esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 5 Power Profiling Model
### Data Extraction  
Electrical usage data in DR systems, acquired from smart meters, is used to extract consumption patterns. We use a real-world dataset of electrical consumption, AMPds2 [18], which includes load data from various electrical appliances (e.g., refrigerator, dryer, stove, and lights) collected over an extended period. The dataset was collected using DENT real-time smart meters, which track short-term power consumption and provide granular insights into daily energy usage patterns. In AMPds2, DENT PowerScout 18 units [66] were installed at the electrical circuit breaker panel to monitor appliance loads, recording data at one-minute intervals. These smart meters capture kWh/kW energy and demand data, enabling diagnostics and monitoring within a smart grid neighborhood area network (NAN) [66]. Each smart meter generates 1440 measurement data points per appliance per day. To balance computational efficiency and pattern extraction fidelity, we downsample the dataset by selecting 10-min interval readings, resulting in 144 data points per appliance per day. This time-series representation allows for efficient pattern recognition and similarity analysis using DTW. The extracted daily power usage time series serves as the basis for profiling and clustering consumption behaviors across different days.
## 3 Results and Discussion
### Aroma Composition
Therefore, the S treatment should be limited to NS or steaming of 1 min, with respect to retaining aromatic components, whereas FD is the preferred drying method carried out at the right temperature to retain all three components. Therefore, MD \(\leq\) 70 \({}^{\circ}\)C should be used for retaining two components (Linalool, _trans_-alpha-bergamotene). Figure 7: GC-MS chromatogram of _Ocinum basililicum_ cv. ‘Genovese’ tea under different tea-making treatment conditions. FD: Freeze-drying (\(-\)56 \({}^{\circ}\)C, 48 h), NS: Non-Steaming, S1: Steaming for 1 min, S2: Steaming for 2 min, S3: Steaming for 3 min, S5: Steaming for 5 min, S10: Steaming for 10 min, SD: Shade-drying (25 \({}^{\circ}\)C, 7 days), MD 40 \({}^{\circ}\)C: Machine-drying at 40 \({}^{\circ}\)C, MD 50 \({}^{\circ}\)C: Machine-drying at 50 \({}^{\circ}\)C, MD 60 \({}^{\circ}\)C: Machine-drying at 60 \({}^{\circ}\)C, MD 70 \({}^{\circ}\)C: Machine-drying at 70 \({}^{\circ}\)C, MD 80 \({}^{\circ}\)C: Machine-drying at 80 \({}^{\circ}\)C, MD 90 \({}^{\circ}\)C: Machine-drying at 90 \({}^{\circ}\)C, and MD 100 \({}^{\circ}\)C: Machine-drying at 100 \({}^{\circ}\)C.  
The analysis of volatile components in this study presents several challenges. The headspace method, which allows for simple analysis, was used, and the values were expressed using only PA without using internal standard reagents. More accurate data could be obtained by extracting volatile components and expressing the ratio of the peak area of volatile compounds to their peak area using an internal standard reagent. Another issue is that the number of measurement repetitions is one. Further investigations of these challenges are warranted. Figure 8: Effects of different treatment conditions on peak areas of aroma compounds in _Ocimium basililicum_ cv. ‘Geno-vese’ tea. (**A.a**) Linalool, (**B.b**) trans-alpha-bergamotene, and (**C,c**) 2-methoxy-3-(2-propenyl)-phenol. FD: Freeze-drying (\(-\)56 \({}^{\circ}\)C, 48 h), NS: Non-Steaming, S1: Steaming for 1 min, S2: Steaming for 2 min, S3: Steaming for 3 min, S5: Steaming for 5 min, S10: Steaming for 10 min, SD: Shade-drying (25 \({}^{\circ}\)C, 7 days), MD 40 \({}^{\circ}\)C: Machine-drying at 40 \({}^{\circ}\)C, MD 50 \({}^{\circ}\)C: Machine-drying at 50 \({}^{\circ}\)C, MD 60 \({}^{\circ}\)C: Machine-drying at 60 \({}^{\circ}\)C, MD 70 \({}^{\circ}\)C: Machine-drying at 70 \({}^{\circ}\)C, MD 80 \({}^{\circ}\)C: Machine-drying at 80 \({}^{\circ}\)C, MD 90 \({}^{\circ}\)C: Machine-drying at 90 \({}^{\circ}\)C, and MD 100 \({}^{\circ}\)C: Machine-drying at 100 \({}^{\circ}\)C.
# Feasibility of LoRa for Smart Home Indoor Localization
## 4 Discussion
### Scalability  
With the increase in the number of anchor nodes and tag nodes, it might be necessary to make localization protocol more efficient to avoid packet collisions. For example, if the number of anchors is considerably lower than the number of tags, we can send packets from anchors only. In this case, the tag nodes only receive on the channel and do not cause any packet collisions. The calculated RSSI then can either be used on the tags (self-localization) or stored and transferred later via another wireless radio to a central server (tracking).
## 3 Results
### Performance of STIP-Search Algorithm  
Time cost is an important metric for evaluating the retrieval performance of protein sequence database search engines. We measured the retrieval time for recalling candidate peptides using 3-mer, 4-mer, and 5-mer tags. The experimental results are shown in Figure 12.  
Figure 11: Block utilization rate comparison of ULP and GLP on NCBI-nr.  
Figure 12: Time cost of retrieving peptide sequence on PXD009449. (**a**) Time cost comparisons of peptide identification using 3-mer tag between TIIP and STIP-Search on PXD009449. (**b**) Time cost comparisons of peptide identification using 4-mer tag between TIIP and STIP-Search on PXD009449. (**c**) Time cost comparisons of peptide identification using 5-mer tag between TIIP and STIP-Search on PXD009449.  
Currently, algorithms that use variable-length tags to recall candidate peptides include PIPI2 and TIIP. TIIP accelerates the retrieval process by precomputing specific enzymatic digestion sites, resulting in the lowest time complexity. In this experiment, TIIP was chosen for comparison to test the time performance of STIP-Search in recalling candidate peptides with variable length tags. The results demonstrate that, compared to TIIP, STIP-Search reduces the peptide recall time by 14.01% to 23.31%, validating the speed advantage of STIP-Search in practical applications of variable length tag retrieval.
# An AMR-Based Liquid Film Simulation with Surfactant Transport Using PLIC-HF Method
## 3 Numerical Methods
### Time Integration  
For temporal discretization in the computations of the present research, a three-stage third-order strong stability-preserving Runge-Kutta (SSP-RK3) scheme was applied. The exception was the evolving pressure projection process, where a first-order Euler scheme was employed. Generally, for a general time-marching partial differential equation of an arbitrary variable \(q\),  
\[\frac{\partial q}{\partial t}=L(q), \tag{50}\]  
where \(L(\cdot)\) is an operator that relies on \(q\). The SSP-RK3 scheme uses the following formulation to update \(q^{n+1}\) from \(q^{n}\):  
\[q^{(1)} =q^{n}+L(q^{n})\Delta t\] \[q^{(2)} =q^{n}+\frac{1}{4}\Big{(}L(q^{n})+L\Big{(}q^{(1)}\Big{)}\Big{)}\Delta y\] \[=\frac{3}{4}q^{n}+\frac{1}{4}q^{(1)}+\frac{1}{4}L\Big{(}q^{(1)} \Big{)}\Delta t \tag{51}\] \[q^{n+1} =q^{n}+\frac{1}{6}\Big{(}L(q^{n})+L\Big{(}q^{(1)}\Big{)}+4L\Big{(} q^{(2)}\Big{)}\Big{)}\] \[=\frac{1}{3}q^{n}+\frac{2}{3}q^{(2)}+\frac{2}{3}L\Big{(}q^{(2)} \Big{)}\Delta t\]Since the present method solves the governing equation with the fully explicit method, the size of the time step \(\Delta t\) is constrained by advection, viscous stress, the propagation of acoustic waves and surface tension waves, and other factors. The simulations carried out in the present research were conducted in the low Mach number region, where the maximum velocity should be much slower than the speed of time. So the time step \(\Delta t\) was mainly constrained by the speed of sound. In the present research, the size of the time step \(\Delta t\) was constrained as follows:  
1. Constraint by advection \[\Delta t_{\text{adv}}\leq\frac{\Delta x}{\max|\mathbf{u}|+c_{s}}\] (52)
2. Constraint by viscous stress \[\Delta t_{\text{visc}}\leq\frac{\Delta x^{2}}{2\
u}\] (53) \(\
u\) is the kinematic viscosity, \(\
u=\frac{\mu}{\rho}\), \(\mu\) is dynamic viscosity and \(\rho\) is density. In Equation (53), the higher kinematic viscosity of heavy and light fluid is used.
3. Constraint by surface tension [45] \[\Delta t_{\text{sf}}\leq\sqrt{\frac{\rho_{h}+\rho_{l}}{2}\frac{\Delta x^{3}}{2 \pi\sigma_{0}}}\] (54)  
Considering the above constraints of the time step \(\Delta t\), the time step \(\Delta t\) can be determined as follows:  
\[\Delta t=\min(CFL_{\text{adv}}\Delta t_{\text{adv}},k_{\text{visc}}\Delta t_{ \text{visc}},k_{\text{sf}}\Delta t_{\text{sf}}) \tag{55}\]  
where \(CFL_{\text{adv}}\) is a non-dimensional number with a value of less than \(1\). In the present research, \(CFL_{\text{adv}}\) was set as \(0.10\) to maintain the numerical stability. As for the other parameters, in the present research, \(k_{\text{visc}}=k_{\text{sf}}=0.10\).
## 6 Performance Evaluation  
In this section, we describe the experiments performed to compare and analyze the performance of the active file mode change mechanism proposed in this paper and the existing manual file mode change mechanism. Because the proposed active file mode change mechanism dynamically changes the local mode and online mode of a file according to factors such as directory activation ratio, used storage ratio, and file access time, we conducted experiments with the following two scenarios. In the first directory deactivation scenario, the client periodically accesses the files in the directory to keep the directory active, then stops accessing the files after a certain period, changing the directory to the inactive state. In the second directory activation scenario, conversely, the client does not access any file keeping the directory inactive, and then starts accessing the files after a certain period to increase the directory activation ratio. Through experiments with these two file access scenarios, we compared and analyzed the differences between the two methods by measuring the local storage usage ratio and file access time.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 5 Performance Evaluation  
In this section, the performance of the proposed EGR is evaluated via computer simulation and then compared to those obtained using conventional protocols. We compare the proposed EGR with GEEC, EEREG, QMOR, AB routing [20], and LEACH-C [21]. Similar to EGR, QMOR considers multiple sinks and data redundancy avoidance. AB routing have clustering mechanism along with data redundancy avoidance in WMSNs. LEACH-C is a widely used clustering-based protocol for centralized WSNs, which is selected for performance study and for investigating how our proposed protocol performs against a real-time protocol that is commonly used in the industry. For simulation, the experimental environment for wireless multimedia sensor networks built upon Castalia/Omnet++ called WISE-Mnet++ is used.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 4 Experiments and Results Analysis
### Experimental Procedure
#### 4.1.5 Fog Removal Performance Evaluation  
The trained dehazing models were evaluated on the O-HAZY evaluation subset (nine real foggy images). Performance was measured using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), standard metrics for image restoration quality. For each model, we computed the average PSNR and SSIM across the evaluation set and visualized the results for qualitative analysis. Example dehazing outputs are shown in Figure 4, with the GridNet-based model demonstrating superior restoration quality.  
Figure 3: Synthetic fog samples generated from the O-HAZY dataset.  
Figure 4: Dehazing results on O-HAZY evaluation set: (**a**) ModelS, (**b**) ModelU, This study primarily focuses on the design and evaluation of a fog generation network suitable for real-time learning, rather than dehazing itself. Further analysis of these visual degradation artifacts and improvements in dehazing performance will be addressed in future work. (**c**) ModelDA, and (**d**) ModelDC. Each image shows the ground truth, foggy input, and dehazed output.
## 1 Introduction
Based on the Airy linear wave theory, a model for interaction of waves and currents has been proposed and widely used to analyze the cases when regular or irregular waves propagate on uniform currents [28; 29]. Since the regular wave has only one frequency, the analysis is contingent upon the subjective choice of the regular wave. In contrast, the irregular wave represented in the form of a wave spectrum facilitates a more comprehensive examination of the impact of various frequency components on the dynamic response of the floating platform. For irregular waves propagating on an opposite current, there is a chance of wave breaking in such particular instances and changing the properties of the wave [30; 31; 32]. The wave-current model with the help of the Airy linear wave theory has been presented and verified by numerical simulations and experiments [33; 34], and applied to offshore and marine structures [23; 35]. For semi-submerged FOWTs, the thorough investigation of the wave-current interaction influence on the dynamic responses of FOWTs should be researched. The purpose of this study is to investigate the impact of wave-current interaction on the dynamic responses of a semi-submerged FOWT constrained by a catenary mooring system, including the platform motions, mooring tension, and tower base forces. To comprehensively assess the coupling effects of waves and currents on the dynamic response of the semi-submerged FOWT and the stress experienced by the mooring cables, the interaction model for irregular wave and uniform current is used in this study. The interaction effect of waves traveling on a positive or opposite current is considered through the utilization of a coupled semi-submerged FOWT model considering the nonlinear dynamics of the mooring system. The operational wind condition is adopted in the coupled model. This paper is structured as follows: Section 2 describes the entire numerical model of the CSS FOWT. Section 3 introduces the interaction models for waves and currents. Numerical simulations and results with comparison and discussion are presented in Section 4. The conclusions are performed in Section 5.
## 4 Proposed Test Simulation
### Virtual Machine-Linked Node Test
The simulation node generates a message according to the standard specifications for the data received from the virtual machine and sends it to the simulation network. The user can create scenarios for specific situations or events according to the test cases. As a result of analyzing the GPS values of the V2X messages that the simulation node collected, we found changes in the data when the GPS spoofing was running, as shown in Figure 12. Various V2X message components, such as heading, speed, and message count, can be used besides GPS data. Although tests were performed by creating simple GPS spoofing scenarios, more diverse and sophisticated attack scenarios can be tested using the simulation environment proposed in this study. Since the virtual machine-linked simulation environment facilitates data transmission and collection, it can be used in various ways. For example, the performance of the misbehavior detection system can be verified by executing the attack scenarios after applying the security system to the virtual machine, or the weaknesses can be supplemented by analyzing the collected attack data. Furthermore, the weaknesses and the routes of cyberattacks performed up to the V2X network can be tested comprehensively using not only V2X messages but also the vulnerabilities of the linked virtual machine's OS.
## 2 Literature Review
### Outlier Detection Method
proposed a real-time monitoring system that is based on smartphone sensors for perishable food [26]. As outliers arise in sensor data due to inadequacies in sensing devices and network communication glitches, [PERSON] et al. used outlier detection that is based on DBSCAN to refine the outlier data. The findings demonstrated that DBSCAN was utilized to effectively recognize/characterize outlier data as isolated from normal sensor data. [PERSON] et al. proposed outlier detection based on DBSCAN for sensor data in wireless sensor networks [27]. The proposed model successfully separated outliers from normal sensors data. Based on the experiment on synthetic datasets, their proposed model showed significant results in detecting outliers, with an accuracy rate of 99%. Finally, [PERSON] et al. proposed an outlier detection method of soft sensor modeling of time series [28]. They utilized DBSCAN for the outlier detection method. The experiment showed that the proposed outlier detection method generated good performance. Utilizing DBSCAN-based outlier detection provides an efficient way for detecting the outlier data. The current literatures showed that removing outliers improves the classification accuracy. Furthermore, the majority of real-world datasets are imbalanced; thus, an oversampling method to generate artificial data from minority class is needed to improve the classification accuracy. A previous study showed that the combination data cleaning (outlier removal) and oversampling method generated a significant result [34]. In this manner, combining DBSCAN-based outlier detection and oversampling technique is predicted to enhance the accuracy of classification model.
## 4 Deformation Characteristics of Site
### Effect of the Shield Tunneling of the Left Tube on the Right One  
As mentioned above, the construction process of this project starts with the shield tunneling of the left tube after completing the excavation of the right tube. In order to study the effect of shield tunneling on the adjacent tunnel, the lateral convergence and the heaving of the right tube are monitored during the shield tunneling of the left one.  
In shield tunneling, the driving pressure makes the surrounding strata float upward, but the unloading caused by the excavation of the tunnel creates a tendency of the surrounding strata to sink. Figure 8a shows the heaving of the tunnel floor of the right tube during the shield tunneling of the left tube, in which the positive value represents the upward floating of the tunnel floor and the negative value represents the sinking of the tunnel floor. As shown in Figure 8a, the heaving of the tunnel floor of the right tube can be characterized by four stages. When the working face is about 8D behind the monitoring point, the right tube begins to float up and down, but the amplitude is very small. When the working face is about 2.5D behind the monitoring point, the right tube uplifts rapidly. When the working face reaches 2.5D in front of the monitoring point, the uplift of the right tube begins to fall back. Finally, it reaches a stable value when the working face reaches 6D in front of the monitoring point. Due to the squeezing effect of synchronous grouting on soil, the final uplift of the right tube is about half of the maximum uplift.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Shield Driving** & **Smax/mm** & **i/m** & **\(V_{s}\)/m\({}^{3}\)** & **\(V_{l}\)/\%** \\ \hline The right tunnel & 6.70–9.01 & 10.48–14.37 & 0.191–0.241 & 0.62–0.76 \\ \hline The left tunnel & 9.68 & 10.21 & 0.248 & 0.80 \\ \hline Double tunnels & 13.87 & 15.94 & 0.555 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameter values of the Peck formula.  
Figure 8b presents the lateral convergence of the right tube during the shield tunneling of the left tube, in which the positive value indicates that the side wall of the tunnel pushes the surrounding strata (i.e., expansion), and the negative value indicates that the side wall of tunnel is separated from the surrounding rock (i.e., contraction). From Figure 8b, the lateral deformation of the right tube shows a tendency to first contract and then expand, and the value of deformation ranges from \(-1.0\) mm to \(3.0\) mm. The lateral convergence of the right tube is also divided into four stages. When the working face is about 8D behind the monitoring point, the right tube begins to expand and contract in a fluctuating state. When the working face is about 6D behind the monitoring point, the right tube begins to contract. When the working face is about 2.5D behind the monitoring point, the right tube begins to expand. Finally, when the working face exceeds 2.5D in front of the monitoring point, the lateral convergence of the right tube reaches a stable value.
## 3 Materials and Methods
### Underlying Software Components
#### 3.2.5 Performance Analysis and Monitoring Tools  
There are four levels of monitoring (containers, virtual machines (VM), end-to-end link quality, and application) for self-adaptative applications to edge computing applications [38]. Due to the heterogeneity of the applications used in this implementation context, the need for performance measurement was critical. This research was implemented for container-based performance monitoring purposes. It assisted the process of pulling and migrating container images across computing nodes in fog or cloud in contrast to virtual machine [39].  
Docker itself had a built-in mechanism to access and convert container metrics into statistical format using Docker stats methods. Those metrics included runtime metrics and resource usage for a given container. This research proposed to use external built-in components to retrieve detailed sets of metrics by accessing exposed remote API using GET methods.  
In monitoring the system behaviors, the following tools were used:  
Figure 2: Architecture with containerization principles.  
**Container advisor**, also known as cAdvisor, is a provided open-source platform. It was used to track, measure, aggregate, process, and display performance monitoring data for the running containers.
* **Prometheus** is a monitoring tool relying on an open-source scheme. On top of monitoring activity, Prometheus can also store persistence time-series-based data for further processing and retrieval using PromQL language.
* **Grafana** is a web-based open-source user interface, used to visualize large-scale performance monitoring information. It can be configured to work with the Prometheus database to visualize the time series data of the metrics.