esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Biosorption Characteristics of 1 M NaOH-Treated \(H\). fusiformis  
#### 3.4.1 Effect of Reaction Time  
The biosorption data for the uptake of metal ions versus contact time at pH 4 are shown in Figure 5 (initial metal concentration: 10 mg/L; pH: 4.0; adsorbent dosage: 1.67 g/L; reaction time: 30-1440 min; agitation speed: 100 rpm; temperature: 25 \(\pm\) 0.5 \({}^{\circ}\)C). The adsorbed amount of metal ions increased rapidly within the first 30 min, and equilibrium was reached after 180 min. After this equilibrium period, the amount of metal biosorption remained constant. The removal efficiencies of Pb\({}^{2+}\), Cd\({}^{2+}\), Cu\({}^{2+}\), and Ni\({}^{2+}\) ions at 180 min were 97.7%, 88.9%, 79.8%, and 61.5%, respectively. High biosorption at the beginning of experiments was also found in other studies: _Chorella vulgaris_ and the freshwater alga _Sceetesemus quadratica_ took up 90-95% of Cu\({}^{2+}\) within the first 15 min of the experiment and reached equilibrium biosorption after about 2 h [46]; _Ecklonia radiate_ took up 90% of Cu\({}^{2+}\) and Pb\({}^{2+}\) ions within 30 min and reached equilibrium biosorption after approximately 1 h [47]. This phenomenon can be explained by the fact that the active sites on the surface of algal materials were not occupied at the initial times, resulting in a high biosorption rate of heavy metals without competition with unadsorbed metals and replacement of adsorbed metals [41; 43].  
The parameters for the PFO, PSO, and Elovich models obtained from the kinetic experimental data from 0 to 180 min are listed in Table 2. For \(H\). _fusiformis_, it can be concluded that the data fitted well with all three models applied in this study with a high correlation (\(R^{2}\) > 0.994). Figure 5a shows that PFO and PSO models seemed to fit better to the experimental data than Elovich models. The good prediction of kinetic data by PFO and PSO models indicates that two or more steps influence the biosorption process [48; 49]. The reaction rates of the PFO and PSO models followed the decreasing order of Cd\({}^{2+}\) > Ni\({}^{2+}\) > Cu\({}^{2+}\) > Pb\({}^{2+}\). Pb\({}^{2+}\) showed a lower reaction rate but higher equilibrium sorption than Ni\({}^{2+}\).  
Figure 5b presents that the biosorption process of heavy metals by 1 M NaOH-treated \(H\). _fusiformis_ involved two stages of diffusion processes. The parameters for the IPD model obtained from the kinetic experimental data from 0 to 1440 min are listed in Table 3. The first step for the diffusion of heavy metals to the external surface of the bios adsorbents was faster than the second step for the diffusion of metals internal pores of the bioadsorbents [50]. Two straight lines in Figure 5b are observed not to pass through the origin, indicating  
Figure 4: FTIR spectra of (**a**) \(H\). _fusiformis_ and (**b**) 1 M NaOH-treated \(H\). _fusiformis_.  
that the biosorption rate of the heavy metals was not only controlled by intraparticle diffusion [51].
## 4 Experimental Results and Comparisons
### Lessons Learned  
We summarize the key observations of the experiments as follows: (1) A standard Inception_v2 architecture was sufficient to improve the defect classification accuracy through simple network training when the dataset is relatively small. (2) Data augmentation can improve the classification accuracy of the model with a certain range. (3) The proposed method has strong robustness in a large-scale complex dataset, which has important implications for practical applications. (4) A standard Inception_v2 architecture can maintain the classification accuracy at a high level with a relatively low percentage of noisy labels if the labels are basically uniformly distributed in the dataset. (5) The classification accuracy of the proposed method is clearly higher than that of the standard Inception_v2 when the percentage of noisy labels does not exceed 60%; however, when the percentage of noisy labels exceeds 60%, the classification accuracy of both the standard Inception_v2 and the proposed method drops rapidly. (6) With 50% noisy labels, the classification accuracy of the proposed method outperforms the recent state-of-the-art label noise learning methods: CleanNet and MentorNet. (7) The proposed method can perform well in practical applications, where the number of noisy labels is uncertain and not necessarily uniformly distributed. This was proven through a steel surface defect detection case in a Chinese real-world cold-rolling steel workshop.
## 4 Methods
### Compensation Factors  
In the overlap area covered by the 3 sensors, 15 samples of 5 \(\times\) 5 pixels each were extracted from every scan of Hyperion, and the time-averaged TOA reflectances were calculated to derive the SBAF (2), as presented by [PERSON] et al. [21]. The Hyperion TOA reflectance was resampled to match the 1-nm sampling interval of the RSR of both sensors. Simulated TOA reflectance for both sensors were obtained by integrating the RSR and Hyperion spectral signatures, weighted by the RSR. The convolution of the numerator corresponds to the simulated TOA reflectance of the reference sensor and the denominator to the sensor to be calibrated; the ratio being an indicator of the difference arising from the RSR mismatch for a given band and target [21; 62].  
\[SBAF=\frac{\left(\int\rho_{\lambda}RSR_{\lambda}\ d\lambda/\int RSR_{\lambda} \ d\lambda\ \right)_{R}}{\left(\int\rho_{\lambda}RSR_{\lambda}\ d\lambda/\int RSR_{ \lambda}\ d\lambda\ \right)_{F}} \tag{2}\]  
\[\rho_{F}^{*}=\rho_{F}\ SBAF \tag{3}\]  
where \(\rho_{\lambda}\) is the hyperspectral TOA reflectance of a sample; \(RSR_{\lambda}\) is the relative spectral response of a sensor; \(R\) is RapidEye MSI, the reference sensor; \(F\) is FASat-C NAOMI-1, the sensor to be calibrated against \(R\); \(\rho_{F}\)* is the reflectance of the sensor \(F\), compensated by the RSR difference effects. The SBAF can be applied to DNs or to at-sensor radiances of \(F\), as well. Two factors were obtained to be applied differentially to calibration (Cal) and evaluation samples (Eva): SBAF\({}_{Cal}\) and SBAF\({}_{Eva}\), respectively. SBAF\({}_{Cal}\) was derived with 5 samples collected at the defined homogeneous zones (Figure 3) and SBAF\({}_{Eva}\) using 10 samples collected over the whole FASat-C scene, covering the range of reflectances available in the study area.  
Figure 3: Mean Hyperion TOA reflectance profile of Frenchman Flat used for the SBAF\({}_{\text{Cal}}\) estimation. The error bars indicate the 1\(\sigma\) standard deviation.  
For the compensation of illumination differences, an adjustment factor was calculated, following [PERSON] et al. [19; 20]. This factor was multiplied by SBAF, and the resulting product was incorporated into a final factor named Ai (Equation (4)).  
\[Ai=SBAF\left(E_{0}cos\theta\right)_{R}/\left(E_{0}cos\theta\right)_{F} \tag{4}\]  
where \(E_{0}\) are the exo-atmospheric solar irradiance coefficients of the respective sensor (R and F) obtained with Equation (1), expressed in W\(\cdot\) m\({}^{-2}\cdot\)\(\mu\)m\({}^{-1}\); \(\theta\) are the zenith angles corresponding to the data acquisition of R and F.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 5 Challenges in Smart Grids  
### Challenges in Communication Channels  
As this paper discussed in Section 4.1, the communication security in the power grid needs to be reconsidered in many old/isolated infrastructures. The smart grid will be secure enough to face the new cyber-physical environments, from secure protocol cryptography [104; 105; 106] to physical layer security [107; 108].  
Smart grid communication reliability and scalability, as well as transmission delay (high QoS), are prioritised features in guaranteeing smart grid communication resilience. Especially, decentralised power infrastructures like electrical vehicles, renewable energy, and microgrids are proliferating nowadays; in many scenarios, the communication channels' resiliency is difficult to guarantee in limited power, bandwidth, or adverse transmission environments [109; 110; 111; 112; 113; 114]. For this challenge, mixing wired and wireless hybrid communication methods may provide more resiliency [115].
## 3 Experimental Section
### Characterization Tests of Immobilized TiO\({}_{2}\)/PEG DSAT  
X-ray diffraction (XRD) spectra were obtained using a Rigakuminiflex II, X-ray diffractometer (Rigaku, Tokyo, Japan). Structural information of the films was obtained in the range of 20 angles from 3\({}^{\circ}\) to 80\({}^{\circ}\) with a step size increment of 1.00 s/step. FTIR spectra of powder samples were recorded on Perkin Elmer Spectrum Version equipped with an attenuated total reflectance device (Perkin Elmer, Waltham, MA, USA) with a diamond crystal. Spectra were collected in a frequency range of 600 to 4000 cm\({}^{-1}\) with 4 scans and a spectral resolution of 4 cm\({}^{-1}\). The morphology of the samples was observed with field-emitting scanning electron microscopy (FE-SEM, JSM-6700F, Akishima, Tokyo, Japan) with an accelerating voltage of 10 kV. The surface area of the immobilized TiO\({}_{2}\) film powders was measured by nitrogen adsorption using the BET equation at 77 K (Micrometrics ASAP 2020M + C, Norcross, GA, USA). A UV-Vis spectrophotometer UV-2550, Shimadzu was used to obtain the UV-Vis reflectance spectrum of the powder sample. X-ray photoelectron spectroscopy (XPS) with a Thermo ESCALAB 250 spectrometer using a radiation source of monochromatic Al K\(\alpha\) with the energy of 1486.6 eV, 200 W and a photoluminescence analyzer (JovinYvon, Chiyoda-ku, Tokyo, Japan) was used to determine the photoluminescence intensity of the samples.
# Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks +
## 4 User Throughput in Multi-Band HetNet
### Parameter Optimization in Multi-Band HetNet  
Figure 9a-d shows the average and 5-percentile user throughput versus \(\text{CSO}_{\text{high}}\) as a function of \(\text{CSO}_{\text{low}}\) for \(\alpha\) = 0.2, 0.4, 0.6, and 0.8, respectively. The ranges of \(\text{CSO}_{\text{low}}\) and \(\text{CSO}_{\text{high}}\) are rather wide compared with those in single-band HetNet.  
When \(\alpha\) = 0.2, the 5-percentile user throughput worsens, although the average user throughput slightly increases compared with that at \(\alpha\) = 0.When \(\alpha\) = 0.6 or 0.8, both 5-percentile and average user throughput worsens compared with those at \(\alpha\) = 0. Thus, from the 5-percentile user throughput perspective, \(\alpha\) = 0.4 provides best performance among \(\alpha\) = 0.2, 0.4, 0.6, and 0.8.  
The behavior for average user throughput against \(\text{CSO}_{\text{low}}\) is the same as in single-band HetNet. When \(\alpha\) = 0.4, the average user throughput hardly depends on \(\text{CSO}_{\text{high}}\). \(\text{CSO}_{\text{low}}\) of 0 or 3 dB provides better average user throughput than that of \(\text{CSO}_{\text{low}}\) of 6 or 9 dB. Thus, considering both the average and 5-percentile user throughput, the parameters of \(\alpha\) = 0.4, \(\text{CSO}_{\text{low}}\) = 3 dB, and \(\text{CSO}_{\text{high}}\) = 12 dB are determined to be the best.  
Figure 8: User throughput of adaptive control CRE compared with the conventional CRE and HomoNet in single-band HetNet.  
Figure 9: Average and 5-percentile user throughput versus CSO\({}_{\text{high}}\) as a function of CSO\({}_{\text{low}}\) (**a**) when \(\alpha=0.2\); (**b**) \(\alpha=0.4\); (**c**) \(\alpha=0.6\); (**d**) \(\alpha=0.8\).
## 3 Results and Discussion
### Effect of KKD-ZnO-NPs on NO Production in LPS-Activated RAW 264.7 Cells  
Macrophages generate nitric oxide (NO) as a primary mediator during inflammation reactions. It is made from the amino acid arginine by the enzyme inducible nitric oxide synthase (iNOS) [29]. NO production is required for a variety of physiological tasks, such as a neurotransmitter and influencing blood flow and synaptic plasticity. In contrast, overexpression of NO production in macrophages can able to abolish and induce dysfunction along with normal cells [65]. NO is a major mediator that is important in the progression of inflammation. As a result, a medication that suppresses NO formation is also recognized as a therapeutic agent for inflammation and cancer management.  
The inhibitory effect of Kakadu plum extract and KKD-ZnO-NPs on LPS-stimulated NO production in RAW264.7 cells were measured using the Griess reagents. At the concentration of 10 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, Kakadu plum extract and KKD-ZnO-NPs were able to suppress the NO production in LPS-stimulated RAW 264.7 cells by 29.28% and 41.46%, respectively, against the LPS-treated group (Figure 10). The standard NO inhibitor, L-NMMA, which was used as a positive control in this work, greatly decreased NO production level. Therefore, KKD-ZnO-NPs can decrease the release of NO level, indicating its anti-inflammatory potential.  
Figure 10: Effects of Kakadu plum extract (KKD-Ext) and KKD-ZnO-NPs on NO production. Control (non-treated cells). LPS (1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL)-treated RAW cells (to induce NO production and cause inflammation). Graph shows mean \(\pm\) SD values of three replicates. ***\(p<0.001\) as compared to the group treated with LPS alone.  
Figure 9: RNA expression of apoptosis pathway genes after treatment of KKD-Ext and KKD-Zn-NPs on A549 cells (24 h).
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 5 Implementation and Evaluation of a Commercial Vehicle Operation System
### CVO System  
This subsection proposes a CVO system composed of OBUs, a CVO server, and a cloud computing database server (shown in Figure 5).  
A GPS receiver and a cellular network module can be equipped in each OBU for periodically transmitting location information (_i.e._, longitude and latitude) determined from GPS and cellular network signals (_i.e._, the ID and RSSI of the connected cell) to the CVO server. When GPS is unavailable, the OBU sends only cellular network signals, which are presented as cell-RSSI pairs. The CVO server can perform the proposed mobile positioning method using the cell-RSSI pair as a key and querying the pre-deployment cloud computing database based on Hadoop [31], MapReduce [31], and Hive [32] techniques. The corresponding location of cell-RSSI pair can then be retrieved for determining the location of the OBU.
# Adaptive Suppression Method of LiDAR Background Noise Based on Threshold Detection
## 4 Experimental Results
From 8 o'clock to 12 o'clock, the comparison results of noise ratio under different methods in the experiment are shown in Figure 13b. The experimental results show that the noise ratio increases to more than 0.045% in the traditional mode, and rapidly increases to 0.08% when the background radiation is strong. The noise ratio measured in the fast mode is more than 0.028%, which is better than the traditional mode. The noise ratio of the adaptive mode is the lowest. Even at 12 a.m., the noise ratio is 0.012%, which proves that the adaptive  
Figure 11: LiDAR point cloud of night (**a**) original data and (**b**) denoised data. method has a good ability to suppress background noise. Figure 13b shows the proportion of noise reduction of these two modes at different times compared with the traditional mode. It can be seen that the proportion of noise reduction of the adaptive mode is more than 80% compared with the traditional mode. In this paper, the adaptive method achieves noise suppression by circuit adjustment at each detection. Compared with the back-end software noise reduction processing  
Figure 12: Comparison of three modes of LIDAR point cloud. (**a**) Point cloud obtained by traditional mode at 8 a.m. (**b**) Point cloud obtained by fast mode at 8 a.m. (**c**) Point cloud obtained by adaptive mode at 8 a.m. (**d**) Point cloud obtained by traditional mode at 10 a.m. (**e**) Point cloud obtained by fast mode at 10 a.m. (**f**) Point cloud obtained by adaptive mode at 10 a.m. (**g**) Point cloud obtained by traditional mode at 12 a.m. (**h**) Point cloud obtained by fast mode at 12 a.m. (**i**) Point cloud obtained by adaptive mode at 12 a.m.  
Figure 13: Comparison of experimental results. (**a**) Noise ratio measured by three methods at different times. (**b**) The percentage of noise reduction at different times for the two methods compared with the traditional method. method, it has great advantages in real-time detection. For the LiDAR with the laser emission frequency of 100 kHz, each adjustment can be completed in a period of 10 us. Per reference [34], a comparison is shown in Figure 10:0
## 1 Introduction  
Cornneal ulcer is an ocular condition associated with eye diseases that can result from various causes, including inflammation, infection, and injury [1]. Corneal ulcers can be categorized into three main types. The first type, point-like corneal ulcer, is characterized by small scar dots distributed on the corneal surface [2]. The severity of this condition correlates with the number of dots on the corneal area. One of the causes of this type is dry eye syndrome [3]. The second type, flaky corneal ulcer, consists of scar areas on the corneal surface. The severity is determined by the size and location of the corneal ulcer [2]. This type can be caused by many factors, such as bacterial infection or corneal surgery. The last type, mixed point-flaky corneal ulcer, has both small scar dots and scar patches on the cornea.  
In diagnosing the severity of corneal ulcers, ophthalmologists commonly use fluorescein dye on the ocular surface. The corneal ulcer becomes stained green, while the non-ulcer area remains sustained. Subsequently, clinicians examine and evaluate the corneal ulcer severity by analyzing images captured through the slit lamp microscope by themselves [3; 4; 5]. However, manual grading is a time-consuming task and can yield inconsistent results depending on the evaluator [6]. Therefore, developing a reliable end-to-end algorithm for grading the severity of corneal ulcers can significantly improve treatment outcomes and save valuable time for ophthalmologists.  
In this study, we propose an automated grading system for flaky corneal ulcers based on their size and location. The algorithm comprises three main components. First, we employ deep learning models to segment the cornea area from eye staining images and apply the Hough Circle Transform (HCT) to refine the result area into a circular shape. The corneal area identified in this process can be used in the corneal ulcer segmentation and severity grading process. Second, the corneal ulcer area is segmented using deep learning techniques. In this process, the novelty of our approach lies in using the predicted corneal area from HCT as additional training data during the learning phase to develop the corneal ulcer segmentation models. This method benefits our models by guiding the weight adjustments to focus on the corneal areas. Third, the severity of the corneal ulcer is graded based on two properties: the percentage of the ulcerated area on the cornea, and the ulcer's distribution across five corneal zones.
## 3 Dataset and Method for Adaptive Neuro-fuzzy Inference System
### Training and Testing  
This phase of ANFIS is more essential in the development of a particular system. Initially, the dataset has been gathered from the experts of renal cancer. After that, the partitioning process has been done on an acquired dataset. This partitioning of the dataset has been done by using the k-fold cross validation. There are 200 different test cases in the acquired dataset.  
For the cross-validation k = 4, the process is known as 4-fold cross validation.  
Hence, the dataset has been divided into four equal parts, from which three parts are used as training data and one part is used as testing data. In each iteration, the training and testing data has been changed according to the partitioning. It can be elaborated, like there are four parts of the dataset i.e., 1, 2, 3 and 4. For the first iteration, the first part is used as testing and the rest of the parts are used for training data. Similarly, in the second iteration, the second part of the dataset has been used to test the system and the rest of the parts, 1, 3 and 4, are used as the training data for the system and so on until the last iteration.  
As there are 200 tests cases in the dataset, 150 test cases are used for the training data and 50 used for testing that particular system. Therefore, 75% of the data from the entire dataset has been used  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure** & \\ \hline The number of Layers of ANFIS & 5 \\ \hline Inputs & 7 \\ \hline Number of Rules & 864 \\ \hline Output & 1 \\ \hline Type of membership functions for input variables & Gaussian \\ \hline \hline \end{tabular}
\end{table}
Table 7: Structure of ANFIS for renal cancer.  
Figure 13: Rules in ANFIS for renal cancer.  
for the training phase of proposed system. The rest of the data (25%) are used for the testing phase of a developed system as the testing data should be different from the training data. The number of epochs for training the system is 4. The validation has been done to check whether the given system is able to classify the patients of renal cancer and non-renal cancer in a correct class or not. The training error after the training phase is shown in Figure 14.
## 2 Materials and Methods
### Measurement of Variables
#### 2.3.4 Chlorophyll Content  
Leaf tissue samples were collected in the morning between 8:00 and 10:00 am to ensure consistency in moisture content and metabolic activity. The collected leaves were immediately placed in sealed plastic bags and stored on ice for transport to the laboratory. Fifty grams of sample were used. They were crushed with 80% acetone in a biker until a homogeneous mixture was obtained. The mixture was centrifuged to separate the liquid extract (containing chlorophyll) from the solid residues. The extract was analyzed with a spectrophotometer. The absorbance was measured at different specific wavelengths of 666 nm, 649 nm and 470 nm using 95% ethanol as a target.  
Chlorophyll content in cocoa seedlings was determined following [PERSON]'s equations by measuring the absorbance of the sample at different wavelengths [22]. The total chlorophyll content, as seen in Equation (3), is the sum of chlorophyll a (Chl a) content (Equation (1)) and chlorophyll b (Chl b) content (Equation (2)). In the formulas, A666 refers to the absorbance measured at 666 nm and A649 to the absorbance measured at 649 nm:  
\[c_{a}=13.77*A_{666}-4.63*A_{649} \tag{1}\]  
Equation (1). Chlorophyll a (Chl a)  
\[c_{b}=24.63*A_{649}-6.66*A_{666} \tag{2}\]  
Equation (2). Chlorophyll b (Chl b)  
\[c_{t}=7.11*A_{649}+20*A_{666} \tag{3}\]  
Equation (3). Total Chlorophyll Content
## 5 Energy Detection Process  
### Detection and False Alarm Probabilities  
According to (5) and (6), the detection and the false alarm probabilities can be expressed as statistical probabilities in order to become [45; 47]:  
\[P_{d_{i}}=Prob\ (\tau_{i}(n)>\lambda_{i}(n))=Q\left(\frac{\lambda_{d_{i}}-\left(P +\sigma_{n_{i}}^{2}\right)}{\sqrt{\frac{2}{\sqrt{\frac{2}{\sqrt{\frac{2}{\sqrt  
\[P_{fa_{i}}=Prob\left(\tau_{i}(n)<\lambda_{i}(n)\right)=Q\left(\frac{\lambda_{fa_{i}}- \sigma_{n_{i}}^{2}}{\sqrt{\frac{2}{N}}\sigma_{n_{i}}^{2}}\right) \tag{12}\]  
where Q(.) is the standard Gaussian complementary cumulative distribution function (CDF). Based on relations (11) and (12) and by taking into account that \(\lambda_{fa_{i}}=\lambda_{d_{i}}=\lambda_{i}\), the interdependence between the detection probability and the false alarm probability can be developed. For the ED process not impacted by NU and performed with a fixed detection threshold, this interdependence can be expressed as:  
\[P_{d_{i}}=Q\left(\frac{Q^{-1}\left(P_{fa_{i}}\right)-\sqrt{\frac{N}{2}}\ SNR}{1+ SNR}\right) \tag{13}\]  
where \(Q^{-1}\) (.) is the inverse standard Gaussian complementary CDF. According to relation (13), for a small sample quantity, low SNR and high false alarm probability, the PU signal detection cannot be achieved for any level of detection threshold \(\lambda_{i}\).