esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 6 Training and Detection Time Comparison
### Discussion  
Based on the experimental results, this paper finds that the BO-SVM and LSTM-BO-SVM models show better detection, even in the CICMalDroid-2020 dataset, where benign and malicious samples are unevenly distributed. This indicates that these two models have high robustness. Moreover, the consistency of the results obtained on different datasets further confirms their generalization ability.  
In the experiments in this paper, the BO-SVM model achieves high accuracy with a short detection time. This feature makes it potentially applicable to resource-constrained environments. For example, the trained model can be deployed to environments such as mobile handsets to effectively defend against potential SSC malware attacks through real-time detection of software when users download or update it.  
Although the training time of the LSTM-BO-SVM model is slightly longer than that of some simple ML and DL models, it outperforms others in terms of detection capability. It may be ideal for detection tasks that require high accuracy and have sufficient computational resources. For example, in areas such as risk management in the financial industry and healthcare information system protection, where devices are usually high-performance, the LSTM-BO-SVM model can provide desirable detection results to effectively defend against persistent threats and malware behaviors from SSC attacks.
## 4 Simulation Results and Discussions
That is to say, in the scenario where there is no memory limitation and the channel noise is generally large, we can replace the list flip decoder with a list decoder. For other scenarios, our decoder is a suitable choice due to its significantly lower complexity and smaller list size. To further verify the advantages of our algorithm, we created Figures 13 and 14, which show the performance comparison of the original D-SCLF2 decoder and our ALF2 decoder for different code lengths. In these figures, solid lines represent the performance of our ALF2 decoder, and dotted lines represent the performance of the original D-SCLF2 decoder. Figure 13 shows that our ALF2 decoder has an FER performance similar to that of the D-SCLF2 decoder with the same code length. Furthermore, the ALF2 decoder also has better FER performance than the D-SCLF2 decoder in some nodes, such as the phenomenon in Figure 10, since the accuracy of the original flip metric has room for further optimization. Figure 14 shows the average complexity performance comparison of the different decoders in Figure 13. We noticed that as \(E_{b}/N_{0}\) is increased, the average complexity of the ALF2 decoder and the DSCLF2 decoder all decrease, regardless of code lengths. In all cases, the average complexity of our ALF2 decoder always decreases faster than the D-SCLF2 decoder. Based on the above simulation results, we can conclude that in the practical FER range and in a wide range of code lengths and code rates, our ALF decoder has a significantly lower average complexity than the D-SCLF decoder with a similar error-correction performance. In particular, the new flip metric employed by our ALF decoder can greatly reduce the nonlinear operations introduced by the original flip metric of the D-SCLF decoder without affecting the error correction performance. Moreover, the \(z\) value of the new flip metric is fixed, and the value is 5. Figure 12: Comparison of the FER performance of the CA-SCL and ALF2, for \(PC(512,256+24)\).
## 6 Conclusions  
This study utilized a combination of field seismic geological surveys, geomorphological measurements, trench excavations, and GPR surveys to determine the spatial distribution and tectonic geomorphology of the Jining River Northern Margin Fault (JRNF). Based on chronological testing results, combined with fault outcrop observations and stratigraphic markers, the activity characteristics of the JRNF since the Quaternary period were determined, leading to the following conclusions:  
1. The JRNF is a NEE-trending frontal normal fault along the northern boundary of the Jining Basin, with a length of approximately 50 km. The fault controls the evolution of the Jining Basin, creating 1.5-1.8 m scarps at Hanqingba and 5.2-7.7 m scarps at Erija Yan.
2. The activity of the JRNF has gradually decreased since the late Middle Pleistocene (approximately 400 ka). During the Late Pleistocene, its activity did not rupture the surface, only exerting limited geomorphological control. The fault is classified as a late Middle Pleistocene to Late Pleistocene fault with no activity during the Holocene.
3. A single surface-rupturing event on the JRNF could result in vertical displacements of up to 1 m. The estimated maximum earthquake magnitude is M\({}_{w}\)g 6.95, indicating a significant seismic risk for the Jining Basin region.  
**Author Contributions: Writing--original draft, Methodology, Formal analysis, Visualization, Investigation, and Software, H.M.; Writing--review and editing, Funding acquisition, Supervision, and Conceptualization, S.D. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by the Investigation of Technological Basic Resources, the Ministry of Science and Technology, China [2021 FY100100]. Data Availability Statement: All data generated or analyzed during this study are included in this published article. Acknowledgments: We extend our gratitude to [PERSON] and his team for providing technical support in the GPR survey. We thank Google Earth for the free access satellite images used in this study. We thank the anonymous reviewers and editors for their helpful comments on the manuscript. Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. All data generated or analyzed during this study are included in this published article.**
###### Abstract
The aim of this study was to develop a new, scalable, data-driven approach for patients with T2D in the form of peer-to-peer support in a virtual environment, based on two principles still largely unexplored in healthcare: patient segmentation and positive deviance. Segmentation is widely used in marketing to subdivide populations based on demographics, psychographics, and behaviors [10]. The use of segmentation in healthcare is limited to, e.g., needs evaluation and service design [11; 12] and is not usually adopted for the provision of tailored interventions, despite growing evidence that it could support patient self-management [13; 14]. Positive deviance is a form of collective intelligence that assumes that solutions can be generated collectively within communities through a process of learning, rather than externally imposed. In healthcare, positive deviance has been used for health services assessment but it has not been used so far for developing group interventions [15; 16; 17]. In this study, we applied positive deviance by including one or two patients with good control of their disease in each peer-to-peer group to let patients with bad control learn from successful peers, with a view to intrinsic and socially-driven behavior change. The rationale was to address whether patients with T2D with poor control of their disease can learn from others who are successful in managing their diabetes and whether this kind of intervention can be more effective, in terms of supporting, learning, and motivation for behavior change, if it is tailored to patients with similar characteristics as opposed to current 'one size fits all' approaches. In this first study, patient segmentation is performed based on medication-taking behavior in order to further validate our preliminary findings [7]; however, the overall intervention is model-agnostic and can be adapted to different patient segmentation approaches.
# Neural Network Exploration for Keyword Spotting on Edge Devices
## 4 Evaluation and Deployment Results
### Evaluation Results
#### 4.1.6 Summary  
Overall, each architecture's relative performance varied depending on the model size and quantization method. The nonquantized models were always the largest in both model size and memory consumption, followed by 16-bit floating-point quantization, dynamic range quantization, integer-float quantization, and full-integer quantization. The DS-CNN architecture consistently produced the smallest model size. In terms of memory consumption, DS-CNN and FFCN models consistently utilized less memory than similar-sized models of other architectures.  
While the architectures performed similarly in accuracy, ResNet consistently distinguished itself as the most accurate architecture when model sizes approached their minimum. For the larger models, accuracy clustered around 90%, except for the full-integer quantization models. Additionally, with full-integer quantization, Inception achieved over  
Figure 8: Memory consumption of neural network models with different quantization schemes: (**a**) 16-bit floating point; (**b**) dynamic range; (**c**) integer-float; (**d**) full integer.  
70% accuracy, outperforming all other architectures by approximately 10%, with model sizes larger than 25 kB.  
In terms of latency, all neural networks, except those using dynamic range quantization, could complete inference in less than 1 ms. Additionally, latency and model size were positively correlated. While latency is an important metric to consider when evaluating a neural network, no architecture was inadequate. For this keyword spotting application, 25 ms would pass before a new mel spectrogram could be produced, meaning that all models would easily complete the inference process before a new spectrogram was available.  
Finally, the number of parameters increased linearly with model size, as expected. However, each architecture exhibited different parameter densities (the number of parameters per byte of model size or the slope of the parameter count versus model size graph). Since the number of model parameters is correlated with the flexibility of a neural network, this comparison may be helpful when training a neural network using a dataset much larger than Google Speech Commands.  
Since the processor within an edge device may not always have a floating-point unit, full-integer quantization is the most attractive option. Otherwise, the latency of the neural network would significantly increase by attempting to compute floating-point operations completely in software. Considering this, the best architecture would be Inception or ResNet, depending on the memory and storage available.
## 5 Discussion
### Limitations and Future Work  
5.3.1. Limitation on Real-Time Detection  
The average calculation time of quality assessment for one fecal microscopic image is shown in Table 3. A fecal microscopic image can be divided into 12 image patches with a fixed size of 512 \(\times\) 768. These patches can be concatenated along the batch channel and be detected in one single inference. The total average calculation time reaches 248 ms per image. In general, an image group captured in the autofocus process contains 10 to 30 images; that is, it takes 2 to 7 s to find the clearest image. Therefore, the proposed model still has a limitation regarding real-time detection.
## 4 Simulation Results and Performance Analysis
### Experimental Results
\begin{table}
\begin{tabular}{c c c c|c c c|c c c} \hline \hline \multirow{3}{*}{**PRN**} & \multicolumn{3}{c|}{**Traditional (Serial search)**} & \multicolumn{3}{c|}{**GCN with adaptive scaling scheme**} & \multicolumn{3}{c}{**GC w/o adaptive scaling scheme**} \\ \cline{3-10}  & \multicolumn{3}{c|}{**step size: 50 Hz)**} & \multicolumn{3}{c|}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{2-10}  & \(\hat{f}_{d}\) & \(\hat{\tau}\) **(samples)** & **PSNR** & \(\hat{f}_{d}\) & \(\hat{\tau}\) **(samples)** & \(\hat{f}_{d}\) & \(\hat{\tau}\) **(samples)** & \(\hat{f}_{d}\) \\ \hline
**11** & 2050 & 12728 & 21.1701 & 2014 & 12731 & 21.2801(15) & 2021 & 12724 & 21.2709(17) \\
**8** & 2250 & 550 & 18.7897 & 2275 & 553 & **18.8675(29)** & 2268 & 548 & **18.8602(57)** \\
**27** & 2000 & 4363 & 21.2701 & 2020 & 4360 & 21.2784(15) & 2010 & 4365 & 21.2712(25) \\
**25** & 1350 & 9790 & 21.0104 & 1370 & 9787 & 21.0341(14) & 1364 & 9782 & 21.0292(25) \\
**28** & 6600 & 12400 & 21.5693 & 6610 & 12403 & 21.5718(14) & 6614 & 12407 & 21.5782(34) \\
**19** & 6050 & 10911 & 21.5326 & 6069 & 10912 & 21.5332(14) & 6049 & 10911 & 21.5301(26) \\
**20** & 2450 & 15905 & 19.1023 & 2441 & 15907 & 19.1238(17) & 2438 & 15906 & 19.1211(25) \\
**17** & 5350 & 4016 & 23.7094 & 5380 & 4014 & 23.7832(11) & 5383 & 4016 & 23.7621(13) \\
**4** & 2900 & 3105 & 18.7036 & 2884 & 3103 & **18.7432 (30)** & 2890 & 3106 & **18.7419(60)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Acquisition results of three methods. Figure 6: Signal search results; **(a)** Doppler frequency evolution _versus_ iterations (PRN-8); **(b)** Code delay evolution _versus_ iterations (PRN-8). Table 3 shows that the proposed method estimates the frequency shift of PRN-8 to be 2.275 KHz, which has better precision than the other method. Although the iteration number of proposed scheme in the signal acquisition of PRN-8 is more than that of other satellites, its computation time is much shorter than that of traditional method (shorter by roughly 2.5 times).
## 3 The Results and Discussion
### Optimization of MICaFVi
(**B**) The efficiency of AS-MNPs in capturing the VM-SPs. (**C**) Heat-denaturation of AS-MNPs in Laemmli does not strip anti-spike Abs (AS-MNPs are still APC-positive after denaturation) but only denatures them. (**D**) VM-SP immuno-capture is specifically mediated by the interaction between anti-spike antibodies (on the surface of AS-MNPs) and spike protein (on the surface of VM-SPs). Denatured AS-MNPs failed to capture VM-SPs. VSV-Gpp, showed that all events were APC-positive and FITC-negative (Figure 5B). These results indicate that AS-MNPs failed to capture VSV-Gpp, which confirms the specificity of these NPs to MERS-CoV spike protein detection. To further show that no VSV-Gpps were depleted by AS-MNPs, viral solutions, before and after incubation with AS-MNPs, were used to infect Huh7 cells. VSV-Gpp carries a GFP reporter gene used to quantify the viral titer. The infection assay results showed no difference in fluorescence intensity before and after incubation with AS-MNPs (Figure 5C), which indicates that the viral titer remained unchanged after incubation with AS-MNPs. In other words, no VSV-G particles were recognized and captured by these nanoparticles. Taken together, our results strongly illustrate the high specificity of AS-MNPs for the capture of the intended target (spike protein).
## 5 Experimental Results
### Routing Protocol Discussion  
Hence, at it can be observed, the proposed \(Sub-LEACH-LMNN\) protocol outperformed the others in both considered fields, i.e., 100 m \(\times\) 100 m and 200 m \(\times\) 200 m. This means that \(Sub-LEACH-LMNN\) works efficiently, and it is robust by nature for two differentiated representative areas. Furthermore, considering these two different areas, the network lifetime has been analyzed in terms of last node dead (LND). Figure 14 shows that the 100 m \(\times\) 100 m area gave better results than the 200 m \(\times\) 200 m area for each protocol--specifically, LEACH (1068), Sub-LEACH (1620), EESR (1666), LEACH-LMNN (1625), \(EESR-LMNN\) (1695) and Sub-LEACH-LMNN (1760). This means that the 200 m \(\times\) 200 m area allowed for less network lifetime than the 100 m \(\times\) 100 m area. Since the number of sensor nodes was fixed, i.e., 100 in both fields, this result was expected and let us evaluate the protocol's efficiency in different field sizes. Therefore, in both scenarios the \(Sub-LEACH-LMNN\) outperformed the rest of the strategies in terms of energy efficiency and end-to-end delay.
Some of the models [12; 17] utilize a transformer-based encoder and a CNN-based decoder, while others [18; 19] apply the hybrid design for both the encoder and decoder. [PERSON] et al. 's study [19] achieved the highest accuracy in liver segmentation with the cost of high model complexity in terms of number of parameters and floating point operations per second (FLOPs). Lastly, [PERSON] et al. [20] proposed a novel efficient paired attention (EPA) block in their hybrid hierarchical model named UNETR++ by applying both spatial and channel attention to reduce model complexity, but slightly sacrificed accuracy in liver segmentation. Table 1 summarizes the accuracy (dice score: DSC) of liver segmentation and complexity of these transformer-based hybrid models over the public Synapse dataset [21]. To address the aforementioned issues, we propose a gradient-enhanced hybrid hierarchical model, named G-UNETR++, that strives to achieve better accuracy with a moderate model complexity similar to TransUNet [17] or UNETR [12]. Based on UNETR++, we add another two gradient-based encoders that take the second-order partial derivatives (the first two elements from the last column of the Hessian matrix of a CT scan) as the input to learn the 3D geometric features such as the boundaries between different organs and tissues. We carefully design the gradient-based encoders to maintain a moderate model complexity while improving liver segmentation accuracy. Furthermore, we design a hybrid loss function that incorporates dice loss, cross-entropy (CE) loss, and Hausdorff distance (HD) loss to address class imbalance and improve the segmentation of challenging cases to further improve liver segmentation accuracy. The contributions of this study can be summarized as follows: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **DSC** & **Parameters (M)** & **FLOPs (G)** \\ \hline TransUNet [17] & 0.9408 & 96.07 & 88.91 \\ UNETR [12] & 0.9457 & 92.49 & 75.76 \\ Swin-UNETR [18] & 0.9572 & 62.83 & 384.2 \\ nnFormer [19] & 0.9684 & 150.5 & 213.4 \\ UNETR++ [20] & 0.9642 & 42.96 & 47.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy and complexity of transformer-based U-Net models. * We introduce a new network architecture named G-UNETR++ to improve accuracy in liver segmentation with moderate model complexity. * We propose gradient-based encoders to learn the 3D geometric features such as the boundaries between different organs and tissues. * We design a novel hybrid loss function that combines dice loss, CE loss, and HD loss to address class imbalance and improve segmentation in challenging cases.
# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
## 4 Experiment Analysis and Results
### Influence of Flood on L4 for GPS Satellites
#### 4.1.2 Influence of Flood on GPS L4 Observation
In addition, the maximum increase rate is G31 satellite, and the RMS increase rate of L4 residuals is approximately 186.67% compared with DOY 199. It should be noted that the RMS increase rate of G02 and G20 is relatively lower than that of other satellites. For example, in terms of the G02 satellite, the RMS increase rate of L4 residuals is only approximately 23.52%. This is mainly because the flood is very small in the starting stage of these two satellites. A similar phenomenon can also be found on the G13 satellite. Based on the above analysis, it can be inferred that the influence of flood on L4 is significant, and the residuals of L4 perform obviously greater fluctuation during the flood. However, there is rainfall on DOY 202, 2021, and whether this abnormal phenomenon is caused by the rainfall or hardware device is not considered. Thus, further analysis should be provided to eliminate the effects of rainfall and hardware devices.
## 4 Experimental Results
### Identifying Sunrise and Sunset Times  
The problem of accurately identifying the sunrise and sunset times is difficult using only light intensity data. Comparing the measured data with the times given by the NOAA celestial model, it can be assumed that when the data is close to 0 \(W/m^{2}\) a transition occurs, that is, a transition from day to night, or vice versa. Using such a threshold to identify the sunrise and sunset times is a very simple solution, and its accuracy depends on the atmospheric conditions for that day since a cloudy day the light intensity value will be lower at the same hour than for a sunny day. However, due to the measurement system offset, setting a threshold of 0 gives accurate sunrise and sunset measurements. Of course, this threshold depends on the hardware employed and should be calibrated.  
However, due to signal noise, using a simple zero crossing condition is not enough, as it may result in many transitions per day. For days with more than two transitions, the mean time of the two transitions closest to those of neighbouring days are used (see Figure 8).  
The strategy for handling fractional days relies on information about the previous day. When this information is not available the sunrise time for the current day is used instead.  
The main steps of the approach can be summarised as follows:  
* The celestial model used is described in Section 2.2.
* The downwelling global solar radiation measurements available at the NOAA FTP server are used.
* Each day is treated independently of each other.
* A time window based algorithm is employed to discover the sunrise and sunset transitions.
* The equinoxes have not been taken into account when running the program (days 81, 82, 265 and 266 for each year).  
Figure 8: Sunrise and sunset times identification (threshold = 0). (**a**) Real NOAA light intensity data for one day; (**b**) Sunrise transitions and how the sunrise time is chosen; (**c**) Sunset transition. In this case there is only one transition.
## Appendix A Appendix
A novel data product of OCO-2 is SIF, which was evaluated at 757 nm and 770.1 nm of the OCO-2 O\({}_{2}\) A-band, and is considered an indicator of the plant functional status [12, 28]. OCO-2 acquires data in three different observation modes: nadir, glint, and target. Nadir mode is used to view the ground directly below the spacecraft. Glint mode is used when tracking near the location with direct, reflected sunlight. Target mode collects a large number of measurements from calibration/validation sites [40].  
In this study, cloud-free nadir observations of offset-corrected OCO-2 SIF (Version 8r) were used to explore the relationship between MODIS products and SIF at a regional scale. We averaged both OCO-2 SIF retrieval results (valid at 757 and 770.1 nm) after multiplying SIF at 770.1 with 1.7 to account for the decrease in the wavelength of SIF in this part of the spectrum, as documented in the OCO-2 Data Product User's Guide and [PERSON], et al. [41]. To make SIF data more reasonable, we deleted SIF values lower than 0.1 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\).
## 4 Interference Analysis
### Parameter Definitions and Relevant Equations  
#### 4.1.1 Parameters  
The key parameters used in this work are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Details (Nominal Values, Units,...)** \\ \hline f & Operating frequency & Uplink: 13.75–14.75 MHz \\  & & Downlink: 10.75–12.75 MHz \\ B & Signal bandwidth & Satellite: 30 MHz \\  & & MVDS: 3 MHz \\ k & Boltzmann’s constant & 228.6 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key parameters used in this work.