esa-sceva/satcom-chunk-collection huggingface dataset:
# Realizing Efficient Security and Privacy in IoT Networks
## 5 Numerical Analysis and Results
### Performance Comparison of Difference Algorithms  
In Figures 9 and 10, we compared our proposed sequential convex estimation optimization (SCEO), which is intended to mitigate the optimization problem in (15) against the Bisection method in [38] and the two-stage cooperative jamming scheme (TSCS) in [44]. A total of 60 transmitting antennas were selected for both experiments. For the rate constraints, about 0.8 of optimal attainable capacity (OAA) was selected for Figure 9, whereas for Figure 10, the power constraint \((\mathbf{p}_{s}=\mathbf{p}_{d}=\mathbf{p})\) is set at 20 dB.  
Figure 9 indicates that mostly if the power constraints are low, our proposed _SCEO_ technique is latent enough to obtain optimal values. Comparing the three different techniques in Figure 10 at different variations of rate constraint. Our technique is observed to outperform the other two compared techniques notwithstanding severe rate constraints. Moreover, as the rate constraints becomes more severe, convergence might be difficult for the TSCS and Bisection techniques but our algorithm converges almost seamlessly.  
Figure 8: _Source-to-Destination_ data rate for multiple sources transmission.  
Figure 9: Performance of the algorithms under different power constraints.  
Finally, the complexity analysis of our swift privacy rate optimization algorithm in a multiple transmission and multiple eavesdropper scenario is shown in Figure 11. We set the optimization parameters as \(A_{E}=A_{Tx}\), \(\beta_{1}=6\), \(P_{n}^{\max}=P_{B}^{\max}=20\) dB. From the result of our investigation, there were no evident patterns in the optimal values, in addition, the optimal parameters are dependent on the outputs of \(\mathbf{G}\). Nevertheless, \(P_{n}\) can be assumed to be frequently and approximately distributed evenly among the channels. Finally, it is observed that as \(A_{E}\) becomes larger, the minimal \(P_{B}\) and \(r\) are respectively favored by the optimization.
## 4 Simulation Results  
### Simulation Parameters  
In this part, we present the numerical results of coarse acquisition for the detection probabilities, mean acquisition time and acquisition performance comparisons during zero-padding, XFAST, DF-XFAST and DC-XFAST, as a function of SNR for fixed false alarm probabilities. Both Monte Carlo results and theoretical results are given when discussing the detection probabilities.  
Though inter-satellite link ranging mode in both GPS and BDS adopt a long PN-code spread spectrum system. GPS IIR satellite ranging mode uses the ultrahigh frequency (UHF) band to measure distance, it applies a broadcast mode to communicate with other satellites [29], while BDS chooses the Ka band, which is an end-to-end way for satellite communication. The transmission of the former can provide additional observation information. Thus the PN-code of GPS ranging mode of inter-satellite link is employed to check the acquisition performance, the PN-code period of which is \(2^{17}-1\) with chip rate 5 MHz, and the generator polynomial is G(X)=\(1+X^{3}+X^{17}\). Since the SNR range of GPSinter-satellite link is \(-21\) dB\(\rightarrow\)\(-1\) dB [30], SNR settings for simulation range from \(-25\) dB\(-0\) dB. To simplify the computation, sampling frequency \(\mathrm{f_{s}}\) is set as 5 MHz and residual code phase offset is set as zero. The quantity of chip for FFT(N\({}_{\mathrm{chip}}\)) is 4096.  
The false alarm probability \(\mathrm{P_{fa}}\) is determined by the choice of threshold, assumed that single peak false alarm probability is \(10^{-5}\), the false alarm probabilities of each threshold \(\mathrm{N_{th}}\) is presented in the following Table 1.
# Patent Analysis and Structural Synthesis of Epicyclic Gear Trains Used in Automatic Transmissions
## 4 Structural Characteristics of EGTs
In accordance with the concept of the tree in graph theory, structural characteristics (5) and (6) can be derived. (5) The number of revolute joints \(N_{r}\) of an \(n\)-link EGT is equal to \(n\)-1. According to the graph-based representation in Section 2.1, the solid vertices, the hollow vertices, and the solid lines denote links, multiple joints, and revolute pairs, respectively. Figure 7 indicates that the number of vertices is seven, where one of the vertices is hollow vertex, which means that there are six links and one multiple joints in the EGT. Based on the definition of the multiple joint, the number of revolute pairs should be one smaller than that of solid lines. In other words, the number of revolute pairs is five. (6) The number of fundamental loops is equal to the number of gear joints \(N_{g}\). The fundamental loops are defined as a loop set that any arbitrary loop of the graph can be expressed as a linear combination of the loops in the loop set. Figure 7 indicates that a fundamental loop is formed by adding a dash line. Therefore, the number of fundamental loops of the Simpson planetary mechanism is 4. (7) Based on the definition of the DOF for the planar mechanism and structural characteristic (5), the mobility \(F_{E}\) of an \(n\)-link EGT can be obtained through the expression \(F_{E}=n-1-N_{g}\). (8) According to the structural characteristic (2), the vertices representing planet gears are incident with a solid line and at least two dash lines so that they cannot connect to the hollow vertex directly. (9) The vertices representing planet gears would not be adjacent to each other by solid lines. The vertices representing the coaxial links would not be adjacent to each other. (10) There are loops formed only by gear joints in some EGTs used in ATs. In other words, DBGs including the loops only formed by dash lines can be used in ATs. These EGTs include the fourth EGT shown in Table 2, the second EGT shown in Table 5, and the second EGT shown in Table 7. In the practical design, strict requirements are needed for the size parameter of gears to satisfy the design purpose. Therefore, the structure is usually applied in special situations. Figure 7: (**a**) The Simpson planetary mechanism shown in Table 2, (**b**) the subgraph after removing dash lines, (**c**) the fundamental loop by adding a dash line. (11) There is one main shaft in each EGT, which means that there is only one hollow vertex in a DBG. Based on the definition of the multiple joints, the lines incident with the hollow vertex should not be presented by dash lines. The hollow vertex has a degree with the maximum number. (12) Nonplanar DBGs can also be used for ATs, such as the DBG in link assortment [2; 8; 1; 1; 0; 1] shown in Table 7 and the DBG in link assortment [3; B; 0; 0; 0; 0; 0; 1] shown in Table 13.  
It should be noted that some of the structural characteristics have been proposed by [PERSON] [14].
## 3 Methodology for Data Pre-Processing
### Feature Selection  
Input feature selection has a significant influence on the convergence of ML models. Battery voltage prediction models are often trained with the terminal current, battery temperature, actual voltage and an indicator of the remaining capacity, such as the SOC or a similar charge counter. Due to the computational cost and inaccuracy of SOC modeling, the proposed model was trained without SOC as an input feature. To bypass the issue of modeling the SOC, some current integration methods have been applied in the literature. The side effect of this is that either the initial SOC or some extra information about the battery state must be known, such as whether the battery is fully charged or fully discharged. This extra information and current integration over time provides a kind of SOC to the net.  
The operation strategy of a 48 V system in automotive applications pre-defines a volatile battery operating area. With rarely stationary states, an adaptable model has to be designed. Therefore, the input features of the model were selected as:  
* Terminal current \(I_{t}\),
* Temperature \(T_{t}\),
* Terminal voltage \(U_{t}\), and
* Voltage trend \(U_{trend}\).  
Table 2 shows an exemplary sequence (with a sequence length of 10) for the target value \(U_{pred}\) and the corresponding input features. The calculation of \(U_{trend}\) contains no new information, but, as an input, it ensures faster and more precise convergence when training the model. The average voltage over the last few steps provides information about which voltage level the battery is actually operating at. Furthermore, when combined with the previous current and voltage, it can provide the trend of overvoltage polarization. The current and temperature determine the overvoltage polarization in the next step. When predicting more than one step at once, the \(U_{trend}\) is recalculated every minute by using the last sequence of predicted voltages. Use of the period of 60 s ensures that small errors in voltage prediction are not fed directly to the next step as input. Updating \(U_{trend}\) in every iteration could result in a drift of voltage and, therefore, an increasing error.  
The input feature set used provides all of the information needed for learning, whereby the future voltages of a battery can be predicted. This approach has two advantages: First, explicit battery  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Step** & \(\mathbf{T}\) & \(\mathbf{I}\) & \(\mathbf{U_{trend}}\) & \(\mathbf{U}\) \\  
modeling knowledge from experts is not necessary to model the behavior. Feature selection and hyperparameter tuning are implemented and can be adapted to any lithium-ion battery by simple data preparation and model adjustments. Second, an online algorithm is provided which is applicable in every condition during operation, with a short initializing time for the sequence length.
## 1 Introduction
The construction of electrochemical biosensors has been improved with the incorporation of carbon nanomaterial due to their feature size (1-100 nm), large surface-to-volume ratio, and high biocompatibility, thus contributing to the improvement of the analytical performance of such biosensors. In addition, carbon nanomaterials are advantageous because they enhance interfacial adsorption properties, increase electrocatalytic activity, and promote electron transfer kinetics compared to many conventional electrochemical biosensor materials [13; 14]. Graphene is a planar sheet with a two-dimensional (2D) carbon nanomaterial hexagonal pattern [13; 15]. The atomic thickness of the graphene sheet and its high surface-to-volume ration allow all carbon atoms to interact directly with the analytes, consequently making such material high sensitive towards the change of local environment conditions, thus promoting higher sensitivity than carbon nanotubes (CNTs) [5; 14]. In opposition to CNTs, graphene has two main advantages regarding its application in electrochemical biosensors. Firstly, graphene does not contain metallic impurities, which interfere with the material electrochemistry and is considered the main drawback of CNTs. Further comprising, graphene compounds can be easily incorporated in biosensor fabrication while it is difficult in the case of CNTs due to its one-dimensional nature [5; 14; 15; 16]. Moreover, graphene-derivatives nanocomposites  
Figure 1: Block diagram of biosensors in order. have the advantages of good stability, wide potential window, low reaction over potential, negligible residual current, and excellent electro-catalytic activity which are beneficial for the construction of high-performance electrochemical biosensors. Since the discovery of graphene was placed as a champion for biosensors electrode development thanks to its superior performance enhancement of the materials such as metals, metal oxides, etc. [5; 13; 17; 18]. Carbonic nanostructures that include various low-dimensional allotropes of carbon including fullerene (0D), carbon nanotube (1D), carbon nano-onion, carbon nanohorn, nanodiamond, and graphene (2D) have unique electrochemical properties. In comparison to other carbon nanostructures graphene has recently been used in the construction of electrochemical biosensors due to its extraordinary characteristics including electrochemical reactivity, conductivity, mechanical strength, and low toxicity [5; 11; 13; 14]. Different types of carbon allotropes crystalline have been presented in Figure 2.
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 2 Related Work
### Arabic Text Clustering
They built a corpus of 1000 documents extracted from a Kuwaiti newspaper website called Alanba [33]. The documents cover different topics, such as health, technology, sports, politics, and others. The authors then compared the evaluation of the three clustering algorithms. They reported that SOM obtained the highest accuracy between the three algorithms with a rate of 93.4%. From this study, it appears that the use of SOM in clustering Arabic text is promising. The Bond Energy Algorithm (BEA) was also used by researchers to cluster Arabic text [34]. The results of this study showed that the BEA algorithm outperforms K-Means clustering in terms of precision, recall, and the F1-score. In the broader field of text clustering, researchers also proposed the use of prototype-based models for text clustering [35]. The results of this work showed that it outperforms K-Means clustering. To conclude, most of the current work on Arabic text clustering used K-Means clustering because it is a simple model and can be applied easily. However, the mechanism that K-Means follows has limitations. For instance, K-Means first initiates centers of clusters and then assigns documents to these clusters. If the initiation process of K-Means is not well formulated, then the risk of incorrect clustering arises. Moreover, techniques that integrate K-Means clustering with Particle Swarm Optimization [26] have promising results. This shows that optimization contributes positively to clustering models. In addition, previous work showed that the use of SOM provided better clustering results than K-Means for Arabic text [11]. We hypothesize that integrating SOM with an optimization method would result in better clustering as we are presenting in this paper. Table 1 presents a summary of the recent work regarding Arabic text clustering.
## 4 Discussion
### Removal Efficiencies of Wastewater Treatment Plants
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Pharmaceutical** & \begin{tabular}{l} **No. of Studies in Which the** \\ **PRs Occur (\% of the Total)** \\ \end{tabular} & \begin{tabular}{l} **Max Removal** \\ **Efficiency (\%)** \\ \end{tabular} & \
\begin{tabular}{l} **Min Removal** \\ **Efficiency (\%)** \\ \end{tabular} &
\begin{tabular}{l} **Average Removal Efficiency** \\ **among the Studies** \\ \end{tabular} \\ \hline Erythromycin & \(5\) (83.3) & \(99.9\)[37] & \(-193.0\)[39] & \(10.3\) \\ Sulfamethoxazole & \(5\) (83.3) & \(100.0\)[39] & \(-504.2\)[37] & \(22.2\) \\ Metronidazole & \(3\) (50.0) & \(99.9\)[37] & \(-320.0\)[5] & \(28.5\) \\ Trimethoprim & \(5\) (83.3) & \(99.9\)[37] & \(-23.0\)[17] & \(54.0\) \\ Clarithromycin & \(4\) (66.7) & \(100.0\)[36; 39] & \(-22.0\)[5] & \(68.1\) \\ Ofloxacin & \(5\) (83.3) & \(100.0\)[39] & \(7.0\)[36] & \(79.9\) \\ Ciprofloxacin & \(4\) (66.7) & \(100.0\)[36; 39] & \(44.5\)[17] & \(88.4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Removal efficiencies of the PRs most commonly found in WW. Given the above, the need to evaluate the occurrence of antimicrobial substances in both effluents and sludge is required to better comprehend the process of sorption and to accurately assess the removal performances of WWTPs.
# Integration of Freeplay-Induced Limit Cycles Based On a State Space Iterating Scheme
## 3 State Space Iterating (SSI) Scheme
### Modification of the Poincare Map and Iteration Plots  
First, an \(N_{T}\)-th time return mapping \(F=F^{N_{T}}\) is proposed based on the Poincare map. We specify a Poincare section \(\Sigma\) at the upper freeplay boundary, \(\Sigma:\ x=\delta\); then, let the system evolve from a point \(\mathbf{x}_{0}(t_{0})\) at \(\Sigma\) to another point \(\mathbf{x}_{0}^{*}(t_{0}^{*})\) at \(\Sigma\) after \(N_{T}\) times crossing \(\Sigma\), where \(N_{T}\) is the \"crossing number\" and can be specified as any positive integer. The orbit of \(\mathbf{x}(t)\), which represents the evolution, is obtained by time integration with the Henon-RK45 method and is illustrated in Figure 5a. The proposition of \(N_{T}\) is based on the consideration that one may encounter a lengthy transient phase of the system responses due to an inappropriate selection of \(\mathbf{x}_{0}\) and the complexity of the aeroelastic system with freeplay. Figure 5b provides a general sense of the freeplay state \(x_{k}\) when the system is evolving from \(\mathbf{x}_{0}(t_{0})\) to \(\mathbf{x}_{0}^{*}(t_{0}^{*})\) following the orbit \(\Gamma\).  
Next, we will observe the orbit \(\Gamma\) from another perspective and focus on the \(j\)-th state \(x_{j}\). When the integration starts with \(\mathbf{x}_{0}(t_{0})\), we have \(x_{j}(t_{0})=x_{0,j}\), while when the integration is terminated at \(\mathbf{x}_{0}^{*}(t_{0}^{*})\), we have \(x_{0,j}^{*}(t_{0}^{*})=x_{0,j}^{*}\). Inspired by the [PERSON] map, we next construct a two-DoF plane, as shown in Figure 6, where point \(A\) represents the orbit \(\Gamma\) by means of its coordinates \((x_{0,j},x_{0,j}^{*})\). This is similar to what [PERSON] did in Figure 4, where [PERSON] used a point with the coordinates \((z_{n+1},z_{n})\) to represent a segment of system behaviors. By means of this method, each I.C. case \(\mathbf{x}_{0}\) is associated with \(\mathbf{x}_{0}^{*}\) and a point on the two-DoF plane. If we select \(N_{S}\) I.C. cases, then we will have \(N_{S}\) points on the plane. When \(N_{S}\) is a large number, sufficient points on the plane could illustrate a spatial pattern, indicating system behaviors. Based on this pattern, we can construct the mapping \(F^{N_{T}}\): \(\mathbf{x}_{0}^{*}=F^{N_{T}}(\mathbf{x}_{0})\). We call this two-DoF plane an \"iteration plot\". Note that we need to construct \(n_{x}-1\) iteration plots (where \(n_{x}\) is the number of system states), with each one corresponding to a state except for the freeplay state \(x_{k}\), because \(x_{k}\) always equals \(\delta\) since both \(\mathbf{x}_{0}\) and \(\mathbf{x}_{0}^{*}\) are at \(\Sigma\).  
Figure 5: Illustration of the \(N_{T}\)-th time return mapping. (**a**) is depicted in state space and (**b**) is depicted on the plane of the freeplay state vs. time. The parameter \(N_{T}\) = 3 is given as an example.
###### Abstract  
In supporting communications with unmanned aerial vehicles (UAVs) as aerial user equipments (aUEs) in cellular systems, the current beamforming schemes based on channel state estimation are facing severe challenges from the pilot contamination effect, especially in 5G and future networks where the cell size becomes small and the user density is high. Beamforming schemes based on signal direction of arrival (DoA) are regarded as a highly promising alternative to solve this problem. However, to achieve optimal performance for DoA-based beamforming, the error to DoA estimation during pilot signal intervals, caused by the high mobility of UAVs, must be addressed. In the meantime, the training overheads of traditional DoA estimation algorithms must be reduced to save the bandwidth for data communication. This paper investigates uplink beamforming performance enhancement based on signal DoA estimation to support UAV-cellular network communication. We propose a novel DoA estimation algorithm to predict angle variations during the intervals, which achieves high precision even when UAVs are at high mobility. The prediction process requires no pilot signals and enables timely adjustment of the steering vector when calculating the beamforming weight vector. The proposed algorithm contributes to the realisation of a beamforming scheme with real-time steering vector updates, which simultaneously maintains high beamforming gains and low training overheads. Simulation results show that, compared with the conventional DoA-based beamforming scheme, the proposed method yields more accurate DoA estimation output and higher gains. Furthermore, simulation experiments also suggests that applying the proposed scheme can reduce up to 100 pilot signal transmissions per second.  
unmanned aerial vehicle (UAV); cellular network; direction of arrival (DoA); training overhead; beamforming; steering vector +
Footnote †: journal: Applied Sciences
## 3 Results and Discussions
### Data-Driven Prediction Model Development and Training
#### 3.2.2 Model Train Flow  
The LSTM was used as the base model for the battery voltage and SOC prediction. To improve the model accuracy, BiLSTM was utilized to process the input data bidirectionally. The BiLSTM model's capacity to integrate both past and future data points grants it a superior performance over conventional RNNs and GRUs for battery SOC prediction. Figure 10 shows the overall flowchart for predicting the battery voltage and SOC based on simulated cycle data derived from the Simulink equivalent circuit model. The dataset contains simulation data under various charge rates, with the input variables being the past 60 time steps of the voltage \(\rm{V_{t-k}}\), current \(\rm{I_{t-k}}\), voltage change \(\rm{dV_{t-k}}\), charge change \(\rm{dQ_{t-k}}\), and state of charge \(\rm{SOC_{t-k}}\). The output variables are the predicted voltage and SOC for the next 30 time steps, with each time step being 10 s, resulting in a total duration of 5 min. The model was trained using a fixed-length sliding window approach, where the past 60 time steps, representing 10 min of input data, were used to predict the subsequent 30 time steps.  
Figure 11 shows the flow of the battery voltage and SOC prediction model using a BiLSTM architecture with MHA. The model's input variables are first processed by an MHA mechanism, which dynamically redistributes the importance of different features across various time steps. By introducing the MHA mechanism, the model's capacity to analyze data over long-time horizons and complex multivariable scenarios, such as the voltage and current, is significantly enhanced, improving the prediction accuracy. After MHA processing, the input variables are fed into the BiLSTM module to capture time sequential data. Finally, the output layer generates predictions for the voltage and SOC.  
The model's hyperparameters, including the learning rate, number of neurons, number of iterations, rate of dropout, batch size, historical data window length, and prediction window length, were set as follows: 0.00025, 60, 5, 0.15, 64, 60, and 30. To assess the predictive performance of different models, this study employs the coefficient of determination (R\({}^{2}\)), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as the model performance comparison criteria.  
Figure 10: Flow chart of the battery voltage and SOC prediction model using BiLSTM architecture with Multi-Head Attention (MHA).