esa-sceva/satcom-chunk-collection huggingface dataset:
# Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection
## 3 Proposed Method
### Parking Line Detection
#### 3.1.2 Parking Line Detection Using a CNN  
For each detected parking corner, a CNN is used to determine whether or not it was a corner point of a parking line [18; 30]. There are various models of CNNs, and there are many models that perform well, such as AlexNet [30], ZFNet [31], VGGnet [32], and GoogLeNet [33]. Because the proposed method must perform automatic parking in real time, a simple real-time model is best. Therefore, because simple operation is more important than a complex model, a modified form of LeNet [34] was used.  
Figure 5 shows the CNN model used in the proposed method. It consists of six convolutional layers and two fully connected layers.  
Figure 4: Corner detection example: (**a**) Hough transform result and (**b**) corner detection result.  
Figure 5: Layers of the CNN.  
To train the CNN model, each corner point and non-corner point in the training images was classified and used for training. Figure 6 shows the examples of data used for learning.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 3 Impulsive Noise Measurements  
Generally, in the signal processing domain, there are at least two types of noise: the white Gaussian noise and impulsive noise [28]. Currently, the statistical behaviour of Gaussian noise is well known. In contrast, impulse noise is a non-Gaussian random process that has a strongly structured form due to its impulsive nature. It often has non-stationary characteristics such as the appearance of random pulses over time with high levels of interference [29]. For measuring the electromagnetic pulses radiated by partial discharges, several experiments have been conducted with antennas [30; 31; 32]. For example, in [33], [PERSON] et al. presented a procedure for measuring impulsive noise in electricity substations with three antennas covering the range 100 MHz-6 GHz. However, since the measurement in power lines is not always possible due to several constraints such as the climatic conditions and the availability of qualified personnel, another alternative has been designed to evaluate the partial discharges. It consists of tests in the laboratory that are done using a Tesla coil or generator bar. In [34; 35], the authors designed a setup for tests in the laboratory, and the results showed that it is possible to use these devices to generate partial discharges or gap noises.
## 3 Methodology  
This study employs PSInSAR, a technique suitable to identify stable radar targets (namely, the PSC) over time, to monitor VUG and its temporal variation in Wuhan using a long time series of COSMO-SkyMed SAR data (2012-2020). To effectively manage this extensive dataset (375 images) and capture detailed temporal VUG dynamics, we developed and implemented a Stack Processing PSInSAR workflow, visualized in Figure 2.  
Our methodology is divided into three distinct phases, each crucial for ensuring the accuracy, consistency, and computational feasibility of analyzing the 8-year data stack.  
Figure 2: Workflow diagram of the PSInSAR stack processing approach for monitoring Vertical Urban Growth (VUG). Notation: Amplitude Stability Index (AS-Index), Atmospheric Phase Screen (APS), Persistent Scatterers (PS), Reference Point (RP).  
Full Stack Processing: The primary goal of this initial phase is to establish a robust and consistent geodetic framework for the entire time series. This involves analyzing all 375 COSMO-SkyMed images together to select the optimal master image and, critically, to identify a highly stable reference point based on long-term signal stability, evaluated using the Amplitude Stability Index (AS-Index) across the full stack. This common reference point serves as the essential anchor for comparing height estimations across different time periods processed in the subsequent phase.  
Sub-Stack Processing: Handling the entire 8-year dataset in a single PSInSAR process is computationally demanding and can obscure finer temporal details of VUG. Therefore, this second phase divides the dataset into 13 smaller, sequential, and manageable stacks (hereafter referred to as'stacks'), each containing 28 images. Each sub-stack is processed independently using consistent parameters and anchored to the common reference point identified in the Full Stack phase. This allows for efficient processing and the detailed extraction of height information over shorter time intervals, capturing the temporal evolution of VUG. This phase directly implements the core of our innovative stack processing approach for detailed temporal analysis.  
Post-Processing: The outputs from the 13 individual sub-stacks require integration and refinement. This crucial final phase involves merging the PSC data, addressing noise and data gaps inherent in time series analysis, applying our multi-scale analysis framework by aggregating results onto a 50-m grid, extracting final VUG metrics (e.g., maximum height per grid, time series), and preparing the results for comparative validation against external datasets.  
The figure details the three phases of the methodology: full-stack processing, sub-stack processing, and post-processing. Key components include the AS-Index for identifying stable scatterers, the reference point for height estimation consistency, and the Atmospheric Phase Screen (APS) for correcting atmospheric disturbances.
## 1 Introduction
Three primary health centers have been piloted in Indonesia by health workers at the centers and volunteer community health workers for NCD monitoring and outreach activities. PHC examinations include health interviews, as well as anthropometric, clinical and laboratory examinations (blood and urine) using portable medical devices and mobile applications. The applications facilitate teleconsultation between doctors and patients at the primary health center based on the examination results that are inputted in real time. The examination results are presented in a triage format, which shows the level of urgency for education and drug administration. Many mobile health or mHealth applications have been developed and implemented by patients or health workers, but few have been facilitated by community health workers to conduct health examinations [20] at research locations. During the pandemic, health professionals prioritized the COVID-19 patients, with the impression of neglecting patients with chronic disease. Community health workers play a significant role in monitoring chronic disease patients in their surrounding area. To fill this gap, PHCs have been introduced to support health workers not only in telemonitoring and teleconsultation but also in providing basic physical examinations and simple lab tests to monitor chronic disease patients through monthly community health service programs. Therefore, the objective of this research was to implement PHCs and assess the challenges and potential improvements to deal with endemic conditions, in order to better control NCDs and support the resilience of the health system.
# Sustainable Fire Protection: Reducing Carbon Footprint with Advanced Coating Technologies
## 1 Introduction
### Mitigating Fire Hazards in Tunnels and Buildings  
The escalating construction of tunnels, driven by the need to enhance transport capacity, introduces heightened risks associated with fires [3; 4; 5]. Enclosed spaces, such as tunnels and buildings, are susceptible to the rapid surface combustion of combustible materials, with potential temperatures exceeding 1000 degrees Celsius. This poses severe threats to structures and individuals, necessitating effective fireproofing solutions.  
Various materials, including natural, synthetic, and organic/inorganic hybrids [5; 6], are employed for achieving high-quality construction finishes. Despite their aesthetic and durable qualities, many of these materials contain combustible substances [7], making them susceptible to ignition through small sparks or radiant heat, leading to widespread fires by combustion [8].  
In response to these challenges, MFR emerges as a robust solution, surpassing traditional organic coatings by eliminating toxic gas emissions during fires. Its application not only addresses fire resistance, but also contributes to thermal envelope layers in buildings, curtailing energy loss and stabilizing indoor temperatures and humidity. MFR's approach is congruent with the scientific goals aimed at emissions reduction, providing an environmentally friendly coating system that propels the journey toward achieving net-zero emissions. The innovative coating not only diminishes the carbon footprint annually, but also leverages the sustainable life cycle of cork trees integral to its production [9; 10].
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 4 SNR Estimators  
After sampling the received signal in Formula (16), the output sequence is expressed as  
\[r_{k}=As_{k}+z_{k}\ \ \ \ \ k=1,2,\cdots,K, \tag{20}\]  
where \(K\) is the observation point after sampling, \(r_{k}\) obeys the Gauss distribution, \(As_{k}\) is the mean, and \(\delta^{2}\) is the variance.
## 3 Studies on Enzyme-Assisted Extraction of Metabolites from Natural Materials
All these carotenoids are obtained and used in large quantities in the food, cosmetic, and pharmaceutical industries. Similarly, other industrially relevant molecules from beetroot (betalains, betacyanin, and betaxanthin) or ginger rhizome (oleoresin and 6-gingerol) can be more effectively obtained with the use of enzymatic pretreatment [16; 72]. It has been observed that changes in the extraction process affect the yield, chemical composition (amount and proportions of metabolites, elution of by-products), and biological activity of the obtained extract/product [13; 16; 62; 73]. Efficient enzymatic hydrolysis requires specified conditions related both to the enzyme and to the natural product (enzyme type, enzymatic mixture; temperature, pH, time, enzyme concentration, sample particle size, and water-to-material ratio). In the case of more lipophilic metabolites, the enzymatic pretreatment is often only the first step, followed by a proper extraction procedure involving the use of organic solvent/-s or equipment [13; 61; 71]. Therefore, the majority of studies of novel applications of enzyme-assisted extraction suggest the need for optimization of conditions and parameters. Employment of statistical methods (response surface methodology (RSM) and orthogonal test design) is a frequently chosen solution, often with great success [14; 56; 74]. Cellulolytic enzymes can be inhibited or even inactivated by several factors, e.g., products of hydrolysis (cellobiose and glucose), oxidants, reductants, phenolic compounds, some solvents and ions (Hg\({}^{2+}\) and Cu\({}^{2+}\)), or surfactants [28; 34; 75; 76]. This should be taken into account when designing or optimizing the enzymatic procedure. Most papers have shown a positive effect of hydrolytic pretreatment on the efficiency and usefulness of the whole process. However, there also reports where enzyme-assisted extraction was less effective that other methods [73; 77; 78]. These \"negative\" effects were explained by the difficulties faced by the enzyme, e.g., enzyme-substrate interactions, release of the plant proteases reducing enzyme activity, or by lack of optimization of, e.g., the water-to-material ratio or the enzyme concentration [14]. In many cases, mixtures of enzymes were found to be more effective than a single enzyme. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Natural Material**} & \multirow{2}{*}{**Target 1**} & \multicolumn{2}{c}{**Optimized**} & \multirow{2}{*}{**Extraction Effects and Yields**} & \multirow{2}{*}{**Ref. **} \\  & & **Conditions** & & **in Optimal** \\  & & **of Enzymolysis** & & **Conditions** \\  & & & **and Extraction** & \\ \hline \multirow{6}{*}{_Malpighia_ _emarginata_ DC. (Malpighiaceae) & \multirow{6}{*}{Phenic compounds and antioxidant activity} & Enzymolysis: & & \\  & & & & \\ \cline{1-1}  & & Enzymolysis
## 4 Discussion
### Effect of Runex Japonicus Houtt. Extract and Nepodin on Hyperglycemia and Insulin Resistance  
The anti-diabetic effect of Nepodin as an 5\({}^{\prime}\) AMP-activated protein kinase (AMPK) activator was already identified. Nepodin can mediate stimulation of solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) translocation via AMPK activation [8]. In our study, the NE supplement decreased blood glucose, plasma insulin and C-peptide, and HOMA-IR. We identified that RU has anti-diabetic effect of which the efficacy was somewhat more potent than NE; this may be due to the synergy effect of multicompounds present in RU, _Runex japonicus_ Houtt. ethanol extract.  
Obesity increases plasma FFA levels because of enlarged adipose tissue. Elevated plasma FFA inhibits insulin-mediated suppression of glycogenolysis. Thereby, obesity can induce insulin resistance [19]. More recently, [PERSON] et al. have reported that brown adipose tissue also strongly influences improved insulin sensitivity via modulating Nrf1 expression [20].  
In this study, NE and RU supplements significantly decreased plasma FFA levels, thereby improving insulin resistance. The fasting blood glucose concentration during the experimental period was increased in the HFD group compared to the ND group. On the other hand, the NE and RU supplements significantly decreased blood glucose levels. Plasma glucose, insulin, and glucagon, C-peptide, HOMA-IR and IPGTT levels were measured to identify the effect of the NE and RU supplements on insulin resistance. Lower HOMA-IR values were considered to indicate higher insulin sensitivity, whereas higher HOMA-IR values were considered to indicate lower insulin sensitivity or insulin resistance [21]. In our results, plasma glucose and insulin levels were reduced by both the RU and NE supplements compared to the HFD group; this result was consistent with immunohistochemistry staining expression of pancreatic insulin. Decreased HOMA-IR by the NE and RU supplements showed similar levels to the ND group. Judging from this, supplementation of both NE and RU can ameliorate insulin resistance. HFD led to glucose intolerance, pre-diabetic symptom compared to ND group by increasing AUC of IPGTT, whereas the RU supplement improved glucose intolerance by decreasing AUC of IPGTT. Although the NE supplement decreased the fasting blood glucose during experimental period, it did not lead to a significant difference in IPGTT.
# Combinatorial Olfactory Signaling in Short-Distance Determines Host Plant Recognition in Locust
## 3 Results
### Combinatorial Olfactory Signaling Controls the Locust's Food Choice  
To directly measure the connection between ORs and cognate ligands in a free-moving locust, we aimed to link gene suppression with the DER assay. First, ddH2O- or dsGFP-injected locusts did not differ significantly from the wild-type groups (Figure 4A), thus providing a solid control for dsORs injection. Next, dsORs-injected locusts were challenged between wheat extraction and the solvent in the DER assay. At least five LmigORs were associated with reduced foraging behavior after RNAi, and the knockdown of dsOR12 and dsOR15 led to the worst behavioral attenuation to the food cue mixture (Figure 4B). We further separated the volatile mixture into single odors and observed that suppression of single ORs was sufficient to reverse the foraging tendency in response to all three odor types. For instance, down-regulation of either OR12, OR21, or OR22 changed the positive valence of host-specific and common odors, as well as the negative valence of nonhost heptanal (Figure 4C-E). However, OR15 was only required for the positive valence of host-specific and common odors, instead of the negative valence of nonhost odors (Figure 4F). Thus, the critical volatiles-induced free-moving preference is also regulated in a combinatorial olfactory coding mode. Taken together, we conclude that locust palps express olfactory receptors that mediate short-range attraction and repellence to specific plant odors to mediate host-plant selection (Figure 4G).  
Figure 3: Multiple odorant receptors (ORs) are required for the odorant-induced palp opening response. (**A**): Spatial and temporal expression patterns of locust palp ORs. LmigActin was the positive control. 5\({}^{\text{th}}\)A: antennae of fifth-instr nymphs, 5\({}^{\text{th}}\)P: mouthpart palps of fifth-instr nymphs, 5\({}^{\text{th}}\)A: antennae of female adults, \(\circ\)P: mouthpart palps of female adults, \(\circ\)A: antennae of male adults; \(\circ\)P: mouthpart palps of male adults. (**B**): Diagrammatic illustration of the palp opening response (POR) assay. (**C**–**E**): POR index of Ant\(-\)/Palo+ locustust toward odorants when injected with dsRNA of OR12 (**C**), OR15/17/19/21 (**D**), and OR22 (**E**). dsGFP was injected as the control. Two-way ANOVA with Bonferroni post-tests. \(N\) = 3 (30 locusts per replicate) for each assay. Error bars represent the SEM. * \(p\) < 0.05, **\(p\) < 0.01, ****\(p\) < 0.0001. (**F**): Summary of POR results in response to plant odors. Circles of the same color represent the ORs that recognize the same plant odors.
# Multiport Single Element Mimo Antenna Systems: A Review
## 3 Isolation Techniques
### Neutralization Lines  
This is a decoupling technique in which a conductive wire or strip is used that links the two antenna radiating elements. The idea of a neutralization line is to minimize the coupling between the two antenna elements by establishing a new coupling path and improves the bandwidth when connected between ground planes [4].  
Neutralization lines with various lengths can be utilized for achieving multi-bands or a wide band operation and to obtain high isolation. Good isolation is achieved for frequency from 1.7 GHz to 2.7 GHz using multiple neutralization lines as shown in Figure 4. Three neutralization lines are used with different lengths to achieve multi-band frequency coverage [29].  
A neutralization line introduced between two antenna elements creates some current on the line path and generates an extra EM field to cancel the mutual coupling as discussed in [30]. Between the C-shaped radiator antennas, a neutralization line is incorporated in parallel. This line cancels out unnecessary coupling by producing opposite coupling and generates imaginary admittance [31].
# Study on Fluctuating Wind Characteristics and Non-Stationarity at U-Shaped Canyon Bridge Site
###### Abstract  
To investigate the non-stationary characteristics of the wind field at the U-shaped canyon bridge site and its impact on fluctuating wind characteristics, a wind observation tower was installed near a cable-stayed bridge. The Augmented Dickey-Fuller (ADF) test was employed to assess the stationarity of wind speed series, while the discrete wavelet transform (DWT) was applied to reconstruct the time-varying mean wind and analyze its effect on fluctuating wind characteristics. Results indicate that wind speeds in this region exhibit bimodal distribution characteristics, with the Weibull-Gamma mixed distribution model providing the best fit. The proportion of non-stationary samples increases with height. Autocorrelation function (ACF), partial autocorrelation function (PACF) tests, and power spectral density (PSD) analysis determined the optimal wavelet decomposition level for wind speed in this region. Analysis of non-stationary samples using db10 wavelet reconstruction reveals that the stationary wind speed model overestimates turbulence intensity but underestimates the turbulence integral scale. The downwind spectrum deviates from the Kaimal spectrum in both low- and high-frequency bands, whereas the vertical spectrum aligns well with the Panofsky spectrum. The findings demonstrate that the wavelet reconstruction method more accurately captures fluctuating wind characteristics under the complex terrain conditions of this canyon area.  
field measurement; fluctuating wind; non-stationary wind; wavelet reconstruction Article
# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 1 Introduction
### Al-Based Approaches to Gait Analysis
#### 1.4.5 Our Own Studies on AI-Based Post-Stroke Gait Analysis  
The in-house research cited in this paper on the methodology and tools for relatively simple, fast, and inexpensive clinical gait analysis using fractal parameters is among the first undertaken in this area. Due to its strong interdisciplinary nature, this research was carried out as a collaboration between the Department of Physiotherapy Ludwik Rydygier Collegium Medicum of the Nicolaus Copernicus University in Torun, the Institute of Mechanics and Applied Informatics at Kazimierz Wielki University in Bydgoszcz, and the Neurocognitive Laboratory at the Interdisciplinary Centre for New Technologies at the Nicolaus Copernicus University in Torun.  
Our own research, cited in this paper, required previous work concerning methodologies and tools for relatively simple, fast, and inexpensive clinical gait analysis using fuzzy logic, specifically ordered fuzzy numbers (OFNs, also called Kosinki's fuzzy numbers (KFNs)), artificial neural networks, and multifractal analysis [53; 54; 55]. Our research is among the earliest attempted in this area. Further research results concerning the use of fuzzy logic, including OFNs, to analyze health-related quality of life (HRQoL) in post-stroke patients are also in print.  
The proposed computational tools have also shown their usefulness, effectiveness, reproducibility, and sensitivity in detecting cases of small and subtle changes in post-stroke patient outcomes during the rehabilitation process. This is very important as post-stroke rehabilitation is lengthy and requires the adequate motivation of the patient and family/carers.  
A potential source of error or inaccuracy that may haveilimetedour studies was the relatively small number of patients we were able to enroll. In addition, concerns were raised about the fidelity of the study/treatment and the rehabilitation methods used (Bobath method in the study group vs. traditional rehabilitation approach in the reference group). A detailed description of the examination and measurement methods, including professional preparation, is necessary. This will enable the replication of the study with the same research protocol.  
The proposed measures based on artificial neural networks, fuzzy models, and fractal dimensions extend the existing capabilities of clinical gait assessment towards more objective clinical reasoning and future-proof them by taking into account the widespread use of wearable health devices [53; 54; 55].