esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Design and Participants  
In this study, we employed the data collected at the Bas van de Goor Foundation challenge, where 23 T1D subjects (10 men and 13 women) from Spain and the Netherlands performed high-intensity PA using mountain bikes in Sierra Nevada [32]. The participants covered a total distance of 263 km at altitudes between 4753 and 11,000 m. Data acquisition was conducted by the authors in [32], a prospective and observational study where the performance of CGM devices was also analyzed during strenuous PA. The study was approved by the Medical Ethical committees in Spain (Hospital Universitario Central de Asturias; 163/18) and the Netherlands (Isala Hospital; NL66388.075.18/180603) and registered in the Dutch trial register ([[https://www.onderzoekmetmensen.nl/en](https://www.onderzoekmetmensen.nl/en)]([https://www.onderzoekmetmensen.nl/en](https://www.onderzoekmetmensen.nl/en)) number NL7133). The authors in [32] obtained the written informed consent by the 23 T1D participants.  
The study was conducted during 12 days (two weeks) in two separate periods. In the first period (week 1), subjects performed high-intensity PA, while in the second period (week 2) after mountain biking, subjects carried out NDA with no sports activities. Three different devices were used to record glucose concentrations during the periods of PA and NDA: (i) the capillary device named Free Style Libre Precision NeoPro strip (shortened to FSLCstrip); (ii) the fluorescence-based and subcutaneously implanted CGM device (shortened to Eversense); and (iii) the glucose-oxidase CGM device (shortened to FSL). In the current study, FSLCstrip was considered as the reference for obtaining glucose levels because in prior research its capillary measurements were comparable with National Institute of Standards and Technology standards to the gold reference method [33].  
To extend the analysis of the performance of each CGM device against FSLCstrip during the periods of PA and NDA, we studied the glucose measurements in four time intervals throughout the day:  
* Morning (\(M_{int}\)) from 6:00 to 12:00 h;
* Afternoon (\(A_{int}\)) from 12:00 to 18:00 h;
* Evening (\(E_{int}\)) from 18:00 to 24:00 h;
* Night (\(N_{int}\)) from 0:00 to 6:00 h.  
We also studied the glucose measurements in three glucose ranges:  
* Hypoglycemia (\(<\)70 mg/dL);
* Euglycemia (in the range \([70,180]\) mg/dL);
* Hyperglycemia (\(>\)180 mg/dL).
## 3 Experimental Results and Analysis
### Hydrodynamic Pressure  
Hydrodynamic pressure was measured along the centerline of the plunge pool by diaphragm-type TS202 pressure transducers and a DASP data collection system. According to the results of previous research [44], a sampling frequency of 100 Hz was used that satisfied [PERSON]'s law [43]. The selection of sample interval and sampling time is related to the ability of the collected data to accurately reflect the reality of the experiment. According to the sampling theorem, the truncation frequency of [PERSON]'s law must be greater than the highest frequency of the sampling signal in the selection. If the sampling interval is too large, the truncation frequency is too small, causing spectrum aliasing. As a result, at least 8192 samples were taken for every experiment in order to provide sufficient data to reduce uncertainty [25]. Figure 6 depicts a portion of the original time history curve of hydrodynamic pressures for Case 3. These data were then decomposed by an IIR filter before proceeding with the analysis.  
Hydrodynamic pressure can be expressed as a combination of mean pressure and fluctuating pressure as  
\[P_{i}=\overline{P}+P^{\prime} \tag{1}\]  
where \(P_{i}\) is the instantaneous pressure at a test point, \(\overline{P}\) is the mean pressure at this test point, and \(P^{\prime}\) is the fluctuating pressure. Mean and fluctuating dynamic pressure on the centerline of the floor slab is affected by the diffusion and air entrainment in the falling jet, and the scale of the turbulence in the plunge pool.  
Figure 5: Schematic diagram of the flow regime.  
Figure 6: The time history curve of maximum hydrodynamic pressures for Case 3.
# A Cooperative Communication Protocol for QoS Provisioning in IEEE 802.11p/Wave Vehicular Networks
However, because of the high cost for the installation of the RSU and geographical problems, the communication range of the RSUs may not include all the roads. This problem can lead to discontinuous Internet connection and disconnection to the intelligent transport system (ITS) server and can also cause severe problems for vehicle safety. The WAVE standard only supports a single-hop transmission scheme, and the RSU can \begin{table}
\begin{tabular}{c c} \hline
**Country/Region** & **Frequency Band** \\ \hline ITU-R (ISM band) & 5725–5875 \\ Europe & 5795–5815, 5855/ 58755905/5925 \\ North America & 902–928, 5850–5925 \\ Japan & 715–725, 5770–5850 \\ \hline \end{tabular}
\end{table} 
Table 1: Spectrum allocation for wireless access in vehicular environments (WAVE)/dedicated short-range communication (DSRC) applications. transmit data frame and safety message for ITS services to vehicles in its visible range. Vehicles outside the communication range of the RSU cannot connect to the RSU and receive data frames for service applications and safety messages for ITS services. In environments where there are many shadow zones of the RSU, the loss of data frames and safety messages increases dramatically. To address these problems, a relay communication technique applicable to the VANET is required. The efficient relay decision scheme is needed to adaptively cope with the problems of link disconnection and overhead increase caused by the high mobility of the vehicle. The existing research deals with multi-hop communications in VANETs, to the best of our knowledge, schemes for multi-hop data delivery have not been proposed for the WAVE standard [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. In this paper, we propose a cooperative communication protocol to reduce performance degradation due to frequent link disconnection in the road environment. The proposed scheme is compatible with the WAVE standard and can provide the quality of service (QoS) required by the network by reducing the delay time. The key features of our proposed protocol include the following. First, we model the path maintenance expectation time (PMET) to predict the link expiration time. Second, when a relay node is selected, a relay node with a longer PMET is selected. The proposed scheme can reduce the link disconnection between devices and improve the network performance. Lastly, since only the reserved devices communicate in the reserved resources, collision by other devices does not occur, and there is no competition for the data transmission. Thus, the proposed scheme is not subject to interference from other devices and can improve network performance. In addition, the proposed scheme is less affected by the network environment.
## 7 Conclusions  
We demonstrated a type of low-frequency low-reflection BSI tunnel, which was constructed by a pair of ultrathin lossy AMs (about \(\lambda/35\)) with the phase gradient \(\mathrm{d}\varphi/\mathrm{d}x=-k\) attached symmetrically on both sides of a straight tunnel. Based on the generalized Snell's law and the phase modulations by both AMs, we designed a tunnel with a low-frequency BSI, in which the transmittances were lower than \(-10\) dB in the range 337-356 Hz, and the minimum value reached about \(-27\) dB at 343 Hz. Such a phenomenon can be explained by the sound reflections and the ABA created by the AMs for the left and right incidences of sound. In addition to the characteristics of low-frequency BSI, the low reflection of sound through the tunnel was simultaneously achieved by the thermoviscous energy loss in the coiled channels of the unit cells. The measured results agreed well with the simulated ones.  
Figure 7: (**a**) Schematic of an open-window structure with low-frequency low-reflection BSI, in which a cylindrical sound source was separately placed at the point O or O\({}^{\prime}\). (**b**) We simulated transmittance spectra through the open window by separately placing a cylindrical sound source at the point O and O\({}^{\prime}\). We also simulated pressure distributions through the open window by separately placing a cylindrical sound source at the points (**c**) O and (**d**) O\({}^{\prime}\) at 348 Hz.  
Finally, the potential application of the tunnel in the design of an open-window structure with low-frequency low-reflection BSI was further simulated in detail.  
The proposed open tunnel and window structures showed high performance characteristic of low-frequency low-reflection BSI, which has potential applications in designing sound insulation rooms. The ultrathin structure of AMs and the highly efficient ventilation of open structures provide high-performance heat dissipation. Beyond that, it can also be applied to design ventilation ducts and corridors of BSI. Future work may focus on performance modulations of open sound insulation structures.  
Conceptualization, Y.-J.G. and H.-X.S.; methodology, Y.-J.G., Y.-W.X. and Y.G.; validation, Y.-J.G., Y.-W.X. and Y.G.; formal analysis, Y.-J.G. and H.-X.S.; writing--original draft preparation, Y.-J.G.; writing--review and editing, H.-X.S.; supervision, H.-X.S., S.-Q.Y. and X.-J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Grant No. 2020 YFC1512403), the National Natural Science Foundation of China (Grant Nos.12174159 and 11834008) and the Practice Innovation Training Program Projects of Jiangsu Province (Grant No. 202110299082Z).  
Not applicable.  
The data are contained within the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# Spectral Correction of CPV Modules Equipped with GaInP/GaInAs/Ge Solar Cells and Fresnel Lenses
###### Abstract  
Photovoltaic (PV) devices are spectrally selective, and their performance is influenced by unavoidable spectral variations. In addition, multijunction-based concentrating photovoltaic (CPV) devices show a strong spectral dependence due to the series connection of various junctions with different absorption bands, and also due to the use of concentrator optics. In this work, the accuracy of a new set of analytical equations that quantify the spectral impact caused by the changes in air mass (\(AM\)), aerosol optical depth (\(AOD\)) and precipitable water (\(PW\)) is discussed. Four different CPV devices based on lattice-matched and metamorphic triple-junction solar cells and a poly(methyl methacrylate) (PMMA) and silicon-on-glass (SoG) Fresnel lenses are considered. A long-term outdoor experimental campaign was carried out at the Centre for Advanced Studies on Energy and Environment (CEAEMA) of the University of Jaen, Spain. Results show a high accuracy in the estimations of the spectral factor (\(SF\)), with an average mean absolute percentage error (\(MAPE\)) within \(0.91\%\) and a mean relative error (\(MRE\)) within \(-0.32\%\).  
Keywords:concentrating photovoltaics; triple-junction solar cells; spectral corrections; air mass; aerosol optical depth; precipitable water; analytical equations +
Footnote †: journal: Applied Physics Letters
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
###### Abstract
CALIPSO provides vertical profiles of clouds and aerosols using the active lidar sensor CALIOP, which is a two-wavelength (532 and 1064 nm) lidar that detects clouds and aerosols from laser backscatter intensity profiles [15; 16]. The slight observational time difference (<2 min) between these two satellites provides near-coincident measurements, and thus a comparison between the satellites can be carried out [17; 18; 19; 20]. The passive imager MODIS can provide daily near-global coverage while the active lidar CALIOP provides a sparse spatial sampling [16]. Assessing the uncertainties in cloud properties retrieved by passive sensors such as MODIS has proved to be a complex and difficult task [20]. Researchers have demonstrated that the CALIOP dataset is valuable for evaluating passive cloud retrievals [17; 19; 20; 21; 22; 23], since CALIOP allows for vertically resolving cloud and aerosol layer discrimination. Previous studies indicated that MODIS cloud detection will be contaminated from high aerosol layers, such as dust and haze pollution in China [21; 22; 23; 24]. Rapid economic developments induce frequent haze pollution in Eastern China [25; 26; 27], and dust storms can pass by Eastern China occasionally, especially in spring [28; 29; 30]. However, the assessment of the effects of haze events on satellite cloud observations is still rare and not quantified. In this study, we compared the MODIS and CALIOP cloud detection data in Eastern China during the period 2013-2016. The purpose of this study is to investigate what differences occurred between passive MODIS and active CALIOP cloud detection over Eastern China, with particular focus on the following: (1) The MODIS cloud detection evaluation over high aerosol concentration regions using CALIOP vertical profiles; (2) the difference in some cloud properties, such as cloud fraction, CTH, between MODIS and CALIOP; and (3) the impacts of air quality on cloud detection by MODIS in detail. The data and methods used in this paper are explained in Section 2. The cloud detection differences between MODIS and CALIOP are shown in Section 3.1. In Section 3.2, we investigate the impacts of aerosols and the solar zenith angle on the differences. Sections 3.3 and 3.4 discuss the contributions of cloud phase and cloud types to the differences. Section 3.5 discusses the spatial distributions of the cloud detection differences and possible reasons for these. Lastly, we summarize our findings in Section 4.
# Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network
## 1 Introduction
[30] proposed a new method for kinematic inverse solution of a three-jointed robotic arm based on a firefly algorithm. [PERSON] et al. [31] proposed particle swarm (PSO) optimized BP neural network algorithm to solve the robotic arm inverse solution problem and improve the convergence accuracy and speed of neural network. However, the meta-heuristic algorithm is often constrained by preset fixed algorithmic parameters when coping with multi-peak high-dimensional problems, which leads to the lack of adaptability of the algorithm and the inability to effectively adjust the exploration strategy to adapt to the different stages of optimization. Aiming at the above problems, this paper proposes an inverse kinematics method for a large hydraulic robotic arm based on an adaptive adjustment SWO optimization BP neural network. The main contributions of this paper are as follows:  
1. By improving the SWO algorithm, an ASWO algorithm is proposed. During the iteration process, the algorithm parameters are dynamically adjusted in real time according to the phase change of population fitness to enhance the adaptability of the algorithm. 2. The ASWO algorithm is used to optimize the initial weights and biases of the neural network, which effectively solves the problem that the BP neural network is easy to fall into the local optimum. 3. Establish the neural network mapping relationship between the actual position information at the end of the robotic arm and the corresponding joint value, so as to reduce the influence of the positive kinematic error on the inverse solution accuracy. This paper is organized as follows. In Section 2, the robotic arm is first modeled with forward kinematics to analyze the positioning error relationship between forward and inverse kinematics, and then the forward solution error model is established to ensure the reliability of the data in the simulation stage. In Section 3, the SWO algorithm is improved through parameter adaptive tuning. In Section 4, the ASWO algorithm is pre-optimized to obtain the optimal initial weights and biases of the neural network through continuous iteration of the population. In Section 5, the validity of the method proposed in this paper is verified by simulation and experiment.
# Leveraging Blockchain Technology for Secure 5G Offloading Processes +
## 3 Blockchain-Based Secure 5G Offloading Mechanism  
This paper extends an existing offloading solution designed for Software Defined Radio (SDN)-based 5G networks which enables operators (named primary operators for the rest of the paper) to autonomously initiate the offloading process based on real-time network conditions and without user intervention [23]. The solution is based on an application with a lateral communication mechanism for exchanging user authentication credentials and offloading information. This solution has already proven to be effective as a basis for more secure and more complex offloading processes such as [49], in which a Blockchain-based marketplace has already been considered to support the offloading process and help to decide which alternative operator (i.e., other 5G operators as well as other kinds of services such as Wi-Fi, LTE, etc.) (named secondary operators for the rest of the paper) to consider. However, this solution presents some limitations gathered in Table 1, where the proposed solutions from this research work are also included. Taking these ideas in mind, an extended secure 5G offloading platform backboned by Blockchain technology is proposed in this paper. Figure 1 shows the updated high-level architecture for both the operators (primary and secondary operators) and the user's equipment, where (i) blue components refer to already existing logic, (ii) orange components refer to new logic for the Blockchain-based marketplace interaction, and (iii) green components refer to the new logic for the SSI-based authentication. On the one hand, each operator participating in this offloading approach requires the following new components:  
* New offloading manager logic which receives the current network state and manages offloading processes when needed. Once an offloading is agreed, it makes a request for resources de-allocation (for primary operator) and allocation (for secondary operator). * A wallet for the correct Blockchain transactions creation and signature. It will be used by operators for network state updates and secondary operators searching processes. * Events listener to receive Blockchain events about the new offloading requests. Each registered operator will receive an event with the offloading characteristics. * SSI controller which will manage the user equipment authentication in any of the operator networks avoiding credentials sharing and identity thefts. The primary operator will issue a credential to the user equipment making the offloading process to the secondary operator, which will verify the identity of the credential prior to the resources allocation.
## 3 Measurement Theory
### PAR Quantities  
The fractions of PAR absorbed by the forest canopy (\(fAPAR_{c}\)), the ground (\(fAPAR_{g}\)), and the whole ecosystem (\(fAPAR_{e}\)), as well as PAR intercepted by the forest canopy (\(fIPAR\)), can be calculated from the relevant PAR components, as shown in Table 6. It should be pointed out that \(fAPAR_{c}\) is the fraction of PAR absorbed by the forest canopy, and it is different from \"green\" \(fAPAR_{c}\) absorbed by photosynthesizing leaves which can be derived from vegetation indices by statistical methods [44]. We observed high \(fAPAR_{c}\) during the winter, and this portion of PAR, partly attributed to the long geometrical pathway due to large sun zenith angles in winter, is obviously not used for photosynthesis.  
Figure 5: Variation of red and NIR mean reflectance (HCRF) and NDVI with sensor off-nadir angle, in comparison with HDRF values from the FRT model along 270\({}^{\circ}\) azimuth. The left graph shows the Abisko forest canopy reflectance and the right graph shows the reflectance from the ground vegetation of the same stand. The HCRF results are computed by using Monte-Carlo integration and the FRT model.  
The measured canopy PAR transmission, TPAR, contains contributions from multiple reflections between ground and canopy bottom (see Figure A2 in Appendix 3), therefore the measured transmittance \(\tau PAR_{m}\) is not the \"true\" transmittance of the canopy. It can be shown that the true transmittance is (see Appendix 3 for details):  
\[\tau PAR=\tau PAR_{m}\cdot\big{(}1-rPAR_{c}\cdot rPAR_{g}\big{)}, \tag{4}\]  
where the pure canopy reflectance \(rPAR_{c}\) is computed by:  
\[rPAR_{c}=\frac{rPAR_{eco}-rPAR_{m}^{2}\cdot rPAR_{g}}{1-\tau PAR_{m}^{2}\cdot r PAR_{g}^{2}}. \tag{5}\]  
Considering the incoming light from ground reflection that may undergo interception by canopy, we express the fraction of canopy totally-intercepted PAR as:  
\[fIPAR=(1-\tau PAR)\cdot\big{(}1+\tau PAR_{m}\cdot rPAR_{g}\big{)}. \tag{6}\]
## 4 Discussion
### Limitations
#### 4.4.2 Limited Number of Studies  
The small number of included studies posed a challenge in conducting a comprehensive systematic review. This limitation reduces the ability to draw strong conclusions about the relationship between orthodontic treatment and TMDs. Furthermore, the limited number of studies amplifies the impact of individual study quality and methodology on the overall findings.
## 2 Materials and Methods
### Script Implementation  
To compute the risk-related metrics defined by the [PERSON] mechanistic model (_OED_, _EAR_, and _LAR_), an in-house plug-in script was developed using the Eclipse Scripting Application Programming Interface (ESAPI). The script was implemented in C#, the programming language required by the platform. It can be launched directly from the EclipseTM user interface, enabling access to the data of the currently open patient. Specifically, the context of the running EclipseTM instance is passed to the script as an input parameter [38], which includes treatment plan data and CT images active in EclipseTM. This direct access allows the script to retrieve the treatment plan information necessary for risk calculations, such as the i-th volume (_V(D\({}_{i}\))_) and i-th dose (_D\({}_{i}\)_) from the OAR differential Dose-Volume Histogram (DVH) (Equation (2)), as well as the patient's age at exposure  
Figure 1: Probability S(age\({}_{i+1}\))S(age\({}_{i}\)) of a healthy individual surviving from _age\({}_{i}\)_ to _age\({}_{i+1}\)_, based on regional survival data from ISTAT (Istituto Nazionale di Statistica) for Sicily, Italy [36].  
(_agee_). Population-specific parameters, including L and _S(agee_)/_S(agee_)_, are predefined within the script.  
Upon execution for a given treatment, the Organ At Risk (_OAR_) of interest (e.g., lungs) can be selected via a scrolling window from the list of previously contoured structures. Following this selection, all calculations defined by [PERSON]'s model are performed automatically. The output includes structure volume (cm\({}^{3}\)), Organ Equivalent Dose (_OED_, Gy), Excess Absolute Risk (_EAR_, per 10,000 person-years), and Lifetime Attributable Risk (_LAR_, per 10,000 persons). Additionally, patient, _OAR_, and risk information are simultaneously saved to an external.txt file, facilitating the creation of a database for subsequent statistical risk analyses. The script workflow is illustrated in Figure 2.