esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Overall CR and Stability Comparison  
In this section, overall CR and stability performance comparison of all widely used algorithms and related researches were conducted. Results are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Researches** & **CR** & **Used Data** & **CR Average \(\pm\) SD** \\ \hline \multirow{4}{*}{UHHT} & 3.69 & \multicolumn{1}{c}{Cholerae} & \\  & 3.74 & \multicolumn{1}{c}{Abscensus} & \\  & 3.66 & \multicolumn{1}{c}{S. cerevisiae} & \\  & 4.8 & \multicolumn{1}{c}{Chr22} & \\ \hline \multirow{4}{*}{MUHTL} & 3.85 & \multicolumn{1}{c}{Cholerae} & \\  & 3.91 & \multicolumn{1}{c}{Abscensus} & \\  & 3.83 & \multicolumn{1}{c}{S. cerevisiae} & 4.09 \(\pm\) 0.55 \\  & 3.8 & \multicolumn{1}{c}{N. crasa} & \\  & 5.07 & \multicolumn{1}{c}{Chr22} & \\ \hline \multirow{4}{*}{Qaip} & 4.78 & \multicolumn{1}{c}{SR2961669} & \\  & 4.98 & \multicolumn{1}{c}{SR2943468} & \\  & 3.0 & \multicolumn{1}{c}{SR3211986} & \\  & 6.06 & \multicolumn{1}{c}{SR3109692} & \\  & 13.89 & \multicolumn{1}{c}{ERR385912} & 5.82 \(\pm\) 3.16 \\  & 5.65 & \multicolumn{1}{c}{ERR386131} & \\  & 3.98 & \multicolumn{1}{c}{SR3043509} & \\  & 5.0 & \multicolumn{1}{c}{ERR174310} & \\  & 5.0 & \multicolumn{1}{c}{ERR391447} & \\ \hline \multirow{4}{*}{CRAM} & 4.57 & \multicolumn{1}{c}{SR2961669} & \\  & 3.79 & \multicolumn{1}{c}{SR294468} & \\  & 2.95 & \multicolumn{1}{c}{SR294468} & \\  & 2.99 & \multicolumn{1}{c}{SR3211986} & \\  & 7.87 & \multicolumn{1}{c}{SR3109692} & 6.59 \(\pm\) 3.5 \\  & 15.15 & \multicolumn{1}{c}{ERR386131} & \\  & 6.02 & \multicolumn{1}{c}{ERR3861341} & \\  & 4.08 & \multicolumn{1}{c}{SR310943509} & \\  & 5.1 & \multicolumn{1}{c}{ERR174310} & \\  & 6.49 & \multicolumn{1}{c}{ERR194147} & \\ \hline \multirow{4}{*}{UHITL} & 3.84 & \multicolumn{1}{c}{Chr26} & \\  & 3.89 & \multicolumn{1}{c}{Abscensus} & \\  & 3.83 & \multicolumn{1}{c}{S. cerevisiae} & 4.08 \(\pm\) 0.55 \\  & 3.8 & \multicolumn{1}{c}{N. crasa} & \\  & 5.06 & \multicolumn{1}{c}{Chr22} & \\ \hline \multirow{4}{*}{FQZCOMP} & 4.69 & \multicolumn{1}{c}{SRR000117} & \\  & 7.04 & \multicolumn{1}{c}{SRR000215,1} & 5.95 \(\pm\) 1.31 \\  & 4.6 & \multicolumn{1}{c}{SRR027520,1} & 5.95 \(\pm\) 1.31 \\  & 7.46 & \multicolumn{1}{c}{SRR05390,1} & \\ \hline \multirow{4}{*}{BAQALC} & 10.98 & \multicolumn{1}{c}{SR3253943} & \\  & 11.24 & \multicolumn{1}{c}{SR3258042} & \\  & 10.0 & \multicolumn{1}{c}{ER16040509} & \\  & 10.25 & \multicolumn{1}{c}{ER16040596} & \\  & 14.7 & \multicolumn{1}{c}{SR3277681} & 12.51 \(\pm\) 2.86 \\  & 13.79 & \multicolumn{1}{c}{SR327681} & \\  & 10.06 & \multicolumn{1}{c}{SR3293589} & \\  & 10.13 & \multicolumn{1}{c}{SR3293588} & \\  & 17.01 & \multicolumn{1}{c}{SR3288195} & \\  & 16.98 & \multicolumn{1}{c}{SR3288194} & \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._Statistical results showed that Pearson's correlation coefficient between CR and stability was 0.62, with a \(p\)-value of 0.018 (statistically significant under 95% confidence interval). In other words, the higher the compression ratio, the lower the stability of the algorithms.
# Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer
## 4 Discussion
This high-cost digital pathology system does not exhibit any benefits, hindering most institutions from establishing this system [33]. Therefore, definite clinical values, such as reduced diagnostic time and improved quality and efficiency, should be achieved in DL implementation to incentivize DL adoption in clinical applications. Second, safety should be guaranteed in the clinical application of the DL model. The robustness and generalizability of the trained DL model are critical for clinical application. Centralized digitalized data archives, which store large-scale biomedical images from various institutions, can be used to overcome this obstacle. The stored digital images can be used for model validation and bias optimization, allowing the algorithms to achieve generalized performance. Digital pathology guidelines for DL implementation and quality assessment have been established [34; 35; 36; 37]. However, the digital pathology system is typically being established in large-scale university hospitals because the capital outlay for the system cannot be borne by every hospital. [PERSON] et al. investigated the accuracy of a deep-learning-based algorithm for datasets from various institutions digitized by different scanner systems. Although a model with high overall accuracy of >98% was used, substantial losses occurred in accuracy because of dependence on HE-staining quality, brightness, and contrast [38]. Therefore, national planning and systemic support for developing large-scale centralized biomedical image archives or databases is crucial for future clinical applications. This study had some limitations. First, the dataset was limited in size. We used 77 WSIs to train the semantic segmentation network for extracting tumors and nerves. However, the proposed model exhibited performance comparable to that of a prior study, where 80k biopsy cores were used to achieve an AUC of 0.98 [21]. Considering the dataset size used in this study and the performance achieved, extremely large-scale data may not be required for the convergence of the proposed pipeline. Second, the results obtained were not externally validated. External validation with additional datasets can improve interpretability and generalizability. Finally, the classifier using distance calculation exhibits a drawback. Since the PNI classifier is a rule-based classifier, distinguishing whether tumor cells actually infiltrated a nerve sheath or are just adjacent to a nerve bundle is not possible. Despite these limitations, the PNI classifier can play a significant role as a screening tool. Therefore, as a trial of PNI detection, this study provides researchers with new possibilities for the development and improvement of data-driven algorithms for PNI detection. Furthermore, by enabling the detection of accurate PNI status, this study can improve the clinical decisions made for individual patients, positively impacting their prognosis.
# Improvement and Performance Evaluation When Implementing a Distance-Based Registration
## 7 Conclusions and Discussion  
This study investigated distance-based registration (DBR), which is known to have a good performance but is not adopted in most mobile communication networks mainly because of difficulty of implementation.  
We proposed a DBR with an LA increasing on a cell-by-cell basis, rather than a conventional LA increasing on a ring-by-ring basis, to obtain optimal performance for DBR. We also presented a semi-Markov process model to analyze the optimal number of cells for an LA in DBR with an LA increasing on a cell-by-cell basis. In this study, to construct an LA increasing on cell-by-cell basis, we did not simply limit the center of the LA to the center of the cell. Instead, we tried to have an LA consisting of a number of cells by newly defining the center of the LA to be possible at any point in the LA.  
Numerical results for various system environments using the presented semi-Markov process model showed that the optimal \(N\) with a minimal total signaling cost exists between four and seven in most cases, thus resulting in a smaller total signal cost than that in a ring-by-ring DBR environment (\(D\) = 2, N = 7) reported in previous studies.  
Therefore, when a DBR is adopted, the LA should be constructed to enable an LA to increase on a cell-by-cell basis as shown in this study. Various numerical results showed that the smaller the registration cost compared to the paging cost (i.e., the larger the CMR value), the smaller the size of the LA with a minimal total signaling cost. Results of this study can be used for implementing DBR with excellent performance in real cellular networks.  
Of note, this study presents only a conceptual idea of constructing an LA that increases on a cell-by-cell basis (not a ring-by-ring basis) in DBR, and it is not easy to apply the proposed method directly to the design of contemporary multi-RAT (radio access technologies) networks. Further studies will be performed to apply to contemporary multi-RAT networks by taking into account the hierarchical cell layouts, universe frequency bands, complex network procedures and so on.  
This research was supported by the Research Base Construction Fund Support Program funded by Jeonbuk National University in 2021. This research was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2016R1D1A1B01014615).  
The authors declare no conflict of interest.  
Figure 17: Performance comparison results of DBR and ZBR (CMR = 1, U = 4).
# Innovative Hydrogeophysical Approaches as Aids to Assess Hungarian Groundwater Bodies
## 2 Materials and Methods
### Study Sites  
The location of the Hungarian study areas is illustrated in Figure 3a. Geoelectric measurements, which are presented in Section 3.1.1, were carried out in Sopron, West Hungary, over a series of faults, thus a step-like structure in limestone (Figure 3b). The ERT profile is perpendicular to the N-S-directed Kohida-fault zone, which is the eastern boundary of the Sopron-Kismarton Basin (NW Hungary). The guessic basement, which outcrops in a few hundred meters away, is overlaid by clay. The surface dips eastwards, most likely following the dipping of the bedrock.  
The measurements, whose results are presented in Section 3.1.2, were carried out at the edge of a large plateau at the village of Kadarta, which lies close to the city of Veszprem, North West Hungary [41] (Figure 3c). The plateau is made up of middle-Triassic dolomite, which is characterized by N-S oriented strike-slip and normal faults [42]. The dolomite surface is overlain by thin (0.1-0.2 m) poor quality soils in this area. The aim of the study was to detect and characterize fracture zones and fractures since they are profoundly significant in hydrological respect because of their high hydraulic conductivity and water-bearing capacity.  
Section 3.1.3 demonstrates the results obtained in Haridwar District, Uttarakhand State, India, in a sedimentary environment consisting of sand, sandy clays, clay and gravel. The investigated site is located at the bank of Solani river (latitude: 29.991266, longitude: 77.77679) in Khubaripur village, which is located about 18 km N38.170 W of Roorkee, Uttarakhand, India (Figure 3d). A well logging approach proposed in this paper is tested in Baktalorathaza-1 well drilled in Szabolcs-Szatmar-Bereg County, in northeast Hungary (Figure 3e). The location of the drilling is 2.1 km from Baktalorathaza village in the direction of the south-south-west (190 degrees). Maximal depth reached by measurements: 1197.70 m, where the bottom hole temperature was 53 Centigrade. The thickness map of Miocene sediments was established by [43]. The borehole did not show a good prospect of hydrocarbon exploration, but it still serves as productive thermal water well. Anunconsolidated shaly sandy sequence of high storage capacity is investigated to conclude on the lithology and petrophysical/hydraulic properties of the groundwater formations.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 5 Experiments and Results Analysis  
### Dataset and Experimental Environment  
In order to verify the efficacy of our method, in the training stage, we selected 80,000 images from the MS-COCO dataset [32] as the first-stage training set and utilized the KAIST dataset [33] as the second-stage training set. In the first-stage training, the balancing parameter \(\lambda\) between the similarity loss and the gradient loss in the loss function was set to 1, and \(\mu\) was set to 100. In the second-stage training, \(\beta\) was set to 500, \(\omega_{\text{ir}}\) was set to 5, and \(\omega_{\text{vi}}\) was set to 3. The model training parameters were set as follows: epochs = 20; batch size = 4.  
During the testing phase, to verify the effectiveness of our method, images were selected from the publicly available infrared and visible-light dataset TNO [34] for experimentation. Six sets of images were chosen for comparative analysis. These images, rich in detail and texture, are suitable for assessing the quality of image fusion. Having been widely used in previous studies, they provide a benchmark for comparing our results with existing methods.  
Our experiments were conducted on a system running Windows 11 with hardware specifications that include an Intel(R) Core(TM) i5-12400F 2.50 GHz processor. The modelwas run on an NVIDIA GeForce RTX 3060 GPU. The software environment for the experiments included Python 3.8.3, PyTorch 1.10.1, CUDA 11.3, and the PyCharm 2020.1 IDE.
## 2 Related Work
### Mass and Microcalcification as Benign or Malignant  
In the task of classifying masses and microcalcifications as benign or malignant, two approaches can be employed: either classifying the ROIs corresponding to segmented masses and microcalcifications or performing classification on the entire mammogram image to determine its benign or malignant nature.  
[PERSON] et al. [17] enhanced the DenseNet architecture for classifying mammogram images, achieving a 94.55% accuracy rate. [PERSON] et al. [18] proposed a method using block-based discrete-wavelet packet transform and principal component analysis, enhanced with a kernel extreme learning machine classifier, achieving accuracy rates above 99%. [PERSON] et al. [30] developed an automated framework for breast cancer classification from mammogram images, employing a novel image enhancement technique and the EfficientNet-b0 model fine-tuned via deep transfer learning. The framework, which included advanced feature extraction and optimization using the Equilibrium-Jaya controlled Regula Falsi algorithm, was tested on the CBIS-DDSM and INBreast datasets, achieving notable accuracies of 95.4% and 99.7%, respectively. [PERSON] et al. [31] combined deep learning with metaheuristic techniques to classify mammography images, achieving up to 97.36% accuracy. [PERSON] et al. [32] utilized ensemble learning techniques with a combination of multiple deep-learning models, achieving an accuracy of approximately 82.4%.
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 2 Materials and Methods  
### Signal Model  
The schematic of the passive system is shown in Figure 1.  
Figure 1: Passive radar system schematic.  
After the signal is received by the reference antenna, it is converted to the digital base band through analog to digital conversion, digital down conversion, filtering extraction, and other operations. Assume that the reference signal only contains reference signal and noise, it can be described as follows:  
\[s_{ref}[n]=A_{ref}d[n]+z_{ref}[n]\quad n=1,\ldots,N \tag{1}\]  
where \(d[n]\) indicates the \(n\)-th sampling of the direct wave signal of the transmitting station; \(A_{ref}\) represents the complex amplitude of the direct wave signal received by the reference antenna; \(z_{ref}\) represents the noise in the reference antenna; and \(N\) represents the length of the received signal.  
Similarly, after the signal received by the echo antenna is converted into the digital base band, it can be described as follows:  
\[x_{sur}[n]=\sum_{i=1}^{N_{i}}A_{i}d[n-\lambda_{i}]+\sum_{m=1}^{N_{M}}a_{m}d[n -\lambda_{m}]e^{j2\pi f_{dm}\frac{n}{f_{s}}}+z_{sur}[n]\ n=1,,N \tag{2}\]  
where \(N_{i}\), \(N_{M}\), and \(N\), respectively, represent the number of clutter multi-paths, the number of targets, and the length of the signal sequence. \(A_{i}\) and \(\lambda_{i}\) are the complex amplitude of the \(i\)-th interference and the delay unit relative to the direct wave signal of the base station. The interference includes direct wave, multi-path, and other types of clutter, where \(\lambda_{1}=0\) indicates that the first interference is a direct wave from the base station. \(a_{m}\), \(\lambda_{m}\), and \(f_{dm}\), respectively, represent the complex amplitude, time delay, and Doppler frequency shift of the \(m\)-th target echo. \(z_{sur}[n]\) represents the noise in the received signal of the echo antenna, and \(f_{s}\) represents the sampling rate.  
Because the direct wave and multi-path interference is much stronger than the target echo signal, interference cancellation is needed before target detection. In this paper, the ECA algorithm is used to cancel the interference of the echo signal [5]. The signal produced after interference cancellation can be expressed as follows:  
\[s_{sur}[n]=\sum_{m=1}^{N_{M}}a_{m}d[n-\lambda_{m}]e^{j2\pi f_{dm}\frac{n}{f_{s }}}+z_{sur}[n] \tag{3}\]  
where the Doppler domain interval is \(1/f_{s}\).
## 3 Characteristics of Magnetic Signal
(**c**) Continuous Wavelet Transform (CWT) in the time-frequency domain, displaying the time-frequency distribution of the magnetic signal during the survey period. The color scale represents signal intensity in (nT\({}^{2}\)) units, with red indicating stronger signals and blue representing weaker signals. The white dashed line in the CWT image represents the confidence interval of the analysis. inside the Cone of Influence (COI; indicated by the white dashed line in Figure 3c) in the frequency range below 0.003 Hz. Similar low-frequency characteristics were present in the Sensor 2 data. However, Sensor 2 additionally displayed periodic high-power features in the frequency range between approximately 0.005 Hz and 0.1 Hz. Consequently, the overall higher power levels observed in the Sensor 2 data indicate greater exposure to EMI from the vessel's systems compared to the Sensor 1 location. Therefore, data from Sensor 1, having relatively less noise impact, were used for the subsequent magnetic correction analysis. Marine magnetic surveys primarily measure the magnetic field resulting from the sum of the geomagnetic field originating from the outer core and the magnetic anomaly vectors within the crust. These magnetic field signals typically exist in very low frequency domains (<0.1 Hz) and do not exhibit rapid changes over short periods or distances under natural conditions. Therefore, based on the CWT analysis results, this study classified frequency components below 0.01 Hz as valid geomagnetic signals and those above this threshold as noise for subsequent analysis.
## 2 Materials and Methods
### Data Analysis Procedure
For determining the principal components (PCs), starting from data matrices, we calculated the eigenvalues and the corresponding eigenvectors of correlation matrix (association matrix based on Pearson coefficient). The eigenvectors represent the mutually orthogonal linear combinations of the original descriptors, and each of them can be considered a new independent variable. In order to investigate the nature of the new variables, or PC, we analyzed the percentage loadings matrix in which each element represents the percentage weight of the original descriptor in the PC. For highlighting differences in the correlation structures among pollutants and meteorological parameters, we used Hot Days and Heat Waves identification procedure for selecting specific subsets of sampling days. We introduced two external variables with double modalities: (HD/noHD) and (HW/noHW). On the base of value assumed by bimodal variables, the original data matrices, including all the summer days \(M_{SS}^{y}=[m\times n]\) (in which \(SS\) indicates the sampling site, \(y\) the year, \(m\) the number of sampling days \(m\leq 92\), and \(n\) the number of descriptors), may be divided in the following submatrices:  
1. HD-Submatrix \((M_{HD})_{SS}^{y}=[m_{HD}\times n]\) and noHD-Submatrix \((M_{noHD})_{SS}^{y}=[m_{noHD}\times n]\), for which \((M_{HD})_{SS}^{y}\cup(M_{noHD})_{SS}^{y}=M_{SS}^{y}\) with \(m_{HD}\leq m\). Basically, we divided the original matrix into two submatrices one containing only HD days and the other containing only non-HD days; and
2. HW-Submatrix \((M_{HW})_{SS}^{y}=[m_{HW}\times n]\) and noHW-Submatrix \((M_{noHW})_{SS}^{y}=[m_{noHW}\times n]\), for which \((M_{HW})_{SS}^{y}\cup(M_{noHW})_{SS}^{y}=M_{SS}^{y}\) with \(m_{HW}\leq m_{HD}\leq m\). Additionally, in this case, we divided the original matrix in two parts: the first that contains only the days that fall within a defined interval HW and another that contains only the days that do not belong to intervals of HW. This procedure may be repeated also introducing the degree of severity for HD and HW, reducing the number of sampling days included in the submatrices, and increasing the level of risk. For all these different submatrices, PCA is applied in order to characterize the different correlation structures.
## 3 Results  
### Fermentation Curve  
In the case of fermented samples with the yogurt culture, a monoculture of _L. plantarum_ 299v, and their mixture (Figure 1a), all samples had an initial pH of 6.5-7.0. Within the first 2 h, there was a rapid decrease in pH, suggesting intensive lactic acid production by the bacteria. After 4 h of fermentation, most samples' pH stabilized at 4.0-4.5. It is worth noting that the milk and dairy-oat samples showed a more remarkable ability to lower pH than oat samples, suggesting milk's beneficial properties for fermentation. The lowest pH values were observed in samples supplemented with _L. plantarum_ 299v, indicating the strong fermentative activity of these bacteria in milk. The research hypothesis was confirmed, as the choice of lactic acid bacteria cultures significantly influenced various quality characteristics of the beverages, including pH reduction, textural properties, and bacterial viability, depending on the base material and storage conditions. Nevertheless, _L. plantarum_ 299v exhibited strong fermentative activity, which may positively impact the quality of fermented beverages.  
Figure 1: Changes in the pH of beverages inoculated with a yogurt culture, a monoculture of lactobacilli, and their mixture at 37 \({}^{\circ}\)C: (**a**) _L. plantarum_ 299v; (**b**) _L. acidophilus_ La-5. Different superscripts show a significant difference at \(\alpha<0.05\).  
In the case of samples fermented with the yogurt culture, a monoculture of _L. acidophilus_ La-5, and their mixture (Figure 1b), at the beginning of fermentation, all samples also had a pH within the range of 6.5-7.0. After 4 h, the pH value in most samples stabilizes at a level of 4.0-4.5, suggesting the end of the main fermentation phase, especially in samples containing milk. The lowest pH values were achieved in samples where _L. acidophilus_ La-5 was present, suggesting its high acidifying ability. These results also confirmed the research hypothesis; that is, the use of _L. acidophilus_ La-5 significantly impacted the pH of fermented beverages, which is crucial for their quality and shelf life. In this case, the milk and dairy-oat beverages showed more intense fermentation than the oat beverage, suggesting that milk favors the activity of lactic acid bacteria. _L. acidophilus_ La-5 demonstrated high fermentative activity, suggesting that it could be an effective starter culture to improve the quality of fermented beverages.