esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study: Practical Implementation of DT for HEMS
### Experiment Setup  
The ICON was applied on a Raspberry Pi 5 (Rpi5) (Cambridge, UK) [36]. The Z-Wave was selected as this protocol is reliable and low power; hence, it can offer flawless communication between devices in a smart home. Sensors and devices were connected to a central hub or gateway through the use of a Z-Wave dangle [37], which efficiently enabled integration and management while keeping the communication stable and reliable. These components were utilized to enable communication with various smart home sensors, functioning as the main gateway that interfaced the Raspberry Pi with a network of devices via the Z-Wave protocol. This controller translated between the Z-Wave communication standard and the Raspberry Pi, facilitating interaction with compatible sensors, such as the MCO Home (Tokyo, Japan) Z-Wave CO\({}_{2}\) sensor [38] for air quality monitoring, the Aeotec Heavy Duty Smart Switch Gen5 (Hamburg, Germany) [39] for managing high-power appliances, the Three-phase Energy Meter [40] for real-time energy consumption data, the Smart Plug Fibaro v2 (Wysogotow, Poland) [41] for remote control of standard appliances, the FIBARO Motion Sensor [42] for motion detection, and the ZXT-600 AC Master (Remotec, Hong Kong, China) [43] for HVAC control. Table 3 below contains a list of all the devices that were used.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Device Name** & **Brand** & **Type / Description** \\ \hline Raspberry Pi 5 [36] & Raspberry & Lightweight desktop computing \\ Aeotec Z-Stick 7 [37] & Aeotec & Z-Wave controller for home automation \\ MCO Home - Z-Wave CO\({}_{2}\)[38] & MCO Home & CO\({}_{2}\) Sensor with Z-Wave compatibility \\ Aeotec Heavy Duty Smart Switch Gen5 [39] & Aeotec & High-power smart switch for appliances (Z-Wave) \\
3-Phase Smart Meter [40] & Qubino & Energy monitoring for three-phase systems \\ Smart Plug Fibaro Z-Wave Plus v2 [41] & Fibaro & Smart plug with energy monitoring (Z-Wave Plus) \\ FIBARO Motion Sensor Z-Wave Plus White [42] & Fibaro & Motion and temperature sensor (Z-Wave Plus) \\ ZXT-600 AC MASTER [43] & Remotec & Z-Wave-to-IR bridge for air conditioner control \\ \hline \hline \end{tabular}
\end{table}
Table 3: Devices and sensors in Z-Wave IoT system.  
The visualization of IoT integration identifies the interconnected network of devices, data flows, and systems that facilitate the exchange of data across the IoT platform. The following graphic shows the main elements of the IoT architecture such as sensors, interconnectivity protocols, cloud infrastructure, and data analytics modules, which make it possible to form real-time insights. Through visualization, these individual components and their complex interactions become clear and enhance efficiency in the data-based decision-making process.  
Figure 7 illustrates the data on an IoT platform, giving real-time information, as well as information about the pattern of events and in-depth information obtained from the data. Therefore, these graphical items helpfully depict the entire project's data flow.
## 2 Materials and Methods
### Mechanical Properties Calculations  
Dissociation energy  
\[G_{t}=\sum_{i}x_{i}G_{i} \tag{2}\]  
Packing density  
\[V_{t}=\frac{\rho}{M}\sum_{i}x_{i}V_{i} \tag{3}\]  
where in, \(G_{i}\) = dissociation energy per unit volume and \(V_{i}\) = packing density with the ith components calculated from the literature [26].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Codes**} & \multicolumn{8}{c}{**Composition Ratios (mol\%)**} \\ \cline{2-10}  & **SrO** & **B\({}_{2}\)O\({}_{3}\)** & **TeO\({}_{2}\)** & **TiO\({}_{2}\)** & **ZnO** & **BaO** & **PbO** & **Density (g/cm\({}^{3}\))** \\ \hline SBT-Ti & 10 & 35 & 35 & 20 & 0 & 0 & 0 & 4.0591 \\ SBT-Zn & 10 & 35 & 35 & 0 & 20 & 0 & 0 & 4.2968 \\ SBT-Ba & 10 & 35 & 35 & 0 & 0 & 20 & 0 & 4.7395 \\ SBT-Pb & 10 & 35 & 35 & 0 & 0 & 0 & 20 & 5.2657 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The composition and density of the prepared glasses.  
[PERSON]'s modulus  
\[E=8.36\ V_{t}G_{t} \tag{4}\]  
Bulk modulus  
\[K=10.00\ V_{t}^{2}G_{t} \tag{5}\]  
Shear modulus  
\[S=3K/(10.2V_{t}-1) \tag{6}\]  
Longitudinal modulus  
\[L=K+\frac{4}{3}\ S \tag{7}\]  
Poisson's ratio  
\[\mu=0.5-(1/7.2V_{t}) \tag{8}\]  
Hardness  
\[H=\frac{1-2\mu}{6\ (1+\mu)} \tag{9}\]  
The elastic moduli are expressed in terms of glass composition, \(G_{t}\), and \(V_{t}\).
# Virtual Sensor for Estimating the Strain-Hardening Rate of
## 5 Conclusions  
Based on the observed relationship between the mechanical properties, chemical composition, and cold thickness reduction for the ASS grades under hardening conditions in previous research [29], a new multiple linear regression (MLR) approach is designed to predict the strain-hardening rate of ASS as a function of their chemical composition. The main conclusions drawn from this work are as follows:  
* The strain-hardening rate of each ASS grade can be accurately predicted (\(R\) coefficient exceeding 0.9800 for all analysed samples, except in three cases), due to the observed linearity between \(Rm\) and the cold thickness reduction;
* A strong linear correlation exists between individual hardening rate values and the chemical composition across the entire austenitic family, encapsulated by a single parameter known as the \(HRHS\) (standing from Hardening Rate of Hot rolled and annealed Stainless steel sheet) coefficient; the MLR indicates a correlation of 0.9983;
* The analysis of the most significant variables in the MLR model indicates that Cr, Mo, Si, Ni, and Nb are the independent variables contributing most to estimating the \(HRHS\) value;
* Validation of the \(HRHS\) parameter is confirmed through the LOOCV strategy, with consistently high \(R\) values and low \(MSE\) across various models using different samples for validation. The correlation coefficients often exceed 0.9980 and the \(MSE\) values are lower than 0.0318;
* A simplified form of the \(HRHS\) parameter is proposed, which can be used as a practical alternative when it is not feasible to obtain chemical analyses of all alloying elements; the \(HRHS_{simplified}\) parameter yields acceptable results with only a modest increase in expected error.  
This study demonstrates that linear regression techniques can establish good correlations between the cold strain hardening and the chemical composition of ASS. It confirms the potential of these methods for modelling the mechanical behaviour of steels.  
The newly developed \(HRHS\) parameter provides a simple and cost-effective way to compare the impact of various chemical compositions on strain-hardening behaviour across different austenitic grades under the same process conditions (such as strain-hardening rate, strain level, and temperature). Therefore, the significance of the \(HRHS\) parameter lies in its ability to enhance the understanding of how a new austenitic steel behaves during cold forming compared to traditional varieties. This is considered as a virtual sensor for designing new steel grades or optimizing current and future processing conditions.  
Conceptualization, J.C.-F; methodology, J.C.-F; software, J.C.-F. and M.I.R.-G.; validation, I.T.; formal analysis, M.I.R.-G.; investigation, R.S.-M.; resources, R.S.-M.; writing--original draft preparation, J.C.-F. and M.I.R.-G.; writing--review and editing, D.L.S., J.F.A. and I.T.; supervision, D.L.S. and I.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 2 Materials and Methods: The Original Version of the Ex-PdBI W-Band Receiver
### Feasibility Study for the Installation on the Gregorian Focus of SRT  
As mentioned above, the ex-PdBI receiver can be installed on the Gregorian focus of the SRT, which offers a focal length-to-diameter ratio equal to 2.34 and can host radio receivers that work in the frequency range of 7.5-116 GHz. At this focal position, the angle subtended by the edge of the sub-reflector as seen from the optical axis is about 12 degrees. The optimum edge taper, the ratio of power at the edge of the SRT sub-reflector with respect to the on-axis value, is approximately 13 dB.  
Some feasibility studies were conducted and the receiver with its original cooling system (based on the helium vessel) and with the upgraded version (explained in the following Section 3.3) were both considered. In [30; 31], the results of these studies are reported in detail.  
As described in Section 2, the HPDE optical system of the receiver matched with its feed horn produces a beam waist that meets the edge taper requirements of the Gregorian focus of the SRT. As a final result, the chosen configuration of installation (presented in Section 3.3) permits a perfect match between the output beam waist of the receiver and the beam waist of the SRT.
## 4 Conclusions  
It could be shown that information about the occurrence of low clouds derived from satellite data on average show good agreement with decreasing 10-min availability of wind measurements conducted with CDWL. Based on this finding we computed availability maps for Germany. To our knowledge, up to now, maps of this kind have not been available and should be a valuable aid in the planning process for lidar measurement strategies. It can be seen that especially more complex terrain like the lower mountain ranges in Germany require special care when using lidar technology because these areas reach critical availability values especially during the winter time. This is especially important as these areas are among the interesting sites for future wind turbines. Additionally, many areas which are still above the critical availability threshold might reach the critical level if other factors like exceptional clean air situations occur during the measurement period.  
Unfortunately, in complex terrain the CBA product is not always able to resolve local cloud cover which most likely leads to mismatches of the cloud base altitude derived from the satellite data compared to the actual cloud base height as present at the site of the lidar measurement. Therefore, we are currently working on improving the spatial resolution of the CBA product to be able to produce maps of higher accuracy.  
Furthermore, it needs to be emphasized that the results shown are only valid for the Leosphere WindCube v1 and v2, as only data from these two type of lidars was used in the analysis. For other types of lidar, possibly with stronger laser units, the results probably differ as the CNR threshold for the WinCube 200S and 400S, for example, are slightly lower (\(-26\,\mathrm{dB}\) and \(-28\,\mathrm{dB}\), respectively) [18]. Therefore, detection efficiency should be slightly higher compared to the WinCube v1 and v2. During moderate visibility reductions (haze), this could result in slightly higher availability but comparable problems in the case of strong fog or dense clouds were reported for the more powerful lidar systems [18; 28].  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] had the idea to develop a lidar availability map; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] contributed materials/analysis tools; [PERSON] wrote the paper; writing--review and editing S.E. All authors have read and agreed to the published version of the manuscript  
**Funding:** This study was performed within the project \"Optimisation of LiDAR-based measurement strategies for site evaluation of wind energy parks\" (LiMeS), generously funded by the Bundesministerium fur Wirtschaft und Energie (BMWI project: 0324159D).  
**Acknowledgments:** The authors also thank EUMETSAT for providing the MSG satellite data used in this study, the DWD for providing the SYNOP station data, and Ramboll and Fraunhofer IEE for supplying aggregated lidar availability data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Selected Research Results
### Time-Frequency Analysis  
Traditional frequency analysis is unsuitable for observing nonstationary signal properties with time-varying parameters. This is because an analysis using combined time-frequency representations of signals is required. These analyses can be divided into time-frequency and time-scalar analyses and interpreted as short-time spectral analyses [3].  
Time-frequency analysis was performed to diagnose crack development, especially recommended for nonstationary and nonlinear signals, which can vary even within a single period of signal oscillation. In the engine under study, there were speed fluctuations. Figure 9a shows the time-frequency analysis for cylinder No. 1 in a suitable state, and Figure 9b shows the analysis for cylinder No. 5 with damage propagation.  
There is little to be gleaned from the continuous wavelet transform and time-frequency analysis; therefore, image color analysis was used as an auxiliary procedure to evaluate them [34]. Thus, image analyses were used to process time-frequency diagrams similar to that for evaluating the wear of injector nozzles. In addition, studies were performed to calculate the global color measure for the suitability of cylinder No. 1 before the damage detection of nozzle No. 5. In this article, the global color measure of time-frequency analyses was determined from the contribution of the three primary colors according to the following relationship:  
\[G_{m}=k_{\mathrm{e}R}R\oplus k_{\mathrm{e}G}G\oplus k_{B}B \tag{11}\]  
where \(G_{m}\) is the global color measure of time-frequency, \(k_{\mathrm{R}}\) is the weighting factor of red color \(R\), \(k_{\mathrm{G}}\) is the weighting factor of green color \(G\), and \(k_{B}\) is the weighting factor of  
Figure 8: The results of the Haar wavelet statistics for the original vibration acceleration signal from cylinder No. 5 (**a**), the sensitivity results \(w_{w}\) of these statistics for individual cylinders (**b**), and more useful maximum values of the Haar wavelet \(W_{h}\) (**c**): _llb_—lower limit value; _ulv_—upper limit value.  
blue color \(B\). For these cases, the variation in color intensity and the values of the global color measure (1.02/1.00) was insignificant. Time-frequency analysis, other than effective visualization, has yet to prove useful.
# Anisotropy and Directivity Effects on Uniaxial Compression of Carbonaceous Slate Form Jinman Mine
###### Abstract  
To determine the anisotropic mechanical characteristics of carbonaceous slate at the Jinman Mine located in Lancang River Gorge, China, uniaxial compression, acoustic emission (AE) monitoring, and scanning electron microscopy (SEM) tests were conducted. The results show that stress loading directions and bedding effects have a significant impact on strain characteristics. The deformation of slate undergoes compaction, elastic, damage accumulation, and failure stages, and there are significant differences in strain paths. The mechanical properties of grey and carbonaceous slate have significant features, and variations of these properties of carbonaceous slate are more than that of grey slate. The discrete degree is significantly related to the direction of stress loading and different types of slate structures. The AE response intensity is related to the loading mode and slate type. A sudden increase in the AE cumulative ring number near peak stress indicates instability and rupture and is a precursor of rock rupture. The failure patterns and fracture characteristics are significantly correlated to the layered structure of the slate. Slates subjected to vertical loading and parallel loading conditions are dominated by shear fracture and tensile fracture, respectively. The peak strains of gray and carbonaceous slate correspond to brittle failure. The SEM tests indicate that slate samples subjected to parallel loading primarily show a tensile failure compared with slate samples subjected to vertical loading, with fractured sections being of lesser roughness, and scattered fractures and sections being smooth without obvious protrusions or cracks.  
carbonaceous slate; anisotropic mechanical characteristics; failure patterns; scanning electron microscope; acoustic emission characteristics +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Antioxidative Capacity  
The stable free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) was used to determine the antioxidant capacity of the produced VOO. The measurements were carried out using electron paramagnetic resonance spectroscopy (EPR) following the method described by [PERSON] et al. (2017) with some minor modifications. In brief, a DPPH' solution (0.15 mM) in ethyl acetate was prepared 24 h before the measurements and kept in the dark at 4 \({}^{\circ}\)C to ensure complete dissolution of the radical. A total of 20 \(\upmu\)L of VOO was added to 980 \(\upmu\)L of DPPH'; this mixture was mixed well and kept in the dark at room temperature for 27 min. Then, the capillary was filled with the mixture, which was then inserted into a standard EPR tube. The EPR spectra were recorded at room temperature using the Magnettech MS--5000 X (Magnettech ESR5000 Bruker, Billerica, MA, USA) after exactly 30 min of contact between the oil and the radical solution. The settings for the spectrometry were magnetic field modulation frequency (100 kHz), central field (337 mT), sweep range (12 mT), sweep time (30 s), microwave power (10 mW), and modulation amplitude (0.2 mT). The scavenging effect of VOO on DPPH radicals was determined by the amplitude (A\({}_{30}\)) of the EPR signal. The antioxidant capacity (AC) of VOO was defined as the percentage of DPPH' reduction and calculated according to Equation (2):  
\[\text{AC}=\frac{\text{A}_{0}-\text{A}_{30}}{\text{A}_{0}}\times 100 \tag{2}\]  
where A\({}_{0}\) is the signal amplitude of the blank sample (DPPH' solution containing 20 \(\upmu\)L of ethyl acetate).
# Design and Optimization of a Toothed-Plate Single-Roller Crushing Device for Waste Plastic Film
## 2 The Structure and Principles of the Crushing Device
### Principles of Crushing Working  
The toothed-plate single-roller crushing device is controlled by electrical control components and driven by a motor. During operation, the motor transmits power through the V-belt and gradually increases the rotation speed of the crushing roller. After the rotation speed stabilizes, the conveyor belt starts at the set speed and begins continuous feeding. Subsequently, the waste plastic film enters the feeding hopper and falls into the crushing component along the inclined plate of the hopper. Then, the moving toothed plate and the fixed toothed plate work together, compressing, cutting, and tearing the waste plastic film in the crushing box. After the first crushing, smaller pieces of waste plastic film are expelled from the discharge hopper by the airflow, while larger pieces are taken by the crushing roller and undergo secondary crushing on the opposite side. Through multiple crushing, the waste plastic film pieces are consistently expelled from the discharge hopper at a stable velocity. When the feeding volume becomes excessive, the motor will stop working under the control of the electrical control system, and the conveyor belt will also stop feeding. Then, the motor undergoes a reverse clearing process according to a predefined control program. Once the blockage is cleared, the motor will return to working mode, and the conveyor belt will resume feeding.
## 2 The Flying Wing Carrier-Launcher System
### Comparison of Ground and Air Launch Systems: Velocity Budgets Estimation
To obtain an estimation of the available payload mass, for both launch systems, we made, as an example, the assumptions summarized in Table 4.  
Finally, the propellant mass and the payload masses for the two examined launch systems are summarized in Table 5.  
The absolute value of the payload mass will depend on effective mass allocation, but whatever the mass allocation, when adopting the proposed air launch system, we would have savings of about 0.32 km/s in the ascent \(\Delta v\), which would translate into at least 0.84% of the total launcher mass becoming available for the payload mass.
## 3 Results
### Instructional Design  
This section presents the instructional design of the 31 studies along with the AI's educational applications in computer science and database instruction. Table 4 provides a comprehensive summary of the research design of these studies, outlining key aspects such as learning topic, research objectives, instructional approaches, AI tools used, participant characteristics, and experiment duration. This structured analysis offers insights into how AI has been integrated into computer science education and highlights trends in its implementation. However, some studies had small sample sizes or lacked details on participant demographics and experiment duration, which affects the reliability and generalizability of the findings. The instructional design approaches used in the selected studies highlight the diverse ways in which AI is integrated into computer science education. As shown in Figure 3, most studies focus on AI applications in programming education, with significantly fewer addressing areas such as database instruction or computational thinking. In terms of learning approaches, Figure 4 illustrates that the predominant instructional strategies include problem-based learning (PBL) and collaborative learning. These approaches encourage active student engagement and promote critical thinking and problem solving in real-world learning contexts. Additionally, experiential learning, project-based learning and inquiry-based learning models are frequently applied, as they provide hands-on experiences that enhance students' practical skills and deepen their understanding of theoretical concepts. Figure 2: Research methodologies applied in the selected studies. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Topic** & **Research Objectives** & **Instructional Approach** & **AI Tool** & **Participants** & **Duration** \\ \hline \multirow{4}{*}{[7]} & & Reducing the cognitive load on students and more seamless, accessible, and effective interaction through Iris & Problem-Based Learning (PBL), Learning Learning (IBL) & Iris, an AI-based virtual teacher designed to support CS education & n/m & 10 days \\  & & Evaluation of a virtual teaching tool to Support students with JAVA programming & PBL, Dependency-Based Learning (CBL) & & & \\ \cline{
## 3 Design of a Wireless Sensor Network for a Power Quality Monitoring System
### Design of Power Quality Information  
Power quality is any power problem manifested in voltage, current, or frequency deviations, that results in failure or malfunctioning of the customer equipment. Power quality information includes harmonics, voltage sags, voltage swells, unbalance of network voltage, electric switching device load effects, and so on. Our power quality system monitors the first three information items. Table 1 shows the information per phase gathered at each pole transformer.  
Figure 4: Block diagram of a wireless sensor node.