esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### Parametric Investigations
#### 3.1.3 Antenna Miniaturization
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Ground** & **f\({}_{\mathbf{r}}\)** & **S\({}_{\mathbf{11}}\)** & **\(-\)10 dB BW** & **Gain (dBi)** & **Gain (dBi)** \\  & **(mm)** & **(GHz)** & **(dB)** & **(GHz)** & **at \({\mathbf{f}}_{\mathbf{r}}\)** & **at 3.5 GHz** \\ \hline Iteration-2 & Full ground & \multirow{2}{*}{9.50} & \multirow{2}{*}{\(-\)21.13} & \multirow{2}{*}{0.43 (9.26–9.69)} & \multirow{2}{*}{6.35} & \multirow{2}{*}{7.48} \\ antenna & (46.15 mm) & & & & & \\ \hline Iteration-2 & 50\% ground & \multirow{2}{*}{\(10.82\)} & \multirow{2}{*}{\(-\)14.68} & \multirow{2}{*}{0.58 (10.54–11.12)} & \multirow{2}{*}{14.24} & \multirow{2}{*}{5.05} \\ antenna & (23.075 mm) & & & & & \\ \hline Iteration-2 & \multirow{2}{*}{\(11.98\)} & \multirow{2}{*}{\(4.88\)} & \multirow{2}{*}{\(-\)32.47} & \multirow{2}{*}{0.25 (4.77–5.02)} & \multirow{2}{*}{8.59} & \multirow{2}{*}{8.46} \\ antenna & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline Iteration-2 & \multirow{2}{*}{\(10.98\)} & \multirow{2}{*}{\(3.26\)} & \multirow{2}{*}{\(-\)26.30} & \multirow{2}{*}{0.96 (2.84–3.80)} & \multirow{2}{*}{12.89} & \multirow{2}{*}{11.18} \\ antenna & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline Iteration-2 & \multirow{2}{*}{\(10.21\)} & \multirow{2}{*}{\(8.18\)} & \multirow{2}{*}{\(-\)39.87} & \multirow{2}{*}{3.68 (5.82–9.47)} & \multirow{2}{*}{6.25} & \multirow{2}{*}{12.22} \\ antenna & & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline Iteration-2 & \multirow{2}{*}{\(10.1\)} & \multirow{2}{*}{\(8.06\)} & \multirow{2}{*}{\(-\)54.64} & \multirow{2}{*}{3.61 (5.82–9.43)} & \multirow{2}{*}{6.29} & \multirow{2}{*}{11.81} \\ antenna & & & & & & & \\  & & & & & & \\  & & & & & & & \\ \hline Iteration-2 & \multirow{2}{*}{\(10.1\)} & \multirow{2}{*}{\(8.18\)} & \multirow{2}{*}{\(-\)20.54} & \multirow{2}{*}{1.27 (2.75–4.02)} & \multirow{2}{*}{10.72} & \multirow{2}{*}{10.71} \\ antenna & & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline Iteration-2 & \multirow{2}{*}{\(10.1\)} & \multirow{2}{*}{\(4.70\)} & \multirow{2}{*}{\(-\)16.50} & \multirow{2}{*}{0.49 (4.36–4.85)} & \multirow{2}{*}{10.29} & \multirow{2}{*}{10.08} \\ antenna + SRR & & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline Iteration-2 & \multirow{2}{*}{\(9.7\)} & \multirow{2}{*}{\(3.26\), \(4.58\)} & \multirow{2}{*}{\(-\)14.34, \(-\)17.66} & \multirow{2}{*}{1.89 (2.89–4.76)} & \multirow{2}{*}{16.66, 9.06} & \multirow{2}{*}{16.86} \\ antenna + SRR & & & & & & \\  & & & & & & \\ \hline Iteration-2 & \multirow{2}{*}{\(6.50\), \(7.52\)} & \multirow{2}{*}{\(-\)27.98, \(-\)29.50} & \multirow{2}{*}{3.55 (5.65–9.20)} & \multirow{2}{*}{12.67, 5.84} & \multirow{2}{*}{15.11} \\ antenna + SRR & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SRR loading with reduced ground.
## 4 The Proposed AAC-IoT System Model
### System Model  
The proposed architecture is designed to assure secure data storage and attribute-based access control using the assistance of a Hyperledger Fabric blockchain. This system model consists of a data owner (DO), a data user (DU),Sity (CA), HLF blockchain and cloud. The proposed AAC-IoT system model is depicted in Figure 2.  
* Data Owner: The data owner first registers with the certificate authority and then uploads meta-data (cipher text of asymmetric key and hash value of identity and number of attributes) into the fabric blockchain followed by an upload of the remaining data into the cloud storage. The data owner has to be authenticated each time to upload the data in the cloud. Let \(N\) be the total number of DOs represented as \(\mathcal{D}_{N}=D_{1},D_{2},\ldots\ldots,D_{N}\). Each owner has an individual identity that is received from a certified authority.
* Data User: The data user also registers with the certificate authority and then submits an access request to the Hyperledger Fabric blockchain. The DUs are \(n\) in total number, and they are denoted as \(\mathcal{U}_{n}=u_{1},u_{2},\ldots\ldots,u_{n}\). The DUs are the mobile devices that submit the query and receive the required response from the cloud. The data user's credentials are validate,d and the access policy is validated.
* Certificate Authority: This entity is the trusted one in the system, and it is responsible for managing certificates of the DOs and the DUs. On the other hand, this entity computes the number of attributes to be considered later for identifying a device account in the blockchain.
* Hyperledger Fabric Blockchain: This type of blockchain is open access, and it is used for authentication of the DO and the DU. In addition, it verifies the access control policy generated based on attribute and allows or denies access.
* Cloud Server: This is a storage system that allows large size data to be stored according to the permission provided. To protect the data, the original data is encrypted and stored in the cloud server.
# Finite Element Analysis of Thermal Stress and Thermal Deformation in Typical Part during SLM
## 5 Experimental Results and Analysis
### Residual Stress  
Figure 10 presents a comparison analysis diagram of the sample with the simulation and experimental data subsequently after forming. In order to unify the comparison range, the simulation data were only focused on 3-8 mm. The samples were tested on the substrates directly (substrate present) as well as subsequently with wire-electrode cutting (substrate removed), and the test data were drawn as a black curve, as presented in Figure 10a. The black solid line shows that the forming stress of the unremoved substrate was identical to the thermal stress trend in the simulation. Simultaneously, the residual stress trend of the stress release after the removal of the substrate was also consistent with the black dotted line trend. It can also be seen in Figure 10b that the values of the two sets of emulation stress deviation before and after the removal of the substrate were very small, and measurement errors of experimental stress might exist. However, the trend of stress release was consistent before and after the removal of the substrate. This proved the simulation algorithm's validity.
## 3 System Design
### Application Server  
The application server provides multiple data processing and analytical functions to support requests from clients in real-time for advanced analyses and visualization of climate data. These functions can be categorized into two modules: data analysis module and data rendering module.
## 2 Materials and Methods
### The Partial Discharge Signal and the Risk Level  
Figure 2 shown the relationship between electrical PD signal and acoustic PD signal which the acoustic PD signal was compared to the electrical PD signal which is based on the real PD signal from the transformer. The electrical signal from the PD is presented in the form of a unipolar pulse, by increasing the time, the unipolar pulse can be shown to the minimum of nanoseconds level. The increase in pulse at the point of origin depends on the type of discharge. The high frequencies are steadily reduced when the signal propagates through the transformer and the pulse shape will be modified because of multiple reflections and the exciting resonant frequency of the transformer components [2; 19]. Furthermore, the severity criteria for partial discharge were called \"Gap time\" and the unit is in milliseconds. The gap time is a space between the end of one burst and the beginning of the next burst. If the gap time has not been generated it was found that a not partial discharge. When the gab time was observed in graph, and it had over 7 ms. which cause the partial discharge was found, but identifier was small partial discharge. The lower the gab time, the greater the partial discharge incidence is, the closer it dangerously becomes to 3 ms., clearly indicating that failure is imminent. Which, burst intervals are critical information in determining the severity of PD [20; 21; 22].  
Table 1 shows the risk level of PD according to the PowerPD reference; the gab time difference in level was presented in the results which it can confirm the determination of the condition level of PD difference. In addition, this table also recommended actions when the PD was generated. The minimum of PD condition level was acceptable when it had not been the gab time and the maximum of PD condition level was s  
Figure 2: The severity criteria of partial discharge; Gap time.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 3 Optimization Problem Analysis
### Algorithm Design
#### 3.3.3 Applying DQN in HWSNs
Despite its theoretical advantages, directly applying the DQN algorithm to optimize energy efficiency in information transmission within HWSNs still presents several challenges. These challenges include ensuring the robustness and generalization capabilities of the algorithm, accurately modeling the dynamic information transmission environment for DQN learning in HWSNs, and addressing the extensive data requirements and prolonged training times needed by the DNNs within the DQN algorithm to achieve a satisfactory performance. Furthermore, the DQN algorithm must be capable of making correct decisions in the rapidly changing information transmission scenarios typical of HWSNs. Therefore, this paper proposes adjustments and optimizations to the DQN algorithm to better accommodate these specific application scenarios. This paper proposes a routing optimization protocol based on deep reinforcement learning, aiming to optimize data transmission decisions in HWSNs. The protocol comprehensively considers the combined effects of the distance between heterogeneous nodes, residual energy states, and the number of relays. Specifically, it first defines the coordinates and energy states of each node and cluster head as the current state. Then, utilizing the DQN algorithm, the protocol learns and selects the next-hop data transmission route based on a reward function until the data are successfully transmitted to the BS.
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 2 Methods
### Statistical Analysis
RF is a well-regarded machine learning tool that has the capacity to identify complex and non-linear relationships in the fitting dataset and offers high classification accuracy [75; 77; 78]. RF models were fitted using the randomForest R package [79], the initial model included all three spectral indices from both platforms and aggregated all data from the entire time-series dataset. The model performance and the relative importance of the predictors were used to provide insight into the sensitivity of the spectral indices to physiological stress. In a subsequent iterative processing step, RF models were fitted using only UAV predictors and only satellite-derived predictors to assess the relative utility of these data. The mean square error calculated from model out-of-bag errors and the coefficient of determination (R\({}^{2}\)) were used to assess model precision. Conditional random forest variable importance calculated using the party R package [80; 81; 82] were used to provide insight into the predictive power of metrics from data from both sources. Conditional importance scores were used as the variable importance scores calculated from RF have been shown to be biased in some situations [81].
## 11 Discussion
[20], even when the features are the MFCC, they deal with only four different faults by using sound signals that have the limitation of being affected by the ambient noise always present in industrial environments. In consequence, a denoising stage is necessary, and the fault signature could be affected by this process. In addition, most applications do not perform formal cross-validation of the DL model but a simple dataset splitting. In centrifugal pumps and reciprocating compressors, vibration signals are affected by non-linear and non-stationary dynamic associated with the complex mechanical system comprising several interacting internal components. In addition, their dynamic operation is also dependent on external driving forces and different working conditions. The combination of these fluctuations in the vibration signals represents the noise interference that hinders the fault signatures and reduces the accuracy of fault classification [65; 66]. The proposed approach attained fault classification with high accuracy in both the centrifugal pump and the reciprocating compressor. However, further research is necessary to quantitatively assess the robustness of the proposed approach, by considering noise or disturbances in the vibration sensors. Figure 15: Cluster structure of the learned features for the reciprocating compressor: (**a**) CNN initial convolution layer, (**b**) CNN softmax layer, (**c**) clusters for the LSTM layer and (**d**) clusters for the BiLSTM layer. The fault classification method we are proposing has several key points where novelty is present:  
* The pseudo-periodic nature of the fault signatures in rotating machines is successfully exploited in our approach to perform a patch-wise fault classification of the features array. This fact suggests that this method could be helpful for online faults detection or even for classifying other types of large images,
* The patch-wise classification that we are proposing can be considered a data size augmentation procedure that is efficient for fault classification with deep learning models, using small balanced data sets of vibration signals,
* The proposed approach can provide high classification accuracy using either CNN, LSTM, or BiLSTM in centrifugal pumps and reciprocating compressors, even for a large set of fault conditions,
* The proposed approach can be easily adapted to other types of rotating machines for classifying faults based on vibration signals (see [PERSON] et al. [33]).
## 4 Discussion
### Limitations and Innovations  
Several methodological limitations warrant careful consideration: (a) The modest sample size (\(n=20\)) taken from a single collegiate athletic program potentially compromises the results' statistical power and limits their generalizability. While this sample size may be adequate for pilot investigations, future research would benefit from larger, more diverse participant pools. (b) The 6-week intervention period, though sufficient for observing initial strength and power adaptations, may be inadequate for capturing comprehensive neuromuscular and biomechanical adaptations. Longer-term studies are needed to fully elucidate the time course of morphological and functional adaptations. (c) Equipment-induced variance in velocity measurements (\(\pm 2.3\%\) according to the manufacturer's specifications) may have introduced a degree of measurement uncertainty that should be considered when interpreting the results.  
Despite these methodological constraints, this investigation makes three notable contributions to the existing literature. First, we conducted the inaugural comparative analysis of the velocity- (VBRT) versus power-based resistance training (PBRT) effects on badminton-specific footwork velocity. Second, this paper establishes practical velocity loss (VL) thresholds (10%) for collegiate athlete programming, offering concrete guidelines for practitioners. Third, it identifies gender-specific adaptation patterns that warrant careful consideration in training program design and implementation.
# Analytical Electromechanical Modeling of Nanoscale Flexoelectric Energy Harvesting
## 3 Closed-Form Expressions of Electromechanical Frequency Response Functions
### Closed-Form Frequency Response Functions  
The motion of the base for the cantilever is typically represented by the translational displacement in the transverse direction with superimposed small rotational displacement. Here, only the translational displacement was considered. Assume the vibration of the base to be a harmonic excitation with a frequency \(\omega\), i.e., \(w_{\rm b}(t)=W_{0}\mathrm{e}^{\mathrm{i}\omega t}\), where \(W_{0}\) is the amplitude of the base vibration and \(\mathrm{j}=\sqrt{-1}\) is the imaginary unit. Then, the modal mechanical force can be written as \(f_{r}(t)=F_{r}\mathrm{e}^{\mathrm{i}\omega t}\), where the amplitude of the modal mechanical force function could be expressed as  
\[F_{r}=W_{0}\omega^{2}\Bigg{[}m\int_{0}^{L}\varnothing_{r}(x_{1})\mathrm{d}x_{1 }+M_{\rm t}\varnothing_{r}(L)\Bigg{]}. \tag{31}\]  
With the small deformation and linear system assumption, the modal mechanical and electrical responses were in steady state and also harmonic with the same frequency as the base excitation. Thus, the modal mechanical response and voltage response can be written as \(\eta_{r}(t)=H_{r}\mathrm{e}^{\mathrm{i}\omega t}\) and \(V(t)=\mathrm{e}^{\mathrm{i}\omega t}\), respectively, in which both \(H_{r}\) and \(v\) are generally complex valued. Then, Equations (28) and (30) lead to  
\[\left\{\begin{array}{l}\left[\left(\omega_{r}^{2}-\omega^{2}\right)+2 \xi_{r}\omega_{r}\mathrm{j}\omega\right]H_{r}-vK_{r}=F_{r},\\ \left(\frac{\kappa_{3}BL}{h}\right)\omega+\frac{1}{R}\right)v+j\omega\sum \limits_{r=1}^{\infty}K_{r}H_{r}=0.\end{array}\right. \tag{32}\]  
Solving Equation (32), \(H_{r}\) and \(v\) are obtained explicitly. The analytical modal expressions of voltage \(V\) and mechanical response \(\eta_{r}(t)\) can be obtained as follows  
\[V(t)=-\frac{\mathrm{j}\omega\sum\limits_{r=1}^{\infty}\frac{K_{r}F_{r}}{\left( \omega_{r}^{2}-\omega^{2}\right)+2\mathrm{j}\omega\xi_{r}\omega_{r}}}{\left( \frac{1}{R}+\frac{\kappa_{3}BL}{h}\mathrm{j}\omega\right)+\sum\limits_{r=1}^ {\infty}\frac{\mathrm{j}\omega K_{r}^{2}}{\left(\omega_{r}^{2}-\omega^{2} \right)+2\mathrm{j}\omega\xi_{r}\omega_{r}}}\mathrm{e}^{\mathrm{i}\omega t}, \tag{33}\]  
\[\eta_{r}(t)=\Bigg{[}\frac{F_{r}}{\left(\omega_{r}^{2}-\omega^{2}\right)+2 \mathrm{j}\omega\xi_{r}\omega_{r}}+V(t)\frac{K_{r}}{\left(\omega_{r}^{2}- \omega^{2}\right)+2\mathrm{j}\omega\xi_{r}\omega_{r}}\Bigg{]}\mathrm{e}^{ \mathrm{i}\omega t}. \tag{34}\]  
We can then substitute \(\eta_{r}(t)\) into Equation (20) to obtain the deflection response \(w(x_{1},t)\).
## 4 Case Study: The Disposal in Finland
Interestingly, with the modified reference point 3 where only eight objectives were considered, mainly solutions with earlier starting time were obtained. In general, the earlier starting time of the disposal improves the objectives (1)-(3) and impairs others compared with the case where the disposal starts later. In general, we notice that the solutions obtained adapt the reference points quite well. In Figure 7, the solutions related to the first group with an early starting time are illustrated. It can be seen that even if all these solutions suggest the early start of the disposal, they still have some differences. One can improve goals (7) and (8) by disposing of spent fuel with a small volume at the beginning. However, this declines goals (1)-(3), (5), (6) and (9) which can be seen from the solution r3q2. It is possible to improve the goals (1)-(3), (5) and (6) by allowing some canister positions to be empty. However, this in its turn declines goals (4) and (7)-(9) which can be seen from the solution r4q4. As the final solution, the decision maker likes to return to the reference point 2 and the solution r2q1 looks like a good compromise when disposal begins early. A similar examination is done for the solutions of the second group with the late starting time. The solutions in terms of the objective function values and the disposal schedules are given in Figure 8. Again, we can observe some differences. The differences depend on the number of years the start of disposal operations is prolonged. It can be seen from Figure 8, that the disposal volume is large in every solution where disposal starts late. On the one hand, one can improve goals (7)-(9) by delaying the start of disposal but on the other hand, this declines goals (2), (3) and (5), as illustrated in the solution r3q8. When the disposal starts late, empty canister positions have only a minor impact on the solution. One can improve goals (2), (3), and (5) by allowing empty canister positions. This yields to the impairing of the goals (4), and (7)-(9) which can be seen from the solution r4q9. Again, the decision maker is willing to return to the reference point 2 and consider the solution r2q9 as a satisfactory solution when the disposal starts late. Additionally, the decision maker selects this solution also for the final solution \(\mathbf{f}^{*}\), since it yields a rather good solution for other objectives than the maximum storage. However, we learned that this is the price of the lower costs and smaller disposal facility area. Moreover, compared with the solution r2q1 also presented from the reference point 2, the later starting does not delay the ending of the disposal. Figure 1.0 The four solutions where disposal starts early.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 3 Overview of Depth Distribution Algorithms  
Depth distribution algorithm is a new feature of linear code first studied by [PERSON] from the perspective of cryptography, and its definition is given as follows:  
**Definition 1**.: _Let \(c=b_{1}\;b_{2}\;\ldots\;b_{n}\) be a code word of block code \(C\) of length \(n\) on \(GF(q)\), defines \(D(c)=(b_{2},-,b_{1},\;\ldots,\;b_{n},-,b_{n-1})\), and calls \(D\) differential operation of code \(c\)[25]._  
**Definition 2**.: _The depth of code word \(c\) is the minimum non-negative integer \(z\) which satisfies \(D^{z}(c)=(0^{n-z})\). If such the \(z\) does not exist, the depth of \(c\) is the codeword length \(n\). Where \(D^{z}(c)\) represents the code words \(c\) to do \(z\) differential element calculation. Mark \(D_{j}\) is the number of code words with depth \(j\), and set \(\left\{\begin{array}{ccc}D_{0}&D_{1}&\ldots&D_{n}\\ \end{array}\right\}\) is called the depth distribution of code \(c\). The subscript set \(\left\{j|1<j<n,D_{j}\
eq 0\right\}\) is called the depth spectrum of the codeword \(c\)[26]._  
Figure 1: General block diagram of the blind recognition.  
**Theorem 1**.: _Let \(C\) be a linear block code of \((n,k)\) on \(GF(q)\). For all non-zero code words in \(C\), there are exactly \(k\) non-zero depth values of \(i_{1},i_{2},\ldots i_{k}\), \(i_{1}<i_{2}<\cdots<i_{k}\), and \(D_{in}=(q-1)q^{m-1}(1\leq m\leq k)\)[27]._  
**Theorem 2**.: _On the finite field \(GF(q)\), there are \(k\) non-zero depth values in the depth spectrum of any \((n,k)\) linear block code \(C\), and the codeword vectors with different depths are linearly independent [28; 29]._  
The overall introduction of the depth distribution algorithm is completed, and the implementation of the specific algorithm principle will be described in the next chapter.