esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Some of the most popular pre-processing procedures are rubber band baseline correction and normalization to the Amide I or Amide II peak [6]. As far as concerns data analysis, many different univariate and multivariate approaches can be adopted [14; 15; 16]. In particular, multivariate analysis of infrared spectra collected from human cells exposed to ionizing radiation can allow the identification of the changes at protein, lipid, and amino and nucleic acid level and their dose-dependent behavior [12; 17]. As an alternative to these multivariate approaches, some authors suggest that a ratiometric approach can be especially advantageous for highlighting and quantifying biochemical changes in \(\mu\)-FTIR spectra related to samples in different experimental conditions [18; 19; 20; 21]. For example, the protein/lipid ratio can be used to monitor sepsis infection in mice [22] and to differentiate cells in various growth phases [23]. Other properly chosen ratios have been also adopted for discriminating normal, borderline, and malignant ovarian tissues [24]; for comparing red blood cells from gastric cancer tissues [25]; for the early diagnosis of oral cancer [26]; and for monitoring breast cancer progression [27]. Hence, a well-done ratio analysis can indicate valuable spectral biomarkers and can provide a quantitative tool also in many biomedical applications. In the present work, we chose a ratiometric approach for analyzing \(\mu\)-FTIR spectra from SH-SYSY neuroblastoma cells after the exposure to different doses of X-ray (0, 2, 4, 6, 8, 10 Gy). This approach can offer a very direct way to monitor changes for specific cellular components. In particular, the spectroscopic investigation has been carried out on cells fixed immediately (t\({}_{0}\) cells) and 24 h after irradiation (t\({}_{24}\) cells), in order to study both the early effects of the irradiation and those that remain after the cellular repair processes have taken place. The SH-SYSY cell line is associated with the tumor pathology of greater incidence in infants, and it is the third most common in children, after leukemia and brain cancer [28]. In addition, it is of considerable importance in studies aiming to elaborate new approaches for treating and/or preventing disorders of the central nervous system [29; 30; 31; 32]. The reported ratiometric analysis of infrared spectra allowed us to detect changes in proteins, lipids, and nucleic acids contributions due to X-ray radiation exposure, as well as to compare the difference arising between t\({}_{0}\) and t\({}_{24}\) cells.
# Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
## 3 Comparison Experiment
### Comparative Experiments with the VGG19 Model
#### 3.2.2 Global Pruning Analysis  
The Top1 accuracy after global model pruning was evaluated for the VGG19 model using three methods: MCP-L1, Lasso regression, and MCP regression. The model was retrained with a pruning ratio of 0.3, an epoch of 20, and a learning rate of 0.001.  
As evident from Table 3 and Figure 13, the VGG19 model trained on CIFAR100 exhibits less parameter redundancy than VGG19_Simple trained on CIFAR10. Consequently, the decreases in Top1 accuracy are more pronounced when pruning the model.  
When the model undergoes pruning using the MCP-L1 method, the Top1 accuracy experiences a significant drop, ranging from 20.54% to 51.15%, at a pruning ratio of 0.1. Upon reaching a pruning ratio of 0.5, the Top1 accuracy plummets to approximately 1%, rendering the model ineffective for a 100-category classification, akin to random classification. In the case of Lasso regression and MCP regression, although the decline in model performance is not as rapid as with the MCP-L1 method, the predictive effect diminishes notably with increasing pruning ratios. Specifically, at a pruning ratio of 0.5, the Top1 accuracies for the Lasso-regression- and MCP-regression-cropped models are 11.69% and 27.21%, representing 60% and 44.48% decreases, respectively.  
Overall, the MCP regression method outperforms Lasso regression both after pruning the model and following fine-tuning. Specifically, at a pruning ratio of 0.3, the Top1 accuracies after pruning are 59.31% and 51.34% for MCP regression and Lasso regression, respectively. Subsequent retraining results in Top1 accuracies of 69.64% and 67.94%, with MCP regression exhibiting superior performance in both scenarios.  
Table 4 displays the parametric quantities and floating-point computations of the VGG19 model with different pruning ratios. In the VGG19 model, a significant number of parameters are attributed to the fully connected layer, whereas a considerable portion of floating-point computations are concentrated in the convolutional layer. In this subsection's experiments, pruning predominantly targets the convolutional channels, excluding the fully connected layer. Consequently, the reduction in the number of parameters after pruning is not pronounced, but the decline in floating-point computations is more substantial. For a pruning ratio of 0.5, the parameter count decreases by approximately 18%, while the floating-point computation volume drops by over 60%, resulting in a computation volume of less than half of the original model. With a pruning ratio of 0.3, the model's Top1 accuracy is restored to 69.64% after only 40 retrains, representing a 2.05% reduction compared with the original model, accompanied by a reduction of about 42% in computation volume.
## 5 Simulation and Discussion
### Day-Ahead DRO Model Simulation Analysis  
#### 5.2.1 Day-Ahead Scheduling Case Studies, Indicators and Analysis Process  
To validate the effectiveness of DHB under the day-ahead DRO model, this paper compares the performance of four different strategies in DRO scheduling: no hydrogen blending, fixed optimal hydrogen blending ratio, THB, and CHB strategies. Four cases are analyzed as follows:  
Case 1: Use only natural gas as the fuel for CHP in DRO scheduling.  
Case 2: Mix natural gas and hydrogen at a fixed volume ratio to optimize the goal for CHP fuel in DRO scheduling.  
Case 3: Apply the THB strategy in DRO scheduling.  
Case 4: Apply the CHB strategy in DRO scheduling.  
In addition, the following seven indicators are used to evaluate the effectiveness of different strategies in day-ahead scheduling: total cost, carbon emissions, carbon trading cost, IDR cost, electricity and gas purchase cost, and operation cost.  
It should be noted that the optimal fixed hydrogen blending Ratio for Case 2 has not yet been determined. Therefore, its value will first be established in Section 5.2.2. Subsequently, Section 5.2.3 will provide an initial qualitative analysis of the CHB Ratio distribution pattern. The following sections will then transition to a quantitative analysis, examining the DHB strategy, the DRO method, the system architecture, and key parameters in the scheduling process.
## 3 Results and Discussion
### Future Scope  
The current research suggests that the Fe(0)-catalytic Fenton oxidation can degrade organic microp pollutants. The process required a less amount of hydrogen peroxide than classical ferrous iron-Fenton oxidation. Thus, Fe(0)-catalyst could be a cost-effective substitute to the conventional ferrous iron-Fenton oxidation. However, to call on Fe(0) catalytic Fenton process as a new approach to the existing technology, it requires more research designed for the treatment of other persistent pollutants. Additionally, further study is needed to explore its sorption effect when using Fe(0) as a catalyst other than its oxidation function. The prime limitation of using acidic pH to perform the Fenton oxidation process could be investigated with coupling Fe(0) with another transition metal. Finally, pilot-scale trials should be run in full scale water treatment plants to collect more technical and economic data before the Fe(0) catalytic Fenton process is to be used in a large scale water treatment.  
Figure 5: (**a**) Floc precipitate at the bottom of the beaker (clearly visible for FeSO4), solid Fe particle adhered to the vessel walls after the treatment; the colour divergence of filtered effluent found after the Fe and FeSO\({}_{4}\) treatment, FeSO\({}_{4}\) treatment persist colour; (**b**) box and whisker plot of sludge volume index (mL/g). \(\times\): Mean value; \(\circ\): Median.
## 3 System Description
### System Model
#### 3.2.2 Human Body Model  
A study that is based on the impact of the human body on VLC has already been presented in [30]. It has been shown that applications requiring high-QoS must consider the effects of human body reflectivity and data rates. Furthermore, it explains that a body possessing high reflectivity value (e.g., 0.7) does not significantly affect the \(SNR\) for data rates lower than 250 Mbps. Even though the human body reflectivity is highly subjected to clothing that is worn by the people and low reflectivity values must also be considered, yet, the inference for low reflectivity values, e.g., 0.1, remains the same, except that the results are more penalizing in terms of increased loss. As the aim of this study is to establish illuminance and \(SNR\) uniformities at various receiver heights for a data rate of 20 Mbps which is much less than 250 Mbps, the following assumptions are made in the DL analysis: (i) the on-body coordinator (receiver) is placed around the patient's arm, such that it is facing the ceiling at all times except when considering the sleeping posture where it is facing the wall, and (ii) the human body has a high reflectivity value. The effects of low reflectivity values will be evaluated in a separate study. Besides, another major issue that is associated with human body modelling is that, when the receiver is worn by a moving person, it may incur random orientations. Moreover, it is likely that the receiver may only collect reflected optical paths; thereby, resulting in ISI. These issues may adversely impact the communication performance by inducing optical path losses and significantly reducing the achievable data rates. While generally the receivers are assumed to be oriented towards the ceiling, a study presented in [31] has considered the impacts of receiver orientations and positions on VLC link performance revealing that even though the effects of receiver orientations in a mobile VLC link are significant, for data rates lower than 30 Mbps, they do not have a strong impact. This is because additional excess delay resulting from one or more reflections does not add to ISI problem and data rates up to 30 Mbps are supported. Since the healthcare applications generally require data rates lower than 1 Mbps [32] and, because our study is limited to a maximum of 20 Mbps, the effects of receiver orientations will not be considered.
# Toward Long-Term Implantable Glucose Biosensors for Clinical Use
## 4 Conclusions  
The implantable glucose biosensor is the key component of continuous glucose monitoring that is a powerful tool to manage disease or blood glucose. The electrochemical biosensor technologies have led innovation in CGMS. The recent enzyme-based CGMSs have reached no calibration before use and even replace standard SMBG for making diabetic treatment decisions. Such innovation has led paradigm shift to \"zero finger-pricking.\" Despite the great progress in glucose monitoring, there have been efforts made toward the development of long-lasting CGMS that can dramatically reduce numbers of self-insertion and costs. Boronic acid-based glucose biosensors have proven their potential for long-term use. However, they still need several calibrations a day. If further technical refinement is required, e.g., calibration algorithm and long-term biostability, we envision that boronic acid-based CGMS also can reach long-term CGM with no calibration and low cost.  
Since the recent low-cost, zero in vivo calibration CGMSs reduce barriers to CGMS use, we expect that CGMS market extends to even healthy people [48]. This easy access to CGMS makes it possible to collect personal health data at various progress of disease and analysis the data that will provide important information to design strategies for effective, proactive diabetic management.  
[PERSON] and [PERSON]-H.K. surveyed the state of art in continuous glucose monitoring and wrote the paper.  
This research was funded by NATIONAL RESEARCH FOUNDATION OF KOREA, grant number NRF-2017R1C1B5076947.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 4: **(a)** Fluorescence hydrogel with boronic acids under the skin of mouse ear. The hydrogel glows through skin. **(b)** The fluorescence intensity of hydrogels responded to blood glucose concentration continuously after 140 days from implantation. Modified figures from those in Ref. [47].  
Figure 3: Glucose sensing mechanism of diboronic-acid-based fluorescence. Modified figures from those in Ref. [46].
# Train Service Design for Rail Transit Cross-Line Operation Applying Virtual Coupling
## 5 Case Study
### A Small-Scale Case Study
#### 5.1.2 Comparison of the Performance of Algorithm  
To evaluate the effectiveness and efficiency of the proposed ASA-GA, its performance is compared with that of GA and Gurobi 11.0.2 solver. For this experiment, GA parameters are set as follows: \(N=100\), \(P_{c}=0.75\), \(P_{m}=0.1\). ASA-GA parameters are set as \(K=10\), maximum number of iterations \(K_{\max}=100\), cooling coefficient \(\theta=0.7\), initial temperature \(T_{0}=800\), and final temperature \(T_{\text{end}}=1\). The encoding and decoding techniques for both ASA-GA and GA are identical. The optimal solution is obtained using the Gurobi 11.0.2 solver on the transformed linear model. Based on the same arithmetic example, the GA, Gurobi 11.0.2 solver, and ASA-GA are carried out 20 times, respectively. Table 4 presents the experimental results, while Figure 11 illustrates the convergence tendency of the objective value.  
As shown in Table 4, the Gurobi 11.0.2 solver outperforms the GA in terms of optimal value, maximum value, and average value by 12.2%, 7.3%, and 11.5%, respectively. Furthermore, the Gurobi 11.0.2 solver achieves a better optimal value and maximum value  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Optimal Value** & **Maximum Value** & **Average Value** & **CPU (s)** \\ \hline GA & 6639 & 8578 & 7127 & 406 \\ Gurobi & 5831 & 7954 & 6308 & 560 \\ ASA-GA & 5870 & 8025 & 6248 & 424 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results when using different algorithms.  
Figure 11: The convergence tendency of the objective value.  
Figure 10: Passenger OD matrix.  
than the ASA-GA algorithm by 0.7% and 0.9%, respectively. The optimal value obtained by the ASA-GA algorithm closely approximates the exact solution of the Gurobi 11.0.2 solver. Figure 11 shows that the Gurobi 11.0.2 solver obtains the optimal value at 560 s, while ASA-GA obtains the optimal solution at 424 s. Consequently, ASA-GA significantly enhances the efficiency of solving the model and obtains a near-optimal solution approximating the exact solution. Therefore, in the following real-world case, we can adopt ASA-GA to produce near-optimal solutions.
## 2 Methods  
### Selection of the Excitation Wavelengths by the Clustering Method  
The typical EEM is described in Table 1. \(E_{x1}\),..., \(E_{xj}\),..., \(E_{yJ}\) are the \(J\) excitation wavelengths, \(E_{\rm{m1}}\),..., \(E_{\rm{m1}}\),..., \(E_{\rm{m1}}\) are the \(I\) emission wavelengths, and \(X_{ij}\) is the fluorescence intensity corresponding to the \(i^{\rm{th}}\) emission wavelength excited by the \(j^{\rm{th}}\) wavelength. Let (\(X_{a1}\), \(X_{a2}\),..., \(X_{aI}\))\({}^{\rm{T}}\) and (\(X_{b1}\), \(X_{b2}\),..., \(X_{bI}\))\({}^{\rm{T}}\) be the emission intensity spectra relating to the \(a^{\rm{th}}\) and the \(b^{\rm{th}}\) excitation wavelengths, respectively. The similarity coefficient \(C_{ab}\) between these two emission spectra is calculated by Equation (1), as follows:  
\[C_{ab}=\frac{\sum\limits_{i=1}^{I}{(X_{ai}-\overline{X}_{a})(X_{bi}-\overline{ X}_{b})}}{\sqrt{\sum\limits_{i=1}^{I}{(X_{ai}-\overline{X}_{a})^{2}\sum \limits_{i=1}^{I}{(X_{bi}-\overline{X}_{b})^{2}}}}} \tag{1}\]  
where \(\overline{X}_{a}\), \(\overline{X}_{b}\) are the mean fluorescence intensity of the \(a^{\rm{th}}\) and the \(b^{\rm{th}}\) emission spectra, respectively.  
Initially there are \(J\) excitation wavelength classes (Figure 1), corresponding to \(J\) excitation wavelengths, then the similarity coefficients between any two emission spectra corresponding to their excitation wavelengths are calculated, after the pair of emission spectra with the largest similarity coefficient are designated as one class, \(J\)-1 excitation wavelength classes are left. The number of classes is reduced by one after each calculation round of the similarity coefficient. This process is performed until the difference of similarity coefficients between any two pair of classes are under the pre-defined tolerant criterion.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(E_{x1}\) &... & \(E_{yj}\) &... & \(E_{yJ}\) \\ \hline \(E_{\rm{m1}}\) & \(X_{11}\) &... & \(X_{j1}\) &... & \(X_{j1}\) \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \(E_{mi}\) & \(When two or more excitation wavelengths are assigned to the same class, the calculation of the similarity coefficient between different classes is different from that between each single emission spectrum. Let \(M\) and \(N\) be two different classes with \(m\) and \(n\) emission spectra in each class, the similarity coefficient calculation steps are: First, the similarity coefficients between one emission spectrum in class \(M\) and the \(n\) emission spectra in class \(N\) are calculated; then another emission spectrum in class \(M\) is assigned to repeat the above step. Once all \(m\times n\) similarity coefficients are calculated, the two excitation wavelengths corresponding to the maximum similarity coefficient between the class \(M\) and \(N\) are accepted to represent each class.  
The EEM of pure organic compound sample is used to select its excitation wavelengths. The spectra corresponding to these wavelengths will be served to select the emission wavelengths.
# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 4 Discussion
### Extended Decision-Making Model with Adaptive Indicators for the System Life Cycle
The graph demonstrates three distinct phases in the architectural performance comparison:  
* Initial phase (years 1-5) where the decentralized architecture maintains a superior score around 0.57 while the centralized solution remains stable at approximately 0.50;
* Transition phase (years 6-8) showing a significant improvement in centralized architecture's performance, crossing over the decentralized architecture's score;
* Maturity phase (years 9-15) where the centralized solution continues to improve gradually, reaching a score of 0.62, while the decentralized approach stabilizes at approximately 0.58. This behavior reflects the impact of the dynamic weight redistribution at the 5-year mark, which shifts emphasis from initial deployment priorities to long-term operational considerations, ultimately favoring the centralized architecture in the later years of system operation.
## 3 Results and Discussion
### \(D_{\mathit{eff}}\)  
Different values of \(D_{\mathit{eff}}\) for different treatments of this study are reported in Figure 2. By increasing the drying temperature, the sample thickness and ultrasonic radiation time during the kiwifruit drying process \(D_{\mathit{eff}}\) had a significantly (\(p<0.05\)) increasing trend. By increasing the air temperature from 50 to 70 \({}^{\circ}\)C, it was observed that the moisture diffusivity coefficient increased from 3.09 \(\times\) 10\({}^{-10}\) to 2.26 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s. The reason for this issue is the significant effect of air temperature in creating more molecular movement and surface suction, and increasing the diffusivity coefficient. Additionally, increasing the temperature increases the enthalpy of the entering air, and increasing the enthalpy increases  
Figure 1: Drying time under different conditions for drying kiwifruit slices.  
the mass and heat transfer, which increases \(D_{\mathit{eff}}\). Further, by increasing the thickness of the samples, the amount of \(D_{\mathit{eff}}\) increased. [PERSON] and [PERSON] reported that superficial hardening generally occurs much faster in thinner samples than in thicker samples, while the rate of moisture evaporation in thinner samples is much higher [39]. Therefore, rapid superficial hardening in the samples with less thickness causes a restriction in moisture transfer compared to thicker samples and thus leads to a decrease in moisture diffusivity coefficient in thinner samples [40; 41]. Similar observations on the effect of thickness on moisture diffusivity coefficient have been reported by other researchers, including [PERSON] and [PERSON], [PERSON] and [PERSON] and [PERSON] et al. [28; 40; 42]. The \(D_{\mathit{eff}}\) increased by prolonging the ultrasonic pretreatment time from 0 to 30 min. Ultrasonic pretreatment causes the opening of the capillary tubes due to the dispersion of surface compounds and the formation of longer microscopic channels due to the deformation of the cells, followed by the formation ofmore open capillary tubes [43]. Therefore, ultrasonic pretreatment accelerates the removal of moisture from the product by deforming the cells and destroying the cell wall.