esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Impact of Enhanced Visibility Through V2X Data  
The VRFormer's performance notably improved by integrating data from other vehicles and infrastructure, compared to scenarios limited to previous vehicles and data from the AVs itself. VRFormer can detect occluded objects and predict potential accidents earlier when operating within the VRDeepSafety platform. For example, when using infrastructure data, the prediction accuracy increased by 10% in one test scenario, demonstrating the value of combining various perspectives.  
Figure 10 shows the increase in visibility achieved through V2X data integration by comparing the detection rates with and without infrastructure information. In addition, V2X communication increases the detection range and accelerates decision-making in VRDeepSafety. This is notably helpful in challenging situations, such as driving behind a large truck or navigating blind spots. VRFormer overcomes the limitations of individual vehicle sensors by accessing shared real-time data, thereby ensuring a safer and more efficient response.
## 4 Enabling Technologies for FSO Communication
### Asynchronous Transmission Scheme
#### 4.8.4 Hybrid Satellite-Terrestrial FSO Cooperative System  
In [378], it is demonstrated that portable cooperative relaying ground stations can be utilized to offer broadband access in disaster-stricken areas. These ground stations receive signals from a satellite and relay them to a hidden destination node via an FSO link. These hybrid satellite-terrestrial FSO cooperative schemes are not only cost effective but also can be deployed rapidly. In [341], an AF protocol-based hybrid satellite-terrestrial FSO cooperative system is analyzed. In this system, the satellite-to-relay link follows the shadowed-Rician land mobile satellite model, while the relay-to-destination FSO link follows the IT fading model. This hybrid system is distinct from a typical tandem RF/FSO link, where the RF link follows simpler fading statistics. The moment generating function (m.g.f.) for the hybrid link is derived, and this m.g.f. is used to approximate the average symbol error rate (SER) of the cooperative system. Additionally, the analytical diversity order of the AF-based hybrid cooperative system is determined.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 3 Results
### GWS Trend in Peru (2003-2023)
#### 3.1.3 Quantitative Impacts of Climatic and Anthropogenic Factors  
_Case Study: Impact of Anthropogenic Extraction on Coastal Aquifers_  
The La Yarada coastal aquifer in Tacna exemplifies the impact of anthropogenic factors on groundwater depletion. With an area of 300 km\({}^{2}\), this aquifer experiences extremely low rainfall--5 mm/year in the lower zone, 50 mm/year in the middle zone, and up to 110 mm/year in the upper zone--resulting in minimal natural recharge [46].  
The expansion of agro-export crops, particularly olive cultivation, has led to an unsustainable annual groundwater extraction rate of 112 h\({}^{3}\)/year (equivalent to 373.33 mm/year), far surpassing replenishment levels and causing declining piezometric levels and seawater intrusion [47].  
_Using Equation (3), the components are defined as follows:_  
* Precipitation (P) = 110 mm/year;
* Extraction (EXT) = 373.33 mm/year;
* ENSO index = 1.5 (dimensionless);
* Coefficients: \(\beta_{1}\) = 0.35, \(\beta_{2}\) = \(-\)20, \(\beta_{3}\) = \(-\)0.80.  
\(\beta_{1}\) and \(\beta_{2}\): dimensionless (relate to mm/mm). \(\beta_{3}\): mm/year per unit of the ENSO index  
_Calculation of \(\Delta\)GWS:_  
* \(\Delta\)GWS = (0.35 \(\times\) 110) + (\(-\)20 \(\times\) 1.5) + (\(-\)0.80 \(\times\) 373.33) + \(\epsilon\)  
* Precipitation effect: 0.35 \(\times\) 110 = 38.5
* ENSO effect: \(-\)20\(\times\)1.5 = \(-\)30
* Extraction effect: \(-\)0.80\(\times\)373.33 = \(-\)298.7  
\(\Delta\)GWS = 38.5 \(-\) 30 \(-\) 298.7 + \(\epsilon\)  
\(\Delta\)GWS \(\approx\) **-290.16 + \(\epsilon\) mm/year**  
_Statistical validation:_  
* R\({}^{2}\) of the model: The regression model indicates that extraction (EXT) accounts for 60-80% of the variability in coastal zones, suggesting R\({}^{2}\)\(\approx\) 0.7 \(-\) 0.8.
* Coefficient significance: The \(\beta\) values are consistent with prior studies [33], and the dominance of \(\beta_{3}\)\(\times\) EXT (\(-\)298.7) indicates extraction as the primary factor (\(p\) < 0.05 implied).
* Residual error (\(\epsilon\)): GRACE's 5% uncertainty suggests a margin of \(\pm\)14.5 mm/year.  
The estimated change in \(\Delta\)GWS is approximately \(-\)290.16 mm/year, indicating a significant decline in aquifer reserves, primarily driven by overextraction. The regression model confirms that human extraction (EXT) exerts a much stronger influence than either precipitation or ENSO-related variability in coastal aquifers like La Yarada. These findings align with trends observed in GRACE satellite data and are strongly validated by in situ measurements from ANA, particularly in high-extraction regions such as Chillon-Rimac, Lurin, and Caplina.
## 5 State of the Art
### Payload Carrying  
The carriage of payloads such as packages is another area of growing interest in the field of logistics and supply chain management. Several companies in the distribution and delivery sector are conducting research on the automation of their delivery services (healthcare delivery, food delivery, postal delivery) by multi-UAV systems [12; 39]. Most of the proposed approaches are adapted mainly for collaborative payload carrying with a set of UAVs.  
[PERSON] et al. [13] presented cooperative grasping and transport using a swarm of quadrotors to move a heavy payload of known structure and weight in a three-dimensional space. Since each UAV knows its position, the task of transporting the payload is decentralized in such a way that each UAV controls its trajectory with respect to the payload stabilization laws. On the other hand, the velocity and the state estimation of the positions of the swarm towards the goal are centralized on the GCS.  
Following the same approach, the authors in [15] addressed the problem of carrying heavy rigid payloads with a multi-UAV system composed of small quadrotors. Each UAV is equipped with a camera and Inertial Measurement Unit sensor. The proposed approach allows them to (1) independently control the motion of each UAV while maintaining the stability of the system regarding the inertial and (2) each UAV benefits from a cooperative scheme, allowing them to measure the positions of the other UAVs.  
[PERSON] and [PERSON] proposed in [14] a method for carrying a flexible structure of payload in three-dimensional space by using a swarm of quadrotors rigidly attached to it. The proposed approach is comparable to previous work [13], except that the stabilization and estimation of the payload deformation are centralized on the GCS. They use a linear-quadratic regulator (LQR) function, which allows them to control and regulate the parameters governing the payload shape.
# Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
## 3 Materials and Methods  
### SURF Feature Points  
SURF extraction is fast with good invariance and robustness and performs well in the feature extraction process [30]. Therefore, we chose the SURF algorithm to extract feature points. The extraction process of the SURF algorithm includes three steps: feature point detection, determining the main direction, and calculating the descriptors.
# Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials
## 1 Introduction
The current work takes into account the detailed information available in HOSS simulations to account for damage evolution, which was not considered in previous works. The proposed probabilistic damage evolution model constructed from HOSS simulations includes interaction between multiple cracks, which makes it robust for upscaling micro-scale models. Our approach utilizes graphs to represent the topology of the system to capture failure paths that is then used to develop a reduced-order model for damage along the failure path. We propose to overcome the computational cost hurdle associated with running high-fidelity dynamic fracture models by representing a fracture network as a graph, with far fewer DOFs (\(\approx\mathcal{O}(10^{4})\) times less), whose nodes and edges contain critical information about the topology and geometry of the cracks obtained from our high-fidelity HOSS model. Our new graph-based approach can be used to incorporate information such as damage statistics describing the evolution of the crack network, including the effects of crack-to-crack interactions, into the continuum codes seamlessly. The main challenge in our work is computing the failure path using a weighted graph to represent crack growth. Typically, in real materials, lengths, locations, and orientations of pre-existing cracks can be random. The structure of the crack network may impact where the dominant flaw in the material forms. Hence, one needs to account for this uncertainty in crack topology when modeling the brittle crack growth. The main contribution of this study is to develop algorithms to predict failure paths and compute damage along the failure paths under tensile loading using weighted graphs at low-strain rates. An advantage of the proposed method is that it is \(\approx\mathcal{O}\left(10^{6}\right)\) times faster than HOSS high-fidelity simulations. Due to the increase in computational savings with reasonable predictions (accuracy of our damage model on unseen data is greater than 90%), our methodology is ideal for usage in comprehensive uncertainty quantification studies which require 1000s of forward model runs. The paper is organized as follows: Section 2 details the set up for the 190 high-fidelity FDEM fracture propagation simulations using the HOSS simulator. This data set is then used for validating our proposed methods. Section 3 describes the proposed algorithm to estimate failure paths based on initial crack configuration. The algorithm is based on a combination of FPZ theory, \(k\)NN, and [PERSON]'s algorithms for weighted shorted paths. Section 4 details a method to construct and estimate damage evolution along the failure path. Section 5 discusses the results of the proposed methods. Failure path predictions are compared against HOSS simulations. Details of training and validation of a damage evolution model are also provided in this section. Prediction accuracy of the failure path algorithm and the damage evolution model are discussed as well. Finally, conclusions are drawn in Section 6.
## 3 Results and Discussion
### Effects on the AE and Failure Behavior
#### 3.3.1 Acoustic Emission Behavior  
The AE and cumulative AE responses over time of the samples during the loading process are shown in Figure 5. The changes in the acoustic emission counts at different chemical erosion times can be broadly categorized into initial, calm, and active phases. The figure shows that when the immersion time is less than 60 days, the initial and calm stages  
Figure 4: (**a**) Typical stress-strain curve and peak stress variation in CGB after chemical erosion. (**b**) Variation in CGB peak stress with erosion time.  
of the specimens are prolonged compared to those of the fresh samples. The trend of the acoustic emission counts change in this process is relatively smooth, while the active phase is compressed, and the acoustic emission counts change faster in this phase. The result suggests that after short-term immersion, the chemical ions did not lead to the deterioration of the internal structure and the creation of pores. Instead, the internal pores and localized microscopic defects may have been filled, strengthening the internal structure. The primary source of the acoustic emission signal may be the result of inter-particle friction during the closure of the initial microcracks inside the CGB. When the initial crack closes, the elastic energy in the specimen begins to accumulate. It continues until the energy or stress required for crack extension is reached, and the specimen starts to break down. The acoustic emission counts in the initial stage showed a positive correlation with increased chemical erosion time when the chemical erosion time was greater than 60 days. During this stage, the chemical erosion action may have caused the deterioration of the internal structure of the specimen or the generation of microcracks, which led to the enhancement of the changes in the acoustic emission counts. With the increase in the chemical erosion time, the density of the acoustic emission counts in the calm period also increases relatively, and more mutation points appear before the destruction of the specimen. In this process, a portion of the energy was released through a stress waveform, resulting in a sharp increase in AE counts. The phenomenon indicates that the specimen is about to enter the unstable crack development stage and gradually reaches the peak stress point to produce damage. In addition, the duration of the active phase is influenced by chemical attacks and tends to increase. Therefore, changes in the acoustic emission behavior caused by the temporal effects of chemical erosion should be considered in engineering designs.  
Figure 5: \(Cont.\)
# The Effects of Aerodynamic Interference on the Aerodynamic Characteristics of a Twin-Box Grider
## 3 Results and Discussions
### Modulation of Surface Pressure Distribution by Leading Body-Height Grids  
First, the surface pressure distributions with the aerodynamic interference of the leading body-height grid were compared with those of the bare twin-box girder, as shown in Figure 10, and the effects of turbulence generated by different body-height grids on the surface pressure distribution were evaluated and analyzed.  
Figure 10 clearly shows that the turbulence intensity, which is an important characteristic of incoming flow, has a significant influence on the time-averaged surface pressure distribution. Compared with the surface pressure distribution of the bare deck, the characteristic of the pressure distribution with the body-height grid aerodynamic interference changed a lot at low Reynolds numbers (Re \(<\) 1.0 \(\times\) 10\({}^{4}\)), because the boundary layer transitioned to the turbulent boundary layer. At low turbulence intensity (\(I\) = 3.57-5.15%), the amplitude of the pressure peak was enhanced. It is also noteworthy that, at the upstream  
Figure 10: Comparisons of the time-averaged surface pressure distribution of the twin-box girder with body-height grid interference and bare deck.  
Figure 9: (**a–c**) The time-averaged drag force coefficient, time-averaged lift force coefficient, and time-averaged moment coefficient at different Reynolds numbers, respectively. (**d–f**) The fluctuating drag force coefficient, fluctuating lift force coefficient, and fluctuating moment coefficient at different Reynolds numbers, respectively.  
girder, the pressure plateaus (appearing in the undisturbed surface pressure distribution) in the upper and lower surfaces were eliminated, because the vortices generated by the body-height grid broke the separation bubbles, and the laminar boundary layer was transformed into turbulence, which cannot maintain a stable pressure to form the separation bubbles. At moderate turbulence intensity (\(I=16.9\)-\(18.3\%\)), the negative pressures are enhanced when \(x/0.5B>-0.7\), indicating that the high turbulence intensity contains more energy to strengthen the flow velocity around the box girder. When the turbulence intensity is further increased (\(I=27.9\)-\(31.4\%\)), the negative pressures are also increased.  
Figure 11 shows the time-averaged surface pressure distribution at different Reynolds numbers when the turbulence intensities are close. It is clearly shown that the turbulence intensity can effectively eliminate the Reynolds number sensitivity of pressure distribution, because the properties of the turbulent boundary layer are dominated by the turbulence intensity of the incoming flows.
# Time-Domain Aeroelasticity Analysis by a Tightly Coupled Fluid-Structure Interaction Methodology
## 3 Structural Model and Solution Method  
### Isogai Wing  
The test case focuses on the cross section of a sweptback wing and investigates its aeroelastic response to the unsteady aerodynamic loads. Isogai wing section model consists in a 2D elastically mounted airfoil, which moves in pitching about its elastic axis and plunging vertically.  
The structural dynamic governing equations of the elastically mounted system can be described as follows:  
\[m\ddot{h}+S_{a}\ddot{\alpha}+K_{h}h=-L \tag{7}\] \[S_{a}\ddot{h}+I_{a}\ddot{\alpha}+K_{a}\alpha=M\]  
where \(h\) and \(\alpha\) are plunging and pitching displacements, respectively, \(K_{h}\) and \(K_{a}\) are mounted spring stiffnesses in two degrees of freedom, \(m\) is the mass of the wing per unit span length, \(S_{a}\) and \(I_{a}\) are the static moment and the inertia moment about the elastic axis, \(L\) and \(M\) are the aerodynamic lift and the moment about the elastic axis, respectively. The equations can be nondimensionalized with the half-chord length b and the free-stream sound speed \(\alpha_{\infty}\), and the resultant equations can be given as:  
\[\ddot{h}+\frac{\chi_{s}}{2}\ddot{\alpha}+\frac{4\
u_{s}^{2}M_{\infty}}{\
u_{s }}^{2}h=-\frac{2}{\pi I_{d}}C_{l} \tag{8}\] \[\ddot{h}+\frac{\
u_{s}^{2}}{2\chi_{a}}\ddot{\alpha}+\frac{2\
u_{s }^{2}M_{\infty}}{\
u_{s}\
u_{s}^{2}}\alpha=\frac{4}{\pi I_{d}\chi_{a}}C_{m}\]where \(V_{f}=\frac{U_{m}}{\omega_{z}b}\) is the reduced velocity, \(r_{d}=m/(\rho\tau b^{2})\), \(r_{\omega}=\frac{\omega_{h}}{\omega_{z}}\), \(C_{l}\) and \(C_{m}\) are lift coefficient and moment coefficient respectively. Using a matrix form, Equation (8) can be written as:  
\[[\mathbf{M}]\Big{\{}\ddot{\mathbf{S}}\Big{\}}+[\mathbf{K}]\{\mathbf{S}\}=\{\mathbf{F}\} \tag{9}\]  
where  
\[[\mathbf{M}]=\left[\begin{array}{cc}1&\frac{x_{h}}{2}\\ 1&\frac{x_{h}}{2x_{a}}\end{array}\right],[\mathbf{K}]=\left[\begin{array}{cc} \frac{4r_{h}^{2}M_{m_{0}}^{2}}{V_{f}^{2}}&0\\ 0&\frac{2r_{h}^{2}M_{m_{0}}^{2}}{x_{a}V_{f}^{2}}\end{array}\right],\ \ \{\mathbf{F}\}=\left[\begin{array}{cc}-\frac{2c_{l}}{ \pi r_{d}}\\ \frac{4c_{m}}{\pi r_{d}x_{a}}\end{array}\right],\ \ \{\mathbf{S}\}=\left[\begin{array}{cc}h \\ \alpha\end{array}\right]\]  
The nondimensional structural dynamic governing equations are solved as follows:  
\[\Big{\{}\mathbf{S}^{p+1}\Big{\}}=\Delta t^{2}+[\mathbf{M}]^{-1}\{\mathbf{F}\}-\Delta t^{2} [\mathbf{M}]^{-1}[\mathbf{K}]+2\{\mathbf{S}^{n}\}-\Big{\{}\mathbf{S}^{n-1}\Big{\}} \tag{10}\]  
where \(p\) is subiteration step number of the pseudo time-stepping method.
# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 3 Background
### Evaluation Metrics
#### 3.5.1 Confusion Matrix  
A confusion matrix is a table that is often used to evaluate the performance of the classification models (Table 1).  
Accuracy is a type of measure to evaluate the model by the ratio between the number of correctly classified images to the total number of images. The accuracy is calculated by Equation (1), in which, TP (true positive) is the number of labeled images that are correctly classified; FP (false positive) is the number of labeled images that are misclassified; FN (false negative) is the number of unlabeled images that are correctly classified; TN (true negative) is the number of unlabeled images that are misclassified.  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
To evaluate the network models' accuracy, we calculate the Precision and Recall values using Equation (2). High precision means that the accuracy of the predicted cases is high. High recall means that the rate of omission of really positive objects is low.  
\[Precision=\frac{TP}{TP+FP};\quad Recall=\frac{TP}{TP+FN} \tag{2}\]
# Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
## 7 Conclusions  
In this paper, we model the MA-Stream problem, which apportions bandwidth to multiple users in a video streaming network, to maximize nonlinear, non-convex objectives such as QoE and fairness objectives. We propose a novel multi-agent reinforcement learning algorithm MAPG-finite that is able to work with nonlinear objective functions to solve this optimization problem.  
Using our testbed implemented in Python, we verify that our proposed solution outperforms existing baseline policies (including \"Even\", \"Adaptive\", and single-agent SARSA) measured by both QoE and fairness. Our algorithm improves QoE by 15.27% and fairness by 22.47% for the 2000 KB/s link data link. Further, it is able to adapt well in collaboration with the existing Adaptive Bitrate (ABR) streaming algorithm by improving the QoE by more than 30% over the adaptive algorithm. The interaction between the bandwidth distribution and ABR policies could be considered in future work to further improve the performance of video streaming. Another interesting future work is to perform large-scale experiments with network scale users.  
Figure 9: QoE reward comparisons with ABR feature activated/deactivated. The total download bandwidth is 1500 KB/s. MAPG-finite achieves 21.94%, 41.25%, and 37.11% more than the “Even”, “Adaptive”, and SARSA policies.  
**Author Contributions:** Conceptualization, Y.W., M.A., T.L. and V.A.; Formal analysis, Y.W. and M.A.; Funding acquisition, T.L. and V.A.; Methodology, V.A.; Software, Y.W.; Writing--original draft, Y.W.; Writing--review & editing, M.A., T.L. and V.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by CISCO, Meta, and the U.S. National Science Foundation under grant CNS-1618335.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Big Data Collection in Large-Scale Wireless Sensor Networks
###### Abstract  
Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.  
Wireless Sensor Networks; data collection; Big Data; IoT 2018  
18 4474 doi:10.3390/s18124474
## 3 Research Methods and Data Sources
### Data Sources  
Indicators suitable for this study were selectively selected from the \"China Science and Technology Statistical Yearbook\", \"China Environmental Statistical Yearbook\", \"China Energy Statistical Yearbook\", the website of the National Bureau of Statistics, and the statistical yearbooks of various provinces, and average interpolation was used for individual missing data.