esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Organic Electrochemical Transistors (OECTs)
Building on this concept but focusing on in situ sensing, [PERSON] and collaborators created a printed OECT-based alcohol sensor on paper, utilizing a low-cost, disposable, and biodegradable material [40]. In a separate study, [PERSON] and colleagues showcased remarkable long-term deep brain cortex recordings using a new OECT design known as the ion-gated electrochemical transistor. The high performance of these devices is largely due to the smart incorporation of a hydrated ion reservoir directly into the polymer composite channel (made of PEDOT:PSS/PEI), which enables faster transistor response times by facilitating rapid ion movement from the reservoir to the semiconducting polymer [41]. In conclusion, electrochemical transistors (OECTs) have witnessed significant advancement and recognition in recent years due to their unique ability to marry electronic device functionality with electrochemical capabilities. This fusion of electronics and chemistry offers revolutionary potential across various domains, from biomedicine to wearable electronics. The development of OECTs has led to significant breakthroughs in biomedical applications, such as in situ monitoring of biomarkers in biological fluids and neural recording in the brain. Their ability to operate in complex biological environments with enhanced sensitivity and selectivity has positioned OECTs as invaluable tools in medical research and clinical practice. However, despite the achievements, OECTs face significant challenges for future development and widespread adoption. Long-term stability optimization, sensitivity and selectivity enhancement, as well as cost reduction in manufacturing and scalability, are critical aspects that require continuous attention. Additionally, integrating OECTs into portable and wearable devices poses additional challenges, such as mechanical compatibility and durability under real-world usage conditions. Overcoming these challenges will require interdisciplinary collaboration among electronics, chemistry, and materials engineering, as well as innovative approaches in the design and manufacturing of these devices. In summary, OECTs represent an exciting frontier at the intersection of electronics and electrochemistry, with the potential to transform numerous fields, from medicine to wearable technology. Addressing the future challenges of OECTs will not only drive their practical application but also open new opportunities for innovation and scientific advancement.
###### Abstract  
Introduction: The skin pick test (SPT) is the first step in the diagnosis of an immunoglobulin E (IgE)-mediated food allergy. The availability of commercial food allergen extracts is very limited, resulting in a need for alternative extraction methods of food allergens. The objective of this study was to compare the SPT results of homemade food allergen extracts with commercially available extracts. Methods: Adult patients with a suspected food allergy were included. Food allergen-specific symptoms were scored using a questionnaire. SPTs were performed with homemade and commercially available extracts (ALK-Abello, Kopenhagen, Denmark) from almond, apple, hazelnut, peach, peanut, and walnut. Serum-specific IgE was measured with ISAC or ImmunoCAP(tm). Intra-class correlation coefficients (ICC) between the SPT results of both extract methods were calculated. The proportion of agreement with food allergen-specific symptoms was analyzed. Results: Fifty-four patients (mean age 36; range 19-69 years; female/male: 42/12) were included. The intra-class correlation coefficient (ICC) between the SPT results of both extract methods were strong for hazelnut 0.79 (\(n\) = 44) and walnut 0.78 (\(n\) = 31), moderate for apple 0.74 (\(n\) = 21) and peanut 0.66 (\(n\) = 28), and weak for almond 0.36 (\(n\) = 27) and peach 0.17 (\(n\) = 23). The proportion of agreement between SPT results and food allergen-specific symptoms was comparable for homemade and commercially available extracts, except for peach; 0.77 versus 0.36, respectively. Conclusion: In the diagnostic procedures to identify an IgE-mediated food allergy, homemade extracts from hazelnut and walnut appear to be a good alternative in the absence of commercially available food allergen extracts.  
diagnosis; extracts; food allergy; oral allergy syndrome; skin pick test; specific IgE
# Experimental Assessment of the Effect of Vertical Earthquake Motion on Underground Metro Station
## 2 Shaking Table Tests
### Model
For the whole soil-structure system, various real-time data were collected, including the acceleration and displacement in soil and on structure, the dynamic soil normal stress on both the left and right side walls, and the strain produced on the surface of the structural members.
## 1 Introduction
Like Landsat, Sentinel-2 data are freely available and could be useful to improve the assessment of crop and rangeland biochemical and biophysical variables. To date, empirical methods for estimating leaf N require basic and complex statistical analysis--from simple to machine learning regression ([PERSON] et al., 2014; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2017). The simple empirical approach assumes that leaf N is significantly related to specific chlorophyll-based vegetation indices ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). The assumption is that leaf nitrogen is related to chlorophyll ([PERSON] et al., 2016) and the red edge based vegetation indices capture subtle changes in vegetation vigour. On the other hand, some methods assume that the estimation of leaf N is not dependent exclusively on one vegetation index or reflectance at a specific band, but also a combination of variables--integrated modelling using reflectance, indices and also environmental variables ([PERSON] et al., 2016; [PERSON] et al., 2017). The latter approach was achieved by using stepwise multiple linear regressions (SMLR), partial least square regression (PLSR), machine learning techniques including artificial neural network (ANN) ([PERSON] et al., 2016; [PERSON] et al., 2016). The machine learning techniques are non-parametric in nature and are known to perform better than the parametric ones for grass N estimation ([PERSON] et al., 2016; [PERSON] et al., 2016), and could be critical for monitoring grass nutrients using Sentinel-2. Deriving leaf N concentration for monitoring purposes using satellite remote sensing is always hindered by the availability of field data collected simultaneously with the image acquisition, which is a common practice for leaf N model development. In this study, we intend to explore the use of the spectrometer data with corresponding field N to calibrate and map grass N using Sentinel-2 data. The spectrometer data will be used to simulate Sentinel-2 spectral bands by a resampling technique and the leaf N model will be developed independent of the Sentinel-2 image. [PERSON] et al. (2016)demonstrated that the simulated Sentinel-2 image coupled with the machine learning techniques improves the estimation of leaf nitrogen concentrations. The red edge and the SWIR bands were found to be important in the estimation of leaf N. In this study, only the visible to near infrared data will be investigated to assess the usefulness of the red edge band in Sentinel-2 data for mapping and monitoring leaf N. The study by [PERSON] et al. [27] demonstrated a consistent performance of red edge based indices on the estimation of herbaceous biomass from HyMap images acquired from different dates, compared to conventional vegetation indices such as NDVI. This study will further test the influence of environmental factors, such as the underlying geological types, climatic, topographic features and fire frequency on leaf N, which are some of the major determinants of savanna ecosystems [28; 29; 30; 31].
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 6 Conclusions  
This paper proposes a classification scheme to distinguish nodes that failed to transmit packets (_i.e._, failed nodes) and nodes that leave the monitoring area (_i.e._, left nodes) under the environments, where mobile nodes can freely move around a monitoring region, leave the region and return to the area repeatedly. If a system to track mobile assets uses heartbeat messages to detect a left node without being aware of possible node failures, the system can generate errors of detecting nodes within the detection region as nodes that leave the area. The proposed method in this paper utilizes two trends that are complementary. One is extracted from a series of neighbor counts generated at each mobile node, and the other is extracted from the ratios of boundary nodes included in the neighbor counts. By using trends of these measurements, the proposed classification method successfully separates failed nodes from mobile nodes that disconnected from wireless sensor networks due to their mobility. The performance of the proposed scheme is compared to those of conventional schemes that use the heartbeat messages and the battery lifetime of mobile nodes. Performance evaluations using real datasets and an ns-2 simulator show that the proposed scheme can detect the mobile nodes that fail to provide proper performance and show that the proposed method has superior performance over conventional schemes. By properly detecting faulty nodes in wireless sensor networks, a system to track mobile assets can improve its reliability and increase its asset management performance. For future research, optimizations for power consumption will be studied to prolong the lifetime of each mobile node. This issue will consider various techniques, such as communication energy consumption, low power sensing devices and small antennas.  
This work was supported by the research fund of Chungnam National University.  
[PERSON] detected the problems that occurred in the medical asset tracking system. [PERSON] and [PERSON] designed the experiments, collected and analyzed the data and prepared the manuscript. [PERSON] revised the manuscript and gave final approval of the version to be submitted.  
The authors declare no conflict of interest.
# Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering
## 2 Problem Description  
We assume there is a target moving in an indoor environment. The target's Cartesian coordinates at time \(t\) are \(y_{t}=[a_{t},b_{t}]^{\top}\in\mathbb{R}^{2}\) and \(\theta_{t}\in(-\pi,\pi]\) is its orientation in radians, as shown in Figure 1. The target's motion model is described by the following non-linear state-transition equation  
\[y_{t+1} =g(y_{t},\theta_{t},u_{t},e_{t}), \tag{1}\] \[\theta_{t+1} =h(y_{t},u_{t},e_{t})+a(y_{t},u_{t},e_{t})\theta_{t}+b(y_{t},u_{t},e_{t})n_{t}, \tag{2}\]  
where \(u_{t}\) is the target's control input and \(e_{t}=\left[\tilde{a}_{t},\tilde{b}_{t}\right]^{\top}\) and \(n_{t}\) are process noises accounting for the model's inaccuracy. To write the above in compact form we define the target's pose \(x_{t}=\left[y_{t}^{\top},\theta_{t}\right]^{\top}\in\mathbb{R}^{3}\) and the process noise \(w_{t}=\left[e_{t}^{\top},n_{t}\right]\). We then write  
\[x_{t+1}=f(x_{t},u_{t},w_{t}), \tag{3}\]  
with \(f\) defined according to (1) and (2). We assume that \(x_{1}=0\) and the sequence \(w_{t}\sim\mathcal{N}(0,Q(u_{t}))\) is i.i.d., with \(Q(u)\) being a diagonal positive definite matrix for each \(u\).  
**Remark 1**.: _We state the motion model in the general form (1) in order to make our presentation valid for the different models used in the literature. In Appendix A we briefly describe the most popular models falling into this general form._  
At each time \(t\in\mathbb{N}\), we obtain the following measurement  
\[z_{t}=m(y_{t})+v_{t}. \tag{4}\]  
Figure 1: Target’s Cartesian coordinates.  
where the map \(m:\mathbb{R}^{2}\rightarrow\mathbb{R}^{N}\) is known and the measurement noise \(v_{t}\sim\mathcal{N}(0,R)\) is i.i.d. and independent of \(v_{t}\).  
**Problem 1**.: _Let \(Z_{T}^{\top}=\left[z_{1}^{\top},\ldots,z_{T}^{\top}\right]\) denote the set of all measurements available up to time \(T\). The dynamic localization problem consists of using, at time \(T\), the measurements \(Z_{T}\) to obtain an estimate \(\hat{x}_{T}\) of \(x_{T}\)._
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 5 Issues and Challenges in 5G and Edge-Based V2X  
It is clear that the introduction of 5G technology and edge computing in vehicular networks support massive data exchanges between vehicles and the edge-clouds based on V2X communications. On one hand, 5G is one of the most important and emerging infrastructures, but it also needs to be the most safe and secure. On the other hand, decentralized MEC effectively provides various advantages, including omnipresent edge computing services, increased scalability, and decreased network complexity operations in order to deal with the proliferation of V2X applications, accelerated demand, and development of 5G services. For 5G-based MEC networks, the primary concern is to prevent system disruption that is caused by an edge node attacks on MEC servers. Hereafter, we discuss the issues and challenges faced by 5G-based MEC in vehicular networks.
# Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research
## 5 PD Detection Performance Test  
### PD Inspection Test Platform  
To verify the performance of the designed Fabry-Perot resonant cavity monopole antenna for detecting PD UHF signals, an industrial frequency, and high voltage testbed without local discharge was built in the laboratory, as shown in Figure 15. The experimental circuit consists of an industrial frequency AC power supply, a filter T\({}_{1}\), a regulator, a step-up transformer T\({}_{2}\), a protection resistor R\({}_{r}\), voltage divider capacitors C\({}_{1}\) and C\({}_{2}\), and a cavity that simulates a GIS structure. The PD signal from the antenna sensor is fed through a coaxial cable to the signal acquisition device Tektronix*MSO44 (oscilloscope with four collectible channels, sampling frequency of 6.25 GS/s, bandwidth of 1.5 GHz) Tektronix high-performance digital oscilloscope for acquisition. The GIS simulation chamber tank is filled with 0.5 MPa of SF\({}_{6}\) gas with 99.999% purity. The experiment uses the air gap discharge model to simulate the insulation fault PD process inside the GIS equipment. The air gap defect is caused by the substandard process of GIS tub insulators, which produces air bubbles during the curing of epoxy resin and the air bubbles evolve into air gaps, and PD will occur in these air gaps during the operation of GIS. The air gap defect model used in this test is as follows: there are three pieces of epoxy resin sandwiched between the high-voltage electrode and the ground electrode, and the middle piece has an air gap defect with a volume of 10 mm \(\times\) 10 mm \(\times\) 5 mm, which is shown schematically in Figure 16.  
Figure 15: Experimental circuit diagram.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Points** & **Maximum Gain** \\ \hline
0.7 GHz & 5.54 dBi \\
1.0 GHz & 7.16 dBi \\
1.4 GHz & 3.46 dBi \\
2.0 GHz & 6.70 dBi \\
2.4 GHz & 6.71 dBi \\
3.0 GHz & 4.79 dBi \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum gain at different frequency points.  
The experiment is based on the State Grid Corporation of China's corporate standard: Q/GDW11304.8-2019 \"Ultra-high frequency method of partial discharge Charged detection technical standards\" [39].
# Measurement of Diurnal Variation in Needle PRI and Shoot Photosynthesis in a Boreal Forest
###### Abstract  
The photochemical reflectance index (PRI) is calculated from vegetation narrowband reflectance in two bands in the visible part of the spectrum. Variations in PRI are associated with changes in the xanthophyll cycle pigments which regulate the light use efficiency of vegetation. Correlations have been found between remotely-sensed PRI and various photosynthetic productivity parameters at the scales from leaves to landscapes. Environmental satellites can provide only an instantaneous value of this index at the time of overpass. The diurnal course of needle (leaf) PRI needs to be known in order to link the instantaneous values robustly with photosynthetic parameters at time scales exceeding one day. This information is not currently available in the scientific literature. Here we present the daily cycle of Scots pine needle and canopy PRI in a southern boreal forest zone in the presence of direct solar radiation during the peak growing season of two consecutive years. We found the PRI of the needles which are exposed to direct radiation to have a distinct diurnal cycle with constant or slightly increasing values before noon and a daily minimum in the afternoon. The cycle in needle PRI was not correlated with that in the incident photosynthetic photon flux density (PPFD). However, when PPFD was above 1000 umol m\({}^{-2}\) s\({}^{-1}\), approximately between 8 a.m. and 5 p.m., needle PRI was correlated with the light use efficiency (LUE), measured with shoot chambers. The timing of the minimum needle PRI coincided with the minimum canopy value, as measured by an independent sensor above the canopy, but the correlation between the two variables was not significant. Our field results corroborate the applicability of needle PRI in monitoring the daily variation in LUE. However, to apply this to remote sensing of seasonal photosynthetic productivity, the daily cycle of leaf PRI needs to be known for the specific vegetation type.  
photochemical reflectance index; light use efficiency; gross primary production; shoot chamber; _Pinus sylvestris_; Scots pine
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### Brisk  
The authors of [53] presented a method, Binary Robust Invariant Scalable Key-Points (BRISK), for key-point detection and description. It uses a novel scale space FAST-based detector for detecting key-points. In this algorithm, key-points are discovered in the image scale-space pyramid. Following that, a circular sampling pattern is created in the vicinity of each detected key-point, which calculates brightness comparisons to form a descriptor string.
## 4 Experiments and Results
### Experimental Results and Analysis
#### 4.5.5 Ablation Experimental
However, if the loss function is set as the proposed method (No.5), both the AUPR and mAUPR showed significant improvements compared with other loss function configurations. In summary, the loss function designed in this paper exhibits better compatibility with MLNet for segment fundus images. In conclusion, the comparative analysis of different model variations in Table 4 and different loss function settings in Table 5 highlights the performance improvements achieved by incorporating additional components and the novel loss function. These enhancements lead to significant improvements in mAUPR and AUPR scores, as well as more accurate segmentation results for specific lesion types. The findings underscore the effectiveness of the proposed components and the novel loss function in enhancing the overall segmentation performance of the algorithm.
## 5 Path-Planning Training
### Environment Modeling  
The environment is modeled as previously presented in Section 4.2. For the learning task, the chance of an obstacle appearing in the environment is randomized in each training episode. In the case of the obstacle's appearance, its size is randomized between 0.5 and 2, and its danger level is randomized between 0 and 1. The entire environment might be scaled along its length (the y axis as in Figure 3) so that the agent could adapt its actions better to different real-life environments. Accordingly, in each training episode, the environment's scale will be randomized between 0.2 and 1.  
We could finish the training episode and reset the environment every time the path is planned. However, this could cause the environment to be much noisier, which could lead to subsequent problems with the training of the neural network. An example of this is when the training environment has an obstacle in an episode, but no obstacle in the next one. In this case, even if the agent could not plan a path that successfully avoids the obstacle in the first episode, it is easy for the agent to do that in the second one and achieve a more favorable reward. This makes the agent accommodate the newer policy despite it may achieve worse results than the previous one. With a noisy environment like this, it would take much longer for the neural network to successfully converge the cumulative reward, and occasionally the policy could not be improved any further due to its inability to notice a better policy over the timesteps. To prevent this, we designed a different resetting mechanism for our environment. Instead of resetting immediately, we only reset the environment if the agent could plan a path without conflicting with the obstacle. Otherwise, the current states are kept so that the agent could try planning again. If the agent takes over a predefined number of steps without being able to plan a successful path, we also need to reset the environment, or the agent could be stuck in finding the appropriate policy.
# A Systematic Review of the Usability of Telemedicine Interface Design for Older Adults
## 3 Results
### Factors Affecting the Usability of Interface Design  
The usefulness of a system for patients largely depends on whether its functions meet user needs [37]. In the design process, it is crucial to ensure that the system's features meet the requirements of older adults, such as teleconsultation, health monitoring, and medication reminders, to enhance the perceived value. Clear interaction logic and effective visual design are critical to enhancing the perceived usefulness in telemedicine systems for older adults, as they facilitate efficient task completion, reduce errors, and strengthen users' belief in the system's effectiveness [16].  
The perceived ease of use refers to users' subjective perception of how easy or difficult it is to use telemedicine services, and the clarity and rationality of the design influence the level of effort required to use the technology. Effective interaction design can reduce the cognitive load, lower the learning curve [16], and improve the ease of learning by simplifying operation processes, incorporating voice interaction, streamlining interface hierarchies, using large fonts, and employing familiar icon-based buttons. These strategies help older users quickly locate target functions and improve their confidence in using the system. The user experience encompasses users' emotional responses, satisfaction, and perceptions of usability during interactions [24]. A well-designed user experience, achieved by integrating emotional design elements and personalized assistance features, not only strengthens the perceived ease of use, but also enhances the overall user satisfaction [41].  
Conversely, technology anxiety may be influenced by functional frameworks, interaction logic, and the overall user experience. Based on the reviewed studies, the TAM was integrated with key interface design elements (as shown in Figure 2) to bridge the identified research gap between influencing factor models and design practices. Additionally, it addresses the lack of detailed design guidelines in existing telemedicine systems, which often fail to fully consider the characteristics and preferences of older users. In the following sections, the findings from the search and review are organized and presented according to the four key design elements of interface design illustrated in Figure 2.