esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Configuration of Hybrid Tramway
### PEMFC Model  
The PEMFC model is the combination of a stack module as a core of the fuel cell system and auxiliary subsystems such as hydrogen delivery, air supplying, humidification, and water-cooling circulation. To construct the dynamic model of PEMFC, the proposed approach in [39] is applied via the MATLAB/Simulink Simscape toolbox, where the effect of reactant flow inside the electrode is neglected to simplify the modeling. In this model, system parameters can be easily set up from the datasheet or by using the simple polarization curve of the testing process.  
Considering the PEMFC model in [40] and the evaluation results in [41], the output voltage of each cell is obtained as follows:  
\[\mathrm{V_{cell}}=\mathrm{V_{oc}}-\mathrm{V_{act}}-\mathrm{V_{oh}} \tag{1}\]  
where \(\mathrm{V_{cell}}\) is the cell output voltage (V), \(\mathrm{V_{oc}}\) is the open-circuit voltage (V), \(\mathrm{V_{act}}\) is the activation voltage loss (V), and \(\mathrm{V_{oh}}\) denotes the resistive and diffusion voltage loss (V).  
The open-circuit voltage can be calculated by:  
\[\mathrm{V_{oc}}=\mathrm{K_{c}}\mathrm{E_{n}} \tag{2}\]  
where \(\mathrm{K_{c}}\) is the voltage constant at the nominal condition of operation (V) and \(\mathrm{E_{n}}\) is the Nernst voltage (V).  
The value of activation voltage loss can be defined through [40]:  
\[\mathrm{V_{act}}=\mathrm{A}\ln\left(\frac{\mathrm{i_{FC}}}{\mathrm{i_{0}}} \right)\frac{1}{\frac{\mathrm{T_{d}}}{3}\mathrm{s}+1} \tag{3}\]  
where A is the Tafel slope (V), \(\mathrm{i_{0}}\) is the exchange current (A), \(\mathrm{i_{FC}}\) is the PEMFC output current (A), and \(\mathrm{T_{d}}\) is the cell settling time to a current step (s).  
Based on the internal resistance of the electrolyte membrane, the resistive and diffusion losses voltage of PEMFC is expressed as follows:  
\[\mathrm{V_{oh}}=\mathrm{R_{ohm}}\mathrm{i_{FC}} \tag{4}\]  
where \(\mathrm{R_{ohm}}\) is the internal resistance (\(\mathrm{\Omega}\)).  
The output voltage of the PEMFC stack is derived according to the number of cells as the following equation:  
\[\mathrm{V_{FC}}=\mathrm{N}\cdot\mathrm{V_{cell}} \tag{5}\]  
where \(\mathrm{V_{FC}}\) is the output voltage of the PEMFC stack (V) and N is the total number of cells.  
The output power of the PEMFC stack is obtained by [27]:  
\[\mathrm{P_{stack}}=\mathrm{\eta}\mathrm{V_{FC}}\mathrm{i_{FC}} \tag{6}\]  
where \(\mathrm{P_{stack}}\) is the supplied power of the PEMFC stack (W) and \(\mathrm{\eta}\) represents the efficiency of the PEMFC stack.  
Based on input parameters of the PEMFC model, simulated polarization curves of stack voltage-current (U-I) and stack power-current (P-I) are obtained, as shown in Figure 2.
## 4 Design of the Soil and Pile Models
### Soil Model
#### 4.1.2 Prototype Soil Parameters  
All of the material properties and modelling specifications were in accordance with those presented by [PERSON] (1998). The target prototype soil designated for the current study was the soil adopted in the reference case study, which was San Francisco Bay mud, a marine clay. This soil was suitable for the implied prototype technique and is also a well-characterised soil according to [PERSON] and [PERSON] (1979), [26] who performed experiments on bay mud samples from Hamilton Air Force Base in Novato, California. The results are shown in Table 3, which also presents the prototype parameters adopted in the current study.  
[PERSON] (1994), [27] investigated the seismic response of bay mud during the 1989 Loma Prieta earthquake and developed an empirical relation for expressing the undrained shear strength of the soil as a function of the soil shear wave velocity, as given in Equation (18):  
\[V_{s}=18\ (S_{u})^{0.475} \tag{18}\]  
where \(V_{s}\) denotes the shear wave velocity in feet per second, and \(S_{u}\) is the undrained shear strength in pounds per square foot. This relationship is illustrated in Figure 10 and was employed to determine the targeted shear wave velocities for the prototype soil. Appropriate shear wave velocities between 114.3 m/s and 160 m/s were determined for the range of prototype soil undrained shear strengths between 28.73 kN/m\({}^{2}\) and 57.46 kN/m\({}^{2}\), respectively.
## 7 Simulation Results
### Results Obtained for Fixed Parameters \(\delta\) and \(\epsilon\)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\bar{\delta}\backslash\epsilon\) & **100** & **10** & **1** & **10\({}^{-1}\)** & **10\({}^{-2}\)** & **10\({}^{-3}\)** \\ \hline \(1.64\) & \(\left(\dagger\right)\) & \(\left(\dagger\right)\) & \(\left(\dagger\right)\) & \(\left(\dagger\right)\) & \(\left(\dagger\right)\) & \(\left(\dagger\right)\) \\ \(1.63\) & \(\left[\begin{matrix}-0.94\\ -0.55\\ -0.48\\ -0.39\end{matrix}\right]\) & \(\left[\begin{matrix}-4.47\\ -2.45\\ -0.72\end{matrix}\right]\) & \(\left[\begin{matrix}-60.58\\ -29.35\\ -1.36\end{matrix}\right]\) & \(10^{3}\times\left[\begin{matrix}-1.20\\ -0.51\end{matrix}\right]\) & \(10^{4}\times\left[\begin{matrix}-2.44\\ -1.02\\ -1.02\end{matrix}\right]\) & \(10^{5}\times\left[\begin{matrix}-4.26\\ -1.76\end{matrix}\right]\) \\ \(1.5\) & \(\left[\begin{matrix}-0.48\\ -0.39\\ -0.41\end{matrix}\right]\) & \(-1.36\) & \(-8.53\) & \(\left[\begin{matrix}-138.25\\ -4.65\\ -0.81\end{matrix}\right]\) & \(10^{3}\times\left[\begin{matrix}-2.82\\ -1.20\end{matrix}\right]\) & \(10^{4}\times\left[\begin{matrix}-5.77\\ -2.40\end{matrix}\right]\) \\ \(1.0\) & \(\left[\begin{matrix}-0.41\\ -0.81\\ -0.35\end{matrix}\right]\) & \(-0.50\) & \(-1.93\) & \(-21.50\) & \(\left[\begin{matrix}-847.50\\ -369.38\\ -0.72\end{matrix}\right]\) & \(10^{4}\times\left[\begin{matrix}-1.72\\ -0.72\\ -0.72\end{matrix}\right]\) \\ \(0.7\) & \(\left[\begin{matrix}-0.41\\ -0.35\\ -0.35\\ -0.50\end{matrix}\right]\) & \(-0.52\) & \(-43.04\) & \(\left[\begin{matrix}-841.15\\ -21.42\\ -21.42\end{matrix}\right]\) & \(\left[\begin{matrix}-841.15\\ -365.35\end{matrix}\right]\) & \(10^{4}\times\left[\begin{matrix}-1.72\\ -0.72\\ -0.72\end{matrix}\right]\) \\ \(0.62\) & \(\left[\begin{matrix}-0.48\\ -0.39\\ -0.72\end{matrix}\right]\) & \(\left[\begin{matrix}-1.35\\ -4.58\end{matrix}\right]\) & \(\left[\begin{matrix}-8.39\\ -4.58\end{matrix}\right]\) & \(\left[\begin{matrix}-136.10\\ -63.12\end{matrix}\right]\) & \(10^{3}\times\left[\begin{matrix}-2.76\\ -1.17\end{matrix}\right]\) & \(10^{4}\times\left[\begin{matrix}-5.64\\ -2.34\end{matrix}\right]\) \\ \(0.61\) & (\(\dagger\)) & (\(\dagger\)) & (\(\dagger\)) & (\(\dagger\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical results, \(\mathcal{K}^{\mathrm{T}}\), of the LMI (77) in Theorem 1 for different values of \(\epsilon\) and \(\bar{\delta}\), and by considering the two LMIs (95a) and (95b) for reducing the size of the gain \(\mathcal{K}\).
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
## 2 Related Work  
The characteristics of scheduling schemes utilizing IC are investigated in [22; 23; 24; 25; 26]. However, they consider the low dense network, so the performance gain is not properly investigated as increasing the nodes in the network. The authors of [27] investigate the network scaling law when multipacket reception is adopted. However, they simplify the interference cancellation condition. The authors of [28; 29; 30] consider the improvement in transmission capacity obtainable with IC. They assume that all interfering signals close to the receiver can be canceled even if the signal strength is under the target SINR threshold. They did not consider the MAC design issue either. In [31], the authors are motivated by the idea of cross-layer design of wireless networks. The authors of [31] examine one aspect of the interaction between the physical and medium access control layers, in particular, the impact of signal processing techniques that enable multipacket reception on the throughput and design of random access protocols. The authors of [32; 33; 34] also consider the multiple access control design with multipacket reception. IC is a practical interference management technique, which is actively discussed as a part of enhanced Inter-Cell Interference Coordination (elCIC) for LTE Advanced (LTE-A) heterogeneous networks [35] and NOMA [8]. IC could also be utilized in distributed random access schemes because IC is a promising technique when the received signal strengths from the different interferers vary severely and it happensfrequently in distributed wireless networks [36; 37; 38]. In the ALOHA system, the concurrent transmitting nodes are randomly distributed in the space, and interferers could exist closer than the desired transmitter. This interferer causes the outage, i.e., signal-to-interference-plus-noise-ratio (SINR) at the receiver is below the target SINR threshold. However, if the receiver has IC capability, the above situation would even be favorable because the interfering signal is easier to cancel as the interferer is getting closer to the receiver. In the CSMA system, the above situation could happen if the sensing range is short, namely, the hidden node problem. Using IC, the hidden node problem can be resolved, and higher ASE could be achieved than that of the conventional CSMA system. In [39], the authors noted that current CSMA design is not suitable to combining IC capability. It also motivates us to design CSNOMA in an ultra-dense network to see if CSNOMA can enhance ASE and other performance metrics. To maximize ASE, we combine the random access schemes with IC capability. This work quantifies the performance improvement on distributed MAC by using IC and provides the insight to design a new random access scheme. Table 1 summaries the aim, proposed solution, pros, cons of the related work.
## 3 Preliminaries  
### Network Model  
Without loss of generality, we assume that all the sensor nodes are homogeneous and have the same capability. Further we assume that all sensor nodes are active in transmission and a portion of these nodes (referred as source nodes in the later text) are active in sensing data. Nodes are partitioned into layers according to their distance to the sink. A cluster is composed of sensor nodes in the same layer and a cluster head in the upstream layer. This layered model is called the PMRC-based WSN [16]. Figure 1 shows the structure of a PMRC-based network with three layers. The shadow area shows the structure of a cluster at layer 2 with node 1 (at layer 1) serving as this cluster's cluster head.  
The operation of a PMRC-based WSN is divided into rounds. Each round begins with a network formation phase when layers and clusters are formed, followed by a data gathering phase. During the network formation phase, multiple iterations are needed to discover all the sensor nodes, with one iteration dedicated for discovering the sensor nodes and forming clusters in just one layer. Each iteration begins with the broadcast of a Control Packet (CP) by the sink. In the first iteration, following the CP, sensor nodes within 1-hop distance to the sink set their layer number to 1 and select the sink as their cluster head. In the second iteration, following the CP, each node at layer 1 sends a Broadcast Packet (BP) to announce their IDs, layer number and residual energy. Upon receiving the BP, sensor nodes within a 2-hop distance to the sink respond with a Head Selection Packet (HP) to select their cluster head(s) following the cluster head selection algorithm [16]. Each selected cluster head receiving one or more HP(s) announces its cluster by broadcasting a Cluster Control Packet (CCP). This process repeats until all the nodes in the network are discovered.  
During the data gathering phase, each source node sends a Data Packet (DP) to its cluster head, either periodically or driven by an event. Each cluster head forwards the data collected from its cluster to the sink through a multi-hop path. Once the residual energy of a cluster head drops below a pre-defined threshold, it sends a beacon to announce its residual energy. Upon receiving the beacon packet, members in the cluster send a network formation request, which will be forwarded to the sink. Upon receiving a network formation request, the sink will initiate the network formation process in a new round. If there is one sensor node that cannot find any candidate cluster head during the process, the network is considered partitioned. Figure 2 illustrates the network operation phases of a PMRC-based WSN.  
In the PMRC structure, two cluster head selection strategies can be used: selecting single cluster head or double cluster heads for each cluster. To further save energy, the clusters in the same layer may rotate to be active in different rounds.  
Figure 1: Structure of PMRC-based WSN.
###### Abstract  
The main aim of this research was to evaluate the impact of the implementation of Fitlight technology in the process of sports training and motor assessment on the improvement in agility and reactive agility of junior basketball players. The age groups studied were under-14 (U14) and under-16 (U16). This study included 70 male basketball athletes, structured in two experimental groups: U14 (18 subjects) and U16 (17 subjects); two control groups: U14 (18 subjects) and U16 (17 subjects). Arithmetic averages of the anthropometric characteristics of the subject groups: experimental group U14: height 172.89 cm, weight 58.22 kg, BMI 19.56; control group U14: height 165.44 cm, weight 50.17 kg, BMI 18.53; experimental group U16: height 179.94 cm, weight 70.82 kg, BMI 20.35; control group U16: height 183.88 cm, weight 73.41 kg, BMI 20.83. An 18-week experimental program that integrates Fitlight technology in order to develop coordination and agility skills and corrective agility was implemented in the experimental groups. This study included six tests: T agility test, T agility test with ball, reactive T agility test, reactive T agility test with ball, Illinois agility test, and Illinois agility test with ball. The results of this study showed statistically significant progress between the initial and final testing for the experimental group, \(p<0.05\). The Cohen's values of the experimental groups were above 0.8, which denotes a large effect size; for the control group, these sizes were small and medium. The comparative analysis of the experimental and control groups, U14 and U16, highlights significant statistical differences in favor of the experimental groups, for all the agility tests of this study. This study highlights the effectiveness of incorporating advanced training tools like Fitlight in sports training, particularly for young basketball players. This approach surpasses traditional methods in enhancing agility, suggesting a paradigm shift towards technology-integrated training in sports.  
agility; reactive agility; Fitlight technology; basketball; sport training program; adapted motor assessment  
1
## 2 Objectives of the Study  
The estimation of the rate and total settlement of embankments is an important engineering task, taking into account the necessity of maintaining the top elevation at the design level. Considering that soft foundation soil is subjected to the processes of consolidation and creep, large settlements can be induced, requiring the raising of the embankment height [4]. The stiffness and strength anisotropy, and the rate-dependent behavior, of soft foundation soil make the analysis challenging and computationally demanding. Most often,sufficient data to describe all the mentioned soil characteristics is not available, and simple constitutive models are preferred in conventional design procedures.  
The aim and the novelty of this study is to investigate the capabilities of the simple HASP constitutive model in predicting the behavior of overconsolidated soft clay during and after embankment construction. Using the state of the soil as an important determinant of its mechanical response, and without introducing anisotropy and creep parameters in constitutive relations, an attempt has been made to address the above-mentioned problem.  
Thus, two examples of soft overconsolidated clay behaviors, due to embankment loading, reported in the literature [6; 8], are analyzed. For the selected embankments, there was a good instrumentation system for monitoring settlement and pore water pressures over time. Also, site investigation and laboratory testing were performed for both sites, and calibrated material parameters for the MCC constitutive model were reported. The same material parameters are adopted for the HASP model. The initial conditions are also well-documented and reproduced in the study.  
The numerical predictions are performed using the PLAXIS 2D software, in which the HASP model was previously implemented via a user subroutine. Coupled hydro-mechanical analyses were conducted with appropriate mechanical and hydraulic boundary conditions. The obtained results are compared with the field measurements. The complete numerical procedures in the study correspond to those described in references [6; 8].  
Additionally, in order to compare the HASP model predictions to predictions of other constitutive models, in PLAXIS 2D, a series of numerical calculations were carried out using the MCC model for clay layers. Although there are many other constitutive models available in PLAXIS 2D that could be used for comparison, the available data from [6; 8] for the clay layers was insufficient to reliably determine the parameters of those models.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 3 An Overview of Recent Development in Position Estimation Methods of SRM
### Magnetic-Model-Free Method Position Sensorless Methods
#### 3.2.2 Methods Based on Pulse Injection  
The pulse injection method is divided into pulse injection into the excitation phase and pulse injection into the non-conduction phase. The theoretical basis that these methods follow is shown in Formula (10).  
\[U_{k}\approx L(\theta_{ph})\frac{\Delta i}{\Delta t} \tag{10}\]  
where \(\Delta i\) and \(\Delta t\) are the current change rate and time interval of the detection coil, respectively.  
The pulse injection method for startup is relatively mature. In [129, 130, 131, 132], an initial position estimation method based on non-conducting phase pulse injection was proposed for the first time. To eliminate the start-up hysteresis, a method of injecting short-duration pulses into all phases was proposed [133]. Later, many studies combined current waveforms to achieve operation over a wider speed range. A general low-speed position sensorless  
Figure 10: Position estimation method based on additional windings.  
based on the principle of phase-locked loop was proposed [134]. There are few pulse injection methods in the high-speed range, and the pulse injection will affect the torque control of the motor. A single-pulse and integrator circuit was combined to broaden the position estimation, addressing the operating frequency limitations of power devices. Non-operating phase injection pulses was proposed [136]. The required pulses are injected into the motor windings via the existing inverter. High-frequency pulse injection was utilized [131]. Different algorithms are used at different velocity stages.  
As shown in Figure 11, a position estimation method based on high frequency sinusoidal signal injection has been proposed [137,138]. The high-frequency sinusoidal signal \(v_{\textit{ff}}\) is superimposed on the driving voltage \(V_{\textit{rf}}\) and compared with the high-frequency triangular wave to generate a SPWM wave signal to drive the inverter, and indirectly obtain the rotor position by responding to the current waveform. No pre-stored magnetic parameters and strong versatility are the advantages of this method. However, the speed regulation range and the execution frequency of power devices represent great challenges for this type of method.
## 3 Application of Integrated Transducer Arrays for Generation of Sound Waves
### Experimental Investigations  
The aim of the experimental studies was to characterize the generation of a directed acoustic wave using the integrated actuator array. Herein, two experimental techniques were utilized, _i.e._ laser Doppler vibrometry (LDV) and microphone measurements in order to identify the actuator-induced wave propagation of the investigated plate and the corresponding acoustic wave.
## 4 Proposed SARP Routing Protocol for BASNs
### Working Principle of Sensors in SARP
#### 4.4.3 Packet Forwarding Over a Set of Optimal Biosensors  
The packet forwarding procedure begins when a biosensor node senses the events and wishes to send the data packet to its cluster head in BASNs. The cluster leader, after receiving the information, first looks in its routing table if a route exists, then it forwards the received data to the next hop cluster head relay node towards the sink. Otherwise, it sends a packet forwarding request (\(reday\_packets\)) message to its neighboring cluster heads, using both low- and high-transmission powers, and waits for the \(rep\_msg\) message for a predefined time interval. The \(rep\_msg\) message contains the sender cluster leader identity, residual energy, location, and level information in the network. Each receiver cluster head, after successfully receiving the \(reday\_packets\) request message from the neighboring cluster leader, marks its identity and hop count in decreasing order, as it is away from the sink in its neighboring routing table only if the entry for a particular node exists in the routing table. Otherwise, a new entry is created for each neighboring head node in the routing table. Only the interested cluster heads reply to the sender request via sending a \(rep\_msg\) message with their identity, residual energy, and level and distance information towards the sink. Then, the receiver cluster leader, upon receiving the \(rep\_msg\) message, computes the probability of each forwarder, updates its routing table with the priority information, and selects the best forwarder towards the sink. A cluster head with high residual energy and shorter distance to the sender and sink has a high probability of being selected as a forwarder node in BASNs. The receiver selects the potential forwarder randomly if more than one forwarder has the same probability. After selecting a potential forwarder, it sends a ready packet receive message (\(ready\_rec\_msg\)) to inform about the arrival of the packet. On packet arrival, at each forwarder, the routing table entries indicate that the data packets already have visited the sender cluster head, thus it avoids sending the same packet to a neighboring cluster head for the second time, which ensures that the discovered routing paths are loop-free in BASNs. This process repeats at each hop, and packets keep moving in the direction of the sink. As soon as the packets are received from the neighboring cluster head, the sink sends an \(ackno\_msg\) message to the source cluster head that includes the identity of all relay nodes along a routing path in BASNs. Thus, a routing path over a set of cluster heads is verified in both upward and downward directions between the source and destination in the network. In the entire routing process, each cluster leader is responsible for maintaining the activities of its member nodes, neighboring cluster head nodes, and the list of routing paths, and it updates it periodically. This periodic information update provides a loop-free network in the discovered routes.