esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, the development of new adsorbents for the adsorption of CR with high selectivity and high adsorption capacity on the modified DE by using LDH is demonstrated. The current work reveals a simple and scalable preparation process that can provide low-cost adsorbent DE-LDH for the removal of anionic CR dye. The maximum removal of CR dye (50 mg/L) from 15% for unmodified DE to 98% for modified DE-LDH was achieved, showing an outstanding improvement in efficiency. The maximum adsorption capacity for DE-LDH was scientifically high (305.8 mg/g) compared to only 23.2 mg/g for DE, showing an excellent adsorption performance as result of LDH modification. Further, the high selectivity removal of CR was achieved during the selectivity study, using other dyes such as EBT and RB by DE-LDH. These results are explained by the chemical binding mechanism between CR and LDH. Desorption studies were carried out using different desorption agents and it showed that the removal of CR by DE-LDH was found to be better using NaOH when the DE-LDH-CR was desorbed than using methanol. Clearly, the current study suggests that DE-LDH can be used as reliable and an efficient adsorbent for the removal of CR from aqueous solutions and it can be a superior adsorbent for wastewater treatment.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/7/2285/s1](http://www.mdpi.com/2076-3417/10/7/2285/s1)]([http://www.mdpi.com/2076-3417/10/7/2285/s1](http://www.mdpi.com/2076-3417/10/7/2285/s1)), Figure S1: Structure of Congo Red., Figure S2. FTIR spectra of after CR adsorption for DE and DE-LDH, Table S1. Intraptice diffusion constants of Congo red adsorption onto DE and DE-LDH.  
Conceptualization: D.L., M.D.K. and M.K., methodology: G.S. and U.T.U. formal analysis/investigation: G.S. and U.T.U., resources: H.-Y.J., D.L., M.D.K. writing--original draft preparation: G.S. and U.T.U., writing--review and editing: D.L., M.D.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Department of Science and Technology (DST), India (DST/BDTD/EAG/2019), DST, India (DST-TM-WTI-2K14-213), and DST Nanomission, India (SR/NM/NS-20/2014), for the financial assistance for this work. [PERSON] would like to acknowledge Chonnam National University (Grant number: 2018-3274), South Korea for the financial assist. D.L. acknowledges the support from ARC Research Hub for Graphene-Enabled Industry Transformation; funding was given under Industrial Transformation Research Hub IH150100003.  
We also thank Jain (Deemed-to-be University), India for providing the infrastructure and other valuable facilities.  
The authors declare no conflict of interest.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 1 Introduction
Secondly, it uses the improved principal component analysis method based on the generalized grey number to determine the weight of the index, so as to overcome the problem of too strong subjectivity in the process of indicator weighting. The performance evaluation of the attitude control system is realized by using the grey-target model.
## 2 Materials and Methodology
### Pre-Processing for Data Analysis  
In step 2, the collected data were pre-processed to evaluate the efficiency of leakage management and to determine the water loss formula. First, four leakage management factors (PR, MR, LR, LD) were classified into independent and dependent variables based on the revenue water ratio increase and the leakage ratio reduction. The correlations of variables (i.e., independent and dependent variables) were then evaluated. Finally, the collected data were standardized using standardization scores (e.g., T-score converting) to alleviate the unit and scale differences between factors.
## 4 Analysis, Results and Discussion
### Incorporating the Noise Data  
In an attempt to further increase the accuracy of the obtained LSTM networks, we decided to try and include the noise data in the models.  
The noise dataset was collected from the Lisbon City Council's API3, where the noise level variable is identified as RULAEQ. There are a total of 80 sensors that measure this variable, and the data collected correspond to the hourly average of the noise level (in dB). These sensors were mostly placed on lampposts, and their locations were chosen with the purpose of collecting a diversified range of data [8].  
Because of the large break in the mobile phone dataset, the noise data were collected for two separate periods: from September 2021 to January 2022, and from April to December 2022.  
Figure 11: Grids corresponding to Cluster 151, the Alfama area.  
Figure 12: Cluster 151, the Alfama area.
## 3 Numerical Simulation Analysis of Demolition by Blasting
### Collapse Process Comparison  
The numerical simulation results were provided with 0.8 s intervals, and the delay-time difference of 0.5 s was outputted. The results were compared and analyzed. Moreover, photos of the collapse process were taken. Finally, the collapse was completed when the structure broke down and touched the ground. The comparison process is shown in Figure 7.  
Figure 7 shows that the numerically simulated time-scale collapse attitude of the small-aspect-ratio reinforced concrete structure from 0 s to 5.1 s maintains a high degree of consistency with the actual collapse attitude. The difference is that the completion time of the disintegration in the numerical simulation is 5.1 s, while the actual collapse time of the building is 5.4 s, as shown by the video. Since these times are very similar, the results are satisfactory, thus indicating that the selected model can reflect the actual collapse of the structure realistically. The comparative analysis between the numerical simulation and actual video shows that the collapse process of this small-aspect-ratio structure can be divided into four stages: blast-incision formation (0.2-2.2 s), destabilization overturning (2.2-2.59 s), incision closure (2.59-4.2 s), and structure-touchdown disintegration (4.2-5.1 s).
## 4 Simulation Results and Discussions
### Multi-Objective OPF Problem
#### 4.2.2. IEEE 118-Bus System  
To guarantee the effectiveness of the MaMPA in the large system, this subsection investigates solving the MOOPF problems in the IEEE 118-bus system. By selecting two and three objectives as the objective functions, cases 15-18, the two- and three-dimensional Pareto fronts are presented in Figures 6 and 7, respectively. For each case study, the generated Pareto fronts are compared with those of PSO [83] and SSA [85] while PESA-II and SPEA2 could not converge on the feasible solution within the set iterations. The setting parameters of the PSO and SSA can be found in the provided references.  
After generating the Pareto fronts in this system, the hypervolume indicators for each case are computed to compare the generated fronts as presented in Table 12 where a higher hypervolume value means better Pareto fronts.  
Figure 6: Two-dimensional Pareto fronts for: (**a**) case 15; (**b**) case 16.  
Thus, for MOOPF problems, MaMPA has a high performance in providing better Pareto fronts than other methods in both tested systems. Although the Pareto fronts of MaMPA were slightly poorer than those of PSO for some cases in the IEEE 30-bus system, MaMPA could generate significantly better Pareto fronts than those of all compared algorithms in the IEEE 118-bus system which could be verified by the hypervolume values.
## 2 Mathematical Formulation
### Set of Constraints  
There are several constraints associated with the optimal integration of dispersed generators in distribution grids to increase the loadability capacities of the network before reaching the point of voltage collapse. These constraints include active and reactive power flow equations, generator capabilities, and possible locations of generators. The complete list of constraints is provided below.  
\[P_{i}^{GS}+P_{i}^{dg}-(1+\lambda)P_{i}^{d} =V_{i}\sum_{i\in\mathcal{N}}Y_{ij}V_{j}\cos\left(\theta_{i}-\theta _{j}-\delta_{ij}\right),\ \forall i\in\mathcal{N}, \tag{2}\] \[Q_{i}^{GS}-(1+\lambda)Q_{i}^{d} =V_{i}\sum_{i\in\mathcal{N}}Y_{ij}V_{j}\sin\left(\theta_{i}-\theta _{j}-\delta_{ij}\right),\ \forall i\in\mathcal{N},\] (3) \[V_{\min}\leq V_{i}\leq V_{\max},\ \forall i\in\mathcal{N},\] (4) \[0\leq p_{i}^{dg}\leq x_{i}p_{\max}^{dg},\ \forall i\in \mathcal{N},\] (5) \[\sum_{i\in\mathcal{N}}x_{i}\leq N_{\max}^{dg},\] (6) \[\sum_{i\in\mathcal{N}}P_{i}^{dg}\leq\beta\sum_{i\in\mathcal{N}}P _{i}^{d},\] (7) \[x_{i}\in\{0,1\},\ \forall i\in\mathcal{N},\] (8) \[\lambda\geq 0, \tag{9}\]  
where \(P_{i}^{GS}\) and \(Q_{i}^{GS}\) denote the active and reactive power injections at the conventional generator, i.e., the slack source; \(p_{i}^{dg}\) is the active power injection in the dispersed generators; \(P_{i}^{d}\) and \(Q_{i}^{d}\) correspond to the active and reactive power demands at each node; \(V_{i}\) and \(V_{j}\) are the voltage magnitudes at nodes \(i\) and \(j\), which have angles \(\theta_{i}\) and \(\theta_{j}\), respectively; \(Y_{ij}\) is the magnitude of the admittance connecting nodes \(i\) and \(j\), with an angle \(\delta_{ij}\); \(V_{\min}\) and \(V_{\max}\) represent the minimum and maximum voltage regulation bounds allowed in all nodes of the network; \(x_{i}\) is a binary variable related to the presence or absence of a dispersed generator at node \(i\); \(p_{\max}^{dg}\) indicates the maximum size allowed for a distributed generator; \(N_{\max}^{dg}\) indicates the maximum number of dispersed generators available for allocation in the network; and \(\beta\) is the maximum penetration level of dispersed generation in the distribution grid. Note that \(\mathcal{N}\) is the set that contains all the demand nodes of the network.
# Design of a 3-Bit Circularly Polarized Reconfigurable Reflectarray
###### Abstract  
In this paper, a 3-bit circularly polarized reconfigurable reflectarray is proposed. The array consists of 64 units in an 8 \(\times\) 8 configuration, with each unit containing a circular metal patch loaded with phase-delay lines and eight PIN diodes. To independently control each unit, a corresponding DC control circuit was designed and tested with the array. In the bandwidth of 3.43-3.71 GHz, the circularly polarized reconfigurable reflectarray achieved a gain of 16 dB, an aperture efficiency of 27%, an axial ratio of \(\leq\)3 dB, an operating bandwidth of 8%, and a beam scanning range of \(\pm\)60\({}^{\circ}\). The circularly polarized reconfigurable reflectarray can also achieve a good dual-beam radiation performance after testing. The 3-bit circularly polarized reconfigurable reflectarray proposed in this paper offers several advantages, including low loss, high aperture efficiency, a wide beam scanning range, and excellent stability in wide-angle oblique incidence. It has potential applications in low-cost phased array, satellite communications, and deep space exploration.  
3-bit; circular polarization; reconfigurable reflectarray Article
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 4 P-DQN-Based Approach  
Traditional RL methods such as DQN, actor-critic, and asynchronous actor-critic (A3C) are designed to handle discrete action spaces. The DDPG, on the other hand, is tailored for dealing with continuous actions. To adapt above RL methods to the discrete-continuous hybrid action space, there are two approaches, i.e., either discretizing the hybrid action space, or relaxing it into a continuous one, which would result in a high-dimensional action space. In this work, we use one prevailing architecture, namely P-DQN, which is directly appropriate for hybrid action space without any approximation or relaxation. In particular, existing P-DQN frameworks (which are predominantly used in the game control [48]), are enabled to address the computation offloading in LEO satellite edge networks. To adapt the classical P-DQN, we integrate offloading decisions with resource allocation into one hybrid action space. More precisely, the MDP with parameterized action space has to be constructed, followed by assessing the satisfaction of tasks and establishing the deferred reward function. This type of parameterized action space facilitates the maximization of satisfied task numbers.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 5 Conclusions  
We showed that remote and proximal sensing technologies allowed to determine that the effect of different irrigation regimes on tree performance and WUE depended on the location within the orchard (Cluster 1 and Cluster 2). The second finding of this study concerns the major role of tree vigour in determining the final fruit yield under optimal soil water availability, whereas soil features become the most important factors under rainfed conditions. Since water availability in the Mediterranean climate is becoming increasingly limited, this information may be crucial to optimize the water management through site-specific irrigation protocols. They should be considered preliminary, as the investigation was done over one growing season only. Furthermore, since olive trees are subject to alternate bearing, long-term studies are needed to evaluate the stability of the orchard zone effects on yield and tree growth in high- (\"on-year\") and low- (\"off-year\") yield crop years.  
G.C.: conceptualization, investigation, methodology, formal analysis, resources, software, writing--original draft and editing. G.P.: investigation, data curation, formal analysis, software, writing--review and editing. R.G.: resources, supervision, validation and writing--review. S.P.: conceptualization, investigation, methodology, formal analysis, resources, software and writing--original draft. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The irrigation system was kindly supplied by Netafim Italia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion  
In this section, the validation of the PCP models introduced in Section 3 was conducted using the dataset mentioned in Section 2. Table 5 presents a comprehensive overview of the processing strategies employed for these PCP models. To ensure statistical significance, the analysis was conducted based on half of the total observation duration. It is important to note that for HP40, only B1I measurements were available for BDS-3. Consequently, the DF-P1I model and TF-P1I models' positionings were identical, just as they were for IFUC12P1I and IFUC123P1I models.
## 3 Numerical Results and Discussion
### EEPN Penalty for M-QAM Formats and Equal Phase Noise in the Transmitter and Receiver Lasers  
The numerical results for the OSNR penalty for model A are shown on Figure 7 for QPSK, 16-QAM, 32-QAM and 64-QAM modulation formats. The number of the BPS test phases was \(B=32\), the length of the averaging filter was \(N=10\), and the baud rate was 25 GBd. For a constant value of the DMD spread, all formats exhibit almost no degradation as the phase noise per laser increases. Note that for all modulation formats, the maximum linewidth of 4 MHz, 800 kHz, 400 kHz and 190 kHz per laser, respectively, is within the range that the BPS algorithm can compensate with the chosen parameters \(B\) and \(N\). However, a small degradation in performance is observed as the DMD value increases and the cause for this may be due to the use of longer equalizers. A sparse MIMO equalizer [2; 43] may help to alleviate this penalty but such analysis is beyond the scope of this work.  
Figure 6: Optimization of the BPS algorithm filter length \(N\) using model A simulator for (**left**) 16-QAM and (**right**) 64-QAM modulation formats. Both plots show the optical signal to noise ratio (OSNR) penalty in dB computed at a bit-error rate (BER) of \(10^{-4}\).  
Figure 8 shows the results for model B and the aforementioned modulation formats. In contrast to model A, all modulation formats are severely degraded as the phase noise increases due to EEPN. Interestingly, for all modulation formats the onset of the penalty degradation corresponds to a DMD threshold value of about 100 symbols.  
Figure 8: OSNR penalties in dB, computed at a BER of \(10^{-4}\), for model B and QPSK, 16-QAM, 32-QAM and 64-QAM modulation formats.  
Figure 7: OSNR penalties in dB, computed at a BER of \(10^{-4}\), for model A and QPSK, 16-QAM, 32-QAM and 64-QAM modulation formats.
# Workload Assessment of Operators: Correlation Between NASA-TLX and Pupillary Responses
## 4 Results
### Effect of Task Type on NASA-TLX Scores and Pupil Size Measurements  
For the second hypothesis on the effect of task type, the visual representation is shown in Figure 4. In the visual tracking task, the participants reported a NASA-TLX score of 47.5 \(\pm\) 25.6; their original mean pupil size was 3.20 \(\pm\) 0.34 mm, and the adjusted pupil size was 0.04 \(\pm\) 0.12 mm. In contrast, when performing the laparoscopic task, the participants reported a NASA-TLX score of 63.4 \(\pm\) 14.3; their original mean pupil size was 4.78 \(\pm\) 0.70 mm, and the adjusted pupil size was 0.31 \(\pm\) 0.48 mm. We conducted t-tests to examine the difference in the NASA-TLX scores and the pupil size between the visual tracking and the laparoscopic task. Significant group differences were observed on the NASA-TLX scores (t = \(-\)6.23, \(p<0.001\)), original mean pupil (t = \(-\)22.57, \(p<0.001\)), and adjusted pupil size (t = \(-\)22.57, \(p<0.001\)). Specifically, the laparoscopic surgery task resulted in higher NASA-TLX scores and larger pupil sizes--both original and adjusted--compared to the visual tracking task.  
Figure 3: Correlation plots between NASA-TLX scores and pupil size measurements. The diagonal panels display density plots illustrating the distributions of each variable. The lower-left panels present scatter plots with corresponding regression lines, showing the relationships between variables. “r” represents the Pearson correlation coefficient (r value), with “arcsec” indicating a significance level of \(p<0.001\).
## 2 Autonomous Orbit Determination Model
### Rotation Correction Model  
In autonomous orbit determination based on only inter-satellite bidirectional ranging, the state parameters of all satellites in the constellation must be estimated, which means a rank deficiency for the solution of the parameters and the lack of the necessary starting benchmark. Thus, it is impossible to determine the absolute positions of all the satellites. In autonomous orbit determination, the observed and real inter-satellite distances of the constellation are the same, meaning the constellation estimation errors cannot be observed theoretically. The unobserved constellation estimation errors of autonomous orbit determination by inter-satellite ranging measurements mainly refer to the unobservability of constellation rotation; on one hand, inter-satellite ranging cannot correct some of the orbital elements and, on the other hand, the unobservability is brought about by using the prediction of EOP [27].  
In the current distributed autonomous orbit determination, the two orbital plane orientation parameters of the right ascension for the ascending point \(\Omega\) and the orbital inclination angle \(i\) are adopted as the constraint conditions to limit the overall constellation rotation satellite specifically, which is the algorithm for the correction of independent satellite constraints. Since the effect (angle) of the constellation rotation error on each satellite is consistent, the independent constraint algorithm eliminates this consistency; therefore, the result is suboptimal. After estimating and obtaining the constellation rotation error of the orbit solved by filtering relative to the reference orbit, the rotation correction algorithm for the global satellite constraints can then directly correct the orbit parameters obtained in autonomous orbit determination to realize the suppression of the overall rotation error of the constellation, which guarantees the integrity of the constellation.
## 1 Introduction
Our study aims to address this limitation by creating high-quality datasets in collaboration with domain experts and using LLMs to address the aforementioned challenges in CP. This study seeks to bridge the research gap by investigating the feasibility of leveraging LLMs to automatically generate constraint codes for constraint satisfaction problems (contribution 1). Support for low-resource, low-level, and domain-specific programming languages [8] has been identified as one of the possible research gaps still present in the area of code generation using LLMs. We propose an approach that extracts semantic information from natural language descriptions of constraint satisfaction problems by employing fine-tuned LLMs (contribution 2). A recent study [17] utilized a different method [18] that did not yield satisfactory results, and the authors suggested that fine-tuning LLMs might be a good alternative. Fine-tuned LLMs are used to generate corresponding CSP models (contribution 3). With this approach, we aim to create complex constraint satisfaction models that describe complex configurations as their primary use case. In this study, we also present the datasets we created to fine-tune our LLMs (contribution 4). Generating synthetic data for a domain-specific programming language for which there is not much data available on the Internet is important, as noted by the authors of [8; 17]. Furthermore,to facilitate future research, we have made our datasets [19] and code publicly available [20] (Supplementary Materials). The remainder of this study is organized as follows: Section 2 presents the research background necessary to understand our methodology, including key concepts such as constraint programming, constraint satisfaction problems, configuration, large language models, fine-tuning, and prompt engineering (PE) methods. Section 3 covers related work and discusses how our approach advances existing research. Section 4 introduces the proposed Automatic Constraint Model Generator, a general framework designed to automatically generate constraint models from natural language prompts. The ACMG framework consists of two core components: semantic entity extraction and constraint model generation. Section 5 presents the evaluation of the proposed approach, including the datasets used, experimental results, and findings from an ablation study. Section 6 discusses key findings, the implications for industry and academia, limitations, and directions for future work. Finally, Section 7 concludes this study.