esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Examples  
### Experiments Design  
In the experiment, selected specimen is hot mix asphalt AC-13. The performance grade of asphalt binder is PG70-22. The aggregate gradation of tested HMA can be seen in Table 1.  
The specimen was compacted by Superpave Gyratory Compactor (SGC). The cylindrical AC sample was 150 mm in diameter and 170 mm in height. Then, these samples were core-drilled and sawed to finished specimens with 100 mm in diameter and 150 mm in height. Three duplicated specimens were prepared for repeated experiments and the air voids were controlled between \(4\%\pm 0.5\%\). Having prepared the specimens, they were heated up to experimental temperature and preserved for one hour, then the dynamic modulus and creep compliance were measured.  
The dynamic modulus and creep compliance were obtained by uniaxial compression tests which were conducted on Material Testing System (MTS). The vertical deformation was measured by four linear variable differential transducers (LVDT) at 90\({}^{\circ}\) radial intervals with a 90 mm gauge length. In the dynamic modulus test, the maximum axial and horizontal strain was controlled in 100 \(\mu\epsilon\). The test was conducted under four different temperatures of 4.4 \({}^{\circ}\)C, 21.1 \({}^{\circ}\)C, 37.8 \({}^{\circ}\)C and 54.4 \({}^{\circ}\)C according to AASHTO PP61 for the preparation of plotting master curve of dynamic modulus. Also, nine different frequencies of 0.1 Hz, 0.2 Hz, 0.5 Hz, 1.0 Hz, 2.0 Hz, 5.0 Hz, 10.0 Hz, 20.0 Hz and 25.0 Hz were under sinusoidal loading at each temperature. The loading time is 100 s and time intervals are 5 min at each temperature. The experimental data of dynamic modulus and phase angle was determined by the average of four LVDTs and the results were the average of three repeated experiments.  
After dynamic modulus test, the specimens were required to stand for one hour waiting for deformation recovery. Then, the creep compliance test can be conducted. The specimens were tested under \(-20\)\({}^{\circ}\)C, \(-10\)\({}^{\circ}\)C, \(0\)\({}^{\circ}\)C, \(10\)\({}^{\circ}\)C, \(20\)\({}^{\circ}\)C and \(30\)\({}^{\circ}\)C. And 100 s at each temperature. The horizontal strain was controlled in 100 \(\mu\epsilon\). The results were also the average of three repeated experiments.
## 3 Methods
### Feature Selection
#### 3.2.1 Pearson Correlation Coefficient Method  
The Pearson correlation coefficient is a statistical method used to measure the strength and direction of the linear relationship between two variables. In feature selection, the Pearson correlation coefficient can be employed to assess the correlation between each feature and the target variable. The goal of feature selection is to choose the most relevant or important features from the original feature set to enhance model performance, reduce dimensionality, and mitigate the risk of overfitting. [PERSON] et al. [52] employed the Pearson correlation coefficient (PCC) to select Points of Interest (POI) information, considering values with an absolute correlation coefficient greater than 0.5 as indicators of social factors influencing Residential Population (RPI). [PERSON] et al. [53] utilized the Pearson correlation coefficient (PCC) to assess the correlation between Numerical Weather Prediction (NWP) variables and wind power. They selected NWP features with PCC values greater than 0.5 to predict future wind power.
## 3 Analytical Instrumentation
### Mass Spectrometry: Interfaces and Analysers
QqLIT allows for the application of the information dependent acquisition (IDA) function, where a targeted screening, monitoring one elected reaction monitoring (SRM) transition per compound, is performed in combination with an enhanced product ion scan (EPI). In this way, MS/MS spectra are achieved, which are afterwards matched with MS/MS spectra recorded in a library [37; 43]. [PERSON] et al. [174] also analyzed 53 antibiotic residues, covering various chemical groups and some of their metabolites in hospital and urban wastewaters, and river waters by UHPLC-QqQLIT. The method showed a good sensitivity with method quantification limit (MQL) between 1.44-44.6 ng/L for river waters and 9.81-272 ng/L for wastewater influents. The sensitivity, reduction of extraction volumes for all matrices, fast separation of a broad spectrum of antibiotics, and selectivity (due to tandem mass spectrometry) were the main advantages of this method. The method's speediness was incredibly advantageous for detecting \(\beta\)-lactam antibiotics (penicillins and cephalosporyrins) that were challenging to analyze in multi-residue methods because of their instability in water.
## 4 Simulation Test
### Parameter Settings  
Because the algorithm's performance is heavily dependent on parameter settings, the parameters suitable for the algorithm proposed in this paper were determined experimentally. The benchmark example proposed by [PERSON] [37] was modified for the multi-variety, small-batch arithmetic example with a number of workpieces of 10, 20, and 50. The parallel sequential mode was used as the workpiece movement mode, and the optimization objectives were the maximum completion time and the number of handling times. The setup time was within 1-10, and the handling time was within 5-30. The joint crossover probabilities for genetic algorithms were 0.65, 0.7, 0.75, and 0.8, and the standard variation probabilities were 0.05, 0.1, 0.15, and 0.2. Therefore, the simulation tests were performed for all combinations of the above probabilities for 16 groups. _Exc_ and _Soj_ measured the algorithm's performance with different parameter values. _Exc_ is the number of non-dominated solutions of the current arithmetic case, which means that the Pareto solution with the current parameter values has _Exc_ non-dominated solutions among all Pareto solutions with the parameter values. _Soj_ is the number of endpoint optima of the current arithmetic case, which means that the simulation results with the current parameter values have _Soj_ optimal single target values. The test results for the three instances with various parameter values are displayed in Table 3.  
The average of _Exc_ and _Soj_ was calculated ten times after simulating each set of parameter values. According to the parameter-taking-value test results, most parameter-taking values may obtain a satisfactory Pareto solution set when the scale of the workpiece is small. However, as the scale of the workpiece rises, various parameter-taking-values will have advantages and disadvantages. The crossover and variation probability of the algorithm proposed in this study were found to be (0.8,0.15) when the Exc and Soj values in Table 2 were combined.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**(Pc, Pm)**} & \multicolumn{3}{c}{_Exc\_avg_} & \multicolumn{3}{c}{_Soj\_avg_} \\  & **J10** & **J20** & **J50** & **J10** & **J20** & **J50** \\ \hline (0.65, 0.05) & 7.4 & 0.8 & 0.7 & 1.2 & 0 & 0.1 \\ (0.65, 0.1) & 10.7 & 2.4 & 2.2 & 1.1 & 0 & 0.1 \\ (0.65, 0.15) & 4.2 & 1.1 & 3.5 & 1.3 & 0.2 & 0 \\ (0.65, 0.2) & 10.9 & 3.3 & 3.1 & 1.1 & 0.1 & 0.2 \\ (0.7, 0.05) & 5 & 3.4 & 3.2 & 1 & 0 & 0 \\ (0.7, 0.1) & 7.4 & 1.1 & 2.3 & 1.3 & 0 & 0.5 \\ (0.7, 0.15) & 6.5 & 1.2 & 0.8 & 1 & 0 & 0 \\ (0.7, 0.2) & 7.7 & 2.3 & 1.2 & 1.5 & 0.2 & 0.1 \\ (0.75, 0.05) & 8.3 & 4.2 & 2.9 & 1.2 & 0 & 0.1 \\ (0.75, 0.1) & 9.6 & 2 & 4.2 & 1.7 & 0.1 & 0.2 \\ (0.75, 0.15) & 10.3 & 5.2 & 1.1 & 1.2 & 0 & 0.3 \\ (0.75, 0.2) & 7.3 & 5.5 & 1.6 & 1.2 & 0.1 & 0.1 \\ (0.8, 0.05) & 6.7 & 1.4 & 1.8 & 1 & 0 & 0.1 \\ (0.8, 0.1) & 8.3 & 7.7 & 1.4 & 1.3 & 1.8 & 0.5 \\ (0.8, 0.15) & 8.9 & 4.7 & 3.8 & 1.8 & 1.7 & 1.1 \\ (0.8, 0.2) & 7.5 & 4 & 2.3 & 1.4 & 1.2 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results with different parameter values.
## 3 Results
### Auxiliary Data-Layers of the Atlas  
Auxiliary data were employed for filling the atlas maps with additional layers to facilitate the visual assessment (more accurate determination of objects' location within the map). Such information included physical and geographical parameters, administrative regions, and infrastructure objects. All operations on employing the auxiliary data were performed in ESRI ArcGIS (ArcMap) software. Let us consider this auxiliary information.  
The physical map of Russia was used as the base for compiling the atlas maps. It includes topographical features of the regions of the western part of the Russian Federation: Northwestern Federal District (NFD), and Ural Federal District (UFD). The most suitable digital representation (at a scale 1:2,500,000) was provided by the Karpinsky All-Russian Research Geological Institute (VSEGEI). Digital geographic bases were prepared using ESRI ArcGIS software in conic equidistant projection [56; 57].  
The digital elevation model (DEM) was added to display the main terrain and relief features of the studied area. Considering the wide coverage of the territory, as well as high resolution, the GEBCO DEM with 30 m resolution was selected. The data is a global DEM for ocean and land showing elevation and depth in meters, on a grid with an interval of 1 angular second (about 30 m) [58; 59]. The model at first was cropped to the territory of the Russian Federation, then to the boundaries of the studied area as part of this work.  
Since there are only six administrative centers of the Russian Federation regions within the studied area, in order to increase the level of detail of the atlas maps, it was decided to also include the basic information on the settlements with a population of over ten thousand people [60]. The initial database includes: the name of the settlement, population, coordinates, administrative codes, and other information. The database was transformed into vector point geodata, and then cropped to the studied area.  
Figure 21: Average changes in snow cover thickness (m) between the periods 1980–1999 and 2000–2021 (difference of average values between these periods) according to MERRA-2 data.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 5 Discussion
### The Effects of the Attention Mechanism on the Model  
To investigate the impact of the attention mechanism on the YOLOv8 model, this experiment compares the YOLOv8 model without the addition of the attention mechanism, and the research results are presented in Table 5. From Table 5, it is evident that the improved model with the CBAM attention mechanism performs better; in particular, the YOLOv8-CBAM model with the attention mechanism shows a 1.7% increase in recall rate and a 3% increase in mean average precision, with its F1 score rising from 0.66 to 0.71.  
It is observed that the attention mechanism enables the model to focus more on the detection target and suppress irrelevant information (Figure 11), by analyzing the heat map of the feature layer with the added attention mechanism. The CBAM attention mechanism, which incorporates the spatial attention mechanism in addition to the channel attention mechanism, exhibits a heightened focus on the landslide surface and enables more accurate localization of old landslides. By combining both spatial and channel information, the CBAM attention mechanism ensures that no location information is lost and facilitates improved detection performance.
## 5 Discussion
### Relevance for Science and Practice
#### 5.2.3 Importance of Strategic Partnerships  
Due to the interoperable claim of DT, the existence of standards is indispensable for technical feasibility. For this reason, many industry partners, and companies within consortia such as the _digital twin association_ or the _digital twin consortium_ are focusing on bundling research activities and establishing standards. Through the results of this work, the acquisition of data and the establishment of standards was identified as a central challenge in the implementation of DTs within DB. For this reason, it seems to be a logical conclusion that companies from the German rail transport participate in the described initiatives to represent interests and possibly establish standards. The initiation of an own consortium with supra-national rail transport partners could also be a solution to jointly research DT and create an industry-specific platform to investigate specifics such as regulatory restrictions in rail transport companies regarding DT. By joining forces, the consortium would potentially combine its bargaining power with manufacturers to enforce standards within rail transport. In addition, the promotion of the DT-based tenders described above could be a strategic field of action for the consortium. It could also make sense to make use of the experience of the aviation industry, which is playing a pioneering role in the use of DTs. Moreover, a corresponding consortium could serve in the discussion of new developments, such as the train-as-a-service business model introduced by Hitachi Rail for the first time in rail transport [60]. The described cooperation of different stakeholders is becoming increasingly important, especially regarding the interoperability of DTs. The aforementioned vision of DB, to be able to digitally represent the entire railway world in order to enable simulations and experiments [12], requires cross-organizational DTAs (see Figure 2) and further research into organizational and technical aspects in order to overcome regulatory restrictions and establish standards.  
In addition to these aspects, a uniform procedure is also highly relevant to enable the targeted establishment of DTs and not to have several individual DTs that have been developed based on diverse interfaces and can thus only be assembled through resource-intensive reprogramming.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 3 The Discrete HMM for SMS Spam Detection
### SMS Property Prediction  
Given the observation sequence and the trained hidden Markov model to find the optimal hidden state sequence, this is a typical decoding problem in HMM. The Viterbi algorithm Viterbi (1996) is applied to find the most likely hidden state sequence based on the input of each word sequence of the testing SMS. In formalization, we are given the testing observation sequence \(\{\omega_{t}\}_{t=1}^{N}\) and trained HMM with parameters \(\lambda\)\(=(\pi,A,B)\) to find the most likely state sequence. That is,  
\[\underset{\{\omega_{t}\}_{t=1}^{N}}{\operatorname{argmax}}p\Big{(}\{\omega_{t }\}_{t=1}^{N}\big{|}\{\omega_{t}\}_{t=1}^{N}\Big{)} \tag{8}\]  
Via the Viterbi decoding algorithm, for each word sequence of the testing SMS, the optimal hidden state sequence is produced. The state sequence is the combination of ham and spam. The prediction of the SMS property is based on the majority role, i.e., an SMS will be labeled as ham if the optimal hidden state sequence has more harms than spams. Otherwise, the SMS will be labeled as spam.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 2 Related Works
### Adaptive Data Transmission
[15] formulated the adaptive transmission problem as a partially observable Markov decision process for underwater acoustic communication systems. And a model-based reinforcement learning strategy was used to develop an online algorithm, which could derive the optimal transmission actions to minimize a long-term cost. A reinforcement learning-based adaptive modulation and coding algorithm was proposed in Reference [16] for underwater communications based on the network states. However, the learning-based methods have high training complexity and usually need a long time to converge. Moreover, the reward function should be designed reasonably, which is also a problem to be studied. Some optimization-based adaptive transmissions have been studied for underwater acoustic communication. In Reference [17], adaptive OFDM modulation and power allocation were investigated to maximize the throughput while maintaining a target average bit error rate (BER). Two schemes were developed with different levels of adaptivity based on a greedy algorithm: One scheme could adjust only the modulation levels and adopted a unified power allocation, while the other scheme could adjust both the modulation levels and the power allocated to each subcarrier. [PERSON] et al. [18] investigated the energy efficiency maximization problem in underwater acoustic channels with periodic dynamics. Assuming that the future channel states were known, a water-filling algorithm was designed to schedule the optimal transmission actions. For channels with causal knowledge, the optimal actions were scheduled based on the predicted channel states and the packet queue condition. However, the computational complexity was relatively high to schedule actions for all time slots.
## 3 Results and Discussion  
### Causality Analysis  
#### 3.1.1 Unit Root Test  
Table 4 presents the results of unit root tests for each of the variables. It is evident from Table 4 that except for older population (OP) data, all the other variables were non-stationary at level but stationary at first difference. Therefore, the older population data will not be used in the VECM development as they did not meet the criteria. Other independent or explanatory variables are suitable for developing VECM models.
###### Abstract  
Wireless communication supports various real-world applications, such as aeronautical navigation, satellite and TV broadcasting, wireless LANs, and mobile communications. The inherent characteristics of the electromagnetic spectrum impose constraints on telecommunication channels and their frequency bandwidths within mobile networks. A persistent challenge in these applications is providing high-demand services to mobile users, where frequency assignment problems, also known as channel assignment problems, assume significance. Researchers have developed several modeling approaches to address different facets of this problem, including the management of interfering radio signals, the assessment of available frequencies, and optimization criteria. In this paper, we present improved algorithms for solving the Minimum Span Frequency Assignment Problem in mobile communication systems using the greedy optimization approach known as F/DR. We solved and evaluated twenty well-known benchmark cases to assess the efficacy of our algorithms. Our findings consistently demonstrate that the modified algorithms outperform the F/DR approach with comparable computational complexity. The proposed algorithm notably achieves the following benchmarks: The modified algorithms consistently produce at least one local optimum better than the traditional algorithm in all benchmark tests. In 95% of the benchmarks evaluated, the probability of discovering a local optimum value (calculated by the modified algorithm) that is better than or equal to the one found by the conventional algorithm exceeds 50%.  
NP-hard problem; channel assignment problem; frequency assignment problem; minimum span frequency assignment problem; heuristic algorithms +
Footnote †: journal: _Mathematics_  
1
Footnote 1: email: {[[https://doi.org/10.3390/math11204243](https://doi.org/10.3390/math11204243)]([https://doi.org/10.3390/math11204243](https://doi.org/10.3390/math11204243))}
# Parameter Calibration and System Design of Material Lifting System for Inclined Shaft Construction
## 4 Case Study
### Lifting System Parameter Calibration
#### 4.2.3 Calibration of Motor and Skywheel Parameters  
The calculated parameters of the hoisting system motor and the skywheel are shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Calculation Parameters** & **Takes Values** \\ \hline Reduction ratio of speed reducer \(i\) & 31.5 \\ Motor speed \(r\) (r/min) & 740 \\ Motor power reserve factor \(K\) & 1.2 \\ Reducer transmission efficiency \(\eta\) & 0.85 \\ Work condition factor \(R\) & 1.5 \\ Safety factor \(S\) & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculated parameters of motor and skywheel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Calculation Parameters** & **Takes Values** \\ \hline Wire rope breaking tension \(Q_{q}\) (KN) & 490.29 \\ Quality of wire rope per meter \(q\) (kg) & 2.75 \\ Lift tilt angle \(\theta\) (\({}^{\circ}\)) & 22 \\ Transporters enhance cargo quality \(G\) (t) & 9.1 \\ Transporter quality \(G_{0}\) (t) & 6.5 \\ Skip operation resistance coefficient \(\omega\) & 0.015 \\ Coefficient of resistance of steel wire rope operation \(\omega^{\prime}\) & 0.2 \\ Wire rope length \(L_{c}\) (m) & 330 \\ Safety factor \(m\) & 7.0 \\ Does it meet the requirements & \(m>6.5\), Meet the requirements \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculated parameters of steel wire rope.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Calculation Parameters** & **Takes Values** \\ \hline Wire rope diameter \(d\) (mm) & 28 \\ Number of winding layers \(k\) & 3 \\ Lift height \(H\) (m) & 620 \\ Wrong number of rope loops \(n^{\prime}\) & 4 \\ Wire Rope Loop Clearance \(\varepsilon\) (mm) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculated parameters of rollers.  
Substitute the calculated parameters into the Formulas (4)-(7) to get the motor lifting speed \(V_{m}=2.45\) m/s \(<5\) m/s, to meet the specification requirements. Motor lifting power \(N=213.7\) KW, reducer permissible input power \(PC=562.5\) KW. According to the calculation results, YTS355L2-8 motor with power \(P=250\) KW, voltage 380 V, speed \(n=740\) r/min is selected.  
The calculated parameters are substituted into the Formulas (8) and (9) to obtain the diameter of the wheel of \(D_{t}\geq 1200\) mm and the depth of the wheel groove of \(h=50\) mm, so the wheel of diameter \(D_{t}=1.2\) m and the depth of the wheel groove \(h=50\) mm is chosen.  
In summary, the lifting system parameters calibration scheme is shown in Table 5.