esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Sulforapahane on Sex-Specific Conditions and Hormone Balance: A Comprehensive Review
## 4 Female Health  
### Estrogen Metabolism  
Estrogen is a generic term used for the body's main estrogens: estradiol (more correctly termed 17\(\beta\)-estradioliol; the dominant form produced by ovaries during reproductive years), estriol (produced placentally during pregnancy), and estrone (the dominant form produced post-menopausal). Both the hormones estrogen and testosterone are steroids that are synthesized from cholesterol. The adrenal glands produce the hormone dehydroepiandrosterone (DHEA) that is then transformed into both testosterone and estrogen. In females, the process starts in the ovaries with androgen synthesis catalyzed by the enzyme aromatase. It converts testosterone to estradiol and converts androspenedione to estrone. In males, estrogens are also synthesized in a variety of testicular cells. In both genders, estradiol is also produced in the brain, adrenal glands, adipose tissue, pancreas, and even skin. It is both biosynthesized and further metabolized in the liver. Women have more circulating estrogen in the bloodstream than men.  
Xenoestrogens are estrogen mimicking compounds which are _not_ naturally produced by the body that disrupt the body's normal hormonal balance. Their environmental presence, e.g., phthalates, bisphenol A (BPA), dichlororibhenyltrichlororethane (DDT), and polybrominated diphenyl ethers (BDEs), raise large concerns due to their potential effects on all stages of human growth and development--particularly on developing fetuses, children, and female health. In addition to the general detoxification effects of SF, primarily via the Keep1-Nrf2 pathway, there are also now many credible studies demonstrating the ability of SF to reduce potential damage from specific xenoestrogens, in particular, BPA [43; 44; 45; 46; 47; 48].  
\begin{table}
\begin{tabular}{l l} \hline
**Normal Symptoms or Conditions** & **Disease States** \\ \hline estrogen metabolism & breast cancer \\ \hline PMS/dysmenorrhea & ovarian cancer \\ \hline mastalgia & uterine cancer \\ \hline fertility & prostate cancer \\ \hline pregnancy & testicular cancer \\ \hline peri-menopausale and menopause & endometriosis \\ \hline sperm and testicular health & polycystic ovarian syndrome \\ \hline hair loss & preeclampsia \\ \hline libido loss & erectile dysfunction \\ \hline mood swings & \\ \hline \end{tabular}
\end{table}
Table 1: Potential sex-related targets of sulforaphane.  
The estrogen metabolite 16\(\alpha\)-hydroxyestrone (16\(\alpha\)-OHE1) acts as a breast tumor promoter, whereas the alternate metabolite, 2-hydroxyestrone (2-OHE1), does not. Consumption of Brassica vegetables (e.g., broccoli) shifts estrogen metabolism towards 2-OHE1 [49]. Decades of research now confirms that SF, indole-3-carbinol (I3C) and its metabolite diin-dolylmethane (DIM) all can play a helpful role in this effect (See Section 8).
## 6 Experiment  
Since the electric field measurement device is not allowed to approach the HV live-operating power cable, we made a proportionally reduced physical model of the mechanical arm to verify the reliability of the simulation results in practical applications. The physical model's length, width, and height are at 1 to 2 scale ratio of the designed mechanical arm, respectively. Therefore, the experiment uses a 220 V voltage level, and the electric field data is collected at the corresponding position of the self-insulating mechanical arm experimental model through an electric field measuring instrument to verify the consistency between the actual results and the simulation. The five measurement positions in Figure 18 are selected according to the simulation model in Figure 13.  
This scheme provides an effective approach for low-voltage experimental verification of the insulation performance of high-voltage equipment.  
The following are the experimental details. The experimental flow chart is shown in Figure 19a. The experimental site picture is shown in Figure 19b. During the experiment, the indoor temperature was maintained at 25 degrees Celsius, and the air humidity was 40%. The positioning accuracy of the electric field measuring instrument was \(\pm\)0.05 mm.  
The electric field measurement results are shown in Table 5.  
The calculated electric field distribution along the mechanical arm is shown as the solid line in Figure 15. The measured electric field distribution at positions 1-5 is shown as the dots and the fitted curved based on the measured results is drawn to compare to the calculated results.  
Figure 20 shows that both the simulation and experiment results have a decreasing tendency along the mechanical arm, indicating that the proposed self-insulating mechanical arm has reliable insulation characteristics in a concealed space working environment. The simulation result is 27.2% larger than the experiment result because the probe of the electric field measurement device is made of stainless steel. The probe itself could interfere with the accuracy of field measurement and cause a decreased offset. However, the electric field tendencies of simulation and experiment results are consistent.  
Figure 18: Electric field measurement system diagram.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Position Number** & **1** & **2** & **3** & **4** & **5** \\ \hline Measured Electric Field V/m & 377 & 121 & 52 & 10 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Electric field measurement experiment data collection results.  
Figure 19: (a) Experimental flow chart; (b) electric field measurement experiment setup.  
Figure 20: Comparison of simulation and experimental results.
## 2 Literature Review
### Early-Warning Systems  
It is clear that implementing CBM provides a significant advantage by proactively identifying components at risk of failure. A strategic approach to utilizing this information for intervention policy planning involves establishing early warning systems. These systems promptly alert maintenance personnel to potential failures and are commonly deployed in high-impact industrial and safety-critical systems [1].  
Several systems are discussed in the literature [33; 34; 35]. However, these systems does not share their methodology. Consequently, defining a standard is essential, and one approach is optimizing decision-making in CBM by jointly minimizing the risk and cost factors associated with predictive and corrective maintenance through PHM [2].  
In Ref. [36], a function dependent on optimal risk and the Weibull parameters of components is proposed. The optimal risk can be derived from a cost function [2], where the fixed-point iteration method introduced by Ref. [37] can be used to obtain the optimal cost and risk. This yields a \"warning-limit function\", a graphical representation outlining component states, distinguishing between safe, warning, or failure-prone zones. In Ref. [2], this function is integral to developing the computational software \"EXAKT\", enabling data input for components and the computation of conditional reliability, remaining useful life, costs, risk, and a maintenance policy.  
While this methodology for deriving maintenance policies is well documented in the literature, it has been observed that few studies have employed this approach to develop such policies in recent decades. This missed opportunity is particularly striking considering the advancements in data processing tools developed in recent years. Consequently, utilizing these tools has the potential to greatly enhance the predictive capability of these policies across different industries. Among the few studies that follow a similar structure, it is worth noting that in Refs. [13; 38], while similar curves to the \"warning-limit\" function are derived, slight variations exist compared to the original proposal due to specific aspects of PHM application. Nevertheless, the interpretation of both graphs is similar to the one shown in Ref. [2].
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 3 Opportunities Given by System-Level Testing
### Opportunities Given by System-Level Testing--Summary  
Summarizing this section, it was shown based on different system-level radiation tests, that such tests might be useful in these areas:  
* to verify the mitigation techniques efficiency,
* to identify new failure modes,
* to define error signatures for the system,
* to assess which parts should be replaced in the system,
* to evaluate general TID performance,
* to provide information on synergistic effects at the system level.
## 3 Noise Analysis
### Determining the Effect of Noise on Population
#### 3.2.2 Determining the Number of People Affected by Noise  
The extent of noise around the route is likely to be limited to parts of the administrative district. In addition, the administrative district is not an ideal shape, such as a square, and residents are not evenly distributed per unit area of the district. Since the current population distribution has statistical data depending on each administrative district, a method to reasonably derive the effect of UAM noise on the population is proposed as follows.  
Figure 9(a) is a hypothetical representation of the noise that eVTOL produces when it passes through an actual city, and Figure 9(b) is modeled in an ideal space of hexagonal tiles.  
The actual city of Figure 9 is a replica of Gangseo-gu, with an area of about 41.4 km\({}^{2}\). In an ideal city, one hexagon has an area of about 0.8 km\({}^{2}\), and the city consists of 52 hexagons. Therefore, the total area of the ideal city is about 41.6 km\({}^{2}\), and it is well fitted because it produces about \(-0.48\%\) of the integral error with the actual city.  
According to the resident registration demographics serviced by the Ministry of the Interior and Safety of Korea, the total number of residents in Gangseo-gu is 572,377 as of 2021, and 182,963 of them commute outside Gangseo-gu, according to the Korean Statistical Information Service. In other words, there will be 389,414 people left in Gangseo-gu, and the population is defined as those who can be affected by noise within the living area. Onthe other hand, because the ideal city consists of 52 hexagons, it can be assumed that there are 7489 people per hexagon.  
At 65 dB and 60 dB in the Miedema curve, the effects of air traffic noise are 27% and 18% of the total population, respectively. In an ideal city, as shown in Figure 10, 65 dB contains six hexagons, and 60 dB contains ten hexagons, and 44,934 and 74,890 represents the number of people living in the hexagons affected by the noise, respectively. As a result, the total number of people affected by the high level of noise generated by the path of the eVTOL vehicle in the ideal city is 12,132 in the 65 dB section and 13,480 in the 60 dB section.  
In this paper, these methods were applied to analyze the impact of UAM noise on resident populations and present a feasible method for setting up actual UAM noise priority routes.
# A Study on Wireless Charging for Prolonging the Lifetime of Wireless Sensor Networks
## 7 Charging Period Determination  
In [15], the charging period of the MC is a constant value \(T\), but in fact, the charging period is a changeable variable. If the charging period \(T\) is too short, the MC may not have enough time to charge all sensor nodes. On the contrary, if the charging period is too long, the MC may stay at the base station for a long time. Therefore, we do not make the charging time a constant in this work.  
The charging period consists of three parts: total moving time of the MC, total charging time of all sensor nodes and the rest time of the MC at base station, which are denoted as \(T_{travel}\), \(T_{charge}\) and \(T_{back}\), respectively. We denote the \(k^{th}\) period as \(T^{k}\), and get the following equation:  
\[T^{k}=T^{k}_{travel}+T^{k}_{charge}+T^{k}_{back} \tag{21}\]  
Let a set of charging nodes are \(V=\{\Phi\}\), the sensor nodes in V are sorted by their remaining energy from low to high, \(s_{1},s_{2},\ldots,s_{n}\). Assume that the node set is \(N\), the number of nodes which need not to be charged is \(N-V\), and they should meet the constraint as follows:  
\[\sum_{i\in|N|-|V|}\Bigl{(}\lambda_{i}-C_{i}\times T^{k}\Bigr{)}>E_{\min},i=1,...,|N|-|V| \tag{22}\]  
This means that even if the sensor nodes in the set of \(N-V\) are not charged in the \(T^{k}\) period, the remaining energy of the nodes within the set is more than the given threshold \(E_{min}\) at any time.  
1. Let \(V=V\cup\{s_{1},s_{2}\}\), calculate \(T^{k}_{travel}\), \(T^{k}_{charge}\) and \(T^{k}_{back}\) according \(s_{1},s_{2}\), then calculate \(T^{k}\).
2. Determine whether \(V\) satisfies the Equation (22)
3. If \(V\) satisfies the Formula (22), let \(V=V\cup\{S_{i+1}\}\) and turn to 1, if not, turn to 4.
4. Return \(V\) and \(T^{k}\).
## 3 Model Construction and Application Scenarios  
In accordance with various application scenarios, devices adhere to varying design principles. Figure 1 illustrates the typical application scenario of the joint radar jamming and communication system proposed in this paper.  
In Figure 1, the integrated system is deployed on the ground in response to an approaching unknown unmanned aerial vehicle (UAV), which may be operating in either tracking search mode or SAR imaging mode. To protect ground targets from reconnaissance, the unknown UAV's signals would have to be jammed, preventing it from detecting the protected targets. Simultaneously, there are known UAVs taking off and departing, necessitating the fulfillment of communication requirements. Transmitting jamming signals and communication signals separately would pose a significant challenge, as the wide beam and high energy of jamming signals can impact the communication process. To ensure optimal communication quality, it is crucial to design jamming signals that incorporate communication data, addressing a critical requirement for the integrated system.  
The ground-based integrated system effectively detects radar signals from jammed objects, followed by the generation of simulated target echo signals strategically designed to deceive the airborne radar systems of the jammed objects. This action disrupts their normal functioning, thereby protecting ground targets from detection. Concurrently, communication data is transmitted to the communication target. The communication target is equipped to assess the real-time behavior of the jammed objects and can execute commands based on the received communication data. Due to the relatively low directional  
Figure 1: Application scenario of joint communication and jamming system.  
requirements of the antenna for communication tasks, the communication target efficiently receives and processes joint signals emitted from sidelobes, ensuring the fulfillment of communication functions.
# A Study on Small-Scale Snake Image Classification Based on Improved SimCLR
## 3 Results
### Experimental Results  
#### 3.3.1 The Impact of Data Augmentation Strategies  
To investigate the effectiveness of different data augmentation strategies in contrastive pretraining, we conducted an ablation study, as shown in Table 4. We started with conventional augmentation methods, including random resized crop, horizontal flip, color jitter, and Gaussian blur. This baseline set had already achieved a relatively high validation accuracy of 0.9676, indicating its ability to provide diverse and effective views for contrastive learning. Building upon this, we added the RandomGrayscale operation to simulate lighting and texture changes. This led to a modest but consistent improvement in accuracy to 0.9712, suggesting that the model benefited from learning invariant representations under grayscale transformations. Furthermore, we incorporated random erasing to simulate occlusion and background clutter. This yielded the highest accuracy of 0.9748, demonstrating that exposing the model to partially erased or corrupted views further enhances its ability to extract robust and generalizable features. Although training losses slightly increased across these configurations, this is expected due to the more challenging training samples and does not negatively impact downstream classification performance. These results highlight that progressively stronger data augmentation strategies, particularly those introducing appearance variation and local occlusion, are beneficial for learning discriminative representations in a contrastive learning framework.
## 2 Study Area and Datasets  
### The Dijon Study Area  
The first study area is Dijon, located in the Cote-d'Or department (with the Dijon prefecture) in the Bourgogne-Franche-Comte region of northeastern France at 05\({}^{\circ}\)01\({}^{\prime}\)E and 47\({}^{\circ}\)17\({}^{\prime}\)N in geographical coordinates (latitude/longitude) on the WGS-84 ellipsoid (Figure 1). The study area, covering a total area of approximately 5000 km\({}^{2}\), features an oceanic climate with a continental influence under the Koppen climate classification, with average temperatures between 6.8 \({}^{\circ}\)C and 16.1 \({}^{\circ}\)C and an annual average precipitation level of 740 mm. These climatic conditions are ideal for growing wheat, rape, grape, and grass.  
Under the Common Agricultural Policy of the European Union, the National Institute of Forest and Geography Information (IGN) of France is responsible for gathering geographical information on the geometry of cultivated crops. The IGN institute has released anonymized parcel geometries and types of cultivated crops under an open license policy. This study used data collected in 2019 to validate the proposed model. In the raw crop type categories, there were 328 distinct crop labels organized into 23 groups. _'Winter wheat'_ (WWT), _'winter barley'_ (WBR), _'winter rapesed'_ (WRP), _'winter triticale'_ (WTT), _'spring barley'_ (SBR), _'corn'_ (CON), _'soy'_ (SOY), _'sunflower'_ (SFL), _'grape'_ (GRA), _'alfalfa'_ (AFF), _'grass'_ (GRS), and _'fallow'_ (FLW) were selected and summarized for the Dijon study area. The two minority classes (_'sunflower'_ and _'winter triticale'_) were also retained to challenge classification methods. This further reflects the significant class imbalance in real-world crop-type-mapping datasets [32]. This study area encompassed approximately 53,400 parcels, of which 20% were selected randomly to serve as labeled samples.  
In addition, Sentinel-2 time-series images (with a Path/Row of T32 TFN) captured between February 2019 and September 2019 were used to record crop growth, as the growth stages of winter crops are also concentrated in the subsequent year. The Sentinel-2 satellite  
Figure 1: The Dijon study area is in the northeast of France. The right-hand section provides an overview of the near-infrared-blue-green Sentinel-2 image acquired on the day of the year (DOY) 263 in 2019.  
image contained four (visible and near-infrared) bands with a spatial resolution of 10 m and six (red edge and shortwave infrared) bands at a 20 m resolution. All 48 images, captured every 5 days, were obtained from the Copernicus Open Access Hub at Level 1C, and 38 of them contained cloud and shadow contamination. Meanwhile, images captured on the days of the year (DOY) 48, 58, 88, 133, 143, 168, 178, 233, 238, 258, and 263 were free from clouds and shadows.
## 3 Calculation Method of Stable Bearing Capacity of Tower  
### Stable Bearing Capacity of Tower without Considering the Influence of Joint Axis Stiffness  
In the engineering design, the transmission tower structure belongs to the truss structure of batten combination, and the influence of joint stiffness is generally not considered. The connection joint between the column limb and batten is generally simplified as hinge joint. When the structure is unstable, each pole in the transmission tower only causes additional axial force. The calculation of the transmission line tower is mostly simplified as a cantilever structure with fixed bottom and upper free part. The calculation model is shown in Figure 10.  
Figure 10: Theoretical calculation model of cantilever tower.  
Figure 9: Load-displacement curve of joint specimen.  
And use the relationship of Equation (10),  
\[d=2b\tan(\theta) \tag{10}\]  
The strain energy of the tower can be written as following Equation (11),  
\[U=\frac{F_{P}^{2}la^{2}}{4E}\bigg{[}\frac{1}{A_{1}b^{2}}+\frac{\pi^{2}}{32l^{2} \tan(\theta)}\cdot\frac{1}{A_{2}\cos^{3}\theta}\bigg{]} \tag{11}\]  
The potential energy of external load is,  
\[U_{P}=-F_{P}\int_{0}^{l}\frac{1}{2}(y^{\prime})^{2}dx=-F_{P}\frac{a^{2}\pi^{2 }}{16l} \tag{12}\]  
According to the energy method [29; 30], stationary condition of potential energy is as shown in Equation (13).  
\[\frac{dE_{P}}{da}=0 \tag{13}\]  
So the expression of critical load can be obtained. As shown in Equation (14).  
\[F_{P\sigma}=\frac{\pi^{2}EI}{l^{2}}\frac{1}{4+\frac{\pi^{2}}{8}\Big{(}\frac{b} {l}\Big{)}^{2}\frac{A_{1}}{A_{2}\sin\theta\cos^{2}\theta}} \tag{14}\]  
In Equation (14), \(I\) is the moment of inertia of the tower section to form mandrel. As shown in Figure 10.  
\[I=4A_{1}\bigg{(}\frac{b}{2}\bigg{)}^{2}=A_{1}b^{2} \tag{15}\]
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
## 2 Split-Spectrum Processing (SSP)
### Implementation of the Filter Bank  
A MatLab program has been written that takes an unprocessed signal in the time domain and converts it to the frequency domain. It then filters the signal using a Gaussian bandpass filter to generate a set of sub-band signals, and applies the recombination algorithms into these sub-bands. The input is the signal to be filtered, with the upper and lower 3 dB cut-off frequencies. Therefore, the lower cut-off frequency \(\mathrm{f_{l}}\), and the higher cut-off frequency \(\mathrm{f_{h}}\), for each sub-band filter, are calculated as:  
\[\mathrm{f_{h}}_{\mathrm{n}}=\left\{\begin{array}{ll}\mathrm{f_{min}}-\ \frac{\mathrm{B_{fit}}}{4}&\mathrm{n=1}\\ \mathrm{f_{h-1}}+\mathrm{F}&\mathrm{n=2,\ 3,\ \ldots N}\end{array}\right. \tag{4}\]  
\[\mathrm{f_{h_{n}}}=\mathrm{f_{h}}+\mathrm{B_{fit}}\ \ \ \mathrm{n=1,\ 2,\ \ldots N} \tag{5}\]  
where \(F\) is the filter separation, N is the number of filters, \(\mathrm{f_{min}}\) is the lower cut-off frequency of B, and \(\mathrm{B_{fit}}\) are the sub-band filters. The lower cut-off frequency for the first sub-band \(\mathrm{f_{h}}_{\mathrm{1}}\), needs to cover the start point of the signal. The selections of these values are inspired by the values that have been employed in UT, and then adjusted for the use in GWT using the brute force search algorithm. Table 1 shows the optimum values of SSP that were proposed in the previous paper [26]. These values are employed in this paper to find the limitations of SSP. The performance of the proposed technique is quantified by measuring the SNR, the spatial resolution, and the defect sensitivity of the output signal.  
\begin{table}
\begin{tabular}{c c c} \hline
**SSP Parameters** & **Symbols** & **Recommended Values** \\ \hline Total bandwidth & B & 99\% of total energy \\ Sub-band filter bandwidth & \(\mathrm{B_{fit}}\) & \(\mathrm{B/11}\) \\ Filter crossover point & \(\mathrm{\delta}\) & \(\mathrm{B_{fit}/3.5}\) \\ Filter separation & F & \(\mathrm{1\ dB}\) \\ Number of filters & N & \(\mathrm{B/F+1}\) \\ \hline \end{tabular}
\end{table}
Table 1: Recommended values for SSP parameters.