esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved PEG-Based Construction of Analog Fountain Codes
## 2 Analog Fountain Codes and the PEG Algorithm
### Preliminaries of AFC  
Consider an information sequence \(\mathbf{b}^{\prime}\) comprising \(k\) information bits \(b^{\prime}_{i}\in\{0,1\}\), where \(i=0,1,\ldots,k-1\). \(\mathbf{b}^{\prime}\) is binary phase shift keying (BPSK) modulated, resulting in \(\mathbf{b}=(b_{0},b_{1},\ldots,b_{k-1})\), where \(b_{i}\in\{-1,1\}\). Each AFC codeword \(\mathbf{c}=\{c_{0},c_{1},\ldots,c_{n-1}\}\) is then generated as  
\[\mathbf{c}=\mathbf{Gb}^{T}. \tag{1}\]  
Here, \(\mathbf{G}\) is the generator matrix:  
\[\mathbf{G}=\begin{bmatrix}g_{0,0}&g_{0,1}&\cdots&g_{0,k-1}\\ g_{1,0}&g_{1,1}&\cdots&g_{1,k-1}\\ \vdots&\vdots&\ddots&\vdots\\ g_{n-1,0}&g_{n-1,1}&\cdots&g_{n-1,k-1}\end{bmatrix} \tag{2}\]  
and \(\mathbf{b}^{T}\) is the transpose of \(\mathbf{b}\). The corresponding Tanner graph is shown in Figure 1.  
The degree \(d\) of AFC is defined as the number of nonzero elements in each row of \(\mathbf{G}\). The \(d\) nonzero elements correspond to a predefined weight set \(\mathrm{W}=\{w_{0},w_{1},\ldots,w_{d-1}\}\), where \(w\in\mathrm{W}\) is called the weight coefficient. Without loss of generality, we set \(w_{0}>w_{1}>\cdots>w_{d-1}>0\).  
After being transmitted through an additive white Gaussian noise (AWGN) channel, \(\mathbf{r}=\mathbf{c}+\mathbf{n}\) is received, where \(\mathbf{n}\) is the noise vector with zero mean and variance \(\sigma^{2}\).  
At the receiver side, a simplified compressive sensing belief propagation (CS-BP) decoder [14] is employed. The CS-BP decoder is a variant of the BP decoding which can be used in compressive sensing and AFC. The decoding process is briefly reviewed as follows. Let \(\mu_{p\to c}(b_{i})\) and \(\mu_{c\to v}(b_{i})\) denote the message transmitted from the VNs \(b_{i}\) to check nodes  
Figure 1: The Tanner graph of an AFC.  
(CNs) \(c_{j}\) and vice versa, respectively. After a fixed number of iterations of \(\mu_{v\to c(b_{i})}\) and \(\mu_{c\to v}(b_{i})\), the marginal distribution \(f(b_{i})\) is obtained by  
\[f(b_{i})=\prod_{j\in\mathcal{J}_{i}}\mu_{u\to v}(b_{i}), \tag{3}\]  
where \(\mathcal{J}_{i}\) denotes the set of CNs connected to \(b_{i}\). Then, the decoder outputs the hard decision based on \(f(b_{i})\). More details of the CS-BP decoder can be reached in [14].
## The Ocean  
As previously mentioned, SARAL/AltiKa's main scientific objective is to provide data products to the oceanographic community with the aim to improve our knowledge of the ocean mesoscale variability, mainly associated to eddies, meandering currents, fronts, filaments and squirts. The mesoscale variability refers to ocean signals with space scales of 50 km to 500 km and time scales from a few days to a few months. The associated kinetic energy is at least an order of magnitude larger than that of the mean circulation. It is now well recognized that the mesoscale processes are a key component of the ocean circulation, shaping the mean currents, controlling the exchanges with the atmosphere and the marine biogeochemistry, and clearly playing a major role in the way the ocean participate to the climatic system.  
The sea level measurements of SARAL/AltiKa are used in theoretical and observational studies, modelling, data assimilation, etc. Because mesoscale and fine scale dynamical processes are particularly important in the coastal ocean, the observational capabilities of SARAL/AltiKa are also expected to bring an important contribution to the study of coastal dynamics. It is particularly important for many downstream applications including operational oceanography which is fed by a large amounts of in-situ and space-based data. Similar to previous altimeters, SARAL/AltiKa not only provide sea level measurements, but also ocean wave observations through the significant wave height (SWH) parameter. Ocean waves play a key role in ocean-atmosphere exchanges at the sea interface. It is essential to have the most accurate knowledge possible of the sea state for climate and meteorological studies.
## 3 Results and Discussions  
### Overview of Spectral Features and Statistics of Reference Analysis  
Figure 4 shows the mean raw spectra of the intact tomato in the spectral range of 1000-1550 nm with resulting second-derivative preprocessed spectral profiles, which is similar with previous studies [34]. The near-infrared region (NIR) was sensitive to the concentrations of organic materials, which involved the response of molecular bonds C-H, O-H, and N-H [35]. The MC, pH, and SSC contain bonds of C-H, O-H, C-O, and C-C. Thus, it is possible to use this region for determination of MC, pH, and SSC in tomatoes. However, their absorption peaks overlap in several parts of the spectral region, resulting in the spectral profiles of tomatoes in the whole spectral region being quite even with some broadband peaks. There are peaks at 1100-1200 nm, and to a lesser extent at 1350-1500 nm, which may be associated with the second overtone of band C-H and the stretching first overtone of bond O-H in H\({}_{2}\)O, respectively [35]. However, these peaks are usually located in wide spectral bands such that those key wavelengths that are helpful for predicting the MC, pH, and SSC of tomatoes cannot be directly identified. The proposed spectral preprocessing is first applied to raw spectral data and the preprocessed spectra were used to develop the PLS regression model.  
An overview of MC, pH, and SSC distributions of tomatoes in the calibration and prediction sets is presented in Table 1. These statistic values include number of samples, range, mean, and standard deviation (SD). In this study, 95 samples were divided into the calibration and prediction sets (60:35). The range of the calibration set was from 91% to 95.9% for MC, 3.9 to 4.4 for pH and 2.7% to 5.5% Brix for SSC, and the range of the prediction set was from 91.2% to 94.4% for MC, 3.9 to 4.3 for pH, and 3.4% to 4.9% Brix for SSC. The range of the calibration set is bigger than that of the prediction set, which is helpful when developing a good model.
# Grain Boundary--A Route to Enhance Electrocatalytic Activity for Hydrogen Evolution Reaction
## 3 Results and Discussion  
### Theoretical Prediction of GB-Assisted HER  
#### 3.1.1 Gibbs Free Energy  
The HER is a multi-step electrochemical process occurring on the surface of an electrode [23]. In acid media, two possible routes have been elucidated for the HER, i.e., the Volmer\(-\)Heyrovsky and the Volmer\(-\)Tafel mechanisms, which can be described as follows [24; 25]:  
\[\mathrm{H_{3}O^{+}+M+e^{-}=H\,\ast-M+H_{2}O} \tag{1}\]  
\[\mathrm{H\,\ast-M+H_{3}O^{+}+e^{-}\rightleftharpoons M+H_{2}+H_{2}O} \tag{2}\]  
\[2H\,\ast-M\rightleftharpoons 2M+H_{2} \tag{3}\]  
Step (1) refers to the Volmer reaction, while steps (2) and (3) refer to the Heyrovsky and Tafel reactions, respectively. We first explore the impact of the GBs on the Au(110) surface on the Volmer reaction, which dominantly determines the overall HER performance; for this, we chose various active sites at and near the Au GBs (Figure 1). Figure 2a shows the Gibbs free energy change (\(\Delta\)G\({}_{\mathrm{H^{+}}}\)) profile of the HER in acid media. The \(\Delta\)G\({}_{\mathrm{H^{+}}}\) values of the flat Au(110) and Pt(111) GB-free surfaces are also presented for comparison (Tables S1 and S2). It should be noted that \(\Delta\)G\({}_{\mathrm{H^{+}}}\) dramatically reduces once the GBs are introduced. Additionally, \(\Delta\)G\({}_{\mathrm{H^{+}}}\) exhibits site-dependent HER activity for sites in the vicinity of the GBs. To be specific, the \(\Delta\)G\({}_{\mathrm{H^{+}}}\) for s1 is 0.18 eV, approximately 0.4 eV less than the flat Au(110) (0.58 eV), whereas s2 and s3 have a \(\Delta\)G\({}_{\mathrm{H^{+}}}\) of about \(-0.07\) eV; this is less than that of Pt (\(-0.09\) eV), which is more close to zero. As is well known, the optimal value of \(\Delta\)G\({}_{\mathrm{H^{+}}}\) for the HER is zero, as hydrogen binding is neither too strong nor too weak. Therefore, the activity for the Volmer reaction is greatly promoted by the GBs. In addition, the sites near the GBs (s4 and s5) reduce \(\Delta\)G\({}_{\mathrm{H^{+}}}\) by \(-0.13\) eV with regard to the Au(110) surface, suggesting that GBs on the Au(110) surface not only greatly promote HER activity on their own (s1-s3) but also activate the sites nearby (s4-s5) to further lower the reaction barrier.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 5 Results
### Link Budget
#### 5.1.4 Link Margin  
For the given minimum C/N required (i.e., \(-120\) dB), and an altitude of 500 m, the link margin was calculated and is shown in Figure 15. From the figure, we observe that the link margin is not positive throughout the change in the elevation angle from \(60^{\circ}\) to \(120^{\circ}\). The 6G communication link can be maintained only when the link margin is positive. Hence, it can be said that, even though the satellite is in the visible range of the receiver drone, it is not always possible to establish a 6G link. The elevation angle needs to be roughly between 80 and 100 to maintain a 6G communication link (\(\theta_{lim-elevation}=\{80^{\circ},100^{\circ}\}\)).  
Figure 14: Path-loss vs. horizontal distance.  
Figure 15: Link margin vs. horizontal distance.  
From the link margin, the range of the 6G satellite-terrestrial network can be determined. The elevation angle and the range where the link margin is positive can be related using the following equations. Figure 16 can be used to understand the calculations.  
\[\theta=90^{\circ}-\theta_{lim-elevation} \tag{20}\]  
\[\alpha=\theta-\arcsin\Big{(}\frac{R_{e}}{R_{sat}}\sin\theta\Big{)} \tag{21}\]  
\[Range_{6G}=R_{e}(2\alpha) \tag{22}\]  
The above equations, Equations (20)-(22), were used to calculate the range of the 6G satellite network at 250 km on the ground.
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 2 System Model
### Conventional NOMA System  
In the conventional NOMA system, the signals of each user are combined at the BS through SC, and the combined signal is sent to users within a single resource block. The signal \(x\) sent from the BS can be expressed as follows:  
\[x=\sqrt{\alpha_{1}}s_{1}+\sqrt{\alpha_{2}}s_{2}, \tag{4}\]  
where \(\alpha_{k}\) denotes the power allocation coefficient (\(\alpha_{1}+\alpha_{2}=1\) and \(\alpha_{2}>\alpha_{1}\), for \(k=1,2\)). Since user 2 is far away from the BS, in general, more power is allocated to \(s_{2}\) to achieve better fairness. According to (1) and (4), the received signal at user \(k\) for the conventional NOMA is represented as follows:  
\[y_{k}^{\text{NOMA}}=\sqrt{\alpha_{1}P_{\text{f}}}h_{k}s_{1}+\sqrt{\alpha_{2}P_ {\text{f}}}h_{k}s_{2}+n_{k}. \tag{5}\]  
Since user 2 decodes \(s_{2}\) without SIC, the term corresponding to \(s_{1}\) is considered as interference. Therefore, the data rate for \(s_{2}\) at user 2 is given as follows:  
\[R_{2}^{\text{NOMA}}=\log_{2}\Biggl{(}1+\frac{\alpha_{2}|h_{2}|^{2}}{\alpha_{1 }|h_{2}|^{2}+\frac{1}{\rho}}\Biggr{)}. \tag{6}\]  
After performing SIC, the data rate of \(s_{1}\) at user \(1\) is calculated as follows:  
\[R_{1}^{\text{NOMA}}=\log_{2}\Bigl{(}1+\rho\alpha_{1}|h_{1}|^{2}\Bigr{)}. \tag{7}\]
## 4 Results and Discussion
### Experiments for Winter Wheat Yield Estimation on a Regional Scale  
Remotely sensed LAI maps with a spatial resolution of 30 m were used to map regional winter wheat yields. The yield map shows that the yield per unit area tended to decrease from the northwest to the southeast of Hengshui (Figure 4). The average yield over the study region was 7069 kg/ha, the yield range was 4957 to 8367 kg/ha, and the standard deviation was 375 kg/ha. The maximum productive capacity was observed in Gucheng county (7884 kg/ha), followed by Zaoqiang (7608 kg/ha) and Jingxian (7456 kg/ha). The minimum yield was 6108 kg/ha in Raoyang.  
Figure 3: **(a)** Comparisons of winter wheat yield estimated by the CERES-Wheat model with no data assimilation, **(b)** the EnKF-based data assimilation, and **(c)** the POD4 DVar-based data assimilation.  
An accuracy verification was performed on pixel and county scales. The estimated yields of 53 pixels better corresponded to the actual yields of field plots, with an \(R^{2}\) of 0.74, an RMSE of 335 kg/ha, and an RE of 3.70% (Figure 5a). The statistical results for the mean yields for 11 counties showed that the \(R^{2}\) was 0.82, the RMSE was 243 kg/ha, and the RE was 2.59% (Figure 5b). The good performance of the CERES-Wheat model with POD4 DVar supports the feasibility and potential of this approach for operational systems that monitor regional crop yield.
## 1 Introduction
In summary, this paper makes significant contributions in the following ways:  
* Implementing the novel RLFOUA framework, an innovative automatic learning environment that pioneers the use of RL techniques in dataset resampling. This approach dynamically determines the next state based on the current state and past rewards, setting it apart from traditional resampling methods. * Proposing the innovative TFRSMOTE resampling algorithm, a pioneering solution for handling imbalanced datasets in a dynamic environment. This algorithm generates synthetic data from the minority class for oversampling and strategically reduces the majority class for effective undersampling, marking a departure from conventional resampling techniques. * Presenting the new IM as an original contribution to the field. This novel metric offers a comprehensive evaluation of algorithm performance, considering a range of critical factors not addressed by existing metrics.
## 5 Results
### Levenberg-Marquardt Neural Network  
The methodology applied for detecting and managing faults in DC motor teeth, utilizing the Levenberg-Marquardt Neural Network (LMNN) model, draws parallels with an approach proposed for extending the longevity of power conversion systems [44]. This method integrates the Levenberg-Marquardt Back Propagation Neural Network (LM-BPNN) for state estimation and power routing, focusing on predictive maintenance in industrial contexts [44]. Specifically addressing fault detection, the LMNN model analyzes motor performance and fault data to discern patterns indicative of tooth faults within the gear system. Employing LMNN, a variant of the Backpropagation Neural Network (BPNN), trained via the Levenberg-Marquardt optimization algorithm, the architecture encompasses input, hidden, and output layers initialized with random weights. Through Levenberg -Marquardt (LM) algorithm-based training on provided data, including features derived from frequency and amplitude measurements alongside corresponding fault statuses, the model strives to optimize weights. However, limitations stemming from the small dataset or fault complexity may impede accurate fault detection and classification, as depicted in Figure 9. The restricted dataset size constrains the LMNN model's ability to capture the variability of fault manifestations and motor performance patterns, thereby limiting its performance. Despite these constraints, this approach outperforms LSTM and LSTM autoencoder-based techniques. Regarding Root Mean Square Error (RMSE), the LMNN method surpasses LSTM and LSTM autoencoder techniques. While the RMSE values for LSTM and LSTM autoencoder are 17 and 21, the LMNN technique achieves a significantly lower RMSE of 12. This disparity in RMSE values underscores the LMNN model's superior predictive accuracy compared with the LSTM and LSTM autoencoder models. Further refinement of the dataset and enhancements to the model's architecture and methodology are crucial to enhancing its effectiveness and reliability in real-world fault detection scenarios for DC motors used in starter applications. To address these challenges, practitioners may explore techniques such as data augmentation, transfer learning, regularization, leveraging pretrained models, and considering simpler model architectures or alternative approaches tailored to the specific constraints of small datasets in gear fault diagnosis for DC motors.  
Figure 8: Evaluating LSTM autoencoder predictions against actual values in DC motor gear fault diagnosis.
## 1 Introduction
This is one of the few papers where both simulation and experimental results are included. The concept of a neural detector is shown in [14]. The detection is based on measured and estimated signals. However, this solution is presented for an induction motor, with only simulation results. The obtained results show that the detection of damage to the speed sensor using a neural network allows for high efficiency. In the literature, many examples where neural detectors are used in drive systems for faults of elements other than the speed sensor can be found. These works show mainly applications for detecting electrical [15; 16] and mechanical [17; 18] damage to the motor itself. In the case of sensors, several works describe the detection of damage to current sensors using neural networks [19; 20; 21]. These works confirm the effectiveness of such solutions, which is why it is the next step to use neural detection to damage the speed sensor. The paper presents a proposal to extend diagnostic systems based on estimators of state variables with damage classification of any speed sensor type. For this purpose, a shallow neural network was used. Such an approach has not been previously described in the literature. The work is focused on the possibilities of using neural networks in the classification of faults of current sensors. The speed estimators used in the work are described as indirect tools. The influence of estimator parameters on estimation accuracy has already been discussed many times in the literature [22; 23; 24]. The use of NN not only allows for classifying the type of damage but also increases the effectiveness of the 0-1 fault detection. Additional diagnostic functionality allows for obtaining additional information about the failure. Which in practice means a faster repair of the damaged element. Three types of failures are considered in the system, which can most often occur in the speed sensor--loss of signal, scaling error, and signal interference. These failures usually have repeatable causes. Such initial diagnostics can significantly speed up the repair process of an element or determine whether it needs to be replaced. The paper shows experimental results that distinguish the proposed work from other works describing the detection of speed sensor faults. The results are presented for different speed and load values. In addition, the article presents an analysis of the damage classifier based on the SMO and MRAS estimators to compare both systems. For both systems, the MLP was used for damage classification. The article is organized as follows. The first part presents the importance of the research problem and describes it against the background of works available in the literature. The next part describes the theoretical background of state variable observers used for fault detection. The third chapter presents the process of designing a neural speed sensor fault classifier using state variable observers described in chapter two. The following parts present the results obtained from experimental tests. Finally, the last chapter contains a brief summary of the obtained results.
## 3 Decentralized Treatment Systems
### Surface, Open Water Systems and Groundwater Treatment
Batch results show that the Fenton reaction could obtain a complete sulfolane oxidation using H\({}_{2}\)O\({}_{2}\) 3% at the H\({}_{2}\)O\({}_{2}\)/ferrous iron molar ratio of 1:0.5 and the initial sulfolane concentration of 72 mg/L. However, the sequential Fenton-like operational pattern could be an alternate option to obtain complete sulfolane removal efficiency. Sulfolane oxidation would produce sulfuric acid, and this would be beneficial to the Fenton reaction. A sulfolane-contaminated groundwater site was selected for the pilot-scale study, and the Fenton oxidation process was applied. Approximately 600 L of 5% H\({}_{2}\)O\({}_{2}\) at the same H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\) molar ratio was injected into the injection well. Up to 97% and 88% of sulfolane could be removed with an initial sulfolane concentration of 68 mg/L, resulting in efficient cleanup of contaminated groundwater [165]. Inorganic contaminants such as lead, arsenic and nitrate in groundwater were removed by using a pilot-scale integrated electrodialysis (ED) and electrolysis (EL) system. Groundwater was firstly treated with the ED device, and the concentrated water was then transferred to the EL device for denitrification. The ED device contained stainless steel coated with a platinum cathode and anode, and each ED cell was packed with 24 pairs of ion exchange membranes. In the EL system, TiO2 was used as the anode, while Zn was the cathode, separated by a cationic exchange membrane. The ED process exhibited sufficient removal of arsenic and common cat(an)ions, being above 95% within 30-45 min. The time-dependent removal of arsenic was the highest, with efficiencies ranging from 71 to \(\sim\)100%. The As removal by ED is currently considered an improvement over other methods: coagulation and flocculation can reach 6-74% removal, while NF removes less than 30% of As (III) and 60-90% As(V). The chloride was removed with a mean efficiency of 84.6%, while nitrate (92.0%) and sulfate (94.2%) had slightly better values. The ED treatment was similarly able to remove cations (except iron species), and 94.1% of the ammonium ion was effectively removed. The EL process can convert ammonium and nitrate ions to nitrogen gas, but the cathode reaction of nitrate only reached \(\sim\)50% of efficiency. The authors affirmed that the possibility of producing safe drinking water from contaminated groundwater using the integrated ED/EL system was accomplished [166].
## 4 Discussion
### Performance Analysis  
The performance of each model was evaluated in terms of recall, precision, F1-Score, sensitivity, and specificity, and the results are represented in Table 1. Confusion matrices of the VGG16, GoogleNet, and Xception-associated fuzzy ensemble techniques are illustrated in Figures 5-7, respectively. Performance comparisons of various transfer learning models with the proposed model using CXR images are presented in Table 2. The following equations were used to calculate the performance metrics:  
\[1.\;Precision=\frac{True\;Positive\;(TP)}{True\;Positive+False\;Positive\;(FP)}\]  
\[2.\;Sensitivity\;or\;Recall=\frac{True\;Positive}{True\;Positive+False\;Negative\;(FN)}\]  
\[3.\;F1-Score=\frac{2\;x(Precision\;x\;Recall)}{(Precision+Recall)}\]  
Figure 4: Loss graph for the training phase of VGG16, Xception, and GoogleNet. Figure 3: Accuracy graph for the training phase of VGG16, Xception and GoogleNet. \[4.\ Specificity=\frac{\textit{True Negative}\ (TN)}{\textit{True Negative}+\textit{False Positive}}\] \[5.\ Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]  
Figure 5: Confusion matrices of VGG16 and associated fuzzy ensemble techniques. VGG16, VGG16 + Majority Voting, VGG16 + Sugeno Integral, and VGG16 + Choquet Fuzzy achieved an accuracy of 96.68%, 96.75%, 96.90%, and 97.04%, respectively. Table 2.0 Confusion matrices of GoogleNet and associated fuzzy ensemble techniques. GoogleNet, GoogleNet + Majority Voting, GoogleNet + Sugeno Integral, and GoogleNet + Choquet Fuzzy achieved an accuracy of 97.91%, 98.05%, 98.27%, and 98.48%, respectively. Table 2.1 Confusion matrices of Xception and associated fuzzy ensemble techniques. Xception, Xception + Majority Voting, Xception + Sugeno Integral, and Xception + Choquet Fuzzy achieved an accuracy of 98.20%, 98.56%, 99.28%, and 99.57%, respectively.