esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
The probability of error curves is developed assuming binary phase shift keying (BPSK) modulation, a path loss exponent of 4, and a bandwidth of 1 MHz. Figure 14 compares the probability of error performance of ONOMA and the RIS-AP-ONOMA _W\!U_ for various \(N\)_. The probability of error expressions for the \(SU\) and _W\!U_ of the ONOMA system are developed in [33]. The probability of error of _W\!U_ of ONOMA system is 0.1956 at 5 dB SNR. The probabilities of error of _W\!U_ of RIS-AP-ONOMA system are 0.0277, 0.0146, 0.0075, 0.0038 and 0.0019 for \(N\) = 32, 64, 128, 256, and 512 respectively at SNR of 5 dB. Comparing the RIS-AP-ONOMA system to the traditional ONOMA system, a noticeable improvement is seen. Even without phase compensation, the array gain facilitated by the RIS-AP-ONOMA system demonstrates the considerable difference in error probability. The probability of error performance of the proposed system improves with an increase in \(N\). Figure 3:4 Sum capacity comparison of blind RIS-SR-ONOMA, blind RIS-AP-ONOMA and ONOMA systems. Figure 3:5 Comparison of probability of error of _WU_ for the blind RIS-AP-ONOMA and ONOMA systems. Figure 3:6 Throughput comparison of blind RIS-SR-ONOMA, FNOMA and ONOMA systems. Similarly, Figure 15 compares the probability of error performance of ONOMA and the RIS-AP-ONOMA \(SU\) for various \(N\). The probability of error of \(SU\) of ONOMA system is 0.1393 at 5 dB SNR. The probabilities of error observed for \(SU\) of RIS-AP-ONOMA system are 0.0026, 0.0013, 0.0006, 0.0003, and 0.0001 for \(N\) = 32, 64, 128, 256, and 512 respectively at SNR of 5 dB. Because of the increased power allocation and higher channel gain, the probability of error for \(SU\) is much lower than for \(W\!U\) in both systems. Additionally, it is noted that the theoretical and simulated probability of error curves closely resemble one another. With an increase in \(N\), the proposed system performs better in terms of probability of error.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 4 Results and Discussion
### Arabic Alphabet Pronunciation Classification  
For Arabic alphabet pronunciation classification without data augmentation, the accuracy of the DCNN model is 96.41% with random split, 95.46% with the hold-out, and 96.37 with the 5-fold CV experiment. The AlexNet transfer learning approach achieved an accuracy of 97.12%, 96.89% with the hold-out, and 96.55% with CV. The BLSTM achieved 72.41% accuracy with random split, 73.54% with the hold-out, and 74.35% with 5-fold CV as shown in Table 8.  
For Arabic alphabet pronunciation classification with data augmentation using the DCNN model, we achieved an accuracy of 97.88% with the random split method, 95.28% with the hold-out validation, and 96.24% with 5-fold CV. The AlexNet transfer learning model achieved an accuracy of 99.14% with random split, 97.42% with the hold-out validation, and 98.43% with CV. The BLSTM model achieved an accuracy of 77.71% with random split, 76.12% with the hold-out validation, and 78.17% with 5-fold CV. In Table 9, the values of mean, standard deviation (SD), and standard mean error (SME) are shown for DCNN, AlexNet, and BLSTM.
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 2 Geolocating GNSS Reflections from Land  
### Surface Criteria for Forward Reflection  
The overall smoothness and uniformity of the ocean allows for the identification of a single specular reflection point at the minimum path difference between the transmitter, surface and receiver using a WGS84 ellipsoidal representation of the Earth, corrected for the mean sea surface height [17]. This technique often works well for flat land surfaces whose heights are not far from the WGS84 approximation [18].  
In these land cases, using a local surface topography model [19] to adjust the estimated WGS84 specular point to the land surface can be adequate for most applications. Generally, for reflections from land with topographic variations, there is often no singular unique specular point as specular reflections may occur from multiple places separated in delay Doppler space. In such conditions, additional criteria are often required to assure that the geolocation estimate is sufficiently accurate for science applications. Empirical criteria are based on matching the CYGNSS-observed delay and Doppler values to values predicted using knowledge of local terrain topography. Additional geometric criteria include the degree to which local incidence and scattering angles are suitable to specular reflection.  
For a given CYGNSS land Delay Doppler Map (DDM), these three conditions (matching of Delay and Doppler values to CYGNSS-observed values, and \"nearness\" to a specular geometry) are evaluated over a local digital elevation map grid centered at the specular reflection point initially estimated using the WGS84 surface model. Points within the grid that satisfy specified delay, Doppler and geometric conditions are then selected as representing the highest probability surface scattering points. For complicated terrain, multiple (even discontinuous) regions may exist for which all the three conditions are met. Additional information on the presence of water bodies within the local surface grid is also relevant to assist in interpreting the likelihood of coherent reflection.
## 3 User Clustering and Power Allocation Co-Design
### Lagrangian Multiplier Based Dynamic Programming Model for Power Allocation
 \tag{20}\]  
After the Lagrangian multiplier derivation, the Karush-Kuhn-Tucker optimality conditions [56,57] can be given as follows:  
* maximum transmission power constraint of the cluster \[\frac{\partial L}{\partial\lambda}=P_{\max}^{j}-\sum\limits_{i=1}^{n^{j}}P_{i }\geq 0,\] (21)
* necessary power constraint for efficient SIC of each user \[\begin{split}\frac{\partial L}{\partial v_{i}}=H_{i-1}^{j}P_{i }^{j}-\sum\limits_{k=1}^  
In Algorithm 2, all \(P^{j}\) that satisfy the corresponding Karush-Kuhn-Tucker optimality conditions are traversed, and the maximum throughput obtained is maintained in \(f(j,p)\). If no \(P^{j}\) satisfies the conditions, the value of \(f(j,p)\) remains at 0, which means that the energy budget is not enough to meet all users' QoS requirements in the \(j\)-th cluster.  
Note that the number of users per cluster is typically limited to a very small value to meet the SIC protocol's decoding needs. Therefore, checking constant combinations of Karush-Kuhn-Tucker optimality conditions in step 7 of the Algorithm 2 is sufficient. Since  
\[\sum_{j=1}^{c}\omega_{j}p_{k}\leq P_{B}, \tag{29}\]  
the computational complexity of Algorithm 2 is \(O(\frac{P_{B}}{\xi})\). Similarly, the computational complexity of the inter-cluster dynamic programming algorithm is \(O((\frac{P_{B}}{\xi})^{2})\). Therefore, the proposed solution is a polynomial-time algorithm that mainly depends on the multiples of system maximum power \(P_{B}\) to step size \(\xi\). By selecting parameter \(\xi\) properly, the energy efficiency maximization problem can be solved with enough precision in a brief period. For example, by setting the parameter \(\xi\) as 0.1% of \(P_{B}\), a BS with typical computational performance can quickly complete the power allocation within a few hundred microseconds, whose response capability is sufficient in the existing NOMA system. ```
0:\(\xi\)
0:\(f(j,p)\)
1:for\(j=1\) to \(c\)do
2:for\(i=0\) to \(\left\lfloor\frac{\omega_{j}p_{k}}{\xi}\right\rfloor\)do
3:\(p\gets i\xi\)
4:\(f(j,p)\gets 0\)
5:if\(p\leq P_{max}^{j}\)then
6:for all\(P^{j}\) calculated by (28)do
7:if\(P^{j}\) satisfies the corresponding Karush-Kuhn-Tucker optimality conditions then
8:\(t\leftarrow\sum_{i=1}^{y}\omega_{j}b\log_{2}\left(1+\frac{H_{i}^{j}p_{i}^{j}} {\omega_{j}+\sum_{k=1}^{y-1}H_{i}^{j}p_{k}^{j}}\right)\)
9:\(f(j,p)\leftarrow\max(f(j,p),t)\)
10:endif
11:endfor
12:endif
13:endfor
14:endfor
```  
**Algorithm 2** Lagrangian multiplier method for calculating \(f(j,p)\)
## 2 Developed Methodology
### Analysed PPF Models
#### 2.1.2 Artificial Neural Networks (ANN)  
The main advantage of ANNs is their capacity to model any system through a combination of non-linear functions, historical databases, and learning iterative algorithms [49]. In addition, ANNs' generalization skill, i.e., their ability to produce accurate forecasts from previously unseen data, makes them suitable not only for activities such as perception, pattern recognition or regression [50; 51], but also for use in many research fields such as renewable generation or energy demand forecasting [8; 15; 16; 22; 24]. A review of the literature on VST forecasters suggests that, among all possible ANNs, PPFs' feedforward and recurrent neural networks provide accurate results [20; 42; 43; 44] and are easy to implement.  
Feedforward Neural Networks (FFNN)  
With regard to FFNNs, processed information flows forward, from forecaster input to output layers. To develop this model, the following set of parameters must be analyzed: the model's number of layers, each layer's number of neurons, the neurons' activation functions, and iterative training algorithm for each layer. In addition, neurons are the smallest unit of the model, and they are responsible for computing the model's mathematical operations. The K-neuron of any ANN will be governed by the following mathematical equation,  
\[\hat{y}_{k}=\lambda_{k}\left(\sum_{j=1}^{M}w_{kj}x_{kj}+b_{k}\right) \tag{2}\]  
where \(\hat{y}_{k}\) refers to the k-neuron's computed value through the use of a certain \(x_{kj}\) set of parameters. \(\lambda_{k}\) represents each layer's neurons' activation user-selected functions, such as linear or sigmoid function [52], while \(w_{kj}\) and \(b_{k}\) are each neuron's synaptic connections and bias value (respectively), which are fitted through the iterative training algorithm. Further information concerning the neurons' modeling and their parameters can be found in [49].  
Recurrent Neural Networks (RNN)  
RNNs have essentially the same set of parameters that FFNNs do, the only difference being the addition of the z\({}^{-t}\) time delay function. In contrast to FFNNs, RNNs' information flows not only forwards but also backwards through the use of loops. Therefore, the hidden layers' outputs are used again as inputs to the previous layers, and their z\({}^{-t}\) parameter needs to be defined. Although the application of loops increases the model's learning skills, it has the disadvantage of increasing training time [45].
# Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark Analysis
## 6 Evaluation
### Inference Latency  
The plots in Figure 5 depict the effect of model scaling on the latency observed by users submitting inference tasks to the DL model serving the application during runtime inference for each of the referenced DL use cases. In these plots, we observe that the linear relationship between model complexity and inference latency holds, especially for the BERT (Figure 5a) and EfficientNet (Figure 5b) use cases, although it is less perfect. The coefficients of determination for these two use cases are \(R^{2}=0.871\) and \(R^{2}=0.972\), respectively. The reduction can be attributed to the fact that although latency is highly correlated with the computational overhead (FLOPs) of the DNN required to output a result, other factors contribute to increased latency. Specifically, latency also depends on data pre-processing to shape the input data points to meet the processing requirements of the DNN and DNN optimizations that can affect memory access patterns. While these factors can (slightly) influence the overall latency, the inference delay observed in the MLP-Regression use case (Figure 5c) is highly affected by overfitting after the global minimum is achieved (\(\gamma=0.6\)), distorting the depicted linear relationship between model complexity and latency (\(R^{2}=0.597\)).  
Finally, a key observation among the three performance axes must be made. A specific model complexity (e.g., \(\gamma=0.2\)) and computational budget (in FLOPs) can be achieved by utilizing different scaling policies (i.e., depth only, compound coefficient on depth/width, etc.). For example, in the case of the BERT models in which two dimensions are available for scaling (network layers (\(d\)), embedding sizes (\(e\))) a \(\gamma=0.2\) can be achieved with \((d,h)\) pairs that are equivalent to (2, 768) and (6, 256). In the first example, the classification accuracy is 74.2%, and latency is 2.2 s, while in the second case, accuracy is 77.8%, and the latency is 2.79 s. Therefore, model scaling can have a significantly different effect on the classification accuracy and inference latency. Hence, benchmarking various model scaling strategies to evaluate the performance outcome during DL model inference is of utmost importance when striving to achieve cost savings in geo-distributed deployments and under resource constraints.
## 2 Experimental Investigation
### Results of Experimental Tests  
All four specimens failed due to overall buckling. This behaviour was expected because the ratio L/D is much higher than 10 (see Table 1), the limit above which the global instability (i.e., buckling) occurs [17]. The diameter to thickness ratio (D/t) for all sections was below the limit prescribed by the EC4 (90-235/fy) so that all sections were not prone to local buckling.  
The measured horizontal displacements at distances 0.35 L (U0.35L), 0.40 L (U0.40L) and 0.50 L (U0.50L) from the top end of the column are shown in Table 2. The maximal horizontal displacements occurred at 0.40 L for all four specimens. Assuming that this position corresponds to the middle point of the equivalent simple beam, the effective buckling length is taken as the length between the top-end of the column and the point of contra flexure. These results indicated that the effective buckling length was approximately equal to 0.80 L for all specimens. Measured critical buckling force values are also presented in Table 2.  
Figure 5 presents the relationship between the measured critical buckling force Pcr and the deformation \(\Delta\)L/L for all four specimens CS1, CS2, CS3 and CS4.  
In order to determine actual boundary conditions at the connection of the CFT column and jacked pile, strains were measured at the bottom end of specimen CS1. Figure 6 shows the positions of strain gauges M1.1-M1.4. Table 3 gives the experimental results for strain gauge measurements.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen** & **U0.35L [mm]** & **U0.40L [mm]** & **U0.50L [mm]** & **Pcr [kN]** \\ \hline CS1 & 25.38 & 25.77 & 24.16 & 266.7 \\ CS2 & 35.01 & 35.56 & 33.34 & 343.0 \\ CS3 & 27.28 & 27.71 & 25.98 & 402.9 \\ CS4 & 38.81 & 39.43 & 36.97 & 503.8 \\ \hline \end{tabular}
\end{table}
Table 2: Measured horizontal displacements at 0.35 L, 0.40 L, 0.50 L and the critical buckling force.  
Figure 5: Pcr-AL/L relationship for specimens CS1-CS4.  
Based on the strain results, it is possible to determine the bending moment \(M_{TEST}\) at the connection of the jacked pile and the CFT column as:  
\[M_{TEST}=\frac{|\varepsilon_{max}-\varepsilon_{min}|\cdot\text{EI}_{eff,II}}{D}=3.62\text{ kNm} \tag{1}\]  
where \(\text{EI}_{eff,II}\) was taken as the second-order effective flexural stiffness proposed by Eurocode 2004 [18]. The values \(\varepsilon_{max}\) and \(\varepsilon_{min}\) are maximal and minimal strains measured by strain gauges placed at the opposite sides of the column surface at the bottom end of the CFT column (e.g., M 1.1 and M 1.3 from Figure 4).
# Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics
## 2 Related Work
### Literature on Sports Betting
#### Forecasting Football League Match Results  
Since this article aims to forcast the results of football league matches based on characteristics and skills of the involved football players, we could identify a few publications related to this topic:  
* Reference [18] extracted time dependent skills of teams of the English Premier League as well as the Spanish Primera Division with a Bayesian dynamic generalized linear model. They employed an algorithm to find the parameters of their model and to predict the next football matches based on results of former matches. In total they examined 3892 football matches from 1993 to 1997 and yielded a final cumulative return of 40% (English Premier League) and 54% (Spanish Primera Division).
* Reference [19] predicted Premier League football games by incorporating Twitter Microposts in which users estiamated the outcome of a football match. Thus, these data were yielded from textual information by a parsing algorithm. For 200 matches in 2013/2014, they could realize a profit of about 30%.
* Reference [20] made predictions for match outcomes of the Dutch Eredivisie from 2000 to 2013. The data set used included of the results of the former matches. Moreover, they added data about whether teams played in a lower leagues in the football season before, about whether a new coach was hired, and whether a top scorer of a team was injured. Different machine learning algorithms were analyzed based on these data regarding their prediction ability (e.g., Decision trees, Neural Networks, and Naive Bayes).
* References [21; 22] predicted the football matche results extracting the characteristics of the corresponding teams. They exploited this information incorporating a risk-averse betting strategy. Based on in total 8082 football matches from 2013 to 2018, the authors could achieve economically as well as statistically significant returns.  
In conclusion, none of these publications analysed the topic this paper focuses on. Some articles aim to connect sports betting with different financial markets [1; 5; 8] while others predict specific sporting events [12; 15; 17]. Currently, there is neither a study about incorporating player characteristics (e.g., age, weight, weight) and player skills (e.g., ball control, dribbling, crossing) nor one which predicts football matches for the big five football leagues (England, France, Germany, Italy, Spain) and the corresponding second leagues. This article fills these and other gaps.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 5 Discussion
### Evaluation of Main Themes  
**Data: We obtained data from EVs under real working conditions, and we utilized the battery signals in a data-driven approach. ** We achieved this using the plethora of data that AVILOO collected from their fleet of EVs' BMS. Within this work, we focused on the car model e-Golf, and this provided a total of 269 full-cycle discharging sessions from deployed EV batteries. Within these sessions, we utilized relevant signals for SoC estimation. This required interpolation and normalization of the signals. Furthermore, segmentation of sessions allowed for partial drive cycle SoC estimation, and it was necessary to process the signals further in meaningful ways. Finally, in order for the data to be trained and evaluated via NNs, different data splitting strategies were used to create train, validation, and test sets. These achievements are all defined in Sections 3.2-3.4. **NN Modeling: We built NNs that could provide robust and resilient estimation of SoC, and we compared their performance. ** We achieved this mainly in Section 4.1, where we documented the uses of architectures such as RNNs like GRU and LSTM, layering of NNs such as CNN-LSTM, and transformer-based NNs such as the Informer. We also explained what is necessary to adapt the Informer for the use case of EV batteries. The results achieved by the different NN architectures are demonstrated in Table 4 and Figures 4a and 5a. Utilizing the results of E1, we continued using the different NN architectures with different signals and even reduced the input window size, as described in Section 4.3. The different NNs continued to show high performance when compared to their performance from E1, which suggests that the NNs can provide robust and resilient estimation of SoC. **Feature Analysis--Fusion of Static and Dynamic Features: We engineered an approach for the fusion of features, specifically in terms of static and dynamic features, in a data-driven manner to estimate SoC. ** We accomplished this in Section 4.2. We previously explained that signals can be considered as features within time series in Section 3.2. In E2, we further expanded upon what dynamic and static features are in relation to time series, and we defined what dynamic and static features would be used in the fusion of features within the context of EV's battery. We engineered a NN that could process the fusion of dynamic and static features, and detailed the relevant parameters along with its training procedure.
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
###### Abstract  
With the rapid development of vertical takeoff and landing (VTOL) aircraft, the blade design of a propeller suitable for VTOL aircraft with a wide range of operating conditions has become a challenging and popular task. This paper proposes a multi-objective optimization framework for a VTOL propeller using an inverse design method at the cruising stage, which is developed from the Betz optimum theory and blade element momentum theory (BEMT). Different from passing studies, the maximum thrust-to-weight ratio at hover (MTWRH) is taken as one of the two objectives in this paper, which is closely related to the wind-resistance capability and maneuverability during takeoff and landing. The other objective is the energy consumption of the whole mission profile. A fixed pitch propeller (FPP) and a variable pitch propeller (VPP) are both optimized using the proposed framework for the Vahana A\({}^{3}\) tilt-wing aircraft and validated by the computational fluid dynamics (CFD) method. The influences of the level flight energy ratio, hover disk loading and cruising speed toward the optimization result are analyzed, respectively. The results show that the MTWRH has a significant impact on the optimization result both for the FPP and VPP. A comparison between the two propeller forms validates the advantages of the VPP both in energy saving and takeoff maneuverability. The quantitative rules of this advantage with the level flight energy ratio are calculated to provide a reference for choosing the appropriate form. Overall, the methodology and general rules presented in this paper support the propeller optimization and form selection for VTOL aircraft.  
VTOL; propeller; BEMT; inverse method; fixed pitch; variable pitch +
Footnote †: journal: Applied Physics Letters  
15 March 2022  
Accepted: 5 April 2022  
Published: 6 April 2022
## 5 Case Study: Propose Three Different Designs
### Third Design: Watchdog Design  
This design is the third design that was developed in this study. The SCH nodes are selected as watchdog nodes at the \"Schedule Creation Phase\" to monitor CHs. Therefore, it is called the Watchdog design. SCH can monitor the CH because it is located within the CH's range (nearby node). The selection criteria of the SCH were mainly based on the Received Signal Strength Indicator (RSSI). This way, the closest node to the CH with the highest remaining energy will be selected as SCH. Watchdog SCH can listen to all sent and received packets from/to the CH. Therefore, this SCH is able to submit all monitored data to the BS which will compare them with the data received by CH to detect whether this CH is a benign node or an attacker.  
This design deploys the monitoring technique instead of the message passing technique. Thus, the nodes are not informed whom the SCH is and only the CH knows its identity without sending extra messages. To clarify it more, Figure 8 illustrates the process flow of this design.  
Figure 7: Broadcast design process.