esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Target Design Based on Complex Target Simulation
## 2 Design Principle  
### Radar Cross-Sectional Area  
The radar cross-section (RCS) means that the radar emits a series of electromagnetic waves, which will generate a reflected echo signal when irradiated on the surface of a target object. The radar obtains the signal characteristics of the target after filtering and discriminating the signal. Under the far-field and high-frequency conditions, the general calculation expression of the field scattered by the target object's surface is given as follows based on the Stratton-Chu integral equation:  
\[E^{s}(r)=\frac{jk}{4\pi}\frac{e^{-jkr}}{r}\int\limits_{\Omega}^{\Lambda}S(M_{s }(r^{\prime})+Z_{0}^{\Lambda}J_{s}(r^{\prime})\exp(jkr^{\prime}(\overset{ \Lambda}{s}-\overset{\Lambda}{i}))d\omega \tag{1}\]  
In (1), \(\overset{\Lambda}{i}\) and \(\overset{\Lambda}{s}\) are the unit vectors in the incident and receiving directions, respectively, \(Z_{0}\) is the free space wave impedance, \(\Omega\) is the cross-sectional area of the wave incident on the surface of the object, and \(J_{s}(r^{\prime})\) and \(M_{s}(r^{\prime})\) are the current and magnetic current vectors, respectively.  
According to (1), the frequency response \(H(\omega)\) can be obtained independent of the radar incident frequency signal. Subsequently, the radar incident signal \(S(t)\) is processed by Fourier transform to obtain the incident signal frequency response \(S(\omega)\). The target echo scattering can be obtained by multiplying \(H(\omega)\) and \(S(\omega)\) to obtain the cross-sectional area \(Y(\omega)\). The inverse Fourier transform (IFFT) of \(Y(\omega)\) is used to obtain the target time domain echo \(Y(t)\).
# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
## 2 Experience Driven MPCC Design
### Key Components of DDPG-MPCC: Engineering Interpretation  
DDPG-MPCC involves several key components, each contributing to its functionality. In the following, as shown in Figure 1, we discuss these components along with a conceptual diagram.  
State SpaceThe state space represents the current state of the system, capturing relevant information for decision making. It includes details such as network conditions, available paths, congestion levels, and historical data.  
Action SpaceThe action space consists of the possible actions the agent can take in a given state. Actions involve selecting the combination of paths for data transmission, adjusting sending rates, and managing congestion control.  
Reward MechanismThe reward mechanism provides feedback to the agent based on its actions, guiding it toward desirable outcomes. Rewards can be derived from achieving efficient data transmission, minimizing congestion, or optimizing network utility.  
_Actor Network_. The actor-network determines the policy or strategy for selecting actions in a given state. In DDPG-MPCC, the actor-network decides the optimal path combination and sending rates based on the current state.  
_Critic Network_. The critic network of DDPG-MPCC evaluates the actions taken by the actor, providing feedback on their effectiveness, which assesses the selected actions' impact on overall system performance. ctor.  
_Experience Replay Buffer_. The experience replay buffer B stores past experiences to break the temporal correlation in sequential data. It retains historical data to improve the stability and efficiency of the learning process.  
_Training Process_. The training process involves iteratively updating the actor and critic networks based on experiences and rewards. It refines the policy and value functions to improve decision-making over time.
## 3 Results
### Social Vulnerability Evaluation  
According to Section 2.2, the socio-economic variables were aggregated trough Equation (1) to obtain a \"synthetic\" score for each census tract and to evaluate the SVI. In Figure 9, the spatial distribution of the SVI is provided, whereas Figure 10 depicts the social vulnerability context in each municipality under investigation. Looking at the general distribution of social vulnerability classes in the study area, most of the census tracts fall into Class 1 (N. = 899), showing a very low SVI, followed by Class 3 (medium level, N. = 181), Class 4 (high level, N. = 154), Class 2 (low level, N. = 84) and Class 5 (very high level, N. = 13). A similar trend is depicted in each municipality, where Class 1 of SVI represents the majority of socio-economic conditions at census tract level succeeded by medium and high classes of the SVI (Classes 3 and 4), especially in relation to census tracts located in town centers areas (Figure 9, yellow circle). As shown in Figure 10, most of the census tracts fall into the class with very low social vulnerability; however, Bernalda, Pisticci, Motescaglioso, Montalbano Jonico, Scanzano Jonico, Policoro, Colobraro, Rotondella, Tursi, Valsinni, and Nova Siri show extended territories affected by medium, high, and very high social vulnerability.  
Figure 8: Classification of the census tracts affected by multi-hazards, derived from the overlap between the landslide hazard layer (LH) and the flood hazard layer (FH) in high- (**a**), medium- (**b**), and low- (**c**) probability hazard scenarios. The class ranges between 2 (very, very low) and 8 (very, very high). Basemap: Google Satellite.
## 4 Related Remarks
Therefore, the selected value of \(P\) affects the performance of the proposed algorithm. Fortunately, when \(P\) is unknown in practical applications, some effective methods can be used to estimate it, such as the Akaike information criterion (AIC) and the minimum description length (MDL) [30]. In the proposed algorithm, the number of emitting sources \(P\) is assumed to be known._  
**Remark 4**. : _After performing the mutual coupling elimination and the reduced dimensional transformation, the received data matrix \(\mathbf{X}\) in Equation (4) turns into \(\mathbf{Y}\) in Equation (15), and the number of effective antenna elements reduces from \((M+N-1)\) to \((\bar{M}+\bar{N}-1)\). Then, by using the real-valued signals that satisfy \(\mathbf{S}_{r}=\mathbf{S}_{r}^{*}\) extracted from the noncircular signals, the extended matrix \(\mathbf{Z}\) with the dimension \(2(\bar{M}+\bar{N}-1)\times J\) is constructed in Equation (17). It implies that the extended data matrix \(\mathbf{Z}\) corresponds to a virtual array, whose virtual antenna number is twice that of the array corresponding to \(\mathbf{Y}\). As the exploitation of the noncircularity of emitted signals enlarges the effective array aperture, the more diversity gain can be obtained in the proposed algorithm. In addition, the reweighted matrix enhances the sparse solution. Thus, the proposed algorithm considerably improves DOA estimation performance in the presence of mutual coupling._  
**Remark 5**: _Mutual coupling coefficients are related to the distance between two antennas. For uniform linear arrays (ULAs) of the considered MIMO radar system in the proposed algorithm, the mutual coupling coefficients between two antenna elements satisfy \(c_{t(q,m)}=c_{t(m,q)}=c_{t|q-m|}\) for the transmit array and \(c_{r(q^{\prime},n)}=c_{r(n,q^{\prime})}=c_{r|q^{\prime}-n|}\) for the receive array [22], in which \(1\leq q,m\leq M\) and \(1\leq q^{\prime},n\leq N\). Consequently, all of the effects of mutual coupling in ULA can be modeled as a banded symmetric Toeplitz matrix in Equation (3) [23], based on which selection matrices are constructed in Equation (8) to eliminate the mutual coupling. Mutual coupling matrix in Equation (3) is modeled under the circumstance of symmetric coupling in ULA. Hence, the proposed algorithm is not suitable for asymmetric coupling._
## 6 On Control Messages Protection
### FLOOD: The Routing Protocol for UAN
#### 6.1.2 The Network Reconfiguration Phase  
FLOOD allows the network to reconfigure, _i.e._, it allows an AUV to move from one part of the network to another. From a network management point of view, this operation requires to: (i) reconstruct the neighbourhood of both the detaching and re-attaching points; (ii) update the routing tables. FLOOD supports this reconfiguration phase under the following assumptions: (a) each mobile node knows when it is going to move and when it has reached its new location; (b) each mobile node does not send/receive messages while moving.  
Before starting moving, a mobile node \(u_{m}\) sends GW a DEL(\(u_{m}\)). Upon receiving the message, GW updates the routing tables accordingly. If some nodes become unreachable through the new routing table, GW triggers a new instance of the Network Discovery Phase. Otherwise, it can continue the Network Reconfiguration Phase. In the latter case, GW sends a BEST message to every node for which the routing table has been updated. The message includes also information about the new next hop to reach the GW.  
Upon reaching its new location, the mobile node broadcasts an ADD(\(u_{m}\)) message. Upon receiving the ADD(\(u_{m}\)) message, each node sends back an ADD-ACK message containing information about link quality. The mobile node acknowledges all the received ADD-ACK messages by sending back unicast ADD-ACK messages, containing information about its link quality. Finally, the mobile node sends an ADD-REPORT message to the GW so that the GW can accordingly update the routing tables and re-distribute them to all the interested nodes by means of ADD-BEST messages.
## 2 Theoretical Derivation
Accordingly, based on the static equilibrium of the cross-section, the relationship between the neutral axis location \(X_{0}\) and the prestress force \(\text{F}\) can be derived from the below equation:  
\[-\int_{0}^{X_{0}}\text{f}(\sigma_{\text{i}})\text{bdy}-\sigma_{\text{bs}}A_{ \text{bs}}-\sigma_{\text{ms}}A_{\text{ms}}-\text{F}+\int_{X_{0}}^{\text{H}_{ \text{b}}}\text{f}(\sigma_{\text{i}})\text{bdy}+\int_{H_{\text{b}}}^{\text{H} }\text{f}(\sigma_{\text{i}})\text{b}_{\text{f}}\text{dy}+\sigma_{\text{ts}}A _{\text{ts}}=0 \tag{4}\]  
where \(\text{f}(\sigma_{\text{i}})\) denotes a function of the stress distribution along the cross-section, which is calculated based on the strain distribution over the cross-section. \(A_{\text{bs}}\) and \(\sigma_{\text{ts}}\) represent the area and stress of the top reinforcements, respectively; \(A_{\text{bs}}\) and \(\sigma_{\text{bs}}\) are the area and stress of the bottom reinforcements, respectively; and \(A_{\text{ms}}\) and \(\sigma_{\text{ms}}\) denote the area and stress of the middle reinforcements, i.e., the reinforcements above the prestress tendon, which may or may not exist in practice, respectively. These reinforcements are included herein for the completeness. \(H_{\text{b}}\) represents the height of the flange, and H is the total height of the cross-section of the structure; b and \(b_{\text{f}}\) are the web thickness and flange width of the T-shaped cross-section, respectively. Therefore, the prestress force of the tendon can be calculated as:  
\[\text{F}=\frac{(\sigma_{\text{t}}+\sigma_{\text{b}})}{2\text{H}}\left(-2 bX_{0} ^{2}+b\text{H}\text{ }_{\text{b}}^{2}+b_{\text{f}}\text{H}\text{ }^{2}-b_{\text{f}}\text{H}\text{ }_{\text{b}}^{2}\right)+\sigma_{\text{b}}\left(2 bX_{0}-bH_{\text{b}}-b_{ \text{f}}H\text{ }+b_{\text{f}}H_{\text{b}}\right)-\sigma_{\text{bs}}A_{\text{bs}}-\sigma_{ \text{ms}}A_{\text{ms}}+\sigma_{\text{ts}}A_{\text{ts}} \tag{5}\]  
In this paper, experimental investigations and numerical verification of this proposed approach for quantifying the prestress force in a segmental beam are conducted. The results are compared to those obtained in the test to verify the reliability and accuracy of the proposed method. The relative errors between experimental measured and theoretically predicted prestress forces, and between numerically simulated and theoretically predicted prestress forces, are calculated and denoted by using the symbol 'RE' in the subsequent studies. Figure 2: Schematic for the force equilibrium of the beam.
# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 3 Artificial Intelligence and RS
#### Decision Support Systems (DSS)  
Agricultural decision support systems (DSS) can simply be identified as the informative resources that facilitate farmers in their decision making, based on knowledge of various crop-growth parameters [79]. DSS involves data collection, organization, and analysis to provide either short-term or long-term decisions. For example, for pest or weed control, DSS can establish a list of prospective fertilizers to use, or even suggest integration of both chemical and non-chemical-based practices, based on the crop type, growth stage, etc. One such practical example is the recommendation of herbicide use along with the suggestion of non-chemical weed control by a DSS, which investigated 100 weed species and more than 30 crops, in Denmark [80].  
Since RS provides near-real-time information, it can play a key role in the development of an efficient DSS, which will further support agricultural sustainability. RS provides the temporal and spatial attributes of a crop that can be used to improve decision making, as they can augment various field data and significant parameters [81]. For instance, DSS may establish a site-specific herbicide application map, predicting specific weed species or invasive plants [80]. Such technological interventions in this 5 th industrial revolution have the potential to combat climate change (by reducing the use of fertilizers) and reduce input costs.  
Despite the promising uses of DSS, its integration into agriculture is not prominent due to a number of factors. Examples of these limiting factors include high economic cost, lack of awareness or lower comprehension by farmers, the gap between the decision-making style required by DSS and the different types of farmer requirements, restrictions in capturing and storing environmental or biological data, etc. [79; 80].
## 2 Materials and Methods
### Experiment Configuration and Validation  
The original MOD17A2H GPP products used the GMAO Reanalysis data as the driving metrological database, and calculated the GPP with the biome based parameters look up table on a global scale. To validate and improve the performances of the MODIS GPP estimations and quantify the uncertainty of the MODIS GPP simulation algorithm (MOD17 model), we replaced the satellite-derived and meteorological inputs in the MOD17 model and compared the modeled GPP estimates with flux tower observations with the following experiment configurations: (1) We firstly assessed the performance of original MOD 17A2H GPP product at spatial resolution of 500m with the tower based GPP. The results of the model validation, in this study, is called GPP_MODIS; (2) we used in situ meteorological data to run the MOD17 algorithm to understand the influence of meteorological inputs (i.e., incoming solar radiation, minimum temperature and vapor pressure deficit) on GPP modelling rather than the GMAO Reanalysis dataset, we called this GPP_Insitu; and (3) we compared the performances between the calibration of one parameter only (\(\epsilon_{\mathrm{max}}\)) and calibration of all parameters of the MOD17 model to examine the sensitivity of the water and temperature-limited parameters on GPP estimation. The results are called GPP_LUEopt and GPP_Fiveopt, respectively. To understand the effects of parameter uncertainty on GPP simulation, we compared the calibrated MOD17 model algorithm with in situ meteorological inputs from the flux tower network. Similar to GPP_Insitu, GPP_LUEopt, and GPP_Fiveopt were also calculated using the in situ meteorology data. However, for GPP_LUEopt, we only optimized the parameter of \(\epsilon_{\mathrm{max}}\) by Bayesian approach and other parameters used the default BPLUT parameters in the MOD17 algorithm. Whereas, for GPP_Fiveopt we optimized all the five parameters using the Bayesian approach.
# Buckling Analysis of Piles in Multi-Layered Soils
## 1 Introduction
In the recent decade, many research groups have studied liquefaction effects on buckling of piles. A method to calculate buckling critical load based on the beam-on nonlinear Winkler foundation (BNWF) model was proposed, considering multi-boundary conditions, the degree of soil layers liquefaction, and piles' nonlinearities. The critical buckling load of a pile in liquefied soils increases with the increase of the soil's relative density and the flexural rigidity of the pile [15; 16]. [PERSON] et al. [17] investigated the dynamic instability of piles in liquefiable soils. A correction factor was suggested to compensate for the natural frequency reduction of piles in a liquefied condition. Utilising the differential transformation method, [PERSON] et al. [18] showed the effects of high and low soil stiffness and intermediate boundary conditions on pile buckling behaviour. To reinforce the foundation of existing structures, micropiles require less effort to apply than do other driven piles, which are longer, and if they are precast, splicing is inevitable according to the site limit space [19]. Furthermore, micropiles enhance the load-bearing capacity of the foundations, even when soil is prone to liquefaction [20; 21]. In this paper, the buckling of piles driven into multi-layer (non-homogeneous) soils for all boundary conditions is investigated. The uneven distribution of the coefficient of the subgrade reaction of a multi-layered soil is described by using a Fourier series. The boundary condition at the pile top can be given by assigning a suitable coefficient of the subgrade reaction to the part of the foundation crossed by the upper part of the pile. This means that the pile can be clamped or partially clamped at the top. The boundary condition at the bottom end is automatically assigned as it depends on the stiffness of the soil around the lower region of the pile. Using the principle of minimum potential energy, a variational approach to find buckling load through the Rayleigh-Ritz method has been proposed (Section 2.1). Two Fourier series were used, one describing the distribution of the coefficient of subgrade reaction and the other describing the displaced shape of the buckled pile. The algorithm was shown to be of general validity for any kind of multi-layer soil, and has been applied to different multi-layered soils, thus allowing for the investigation of the influence of position and thickness of layers of soft soils on buckling load (Section 3.1). The influence of the rotation of the pile sections in the closeness of the interface between layers with different stiffness, as well as of the rotation of the pile's lower end depending on the pile embedding in a stiffer soil layer close to its base were also considered (Section 3.1). This study shows how pile buckling can be an issue when slender piles pass through cavities or very soft soils, especially peaty soils, even if some silty sands and normally consolidated clays can be soft enough to allow for buckling of the slender piles driven in them (Section 3.1).
## 4 Discussion
### Model Accuracy Evaluation and Limitations  
Previous studies suggest that an RMSE value within 10-35% of the observed mean is acceptable for geological hazard modeling [84; 85; 86; 87]. In this study, Random Forest models were constructed using 1-500 trees to determine the optimal number of features. When the number of trees reached the optimal value, the model achieved minimum RMSE across all zones, with values remaining below 35% of the true mean. This result aligns with [PERSON] et al. (2020)'s conclusions in rainstorm disaster modeling (RMSE \(\approx\) 30%) [88], further affirming the robustness of the Random Forest approach in multi-hazard contexts.  
However, the model does have limitations. In the northwestern and central Plateau, where terrain is flat and population density is low, disaster occurrence is infrequent, leading to sparse and unevenly distributed data. This reduces the model's ability to learn relevant hazard patterns, introducing uncertainty into the results and weakening predictive accuracy in these areas. To address this challenge, the following optimization strategies are proposed: (1) Supplementing environmental factors: Integrate critical variables such as permafrost distribution, active layer thickness, and dynamic groundwater levels, extractable via high-resolution remote sensing technologies (e.g., InSAR, MODIS, Sentinel-2), to enhance factor completeness in data-scar areas [89]. (2) Balanced sampling design: Implement spatially balanced sampling to expand representative datasets in key geometric and structural units, improving generalization and model training quality. (3) Transfer learning optimization: Apply regional adaptive transfer learning by leveraging high-quality training samples from southeastern zones as source domains. Domain adaptation techniques can improve prediction in low-sample western regions while maintaining model structure integrity [90]. (4) Multi-source data integration: Combine geological maps, historical hazard archives, UAV data, and public disaster perception reports to create a multi-scale multi-dimensional hazard-inducing factor database, improving regional adaptability and predictive robustness. These strategies will not only reduce uncertainty in model outputs for the central and northwestern Plateau, but also offer more reliable scientific support for comprehensive regional geological hazard risk assessments.
## 4 Result and Discussion
### Battery Based Power Levelling During Solar Radiation and Temperature Change  
#### 4.4.1 Solar Radiation Change  
The solar radiation is changed from 1000 Wm\({}^{-2}\) to 100 Wm\({}^{-2}\) to evaluate the efficacy of the battery controller. As can be seen from Figure 28, battery compensates the shortage of PV power due to the reduction of solar radiation. PV power is reduced from 1.74 MW to 0.1 MW, whereas battery power is increased from zero to 1.6 MW. During this period, the battery state of charge is decreased from 81.1% to 80.6%. The real power delivery through the MMC1 remains almost constant. Similarly, delivered battery power is reduced from 1.6 MW to zero during the solar radiation that changed from 100 Wm\({}^{-2}\) to 1000 Wm\({}^{-2}\), as depicted in Figure 29. During this time, PV power is increased from 0.1 MW to 1.74 MW, and the battery state of charge is decreased from 73.4% to 72.85%. The delivered real power of MMC1 encounters negligible overshoot during the transient but maintains constant power in the steady-state.