esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
High dewatering costs, resulting from the harvesting and separation of microalgae from the cultivation medium, pose a significant challenge to the large-scale commercial production of algae-based products, accounting for 20-60% of total cultivation expenses. This study presents research findings on the recovery of Baltic green microalgae _Chlorella vulgaris_ BA-167 from water under static sedimentation conditions, evaluating its potential as a cost-effective harvesting method. The study investigates the effect of suspension concentration on the kinetics and efficiency of sedimentation under both autofocculation and targeted flocculation conditions, using the Flokor 1.2A coagulant, which is commonly employed in industrial water treatment processes in Poland. The novelty of this research lies in the application of the new coagulant Flokor 1.2A to explore its potential for harvesting _Chlorella vulgaris_ BA-167 cultivated under laboratory conditions. The results demonstrate a strong correlation between the algae removal rate and their initial concentration in the suspension, within the range of 0.375-2.380 g/L. Under autof flocculation conditions, the final minimum algae concentration in the liquid after sedimentation ranged from 0.078 to 0.148 g/L, corresponding to initial concentrations of 0.960 g/L and 0.615 g/L, respectively. Experimental results indicate that combining sedimentation with targeted flocculation significantly increases microalgae harvesting efficiency. Flokor 1.2A facilitates the coagulation and agglomeration of microalgae cells, promoting the formation of larger aggregates (flocs) ranging from 20 \(\upmu\)m to 690 \(\upmu\)m, which settle more easily during gravity-driven sedimentation. Within the coagulant concentration range (\(C_{F}\)) of 0.01-0.36 g/L, sedimentation time was reduced by 3-7 times, and algae harvesting efficiency exceeded 92%. The greatest reductions in algae concentration occurred with 0.12 g/L of coagulant for 0.615 g/L algae and 0.17 g/L for 0.960 g/L algae, achieving maximum harvesting efficiencies of 83.2% and 92.9%, respectively. These results represent a 2.02-2.53-fold improvement over autofocculation.  
algae harvesting; sedimentation; flocculation; microalgae _Chlorella vulgaris_ BA-167
# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 5 Conclusions  
The 4 DT-Wx prototype system developed in this study presents an innovative approach to integrating weather information for TBOs. Implementing multi-model-based trajectory services and buffer zone information offers a more comprehensive and flexible weather information integration method than existing services. The use of Apache Spark for a distributed processing system enables the efficient handling of large-scale weather data. These research results can contribute to the implementation of a high-performance ATM system, as required by ICAO's GANP, and introduce a new paradigm for weather information integration in a TBO environment. Therefore, these analysis results can serve as important foundational data for the future implementation and optimization of the 4 DT-Wx system's API services, ensuring seamless weather data integration in TBOs utilizing geospatial information.
## 1 Introduction  
### Background  
Liquefied petroleum gas (LPG) is a flammable gaseous hydrocarbon widely used around the world, as it is a non-toxic and sulfur-free fuel. It can be easily stored in liquid form under moderate pressure, making it a very portable fuel and a versatile energy source for consumers located in areas where pipe gas supply is not available [1].  
In Latin America, unlike other more developed regions of the world, LPG demand is driven by residential and commercial customers, who consume around 80% of the region's LPG. In Mexico, for example, residential and commercial consumption accounts for almost 90% of the domestic market [2]. In the region, LPG distribution for residential and commercial use can be divided into two categories: (1) distribution of LPG in portable cylindrical tanks and (2) refilling LPG distributed by tanker trucks to domestic stationary tanks. Portable cylindrical tanks are widely used, especially in rural or remote areas, where it is complicated or expensive to refill a stationary tank. On the other hand, stationary tanks are more popular in urban areas and offer a more practical way to store LPG on the property [3].  
Both forms of distribution are operationally inefficient and susceptible to theft in their current state [4; 5; 6], since: (1) in the case of returnable portable cylinders, less gas might be supplied than what was paid for (which is a form of theft), (2) the truck that transports the cylinders might not be circulating near or at the time when consumers require it, and (3) usually, domestic stationary tanks are installed on the roofs of buildings, which makes it difficult for consumers to check the dial gauge of LPG on the tank.  
Thus, to try to alleviate these inconveniences, mainly in the case of refilling stationary tanks at home, different wired and wireless solutions have already been developed. For comparison purposes, this work only considers existing wireless solutions in the Latin American market.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 4 Experiment  
### Experimental Setup  
We used a Windows 10 operating system, with an Intel I7-8750H CPU, 16 GB of RAM, and an Nvidia GTX 1060 graphics card. We also used the Keras 2.2.4 deep learning framework with Python version 3.7. The batch size was set to 2048. We split the training and testing sets using five-fold cross-validation and ran 20 iterations of five-fold cross-validation in each experiment.  
Traditional model hyperparameters were as follows:  
1. KNN: k = 3. Euclidean distance was used for distance calculation, and all sample points had equal weights.
2. Decision Tree: Gini impurity was used as the measure of classification quality. After multiple experiments, it was found that the tree performed best when the maximum depth was set to 30 for the test set.
3. SVM: Gaussian kernel and polynomial kernel used default parameters. The polynomial SVM performed best when the degree was set to 3.
# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 3 Methods
### Model Optimization  
The overall loss function is shown in Equation (16), the loss function consists of contrastive learning loss and a task-specific loss.  
\[L=L_{BCE}+L_{ct}+L_{cv} \tag{16}\]  
For the task-specific loss, MGKGR reformulates the link prediction task as a Masked Language Modeling (MLM) task. In this task. Consequently, binary cross-entropy loss \(L_{BCE}\) is used as the task-specific loss function.  
\[L_{BCE}=-\frac{1}{N}\sum_{i=1}^{N}y_{i}\log(p(y_{i}(H_{m})))+(1-y_{i})\log(1-p (y_{i}(H_{m}))), \tag{17}\]  
where \(y_{i}\) represents the true label of the [MASK] entity, and \(p(y_{i}(H_{m}))\) denotes the probability that the sample belongs to this label, using the probability distribution of \(H_{m}\) at the [MASK] position. In the MGKGR model, the loss from this task is jointly trained with the two contrastive learning loss functions.  
MGKGR encodes the structure, text, and image features of the multimodal GeoKG separately, followed by a two-stage feature fusion process. In the first stage, the AdaptiveFeature Fusion method is used to selectively integrate structural features into the text and visual features. During the fusion process, contrastive learning is employed to mitigate the noise introduced by distributional differences among the features. In the second stage, features with a higher text-image correlation are fused to alleviate the impact of modal heterogeneity. Finally, MGKGR utilizes the fused multimodal features as the feature representation of the multimodal geographic knowledge graph and performs a link prediction task. The overall training procedure of MGKGR is provided in Algorithm 1.  
```
0: Multimodal Geographic Knowledge Graph \(G_{geo}=\{V,R,E,M\}\)
0: Model parameters \(\Theta\), multimodal geographic knowledge graph embedding \(\mathbf{H_{m}}\)
1: Initialize model parameters \(\Theta\)
2: Construct the text and triple in the format of \(\mathit{Text}(e_{h,r,?})\)
3:while not converge do
4: Encode \(G_{geo}\), \(\mathit{Text}(e_{h,r,t})\), and \(\mathit{Picture}\) as \(\mathbf{H^{s}}\), \(\mathbf{H^{t}}\), and \(\mathbf{H^{v}}\)
5: Fuse \(\mathbf{H^{s}}\) with \(\mathbf{H^{t}}\) and \(\mathbf{H^{v}}\) to obtain \(\mathbf{H^{gt}}\), \(\mathbf{H^{sv}}\)
6: Compute the contrastive learning loss \(L_{ct}\) and \(L_{cv}\) with Equations (8) and (9)
7: Calculate \(\mathbf{H^{gt}}\), \(\mathbf{H^{sv}}\) multi-head attention by Equations (10) and (11)
8: Obtain fusion feature \(\overline{h^{gt}}\), \(\overline{h^{sv}}\) with Equations (12) and (13)
9: Combine the vision feature: \(Aggg^{v}=\cos(\overline{h^{gt}_{l}},\overline{h^{sv}_{l}})\cdot\overline{h^{sv} _{l}}\)
10: Compute multimodal fusion features \(\mathbf{H_{m}}\) by Equation (15)
11: Calculate the task loss function \(L_{BCE}\)
12: Update \(\Theta\) by minimizing \(L=L_{BCE}+L_{ct}+L_{cv}\)
13:endwhile
14:return\(\Theta\), \(\mathbf{H_{m}}\)
```  
**Algorithm 1** The training procedure of MGKGR
## 5 Results  
### Spatio-Temporal Evolution of NPP in Northeast China  
#### 5.1.1 Variation Characteristics of Forests NPP from 2001 to 2019  
In order to reveal the evolution characteristics of NPP in northeast forests over time, this study selected the average NPP as an indicator of ecological environment quality from 2001 to 2019. Overall, this study found an increasing trend with an average increase of 3.95 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) (Figure 6) in the analyzed years. Specifically, there was a downward trend from 2001 and 2005, with a decrease of 19.85%. Then, due to the large-scale implementation of natural forests protection and other ecological restoration projects in Northeast China, the ecological environment quality rapidly increased during the period of 2005-2008 and the NPP reached 859.16 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) in 2008. However, the average NPP did not show a trend of continuous increase after 2008. Instead, it showed a severe decline with a reduction of \(-\)17.22% from 2008 to 2010, which may be related to large-scale drought events. Subsequently, the NPP gradually increased again from 2010 to 2015, mainly due to the climate and the national attention to the protection of natural forests. During the period of 2016 to 2019, the mean NPP had a slight drop in 2016, 2017. Subsequently, the mean NPP reached the maximum 976.17 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) in 2019. It is likely to be affected by a range of climate changes, such as rising regional temperatures, permafrost degradation, shorter freezing-thawing times and so on [58; 59; 60].  
Figure 5: Comparison between simulated NPP and MOD17 NPP, based on different raster pixels, in 2003 and 2008.
# Mod2 VQLS: A Variational Quantum Algorithm for Solving Systems of Linear Equations Modulo 2
## 5 Experimental Results  
This section describes the results of experiments evaluating the performance of Mod2 VQLS solvers using both the rotation and brickwork ansatze.  
In each dimension from 1 to 9, we generated ten consistent binary linear systems by constructing \(n\times n\) matrices \(A\) and \(n\)-vectors \(x\) with independent and uniformly selected entries in \(\{0,1\}\) and then computing \(Ax=b\). Then, we solved the systems using Mod2 VQLS, using both the brickwork and rotation ansatze. Solving a linear system entails optimizing the model's variational parameters and then measuring the optimized state \(|\psi(\theta^{*})\rangle\). We tested all the computational states observed in the optimized superposition in order to determine whether they were valid solutions to the linear system in question. We counted the number of distinct valid and invalid solutions proposed, as well as the average number of iterations required for convergence of the variational method. We used SciPy 1.11's COBYLA implementation to update the variational parameters in our circuits; we note this optimization routine only requires a single quantum circuit execution per iteration. Circuits were coded and simulated using the Qiskit Python package [22].  
In the brickwork case, the number of layers in the variational ansatz is an extra configuration parameter. From experimenting, we found that at least two layers were needed and that matching the number of layers/parameters to the number of dimensions produced a reasonable tradeoff between solution iterations and correctness.  
Our results are presented in Table 1. The rotations ansatz performed quite simply and effectively, always finding a correct solution, and occasionally finding more than one, with a reasonably small number of iterations. The rotations ansatz proposed no invalid solutions. The brickwork ansatz was more costly and error-prone, using more iterations and producing some invalid solutions. The brickwork ansatz was also able to find a greater variety of valid solutions, though not dramatically so.  
Figure 7 shows the growth in the number of iterations used by Mod2 VQLS with the rotations ansatz. The growth is roughly linear in small dimensions, with a slope of (just under) 4. This allows for comparison with block Wiedemann, as explained in Section 6 below.  
These experiments demonstrate proof-of-concept, in that the Mod2 VQLS system does find correct solutions. They also demonstrate that there are performance tradeoffs, and that different choices of ansatz may be appropriate, depending on whether the task requires searching for any solution, or a more exhaustive search for all solutions.  
These results are preliminary. The Mod2 VQLS algorithm should be tested on larger systems of linear equations and compared with the results of classical solvers. The robustness of the circuits at larger scales with different kinds of noise has not yet been analyzed.
## 5 Conclusions  
The present work introduces two computer vision techniques (SIFT-based and YOLO-based) aimed at automating MRI slice planning to aid operators in decision making. These techniques are crucial for MRI, where precise alignment and planning are essential for high-quality images needed for accurate diagnosis and treatment planning. By reducing manual intervention, the methods enhance efficiency and consistency, leading to more reliable outcomes. Both techniques can perform real-time slice planning, with SIFT-based models being more accessible for healthcare facilities with limited resources due to their lower computational requirements compared to YOLO-based methods. The models were developed using a limited set of images for the sagittal plane, suggesting they can be easily adapted to other anatomical structures. The approaches use keypoints and descriptors to identify and align anatomical structures, providing an effective solution for slice planning. The study demonstrated that the methods' performance is comparable to expert human operators, which is crucial for clinical acceptance. The computer vision-based approach is practical, easily integrated into existing MRI systems, and scalable across various healthcare settings. The results also open avenues for future research, with potential for greater accuracy and efficiency through algorithm refinement and dataset expansion. By integrating these techniques into clinical practice, healthcare providers can enhance diagnostic imaging, ultimately improving patient care.  
Conceptualization, A.W.V. and M.F.S.V.D.; methodology, A.W.V., H.R.d.O. and M.F.S.V.D.; software, R.A.A. and J.C.P.d.C.; validation, A.W.V., H.R.d.O. and M.F.S.V.D.; formal analysis, A.W.V., H.R.d.O. and M.F.S.V.D.; investigation, R.A.A., J.C.P.d.C., A.W.V., H.R.d.O. and M.F.S.V.D.; focuses, R.A.A., J.C.P.d.C., A.W.V., H.R.d.O. and M.F.S.V.D.; writing--original draft preparation, R.A.A., J.C.P.d.C., A.W.V., H.R.d.O. and M.F.S.V.D.; writing--review and editing, R.A.A., J.C.P.d.C., A.W.V., H.R.d.O. and M.F.S.V.D.; visualization, R.A.A., J.C.P.d.C., A.W.V., H.R.d.O. and M.F.S.V.D.; supervision, A.W.V. and  
Figure 9: Scenarios for line adjustment. In (**a**), neither method successfully identified the points marked by the expert in yellow. In (**b**), both methods were successful, with YOLO achieving greater accuracy—shown by the red line. In (**c**), both methods were successful, with SIFT achieving better accuracy—indicated by the blue line.  
M.F.S.V.D.; project administration, A.W.V., H.R.d.O. and M.F.S.V.D.; funding acquisition, M.F.S.V.D. All authors have read and agreed to the published version of the manuscript.  
This study was supported by grants from the CNPq (Grant 308265/2022-0), CAPES, and FAPEMIG (Grants APQ-01817-22, APQ-00528-24, APQ-04310-24, APQ-03595-22, APQ-00691-23, and APQ-04096-22).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors acknowledge the support of CNPq, FAPEMIG, and CAPES.  
The authors declare no conflicts of interest.
# Deep-Learning-Based Identification of Broad-Absorption Line Quasars
## 1 Introduction
The absorption lines observed in BAL quasars predominantly originate from highly ionized atoms, such as C IV, Si IV, N V, and O IV, providing valuable insights into large-scale galactic processes [18]. However, BAL quasars have sparked widespread interest as an important subclass in the quasar research process. They exhibit broad- and deep-absorption lines in their spectra, and the mechanism by which these lines are produced may involve the motion of high-speed gas clouds around black holes. A team headed by the British astronomer [PERSON] first characterized BAL quasars in detail in 1991 [19]. They found that the spectra of BAL quasars differed markedly from those of typical quasars, particularly in the characteristics of the absorption lines, such as their widths and depths. These distinctive features indicated the unique physical processes occurring within a quasar's central region, offering valuable insights into the dynamics of the material surrounding supermassive black holes. With the continuous development of observational techniques, several survey programs, such as the Sloan Digital Sky Survey (SDSS), have provided more observational data for quasar research. However, with the rapid increase in the amount of data, traditional manual classification methods become difficult and subjective. Therefore, automatedclassification methods have become particularly important. Principal component analysis (PCA), spectral template matching, and deep learning techniques have become widely employed tools in quasar classification. These methods enable researchers to efficiently process and analyze vast amounts of observational data, facilitating the identification and categorization of quasars. By leveraging these advanced computational approaches, astronomers can extract key features from quasar spectra, enabling more rapid and accurate classification while also uncovering subtle patterns and correlations that may otherwise be overlooked [20; 21; 22]. This study applied deep learning techniques to classify the quasar spectrum in the 16 th SDSS data publication (DR16) using tags that distinguished the BAL quasars from the non-BAL quasars. We investigated various methods of data pre-processing, including various downscaling methods, to optimize inputs for the formation of the deep learning models. The goal was to build robust models that could accurately classify these quasar samples, with the aim of using the formed models when datasets became available in the future. By completing this study, we sought to improve our ability to differentiate quasar types, which, in turn, would deepen our understanding of the activity of supermassive black holes and open up new possibilities for studying complex phenomena in the most dynamic regions of the universe.
## 2 Materials and Methods  
### Study Areas  
This study covers a nationwide set of geodetic control points distributed across the entire territory of the Republic of Korea. The approximately 1700 unified geodetic control points (UGCPs) used in the analysis are spatially well distributed, encompassing all major administrative regions, including the seven metropolitan cities and nine provinces, as well as Jeju Island.  
The broad spatial coverage enables not only an assessment of overall coordinate consistency between GNSS processing software and the KGD2002 reference frame but also a region-by-region analysis of systematic or random discrepancies. Since local network geometry, observation density, and environmental conditions may vary geographically, evaluating spatial patterns in coordinate differences is essential for understanding the performance of each software in practical geodetic applications. Regional metrics such as mean offsets and RMSE values are therefore computed separately for each administrative unit to identify potential patterns of bias or distortion.  
Figure 1 presents the spatial distribution of the unified geodetic control points (UGCPs) used in this study, totaling 3164 stations. These points represent the subset of nationwide geodetic control stations for which coordinates were successfully computed using both the GAMIT/GLOBK and BERNESE GNSS processing software. To ensure data quality and comparability, only stations with observation durations of eight hours or longer were included in the analysis. The selected UGCPs are evenly distributed across the entire territory of the Republic of Korea, encompassing all major administrative regions including the seven metropolitan cities, nine provinces, and Jeju Island.  
The map clearly demonstrates the extensive spatial coverage of the dataset, which provides a solid foundation for both national-scale and regional analyses of coordinate consistency. The dense distribution of points allows for high-resolution assessment of regional discrepancies, which may result from differences in network geometry, satellite visibility, multipath effects, or localized environmental factors. To aid in regional interpretation, approximate administrative boundaries and area labels have been included on the map. The GNSS data quality and network stability of these control points have already been thoroughly evaluated in studies [13; 14], thereby reinforcing the reliability of the dataset. This spatial visualization not only underscores the comprehensive nature of the dataset but also provides a geographic reference for interpreting the statistical results of coordinate differences--such as mean offsets and RMSE values--discussed in subsequent sections.