esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 5 Fuzzy-Based Dynamic Time Slot Allocation
### Fuzzification
#### 5.1.3 Packet Arrival Rate  
The packet arrival rate (_PAR_) in the network is estimated with the help of the exponentially weighted moving average (EWMA) method. It is defined as follows:  
\[PAR=\alpha_{1}\times pr_{avg}+(1-\alpha_{1})\times pr_{cur}, \tag{14}\]  
where \(\alpha_{1}\) is the weighting factor that takes the value within the range from 0.1 to 0.9. \(pr_{avg}\) is the average of the previously arrived packet rate, \(pr_{cur}\) is the current packet arrival rate. Equations (15)-(17) explain the partitioning of the input variable _PAR_. Fuzzy rules for _PAR_ and slot allocation are as follows:  
1. If PAR is high, then the slot allocated value is high.
2. If PAR is medium, then the slot allocated value is medium.
3. If PAR is low, then the slot allocated value is low.  
\[u_{l}(x)=\begin{cases}\begin{array}{cc}1&x\in(0,0.15)\\ 1-2(\frac{x-0.15}{0.35})^{2}&x\in(0.15,  
Table 1 defines the fuzzy inference rules for the selection of the optimal slot value for each node. The fuzzy rules consist of a series of conditional statements of \"if-then\" type. The rating is given as \"low\", \"rather low\", \"medium\", \"rather high\", and \"high\". If the normalized input variables for ER, BMR, and PAR is all low, then the chance value for the required number of slots for the particular node is expected to be low. Similarly, if the normalized input variables are all high, then the chance value for the required number of slots for a certain node is expected to be low. The remaining chances occur between these two extremes. The inference system used to find the chance value fuzzy variable is the Mamdani fuzzy inference system. The results of all fuzzy rules are in fuzzy values and are converted into crisp values based on the centroid of area \(U_{CoA}\):  
\[U_{CoA}=\frac{\sum_{x=1}^{n_{r}}u_{x}Z(u_{x})}{\sum_{x=1}^{n_{r}}Z(u_{x})}, \tag{23}\]  
where \(Z(u-x)\) is the membership function of all aggregated outputs, \(u_{x}\) is the centroid of the area, and \(n_{r}\) is the number of fuzzy rules.
## 3 Experimental Results
### Coal Mine Tremors and Prevention Cases
The purpose is to periodically cut off hard rock strata 8 and 10 to avoid the development of large-scale hanging roofs and chains of fractures formed in rock strata 8, 10, and 12. This greatly weakens the interlayer shear slip, reducing the duration of the vibrations. We loaded 77, 92, 224, and 226 kg of explosives, respectively, in blast holes A1, A2, B1, and B2. After the implementation of roof blasting measures, the distribution of the microseismic events with energies of greater than 10,000 J within the initial 700 m mining range of working face 3106 are shown in Figure 20. The maximum energy of the microseismic events is only 8.17 \(\times\) 10\({}^{5}\) J, and no mining tremor events have occurred. This means that the treatment measures for the roof have played a certain role in preventing mine tremors. In addition, the working resistance of the fully mechanized hydraulic support in the working face increases, but the dynamic load characteristics of periodic weighting are no longer significant. In the roadway ahead of the working face, the deformation of the  
Figure 19: Borehole layout of presplitting blasting on the roof of the Return Airway 3106.  
roadway walls increases slightly. They indicate that part of the pressure of the roof treated by blasting is transferred to the lower coal seam, which is beneficial to the management of the goat. In fact, the occurrence of mining tremors is sudden and difficult to effectively and accurately predict, which increases the difficulty of prevention and control. There is currently no particularly effective method for the prevention and control of roof mining tremors, especially high-level roof mining tremors. Although most mines have adopted methods for preventing and controlling coalbursts, such as ground hydraulic fracturing, separation layer grouting, roof blasting, filling mining, and increased support [31, 32], new and more effective prevention and control techniques still need to be developed.
# Dynamic Viscosity and Transverse Ultrasonic Attenuation of Engineering Materials
## 3 Results and Discussion
### Attenuation Behavior of Iron and Ferrous Alloys
Ductile iron (grade 80-55-06: Green curve) and ferritic malleable iron (purple) showed the Mason-McSkimin relation for the transverse mode. The same relation was found previously for the longitudinal mode. The transverse attenuation coefficients for the ductile and malleable irons were 2 to 2.9 times higher than their longitudinal counterparts. As was the case in steels, the ratio of damping factors, \(\eta_{\rm t}\)/\(\eta_{\rm t}\), was smaller than the C\({}_{\rm dt}\)/C\({}_{\rm d}\) ratios since the \(\eta_{\rm t}\)/\(\eta_{\rm t}\) ratio factors in the velocity ratio of less than unity. The frequency-squared dependence in gray iron was attributed to two mechanisms in [13]. One was due to multiple scattering according to the Datta-Kinra theory [17]. Another proposed mechanism was based on the reflection from rough surfaces [41]. In both explanations, the transverse wave scattering or reflection needs to be stronger. It is unclear how such changes can be produced. The increased damping terms in the transverse attenuation of ductile iron and ferritic malleable iron actually give \(\eta_{\rm t}\)/\(\eta_{\rm t}\) ratios closer to unity. Thus, dislocation damping due to bow-out [13] can account for the difference. It is noted here that gray irons have ultrasonic attenuation comparable or lower than ductile or malleable iron below 1 MHz. This is opposite to the vibration damping behavior of these materials [42]. At sub-kHz frequencies, gray irons have more than ten-fold increase in damping. The main source of the contrary behavior comes from much higher vibration amplitudes at low frequencies, introducing frictional damping processes at graphite flakes. Figure 5: Anisotropic transverse attenuation (\(\alpha_{\rm t}\)) spectra of a high-strength low alloy steel plate with curve fitting. Blue: Longitudinal (L) direction; red: Transverse (T) direction; green: Thickness (S) direction. Longitudinal attenuation (\(\alpha\)) spectra are shown in comparison. Purple: Transverse direction; light green: Thickness direction.
# State and Parameter Estimation of a Mathematical Carcinoma Model under Chemotherapeutic Treatment
## 2 Materials and Methods
### The Applied Tumor Growth Model  
The investigated tumor growth model is a third-order nonlinear ordinary differential equation. The model differentiates between living and dead tumor cell volumes by introducing separate state variables: \(x_{1}\) [mm\({}^{3}\)] and \(x_{2}\) [mm\({}^{3}\)], respectively. The third variable \(x_{3}\) [mg/kg] describes the drug concentration in the host. The dynamics is described by the equations given in [6,7] at time \(t\) as  
\[\dot{x}_{1}(t) =(a-n)x_{1}(t)-b\frac{x_{1}(t)x_{3}(t)}{ED_{50}+x_{3}(t)}, \tag{1}\] \[\dot{x}_{2}(t) =nx_{1}(t)+b\frac{x_{1}(t)x_{3}(t)}{ED_{50}+x_{3}(t)}-wx_{2}(t),\] (2) \[\dot{x}_{3}(t) =-c\frac{x_{3}(t)}{K_{B}+x_{3}(t)}-b_{k}\frac{x_{1}(t)x_{3}(t)}{ ED_{50}+x_{3}(t)}+u(t), \tag{3}\]  
where \(a\) describes the proliferation rate of the living tumor cells, \(n\) is the tumor cell necrosis rate, \(w\) is the dead tumor cell washout rate, \(c\) is the drug depletion rate. The pharmacodynamics is affected by the \(b\) and \(ED_{50}\) (median effective dose) parameters. The effect of the drug and the depletion of the drug is characeterized by equations following the dynamics of Michaelis-Menten kinetics, thus \(ED_{50}\) and \(K_{B}\) are Michelis-Menten parameters. Resistance is not modeled explicitly, but the ineffectiveness of the drug appears as a specific combination of the parameters. When the difference \(a-b-n>0\), the drug is not able to shrink the volume, it only decreases the growth rate. Qualitative analysis of the model has been previously done in [15]. The values of the parameters for the mice identified based on a mixed-effect model [7] are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameter** & **PLD1** & **
## 1 Introduction
However, the currently proposed resource management structures need to thoroughly consider SAGIN's complex network structure and multiple heterogeneous devices. Moreover, after the introduction of the SDN and NFV, SFC orchestration studies have focused on the load of the ground and air nodes, and there is no study on the energy consumption and delay of heterogeneous devices. However, the resources of the air devices are limited, and the air devices also introduce different degrees of delay when communicating with the ground devices, so it is necessary to study them further. Therefore, to address the problem of heterogeneous resource management and scheduling in SAGIN scenarios, we propose a SAGIN resource management structure that introduces the SDN, NFV, and MEC to improve the utilization efficiency of the heterogeneous resources. In addition, we researched the resource scheduling and QoS optimization problem under this structure and designed a scheduling algorithm that takes into account both energy consumption and latency. The primary contributions of this article are as follows:  
1. We propose a SAGIN-MEC structure based on SDN/NFV and MEC. It has a multi-level distributed SDN control structure to achieve the harmonious scheduling of heterogeneous resources;
2. An optimization model is developed by comprehensively considering resource scheduling in SAGIN scenarios. The model is designed to reduce the system's energy consumption while meeting the constraints of the latency and network resources;
3. A hybrid algorithm DRL-G based on Deep Reinforcement Learning (DRL) and a greedy algorithm are proposed to optimize the SFC resource scheduling under SAGIN, reducing the energy consumption and cost while ensuring the efficient use of network resources and tolerable latency. The other parts of this article are organized as follows: Section 2 introduces the related work of the SAGIN structure and SFC orchestration; Section 3 introduces the structure and topology of SAGIN; Section 4 sets up the mathematical model; Section 5 proposes the optimization algorithm; Section 6 carries out the experimental verification and analysis, and Section 7 finally summarizes this article.
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
###### Abstract  
Among various consensus algorithms, the Byzantine Fault Tolerance (BFT)-based consensus algorithms are broadly used for private blockchain. However, as BFT-based consensus algorithms are structured for all participants to take part in a consensus process, a scalability issue becomes more noticeable. In this approach, we introduce a consensus coordinator to execute a conditionally BFT-based consensus algorithm by classifying transactions. Transactions are divided into equal and unequal transactions. Moreover, unequal transactions are divided again and classified as common and trouble transactions. After that, a consensus algorithm is only executed for trouble transactions, and BFT-based consensus algorithms can achieve scalability. For evaluating our approach, we carried out three experiments in response to three research questions. By applying our approach to PBFT, we obtained 4.75 times better performance than using only PBFT. In the other experiment, we applied our approach to IBFT of Hyperledger Besu, and our result shows a 61.81% performance improvement. In all experiments depending on the change of the number of blockchain nodes, we obtained the better performance than original BFT-based consensus algorithms; thus, we can conclude that our approach improved the scalability of original BFT-based consensus algorithms. We also showed a correlation between performance and trouble transactions associated with transaction issue intervals and the number of blockchain nodes.  
Keywords:consensus coordination; consensus algorithm; byzantine fault tolerance; PBFT; IBFT; hyperledger besu +
Footnote †: journal: Applied Sciences
# Networking of Multi-Robot Systems: Architectures and Requirements
## 4 Communication Issues and Requirements for MRS Systems
### Communication Links in MRS Systems  
Figure 2 shows the R2R and R2I communication links along with the different networking protocols that may be chosen for each one. The various networking protocols that may be selected by the network designers are shown in Table 1 along with the corresponding characteristics and specifications for the physical layer, data link layer, data rate, transmission range, and the appropriate link for each one [40; 41; 42].  
Figure 2: Various protocols for MRS systems: (**a**) Robot-to-Robot (R2R) links, and (**b**) Robot-to-Infrastructure (R2I) Links.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Main Characteristics** & **Physical Layer Specs** & **Data Link** & **Data Rate** & **Transmission Range** & **MRS System Communication** \\  & & & **Layer Specs** & **Range** & **and Link Types** \\ \hline IEEE 802.15.1 & Low- to medium-rate short-range communication & 2.4 GHz band, FHS/PSK & Master/Slave, PIcomet/Sattternet architecture & 1 to 24 Mbps & 10 m communication (e.g., task synchronization, small-size telemetric data exchange, etc.) \\ \hline IEEE 802.15.3 & High rate cable replacement & 2-9 GHz and 57-6 GHz range bands, PIcomet/Sattternet architecture & Master/Slave, PIcomet/Sattternet architecture & 11–55 Mbps & 10 m communication (e.g., audio/video data exchange, etc.) \\ \hline IEEE 802.15.3 & High rate cable replacement & 2-9 GHz and 57-6 GHz range bands, PIcomet/Sattternet architecture & 11–55 Mbps & 10 m communication (e.g., audio/video data exchange, etc.) \\ \hline IEEE & Local Area Network, medium range & 2.4 GHz and 5 GHz band, DSSS, OFDM & CSMA/CA, DFS/PFS & 15, 30, 45, 60, 90, 120, outdoors & R2R and R2 links with medium random random communication and medium to high data rate. \\ \hline IEEE 802.16/ red 1
## 2 Microplastics and Microfibres in WWTPs
### Analytical Methods Used to Analyse MPs from Wastewater and Sludge Samples
Such a device allows the processing of large quantities of aqueous samples, which means that the sample taken is more representative. In addition, authors reported the use of sieves with different mesh sizes, affecting the size range of MPs [62; 67; 69; 70; 72; 74; 78; 79; 80; 91; 100]. Samples are pretreated before MPs detection in order to remove from MPs the organic and inorganic impurities. The most common way to purify samples implies an oxidation using the reagent of Fenton [51; 64; 66; 94; 97; 100; 109; 114], although some authors reported the use of enzymatic digestion [76; 81; 86; 111; 116] or only hydrogen peroxide as oxidizing agent [72; 73; 75; 78; 89; 90; 91; 98]. After this digestion step, a subsequent separation using a brine solution is frequently carried out. Some authors mentioned omitting the oxidation process and directly using the brine solution [48; 85; 99; 106]. In all cases, samples are filtered after being treated with the brine solution. Finally, a visual sorting of MPs is conducted using an optical microscope [89; 100; 108; 109; 115] or stereomicroscope [92; 95; 96; 98; 100; 117], although some authors reported the use of a fluorescence microscope to differentiate MPs from impurities [75; 91; 93; 94; 103]. To complete the study, FTIR [45; 48; 51; 61; 62; 63; 64; 65; 72; 73; 77; 78; 82; 84; 86; 87; 89; 90; 92; 95; 96; 97; 98; 99; 100; 105; 106; 107; 108; 109; 110; 111; 109; 112; 113; 114; 115; 116; 117], Raman spectroscopy [57; 69; 71; 74; 75; 76; 80; 81; 85; 88; 91; 94] or a combination of both techniques are often used to determine the chemical composition of MPs [51; 66; 103; 121]. Regarding dewatered or wet sludge samples, they are stored in containers. A pretreatment is necessary to purify MPs, and the most common alternative is using an oxidizer-like reagent of Fenton or hydrogen peroxide (references in Table 2). After that, a brine solution is used to isolate MPs from inorganic impurities. Finally, the detection techniques are the same as those employed for water samples.
###### Abstract
The proposed deep learning architecture for multimodal gym exercise activity identification offers the following significant advancements:  
* This research introduces a novel hybrid deep learning design called CNN-ResBiGRU. This architecture effectively combines a CNN, residual connections, and bidirectional gated recurrent units (BiGRUs) to accurately identify gym workout patterns using multimodal sensor data. The proposed model effectively captures essential spatial and temporal features from EMG and IMU data, focusing on speed and computational efficiency. * An extensive evaluation of the CNN-ResBiGRU model was conducted using the Myogym dataset, which serves as a thorough benchmark comprising data from 10 individuals performing 30 different gym exercises. The model showcased outstanding performance, achieving a remarkable accuracy of 97.29% and an F1-score of 92.68% when combining IMU and EMG data. This outperforms existing deep-learning approaches across various sensor configurations. * To explore the impacts of different elements within the CNN-ResBiGRU structure, thorough ablation experiments are underway. These studies play a crucial role in unraveling the contributions of convolutional blocks in capturing spatial patterns and the ResBiGRU block in modeling temporal relationships, especially concerning EMG signals. The findings highlight the importance of each component in enhancing the model's effectiveness and robustness. * The primary objective of this research is to investigate the cooperative interaction between IMU and EMG sensor types when identifying gym activities. Through extensive testing, this study demonstrates the enhanced classification accuracy attained by integrating IMU and EMG data. It emphasizes the importance of utilizing multiple data sources for accurate activity detection. The subsequent parts of this research are organized as follows: Section 2 outlines HAR; delves into relevant sensor types, particularly IMU and EMG sensors; and assesses prior deep learning methods, along with their limitations. Section 3 details the proposedapproach. It illustrates how the CNN-ResBiGRU architecture is tailored for simulating gym workouts and highlights significant enhancements in the model components tailored for this purpose. Section 4 elaborates on the experimental setup, dataset attributes, implementation specifics, and comprehensive quantitative results, comparing them to state-of-the-art techniques. Thorough ablation studies offer valuable insights into the specific contributions of each model component, complemented by qualitative visualizations showcasing distinctive learned features. Section 5 analyzes the experimental findings, while Section 6 provides an overall summary, identifies potential constraints, and suggests avenues for further research in this domain.
## 2 Sensor Configuration  
Figure 1 illustrates the sensing device used to detect the dump truck cargo box. Its dimensions were 20 \(\times\) 10 \(\times\) 30 cm in terms of width, length, and height, respectively. The device consisted of a stereo camera (ZED-1) and two LiDAR (Hokuyo UST-10 LX1-CH) sensors and was placed on top of the excavator's driver seat to estimate the plane of the cargo box's rear. The stereo camera provides one color image and one 3D depth map. The data acquired by the stereo camera were used to recognize the 3D position of the dump truck's cargo box, determine the loading space available, and calculate the volume of the load. Because most excavators operate outdoors, a commercial outdoor camera was selected. It had a resolution of 1920 \(\times\) 1080 pixels and provided red green blue video information as a depth map at a rate of 30 fps. The depth map had sufficient sensitivity between 0.5 and 20 m.  
An excavator's working environment comprises dust and solar reflections. Consequently, the information acquired from a camera in such an environment contains noise; hence, errors occur in the distance estimates of the stereo algorithm. Therefore, for the purposes of this research, it was not possible to rely solely on the information captured by the camera. The two LiDAR sensors were attached perpendicular to the device. They are widely used with automated driving technologies, drones, and robots. One disadvantage of LiDAR is its price, which increases with resolution. Although LiDAR acquires sparse depth information, it has the advantage of consistent accuracy. When combined with camera data, superior dense depth information is acquired. The LiDAR sensor has a 270\({}^{\circ}\) angle and can see distances between 0.02 and 10 m. It provides users with a relatively high level of depth accuracy at \(\pm\)40 mm (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Component** & **Manufacture** & **Model** & **Description** \\ \hline \multirow{4}{*}{3D Sensing Device} & \multirow{2}{*}{LIDAR} & \multirow{2}{*}{HOKUYO} & \multirow{2}{*}{UST-10 LX1-CH} & Field of view: 270\({}^{\circ}\) \\ \cline{3-4}  & & & & Depth Range: 0.02–10 m \\ \cline{3-4}  & & & & Accuracy: \(\pm\)40 mm. \\ \cline{2-4}  & \multirow{2}{*}{Camera} & \multirow{2}{*}{STEREOLABS} & \multirow{2}{*}{ZED 1} & Resolution: 1920 \(\times\) 1080 \\ \cline{3-4}  & & & & Depth Range: 0.5–20 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the proposed dump truck 3D position recognition system.  
Figure 1: Dump truck cargo box 3D position recognition system.