esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Grain Size on the Abrasive Wear Resistance of Hardox 500 Steel
## 2 Materials and Methods
### Abrasive Wear Resistance Testing  
Laboratory testing of abrasive wear resistance was carried out using a T-07 abrasive wear tester in the presence of loose abrasive, in accordance with GOST23.208-79 [39], a standard analogous to ASTM G65 [40]. The choice of this testing methodology was driven by its practical relevance, as the analyzed group of low-alloy martensitic steels is commonly used in excavator bucket parts and agricultural machinery components, which operate under intense wear conditions when extracting mineral deposits and processing soil abrasives. This method aligns well with field test results [41]. Each test was performed in six repetitions. The T-07 device, manufactured by the Institute for Sustainable Technologies,  
Figure 1: Schematic of the T-07 Tribotester operation: 1, sample; 2, rubber-rimmed steel wheel; 3, abrasive; 4, load, P1, P2, P3, points of surface reconstruction.  
a research institution in Poland, differs from the device described in ASTM G65 in that the sample is positioned horizontally instead of vertically. The T-07 tribotester comprises a rubber-rimmed steel wheel of diameter \(\mathcal{O}\) = 50 (+0.2) mm and width 15 (\(-\)0.1) mm, an abrasive reservoir allowing for adjustable abrasive flow, and a lever with weights that generates a vertical pressing force of the sample against the roller. The hardness of the rubber applied to the roller falls within the range of 78-85 ShA. Testing was conducted under a constant load of F = 44 N (\(\pm\)0.25 N). Electrofused alumina with a particle size of #90, according to PN-M-59115:1976 [42], was used as the abrasive. The test duration, dependent on the hardness of the tested material, was set to 30 min (1800 roller cycles). The sample dimensions were 30 \(\times\) 30 \(\times\) 3 mm. The goal of the study was to determine the relative abrasive wear resistance coefficient \(K_{b}\) in relation to the reference sample, which was Hardox 500 steel in its as-delivered state. This wear resistance coefficient was defined by the following Formula (5):  
\[K_{b}=\frac{Z_{w}\times\rho_{b}\times N_{b}}{Z_{b}\times\rho_{w}\times N_{w}} \tag{5}\]  
where:  
\(K_{b}\) is the relative abrasive wear resistance coefficient,  
\(Z_{w}\) is the weight loss of the reference samples [g],  
\(Z_{wb}\) is the weight loss of the tested material [g],  
\(N_{w}\) is the number of roller revolutions during testing of the reference sample,  
\(N_{b}\) is the number of roller revolutions during testing of the sample, and  
\(\rho_{w}\) and \(\rho_{b}\) represent the density of the reference and test sample materials [g/cm\({}^{3}\)].  
The differences in density for Hardox 500 steel after heat treatment are negligible and therefore do not significantly impact the results.  
A schematic of the testing method is shown in Figure 1.
_Article_  
**Terahertz Thermal Sensing by Using a Defect-Containing Periodically Corrugated Gold Waveguide**  
**[PERSON] \({}^{1,2,\dagger}\), [PERSON] \({}^{1,\dagger}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2,3,\ast}\) and [PERSON] \({}^{2,3,\ast}\)\({}^{\dagger}\)**  
Key Lab of In-Fiber Integrated Optics, Ministry Education of China, Harbin Engineering University, Harbin 150001, China; [EMAIL_ADDRESS] (J.-L.X.); [EMAIL_ADDRESS] (L.-L.X.);[EMAIL_ADDRESS] (T.-T.W.)  
Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin University of Electronic Technology, Guilin 541004, China  
Academy of Marine Information Technology, Guilin University of Electronic Technology, Beihai 536000, China  
Correspondence: [EMAIL_ADDRESS] (Y.-X.F.); [EMAIL_ADDRESS] (Z.-Y.T.)  
These authors contributed equally.  
Received: 27 May 2020; Accepted: 21 June 2020; Published: 25 June 2020  
**Featured Application: We used the \"COMSOL Multiphysics\" software in the simulation process. This software enables the researcher to study the combined effect of a multitude of physical parameters in just one calculation (and for a specific model system). It has unparalleled capabilities, so that all physical phenomena can be perfectly reproduced by using the computer.**  
**Abstract: A terahertz (THz) thermal sensor has been developed by using a periodically corrugated gold waveguide. A defect was positioned in the middle of this waveguide. The periodicities of waveguides can result in Bragg and non-Bragg gaps with identical and different transverse mode resonances, respectively. Due to the local resonance of the energy concentration in the inserted tube, a non-Bragg defect state (NBDS) was observed to arise in the non-Bragg gap. It exhibited an extremely narrow transmission peak. The numerical results showed that by using the here proposed waveguide structure, a NBDS would appear at a resonance frequency of 0.695 THz. In addition, a redshift of this frequency was observed to occur with an increase in the ambient temperature. It was also found that the maximum sensitivity can reach 11.5 MHz/K for an optimized defect radius of 0.9 times the mean value of the waveguide inner tube radius, and for a defect length of 0.2 (or 0.8) times the corrugation period. In the present simulations, a temperature modification of the Drude model was also used. By using this model, the thermal sensing could be realized with an impressive sensitivity. This THz thermal sensor is thereby very promising for applications based on high-precision temperature measurements and control.**  
**Keywords: temperature tunability; defect states; mode interactions; frequency manipulations**
## 6 Simulation Setup and Results
### Uplink Use Case
#### 6.7.3 DSS1+  
With the introduction of FWA alarm messages in CP of DSS1 technique, we can see from the SINR graphs in Figure 12, that FWA links are much better protected now. As there is no side effect of the extra FWA protection mechanism on satellite links, SINR graphs  
Figure 11: SINR graphs in the uplink use case (DSS1). (**a**) GSO-SAT 4. (**b**) NGSO-SAT 5. (**c**) NGSO-SAT 6. (**d**) FWA 8. (**e**) FWA 10.  
Figure 10: SINR graphs in the uplink use case (baseline). (**a**) GSO-SAT 4. (**b**) NGSO-SAT 5. (**c**) NGSO-SAT 6. (**d**) FWA 8. (**e**) FWA 10.  
of satellite links are not presented here (same as in Figure 11). The protection mechanism offers a significant improvement, returning the FWA links almost back to optimal operation, with FWA links operating close to 99% uptime.  
The introduction of additional CP messages is also not causing a significant increase in control channel overhead. FWA alert messages are used sparsely, only in case interference is detected, in line with the event-based design approach of the CP. Hence, as expected, the induced overhead is minimal (see Figure 13).
# Construction of Ensemble Learning Model for Home Appliance Demand Forecasting
## 3 Experiment and Discussion
### Data Distribution Analysis  
After data collection is completed, due to the possibility of sampling bias [33], demand distribution needs to be analyzed to determine the distribution of the data and allow for the detection of any significant cyclical trends [34].  
Therefore, in the process of data processing, the data are summarized according to the monthly granularity, and the results of the demand distributivity analysis are shown in Figure 5. As can be seen in Figure 5, it is evident that there are variations in the sales figures. The market demand falls before and after promotional events such as 618 (China's e-commerce midyear promotion day), Double 11 (China's e-commerce end-of-year promotion day), and other e-commerce festivals; nonetheless, this reduction is explainable. Because consumers anticipate promotional events, they often wait for them to begin before making purchases, resulting in a fall in demand. Following the conclusion of the advertising period, a considerable number of consumers have already made household appliance purchases, thereby meeting the market's demand, subsequently leading to a decline in purchasing activity.  
The long-term pattern of demand is stable and slightly increasing, but the monthly granularity observation demonstrates that there are noticeable peaks and troughs. Peaks typically occur during hot weather months and sales promotions, while troughs occur during holiday months such as the Chinese New Year. Nevertheless, consumer demand may dip during the Chinese New Year, which is a natural market fluctuation. This pattern indicates that customer behavior and sales promotions have a substantial impact on the demand for air cooling equipment. Meanwhile, the reduced annual demand in 2020 shows that the COVID-19 outbreak had a significant influence on the broader home appliance industry, contributing to a market downturn. Consumers' propensity to buy was significantly impacted by the pandemic, and they became more cautious and conservative in the face of the risk of economic instability and uncertainty. Furthermore, the government's efforts to prevent and contain the outbreak had a substantial impact on home appliance sales and production, limiting market expansion and releasing demand.  
Figure 5: Demand distribution map.
# Wireless Remote Control for Underwater Vehicles
## 3 Requirements and Definition of Working Modes for ROV Control  
In this section, we analyze the requirements for operational ROV control. First, we report the raw bit rate measurements of the communication streams used in an inspection-class ROV; specifically, we present the case of the BlueROV used by the Fraunhofer Center for Maritime Logistics and Services (CML) [107] during the Martera RoboVaaS project [108]. Although this model of ROV has an extremely low cost and does not motivate the use of expensive underwater modems (the price of one unit of BlueROV is less then 5 kEUR, i.e., about half the price of a commercial acoustic modem), its application streams are similar to the ones employed by more sophisticated inspection-class ROVs, such as the I-ROV used in the same project as the Centre for Robotics and Intelligent Systems (CRIS) of the University of Limerick [109]. After the analysis of the raw data streams, we define different working modes (Table 4), each able to provide a different QoS at a different working range.
## 1 Introduction
The improved model achieved an mAP value of 93.6%, with a recognition accuracy of 96% for missing seedlings. [PERSON] et al. [13] utilized low-altitude drone aerial photography, followed by training based on the GoogLeNet network model for the real-time classification and detection of single-hole seedlings. This enabled the timely judgment of floating seedlings, injured seedlings, and qualified seedling forms. Although the above methods are capable of accurately identifying targets, the studied network model has a large number of parameters, runs slowly, has low real-time performance, and fails to achieve an effective balance between identification efficiency and accuracy in paddy fields. Meanwhile, the target features in the image have small differences, high density, and complex backgrounds. Especially, the recognition environment is very harsh. In summary, this paper focuses on the tasks of recognizing the morphology of floating seedlings, identifying qualified seedlings, and detecting missing seedlings. The proposed approach utilizes the YOLOv8 network as the foundational model for recognizing floating and qualified seedling targets. The original model is improved based on issues encountered during the training and prediction processes. Given the complex and variable nature of missing seedling situations, image processing techniques are more effective in accurately locating the positions of missing seedlings. Therefore, this paper integrates the algorithms for seedling planting condition recognition and missing seedling detection, ensuring real-time detection while enhancing the overall accuracy of the detection process. This integrated approach is more in line with practical scenario requirements, exhibits strong generalization, and provides a basis for optimizing the operation quality strategy for driving transplanting machines. The technology roadmap is shown in Figure 1.
## 5 Computational Experiments  
The procedure of the numerical experiment is explained in this section. First, we generate the input data. Then, we solve the scheduling problem using the exact solution and approximate solution. We create a dataset from the input data and the obtained output data and apply it to each model. Finally, we evaluate the accuracy of the model by the MSE of the estimated weighting factor with the true weighting factor. MSE is expressed by Equation (1). For machine learning, the dataset was divided into 5 parts and 5-fold cross validations were performed.  
For small-scale problems, the number of machines is 3 and the number of jobs is 7. For large-scale problems, the number of machines is 5 and the number of jobs is 50. The parameters of the scheduling problem are given randomly in the range of Tables 1 and 2. Table 1 shows small-scale problems and Table 2 shows large-scale problems. A total of 100 scheduling problems were prepared for each case. The weighting factor was changed by 0.1 from 0.1 to 0.9. For the small-scale problems, CPLEX solver was used to obtain an exact solution, and for the large-scale problems, simulated annealing was used to obtain an approximate solution. In machine learning, 900 data sets are used for the 2 objectives and 3600 data sets are used for the 3 objectives. MSE is obtained from these numbers of data. In the random forest, the number of decision trees is set to 100 and the tree depth to 5. \(\varepsilon\) in SFS is set to 0.001. In inverse optimization, the maximum number of iterations \(I\) is 15, the learning rate is 0.5, the number of problem instances is 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Weight of Job** & **Processing Time** & **Delivery Time** & **Setup Cost** \\ \hline
1–5 & 50–500 & 50–200 & 10–1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter of input data in small-scale problems.
## 3 Description of the Method
### Definition of the Chine Curves  
#### 3.3.1 Definition of the Inner Chine  
Fitting the discrete points \(\mathbf{Q}_{ci}\) that were calculated in Section 3.2.2 can establish the inner chine curve. There are two main methods for fitting 3D curves with discrete points: approximation and interpolation. The approximation method constructs curves with fewer nodes, establishes an overdetermined system of equations, and utilizes algorithms such as least squares to evaluate the curve control points. In the context of this approach, the curve does not entirely pass through the discrete points; hence, this is regarded as an approximate approach. The other method is interpolation, where a specified number of nodes are employed to construct a curve that satisfies the requirement of passing through the discrete points. Therefore, it represents a relatively accurate analytical method. In the present study, a cubic B-spline curve interpolation is employed to establish the inner chine curve in the following form:  
\[\mathbf{S}(u)=\sum_{i=0}^{n}N_{i,3}(u)\mathbf{P}_{i},\ \ 0\leq u\leq 1 \tag{25}\]  
In the above relation, the curve is interpolated at the nodes \(\mathbf{Q}_{ci}\). In the case of \(n_{k}\) discrete points, the number of nodes on the curve is determined as \(n_{k}+2\). The node vector \(\boldsymbol{U}=\{u_{0},u_{1},\ldots u_{n+4}\}\) is obtained via the chord-length parameterization method. The interpolation curve interpolates at all nodes with the discrete points \(\mathbf{Q}_{ci}\). The nodes at both the beginning and the end should satisfy the following condition:  
\[\left\{\begin{aligned} \mathbf{P}_{0}=\mathbf{Q}_{0}\\ \mathbf{P}_{1}=\frac{u_{4}}{3}\mathbf{D}_{0}+\mathbf{P}_{0}\\ \mathbf{P}_{n}=\mathbf{Q}_{n-2}\\ \mathbf{P}_{n-1}=\mathbf{P}_{n}-\frac{1-u_{n+1}}{3}\mathbf{D}_{n} \end{aligned}\right. \tag{26}\]  
where \(\mathbf{D}_{0}\) and \(\mathbf{D}_{n}\) represent the first derivative vectors at the beginning and end points of the curve.  
By obtaining the diagonal equation system based on the discrete point conditions, as displayed by Equation (27), the control points for the 3D curve of the chine line can be obtained by solving the following set of equations:  
\[\left[\begin{aligned} \mathbf{Q}_{1}-a_{1}\mathbf{P}_{1}\\ \mathbf{Q}_{2}\\ \vdots\\ \mathbf{Q}_{n-4}\\ \mathbf{Q}_{n-3}-c_{n-3}\mathbf{P}_{n-1}\end{aligned}\right]= \left[\begin{aligned} \begin{matrix} b_{1}& c_{1}& 0&\cdots& 0& 0& 0\\ a_{2}& b_{2}& c_{2}&\cdots& 0& 0& 0\\ \vdots&\vdots&\vdots&&\vdots&\vdots&\vdots\\ 0& 0& 0&\cdots& a_{n-4}& b_{n-4}& c_{n-4}\\ 0& 0& 0&\cdots& 0& a_{n-3}& b_{n-3}\end{matrix}\right]\left[\begin{aligned} \mathbf{P}_{2}\\ \mathbf{P}_{3}\\ \vdots\\ \mathbf{P}_{n-3}\\ \mathbf{P}_{n-2}\end{aligned}\right] \tag{27}\]
# Simultaneous Quantitation of Isoprenoid Pyrophosphates in Plasma and Cancer Cells Using LC-MS/MS
## 3 Results and Discussion
### Calibration Curve and Carry-Over  
The method was validated for each analyte using three calibration curves prepared on three separate days. The calibration curved ranged from 0.04-20 ng/mL, which encompassed the relevant  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Bio-Matrix**} & \multirow{2}{*}{**Analytes**} & \multirow{2}{*}{**LLOQ (0.04 ng/mL)**} & \multicolumn{2}{c}{**LLO (1 ng/mL)**} & \multicolumn{2}{c}{**MQC (5 ng/mL)**} & \multicolumn{2}{c}{**HQC (20 ng/mL)**} \\ \cline{3-10}  & & & **Accuracy** & **\% RSD** & **Accuracy** & **\% RSD** & **Accuracy** & **\% RSD** \\ \hline \multirow{3}{*}{**Charcoalated**} & GPP & 97.8 & 8.2 & 102.3 & 3.2 & 98.3 & 7.6 & 102.5 & 7.8 \\  & FPP & 91.9 & 7.4 & 92.5 & 4.7 & 104.6 & 8.5 & 108.4 & 2.9 \\  & GGPP & 100.7 & 6.2 & 112.7 & 8.3 & 97.2 & 5.4 & 91.1 & 9.1 \\ \hline \multirow{3}{*}{**Buffer**} & GPP & 101.2 & 6.6 & 105.2 & 5.8 & 97.8 & 10 & 97.3 & 1.8 \\  & FPP & 110.2 & 2.5 & 112.5 & 7.7 & 101.3 & 2.5 & 95.2 & 4.9 \\ \cline{1-1}  & GGPP & 94.4 & 7.8 & 98.4 & 2.9 & 105.2 & 5.4 & 103.1 & 7.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inter-day precision (% RSD) and accuracy for GPP, FFP and GGPP in human plasma and buffer solution.  
Figure 4: Representative MRM ion-chromatograms of (**a**) blank neat (GPP), (**b**) GPP spiked in neat solution (rt, 3.6 min, 10 ng/mL), (**c**) blank neat (FFP), (**d**) FFP spiked in neat solution (rt, 5.8 min, 10 ng/mL), (**e**) blank neat (GGPP), (**f**) GGPP spiked in neat solution (rt, 7.5 min, 10 ng/mL), (**g**) blank neat (RAM1147), (**h**) RAM1147 spiked in neat solution (rt, 4.7 min, 100 ng/mL).  
physiological concentrations [17; 23]. For reference, 0.04 ng/mL represents 0.13, 0.10, and 0.09 nM for GPP, FPP and GGPP, respectively. A linear plot of the relation between the concentration and peak area ratio was fitted using a linear regression weighted (1/\(\times^{2}\)). The calibration curve had to have a correlation coefficient (r\({}^{2}\)) of 0.998 or better for all analytes. The analytes showed no significant carry-over effect and there was no significant peak (>20% of the LLOQ) in zero samples injected after the HQC samples.
# An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet +
## 4 Propaganda Detection in Mixed-Code Text
### Dealing with the Propaganda Detection
#### 4.3.1 Machine Learning Approach  
Among the available analysis techniques, machine learning (ML) is one of the most popular ones. It is adopted in different research fields by facing with (i) computational finance for the evaluation of credit risk and algorithmic trading; (ii) image processing and artificial vision for facial recognition, motion detection and object identification; (iii) computational biology for the diagnosis of tumors, pharmaceutical research and DNA sequencing; (iv) energy production for price and load forecasts; (v) automotive, aerospace and manufacturing sectors, for predictive maintenance; (vi) natural language processing for speech recognition applications and so on.  
Furthermore, from the literature review, which is described in the related work section, it emerged that four main ML techniques represent the most popular algorithms in the automatic detection field, that is: (i) _Multinomial Naive Bayes (MNB)_: which is a variant of Naive Bayes classifiers which uses a multinomial distribution for each of the features, (ii) _Support Vector Machine (SVM)_: that is a linear model for classification and regression problems which is used to solve linear and nonlinear problems, (iii) _Logistic Regression (LR)_: which is a linear predictive analysis algorithm based on the concept of probability to face with classification problems (iv) _Convolutional Neural Network (CNN)_: that is a type of artificial neural network, which is used in image recognition and processing, centred on deep learning to perform both generative and descriptive tasks.  
As a consequence, given a sentence S, to be able to assess whether it is Propaganda or Nonpropaganda related, four different classifiers have been trained and evaluated (one for each of the above mentioned ML techniques respectively) regarding propaganda detection. The best one has been then selected.
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 6 Experimental Results
### All-4 Training Results  
The system completed a learning process consisting of 500 episodes, and the experimental results are shown in Figure 10a. Negative rewards indicate that the waiting time increases after the action was selected. The graph shows a decreasing trend in the total value of the negative rewards per episode. This indicates that the system has already been learned.  
In Figure 10b-d, the graph shows that after learning, the proposed method outperformed the original DQN with the fuzzy logic method.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 2 Related Work  
Both types of MANET routing protocols, namely, proactive routing and reactive routing, deal with the issue of redundant routing packet broadcasts. The proactive routing protocols can use the available global routing states in tackling the issue, for example, using the selected multiple point relays as in Optimized Link State Routing Protocol (OLSR) [9] or landmark nodes as in LANMAR [10]. In reactive routing protocols, most efforts focus on the routing discovery phase where flooding is used to search for the destination. Since Ad-hoc NDN is reactive, this work shares common aspects with our work. Among them, two categories are closely related to our work in terms of mitigating broadcast storm. One category involves selecting a subset of neighbors, while the other category uses probability in selecting nodes [11; 12; 13; 14; 15]. Works to mitigate broadcast storm in ad-hoc NDN can benefit from the work on solving traditional MANETs broadcast problems, yet with a focus on their unique issues. The existing work generally falls into three approaches, namely: selective broadcast, probabilistic broadcast and deferred broadcast.