esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Numerical Results
We analyzed differentmaterials and geometric shapes and observed that some geometries required significantly more time to simulate. Among the materials tested, graphite took the longest to simulate, followed by glycerin and DW, respectively. The probe-fed patch antenna is mounted on an FR-4 substrate, characterized by \(\varepsilon_{\mathrm{r}}=4.5\) and \(\tan\delta=0.013\), \(\mu_{\mathrm{r}}=1\), and operates at a resonant frequency of \(f_{r}=2.45\) GHz. Figure 3 illustrates the probe-fed patch antenna, which has dimensions of \(L=28.83\) mm and \(W=15.00\) mm, with the probe-fed point located 9.61 mm from the lower edge and 7.50 mm from the side edges. The resonator is positioned above the radiating element of the probe-fed patch antenna, with its bottom center aligned with the center of the radiator. For cylindrical and prism-shaped geometries, alignment can occur along either LPA or WPA, where LPA refers to the resonator's length aligned with the patch antenna's length, and WPA refers to the resonator's length aligned with the patch antenna's width. To evaluate the effects of these configurations, specific dimensions of the geometric shapes were varied to assess their influence on performance. The chosen dimensions and their variation ranges are provided in Appendix A. Moreover, the key properties of the DW, glycerin, and graphite materials used in the resonators are summarized in Table 3.  
The performance comparison among the combinations primarily relies on the radiation pattern and the magnitude of the scattering parameter \(S_{11}\) (\(|S_{11}|\)). We conducted the numerical simulations under identical conditions and parameters. Additionally, we assume that an acceptable value for \(|S_{11}|\) is below \(-10\) dB. Consequently, the BW of the overall patch antenna is defined as the frequency BW around \(f_{r}\) that satisfies this criterion. All reported numerical results consider the resonator with dimensions varying within the intervals and resolutions listed in Table A1 in Appendix A. Moreover, the discussed numerical results correspond to the highest value of the MLM, where MLM represents the realized gain. The radiation patterns in polar form at a frequency of 2.45 GHz were analyzed at an azimuth angle of \(\phi=0^{\circ}\), with the elevation angle varying from \(\theta=0^{\circ}\) to \(\theta=360^{\circ}\). Figure 3: The probe-fed patch antenna. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & \(\varepsilon_{\mathrm{r}}\) & \(\mu_{\mathrm{r}}\) & \(\sigma\) **(S/m)** \\ \hline DW & 78.4 & 1 & 5.55 \(\mu\) \\ \hline Glycerin & 50 & 1 & 0 \\ \hline Graphite & 12 & 1 & 100 k \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Material properties used in the numerical simulations. This section is organized as follows: Section 4.1 addresses the resonator based on DW, Section 4.2 addresses the resonator based on glycerin, Section 4.3 addresses the resonator based on graphite, and Section 4.4 provides general discussions about the numerical results obtained with the considered materials.
## 4 Experiments and Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Type of Attack** & **PSNR** & **Marked Tampered/Total \# of Blocks** & **Success \%** \\ \hline No attack & 40.577 & 0/2932 & 100 \\ Tampered & 30.902 & 54/2932 & 100 \\ JPEG 75\% compression & 35.107 & 6 & 99.8 \\ JPEG 50\% compression & 33.346 & 181/2932 & - \\ JPEG 25\% compression & 31.640 & 627/2932 & - \\ Gaussian filter & 29.981 & 16/2932 & 99.5 \\ Histogram equalization & 17.458 & 0/2932 & 100 \\ Intensity Adjustment & 22.114 & 3/2932 & 99.9 \\ Scale & 29.071 & 186/2932 & - \\
3 \(\times\) 3 Average filter & 29.748 & 129/2932 & - \\ Gamma correction & 18.704 & 11/2932 & 99.63 \\ Wrong key & - & 2111/2932 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Success Rate of Our Method for Different Attack Types. Figure 4: (**a**) Original image (**b**) Binary watermark logo inserted into inner part (**c**) Watermarked image with both ownership and authentication watermark. Figure 5: (**a**) JPEG 75 authentication (**b**) JPEG 50 authentication (**c**) JPEG 25 authentication. Figure 6: (**a**) Re-scale authentication (**b**) Intensity adjustment authentication (**c**) Histogram equalization authentication. With respect to tamper localization, our method can localize tamper detection in 4 \(\times\) 4 block precision, which can be considered as very sensitive. The fidelity metric PSNR is around 40 for watermarked images with our method, which is an acceptable fidelity value and only worse than one of the previous methods. Our method only needs the scrambling key, which is a prime number, and the PSNR, which is not a long sequence. For the security of the watermarking method, these numbers must be passed to the authenticator. When one looks at the previous methods, all the previous methods must pass some secret value to the authenticator. The proposed algorithm and Chamlavi [36] are semi-fragile embedding methodologies. Both algorithms need a watermark and secret key for the extraction process. The proposed algorithm resists JPEG 75 compression, while the Chamlavi algorithm resists JPEG 70 compression. The proposed algorithm has a better PSNR value than the other algorithms, which shows the high quality of the watermarked image. Figure 8: (a) 3 \(\times\) 3 Average filter authentication (b) Gaussian filtering authentication. Figure 7: (a) Tampered watermarked image (b) Tampered watermarked image authentication (c) Authentication with wrong key.
## 3 Base Station Deployment and Self-Optimization Positioning Method
### Experiment and Results  
#### 3.4.1 Simulation Experiment on Positioning Accuracy of A-Type Central Area  
In accordance with the base station self-optimization trilateration positioning method, this paper conducts experimental validation through simulation. To verify the accuracy of the weighted average positioning coordinate calculation method under the new weight assignment method, this paper utilizes data recorded from a circuit around the peripheral areas within a 12 m \(\times\) 20 m venue as the true values for positioning coordinates in the simulation experiment. During the experiment, a uniformly distributed random ranging error of [0, 0.03 m] is added to each ranging information point, serving as the input for the ranging distance in the base station self-optimization positioning algorithm. Ultimately, the positioning coordinates were obtained based on the self-optimization positioning algorithm described in Section 3.3. The results are shown in Figure 4.  
As depicted in Figure 4, the mean error of the new base station self-optimization positioning algorithm is only 3.40576 cm, with a variance of 0.0412677 m\({}^{2}\), achieving a positioning accuracy at the centimeter level and exhibiting good stability. Observing the overall trajectory, the positioning path is not smooth, indicating the presence of certain jitter errors, suggesting that there is still room for improvement in the algorithm.
## 3 The Diffraction Continua of Vector Cylindrical Modes  
Thanks to the axial symmetry of the array we can exploit the vector cylindrical modes for description of the diffraction continua which are doubly degenerate in TM and TE polarizations \(\sigma\). The modes can be expressed through a scalar function \(\psi\)[1].  
\[\psi_{m,n}(r,\phi,z)=H_{m}^{(1)}(\chi_{n}r)e^{im\phi+ik_{z,n}z}. \tag{17}\]  
Then for the TE modes we have  
\[E_{z}=0,\;\;H_{z}=\psi_{m,n},\] \[E_{r}=\frac{ik_{0}}{\chi_{n}^{2}}\frac{1}{r}\frac{\partial\psi_{ m,n}}{\partial\phi},\;\;H_{r}=\frac{ik_{z}}{\chi_{n}^{2}}\frac{\partial\psi_{ m,n}}{\partial r},\] \[E_{\phi}=\frac{-ik_{0}}{\chi_{n}^{2}}\frac{\partial\psi_{m,n}}{ \partial r},\;\;H_{\phi}=\frac{ik_{z}}{\chi_{n}^{2}}\frac{1}{r}\frac{\partial \psi_{m,n}}{\partial\phi}, \tag{18}\]  
and for the TM modes\[E_{z}=\psi_{m,n},\;\;H_{z}=0,\] \[E_{r}=\frac{\hbar z}{\chi_{n}^{2}}\frac{\partial\psi_{m,n}}{ \partial r},\;\;H_{r}=\frac{-ik}{\chi_{n}^{2}}\frac{1}{r}\frac{\partial\psi_{m,n}}{\partial\phi},\] \[E_{\phi}=\frac{\hbar z}{\chi_{n}^{2}}\frac{1}{r}\frac{\partial \psi_{m,n}}{\partial\phi},\;\;H_{\phi}=\frac{ik}{\chi_{n}^{2}}\frac{\partial \psi_{m,n}}{\partial r}, \tag{19}\]  
where  
\[\chi_{n}^{2}=k_{0}^{2}-k_{z,n}^{2} \tag{20}\]  
and  
\[k_{z,n}=\beta+2\pi n,\;\;n=0,\pm 1,\pm 2,\ldots. \tag{21}\]  
In what follows we consider the BSCs in the diffraction continua specified by two quantum numbers \(m\) and \(n\) where the \(m\) is the result of the axial symmetry and \(n\) is the result of translational symmetry of the infinite linear array of the dielectric spheres. Note that each diffraction continuum is doubly degenerate relative to the polarization \(\sigma\). As a result of the interplay between the frequency \(k_{0}\) and the wave number \(k_{z,n}\) the continua can be open (\(\chi\) is real) or closed (\(\chi\) is imaginary). In the present paper we restrict ourselves by the case of one, two and three open continua.
## 4 Results and Interpretation
### Meso-Scale Wettability Characteristics  
Rocks are considered water-wet when the aqueous phase contact angles are less than 75\({}^{\circ}\), exhibit intermediate wettability between 75\({}^{\circ}\) and 105\({}^{\circ}\), and are oil-wet between 105\({}^{\circ}\) and 180\({}^{\circ}\)[36]. Contact angle measurements were taken at multiple sites on the surfaces of four samples, as illustrated in Figure 5. The results revealed that water contact angles range from 12.6\({}^{\circ}\) to 89.7\({}^{\circ}\), reflecting a transition from water-wet to intermediate wettability. Notably, Sample C2 is characterized by contact angles between 65.4\({}^{\circ}\) and 89.7\({}^{\circ}\), indicating an intermediate water-wet state at millimeter scale. The remaining samples displayed lower contact angles, ranging from 12.6\({}^{\circ}\) to 49.2\({}^{\circ}\) with an average of 28.6\({}^{\circ}\), indicative of a water-wet state.  
Figure 4: ESEM images showing typical water vapor condensation scenarios at pore walls. Images (**a**–**c**) from Sample C1 showing low-dome morphology of water droplets; images (**d**–**f**) from Sample C2 showing globular shaped water droplets; images (**g**–**i**) from Sample C3 showing low-dome morphology of water droplets; images (**j**–**l**) from Sample C4 showing water films on mineral surfaces.
## 4 Construction of a Timbre Perception Feature Model
### Experiment B: A Timbre Evaluation Experiment Based on the Method of Successive Categories  
A subjective evaluation experiment was conducted in a standard listening room with a reverberation time of 0.3 s, which conforms to listening standards [59]. A total of 34 subjects (16 males) with a professional music background participated in the experiment. Their ages ranged from 18 to 35 and they had no history of hearing loss. The experimental subjective evaluation process was conducted as follows. Material fragments were played, and the subjects judged the psychological scale of the piece for each timbre perception feature (evaluation term) in sequence, scoring it on a nine-level scale. All experimental materials were played prior to the formal experiment to familiarize subjects with the samples in advance. This was done to assist each subject in mastering the evaluation criteria and scoring scale, reducing the discretization of evaluation data for the same sample. Each piece was played twice with an interval of 5 s and a sample length of 6-10 s. Each evaluation term was tested for 10 min, with a 15-min break every half hour. The validity and reliability of data from these 34 samples were analyzed to calculate a correlation coefficient between the scores for each subject. The Euclidean distance between the evaluation terms was calculated using cluster analysis to identify the two subjects with the largest difference in each group. Some subjects may not have had a sufficient understanding of the purpose of the experiment. Data from these subjects were excluded and not used for subsequent timbre perception feature modeling. The method of successive categories was used to conduct a statistical analysis of the experimental data [67]. The theoretical basis for this approach assumes the psychological scale to be a random variable, subject to a normal distribution. The boundary of each category was not a predetermined value, but a random variable identified from the experimental data. The Thurstone scale was then used to process the data and produce a psychological scale for all timbre materials and each perception feature for modeling purposes. Figure 4 shows the resulting scale for 72 musical instruments in 5 timbre evaluation dimensions. In each image, the dotted line represents the average value of each instrument in the corresponding dimension. It is evident from Figure 4 that the distribution of timbre values for Chinese instruments differed significantly from Western instruments. For example, raspy/mellow and hoarse/consonant exhibited drastically different scales. This suggested the timbre database containing Chinese instruments had a richer variety of timbre types than a conventional Western instrument database. In addition, the distribution of timbre samples in the five timbre evaluation scale pairs was relatively balanced. This suggested the proposed evaluation terminology was representative of multiple timbre types and could better
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Performance Metrics for Classification
#### 3.3.3 F-Score  
To evaluate a model's performance, it needs a metric that combines precision and recall. F-score is a generally accepted way, though it is far away from perfect. It is calculated from the precision and recall of the test, where the precision is the number of True Positive results divided by the number of all positive results, including those not identified correctly, and the recall is the number of True Positive results divided by the number of all samples that should have been identified as positive. Precision is also known as a positive predictive value, and recall is also known as sensitivity in diagnostic binary classification. Among F-score methods, the most popular one is the \(F_{1}\) score, which is the harmonic mean of precision and recall:  
\[F_{1}=2\frac{Precision\cdot Recall}{Precision+Recall} \tag{7}\]  
Expanding upon the basic \(F_{1}\) score, the \(F_{\beta}\) allows for varying the emphasis on precision versus recall, accommodating the specific needs of different testing environments. Here, \(\beta\) is a parameter that sets the weight of recall relative to precision. A \(\beta\) value greater than 1 gives more weight to recall, making it more crucial in scenarios where missing a positive instance carries greater consequences than FPs. Conversely, a \(\beta\) less than 1 emphasizes precision, suitable for cases where FPs are more costly or detrimental than FNs. This flexibility makes the \(F_{\beta}\) score adaptable to diverse applications, providing a tailored metric that can reflect the unique cost dynamics associated with particular diagnostic tests or other classification tasks. The highest value of an \(F_{\beta}\) score is 1.0, indicating perfect precision and recall, while the lowest is 0, occurring when either the precision or the recall is zero, signifying a complete failure in either identifying TPs or excluding FPs effectively [26]:  
\[F_{\beta}=(1+\beta^{2})\cdot\frac{Precision\cdot Recall}{(\beta^{2}\cdot Precision )+Recall} \tag{8}\]
## 5 Conclusions  
In summary, our study contributes to the growing body of evidence exploring the prognostic value of inflammatory indices in patients undergoing robot-assisted thoracic surgery for lung cancer. Our findings suggest that inflammatory indices derived from blood, such as PLR, NLR, and LMR, do not appear to be reliable predictors of postoperative complications, air leak, or mortality in patients undergoing robot-assisted anatomical lung resection for lung cancer. These results emphasize the need for further research with larger sample sizes, longer follow-up periods, and more rigorous study designs to better understand the prognostic value of these indices in clinical practice. Additionally, given the inconsistent findings across different studies and the lack of standardized cut-off values, future research should focus on identifying robust prognostic indices and establishing clear criteria for their clinical utility.  
Conceptualization, R.R., N.I.Q. and M.B.; methodology, R.R. and L.G.; formal analysis, M.B. and A.U.; investigation, L.G., X.M. and I.B. ([PERSON]); writing--original draft preparation, N.I.Q.; writing--review and editing, A.G., L.M., R.N. and R.G.; visualization, A.U. and I.B. ([PERSON]); supervision, R.R. and M.B.; project administration, N.I.Q., M.B. and R.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee \"Comite de Etica de la Investigacion--HCB\" with the number: HCB/2023/0744, 27 July 2023.  
Patient consent was waived because this is a retrospective study analyzing laboratory parameters. All data were anonymized prior to analysis, ensuring patient confidentiality and compliance with ethical standards.  
Data presented in this study are available upon request from the corresponding author. Data are not available to the public due to the internal policies of the institution and the data protection law 3/2018.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
MDPI Multidisciplinary Digital Publishing Institute  
NLR Neutrophil-Lymphocyte Ratio  
PLR Platelet-Lymphocyte Ratio  
LMR Lymphocyte-Monocyte Ratio  
SII Systemic Immune-Inflammatory Index  
RATS Robot-Assisted Thoracic Surgery  
FEV1 Forced Expiratory Volume in 1 s  
DLCO Diffusing Capacity of the Lung for Carbon Monoxide  
BMI Body Mass Index  
COPD Chronic Obstructive Pulmonary Disease  
TNM Tumor-Node-Metastasis (8 th edition)  
PAL Persistent Air Leak  
ARDS Adult Respiratory Distress Syndrome
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 4 Discussion  
### Temporal Analysis of the Post-Fire Vegetation Trajectory  
In this study, the EVI was used to measure the vegetation growth and the LST and LSWI were used to monitor the post-fire hydrothermal conditions. Therefore, analysis of post-fire forest-regeneration trajectories with the EVI, LST and LSWI was expected to reveal different characteristics of post-fire forest-successful processes and patterns. Figure 4 confirms the utility of the MODIS (EVI, LST and LSWI) data for monitoring fire-induced vegetation changes because the data yielded different results at different burn severities (see Figures 6-8). The magnitude of the changes in the LSWI and LST was significant and closely related to the burn severity, as reflected in the EVI changes (see Figures 6-8). The annual time-series values of the indices, however, were also subject to seasonal variations. The changes in the MODIS index were minor in winter because of weak vegetation activity, which produced imperceptible changes from fire disturbances. The average EVI value in the focal pixels with high burn severity was 0.21 less than that in the control pixels. The results were very similar, changing from 0.15 to 0.35 [(9)]. The mean of the focal pixels exceeded the mean of the control pixels by 1.1-6.9 \({}^{\circ}\)C, similar to the 2-8 \({}^{\circ}\)C immediate post-fire temperature increase during the daytime that has been reported by other studies [(29; 47; 48; 49; 50)]. In this study, the EVI continued to decrease and the LST continued to increase during the second month after the fire (Figure 4); however, no clear recovery trend was evident during the next two years (Figure 5). This result differed from previous research results, where the EVI and LST immediately fell after recovery. This difference might have been related  
Figure 7: Comparison of the correlation between the MODIS index products (EVI, LSWI and LST) and C-flux data (GPP and NPP) under difference burn severities: (**a–c**) high burn severity; (**d–f**) moderate burn severity; and (**g-i**) light burn severity. The correlation between EVI and GPP was slightly higher than those of the other parameters.  
to the location of the fire region, specifically, a boreal forest in a northern region, whose cold climate, strong seasonality and short growing season result in slower recovery times. The greatest differences in the EVI, LST and LSWI were observed in August and September, which were not the fire month or the next month. The results showed that the seasonality aggravated the effects of fire interference. Additionally, the changes in the LST depend on the burn severity and meteorological conditions, which limits the ability of the LST to characterize vegetation changes after fire disturbances [23; 24; 25].
# Open-Bite Correction Using a Mid-Palatal TAD with an Intrusion TPA: Anteroposterior Position of TAD
## 4 Discussion
### In What Cases Is Total Intrusion Required?  
_Case 4_ (total intrusion, Figures 10, 11, 13 and 14) shows an example explaining why total intrusion is necessary. This patient wanted to correct her gummy smile (Figure 13A) and open bite (Figure 13C). After treatment, both resolved well (Figure 13B,D).  
Before treatment, the intraoral photo showed a maxillary compensating curve (Figure 13C). After leveling, the open bite was resolved due to the extrusion of the maxillary anterior teeth (Figure 14A). However, the problem was that the gummy smile still needed to be treated. Furthermore, it was impossible to decrease the lower anterior facial height using a counterclockwise rotation of the mandible as the upper and lower anterior teeth began to touch. Maximly posterior intrusion is not a solution in this situation. Total intrusion, including the anterior teeth, was required to resolve the gummy smile and reduce the lower facial height.  
A mid-palatal TAD was placed anterior to the maxillary first molar to perform the total intrusion of maxillary dentition (Figure 14A). The TPA hook was directed toward the mesial. Using this mechanic, some intrusion of the maxillary incisors also occurred (Figure 14B), and a counterclockwise rotation of the mandible was possible (Figure 14C).  
The effects of the anteroposterior positioning of the mid-palatal TAD can be different depending on the characteristics of each patient and the tooth extraction. Systematic and objective studies, including many samples, are required in the future.
## AI-Enabled 6G Network Challenges  
### Channel Estimation  
Channel estimation is an essential receiver function in 6G networks. Many of the primary 6G technologies under consideration pose novel channel-estimation problems that are intractable by means of the techniques already in use. For instance, due to the extreme noise, THz communication has very lengthy channel responses and low SNR.  
Figure 4: Virtual reality.  
Furthermore, even slight environmental modifications might result in large inaccuracies in channel estimates.  
The emergence of beam-split phenomena in large MIMO systems operating in the THz region can result in significant reductions in array gain. As a result of this phenomenon, route components are divided into various spatial directions across distinct subcarrier frequencies. Because they cannot send, receive, or analyze pilot signals, passive Reflective Intelligent Surface (RIS) components complicate channel estimates even more. As a result, the development of channel-estimate approaches capable of dealing with this complexity becomes critical. These approaches must strike a compromise between great spectrum efficiency and computational simplicity, while maintaining estimate accuracy. The following sections will go into the current state of channel estimate in the context of 6G networks [30].