esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
The primary goal of neoadjuvant therapies is shrinking tumor size to improve surgical outcomes, eliminating micro-metastases to lower recurrence risk, and assessing tumor response to specific treatments [53; 54]. In this setting, the MVMSGAT model excels. Its recall rate is the second highest among its counterparts. More importantly, it leads in critical metrics like AUC-ROC, Accuracy, and F1 Score. This model's overall effectiveness ensures it accurately identifies many patients likely to benefit from treatment. It reduces false diagnoses and ensures that all eligible patients receive the right care. This accurate and comprehensive diagnostic capability is vital for the success of neoadjuvant therapies, significantly affecting patient prognosis and quality of life. The effectiveness and accuracy of the MVMSGAT model mainly come from two areas: its handling of multi-scale gene cluster features and the integration of graph inputs from various biological perspectives at the mRNA level. Traditional methods for graph-level prediction often use simple readout operations, like averaging or maximizing node values, which can lead to a loss of important structural information. The MVMSGAT model overcomes this with multi-scale pooling and hierarchical pooling operations. These allow for data integration at different levels of detail, preserving crucial structural information. The model's ability to combine gene clusters at multiple scales captures finer biological details and biomarker features. Additionally, by specifically integrating gene co-expression networks and protein-protein interaction networks at the mRNA level, the MVMSGAT model offers a more thorough analysis of the biological processes involved in new adjuvant therapies. This combination of technical advancements and focus on mRNA-level interactions greatly improves the model's performance in complex biological studies. This aligns well with the goals of neoadjuvant therapies to enhance patient outcomes. Furthermore, the relevance of selected genes, like VCAM1, IL6R, CD46, and ATF3, is supported by clinical research. VCAM1, a cell adhesion molecule, is upregulated in several cancers, including bladder cancer. It plays a crucial role in tumor cell adhesion, migration, and invasion, possibly interacting with immune cells in the tumor microenvironment [55; 56]. IL6R, a cytokine receptor, regulates immune and inflammatory responses. Its activation in bladder cancer may encourage tumor growth and progression by affecting cell survival and proliferation [57; 58; 59]. CD46, regulating immune responses, shows abnormal expression in some cancers and relates to the aggressiveness and prognosis of bladder cancer [60]. ATF3, a stress-responsive transcription factor, is activated in various cell stress and pathological conditions. It may regulate cellular stress responses, affecting the survival and death of tumor cells in bladder cancer [61; 62]. These insights shed light on the complex biological processes in bladder cancer and validate the MVMSGAT model's ability to accurately predict patient responses to therapy.
## 1 Introduction
### Interfacial Interactions Between the Fillers and the Matrix  
High quality -nanocomposites require the uniform dispersion of carbon fillers within the polymeric matrix which can be achieved by strong interfacial contact between the inorganic fillers and the organic polymer and high nanofiller loading [15]. [PERSON] et al. [12] showed that polystyrene (PS)/multi-wall carbon nanotube (MWCNT) nanocomposites with an 80%-CNT content resulted in a very inhomogeneous coating. By contrast, lowering the CNT content by a factor of two resulted in uniform grafting of the PS onto the CNT surface, where -40% of the CNT content remained high. Thus, differences in filler content have a real impact on good carbon nanomaterial dispersion within the matrix.  
High surface contact within polymer composites filled with carbon nanomaterials can be achieved by employing both non-covalent and covalent approaches. To obtain covalent linkages, the most common method is grafting and in particular the \"grafting-from\" approach. The main technique for producing non-covalent polymer and carbon bonding is known as in situ polymerization. These two approaches are discussed separately below.
###### Abstract  
An electrically tuned phase shifter based on the single slot unit cell and liquid crystal for a reconfigurable reflectarray antenna was presented. The simulation and measured results obtained at the F-band were used to demonstrate that the dielectric properties of a nematic state liquid crystal could be exploited to realize a slot unit cell phase shifter for the reconfigurable reflectarrays antennas. By reducing the inhomogeneous of the external electric field in the liquid crystal layer, a lower control voltage and improved model accuracy was obtained. In the experiments test, the achieved saturation bias voltage (10 V) was smaller than the previously reported structure, and the phase shift of the reflectarray greater than 300\({}^{\circ}\) was achieved in the frequency range 121.5-126 GHz. The maximum phase shift of 306\({}^{\circ}\) occurred at 124.5 GHz.  
phase control; phase shifter; liquid crystal device; reconfigurable reflectarray
# Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
## 5 Discussion
### Bit Resolution  
Regarding the first point, the two solutions can be compared in terms of ENOB degradation. As reported in Sections 4.1 and 4.2, no significant distortions nor noise are introduced by the PICs, being the optical loss the main source of ENOB degradation. This was expected since the devices are composed of passive elements and reconfigurable by means of thermal shifters. For this reason, the bit resolution comparison focuses on the IL determined by the MZI loss, the processor depth, and the optical I/O loss. The graphs in Figures 6 and 8 show that the silicon nitride chip can perform the analog processing with higher resolution (about 1 ENOB) due to lower losses with respect to the silicon-on-insulator device.  
Regarding the circuit topology, the \"Clements\" architecture is more convenient than the \"Reck\" architecture, given the same number of MZIs and the lower processor depth. The better scalability of \"Clements\" is apparent when devising larger size processors, since its processor depth is equal to the number of inputs \(N\), while the processor depth in the \"Reck\" architecture is almost double, growing as \(2N-3\), as discussed in Section 3. Indeed, given the single element loss and neglecting optical I/O losses, the processor depth determines the ENOB reduction for all SNR-limited analog processors (not restricted to the case of PICs based on MZI meshes), as reported in Figure 9. In this analysis that neglects  
Figure 8: ENOB reduction as a function of laser power for the reference signal, and the best and the worst path in the silicon nitride-based \(4\times 4\) linear optical processor.  
Figure 7: Characterization of silicon nitride-based \(4\times 4\) linear optical processor. **(a)** Optical processor bandwidth on the longest path; **(b)** normalized transmission of MZI 4 in cross state; (c) normalized transmission of MZI 7 in bar state.  
optical I/O loss, the silicon-on-insulator chip, with an average MZI element loss of 0.7 dB, appears to be more scalable than the silicon nitride chip, with an average MZI element loss of 1.5 dB (more than double of the former). For instance, by restricting the ENOB reduction to be <2, this leads to a maximum silicon nitride processor depth of 8 with respect to 17 for the silicon-on-insulator processor.
###### Abstract
One of these studies [29] also investigated the accuracy according to the implant placement depth and the exposed length of the ISB, but the authors used implant placement depths of 0 mm, 3 mm, and 6 mm, which did not reflect the diverse range of depths at which implants are actually placed in clinical practice, and so the results were not suitable for utilization in guidelines. Moreover, there was the limitation that it was difficult to evaluate only the change in accuracy according to the exposed length of the ISB, because factors such as the angle, shape, and direction of the ISB were not controlled [29]. Another previous study found that the accuracy of implant position reproduction differs with the exposed length of the ISB, which was revealed by dividing the exposed length of the ISB into groups from fully exposed to 2.5 mm less exposed [24]. However, since only one type of tabletop scanner was used in that study, only the errors generated in the process of scanning the model were evaluated. Therefore, the previous studies have had the limitation of the error that occurs when direct digitalization is performed using an intraoral scanner in the oral cavity being unknown. In clinical practice, the implant placement depth can vary greatly depending on the condition of the alveolar bone and surrounding gingiva, and so it is necessary to evaluate how the accuracy of direct digitalization varies with the level of implants and the exposed length of the ISB, and to produce a guideline that can be used by clinicians. Therefore, the purpose of this study was to adjust the exposed length of the ISB in the model by placing implants at the normal bone level, submerged 1.5 mm, submerged 3.0 mm, submerged 4.5 mm, and submerged 6.0 mm at the positions of the lateral incisor, first premolar, and first molar in a maxillary unilateral master model. The accuracy of implant position reproduction of the data scanned with the oral scanner was then evaluated. The null hypothesis of this study was that the accuracy of implant position reproduction does not vary with differences in the exposed length of the ISB, scanner type, or implant placement position.
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 5 Results
### Multiple Damage Severity Assessment  
Figures 8-10 report the results for increasing damage level in damage #1, #2, and #3 (in the same order), with and without added noise.  
Figure 7: Noisy measurements (10% of noise): (**a**) 80%/20%/0%; (**b**) 40%/0%/80%.  
Figure 8: Increasing stiffness reduction (20%, 40%, 60%, and 80%) for damage #1. (**a**) noise-free; (**b**) noise = 10%. Graphs are limited between 7 m and 13 m to show the increase of the intensity of damage for increasing stiffness reduction.  
Figure 9: Increasing stiffness reduction (20%, 40%, 60%, and 80%) for damage #2. (**a**) noise-free; (**b**) noise = 10%. Graphs are limited between 7 m and 13 m to show the increase of the intensity of damage for increasing stiffness reduction.  
Figure 10: Increasing stiffness reduction (20%, 40%, 60%, and 80%) for damage #3. (a) noise-free; (b) noise = 10%. Graphs are limited between 7 m and 13 m to show the increase of the intensity of damage for increasing stiffness reduction.  
It is possible to notice how the entropy-based health state indicator increases monotonically for an increasing damage level, showing thus a correlation between the stiffness reduction and the entropy variation. In contrast, far from the damage, the variability induced by the noise affects the baseline and the damaged structure in a similar fashion. In this way, the two effects cancel each other out, resulting in a near-zero variation. For this reason, the proposed damage indicator is robust to noise in terms of avoided false positives.  
The noise affects the sensibility of the indicator, similarly to what is seen in the previous subsection. The same remarks can be applied here: while the damage becomes less detectable from a longer distance, its short-range effects seem to be positively affected by the additional noise, making it more pronounced.
## 3 Innovation
### Innovative Research Focusing on Variations in Predicted Target Depths and Unexplored Area
#### 3.1.3 Remote Sensing-Based Salinity Monitoring Studies in New Geographical Areas  
Research on remote sensing for monitoring soil salinization in underexplored areas has gained attention in recent decades. Most studies have concentrated on specific regions such as China, Kazakhstan, Iran, Iraq, the USA, the Netherlands, and Australia [48]. These studies have demonstrated the effectiveness of remote sensing techniques and comprehensive indicators. Existing regional or global remote sensing prediction maps are primarily based on field data collected from these areas. However, significant knowledge gaps remain between different regions of the world, and soil conditions in many areas are either unexplored or insufficiently studied. This is particularly critical in light of anticipated drastic climate changes, which will exacerbate issues related to the sustainable management of soil and water resources in arid and semi-arid zones [49]. Furthermore, scenarios such as seawater intrusion contaminating soil and groundwater in coastal regions are likely to lead to cascading changes in soil salinity [50]. Accurate soil salinity mapping will necessitate years of routine monitoring in each region. For instance, the National Dryland Soil Salinity Monitoring Program in Western Australia ([[https://data.wa.gov.au/land-monitor/](https://data.wa.gov.au/land-monitor/)]([https://data.wa.gov.au/land-monitor/](https://data.wa.gov.au/land-monitor/)) (accessed on 15 July 2024)) has produced a variety of relevant datasets for soil salinity monitoring since 1998. In contrast, the application of combined ML and remote sensing techniques for soil salinity mapping in coastal Bangladesh has been limited [51]. Although some recent studies have investigated soil salinity variations in southwestern coastal areas (specifically the Comilla and Noakhali districts), similar research has not yet been conducted in the southern districts of Pataukhali, Borguna, Jalakhati, and Barishal [52].
## 4 Experimental Results
### Comparison of Performance Evaluation Results by Features
The best model in precision was linear SVC increased by 781.2%, while the worst model was gradient boosting which increased by 135.2%. The best model in recall in was gradient boosting which increased by 3642.1%. On the other hand, recall showed that linear SVC model suffered poor evaluations of Datasets with elapsed time, which decreased to 21.8%. The best model in F1-score was gradient boosting which increased by 2175%, while the worst model was Linear SVC increased by 150.8%. The best model in AUC was KNN increased by 295.1%, while the worst model was gradient boosting increased by 143.3%. Except for the linear SVC model in recall, all performance results were increased in Dataset 1, and the highest increase rate was 3642.1%. In conclusion, using the dataset with elapsed time, we can effectively classify random keyboard data with up to 96.2% accuracy. This means that an attacker steals the real keyboard data input by the user in the real world. Consequently, the proposed attack technique discussed in this paper has derived a security threat and a new vulnerability that effectively steal user authentication information in password authentication.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 5 Results
### XGBoost  
The hyperparameters that best fit the XGBoost model were found, and the classification results are presented in Table 11. Subsequently, the machine was trained and tested on a separate dataset, which yielded the results shown in Figure 7.  
The general result of 99% accuracy with a standard deviation of 0.02% demonstrated the good performance of this technique, which worked with a series of classifying trees for residues. Such an accuracy is adequate for using this model in practical cases.  
Achieving a high accuracy across multiple tests indicates that the model generalized well to unknown data. This suggests that the model learned meaningful patterns from the training data that were applied to new, unseen examples. The model performed consistently well across multiple subsets of data, with a high accuracy maintained across the different separations of the data, which indicates its generalization capacity.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & \multicolumn{2}{c}{**Lofar**} & \multicolumn{2}{c}{**PCA**} & \multicolumn{2}{c}{**NLPCA**} \\  & **w/ Dec** & **w/o Dec.** & **w/ Dec** & **w/o Dec.** & **w/ Dec** & **w/o Dec.** \\ \hline Components & 10 & 10 & 5 & 5 & 5 & 5 \\ \hline Solver & SVD & SVD & SVD & SVD & SVD & SVD \\ \hline Mean Accuracy \(\tilde{Acc}\) & 0.859 & 0.995 & 0.859 & 0.995 & 0.859 & 0.995 \\ \hline Standard Error (SE) & \(8.39\cdot 10^{-2}\) & \(4.69\cdot 10^{-3}\) & \(8.39\cdot 10^{-2}\) & \(4.69\cdot 10^{-3}\) & \(8.39\cdot 10^{-2}\) & \(4.69\cdot 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: LDA hyperparameters and the model’s performance for the Lofar, PCA, and NLPCA variations of the database.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & \multicolumn{2}{c}{**Lofar**} & \multicolumn{2}{c}{**PCA**} & \multicolumn{2}{c}{**NLPCA**} \\  & **w/ Dec** & **w/o Dec.** & **w/ Dec** & **w/o Dec.** & **w/ Dec** & **w/o Dec.** \\ \hline alpha & \(10^{-6}\) & \(10^{-6}\) & \(1.359\cdot 10^{-5}\) & \(1.359\cdot 10^{-5}\) & \(10^{-6}\) & \(10^{-6}\) \\ \hline colsample\_bytree & 0.934 & 0.934 & 1.0 & 1.0 & 0.470 & 0.470 \\ \hline gamma & 1 & 1 & \(10^{-6}\) & \(10^{-6}\) & 0.0603 & 0.0603 \\ \hline lambda & 1 & 1 & \(3.637\cdot 10^{-6}\) & \(3.637\cdot 10^{-6}\) & 1 & 1 \\ \hline learning\_rate & 0.051 & 0.051 & 0.245 & 0.245 & 0.01 & 0.01 \\ \hline max\_depth & 10 & 10 & 1 & 1 & 3 & 3 \\ \hline min\_child\_weight & 4 & 4 & 4 & 4 & 1 & 1 \\ \hline n\_estimators & 1000 & 1000 & 560 & 560 & 849 & 849 \\ \hline subsample & 1.0 & 1.0 & 0.937 & 0.937 & 0.426 & 0.426 \\ \hline Mean Accuracy \(\tilde{Acc}\) & 0.997 & 0.996 & 0.886 & 0.994 & 0.997 & 0.997 \\ \hline Standard Error (SE) & \(1.38\cdot 10^{-2}\) & \(4.61\cdot 10^{-3}\) & \(1.38\cdot 10^{-2}\) & \(5.75\cdot 10^{-3}\) & \(1.38\cdot 10^{-2}\) & \(3.66\cdot 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: XGBoost hyperparameters and the model’s performance for the Lofar, PCA, and NLPCA variations of the database.
# A Dual-Band 8-Antenna Array Design for 5G/WiFi 5 Metal-Frame Smartphone Applications
## 2 Proposed MIMO Antenna  
### Antenna Structure  
The structure and physical dimensions of the proposed antenna are depicted in Figure 1a. The MIMO antenna design incorporates three FR-4 substrates, each exhibiting a relative permittivity of 4.4 and a loss tangent of 0.02. These substrates consist of a central main substrate and two additional side-edge substrates. The main substrate measures \(150\,\mathrm{mm}\times 73.4\,\mathrm{mm}\times 0.8\,\mathrm{mm}\), while the side-edge substrates have dimensions of 150 mm \(\times\) 7 mm \(\times\) 0.8 mm. Precisely printed on the two side-edge substrates are eight antenna elements, strategically positioned perpendicular to the main substrate.  
Figure 1: _Cont._
## 3 Experiment and Results
### Experimental Environment  
The experimental environment in this study required the use of several critical components, including a GPU with CUDA support and the PyTorch framework. To perform the learning process and object detection quickly and accurately, the YOLOv5 deep learning model required parallel computing. As a result, a GPU and CUDA were used to accelerate the computing, while the PyTorch framework was used to build and train models. Table 1 shows the configuration details for this experimental environment.
# Glassy Carbon Electrochemical Sensor for Gallic and Vanillic Acid Detection in Aqueous Solutions
In addition, a variety of studies on bare or modified glassy carbon electrodes in electrochemical sensors for copper(II) ions detection was reported in the recent literature [25; 26; 27]. The main disadvantage of the electrochemical detection of phenols present in wastewater is the fast deactivation of the electrode by the adsorption of undesirable byproducts. The formation of an inactive film on the electrode surface hinders the electron transfer process resulting in lower current density values and poor sensitivity to the phenol content of the solution. Therefore, increased research effort is necessary to modify and activate materials by the addition of functionalized groups, tune their electrochemical properties, and increase their stability and sensitivity. In this work, a bare glassy-carbon electrode was studied as an electrochemical sensor for the detection and quantification of gallic and vanillic acid in acidic aqueous solutions individually or as a mixture. These acids are mainly contained in olive and wine wastewaterand considered as environmental pollutants with high ecological impact. The typical value of total phenolic content in olive wastewaters is in the range of 1.6 to 10.7 g of gallic acid per L and 0.001 to 0.1 g of vanillic acid per L [28]. According to the international regulatory body, wastewater has to be treated before discharge, with a permissible limit of phenols at 1 mg\(\cdot\)L\({}^{-1}\)[29]. Since, both phenolic compounds have antioxidant and anti-inflammatory properties with beneficial effect on human health, their recovery from wastewater is an intriguing process for the production of high-added-value products [11]. The aim of the study was to find an easy, rapid, and handy way to identify the above phenolic compounds in model wastewaters.