esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Comparing the Predication Results of the Three Algorithms Tested  
The results obtained from testing the three algorithms are all compared in this section. Figure 5 shows the true positive results of all the algorithms when tested against all the datasets, and Figure 6 shows the true negative results when tested against all the datasets.  
The Zeus malware prediction accuracy for dataset1, dataset2, dataset3, dataset7, dataset8 and dataset9 were all above 90%, with the random forest algorithm performing the best with an average accuracy prediction result of 97% across these datasets. The three 2014 Zeus datasets (dataset4, dataset5 and dataset6) produced mixed results with the deep learning algorithm, performing better than the other two, with a detection result of 86% for dataset6. For dataset4 and dataset5, the random forest algorithm performed the best with a result of 71% and 74%, respectively.  
For the benign traffic, the prediction results showed that for dataset1, dataset2, dataset3, dataset5, dataset6, dataset7, dataset8 and dataset9, the prediction accuracy for all the algorithms were above 90%, with the random forest algorithm performing the best with an average accuracy prediction result of 98% across these datasets. For dataset4 and dataset8, the deep learning algorithm performed best with a result of 95% and 94%, respectively, and the decision tree algorithm had the lowest prediction with a result of 87% for both these datasets.  
This paper has demonstrated a methodology that could be used to detect the Zeus malware and its variants and has demonstrated that the methodology does work across multiple datasets and three other variants of the Zeus malware. The next section (Section 4.4) investigates the impact of the prediction accuracy when the number of features used during testing and training are reduced.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Benign** & **Benign** & **Benign** & **Malware** & **Malware** & **Malware** \\
**Name** & **Precision Score** & **Recall Score** & **f1-Score** & **Precision Score** & **Recall Score** & **f1-Score** \\ \hline Dataset1 & 0.98 & 0.97 & 0.98 & 0.97 & 0.98 & 0.98 \\ Dataset2 & 0.92 & 0.97 & 0.94 & 0.97 & 0.91 & 0.94 \\ Dataset3 & 0.92 & 0.98 & 0.95 & 0.97 & 0.92 & 0.95 \\ Dataset4
## 3 Methods  
### Data Acquisition  
In this study, we investigated data acquired through two separate MRI examinations conducted on different imaging systems. For the first acquisition, raw MRI data were obtained using a 3T MRI scanner (MAGNETOM Prisma, Siemens Healthineers, Erlang, Germany). Three volunteers were enrolled, and three datasets \((D_{1},D_{2},D_{3})\) with non-rigid motion were acquired using a T1-weighted 3D fast low-angle shot GRadient Echo (GRE) sequence covering the breast region in a 90 s free-breathing scan. Additional details regarding this acquisition and its ethical approval are available in [13; 16]. For the second acquisition, MRI data were acquired using a 7T MRI scanner (Magnetom 7T, Siemens Healthineers) equipped with an in-house built head RF coil having 8 transmit and 32 receive channels [17]. Here, 3 datasets \((D_{4},D_{5},D_{6})\) containing subtle rigid motion from 3 volunteers were obtained with a 3D Magnetization Prepared Rapid Gradient Echo (MPRAGE) [18] sequence covering the head at a resolution of \(1\times 1\times 1\) mm\({}^{3}\). To enhance motion sensitivity, the MPRAGE sequence was modified to randomize the \(k\)-space sampling order. This randomization evenly distributes motion-induced artifacts, effectively transforming them into less noticeable, noise-like distortions and making them more suitable to advanced reconstruction techniques [19]. Additionally, it improves temporal resolution by enabling more flexible sampling strategies that adapt to motion dynamics and capture more relevant temporal information. A navigator (the central \(k\)-space line, a line scan oriented along the inferior-to-superior IS direction) was acquired at the beginning of each RAGE shot and was processed in order to split the \(k\)-space data into \(N_{l}\) motion states. All participants in these studies provided written informed consent. Volunteers were not instructed to move. A summary of the dataset parameters is depicted in Table 1.  
Figure 1: Example of a roofline model. The performance characteristics of two computational kernels (\(k_{1}\) and \(k_{2}\)) with respect to the arithmetic intensity and attainable performance are highlighted. \(k_{1}\) is bandwidth-bound, limited by memory bandwidth. \(k_{2}\) is in a compute-bound region, approaching peak computational performance. Cache and main memory bandwidth ceilings are represented to highlight resource utilization boundaries.
## 5 Evaluation of Keypoint Matching  
### Objective of the Evaluation  
The objective of this experiment was to quantitatively evaluate the accuracy of the corresponding points to verify the effectiveness of the proposed method. Specifically, the accuracy of the positions where the corresponding points were extracted was evaluated. We used co-registered SAR and optical images for this experiment. In the evaluation scale, the average of the Euclidean distances \(\overline{d}\) of the positions of the corresponding points in the SAR and optical image was used, which is given by the following:  
\[\overline{d}=\frac{1}{n}\sum_{i}^{n}\parallel\mathbf{p}_{i}-\mathbf{p}_{i}^{\prime} \parallel, \tag{4}\]  
where \(\mathbf{p}_{i}\) and \(\mathbf{p}_{i}^{\prime}\) are the positions of the corresponding points in the optical and generated optical image, respectively. If the registration was perfect, the corresponding points ideally had the same positions in each coordinate; hence, the closer the evaluation scale \(\overline{d}\) was to 0, the higher the accuracy.  
The results of the proposed method were compared with those of six other methods, which were as follows: (1) DLSC [14]--a method for finding corresponding points using dense local self-similarity; (2) HOPC [15]--a method for finding corresponding points using a histogram of orientated phase congruency (HOPC), which is based on the structural properties of images; (3) CFOG [16]--a method for finding corresponding points using CFOG, which is an extension of the pixel-wise HoG (histogram of Gaussian) descriptor; (4) Pix2 pix [11]--a method that uses SIFT [24] for keypoint detection and description, and images obtained by a Pix2 pix prediction; (5) Pix2 pix + Edge Enhancement (EE)--a method that uses SIFT and edge-enhanced images obtained by applying an edge-enhancement filter to the Pix2 pix prediction; and (6) Proposed--our method that uses SIFT for keypoint detection and description, and a discriminator trained by edge-enhanced images.  
Figure 5: An example of a local correspondence between two images. The yellow lines are the connectors of corresponding points and the blue points are the points without corresponding points.
## 1 Introduction
### Network Properties  
In general, ONs are intended to support communications over _Challenged Networks_[5], which are described by the following properties:  
**Disconnection:** Nodes can be outside other nodes' range for long periods. This can happen for single nodes or groups.  
**Low duty cycle:** Nodes can shut down network interfaces to save battery, only briefly coming online.  
**Limited resources:** Nodes can have limited battery power supply and low computational resources, such as CPU and storage.  
**Low bandwith:** Usually over wireless links, subject to high losses and latencies.  
The characteristics mentioned above lead to the need for long queuing times, where messages must be held in buffers for minutes, hours, or days at a time, unlike the milliseconds usual in structured networks. Because of this, routing algorithms supporting ONs are called \"carry-and-forward\" algorithms.  
ONs are usually highly stochastic systems. There are three main sources of randomness. In the first place, mobility. While there are ONs with deterministic behavior (e.g., orbiting spaceships [6]), most mobile devices do not exactly have repeating trajectories. Examples include service robots in a warehouse, cars in a city, or people in a building.  
The second source of randomness is the generation of data to be transmitted. Data generation at the nodes is usually an external process outside the control of the network.  
Finally, wireless communications are subject to interference from external devices and other environmental conditions.
# A Deep Learning-Based Password Security Evaluation Model
## 2 Related Studies  
### The Cracking Process and Method of the Password Attacker  
In a typical system, a password selected by a user is converted into a hash value through a hashing algorithm and then stored [16]. Afterwards, when the user inputs the same password, an authentication process is performed to verify whether the user is a registered user. If the hash value matches, it is approved, but if it does not match, authentication is rejected. Since an inverse function for the hashing algorithm does not exist, the password cannot be inferred from the converted hash value [17]. Therefore, a password attacker must attempt all cases to infer the user's password. Figure 1 shows the brute-force attack process in which an attacker attempts all cases to crack a user's password. However, since the brute-force attack can take a lot of time as the password length becomes longer and the number of possibilities increases significantly, the attacker may attempt a variety of methods.  
Since it is difficult to memorize passwords that are composed completely randomly, users tend to set passwords by using words in the dictionary and adding or deleting several letters, numbers, and special symbols [18]. In some cases, individual users may reuse previously used passwords, and they may also select passwords based on personally identifiable information (PII) using keywords that only they know, or their date of birth [19; 20]. Since passwords like these are easier to predict than randomly composed letter strings, they can lead to a decrease in security. Therefore, an attacker may exploit this by building a list of passwords a user frequently uses, and attempt cracking based on this [21; 22]. This is the targeted guessing attack, and existing leaked passwords can be abused by attackers to create user-specific password lists [20].  
Ultimately, since password attackers can attempt cracking by employing a variety of methods, selecting a password directly after becoming fully familiar with the security guidelines for passwords is not an easy task for users [23]. Therefore, a password evaluation indicator that can evaluate how safe a password is in advance is needed.
# The Influence of the Load Term of the Strain Energy Sensitivity in the Shell Shape Optimization
## 6 Case Study
### Example 1: the Shape Optimization of A Spherical Shell
The ratio and size of the self-weight and the vertical load of the structure did not affect the structural evolution process. In order to verify the role of the thickness of shell, we set the thickness of the shell as 0.02 m, 0.1 m, and 0.2 m, and recalculated the first example from Schemes A and B. The difference in the strain energy between Schemes A and B for the same step for different thickness t was shown in Figure 1:0 For the thickness t = 0.02 m, the maximum was 24.42% of the strain energy of Scheme B at Step 223; for the thickness t = 0.05 m, the maximum was 6.6% at Step 112; for the thickness t = 0.1 m, the maximum was \(-6.1\%\) at Step 135; for the thickness t = 0.2 m, the maximum was \(-4.5\%\) at \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Step** & **Load Term \(\rm S_{NF}\)** & **Stiffness Term \(\rm S_{NG}\)** & **Whole Sensitivity \(\rm S_{NT}\)** & \(\rm S_{NF}/S_{NT}\times 100\) (\%)** \\ \hline
1 & 116.54 & \(-\)1486.47 & \(-\)1369.92 & \(-\)8.51 \\
10 & 55.22 & \(-\)555.56 & \(-\)500.34 & \(-\)11.04 \\
20 & 27.67 & \(-\)149.31 & \(-\)121.64 & \(-\)22.75 \\
30 & 16.18 & \(-\)31.12 & \(-\)14.94 & \(-\)108.30 \\
40 & 9.68 & \(-\)26.25 & \(-\)16.57 & \(-\)58.41 \\
120 & 6.18 & \(-\)8.74 & \(-\)2.56 & \(-\)241.40 \\
248 & 5.18 & \(-\)6.37 & \(-\)1.19 & \(-\)435.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Node 17’s strain energy sensitivity values for partial evolution steps. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Step** & **Load Term \(\rm S_{NF}\)** & **Stiffness Term \(\rm S_{NG}\)** & **Whole Sensitivity \(\rm S_{NT}\)** & \(\rm S_{NF}/S_{NT}\times 100\) (\%)** \\ \hline
1 & 34.04 & \(-\)20.59 & 13.45 & 253.09 \\
10 & 11.94 & \(-\)6.57 & 5.37 & 222.35 \\
20 & 5.93 & \(-\)3.09 & \(-\)2.83 & \(-\)209.54 \\
30 & 4.55 & \(-\)5.24 & \(-\)0.69 & \(-\Step 47. Moreover, the optimization in Scheme B would terminate early compared to that of Scheme A when the convergence condition was the same. The above differences indicate that the thickness of the shell plays an important role in the relationship between the load term and the stiffness term in the nodal strain energy sensitivity, which was nonlinear and complex; the influence of the thickness on the optimization process would become small when the thickness becomes large. In summary, the load term had an obvious influence on the strain energy sensitivity at the convergent stage. This produced a cumulative effect in the iteration process as a part of the structural evolutionary direction.
## 7 ARF Is Involved in Crosstalk between Plant Hormones  
ARFs are involved in regulating many physiological processes in plant by mediating auxin signaling. However, ARFs can function in more ways than this. Many studies have been carried out on the involvement of ARFs in the crosstalk between plant hormones, providing new insights and ideas for the function of ARFs.  
The regulation of plant growth and development by plant hormones is complex, and more than one plant hormone is often required to act simultaneously. The crosstalk and synergy of plant hormones have high research value. In recent years, some results have been obtained on the involvement of auxin response factors in phytohormone crosstalk. In terms of synergistic effects of plant hormones, ATARF6/8, ATARF2/19, and ATARF2 are considered to be key factors linking auxin with jasmonic acid, ethylene, and abscisic acid [58; 59]. [PERSON] et al. [60] proposed a model for the effect of ABA-auxin interaction on seed dormancy/germination control; as shown in Figure 4, the strength of the auxin signal determines whether the response factor ARF10/16 can be released from inhibition, which in turn initiates the regulatory effect on the key factor ABSCISIC ACID INSENSITIVE 3 (ABI3) in the downstream ABA pathway, indirectly regulating seed dormancy or germination. In tomato, SIARF5/7 and SIARF2/6A were reported to be associated with auxin synergism with gibberellic acid (GA) and ethylene [10; 36; 42]. In the previous sections, we mentioned that SIARF7 has an inhibitory effect on tomato fruit set and development, and this effect is achieved precisely through the signaling crosstalk between auxin and gibberellin and ethylene. [PERSON] et al. [44] found that auxin signaling repressor complex SIARF7/SIIAA9 and GA signaling repressor SIDELLA additively inhibit tomato fruit set and fruit development by repressing the expression of growth-related genes and activating ethylene biosynthesis gene (\(ACO4\)). In apple, auxin induces ethylene biosynthesis in apple fruit by activating the expression of MdARF5. MdARF5 directly binds to the promoters of ethylene biosynthesis genes \(M\)  
effect is direct or indirect requires further exploration. The relevant genes or proteins may be directly regulated by hormonal molecules, while if they are indirectly regulated, ARFs are likely to be important participants. Studying the mechanism of ARFs at the molecular level will help us better understand the regulatory effect of auxin on plants.
## 4 Experimental Results
### Device Orientation Experiment  
When varying the device position and orientation, we are unable to replicate the findings of _Movement Data Experiment_ and the results reported in [9] with our training and test datasets. In contrast with [9], our results show a drastic degradation of prediction accuracy from 99% to 15%, as seen in Figure 10 when the device position and orientation is altered between the training and test session. This suggests that device position and orientation is a key component in the movement data [10] and this reduces the generalisability of the model for other orientations.  
Analysing movement data for the training and test session in Figure 11, we observe a separation in the feature space between readings from different sessions for the rotation vector. This suggests that the classification model derived from one session cannot be used to predict touch input location from another session as the data from each session occupies a different feature space. Now, the rotation vector is a composite of various  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Percentage** \\ \hline R\({}_{\text{x}}\) & 14.8\% \\ R\({}_{\text{y}}\) & 18.4\% \\ R\({}_{\text{z}}\) & 8.0\% \\ R\({}_{\text{w}}\) & 4.6\% \\ L\({}_{\text{x}}\) & 9.6\% \\ L\({}_{\text{y}}  
environment sensors which includes the magnetometer; the magnetometer measures the direction, strength, or relative change of the earth's magnetic field at a particular location and is dependent on the absolute (global) position of the device [24]. Slight changes to the device position will result in a large deviation in the sensor readings. The feature space corresponding to the testing and training session is shown in Figure 11. Readings from the linear acceleration in each axis shown in Figure 11a-c overlap for both training and test sessions. Likewise, gyroscope sensor readings along each axis from the the training and test sessions seen in Figure 11g-i are distributed similarly across the entire range. In contrast, a clear separation between the training and testing sensor readings in all three axes is observed in Figure 11d-f of the rotational vector.  
Figure 11: Typical histogram representing the sensor data across device orientation. Horizontal range of each plot reflects the range of the points observed, automatically adjusted in scale to maximise the range of values observed; vertical range is the frequency of occurrence. Note the bi-modal distribution for rotation vectors vs. linear acceleration and gyroscope readings suggesting the rotation vector occupies separate feature spaces.  
Figure 10: Confusion matrix for Device Orientation Experiment.
## 2 Methodology
### Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE)  
MAE, MSE and RMSE are all metrics used to evaluate the performance of a surrogate model, particularly in regression problems.  
MAE (Mean Absolute Error) is a commonly used metric in statistical modeling to assess the average difference between predicted and actual values.  
\[\text{MAE}= \frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{14}\]  
MSE (Mean Squared Error) is a commonly used metric in statistical modeling to assess the average squared difference between predicted and actual values.  
\[\text{MSE}= \frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{15}\]  
RMSE (Root Mean Squared Error) is a widely used metric in statistical modeling to evaluate the average square root of the difference between predicted and actual values.  
\[\text{RMSE}= \sqrt{\text{MSE}}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{ i})^{2}} \tag{16}\]
###### Abstract  
The separation and recycling of effective resources in Fischer-Tropsch wax residue (FTWR) are urgent because of the environmental hazards and energy waste they bring. In this study, organic solvents are used to separate recyclable resources from FTWR efficiently, achieving the goals of \"Energy Recycle\" and \"Fisher-Tropsch Wax Residue Treatment\". The response surface methodology (RSM) response surface analysis model accurately evaluates the relationship among temperature, residence time, liquid-solid ratio, and desorption rate and obtains the best process parameters. The results show that the product yield can reach 82.28% under the conditions of 80 \({}^{\circ}\)C, 4 h, and the liquid-solid ratio of 24.4 mL/g. Through the kinetic analysis of the desorption process of FTWR, the results show that the desorption process conforms to the pseudo second-order kinetic model and the internal diffusion model. The thermodynamic function results showed that there were not only van der Waals forces in the desorption process, but other strong interaction forces such as hydrogen bonds. In addition, Langmuir, Freundlich, and BET equations are used to describe the desorption equilibrium. Scanning electron microscopy (SEM) were used to analyze the pore structure of FTWR during desorption. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and Gas chromatography-mass spectrometer (GC-MS) analysis confirmed that the desorption product's main component was hydrocarbons (50.38 wt%). Furthermore, naphthalene (22.95 wt%), primary alcohol (11.62 wt%), esters (8.7 wt%), and aromatic hydrocarbons (6.35 wt%) compounds were found and can be further purified and applied to other industrial fields. This study shows that using petroleum ether to separate and recover clean resources from Fischer-Tropsch wax residue is feasible and efficient and has potential industrial application prospects.  
desorption; Fischer-Tropsch wax residue; thermodynamic; kinetic analysis +
Footnote †: journal: Applied Sciences