esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Absorption Spectra of B-co-MP  
Figure 2 reveals the UV-vis absorption spectra of B-co-MP in the THF solution at a concentration of 1 \(\upmu\)M, pre-and post-different doses of \(\upgamma\)-irradiation. The non-irradiated sample showed double peaks, one around 305 nm and the other around 450 nm. These absorption peaks were attributed to \(\upgamma\)-\(\upgamma\)\({}^{\star}\) (HOMO-LUMO) transitions [4]. The same sample was then irradiated with doses ranging from 5 to 20 kGy at a constant temperature (25 \({}^{\circ}\)C). The results showed that the central peak at 450 nm was blue-shifted (shorter wavelength), with a significant decrease in the absorbance after irradiation, as demonstrated in Figure 2. As shown in the inset of Figure 2, the peak position shift was linearly proportional to the gamma radiation dose.  
Following the same procedure, five different solutions of various concentrations (1, 10, 100, 200, and 400 \(\upmu\)M) were prepared and irradiated with four different doses (5, 10, 15, and 20 kGy) for each solution. Figure 3 displays the shift in the central maximum wavelength peak (450 nm) measured as a function of the solution concentration. The shift in the peak was linearly dependent on the gamma dose for each concentration studied. Furthermore, it is noteworthy that the shift amount also relied linearly on the concentration of the prepared solutions; for example, at a fixed \(\upgamma\)-irradiation dose (10 kGy), the concentrations of 1, 10, 100, 200, and 400 \(\upmu\)M yielded blue shifts corresponding to 7, 11, 14, 22, and 23 nm, respectively (see inset Figure 3). The plausible explanation for the blue shift observed in the primary absorption peak is the change in the polymer structure caused by a reduction in the copolymer's conjugation length due to a free radical interaction with the polymer backbone caused by solvent radiolysis [25, 26].  
Figure 2: UV–vis absorption spectra of B-co-MP in THF at a concentration of 1 \(\upmu\)M at different gamma doses.
# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
## 4 Experimental Results and Analyses
### RMS Entropy Features Extraction and Blockage Recognition
Figure 9: Identification accuracy of different IMF components screening methods. In order to further investigate the ability of VMD-IG-RMSE algorithm to identify pipe operational conditions and the details of blockage misjudgment, a multi-classification confusion matrix is introduced to quantitatively analyze the results of pipe operational conditions recognition in detail. The confusion matrix comprehensively reflects the recognition accuracy and number of misjudgments of pipelines at different blockage levels, as well as the misjudgment type of real blockage type. The quantization diagram of the confusion matrix of four classifiers KNN, SVM, ELM, and RF is shown in Figure 10.  
It is obvious from Figure 10: Class 1 and Class 2, respectively, represent the normal operating condition of the pipe clean and lateral connection, and the recognition accuracy of both the normal operation of the pipe on the test set reaches 100%. Therefore, the algorithm achieves 100% recognition accuracy between the normal operating conditions and blocked. Class 3, Class 4, and Class 5 represent a 20 mm blockage, a 40 mm blockage, and a 50 mm blockage for a single blockage in the pipe, respectively. By analyzing the types of misjudgment of blockage, it can be seen that the above misjudgment is the error between single blockage categories, which belongs to the misjudgment of different degrees of blockage of single blockage, and there is no misjudgment to multiple blockage. Class 6, Class 7, Class 8, and Class 9 represent a 40 mm blockage and a LC, a 55 mm blockage and a LC of multiple blocked pipes, a 40 mm blockage and a 55 mm blockage and a 40 mm blockage, a 55 mm blockage, and LC respectively. By analyzing the types of multiple blockage misjudgments, it can be seen that the above misjudgments are the misjudgments of multiple blockage categories with different degrees of blockage, and there is no misjudgment to a single blockage. It can be seen that the recognition rate of comprehensive pipeline operation status can reach 99.56%. Through experimental verification, the improved VMD-IG-RMSE-RF algorithm has superior recognition ability and high diagnosis accuracy for pipeline blockage.
# Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse
## 6 Conclusions  
In this paper, the spatial distribution of crack clusters has been analyzed using the microseismic monitoring technique. Through clustering analysis, the temporal and spatial evolution of cracks has been examined, providing valuable insights into the mechanism of crack propagation and instability. The study also validates the applicability of this method through a case study on roof caving. Based on the findings, the following conclusions can be drawn:  
1. Microseismic equipment proves to be an effective tool for monitoring the crack evolution of rock masses. By utilizing machine learning methods, it becomes possible to classify and characterize individual cracks. Furthermore, when microseismic events are located on a plane, they are considered to be potential precursors to cracks and rock mass instability.
2. Different morphologies of crack clusters reflect the complexity of crack development in the analyzed region. The identified crack development can be categorized into three distinct types, illustrating the dynamic evolution process of the rock mass. This process involves independent development at nucleation positions, mutual influence between adjacent locations, and subsequent crack growth and propagation.
3. The proposed model is demonstrated to effectively predict the direction and angle of crack propagation. Its successful engineering application provides valuable guidance for studying rock mass stability and informing related research efforts.  
Conceptualization, Y.C.; methodology, Z.C.; software, Z.C.; investigation, Z.C., P.W., Z.L. and Y.C.; resources, P.W. and Z.C.; data curation, Y.C.; writing--original draft preparation, Y.C., Z.C. and Z.L.; writing--review and editing, Y.C., Z.C. and Z.L.; supervision, Z.L. and P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Science and Technology Innovation Fund, BGRIMM Technology Group (Grant No. 04-2228), and the National Key Research and Development Program of China (Grant No. 2022 YFC2903804).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
Figure 9: Simulation results of three-dimensional crack morphology. The different colors in the right image represent different fracture surfaces.
# Industry 4.0 and Lean Six Sigma Integration: A Systematic Review of Barriers and Enablers
## 4 Review and Discussion
### Analysed Papers' Main Features  
In total, academic contributions from 22 countries were identified, as shown in Figure 4. The findings show that studies on I4.0 and LSS integration have mainly been carried out in Germany, Brazil, Italy (eight articles each), the United States (six articles), and China (five articles). In the case of Oceania, an analysis in [60] found that, from 2017 onwards, I4.0 has started to gain attention in companies; however, no journal article has addressed its integration with LSS in the industries of this continent so far, as shown in Figure 4.  
Additionally, the industries concentrating most of the research on I4.0 and LSS were identified. As shown in Figure 5, metal mechanical (33.8%) and automotive (25.7%) are  
Figure 4: I4.0 and LSS integration research by countries.  
Figure 3: Search results summary.  
the two most addressed sectors, followed by food and beverage (17.6%) and construction (16.2%). The automotive sector is familiar with Lean manufacturing because of its origins with the Toyota car manufacturer [61], which places it in a favourable position to become a primary research source for I4.0 and LSS integration. Other sectors are grasping the importance of this integration in their organisations. One example is the healthcare sector, where integrating I4.0 and LSS may help improve patient flow and enhance supply chain performance, especially in high-demand scenarios [62]. Twenty-four industrial sectors were identified, with some papers addressing more than one (hence, percentages in Figure 5 do not add up to one). Nevertheless, 29.7% of the research did not mention the sector in their study.  
Another aspect of interest is the type of research conducted in the reviewed papers. Case studies accounted for 39.2% of the research. For example, in [63], JIT and robotics in the automotive industry were analysed to optimise the cycle time in a hybrid (human-robot) assembly line system. It was found that 23.0% of the authors used the review method to address the interaction between I4.0 and LSS. Some studies assessed integration feasibility through surveys (21.6%) with the application of structural equation methods [35; 40] and correlation and regression analyses [62; 64]. Finally, 16.2% used conceptual frameworks and focus groups, as shown in Figure 6.  
Figure 5: Primary industries identified by the reviewed articles.
# The Influence of Sebum on Directional Reflectance of the Skin
## 5 Conclusions  
By analyzing the directional reflectance of the skin and the amount of skin sebum, the following conclusions can be drawn:  
1. The amount of sebum does not affect the directional reflectance of the skin at the wavelength 335-380 nm;
2. With an increase in the amount of sebum, the directional reflectance of the skin decreases at the wavelengths 400-540 nm and 480-600 nm;
3. With an increase in the amount of sebum, the directional reflectance of the skin increases at the wavelengths 590-720 nm, 700-1100 nm, 1000-1700 nm, and 1700-2500 nm;
4. The closest relationship between amount of sebum and directional reflectance of the skin was observed at the wavelength 700-1100 nm;
5. Sebum may have a weak sunscreen effect in the near-infrared spectral range;
6. The increased absorption of radiation by sebum in the spectral range 400-600 nm may cause skin reddening.  
The obtained results are an interesting starting point for continuing research on the effect of sebum content on skin reflectance. Reflecting/scattering radiation from the skin surface depending on sebum content may be clinically significant not only in the context of exposure to solar radiation (which in turn may contribute to photoaging and even skin cancer) but also in the context of numerous therapeutic methods based on artificial sources of radiation. In this area, it is desirable for radiation to penetrate the skin as effectively as possible. The obtained preliminary results confirm that the used method is an interesting alternative to spectroscopic methods.  
**Author Contributions:** Conceptualization, A.B., K.K., M.K. and S.W.; methodology, A.B., M.H.-P., B.K.-S. and K.K.; software, P.P., M.H.-P. and S.W.; validation, A.B. and M.H.-P.; formal analysis, A.B., M.K. and B.K.-S.; investigation, A.B., M.K. and K.K.; resources, S.W.; data curation, A.B., M.H.-P. and K.K.; writing--original draft preparation, A.B., K.K., A.L. and M.H.-P.; writing--review and editing, A.B., A.L. and S.W.; visualization, A.B. and M.H.-P.; supervision, S.W.; project administration, A.B. and S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Medical University of Silesia, grant numbers PCN-1-128/N/2/O; PCN-1-057/K/2/O.  
**Institutional Review Board Statement:** This study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethics Committee of the Medical University of Silesia (PCN/CBN/0022/KB1/27/III/16/17/21).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Optimization Formulation
Note that for each RMS vertex, this equation needs to be solved for all the \(2C_{m}^{2}\) facets of the AMS in the worst case, and in each optimization step all the RMS vertices need to be considered to evaluate the cost function. From [8], the RMS takes \(12\times 12\times 24=3456\) vertices to precisely represents its contour, and the AMS of a system with only 8 inputs has 56 facets. This means to evaluate the cost function defined in (3) for an 8-input system, \(3456\times 56=193,536\) 4-by-4 linear systems must be evaluated in each optimization step for the worst case. Another solution to the intersection problem is to formulate it as an optimization problem by itself:  
\[ \begin{array}{c}\text{max}\lambda,\\ \text{subject to, }\left\{\begin{array}{c}\boldsymbol{B}u=\lambda d\\ \boldsymbol{u}\in\boldsymbol{\mathcal{A}}_{c},\;\lambda d\in\boldsymbol{ \mathcal{A}}_{M}\end{array}\right..\end{array}  \tag{5}\]  
This problem is also known as direct allocation in the control allocation domain, which can be transformed into a standard linear programming problem and solved by either simplex algorithm or interior-point algorithm [1; 13; 14]. However, this also means to solve for 3456 times linear programming problem for all the RMS vertices in each step of our \"outer\" optimization framework and is therefore not an acceptable means. In this paper, we focus on the application in \(\mathbb{R}^{3}\) space of the optimization framework and propose an efficient \(3D\) intersection solver to tackle the computation issue. As will be elaborated in the next chapter, the method starts from geometry intuition to locate the intersection facet by coordinate transformation and dimension reduction. Instead of solving a 4-by-4 linear system for each facet, the method solves only a point in triangle problem in \(\mathbb{R}^{2}\) space. Once the facet is determined, the intersection is easily located by \"ray-tracing\" solver often used in the computer graphics domain, which only involves vector dot products. Given correct evaluations of the cost function, the optimization problem can be solved by surrogate optimization functions provided by MATLAB [11].
## 4 Research Contributions and Novelty  
The first contribution of this study is to examine and assess the efficacy of our proposed protocol for assisting and improving dark and deep web forensic investigations. The findings and results show that the proposed protocol extracts more artefacts than standard automation. The second contribution is to analyse and document how to investigate dark and deep web forensics using the strategy outlined in our proposed protocol, such as extracting artefacts from operating systems, network traffic, malicious browser plugin behaviour, and memory dump analysis. The third contribution of this study is to draw attention to the flaws of privacy tools, particularly TOR, such as its tendency to take longer to load when connecting to available node servers, slow performance because of routing data through different nodes, and the fact that data inserted on web pages are not encrypted. Lastly, the fourth contribution is significant because we value knowledge exchange and aligning teaching to industrial practice, which will be a direct output of this study. We aim to use both the proposed D2 WFP protocol and evidence files created from simulated deep and dark forensic scenarios in the new cyber threat intelligence and dark web forensic curriculum to be delivered for undergraduate, postgraduate, and CPD students as well as for conducting future research works.  
The initial protocol is elaborated based on theoretical knowledge and has been inspired by previous research and established forensic principles, as explained in Figure 3. We then proceeded with the elaboration of eight (08) forensic scenarios created on different OS, respectively, Windows, Linux, Android, and iOS, where we used TOR to access emulated deep and dark web addresses and content. At the end of the simulation, we dealt with forensic imaging by running an anti-forensic tool to delete artefacts and forensic re-imaging using FTK imager and UFED Extractor.  
We then moved to the forensic examination, recovery, and analysis using FTK, AXIOM, and UFED Physical Analyzer (PA) software packages following the application of the  
Figure 3: Standard digital forensics process.  
principles outlined within the protocol. The identification phase differentiates between valuable data such as transaction records, timestamps, login credentials, browsing caches holding posts and/or comments as well as networking activities including pertinent IP addresses which are often related to higher layers in the order of volatility (OoV) and primary and secondary memory evidence, such as additional MicroSD or cloud storage. The evidence is cloned, preserved while write-protected, and securely stored when forensic processing is applied to the clones. We reviewed and analysed the data using forensic methods and solutions to the forensic copy of the evidence. However, the reporting must properly communicate all pertinent information in a way that non-experts may understand.
## 2 Experimental Program
### Experimental Methods
#### 2.4.6 Electrical Resistance Test  
In general, two-probe and four-probe methods are used as a means to measure for the electrical conductivity test of cement mortar. Although the circuit of the two-probe method is simpler, the four-probe method is preferred because it relieves the contact resistance between the specimen and electrode [54; 55]. For cement mortar, the four-probe method has a pair of electrodes on the outside and a pair of electrodes on the inside, that is, two pairs of electrodes as a whole. While arranging constant current to flow through the outer pair of electrodes, the difference in voltage generated by the supply of current from the inner pair of electrodes is measured. Electricity introduction can be divided into the direct current (DC) and alternating current (AC) methods, and the use of the direct current (DC) method is the simplest of both methods for conducting electrical conductivity tests. In the present study, the four-probe method was adopted to perform the electrical conductivity test of cement mortar. Figure 5 displays the electrical resistance measurement procedure and test equipment of a cement mortar specimen. The electrical resistance of all specimens was measured at each age (28, 56, and 91 days). To measure electrical resistance, a digital multimeter (precision current source, Keithley 6220) supplies DC (I) to the two outer electrodes, and Keithley 2182A (nanovoltmeter) uses the two inner electrodes to measure voltage (V). The authorized current is available in the range of \(\pm\)100 fA to \(\pm\)100 mA, and the measurement range of electrical resistance value is available in the range of 10 n\(\Omega\) to 200 M\(\Omega\). Thus, it may be said that electrical resistivity is a value that evaluates the properties of a material through which current flows. Meanwhile, electrical conductivity is expressed as \(\sigma\), which is the reciprocal of electrical resistivity, and the unit is S/cm. The electrical conductivity \(\sigma\) (in S/cm) of cement mortar was obtained from the reciprocal of the electrical resistivity by applying the Equation (4).  
\[\sigma=\frac{1}{\rho}=\frac{L}{R.A} \tag{4}\]  
here, \(\rho\) is the electrical resistivity (in \(\Omega\).cm), \(L\) is the distance between the two inner electrodes (4 cm), \(A\) is the area of the electrodes (16 cm\({}^{2}\)), and \(R\) is the measured electrical resistance based on [PERSON]'s law (i.e., R = V/I).
## 3 Materials and Methods
### Classification Methods  
The aim of this work is to detect tweets related to hate speech from the Twitter social media platform when a user posts a tweet. This problem can be represented as a binary classification, classifying tweets as hate speech or non-hate speech. Each tweet can be represented as \(\{x_{1},x_{2},x_{3},\,\dots\,x_{n}\}\) where \(x_{i}\) is the vector encoding the \(i\)th tweet, and \(n\) is the number of tweets. In our work, examples are categorized into two classes. Therefore, \(y_{i}\in\{\)hate speech, non-hate speech\(\}\) is the corresponding label of \(x_{i}\). To achieve the goal  
Figure 1: Proposed framework of Twitter hate-speech detection.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Label** & **Id-Count** & **Mean** & **SD** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline
0 & 29720 & 15,974.45 & 9223.78 & 1.0 & 7981.75 & 15,971.5 & 23,965.25 & 31,962.0 \\ \hline
1 & 2242 & 16,074.90 & 9267.956 & 14.0 & 8075.25 & 16,095.0 & 24,022.00 & 31,961.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A statistical description of the train data of Twitter. SD is the standard deviation.  
of this work in detecting the hate speech from Twitter, we adopt several ensemble methods explained in the following.
## 2 Experiments  
### Injection Molding Experiments  
In the injection molding process, plastic in granular form is fed to the injection unit through a hopper. The plastic passes through a chamber where it is simultaneously mixed and heated until it melts. The plastic melt is then injected into a mold cavity and subjected to packing pressure for a set duration followed by cooling and is eventually ejected as a finished solid product. The entire process requires specifying appropriate values for the process parameters to ensure acceptable product quality with minimal defects (such as jetting, surface delamination, warpage, volumetric shrinkage, etc.).  
The aim of this study was to conduct injection molding experiments aimed at determining the ideal values for process parameters to minimize both volumetric shrinkage and surface roughness. Seven critical process parameters were examined: injection pressure, packing pressure, mold temperature, packing duration, cooling duration, melt temperature, and injection speed.  
The experimental phase of this study was conducted using an Arburg Allrounder 420 C Gold Edition injection molding machine, produced by ARBURG GmbH + Co KG, based in Lossburg, Germany, as shown in Figure 1[35]. The mold cavity used in this study was cuboid in shape, resulting in a product with dimensions of 3 mm in thickness, 117 mm in length, and 97 mm in width. Figure 2a illustrates the two halves of the mold, while Figure 2b provides the dimensions of the resulting product. Although a rectangular-shaped mold cavity was used in this study, this method can be adapted for other shapes as well.  
High-density polyethylene (HDPE), specifically the M80064 grade produced by the Saudi Basic Industries Corporation (SABIC), a global chemical company headquartered in Riyadh, Saudi Arabia, was selected as the injection molding material. It should be noted, however, that the procedure outlined in this study can also be applied to other types of plastics. Table 2 presents a few fundamental properties of this polymer. A detailed list of its properties can be found in [36].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Property** & **Units** & **Magnitude** \\ \hline Flow Rate of the Melt (conducted at 2160 g and 190 \({}^{\circ}\)C) & g/10 min & 8 \\ Density & g/mm\({}^{3}\) & \(9.64\times 10^{-4}\) \\ Vicat Softening Temp. (performed at 10.0 N) & \({}^{\circ}\)C/\({}^{\circ}\)F & 128/262 \\ Stress at Breaking Stress Point & Pa & \(15\times 10^{6}\) \\ Stress at Yield & Pa & \(33\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of material properties of HDPE M80064 Series [36].  
Figure 1: A depiction of the Arburg Allrounder 420 C injection molding unit utilized in the experiments.