esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of the Proposed Method  
### INPSO Algorithm for Numerical Function Optimization  
In order to strictly investigate the capabilities of the INPSO, eight classical benchmark functions were selected for simulation. The equations of selected functions and their brief descriptions are provided in Table 2, where \(F_{1}\sim F_{5}\) are the unimodal benchmark functions, and \(F_{6}\sim F_{8}\) are the multimodal functions. To substantiate the efficiency of INPSO, the SCA, CS and PSO were utilized to optimize these benchmark tasks and the experimental results were compared with those obtained by INPSO. For fair comparisons, the common parameter settings of the four algorithms were the same: population size \(N=30\), dimensions \(d=30\) and the maximum number of iterations was 500. For SCA, the parameters were set to be: \(a=1,r_{1},r_{2},r_{3}\) and \(r_{4}\) were random numbers. The parameters of CS were set to be \(pa=0.25\). For PSO, the parameters were set to be: \(w_{\max}=0.9\), \(w_{\min}=0.2\), and \(c_{1}=c_{2}=2\). Then we separately simulated 30 times and recorded the average values and standard deviations. The average values of the experiments were used to reflect the convergence accuracy and speed of the algorithms with a given number of iterations. The standard deviations were used to reflect the stability of the algorithms. The results are shown in Table 3.  
To further illustrate the optimization performance of the proposed INPSO, the convergence curves are exhibited in Figure 4. The curves shown in these figures were the average fitness values achieved from 30 independent experiments. As can be seen from Table 3 and Figure 4, in terms of \(F_{1}\sim F_{4}\), the INPSO algorithm was superior to the other algorithms and could obtain solutions quite close to the global optimums. The solution accuracy of \(F_{1}\) function even reached the level of \(5.97\times 10^{-63}\), which showed that the algorithm had obvious superiorities and made up for the deficiencies of the basic PSO algorithm. On two multimodal functions (\(F_{6}\) and \(F_{7}\)), though the INPSO algorithm could not get the minimum standard deviations under the specified maximum number of iterations, the best and mean of the objective values performed better than the other four algorithms. For the convergence curves of \(F_{5}\) and \(F_{8}\), its convergence speed was obviously faster than the other three algorithms during the initial  
Figure 3: INPSO-SVM flow chart. stage, and the curves showed that the INPSO algorithm could reveal a promising exploitation potential. To sum up, all simulation results indicated that the proposed INPSO had better performances in search accuracy and speed.
## 4 Design of Roll Angle Detection Module  
Referring to the signal communication processing method, a Frequency-Locked Loop (FLL) assisted Phase-Locked Loop (PLL)-based Costas loop is applied to track the roll modulation signals. The measurements of the tracking loops are the phases and frequencies of roll modulation signals, which correspond to the relative angles and rotational speeds of a spinning vehicle. The theoretical analysis, simulations and error analysis of the loops have also been carried out.
## 2 Materials and Methods
### Experimental Setup  
The experiments in this work are performed in a DN300 stirred tank shown in Figure 3. The tank is made of transparent polymethyl methacrylate (PMMA) and the geometry is based on a setup published by [PERSON] et al. [53; 54] with detailed measured velocity profiles [53; 54]. Due to the same geometry, the data obtained in this study can be considered complementary to data from [54; 55]. Therefore, an extensive database is available for CFD investigations, which have been presented on the same test case by, e.g., [PERSON] et al. [55].  
The tank has an inner diameter of \(T\) = 290 mm and a height of \(H_{0}\) = 400 mm. The tank is filled to \(H\) = 290 mm height to ensure a ratio of \(H/T\) = 1. The tank is surrounded by another square tank, which is filled with water and ensures a constant temperature in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Substance**} & **Density** & **Kinematic Viscosity** & **Interfacial Tension** \\  & \(\rho\) [kg/m\({}^{3}\)] & \(\
u\) [mm\({}^{2}\)/s] & \(\sigma_{d,c}\) [mN/m] \\ \hline Water + Na\({}_{2}\)SO\({}_{4}\) & 1000 & 1.0 & - \\ (50 mmol/L) & 825 & 13.1 & 53 \\ Paraffin oil FC 2006 & 1000 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of the used substances at a temperature of 20 \({}^{\circ}\)C.  
Figure 3: Experimental setup of the DN300 stirred vessel and the measurement position \(MP_{1}\) of the OMOP probe; (**a**) side view and (**b**) top view of the vessel.  
inner tank by means of thermostat \(T1\) and pump \(P1\). A six-blade Rushton impeller with a diameter of \(D=98\) mm (\(D/T\approx 0.3\)) is positioned at an off-bottom-height \(C=T/2=145\) mm and driven by a stirring unit (Heidolph RZR 2052 control). The impeller has the identical dimensions as in the work of [PERSON] et al. [53; 54], a disc diameter of 73.5 mm, a blade height of 19.6 mm, and a blade width of 24.5 mm. The tank is equipped with four radial equally distributed baffles. They have a width of \(B=T/10=29\) mm, a thickness of 3 mm and cover the entire height \(H_{0}\) of the tank.  
The small single-sided probe (see Figure 2) is inserted vertically from above such that its measurement gap is located at the height of the impeller at measuring point \(MP_{1}\). The measuring point is arranged radially at 87 mm from the center of the impeller shaft with an offset of 45\({}^{\circ}\) between two baffles. The probe is fitted with a Basler Aca 1300-60 gm camera and 1\(\times\) telecentric lens. The chosen resolution is 1024 pixel \(\times\) 1024 pixel, which equals 5.4 mm \(\times\) 5.4 mm.  
The camera with this lens results in a typical error range of \(+/-20\) um for measured lengths and diameters. The probe captures 1500 pictures at each measurement point with 10 fps. The gap between the probe heads, which form the measurement volume, was set to 2 mm. A further measuring point \(MP_{2}\) near the liquid surface was additionally investigated experimentally up to 10 vol.-% and simulations are reported by [PERSON] et al. [21].
## 2 Methodology
### Machine Learning Models
#### 2.3.1 Support Vector Machine (SVM)  
The support vector machine is a widely used machine learning algorithm in classification tasks. It is suitable for datasets with small sample size. Its core idea is to find a classification hyperplane that maximizes the margin between different classes. Support vectors are the data points closest to the hyperplane, and they determine the position of the hyperplane during model construction. For linearly separable problems, classification decisions can be made by evaluating the distance between data points and the hyperplane. However, in practical applications of well logging data analysis, data are often not completely linearly separable and may contain some noise [48]. To address these situations, kernel functions can be employed in SVM to map data features into a higher-dimensional space, allowing nonlinear relationships to be separated by a linear hyperplane in this higher-dimensional space. The SVM model used in this study employed the radial basis function (RBF) kernel, which can be represented by the following equation:  
\[K\big{(}x_{i},x_{j}\big{)}=exp(-\frac{\left\|x_{i}-x_{j}\right\|^{2}}{2\sigma^ {2}}) \tag{8}\]  
Figure 5: Schematic diagram of candidate classifiers.  
where \(x_{i}\) and \(x_{j}\) are data points and \(\sigma\) is a parameter controlling the kernel width.  
The soft margin of SVM allows a small number of samples to be misclassified within a certain range, which helps maintain the model's generalization ability and prevent overfitting. During model training, parameters related to the kernel function and soft margin have the most direct impact on model performance.
## 3 Use Case. CEDER-CIEMAT Microgrid
### Results
#### 3.5.2 Eliminating Power Peaks  
Figure 10 shows the consumption data of the CEDER microgrid on 12 January 2023, without using the storage systems. There is a consumption peak between 9:19 and 10:25, and it can be seen how consumption goes from levels of around 80-90 kW to values of up to 180 kW. This peak has been reduced using the methodology described in this article.  
In order to eliminate the peak, the storage systems have been started up according to their efficiency: battery PEPA I, battery PEPA II, LFP batteries, and the Hydraulic system. The power generated by each of the storage systems to compensate for the power peak can be seen in Figure 11. The green line represents the total energy stored, which is the sum of the energy stored in PEPA I lead-acid battery (dark red line), energy stored in PEPA II lead-acid battery (red line), energy stored in LFP battery (yellow line), plus energy stored in the Pumping-Turbine system (blue line).  
Figure 12 shows how the power peak has been eliminated using ESS, and the energy savings obtained by eliminating the peak power are depicted in green, totaling 51.6 kWh. The blue line represents the consumption of the microgrid if energy stored systems were stopped, the yellow line represents the real consumption of the microgrid with energy storage systems working, and the green area is the energy saved due to the use of storage systems.  
Figure 10: CEDER microgrid consumption without using ESS (12 January 2023).
## 1 Introduction  
In Industry 4.0, following the growing public demand for industrial products that significantly contribute to economic perspectives [1; 2], manufacturers are striving to produce highly customizable products while maintaining production accuracy and speed [2; 3]. In line with this, government regulations have been established with the purpose of regulating the responsibility of company owners to ensure worker safety during working hours [4]. Likewise, monitoring manufacturing environments, particularly human workers, is crucial [5; 6; 7]. Implementing these measures enhances quality control, worker productivity, and cost efficiency in addition to workplace safety. Hence, it is necessary to focus on aspects such as human error, machine malfunction, or other possibilities that significantly impact product quality, efficiency, and workplace safety.  
As a matter of fact, human error significantly contributes to manufacturing production disturbances and safety problems [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. In addition, humans are considered the most active objects; thus, detecting worker behavior is vital. Anomaly detection, which is a key task of modern automated monitoring systems, highly impacts diverse fields such as health care, sports analysis, industries, and security. Therefore, this study aims to develop a model that detects anomalous worker behavior based on human-object interactions. The outputs are in the form of descriptive texts that indicate detection results. This supports management teams in mitigating production failure and improving worker performance, as well as workplace safety. Accordingly, an algorithm has been developed for anomaly detection and text generation.  
To provide a clear structure and logical flow, this paper is organized into different sections. Section 2 presents a description of related research, and Section 3 contains an explanation of the concept and detection of anomalous behavior. Next, the proposed method is discussed in Section 4, and the experimental results are provided in Section 5. Finally, Section 5 also presents the conclusion and future study directions.
## 3 System Identification  
System identification is able to provide a mathematical description of the system dynamics valid for a wide variety of operating scenarios. The identification procedure requires several steps:  
* Model purpose identification;
* Design of the experiment;
* Collection of data;
* Choice of the model structure;
* Selection of the model parameters estimation method;
* Model training;
* Validation of the obtained model.  
It is an iterative procedure in which the a priori assumptions and the structure selection are tested and rectified until a satisfactory model behavior is not achieved. The candidate models are selected based on a priori knowledge, engineering experience, or physical background, and the decision must reflect the purpose of the model.  
Suppose there is a nonlinear relationship between the current output, the past output, and input values. The general identification problem can be expressed as a nonlinear autoregressive with the moving average and exogenous input model (NARMAX) as  
\[y_{k}=f(y_{k-1},\dots,y_{k-n_{a}},u_{k-n_{k}},\dots,u_{k-n_{k}-n_{b}},e_{k-1}, \dots,e_{k-n_{e}})+e_{k} \tag{10}\]  
with \(y_{k}\) the current output value, \(y_{k-i}\), the past output values, \(i=1,\dots,n_{a}\), \(u_{k-j}\) the input values, \(j=n_{k},\dots,n_{b}\), \(n_{k}\), the delay, \(e_{k}\), the noise term, \(n_{e}\), and the number of considered noise terms; they are included to accommodate the presence of the measurement noise, uncertainties in the model, or other unknown disturbances. When a linear independent additive noise term \(e_{k}\sim\mathcal{N}(0,\Sigma_{e})\) is considered, a simplification can be done and a nonlinear autoregressive with exogenous input model (NARX) can be obtained. This is expressed as [4]  
\[y_{k}=f(y_{k-1},\dots,y_{k-n_{a}},u_{k-n_{k}},\dots,u_{k-n_{k}-n_{b}})+e_{k} \tag{11}\]  
Parameters \(n_{a}\) and \(n_{b}\) are referred to as the orders of the NARX model and represent the number of past time instances that influenced the current system output. They are degrees of freedom in the model structure settings, together with the selection of the nonlinear relationship \(f(\cdot)\) and the \(n_{k}\) delay value. In the following, two kinds of nonlinear mapping are considered. The first one is a Gaussian process-based NARX model, the second one a wavelet-based NARX, in which a GP function and a wavelet network are employed, respectively.
## 4 Evaluation  
### Cooperative Eco-Driving Speed Guidance Application  
A representative application of a cooperative eco-driving system is a cooperative eco-driving speed guidance application. The cooperative eco-driving speed guidance application provides the driver with an eco-speed that improves the fuel efficiency by utilizing the dynamic information contained in the V2X messages. A CHEV can perceive objects within a distance of up to 1 km using V2X communication [27]. Therefore, a CHEV recognizes upcoming events by receiving V2X messages from surrounding nodes within the communication range, while the cooperative eco-driving speed guidance application provides an eco-speed for driver to drive economically in response to the events.  
To demonstrate the performance of the proposed IDHIL simulator, we considered a scenario of exchanging traffic light signal information through V2I communication between a HV and traffic lights in an urban intersection driving environment. The cooperative eco-driving speed guidance application in DUT is operated to calculate and provide the driver with the eco-speed using the received V2I messages from the traffic lights, which are the signal phase and timing (SPaT) messages. A flowchart of the implemented cooperative eco-driving speed guidance application is shown in Figure 5. The application operates whenever the HV receives a SPaT message. The performance of the cooperative eco-driving speed guidance application is evaluated by comparing the total fuel consumption between driving with eco-speed and when driving without it. Consequently, the comparison results prove the effectiveness of the IDHIL simulator.  
Figure 4: Data flow of IDHIL simulator.
## 3 Results and Analysis
### Evaluation of Using POTS Test for Determining Moisture Damage Resistance  
In this section, the POTS test was conducted to apply a new method for measuring the moisture damage resistance in asphalt mixture. This method was adapted from the standard specified in ASTM C 1583 that is measured in concrete. The test was proceeded in the concept of direct tensile method, thus enhancing the reliability of moisture damage resistance. The advantage of this method is that the device is portable, inexpensive, and applicable to field testing. Two aggregates, granite and limestone with only granite filler, were tested in this study. Fillers were added to the asphalt binder at 20% and 30% by volume of asphalt mastic. Table 4 shows the values of POTS test on asphalt mixtures and different filler contents (i.e., 20% and 30% by volume) for both aggregate types in the dry and wet conditions. Results show that in the dry condition, the value of POTS of 20% filler for limestone (i.e., 2.00 kPa) was about three-times lower than that for granite (i.e., 6.15 kPa), while that of POTS of 30% filler for limestone (i.e., 7.25 kPa) showed higher strength than that of granite (i.e., 4.52 kPa). Similarly, the results show the same trend in the wet condition. Results show that the POTS value of 20% filler after the wet conditioning was higher than that of 30% filler for limestone, while it shows converse results for granite.  
Figure 6: Tensile strength ratios (TSR) of ITS test after moisture of asphalt mixtures with limestone aggregate for all filler types.  
Figure 7: TSR of ITS test after moisture of asphalt mixtures with granite aggregate for all filler types.  
Figure 8 displays the TSR of POTS after the wet conditioning of asphalt mixtures with granite aggregate for both filler types. Results show that the TSR were in range of 0.35-0.65, in which the 20% limestone filler gave the highest TSR value, while the 20% granite filler showed the lowest value. This indicates that the TSR is dependent on filler type and filler content. The results correspond to those measured by the ITS test. However, the specification of 0.7 TSR as a threshold for the ITS test cannot be standardized on the POTS test, as many factors for two tests are different. Therefore, to confirm that the results from POTS test are valid to determine moisture damage resistance, the TSR values for the two methods, the ITS and POTS tests, need to be correlated.
## 2 Data and Methods  
### Data  
Sea-level anomaly (SLA) along-track measurements obtained from eight satellites--European Remote Sensing 1 (ERS-1), European Remote Sensing 2 (ERS-2), Environmental Satellite (Envisat), Geosat Follow-On (GFO), Topex/Poseidon, Jason-1, Jason-2, and Cryosat-2--and their tandem missions were used to extract the features of mode-1 \(M_{2}\) internal tides in the Arabian Sea. All datasets were obtained from the Online Data Extraction Service (ODES) of the Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) project and are available online. The aforementioned satellites have different data coverage. For instance, Jason-1 has a repeat cycle of 9.9156 days with 254 passes per cycle. The repeat cycle of Cryosat-2 is 369 days with 30-day pseudo-subcycles and ground-track drift within a 5 km band. Cryosat-2 provides the densest track coverage.  
The merged satellite data thus obtained provide extensive spatial coverage, as shown in Figure 1a. We analyzed the satellite data for the area within [0\({}^{\circ}\), 30\({}^{\circ}\)N] and [30\({}^{\circ}\)E, 80\({}^{\circ}\)E] from January 1993 to January 2016. Figure 1b shows the topography of the study domain, including the northern Indian Ocean, Arabian (or Persian) Gulf, and the Red Sea.
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
## 5 Conclusions  
To enhance the precision of the output displacement affected by hysteresis in PAMs, the FN-QUPI model and the inverse model of QUPI are introduced. The modeling outcomes indicate that the FN-QUPI model is able to efficiently forecast the output displacement corresponding to the PAM input signals under different conditions. Based on this, to make up for the displacement deviation caused by hysteresis in PAMs, this paper proposes a composite control of feedforward control-incremental PID-type iterative learning feedback control of the QUPI inverse model, proving that the model has high accuracy for different input signals. The conclusions can be reached as follows:  
1. The FN-QUPI model reduces the number of unknown parameters and simplifies the process of parameter identification, effectively predicting the output displacement corresponding to the input signals of the pneumatic artificial muscle under different conditions.
2. The QUPI inverse model proposed in this paper, combined with the incremental PID-type iterative learning composite control model, can achieve high-precision displacement tracking of the pneumatic artificial muscle, and the control system demonstrates good robustness to different input signals of the PAM.
3. The results demonstrate that the FN-QUPI model and composite control model have high accuracy and fast convergence speed for different input signals. The FN-QUPI model can be used for hysteresis modeling of pneumatic artificial muscles or other flexible actuators. Composite tracking control is significant in improving control accuracy and robustness, adapting to various working conditions, and effectively compensating for hysteresis and nonlinear effects. It is widely applicable in fields such as robotics, rehabilitation devices, and industrial automation.  
Methodology, G.M.; Validation, G.M. and H.J.; Formal analysis, D.X.; Investigation, D.X.; Writing--original draft, G.M.; Writing--review & editing, L.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Patient Selection  
Among the patients who visited the periodontal department of Chosun University Dental Hospital between September 2016 and April 2018, we selected those who underwent alveolar bone augmentation with 3-D preformed Ti mesh (Oss-Builder(r) type I; Osstem, Busan, Korea) and freeze-dried particulate bone allografts (Do bone(r), Renew Medical Co., Bucheon, Korea) for non-contained horizontal defects of the buccal region around the implant at the same time the implant was placed in this study. One hundred patients (39 men, 61 women) were selected, and their ages ranged from 20 to 82 years (average, 57.1 \(\pm\) 15.5) (Table 1). The patients were selected based on the following criteria: (i) having non-contained horizontal defects in the buccal area at the platform level after implant placement. On the other hand, patients satisfying the following criteria were excluded: (i) vertical bone augmentation and (ii) non-contained horizontal defects with no implant thread exposure. The selected patients were grouped into three: a group using only a 3-D preformed Ti mesh for GBR, a group using a cross-linked collagen membrane above the Ti mesh, and a group using an additional non-cross-linked collagen membrane. This study was conducted with the approval of the Institutional Review Board (IRB) of the Dental Hospital of Chosun University in Gwangju, Korea (CUDHIRB-1806-005). All treatment plans and procedures were explained to the patients, and they provided consent for surgery.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Only** & **3-D-PFTM + CCM** & **3-D-PFTM + NCCM** & **Total** \\  & **(Group 1)** & **(Group 2)** & **(Group 3)** & **Total** \\ \hline N of subjects (N\({}_{\text{s}}\)) & 15 & 56 & 29 & 100 \\ N of sites (N\({}_{\text{i}}\)) & 17 & 72 & 40 & 129 \\ Mean age (range) & 64.5 \(\pm\) 16.