esa-sceva/satcom-chunk-collection huggingface dataset:
# \(HM^{3}a1D\): Polymorphic Malware Detection Using Program Behavior-Aware Hidden Markov Model
## 4 Proposed Method: \(HM^{3}aID\)
### Training Phase
#### 4.1.3 Clustering Action Sequences  
Since learning techniques (i.e., HMM) have high performance when input samples are more similar to each other and since malware programs may have different classes of action sequences, we therefore partition all action sequences of malware set to some clusters (i.e., \(\mathcal{C}=\{c_{1},c_{2},\ldots,c_{k}\}\)) using complete-link agglomerative hierarchical clustering method [33], where each cluster has high cohesion (i.e., high similarity) and less similarity with other clusters. In the process of clustering, we compute the normalized similarity of every two action sequences \(AS_{i}\) and \(AS_{j}\) based on Equation (1). Here, \(ED(AS_{i},AS_{j})\) is the edit distance of \(AS_{i}\) and \(AS_{j}\), which is computed by the Levenshtein technique [34].  
\[Sim(AS_{i},AS_{j})=1-\frac{ED(AS_{i},AS_{j})}{max(length(AS_{i}),length(AS_{ j}))}. \tag{1}\]  
Figure 1: The architecture of \(HM^{3}aD\).  
```
0:
1:\(S=\) A system call sequence \(S=\{s_{1},s_{2},\ldots,s_{n}\}\).
2:
3:\(AS=\) An action sequence for system call sequence \(S\).
4:Define an empty array \(\mathcal{B}\) such that each array element specifies a system call sequence during the execution of algorithm.
5:Initialize \(A\) with 26 base actions: \(A=\{v_{1},v_{2},\ldots,v_{26}\}\)
6:for each \(s_{i}\in S\)do
7:\(\ell=\)OS_Handle(\(s_{i}\)) \(\triangleright\) get the Operating System handle pointer of \(s_{i}\)
8:insert(\(\mathcal{B}(\ell)\),\(s_{i}\)) \(\triangleright\) for any system call, insert it to \(\mathcal{B}(\ell)\)
9:if\(s_{i}\) is a releasing system call then\(\triangleright\) A releasing system call releases the dependent resources and invalidate the handle.
10:\(v=\)Match(\(A\),\(\mathcal{B}(\ell)\)) \(\triangleright\) Match \(\mathcal{B}(\ell)\) to the corresponding action in \(A\) by a hash function
11:insert(\(AS\),\(v\)) \(\triangleright\) Insert action (\(v\)) to \(AS\)
12:endif
13:endfor
```  
**Algorithm 1**Preprocessing, generating an action sequence from a system call sequence  
In the hierarchical clustering method, we plot the reconstruction error as a function of \(k\) and look for an elbow. Then, we set \(k\) at elbow. \(k\_Set\) denotes the set of all candidate \(k\) values that points to the one of the elbow points. In \(HM^{3}alD\), the principle rule is to select the smallest possible \(k\) that leads to set of suitable HMMs. We select the smallest \(k\) such that: (1) \(k\in k\_Set\); and (2) there is only at most one cluster \(c_{i}\) where its members are not similar enough and has just few action sequences. Note that, according to our experience on HMM, if the average similarity of a cluster is less than 0.5, then the corresponding HMM is not converged accurately and leads to a high false alarm rate.
# Motion Sickness in Mixed-Reality Situational Awareness System
## 5 Results
### Field of View  
In total, 49 tests were conducted using varying FoV settings. As the subjects were randomly assigned their test FoVs, the number of samples in each category was not equal. All of these tests were exactly 15 min long.  
The FoV experiment scores were scaled by MSQ and can be seen in Figure 13. The SSQ scores were also scaled by individual users to remove the effect of random chance causing more susceptible users to be given the same FoV setting.  
With the data scaled by users, the results suggest there is a minor local minimum of motion sickness at 49/54 deg and two very significant local minima at 26 deg and 84 deg. The very low FoV performing well is explained by very susceptible users who tended to prefer lower FoV settings in general. This coincides with the findings of [PERSON] et al. (2018) where FoV reduction on MS susceptible people had a very significant impact on MS reduction. The 84 deg FoV generating a significant minimum is currently unexplained and can only be explained by a lack of data. The primary local minima being between 49 and 54 deg is also similar to the findings of [PERSON] et al. (2018), where lower FoV values down to 60 deg were suggested and values below 45 deg proved to be hampering.  
The raw SSQ values, shown in Table 2, indicate very similar results but with a larger preference for 49 and 84 deg FoV values than the MSQ-scaled or user-scaled versions.
# Exploring Structural Changes in Ge-Te Amorphous Films Through Small-Angle Neutron Scattering
## 1 Introduction
This research has identified two structural singularities at approximately 22-25 at.% and about 33 at.% Ge. The first singularity, at 22-25 at.% Ge, corresponds to the minimum bond variability and homopolar bond fractions, indicating a high level of structural organisation. This behaviour aligns with the flexible-to-rigid transition predicted by rigidity theory, initially proposed by [PERSON] and [PERSON] [27; 28]. Experimental evidence supports this interpretation, with maximum values of thermal stability (\(\Delta T=T_{\rm x}-T_{\rm g}\)), where \(T_{\rm x}\) is the temperature of crystallisation onset and \(T_{\rm g}\) the glass transition temperature and the optical band gap (\(E_{\rm g}\)) observed near 22 at.% Ge [27]. The second singularity, occurring at compositions above about 33 at.% Ge, signifies a pronounced trend towards crystallisation, as evidenced by the disappearance of the glass transition temperature (\(T_{\rm g}\)) and a sharp decrease in the crystallisation temperature (\(T_{\rm x}\)). Structurally, this Ge-rich region is marked by increased disorder, reflected in a constant distribution of heteropolar and homopolar bonds, significant bond length variability, and the consistent intensity of the First Sharp Diffraction Peak (FSDP) in the concentration-concentration structure factor [25]. The enhanced Ge-Ge correlations observed at intermediate length scales (\(\sim\)7.7 A) in this region further suggest the potential for phase separation. In this study, we aim to extend the structural investigation of the Ge-Te system by exploring potential rearrangements at the nanometre scale using small-angle neutron scattering (SANS). The objective is to clarify the structural evolution of Ge\({}_{x}\)Te\({}_{1-x}\) amorphous films. Covering a wide range of momentum transfer, we monitored structural changes from large structural units to short-range order (SRO, corresponding to interatomic distances), also covering intermediate-range order (IRO, corresponding to connections and relative orientations of groups of atoms) [29; 30]. Within this context, fitting [PERSON]'s region [31] in the SANS diffractograms also aims to reveal the topology of the system and to provide insights into its morphology and dimensionality.
## 3 Related Work
### Vulnerability Severity Assessment Studies  
To compensate for the limitations of existing scoring systems, vulnerability severity assessment studies have been conducted, considering factors, such as patch information and release dates. Many of these studies utilize the level of availability of exploit code as an attribute to gauge an attacker's ease of exploiting a vulnerability. As part of our research objective to evaluate vulnerabilities from an offensive security perspective, we examined studies that dynamically evaluated vulnerability scores by considering exploit information.  
[PERSON] et al. [15] defined evaluation criteria for the \"exploit code maturity\" attribute of CVSS's temporal metric using reference URLs and tag information from publicly available vulnerabilities. They automated the evaluation process and prioritized patches based on scores to leverage contextual information on ease of exploitation. However, the evaluated scores merely lower severity and do not track vulnerability weaponization levels. [PERSON] et al. [16] calculated CVSS's temporal metric scores using exploit code maturity and patch-level information derived from vulnerability data. These scores, in combination with base metric information, were used to calculate the exploit frequency and estimate the quantitative security risk. Nevertheless, the criteria for assessing exploit code maturity lack standardization and heavily rely on empirical judgment. In a different approach,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tactic ID** & **Tactic Name** & **Technique** & **Sub-Technique** & **Total** \\ \hline TA0043 & Reconnaissance & 10 & 33 & 43 \\ TA0042 & Resource Development & 8 & 37 & 45 \\ TA0001 & Initial Access & 9 & 10 & 19 \\ TA0002 & Execution & 14 & 22 & 36 \\ TA0003 & Persistence & 19 & 94 & 113 \\ TA0004 & Privilege Escalation & 13 & 83 & 96 \\ TA0005 & Defense Evasion & 42 & 142 & 184 \\ TA0006 & Credential Access & 17 & 46 & 63 \\ TA0007 & Discovery & 31 & 13 & 44 \\ TA0008 & Lateral Movement & 9 & 13 & 22 \\ TA0009 & Collection & 17 & 20 & 37 \\ TA0010 & Exfiltration & 16 & 9 & 25 \\ TA0011 & Command and Control & 9 & 23 & 32 \\ TA0040 & Impact & 13 & 13 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tactics in MITRE ATT&CK and number of (sub-)techniques in each tactic.  
[PERSON] et al. [17] introduced three attributes, namely, Weaponized Exploit (WX), Utility, and Opportune, to evaluate vulnerability exploitability, relying on sources, such as Exploit DB, Metasploit, GitHub, and expert judgment, for their assessment. Although these attributes were incorporated as weighting factors in severity score calculations, reliance on expert judgment hinders consistent evaluations and quantification of risk scores.
## 5 Comparative Analysis of Traditional and Modern Methods
### LLM-Based Methods
#### 5.3.5 Multi-Modal Approaches for Fake Profile Detection  
The authors in [41] developed a dataset of 1800 legitimate LinkedIn profiles (LLPs), 600 fake LinkedIn profiles (FLPs) and 1200 profiles generated by ChatGPT (CLPs). They used word embeddings and trained a classifier to evaluate the set of LLM-generated user profiles. Their proposed method, SSTE achieved an accuracy of 72.26% when using BERT and 76.12% when using RoBERTa. They also used CLPs as fake profiles in the training phase to identify FLPs in the testing phase and achieved 65.39% accuracy with BERT embeddings and 60.38% accuracy with RoBERTa. Finally, when the task was to discriminate between LLPs and FLPs the accuracy of SSTE was 96.33% and 95% with BERT and ROBERTa, respectively.  
The approach proposed in [86] used pre-trained BERT in combination with Logistic Regression for classifying profiles as fake or real. They considered a rich set of impactful features: attribute-based, network-based, and user features and used LLMs to extract a few more features such as gender. The method achieved an accuracy score of 94.65% with the additional extracted features. In contrast, the accuracy with the original features was only 87.32%.  
Table 5 provides a summary of all the traditional and LLM-based methods presented in the previous subsections in the two tasks of detecting Fake user profiles and Fake content.
# Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals
## 2 Background Theory  
### Continuous Wavelet Transform (CWT)  
CWT is a method used for time-frequency domain analysis. It can overcome the fixed-resolution problem of the short-time Fourier transform (STFT). The STFT has the same resolution throughout the frequency ranges because of the fixed window size. The CWT can have a different resolution by adjusting its basis functions. The difference in the window between the STFT and CWT is illustrated in Figure 1. The CWT is defined using Equation (1) [8].  
\[W_{b}(a)=<\psi_{a,b}^{*}(t),x(t)\geq|a|^{-\frac{1}{2}}\int x(t)\psi_{a,b}^{*} dt\text{ where }\psi_{a,b}(t)=\psi\bigg{(}\frac{t-b}{a}\bigg{)} \tag{1}\]  
In Equation (1), \(x(t)\) is an arbitrary input signal, and \(\psi(t)\) is the mother wavelet or basis function. Along with the type of basis function, \(\psi(t)\) can take various forms. \(\psi_{a,b}^{*}\) is a complex conjugate of \(\psi_{a,b}(t)\), where \(\psi_{a,b}(t)\) is the dilated and translated version of the mother wavelet \(\psi(t)\). The parameter \(a\) is a dilation factor. The parameter \(b\) is a translation factor that controls the amount of translation along the time axis. In addition to the dilation and translation factors, the type of mother wavelet itself is an important parameter of the CWT. This study uses a Morlet wavelet, which is defined in Equation (2), for the mother wavelet. The coordinates of the center point of the mother wavelet in the time-frequency domain are \((0,\ \eta/2\pi)\). For example, if \(\eta=6\), the \(t\)-axis coordinate of the center point is 0.  
Figure 1: Time–Frequency resolution of (**a**) STFT and (**b**) CWT.  
The y-axis coordinate (also called the central frequency, \(\mu_{f}\)) was \(6/(2\pi)\). Further discussion about the theory and another application case of the continuous wavelet transform can be found in [9; 10].  
\[\psi(t)=\pi^{-\frac{1}{4}}\exp\biggl{(}-\frac{t^{2}}{2}+j\eta t\biggr{)} \tag{2}\]
## 4 Experimentation and Results
### Experiment 1: How Well SAFEPA Performs in Pain Assessment with a Frontal and Partial Veiv  
In Experiment 1, we conducted a rigorous evaluation of SAFEPA's pain level recognition capabilities. Leveraging a subset of the UNBC-McMaster dataset containing 6000 samples, we utilized the same training and testing process as described in [9]. Specifically, we trained SAFEPA for 20 epochs using the SGD optimizer and a batch size of 32 and measured 10-fold cross-validation for each epoch.  
In addition, we trained the three distinct models VGG16, ResNet50, and Ensemble in [17] to 500 epochs using the same parameters as mentioned in [17]. Specifically, we classified extracted features by VGG16 with 50 neurons, ResNet50 with 15 neurons, and the concatenation features extracted by VGG16 and ResNet50 with 50 neurons. These models were run on images of size 300 \(\times\) 300, and the experiment was repeated for size 150 \(\times\) 150 to match the input size of the SAFEPA models. Once again, we measured 10-fold cross-validation for each epoch. After running the models on the two different image sizes, the best model from each type was selected based on their performance. The results in Table 2 show that the SAFEPA demonstrated the highest accuracy of 98.93% for 10-fold cross-validation.  
Figure 9 presents the confusion matrix for the best performing model achieved through 10-fold cross-validation of the SAFEPA on the UNBC-McMaster dataset. This matrix provides valuable insights into the model's performance in classifying four pain levels: 'No Pain', 'Low Pain', 'Moderate Pain', and 'Severe Pain'. Upon examination, it is observed that the model exhibits a high accuracy in pain classification. Specifically, there is one misclassification between the 'No Pain' and 'Low Pain' classes and one additional misclassification between the 'Low Pain' and 'No Pain' classes. Notably, two misclassifications occur between the 'Moderate Pain' and the 'Low Pain' classes, reflecting a challenge in accurately distinguishing between these categories. However, it is noteworthy that the model demonstrates exceptional performance for the 'Severe Pain' class.  
Figure 10 presents the Receiver Operating Characteristic (ROC) curve for the best performing model achieved through 10-fold cross-validation of the SAFEPA on the UNBC-McMaster dataset. The ROC curve provides a comprehensive visualization of the model's performance in distinguishing between different pain levels.
## 4 Computational Experiments and Results Analysis
### Evaluation of Model Parameters  
Various instances of the deterministic model were executed, with changes made to one of the model parameters or the location data in each instance. Subsequently, the average results of all the solutions from these instances were obtained to assess the impact of the representative parameters.  
First, the effect of considering tax incentives on average renewable generation was evaluated. Table 3 illustrates that the use of renewable energies to supply the demand increases, while diesel decreases when the tax incentive \(\Delta\) is introduced. This finding underscores the significance of public policies in facilitating the energy transition. It is important to note that when using batteries, the sum is different from 100% since a portion is allocated to covering the demand, and another portion charges the batteries.  
As mentioned in the mathematical formulation, the \(\delta\) parameter is utilized to adjust the maximum level of ENS, making it more flexible or rigid. Figure 11 provides a comparison with varying values of this parameter. Initially, the value is set to 1, indicating that the ENS cannot exceed the specified limit in any given hour (see the left side of Figure 11). Subsequently, the parameter is adjusted to allow for weekly flexibility, 168 h, meaning that the weekly average of ENS must be adhered to, but individual hours may exceed this limit (see the right side of Figure 11).  
Figure 11 illustrates that the use of renewable energies increases when \(\delta\) is more flexible. Note that generation resources (Diesel, Wind and Solar) add up to 100%. In this case, the surplus in the pie charts corresponds to the battery storage. This implies that if there is a high level of rigidity in reliability requirements, diesel generators must be installed to ensure demand coverage, thereby affecting environmental objectives.  
It is then observed that increasing the allowable hourly ENS results in the model installing more renewable generators, as reliability becomes more flexible as indicated in Figure 12 (the percentages add up more than 100% due to the battery storage).  
Figure 11: Comparison of different \(\delta\) values considering different limits of ENS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & \(\Delta\)**OFF** & \(\Delta\)**ON** \\ \hline Battery & 0\% & 6\% \\ Diesel & 78\% & 75\% \\ Solar & 12\% & 7\% \\ Wind & 10\% & 19\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact tax incentive.
###### Abstract  
A DSSC's performance depends on its working electrode. Since this working electrode functions as an electron carrier, it affects how well DSSC solar cells perform. This study focused on working electrodes that use the dye DN-F05 and the Ag ions in the hybrid dye DN-F05 as a sensitizer. This research was conducted using the spin-coating method. Based on structural, optical, and electrical data, the results obtained were related to each other. It was determined that Ag has a significant influence on DSSC performance, and this discovery was also the main goal of this study. Previous studies have shown that the addition of Ag as a doping agent is an effective strategy for improving chemical and electronic properties. This can be proven through electrical measurements, which have shown an efficiency of 5.33%. However, when a significant amount of Ag is hybridized in the dye DN-F05, it will cause a decrease in DSSC performance due to the accumulation of Ag particles on the semiconductor's surface, creating a barrier to light absorption and aggregation. This is what led to the selection of a hybrid material (Ag metal), as Ag is unique and offers great potential with respect to boosting solar cells' performance.  
d  
type DN-F05; Ag metal; TiO\({}_{2}\) (anatase); spin coating
## 3 Results and Discussion
### Degradability and pH  
The CaSO\({}_{4}\) scaffolds containing various contents of nHAp were soaked in SBF solution for different periods to evaluate its degradability (Figure 4). It was found that the scaffolds without nHAp were nearly completely degraded within 21 days. Fast degradability of CaSO\({}_{4}\) was also confirmed by [PERSON] et al. [30]. After adding the nHAp, the degradation was obviously delayed and slowed gradually with increasing nHAp. Therefore, controlling the nHAp content is an efficient way to adjust the degradation rate of the scaffolds. The degradation rate decrease could be attributed to the fact that nHAp possesses a quite low degradability. Meanwhile, nHAp adherence to CaSO\({}_{4}\) could effectively reduce the contact of the CaSO\({}_{4}\) with the immersion solution and thus decrease the degradation rate of scaffolds.  
Figure 4: Degradation of the scaffolds after soaking in simulated body fluid (SBF) for different times. Significant difference between the composite scaffolds and pure CaSO\({}_{4}\) scaffolds (* \(p<0.05\)).  
Figure 3: Cross-section of the scaffolds containing (**a**) 0 wt %; (**b**) 5 wt %; (**c**) 10 wt %; (**d**) 15 wt %; and (**e**) 20 wt % nHAp. The nanoparticles in circles indicate nHAp.  
The pH value changes of the SBF solution during the scaffolds immersion are shown in Figure 5. The solution presented an alkaline to acidic pH transition after soaking the scaffolds without nHAp, while the acidification progress of the immersion solution was alleviated effectively by introducing nHAp into the scaffolds. Moreover, the solution showed a mild alkaline pH (from 7.4 to 7.0) when nHAp contents increased to 15 or 20 wt %. This might be explained by nHAp presenting slight alkalinity, which was able to neutralize the acidic product of CaSO4 and thus hinder the pH decrease. The improved pH environment could prevent inflammatory responses in vivo. In addition, it was reported that an alkaline environment was helpful for apatite formation and promoted its nucleation rate.
# Superconducting Sub-Terahertz Oscillator with Continuous Frequency Tuning
## 4 Conclusions  
We proposed and realized a method for suppressing resonances in a sub-terahertz superconducting local oscillator based on long Josephson junctions. The suggested method is based on the introduction of normal metal layers near the junction. Modeling of the propagation of electromagnetic waves in the proposed integrated circuits was performed; experimental samples were fabricated and tested. Complete suppression of resonances and the possibility of continuous tuning of the frequency of a superconducting local oscillator in the range of 200-700 GHz at arbitrary bias current have been demonstrated. The linewidth of the FFO radiation in the range 4-12 MHz was measured, allowing the realization of FFO phase locking, which is vitally important for spectral studies. Geometry with a matching circuit made of normal metal connected to one of the ends of the FFO was proposed and modeled. This geometry will help to improve the suppression even further, without a significant change in losses compared with the standard FFO geometry.  
Figure 11: Dependencies of the free-running FFO linewidth on FFO frequency recorded for frequency ranges near 365 (**a**) and 515 GHz (**b**). Experimental points are connected by lines as a guide for the eye.  
Figure 10: Down-converted spectra of the Nb-AlN-NbN FFO recorded for points A and B (see Figure 9); spectra are measured for free-running and phase-locked FFO (blue and red curves, respectively).