esa-sceva/satcom-chunk-collection huggingface dataset:
# The Clinical Role of SRSF1 Expression in Cancer: A Review of the Current Literature
## 3 Results
### Review
#### 3.2.3 Lung Cancer
The authors of this work found that SRSF1 plays a key role in the regulation of HIF (Hypoxia-inducible factors) alternative splicing, leading to a shift from HIF-1 alfaL to HIF-1 alfaS; in conclusion, the authors examined migration and invasion of cells in vitro with HIF-1 alfa-targeting vectors and they found that isoform HIF-1 alfaS has a greater metastatic potential in LC cells. They found that SRSF1 expression could play a clinical role enhancing metastatic activities of LC [36]. Another role of SRSF1 on NSCLC is to regulate an aberrant alternative splicing of the protein BIN1 (Bridging Integrator 1), a protein that could suppress immune escape in NSCLC and malignant behaviors [37]; in particular, SRSF1 activates the expression of the BIN1+12a isoform through 12a exon inclusion. The authors demonstrate that this aberrant isoform eliminates NSCLC cell apoptosis and suppression of invasion, leading to neoplasia free progression [38]. The most recent finding regarding SRSF1 and LC is the role of this proto-oncogene in autophagy, which is an intracellular degradation system that has the role of recycling and destroying old and aberrant proteins and organelles [39]. The authors demonstrated that SRSF1 regulates the alternative splicing of the long isoform of the protein Bcl-X, well known for play a role in cell survival [40]; the Bcl-X long isoform suppresses apoptosis interacting with the Beclin-1 protein and preventing Beclin1-PIK3C3 interaction. Beclin1-PIKeC3 interaction is a well-known complex that stimulates the formation of autophagosomeand autophagy, playing a key clinical role in several settings such as tumorigenesis and neurodegeneration [41; 42; 43]. It was demonstrated that SRSF1 mediates the alternative splicing of the Bcl-X long isoform that suppresses apoptosis interacting with Beclin-1 protein and preventing Beclin1-PIK3C3 interaction, furthermore, SRSF1 interacts directly with PIK3C3 to disrupt this interaction [44]. In conclusion SRSF1 plays a key clinical role in tumorigenesis and progression for several subtypes of lung cancer with various mechanisms of action and interferes with different pathways. This evidence is summarized in Figure 4.
## 3 Results and Discussion
### Ablation Study  
#### 3.6.1 Attention Mechanism  
To validate the impact of PHAM on the detection performance of the model within the proposed improvement methods, ablation experiments were designed using YOLOv8n as the baseline model on the SBDS dataset. In the experiments, the ELA and LMSA methods were added separately at the connection between the lowest resolution layer of the PAN-FPN feature pyramid structure and the backbone network for comparison, as shown in Table 3. The integration of either LMSA or ELA improved the precision recall and mAP of the model. Notably, when LMSA and ELA were combined to form the complete PHAM mechanism, the model achieved the highest detection performance, with the precision, recall, and mAP\({}_{0.5}\) improving by 0.6%, 0.6%, and 0.7%, respectively. This result not only demonstrated that the integration of certain mixed attention mechanism modules could enhance the feature fusion capability of the FPN but also confirms the advantage of PHAM in reinforcing the contextual information perception of targets with varying maturity levels in strawberries, thereby effectively improving the detection performance.
## 5 Discussion
### Usefulness, Applicability, and Performance of the Proposed Technique  
The usefulness of the proposed method is that, as described in Section 3.1, we constructed the attack system and verified the practicality of keyboard data exposure using machine-learning models. We collected 28,590 scancodes from three datasets and increased the attack success rate of keyboard attack by defining elapsed time and scancode as features. In particular, keyboard data collected from keyloggers installed by an ignorant attacker described in Section 5.1 cannot be used to steal passwords input from the user on the system that deployed the keyboard data defense technique described in Section 2.3. Nevertheless, if the same attacker uses the proposed attack technique, the attacker has a high attack success rate. Therefore, the proposed technique has usefulness.  
If the attacker constructs the attack system shown in Figure 3, the attack succeeds. If the ignorant attacker, as described in Section 5.1, can install keyloggers and use machine-learning models, the proposed technique can be applicable. Therefore, the proposed technique means is applicable. Moreover, with regard to malware and vulnerability detection, the proposed attack method is applicable, because the method bypasses detection in the way of detecting malicious code or vulnerabilities presented by [15; 16]. Specifically, this approach uses system-provided functions that are free from having vulnerabilities, so that an attack tool is not vulnerable or detected as malware.  
Keyboard data collected from keyloggers installed by the ignorant attacker alone shows the attack success rate as low as 21.5% and up to 88.0%, even with machine-learning models used. On the contrary, defining elapsed time as a feature proposed in this paper, the attack success rate is as low as 90.4% and up to 96.2%. Moreover, the performances proposed in this paper by the performance evaluation measures were significantly better. Therefore, we verified that the proposed technique has high performance.
## 4 RFQ-Decelerator for the GSI HITRAP Facility
### RFQD Test at an External Facility  
In a next step the obtained simulation results were confirmed experimentally. The HITRAP RFQD was transported to MPI-K (Heidelberg, Germany) and tested at the beam line behind the Pelletron accelerator, which provides for an almost mono-energetic H\({}_{2}\)\({}^{+}\) beam with a variable energy, in particular inside the required range from 450 keV/u to 550 keV/u at a sufficient repetition rate to carry out the necessary investigations. Additionally, the energy-analyzing detector, usually installed behind the HITRAP IH-DTL at GSI, has been calibrated at MPI-K facility with several beams of precisely known energy. Furthermore, the data, taken during previous beam times at GSI facility, has been proved. In particular, the beam energy behind IH-DTL, operated in a nominal mode, has been confirmed as only a few keV/u below 500 keV/u, which is well consistent with the design parameters [15].  
The beam experiments with the HITRAP RFQD, performed at MPI-K for a wide spectrum of the input beam energy and rf-power (rf-voltage), have impressively confirmed the results of the DYNAMION simulations:  
* RFQD steadily decelerates ions from 525 keV/u to the design energy of 6 keV/u;
* the acceptable input energy of ions varies from 520 to 530 keV/u;
* no deceleration was observed in the vicinity of the design energy of 500 keV/u.  
Therefore, in addition to the DYNAMION simulations, this machine study also confirms why the decelerated ions behind the fabricated original RFQD could not be observed during dedicated experimental campaigns at the GSI-HITRAP setup.
## 3 Results
### Model Performances and Comparisons
#### 3.3.2 The Case of Smaller Inventory Polygons  
The performances of the No. 1 model based on the RF method with a cost of 5000 and the No. 3 model with the RF algorithm were assessed, as presented in Table 6, to predict the samples extracted from the polygon areas equal to or larger than 50,000 and smaller than 100,000 m\({}^{2}\). The accuracies were lower than those displayed in Figure 13a, especially for the runout UA and PA of  
Figure 12: Performances (Y- axis) of the No. 5 model, where OA, AUC, PA, UA, S, and R represent the overall accuracy, area under ROC curve, producer’s accuracy, user’s accuracy, landslide source, and runout-out, respectively. (**a**) Using SVM with linear, the first- to third-order polynomial, and radial basis (RBF) functions, (**b**) comparing the optimal best results of Table 5 and (**a**) with logistic (Logis) regression.  
Figure 13: Comparison of the representative results using the RF method for the No. 2, 3, and 4 models, with costs of 5000 and 2000 for No. 1 and 5 models, respectively. (**a**) User’s accuracy (UA), producer’s accuracy (PA), and overall accuracy (OA); (**b**) area under the ROC curve (AUC) for landslide source (S) and runout (R) detections.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **UA(R)** & **PA(R)** & **UA(S)** & **PA(S)** & **AUC** & **OA** \\ \hline RF & 1 & 0.49 & 0.38 & 1 & 0.76 & 61.44\% \\ RF\_2000 & 1 & 0.76 & 0.56 & 1 & 0.91 & 81.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the No. 5 model using the RF method with a cost of 2000, where OA, AUC, PA, UA, S, and R represent the overall accuracy, area under the ROC curve, producer’s accuracy, user’s accuracy, landslide source, and runout, respectively.  
the No. 1 model. Furthermore, the No. 3 model with the RF method outperformed the No. 1 model, indicating accurate results. The topographic roughness index (TRI) is commonly used for assessing landslide susceptibilities [61]. TRI was further used to improve this case as also listed in Table 6. However, the significant topographic attributes [62; 63; 64] and various indices [65] for separating landslide source and runout signatures need to be further explored. These results reveal the uncertainty between the training data and prediction targets. More precisely, the area size of the training inventory polygon affected the capability and applicability of the constructed models. In practice, manually interpreting the larger targets of landslide source and runout from remotely sensed images to produce the training data was easier than selecting smaller objects, which was a clear limitation of using the supervised strategy with four topographic variables in this study. Addressing this problem requires further consideration regarding the tradeoff between the cost of producing training data and the applicability of the developed models. The developed models were suggested for different area sizes of the inventory polygons.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Training with Position Data Only
#### 6.4.2 Achieved Results  
By relying on position data only and applying the previously mentioned improvements our implemented system is able to achieve an AUC of approximately 84% and an EER of about 25% using 25 consecutive strokes and gestures with more than 50 data points only as illustrated in Figure 5. To further improve on those first results, the next sections are considering more features for the classification process.
## 2 Materials and Methods
### Data Processing Methods
#### 2.3.4 Precision Evaluation Method (ROC Curve)  
In assessing model predictive performance, the receiver operating characteristic curve (ROC curve) serves as a widely adopted metric to evaluate the model's classification capability across varying thresholds. The ROC curve assesses model accuracy by illustrating the relationship between the true positive rate (TPR) and the false positive rate (FPR), with the area under the curve (AUC) providing a quantitative measure of classification performance. AUC values approaching 1 indicate superior model classification performance [33]. Specifically, the true positive rate (TPR) and false positive rate (FPR) are calculated as follows:  
\[TPR=\frac{TP}{TP+FN},FPR=\frac{FP}{FP+TN} \tag{4}\]  
where TP denotes the true positive, FP represents the false positive, TN is the true negative, and FN is the false negative. These metrics form the foundation for evaluating model performance. In this study, the accuracy of the ResNet18 model in assessing collapse hazard risk is evaluated by computing the area under the curve (AUC) value. The AUC values of different models are compared to determine the optimal model. Furthermore, the Receiver operating characteristic (ROC) curve is employed to detect potential overfitting or underfitting, thereby facilitating the optimization of model parameters [34].
## 8 Conclusions  
1. FLAC3D numerical simulation was used to analyze the law of land subsidence under the condition of repeated mining in multiple coal seams of the Ehuobulake Coal Mine. Under the condition of multiple coal seam mining, land subsidence presented obvious asymmetry. The size and scope of land subsidence further increased due to the mining of lower layer coal. There were two obvious settlement funnels on the ground, with maximum displacement of \(-180\) mm and \(-211.8\) mm, respectively.
2. The land subsidence results of the Ehuobulake Coal Mine monitored by SBAS-InSAR technology were of high accuracy. The results showed two obvious subsidence areas of \(1\#\) and \(2\#\) in the mining area during the study period. The maximum displacement was \(-225\) mm, and the maximum displacement rate was \(-67\) mm/a. With the advancement of underground mining working face, the range of land subsidence area gradually expanded.  
Figure 18: FLAC3D numerical simulation versus SBAS-InSAR monitoring ground vertical displacement.  
3. The method of combining FLAC3D and InSAR technology can accurately and reliably monitor and analyze the land subsidence situation under the repeated mining of multiple coal seams in mining areas, as well as provide an effective approach for the prediction of the land subsidence law in the later period.  
Conceptualization, S.Z. and H.W.; methodology, H.W. and S.Z.; validation, S.Z., C.S., H.K. and H.L.; investigation, S.Z., G.L. and Y.L.; data curation, S.Z., F.Y. and G.X.; writing--original draft preparation, S.Z., H.L. and G.L.; writing--review and editing, S.Z., H.W. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 51964043, the Natural Science Foundation of Xinjiang Uyghur Autonomous Region (2022D01E31), the Xinjiang Uygur Autonomous Region \"Tanshan Talent Training\" Program (2022 TSYCCX0037), and the Xinjiang Uygur Autonomous Region Special Program for Key R&D Tasks (2022B01034,2022B01051).  
The research data used to support the findings of this study are currently under embargo while the research findings are commercialized. Requests for data, 12 months after publication of this article, will be considered by the corresponding author.  
The authors declare no conflict of interest.
## 3 Proposed Methodology
### Image Enhancement  
The visual signal distortion of mammogram images is considered a major problem of diagnosis due to the coherent nature of the transferred wave. This distortion is called noise. Images can be affected by either adaptive or multiplicative noise [46]:  
\[Additive\;noise:w(x,\;y)=s(x,\;y)+n(x,y) \tag{1}\]  
\[Multiplicative\;noise:w(x,\;y)=s(x,\;y)\times n(x,y) \tag{2}\]  
where the original image is represented by \(s(x,y)\), and noise introduced in the image is indicated by \(n(x,y)\), resulting in the corrupted image \(w(x,y)\). The position of pixels is represented by \((x,y)\). The concept of denoising images is shown in Figure 2. The denoising technique has been applied to form the denoised image \(z(x,y)\).  
Random noise that affects mammogram images during data acquisition can lead to corrupted images. Thus, image processing filters must be implemented to enhance and eliminate existing noise. In this study, the EWBFEM model is proposed to eliminate noise and enhance mammogram images. EWBFEM can reduce the noise of images by removing high-frequency bands and enhance the mammogram based on the threshold value. The Bayes-Shrink technique is used to minimize the Bayesian risk via soft thresholding, which is a smoothness-adaptive strategy that depends on sub-bands. The thresholding operator in the decomposition of WT is performed at each resolution band. The \(t_{B}\) of the Bayes threshold is defined in Equation (3).  
\[t_{B}=\sigma^{2}/\sigma_{s}^{2}, \tag{3}\]  
where \(\sigma^{2}\) represents variance of noise and \(\sigma_{s}^{2}\) represents the signal variance without noise. Using Equation (4), \(\sigma_{y}^{2}\) can be calculated. The signal variance \(\sigma_{s}^{2}\) can be calculated using Equation (5). Using \(\sigma^{2}\) and \(\sigma_{s}^{2}\), the Bayes threshold has been calculated as in Equation (5).  
\[\sigma_{w}^{2}=\frac{1}{n^{2}}\;\sum_{x,y=1}^{n}w^{2}(x,y), \tag{4}\]  
\[\sigma_{s}=\sqrt{max(\sigma_{w}^{2}-\sigma^{2},0)}, \tag{5}\]  
High-frequency bands are thresholded within one level of decomposition. The HH, LH, and HL bands are contained in the high-frequency mammogram component. The EWBFEM uses four different filters under the same threshold value. First, only the HH band is denoised based on the threshold. Second, the HH-LH bands are denoised using the threshold value. Third, the HH-HL band is denoised using the same threshold value. Lastly, all bands containing high frequencies are denoised (LH-HH-HL).
###### Abstract  
A portable instrumental system was designed for the routine environmental monitoring of toxic volatile organic compounds (VOCs) in atmospheric conditions based on changes in the photoluminescence emission of semiconductor nanoparticles (quantum dots) entrapped in a sol-gel matrix as the solid sensing material. The sol-gel sensing material displayed a long-lived phosphorescent emission, which is quenched in the presence of trace levels of a volatile organic compound (acetone) in gaseous atmospheres. The developed instrument could measure and process the changes in the photoluminescence of the sensing material after exposure to gaseous acetone. The developed prototype device consists of a deep-ultraviolet light-emitting diode (UV LED), which excites the chemical sensing material; an optical filter to remove scattered light and other non-desirable wavelengths; a photomultiplier tube (PMT) to convert the phosphorescence emission of the sensor phase to an electrical signal; and a microcontroller to correlate the signal with the analyte concentration. The developed prototype was evaluated for its ability to measure low levels of gaseous acetone in contaminated atmospheres with high sensitivity (detection limit: 9 ppm). The obtained results show the feasibility of this type of instrument for environmental analytical control purposes.  
instrumentation; nanoparticles; photomultiplier; quantum dots (QDs); room temperature phosphorescence (RTP); UV LED +
Footnote †: journal: Applied Sciences
# PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm
## 2 System Model  
Figure 1 illustrates the structure of the PDASTSGAT-MPTCP algorithm, which consists of an MPTCP server, a link splitting mechanism, an MPTCP client, \(N\) links, and their corresponding link states. To select the optimal subflow, the packet-splitting mechanism utilizes the link states of the individual subflows and determines the best link choice for the current data packet. The notations for this article are shown in Table 1.  
The primary objective of the overall system design is to determine the optimal transmission path from a set of multiple subflows. The exposition pertaining to the management and scheduling of multiple paths is as follows:  
1. Obtaining the subflow state: The MPTCP server concurrently distributes packets to established subflows and transmits them to the MPTCP client [10]. At the MPTCP client, we can retrieve the current state matrix for each individual subflow.
2. The mechanism for packet-splitting: The current state matrix, obtained for each subflow, combined with the localized spatiotemporal graph, \(A^{{}^{\prime}}\), representing the local spatial-temporal graph and the transmitted packet information, forms the input at the current time step. By utilizing the learned link graph spatial-temporal features through the packet-splitting mechanism, the optimal link selection for the current packet is determined.
3. Updating the subflow state: The packets are distributed to the optimal subflow, and the MPTCP client updates the current state of each subflow and the subsequent subflow state matrix for the next time step.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Meaning** \\ \hline \(N\) & The number of nodes \\ \(C\) & The number of features \\ \(T\) & The length of time slots \\ \(A\in\mathbb{R}^{N\times N}\) & The adjacency matrix of the spatial graph \\ \(A^{{}^{\prime}}\in\mathbb{R}^{3N\times N}\) & The adjacency matrix of the localized spatiotemporal graph \\ \(X_{C}\in\mathbb{R}^{N\times C\times T}\) & The spatiotemporal network sequence \\ \(T_{cntk}\in\mathbb{R}^{C\times T}\) & The learnable time embedding matrix \\ \(S_{cntk}\in\mathbb{R}^{N\times C}\) & The learnable spatial embedding matrix \\ \(e_{ij}\) & The correlation between node \(j\) and node \(i\) \\ \(\overline{a}^{k}\in\mathbb{R}^{2E^{\prime}}\) & The weight vector employed in the same self-attention mechanism \\ \(a^{k}_{ij}\) & The correlation between node \(j\) and node \(i\) within the \(k\)th self-attention mechanism \\ \(W^{k}_{i}\) & The linear transformation matrix within the \(k\)th self-attention mechanism \\ \(\overline{b}^{k}_{i}\) & The output feature in the multi-headed attention mechanism STSGAT network \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
Figure 1: System model.
## 3 Results
### Climate Data Trend  
We used the Mann-Kendall test and Sen's slope estimator for detecting monotonic trends in rainfall and temperature. The total monthly rainfall and average monthly temperature data from 2010 to 2019 were used for this purpose. The data, including trend lines, were plotted in Figure 8. The trend line used for both plots in this figure is a Loess regression line, commonly used for smoothing a volatile time-series such as climate data.  
From the Mann-Kendall test result, a statistically significant \(p\)-value (\(p\) < 0.05) was found for both climate data, indicating the presence of monotonic trend. The Tau values for rainfall and temperature are \(-\)0.162 and 0.387, respectively. The positive Tau value denotes an upward trend, while the negative value indicates a downward trend. These values showed that the study area experienced a decrease in monthly rainfall and an increase in monthly mean temperature over the study period. The Sen's slope results confirmed the Tau values by giving an estimate of a total monthly rainfall data decrease of 0.93 mm and average monthly temperature increase of 0.008 \({}^{\circ}\)C, per month. The single change-point in a monthly rainfall and temperature series based on the [PERSON]'s test was identified in December 2013 and June 2014, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Phenological Metrics**} & \multicolumn{3}{c}{**Growing Season 8 (2017/18)**} & \multicolumn{3}{c}{**Growing Season 9 (2018/19)**} \\ \cline{2-7}  & **MODIS** & **Sentinel** & **Differences in DOY** & **MODIS** & **Sentinel** & **Differences in DOY** \\ \hline SOS & 226 & 234 & 8 & 212 & 207 & 5 \\ EOS & 170 & 169 & 1 & 192 & 188 & 4 \\ LOS & 309 & 300 & 9 & 345 & 346 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of SOS, EOS, and LOS (in day of year) for growing seasons 8 and 9.  
Figure 8: Ten-year monthly climatic data: (**a**) total monthly rainfall; and (**b**) average monthly temperature. The blue line is a trend line of Loess regression.