esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Approach for the Definition of an Integrated Visual Quality Index for Residential Buildings
## 3 Methods
### Qualitative Parameters
#### 3.4.1 View Out  
It is widely accepted that a window view elicits a positive effect on the user of an indoor environment. The first research in this field was from [PERSON] [58], who examined patients in a suburban Pennsylvania hospital between 1972 and 1981 to determine whether assignment to a room with a window view of a natural setting might have restorative influences. Moreover, [PERSON] and [PERSON] [25] have demonstrated that a view out in a workplace leads to less nervous and anxious workers. [PERSON] and [PERSON] [30] add that three characteristics of outside views are generally appreciated: natural views, distant views, and views containing water. Similar considerations also  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **sDA (\%)** & **sDGP** & **UGR** & **CCT (K)** \\ \hline Class I & \(\geq\)75 & \(\leq\)0.35 & \(\leq\)16 & \(\geq\)2500 \(\cap\) \(\leq\)3000 \\ \hline Class II & \(\geq\)55 \(\cap\) \(<\)75 & \(\leq\)0.40 \(\cap\) \(>\)0.35 & \(\leq\)19 \(\cap\) \(>\)16 & \(>\)3000 \(\cap\) \(\leq\)3300 \\ \hline Class III & \(\geq\)35 \(\cap\) \(<\)55 & \(\leq\)0.45 \(\cap\) \(>\)0.40 & \(\leq\)22 \(\cap\) \(>\)19 & \(>\)3300 \(\cap\) \(\leq\)5300 \\ \hline Class IV & \(<\)30 & \(>\)0.45 & \(>\)22 & \(>\)5300 \(\cup\) \(>\)2500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thresholds for each class regarding spatial daylight autonomy (sDA), simplified daylight glare probability (sDGP), unified glare ratio (UGR) and correlated color temperature (CCT).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{3}{l}{**Maintained Illuminance (lux)**} \\ \cline{2-5}  & _Living Room_ & _Kitchen_ & _Sleeping Room_ & _Bathroom_ \\ \hline Class I & \(>\)172.5 \(\cap\) \(<\)222.5 & \(>\)170 \(\cap\) \(<\)230 & \(>\)115 \(\cap\) \(<\)175 & \(>\)115 \(\cap\) \(<\)175 \\ \hline Class II & \(>\)127.5 \(\cap\) \(<\)172.5 & \(>\)130 \(\cap\) \(<\)170 & \(>\)85 \(\cap\) \(<\)115 &apply to residential spaces where the effect of view out on human comfort is often neglected or at most is accounted for in a quantitative way [30]. With reference to the new European standard EN 17037 and to the LEED v4 protocol [33], the VQI includes the following checks:  
* View out: at least 75% of the floor of the rooms has a direct line of sight to the outdoors;
* Qualitative view out: window views includes at least two of the following elements: flora, fauna, sky, movement, and objects at least 7.5 m from the glazing.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### Comparison of the Methods  
To visualize and compare the results, we created a graph (Figure 10). The \(x\)-axis represents the classifier methods, while the \(y\)-axis shows the recognition rate. The color shows the number of training images used.  
It is apparent that individual features alone were not enough to achieve satisfactory results; rather, a combination of multiple features was necessary. None of the base features, when used independently, met the desired performance criteria. Moreover, the quantity of training images played a critical role in enhancing classification accuracy. In this study, each weed species was represented by 500 images, of which 5-160 were allocated for training purposes. To rigorously assess the effectiveness of our method, future experiments should explore the use of different training set sizes, such as 300 or 500 images per weed. Should these smaller sets fail to maximize the potential of the method, it would indicate that more than 80-160 images are essential for optimal training. However, it is important to note that even the use of 160 training images per category is minimal compared to the extensive data requirements typical of neural network systems.  
Figure 9: Confusion matrix (green: correct recognition; red: incorrect recognition).
# Wing Geometry and Kinematic Parameters Optimization of Flapping Wing Hovering Flight
## 4 Extended Quasi-Steady Aerodynamic Model  
Here, we assume that flapping wing fruit fly can always keep hovering in flight, so the revised quasi-steady aerodynamic model can be adopted to estimate the aerodynamic force of the flapping wing thin plate passing through unsteady flow [32; 34; 35]. Considering the assumption and applicability of revised quasi-steady aerodynamic model, a nonlinear constraint for posterior optimization analysis of minimum energy is defined via Reynolds number, \(Re=U_{\rm{aver}}C_{\rm{aver}}/\
u\). Here \(U_{\rm{aver}}\) is mean translating velocity of wing tip expressed as \(4\phi_{m}/R_{\rm{eff}}\), and \(\
u\) is kinematic viscosity of air (\(1.48\times 10^{-5}\) m\({}^{2}\)/s). The hovering flight of flapping wing insects including fruit fly usually has a low \(Re\) approximate to 100-3000 [25; 36; 37], in which range the quasi-steady aerodynamic model has been proved to be able to estimate experimental result very well [32; 35]. Here, we shall derive aerodynamic forces and moments component analytical formulas for three aerodynamic mechanisms, namely, translational and rotational circulation mechanism and added mass effect. It is worth noting that the aerodynamic damping moment, which plays an important role in realizing smooth pitch rotation of wing planform [25], is introduced into the pitch-axis aerodynamic moment. Moreover, the aerodynamic damping moment that stems from the velocity gradient of each strip along chordwise differential elements due to pitch motion of wing planform [8; 25; 38; 39; 40] is also included to complete the development of aerodynamic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Description** & **Min** & **Max** \\ \hline \(R_{\rm{eff,vari}}\) (mm) & wing effective length & 2 & 4 a  \\ \(C_{\rm{aver,vari}}\) (mm) & mean chord length & 0.5 & 2 a  \\ \(x_{\rm{t,vari}}\) (mm) & \(x\)-root offset & 0 & 2 a  \\ \(\hat{x}_{0}\) & rotational axis location & 0 & 0.36 \\ \(f\) (Hz) & Frequency & 0 & \(\infty\) \\ \(\phi_{m}\) (rad) & Flapping amplitude & 0 & 1.309 \\ \(K_{\phi}\) & Regulating parameters of flapping angle profiles & 0 & 1 \\ \(\psi_{m}\) (rad) & Pitch amplitude & 0 & \(\pi/2\) \\ \(\zeta\) (rad) & Pitch phase offset & \(-\pi/2\) & 0 \\ \(C_{\phi}\) & Regulating parameters of pitch angle profiles & 0 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Independent variable wing geometry parameters (WGP) and wing kinematics parameters (WKP) and their constraints.  
Figure 4: The wing kinematic pattern: (**a**) flapping angle changed with \(K_{\phi}\); (**b**) pitch angle is regulated by with \(C_{\phi}\). \(\zeta\) is limited in gray region.  
moments. Thus, the current aerodynamic model can be termed an extended version of quasi-steady aerodynamic model.
## 4 Summary and Conclusions  
In this study, the dynamic ground behavior determined through 1 \(g\) shaking table tests was numerically analyzed using DEEPSOIL and ABAQUS. The conclusions of this study are summarized as follows:  
(1) In the ground acceleration profile, the experiment and analysis of DEEPSOIL and ABAQUS showed a consistent tendency to increase as the depth becomes smaller overall. In particular, the analysis of ABAQUS almost coincides with the ground acceleration of the experiment regardless of the input motions.  
(2) For the input motion coinciding with the natural frequency of the ground, the spectral accelerations of the experiment and analysis of DEEPSOIL are almost identical and the maximum amplification is also the same. This indicates that using laminar shear box is a good tool to understand the dynamic behavior of soil.  
(3) In order to check the amplification effect for the overall frequency period, the spectral acceleration obtained from both DEEPSOIL and ABAQUS are pretty close to that from 1 \(g\) shaking table test. Therefore, in order to verify the behavior characteristic of ground and dynamic experiment like shaking table test, it would be reasonable to perform different types of analyses and to compare the results comprehensively.  
(4) The slope model shows much larger difference in spectral accelerations between experimental and ABAQUS results at the top than that with the flat model. This difference may be due to different boundary conditions and geometry used for the flat and slope model.  
(5) In this study, 1 \(g\) shaking table experiments were performed to simulate flat grounds and slope grounds. The method of experiment and simulation ensures that the data obtained is fairly accurate. For the flat model, the comparison between numerical analysis results with DEEPSOIL and ABAQUS and the experimental results verified the  
Figure 13: Spectral acceleration for experiment and analysis in slope: (**a–e**) Sine 10 Hz at depth 0.5 m, 0.3 m, 0.2 m, 0.1 m, 0.05 m; (**f–h**) Sine 10 Hz at slope 0.2 m, 0.1 m, 0.05 m.  
accuracy of numerical analysis for the flat model modeling. For the slope model, the comparison between numerical analysis results with ABAQUS and experimental results verified the accuracy for the slope model modeling, which provided a good guidance for the future research.  
Writing--original draft preparation, Y.J., H.K. ([PERSON]) Review and editing, D.K. Data acquisition H.K. ([PERSON]), Y.L. and H.K. ([PERSON]) All authors read and agreed to the published version of the manuscript.  
This study was supported by Chosun University, 2020.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Discussion of Results
As explained in [70], LSTM was fed previous values, and, in that case, the time-step was set to be 364 values to predict the next 364. For validation, 20% of random data from the training dataset was used, and for testing, the same dataset that was used for the ANN, RCGA-FCM, SOGA-FCM, and hybrid FCM-ANN, as well as with their ensemble structures implementation. In [70], various experiments with different numbers of units, number of layers, and dropout rates were accomplished. Through the provided experimental analysis, the best results of LSTM emerged for one layer, 200 units, and dropout rate = 0.2. These results are gathered in Table 9 for the three cities. In Table 9, it is clear that both ensemble forecasting methods can achieve high accuracy in the predictions of the energy consumption patterns in a day-ahead timescale. Additional exploratory analysis and investigation of other types of ensemble methods, as well as other types of neural networks, such as convolutional neural networks (CNNs), could lead to a better insight of the modeling the particular problem and achieve higher prediction accuracy.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 5 Discussion  
### State of the Art in the Field of Radiation Thermometry  
Currently the field of radiation thermometry appears to be technologically well supported. Radiation thermometers with state-of-the-art measuring capabilities are available for scientific, professional and consumer use.  
Metrological conformity is in practice established for radiation thermometry of black bodies. Calibrations conducted on black body sources are well established and documented in the literature, often also accredited and traceable to national or international standards. Traceable use of radiation thermometers usually only includes BBRT; however, it is often also applied to GBRT while neglecting spectral interactions.  
Through theory and scalar inverse models, the use of radiation thermometers can be extended to grey bodies while retaining traceability; however, this is generally not  
Figure 19: Relative measurement uncertainty intervals for grey body radiation thermometry of surfaces with different spectral emissivities and homogeneities, expressed relative to the temperature difference between the measured object and the 25 \({}^{\circ}\)C environment. The uncertainty contributions correspond to the probability of occurrence of the spectral emissivity within the specified uncertainty interval. The singularity of the relative error at temperature 25 \({}^{\circ}\)C due to the division of the absolute error with 0 \({}^{\circ}\)C thermal difference was omitted. If the temperature of the object and the reflected radiance are equal, emissivity is irrelevant and the measurement uncertainty due to the uncertainty of the emissivity does not exist.  
applicable to real conditions. The literature search for theoretically traceable radiation thermometry protocols for real bodies (RBRT) did not yield any existing results.  
The Use of the Equilibrium Equation for Instrumental Settings in Radiation Thermometry under Real Conditions Is Theoretically Incorrect  
As demonstrated by the simulation in Figure 15, the effective scalar equivalents of the spectral parameters do not necessarily sum to 1 (Figure 16) under real conditions. Therefore, the usual simplification of the equilibrium equation of the scalar inverse model (\(i\epsilon+i\tau+i\varrho=1\)) is not valid under real conditions, when only a slight spectral non-homogeneity of the spectral factors exists.  
The use of this equation under real conditions inevitably results in errors since the data of the spectral distribution of the signals are not taken into account, as shown in Figure 14.
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 3 Experiments and Results
### Continuity of B2b Corrections
#### 3.1.2 Jumps in Clock Corrections  
Figure 4 presents the time series of the clock corrections on DOY 192 in 2022. For most BDS-3 satellites, the values of the clock corrections were in the range of -5 to 5 ns, and only one satellite had a value of approximately -9 ns; for the GPS satellites, the values of the clock corrections were in the range of -1 to 15 ns. As shown in Figure 4, the clock corrections of both the BDS-3 and the GPS satellites contained frequent jumps due to changes in the reference satellite used to calculate the clock offsets. For the BDS-3 and GPS satellites, the mean magnitudes of the jumps were 0.72 ns and 1.43 ns, respectively. Due to the existence of these frequent jumps, the clock corrections were divided into several consecutive arcs. Figure 5 shows the number of clock arcs with different lengths of time in the studied period. For the BDS-3 satellites, most arcs were over 50 min, while for the GPS satellites, most were within 10 min. This phenomenon was also shown in the paper by [PERSON] et al. [19]. Since the clock corrections are estimated based on a regional ground observation network, we speculate that this phenomenon occurs because the GPS reference satellites change more frequently compared to the BDS-3 satellites.  
Figure 4: Time series of the clock corrections of the BDS-3 and the GPS satellites on DOY 192 in 2022.  
Figure 5: Number of consecutive periods for clock arcs of the BDS-3 and the GPS satellites with different lengths of time on DOY 192 in 2022. Different colors represent different continuous time periods.  
It should be noted that since the common jumps in the clock corrections do not affect the coordinate solution, we fixed the common jumps in Figure 6, which become much smoother than those shown in Figure 4. Although some jumps still occur on some individual satellites, and these jumps will have an impact on the positioning solution, many studies have shown that even if these jumps are included, the clock corrections are sufficient to support decimeter-level positioning accuracy standards [18; 19; 20; 21].  
However, both common jumps and jumps present in individual satellites lead to discontinuities in the clock corrections, reducing the lengths of continuous arcs that can be used for predicting, and making it extremely difficult to predict the clock corrections accurately. The jumping phenomenon of clock corrections during DOY 192-198 in 2022 was further investigated. For the BDS-3 satellites, the mean value of the jumps was 0.84 ns; for the GPS satellites, the mean value of the jumps was 1.05 ns. In addition, the clock corrections of the BDS-3 satellites usually contained approximately 11 jumps daily. In comparison, the GPS satellite clock corrections contained approximately 33 jumps daily, and approximately 65% of these arcs lasted for less than 10 min. These frequent jumps in the clock corrections make their prediction very difficult and unreliable.
# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
###### Abstract  
Telco content delivery networks (CDNs) have envisioned building highly distributed and cloudified sites to provide a high-quality CDN service in the 5G era. However, there are still two open problems to be addressed. First, telco CDNs are operated upon the underlay network evolving towards information-centric networking (ICN). Different from CDNs that perform on the application layer, ICN enables information-centric forwarding to the network layer. Thus, it is challenging to take advantage of the benefits of both ICN and CDN to provide a high-quality content delivery service in the context of ICN-based telco CDNs. Second, bandwidth pricing and request mapping issues in ICN-based telco CDNs have not been thoroughly studied. In this paper, we first propose an ICN-based telco CDN framework that integrates the information-centric forwarding enabled by ICN and the powerful edge caching enabled by telco CDNs. Then, we propose a location-dependent pricing (LDP) strategy, taking into consideration the congestion level of different sites. Furthermore, on the basis of LDP, we formulate a price-aware request mapping (PARM) problem, which can be solved by existing linear programming solvers. Finally, we conduct extensive simulations to evaluate the effectiveness of our design.  
Telco CDN; ICN; pricing; request mapping; optimization +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 125; doi:10.3390/611060125
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 2 The System Model
### The Coverage Area  
By introducing relay ships to forward the signals, the coverage range between remote ships and shore stations can be significantly expanded, thus improving the reach and stability of communication. Based on the assumption in Section 2.3, the probability of (SNR \(>\)\(\gamma_{0}\)) in traditional single-hop networks is given by  
\[P_{y}=P\Bigg{\{}|h|^{2}>\frac{n_{0}\gamma_{0}}{P_{s}C_{0}d_{0}^{-\alpha}} \Bigg{\}}=e^{-\lambda\frac{n_{0}\gamma_{0}}{P_{s}C_{0}d_{0}^{-\alpha}}} \tag{16}\]  
Comparing the performance of traditional single-hop and dual-hop schemes, the following condition is obtained:  
\[P_{2 hop}>P_{y}\to e^{-n_{0}\gamma_{0}}\Bigg{(}\frac{\lambda_{1}}{P_{s}C_{1}d_{ 1}^{-\alpha}}+\frac{\lambda_{2}}{P_{r}C_{2}d_{2}^{-\alpha}}\Bigg{)}>e^{-n_{0} \gamma_{0}\frac{\lambda_{0}}{P_{s}C_{0}d_{0}^{-\alpha}}} \tag{17}\]  
Simplifying the equation leads to  
\[\frac{\lambda_{1}}{P_{s}C_{1}d_{1}^{-\alpha}}+\frac{\lambda_{2}}{P_{r}C_{2}d_ {2}^{-\alpha}}<\frac{\lambda_{0}}{P_{s}C_{0}d_{0}^{-\alpha}} \tag{18}\]  
The above formula demonstrates a condition under which the dual-hop channel outperforms the traditional single-hop channel, considering changes in the channel conditions and relay configuration parameters.  
Based on the derived formula, this paper uses Figure 2 to show the coverage area of a dual-hop communication system in polar coordinates compared to that of a traditional single-hop communication system. The red dot in the figure marks the position of the source transmitter, typically the shore station. The triangle symbol represents the relay ship's position, acting as an intermediate node in the signal transmission path. The solid line area represents the maximum coverage area directly connected to the source transmitter. The light blue-shaded region indicates the extended range achievable through relay transmission. This illustrates that compared to traditional single-hop communication, the dual-hop system can extend its coverage and achieve more stable communication.  
Figure 2: Coverage range of communication system.
## 3 Network Models and Related Definitions
### Network Models
#### 3.1.2 Node Perception Model  
The Boolean perception model describes the node sensing in this study. The coordinate of node \(S_{i}\) is \(\left(x_{i},\ y_{i},\ z_{i}\right)\), and the Euclidean distance between \(S_{i}\) and point \(p(x,\ y,\ z)\) is \(d(s_{i},p)=\sqrt{\left(x-x_{i}\right)^{2}+\left(y-y_{i}\right)^{2}+\left(z-z_{ i}\right)^{2}}\). Therefore, under this perception model, the probability of point \(p\) is perceived by node \(S_{i}\) and can be presented as follows:  
\[c_{p}\left(S_{i}\right)=\left\{\begin{array}{ll}1,&d\left(S_{i},p\right)<Rs \\ 0,&d\left(S_{i},p\right)\geq Rs\end{array}\right. \tag{1}\]  
where \(Rs\) denotes the sensing radius of node \(S_{i}\).
## 3 Results and Discussion  
### Electric Performance of Oil  
The DDF (\(\tan\delta\)) is closely related to the structure and properties of insulating materials. It is a macroscopic physical quantity which comprehensively reflects the polarization behavior of the dielectric. Figure 1 shows that the DDF of oil increases during aging. After 24 d of aging, the DDFs of the PTFE and TPEE-coated optical fibers were 2.5 and 4.5 times that of pure oil, respectively.  
Volume resistivity (\(\rho\)) is a quantitative physical description of dielectric conductivity. For the oil, the volume resistivity partly reflects the degree of contamination and aging. As shown in Figure 2, volume resistivity decreases significantly during aging. The decreasing trend of oil samples containing optical fibers is steepest in the early stage of aging and is moderate as time progresses. The volume resistivity of the two kinds of oil samples containing fibers does not change significantly during thermal aging which were both 33.1% of pure oil.  
According to the test method of the electrical parameters, the oil sample was subjected to a dielectric strength test. For each set of insulating oil samples, six breakdown voltage tests were performed. The arithmetic mean value was calculated to obtain the corresponding average breakdown voltage (\(U\)). The results are shown in Figure 3.  
Figure 1: The dielectric dissipation factor (DDF) of oil. PTFE is poly tetra fluoroethylene; TPEE is thermoplastic polyester elastomer.  
Figure 2: Volume resistivity of oil.  
It can be seen from Figure 3 that the breakdown voltages of all three oil types show a gradual decline along with increasing heat aging time. However, there is a difference in the dielectric strength of the fiber-containing oil compared to the pure oil. The breakdown voltage of the fiber-containing oil is lower than pure oil, and this phenomenon is most obvious in the later stages. At the end of thermal aging (24 days), compared with pure oil, the breakdown voltages of the PTFE- and TPEE-containing oil decreased by 14.3% and 33.1%, respectively. The influence of the optical fiber sheath on the dielectric strength of the oil can be classified as follows: in the initial stage of thermal aging, the impact of the sheath on the breakdown voltage of the insulating oil is similar to that of the pure oil sample; in the middle and end stages, compared with the pure oil sample, the sheath greatly affects the breakdown voltage of oil.