esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a NARX-ANN-Based SP Controller for Control of an Irrigation Main Canal Pool
## 1 Introduction
The main objective of this paper is the design of modified SP controllers that use NARX-ANN models as nonlinear internal models for the accurate and reliable control of water distribution in an irrigation main canal pool. The major contributions of this paper are: (1) the proposal for the first time of a water distribution controller in an irrigation main canal pool founded on a modified SP with a NARX-ANN based model that describes the nonlinear dynamic behavior of the canal pool with an arbitrarily small error and (2) the improvements in the SP rejection features to load disturbances by means of including a gain tuning block \(K(t)\) that allows an adequate estimation of the load disturbances to be removed. The proposed controller has the advantages of increasing the operability and reliability of the water distribution control systems, as well as reducing current water losses due to operation in the irrigation main canals pools. This article is organized as follows. Section 2 proposes mathematical models of the studied process through the application of systems identification procedures. Section 3 develops the design of the NARX-ANN-based SP controller. Section 4 shows the discussions of the obtained results. Finally, some conclusions are offered in Section 5.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 5 Management of WSNs Based on SDN
### QoS Management  
The Quality of Service management covers all aspects of ensuring an acceptable delivery of service in a network. This includes management of system tolerance and reliability, packet latency, event detection, robustness and maintenance. The SDN architecture allows for the QoS management tasks to take place in the controller by virtue of being logically centralized. The SDN-based WSN environment varies due to factors such as topology changes, node failure, bandwidth availability and energy fluctuations therefore, there is need to integrate a mechanism for reliability of the network. [PERSON] et al. [127] study the reliability of the WSN architecture based on SDN through the use of models based on continuous time Markov chain and continuous stochastic logic techniques. The results show that network reliability is affected by the number of controllers and sensors and their respective failure rates. From the results a proposal to integrate more than one reliable controller as a reliability management strategy is made.  
Management of reliability has been classified into the reliability of communication systems and reliability of tasks [87]. Management of communication reliability can done through methods such as providing back up nodes and redundant links [87]. Function alternation has been proposed to manage reliability of sensor tasks by enabling neighbouring sensor nodes to take over damaged sensor node tasks automatically providing a self healing mechanism [65]. The SDN controller can also provide a back-up for sensing tasks and information to improve the reliability of tasks [87]. The back-up information can be used for re-tasking neighbouring sensor nodes to take up the task of damaged or unreliable nodes in SDN-based WSNs. The SDN OpenFlow concept has been demonstrated and exploited to address issues of performance reliability in WSNs [128]. The idea presented is based on the inherent characteristic of OpenFlow to control and monitor sensor traffic enabling a reliable routing and load balancing mechanism. In ordinary WSNs the reliability of multi-hop and end to end communications is still an unsolved and difficult issue to manage and hence Flow-sensor has been proposed to produce better reliability as compared to typical sensors by generating a lower amount of packets reducing the traffic overhead in constrained networks [128]. Another QoS parameter is robustness which is a characteristic that defines a system's ability to meet its expected performance [24] under unreliable environmental conditions. To manage system robustness it is necessary to consider factors affecting and interfering with the system and monitor the interference patterns in a proposed statistical machine learning technique [24]. This technique would allow for the prevention of possible interference based on previous behavioural patterns of interference. In managing latency TinySDN [69; 118] has been proposed however, there is need for further research on managing latency and event detection.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 3 Hardware and Software of WT in Sport
### Sensors Layer
#### 3.1.3 Location Sensors  
Location sensors are used to track athletes' position changes and movements. The most fundamental wearable positioning systems include Global Navigation Satellite System (GNSS), Ultra-wideband (UWB) positioning systems, Wi-Fi, Bluetooth, RFID, and wearable marker positioning systems [68; 69; 70]. The most used methods among these are GNSS, UWB, and camera-based wearable marker positioning systems.  
GNSS refers to satellite-based navigation systems, with the Global Positioning System (GPS) being the most widely used. A GNSS receiver receives satellite signals, analyzes the timing and location of the signals, and determines the user's position accordingly. GNSS is suitable for open-field applications, but it may not work or may be misleading in indoor environments due to signal weakening and reflections. In sports, it is used for position determination, speed and distance measurement, and activity analysis studies [69; 71; 72].  
The UWB positioning system consists of a transmitter and one or more UWB receivers. The UWB transmitter generates short-duration pulse signals and transmits them over a wide frequency band. UWB receivers receive the signals from the UWB transmitter and analyze their arrival time, power, and frequency spectrum. Through this analysis, the position of the user wearing UWB can be determined based on the time, path, and obstacles through which the signals propagate [69; 73]. UWB positioning provides high accuracy and precision and is also inexpensive and easily portable. UWB modules combined with IMUs are used for applications such as motion capture, biomechanics, and action recognition [74; 75; 76].  
Camera-based wearable marker positioning systems involve tracking multiple wearable markers with multiple cameras to monitor the user's position and movements. These systems allow real-world movements to be reflected in virtual environments. While these systems are designed for positioning purposes, they are primarily used in biomechanical analysis and motion recognition due to their high precision and accuracy [77; 78; 79].
## 4 Results and Comparison
### Comparison  
Table 3 compares the proposed design with previous studies which could perform the same FFT operations (i.e., maximum FFT length of 128). An SDF 128-point FFT processor was developed based on the radix-2\({}^{4}\) algorithm [30] and its performance was extended to four streams for evaluation. That which is compared deals with devices based on different technologies; therefore, the area was normalized [Equation (8)] based on the scheme in [27; 33]. Although the GI duration is relatively short (e.g., 1/4 symbol duration) in timeliness considerations, the proposed FFT kernel could lend support to a throughput gain of (6/4) for the number of streams, only mainly needing additional memory elements for the two-stream data storage. The comparison results demonstrated the per-stream area efficiency of the proposed design by using area-efficient Modules 2 and 3 (sorting buffer and CMU), resource sharing and hardware use GI duration. Table 3 shows the improved area efficiency of the proposed design because the proposed scheme achieved the highest throughput by using modest hardware resources (i.e., throughput per area).  
\[Nor.\,Core\,Area=\frac{Core\,Area}{\left(Tech./90\,nm\right)^{2}} \tag{8}\]  
Figure 15: Layout and performance summary of FFT kernel test chip.
## 4 Dynamic Model Updating Examples  
### Proof Examples: Dynamic Model Updating of a Thin Plate  
A thin plate with non-uniform thickness and small holes is investigated to test the proposed CMPES method, as shown in Figure 2(a). The average thickness, length, and width are 8, 530, and 250 mm. The diameter of all the holes is 24 mm, and the location of these holes can be determined as follows: \(x_{1}=84\) mm, \(x_{2}=74\) mm, \(x_{3}=156\) mm, \(x_{4}=129\) mm, \(y_{1}=y_{2}=68\) mm, \(y_{3}=y_{4}=122\) mm, \(y_{5}=y_{6}=141\) mm.  
The initial FE model, ignoring the irregular geometrical characteristics, was produced by 2048 solid elements, as illustrated in Figure 2(b). The equivalent areas with different material parameters were employed to present the hole areas distinguished by colors. The density, modulus of elasticity, and Poisson's ratio of steel material are 7850 kg/m\({}^{3}\), 210 GPa, and 0.3, respectively, and the boundary condition is free-free.  
Non-dimensional ratios of material parameters to the corresponding steel values were adopted as updating parameters, as listed in Table 1. The initial FE model was updated based on the simulated \"experimental\" and impact modal test results by the MUUM [23] and CMPES method to validate the accuracy and effectiveness of the proposed method.  
Figure 3: Real structure and initial FE model of the thin plate. (**a**) Dimension. (**b**) Initial FE model.
## 3 Methodology
### Build Prediction Methods
#### 3.5.1 Bidirectional LSTM (BiLSTM)  
A BiLSTM is a type of recurrent neural network where the signal is propagated backward and forward in time, i.e., it allows additional training by traversing the input data twice: from left to right and from right to left [28]. BiLSTM has demonstrated good results in many fields, such as natural language processing, semantic segmentation of the QRS complex, prediction or forecasting of time series, and phoneme classification, among others.  
As the output sequence of the forward LSTM layer \(\overset{\rightarrow}{h}\) is commonly obtained as the unidirectional one, the output sequence of the reverse LSTM layer \(\overset{\leftarrow}{h}\) is calculated using the reverse inputs from time \(t-1\) to time \(t-n\). These output sequences are then fed to the \(\sigma\) function to combine them into an output vector \(y_{t}\)[29]. Similar to the LSTM layer, the final output of a BiLSTM layer can be represented by a vector, \(Yt=[y_{t-n},\ldots,y_{t-1}]\), in which the last element, \(y_{t-1}\), is the estimated unemployment rate for the next iteration.  
\begin{table}
\begin{tabular}{c c} \hline
**Economic Indicator** & **Monthly Unemployment Rate** \\ \hline Monthly inflation rate & 0.241 \\ Monthly minimum wage & \(-0.804\) * \\ Monthly GDP & \(-0.863\) * \\ Monthly GFCF & \(-0.877\) * \\ \hline \end{tabular}
\end{table}
Table 3: Significant variables.  
Figure 2: Heat map of unemployment rate vs. inflation, minimum wage, GDP, and GFCF.
# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 3 Results  
This section presents the experimental outcomes of the proposed xLSTM-based traffic forecasting framework. The results are analyzed across multiple metrics, including accuracy, MAE, RMSE, F1-score, and inference latency, to comprehensively assess the model's effectiveness and real-time applicability. All reported values represent the average of three independent experimental runs to ensure statistical robustness.
# Bionic Design of the Vertical Bracket of Wide Angle Auroral Imager by Additive Manufacturing
## 2 Original Structure Design of the Vertical Bracket of Wide Angle Auroral Imager  
Generally, the vertical bracket of the wide-angle imager needs to be specifically designed based on experience according to the overall design input. The tracking shafting of the wide-angle imager is composed of two independent shafting: the fixed shafting and the drive shafting. The top of the vertical bracket needs to provide installation interfaces for them respectively. A rotatable imaging system is installed in the middle of the tracking shaft system, and the vertical bracket needs to provide enough space for the imaging system. The bottom surface of the vertical bracket is the interface surface between the vertical bracket and the satellite platform, with 12 screw holes evenly distributed. Therefore, the upper part of the vertical support adopts U-shaped structure, and the lower part adopts cone-shaped structure, which is reinforced with inclined stiffeners to improve the stability. A plurality of ring stiffeners and cylindrical stiffeners are set on the cone to further improve the structural rigidity. At the same time, multiple lightweight holes are reasonably set on the conical surface, inclined stiffeners, and other parts to reduce the weight of the structure. The original design of the vertical bracket and its boundary dimensions is shown in Figure 1.
## 2 Related Backgrounds
### Multiobjective Evolutionary Algorithm Based on Decomposition
Firstly, two individuals are produced by the parent with the cloud generator. Then, the search direction of the offspring is guided by the two individuals and differential operation. Finally, the new individual is updated by cloud particles mutation or polynomial mutation for improving the population diversity. In this way, the proposed algorithm can not only accelerate convergence speed but also improve the local exploitation. ### Cloud Generator

The cloud model is proposed by [PERSON] (2015) and has three parameters, including Expectation (\(E_{x}\)), Entropy (\(E_{n}\)) and Hyper-entropy (\(H_{e}\)), in which \(E_{x}\)_is the expectation value of the distribution for all cloud particles in the domain, and \(E_{n}\) is the range of domain which can be accepted by linguistic values (qualitative concept). In other words, \(E_{n}\) is ambiguity and \(H_{e}\) is the dispersion degree of entropy (\(E_{n}\)). That is to say, \(H_{e}\) is the entropy of entropy. The cloud generator works according to (2) and (3), \(\Phi(\mu,\delta)\) is the normal random variable that has an expectation \(\mu\) and a variance \(\delta\); \(N\) is the number of cloud particle; \(C\) is the cloud particle generated by the cloud generator. \[\mathrm{S} = \{s_{i}|\Phi(E_{n},\ H_{e}),\ i=1,2,\cdots N\} \tag{2}\] \[\mathrm{C} = \{c_{i}|\Phi(E_{x},\ s_{i}),\ s_{i}\in\mathrm{S},\ i=1,2,\cdots N\} \tag{3}\]

### Cloud Particle Differential Evolution

First, the best solution found so far acts as a seed. Then, each seed produces two individuals (\(c_{1}\) and \(c_{2}\)) by cloud generator. The indices \(r_{1}\) and \(r_{2}\) are mutually exclusive integers randomly chosen within \([1,N]\). \(F\) is a mutation factor that controls the balance between exploration and exploitation. \(u_{r}\in[0,1]\) is a uniformly random number. Let \(y_{i}\) be the decision variable to be evolved. Then \(y_{i}\) is computed as follows:

\[y_{t}= \begin{cases}x_{i}+F(x_{r2}-x_{r1})+\sqrt[3]{(0.5-u_{r})}*(\ c_{1}-c_{2}),&\quad\text{if $u_{r}\geq 0.5$;}\\ x_{i}+F(x_{r1}-x_{r2})+\sqrt[3]{(0.5-u_{r})}*(\ c_{1}-c_{2})\,,&\quad\text{ otherwise.}\end{cases}  \tag{4}\]

### Cloud Particle Mutation Operator

\[y_{i}^{k}= \begin{cases}y_{i}^{k}+(u_{r}-0.5)*log_{2}(u_{r})*(u_{k}-l_{k}),& \quad\text{if $u_{r}\ \leq\frac{1}{D}$;}\\ y_{i}^{k}\,,&\quad\text{otherwise.}\end{cases}  \tag{5}\]

where \(u_{k}\) and \(l_{k}\) are the upper bound and lower bound of the \(k\)th decision vector respectively. \(D\) is the dimension of decision space. \(u_{r}\in[0,1]\) is a uniformly random number. ```  
**Algorithm 1** The procedure of MOEA/D-CPDE
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 3 Results and Discussion
### Classification Tests
With the precision, recall and specificity criteria, one must acknowledge that the hyperparameters in the classifiers were optimized by maximizing the test accuracy in the inner resampling loop. Different results may be obtained by using a different criterion. In the data set of thruster 2, the predictive power of the different feature sets was more equal than in the other data set. This can be inferred especially from the results of \"two distributions\" approach, the performance of which was relatively similar with different feature sets, as shown in Figure 16. Other approaches like SVM and k-NN supposedly give (fainter) indications of the more equal predictive power as well when all the performance criteria are examined together. With bearing features, the highest test accuracy, 96.51%, was obtained by using the \"two distributions\" approach, while the second highest accuracy, 89.62%, was achieved by using the \"probabilistic threshold\" approach with \(\alpha\) = 0.001. The reference methods had accuracies in the range 83.84% (AdaBoost M1) to 87.88% (SVM). With time domain features, the highest test accuracy, 91.91%, was achieved by using SVM. Then again, two other reference methods, LDA and AdaBoost M1, had test accuracies below 50.40%. The test accuracy of \"two distributions\" approach was 90.55%, while the \"probabilistic threshold\" approach had accuracies in the range 68.69% (\(\alpha\) = 0.001) to 80.30% (\(\alpha\) = 0.1). With gear features, the highest test accuracy, 94.68%, was achieved by using the \"two distributions\" approach. The \"probabilistic threshold\" approach had test accuracies in the range 75.97-84.99% and the reference methods had accuracies in the range 65.79% (LDA) to 81.54% (SVM). The highest precision (98.58%) was achieved by using the \"probabilistic threshold\" approach with \(\alpha\) = 0.001 when the gear features were used as input variables. The highest recall (98.76%) was achieved by using the \"two distributions\" approach with the bearing  
Figure 16: Thruster 2 classification results. For interpretation, see the caption of Figure 15.  
features as input variables. The highest specificity (99.98%) was achieved with AdaBoost M1 with the time domain features as input variables, but the accuracy was only 50.35%, indicating that the classification was unsuccessful. Based on Figures 15 and 16, the precision and specificity increased together with decreasing \(\alpha\) in \"probabilistic threshold\" approach, apart from the precision with the time domain features in Figure 14.0 This suggests that the number of false positive predictions go down when the limit value for \(D\) is set higher (i.e., \(\alpha\) is set lower). On the other hand, this increases the risk that the number of false negatives grows and then recall decreases, which is shown in Figures 15 and 16.
## 2 Brachytherapy as Radical Treatment  
### Intraductal Brachytherapy as an Independent Treatment in Early Esophageal Cancer  
The role of brachytherapy (BT) alone in the treatment of early esophageal cancer involving only the mucosa or submuc has not been determined. Currently, the obvious and recommended method is surgery.  
Surgical treatment is the standard treatment for early esophageal cancer (T1-T2 lesions, <3 cm, N0M0), but it is associated with a high risk of complications and mortality. For this reason, the endoscopic removal of esophageal tumor is increasingly used as an alternative procedure. But treatment results are ambiguous regarding the effectiveness of this procedure. Meta-analysis compares 15 studies covering a total of 2467 patients after endoscopic treatment and 2264 after surgical treatment. Endoscopic treatment is a safe procedure in early esophageal cancer (we observe fewer side effects--RR 0.46 at 95% CI and lower-treatment-related mortality--RR 0.27 at 95% CI); however, after esophagectomy, we observe fewer relapses, more R0 treatments and longer overall survival [8].  
The question that is increasingly being asked is that of whether brachytherapy can be an alternative, especially to esophagectomy in early esophageal cancer.  
Few studies from many years ago cover small groups of patients and in a short observation period indicate a local response after the use of independent brachytherapy. In the study by [PERSON] et al. [9], out of 25 patients, 12 had tumor recurrence, 11 of  
Figure 1: The targets of the manuscript.  
which were in the irradiated area. mDFS (median disease-free survival) was 14 months and 1-, 2-, and 3-year OS was 76%, 37%, and 14%, respectively. Unfortunately, despite the low stage of advancement, overall survival was very low and did not differ significantly between patients with stage T1 and Tis 20% vs. 24%, \(p\) = 0.83. In the study by [PERSON] et al. [10], 44 patients with stage T1a cancer were treated with intraductal brachytherapy, administering 25-35 Gy in 5-14 fractions. The 5-year locoregional control (LC), cancer specific survival (CSS), and overall survival (OS) rates were 75%, 97%, and 84%, respectively. [PERSON] et al. [11] described six patients who were irradiated to 24 Gy in a 4 \(\times\) 6 Gy/2\(\times\) week schedule. All patients had a complete local response, but the follow-up period was insufficient to fully assess the treatment results. Excellent results were described in the analysis of 23 patients with early esophageal cancer in the study by [PERSON] et al. [12], which compared various radiotherapy techniques, including HDR brachytherapy alone. The 5-year LC in stage T1a was 100% and T1b was 54%. Based on all the above data, brachytherapy alone cannot be recommended for the radical treatment of early esophageal cancer. The role of brachytherapy in this indication may only be limited to selected patients whose general condition prevents the use of radical surgical treatment, radiochemotherapy or teleral radiotherapy, and who do not consent to endoscopic treatment.
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 2 Original Output Characteristics Test
### Relational Model of Displacement and Acquisition Voltage
Meanwhile, combining the approximately linear relationship between the reduced impedance and the displacement, which is obtained by Equation (5), it can be concluded that the voltage difference \(d(\Delta U)\) and the displacement \(x\) are linear.
## 6 Discussion  
### Energy Consumption Per Round  
Energy consumption per round is critical as it directly impacts the overall network lifetime. The energy consumption of MFG-LEACH is notably lower compared to EZ-SEP, EAMR and BRE across all node densities, as illustrated in Figures 3a, 4a, 5a and 6a. For all the node densities, 0.001, 0.01, 0.05 and 0.1, the plots show that the other protocols consumed more energy per round than MFG-LEACH. For instance, after 1000 rounds at a node density of 0.1, EAMR, EZ-SEP and BRE have consumed 0.547 J, 0.487 J and 1.22 J, respectively, while node energy consumption under MFG-LEACH routing is 0.375]. The other protocols consume more energy per round because they do not have MFG-LEACH's adaptive control policy, which dynamically adjusts transmission energy in response to changes in network dynamics.  
Figure 4: Performance comparison of energy consumption and throughput at 0.05 node density of BRE, EAMR, EZ-SEP, and MFG-LEACH protocols. **(a)** Comparison of energy consumption at 0.05 node density between BRE, EAMR, EZ-SEP, and MFG-LEACH.  
Figure 3: Performance comparison of energy consumption and throughput at 0.01 node density of BRE, EAMR, EZ-SEP, and MFG-LEACH protocols. **(a)** Comparison of energy consumption at 0.01 node density between BRE, EAMR, EZ-SEP, and MFG-LEACH. **(b)** Comparison of throughput at 0.01 node density between BRE, EAMR, EZ-SEP, and MFG-LEACH.  
of nodes across all node densities compared to EZ-SEP, EAMR and BRE. This delay in FND and LND is attributed to the lower energy consumption observed in the protocol, resulting in longer node activity compared to other approaches. Extending FND and LND means that the network remains operational for a longer period, which is crucial for applications requiring sustained sensor activity and data collection.