esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Oxidative Stress  
Oxidative stress plays an important pathophysiological role in intracellular mechanisms in all of the aforementioned cells and processes [68; 81; 82]. Most of the involved proteins and molecules in oxidative stress are difficult to assess due to their short lifespan and rapid reactivity with redox state regulating components [83]. Hence, a robust analysis of the role of oxidative stress in epilepsy pathmechanisms using 2 PI requires assessment of non-degradable fluorescent or autofluorescencement composites. In this regard, lipofuscin forms an interesting end-product of oxidative stress as it is a nondegradable substance with strong and specific autofluorescencement properties [84; 85]. It is most commonly found in the cytoplasm of cells with a low rate of mitosis, such as neurons [86]. Its aggregation is associated with the pathophysiology of epilepsy as well as with neurodegenerative disorders [22; 87]. We recently associated lipofuscin with the pathophysiology of epilepsy using 2 PI in vascular and tissue samples of epilepsy patients that were harvested following surgical resection of epileptic brain regions [21]. We found that lipofuscin is present in the brain's pial arterial wall and neocortical parenchyma in young epilepsy patients and age-matched controls. In the three-dimensional reconstruction of the 2 PI image stack, the particles appeared to be mainly located within the adventitia and to a lesser extent in the tunica media (shown in Figure 5) [21]. Further quantitative analysis of lipofuscin particles suggested that progressive accumulation of lipofuscin in the vascular wall may be related to the pathophysiology of epilepsy.  
Previous studies also reported on lipofuscin aggregation in association with human epilepsy. [PERSON] et al. [88] described excessive lipofuscin accumulation within dysmorphic neurons in young adult patients with focal frontal lobe epilepsy. Furthermore, lipofuscin has been found in mesial temporal lobe structures of epilepsy patients. Here, oxidative damage and neuronal degeneration induced by seizures seemed to contribute to the formation of lipofuscin in the hippocampus of kainate-treated mouse epilepsy models [89]. In patients with temporal lobe epilepsy, the hippocampal antioxidative system was upregulated [90]. This finding was most pronounced in patients with hippocampal sclerosis [90]. Another article reported on the identification of lipofuscin granules within the entorhinal regions and amygdala of epileptic tissue [91].  
These findings provide a basis for understanding the potential involvement of oxidative alterations in the pathophysiology of epilepsy. Analysis of lipofuscin could be a new method to detect and quantify oxidative stress. Using 2 PI, lipofuscin is recognized relatively easily based on its specific autofluorescencement and lifetime characteristics [21]. Furthermore, the finding of lipofuscin accumulation within the vascular wall in addition to lipofuscin-accumulation in post-mitotic cells such as neurons warrants further assessment.
## 3 Results and Discussion
### Elemental Analysis
#### 3.3.1 High-Altitude Samples
A similar observation is made for the particulate featured in Figure 6(b2-b5) with the same elements being present in a different physical state suggesting entrapment by the airborne method either in a different state of formation or distinct conditions during particulate formation. The well-mixed nature of the various present elements indicates that the firework shells of origin were of distinct color and effect, as is to be expected in a large display of this nature. The theorized mechanism by which the particulates form is discussed in Section 3.4. Many of these particulates were simple in composition, consisting of two or three different metallic elements, while others, as seen in Figure 6(c2-c7), were far more complex, containing specific regions that were associated with certain elements. Figure 6(c1-c8) shows a secondary electron image of the particulate and comparing this against other images, specifically Figure 6(c4,c6), illustrating how the particulate's magnesium and copper appear to occupy distinct regions of the particulate. The elemental peaks observed in the EDX spectra of Figure 6(a6,b6,c8) display prominent oxygen peaks with respect to the other present metals, indicating that there are likely metal oxides present in the observed particulates, though this cannot be fully verified without further analysis using Electron Energy Loss Spectroscopy (EELS). It should be noted that the spectra also feature a very prominent carbon peak while the elemental maps do not include carbon, and this is a result of the carbon tape used to trap the particulates rather than a reflection of the particulates themselves. Exposure to certain heavy metals has been specifically highlighted in published medical literature due to the especially large risk from those particular elements [32]. Several metals identified through EDX analysis have been included as presenting risks for human health when present in elevated concentrations, specifically Fe and Al. Increased exposure to Fe, despite its crucial role within the human body, has been known to cause the presence of extra radicals that damage biomolecules, cells, and tissue within the body. Aluminum exposure presents a risk mainly for those with preexisting conditions like kidney disease, but extended exposure has also been tied to other conditions like aluminum-induced advamic bone disease and aluminum-induced osteomalacia. For the majority of the population, however, brief exposure to aluminum can cause nausea, ulcers, and vomit ing. These symptoms are reported to be brief and mild, unlike the more potent risks of extended exposure.
## 4 Simulation Test Analysis
### Simulation Results of High Frequency Part of Power Data
#### 4.3.1 High Frequency Steady-State Component Simulation Results  
Decomposed by the Mallat algorithm, the frequency of the D3 layer is 400-800 Hz. According to the signal model, it can be seen that there are only the 11 th and 13 th harmonic components. The D3 layer reconstruction is shown in Figure 8.  
Using the method designed in this paper, each peak is extracted and divided into two parts. After the average value is obtained, the two parts are subtracted to obtain the attenuation rate. The average value of the first half of the peak is equal to the average value of the latter part of the peak, so the attenuation rate is 0. It can be judged that the D3 layer only contains steady-state components, and parameters are extracted from the data after windowing DFT after the reconstructed signal, and the high frequency harmonic signals are restored. The waveform diagram is shown in Figure 9.  
Based on the Hanning window function interpolation DFT and DWT mixed harmonic detection methods, the steady state components in the high frequency part cannot be restored, so the two methods cannot be compared. It can be seen from Figure 10 that the simulation result is very close to the theoretical value, and its specific data are shown in Table 5. The data obtained in the table are the average of three sets of data.  
Figure 8: D3 layer reconstruction waveform.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **Theory** & **Actual** & **Theory** & **Actual** & **Accuracy (\%)** \\
**Harmonic** & **Frequency (Hz)** & **Frequency (Hz)** & **Amplitude (V)** & **Amplitude (V)** & **Accuracy (\%)** \\ \hline
11 & 550 & 549.13 & 8 & 7.71 & 96.365 \\
13 & 650 & 650.32 & 5 & 4.77 & 95.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis result table of high frequency steady-state harmonic component of D3 layer.  
Figure 9: D3 layer high frequency steady-state spectrum.
## 8 Challenges and Directions for Future Research
### Emergence of AI-Powered C&C  
As discussed in Section 6.8, AI-powered C&C emerged in 2022, indicating that attackers are utilizing AI to abuse Twitter as a C&C infrastructure. Although its current observation is limited to Twitter, it is conceivable that threat actors may employ AI in their abuses across various platforms. This highlights the importance of future research focusing on developing defenses against AI-powered C&C.
## 4 Nanodelivery Platform for Vaccine
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types of Cancer** & **Vaccine Types** & **Vaccine Name** & **Composition** & **Combination with** & **Ref.** \\ \hline \multirow{9}{*}{Melanoma} & NP Vacc & PEI-PEG & STING agonist & [62] \\  & & nanoSTING-peptide & & nanoSTING-vax & ICIs & [63] \\  & & antigens-cGAMP & & & \\  & NTV & NT2 & ICIs & [67] \\  & & GP-M30 & GPs & — & [68] \\  & & MSR-PEI E7 & PEI & — & [69] \\  & & AuNP@B16F10 & AuNP & PTT & [70] \\  & peptides & PeptiCRad-SIINFEKL & OVs & — & [71] \\  & & EVX-01 & cationic liposome & & \\  & & (CAF@09b) & & \\  & & DSPE-PEG2000-peptide-NIR797 & DSPE-PEG2000 & ICIs & [73] \\  & & nanoparticles & polyanion pAA & — & [74] \\  & & peptide/pAA nanplexes & & & \\   Melanoma & & multi-target VLP-based vaccine & & & \\  & & (MTV) & Q\(\beta\)-VLP/CpG & — & [75] \\  & & M-NP-Ag & PLGA nanoparticles & DCV & [76] \\ \hline protein & FA-TSL/AuNc/SV & AuNCs & PTT & [77] \\ \hline RNA & LNP-mRNA & LNPs & CpG2018B & [78] \\ \hline DNA & DD-TMG-IL12/CpG & liposome & — & [79] \\ \hline WTCV & PC-Cell@gel & FK-PBA hydrogel & PDT & [80] \\  & CpG@LL-B16F10 & CpG@eeBSA NPs & PDT & [81] \\ \hline \multirow{2}{*}{Kidney cancer} & tumor cell & \multirow{2}{*}{Gel-BPQD-CCNVs} & \multirow{2}{*}{BPQD-CCNVs} & \multirow{2}{*}{ICB, PTT} & [82] \\  & membrane & & & & \\ \hline Kidney cancer & RNA & DOTAP/DP7-C/mRNA & DOTAP liposome & — & [83] \\ \hline \multirow{3}{*}{Cervical cancer} & \multirow{3}{*}{peptide} & ferritin-E7 (43–62) NP/ ferritin- & \multirow{3}{*}{SC- ferritin NP} & \multirow{3}{*}{ICIs} & [58] \\  & & Reps1/Adpgk/Dpagt1 NP & & & \\  & Mn*+-SNPs + GF001 & Mn*+-SNPs & — & [84] \\  & & cGAMP/antigen-cod delivering NVs & NP & ICB & [85] \\ \hline \multirow{3}{*}{Breast cancer} & peptide & GP-M25 & GPs & — & [68] \\  & & M-NP-Ag & PLGA nanoparticles & DCV & [76] \\ \cline{2-5}  & RNA & CM-RNA@Cee/PLGA & Ce6/PLGA & USI & [86] \\ \hline \multirow{3}{*}{Lung cancer} & — & hEX@BP & hEX & PTT & [87] \\ \cline{2-5}  & peptide & neantigen peptide & DSPE-PEG2000- & & \\  & DSPE-PEG2000-NHS & NHS & — & [88] \\ \cline{2-5}  & DC & DP7-C/neoantigen-pulsed DCs & & & [89] \\  & & vaccine & & & \\ \hline \multirow{3}{*}{Hepatocellular carcinoma} & \multirow{3}{*}{peptide} & DEX\({}_{\text{PFA}\text{-}\text{2 kN}}\) & DEX & — & [90] \\  & & NGC-gels vaccines & silk gels & TIM-3 blockade & [91] \\  & & thiolated nanovaccine & CpG-ODN NPs & ICB & [92] \\  & & mD@cSMN & cSMN & PDT & [93] \\ \hline AML & DC & \(\alpha\)CD40-DC & bacterial flagellin & — & [94] \\ \hline Lymphoma & peptide & VLP-neoantigen peptide & P22 VLPs & — & [95] \\ \hline Glioma & peptide & NeoAgs-CpG-nanodisc & sHDL nanodiscs & ICIs & [96] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution
## 2 The Proposed Method
### Feature Construction  
In the proposed technique, the chromosomes of grammatical evolution are used as a set of functions that create artificial features as nonlinear combinations of the existing ones. This process can also be considered a feature selection method, since it is possible that only a part of the original features can be used in the generated features. The proposed method creates \(N_{f}\) artificial features from the original ones, and the process for any chromosome \(p\) is as follows:  
1. **Divide**\(p\) into \(N_{f}\) parts. Every part is denoted as the \(p_{i}\) sub-particle.
2. **For** each sub-particle \(p_{i}\), a new artificial feature \(g_{i}(\overrightarrow{x},p_{i})\) is constructed with the grammar of Figure 1 as a nonlinear combination of the original set of features \(\overrightarrow{x}\).  
The final set of features will be considered mapping functions of the original ones. For example, the set  
\[g(\overrightarrow{x},p)=\left\{\begin{array}{rcl}g_{1}(\overrightarrow{x},p_ {1})&=&x_{1}^{2}+2x_{3}\\ g_{2}(\overrightarrow{x},p_{2})&=&3\cos(x_{2})\end{array}\right.\]  
is a set of mapping functions for the original features \(\overrightarrow{x}=(x_{1},x_{2},x_{3})\). However, sometimes the generated features can lead to extreme values, and this will result in generalization problems from the used machine learning models. For this reason, and in the present work, penalty factors are used so that the mapping functions do not lead to extreme values. These penalty factors also modify the fitness function that the particle swarm optimization technique will minimize each time and are considered next.
## 4 Experiment Arrangement and Results
### Performance Metrics
#### 4.1.2 Forecasting Effectiveness  
To provide a more comprehensive evaluation of the forecasting models beyond traditional accuracy metrics, the research incorporates the concept of forecasting effectiveness  
\begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{CEEMDAN} & Maximum number of iterations & 1000 \\  & Noise standard deviation & 0.2 \\  & Moderate bandwidth constraint & 200 \\  & Number of modes & 4 \\  & Population scale & 10 \\ \multirow{2}{*}{MPSO} & Maximum number of iterations & 10 \\  & Acceleration constants & 0.6 \\  & Precision & \(<\)10\({}^{-8}\) \\  & Neuron number in the input layer & 40 \\  & Neuron number in the hidden layer & 12 \\ \multirow{2}{*}{BPNN} & Neuron number in the output layer & 1 \\  & Learning rate & 0.07341 \\  & Regularization coefficient & 0.126195 \\ \hline \end{tabular}
\end{table}
Table 4: Experimental parameter values.  
\begin{table}
\begin{tabular}{c c c} \hline
**Etric** & **Formulas** & **Best Performance Value** \\ \hline \multirow{3}{*}{RMSE} & \multirow{3}{*}{\(R^{2}=1-\sum\limits_{i=1}^{N}{{{\left({y_{i}-\hat{y}_{i}}\right)}^{2}}/\sum \limits_{i=1}^{N}{{{\left({y_{i}-\overline{y_{i}}}\right)}^{2}}}}\)} & \multirow{3}{*}{Closest value to 1} \\  & & Minimum \\ \cline{1-1}  & & Minimum \\ \cline{1-1}  & MAE & Minimum \\ \cline{1-1}  & MAPE & Minimum \\ \cline{1-1}  & U1 & Minimum \\ \cline{1-1}  & U2 & Minimum \\ \cline{1-1}  & RE & \({RE(\%)=\frac{{\hat{y}_{i}-y_{i}}}{{y_{i}}}}\times 100\%\) & \\ \hline \end{tabular}
\end{table}
Table 5: Performance measures.  
(FE). This approach leverages the sum of squared errors and the mean squared error of prediction accuracy to offer a nuanced perspective on model performance [41].  
The kth-order forecasting validity is defined as  
\[m^{k}=\sum\limits_{n=1}^{N}Q_{n}A_{n}^{k} \tag{5}\]  
where \(Q_{n}\) represents a discrete probability distribution and \(\sum\limits_{n=1}^{N}Q_{n}=1(A_{n})\) is the prediction precision. \(Q_{n}\) is defined as \(1/N\) if the distribution in the information before is unknown.  
The kth-order predictive validity is then calculated as  
\[KFV=H\Big{(}m^{1},m^{2},\cdot\cdot\cdot,m^{k}\Big{)} \tag{6}\]
## 3 Results and Discussion
### UV-Visible Spectroscopy  
The determination of the band gap is a crucial characterization for photocatalytic materials, as it plays a significant role in the photocatalytic reaction--specifically, in light absorption and the generation of electron-hole pairs [52]. According to the Kubelka-Munk model and the extrapolation of the linear adjustment to zero, the corresponding Tauc plot was created for commercial pure TiO\({}_{2}\) (Figure 6a), synthesized TiO\({}_{2}\) (Figure 6b), and TiO\({}_{2}\)/zeolite (Figure 6c) with Equations (1) and (2). The results, which are summarized in Table 2, reveal that the calculated band gap for TiO\({}_{2}\)/zeolite is 3.14 eV, which is smaller than the values obtained for commercial TiO\({}_{2}\) (3.29 eV) and synthesized TiO\({}_{2}\) (3.18 eV) and is in proximity to the reported values [53; 54; 55]. These values imply activation only with a UV light source [56]. In addition, the band gap of TiO\({}_{2}\)/zeolite implies heightened light absorption, which leads to an increased generation of excited charge carriers and, consequently, enhances photocatalytic activity.  
Figure 5: SEM micrograph of (**a**) the TiO\({}_{2}\)/zeolite photocatalytic material and (**b**) the analyzed nanoparticle area. (**c**) EDS of the entire area analyzed in (**b**).
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 4 Protocol Tunneling Detector
### General Approach
As already outlined in Section 3, one-class SVMs are successful in identifying outliers; for this reason, we have extensively used them to sanitize our training sets with remarkable results. Figure 3: Input sanitization module. This sanitized dataset is then split into training and validation sets to essentially build two different models: (i) an artificial neural network (ANN) able to classify clear-text protocols (e.g., DNS) and (ii) an SVM that is a compression/encryption detector for identifying, respectively, compressed and encrypted packets. As later shown in Section 5, after construction, the training set is considerably unbalanced towards secure protocols. For this reason, we apply the SMOTE data augmentation technique [35] to increase the samples of those protocols belonging to minority classes. During the test phase, performed light labeling based on connection's destination port is not taken into account, and the resulting bit streams are grouped by connection. Each packet is given in input to a trained ANN (whose training process is detailed in Section 4.3) and the analytics both verifies if, in the connection, there are some packets that have been classified with low confidence and more than one protocol is present. While in this latter case, the co-presence of multiple protocols might signal a possible tunneling attack, when the ANN classifies packets with low confidence, then, the connection could contain either compressed/encrypted packets or packets whose byte sequences differ from the ones usually observed in the network. To distinguish between these two cases, a more in depth verification is carried out: if the connection is not entirely encrypted, meaning that it is a not a secure communication, the prototype checks if the packets signaled as anomalous (i.e., with low confidence) by the ANN are either encrypted or belongs to another protocol. If either encryption or compression is detected, the anomaly is notified to security analysts. On the other hand, if the entire connection is encrypted, it is collected and stored in a database, periodically accessed in order to retrieve data and metadata about X.509, SSL, and TLS exchanged protocol messages in order to be analyzed by the analytics described in [11]. As outlined earlier, all the compression/encryption tests are performed using an SVM, capable of correctly classifying network packets, but the proposed classifier could be substituted with other valid alternatives, such as random forest models.
# Probabilistic Analysis as a Method for Ground Freezing Depth Estimation
## 2 Material and Methodology
### Methods of Probabilistic Analysis  
The aim of the analysis is to find such values of the position of zero centigrade temperature which would be exceeded with the accepted probability in appropriate reference period. Nowadays, the commonly accepted annual probability of exceedance of climatic actions is 0.02 which means 50-year return period. Such an assumption may also be used for the probabilistic calculation of freezing depths. The probability that a value \(Z_{k}\) will be exceeded once in \(t\) years is:  
\[P(Z>Z_{k})=\frac{1}{t} \tag{3}\]  
and the probability of not exceeding it is:  
\[P(Z\leq Z_{k})=1-\frac{1}{t}=F(Z_{k}) \tag{4}\]  
In order to find \(Z_{k}\) one has to use the annual (winter) maxima of the position of the zero centigrade temperature and the appropriate probability distribution. The Gumbel distribution [22] has been preferred here. The Gumbel extreme--value distribution was used to analyze the data on locations of the zero isotherm. The purpose of this study was to fit an extreme-value distribution to a given set of winter maxima of soil freezing depth denoted by \(Z_{i}\). The cumulative distribution function (CDF) is given as:  
\[F(Z)=\exp\{-\exp[-\alpha(Z-U)]\} \tag{5}\]  
where: \(\alpha\) and \(U\) are parameters of the distribution estimated using the maximum likelihood method.  
This distribution and procedure have been widely used to forecast wind velocity and snow load, e.g., [23]. It was also used to forecast soil freezing [24]. After being doubly logarithmized, it takes the form of a linear function with respect to \(\alpha\), \(U\). The value \(Z_{k}\) of the depth of zero centigrade temperature and 50-year return period is calculated using the following equation:  
\[Z=U-\frac{1}{\alpha}\ln[-\ln F(Z)] \tag{6}\]  
The unknown Gumbel distribution parameters (\(\alpha\), \(U\)) were estimated using the least squares method, the maximum likelihood method, the method of moments, and the method described by [PERSON] in his work [25].  
Figure 2: Example of extrapolation of the data on the soil temperature made in order to define maximal winter value adapted from [19].
## 4 Discussion
Compared with these studies, our proposed method concentrated on detecting and identifying the four typical crack categories: transverse, longitudinal, block, and alligator. Moreover, the network structure of YOLOv5 was also improved from various aspects, and the detection performance of small-sized cracks was further compared and analyzed. On the other hand, determining the severity levels of the detected and identified cracks is also essential for pavement maintenance work. In this study, the severity levels of cracks in images can be roughly evaluated according to the detection results output by the proposed model. It is obvious that the pavement has seriously deteriorated when block and alligator cracks exist, and the alligator is more destructive than the block because of its development pattern. Because the longitudinal tends to propagate downwards to cause top-down cracking (TDC), it is more destructive than the transverse [62]. Consequently, in this paper, it can be preliminarily thought that the order of the severity levels of cracks from high to low is alligator, block, longitudinal, and transverse. As for the severity levels of alligator and block cracks, it can be considered that the larger the size of the detection bounding box output by the model, the larger the crack's coverage area and the higher the severity. As for transverse and longitudinal cracks, which typically appear as one or several adjacent curves in images, it can be considered that when the aspect ratio of the detection bounding box is too large or too small, the crack has a longer length and a higher severity. However, this is just a crude method for severity levels' judgment combined with pavement crack detection results; we can further figure out the thresholds for classifying the severity levels of each category of crack in the future.