esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Vision Technology in Citrus Production
## 2 Pest and Disease Detection
### Identification of Pests and Diseases Based on Spectral Technology
[32] collected hyperspectral images of citrus visible and near-infrared light from three types of tissues with a wavelength range of 325-1000 nm. The spectral variables were optimized using a combination of bootstrap soft shrinkage (BOSS) and BOSS sequential projection algorithm (BOSS-SPA) algorithms. PLS-DA models based on three types of tissues and two types of tissues were constructed, and a fast multispectral image processing algorithm combined with global threshold theory was used for detection of decayed citrus. Table 2 compares three methods--traditional method, deep learning method and hyperspectral imaging technique.
# The Construction of a Digital Agricultural GIS Application Suite
## 2 Design Considerations and Methods
### Implementation Method
#### 2.2.2 Low-Code Development  
Low-code development is an efficient and flexible approach to application development [37; 38], emerging as a crucial strategy for modern enterprises to cope with rapidly changing markets and technological advancements. Low-code development platforms enable developers to create applications using graphical user interfaces and visual modeling tools without the need to write extensive traditional code, allowing for faster application construction.  
Prominent low-code development platforms include Microsoft's Power Platform [39], Alibaba's LowCodeEngine [40], Huawei's AstroZero [41], and Tencent's TMagic-Editor [42]. Notably, TMagic-Editor is an open-source, WYSIWYG (What You See Is What You Get) page visual editor that enables zero-code/low-code page generation, facilitating the rapid establishment of a visual page production platform. TMagic-Editor supports custom components, the free nesting and layout of components, and component linkage, offering rich configuration capabilities for various components within a page. Components in TMagic-Editor exist as npm packages, and as long as they are developed according to the specified norms, they can be added to the TMagic-Editor runtime and correctly rendered. Components developed once can be reused across multiple applications. Custom components should provide configuration options to ensure flexibility and meet the customization needs of different applications using the component.
# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 3 Methodology
### Notations and Problem Formalization  
#### 3.1.1 Preliminary Knowledge of FCA  
For better understanding, we first provide a brief introduction to Formal Concept Analysis [19] (FCA). FCA is a method for knowledge representation, information management and data analysis. Generally, we regarded FCA as a conceptual clustering method used to determine implicit associations between objects and attributes. Formal context, formal concept, and concept lattice are three central notions in FCA. The following are the key definitions:  
Figure 2: An overview of the SFCA framework.  
**Definition 1**.: _Let \(G\) be the set of objects, \(M\) be the set of attributes, and \(I\) be the binary relationship between object set \(G\) and attribute set \(M\). Then, the triple \((G,M,I)\) is a formal context._  
**Definition 2**.: _Given a formal context \(K\) = (\(G,M,I\)), for \(A\subseteq G\), \(B\subseteq M\), the following operations are defined:_  
\[A^{\prime}=\left\{m\in M|gIm,\forall g\in A\right\} \tag{1}\]  
\[B^{\prime}=\left\{g\in G|gIm,\forall m\in B\right\} \tag{2}\]  
\(A^{\prime}\) _is the set of attributes shared by all objects in \(A\); \(B^{\prime}\) is the set of objects corresponding to all attributes in \(B\)._  
_If \(A^{\prime}=B\), \(B^{\prime}=A\), then the binary \((A,B)\) is a concept in the formal context, where \(A\) is the intent of the concept \((A,B)\) and \(B\) is the extent of the concept \((A,B)\)._  
**Example 1**.: _A formal context \(K\) = (\(G,M,I\)) is shown in Table 1, where the object set \(G\) = (1, 2, 3, 4, 5) and the attribute set \(M\) = {a, b, c, d, e}._  
According to Definition 2, We can get all the concepts in the formal context K: (G, \(\O\)), (1, ab), (2, bde), (3, bcd), (4, ce), (23, bd), (245, e), (235, d), (135, b), (\(\O\),M).  
**Definition 3**.: _Let (\(A_{1}\), \(B_{1}\)) and (\(A_{2}\), \(B_{2}\)) be two concepts on the formal context \(K\) = (\(G\), \(M\), \(I\)), and \(A_{1}\subseteq A_{2}\) (equivalent to \(B_{1}\supseteq B_{2}\)), then we call (\(A_{1}\), \(B_{1}\)) the subconcept of (\(A_{2}\), \(B_{2}\)) and (\(A_{2}\), \(B_{2}\)) the parent concept of (\(A_{1}\), \(B_{1}\)), denoted as (\(A_{1}\), \(B_{1}\)) \(\leq\) (\(A_{2}\), \(B_{2}\))._  
The concept lattice can be visualized by a Hasse diagram. For example, the corresponding concept lattice in Example 1 is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **a** & **b** & **c** & **d** & **e** \\ \hline \(1\) & \(1\) & \(1\) & \(0\) & \(0\) & \(0\) \\ \(2\) & \(0\) & \(1\) & \(0\) & \(1\) & \(1\) \\ \(3\) & \(0\) & \(1\) & \(1\) & \(1\) & \(0\) \\ \(4\) & \(0\) & \(0\) & \(1\) & \(0\) & \(1\) \\ \(5\) & \(0\)
## 3 Results and Discussion
### Cylindrical Shape  
Figure 9 indicates the contact angles of AAO, AAO-O, and AAO-O-PT (pressure test) for the cylindrical shape of aluminum 7075. After anodizing, the contact angle was decreased. After oil impregnation, the contact angle was increased to 50.0\({}^{\circ}\)\(\pm\) 4.4\({}^{\circ}\) like that in the plate shape. The contact angle of the AAO-O-PT was approximately 89.1\({}^{\circ}\)\(\pm\) 13.6\({}^{\circ}\). Although the pressure test was conducted on AAO-O, the contact angle was increased by 39.1\({}^{\circ}\). This result showed that some oil remained in the nanoporous structure, even though the oil on the top of the nanoporous structure was removed. The combination of the nanoporous structure and the oil in the pores caught the water droplet. Therefore, the high contact angle was investigated in the case of oil impregnation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Bare Aluminum** & **AAO** & **AAO-O-SST** \\ \hline Diagram & & & \\ Interfacial tension & 55.2 & 7853.0 & 53.7 \\ (mN/m) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Interfacial tensions between water and bare aluminum, AAO (anodic aluminum oxide), and AAO-O-SST (oil impregnation—salt spray test).
# New Vector-Space Embeddings for Recommender Systems
## 2 Related Work
### Neighborhood-Based Collaborative Filtering  
This is the other major methodology for tackling the Recommender Systems problem. In this approach, the user-item interactions are directly used to predict the potential interest in new items. Two strategies are typically adopted: user-based or item-based recommendations. In user-based methods, the prediction is based on the ratings of other users for a specific item. These users are the neighbors, that is, the users with similar taste to the given user. In item-based methods, the roles of items and users are reversed, and the prediction is based on the given user's ratings for similar or neighbor items.  
In user-based models, the set of \(k\) neighbors for a given user are first extracted based on the used similarity measure. The rating prediction can then be performed using aggregation approaches such as the average and the weighted sum. In item-based models, the \(k\) neighbors for each item in the system are first determined. The rating prediction is based on the ratings the given user has assigned to the neighboring items (by averaging or other aggregation methods). This approach is known for its simplicity and reasonably good results. However, it suffers from low scalability and sparsity of the Recommender Systems databases [23; 24; 25].  
Some variants and modifications of the neighborhood approach have also been proposed. Again, only a few examples are given below. In [26], a neighborhood-based CF approach is proposed. The approach aims at enhancing the computed neighborhood such that fake neighbors are eliminated. Fake neighbors are users who have spurious similarities, but the resemblance is not genuine. In addition, semantic similarity is also deployed, in the sense that users do not need to have ratings for the same items in order to be compared.  
In [27], an adaptive k-nearest neighbors collaborative filtering is proposed for recommender systems. A knowledge-based domain specific ontology is devised to obtain customized recommendations.  
In [28], a user-based recommender system is proposed. A method is developed to select the optimal neighborhood for a given user. The selection is performed using two strategies: k-nearest neighbors and threshold-based neighbors. The threshold is computed as the distance between a given user and its neighbors, proving that using higher thresholds is more error prone.  
In [29], traditional user-based collaborative filtering approaches are extended to achieve better accuracy and coverage levels. The proposed method relies on covering-based rough set theory where redundant users are removed. Since popular items appeal to many users, this can generate many non-genuinely similar users, which preferably have to be eliminated.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 6 Results and Discussion
### System Efficiency and Endurance  
Power consumption varied significantly based on operational mode, with the highest consumption occurring during simultaneous audio streaming and motor operation. Table 4 details the measured consumption across different operational scenarios.  
The battery endurance testing confirmed these estimates, with the fully charged LiPo battery supporting approximately 4.3 h of combined operation before the voltage dropped  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Operation Mode** & **Average Current Draw** & **Estimated Runtime (2200 mAh)** \\ \hline Standby (connected) & 115 mA & 19.1 h \\ Audio streaming only & 175 mA & 12.6 h \\ Motor operation only & 390 mA & 5.6 h \\ Combined operation & 485 mA & 4.5 h \\ \hline \hline \end{tabular}
\end{table}
Table 4: Power consumption in various operational modes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distance** & **Command Latency** & **Command Execution Accuracy** & **Interference with Audio** \\ \hline
2 m & 105 \(\pm\) 12 ms & 98.7\% & Minimal \\
5 m & 123 \(\pm\) 18 ms & 97.2\% & Slight \\
10 m & 142 \(\pm\) 25 ms & 95.8\% & Moderate \\ \hline \hline \end{tabular}
\end{table}
Table 3: Motor control performance at varying distances.  
Figure 7: Motor noise spectral impact on audio recording.  
below the operational threshold. This endurance is sufficient for typical field recording sessions and can be extended through power management strategies such as the selective activation of audio streaming only when necessary.  
CPU utilization remained within manageable limits throughout all operational scenarios, with Core 0 averaging 65% utilization and Core 1 averaging 72% utilization during combined operation. This headroom suggests potential for additional on-device processing, such as basic audio analysis or feature extraction, in future iterations of the system.
# Non-Local Effects in Brillouin Optical Time-Domain Analysis Sensors
###### Abstract  
Brillouin optical time-domain analysis (BOTDA) sensors have great potential to provide distributed measurements of temperature and strain over large structures with high spatial resolution and measurement precision. However, their performance ultimately depends on the amount of probe and pump pulse power that can be injected into the sensing fiber, which determines the signal-to-noise ratio of the detected measurement signal. The probe wave power is constrained by the generation of noise induced by spontaneous Brillouin scattering and at lower power by the so-called non-local effects. In this work, we focus on the latter. We review the physical origins of non-local effects and analyze the performance impairments that they bring. In addition, we discuss the different methods that have been proposed to counteract these effects comparing their relative merits and ultimate performance. Particularly, we focus on a technique that we have devised to compensate non-local effects which is based on introducing an optical frequency modulation or dithering to the probe wave. This method is shown to provide a comprehensive solution to most of the impairments associated with non-local effects and also to enable some side benefits, such as amplification of the pump pulses to compensate the attenuation of the fiber.  
s +
Footnote †: journal: Applied Physics Letters  
_Applied_  
_sciences_
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
As the core bearer of future space-based network, the optical backbone network must meet the following demands: (1) Global seamless connections with high data rates and large communication capacities should be provided. (2) The transmission of various services should be supported. Furthermore, as a space network mainly consisting of satellites, the space-based optical backbone network is subject to several constraints, such as the limited resources and restricted performance due to the characteristics of satellites. In order to meet the demands mentioned above, we must resolve the following challenges: (1) How to support efficient management and control for a resource-limited optical network? (2) How to take full advantage of the restricted performance of satellites in a space-based optical backbone network? (3) How to provide transparent transmission for different kinds of services? In order to solve these problems, we have undertaken various studies [10]. Based on such studies, we further propose a detailed design of a protocol stack that is suitable for the space-based optical backbone network. Then, we used software to simulate and built a hardware platform to test this protocol stack, before finally analyzing the results.
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 4 Standards Evaluation Results
### Standards Selected for Analysis
#### 4.1.3 Incident Object Description Exchange Format  
IODEF is an Internet Engineering Task Force (IETF) initiative that aims to facilitate information sharing between organizations and increase the possibility of mitigating cyber threats [51]. In the first version, the data model was focused on representing incidents. Bringing a more holistic approach, the second version of IODEF was designed with significant evolution in its structural part, which now includes structures for the description of indicators, attackers and incident response methodologies [52]. Both versions use the XML format.
## 3 Materials and Methods
### Antioxidant Properties
#### 3.5.3 Detection of Intracellular Levels of Reactive Oxygen Species (ROS)  
In order to determine the ability of the analyzed hydrogels (H-CON, H-CBD, H-TER, and H-CBD-TER) to inhibit the intracellular production of reactive oxygen species in keratinocytes (HaCaT) and fibroblasts (BJ), the fluorogenic H\({}_{2}\)DCFDA dye was used based on the procedure described by [PERSON] et al. (2018). Briefly, cells were seeded in 96-well flat-bottom plates at a density of 2 \(\times\) 10\({}^{4}\) cells per well. The cells were then cultured in an incubator for 24 h to adhere them to the bottom of the wells. The DMEM medium was then removed and replaced with 10 uM H\({}_{2}\)DCFDA indicator solution (Sigma Aldrich, Sant Louis, MO, USA) dissolved in serum-free DMEM medium (FBS). HaCaT and BJ cells were incubated with H\({}_{2}\)DCFDA for 45 min and then treated with H-CON, H-CBD, H-TER, and H-CBD-TER hydrogels in 0.1-5.0% concentration range. Cells treated with 1 mM hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) solution were the positive control, while cells untreated with analyzed hydrogels were the control sample. Fluorescence of 2\({}^{\prime}\),7\({}^{\prime}\)-dichlor fluorescein (DCF) in individual wells was measured every 30 min for 90 min at excitation wavelength \(\lambda\) = 485 nm and emission \(\lambda\) = 530 nm using a microplate reader (FilterMax F5, Thermo Fisher Scientific, Waltham, MA, USA). Three independent experiments were performed, and each sample was tested in three replications.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 5 AI and Different Applications in Ophthalmology and Other Fields
### Cardiovascular Risk Factors
#### 5.1.6 AI in Tele-Ophthalmology  
AI applications in tele-ophthalmology have the power to increase access to healthcare, enable cost savings for patients, providers, and governmental bodies. Three delivery methods for the tele-ophthalmology domain have been described:  
* Asynchronous: This \"store-and-forward\" method uses clinical imaging information and sends it to the ophthalmologist for evaluation. This method is currently applied for diabetic retinopathy [141].
* Synchronous: This approach may provide real-time telemedicine between patients and ophthalmologists through certain communication channels (e.g., video chat, phone calls, and smartphone and internet apps). This method has been implemented with success in many intensive care units (ICUs) where remote eye care is offered to centers that do not have such services.
* Remote monitoring: This type of service permits suppliers to monitor sick individuals at home or remotely. Intraocular pressure (IOP)-measuring contact lenses, home IOP monitors, IOP sensors, visual field devices, and age-related macular degeneration (AMD) devices are examples of instruments that automatically send acquired data to providers in order to enhance clinical services. AI is permeating the entire ophthalmic healthcare sector, i.e., in
* Administrative work: AI programs reduce the administrative workload through intelligent scheduling, automatic invoicing, patient tracking, and claims management;
* Robotics and procedures: AI software can be found in many specific devices, such as alignment automatic guides, and focus and data acquisition. In addition, AI has been used in minimally invasive surgery, remote surgery, and slit-lamp examinations. Robotic tools such as co-manipulators, tele-manipulators, and highly stable hands are being developed;
* Diagnosis and screening: Integrating the interpretive and predictive skills of AI for tele-diagnosis significantly increases access to healthcare, decreases unnecessary eye doctor consultations, and saves money and time for sick individuals, providers, and the healthcare system. Tele-ophthalmology and AI have so far shown effectiveness for diabetic retinopathy and are being used, for instance, for glaucoma screening, AMD monitoring, corneal abnormal topography identification, and pediatric cataract screening.
* Remote Monitoring: Telemonitoring is an important challenge in tele-ophthalmology. AI programs can provide personalized information to a large number of patients and assist providers in making clinical decisions.
## Materials and Methods
### BICOS Algorithm  
This section contains the details of our BICOS algorithm. We add a pre-calculation before the actual correspondence search which allows us to reduce the number of required calculations for each pixel comparison. Our coarse correspondence search comprises three steps:  
1. **Calculation of binary features.** For each pixel, we first calculate a bit string of \"binary features\". A binary feature is generated by comparing two of the brightness values of a pixel with each other (Figure 2a,b). For example, comparing brightness values \(b_{1}\) with \(b_{2}\) yields a binary feature with a value of 1 if \(b_{1}>b_{2}\) and 0 otherwise. It does not matter, if a \(>\), \(<\), \(\geq\) or \(\leq\) operator is used, as long as it is consistent.  
We can compare every brightness value with every other brightness value within the sequence. For instance, for a sequence length of 10, we compare brightness value \(b_{1}\) with \(b_{2}\), \(b_{2}\) with \(b_{3}\),..., \(b_{9}\) with \(b_{10}\), \(b_{10}\) with \(b_{1}\), \(b_{1}\) with \(b_{3}\) and so forth. This yields 45 binary features. In addition, each brightness value can be compared to the mean brightness value of the sequence, yielding another 10 binary features for a sequence length of 10 (Figure 2c). We restrict the number of binary features to 64 to allow fast computation.
2. **Comparing the binary features.** To find a correspondence we do not compare the brightness values but we compare the binary features of a pixel in the left camera with the binary features of a pixel in the right camera. The more binary features coincide, the better the match. Thus the pixel similarity measure for BICOS (and BICOS+) is the Number of Equal Binary Features (NEBF). Calculation of the NEBF is very fast because only two operations are required: compare and count. Like in the NCCCOS, we perform the correspondence search in both directions, that is, first from left to right, then from right to left. We accept only consistent results.
3. **Filtering the coarse correspondences.** The coarse disparity map which we generate by using the NEBF contains more outliers and holes than the one created with the NCCC which is used in NCCCOS. For an explanation why this is the case, see Section 3.2. We compensate for this by applying a 3 \(\times\) 3 median filter to the disparity map, which also fills the holes. Please note that this does **not** mean that the final 3D result is filtered because we apply this filter only to the coarse disparity map which is used as a start solution for the refinement step.  
Figure 2: Creation of binary features from a pixel’s sequence of brightness values. (**a**) Comparison of temporally adjacent brightness values. (**b**) Comparison of each brightness value with every second brightness value. (**c**) Comparison of each brightness value with the mean brightness of the pixel (BICOS and BICOS+).
## 3 Results  
### Improving Image Quality by Removing Compression Artifacts  
In all analyses, we compressed the original images with JPEG compression quality factor \(QF\) values from 10 to 90 in increments of 10. We then tested each described method for removing compression artifacts in terms of the quality of reconstructed images and compared them to results obtained for JPEG compressed images. The results for histopathological H&E stained images are presented in Table 1. We included the best mean \(SSIM\) and \(PSNR\) scores obtained by each method for the tested parameter sets. Figure 3 depicts fragments of uncompressed histopathological images, images after compression, and the effects of removing compression artifacts by methods that obtained the highest image quality: EPIIC, EPIIC Sobel, FBCNN, the Gaussian filter, and the Guided filter. As can be seen, the deep learning-based solution significantly outperformed the other methods. However, our proposed artifact removal methods improved image quality for all tested compression quality parameters. They were also slightly better than the Gaussian filter in most cases. The EPIIC method performed worse than the Sobel variant. The Guided filter gave more satisfying results for images compressed with lower \(QF\) values than the mentioned filtering methods. If we carefully look at the images processed by the network, we can see some smoothing and unification of the color of the structures visible in the image. The images processed by the developed methods are blurred. However, they allowed slightly better preservation of the minor color variations in the actual tissue image.