esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Mechanical Beam Steering with Polarization Gratings: A Review
## 3 Applications of Non-Mechanical Beam Steering with PGs
### Tiling Fields of View in Time-of-Flight and Flash Lidar Sensors
Another LC quarterwave retarder at the exit aperture of the stack converts the light back to a linear state. This stack arrangement steers the transmitted light to one view angle and steers the linearly polarized light reflected from that angle back on axis where it focused onto the camera's FPA after it passes through a cleanup polarizer. It has been our experience that if the transmitter and receiver are properly aligned before the scanner is inserted into the system, the LCPG scanner maintains the alignment as it steers to any of the view angles. Figure 10 shows a result from the modified camera after the 3-stage stack is integrated with the narrow-FOV camera. The camera captures eight high-resolution images as the 1D LCPG scanner steps across the field of regard. There is approximately a 0.8\({}^{\circ}\) overlap of the captured images to ensure continuous and uniform coverage across the field of regard. This angular overlap is 30 pixels. Therefore, the overlapping pixels are removed to stitch the eight images together to form a 2080 \(\times\) 240 image with each image tile being 260 \(\times\) 240. A point cloud is generated from the corresponding distance data, which can be rotated and analyzed as a 3D object. The 3-stage scanner fabricated for this first prototype has a transmission of approximately 75% (or 2.5 dB round-trip loss), which is not optimized. With four LC cells and 4 PGs in the stack (where 2 PGs are used to form the largest steer angle), the transmission can be \(\sim\)86% (3-4% per cell-grating pair). The additional loss in the prototype is mostly due to voltage-dependent back reflections observed while testing the LC cells, which can be significantly reduced by carefully selecting the cell gap to avoid the loss. To show this is the case, a new TOF camera is being developed, which uses an 8 \(\times\) 8 scan pattern to extend the camera's field of regard in two dimensions (2D). The 2D PG steering assembly, made to steer the camera's FOV 48\({}^{\circ}\) in azimuth and 40\({}^{\circ}\) in elevation using 6\({}^{\circ}\) and 5\({}^{\circ}\) steps, respectively, is shown in Figure 11. This stack has 8 PGs and 7 LC switches and meets expectations at the design wavelength with an average transmission of 74% over the field of regard. Additionally, shown in Figure 11 are thinner PGs and LC cells built with 0.2 mm thick substrates, which can be used to reduce the size and weight of the stack for SWaP-sensitive applications.
## 3 Results and Discussion
### Correction Factors Used for CO\({}_{2}\) Uptake
#### 4.1.3 Correction Factor for Surface Treatment  
A correction factor is introduced to cater to concrete that is not bare, such as painted or covered walls. These correction factors are credible because painted surfaces exhibit lower carbonation than bare surfaces. Table 7 lists the values [19].
# Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
## 4 Test Scenarios and Validation Results
### Multi-Device Automation Execution  
The final test scenario involves a more complex automation request that requires coordination across multiple devices. This scenario is particularly relevant for validating the scalability and adaptability of the system, as it must parse a single user instruction that triggers distinct actions on two different devices.  
In this case, the user requested: \"Adjust the brightness in the living room to 58, turn the speaker on, and set the volume to 30\". When the orchestrator received the command, it performed a semantic analysis to identify both a dimmable light and a speaker in the same room. As shown in Figure 17, the system logged the user input and confirmed that the Living Room Lamp and Living Room Speaker were the devices best suited to fulfil these requirements.  
To execute the multi-device request, the orchestrator generated a new bundle named _LivingRoomAutomation_, which contained the coordination logic for both the lamp and the speaker. The AI component produced Java code specifying how the lamp's brightness level should be set to 58 and how the speaker's power and volume should be configured. Figure 18 shows the output logs confirming the successful creation and compilation of this bundle.  
Upon installation in the Apache Felix container, the orchestrator verified that the newly generated bundle included the relevant service references for both devices. Figures 19-21 display the Java code, manifest file, and component XML, confirming that the generated orchestration service recognised and used the devices' interfaces. When the LivingRoomAutomation component activated, the log messages in Figure 22 validated that  
Figure 18: Terminal output confirming successful code generation, compilation, and deployment of the generated service bundle (LivingRoomAutomation) handling multiple devices.  
the tasks were carried out in the correct sequence: the brightness was set first, followed by the speaker's power-on event and volume adjustment.  
Finally, Figures 23 and 24 illustrate the new service's entry in the OSGi Service Registry and its Active status in the Apache Felix console. These confirmations show that the orchestrator handles multi-device scenarios without conflicts or execution delays. By coordinating the actions of separate devices within a single request, the system demonstrates the scalability and adaptability required for more advanced applications, where multiple devices often collaborate to provide a seamless user experience.  
Figure 19: Generated Java code for the service bundle, illustrating multi-device orchestration logic embedded within an OSGi Declarative Service component.  
Figure 20: Manifest file created for the service bundle, documenting the necessary imports and bundle metadata for multi-device orchestration.
# Model Updating of Bridges Using Measured Influence Lines
###### Abstract  
In developing a digital twin of a real structure, finite element model updating (FEMU) is essential for refining the model's response based on measured data, enabling the detection of structural damage or hidden reserves over time. This case study focused on a 40-year-old multi-span concrete roadway bridge, equipped with permanent bridge weigh-in-motion (B-WIM) and structural health monitoring (SHM) systems. Bridge responses from two calibration vehicles were used to derive strain influence lines (ILs) from mid-span B-WIM strain transducers mounted on the main girders. The error-domain model falsification (EDMF) methodology was applied to perform strain IL-based FEMU and the more conventional frequency-based, MAC-based, and combined frequency and MAC-based FEMU. Boundary conditions and three Young's modulus adjustment factors, representing different groups of structural elements, were updated. The strain IL-based updated FE model, with averages of 35% and 50% stiffness increases for the two main girders, showed strong agreement with independently measured mid-span vertical displacements. Maximum values deviated not more than 5%. In contrast, the frequency and MAC-based updated FE model underestimated displacements by 25-30%. These findings highlight the potential of using B-WIM for FEMU and SHM on such types of bridges, particularly when the response under traffic load is of interest.  
finite element model updating (FEMU); calibration; bridge; bridge weigh-in-motion (B-WIM); structural health monitoring (SHM); error-domain model falsification (EDMF); influence line (IL) +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Model Evaluation and Simulations  
The ability of our process-based model to predict NPP was evaluated against a tree-ring width index, extracted from the increment cores, from the 1980-2020 period. Our process-based model was also used to simulate carbon fluxes during the same period, forced with the climate dataset from the Lesvos airport. In each plot, a constant LAI was used (i.e., regeneration, growth and mortality were not considered). The simulated NPP from the process-based model was then compared with the simulated annual tree-ring width index, using a Pearson correlation analysis. As diameter tree-growth indices are related to NPP [69], we assumed that a linear association between the tree-growth index and our simulated NPP would indicate an agreement with an independent primary productivity estimation method. At this point, we note that we compared two independent data flows from the same site, i.e., annual tree-rings and process-based estimations in the case of our model.  
To disentangle the effect of elevation (i.e., temperature) and leaf area index variation on primary productivity across the study sites, we conducted a simulation experiment where each factor was kept constant and simulation outputs were comparatively considered. The model setups were implemented. The conLAIvarT setup controlled for the effect of stand LAI variation by setting a constant LAI value (average across plots, 1.96 m\({}^{2}\) m\({}^{-2}\)) for each plot and allowing temperature to vary based on the elevation of each plot. The varLAIconT setup controlled for the effect of temperature by setting the seasonal variation of temperature as identical for all plots (as per the 550 msl plot) and setting the LAI specific value to be the one measured in the field. The varLAIvarT setup represented the most realistic model parameterization (the one used in the model evaluation exercise), where both LAI and temperature were allowed to vary based on the observed variation across the elevation gradient. The outputs of these simulations were comparatively considered to test which of the first two setups was closer to our most realistic parameterization.  
The process-based model was additionally used to simulate carbon fluxes over the course of the 21 st century following a baseline (current climate), an RCP4.5 and an RCP8.5 scenario. In this study, we mainly focused on the GPP, R\({}_{\text{het}}\) and NEP model simulations over three reference periods: baseline (from 1996 to 2005), forced with the current climate conditions; mid-term (from 2051 to 2060, referred to also as 2060), forced with both the RCP4.5 and RCP8.5 climate scenarios; and long-term (from 2091 to 2100, referred to also as 2100), forced with both the RCP4.5 and RCP8.5 climate scenarios.
## 4 Discussion
### Discussing the Fine-Root Lifespan and Turnover Rate  
Although the differences in the overall survival probability were about 10% to 15%, our results showed significant differences in the fine-root lifespan and the turnover rate among species with a different life strategy. Foremost, our results showed that the fine-root lifespan and the turnover rate in beech and oak were greater and smaller, respectively, than those of birch. In fact, our estimations of the fine-root lifespan and the turnover rate in birch deviated notably from the values we expected based on the literature. Our results do correspond to the observation that the fine-roots of late-successful species usually live longer than those of pioneer species [40]. The fine-root lifespan and the turnover rate of beech, a species of which the establishment is considered to be the latest phase in a forest's succession, was even the highest and the lowest, respectively, of all three species.  
The results shown in the literature indicate an extremely wide variability in the estimations of the fine-root lifespan and the turnover rate among and within deciduous tree species [13; 27; 50; 53; 54; 55; 56; 57; 58; 59; 60; 61; 171]. Nevertheless, our estimations of the fine-root turnover rate in beech, oak, and birch lie remarkably close to the universal fine-root turnover value (0.7 year\({}^{-1}\)) suggested by [PERSON] et al. [172] for deciduous broadleaf forests. In fact, our estimations corroborate with the fine-root turnover rate (1 year\({}^{-1}\)) usually set in global vegetation models [50; 65; 173; 174].  
We found considerable variation in our estimations of the fine-root lifespan and the turnover rate within species, especially whenever the estimations were split by root diameter class or the season of root birth. While we expected roots with a larger diameter to have a greater fine-root lifespan, this was not always the case [3; 40]. We did not observe that fine-roots born in spring always lived shorter than those born later [8; 32]. In fact, fine-roots born in spring only lived shorter in beech and oak but not in birch, pointing towards a species-specific effect. Nevertheless, we might say that our estimations of the fine-root lifespan were relatively high, given the drought stress [50; 175; 176; 177]. Unlike for roots with a diameter below 0.5 mm, our estimations of the fine-root lifespan and the root turnover rate in fine-roots with a diameter above 1 mm but below 2 mm also seemed to be rather stable among species.
# Study of the Relationship between Mode I Fracture Toughness and Rock Brittleness Indices
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equation** & **No.** & **Figure No.** & **RMSE** & \
\begin{tabular}{c} _p-Value_ \\ **(Pearson)** \\ \end{tabular} &
\begin{tabular}{c} _p-Value_ \\ **(Spearman)** \\ \end{tabular} \\ \hline \(K_{IC}=(0.6333)\)\(exp^{0.032\text{ }B^{4}}\), \(R^{2}=0.5044\) & (11) & Figure 4 & 0.3375 & \(1.57\times 10^{-8}\) & N.A. \\ \hline \(K_{IC}=(0.7745)\)\(exp^{0.0033\text{ }B^{9}}\), \(R^{2}=0.5014\) & (12) & Figure 9 & 0.3385 & \(1.81\times 10^{-8}\) & N.A. \\ \hline \(K_{IC}=(0.751)+(0.0045)\text{B9}\), \(R^{2}=0.5035\) & (13) & Figure 9 & 0.4543 & \(1.64\times 10^{-8}\) & N.A. \\ \hline \(K_{IC}=(0.6498)exp^{0.0498\text{ }B10}\), \(R^{2}=0.5616\) & (14) & & 0.3174 & N.A. & \(9.86\times 10^{-11}\) \\ \hline \(K_{IC}=(-0.8719)+(0.9278)\)\(ln(B10)\), \(R^{2}=0.5739\) & (15) & Figure 10 & 0.4209 & N.A. & \(9.86\times 10^{-11}\) \\ \hline \(K_{IC}=(0.4992)+(0.0681)B10\), \(R^{2}=0.5813\) & (16) & & 0.4173 & N.A. & \(9.86\times 10^{-11}\) \\ \hline \(K_{IC}=(0.2205)\)\(B10^{0.7087}\), \(R^{2}=0.6058\) & (17) & & 0.1307 & N.A. & \(9.86\times 10^{-11}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Regression results of \(\mathrm{K_{IC}}\)—rock brittleness index relationships with (R\({}^{2}>0.5\)).
# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 1 Introduction
### General Context
They can also provide rapid response to deviations of frequency, helping to maintain the grid's stability and maintain voltage levels within the desired range, which is important for the grid's stability and efficiency. In sustainability terms, the RES integration reduces the reliance on fossil fuels, lowering greenhouse gas emissions. ESS mitigates the intermittency of RES by storing the excess energy and providing it when RES are not generating. The result is the reduction of carbon footprint and lower emissions of pollutants and greenhouse gases. The evolution of electricity markets, especially in liberalized retail environments, fosters competition between suppliers, leading to innovative tariff structures. Consumers benefit from a wider selection of tariffs tailored to their consumption patterns and risk preferences, thereby facilitating the adoption of RTP strategies. This competitive landscape transforms the electricity market from a static, vertical structure with fixed-rate tariffs to a dynamic and resilient market where consumers are potential producers and energy transactions occur in real time, exploiting the advantages given by ICT, like smart meters and systems of energy management [12]. Prosumers in an SG contribute actively to its stability and performance. They produce and store electricity for self-consumption and engage in energy transactions when production exceeds or falls short of their needs. This requires efficient negotiation processes to determine the terms of energy transactions. This context's negotiation involves complex Decision Making (DM) where multiple parties seek to resolve conflicting interests. This process includes pre-negotiation, intention (offers and counter-offers), and post-optimization phases to achieve mutually satisfying agreements. Artificial Intelligence (AI) technologies offer significant potential for optimizing these negotiation processes. AI techniques such as Machine Learning, Metaheuristic Algorithms (MA), and Fuzzy Logic (FL) systems can improve DM efficiency and adaptability in environments characterized as dynamic and uncertain. Especially, integrating FL systems like q-Rung Orthopair Fuzzy Sets (q-ROFS) with MA can enhance the handling of uncertainty and provide robust solutions in negotiation scenarios.
## 3 Proposed Method
### Description of the Individual Steps of the Compatibility Improvement Approach
#### 3.2.2 Second Step--Definition of Conditions  
In this step, it is necessary to highlight the differences between SSI and TSI by defining opposing conditions. Analyzing the determined structures from the previous step provides the basis for identifying error causes during imports. The relationships are, in many cases, non-trivial. Moreover, repeated modification of the exchange file from the source system according to the required instances for an error-free import into the target system helps trace correlations.  
An examination of various use cases shows that the differentiation of the five conditions is sufficient to cover all the considered scenarios. To begin, we distinguish between conditions for instances that exist in both the SSI and the TSI:  
* Homogeneous--An instance is needed in the source and the target system to interpret correctly and is identical in both setups.
* Heterogeneous--An instance is present in both structures to be compared but differs in certain places. For example, a reference to another instance is missing.  
Moreover, we develop the following three conditions to identify instances found either in the SSI or TSI:  
* Positive--An instance only exists in the TSI, and this is necessary for an error-free import.
* Negative--An instance only exists in the SSI, and this leads to a misinterpretation in the target system.
* Neutral--An instance only exists in the SSI, and this does not lead to a misinterpretation in the target system.  
We assume that this condition classification is sufficient to describe a large number of different use cases. To our knowledge, there are no more cases to distinguish between.
# Directional Active Noise Control with a Local Minimax Error Criterion
## 6 Conclusions  
To achieve a directional noise reduction, as well as an overall noise reduction, this paper presented a local minimax error criterion that can balance local and global reductions with the optimization of the Nelder-Mead algorithm. The initial values of the traditional method were set as the starting points of the local optimization. Analyses were conducted for the dynamic residual sound, which resulted from the changes in the initial values. Based on the initial values of the traditional method, the new error criterion produced a satisfactory result, meeting the mentioned performance requirements. This research shed new light on the balanced ANC system.  
Future works should focus on the following problems. First, to get a stable solution of the Nelder-Mead algorithm, stable initial values should be guaranteed. Second, remote microphone technology should be adopted when it is impossible to position microphones at a distance. Third, adaptive control and online identification are required for complex and variable sound environments.  
The author gratefully acknowledges the co-authors for their support and contribution to this work. [PERSON] and [PERSON] conceived of the work; [PERSON] helped with the Nelder-Mead algorithm; [PERSON] and [PERSON] conducted the experiments; [PERSON] and [PERSON] are [PERSON]'s thesis supervisors and provided critical comments and guidance for the work. All authors have given final approval of the version to be published.  
This work was supported by the National Key Research and Development Program of China (Grant No. 2016 YFB1200503) and the National Natural Science Foundation of China (Grant No. 11474306, No. 11404367, and No. 11474307).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Areas** & \(Rd_{1}\) & \(Rd_{2}\) & \(Rd_{3}\) \\ \hline ANC for Directional Area 1 & 7.8 dB & 15.1 dB & 15.1 dB \\ ANC for Directional Area 2 & 9.3 dB & 5.9 dB & 15.5 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of reduction parameters.
## 3 Robust Co-Optimization Considering N-1 Constraints and Active Set Method
### N-1 Contingency Constraints for Transmission Lines  
Assuming that the \(s\)-th transmission line in the pre-defined set is out of service, the topology of grid will be changed. Consequently, the generation shift distribution factor \(\mathbf{H}^{s}\) must be renewed and recalculated. It should be noted that transformers can be modeled as transmission lines in the optimization model, and here the transformers are taken as transmission lines in N-1 contingencies. Therefore, the transmission lines under N-1 contingency are subjected to:  
\[-P_{l_{k}\max}\leq P_{l_{k}}^{s}\leq P_{l_{k}\max} \tag{18}\]  
\[P_{l_{k}}^{s}=\sum_{i=1,j\in\Omega_{g}}^{N_{g}}H_{ki}^{s}P_{i}-\sum_{j=1,j\in \Omega_{d}}^{N_{d}}H_{kj}^{s}D_{j}+\sum_{m=1,m\in\Omega_{w}}^{N_{w}}H_{km}^{s} W_{m}^{f}+\sum_{a=1,a\in\Omega_{a}}^{N_{w}}\sum_{m=1,m\in\Omega_{w}}^{N_{w}}(H_{ka} ^{s}\beta_{a}+H_{km}^{s})E_{m} \tag{19}\]  
where \(P_{l_{k}}^{s}\) is the active power flow of the transmission line \(k\) when the pre-defined transmission line \(s\) is broken and \(H_{ki}^{s}\) is the element located in (\(k\), \(i\)) in the renewed shift distribution factor matrix \(\mathbf{H}^{s}\).
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 5 Discussion  
The performance improvements of the proposed GS-SA-SCC CM schemes are summarized in Table 2, when compared to the existing CM schemes with standard SCC decoding and QAM modulations. Due to the greater sensitivity to the fiber nonlinear effect, the performance degradation of the system with the higher order modulation format is more serious than that of system with the lower order modulation format. Therefore, the observed distance increase is not significant as the SNR gain in the AWGN channel for constellation with 256 points. However, we can see that the proposed CM scheme can cover the distance from 100 km to 1000 km and provide a net data rate of up to 666 Gbit/s, which are suitable for short-reach DCI and metro links. One of the advantages of the proposed CM schemes is the 50 GHz grid compatibility, which is very common in most of optical network. For future the transponder with a 75 GHz fixed grid, these CM scheme could be also extended to beyond 1 Tbps without significant modification.  
Figure 10: Post-FEC BER as a function of the transmission distance for _M_QAM and _M_-GS modulation formats (\(M\in\{64,128\}\)) and FEC codes. White markers indicate the BERs after the EDFAs. Filled markers show BER between two amplifiers (obtained by noise loading). SCC rates are \(R_{s}^{1}=0.925\) and \(R_{s}^{2}=0.833\). LDPC rate is \(R_{l}=0.9\).