esa-sceva/satcom-chunk-collection huggingface dataset:
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 6 Open Research IIoT Application Domains, Fog Computing as an Enabler
### Smart-Grids (SGs)  
Due to an increase in the number of devices required by a user at home, residential usage of electricity has been increasing with time. The proposed modern solutions, such as SGs, greatly improve the reliability, efficiency, and sustainability of the system in an automated way. Using communication technologies, SGs works on gathered information about consumer's and supplier's behavior. As SGs are distributed systems implemented on a very large scale, fog computing is ideal to deal with such a scenario. For this purpose, [PERSON] et al. [135] have discussed how IoTs and fog computing can be integrated into the power systems without compromising user privacy. The proposed architecture works on demand-response service to ensure privacy by using standard protocols and open-source resources. For devising a power plan for all the devices, a load scheduler is placed in the system while data management performs all the necessary actions that are required to process the data. The software architecture of the proposed solution is based on Lambda architecture which embodies edge computing. To meet the demands of real time processing, collecting information, computing and storing the data generated by multiple smart meters, the aforementioned researchers have proposed a fog-based solution. Fog networking acts as a bridge between the cloud and the SG. With fog networking, geographically distributed smart meters can be employed. Latency is reduced while improving location awareness and privacy for SG. Furthermore, [PERSON] et al. [136] have worked on how to maintain the QoS by minimizing the expense of electricity using coordination between data centers.
# Terpenes as Potential Anti-Alzheimer's Disease Agents
## 2 Activities of Terpenes on the Mechanisms Associated with AD
### Inhibition of Acetylcholinesterase (AChE)
#### 2.4.3. Diterpenes  
Several diterpenes isolated from plants have presented high inhibitory activity against AChE.  
From _Caryopteris mangolica_ Bunge, distributed in Mongolia and Inner Mongolia in China, 12-O-demethylcryptojaponol (**135**) and 6\(\alpha\)-hydroxydemethylcryptojaponol (**136**) inhibited human erythrocyte AChE. The former compound (**135**) inhibits AChE with an IC\({}_{50}\) value of 50.8 uM, whereas the latter compound (**136**) is a more active inhibitor of AChE (IC\({}_{50}=19.2\) uM), suggesting that the presence of the hydroxyl group at the C6 position is important for the inhibition of this cholinesterase [114; 128]. Lycocasuarinone A (**137**), another abietane-type dipterpene, isolated from _L. casuarinoides_, commonly known as Shu Jin Cao in Chinese, existing in South China, inhibits AChE with an IC\({}_{50}\) value of 26.8 uM [127].  
Another class of abietane-type dipterpene is constituted by the tanshinones, which can be isolated from the roots of _Salvia_ and _Perovskia_ species. These compounds have high permeability to cross the BBB. From _Perovskia atribicifolia_ Benth and _Salvia glutinosa_ L., 15,16-dihydrotanshinone (**138**) was a very potent inhibitor of AChE with an inhibition of 65.17% at a concentration of 35.8 uM. Militrone (**139**), another tanshinone extracted from the same plants, is inactive as inhibitor of AChE. Cryptotanshinone (**140**), 1\(\beta\)-hidroxicryptotanshinone (**141**), and 1,2-didehydromilitrone (**142**) were also inactive as inhibitors of AChE [129]. These results suggest that the presence of the two aromatic rings and the presence of the furan ring from C15, making 15,16-dihydrotanshinone (**138**) very planar, is responsible for its AChE inhibition.  
Pentylcurcumene (**143**), the (6',10'-dimethylundec-5'-en-2'\(\alpha\)-yl)-4-methylbenzene, exists in the aerial parts of the leaves of _Geophila repens_ (L.) [PERSON] of India. Pentylcurcumene (**143**) is a weak inhibitor of AChE exhibiting an IC\({}_{50}\) value of 268.6 uM [130].
## 3 Experimental Result Analysis
### Energy Characteristics
#### 3.3.3 Relationship Between Energy and Failure Mechanism  
Figure 5 shows the relationship between the energy and the destruction mechanism. Because sandstone is a typical heterogeneous multiphase geological rock, its mechanical response is controlled by both mineral components and energy transfer paths. [PERSON] et al. [27] found that sandstone is mainly composed of minerals such as quartz crystals and clay substances. Among them, quartz crystals are connected through clay substances. The energy distribution characteristics of this dual-phase structure show significant differences under the effect of the strain rate, resulting in changes in the failure mode. At a low strain rate, the mechanical properties of clay substances dominate the initial damage evolution process. The longer stress action time provides sufficient conditions for the internal stress redistribution of the clay material, promoting the priority initiation of secondary cracks at the weak interface. With the increase in the loading stress, secondary cracks extend slowly along the clay-quartz interface by selectively choosing the dominant propagation path. During this process, the elastic strain energy is mainly released slowly in the form of frictional heat, eventually forming a through main crack extending along the boundary of the quartz crystal, and the rock sample undergoes intergranular failure, as shown in Figure 5a,b. At a high strain rate, the energy transfer mechanism undergoes a transformation. The high strain rate significantly inhibits the stress relaxation ability of the clay material, forcing the energy distribution to shift to the quartz crystal. At this point, the clay material is unable to regulate the local stress concentration, resulting in the secondary cracks being forced to enter a non-equilibrium propagation state. The tip of the secondary crack rapidly expands under the drive of high elastic strain energy. This energy pulse prompts the secondary crack to break through the energy failure threshold of the mineral interface and directly penetrate the internal lattice structure of the quartz crystal, thereby causing a transgranular failure of the rock sample, as shown in Figure 5c,d.  
Figure 4: Relationship between strain energy and loading rate.
# Generating Super Spatial Resolution Products from Sentinel-2 Satellite Images +
## 3 Result  
To compare our proposed method and the ESRGAN network, the three metrics PSNR, SSIM, and SAM were considered, and the outcomes are shown in Table 1.  
According to Table 1, the provided method could achieve incredible results for SAM and SSIM metrics, with 0.10 and 0.92 radians, respectively, which was better than for the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Metric** & **ESRGAN** & **Proposed Method** \\ \hline PSNR (dB) & \(\uparrow\begin{subarray}{c}\infty\\ -1\end{subarray}\) & \(37.34\pm 3.95\) & \(37.23\pm 4.23\) \\ SSIM & \(\uparrow\begin{subarray}{c}\uparrow\begin{subarray}{c}1\\ -1\end{subarray}\) \\ SAM (radian) \\ \(\downarrow\begin{subarray}{c}0\end{subarray}\) \\ \end{tabular} & \(0.90\pm 0.05\) & \(0.92\pm 0.05\) \\ \(\downarrow\begin{subarray}{c}\infty\\ 0\end{subarray}\) & \(0.12\pm 0.06\) & \(0.10\pm 0.06\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of quantitative evaluation metrics on the test dataset between the ESRGAN network and the proposed method. The entered values in the table represent the calculated mean and standard deviation (\(\mu\pm\sigma\)).  
Figure 2: The order of computed features and their utilized elements.  
ESRGAN network, with 0.12 radian in SSIM and 0.90 in SAM. By contrast, the PSNR metric ESRGAN gained higher values, with 37.34 dB, and the approach provided shows 37.23 dB.  
On the other hand, to compare the visualized images, the real outcome, the approach provided, and the ESRGAN network, two samples are selected from the test set. Figure 3a-c corresponds to an area with dense vegetation and Figure 3d-f is selected from a desert area with scattered bushes. By comparing the outputs obtained with WorldView images, it can be seen that our proposed model performs better than the ESRGAN in the vegetated area, but has generated extra noise in the desert area. This issue is most likely due to the lack of training data from desert areas. Moreover, to survey and evaluate the spectral images, the RGB bands' histograms were considered, as shown in Figure 4. By comparing the histograms, we reach similar results.  
Figure 3: Visual comparison (RGB) of Worldview images (**a,d**), images super-resolved by the proposed model (**b,e**), and images super-resolved by ESRGAN (**c,f**); the values of PSNR, SSIM and SAM are provided, respectively: (**b**) 38.07 dB/0.93/0.16 rad., (**c**) 37.89 dB/0.93/0.16 rad., (**e**) 34.80 dB/0.84/0.12 rad., (**f**) 37.91 dB/0.91/0.09 rad.
## 3 Experiments
### Evaluation Parameters and Indicators  
This study assessed the performance of the manhole cover localization network utilizing metrics such as precision, recall, and mean average precision (mAP). The image super-resolution reconstruction was evaluated using peak signal-to-noise ratio (PSNR) and a mean squared error (MSE) as the benchmark indices. Additionally, the text classification network was gauged through parameters including precision, recall, and F1_Score. Detailed computational methods for these indicators are delineated below.  
\[Precision=\frac{TP}{TP\;+\;FP} \tag{1}\]  
\[Recall=\frac{TP}{(TP\;+\;FN)} \tag{2}\]  
\[mAP=\frac{\sum_{i=1}^{n-1}AP_{i}}{k} \tag{3}\]  
\[top-1\;accuracy=\frac{TP+TN}{TP\;+\;FP\;+\;FN\;+\;TN} \tag{4}\]  
\[F1\_Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{5}\]  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\left(\sigma_{x}^{2 }+\sigma_{y}^{2}+c_{2}\right)\right.} \tag{6}\]  
\[PSNR=20\times log_{10}\frac{MAX_{I}}{\sqrt{MSE}} \tag{7}\]  
In this study, as shown in Figure 14. TP (true positive) quantifies the number of instances where the model accurately identifies actual positive cases as such. Conversely, FP (false positive) counts the instances where the model erroneously labels actual negative cases as positive. FN (false negative) tabulates the number of cases in which actual positive instances are misclassified as negative, while TN (true negative) records the correct classification of actual negative cases. Here, \(x\) denotes the original image, and \(y\) signifies the reconstructed image. \(\mu_{x}\) and \(\mu_{y}\) are the mean values of the respective images, while \(\sigma_{x}\) and \(\sigma_{y}\) represent their variances. \(\sigma_{xy}\) is the covariance between \(x\) and \(y\). The constants \(c_{1}\) and \(c_{2}\) are utilized to maintain computational stability. Precision is the metric that quantifies the ratio of true positive predictions out of all positive predictions made by the model. Recall assesses the fraction of relevant instances that the model correctly identifies out of the total number of actual positives, reflecting the model's sensitivity. The mean average precision (mAP) is an aggregate measure that computes the average of the individual mean average precisions across multiple categories, with a value range from 0 to 1. A higher mAP value indicates superior model precision across various classes. Top-1 accuracy is the metric that evaluates the model's overall predictive accuracy by measuring the ratio of the number of correctly predicted samples to the total number of samples. F1 score is an evaluation index that considers both precision and recall. The structural similarity index (SSIM) serves as an indicator for assessing the perceptual similarity between two images.
# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 3 Error Analysis Model and Influencing Factors of Positioning Algorithm
### Simulation Analysis Method and Evaluation Index of Positioning Algorithm
#### 3.1.2 Monte Carlo Method  
With the development of computer technology, the Monte Carlo method is gradually used in error analysis, and its accuracy and calculation speed are both better than that of the total differential method. It is also known as the statistical simulation method and is an approximate calculation method based on the theory of probability and statistics, which uses a computer to generate pseudo-random data that meets the requirements to replacethe actual data that is difficult to obtain. The original Monte Carlo idea came from a large number of practical experiments. With the improvement of computer calculation power, scholars now increasingly realize the random process required by the algorithm can be satisfied through the computer simulation environment.  
For the simulation requirements of the target positioning algorithm, the Monte Carlo method can unify various random errors into one model without differential approximation calculation and error synthesis. The random error generated in the process of target positioning usually obeys the normal distribution, which can be used to generate corresponding pseudo-random number sequences by computer for simulation analysis. The general form of the error analysis model of the positioning algorithm established by the Monte Carlo method is shown in Equation (33) [85,86].  
\[\Delta y=f(x+\Delta x_{1},x_{2}+\Delta x_{2},\ldots,x_{n}+\Delta x_{n})-f(x_{1 },x_{2},\ldots,x_{n}) \tag{33}\]  
In the equation, \(\Delta y\) is the error of the function value \(y\), which represents the distance between the result calculated by the target positioning algorithm and the actual position of the target. \(x_{1},x_{2},\ldots,x_{n}\) is the measured value of each angle or distance parameter, \(\Delta x_{n}\) is the random error conforming to the normal distribution, indicating various errors that tend to occur when the aerial camera actually works (aerial carrier position error, frame angle error, attitude angle error). The result of Equation (33) is equivalent to the positioning distance error calculated by Equation (34), which means the distance between two points in space. In Equation (35), \(T_{E}^{R}=\begin{bmatrix}x_{r}&y_{r}&z_{r}\end{bmatrix}\) is the reference coordinate value of the target in the earth coordinate system, indicating the real position of the target, and \(T_{E}^{S}=\begin{bmatrix}x_{s}&y_{s}&z_{s}\end{bmatrix}\) is calculated by the positioning algorithm in the simulation environment Targeting results.  
\[d=\sqrt{\left(x_{r}-x_{s}\right)^{2}+\left(y_{r}-y_{s}\right)^{2}+\left(z_{r}- z_{s}\right)^{2}} \tag{34}\]
# Development of a Clock Generation and Time Distribution System for Hyper-Kamiokande +
## 3 Results
### Time Distribution  
The time distribution system will be built in two stages, based on the same technology. First, a 96-port clock distributor will pass on the signal to the second stage, where about 70 modules will then distribute it to the front-end electronics. A total of 24 photomultipliers are connected to each front-end electronics. The distributor prototype was produced and the first tests show that the jitter level at second stage is of 40.6 ps, which can be reduced to 2.4 ps by filtering the signal with a Phase-Locked Loop. It is in good agreement with what is needed for the experiment.
# Mesh Stiffness and Dynamic Modeling and Analysis of Modified Straight Bevel Gears
## 4 Dynamic Model of Straight Bevel Gears
### Element Model of the Shaft Element  
As shown in Figure 15, for continuous rotor systems with variable cross-sections, it is common in engineering to discretize the physical prototype and then approximate the solution by rigorously handling the discrete model mathematically. In this section, we will use a discrete Timoshenko beam-axis finite element formulation for dynamic modeling of the axial elements. The entire continuous shaft is divided into N nodes and N-1 elements.  
The elastic support shaft is divided into several shaft segments, and each shaft segment is modeled using two-noded Timoshenko beam elements. By using the Lagrangian equations, the motion equations for the undamped system can be obtained as follows [40]:  
\[\mathbf{M}^{e}\ddot{\mathbf{q}}^{e}+\mathbf{\Omega}\mathbf{G}^{e}\dot{\mathbf{ q}}^{e}+\mathbf{K}^{e}\mathbf{q}^{e}=\mathbf{F}^{e} \tag{43}\]  
where \(\mathbf{q}^{e}\), \(\mathbf{M}^{e}\), \(\mathbf{G}^{e}\), and \(\mathbf{K}^{e}\) represent the displacement vector, mass matrix, gyroscopic matrix, and stiffness matrix of the Timoshenko beam element, respectively [2].
## 3 FGRL for Mapless Navigation
### Federated Growing Reinforcement Learning (FGRL)
To realize the sensor-level mapless navigation problem, the proposed FGRL approach combines TD3 with our model aggregation strategy, and Algorithm 1 details the computing pseudo-code. ```
1 Initialize \(2K\) critic networks \(Q_{\theta_{11,\cdots,1k^{\prime}}}\), \(Q_{\theta_{21,\cdots,2K}}\) and \(K\) actor networks \(\pi_{\phi_{1,\cdots,k}}\), initialize \(2K\) target critic networks \(Q_{\theta^{\prime}_{11,\cdots,1k^{\prime}}}\), \(Q_{\theta^{\prime}_{21,\cdots,2K}}\) and \(K\) target actor networks \(\pi_{\phi^{\prime}_{1,\cdots,K}}\), and initialize replay buffer \(B_{1,\cdots,K}\). 2 for each global aggregation round \(m=1,2,\cdots,M\)do
3 foreach agent \(k\) in paralleldo
4 for\(t=1,2,\cdots\)do
5 Select action with noise \(a_{k}\sim\pi_{\phi_{k}}(s)+\epsilon,\epsilon\sim N(0,\sigma)\) and store transition tuple \((s_{k},a_{k},r_{k},s^{\prime}_{k})\) in \(B_{k}\)
6 Sample mini-batch of N transition \((s_{k},a_{k},r_{k},s^{\prime}_{k})\) from \(B_{k}\)
7\(\tilde{a_{k}}\leftarrow\pi_{\phi^{\prime}_{k}}(s^{\prime})+\epsilon,\epsilon \sim clip(N(0,\sigma),-c,c)\)
8\(y_{k}\gets r_{k}+\gamma min_{i=1,2}Q_{\theta^{\prime}_{k}}(s^{\prime}_{k}, \tilde{a_{k}})\)
9 Update critics \(\theta_{ik}\gets argmin\theta_{ik}N^{-1}\sum(y_{k}-Q_{\theta_{ik}}(s_{k}, a_{k}))^{2}\)
10 if\(t\) mod \(d\)then
11 Update \(\phi_{k}\) by the deterministic policy gradient: \(\
abla_{\phi_{k}}J(\phi_{k})=N^{-1}\sum\
abla_{a}Q_{\theta_{ik}}(s_{k},a_{k}) |_{a=\pi_{\phi_{k}}(s)}\
abla_{\phi_{k}}\tau_{\phi_{k}}(s_{k})\)
12 Update target networks \(\theta^{\prime}_{ik}\leftarrow\tau\theta_{ik}+(1-\tau)\theta^{\prime}_{ik^{ \prime}}\)
13\(\phi^{\prime}_{k}\leftarrow\tau\phi_{k}+(1-\tau)\phi^{\prime}_{k}\)
14 for each agent \(k\) in paralleldo
15 if\(m=1\)then
16 After \(E\) local episodes calculate \(\zeta^{k}_{m}\), \(\varphi^{k}_{m}\), \(\eta^{k}_{m}=1\)
17 else
18 After \(E\) local episodes calculate \(\zeta^{k}_{m}\), \(\varphi^{k}_{m}\)
19 Update growth value \(g^{k}_{m}=\psi\frac{\varphi^{k}_{m}-\varphi^{k}_{m-1}}{\varphi^{k}_{m}}+(1- \psi)\frac{\zeta^{k}_{m-1}-\zeta^{k}_{m}}{\zeta^{k}_{m-1}}\)
20\(\eta^{k}_{m}=\frac{1}{1+e^{-\zeta^{k}_{m}}}\)
21 Global model aggregation \(w_{m}^{\text{global}}=\sum_{k=1}^{K^{\prime}}\frac{\eta^{k}_{m}}{\sum_{k=1}^{K^ {\prime}}\eta^{k}_{m}}w_{m}^{k}\)
22 Local model soft update \(w_{m+1}^{k}=\lambda w_{m}^{k}+(1-\lambda)w_{m}^{\text{global}}\)
```  
**Algorithm 1**Federated growing reinforcement learning algorithm. (\(K\) agents are indexed by \(k\), \(M\) global aggregation rounds are indexed by \(m\), \(E\) is the number of local episodes.)
## 1 Introduction  
Most of the antenna arrays are designed for wide-angle (full field-of-view (FFoV)) scanning. However, there are antenna systems that operate fulfilling certain restrictions in limited scan coverage. Radiating equipment with limited-field-of-view (LFoV) antennas is usually incorporated in space-borne systems for satellite communications [1, 2, 3].  
Conventional design of these antennas follows two steps. In the first step a judicious choice of the size of the radiating aperture and the accompanying continuous excitation is made. The choice is the one that exhibits far-field indexes that are closer to the design requirements. Tabulated data such as those presented, for example, in Table 1.2 of [4], can be very helpful during this step. In the second step, the chosen continuous excitation is sampled to produce a space tapering that achieves approximately the same far-field performance. The sampling can follow deterministic or even stochastic routes [4, 5, 6, 7, 8, 9, 10, 11].  
Admittedly, those methods give adequate practical solutions but somehow are rather limited in number, and also lack the degree of control in the final result (the far-field pattern and its indices) when compared to the ones developed for the full field of view.  
The key point of our work is the use of the scaling transformation that makes it possible to map FFoV designs to LFoV ones. Thus, the current study, apart from introducing design procedures to cope with arrays with restriction in the scan coverage, focuses on transforming solutions by using a scaling transformation that produces LFoV systems after borrowing developed designs from the FFoV set. It must be noted that we can use any of the well-known array synthesis methods, like Dolph-Chebyshev [12], Villeneuve [13], Orchard [14], and orthogonal [15], while design methods which are based on sampling of planar aperture distributions, like parabolic, truncated Gaussian [16], [PERSON] [17; 18], [PERSON] [19] and [PERSON] [20], are also applicable.  
The paper is structured as follows. In Section 2, the scaling transformation is analyzed and the employed design procedure is presented. Detailed representative design examples, which verify the potential of the method, are given in Section 3. The overall evaluation of the method is discussed in the conclusions.
# Uniaxial Testing of Soil-Cement Composites to Obtain Correlations to Be Used in Numerical Modeling
## 4 Laboratory Test Results and Discussion of the Obtained Results
### Test Results of Cylindrical Samples from the Test Site  
The vast majority of results presented below refer to the cylindrical samples achieved from \"pipe in pipe\" probing, and they correspond to a curing time equal to 28 days. Some samples were extracted by \"core drilling\" and were also tested after 28 days. All the samples are listed in Table 5, which juxtaposes the averaged values of all the cylindrical samples gathered (or cut) from \"in situ\" columns.  
The relationship between elastic modulus and compressive strength was established consequently for all the results given in Table 6 and in Formula (5). The authors only decided to exclude two evident outliers: samples named 9 and 12, because the achieved values seemed to be completely unrealistic for DSM composites in cohesive (clayey) soil.  
\[\text{E}=100\times\text{R}_{\text{c}}-17.9\text{ [MPa]}, \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **the Sample** \\ **g/cm\({}^{3}\)** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **kPa** \\ \end{tabular} & \begin{tabular}{c} **Compressive Strength** \\ **\%** \\ \end{tabular} & \
\begin{tabular}{c} **Critical Deformation** \\ **MPa** \\ \end{tabular} &
\begin{tabular}{c} **Elastic Modulus** \\ **MPa** \\ \end{tabular} \\ \hline
1 & 1.547 & 491.32 & 2.70\% & 18.571 \\
2 & 1.444 & 796.96 & 2.80\% & 54.026 \\
3 & 1.658 & 565.76 & 3.20\% & 61.36 \\
4 & 1.628 & 226.44 & 3.00\% & 9.193 \\
5 & 1.407 & 941.14 & 4.20\% & 74.91 \\
6 & 1.447 & 949.72 & 2.70\% & 69.351 \\
7 & 1.508 & 730.91 & 3.70\% & 29.729 \\
8 & 1.631 & 359.22 & 3.10\% & 16.204 \\
9 & 1+631 & 3246.1 & 2+40\% & 4050.901 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Averaged values of all tests performed on real-world samples.  
Figure 11: Compressive strength \(\text{R}_{\text{c}}\) increasing in time domain with large variety.  
Figure 12: Elastic modulus E increasing in time domain with large variety.  
It is important to understand that the correlation of elastic modulus and compressive strength given in Formula (5) is in accordance with the former authors' experiences [29, 30] and also with other researchers' findings, e.g.,  
* E \(\approx\) 143 \(\times\) R\({}_{\rm c}\) [MPa] in reference [15].
* E \(\approx\) 120 \(\times\) R\({}_{\rm c}\) [MPa] in reference [17].