster; nonlinear power amplifier; energy efficiency
## 3 Methodology
### FWI Objective Function with First-Arrival Constraints  
The conventional full waveform inversion (FWI) objective function (Equation (10)) quantifies the data misfit between the observed and synthetic seismic traces across all receivers. However, this formulation may result in inversion instability owing to initial model dependencies. To mitigate this limitation, we introduced a dynamic objective function that incorporates first-arrival constraints:  
\[J(\mathbf{v})=\sum_{s\in\mathcal{S}_{\rm selected}}\sum_{t=1}^{T}\|\ \mathbf{d}_{\rm cal}^{\rm s}(t;\mathbf{v})-\mathbf{d}_{\rm obs}^{\rm s}(t)\|^ {2}, \tag{16}\]  
where \(\mathbf{d}_{\rm cal}^{\rm s}(t;\mathbf{v})\) denotes the synthetic seismogram at time step t for the s-th trace, \(\mathbf{d}_{\rm obs}^{\rm s}(t)\) represents the corresponding observed seismogram, and T denotes the total number of temporal samples. This loss function exclusively incorporates seismic traces from the subset \(\mathcal{S}_{\rm selected}\), thereby amplifying the contribution of the first-arrival phase matching and attenuating the influence of the initial model uncertainties.
## 4 Conclusions
* Macroscale analysis: The homogenised mechanical properties obtained for PUFs with varying volume fractions were then implemented in large-scale simulations. These simulations involved a sandwich cantilever beam with a homogeneous PUF core and another with an approximated functionally graded PUF core. The analysis revealed that near the domain boundary, NNRPIMv2, with its larger influence domains, yielded lower local stress values. However, the stress distributions across the beam thickness exhibited convergence among all the investigated formulations. The findings consistently indicated that NNRPIMv1 generated results closely matching FEM-4n for the macroscale examples. Comparing the displacement obtained with the FEM and both NNRPIM versions, it can be concluded that for uniform PUF cores, NNRPIMv1 provides results with \(d_{v1}\in\) \(]\) 3.0%, 6.6% [ and \(d_{v1}\in\) \(]\) 5.5%, 6.1% [ for the \(u\) and \(v\) components, respectively, and for the same \(u\) and \(v\) components, NNRPIMv2 is capable to deliver results with \(d_{v2}\in\) \(]\) 3.2%, 7.6% [ and \(d_{v2}\in\) \(]\) 5.2%, 6.6% [, respectively. Regarding the approximated functionally graded PUF cores, the difference between the FEM and both NNRPIM versions is much lower. For the \(u\) and \(v\) displacement components, the results obtained with NNRPIMv1 are \(d_{v1}\in\) \(]\)3.4%, 3.7% [ and \(d_{v1}\in\) \(]\) 3.0%, 3.4% [, respectively, and for the NNRPIMv2 it was observed that \(d_{v2}\in\) \(]\) 3.2%, 3.3% [ and \(d_{v2}\in\) \(]\) 2.7%, 3.2% [, respectively, for the \(u\) and \(v\) displacement components. To conclude, the obtained results show that both NNRPIM formulations, especially the NNRPIMv1 version, are solid alternatives to the FEM. Particularly in the microscale examples, the results also show that if the objective is to obtain a smooth representation of the stress field, the number of integration points per quadrilateral sub-area integration cell should be increased. However, this approach is not very interesting numerically, since it increases the computational cost of the analysis without a significant gain in accuracy. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The author acknowledges the funding provided by Ministerio da Ciencia, Tecnologia e Ensino Superior--Fundacao para a Ciencia e a Tecnologia (Portugal) and LAETA, under internal project UIDB/50022/2020. The author declares no conflicts of interest.
## 5 Simulation Analysis
### End-end delay  
The time it takes packets to propagate from the source to the destination is known as delay in wireless networks. It is made up of the total time spent on route finding, queuing, propagation, and transfer time. Figure 13 represents end-to-end delay at various simulation timings. The final results of the Zigbee simulation are clearly illustrated in the Table 3.  
Figure 13: (a,b) Queue size and queuing delay.  
Figure 14: End-to-End delay.
# Designing a Reliable UAV Architecture Operating in a Real Environment
## 4 Collision Avoidance Algorithm
### Modifications to the Collision Avoidance Algorithm
#### 4.3.2 Spheroid Separation Areas  
576 Most airplanes measure altitude positions with higher accuracy than other coordinates. Therefore most of the norms assume safe ellipsoid areas. For example, Reduced Vertical Separation Minimum (RVSM) [28] introduces lowered 1000 ft. vertical separation for flights under 41,000 ft. and 2000 ft. above that threshold. Meanwhile, horizontal separation is defined as more complex due to different definitions. Usually, horizontal separation requires a distance of 15 nautical miles (27.8 km--lateral separation) or 15 min of flight (longitudinal separation) [29].  
There are many definitions for the safety radii, varying for countries, heights, speeds or weather conditions (e.g., [30], or [31] presented in Figure 9b). In this paper, separation values, due to low altitude flights, is 9260 m for horizontal distance and 600 m for vertical distance, defining spheroid presented in Figure 9a.  
Additionally, the distances depend on object type (UAVs usually assume smaller collision spheroids) or flight conditions (IFR-Instrument flight rules versus VFR-visual flight rules).  
To solve this problem, simple coordinate transformation can be used. The operations on the ellipsoid or spheroid are more complex than for sphere (for example, requiring iterative calculations for projections), and the original algorithm does not consider variable \(r_{safe}\) distance. It is the easiest to assume a spheroid separation zone (\(r_{safe-H},r_{safe-V}\)) and stretch the Z-axis with coefficient \(\frac{r_{safe-H}}{r_{safe-V}}\) before the appliance of the calculations for the sphere. Afterward, the Z-axis has to be brought back.  
That yields few profits: dodge maneuvers in the Z-axis are smaller than initially, so it is simpler to test the algorithm and prove its efficiency. The \(r_{mt}\) still represents the violation grade of the separation distance as it can be compared to \(r_{safe-H}\).  
Figure 8: Example of the problematic situation after introduction of a collision time limit. (**a**) Detection of collision over a very long time, (**b**) safe area violation after a short period.
# Solar Power System Planning and Design
## 3 Site Evaluation  
It is necessary to increase the effectiveness of installing solar power plants by prioritizing and selecting suitable locations to maximize electricity generation and minimize the damage that may occur. The results of such site evaluation can help solar utility companies, energy companies, and policymakers select potential sites for the construction of solar power plants [3].  
[PERSON] et al. [7] proposed an evaluation model of demand-side energy resources (DSER) for urban power grids based on geographic information. The commonality and individuality indices for five kinds of DSER, revolving wind power generation, photovoltaic power generation, electric vehicles, energy storage, and flexible load, were selected based on geographic information. Then, the weight of each sub-index of the commonality and individuality indices was determined by the analytic hierarchy process (AHP) and entropy weight method. Finally, the weighted overlay was generated according to the weights and quantized values of each index, and a comprehensive score was obtained from the commonality indices. The results depicted that the evaluation model is beneficial for the planning of the city and the power grid.  
The installation of PV panels on the ground can cause some problems, especially in countries where there is not enough space for installation. As an alternative, floating PV, with advantages in efficiency and for the environment, attracted attention. [PERSON] et al. [8] analyzed the water-level data from 3401 reservoirs in South Korea and selected suitable reservoirs for floating PV systems, with an average reservoir water depth greater than 5 m and minimum water depth greater than 1 m. The results were utilized to estimate priorities and potentiality prior to the actual floating PV installation and detailed analysis.  
GIS is useful for site evaluations when installing solar power plants for PV or CSP on the regional scale. [PERSON] et al. [3] reviewed the GIS-based methods for determining suitable locations for solar power plants. In most site evaluation studies, solar radiation is the primary consideration. However, it is also necessary to consider economic, environmental, technical, social, and risk factors. These factors can be used to exclude unsuitable regions through a Boolean overlay and can be employed in various multiple-criteria decision analysis (MCDA) methods to estimate suitability indices [3].
## 3 Data and Methodology  
This study follows a comprehensive analysis approach, as outlined in Figure 5. The steps include (1) acquiring geological and geomorphological data of the mining area using on-site geological surveys; (2) interpreting landslide deformation features using multiperiod unmanned aerial vehicle (UAV) 3D models; (3) comprehensively determining the geological structure of the pit slope by combining physical exploration and drilling techniques; (4) conducting indoor physical-mechanical experiments and microscopic analyses to assess the geotechnical properties; and (5) employing FLAC3D for analyzing the stress-strain characteristics of the pit slope, followed by an assessment of the deformation patterns and damage mechanisms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Number of** & **Pit Floor** & **Pit Top** & **Relative** & **Terrain** \\  & **Steps** & **Elevation/m** & **Elevation/m** & **Elevation/m** & **Slope/\({}^{\circ}\)** \\ \hline North pit & 6 & 1580 & 1637 & 57 & 32 \\ South pit & 8 & 1545 & 1628 & 83 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Status of mine excavation (2023.03).  
Figure 4: Average monthly precipitation and average monthly temperature from 2018 to 2023.
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
###### Abstract  
This article addresses one of the main challenges related to the practical deployment of Internet of Things (IoT) solutions: the coordinated operation of entities at different infrastructures to support the automated orchestration of end-to-end Internet of Things services. This idea is referred to as \"Internet of Things slicing\" and is based on the network slicing concept already defined for the Fifth Generation (5G) of mobile networks. In this context, we present the architectural design of a slice orchestrator addressing the aforementioned challenge, based on well-known standard technologies and protocols. The proposed solution is able to integrate existing technologies, like cloud computing, with other more recent technologies like edge computing and network slicing. In addition, a functional prototype of the proposed orchestrator has been implemented, using open-source software and microservice platforms. As a first step to prove the practical feasibility of our solution, the implementation of the orchestrator considers cloud and edge domains. The validation results obtained from the prototype prove the feasibility of the solution from a functional perspective, verifying its capacity to deploy Internet of Things related functions even on resource constrained platforms. This approach enables new application models where these Internet of Things related functions can be onboarded on small unmanned aerial vehicles, offering a flexible and cost-effective solution to deploy these functions at the network edge. In addition, this proposal can also be used on commercial cloud platforms, like the Google Compute Engine, showing that it can take advantage of the benefits of edge and cloud computing respectively.  
Internet of Things (IoT); network slicing; orchestration; edge computing; microservice; Small Unmanned Aerial Vehicle (SUAV), IoT slice +
Footnote †: journal:
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 3 Methodology  
Modern industrial environments often deploy numerous machines (e.g., \(m=1,\ldots,M\)) equipped with diverse sensors, alongside textual or categorical information such as maintenance logs or batch IDs. Over time, these machines can experience drift--a shift from their normal operational parameter distribution to a new, possibly abnormal distribution. Detecting this shift (drift) early is paramount to avoiding quality degradation, production downtime, or catastrophic failures [16].  
Formally, let \(\mathbf{x}^{(m)}(t)\in\mathbb{R}^{d}\) denote the multi-modal feature vector for machine mmm at time \(t\). Under normal conditions, the data follow the distribution \(p_{m}(\mathbf{x})\). When a drift occurs, the distribution changes to \(p^{\prime}_{m}(\mathbf{x})\
eq p_{m}(\mathbf{x})\). The key objective is to detect the earliest time \(t^{*}\) at which  
\[p_{m}(\mathbf{x},t)\stackrel{{?}}{{\rightarrow}}p_{m}(\mathbf{x}) \tag{1}\]  
no longer holds, thereby generating a drift warning \(y^{(m)}(t^{*})=1\). We seek a methodology that accommodates the following aspects:  
1. Multi-modal data (e.g., numeric sensors, maintenance logs in text, and categorical features).
2. Phase-by-phase processes for data cleaning, baseline modeling, real-time detection, and long-term monitoring.
3. Explainability and active learning to continually refine the detection model with new feedback and anomalies.  
The following sections describe our proposed M2D2 framework, which organizes the solution into seven phases, from data preprocessing to optional federated learning and maintenance strategies.
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 4 Tamed Warping Network
### The Correction Stage
#### 4.3.1 Context Feature Rectification (CFR)  
We introduce a lightweight module called CFR to explicitly correct the warped context features \(\widehat{\mathcal{F}_{t}}\) by considering the following observations. First, the contextual information of the warped features is generally correct, except for the _edges of moving objects_. Second, the low-level features contain the spatial information, such as \"edge\" and \"shape\", which can help to correct the context features. Thus, we make CFR take as the input the warped context features \(\widehat{\mathcal{F}_{t}}\) as well as the spatial features of the current frame \(f_{t}\) and output the corrected context features \(\mathcal{F}_{t}\), as shown in Figure 4a. Specifically, CFR adopts a single-layer network, \(\phi_{r}\), which takes the concatenation \([\widehat{\mathcal{F}_{t}},\,f_{t}]\) as the input and outputs \(Res\mathcal{F}_{t}\), the residuals in feature space, as follows:  
\[\begin{split}\mathcal{F}_{t}&=\widehat{\mathcal{F} _{t}}+\phi_{r}([\widehat{\mathcal{F}_{t}},f_{t}])\\ &=\widehat{\mathcal{F}_{t}}+Res\mathcal{F}_{t}.\end{split} \tag{4}\]
## 5 Results
### Topology 1: Results
#### 5.1.3 Scenario 3  
In Scenario 3, we used rate limiting in the left path from h26->h30->h27 to slow down the traffic on the left path, and the right path was configured with less delay and packet loss on the links h26->h29->h27.  
Figure 7a shows that snnr performed worse than snndp and snnd at lower and higher bundle rates, but performed 5-19% and 2-7% better than snndp and snnd at bundle rates of 3-7 bundles/s. snnr showed inferior performance to snnp at lower bundles rates, but performed 36-68% better at 7-10 bundles/s. The performances of snndp and snnr were 1-47% and 23-54% better than static at bundle rates of 9-10 bundles/s, whereas static was better than both methods at lower bundle rates. snndp was 5-20% better than snnd at 9-10 bundles/s and 43-70% better than snnp at 7-10 bundles/s.  
Figure 7b shows the packet loss percentage with snnr being 1-38% better than snndp and snn, and 10-50% better than static. snndp was 7-32% better than static. snnp showed 11-440% and 16-758% better performance than snnr and snndp.  
Figure 7c shows the throughput with snnr being up to 25% better than snndp and snnd and 10-50% better than static. Throughput with snnp was 7-40% better than snnr and snndp. snndp showed 7-30% better throughput than static.  
Figure 8 depicts the link utilization at Node h26 in Scenario 3. We can see that snnp selected h30, the most to avoid packet loss on the link connecting h29, whereas other SNN algorithms selected h29 more since their optimization goal includes delay.  
Figure 6: Link selection at Node h26: Scenario 2  
Figure 7: Routing performance: Scenario 3. (**a**) Average response time; (**b**) Packet loss ratio (%); (**c**) Throughput.
# Assessing Landsat Images Availability and Its Effects on Phenological Metrics
###### Abstract  
Landsat imagery offers the most extended continuous land surface observation at 30 m spatial resolution and is widely used in land change studies. On the other hand, the recent developments on big data, such as cloud computing, give new opportunities for carrying out satellite-based continuous land cover monitoring including land use/cover change and more subtle changes as forest degradation, agriculture intensification and vegetation phenological patterns alterations. However, in the range 0-10\({}^{\circ}\) south latitude, especially in the summer and autumn, there is a high rainfall and high clouds presence. We hypothesise that it will be challenging to characterise vegetation phenology in regions where the number of valid (cloud-free) remotely-sensed observation is low or when the observations are unevenly distributed over the year. This paper aims to evaluate whether there is sufficient availability of Landsat 7 and 8 images over Brazil to support the analysis of phenodynamics of vegetation. We used Google Earth Engine to assess Landsat data availability during the last decades over the Brazilian territory. The valid observations (excluding clouds and shadow areas) from Landsat 4/5/7/8 during the period 1984-2017 were determined at pixel level. The results show a lower intensity of Landsat observations in the northern and northeastern parts of Brazil compared to the southern region, mainly due to clouds' presence. Taking advantage of the overlapping areas between satellite paths where the number of observations is larger, we modelled the loss of information caused by a lower number of valid (cloud free) observations. We showed that, in the deciduous woody formations of the Caatinga dominium, the scarcity of valid observations has an adverse effect on indices' performance aimed at describing vegetation phenology. However, the combination of Landsat data with satellite constellation such as Sentinel will likely permit to overcome many of these limitations.  
Landsat; dynamics of land cover; time series; monitoring; phenology 2021