esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The scientific community has shown an ever growing interest towards natural epigenetic modulators; among them, natural molecules as curcumin, sulforaphane, resveratrol, genistein, and polyphenols have proved to be able to act as epigenetic modulators, influencing the activity of chromatin modifiers and transcriptional factors [1]. The effects of these molecules have been exploited for therapeutic purposes and these compounds have been also employed to increase the sensitivity of cancer cells to conventional chemotherapeutic agents [2]. The recently coined term \"nutriepigenomics\" is used to describe the study of the interactions between dietary compounds and gene expression modulated by epigenetics and it represents a research field that has been developing for the past decades with the advent of genome-wide expression profiling. These natural molecules that are introduced as part of the diet providing extra health benefits are therefore called \"nutraceuticals\". In the last yearsnumerous experiments and clinical trials had proven the efficacy of these compounds in the treatment of different pathologic conditions. In particular, several trials confirmed the beneficial anti-inflammatory and anti-oxidant properties of curcumin in the treatment of osteoarthritis, rheumatoid arthritis, and vascular endothelial function [3; 4; 5]. Some promising results of curcumin have been obtained also in oncology [6; 7; 8] in terms of safety and anti-cancer effects of curcumin as a complementary therapy in combination with gemcitabine in pancreatic cancer patients [9]. Moreover, curcumin has been found effective also on diabetes [10] since it reduces diabetes complications through decreasing triglyceride levels as well as indicators of inflammation; supplementation with curcumin improves also liver fat and transaminase levels in patients with non-alcoholic fatty liver disease (NAFLD) [11] and it is active in improving memory and attention [12] and in preventing cognitive decline in the elderly [13]. Clinical trials have demonstrated that sulforaphene has anti-oxidant [14; 15] and tumor-protective [16; 17] properties. Moreover, dietary supplementation of sulforaphene ameliorates liver function and insulin resistance in diabetes II patients [18; 19]. Because of their high efficacy and safety, at the moment there are 64 ongoing clinical trials on curcumin and 19 on sulforaphene ([[https://clinicaltrials.gov/](https://clinicaltrials.gov/)]([https://clinicaltrials.gov/](https://clinicaltrials.gov/))).  
This review is aimed at illustrating the ability of three nutraceuticalals to modulate the expression of vitamin D receptor and their beneficial effects on health due to the enhanced ubiquitous actions exerted by vitamin D; this collection of initial observations is meant to stimulate the search for other natural compounds with similar activity.
# Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation
## 2 Dataset and Method  
### Dataset and Preprocessing  
The dataset was obtained from the experimental rice fields at the Anhui Agricultural Meteorological Center Hefei Branch experimental base, Hefei, China (117\({}^{\circ}\)03\({}^{\prime}\)26\({}^{\prime\prime}\) E, 31\({}^{\circ}\)57\({}^{\prime}\)20\({}^{\prime\prime}\) N) [7]. Images of the whole process from transplanting to the harvesting of four varieties of one-season rice (Dangyujing 10, Xuanjing Nuo 1, Chuangliangyou 699 and Liangyou 631) were collected in 2019 and 2020. They were planted in six different sowing periods in 12 plots of equal area and proportion (each plot size was 12 m \(\times\) 5 m). As shown in Figures 1 and 1, network cameras were set up at the diagonal points of the rice field. Camera 1 took images of the whole growth period of the rice in areas 1-6, camera 2 took images of the whole growth period of the rice in areas 7-12, the blurred images taken in area 6 and 7 are not available. The Hikvision iDS-2 DF8825 IX-A(T5) camera (made in Hangzhou, Zhejiang, China) was used to acquire a total of 300 images for creating a rice milk ripening stage image dataset. The camera video output supports 3840 \(\times\) 2160 @25 fps, 2100 lines, 37\(\times\) optical zoom, a maximum of 300 preset positions, 18 cruise paths, and a mounting riser 2.5 m above ground [7]. Ten shooting points were set in each sowing period block of each experimental field, and the images were automatically uploaded to the FTP server after a timely fixed-point collection, as to avoid as many substantial changes in light intensity caused by direct sunlight as possible. Due to the large size of the original images used for segmentation, they were randomly segmented into areas of 573 \(\times\) 880 pixels; each image was cropped only once to avoid training overfitting. To improve the algorithm robustness, the dataset was expanded using random cropping, mosaicking, and scaling to simulate images obtained at different angles and distances and in different environments.
## 1 Introduction
The distortion and deformation of BaTiO\({}_{3}\) structure induced by the substitution of Na\({}^{+}\) and Bi\({}^{3+}\) into Ba sites by (Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\) addition improves the stable temperature characteristics of the dielectric properties of BaTiO\({}_{3}\). With the addition of Co\({}_{3}\)O\({}_{4}\) and Nb\({}_{2}\)O\({}_{5}\), the stable dielectric properties of BaTiO\({}_{3}\)-(Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\) were tailored to meet the X9R specification by adjusting the core-shell structure [14; 15]. It was also shown in the literature that the dielectric properties of BaTiO\({}_{3}\) or bismuth-based compounds can be significantly enhanced with the addition of Ta\({}_{2}\)O\({}_{5}\), through tailoring the microstructure, improving densification, and shifting Curie temperature [23; 24]. In thisstudy, BaTiO\({}_{3}\)-(Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\) dielectric ceramics incorporating various amounts of Ta\({}_{2}\)O\({}_{5}\) additive were investigated to enhance their high temperature stability to meet the X9R specifications. The effect of the Ta\({}_{2}\)O\({}_{5}\) additive on the densification, crystalline phase, microstructure development, and dielectric properties of BaTiO3-(Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\) ceramics compared to those of the common Nb\({}_{2}\)O\({}_{5}\) additive under the same processing conditions were investigated and are discussed below.
## Instrument Description
### Clock Synthesizer  
A block diagram of the in-house designed clock synthesizer is shown in Figure 3. An oven controlled crystal oscillator (OCXO) generates the 100 MHz reference clock from which the local oscillator and the sampling frequency are derived. Its actual frequency can be slightly adjusted by the controller system, by using a PWM signal, which is low-pass filtered at the input of the OCXO. The reference clock is split into two: the first replica is delivered to the FPGA, which internally generates the 80 MHz sampling clock, and the second one is used to generate the local oscillator, through a phase-locked loop (PLL) and a bank of voltage controlled oscillators, one for each instrument band. The local oscillator is then amplified in several stages and delivered to the 16 receiver chains using an external 1:16 passive splitter. A digital calibration signal (pseudorandom noise with Gaussian statistics) provided by the controller is converted to the analogue domain and shifted to RF by using a passive mixer and a replica of the local oscillator. An external reference clock at 10 MHz, coherent with the 100 MHz clock, is also generated at the controller system and buffered through the clock synthesizer board. The function of this clock is to provide a coherent reference to the external electronics, so that the recorder can be used at higher frequency bands using external Low Noise Blocks (LNB). This will enable the use of SPIR with sources of opportunity at different frequency bands in the future.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 4 Edge Node Deployment Algorithm Design
### Improved Dual Population Genetic Algorithm
#### 4.1.2 Population Initialization Based on the Cubic Chaos Map  
The traditional approach relies on random initialization of the search population, often leading to limited diversity and increasing the chances of the algorithm becoming trapped in a local maximum. Cubic mapping chaos can lead to a more uniform population, expand the search space, and improve the convergence speed and accuracy of the algorithm. The formula for the cubic mapping chaotic sequence is as follows:  
\[z(i+1)=4z(i)^{3}-3z(i),-1\leq z(i)\leq 1,i=0,1,2,... \tag{13}\]  
\[u_{i}=l_{min}+(l_{max}-l_{min})(z(i)+1)/2 \tag{14}\]  
where \(z(i)\) represents the chaotic variable, \(l_{max}\) and \(l_{min}\) indicate the upper and lower bounds of the mapping interval, respectively, and the mapped variable value is denoted as \(u_{i}\).
## 2 State of the Art
### Characteristics and Metrics in OppNets  
In this section, the article explains the characteristics and metrics used in OppNets and why they are fundamental concepts in OppNets. This section also describes the relationship between the characteristics and the metrics.  
OppNets are heterogeneous. A specific OppNet instance can be expressed with a set of characteristics. However, it cannot be said a priori that one instance of an OppNet is necessarily equal to or different from another OppNet. One way to establish the differences between OppNets is to compare the characteristics that describe each OppNet.  
The more characteristics are used, the more accurate is the description of an OppNet's behavior. In other words, this feature-based description somewhat simplifies a real-world OppNet. As said in [23], characteristics are deployment facts expressed in numbers for a network.  
Metrics return quantitative information about a feature or behavior. Moreover, a metric is a measure function whose output is a numerical value that can be interpreted as the degree to which the routing algorithm has a given attribute [24]. Researchers use metrics to evaluate, compare, or measure the behavior of OppNet routing algorithms [25; 26]. Metrics quantify, among others, the performance and the ratio that routing algorithms achieve when the messages are interchanged between source and destination. It can quantify a specific attribute (such as a count of successful processes, time consumption, and messages delivered), providing a quantitative indication.  
There are some metrics that most OppNet researchers tend to use to prove performance hypotheses. Among them, three stand out because of their presence in most works related to OppNets this work has found. Those metrics are: Delivery ratio, delivery delay and delivery cost [16]. However, some authors do not use these metrics but rather modified versions of them to fit specific hypotheses. In other cases, some authors even find it necessary to establish entirely new metrics to measure the behavior of their work [27; 28].  
Characteristics and metrics have a close relationship. This relationship is given because routing algorithms work in OppNets instances. Moreover, since OppNet instances are described by a set of characteristics and those characteristics, in a way, produce a routing algorithm behavior measured by the metrics, the characteristics of an OppNet influence the metrics that a routing algorithm has. For example, there are equivalent network configurations where the same message routing algorithm would be equally efficient. However, the metrics will tell if two scenarios are the same or different from a routing point of view. An adequate characterization of an OppNet withdraws the attention on details that can give a wrong cognitive impression of the difference between networks.  
This section has described the characteristics and metrics in OppNets, and it also has described the relationship between them. The following section shows the current performance evaluation techniques among OppNet routing algorithms.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 5 A Taxonomy for Gait, Balance, RoM Analysis
### Gait Analysis
#### 5.1.3 Gait Analysis: Variability  
Variability can be expressed in terms of measures of dispersion, such as standard deviation and/or coefficient of variation [114]. Variability can be detected temporally or spatially, similar to asymmetry. Research informs that variability in spatiotemporal parameters predicts mobility deficits and future falls better than other gait parameters [104]. On the other hand, researchers have concerns about the best way to measure variability. This leads to questions about how many parameters to use to measure variability; for example, whether to measure it temporally or spatially, and whether to measure variability for each lower limb separately or combined.  
[PERSON] et al. proposed a new way to quantify fluctuation magnitude using the Gait Deviation Index as reference and developed a _Gait Variability Index_ (GVI) [115; 116]. The GVI is based on nine weighted (using Principal Component Analysis) gait parameters: step length, stride length, step time, stride time, swing time, stance time, single support time, double support time, and velocity. It uses the difference between the variability of an individual compared with a reference group. The value obtained is transformed into a score with 100 and 10 representing the mean and standard deviation, respectively, of the reference.  
Non-linear variability is also gaining acceptance within the gait analysis community, such as Lyapunov exponent (LyE). [PERSON] et al. quantified the temporal structure of the trunk acceleration time series from both direction using LyE and approximate entropy [117]. The largest LyE is a measure of the rate at which nearby trajectories in state space diverge s lack of divergence in the acceleration pattern will produce small values for the LyE and vice versa [117]. They demonstrated that in people with multiple sclerosis the acceleration time series increased LyE in both medio-lateral and antero-posterior directions, which indicates excessive divergence and reduced behavioral complexity as compared to healthy subjects [117].  
Another way to measure variability using wearable devices is to extract statistical parameters. Two of the most common statistical parameters used to represent variability are standard deviation and coefficient of variation [35; 36; 48; 116]. These are calculated by using spatiotemporal parameters such as step length, step time, single support time, and others, since they have shown in the literature to being able to assess fall risk. However, these are sometimes not recommended since these measures of dispersion can present bias and alter the results: standard deviation is sensitive to the scale, and coefficient of variation tends to infinity when the mean is close to 0 [116].
# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 6 Experimental Results
### Experiment III: The Influence of Sensing Technology  
This experiment points out the influence of the tested sensing technologies on the proposed PAD subsystem efficiency. The comparison focuses on two main aspects:  
I. The overall PAD mechanism accuracy is determined using BPCER at fixed APCER values. Hence, the most effective feature extractors are selected, then the sensors are compared using BPCER20 (Figure 12);  
II. The mechanism's capability of eliminating specific PAI species. The comparison is carried out by determining the number of eliminated PAI species considering the different sensors and feature extractors (Figure 13).  
The comparison here is not as straightforward as it seems. Even though the optical sensor is demonstrating a higher accuracy in part of the DET curves, the PAD mechanism has shown the vulnerability to all attack species, and in its best case, the PAD mechanism rejected two species. On the other hand, utilizing the PAD mechanism at the thermal sensor had proven a higher capability to reject more attack species, where four PAI species were rejected using VLBP, LBP-TOP, VLPQ, and LPQ-TOP features.  
Figure 12: PAD accuracy for the two sensors in terms of BPCER20.
# A Novel Device-Free Counting Method Based on Channel Status Information
## 1 Introduction
Meanwhile, people-counting also plays an significantly role in security monitoring and energy management for smart homes. We aim to find a baseline indicating the relationship between CSI and people number. In addition, it is worth noting that the widespread deployment of WiFi networks can also further boost our study. However, the same environment and conditions should be required (e.g., the same transmitter and receiver, as well as the same indoor environment) in this scenario, which has been widely considered in existing works [11; 12]. The main contributions of our work are listed as follows:  
* We present a device-free indoor people-counting method based on CSI. Since CSI is relatively stable in the static environment and sensitivity to human bodies, we leverage it to exactly achieve the crowd/people-counting. * We utilize the linear transformation to obtain available phase information. In addition, the wavelet denoising is introduced to mitigate environment noises (e.g., indoor settings) and get relatively transparent CSI measurements. For multi-dimensional sample data, we extract the amplitude or phase covariance matrix as the eigenmatrix. Since covariance matrix can not only describe multi-dimensional data, but the diagonal elements represent the variance of each dimension. * We attempt to combine both space diversity provided by MIMO and frequency diversity supplied by OFDM subcarriers, to enable more robust and accurate counting. * We validate a monotonic relationship that lies between the CSI variations and crowd size. In addition, we utilize the percentage in the extension covariance matrix of amplitude and phase as baseline that estimate this relationship. Meanwhile, we compare the differences and similarities between phase and amplitude information in crowd counting.
## 3 Results and Discussion  
### Preliminary Analysis  
The performance of the main sub-systems was investigated via the preliminary analysis. At first, the collector performance was determined via the thermal losses analysis in steady-state conditions, as it is presented in Ref. [29]. According to these calculations, the collector thermal efficiency can be approached through a polynomial expression, as described in Equation (6). The defined polynomial equation, for which the coefficient of determination (\(R^{2}\)) was found at almost 1, is presented below:  
\[\eta_{th,col}=0.7358\cdot K(\theta)-0.0789.\frac{T_{col,in}-T_{amb}}{G_{bn}}+3.407\cdot 10^{-4}.\frac{\left(T_{col,in}-T_{amb}\right)^{2}}{G_{bn}}-1.771\cdot 10^{-6}.\frac{\left(T_{col,in}-T_{amb}\right)^{3}}{G_{bn}} \tag{39}\]  
The PTC thermal efficiency as a function of the temperature difference between the collector inlet and the ambient temperature is depicted in Figure 4. The collector efficiency decreased with the increase in the aforementioned temperature difference, while the maximum achieved value was equal to 72.8%. Additionally, through steady-state calculations, the ORC thermal efficiency was also specified, taking into account two important parameters, i.e., the evaporator saturation temperature (\(T_{sat}\)), and the ambient temperature. These two temperature levels defined both the higher and the lower pressure values, which strongly affected the cycle's efficiency. The ORC efficiency, which is illustrated by a tri-dimensional plot in Figure 5, increased if the evaporator saturation temperature increased and the ambient temperature decreased. The calculated values ranged from 19.8% to 35.4%. Moreover, the main output of the PEM electrolyzer was the hydrogen production rate, which highly depended on the electricity input; therefore, according to the results that are shown in Figure 6, there was almost a linear correlation between the produced hydrogen mass flow rate and the electricity load provided to the PEM water electrolyzer. All the above results were exploited as inputs to the transient operation simulation.  
Figure 3: Validation of the PEM water electrolyzer model with experimental data from the literature [47].  
Figure 4: Collector thermal efficiency depending on the temperature difference between the collector inlet and the ambient temperature for solar beam irradiation \(G_{\text{bn}}\) = 800 W/m\({}^{2}\), and incident angle \(\theta\) = 0\({}^{\circ}\).  
Figure 5: ORC thermal efficiency as a function of the evaporator saturation temperature and the ambient temperature.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Performance Metrics Comparison  
We use a variety of performance criteria, like accuracy, precision, recall, and F1-score, to assess the efficacy of domain adaptation approaches. These measures are essential gauges of how well the models can deal with domain shift issues and achieve strong generalization across various data distributions [301].  
The results show that, for a variety of computer and robotic vision tasks, deep learning-based approaches, such as DANN and CycleGAN, consistently outperform more established techniques, such as TCA and MMD. DANN and CycleGAN successfully learn domain-invariant feature representations by using the strength of deep neural networks, resulting in appreciable performance gains [295]. For instance, DANN exhibits an average accuracy gain of around 15% compared to TCA and MMD in object identification tasks using cross-domain picture datasets [296]. This demonstrates how effective deep learning-based approaches are in overcoming domain shift difficulties and achieving improved generalization [302].  
Additionally, DANN outperforms conventional approaches in the accuracy and recall analyses of domain adaptation strategies in object detection tasks. Comparing DANN to TCA and MMD, there is an average 10% gain in precision and an 8% improvement in recall [303]. Such enhancements demonstrate DANN's capacity to precisely recognize and recall items, even under conditions with substantial domain variance. Further highlighting the supremacy of deep learning-based approaches is the F1-score study [304]. In comparison to conventional techniques, DANN yields a significant F1-score improvement of 12%, demonstrating its effectiveness in achieving a balance between accuracy and recall, even in highly dynamic and complicated robotic vision environments [305].