esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Ion Release on Color Stability of Zirconia: A Comparative Study
## 2 Materials and Methods
### Assessment of pH Variation and Ion Release Dynamics  
To assess the effect of coloring solution aging, additional specimens were immersed in solutions that had been aged for three months under laboratory conditions. Surface brightness (L*) and color change (\(\Delta\)E) were compared between fresh and aged solution groups. pH measurements of the coloring liquids were conducted at eight time points over the 90-day period (days 1, 14, 28, 42, 56, 70, 84, and 90) using a calibrated digital pH meter (a-AB41 PH ZH, OHAUS, Changzhou, China). Specimens were stored in individual containers at 37 \({}^{\circ}\)C in a shaking incubator, and extraction media were replaced every 14 days.  
For ion release analysis, specimens were immersed in 10 mL of A2 coloring liquid at 37 \({}^{\circ}\)C, and leachate samples were collected at baseline, 30 days, and 90 days. Ion concentrations of iron (Fe) and erbium (Er) were measured using inductively coupled plasma mass spectrometry (ICP-MS; iCAP Qc, ThermoFisher Scientific, Waltham, MA, USA). At each interval, 5 mL of immersion liquid was withdrawn for analysis and replaced with fresh solution to simulate dynamic clinical exposure. Ion release values were recorded in parts per billion (ppb).
## 3 First-Passage Reliability Problem
### Parameter Calibration Using Observational Data
#### 3.2.4 Test of Fitting Accuracy  
In order to evaluate the accuracy of the fitting-based prediction of the quantities, the coefficient of determination (\(R\)-square) is a frequently-used tool [50], which is computed as follows,  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\widetilde{y}_{i}\right)^{2}}{\sum_{ i=1}^{n}\left(y_{i}-\widetilde{y}\right)^{2}}, \tag{17}\]  
where \(\widetilde{y}_{i}\) is the fitted value, and \(\tilde{y}=\frac{1}{n}\sum_{i=1}^{n}y_{i}\). This coefficient varies within \([0,1]\). A value of \(R^{2}\) approaching 1 indicates a well-fitted prediction model.
## 4 Discussion
### Cross-Farm TR/ACC for Unavailable Data Situation  
Comparison of the OA results between the same-farm and the cross-farm TR/ACC scenarios (Figure 9) showed significant differences in the classifiers' performances. Regarding PRISMA data (reported in panels (a) and (b)), apart from a general decrease in accuracy, it should be noted that the CNN algorithms presented a limited reduction of about 6% in OA, while for the SVM, RF, and MNB algorithms, the OA was reduced by 17%, 15%, and 22%, respectively. The stability of CNNs in the cross-farm TR/ACC scenario seems quite promising and deserves consideration in future studies. The results (Figure 9) show that the classifiers' performances in terms of accuracy were lower with respect to the cross-farm TR/ACC compared with the same-farm scenario. With respect to the same-farm scenario, just a limited reduction of 6% OA was observed by using the CNN algorithms, while OA was significantly reduced by 17%, 15%, and 22% for SVM, RF, and MNB, respectively. The reason behind the stability of CNNs in comparing the ML algorithms in the cross-farm TR/ACC scenario needs additional consideration.  
The transfer learning scheme based on deep learning approaches helps to gain crop mapping results in transfer sites, even though abundant historical labels of crop type are required. This study states an innovative data set offered by the combination of hyperspectral imagery and known related ground truth. As a preliminary exploitation of the hyperspectral time series potential, we decided to use trivial cross-farm approaches to simply mimic more complex learning approaches testing different TL types and theories (e.g., fine-tuning, feature extraction, few-shot learning, domain adaptation).  
Moreover, this study highlights some points that are to be considered for further study. Dimension reduction/feature extraction being performed at present with a trivial PCA could be enhanced with more sophisticated feature extraction methods, better preserving the spectral information contents, or balanced with techniques like stacked auto-encoder (SAE). Furthermore, the self-attention mechanism could be used to focus on the most informative features. Future work should explore the capability of developed foundation models (e.g., SAM, GPT, DINO) in excluding the feature extraction step within the present ML workflow.
## 3 Empirical Results
### Robustness Test
#### 3.6.3 Adding Control Variables  
The agricultural technology level could promote the efficiency of agricultural production. Therefore, this paper refers to the relevant literature and adopts the proportion of agricultural R&D of expenditure in the GDP to measure the level of agricultural technology (tech) [15; 47]. The estimation results only show little change of coefficient, indicating that the above verification results are credible.
## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on the Sample Type
#### 4.1.3 HCR/PCR Products  
Polymerase chain reaction (PCR) and hybrid chain reaction (HCR) are considered to be the most sensitive and precise detection tests for hepatitis [83]. These chain reactions are used for nucleic acid amplification, providing quantitative and highly sensitive biosensing of the target nucleic acid (DNA/RNA) in a sample [84]. Various efforts have been made to diagnose hepatitis using Raman spectroscopic analyses of HCR and PCR products over the last decade. [PERSON] et al. used the ultrasensitive detection of HCV nucleic acid for hepatitis C diagnosis in 2024 [69]. A novel methodology was developed using Ag@AuNP-based SERS combined with an HCR. The scheme was designed by attaching a Raman reporter (4-ethylbenzaldehyde) to the Ag@AuNPs and two HCR sequences, H1 and H2 hair pins. On the detection plate, another Ag@AuNP layer was developed, bearing an immobilized capture DNA sequence. If the target DNA is present in the analyte, the SERS tags bearing H1 and H2 are captured in the detection well on the plate, providing a large number of \"hotspots\" through the HCR, generating a strong SERS signal for HCV nucleic acid detection. This method achieved a limit of detection of 0.47 fM within a linear range of 1-10\({}^{5}\) fM.  
In 2021, [PERSON] et al. explored the potential of SERS for the diagnosis of hepatitis B using PCR-amplified DNA products of HBV as the test samples [27]. SERS spectral features were identified for the detection of hepatitis B viral DNA, and a comparison was made with SERS data on healthy individuals. Clinical viral loads ranging from 1 to >10,000 were divided into three groups, classified as low, medium, and high viral loads. The data were analyzed using PCA, PLS-DA, and PLSR multivariate models. The PCA successfully discriminated between all three classes of viral loads, while the PLS-DA showed 89% sensitivity and 98% specificity in discrimination between the classes. Further validation was conducted using a PLSR model, and viral load predictions were achieved with a 0.9031 value of goodness and a minimal root mean square error (RMSE). A few months later, [PERSON] et al. applied a similar SERS strategy to the diagnosis of hepatitis C using the PCR products of HCV RNA as the test samples [28]. The PCA successfully differentiated between healthy and diseased samples, while PLSR predicted the viral load with an accuracy of 99%. In the same year, [PERSON] et al. presented a comparison of HBV DNA and HCV RNA PCR products with healthy and negative control samples [29]. Clear differentiation among the four groups was shown using the PCA, and differential features were identified in the SERS spectra. Further validation was performed using PLS-DA, and a higher sensitivity, specificity, and accuracy of 96%, 94%, and 98%, respectively, were claimed.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### Multiple Access Techniques for BackCom
#### 5.3.1 Time Division Multiple Access  
TDMA-based multiple access scenario for BackCom considers the transmission of signals to many users via wireless access point (AP) [100]. This scenario enables tags to harvest energy first, and harvested energy is then used to backscatter signals received from wireless AP. [PERSON] et al. used TDMA to uplink data from two users [99]. These two distributed users harvested energy in the downlink broadcasted by the H-AP, while TDMA was used in the data uplink to transmit users' data individually. A multiple access technique was used in BST to show a sequence of edges in time [68]. BST is a physical layer scheme for BackCom networks, which enables concurrent transmissions from multiple devices by leveraging temporary interleaved signal edges. Furthermore, BST works on OOK signals received from various tags.  
As shown in Figure 25, a model based on the bifurcation of the time block was proposed to have two segments: (i) the data backscattering segment and (ii) the transmission period [101]. The first time segment is used for data backscattering in BackCom mode, while the other segment considers energy harvesting. TDMA is employed in data transmission and data backscattering periods. Subsequently, to improve system throughput, some users harvest energy from the power station, while others reflect/backscatter data. The notation \(U_{i}\) in Figure 25 denotes the number of users, where \(i=1,....,K\). The duration of the two periods is denoted by \(t_{1}\) and \(t_{2}\). The overall time for energy harvesting of \(U_{i}\) during the data backscattering period is denoted by \(t_{1}-\alpha_{i}\). The multiple access system for Amb-BackCom was proposed by [PERSON] et al. [117]. This system detects information from the source and the tag simultaneously. Moreover, they also introduced a new channel model named as the multiple-access channel.  
Figure 24: Multiple access in BackCom.  
Figure 25: TDMA in BackCom.
# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
## 3 System Model  
### Security Situational Awareness in Smart Grids  
In the network environment of smart grids systems, the acquired information includes grid system dynamic data, power terminal's security states, network topology, operation environments, temporary malfunction, and steady-state operation, etc. By leveraging network SSA technology, the current security state of the network system is accurately monitored and predicts future security development trends. Active and effective defense and countermeasures are available in advance to deal with the upcoming large-scale attacks. It will significantly transform the unfavorable situation of after-the-fact handling and passive protection of power network security management over the past.  
We consider the wide-area situational awareness (WASA) [27] technology to acquire and combine information on the smart grids' situational elements. Specifically, it includes external attack information, the internal vulnerability of power terminal nodes, and threat awareness of possible attacks, as shown in Figure 1.  
The external attacks mainly consist of the network port traffic information and the logs of various power terminals. When the power terminal's service port encounters continuous malicious connection requests, the port will not respond to new legitimate connection requests rapidly, causing the power system to fail to provide standard network services. Port traffic information can timely reflect port threat information. Also, log information records the operation and usage of power terminals, including alarm information of abnormal events. As the topological distribution of a massive number of power terminals forms the power network, the vulnerability of the power terminal nodes in the network constitutes the internal environmental elements. The external and internal elements are considered from the perspective of the power terminal itself, and this information is accessible from the terminal directly or from the network constituted by the terminals. However, there may still be deliberate attacks by attackers in the physical grid, such as the injection of malware, viruses, and advanced persistent threats, etc., to the power information management platform. Therefore, the behavior of possible attacks is also necessary as situational elements. By accurately grasping the behavior of potential attacks, it is capable of suppressing threats. The known types of possible attacks can have a large impact on the power grid. Controlling the information of situational elements in the smart  
Figure 1: Wide-Area Situational Awareness (WASA).  
grids is crucial to the analysis of security situational awareness. Based on those situational elements, one can obtain assessment results, to accurately grasp the smart grids' security situation, and provide a basis for an active defense against threats.
## 4 Grounding Grid Imaging Results and Evaluation Analysis
### Ground Grid Imaging Evaluation Index  
In order to intuitively reflect the effectiveness and superiority of this algorithm, this paper analyzes the imaging results of simulation data from a quantitative perspective, using the structural similarity index (SSIM) and peak signal to noise ratio (PSNR) to evaluate the effect of image reconstruction. The equation is as follows:  
\[S(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+\mu_{y} ^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{15}\]  
where \(u_{x}\) and \(\sigma_{x}\) represent the mean and standard deviation of the \(x\) image, \(u_{y}\) and \(\sigma_{y}\) represent the mean and standard deviation of the y image, \(\sigma_{xy}\) represents the covariance of the \(x\) and y images, and \(c_{1}\) and \(c_{2}\) are constants, usually 6.5025 and 58.5225. The SSIM value is between 0 and 1, where 1 means that the two images are exactly the same.  
When solving the PSNR value, you need to first calculate the mean square error (MSE) of each target image, as follows:  
\[M(i,j)=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}I((i,j)-K(i,j))^{2} \tag{16}\]  
Figure 12: Algorithmic imaging after adding different noises.  
In Equation (16), \(I(i,j)\) and \(K(i,j)\) represent the pixel values of the original image and the contrast image, respectively. Then, the peak signal-to-noise ratio is expressed as follows:  
\[P=10\log_{10}(\frac{MAX^{2}}{M}) \tag{17}\]  
\(MAX\) is the maximum pixel value of the image, \(MAX=2^{n}-1\); the higher the image peak signal-to-noise ratio, the better the image quality and the less distortion.  
Based on the relatively regular characteristics of the grounding grid branches, this paper segmented the conductivity images after CGAN imaging to better diagnose corrosion. The conductivity value and corrosion degree of the corroded branch can be calculated based on the divided branches. The conductivity value is solved as Equation (18):  
\[\sigma=\frac{1}{N}\sum_{i=1}^{N}(k\cdot(C_{i}-C_{\text{min}})+\sigma_{\text{ min}}) \tag{18}\]  
\(N\) is the total number of branch units, \(C_{i}\) is the unit color value, \(C_{\text{min}}\) is the minimum color reference value, \(k\) is the mapping ratio coefficient, \(\sigma_{\text{min}}\) is the minimum conductivity value, which is \(1\times 10^{6}\) S/m, and \(\sigma_{\text{max}}\) is the corrosion upper limit conductivity value, which is \(1\times 10^{7}\) S/m. The corrosion degree can be calculated based on the solved conductivity value \(\xi\):  
\[\xi=\frac{\sigma-\sigma_{\text{min}}}{\sigma_{\text{max}}-\sigma_{\text{min}} }\times 100\% \tag{19}\]  
According to the determined corrosion branch and corrosion degree, the judgment accuracy \(A\) and judgment error \(R\) are obtained:  
\[A=\frac{TP}{TP+FN} \tag{20}\]  
\[R=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(\xi_{x}-\xi_{y}\right)^{2}} \tag{21}\]  
\(TP\) represents the correctly detected corroded branch, \(FN\) represents the missed corroded branch, \(\xi_{x}\) represents the calculated corrosion degree and \(\xi_{y}\) is the actual corrosion degree.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Back-End
#### 2.2.7 Weather and Risk Rasters  
The measured agrometeorological values are interpolated at a resolution of 100 m for the entire territory of Sardinia and 10 m for smaller, specific areas like rice field districts. Hourly raster images are generated for the mean temperature, mean relative humidity, and precipitation, along with daily maps for rainfall accumulation, daily maximum temperature, and daily mean temperature. Additionally, maps depicting the risk index for rice blast disease are generated for the previous, current, and following days, as well as a raster map indicating the alert level. Finally, maps are produced to illustrate the variables necessary for predicting the date of locust egg hatching.
## 1 Introduction
In addition, it has been reported that Lc also enhances the ferric-reducing antioxidant power (FRAP) and 2,2'-azinobis-3-ethylbenzotiazo-line-6-sulfonic acid (ABTS) assay. However, to date, none of the studies have been conducted on the enhancement of phenylpropanoid production and antioxidant activities on _ZmLc_ overexpression in _A. rugosa_ transgenic hairy root cultures after exposure to light conditions. The main aim of this study was to produce a _ZmLC_-overexpressing transgenic hairy root line via _Agrobacteriumrium rhizogenic_-mediated transformation. The best hairy root line was selected, and expression levels of phenylpropanoid biosynthetic pathway genes and accumulation of phenylpropanoid compounds were analysed using qRT-PCR and high-performance liquid chromatography (HPLC). In addition, antioxidant activities reducing power assay (RPA), ABTS, and 2,2-Diphenyl-1-Picryl Hydrazyl (DPPH), total phenolic content (TPC), and total flavonoid content (TFC) were also analysed. These results show that _ZmLC_ can be used to enhance phenylpropanoid compounds and antioxidant activities in transgenic _A. rugosa_ hairy root lines via the genetic engineering approach.
# Evaluation Model of Parking Equipment Planning and Design Based on Object-Oriented Technology
## 1 Introduction
### Research on Object-Oriented Technology  
Object-oriented technology refers to the remote sensing image and other related geospatial data of the surface area obtained through satellites, etc., combined with corresponding features and remote sensing imaging mechanism, using spectrum, shape, texture and other characteristics through image segmentation technology, using the object as the image processing unit classification, so as to quickly obtain the high-precision feature distribution [18; 19; 20]. At present, object-oriented technology has been widely used in various spatial planning fields. [PERSON] et al. proposed an object-oriented recognition method for monitoring ion-type rare earth mining using high spatial resolution remote sensing images [22]. [PERSON] and [PERSON] used GF-1 WFV sea surface oil spill images as data sources, and used four classic supervised classification algorithms to extract sea surface oil spill information [23]. [PERSON] and [PERSON] et al. used high spatial resolution GF (high score)-1 remote sensing images to finely classify the construction land in the planned restricted area of nuclear power plants [24]. The object-oriented landslide individual separation method proposed by [PERSON] et al. can well solve the problem of rapid risk assessment of landslide hazards [25]. [PERSON] et al. used remote sensing technology and object-oriented classification algorithms to evaluate the desertification of the desert in central Iran [26]. [PERSON] et al. used optical satellite images and object-oriented convolutional neural networks to classify open water features [27]. [PERSON] et al. applied digital photogrammetry and object-oriented imaging technology to evaluate the soil erosion risk of olive groves [28]. [PERSON] et al. evaluated the accuracy of forest area boundary location based on object-oriented classification of multispectral images [29]. [PERSON] and [PERSON] et al. extracted seismic road blockade information based on convolutional neural networks and high-resolution satellite images [30].
# Inactivation of Contaminated Fungi in Rice Grains by Dielectric Heating
## 2 Materials and Methods
### Experimental Desing for Dielectric Heating System
#### 2.3.3 The Study of Relative Electric Field Intensity and Temperature  
The dielectric heating system was set the optimized ISM frequency band as 40.68 MHz. The electric field intensity was studied and calculated according to Equation (2):  
\[E=\frac{V}{\Delta d} \tag{2}\]  
where \(E\) is the electric field intensity (V/m), \(V\) is the voltage during the heating process (V), \(\Delta d\) is the distance between electrodes (m).  
In total, 75 dehulled polished rice samples (60 g with 8.33% moisture content) were prepared in sterile glass Petri dish and then placed at the central position of the bottom electrode under the dielectric heating system at 40.68 MHz frequency. The temperature was monitored and recorded using the infrared thermography camera (U5857A, Keysight  
Figure 3: Schematic diagram of dielectric heating system: (**a**) Dielectric heating system; (**b**) Samples in dielectric heating system.  
Technologies Inc., Santa Rosa, CA, USA). At different five electric field intensities (150, 190, 225, 300, and 450 kV/m), heating time (\(t\)) was recorded three replicates at each five temperatures (\(T\)) including 70, 80, 90, 100, and 110 \({}^{\circ}\)C and calculated for heating rate (\({}^{\circ}\)C/min) according to Equation (3):  
\[\text{Heating rate}=\frac{\Delta T}{\Delta t} \tag{3}\]  
where \(T\) is the temperature in sample (\({}^{\circ}\)C) and \(t\) is the heating time (min).  
The energy consumption (\(W\)) was calculated according to Equation (4):  
\[W=\frac{VI\Delta_{t}}{m} \tag{4}\]  
where \(W\) is the energy consumption (kWh g\({}^{-1}\)), \(V\) is the voltage during the heating process (V), \(I\) is the current during the heating process (A), \(t\) is the heating time (hour), and \(m\) is the sample mass (gram).
## 3 Results and Discussion
### Zeta (\(\zeta\)) Potential Characterization  
Zeta potential is employed to assess the surface charge and stability of nanoparticles, indicating their capacity to withstand agglomeration and the level of repulsion between particles carrying the same charge in dispersion. The (CNCs-PEG/NaOH sample, as illustrated in Figure 13, revealed a negative zeta potential of approximately \(-0.552\) mV. Therefore, the negatively charged surface of the (CNCs-PEG/NaOH generates electrostatic repulsion between the nanocomposite layers. This characteristic leads to the effective dispersion of the negatively charged surface nanocomposite in a biological solution, which has a crucial impact on the interaction between nanoparticles and cells.  
Figure 12: **(a)** FESEM images and **(b)** size distribution histogram of CNCs-PEG/NaOH.  
Figure 13: The zeta potential (\(\zeta\)) of CNCs-PEG/NaOH.