esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 4 Problem Formulation
The constraints (5) and (6) state that a D2D device can send to at most one receiver or receive from at most one sender; they also state that each device terminating a link must be D2D-capable, whereas the constraints (7) and (8) formulate, respectively, the SINR requirement of the cellular device and D2D devices. The use of a binary variable to control whether a linear constraint is active is a very well-known modeling trick in integer linear programming [36]. The solution of the problem [_MaxL_] can be obtained by using an exact algorithm or a heuristic one. The exact algorithm is typically based on the use of the branch and bound [37] and dynamic programming methods [38]. Though such methods can obtain an optimal solution, their computational complexity is large and only suitable for small scale problems. On the other hand, the heuristic algorithm is not based on finding the optimal solution of the problem, but it expects to obtain a near global optimal solution in an acceptable time. If \(x_{ij}=\ 1\), (8) constrains the SINR to be at least \(\gamma_{D}\) for \(x_{ij}=0\). When no D2D links are active for the pair \((i,j)\), the constraint is always satisfied for a sufficiently large \(M_{ij}\). Large values for \(M_{ij}\) can easily lead to numerical problems and to weak linear relaxations ([39; 40]). The choice of such big numbers for \(M_{ij}\), known as Big-M in integer programming, is used to turn on or off some inequality constraints when necessary and can potentially result in difficulties when trying to solve an integer linear programming problem like _MaxL_ that relies heavily on the bounds of continuous relaxation to be solved in a computationally acceptable time. That is, an improper selection of the \(M_{ij}\) values can potentially result in a very weak continuous relaxation. Moreover, the gain values in (8) may vary significantly in magnitude and, hence, may introduce other numerical difficulties in solving the problem.
## 4 Experimental Design and Results Analysis
### Experimental Results and Performance Analysis  
We tested 24 experimental scenarios recorded in Table 3 using MAPSO, NSGA-II, MOEA/D, and MOGA algorithms. The NSGA-II parameters were set as population size 100, maximum iterations 100, crossover probability 0.9, mutation probability 0.1, and roulette wheel selection method. The optimization parameters of MAPSO were population size 100, inertia weight 0.7, and learning factors c1 = c2 = 1.5. MOEA/D parameters were population size 100, neighborhood size 20, mutation probability 0.02, crossover probability 0.9, and maximum iterations 100. MOGA parameters were population size 100, crossover probability 0.9, mutation probability 0.02, maximum iterations 100, and roulette wheel selection. Table 4 records the time consumed by each algorithm under different experimental scenarios, and Table 5 records the time each algorithm takes to schedule data transmission tasks in different scenarios. The MAPSO algorithm improved data transmission efficiency using the minimum angle method to select particles with the best fitness for _gbest_ and _pbest_. The archive update operation also updated the archive set, making MAPSO the fastest in solving satellite data transmission problems across all scenarios. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experiment** & **MAPSO** & \begin{tabular}{c} **DRSC-** \\ **MOAGDE** \\ \end{tabular}  & \ \begin{tabular}{c} **DSC-** \\ **MOPSO** \\ \end{tabular}  & \begin{tabular}{c} **MO-Ring-** \\ **PSO-SCD** \\ \end{tabular}  & **NSGA-II** & **MOEA/D** & **MOGA** \\ \hline c1 & 68.721 & 60.018 & 63.879 & 101.592 & 166.0427 & 207.0557 & 120.4097 \\ c2 & 104.373 & 95.499 & 99.312 &  
We selected the most complex scenarios from Table 1: C4 (one station, 40 satellites), C9 (two stations, 50 satellites), C15 (three stations, 60 satellites), and C24 (four stations, 90 satellites) to plot Pareto fronts. Figure 7 displays the Pareto fronts obtained by the four optimization algorithms under different scenarios. Scenarios C1 to C4 were not chosen because when there is only one ground station, satellites transmit data sequentially within the time window, resulting in fixed values for the shortest transmission time and maximum priority between satellites and ground stations, making it impossible to plot corresponding Pareto fronts. Compared to NSGA-II, MOEA/D, and MOGA, the MAPSO algorithm shows significantly better performance in terms of Pareto front diversity and uniformity.
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 1 Introduction
An advanced two-dimensional (2D) parameter estimation framework that combines virtual array interpolation techniques with coprime EMVS array processing to simultaneously estimate both DOA and polarization states was presented [26]. It is worth noting that an advantage of these approaches is their gridless nature, which avoids the requirement for dense sampling grids. However, they usually require computationally expensive steps, such as \(l_{1}\)-norm minimization, NNM minimization, and the convex semi-definite programming (SDP) solving problem. A computationally efficient coarray-based approach for off-grid DOA estimation that achieves accurate results with minimal snapshot requirements is proposed. The main contributions of this paper are threefold: (i) A noise elimination technique is proposed, in which a selection matrix is left-multiplied to reduce noise effects in DOA estimation. (ii) An iterative correction method for angle and power estimation is developed. In each iteration, the target signal is isolated by removing other signals from the noise cancellation data, and the parameters are refined using an alternating minimization framework to improve the estimation performance. (iii) The CRB is theoretically derived for a deterministically orthogonal signal model, providing a rigorous performance metric for the proposed estimator. Comprehensive simulations are conducted to validate the enhanced estimation accuracy and robustness of the proposed approach compared to state-of-the-art techniques. This paper proceeds with the following structure. Section 2 formulates the coarray-based signal model for off-grid DOA estimation, providing mathematical foundations for the subsequent algorithm. Section 3 introduces the proposed iterative correction method. The theoretical analysis is given in Section 4, including the derivation of the CRB for the deterministically orthogonal signal model. Section 5 presents comprehensive simulation results validating the proposed method, while Section 6 provides concluding remarks and outlines promising directions for future research.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 7 Conclusions  
In this contribution, a new compact CRB for the joint delay, Doppler and complex amplitude estimation of a dual source narrowband CSM was derived. A handy closed-form expression of these CRBs that only depends on the baseband signal samples was also proposed. Such formulation is especially easy to use whatever the baseband signal considered, therefore being valid for a large variety of applications and of broad interest. The new formulation was validated using the asymptotic properties of the dual source CMLE and a sub-optimal algorithm, the CRE, that behaves well under certain assumptions. The different simulations show a satisfying fit between the theoretical bounds and the statistical output of the estimators applied to two representative navigation and radar signals, which validated the proposed CRBs. In addition, a brief comparison with the existing literature was provided, showing the consistency with CRBs previously derived for GNSS-R applications. A number of applications can be thought of based on these CRBs, which were also discussed for completeness. For instance, the new CRBs can be exploited for optimal signal design, multipath characterization, precise positioning methods assessment under challenging propagation conditions, or for the estimation of specific application-dependent parameters that are function of the signals parameters (delay, Doppler shift, complex amplitude).  
**Author Contributions:** Conceptualization, E.C.; Formal analysis, C.L.; Funding acquisition, J.V.-V., L.L. and E.C.; Methodology, E.C.; Software, C.L. and L.O.; Supervision, J.V.-V. and E.C.; Validation, C.L., L.O., J.V.-V. and E.C.; Writing--original draft, C.L.; Writing--review & editing, L.O., J.V.-V. and E.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially supported by the CNES and the DGA/AID projects (2019.65.0068.00.470.75.01, 2018.60.0072.00.470.75.01).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion
### Choice of Functional Unit and System Boundaries  
According to ISO guidelines, selecting a functional unit aligned with the study's goal is crucial [95]. Depending on the focus, studies use different units: digestate applications use m\({}^{3}\), kg, or tonnes of digestate (solid or liquid), compost studies use kg of compost or soil conditioner, and energy recovery studies use MJ or m\({}^{3}\) of biogas/biomethane. Waste treatment studies rely on kg of processed waste (manure, organic, or food waste). The functional unit determines environmental credit allocation when a product displaces another, but limits quantitative comparison across studies.  
System boundaries define which lifecycle stages are included in LCA assessments and vary based on study goals. For energy recovery studies, boundaries typically end at biogas or biomethane production [110; 111], excluding digestate post-treatment. In contrast, studies on digestate-based fertilizer substitution require boundaries covering the full value chain [88; 94].  
Clear inclusion/exclusion of system processes is essential, as it impacts study conclusions. Considering upstream waste generation enables a full assessment of total system impact, whereas excluding it limits the study to waste treatment and disposal. Transportation of feedstock and digestate significantly affects energy consumption and GHG emissions, especially for long distances [92; 96]. Furthermore, including infrastructure construction (digesters, storage, and transport) influences cumulative LCA impacts, particularly in long-term or technology comparison studies [88]. Thus, clearly defining system boundaries is key for accurate LCA interpretation and reproducibility.
# Defining Breast Tumor Location Using a Four-Element Wearable Circular UWB MIMO Antenna Array
## 4 Results and Discussion
### SAR Results  
According to [PERSON]'s law, when a coil's magnetic field interacts with tissues in the body, it induces radio frequency energy, leading to the generation of an electric field within the tissues. This radio frequency energy is absorbed by the tissues and converted into heat [25]. Exposure to radiofrequency radiation can potentially have health impacts due to tissue heating, and these effects can be monitored and managed using a parameter called the specific absorption rate (SAR) [26; 27].  
The SAR is a measurement of how quickly biological tissues absorb radio frequency power when exposed to RF radiation. To calculate the SAR value, the IEEE Standard 1528 [28] requires the consideration of a volume, either 1 g or 10 g in mass, which is cubical in shape and includes the peak electric field. The local SAR, denoted as \(SAR_{local}\)\((r,\omega)\) represents the SAR value at each position inside the phantom tissues and is expressed in units of watts per kilogram (W/kg) [29; 30]. It can be defined as follows:  
\[SAR_{local}\)\((r,\omega)\)=\(\dfrac{\sigma(r,\omega)|E(r,\omega)|^{2}}{2\rho(r)}\) (5)  
Here, \(\sigma(r,\omega)\) represents the conductivity of the phantom material in siemens per meter (S/m), \(\rho(r)\) is the mass density of the tissue in kilograms per cubic meter (kg/m\({}^{3}\)), r is the position vector, \(\omega\) is the frequency, and \(E(r,\omega)\) represents the electric field in volts per meter (V/m).  
The average SAR denoted as \(SAR_{average}\)\((r,\omega)\) is obtained by dividing the mass of the cube by the integral of the local SAR at each point on the cube. It can be calculated using the following equation:  
\[SAR_{average}\)\((r,\omega)\)=\(\dfrac{1}{v}\dfrac{\sigma(r,\omega)|E(r,\omega)|^{2}}{2\rho(r)}dr\) (6)  
In Equation (6), \(v\) represents the volume of the cube.  
To analyze the SAR in the heterogeneous phantom, the CST software is employed in the biomedical context. At a frequency of 2.8 GHz, an input power of 0.5 watts is considered  
Figure 12: Detection scenario flow chart of the proposed system.  
at the antenna input. The spatial average SAR values for volumetric samples of 1 g and 10 g in the heterogeneous phantoms are investigated at this frequency. The results depicted in Figure 13 demonstrate that at 2.8 GHz and a 0.5-watt input power, the highest spatial average SAR values for the 1 g and 10 g samples in the phantom are 0.804 W/kg and 1.75 W/kg, respectively.  
Regulatory bodies such as the Federal Communications Commission (FCC) have established maximum restrictions for the SAR. In the United States, the limit is set at a maximum average SAR of 1.6 W/kg over a 1 g volume, while in Europe, the standard is 2 W/kg averaged over a 10 g volume. The obtained SAR values in the study remain below these regulatory restrictions.
## 4 Performance Evaluation  
### Simulation Design  
The proposed protocol's performance was evaluated using MATLAB R2022a computer simulations. The simulation settings that were used in the evaluation are listed in Table 2. [PERSON]'s empirical model of the submerged acoustical channel in [53] is used to accurately represent the properties of the submerged channel. Moreover, our research also takes into consideration the following interference situations. In this stage, there are two different scenarios.  
**Scenario 1** : The research makes the assumption that even in situations when there is partial overlapping between control data packets, the communicating data packet cannot be effectively decoded until the criteria mentioned in (7) is accomplished [54].  
**Scenario 2**: The second assumption is that the interference and receiving signals are uncorrelated with one another and that the interference signals may be thought of as cumulative values [54]. The Tx anchor node and Rx node energies, as well as the communicating data packet rate, were established by the specifications of the ATM-903 submerged modem. As illustrated in Figure 5, every Rx node creates a network of stars on its topology, with the sink node at its center, after being randomly placed inside any of the four aforementioned clusters. The four clusters are carefully deployed to take into consideration any inter-cluster interference situations. Additionally, a particular simulation scenario is considered, such as a variable average traffic load with a certain amount of Rx nodes in a cluster. As an example, cluster-1 has 12 Rx nodes that generate network traffic. This traffic load has a communicating data packet rate of between 0.005 and 0.015 packets/s and follows a Poisson distribution. Three thousand repetitions of each simulation are run to increase the accuracy of the findings.
## 3 Results
### Unconfined Compressive Strength  
To study the effect of compaction on the compressive strength of the soil, cylindrical specimens of 5 cm diameter and 10 cm in height were prepared after the Proctor tests. For each soil and for each test period, three specimens were tested in unconfined compression at maximum dry density and OMC for each condition. Figure 8 shows the mean values and standard deviations of the compressive strength of BE42, Morne Coco, and Cabale. The tests could not be carried out on the MTI soil because the specimens could not be prepared due to a lack of cohesion.  
The standard deviation values show that the measurements are homogeneous and repeatable. For the specimens made with standard Proctor energy, an increase in unconfined compressive strength was observed from 1 to 7 days. However, from 7 to 28 days, no improvement in compressive strength was observed for the BE42 and Morne Coco soils, whereas the compressive strength of the Cabale soil practically doubled during this period. The increase in values over time is related to water loss even in a controlled environment. For BE42, the percentages of water content at crushing in simple compression are 23.2, 5.7, and 5.7 at 1, 7, and 28 days, respectively. The specimens were placed without a case in the environmental chamber, and water loss occurred; however, the value of water content at crush was not available for all specimens and therefore is not presented. Furthermore, the curves show the improvement of the unconfined compressive strengths of the three soils by considering the modified Proctor energy. This compaction energy considerably increased the unconfined compressive strength from 50% to 180% at 28 days compared to that of the standard Proctor test.  
Figure 8: Compressive strength of studied soils (**(A)**: BE42, **(B)**: Morne coco, **(C)**: Cabale soils).
## 3 The Experiment of Similar Physical Models
### Fiber Optic Test System Accuracy  
To validate the data reliability, post-construction calibration of the optical sensing elements is conducted following the physical model assembly [29; 30]. The embedded fiber optic sensors within the critical stratum undergo quintuple verification tests. With the initial measurement serving as the reference baseline, comparative analysis of the four subsequent datasets enables the quantification of systematic strain variations, as illustrated in Figure 5. The graphical representation depicts the monitored length along the model axis excluding coal support elements on the x-axis, while the y-axis quantifies the strain measurements from fiber optic instrumentation. The measurement consistency demonstrates the robust survivability of the fiber optic sensing array and indicates stable instrumentation performance with minimal systematic deviation. The monitoring system exhibits high  
Figure 4: Monitoring system of the model.  
stability characteristics, with strain measurement precision maintained within the \(\pm 30\)\(\upmu\epsilon\) range, excluding isolated anomalous readings. This calibration outcome substantiates the measurement fidelity of the fiber optic sensor network, establishing a reliable foundation for subsequent rock mass deformation monitoring and analysis.  
Through the design and construction of similar experimental models, as well as the layout and accuracy verification of the monitoring system, we have established a reliable technical foundation for subsequent experiments. Having ensured the stability of the monitoring system and the accuracy of data acquisition, we will now focus on analyzing the main phenomena observed during the experiment and their underlying mechanisms, delving deep into the key scientific questions revealed by the experimental results. This not only helps validate our theoretical hypotheses but also provides important reference points for engineering practice.
# Chromosome Number and Genome Size Evolution in _Brasolia_ and _Sobralia_ (Sobralieaee, Orchidaceae)
## 3 Discussion
### Genome Size Reconstruction: The Role of Genome Size in Species Evolution  
The genera _Brasolia_ and _Sobralia_ did not differ in genome size, while _Eleantthus_ differs from both genera, probably due to the reduced 1C-value of _E. crimipes_. The genome size seems not to have any association with chromosome number variation. For example, _Sobralia_ species with \(2n=48\) vary 2-fold in their genome size (1C-value from 4.34 pg to 8.08 pg), while _Brasolia_ species with \(2n=46\) vary from 4.97 to 6.37 pg, and between the two _Ellanthus_ species, both with \(2n=50\), the genome size varies more than 2-fold (from 2.04 to 4.79 pg). The difference observed in genome size is probably due to differences in repetitive sequences as transposable elements and satellite DNA families [34, 35]. The homogenisation and elimination of such redundant sequences are characteristics of the diplodisation process [35] and probably shaped the genomes in alternative ways resulting in genome size differences, even with the same chromosome number [36].  
The chromosome number and genome size are essential as reproductive barriers, ensuring the species' integrity [37]. Here, the most significant difference in genome size, conserving the same chromosome number, was observed in _Ellanthus_. The two studied _Ellanthus_ species, besides differing in genome size, also differ in their distribution. While _E. brasiliensis_ ranges from French Guiana to southern Brazil and in the Brazilian Atlantic Rain Forest occurring from the sea level until 1000 m, _E. crimipes_ is endemic to the highland forest areas in Brazilian Atlantic Rain Forest, occurring only above 800 m [38, 39]. Despite the region of sympatry between the two species that present simultaneous blooming and partially share pollinators, no hybrids were described between these two species, with no fruit being formed after manual interspecific crossings [38]. Despite the two species presenting the same chromosome number with similar karyotype morphologies, the two-fold genome size difference should configure as a strong post-zygotic barrier preventing hybridisation, highlighting the importance of genome size as a reproductive barrier and its importance in speciation (reviewed [36]).
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 2 Polymer Flooding (PF)
### Recovery Mechanism  
The main recovery mechanism involved in PF is viscosification of the displacing fluid resulting from the addition of polymer, which is favorable for hydrocarbon recovery [39]. The addition of polymer to the displacing fluid increases its viscosity, resulting in a decreased mobility ratio, as per the fundamental relationship shown below:  
\[\textit{Mobility ratio},\ M=\frac{\textit{Mobility of the displacing fluid},\lambda_{D}}{\textit{Mobility of the displaced fluid},\lambda_{d}}=\frac{\mu_{o}k_{rw}}{\mu_{w}k_{ro}} \tag{1}\]  
where \(\mu_{o}\) and \(\mu_{w}\) are the viscosity values of oil and water, respectively; while \(k_{ro}\) and \(k_{rw}\) are the corresponding relative permeability values of oil and water. For the mobility ratio \(M>1\), the water moves faster, causing an unstable front advancement, early breakthrough of water, and lower ultimate oil recovery. The polymer on the other hand has relatively stable front movement, delayed water breakthrough, and better sweep efficiency. Much research has been performed in the area of PF to improve its applicability and outcomes [40; 41]. Many researchers have studied the chemical and physical properties of polymers and their dependence on various subsurface reservoir conditions [42; 43].