esa-sceva/satcom-chunk-collection huggingface dataset:
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 3 Results
### GF3-02 SAR Wind Speed from VV Polarization
#### 3.1.3 Compared with HY-2B SCA Wind Speed  
Figure 5 shows a comparison of the wind speeds retrieved from GF3-02 VV polarization data and the HY-2B SCA wind speeds. Among them, the abscissa is the wind speed of HY-2B SCA, and the ordinate is the wind speed retrieved by the GF3-02 SAR. Table 3 is the wind speed retrieval accuracy of GF3-02 VV polarization data based on each GMF model compared with HY-2B SCA wind speed. It can be seen that the deviation is negative, which means that the SAR wind speed is generally lower than the SCA wind speed. The RMSE of the retrieved wind speeds are 1.88 m/s, 1.66 m/s, 1.24 m/s, 1.73 m/s, and 1.70 m/s, respectively. Consistent with the comparison results of buoy wind speed and ERA5 wind speed, the RMSE of CMOD5N is also the smallest, the deviation is \(-\)0.53 m/s, and the correlation coefficient is 0.80. Furthermore, the RMSE of the CMODH_VV is 1.70 m/s and the correlation coefficient is 0.81, which are also a little better than those of the CSARMOD2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**GMFs** & **Bias (m/s)** & **RMSE (m/s)** & **Corr** \\ \hline CMOD\_IFR2 & \(-\)0.83 & 1.53 & 0.83 \\ CMOD5 & \(-\)0.67 & 1.35 & 0.84 \\ CMOD5.N & 0.03 & 1.18 & 0.84 \\ CSARMOD2 & \(-\)0.19 & 1.50 & 0.82 \\ CMODH\_VV & \(-\)0.61 & 1.32 & 0.83 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of wind speed retrieval results of the GF3-02 VV polarization data based on each GMF model and ERA5 wind data.  
Figure 3: Comparison of wind speed retrieval results of the GF3-02 SAR data based on each GMF model and NDBC speed wind. (**a**) VV polarization; (**b**) HH polarization.  
\begin{table}
\begin{tabular}{c c c c} \hline
**GMFs** & **Bias (m/s)** & **RMSE (m/s)** & **Corr** \\ \hline CMOD\_IFR2 & \(-1.34\) & \(1.88\) & \(0.77\) \\ CMOD5 & \(-1.23\) & \(1.66\) & \(0.80\) \\ CMOD5.N & \(-0.53\) & \(1.24\) & \(0.80\) \\ CSARMOD2 & \(-0.72\) & \(1.73\) & \(0.79\) \\ CMODH\_VV & \(-1.36\) & \(1.70\) & \(0.81\) \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of wind speed retrieval results of the GF3-02 VV polarization data based on each GMF model and HY-2B SCA wind speed.  
Figure 4: Comparison of wind speed retrieval results from GF3-02 VV polarization data based on (**a**) CMOD_IFR2, (**b**) CMOD5, (**c**) CMOD5.N, (**d**) CSARMOD2, and (**e**) CMODH_VV and ERA5 wind data.  
In conclusion, the comprehensive analysis of the wind speed retrieved from the GF3-02 SAR VV polarization data with the NDBC, ERA5, and HY-2B SCA wind speeds shows that CMOD5.N exhibits the overall best performance of the GF3-02 SAR VV polarization. It has the smallest RMSEs of 1.40 m/s, 1.18 m/s, and 1.24 m/s, which can fully meet the operational requirements of GF3-02 SAR. In addition, among the models proposed from SAR data, CMODH_VV performs better than CSARMOD2 for GF3-02 SAR VV polarization.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 6 Network Data Analytics Function (NWDAF)
### Challenges of Federated Learning
#### 6.2.3 Varying Client Sets  
The assumption that all the clients remain active all the time may not hold in many cases. For instance, in autonomous driving settings, the clients, i.e., autonomous vehicles, in specific regions may not be part of FL for every iteration. Hence, a more realistic assumption is that only a fraction of the clients will be part of the learning at a particular time. Furthermore, those active clients may also drop out of the learning process due to poor connectivity or inadequate computing resources. Major problems under this category are accurately estimating active clients and creating a framework supporting hardware with heterogeneous characteristics.
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 5 The ALPR System
### Segmentation
#### 5.2.2 Image De-Rotation  
The resulting image from the previous stage is almost elliptical. Once the angle between the \(x\)-axis and the major axis of the ellipse shape of the plate is found, it is used as the rotation angle to the plate. Figure 7 shows an example of a rotated image that is based on the calculated angle of the ellipse shape.
# A Kriging Model Based Finite Element Model Updating Method for Damage Detection
###### Abstract  
Model updating is an effective means of damage identification and surrogate modeling has attracted considerable attention for saving computational cost in finite element (FE) model updating, especially for large-scale structures. In this context, a surrogate model of frequency is normally constructed for damage identification, while the frequency response function (FRF) is rarely used as it usually changes dramatically with updating parameters. This paper presents a new surrogate model based model updating method taking advantage of the measured FRFs. The Frequency Domain Assurance Criterion (FDAC) is used to build the objective function, whose nonlinear response surface is constructed by the Kriging model. Then, the efficient global optimization (EGO) algorithm is introduced to get the model updating results. The proposed method has good accuracy and robustness, which have been verified by a numerical simulation of a cantilever and experimental test data of a laboratory three-story structure.  
model updating; frequency response function; Kriging model; damage detection Article
# Dynamic Performance Analysis for an Absorption Chiller under Different Working Conditions
## 4 Dynamic Performance Analysis
### Generator Inlet Temperature (\(T_{gin}\))  
#### 4.1.1 Dynamic Response Process  
To investigate the effects of the mass and thermal storage, a step change of 10 \({}^{\circ}\)C (from 90 \({}^{\circ}\)C to 100 \({}^{\circ}\)C) for \(T_{gin}\) appears at time 0, and then the dynamic response of the AC is observed and shown in Figure 5. In the meanwhile, the cooling water inlet temperature \(T_{cin}\) and the evaporator inlet temperature \(T_{cin}\) remain at 32 \({}^{\circ}\)C and 12 \({}^{\circ}\)C, respectively.  
At the beginning, the AC is on steady-state, but the generator inlet temperature \(T_{gin}\) increases by 10 \({}^{\circ}\)C steeply at time 0 in Figure 5a, resulting in gradually growing for both the generator outlet temperature and the strong solution temperature. So, the solution concentration difference in Figure 5d and the evaporated refrigerant mass flow rate \(m_{e,v}\) in Figure 5b become large. The strong solution mass flow rate decreases at first because of the increased refrigerant. However, the solution density becomes high with the rising concentration, so a slight inversion trend appears in the curve after 100 s in Figure 5b. The volumetric flow rate of the weak solution is constant, but its density also grows, thus the mass flow rate increases gradually.  
In Figure 5c, all the external heat exchange rates of the condenser, the evaporator and the absorber keep rising as a consequence of more refrigerant production. And the temperature steep change is followed by a steep increase of the external generation heat \(Q_{g,ext,i}\) due to the improved temperature difference in the generator. Accordingly, the \(COP_{i}\) initially shows a sudden decrease in Figure 5d. Nevertheless, such a fall can be recovered progressively as long as the \(Q_{g,ext,i}\) decreases and \(Q_{e,ext,i}\) increases. Finally, all these parameters reach a new steady-state, and their values are almost coherent with what can be obtained from the steady-state model.  
Figure 5: Dynamic response to 10 \({}^{\circ}\)C step change of \(T_{gin}\) (\(T_{cin}\) = 32 \({}^{\circ}\)C, \(T_{cin}\) = 12 \({}^{\circ}\)C); (**a**) The temperatures in the generator; (**b**) The mass flow rate. M; (**c**) The external heat exchange rates; (**d**) The \(COP\) and solution concentrations.
# Recent Advances in Flexible Resistive Random Access Memory
## 1 Introduction  
Flexible RRAM is a new non-volatile information storage technology [1] which has the characteristics of high speed, durability, stability, low power consumption, the non-volatile and a wide selection of materials, and it is regarded as one of the important candidates for future flexible memory devices. The integration of flexible RRAM is usually divided into the active array and passive array [2]. The active array eliminates cross-talk between storage cells but requires high-temperature preparation and cannot achieve three-dimensional (3D) integration [3, 4]. In contrast, the passive array has a simple fabrication process and can realize 3D multilayer integration. Meanwhile, the minimum storage unit of the passive array is smaller than that of the active array. Therefore, the passive array with a cross structure is the lowest cost integration of memory devices at present, and it is also the focus of flexible RRAM research in the future [5, 6, 7]. The organic-inorganic composite medium has both stable storage performance and good flexibility. The metal electrode has good conductivity, the conductive oxide electrode has high transmittance, and the carbon/nitrogen electrode has excellent flexibility. It can be seen that the material system of flexible RRAM has great development potential. In this paper, the research status of flexible RRAM is summarized. The second part summarizes its basic working principle, the third part focuses on the current research status of the flexible RRAM material system, the fourth part briefly introduces the preparation process of flexible RRAM andthe fifth part summarizes the challenges and opportunities for the further development of flexible RRAM.
## 4 Case Study
### Characterization of the Fuzzy Logic System
#### 4.1.1 Determining the Knowledge Base  
To determine the knowledge base, expert knowledge on FFH is considered in order to select variables to support the problem at hand. For example, a worker closer than 75 cm to his/her lifeline is defined as attached. Similarly, if a construction worker is closer than 50 cm to an edge defined by a virtual barrier of beacons, the risk of FFH is accepted as high. If the worker is farther, the risk value will be medium or low. Then, to manage this gradual concept on the distance, domain fuzzy variables associated with sensors and actuators are characterized (four input and one output):  
* _Harness detection_ is associated with appropriate use of the hardness by the construction worker. The values of the input variable are: _\"Attached\"_, _\"Umatached\"_.
* _Virtual fence distance_ depicts the average distance of the construction worker to the virtual barrier. Then, the input variable is composed of the fuzzy terms _\"Near\"_, _\"Medium\"_, and _\"Far\"_ in the domain \([0,200]\) and expressed in centimeters.
* _Altitude_ is defined as the related distance between the ground level and the worker. It is an input variable defined by the fuzzy terms _\"Little\"_, _\"Medium\"_, or _\"Tall\"_ in the domain \([0,15]\) represented in meters.
* _Wind velocity_ is the speed of the wind in the construction place. An input variable defined by the fuzzy terms _\"Low\"_, _\"Medium\"_, or _\"High\"_ in the domain \([0,120]\) and represented in km/h.
* _Risk_ represents the level of FFH risk. It is an output variable defined by the fuzzy terms _\"Low Risk\"_, _\"Medium Risk\"_, _\"High Risk\"_, and _\"Very High Risk\"_ in the domain \([0,10]\).  
Table 4 shows the different variables described and their relation with respect to the level of FFH risk.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 3 Proposed Method
### FDNN Model
#### 3.2.3 Feedback Module  
The feedback module is responsible for completing the reconstruction of the detection signal. The decision function \(f_{\sigma}\) binarizes the output vector into a unit vector. By multiplying the binary unit vector with the reconstruction gain matrix, the reconstruction signal can be obtained. The reconstruction module can be expressed as  
\[\mathrm{H}(\hat{\mathbf{x}}_{i})=f_{\sigma}(\hat{\mathbf{x}}_{i})\mathbf{V}_{i}+\mathbf{c}_{i}\,, \tag{22}\]  
in which \(\mathbf{V}_{i}\) and \(\mathbf{c}_{i}\) represent the reconstruction gain matrix and reconstruction bias vector. Since the channel parameter information of each user is known, \(\mathbf{V}_{i}\) and \(\mathbf{c}_{i}\) can be constructed based on the channel parameter information and updated only based on the detected users. Typically, modulation symbols are unbiased, so the \(\mathbf{c}_{i}=0\). \(\mathbf{V}_{i}\) can be denoted as  
\[\mathbf{V}_{i}=[\sqrt{P_{i}}\mathbf{h}_{i}s_{i}^{(1)},\sqrt{P_{i}}\mathbf{h}_{i}s_{i}^{(2 )},\cdots,\sqrt{P_{i}}\mathbf{h}_{i}s_{i}^{(M)}]^{T}\,, \tag{23}\]  
in which \(P_{i}\) and \(\mathbf{h}_{i}\) are the power and the channel parameter for user \(i\). \(s_{i}^{(1)},s_{i}^{(2)},\cdots,s_{i}^{(M)}\) denoted the \(M\) modulation symbols of user \(i\).
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 3 Robust Spectral Dictionary Learning and Sparse Representation
### Robust Spectral Dictionary Learning
According to the IRLS, Equation (17) can be converted into the following two problems:  
\[\mathbf{D}(j,:)=\arg\underset{\mathbf{D}(j,:)}{\min}\frac{1}{h}\sum\limits_{i=1}^{h} \omega_{ij}^{t}\Big{(}\overline{y}_{c,ij}^{t}-\mathbf{D}(j,:)\overline{a}_{i}^{t} \Big{)}^{2} \tag{18}\]  
\[\omega_{ij}^{t}=\frac{1}{\sqrt{\Big{(}\overline{y}_{c,ij}^{t}-\mathbf{D}(j,:) \overline{a}_{i}^{t}\Big{)}^{2}+\delta}}, \tag{19}\]  
where \(\delta\) is a small positive quantity (the machine precision is chosen to be the experimental value). For robust statistical properties, the weighted square in Equation (5) is a reasonable approximation of the \(l_{1}\) norm. Each iteration of Equation (5) involves minimizing the objective function twice. A global optimum can be achieved by taking the derivatives and setting them to zero. In the online processing method, each update uses a small batch of new data of a fixed size, and thus, it is necessary to consider the statistical information of the historical data. Therefore, the information in the current iteration can updated as follows:  
\[\mathbf{M}_{j}^{t}=\mathbf{M}_{j}^{t-1}+\sum\limits_{i=1}^{h}\omega_{ij}^{t}\overline{ a}_{i}^{t}\Big{(}\overline{a}_{i}^{t}\Big{)}^{\mathrm{T}} \tag{20}\]  
\[\mathbf{C}_{j}^{t}=\mathbf{C}_{j}^{t-1}+\sum\limits_{i=1}^{h}\omega_{ij}^{t}\overline{ y}_{c,ij}^{t}\Big{(}\overline{a}_{i}^{t}\Big{)}^{\mathrm{T}} \tag{21}\]  
\[\overline{\mathbf{y}}_{c,q}=\mathbf{D}\overline{\mathbf{\alpha}}_{q}+\overline{\mathbf{n}}_{c,q}. \tag{13}\]0  
\[\overline{\mathbf{y}}_{c,q}=\mathbf{D}\overline{\mathbf{\alpha}}_{q}+\overline{\mathbf{n}}_{c,q}. \tag{13}\]1  
Here, \(\sum\limits_{i=1}^{h}\omega_{ij}^{t}\overline{a}_{i}^{t}\Big{(}\overline{a}_{ i}^{t}\Big{)}^{\mathrm{T}}\) and \(\sum\limits_{i=1}^{h}\omega_{ij}^{t}\overline{y}_{c,ij}^{t}\Big{(}\overline{a}_ {i}^{t}\Big{)}^{\mathrm{T}}\) are the information of the observed data for the current iteration. Therefore, a solution can be sought in the following linear system \(\mathbf{D}(j,:)\):  
\[\overline{\mathbf{y}}_{c,q}=\mathbf{D}\overline{\mathbf{\alpha}}_{q}+\overline{\mathbf{n}}_{c,q}. \tag{13}\]2  
To solve Equation (14), a conjugate gradient method was used. The value of \(\mathbf{D}^{t-1}\) in the previous iteration was used as the starting value for the current iteration. Because the matrix \(\mathbf{M}_{j}^{t}\) was usually diagonally dominant, a reasonable initialization allowed the conjugate gradient update to converge quickly. Through the above analysis, the specific steps of the online robust spectral dictionary learning algorithm (ORSDL) can be summarized. Algorithm 1 shows the pseudo code for the ORSDL. Because the value of the initialization dictionary has a negligible effect on the learning results of the spectral dictionary, the first \(k\) pixel spectral vectors of \(\overline{Y}_{c}\) are usually chosen for the initialization of the spectral dictionary for convenience.
## 6 Results and Discussion  
### Reliability Analysis  
To ensure the reliability of the questionnaire used to evaluate the proposed framework, a reliability analysis was conducted using Cronbach's Alpha. As shown in Table 11, the scale, which consists of 21 questions, demonstrated acceptable internal consistency, yielding a Cronbach's Alpha value of 0.757 [80]. This suggests that the scale is sufficiently reliable for measuring perceptions of the framework's effectiveness.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Occupation** & **Profession (Grade)** & **Field of Work** &
\begin{tabular}{c} **Country of** \\ **Residence** \\ \end{tabular} & **Experience** \\ \hline \hline
1 & Lean Construction Consultant, & Architect, MSc, MBA & Lean Construction, BIM, Design, & & +15 years \\
2 & Lean Construction Consultant & Civil Engineer, PhD & Lean Construction, BIM & United States & +15 years \\
3 & Director of Study and & Architect, PhD & Lean Construction, BIM, Design, & Saudi Arabia & +20 years \\  & Design, Consultant & Architect, PhD & Construction Management & Saudi Arabia & +20 Years \\
4 & Engineering Director, & Architect, PhD & BIM, Construction Management & Saudi Arabia & +20 years \\  & Consultant & Construction & Strategic Project Management & Saudi Arabia & +20 years \\
5 & Academic, Consultant & Construction & Management, PhD & Lean Construction 4.0 & Chile & +15 years \\  & Academic, Consultant, & Management, MSc, & Lean Construction, BIM, & United State & +25 years \\  & Owner Representative & PhD candidate & Technological Expert & United States & +25 years \\  & Head of the Department of BIM & Civil Engineer, & Lean Construction, BIM, GIS, & & \\
9 & Infrastructure, GIS, & PhD, MBA & Construction Management & Germany & +10 years \\  & Mapping, Consultant & Civil Engineer, & Construction 4.0 & Chile & +25 years \\
10 & Director of PhD engineering & Building Engineer, & Knowledge and & & \\
11 & Academic, Consultant & PhD & construction management & Italy & +10 years \\
12 & Academic, Consultant & Civil Engineer, MSc & Lean Methods Implementation, & Spain & +40 years \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of expert participants’ profiles.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 6 Result and Discussions
### Simulation Results
#### 6.2.8 Packet Loss Ratio  
Figure 12 depicts the packet loss ratio with respect to the network size. In our simulation, we observed that packet loss ration increases dramatically, while increasing the network size. It is noted that the RPL, LA-RPL, and EDADA-RPL packet loss ratios are 12%, 8%, and 6% respectively, for a network size of 120. It confirms that the data packet loss is also found to increase, as the network size increases. It is due to the lack of neighbor nodes, coverage issues and difficult to find alternate nodes. However, EDADA-RPL finds an alternate node to transfer the data quickly to the DODAG root.  
Figure 11: Number of observations vs. reconstruction MSE.  
Figure 10: Node energy consumption vs. number of nodes.
## 4 Analysis of Test Results
### Analysis of the Failure Strength of Fiber-Reinforced Soil and the Empirical Prediction Model  
To better predict the strength characteristics of the fiber-reinforced soil at the time of failure, the stress-strain relationship of the fiber-reinforced soil was obtained from the triaxial tests, and the stress value corresponding to 15% of the axial strain was taken as the failure stress.  
(1) Effect of fiber content  
Figure 7 shows the relationship between the failure stress and the fiber content for the fiber-reinforced soil with 12-mm-long fibers. The failure stress of fiber-reinforced soil did not increase with the increase in fiber content; instead, it first increased and then decreased, approximating a sinusoidal trigonometric function, which indicated an optimal fiber content. When the fiber content was 0.2%, the failure strength of the fiber-reinforced soil peaked.  
(2) Effect of fiber length  
Figure 8 shows the relationship between the failure stress and the fiber length of the fiber-reinforced soil with a fiber content of 0.2%. As fiber length increased, the failure stress of the fiber-reinforced soil first increased and then decreased, approximating a Gaussian function. This indicates that when the fiber length is too long or too short, the fiber length limits the failure strength of the fiber-reinforced soil. That is, the failure strength is highest at an intermediate length. In this study, the peak failure strength of the fiber-reinforced soil was reached at a fiber length of 12 mm.  
(3) Effect of confining pressure  
Figure 9 shows the relationship between the failure stress and confining pressure of the studied fiber-reinforced soil. As the confining pressure increased, the failure stress of the fiber-reinforced soils  
Figure 8: Relationship between the failure stress and fiber length of the fiber-reinforced soil.  
Figure 7: Relationship between the failure stress and fiber content of the fiber-reinforced soil.  
showed a linear increasing trend. That is, the greater the confining pressure, the greater the failure strength of the fiber-reinforced soil and the better the reinforcement effect were.  
(4) Empirical model for predicting the failure strength of fiber-reinforced soil  
Multivariate regression analysis was used to develop an empirical model for predicting the failure strength of fiber-reinforced soil considering three factors:  
\[\frac{\left(\sigma_{1}-\sigma_{3}\right)_{\mathrm{f}}}{P_{\mathrm{a}}}=2.37 \cdot\sin(3.48\beta_{\mathrm{f}}+0.88)\cdot\mathrm{e}^{-(\frac{L_{\mathrm{f}}- 132}{28.9})^{2}}\cdot\left(\frac{\sigma_{3}}{P_{\mathrm{a}}}+1.43\right) \tag{1}\]  
where \((\sigma_{1}-\sigma_{3})_{\mathrm{f}}\) is the failure stress of fiber-reinforced soil; \(\beta_{\mathrm{f}}\) is fiber content; \(L_{\mathrm{f}}\) is fiber length; \(\sigma_{3}\) is confining pressure; and \(P_{\mathrm{a}}\) is the standard atmospheric pressure of 0.101 MPa.