esa-sceva/satcom-chunk-collection huggingface dataset:
# Fracture Properties of Concrete in Dry Environments with Different Curing Temperatures
## 1 Introduction
This is useful for a comprehensive understanding of the fracture properties of concrete exposed to realistic conditions, which is the key premise for assessing crack stability and predicting concrete structure failure based on fracture mechanics. However, so far, less information is available regarding the influence of environmental conditions on fracture properties of concrete, and some of the conclusions are contradictory. For instance, [PERSON] et al. [19] clarified that due to the action of the pore water pressure inside the specimen, the fracture energy of concrete increases with reducing moisture. [PERSON] et al. [20] observed that the value of fracture energy of concrete under dry conditions was smaller than that under saturated curing conditions. Besides, [PERSON] et al. [21] obtained fracture energy gain data for concrete exposed to 14, 23, and 35 \({}^{\circ}\)C, and they clarified that concrete fracture energy improved dramatically with increasing temperature. Unfortunately, they did not investigate the fracture energy of concrete after 20 d. Analogously, [PERSON] et al. [22] observed that the fracture energy of full-graded concrete cast in hot weather was larger than that of concrete cast in cold weather from 28 d to 180 d. From the above results, it can be seen that fracture energy of concrete increased with increasing curing temperature. Conversely, [PERSON] [23] clarified that the fracture energy of concrete stored at elevated temperature in summer was much smaller than that at 20 \({}^{\circ}\)C after 56 d, and the former was only half of the latter. This indicated that the elevated temperature deteriorated the fracture properties of concrete in later ages, which was in line with the results observed by [PERSON] et al. [24]. Further, [PERSON] [25] demonstrated that concrete fracture energy was less dependent on the temperature from 2 \({}^{\circ}\)C to 50 \({}^{\circ}\)C. It is clear that there is no consensus concerning the effect of ambient conditions on concrete fracture properties. What's more, there are very litter or no studies explaining the influence of temperature and humidity coupling on the fracture properties of concrete. Therefore, the current paper presents the investigation of the fracture characteristics of concrete in dry environments with different curing temperatures. The fracture properties of concrete including fracture toughness and fracture energy were measured under a constant relative humidity of 50% with four different temperatures. The information about the fracture properties of concrete under hot-dry (or cold-dry) environments was not available in previous publications. The experimental data is helpful for a deeper and comprehensive understanding of the fracture properties of concrete under extreme climate environment, which can greatly enhance the numerical simulation of cracking behavior of concrete members in harsh environments and promote the development of cracking design specifications for concrete structures.
## System Model
### Computation Task Model  
Here, it is supposed that a UE generates task \(m\) to offload into the MEC server. The computational tasks can be parameterized as triplets in (2):  
\[\chi\triangleq[d_{m},c_{m},t_{m}] \tag{2}\]  
where \(d_{m}\) specifies the total data size of task \(m\) (in bits). The computation intensity \(c_{m}\) refers to the number of required CPUs to accomplish computing one-bit data of a task. The \(t_{m}\) indicates the computation deadline time allowed for executing task \(m\) (in seconds). As the size of the computed result is generally small, it is omitted from the equation. To consider large input data sizes, \(d_{m}\) can be divided into \(k_{m}\) number of equal-sized sub-tasks. Therefore, \(d_{m}=k_{m}d_{sub}\), and \(d_{sub}\) denote to each sub-task data size.  
As the UEs decide to offload the computational task, the MEC server is responsible for resource allocation based on available resource constraints. The computation resource inside the MEC server is indicated based on the CPU cycle frequency. Let \(F(n)\) represent the total computing constraint of the MEC server \(n\), which indicates the maximum available CPU cycle frequency of the MEC server. The computation frequency \(f_{m,n}\) (denoted as CPU frequency) is the total resource allocated by MEC server \(n\) into the UE with requesting task \(m\) for computation. Accordingly, the time cost for executing a sub-task of \(m\) by the MEC server \(n\) is given as (3):  
\[t_{m,n}^{e}=d_{sub}\times\frac{c_{m}}{f_{m,n}} \tag{3}\]  
where \(d_{sub}\) specifies the sub-task data size. Once all sub-tasks are computed by MEC server \(m\), which takes a total duration of \(T_{m,n}^{tot}=\sum_{k=1}^{k_{m}}t_{m,n}^{e}\), it deallocates resources so other UEs could use deallocated CPU for their tasks.
###### Abstract  
In this study a new built-in ultrahigh frequency (UHF) antenna sensor was designed and applied in a high-voltage switchgear for partial discharge (PD) detection. The casing of the switchgear was initially used as the ground plane of the antenna sensor, which integrated the sensor into the high-voltage switchgear. The Koch snowflake patch was adopted as the radiation patch of the antenna to overcome the disadvantages of common microstrip antennas, and the feed position and the dielectric layer thickness were simulated in detail. Simulation results show that the antenna sensor possessed four resonant points with good impedance matching from 300 MHz to 1000 MHz, and it also presented good multi-frequency performance in the entire working frequency band. PD detection experiments were conducted in the high-voltage switchgear, and the fabricated antenna sensor was effectively built into the high-voltage switchgear. In order to reflect the advantages of the built-in antenna sensor, another external UHF antenna sensor was used as a comparison to simultaneously detect PD. Experimental results demonstrated that the built-in antenna sensor possessed high detection sensitivity and strong anti-interference capacity, which ensured the practicability of the design. In addition, it had more high-voltage switchgear PD detection advantages than the external sensor.  
ultrahigh frequency (UHF); high-voltage switchgear; partial discharge; Koch snowflake; multi-frequency +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Analysis of Aroma-Active Compounds Based on AEDA and MF
#### 3.4.2 Modified Frequency (MF) Method Results  
A total of 28 aroma-active compounds with MF > 65% were selected as key odorants and plotted into olfactograms based on MF (Figure 3B). As expected, the MF olfactogram obtained from HY EO was more complex and intense than that of GF EO. Moreover, among these compounds, \(\beta\)-sinensal was detected at full frequency and aroma intensity (i.e., MF = 100%) in HY EO, presenting a strong fishy note, but was not detected in GF EO. Contrarily, in AEDA results, \(\beta\)-sinensal had a small FD indicating little contribution to the overall aroma. This highlights differences between two olfactory analysis methods, which was also the case in the previous study [41]. Consequently, combining two strategies allowed us to screen out key odorants more accurately from multiple perspectives. Nooftatome (MF = 99.22%) was another key odorant with a second high MF of 99.22% in HY EO, characterized by a strong woody flavor with a significant contribution to HY EO, which was consistent with AEDA. However, compared to AEDA results, \(\gamma\)-terpinene (MF = 73.95%, engine oil), unknown 2 (MF = 72.89%, bedbug/tallowy/pungent) and unknown 11 (MF = 73.95%, stinky) were newly identified key odorants in HY EO, with neral (MF = 67.31%, citrus/ floral/fresh) and elemol (MF = 75.00%, spicy/stinky) in GF EO. In addition, the common key aroma-active compounds in HY EO and GF EO were linalool (MF = 83.45%, 85.70%), citronellal (MF = 87.50%, 83.85%), decanal (MF = 84.78%, 84.78%), 7-methoxycomarin (MF = 81.01%, 81.97%), dodecanal (MF = 79.06%, 76.03%), \(\alpha\)-terpineol (MF = 68.47%, 77.06%), unknown 4 (MF = 70.71%, 66.14%) and unknown 5 (MF = 68.47%, 76.03%).  
The Venn diagram (Figure 3C) showed that 23 key aroma-active compounds were determined jointly by both AEDA and MF, i.e., \(\beta\)-myrcene, octanal, \(D\)-limonene, linalool, unknown 4, \(\alpha\)-terpineol, decanal, geraniol, unknown 5, carvyl acetate, copaene, dodecanal, _cis-\(\beta\)_-farnesene, _trans-_2-dodecenal, germacrene D, elemol, germacrene B, unknown 12, \(T\)-murolrol, \(\alpha\)-cadinol, \(\beta\)-sinensal, 7-methoxycomarin and nootkatone.
# A Large-Class Few-Shot Learning Method Based on High-Dimensional Features
## 3 Methodology  
### The Large-Class FSL Definition and Symbol Representation  
Formally, we use \(x\) to represent input data and \(y\) to represent the supervision target. A few-shot classification task, \(T\), usually consists of a \(T\)-independent auxiliary dataset, \(D_{base}\), and a \(T\)-dependent dataset, \(D_{novel}\). The goal of few-shot learning is to transfer knowledge learned from \(D_{base}\) to \(D_{novel}\), where \(D_{novel}\) contains limited amounts of available data per category. \(D_{novel}\) consists of support sets, \(S_{spt}\), and query sets, \(S_{spt}\), i.e., \(D_{novel}=\{S_{spt},S_{qry}\}\), where \(S_{spt}=\{(x_{i},y_{i})\}_{i=1}^{n}\), \(S_{qry}=\{x_{i}\}_{j=1}^{N_{spt}}\). If \(S_{spt}\) contains \(C\) categories, and each category contains \(K\) samples, \(T\) is called a \(C\)-way \(K\)-shot task. Common settings for traditional few-shot learning are 5-way 1-shot and 20-way 1-shot. When the value of \(C\) reaches hundreds or thousands it is called a large-class few-shot learning task. In this paper, \(\phi(x)\) represents the feature embedding of sample \(x\), where \(\{(x_{i},\hat{x}_{i})\}_{i=1}^{n}\) is the sample pair formed by pairs of samples from different categories.
# A Scientometric Review of Obstructive Sleep Apnea and Obesity
## 4 Discussion
### Cluster #23: Biological Markers of OSA  
The major citing articles in cluster #23 were authored by [PERSON] et al. [225], with a coverage of 5 articles and a GCS of 2; [PERSON] et al. [226], with a coverage of 4 articles and a GCS of 0; and [PERSON] et al. [227], with a coverage of 3 articles and a GCS of 2. Cluster #23 appears to be an emerging cluster focused on identifying biological markers of OSA. The identification of biomarkers of OSA is important as it aims to overcome some limitations that are inherent in the traditional OSA screening instruments. Specifically, while polysomnography is the gold standard for OSA diagnosis, it remains quite inaccessible for many patients with suspect OSA. In fact, polysomnography has the disadvantage of being quite expensive, time-consuming, and uncomfortable for patients [225]. Similarly, the validity of traditional questionnaires for OSA screening when used in different populations is still not well established. This attention on tackling the embedded shortcomings of traditional instruments can be observed from how the cited references of this cluster examined diagnostic tools for OSA [63; 228; 229]. For example, [PERSON] et al. [228] conducted a meta-analysis on existing scales (e.g., Berlin Questionnaire, STOP-BANG, STOP, and the Epworth Sleepiness Scale) to assess their diagnostic accuracy. Their findings recommended the use of STOP-BANG, although there were varying degrees of sensitivity and specificity across OSA of different severity. To address the problem of instruments validity and accessibility, the citing articles also investigated various other markers (e.g., serum lipid profiles [227]) and techniques for the diagnosis of OSA. For instance, [PERSON] et al. [225] attempted to develop a nomogram based on a series of predictors of OSA in bariatric surgery candidates (i.e., gender, habitual snoring, type 2 diabetes mellitus, neck circumference, body mass index, and age). With this nomogram, a good classification accuracy was obtained for the training and validation cohort, proving the clinical usefulness of the developed instrument. In a similar vein, other citing articles investigated portable sleep monitor testing [230] and skeletal muscle lipid content [231].
# Performance Analysis of Physical Layer-Based Multiple-Input Multiple-Output on WiMAX (MIMO-WiMAX) +
## 2 WiMAX Physical Layer Model
### Modulation  
The QPSK, 16-QAM, and 64-QAM bit modulations may be supported by the bits that are interleaved and given serially to the constellation mapper [10]. To guarantee that the symbols all have the same amount of average powers, complex constellations are normalized using predetermined multiplication factors for each modulation technique. The mapped data in the constellation are distributed evenly throughout all OFDM symbol's data sub-carriers, with the distribution order determined by the rising frequency offset index. Because the channel operates in the time domain, the data change from being in frequency domains to time domains, so that they may be represented as OFDM subcarriers [11]. This creates a waveform that meets the orthogonality criteria automatically. In the same way that IFFT acts in the opposite direction, FFT turns data from the time domain into data from the frequency domain. This is necessary, since our work is done in the frequency domain. Figure 3 depicts the Convolutional encoder with a code rate of 1/2.
To our knowledge, no article has analyzed and compared in detail the influence of the different methodological issues in the results of the automatic detection of PD employing different corpora in a single work. With these premises, the purposes of this study are:  
* To analyze the influence of certain factors, such as age difference between classes, speaker identity in training/testing folds, or feature vector length that might bias results;
* To study how different validation and classification methodologies influence the PD detection results;
* To study the differences between corpora that might lead to differences in results and conclusions. A graphical summary of the purposes of this work is included in Figure 1.  
To achieve these objectives, we performed PD detection employing sustained vowels as acoustic material obtained from three different corpora, of which two are publicly available. We calculated the most commonly used features in these types of studies found in literature [4], also employing publicly available feature extraction and machine learning code for reproducibility. We repeated the experiments several times, changing the methodology in order to evaluate how the identified methodological issues can influence the results. We started from a baseline experiment and compared its results with each methodological variation; i.e., including the same speaker in training and testing, using different average age differences between speakers with and without PD, or using different parts of the acoustic signal, among other experiments. The goal of this study is not to identify or point out which studies have methodological issues. Instead, we aim to provide a comprehensive analysis of how the outlined issues may be affecting results in order to avoid repeating them in future work. Some seminal studies can be of capital importance because these paved the path for the automatic detection of PD through speech and yet have methodological issues that need to be addressed for iterative scientific advancement. This document is organized as follows: Section 2 describes the three corpora used in this study. Section 3 provides an overview of our experimental processes. This includes how we calculate our coefficients and employ dimensionality reduction (Section 3.2), construct our classification architecture (Section 3.3), and vary our methodological strategy between several different scenarios (Section 3.4). Section 4 contains tables and graphics which demonstrate the impact of each methodological difference on classification accuracy. Finally, Section 5 includes the conclusions of the study.
# Global GNSS-RO Electron Density in the Lower Ionosphere
## Appendix A Algorithm Flow Diagram  
The algorithm developed in this study shares the similar flow to one published by [PERSON] [31] in terms of data processing and retrieval modules. As shown in Figure A1, in this study we leveraged many modules from the previous development, including data quality control, \(F\)-region component estimation, and optimal estimation inversion, while the algorithm is extended to use of both \(\phi_{exL1}\) and \(\phi_{exL2}\) profiles. Slightly different thresholds for quality control are used in the new algorithm. Because of the need of quality control for both \(\phi_{exL1}\) and \(\phi_{exL2}\) profiles, the total number of \(N_{e}\) retrievals is slightly lower than the algorithm that uses the \(\phi_{exL1}\) profile only.  
Although the inversion module bears essentially the same architecture, the weighting functions and linearization vectors are somewhat different. The new algorithm is linearized on a climatological profile derived from the IRI and FIRI models, instead of zero values. The weighting functions in the new algorithm are extended to a higher altitude such that it can handle the measurement and state vector up to 800 km. Both \"bottom-up\" and Abel weighting functions are computed and stored in the new algorithm, making it flexible to retrieve \(N_{e}\) from different input measurement vectors, namely \(\Delta hTEC\) (without absolute \(\phi_{ex}\) calibration) and \(HTEC\) (with absolute \(\phi_{ex}\) calibration) profiles. The capability of retrievingthe entire ionospheric \(N_{e}\) from \(h\)_ITEC_ allows the new algorithm to process the podTEC data produced at CDAAC for COSMIC-1 and COSMIC-2. Initial testing of the retrieval from podTEC data shows that the optimal estimation inversion with the Abel weighting function produces better quality (much less oscillations) \(N_{e}\) in the lower ionosphere than the inversion from the onion-peeling approach [33; 87; 38]. The algorithm architecture in this study can readily accommodate further extensions of the \(N_{e}\) retrieval from the podTEC data, such as the use of podTEC from both positive and negative elevation angles.  
This study added a new module to retrieve \(TEC\) from the high-rate RO data with a top up to 120 km. Preliminary results from this module were presented in Section 4.2, revealing scientifically-useful \(TEC\) measurements on a global basis. Unlike the \(TEC\) retrieval derived from the \(N_{e}\) profile retrieved from the podTEC data, this \(TEC\) retrieval method does not require absolute calibration in the \(h\)_ITEC_ or \(\phi_{ex}\) profile, as in most of the high-rate RO measurements. However, it does require the RO top to reach at least 120 km in order to observe a sufficient vertical gradient in the \(h\)_ITEC_ profile. This added \(TEC\) retrieval capability will significantly improve the spatiotemporal sampling of global ionosphere on a daily basis. Thus, further development and refinement are expected for this \(TEC\) module as the GNSS-RO constellations produce more the high-quality high-rate RO profiles.
## 3 Crack Detection in Plates Using Dynamic Response (DR)
### Forward Problem for the DR of the Cracked Plate  
Various research initiatives have been conducted to examine changes in the DR of plate structures due to damage caused by cracks, a situation identified as a forward problem. These studies can be systematically divided into two categories, depending on the thickness of the plates being analyzed.
## 3 Design of the Algorithm  
The effectiveness of image segmentation algorithms can vary depending on a range of factors, such as image complexity, image quality, the types of features being sought, noise present in the image, and the required accuracy for the specific task. In this case, a simple algorithm has been implemented to detect pixels belonging to the pulp chamber. Similar to any other image segmentation algorithm, it is based on the discontinuity or similarity between the gray levels of neighboring pixels, allowing it to locate changes in the gray levels of adjacent pixels. This algorithm can find isolated pixels, detect edges, and divide the image into zones that have similar gray values based on a certain threshold.
## 4 Discussion
### Applications in the Field of Sensing
[146] proposed a biosensor that can be easily used to detect Salmonella typhimurium in vegetables with high sensitivity and specificity, and the detection limit can reach 10.2 ug/mL. In 2013, a label-free biosensor based on a polymer-coated optical waveguide was reported in ref. [147], which can detect Escherichia coli O157:H7. The sensor has a detection limit of 20 CFU/mL (Colony Forming Units/mL) and a response time of 30 min. In 2012, ref. [148] reported a portable, easy-to-use, and highly sensitive lab-on-a-chip biosensing device with an operability that has a detection limit of 2.5 \(\times\) 10\({}^{-6}\) fIU. Other relevant literature review reports also show that optical waveguide refractive index sensors have been applied to the quantitative detection of specific chemicals, viruses, and bacteria with high sensitivity and specificity. The performance index is improving with a lower detection limit, shorter detection time, and higher sensitivity, making this type of sensor increasingly popular and widely used in biomedicine and food safety fields. However, the detection limit of the sensor is inversely proportional to the quality factor (\(Q\)) and the detection sensitivity (\(S\)) of the sensor. Therefore, to achieve a lower detection limit, the focus should be on improving the quality factor and detection sensitivity of the refractive index sensor.
## 4 Discussion
### Practical Implications  
* Therapeutic and Recreational Integration: The combination of VR and aromatherapy has the potential to significantly enhance therapeutic and recreational interventions for older adults. Care providers should tailor personalized VR experiences with complementary aromatherapy scents based on individual preferences. For example, calming lavelander scents for relaxation. These multisensory experiences can improve cognitive and emotional well-being, offering innovative ways to promote mental and physical health, especially in senior facilities like nursing homes and retirement communities.
* VR Aromatherapy Experience as a non-pharmacological approach: The congruence between VR content and aromas, such as the use of pine scents in forest environments, is essential for creating immersive experiences. Practitioners should consider the specific aromas used, the specific VR content, and how they match each other to maximize therapeutic benefits. For example, calming lavelander and a garden for relaxation; pine and a forest for cognitive stimulation.
* Enhancing Engagement and Socialization: VR and aromatherapy interventions can improve social interactions and emotional engagement. Studies indicate that these multisensory experiences encourage personal connections, reminiscences, and socialization, making them valuable for fostering social bonds among older adults, particularly those with mobility issues or cognitive impairments [36; 37; 39].
* Limited Clinical Implementation Evidence: The current body of research on VR and aromatherapy lacks robust clinical implementation evidence to fully support its widespread use in practice. For example, care staff should be adequately trained in the appropriate use of VR equipment, including setup, troubleshooting, and tailoring experiences to individual preferences. Similarly, the safe and effective application of aromatherapy requires knowledge of essential oil selection, dosage, and contraindications to prevent adverse reactions.