esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
This study aimed to predict GEE models for exercise and resting periods during three dumbbell exercises including shoulder press, deadlift, and squat for untrained and trained healthy participants. During exercise, RER, HR, SBP, and BL predicted significant models with MET for untrained and trained groups (Table 3). While RER, HR, SBP, DBP, and RPE reported significant models during the resting period for the untrained and BL for the trained groups (Table 4). Most of the available literature found regression models to predict EE using body composition (\(p\) < 0.001), ambient temperature (\(p\) = 0.004) [10], and exercise intensity-loads and volume (R\({}^{2}\) = 0.61 and \(p\) < 0.05) [9] etc. A study by [PERSON] and [PERSON] reported that total energy expenditure (EE) is mainly affected by muscle mass (\(p\) > 0.001) during five sets of ten repetitions with 15 RM loads of moderate-intensity horizontal leg press and chest fly with 1 and 3 min rest intervals [21]. Another study predicted regression models for bench press Y = 0.132 + (0.031) (X\({}_{1}\)) + (0.01) (X\({}_{2}\)), R\({}^{2}\) = 0.728 and parallel squat Y = \(-\)1.424 + (0.022) (X\({}_{1}\)) + (0.035) (X\({}_{2}\)), R\({}^{2}\) = 0.656 with low intensity for consecutive 5 min exercise--where Y is VO\({}_{2}\), X\({}_{1}\) is the load measured in kg and X\({}_{2}\) is the distance in cm [1]. One published study predicted EE during traditional strength training in 30 trained healthy participants using triaxial accelerometry found a correlation with the axis and location of accelerometer as vertical axis (R = 0.67), the horizontal axis (R = 0.43), third axis (R = 0.36), and the sum of three axes (R = 0.50) counts at the waist [22]. Not one study has predicted MET from cardiorespiratory variables during moderate-intensity interval resistance training. It is challenging to compare our predicted models due to the lack of research on the GEE modeling during resistance training exercises. The primary goal of this study was to estimate GEE models for MET. These models were computed into two categories: (1) exercise and (2) resting models. During exercise, group models have low QIC (UTr = 74) for untrained group and predicted MET significantly with two parameters RER and HR (95% CI = 0.009\(-\)0.789, \(p\) = 0.000*-0.004*) while trained group has QIC (Tr = 187) and predicted MET with RER, HR, SBP, and BL (95% CI = 0.000*-0.000*). The results of the experiments are summarized in Table 5.
## 2 Numerical Mean Echo Power Model for SAR Altimetry
### Along-Track FFT  
SAR altimeters can achieve higher along-track resolution through Doppler beam sharpening, which is accomplished using the inter-pulse Fourier transform. For convenience, we assume that \(N\) is an even number. Following discrete Fourier transformation, the signal output is  
\[\begin{split}\gamma(t_{r},k)&=\frac{1}{N}\sum_{n=0 }^{N-1}C(t_{r},n)\exp\bigg{(}-j\frac{2\pi}{N}nk\bigg{)}\\ &=A_{2}W_{r}\bigg{(}t_{r}-\frac{2(R_{0}-z)}{c}\bigg{)}W_{a}(k- Nf_{d}T_{r})\end{split} \tag{10}\]for \(k=-N/2,\ldots,N/2-1\). Here, \(A_{2}=A_{1}\exp[j(N-1)\pi(f_{d}T_{r}-k/N)]\) and the point target response \(W_{a}(\cdot)\) in the Doppler domain is provided by  
\[W_{a}(k^{\prime})=\frac{1}{N}\frac{\sin(\pi k^{\prime})}{\sin(\pi k^{\prime}/N )}\approx\text{sinc}\big{(}k^{\prime}\big{)}. \tag{11}\]
## 2 Multi-Loop Model Reference PID-MIT (ML-MR PID-MIT) Control: Analytical Tuning and Nonlinearity
In former studies, a ML-MR control structure using the MIT rule was experimentally verified for control of an experimental magnetic levitation system [26] and control of an experimental electrical rotor [28]. We conducted an experimental study to validate the contribution of the ML-MR PID-PID-PFA control structure to the disturbance rejection performance of classical PID control systems. A twin-rotor multi-input multi-output (MIMO) system (TRMS) is a popular rotor control test platform that is preferred for electrical rotor control experiments [37; 38]. The TRMS is composed of two electrical  
Figure 18: Comparisons of disturbance rejection performances of classical PID control and ML-MR PID-PID-PFA control structures for (**a**) step disturbance, (**b**) sinusoidal disturbance with a frequency of 0.001 rad/s, (**c**) sinusoidal disturbance with a frequency of 0.01 rad/s, and (**d**) sinusoidal disturbance with a frequency of 0.1 rad/s. rotors [39]. These are pitch and yaw rotors, as illustrated in Figure 19. The angles of the rotors are controlled by regulating the input voltages of the DC electric motors. This adjusts the rotational speed of the propellers so that the rotors can hover. Figure 20 shows the experimental results. After settling the pitch angle to 0.5 rad, an input disturbance with a unit step waveform was applied at 25 s. One can observe that the step-point control performances of the classical PID controller and ML-MR PID-PID-PFA control structure are almost the same. The slight differences are mainly caused by internal system noise and limitations of the reference model in representing whole dynamics in the main rotor of the TRMS experimental setup. When disturbance is applied to the system, the response of ML-MR PID-PID-PFA control structure differs from the response of the PID controller and better rejects the disturbance at the system output, as shown in the figure. One of major technical complications that was observed in the experimental study was the model mismatch problem in the real system. We used a nonlinear model of TRMS that is a good representation of experimental systems. However, inherent limitations of mathematical modeling and changes of operating conditions reduce the model consistency and lead to model mismatching problems. Since the response of reference model does not adequately match with the response of the real TRMS setup, the PID controller of the adaptation loop, which generates the adaptation gain \(\theta\), can cause instability  
Figure 19: Experimental twin-rotor multi-input multi-output system (TRMS) setup [39]. Figure 20: Experimental results obtained for control of the main rotor of TRMS setup. in the system for the same PID controller coefficients for the control loop. This stability problem can be solved by retuning the PID controller to the adaptation loop.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 3 Locata Development History and Mathematical Models  
### Locata's Prototype and Second Generation System  
A prototype system had been built and tested between 2002 to 2005 as a proof-of-concept demonstration of the Locata positioning technology, and to verify key system concepts and algorithms. This first generation system used single-frequency transceivers (LocataLites) and a modified standard GPS receiver as the mobile unit (the Locata Rover receiver). Commercially available GPS patch antennas were used for the receiver and transmitter, in addition to a custom built \(\mathbb{V}\) wave antenna for one of the LocataLite transmitters, as shown in Figure 4.  
Figure 4: First generation LocataLite hardware and antennas [18].  
Figure 3: The prototypes and current production model of the Locata beam-forming VRay antenna.  
Although the proof-of-concept was successfully demonstrated, the Locata system had some significant limitations, including: (1) the challenge of interoperability with GPS--due to the prototype Locata signal transmissions being at the GPS L1 frequency; (2) OTF carrier phase ambiguity resolution based on KPI; (3) limited multipath mitigation--using a standard GPS L1 C/A code signal structure; and, (4) limited transmitter range and penetration--limited transmission power allowed in the GPS L1 frequency band, so as to not interfere with GPS signals [16; 18].  
To address these limitations Locata designed and built a second generation system, which is the current commercially available system, as described in Section 2. This new design incorporates Locata's own proprietary signal transmission structure, different transmission frequencies, and allows the complete control over both the signal transmitter and receiver, offering enormous flexibility. Table 1 provides a summary of Locata's prototype and second generation system.
# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 3 Methodology
### Simulation Input and Experimental Scenario
The program also provides the status of parked cars for all stations during the simulation on an hourly basis for the week, as shown in Figure 6 for the case of 2800 total reservations in a week, 100 cars, and 10 stations. Figure 6a shows an example of a non-relocation simulation where the initial number of parked cars was set as 10 cars in each station. The number of parked cars at each station is changed once the simulation is complete, and the unbalanced distribution is clear. Figure 6b shows a rebalancing relocation with the same inputs. The number of parked cars at the various stations changes once the simulation is run. However, at midnight the rebalancing relocation is triggered, and the number of cars at each station resets to 10 cars again. Thus, to implement this model the operator needs to relocate approximately 30 cars per day. Figure 6c shows forecasting relocation with the same inputs. However, the initial cars in each station was not set to be equal, but was based on prediction from the customer demand history. Thus, the number of cars initially at each station are different. Forecasting relocation is triggered at midnight, and the total number of cars in each station for the next day will be different based on customer demand at a particular station, calculated using the proposed MLP method. The relocation cost for this model was approximately 60 relocated cars per day. Figure 7 show the status of all 100 cars for each different simulation scenario. The total number of cars is a combination of booked, parked, and on road cars. Figure 7a shows that omitting a relocation scheme results in a high number of parked cars and a low number of on road cars. Cars in each station become unbalanced, with most cars ending up in unpopular departure stations, and most customers (from high demand stations) are unable to make a reservation for the parked cars. The average car utilization was 14.32% with an acceptance ratio of 34.25%. Figure 7b shows that rebalancing relocation reduced the number of parked cars, and increased the utilization and acceptance ratio to 19.31% and 47.6%, respectively. Figure 6: Simulation time history for cars present at each station (**a**) without relocation; (**b**) rebalancing relocation; and (**c**) proposed forecasting relocation. Figure 7c shows that implementing forecasting relocation generates the lowest number of parked cars compared to the previous scenarios, and achieves the highest acceptance and utilization ratios, 26.95% and 67.53%, respectively.
## 4 Results and Discussion
### Dynamic Performances
#### 4.2.1 Hwfet  
The heat transfer rate and its effectiveness showed comparable trends as a consequence of the performance calculation; however, fin efficiency showed contrary trends. In addition, the pressure drop in the air side shows a similar trend to the mass flow rate of air in Figure 8a. Moreover, as shown in Figure 8a, because there was no change in the coolant mass flow rate, the pressure drop on the coolant side was found to be minimal. Other than the air side pressure drop in the HWFET scenario, the overall performance difference is minor.
## 7 Evaluation and Findings
In the external phase, the MITIGATE system was demonstrated to external professionals (experts from Transport and Logistics enterprises and security consultants) who evaluated and assessed the MITIGATE system and its corresponding services. Their comments and suggestions have been collected. The feedback and experience gained have been continuously and promptly passed on to the developers, who then incorporated it into system improvements. Moreover, the tests sites reported in total the participation of 235 internal and 452 external participants, mostly representatives from maritime, transport and logistics sectors, which are spread over the individual test sites (Table 3). A total of 113 non-technical and four technical questionnaires have been collected (Figures 16 and 17). However, totally 18 of the respondents can be assigned an IT background based on their positions they entered in the questionnaire, e.g., \"SCADA/EMS Operator\", \"Senior Software Architect\" or \"Manager Network, Security & Infrastructure IT\". Furthermore, at least 16 respondents can be assumed to have a security-related background, since \"security\" and/or \"safety\" is a part of their job title, e.g., \"PPSO\" (\"Port Facility Security Officer\"), \"Chief Security Officer\" or \"Head of Safety and Security\". Summarizing, 34 out of 113 are considered to have IT or Security related knowledge. Respondents answered questionnaires and provided their assessment using a 4-point Likert scale; A. Strongly disagree; B. Disagree; C. Agree; D. Strongly agree.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 2 Materials and Methods
### Data and Processing  
#### 2.2.1 Seismic Records  
Seismic activity data, reconnaissance reports, and remotely sensed damage assessments from multiple resources were collected for the target events in order to qualitatively assess the possible relations between the soil moisture content dynamics of the shallow surface and the geotechnical characteristics of the earthquakes. The USGS Earthquake Hazard Program Data Center provides comprehensive near-real-time seismic data. For this study, peak ground acceleration (PGA) and MMI contours and earthquake-induced ground failure (landslide and liquefaction) estimates, which are publicly available at the USGS [70], were collected and classified for each study region and focus zone.  
Figure 1: Target earthquakes.  
Figure 2: Focus zone of Petrinja, Croatia 29 December 2020 earthquake.  
For all the target earthquakes in this paper except the Petrinja, Croatia 2020 earthquake, Geotechnical Extreme Events Reconnaissance (GEER) Association, funded by the National Science Foundation (NSF), assembled reconnaissance teams to record geotechnical aspects of the events [71]. For the Petrinja, Croatia 2020 earthquake, the Structural Extreme Events Reconnaissance (STEER) product [72] was used in this study. These reconnaissance reports mainly reviewed the earthquake history and geologic background of the study regions and also mapped geotechnical and, in some cases, structural damage by on-ground mapping and/or aerial imagery using small unmanned aerial vehicles.  
Following major natural events, NASA's Advanced Rapid Imaging and Analysis [73] team produces damage proxy maps (DPM) to identify the extent of potentially damaged areas. These maps are produced based on synthetic aperture radar (SAR) images [40]. DPMs were downloaded and used for three seismic zones in this research, including Samos in Greece, Palu in Indonesia, and Accumoli in Italy [73].
## 3 The Algorithm
### Base Language Construction  
In the first stage, the message \(M\) is separated into single characters. The resulting set of characters along with their frequency distribution constitute the first attempt to obtain a good language and it will be denoted as \(\textbf{{B}}_{\textbf{1}}\). The sub-index indicates the maximum length that any symbol can achieve.
## 2 Numerical Methods
### Validation  
To verify the numerical method used in this paper, confirmatory calculations were carried out for the AITD model (Figure 5) experimentally studied in the paper [32]. Figure 6 compares the predicted limiting streamlines and the oil flow visualization on the surface of the shroud and hub. The bending position and the trend of the limiting streamlines  
Figure 4: Calculation domain and grid of the integrated AITD.  
driven by the circumferential pressure gradient were in good agreement with the oil flow visualization. Figure 7a shows the total pressure coefficient (Cp\({}_{0}\)) contour on Sections C1 and C2, and the measurement boundary of each section is marked with the dotted line. The predicted Cp0 distribution had good similarity with the experimental results, especially in the freestream region. However, the measured total pressure in the regions of wake and passage vortices was lower than the predicted value, and the range of this low total pressure region was larger than that of the numerical result. Figure 7b shows the radial distribution of the predicted pitchwise-averaged static pressure coefficient (Cp\({}_{\text{s}}\)) and flow angle at the AITD outlet (Section C3) and compares them with the experimental results. The predicted Cp\({}_{\text{s}}\) was in good agreement with the experimental result in Section C3. The trend of the predicted flow angle was basically consistent with the experimental results, especially in the middle region. However, the measured flow angle in the regions with large velocity gradients (such as the endwall region and passage vortices) was larger than the predicted flow angle. The reasons for the above differences might be that for the seven-hole probe with a diameter of 2.5 mm, its different measuring holes would be in different velocity zones in the regions with large velocity gradients (such as the wake, boundary layer, and passage vortices), and this velocity difference was interpreted as the flow angle when solving the measured flow field, resulting in the measured flow angle being larger than the actual value and the measured total pressure being lower. Therefore, the numerical method used in this paper could accurately model the flow field of an AITD with a high-curvature geometry.  
Figure 5: Sketch and grid of the AITD.  
Figure 6: Flow visualization and computed limiting streamline on the ITD.
## 5 Results and Discussion
### Comparative Analysis  
In this section, a comparative analysis is performed to show the validity of the proposed methodology. The first ten transcendental equation roots (\(\beta_{n}l\)) values, i.e., \(n=\) 1-10, have been evaluated as an example. The following is the step-by-step procedure for comparative analysis:  
* Frequency (\(\beta_{n}\)) datasets, based on the prismatic cantilever beam properties mentioned in Table 1, are generated using FEM ANSYS.
* The Euler-Bernoulli Equation (19) is then employed to generate transcendental equation roots (\(\beta_{n}l\)).
* For the cross-validation of datasets,\(f_{n}\) and \(\beta_{n}l\) values are calculated numerically using the MATLAB programming platform.
* Based on the input and output target datasets, transcendental roots (\(\beta_{n}l\)) values are predicted using the LSTM-RNN model.
* Finally, the transcendental roots (\(\beta\)nl) values are compared among ANSYS (datasets), MATLAB (numerical) and the proposed LSTM-RNN model.  
Figure 8: Validation accuracy vs. training loss and training accuracy results with 500 epochs.  
Figure 9: Validation accuracy vs. training loss and training accuracy results with 1000 epochs.  
The predicted \(\beta_{n}l\) parameter values based on the LSTM-RNN model are tabulated in Table 5 with a comparative analysis in terms of absolute % error. The \(\beta_{n}l\) results are obtained based on 98.55% accuracy, described in the analysis mentioned above.  
Table 5 shows that % errors are minimal when a comparison is made between analytical and ANSYS results. The lowest and highest % errors for \(\beta_{n}l\) are 0.07% and 1.31% for \(n=1\) and 5, respectively. On the other hand, compared with analytical and ANSYS \(\beta_{n}l\) results, the proposed LSTM-RNN methodology shows a slightly higher % error, but the highest error is still less than 1.5%.  
In fact, it is a more complex and time-consuming process to find a large number of transcendental roots for different beam modes (\(n\)) through conventional or analytical methodologies. At the same time, the proposed LSTM-RNN methodology based on ANSYS datasets provides an efficient and simple approach for approximating the roots of the transcendental equation. From the results, it is also concluded that the model accuracy can be further improved by increasing (1) the number of epochs and (2) input and output datasets but at the cost of an increased execution time and a higher demand of system resources.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 4 Challenges Faced by the Acoustic Signal in Underwater Communication Technology
### Challenges in Acoustic Signal  
1. Low bandwidth: In underwater environments, the noise is very high because of very low medium frequencies. The bandwidth is highly dependable on the distance of the transmission, that can be lesser than 1 KHz [1].
2. High channel error rate: The interference in underwater acoustic channels is due to fading and multipath. This can cause connection losses due to high channel error rates in acoustic communication [1].
3. Large propagation delay: The speed of underwater acoustic communication varies from 1480 m/s to 1540 m/s [2; 3]. Hence, the propagation delay is five times higher than the radio frequency (RF) in terrestrial area networks. Similarly, there is an extreme level of variation in propagation delay depending on the water pressure, temperature, salinity, etc. Though propagation delay is the critical point for underwater acoustic sensor networks, deep designs for MAC protocols are considered [4].
4. High energy consumption: In acoustic communication, the magnitude is higher when compared to the terrestrial environment. The ratio of power usage is even higher when compared to the terrestrial environment. The batteries in the constrained environment cannot be rechargeable [5].
5. Low memory storage: The memory storage level of the underwater nodes is less than that of terrestrial nodes [6].
6. Problems with sensors due to fouling: The sensor nodes that are deployed in underwater environments can accumulate waste materials such as soil, fish waste, oils, etc. This can affect the operation of sensor nodes [6].
7. Multipath and surface scattering: Due to multipath and surface scattering, when the signal is sent from the source to destination via different paths, the strength of the signal could decrease. This causes problems in data transmission [7].
8. Problems in routing: Routing in underwater communication faces challenges such as reliability in data transmission, network connectivity, forwarding of data, and variations in the link. Therefore, in underwater communication, routing is the most difficult issue. Hence, a multi-channel/multi-band adaptation layer is needed in underwater communication.
# Dynamical Manipulation of Surface Plasmon Polaritons
## 2 Directional Excitation of Surface Plasmon Polaritons (SPP)
### Directional SPP Excitation Determined by the Topological Charge of Vortex Beam  
When the incident light is vortex beam, the sign of the topological charge can also determine the propagation direction of SPP. As shown in Figure 2a, two gratings with different periods \(\Lambda\) (denoted by the green and purple triangles) are utilized to compose the directional SPP coupler [82]. According to the wavevector matching condition, the reciprocal vector of grating \(\overset{\rightarrow}{G_{1,0}}=2\pi/\Lambda\), wavevector of the azimuth component of the incident beam \(\overset{\rightarrow}{k}_{OM}\) and \(\overset{\rightarrow}{k}_{sp}\) should satisfy  
\[\overset{\rightarrow}{k}_{sp}=\overset{\rightarrow}{k}_{OM}+\overset{ \rightarrow}{G_{1,0}}. \tag{5}\]When the relation between the grating periods and the topological charge \(l\) follow the curves in Figure 2a, the above wave vectors are collinear. From the analyses of the wave vectors, it can be seen that the wave vectors of SPP synthesized by the upper grating and the lower grating are the same and only the SPP propagating to the right satisfies Equation (5) for the \(l=1\) incident vortex beam. If the topological charge is switched to \(l=-1\), the direction of \(\overset{\rightarrow}{G_{1,0}}\) will be reversed and SPP will propagates leftward. The experimental results in Figure 2a verify the feasibility of the directional SPP coupler.