esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In recent years, the use of machine-learning techniques for EEG analysis for the automatic differentiation of PNES and epileptic subjects has attracted a lot of interest in the scientific community.  
This work proposed an analysis pipeline that, starting from raw EEG acquired in resting conditions, combines signal processing and machine learning techniques for the automatic classification of PNES, epileptic and healthy subjects. Our analysis pipeline underwent testing on a large dataset comprising approximately 225 subjects, resulting in classification accuracy exceeding 83% for distinguishing between control subjects, PNES, and epileptics.  
The study also focused on the effects of segmenting the EEG in epochs and in particular showed that, under the Kruskal-Wallis test assumptions, we found no statistically significant difference in segmented epochs among the three groups, \(\chi^{2}(X)=1.17\), \(p=0.56\), \(\alpha=0.05\).  
Then, different evaluation strategies were compared to quantify how much the segmentation of the EEG recordings in epochs may affect the performance.  
The results indicate the presence of overfitting in the best-performing model when validated using patient-based 10-fold cross validation. Specifically, the learning curve of this model exhibits signs of overfitting, with the training curve showing a large gap between the accuracy on training and test set. However, by only changing the validation method on the same dataset, the accuracy is doubled and the learning curve did show good convergence. These findings aligned well with recent studies that highlight the critical importance of carefully selecting an appropriate validation strategy to ensure the reproducibility and applicability of the method. It emphasizes the need to investigate and compare different validation criteria to determine the most effective approach for evaluating the model's well as the importance of defining rigorous evaluation protocols to ensure reproducibility and good generalization of the proposed model on unseen data.  
Therefore, we plan to conduct more in-depth analyses that extend the comparison of different validation approaches to several EEG datasets, to investigate the cause of the strong difference in performance between different validation approaches.  
Conceptualization, B.C., C.Z. and M.C.; methodology, C.Z., B.C. and M.C.; validation, C.Z. and B.C.; data curation, M.S. writing--original draft preparation, B.C. and C.Z.; writing--review and editing, B.C., C.Z., M.C., R.M., M.S., F.P. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data sharing not applicable The data are not publicly available due to privacy regulation.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Evaluation  
#### 3.3.1 Content-Based Model  
As compared to other regression and classification algorithms, content-based methods do not have a direct method for evaluating the feasibility of recommendations using well-known metrics such as RMSE, MAE, Recall, and Precision. As a result, the evaluation of this model must be completed with the help of the user, which is not always reliable as it is based on personal preferences rather than objective facts. To evaluate the content-based recommender system, we created a custom metric to assess the recommendations' quality. We have used the test set to evaluate the system. We hid 40% of the user's data from  
Figure 5: (**A**) Actual rating given by user 433 for a particular restaurant (restaurant id 13,627) and (**B**) rating prediction by all three collaborative-based filtering models (NMF, SVD, and SVD++).  
the system to use them as testing data. Then, we derived the recommendations from the remaining 60% and selected the top 30 recommended restaurants. We then searched for the presence of the 40% of restaurants among the top 30 recommended restaurants and calculated the ratio of the present restaurants among the 30 recommended restaurants. This metric is shown in Figure 6.  
We attempted to calculate metrics such as RMSE, MAE, Precision, and Recall, but since the resulting predicted data are not numerical (they are the names of the restaurants that might suit the user's preferences), it is not possible to calculate the difference between the predicted and real values. Additionally, users may input their preferences without considering the order of their most preferred restaurants, which makes the calculation of these metrics unviable as the most preferred restaurant by the user may not necessarily be the top recommendation in the list of recommended restaurants.
# Origin and Function of Structural Diversity in the Plant Specialized Metabolome
## 4 Decoration and Conjugation in Specialized Metabolism
### Decoration Pathways  
In order to better understand the role of specialized metabolite modifications and conjugation, and the control mechanisms governing those, it can be of interest to know the order of the different modifications leading to the complex compounds. Large scale untargeted metabolite profiling by LC-MS or GC-MS, whether or not in combination with the construction of a mass difference network, a spectral similarity network, a structural similarity network or an abundance-based correlation network, may reveal potential pathway intermediates and be a first step in the reconstruction of specialized metabolite modification pathways [143]. For example, in the tropane alkaloid spectrum in _Datura inmoxia (Solanaceae)_, among the high diversity of simple and multiple esters of tropine or pseudotropine, no correlation clustering of tropine or pseudotropine esters with common acyl moieties (for example: all tigloyl esters) was observed, suggesting that the availability of acyl donors is not the main or only factor determining the accumulation of the esters. Instead, robust correlations suggested that 3\(\alpha\)-tigloyloxy-6-propionyloxy-7-hydroxytropane is formed by 3\(\alpha\)-hydroxylation of tropine, followed by 3\(\alpha\)-O-tigloylation, 6-hydroxylation, 6-O-propionylation and, finally, 7-hydroxylation. The fact that alternative precursors, such as 3\(\alpha\) -hydroxy-6-propionyloxytropane, were not detected corroborated this order of modifications [146]. In general, knowledge about the presence or absence of specific metabolites in a particular plant organ is a crucial first step in the construction of putative pathways, whose relevance can then be further tested by gene function analysis [6].
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Complex Indoor and Outdoor Scene Classification Algorithm Based on Spatio-Temporal Features  
In this paper, we propose a complex indoor and outdoor scene classification model based on spatio-temporal features. This model leverages data collected from cellular networks, satellite signals, Wi-Fi signals, light sensors, and inertial sensors found in smartphones. By performing spatio-temporal characterization and feature extraction on the collected information, we develop a network that integrates a multi-scale convolutional neural network (CNN) with a bidirectional long short-term memory (BiLSTM) network. The multi-scale CNN captures spatial features, while the BiLSTM further extracts spatio-temporal features from time series data.  
The overall architecture of the algorithmic model is illustrated in Figure 10. In this model, features from the analyzed data are extracted through convolution, resulting in multiple feature sets that are then flattened. These features are passed through the BiLSTM network, and scene classification and identification are performed using a fully connected layer followed by a softmax activation function. Additionally, an intelligent optimization algorithm known as the whale optimization algorithm (WOA) is employed to determine the optimal parameter values. The data feature extraction process has been thoroughly described and standardized in the preceding sections.  
To analyze the relationship between various sensor features and environmental characteristics, we introduced [PERSON]'s correlation coefficient to quantify the degree of linear correlation between two sensors [29]. This coefficient is expressed as follows:  
\[\mathrm{R}=\frac{\mathrm{n}\sum_{i=1}^{n}\mathrm{x}_{i}\mathrm{y}_{i}-\sum_{i= 1}^{n}\mathrm{x}_{i}\sum_{i=1}^{n}\mathrm{y}_{i}}{\sqrt{\left(\mathrm{n}\sum_{ i=1}^{n}\mathrm{x}_{i}^{2}-\left(\sum_{i=1}^{n}\mathrm{x}_{i}\right)^{2}\right) \left(\mathrm{n}\sum_{i=1}^{n}\mathrm{y}_{i}^{2}-\left(\sum_{i=1}^{n}\mathrm{ y}_{i}\right)^{2}\right)}} \tag{10}\]  
In the equation, \(x_{i}\) and \(y_{i}\)denote the first and second sensor variables, respectively, while n represents the number of samples. The calculation yields the correlation diagram of sensors and scene features, as shown in Figure 11. The correlation coefficients indicate that the relationships between the sensors are generally low with the GNSS sensors and light  
Figure 10: An algorithmic model for the classification of complex indoor and outdoor scenes based on spatio-temporal features.  
sensors exhibiting a stronger correlation. Furthermore, the GNSS sensors show the highest correlation with the scene, whereas the IMU sensors demonstrate the weakest correlation.
## 3 Results and Discussion
### Effect of pH on Hg(II) Adsorption  
Figure 5a,d both showed that the Hg uptake reached maximum Hg(II) uptake values after 20 min. Figure 5d shows the effect of pH on Hg(II) adsorption by 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\), revealing that removal capacity is highly pH dependent. The aqueous formation of charged Hg(II) species (Hg\({}^{2+}\), HgOH\({}^{+}\)) and neutral Hg(OH)\({}_{2}\) can result at different pH values, although these are all capable of reacting with L-cysteine thiol groups due to the high affinity of Hg for sulfur [31]. As shown in Figure 5e, speciation curves calculated using the Visual MINTEQ software [26] indicate that free Hg(II) ions predominate at pH \(\leq\) 2, which in the presence of thiol groups may form positively charged complexes (S-Hg\({}^{+}\)). The presence of large numbers of such complexes on the sorbent surface may, at some point, inhibit further binding based on electrostatic repulsion. At higher pH values mercuric hydroxide species (HgOH\({}^{+}\) and Hg(OH)\({}_{2}\))) become more prevalent, which are able to bind to cysteine thiol groups forming neutral complexes (-S-HgOH) [57]. The buildup of uncharged Hg(II) complexes on the sorbent surface does not retard further Hg(II) adsorption in the same way as accumulation of positive charge. Accordingly, 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\) exhibits greater Hg(II) removal performance at higher  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Uncoated Fe\({}_{3}\)O\({}_{4}\)** & **C@ Fe\({}_{3}\)O\({}_{4}\)** & **500 Cys-C@ Fe\({}_{3}\)O\({}_{4}\)** \\ \hline
**Langmuir model** & & & \\ \(\mathbf{q_{e(exp)}}\) (mg.g\({}^{-1}\)) & 12.27 & 21.01 & 90.03 \\
**Pseudo-first-order model** & & & \\ \(K_{1}\) (min\({}^{-1}\)) & 0.028 & 0.034 & 0.041 \\ \(q_{e(all)}\) (mg.g\({}^{-1}\)) & 9.526 & 7.874 & 35.07 \\
**\(R^{2}\)** & 0.986 & 0.945 & 0.601 \\
**Pseudo-second-order model** & & & \\ \(K_{2}\) (g.mg\({}^{-1}\).min\({}^{-1}\)) & 0.007 & 0.014 & 0.004 \\ \(q_{e(all)}\) (mg.g\({}^{-1}\)) & 13.95 & 22.03 & 95.24 \\ \(R^{2}\) & 0.998 & 0.998 & 0.995 \\ \hline
**Freundlich model** & & & \\ \(K_{f}\)(mg.g\({}^{-1}\)) & 11.12 & 35.11 & 265.58 \\ \(n\) & 7.41 & 3.86 & 1.98 \\ \(R^{2}\) & 0.8212 & 0.9626 & 0.9100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adsorption isotherm parameters and sorption rate constants for Hg(II) adsorbed on uncoated Fe\({}_{3}\)O\({}_{4}\), C@ Fe\({}_{3}\)O\({}_{4}\), and 500 Cys-C@ Fe\({}_{3}\)O\({}_{4}\).  
pH values, as shown in Figure 5d. These findings are in agreement with zeta potential measurements with 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\) (Figure 5f), which indicate that the particle surface is positively charged at a solution pH lower than the Point Zero Charge (_PZC_, -5.4) and becomes negatively charged only above this value.
# Strength Analysis of Real-Life Passwords Using Markov Models
## 6 Discussion
### Limitations  
When performing the experiments, we limit the number of states, i.e., the number of distinct characters that we search in the datasets and use in our models, to 224, coinciding with the set of ASCII characters from character code 32 to 255. This character set includes all characters from the extended ASCII table, except for the initial 32 characters which are basically unprintable control codes and are used to control peripherals such as printers.  
It is necessary to note that when Markov models are used for password cracking they are usually used as an aid when dictionaries are exhausted and when the search space becomes very large. We assume that an average attacker would have access to password training sets that are as good and effective as the best of all our training sets. If Markov models are used the other way around (as a password scoring tool), one should do an analysis of the datasets similar to ours, in order to find the right way to combine the datasets and to create a more effective password scoring tool.  
Furthermore, the datasets that we used in our research are publicly available datasets of passwords that were illegally collected. This increases the doubt that these datasets are partial (only a part of larger datasets that also contain stronger passwords) and biased datasets that contain only the most common and weaker passwords that are easy to predict. Since these are publicly available datasets of previously compromised accounts, we can argue that future attackers can also use only the datasets that are available online in order to build their tool and strategy for an attack. This implies that a solution for password restriction based on multiple Markov models should be able to recognize and prevent the weak passwords from entering the system. The problem in information security are not strong passwords that are not yet cracked and leaked, but weak passwords that are publicly available. A good password checker should not let weak passwords through. Hence, our models should be able to recognize and assess these passwords as weak. This implies that our models should be trained on weak, biased and similar passwords that are available for most of the attackers.
## 3 System Model Analysis
### Network Model  
In the network model, we consider three types of nodes, namely Underwater Sensor Nodes (USNs), Sink Nodes (SNs), and Base Stations (BSs), as shown in Figure 1. The USNs and SNs are deployed randomly underwater, and BSs are deployed on the ships above the water's surface. The USNs, SNs, and BSs are represented as \(\mathcal{U}=\{u_{1},u_{2},\ldots u_{l}\}\), \(\mathcal{N}=\{n_{1},n_{2},\ldots,n_{N}\}\), and \(\mathcal{B}=\{b_{1},b_{2},\ldots b_{b}\}\). A coalition is formed among the nodes of USNs, SNs, and BSs, which is depicted in Section 4. The computational task is offloaded by SNs to BSs. The communication time is calculated according to the attenuation model of the acoustic channel. Since the nodes of USNs and SNs have limited communication capability and computing power, while BSs have enough communication capability and computing power, BSs can be used to provide computational services to SNs and USNs in a process called ocean computing. The research objective of this paper is to provide computational services and reliable connectivity for SNs to guarantee high computing capability and low communication time. In our scheme, the task performed by the SNs is denoted by \(A_{t}\), and we define a certain time span, \(T_{s}\). It is assumed that, within a \(T_{s}\), the task \(A_{t}\) is offloaded by \(\text{n}\in\mathcal{N}\) to \(\text{b}\in\mathcal{B}\). \(A_{t}\) must be offloaded to BS before the next \(T_{s}\). Therefore, an offloading and computing policy is used for \(\mathcal{N}\). The BS is considered to be the main controller that makes the offloading and computing decisions for USNs and SNs in a decentralized manner. Within a \(T_{s}\), the offloading decision for \(\mathcal{N}\) is denoted by a binary vector, i.e., \(\text{V}_{\text{n}(T_{s})}\), \(\forall\text{n}\in\mathcal{N}\). For instance, if \(\text{V}_{\text{n}(T_{s})}=1\), the computation task of \(\text{n}\in\mathcal{N}\) is offloaded to \(\text{b}\in\mathcal{B}\). Otherwise, if \(\text{V}_{\text{n}(T_{s})}=0\), the computation task of the SN node \(\text{n}\) (\(\text{n}\in\mathcal{N}\)) is not offloaded to the BS node \(\text{b}\) (\(\text{b}\in\mathcal{B}\)). A computation task can be jointly processed by more than one BS, \(\text{b}\in\mathcal{B}\), in which the results of computation task are subject to being transmitted to the SNs.
# Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model
## 3 Related Work
### Symbol Execution  
Symbolic execution [38] symbolizes the code variables and replaces the specific value input with the symbolic input to realize the analysis of the program. Oyente [21] is the first smart contract vulnerability detection tool based on symbolic execution, which can detect smart contract transaction sequence dependency, timestamp dependency, reentrancy, and other vulnerabilities, and output the corresponding symbolic paths. The results of Oyente vulnerability detection show that the code coverage is low, and there is a high false positive rate and false negative rate in the detection of integer overflow vulnerability. The MAIAN [22] analysis tool completes the detection task in two steps. It first enters the contract bytecode and analysis strategy into the symbol analysis component and executes all possible execution paths for each candidate contract symbolically until it finds and marks a path that satisfies a set of predetermined properties, and then executes the contract to verify that the results are consistent. Osiris [23] is an improvement on the basis of Oyente, which aims at detecting integer overflow vulnerabilities in smart contracts, combining symbolic execution and taint analysis techniques. The tool takes as input the smart contract's bytecode or Solidity source code, which is further compiled into bytecode for analysis and outputs the integer overflow information that exists in the contract. The vulnerability detection function consists of three modules, i.e., symbolic execution, taint analysis, and integer overflow detection. On top of that, Securify [39] and Slither [40] are also Ethereum smart contract security analysis tools based on symbolic execution technology.
## 5 Simulation and Analysis
### The Impact of Spread-Spectrum Sequence Depth on System Sensitivity  
In order to verify the relationship between spreading gain and the sensitivity of the detector system, signals with different spreading gains were designed and transmitted. These included spread signals with PRN depths of 1023, 127, and 31. At a signal bandwidth of 10 MHz, the corresponding effective data rates for these spread signals are 9.7 kbps, 78.74 kbps, and 322.58 kbps, respectively. The non-spread signal and spread signals maintain the same bandwidth, with a data rate of 10 Mbit/s. The experimental results obtained for the received optical power and beacon communication data error rate under different spreading gains are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Pseudocode** \\ **Sequence Depth** \\ **(\(\upmu\)m)** \\ \end{tabular} } & \begin{tabular}{c} **Received Optical** \\ **Power is** \\ **-34 dBm, \(\sigma\)** \\ **(\(\upmu\)m)** \\ \end{tabular} & \begin{tabular}{c} **The Improvement** \\ **in Position** \\ **Detection Precision** \\ **(\(\upmu\)m)** \\ \end{tabular} & \begin{tabular}{c} **Received** \\ **Optical Power is** \\ **-37 dBm, \(\sigma\)** \\ **(\(\upmu\)m)** \\ \end{tabular} & \
\begin{tabular}{c} **The Improvement in** \\ **Position-Detection** \\ **Precision** \\ **(\(\upmu\)m)** \\ \end{tabular} &
\begin{tabular}{c} **Theoretical** \\ **Precision** \\ **\(\sigma_{G_{p}=1}\)/\(\sigma\) **(dB)** \\ \end{tabular} \\ \hline \(1\) & 15.395 & 1 & 30.811 & 1 & 0 \\ \(31\) & 3.641 & 6.26 & 7.275 & 6.26 & 7.45 \\ \(127\) & 1.425 & 10.33 & 2.876 & 10.29 & 10.52 \\ \(1023\) & 0.525 & 14.67 & 0.983 & 14.96 & 15.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Position-detection precision achieved by the four-quadrant detector under different spreading gains.  
It can be observed in Figure 10 that different spreading gains result in varying degrees of improvement in the reception sensitivity. In addition, higher spreading gains lead to a superior enhancement in sensitivity. Compared to the non-spread signal (with a sensitivity of \(-39.6\) dBm), the spread signal with a modulation PRN of 1023 achieves a reception sensitivity of \(-54.2\) dBm, resulting in an improvement of 14.6 dB.  
Additionally, Table 5 presents the sensitivity of spread signals with PRN depths of 1023, 127, and 31, achieving a system-detection error rate of \(1\times 10^{-6}\). It also presents their improvements in sensitivity when compared to those achieved using direct detection with a spreading gain of 1. The improvements in sensitivity achieved are 14.6 dB, 10.1 dB, and 6.4 dB for PRN depths of 1023, 127, and 31, respectively. The enhancement in sensitivity is approximately proportional to \(\sqrt{G_{p}}\). Therefore, spread-spectrum detection can effectively improve the sensitivity of the detector, and the received optical power required by the QD is lower when the communication bit error rate is met.