esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Mechanical Properties of Xinyang Red Clay Improved by Lime and Fly Ash
## 3 Test Results and Analysis
### Compression Test  
Unidirectional consolidation soil is used for the unconfined compression test, and four stage loads of 50 kPa, 100 kPa, 200 kPa, and 400 kPa are applied. The corresponding void ratio is calculated according to Formula (1), and the compression coefficient a\({}_{1\_2}\) is calculated according to Formula (2). The variation law of the compression coefficient a\({}_{1\_2}\) with the incorporation amount of fly ash and lime is shown in Figure 7.  
\[e_{i}=e_{0}-\frac{s}{H_{0}}(1+e_{0}) \tag{1}\]  
where, \(e_{i}\) is the porosity ratio of samples under grade \(i\) load compression and stability; \(e_{0}\) is initial pore ratio of sample; \(H_{0}\) is initial height of sample; s is the cumulative compression value of the sample.  
\[a_{1-2}=\frac{e_{1}-e_{2}}{p_{2}-p_{1}} \tag{2}\]  
where, \(p_{1}\) and \(p_{2}\) are taken as 100 kPa and 200 kPa, respectively; \(e_{1}\) and \(e_{2}\) are void ratios under compression and stability of loads \(p_{1}\) and \(p_{2}\), respectively.  
As can be seen from Figure 7, the addition of lime and fly ash can effectively reduce the compressibility of soil. When only lime is added, the compression coefficient first decreases with the increase of lime content, and then increases. When lime content is 5%, the compression coefficient is 0.09 MPa\({}^{-1}\), indicating low compressibility. When fly ash is added, the compression coefficient of modified red clay increases with the increase of fly ash content, and then tends toward a stable value of 0.22 MPa\({}^{-1}\), indicating that the improved soil has medium compressibility. When lime and fly ash are mixed together, increasing the amount of fly ash in the mixture leads to a decrease in the compression coefficient. When the ratio is 1:3:6, the compression coefficient reaches a minimum of 0.1 MPa\({}^{-1}\). Continuing to increase the proportion of fly ash, the compression coefficient starts to increase again. The main reason is that the fly ash is mixed in a large amount, and the active ingredients in the fly ash cannot react fully, resulting in some fly ash particles  
Figure 7: The compression coefficient varies with modified materials incorporated: (**a**) single lime, (**b**) single fly ash, and (**c**) mixed lime and fly ash.  
filling in the pores of the red clay. These fly ash particles is not cohesive. It is beneficial to the relative migration of soil particles, leading to the increase of the compression coefficient.
## 4 Results and Discussion
### Discussion
However, many studies utilize the FEMNIST dataset, which is a federated version of the EMNIST dataset built by partitioning the data between the clients based on their handwriting; the FEMNIST dataset was utilized in 14% of non-IID data studies, 9% of communication-efficient studies, and 12% of studies that provided solutions to both challenges. Other than image datasets, text datasets such as the Shakespeare dataset were utilized in 9% of the non-IID data studies; Sentiment140 and Wikitext-2 are also text datasets used in 3% of communication-efficient studies. The MNIST and Cifar-10 datasets are widely used with the CNN model, as shown in Figures 11, 15 and 19. The deeper neural networks ResNet and VGG are commonly used with the colored image datasets Cifar-10 and Cifar-100 as they have more features than the greyscale MNIST dataset. Based on the datasets utilized, we can conclude that image classification tasks are the most used tasks in the selected studies. Figure 22: Percentage of the objective used to provide efficient communication in federated learning.
# The Effect of Speed Humps on Instantaneous Traffic Emissions
## 3 Traffic Flow and Instantaneous Emission Models  
We use the modified version of the NaSch model presented in [47] to study the effect of speed humps on instantaneous traffic emissions. To characterize this effect we neglect any other factors such as traffic lights, pedestrians crossings and intersections. The traffic flow model defines the rules that describe the dynamics of the system. At any single time, the state of the system can be represented by the occupancy of the lane and the current velocity of the vehicles. The vehicles move at discrete time steps following the rules of the cellular automata defined in Section 3.1. Each vehicle has a velocity and a position on the lane. The velocity is the number of cells that the vehicle can move forward per simulation step. In order to estimate the total pollutant emissions of the vehicles on the lane we feed the velocity and the acceleration of each vehicle into the instantaneous traffic emissions model [51].  
In this study, we consider speed humps that span the width of the road and gradually raise the road surface up to a height range from 7 to 10 cm (about 3 to 4 inches) [23; 24; 47]. We only consider speed humps because they effectively reduce vehicles speed, conversely, speed humps, composed of an abrupt raised area, are not recommended for use on public roads [18; 25]. The length and height of the speed humps are depicted in Figure 1. In our cellular automata model each speed hump occupies one cell.  
Figure 1: Speed humps length and height with trapezoidal shape.
## 5 Evolution of HCI Mode
### Voice Control  
Voice control is a method of human-machine interaction in which the human exchanges information with the machine through voice in order to ultimately achieve user goals. Voice interaction can effectively control surgical robots while freeing the users' hands [41], reducing hand tremors due to fatigue, and helping surgeons overcome the dilemma of having to interrupt operations to move related equipment [39]. Representative  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Name** & **State** & **Main Modes of Interaction** \\ \hline
2015 & Mako & USA & GUI, haptic feedback \\
2015 & Neuroarm & Canada & GUI, haptic feedback \\
2016 & Tirobot & China & GUI \\
2016 & HeroSurg & Australia, USA & GUI, haptic feedback \\
2017 & Versius & UK & GUI, haptic feedback \\
2017 & Da Vinci X & USA & GUI \\
2017 & Senhance & USA & GUI, haptic feedback, eye tracking \\
2017 & The TSolution One & USA & GUI \\
2018 & Remebot & China & GUI \\
2019 & ROSA One Brain & USA & GUI \\
2019 & ROSA ONE Spine & USA & GUI \\
2019 & Hugo RAS & USA & GUI \\
2019 & Avatera & Germany & GUI, haptic feedback \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._products include the AESOP series of laparoscopic surgery robots [52; 53] and the KaLAR surgical assistant [54].  
Current research in this field mainly focuses on the following two aspects:  
1. The optimization of operation procedures and experience through the development of voice modules: [PERSON] [55] established a voice-based HCI mode that enables robots to perform corresponding actions through voice control, solving the problem of poor co-ordination between assisting surgeons and primary surgeons. [PERSON] et al. [56] developed a voice interface to assist in controlling autonomous camera systems, providing surgeons with high-quality surgical camera views.
2. The enhancement of voice control effects: [PERSON] et al. [57] proposed a new intentional voice control method to control the long-distance motion of the robotic arm and proposed a method for voice-to-motion calibration to reduce motion ambiguity.  
Voice interaction may result in misrecognition, and, compared with artificial assistance, it has a slower response speed [58], presenting certain safety risks.
# Wireless Sensor Networks for Building Information Modeling
## 2 BIMERR Wireless Sensor Network Description  
In the context of the BIMERR project, pilot Wireless Sensor Networks (WSNs) were deployed in apartments of pilot buildings located in Poland, Spain and Greece. The WSN of each apartment is an autonomous wireless network, based on the Z-Wave Radio Access Technology (RAT), and consists of several Z-Wave enabled sensors. The sensors exchange data over the Internet with the BIMERR Middleware via a local Gateway (one Gateway per apartment), as depicted in Figure 1.  
The International Telecommunication Union (ITU) included the Z-Wave PHY and MAC layers as an option in the ITU-T G.9959 standard for wireless devices under 1 GHz [27]. The Z-Wave transceivers were supplied exclusively by Silicon Labs. Z-Wave operates in the unlicensed Industrial, Scientific and Medical (ISM) band, and the frequency used in Europe is 868.42 MHz. This band competes with some cordless telephones and other consumer electronics devices, but avoids interference with Wi-Fi, Bluetooth and other systems that operate in the \"crowded\" 2.4 GHz band. Z-Wave uses a source-routed mesh network architecture. In such networks, the source device (the \"initiator\") uses the Z-Wave wireless interface to transmit messages which are relayed to the destination device of the network via neighboring Z-Wave devices in a \"wave-like\" fashion. In the indoor environments of the BIMERR project, the Z-Wave sensors can communicate with the Gateway by using intermediate sensors to actively route around and circumvent household obstacles or radio \"dead spots\" that might occur in the multipath environment. In this mesh network, a message from sensor A can be successfully delivered to Gateway G, even if the two devices are not within range, provided that a Sensor B can communicate both with A and G, as depicted in Figure 2. Therefore, a Z-Wave network can have a longer range than the radio  
Figure 1: BIMERR WSN concept.  
When it comes to how many devices can be connected at once on the same mesh network or to the achievable data rates, _Z_-Wave can support up to 232 nodes (not considering the case of bridging networks), while ZigBee can support up to 65,000 nodes. Moreover, the max data transfer that can be achieved by Z-Wave is 100 Kbps, which is much lower compared to the 250 Kbps of ZigBee. However, since the number of nodes and data rates required by the project were rather low, and considering that ZigBee operates in the \"crowded\" 2.4 GHz band, _Z_-Wave was chosen as the most appropriate technical solution to be employed at the pilot sites.  
In the BIMERR project, the Gateway of each apartment is the Primary Controller of the Z-Wave network. The network is identified by a unique Network ID. A Z-Wave device must first be \"included\" in the network in order to be able to exchange data over it (commissioning). Commissioning of a Z-Wave device needs to be performed only once during network setup. After successful commissioning, the device acquires the Network ID (4 bytes) and a Node ID (1 byte) by the Primary Controller.
## 3 Proposed System Model
### BPMR Channel  
Before putting data through the channel, input data \(u[k]\in\{0,1\}\) are magnetized into 2D data \(a[j,k]\in\{-1,1\}\). Data \(a[j,k]\) are then passed through the BPMR channel. At the receiver, the received data are modeled as additive white Gaussian noise (AWGN). For the simulations, a 2D Gaussian pulse response, representing the 2D island response for the BPMR channel, is expressed as follows [19; 20; 21]:  
\[P(x,z)=A\exp\left(-\frac{1}{2c^{2}}\left[\left(\frac{x+\Delta_{x}}{PW_{x}} \right)^{2}+\left(\frac{z+\Delta_{z}}{PW_{z}}\right)^{2}\right]\right), \tag{4}\]  
where \(x\) and \(z\) are the down- and cross-track directions, respectively; \(\Delta_{x}\) and \(\Delta_{z}\) are the down- and cross-track bit location fluctuations, respectively; \(c\) represents the relationship between the standard deviation of a Gaussian function and \(PW_{50}\), which is the pulse width at half the peak amplitude, and \(c\) is \(1/2.3548\); and \(PW_{x}\) and \(PW_{z}\) are the \(PW_{50}\) components of the down- and cross-track pulses, respectively.  
The BPMR 2D channel island pulse response is expressed as follows:  
\[h[j,k]=P\left(jT_{x},kT_{z}\pm\Delta_{off}\right), \tag{5}\]  
where \(j\) and \(k\) are the discrete indices in the down- and cross-track directions, respectively; \(T_{x}\) and \(T_{z}\) are the bit period and track pitch, respectively; and \(\Delta_{off}\) is the read-head offset for the cross-track. Track misregistration (TMR) is defined as the head offset size divided by the magnetic-island period and can be expressed as follows:  
\[\text{TMR}(\%)=\frac{\Delta_{off}}{T_{z}}. \tag{6}\]  
Readback signal \(y[j,k]\) for BPMR is given by  
\[y[j,k]=a[j,k]*h[j,k]+n[j,k], \tag{7}\]  
where \(a[j,k]\), \(h[j,k]\), and \(n[j,k]\) are the 2D discrete input data, 2D channel response, and electronic noise modeled as AWGN with zero mean and variance \(\sigma^{2}\), respectively.
## 2 Materials and Methods
### Instrumental Analysis
#### 2.3.2 Volatile Compounds (HS-SPME, GC/MS)  
Two milliliters of coffee samples was contained in a 10 mL of vial with silicon/ polytetrafluoroethylene (PTFE) septum (18 mm diameter \(\times\) 3.2 mm thickness, Varian, Inc., Walnut Creek, CA, USA) and 5 \(\upmu\)L of 0.045 mg/mL 1,3-dichlorobenzene (Sigma-Aldrich, St. Louis, MO, USA) solution was added to the sample as an internal standard. After equilibration for 10 min at 60 \({}^{\circ}\)C at 250 rpm, a 50/30 \(\upmu\)m three phase (divinylbenzene/carboxen/polydimethylbenzene, DVB/CAR/PDMS) SPME fiber (Supelco, Bellefont, PA, USA) was used to extract volatile compounds at 60 \({}^{\circ}\)C for 45 min. After that, fiber was desorbed into the injection port of a GC/MS (Model 7890A, Agilent Technologies, Santa Clara, CA, USA) equipped with spectrophotometer detector (Model 5977A, Agilent Technologies, Santa Clara, CA, USA). A HP-5 ms column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m thickness, Agilent Technologies, Santa Clara, CA, USA) was used to separate the analytes. Initial temperature of the column was 40 \({}^{\circ}\)C for 10 min and changed to 8 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C, 10 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C, and held for 10 min at 280 \({}^{\circ}\)C. The MS detector scanned a mass range (m/z) from 30 to 400 m/z with scan speed 1.562 \(\upmu\)s. Identification of compounds was based on both the mass spectra database (NIST/EPA/NIH mass spectral library, Version 2.2, 2014). Semi-quantification was done for the identified compounds, and the relative concentration was reported based on the area of the 1,3-dichlorobenzene, which was used as the internal standard. Three replications were carried out.
# Modeling and Analysis of a Generic Internal Cargo Airdrop System for a Tandem Helicopter
## 2 Internal Cargo Airdrop System  
The general fixed-wing transport aircraft extracts the internal cargo from the cabin using a recovery parachute during airdropping. Due to the low flight speed of the helicopter, especially the influence of the downwash generated by the main rotor, the recovery parachute, if used for helicopter internal cargo airdrops, is difficult to open smoothly. This is the practical difference between the helicopter airdrop and the fixed-wing aircraft airdrop. Thus, usually combining with both the manpower and sliding rails, the cargo can be extracted from the helicopter cabin. Generally, two workers are employed to push the cargo that is installed on the sliding rails.  
In this article, a generic internal cargo airdrop system using both manpower and sliding rails for a CH-47 series tandem rotor helicopter is studied. The cargo compartment of the helicopter, with a length of 366 in. (9.2964 m), width of 90 in. (2.286 m), and height of 78 in. (1.9812 m), has an internal load capacity of 16000 pounds (7257.5 kg) at helicopter maximum gross weight of 33,000 pounds (14,968.6 kg) [28]. A rectangular container, with a length of 2.6 m, width of 2 m, height of 1.4 m, and mass of 3000 kg, is selected as a basic cargo configuration, assuming that the cargo is in the longitudinal symmetry plane of the helicopter with no lateral movement, and the initial position of the cargo is directly under the center of gravity of the helicopter.  
Considering the general non-uniform distribution of the mass of the cargo, we develop a discretization method of the separation method of the vector mechanics in order to analyze this generic internal cargo airdrop system. Compared with the traditional separation method, our method has some novelty. In detail, by using the discretization method, the cargo can be divided into N discrete sections along the longitudinal moving direction. After analyzing the working condition in each section and synthesizing all the sections, a novel cargo airdrop analysis technique can be developed. Figure 1 shows the sketch of this analysis technique. Some of the aspects are considered thoroughly as follows, including the descriptions of the linear and rotational dynamics of the cargo, the kinematics of the cargo, and the forces and moments of the worker-cargo system acting on the helicopter.
## 6 Numerical Simulations
### Straight-Line Path Following  
The control parameters are selected as follows: \(u_{d}=1m/s\), \(\kappa=0.01\), \(\gamma=80\), \(\Delta_{\min}=4\), \(\Delta_{\max}=7\), \(k_{1}=30\), \(k_{ue}=900\), \(k_{re}=50\), \(\gamma_{u}=100\), \(\kappa_{u}=0.1\), \(\gamma_{r}=100\), \(\kappa_{r}=0.1\), \(K_{eu}=1\), \(\xi_{u}=0.1\), \(K_{er}=1\), \(\xi_{r}=0.1\), \(\tau_{u\max}=600\)\((N)\), \(\tau_{u\min}=-600\)\((N)\), \(\tau_{r\max}=480\)\((N/m)\), \(\tau_{r\min}=-480\)\((N/m)\), and the neural shunting model design parameters are taken as \(A=B=D=5\). The initial state of USV is \([x(0),y(0),\psi(0),u(0),v(0),r(0)]=[0,\;100,\;1,\;0,\;0,\;0]\). The desired geometrical path is a straight-line expressed as \(p_{d}(\theta)=[\theta,\theta]^{T}\). In addition, according to [33], more complicated time-dependent disturbances are considered as:  
\[\left\{\begin{array}{l}b_{u}=1+0.1\sin(0.2t)+0.3 cos(0.5t)\\ b_{v}=1+0.2\sin(0.2t)+0.1 cos(0.4t)\\ b_{r}=1+0.1\sin(0.1t)+0.1 cos(0.2t)\end{array}\right. \tag{65}\]  
The simulation results of straight-line path following are plotted in Figure 4.  
Figure 4a shows that the USV tracks the reference straight-line path accurately, which would be impossible without sideslip angle compensation. Meanwhile, it is observed that the USV converges to the reference path in a short time without obvious overshoot. The reference heading generating by the guidance system and the actual heading are depicted in Figure 4b. We can see that the actual heading can perfectly track the reference heading in the early stage of control, and even if there are external disturbances, the yaw rate controller still has a good control effect. Figure 4c shows the value of \(\Delta\), and the same as the expected theoretical assumption, \(\Delta\) decreases as the error decreases. The reason why its value fluctuates is due to the presence of disturbances. From Figure 4d, it is observed that the estimated value of sideslip angle has a large range of variation, which eventually converges at about \(-1.2\) rad. We can fully expect the collapse of the entire control system if the sideslip angle is not effectively compensated. The control inputs of force \(\tau_{u}\) and moment \(\tau_{r}\) are depicted in Figure 4e,f. In the early stage of control, they have larger initial values, which are determined by factors such as error, control gain, the algorithm itself, and so on. If both error and control gain increase, \(\tau_{u}\) and \(\tau_{r}\) may produce a value greater than the actuator output capability. Therefore, it is necessary to consider the actuator saturation in the design of the controller. Besides, it is observed that they converge to an ideal range in a very short time, and certain fluctuations exist to compensate for external disturbances.
## 3 Results and Discussion
### Fusion Model Building and Discriminant
#### 3.3.4 Discriminant Model Comparison Analysis  
The data fusion model of citrus greening identification was evaluated with 52 prediction samples, which was established based on three levels; the identification ability of the fusion model was better than the single band model on the whole. According to the model evaluation index of the identification accuracy, penalty coefficient and kernel parameter at the spectrum data layer, the fusion model based on the derivative spectrum has a better effect using the LS-SVM method. The discrimination effect of the feature level fusion model is best among the three levels, and the LS-SVM fusion model with PCAcompression variables is optimal. However, the fusion model based on PLS-DA, which adopts SPA method to select variables, has a poorer effect. The main reason is that the new feature variables, acquired after the PCA compressing variables, contains more effective information. The information of target factors decreased when derivative spectral was used to remove the non-target factor's influence on the spectrum. Moreover, in these LS-SVM models, linear kernel function is better than radial basis kernel function because there is a better linear relationship between spectral variables. The NNLS fusion model at the decision level has a medium effect, which may be due to the model not taking into account the proportion of each spectral variable to the model.
## 3 Numerical Analysis of Damped HPL Panels
### Introduction of the Simulation Model  
The simulation model represents the mechanical and acoustic mathematical relationships. The model was set up with the software tool wave6 [18] and is composed of six subsystems; three subsystems to model the prototype and additional three subsystems to model the exciter with free moving mass as shown in Figure 8. The panel FE-subsystem is the central part of this model, which is coupled via area junctions to the panel volume (3D-FEA subsystem), the BEM-subsystem and the excitation force from the exciter model. The moving panel is modeled as a 2D finite element (FE) subsystem with dimensions of 0.3 m\({}^{2}\) and a ratio of 2. Table 1 shows the used properties of the HPL panel according to [10]. The element type is CQUAD8 with an element length of 5 mm to guarantee an error of less than 1% with the h-refinement method by comparing the difference in the eigenfrequencies. The FE acoustic subsystem is modeled to represent the internal air volume of the closed cavity; therefore, it has smaller dimensions than the BEM subsystem and affects the model at low frequencies only. The boundary element model (BEM) subsystem is used to describe the free-field radiation; it has the outer dimensions of the prototype. The mesh size of the FEA model and the BEM subsystem is chosen to guarantee more than six elements per acoustic wavelength up to 5 kHz according to [19]. Therefore, the chosen element length is 10 mm. The driving force is modeled as an exciter with a separated moving mass according to [20]. Every subsystem describes a section of the electro-mechanical network to model the electric and mechanic domain of the voice coil (VC). The subsystems are linked with transfer impedances describing the magnetic field (Bl-factor) or the suspension (Kms, Rms) of the free magnet. A detailed analysis of the fitting error of this model is intensively described in earlier studies [21].  
Figure 8: (**a**) Exciter model made up of three subsystems (VC-electric, VC-mechanic and magnet mass); and (**b**) prototype model composed of 2D-FE, 3D-FE acoustic and BEM subsystems.