esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## 5 Results
### Simulation Results
#### 5.1.2 Multidimensional Functional Data Simulation
This limitation arises because the introduction of coefficient noise \(\xi\) biases the model toward eliminating signals that are deemed insignificant. Moreover, when comparing the three fixed-\(\xi_{k}\) variants of the fast algorithm, a clear trade-off emerges: a smaller \(\xi_{k}\) reduces the overall error but slows down the optimization due to increased dependency among variables. These results collectively demonstrate the effectiveness of our chosen \(\xi_{k}\) schedule in BSFDAFast, as it balances both efficiency and accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{N_{i}}\) & \(\mathbf{\text{AIC}^{2022}_{\text{PACE}}}\) & \(\mathbf{\text{BIC}^{2022}_{\text{PACE}}}\) & \(\mathbf{\text{fpca}}\) & \(\mathbf{\text{refund.sc}}\) & \(\mathbf{\text{BSFDA}}\) & \(\mathbf{\text{BSFDA}^{\text{Fast}}}\) \\ \hline
5 & 12.373 \(\pm\) 4.026 & 12.377 \(\pm\) 4.031 & \(\mathbf{5.192\pm 6.166}\) & 8.833 \(\pm\) 4.730 & 5.814 \(\pm\) 3.535 & 10.292 \(\pm\) 12.717 \\ \hline
10 & 10.391 \(\pm\) 2.521 & 10.391 \(\pm\) 2.521 & 2.098 \(\pm\) 1.425 & 5.314 \(\pm\) 3.501 & \(\mathbf{2.068\pm 1.427}\) & 2.656 \(\pm\) 1.712 \\ \hline
50 & 9.054 \(\pm\) 1.683 & 9.054 \(\pm\) 1.683 & 1.642 \(\pm\) 1.240 & N/A & \(\mathbf{1.638\pm 1.247}\) & 1.770 \(\pm\) 1.275 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean squared error of covariance \(\text{Error}_{\text{CovFunc}}\) for Scenario 5. Figure 4: Convergence plots for Scenario 5 in Yéhua and the 4D simulation. The upper row displays the covariance error against time, and the lower row illustrates the difference between the estimated and true numbers of components.
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 3 Materials, Methods and Field Tests in a Greenhouse
### Architecture Schema of WSN Communication in Greenhouse  
In Figure 2, we illustrate the high-level architecture and deployment setup in the greenhouse. It comprises of the on-field deployment of the sensors and gateway and processing of the data at the back end. The hardware setup used for the deployment are shown in Figure 7. This hardware setup is mounted inside the airflow boxes and are deployed in the PCH greenhouse. We used the imec's OCTA-Connect board stacked on top of Nucleo board. The OCTA-Connect board provides the provision to stack further the wireless connectivity and sensor module. We have connected the LoRa shield and sensors as depicted in Figure 7. This hardware takes the power source from the battery attached inside the airflow box. The LoRaWAN Kerlink gateway [51] is installed inside the greenhouse with an ethernet and power connection. The technician of the greenhouse can install the sensors and gateway as there is no manual configuration required, but just switching on the OCTA-Connect board. While turned on, all the deployed devices automatically start sending data along with battery status. On the back end, the raw sensor data is parsed, stored in MongoDB database following which it is forwarded to a custom ThingsBoard dashboard.  
The data sequence from reading the sensor values until its visualization is shown in the Figure 8. The OCTA-Connect platform board used in this experiment has several onboard sensors, as well as the ability to stack a variety of shields onto it. The system architecture for the deployment is shown in Figure 2, and overview of the available sensors, both embedded on the OCTA-Connect board and as stackable shields are shown in Table 4. Every setup has a LoRaWAN communication shield attached, of which the configuration is shown in Table 3. The STM32L496 ZG Nucleo 144 board is used to program and debug the OCTA-Connect board. The readings of the sensors are taken every  
Figure 6: The airflow box.  
Figure 7: Deployment setup (**a**) Hardware setup overview. (**b**) Example configuration with CO\({}_{2}\) sensor shield.  
five minutes and then forwarded to a Kerlink LoRa gateway. This gateway has a power and ethernet connection, being placed inside the technician office next to greenhouse chambers. The data from the gateway is forwarded to the Things network. At the back end (local database), we subscribe to the data using the MQTT protocol. This data is then used in two ways; to store in the MongoDB and to show the data on the customized ThingsBoard-based dashboard.
## 3 The Concept of the ASI
### Accurate
#### Intelligent  
This refers to the fact that the operation of ASI depends ultimately on the imbedded algorithms. Each component of the ASI is equipped with appropriate algorithms to carry out the associated functions.  
The objective, therefore, is to design a device that would have the above characteristics. Based on this objective, the design of ASI is presented in terms of its architecture (components) and the associated methodology (functions). As a prelude to this design subject, one could define the concept of ASI in the following statement or summary.  
The basic concept of ASI is to simulate a pre-specified range of system contingencies so as to generate patterns in areas of vulnerability. The ASIs then compare these actual measurements (system parameters at these locations) against known patterns from which the stress levels of disturbances are predicted. These stress levels are defined in terms of performance levels (status of a system) and the resulting levels sent to the System Operator as required.  
Referring to Fig.1, a summary of this concept follows:  
1. Simulate off-line contingencies and generate patterns for the Predictive Module as described in Section 5.
2. Generate on-line system parameters on a continuous basis.
3. Compare the on-line parameters with the many simulated patterns.
4. Predict the corresponding stress levels.
5. Initiate alarms.
6. Provide the System Operator with information as requested.
## 5 Results
### Overall System BER Improvement  
As evident from Figure 4, the use of DDA-FRESH equalizer enhances the BER performance of the NOMCR system as compared to RLS-FRESH scheme. This is attributed to the backpropagation scheme which is involved in updating the weights and biases of the neural network. Moreover, the use of multiple layers of neurons (hidden nodes) inside the auto-encoder leads to enhanced background noise cancellation, which improves the cyclostationarity based correlation maximization operation of the FRESH filter structure. It is observed from Figure 4 that for injection level \(-10\) dB and SNR higher than 5 dB, the BA-DDA-FRESH equalizer out-performs the BA-RLS-FRESH by 5 dB. But for a lower injection level (\(-5\) dB) and SNR higher than 5 dB, the BA-DDA-FRESH outperforms the BA-RLS-FRESH by almost 10 dB. As a matter of fact, the performance of the BA-DDA-FRESH is very close to the optimum RLS-FRESH equalizer for an injection level of \(-5\) dB. This improvement in performance of the BA-DDA FRESH can be attributed to the higher non-linearity cancellation via iterative interference estimation and the subtraction procedure adopted.  
\begin{table}
\begin{tabular}{c c} \hline \hline SU Symbol Level Modulation Type & 16-QAM \\ PU Symbol Level Modulation Type & 4-QAM \\ SU pulse shaping filter & Root Raised Cosine (RRC) \\ PU pulse shaping filter & Rectangular \\ SU Injection level & 5 dB, 10 dB \\ Total frame length of PU-OFDM (without CP) & 32 samples \\ CP length of PU-OFDM & 8 samples \\ Total sub-block length of SU-OFDM (without CP) & 128 samples \\ CP length of SU-OFDM sub-block & 32 samples \\ Total number of samples used & 10000 \\ Number of hidden layer nodes in 1 st layer & 196 \\ Number of hidden layer nodes in 2 nd layer & 20 \\ Sparsity Parameter & 0.05 \\ Weight decay parameter & 0.003 \\ Sparsity Penalty term Weight & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 4: BER performance comparison between RLS based and DDA based Cyclic FRESH Equalizers for injection levels \(-5\) dB and \(-10\) dB.
# Application of Wavelet Transform and Fractal Analysis for
### Wavelet Transform  
The wavelet transform (WT) method used in this paper is a well-known method for decomposing a source signal and its subsequent representation by a linear combination of given base functions, known in the literature as wavelets. In a mathematical sense, wavelets are functions characterised by zero mean value, finite signal power, fast decay, and finite length. Wavelets are well localised in both the time (or space) and the frequency domains, which makes them particularly useful in the analysis of non-periodic and non-stationary signals with omnipresent singular points. Those features are characteristic for biomedical signals.  
One of the key parameters that has a strong influence on the quality and accuracy of the wavelet transform calculation results is the correct selection type of the basis wavelet. In the present work, it was decided to use the Haar wavelet (which is synonymous with the Daubechies wavelet [32]), which is useful for the analysis of discontinuous, non-stationary signals of high variability [33; 34; 35; 36; 37; 38; 39].  
To measure the quality of the assessment, the typical medical parameters were used: sensitivity and specificity [2; 5; 6]. Sensitivity (1) is measured as the percentage of correctly diagnosed sick patients (true positives). This parameter is crucial in medical diagnostics since it gives the best outlook on the used method. It shows the probability of finding a sick patient from a population. Specificity (2), on the other hand, refers to the percentage of rightfully healthy patients out of all the patients diagnosed as healthy. This parameter describes a test's ability to identify healthy patients from a population. This is helpful as such patients do not have to undergo other, often more time-consuming and expensive, tests to rule out a doubtful diagnosis.  
\[sensitivity=\frac{\textit{correctly diagnosed sick patients}}{\textit{all sick patients in the population}}\times 100\% \tag{1}\]  
\[specificity=\frac{\textit{correctly diagnosed healthy patients}}{\textit{all healthy patients in the population}}\times 100\% \tag{2}\]
## 3 Results  
### Study Screening and Selection  
Initially, 8388 records were identified from database searches. After removing duplicates, 6524 records were screened based on titles and abstracts, and 6460 of these records were excluded according to pre-determined exclusion criteria. Finally, 64 records were examined based on the full -texts, and 17 randomised controlled trials (RCTs) [76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92] were included in this systematic review. The study screening and selection process is illustrated in a PRISMA 2020 flow diagram (Figure 1).
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 5 Discussion
### Outlook and Future work  
In future work, we will propose an appropriate metric to judge the influence weight of each neighboring node of the calculating vertex, and more metrics for mesh quality will be discussed. Moreover, we will further expand the application scopes of the adaptive Laplacian mesh smoothing algorithm accelerated on the GPU for more complex and irregular hexahedral mesh models. In fact, hexahedral meshes have higher accuracy and applicability; at present, the generation and optimization technologies of tetrahedral meshes are relatively mature, but because studies on hexahedral mesh less common, we will focus on the optimization of hexahedral meshes in the future.
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
## 5 Results and Analysis
### Encryption Quality Analysis
#### 5.1.3 MSE and PSNR  
The mean squared error (MSE) measures the average of the squared differences between the original and the encrypted image. A lower MSE value indicates that the encrypted image is more similar to the original image, and so a lower MSE value image has a lower level of distortion.  
The peak signal-to-noise ratio (PSNR) is a measure of the quality of the encrypted image compared to the original image. It is calculated as the ratio of the maximum possible power of a signal and the power of the distortion caused by the encryption process. A higher PSNR value indicates that the encrypted image is of higher quality and has less distortion compared to the original image as a result of encryption.  
To further demonstrate the robustness of the proposed method, the previously mentioned tests were conducted by making slight changes to keys \(k_{1}\) and \(k_{2}\). The mean square error (MSE) and peak signal-to-noise ratio (PSNR) were calculated using Equations (11) and (12):  
\[MSE=\frac{1}{mn}\sum_{i=1}^{m}\sum_{j=1}^{n}(p_{i,j}-q_{i,j})^{2} \tag{11}\]  
\[PSNR=20 log_{10}\frac{(MAX_{p})}{\sqrt{MSE}} \tag{12}\]  
where \(m\) and \(n\) are the number of rows and columns in the image, \(p\) is the original image, and \(q\) is the embedded image. The results of these calculations are listed in Table 1, showing two encrypted images \(c_{1}\) and \(c_{1}\) as a function of variations in the keys \(k_{1}\) and \(k_{2}\). The obtained numbers confirm the sensitivity of the scheme to small changes in the key.  
Figure 7: Sensitivity of the encryption algorithm to small change in the key \(K\). (**a**) Original high-resolution image \(3264\times 4841\). (**b**) Encrypted image \(I^{\prime}_{1}\) obtained using \(K_{1}\). (**c**) Encrypted image \(I^{\prime}_{2}\) obtained using \(K_{2}=K_{1}+10^{-8}\) in one ot its control parameters \(c\). (**d**) The absolute pixel-by-pixel difference between \(I^{\prime}_{1}\) and \(I^{\prime}_{2}\), which confirms the production of two distinct encrypted images.  
Since \(k_{1}\) was used for permuting the pixels and \(k_{2}\) was used for changing the gray levels, the results in the table demonstrate that the scheme showed a good level of confusion-diffusion properties.  
Table 1 shows the MSE and PSNR values of two encrypted images that were generated by changing the parameters \(k_{1}\) and \(k_{2}\) in the image encryption scheme. Both MSE values are relatively high, indicating that the ciphered images were significantly different from the original image. Moreover, both PSNR values are relatively low, indicating that the level of distortion in the ciphered images was high compared to the original image.  
This is a desirable property of a good image encryption scheme since it implies that the ciphered image underwent both confusion and diffusion.
## 7 Analysis of Topology Optimization  
Solid-surface support, a volume fraction of 0.7, and an external load of 100 N were applied at the edge point of the cantilever beam, which was positive and upwards along the Y-axis. The structure of the facility is shown in Figure 5. The left surface is fixed, and the bottom is connected to the ground. The output voltage is measured through the top point.  
Figure 6 shows the MSE-SE multi-objective optimization iteration diagram and model shape change diagram. Figure 6a shows the multi-objective value, Figure 6b shows the voltage, Figure 6c shows the SE value, and Figure 6d shows the MSE value. Topological optimization achieved the goal that the objective value could be reduced, and that the value of SE and MSE could be reduced and increased, respectively. Furthermore, the voltage was tested and the result showed that the voltage value was raised successfully. The value of MSE increased by 23.6%, the SE value decreased by 50.9%, and the voltage value increased by 30%. The bottom part of the diagram shows the corresponding structure diagram in each iteration state. It could be seen that the conclusion conformed to the expectation. The weakening connection between the structure and the base would help to improve the flexibility, thus increasing the voltage output. In addition, the x value at all points in the initialization was reduced to 70% of the original value. Therefore, the SE decline does not mean that the optimized structure was more robust (less compliant) than the original fully existing state with an initial x of 1, but rather, that the structure was adjusted to a better state under a fixed volume fraction of 0.7. Additionally, it can synchronously work as a sensor with a higher voltage output.  
Figure 5: 3D model of the piezoelectric harvester.  
Figure 6: Multi-objective optimization iteration diagram and model shape change diagram of mutual strain energy (MSE)–strain energy (SE). (**a**) Multi-objective figure. (**b**) Voltage output (**c**) SE value (**d**) MSE value.  
Figure 7 shows the optimization results with SE as the only objective value. The SE objective value and the voltage value are shown in Figure 7a,b, respectively, and the corresponding model diagram is shown below. Compared to Figure 6, the above conclusions were confirmed. Namely, that the growing connection to the base increases the structural stiffness (and reduces compliance), and that the corresponding design of piezoelectric transducers supports the conclusion that adding a mass block at the end of the cantilever increases the voltage output. The decreasing voltage in Figure 7 confirms the effectiveness and necessity of MSE as the objective. With a higher voltage output, the piezoelectric harvester could work more efficiently.  
Figure 7: Optimization iteration diagram and structure diagram with SE as the objective only. (**a**) Voltage output (**b**) objective value.
## 4 Proposed RL-Based Antenna Beamwidth and Beam Alignment Interval Optimization
### Reinforcement Learning  
A agent in RL observes the state in an environment that satisfies the Markov Decision Process (MDP) and takes an action according to the given policy. In return, the environment gives a reward to the agent and transit to a new state. Through these interactions, we obtain a sequence of states, actions, and rewards as \(s_{0},a_{0},r_{1},s_{1},a_{1},r_{2},\cdots\). The goal of RL is aiming to learn the policy that yields maximal expected cumulative rewards within an episode, as follows [48]:  
\[r_{t:T}=r_{t}+\gamma r_{t+1}+\gamma^{2}r_{t+2}+\dots+\gamma^{T}r_{t+T-1}, \tag{7}\]  
where \(0\leq\gamma\leq 1\) is the discount factor, \(T\) is the number of time steps, and \(r_{t}\) is the reward obtained at time \(t\). If \(\gamma=0\), the agent is concerned with the immediate reward irrespective of the future rewards. The structure of the RL is shown in Figure 3.  
To solve the decision problem of (6), we use a Q-learning model, which is a representative algorithm of the RL. The Q-learning model involves estimating the value of action for the state and managing these values as the Q-value in terms of the expected cumulative reward. The Q-value is stored in the Q-table as follows:  
\[Q_{\pi}(s,a)=\mathbb{E}[r_{t:T}|s_{t}=s,a_{t}=a,\pi]. \tag{8}\]  
The Q-table is iteratively updated via exploration and calculated using the Bellman equation as follows:  
\[Q_{\pi}(s_{t},a_{t})\leftarrow(1-\alpha)Q_{\pi}(s_{t},a_{t})+\alpha\Big{[}r_{t }+\gamma\max_{a}Q_{\pi}(s_{t+1},a)\Big{]}, \tag{9}\]  
where \(\alpha\in(0,1]\) denotes the learning rate. The agent learns the policy \(\pi^{*}\), when the Bellman optimality equation is satisfied as follows:  
\[Q_{\pi^{*}}(s_{t},a_{t})=\max_{a_{t+1}}\big{(}\mathbb{E}[r_{t+1}+\gamma Q_{\pi ^{*}}(s_{t+1},a_{t+1})]\big{)}. \tag{10}\]
## 6 Results and Discussion
### Simulation Runs and Sensitivity Analysis
#### 6.2.2 Collaborative Network Scenario Run  
This experiment starts with the configuration of the initial values of the parameters of the collaborative network _SimulCN_. Figure 22, illustrates the scenarios S2.1, S2.2 and S2.3 that were simulated for the SimulCN agent.  
Initially, with the simulation run of the initial conditions (S2.1), it can be seen that both valence and arousal took a period of time before reaching the _contentment_ ANE state (as expected) around t = 35. This unstable period has to do with the initial dynamics of the involved members. When Scenario S2.2 is put in practice, i.e., when the only VO under creation fails and the levels of trust and values alignment of the VO decrease substantially, both valence and arousal decrease and the ANE remains in the _contentment_ state, although with a reduced value of valence (it can suddenly change to depression, which is what was expected). After a period of days, changes in member's IME affects the dynamics of the ANE, as simulated with the S2.3. What happens is a shift from contentment to depression states in two members, which is translated into a negative reaction of both valence and arousal, conducting the ANE state in SimulCN to _depression_.  
As a final remark on this analysis and comparing with what was expected from the experiments design, it can be said that the resulting behavior of both the IMEA and ANEA SD models are adequate and positively valid. Nevertheless, there are some improvements that are needed for future development in order to transform them into more accurate models. Some examples are: (_i_) refinements of the IMEA and ANEA SD models, in order to have smoother transitions whenever the events occur; (_ii_) the creation of a new dimension of CNE (collaborative network emotion) that represents intensity of emotion (e.g., strong, moderate and weak). This dimension, in conjunction with the other two (arousal and valence), could give more information about the emotion that is being felt.
## 3 Experimental Results and Analysis
### Failure Process and Morphology
By analysing Figure 3, evident ductile failure characteristics can be observed in the steel fibre-reinforced steel slag micronized UHPC specimens. The surface of the specimens shows numerous intersecting wide and deep cracks. However, the micro-reinforcement effect of the steel fibres on the cracks, combined with the anchoring effect created by the steel fibres between the coarse aggregate and the matrix, contributes to the overall improved performance of the concrete system. Despite specimen failure, the concrete structure remains relatively intact, with only some surface debris spalling. As the volume fraction of steel fibres increases, the width of cracks on the specimen surface also increases. The wide cross-section of the cracks reveals a significant presence of steel fibres exhibiting bending deformation. This is attributed to the strengthening bridging effect of the steel fibres on both sides of the developing microcracks in the specimen. The increased content of steel fibres effectively impedes the initiation and propagation of primary cracks in the specimen. Consequently, as the volume fraction of steel fibres rises, the width of cracks during specimen failure widens, indicating an enhanced toughening effect. Furthermore, by combining the information from Table 2 and Figure 3, it can be inferred that the content of coarse aggregate embedded in the UHPC specimens also influ  
Figure 3: Steel slag micronized UHPC prismatic experimental group. ences crack development. As the content of coarse aggregate in the specimens increases, the quantity of macroscopic cracks decreases, and the specimens demonstrate radial expansion failure with severe and uneven deformation. This is attributed to the impact of increased coarse aggregate on the distribution of steel fibres within the specimen. The higher agglomeration of steel fibres caused by the increased coarse aggregate disrupts the even distribution of stress, resulting in internal cracks predominantly forming along the weakest points subjected to stress.