esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle  
As shown in Figure 1, when a target object is hidden behind a scattering medium, an active light is used to illuminate the object. In such a case, the incident plane wave has gone through a round-trip distortion, and the optical field reflected by the object can be expressed as  
\[R(\xi,\eta)=S(\xi,\eta)*O(\xi,\eta), \tag{1}\]  
where the \"* operation indicates that the corresponding \((\xi,\eta)\) elements are multiplied. \(O(\xi,\eta)\) is the complex amplitude of the target object, and \(S(\xi,\eta)\) is the complex field distribution of the illumination wave which is seriously distorted due to the on-the-way-in process via the scattering medium. Based on these definitions, the final output optical field can be expressed as  
\[E(x,y)=\sum_{\xi,\eta}K_{R\to E}(x,y;\xi,\eta)*R(\xi,\eta). \tag{2}\]  
The \(K_{R\to E}(x,y;\xi,\eta)\) is a traditional one-way TM of scattering media, which has given the relationship between the reflected optical field \(R(\xi,\eta)\) and the output optical field \(E(x,y)\)[15].  
As long as \(K_{R\to E}(x,y;\xi,\eta)\) is measured in advance, the reflected optical field \(R(\xi,\eta)\) can be recovered from \(E(x,y)\) directly [16]. Nevertheless, the object \(O(\xi,\eta)\) cannot be recovered yet since \(S(\xi,\eta)\) is still unknown. To overcome this problem, specially, we construct a new TM  
\[T_{O\to E}(x,y;\xi,\eta)=K_{R\to E}(x,y;\xi,\eta)*S(\xi,\eta) \tag{3}\]  
as the round-trip TM of the scattering medium. It can be seen from Equation (3) that, different from the traditional one-way TM \(K_{R\to E}(x,y;\xi,\eta)\), this new TM \(T_{O\to E}(x,y;\xi,\eta)\) has additionally recorded the complex field distribution \(S(\xi,\eta)\) of the distorted illumination wave. In this way, Equation (2) can also be expressed as  
\[E(x,y)=\sum_{\xi,\eta}T_{O\to E}(x,y;\xi,\eta)*O(\xi,\eta). \tag{4}\]  
Now, once the \(T_{O\to E}(x,y;\xi,\eta)\) is measured, the relationship between \(O(\xi,\eta)\) and \(E(x,y)\) can be given directly.  
Furthermore, for the convenience of calculation, the Equation (4) is converted to the matrix form as  
\[E(x,y)=T_{O\to E}(x,y;\xi,\eta)\times O(\xi,\eta), \tag{5}\]  
where the'\(\times\)'represents a multiplication operation between two matrices. In this way, the object information \(O(\xi,\eta)\) can be recovered directly from the distorted output optical field \(E(x,y)\) by the inverse operation:  
\[O_{\text{rec}}(\xi,\eta)=T_{O\to E}(x,y;\xi,\eta)^{-1}\times E(x,y), \tag{6}\]  
where \(T_{O\to E}(x,y;\xi,\eta)^{-1}\) is the inverse matrix (or pseudoinverse matrix for any \((\xi,\eta)/(x,y)\) segments ratio) of.
## 3 Results
### Study of TBA Concentration  
In order to obtain relevant results and to improve the experimental conditions, different values of the TBA concentration (0.3%, 0.5%, and 0.8%) were tested. The results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TBA 1 Concentration** & **A450 (nm) 2** & **A495 (nm) 3** & **A532 (nm) 4** & **MDA (\#g/L) 5** \\ \hline
0.3\% & 0.2679 & 0.1507 & 0.1326 & 144.93 \\
0.5\% & 0.2909 & 0.1552 & 0.1402 & 125.08 \\
0.8\% & 0.3419 & 0.1416 & 0.1544 & 144.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The absorbances and MDA concentration with different TBA concentrations (\(n=3\)).  
Figure 1: The absorbances obtained at the three incubation times.  
As shown in Table 3, in all three situations, the maximum absorption values were read at 450 nm, followed by results obtained at 495 nm. In addition, the lowest absorbance values appeared at 532 nm. At the same time, it was observed that, overall, the highest absorbance values were obtained using 0.8% TBA. Therefore, the optimum TBA concentration selected was 0.8%.  
According to the data obtained, considering the evaluation of steps previously described, the optimized method presented the following parameters: 5 mL of each milk sample was treated with 10 mL of 20% TCA, without adding antioxidant. After centrifugation at 3000 rpm for 5 min, the samples were filtered and then 2.5 mL of filtrate was incubated with 1.5 mL of 0.8% TBA for 90 min at 80 \({}^{\circ}\)C. After incubating and cooling of samples, absorbance values were read at different wavelengths, specific for milk degradation products (450 nm--saturated aldehydes, 495 nm--dienals, and 532 nm--MDA). By using the protocol described, milk lipids' oxidation can be evaluated not only based on MDA concentrations (minor oxidation product of milk) but also, saturated aldehydes and dienals can be considered for quantification.
# Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules
###### Abstract  
Free space optical communication (FSO) is widely deployed to transmit high data rates for rapid communication traffic increase. Asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) modulation is a very efficient FSO communication technique in terms of transmitted optical power. However, its performance is limited by atmospheric turbulence. When the channel includes strong turbulence or is non-deterministic, the bit error rate (BER) increases. To reach optimal performance, the ACO-OFDM decoder needs to know accurate channel state information (CSI). We propose novel detection using different deep learning (DL) algorithms. Our DL models are compared with minimum mean square error (MMSE) detection methods in different turbulent channels and improve performance especially for non-stationary and non-deterministic channels. Our models yield performance very close to that of the MMSE estimator when the channel is characterized by weak or strong turbulence and is stationary. However, when the channel is non-stationary and variable, our DL model succeeds in improving the performance of the system and decreasing the signal to noise ratio (SNR) by more than 8 dB compared to that of the MMSE estimator, and it succeeds in recovering the received data without needing to know accurate CSI. Our DL decoders also show notable speed and energy efficiency improvement.  
free space optical communication; asymmetrically clipped optical orthogonal frequency division multiplexing; channel state information; minimum mean square error; fully connected neural network; fully convolutional neural networks; additive white gaussian noise +
Footnote †: journal: Applied Physics Letters
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 6 Investigating Reinforcement Learning-Based Routing Protocols
### Ppmac+rlsrp  
[PERSON] et al. [76] have proposed an adaptive communication protocol--namely, the position-prediction-based directional MAC protocol (PPMAC) and the RL-based self-learning routing protocol (RLSRP) for FANETs. This routing method tries to quickly form communication paths and deliver data packets with low delay. PPMAC has three steps: position prediction, communication control, and data transfer. In the first step, UAVs obtain their speed and position using GPS. In PPMAC, each UAV periodically shares a position packet including ID, position information, antenna and status information, and path information with its own single-hop neighboring nodes. Thus, each node knows the position of its neighboring UAVs and can predict their future position. In the second step, the communication control process is performed by exchanging control packets. In the third step, the data are transferred from a flying node to the neighboring node. In the routing process, RLSRP updates the routing policy based on the position of UAVs and designs a reward function based on the transmission delay. RLSRP searches the shortest routes with the least delay. In this routing method, the routing process is defined as a partially observable Markov decision process (POMDP). In RLSRP, each node plays the agent role, and the status of the nodes is considered as the state space. Furthermore, the action indicates the transmission of the packet from the current node to the next-hop node. This learning process is shown in Figure 35. In this routing process, a greedy strategy is used to select the next-hop node, and the neighboring node with the highest value is selected as the next-hop node. In RLSRP, learning parameters, including learning rate and discount factor, have constant values. The most important advantages and disadvantages of PPMAC+RLSRP are outlined in Table 17.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Advantage** & **Disadvantages** \\ \hline \hline Q-FANET [75] &
\begin{tabular}{c} No having a mechanism for controlling the hello \\ updating interval, high routing overhead, not \\ managing Q-table size in large-scale networks and \\ reducing convergence speed, low scalability, \\ considering constant learning parameters, ignoring the \\ energy of UAVs in the routing process, considering a \\ flat topology network and ignoring the clustering \\ process, not applying a mechanism to prevent \\ congestion in the network. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 16: The most important advantages and disadvantages of Q-FANET.  
Figure 34: Learning process in Q-FANET.
## 4 Model Analysis
### Mean Time to Failure (MTTF) Analysis
Table 3 and Figure 4 illustrate that implementing this reduction technique contributes to enhancing the original system's reliability, particularly when comparing Cases I and II. Figure 5 illustrates how the mean time to failure (MTTF) of the system changes in response to variations in \(\sigma_{1}\), \(\sigma_{2}\), \(\sigma_{3}\), and \(\sigma_{4}\), while keeping the other parameters constant. The observed variation in MTTF for different failure rates suggests that a gradual increase in the value of a parameter leads to a decrease in the MTTF of the repairable system. Table 5 and the corresponding Figure 6 indicate that an increase in failure rate values correlates positively with enhanced sensitivity. It is crucial to highlight that the system becomes more responsive as failure rates rise. Figures 7 and 8 show that the anticipated profit rises over time, with the expected profit attributed to copula repair exceeding that of general repair. This strongly suggests that copula repair not only enhances system availability, but also leads to higher profits from the operation of repairable systems. Finally, it is noticeable that, as service costs rise, there is a corresponding decrease in profit. The findings suggest that reliability modeling can effectively evaluate and improve the robustness, efficiency, and performance of an Apache Kafka system. To alleviate congestion in the repair facility and resolve issues using supplemental variable techniques, this research could be expanded to encompass a system with multiple subsystems and diverse repair machines. This work holds promise for benefiting industrial applications and addressing human needs. The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2024-9/1). The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2024-9/1). The authors declare no conflict of interest.
## 4 Experimental Results and Analysis
### Impacts of the Orbital Parameters on LEO-LEO Occultation Events
#### 4.1.5 Impact of the Height  
For the polar orbit constellations, one can see from Figure 2c that the number of occultation events that accumulated over 3 months gradually varies with the Rx and Tx height from about 4500 to 5500 events. The basic rule is that the lower the orbit is, and hence the shorter the orbital period, the more quickly the occultation number increases, since more orbit revolutions occur per day.
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 7 Communication in EV Charging Coordination
Another semi-centralized V2V charging navigation strategy is proposed in [127], in which EVs are augmented with mobile edge computing (MEC) capabilities to adaptively choose their optimal route based on different traffic models and other information received through vehicular communications. Nevertheless, the specifications of the underlying vehicular communication networks has received less attention in the literature, with most of the published research assuming an effective vehicular communication infrastructure through which coordination information is exchanged [119]. In addition, previously discussed papers on EVs charge scheduling and coordination mainly focus on static wired and wireless charging while only a few papers consider charge management in dynamic charging scenarios. In this section, the authors review the existing literature on the utilization of the different communication technologiesto facilitate mobility-aware charging coordination for both stationary and dynamic EV charging systems.
## 2 Materials and Methods
### Signal Features Extraction  
For each burst ROI, the signal features were calculated in MATLAB [17] considering amplitude [11], root mean square (RMS) [15], median frequency (MF) [12], and signal-to-noise ratio (SNR) [4].  
Amplitude represents the maximum absolute value of the signal expressed in [mV]. RMS represents the mean square of signal values used to determine the magnitude of a signal, varying and expressed in [mV]. RMS is calculated using Formula (1):  
\[RMS=\sqrt{\frac{1}{N}{\sum_{n=1}^{N}{(x(n))}^{2}}} \tag{1}\]  
MF represents a frequency value at which the sEMG power spectrum is divided into two regions with equal integrated power. MF is expressed in Hz and was calculated using a Formula (2):  
\[MF=\sum_{j=1}^{MF}{P_{j}}=\sum_{j=MF}^{M}{P_{j}}=\frac{1}{2}{\sum_{j=1}^{M}{P_{ j}}} \tag{2}\]  
where \(P_{j}=\frac{1}{N^{2}}|X_{k}|^{2}\) is the signal power spectrum at the frequency bin \(j\) and \(M\) is the length of the frequency bin, defined as the next power of 2 from the length of the signal in the time domain.  
SNR, measured in decibels [dB], determines the ratio of the signal (segments of burst named signal ROIs) to the background signal (segments of noise named noise ROIs). SNR is approximated using Formula (3):  
\[SNR_{i}=10\cdot\log 10\frac{RMS(signal\ ROIs_{i})^{2}}{RMS(noise\ ROIs_{i})^{2}}, \tag{3}\]  
where \(i\) is the \(i^{th}\) extracted segment of signal.
## 4 Results
### Analytical Experiments  
To gain a deeper understanding of the impact of various parameters in the model and delve into the inner workings of the model, we meticulously designed and executed a series of analytical experiments.  
**The impact of embedding dimension size.** As Figure 4 demonstrates, we conducted an experimental analysis to investigate the impact of the embedding dimension of entities, predicates, and entity-links within Relgraph on its performance. The embedding dimensions of the model were set to 50, 100, 150, and 200, respectively. The results indicate that the performance improvement plateaus at a dimension of 200.  
**The impact of training epoch.** To analyze the learning efficiency of the Relgraph model, the MRR performance of each training epoch model was sampled during experiments on the FB15k-237 dataset, as displayed in Figure 5. It is evident that the model achieved satisfactory performance at approximately 500 epochs and reached optimal fitness at 3000 epochs.  
**Comparison of schemes for calculating the rotation angles in the Relgraph-RotatE.** To verify the effectiveness of the scheme to calculate rotation angles in the Relgraph-RotatE model as per Equation (23), we conducted a comparative experiment. We established two modified versions of the model, Relgraph-RotatE-min and Relgraph-RotatE-max, based on  
Figure 4: MRR of Relgraph under different embedding dimensions on FB15K-237 dataset.  
Figure 5: The learning curve of Relgraph under different epochs on FB15K-237 dataset.  
their distinct approaches to processing relation embeddings. The corresponding calculation formulas for rotation angles in RotatE are as follows:  
\[\begin{array}{l}p_{r}^{\theta,min}=\min(p_{r}^{u},p_{r}^{d})\\ p_{r}^{\theta,max}=\max(p_{r}^{u},p_{r}^{d})\end{array} \tag{23}\]  
The performance comparison between the Relgraph-RotatE-min, Relgraph-RotatE-max, and Relgraph-RotatE models on the FB15k-237 dataset is displayed in Figure 6. It is evident that the rotation angle calculation scheme proposed in this paper offers several advantages.  
**Comparison of different similarity algorithms in the Relgraph model.** We also conducted an analysis and verification of the performance impact of different similarity algorithms in scoring functions (Equations (16) and (17)) on the Relgraph model. For this experiment, we utilized cosine similarity, F1 norm, and F2 norm in scoring functions to calculate the similarity between embeddings. The comparison of their performance on the FB15K-237 dataset is presented in Figure 7.  
Figure 6: Comparison of model performance under different rotation angle calculation schemes on the FB15k-237 dataset.  
Figure 7: Comparison of model performance under different similarity algorithms on the FB15k-237 dataset.
## 3 Network Model
### Network Reliability  
We consider the network reliability formulation in [54], which is defined based on the number of disjoint paths between a given sensor and the sink node through [PERSON]'s Algorithm [55]. Thus, the reliability \(re_{i}\) of a sensor \(i\) of the initial sensor sets \(\tilde{S}_{s}\) is  
\[re_{i}=1-\prod_{l=1}^{d_{jp}^{c}}\left(1-(1-err)^{\frac{k_{i}c}{l}}\right), \tag{9}\]where \(djp_{i}^{c}\) denotes the number of disjoint paths between the sensor \(i\in\tilde{S}_{s}\) and the sink node, \(err\in[0,1]\) is the local channel error, and \(h_{l}^{i,c}\) is the number of hops in the \(l\)-th disjoint path between \(i\in\tilde{S}_{s}\) and the sink node.
## 3 The Radial Basis Function
By using Equation (11) and boundary data as shown in Equations (2) and (3), we obtained the following linear system:  
\[\mathbf{Pa}\ =\ \mathbf{Q}, \tag{12}\]  
in which \(\mathbf{P}\) is an \(n\times m\) matrix, where \(m=M_{O}\times M_{N}\), \(n=n_{I}+n_{D}+n_{N}\), \(n_{I}\) is the inner collocation number, \(n_{D}\) is the boundary point number for Dirichlet boundary, \(n_{N}\) is the boundary point number for Neumann boundary, \(\mathbf{a}\) is an \(m\times 1\) vector that includes unknown coefficients described as \(\mathbf{a}=\left[a_{1}\ a_{2}\ \ldots\ a_{m}\right]^{T}\), \(\mathbf{Q}\) is an \(n\times 1\) vector, \(n\) is the total number of points, and \(a_{1},a_{2},\ldots,a_{m}\) are the unknown coefficients to be solved. The input data with noises are considered as follows:  
\[\hat{g}(\mathbf{x})\ =\ g(\mathbf{x})\ \times\ (1\ +\ \delta\ \times\ rand)\ \text{and}\ \hat{h}(\mathbf{x})\ =\ h(\mathbf{x})\ \times\ (1\ +\ \delta\ \times\ rand), \tag{13}\]  
where \(\hat{g}(\mathbf{x})\) and \(\hat{h}(\mathbf{x})\) are the input data with noises, \(rand\) is a random number ranging from \(-1\) to \(1\), and \(\delta\) is the noise level. The maximum absolute error (MAE) is  
\[\text{MAE}\ =\ \max\big{|}\hat{u}\big{(}\mathbf{x}_{j}\big{)}-u\big{(}\mathbf{x}_{ j}\big{)}\big{|},\ 1\ \leq\ j\ \leq\ n_{t}, \tag{14}\]  
and the root mean square error (RMSE) is  
\[\text{RMSE}\ =\ \sqrt{\sum\limits_{j=1}^{n_{t}}\big{[}\hat{u}\big{(}\mathbf{x}_{ j}\big{)}-u\big{(}\mathbf{x}_{j}\big{)}\big{]}^{2}/n_{t}}, \tag{15}\]  
where \(n_{t}\) is the validation point number and \(\hat{u}(\mathbf{x}_{j})\) and \(u\big{(}\mathbf{x}_{j}\big{)}\) are the analytical and numerical solutions at the \(j\)th validation point, respectively. The validation points coincided with the inner points where Equation (11) is imposed.
## 5 Numerical Results
### Radar Detection Performance Analysis
#### 5.1.2 Different Methods' Output Responses  
In this subsection, the integration output responses for each method are provided in \((SNR=20\) dB). The normalized results are as follows: Figure 6a shows the result of the moving target detection (MTD) method, where the target integration is not apparent. Figure 6b displays the result of the multi-frame integration method, where the target is integrated but noise and sidelobes are prominent. Figure 6c presents the result of the proposed method, demonstrating lower sidelobes.  
Figure 5: Intra-frame integration results. (**a**) Range across echo, (**b**) intra-frame integration, (**c**) one frame integration result of a single target, (**d**) the distance slice of (**c**), (**e**) the Doppler slice of (**d**).
## 4 Experiment and Analysis
### Predicting Rock Surface Strength
#### 4.4.1 Predictions of Batch A  
First, Batch A was used as the input to test the method. The regression algorithms were used as follow: (1) in the SVM, the Gaussian kernel function was adopted; (2) in the KNN, the number of N was set to 20; (3) in the RF, the number of trees was 800, and the maximum depth was nine. Figure 9 displays the regression results.  
Figure 8: Re-training of the Inception-ResNet-v2 model.  
The R\({}^{2}\) is used to measure the goodness of fit (GOF). Figure 9a,c, and e show that all the three algorithms can reach a high R\({}^{2}\) of more than 0.95. The R\({}^{2}\) of the KNN algorithm is the largest and is more than 0.98. The norm of residual is another measure of GOF, and a lower norm signifies a better fit. Among the three algorithms, KNN has the smallest norm of residual. Figure 9b,d,f are the distributions of the errors of the three algorithms. It also indicates that KNN is more accurate than the other two algorithms: the range of the errors is within [-20, 20], the errors that larger than \(-2.5\) and smaller than 2.5 occupy 96.88%, and the errors in the range of [-5, 5] occupy 97.86%. Comprehensively, KNN gets the best performance, and the followings are the SVM and the RF.
###### Abstract  
Hydrogen (H2) production from magnesium and its alloys offers an efficient alternative to traditional methods. In magnesium-based hydrolysis, the formation of a Mg(OH)\({}_{2}\) passivation layer significantly hinders the reaction, thereby reducing hydrogen production efficiency. To address this issue, increasing the reaction temperature and selecting appropriate materials are essential. This study adjusted the compositions of magnesium alloys (pure Mg, Mg-Al, Mg-Zn) to form low-melting phases (Mg\({}_{17}\)Al\({}_{12}\) and Mg\({}_{7}\)Zn\({}_{3}\)). The results demonstrate that as-cast Mg-Al and Mg-Zn alloys exhibit faster reaction rates and lower critical temperatures compared to pure Mg. Specifically, Mg-30 wt.% Al and Mg-30 wt.% Zn show the fastest reaction rates, with hydrogen purity reaching 99%. Mg-Zn alloys have an induction time of merely 98 s and can achieve complete reaction at 300 \({}^{\circ}\)C, while Mg-Al alloys require 174 s and 420 \({}^{\circ}\)C. Pure Mg, on the other hand, requires 270 s and 520 \({}^{\circ}\)C. Therefore, regarding the critical reaction temperature, the Mg-Zn alloy requires the lowest critical reaction temperature, the Mg-Al alloy requires a moderate reaction temperature, and pure Mg requires the highest critical reaction temperature. In addition, in terms of the performance of hydrogen production through hydrolysis, compared with pure Mg, Mg-30 wt.%Al, Mg-20 wt.%Zn, and Mg-30 wt.%Zn all exhibit good hydrogen production performance through hydrolysis. Their final conversion rates are all higher than that of pure Mg. Among them, Mg-30 wt.%Zn has the most excellent hydrogen production performance, followed by Mg-30 wt.%Al. The specific conclusions will be analyzed and discussed in detail in the subsequent text.  
hydrogen generation; pure magnesium; Mg-Al alloys; Mg-Zn alloys; low-melting alloy phases; high temperature Article