esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The histological examination identified 22 benign breast lesions (23/92, 25%), 7 lesions classified as B3 (7/92, 7.6%), and 62 malignancies (62/92, 67.3%). Benign lesions included fibroadenomas (13), fibrocystic mastopathy (6), adenosis (2), fibrotic lesions (1), and inflammatory lesions (1). B3 nodules included atypical (1) and typical (1) intraductal papilloma, sclerosing adenosis (3), and atypical ductal hyperplasia (2). The malignant tumors included infiltrating ductal carcinomas (30), infiltrating ductal carcinomas with foci of in situ ductal carcinoma (18), ductal carcinoma in situ (3), invasive lobular carcinomas (7), infiltrating mucinous carcinoma of mixed type with foci of ductal carcinoma in situ (3), and pleomorphic sarcoma (1).  
In the analysis of BI-RADS parameters, [PERSON]'s correlation coefficient showed a moderate agreement between the \"SmartBreast\" and the operator in the evaluation of the dimensions (0.90, \(p\) = 0.07), as also seen in the Bland-Altman agreement in Figure 3.  
Calculating [PERSON]'s k for the other BI-RADS parameters, the following criteria were used: if k assumed values between 0 and 0.4, the agreement was considered poor; if k assumed values between 0.4 and 0.6, the agreement was moderate; if k values were between 0.6 and 0.8, the agreement was good; if k values were between 0.8 and 1, the agreement was considered excellent.  
In our study, the agreement between the software and the investigator was excellent in the identification of the posterior features of breast lesions ([PERSON]'s k = 0.94); good in the evaluation of the shape and vascular signal at color Doppler ([PERSON]'s k, respectively, of 0.6 and 0.65); and poor in the analysis of orientation, margins, and echo-pattern ([PERSON]'s k, respectively, of 0.28, 0.33, and 0.48). In the identification of microcalcifications, [PERSON]'s k was 0.36, with the area under the ROC curve equal to 0.64.  
The analysis of the agreement in the definition of the final BI-RADS score between the software and the human investigator showed a [PERSON]'s k of 0.35 (poor), 0.28 between the software and the histological results (poor), and 0.71 between the operator and the histological results (good).  
Elastography analysis in the discrimination between malignant and benign lesions showed an AUC of 0.84 for the strain ratio, 0.64 for the 2D SWE, and 0.61 for the pSWE, with the cut-off values respectively equal to 1.69 (sensitivity: 0.81, specificity: 0.86), 25 Kpa (sensitivity: 0.73, specificity: 0.55), and 64.34 kPa (sensitivity: 0.71, specificity: 0.50). The results are shown in Table 1.  
Figure 3: [PERSON]–Altman correlation. Breast lesion diameters measured by “SmartBreast” and by radiologist. The B-A plot represents every difference between the measurement by the software and the measurement by the operator (Y-axis) against the average of the measurement (X-axis). Most of the points, except three, lie between the two confidence limits (lower limit: mean difference-2 SD; upper limit: mean difference + 2 SD), on both the lower and higher average values (good agreement).
# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 4 Clustering Applications of the HSA
### Clustering Applications for Gene Expression  
The clustering design is a diverse procedure for data mining. K-harmonic clustering is another version of the K-means technique; however, it falls into local optima. The HSA can address this issue, as it is a random optimization system. [PERSON] et al. [79] proposed a hybrid clustering algorithm known as HSKHM. The proposed set of rules is used along with the basic HSA and the K-means to maximize their advantages. The results on four gene expression datasets illustrated that the proposed HSKHM was superior to KHM and fundamental HSA in most cases. The HSKHM algorithm increased the convergence of the primary HSA and avoided the local optima.  
The PSO algorithm is used for many applications; however, it fails to obtain the inputs for producing clusters and thus decreases the clustering precision. HSA is a conventional optimization algorithm that avoids divergence and can find the near-ideal solution by exploring the optimal solution region. [PERSON] and [PERSON] [85] presented a novel HSA called HSPSO, which is used as a clustering algorithm for gene expression datasets for investigating brain tumors, Leukemia, colon cancer, lung cancer, etc. The obtained results showed that the proposed algorithm createdclusters with better compactness and precision than the K-means, PSO, and fuzzy PSO clustering algorithms. [PERSON] et al., in [87], claimed that the Self Organizing Map (SOM) dimension for the drawing schooling technique is not an appropriate technique, as it may not make specific a map with a shadow of quantization and topographic mistakes. They presented a SOM-concord Hybrid algorithm that calculated the maximum size of the SOM network with minimum topographic and quantization errors. The results indicated that the proposed method achieved better results.  
[PERSON] et al., in [58], introduced a new clustering method based on HSA for clustering gene expressions called EHS. In this method, the local and global search processes are combined by decreasing the pitch rates and returning a part of devices with new tools. Experiments were performed with optimization test functions and gene expression datasets. The results revealed that the proposed EHS exhibits considerably better performance than the basic HSA for both benchmarks. Moreover, this study defined the biological validation of groups with gene ontology considering the function, process, and component.  
[PERSON] and [PERSON] [96] proposed a new meta-heuristic clustering method for separating gene expression. A variance-based HSA was introduced as an underlying optimization technique. The performance of the proposed method was compared with twelve standard clustering methods on six benchmark datasets and matched. The results confirmed that the proposed method performed considerably better than other similar methods. The statistical significance tests also illustrated the superiority of the proposed method.
# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 4 Proposed System Model
### Fourier-Based Peak Detection for Feature Extraction  
Digital signal processing (DSP) has been a reliable condition monitoring paradigm in a variety of applications for decades. Particularly for induction motors, current signatures are often stationary with different periodic components that are affected by changing operating conditions. Most signals are composed of complex synthesis of sine and cosine functions under relaxable assumptions, which provides a reliable avenue for FFT to flourish.  
Different parameters can be extracted from the spectra to make the necessary discriminative inferences for diagnosis. Often, there is a change in magnitude of the spectral components as the operating conditions of the induction motor change, and this presents an opportunity to exploit spectral peaks and their coordinate frequency values as repre  
Figure 1: Proposed diagnostic model.  
sentative features. Given a time-recorded signal (one-dimensional digitized current signal) \(f(x)=\{x_{1},x_{2},\ldots,x_{m}\}\), the Fourier transform of \(f(x)\) is traditionally denoted \(F(k)\) and is computed using Equation (1):  
\[F(k)=\int_{-\infty}^{\infty}f(x)e^{-j\left(\frac{2\pi mk}{N}\right)}dx,0\leq m\leq N \tag{1}\]  
where \(f(x)\) is the input signal, \(k\) is the length of the transform, and \(F(k)\) is the corresponding frequency-domain output of the signal.  
PSD exaggerates the impact of high-energy components while reducing it for lower-energy components by computing the energy densities of the constituent frequencies. Mathematically, PSD generates a spectrum by squaring the magnitude of the FFT outputs from Equation (1), and is obtained using Equation (2):  
\[PS(k)=|F(k)|^{2} \tag{2}\]  
where \(PS(K)\) is the PSD-domain output from the FFT of the signal.  
High autocorrelation (a maximum of 1) implies high similarity between the signal and its delayed component, while the reverse is the case if autocorrelation is close to zero. ACF can be computed via a convolution theorem using Equation (3):  
\[y(\tau)=\sum_{i=0}^{i=N-1}f(x_{i})f(x_{i}-\tau)=iFFT(F(k)F(k)^{*}) \tag{3}\]  
where * means complex conjugation and \(iFFT\) is the inverse FFT.  
Feature extraction from FFT, PSD, and ACF exploits peak coordinates such that for the first \(l\) tallest peaks in each of these three functions, their coordinates are concatenated to form the feature set:  
\[A=\{[k_{1},F(k)_{1},k_{1},PS(k)_{1},\tau_{1},y(\tau_{1})],[k_{2},F(k)_{2},k_{2 },PS(k)_{2},\tau_{2},y(\tau_{2})],\ldots,[k_{n},F(k)_{n},k_{n},PS(k)_{n},\tau_{ n},y(\tau_{n})]\}.\]
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 3 Design of the Protocol Stack  
In this paper, the design of the protocol stack focuses on the control plane and transport plane.
## 3 Results
### Performance of Different Correction Factors for \(\mathrm{f_{esc}}\) Estimation
#### 3.2.2 Validation of the \(\mathrm{f_{esc\_GPR\_SR}}\) Model Using DART Simulations  
The reliability of the \(\mathrm{f_{esc\_GPR\_SR}}\) model was further assessed using leaf-level and canopy-level SIF of maize plants simulated by the DART model under sparse vegetation conditions (LAI = 2). The comparison of SIF at canopy and leaf levels is shown in Figure 8. For ease of comparison, the unit of leaf-level SIF was converted from mW/m\({}^{2}\)/nm to mW/m\({}^{2}\)/nm/sr. The leaf-level SIF estimated by the \(\mathrm{f_{esc\_GPR\_SR}}\) model (the mean value is 1.818 mW/m\({}^{2}\)/nm/sr) is found to be closer to the SIF simulated by the DART model (\(\mathrm{SIF_{leaf}^{DART}}=1.813\) mW/m\({}^{2}\)/nm/sr). The interquartile range (IQR) of leaf-level SIF calculated by the \(\mathrm{f_{esc\_GPR\_SR}}\) model is significantly smaller than that of multi-angle canopy SIF, indicating that the \(\mathrm{f_{esc\_GPR\_SR}}\) model can eliminate the directional effect of the canopy SIF effectively. Figure 9 displays the comparison of \(\mathrm{f_{esc}}\) in the near-infrared band estimated by the NIRv/FAPAR model and the \(\mathrm{f_{esc\_GPR\_SR}}\) model with that simulated by the DART model. The \(\mathrm{f_{esc}}\) estimated by the \(\mathrm{f_{esc\_GPR\_SR}}\) model is closer to the 1:1 line, with similar R\({}^{2}\) values but the RMSE decreasing significantly from 0.070 to 0.026, in comparison with the NIRv/FAPAR model. These results of the 3-D radiation transfer model also verify that the \(\mathrm{f_{esc\_GPR\_SR}}\) model can effectively improve the accuracy of \(\mathrm{f_{esc}}\) estimation.  
Figure 8: Boxplot of maize canopy SIF in the near-infrared band (760 nm) simulated by the DART model (\(\mathrm{SIF_{canopy}}\)) and leaf-level SIF estimated by the NIRv/FAPAR model (\(\mathrm{SIF_{leaf}^{NIRv/FAPAR}}\)) and that estimated by the \(\mathrm{f_{esc\_GPR\_SR}}\) model (\(\mathrm{SIF_{leaf}^{CPR\_SR}}\)). The red dotted lines represent the leaf-level SIF of maize in the near-infrared band (760 nm) simulated by the DART model (\(\mathrm{SIF_{leaf}^{DART}}\)). For the convenience of comparison, the unit of leaf-level SIF is converted to mW/m\({}^{2}\)/nm/sr (consistent with the unit of canopy SIF).  
Figure 7: The coefficients of variation (CVs) of the canopy SIF simulated by the SCOPE model and the leaf SIF estimated by the \(\mathrm{f_{esc\_GPR\_SR}}\) model and NIRv/FAPAR model for each set of different LAI values with only the VZA changing and the remaining parameters fixed (LIDF is plagiophile, \(\mathrm{C_{ab}}=60\)\(\mathrm{\SIUnitSymbolMicro m^{2}/\SIUnitSymbolMicro m^{2}/\SIUnitSymbolMicro m ^{2}/\SIUnitSymbolMicro m^{2}/\SIUnitSymbolMicro m^{2}/\SIUnitSymbolMicro m^{2}/ \SIUnitSymbolMicro m^{2}/\SIUnitSymbolMicro m^{2}/\
## 1 Introduction
The electrochemical circuit is completed by connecting the electrodes to a sensitive voltmeter using special low-noise cables and connectors. A potential difference is developed as the selected ions diffuse onto the ISE membrane surface and interact with the molecules imbedded in the membrane. Most ISEs use a polyvinylchloride (PVC) based membrane, but crystalline and other special membranes do exist. Sensor distributors such as NexSens Technology (Fairborn, OH, USA), Thermo Fisher Scientific (Waltham, MA, USA), Fondriest Environmental (Fairborn, OH, USA), NICO2000 (London, UK), etc., market ISE electrodes for various metals and ions e.g., nitrates, chlorides, mercury, etc. The ISE electrodes can be calibrated for high-to-low concentration. The main advantage of the ISE is the flexibility to calibrate and monitor very high (mg/L) to very low concentration (ng/L). However, ISE are sensitive to the environment, especially operating temperature and pH. In some cases, a one-degree Celsius increase of water temperature may lead towards 1.5% detection error. Most of the ISE are calibrated with individual water solution and not tested with different atmospheres. FGD wastewater quality varies dramatically in different locations in the same FGD wastewater treatment plant. Off-the-shelf made ISE sensors are suitable for clean, room temperature water, whereas the FGD water is contaminated with various chemicals and temperature can be much higher than room temperature (76-125 \({}^{\circ}\)F/24-52 \({}^{\circ}\)C). Previous studies have investigated the use and suitability of ISEs with various media, and have found that ISEs are typically cheaper and quicker for mass data acquisition compared to traditional methods such as inductively coupled plasma (ICP) analysis [2; 3]. However, certain complications have occurred, such as the ionic strength of the undiluted solution either damaging the electrode membranes or causing poor quality in readings. Therefore, ISEs must be evaluated for use with FGD wastewater to determine if they would be a more viable option than standard procedures currently in use.
## 3 Results
### Comparison of Pre- and Post-Treatment Changes in the ASO and NASO Groups (Table 5)  
To account for the differences between the initial conditions of the ASO and NASO groups, the pre- and post-treatment changes within each group were examined. This analysis facilitated evaluation of the skeletal and dental effects based on the treatment method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**ASO Group**} & \multicolumn{2}{c}{**Non-ASO Group**} & & \\ \hline
**Measurements** & **Mean** & **SD** & **Mean** & **SD** & **Difference** & \(p\) \\ \hline SNA & 78.04 & 4.14 & 79.15 & 3.32 & \(-1.11\) & 0.23 \\ SNB & 74.82 & 3.92 & 75.88 & 2.98 & \(-1.05\) & 0.22 \\ ANB & 3.22 & 1.22 & 3.28 & 1.37 & \(-0.06\) & 0.85 \\ FMA & 28.79 & 3.84 & 28.46 & 4.42 & 0.33 & 0.75 \\ SN-GoMe & 37.33 & 4.65 & 38.67 & 4.95 & \(-1.34\) & 0.26 \\ A to N-perp & \(-3.86\) & 2.76 & \(-0.72\) & 2.78 & \(-3.13\) & 0.00 ** \\ A to FH & 34.22 & 3.47 & 35.30 & 4.16 & \(-1.09\) & 0.13 \\ B to N-perp & \(-13.10\) & 5.29 & \(-7.05\) & 4.36 & \(-6.06\) & 0.00 ** \\ B to FH & 82.57 & 5.94 & 72.98 & 6.80 & 9.59 & 0.00 ** \\ Pog to N-perp & \(-11.13\) & 5.80 & \(-8.16\) & 5.52 & \(-2.97\) & 0.04 * \\ FABA & 79.15 & 3.67 & 80.50 & 3.60 & \(-1.34\) & 0.14 \\ \hline U1 to FH (\({}^{\circ}\)) & 110.31 & 6.06 & 110.00 & 7.53 & 0.31 & 0.86 \\ U1 to FH (mm) & 62.90 & 4.42 & 57.26 & 5.60 & 5.65 & 0.00 ** \\ L1 to FH (\({}^{\circ}\)) & 51.86 & 6.34 & 62.99 & 7.97 & \(-11.13\) & 0.00 ** \\ L1 to FH (mm) & 60.23 & 4.34 & 55.08 & 5.67 & 5.14 & 0.00 ** \\ U1 C to N-perp & 1.95 & 4.66 & 2.13 & 3.86 & \(-0.18\) & 0.86 \\ U1 R to N-perp & \(-7.58\) & 3.81 & \(-5.64\) & 3.00 & \(-1.93\) & 0.02 * \\ L1 C to N-perp & \(-1.16\) & 4.75 & \(-1.00\) & 3.75 & \(-0.16\) & 0.88 \\ L1 R to N-perp & \(-12.52\) & 5.36 & \(-9.71\) & 4.27 & \(-5.52\) & 0.00 ** \\ Interincisal angle & 121.56 & 9.49 & 132.99 & 11.88 & \(-11.44\) & 0.00 ** \\ BOP & 12.17 & 3.69 & 10.88 & 4.09 & 1.29 & 0.18 \\ FOP & 12.29 & 3.87 & 10.68 & 4.67 & 1.61 & 0.13 \\ IMPA & 99.34 & 7.39 & 88.54 & 7.54 & 10.80 & 0.00 ** \\ \hline \end{tabular} Independent \(t\)-tests were performed to compare variables between the two groups after treatment * \(p<0.05\), ** \(p<0.01\). IMPA, incisor mandibular plane angle; ASO, anterior segmental osteotomy; SD, standard deviation; FH, Frankfort horizontal.  
\end{table}
Table 4: Comparison of skeletal and dental measurements between the ASO and NASO groups after treatment.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 6 Ablation Study
### Ablation Results
#### 6.2.4 Style Fidelity Loss Enhancement  
Implementation Details: The final variant incorporates a style fidelity loss on top of Variant D. This loss function is designed to ensure that the generated fonts adhere closely to the fine-grained stylistic nuances of the target fonts, further refining the stylistic alignment of the generated glyphs.  
Results: The integration of style fidelity loss significantly enhanced style accuracy Acc (S) from 53.72% to 78.42%, demonstrating its effectiveness in refining stylistic fidelity. However, its impact on content accuracy Acc (C) was minimal, with a slight increase from 96.91% to 97.16%, reflecting that the loss focuses primarily on style refinement without affecting structural content. Perceptual quality, as measured by LPIPS, improved to 0.132, and SSIM increased to 0.758, indicating better perceptual and structural similarity to the target fonts. Notably, FID remained stable at 98, as this metric evaluates the overalldistributional alignment, which is less sensitive to the localized stylistic nuances targeted by the style fidelity loss.  
The ablation study highlights each component's essential contributions. The patch encoder and attention mechanisms significantly boost style accuracy by enabling localized style encoding, while the triplet loss improves style disentanglement without compromising content fidelity. Finally, the style fidelity loss ensures adherence to the intricate stylistic details of the target fonts, further elevating the model's performance. Together, these enhancements result in a robust font generation model capable of effectively balancing style and content accuracy.
## 2 Study Area and Data Processing  
### Study Area  
For this study, the NWPO was selected as the study area. Specifically, the area between 105-180\({}^{\circ}\)E and 5-60\({}^{\circ}\)N, as shown in Figure 1. The hydrographic setting in the NWPO is complex. It is one of the most tropical cyclone-prone oceans in the world [11], and is also strongly associated with ocean features such as eddies [22]. There is a mixed water region, where the warm Kuroshio current from the south mixes with the cold Oyashio current from the north, and a recirculation region in the KE [23]. The KE region has a zonal band of high eddy kinetic energy [24], and the short-term formation of the Kuroshio meander appears to be closely related to these eddy activities [25; 26].
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
## 4 Results and Discussion
### Intermediate Scale Modelling  
As stated earlier, these models were designed for comparison and validation purposes. The model simulated a low salinity waterflooding scenario. The results of this case were compared with the results of the single block model to validate the findings of the single block model against the intermediate scale model under the same condition using the dual-porosity solver. The simulation outcomes show almost identical oil recovery despite the differences in the scale of the two models, as shown in Figure 11.  
On the other hand, the oil recoveries of both intermediate models (i.e., Case A and Case B in Figure 6) were compared to assess the matrix recovery under the sweep process by the injected low salinity brine scenario and the flooded scenario when the fluid exchange occurred under static condition. The three levels of uncertainty in the change to the relative permeability due to the impact of low salinity brine were simulated to evaluate oil recovery under the above-explained conditions. The simulation results indicate faster oil recovery in the flooded case compared with the injection case  
Figure 10: Water saturation profile in the matrix after 10 days of fluid exchange between the matrix and fractures (**A**) using the dual-porosity model for a single matrix block, and (**B**) using the single-porosity model for the same matrix block in (**A**) after discretisation. The corner grid cells in the fine-scale model have shown a higher water saturation compared to the remaining cells, as they have two faces in contact with fractures for fluid exchange.  
as all matrix blocks were in contact with the low salinity brine that filled fractures. Meanwhile, in the injection case, the matrix blocks were in contact with the oil in fractures. Then, the injected low salinity brine displace the oil from fractures and become in contact with additional matrix blocks as the low salinity waterfront advanced. The simulation results are illustrated in Figure 12.
## 3 The Scheduling Method Based on Time Shifting Constraints and Pulse Interleaving
### The Description of Proposed Methodology
Furthermore, the use of the time pointer and shifting constraints helps prune infeasible options early, reducing unnecessary comparisons. Therefore, the algorithm achieves good real-time performance and is well suited for deployment in resource-constrained radar-scheduling scenarios. With the above model and optimization formulation in place, we now proceed to evaluate the proposed algorithm through simulation experiments under representative radar scenarios.