esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method of Estimating an Object's Parameters Based on Simplication with Momentum for a Manipulator
## 4 Parameter Estimation Simulation
### Comparison with Previous Studies  
The previous studies compared with the proposed method are [13; 18; 26]. Due to the use of different robots in these studies, direct comparison of the estimation results is challenging. Additionally, the differences in the degrees of freedom of the robots make it difficult to apply the same estimation motion across all platforms. Therefore, the accuracy of the previous methods is acknowledged, and the comparison focuses on the estimation motion, demonstrating that the proposed method achieves precise estimates with smaller motions than the earlier approaches. In [13], estimation was conducted using a PUMA 600 robot, with a minimum jerk trajectory for the estimation motion, utilizing the least squares method. The total estimation time was 2 s.  
In [18], object parameters were estimated by integrating force and moment data over time, using the least squares method. The joint trajectory was defined by a 5 th-order polynomial, though exact values for joint velocity and acceleration were not provided. Therefore, the maximum joint velocity and acceleration are calculated by assuming a 5 th-order polynomial trajectory with constraints ensuring zero initial and final velocity and acceleration. The estimation process took 1.83 s using an IRP-6 robot. In [26], object parameters were estimated using machine learning with maximum-likelihood estimation. The estimation motion followed sinusoidal functions, with different motion range and frequencies for each joint's trajectory. A Franka Emika Panda robot was used for this estimation.
# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
## 1 Introduction
This sampling rate can guarantee even further reduction in the estimation error. Another important point to note is that the rate of frequency swing is smaller in the normal operation than when a disturbance occurs. Modern power systems have a large number of renewable energy generation power plants. This affects the overall inertia of the system since power electronic interfaced devices are known to have very low inertia. Both the disturbance and inertia estimation are done using the swing equation. In disturbance estimation, the inertia is assumed a constant and this results in errors. Therefore, it is better to use the dynamics of the whole system rather than just the swing equation to estimate the disturbance. By making use of wide area measurements, an SFR model that accounts for the dynamics of the power system can be realized online. An observer, which replicates the system, can then be designed to estimate the disturbance. The use of observers is commonly applied to direct load frequency control (LFC), for example in [32]. However, we propose the use of the disturbance observer for systems subjected to severe disturbances. In this paper, we propose two concepts to enhance disturbance estimation. Firstly, instead of only using the swing equation or simply the frequency, different system states are used in the estimation. Based on the developed system frequency response model the equations, which describe the dynamics of the system, are first derived. Using the derived equations, the system matrix, input matrix and the disturbance matrix of the system are developed. Taking into consideration that not all the system states are directly accessible, a state observer is also developed to estimate the unknown states. After which a robust disturbance observer is designed to accurately estimate the disturbance magnitude. In addition to the disturbance estimation, it is shown in the simulations that the disturbance observer can also estimate different sized disturbances, which occurred in different areas. In our simulations a four area interconnected power system is considered. The disturbance observer is designed such that it can estimate a disturbance as well as its location area. Multiple disturbances occurring in different areas can also be measured accurately. With the use of the disturbance observer, the estimation error can be effectively zeroed. This paper is organized as follows: In Section 2 power system modelling is presented. Section 3 describes the design of the robust disturbance observer and the considered test system is presented in Section 4. Simulations and results are discussed in Section 5. This paper concludes with Section 6 where the advantages and disadvantages of using the disturbance observer are outlined. Furthermore, intended future works are also presented in the last section.
## 6 Experimental Results and Analysis
### Influence of V-Rib Attack Angles (\(\beta\))
#### 6.2.3 Thermal Performance Index Results  
The thermal performance indices (_TPI_) for continuous V-rib twisted tapes (CVRTs) at attack angles (\(\beta\)) of 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\) are approximately 1.13, 1.11, and 1.10, respectively, all exceeding unity. This indicates that the twisted tape structure effectively improves thermal performance in heat exchanger tubes. Figure 12 illustrates changes in the _TPI_ for CVRTs with varied attack angles at _Re_ from 6000 to 20,000. The figure illustrates that the _TPI_ for all twisted tapes increased with decreased Reynolds numbers. The variation is initially more noticeable but gradually stabilizes as _Re_ increases. This behavior results from a combination of factors, including the transition of the flow regime, the saturation of the thermal boundary layer, a nonlinear increase in frictional losses, and a diminishing influence of geometric disturbances. At lower Reynolds numbers, viscous forces dominate the flow field, and twisted tapes can effectively induce secondary flows and vortices. These flow disturbances significantly enhance mixing near the wall, making the Nusselt number highly sensitive to increases in Reynolds number. However, as the Reynolds number increases further, the baseline turbulence intensity within the exchanger tube becomes comparable to, or exceeds, that induced by the enhancement devices. In this regime, the incremental contribution of the twisted tape to turbulence generation is reduced. This convergence leads to a plateau in the thermal performance index (_TPI_), indicating that further increases in the Reynolds number yield diminishing returns in terms of thermal enhancement relative to pressure loss. At a constant _Re_, the _TPI_ value reduces as the attack angle increases. The CVRT at \(\beta=15^{\circ}\) exhibits the highest _TPI_ value, exceeding the CVRTs at \(\beta=30^{\circ}\) and \(\beta=45^{\circ}\) by 0.6 and 2.6%, respectively. The difference is smaller at lower Reynolds numbers, indicating that the attack angle is indeed a crucial factor affecting the heat transfer of the CVRTs. A lower attack angle results in improved thermal performance indices (_TPI_).  
Figure 11: Effect of V-rib attack angle (\(\beta\)) on the friction factor.
## 4 Discussion
### Hand Motor Function Effects  
In _MIPAS_, we highlight the fact that all three interventions induced improvements in paretic wrist aROM. PAS/MI induced an immediate and large increase in paretic wrist aROM that lasted 30 min postintervention. Similar motor effects were observed in healthy subjects when MI was combined with anodal transcranial direct current stimulation (tDCS) [50]. However, the effect in _MIPAS_ has neither a higher effect size nor a longer-lasting effect than PAS alone. This result again supports the idea that both interventions are not synergistic, presumably through a mechanism of regulatory homeostatic plasticity [39; 40; 41].  
PAS induced a gradual and significant increase in active paretic wrist aROM, lasting from 15 to 30 min postintervention, compared to pre-intervention values. To our knowledge, this is the first evidence of a beneficial effect of PAS on the UE motor function in stroke participants. As compared to MI, PAS is supposed to induce a more specific strengthening of the corticospinal connections of the targeted muscle [6; 9]. A beneficial effect of PAS on motor performance has been observed in SCI individuals [12; 13; 51] but not in stroke patients [10; 18]. These differences might be explained by the difference in the lesion location/size between SCI and stroke and is in line with the fact that PAS might not be suitable for individuals with large cortical lesions. Additionally, the assessment method is critical and especially in single-session designs. Indeed, the use of kinematic aROM measurements is more specific to the tested muscle and more sensitive to single-intervention effects than the standardized clinical scales, such as the FMA [18].  
ShamPAS/MI induced less clear improvement of paretic aROM compared to PAS and PAS/MI, although still significant at 15 min postintervention. This effect is of interest given the easy use and implementation of MI-based exercise in stroke neurorehabilitation [15]. The repetition of MI-based exercises over a greater number of sessions could induce greater functional improvements.
## 2 Literature Review
### Discussion
#### 2.2.2 Unmanned Aerial Vehicles (UAV)
In theory, the flight time increases proportionally with flight altitude, explaining the stark difference in maximum flight times achieved in HALEs vs LALEs. However, HALEs have expansive wingspans and require a runway, which make them impractical for many applications. Using an ATmega328 microcontroller-based photovoltaic power management system, researchers were able to achieve a continuous surveillance of about 6 h using a swarm of six LALE UAVs [74]. XSun's SolarXOne claims to be able to fly continuously for 12 h, powered by solar cells [75]. It can support a payload of 5 kg, and provides customizable options including RGB or IR cameras, and other sensors which can be used to detect wildfires. [PERSON] et al., proposed a thoroughly detailed method for designing a solar-powered, hand-launched UAV considering payload capacity, expected solar irradiance, aerodynamic efficiency considerations at proposed flight altitude and flight control [54]. Using their model, they were able to achieve flight endurance durations of several hours at an altitude of 15,000 ft.  
_Advantages:_  
The biggest advantage of UAVs is flexibility in repositioning, which allows dynamic readjustment of areas surveyed as deemed necessary. UAVs can also be programmed to provide high temporal and spatial resolution over areas determined to be at high risk. Unlike static nodes or camera networks, UAVs' usage often extends past the initial detection, as they can follow the spreading patterns of fires from a safe distance. Using a limited number of UAVs to scan an area through multiple passes can be more efficient than installing a vast sensor node network. They are less expensive to implement than satellites and may also be more capable of identifying smaller wildfires than the latter technology. _Challenges:_  
Most UAVs still need some form of human involvement in and throughout their operation. The major hurdle for this technology is distance and duration of flight. Despite the advancements in assimilating solar panels into the device, the flight times are practically limited to a few days for hand-launched LALEs. Therefore, this technology is likely to be unsuitable when uninterrupted surveillance is needed in wildfire-prone areas. The technology is also relatively new; therefore, the costs of operating and maintaining a UAV fleet can add up exponentially. UAVs are also mired in frequent software and hardware issues that have impeded their adoption as a widespread firefighting solution.
# Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation
## 3 Data  
To generate more training data, we prepare a horizontally layered model adapted from a field downhole monitoring of five-stage hydraulic fracturing [10], as shown in Figures 2 and 3a. There are 395 events in total and the event numbers from stage 1 to stage 5 are 105, 116, 48, 66, and 60, respectively. The field microseismic data contain three components and we consider only the Z component to reduce the number of operations. The acquisition system consists of 15 receivers (black reverse triangles) placed at a constant spacing of 20 m in a vertical linear array. Each trace has 1201 samples with a time interval of 0.5 ms. Four-layer velocity models are constructed referring to the velocity model from traveltime inversion with eight ball-hit events [10]. We obtain 200 velocity models by adding random \(\pm\)10% perturbations to the P- and S-wave velocities with fixed layer depths. We randomly set 30 source locations in the source region (Figure 3a) for each velocity model. The velocity model has a size of 64 \(\times\) 200, with a grid spacing of 5 m. A Ricker wavelet with a peak frequency of 100 Hz is used as the source function. We use 6000 synthetic gathers (200 models \(\times\) 30 sources) as the initial training dataset. The testing dataset included 105 field microseismic events from stage 1 (corresponding to a single reference velocity model).  
Figure 3b shows the results of the power spectra comparison. The augmented data approaches the real data in terms of energy distribution by retaining more high-frequency contents of the real data. The exemplary synthetic and real microseismic waveform data are shown in Figure 3c-f.  
Figure 3: Model and data. (**a**) A horizontally layered model for downhole microseismic monitoring. The black rectangle indicates the region where the sources are located, the red pentagram indicates an arbitrary source, and black reverse triangles indicate the receivers. (**b**) Power spectra comparison. (**c**) The original noise-free synthetic waveforms generated by ray-tracing. (**d**) Real microseismic data. (**e**) Result of the real data autocorrelation. (**f**) The augmented data for the synthetic waveforms in (**c**).  
Figure 2: The layout of a real downhole microseismic monitoring project. (**a**) Three-dimensional view. (**b**) Side view of (**a**). Black reverse triangles indicate the receivers and the dots are microseismic events.
# Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists
## 2 Materials and Methods  
### Formulation of the Objective Functions  
This study solves a multi-objective optimization problem (MOOP) of finding the best cycling routes between two specific origin and destination points. The A Posteriori method using multi-objective evolutionary algorithms (MOEAs), specifically the NSGA-II algorithm, is chosen to solve this problem. Multi-objective evolutionary algorithms (MOEAs) are optimization techniques designed to solve problems involving multiple conflicting objectives. These algorithms utilize evolutionary strategies to explore a diverse solution space, seeking optimal trade-offs among objectives [26]. By employing NSGA-II, the complexities associated with optimizing various transportation routes--while simultaneously balancing multiple objectives--are effectively navigated. The results of [PERSON]'s study indicate that NSGA-II not only efficiently generates a diverse set of optimal solutions but also enhances decision making in transportation planning. Their study underscores the potential of NSGA-II as a robust tool for addressing intricate optimization challenges in the field of transportation engineering, paving the way for this study [27]. First, the criteria variables that bicycle users consider are identified. For each of them, an objective function is created. In optimization, an \"objective function\" is a mathematical expression that defines the goal or outcome that the optimization algorithm seeks to achieve or maximize/minimize. Specifically, in multi-objective optimization, this can involve several objective functions, each reflecting different, and sometimes conflicting, goals, such as minimizing cost or maximizing efficiency [28]. These are presented below.
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
## 4 Results and Discussion
### Start-up Response of the Buck Converter  
The CI algorithm was used to optimize the voltage response of the buck converter circuit at start-up in SIMULINK with the FOPID parameters as design variables. The initial runs showed better results with the sampling interval chosen as \([0,200]\) for \(K_{p}\), \(K_{i}\) and \(K_{d}\), and \([0,1]\) for \(\lambda\) and \(\mu\). The start-up response of the buck converter was optimized with different values of the reduction factor \(r_{i}\). Figure 5 shows the most optimum time-domain response for the cost function ISE using reduction factors of \(r_{i}=0.55\) and \(r_{i}=0.45\).  
For the cost function \(J_{1}=ISE\), the value of overshoot was greatly reduced by setting the reduction factor \(r_{i}\) to 0.45 and 0.55 compared to higher values of \(r_{i}\). The inductor current ripple was also limited to less than 20% of the output current (Figure 6). With \(r_{i}=0.45\), the best result gave zero overshoot, and the rise time was \(3.7\times 10^{-5}\) seconds and settling time around 0.000265 s, which shows a fast transient response. The output voltage ripple was around 75 mV. For \(r_{i}=0.55\), the overshoot and settling time slightly increased to 1.86% and 0.00061 s, respectively, while the rise time remained roughly the same.  
Similarly, the responses were found to be better for all the cost functions with \(r_{i}=0.55\) and \(r_{i}=0.45\).The optimal parameters for each cost function has been tabulated in Table 3. It is seen that while the rise time and settling time remains almost the same, the overshoot is more for the cost functions ITAE and IAE. The responses of the CI optimized system with different cost functions is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline No. of cohort candidates & 4–5 \\ \hline No. of variables & 5 \\ \hline Reduction factor \(r_{i}\) & 0.45, 0.55 \\ \hline Convergence constant & 0.001 \\ \hline Maximum iterations & 25 \\ \hline \end{tabular}
\end{table}
Table 2: CI algorithm specifications.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **ISE (\(r_{i}=0.45\))** & **ITSE (\(r_{i}=0.55\))** & **ITAE (\(r_{i}=0.45\))** & **IAE (\(r_{i}=0.45\))** \\ \hline \(K_{p}\) & 162.08 & 27.9709 & 123.0768 & 197.3838 \\ \hline \(K_{i}\) & 133.84 & 112.7302 & 122.9522 & 115.0357 \\ \hline \(K_{d}\) & 0.5851 & 0.7737 & 54.0215 & 63.0392 \\ \hline \(\lambda\) & 0.0673 & 0.1022 & 0.4738 & 0.3730 \\ \hline \(\mu\) & 0.6107 & 0.5868 & 0.2537 & 0.1874 \\ \hline Mp (\%) & 0 & 0 & 20 & 21.3 \\ \hline Tr (ms) & 0.037 & 0.035 & 0.0365 & 0.0354 \\ \hline Tss (ms) & 0.265 & 0.35 & 0.27 & 0.3 \\ \hline Cost function value & 0.006 & 0.0288 & 0.0004 & 0.0119 \\ \hline \end{tabular}
\end{table}
Table 3: Response of CI optimized converter with different cost functions.  
Figure 5: Startup response of CI-ISE optimized Buck converter with variation of reduction factor.  
Figure 6: Inductor current ripple of system with CI-ISE and \(r_{i}=0.45\).
## 4 Distance-Based Energy Aware Routing (DEAR) Algorithm  
### Theoretical Analysis of Hotspot Problem  
#### 4.1.1 Event-based Traffic Model  
We first study the one dimensional linear network for simplicity, which can be used in linear applications like highway traffic monitoring, congestion control _etc._ In Figure 4, there are \(n\) sensor nodes placed along a line from source node to sink node with individual distance \(\{d_{I},\,d_{2},\,\cdots,\,d_{n}\}\). The source to sink node distance is \(d\) and \(\sum\limits_{i=1}^{n}d_{i}=d\).  
Once an event is detected by the source node, it will send an _l_-bits message through either direct transmission or multi-hop transmission to the remote sink node. Since multi-hop transmission is more energy efficient when \(d\) is large, here we study the _n_-hop transmission from source to sink node. Our first objective is to find the optimal multi-hop number and each individual distance \(d_{i}\) so that each node consumes the least energy at similar rate. Based on Equation (1) to (3), the total energy consumption to transmit one bit data (\(l=1\)) over _n_-hop route will be:  
\[E(n)=(E_{elec}+\varepsilon_{amp}\cdot d_{i}^{\alpha})+\sum\limits_{i=2}^{n}{(2E _{elec}+\varepsilon_{amp}\cdot d_{i}^{\alpha})}=(2n-1)E_{elec}+\sum\limits_{i=1 }^{n}{\varepsilon_{amp}\cdot d_{i}^{\alpha}} \tag{4}\]  
where, \(\varepsilon_{amp}=\varepsilon_{fos}\) when \(\alpha=2\) and \(\varepsilon_{amp}=\varepsilon_{mp}\) when \(\alpha=4\). For fixed \(\sum\limits_{i=1}^{n}d_{i}=d\), \(\sum\limits_{i=1}^{n}d_{i}^{\alpha}\) in Equation (4) has a minimal value when \(d_{I}=d_{2}=\cdots=d_{n}=d/n\). Therefore, \(E(n)\) is finally equal to:  
\[E(n)=(2n-1)\cdot E_{elec}+\varepsilon_{amp}\cdot n^{1-\alpha}\cdot d^{\alpha} \tag{5}\]  
Equation (5) has the minimum when \(E(n)=0\) or  
\[2E_{elec}+\varepsilon_{amp}\cdot(1-\alpha)\cdot(d/n)^{\alpha}=0\]  
Thus, we can get the final optimal hop number as:  
\[n_{opt}=d\cdot(\varepsilon_{amp}\cdot(\alpha-1)/2E_{elec})^{1/\alpha}. \tag{6}\]  
and the corresponding distance [5, 22]:  
\[d_{i}=d/n_{opt}=\sqrt[2E_{elec}]{\varepsilon_{amp}(\alpha-1)} \tag{7}\]  
Since the distance \(d_{i}\) as well as the traffic length is the same for all sensor nodes, the only difference of energy consumption is \(E_{Rx}(l)=l\)\(\cdot E_{elec}\), as can be seen from Equation (1) to (3). Therefore, we can get \(E_{I}\approx E_{2}=E_{3}\cdot\cdots=E_{n}\).  
Figure 4: One dimensional linear network.  
The query-based traffic model is similar to the event-based model in terms of energy consumption since the length of data message as well as the individual distance is the same for all sensors. In both cases, all involved sensors will consume their energy at similar rate, which can effectively alleviate the hotspot problem and prolong the network lifetime.
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 2 Popular Pooling Methods  
### Max Pooling Method  
The max pooling method [20] is simple and extensively applied in CNNs because there are no parameters that need to be tuned  
Max pooling is a mechanism that optimizes the spatial size of a feature map while also providing the network with translation invariance. This is performed by exhibiting the greatest value in the feature map mainly within a k x k neighborhood. The max pooling technique identifies the biggest element in each pooling region [21; 22]. Considering sparse codes and simple linear classifiers, max pooling shows better performance. Its popularity has increased in recent years as a result of the aforementioned factor. The equation for max pooling is:  
\[f_{max}(X)=max_{i}x_{i} \tag{1}\]  
\(J\) related filters are used for the composition of \(m\)-th max pooling band \(pm=[p1,m,\ldots,pj,m,\ldots,pJ,m]\)\(\epsilon\)\(R^{J}\):  
\[pj,m=\max\bigl{(}\ h_{j},(m-1)N+r\bigr{)} \tag{2}\]  
Here, \(N\)\(\epsilon\)\(\{1,\ldots,R\}\) is termed as a pooling shift which allows enabling overlap within concerning pooling regions when \(N<R\). The biggest limitation of max pooling is that all the other items are ignored completely whereas only the biggest element from the pooling region is assessed [23]. If most of the components in the pooling region are of large magnitude, the distinguishing traits vanish once the max pooling process has been completed. As a result of the loss of information, the scenario leads to unacceptably bad outcomes.  
An example to further clarify the concept of max pooling operation is shown below in Figure 2, which depicts the input sample size of the pooling region as 4 \(\times\) 4 while the filter size with a stride of 2 is 2 \(\times\) 2. The first 2 \(\times\) 2 parts (green) have a maximum value of 20 chosen by max pooling. The maximum value for each zone is selected accordingly and an output channel is created. The problem is that max pooling only considers the largest element and ignores the others, as we can see in the example. In some cases, after max pooling, salient features disappear when most elements have high magnitudes, which can lead to unacceptable results [24].  
Figure 2: Max pooling operation illustration.