esa-sceva/satcom-chunk-collection huggingface dataset:
# Use Case Based Blended Teaching of IIoT Cybersecurity in the Industry 4.0 Era
## 4 Shodan for Practical IIoT/Industry 4.0 Cybersecurity Labs  
### Lab 1: Shodan Basics  
Shodan is an Internet scanner that searches for text banners, which are shown by a device when a remote user connects to them (for instance, the welcome message of an FTP server) [81]. Such banners usually contain information that allow for identifying certain characteristics of an Internet service (e.g., the software version of an FTP server) or of the computer where it runs (e.g., the version of the operating system). The collected information can be used later for detecting vulnerabilities and for defining potential cyberattacks.  
Shodan can be used like other web search engines, but the results it produces are conditioned by the type of user that performs the query. For instance, non-registered users have a limited number of queries and cannot use certain Shodan features. One of such features are filters, which allow for narrowing down search results geographically (e.g., by country, city), by IP (e.g., specific IP or network), by port or organization [82].  
An example of Shodan search with filters is illustrated in Figure 2, which also indicates the different parts of a Shodan main result page. As it can be observed in Figure 2, the result page lists more than 22,000 devices in which port 102 is open. Such a Shodan query is really relevant for the industrial field, since it allows for discovering Siemens S7 PLCs (port 102 is used by the S7 protocol to communicate PLCs of the Siemens S7 family).  
It is important to indicate the students that not all the discovered devices are'real' S7 PLCs: there is a significant amount of honeypots that are deployed with the goal of determining when cyberattacks are performed, thus gathering information on the strategies of the attackers. This is a common problem in previous Shodan literature: statistics on detected devices are given based on a specific query, but honeypots are not filtered out. For instance, the previous Siemens S7 query can be refined to determine which of the previous detected devices were actually making use of the ICS/SCADA honeypot Conpot [83]. It is also worth mentioning that Shodan provides a tool that helps to identify to a certain extent (i.e., with an estimated probability called 'Honeyscore') whether an IP address is a honeypot [84].
# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 4 Conclusions and Future Work  
We presented a constructive adjustable image data hiding scheme for HDR RGBE images and LDR images, where secret messages are concealed by directly synthesizing a stego abstracted image. Depending on the E channel in an HDR image, our six-level adaptive message embedding approach can conceal various secret messages. A two-level adaptive technique was presented for LDR images, where adaptivity is on the basis of the mean value of each channel. This encourages our scheme to convey more data in lower luminance pixels and fewer in higher luminance ones without upsetting the human visual system. Without degrading the quality of the stego image, our scheme achieves an embedding rate that is 14% to 33% higher than current state-of-the-art schemes.  
A novel image encryption method was introduced to protect the hidden messages carried in an abstracted image. Our new scheme incorporates a 2D logistic tent modular map with a bit-level random permutation technique. The former has a continuous chaotic range, robust chaotic behavior and hyperchaotic properties, while the latter is flexible and exhibits the time complexity and the space complexity of \(O(N)\) and \(O(1)\), respectively. The proposed algorithm supports excellent security and is robust against statistical, differential, brute force, and chosen key attacks. Our proposed adaptive data hiding and secure encryption scheme is feasible in seeking to extend the HDR and LDR data hiding applications.  
Possible future work includes extending our algorithm to provide the feature of reversibility, which includes the ability to recover the initial image followed by message extraction.  
Conceptualization, C.-F.L. and C.-M.W.; methodology, C.-F.L.; software, C.-F.L.; validation, C.-F.L.; writing--original draft preparation, C.-F.L. and C.-M.W.; writing--review and editing, C.-F.L. and C.-M.W.; project administration, C.-M.W. and W.L.; Supervision, W.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available in a publicly accessible repository.  
This work is supported in part by the Ministry of Science and Technology, Taiwan, under Grant MOST 109-2221-E-005-062, MOST 110-2221-E-005-069, and MOST 111-2221-E-005-076.  
The authors declare no conflict of interest.
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 5 Simulation Verification
### Critical Task Scheduling Simulation  
In order to compare the completion of important tasks when the resources of the edge computing system are limited, the scheduling processing of the fault handling tasks is simulated according to the actual operation scenario of the distribution station area. The  
Figure 5: The effect of the number of concurrent containers in the edge device on the task completion time.  
Figure 6: Impact of cloud-edge communication time on the number of tasks completed by different algorithms.  
Figure 7: Impact of cloud-edge communication time on the completion time of tasks by different algorithms.  
parameters and structure of the tasks are shown in Figure A1 and Table A2. The scheduling was performed using the strategy of this paper (denoted as strategy 1) and the strategy of the literature [23] (denoted as strategy 2), respectively. Thirty random tests were conducted to observe the task processing, and the results are shown in Figure 8.  
In Figure 8, the processing time of the task under strategy 1 is significantly smaller than the processing time of the task under strategy 2. The task completion rate is 83.3% under strategy 1 and 23.3% under strategy 2, with a task delay requirement of 3.2 s. This is because strategy 2 is only based on the order of the scheduling list to select the microservice that can be executed at the earliest, which is less flexible and cannot adapt to the special task requirements. During the operation of the grid, tasks such as fault handling will have a large impact on the grid if they are not handled in a timely manner [24]. The strategy proposed in this paper can reduce the processing time of important tasks when the resources of the edge computing system are limited, and thus can improve the stability and security of the grid operation.  
In summary, the cloud-edge collaborative online scheduling strategy for station tasks can meet the needs of various tasks under different resource conditions of the system with higher task processing efficiency. The method provides a solution to the task scheduling problem when the resources of the edge devices are limited.
## 1 Introduction  
Latest developments and applications of solid-state lighting have begun gaining greater attention due to the requirements for efficient energy usage nowadays [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. With features of energy-savings, lower maintenance costs and long lifetime, light-emitting diode (LED) tube lamps have begun to be favorable alternatives to replace traditional T8/T10-type fluorescent lamps in building-lighting applications [17; 18; 19]. According to the specifications shown in [20; 21] with almost the same color temperature and color-rendering index, the LED tube lamp has better lighting efficiency, less power consumption, longer lamp lifetime, and contains no mercury inside the lamp tube, as compared to its T8-type counterparts. Therefore, T8-type LED tube lamps have become increasingly popular lighting sources for indoor lighting applications such as public architecture, offices, classrooms, and so on [17; 18; 19; 20; 21]. Figure 1 shows the commercial two-stage driver for supplying a T8-type LED tube lamp; it is composed of an AC-DC (alternating current-direct current) converter (typically a boost converter) with power-factor corrections (PFCs) as the first stage, and a DC-DC converter (typically a buck converter) as the second stage for regulating the voltage/current of the LED tube lamp. Separate controllers for each stage are required, and the circuit efficiency is restricted due to the two-stage power conversion. Some single-stage AC-DC drivers for providing a T8-type LED tube lamp to replace T8/T10-type fluorescent lamps have been presented: reference [18] utilizes a flyback PFC converter, and a buck converter with input-current shaping is presented in reference [19]. These single-stage versions are cost-effective in comparison to their two-stage counterpart; however, their power switches do not include the soft-switching function, which results in limited efficiency.  
In response to these concerns, this paper presents a single-stage LED tube lamp driver suitable for indoor lighting applications with features of power-factor corrections and soft-switching characteristics. Theoretical analyses of operating modes, design equations for key circuit parameters, and experimental results from a prototype driver circuit for supplying an 18 W-rated T8-type LED tube lamp with input voltages ranging from 100 V to 120 V are demonstrated. This paper is organized as follows. Section 2 introduces and analyzes the presented single-stage LED tube lamp driver. Section 3 shows design equations of key components in the presented driver. Section 4 demonstrates experimental results of a prototype circuit for supplying an LED tube lamp. Finally, some conclusions are provided in Section 5.
### Energy Scalable Pulse Cleaning Techniques  
#### 1.3.1 Plasma Mirror  
The plasma mirror technique [27] was proven to be an efficient method to remove prepulses. The low intensity part of the beam, arriving before the main pulse, is transmitted through an antireflection-coated transparent target. Above the ionization threshold, an overdense plasma is generated, reflecting the laser beam, leading to improved temporal contrast. Since reflectivities up to 80% are reached [35], plasma mirrors, even in a double arrangement, are often used [36]. Recently, reflectivity of up to 97% was reached by [37], using a prepulse some picoseconds before the main pulse of the PHELIX laser [38]. Plasma mirrors are used mainly for infrared laser systems, as the deeper penetration depth of a UV beam results in a lower reflectivity. Recently, the plasma mirror effect was also demonstrated for a short-pulse _KrF_ laser [39; 40]. In an improved experiment, reflectivity up to 70% was obtained for the 248 nm pulse [41].  
The plasma mirror technique, however, has shortcomings, that the contrast improvement in a single stage is moderate (roughly two orders of magnitude), moreover, a fresh target area is needed for each shot.
## 4 Experiment Results and Comparison
### The Experimental Setup
#### 4.4.3 Optimisation of Threshold  
Although practical IDSs are typically configured using multiple threshold parameters, choosing an appropriate threshold value can prove to be a challenging problem.  
In our case, by tuning the threshold parameters, we could either maximize or minimize the accuracy and efficiency of the labeling process. To answer this question, we will investigate and analyze the impact of applying five values of \(\alpha\) threshold ranging from 0.90 to 1, three values of \(\beta\), and two values of \(\gamma\) in the performance of the proposed approach. Thus, in our case, the model will be retrained 30 times.  
\[5(\alpha\text{-values})*3(\beta\text{-values})*2(\gamma\text{-values})=30 \tag{15}\]  
After we performing an exhaustive analysis, we conclude that the best set of parameters were (\(\alpha=0.9\), \(\beta=1.2\%\), \(\gamma=0.01\))
## 3 Results and Discussion
### ESI-MS of Uranyl Complexes with Glycine, Aspartic Acid, Cysteine, and Histidine
#### 3.3.2 Competitive CID and Reactivity of Anionic Uranyl Mixed Amino Acid Complexes
The mixed ligand complex [UO\({}_{2}\)(His-2H)(Gly-H)]\({}^{-}\) also reacted quickly with water (\(k\)-15 s\({}^{-1}\)), whereas the complexes with Asp/His and Cys/His were unreactive, which suggests that the doubly deprotonated amino acids are Asp-2H and Cys-2H.
## 3 Results  
### Physical Characteristics of Ecological Concrete  
The optimization of ecological concrete properties yielded three distinct strength grades (C20, C25, and C30), with systematically varying compositions as detailed in Table 1. The C25 grade emerged as the optimal formulation, achieving a balanced cement content of 378 kg m\({}^{-3}\), reinforcing agent content of 12 kg m\({}^{-3}\), aggregate content of 1650 kg/m\({}^{-3}\), and water content of 137 L m\({}^{-3}\). This formulation achieved the target 28-day compressive strength of 20-30 MPa while maintaining porosity above 15% and permeability coefficient exceeding \(1\times 10^{-4}\) cm s\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property Type** & **Parameter** & **C20** & **C25** & **C30** \\ \hline \multirow{4}{*}{**Mix Proportions (kg m\({}^{-3}\))**} & Cement & 358 & 378 & 398 \\  & Reinforcing Agent & 10 & 12 & 15 \\  & Aggregate & 1650 & 1650 & 1650 \\  & Water & 130 & 137 & 145 \\ \hline \multirow{4}{*}{**Performance Parameters**} & Strength Grade & C20 & C25 & C30 \\  & 28-day Compressive Strength & 15–25 MPa & 20–30 MPa & 25–35 MPa \\  & Porosity & \textgreater{}18\% & \textgreater{}15\% & \textgreater{}12\% \\  & Permeability Coefficient & \textgreater{}2 \(\times\) 10\({}^{-4}\) cm s\({}^{-1}\) & \textgreater{}1 \(\times\) 10\({}^{-4}\) cm s\({}^{-1}\) & \textgreater{}5 \(\times\) 10\({}^{-5}\) cm s\({}^{-1}\) \\ \hline \hline \end{tabular} Note: mix proportions are given in kg m\({}^{-3}\). Strength grade (C20, C25, C30) indicates characteristic compressive strength in MPa. Performance parameters include 28-day compressive strength (MPa), porosity (%), and permeability coefficient (cm s\({}^{-1}\)). Higher permeability coefficients and porosity values indicate better water infiltration capacity suitable for ecological concrete applications.  
\end{table}
Table 1: Mix proportions and properties of ecological concrete with different strength grades.  
The systematic preparation and characterization process of the ecological concrete specimens is illustrated in Figure 1. The fresh concrete mixture demonstrated uniform consistency with visible pore formation (Figure 1A). Special molds equipped with drainage ports facilitated controlled water release during setting (Figure 1B), crucial for achieving the desired pore structure. The specimen preparation process involved careful compaction and surface finishing to ensure uniformity (Figure 1C,D). The comparative view of three strength grade specimens shows distinct visual characteristics (Figure 1E), with C25 specimens exhibiting optimal surface texture and pore distribution for the heavy metals' removal experiment (Figure 1F,G).
Molecules201520047359  
_molecules_  
**ISSN 1420-3049**  
www.mdpi.com/journal/molecules  
Investigation of a Quantitative Method for the Analysis of Chiral Monoterpenes in White Wine by HS-SPME-MDGC-MS of Different Wine Matrices  
**[PERSON], [PERSON] and [PERSON] ***  
Department of Food Science & Technology, Oregon State University, Wiegand Hall, Corvallis, OR 97331, USA; E-Mails: [PERSON]DDRESS] (M.S.); [EMAIL_ADDRESS] (Y.X.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-541-737-4866.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 16 March 2015 / Accepted: 16 April 2015 / Published: 22 April 2015_  
**Abstract:** A valid quantitative method for the analysis of chiral monoterepenes in white wine using head-space solid phase micro-extraction-MDGC-MS (HS-SPME-MDGC-MS) with stable isotope dilution analysis was established. Fifteen compounds: (S)-(-)-limonene, (R)-(+)-limonene, (+)-(2R,4S)-_cis_-rose oxide, (-)-(2S,4R)-_cis_-rose oxide, (-)-(2R,4R)-_trans_-rose oxide, (+)-(2S,4S)-_cis_-rose oxide, furanoid (+)-_trans_-linalo oxide, furanoid (-)-_cis_-linalo oxide, furanoid (+)-_trans_-linalool oxide, (+)-_cis_-linalool oxide, (-)-linalool, (+)-linalool, (-)-a-terpineol, (+)-a-terpineol and (R)-(+)-\(\beta\)-citronello were quantified. Two calibration curves were plotted for different wine bases, with varying residual sugar content, and three calibration curves for each wine base were investigated during a single fiber's lifetime. This was needed as both sugar content and fiber life impacted the quantification of the chiral terpen. The chiral monoterepene content of six Pinot Gris wines and six Riesling wines was then analyzed using the verified method. ANOVA with Tukey multiple comparisons showed significant differences for each of the detected chiral compounds in all 12 wines. PCA score plots showed a clear separation between the Riesling and Pinot Gris wines. Riesling wines had greater number of chiral terpen in comparison to Pinot Gris wines. Beyond total terpene content it is possible that the differences in chiral terpene content may be driving the aromatic differences in white wines.  
**Keywords:** monoterepenes; chiral; MDGC-MS; Riesling; Pinot Gris
# Self-Adaptive Differential Evolution with Gauss Distribution for Optimal Mechanism Design
## 5 Numerical Simulation
### Modified Differential Evolution for Real-World Problem
#### 5.4.4 Optimization Problem of Gear Reducer
Design variable: \(x_{1}\) = [17; 30]; \(x_{2}\) = [2; 5]; \(x_{3}\) = [55; 61]; \(x_{4}\) = [0.2, 0.5]; \(x_{5}\) = [5; 30]; \(x_{6}\) = [17; 30]; \(x_{7}\) = [2, 5]; \(x_{8}\) = [140, 200]; \(x_{9}\) = [0.2, 0.5]  
Minimum:  
\[f(x)=\frac{\pi}{4}\times(x_{3}(((x_{1}+2(1+x_{4}))x_{2})^{2}+3( (0.5(x_{5}-2)x_{1}+2(1+x_{4}))x_{2})^{2})\\ +x_{8}(((x_{6}+2(1+x_{9}))x_{7})^{2}+((0.5(\frac{u}{x5}-2)x6+2(1+ x_{9}))x_{7})^{2})) \tag{50}\]  
where \(u\) is the total transmission ratio of the planetary gear system, \(u\)=30. Subject to  
\[g_{1}\ =\ \frac{2\times 2.89\times 5.69\times 10^{5}\times(x_{5}+1)}{x_{3}( x_{1}x_{2})^{2}x_{5}}-(\frac{1033.41}{2.22\times 189.98\times 0.95})^{2}\leq 0; \tag{51}\]  
\[g_{2}\ =\ \frac{2\times 2.95\times 1.78\times 10^{6}\times(\frac{u}{x_{5}}+1)}{x _{8}(x_{6}x_{7})^{2}\frac{u}{x_{5}}}-(\frac{1104.47}{2.25\times 189.98\times 0.9 4})^{2}\leq 0; \tag{52}\]  
\[g_{3}\ =\ \frac{2\times 2.89\times 5.69\times 10^{5}}{x_{1}\times x_{2}^{2}\times x _{3}}-\frac{499.39}{2.29\times 1.73\times 1.12}\leq 0; \tag{53}\]  
\[g_{4}\ =\ \frac{2\times 2.89\times 1.78\times 10^{6}}{x_{6}\times x_{7}^{2}\times x _{8}}-\frac{521.53}{2.32\times 1.73\times 1.08}\leq 0; \tag{54}\]  
\[g_{5}\ =\ x_{1}\times(x_{5}-\frac{\sqrt{3}}{2}(x_{5}+1))-2\times x_{4}+4\leq 0; \tag{55}\]  
\[g_{6}\ =\ x_{6}\times(\frac{u}{x_{5}}-\frac{\sqrt{3}}{2}(\frac{30}{x_{5}}+1))-2 \times x_{9}+4\leq 0; \tag{56}\]  
\[g_{7}\ =\ 0.6\times x_{1}\times x_{2}-x_{3}\leq 0; \tag{57}\]  
Figure 27:0  
Figure 27:1  
Figure 27:2  
The obtained solution is compared with the original DE, as shown in Figure 27:3 Our optimal solution is better than the candidate generated by the original DE. \begin{table}
\begin{tabular}{c c c} \hline
**Design Variables** & **DE** & **G-ISADE** \\ \hline \(x_{1}\) & 13 & 20 \\ \(x_{2}\) & 2.47044 & 4.957651 \\ \(x_{3}\) & 60.7104 & 59.557864 \\ \(x_{4}\) & 0.238434 & 0.5 \\ \(x_{5}\) & 12.9891 & 5.423887 \\ \(x_{6}\) & 13 & 24 \\ \(x_{7}\) & 6.9805 & 4.907367 \\ \(x_{8}\) & 110.067 & 147.951034 \\ \(x_{9}\) & 0.2408021 & 0.5 \\ \hline \(f(x)\) & 14,555,831.29 & **14,204,000** \\ \hline \end{tabular}
\end{table} 
Table 11: Best solution for two-stage planetary gear reducer problem.
## 3 Materials and Methods
### Model Construction
#### 3.2.5 Multi-Scale Feature Extraction Mechanism  
To enhance the feature extraction capability of DCSInformer across different time scales, the model adopts a multi-scale feature extraction mechanism [38]. Specifically, the input time series \(X\) is divided into three distinct parts: the full series, the half series, and the quarter series. These sub-series correspond to features extracted at different time granularities. Assuming the total time steps of the input series is \(T\), the three series are represented as follows:  
\[\text{Full sequence: }X_{\text{full}}=X \tag{23}\]  
\[\text{Half sequence: }X_{\text{half}}=\{x_{1},x_{3},\dots,x_{T/2}\} \tag{24}\]  
\[\text{Quarter sequence: }X_{\text{quarter}}=\{x_{1},x_{5},\dots,x_{T/4}\} \tag{25}\]  
These sub-series are processed independently through Multi-Head ProbSparse Self-Attention, which prioritizes computations by selecting the most relevant query-key pairs for each sub-series. While the model reduces the computational burden, it retains key contextual information from the data at each granularity, ensuring that important temporal dependencies are effectively captured. By combining features from all three series, the model achieves a comprehensive representation of the input time series, capable of addressing variations across multiple time scales.  
For each sub-series \(X_{\text{sub}}\), the attention mechanism computes the weighted sum of the values \(V\), given the queries \(Q\) and keys \(K\):  
\[A(Q,K,V)=\text{softmax}\left(\frac{\mathcal{Q}K^{\top}}{\sqrt{d}}\right)V \tag{26}\]  
Here, \(d\) is the feature dimension. The computation is independently performed for \(X_{\text{full}},X_{\text{half}}\), and \(X_{\text{quarter}}\).  
After extracting features from each sub-series, the outputs are concatenated and fused to form the final representation:  
\[H=[H_{\text{full}},H_{\text{half}},H_{\text{quarter}}] \tag{27}\]  
where \(H_{\text{full}},H_{\text{half}},H_{\text{quarter}}\) are the attention-weighted representations for the respective sub-series.  
Compared to the traditional Informer model, DCSInformer demonstrates significant improvements in capturing temporal dependencies across different time scales. By introducing the dilated causal convolution and multi-scale extraction mechanisms, the model enhances its ability to focus on key features over longer time spans while maintaining sensitivity to short-term variations [39]. This capability is especially valuable for medium  
Figure 5: Dilated causal convolution.  
and long-term predictions, where balancing short-term dynamics and long-term trends is critical for improving forecast accuracy.
# Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion
## 2 Using Genetic Algorithm to Optimize Spatiotemporal Correlation Forecast Model
### Multi-Channel and Multi-Location Attention Mechanism
#### 2.2.2 Attention Module in Spatial Dimension  
The structure of the spatial attention module (SAM) is shown in Figure 7. First of all, the global maximum pooling and maximum average pooling operations are performed on the input feature map to obtain two sub-feature maps, which are aggregated into a 2-channel feature map by channel. They are then connected through standard convolutional layers and subjected to convolution operations to generate a spatial attention feature submap Ms(F) with a channel size of 1 \(\times\) 4. The calculation process of SAM in this model can be expressed as Equation (9).  
\[\begin{array}{ll}\mathrm{Ms(F)}&=\sigma\Big{(}\vec{f}\left([\mathrm{AvgPool( F);MaxPool(F)]}\right)\Big{)}\\ &=\sigma\Big{(}\vec{f}\left(\Big{[}\mathrm{F}_{\mathrm{avg}}^{\mathrm{s}}; \mathrm{F}_{\mathrm{max}}^{\mathrm{s}}\Big{]}\right)\Big{)}\end{array} \tag{9}\]  
where \(\sigma\) refers to the sigmoid function, and \(f^{\prime}\) represents a one-dimensional convolution operation with a filter size of 7. After the sigmoid activation function, the spatial attention feature map Ms(F) is obtained. Ms(F) is multiplied by the feature map F\({}^{\prime}\) generated by channel attention, and the final feature map F\({}^{\prime}\) is output.