esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 1 Introduction
Explainable AI with Grad-CAM: Provides visual justification of the model's outcomes, enhancing transparency and clinician trust. 4. Comprehensive training over the HAM10000 dataset: Utilizes a distinct dataset to improve the generalization of the model to new images. 5. Optimal feature collection and fusion: Combines the most informative features for specialized dermatoscopic analysis. 6. Rigorous evaluation: Validates the model's effectiveness through extensive testing and performance metrics. The contributions of this study are as follows:  
1. Proposed DCAN-Net framework: This study introduces DCAN-Net, an advanced deep learning framework for skin cancer detection. It incorporates a tailored attention mechanism to focus on critical regions of dermatoscopic images, improving diagnostic accuracy and providing visual attention maps for enhanced explainability. 2. Integration of ConvNeXt and modified Grad-CAM: ConvNeXt serves as the core feature extraction architecture, offering state-of-the-art efficiency, while a modified Grad-CAM explainable AI enhances interpretability. This combination improves the diagnostic precision and empowers clinicians with insights into the model's decision-making process. 3. Parallel feature fusion strategy: An advanced parallel feature fusion strategy amplifies essential feature extraction, enabling accurate skin cancer classification by distilling critical details from dermatoscopic images. 4. Comprehensive ablation study: Rigorous ablation studies evaluate the impact of hyperparameters and model components, resulting in reduced false positives and negatives and overall improved accuracy. 5. Evaluation on the HAM10000 dataset: Extensive testing on the HAM10000 dataset including its augmented version was conducted to validate the model's robust generalization, precision, and potential to transform healthcare diagnostics. 6. Addressing limitations of traditional CNNs: The study tackles challenges in traditional CNNs including the lack of explainability, overfitting, and sensitivity to data quality. It ensures improved reliability for diverse real-world clinical scenarios. 7. Enhanced trustworthiness: By incorporating Grad-CAM for visual explanations, robust preprocessing, balanced training, and continuous evaluation, DCAN-Net can establish a transparent, reliable, and clinically applicable system for skin cancer detection. The remainder of the article is organized as follows. The mainstream approaches are briefly explained in Section 2, and the technical information of the proposed model is provided in Section 3. The empirical results are presented in Section 4, and Section 5 concludes the paper.
## 3 Results and Discussion
### Analytical Study  
#### 3.4.1 Linear Range Detection  
The developed GCE/MWCNT/UN-MNPs were used to detect H\({}_{2}\)O\({}_{2}\) at an applied potential of \(-0.3\) V with a concentration range from 99.9 \(\upmu\)mol L\({}^{-1}\) to 792.86 \(\upmu\)mol L\({}^{-1}\). The cathodic current response increased linearly with the consecutive injection of H\({}_{2}\)O\({}_{2}\) (\(R^{2}\)= 0.995) in Figure 4. The slope of the calibration curve is regarded as the sensitivity of the electrode. The developed sensor had a sensitivity of 0.0676 \(\upmu\)A \(\upmu\)M\({}^{-1}\). The limit of detection (LOD) was calculated by using \"3 s/m\", where \"s\" is defined as the standard deviation of the current response and \"m\" is the slope of the calibration curve. The LOD was found to be 27.02 \(\upmu\)mol L\({}^{-1}\). The limit of quantification (LOQ) was calculated as 89.26 \(\upmu\)mol L\({}^{-1}\) by using the formula \"10 s/m\" and is presented in Table 1.  
Figure 4: (**A**) Chronoamperogram and (**B**) related concentration dependence of peak current obtained using GCE/MWCNT/UN-MNPs. Successive injections of 100 \(\upmu\)mol L\({}^{-1}\) H\({}_{2}\)O\({}_{2}\); applied potential of \(-0.3\) V in 10 mL 0.01 M PBS buffer.  
Figure 3: Comparing the sensitivity of the sensor at different applied potentials: 0.01 M phosphate-buffered saline (PBS), 7.4 pH with continuous injection of H\({}_{2}\)O\({}_{2}\) containing 100 \(\upmu\)mol L\({}^{-1}\) in each injection.
# Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics
## 4 Conclusions  
Diabetes is now one of the most alarming diseases in the world. Data mining and ML are now being used to predict diabetes alongside traditional clinical tests. Inspired by this, this study aimed to build an automated model to predict diabetes at an early stage. To fulfill this objective, six ML approaches were applied and compared to their performances to find the best-fit classifier that will predict diabetes based on a socio-demographic attribute in an early stage with significant accuracy. It was observed that the best-fit classifier is RF with an accuracy of 99.37%. This study also aimed to find the features' impact on model prediction, and that has been successfully achieved in this study. The proposed method will be further developed with more state-of-the-art technology and with more data in the future. This study presents limitations regarding the data. The findings will be more beneficial for researchers who have an interest in diabetes disease research based on ML techniques and will also be helpful for physicians to diagnose diabetes at very early stages.  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] provided the concept and performed the experiments; wrote the paper; analyzed and interpreted the data. [PERSON] and [PERSON] interpreted the data. [PERSON] and [PERSON] handled the manuscript and analyzed the data. [PERSON] and [PERSON] edited and reviewed the manuscript. F.M.B. provided funding for the project. [PERSON], [PERSON] and [PERSON] designed the experiments and supervised the whole project. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).  
The corresponding author can provide the data that were utilized to support the study upon request.  
No conflict of interest are disclosed by the authors.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Author** & **Accuracy** & **Precision** & **Recall** & **F1-Measure** & **Kappa** & **MCC** \\ \hline [PERSON] et al. [7] & 83.00\% & 0.7663 & 0.7193 & 0.7420 & - & - \\ [PERSON] et al. [10] & 88.40\% & 0.4490 & 0.8560 & - & - & - \\ [PERSON] et al. [5] & 88.60\% & - & - & - & - & - \\ [PERSON] et al. [9] & 88.84\% & 0.8420 & 0.7890 & 0.8146 & - & - \\ [PERSON] et al [13] & 94.00\% & - & 0.9300 & - & - & - \\ [PERSON] et al. [12] & 94.25\% & 0.9429 & 0.9954 & 0.9684 & - & - \\ [PERSON] et al. [11] & 94.87\% & 0.9593 & 0.9555 & 0.9574 & - & - \\ [PERSON] et al. [8] & 95.94\% & - & 100\% & - & - & - \\ [PERSON] et al. [6] & 99.00\% & 0.990 & 0.990 & 0.980 & - & - \\
**This study** & **99.37\%** & **1.0000** & **0.9902** & **0.9951** & **0.9859** & **0.9860** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison the proposed model with existing methods.
# Boundary-Aware Hashing for Hamming Space Retrieval
## 4 Experiments
### Datasets and Settings
According to the standard evaluation protocol, Hamming space retrieval constitutes two phases: (1) pruning: return all data points within the Hamming radius 2 by hash table lookups of each query; (2) re-ranking: re-rank the continuous code of the pruning data points in ascending order of their distances to each query point. We evaluate the performance of pruning by Recall and Precision within Hamming radius 2 (R@\(H\leq 2\), P@\(H\leq 2\)) while employing Mean Average Precision of Re-ranking within Hamming Radius 2 (MPA@\(H\leq 2\)) on Re-ranking. In addition, we use the F1-score [43] to evaluate the harmonic mean of precision and recall. The ratios of returning zero images evaluate the efficiency of the hash table lookups of the various method. We implement BAH based on the PyTorch framework and adopt the AlexNet architecture [44] as the backbone for all comparison methods. All experiments are completed on an RTX 3090 GPU server. We employ back-propagation to train the hash layer _fch_ and finetune the backbone model pre-trained on ImageNet. As the hash layer _fch_ is trained from scratch, we set its learning rate to be 10 times higher than that of the other layers. We set the mini-batch size of images as 128 and use Root Mean Square prop (RMSprop) as the optimizer with a weight decay of 0.00001. Furthermore, we tune the learning rate from \(10^{-6}\) to \(10^{-3}\) using a 5-fold cross-validation strategy and obtain the hyper-parameter \(m=0.33\). We set the hyper-parameters \(\alpha\) to 0.01, 0.01, 0.05, and 0.01 for the MS-COCO, NUS-WIDE, CIFAR-10, and ImageNet datasets, respectively. Three variants of BAH, namely BAH+T, BAT-C, and BAT-Q, have been investigated and are shown in Figures 4-6, Tables 2 and 3. BAH+T adds a tanh activation function behind the hash layer to learn the hash code using BAH. BAH-C is a BAH variant that does not use \(c_{ij}\) Equation (1). BAH-Q is a variant of BAH that does not utilize the \(l_{2}\) loss Equation (3). We will discuss them in detail in Section 4.4. Ablation studies were used to analyze the impact of various BAH variants on the hash model.
## 3 Results and Discussion
### Correlation Analysis of Physicochemical Properties with Significant Differences  
Pearson correlation analysis was performed on physicochemical characteristics with significant differences. As presented in Figure 7a, significantly positive correlations were observed between W2S and cohesiveness, as well as W2W and W1W, in the control samples after 24 h of low-temperature storage. Gumminess negatively correlated with springiness and W5S, and sourness showed similar correlations with W1W and W2W (Figure 7a). Interestingly, there were no significant correlations among the physicochemical characteristics in the set yoghurt with the 0.05% RE samples (Figure 7b). With increasing RE concentrations, W1W displayed a strong positive correlation with WHC in the 0.10% RE samples, and significantly negative correlations were shown between adhesiveness and gumminess, as well as W5S and springiness (Figure 7c). When the concentration of RE increased to 0.15% in the set yoghurt, W2S showed a significant correlation with hardness, while significantly negative correlations were presented between adhesiveness and springiness, W5S and W2W, and sourness and W2S (Figure 7d). Based on the correlation analysis, there were significant differences in the correlations among varied physicochemical characteristics under different RE concentrations, indicating that RE concentrations should be selected according to the physicochemical properties of the ultimately obtained set yoghurt.  
Figure 6: Principal component analysis of the electronic nose (**a**) and electronic tongue (**b**) after 24 h of low-temperature storage.
## 3 Materials and Methods
### Implementation
#### 3.2.2 Extractive Text Summarization  
We consider text summarization as the second step of our information extraction pipeline for two reasons: i. extracting triples directly from large corpora (such as the CORD-19 dataset) is both costly and time-inefficient due to the increased computational resources required by information extraction engines, and ii. in most cases, only a small fraction of the extracted triples is useful and/or relevant to the discussed topic. Relying on the articles' abstracts--whenever applicable--is a reasonable alternative; however, we run the risk of processing text that contains short descriptions of the article's purpose, along with broad, high-level specifications of little contextual value. Hence, we argue that an extractive summarization method is the optimal way to reduce a document's text length by omitting peripheral or inappropriate information, while highlighting its key features for triple extraction. To this end, we relied on the HuggingFace extractive summarizer library based on [PERSON]'s transformer-based approach [36]. For our implementation, and since we were dealing mainly with biomedical articles, we substituted the vanilla BERT model with AllenAI's SciBERT pretrained model for scientific text, which significantly outperforms BERT-base on most NLP-related tasks including named entity recognition, sequence labeling and text classification [70]. We iteratively passed the CORD-19 articles (previously submitted to in-place coreference resolution) through the summarizer, where all sentences were embedded into the multi-dimensional space using SciBERT embeddings. Subsequently, \(k\)-means clustering was used on the sentence representations to identify those sentences that were closest to the cluster's centroids for summary selection. The ratio of sentences comprising each summary to the original text was provided as parameter (0.2) to the model. The result was an extractive summary for each one of the 9365 ingested articles, with an average count of 843 words per summary, which resulted in a significantly more condensed corpus, compared to an
## 5 Conclusions and Future Work  
Targeting the methods for the monitoring of conveyor belt deviation states, this paper proposes a belt edge straight-line detection method based on diagonal features, and the Yolov5 network was used to complete the tests. The proposed method was fully combined with the prediction box results of the general target detection network. The main contributions of this paper are as follows.  
1. A new dataset containing 5000 images and a labeling method for conveyor belt deviation detection were presented.
2. We designed a quantitative characterization method and a judgment method for the determination of deviation states. These methods effectively balance the detection speed and accuracy, achieving detection accuracy of 99% on the self-made dataset and a detection speed of 67.7 FPS on the RTX2060.
3. Through the Grad-CAM technique, the effects of the types, positions, and quantities of the three attention mechanisms on the improvement in the deviation detection accuracy were explored, and the characteristic positions that affected the detection results were identified.
4. The proposed method has a strong anti-interference ability and can effectively resist environmental interference such as low illumination and motion blur and achieve the stable and reliable monitoring of operational states.  
This work is of great significance in ensuring the safety and efficiency of the transportation system. In future work, we will continue to expand the corresponding dataset and continuously enhance the anti-interference ability of the network.  
Conceptualization, M.Z. ([PERSON]) and Y.Y.; methodology, M.Z. ([PERSON]); software, X.Z. and Z.Y.; validation, M.Z. ([PERSON]); investigation, Z.Y.; writing--original draft preparation, M.Z. ([PERSON]) and X.Z.; writing--review and editing, M.Z. ([PERSON]) and Y.Z.; visualization, X.Z. and Z.Y.; supervision, [PERSON] ([PERSON]) and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data in this study are available on request from the corresponding author.  
Author [PERSON] and [PERSON] was employed by the company Libo Heavy Industries Science and Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract  
In this paper, the physical adsorption of volatile organic compounds (VOCs) such as methyl chloride (CH\({}_{3}\)Cl), carbon disulfide (CS\({}_{2}\)), dimethyl sulfide (C\({}_{2}\)H\({}_{6}\)S), and benzene (C\({}_{6}\)H\({}_{6}\)) by three models of activated carbon (without a functional group, with a pyridine-containing functional group, and with a pyrrole-containing functional group) was simulated. The surface electrostatic potential (ESP), physical adsorption energy, and non-covalent interaction between activated carbon and VOC molecules were analyzed based on the density functional theory (DFT). The effect mechanism of nitrogen-containing functional groups on VOC adsorption by activated carbon was determined. Our simulations showed that nitrogen-containing functional groups can change the surface ESP and polarity of activated carbon. The pyrrole functional group is conducive to CH\({}_{3}\)Cl and CS\({}_{2}\) adsorption on the activated carbon plane, while the pyridine functional group is relatively small or even unfavorable for CH\({}_{3}\)Cl and CS\({}_{2}\) adsorption on the activated carbon plane. The promotional effect of the pyrrole functional group on the adsorption of C\({}_{2}\)H\({}_{6}\)S is more significant than that of the pyridine functional group. The adsorption of C\({}_{6}\)H\({}_{6}\) on activated carbon occurs through parallel-displaced \(\pi\)-\(\pi\) stacking interactions, in which functional groups have little influence on it. The adsorption energy of VOCs on the activated carbon plane is higher than that at the edge, so VOCs are more likely to be adsorbed on the activated carbon plane.  
activated carbon; functional group; VOC; DFT; adsorption +
Footnote †: journal: Applied sciences
# THz Range Low-Noise SIS Receivers for Space and Ground-Based Radio Astronomy +
## 2 Materials and Methods
### Mixer and Mixer Block Designs
Due to the high current density of the AlN barrier, a lower \(R_{n}\) value is realized, which gives a higher \(1/R_{n}C\) ratio for junctions (with \(C\)-junction capacitance), providing a wider reception bandwidth. A twin mixer circuit was formed by two SIS junctions with 6.5 \(\upmu\)m junction spacing, embedded in a 4.5 \(\upmu\)m wide microstrip line, which is connected to the antenna by a \(7\times 27\)\(\upmu\)m impedance transformer [44] designed to match the high current density junctions to the waveguide probe (see Figure 8).
# Software Development Process Considerations in GNSS-Denied Navigation Project for Drones
## 2 Process Fundamentals
### Software Development Process  
First, let us explain what the software development process means from the system engineering and software engineering point of view.  
The software development process and its activities are documented in [13; 18]. Some more specialised processes are targeted and tailored to the specific needs of a given domain. For instance, the considerations regarding the software development process for aviation are documented in [6]. They should be used for drones with a maximum take-off weight between 150 and 20,000 kg intended to operate in a non-segregated airspace [28]. Considerations for space applications can be found in [16; 20; 21; 24].  
We can sum up that any software development process usually concerns the following aspects of software engineering [21]:  
* Requirements definition;
* Design;
* Production (implementation);
* Verification and validation;
* Transfer (installation);
* Operation and maintenance (utilisation).  
Sometimes, other aspects are also considered during the software development process, for instance, as addressed in [6], software planning, certification, integration or disposal [13]. They are the key aspects of the certified drones applied in civil and military areas. Some processes, like quality management or configuration management [15], are also considered during software development. How much support processes are incorporated into the software development process designed for the project depends on the need (contract regulations, company rules, etc.) and tailoring [9; 13; 15] associated with it. Knowledge areas like quality management and assurance [16], version, configuration or change management [1; 18; 29] are also within the scope of software engineering [18] and, thus, shall not be omitted while considering a software development process. As mentioned in [6; 13; 15], they are crucial to achieving an acceptable quality of a product or service and then for certifying the software and the drone.
## 3 Methodology
### Channel Additive White Gaussian Noise/Rayleigh  
For simplification and processing reasons, the simulations are baseband signal transmissions. Consequently, a signal in the passband can be represented by a complex signal in the baseband. The noise variation is applied to AWGN and Rayleigh channels, subjecting different video segments to varying channel conditions. The noise level increases gradually as the video frame sequence progresses, achieved by adjusting the Signal-to-Noise Ratio (SNR). The relationship between SNR and the noise variance can be defined by Equation (1) [37].  
\[SNR_{\text{dB}}=10\log_{10}\left(\frac{S}{\sigma^{2}}\right) \tag{1}\]  
where the following abbreviations apply:  
* \(SNR_{\text{dB}}\) is the SNR in decibels;
* \(S\) is the signal power;
* \(\sigma^{2}\) is the noise variance.  
The methodology employed results in a significant loss of quality in the information, as depicted in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline Parameters & Value \\ \hline FFT Length & 2048 \\ \hline Number of Subcarriers & 1705 \\ \hline Size of Cyclic Prefix & 512 \\ \hline Modulation Scheme & QPSK, 16 QAM, and 64 QAM \\ \hline Channel & AWGN and Rayleigh \\ \hline Configuration Reed–Solomon & 188/204 \\ \hline Code Rate & 1/2 \\ \hline Configuration BCH & 32,208/32,400 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 4: OFDM symbol generation.  
The simulations use the Rayleigh channel gain and delay values obtained from field tests performed by the Brazilian Association of Radio and Television Broadcasters (ABERT) and Mackenzie University [38]. Table 3 presents the gains and delays.
## 4 Dataset  
Throughout the South Field mine, several hundreds of sampling boreholes were drilled by the Public Power Corporation of Greece--PPC (Figure 5). During the present study and based on the above, all the borehole data were analyzed and interpreted in order to create a dataset including the characterization of distinguished horizons and their range of main quality properties. This dataset includes information on 66,376 samples taken from 825 boreholes. Sample lengths range from 0.01 to 19.43 m, with an average value of 1.13 m. Only 143 samples are smaller than 0.1 m and 48 samples are greater than 4 m.  
The selected samples have a set of information comprising the four most relevant quality properties--moisture percentage (%), ash percentage (%), CO\({}_{2}\) content (%), and Lower Calorific Value (kcal/kg). However, not all attributes are measured on all samples. More specifically, LCV, which is the main variable of interest, has been measured only in a portion of the total number of samples, as shown in Table 1. Usually, samples with LCV values are also analyzed for moisture, ash, and CO\({}_{2}\).  
The large number of analyzed samples per borehole is due to the multi-seam structure of the lignite deposit and the significant variation of the main quality characteristics between these seams (Figure 6). Nevertheless, the lignite seams need to be excavated either separately or together, depending on the thickness of the waste seams that are intercalated between them. The co-excavation of lignite and waste seams of limited thickness is considered as the optimum operating regime for increasing the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quality Properties** & **Number of Samples** & **Minimum** & **Maximum** & **Mean** & **Std. Dev** \\ \hline Moisture (\%) & 23,027 & 17.00 & 96.80 & 54.62 & 4.65 \\ Ash percentage (\%) & 23,027 & 4.71 & 81.60 & 28.35 & 10.40 \\ CO\({}_{2}\) content (\%) & 9136 & 0.10 & 71.40 & 16.99 & 9.66 \\ Lower Calorific Value (kcal/kg) & 11,186 & 93.00 & 2610.00 & 1419.59 & 306.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of samples for each quality property.  
Figure 5: Positions of boreholes (red color) and mining area (green color).