esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Analysis of Results
### Extraction of Entropy Value, Time Domain, and Frequency Domain Features  
Due to the multiple limitations of the acquisition scenario and sampling conditions, the raw power samples suffered from severe data redundancy and high data dimensionality. Feature extraction can decrease data complexity and computation time of the model, while improving the classifier's performance. For the data type of industrial power loads, the classical time-frequency domain features have problems such as high complexity and insufficient accuracy. To prevent the loss of important information from the original power data during feature conversion, the authors introduced entropy value features after extracting time-frequency domain features.  
Based on the feature principle, the authors extracted 72 dimensional features from the raw power data and aggregated them into a joint feature set. These included 14 dimensional entropy value features (7 dimensional active power and 7 dimensional reactive power), 32 dimensional time-domain features (16 dimensional active power and 16 dimensional reactive power), and 26 dimensional frequency-domain features (13 dimensional active power and 13 dimensional reactive power) of the power load.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 4 Significance of Communications in IoMT  
The communication layer in IoMT systems serves as a foundation for enabling seamless connectivity, data exchange, and interaction between distributed devices, sensors, and systems within an ambient intelligent environment. This layer encompasses various communication technologies, protocols, and standards tailored to the specific requirements of IoMT applications. In other words, the communication medium layer is responsible for managing the underlying communication infrastructure, protocols, and technologies to ensure smooth connectivity, efficient data transmission, and reliable operation of IoMT systems.
# Generalized Electron Emission Theory for One-Dimensional Conducting Materials
## 7 Conclusions  
We formulated a comprehensive electron emission theory that covers arbitrary thermal energy and electric potential energy across various one-dimensional materials. The analysis included the spectral radiance for one-dimensional thermal radiation, illustrated by the dimensionless photon energy across various temperatures in such materials. Additionally, calculations for the thermionic and field emission current densities were conducted for one-dimensional conductors. The generalized current densities, applicable at any temperature and under an applied electric field, were derived and computed for one-dimensional materials. The one-dimensional current for thermionic emission was computed as a function of the temperature across different materials with work functions of 2, 3, 4, and 5 eV. Similarly, the one-dimensional current for field emission was calculated as a function of the electric field for materials with the same range of work functions. The one-dimensional current of a carbon nanotube was displayed as a function of the electric field for temperatures spanning from 500 K to 2500 K. Furthermore, the one-dimensional current at a constant temperature of 1000 K was depicted as a function of the electric field for materials with work functions of 2, 3, 4, and 5 eV.  
H.K.: Conceptualization, data curation, investigation, writing--original draft, writing--review and editing. C.-S.P.: data curation, investigation, validation, writing--review and editing. S.J.Y.: investigation, validation, funding acquisition, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This study was carried out as part of an inter-ministerial collaboration project supported by the Korea Institute for Advancement of Technology (KIAT) under the Ministry of Trade, Industry and Energy (MOTIE) (Project No. P0022203).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to thank all the researchers who participated in discussions about electron emissions in RAON.  
The authors declare no conflicts of interest.  
Figure 6: One-dimensional current plotted against the applied electric field for materials featuring various work functions, all at a constant temperature of 1000 K. In the sequence from top to bottom, the depicted work functions correspond to 2, 3, 4, and 5 eV.
# Daydirex: Translating Nighttime Scenes towards Daytime Driving Experience at Night
## 4 Proposed Solution
### Filling a Missing Area  
In the process of cropping a street view image for hint information, a partial area of the cropped image may be missing. Main reasons are two-fold: First, the street view images are not continuous; we find that they are often captured at 10-meter intervals on average or sometimes even longer. Second, the window to obtain a hint image is a trapezium-shaped as shown in Figure 8.  
Figure 10 shows an example case that a missing area can be included in the cropped image. Assume that a car is currently moving past the center coordinates of the i-th street view image. The hint image is cropped as the window for the current location moves in the moving direction of the car. However, if the car moves more than a certain distance from the center coordinates of the street view image, a hint image cropped by the window can include some empty area as shown in Figure 10. In this case, hint information for the area cannot be provided.  
To address the problem, we adopt a panoramic image construction method. Since street view services usually provide a 360-degree view of image at a location, we can obtain an image with a view parallel to the road (i.e., front or rear view) as well as an image with a view to the roadside. To fill a missing part from a roadside view image at a certain location, we stitch two images, a side view image and a front (or rear) view image obtained at the same location to construct a panoramic image. Then, we crop a part of image from the constructed panoramic image to get a hint image without any missing area. For panoramic image construction, we use a technique proposed in [27].  
Figure 8: A vehicle to capture street view images (Photo from Pixabay) (**a**) and an example of trapezium-shaped window overlaid on an example street view image (**b**).  
Figure 10: An example case showing that some part of hint image is missing. This can happen when the distance between the vehicle location and the street view image location is larger than a certain range.  
Figure 9: An example to show the results of perspective transformation with two different windows. (**c**,**d**) show that the red trapezium-shaped window is better than the yellow rectangular window to provide more appropriate hint images.
## 5 Results and Discussion  
### Comparison of the Response Spectra between Two Pairs of Stations: JJU1 and JJU2, and SGP1 and SGP2  
To further investigate the causes of the large variability in the response spectra of the four stations, we compared the responses of two stations located close to each other under similar site effect conditions, which helps simplify and focus on the other two factors, considering the influence of the site effect among the three factors affecting the response spectra becomes negligible.  
On 16 th December 2016, the Korea Meteorological Administration (KMA) closed JJU1 and moved to JJU2, which was at a distance of approximately 200 m from JJU1. Similarly, SGP1 was closed and moved to SGP2, which was approximately 200 m from SGP1. We compared the responses of JJU1 and SGP1 for the Kumamoto series and those of JJU2 and SGP2 for the Pohang series.  
Figure 6a,b show the horizontal responses of JJU1 and JJU2, and SGP1 and SGP2, before and after relocation. Compared to the responses of JJU1 and SGP1, the results of JJU2 and SGP2 showed a significantly higher variability for all frequency bands. Assuming the movement was not only within the \(\sim\)200 m distance, and that the conditions of the new site generally improve through ground surveys, the difference of the site effect on the responses before and after relocation can be neglected.  
In addition, the wave propagation paths from the Kumamoto and Pohang earthquakes to the Jeju region exhibit the same intra-plate tectonic characteristics ([PERSON] et al., 1999) [22]. Since similar tectonic environments show similar elastic and inelastic properties of wave propagation, the difference in the ground motions reflected in the response spectra of the two paths is almost negligible. Considering this, the increased variability between the before and after movement suggests that seismic sources are most significantly involved. The importance of earthquake magnitude, which is one of the components of the seismic sources, is confirmed again in addition to the previous section.
# Ultrasound-Guided Procedures in Common Tendinopathies at the Elbow: From Image to Needle
## 3 Common Elbow Pathologies
### Golfer's Elbow
#### 3.2.1 Essential Anatomy  
The common flexor tendon (CFT), also known as the caput commune ulnare, is a conjoint tendon of the flexor-pronator musculature on the medial humeral epicondyle (ME) (Figure 5). The CFT attaches proximally to the anterior bundle of the medial collateral ligament (MCL). The part of the CFT that is the ulnar head of the pronator teres muscle (PT) is confluent with the anteromedial joint capsule [61]. The CFT is on average 3 cm long and crosses the humeroulnar joint medially [62]. Flexor-pronator musculature is a group of four muscles of the superficial layer of the anterior forearm compartment. These muscles are the PT, FCR, PL, and FCU. The PT and FCR originate from the most proximal and anterior part of the medial humeral epicondyle. They are the most susceptible to tendinopathy development from the CFT [38]. Repetitive eccentric loading of muscles, conducting wrist flexion, and forearm pronation combined with valgus overload at the elbow is considered the leading cause of golfer's elbow [63]. The PT is inserted on the lateral border of the radius in the middle of the radial diaphysis. Other tendons of the flexor-pronator musculature undergo the flexor retinaculum of the wrist joint. The FCR tendons are inserted in the ventral part of the base of the second and third metacarpals. The PL inserts into the palmar aponeurosis. FCU is inserted onto the pisiform bone. PT, FCR, and PL are innervated by the median nerve. The FCU is innervated by the ulnar nerve. The ulnar nerve at the elbow level runs posterior to the medial epicondyle within the cubital tunnel.
## 1 Introduction
### Background
For this purpose, we used Sentinel-1 data and two different approaches: a robust and supervised approach and an unsupervised and automatic approach. In this way, the combined use of both software methodologies is due to the necessity: (i) to avoid unmonitored areas, increasing the number of PS (velocity points) detected, and (ii) to provide and improve the interpretation of the A-DInSAR dataset. In addition, and as a novelty of this study, we demonstrate the high capability and utility of the unsupervised service to detect and monitor ground movements at a local scale.
# Risk Identification of FPSO Oil and Gas Processing System Based on an Improved FMEA Approach
###### Abstract
However, there is no relevant research combining fuzzy AHP method with fuzzy evidence reasoning (FER) and TOPSIS, especially for failure assessment in FMEA, and FER is well-suited for handling incomplete assessment of uncertainty. For determining the weights of risk factors, the available literature provides details for methods such as AHP [21], fuzzy AHP [22; 23], and analytic network process (ANP) [24]. [PERSON] and [PERSON] [7] introduced the fuzzy theory into FMEA analysis and used the fuzzy analytic hierarchy process to determine the weight of risk factors, but ignored the objective information of risk factors themselves. AHP was used to derive the relative priorities of evaluation criteria in the FTOPSIS approach to rank failure modes by [PERSON] et al. [23]. [PERSON] et al. [25] used [PERSON]'s fuzzy AHP method to calculate the fuzzy weights of the risk factors. [PERSON] and [PERSON] [26] used an example to verify the variable weight effect of variable weight vector in the comprehensive decision-making. To weigh the risk factors, [PERSON] et al. [22] integrated the fuzzy analytic hierarchy process (AHP) and the entropy method. [PERSON] [27] proposed best-worst method (BWM) to determine the importance weights of criteria, flexibly. This approach is a comparison-based method that establishes the comparisons between items in a particular way. The traditional BWM uses crisp values to conduct the comparisons so it fails to determine weights under uncertain environment. Thus, to obtain the weights of risk factors, fuzzy BWM [28; 29; 30] is employed. However, BWM involves tedious processes and a few pair-wise comparisons to achieve consistent results. Moreover, in some FMEA models, the weights of FMEA team members are assumed to be known or equal; such an approach cannot avoid subjective randomness. The current study proposes an integrated approach for FMEA. In the proposed framework, using the fuzzy evidence reasoning method, the ratings of O, S, and D are represented in fuzzy belief-structures to express the diverse and the uncertain. To determine the weights of O, S, and D, the subjective experience of the experts and the objective information of the ratings are considered comprehensively. The variable-weight method and entropy-weight method are used to calculate the weights reflected by the objective information. Additionally, the objective and the subjective weights are combined and integrated to obtain the comprehensive weight of O, S, and D. To prioritize the risk of potential failure modes, the fuzzy TOPSIS method is adopted. The rest of the paper is organized as follows: FPSO oil and gas processing system is introduced in Section 2. The proposed methodology is described in Section 3. Risk identification of FPSO oil and gas processing system is offered in Section 4, and the analysis of results is made in Section 5. Section 6 gives the conclusions of this paper.
## 3 Results and Discussion
### Analysis of Equivalent Circuit of Immune Sensing  
Figure 4a shows the equivalent circuit model of impedance data fitting. The dashed line and solid line in Figure 4b represent the Nyquist plot of the measured impedance and the comparison of the fitted data according to the equivalent circuit. The two curves are highly coincident and have a good fitting effect. This circuit consists of five circuit elements: Solution resistance \(\mathrm{R_{s}}\), constant phase elements \(\mathrm{CPE_{1}}\) and \(\mathrm{CPE_{2}}\), constant resistance \(\mathrm{R_{a}}\) and charge transfer resistance \(\mathrm{R_{ct}}\). It can be understood here that we divide the surface of the electrode into an internal SAM layer and external biomolecules (antigen and antibody). The internal part is composed of a constant resistance and a constant phase element \(\mathrm{CPE_{2}}\), and the external part is composed of a charge transfer resistance \(\mathrm{R_{ct}}\) and Another constant-phase original \(\mathrm{CPE_{1}}\), and \(\mathrm{R_{s}}\) represents the resistance of the electrolyte solution [28].  
In the immunoassay experiment, Faraday impedance was measured using \(\mathrm{[Fe(CN)_{6}]^{3-/4-}}\) as the REDOX probe, and \(\mathrm{R_{ct}}\) stands for the charge transfer resistance of the REDOX probe \(\mathrm{[Fe(CN)_{6}]^{3-/4-}}\). The formation of biomolecular membrane on the electrode surface would block the transfer of \(\mathrm{[Fe(CN)_{6}]^{3-/4-}}\) charge, resulting in the increase of the \(\mathrm{R_{ct}}\) of the charge transfer resistance. Since the ability of the  
Figure 4: (a) the equivalent circuit model of the immune analysis in this paper; (b) Nyquist plots of actual measured and fitted values of impedance after incubation of different concentrations of IgG.  
Figure 3: Shows the biofunctionalization steps of the interdigitated gold electrode.  
antibody on the sensor surface to capture the antigen is related to the concentration of the antigen itself, we can judge the concentration of the antigen by monitoring the change in charge transfer resistance of the reduction probe [Fe(CN)\({}_{6}\)]3-/4- before, and after, the immune response.
# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 4 Methodology  
### Overview  
This section details the method of Joint embedding of Attributes and Relations for User Alignment (JARUA), which includes three components: multi-granularity attribute embedding, attention-based structure embedding, and iterative training process. First, all text attributes of users are automatically divided into two granularities: the subword level and the word level. Simple and intuitive multi-layer perceptrons (MLPs) are then designed to extract attribute features of each granularity level. Second, a GAT approach is employed to capture users' structural characteristics, and the attribute and structure features are combined to achieve the embeddings of users, based on which a unified objective function is designed for optimization. Third, a large number of unlabeled samples is utilized because of the expected difficulty in obtaining adequate labeled data on the UA problem. We outline an iterative training algorithm to bootstrap the model performances using the unlabeled samples. Finally, each source user's alignment can be found by searching the nearest neighbors in the target network. Figure 2 depicts the overall framework of our proposed approach. The following parts of this paper focus on the elaboration of the multi-granularity attribute embedding, the attention-based structure embedding, and the iterative training algorithm.
# Soy Isoflavones and PCOS: Role in Hormonal and Metabolic Mechanisms
## 4 Polycystic Ovary Syndrome and Soy Isoflavones
### Endocrine Mechanisms
#### 4.1.2 Hypothalamic-Pituitary-Ovarian Axis  
In addition to hyperandrogenism resulting from ovarian factors, non-ovarian factors also influence the pathogenesis of PCOS and the increase in androgen levels, including abnormalities in the hypothalamic-pituitary-ovarian (HPO) axis [114]. Under physiological conditions, this axis regulates normal hormone concentrations and function, including ovulation [115]. Gonodotropin-releasing hormone (GnRH) is released in pulses in the hypothalamus, stimulating the pituitary gland to release follicle-stimulating hormone (FSH) and luteinizing hormone. Follicle-stimulating hormone and lutropin regulate ovarian function. The former is responsible for the formation of ovarian follicles (Graafian follicles) and stimulating the production of oestrogen in the granulosa cells of the ovaries and progesterone [115]. FSH also increases the activity of aromatase, an enzyme that helps convert androgens into oestrogens. The second hormone, LH, plays a key role in ovulation. If ovulation proceeds normally--in the production of the corpus luteum and thus progesterone, and also stimulates the tleca cells of the ovaries to produce androgens (testosterone, androstenedione, dehydroepiandrosterone (DHEA)--in small amounts) [115]. FSH and LH in each phase of the menstrual cycle are precisely regulated and controlled by variations in the amplitude and frequency of GnRH pulses [114]. In women with polycystic ovary syndrome, LH and FSH levels deviate significantly from normal values. Usually, LH levels increase significantly, while FSH levels decrease, although they may also remain largely unchanged. This situation causes an increased LH/FSH ratio, which is observed in most patients with PCOS [115; 116; 117; 118]. The cause of this condition is thought to be increased frequency and amplitude of LH pulses caused by increased frequency of GnRH pulses. On the other hand, increased GnRH secretion in PCOS may be caused by hyperinsulinemia, which increases the pituitary gland's response to GnRH. Low progesterone levels caused by anovulation or hyperandrogenism affecting the hypothalamus through a negative feedback mechanism may play a similar role [116]. There are also reports concerning kissepeptin, a neuropeptide produced in the hypothalamus, which shows elevated levels in women with PCOS. It affects HPO axis dysfunction by binding to the GPR54 (KISS1R) receptor on GnRH neurons and causing an increase in the frequency and amplitude of GnRH pulses. As a result, there is an increase in LH and androgen levels [119; 120]. Another neurotransmitter, gamma-aminobutyric acid (GABA), which has an inhibitory effect in other regions of the CNS, may have a stimulating effect in the hypothalamus, stimulating the release of GnRH and thus leading to an increase in LH/FSH [121].  
Figure 5: Selected pathways leading to hyperandrogenism in PCOS [38; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56], \(\uparrow\) indicates an increase; \(\downarrow\) indicates a decrease.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 9 Future Directions and Development Trends
### Robustness and Adversarial Attacks  
LLMs are susceptible to adversarial attacks. Small but carefully crafted perturbations can cause model misinterpretation. Addressing these issues is critical for ensuring the reliability and trustworthiness of LLMs. Ensuring consistent performance under perturbations requires eliminating susceptibility to adversarial manipulation. Mitigation approaches include input preprocessing and transformation, adversarial training, robust optimization techniques, adversarial example detection, defensive distillation, model ensembling, adaptive adversarial training and transferability analysis, adversarial attack-aware training data augmentation, certified robustness, explainable robustness mechanisms, and benchmarking and evaluation metrics.  
In the input preprocessing and transformation approach, certain transformations are applied to the datasets to make the models robust to perturbations. For example, input denoising or transformation-based defenses modify inputs to remove adversarial perturbations. In adversarial training, the datasets are augmented with adversarial samples. This enhances a model's resilience to adversarial attacks. Robust optimization techniques, such as adversarial regularizations, modify the training objective functions to make the models more robust against adversarial perturbations.  
Adversarial example detection involves methods to detect and flag adversarial examples during model inference. Techniques for this task include input reconstruction, uncertainty estimation, and anomaly detection. Defensive distillation and model ensembles combine predictions from multiple models to mitigate the impact of adversarial attacks. Also, ensembling diverse models reduces vulnerability to specific attack strategies. The adaptive adversarial training and transferability analysis approach employs adaptive adversarial training. Adversarial examples are dynamically generated during training to enhance a model's robustness. Analyzing the transferability of attacks across models provides insights into developing more universally robust defenses.  
In adversarial attack-aware training data augmentation, the training data are enhanced with adversarial attack-aware data. Certified robustness methods provide formal guarantees on the model's robustness against certain kinds of adversarial attacks. This emerging research area offers provable bounds on the model's performance under attack. If the model design incorporates explainable robustness mechanisms, then examining how the model handles adversarial attacks is feasible. Lastly, the availability of benchmarks and evaluation metrics contextualized to adversarial attacks helps to compare the effectiveness of different models and techniques. The techniques mentioned above originally came from the traditional machine learning domain. Research is needed to adapt these to the LLMs context. Moreover, research is needed to develop new approaches to adversarial attacks given the unique characteristics of LLMs.