esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Qualitative Validation--Results and Analysis
### Analysis  
#### 5.2.1 Normal Operation Condition  
As a baseline, the normal operation scenario presents a smooth reduction on the aircraft speed achieved through proper control and modulation of the EMA by the actuator controller. The actuator provides enough force to the brake, which is capable of providing the required torque to stop the aircraft gradually. The wheel slip value is properly stabilized at 0.2 and provides a good difference in speed between the wheel and the aircraft.  
With the actuator controller set at the specified parameters, the electric power is maintained at proper levels without having peak loads. The electric motor temperature increase is minimum, which suggests a good performance achieved by the controller. Moreover, the simulation shows the stopping distance of 2.1 km achieved under this scenario, which can be used to compare with the scenarios on which the aircraft actually achieves a full stop, such as the ABS OFF scenario, which stops after 2.6 km, showing a reduction in performance.  
When comparing with the scenario in which the ABS system is OFF, the EBS fails to reduce the aircraft speed at the same pace as the wheel stops with the wheel slip at 1 and continues on that value until the end of the simulation. The force provided to the brake by the EMA is eventually reduced, which can be also seen on the braking torque, taking a longer time to stop the aircraft. The electric power from the actuator increases gradually to higher levels under the ABS ON scenario, which suggests a reduction in the performance of the system due to increase in the motor temperature.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Operational** & **Temperature** & **Voltage** & **Current** & **Torque** & **Shaft** & **Angular** \\
**Condition** & **Increase** & **Signal** & **Signal** & **Signal** & **Velocity** & **Displacement** \\  & & **Change** & **Change** & **Change** & **Change** & **Change** \\ \hline Normal Condition & & 1 & 1 & 1 & 1 & 1 \\ (ABS ON) & & 3 & 3 & 3 & 3 & 3 \\ Open Circuit & & 2 & 2 & 2 & 2 & 2 \\ Intermittent Open & & 2 & 3 & 4 & 4 & 4 \\ Circuit & & & & & & \\ Jamming & 3 & 2 & 3 & 4 & 4 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fault modes qualitative validation comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Behaviour** & **Numerical Value** & **Behaviour Description** \\ \hline No Change & 1 & The performance is similar to the one obtained from the normal condition simulation. \\ \hline Change Moderately & 2 & The signal increases or decreases moderately. Not much difference in the results can be visible, but some change in performance exists. \\ \hline Change Greatly & 3 & Great change in the signal obtained from the simulation. It is clear that some abnormal condition exists and there is great variation in performance. \\ \hline Signal Abruptly Cut & 4 & The signal does not provide any information as it remains as it started the simulation. Great change in performance is observed.  
\end{table}
Table 3: Qualitative validation method.
## 3 System Overview
### Sensor Calibration  
As shown in Figure 3a, the emitter of the laser ToF sensor emits light, and the collector measures the reflected light. The shortest distance that the sensor can measure is calculated as  
\[h=\frac{a}{\tan 17.5}=\frac{b}{\tan 12.5} \tag{1}\]  
where \(a+b\) is the distance between the collector and the emitter (2.7 mm), and \(h\) is the distance from the sensor baseline to the intersection of the emitter's light spread cone and the collector's input cone.  
The distance \(h\) calculated from Equation (1) is 5.03 mm. The offset calibration for our system is 10 mm. Thus, the actual offset calibration is \(5.03+10=15.03\) mm. The distance from the sensor to a full roll of paper is 15 mm in our proof of concept experiment. Thus, this sensor is suitable for our system. Actual distances between 20 mm and 120 mm were measured for different objects using the laser ToF sensor. We found a 1 to 2 mm error for short distances. A sensor calibration curve is shown in Figure 3b.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Component** & **Technical Details** \\ \hline
1. & Laser ToF distance sensor & Operating voltage range: 2.6 V to 3.5 V \\
1. & Laser ToF distance sensor & Operating temperature : \(-\)20 \({}^{\circ}\)C to 70 \({}^{\circ}\)C \\  & & Measures distance between 10 cm and 2 m \\  & & Operating voltage range: 3 V to 3.6 V \\
2. & ESP32 SoC & Operating temperature : \(-\)40 \({}^{\circ}\)C to 85 \({}^{\circ}\)C \\  & & Built-in Wi-Fi module, \({}^{\circ}\)C, and GPIOs \\  & & Consumes 10 uA in sleep mode \\  & & Output voltage range: 2.7 V to 4.2 V \\
3. & Li-Po 420 mAh battery & Operating temperature : \(-\)20 \({}^{\circ}\)C to 60 \({}^{\circ}\)C \\  & & Dimensions : 6.7 mm\(\times\) 43 mm\(\times\) 61 mm \\  & & Weight : 45 \({}^{\circ}\)S \\
4. & MCP 1700 low dropout (LDO) regulator & Operating voltage range: 2.3 V to 6 V \\  & & Operating temperature : \(-\)40 \({}^{\circ}\)C to 125 \({}^{\circ}\)C \\  & & Regulates output voltage to not exceed 3.3 V \\
5. & TP4056 charger module & Present to 4.2 V charge voltage with 1.5\({}^{\circ}\)s accuracy \\  & & Operating temperature : 0 \({}^{\circ}\)C to 85 \({}^{\circ}\)C \\  & & USB bus-powered \\  & & Typical operating voltage: 3.2 V \\
6. & Light emitting diode (LED) & Operating temperature : \(-\)40 \({}^{\circ}\)C to 95 \({}^{\circ}\)C \\  & & Size: 5 mm \\ \hline \end{tabular}
\end{table}
Table 1: Technical details.  
Figure 3: Sensor calibration. (**a**) Sensor parameters. (**b**) Sensor calibration curve.
# Improved Electrochemical-Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models
## 5 Conclusions  
In this study, a parameter estimation framework was developed by integrating both electrochemical and mechanical measurements into the parameter estimation process. The inclusion of mechanical data, such as the battery thickness change, introduces additional constraints that significantly enhance the identifiability of key parameters, particularly those related to diffusion and reaction kinetics.  
Two scenarios were considered: one where the battery underwent teardown to measure electrode thicknesses and the number of layers, and one without teardown. In the first case, the electrochemical-only estimation, constrained by fewer free parameters, converged to incorrect solutions, while the electrochemical-mechanical approach reached satisfying  
Figure 12: Variation in the estimated values of the diffusion coefficients and reaction rates when using different initial guesses of diffusion coefficients. The green asterisk indicates the best parameter set, corresponding to the lowest RMSE.  
estimates with lower RMSEs and parameter variability. In the second case, where no tear-down was performed and a greater number of parameters remain uncertain and must be identified, the results obtained with both electrochemical and electrochemical-mechanical approaches are comparable but the first showed higher uncertainty.  
In conclusion, integrating mechanical data consistently improves accuracy and robustness of the estimation, reducing parameter variability and sensitivity to initial guesses.  
Conceptualization, S.S., D.C. and F.P.; methodology, S.S.; software, S.S.; validation, S.S. and D.C.; formal analysis, D.C.; investigation, S.S., D.C. and F.P.; resources, A.S.; data curation, D.C.; writing--original draft preparation, S.S.; writing--review and editing, D.C.; visualization, S.S.; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LIB & Lithium-Ion Battery. \\ LSOF & Least Square Objective Function. \\ DFN & Doyle-Fuller-Newmann. \\ P2D & Pseudo-Two-Dimensional. \\ PDE & Partial Differential Equation. \\ EC & Electrochemical. \\ ECM & Electrochemical-mechanical. \\ GA & Genetic Algorithm. \\ RMSE & Root Mean Square Error. \\ SOC & State of Charge. \\ DOD & Depth of Discharge. \\ \end{tabular}
# Group Pile Effect on Temperature Distributions inside Energy Storage Pile Foundations
## 4 Analytical Results and Discussions
### Temperature Distribution in Soil
The thermo-active pile and isolated energy storage pile show a similar temperature distribution trend: decreasing when moving away from the pile center, while the group energy storage pile shows a different uniform distribution due to the significant group pile effect. Figure 14 illustrates the largest temperature ever reached within each cycle for various \(s\) at (a) the soil inner surface (\(r_{o}\)); (b) the quarter of the soil layer (\(\frac{s}{4}+\frac{r_{o}}{2}\)); and (c) the center between two piles (\(\frac{s}{2}\)) for both the horizontal (\(\theta=0^{\circ}\)) and diagonal (\(\theta=45^{\circ}\)) directions. As expected, the maximum temperature increases with the loading cycles but starts to slow down with increase of the cycles until it reaches a constant value. The cases with larger \(s\) show a slower convergence and converge at a similar but slightly lower temperatures compared to the cases with smaller \(s\). In comparing the curves at different locations, it is seen that more cycles are required for temperature convergence when moving far from the pile. In addition, the cases with \(\theta=45^{\circ}\) (Figure 14d-f) show similar convergence temperatures to those in the horizontal direction (Figure 14a-c). Figure 15 shows \(N_{G}\), calculated using the similar procedure demonstrated in Figure 10a, for each point of the soil layer for (a) \(\theta=0^{\circ}\) and (b) \(\theta=45^{\circ}\). As expected, \(N_{G}\) decreases when moving far away from the concrete pile, which indicates that the locations near the center between two piles (\(r=s/2\)) first start to be influenced by the group pile effect. Then as the cycle increases, the heat from the adjacent pile starts to transfer into the soil layer further from the center line of adjacent piles, beginning to affect the temperature there. In addition, it is noted that the soil along the horizontal direction (Figure 15a) has lower \(N_{G}\) values than the soil along \(\theta=45^{\circ}\) (Figure 15b), which means that the points at the horizontal line are affected first, since they are closer to the adjacent pile. As for pile spacing, piles located closer to each other show smaller \(N_{G}\). Figure 13: Temperature distribution along the soil section for different \(s\) at the end of the 1 st, 10 th, 50 th, 200 th, and 1000 th loading cycles for (**a**) \(\theta=0^{\circ}\) and (**b**) \(\theta=45^{\circ}\).
# Zero-Voltage Ride-Through Capability of Single-Phase Grid-Connected Photovoltaic Systems
## 2 System Configuration  
In the low-voltage DRE system, the single-phase configuration is a more competitive solution [11; 12; 13; 14]. A generic control structure of the single-phase grid-connected PV system is shown in Figure 1, with an option of a DC-DC converter, which is used to boost up the PV panel voltage to a suitable level of the following-stage DC-AC converter. The choice of single- or two-stage (i.e., without or with the DC-DC converter) is dependent on the control strategy, efficiency, cost, size and weight, etc. [13; 14; 30]. To guarantee a high-quality sinusoidal grid current, the inductor-capacitor-inductor (LCL) filter is adopted to improve the switching harmonic with lighter and smaller inductors [33].  
In normal conditions, the PV system draws the maximum power from the PV arrays (i.e., in MPPT operation) and transfers it to the grid at unity power factor by using control strategy. The widely employed control strategy in single-phase inverters has two cascaded control loops [3; 30]. The inner loop is a current loop, in which the grid current quality can be guaranteed and the overcurrent protection is also ensured. The outer loop is a voltage or power control loop, in which the voltage of the DC-side can be ensured and a reference of the inner current loop is calculated simultaneously in the outer loop.  
According to Figure 1, the current control loop is responsible for the quality of the injected sinusoidal current, i.e., it needs to satisfy the requirements of the grid code regarding power quality [34]. The control diagram of the current control loop is presented in Figure 2. The capacitor-current-feedback is measured for active damping [35]. The error of the grid-current reference \(i_{\text{ref}}\) and the measured grid-current \(i_{g}\) is sent to the current controller \(G_{c}(s)\). For the digital implementation, the time delay should be considered, and \(G_{\text{delay}}\) is used to reflect the total time delay including pulse width modulation (PWM) processing and the delay because of computation [36]. Considering that the switching-frequency is much higher than the fundamental frequency of the grid voltage, \(K_{\text{PWM}}\) is then the equivalent transfer function of the inverter. As a consequence, the closed-loop current transfer function (i.e., the transfer function for \(i_{g}\) and \(i_{\text{ref}}\)) can be derived as  
\[G_{i}(s) = \frac{i_{g}(s)}{i_{\text{ref}}(s)}=\frac{K_{\text{PWM}}G_{c}(s)G_{\text{delay}}(s)}{s^{3}CL_{1}(L_{2}+L_{g})+s^{2}K_{ \text{PWM}}C(L_{2}+L_{g})G_{\text{delay}}(s)+s(L_{1}+L_{2}+L_{g})+K_{\text{ PWM}}G_{c}(s)G_{\text{delay}}(s)} \tag{1}\]  
where \(L_{1}\) is the inverter-side inductor, \(L_{2}\) is the grid-side inductor, \(L_{g}\) is the grid impedance, and \(C\) is the \(LCL\) filter capacitor.  
Figure 1: Generic control structure of the single-phase grid-connected photovoltaic (PV) system. Maximum power point tracking (MPPT).
## 4 IoT Multi-Sensor System for Remote Patient Monitoring
### Wearable IoT Multi-Sensor Device for Patient Monitoring
The medical staff can adjust, from the monitoring application, different threshold values that can be applied individually per each monitored vital sign. These threshold values of monitored parameters allow the system to generate only relevant alarms and notifications, excluding false triggers. Additionally, the monitored person can make an emergency call for urgent medical services in case of rapid deterioration of their health status. This is achieved by simply pressing the panic button on the device worn by the patient. After the IoT wearable device joins the network, it starts monitoring the parameters: GPS location, heart rate, oxygen saturation level, and temperature of the patient.
# An Approach for Setting Parameters for Two-Degree-of-Freedom PID Controllers
## 2 The Related Tuning Methods
### Generalized Frequency Method (GFM)  
The GFM can calculate the controller parameters directly and analytically and can accurately guarantee the specified stability margin with only one parameter, which is called the attenuation index \(m\). The attenuation index \(m\) determines the two rays, which shape a sector area shown in Figure 2, and the GFM can make all the closed-loop poles lie in the resulting sector area. The equation of the two rays is \(s=-|m\omega|+j\omega\), where \(m>0\), \(m=\tan\alpha\). \(m\) is a constant and \(\alpha\) is the included angle between the ray and the imaginary axis.  
The stability margin increases with attenuation index \(m\). Each pair of conjugate complex poles have a corresponding \(m_{n}\), \(m_{n}=\tan\beta\) and the corresponding \(m_{n}\) equal to infinity for the real poles. The minimum \(m_{n}\) is equal to the specified attenuation index \(m\), i.e., \(\min\{m_{n}\}=m\).  
Next, we will illustrate the relationship between the maximum sensitivity (\(M_{s}\)) and the attenuation index \(m\) to explain the rationality of the attenuation index \(m\) as the stability margin criterion. \(G(s)=1/(s+1)^{4}\) is taken as an example. Substitute \(s=-m\omega+j\omega\) into \(G(s)\), and the Nyquist plots of \(m=0,0.1,0.2\) are shown in Figure 3.  
In Figure 3, the Nyquist plots are shown with full lines, and \(m=0\), \(m=0.1\), and \(m=0.2\) are respectively shown with a red line, green line, and blue line; dashed lines represent the maximum sensitivity \(M_{s}=1/R\) and the dashed red line, green line, and blue line respectively represent \(R_{0}\), \(R_{1}\), and \(R_{2}\). The stability margin of \(G(-m\omega+j\omega)\) decreases with the attenuation index \(m\) increasing. So, when designing the controller parameters, make the transfer function \(G(-m\omega+j\omega)C(-m\omega+j\omega)\) to give \(G(j\omega)C(j\omega)\) as the stability margin, where \(C\) represents the transfer function of the controller. From above, the attenuation index \(m\) can give a system stability margin in parameter design stage, and thus, it can be a stability margin criterion.  
Figure 2: Generalized frequency characteristic.
# Surgical Instrument Recognition Based on Improved YOLOv5
## 2 Materials and Methods
### YOLOv5 and Improve Methods  
YOLO is currently a commonly used target detection neural network [14; 15; 16]. YOLOv5 is an object detection model that is designed for real-time object detection and is known for its speed and accuracy [17]. Regarding its applications, YOLOv5 has garnered extensive adoption across diverse domains. It has proven effective in real-time vehicle detection [18] and object detection in maritime datasets [19]. The model's remarkable capability to deliver rapid inference speeds renders it highly suitable for real-time scenarios requiring swift and precise object detection. The architecture of YOLOv5 consists of several key components: The backbone is responsible for extracting features from the input image. YOLOv5 utilizes CSPNet (Cross Stage Hierarchical Networks) as the backbone, which is an efficient and scalable architecture for feature extraction. The neck connects the backbone to the detection head and is responsible for aggregating and refining the features extracted by the backbone. YOLOv5 employs PANet (Path Aggregation Network) as the neck, which helps in improving the information flow between layers. The detection head is responsible for predicting the object bounding boxes, class probabilities, and objectness scores. YOLOv5 utilizes anchor boxes to predict the bounding box coordinates and applies a sigmoid activation function to the output for better accuracy. The architecture of the improved YOLOv5 is shown in Figure 4.
## 4 Case Study
### Results
OMSS covers maintenance activities of seven bridges with the lowest cost, i.e., 1450 fund-units generating the minimum traffic delays during the makespan of 25 days. WFSS exhausts the largest portion of the budget, which can only cover six bridges. FFSS has the maximum makespan and brings about the maximum traffic delays. Obviously, OMSS outperforms the two empirical strategies, which illustrates that the proposed method can make the bridge maintenance decision with lower cost, fewer traffic delays and shorter maintenance period.
## 5 Discussion
### Methods: Algorithms and Machine Learning Models
#### 5.1.1 Neural Networks  
Neural networks are made up of layers of interconnected nodes. Their scheme essentially consists of an input layer, one or more hidden layers, and an output layer. During the training phase, the output is compared with the predicted result and the obtained error is calculated. This error is then propagated through the hidden layers and the weights of the nodes are modified in order to obtain better results. This process is repeated to improve accuracy.  
In the articles we analyzed, there is no standard optimum configuration. Researchers perform different tests until they obtain a result that satisfies the two desired properties of accuracy and computation time. In [30], the authors use six layers with 512 nodes in every layer to achieve an accuracy of 2.4 m. However, the authors of [31] instead use only four layers, without specifying the number of nodes, while in [32] only two layers of 50 nodes each are used.  
Thus, there is no a standard configuration. Nevertheless, it is important to take into account the difficulty of finding an optimal configuration, as it is influenced by different variables, such as the type of scenario where the experiments are performed, its shape, whether or not there are obstacles, the number of APs used, etc.  
Figure 4: Most widely used algorithms and Machine Learning models.
## 1 Introduction
### Topology Optimization Techniques  
TO definition dates back to the early twentieth century [7]. Its first computational application contributions are due to [8] in the eighties; nevertheless, its full application in real cases has always been hindered by design software and technological constraints. TO is a numerical method to effectively distribute material into a control volume, which aims to maximize or minimize specific criteria (e.g., weight, stiffness, thermal conductivity, resonance frequency) according to set constraints. In applications for lightweight design, specific design tools for TO can be used to obtain a lighter structure for a set stiffness or to minimize its compliance with a given target mass. Nowadays, many algorithm categories have been developed to solve a TO problem [9]. The most prominent ones have been classified and described by [PERSON] et al.: Density-based; Level Set Evolutionary/Genetic Algorithms; Topological Derivatives and Phase Field [10]. Most widely used [7] are homogenization density-based methods such as the Solid Isotropic Material with Penalization (SIMP), even though several research works concern the Evolutionary Structural Optimization (ESO) development, mainly in its more known Bi-dimensional version (BESO). The SIMP method is actually the most implemented algorithm in commercial software [11], due to its computational efficiency and its ability to generate excellent results in terms of mechanical performance and aesthetics. A lot of research effort has been contributing to the development of the algorithm adding features suitable in industrial applications in real cases [12; 13]. Moreover, technological and manufacturing features have also been  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Definition** \\ \hline AM & Additive Manufacturing \\ TO & Topology Optimization \\ DfAM & Design for Additive Manufacturing \\ KPI & Key Performance Indicator \\ TS & Tools System \\ GP & Geometry Preparation \\ OS & Optimization Setting \\ R\&PP & Results and Post-Processing \\ IT & Interface and user experience \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of abbreviations and acronyms used in the paper.  
involved, such as constraints for machining [14] or casting [15]. The last decade highlights an increased interest in the improvement of AM technologies [2].