esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue
## 3 Results
### Explainability  
One of the major issues concerning DL AI designs is their innate \"black box\" nature, making it difficult to estimate the relationships between the input features, which provides reasoning behind the results that are computed. In this view, the colossal number of layers that are contained in the majority of DL architectures hinders the importance of each feature that is internally computed (as each layer transforms the input data). As a result, the interpretability and, therefore, the trustworthiness of such models can be compromised. Shallow artificial networks alleviate these concerns, since the relatively small number of layers allows the decisions incorporated in each step of the model to be estimated and thus explained. In this regard, explainability tools, such as Shapley additive explanations (SHAP), values provide a visualization of the internal model processes [27],  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**AI Model** & **Authors** & **Training Time Required** & **Prediction Time** \\ \hline UNET(CNN) + LSTM & [12] & 281 min & 25 ms \\ Dual CNN & [11] & 112 min & 12 ms \\ PCANet + SVM & [26] & 221 min & 45 ms \\
**This study (TF + CNN)** & **-** & **44 min** & **4 ms** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithmic Cost-Efficiency Comparison.  
highlighting the importance of each region or feature. In this study, SHAP values estimation was performed by utilizing an enhanced method of DeepLift (deep learning important features) [28] with the DeepExplainer Tool. DeepExplainer uses a sampling technique to approximate the Shapley values for a DL model. Specifically, a large number of random samples is generated from the model's input space and using the resulting samples to estimate the Shapley values. In this study, 100 samples were utilized to generate the SHAP values. For each individual input/observation, the SHAP values are computed for each of the output classes, with the positive values indicating the contribution in model selection of the correspondent class, while the negative values denote the features inhibitory nature. In this study, SHAP values have been employed to identify what the model relies on (increasing the probability that the input feature map is the predicted class). An example regarding the positive and negative SHAP values observation is presented in Figure 4. Interestingly, in the majority of SHAP values computation, the range between 5-15 Hz is contributing positively in predicting the Fatigued class. In a similar way, activation in the range of 3-7 Hz on time-frequency map suggests an inhibitory factor for the Fatigued class and positive values for the Rested class. The importance of visually interpreting SHAP values comes along with visually inspecting TFR power maps. Visually interpreting TFR power maps is an invaluable tool for neuroscientists, facilitating the reasoning behind the AI model predictions and shedding light to the understanding of brain activity, EEG states, conditions, and mental illness [29].
# Revisiting the Detection of Lateral Movement through Sysmon
## 6 Methodology
### Exploitation of Remote Services
#### 6.1.1 Exploitation of ms17-010  
This was conducted successfully against a vulnerable MS Windows server with the aid of Metasploit. It is worth mentioning that the targeted server responded with a successfully activated _Meterpreter_ session of the EternalBlue vulnerability after several repeated attempts (at least ten). The attack is deemed successful when PowerShell admin rights are available for the execution of core Windows commands and LM expansion to other hosts.  
_Discussion:_ With reference to malicious endpoint command abuse, the analysis revealed the Process Creation vector, i.e., _Event ID 1_ with reference to Table 3, as the most prone to exploitation from opponents. Specifically, more than 25% or 7500 logs were related to the creation of processes on either the source or destination hosts. Command line administrative tools were among the most recognized on the logs related to the attacker's machine, namely _sc.exe_, _schtasks.exe_, _winrs.exe_, _PowerShell_, _cmd.exe_ and many others. On the other hand, among the most identified parent processes linked to the aforementioned potentially malicious Windows Services features were _services.exe_ (_SCM_), _svchost.exe_ (_Scheduled Tasks_),wumipsrve.exe (WMI), mmc.exe (DCOM), wsmprovhost.exe (WinRM), and explorer.exe (RDP). In addition, the existence of _svchost.exe_ and _mmc.exe_ process features in the _Sysmon_ logging activity may also be a distinctive indicator of remote execution of _Impacket Python scripts_. That is, _Impacket_ Python classes are used for Microsoft's network protocol implementation and are popular among threat actors regarding concealing their presence during an attack.
## 2 Materials and Methods
### Enumeration and Identification of Coagulase-Positive Enterotoxicing Staphylococcusococci  
Each analytical unit of cheese was homogenized with 225 mL of 0.1% peptone water in a Smasher (BioMerieux, Marcy-I'Etoile, France), and the homogenates were subjected to serial decimal dilutions in 0.1% peptone water up to 10\({}^{-5}\). From each dilution, 1 mL was transferred to STX Petrifilm(r) plates (3M, Sumare, Brazil) and incubated at 37 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C for 24 \(\pm\) 2 h, and then the red-violet colonies were counted. The Petrifilm(tm) Staph Express Disk was used whenever colonies other than red-violet were also present, followed by incubation at 37 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C for an additional 1-3 h. Colonies surrounded by a pink zone were considered positive and included in the counts. Five colonies were selected from each STX plate and subjected to the coagulase reaction according to Bennett and Lancette [30]. Based on the results, the counts of coagulase-positive staphylococci in the cheese samples were calculated. The results were expressed as CFU/g.  
For identification of the species of the coagulase-positive staphylococci isolates, total DNA was extracted with the GenElute(tm) extraction kit (Merck, Darmstadt, Germany) and submitted to 16S rDNA sequencing at the Human Genome and Stem Cell Research Center at the University of Sao Paulo, Brazil. The sequences were analyzed with the BLAST algorithm (National Centre for Biotechnology Information, USA)  
The enterotoxicing of the coagulase-positive isolates obtained from the cheeseses was evaluated by investigating the presence of the genes _sea_, _seb_, _sec_, _sed_, _see_, _seg_, _sel_, _sei_ and _sej_ by PCR, using the primers listed in Table 1.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Gene** & **Primers** & **Sequences** & **Amplicon (pb)** & **Ref** \\ \hline _saa_ & SEA-1 & GAAAAAAGCTGAATTGCAGGAACA & & \\  & SEA-2 & CAAATAAAATCGTAATTAACCGAAGGTTC & & \\ \hline _seb_ & SEB-1 & ACACCCCAACGTTTAGCAGAGAGATCA & & \\  & SEB-2 & TCCGTGTGCAGGCATCATGTCA & & \\ \hline _sec_ & SEC-1 & GTAAAGTTACAGGTGCAAACTTG & & \\  & SEC-2 & CATATCATACCAAAAAGATATTGCCGT & & \\ \hline _sel_ & SED-1 & GTGGTGAATAGATAGGACTGC & & \\  & SED-2 & ATATGAAGGTGTCTGTGG & & \\ \hline _sec_ & SSE-1 & CAAAGAATGCTTAAAGCAATTTAGGC & & \\  & SEE-2 & CACCTTACCGCCAAAGCTG & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primers used in the investigation of enterotoxin genes in coagulase-positive staphylococci.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 6 Simulations and Results
### Simulation Scenarios
#### 6.1.5 Results of Simulation Scenario 2
This result complements the informative value of the first result, as the achieved delivery times depend on the number of hops. Based on hops, it is possible to see why the time contribution of the MANET network in Position 3 was the highest. The reason is the fact that most of the data transmission took place via the MANET network. On average, almost 6 hops within the MANET network in Position 3 compared to 1 to 3 hops within the MANET network in Position 1 and 2. In the overall comparison with the WSN network, the number of hops was reduced by 40 to 45% in the case of WSN-MANET-DRONET network. The third result presented in a tabular illustration (Table 5) shows the average data rates achieved on optimal routing paths when transmitting 100 Kb of urgent data. In the case of the WSN network, data rate ranges from approximately 27-28 Kbps, while interconnection of the WSN-MANET-DRONET network resulting in an average data rate ranges from 44 to 87 Mbps. An important factor of this increasing trend are the higher data rates of MANET and DRONET networks. This shows a great advantage over the deployment of the classic WSN network in the case of fixed infrastructure disruption.
## 3 Results
### Effects of Organic Amendments on Soil Water-Holding Capacity  
The soil water-holding capacity is recognized as one of the most important parameters to facilitate vegetation and microbial activities [27; 28]. However, the average water-holding capacity of the gob spoil soil was below 40% during the soil incubation study due to its loamy sand soil texture (Figure 3A). Regardless of amendment rates, biochar increased the soil water-holding capacity to approximately 60%, indicating a 50% increase. Biochar has been widely reported to enhance soil water-holding capacity [29; 30; 31; 32]. Biochar generated from yellow pine scrap lumber was reported to double the water-holding capacity of a loamy sand soil at an amendment rate of 9% [31]. Although no significant differences (\(p>0.05\)) were seen among the three biochar amendment rates (5%, 7.5%, and 10%) during the 90-day incubation period (Table 2), the increase in water-holding capacity we observed (\(\sim\)50%) was slightly higher than the 38% reported in sandy soils, using red oak biochar amendment at 6% during a 91-day incubation [30]. In comparison, both soils amended by compost or by the mixed amendment had average water-holding capacities at around 70%, which was not significantly higher (\(p>0.05\)) than those amended by biochar only (Figure 3A, Table 2). [PERSON] et al. (2023) [33] reported a better water-holding capacity by biochar under insufficient water stress in a field study, probably due to the increased soil aggregation and the porous structure of biochar. However, the efficiencies of biochar or compost in enhancing soil water-holding capacity also depend on the types of biochar or compost used [34]. In this study, all the treatments showed significant impacts (\(p<0.05\)) on enhancing the water-holding capacity of the gob spoil soil (Table 2).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Sample Type**} & \multicolumn{1}{c}{**Water-Holding Capacity**} & \multicolumn{1}{c}{**Total Phosphorus**} & \multicolumn{1}{c}{**Plant-Available**} \\  & \multicolumn{1}{c}{**Phosphorus**} & \multicolumn{1}{c}{**Nitrogen**} \\ \hline Soil & 33.30 \(\pm\) 7.17 c & 221.01 \(\pm\) 22.63 de & 122.43 \(\pm\) 24.27 d & 175.93 \(\pm\) 15.85 b \\ Soil + 5\% biochar & 49.95 \(\pm\) 12.18 bc & 223.36 \(\pm\) 15.39 de & 164.19 \(\pm\) 25.65 d & 512.29 \(\pm\) 43.63 a \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical analytical results for soil amended with different amendments after 90 days of incubation.  
Figure 2: The impact of organic amendments on (**A**) soil pH; and (**B**) electrical conductivity. Data are shown as mean (n = 3) \(\pm\) standard deviation.
## 2 Materials and Methods
### Determination of Fucaxanthin Content  
Raw and cooked dried \(L\). _japonica_ samples were mixed with 80% ethanol (1:10, \(w/v\)) and sonicated for 30 min. The resulting mixture was centrifuged at 4000 rpm for 10 min. The supernatant was then evaporated (200 rpm, 50 \({}^{\circ}\)C) in a rotary evaporator (SY-2000, Shanghai Yarong Biochemical instrument Factory, Shanghai, China). Then, an equal proportion of chromatographic methanol was used to reconstitute. The obtained solution was filtered through 0.22 \(\upmu\)m nylon microporous filter membrane, and then, 1 mL was transferred to a brown sample bottle until further use.  
Preparation of standard curves for fucoxanthin: Firstly, 1 mg of fucoxanthin standard was dissolved in 1 mL of chromatographic methanol to prepare a stock solution of 1 mg/mL of algal fucoxanthin, which was stored at \(-\)20 \({}^{\circ}\)C at low temperature and protected from light. The stock solution was then diluted to 0.05, 0.1, 0.5, 1.0, 5.0, 10.0, and 50.0 \(\upmu\)g/mL and placed in a brown injection vial for measurement. The standard curve equation was obtained as y = 223,619x \(-\) 42,866, where y is the peak area, and x is the concentration of fucoxanthin, mg/mL; the correlation coefficient R\({}^{2}\) = 0.9999.  
Determination of the fucoxanthin of kelp: According to [PERSON] et al. [26], with minor modifications, the high-performance liquid chromatography (HPLC, Shimadzu-SPD-20A, Japan) was used for the determination of fucoxanthin in a C18 column (4.6 \(\times\) 250 mm, 5 \(\upmu\)m, Shimadzu, Japan) and at 40 \({}^{\circ}\)C. Methanol was used as the mobile phase at 0.5 mL\(\cdot\)min\({}^{-1}\), and the elution gradient was 100% methanol in 10 min. The injection volume of sample volume was 10 \(\upmu\)L. Detection was performed by a diode array detector (DAD) at 450 nm. The corresponding peak areas of the untreated and processed kelp samples were then determined according to the method described above, and the content of fucoxanthin in the kelp extract was calculated by substituting into the standard curve. The fucoxanthin content of kelp processed by each method was measured three times.
## 3 Analysis and Numerical Results
### SINR and Power Analysis
 \tag{7}\]For the node immediately before the sink node in Equation (8), interference comes from the third box to the left and another interference comes from the third box to the right ( \(P_{{}_{M+3}}=P_{{}_{M-1}}\)). As the dominant interference cannot be identified at this point, both are included in interferences. For the sink node in Equation (9), one interference comes from the third box to the left and another interference comes from the second box to the right ( \(P_{{}_{M+3}}=P_{{}_{M-1}}\)). Next, the \(SINR_{i}\) for Scheme 3 in Figure 4c is as follows:  
\[SINR_{i}=\frac{P_{i-1}}{P_{N}}\,\ \ \ \ \ i=2\,3\,A \tag{10}\]  
The first three SINRs received are expressed as Equation (10) because they have only noises without any interference:  
\[SINR_{i}=\frac{P_{i-1}}{P_{i-4}\left(\frac{1}{4}\right)^{1.5}+P_{ N}}\,\ \ \ \ i=5,6,...,M-1 \tag{11}\]  
In most cases, the dominant interference comes from the fourth box to the left, as shown by Equation (11):  
\[SINR_{M}=\frac{P_{{}_{M-1}}}{P_{M-4}\left(\frac{1}{4}\right)^{1.5}+P_{M-1} \left(\frac{1}{3}\right)^{1.5}+P_{{}_{N}}} \tag{12}\]  
\[SINR_{M+1}=\frac{P_{{}_{M}}}{P_{M-3}\left(\frac{1}{4}\right)^{1.5}+P_{M-1} \left(\frac{1}{2}\right)^{1.5}+P_{{}_{N}}} \tag{13}\]  
Although Figure 4c is not precisely bilaterally symmetrical centering on the sink node, it is regarded as being approximately symmetrical and the SINRs on the left of the sink node are obtained. For the node immediately before the sink node in Equation (12), an interference comes from the fourth box to the left and another interference comes from the third box to the right ( \(P_{{}_{M+3}}=P_{{}_{M-1}}\)). For the sink node in Equation (13), an interference comes from the fourth box to the left and another interference comes from the second box to the right ( \(P_{{}_{M+3}}=P_{{}_{M-1}}\)). The SINRs received by the individual nodes for Schemes 1-3 are obtained through Equations (3)-(13) above and these SINRs should satisfy Equation (2). Here, noise \(P_{N}\) is considered to be 1 and the relative \(P_{S}\) are obtained based on the value. If the \(SINR\)s are substituted into Equation (2), \(P\)s will be obtained in sequence.
## 2 Materials and Methods
### Experimental Protocol  
To avoid familiarization effects and to train the reproducibility of steady submaximal torque production, subjects had to do a training session before the test session. The test session started with four maximum voluntary contractions (MVC) (\(\sim\)3 s) at two different angular positions in a randomized order:  
1. Position_1: 3\({}^{\circ}\) dorsiflexion;
2. Position_2: 13\({}^{\circ}\) plantarflexion, with 0\({}^{\circ}\) defined as tibia axis perpendicular to the plantar aspect of the foot.  
To ensure standardized conditions, the examiner always gave the same verbal starting command and provided maximal verbal encouragement during the MVCs. Participants had to contract as hard and fast as possible.  
Subsequently to the MVCs, each subject had to perform six submaximal isometric contractions for 40 s at 30% of individually assessed maximum torque level (three at Position_1 and Position_2, respectively; randomized order). During contractions, torque feedback was provided on a screen in front of the subjects. Between every contraction, subjects rested as long as required; the minimum rest was set to 3 min [32].  
Near-Infrared Spectroscopy  
The local oxygen consumption of the tibialis muscle was estimated based on measurements with a CW-NIRS device (PortaMon, Artinis Medical Systems, Elst, The Netherlands) using a sample rate of 10 Hz. The device consisted of three light sources and one light detector, and used wavelengths of 780 and 855 nm. The maximal penetration depth was about 2 cm (distance light source \(-\) light detector = 4 cm). The signals used in this study were HHB, O\({}_{2}\)Hb, TSI, and the total blood volume (tHB = HHB + O\({}_{2}\)Hb).
## 2 SISO Impedance Measurement and Oscillation Analysis of FSC-VSPSU
### SISO Impedance Measurement Method  
As for the FSC-VSPSU, the impedance measurement procedures are shown in Figure 5, which can be divided into four steps: (1) inject the controllable harmonic disturbance into the grid-connected point; (2) collect the voltage/current responses of the FSC-VSPSU; (3) extract the spectrum of the collected response data; (4) calculate the impedance with the extracted spectrum data. Specifically, to achieve more precise impedance measurement results, this study employs a frequency-sweeping disturbance method targeting a frequency band up to 100 Hz [22]. Additionally, for the collection of response data, the data buffer size is determined based on the quantity of data to be sampled (the product of the sampling rate and the sampling duration). The sampling rate is required only to comply with the Nyquist sampling theorem. The measurement period is defined as the reciprocal of the desired frequency resolution.  
For a simpler impedance measurement procedure of the FSC-VSPSU, the SISO impedance can be calculated by  
\[\overset{\rightarrow}{Z}_{\text{pump}}(\mathrm{j}\omega)=\overset{ \rightarrow}{U}(\mathrm{j}\omega)/\overset{\rightarrow}{I}(\mathrm{j}\omega) \tag{3}\]  
where \(\overset{\rightarrow}{Z}_{\text{pump}}(\mathrm{j}\omega)\) is the SISO impedance of the FSC-VSPSU and \(\overset{\rightarrow}{U}(\mathrm{j}\omega)\) and \(\overset{\rightarrow}{I}(\mathrm{j}\omega)\) are the voltage and current spectrums of the collected response data, respectively.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 4 Definitions and Problem Statement
### VEPO-S2S Model
#### 4.3.2 Feature Fusion and Decoding Module
However, this approach usually fails to yield satisfactory results due to the lack of flexibility in controlling the degree of characteristic involvement introduced. Therefore, we introduce the Feature Fusion Component to optimize the utilization of the characteristics. Compared with directly connecting those characteristics, we used a more flexible way to integrate them. Here, we applied an LSTM to fuse the prior distribution _prior_ and the historical trajectory \(H\), and the results of the LSTM took into account the continuity and correlation between characteristics over time. Additionally, _prior_ served as the initial hidden state of the LSTM, and the trajectory representation \(H\) obtained by the trajectory encoder was used as the input at each step. Finally, we obtained the fused semantic vector \(c_{t}^{k}\), and the process can be expressed through the following following Equation (10)  
\[c_{t}^{k}=LSTM(prior,H) \tag{10}\]  
To regulate the involvement of Multi-level Vessel Characteristics in the prediction, we introduced the Multi-head Decoding Component; the framework diagram is depicted in Figure 7. This component comprises two GRU blocks and a fusion unit that efficiently synthesizes the hidden states generated by the two GRU blocks to predict future trajectories. The design is formulated to adjust the weighting between the trajectory information and the vessel characteristics during the prediction process. The orange region is a standard GRU module that receives the trajectory information \(h\). Additionally, it takes the preceding prediction value \(y-1\) as input, producing its hidden state \(T_{i}^{n}\). Another GRU is dedicated to adding the prior distribution _por_ into the predictions; it also takes the fused semantic vector \(c_{t}^{k}\), trajectory information \(h\), and the preceding prediction value \(y-1\) as inputs, generating the feature representation \(T_{i}^{p}\). Ultimately, \(T_{i}^{n}\) and \(T_{i}^{p}\) are fused through the fusion gate to produce the final trajectory. This process can be expressed by Equation (11):  
\[T_{i}^{n} =GRU^{n}(y-1,h) \tag{11}\] \[inp_{p} =concat(y-1,c_{t}^{k},prior)\] \[T_{i}^{p} =GRU_{p}(inp_{p},h)\] \[Z_{prior}(por=por_{i}|h^{*})=\frac{exp(por_{i}*h^{*})}{\sum_{i=1}^{7}exp(por_{i}* h^{*})} \tag{7}\]0 \[Z_{prior}(por=por_{i}|h^{*})=\frac{exp(por_{i}*h^{*})}{\sum_{i=1}^{7}exp(por_{i}* h^{*})} \tag{7}\]1where \(i\in(11,12,\ldots,15)\), and \(W_{z}\), \(W_{y}\), and \(W_{k}\) correspond to weight matrices with different coefficients. \(\sigma\) and \(tanh\) are the sigmoid activation function and tanh activation function, respectively.