esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Compared with former research [20] which used four NB antennas to cover the entire UWB communication band, this research used only three NB antennas to cover the full UWB communication band. In addition, the proposed antenna can still maintain high isolation performance between the UWB antenna and the NB antennas, despite the compact common ground, without additional decoupling structures or cutting the ground plane. The high isolation characteristics of the proposed antenna are realized by designing the UWB antenna as an electric source and the NB antennas as magnetic sources. This results in a fundamentally orthogonal design [27; 28; 29; 30; 31] (orthogonal characteristic mode).
## 5 Experiment
### Experimental Setup
#### 5.1.3 Evaluation  
We used average-based and correlation-based metrics to evaluate the local and global preservation quality. The average-based metric is calculated by averaging the preservation quality around each data point and evaluates the local preservation around them. The correlation-based metric is a correlation value between the observed and embedding spaces and quantifies the global similarity between two spaces. We used (1) trustworthiness [57] as an average-based metric and (2) Shepard goodness [58] as a correlation-based one, which have also been used in previous studies [58; 59]. Trustworthiness \(T(k)\in[0,1]\) is defined as follows:  
\[T(k)=1-\frac{2}{Nk(2N-3k-1)}\sum_{i=1}^{N}\sum_{j\in N_{i}^{k}}\max(0,r(i,j)-k), \tag{31}\]  
where \(N_{i}^{k}\) denotes an index list of the \(k\)-nearest neighbors in the low-dimensional space, and \(r(i,j)\) indicates that samples \(i\) and \(j\) are the \(r(i,j)\)-th nearest neighbors in the observed space. Therefore, trustworthiness penalizes unexpected neighbors in the low-dimensional space and can evaluate the preservation quality of the local structures of data. We set the number of neighbors as \(k=5\), but we observed that the results are robust to this setting. Shepard goodness is a global metric and is calculated with the Shepard diagram [60]. The Shepard diagram is a scatterplot of the point-wise distances of all points in the observed and embedding spaces. The Shepard goodness is the Spearman rank correlation of the Shepard diagram and can evaluate the global similarity between the original data and embedding. The Shepard goodness is computed as follows:  
\[\rho=1-\frac{6\sum_{n=1}^{N_{\rm s}}D_{n}^{2}}{N_{\rm s}^{3}-N_{\rm s}}, \tag{32}\]  
where \(N_{\rm s}=\frac{N(N-1)}{2}\) is the number of the points on the Shepard diagram and \(D_{n}\) is the distance between the ranks of two features of the \(n\)-th point on the Shepard diagram. Both metrics are within \([0,1]\), and a higher value indicates better results. Importantly, the global and local metrics are tradeoffs of each other and should be balanced to realize reliable visualization.
Underwater DTNs have been studied by different research groups [11; 12; 13]; however, most of the work has been focused on routing protocols for opportunistic and predicted contact between nodes, rather than on solutions for high bitrate wireless transfer. Autonomous underwater data muling systems have been considered in a few works [14; 15]. However, all of them used data muling to retrieve data from static nodes. The GROW solution advances the state of the art by considering data retrieval from mobile AUVs. The ability to accurately simulate the data mules' motion and the communications network performance is relevant for studying how a data muling system is affected by the  
Figure 2: The GROW concept, which consists of Data Mule Units that operate between a Survey Unit and a Central Station Unit. variation of parameters such as the number of data mules, the distance between the CSU and the SU, the amount of data to be transferred, and the control laws for a timely and accurate approach. The main contribution of this paper is an enhanced version of UDMSim, which is a simulation platform for underwater data muling oriented systems that combines the AML and ns-3 simulators. This new version considers the following:  
* A more realistic AML simulator, considering the designed suite of sensors and emulating their expected performances;
* The simulation of the designed estimation layer and the control under the influence of measurement noise;
* The inclusion of the antenna radiation patterns to help in evaluating the design and relative positioning of underwater antennas. UDMSim is validated against a theoretical model and lab experiments. The results show a good match between UDMSim, the theoretical model, and the experimental results obtained using an underwater lab testbed considering no localization errors. UDMSim is also capable of reproducing scenarios with localization errors, either simulated or from real traces. UDMSim is made available to the community [6] to support easy and faster evaluation of data muling oriented underwater communications solutions such as GROW and enables offline replication of real world experiments. The rest of the paper is organized as follows. Section 2 presents the related work. Section 3 provides an overview of the GROW solution. Section 4 presents the simple theoretical model of a data muling oriented system. Section 5 presents the UDMSim. Section 6 evaluates the equivalent throughput results. Section 7 draws the conclusions and points out future work directions.
## 6 Comparative Results and Analysis
### Impeller Performances  
Considering the difference of streamline patterns in different impellers, it is necessary to compare the impeller performance based on the initial impeller [32]. Figure 9 compares the difference in pump head, as an example of the performance. The pump impeller head, \(H\), can be calculated as follows:  
\[H=\frac{\Delta p_{total}}{\rho g} \tag{13}\]  
where \(\Delta p_{total}\) is the difference of total pressure in stationary frame between the impeller outlet and inlet, \(\rho\) is the density of fluid medium, and \(g\) is the acceleration of gravity. As shown in Figure 9, \(H_{ini}\) is the CFD-predicted head of the initial impeller. The \(H\) values of the blunt and sharp impellers were lower than the initial impeller by 0.21% and 0.33%, respectively. The \(H\) values of the ellipse and round impellers were higher than those of the initial impeller by 1.31% and 1.54%, respectively.  
Figure 8: Streamlines of the alternating stall patterns in the impellers with different leading-edge shape: (**a**) blunt, (**b**) sharp, (**c**) round, and (**d**) ellipse.  
Figure 10 comparatively shows the hydraulic efficiency of different impellers. The hydraulic efficiency \(\eta\) can be calculated as follows:  
\[\eta=\frac{\rho gQH}{M\omega} \tag{14}\]  
where \(Q\) is the flow rate; \(M\) is the total torque on blades, impeller hub, and shroud; and \(\omega\) is the rotational angular speed of impeller. The \(\eta\) values were between 0.901 and 0.905. The differences were very slight. The ellipse impeller has the highest value, \(\eta\) = 0.905, which is also higher than the initial impeller (\(\eta\) = 0.904). Generally, the hydraulic efficiency has no obvious law among these impellers.  
Figure 10: Comparison of the impeller hydraulic efficiency, \(\eta\), among different impellers.  
Figure 9: Comparison of the impeller head, \(H\), against the initial impeller head, \(H_{ini}\).
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## 3 Control Algorithms  
Three different semi-active suspension settings are tested. First, MR dampers are controlled by different values of constant current reflecting different soft and hard passive suspension configurations. Next, two types of semi-active control are tested that are based on different variants of the Skyhook algorithm.  
The control algorithms dedicated to MR dampers are generally divided into a lower force control layer and a higher vibration control layer, as shown in the block diagram presented in Figure 6. In the case of the lower layer an open-loop inverse model was implemented by reformulating the Tanh model of the MR damper defined in Section 2.  
In order to obtain a desired current \(i_{mr}\) for a given desired force \(F_{\text{alg}}\) generated by a control algorithm the MR damper model defined in Equation (18) needs to be reformulated into the inverse form, as follows:  
\[\sqrt{i_{mr}}=-\frac{F_{\text{alg}}-\alpha_{0}\cdot\tanh(\beta_{0}\cdot v_{us})- c_{0}\cdot v_{us}}{\alpha_{1}\cdot\tanh(\beta_{0}\cdot v_{us})+c_{1}\cdot v_{us}+ \zeta}, \tag{26}\]  
where the control current \(i_{mr}\) is limited to the range from 0 to 1.3 amperes, according to the specification of MR dampers of type RD-8041 from Lord Corporation. Parameter \(\zeta\) is used in order to avoid dividing by zero in the case of damper piston velocity \(v_{us}=0\).
## 4 Results
### Analysis of Evaluation Metrics
#### 4.2.2 Chatbot Engagement Assessment Using the UES-SF
Figure 4 illustrates the engagement scores across four dimensions (FA, PU, AE, and RW) for three interaction modes (text-based, menu-based, and hybrid) and three levels of question complexity (simple, intermediate, and complex). The analysis showed that the hybrid-based interaction mode performed strongly across multiple dimensions, particularly excelling in aesthetic appeal (mean = 89.27) and reward (mean = 82.4), indicating that users found the interface to be visually engaging and satisfying. However, the perceived usability (PU) scores were relatively low, ranging from 44.0 to 54.9 across different task complexities, suggesting that, while users appreciated the visual and rewarding aspects of the interface, there were areas where usability could be improved to enhance intuitiveness and ease of use. Despite this, the focused attention (FA) scores remained high, ranging from 68.2 to 74.9, reflecting consistent user engagement. Overall, the hybrid-based mode remains a reliable and adaptable interface, particularly for users who prioritize engaging and visually appealing experiences. The text-based interaction mode demonstrated robust performance in complex tasks, where it achieved the highest focused attention (mean = 75.8) and aesthetic appeal (mean = 74.00) scores, indicating that users found it engaging and visually appealing. However, the perceived usability (PU) scores varied across task complexities, with a relatively low score of 40.0 for simple tasks, and improving to 63.3 for complex tasks. This suggests that, while the text-based mode can be engaging, there are opportunities to enhance its usability, particularly for more superficial interactions, to ensure a smoother and more intuitive experience across all task difficulties. The menu-based interaction mode excelled primarily in perceived usability (77.8) for complex tasks, highlighting its strength in straightforward, efficient interactions, which users can complete quickly. However, the reward scores were lower (73.3), indicating that  
Figure 4: UES-SF performance over sub-dimensions. although the menu-based mode supports efficient task execution, it may lack satisfaction or engagement in other modes. Therefore, the menu-based mode facilitates fast task completion but requires improvements in visual appeal and overall engagement. Thus, each interaction mode has distinct strengths: the hybrid-based mode excels in aesthetic appeal and reward, offering a visually engaging experience; the text-based mode leads in focused attention, ideal for ease of use and engagement, although it may need better usability for complex tasks; the menu-based mode is highly efficient, with strong perceived usability, but could improve its appeal and satisfaction for handling more complex interactions.
## 2 Problem Description
### Mathematical Model  
Here, we establish a multi-objective mathematical model for the dynamic observation-mission-scheduling problem. Two dummy missions without profit, i.e, a dummy start mission \(s\) and a dummy end mission \(e\), are introduced for the ease of modeling. The multi-objective mathematical model is described as follows:  
\[\max f_{1}=\sum_{i=1}^{N}\sum_{j=1}^{n_{i}}x_{ij}\cdot p_{i} \tag{3}\] \[\min f_{2}=\sum_{u=1}^{N_{s}}\sum_{v=1}^{3}w_{u}\sigma_{u}^{v}\] (4) \[\sum_{j=1}^{n_{i}}x_{ij}\leq 1,\forall t_{i}\in T\] (5) \[eot_{i}\leq st_{i},st_{i}+d_{i}\leq\mathit{lot}_{i},\ \forall i\in T\] (6) \[s_{ij}\leq st_{i},st_{i}+d_{i}\leq\ e_{ij},\ \forall t_{i}\in T,\ \text{if}\ x_{ij}=1,\] (7) \[st_{i}+d_{i}+z_{ii^{\prime}}-st_{i^{\prime}}\leq V\Big{(}1-\rho_{ ii^{\prime}}^{jk}\Big{)},\ \forall t_{i},t_{i^{\prime}}\in T\] (8) \[\sum_{i^{\prime}\in T\cup\{s\}}\rho_{ri}^{jk}\leq 1,\ \forall t_{i}\in T,s_{j}\in S,o_{jk}\in O_{j}\] (9) \[\sum_{i^{\prime}\in T\cup\{e\}}\rho_{ii^{\prime}}^{jk}\leq 1,\ \forall t_{i}\in T,s_{j}\in S,o_{jk}\in O_{j}\] (10) \[\sum_{i^{\prime}\in T\cup\{s\}}\rho_{ri}^{jk}=\sum_{i^{\prime}\in T \cup\{e\}}\rho_{ii^{\prime}}^{jk},\forall t_{i}\in T,s_{j}\in S,o_{jk}\in O_{j}\] (11) \[\sum_{i\in T}\rho_{si}^{jk}=\sum_{i\in T}\rho_{ie}^{jk}=1,\ \forall s_{j}\in S,o_{jk}\in O_{j}\] (12) \[x_{ij}\in\{0,1\},\forall\ t_{i}\in T,j\leq n_{i}\] (13) \[\rho_{ii^{\prime}}^{jk}\in\{0,1\},\ \forall\ t_{i},t_{i^{\prime}}\in T,s_{j}\in S,O_{ik}\in O_{j} \tag{14}\]  
The objective function \(f_{1}\) aims to maximize the total profits of the scheduled missions and the objective function \(f_{2}\) aims to minimize the perturbation to the initial observation plan. Constraint (5) denotes that each observation mission can only be observed at most once, and Constraint (6) limits the observation time for each mission to its effective time window. Constraint (7) indicates that each mission must be observed in the VTW. Constraint (8) is the transition time constraint, which indicates that there must be a sufficient time interval for the EOS to observe any two adjacent observation missions, where \(V\) is a sufficiently large positive number. Constraint (9) and Constraint (10) denote that there is at most one predecessor mission and one successor mission for each real observation mission. Constraint (11) is the observation mission flow balance limit, which indicates that the number of predecessor missions is equal to the number of successor mission for each mission. Constraint (12) guarantees that the observation on each orbit start from the dummy start mission \(s\) and ends at the dummy end mission \(e\). Constraints (13-14) define the domains of the decision variables.
## 2 Materials and Methods
### Statistical Metrics  
All data given in the diagrams are expressed as mean values \(\pm\) standard deviation (SD). The deviation in ROM (\(\Delta_{\text{ROM}}\)) between the postoperatively reconstructed and preoperatively planned situation was calculated using the median of differences. Statistical analyses were performed using SPSS V27.0 (SPSS() Inc., Chicago, IL, USA), to determine statistically significant differences in the ROM of the preoperatively planned situation and postoperatively reconstructed situation for each of the three movements. Both situation groups within the movements, maxFlex, IR@90 Flex, and ER@90 Flex, were examined for statistically significant differences with the Wilcoxon test. The preoperatively planned situation and postoperatively reconstructed situation of the same patient were assumed to be combined samples. A level of \(p\leq 0.05\) was considered statistically significant.  
Figure 3: Depiction of the analyzed movements considered in the ROM analyses: maximum flexion starting from neutral (maxFlex) (**A**), internal rotation at 90\({}^{\circ}\) flexion (IR@90 Flex) (**B**), and external rotation at 90\({}^{\circ}\) flexion (ER@90 Flex) (**C**).
## 2 Research Problem
### RF OFDM vs. Optical OFDM  
OFDM is a popular technique that is designed for RF systems due to its robustness to intersymbol interference and frequency selective channels [25]. The optical OFDM works as a candidate for high-speed transmission and has attracted more and more attention [26]. However, the conventional RF OFDM cannot be directly adopted in optical systems. For RF systems, both negative and positive complex-valued signals can be transmitted. Unlike the RF systems, optical wireless communication systems can only generate non-negative and real-valued signals. Therefore, Hermitian symmetry is usually used to generate real-valued signals [22]. Compared with RF OFDM, the bandwidth efficiency of optical OFDM is reduced by half due to the Hermitian symmetry.
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 8 Control Methods Used in the Active Seating Suspension System  
In the last few decades, researchers have made considerable efforts in the seating suspension system control algorithms to achieve ride comfort and reduce vibration. The control algorithms and techniques of active seating suspension system have been improved from the conventional controllers such as PID controllers to a modern control system like Linear Quadratic Regulator (LGR), then to the smart and intelligent controller such as Fuzzy Logic Controller (FLC) and Artificial Neural Network (ANN) to an advanced control algorithm such as H infinite (H\({}_{\infty}\)) and Sliding Mode Controllers (SMC). In addition, hybrid controller methods such as PID-FLC and SMC-PI have been designed by many researchers. Generally speaking, the main aim of the researches is to come up with a simple structure and robust performance controller to achieve the required design. In this section, several control strategies of the active seating suspension system will be discussed in detail.
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
## 4 Joint Simulation of the Calibration Process
### Frequency Domain Simulation  
The time domain simulation can only get the phase sensitivity at a certain frequency, while the frequency domain simulation will give the whole picture of it, so this is further conducted.  
Figure 14: Waveform fitting at 0.1 Hz.  
Figure 15: (**a**) Waveform of the piston displacement and the calibrating sound pressure at 0.1 Hz.  
(**b**) Waveform of the diaphragm deformation at 0.1 Hz in two venting states.  
Firstly, the influence of the microphone geometries on the phase sensitivity of the microphone is studied, as shown in Figure 16a, while the pistonphone model adopts more realistic heat conduction and leaking conditions. The sealing features of the microphone are labeled as sealing (seal.), small leaking (small-leak.) and large leaking (large-leak.). Meanwhile, the heat conduction features are labeled as adiabatic (adia.) and heat conduction (heat.).  
Secondly, the influence of the pistonphone geometries on the phase sensitivity of the microphone is studied, as shown in Figure 16b, while the microphone model adopts more realistic heat conduction and leaking conditions. The sealing features of the pistonphone are labeled as sealing (seal.) and leaking (leak.).  
From Figure 16, it can be found that the microphone phase sensitivity for the vent in field and vent out field calibrations differ greatly at low frequencies. A phase lead limit of 90\({}^{\circ}\) will happen for the vent in field calibration at very low frequencies, while it is almost unaffected for the vent out field calibrations. The tiny phase lag for the vent out field calibrations is caused by the diaphragm pushing the air flow through the hole into the atmosphere. Thus, a sealed microphone will not have such a phenomenon.  
Figure 16a shows the influence of the pressure leakage and heat conduction effects in the microphone back chamber on its phase sensitivity, while Figure 16b shows the influence of the pressure leakage and heat conduction effects in the pistonphone on the microphone phase sensitivity. It can be concluded that the pressure leakage loss in the microphone back chamber is the dominating factor that affects its phase sensitivity in the vent in field calibrations, while the influence of the heat conduction effect on the phase sensitivity is much less. The properties of the pistonphone will not affect the phase sensitivity of the microphone.
## 2 Experimental Preparation for Fuzzy Logic Application
### Battery Charging Current
Changing the weight of the vehicle causes the regenerative braking energy to change. In this case, the charging current value of the energy gained at different loads to the battery cell is given in Table 2. The weight of a passenger is assumed to be 80 kg in the calculations. In the calculations made under different road conditions and different loads from the three scenarios developed, the charging current gained by regenerative braking to the EV battery cell reached a high charging current value with a C value of 2.275. Low C rates during battery charging increase the battery capacity and battery life. In this context, the SC will be preferred primarily for energy storage from regenerative braking. As soon as the instantaneous average consumption exceeds 16.9 kWh, the SC discharges the load by feeding the electric motor. Thus, the SC will remain empty continuously. When the SC is full, if the temperature and SoC values are suitable for the battery, charging by limiting the charging current value by 0.5 C will improve the battery life. If the SC is full of the battery and is not suitable for energy storage, safe deceleration or stopping will be achieved by mechanical braking. In the current applications to preserve battery life, the efficiency of the energy to be stored is lower than that of the method used in this study because of the lack of a HESS. Considering all these scenarios, the SC capacity was calculated to optimally store the energy gained from regenerative braking. The capacity of a Maxwell brand BCAP0350 model 350 F SC is 0.354 Wh. The SC pack voltage is 400 V, equivalent to the battery pack voltage, and consists of 150 series connections. To store the energy from regenerative braking and to direct the peak current value during braking to the SC, three parallel connections are considered appropriate. In this case, the total capacity of 450 SCs is 159.3 Wh. When using two SC packs connected in parallel, a capacity of 106.2 Wh is obtained. This capacity value can store 23.49% of the braking of Scenario 1 with only the driver in the vehicle. This may cause us to recharge the battery with high current values without being able to sufficiently transfer the high current values at the beginning of the braking to the SC. Therefore, the number of SCs is set to 450. Thus, at maximum regenerative braking, 35.24% of the energy will be transferred to the SC, and high current values will be prevented from damaging the battery. The parameters of the Maxwell brand BCAP0350 model 350 F SC are given in Table 3.  
The continuous maximum current value of the SC is 170 A. In all calculated scenarios, a maximum charging current of 92.38 A will be obtained for 150 series and three parallel-connected SC cells. In this case, safe storage will be provided without exceeding the maximum current of the SCs.