esa-sceva/satcom-chunk-collection huggingface dataset:
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 4 Experimental Analysis
### State Selection  
In Section 3.2, we split the states into two according to their importance. One type includes high-priority states, and the other includes low-priority states. In each cycle, the state selector only focuses on high-priority states and ignores low-priority states. To verify the effectiveness of this method, we conducted a comparative experiment with three state selection criteria. To reduce the impact of randomness, we ran 10 isolated instances of each group for each subject. We set the test time to 12 h, and the experimental results of the three groups are shown in Figure 4.  
The state selection criterion for group one (G1 for short) includes low-priority states only. Group two (G2 for short) chooses round-robin scheduling, which is the same technique as that used in AFLnet. Group three (G3 for short) realizes our method. It can be seen from Figure 4(a) that, for LightFTP, in terms of vulnerability discovery efficiency, G1 decreases by 57.51% on average compared to G2, while G3 increases by 34.27% on average compared to G2. Figure 4(b) shows that, for Live555, in terms of vulnerability discovery efficiency, G1 is zero, while G3 and G2 have the same value.  
Figure 4: Comparative experimental results obtained with different state selection criteria. (**a**) LightFTP. (**b**) Live555.  
By analyzing the experimental results, we find that high-priority states performed much better in exploring the state space than low-priority states, which verifies the effectiveness of our state selection method. In addition, the performance of our method on Live555 was worse than that on LightFTP because, when the SFSM is applied to Live555, the status code set does not contain low-priority states, a condition for which G3 and G2 have the same state selection criterion; in contrast, the state selection criterion of G1 fails to take effect.
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 5 Conclusions  
This study introduced a novel approach for detecting skin cancer by combining a ConvNeXt-based CNN model with a modified attention mechanism. This method is crucial for early detection and distinguishing between visually identical skin conditions. The ConvNeXt architecture is effective for feature extraction, and a custom attention mechanism was integrated to distinguish between malignant and benign lesions in dermatoscopic images. The experimental findings on the HAM10000 dataset demonstrated a dramatic improvement in accuracy and a decrease in false positives and false negatives. This study  
Figure 7: Visualized results of the proposed model for effective skin cancer localization and classification.  
also highlighted the potential of deep learning models for medical image processing, particularly when paired with attention mechanisms. This method may aid in early diagnosis, reduce the impact of skin cancers on patients, and increase the chances of positive results. This study established a framework for more accurate and efficient skin cancer diagnoses by demonstrating the potential of ConvNeXt-based CNN models with attention processes. The investigation of ConvNeXt-based CNN models featuring attention mechanisms has revealed encouraging results for skin cancer recognition. However, further improvements are required including expanding the dataset to include a wider range of skin conditions, incorporating transfer learning from other medical imaging domains, adapting the model for real-time applications, developing interpretable AI methods, conducting clinical trials, and integrating multimodal data fusion to improve the diagnostic accuracy. These advancements will benefit patients and healthcare professionals in the early diagnosis of skin cancer.  
Conceptualization, S.M.T., H.-S.P. and S.H.S.; Methodology, S.M.T. and H.-S.P.; Software, S.M.T., H.-S.P. and S.H.S.; Validation, S.M.T., H.-S.P. and S.H.S.; Formal analysis, S.M.T., H.-S.P. and S.H.S.; Investigation, S.M.T. and H.-S.P.; Resources, H.-S.P. and S.M.T.; Data curation, H.-S.P. and S.H.S.; Writing--original draft preparation, S.M.T.; Writing--review and editing, S.M.T., H.-S.P. and S.H.S.; Visualization, S.M.T. and H.-S.P.; Supervision, H.-S.P. and S.H.S.; Funding acquisition, H.-S.P. and S.H.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Ablation Experiment and Analysis  
Since the construction of HFDF-EffNetV2 involved multiple methods, ablation experiments were conducted on the CWRU bearing dataset to more intuitively visualize the impact of each method on the model's diagnostic performance. As shown in Table 10, The BASE refers to the model constructed based on EfficientNetV2-S. The BASE + A and BASE + B models incorporated the methods of Fused-MBPyConv and BSMBConv-MLCA, respectively. The BASE + C model employed the HFDF strategy, and the BASE + A + B + C represents the proposed model.  
The experimental results are presented in Figure 19 and demonstrate that the application of the three improvement methods significantly enhanced the diagnostic accuracies under strong noise interference. At 2 dB, the accuracy of the BASE + A + B + C was improved by 7.79% compared to the BASE. At 4 dB, BASE + C with the HFDF strategy applied showed improvements of 1.4%, 0.66%, and 0.09% compared to BASE, BASE + A, and BASE + B, respectively. At 6 dB and 8 dB, all models showed improved performance, making the relative improvement of the three enhanced methods compared to BASE non-significant. However, the average accuracies of the three enhanced methods were increased by 1.22%, 1.71%, and 1.46%, respectively, compared to BASE. Overall, all three improvement methods were effective in enhancing the model's performance under noise interference, with the most significant contributions coming from the application of BSMBConv-MLCA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **BASE** & **BASE + A** & **BASE + B** & **BASE + C** & **BASE + A + B + C** \\ \hline Parameters/M & 2.31 & 2.37 & 1.67 & 2.44 & 1.85 \\ Time/ms & 15.24 \(\pm\) 0.29 & 17.43 \(\pm\) 0.31 & 17.18 \(\pm\) 0.30 & 15.80 \(\pm\) 0.29 & 18.67 \(\pm\) 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The number of parameters and diagnostic time for ablation models.
# Signal Detection for GSTFIM Systems with Carrier Frequency Offset +
## 4 Proposed Detectors Based on the MP Criterion
That is, the basic unit is a spatial- and time-domain symbol in the proposed detectors, while it is a spatial- and frequency-domain symbol in [30]; (b) The GSTFIM transmit symbols can be reconstructed by combining three-dimensional information including \(N_{r}\) antennas, \(N_{b}\) subcarriers, and \(T\) symbol durations, rather than only combining \(N_{r}\) antennas and \(N_{f}\) subcarriers in [30], and the set of \(\mathbf{X}_{l}\) includes the zero matrix, which is not existent in [30]. Specifically, in the detector of [30], the posterior probability of the CSFIM symbol \(\mathbf{X}_{g}\in\mathbb{C}^{N_{l}\times N_{f}}\) obtained via the MP criterion is directly utilized to reconstruct the detected GSFIM symbol by comparing the posterior probabilities. However, in the proposed SC-MP detector, the posterior probability \(P(\mathbf{X}_{k}=\mathbf{fi}_{i})\) is used to first select a possible GSTSK symbol for all the subcarriers, and then the activated subcarrier is obtained by comparing the posterior probability of the possible GSTSK symbol in different subcarriers. In the proposed BC-MP detector, the probability of block carriers is calculated by two parts, namely the probability of the GSTSK (activated) symbol and the all-zero matrix, which are obtained by the posterior probability \(P(\mathbf{X}_{k}=\mathbf{fi}_{i})\). Finally, the sum of the probabilities of the GSTSK symbol and all-zero matrix is considered as the probability of the block subcarriers. However, due to the unique advantage of MP, we will disclose the performance improvement of the MP detector over its counterpart MMSE-based one, but with reduced computational complexity.
# Integration of _Salmonella_ into Combination Cancer Therapy
## 4 Clinical Application of _Salmonella_ in Combination Cancer Therapy
### Clinical Trials
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _Salmonella Strain_ & \begin{tabular}{c} **Route of** \\ **Administration** \\ \end{tabular} & \begin{tabular}{c} **Cancer Model** \\ \end{tabular} & \begin{tabular}{c} **Phase** \\ \end{tabular} & \begin{tabular}{c} **Recruitment Status** \\ \end{tabular} & \
\begin{tabular}{c} **Identifier** \\ **(Net Number)** \\ \end{tabular} &
\begin{tabular}{c} **Ref** \\ \end{tabular} \\ \hline VNP20009 & Intraumoral & Refractory, superficial solid tumors & 1 & Completed & NCT00004216 & [175] \\ \hline VNP20009 & Intravenous & Advanced or metastatic cancer & 1 & Completed & NCT00004988 & [176] \\ \hline VNP20009 & Intravenous & Metastatic melanoma and metastatic & & & & \\  & & renal cell carcinoma (RCC) & 1 & Completed & NCT00006254 & [177] \\ \hline _S. typhimurium_ SalpPL2 (expressed human IL-2) & Oral & Liver metastasis of solid tumors & 1 & Completed & NCT01099631 & [178] \\ \hline VX001 vaccine & Oral & Advanced pancreatic cancer & 1 & Completed & NCT01486329 & [179] \\ (_S. typhimurium_ T212 expresses VEGFR) & Oral & Multiple myeloma & 1 & Not yet recruiting & NCT03762291 & [180] \\ \hline _S. typhimurium_ strain (S2017) expressing & Oral & Neurobastoma & Early phase I & Recruiting & NCT04049864 & [181] \\ tumor DNA vaccine & & & & & & \\ Salikva & & & & & & \\ (_S. typhimurium_ expenses human IL-2) & Oral & Metastatic pancreatic cancer & II & Recruiting & NCT045899234 & [182] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of previous and ongoing bacterial-therapy clinical trials utilizing _Salmonella_.
## 3 Results and Analysis
### Correlation between the Fractal Dimension of Particle Size Distribution and the Water Content  
According to different repeated compaction times, the fractal dimension of particle size distribution and water content were analyzed, and the obtained results are shown in  
Figure 8: Distribution of fractal dimension against the repeated compaction time.  
Figure 9. It is observed that as the water content increases, the fractal dimension of the particle size distribution first increases and then decreases, which is consistent with the distribution of dry density and water content.  
It was observed that at a constant repeated compaction time, the water content of graded crushed stone has a greater influence on the fractal dimension. Using the repeated compaction time of three as an example, when the water content increased from 2.0% to 3.5%, the fractal dimension increased by 0.11 g/cm\({}^{-3}\); when the water content increased from 5.0% to 6.5%, the fractal dimension increased by 0.011 g/cm\({}^{-3}\). It can be seen that the closer the water content of the graded crushed stone is to the optimal water content, the smaller the influence of water content on the change of the fractal dimension, and the more easily the graded crushed stone is compacted. When the water content of the graded crushed stone is too low or too high, it is difficult for the graded crushed stone to achieve a better compact state.  
Figure 9 indicates that there is a strong correlation between the fractal dimension of particle size distribution and water content. In this regard, a quadratic function \(D=a\omega^{2}+b\omega+c\) was used to fit the data. The correlation coefficients of the fitting results are above 0.97, indicating the accuracy of the curve fitting. The obtained list for different items is provided in Table 5.
## 4 Performance Analysis
### Impact of Selection Time-Slot in Co-Carrier-Based SC  
In downlink time division multiplexing (TDM) SATCOM, the system would allocate transmission time-slots for the selection process in the co-carrier-based SC method. The length of each time-slot could be adjusted according to the transmission rate. Generally speaking, lower carrier rates require a longer time-slot. In this subsection, we compare the advancement of DC and SC methods in terms of the length of time-slot \(\tau\).  
**Definition 1**.: _For the beam-edge UT, the ergodic rate gain of the co-carrier-based SC method is defined as_  
\[G=10\log\frac{\mathbb{E}[R_{k}^{{}^{\prime}}]}{\mathbb{E}[R_{k}]}. \tag{39}\]  
To obtain a positive value of \(G\), \(\mathbb{E}[R_{k}^{{}^{\prime}}]-\mathbb{E}[R_{k}]>0\) should be met. In this case, the length of each time-slot satisfies  
\[\tau<\frac{\int_{0}^{\infty}\frac{1-(1-e^{-\frac{x}{|\beta_{k}|^{2 }\delta(1-ax)}})M}{1+x}-\delta|\beta_{k}|^{2-2M}\sum_{i=1}^{n}\omega_{i}B_{k}( \delta M|\beta_{k}|^{2}x_{i}). \tag{40}\]
## 1 Introduction
### Correlation between Uniaxial Compressive Strength and Point Load Index  
The point load index is an indirect way of estimating the \(\sigma_{c}\) of rock, and there is a wide range of correlations for this. During the early days of this test, it was determined based on the uncorrected point load index (\(I_{s}\)) [12; 13], but later the use of the point load index corrected for a diameter of 50 mm (\(I_{s(50)}\)) was widespread due to the publication of the scale effect of the point load index by [PERSON] [16], analogous to that of [PERSON] and [PERSON] [33; 34] for the \(\sigma_{c}\) from cylindrical specimens of intact rock.  
Table 1 shows the different correlations for limestone and traverine, from which the dimensions of the test specimens were extracted to determine \(\sigma_{c}\) and the corresponding ones to determine \(I_{s}\) or \(I_{s(50)}\). The point load tests were mainly carried out with diametrically loaded cylinders (Equations (1)-(5), (10), (15) and (16)), but also with axially and diametrically loaded cylinders (Equation (9)) or with regular blocks (Equations (11)-(13)). Another case is the correlations obtained from the results of other publications, such as that of [PERSON] [16] (Equation (8)), analogous to his method of the resistance index for a minimum area of 500 mm\({}^{2}\) (\(T_{500}\)) (Equation (7)) or those that do not define the geometry or the type of test (Equations (6) and (14)).  
It can be observed that most correlations are linear and pass through the origin of the coordinates, but both ASTM (Equation (18)) and ISRM (Equation (17)) do not clarify the cutoff point with the horizontal axis: ASTM [22] indicates that, if not there are experimental values of \(\sigma_{c}\), \(I_{s}\) multiplied by 23 to estimate the uniaxial compressive strength at a diameter of 50 mm (\(\sigma_{c(50)}\)) based on Bieniawski [13], while the ISRM [3] indicates that \(I_{s(50)}\) must be multiplied by a value between 20 and 25, and can vary between 16 and 60 for anisotropic rocks.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Correlation**} & \multirow{2}{*}{**R\({}^{\prime}\)**} & \multirow{2}{*}{**Back Type**} & \multirow{2}{*}{\(\sigma_{e}\)} & \multicolumn{2}{c}{**Genometry**} & \multicolumn{1}{c}{**F1\({}^{*}\)**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Equation**} \\ \cline{5-
## 3 Results of Case Study Research
### Investigations in the Vertical Plane  
The distribution of the RF EMF power density of the mobile antenna (ERP = 1428 W, height of 21.5 m) in the vertical plane is illustrated in Figure 13.  
It is evident that the mobile antenna (ERP = 1428 W, height = 21.5 m) at a distance of 35 m and a height of 13 to 23 m creates a 20-m basic radiation (coverage) zone in the vertical plane. At this height, the values of the power density of the RF EMF are distributed in the interval between 5.5 and 7.5 \(\mathrm{\SIUnitSymbolMicro W}\)/\(\mathrm{cm}^{2}\). As shown, the maximum value of the RF EMF power density occurs at a height of 16.5 m, but is not at the same level as the antenna's geometric centre, that is, at a height of 21.5 m above the ground. It is determined by the basic antenna characteristics of the directional diagram in the vertical plane. This diagram is a parameter that defines at what height and how widely the antenna radiates RF EMR. The distribution of the mobile antenna's diagram depends on the antenna's electrical and mechanical downtilt angle [23]. The mobile antenna's (ERP = 1428 W, height = 21.5 m) directional diagram in the vertical plane is formed by inclining the antenna at the angle of the electrical downtilt (-4\({}^{\circ}\)). The antenna's downward direction forms the basic coverage zone between the heights of 13 and 23 m. The electromagnetic power density limits were not exceeded.  
Figure 14 illustrates the contributions of the GSM-900, GSM-1800, and UMTS-2100 mobile antenna technologies (ERP = 1428 W, height = 21.5 m) and background to the RF EMR distribution, as a percentage, depending on height. As the figure shows, the distributions of the GSM-900 MHz, GSM-1800 MHz, and UMTS-2100 MHz mobile antenna technologies' RF EMR and background RF EMR are practically the same for all possible cases of height. If we compare the percentage RF EMF power density distribution from distance with the percentage RF EMF power density distribution from height, we can see that when moving further away from the mobile antenna, the background RF EMR increases (Figure 14), whereas when the height increases above the ground, the background radiation changes very little. This proves that the RF EMF power density created by the mobile antenna, depending on the electric and mechanical downtilt angles, is distributed in the horizontal plane.  
The uneven RF EMR distribution of the mobile antenna (ERP = 1428 W, height = 21.5 m) depends on the antenna technology used. This mobile antenna uses three technologies, each characterized by different RF EMR as follows: for the GSM-900 technology, the ERP is 407 W; for the GSM-1800  
Figure 13: Distribution of the electromagnetic power density of the mobile telephony base station antenna (ERP = 1428 W) at different heights.  
technology it is 675 W; and for the UMTS-2100 technology it is 346 W. The calculated EQ is less than 1 and therefore the environment is considered safe against radio frequency radiation.
## 4 GNSS-Based Location Context Profiling
### Feature Engineering Pipeline
#### 4.1.2 Satellite Position-Based Feature Packing  
As we have elaborated in Section 2.5, packing samples for all satellites into one compact and meaningful representation that is highly non-trivial. Fortunately, the preliminary studies in Section 3.1 indicate that the satellite pattern might be a good way of indexing the complex data structure. Further inspired by the fact that most deep learning models take images (i.e., 2D pixel maps) as a common input, we design a pixel-position-based packing mechanism. Essentially, we represent each feature of all visible satellites as a _snapshot_, i.e., an image with certain pixels representing the satellites and the other pixels taking their values from the features. Each snapshot is centered at the receiver's true location, and the relative satellite positions derived from _SvPosition_ are normalized to determine the corresponding pixel positions. As a result, each sample (for all satellites) contains \(\mathcal{M}\) snapshots and a record has \(\mathcal{T}\times\mathcal{M}\) of them. We could further pack the temporal dimension into these snapshots, but that would require a much higher resolution and may also lose track of temporal correlations. We show a few snapshot samples in Figure 1(a).  
To ensure the scale and trends of features are consistent, normalization and re-ranking are done before packing them into snapshots. The normalization phase transforms feature values within the range \([0,1]\)--it should produce a \"scattered\" distribution for all features  
Figure 2: Snapshots and corresponding representations (the \(x\)-axis is the feature index and the \(y\)-axis represents the feature magnitude, normalized to a scale of \([0,10]\)).  
and we use stereographic projection to map the celestial sphere to a plain. In particular, most uncertainty features follow a log-normal distribution, so we convert them logarithmically to obtain a quasi-normal distribution. The re-ranking phase makes sure all the features at each pixel have a consistent trend in their physical meanings. Since pixel values are in greyscale (i.e., non-negative integers) and the zero value indicates no valid data presented, a smaller value should correspond to greater uncertainty. Therefore, all the uncertainty and ambiguity features are reversely ordered to maintain consistency.
# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 3 Data Analysis and Model Construction
### Learning Model Description  
#### 3.2.1 Preprocessing Analyzed Data  
Data preprocessing involved several essential steps to ensure the dataset's quality and suitability for training the regression model. Firstly, the phase drift values were processed to extract their means and differentiations. The mean values were calculated as (pa1 + pa2)/2, and the difference values were calculated as pa1 \(-\) pa2. As shown in Figure 6, the overall system overview of the PLAE model illustrates the processing of raw phase drift value signals through feature extraction and subsequent angle detection. This includes calculating the mean and difference values, performing Z-score outlier removal, and preparing the data for the model's input layer. In Figure 6, the workflow of time series signal processing is further detailed, showing the structured sequences of 100 time steps using a sliding window approach, followed by min-max scaling and feeding into the model architecture, which comprises GRU and dense layers. This approach captures temporal dependencies and organizes the data for time series analysis.  
To ensure the data were on a consistent scale, min-max scaling was applied to the dataset. Due to the different ranges of the mean and difference values, min-max scaling was applied separately to each. This normalization process is crucial for enhancing the performance and convergence of machine learning models by ensuring that all input features contribute equally to the model's learning process. The scaled data were then divided into sequences, with the angle values serving as labels for each sequence. This  
Figure 5: Difference of pa1 and values across angles (\(-90^{\circ}\), \(-40^{\circ}\), \(0^{\circ}\), \(40^{\circ}\), and \(90^{\circ}\)).  
Figure 6: System overview of the PLAE model for angle estimation.  
structured approach enabled the segmentation of the dataset into training and testing sets, supporting effective regression analysis and model evaluation.