esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study investigated the efficacy of ML and DL techniques for lung cancer prediction, addressing the critical need for early and accurate diagnosis to improve the patient outcomes. The research provides a comparative analysis of various ML algorithms (DT, RF, KNN, NB, AB, LR, and SVM) and NN models for lung cancer prediction using a dataset of patient symptoms and lifestyle factors. This shows how a rigorous data preprocessing pipeline, including outlier removal, normalization, and feature selection with Pearson's correlation, improves the model prediction performance and reduced noise. The study identifies coughing, wheezing, alcohol consumption, swallowing difficulty, and allergies as the key predictive features for lung cancer, highlighting the significant risk indicators. It evaluates the performance of NN models with varying architectures (one, two, and three hidden layers) and training parameters, determining the optimal configuration for lung cancer prediction.  
Several ML models, including DT, NB, and KNN, achieved a high classification accuracy (above 85%), demonstrating their potential for effective lung cancer prediction. DL models, notably a three-hidden-layer NN, outperformed the traditional ML classifiers, reaching a maximum accuracy of 92.86%, highlighting the superior capability of NNs to capture complex patterns in the data.
## 3 Experiments and Results Analysis
### Experimental Results and Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{
\begin{tabular}{c} **Sampling** \\ **Scale** \\ \end{tabular} } & \multirow{2}{*}{**Parameters (k)**} & **Set5** & **Set14** & **BSD100** & **Urban100** & **Manga109** \\ \cline{3-8}  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline CARN [29] & & 1592 & 34.33/0.926 & 30.33/0.8410 & 29.09/0.8031 & 27.35/0.8400 & — \\ CARFT [30] & & 737 & 34.74/0.930 & 30.55/0.8473 & 29.20/0.8089 & 28.75/0.8640 & 34.31/0.9487 \\ EDSR [4] & & 1555 & 34.41/0.9270 & 30.25/0.8415 & 29.11/0.8060 & 28.20/0.8353 & 33.45/0.9444 \\ ELAN-light [31] & & 582 & 34.64/0.9284 & 30.55/0.8463 & 29.22/0.8082 & 28.70/0.8626 & 34.00/0.9475 \\ IMDN [32] & \(\times\)3 & 694 & 34.36/0.9269 & 30.34/0.8415 & 29.11/0.8048 & 28.19/0.8521 & 33.63/0.94457 \\ LAPAR-A [33] & & 548 & 34.39/0.9269 & 30.36/0.8423 & 29.13/0.8056 & 28.17/0.8525 & 33.53/0.9444 \\ LatticeNet [34] & & 756 & 34.54/0.9285 & 30.41/0.8424 & 29.15/0.8059 & 28.35/0.8540 & — \\ MAFFSRN-L [34] & & 790 & 34.48/0.9280 & 30.42/0.8433 & 29.15/0.8063 & 28.27/0.8553 & — \\ SwinIR-light [11] & & 878 & 34.65/0.9291 & 30.56/0.8465 & 29.22/0.8084 & 28.68/0.8626 & 33.99/0.9479 \\
**PCCSTSR (ours)** & & **578** & **34.78/0.9308** & **30.58/8477** & **29.28/0.8098** & **28.87/0.8660** & **34.42/0.9492** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of image SR reconstruction results (\(\times\)3). Figure 5: Comparison of reconstruction results between advanced algorithms and PCCSTSR on the Urban100 dataset (\(\times\)4). Figure 6: \(Cont\). The results of the average running time on the Urban100 dataset (\(\times\)4) are shown in Table 3. Clearly, the proposed PCCSTSR network has the fastest reconstruction speed, with only \(5.78\times 10^{5}\) parameters. Compared to performance-oriented methods like IDMN and SwinIR, the proposed network achieves similar performance, while its inference time is only 85% and 14% that of IDMN and SwinIR, respectively, with an inference speed of 55 frames/s. This demonstrates that the proposed network is a lightweight model with significant performance advantages.
## 3 Results and Discussion
### Model Comparison
[14], no substantial disparity was observed in the accuracy scores of the videos analyzed at various times of the day. The reason for this is that, unlike our study, this study was conducted during the summertime when there was minimal variation in lighting conditions.
## 5 Conclusions  
In this paper, a flexible demand response dispatch strategy considering multiple response modes and wind power uncertainty is presented. First, a DR resource operation decision model based on multiple response characterization of DR resources is formulated. Second, the flexible demand response dispatch strategy is proposed by combining wind power scenario generation and the reduction method. Finally, the effectiveness of the proposed strategy is verified with simulation results.  
From the work presented in this paper, general conclusions can be drawn as follows:  
1. Multiple DR integration has a notable impact on power system dispatch. Meanwhile, when coordinated with thermal units, DR effectively improves the function of peak shaving and valley filling;
2. This flexible DR dispatch strategy provides a quantitative assessment of DR integration impacts on system operation cost and wind power consumption;
3. It can be applied in day-ahead power system dispatch to help operators effectively evaluate the system state and design demand response mechanisms.  
In the future, the energy storage and carbon emission index will be included in our model. Additionally, we plan to combine the load forecast uncertainty and numerical weather prediction model with our framework and make the proposed dispatch strategy more appropriate for real-world scenarios.  
Conceptualization, H.H. and C.W.; Literature review, Y.Z. and T.W.; methodology, H.H., Y.Z. and T.W.; writing--original draft, H.H., Y.Z. and T.W.; writing--review & editing, H.Z., G.S. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by Fundamental Research Funds for the Central Universities under Grant B200201016 and in part by the Postdoctoral Research Funding Program of Jiangsu Province under Grant 2021K622C.  
Not applicable.  
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 2 Materials and Methods
### Validation
#### 2.5.2 Holdout Validation  
To further evaluate the algorithm's performance, we divided the point dataset into two distinct subsets: a training set and a testing set. This partitioning was accomplished through a random split, assigning 70% of the data for training and reserving 30% for testing purposes.  
The choice of these specific percentages is grounded in the widely recognised efficacy of the 70-30% split. This configuration consistently yields lower mean standard errors, ensuring a high precision in our analysis. This statistical advantage has been well-established in previous research, as demonstrated by [PERSON] et al. (2019). The 70-30 ratio allows the detection of data patterns within a sufficiently large training set while retaining a substantial test set for a reliable estimate of the model's performance. In other words, this ratio was chosen to maintain a balance between reducing bias and controlling model variance. A smaller test set might not yield reliable performance estimates, while a larger training set could lead to overfitting, where the model excels on training data but underperforms on new data.  
The test set was then utilised to compute a confusion matrix and standard performance evaluation metrics, including OA, user and producer accuracy, and the kappa coefficient ([PERSON] et al., 2019). OA serves as a comprehensive gauge of the total classification accuracy, essentially reflecting the likelihood of correctly classifying a randomly selected location on a map ([PERSON] et al., 2019). Producer's accuracy and User's accuracy are distinct metrics used to assess classification accuracy. Producer's accuracy denotes the probability that a real-world feature is accurately represented on the map, while User's accuracy signifies the probability that a class depicted on the map actually exists in the real world. The Kappa coefficient offers quantification of how superior the classification performed compared to the probability of randomly assigning pixels to their correct categories, providing a valuable indicator of classification quality ([PERSON] et al., 2019).
# The BCL Method for DSSC: Basis and Applications +
## 7 The Role of Lithium on the Photo Conversion Efficiency of Ruthenium Dyes
### Adduct Generation
#### 7.2.3 Absorption Spectra and Stark Shift  
By means of TDDFT calculations the effect of Lithium on the absorption spectrum of the 1S0 complex can be simulated, as shown in Figure 34a. According to these calculations, the complex presents an electronic transition \(\Gamma_{1}\) with MLCT character at \(\lambda_{\rm max}\) = 486 nm, which moves up to \(\Gamma_{\rm 1 Li}\) with \(\lambda_{\rm max}\) = 509 nm when lithium is present.  
Figure 34: (**a**) Stark effect caused by Li\({}^{+}\) on the \(\Gamma 1\) transition. (**b**) Annihilation of the Stark effect by an external potential of E0.  
Figure 33: (**a**) Energy levels diagram for the lithium adduct. (**b**) Molecular orbital composition in terms of fragments dcb, dmb, Ru and Ti(OH)\({}_{3}\).  
Table 7 shows the composition of both bands, which are similar since they contain 60% of the same excitation from HOMO-2 to LUMO + 2 (Ru\(\rightarrow\)dcb). The difference between them arises because \(\Gamma_{1}\) contains 29% of an excitation between HOMO to LUMO + 2 and LUMO + 3, while \(\Gamma_{1\mathrm{Li}}\) only contains 26% of an excitation from HOMO to LUMO + 1. According to this, the 0.11 eV shift on the \(\lambda_{\mathrm{max}}\) of the absorption spectra is identified with a Stark effect caused by the electric field of Li\({}^{+}\).  
This value is consistent with the 0.12 eV displacement in \(\lambda\)max observed experimentally. Besides the existence of the electronic excitation from HOMO-2 to LUMO previously mentioned, there are also small 10-15% contributions to \(\Gamma\) from the HOMO to the LUMO + 1 and LUMO + 2. The HOMO to LUMO + 3 excitation is present in the free complex but absent in the adduct.  
As indicated above, the signal observed at 509 nm is not a new signal but rather a ghost signal, caused by the electric field of lithium, on the initial transition at 489 nm. One way to demonstrate the existence of this field is to neutralize it with a field of the same intensity but in the opposite direction. The bathochromic shift observed on going from 1(S0) to 1(S0)Li can be neutralized (Figure 34b) by applying an electric field pointing to the negative side of the Z-coordinate axis, with a magnitude of EZ = E0 = 7.3 MV/cm.  
This value for the electric field is in good agreement with the 1-10 MV/cm values previously reported for this class of molecules bound to TiO\({}_{2}\). With application of this external field, the transition \(\Gamma_{1\mathrm{a}}\) disappears and gives rise to the unshielded transition \(\Gamma_{1}\) observed at 489 nm.
## 3 Field Test Study on Liquid CO2 Phase-Transition Rock Fracturing
### Fracturing Effects
#### 3.3.2 Distribution Characteristics of the Granularity of the Rock after Fracturing
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Project**} & \multicolumn{8}{c}{**Cumulative Percentage of Volume under the Rock Sieve/\%**} & \multirow{2}{*}{**b**} & \multicolumn{2}{c}{**Fractal Dimension**} & \multirow{2}{*}{**Correlation Coefficient**} \\ \cline{2-2} \cline{5-12}  & **\(<\)5** & & & & & & & & & & & **(D)** & **Coefficient** \\ \hline
95-type single-hole & 0.71 & 7.19 & 21.92 & 42.45 & 42.45 & 99.99 & & & & & & 2.030 & 0.970 & 0.982 \\
95-type several-hole & 0.01 & 0.26 & 1.56 & 2.90 & 5.59 & 8.10 & 11.52 & 25.14 & 25.14 & 42.64 & 100.00 & 2.073 & 0.927 & 0.978 \\
108-type single-hole & 0.08 & 1.36 & 11.39 & 26.89 & 59.26 & 100.00 & & & & 2.817 & 0.183 & 0.988 \\
108-type several-hole & 0.02 & 0.40 & 2.38 & 3.49 & 6.79 & 11.36 & 14.45 & 20.53 & 37.67 & 99.99 & 1.959 & 1.041 & 0.978 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The cumulative percentage under the rock sieve and fractal results of liquid CO\({}_{2}\) phase-transition rock fracturing. see that the higher the proportion of small fragments after fracturing was, the larger the fractal dimensions would be. In contrast, it was observed that the lower the proportion of small fragments after fracturing were, the smaller the fractal dimensions would be. The fractal dimension of the 108-type single-hole test was determined to be 0.183. Meanwhile, the fractal dimension of the 95-type single-hole test was 0.970, which was abnormal. The reason for these results was determined to be that the 95-type single-hole test boreholes had a high content of clay, which was a soil-rock accumulation layer and strong weathering layer with poor rock properties. This had led to increases in the fractal dimensions. The fracturing effects of rock mass excavations using liquid CO2 phase transition methods are better than those obtained using conventional non-blasting excavation methods. The fracturing volumes have been found to be more ideal. Further, the fractal dimension D can be used to characterize the granular distributions, which provides favorable conditions for the subsequent optimization of the fracturing modes.
# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 4 Experimental Results
### Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model Type**} & \multicolumn{2}{c}{**Model Name**} & \multicolumn{2}{c}{**\#Parameters (Millions)**} \\ \hline \multirow{4}{*}{CLIP} & RN50-quickgelu & RN50-quickgelu & 102.54 M & RN101-quickgelu & RN50-quickgelu & RN50-quickgelu & RN50-quickgelu & RN50-quickgelu \\ \cline{2-8}  & RN101-quickgelu & 55 & 50 & 56 & 54 & 53 & 59 & 54.50 \\ \cline{2-8}  & RN50x64 & 64 & 64 & 68 & 68 & 74 & 65 & 67.17 \\ \cline{2-8}  & ViT-B-32 & 58 & 56 & 60 & 56 & 59 & 60 & 58.17 \\ \cline{2-8}  & ViT-B-16 & 62 & 62 & 64 & 60 & 62 & 65 & 62.50 \\ \cline{2-8}  & ViT-L-14 & 70 & 68 & 73 & 69 & 71 & 77 & 71.33 \\ \hline \multirow{4}{*}{Open-Clip} & convnext\_base\_w & 54 & 53 & 60 & 58 & 60 & 60 & 57.50 \\ \cline{2-8}  & convnext\_large\_d & 62 & 67 & 69 & 64 & 70 & 71 & 67.17 \\ \cline{2-8}  & ViT-L-14’ & 60 & 58 & 65 & 62 & 63 & 62 & 61.67 \\ \cline{2-8}  & ViT-B-16’ & 52 & 54 & 54 & 56 & 60 & 51 & 54.50 \\ \cline{2-8}  & ViT-B-32’ & 45 & 54 & 48 & 51 & 56 & 47 & 50.17 \\ \cline{2-8}  & ViT-H-14’ & 67 & 65 & 76 & 72 & 73 & 79 & 72.00 \\ \cline{2-8}  & xlm-roberta-large & 79 & 81 & 76 & 78 & 75 & 73 & 77.00 \\ \hline \multicolumn{2}{c}{Average} & 58.08 & 58.08 & 61.83 & 59.67 & 62.42 & 62.25 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Zero-shot classification results for (A) Merced, (B) AID, and (C) NWPU datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Type** & **Model Name** & **\#Parameters (Millions)** \\ \hline \multirow{4}{*}{CLIP} & RN50-quickgelu & 102.54 M \\ \cline{2-4}  & RN101-quickgelu & 120.22 M \\ \cline{2-4}  & RN50x64 & 623.67 M \\ \cline{2-4}  & ViT-B-32 & 151.81 M \\ \cline{2-4}  & ViT-B-16 & 150.15 M \\ \cline{2-4}  & ViT-L-14 & 428.15 M \\ \hline \multirow{4}{*}{Open-CLIP} & Convnext\_base\_w & 179.92 M \\ \cline{2-4}  & Convnext\_large\_d & 352.30 M \\ \cline{2-4}  & ViT-L-14’ & 428.15 M \\ \cline{2-4}  & ViT-B-16’ & 150.15 M \\ \cline{2-4}  & ViT-B-32’ & 151.81 M \\ \cline{2-4}  & ViT-H-14’ & 986.64 M \\ \cline{2-4}  & xlm-roberta-large & 1.19 B \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Models investigated for zero-shot classification.
# Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System
###### Abstract  
Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI) application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG) signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA) was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.  
Keywords:brain-computer interface; steady-state visual evoked potentials; fuzzy logic; CCA +
Footnote †: journal: Applied Sciences
# Temperature Stable, High-Quality Factor Li2 TiO3-Li4 NbO4F Microwave Dielectric Ceramics
###### Abstract  
In this work, (1-x)Li2 TiO3-xLi4 NbO4F ceramics were prepared by the conventional solid-state ceramic route. With the increase of Li4 NbO4F content, the phase structure transformed from ordered monoclinic to disordered cubic. By increasing Li3 NbO4F content, the temperature coefficient of resonant frequency (\(\tau_{\mathrm{f}}\)) was successfully adjusted closer to zero, while the dielectric constant (\(\epsilon_{\mathrm{r}}\)) and microwave quality factor (Q/f) decreased to some degree. Outstanding microwave dielectric properties with a \(\epsilon_{\mathrm{r}}\) = 18.7, Q/f = 61,388 GHz (6.264 GHz), and \(\tau_{\mathrm{f}}\) = 0.9 ppm/\({}^{\circ}\)C were obtained for 0.9 Li2 TiO3-0.1 Li4 NbO4F ceramics sintered at 1050 \({}^{\circ}\)C for 2 h, which indicated that these ceramics are suitable for practical applications in the field of microwave substrates and components.  
microwave dielectric properties; quality factor; Li2 TiO3; Li4 NbO4F
# A Survey on Satellite Communication System Security
## 7 Future Research Directions
### Full Quantum Networks
Furthermore, through these advancements, we can expect a significant increase in security for satellite communication systems.
## 3 Results and Discussion
### Topic 2: Healthcare Delivery
#### 3.3.2 Primary Healthcare  
Industry bodies such as IOGP have guidelines for the provision of primary care which offer a useful framework for developing healthcare strategies in a manner that is based on risks, healthcare needs, and available resources [26]. Within this framework, primary care typically relates to capacity-building of existing healthcare or establishing a clinical service on or near the work site. Capacity building may involve upskilling local clinicians and/or purchasing equipment to increase diagnostic capacity. On the other hand, a'sole proprietor' model may be employed whereby the organisation builds and operates their own clinic, or they may choose to use a 'congolomerate model' by partnering with another company [26]. No model of primary care is considered to be superior, but instead, the ideal model should be developed via a thorough assessment of resources, risk assessments, and insufficiencies of the community's local primary care services. What is recommended within the framework, however, is that, at times where healthcare needs are lower, other activities may be conducted such as health promotion and occupational health services [26]. This recommendation aligns with a significant volume of published evidence which has demonstrated that preventative healthcare strategies such as interventions targeting behaviour change and employee well-being at the organisation level can have significant benefits to employee wellness and health status [16; 27; 28].
# X-ray Image Enhancement Based on Adaptive Gradient Domain Guided Image Filtering
## 3 The Proposed Method  
In this section, an adaptive amplification factor is proposed. From Section 2, we can denote that the detail layer \(s\) is defined as the following [25; 28]:  
\[s=G-\hat{Z} \tag{11}\]  
where \(\hat{Z}\) presents the base layer.  
Multiplying the detail layer by an amplification factor \(\beta\), the corresponding equation is defined as follows:  
\[s_{1}=\beta\times s=\beta\times\left(G-\hat{Z}\right) \tag{12}\]  
where \(s_{1}\) shows the enhanced detail layer, and \(\beta\) is an adjustable parameter, which can influence the enhanced result. In traditional gradient domain guided image filtering, the value of \(\beta\) is set to 5 [25].  
The output image \(f\) can be computed by the following:  
\[f=\hat{Z}+s_{1} \tag{13}\]  
Substituting Equation (8) into Equation (12):  
\[s_{1}=\beta\times\left(G-\overline{a}G-\overline{b}\right)=\beta\times(1- \overline{a})G-\beta\times\overline{b} \tag{14}\]  
The gradient of the enhanced detail layer can be defined as follows:  
\[\
abla s_{1}=\beta\times(1-\overline{a})\times\
abla G \tag{15}\]From Equation (1), we can define the gradient of base layer, and it is defined as follows:  
\[\
abla\dot{Z}=\overline{a}\times\
abla G \tag{16}\]  
In general, the \(\
abla s_{1}\) is less than \(\
abla\dot{Z}\). If not, the noise in the detail layer will be amplified. We have the following equation:  
\[\
abla s_{1}\leq\
abla\dot{Z} \tag{17}\]  
Substituting Equations (15) and (16) into Equation (17):  
\[\beta.(1-\overline{a})\times\
abla G\leq\overline{a}\times\
abla G \tag{18}\]  
From Formula (18), we can have the following equation:  
\[\beta\leq\frac{\overline{a}}{1-\overline{a}} \tag{19}\]  
When the value of \(\beta\) is large, the noise can be amplified; when the value of \(\beta\) is small, the details will be suppressed. From the above analysis, we can see that a proper value of \(\beta\) is very important to balance the noise suppression and detail enhancement. In this section, the value of \(\beta\) is defined as follows:  
\[\beta=\frac{\overline{a}}{1-\overline{a}} \tag{20}\]  
where \(\beta\) is an adaptive parameter, and its value does not need to be set manually.