esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Models to Predict Google Stock Prices
## 4 Results
### Lstm  
We then applied the LSTM model separately to each of the three datasets. The results are reported in Table 3.  
From Table 3, it is possible to note a decline in the results of the LSTM going from the first to the third dataset. We remark that the decrease in accuracy may be compensated by an improvement of explainability and robustness. To understand whether this is true, we present the application of the S.A.F.E. AI metrics, based on the unifying concept of Lorenz inequality, in Table 4, for the LSTM model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**LSTM**} \\ \hline  & **Dataset1** & **Dataset2** & **Dataset3** \\ \hline RGA & 0.9955 & 0.9652 & 0.9797 \\ RGE Tot & 0.0014 & 0.4124 & 0.2479 \\ RGR All & 0.9996 & 0.9955 & 0.9986 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SAFE AI metrics for LSTM models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Train**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-11}  & **MSE** & **RMSE** & **MAE** & **MAPE** & \(\mathbf{R^{2}}\) & **MSE** & **RMSE** & **MAE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline LSTM & 3.5651 & 1.8882 & 1.4078 & 2.43\% & 0.9969 & 66.8111 & 8.1738 & 6.6792 & 6.01\% & 0.8686 \\ ANN & 1.7655 & 1.3287 & 1.0444 & 2.12\% & 0.9985 & 123.2245 & 11.1007 & 8.8661 & 8.25\% & 0.7556 \\ XG Boost & 0 & 0.0014 & 0.0009 & 0\% & 1 & 144.3545 & 12.0148 & 9.6100 & 8.52\% & 0.7137 \\ Ridge & 9.2765 & 3.0457 & 2.4585 & 5.03\% & 0.9920 & 123.9198 & 11.1319 & 8.6786 & 8.20\% & 0.7542 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model comparison using all variables.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**LSTM**} \\ \hline  & \multicolumn{3}{c}{**Train**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-11}  & **MSE** & **RMSE** & **MAE** & **MAPE** & \(\mathbf{R^{2}}\) & **MSE** & **RMSE** & **MAE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline Dataset1 & 2.1799 & 1.4764 & 0.9852 & 1.55\% & 0.9981 & 9.2161 & 3.0358 & 23.322 & 2.00\% & 0.98.12 \\ Dataset2 & 14.878 & 3.8572 & 2.8738 & 5.73\% & 0.9871 & 134.5846 & 11.6011 & 9.1761 & 7.18\% & 0.7353 \\ Dataset3 & 7.4785 & 2.7347 & 2.0244 & 3.26\% & 0.9936 & 177.4575 & 13.3213 & 10.686 & 8.08\% & 0.6510 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of Long-Short Term models.  
From Table 4, it appears that the second dataset shows the highest total explainability. Connecting Table 3 with Table 4, this means that the high accuracy of the LSTM for the first database mainly derives from the lagged price variables, whereas the second database gives a more structural explanation. Finally, note that the LSTM model has a similar robustness across all datasets.
## 2 Materials and Methods
### Analysis of Volatile Flavor Compounds in Fermented Grains  
Saturated sodium chloride solution (5 mL) was added to the fermented grains (2 g) in a 20 mL bottle. The internal standard 4-octanol was added, with a concentration of 4 mg/L, followed by ultrasonicating for 10 min. The volatile compounds in the fermented grains were assayed by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) (TSQ 8000 Evo, Trace MS/GC, Thermo Fisher Scientific, Waltham, MA, USA) equipped with a flame ionization detector. The volatiles of the fermented grains were extracted with a 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA), and after equilibrating at 60 \({}^{\circ}\)C for 20 min, a fiber was used to extract the volatiles for 30 min, followed by desorbing at the inlet for 5 min, following our previous studies [26; 27]. The initial column temperature was kept at 50 \({}^{\circ}\)C for 2 min, followed by increasing it to 85 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C/min, and then kept at 85 \({}^{\circ}\)C for 5 min, and then increased to 150 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and maintained there for 10 min, and finally increased to 250 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and kept there for 20 min. The flow rate of the helium carrier gas was 1 mL/min [26]. The column was a TG-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, J&W Scientific, Folsom, CA, USA). Mass spectrometry (MS) was generated with an electron impact ionization energy of 70 eV and a full scan range from 30 to 400 amu [26]. The flavor chemicals were identified by matching the spectra to the NIST05 spectrum database.
# HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks
## 1 Introduction
Attackers may ignore these relationships when deploying fake sites, in order to reduce the cost of the attack, thus leading to implicit differences between phishing sites and normal sites. As shown in Figure 2, phishers retain the resource links of the original pages. Of course, they can modify the links to their own domain name, but this will cost more. In particular, HinPish models use a Heterogeneous Information Network (HIN) where we naturally model the page with domain nodes, resource nodes, and design three relations among the four links: outlier (foreign link and null link), local (local link), and relative (relative link). Then, we apply a modified Authority Ranking algorithm [7] to compute the influence of different relations to each node and obtain a quantified score for every node. Finally, machine learning algorithms are applied to make use of the node score to achieve phishing detection. By integrating  
Figure 1: Trend of phishing attacks. semantic meaning into the model, HinPhish not only improves the detection accuracy, but also increases the cost of attacks. In summary, we make the following contributions in this research:  
* An effective representative model--HinPhish models the domains and resources links in a webpage as a HIN, in order to describe the relation of the links, which effectively preserves the deeper semantics between the links. * An effective phishing detection approach--HinPhish applies the modified algorithm to leverage the characteristics of different link types to calculate the phish-score of the target domain in a webpage, then leverages HIN and a machine learning algorithm to detect phishing attacks, which significantly increases the evasion cost. * An adequate experiment--to demonstrate the effectiveness and superiority of the proposed method, we conducted sufficient comparative experiments with various state-of-the-art techniques. HinPhish exhibited an excellent performance, with an accuracy of 0.9856 and F1-score of 0.9858. The remainder of this paper is organized as follows: Section 2 describes the related work in the literature. After introducing necessary preliminaries in Section 3, we describe HinPhish's framework and the technical details of each component in Section 4. Section 5 reports the experimental results and provides a comparison of the proposed method with other methods. We discuss the limitations of the study and future work in Section 6 and summarize our work in Section 7.
## 3 Results and Discussions  
The study analysed controller parameters from the previous literature applied to a proposed complex-order controller [5]. The research included various processes with different transfer functions and behaviours. The transient response characteristics of thecomplex-order controller, such as overshoot (%OS), rise time (\(t_{r}\)), and settling time (\(t_{s}\)), were quantified and compared to other controllers, including PID, PI-PD, FOPID, FOPI-PD, and COPI-PD. Set-point tracking is used to evaluate the performance of the controller's tracking and flexibility abilities. In most of the following simulations and experimentations, initially, the set point is set to 1; after 300 s, it changes to 0.5 and then to 2 after 700 s. Similarly, a disturbance of 0.5 to 2 magnitude is injected into the controller structure to study the disturbance rejection abilities. Unlike the other controllers, the complex-order controller used complex parameters obtained through MATLAB commands. The controller parameters and gain were constant across all controllers except for the complex-order variant. This study sheds light on the efficacy of the complex-order controller in handling diverse processes with distinct transfer functions and behaviours, providing valuable insights into its performance compared to traditional and fractional-order controllers.
## 4 Results and Discussion
### Establishment and Application of Evaluation Models
#### 4.1.1 Determination of the Weights of Indicators
\begin{table}
\begin{tabular}{c c c c} \hline
**Term (in a Mathematical Formula)** & **Average Value** & **TTL (Indicator Score)** & **Weighting** \\ \hline Drilling fluid consumption (m\({}^{3}\)/h) & 0.097 & 0.500 & 6.250 percent \\ \hline Grouting final pressure & 0.311 & 2.500 & 31.250 percent \\ \hline Dry material per unit length (t/m) & 0.142 & 1.500 & 18.750 percent \\ \hline Permeability & 0.727 & 3.500 & 43.750 percent \\ \hline \end{tabular}
\end{table} 
Table 2: Calculation results of the weights of PC. \begin{table}
\begin{tabular}{c c c c c} \hline
**Term (in a Mathematical Formula)** & **Indicator Variability** & **Conflicting** & **Volume of** & **Weights** \\  & & **Indicators** & **Information** & **Weights** \\ \hline Drilling fluid consumption (m\({}^{3}\)/h) & 0.203 & 3.053 & 0.618 & 19.26 percent \\ \hline Grouting final pressure & 0.302 & 3.598 & 1.086 & 33.83 percent \\ \hline Dry material per unit length (t/m) & 0.205 & 3.162 & 0.649 & 20.21 percent \\ \hline Permeability & 0.233 & 3.680 & 0.857 & 26.70 percent \\ \hline \end{tabular}
\end{table} 
Table 3: CRITIC weighting calculation results. The combined subjective and objective weights, calculated using Equation (9), are presented in Table 4. The resulting combined weights for the four indicators--drilling fluid consumption, grouting final pressure, dry material per unit length, and water permeability--were 7.744%, 4%, 39%, 14%, and 43%, respectively. It can be seen that the weight values of grouting final pressure and permeability are relatively large, which is consistent with previous research [4].
# Strategies for Reducing Sodium Intake in Bakery Products, a Review
## 2 The Physiological Role of Sodium Chloride and Legislative Recommendations on Its Consumption
### Legislative Norms on Recommended Daily Sodium Intake  
Globally, institutions such as the European Union are actively promoting the reduction of salt content in food. The European Parliament and the EU Council approved in 2006 a regulation on nutrition and healthy food (EC) No. 1924/2006 (European Commission (EC), 2006), this document allowing, among other things, the use of nutritional claims regarding the sodium/sodium chloride content of foods. These regulations allow consumers to focus on the salt content of food with the help of inscriptions found on product packaging [1]. Article 8 of EU Regulation 1924/2006 lists the following restrictions for sodium/sodium chloride claims it's shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Salt Content, g/(100 g/100 mL)** & **Sodium Content, g/(100 g/100 mL)** & **Nutrition Claims** \\ \hline - & - & Low content (sodium chloride/sodium) certifies that the sodium or equivalent salt has been reduced by at least 25\% compared to a similar product. \\ \hline \hline
0.30 & 0.12 & Low sodium chloride/sodium centertify that the product does not contain more than 0.12 g of sodium or the equivalent value for sodium chloride, 0.3 g per 100 g or per 100 mL. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nutrition claims regarding salt/sodium content—per Article 8 of EU Regulation 1924/2006 [1; 5].
# Analysis of ICS and SCADA Systems Attacks Using Honeypots
## 3 Background
All of the aforementioned factors create vulnerabilities in the communication systems of industrial plants. Insecure software vulnerabilities arise from the absence of access control in real-time operating systems used in programmable logic controllers (PLCs) and remote terminal units (RTUs). This lack of access control provides all users with root access, thereby leaving the system open to various attacks. Insecure software vulnerabilities can also stem from vulnerabilities in Windows and Linux. Stuxnet is an example of a cyber-attack that exploited Windows vulnerabilities, such as Windows Server Service remote code execution. Additionally, buffer overflow vulnerabilities are common [14]. Software applications are also vulnerable to attacks due to the absence of digital signatures, which exacerbates the problem of vulnerabilities in the software. Honeypots can be thought of as traps to attract attackers. They emulate physical or virtual network devices to help monitor attackers' behaviors. Honeypots help security administrators to identify and study their enemies. Additionally, honeypots help determine how attackers can gain access and escalate their privileges, what attackers leave in the attacked network to come back again (e.g., rootkits, Trojans, and exploits), and how to defend against these attacks [14]. The authors divided honeypots and honeynets for IoT, IIoT, and CPS into several categories based on their function, level of interaction, scalability, resource level, source code accessibility, and application, as shown in Figure 1[15]. They also considered simulated services, connections between honeypots and honeynets based on inheritance, the platforms on which they were built, and the programming languages used. Additionally, honeypots may be categorized based on their level of interaction, ranging from low to high, as shown in Table 2[16].
# A Novel Decision Support System for Long-Term Management of Bridge Networks
## 2 Materials and Methods
Analysis of historical element condition states to predict the future condition using a time series forecasting model that estimates the damage growth;
* A novel, adaptive decision ranking implementation for bridge maintenance decisions using bridge appraisals and a deep learning-based ranking algorithm;
* Adapting the infrastructure owner's maintenance practice through periodic model updates to fine-tune the decision ranking weights using automatically generated data from users' decision actions;
* Decision tree implementation to produce maintenance/repair strategies with alternative actions and associated cost calculation;
* Damage visualization on realistic 3D bridge model with a timeline feature demonstration of both past and future conditions;
* Data exchange and synchronization with infrastructure owner's bridge management software and the NBI database. The novel decision support framework discussed in this paper uses bridge deck deterioration as an example; however, the methodology applies to any bridge component. After the system integrates the NDE data for all bridges in a defined transportation network, the condition history of each bridge component (i.e., deck, columns, railings, girders) is used to calculate the structural health index of the bridge. Then, the system predicts the future condition of each component using a deep learning-based approach (a cracking defect was shown in the paper as an example, but it can easily be applied to other deterioration  
Figure 4: Web-based software implementation of the decision support framework (bridge information retrieval). modes). Before the system generates maintenance decisions, the bridges in the network are ranked using a deep learning-based ranking model. Lastly, the decision strategies are generated for these prioritized bridges. The proposed system will, however, possess limitations in updating the ranking if the bridge owner prioritizes a certain bridge for an unfactored decision (e.g., impact on the bridge or flooded bridges on water) in which the periodic inspection using NDE fails to provide enough data.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 3 Risk Analysis
### Qualitative Risk Assessment (QRA)  
Insufficient data leads to a qualitative risk assessment that uses some information about hazards, causes, and outcomes of failure, in addition to the probability of failure events, to produce reliability. The most used methods for QRA are Hazard and Operability Study (HAZOP) [50], What-if/Checklist, Logic diagrams, and Failure Modes and Effects Analysis (FMEA) [51].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \hline
**Likelihood of** & **Catas-Rophic:** & **Serious: Extensive** & **Moderate: Medical** & **Minor: First** & **Negligible: No** \\
**Occurrence (\(\psi\))** & **Death, Injuries** & **Toxic Release** & **Treatment Required** & **Aid Treatment** & **Injuries or Illness** \\ \hline Very Likely & H & H & H & S & S \\ \(\psi>=10^{-1}\) & & & & & & \\ \hline Likely & H & H & S & S & M \\ \(10^{-3}<=\psi<10^{-1}\) & & & & & & \\ \hline Moderate & H & H & S & M & L \\ \(10^{-6}<=\psi<10^{-3}\) & & & & & & \\ \hline Unlikely & H & S & M & L & L \\ \(10^{-9}<=\psi<10^{-6}\) & & & & & & \\ \hline Rare & S & S & M & L & L \\ \(\psi<10^{-9}\) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Risk Level Classification.  
Figure 2: A classification of the presented reliability-based methods for risk analysis.
# Influence of the Approach Direction on the Repeatability of an Industrial Robot
## 1 Introduction
The measurement results were 3D trajectories of three joint nodes and vibration responses in these nodes during robot movement. The authors in [21] proposed an algorithm of simple iterative learning control of a robot, whose aim was to reduce geometric errors occurring in the technology process known as single point incremental sheet forming. They apply the 3D digital image correlation method to measure the geometric error along the tool path. In addition to position measurement, the DIC method can also be used in operational vibration analysis or experimental modal analysis. Several papers have been published on this topic [22; 23; 24; 25; 26; 27]. In the paper [26], the authors proposed a highly efficient procedure for processing large amounts of data representing the vibration time responses of the analyzed structure. This procedure was subsequently implemented in the DICMAN 3D software application for modal test evaluation. In another work [27], the authors went even further by presenting a technique that makes it possible to use a DIC system to measure vibration responses during body movement. It is based on post-processing numerical elimination of the rigid body motion components. They used this technique to measure the modal shapes and operating shapes of the rotating disk. These approaches can be advantageously used in the analysis of dynamic behavior of robots and manipulators, e.g., to reduce dynamic load when accelerating, decelerating, or optimizing trajectory. Most of the scientific publications are focused on measurement and increase of the robot absolute accuracy. According to the aim of this article, it is also necessary to mention those that address the measurement of repeatability. A team of researchers from the University of Zilina described a methodology of pose-repeatability measurement, based on the use of laser interferometer and digital indicator [28]. This paper uses the recommended procedure described in the ISO 9283:1998--international standard that describes how performance characteristics should be specified [29]. Experimental measurement of robot repeatability is also described in a paper from Robert Morris University, USA [30]. We must not forget to mention articles focused on a comparative evaluation of industrial robots using different types of methods [31; 32; 33]. It is clear from the above overview that the area of increasing the accuracy or repeatability of manipulators is an area, that has long been paid attention to, and several possible solutions have been presented to increase the precision. However, none of these methods examines in more depth the influence of arrival direction at the desired target. The paper aims to prove the dependence of the arrival direction to the target on repeatability and to determine the range of directional deviation for a specific robot by a set of measurements.
# Inspection of Coated Hydrogen Transportation Pipelines
## 2 Background
### The Effects of Hydrogen on Steels  
Previous studies have hinted at the choice of pipeline material being a key determining factor in the susceptibility of the pipeline to hydrogen degradation. This sub-section provides a summary of the behaviour of different classes of pipeline steels under various conditions in the context of hydrogen degradation. For pipelines in general, key findings from literature include:  
1. In the case of blended hydrogen-natural gas pipelines, before existing gas in the pipeline can be replaced, the feasibility of doing so has to be considered with respect to material fatigue, pipeline failure due to overpressure, and HE [20]. Using nitrided steel or higher grade pipeline steel was also said to improve safety [20]. The effect of coatings, however, needs further study to determine its effectiveness in preventing HE [20].
2. It has been suggested that scratches suffered by pipelines as a result of pigging are not likely to change their susceptibility to SCC [21]. However, it is said that if pigging were conducted in the presence of H2S, the negative impact of SCC on the pipelines could be more severe [21].
3. HE susceptibility is a contested issue, with some having reported that the HE susceptibility of structural metals rises as materials strength rises [22]. Others separately reported that fatigue crack growth depended on the pipeline steel and its \"specific microstructural characteristics\" [22].
4. A method was proposed to predict the changes in fracture toughness arising due to changes in \"hydrogen concentration\" [23]. This involved using \"small punch test data of HE specimens\", and finite element damage analysis \"based on the multi-axial fracture strain-based damage model\" [23]. While analysis of literature relating to each API 5 XL grade has not been done for this review, existing literature relating their behaviour in various conditions in the context of hydrogen damage has been categorised in Table 4.