esa-sceva/satcom-chunk-collection huggingface dataset:
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 3 PV Floating Monitoring Issues  
### Monitoring FPV Systems for Evaluating the Water Environment Impact on Energy Production  
The interactions between an FPV system and the aquatic environment must be closely observed and monitored from both perspectives: the impact of water on the floating system, and conversely, the impact of the floating system on the aquatic environment. In this paragraph, the first case is considered.  
The interactions to be monitored, considering the environmental influence on FPV systems, include:  
1. Positioning of the PV Modules: The position of the photovoltaic (PV) modules can be affected by wave movements, which vary significantly depending on whether the system is located offshore or in freshwater basins, as well as the type and design of the anchoring system. Consequently, the modules exhibit variable positioning.
2. Temperature of the PV Modules: The temperature of the PV modules is affected by their surrounding microclimate, in turn depending on the water temperature and the thermo-hygrometric conditions induced by the presence of water.
3. Efficiency Degradation of the PV Modules over time. The hygrometric conditions in which the PV modules operate can impact their efficiency over time, potentially leading to a loss of performance.  
Considering the aforementioned factors, additional environmental and operational measurements are essential for monitoring an FPV system. These measurements are crucial not only for assessing the system's short- and long-term energy performance but also for ensuring its safety and reliability.
# Aluminum-Based Plasmonic Photodetector for Sensing Applications
## 2 Materials and Methods
### Morphological Characterization  
The surfaces of the gratings were morphologically characterized using a Dimension 3000 Scanning Probe atomic force microscope (Digital Instruments Inc., Tonawanda, NY, USA) in the tapping mode. When determining the profile shape of grating grooves using a profilometer or AFM [29], the ratio between the curvature radius of the probe tip and the period and depth of the groove is of great importance. The smaller the grating period is and the greater the depth of its grooves, the greater the deviation of the probe trajectory during the scanning of the sample relief from the real groove profile [29]. In our case, the curvature radius of the probe tip was 7 nm, two orders of magnitude smaller than the grating period (633 nm). Moreover, the gratings were rather shallow: the relief depth was less than 12% of the period, and the minimal slope of the groove side sections relative to the normal to substrate plane was more than 30\({}^{\circ}\). At the same time, the cone angle of the probe tip used by us was 22\({}^{\circ}\). Thus, these factors do not affect our profilometry results. Figure 2 shows a cross-section of the surface microreelief of several manufactured PPPDs with a plane perpendicular to the Al grating grooves.  
Figure 2: AFM cross-sections of the Al gratings on the surface of the Si wafer. The inset shows the grating groove profile and its approximation with a trapezoid.  
It can be seen (see inset in Figure 2) that the cross-sectional shape of the grating grooves is close to a trapezoid with a varying filling factor (i.e., the ratio of the groove width to the grating period). The parameters of the trapezoid and filling factor were determined by the parameters of the interference lithography process: exposure time, etching time, characteristics of the resist, selective etchant, and etchant for removing the photoresist mask. By varying these parameters, the characteristics of the grating were optimized in accordance with the results of numerical modeling, which are presented in the following section of this article.
# On the Spectral Efficiency for Distributed Massive MIMO Systems
## 4 Correlation and Los on Emulated D-MIMO Systems above 6 Ghz
### Correlation and LoS Analysis
A D-MIMO formed by 50 TX and 10 RX is analyzed, where 2 BS are considered (25 TX per BS). Both BS are moved simultaneously from 50 to 1000 m away from the UE. The relative distance between BSs is set to be the BS-UE distance. According to the previous test, the antenna spacing is set to \(\lambda/2\). Two environments were tested (UMa and UMi) and two frequencies were considered (10 GHz and 28 GHz). Figure 5a,b show the spectral efficiency of both frequencies and visibility conditions in function of the distance between the UE and both BSs for the UMa and UMi scenario respectively. For the UMa and UMi environment (LoS and NLOS cases), the spectral efficiency slightly  
Figure 4: Distance to the spectral efficiency theoretical limit for three frequencies (28 GHz, 40 GHz and 78 GHz) and three TX/RX antenna spacing (\(\lambda/10\), \(\lambda/2\) and \(\lambda\)) for several SINR values in a D-MIMO (50 \(\times\) 10). (a) UMa scenario and (b) UMi scenario. decreases over distance due to attenuation in the propagation path. This decrease is not linear. Due to fading effects, there are distances where a constructive contribution of the multipath environment implies an increase in the efficiency. Likewise, at other distances, a destructive contribution from the multipath environment decreases the efficiency. Note that the 28 GHz channel also has lower efficiency than the 10 GHz channel due to higher attenuation. However, besides all effects mentioned, the main one is the LoS/NLoS condition. For both environments, the spectral efficiency is increased by approximately 25% when the propagation path includes the Line-of-Sight. In conclusion, the network deployment for massive D-MIMO systems should ensure a spacing antenna above \(\uplambda/2\) and should provide the UE with LoS to maximize the spectral efficiency of the communications.
## Abbreviations  
The following abbreviations are used in this manuscript:  
6 LoWPANIPv6 over Low-Power Wireless Personal Area Network  
6 TiSCHIPv6 over the TSCH mode of IEEE 802.15.4e  
ACKAcknowledgment  
CACollision Avoidance  
CSMACarrier-Sense Multiple Access  
DADDuplicate Address Detection  
DAODestinationAdvertisementObject  
DIODODAGInformationObject  
DISDODAGInformationSolicitation  
DODAGDestination-OrientedDirectedAcyclicGraph  
DODAGIdDODAGIdentifier  
DRiPLDualRadio-Interface Routing Protocol for LLNs  
DRiPLORiPLORiPLOplective Function  
ETXExpected number of Transmissions  
GHzGigahertz  
ICMPInternetControlMessage Protocol  
ICMPVersion 6  
IEEEInstitute of Electrical and Electronics Engineers  
IETFInternetEngineering Task-Force  
IOInterface Oriented  
IPInternetProtocol  
IPV6 IPVision 6  
IoTInternet of Things  
JNIJava Native Interface  
LLNLow-power and Lossy Network  
LQLLink-Quality Level  
MACMedium Access Control  
MHzMegahertz  
MIMultiple RPLInstances  
MOPModeOf Operation
## 4 Simulation Experiments and Discussion  
### Experimental Design  
To verify the overall performance of the proposed ABP determination model and analyze the impact of different reflector TDEs on the BPE, two groups of experiments are presented in this section.  
Experiment 1. The proposed ideal ABP determination model and the reflector antenna analysis software (i.e., GRASP) were used to conduct a comparative experiment for ABP determination. The outputs from our MO-based model were compared with the simulation results of the GRASP software to evaluate the ABP determination accuracy of our model. In addition, to verify the advantage of our model in computational efficiency, we also measured the average time for a single ABP spent by our model and the GRASP software. Since the correctness of the BPE analysis is based on the correctness of the validation of the deduced ABP determination models, this experiment also set the basis for the correctness of the beam-pointing error analyses to be presented in Experiment 2.  
Experiment 2. The MO-based method for rapid calculation of the thermally induced BPE was used to quantify the independent impact of each TDE on the BPE. Based on these independent impact assessments, the TDEs were ranked according to the degree of impact on the BPE. The purpose of this experiment was to identify the TDEs that had a significant impact on the BPE, thus providing basic guidelines for the on-board thermal distortion compensation scheme design of the examined GEO microwave radiometer.
## 4 Study on MS Events in the Surrounding Rock during Excavation and Unloading
### Fracture and Damage Mechanism of the Surrounding Rock  
The seismological theory holds that the ratio of S-wave energy to P-wave energy (ES/EP) is regarded as an important indicator to study the type of rock fractures, and this indicator is often used in MS monitoring to analyze the fracture and damage mechanism of the surrounding rock. In the field of MS monitoring, both ES and EP can be calculated by using the following formula:  
\[E=4\pi\rho cr^{3}\frac{J_{c}}{F_{c}^{2}}\]  
where \(\rho\) is the rock density (kg/m\({}^{3}\)); \(c\) is the wave velocity (m/s); \(r\) is the distance from the source to the sensor (m); \(J_{c}\) is the integral of the particle motion velocity; \(F_{c}\) is the empirical coefficient of seismic wave radiation types, usually F\(\alpha\) = 0.52 (P-wave) and F\(\beta\) = 0.63 (S-wave) in the case of unknown source mechanism [25].  
A lot of research work has been carried out at home and abroad on the ES/EP value of MS signals. According to [PERSON] [26], the ES/EP value was greater than 10 for fault-slip and shear failure, less than 3 for non-shear failure, and ranging from 3 to 10 for mixed failure. Figure 9 shows the distribution of the ES/EP value of MS events in the spillway tunnel. The figure shows that the ES/EP value was less than 3 for 32.7% of the MS events, less than 10 for 88.2%, greater than 10 for only 11.8%, and ranging from 3 to 10 for more than half. Thus, the failure mode in the surrounding rock of the spillway tunnel was mainly tensile failure and mixed failure, with traces of shear failure. Due to the excavation of the spillway tunnel, a free face was formed on the side wall, and the surrounding  
Figure 8: Spatial distribution of MS events.  
rock completed stress self-adjustment due to horizontal unloading, which was prone to tensile failure.
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 3 Radar Fundamental Equations and Parameters
On the contrary, forest canopies, mountains, and buildings are generally rough and therefore tend to produce brighter images [43; 44; 45].
## 3 Results
### The Performance of Liquid Crystal Optical Power Limiter with Integrated Thin-Film Polarizers
According to the collected data, the threshold power exhibits a considerable decrease with an increase in temperature, which aligns with the  
Figure 3: Light transmission as a function of beam power (intensity) at the output of a TN-textured nematic liquid crystal cell with internal film polarizers: experimentally obtained values for **(a)** pure 6 CHBT NLC and cells of 12 \(\upmu\)m (black circles) and 30 \(\upmu\)m (brown squares) thickness; **(b)** Corresponding plot to **(a)** for 6 CHBT doped with 1% by weight Sudan Blue II; **(c,d)** Light transmission obtained at different ambient temperatures: 20 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C (blue, purple, and red circles) as a function of the beam power: data obtained for TN-textured NLC cells filled with 6 CHBT doped with 1% by weight of Sudan Blue II, internal film polarizers and a thickness of **(c)** 12 \(\upmu\)m and **(d)** 30 \(\upmu\)m, analyzed in **(b)**. theoretical concept of order parameter modification. This phenomenon is further enhanced by the birefringence modification resulting from thermal effects. The findings revealed in Figure 3 indicate a trade-off between transmittance and the P\({}_{\text{th}}\) value.
# FSI Application in the Parametric Analysis of Blade-Disk Connection of Turbojet Engine Compressor
## 4 Results  
### Influence of the Blade Root Width Angle (P1) on the Maximum Stresses  
The parameter P1 defines the angle of inclination of the side surfaces of the trapezoidal blade root. Figure 10 shows the effect of changing the value of this parameter on the maximum stresses in the rotor stage of the axial compressor at fixed values of other parameters. Parameter P1 varied from 92\({}^{\circ}\) to 98\({}^{\circ}\).  
Figure 10: Effect of P1 change on stresses.  
Figure 9: Geometrical parameters of the blade root.  
The analyses were performed for various values of the P2 parameter. The results for three selected P2 values are shown below: 1.26\({}^{\circ}\), 1.4\({}^{\circ}\), and 1.6\({}^{\circ}\), where 1.26\({}^{\circ}\) was the reference value of the parameter, resulting from the mapped real geometry of the blade root. The value of 1.6\({}^{\circ}\) was the maximum value of the P2 parameter that could be assumed while keeping the condition of the engine dimensions unchanged.  
In the case of von Mises stresses (Figure 10) at P2 = 1.26\({}^{\circ}\) and 65% N2, the lowest values were obtained in the range of the variation of the breech width angle from 93\({}^{\circ}\) to 95\({}^{\circ}\), while the highest values occurred at the angle of 98\({}^{\circ}\). Depending on the value of aerodynamic loads (150 and 200 kPa), the maximum von Mises stresses were 500 and 540 MPa, respectively. The increase in rotational speed to 100% N2 resulted in an increase in the maximum stress values in stage to 1160 MPa at 150 kPa and to about 1180 MPa at 200 kPa. In the angle variation range from 93\({}^{\circ}\) to 95\({}^{\circ}\), maximum stress values were obtained for the lower flow load (pressure 150 kPa). However, for the higher load of 200 kPa, the maximum values were obtained for the extreme values of the P1 parameter, i.e., 92\({}^{\circ}\) and 98\({}^{\circ}\), and the lowest values were for the angle 94\({}^{\circ}\).  
For parameter P2 of 1.4\({}^{\circ}\), the stress values did not exceed 630 MPa at idle speed. In the case of maximum rotor speed, higher values occurred for P1 = 92\({}^{\circ}\) at over 1400 MPa. Only after exceeding an angle of 97 degrees did the stresses at a pressure of 150 kPa increase slightly, and at 200 kPa, they began to decrease.  
Increasing the value of the P2 parameter to 1.6\({}^{\circ}\) had a significant impact on the character of the trend and the values of stress for both assumed rotational speeds. Relatively high stress values were achieved, especially at maximum rotational speed. The lowest values of about 1700 MPa were obtained for P1 = 98\({}^{\circ}\), and the highest were over 1900 MPa for P1 = 92\({}^{\circ}\). These are values that significantly exceed the strength of the material used.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 2 Model Development  
In urban road network systems, straight road segments constitute the primary road form. Therefore, this chapter focuses on the driving states of traffic participants on straight-road segments. A fixed reference frame is employed in this study, with the origin fixed at time t at the geometric center of the target vehicle's vertical projection onto the road surface [28], as shown in Figure 1. In this reference frame, the \(y\)-axis aligns with the direction of the target vehicle's motion on the road, while the \(x\)-axis is perpendicular to the vehicle's motion direction.  
The historical trajectory of a traffic participant is defined as a numerical vector, where the values alternate between \(X_{i}\) and \(Y_{i}\) and are separated by fixed time steps corresponding to the device's sampling frequency. A trajectory consisting of n points is represented as a sequence of \(2n\) values \(X_{1}\), \(Y_{1}\), \(X_{2}\), \(Y_{2}\),...,\(X_{n}\), \(Y_{n}\), all of which are elements of the set \(T\), defined as follows:  
\[T=X_{i},Y_{i}:i\in[1\ldots n] \tag{1}\]  
The trajectory point indices are mapped to timestamps through the function \(time(x)\), which follows the conditions specified in Equation (2), where \(K\) is the fixed time step:  
\[time(i)-time(j)=K\cdot(i-j) \tag{2}\]  
In this chapter, the trajectory prediction problem is defined as the following sequence generation task: Given an observed trajectory of \(m<n\) points as the sequence \(X_{1}\), \(Y_{1},i\in[1,\ldots,m]\), the goal is to predict each \(X_{1}\), \(Y_{1},i\in[m+1,\ldots,n]\) as accurately as possible by minimizing some error metric between the actual and predicted trajectories.  
In the HTSA-LSTM trajectory prediction model proposed in this chapter--based on LSTM, integrating spatiotemporal attention mechanisms, and accounting for differences in driver behavior--a fixed reference frame is used, as illustrated in Figure 2. The model's output is defined as:  
\[T=X_{i},Y_{i}:i\in[1\ldots m] \tag{3}\]where \((X_{i},Y_{i})\) represents the instantaneous coordinates of the target vehicle and the neighboring vehicles in adjacent lanes within a distance \(D\) meters at time \(i\). The specific value of \(D\) must comply with local traffic safety regulations, matching the safe distance required at the current driving speed.  
The model's output is a probability distribution of the possible trajectory:  
\[T_{pre}=T_{i}:i\in[m,m+1,\ldots n] \tag{4}\]  
where \(T_{i}=(X_{i},Y_{i})\) represents the coordinates that the target vehicle may reach within the prediction time steps \([m,n]\).
## 1 Introduction
[8] confirmed the excellent validity and reliability of the MyJump 2 application for assessing squat jump (SJ) and countermovement jump (CMJ) height performance in both junior and adult athletes, showing near-perfect correlations (r = 0.99) with an infrared platform. These findings support the app's applicability across different age groups in sports performance testing. Similarly, [PERSON] et al. [9] demonstrated that the MyJump 2 app is a valid and reliable instrument for assessing drop jump performance and interlimb asymmetry in young female basketball players, with nearly perfect correlations (r = 0.98-0.99) and excellent inter-device reliability (ICC = 0.99). These results could reinforce the app's utility for performance monitoring in youth athletic populations. While the use of the MyJump 2 application for assessing jump performance has been explored in previous research, the present study offers a meaningful contribution by extending this evaluation to a broader age spectrum of soccer players, encompassingboth adolescent and junior athletes under the age of 18. Importantly, few studies to date have included mixed samples of adult and youth athletes. As such, this study provides valuable insights into the accuracy and consistency of MyJump 2 across different stages of athletic development. Thus, the aim of this study was to examine the validity and reliability of counter-movement jump (CMJ) and squat jump (SJ) performance measurements obtained using the MyJump 2 smartphone application in comparison with the Chronojump system. Both squat jump (SJ) and countermovement jump (CMJ) were included to capture distinct neuromuscular characteristics. While CMJ involves the utilization of the stretch-shortening cycle and reflects elastic energy contributions, SJ isolates concentric muscle action, providing complementary insight into lower-limb power output. Evaluating both jump types enables a more comprehensive assessment of the application's measurement accuracy across different motor strategies, as commonly applied in sports performance testing. Moreover, this investigation focused on youth soccer players (U14-U19), a developmentally dynamic population undergoing neuromuscular maturation and movement variability, where validating such applications is crucial to support reliable performance monitoring, longitudinal development and early talent identification. The objectives of this study were to assess the agreement between CMJ and SJ values recorded by MyJump 2 and Chronojump, determine the intra-session reliability of MyJump 2 across repeated trials for both jump types and evaluate its practicality and validity for monitoring jump performance in soccer players of different age groups.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 3 Duplication Suppression and Re-Queuing
### The Definition and Characteristics of the Duplication Ratio
#### 3.1.2 The Approximation of the Duplication Ratio  
The duplication ratio can be obtained based on the duplication index in (1), which relies strongly on assuming that node \(i\) can estimate \(n_{ij}\). The approach of designing a signaling mechanism to estimate \(n_{ij}\) is not desirable because it tends to incur significant overhead, especially in dense networks, and the feedback information may be stale or incorrect due to the signaling delay. Even with a suitable signaling mechanism, it is difficult to estimate \(n_{ij}\) accurately if any kind of duplication suppression scheme is applied, which may affect \(n_{ij}\) and lead to a distortion in the estimation. To avoid these problems, we propose a simple method of approximating the duplication ratio based on the number of adjacent nodes. It is important to note from Figure 3b that the dominant factor affecting \(DR_{i}(\tau)\) is the maximum value of \(\tau\), which mostly depends on \(N_{i}\). By taking this point into account, we propose an approximated duplication ratio \(ADR_{i}(\tau)\) defined as  
\[ADR_{i}(\tau)=\delta+\frac{(1-\delta)\log\left(1+\mu\frac{\tau-1}{N_{i}-1} \right)}{\log(1+\mu)}. \tag{3}\]  
This approximation is independent of \(n_{ij}\), but requires the estimation of \(N_{i}\), which can be easily estimated by identifying the address fields in the MAC header of a frame received from an adjacent node. In (3), \(ADR_{i}(\tau)\) is derived by modifying the \(\mu\)-law algorithm [36], which is characterized by two design parameters: \(\delta\) and \(\mu\). Please note that \(ADR_{i}(1)=\delta\), i.e., \(\delta\) corresponds to the value of the approximated duplication ratio when the node receives a new frame. Therefore, \(\delta\) can be set to a small value close to 0 from the observations in Figure 3b. Meanwhile, \(\mu\) determines the slope of \(ADR_{i}(\tau)\).  
Figure 4: The effect of the duplication-index-decision factor \(\alpha\) on the duplication ratio.  
Figure 3: Examples of duplication ratio: (**a**) the network topology to measure the duplication ratio in the simulation, and (**b**) examples of duplication ratio \(DR_{i}(\tau)\) (\(\alpha\) = 1) measured in several nodes.  
Figure 5 compares the \(DR_{i}(\tau)\) (\(\alpha=1\)) specifically measured at a node 8 (represented as a solid line) to the values of \(ADR_{i}(\tau)\) calculated with different values of \(\mu\) (represented as dotted lines). Here, \(\delta\) was set to 0.1. When \(\mu\) was very small, \(ADR_{i}(\tau)\) linearly increased as \(\tau\) increased, and \(ADR_{i}(\tau)\) increased and became saturated at 1 more rapidly as \(\mu\) became larger. We verified through extensive simulations that the performance of the proposed mechanism with \(ADR_{i}(\tau)\) was not much affected by the values of \(\delta\) and \(\mu\) as long as \(\delta\) was close to 0 and \(\mu\) was sufficiently large.