esa-sceva/satcom-chunk-collection huggingface dataset:
# Proposal for a System Model for Offline Seismic Event Detection in Colombia
## 4 Seismic Detection Model Proposal  
This research paper proposes the model presented in Figure 2--a set of modules in which seismic signals can be processed--from the seismic data collection to the detection mechanism expressed in the classification process module. The applicability of the model is directly related to the selection of the geographical zone whose seismicity is to be studied--in this case, the northeastern region of Colombian. The size of the study area, the rate of occurrence of events, and the homogeneity of the subsoil are some of the variables that directly influence the number of observations to be analyzed and the performance of each of the modules, which is why a careful selection of the region of interest must be first carried out. Applicability of the model on a different geographical region would require historic seismic arrays specific to the location.  
The modules are described below as follows: Seismic data seeking and gathering, Reading and interpretation of the seismic data, Analysis of seismological stations, Sample selection, and Classification process.
# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 3 Database Creation
### Bluied  
Among all the datasets available in literature, in this work the BLUED dataset [23] has been considered. This dataset was built in 2011 by monitoring a whole house located in Pennsylvania (USA), for 8 days. It contains the samples of voltage and current signals, and the corresponding timestamps, collected with a sampling frequency of \(f_{s}=12\) kHz. During the creation of the data set, all the changes in the state of power consumption higher than 30 W and lasting at least 5 s, for almost 50 appliances, have been labelled and recorded. This results in 2355 events recorded from known sources, and additional 127 events recorded from unknown sources, for a total of 2482 events. In this work, only the events involving appliances with a nominal active power higher than 30 W, with more than 10 recorded events, have been considered, resulting in 1585 events. In Table 2, the list of the considered appliances is reported, together with the identification code in the BLUED dataset (appliance label), and the number of recorded events.
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 2 Related Work
A more feasible method is to store the data of the targets in a databaseand then load amounts of data that can be processed into the program one by one according to the capacity of the computer. In [21], the authors considered that the number of targets was very high, so the targeted data were stored in a database system. Then, several small areas were planned, and the ranges of these small areas could cover the entire targeted area. Since each small area had the shape of a circle, the method was represented as c\({}_{-}\). Then, the targets within a small area were retrieved from the database. In addition, this method also included a mechanism that allowed some targets within a small area not to be confirmed by the PtInPy function. However, this method had a major disadvantage because the small areas overlapped. This led to a bottleneck in its performance. GIS is a broad subject, combining geography and cartography, and has been widely used in map production, information management, and registration management of geographic-related data in various businesses. The key to any GIS is a database containing representations of geographic phenomena, modeling their geometry (location and shape) and their properties or attributes. A GIS database can be stored in many forms, such as collecting individual data files or a single spatially enabled relational database because it has pre-collected and managed these data and cooperated with the development of many tools. When new information from the real world enters the system, the system can quickly reflect the answer. Its characteristic is that the pre-processed data are stable, and the value of the data is inquiring about changing rapidly. However, in our research, the time-space relationship between targets and areas is easy to change. Therefore, it is hard to use prior modeling to achieve time efficiency. The planning of a simulation requires an extensive range of areas to be used as targeted areas and many geographical points to be used as targets. Well-known check-in places posted on a social network service (SNS) were used as targets, and three administrative cities in Taiwan were used as the targeted areas. According to this study's methods, administrative regions could also be used as check-in places. These check-in places were locations where people had socialized in the past. The positioning and classification of the administrative areas for many check-in places can also be extended to many valuable applications [22; 23; 24; 25; 26], such as finding delicious food or locations related to religious beliefs. In [22], what drives users to check in on Facebook was discussed. For example, belief in Wang-Ye is one of the most popular folk religions among the Chinese people. In [23], check-in places were used as an example to find locations related to this belief. In [26], a method for collecting big data from Facebook check-in places and finding places related to Taiwanese beef noodles was proposed.
## 2 The ELD Problem Formulation
### Objective Function of the ELD Problem with MFs  
Because coefficients of each TGU are modified according to the different types of fuel being used, the fuel cost function form is the sum of different quadratic functions, as in (4). When VPEs are considered, the form is more complex with the presence of a sinusoidal function, and the objective function can be expressed as in (5):  
\[f_{i}(p_{i})=\begin{cases}\alpha_{1}p_{i}^{2}+\beta_{1}p_{i}+\gamma_{i1}&for\;p _{i,1,\min}\leq p_{i}\leq p_{i,1,\max}\\ \alpha_{2}p_{i}^{2}+\beta_{2}p_{i}+\gamma_{2}&for\;p_{i,2,\min}\leq p_{i}\leq p _{i,2,\max}\\ \ldots\\ \alpha_{im}p_{i}^{2}+\beta_{im}p_{i}+\gamma_{im}&for\;p_{i,m,\min}\leq p_{i} \leq p_{i,m,\max}\end{cases} \tag{4}\]  
\[f_{i}(p_{i})=\begin{cases}\alpha_{1}p_{i}^{2}+\beta_{1}p_{i}+\gamma_{11}+\left| \delta_{1}\times\sin(\varepsilon_{1}\times(p_{i,\min}-p_{i}))\right|&for\;p_{i,1,\min}\leq p_{i}\leq p_{i,1,\max}\\ \alpha_{2}p_{i}^{2}+\beta_{2}p_{i}+\gamma_{2}+\left|\delta_{2}\times\sin( \varepsilon_{2}\times(p_{i,\min}-p_{i}))\right|&for\;p_{i,2,\min}\leq p_{i} \leq p_{i,2,\max}\\ \ldots\\ \alpha_{im}p_{i}^{2}+\beta_{im}p_{i}+\gamma_{im}+\left|\delta_{im}\times\sin( \varepsilon_{im}\times(p_{i,\min}-p_{i}))\right|&for\;p_{i,m,\min}\leq p_{i} \leq p_{i,m,\max}\end{cases} \tag{5}\]  
where \(\alpha_{im}\), \(\beta_{im}\), and \(\gamma_{im}\) are the known coefficients of FCF of the \(i\)th TGU corresponding to the \(m\)th fuel type for the case without VPEs; \(\delta_{im}\) and \(\varepsilon_{im}\) are the known coefficients of FCF of the \(i\)th TGU corresponding to the \(m\)th fuel type for the case with VPEs; and \(p_{i,m,min}\) and \(p_{i,m,max}\) are the minimum and maximum power outputs of the \(i\)th TGU corresponding to the \(m\)th fuel type.
# Improved Q Network Auto-Scaling in Microservice Architecture
## 3 Introducing Intelligent Kubernetes Framework Design Methods and Workloads
### Proposed Microservice Architecture for Auto-Scaling Performance Evaluation  
Microservice computing is used for customized applications that have a variety of resource requirements. For example, high traffic loads require significant memory and computing power high parallel analysis operations to process video materials such as Netflix and YouTube videos. Meanwhile, applications like chatbots tend to be low in computing intensity. However, as the number of users increases, the number of requests also increases, thereby requiring more responses. To investigate the auto-scaling impact of various workloads, a synthesized and reliable workload profile that can simulate microservice applications needs to be created. In this study, an API YAML (API Version: v1 bera1, Kind: Deployment) file was created to specify a target port. A load operation was then run on the PHP-Apache server. Figure 2 illustrates the Intelligence Microservice Architecture proposed in this study.  
Two separate clusters were set up using Kubernetes to test the auto-scaling capabilities in a high-performance computing environment (Intel i9 X processor, 128 GB RAM, NVIDIA GPU RTX 3090 * 2). The sample services used in the experiment were distributed to the service cluster. Each cluster is designed to provide sufficient capacity to host all components as well as avoid performance limitations. Based on the collected metrics, the agent manages the activities of the two clusters, such as updating the configuration of the sample service and adjusting the process flow of the experiment by acting as a Kubernetes user. The existing HPA controls the statuses of the deployed Kubernetes services and enables automatic scaling of additional pods.  
ML's Reinforcement Learning method was used to update the auto-scaling configuration of the service in each iteration to create a new output each time. To comprehensively test the auto-scaling function, we increased the number of copies of the receiving gateways to process load balancing, while focusing only on the auto-scaling function. We also experimentally investigated the Auto-Scaling method using the existing reinforcement learning algorithm and the newly proposed Q Network algorithm.
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 3 Image Preprocessing  
The digital radiographic images often show low contrast, existence of noise, inconsistent distribution of grays. However, the quality of images influenced the detection of weld defects to a large extent especially for those small defects which can be easily drawn in the noise. Many processing methods were applied to eliminate or relieve the existence of such problems. It should be mentioned that the processing must be carefully so that the important information is not lost. For instance, the original shape and brightness distribution of the defect which is important for the discrimination of the defect types may be lost when the image enhancing methods, such as normalization and histogram equalization are applied. The pre-processing methods mainly involve noise reduction and contrast enhancement. There are several key tasks in this phase: noise filtering, object highlighting and vision improvement.
## 4 Theoretical Analysis of the Proposed PCSDF Cooperation Scheme
### Analyses of Outage Probability in Proposed Scheme
\begin{array}{l}P_{3}=\Pr(\Omega_{k}\
eq\varnothing)\Pr(\beta_{r_{opt}}/ \beta_{sd}>\theta)\\ \qquad\times\Pr(\gamma_{sd(1)}+\gamma_{rd_{opt}}<2^{2V}-1)\\ \qquad=C_{M}^{K}\exp(-\frac{2^{V}-1}{\Gamma_{M}})^{K}(1-\exp(-\frac{2^{V}-1}{ \Gamma_{M}}))^{M-K}\\ \qquad\times(1-(1-\exp(-\frac{\theta_{T_{sd}}}{\Gamma_{rd_{opt}}}))^{K})(1- \exp(-\frac{2^{2V}-1-\gamma_{rd_{opt}}}{\gamma_{sd(1)}}))\end{array} . \tag{32}\]  
According to the above analysis, the interrupt probability of the proposed PCSDF scheme is proportional to the cooperation threshold \(\theta\) when the information transmission rate \(V\) and all channel conditions are known. As \(\theta\) increases, the number of optimal relays participating in cooperative communication decreases, which is accompanied by an increase in interruption probability for the whole system.
# Wobble Board Performance: A Practical and Useful Quantification in Balance Assessment
## 2 Materials and Methods
### Data Collection  
Each participant completed three identical sessions of WB balance testing on a force plate to collect data simultaneously from both devices. The three sessions took place in 6-week intervals to represent a duration that is often required between measurements for various training interventions. The repeated sessions allowed for the investigation of long-term familiarization effects and testing whether the relationship between devices changed over time. Data from the final session were used for in-depth, within-session analysis.  
Each session followed a validated protocol [17], including a mandatory familiarization with the WB by performing test trials in accordance with the protocol specifications. Then,the barefooted participants performed three unipedal stance tests per leg on the WB with their hands at their hips. A block of three trials with one leg was finished before a second block of another three trials on the other leg was conducted, and the sequence of blocks was randomized. This resulted in a total of six trials per participant. Each trial lasted 30 s, with a 1 min break between trials to prevent fatigue. During the entire duration of each the trial, the participants attempted to spend as much time as possible in a stable horizontal WB position (i.e., \(\sim\)0\({}^{\circ}\) WB tilt angle).  
The WB (Balance Board WSP, GSJ Service, Rome, Italy; maximal tilt angle = 20\({}^{\circ}\)) had a circular even surface of 40 cm diameter to stand on and a plastic hemisphere of 6 cm height and 20 cm width under the board to allow for 360\({}^{\circ}\) freedom of movement. It was equipped with tri-axis accelerometers (Photiget Spatial 0/0/3 Basic 1041, Phidgets Inc. 2016, Calgary, AB, Canada) placed in the hemisphere to measure mediolateral and anterior-posterior tilt angles at a sampling frequency of 200 Hz [17]. A USB connection with a computer via proprietary software (Software WSP version 1.0.0.1, GSJ Service, Rome, Italy) allowed for data collection and real-time display of the tilt angle. Visualized performance was displayed in real time on a screen in front of the participants via a validated software (GSJ Service, Rome, Italy), showing a moving dot that represented the CoP and a target zone that represented the \(\sim\)0\({}^{\circ}\) WB tilt area [17]. A screen presented the dot and the target zone to the participants in real time during the trials, approximately one meter in front of the participants and one meter above the ground [17]. The WB was placed on an AMTI force plate (Watertown, MA, USA), obtaining ground reaction forces simultaneously at 1000 Hz. Visual3D v6 software (C-Motion Inc., Rockville, MD, USA) was used to calculate the CoP from ground reaction forces measured by the force plate. Force plate data were filtered in accordance with recommendations for CoP filtering [13] and the results of residual analysis performed for the specific data [30]. A fourth-order zero-lag Butterworth low-pass filter with a cut-off frequency of 10 Hz was applied.
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 3 Case Study
### Results
#### 3.7.2 Heat Load Prediction Based on AE-GWO-GRU  
The GWO algorithm is employed to optimize the GRU model, resulting in the determination of optimal parameters: 121 neurons for the first GRU layer, 156 neurons for the second GRU layer, 71 neurons for the hidden layer, a learning rate of 0.007, a dropout rate of 0.2 and a training batch size of 24. The GRU model is retrained using these optimal parameters, and heat load prediction is conducted alongside comparison models. The relative errors of the predictions from different models are shown in Figure 16, and the MAPE and SDAPE are shown in Figure 17.  
Through experimental analysis, the prediction errors of AE-BP and AE-GRU are relatively large, while the prediction error of AE-GWO-GRU is the smallest, with maximum relative errors of 8.40%, 7.43%, and 6.73%, respectively. The AE-GWO-GRU model has the smallest SDAPE, indicating that the prediction errors are more concentrated. This demonstrates the model's superior stability and better generalization ability. To further validate the model's prediction accuracy, the results of 24 test samples are examined. The prediction curves of different models, including AE-BP, AE-GRU, and AE-GWO-GRU, are shown in Figure 18. The accuracy analysis values of heat load prediction results under different models are presented in Table 3.  
Figure 16: The relative errors of heat load prediction of different models.  
Figure 17: The MAPE and SDAPE of different heat load prediction models.  
Comparing the prediction results, the AE-GWO-GRU model yields values closest to the actual data, demonstrating the best predictive performance, as shown in Figure 18. Compared with the BP, AE-BP, GRU, and AE-GRU models, the AE-GWO-GRU model exhibits lower error values in terms of RMSE, MAE, and MAPE, with values of 47.90, 36.90, and 2.17%, respectively. This demonstrates the superiority of the AE-GWO-GRU model, achieving the expected effectiveness.  
As shown in Table 3, a significant error exists between the predicted and actual values before using AE for data augmentation. After data augmentation, the errors of the AE-BP and AE-GRU models are reduced. Specifically, the RMSE and MAE of the BP model decrease by 3.03 and 1.29, respectively, while those of the GRU model decrease by 2.71 and 0.59, respectively. The SDAPE of BP and GRU both decrease after data augmentation. This demonstrates that utilizing AE to augment data in scenarios with insufficient data volume effectively reduces the prediction error and enhances the prediction stability. Moreover, compared with AE-GRU, the AE-GWO-GRU model's predicted values are closer to the real values, resulting in a higher prediction accuracy. The RMSE and MAE are reduced by 6.08 and 4.28, respectively, indicating that using GWO to optimize GRU parameters enables adjustments that best match the heat load data, thereby further enhancing prediction accuracy.
# An Integrated Program to Improve Cognitive and Physical Abilities in Older People
## 4 Statistics
### Physical Performance  
RM ANOVA was conducted to determine whether the integrated program influenced subjects' performance. The results showed that TUG, FTSTS, and 10 MWT all showed significant increases compared to the pre-program (\(p\) = 0.001). This aligns with [PERSON]'s [40] findings that integrated programs positively affect both cognition and daily physical performance in older adults. Stride length and gait speed also increased significantly, which was consistent with [PERSON] et al.'s [30] findings that a fall prevention program using a four-way stepping game improved walking ability and lower extremity muscle strength. The results showed that the integrated program influenced physical performance and walking ability in older adults (Table 11).  
In addition, while step length and speed increased gradually over time, the results for TUG, FTSTS, and 10 MWT showed a significant decrease at the mid-test and only a slight increase at the post-test, which may be the result of the subjects becoming less nervous as they gained proficiency with the program. However, ultimately, we found a significant increase in the post-test scores related to the pre-test (Figure 6a-e).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sections**} & \multicolumn{2}{c}{**M \(\pm\) SD**} & \multicolumn{1}{c}{**t**} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{\(\mathbf{d}\)} & \multirow{2}{*}{\(\mathbf{n^{2}}\)} \\ \cline{2-2} \cline{5-8}  & **Pre** & **Post** & **(Post-Pre)** & & & \\ \hline Visuospatial function & 1.55 \(\pm\) 0.68 & 1.88 \(\pm\) 0.32 & 2.062 & 0.055 & 0.486 & 0.200 \\ \hline Executive function & 5.15 \(\pm\) 0.98 & 5.05 \(\pm\) 0.8 & \(-\)0.766 & 0.454 & \(-\)0.180 & 0.033 \\ \hline Memory & 8.29 \(\pm\) 1.68 & 9.38 \(\pm\) 0.77 & 3.249 & 0.005 & 0.766 & 0.383 \\ \hline Language function & 3.6 \(\pm\) 0.5 & 3.77 \(\pm\) 0.42 & 2.204 & 0.042 & 0.519 & 0.222 \\ \hline Total & 26.3 \(\pm\) 2.73 & 27.66 \(\pm\) 1.78 & 3.079 & 0.007 & 0.726 & 0.358 \\ \hline \hline \end{tabular}
\end{table}
Table 10: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Type III SS** & **DF** & **MS** & **f** & **\(p\)** & **d** & **n\({}^{2}\)** \\ \hline TUG (s) & 28 & 2 & 13.998 & 29.107 & 0.001 & 1.308 & 0.631 \\ \hline Error & 16.35 & 34 & 0.481 & & & & \\ \hline FTSTS (s) & 125.6 & 2 & 62.780 & 45.775 & 0.001 & 1.639 & 0.729 \\ \hline Error & 46.63 & 34 & 0.481 & & & & \\ \hline
10 MWT (s) & 1.25 & 2 & 0.627 & 12.894 & 0.001 & 0.087 & 0.431 \\ \hline Error & 1.65 & 34 & 0.049 & & & & \\ \hline Step length (cm) & 186 & 2 & 93.205 & 12.563 & 0.001 & 0.086 & 0.411 \\ \hline Error & 252.24 & 34 & 7.419 & & & & \\ \hline Speed (m/s) & 0.09 & 2 & 0.046 & 11.840 & 0.001 & 0.833 & 0.425 \\ \hline Error & 0.13 & 34 & 0.004 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: ANOVA of TUG, FTSTS and 10 MWT (\(n\) = 18).
# Instant Center Identification of Single-Loop Multi-DOF Planar Linkage Using Virtual Link
###### Abstract  
Instant center is an important kinematic characteristic which can be used for velocity and singularity analysis, configuration synthesis and dynamics modeling of multi-degree of freedom (multi-DOF) planar linkage. The Aronhold-Kennedy theorem is famous for locating instant centers of four-bar planar linkage, but for single-loop multi-DOF linkages, it fails. Increasing with the number of the links of single-loop multi-DOF planar linkages, the lack of link relationship makes the identification of instant center become a recognized difficulty. This paper proposes a virtual link method to identify instant centers of single-loop multi-DOF planar linkage. First, three types of instant centers are redefined and the instant center identification process graph is introduced. Then, based on coupled loop chain characteristic and definition of instant center, two criteria are presented to convert single-loop multi-DOF planar linkage into a two-loop virtual linkage by adding the virtual links. Subsequently, the unchanged instant centers are identified in the virtual linkage and used to acquire all the instant centers of original single-loop multi-DOF planar linkage. As a result, the instant centers of single-loop five-bar, six-bar planar linkage with several prismatic joints are systematically researched for the first time. Finally, the validity of the proposed method is demonstrated using loop equations. It is a graphical and straightforward method and the application is wide up to single-loop multi-DOF N-bar (N \(\geq\) 5) planar linkage.  
loop chain; virtual link; instant center; multi-degree-of-freedom; Aronhold-Kennedy theorem 2021  
12 May 2021  
14 May 2021