esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Cost-Effective Auto-Scaling (CEAS) Framework
### Monitoring and Provisioning Amazon EMR Clusters for Auto-Scaling
#### 3.3.2 Rules for Auto-Scaling  
For an instance group, when a scaling activity is triggered by a scale-out, Amazon EC2 instances are added to the instance group based on the scaling rules, whereas the termination and removal of instances can be done by setting up a scale-in rule. With simple scaling policies available with Amazon AWS, the threshold values for the CloudWatch alarms that trigger the scaling process as well as define how the Auto Scaling group should be scaled when a threshold is in breach for a specified number of evaluation periods. In our framework, we have chosen the metric _PercentChangeInCapacity_, where we specify the minimum number of instances to scale (using the MinAdjustmentMagnitude parameter, Add instances in increments). The autoscaling policy is based on the two common metrics of CloudWatch--_YarmMemoryAvailablePercentage_ and _ContainerPendingRatio_ provided within the AWS.  
The scaling behaviour for each rule in a scaling policy is identified by the following parameters:  
* _Scale-out and scale-in rules_: apply to the maximum and minimum instances, respectively.
* _Unique rule name_: should be configured in the scaling policy.
* _Resizing of EC2 instances_: must be determined by the scaling adjustment.
* _CloudWatch_: monitors for an alarm threshold condition and compares the performance metric against a threshold value.
* _Evaluation period_: is the duration when CloudWatch metric is evaluated and in triggered condition before prompting the auto-scaling activity.
* _Cooldown period_: is the time passed by the instance between the period when the auto-scaling activity is triggered by the scaling rule and the beginning of the next scaling activity.
* _Performance metrics_: are variables that need to be monitored, such as CPU usage, traffic, etc.
* _Cloud watch alarm_: is an object that controls a single metric over a given period. An example of a threshold value to create an alarm is shown in Figure 2. The scaling of resources can be thus triggered within the EMR.
* _Alarm_: is used to give warning when the value of the EMR metric breaches a defined range and maintains the change for a specified duration.  
Within each Hadoop cluster, by enabling the Ganglia web interface, the performance of the entire Hadoop cluster can be evaluated and displayed by visual reports. Also, the performance of each individual instance can be inspected. Here, Ganglia is referred to as an open-source tool for high-performance monitoring of grids and clusters. It consists of gmond, gmetad and a front-end web. Gmond is a multi-threaded daemon that runs over all nodes and information is reported back to gmetad which helps in collecting the metrics and also generating the report. In the CEAS framework, the Ganglia is initially used for graphing and data logging. The data is visualized by the front-end web bundled with Ganglia which offers a complete solution for archiving, monitoring and visualisation of the system metrics without any special coding.
## 5 ENO Controller Design  
The validated energy consumption model of the CTT-1000a was used to design and test a controller with two goals. First, the battery SOC should be maintained at 80% for optimum life according to [6]. Second, collection rate should be constant or only change in short (once per day) increments. The two design goals needed a scheme that would feed back to the SOC, compare it against a set-point, and then calculate a change in the fix rate, if needed. There are many known control methods that may not have been specifically designed to maintain ENO for the investigated system, such as artificial intelligence [38], adaptive control [32; 39], LQR, non-linear control, and feedback control [40]. The various control methods found were compared for processing execution based on complexity of integration. Feedback control in the form of PID was found the be the simplest, yet was capable of successfully controlling the modeled wildlife sensor system, shown for ENO in Figure 6. It should be noted that PID is known to work for linear time-invariant systems and the model being used here is a time-varying non-linear system, meaning that extra precautions need to be implemented to avoid output oscillations and asymptotes. The ENO controller developed uses the difference between the desired or set point SOC and the measured SOC to suggest an increase or decrease in the GPS fix interval. At initialization of the system, the GPS fix interval is selected based on predicted knowledge of the device's location. During operation, the controller is given calculation time after each data collection interval (in this case the GPS fix interval). The controller is then able to increase or decrease the collection interval in order to utilize more available energy or decrease to save energy. The controller consisted of a gain scheduled PID with two sets, upper/lower boundary control, and asymptote detection for a reset. Gain scheduling was done to allow fast adjustments  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Device \#**} & \multicolumn{3}{c}{**Absolute Difference**} \\ \cline{2-4}  & **Minimum** & **Maximum** & **Average** \\ \hline
1 & 0.3\% & 2\% & 1\% \\
2 & 0.1\% & 5\% & 1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of device and modeled battery SOC for golden eagles in eastern North America.  
Figure 5: Model validation with (**a**) empirical PVC current data; (**b**) simulated and measured SOC; and (**c**) absolute percent difference in SOC.  
with large errors and very small reactions once the error or desired set point was reached. Typical automated or deterministic PID parameter tuning methods do not work for the developed model due to the non-linearity. Therefore, trial and error simulations using the model will determine the PID gain values and schedule points.
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 4 Results
### Efficiency Comparisons  
Since the vast majority of computation in the model comes from convolutional operations, this section only counts the calculated quantities of the convolutional layers in the model. Unlike traditional 3D convolution, we use the following formula to calculate the number of operations for sparse tensor-based convolution operations.  
\[\text{Flops}=2*(\text{C}_{\text{in}}*\text{K}-1)*\text{N}*\text{C}_{\text{out}} \tag{18}\]  
where the number of input and output channels of the convolution layer are denoted by \(\text{C}_{\text{in}}\) and \(\text{C}_{\text{out}}\), respectively. For rasterized data, we express the size of the feature map in terms of the number of sparse rasters. Therefore, N is the number of output rasters in the convolution layer. K is the average amount of activation points. K is calculated by setting the convolution kernel parameter of each convolution layer to 1 and averaging the output to obtain the amount of activation points around each point.  
In this section, the efficiency of LFNet is compared with existing models, and the specific statistics are shown in Table 4. Compared to the 3D-Unet model, LFNet introduces new convolutional layers due to the addition of the feature-learning and fusion modules, which increase the parameters of the model and require more computations. As a result, LFNet has a slight increase in FLOPs and single-frame prediction times, but the model still meets the requirements of real-time computing.  
By analyzing the experimental data, it can be seen that LFNet increases the number of parameters by 5.2 M compared to the SPVNAS model, but it reduces the amount of computation by nearly 83.6 G. The sparsity of the point cloud is the main reason for this. LFNet uses sparse tensor-based implementation, meaning that the model is able to avoid performing a large number of invalid operations in blank areas and therefore effectively reduces computational effort. The results demonstrate that LFNet can reduce the model computation, improve the operation efficiency, and essentially meet the application requirements of UGV for real-time semantic segmentation.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 3 Dangampo Site: Equipment Testing
### Experimental Setup
#### 3.1.2 Echo Sounder  
An echo sounder records water depth by measuring the time required for a sound wave to travel from a transducer to the seafloor and back. An MBES emits multiple beams and collects a large range of information, which can be used as data for the 3D mapping of the seafloor topography (Figure 1).  
Figure 1: Photograph of a multibeam echo sounder transducer.  
The MBES survey at the Dangampo site was conducted on October 27, before the scheduled sediment removal survey, using an R2 Sonic 2024 Wideband Multibeam Echosounder. The multibeam transducer of this model emits 256 beams at 400 kHz in one sweep. We installed the MBES on the ship Sea Muse (18 ton) as it was too cumbersome for a small boat, and we conducted the survey during the high tide. In the northern part of the site, the water was too shallow for the survey vessel to approach. Hence, only half of the site (500 m (latitudinal) \(\times\) 100 m (longitudinal)) was surveyed. The swath of the MBES varied depending on the water depth; typically, the swath was approximately four times the water depth. Given that the water depth at the Dangampo site ranged from 4 to 10 m, the swath of the echo sounder ranged between 16 and 40 m. Accordingly, the survey line, spacing was set to 10 m along the longitudinal axis, and the seafloor was surveyed while the ship navigated back and forth 7.5 times along the latitudinal axis.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 2 Vehicular Named Data Networking (VNDN)
### Consumer Vehicle  
In VNDN, any node/vehicle that generates an interest packet (request) for any content or information is called a consumer vehicle. Based on this NDN forwarding strategy, a consumer initiates an interest packet and broadcasts it to all the neighboring vehicles. The data packet will be received if the interest is fulfilled by the intermediate vehicles or the producer. The consumer vehicle is hence the data requester in a VNDN system.
## 1 Introduction
In the second stage, the damage location is further identified by comparing the difference in boundary modal curvature (BMC). Firstly, two panel units with different sizes were investigated by numerical test. Considering the influence of the damage severity and measurement noise, a threshold of damage identification on the ACBMC is proposed. Then, model tests were carried out on the two panel units. Both numerical and experimental results demonstrate that the proposed method effectively identifies the damage location of structural sealants.
## 5 Simulink Results and Discussion
### Analysis of FLOS Simulation Results
#### 5.1.2 FLOS Navigation Algorithms in Case 2  
In Case 2, a more complex scenario hexagonal expectation tracking path is designed. In Case 1, the adaptability of FLOS to the situations with lateral velocity has been verified, so in this scenario lateral velocity will not be imposed. Only the tracking accuracy and the convergence speed of \(y_{e}\) are compared and analyzed. The results can be found in Figures 9 and 10. In Figure 9, the starting point is (2 m, 3 m), and Point \(I\) (\(I=1\,\dots\,6\)) is the navigation point.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Performance Parameter** & **LOS** & **ELOS** & **ALOS** & **FLOS** \\ \hline
0–15 s, standard deviation \(y_{e}\) (m) & 0.1843 & 0.1685 & 0.2306 & 0.16 \\ \hline
0–15 s, max \(y_{e}\) (m) & 0 & 0.018 & 0.2053 & 0 \\ \hline Time (s) for \(y_{e}<\) 0.01 m in 0–15 s & 4.35 & 4.00 & 8.49 & 3.72 \\ \hline
15–28 s, standard deviation \(y_{e}\) (m) & 0.1032 & 0.0473 & 0.1322 & 0.0264 \\ \hline
15–28 s max \(y_{e}\) (m) & 0.5164 & 0.1588 & 0.1154 & 0.3629 \\ \hline Time(s) for \(y_{e}<\) 0.01 m in 15–28 s & NAN & 4.4970 s & 7.404 & 2.784 \\ \hline
0–15 s, time(s) to reach the ideal yaw angle of 0.785 rad & 6.3470 & 6.4910 & 10.6360 & 5.7662 \\ \hline
15–28 s, time(s) to reach ideal yaw angle 1.031 rad & 7.270 & 6.1650 & 9.2110 & 4.9750 \\ \hline
0–15 s, yaw angle overshoot 0.785 rad & 51.44\% & 196.66\% & 55.37\% & 114.28\% \\ \hline
0–15 s, yaw angle overshoot 1.031 rad & 0.75\% & 4.38\% & 7.08\% & 5.48\% \\ \hline \end{tabular} According to Table 2, the fastest convergence algorithm is the FLOS, which takes 3.72 s for \(y_{e}\) to converge to zero. In the presence of lateral velocity, it takes 2.784 s for \(y_{e}\) converge to zero. The highest tracking accuracy is based on the FLOS, which the standard deviation is 0.16 m in 0–15 s and 0.0264 m in 15–28 s. The time to reach the ideal yaw angle are, respectively, 5.76 s and 4.975 s, which are better than LOS, ELOS and ALOS. However, using FLOS produces overshoot in the initial conditions, which is less than using ELOS, but higher than using LOS and ALOS.  
\end{table}
Table 2: Quantitative comparison of performances for the proposed FLOS with LOS, ALOS, and ELOS in Case 1.  
Figure 9: Tracking a hexagonal desired trajectory. The robot follows the hexagonal desired trajectory to the navigation points one by one.  
As shown in Figure 10, after reaching the navigation point, the CDR calculates the desired heading angle to move to the next target point. Since the desired trajectory is regular hexagonal, the desired heading angle is (0, 0.785, 2.355, 3.14 or \(-3.141\), \(-2.355\), \(-0.785\)). It should be noted that at 38-50 s, the planned yaw angle is constrained to \((-\pi,\pi)\) and the switch from \(\pi\) to \(-\pi\) occurs when it switches from the second quadrant to the third quadrant.  
To introduce the advantages of the FLOS algorithm we proposed in the Case 2, the quantitative comparison is shown as Table 3.
## 3 Description of the Measurement Set-Up
The total distortion of the current drawn from the power grid can be characterized by the total harmonic distortion coefficient, \(THD_{I}\), of the supply current, which is described by the following formula [8; 39]:  
\[THDI=\sqrt{\frac{\sum\limits_{k=2}^{n}I_{k}^{2}}{\sum\limits_{k=1}^{n}I_{k}^{2} }\cdot 100\%} \tag{1}\]  
where \(I_{k}\) is the \(k\)-th harmonic of the supply current. One of the important parameters describing the performance of the selected power electronic system is energy efficiency \(\eta\), which can be calculated using the following formula [7; 39]:  
\[\eta=\frac{P_{out}}{P_{in}}=\frac{P_{out}}{\frac{1}{\frac{1}{\gamma}\int\limits _{0}^{T}V_{in}(t)\cdot I_{in}(t)dt}}\cdot 100\% \tag{2}\]  
where \(P_{out}\) is the output power of the power supply system under test, and \(P_{in}\) is the power drawn from the power grid, the value of which was measured with a PA1000 power analyzer. Figure 4: View of the measurement set-up. The adverse effect of time-domain disturbances in the current drawn from the power grid is usually described by the power factor (_PF_) using a suitable formula implemented in a PA1000 power analyzer [7]. \[PF=\frac{1}{T}\cdot\frac{\int\limits_{t}^{T}Vin(t)\cdot lin(t)dt}{VinRMS\cdot linRMS} \tag{3}\]  
where \(V_{in}(t)\) and \(I_{in}(t)\) denote the instantaneous values of the waveforms of voltage and current drawn from the power grid, while \(V_{inRMS}\) and \(I_{inRMS}\) denote the RMS values of voltage and current drawn from the power grid, respectively. The paper [8] presents a method for determining the power factor using the \(THD_{I}\) current harmonic distortion coefficient, which was used in further experimental studies [8]. \[PF=\frac{1}{\sqrt{1+\left(\frac{THDI}{100\%}\right)^{2}}} \tag{4}\]  
where \(THD_{I}\) is the total harmonic distortion coefficient of the time course of the current drawn from the power grid and determined from the spectrum measured with a GwInstek GDS2104 oscilloscope and Formula (1). The uncertainty of the measurement of energy efficiency is equal to the product of this efficiency and the sum of a relative error of measurement of output power and input power. In turn, the uncertainty of the measurement of \(THD_{I}\) factor can be estimated using the total differential method [40]. It can be described using the following formula:  
\[\Delta THD_{I}=\frac{I_{1}}{THD_{I}}\cdot\left[\frac{I_{1}\cdot\sum\limits_{k= 2}^{n}\left(I_{k}\cdot\Delta I_{k}\right)+\Delta I_{1}\cdot\sum\limits_{k=2}^{ n}I_{k}^{2}}{\left(\sum\limits_{k=1}^{n}I_{k}^{2}\right)^{2}}\right] \tag{5}\]  
where \(\Delta I_{k}\) denotes the uncertainty of measurement of RMS values of harmonics \(I_{k}\). The analogue formula can be used for calculating the uncertainty of \(THD_{V}\).
# Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability
## 2 Reliability Assessment Approach
### Interruption Indices  
System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI) are the most widely used indices by utility companies in order to reach their targets in terms of customer satisfaction, service restoration, and quality of services [29]. Throughout this study, these mentioned indicators are used for assessing the reliability improvements.  
SAIFI gives the average number of interruptions per end-user per year in the system and can be decreased by reducing outages at the load points [27]. SAIFI is represented as given in Equation (4) where \(i\) indicates load point, while \(N_{i}\) is the total number of end-users served.  
SAIDI index is expressed in Equation (5) that means the average interruption duration per end-user experienced during the year [30]. It is possible to achieve improvements in SAIDI by reducing failure restoration times with effective crew management strategies.  
\[SAIFI=\frac{\sum_{i}\lambda_{i}N_{i}}{\sum_{i}N_{i}} \tag{4}\]  
\[SAIDI=\frac{\sum_{i}U_{i}N_{i}}{\sum_{i}N_{i}} \tag{5}\]
## 3 Developments of CIM  
CIM represents a holistic digital framework that seamlessly integrates urban planning, infrastructure management, and environmental monitoring, with the overarching goal of enhancing the intelligence and sustainability of urban management systems. Its evolution transcends mere technological innovation, encompassing critical dimensions such as data standardization, system interoperability, policy support, and multi-stakeholder collaboration. This multifaceted approach ensures more efficient, coordinated, and resilient urban governance.
## 2 Methods
### Evaluation Parameters and Numerical Modeling
#### 2.2.3 Control Equations and Turbulence Modelling in Fluid Domains  
The numerical calculations in this paper are divided into transient solutions in the fluid and solid domains. In the fluid domain computation, considering the computation as an in-compressible flow turbulence problem, the equations of motion control are the Reynolds-averaged Navier-Stokes (RANS) equations and the time-averaged continuity equations [14]:  
\[\frac{\partial\overline{u}_{i}}{\partial x_{i}}=0, \tag{10}\]  
\[\frac{\partial\mathbf{v}}{\partial t}+\mathbf{v}\cdot\
abla\mathbf{v}=-\frac{1}{\rho} \
abla\overline{p}+(\gamma+\gamma_{t})\
abla^{2}\mathbf{v} \tag{11}\]  
where \(\overline{p}\) is the time-averaged pressure of the fluid, Pa. \(\mathbf{v}\) is the average velocity of the fluid movement, m/s. \(\gamma\) is the laminar flow viscosity coefficient, Pa-s. \(\gamma_{t}\) is the turbulent flow viscosity coefficient, Pa-s.  
Figure 4: Flexible hydrofoil grid deformation: (**a**) Solid domain mesh deformation; (**b**) Fluid domain mesh deformation.  
Figure 5: Partitioned iterative solution method flow.  
Since the number of unknowns in the above system of equations is larger than the number of equations, a turbulence model was introduced to close the system of equations to find a definite solution. Commonly used turbulence models include the k-\(\omega\) model and k-\(\epsilon\) model, etc. The k-\(\epsilon\) model is the most stable and widely used model among many turbulence models, which can improve the calculation speed while guaranteeing accuracy, so this paper chose the k-\(\epsilon\) model in the numerical simulation. There are three kinds of k-\(\epsilon\) models, namely, the standard k-\(\epsilon\) model, the RNG k-\(\epsilon\) model, and the realizable k-\(\epsilon\) model. Since the fluid medium in this study was a viscous incompressible fluid, for the viscous problem, the realizable k-\(\epsilon\) model adds an equation for the viscous term, which improved the accuracy and precision of the calculation results and was more in line with the computational needs of this paper. In summary, the realizable k-\(\epsilon\) turbulence model was chosen to solve the N-S equations in this paper. The relevant equations can be found in a study by [PERSON] et al. [14]. The utilization of bi-directional fluid-structure interactions to investigate the passive deformation of actively oscillating flexible hydrofoils is not a novel concept from the perspective of contemporary computational fluid dynamics (CFD) techniques. However, the present study contributed to the advancement of these methods by applying them to flexible hydrofoils under ultra-low head conditions. The integration of passive deformation analysis with Shore hardness and oscillation mode optimization under such conditions remains an under-explored area in the field. While the method itself is widely recognized in general CFD simulations, its application to solve the actual flexible deformation problem of oscillating hydrofoils is also of great importance in this field.