esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Forecasting Methods  
As explained, to predict the future evolution of the cell capacity, it is necessary to apply suitable mathematical methods. In this section, it is proposed to apply two approaches, namely, linear fitting model (LF) and autoregressive integrated moving average (ARIMA).  
As shown in Figure 3, the forecasting algorithm predicts the value of \(\Delta C\) or \(\Delta V_{RMS}\) of the cell at the current cycle according to the degradation model built from the evolution of \(\Delta C\) or \(\Delta V_{RMS}\). Positioned at the current cycle, the forecasting algorithm takes the past values of \(\Delta C\) or \(\Delta V_{RMS}\) in the training interval (see the gray area in Figure 3) and fits these measured values to a straight line in the case of the LF method or uses lagged values of \(\Delta C\) or \(\Delta V_{RMS}\) and lagged errors in the case of ARIMA, as explained in the following paragraphs.  
ARIMA was developed in the 1970s [33] and has since been widely used for forecasting and time series analysis [34; 35]. ARIMA builds a time domain forecasting model based on the most recent measurements combined with the long-term historical trend [36]. ARIMA models have been used to forecast time series of economic variables [37], pandemic data [38], climate-related variables [26], the remaining useful life of fuel cells [39], and the behavior of electrochemical batteries, among other applications. Nonseasonal (\(p\), \(d\), \(q\)) ARIMA models  
Figure 3: Forecasting algorithm based on historical data—the training data.  
Figure 2: Evolution of the capacity fade \(\Delta C\) and the proposed estimator \(\Delta V_{RMS}\) during the lifetime of cell B0005.  
are based on \(p\) autoregressive terms, \(d\) nonseasonal differences, and \(q\) lagged forecast errors. A (\(p\), \(d\), \(q\)) ARIMA model can be expressed as  
\[\hat{y}_{j}=\alpha_{1}\Delta^{d}y_{j-1}+\ldots+\alpha_{p}\Delta^{d}y_{j-p}+ \varepsilon_{j}+\gamma_{1}\varepsilon_{j-1}+\ldots+\gamma_{q}\varepsilon_{j-q} \tag{10}\]  
where \(y_{j}\) and \(\hat{y}_{j}\) denote the measured and predicted values of \(\Delta C\) and \(\Delta V_{RMS}\), respectively, of the cell at the time instant \(j\Delta T\), where \(\Delta T\) is the sampling time; \(\varepsilon_{j}=y_{j}-\hat{y}_{j}\) is an error term; \(\Delta^{d}y_{j}=(1-L)^{d}y_{j}\); and \(L\) is the lag operator, i.e., \(Ly_{j}=y_{j-1}\) and \(L^{d}y_{j}=y_{j-q}\). Thus, \(y_{j}\) is obtained by applying \(d\) successive integrations to \(\Delta^{d}y_{j}\).
## 2 Problem Description  
The parameter identification of USV refers to the process of estimating and determining the unknown parameters in the USV model using real-time navigation data. The motion state data collected during USV operations, including sensor-acquired state data and corresponding control commands, are related to specific motion parameters in the USV mathematical model. By employing parameter identification algorithms, the unknown parameters in the model can be determined, leading to a more accurate USV motion model. There are three crucial steps that precede parameter identification: the establishment of the USV Nomoto model, the construction of the identification model, and the design of the LWR filtering algorithm.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 5 Performance Evaluation
### Simulation Configuration  
This work considers a WSN with node deployment following the Motoscope testbed [34] called the Soda network. As seen in Figure 3, 78 nodes were deployed in a \(40\times 50\) m area, where the dark star and the circles represent the sink and the sensor nodes, respectively. With this node deployment, the maximum group index in the Soda network is 6 (i.e., \(g=6\)).  
The simulation setup is listed in Table 3. Sensor nodes are equipped with a Preon32 wireless module [35]. The transmission range and bandwidth of the sensors are 10 m and 2 Mbps, respectively. The power consumption of the Preon32 in the sleep, receiving, and transmitting modes is 60 nW, 36.9 mW, and 72 mW, respectively. The initial energy of each node is set to 500 J, which is enough to ensure that no node consumes all energy before the simulation finishes. In the data transmission phase, an event randomly occurs at an arbitrary node. The number of events occurring in a given time follows the Poisson distribution with a mean of \(\lambda\). For example, if the event rate is set to 0.5 packets/s, there is 0.5 event per 1 s on average, i.e., \(\lambda=0.5\). Then, this node packs information into a 46-byte data packet to transmit to the sink. During a duty cycle interval, nodes are awake for the active period \(\tau=0.4\) s. We used the _ns-2_ network simulator [36] version 2.35 for the simulation, in which the final result is the average of 30 execution results. The main features for the transmission model setup in _ns-2_ are also shown in Table 3.  
Network performance was analyzed under the evaluation parameters shown in Table 4. In order to evaluate the effects of each evaluation parameter on network performance, only the value of one selected parameter was varied, while the other parameters remained at default values. The default values for the delay bound, the required DSR, and the event rate were 30 s, 95%, and 0.5 packet/s, respectively.  
Figure 3: In the Soda network, 78 nodes were deployed following the Motoscope testbed in 2007 in Soda Hall, University of California, Berkeley, California, USA.
## 1 Introduction
Nevertheless, such networks effectively fuse multi-level features but do not fully utilize the contextual information. The primary motivation behind this work is to introduce a new UNet-based network that will fully explore useful features of the channel and capitalize on the contextual information with an overall aim of increasing segmentation accuracy and training performance. To achieve this, we introduce three modifications to the original UNet. First, we add residual connections to each layer of the encoder and decoder to help network training. Second, we introduce a self-attentive mechanism in skip connections to capture feature dependencies in the channel dimension while ensuring effective fusion of multi-level features. This is achieved by assigning the specific attention weight to channels, which reduces noise and gives more attention to essential regions. Third, we use ASPP blocks as the bottom structure of UNet to effectively increase the receptive field and reduce the impact of learned redundant features, with the overall aim of obtaining higher segmentation accuracy.
# Current Context and Research Trends in Linear DC-DC Converters
## 1 Introduction  
Most electronic circuits, including portable devices, require a clean and constant voltage DC power supply. In a successful electronic design, the power supply should not be considered an afterthought or the final stage of the design process, because it is the most vital part of a system for ensuring reliable performance under worst-case circumstances. As shown in Figure 1[1], the main energy source available for the system may be a commercial AC supply, a battery pack, or a combination of the two. After this stage, various types of DC-DC converters are used to match the various load power requirements. Primarily, three basic types of DC-DC converters, linear regulators, switch-mode power supplies (SMPS), and switched capacitor converters (SCCs), are mixed and matched according to the nature of power supply requirements.  
Most power supply design issues are due to resource and component limitations within the power supply and the power management system (PMS). Non-ideal components, particularly passive devices, have commercial limitations in providing sufficient backup energy storage within the battery pack. Furthermore, unexpected surges and transients from the commercial AC supply and fast load current transients could create extreme and unexpected conditions within the system unless the PMS adequately addresses all the possible worst cases at an early design stage.
## 3 Test Results and Analysis  
### Shear Characteristics Analysis  
The shear displacements and shear stress curves of the displacements meet expectations, and can better reflect the shear characteristics of irregular sawtooth displacements  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial No** & **Normal Stress/MPa** & **Undulating Height/mm** & **Undulating Angle/\({}^{\circ}\)** & **Research Objective** \\ \hline
1 & 2 & 6–10 & 40 & \\
2 & 3 & 6–10 & 40 & Effect of normal stress \\
4 & 3 & 6–10 & 40 & \\
5 & 3 & 6–10 & 50 & Effect of undulating height \\
6 & 3 & 6–10 & 60 & \\
7 & 3 & 6 & 40–70 & \\
8 & 3 & 8 & 40–70 & Effect of undulating angle \\
9 & 3 & 10 & 40–70 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test Scheme.  
Figure 4: Test equipment diagram.  
under different influencing factors (normal stress, undulating angle, undulating height). According to the test results, the shear displacement-shear stress curve of the rock discontinuity is shown in Figure 5.
# Performance Evaluation of Carbon Black Nano-Particle Reinforced Asphalt Mixture
## 2 Experimental Work
### Preparation of Asphalt Mixtures  
Midpoint aggregate gradation of National Highway Authority (NHA) class B (19.5 mm nominal maximum aggregate size) was used in this study. It is specified as a finer gradation and is generally used by the local pavement industry as an asphalt wearing course. The midpoint gradation curve for NHA class B is shown in Figure 2. Marshall mix design was performed for two CBNPs percentages (5% and 10% by weight of asphalt binder) to get the optimum binder content for asphalt mix design. In this test, a standard hammer of 4.5 lbs was used and it was dropped from 18 in height; 75 blows of the hammer were applied on each side of the specimen (specified for heavy traffic in the specifications of the Asphalt Institute). Midpoint of the in-place air voids range (3 to 8 percent) as specified in MS-2 series, i.e., 5.5% of air voids were maintained in the design. Optimum bitumen contents for the two CBNPs percentages used in asphalt performance testing are shown in the Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Container** & **5\% CBNPs** & **10\% CBNPs** \\ \hline
**Top Portion** & PG 64 (64.5 \({}^{\circ}\)C) & PG 70 (71.0 \({}^{\circ}\)C) \\
**Middle Portion** & PG 64 (64.2 \({}^{\circ}\)C) & PG 70 (70.9 \({}^{\circ}\)C) \\
**Bottom Portion** & PG 64 (64.7 \({}^{\circ}\)C) & PG 70 (70.6 \({}^{\circ}\)C) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance grades showing homogeneous dispersion.  
Figure 2: Aggregate gradation curve.  
Generally an increase in the filler content in an asphalt mixture results in the reduction in the optimum binder content requirement for that mix because filler material acts as an 'extender' and requires less bitumen in order to fulfil the asphalt volumetric requirements [20]. However, researchers have experienced both an increase and decrease in the optimum binder content requirements while utilizing nano materials. Previous studies have shown that different fillers differently affect the properties of asphalt mixtures. Such changes in test properties can be associated with changes in viscosity of the filler-binder containing different fillers. However, it is difficult to get a direct correlation between filler-binder viscosity and compacted mixture properties. The reason for this could be attributed to the variable effects of different fillers on the compaction and volumetric properties of different asphalt mixtures [21].  
An increase in optimum binder content was experienced with the increase in CBNPs dosage. [PERSON] and [PERSON] [22] observed the same trend. An increase in optimum binder content requirement could be attributed to large surface area of CBNPs used in this study. This could also be attributed to the increase in viscosity of the binder after addition of the nano material. CBNPs, because of their small size, enhance the viscosity of bitumen making it stiffer. A higher viscosity leads to a thicker binder film of the modified bitumen in the mix thus increasing the binder volume in the mix [23].
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 4 Results and Discussion
### Evaluating the Model  
We evaluate the model by using the performance evaluation metrics and validating the percentage of customer churn that our model predicted versus that provided by the company.  
The company presented a customer churn percentage of 27% from January 2017 to June 2017. Our model predicted the customer churn for the same period as 31.6%, which is close to the real percentage.  
The model predicts the customer churn percentage based on the following equation:  
\[\text{cust\_churn}=\text{total\_churner}/(\text{num\_customers})\times 100 \tag{3}\]  
where total_churner is the total number of churners in the dataset, and num_customers is the total number of all the customers in our dataset.  
After validating the customer churn percentage by using the historical data of customers and the customer satisfaction percentage predicted by Twitter mining, we were able to answer the RQ1, 'Is it possible to predict the customer churn of telecommunication companies in Saudi Arabia by analysing customers' tweets?  
Figure 10: Log loss score versus probability distribution for the SentiChurn model.  
Figure 9: Normalised confusion matrix of the SentiChurn model.
## 3 Deep-Learning-Based Channel Estimation Methods
### Toy Example of Deep Learning Application in Channel Estimation  
Before reviewing the research on deep-learning-based channel estimation, we provide a toy example to illustrate the application of deep learning in channel estimation. The example aims to demonstrate the ability of deep learning to enhance channel estimation performance.  
This simulation is set in a vehicular communication scenario within an OFDM system that conforms to the IEEE 802.11p standard. In this environment, the channel exhibits time-varying characteristics, and there is a strong correlation between adjacent OFDM symbols. The classic LS algorithm, performed based on two preambles in an OFDM frame, struggles to track rapid changes in the channel, resulting in poor performance. In contrast, the constructed data pilot (CDP) algorithm constructs virtual pilots from data subcarriers and uses the correlation between adjacent OFDM symbols. It treats the data subcarriers of the previous symbol as a preamble to conduct the current symbol's channel estimation. This algorithm somewhat improves the channel estimation performance but reduces reliability due to error propagation from one symbol to the next.  
The flowchart of our example is shown in Figure 11. In our example, we aim to employ a DNN to capture more channel time-frequency features, thereby reducing the error between the CDP estimation results and the actual channel response and improving the channel estimation accuracy. This DNN comprises three hidden layers, each with 20 neurons, and an output layer, and it takes the coarse estimation result from the CDP algorithm as its input.  
In the experiments, we used the VTV Urban Canyon (VTV-UC) channel model with a velocity setting of 48 km/h and a Doppler shift of 500 Hz. To obtain a robust DNN model, we trained it at a high SNR of 40 dB. Then, we tested it in a SNR range of 0-40 dB and compared its performance with the LS algorithm and the original CDP algorithm. Figure 12a,b display the performance comparison in terms of BER and NMSE, respectively. These results indicate that the DNN-enhanced CDP approach significantly improves the channel estimation accuracy in most scenarios. Even though the performance is not the best in a few low-SNR conditions, it still outperforms the other two traditional algorithms in general, showing the potential of deep learning in channel estimation applications.  
Figure 11: Flowchart of the toy example.
## 4 Inverse Problems
### Summary  
In Table 3 we summarize the highlights of direct and iterative solution methods of inverse problems.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Section** & & \\ \hline \hline
4. & Solving an inverse problem consists of using measured effects to estimate the causes. \\ \hline  & The main difficulties with inverse problems are the possible nonlinearity of the inverse mapping function, the multiplicity of solutions, and the sparsity and noise of the measured effect data. \\ \hline  & The direct solution of inverse problems by the deduction of the inverse mapping function is only possible for simple cases. \\ \multirow{4}{*}{4.1} & There are specialized mathematical solutions for specific problems of finite elasticity. Some relevant problems of inverse elasticity that have direct inverse solutions are (1) the solution of material properties from boundary loads and domain displacements. (2) The solution of the unloaded configuration from the applied loads, material properties and deformed configuration. \\ \multirow{4}{*}{4.2} & Direct solutions of inverse problems are computationally efficient. However, direct solutions are not generalizable and require continuous smooth functions of the measured input often not compatible with noise and scarce experimental data. \\ \hline  & Inverse problems can also be solved through an iterative weak approach. This consist of iteratively solving a forward simulation problem to minimize an error function between simulation outputs and target measurements while fitting the sets of unknowns. The iterative solution methods of inverse problems are generalizable, can handle noisy and scarce experimental target data, and can operate on top of existing simulation software. However, iterative methods are computationally expensive. \\ \multirow{4}{*}{4.2} & The selection of the target function to be minimize needs to be consistent with the nature of the problem and the characteristics of the biomechanical model. \\ \multirow{4}{*}{4.2} & The inverse method can be implemented though a variety of optimization methods. For the solutions of biomechanical inverse problems, optimization methods with no differentiation of the target function are preferred. \\ \multirow{4}{*}{4.2} & Population-based optimization algorithms can solve global minima of multiparametric functions with an increased toll of computational expense. \\ \multirow{4}{*}{4.3} & Statistic-based optimization method can incorporate previously reported data which can reduce convergence time and provide probability distributions of results rather than single deterministic values. \\ \multirow{4}{*}{Convergence times can be improved and solution multiplicity narrowed by the implementation of solution constraints. Constraints can be based on physical laws and limits or on previous experience. \\ \multirow{4}{*}{Constraints can also be implemented to promote numerical stability and smoothness of the converged solution.} \\ \cline{2-2}  & \(\mathcal{L}\)-\(\mathcal{L}\
## 2 Chemical Synthesis of Nucleoside Di- and Triphosphates
### Synthesis of Nucleoside Di- and Triphosphates via Phosphoramidate Intermediates
The in situ-generated phosphorimidazoles of type **10** easily reacted with various phosphate nucleophiles, leading to NDP or NTP. The reaction occurred under mild conditions and did not require the use of protective groups in the nucleoside moiety. This synthetic protocol was further developed by [PERSON] and [PERSON], who applied it to the preparation of 5\({}^{\prime}\)-O-triphosphates of deoxynucleosides and oligodeoxynucleotides. The yields of isolated products reached 70%, although in some cases were as low as 20% [61]. Phosphorimidazoles **10** intermediates were used also for the synthesis of nucleotide derivatives, with various natural and unnatural sugar residues at the P\({}_{\beta}\) phosphate moiety (Scheme 7) [62]. An effective modification of this approach was proposed by [PERSON] and colleagues [63]. For the formation of phosphorimidazolide **10** they used an imidazole/triphenylphosphine/2,2\({}^{\prime}\)-difluoridinepyridine reagent system (**B**, Scheme 7), which was previously developed by [PERSON] et al. [64]. After the generation of nucleotide derivative **10**, inorganic phosphate and a catalyst (zinc chloride, to increase the leaving group ability) were added. The condensation to NDP was usually complete within a few hours, and the final products were formed in very good yields (95-97%) [63]. Currently, there are several methods available for the synthesis of di- and triphosphates from the corresponding phosphate monoesters, differing in the choice of the reagent used to generate phosphorimidazolide **10**[3; 65]. One of the recent proposals is 2-imidazolyl-1,3-dimethylimidazolium chloride [66] or hexafluorophosphate [2] (ImIm, **C**, Scheme 7), which were both prepared in situ and were used in the synthesis of NDPs, NTPs, symmetrical bis-5\({}^{\prime}\),5\({}^{\prime}\)-O-dinucleoside diphosphates, and nucleoside di- and triphosphates with various sugar residues attached to the terminal phosphate. The advantage of this strategy is that unprotected nucleotides (commercially available sodium or potassium salts) can be used as starting materials, and the reactions can take place in an aqueous medium. The yields of this method are 20-60%. To sum up, in most of the methods based on the activation of a phosphorus center in the form of phosphoramidates, the transformation of the substrate into the desired product (NDP or NTP) takes quite a long time (in some cases even several days), and the yield of isolated compounds is not always satisfactory.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 3 Proposed Algorithm
### CJ Joint Processing  
CJ joint processing combines CG and Jacobi to pre-iterate the initialized signal, taking advantage of the good convergence of the CG initial iteration. It is CJ joint processing that reduces the number of cycles required for subsequent iterative estimation.  
The CG [8] detection algorithm searches in the direction conjugate to the gradient until the search causes the residuals to converge to zero. The CG iteration algorithm gives a good initial detection direction, but the pre-processing step generates high complexity over many iterations of the loop. Therefore, we choose one conjugate gradient iteration to avoid the increase in complexity.  
In the CG iterative algorithm, it is known that the initial residuals and the starting conjugate search direction are assumed to be \(r^{(0)}=y_{MF}-As^{(0)}\) and \(r^{(0)}=p^{(0)}\), respectively. Then, the result for the CG iterative algorithm after one iteration is [28]  
\[s^{(1)}_{CG}=s^{(0)}+u^{(0)}p^{(0)} \tag{11}\]  
where \(u^{(0)}=\frac{(r^{(0)},r^{(0)})}{(r^{(0)},Ap^{(0)})}\) is the search step.  
The expression for the signal estimate of the Jacobi iterative algorithm [10] applied to massive MIMO communication is [28]  
\[s^{(i+1)}=D^{-1}(y_{MF}+(E+F)s^{(i)}) \tag{12}\]  
Using the CG iterative algorithm as the starting value of the Jacobi iterative scheme, the result after one repetition of Jacobi can be given,  
\[s^{(1)}=D^{-1}(y_{MF}+(E+F)s^{(1)}_{CG}) \tag{13}\]  
Combined with the description of the above, the CJGS iterative algorithm is shown in Algorithm 1.  
```
Input: y H\(\sigma^{2}\)B U Initialization: 1. A = H\({}^{4}\)H+\(\sigma^{2}\)I
2. \(y_{MF}=H^{H}y\)
3. D = diag(diag(A))
4. \(E=-tri(A,1)\)
5. \(F=-tril(A,-1)\)
6. CJ joint processing:
6. \(r^{(0)}=y_{MF}-As^{(0)}\)
7. \(r^{(0)}=p^{(0)}\)
8. \(u^{(0)}=\frac{(r^{(0)},r^{(0)})}{(r^{(0)},Ap^{(0)})}\)
9. \(s^{(1)}=D^{-1}(y_{MF}+(E+F)(s^{(0)}+u^{(0)}p^{(0)}))\) GS iterative estimation:  For i = 2 do
10. \(s^{(i)}=(D-F)^{-1}(Es^{(i-1)}+y_{MF})\) End Output: \(s^{i}_{end}\)
```  
**Algorithm 1** CJGS iterative algorithm