esa-sceva/satcom-chunk-collection huggingface dataset:
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 2 Preliminaries
### XML External Entity  
XXE attack involves three concepts: XML, DTD (Document Type Definition), and entity. XML is a marked language and file format for storing, transmitting, and reconstructing data, which is widely used in web applications. DTD is used to control the format specification of XML, which can define tags and entities in XML. Entity can be regarded as the variable in XML, which is divided into an internal entity and external entity.  
A major feature of the entity is that users can refer to the external entity (i.e., the entity defined in the external DTD file), ensuring that any changes in external resources can be automatically updated in the XML. Unfortunately, attackers can also use this feature to realize XXE attacks. Attackers typically construct malicious payloads to disrupt the parsing and processing of XML data, utilizing external entities to perform malicious behaviors such as accessing the file system contents of the application server and interacting with other hosts.  
Figure 2 is an XXE example. Figure 2a is the request packet containing malicious XML data constructed by the attacker, and Figure 2b is the malicious DTD content. The attacker finds that there is an injection point in the request parameter q, so he sends the well-constructed malicious XML data to the victim server for controlling the execution flow of the web application and forcing the victim server to request malicious DTD content from the host controlled by the attacker so as to realize the purpose of reading and returning the contents of the etc/passwd file.  
Figure 1: The RCE attack process for web applications.  
Figure 2: An XXE example.
# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 4 Experiments
### Main Results
#### 4.3.1 Embedding vs. Front-End
This strongly supports our hypothesis that the domain mismatch issue can largely be attributed to the distributional distortion in the embedding space, and a simple linear mapping can largely eliminate this distortion. * C-FT vs. C-LDA (11.08% vs. 10.66%): Both of which are unsupervised and rely on pseudo-labels. It can be seen that the embedding adaptation method C-LDA outperforms the front-end adaptation method C-FT. On one hand, it reaffirms that a linear transform such as full-rank LDA can largely eliminate the distributional distortion caused by domain mismatch, even though the LDA is trained with inaccurate speaker labels. On the other hand, more importantly, it indicates that for noisy pseudo-labeled data, compared to fine-tuning the entire front-end network, the simpler embedding adaptation is perhaps more effective. This relative superiority of C-LDA might be attributed to its simple functional form that prevents over-fitting to the errors in the pseudo labels. * The performance of the SSL-DA approach is relatively weaker (11.54%) compared to other unsupervised methods, though it is still much better than the pre-trained model (14.22%). We guess it is because self-supervised learning is not powerful enough to address the complex multi-domain adaptation problem due to the weak supervision signal from contrastive pairs. It should be highlighted that among all these adaptation methods, C-LDA is the only one that does not require any supervision and knowledge of the source-domain model, even though its performance (10.66%) is substantially better than the pre-trained model (14.22%) and close to the supervised fine-tuning (9.50%). It also substantially outperforms the more complex SSL-DA approach that not only requires knowledge of the embedding model but also needs genre labels.
## 5 Conclusions  
Technology acceptance research is of critical importance, especially in today's society were technologies such as the smartphone gain increasing importance. Prior research revealed that user personality may significantly affect technology acceptance perceptions and decisions. However, prior evidence on the moderating influence of user gender on the relationship between personality and technology acceptance is hardly available. Against this background, we conducted a survey study in which we examined the relationship between personality and technology acceptance from a gender perspective. Applying the HEXACO-PI-R ([[http://hexaco.org/](http://hexaco.org/)]([http://hexaco.org/](http://hexaco.org/))) along with established technology acceptance measures, we found that honesty-humility was negatively related to all TAM constructs, whereas extraversion was positively related to all TAM constructs. Moreover, we did not find significant influence of user gender on the relationship between personality and technology acceptance, except for one notable aspect of personality, namely altruism. We recommend that future research replicates our exploratory findings, based on actual technology usage data (that complement our self-reported data) and with a focus on more narrow personality traits (this could eventually lead to identification of possible moderator effects of gender in the personality-TA relationship). In this way, it is possible to shed further light on influential variables of the acceptance of technologies such as the smartphone.  
Conceptualization, R.R. and C.M.; data curation, C.S.; formal analysis, C.S.; funding acquisition, C.M.; investigation, C.S. and C.M.; methodology, C.S., R.R. and C.M.; project administration, C.M.; validation, C.S. and C.M.; visualization, C.S.; writing--original draft, C.S. and R.R.; writing--review and editing, C.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Research Foundation, grant number MO2363/3-2.  
The authors declare no conflicts of interest.  
Appendix A Partial Correlations between the HEXACO-PI-R Scales and the Scales to Assess Technology Acceptance
## 3 Method
### PrimeNet
The other layers with higher losses are soft pruned, and their weights are locally updated. where \(x\) is the input image, and \(f_{(1,2,3,4)}\) and \(\theta(_{1,2,3,4})\) represents the transformation operation (\(conv\to fc\to softmax\)) for \(y_{i}\) classifier. Now, each capsule operates on same input images for the same classifier output, and Equation (2) can be reduced as following:  
\[[y]=f_{i}(x;\theta)]; \tag{3}\]  
here, \(f_{i}\) represents the transformation operation at \(i\)-\(th\) convolution scale for the same \(y\) target output. Similarly, from Equation (1), the loss function can be expanded here as  
\[L(y,f(x;\theta))=CE[y_{1}(f_{1}(x;\theta_{1})),...,(y_{4}(f_{4}(x;\theta_{4}))]; \tag{4}\]  
which is further reduced as:  
\[L_{i}(y,f_{i}(x;\theta))=CE[y(f_{i}(x;\theta_{1}))] \tag{5}\]  
here, \(L_{i}(y,f_{i}(x;\theta))\) represents the loss as a result of a cross-entropy operation between the \(i\)-\(th\) scale convolution output prediction and the expected prediction. After this, the loss based adaptive inference is implemented as a simple _if-then-else_ operation, and the following selected convolution layer is written as:  
\[f_{min}(x;\theta)=MIN[L(y,f(x;\theta))]. \tag{6}\]  
Once the local predictions are made, and the min-loss layer is identified, the next challenge is to extract the min-loss features. To address this issue, we provisioned our capsule network instances to generate predictions along with their raw convolution feature output. After the min-loss layer is identified, the generated convolution feature output is taken and forwarded to be used by the next layer. At this stage, we obtain the most prominent visual indicators. Typically, an iterative algorithm overfits when the network is too complex or algorithm runs for too long [32]. By complex network, we refer that it is over parameterized. The capsule network's decoupled local weight training and update have an advantage here: it prevents the classifier model's overlearning. The disconnected training ensures that the model is simple and not too complex. In addition, the capsule instances do not participate in classifier model weight training which prevents over parameterization of the network. This means the capsule instances are trained per batch per iteration, and their previous weights are discarded once the min-loss features are extracted for every epoch. By disconnected training design, the simplicity of the network with reduced parameters is achieved. Now, the _translation invariant feature projection_ can be applied as follows:  
\[f_{next}(x;\theta)=[f_{min}(x;\theta)^{\frown}S(x;\theta)], \tag{7}\]  
where \(f_{next}(x;\theta)\) is the next layer input after concatenation (\(\frown\)) of convolution output with minimum loss \(f_{min}(x;\theta)\) and pool projection \(S(x;\theta)\).
## 5 Conclusions  
We have quickly summarized some of the challenges and proposed solutions for beam diagnostics and monitoring of proton FLASH experiments. These experiments may represent a revolution in the way the dose is delivered to a patient, going from treatments taking weeks and a low dose per session, to treatments of high-dose administered in one or few sessions.  
Since most of the evidence seems to show that short treatment time and high doses are in general needed to trigger a reproducible FLASH effect, we hypothesized what such scenarios could look like in a modern clinical facility. State-of-the-art proton facilities, based on cyclotrons and synchro-cyclotrons, have already shown their ability to reach FLASH dose rates in several experiments, and first proof-of-principle experiments in synchrotrons have also been successful. Beam current monitoring at these facilities is often able to cope with large beam currents, even though the commonly used dose control systems based on ionization chambers will require either dose-rate-dependent corrections to recombination effect or new, dose-rate-independent detectors. A big challenge for monitoring and safety will be the very short irradiation times, which will require the diagnostics to work at much higher sampling rates. For first experiments and patients, an external validation of the absolute delivered dose will likely be required, where we also illustrated the different possibilities.  
The potential of FLASH therapy is still being explored, and current proton therapy machines are generally able to provide FLASH beams and a reasonably precise diagnostics system. The 'first in human' FLASH trial (FAST-01 [13]) has already started recruiting patients. Indeed, proton therapy seems to be the radiation therapy platform with the fastest pathway to the application of FLASH irradiation to a large variety of tumors. On the other hand, the exact beam parameters needed to trigger the FLASH effect are still being investigated [10]. Biological experiments have so far investigated only a handful of tissues and tumor models, and different tissues/organs might show different dose and/or dose-rate thresholds. New findings might lead to even more stringent requirements in terms of dose linearity, dose precision, and diagnostics speed, for example. Therefore, we expect still a few years of development will be needed before patients can reliably be treated with this technique.  
Both authors contributed equally to the concept, literature research, writing and editing of the manuscript. Both authors have read and agreed to the published version of the manuscript.  
The APC was funded by the Lib4 RI Open-Access Fund.  
Not applicable.  
We would like to thank [PERSON] and [PERSON] from the Center for Proton Therapy, PSI, for reviewing the manuscript.  
The authors declare no conflict of interest.
## 4 Experimental System
### Signal Acquisition and Processing  
The pressure signal of the rodless chamber of two hydraulic cylinders was collected separately by two high-precision sensors [38]. The sampling frequency is 40 Hz, and 30 groups of samples were collected individually under each of the four working conditions (healthy, minor leakage, medium leakage and serious leakage). In all tests, the directional valve was used to control the lifting and retraction of the hydraulic cylinders, and the pressure of rodless chambers was collected when the hydraulic cylinders enter the pressure maintenance phase from the lifting state.  
The pressure in the pressure maintaining stage is the focus of this paper, and then one experimental process is as follows:  
Step 1: At the beginning, the directional valve is in the middle position, the piston rod is stationary, and the cantilever beam is 30 degrees with the horizontal position. Then, the directional valve is switched to the left position, the piston rod is pushed out to lift the cantilever beam up.  
Step 2: Then, when the cantilever beam is lifted to 60 degrees with the horizontal position, the directional valve is switched to the middle position, and the hydraulic cylinders are at the pressure maintaining state, which should last for more than 20 s to produce a sufficient number of oscillation cycles for the cantilever beam. Finally, the directional valve is switched to the right position and the cantilever beam is reset to 30 degrees with the horizontal position.  
Figure 10 shows the collected rodless chamber pressure of the leakage hydraulic cylinder under four working conditions. It is clear that the pressure signal in the maintenance phase oscillates obviously, which is related to the mechanical inertia. When no internal leakage exists, the pressure signal has a strong transient response, which tends to be stable after a long time. When the internal leakage exists, the damping effect of the internal leakage on the pressure oscillation increases with the growth of the internal leakage, and the pressure oscillation declines quickly. In healthy state, the pressure signal tends to slightly decrease in the pressure maintenance phase, which is caused by a slight external leakage of the hydraulic cylinders. However, with the increase in leakage, the pressure in the pressure maintenance phase still rises, and the test results are consistent with the above analysis. The specific steps for processing the collected signals are as follows:  
Figure 9: Three orifices for different internal leakages.  
Step 1: the pressure signal in the pressure maintenance phase is separated out for analysis, which starting from the moment when the pressure drops sharply; then, the data length is 20 vibration periods (2.4 Hz);  
Step 2: Five fault features are extracted by wavelet packet transform.
## 3 Experiments and Comparisons
### Experimental Results Analysis
#### 3.4.2 Analysis of 10-Fold Cross-Validation Experimental Results  
The model's generalization performance was evaluated using 10-fold cross-validation, dividing the bearing fault dataset into ten equal subsets, with nine subsets used for training and the remaining subset for evaluation. This process was repeated 10 times, with a different subset selected as the test set each time, ensuring that each data point had the opportunity to be used as part of the test set.  
The results of the cross-validation experiment are shown in Table 3 and Figure 5b, within the cross-validation section. The average diagnostic accuracy of the ChebyAT reaches 99.72%, representing increases of 9.79%, 9.33%, 10.31%, 9.89%, and 17.96% over the ChebyNet, GAT, GCN, GIN, and GraphSAGE, respectively. The ChebyAT demonstrates a significant advantage in average diagnostic accuracy, showing its ability to stably and accurately extract relevant features for classification tasks across various subsets, leading to precise predictions. In comparison, although the ChebyNet, GAT, GCN, and GIN also achieved an average diagnostic accuracy of nearly 90%, this outcome may obscure their subpar performance with specific subsets.  
The standard deviation of the ChebyAT is 0.0005, representing reductions of 0.0277, 0.014, 0.0287, 0.0297, and 0.018 compared to the ChebyNet, GAT, GCN, GIN, and GraphSAGE, respectively. The standard deviation of the ChebyAT model is significantly lower than those of the other models, reduced by several orders of magnitude, indicating that its performance exhibits less fluctuation across different subsets and demonstrates greater stability. The high standard deviations of the other five GNNs reveal substantial fluctu  
Figure 7: t-SNE visualization experimental results of six models. (**a**) ChebyNet. (**b**) GAT. (**c**) GCN. (**d**) GIN. (**e**) GraphSAGE. (**f**) ChebyAT.  
ations in their performance across different dataset partitions. Such fluctuations often indicate that the models are heavily reliant on specific data features, thereby restricting their generalization capability.  
As demonstrated through the rigorous evaluation with cross-validation, the ChebyAT not only achieves high accuracy on the current dataset but, more importantly, demonstrates robust performance across various datasets, indicating that it can maintain stable predictive capabilities when encountering diverse data distributions, which is particularly crucial for complex scenarios in practical applications. In summary, this further demonstrates that the ChebyAT model excels not only with specific datasets but also possesses superior generalization capabilities.
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 5 Study on the Deformation and Instability Modes of the Slope Under Fluid-Structure Coupling
### Displacement Response Analysis  
Figures 16 and 17 show the displacement contour plots in the X and Z directions for the slope under different rainfall durations. After 1 day of rainfall, the maximum negative displacement in the X direction occurs at the exposed surface of two weak interlayers, reaching approximately 3.71 cm, which is several times larger than the displacement under dry conditions. In the middle of the ID1 contact surface, the soil and rock mass experience the maximum positive displacement in the X direction, reaching 12.62 cm. A local failure without continuity occurs along the ID5 contact surface below the excavation level. After 3 days of rainfall, the overall displacement in the X direction increases. Negative displacement appears at the top of the slope, with the maximum negative displacement in the upper weak interlayer reaching 18.04 cm, indicating a significant sliding phenomenon at this location. The positive displacement zone in the X direction extends along the contact surface into the slope body, but the maximum displacement decreases. In the Z direction, the displacement shows a positive correlation with the slope elevation and increases with the duration of rainfall. The Z-direction displacement becomes larger as the rainfall duration increases. Specifically, the Z-direction displacement at the four topmost layers of the slope is considerable, reaching a maximum of 55.40 cm after 0.5 days of rainfall and 79.36 cm after 3 days.  
Overall, the displacement contour plots for both directions exhibit a layered phenomenon along the contact surfaces, with displacement mainly controlled by vertical displacement, while inter-layer shear displacement is relatively small. This suggests that during rainfall, the reduction in rock strength at the joints plays a dominant role in slope instability. However, with the increase in rainfall duration, the downward sliding force in the soil and rock mass intensifies, leading to shear failure between the contact surfaces.  
Figure 16: Contour plot of X direction displacement under different rainfall durations: (**a**) 1 day of rainfall; (**b**) 3 days of rainfall.  
Figure 17: Contour plot of Z direction displacement under different rainfall durations: (**a**) 1 day of rainfall; (**b**) 3 days of rainfall.  
Using the displacement magnification function in FLAC3D, it is observed that the slope undergoes a sliding-collapse failure, with the excavation-exposed surface following the trend of shear failure along the contact surface.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 1 Introduction
### Preparation of Data for the System
#### 1.3.7 Data Sources  
Interview data were obtained from several different sources to ensure the highest possible quality and comprehensiveness of the information used in the diagnosis. Sources include the following:  
* **Surveys** conducted by doctors based on interviews with patients.
* **Anonymised disease epics**, which are detailed descriptions of disease cases.
* **Diagnostic charts** from the medical literature, especially from the book 'Interna Szczeklika' [8], which contains recognised assessment schemes for many diseases.  
These sources provide the basis for the development of questions and enable the construction of a system of questions linked to specific disease entities.
## 4 Experiment and Results  
### Experimental Setup  
By comparing with some existing optical measurement systems, this system's light source was a broadband halogen lamp (OSL2 from Thorlabs), and a laser line filter (FL632.8-1, Thorlabs) created quasi-monochromatic light with a central wavelength of 632.8 nm. The collimating and the imaging lens used were achromatic. The polarizer and analyzer were Glan-Taylor polarizers (PGT6315, Joint Optics), and the objective lens of _NA_ 0.9 from Olympus (semidecolorizer, 100\(\times\)) were used. As for the detector, an MER-500 CMOS camera was used. The sample was clamped on an electric displacement table (model Zolix-CXP15-40) with a minimum step distance of 5 \(\upmu\)m.
# A Study of EEG Feature Complexity in Epileptic Seizure Prediction
## 2 Materials and Methods
### Complexity Metrics for Cross-Subject Variability Assessment  
Alternatively, we resorted to the extension of complexity measures designed for a binary problem to multiple-class classification in order to study the variability of the features of the pre-ictal state between patients where the classification task is to identify to which patient a pre-ictal instance belongs. The complexity of the patient's classification problem points out to the level of variability within patients. By converting the multi-class problem to many two-class sub-problems. The complexity metrics become:  
* The Fisher discriminant ratio, F1, for \(C\) classes extended from Equation (1) as: \[F1_{i}=\frac{\sum_{j=1,k=1,j\
eq k}^{C}p_{ij}p_{ik}(n_{ij}-\mu_{ik})^{2}}{ \sum_{j=1}^{C}p_{ij}\sigma_{ij}^{2}},\] (4) where \(\mu_{ij}\), \(\mu_{ik}\), \(p_{ij}\), \(p_{ik}\), and \(sigma_{ij}^{2}\) are the means, the proportions, and the variance of the feature \(i\) for the two classes \(j\) and \(k\), respectively.
* The volume of overlap region, F2, for a multi-class problem is given by: \[F2_{i}=\sum_{j=1,k=1,j\
eq k}^{C}\frac{min(max_{ij},max_{ik})-max(min_{ij}, min_{ik})}{max(max_{ij},max_{ik})-min(min_{ij},min_{ik})},\] (5) where \(max_{ij}\), \(max_{ik}\), \(min_{ij}\), \(min_{ik}\) are the maximums and minimums values of the feature \(i\) for the two classes \(j\) and \(k\), respectively.
* The individual feature efficiency for multiple classes can be written as: \[F3_{i}=\sum_{j=1,k=1,j\
eq k}^{C}\frac{|fi\in[min(max_{ij},max_{ik}),max(min_{ ij},min_{ik})]|}{n_{j,k}},\] (6) where \(max_{ij}\), \(max_{ik}\), \(min_{ij}\), \(min_{ik}\) are the maximums and minimums of the attribute \(i\) for the classes \(i\) and \(j\), respectively, and \(n_{j,k}\) is the total number of samples.  
Furthermore, for each complexity measure evaluated for all 88 extracted features, we estimate the data distribution by fitting the data with 82 distribution functions available in the SciPy 0.12.0 Package. To test the goodness of fit, we perform a Kolmogorov-Smirnov test, with a significance level of 0.05. Lower and upper thresholds, on which the decision whether the feature is complex or not relies, are set according to the rule of thumbs to the 5 th and 95 th quantiles of the probability distribution which fits the best the data. The feature that exceeds the lower or higher threshold, depending on the complexity metric, is considered a potential discriminant feature.