esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 2 Unmanned Aerial Vehicle Mobility Model  
FANET is a type of mobile ad hoc network (MANET). MANETs typically use the random waypoint (RWP) mobility model [11]. The RWP chooses the velocity of the node and the direction randomly, and then the mobile node moves to the destination in a straight line. When the node reaches its destination, it pauses and chooses a new destination. Thus, using the RWP, the node is subject to sudden changes in speed and directions and sudden stops. Moreover, with RWP, a node can undergo a 180\({}^{\circ}\) turn. Figure 1a shows an example of a path generated using this model. However, the UAVs have aerodynamic and mechanical restrictions that prevent them from performing such turns (illustrated in red circles in Figure 1a) [12]. Obviously, this mobility model is unrealistic for UAVs and may lead to inaccurate results when using simulations to study protocol performance. Additionally, the work in [13] shows that using typical MANET mobility models may produce undesirable trajectories for UAV applications. Different UAV formations and movements directly affect the mobility of multi-UAV systems. To address this issue, FANET mobility models are proposed.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 5 Conclusions and Future Work
### Limitations of the Study
#### 5.2.2 External Validity  
The main limitations to the external validity of MC-BDP's evaluation were the choice of technology, the data transfer configuration, and the complexity of cloud pricing models. The need to limit the experimental part of this research in terms of both time and scope meant that only one prototype was developed and evaluated. However, since MC-BDP is modular and technologically agnostic by design, a number of alternative technologies could have been used instead of the ones selected. Better yet, controlled experiments comparing the performance of equivalent technology would have given architects and potential implementers a good understanding of the advantages and disadvantages of using the different options. Worthwhile substitutions in the prototype implementation would have been the big data framework, the container technology, the orchestrator, or even the platform/operating system.  
In terms of data transfer configuration, the current experiments were set up with no resource sharing between containers deployed to the same node, so container co-location did not translate into reduced network utilisation. This study believes, however, that improvements could have been made if resources deployed to the same node had been able to transfer data without using the network. Additionally, it would have been beneficial to measure the reduction in network resource consumption when the container running the job manager service is co-located in the same machine as some of the containers running the task manager service. Although there are disadvantages to this approach, such as increased coupling between the services, it would have been advantageous to understand exactly how much could be gained in terms of reduced network consumption. A more complete picture would indeed be achieved if these savings could be measured not only in terms of a reduction in the number of gigabytes sent and received over the network, but also in terms of the cost incurred. Ref. [106]'s optimisation framework aimed at minimising data transfer costs across clouds is an important step towards a cost-centric approach to task distribution, as opposed to the traditional computational-centric algorithms.  
The final external limitation is related to the complexities of cloud resource pricing. Since cloud resources are charged on a pay-as-you-go basis, one could argue that estimation models offering cost predictions are more useful to implementers than those which focus on initial capital expenditure. However, the great complexity and considerable variety in pricing models adopted by different cloud providers mean it is difficult to find a cost prediction model with generic application across providers.
## 1 Introduction
This distributed sensing technology has been effectively applied in many industries and has attracted interest from industry practitioners all over. MMF has been effectively implemented in reservoir environments to monitor and enhance VSP. The use of fiber optics for VSP surveys for optimum data enhancement has evolved into a reasonably established application for DAS [12; 13]. Some merits of these fiber optic technologies include their capability within a narrow instrument deployment, offering distinct advantages, such as small size, light weight, immunity to electromagnetic interference (EMI), and embedding capability [14; 15; 16; 17]. Therefore, in this paper, we provide a comprehensive comparative review of the use of SMF and MMF in geophysics exploration. The literature research used in this review is dependent on the type of geophysics exploration conducted, mostly focused on the deployment of fiber optic cables, as well as their performance and measurement efficiency, amongst other parameters. This comparative review also considers the ML approach using data acquired from both types of optic cable.
## 2 Study Area and Materials
### In Situ Data  
In situ data for the Ebre Basin are accessible through the Automatic Hydrological Information System, known as the 'Sistema Automatico de Informacion Hidrologica' (SAIH Ebre), operated by the Ebre Hydrographic Confederation (CHE) and accessible at www.saihebro.com. SAIH Ebre is an online platform providing publicly available hydrological information across the Ebre Basin. It offers data at various temporal frequencies, including river gauge readings, reservoir levels, rainfall measurements, temperatures, and other relevant variables.
## 6 Conclusions  
This paper proposes a PV-HESS system sizing method considering power difference and system economy. By setting four scenarios, the minimum operating costs of the system configurations of each scenario under different sizing configurations are calculated and compared, and a reasonable sizing configuration is obtained. The following are the main findings of this study:  
1. Considering the saving of battery degradation, under the optimal system component size, the proposed method can effectively monitor the operating status of the battery system. The constant current charging and discharging period accounts for 32.24% of the total simulation period, effectively reducing the system operating cost.
2. It can be concluded that when the user budget is low or the electricity consumption is low, the lowest cost of the system can be obtained when the photovoltaic capacity is 5-7 kW under configuration 2. This further improves the economy of the PV-HESS system.
3. By comparing the four system configurations, it can be proven for the whole system that the impact of each component on the system economy is as follows (high to low): photovoltaic panels, lithium-ion batteries, and supercapacitors. The combined operation of the lithium-ion battery and SC can properly improve the system economy and prolong the service life of the lithium-ion battery. However, under the constraints of system economy, the degree is low, and the working period of the SC only accounts for 22.39% of the total period. In addition, the ability to improve the energy efficiency is limited.  
The proposed method can effectively take into account the power difference due to the output change in PV generation, the degradation of the battery due to irregular charging and discharging, and the effect of bad weather on PV generation. Through the proposed method, a reasonable capacity allocation scheme for PV-HESS is obtained. In future research, predictive and control algorithms can be involved to design an energy  
Figure 18: (\(C_{\text{PV}}=11\), \(C_{\text{SC}}=1.3\), \(C_{\text{bat}}=48\)) The power of HESS.  
management strategy based on rolling optimization that matches the optimal component size of the system.  
Conceptualization, Y.L. and C.T.; methodology, Y.L.; software, Y.L.; validation, Y.L., C.T. and Y.Z.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L.; visualization, Y.L.; supervision, Y.L.; project administration, Y.L. and Y.L.; funding acquisition, C.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable. No new data were created or analyzed in this study.  
The authors declare no conflict of interest.
## 4 The Design of Routing Protocol
### The Packet Structure  
Figure 6 illustrates the packet structure used in the network; it comprises a header composed of the packet identification, the routing information, and the node status information.  
The packet identification fields include:  
1. Source ID, identifying the source node.
2. Packet sequence number, providing a unique identifier for the packet.  
These fields are node-specific and utilized to differentiate data packets during data forwarding, remaining constant throughout the packet's lifetime.  
Figure 5: Illustration of recovery mode.  
Figure 6: The structure of packet delivery.  
Routing information is used to determine the routing pipe radius, select the next hop, and assist the forwarding candidates in the transmitted data packets. The routing information comprises the following fields:  
1. [label=(0)]
2. Sender ID, identifying the current node.
3. Receiver ID, identifying the optimal next hop.
4. Position of receiver, providing the 3D coordinates of the optimal next hop.
5. Routing pipe radius, specifying the routing pipe radius.  
The routing pipe radius size controls the number of forwarding candidates, as discussed previously. The sink node determines the size through the calculation of the PDR.  
Each node must embed its status information into the following fields before sending a data packet.  
1. [label=(0)]
2. Depth, providing the depth information of the current node.
3. Position, providing the 3D coordinates of the current node.
4. Residual energy, providing the remaining energy of the current node.
5. Neighboring nodes number, indicating the number of neighboring nodes of the current node.
6. Largest decision value, providing the largest decision value among the neighboring nodes of the current node.  
Upon the reception of a data packet, every node extracts the relevant fields from the packet header and refreshes its neighboring information with the most up-to-date routing details. This process aids the nodes in making informed routing decisions that optimize their routing paths.  
Apart from the packet header, there is an optional data field that can be included. This field carries the message intended for transmission to the destination. If the data field is not present, the packet serves the sole purpose of exchanging routing information, as further detailed in the subsequent subsection.
## Appendix A Parameters of \(\gamma_{D}\)
### Expected Value of Each Fading Coefficient  
Since the expected value is a linear operator, then  
\[\mathbb{E}[h_{k}]=\mathbb{E}[\sum_{i=1}^{N}|h_{ki}^{LD}|\big{|}h_{i}^{SL}\big{|} e^{i\theta_{ki}}+h_{k}^{SD}]=N\mathbb{E}[\big{|}h_{ki}^{LD}h_{i}^{SL}\big{|}e^{i \theta_{ki}}]+\mathbb{E}[h_{k}^{SD}]. \tag{12}\]  
Figure 8: First order approximation of SOP for \(K=2\) antennas and different number of reflectors (\(N\)). Since the channels are independent and identically distributed, \(\mathbb{E}[h_{k}^{SD}]=0\), and \(\mathbb{E}[e^{i\theta_{ki}}]=\alpha_{1}\), thus  
\[\mathbb{E}[h_{k}]=N\;\mu_{LD}\times\mu_{SL}\times\alpha_{1}, \tag{20}\]  
where  
\[\mu_{LD}=\mathbb{E}[\left|h_{ki}^{LD}\right|]=\frac{\Gamma(m_{LD}+0.5)}{\Gamma( m_{LD})}(\frac{\Omega_{LD}}{m_{LD}})^{\frac{1}{2}}, \tag{21}\]  
and  
\[\mu_{SL}=\mathbb{E}[\left|h_{i}^{SL}\right|]=\frac{\Gamma(m_{SL}+0.5)}{\Gamma( m_{SL})}(\frac{\Omega_{SL}}{m_{SL}})^{\frac{1}{2}}, \tag{22}\]  
are the expected values of each Nakagami-\(m\) channel. To obtain the variance of the overall channel fading coefficient, the mean of \(c_{k}=Re\{h_{k}\}\) and \(s_{k}=Im\{h_{k}\}\) needs to be calculated, i.e., the in-phase and quadrature components of the fading coefficient, respectively. The in-phase component can be written as  
\[c_{k}=\sum_{i=1}^{N}\left|h_{ki}^{LD}\right|\left|h_{i}^{SL}\right|\cos\theta_ {ki}+Re\{h_{k}^{SD}\}, \tag{23}\]  
while the quadrature component is  
\[s_{k}=\sum_{i=1}^{N}\left|h_{ki}^{LD}\right|\left|h_{i}^{SL}\right|\sin\theta_ {ki}+Im\{h_{k}^{SD}\}. \tag{24}\]  
Then, the expected value of \(c_{k}\) is  
\[\mathbb{E}[c_{k}]=\sum_{i=1}^{N}\mathbb{E}[\left|h_{ki}^{LD}\right|\left|h_{i }^{SL}\right|\cos\theta_{ki}]+\mathbb{E}[Re\{h_{k}^{SD}\}]. \tag{25}\]  
Since \(\mathbb{E}[Re\{h_{k}^{SD}\}]=0\) and all the summation terms are independent,  
\[\mathbb{E}[c_{k}]=N\times\mathbb{E}[\left|h_{ki}^{LD}\right|\left|\mathbb{E}[ \left|h_{i}^{SL}\right|\left|\mathbb{E}[\cos\theta_{ki}]N\times\mu_{LD}\;\mu_{ SL}\;\alpha_{1},\right. \tag{26}\]  
as well as \(\mathbb{E}[h_{k}]\). In its turn, the expected value of \(s_{k}\) is given by  
\[\mathbb{E}[s_{k}]=\sum_{i=1}^{N}\mathbb{E}[\left|h_{ki}^{LD}\right|\left|h_{i }^{SL}\right|\sin\theta_{ki}]+\mathbb{E}[Im\{h_{k}^{SD}\}]. \tag{27}\]  
Since \(\mathbb{E}[Im\{h_{k}^{SD}\}]=0\) and all the summation terms are independent,  
Figure 8:0  
since \(\mathbb{E}[\sin\theta_{ki}]=0\). _Appendix A.2. Variance of the In-Phase and Quadature Components of Each Fading Coefficient_  
The variance of the in-phase component is written as  
Figure 8:1Since \(\text{var}(h_{k}^{SD})=\sigma_{SD}^{2}\), the summation terms and \(Re\{h_{k}^{SD}\}\) are independent and the \(h_{k}^{SD}\) coefficient is zero mean, then  
Figure 8:2  
Next, the variance of the term \(\big{|}h_{ki}^{LD}\big{|}\big{|}h_{i}^{SL}\big{|}\cos\theta_{ki}\) is needed, considering that the variance of the product of two random variables \(X\) and \(Y\) is \(\text{var}(XY)=\text{var}(X)\text{var}(Y)+\text{var}(X)\text{E}[Y]^{2}+\text{ var}(Y)\text{E}[X]^{2}\) and the phase noise is independent of the fading magnitudes.
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 4 System Architecture  
Fed-Hetero is a self-evaluating framework that provides recommendations to clients, guiding them to perform clustering or data augmentation to enhance accuracy whenever the performance of federated learning is decreased due to the diversity in the data. Fed-Hetero enables clients with limited data to actively participate in the FL process by employing appropriate strategies that improve the accuracy of the model. Clients with limited data may represent rare or critical cases and also provide valuable insight that is essential to improve the generalization of the model in diverse scenarios. The federated learning process begins when the server makes a request to the clients with initial global model parameters \(\mathbf{w}_{0}\). The clients participating in the study, ranging from client 1 to client m (where m = 4), train the CNN model. Each client then sends its model weights \(\mathrm{W}_{t}^{m}\) (where \"t\" indicates the number of rounds) to the server, which uses the FEDAVG algorithm to aggregate the weights and sends \(\mathrm{W}_{(t+1)}^{m}\) to the clients after each round. After \"nt\" rounds of the federated learning process, the server calculates the weight divergence (WD) factor between clients. Based on WD, the server requests the data distributions, subset of samples from each client. The client then sends the probability of data distributions--PDm, probability distributions between classes--PDm0, PDm1, and subset of samples--\(\mathrm{Dpm}^{(Sub)}\), and the server computes the JSD between clients--\(\mathit{JSD}_{d}\), JSD between classes in the clients--\(\mathit{DjS},\mathit{0}\), \(\mathit{DjS},\mathit{1}\) and SSIM between the subset of images from the clients--\(\mathit{SSIM}\) values. The server notifies the clients about the heterogeneity present in their data and recommends data augmentation and clustering to mitigate it. The system architecture diagram in Figure 1 illustrates the flow of communication and data exchange between the clients and the server during the federated learning process.
# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 4 Multi-Hop Transmission and Synchronization  
Since the energy consumption of data transmission increases exponentially with the distance between nodes, it is necessary to form a backbone network among the nodes in the cluster head and the hot spot area to realize the multi-hop communication so as to save energy consumption. In this paper, a particle swarm optimization algorithm is designed, which makes use of a multi-objective adaptive value function to find the optimal routing tree and satisfy the minimum energy consumption condition.
## 4 Results
### Scenario 2-Dam Removal  
Post-dam removal scenario (Post-DR, Table 3) was carried out over a 20-year period to perform a direct comparison with the sediment budget related to Scenario 1. Decadal-scale modelling of the geometric response to dam-removal predicts a strong modification of the fluvial system, with a general increase of topographic changes than the pre-dam removal scenario (Cfr. Figures 4 and 6). Altitude difference map (Figure 6) clearly highlights a significant increase of erosion processes in both the headwaters and mid sectors of the drainage net, which are mainly related to the higher incision ability of the main channels. In fact, a comparison between the results of the two simulations shows that lateral migration of channels appears to be limited (cf. Figures 4 and 6). LEM predicts the development of a wide floodplain in the flat area of the reservoir with a well-defined incision of the infill by the three main channels of the study area. In addition, analysis of the spatial pattern of erosion and deposition shows the occurrence of small sedimentation areas upstream to the reservoir infill (Figure 6), which contributed to a slight decrease of the sediment connectivity of the entire catchment.  
Figure 5: Erosion/deposition classes in the catchment deriving from the analysis of the altitude difference map of Figure 4. In the frame: distribution of the eroded volumes from the three sub-basins of the study area (numbering is shown in Figure 4).  
The frequency distribution of the altitude difference map (Figure 7) illustrates a higher erosional ability of the geomorphic system after the lowering of the base level.  
Figure 6: (**A**) Landscape evolution models (LEM)-based elevation difference map for the post-dam removal scenario (simulation period: 20 years). (**B**) Numbering of the post-dam removal catchments.  
Figure 7: Statistical distribution of the altitude difference map of Figure 6 (Post-dam removal scenario). In the frame: distribution of the eroded volumes from the three sub-basins of the study area (numbering is shown in Figure 6).  
In fact, the model predicts an increase of the total eroded volumes by a factor of 4.4 for the post-dam removal scenario whereas the mean annual erosion volumes increase from 4203 m\({}^{3}\)/year to 18465 (Figure 7). Caesar-Lisflood simulation infers topographic modifications for a percentage of the total area of about 7.1% (corresponding to an area of 0.74 kmq). In this scenario, erosion classes with a value ranging from 0.1 to 0.5 m represent the maximum of the altitude difference map but the comparison with Scenario 1 shows a relative increase of the erosion classes with a higher value. Analysis of the spatial pattern of erosion for the different sectors of the study area shows that sub-basin 4 and 3 contribute to 45.7% and 18.7% of the total amount of eroded volumes yielded in the upland sectors.  
Then, approximately 81.3% of the total amount of sediment eroded comes from sub-basin n.3 and n.4 (i.e., the easternmost and the central one), highlighting a higher erosion ability of these catchments for the dam removal scenario.
# Numerical Analysis of the Installation Process of Screw Piles Based on the FEM-SPH Coupling Method
###### Abstract  
The installation of screw piles can cause damage to the soil, which is a dynamic and large deformation problem. In this paper, a FEM-SPH numerical model for the analysis of this large deformation problem was developed in LS-DYNA to simulate the installation process of screw piles. In addition, field installation tests of screw piles were carried out. By comparing the FEM-SPH simulation results with the experimental results and traditional FEM simulation results, it was found that the FEM-SPH coupling method has higher efficiency and accuracy in dealing with the large deformation problems caused by the installation of screw piles. Then, numerical simulations of screw piles with various parameters were conducted to analyze the differences in the installation process. The results show that the spiral pitch and pile diameter have a significant effect on the installation torque, soil stress and soil pressure during the installation process. During the installation process, the interaction between the screw pile and the soil is transferable. The installation of the screw pile will lead to the movement of soil particles in the radial and axial directions, resulting in heave damage to the shallow soil and cylindrical shear damage to the middle and deep soil. The influence range on the soil by the heave failure mechanism (HFM) and the cylindrical shear failure mechanism (CSFM) caused by the installation of screw piles is affected by pile parameters. The change in pile diameter will act on both HFM and CSFM, whereas the variation in spiral pitch will only have an influence on CSFM.  
screw pile; large deformation problem; installation process; pile-soil interaction; numerical simulation; FEM-SPH
## 2 Parabolic Antenna Analysis Method
### Induced Phase Calculation  
In the electric field integral equation (EFIE) [31] used to determine the electric field intensity at observation positions (positions of each charge point source) due to wave propagation from the feeder, the phase of the electric field intensity is related to the distance between the feeder and the observation point. The induced signal phase at each charge point source can be defined using Equation (15), considering the phase values along the propagation path and the boundary conditions on the reflecting surface.  
\[a_{n}=-k\Big{|}\overset{\rightarrow}{r_{f}}-\overset{\rightarrow}{r_{n}} \Big{|} \tag{15}\]  
where \(k\) is the wave propagation constant.  
Figure 5 illustrates the induced phase distribution on both the parabolic reflector's surface and the parabolic aperture, as determined using Equation (15). The antenna configuration presented here corresponds to that depicted in Figure 3a. In the induced phase distribution on the parabolic aperture in Figure 5b, the non-uniform distribution arises not only due to the displacement of the feed axis but also due to the possibility that the beam axis may not steer toward the center of the reflector.