esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Concept of Reconfigurable Antenna Structure Based on an Array of RF-MEMS Switches
## 2 Reconfigurable Antenna Structure  
In this section, the reconfigurable matrix structure is detailed. It consists of gold square patch sets that can be interconnected using radio frequency MEMs. Each gold patch has four and three MEMS switches for the central and boundary patches, respectively. The total length of the structure L is given by the following:  
\[L=N_{p}\times D+\big{(}N_{p}-1\big{)}G \tag{1}\]  
where \(N_{p}\) and \(D\) are the number and size of gold square patches, respectively. \(G\) is the MEMs length. As can be noticed from Figure 1, the structure is modular. Different matrices can be combined in order to create complex geometries. In particular, conductive traces can be obtained by properly activating the MEMS switches. Figure 2 reports an example of a patch antenna equipped with a quarter wavelength transformer and a feeding microstrip, as it can be noticed that all the elements have been realized by properly activating the MEMS. This structure is very versatile, and it is possible to obtain different complex geometries with a suitable number of matrices.  
Another big advantage of this structure is that all the components can be fabricated directly on the substrate, power consumption is very low since MEMS are voltage-driven, no current is required for their activation, and they can operate from low frequency up to 100 GHz. The main drawback of the reconfigurable structure is the complexity of the switch control network. The following Figure 3 shows the whole CAD of a \(7\times 7\) matrix  
Figure 1: Schema of the reconfigurable antenna structure based on a matrix of RF-MEMS switches.  
structure, with MEMS switches and control pins aimed at activating rows and columns of gold patches. The CAD has been obtained with the professional software Cadence. In the following, we will consider the \(7\times 7\) matrix as a basic structure since it can be combined with similar ones to obtain more complex geometries. The next section will detail a prototype fabrication of a reconfigurable matrix.  
Figure 3: CAD schema of a \(7\times 7\) elements reconfigurable matrix.  
Figure 2: Matrix configured as patch antenna equipped with a quarter wavelength transformer and feeding microstrip.
## 2 An Overview of the LCPV System Based on an SSLFR
### The Longitudinal Tilt Angle of the Secondary System  
Several studies have shown the positive effects of \(SSLFR\) configurations with a longitudinal tilt angle on the primary reflector system and/or the secondary system [23; 24; 28]. These positive effects are related to the energy absorbed by the secondary system and the surface area required for installation.  
A study on the influence of the longitudinal tilt angle on the energy absorbed by the secondary system was presented in [23]. The study was carried out in five cities in the Northern Hemisphere: Almeria (Spain), Rome (Italy), Budapest (Hungary), Berlin (Germany), and Helsinki (Finland). The results obtained were compared with the longitudinal tilt angle used in large-scale \(LFRs\), i.e., \(\beta_{S}=0\) (\({}^{\circ}\)). When \(\beta_{S}=\lambda\) (\({}^{\circ}\)), the increase in the energy absorbed by the secondary system was maximum [24]. Specifically, there was an increase of 164.14% for Almeria [23]. However, the useful energy was not determined in that study, i.e., thermal losses were not considered, hence the aim of this paper.
# Cost Analysis and Optimization of Modern Power System Operations
## 3 Implementation and Case Studies
### Case 4: Unit Commitment Demand Flexibility Constraint  
In this case, demand-side flexibility (DSF) is investigated as a mechanism to improve the operational efficiency and economics of power systems. Demand-side flexibility refers to the extent to which electricity demand can be reduced, increased, or shifted within a given time window in response to supply conditions, market signals, or system constraints.  
Traditionally, power systems have relied heavily on supply-side management, where generation is adjusted to match demand in real time. However, with the rise of smart grids, the integration of information and communication technologies (ICT), and the deployment of smart appliances and devices at the consumer end, it is now increasingly feasible to also manage demand in a dynamic and coordinated way.  
In this study, the focus is on analyzing how operating costs vary with the degree of demand-side flexibility. Flexibility can be introduced both  
* At the consumer side through demand response programs and smart appliances,
* And at the supply side via technologies like battery energy storage systems (BESS).  
To model demand-side flexibility within the unit commitment problem, additional constraints are introduced to allow demand shifting and modulation across the 24 h horizon. These constraints govern  
* Maximum allowable deviation from baseline demand (upward or downward),
* Temporal balancing to ensure total shifted demand is met within the operational window,
* And potential cost penalties or incentives associated with shifting loads.  
It is good to note that for sustainable resource participation in demand flexibility, fairness and ongoing incentivization must be maintained. For that, contribution-based sharing mechanisms that distribute collective benefits (cost savings or revenues through program participation) are preferred [32].  
The mathematical formulation of these constraints is provided in the following section.  
\[(1-\varsigma\textit{min})\mathbf{L}\mathbf{t}\leq\mathbf{D}\mathbf{t}\leq(1+\varsigma\textit{ max})\mathbf{L}\mathbf{t} \tag{34}\]  
\[\sum_{t}\mathbf{D}\mathbf{t}=\sum_{t}\mathbf{L}\mathbf{t} \tag{35}\]  
where \(\varsigma_{min/max}\) determines min/max demand flexibility. For study, \(\varsigma_{min/max}\) in this case is taken between 0 to 20%. Figure 11 depicts the comparison of flexible demand and actual demand.  
Figure 10: Unit commitment for 24 h in Case 3.  
In this case, for different values of demand flexibility, operating cost is calculated and shown in Figure 12 below.
## 5 Conclusions  
This paper presented an experimental analysis of a DFIG-based wind turbine control system that receives power references from the grid operator, by means of a GSM network (a consolidate wireless communication standard). Using SMS to transmit the references (each one with two messages, \(P_{ref}\) and \(Q_{ref}\)), two tests were performed: A first one using \(Q_{ref}\) steps with \(P_{ref}\) constant, and another one using \(P_{ref}\) steps with \(Q_{ref}\) constant. The test results showed that the DFIG controllers satisfactorily followed the received references, and that the noise introduced by the wireless communication system did not affect the control system's performance. The results are in agreement to a control without wireless reference transmissions, which validates the proposed system.  
Due to its flexibility and reliability of information, the implemented system is an interesting solution--it offers a significant reduction in deployment and operating costs of a communication system for the implementation of the SG concept in wind power generation systems. Moreover, looking to a more traditional implementation, the proposed configuration can be used as a secondary communication link, implementing redundancy in order to improve the system security.  
As a complementary and future proposal, a 3G LTE (Long Term Evolution) network infrastructure may also be employed, reducing the transmission delay at the expense of increasing operation costs and power consumption of equipments and devices [54].  
Conceptualization, M.B.C.S., A.J.S.F., C.E.C., and I.R.S.C.; methodology, J.A.T.A.; software, L.A.G.G., S.C.P., A.L.L.F.M., and H.S.F.; validation, J.A.T.A., M.B.C.S., and I.R.S.C.; formal analysis, J.A.T.A. and M.B.C.S.; investigation, L.A.G.G., S.C.P., A.L.L.F.M., and H.S.F.; resources, M.B.C.S., A.J.S.F., and I.R.S.C.; data curation, L.A.G.G., S.C.P., A.L.L.F.M., and H.S.F.; writing--original draft preparation, L.A.G.G., S.C.P., and A.L.L.F.M.; writing--review and editing, I.R.S.C.; visualization, I.R.S.C., L.A.G.G., and S.C.P.; supervision, M.B.C.S., A.J.S.F., C.E.C., and I.R.S.C.; project administration, A.J.S.F., C.E.C., and I.R.S.C.; funding acquisition, M.B.C.S., A.J.S.F., and I.R.S.C.  
The authors declare no conflict of interest.
## 3 Experimental Results
### Displacement Ductility  
The average displacement ductility for each SCMSW specimen is shown in Table 4. These values were calculated according to the elasto-plastic idealization (see Figure 9) based on the area under the load-displacement envelope determined using the trapezoidal rule [21]. The load-displacement envelope was composed of the pick lateral load at the first cycle for each displacement level until failure. The yield displacement was defined as the intersection of the secant stiffness through the first yield of the outermost vertical steel bar to the yield force of the elasto-plastic idealization. Furthermore, the displacement ductility was calculated for each load direction and then averaged as presented in Table 4 for each SCMSW specimen. The lowest value of the displacement ductility was found in SW5, which had relatively higher flexural reinforcement ratio. Meanwhile, the highest value of the displacement ductility was found in SW6. This was due to the existence of the boundary elements at wall ends. The results revealed that the effect of axial load ratio and aspect ratio in the constant of flexural reinforcement ratio on the displacement ductility was not significant.
###### Abstract  
The goal of this paper is to explore the effect of various parameters on the information geometric structure of the phase-locked loop (PLL) statistics, both transient and stationary. Comprehensive treatment on the behavior of PLL statistics will be given. The behavior of the phase-error statistics of the first-order PLL, in the presence of additive white Gaussian noise (WGN) is investigated through solving the differential equations known as the Fokker-Planck (FP) equation using the implicit Crank-Nicolson finite-difference method. The PLL is one of the most commonly used circuits in electrical engineering. A full knowledge of probability density functions (PDFs) of the phase-error statistics becomes essential in understanding the PLLs. Several illustrative examples are presented to provide profound insights on understanding the PLL statistics both qualitatively and quantitatively. Results covered include the transient and stationary statistics for the nonmodulo-\(2\pi\) probability density function, modulo-\(2\pi\) probability density function, and cycle slipping density function, of the phase error. Various numerical settings of PLL parameters are involved, including the detuning factor and signal-to-noise ratio (SNR). The results presented in this paper elucidate the link between various parameters and the information geometry of the phase-error statistics and form a basis for future investigation on PLL designs.  
phase-locked loop; Fokker-Planck equation; probability density function; cycle-slipping +
Footnote †: journal: Applied Physics Letters
# A Metric for Secrecy-Energy Efficiency Tradeoff Evaluation in 3 GPP Cellular Networks
## 4 Results
In particular, with a distance of Bob of 50 m and a target secrecy rate of 20% of the capacity of the legitimate link, \(1/3\) of the BS area is unsecure. Figure 5: Effective secrecy area versus transmit power as a function of Bob’s distance and transmit power.
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 2 Materials and Methods
### Methods
The most important textural features were selected based on the ranking of mean decrease accuracies [36] of the following metrics: contrast, dissimilarity, homogeneity, angular second moment, energy, entropy, mean, variance, correlation, and maximum probability. The equations and the definitions of each feature can be found in [PERSON] et al. [46]. All attributes summarized in Table 1 were used as the input parameters for non-parametric, machine learning RF classification, with a 60%/40% training and test subset ratio. RF was proposed by [PERSON] [36] to improve the accuracy of image classification and regression trees through several combinations of random subsets of trees. Each tree contributes with one vote, and the final classification is produced considering the votes from all forest trees. We used the default value for the number of variables in the random subset at each node (mtry). The number of trees (ntree) was set as 300, which is the square root of the number of variables. Preliminary tests with 100, 300, and 500 trees showed that changing from 100 to 300 trees resulted in small improvements in the classification results. The ten most relevant combinations of spectral bands, spectral indices, and textural features were selected for the RF classification. Using the validation dataset, we obtained the overall classification accuracy, Kappa index, F1-score, and omission and commission errors derived from a confusion matrix with a level of significance of 0.05. The overall accuracy and Kappa index provide an overview of the classification performance, while the omission and commission errors and the F1-score provide a by-class analysis. The overall accuracy refers to the number of correctly classified samples divided by the total number of samples, while Kappa index measures the degree of agreement of an image classification, and its values typically range from 0 (no agreement) to 1 (complete agreement). The main diagonal of the matrix indicates the correct classifications, while the off-diagonal elements indicate omission and commission errors [49]. F1-score is derived from the precision (a relation between true positives and the total number of true positives and false positives) and recall (a relation between true positives and total number of true positives and false negatives) values and is considered as the harmonic mean of these two values. In addition to the classification performance estimators, a confidence map was also produced for the municipality of Goiatuba. This map indicates, on a per-pixel basis, the percentage of votes for the majority class (i.e., the class chosen for the pixel) out of the total votes from the RF decision trees [50]. High confidences indicate that the class is more likely to be correct as there was a clearer majority of votes by the ensemble of trees.
# Robust Optimization of Transport Organization for China Railway Express
## 2 Problem Description  
The 'Plan for the Construction and Development of China-Europe Railway Services (2016-2020)' strategizes the adoption of a 'trunk and branch integration and hub distribution' model for enhancing China-Europe railway services. A key feature of this strategy is the establishment of 'point-to-point' direct trains operating regularly between major inland cargo origins and significant coastal ports. This approach consolidates goods destined for the same location at a single terminal, streamlining train operations and boosting efficiency. In 2020, the National Development and Reform Commission dedicated CNY 200 million to support the China-Europe Railway Line Assembly Center demonstration project. This project is operational in five critical hub cities: Zhengzhou, Chongqing, Chengdu, Xi'an, and Urumqi. The Central Europe Freight Train Assembly Center, serving as a hub for freight train assembly and transfer, plays a crucial role in this network. It coordinates dispersed cargo sources, consolidates containers, and enhances train operations, thereby reducing operational costs. In light of these developments, this section proposes a transportation organization optimization model for the China-Europe freight trains, accommodating both direct and aggregate transfer modes and addressing the robust optimization challenge posed by uncertain cargo transportation demand.
## 2 Proposed Equalization Algorithms
### Radius-Directed Equalization  
The radius-directed equalizer (RDE) is proposed to improve the equalization performance for the QAM constellations. Since QAM constellation does not present a constant modulus or magnitude, the RDE is implemented as an alternative algorithm to CMA-based equalizers, demonstrating potential enhancements in steady-state performance. In RDE, the idea is to adjust the amplitude of the received, possibly distorted, QAM symbol so that its modulus is closer to the ideal value for that symbol.  
The RDE optimization relies on the output from the equalizer and the nearest constellation radius. The error criterion for the RDE is defined as follows [45].  
\[e(\mathrm{k})=u(\mathrm{k})\left(R-\mid y(\mathrm{k})\mid{}^{2}\right) \tag{7}\]  
where e(k) is the error signal, \(R\) is the radius of the nearest constellation symbol, u(k) is the equalized signal, and \(\mid y(\mathrm{k})\mid{}^{2}\) is the squared magnitude of the distorted signal.  
RDE is the best-performing algorithm in our setup for QPSK and 16-QAM constellations, as will be shown in Section 4.3, and provides a faster conversion than the CMA algorithm for QAM signals [46].
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 4 Results
### Impact on Quality
#### 4.3.1 VMAF Decrease
An initial observation that can be made is that, for relatively low QPNS and QPCS values, the average VMAF decrease is lower for KINS than for KIsrc. This observation was also made in Figures 4 and 5. Since the frame size increase of KINS is smaller than the increase in KIsrc, KINS is the best performing mode for relatively low QPNS and QPCS values, assuming many frames are streamed before a new keyframe is introduced. In contrast, when using large QPNS and QPCS values, the quality decrease is slightly larger for KINS than for KIsrc. The most extreme difference happens for QPNS \(=37\) and QPCS \(\leq 32\). In that case, the quality of KIsrc even increases rather than decreases. That is because the inserted keyframe is of a much higher quality than NSsrc, and therefore has a slightly positive impact on the quality. However, as seen in Table 1, the frame size increase of the inserted keyframe in those cases is between 50.7 and 149.4. Therefore, one can argue that the quality increase is not worth the extra bitrate overhead in those cases. For the other cases where the quality decrease of KIsrc is smaller than the decrease of KINS, such as for QPNS \(\leq 27\) and QPCS \(=32\), the corresponding bitrate increase is (slightly) larger as well. In general, one can observe that a higher QPNS value (with an equal QPCS value) usually results in a larger VMAF score decrease. As those cases also result in a larger bitrate increase, the proposed keyframe insertion method is least effective for high QPs. Last, using a higher QPCS value (while keeping QPNS constant) decreases the quality more than when using a lower QPCS value, which is as expected.
## 2 Methods
### Monte Carlo Simulation  
The simulated beam parameters were defined according to proposed experimental designs for MeV-STEM [12; 15]. The following parameters were employed: 2 pm geometrical emittance, 3 \(\times\) 10\({}^{-5}\) energy spread, optimized chromatic (1.8 cm) and spherical (16 cm) aberrations of the STEM column, 1 nm of the minimum root-mean-square (RMS) beam size, and 1-10 mrad of semi-convergence angle at the focal position.  
In a single MC simulation, 10,000 electrons were each randomly assigned an event at each depth with the probability determined by the cross-section of each event type (elastic, inelastic, and unscattered). A simulation (i.e., 10,000 electrons traced) was performed for each variation in experimental conditions. It is worth noting that there are discrepancies between the calculated and theoretical ratios of \(\sigma_{\text{inel}}/\sigma_{\text{el}}\). It is predicted to be \(\sim\)26/Z [21] and \(\sim\)20/Z [13]. In this study, this ratio was defined as lying between these predictions but remains an approximation and may be considered a potential source of error: 3 for amorphous ice, 3.8 for amorphous carbon, 1.6 for crystalline silicon, and 3 for the oxygen atoms and 1.6 for the silicon atoms in silicon dioxide. The total inelastic cross-section was calculated as a multiple of the total elastic cross-section based on these ratios. Sample thickness \(dt\) was set at 0.5 nm. For ice, the inelastic cross-section of hydrogen was demonstrated to be inaccurate [20]; thus, the inelastic cross-section (total and incremental) of ice was set to 110% of that of oxygen-only ice. The scattering angle for each event was probabilistically determined using differential cross-sections calculated using Equations (1) and (3). The angular intervals were set to 0.001 mrad up to 0.6 mrad, then 0.01 mrad up to 6.3 mrad, followed by 0.1 mrad to 67 mrad, 1 mrad to 600 mrad, and 10 mrad for remaining angles. The position and angle were recorded for each event as the electron traveled through the sample, so they could be plurally scattered with each event type recorded accordingly.  
Since the incoming electron beam is circularly symmetric about the incident axis, only a cross-section parallel to and across the incident axis was studied in the simulations. The beam width at a selected depth \(t\) was defined as the width containing 68% of the electrons. Note that for a defined collection angle that excludes some number of electrons, the beam width refers to the beam size within which 68% of the collected electrons are contained.