esa-sceva/satcom-chunk-collection huggingface dataset:
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
## 5 Direct Identification of Bacterial Species from the Blood Culture Medium  
In general, after the liquid culture becomes positive, the sample is subcultured on solid medium for a day and a night to permit the growth of colonies. While most identification is performed using plate culture, the most clinically useful application of MALDI-TOF MS is in the identification of bacterial species directly from blood culture medium. The rapidity of this method is particularly useful in blood culture, where identification results can be obtained within one hour after a positive reaction in the blood culture bottle. This method is expected to improve the prognosis of patients and shorten their hospital stay because this approach can identify the organism 1.5 to 2 days earlier than the conventional method. Pretreatment to separate and concentrate the bacteria from the blood culture medium is necessary, but this procedure can be done in a short time.  
Pretreatment methods for bacterial identification using MS techniques are required for direct identification of microorganisms from blood culture bottles [58; 59; 60; 61; 62; 63]. Kits have been developed for this purpose. The MALDI Sepsityper(r) kit (Bruker Daltonics), which collects bacteria by lysing blood cell components in lysis buffer followed by high-speed centrifugation, is widely used in Japan and overseas [64; 65; 66; 67; 68]. An alternative method is to capture and collect bacteria using positively charged polyacrylamide (cationic particles) [69]. The cationic particle technique has been improved and put to practical use; following clinical evaluation, a kit incorporating this technology is sold in Japan and overseas as the pretreatment rapid kit BACpro(r) II (Nitto-Bo Medical Inc.) [20; 70; 71; 72]. However, these methods cannot be used for drug susceptibility testing, given that the bacteria are killed during the pretreatment process, which is a major drawback. In contrast, if viable bacteria can be recovered efficiently, these organisms can be used for species identification and rapiddrug susceptibility testing using automated equipment. Therefore, further improvement of new pretreatment kits is expected.  
Gene-level methods for rapid identification from blood culture-positive bottles have also progressed [73]. An automated multi-item simultaneous gene-related testing system capable of simultaneous detection of causative organisms and drug resistance genes has been reported, and comparative studies with MS-based methods have begun [68]. However, both methods are still in their early stages, and further investigations of the use of genetic and protein level methods in clinical practice will be necessary.
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Nanofiltration (NF)  
Nanofiltration is a membrane filtration process used to remove dissolved ions or organic matter to produce soft water, i.e., water with a limited number of ions that are responsible for scaling (Ca2+, Mg2+...). This technique is conceptually similar to RO. The main difference is the action used to remove the ions from the saltwater, as shown in Figure 22.  
NF is used in several applications such as water and wastewater, pharmaceutical and food processing [52]. The applications for the desalination of seawater are limited, since these semipermeable membranes are more porous, allowing the passage of some dissolved solids [23].  
As shown in Figure 23, filtration technologies are classified according to the size of the particles and molecules that are stopped by the membrane [36].  
The prefix \"Nano\" is related to the pore sizes, ranging from 1 to 10 nanometers, so smaller than other filtration techniques (microfiltration and ultrafiltration) but larger than in the RO. As a consequence, this technology removes mostly divalent ions (e.g., Ca2+ and Mg2+), with an efficiency of between 90% and 98%. The removal of monovalent ions is limited (between 60% and 85%) [2].  
As the soft water produced by the NF process has a greater ion concentration than RO, a lower pressure gradient must be applied to the semipermeable membrane (between 34 and 48 bar) [2]. As NF requires a lower energy demand than RO, this solution is under investigation for seawater desalination, introducing a dual-stage unit [46; 52].  
Figure 21: A schema of a Forward Osmosis desalination unit.  
Figure 22: The working principles and schema of a nanofiltration unit.
## 9 Conclusions  
This study presented a comprehensive analysis of received power and SNR performance in indoor VLC systems employing IRS position optimization via an RNN for different ADR geometries and processing mechanisms. The results demonstrated that the incorporation of the RNN significantly improves both metrics compared to scenarios without IRS optimization. Specifically, ADRs with MRC processing consistently outperformed SBC in terms of received power and the SNR, regardless of ADR geometry, achieving maximum power and SNR values of 12.11 dB and 105.94 dB, respectively. Furthermore, hemispherical ADRs generally exhibited superior performance compared to pyramidal ones under the same conditions, highlighting the benefits of this geometry in achieving broader and more uniform distributions.  
The impact of RNN-based optimization on system stability across epochs was also evident. MRC-based ADRs achieved consistent received power levels (between 10.92 and 12.11 dB) with minimal fluctuation over 50 epochs, showcasing the robustness of this approach for adaptive optimization in dynamic environments. In contrast, SBC exhibited limited performance gains, suggesting that advanced combination schemes are critical to take advantage of the full potential of RNN-optimized IRS systems. The findings underscore the pivotal role of IRS positioning and signal processing mechanisms in maximizing the performance of VLC systems.  
Future studies could extend this work by exploring hybrid optimization frameworks that combine RNNs with other deep learning techniques, such as convolutional neural networks (CNNs) or transformers, to further refine IRS positioning strategies. Furthermore, incorporating user mobility and dynamic changes in channel conditions would provide deeper insight into the adaptability and scalability of RNN-optimized systems in real-world applications. Finally, experimental validation of these simulations in physical setups, including the deployment of practical ADR designs, would bridge the gap between theoretical models and practical implementations, paving the way for advanced VLC systems in next-generation wireless networks.  
Conceptualization, M.R.C.; methodology, M.R.C.; software, M.R.C. and P.P.J.; validation, P.P.J. and C.A.-M.; formal analysis, M.R.C.; investigation, M.R; resources, P.P.J. and C.A.-M.; data curation, M.R.C.; writing--original draft preparation, M.R.C.; writing--review and editing, P.P.J., C.A.-M., D.Z.-B., and I.S.; supervision, P.P.J. and C.A.-M.; project administration, P.P.J. and C.A.-M.; funding acquisition, P.P.J. and C.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ANID/FONDECYT Iniciacion No. 11240799, ANID FONDECYT Regular 1211132, Project ANID Vinculacion Internacional FOVI240009, SENESCYT \"Convocatoria abierta 2014-primera fase, Acta CIBAE-023-2014\", and UDLA Telecommunications Engineering Degree FICA, UDLA.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Combining ground data and VIs to scale biophysical processes over large areas  
### Flux Towers and Other Methods to Measure Carbon Assimilation and ET at Plot Scales of Measurement  
An alternative to solving complex SVAT and SEB equations with limited remote sensing data is to combine remote sensing measurements with ground data. When ground data are available, remote sensing serves as a scaling tool rather than as a complete physical model. Before the 1990s, carbon and moisture fluxes were difficult to measure at scales relevant to agriculture or ecology. As mentioned, leaf-level measurements were available in the 1980s [19], but it is a daunting task to scale these measurements to whole canopies and larger landscape units. From the 1990s to the present, an increasing number of micrometeorological flux towers have been deployed in agricultural and natural ecosystems around the world [49-51]. These flux towers are now organized into networks such as Euroflux, Ameriflux, MEDEFLUX (the Mediterranean region), AsiaFlux, and OzNet (Australia). In 1997 FLUXNET was formed as a \"partnership of partnerships\" to compile long-term measurements of carbon dioxide, water vapor and energy flux into high-quality data sets for ecosystems around the world. FLUXNET currently contains 400 towers, and it has the specific goal of validating the remote sensing ecosystem products from the Terra satellite [50].  
The existence of flux tower data simplifies the job of scaling plant-related processes over large landscape units. As top-of-canopy measurements, flux towers do not require knowledge of LAI, f\({}_{\rm c}\), or details of canopy architecture to produce results, and the measurement footprint of flux towers at least partially overlaps the pixel size of daily-return satellites (e.g., 250 m for MODIS).  
Satellite measurements are spatially continuous at their pixel scale but temporally provide only daily or less frequent snapshots of data, whereas tower-based flux data are temporally continuous at their recording intervals (typically 20-30 minutes), but are essentially point measurements spatially. Hence, combined remote sensing and flux tower data can provide opportunities to upscale the tower data and to constrain the satellite data. Satellites can uniformly sample the entire study area of interest, whereas flux towers seriously under-sample the landscape spatially, but they provide detailed data on fluxes as well as micrometeorological data at frequent intervals over the course of a day.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 2 Related Works  
In this section, previous works related to the proposed method are reviewed. At present, object detection technology is used in many fields in combination with object detection, such as in forest fire detection [9], identification of insulator defects on pylons [10], and aerial vehicle detection [11]. At the same time, there have been many studies on object detection for wildlife detection, such as O-YOLOv2, YOLOv2 [12], YOLOv3, TinyYOLOv3 [13], YOLOv4-uw [14], Faster R-CNN, Modified Faster R-CNN, RetinaNet [15], CenterNet, improved CenterNet [16], and other models, the performances of which are shown in Table 1. Although many models have high detection accuracy, the large scale of the models and the large number of parameters leads to their ability to perform real-time  
Figure 1: (**a**) The targets are small in the image; (**b**) The targets are large in the image.  
detection in application being insufficient. [PERSON] et al. [15] used Faster R-CNN and modified Faster R-CNN models, respectively, to detect wild animals. Although the detection accuracy was high, the detection speed was very low. The detection speed of the Faster R-CNN model was 3 fps, and the detection speed of the Modified Faster R-CNN model was 2 fps.  
With the advancement of technology, the application of UAVs is everywhere in daily life, and research based on UAV vision object detection is common. The current application is more based on the detection of pedestrians and vehicles by drones [17; 18; 19]. The SlimYOLOv3 model proposed by [PERSON] et al. [20] not only has a high detection accuracy but also meets the practical needs of UAVs in real-time. [PERSON] et al. [21] applied the object detection model to UAV countermeasures, which is a new research direction based on UAV object detection and also achieved good results in terms of real-time and accuracy. Small target detection based on UAV vision is also a research hotspot. The UAV-YOLO model proposed by [PERSON] et al. [22] improves the accuracy of small target detection by adding spatial information. [PERSON] et al. [23] provide a multi-scale dataset based on UAV vision, named MOHR, and this dataset is of great significance for monitoring in the industry.  
The purpose of this paper is to design a lightweight real-time object detector that can be deployed to an embedded platform and better integrated with UAVs. At the same time, the detector should accommodate as much as possible the change in altitude of the UAV during actual flight.
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 3 Insulator Damage Detection Based on Zernike Moment
### Hu Moment and Zernike Moment
#### 3.2.1 Hu Moment  
For a single-channel digital image _f_(_x_,_y_), its (_p_ + _q_) order moment _mpq_ and central moment _mpq_ are expressed as:  
\[m_{pq}=\sum_{x}\sum_{y}x^{p}y^{q}f(x,y) \tag{3}\]  
\[\mu_{pq}=\sum_{x}\sum_{y}\,(x-\overline{x})^{p}(y-\overline{y})^{q}f(x,y) \tag{4}\]  
where \(\overline{x}=m_{10}/m_{00}\), \(\overline{y}=m_{01}/m_{00}\), and they represent the abscissa and ordinate of the image centroid, respectively. In addition, \(p\), \(q\) = 0, 1, 2,...  
The normalized central moment _mpq_ is expressed as:  
\[\eta_{pq}=\frac{\mu_{pq}}{\mu_{00}^{r}} \tag{5}\]  
where \(r\) = (_p_ + \(q\) + 2)/2 and \(p\) + \(q\) = 2  
where \(R_{n,m}(\rho)\) is an orthogonal radial polynomial, which can be expressed as:  
\[R_{n,m}(\rho)=\sum\limits_{s=0}^{(n-|m|)/2}\frac{{{(-1)}^{s}(n-s)!\rho^{n-2s}}}{{ s!(\frac{n+|m|}{2}-s)!(\frac{n-|m|}{2}-s)!}} \tag{15}\]  
In order to ensure the scale and translation invariance of the algorithm, the calculation origin of the image boundary points is moved to the centroid of the image and normalized at the same time, that is:  
\[\rho=\frac{{\sqrt{{{(x-\overline{x})}^{2}+{(y-\overline{y})}^{2}}}}}{{\max( \sqrt{{{(x-\overline{x})}^{2}+{(y-\overline{y})}^{2}}})}} \tag{16}\]  
\[\theta=\tan^{-1}(\frac{x-\overline{x}}{y-\overline{y}}) \tag{17}\]  
Combining Equations (13)-(16), the Zernike moments of any order of single-channel digital images can be calculated. Generally speaking, low-order moments can be used to describe the basic features of the object shape, and high-order moments can be used to describe the detailed information on the object shape. Compared with the Hu moment, which can only calculate the seventh-order characteristic moments, Zernike can construct any higher order as an orthogonal moment, so it can more accurately distinguish the shape of normal insulators and damaged insulators.
# A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks
## 6 Results and Analysis
### Performance of Network Visualization of the HGE Model  
Network visualization is also one of the most important means to detect the quality of the learned embedding. Network visualization maps a network into two-dimensional space. In this section, for better visualization, we visualize a sub-network which is selected from the BlogCatalog dataset with 175 nodes and 150 edges.  
From Figure 6, we find that our HGE model preserves the default hierarchical structure, and distributes the nodes of each layer more uniformly in a fan-shaped area.
## 5 Results and Discussion  
To simulate ARL, SDRL and RL percentiles of IT2 TFN \(\overline{X}\) and R control charts, sample size \(n\) = 5, process mean 0.0 and standard deviation 1.0 were selected. \(L_{1}\) = \(L_{2}\) = 0.05 and FOU = 0.30 were used in the fuzzification process according to experts, as they provide a fast response while at the same time reducing overshoots according to [18]. Average Run Length (ARL) and Standard Deviation Run Length (SDRL) for \(\overline{X}\)-R Control Charts  
The values of \(\delta\) range from 0 to 1.6 with successive increments of 0.2, and \(\lambda\) ranges from 1 to 2.5. According [34], 10,000 iterations were performed in the simulation in order to reach a more accurate result. Table 2 presents the theoretical values of ARL and SDRL performance measures found for traditional control charts. It is observed that the SDRL value is very close to that of ARL, which explains the high variability of ARL as a performance measure. Figure 8: Simulation algorithm for calculating the ARL, SDRL and RL percentile parameters for the IT2 TFN \(\overline{X}\) and \(R\) control charts. Although not starting from an ARL = 370.50, IT2 TFN control charts showed an average reduction by 0.31% and 0.33% in ARL and SDRL values, respectively, when compared to values found using traditional control charts. ARL values whose \(\delta\) = 0.8 showed 1.37% lower values (on average) with respect to the same \(\delta\) value simulated in traditional control charts. Regarding \(\lambda\), ARL values were found when \(\lambda\) = 1.40 and showed 0.76% lower ARL considering the same \(\lambda\) value simulated in traditional control charts, as shown in Table 3.  
For SDRL, there is an average reduction by 2.13% considering \(\delta\) = 1.6, but 0.98% in SRDL when \(\lambda\) = 1.2 in comparison with traditional control charts. Such reductions in ARL and SDRL demonstrate that IT2 TFN \(\overline{\text{X}}\) and R control charts are capable of detecting out-of-control points more accurately than traditional control charts. Thus, the presently proposed model has superior performance. A rapid response is observed when the purpose of process monitoring is to detect defects from a quality characteristic, which is a variable able to be obtained by a measurement system in the case under study. Regarding the behavior of RLs, [29] reports that ARL and SDRL values at every \(\delta\) and \(\lambda\) value should be close, since ARL and SDRL performance metrics are in accordance with a geometric distribution. Figures 9 and 10 illustrate the values of ARL and SDRL as a function of \(\delta\) and \(\lambda\), respectively. It is worth mentioning that they are quite similar in terms of shape and numerical scale.
# Blockchain Applications in Agribbusiness: A Systematic Review
## 3 Results and Discussion
### A Qualitative Systematic Review
#### 3.2.4 Blockchain Application for Environmental Management in Agribusiness  
Agribusiness fights against the image that it negatively impacts the environment [55]. Blockchain can play a key role in the environment due to its data monitoring capability. Research suggests that its adoption can make ecological and sustainable practices in the supply chains easier [56; 57]. [PERSON] et al. [24] presented an evaluation tool with social and technical requirements applying blockchains in Information and CommunicationsTechnology (ICT) systems in agriculture, using an interface to visualize the application in its operation, which can promote better development and incorporation of data into local climate, energy use, pesticides, soil quality, production costs, and biodiversity conservation.  
[PERSON] and [PERSON] [58] proposed studying advances in Information Technology (IT) using social media, identifying the feasible options for an alternative global transnational climate policy. In the research, they included consumers, fossil fuel industries, non-governmental environmental organizations (NGOs), insurance industries, IT companies, representatives of public administrations, and UN agencies. The label accurately shows the reliability of climate impact and the total energy incorporated into final goods and services. Since the monitoring system is transparent, consumers have more trust in the label.  
[PERSON] et al. [59] present a management proposal for the Jundiai River Basin, based on the adoption of a complementary currency created with the support of blockchains. Technological innovation, such as payment schemes for environmental systems (PES), does not depend on the traditional financial system. Thus, it is immune to economic crises because its creation and management are independent of banks, with the rewards being paid to the makers in the form of cryptocurrency.  
[PERSON] et al. [60] implemented a blockchain prototype for open-source electronic wood traceability, using Radio-Frequency Identification (RFID) Technology, in which the process is tracked from the standing tree to the end user. In the first step, the tree is identified in the forest and this first stripe is associated with the information in the database: marking date, Global Positioning System (GPS) point of the tree, species, diameter, and height. Other information such as cutting, labels, stacking and production flows are detected by an antenna where the information is again associated with the database. Therefore, labels are produced during production and sale and applied on the final products destined for the end consumer.  
It was identified that the utilization of blockchain in the environment is essentially focused on payments for environmental services, forest mapping, traceability, and climate and soil control. However, it has the potential to be explored in several other ways, including problems of climate change and carbon sequestration.