esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion and Analysis
### The Limitations of the Numerical Model  
On the one hand, during the navigation of ships in polar regions, ballast water is typically seawater with a specific salinity. Although some researchers have studied the freezing of brine, salts precipitate during the freezing process, causing the salinity of ballast water to fluctuate and subsequently affecting the distribution of ice formations. This issue still requires in-depth exploration [36; 37]. Consequently, the assumption in this paper that ballast water is fresh water does not accurately reflect reality. Additionally, the ballast water in the ballast tank is often in a dynamic state. While some researchers have conducted experimental studies on the freezing of flowing water, the environmental conditions in polar regions are complex and variable, and current technology cannot precisely determine the movement patterns of ballast water [38]. Therefore, this paper does not consider the influence of ballast water flow on freezing, which may significantly impact the actual distribution of ice formations.  
On the other hand, due to limitations in technical resources and testing conditions, there are currently few experiments on ballast water freezing simulation, ice pool freezing simulation, and reservoir freezing simulation. The numerical calculation model established in this paper has only been validated against the numerical results of other researchers, lacking comparison and verification with empirical test data. Consequently, the reliability of the numerical model is insufficient, and the data supporting the results are inadequate.  
In the future, a numerical model of ballast water tanks that accounts for liquid flow will be developed. Additionally, the impact of salinity variations on freezing will be considered, thereby enhancing the accuracy of predictions regarding the ice shape distribution within ballast water tanks.
## 2 Resonant Element Design  
The first step in the design of the band-pass filter (henceforth referred to as BPF) is the design of the resonant element. In [16], the resonant element--replicated to model an infinite resonant structure--is characterized as an Artificial Magnetic Conductor (AMC) material [17; 18; 19; 20] and the in-phase reflection property was studied. This feature enables efficient radiation for antennas placed closed to the periodic structure. In this contribution the resonant structure is seen from another point of view: as a material that has the possibility to block the propagation of electromagnetic waves in certain frequency bands and guide them in a desired direction [_i.e._, electromagnetic band-gap (EBG)].  
EBG structures [21; 22] exhibit allowed and forbidden bands of modes' propagation and can be characterized by the dispersion diagram. The dispersion diagram for a unit-cell of period W will show the relation between the wave number and frequency, giving information about the propagating modes and band-gaps that can potentially exist between such modes [23; 24]. The eigenmode solver of High Frequency Structural Simulator (HFSS) together with proper boundary conditions along the sides of the unit-cell (resembling an infinite structure) is used in order to determine the dispersion diagram of the structure (see Figure 1a). Due to symmetry of the unit-cell, the dispersion diagram is computed along the lines connecting the R, X and M points, to form the irreducible Brillouin triangle. Thus the dispersion analysis of periodic structures is reduced to find only the propagation modes in the direction of the vectors of the irreducible Brillouin triangle. The dispersion characteristics, _i.e._, the position and width of the band-gap and the frequency of the propagating modes are primarily defined by the geometry of the unit-cell. ARLON25N with relative dielectric permittivity 3.28, loss tangent less than 0.0025 and a thickness of 0.762 (30 mils) is used as dielectric substrate. The unit-cell dimensions are \(W\times W=11.52\) mm \(\times\) 11.52 mm and its geometry exhibits four symmetry planes. The metallization thickness is 18 \(\upmu\)m.  
The dispersion diagram for three lowest modes is depicted in Figure 1b. The first mode propagates in the frequency range from 5 to 6.6 GHz, whereas the second mode propagates from 5 to 7 GHz.  
The resonance frequency takes place between the first and second mode, being around 6 GHz. Moreover, the bandgap is presented in a frequency range from 7 to 7.8 GHz. Within the bandgap, the electromagnetic waves cannot propagate at any direction in the EBG structure.
## 3 Optimization of Channel MIMO Capacity with Dual-Polarization and XPI Power Allocation
where \(N_{Y}\) is the number of the receive antennas, \(P_{t}\) is the total transmit power, \(\sigma_{n}^{2}\) is the variance of the independent complex Gaussian random variable noise, and \(\lambda_{i}^{2}\) is the i'th eigenvalue of \(HH^{*}\). In Figure 3, numerical-results of the proposed simulator, i.e., simulation of MIMO-channel-capacity vs. \(\frac{E_{t}}{N_{0}}\) (Ratio of energy per bit to white Gaussian noise density), in addition to rain distortion effects are illustrated. The red, green, and blue graphs describe the four points of global maximum, intermediate value, and most distortion value, respectively. Note that the simulation result of all four points of global maximum is identical. From the analysis of the results, one can deduce the first two main insights: The first one, the simulation results, represents the analytical analysis we performed. The red graph describes the maximum average capacity of the wireless communication system. By optimizing XPI values under rain distortion, we design the optimal conditions to obtain the total capacity. The green graph represents the intermediate values of the optimization results (\(\phi=\frac{\pi}{8},\theta=\frac{3\pi}{8}\)). Note that in this situation, the performance of the capacity channel is still high relative to the blue graph. Similar to the red graph, and in contrast to the blue graph, the green graph increases with an increase in the value of \(\frac{E_{t}}{N_{0}}\). The blue graph represents the maximum shortfall capacity performance led by the most destructive value (\(\phi=\frac{\pi}{4},\theta=\frac{\pi}{4}\)). The second insight is the relatively constant behavior of the blue graph. The constant behavior of the blue graph is reflected starting from the value of \(\frac{E_{t}}{N_{0}}\sim 14\) dB, while in these values in the green and red graphs, there is an increase to significantly higher values. The direct conclusion is that in the presence of a destructive effect on the wireless channel, improper tuning of an XPI mechanism leads to lousy system performance.
## 4 Simulation Results
### BER and Complexity Analysis  
Figure 7a shows a BER performances comparison between the proposed HGA without and with layering and the basic GA (BGA) without layering in a 4 \(\times\) 4 MIMO system with 64-QAM, 256-QAM, and 1024-QAM. It can be seen that compared to the basic GA without layering, the proposed layered HGA approach significantly improves BER performance. For example, the basic GA without layering needs an SNR of 39 dB to achieve a \(10^{-3}\) BER for 64-QAM, whereas the proposed HGA without layering achieves the same BER at 31 dB, which amounts to an SNR gain of 8 dB. Moreover, the LHGA further improves BER performance. For 256-QAM and 1024-QAM, this SNR gain is equal or even higher.  
Figure 7b shows a computational complexity comparison between the algorithms, where we have plotted the average number of searched vectors in each algorithm. Since the number of searched vectors greatly affects the computational complexity, we compare and analyze the computational complexity through the number of searched vectors. In other words, as the number of search vectors decreases, the amount of computation required to select one of the searched vectors decreases. For example, it is much simpler to detect 1 out of 100 searched vectors than to detect 1 out of 10,000 searched vectors. The BGA and HGA have fixed numbers of searched vectors according to the modulation order regardless  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **64-QAM** & **256-QAM** & **1024-QAM** \\ \hline \(G\) & 100 & 100 & 200 \\ \hline \(P\) & 371 & 901 & 1801 \\ \hline Selection probability & 0.01 & 0.01 & 0.01 \\ \hline Crossover probability & 0.13 & 0.11 & 0.11 \\ \hline Mutation probability & 0.86 & 0.88 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the basic GA.  
Figure 6: For a 4 \(\times\) 4 MIMO with 64-QAM at SNR = 30 dB, (**a**) BER performance and (**b**) the number of vectors to be searched in the HGA process with various parameters.  
of SNR, while the number of searched vectors in the LHGA depends on SNR. In the low SNR region, the LHGA has higher computational complexity than the BGA and the HGA without considering the layered process. However, the computational complexity of the LHGA decreases as the SNR increases. Especially, in the high SNR region, the LHGA has much less complexity than the algorithms without a layered process. As the SNR increases, the accuracy of SIC detection also improves, which reduces the probability of entering the HGA phase in the layered process. Considering the target BER as \(10^{-4}\), the LHGA has significantly lower computational complexity than the BGA and HGA.
# A Study of Emergency Aircraft Control During Landing
## 2 Related Work
It was noted that longitudinal and lateral thrust control was significantly limited by the utilisation of the same engines in both control channels. On this account, a control priority algorithm was developed, in which an absolute value of the lateral channel control output was used to determine the limits of the longitudinal channel control output (the lateral channel control was prioritised as opposed to the PCA system) [9]. This approach ensured the sum of the control signals would not exceed the limits of each engine and the commanded asymmetric thrust would override the collective thrust. Particularly worth mentioning is the approach taken during the evaluation of the developed system. During the first part of the in-simulator trials, the test pilots were not instructed on the system's operation in order to test its accessibility. The system was also demonstrated in flight on a VFW-Fokker 614 [10]. Each of the aforementioned concepts presented satisfactory control properties under realistic flight conditions. However, no similar system has been implemented in any airliner so far. The motivation of this paper is to explore the introduced control problem and to identify the capabilities and limitations associated with the control system design. The authors' objective is to propose a concept of an emergency thrust control system, which would ensure the recovery of a twin-engine airliner under simulated controls failure, a safe aircraft diversion to a selected airport, and an automatic landing using available on-board equipment.
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 2 System Description
### System Model
#### 2.3.3 Receiver  
At the receiver, we have adopted a soft-in-soft-out (SISO) to compute the log-likelihood ratio (LLR) of the information. The data detection algorithm mainly consists of a soft demapper, which provides the bit metric to the detector. The concept of the system depends on the demapper taking soft values \(\mathbf{y}_{k}\) from the channel output and producing extrinsic information \(\mathbf{E}_{k}^{m}\), which is passed through a bit centerleaver to become a priori decoder input \(\mathbf{A}_{k}^{c}\). The decoder's extrinsic information of the coded bits \(\mathbf{E}_{k}^{c}\) is fed back to the demapper as a priori knowledge \(\mathbf{A}_{k}^{m}\) through the bit interleaver, reducing the BER further in subsequent iterative processes.
# Analysis on Influences of Squeeze Film Damper on Vibrations of Rotor System in Aeroengine
## 4 The Influence of SFD on the Vibration of the Rotor System
### The Unbalanced Vibration Response of the Rotor without SFD  
The sub-critical and over-critical of the rotor system, respectively, refer to the state when the speed is slightly lower and higher than the critical speed, which is of great significance for the analysis of the rotor's dynamic stability. This paper first analyzes the unbalanced vibration response of the rotor system under sub-critical and over-critical conditions without SFD. The vibration response under low speed \(r_{n}=2000\) r/min is shown in Figure 6. The vibration amplitude is about 2.1 \(\upmu\)m, and the orbit is a complete circle, as shown in Figure 7.  
Figure 6: Response of high-pressure rotor at low speed. (**a**) time domain response; (**b**) frequency domain response.  
In the case of high speed \(r_{n}\) = 12,800 r/min, the vibration response is shown in Figure 8. The amplitude is about 3 \(\upmu\)m, and the orbit is a complete circle, as shown in Figure 9. It can be found that the vibration response amplitude of the rotor system without SFD is very small (less than 5 \(\upmu\)m) at different speeds, and the orbits are complete circles.
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Filtration  
During filtration, a filter separates suspended solids from a solid-liquid mixture as it flows through the filter medium. Filters can remove suspended solid particles, dissolved organic carbon/natural organic matter (DOC/NOM), and TOC from untreated water.  
Various media are utilized in filtration, including GAC, sand, fabric, and ceramic filters. The depth of a slow sand filter is significant for effectively removing DOC from untreated water. Studies have shown that the efficiency of removing solid particles varies depending on the type of filtering media, their depth, and the duration of filter operation. Biodegradation occurring within GAC filters also plays a crucial role in enhancing the efficiency of TOC removal. Initially, the DOC removal efficiency in GAC filters ranges from 77 to 81%, but after 200 days, it decreases to 13% [29].  
However, the removal's effectiveness depends on the nature of the organic matter, and despite mitigation efforts, more than 800 DBPs have been identified in treated water [1].
###### Abstract  
Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. Combinations of seven vegetation indices from Sentinel-2 imagery and seven phenology metrics were evaluated for the prediction of maize and soybean yield potential. Ground truth yield data were provided by the Quantile Loss Domain Adversarial Neural Network (QDANN) database, with 1000 samples randomly selected per year from 2019 to 2022 for Iowa and Illinois. Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). Across all evaluations, RF was found to outperform the other models in both cross-validation and final model accuracy metrics. Vegetation index values at peak of season (POS) and phenological timing, expressed as the day of year (DOY) of phenological events, were identified as the most influential covariates for predicting yield potential in particular years for both maize and soybean.  
p _Applied sciences Article  
1  
[PERSON], [PERSON], M. Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series. _Appl. Sci._**2025**, 15, 7216. [[https://doi.org/10.3390/app15137216](https://doi.org/10.3390/app15137216)]([https://doi.org/10.3390/app15137216](https://doi.org/10.3390/app15137216))  
1
## 4 Discussion  
Compared to classical control structures that use only active or reactive power for grid voltage control, the proposed control scheme includes:  
* Utilization of active and reactive power for voltage regulation;
* Minimization of the conditioner current amplitude;
* Estimation of the grid impedance;
* Voltage balancing.  
The injection of active and reactive components proportional to the R/X grid impedance parameters helps to minimize the storage capacity and minimize line current/losses compared to voltage regulation using only active or reactive power. The proposed method is, therefore, superior to others that only utilize storage active power control or reactive power compensation.  
Universal power quality improvement is performed in terms of voltage magnitude and unbalance as well as in static and dynamic states. The low-level converter control results in very high dynamics of voltage regulation and the mitigation of temporary events. As indicated in Figure 3, an additional harmonic controller can be implemented in the  
Figure 11: Three-phase voltage unbalance: (**a**) without compensation and (**b**) with compensation.  
Figure 12: Single-phase 70% dip compensation.  
proposed solution, in parallel to the fundamental component. It was proven that voltage harmonic reduction is possible using control methods similar to the ones described in [34].  
The most proper implementation of this method is by using ESS or V2G interfaces in rural areas, i.e., for compensation of voltage fluctuations caused by PV and heavy loads. The proposed method can be also adopted to DER-interfacing converters. Their only flexibility is reactive power and unbalance control, and, hence, minimization of the compensating current would not be possible. Moreover, to compensate unbalance, the DC-link capacitor needs to be oversized due to the double-grid-frequency power oscillations.  
A short-term balancing is shown in the given examples; however, with an extended storage capacity, long-term balancing is also possible. A feasible option would be to minimize the daily voltage fluctuations related to the load profiles or PV generation--performing peak shaving. Practically, this would mean storing energy during high production/low consumption hours and releasing energy during low production/high consumption hours. In case of high PV penetration, this would exhibit daily profiles similar to those reported in [17]. This has, therefore, the advantage of minimizing feeder loading and increasing hosting capacity of DERs.  
To minimize the storage capacity, power rating and energy circulation in the system, an adaptive reference voltage can be used, as shown, e.g., in [30]. The reference voltage can be gradually varied between a minimum and a maximum value, as permissible by the standards, which will prevent violations at a minimal implementation cost. If more compensating devices would be connected along one feeder, a coordinated droop method or voltage dead zones should be applied to allow for power sharing. The abovementioned issues related to the parallel operation of multiple storage devices are considered as part of the authors' future work.
## 2 Related Work
### Text Rendering in T2I Models  
Precise text rendering in text-to-image generative models has advanced significantly through approaches such as character-aware text encoders [35; 36], glyph-controlled mechanisms [37; 38], and two-stage frameworks [39; 40], which effectively address spelling errors, glyph alignment, and multilingual rendering challenges, achieving substantial improvements in accuracy and usability across diverse scenarios. Innovations like Glyph-ByT5 [35] and UDiffText [36] use character-aware encoders to boost spelling accuracy to nearly 90% by leveraging glyph-text pairs and segmentation maps, ensuring accurate sequence rendering. Glyph-controlled methods, exemplified by GlyphControl [37], provide training-free solutions for precise customization of text content, size, and location without the need for retraining base models. Two-stage frameworks such as TextDiffuser [39] separate layout generation from rendering, significantly enhancing text placement and integration into complex visual backgrounds. For multilingual and non-Latin scripts, models like Glyph-ByT5-v2 [35] and TextMastero introduce specialized datasets and OCR-driven refinement techniques, excelling in languages like Chinese while maintaining aesthetic fidelity. Benchmarks such as MARIO-10M [39] and LenCom-Eval [40] have been instrumental in evaluating text fidelity, measuring edit distance, glyph precision, and layout coherence. Concurrently, diffusion model enhancements such as localized attention mechanisms [38] and glyph-aware training losses [41] address structural misalignments and spelling errors with high precision. Emerging methods prioritize user-driven customization, as seen in frameworks like AnyText2 [42], which enable control over font styles, colors, and background, fostering creativity and adaptability. The field continues to evolve, balancing the scalability of training-free methods [37] with the performance gains of fine-tuned approaches [35; 36], paving the way for broader real-world applications in creative, multilingual, and complex text rendering scenarios.  
The review of existing work highlights three fundamental aspects of text-to-image evaluation: the emerging role of LLMs as evaluators offering enhanced scalability and explainability, the evolution of evaluation frameworks from basic metrics to comprehensive benchmarks addressing compositional and ethical challenges, and the advancement of text rendering through character-aware encoders and glyph-controlled mechanisms. Despite significant progress, challenges persist in achieving reliable evaluation across complex tasks and handling nuanced compositions for specialized applications. These limitations underscore the need for a more systematic approach to assessing text-to-image models, particularly for specialized content generation. A comprehensive review of text-to-image evaluation and text rendering literature from 2017 to 2025 is presented in Appendix A, which summarizes the key methodological advances and findings in this field.