esa-sceva/satcom-chunk-collection huggingface dataset:
# Changes in Sprint Momentum in Elite Rugby Union Players over a Three-Season Period
## 3 Results
### Effects of Changes in Body Mass, Speed, and Momentum on Changes in Technical Performance Indicators  
In forwards, significant positive effects were detected of changes of sprinting speed on changes in effective carries (\(p\) = 0.001, ES = 0.85, moderate), ineffective carries (\(p\) = 0.032, ES = 0.54, small), carries (\(p\) = 0.001, ES = 0.83, moderate), linebreaks (\(p\) = 0.011, ES = 0.48, \begin{table}
\begin{tabular}{c c c c c} \hline  & **Season** & **Body Mass (kg)** & **Sprinting Speed (m/s)** & **Sprint Momentum (kg\(\cdot\)m/s)** \\ \hline \multirow{4}{*}{Forwards} & 18/19 & 111.8 \(\pm\) 6.9 & 5.64 \(\pm\) 0.21 & 629.6 \(\pm\) 42.1 \\  & 19/20 & 111.0 \(\pm\) 7.2 & 5.63 \(\pm\) 0.24 & 624.4 \(\pm\) 47.0 \\  & 20/21 & 109.9 \(\pm\) 7.2 & 5.59 \(\pm\) 0.20 & 614.5 \(\pm\) 43.9 \\ \hline \multirow{4}{*}{Backs} & 18/19 & 92.7 \(\pm\) 7.1 & 5.79 \(\pm\) 0.22 & 537.4 \(\pm\) 47.4 \\  & 19/20 & 92.0 \(\pm\) 5.8 & 5.84 \(\pm\) 0.26 & 537.6 \(\pm\) 42.6 \\ \cline{1-1}  & 20/21 & 91.2 \(\pm\) 5.4 & 5.78 \(\pm\) 0.19 & 527.1 \(\pm\) 36.9 \\ \hline \end{tabular}
\end{table} 
Table 1: Mean \(\pm\) SD values of body mass, sprinting speed, and momentum across the three examined seasons. small), and offloads (\(p=0.005\), ES = 1.17, moderate) (Table 2), while in backs, a significant negative effect of changes in sprinting speed was observed on changes in number of tackles (\(p=0.002\), ES = \(-1.29\), large) (Table 3). Body mass changes, in forwards, had an effect on the changes in number of tackles (\(p=0.006\), ES = \(-0.22\), small) (Table 2), while in backs it had an effect on changes in effective carries (\(p=0.018\), ES = 0.89, moderate) and on changes in numbers of carries (\(p=0.013\), ES = 0.75, moderate). Some effects were detected of changes in momentum on changes in performance indicators. Changes in tackles (\(p=0.016\), ES = \(-0.26\), small), ineffective contests (\(p=0.019\), ES = \(-0.27\), small), and offloads (\(p=0.048\), ES = 0.61, moderate) were, respectively, observed to be associated with changes in momentum in forwards, while in backs changes in momentum affected changes in effective carries (\(p=0.048\), ES = 0.59, moderate) and of carries (\(p=0.032\), ES = 0.51, moderate).
## 3 Hardware Development
### System Realization  
The hardware is designed and manufactured based on previous sections. It is a lightweight, power-efficient, and eight-channel SHM system, as illustrated in Figure 6. The hardware system has four SMA interfaces on the left and right sides for connecting the eight PZT sensors. There is an additional SMA interface in the upper right corner of the PCB to introduce an external clock signal to the AD5933. This signal is used to drive the AD5933 to measure the impedance of the sensor over 100 kHz. In addition, five LEDs are used to indicate the system status: power supply, impedance detection, impact detection, and two IIC communication statuses.  
A double-layer design is adopted in the PCB configuration to reduce the crosstalk. The top layer is used for the EMI, and the bottom layer is for impact detection. Moreover, the components in the  
Figure 5: The circuit principle of the impedance measurement part: **(a)** Measuring mode: the mode selection part, under the control of the software by digital pin, leads the signal to the sensors. The dotted lines indicate the calibration resistors are not measured; **(b)** Calibration mode: the mode selection part leads the signal to the calibration resistors to acquire the reference signals. The dotted lines indicate the sensors are not measured.  
Figure 6: Photo of the designed hardware system.  
different layers are designed not to be overlapped. On the other hand, chip resistors and chip capacitors are employed to improve high-frequency performance and resistance to electromagnetic interference. Furthermore, the entire PCB will be protected by an aluminium metal case during service.  
The impedance measurement system can measure impedance at the frequency range of 10 Hz to 100 kHz with a resolution of less than 0.1 Hz. The measurement relative error is less than 0.5% under the configuration shown in Table 1.  
The MCU employed here is STM32F103C8T6 (Shenzhen STS Microelectronics Co. Ltd, Shenzhen, China), whose ADC modules are directly utilized to monitoring the impact signals from charge amplifiers. The ADC is used because it is a 12-bit, 10-channel ADC with a 1 MHz convert rate and direct memory access (DMA) of STM32F103C8T6 can be applied on it. This could improve data read speed and reduce the size of the hardware system. With the power supply voltage of 3.3 V, the maximum error of ADC is \(\pm\)4 mV and the average error is \(\pm\)1 mv. The size, power consumption, and other parameters of the system are listed in Table 2.
## 4 Simulation Analysis
### Network model and performance metrics  
The superiority of the proposed algorithm was verified with the environment settings shown in Table 2. In particular, simulations were run for 200 simulated seconds in a 10 km \(\times\) 10 km large rectangular network space.  
Three metrics were used to evaluate the performance: the percentage of data packets that were received at the destination node, the control messages overhead, and the average number of control messages being used to deliver a message to a destination. The data packet delivery ratio refers to the percentage of the number of packets that are received at the destination to the number of packet sent by the source. The larger this metric is, the more efficient the MANET will be. The control message overhead metric represents the total number of control message packets per second in the experiment network. By combining the two metrics, the average number of control messages being used to deliver a message to a destination, can be calculated and finally used to present the performance of each algorithm. The simulation results of the proposed scheme are compared with the results of AODV, LAR, ZRP and AODV-DFR.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Environments** & **Value** \\ \hline Simulation time & 200 sec. \\ Area size & 10 km \(\times\) 10 km \\ Transmission range & 250m \\ Movement speed & 5 m/s, 20 m/s \\ Packet size & 512 Bytes \\ Number of nodes & 50\(\sim\)200 \\ Number of UDP connection & 5\(\sim\)20 \\ MAC & IEEE 802.11 WLAN DCF \\ Data sending rate & 4 kBps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation environments.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 5 Evaluation Experiment and Analysis
### Parameter Sensitivity Analysis  
In this section, a parameter sensitivity assessment of the grey-target assessment model based on improved principal components is carried out, using the operating data of the satellite in the sun-pointing mode, and the inputs of the assessment model are the values of the six indicators in the attitude tracking mode performance evaluation indicator system, and the output of the model is the off-target distance. The main effect coefficients and full effect coefficients of each performance indicator are calculated by the Sobel indicator method based on the Monte Carlo algorithm; the results are shown in Table 5.  
Based on the results of the Sobel sensitivity indicator calculations, the following conclusions can be drawn:  
(1) In the grey-target evaluation model based on improved principal components, the results of the main effect coefficient and the full effect coefficient ranking of each performance indicator are identical. The two performance indicators with the highest sensitivity are attitude pointing accuracy and attitude control response time, and the sensitivity indexes of these two performance indicators are obviously larger than other  
Figure 5: Comparison of high and low performance processes of (**a**) attitude angle and (**b**) cumulative energy consumption.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicators** & **Main Effect Coefficients** & **Sort** &
\begin{tabular}{c} **Full Effect** \\ **Coefficients** \\ \end{tabular} & **Sort** \\ \hline Attitude pointing accuracy & 0.3753 & 1 & 0.3618 & 1 \\ \hline Relative attitude pointing accuracy & 0.1334 & 4 & 0.1337 & 4 \\ \hline Pointing stability & 0.0313 & 5 & 0.0636 & 5 \\ \hline Attitude control response time & 0.3291 & 2 & 0.3066 & 2 \\ \hline Maneuvering angular velocity & 0.0295 & 6 & 0.0069 & 6 \\ \hline Attitude control energy consumption & 0.1654 & 3 & 0.1651 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculation of sensitivity indices for evaluation indicators.  
indicators, with a greater impact on the comprehensive performance of the satellite attitude control system in tracking mode; these two aspects should be given priority in order to improve the performance of the system in attitude tracking mode.  
(2) In the grey-target assessment model based on improved principal components, the two performance indicators with the lowest sensitivity are maneuvering angular velocity and pointing stability, and the main effect coefficients and full effect coefficients of these two performance indicators are obviously smaller than those of other indicators, with a relatively small impact on the comprehensive performance of the satellite attitude control system in tracking mode. The priority of these two indicators can be appropriately lowered in the design of satellite platforms or in the research of performance evaluation.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### LV and MV Monitoring and Management
#### 4.7.2 Sustainable  
Another project that dealt with grid management has been the \"SUSTAINABLE\" project that had to do with the distribution network management and its real-time monitoring. The project involved participants from five countries. Two control levels have been implemented, an intermediate control level at the HV/MV primary substation and one at the MV/LV secondary substation. Smart meters have also been used for the project realization and mainly PRIME smart meters. However, for specific functionalities, like the voltage control functionality, smart meter communication has been achieved via GPRS [119].
## 2 Fundamentals of Non-Equally Proportional Discounting of Strength Parameters
### Relationship of Non-Equivalent Reduction Factors  
The evolution law of the strength parameters of a failed mass can generally be obtained through experiments. The stress-strain curve of the soil mass is shown in Figure 1. If we can obtain the weakening law of the parameters of the potential failed mass under a natural condition, then we can reduce the parameters of the failed mass according to the actual situation. However, for most projects, we can only obtain the strength parameters of the soil's peak and residual moments through experiments, as it is more difficult to obtain the parameters between the peak and residual moments. Overall, reports in the literature assume a segmented linear functional relationship between the strength parameters and the softening parameters \(\eta\) (equivalent plastic strain, maximum plastic principal strain, and plastic shear strain) [21] (Figure 2).  
The expression of the strength parameters (\(c\) and \(\varphi\)) and the softening parameter (\(\eta\)) is given below:  
\[c(\eta)=\left\{\begin{array}{ccc}c_{p}&\eta\leq\eta_{p}\\ \frac{c_{r}-c_{p}}{\eta_{r}-\eta_{p}}(\eta-\eta_{p})+c_{p}&\eta_{p}<\eta<\eta _{r}\\ c_{r}&\eta\geq\eta_{r}\end{array}\right. \tag{4}\]  
\[\tan\varphi(\eta)=\left\{\begin{array}{ccc}\tan\varphi_{p}&\eta\leq\eta_{p} \\ \frac{\tan\varphi_{r}-\tan\varphi_{p}}{\eta_{r}-\eta_{p}}(\eta-\eta_{p})+\tan \varphi_{p}&\eta_{p}<\eta<\eta_{r}\\ \tan\varphi_{r}&\eta\geq\eta_{r}\end{array}\right. \tag{5}\]  
where the subscript \"\(p\)\" denotes the parameter corresponding to the peak moment and the subscript \"\(r\)\" denotes the parameter corresponding to the residual moment.  
Equations (6) and (7) can be obtained from Equations (4) and (5).  
\[\frac{\eta-\eta_{p}}{\eta_{r}-\eta_{p}}=\frac{c(\eta)-c_{p}}{c_{r}-c_{p}}=\frac{ \tan\varphi(\eta)-\tan\varphi_{p}}{\tan\varphi_{r}-\tan\varphi_{p}} \tag{6}\]  
\[\frac{c_{p}\big{(}\tan\varphi_{r}-\tan\varphi_{p}\big{)}}{\tan\varphi_{p}\big{(} c_{r}-c_{p}\big{)}}=\frac{SRF_{c}\big{(}1-SRF_{\varphi}\big{)}}{SRF_{\varphi}(1- SRF_{c})} \tag{7}\]  
and  
\[\lambda=\frac{c_{p}\big{(}\tan\varphi_{r}-\tan\varphi_{p}\big{)}}{\tan\varphi _{p}\big{(}c_{r}-c_{p}\big{)}} \tag{8}\]  
Then,  
\[SRF_{c}=\frac{\lambda SRF\varphi}{1-(1-\lambda)SRF_{\varphi}} \tag{9}\]  
When \(\lambda\) equals 1, the \(SRF_{c}\) is equal to \(SRF_{\varphi}\), which represents the traditional equiproportional strength discounting method. By introducing the assumption that the strength parameter obeys a linear relationship with the softening parameter and then constructing a non-equal proportional discounting relationship between the cohesion and internal friction angle, the uncertainty discount path between the discount coefficients can be avoided.  
Figure 1: The full stress–strain curve of the uniaxial compression of soil mass.  
Figure 2: Evolution curves of strength parameters c and \(\varphi\).
## 1 Introduction
### Motivation/Objective of This Paper  
The reviewed scientific works indicate that swab and surge experimental have been performed based on the rheological models (i.e., power law, yield power law, Bingham plastic) in concentric and eccentric well-annular configurations. The pressure loss in an eccentric annulus is commonly computed based on the pressure loss in the concentric annulus and using the correlation factor. Among others, [PERSON] and [PERSON] (1990) [25] developed empirical R-factors based on power-law fluids. It is important to note that in general, the reviewed literature often does not encompass all operational, fluid property, and wellbore geometry variations. Consequently, the applicability of the swab and surge models is valid within the scope of the assumed conditions and experimental setups.  
For instance, [PERSON] et al. (2017) [12] conducted experimental work demonstrating how trip speed, drilling fluid properties, annular clearance, and pipe eccentricity influence surge and swab pressures. The authors have also developed a model based on a narrow-slot approach to predict their experimental measured dataset. The model predicted the experimental data with certain discrepancies. Due to the availability of [PERSON] et al.'s (2017) [12] experimental and model prediction dataset; this paper aims to develop a new pressure loss model based on a different approach and compare the model performances.
###### Abstract  
Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combined the continuous and binary representations of PSO and SMO to effectively manage trade-offs between accuracy, feature selection, and hyperparameter tuning. We evaluated several CAD models and investigated the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Filters, and Edge Detection. We further shed light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. With HSMO, our models achieved an accuracy of 98.27% and F1-score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.  
breast cancer detection; thermography; XAI; HPSO; HSMO; feature extraction; feature attribution; multi-objective optimization; continuous; binary; feature selection; hyperparameter tuning +
Footnote †: journal: _Algorithms_
# A Semantic and Knowledge-Based Approach for Handover Management
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Work**} & \multirow{2}{*}{**Description**} & \multicolumn{2}{c}{**Making-Decision**} \\ \cline{3-4}  & & **Control** & **Method** & **Wireless Technology** \\ \hline \multirow{4}{*}{[9]} & The end-user device switches to another Access & & & Universal Mobile Telecommunications System, Wireless Local Area Network (WLAN) \\  & Point (AP) or Base Station (BS) when the RSSI & & & \\  & level of the serving network is lower than & & & \\  & a threshold & & & \\ \hline \multirow{4}{*}{[13]} & A Software-Defined Networking (SDN) & & & \\  & controller uses a fuzzy system to score & & & \\  & candidate networks for staying in the current & & & \\  & network or connecting to a better one & & & \\ \hline \multirow{4}{*}{[14]} & An algorithm is proposed to reduce the & & TOPSIS, & 5G, Long-Term Evolution (LTE), WLAN \\  & handovers by multicriteria decision-making & & & \\  & algorithms improved with a context-aware and & & & \\  & threshold-based scheme & & & \\ \hline \multirow{4}{*}{[15]} & A framework, based on a quantum-inspired & & & \\  & immune clonal algorithm and network-related & & & \\  & and user-related parameters with different & & & \\  & weights, is proposed for improving the access & & & \\  & network selection in wireless networks & & & \\ \hline \multirow{4}{*}{[16]} & A fuzzy logic and reinforcement learning-based & & & \\  & mechanism is introduced to address & & & \\  & unnecessary and frequent handovers by & & & \\  & adjusting the handover hysteresis margin and & & & \\  & time-to-trigger & & & \\ \hline \multirow{4}{*}{[17]} & A solution, based on SDN, Binary Integer Linear & & & \\  & Programming (BILP), user criteria and network & & & \\  & packet error rate data, is proposed to rank & & & \\ \cline{1-1}  & candidate BSs and to enhance the handover & & & \\ \cline{1-1}  & selection phase & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related Work.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 5 Performance Evaluation
### Time Complexity Analysis  
The time complexity of Algorithm 1 K-means Clustering in the proposed DTPCH-C scheme is \(O(KNI)\), where \(N\) is the number of UAVs, \(K\) is the number of clusters, and \(I\) is the number of iterations which is set to 50 in the simulation. The time of Algorithm 2 Cluster Head Selection is \(O(N)\). The time complexity of Algorithm 3 Dynamic Transmission Power of Cluster Heads is \(O(K\times\frac{N}{K}\times K)\)=\(O(KN)\). The time complexity of Algorithm 4 Cluster Maintenance is dominated by Algorithm 2.  
Figure 10: Lifetime of FANET in different clustering schemes versus the number of UAV nodes.  
Table 6 summarizes the time complexity of the proposed DTPCH-C scheme and the two benchmark methods, namely, GWO and DSWCA.
## 4 Model Validation and Results Analysis
### Prediction Results Analysis
#### 4.4.1 WOA Hyperparameters Selection and Optimization Results  
The initial WOA-Attention-BILSTM model built in this paper consists of an input layer, two BILSTM hidden layers, an attention layer, a fully connected layer, and an output layer, and the Adam optimizer is used to update the model parameters. The search iteration and population size settings usually affect the convergence speed and performance of the  
Figure 8: Trajectory repaired by cubic spline interpolation.  
And the performance of the WOA-Attention-BILSTM on the training sets and test sets is shown in Figure 10. The results show that the WOA-Attention-BILSTM has a low MSE, and the model converges faster because the ship routinely travels in the channel, and there is some similarity in the trajectory.
## 5 Conclusions  
The experimental investigation of electrically pumped THz emission from AlGaN/GaN HEMTs grown on sapphire substrate revealed that emission signal has a minima at \(T_{\rm s}=155\) K. The comparison of experimentally obtained THz spectroscopy data and theoretically calculated spectra showed that at temperatures above 155 K emission is caused by the thermal radiation of the sample. Also, at such temperatures, the emission spectra is mainly influenced by the thermal radiation coming from the sapphire substrate, which is much thicker than the AlGaN/GaN heterostructure. Other emission phenomena are dominating the net signal when the sample temperatures are below 155 K. Emission due to optical sapphire phonons is observed, also electroluminescence from shallow residual impurities could be responsible for the observed broadband THz emission. The results of our study also suggest that a better heat management should be provided in order to increase dissipation of electrical power and reduce thermal emission from the heated conductive channel, for example, the growth of AlGaN/GaN HEMT structures on native or SiC substrates.  
Conceptualization, I.K.; Methodology, I.K. and I.G.; Formal analysis, I.G.; investigation, I.G.; writing--original draft preparation, I.G.; writing--review and editing, I.K.; visualization, I.G.; supervision, I.K.; funding acquisition, I.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Social Fund under the No 09.3.3-LMTK-712 \"Development of Competences of Scientists, other Researchers and Students through Practical Research Activities\" measure.  
The authors are grateful to [PERSON] for sample contacts processing and to [PERSON] for the helpful advices concerning the RCWA calculations.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} THz & terahertz \\ FET & field-effect transistor \\ HEMT & high-electron mobility transistor \\
2 DEG & two-dimensional electron gas \\ RCWA & rigorous coupled wave analysis \\ \end{tabular}  
Figure 3: Symbols denote Fourier transform terahertz emission spectra at cryostat temperatures of 22 K, 52 K, 121 K, and 131 K (panels (**A**–**D**), respectively) for pulse bias voltages of 60 V and 90 V. Solid curves denote the calculated thermal sample emission for cryostat base temperatures and bias voltages mentioned above.