esa-sceva/satcom-chunk-collection huggingface dataset:
# Hot-Pressing of Ti-Al-N Multiphase Composite: Microstructure and Properties
## 4 Conclusions  
The main findings of the research were as follows:  
* The hot pressing process successfully synthesized a AlN-TiN(BN) composite with a uniform and dense microstructure. The addition of boron nitride significantly influenced the distribution of phases and contributed to the overall integrity of the material.
* The composite exhibited desirable mechanical properties, including a homogeneous hardness distribution (500-540 HV2), suitable for industrial applications requiring durable cutting tools. These properties result from the synergistic interaction of the TiN, Ti2 AlN, Ti3 AlN, and BN phases within the matrix.
* Electrochemical analysis demonstrated that the composite forms a passive oxide layer in neutral chloride environments, providing a moderate level of protection. However, under high chloride concentrations, localized pitting corrosion was evident, primarily in regions of electrochemical heterogeneity.
* Despite the promising results, improvements are needed to fully integrate the benefits of BN admixture. Future studies should focus on refining sintering parameters and exploring post-treatment processes to enhance corrosion resistance and mechanical durability, particularly in aggressive environments.  
**Author Contributions:** Conceptualization, R.S., J.K., K.B. and P.M.; methodology, R.S., G.P.K., J.K., P.M. and K.B.; validation, R.S., K.Z. and J.K.; formal analysis, R.S., K.Z., M.M., M.K. and P.M.; investigation, R.S., J.K., K.B., G.P.K. and M.M.; data curation, R.S., M.K. and J.K.; writing--original draft preparation, R.S., J.K., P.M., G.P.K. and K.B.; writing--review and editing, R.S., J.K., K.B. and M.K.; visualization, R.S., K.B., K.Z. and M.K.; supervision, R.S.; project administration, R.S. and P.M. All authors have read and agreed to the published version of the manuscript.  
The scientific research was financed from the statutory work Ref. No. 504/04812/1090/44.00000.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
We sincerely thank [PERSON] for her invaluable assistance with the XRD analyses conducted for this study.  
The authors declare no conflicts of interest.
# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 1 Introduction
If we can simulate the growth behavior of plant communities on a computer, we could also use this artificial plant community algorithm to search for the image edges on an original image. The main contributions of this paper are as follows:  
* First, the image edge is described as an area rich in water and nutrients, and the edge detection process is modeled as an objective function that can be used to search for water sources and nutrients. Hence, edges with higher habitable value are detected, and nonedges with lower habitable value are removed due to being unimportant details. * Second, an artificial plant community (APC) algorithm is designed to solve the objective function by simulating the growth process of a true plant community. The growth behavior of an artificial plant community includes three operations: seeding, growing, and fruiting. These three operations correspond to three fitness comparisons, and the population size will also decrease or recover accordingly. Only seeds, individuals, and fruit with high fitness levels can survive, while those with low fitness levels will die. The individuals with the highest fitness levels can produce identical offspring fruit, other individuals can learn from each other to produce new fruit, and individuals with higher fitness levels can produce more fruit. Through this continuous cycle of three operations, the artificial plant community finally identifies the optimal image edge. * Third, the proposed algorithm is verified by a series of experiments and is compared with other image edge detection algorithms. Comparative analysis results are provided and discussed. The organization of the paper is as follows: The relevant work is briefly reviewed in Section 2. A description of the model is given in Section 3. The architecture of the proposed APC algorithm is developed in Section 4. The experimental results of the proposed scheme are presented and compared with other algorithms in Section 5. Finally, this study and some limitations are summarized, and future directions are suggested in Section 6.
###### Abstract  
This study examines Retrieval-Augmented Generation (RAG) in large language models (LLMs) and their significant application for undertaking systematic literature reviews (SLRs). RAG-based LLMs can potentially automate tasks like data extraction, summarization, and trend identification. However, while LLMs are exceptionally proficient in generating human-like text and interpreting complex linguistic nuances, their dependence on static, pre-trained knowledge can result in inaccuracies and hallucinations. RAG mitigates these limitations by integrating LLMs' generative capabilities with the precision of real-time information retrieval. We review in detail the three key processes of the RAG framework--retrieval, augmentation, and generation. We then discuss applications of RAG-based LLMs to SLR automation and highlight future research topics, including integration of domain-specific LLMs, multimodal data processing and generation, and utilization of multiple retrieval sources. We propose a framework of RAG-based LLMs for automating SRLs, which covers four stages of SLR process: literature search, literature screening, data extraction, and information synthesis. Future research aims to optimize the interaction between LLM selection, training strategies, RAG techniques, and prompt engineering to implement the proposed framework, with particular emphasis on the retrieval of information from individual scientific papers and the integration of these data to produce outputs addressing various aspects such as current status, existing gaps, and emerging trends.  
retrieval-augmented generation; large language models; systematic literature review Article
## 2 The Proposed Methodology
### Optimization Problem Formulation
#### 2.2.1 Retrofit Planning in a Single-Period Setting  
The single-period decision model relies on several decision variables, reflecting the choices on the retrofitting interventions to be implemented in the existing street lighting system at the beginning of the current period \(k=1\). Let \(\mathbf{x}_{1}=\big{(}x_{11},\ldots,x_{1j},\ldots,x_{1j}\big{)}\) denote the column vector of integer decision variables, reflecting the quantitative choices on retrofit actions implementation. For instance, if action \(j\) denotes the deployment of energy harvesting modules, \(x_{1j}\) represents the quantity of modules to be deployed in the given street lighting system at the beginning of the current period \(k=1\). All the variables are lower bounded by zero (i.e., no intervention) and are upper bounded by the application potential of the given intervention. Indicating by \(\mathbf{0}_{j}\) the \(J\)-dimensional zero column vector, we introduce set \(\chi\) to compactly define the decision space:  
\[\chi=\Big{\{}\mathbf{x}_{1}\in\mathbb{N}^{J},\quad\mathbf{0}_{j}\leq\mathbf{x}_{1}\leq\bm {u}\Big{\}}. \tag{1}\]  
Furthermore, the choice of the decision variables is constrained by the financial resources limitation. Assuming that different actions produce additive effects on cost and calling \(b\) the budget allocated to the retrofit actions plan of the whole system at the beginning of the planning phase, the following inequality must be satisfied:  
\[\sum_{j=1}^{J}c_{j}x_{1j}=c^{T}\mathbf{x}_{1}\leq b. \tag{2}\]  
In addition, the objective function, to be maximized, is the total energy saving achieved at the end of the current period. Assuming that different actions produce additive effects on payoff, the objective function is defined by summing the payoffs resulting from implementing all the planned actions in the given street lighting system:  
\[f_{1}(\mathbf{x}_{1})=\sum_{j=1}^{J}p_{j}x_{1j}=\mathbf{p}^{T}\mathbf{x}_{1}. \tag{3}\]  
Summing up, the optimization problem aimed at selecting the optimal energy retrofit of an existing street lighting system turns into determining the \(J\) integer decision variables in \(\mathbf{x}_{1}\) to maximize the linear objective function in (3) and satisfy the \(2J\) bounding constraints (1) and the inequality constraint (2):  
\[\max_{\mathbf{x}_{1}\in\chi}\mathbf{p}^{T}\mathbf{x}_{1}\text{s.t. }\mathbf{c}^{T}\mathbf{x}\leq b. \tag{4}\]  
Problem (4) is a linear integer programming problem, also known as integer mono-constrained Knapsack problem [21]. A nontrivial solution of (4) can be determined by exact methods (e.g., the branch and bound method) or heuristic approaches (e.g., greedy algorithms) [21].
# Exploring an Integrated Manure-Sewater System for Sustainable Cyanobacterial Biomass Production
## 2 Materials and Methods
### Measurement of Biomass, Exo-Polysaccharides (EPSs) and Photosynthetic Pigments  
For biomass measurement, 50 mL of cyanobacterial growth culture was collected and centrifuged at 5000 rpm for 10 min. After the liquid supernatant was discarded, the biomass was exposed to room temperature on aluminum foil to reduce the water content, and then the biomass was measured as a fresh weight using the weight balance. In order to determine the exo-polysaccharide (EPS) content, an extract was taken from the fresh biomass using the formaldehyde-NaOH method as described by [26], and then the polysaccharide content in the extract was measured using the phenol-sulfuric acid method as described by [27]. To measure the photosynthetic pigments (chlorophyll a and carotenoids), extraction and spectrophotometric absorbance measurements were conducted using the protocol described by [28], and the chlorophyll a content was calculated from the absorbance value using the formula Chla [\(\mu\)g/mL] = 12.9447 (A665-A720) as described by [29]. Carotenoid content was calculated using the formula, carotenoids [\(\mu\)g/mL] = [1000 (A470-A720)--2.86 (Chla [\(\mu\)g/mL])]/221 as described by [30].
# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
## 4 Deep Learning Methods for Nested NER
### Span Labeling Approaches
#### 4.2.1 Enumeration Model  
The enumeration model considers all spans shorter than a threshold as potential NE candidates (Figure 3b). All NE candidates are transformed into high-quality representations based on the span representation strategy and then classified as either specific entity types or non-entities through a span classification layer. The enumeration model is a straightforward approach that can consider all valid spans. Consequently, its performance heavily depends on the quality of the span representation.  
[PERSON] and [PERSON] define a span representation as a concatenation of the head and tail token representations, along with a learned representation of its width feature [85]. This concatenation strategy has the disadvantage of ignoring information from intermediate tokens within the span. To overcome the limitation, [PERSON] et al. employ a max-pooling representation [86]. The max-pooling representation is defined as collecting the maximum values for each dimension across the token representations within the span. It is then concatenated with the representations of the head token, the tail token, and the width feature to construct the span representation.  
The concatenation and pooling representations are defined by combining token representations generated by the encoding layer. However, these strategies have limitations in accounting for dependencies among spans. [PERSON] et al. emphasized the importance of these interrelationships and introduced a packed levitated marker (PL-Marker) [87]. In their model, each span is assigned two levitated markers that contain the position information of its head and tail tokens, respectively. These markers are paired and appended to the end of a sentence with other levitated marker pairs from adjacent spans together. The sentence, along with the group of levitated markers, is passed through the encoding layer. Subsequently, the representations of the encoded head and tail levitated markers are concatenated together and utilized as a span representation.  
Enumeration approaches can consider as many spans as possible. However, this approach is inherently expensive due to the large number of spans processed. The representation strategies that account for dependencies between spans, such as PL-marker, further increase computational and time costs. Additionally, these approaches are constrained by their inability to predict long entities that exceed a certain threshold. While enumeration methods can intuitively handle nested NEs, their critical disadvantage lies in the substantial resource requirements, making them less efficient for time-sensitive applications.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 4 Simulation Analysis
### DOP Value Distribution Analysis of Resonant Constellations
#### 4.2.1 Performance Distribution Analysis in the Near-Earth Region  
An analysis of the DOP performance in the near-Earth region yielded the constellation's trajectory and DOP mean distribution results, as shown in Figure 8.  
From a spatial perspective, the DOP temporal averages were calculated by unwrapping all target spherical surfaces to evaluate each latitude-longitude coordinate. Notably, the latitude and longitude definitions here differed from the standard geodetic coordinates:  
* Longitude was defined within the Earth-Moon rotating frame, with its origin set at the intersection line of the X-Z plane and the spherical surface. This line is bisected by the spherical poles, and the longitude origin corresponds to the positive X-axis direction.
* Latitude originated from the lunar orbital plane (i.e., the \"White Circle\" plane), with the positive direction pointing toward the spherical north pole.  
The resulting DOP distributions across the near-Earth region's spherical surfaces are illustrated in Figure 9.  
The figures reveal consistent DOP distribution patterns across all spherical layers when unwrapped. The DOP values exhibit symmetry about the Earth-Moon axis and the 0\({}^{\circ}\) latitude plane (the spherical equator), which is termed dual symmetry in this study. This symmetry arises because the optimal constellation is symmetric about both the Earth-Moon axis and the lunar orbital plane (White Circle plane), indicating a direct correlation between the constellation's structural symmetry and the DOP distribution symmetry. Notably, target points within the 100\({}^{\circ}\)-250\({}^{\circ}\) longitude range exhibit higher DOP values, attributed to the constellation's concentration near the 0\({}^{\circ}\) longitude side, thereby degrading the performance on the opposite side. Additionally, the DOP values decrease and their variability diminishes as the spherical radius of the target region decreases.  
Further analysis of the mean DOP values for each spherical layer (plotted against radius) is presented in Figure 10. The results demonstrate a nonlinear, monotonically increasing relationship between mean DOP values and spherical radius, indicating superior service quality for low-altitude users within the near-Earth region.  
Figure 8: Schematic diagram of optimal resonant constellation configurations and time-averaged DOP distribution in the combined region.  
Figure 9: Spatial distribution of DOP values in the near-Earth region. Subfigure **(a)** illustrates the distribution on a spherical surface with a radius of 100,000 km. Subfigure **(b–j)** consists of a series of plots showing the DOP value distributions on spherical surfaces with radii ranging from 10,000 to 90,000 km.
## 3 Results and Discussion
### Analysis and Discussion of Results
The obtained results were tested against the most recent satellite hourly data available within the PVGIS platform for Rabat, corresponding to the year 2020 SARAH2 database. The accumulated monthly GHI for the October 2020 benchmark was then obtained by adding all the hourly data for this month, resulting in 139.12 kWh/m\({}^{2}\). Next, from the PDF obtained for the city of Rabat in the month of October depicted at Figure 12, a Monte Carlo simulation was run. Figure 12: Examples of the extreme value, normal and kernel PDFs’ fitting test results for the month of February in Ifrane, the month of October in Nador and the month of March in Tetouan (GHI case). Source: own elaboration. Figure 13 shows the basic structure of the algorithm. Initially, for the selected city and month, the appropriate PDF is fed into the probabilistic model. An iterative random sampling process is then started for obtaining irradiation values. This process ends when the predefined number of samples (_Nsamples_) is achieved. In this case, 10,000 samples have been obtained. As can be seen in Figure 14, the mean value of the simulated accumulated monthly GHI for October in the city of Rabat was 137.01 kWh/m\({}^{2}\). This figure also shows the evolution of the average of all the simulated values as the number of samples grows. The obtained mean of 137.01 kWh/m\({}^{2}\) represents a relative difference of 1.5% regarding the 2020 benchmark data of 139.12 kWh/m\({}^{2}\). The interpretation of this result is that the PDF in Figure 12 can provide a set of _Nsamples_ feasible values of accumulated monthly GHI for the benchmark city and month, whose behaviour properly reflects the implied stochastic pattern of the irradiation. In this sense, the determined PDFs can be considered as a new solar irradiation database for the corresponding locations and time, since random values can be generated from them, all of which reflect the variability of the solar resource of the site. It must be clear that this methodology is not intended for predicting the real irradiation value for a specific location and month, but for providing feasible irradiation data able to characterize the irradiation variability. This methodology may be more than sufficient when assessing the economic profitability of solar assets, and far better than the deterministic approaches usually employed. The same method can be applied for studying other phenomena, such as the variability of wind speeds, the ambient temperature of a location, or the seasonal humidity. This method is not limited to the energy sector but can be applied in other areas of science as well. Figure 13: Basic structure of the Monte Carlo simulation algorithm for testing the PDFs. Source: own elaboration.
# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 2 Target Detection in Passive Forward Scattering Radar
### Received Signal and Target's Features in Passive FSR  
In FSR system, high Doppler resolution can be achieved by long received signal coherent integration time [1; 2]. The coherent integration time is determined by the duration of target's visibility within the FSR signal beam width. Moreover, the target's relative speed, which is beyond radar control, is an important parameter in passive FSR. Based on previous work, in practice, information on the target speed can be obtained from the received signal, as explained by Equation (9) and investigated in the next section. The experimental time domain signal scattered from twodifferent cars: Compact and Saloon are shown in Figure 71 respectively. The envelope of the received signal's amplitude in time  
Figure 6: Passive Forward Scattering Radar sensor system based on LTE signal transmission.  
domain is used for vehicle detection in passive FSR. The signal also shows that the horizontal (time) scale of the waveform proportional to the vehicle's speed. Both signals showed the target crossing the baseline with an almost symmetrical shape for the Doppler frequency. Fundamentally, the obtained results show two significant relationships:  
1. the amplitudes of Doppler signatures to the target's forward scatter cross section, hence, the Saloon car had a bigger forward scatter cross section, which was reflected in the amplitudes of their signature,
2. the signal span in time domain to the length of the target, hence, the Saloon car also had a longer body, which was reflected in the signal span of the forward scatter main lobe.  
In addition, the Doppler modulated in the signal can be clearly seen at both ends of the signals when the car was entering and leaving the baseline, and when the antenna started to receive the target's forward scatter lobe.  
The signal's power spectra densities of the same cars at different speeds are shown in Figure 7 ii. From the FSR theory, the position of the first null can be approximately determined from the ratio between the speed and the length of the vehicle [2]. This relationship can be used to estimate the vehicle's speed. The Doppler spectrum signature is unique for specific vehicle's shapes and dimensions. It is used as the vehicle's feature vector for classification system in this paper. Several meaningful criteria can be analyzed and selected as features, for example, the number of spectrum lobes, the number of principal components to be used and minimum power level.  
Figure 7 iii shows the signal spectrograms for each vehicle. Two main characteristics based on the spectrograms can be point out: the size of the vehicle's forward scatter main lobe can be estimated, and it also illustrates that each vehicle has unique time-frequency attributes and features that can be utilized for advanced vehicle recognition.  
Figure 7: _Cont._
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Data Collection and Analysis  
An advanced smart metering system is essential for the success of the smart grid. This system is one of the important components for data collecting and analyzing in smart grids. Hence, it plays an important role in ensuring the safe, stable, and efficient operation of smart grids. In this subsection, the emerging applications for data collection and analysis using an advanced smart metering system will be discussed.  
Connectivity verification of the distribution system is an urgent problem. To solve this problem, [PERSON] et al. [101] introduced a cloud-based collaboration mechanism to determine outlier users and valid connections. In this paper, the authors came up with an affinity propagation clustering-based local outlier factor (AP-LOF) algorithm that can effectively identify and verify the voltage outlier. Moreover, they set up a mechanism in the cloud center to correct the identified outliers. The experiments are based on actual smart meter data. The results show that the performance of this algorithm is better compared with traditional methods. Additionally, the calculation efficiency is improved significantly because of the cloud-edge collaboration scheme.  
Improving the analyzing capability of the smart meter is another notable research topic. [PERSON] et al. (2012) presented that traditional smart meters are only able to measure and show the digital number of energy usage. To enable the smart meter to function in modern advanced metering systems, the authors proposed an embedded edge computing paradigm in the smart meter. This paradigm improves accuracy, latency, and bandwidth significantly by bringing data analytics to the smart meters.