esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Evolution Policy-Based Approach for RIS-Enhanced Communication System
## 4 Joint Active and Passive Beamforming Optimization Based on DEP
Consequently, \(P\) agents characterized by varying parameter vectors can be generated within the new population following the crossover and mutation processes. As the generation number increases to \(G\), the agent exhibiting the highest reward can be decomposed into distinct weights \(w\) and \(b\), which can subsequently be utilized to construct a new DNN. The proposed DEP-based approach is summarized as Algorithm 2. Figure 5: Crossover process. The boxes in gray and cyan represent the genetic information of \(\text{Para}_{1}\) and \(\text{Para}_{2}\), respectively. Figure 6: Mutation process. The boxes in gray, cyan and yellow represent the genetic information of \(\text{Para}_{1}\), \(\text{Para}_{2}\), and mutation respectively. Assume that the generation number of the DEP algorithm is \(G\), and each generation consists of \(P\) individuals. The computational complexity of the DEP mainly comes from the fitness evaluation in each generation, and the complexity of matching, crossover, and mutation can be neglected. From (2), the computational complexity of the fitness evaluation is \(\mathcal{O}(KN)\), and the proposed DEP has an approximate computational complexity of \(\mathcal{O}(GPKN)\). In addition, the computational complexity of the DDPG and SAC algorithms mainly comes from the fitness evaluation and the network updates, and the target network updating and the experiment reply can be neglected. Assume that the batch size and the number of network parameters are \(B\) and \(D\), respectively. With \(T\) time steps, the computational complexity of DDPG and SAC is \(\mathcal{O}(T(BD+KN))\)
## 3 Results
### Clustering of the HVSRs and Their Spatial Distribution in Durres  
In Seismic Zonation maps spatial grouping of certain geophysical or/and geotechnical parameters (e.g., V\({}_{\rm S30}\), f\({}_{\rm o}\), A\({}_{\rm 0}\), etc.) is usually attempted either in urban environment or even in wider regional scale [65; 66].  
To cluster spatially the 76 HVSRs, the entirety of their curve (in frequency and amplitude) was considered. The best HVSR grouping has been achieved in four categories, as shown in Figure 14. For Category 1 (0.20 Hz \(<\) f\({}_{\rm o}\)\(<\) 0.60 Hz) an average f\({}_{\rm o}\)\(\sim\) 0.35 Hz with average A\({}_{\rm o}\)\(\sim\) 3, is observed. For Category 2 (0.61 Hz \(<\) f\({}_{\rm o}\)\(<\) 0.98 Hz) an average f\({}_{\rm o}\)\(\sim\) 0.9 Hz with average A\({}_{\rm o}\)\(\sim\) 3.2 while for Category 3 (0.99 Hz \(<\) f\({}_{\rm o}\)\(<\) 1.70 Hz) f\({}_{\rm o}\)\(\sim\) 1.2 Hz with A\({}_{\rm o}\)\(\sim\) 2.5, and for Category 4 (1.89 Hz \(<\) f\({}_{\rm o}\)\(<\) 20 Hz) f\({}_{\rm o}\)\(\sim\) 3 Hz with A\({}_{\rm o}\)\(\sim\) 2.0 (almost flat), are observed.  
Two out of seventy-six HVSR curves exhibited a double peak (Figure 15 left), while four out of seventy-six were affected by industrial noise (Figure 15 right) and were not included in the clustering procedure.  
Figure 14: Four categories of HVSRs for the city of Durres (average red line \(\pm\) one standard deviation blue lines).  
Figure 15: HVSRs of two sites exhibited double peaks (**left**); HVSRs of four sites affected by industrial noise (**right**).  
Nevertheless, fundamental frequency with depth-to-bedrock distribution shows a satisfactory correlation with large dispersion (Figure 16).  
In Figure 17, all HVSRs clustered in four categories are presented for the city of Durres. It is evident that Category 1 (0.20 Hz \(<\) f\({}_{\rm o}\) \(<\) 0.60 Hz) corresponds to the deepest part of the basin while Category 2 (0.61 Hz \(<\) f\({}_{\rm o}\) \(<\) 0.98 Hz) corresponds to both western and eastern edge of the basin. However, in the eastern part of the basin, a gradual transition from Category 2 to Category 3 (0.99 Hz \(<\) f\({}_{\rm o}\) \(<\) 1.70 Hz) is observed. Category 4 (1.89 Hz \(<\) f\({}_{\rm o}\) \(<\) 20 Hz) is mainly seen in the eastern hilly area. Categories 1 and 2 exhibit an average amplitude slightly higher than that of 3, Category 3 higher than 2 and Category 4 up to that of 2.
## 3 Cause of Network Scan Failures and Delay
#### Scan Response Data Acquisition  
In order to acquire scan response data that can be used to estimate the cause of scan failure, it is important to first perform a network scan and acquire a scan response according to the description presented in Section 3. The process of acquiring the scan response RTT is summarized below:  
1. The list of target terminals to be scanned (i.e., scan list) is acquired from the targeted IoT wireless network.
2. Scans are performed on all the ports of the terminals included in the list.
3. The scan response results (such as success, failed, response latency, and round-trip time (RTT)) are measured.
4. The scan response features (such as response latency, RTT, jitter, maximum RTT, and minimum RTT) are extracted in order to estimate the cause of scan failure/delay.  
In usual cases, the scan response is obtained as multidimensional results (i.e., the scan returns RTT, host type, port status, etc.). However, in our method, additional features, such as minimum RTT, maximum RTT, jitter, etc., are measured from the scan. Thereafter, during the data preparation process, we extract the RTT and other features of each terminal from the multidimensional result of a scan response into a one-dimensional array per feature. Table 1 shows an example of scan response data obtained by performing network scans in a simulation environment using the process described above. Each row in Table 1 corresponds to each terminal scanned per base station/access point. The values of the features in columns 3-6 correspond to the number of ports of each terminal (i.e., one packet per each port) that are scanned.
## 3 Methodology
### Spatiotemporal Fusion with DBNs  
A deep DBN model was constructed by concatenating the outputs of the spatial and temporal CNNs in the fc7 layers [54]. The FER is represented using a deep DBN model based on a combination of discriminative features.  
RBMs are bipartite graphs stacked in DBN models to form multilayered neural networks [55]. Two RBMs comprised two hidden layers and one visible layer. This illustrates the structure of a DBN by showing two RBMs with two hidden layers, and DBNs can be used to learn multilayer generative models using multiple RBMs. Multiple RBMs can be used to learn multilayer generative models. Thus, DBNs can be used to discover distribution properties and learn hierarchical feature representations.  
The DBN fusion network is trained in two steps [56].  
1. As a bottom-up method of unsupervised pretraining, greedy layer-wise training is used. The logarithm of the probability of derivatives is used to update the weights of each RBM model: \[\Delta w=\varepsilon\big{(}<v_{i}h_{j}>_{data}-v_{i}h_{j}>_{model}\big{)}\] (4) The learning rate \(\varepsilon\) is represented by \(\varepsilon\), while the data expectation is represented by \(<>\). A \(v_{i}\) indicates that a node is visible and a \(h_{j}\) indicates that a node is hidden.
2. Network parameters are updated through the supervised fine-tuning stage with backpropagation. Specifically, supervised fine-tuning can be achieved by comparing input data to the reconstructed data using the following loss function. \[L\big{(}x,x^{\prime}\big{)}=\big{|}|x,x^{\prime}\big{|}\big{|}_{2}^{2}\] (5) A reconstruction error \(||~{}||_{2}^{2}\) is the L2-norm reconstruction error, where \(x\) and \(x^{\prime}\) are the input data.
## 2 Physicochemical Properties of SSGP  
As mentioned earlier, because DGP is inherently prone to microbial instability, whether produced by steam distillation or countercurrent leaching, drying is always required if it is not intended for composing [36; 37; 38]. For this reason, the final physicochemical characteristics of SSGP are only marginally influenced by grape variety and winemaking method and are primarily determined by thermal and oxidative effects (Table 2).  
A comparison of GP (Table 1) and SSGP (Table 2) reveals significant physicochemical transformations resulting from processing. Most notably, SSGP exhibits a drastically lower moisture content (3-7%) compared to the high moisture levels of fresh GP (50-72%), indicating extensive drying. This drying and leaching process also leads to the complete removal of soluble sugars from SSGP, which are present at 1.5-6.2% in fresh GP. Phenolic compounds are substantially reduced, decreasing from 5-10% in GP to just 0.5-0.6% in SSGP. Conversely, the removal of moisture and soluble components results in a relative concentration of other constituents: lignin content increases from 16.8-24.2% in GP to a much higher 27-56% in SSGP. Protein levels remain relatively consistent (2.7-3.8% in GP vs. 2.8-3.0% in SSGP), while mineral content in SSGP (1.5-10%) exhibits a broader range and a potentially higher upper limit than in GP (1.8-4.6%). Pectic substances, prominent in GP (\(\sim\)20%), are not reported for SSGP, suggesting their removal or significant alteration.
# Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System
## 4 Experimental Verification
### Comparison of the Ability to Handle Uncertainties  
Next, compare the uncertainties performance of each control mode. The uncertainties of the experimental platform mainly come from the following aspects:  
1. The flexible mirror will be slightly deformed during the movement, resulting in uncertain position output.
2. The friction between shaft systems and the characteristics of motor output brings uncertainties.
3. The uncertainties are caused by environmental noise and mechanical noise.  
Figure 14a and Table 4 show the step response state in the mode of Cp only. In the case of the same control parameters, the shape of the step response curve is quite different, and the indicators are also very different, which shows that the Cp has a poor ability to handle uncertainties.  
Figure 12: Cp and T1 and T2 step response comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TS** & **ESS** & **VAR** & **J(ITAE)** \\ \hline
**CP** & 0.136 & 0.3083 & 1.6174 & 1251.8 \\
**T1** & 0.120 & 0.0031 & 1.3003 & 1200.9 \\
**T2** & 0.069 & 0.0005 & 1.2038 & 979.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cp and T1 and T2 step response comparison.  
Figure 14: Comparison of three experiments with the same parameters in (**a**) Cp mode; (**b**) T1 FLC mode; (**c**) IT2 FLC mode.  
Figure 13: FLC dynamic response.  
Figure 14b and Table 5 show the step response under the T1 FLC mode. Its various indicators are more stable and better than the Cp mode, but the dynamic curve shape is still quite different.  
Figure 14c and Table 6 show the step response in T2 FLC control mode. The dynamic curve shape difference is small, the steady-state error is reduced to 4 decimal places, and the fluctuation range is not large. In addition, the steady-state accuracy is improved, the adjustment time can be maintained at 2 decimal places, and VAR and ITAE values keep within a small fluctuation range, which is more excellent than the other two control modes.  
In summary, IT2 FLC can better handle system uncertainties than Cp and T1 FLC.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 3 Singular Value Decomposition (SVD)
### Sensing  
SVD is constituted from two orthogonal dominant and subdominant subspaces. The only part of \(x\) that matters is the component that lies in the N-dimensional subspace of \(R^{N}\) spanned by the first columns of \(V\). Thus, the addition of any components that lies in the null-space of \(A\) will make no difference.  
In standard compression sensing, to recover the signal, a priori knowledge of the seed that generates \(\Phi\) is required, and the dictionary \(\Psi\)[30], which could be a circular matrix or particular kind of Toeplitz matrix is, in this paper, the seed, and the dictionary comes as a first approximation of the  
Figure 5: The singular value decomposition (SVD). Each singular value in \(S\) has an associated left singular vector in \(U\) and right singular vector in \(V\).  
signal by using a non-symmetric Toeplitz matrix that replaces the conventional random sensing matrix. We will call it the Toeplitz matrix or dictionary.  
The matrix \(Tz\in\mathbb{C}^{m\times n}\) is called the Toeplitz matrix if each diagonal parallel to the principal diagonal is constant. In mathematical terms:  
\[\forall\ a_{i,j}\ \in T\to a_{i,j}\ =a_{i+1,j+1} \tag{6}\]  
\[Tz=\left[\begin{array}{cccc}x_{0}&x_{1}&x_{n}\\ x_{1}&x_{0}&x_{1}\\ x_{2}&x_{1}&x_{0}\\ x_{3}&x_{2}&x_{1}\\ x_{m}&x_{3}&x_{2}\end{array}\right] \tag{7}\]  
Once the Toeplitz dictionary is established, and the SVD computed, the signal can be compressed by using the \(U\) left singular vector (LSCs) of the GNSS signal, which are the eigenvectors of \(XX^{T}\). The compression is done by multiplying the transpose of \(U\) to the observed signal, \(U^{T}x\), where \(T\) stands for transpose of the matrix.
## 3 Results and Discussion  
### Microstructure and Phase Analysis  
Figure 4a,b shows the microstructures of the F and the FH specimens. In the F specimen, the matrix was composed of both coarse and fine grains. After homogenization heat treatment, the grain distribution became relatively uniform, and the average grain size was approximately 5\(\upmu\)m. By contrast, after the as-extruded material was hot-rolled, grain refinement and deformation twinning occurred, as depicted in Figure 4c, and as the annealing time increased, the grains tended to grow.  
Figure 3: In vivo test of ZKX500 Mg bone plate and screws: (**a**) animal on the operation table with red line denoting the incision position, (**b**) insertion of a magnesium screw into the magnesium bone plate, (**c**) complete insertion of the screw, and (**d**) appearance of the animal after suturing of the wound.  
Figure 4: Microstructure of ZKX500 Mg alloy: (**a**) F, (**b**) FH, (**c**) FR, (**d**) FRH05, (**e**) FRH10, (**f**) FRH30, and (**g**) FRH60.  
Figure 5a shows the results of the XRD analysis of the F and FH specimens. The diffraction peaks represent mainly the \(\alpha\)-Mg phase and a few MgZn\({}_{2}\) phases. Homogenization heat treatment caused the second phase to dissolve into the matrix, which reduced the magnitudes of the MgZn\({}_{2}\) phase peaks; moreover, splitting of high-angle peaks can be observed as well. Figure 5b shows the results of XRD analysis of the FR and FRH specimens. Mg alloys have the HCP structure during the rolling process, moving along the basal slip system; as a result, at approximately 30\({}^{\circ}\), the Mg(0002) signal intensified [17]. The experimental results indicated that annealing can reduce not only the residual stress induced by rolling, but also cause grain growth. However, the rolling texture did not change significantly.  
Figure 5: XRD patterns of ZXX500: (**a**) F and FH, and (**b**) FR and RH.
## 3 Results
### Comparative Analysis
As depicted in Figure 10, the algorithm's observed behavior ratified this comparative analysis of quantitative results. On the top row, for the indoor sequence, it can be noticed that the sparse-direct methods outperformed the other evaluated methods starting and ending their trajectory pretty close to the ground truth. The indirect methods behaved completely differently, where the system constantly lost the trajectory, accumulating drift and obtaining the wrong scale measures concatenated erroneously when the system achieved relocalization. The DynaSLAM represents an important contribution to the ORB-SLAM2 system because it estimated the trajectory better than the rest, closing the trajectory close to the ground truth, while the rest of the indirect systems lost their trajectories. On the other hand, the hybrid methods performed considerably worse indoors; so, many of the algorithms' runs did not complete the full frame sequence, and the algorithm typically finished its trajectory pretty far from the end-segment ground truth. In the bottom row of Figure 10, it can be noticed again that the sparse-direct methods outperformed the rest of the evaluated systems, with an appropriate bootstrapping in the start segment and a small amount of accumulated drift in the end segment. In Figure 10e, similar to indoors, the indirect methods suffered from trajectory loss issues despite the fact that the relocalization module typically was able to continue the system execution, accumulating a critical amount of drift during relocalization, making the  
Figure 10: Trajectories in the TUM-Mono dataset for the compared sparse-direct (**a**,**d**), indirect (**b**,**e**), and hybrid methods (**c**,**f**). The top row displays the results for the indoor sequence _seq-02_, and the bottom row displays the results for the outdoor sequence _seq-29_. The solid lines represent the trajectory estimated by each algorithm; the dashed lines represent the aligned ground truth. estimated trajectory end far from the groundtruth end segment. In the hybrid methods, the SVO suffered from similar issues to the indirect methods. However, it can be noticed that the CNN version of the SVO improved its performance outdoors, differently from indoors, which can be explained because the added CNN MonoDepth module was trained in the Cityscapes dataset, which was mainly trained from outdoor sequences.
## 3 Results and Discussion
### Mineral Content  
According to our results, the potassium content was higher in _C. squammulose_, _L. indigo_, and _R. brevipes_ (200 mg 100 g\({}^{-1}\) DW) than in the other four analyzed mushrooms (Figure 5a). _C. nuda_, _C. subclavipes_, _R. delica_, and _A. jacksonii_ presented low levels of this element (<200 mg 100 g\({}^{-1}\) DW) in comparison with those determined in _L. edodes_, _Pleurotus florida_, and _P. djamor_, which are frequently consumed in India [23]. Our results support that edible mushrooms can be a rich source of potassium, as stated in previous investigations [23; 24; 25]. Calcium was another abundant element in the seven studied samples (Figure 5b). However, _L. indigo_ stored more endogenous levels of this mineral than the other mushrooms (>400 mg 100 g\({}^{-1}\) DW). _C. nuda_, _C. subclavipes_, _C. squammulose_, and _A. jacksonii_ stored levels higher than 300 mg 100 g\({}^{-1}\), whereas _R. delica_ and _R. brevipes_ accumulated approximately 200 and 100 mg 100 g\({}^{-1}\) DW calcium, respectively. These levels were higher than those reported in _L. edodes_, _P. florida_, and _P. djamor_[23]. The iron levels were high in all analyzed mushrooms (Figure 5c). _Clitocybe subclavipes_ accumulated more amounts of this micronutrient (>70 mg 100 g\({}^{-1}\) DW) than the other mushrooms. _L. indigo_ stored approximately 50 mg 100 g\({}^{-1}\) iron, whereas _C. nuda_, _R. delica_, _R. brevipes_, _C. squammusa_, and _A. jacksonii_ accumulated less than 40 mg 100 g\({}^{-1}\) DW. These results strongly suggest that these traditional foods may contribute to the recommended daily iron intake (20-30 mg/day) [23]. This finding could be useful in formulating new diets based on these foods to prevent or treat anemia, which shows a high incidence in children from rural areas of Mexico [24]. Nevertheless, the consumption of sources rich in iron should be carefully standardized to avoid possible toxic effects for consumers and extensive studies should be performed to demonstrate their effect to prevent or alternatively treat anemia. The amount of sodium ranged from 5 to 40 mg 100 g\({}^{-1}\) DW, and this range was lower than that observed for _Boletus badius_ and _B. edulis_, which are popularly consumed in Poland [25]. Interestingly, _A. jacksonii_ was the mushroom with the lowest levels of this mineral (Figure 5d). The zinc levels ranged from 70 to 160 mg 100 g\({}^{-1}\) DW (Figure 5e). _C. nuda_ and _A. jacksonii_ were rich sources of this mineral, and their levels were comparable to those found in _B. badius_ and _B. edulis_[25]. The magnesium concentration was noticeably high in _L. indigo_ (300 mg 100 g\({}^{-1}\) DW). The levels of this element were similar in _C. nuda_, _C. subclavipes_, and _R. delica_ (approximately 200 mg 100 g\({}^{-1}\) DW), whereas those found in _R. brevipes_, _C. squammusa_, and _A. jacksonii_ were below 150 mg 100 g\({}^{-1}\) DW (Figure 5f). These levels were threefold higher than those determined in _B. badius_ and _B. edulis_[25].
## 2 Theoretical Basis and Related Work
### Greedy Reconstruction Algorithms
For example, some algorithms do not exert a specific \"selection strategy\" when selecting elements of the correlation vector, which limits accuracy to some extent. Additionally, variable step-sizes adopted above are relatively simple and do not take multiple phases in single iteration into consideration. In addition, these methods do not further filter results for higher accuracy, and the algorithms mentioned above do not employ the preselection step, which may lead to poor reconstruction performance. In contrast with these algorithms, the novel reconstruction algorithm proposed in this paper integrates strategies of regularization and variable adaptive step size without requiring any prior information of the signal sparsity. Furthermore, optimized reconstruction results can be achieved by a filtering step. Therefore, it can perform reconstruction more accurately and efficiently.
## 4 Analysis of Experimental Results and Discussion
### The Effectiveness of RS-DFV  
Based on the rough set and the DFV extraction tree, the RS-DFV extraction tree (RS-DET) of the six REB faults (RF\({}_{1}\), RF\({}_{2}\), RF\({}_{3}\), RF\({}_{4}\), RF\({}_{5}\), RF\({}_{6}\)) was built as illustrated in Figure 14. Through depth-first traverse of this RS-DET, the RS-DFV of the six REB faults was obtained as \((f_{32},f_{6},f_{95},f_{19},f_{2})\). The RS-DFV had the same structure, the same valid items and the same invalid items as the DFV. The Eulerian distances based on the RS-DFV among the six faults were recorded in Table 7, and the spatial distribution map based on RS-DFV is illustrated in Figure 13b. The statistics of abnormal termination in DET and RS-DET are presented in Table 8.  
Figure 14: The RS-DET of the REB faults.  
Figure 13: The spatial distribution map of the six REB faults based on \((f_{2},f_{6},f_{19},f_{32},f_{95})\).  
In Table 7, the average Euler distances among the samples of the same type were much smaller than the corresponding record in Table 5, and the average Euler distances between different fault types were much larger than the corresponding record in the same table. In Table 9, the minimum DA and average DA based on RS-DFV of each fault were much larger than the corresponding record based on \((f_{2},f_{6},f_{19},f_{32},f_{95})\) in Table 6. Compared with Figure 13a, the fault samples of the same type are much more concentrated in Figure 13b, and the different faults were much more scattered. All these revealed that RS-DFV is much more remarkable in fault differentiation than the traditional feature vector.  
In Table 8, there were always a few abnormal interruptions in the DFV extraction based on the DFV extraction tree (DET), but the abnormal interruptions were eliminated in the RS-DFV extraction based on RS-DFV. This indicates that a rough set can effectively overcome the problem of abnormal interruptions in DFV extraction, and RS-DFV is a more efficient fault description method.