esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 SSM-GA Optimization for Energy Management  
In essence, GA is inspired by natural selection and genetic mechanisms in biological evolution. It is a stochastic optimization method that was introduced and developed by Professor [PERSON] in 1975. It uses crossover and mutation to manipulate a genetic operator to search and find the best solution candidates while its fitness function reaches its optimal value (maximum or minimum) over the horizon. In this study, GA is used to find a set of \(ESOC_{S}(k)\) allowing the maximum wind power be consumed locally over the optimization process. In order to derive the relationship between each energy component (an SSM expression), a conventional method to describe the complex multi-vector energy system is used. In addition to the constraints in the operation, the SSM is also the main constituent of the GA fitness function to derive the relationship between the variables.  
A flow chart for the GA optimization is illustrated in Figure 6.  
In the process, the best solution is found untill the stopping criterion is met. There are 6 steps as follows:  
1. An initial solution is randomly selected between 0.2 and 0.9. For instance, the state variable in the SSM could be \(\left\{x(i)_{i=1-24}\middle|\left[0.213,0.334,0.257\ldots 0.720\right]\right\}\);
2. A new generation of solution candidate is applied to the fitness function. The fitness function in this study consists of the SSM function and a series of constraints. An SSM function represents the correlation between the variables \(x(i)\), \(x(i-1)\) and \(y(i)\) while the constraints are defined;
3. If the solution candidate satisfies the stopping criterion and the objective function is met;
4. Crossover and mutation methods are used to generate a new solution candidate;
5. Under the operation of the GA function, a new generation is created;
6. A final solution is found when the GA optimization ends.  
In this study, the GA optimization and the associated fitness function are programmed by MATLAB.
## 4 Cryptanalysis of Son et al.'s Son et al. (2019) Protocol
### Traceability Attacks  
Ensuring vehicle untraceability in VANETs is crucial for protecting privacy. This means preventing attackers from tracing a specific vehicle's route [PERSON] et al. (2019). Specifically, an attacker should not be able to determine which RSUs a vehicle has passed.  
It is generally assumed that RSU administrators/managers will not engage in \"active\" attacks. However, they may act as \"honest-but-curious\" adversaries \(\mathcal{A}\) and initiate \"passive\" attacks. To acquire a vehicle's RID, we now present two methods through which \(\mathcal{A}\) can acquire a vehicle's pseudo-identity RID:  
Case1: With the secret key \(s_{j}\) of \(RSU_{j}\) and intercepted data \((M_{1},M_{2},P_{i},T_{1})\) from a public channel, \(\mathcal{A}\) calculates \(P_{ij}=s_{j}\cdot P_{i}\) and the pseudo-identity \(RID_{i}=M_{1}\oplus h(ID_{j}||P_{ij}||T_{1})\).  
Case2: Using the secret key \(k\) from a compromised RSU, \(\mathcal{A}\) accesses blockchain transaction data to extract \((TID_{i},n_{j},ID_{j},Sig_{j}(h(TID_{i}||RID_{i}||n_{j})))\). Then, \(\mathcal{A}\) computes \(RID_{i}=TID_{i}\oplus h(n_{j}||ID_{j}||k)\), thus acquiring any vehicle's pseudo-identity RID authenticated by an RSU.  
This enables \(\mathcal{A}\) to associate specific \(TID\)s with a given pseudo-identity RID. Since each vehicle has a unique pseudo-identity, \(\mathcal{A}\) can then identify all RSUs (through their \(ID\)s) that a particular vehicle has encountered. Consequently, \(\mathcal{A}\) effectively compromises the trace privacy of vehicles.
## 2 Materials and Methods
### Features Evaluation
#### 2.3.2 Effects of the Volume of Training Dataset  
There were three cases in this section. _Case 0_ was that 75% of data (444 from non-CAD subjects and 429 from CAD subjects) were used for training, _Case 1_ was that 50% of data (296 from non-CAD subjects and 286 from CAD subjects) were used for training, and _Case 2_ was that 25% of data (148 from non-CAD subjects and 143 from CAD subjects) were used for training. Given that the different data sampling did not affect the classification performances (See Results below), in order to simplify the subsequent description, we further defined the subsets of non-CAD subjects as \(C1=A1+A2\), \(C2=A3+A4\); and the subsets of CAD subjects as \(D1=B1+B2\), \(D2=B3+B4\). _Case 0_: we randomly selected three subsets from (\(A1\), \(A2\), \(A3\), \(A4\)) and (\(B1\), \(B2\), \(B3\), \(B4\)) as training data sets, respectively, the remaining morphometric data were used as the testing data sets; _Case 1_: we randomly selected one subset from (\(C1\), \(C2\)) and (\(D1\), \(D2\)) as training data sets, respectively, the remaining subsets were used as the testing data sets; _Case 2_: similarly, we randomly selected one subset from (\(A1\), \(A2\)) and (\(B1\), \(B2\)) as training data sets, respectively, the remaining morphometric data (including (\(A3\), \(A4\)) and (\(B3\), \(B4\))) were used as the testing data sets. This part performed by using the best classification algorithm among the algorithms given above.
## 3 Results and Discussion
### Effect of Breed and Finishing Diet on Volatile Compounds
#### 3.2.4 Aldehydes
Thus, D2 seems to be a promising combination able to limit meat lipid oxidation and as a consequence, the generation of aldehydes. In the same way, the predominance of the D1 group on the D2 one was also observed in most of the individual aldehydes, apart from propanal, 2-propyl and pentanal. In particular, the main and predominant aldehyde in both diets was hexanal, representing one of the best indicators of lipid oxidation as previously commented [22; 29]. This compound occupied the highest percentages within this family, and D1 foals presented significantly higher values than the D2 group (82.30 _vs._ 80.46%, respectively). Similarly, this tendency was observed also for other relevant aldehydes, such as heptanal, octanal and nonanal, described by [PERSON] et al. [29] as characteristic volatile compounds of oxidizing oleic acid. These aldehydes in fact reported the highest (\(p<0.001\)) values in D1 foals, which in turn presented also the greatest (\(p<0.001\)) concentrations of this fatty acid (Table 2). Finally, also in this case, this family of compounds represented the third most plentiful in relation with the total volatile compounds of both groups. Thus, considering the low odor threshold of aldehydes, these volatile substances could have a relevant influence on the aromatic profile of our samples. According to the data published by some authors [22], hexanal can give rancid aroma at high levels, while in low quantities it has a pleasant grassy aroma. In addition, heptanal, octanal and nonanal have been related to pleasant meaty notes; octanal contributes meat-like, green, fresh, grassy and fruity notes, whereas nonanal imparts sweet and fruity aromas [22]. As previously commented, our values are lower than those observed by other authors [54; 65; 66]; consequently, from our samples, a pleasant grassy aroma could probably be detected. Nevertheless, it is complicated to associate an aldehyde compound to an animal feeding regime and be recognized as a source of a specific aroma in our samples. As regards to the four groups of treatment, the highest amounts of aldehydes were generated from JN-D1 (349.6 AU \(\times\) 10\({}^{4}\)/g fresh meat), followed by BU-D1 (262.6 AU \(\times\) 10\({}^{4}\)/g fresh meat), BU-D2 (252.4 AU \(\times\) 10\({}^{4}\)/g fresh meat) and JN-D2 (169.9 AU \(\times\) 10\({}^{4}\)/g fresh meat). On the whole, total contents and most of singular aldehydes were affected (\(p<0.05\)) by the interaction of breed and type of diet, except for 2-propyl, heptanal, octanal and 2-ocenal, (E)-.
## 5 Discussion  
As seen in the previous section, the three methods generated similar deformed bodies when solving the plane strain problem. With a fine mesh, the maximum displacement in the Y-axis with FEM was \(-0.094\) mm, while with the conventional BEM, it was \(-0.023\) mm, and with the proposed IGA-BEM method, it was \(-0.073\) mm. The maximum displacement was seen where the P load was applied. To solve the problem, the FEM software used 2566 nodes or analysis points, while BEM used 71 analysis points, and the proposed IGA-BEM used 24 analysis points. Internal points were added in BEM and proposed IGA-BEM to obtain the internal displacements. In contrast, given the nature of FEM, the analysis did not require adding additional points.  
Regarding the contact problem, Table 9 shows a comparison of the contact area widths obtained with the Hertz model, the FEM software, and the proposed IGA-BEM of contact. Taking the analytical solution as a reference, the error obtained with FEM was 16%, while the proposed method was 10%. In both cases, interference between the bodies was eliminated after applying the load. With FEM, the problem was solved by using a total of 2184 nodes, equivalent to 4368 Degrees of Freedom (DOF). With the proposed IGA-BEM, the problem was solved using 24 analysis points (48 DOF), but the control points that define the bodies' shape increased 9-13. On the other hand, the deformed figure was obtained after the PSO algorithm converged in a value gap.  
Although the proposed method solves the problems with fewer degrees of freedom compared to FEM, it requires more robustness to avoid sensitivity problems in the calculation of \(n_{x}\) and \(n_{y}\); The same issue was also observed with the conventional BEM.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 5 Conclusions  
In this paper, we proposed an improved CNN transformer combination network for image deblurring. It can extract richer local features and global features simultaneously, which can ameliorate the details loss and enlarge the receptive field. A series of experiments demonstrate that our model performs well on image deblurring and achieves competitive evaluation results (PSNR and SSIM). When compared with other models, our model does not achieve the best performance in every aspect, but on the whole, it also shows superiority in regard to accuracy and speed. Furthermore, our deblurring model can be considered a pretreatment technique for object detection to improve performance.  
Conceptualization, X.C., Y.W. ([PERSON]), D.W. and Y.W. ([PERSON]); methodology, X.C. and Y.W. ([PERSON]); software, X.C. and Y.W. ([PERSON]); validation, X.C. and D.W.; formal analysis, X.C. and Y.W. ([PERSON]); investigation, X.C. and D.W.; resources, X.C. and D.W.; data curation, X.C. and Y.W. ([PERSON]); writing\(-\)original draft preparation, X.C., Y.W. ([PERSON]), and Y.W. ([PERSON]); writing\(-\)review and editing, X.C., Y.W. ([PERSON]), and D.W. All authors have read and agreed to the published version of the manuscript.  
Figure 9: The visual example of object detection. From top left to bottom right: blurry images, the restoration of SRN [12], MPRNet [2], MIMO-Unet+ [8], Restomer [18], and our model.  
**Funding:** This research was funded by \"Research and application of key technologies for online detection of aerosol particulate matter based on precision medicine\" OF Jilin Province Science and Technology Department, grant number: 20210204134 YY.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Millimeter-Wave 4 th-Harmonic Schottky Diodes Mixer with Integrated Local Oscillator
## 2 Mixer Design
### Local Oscillator  
The proposed local oscillator (LO) is based on a transmitter for automotive radar developed with low-cost COTS MMICs, namely Triquint's chipset [13]. It consists of the TGV2204-FC 19 GHz VCO followed by the TGV4703-FC and TGV4704-FC frequency doublers. This chain generates a signal from 74 to 77 GHz controlled by a phase locked loop based on the fractional frequency synthesizer ADF4158 from Analog Devices. The MMICs are mounted on a Rogers 3210 PCB and provide around 10 dBm power.  
Figure 1: Configuration of the whole fourth-harmonic mixer with integrated MMIC local oscillator. (**a**) Schematic of the fourth-harmonic mixer circuit. (**b**) Top view of the design prototype.
## 4 MIMO Performance Parameters
### Channel Capacity Loss (CCL)  
Correlation loss is a prime parameter for the CCL. The standard limit of CCL is less than 0.5 bps/Hz. The CCL response of the circular disk MIMO antenna is near zero at 4.95 GHz, and in the CSRR-based MIMO antenna, the CCL value is near zero at 5.08 GHz. Overall for the broad range up to 15 GHz, the CCL value is within the allowed range. Equations (5) and (6) are used to calculate the CCL. The CCL of the presented design is represented in Figure 17.  
\[C_{\text{Loss}}\ =-\log_{2}\det\!\left[\alpha^{R}\right] \tag{5}\]  
\[\left[\alpha^{R}\right]=\left[\begin{array}{cc}\alpha_{11}&\alpha_{12}\\ \alpha_{21}&\alpha_{22}\end{array}\right]\]  
\[\alpha_{11}=1-(|S_{11}|^{2}+|S_{12}|^{2}),\]  
\[\alpha_{12}=S_{11}S_{12}+S_{21}S_{12},\]  
\[\alpha_{21}=S_{22}S_{21}+S_{12}S_{21}\]  
\[\alpha_{22}=1-(|S_{22}|^{2}+|S_{21}|^{2})\]  
The assessment of the two proposed and published works are presented in Table 2. The proposed CSRR metasurface design shows the highest isolation of 55 dB, and its overall complexity of the design is less than other available works.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Dimension (mm2)** & **Ports** &
\begin{tabular}{c} **Isolation** \\ **(dB)** \\ \end{tabular} & **Complexity** \\ \hline Proposed design with CSRR metasurface & \(40\times 25\) & 2 & 55 & Less \\ Proposed design with circular disk metasurface & \(40\times 25\) & 2 & 29 & Less \\
[37] & \(30\times 15\) & 2 & 35.8 & Medium \\
[18] & \(48\times 31\) & 4 & 21 & More \\
[38] & \(20\times 20\) & 2 & 24 & Medium \\
[39] & \(19\times 19\) & 4 & 35 & More \\
[40] & \(31\times 31\) & 8 & 16.1 & More \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assessment of the presented MIMO designs with other MIMO structures.  
Figure 17: The CCL response of the presented MIMO antenna structure.
## 1 Introduction
Further, rigorous deformation analysis and tests of significance of the results are missing [12; 13]. Tests in laboratory conditions of the latest generation of laser scanners have concluded that the laser scanner can be used to detect vertical displacement non-natural targets marked with photogrammetry markers with millimeter accuracy [14]. Image analysis techniques, in contrast, are very sensible for identifying displacements in transverse direction. Hence, a combined acquisition and analysis of point cloud and image data complement each other very well [15]. In a research study [15; 16] a novel approach of fusing image and laser scan data from the same instrument, such as a MS, to create an RGB+D image was proposed. D channel represents the distance channel interpolated from the laser scanner data. Detection and matching of identical image features from RGB+D images gathered from different measurement epochs is a use case for computer vision algorithms. 3D coordinates of the detected features can easily be calculated using RGB+D images. Matched image features can be used as substitutes for classic discrete points in displacement monitoring tasks and rigorous geodetic deformation analysis with tests of significance. Comparatively, the lack of discrete points is the biggest drawback of using laser scanner data (3D point clouds) for displacement monitoring and deformation analysis. The proposed methodology and all needed steps to create RGB+D images and their subsequent use were field-tested in this research. The research, i.e., this article, is organized in five sections. After the introduction in section two the used instrument Trimble multi station SX10 is presented with its basic characteristics and possibilities. Basic principle of fusion of laser scan and image data from multi stations, as well as deformation analysis using point clouds and matching algorithms for RGB+D data in displacement monitoring is explained in section two also. In section three, the experiment test procedure is elaborated upon in detail. The focus of the research is to determine the achievable precision of the method in field conditions. The testing aims to compare RGB+D detected displacement of a tested wooden beam to the control data, extracted from a numerical model of the beams, based on measurements by a linear variable differential transformer (LVDT) sensor, all presented in section three. The achieved results are shown in section four regarding the optimal algorithm used for image feature and matching identical points between different measurement epochs obtained from RGB+D data. Data comparison of obtained results regarding the numerical and measured displacements of the beam, i.e., the differences between them are also shown in section four. Our aim was to verify that fusion of laser scans and image data (RGB+D) is applicable for structural health monitoring of engineering structures. The gap in the current research is the lack of numerical testing and validation of the RGB+D method, which the authors have tried to fill in this paper. In the end in section five discussion and some conclusions are derived.
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 3 Applying AI/ML into 5G RAN: Proposed Methodology
### Data Filtering  
Many algorithms have been proposed to limit data collection in the machine learning and data science literature [11]. For instance, principal component analysis (PCA) is a well-known mathematical technique that transforms a set of correlated variables into a set of uncorrelated variables known as principal components. Thus, PCA can be used as a 5G radio characteristic extraction technique to reduce the dataset to fewer dimensions. Technically, PCA identifies the given vectors transformed with the highest eigenvalues of a covariance matrix and uses those to project the input parameters into a new subspace of a lower number of indicators.  
The RAN optimization-driven task, with \(RSRP\), \(RSRQ\), and \(SINR\) parameters (i.e., the input data denoted as \(b_{\textit{Coverage}}\) and \(b_{\textit{Mobility}}\)), qualifies to apply the correlation coefficient measure due to the normalized meaning of the parameters. Table 3 shows the different values of the parameters, which correspond to varying levels of radio signal quality, where a very good signal level is associated with an excellent perception of the network by the end user (e.g., seamless coverage, close to the base station), while a very poor signal level is associated with a network perception having insufficient quality, lacking continuous coverage (e.g., at cell edge far from the base station).  
Each parameter that determines the quality of the 5G signal can help to read the detailed characteristics of the connection. \(RSRP\)--the average power of the received pilot signals (reference signal) or the level of the received signal from the base station. For instance, when \(RSRP\) = \(-120\) dBm and below, the connection may be completely unavailable. \(RSRQ\) indicates the quality of the received reference signal. If its range is below \(-20\) dB (poor), it indicates typically high cell load or high noise level. \(SINR\) is the 5G signal-to-noise ratio. For \(SINR\), the higher the value, the better the signal quality. At \(SINR\) values below \(0\), the connection perception will be very poor (e.g., very slow connection), since this value characterizes the situation when there is more noise in the received signal than the useful part, and the probability of losing a connection is high [15].  
A correlation coefficient for \(RSRP\), \(RSRQ\), and \(SINR\) can result in proof that the performance indicators in some scenarios depend on each other [16]. Thus, in the proposed data selection model, practically, the function of data filtering can be performed on a data set, B, to reduce the data dimension in RAN optimization-related data collection.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### High-Resolution AGB Maps from Field Measurements and LiDAR Data
#### 3.3.3 LiDAR-Derived Aboveground Biomass, Canopy Height and Cover for Maryland, 2011  
[PERSON] et al. [81] generated a Maryland forest biomass map at 30 m spatial resolution in 2011 by relating field measured biomass to LiDAR metrics using Random Forest regression models. The results of the comparison with the 848 independent field data showed that the R\({}^{2}\) was 0.49 and the RMSE was 89.3 Mg/ha. Overestimation of biomass occurred, particularly for low biomass estimations.
# Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
## 3 Experimental Results
### Compression Performance Analysis
#### 3.2.1 Compression Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Sensor**} & \multirow{2}{*}{**Entropy**} & \multirow{2}{*}{**\(\Lambda\) Values**} & \multirow{2}{*}{**CCSDS-123-AC**} & \multirow{2}{*}{**NL-CCSDS-123**} & \multirow{2}{*}{**M-CALIC**} & \multirow{2}{*}{**NLRLS**} \\ \cline{5-6} \multicolumn{1}{c}{**Abbreviation**} & & & & & & \\ \hline \multirow{5}{*}{AC} & \multirow{5}{*}{9.77} & \(\Lambda=0\) & 3.66 (0.14) & 3.73 (0.21) & 4.03 (0.51) & **3.52** \\  & & \(\Lambda=1\) & 2.45 (0.38) & 2.54 (0.47) & 2.87 (0.80) & **2.07** \\  & & \(\Lambda=10\) & 0.58 (0.10) & 0.94 (0.46) & 0.88 (0.40) & **0.48** \\  & & \(\Lambda=20\) & 0.35 (0.07) & 0.72 (0.44) & 0.53 (0.25) & **0.28** \\  & & \(\Lambda=30\) & 0.26 (0.06) & 0.63 (0.43) & 0.40 (0.20) & **0.20** \\ \hline \multirow{5}{*}{AU} & \multirow{5}{*}{12.13} & \(\Lambda=0\) & 5.87 (0.14) & 5.95 (0.22) & 6.13 (0.40) & **5.73** \\  & & \(\Lambda=1\) & 4.80 (0.66) & 4.89 (0.75) & 5.05 (0.91) & **4.14** \\  & & \(\Lambda=10\) & 1.96 (0.38) & 2.24 (0.66) & 2.22 (0.64) & **1.58** \\  & & \(\Lambda=20\) & 1.14 (0.22) & 1.46 (0.54) & 1.40 (0.48) & **0.92** \\  & & \(\Lambda=30\) & 0.83 (0.17) & 1.18 (0.52) & 1.05 (0.39) & **0.66** \\ \hline \multirow{5}{*}{AIRS} & \multirow{5}{*}{11.39} & \(\Lambda=0\) & 4.25 (0.41) & 4.31 (0.47) & 4.38 (0.54) & **3.84** \\  & & \(\Lambda=1\) & 3.08 (0.77) & 3.13 (0.82) & 3.21 (0.90) & **2.31** \\ \cline{1-1}  & & \(\Lambda=10\) & 0.61 (0.28) & 0.98 (0.65) & 0.70 (0.37) & **0.33** \\ \cline{1-1}  & & \(\Lambda=20\) & 0.29 (0.15) & 0.66 (0.52) & 0.36 (0.22) & **0.14** \\ \cline{1-1}  & & \(\Lambda=30\) & 0.20 (0.10) & 0.57 (0.47) & 0.26 (0.16) & **0.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different near-lossless schemes for several different peak absolute errors.